diff --git "a/segmentation/upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.log" "b/segmentation/upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.log" new file mode 100644--- /dev/null +++ "b/segmentation/upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.log" @@ -0,0 +1,24475 @@ +2024-06-18 02:24:58,908 - mmseg - INFO - Multi-processing start method is `None` +2024-06-18 02:24:59,089 - mmseg - INFO - OpenCV num_threads is `128 +2024-06-18 02:24:59,833 - mmseg - INFO - Environment info: +------------------------------------------------------------ +sys.platform: linux +Python: 3.9.19 (main, May 6 2024, 19:43:03) [GCC 11.2.0] +CUDA available: True +GPU 0,1,2,3,4,5,6,7: NVIDIA A100-SXM4-80GB +CUDA_HOME: /mnt/petrelfs/share/cuda-11.7/ +NVCC: Cuda compilation tools, release 11.7, V11.7.99 +GCC: gcc (GCC) 7.3.0 +PyTorch: 1.12.0+cu113 +PyTorch compiling details: PyTorch built with: + - GCC 9.3 + - C++ Version: 201402 + - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications + - Intel(R) MKL-DNN v2.6.0 (Git Hash 52b5f107dd9cf10910aaa19cb47f3abf9b349815) + - OpenMP 201511 (a.k.a. OpenMP 4.5) + - LAPACK is enabled (usually provided by MKL) + - NNPACK is enabled + - CPU capability usage: AVX2 + - CUDA Runtime 11.3 + - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86 + - CuDNN 8.3.2 (built against CUDA 11.5) + - Magma 2.5.2 + - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.3, CUDNN_VERSION=8.3.2, CXX_COMPILER=/opt/rh/devtoolset-9/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -DEDGE_PROFILER_USE_KINETO -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-unused-parameter -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Werror=cast-function-type -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.12.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=OFF, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, USE_ROCM=OFF, + +TorchVision: 0.13.0+cu113 +OpenCV: 4.9.0 +MMCV: 1.7.0 +MMCV Compiler: GCC 7.3 +MMCV CUDA Compiler: 11.7 +MMSegmentation: 0.27.0+6d3ca17 +------------------------------------------------------------ + +2024-06-18 02:24:59,833 - mmseg - INFO - Distributed training: True +2024-06-18 02:25:00,854 - mmseg - INFO - Config: +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + type='EncoderDecoder', + pretrained=None, + backbone=dict( + type='PIIPTwoBranch', + n_points=4, + deform_num_heads=16, + cffn_ratio=0.25, + deform_ratio=0.5, + with_cffn=True, + interact_attn_type='deform', + interaction_drop_path_rate=0.4, + interaction_proj=False, + norm_layer='none', + branch1=dict( + real_size=512, + img_size=512, + pretrain_img_size=224, + patch_size=16, + pretrain_patch_size=14, + depth=48, + embed_dim=3200, + num_heads=25, + mlp_ratio=4, + qkv_bias=False, + init_values=0.1, + with_cp=True, + use_flash_attn=True, + qk_normalization=True, + layerscale_force_fp32=False, + with_fpn=False, + drop_path_rate=0.4, + interaction_indexes=[[0, 3], [4, 7], [8, 11], [12, 15], [16, 19], + [20, 23], [24, 27], [28, 31], [32, 35], + [36, 39], [40, 43], [44, 47]], + pretrained='pretrained/intern_vit_6b_224px.pth', + norm_layer_type='RMSNorm', + mlp_type='fused_mlp'), + branch2=dict( + real_size=512, + img_size=512, + pretrain_img_size=224, + patch_size=16, + pretrain_patch_size=14, + depth=32, + embed_dim=1280, + num_heads=16, + mlp_ratio=4, + qkv_bias=True, + init_values=1.0, + with_cp=True, + use_flash_attn=True, + qk_normalization=False, + layerscale_force_fp32=False, + with_fpn=False, + drop_path_rate=0.4, + interaction_indexes=[[0, 1], [2, 3], [4, 5], [6, 7], [8, 10], + [11, 13], [14, 16], [17, 19], [20, 22], + [23, 25], [26, 28], [29, 31]], + pretrained='pretrained/mae_pretrain_vit_huge.pth', + norm_layer_type='LayerNorm', + mlp_type='fused_mlp')), + decode_head=dict( + type='UPerHead', + in_channels=[3200, 3200, 3200, 3200], + in_index=[0, 1, 2, 3], + pool_scales=(1, 2, 3, 6), + channels=1536, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=dict(type='SyncBN', requires_grad=True), + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), + auxiliary_head=dict( + type='FCNHead', + in_channels=3200, + in_index=2, + channels=1536, + num_convs=1, + concat_input=False, + dropout_ratio=0.1, + num_classes=150, + norm_cfg=dict(type='SyncBN', requires_grad=True), + align_corners=False, + loss_decode=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), + train_cfg=dict(), + test_cfg=dict(mode='slide', crop_size=(512, 512), stride=(341, 341))) +dataset_type = 'ADE20KDataset' +data_root = 'data/ade/ADEChallengeData2016' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +crop_size = (512, 512) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 512), + flip=False, + transforms=[ + dict( + type='SETR_Resize', + keep_ratio=True, + crop_size=(512, 512), + setr_multi_scale=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=4, + train=dict( + type='ADE20KDataset', + data_root='data/ade/ADEChallengeData2016', + img_dir='images/training', + ann_dir='annotations/training', + pipeline=[ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', reduce_zero_label=True), + dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)), + dict(type='RandomCrop', crop_size=(512, 512), cat_max_ratio=0.75), + dict(type='RandomFlip', prob=0.5), + dict(type='PhotoMetricDistortion'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size=(512, 512), pad_val=0, seg_pad_val=255), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_semantic_seg']) + ]), + val=dict( + type='ADE20KDataset', + data_root='data/ade/ADEChallengeData2016', + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 512), + flip=False, + transforms=[ + dict( + type='SETR_Resize', + keep_ratio=True, + crop_size=(512, 512), + setr_multi_scale=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ]), + test=dict( + type='ADE20KDataset', + data_root='data/ade/ADEChallengeData2016', + img_dir='images/validation', + ann_dir='annotations/validation', + pipeline=[ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 512), + flip=False, + transforms=[ + dict( + type='SETR_Resize', + keep_ratio=True, + crop_size=(512, 512), + setr_multi_scale=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ])) +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook', by_epoch=False), + dict(type='TensorboardLoggerHook') + ]) +dist_params = dict(backend='nccl') +log_level = 'INFO' +load_from = None +resume_from = None +workflow = [('train', 1)] +cudnn_benchmark = True +optimizer = dict( + type='AdamW', + lr=4e-05, + betas=(0.9, 0.999), + weight_decay=0.05, + constructor='CustomLayerDecayOptimizerConstructor', + paramwise_cfg=dict(num_layers=48, layer_decay_rate=0.95, skip_stride=1.5)) +optimizer_config = dict() +lr_config = dict( + policy='poly', + warmup='linear', + warmup_iters=1500, + warmup_ratio=1e-06, + power=1.0, + min_lr=0.0, + by_epoch=False) +runner = dict(type='IterBasedRunner', max_iters=80000) +checkpoint_config = dict( + by_epoch=False, interval=2000, deepspeed=True, max_keep_ckpts=1) +evaluation = dict(interval=1000, metric='mIoU', pre_eval=True, save_best=None) +deepspeed = True +deepspeed_config = 'zero_configs/adam_zero1_bf16.json' +pretrained = None +custom_hooks = [dict(type='ToBFloat16Hook', priority=49)] +work_dir = './work_dirs/upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5' +gpu_ids = range(0, 8) +auto_resume = True + +2024-06-18 02:25:06,246 - mmseg - INFO - Set random seed to 1473895943, deterministic: False +2024-06-18 02:26:09,166 - mmseg - INFO - _IncompatibleKeys(missing_keys=[], unexpected_keys=['cls_token', 'clip_projector.norm1_q.weight', 'clip_projector.norm1_q.bias', 'clip_projector.norm1_k.weight', 'clip_projector.norm1_k.bias', 'clip_projector.norm1_v.weight', 'clip_projector.norm1_v.bias', 'clip_projector.cross_attn.q_bias', 'clip_projector.cross_attn.k_bias', 'clip_projector.cross_attn.v_bias', 'clip_projector.cross_attn.q.weight', 'clip_projector.cross_attn.k.weight', 'clip_projector.cross_attn.v.weight', 'clip_projector.cross_attn.proj.weight', 'clip_projector.cross_attn.proj.bias']) +2024-06-18 02:26:18,651 - mmseg - INFO - _IncompatibleKeys(missing_keys=['blocks.0.ls1.gamma', 'blocks.0.ls2.gamma', 'blocks.1.ls1.gamma', 'blocks.1.ls2.gamma', 'blocks.2.ls1.gamma', 'blocks.2.ls2.gamma', 'blocks.3.ls1.gamma', 'blocks.3.ls2.gamma', 'blocks.4.ls1.gamma', 'blocks.4.ls2.gamma', 'blocks.5.ls1.gamma', 'blocks.5.ls2.gamma', 'blocks.6.ls1.gamma', 'blocks.6.ls2.gamma', 'blocks.7.ls1.gamma', 'blocks.7.ls2.gamma', 'blocks.8.ls1.gamma', 'blocks.8.ls2.gamma', 'blocks.9.ls1.gamma', 'blocks.9.ls2.gamma', 'blocks.10.ls1.gamma', 'blocks.10.ls2.gamma', 'blocks.11.ls1.gamma', 'blocks.11.ls2.gamma', 'blocks.12.ls1.gamma', 'blocks.12.ls2.gamma', 'blocks.13.ls1.gamma', 'blocks.13.ls2.gamma', 'blocks.14.ls1.gamma', 'blocks.14.ls2.gamma', 'blocks.15.ls1.gamma', 'blocks.15.ls2.gamma', 'blocks.16.ls1.gamma', 'blocks.16.ls2.gamma', 'blocks.17.ls1.gamma', 'blocks.17.ls2.gamma', 'blocks.18.ls1.gamma', 'blocks.18.ls2.gamma', 'blocks.19.ls1.gamma', 'blocks.19.ls2.gamma', 'blocks.20.ls1.gamma', 'blocks.20.ls2.gamma', 'blocks.21.ls1.gamma', 'blocks.21.ls2.gamma', 'blocks.22.ls1.gamma', 'blocks.22.ls2.gamma', 'blocks.23.ls1.gamma', 'blocks.23.ls2.gamma', 'blocks.24.ls1.gamma', 'blocks.24.ls2.gamma', 'blocks.25.ls1.gamma', 'blocks.25.ls2.gamma', 'blocks.26.ls1.gamma', 'blocks.26.ls2.gamma', 'blocks.27.ls1.gamma', 'blocks.27.ls2.gamma', 'blocks.28.ls1.gamma', 'blocks.28.ls2.gamma', 'blocks.29.ls1.gamma', 'blocks.29.ls2.gamma', 'blocks.30.ls1.gamma', 'blocks.30.ls2.gamma', 'blocks.31.ls1.gamma', 'blocks.31.ls2.gamma'], unexpected_keys=['cls_token', 'norm.weight', 'norm.bias']) +2024-06-18 02:26:52,381 - mmseg - INFO - initialize UPerHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} +2024-06-18 02:26:53,433 - mmseg - INFO - initialize FCNHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} +Name of parameter - Initialization information + +backbone.w1 - torch.Size([]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.w2 - torch.Size([]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.pos_embed - torch.Size([1, 1025, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.patch_embed.proj.weight - torch.Size([3200, 3, 16, 16]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.patch_embed.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.0.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.1.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.2.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.3.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.4.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.5.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.6.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.7.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.8.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.9.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.10.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.11.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.12.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.13.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.14.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.15.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.16.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.17.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.18.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.19.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.20.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.21.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.22.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.23.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.24.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.25.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.26.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.27.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.28.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.29.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.30.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.31.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.32.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.33.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.34.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.35.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.36.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.37.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.38.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.39.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.40.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.41.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.42.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.43.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.44.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.45.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.46.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.norm1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.attn.qkv.weight - torch.Size([9600, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.attn.proj.weight - torch.Size([3200, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.attn.proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.attn.q_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.attn.k_norm.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.ls1.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.norm2.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.mlp.fc1.weight - torch.Size([12800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.mlp.fc1.bias - torch.Size([12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.mlp.fc2.weight - torch.Size([3200, 12800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.mlp.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch1.blocks.47.ls2.gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.pos_embed - torch.Size([1, 1025, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.patch_embed.proj.weight - torch.Size([1280, 3, 16, 16]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.patch_embed.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.0.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.1.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.2.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.3.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.4.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.5.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.6.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.7.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.8.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.9.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.10.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.11.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.12.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.13.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.14.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.15.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.16.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.17.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.18.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.19.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.20.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.21.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.22.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.23.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.24.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.25.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.26.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.27.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.28.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.29.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.30.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.norm1.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.norm1.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.attn.qkv.weight - torch.Size([3840, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.attn.qkv.bias - torch.Size([3840]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.attn.proj.weight - torch.Size([1280, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.attn.proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.ls1.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.norm2.weight - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.norm2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.mlp.fc1.weight - torch.Size([5120, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.mlp.fc1.bias - torch.Size([5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.mlp.fc2.weight - torch.Size([1280, 5120]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.mlp.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.branch2.blocks.31.ls2.gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.0.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.1.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.2.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.3.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.4.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.5.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.6.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.7.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.8.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.9.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.10.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ca_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.cffn_gamma - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight - torch.Size([128, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.attention_weights.weight - torch.Size([64, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.output_proj.weight - torch.Size([3200, 1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.output_proj.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.value_proj.weight - torch.Size([1600, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.attn.value_proj.bias - torch.Size([1600]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.fc1.weight - torch.Size([800, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.fc1.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight - torch.Size([800, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.bias - torch.Size([800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.fc2.weight - torch.Size([3200, 800]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch2to1_injector.ffn.fc2.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ca_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.cffn_gamma - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.sampling_offsets.weight - torch.Size([128, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.sampling_offsets.bias - torch.Size([128]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.attention_weights.weight - torch.Size([64, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.attention_weights.bias - torch.Size([64]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.output_proj.weight - torch.Size([1280, 640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.output_proj.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.value_proj.weight - torch.Size([640, 3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.attn.value_proj.bias - torch.Size([640]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.fc1.weight - torch.Size([320, 1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.fc1.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.weight - torch.Size([320, 1, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.dwconv.dwconv.bias - torch.Size([320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.fc2.weight - torch.Size([1280, 320]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.interactions.11.interaction_units_12.branch1to2_injector.ffn.fc2.bias - torch.Size([1280]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch1.0.weight - torch.Size([3200, 3200, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch1.1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch1.1.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch1.3.weight - torch.Size([3200, 3200, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch1.4.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch1.4.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch2.0.weight - torch.Size([3200, 1280, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch2.1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch2.1.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch2.3.weight - torch.Size([3200, 3200, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch2.4.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.merge_branch2.4.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.fpn1.0.weight - torch.Size([3200, 3200, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.fpn1.0.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.fpn1.1.weight - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.fpn1.1.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.fpn1.3.weight - torch.Size([3200, 3200, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.fpn1.3.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.fpn2.0.weight - torch.Size([3200, 3200, 2, 2]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +backbone.fpn2.0.bias - torch.Size([3200]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.conv_seg.weight - torch.Size([150, 1536, 1, 1]): +NormalInit: mean=0, std=0.01, bias=0 + +decode_head.conv_seg.bias - torch.Size([150]): +NormalInit: mean=0, std=0.01, bias=0 + +decode_head.psp_modules.0.1.conv.weight - torch.Size([1536, 3200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.0.1.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.0.1.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.1.1.conv.weight - torch.Size([1536, 3200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.1.1.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.1.1.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.2.1.conv.weight - torch.Size([1536, 3200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.2.1.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.2.1.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.3.1.conv.weight - torch.Size([1536, 3200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.3.1.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.psp_modules.3.1.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.bottleneck.conv.weight - torch.Size([1536, 9344, 3, 3]): +Initialized by user-defined `init_weights` in ConvModule + +decode_head.bottleneck.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.bottleneck.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.lateral_convs.0.conv.weight - torch.Size([1536, 3200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.lateral_convs.0.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.lateral_convs.0.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.lateral_convs.1.conv.weight - torch.Size([1536, 3200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.lateral_convs.1.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.lateral_convs.1.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.lateral_convs.2.conv.weight - torch.Size([1536, 3200, 1, 1]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.lateral_convs.2.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.lateral_convs.2.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_convs.0.conv.weight - torch.Size([1536, 1536, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_convs.0.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_convs.0.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_convs.1.conv.weight - torch.Size([1536, 1536, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_convs.1.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_convs.1.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_convs.2.conv.weight - torch.Size([1536, 1536, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_convs.2.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_convs.2.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_bottleneck.conv.weight - torch.Size([1536, 6144, 3, 3]): +Initialized by user-defined `init_weights` in ConvModule + +decode_head.fpn_bottleneck.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +decode_head.fpn_bottleneck.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +auxiliary_head.conv_seg.weight - torch.Size([150, 1536, 1, 1]): +NormalInit: mean=0, std=0.01, bias=0 + +auxiliary_head.conv_seg.bias - torch.Size([150]): +NormalInit: mean=0, std=0.01, bias=0 + +auxiliary_head.convs.0.conv.weight - torch.Size([1536, 3200, 3, 3]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +auxiliary_head.convs.0.bn.weight - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder + +auxiliary_head.convs.0.bn.bias - torch.Size([1536]): +The value is the same before and after calling `init_weights` of EncoderDecoder +2024-06-18 02:26:53,472 - mmseg - INFO - EncoderDecoder( + (backbone): PIIPTwoBranch( + (branch1): InternViT6B( + (patch_embed): PatchEmbed( + (proj): Conv2d(3, 3200, kernel_size=(16, 16), stride=(16, 16)) + (norm): Identity() + ) + (pos_drop): Identity() + (blocks): ModuleList( + (0): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): Identity() + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): Identity() + ) + (1): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.009) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.009) + ) + (2): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.017) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.017) + ) + (3): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.026) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.026) + ) + (4): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.034) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.034) + ) + (5): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.043) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.043) + ) + (6): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.051) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.051) + ) + (7): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.060) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.060) + ) + (8): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.068) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.068) + ) + (9): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.077) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.077) + ) + (10): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.085) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.085) + ) + (11): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.094) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.094) + ) + (12): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.102) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.102) + ) + (13): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.111) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.111) + ) + (14): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.119) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.119) + ) + (15): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.128) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.128) + ) + (16): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.136) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.136) + ) + (17): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.145) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.145) + ) + (18): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.153) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.153) + ) + (19): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.162) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.162) + ) + (20): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.170) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.170) + ) + (21): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.179) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.179) + ) + (22): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.187) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.187) + ) + (23): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.196) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.196) + ) + (24): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.204) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.204) + ) + (25): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.213) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.213) + ) + (26): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.221) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.221) + ) + (27): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.230) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.230) + ) + (28): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.238) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.238) + ) + (29): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.247) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.247) + ) + (30): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.255) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.255) + ) + (31): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.264) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.264) + ) + (32): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.272) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.272) + ) + (33): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.281) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.281) + ) + (34): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.289) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.289) + ) + (35): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.298) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.298) + ) + (36): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.306) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.306) + ) + (37): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.315) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.315) + ) + (38): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.323) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.323) + ) + (39): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.332) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.332) + ) + (40): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.340) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.340) + ) + (41): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.349) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.349) + ) + (42): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.357) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.357) + ) + (43): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.366) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.366) + ) + (44): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.374) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.374) + ) + (45): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.383) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.383) + ) + (46): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.391) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.391) + ) + (47): Block( + (norm1): RMSNorm() + (attn): Attention( + (qkv): Linear(in_features=3200, out_features=9600, bias=False) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=3200, out_features=3200, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): RMSNorm() + (k_norm): RMSNorm() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.400) + (norm2): RMSNorm() + (mlp): FusedMLP( + (fc1): Linear(in_features=3200, out_features=12800, bias=True) + (fc2): Linear(in_features=12800, out_features=3200, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.400) + ) + ) + ) + (branch2): InternViT6B( + (patch_embed): PatchEmbed( + (proj): Conv2d(3, 1280, kernel_size=(16, 16), stride=(16, 16)) + (norm): Identity() + ) + (pos_drop): Identity() + (blocks): ModuleList( + (0): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): Identity() + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): Identity() + ) + (1): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.013) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.013) + ) + (2): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.026) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.026) + ) + (3): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.039) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.039) + ) + (4): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.052) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.052) + ) + (5): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.065) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.065) + ) + (6): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.077) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.077) + ) + (7): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.090) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.090) + ) + (8): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.103) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.103) + ) + (9): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.116) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.116) + ) + (10): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.129) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.129) + ) + (11): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.142) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.142) + ) + (12): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.155) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.155) + ) + (13): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.168) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.168) + ) + (14): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.181) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.181) + ) + (15): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.194) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.194) + ) + (16): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.206) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.206) + ) + (17): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.219) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.219) + ) + (18): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.232) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.232) + ) + (19): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.245) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.245) + ) + (20): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.258) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.258) + ) + (21): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.271) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.271) + ) + (22): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.284) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.284) + ) + (23): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.297) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.297) + ) + (24): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.310) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.310) + ) + (25): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.323) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.323) + ) + (26): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.335) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.335) + ) + (27): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.348) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.348) + ) + (28): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.361) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.361) + ) + (29): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.374) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.374) + ) + (30): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.387) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.387) + ) + (31): Block( + (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (attn): Attention( + (qkv): Linear(in_features=1280, out_features=3840, bias=True) + (attn_drop): Dropout(p=0.0, inplace=False) + (proj): Linear(in_features=1280, out_features=1280, bias=True) + (proj_drop): Dropout(p=0.0, inplace=False) + (inner_attn): FlashAttention() + (q_norm): Identity() + (k_norm): Identity() + ) + (ls1): LayerScale() + (drop_path1): DropPath(drop_prob=0.400) + (norm2): LayerNorm((1280,), eps=1e-06, elementwise_affine=True) + (mlp): FusedMLP( + (fc1): Linear(in_features=1280, out_features=5120, bias=True) + (fc2): Linear(in_features=5120, out_features=1280, bias=True) + ) + (ls2): LayerScale() + (drop_path2): DropPath(drop_prob=0.400) + ) + ) + ) + (interactions): Sequential( + (0): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (1): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (2): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (3): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (4): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (5): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (6): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (7): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (8): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (9): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (10): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + (11): TwoBranchInteractionBlock( + (interaction_units_12): BidirectionalInteractionUnit( + (branch2to1_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=3200, out_features=128, bias=True) + (attention_weights): Linear(in_features=3200, out_features=64, bias=True) + (output_proj): Linear(in_features=1600, out_features=3200, bias=True) + (value_proj): Linear(in_features=1280, out_features=1600, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=3200, out_features=800, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(800, 800, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=800) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=800, out_features=3200, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + (branch1to2_injector): Injector( + (query_norm): Identity() + (feat_norm): Identity() + (attn): MSDeformAttn( + (sampling_offsets): Linear(in_features=1280, out_features=128, bias=True) + (attention_weights): Linear(in_features=1280, out_features=64, bias=True) + (output_proj): Linear(in_features=640, out_features=1280, bias=True) + (value_proj): Linear(in_features=3200, out_features=640, bias=True) + ) + (ffn): ConvFFN( + (fc1): Linear(in_features=1280, out_features=320, bias=True) + (dwconv): DWConv( + (dwconv): Conv2d(320, 320, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=320) + ) + (act): GELU(approximate=none) + (fc2): Linear(in_features=320, out_features=1280, bias=True) + (drop): Dropout(p=0.0, inplace=False) + ) + (ffn_norm): Identity() + (drop_path): DropPath(drop_prob=0.400) + ) + ) + ) + ) + (merge_branch1): Sequential( + (0): Conv2d(3200, 3200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(32, 3200, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(3200, 3200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(32, 3200, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + (merge_branch2): Sequential( + (0): Conv2d(1280, 3200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (1): GroupNorm(32, 3200, eps=1e-05, affine=True) + (2): ReLU(inplace=True) + (3): Conv2d(3200, 3200, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (4): GroupNorm(32, 3200, eps=1e-05, affine=True) + (5): ReLU(inplace=True) + ) + (fpn1): Sequential( + (0): ConvTranspose2d(3200, 3200, kernel_size=(2, 2), stride=(2, 2)) + (1): GroupNorm(32, 3200, eps=1e-05, affine=True) + (2): GELU(approximate=none) + (3): ConvTranspose2d(3200, 3200, kernel_size=(2, 2), stride=(2, 2)) + ) + (fpn2): Sequential( + (0): ConvTranspose2d(3200, 3200, kernel_size=(2, 2), stride=(2, 2)) + ) + (fpn3): Sequential( + (0): Identity() + ) + (fpn4): Sequential( + (0): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False) + ) + ) + (decode_head): UPerHead( + input_transform=multiple_select, ignore_index=255, align_corners=False + (loss_decode): CrossEntropyLoss(avg_non_ignore=False) + (conv_seg): Conv2d(1536, 150, kernel_size=(1, 1), stride=(1, 1)) + (dropout): Dropout2d(p=0.1, inplace=False) + (psp_modules): PPM( + (0): Sequential( + (0): AdaptiveAvgPool2d(output_size=1) + (1): ConvModule( + (conv): Conv2d(3200, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + (1): Sequential( + (0): AdaptiveAvgPool2d(output_size=2) + (1): ConvModule( + (conv): Conv2d(3200, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + (2): Sequential( + (0): AdaptiveAvgPool2d(output_size=3) + (1): ConvModule( + (conv): Conv2d(3200, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + (3): Sequential( + (0): AdaptiveAvgPool2d(output_size=6) + (1): ConvModule( + (conv): Conv2d(3200, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + ) + (bottleneck): ConvModule( + (conv): Conv2d(9344, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + (lateral_convs): ModuleList( + (0): ConvModule( + (conv): Conv2d(3200, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU() + ) + (1): ConvModule( + (conv): Conv2d(3200, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU() + ) + (2): ConvModule( + (conv): Conv2d(3200, 1536, kernel_size=(1, 1), stride=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU() + ) + ) + (fpn_convs): ModuleList( + (0): ConvModule( + (conv): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU() + ) + (1): ConvModule( + (conv): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU() + ) + (2): ConvModule( + (conv): Conv2d(1536, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU() + ) + ) + (fpn_bottleneck): ConvModule( + (conv): Conv2d(6144, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} + (auxiliary_head): FCNHead( + input_transform=None, ignore_index=255, align_corners=False + (loss_decode): CrossEntropyLoss(avg_non_ignore=False) + (conv_seg): Conv2d(1536, 150, kernel_size=(1, 1), stride=(1, 1)) + (dropout): Dropout2d(p=0.1, inplace=False) + (convs): Sequential( + (0): ConvModule( + (conv): Conv2d(3200, 1536, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) + (bn): SyncBatchNorm(1536, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) + (activate): ReLU(inplace=True) + ) + ) + (norm): Identity() + ) + init_cfg={'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} +) +2024-06-18 02:26:54,030 - mmseg - INFO - Loaded 20210 images +2024-06-18 02:26:55,664 - mmseg - INFO - {'num_layers': 48, 'layer_decay_rate': 0.95, 'skip_stride': 1.5} +2024-06-18 02:26:55,664 - mmseg - INFO - Build LayerDecayOptimizerConstructor 0.950000 - 50 +2024-06-18 02:26:55,676 - mmseg - INFO - Param groups = { + "layer_49_decay": { + "param_names": [ + "backbone.w1", + "backbone.w2", + "backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.sampling_offsets.weight", + "backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.attention_weights.weight", + "backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.output_proj.weight", + "backbone.interactions.0.interaction_units_12.branch2to1_injector.attn.value_proj.weight", + "backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.fc1.weight", + "backbone.interactions.0.interaction_units_12.branch2to1_injector.ffn.dwconv.dwconv.weight", + 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02:28:10,803 - mmseg - INFO - Hooks will be executed in the following order: +before_run: +(VERY_HIGH ) PolyLrUpdaterHook +(49 ) ToBFloat16Hook +(49 ) ToBFloat16Hook +(NORMAL ) DeepspeedCheckpointHook +(LOW ) DeepspeedDistEvalHook +(VERY_LOW ) TextLoggerHook +(VERY_LOW ) TensorboardLoggerHook + -------------------- +before_train_epoch: +(VERY_HIGH ) PolyLrUpdaterHook +(LOW ) IterTimerHook +(LOW ) DeepspeedDistEvalHook +(VERY_LOW ) TextLoggerHook +(VERY_LOW ) TensorboardLoggerHook + -------------------- +before_train_iter: +(VERY_HIGH ) PolyLrUpdaterHook +(LOW ) IterTimerHook +(LOW ) DeepspeedDistEvalHook + -------------------- +after_train_iter: +(ABOVE_NORMAL) OptimizerHook +(NORMAL ) DeepspeedCheckpointHook +(LOW ) IterTimerHook +(LOW ) DeepspeedDistEvalHook +(VERY_LOW ) TextLoggerHook +(VERY_LOW ) TensorboardLoggerHook + -------------------- +after_train_epoch: +(NORMAL ) DeepspeedCheckpointHook +(LOW ) DeepspeedDistEvalHook +(VERY_LOW ) TextLoggerHook +(VERY_LOW ) TensorboardLoggerHook + -------------------- +before_val_epoch: +(LOW ) IterTimerHook +(VERY_LOW ) TextLoggerHook +(VERY_LOW ) TensorboardLoggerHook + -------------------- +before_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_iter: +(LOW ) IterTimerHook + -------------------- +after_val_epoch: +(VERY_LOW ) TextLoggerHook +(VERY_LOW ) TensorboardLoggerHook + -------------------- +after_run: +(VERY_LOW ) TextLoggerHook +(VERY_LOW ) TensorboardLoggerHook + -------------------- +2024-06-18 02:28:10,803 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters +2024-06-18 02:28:10,829 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/PIIP/mmsegmentation/work_dirs/upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5 by HardDiskBackend. +2024-06-18 02:31:04,070 - mmseg - INFO - Iter [50/80000] lr: 1.306e-06, eta: 2 days, 2:16:21, time: 2.264, data_time: 0.012, memory: 72263, decode.loss_ce: 4.1069, decode.acc_seg: 0.4028, aux.loss_ce: 1.6291, aux.acc_seg: 0.6581, loss: 5.7360 +2024-06-18 02:32:42,929 - mmseg - INFO - Iter [100/80000] lr: 2.637e-06, eta: 1 day, 23:03:43, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 3.9120, decode.acc_seg: 5.6235, aux.loss_ce: 1.5738, aux.acc_seg: 3.2437, loss: 5.4858 +2024-06-18 02:34:21,748 - mmseg - INFO - Iter [150/80000] lr: 3.966e-06, eta: 1 day, 21:58:01, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 3.6058, decode.acc_seg: 23.9975, aux.loss_ce: 1.5282, aux.acc_seg: 14.0186, loss: 5.1340 +2024-06-18 02:36:00,626 - mmseg - INFO - Iter [200/80000] lr: 5.294e-06, eta: 1 day, 21:24:45, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 2.9653, decode.acc_seg: 35.9420, aux.loss_ce: 1.3762, aux.acc_seg: 29.3699, loss: 4.3415 +2024-06-18 02:37:39,542 - mmseg - INFO - Iter [250/80000] lr: 6.619e-06, eta: 1 day, 21:04:20, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 2.3341, decode.acc_seg: 45.2672, aux.loss_ce: 1.0823, aux.acc_seg: 39.1611, loss: 3.4165 +2024-06-18 02:39:18,591 - mmseg - INFO - Iter [300/80000] lr: 7.944e-06, eta: 1 day, 20:50:46, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 1.9594, decode.acc_seg: 53.2923, aux.loss_ce: 0.9035, aux.acc_seg: 47.6422, loss: 2.8629 +2024-06-18 02:40:57,557 - mmseg - INFO - Iter [350/80000] lr: 9.266e-06, eta: 1 day, 20:40:17, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 1.7280, decode.acc_seg: 57.6607, aux.loss_ce: 0.7891, aux.acc_seg: 53.1096, loss: 2.5171 +2024-06-18 02:42:36,529 - mmseg - INFO - Iter [400/80000] lr: 1.059e-05, eta: 1 day, 20:32:02, time: 1.979, data_time: 0.009, memory: 72263, decode.loss_ce: 1.5175, decode.acc_seg: 61.7816, aux.loss_ce: 0.6799, aux.acc_seg: 59.3977, loss: 2.1975 +2024-06-18 02:44:15,589 - mmseg - INFO - Iter [450/80000] lr: 1.191e-05, eta: 1 day, 20:25:30, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 1.3480, decode.acc_seg: 64.2414, aux.loss_ce: 0.6031, aux.acc_seg: 62.4217, loss: 1.9511 +2024-06-18 02:45:54,629 - mmseg - INFO - Iter [500/80000] lr: 1.322e-05, eta: 1 day, 20:19:54, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 1.3377, decode.acc_seg: 64.1809, aux.loss_ce: 0.5920, aux.acc_seg: 62.4357, loss: 1.9298 +2024-06-18 02:47:33,725 - mmseg - INFO - Iter [550/80000] lr: 1.454e-05, eta: 1 day, 20:15:09, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 1.1968, decode.acc_seg: 66.4990, aux.loss_ce: 0.5206, aux.acc_seg: 65.3573, loss: 1.7174 +2024-06-18 02:49:12,746 - mmseg - INFO - Iter [600/80000] lr: 1.585e-05, eta: 1 day, 20:10:45, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 1.1751, decode.acc_seg: 66.7807, aux.loss_ce: 0.5103, aux.acc_seg: 65.8867, loss: 1.6854 +2024-06-18 02:50:51,790 - mmseg - INFO - Iter [650/80000] lr: 1.717e-05, eta: 1 day, 20:06:49, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 1.0572, decode.acc_seg: 69.2241, aux.loss_ce: 0.4524, aux.acc_seg: 68.9092, loss: 1.5095 +2024-06-18 02:52:30,872 - mmseg - INFO - Iter [700/80000] lr: 1.848e-05, eta: 1 day, 20:03:17, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 1.0423, decode.acc_seg: 69.8651, aux.loss_ce: 0.4453, aux.acc_seg: 69.3759, loss: 1.4877 +2024-06-18 02:54:09,925 - mmseg - INFO - Iter [750/80000] lr: 1.979e-05, eta: 1 day, 19:59:58, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 1.0033, decode.acc_seg: 69.2320, aux.loss_ce: 0.4315, aux.acc_seg: 68.9173, loss: 1.4348 +2024-06-18 02:55:49,084 - mmseg - INFO - Iter [800/80000] lr: 2.109e-05, eta: 1 day, 19:57:01, time: 1.983, data_time: 0.009, memory: 72263, decode.loss_ce: 0.9282, decode.acc_seg: 72.0327, aux.loss_ce: 0.3969, aux.acc_seg: 71.4682, loss: 1.3251 +2024-06-18 02:57:28,178 - mmseg - INFO - Iter [850/80000] lr: 2.240e-05, eta: 1 day, 19:54:07, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.8981, decode.acc_seg: 72.1455, aux.loss_ce: 0.3798, aux.acc_seg: 71.6392, loss: 1.2779 +2024-06-18 02:59:07,225 - mmseg - INFO - Iter [900/80000] lr: 2.370e-05, eta: 1 day, 19:51:17, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.8942, decode.acc_seg: 72.8289, aux.loss_ce: 0.3731, aux.acc_seg: 72.6577, loss: 1.2673 +2024-06-18 03:00:46,220 - mmseg - INFO - Iter [950/80000] lr: 2.501e-05, eta: 1 day, 19:48:31, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.8475, decode.acc_seg: 73.4077, aux.loss_ce: 0.3495, aux.acc_seg: 73.5926, loss: 1.1971 +2024-06-18 03:02:25,269 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 03:02:25,269 - mmseg - INFO - Iter [1000/80000] lr: 2.631e-05, eta: 1 day, 19:45:55, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.8305, decode.acc_seg: 73.4162, aux.loss_ce: 0.3404, aux.acc_seg: 73.6835, loss: 1.1709 +2024-06-18 03:05:15,985 - mmseg - INFO - per class results: +2024-06-18 03:05:16,008 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 67.36 | 75.33 | +| building | 76.84 | 92.7 | +| sky | 90.51 | 94.37 | +| floor | 75.26 | 86.36 | +| tree | 70.76 | 86.96 | +| ceiling | 78.01 | 93.96 | +| road | 78.22 | 86.58 | +| bed | 84.52 | 95.46 | +| windowpane | 56.13 | 79.65 | +| grass | 54.42 | 61.21 | +| cabinet | 53.15 | 75.2 | +| sidewalk | 56.88 | 84.26 | +| person | 70.1 | 88.3 | +| earth | 34.07 | 48.88 | +| door | 45.76 | 63.41 | +| table | 50.71 | 62.61 | +| mountain | 60.02 | 78.99 | +| plant | 44.78 | 54.88 | +| curtain | 61.5 | 89.38 | +| chair | 44.83 | 54.09 | +| car | 75.19 | 89.76 | +| water | 42.4 | 57.38 | +| painting | 60.66 | 85.4 | +| sofa | 65.79 | 88.04 | +| shelf | 27.9 | 41.34 | +| house | 31.52 | 45.0 | +| sea | 45.46 | 56.38 | +| mirror | 38.34 | 88.21 | +| rug | 59.63 | 77.06 | +| field | 22.53 | 78.54 | +| armchair | 39.09 | 68.81 | +| seat | 59.94 | 82.91 | +| fence | 36.89 | 57.26 | +| desk | 35.33 | 58.59 | +| rock | 55.12 | 74.8 | +| wardrobe | 45.23 | 82.08 | +| lamp | 47.73 | 64.69 | +| bathtub | 72.85 | 82.71 | +| railing | 25.02 | 32.99 | +| cushion | 47.58 | 67.13 | +| base | 0.34 | 0.34 | +| box | 7.45 | 8.11 | +| column | 5.93 | 5.94 | +| signboard | 15.17 | 17.81 | +| chest of drawers | 35.81 | 68.26 | +| counter | 30.78 | 35.65 | +| sand | 44.38 | 54.72 | +| sink | 52.04 | 75.46 | +| skyscraper | 43.57 | 58.41 | +| fireplace | 57.69 | 93.12 | +| refrigerator | 63.92 | 79.24 | +| grandstand | 15.58 | 16.33 | +| path | 0.07 | 0.07 | +| stairs | 3.62 | 3.71 | +| runway | 62.17 | 89.89 | +| case | 27.27 | 93.4 | +| pool table | 77.07 | 98.33 | +| pillow | 27.25 | 29.32 | +| screen door | 47.11 | 53.18 | +| stairway | 24.91 | 61.22 | +| river | 15.17 | 65.96 | +| bridge | 59.19 | 72.74 | +| bookcase | 27.89 | 70.69 | +| blind | 0.0 | 0.0 | +| coffee table | 46.48 | 77.01 | +| toilet | 76.8 | 92.92 | +| flower | 11.54 | 21.11 | +| book | 17.12 | 18.77 | +| hill | 0.0 | 0.0 | +| bench | 41.58 | 48.06 | +| countertop | 13.29 | 13.5 | +| stove | 60.57 | 82.64 | +| palm | 43.35 | 77.32 | +| kitchen island | 12.18 | 12.91 | +| computer | 54.8 | 81.23 | +| swivel chair | 37.25 | 62.24 | +| boat | 43.53 | 81.11 | +| bar | 46.48 | 85.31 | +| arcade machine | 84.63 | 96.16 | +| hovel | 9.76 | 10.96 | +| bus | 77.58 | 95.74 | +| towel | 9.02 | 9.06 | +| light | 0.0 | 0.0 | +| truck | 36.44 | 45.11 | +| tower | 18.93 | 28.77 | +| chandelier | 52.01 | 66.72 | +| awning | 7.97 | 8.92 | +| streetlight | 0.0 | 0.0 | +| booth | 0.0 | 0.0 | +| television receiver | 51.15 | 61.9 | +| airplane | 40.25 | 43.52 | +| dirt track | 0.0 | 0.0 | +| apparel | 13.95 | 16.18 | +| pole | 0.0 | 0.0 | +| land | 0.0 | 0.0 | +| bannister | 0.0 | 0.0 | +| escalator | 0.0 | 0.0 | +| ottoman | 1.28 | 1.28 | +| bottle | 0.48 | 0.48 | +| buffet | 0.0 | 0.0 | +| poster | 0.0 | 0.0 | +| stage | 0.0 | 0.0 | +| van | 0.16 | 0.16 | +| ship | 0.0 | 0.0 | +| fountain | 0.03 | 0.03 | +| conveyer belt | 70.88 | 76.08 | +| canopy | 8.92 | 9.0 | +| washer | 82.38 | 96.47 | +| plaything | 0.0 | 0.0 | +| swimming pool | 36.95 | 38.81 | +| stool | 0.0 | 0.0 | +| barrel | 3.37 | 60.61 | +| basket | 0.0 | 0.0 | +| waterfall | 50.16 | 73.54 | +| tent | 88.59 | 98.04 | +| bag | 0.0 | 0.0 | +| minibike | 5.43 | 5.47 | +| cradle | 65.02 | 97.65 | +| oven | 0.0 | 0.0 | +| ball | 15.34 | 15.7 | +| food | 1.48 | 1.49 | +| step | 0.0 | 0.0 | +| tank | 0.0 | 0.0 | +| trade name | 0.0 | 0.0 | +| microwave | 61.86 | 63.75 | +| pot | 0.0 | 0.0 | +| animal | 0.0 | 0.0 | +| bicycle | 0.0 | 0.0 | +| lake | 0.0 | 0.0 | +| dishwasher | 2.4 | 2.4 | +| screen | 40.69 | 41.16 | +| blanket | 0.0 | 0.0 | +| sculpture | 0.0 | 0.0 | +| hood | 0.0 | 0.0 | +| sconce | 0.0 | 0.0 | +| vase | 0.0 | 0.0 | +| traffic light | 0.0 | 0.0 | +| tray | 0.0 | 0.0 | +| ashcan | 0.0 | 0.0 | +| fan | 0.0 | 0.0 | +| pier | 0.0 | 0.0 | +| crt screen | 0.0 | 0.0 | +| plate | 0.0 | 0.0 | +| monitor | 0.0 | 0.0 | +| bulletin board | 0.0 | 0.0 | +| shower | 0.0 | 0.0 | +| radiator | 0.0 | 0.0 | +| glass | 0.0 | 0.0 | +| clock | 0.0 | 0.0 | +| flag | 0.0 | 0.0 | ++---------------------+-------+-------+ +2024-06-18 03:05:16,008 - mmseg - INFO - Summary: +2024-06-18 03:05:16,008 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 76.9 | 29.16 | 40.02 | ++------+-------+-------+ +2024-06-18 03:05:16,009 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 03:05:16,009 - mmseg - INFO - Iter(val) [250] aAcc: 0.7690, mIoU: 0.2916, mAcc: 0.4002, IoU.wall: 0.6736, IoU.building: 0.7684, IoU.sky: 0.9051, IoU.floor: 0.7526, IoU.tree: 0.7076, IoU.ceiling: 0.7801, IoU.road: 0.7822, IoU.bed : 0.8452, IoU.windowpane: 0.5613, IoU.grass: 0.5442, IoU.cabinet: 0.5315, IoU.sidewalk: 0.5688, IoU.person: 0.7010, IoU.earth: 0.3407, IoU.door: 0.4576, IoU.table: 0.5071, IoU.mountain: 0.6002, IoU.plant: 0.4478, IoU.curtain: 0.6150, IoU.chair: 0.4483, IoU.car: 0.7519, IoU.water: 0.4240, IoU.painting: 0.6066, IoU.sofa: 0.6579, IoU.shelf: 0.2790, IoU.house: 0.3152, IoU.sea: 0.4546, IoU.mirror: 0.3834, IoU.rug: 0.5963, IoU.field: 0.2253, IoU.armchair: 0.3909, IoU.seat: 0.5994, IoU.fence: 0.3689, IoU.desk: 0.3533, IoU.rock: 0.5512, IoU.wardrobe: 0.4523, IoU.lamp: 0.4773, IoU.bathtub: 0.7285, IoU.railing: 0.2502, IoU.cushion: 0.4758, IoU.base: 0.0034, IoU.box: 0.0745, IoU.column: 0.0593, IoU.signboard: 0.1517, IoU.chest of drawers: 0.3581, IoU.counter: 0.3078, IoU.sand: 0.4438, IoU.sink: 0.5204, IoU.skyscraper: 0.4357, IoU.fireplace: 0.5769, IoU.refrigerator: 0.6392, IoU.grandstand: 0.1558, IoU.path: 0.0007, IoU.stairs: 0.0362, IoU.runway: 0.6217, IoU.case: 0.2727, IoU.pool table: 0.7707, IoU.pillow: 0.2725, IoU.screen door: 0.4711, IoU.stairway: 0.2491, IoU.river: 0.1517, IoU.bridge: 0.5919, IoU.bookcase: 0.2789, IoU.blind: 0.0000, IoU.coffee table: 0.4648, IoU.toilet: 0.7680, IoU.flower: 0.1154, IoU.book: 0.1712, IoU.hill: 0.0000, IoU.bench: 0.4158, IoU.countertop: 0.1329, IoU.stove: 0.6057, IoU.palm: 0.4335, IoU.kitchen island: 0.1218, IoU.computer: 0.5480, IoU.swivel chair: 0.3725, IoU.boat: 0.4353, IoU.bar: 0.4648, IoU.arcade machine: 0.8463, IoU.hovel: 0.0976, IoU.bus: 0.7758, IoU.towel: 0.0902, IoU.light: 0.0000, IoU.truck: 0.3644, IoU.tower: 0.1893, IoU.chandelier: 0.5201, IoU.awning: 0.0797, IoU.streetlight: 0.0000, IoU.booth: 0.0000, IoU.television receiver: 0.5115, IoU.airplane: 0.4025, IoU.dirt track: 0.0000, IoU.apparel: 0.1395, IoU.pole: 0.0000, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.0000, IoU.ottoman: 0.0128, IoU.bottle: 0.0048, IoU.buffet: 0.0000, IoU.poster: 0.0000, IoU.stage: 0.0000, IoU.van: 0.0016, IoU.ship: 0.0000, IoU.fountain: 0.0003, IoU.conveyer belt: 0.7088, IoU.canopy: 0.0892, IoU.washer: 0.8238, IoU.plaything: 0.0000, IoU.swimming pool: 0.3695, IoU.stool: 0.0000, IoU.barrel: 0.0337, IoU.basket: 0.0000, IoU.waterfall: 0.5016, IoU.tent: 0.8859, IoU.bag: 0.0000, IoU.minibike: 0.0543, IoU.cradle: 0.6502, IoU.oven: 0.0000, IoU.ball: 0.1534, IoU.food: 0.0148, IoU.step: 0.0000, IoU.tank: 0.0000, IoU.trade name: 0.0000, IoU.microwave: 0.6186, IoU.pot: 0.0000, IoU.animal: 0.0000, IoU.bicycle: 0.0000, IoU.lake: 0.0000, IoU.dishwasher: 0.0240, IoU.screen: 0.4069, IoU.blanket: 0.0000, IoU.sculpture: 0.0000, IoU.hood: 0.0000, IoU.sconce: 0.0000, IoU.vase: 0.0000, IoU.traffic light: 0.0000, IoU.tray: 0.0000, IoU.ashcan: 0.0000, IoU.fan: 0.0000, IoU.pier: 0.0000, IoU.crt screen: 0.0000, IoU.plate: 0.0000, IoU.monitor: 0.0000, IoU.bulletin board: 0.0000, IoU.shower: 0.0000, IoU.radiator: 0.0000, IoU.glass: 0.0000, IoU.clock: 0.0000, IoU.flag: 0.0000, Acc.wall: 0.7533, Acc.building: 0.9270, Acc.sky: 0.9437, Acc.floor: 0.8636, Acc.tree: 0.8696, Acc.ceiling: 0.9396, Acc.road: 0.8658, Acc.bed : 0.9546, Acc.windowpane: 0.7965, Acc.grass: 0.6121, Acc.cabinet: 0.7520, Acc.sidewalk: 0.8426, Acc.person: 0.8830, Acc.earth: 0.4888, Acc.door: 0.6341, Acc.table: 0.6261, Acc.mountain: 0.7899, Acc.plant: 0.5488, Acc.curtain: 0.8938, Acc.chair: 0.5409, Acc.car: 0.8976, Acc.water: 0.5738, Acc.painting: 0.8540, Acc.sofa: 0.8804, Acc.shelf: 0.4134, Acc.house: 0.4500, Acc.sea: 0.5638, Acc.mirror: 0.8821, Acc.rug: 0.7706, Acc.field: 0.7854, Acc.armchair: 0.6881, Acc.seat: 0.8291, Acc.fence: 0.5726, Acc.desk: 0.5859, Acc.rock: 0.7480, Acc.wardrobe: 0.8208, Acc.lamp: 0.6469, Acc.bathtub: 0.8271, Acc.railing: 0.3299, Acc.cushion: 0.6713, Acc.base: 0.0034, Acc.box: 0.0811, Acc.column: 0.0594, Acc.signboard: 0.1781, Acc.chest of drawers: 0.6826, Acc.counter: 0.3565, Acc.sand: 0.5472, Acc.sink: 0.7546, Acc.skyscraper: 0.5841, Acc.fireplace: 0.9312, Acc.refrigerator: 0.7924, Acc.grandstand: 0.1633, Acc.path: 0.0007, Acc.stairs: 0.0371, Acc.runway: 0.8989, Acc.case: 0.9340, Acc.pool table: 0.9833, Acc.pillow: 0.2932, Acc.screen door: 0.5318, Acc.stairway: 0.6122, Acc.river: 0.6596, Acc.bridge: 0.7274, Acc.bookcase: 0.7069, Acc.blind: 0.0000, Acc.coffee table: 0.7701, Acc.toilet: 0.9292, Acc.flower: 0.2111, Acc.book: 0.1877, Acc.hill: 0.0000, Acc.bench: 0.4806, Acc.countertop: 0.1350, Acc.stove: 0.8264, Acc.palm: 0.7732, Acc.kitchen island: 0.1291, Acc.computer: 0.8123, Acc.swivel chair: 0.6224, Acc.boat: 0.8111, Acc.bar: 0.8531, Acc.arcade machine: 0.9616, Acc.hovel: 0.1096, Acc.bus: 0.9574, Acc.towel: 0.0906, Acc.light: 0.0000, Acc.truck: 0.4511, Acc.tower: 0.2877, Acc.chandelier: 0.6672, Acc.awning: 0.0892, Acc.streetlight: 0.0000, Acc.booth: 0.0000, Acc.television receiver: 0.6190, Acc.airplane: 0.4352, Acc.dirt track: 0.0000, Acc.apparel: 0.1618, Acc.pole: 0.0000, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.0000, Acc.ottoman: 0.0128, Acc.bottle: 0.0048, Acc.buffet: 0.0000, Acc.poster: 0.0000, Acc.stage: 0.0000, Acc.van: 0.0016, Acc.ship: 0.0000, Acc.fountain: 0.0003, Acc.conveyer belt: 0.7608, Acc.canopy: 0.0900, Acc.washer: 0.9647, Acc.plaything: 0.0000, Acc.swimming pool: 0.3881, Acc.stool: 0.0000, Acc.barrel: 0.6061, Acc.basket: 0.0000, Acc.waterfall: 0.7354, Acc.tent: 0.9804, Acc.bag: 0.0000, Acc.minibike: 0.0547, Acc.cradle: 0.9765, Acc.oven: 0.0000, Acc.ball: 0.1570, Acc.food: 0.0149, Acc.step: 0.0000, Acc.tank: 0.0000, Acc.trade name: 0.0000, Acc.microwave: 0.6375, Acc.pot: 0.0000, Acc.animal: 0.0000, Acc.bicycle: 0.0000, Acc.lake: 0.0000, Acc.dishwasher: 0.0240, Acc.screen: 0.4116, Acc.blanket: 0.0000, Acc.sculpture: 0.0000, Acc.hood: 0.0000, Acc.sconce: 0.0000, Acc.vase: 0.0000, Acc.traffic light: 0.0000, Acc.tray: 0.0000, Acc.ashcan: 0.0000, Acc.fan: 0.0000, Acc.pier: 0.0000, Acc.crt screen: 0.0000, Acc.plate: 0.0000, Acc.monitor: 0.0000, Acc.bulletin board: 0.0000, Acc.shower: 0.0000, Acc.radiator: 0.0000, Acc.glass: 0.0000, Acc.clock: 0.0000, Acc.flag: 0.0000 +2024-06-18 03:06:55,388 - mmseg - INFO - Iter [1050/80000] lr: 2.761e-05, eta: 1 day, 23:17:48, time: 5.402, data_time: 3.431, memory: 72263, decode.loss_ce: 0.7813, decode.acc_seg: 73.9835, aux.loss_ce: 0.3242, aux.acc_seg: 73.9713, loss: 1.1055 +2024-06-18 03:08:34,714 - mmseg - INFO - Iter [1100/80000] lr: 2.890e-05, eta: 1 day, 23:05:50, time: 1.987, data_time: 0.010, memory: 72263, decode.loss_ce: 0.8446, decode.acc_seg: 72.4126, aux.loss_ce: 0.3444, aux.acc_seg: 72.8523, loss: 1.1891 +2024-06-18 03:10:13,863 - mmseg - INFO - Iter [1150/80000] lr: 3.020e-05, eta: 1 day, 22:54:34, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.8325, decode.acc_seg: 73.0889, aux.loss_ce: 0.3383, aux.acc_seg: 73.5718, loss: 1.1708 +2024-06-18 03:11:53,010 - mmseg - INFO - Iter [1200/80000] lr: 3.149e-05, eta: 1 day, 22:44:05, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.7454, decode.acc_seg: 74.6712, aux.loss_ce: 0.3020, aux.acc_seg: 75.2617, loss: 1.0474 +2024-06-18 03:13:32,074 - mmseg - INFO - Iter [1250/80000] lr: 3.279e-05, eta: 1 day, 22:34:14, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.7454, decode.acc_seg: 74.8379, aux.loss_ce: 0.3005, aux.acc_seg: 75.2014, loss: 1.0459 +2024-06-18 03:15:13,928 - mmseg - INFO - Iter [1300/80000] lr: 3.408e-05, eta: 1 day, 22:27:49, time: 2.037, data_time: 0.061, memory: 72263, decode.loss_ce: 0.7415, decode.acc_seg: 75.4053, aux.loss_ce: 0.3005, aux.acc_seg: 75.7516, loss: 1.0420 +2024-06-18 03:16:53,051 - mmseg - INFO - Iter [1350/80000] lr: 3.537e-05, eta: 1 day, 22:19:07, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.7412, decode.acc_seg: 75.2192, aux.loss_ce: 0.2998, aux.acc_seg: 75.9446, loss: 1.0411 +2024-06-18 03:18:32,148 - mmseg - INFO - Iter [1400/80000] lr: 3.665e-05, eta: 1 day, 22:10:53, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.7325, decode.acc_seg: 75.0845, aux.loss_ce: 0.2925, aux.acc_seg: 75.6497, loss: 1.0250 +2024-06-18 03:20:11,283 - mmseg - INFO - Iter [1450/80000] lr: 3.794e-05, eta: 1 day, 22:03:08, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.7114, decode.acc_seg: 74.9494, aux.loss_ce: 0.2839, aux.acc_seg: 75.6260, loss: 0.9953 +2024-06-18 03:21:50,291 - mmseg - INFO - Iter [1500/80000] lr: 3.922e-05, eta: 1 day, 21:55:41, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.7185, decode.acc_seg: 75.1041, aux.loss_ce: 0.2865, aux.acc_seg: 75.6189, loss: 1.0050 +2024-06-18 03:23:29,412 - mmseg - INFO - Iter [1550/80000] lr: 3.923e-05, eta: 1 day, 21:48:42, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6565, decode.acc_seg: 77.1489, aux.loss_ce: 0.2617, aux.acc_seg: 77.5598, loss: 0.9182 +2024-06-18 03:25:08,405 - mmseg - INFO - Iter [1600/80000] lr: 3.920e-05, eta: 1 day, 21:41:57, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6856, decode.acc_seg: 76.4560, aux.loss_ce: 0.2719, aux.acc_seg: 77.5095, loss: 0.9575 +2024-06-18 03:26:47,530 - mmseg - INFO - Iter [1650/80000] lr: 3.918e-05, eta: 1 day, 21:35:37, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6703, decode.acc_seg: 75.8606, aux.loss_ce: 0.2666, aux.acc_seg: 76.5971, loss: 0.9369 +2024-06-18 03:28:26,533 - mmseg - INFO - Iter [1700/80000] lr: 3.915e-05, eta: 1 day, 21:29:28, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.7354, decode.acc_seg: 74.7617, aux.loss_ce: 0.2924, aux.acc_seg: 75.2811, loss: 1.0278 +2024-06-18 03:30:05,617 - mmseg - INFO - Iter [1750/80000] lr: 3.913e-05, eta: 1 day, 21:23:38, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6584, decode.acc_seg: 76.3245, aux.loss_ce: 0.2622, aux.acc_seg: 76.8736, loss: 0.9206 +2024-06-18 03:31:44,690 - mmseg - INFO - Iter [1800/80000] lr: 3.910e-05, eta: 1 day, 21:18:01, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6634, decode.acc_seg: 77.1277, aux.loss_ce: 0.2625, aux.acc_seg: 77.8748, loss: 0.9259 +2024-06-18 03:33:23,651 - mmseg - INFO - Iter [1850/80000] lr: 3.908e-05, eta: 1 day, 21:12:32, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6414, decode.acc_seg: 76.7382, aux.loss_ce: 0.2530, aux.acc_seg: 77.3920, loss: 0.8944 +2024-06-18 03:35:02,667 - mmseg - INFO - Iter [1900/80000] lr: 3.905e-05, eta: 1 day, 21:07:18, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6935, decode.acc_seg: 75.1789, aux.loss_ce: 0.2726, aux.acc_seg: 75.7413, loss: 0.9662 +2024-06-18 03:36:41,790 - mmseg - INFO - Iter [1950/80000] lr: 3.903e-05, eta: 1 day, 21:02:19, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6901, decode.acc_seg: 75.6762, aux.loss_ce: 0.2729, aux.acc_seg: 76.3002, loss: 0.9630 +2024-06-18 03:38:20,785 - mmseg - INFO - Saving checkpoint at 2000 iterations +2024-06-18 03:39:36,585 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 03:39:36,586 - mmseg - INFO - Iter [2000/80000] lr: 3.900e-05, eta: 1 day, 21:46:41, time: 3.496, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6758, decode.acc_seg: 76.0678, aux.loss_ce: 0.2665, aux.acc_seg: 76.6675, loss: 0.9423 +2024-06-18 03:41:27,021 - mmseg - INFO - per class results: +2024-06-18 03:41:27,027 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 74.59 | 84.56 | +| building | 79.93 | 94.8 | +| sky | 92.24 | 95.97 | +| floor | 79.04 | 90.59 | +| tree | 65.18 | 84.79 | +| ceiling | 82.55 | 92.03 | +| road | 80.23 | 85.87 | +| bed | 86.66 | 93.07 | +| windowpane | 56.84 | 85.92 | +| grass | 59.35 | 92.96 | +| cabinet | 57.55 | 66.15 | +| sidewalk | 59.62 | 83.19 | +| person | 74.74 | 89.09 | +| earth | 20.63 | 23.97 | +| door | 45.52 | 51.48 | +| table | 53.78 | 62.77 | +| mountain | 58.33 | 65.8 | +| plant | 29.47 | 32.3 | +| curtain | 71.32 | 79.05 | +| chair | 49.06 | 59.0 | +| car | 58.22 | 59.96 | +| water | 54.43 | 77.13 | +| painting | 71.06 | 85.07 | +| sofa | 70.99 | 91.3 | +| shelf | 38.5 | 75.07 | +| house | 27.35 | 31.04 | +| sea | 52.51 | 81.91 | +| mirror | 70.68 | 84.69 | +| rug | 65.09 | 73.15 | +| field | 7.61 | 10.61 | +| armchair | 41.65 | 70.7 | +| seat | 63.02 | 86.23 | +| fence | 44.46 | 53.27 | +| desk | 40.12 | 73.13 | +| rock | 53.54 | 58.28 | +| wardrobe | 46.65 | 60.8 | +| lamp | 53.77 | 70.24 | +| bathtub | 72.91 | 83.85 | +| railing | 37.07 | 47.85 | +| cushion | 56.52 | 72.8 | +| base | 30.39 | 44.44 | +| box | 16.62 | 18.64 | +| column | 45.13 | 73.78 | +| signboard | 27.19 | 32.8 | +| chest of drawers | 26.34 | 75.76 | +| counter | 30.83 | 36.42 | +| sand | 41.87 | 45.92 | +| sink | 66.31 | 79.02 | +| skyscraper | 56.48 | 83.69 | +| fireplace | 62.46 | 93.87 | +| refrigerator | 72.27 | 86.56 | +| grandstand | 41.96 | 82.39 | +| path | 15.64 | 21.91 | +| stairs | 36.59 | 60.25 | +| runway | 66.15 | 90.09 | +| case | 51.39 | 63.07 | +| pool table | 84.73 | 95.7 | +| pillow | 51.26 | 55.66 | +| screen door | 57.88 | 97.15 | +| stairway | 30.33 | 34.4 | +| river | 22.74 | 32.8 | +| bridge | 57.77 | 89.98 | +| bookcase | 17.64 | 20.82 | +| blind | 4.12 | 4.12 | +| coffee table | 45.27 | 84.84 | +| toilet | 80.93 | 96.36 | +| flower | 28.85 | 45.54 | +| book | 41.16 | 56.74 | +| hill | 12.15 | 17.13 | +| bench | 44.58 | 50.74 | +| countertop | 48.66 | 67.43 | +| stove | 67.96 | 92.17 | +| palm | 45.11 | 52.28 | +| kitchen island | 37.06 | 57.69 | +| computer | 72.14 | 84.62 | +| swivel chair | 41.93 | 75.02 | +| boat | 37.22 | 88.97 | +| bar | 52.43 | 80.51 | +| arcade machine | 84.08 | 94.06 | +| hovel | 6.65 | 6.67 | +| bus | 87.22 | 94.57 | +| towel | 63.62 | 79.96 | +| light | 34.24 | 42.89 | +| truck | 8.78 | 83.46 | +| tower | 24.76 | 35.44 | +| chandelier | 54.96 | 86.44 | +| awning | 27.34 | 65.44 | +| streetlight | 11.6 | 16.35 | +| booth | 33.43 | 64.57 | +| television receiver | 71.63 | 79.67 | +| airplane | 51.09 | 57.57 | +| dirt track | 0.0 | 0.0 | +| apparel | 40.87 | 72.05 | +| pole | 1.47 | 1.5 | +| land | 0.0 | 0.0 | +| bannister | 0.0 | 0.0 | +| escalator | 44.74 | 54.54 | +| ottoman | 42.26 | 67.56 | +| bottle | 10.74 | 11.42 | +| buffet | 0.0 | 0.0 | +| poster | 11.91 | 12.47 | +| stage | 18.69 | 31.23 | +| van | 1.23 | 1.32 | +| ship | 0.0 | 0.0 | +| fountain | 58.26 | 65.45 | +| conveyer belt | 62.5 | 97.84 | +| canopy | 21.3 | 22.91 | +| washer | 85.05 | 96.12 | +| plaything | 30.91 | 38.53 | +| swimming pool | 57.51 | 80.73 | +| stool | 22.21 | 28.96 | +| barrel | 3.87 | 64.82 | +| basket | 31.28 | 45.61 | +| waterfall | 49.25 | 75.18 | +| tent | 85.43 | 96.43 | +| bag | 0.0 | 0.0 | +| minibike | 66.31 | 78.94 | +| cradle | 74.27 | 96.68 | +| oven | 7.22 | 7.32 | +| ball | 33.98 | 64.67 | +| food | 50.61 | 58.11 | +| step | 0.0 | 0.0 | +| tank | 30.71 | 31.94 | +| trade name | 0.02 | 0.02 | +| microwave | 78.3 | 95.03 | +| pot | 22.46 | 23.93 | +| animal | 45.29 | 47.57 | +| bicycle | 49.85 | 67.95 | +| lake | 0.0 | 0.0 | +| dishwasher | 54.2 | 66.02 | +| screen | 60.75 | 83.09 | +| blanket | 0.0 | 0.0 | +| sculpture | 23.3 | 23.62 | +| hood | 64.41 | 88.45 | +| sconce | 0.01 | 0.01 | +| vase | 15.65 | 17.32 | +| traffic light | 0.34 | 0.34 | +| tray | 0.32 | 0.34 | +| ashcan | 25.75 | 26.38 | +| fan | 45.42 | 58.87 | +| pier | 52.9 | 70.32 | +| crt screen | 0.0 | 0.0 | +| plate | 37.97 | 66.14 | +| monitor | 57.55 | 68.89 | +| bulletin board | 19.69 | 25.25 | +| shower | 0.0 | 0.0 | +| radiator | 25.6 | 25.63 | +| glass | 0.0 | 0.0 | +| clock | 1.19 | 1.19 | +| flag | 42.35 | 42.66 | ++---------------------+-------+-------+ +2024-06-18 03:41:27,027 - mmseg - INFO - Summary: +2024-06-18 03:41:27,028 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 80.01 | 41.32 | 54.92 | ++-------+-------+-------+ +2024-06-18 03:41:27,028 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 03:41:27,029 - mmseg - INFO - Iter(val) [250] aAcc: 0.8001, mIoU: 0.4132, mAcc: 0.5492, IoU.wall: 0.7459, IoU.building: 0.7993, IoU.sky: 0.9224, IoU.floor: 0.7904, IoU.tree: 0.6518, IoU.ceiling: 0.8255, IoU.road: 0.8023, IoU.bed : 0.8666, IoU.windowpane: 0.5684, IoU.grass: 0.5935, IoU.cabinet: 0.5755, IoU.sidewalk: 0.5962, IoU.person: 0.7474, IoU.earth: 0.2063, IoU.door: 0.4552, IoU.table: 0.5378, IoU.mountain: 0.5833, IoU.plant: 0.2947, IoU.curtain: 0.7132, IoU.chair: 0.4906, IoU.car: 0.5822, IoU.water: 0.5443, IoU.painting: 0.7106, IoU.sofa: 0.7099, IoU.shelf: 0.3850, IoU.house: 0.2735, IoU.sea: 0.5251, IoU.mirror: 0.7068, IoU.rug: 0.6509, IoU.field: 0.0761, IoU.armchair: 0.4165, IoU.seat: 0.6302, IoU.fence: 0.4446, IoU.desk: 0.4012, IoU.rock: 0.5354, IoU.wardrobe: 0.4665, IoU.lamp: 0.5377, IoU.bathtub: 0.7291, IoU.railing: 0.3707, IoU.cushion: 0.5652, IoU.base: 0.3039, IoU.box: 0.1662, IoU.column: 0.4513, IoU.signboard: 0.2719, IoU.chest of drawers: 0.2634, IoU.counter: 0.3083, IoU.sand: 0.4187, IoU.sink: 0.6631, IoU.skyscraper: 0.5648, IoU.fireplace: 0.6246, IoU.refrigerator: 0.7227, IoU.grandstand: 0.4196, IoU.path: 0.1564, IoU.stairs: 0.3659, IoU.runway: 0.6615, IoU.case: 0.5139, IoU.pool table: 0.8473, IoU.pillow: 0.5126, IoU.screen door: 0.5788, IoU.stairway: 0.3033, IoU.river: 0.2274, IoU.bridge: 0.5777, IoU.bookcase: 0.1764, IoU.blind: 0.0412, IoU.coffee table: 0.4527, IoU.toilet: 0.8093, IoU.flower: 0.2885, IoU.book: 0.4116, IoU.hill: 0.1215, IoU.bench: 0.4458, IoU.countertop: 0.4866, IoU.stove: 0.6796, IoU.palm: 0.4511, IoU.kitchen island: 0.3706, IoU.computer: 0.7214, IoU.swivel chair: 0.4193, IoU.boat: 0.3722, IoU.bar: 0.5243, IoU.arcade machine: 0.8408, IoU.hovel: 0.0665, IoU.bus: 0.8722, IoU.towel: 0.6362, IoU.light: 0.3424, IoU.truck: 0.0878, IoU.tower: 0.2476, IoU.chandelier: 0.5496, IoU.awning: 0.2734, IoU.streetlight: 0.1160, IoU.booth: 0.3343, IoU.television receiver: 0.7163, IoU.airplane: 0.5109, IoU.dirt track: 0.0000, IoU.apparel: 0.4087, IoU.pole: 0.0147, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.4474, IoU.ottoman: 0.4226, IoU.bottle: 0.1074, IoU.buffet: 0.0000, IoU.poster: 0.1191, IoU.stage: 0.1869, IoU.van: 0.0123, IoU.ship: 0.0000, IoU.fountain: 0.5826, IoU.conveyer belt: 0.6250, IoU.canopy: 0.2130, IoU.washer: 0.8505, IoU.plaything: 0.3091, IoU.swimming pool: 0.5751, IoU.stool: 0.2221, IoU.barrel: 0.0387, IoU.basket: 0.3128, IoU.waterfall: 0.4925, IoU.tent: 0.8543, IoU.bag: 0.0000, IoU.minibike: 0.6631, IoU.cradle: 0.7427, IoU.oven: 0.0722, IoU.ball: 0.3398, IoU.food: 0.5061, IoU.step: 0.0000, IoU.tank: 0.3071, IoU.trade name: 0.0002, IoU.microwave: 0.7830, IoU.pot: 0.2246, IoU.animal: 0.4529, IoU.bicycle: 0.4985, IoU.lake: 0.0000, IoU.dishwasher: 0.5420, IoU.screen: 0.6075, IoU.blanket: 0.0000, IoU.sculpture: 0.2330, IoU.hood: 0.6441, IoU.sconce: 0.0001, IoU.vase: 0.1565, IoU.traffic light: 0.0034, IoU.tray: 0.0032, IoU.ashcan: 0.2575, IoU.fan: 0.4542, IoU.pier: 0.5290, IoU.crt screen: 0.0000, IoU.plate: 0.3797, IoU.monitor: 0.5755, IoU.bulletin board: 0.1969, IoU.shower: 0.0000, IoU.radiator: 0.2560, IoU.glass: 0.0000, IoU.clock: 0.0119, IoU.flag: 0.4235, Acc.wall: 0.8456, Acc.building: 0.9480, Acc.sky: 0.9597, Acc.floor: 0.9059, Acc.tree: 0.8479, Acc.ceiling: 0.9203, Acc.road: 0.8587, Acc.bed : 0.9307, Acc.windowpane: 0.8592, Acc.grass: 0.9296, Acc.cabinet: 0.6615, Acc.sidewalk: 0.8319, Acc.person: 0.8909, Acc.earth: 0.2397, Acc.door: 0.5148, Acc.table: 0.6277, Acc.mountain: 0.6580, Acc.plant: 0.3230, Acc.curtain: 0.7905, Acc.chair: 0.5900, Acc.car: 0.5996, Acc.water: 0.7713, Acc.painting: 0.8507, Acc.sofa: 0.9130, Acc.shelf: 0.7507, Acc.house: 0.3104, Acc.sea: 0.8191, Acc.mirror: 0.8469, Acc.rug: 0.7315, Acc.field: 0.1061, Acc.armchair: 0.7070, Acc.seat: 0.8623, Acc.fence: 0.5327, Acc.desk: 0.7313, Acc.rock: 0.5828, Acc.wardrobe: 0.6080, Acc.lamp: 0.7024, Acc.bathtub: 0.8385, Acc.railing: 0.4785, Acc.cushion: 0.7280, Acc.base: 0.4444, Acc.box: 0.1864, Acc.column: 0.7378, Acc.signboard: 0.3280, Acc.chest of drawers: 0.7576, Acc.counter: 0.3642, Acc.sand: 0.4592, Acc.sink: 0.7902, Acc.skyscraper: 0.8369, Acc.fireplace: 0.9387, Acc.refrigerator: 0.8656, Acc.grandstand: 0.8239, Acc.path: 0.2191, Acc.stairs: 0.6025, Acc.runway: 0.9009, Acc.case: 0.6307, Acc.pool table: 0.9570, Acc.pillow: 0.5566, Acc.screen door: 0.9715, Acc.stairway: 0.3440, Acc.river: 0.3280, Acc.bridge: 0.8998, Acc.bookcase: 0.2082, Acc.blind: 0.0412, Acc.coffee table: 0.8484, Acc.toilet: 0.9636, Acc.flower: 0.4554, Acc.book: 0.5674, Acc.hill: 0.1713, Acc.bench: 0.5074, Acc.countertop: 0.6743, Acc.stove: 0.9217, Acc.palm: 0.5228, Acc.kitchen island: 0.5769, Acc.computer: 0.8462, Acc.swivel chair: 0.7502, Acc.boat: 0.8897, Acc.bar: 0.8051, Acc.arcade machine: 0.9406, Acc.hovel: 0.0667, Acc.bus: 0.9457, Acc.towel: 0.7996, Acc.light: 0.4289, Acc.truck: 0.8346, Acc.tower: 0.3544, Acc.chandelier: 0.8644, Acc.awning: 0.6544, Acc.streetlight: 0.1635, Acc.booth: 0.6457, Acc.television receiver: 0.7967, Acc.airplane: 0.5757, Acc.dirt track: 0.0000, Acc.apparel: 0.7205, Acc.pole: 0.0150, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.5454, Acc.ottoman: 0.6756, Acc.bottle: 0.1142, Acc.buffet: 0.0000, Acc.poster: 0.1247, Acc.stage: 0.3123, Acc.van: 0.0132, Acc.ship: 0.0000, Acc.fountain: 0.6545, Acc.conveyer belt: 0.9784, Acc.canopy: 0.2291, Acc.washer: 0.9612, Acc.plaything: 0.3853, Acc.swimming pool: 0.8073, Acc.stool: 0.2896, Acc.barrel: 0.6482, Acc.basket: 0.4561, Acc.waterfall: 0.7518, Acc.tent: 0.9643, Acc.bag: 0.0000, Acc.minibike: 0.7894, Acc.cradle: 0.9668, Acc.oven: 0.0732, Acc.ball: 0.6467, Acc.food: 0.5811, Acc.step: 0.0000, Acc.tank: 0.3194, Acc.trade name: 0.0002, Acc.microwave: 0.9503, Acc.pot: 0.2393, Acc.animal: 0.4757, Acc.bicycle: 0.6795, Acc.lake: 0.0000, Acc.dishwasher: 0.6602, Acc.screen: 0.8309, Acc.blanket: 0.0000, Acc.sculpture: 0.2362, Acc.hood: 0.8845, Acc.sconce: 0.0001, Acc.vase: 0.1732, Acc.traffic light: 0.0034, Acc.tray: 0.0034, Acc.ashcan: 0.2638, Acc.fan: 0.5887, Acc.pier: 0.7032, Acc.crt screen: 0.0000, Acc.plate: 0.6614, Acc.monitor: 0.6889, Acc.bulletin board: 0.2525, Acc.shower: 0.0000, Acc.radiator: 0.2563, Acc.glass: 0.0000, Acc.clock: 0.0119, Acc.flag: 0.4266 +2024-06-18 03:43:06,519 - mmseg - INFO - Iter [2050/80000] lr: 3.898e-05, eta: 1 day, 22:51:01, time: 4.199, data_time: 2.227, memory: 72263, decode.loss_ce: 0.6941, decode.acc_seg: 75.5417, aux.loss_ce: 0.2746, aux.acc_seg: 76.2000, loss: 0.9687 +2024-06-18 03:44:45,681 - mmseg - INFO - Iter [2100/80000] lr: 3.895e-05, eta: 1 day, 22:43:38, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6342, decode.acc_seg: 76.4413, aux.loss_ce: 0.2527, aux.acc_seg: 76.6261, loss: 0.8869 +2024-06-18 03:46:24,751 - mmseg - INFO - Iter [2150/80000] lr: 3.893e-05, eta: 1 day, 22:36:28, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6515, decode.acc_seg: 76.6006, aux.loss_ce: 0.2600, aux.acc_seg: 76.8933, loss: 0.9116 +2024-06-18 03:48:03,898 - mmseg - INFO - Iter [2200/80000] lr: 3.890e-05, eta: 1 day, 22:29:36, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6327, decode.acc_seg: 76.6461, aux.loss_ce: 0.2494, aux.acc_seg: 77.2419, loss: 0.8822 +2024-06-18 03:49:42,929 - mmseg - INFO - Iter [2250/80000] lr: 3.888e-05, eta: 1 day, 22:22:53, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6430, decode.acc_seg: 76.8676, aux.loss_ce: 0.2543, aux.acc_seg: 77.3447, loss: 0.8973 +2024-06-18 03:51:22,081 - mmseg - INFO - Iter [2300/80000] lr: 3.885e-05, eta: 1 day, 22:16:28, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6959, decode.acc_seg: 75.2687, aux.loss_ce: 0.2758, aux.acc_seg: 75.8851, loss: 0.9718 +2024-06-18 03:53:01,007 - mmseg - INFO - Iter [2350/80000] lr: 3.883e-05, eta: 1 day, 22:10:07, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6686, decode.acc_seg: 75.8893, aux.loss_ce: 0.2649, aux.acc_seg: 76.3267, loss: 0.9335 +2024-06-18 03:54:40,265 - mmseg - INFO - Iter [2400/80000] lr: 3.880e-05, eta: 1 day, 22:04:09, time: 1.985, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5913, decode.acc_seg: 77.9903, aux.loss_ce: 0.2346, aux.acc_seg: 78.4318, loss: 0.8259 +2024-06-18 03:56:19,320 - mmseg - INFO - Iter [2450/80000] lr: 3.878e-05, eta: 1 day, 21:58:15, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6068, decode.acc_seg: 78.0585, aux.loss_ce: 0.2385, aux.acc_seg: 78.5296, loss: 0.8453 +2024-06-18 03:57:58,371 - mmseg - INFO - Iter [2500/80000] lr: 3.875e-05, eta: 1 day, 21:52:31, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5840, decode.acc_seg: 78.3646, aux.loss_ce: 0.2313, aux.acc_seg: 78.6720, loss: 0.8153 +2024-06-18 03:59:39,869 - mmseg - INFO - Iter [2550/80000] lr: 3.873e-05, eta: 1 day, 21:48:11, time: 2.030, data_time: 0.058, memory: 72263, decode.loss_ce: 0.6302, decode.acc_seg: 77.5726, aux.loss_ce: 0.2508, aux.acc_seg: 77.8573, loss: 0.8810 +2024-06-18 04:01:18,877 - mmseg - INFO - Iter [2600/80000] lr: 3.870e-05, eta: 1 day, 21:42:43, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5860, decode.acc_seg: 78.7726, aux.loss_ce: 0.2303, aux.acc_seg: 79.4414, loss: 0.8162 +2024-06-18 04:02:57,993 - mmseg - INFO - Iter [2650/80000] lr: 3.868e-05, eta: 1 day, 21:37:27, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5662, decode.acc_seg: 79.3883, aux.loss_ce: 0.2239, aux.acc_seg: 79.9313, loss: 0.7901 +2024-06-18 04:04:37,018 - mmseg - INFO - Iter [2700/80000] lr: 3.865e-05, eta: 1 day, 21:32:16, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5737, decode.acc_seg: 78.4642, aux.loss_ce: 0.2269, aux.acc_seg: 79.1480, loss: 0.8006 +2024-06-18 04:06:16,028 - mmseg - INFO - Iter [2750/80000] lr: 3.863e-05, eta: 1 day, 21:27:13, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5987, decode.acc_seg: 77.3420, aux.loss_ce: 0.2361, aux.acc_seg: 77.8094, loss: 0.8348 +2024-06-18 04:07:55,205 - mmseg - INFO - Iter [2800/80000] lr: 3.860e-05, eta: 1 day, 21:22:21, time: 1.984, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5464, decode.acc_seg: 79.2840, aux.loss_ce: 0.2142, aux.acc_seg: 79.8458, loss: 0.7606 +2024-06-18 04:09:34,256 - mmseg - INFO - Iter [2850/80000] lr: 3.858e-05, eta: 1 day, 21:17:33, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5676, decode.acc_seg: 79.1495, aux.loss_ce: 0.2255, aux.acc_seg: 79.3757, loss: 0.7930 +2024-06-18 04:11:13,353 - mmseg - INFO - Iter [2900/80000] lr: 3.855e-05, eta: 1 day, 21:12:52, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.5823, decode.acc_seg: 78.1054, aux.loss_ce: 0.2296, aux.acc_seg: 78.8118, loss: 0.8119 +2024-06-18 04:12:52,377 - mmseg - INFO - Iter [2950/80000] lr: 3.853e-05, eta: 1 day, 21:08:16, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5787, decode.acc_seg: 78.3711, aux.loss_ce: 0.2290, aux.acc_seg: 78.7600, loss: 0.8077 +2024-06-18 04:14:31,472 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 04:14:31,472 - mmseg - INFO - Iter [3000/80000] lr: 3.850e-05, eta: 1 day, 21:03:47, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5973, decode.acc_seg: 78.0479, aux.loss_ce: 0.2355, aux.acc_seg: 78.3693, loss: 0.8328 +2024-06-18 04:16:20,718 - mmseg - INFO - per class results: +2024-06-18 04:16:20,724 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 76.07 | 86.97 | +| building | 81.74 | 89.7 | +| sky | 92.89 | 96.07 | +| floor | 80.04 | 87.26 | +| tree | 72.51 | 89.48 | +| ceiling | 83.28 | 93.74 | +| road | 82.28 | 88.52 | +| bed | 87.97 | 96.72 | +| windowpane | 59.73 | 69.46 | +| grass | 61.62 | 95.38 | +| cabinet | 54.94 | 59.16 | +| sidewalk | 63.72 | 79.2 | +| person | 77.91 | 86.88 | +| earth | 27.59 | 35.01 | +| door | 50.87 | 74.68 | +| table | 56.23 | 71.33 | +| mountain | 62.96 | 71.01 | +| plant | 52.03 | 62.0 | +| curtain | 70.84 | 90.42 | +| chair | 54.66 | 66.31 | +| car | 81.17 | 92.65 | +| water | 62.31 | 85.64 | +| painting | 75.22 | 86.43 | +| sofa | 73.9 | 85.82 | +| shelf | 35.67 | 47.62 | +| house | 48.48 | 74.13 | +| sea | 71.23 | 87.85 | +| mirror | 70.88 | 81.83 | +| rug | 67.45 | 85.73 | +| field | 18.31 | 23.67 | +| armchair | 51.15 | 65.02 | +| seat | 64.01 | 84.6 | +| fence | 17.34 | 19.1 | +| desk | 46.6 | 70.8 | +| rock | 58.78 | 77.59 | +| wardrobe | 46.59 | 85.5 | +| lamp | 56.94 | 66.59 | +| bathtub | 76.15 | 83.27 | +| railing | 30.27 | 47.31 | +| cushion | 59.87 | 69.82 | +| base | 29.37 | 33.37 | +| box | 23.47 | 28.84 | +| column | 48.6 | 60.43 | +| signboard | 29.52 | 36.72 | +| chest of drawers | 42.96 | 64.95 | +| counter | 36.43 | 48.4 | +| sand | 37.31 | 78.06 | +| sink | 66.73 | 81.63 | +| skyscraper | 47.89 | 82.44 | +| fireplace | 63.49 | 94.02 | +| refrigerator | 69.48 | 90.2 | +| grandstand | 61.44 | 67.3 | +| path | 20.98 | 31.33 | +| stairs | 26.6 | 37.12 | +| runway | 67.8 | 87.67 | +| case | 54.09 | 66.77 | +| pool table | 87.88 | 97.68 | +| pillow | 58.96 | 67.23 | +| screen door | 68.58 | 95.57 | +| stairway | 33.23 | 79.13 | +| river | 9.01 | 9.68 | +| bridge | 73.8 | 84.62 | +| bookcase | 19.54 | 27.27 | +| blind | 42.19 | 51.22 | +| coffee table | 46.44 | 85.35 | +| toilet | 85.74 | 92.86 | +| flower | 32.36 | 44.43 | +| book | 46.36 | 70.38 | +| hill | 3.9 | 5.13 | +| bench | 61.02 | 67.72 | +| countertop | 49.8 | 63.55 | +| stove | 61.78 | 93.4 | +| palm | 44.18 | 72.57 | +| kitchen island | 29.24 | 83.84 | +| computer | 71.53 | 89.83 | +| swivel chair | 43.2 | 82.7 | +| boat | 53.77 | 88.74 | +| bar | 54.1 | 65.82 | +| arcade machine | 87.79 | 94.57 | +| hovel | 26.76 | 27.1 | +| bus | 87.23 | 96.08 | +| towel | 63.06 | 83.63 | +| light | 24.2 | 25.34 | +| truck | 34.2 | 61.1 | +| tower | 1.27 | 1.27 | +| chandelier | 59.3 | 78.08 | +| awning | 20.95 | 22.44 | +| streetlight | 15.5 | 21.03 | +| booth | 38.04 | 74.58 | +| television receiver | 69.33 | 85.5 | +| airplane | 72.34 | 94.93 | +| dirt track | 15.23 | 16.39 | +| apparel | 25.21 | 31.52 | +| pole | 2.16 | 2.18 | +| land | 0.52 | 0.53 | +| bannister | 4.52 | 6.53 | +| escalator | 60.0 | 79.35 | +| ottoman | 42.21 | 74.53 | +| bottle | 25.87 | 77.76 | +| buffet | 48.67 | 67.52 | +| poster | 22.57 | 25.39 | +| stage | 12.73 | 18.67 | +| van | 39.34 | 48.33 | +| ship | 71.15 | 80.01 | +| fountain | 11.2 | 11.28 | +| conveyer belt | 55.37 | 99.39 | +| canopy | 39.33 | 46.59 | +| washer | 80.97 | 97.52 | +| plaything | 22.18 | 29.11 | +| swimming pool | 59.51 | 87.09 | +| stool | 26.3 | 66.52 | +| barrel | 35.05 | 69.7 | +| basket | 35.27 | 57.4 | +| waterfall | 63.2 | 77.38 | +| tent | 92.89 | 95.13 | +| bag | 17.7 | 18.51 | +| minibike | 68.02 | 82.97 | +| cradle | 72.8 | 98.26 | +| oven | 20.89 | 28.2 | +| ball | 31.47 | 66.54 | +| food | 55.19 | 67.61 | +| step | 0.21 | 0.21 | +| tank | 61.33 | 93.16 | +| trade name | 0.0 | 0.0 | +| microwave | 81.31 | 91.96 | +| pot | 47.81 | 58.26 | +| animal | 65.79 | 69.33 | +| bicycle | 49.49 | 62.7 | +| lake | 0.0 | 0.0 | +| dishwasher | 53.49 | 58.08 | +| screen | 58.56 | 85.43 | +| blanket | 0.08 | 0.08 | +| sculpture | 53.46 | 59.07 | +| hood | 65.95 | 89.29 | +| sconce | 23.46 | 25.92 | +| vase | 31.21 | 43.1 | +| traffic light | 19.58 | 33.04 | +| tray | 7.44 | 13.69 | +| ashcan | 44.71 | 54.75 | +| fan | 23.26 | 24.62 | +| pier | 40.14 | 54.45 | +| crt screen | 0.0 | 0.0 | +| plate | 44.34 | 68.73 | +| monitor | 63.37 | 82.86 | +| bulletin board | 43.8 | 52.3 | +| shower | 0.0 | 0.0 | +| radiator | 50.52 | 56.27 | +| glass | 0.0 | 0.0 | +| clock | 20.19 | 20.24 | +| flag | 58.57 | 64.78 | ++---------------------+-------+-------+ +2024-06-18 04:16:20,724 - mmseg - INFO - Summary: +2024-06-18 04:16:20,724 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 82.12 | 46.87 | 61.16 | ++-------+-------+-------+ +2024-06-18 04:16:20,725 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 04:16:20,725 - mmseg - INFO - Iter(val) [250] aAcc: 0.8212, mIoU: 0.4687, mAcc: 0.6116, IoU.wall: 0.7607, IoU.building: 0.8174, IoU.sky: 0.9289, IoU.floor: 0.8004, IoU.tree: 0.7251, IoU.ceiling: 0.8328, IoU.road: 0.8228, IoU.bed : 0.8797, IoU.windowpane: 0.5973, IoU.grass: 0.6162, IoU.cabinet: 0.5494, IoU.sidewalk: 0.6372, IoU.person: 0.7791, IoU.earth: 0.2759, IoU.door: 0.5087, IoU.table: 0.5623, IoU.mountain: 0.6296, IoU.plant: 0.5203, IoU.curtain: 0.7084, IoU.chair: 0.5466, IoU.car: 0.8117, IoU.water: 0.6231, IoU.painting: 0.7522, IoU.sofa: 0.7390, IoU.shelf: 0.3567, IoU.house: 0.4848, IoU.sea: 0.7123, IoU.mirror: 0.7088, IoU.rug: 0.6745, IoU.field: 0.1831, IoU.armchair: 0.5115, IoU.seat: 0.6401, IoU.fence: 0.1734, IoU.desk: 0.4660, IoU.rock: 0.5878, IoU.wardrobe: 0.4659, IoU.lamp: 0.5694, IoU.bathtub: 0.7615, IoU.railing: 0.3027, IoU.cushion: 0.5987, IoU.base: 0.2937, IoU.box: 0.2347, IoU.column: 0.4860, IoU.signboard: 0.2952, IoU.chest of drawers: 0.4296, IoU.counter: 0.3643, IoU.sand: 0.3731, IoU.sink: 0.6673, IoU.skyscraper: 0.4789, IoU.fireplace: 0.6349, IoU.refrigerator: 0.6948, IoU.grandstand: 0.6144, IoU.path: 0.2098, IoU.stairs: 0.2660, IoU.runway: 0.6780, IoU.case: 0.5409, IoU.pool table: 0.8788, IoU.pillow: 0.5896, IoU.screen door: 0.6858, IoU.stairway: 0.3323, IoU.river: 0.0901, IoU.bridge: 0.7380, IoU.bookcase: 0.1954, IoU.blind: 0.4219, IoU.coffee table: 0.4644, IoU.toilet: 0.8574, IoU.flower: 0.3236, IoU.book: 0.4636, IoU.hill: 0.0390, IoU.bench: 0.6102, IoU.countertop: 0.4980, IoU.stove: 0.6178, IoU.palm: 0.4418, IoU.kitchen island: 0.2924, IoU.computer: 0.7153, IoU.swivel chair: 0.4320, IoU.boat: 0.5377, IoU.bar: 0.5410, IoU.arcade machine: 0.8779, IoU.hovel: 0.2676, IoU.bus: 0.8723, IoU.towel: 0.6306, IoU.light: 0.2420, IoU.truck: 0.3420, IoU.tower: 0.0127, IoU.chandelier: 0.5930, IoU.awning: 0.2095, IoU.streetlight: 0.1550, IoU.booth: 0.3804, IoU.television receiver: 0.6933, IoU.airplane: 0.7234, IoU.dirt track: 0.1523, IoU.apparel: 0.2521, IoU.pole: 0.0216, IoU.land: 0.0052, IoU.bannister: 0.0452, IoU.escalator: 0.6000, IoU.ottoman: 0.4221, IoU.bottle: 0.2587, IoU.buffet: 0.4867, IoU.poster: 0.2257, IoU.stage: 0.1273, IoU.van: 0.3934, IoU.ship: 0.7115, IoU.fountain: 0.1120, IoU.conveyer belt: 0.5537, IoU.canopy: 0.3933, IoU.washer: 0.8097, IoU.plaything: 0.2218, IoU.swimming pool: 0.5951, IoU.stool: 0.2630, IoU.barrel: 0.3505, IoU.basket: 0.3527, IoU.waterfall: 0.6320, IoU.tent: 0.9289, IoU.bag: 0.1770, IoU.minibike: 0.6802, IoU.cradle: 0.7280, IoU.oven: 0.2089, IoU.ball: 0.3147, IoU.food: 0.5519, IoU.step: 0.0021, IoU.tank: 0.6133, IoU.trade name: 0.0000, IoU.microwave: 0.8131, IoU.pot: 0.4781, IoU.animal: 0.6579, IoU.bicycle: 0.4949, IoU.lake: 0.0000, IoU.dishwasher: 0.5349, IoU.screen: 0.5856, IoU.blanket: 0.0008, IoU.sculpture: 0.5346, IoU.hood: 0.6595, IoU.sconce: 0.2346, IoU.vase: 0.3121, IoU.traffic light: 0.1958, IoU.tray: 0.0744, IoU.ashcan: 0.4471, IoU.fan: 0.2326, IoU.pier: 0.4014, IoU.crt screen: 0.0000, IoU.plate: 0.4434, IoU.monitor: 0.6337, IoU.bulletin board: 0.4380, IoU.shower: 0.0000, IoU.radiator: 0.5052, IoU.glass: 0.0000, IoU.clock: 0.2019, IoU.flag: 0.5857, Acc.wall: 0.8697, Acc.building: 0.8970, Acc.sky: 0.9607, Acc.floor: 0.8726, Acc.tree: 0.8948, Acc.ceiling: 0.9374, Acc.road: 0.8852, Acc.bed : 0.9672, Acc.windowpane: 0.6946, Acc.grass: 0.9538, Acc.cabinet: 0.5916, Acc.sidewalk: 0.7920, Acc.person: 0.8688, Acc.earth: 0.3501, Acc.door: 0.7468, Acc.table: 0.7133, Acc.mountain: 0.7101, Acc.plant: 0.6200, Acc.curtain: 0.9042, Acc.chair: 0.6631, Acc.car: 0.9265, Acc.water: 0.8564, Acc.painting: 0.8643, Acc.sofa: 0.8582, Acc.shelf: 0.4762, Acc.house: 0.7413, Acc.sea: 0.8785, Acc.mirror: 0.8183, Acc.rug: 0.8573, Acc.field: 0.2367, Acc.armchair: 0.6502, Acc.seat: 0.8460, Acc.fence: 0.1910, Acc.desk: 0.7080, Acc.rock: 0.7759, Acc.wardrobe: 0.8550, Acc.lamp: 0.6659, Acc.bathtub: 0.8327, Acc.railing: 0.4731, Acc.cushion: 0.6982, Acc.base: 0.3337, Acc.box: 0.2884, Acc.column: 0.6043, Acc.signboard: 0.3672, Acc.chest of drawers: 0.6495, Acc.counter: 0.4840, Acc.sand: 0.7806, Acc.sink: 0.8163, Acc.skyscraper: 0.8244, Acc.fireplace: 0.9402, Acc.refrigerator: 0.9020, Acc.grandstand: 0.6730, Acc.path: 0.3133, Acc.stairs: 0.3712, Acc.runway: 0.8767, Acc.case: 0.6677, Acc.pool table: 0.9768, Acc.pillow: 0.6723, Acc.screen door: 0.9557, Acc.stairway: 0.7913, Acc.river: 0.0968, Acc.bridge: 0.8462, Acc.bookcase: 0.2727, Acc.blind: 0.5122, Acc.coffee table: 0.8535, Acc.toilet: 0.9286, Acc.flower: 0.4443, Acc.book: 0.7038, Acc.hill: 0.0513, Acc.bench: 0.6772, Acc.countertop: 0.6355, Acc.stove: 0.9340, Acc.palm: 0.7257, Acc.kitchen island: 0.8384, Acc.computer: 0.8983, Acc.swivel chair: 0.8270, Acc.boat: 0.8874, Acc.bar: 0.6582, Acc.arcade machine: 0.9457, Acc.hovel: 0.2710, Acc.bus: 0.9608, Acc.towel: 0.8363, Acc.light: 0.2534, Acc.truck: 0.6110, Acc.tower: 0.0127, Acc.chandelier: 0.7808, Acc.awning: 0.2244, Acc.streetlight: 0.2103, Acc.booth: 0.7458, Acc.television receiver: 0.8550, Acc.airplane: 0.9493, Acc.dirt track: 0.1639, Acc.apparel: 0.3152, Acc.pole: 0.0218, Acc.land: 0.0053, Acc.bannister: 0.0653, Acc.escalator: 0.7935, Acc.ottoman: 0.7453, Acc.bottle: 0.7776, Acc.buffet: 0.6752, Acc.poster: 0.2539, Acc.stage: 0.1867, Acc.van: 0.4833, Acc.ship: 0.8001, Acc.fountain: 0.1128, Acc.conveyer belt: 0.9939, Acc.canopy: 0.4659, Acc.washer: 0.9752, Acc.plaything: 0.2911, Acc.swimming pool: 0.8709, Acc.stool: 0.6652, Acc.barrel: 0.6970, Acc.basket: 0.5740, Acc.waterfall: 0.7738, Acc.tent: 0.9513, Acc.bag: 0.1851, Acc.minibike: 0.8297, Acc.cradle: 0.9826, Acc.oven: 0.2820, Acc.ball: 0.6654, Acc.food: 0.6761, Acc.step: 0.0021, Acc.tank: 0.9316, Acc.trade name: 0.0000, Acc.microwave: 0.9196, Acc.pot: 0.5826, Acc.animal: 0.6933, Acc.bicycle: 0.6270, Acc.lake: 0.0000, Acc.dishwasher: 0.5808, Acc.screen: 0.8543, Acc.blanket: 0.0008, Acc.sculpture: 0.5907, Acc.hood: 0.8929, Acc.sconce: 0.2592, Acc.vase: 0.4310, Acc.traffic light: 0.3304, Acc.tray: 0.1369, Acc.ashcan: 0.5475, Acc.fan: 0.2462, Acc.pier: 0.5445, Acc.crt screen: 0.0000, Acc.plate: 0.6873, Acc.monitor: 0.8286, Acc.bulletin board: 0.5230, Acc.shower: 0.0000, Acc.radiator: 0.5627, Acc.glass: 0.0000, Acc.clock: 0.2024, Acc.flag: 0.6478 +2024-06-18 04:18:00,101 - mmseg - INFO - Iter [3050/80000] lr: 3.848e-05, eta: 1 day, 21:45:28, time: 4.173, data_time: 2.200, memory: 72263, decode.loss_ce: 0.5611, decode.acc_seg: 79.1292, aux.loss_ce: 0.2218, aux.acc_seg: 79.6101, loss: 0.7829 +2024-06-18 04:19:39,242 - mmseg - INFO - Iter [3100/80000] lr: 3.845e-05, eta: 1 day, 21:40:25, time: 1.983, data_time: 0.011, memory: 72263, decode.loss_ce: 0.5632, decode.acc_seg: 79.1683, aux.loss_ce: 0.2248, aux.acc_seg: 79.3827, loss: 0.7880 +2024-06-18 04:21:18,453 - mmseg - INFO - Iter [3150/80000] lr: 3.843e-05, eta: 1 day, 21:35:30, time: 1.984, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5668, decode.acc_seg: 78.2749, aux.loss_ce: 0.2230, aux.acc_seg: 78.8272, loss: 0.7898 +2024-06-18 04:22:57,413 - mmseg - INFO - Iter [3200/80000] lr: 3.840e-05, eta: 1 day, 21:30:36, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5669, decode.acc_seg: 79.0737, aux.loss_ce: 0.2241, aux.acc_seg: 79.4182, loss: 0.7909 +2024-06-18 04:24:36,409 - mmseg - INFO - Iter [3250/80000] lr: 3.838e-05, eta: 1 day, 21:25:48, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5630, decode.acc_seg: 79.2185, aux.loss_ce: 0.2211, aux.acc_seg: 79.7448, loss: 0.7841 +2024-06-18 04:26:15,449 - mmseg - INFO - Iter [3300/80000] lr: 3.835e-05, eta: 1 day, 21:21:07, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5759, decode.acc_seg: 78.6021, aux.loss_ce: 0.2280, aux.acc_seg: 79.0444, loss: 0.8039 +2024-06-18 04:27:54,546 - mmseg - INFO - Iter [3350/80000] lr: 3.833e-05, eta: 1 day, 21:16:32, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5661, decode.acc_seg: 79.0833, aux.loss_ce: 0.2247, aux.acc_seg: 79.4040, loss: 0.7908 +2024-06-18 04:29:33,585 - mmseg - INFO - Iter [3400/80000] lr: 3.830e-05, eta: 1 day, 21:12:02, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6004, decode.acc_seg: 77.5954, aux.loss_ce: 0.2373, aux.acc_seg: 78.1387, loss: 0.8377 +2024-06-18 04:31:12,747 - mmseg - INFO - Iter [3450/80000] lr: 3.828e-05, eta: 1 day, 21:07:39, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5650, decode.acc_seg: 79.3699, aux.loss_ce: 0.2225, aux.acc_seg: 79.8250, loss: 0.7875 +2024-06-18 04:32:51,756 - mmseg - INFO - Iter [3500/80000] lr: 3.825e-05, eta: 1 day, 21:03:18, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5940, decode.acc_seg: 78.3136, aux.loss_ce: 0.2344, aux.acc_seg: 78.7895, loss: 0.8284 +2024-06-18 04:34:30,737 - mmseg - INFO - Iter [3550/80000] lr: 3.823e-05, eta: 1 day, 20:59:00, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5705, decode.acc_seg: 78.9191, aux.loss_ce: 0.2232, aux.acc_seg: 79.5124, loss: 0.7937 +2024-06-18 04:36:09,740 - mmseg - INFO - Iter [3600/80000] lr: 3.820e-05, eta: 1 day, 20:54:48, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5589, decode.acc_seg: 78.3611, aux.loss_ce: 0.2205, aux.acc_seg: 78.7292, loss: 0.7794 +2024-06-18 04:37:48,770 - mmseg - INFO - Iter [3650/80000] lr: 3.818e-05, eta: 1 day, 20:50:40, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5587, decode.acc_seg: 79.1790, aux.loss_ce: 0.2203, aux.acc_seg: 79.6042, loss: 0.7790 +2024-06-18 04:39:27,794 - mmseg - INFO - Iter [3700/80000] lr: 3.815e-05, eta: 1 day, 20:46:36, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.6126, decode.acc_seg: 77.7937, aux.loss_ce: 0.2406, aux.acc_seg: 78.3928, loss: 0.8532 +2024-06-18 04:41:06,858 - mmseg - INFO - Iter [3750/80000] lr: 3.813e-05, eta: 1 day, 20:42:37, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5690, decode.acc_seg: 78.6517, aux.loss_ce: 0.2257, aux.acc_seg: 79.1615, loss: 0.7947 +2024-06-18 04:42:48,103 - mmseg - INFO - Iter [3800/80000] lr: 3.810e-05, eta: 1 day, 20:39:25, time: 2.025, data_time: 0.052, memory: 72263, decode.loss_ce: 0.5416, decode.acc_seg: 80.2892, aux.loss_ce: 0.2135, aux.acc_seg: 80.6582, loss: 0.7550 +2024-06-18 04:44:27,085 - mmseg - INFO - Iter [3850/80000] lr: 3.808e-05, eta: 1 day, 20:35:31, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5244, decode.acc_seg: 80.8010, aux.loss_ce: 0.2072, aux.acc_seg: 81.1131, loss: 0.7316 +2024-06-18 04:46:06,098 - mmseg - INFO - Iter [3900/80000] lr: 3.805e-05, eta: 1 day, 20:31:41, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5558, decode.acc_seg: 79.0607, aux.loss_ce: 0.2198, aux.acc_seg: 79.3394, loss: 0.7756 +2024-06-18 04:47:45,079 - mmseg - INFO - Iter [3950/80000] lr: 3.803e-05, eta: 1 day, 20:27:53, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5351, decode.acc_seg: 80.2784, aux.loss_ce: 0.2101, aux.acc_seg: 80.6213, loss: 0.7452 +2024-06-18 04:49:24,028 - mmseg - INFO - Saving checkpoint at 4000 iterations +2024-06-18 04:50:53,113 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 04:50:53,113 - mmseg - INFO - Iter [4000/80000] lr: 3.800e-05, eta: 1 day, 20:52:21, time: 3.761, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5185, decode.acc_seg: 80.4367, aux.loss_ce: 0.2038, aux.acc_seg: 80.6344, loss: 0.7223 +2024-06-18 04:52:44,337 - mmseg - INFO - per class results: +2024-06-18 04:52:44,344 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 77.52 | 85.13 | +| building | 82.55 | 92.38 | +| sky | 93.05 | 95.28 | +| floor | 80.39 | 90.24 | +| tree | 74.05 | 86.59 | +| ceiling | 82.78 | 92.08 | +| road | 82.68 | 90.3 | +| bed | 87.7 | 97.25 | +| windowpane | 61.78 | 77.62 | +| grass | 62.66 | 83.51 | +| cabinet | 59.66 | 67.07 | +| sidewalk | 63.08 | 80.29 | +| person | 77.57 | 93.34 | +| earth | 33.54 | 45.73 | +| door | 55.74 | 68.09 | +| table | 59.16 | 74.72 | +| mountain | 60.85 | 77.2 | +| plant | 55.5 | 72.07 | +| curtain | 69.89 | 88.7 | +| chair | 55.15 | 66.55 | +| car | 79.6 | 93.84 | +| water | 63.42 | 81.7 | +| painting | 70.62 | 85.14 | +| sofa | 70.74 | 88.85 | +| shelf | 40.0 | 60.37 | +| house | 43.28 | 48.75 | +| sea | 64.43 | 75.5 | +| mirror | 73.64 | 84.28 | +| rug | 56.4 | 65.45 | +| field | 32.05 | 65.63 | +| armchair | 47.75 | 67.88 | +| seat | 67.51 | 78.77 | +| fence | 48.12 | 69.74 | +| desk | 48.07 | 64.0 | +| rock | 44.33 | 50.44 | +| wardrobe | 54.28 | 80.3 | +| lamp | 58.12 | 72.4 | +| bathtub | 82.03 | 86.6 | +| railing | 39.6 | 59.78 | +| cushion | 60.09 | 84.99 | +| base | 38.33 | 49.77 | +| box | 28.44 | 34.39 | +| column | 48.61 | 60.9 | +| signboard | 32.98 | 42.92 | +| chest of drawers | 43.51 | 73.28 | +| counter | 38.3 | 40.82 | +| sand | 55.55 | 83.21 | +| sink | 62.73 | 73.49 | +| skyscraper | 45.22 | 69.7 | +| fireplace | 67.9 | 88.24 | +| refrigerator | 75.09 | 83.64 | +| grandstand | 40.89 | 91.75 | +| path | 23.83 | 34.18 | +| stairs | 34.86 | 40.19 | +| runway | 64.38 | 74.36 | +| case | 51.09 | 90.67 | +| pool table | 89.59 | 94.84 | +| pillow | 54.6 | 59.01 | +| screen door | 74.1 | 92.54 | +| stairway | 49.45 | 55.04 | +| river | 31.9 | 48.95 | +| bridge | 71.83 | 83.92 | +| bookcase | 20.4 | 25.61 | +| blind | 18.62 | 19.02 | +| coffee table | 53.7 | 82.08 | +| toilet | 82.97 | 96.89 | +| flower | 34.7 | 39.5 | +| book | 44.86 | 73.59 | +| hill | 3.35 | 3.51 | +| bench | 55.23 | 68.11 | +| countertop | 58.21 | 76.14 | +| stove | 77.32 | 90.91 | +| palm | 46.72 | 79.02 | +| kitchen island | 38.99 | 86.66 | +| computer | 72.67 | 82.95 | +| swivel chair | 44.69 | 64.29 | +| boat | 42.18 | 97.41 | +| bar | 60.55 | 80.76 | +| arcade machine | 72.23 | 88.02 | +| hovel | 41.0 | 79.44 | +| bus | 89.1 | 91.83 | +| towel | 55.49 | 62.86 | +| light | 20.24 | 21.07 | +| truck | 36.57 | 46.3 | +| tower | 3.85 | 4.68 | +| chandelier | 60.53 | 80.68 | +| awning | 25.71 | 65.43 | +| streetlight | 18.92 | 25.92 | +| booth | 34.08 | 71.07 | +| television receiver | 69.32 | 78.51 | +| airplane | 71.6 | 90.79 | +| dirt track | 0.0 | 0.0 | +| apparel | 41.78 | 52.69 | +| pole | 11.73 | 13.68 | +| land | 0.0 | 0.0 | +| bannister | 3.85 | 5.18 | +| escalator | 54.6 | 89.52 | +| ottoman | 52.41 | 68.14 | +| bottle | 34.91 | 73.58 | +| buffet | 44.69 | 59.56 | +| poster | 25.42 | 47.34 | +| stage | 19.71 | 33.15 | +| van | 33.8 | 47.05 | +| ship | 0.0 | 0.0 | +| fountain | 58.35 | 74.1 | +| conveyer belt | 74.59 | 97.29 | +| canopy | 38.71 | 45.61 | +| washer | 85.52 | 94.97 | +| plaything | 25.74 | 44.51 | +| swimming pool | 59.34 | 78.77 | +| stool | 35.2 | 52.74 | +| barrel | 49.21 | 65.38 | +| basket | 37.36 | 53.15 | +| waterfall | 50.28 | 98.67 | +| tent | 54.73 | 99.75 | +| bag | 9.78 | 10.05 | +| minibike | 67.58 | 84.58 | +| cradle | 72.09 | 98.76 | +| oven | 51.26 | 70.88 | +| ball | 35.14 | 67.9 | +| food | 48.04 | 58.97 | +| step | 9.64 | 15.09 | +| tank | 47.39 | 70.03 | +| trade name | 1.57 | 1.57 | +| microwave | 82.72 | 95.4 | +| pot | 46.74 | 59.03 | +| animal | 57.96 | 60.91 | +| bicycle | 54.35 | 68.96 | +| lake | 0.0 | 0.0 | +| dishwasher | 62.54 | 80.13 | +| screen | 52.23 | 95.81 | +| blanket | 3.43 | 3.84 | +| sculpture | 54.73 | 66.01 | +| hood | 64.14 | 77.62 | +| sconce | 27.07 | 31.25 | +| vase | 37.71 | 55.23 | +| traffic light | 23.14 | 50.87 | +| tray | 4.86 | 6.34 | +| ashcan | 45.74 | 53.48 | +| fan | 36.84 | 40.7 | +| pier | 33.45 | 49.63 | +| crt screen | 0.0 | 0.0 | +| plate | 46.27 | 51.23 | +| monitor | 53.06 | 67.2 | +| bulletin board | 46.1 | 65.47 | +| shower | 0.0 | 0.0 | +| radiator | 59.48 | 66.55 | +| glass | 3.24 | 3.29 | +| clock | 32.62 | 33.42 | +| flag | 53.52 | 81.14 | ++---------------------+-------+-------+ +2024-06-18 04:52:44,344 - mmseg - INFO - Summary: +2024-06-18 04:52:44,344 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 82.76 | 48.08 | 63.09 | ++-------+-------+-------+ +2024-06-18 04:52:44,345 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 04:52:44,345 - mmseg - INFO - Iter(val) [250] aAcc: 0.8276, mIoU: 0.4808, mAcc: 0.6309, IoU.wall: 0.7752, IoU.building: 0.8255, IoU.sky: 0.9305, IoU.floor: 0.8039, IoU.tree: 0.7405, IoU.ceiling: 0.8278, IoU.road: 0.8268, IoU.bed : 0.8770, IoU.windowpane: 0.6178, IoU.grass: 0.6266, IoU.cabinet: 0.5966, IoU.sidewalk: 0.6308, IoU.person: 0.7757, IoU.earth: 0.3354, IoU.door: 0.5574, IoU.table: 0.5916, IoU.mountain: 0.6085, IoU.plant: 0.5550, IoU.curtain: 0.6989, IoU.chair: 0.5515, IoU.car: 0.7960, IoU.water: 0.6342, IoU.painting: 0.7062, IoU.sofa: 0.7074, IoU.shelf: 0.4000, IoU.house: 0.4328, IoU.sea: 0.6443, IoU.mirror: 0.7364, IoU.rug: 0.5640, IoU.field: 0.3205, IoU.armchair: 0.4775, IoU.seat: 0.6751, IoU.fence: 0.4812, IoU.desk: 0.4807, IoU.rock: 0.4433, IoU.wardrobe: 0.5428, IoU.lamp: 0.5812, IoU.bathtub: 0.8203, IoU.railing: 0.3960, IoU.cushion: 0.6009, IoU.base: 0.3833, IoU.box: 0.2844, IoU.column: 0.4861, IoU.signboard: 0.3298, IoU.chest of drawers: 0.4351, IoU.counter: 0.3830, IoU.sand: 0.5555, IoU.sink: 0.6273, IoU.skyscraper: 0.4522, IoU.fireplace: 0.6790, IoU.refrigerator: 0.7509, IoU.grandstand: 0.4089, IoU.path: 0.2383, IoU.stairs: 0.3486, IoU.runway: 0.6438, IoU.case: 0.5109, IoU.pool table: 0.8959, IoU.pillow: 0.5460, IoU.screen door: 0.7410, IoU.stairway: 0.4945, IoU.river: 0.3190, IoU.bridge: 0.7183, IoU.bookcase: 0.2040, IoU.blind: 0.1862, IoU.coffee table: 0.5370, IoU.toilet: 0.8297, IoU.flower: 0.3470, IoU.book: 0.4486, IoU.hill: 0.0335, IoU.bench: 0.5523, IoU.countertop: 0.5821, IoU.stove: 0.7732, IoU.palm: 0.4672, IoU.kitchen island: 0.3899, IoU.computer: 0.7267, IoU.swivel chair: 0.4469, IoU.boat: 0.4218, IoU.bar: 0.6055, IoU.arcade machine: 0.7223, IoU.hovel: 0.4100, IoU.bus: 0.8910, IoU.towel: 0.5549, IoU.light: 0.2024, IoU.truck: 0.3657, IoU.tower: 0.0385, IoU.chandelier: 0.6053, IoU.awning: 0.2571, IoU.streetlight: 0.1892, IoU.booth: 0.3408, IoU.television receiver: 0.6932, IoU.airplane: 0.7160, IoU.dirt track: 0.0000, IoU.apparel: 0.4178, IoU.pole: 0.1173, IoU.land: 0.0000, IoU.bannister: 0.0385, IoU.escalator: 0.5460, IoU.ottoman: 0.5241, IoU.bottle: 0.3491, IoU.buffet: 0.4469, IoU.poster: 0.2542, IoU.stage: 0.1971, IoU.van: 0.3380, IoU.ship: 0.0000, IoU.fountain: 0.5835, IoU.conveyer belt: 0.7459, IoU.canopy: 0.3871, IoU.washer: 0.8552, IoU.plaything: 0.2574, IoU.swimming pool: 0.5934, IoU.stool: 0.3520, IoU.barrel: 0.4921, IoU.basket: 0.3736, IoU.waterfall: 0.5028, IoU.tent: 0.5473, IoU.bag: 0.0978, IoU.minibike: 0.6758, IoU.cradle: 0.7209, IoU.oven: 0.5126, IoU.ball: 0.3514, IoU.food: 0.4804, IoU.step: 0.0964, IoU.tank: 0.4739, IoU.trade name: 0.0157, IoU.microwave: 0.8272, IoU.pot: 0.4674, IoU.animal: 0.5796, IoU.bicycle: 0.5435, IoU.lake: 0.0000, IoU.dishwasher: 0.6254, IoU.screen: 0.5223, IoU.blanket: 0.0343, IoU.sculpture: 0.5473, IoU.hood: 0.6414, IoU.sconce: 0.2707, IoU.vase: 0.3771, IoU.traffic light: 0.2314, IoU.tray: 0.0486, IoU.ashcan: 0.4574, IoU.fan: 0.3684, IoU.pier: 0.3345, IoU.crt screen: 0.0000, IoU.plate: 0.4627, IoU.monitor: 0.5306, IoU.bulletin board: 0.4610, IoU.shower: 0.0000, IoU.radiator: 0.5948, IoU.glass: 0.0324, IoU.clock: 0.3262, IoU.flag: 0.5352, Acc.wall: 0.8513, Acc.building: 0.9238, Acc.sky: 0.9528, Acc.floor: 0.9024, Acc.tree: 0.8659, Acc.ceiling: 0.9208, Acc.road: 0.9030, Acc.bed : 0.9725, Acc.windowpane: 0.7762, Acc.grass: 0.8351, Acc.cabinet: 0.6707, Acc.sidewalk: 0.8029, Acc.person: 0.9334, Acc.earth: 0.4573, Acc.door: 0.6809, Acc.table: 0.7472, Acc.mountain: 0.7720, Acc.plant: 0.7207, Acc.curtain: 0.8870, Acc.chair: 0.6655, Acc.car: 0.9384, Acc.water: 0.8170, Acc.painting: 0.8514, Acc.sofa: 0.8885, Acc.shelf: 0.6037, Acc.house: 0.4875, Acc.sea: 0.7550, Acc.mirror: 0.8428, Acc.rug: 0.6545, Acc.field: 0.6563, Acc.armchair: 0.6788, Acc.seat: 0.7877, Acc.fence: 0.6974, Acc.desk: 0.6400, Acc.rock: 0.5044, Acc.wardrobe: 0.8030, Acc.lamp: 0.7240, Acc.bathtub: 0.8660, Acc.railing: 0.5978, Acc.cushion: 0.8499, Acc.base: 0.4977, Acc.box: 0.3439, Acc.column: 0.6090, Acc.signboard: 0.4292, Acc.chest of drawers: 0.7328, Acc.counter: 0.4082, Acc.sand: 0.8321, Acc.sink: 0.7349, Acc.skyscraper: 0.6970, Acc.fireplace: 0.8824, Acc.refrigerator: 0.8364, Acc.grandstand: 0.9175, Acc.path: 0.3418, Acc.stairs: 0.4019, Acc.runway: 0.7436, Acc.case: 0.9067, Acc.pool table: 0.9484, Acc.pillow: 0.5901, Acc.screen door: 0.9254, Acc.stairway: 0.5504, Acc.river: 0.4895, Acc.bridge: 0.8392, Acc.bookcase: 0.2561, Acc.blind: 0.1902, Acc.coffee table: 0.8208, Acc.toilet: 0.9689, Acc.flower: 0.3950, Acc.book: 0.7359, Acc.hill: 0.0351, Acc.bench: 0.6811, Acc.countertop: 0.7614, Acc.stove: 0.9091, Acc.palm: 0.7902, Acc.kitchen island: 0.8666, Acc.computer: 0.8295, Acc.swivel chair: 0.6429, Acc.boat: 0.9741, Acc.bar: 0.8076, Acc.arcade machine: 0.8802, Acc.hovel: 0.7944, Acc.bus: 0.9183, Acc.towel: 0.6286, Acc.light: 0.2107, Acc.truck: 0.4630, Acc.tower: 0.0468, Acc.chandelier: 0.8068, Acc.awning: 0.6543, Acc.streetlight: 0.2592, Acc.booth: 0.7107, Acc.television receiver: 0.7851, Acc.airplane: 0.9079, Acc.dirt track: 0.0000, Acc.apparel: 0.5269, Acc.pole: 0.1368, Acc.land: 0.0000, Acc.bannister: 0.0518, Acc.escalator: 0.8952, Acc.ottoman: 0.6814, Acc.bottle: 0.7358, Acc.buffet: 0.5956, Acc.poster: 0.4734, Acc.stage: 0.3315, Acc.van: 0.4705, Acc.ship: 0.0000, Acc.fountain: 0.7410, Acc.conveyer belt: 0.9729, Acc.canopy: 0.4561, Acc.washer: 0.9497, Acc.plaything: 0.4451, Acc.swimming pool: 0.7877, Acc.stool: 0.5274, Acc.barrel: 0.6538, Acc.basket: 0.5315, Acc.waterfall: 0.9867, Acc.tent: 0.9975, Acc.bag: 0.1005, Acc.minibike: 0.8458, Acc.cradle: 0.9876, Acc.oven: 0.7088, Acc.ball: 0.6790, Acc.food: 0.5897, Acc.step: 0.1509, Acc.tank: 0.7003, Acc.trade name: 0.0157, Acc.microwave: 0.9540, Acc.pot: 0.5903, Acc.animal: 0.6091, Acc.bicycle: 0.6896, Acc.lake: 0.0000, Acc.dishwasher: 0.8013, Acc.screen: 0.9581, Acc.blanket: 0.0384, Acc.sculpture: 0.6601, Acc.hood: 0.7762, Acc.sconce: 0.3125, Acc.vase: 0.5523, Acc.traffic light: 0.5087, Acc.tray: 0.0634, Acc.ashcan: 0.5348, Acc.fan: 0.4070, Acc.pier: 0.4963, Acc.crt screen: 0.0000, Acc.plate: 0.5123, Acc.monitor: 0.6720, Acc.bulletin board: 0.6547, Acc.shower: 0.0000, Acc.radiator: 0.6655, Acc.glass: 0.0329, Acc.clock: 0.3342, Acc.flag: 0.8114 +2024-06-18 04:54:23,720 - mmseg - INFO - Iter [4050/80000] lr: 3.798e-05, eta: 1 day, 21:23:11, time: 4.212, data_time: 2.241, memory: 72263, decode.loss_ce: 0.5264, decode.acc_seg: 79.6010, aux.loss_ce: 0.2087, aux.acc_seg: 79.6709, loss: 0.7351 +2024-06-18 04:56:02,742 - mmseg - INFO - Iter [4100/80000] lr: 3.795e-05, eta: 1 day, 21:18:46, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5082, decode.acc_seg: 80.3638, aux.loss_ce: 0.2006, aux.acc_seg: 80.8646, loss: 0.7088 +2024-06-18 04:57:42,010 - mmseg - INFO - Iter [4150/80000] lr: 3.793e-05, eta: 1 day, 21:14:28, time: 1.985, data_time: 0.011, memory: 72263, decode.loss_ce: 0.5220, decode.acc_seg: 80.5551, aux.loss_ce: 0.2053, aux.acc_seg: 80.8191, loss: 0.7273 +2024-06-18 04:59:21,069 - mmseg - INFO - Iter [4200/80000] lr: 3.790e-05, eta: 1 day, 21:10:11, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5199, decode.acc_seg: 80.4111, aux.loss_ce: 0.2044, aux.acc_seg: 80.8326, loss: 0.7242 +2024-06-18 05:01:00,057 - mmseg - INFO - Iter [4250/80000] lr: 3.788e-05, eta: 1 day, 21:05:56, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5379, decode.acc_seg: 79.5701, aux.loss_ce: 0.2113, aux.acc_seg: 80.1485, loss: 0.7492 +2024-06-18 05:02:38,970 - mmseg - INFO - Iter [4300/80000] lr: 3.785e-05, eta: 1 day, 21:01:44, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5129, decode.acc_seg: 80.4379, aux.loss_ce: 0.2020, aux.acc_seg: 80.9519, loss: 0.7149 +2024-06-18 05:04:17,950 - mmseg - INFO - Iter [4350/80000] lr: 3.783e-05, eta: 1 day, 20:57:36, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5330, decode.acc_seg: 80.0518, aux.loss_ce: 0.2111, aux.acc_seg: 80.5223, loss: 0.7441 +2024-06-18 05:05:56,914 - mmseg - INFO - Iter [4400/80000] lr: 3.780e-05, eta: 1 day, 20:53:32, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5294, decode.acc_seg: 79.6813, aux.loss_ce: 0.2094, aux.acc_seg: 79.8065, loss: 0.7389 +2024-06-18 05:07:35,944 - mmseg - INFO - Iter [4450/80000] lr: 3.778e-05, eta: 1 day, 20:49:31, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5116, decode.acc_seg: 80.9061, aux.loss_ce: 0.2017, aux.acc_seg: 81.1908, loss: 0.7134 +2024-06-18 05:09:15,149 - mmseg - INFO - Iter [4500/80000] lr: 3.775e-05, eta: 1 day, 20:45:37, time: 1.984, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5418, decode.acc_seg: 79.4937, aux.loss_ce: 0.2139, aux.acc_seg: 79.7498, loss: 0.7557 +2024-06-18 05:10:54,237 - mmseg - INFO - Iter [4550/80000] lr: 3.773e-05, eta: 1 day, 20:41:44, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4978, decode.acc_seg: 80.7894, aux.loss_ce: 0.1958, aux.acc_seg: 81.3957, loss: 0.6935 +2024-06-18 05:12:33,391 - mmseg - INFO - Iter [4600/80000] lr: 3.770e-05, eta: 1 day, 20:37:55, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5008, decode.acc_seg: 80.4616, aux.loss_ce: 0.1976, aux.acc_seg: 80.8409, loss: 0.6984 +2024-06-18 05:14:12,371 - mmseg - INFO - Iter [4650/80000] lr: 3.768e-05, eta: 1 day, 20:34:06, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5361, decode.acc_seg: 80.3977, aux.loss_ce: 0.2129, aux.acc_seg: 80.6299, loss: 0.7489 +2024-06-18 05:15:51,423 - mmseg - INFO - Iter [4700/80000] lr: 3.765e-05, eta: 1 day, 20:30:20, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5286, decode.acc_seg: 80.3535, aux.loss_ce: 0.2083, aux.acc_seg: 80.7959, loss: 0.7369 +2024-06-18 05:17:30,412 - mmseg - INFO - Iter [4750/80000] lr: 3.763e-05, eta: 1 day, 20:26:37, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5409, decode.acc_seg: 79.4691, aux.loss_ce: 0.2124, aux.acc_seg: 79.9527, loss: 0.7533 +2024-06-18 05:19:09,608 - mmseg - INFO - Iter [4800/80000] lr: 3.760e-05, eta: 1 day, 20:22:59, time: 1.984, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5332, decode.acc_seg: 79.8225, aux.loss_ce: 0.2080, aux.acc_seg: 80.2255, loss: 0.7412 +2024-06-18 05:20:48,651 - mmseg - INFO - Iter [4850/80000] lr: 3.758e-05, eta: 1 day, 20:19:21, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5208, decode.acc_seg: 79.9953, aux.loss_ce: 0.2047, aux.acc_seg: 80.3992, loss: 0.7255 +2024-06-18 05:22:27,634 - mmseg - INFO - Iter [4900/80000] lr: 3.755e-05, eta: 1 day, 20:15:45, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5221, decode.acc_seg: 79.7698, aux.loss_ce: 0.2065, aux.acc_seg: 79.9926, loss: 0.7286 +2024-06-18 05:24:06,620 - mmseg - INFO - Iter [4950/80000] lr: 3.753e-05, eta: 1 day, 20:12:11, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5214, decode.acc_seg: 80.6209, aux.loss_ce: 0.2060, aux.acc_seg: 80.9660, loss: 0.7274 +2024-06-18 05:25:45,791 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 05:25:45,791 - mmseg - INFO - Iter [5000/80000] lr: 3.750e-05, eta: 1 day, 20:08:42, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5116, decode.acc_seg: 80.5415, aux.loss_ce: 0.2010, aux.acc_seg: 80.9018, loss: 0.7126 +2024-06-18 05:27:34,939 - mmseg - INFO - per class results: +2024-06-18 05:27:34,945 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 77.51 | 87.7 | +| building | 82.16 | 90.15 | +| sky | 93.2 | 96.15 | +| floor | 80.59 | 89.06 | +| tree | 74.27 | 86.1 | +| ceiling | 83.62 | 91.99 | +| road | 83.16 | 86.78 | +| bed | 88.22 | 95.87 | +| windowpane | 60.03 | 73.38 | +| grass | 66.28 | 74.96 | +| cabinet | 62.72 | 71.46 | +| sidewalk | 63.75 | 88.0 | +| person | 78.95 | 90.02 | +| earth | 40.8 | 64.03 | +| door | 53.53 | 76.56 | +| table | 62.78 | 80.39 | +| mountain | 54.23 | 59.81 | +| plant | 54.99 | 63.56 | +| curtain | 70.51 | 90.24 | +| chair | 57.11 | 69.83 | +| car | 82.58 | 89.83 | +| water | 61.54 | 89.23 | +| painting | 72.62 | 81.54 | +| sofa | 76.75 | 89.32 | +| shelf | 38.27 | 52.56 | +| house | 52.84 | 81.13 | +| sea | 64.63 | 71.9 | +| mirror | 72.52 | 81.55 | +| rug | 65.24 | 84.65 | +| field | 29.31 | 51.23 | +| armchair | 56.7 | 75.65 | +| seat | 61.54 | 87.79 | +| fence | 49.24 | 58.52 | +| desk | 46.51 | 56.22 | +| rock | 54.18 | 80.93 | +| wardrobe | 54.63 | 77.51 | +| lamp | 61.2 | 76.82 | +| bathtub | 80.73 | 84.63 | +| railing | 36.7 | 48.8 | +| cushion | 62.45 | 75.45 | +| base | 41.07 | 60.92 | +| box | 32.27 | 40.99 | +| column | 48.6 | 69.89 | +| signboard | 31.06 | 39.03 | +| chest of drawers | 38.34 | 70.2 | +| counter | 46.17 | 53.77 | +| sand | 35.84 | 55.24 | +| sink | 71.68 | 78.32 | +| skyscraper | 34.39 | 35.86 | +| fireplace | 68.67 | 91.35 | +| refrigerator | 73.59 | 87.11 | +| grandstand | 37.42 | 89.85 | +| path | 19.03 | 21.67 | +| stairs | 31.87 | 33.71 | +| runway | 71.46 | 94.85 | +| case | 56.46 | 78.29 | +| pool table | 90.19 | 97.18 | +| pillow | 63.32 | 75.07 | +| screen door | 65.17 | 90.53 | +| stairway | 45.03 | 71.1 | +| river | 9.13 | 10.19 | +| bridge | 46.52 | 58.12 | +| bookcase | 30.18 | 70.08 | +| blind | 7.22 | 7.33 | +| coffee table | 57.92 | 64.09 | +| toilet | 86.65 | 94.8 | +| flower | 36.36 | 52.79 | +| book | 43.82 | 55.79 | +| hill | 5.49 | 6.66 | +| bench | 57.92 | 63.71 | +| countertop | 60.06 | 71.6 | +| stove | 78.47 | 93.34 | +| palm | 44.86 | 83.7 | +| kitchen island | 38.46 | 64.58 | +| computer | 71.27 | 89.88 | +| swivel chair | 48.21 | 90.5 | +| boat | 70.27 | 89.32 | +| bar | 56.12 | 76.74 | +| arcade machine | 83.42 | 99.13 | +| hovel | 48.31 | 58.16 | +| bus | 76.21 | 96.72 | +| towel | 60.67 | 69.71 | +| light | 44.0 | 55.96 | +| truck | 8.59 | 9.56 | +| tower | 26.0 | 45.46 | +| chandelier | 62.27 | 84.32 | +| awning | 39.84 | 64.51 | +| streetlight | 18.23 | 23.93 | +| booth | 27.58 | 54.39 | +| television receiver | 73.71 | 78.57 | +| airplane | 75.48 | 93.28 | +| dirt track | 0.0 | 0.0 | +| apparel | 37.27 | 52.82 | +| pole | 8.01 | 9.04 | +| land | 8.42 | 20.82 | +| bannister | 6.24 | 11.24 | +| escalator | 58.5 | 87.77 | +| ottoman | 52.77 | 72.02 | +| bottle | 38.59 | 66.47 | +| buffet | 46.92 | 63.0 | +| poster | 39.07 | 53.09 | +| stage | 14.02 | 21.45 | +| van | 44.49 | 73.79 | +| ship | 7.73 | 7.99 | +| fountain | 35.83 | 43.7 | +| conveyer belt | 73.16 | 96.35 | +| canopy | 59.48 | 65.4 | +| washer | 79.26 | 86.71 | +| plaything | 23.58 | 76.2 | +| swimming pool | 59.01 | 91.46 | +| stool | 39.66 | 51.05 | +| barrel | 39.3 | 65.12 | +| basket | 37.79 | 49.91 | +| waterfall | 26.32 | 26.47 | +| tent | 83.19 | 99.62 | +| bag | 20.96 | 23.17 | +| minibike | 68.42 | 86.12 | +| cradle | 78.48 | 97.74 | +| oven | 36.38 | 39.85 | +| ball | 0.77 | 0.77 | +| food | 54.36 | 77.33 | +| step | 1.93 | 2.03 | +| tank | 51.18 | 76.23 | +| trade name | 27.73 | 47.21 | +| microwave | 81.04 | 96.41 | +| pot | 46.93 | 53.69 | +| animal | 69.75 | 74.64 | +| bicycle | 49.34 | 57.74 | +| lake | 0.0 | 0.0 | +| dishwasher | 67.96 | 74.6 | +| screen | 57.41 | 66.78 | +| blanket | 6.24 | 6.75 | +| sculpture | 57.9 | 65.69 | +| hood | 70.16 | 89.04 | +| sconce | 43.0 | 53.5 | +| vase | 35.51 | 43.45 | +| traffic light | 23.84 | 46.18 | +| tray | 10.88 | 15.68 | +| ashcan | 46.09 | 55.1 | +| fan | 53.93 | 66.55 | +| pier | 63.09 | 76.86 | +| crt screen | 10.45 | 15.28 | +| plate | 53.18 | 75.06 | +| monitor | 51.81 | 74.63 | +| bulletin board | 56.37 | 65.56 | +| shower | 0.0 | 0.0 | +| radiator | 63.38 | 78.87 | +| glass | 6.6 | 6.78 | +| clock | 39.95 | 44.06 | +| flag | 64.18 | 70.42 | ++---------------------+-------+-------+ +2024-06-18 05:27:34,945 - mmseg - INFO - Summary: +2024-06-18 05:27:34,945 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 83.1 | 49.68 | 63.66 | ++------+-------+-------+ +2024-06-18 05:27:34,946 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 05:27:34,946 - mmseg - INFO - Iter(val) [250] aAcc: 0.8310, mIoU: 0.4968, mAcc: 0.6366, IoU.wall: 0.7751, IoU.building: 0.8216, IoU.sky: 0.9320, IoU.floor: 0.8059, IoU.tree: 0.7427, IoU.ceiling: 0.8362, IoU.road: 0.8316, IoU.bed : 0.8822, IoU.windowpane: 0.6003, IoU.grass: 0.6628, IoU.cabinet: 0.6272, IoU.sidewalk: 0.6375, IoU.person: 0.7895, IoU.earth: 0.4080, IoU.door: 0.5353, IoU.table: 0.6278, IoU.mountain: 0.5423, IoU.plant: 0.5499, IoU.curtain: 0.7051, IoU.chair: 0.5711, IoU.car: 0.8258, IoU.water: 0.6154, IoU.painting: 0.7262, IoU.sofa: 0.7675, IoU.shelf: 0.3827, IoU.house: 0.5284, IoU.sea: 0.6463, IoU.mirror: 0.7252, IoU.rug: 0.6524, IoU.field: 0.2931, IoU.armchair: 0.5670, IoU.seat: 0.6154, IoU.fence: 0.4924, IoU.desk: 0.4651, IoU.rock: 0.5418, IoU.wardrobe: 0.5463, IoU.lamp: 0.6120, IoU.bathtub: 0.8073, IoU.railing: 0.3670, IoU.cushion: 0.6245, IoU.base: 0.4107, IoU.box: 0.3227, IoU.column: 0.4860, IoU.signboard: 0.3106, IoU.chest of drawers: 0.3834, IoU.counter: 0.4617, IoU.sand: 0.3584, IoU.sink: 0.7168, IoU.skyscraper: 0.3439, IoU.fireplace: 0.6867, IoU.refrigerator: 0.7359, IoU.grandstand: 0.3742, IoU.path: 0.1903, IoU.stairs: 0.3187, IoU.runway: 0.7146, IoU.case: 0.5646, IoU.pool table: 0.9019, IoU.pillow: 0.6332, IoU.screen door: 0.6517, IoU.stairway: 0.4503, IoU.river: 0.0913, IoU.bridge: 0.4652, IoU.bookcase: 0.3018, IoU.blind: 0.0722, IoU.coffee table: 0.5792, IoU.toilet: 0.8665, IoU.flower: 0.3636, IoU.book: 0.4382, IoU.hill: 0.0549, IoU.bench: 0.5792, IoU.countertop: 0.6006, IoU.stove: 0.7847, IoU.palm: 0.4486, IoU.kitchen island: 0.3846, IoU.computer: 0.7127, IoU.swivel chair: 0.4821, IoU.boat: 0.7027, IoU.bar: 0.5612, IoU.arcade machine: 0.8342, IoU.hovel: 0.4831, IoU.bus: 0.7621, IoU.towel: 0.6067, IoU.light: 0.4400, IoU.truck: 0.0859, IoU.tower: 0.2600, IoU.chandelier: 0.6227, IoU.awning: 0.3984, IoU.streetlight: 0.1823, IoU.booth: 0.2758, IoU.television receiver: 0.7371, IoU.airplane: 0.7548, IoU.dirt track: 0.0000, IoU.apparel: 0.3727, IoU.pole: 0.0801, IoU.land: 0.0842, IoU.bannister: 0.0624, IoU.escalator: 0.5850, IoU.ottoman: 0.5277, IoU.bottle: 0.3859, IoU.buffet: 0.4692, IoU.poster: 0.3907, IoU.stage: 0.1402, IoU.van: 0.4449, IoU.ship: 0.0773, IoU.fountain: 0.3583, IoU.conveyer belt: 0.7316, IoU.canopy: 0.5948, IoU.washer: 0.7926, IoU.plaything: 0.2358, IoU.swimming pool: 0.5901, IoU.stool: 0.3966, IoU.barrel: 0.3930, IoU.basket: 0.3779, IoU.waterfall: 0.2632, IoU.tent: 0.8319, IoU.bag: 0.2096, IoU.minibike: 0.6842, IoU.cradle: 0.7848, IoU.oven: 0.3638, IoU.ball: 0.0077, IoU.food: 0.5436, IoU.step: 0.0193, IoU.tank: 0.5118, IoU.trade name: 0.2773, IoU.microwave: 0.8104, IoU.pot: 0.4693, IoU.animal: 0.6975, IoU.bicycle: 0.4934, IoU.lake: 0.0000, IoU.dishwasher: 0.6796, IoU.screen: 0.5741, IoU.blanket: 0.0624, IoU.sculpture: 0.5790, IoU.hood: 0.7016, IoU.sconce: 0.4300, IoU.vase: 0.3551, IoU.traffic light: 0.2384, IoU.tray: 0.1088, IoU.ashcan: 0.4609, IoU.fan: 0.5393, IoU.pier: 0.6309, IoU.crt screen: 0.1045, IoU.plate: 0.5318, IoU.monitor: 0.5181, IoU.bulletin board: 0.5637, IoU.shower: 0.0000, IoU.radiator: 0.6338, IoU.glass: 0.0660, IoU.clock: 0.3995, IoU.flag: 0.6418, Acc.wall: 0.8770, Acc.building: 0.9015, Acc.sky: 0.9615, Acc.floor: 0.8906, Acc.tree: 0.8610, Acc.ceiling: 0.9199, Acc.road: 0.8678, Acc.bed : 0.9587, Acc.windowpane: 0.7338, Acc.grass: 0.7496, Acc.cabinet: 0.7146, Acc.sidewalk: 0.8800, Acc.person: 0.9002, Acc.earth: 0.6403, Acc.door: 0.7656, Acc.table: 0.8039, Acc.mountain: 0.5981, Acc.plant: 0.6356, Acc.curtain: 0.9024, Acc.chair: 0.6983, Acc.car: 0.8983, Acc.water: 0.8923, Acc.painting: 0.8154, Acc.sofa: 0.8932, Acc.shelf: 0.5256, Acc.house: 0.8113, Acc.sea: 0.7190, Acc.mirror: 0.8155, Acc.rug: 0.8465, Acc.field: 0.5123, Acc.armchair: 0.7565, Acc.seat: 0.8779, Acc.fence: 0.5852, Acc.desk: 0.5622, Acc.rock: 0.8093, Acc.wardrobe: 0.7751, Acc.lamp: 0.7682, Acc.bathtub: 0.8463, Acc.railing: 0.4880, Acc.cushion: 0.7545, Acc.base: 0.6092, Acc.box: 0.4099, Acc.column: 0.6989, Acc.signboard: 0.3903, Acc.chest of drawers: 0.7020, Acc.counter: 0.5377, Acc.sand: 0.5524, Acc.sink: 0.7832, Acc.skyscraper: 0.3586, Acc.fireplace: 0.9135, Acc.refrigerator: 0.8711, Acc.grandstand: 0.8985, Acc.path: 0.2167, Acc.stairs: 0.3371, Acc.runway: 0.9485, Acc.case: 0.7829, Acc.pool table: 0.9718, Acc.pillow: 0.7507, Acc.screen door: 0.9053, Acc.stairway: 0.7110, Acc.river: 0.1019, Acc.bridge: 0.5812, Acc.bookcase: 0.7008, Acc.blind: 0.0733, Acc.coffee table: 0.6409, Acc.toilet: 0.9480, Acc.flower: 0.5279, Acc.book: 0.5579, Acc.hill: 0.0666, Acc.bench: 0.6371, Acc.countertop: 0.7160, Acc.stove: 0.9334, Acc.palm: 0.8370, Acc.kitchen island: 0.6458, Acc.computer: 0.8988, Acc.swivel chair: 0.9050, Acc.boat: 0.8932, Acc.bar: 0.7674, Acc.arcade machine: 0.9913, Acc.hovel: 0.5816, Acc.bus: 0.9672, Acc.towel: 0.6971, Acc.light: 0.5596, Acc.truck: 0.0956, Acc.tower: 0.4546, Acc.chandelier: 0.8432, Acc.awning: 0.6451, Acc.streetlight: 0.2393, Acc.booth: 0.5439, Acc.television receiver: 0.7857, Acc.airplane: 0.9328, Acc.dirt track: 0.0000, Acc.apparel: 0.5282, Acc.pole: 0.0904, Acc.land: 0.2082, Acc.bannister: 0.1124, Acc.escalator: 0.8777, Acc.ottoman: 0.7202, Acc.bottle: 0.6647, Acc.buffet: 0.6300, Acc.poster: 0.5309, Acc.stage: 0.2145, Acc.van: 0.7379, Acc.ship: 0.0799, Acc.fountain: 0.4370, Acc.conveyer belt: 0.9635, Acc.canopy: 0.6540, Acc.washer: 0.8671, Acc.plaything: 0.7620, Acc.swimming pool: 0.9146, Acc.stool: 0.5105, Acc.barrel: 0.6512, Acc.basket: 0.4991, Acc.waterfall: 0.2647, Acc.tent: 0.9962, Acc.bag: 0.2317, Acc.minibike: 0.8612, Acc.cradle: 0.9774, Acc.oven: 0.3985, Acc.ball: 0.0077, Acc.food: 0.7733, Acc.step: 0.0203, Acc.tank: 0.7623, Acc.trade name: 0.4721, Acc.microwave: 0.9641, Acc.pot: 0.5369, Acc.animal: 0.7464, Acc.bicycle: 0.5774, Acc.lake: 0.0000, Acc.dishwasher: 0.7460, Acc.screen: 0.6678, Acc.blanket: 0.0675, Acc.sculpture: 0.6569, Acc.hood: 0.8904, Acc.sconce: 0.5350, Acc.vase: 0.4345, Acc.traffic light: 0.4618, Acc.tray: 0.1568, Acc.ashcan: 0.5510, Acc.fan: 0.6655, Acc.pier: 0.7686, Acc.crt screen: 0.1528, Acc.plate: 0.7506, Acc.monitor: 0.7463, Acc.bulletin board: 0.6556, Acc.shower: 0.0000, Acc.radiator: 0.7887, Acc.glass: 0.0678, Acc.clock: 0.4406, Acc.flag: 0.7042 +2024-06-18 05:29:14,398 - mmseg - INFO - Iter [5050/80000] lr: 3.748e-05, eta: 1 day, 20:32:20, time: 4.172, data_time: 2.199, memory: 72263, decode.loss_ce: 0.5392, decode.acc_seg: 79.7322, aux.loss_ce: 0.2132, aux.acc_seg: 80.1339, loss: 0.7524 +2024-06-18 05:30:55,635 - mmseg - INFO - Iter [5100/80000] lr: 3.745e-05, eta: 1 day, 20:29:09, time: 2.025, data_time: 0.052, memory: 72263, decode.loss_ce: 0.4925, decode.acc_seg: 81.3439, aux.loss_ce: 0.1935, aux.acc_seg: 81.6255, loss: 0.6860 +2024-06-18 05:32:34,592 - mmseg - INFO - Iter [5150/80000] lr: 3.743e-05, eta: 1 day, 20:25:27, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4841, decode.acc_seg: 81.5066, aux.loss_ce: 0.1912, aux.acc_seg: 81.6819, loss: 0.6753 +2024-06-18 05:34:13,587 - mmseg - INFO - Iter [5200/80000] lr: 3.740e-05, eta: 1 day, 20:21:47, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4825, decode.acc_seg: 81.5851, aux.loss_ce: 0.1910, aux.acc_seg: 81.6944, loss: 0.6735 +2024-06-18 05:35:52,587 - mmseg - INFO - Iter [5250/80000] lr: 3.738e-05, eta: 1 day, 20:18:10, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4880, decode.acc_seg: 81.1458, aux.loss_ce: 0.1933, aux.acc_seg: 81.2328, loss: 0.6813 +2024-06-18 05:37:31,525 - mmseg - INFO - Iter [5300/80000] lr: 3.735e-05, eta: 1 day, 20:14:34, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4827, decode.acc_seg: 81.1650, aux.loss_ce: 0.1898, aux.acc_seg: 81.5773, loss: 0.6725 +2024-06-18 05:39:10,621 - mmseg - INFO - Iter [5350/80000] lr: 3.733e-05, eta: 1 day, 20:11:02, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4951, decode.acc_seg: 81.2125, aux.loss_ce: 0.1954, aux.acc_seg: 81.5070, loss: 0.6904 +2024-06-18 05:40:49,598 - mmseg - INFO - Iter [5400/80000] lr: 3.730e-05, eta: 1 day, 20:07:31, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4690, decode.acc_seg: 82.2968, aux.loss_ce: 0.1861, aux.acc_seg: 82.1813, loss: 0.6551 +2024-06-18 05:42:28,661 - mmseg - INFO - Iter [5450/80000] lr: 3.728e-05, eta: 1 day, 20:04:04, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4840, decode.acc_seg: 80.4110, aux.loss_ce: 0.1907, aux.acc_seg: 80.9127, loss: 0.6746 +2024-06-18 05:44:07,657 - mmseg - INFO - Iter [5500/80000] lr: 3.725e-05, eta: 1 day, 20:00:37, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4861, decode.acc_seg: 81.3753, aux.loss_ce: 0.1920, aux.acc_seg: 81.9558, loss: 0.6781 +2024-06-18 05:45:46,559 - mmseg - INFO - Iter [5550/80000] lr: 3.723e-05, eta: 1 day, 19:57:11, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4907, decode.acc_seg: 81.4836, aux.loss_ce: 0.1952, aux.acc_seg: 81.7330, loss: 0.6859 +2024-06-18 05:47:25,509 - mmseg - INFO - Iter [5600/80000] lr: 3.720e-05, eta: 1 day, 19:53:47, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4908, decode.acc_seg: 81.4411, aux.loss_ce: 0.1939, aux.acc_seg: 81.8680, loss: 0.6847 +2024-06-18 05:49:04,426 - mmseg - INFO - Iter [5650/80000] lr: 3.718e-05, eta: 1 day, 19:50:25, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5044, decode.acc_seg: 80.7713, aux.loss_ce: 0.1986, aux.acc_seg: 81.2069, loss: 0.7030 +2024-06-18 05:50:43,474 - mmseg - INFO - Iter [5700/80000] lr: 3.715e-05, eta: 1 day, 19:47:07, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4931, decode.acc_seg: 81.5000, aux.loss_ce: 0.1950, aux.acc_seg: 81.6338, loss: 0.6881 +2024-06-18 05:52:22,462 - mmseg - INFO - Iter [5750/80000] lr: 3.713e-05, eta: 1 day, 19:43:49, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5088, decode.acc_seg: 80.9677, aux.loss_ce: 0.2003, aux.acc_seg: 81.2096, loss: 0.7091 +2024-06-18 05:54:01,463 - mmseg - INFO - Iter [5800/80000] lr: 3.710e-05, eta: 1 day, 19:40:34, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5120, decode.acc_seg: 80.4640, aux.loss_ce: 0.2012, aux.acc_seg: 80.9295, loss: 0.7131 +2024-06-18 05:55:40,522 - mmseg - INFO - Iter [5850/80000] lr: 3.708e-05, eta: 1 day, 19:37:20, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4909, decode.acc_seg: 81.3698, aux.loss_ce: 0.1948, aux.acc_seg: 81.6612, loss: 0.6857 +2024-06-18 05:57:19,458 - mmseg - INFO - Iter [5900/80000] lr: 3.705e-05, eta: 1 day, 19:34:07, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5105, decode.acc_seg: 81.1496, aux.loss_ce: 0.2000, aux.acc_seg: 81.4029, loss: 0.7105 +2024-06-18 05:58:58,429 - mmseg - INFO - Iter [5950/80000] lr: 3.703e-05, eta: 1 day, 19:30:56, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4898, decode.acc_seg: 81.7072, aux.loss_ce: 0.1943, aux.acc_seg: 81.7320, loss: 0.6841 +2024-06-18 06:00:37,392 - mmseg - INFO - Saving checkpoint at 6000 iterations +2024-06-18 06:02:02,733 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 06:02:02,733 - mmseg - INFO - Iter [6000/80000] lr: 3.700e-05, eta: 1 day, 19:45:18, time: 3.686, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4876, decode.acc_seg: 81.7363, aux.loss_ce: 0.1927, aux.acc_seg: 81.9879, loss: 0.6803 +2024-06-18 06:03:51,505 - mmseg - INFO - per class results: +2024-06-18 06:03:51,511 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 77.48 | 85.23 | +| building | 82.14 | 92.08 | +| sky | 93.61 | 96.96 | +| floor | 81.87 | 88.13 | +| tree | 74.52 | 88.04 | +| ceiling | 83.03 | 94.99 | +| road | 84.34 | 89.29 | +| bed | 85.81 | 98.53 | +| windowpane | 62.73 | 80.46 | +| grass | 60.89 | 71.77 | +| cabinet | 60.12 | 66.09 | +| sidewalk | 63.97 | 88.24 | +| person | 80.2 | 90.36 | +| earth | 37.56 | 51.17 | +| door | 53.37 | 65.31 | +| table | 62.01 | 77.27 | +| mountain | 59.64 | 68.11 | +| plant | 52.51 | 62.07 | +| curtain | 71.87 | 87.58 | +| chair | 59.94 | 80.09 | +| car | 82.06 | 93.59 | +| water | 63.64 | 83.8 | +| painting | 70.44 | 86.54 | +| sofa | 75.68 | 89.91 | +| shelf | 44.0 | 69.08 | +| house | 52.15 | 79.66 | +| sea | 68.72 | 87.05 | +| mirror | 73.34 | 82.33 | +| rug | 67.17 | 80.51 | +| field | 26.55 | 79.64 | +| armchair | 50.11 | 56.71 | +| seat | 69.08 | 85.17 | +| fence | 48.84 | 55.35 | +| desk | 49.11 | 70.05 | +| rock | 53.8 | 70.6 | +| wardrobe | 57.23 | 71.04 | +| lamp | 60.82 | 71.01 | +| bathtub | 81.83 | 85.66 | +| railing | 38.56 | 52.57 | +| cushion | 52.76 | 57.69 | +| base | 42.26 | 67.26 | +| box | 23.34 | 25.3 | +| column | 47.62 | 60.5 | +| signboard | 33.85 | 41.45 | +| chest of drawers | 35.87 | 75.77 | +| counter | 42.42 | 74.54 | +| sand | 52.09 | 83.92 | +| sink | 71.25 | 81.45 | +| skyscraper | 47.89 | 68.49 | +| fireplace | 67.55 | 93.38 | +| refrigerator | 67.2 | 89.82 | +| grandstand | 48.31 | 77.97 | +| path | 10.88 | 12.28 | +| stairs | 37.72 | 63.48 | +| runway | 69.12 | 94.23 | +| case | 53.45 | 83.35 | +| pool table | 90.42 | 98.25 | +| pillow | 44.12 | 50.1 | +| screen door | 72.46 | 85.99 | +| stairway | 29.44 | 31.53 | +| river | 31.45 | 38.06 | +| bridge | 74.05 | 86.48 | +| bookcase | 33.3 | 50.73 | +| blind | 22.86 | 23.93 | +| coffee table | 50.31 | 89.7 | +| toilet | 85.68 | 94.27 | +| flower | 21.13 | 22.27 | +| book | 49.88 | 69.79 | +| hill | 6.62 | 13.72 | +| bench | 54.33 | 60.96 | +| countertop | 60.14 | 79.77 | +| stove | 76.19 | 92.44 | +| palm | 53.85 | 73.03 | +| kitchen island | 40.38 | 76.12 | +| computer | 72.34 | 88.2 | +| swivel chair | 48.26 | 64.66 | +| boat | 40.32 | 42.18 | +| bar | 43.91 | 52.16 | +| arcade machine | 88.29 | 94.1 | +| hovel | 14.04 | 15.48 | +| bus | 89.5 | 95.49 | +| towel | 62.84 | 72.41 | +| light | 42.71 | 49.59 | +| truck | 40.85 | 49.01 | +| tower | 8.74 | 11.58 | +| chandelier | 61.71 | 85.8 | +| awning | 40.31 | 49.78 | +| streetlight | 20.48 | 26.49 | +| booth | 34.77 | 64.03 | +| television receiver | 72.29 | 82.83 | +| airplane | 62.36 | 72.15 | +| dirt track | 4.52 | 8.13 | +| apparel | 37.01 | 45.18 | +| pole | 7.44 | 7.91 | +| land | 0.67 | 0.8 | +| bannister | 2.84 | 3.12 | +| escalator | 59.22 | 88.79 | +| ottoman | 55.27 | 69.15 | +| bottle | 25.68 | 27.43 | +| buffet | 46.24 | 81.93 | +| poster | 9.9 | 10.08 | +| stage | 24.19 | 72.51 | +| van | 27.63 | 34.43 | +| ship | 43.49 | 65.8 | +| fountain | 9.25 | 9.31 | +| conveyer belt | 69.33 | 98.33 | +| canopy | 27.11 | 33.67 | +| washer | 83.8 | 90.38 | +| plaything | 39.36 | 67.59 | +| swimming pool | 55.82 | 82.03 | +| stool | 33.83 | 45.73 | +| barrel | 52.62 | 65.0 | +| basket | 37.29 | 54.66 | +| waterfall | 61.79 | 70.54 | +| tent | 86.05 | 99.62 | +| bag | 28.22 | 30.94 | +| minibike | 70.36 | 82.51 | +| cradle | 86.15 | 95.91 | +| oven | 51.95 | 61.67 | +| ball | 30.6 | 71.75 | +| food | 44.37 | 49.53 | +| step | 10.47 | 10.9 | +| tank | 54.53 | 77.06 | +| trade name | 6.38 | 6.45 | +| microwave | 80.68 | 96.42 | +| pot | 46.27 | 52.3 | +| animal | 54.38 | 55.6 | +| bicycle | 56.89 | 78.92 | +| lake | 0.0 | 0.0 | +| dishwasher | 56.21 | 83.98 | +| screen | 57.12 | 90.65 | +| blanket | 18.67 | 21.47 | +| sculpture | 65.84 | 83.68 | +| hood | 5.07 | 5.07 | +| sconce | 45.76 | 55.33 | +| vase | 38.56 | 55.51 | +| traffic light | 26.55 | 43.85 | +| tray | 8.61 | 10.2 | +| ashcan | 44.19 | 55.89 | +| fan | 57.36 | 74.49 | +| pier | 38.5 | 42.78 | +| crt screen | 6.37 | 7.1 | +| plate | 50.83 | 55.88 | +| monitor | 56.52 | 77.69 | +| bulletin board | 52.39 | 59.69 | +| shower | 0.1 | 0.19 | +| radiator | 59.2 | 67.11 | +| glass | 4.43 | 4.48 | +| clock | 41.93 | 47.29 | +| flag | 49.71 | 51.06 | ++---------------------+-------+-------+ +2024-06-18 06:03:51,511 - mmseg - INFO - Summary: +2024-06-18 06:03:51,511 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 83.09 | 49.13 | 62.48 | ++-------+-------+-------+ +2024-06-18 06:03:51,512 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 06:03:51,512 - mmseg - INFO - Iter(val) [250] aAcc: 0.8309, mIoU: 0.4913, mAcc: 0.6248, IoU.wall: 0.7748, IoU.building: 0.8214, IoU.sky: 0.9361, IoU.floor: 0.8187, IoU.tree: 0.7452, IoU.ceiling: 0.8303, IoU.road: 0.8434, IoU.bed : 0.8581, IoU.windowpane: 0.6273, IoU.grass: 0.6089, IoU.cabinet: 0.6012, IoU.sidewalk: 0.6397, IoU.person: 0.8020, IoU.earth: 0.3756, IoU.door: 0.5337, IoU.table: 0.6201, IoU.mountain: 0.5964, IoU.plant: 0.5251, IoU.curtain: 0.7187, IoU.chair: 0.5994, IoU.car: 0.8206, IoU.water: 0.6364, IoU.painting: 0.7044, IoU.sofa: 0.7568, IoU.shelf: 0.4400, IoU.house: 0.5215, IoU.sea: 0.6872, IoU.mirror: 0.7334, IoU.rug: 0.6717, IoU.field: 0.2655, IoU.armchair: 0.5011, IoU.seat: 0.6908, IoU.fence: 0.4884, IoU.desk: 0.4911, IoU.rock: 0.5380, IoU.wardrobe: 0.5723, IoU.lamp: 0.6082, IoU.bathtub: 0.8183, IoU.railing: 0.3856, IoU.cushion: 0.5276, IoU.base: 0.4226, IoU.box: 0.2334, IoU.column: 0.4762, IoU.signboard: 0.3385, IoU.chest of drawers: 0.3587, IoU.counter: 0.4242, IoU.sand: 0.5209, IoU.sink: 0.7125, IoU.skyscraper: 0.4789, IoU.fireplace: 0.6755, IoU.refrigerator: 0.6720, IoU.grandstand: 0.4831, IoU.path: 0.1088, IoU.stairs: 0.3772, IoU.runway: 0.6912, IoU.case: 0.5345, IoU.pool table: 0.9042, IoU.pillow: 0.4412, IoU.screen door: 0.7246, IoU.stairway: 0.2944, IoU.river: 0.3145, IoU.bridge: 0.7405, IoU.bookcase: 0.3330, IoU.blind: 0.2286, IoU.coffee table: 0.5031, IoU.toilet: 0.8568, IoU.flower: 0.2113, IoU.book: 0.4988, IoU.hill: 0.0662, IoU.bench: 0.5433, IoU.countertop: 0.6014, IoU.stove: 0.7619, IoU.palm: 0.5385, IoU.kitchen island: 0.4038, IoU.computer: 0.7234, IoU.swivel chair: 0.4826, IoU.boat: 0.4032, IoU.bar: 0.4391, IoU.arcade machine: 0.8829, IoU.hovel: 0.1404, IoU.bus: 0.8950, IoU.towel: 0.6284, IoU.light: 0.4271, IoU.truck: 0.4085, IoU.tower: 0.0874, IoU.chandelier: 0.6171, IoU.awning: 0.4031, IoU.streetlight: 0.2048, IoU.booth: 0.3477, IoU.television receiver: 0.7229, IoU.airplane: 0.6236, IoU.dirt track: 0.0452, IoU.apparel: 0.3701, IoU.pole: 0.0744, IoU.land: 0.0067, IoU.bannister: 0.0284, IoU.escalator: 0.5922, IoU.ottoman: 0.5527, IoU.bottle: 0.2568, IoU.buffet: 0.4624, IoU.poster: 0.0990, IoU.stage: 0.2419, IoU.van: 0.2763, IoU.ship: 0.4349, IoU.fountain: 0.0925, IoU.conveyer belt: 0.6933, IoU.canopy: 0.2711, IoU.washer: 0.8380, IoU.plaything: 0.3936, IoU.swimming pool: 0.5582, IoU.stool: 0.3383, IoU.barrel: 0.5262, IoU.basket: 0.3729, IoU.waterfall: 0.6179, IoU.tent: 0.8605, IoU.bag: 0.2822, IoU.minibike: 0.7036, IoU.cradle: 0.8615, IoU.oven: 0.5195, IoU.ball: 0.3060, IoU.food: 0.4437, IoU.step: 0.1047, IoU.tank: 0.5453, IoU.trade name: 0.0638, IoU.microwave: 0.8068, IoU.pot: 0.4627, IoU.animal: 0.5438, IoU.bicycle: 0.5689, IoU.lake: 0.0000, IoU.dishwasher: 0.5621, IoU.screen: 0.5712, IoU.blanket: 0.1867, IoU.sculpture: 0.6584, IoU.hood: 0.0507, IoU.sconce: 0.4576, IoU.vase: 0.3856, IoU.traffic light: 0.2655, IoU.tray: 0.0861, IoU.ashcan: 0.4419, IoU.fan: 0.5736, IoU.pier: 0.3850, IoU.crt screen: 0.0637, IoU.plate: 0.5083, IoU.monitor: 0.5652, IoU.bulletin board: 0.5239, IoU.shower: 0.0010, IoU.radiator: 0.5920, IoU.glass: 0.0443, IoU.clock: 0.4193, IoU.flag: 0.4971, Acc.wall: 0.8523, Acc.building: 0.9208, Acc.sky: 0.9696, Acc.floor: 0.8813, Acc.tree: 0.8804, Acc.ceiling: 0.9499, Acc.road: 0.8929, Acc.bed : 0.9853, Acc.windowpane: 0.8046, Acc.grass: 0.7177, Acc.cabinet: 0.6609, Acc.sidewalk: 0.8824, Acc.person: 0.9036, Acc.earth: 0.5117, Acc.door: 0.6531, Acc.table: 0.7727, Acc.mountain: 0.6811, Acc.plant: 0.6207, Acc.curtain: 0.8758, Acc.chair: 0.8009, Acc.car: 0.9359, Acc.water: 0.8380, Acc.painting: 0.8654, Acc.sofa: 0.8991, Acc.shelf: 0.6908, Acc.house: 0.7966, Acc.sea: 0.8705, Acc.mirror: 0.8233, Acc.rug: 0.8051, Acc.field: 0.7964, Acc.armchair: 0.5671, Acc.seat: 0.8517, Acc.fence: 0.5535, Acc.desk: 0.7005, Acc.rock: 0.7060, Acc.wardrobe: 0.7104, Acc.lamp: 0.7101, Acc.bathtub: 0.8566, Acc.railing: 0.5257, Acc.cushion: 0.5769, Acc.base: 0.6726, Acc.box: 0.2530, Acc.column: 0.6050, Acc.signboard: 0.4145, Acc.chest of drawers: 0.7577, Acc.counter: 0.7454, Acc.sand: 0.8392, Acc.sink: 0.8145, Acc.skyscraper: 0.6849, Acc.fireplace: 0.9338, Acc.refrigerator: 0.8982, Acc.grandstand: 0.7797, Acc.path: 0.1228, Acc.stairs: 0.6348, Acc.runway: 0.9423, Acc.case: 0.8335, Acc.pool table: 0.9825, Acc.pillow: 0.5010, Acc.screen door: 0.8599, Acc.stairway: 0.3153, Acc.river: 0.3806, Acc.bridge: 0.8648, Acc.bookcase: 0.5073, Acc.blind: 0.2393, Acc.coffee table: 0.8970, Acc.toilet: 0.9427, Acc.flower: 0.2227, Acc.book: 0.6979, Acc.hill: 0.1372, Acc.bench: 0.6096, Acc.countertop: 0.7977, Acc.stove: 0.9244, Acc.palm: 0.7303, Acc.kitchen island: 0.7612, Acc.computer: 0.8820, Acc.swivel chair: 0.6466, Acc.boat: 0.4218, Acc.bar: 0.5216, Acc.arcade machine: 0.9410, Acc.hovel: 0.1548, Acc.bus: 0.9549, Acc.towel: 0.7241, Acc.light: 0.4959, Acc.truck: 0.4901, Acc.tower: 0.1158, Acc.chandelier: 0.8580, Acc.awning: 0.4978, Acc.streetlight: 0.2649, Acc.booth: 0.6403, Acc.television receiver: 0.8283, Acc.airplane: 0.7215, Acc.dirt track: 0.0813, Acc.apparel: 0.4518, Acc.pole: 0.0791, Acc.land: 0.0080, Acc.bannister: 0.0312, Acc.escalator: 0.8879, Acc.ottoman: 0.6915, Acc.bottle: 0.2743, Acc.buffet: 0.8193, Acc.poster: 0.1008, Acc.stage: 0.7251, Acc.van: 0.3443, Acc.ship: 0.6580, Acc.fountain: 0.0931, Acc.conveyer belt: 0.9833, Acc.canopy: 0.3367, Acc.washer: 0.9038, Acc.plaything: 0.6759, Acc.swimming pool: 0.8203, Acc.stool: 0.4573, Acc.barrel: 0.6500, Acc.basket: 0.5466, Acc.waterfall: 0.7054, Acc.tent: 0.9962, Acc.bag: 0.3094, Acc.minibike: 0.8251, Acc.cradle: 0.9591, Acc.oven: 0.6167, Acc.ball: 0.7175, Acc.food: 0.4953, Acc.step: 0.1090, Acc.tank: 0.7706, Acc.trade name: 0.0645, Acc.microwave: 0.9642, Acc.pot: 0.5230, Acc.animal: 0.5560, Acc.bicycle: 0.7892, Acc.lake: 0.0000, Acc.dishwasher: 0.8398, Acc.screen: 0.9065, Acc.blanket: 0.2147, Acc.sculpture: 0.8368, Acc.hood: 0.0507, Acc.sconce: 0.5533, Acc.vase: 0.5551, Acc.traffic light: 0.4385, Acc.tray: 0.1020, Acc.ashcan: 0.5589, Acc.fan: 0.7449, Acc.pier: 0.4278, Acc.crt screen: 0.0710, Acc.plate: 0.5588, Acc.monitor: 0.7769, Acc.bulletin board: 0.5969, Acc.shower: 0.0019, Acc.radiator: 0.6711, Acc.glass: 0.0448, Acc.clock: 0.4729, Acc.flag: 0.5106 +2024-06-18 06:05:30,937 - mmseg - INFO - Iter [6050/80000] lr: 3.698e-05, eta: 1 day, 20:04:16, time: 4.164, data_time: 2.193, memory: 72263, decode.loss_ce: 0.4957, decode.acc_seg: 81.2834, aux.loss_ce: 0.1958, aux.acc_seg: 81.6670, loss: 0.6914 +2024-06-18 06:07:09,918 - mmseg - INFO - Iter [6100/80000] lr: 3.695e-05, eta: 1 day, 20:00:48, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5261, decode.acc_seg: 79.9115, aux.loss_ce: 0.2060, aux.acc_seg: 80.3336, loss: 0.7321 +2024-06-18 06:08:49,017 - mmseg - INFO - Iter [6150/80000] lr: 3.693e-05, eta: 1 day, 19:57:24, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5109, decode.acc_seg: 80.4148, aux.loss_ce: 0.1988, aux.acc_seg: 80.7083, loss: 0.7097 +2024-06-18 06:10:28,085 - mmseg - INFO - Iter [6200/80000] lr: 3.690e-05, eta: 1 day, 19:54:00, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5444, decode.acc_seg: 79.1239, aux.loss_ce: 0.2123, aux.acc_seg: 79.9601, loss: 0.7567 +2024-06-18 06:12:07,116 - mmseg - INFO - Iter [6250/80000] lr: 3.688e-05, eta: 1 day, 19:50:38, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.4784, decode.acc_seg: 81.6324, aux.loss_ce: 0.1886, aux.acc_seg: 81.9149, loss: 0.6670 +2024-06-18 06:13:46,082 - mmseg - INFO - Iter [6300/80000] lr: 3.685e-05, eta: 1 day, 19:47:17, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4775, decode.acc_seg: 81.3792, aux.loss_ce: 0.1880, aux.acc_seg: 81.6962, loss: 0.6655 +2024-06-18 06:15:27,248 - mmseg - INFO - Iter [6350/80000] lr: 3.683e-05, eta: 1 day, 19:44:23, time: 2.023, data_time: 0.053, memory: 72263, decode.loss_ce: 0.4525, decode.acc_seg: 82.1001, aux.loss_ce: 0.1792, aux.acc_seg: 82.4794, loss: 0.6317 +2024-06-18 06:17:06,250 - mmseg - INFO - Iter [6400/80000] lr: 3.680e-05, eta: 1 day, 19:41:06, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4477, decode.acc_seg: 82.6984, aux.loss_ce: 0.1768, aux.acc_seg: 83.0267, loss: 0.6245 +2024-06-18 06:18:45,258 - mmseg - INFO - Iter [6450/80000] lr: 3.678e-05, eta: 1 day, 19:37:49, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4794, decode.acc_seg: 81.5903, aux.loss_ce: 0.1884, aux.acc_seg: 81.9318, loss: 0.6678 +2024-06-18 06:20:24,415 - mmseg - INFO - Iter [6500/80000] lr: 3.675e-05, eta: 1 day, 19:34:36, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4391, decode.acc_seg: 83.0884, aux.loss_ce: 0.1739, aux.acc_seg: 83.4080, loss: 0.6130 +2024-06-18 06:22:03,512 - mmseg - INFO - Iter [6550/80000] lr: 3.673e-05, eta: 1 day, 19:31:24, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4700, decode.acc_seg: 81.7832, aux.loss_ce: 0.1842, aux.acc_seg: 82.3652, loss: 0.6542 +2024-06-18 06:23:42,569 - mmseg - INFO - Iter [6600/80000] lr: 3.670e-05, eta: 1 day, 19:28:13, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4417, decode.acc_seg: 83.3460, aux.loss_ce: 0.1747, aux.acc_seg: 83.4788, loss: 0.6164 +2024-06-18 06:25:21,639 - mmseg - INFO - Iter [6650/80000] lr: 3.668e-05, eta: 1 day, 19:25:03, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4749, decode.acc_seg: 82.3263, aux.loss_ce: 0.1876, aux.acc_seg: 82.4244, loss: 0.6625 +2024-06-18 06:27:00,863 - mmseg - INFO - Iter [6700/80000] lr: 3.665e-05, eta: 1 day, 19:21:57, time: 1.984, data_time: 0.011, memory: 72263, decode.loss_ce: 0.4362, decode.acc_seg: 82.7164, aux.loss_ce: 0.1722, aux.acc_seg: 83.0778, loss: 0.6084 +2024-06-18 06:28:39,944 - mmseg - INFO - Iter [6750/80000] lr: 3.663e-05, eta: 1 day, 19:18:50, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4818, decode.acc_seg: 81.2311, aux.loss_ce: 0.1907, aux.acc_seg: 81.3731, loss: 0.6725 +2024-06-18 06:30:18,971 - mmseg - INFO - Iter [6800/80000] lr: 3.660e-05, eta: 1 day, 19:15:44, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4944, decode.acc_seg: 81.4626, aux.loss_ce: 0.1956, aux.acc_seg: 81.7121, loss: 0.6900 +2024-06-18 06:31:58,163 - mmseg - INFO - Iter [6850/80000] lr: 3.658e-05, eta: 1 day, 19:12:40, time: 1.984, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4808, decode.acc_seg: 81.7021, aux.loss_ce: 0.1882, aux.acc_seg: 82.2322, loss: 0.6690 +2024-06-18 06:33:37,305 - mmseg - INFO - Iter [6900/80000] lr: 3.655e-05, eta: 1 day, 19:09:38, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4654, decode.acc_seg: 83.0654, aux.loss_ce: 0.1848, aux.acc_seg: 83.0556, loss: 0.6502 +2024-06-18 06:35:16,314 - mmseg - INFO - Iter [6950/80000] lr: 3.653e-05, eta: 1 day, 19:06:35, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4656, decode.acc_seg: 81.5747, aux.loss_ce: 0.1847, aux.acc_seg: 81.8895, loss: 0.6502 +2024-06-18 06:36:55,413 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 06:36:55,413 - mmseg - INFO - Iter [7000/80000] lr: 3.650e-05, eta: 1 day, 19:03:35, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4834, decode.acc_seg: 80.9666, aux.loss_ce: 0.1892, aux.acc_seg: 81.3561, loss: 0.6725 +2024-06-18 06:38:44,772 - mmseg - INFO - per class results: +2024-06-18 06:38:44,779 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 75.05 | 81.16 | +| building | 83.11 | 91.72 | +| sky | 93.78 | 97.38 | +| floor | 81.49 | 87.19 | +| tree | 74.19 | 85.11 | +| ceiling | 82.59 | 94.7 | +| road | 84.85 | 89.41 | +| bed | 90.18 | 95.64 | +| windowpane | 62.13 | 80.39 | +| grass | 63.15 | 86.68 | +| cabinet | 61.3 | 74.18 | +| sidewalk | 65.55 | 79.71 | +| person | 80.53 | 91.64 | +| earth | 33.42 | 40.77 | +| door | 45.53 | 84.01 | +| table | 59.73 | 70.61 | +| mountain | 54.67 | 71.18 | +| plant | 55.5 | 66.08 | +| curtain | 72.46 | 89.12 | +| chair | 58.64 | 72.75 | +| car | 82.61 | 93.61 | +| water | 57.52 | 73.32 | +| painting | 71.95 | 85.53 | +| sofa | 76.94 | 85.71 | +| shelf | 39.66 | 51.23 | +| house | 46.01 | 66.12 | +| sea | 65.62 | 94.01 | +| mirror | 71.13 | 88.07 | +| rug | 65.17 | 87.76 | +| field | 31.77 | 58.85 | +| armchair | 57.09 | 75.49 | +| seat | 64.11 | 84.96 | +| fence | 48.94 | 57.1 | +| desk | 47.73 | 78.33 | +| rock | 59.89 | 73.47 | +| wardrobe | 51.07 | 73.93 | +| lamp | 63.85 | 79.38 | +| bathtub | 86.92 | 94.14 | +| railing | 33.37 | 45.5 | +| cushion | 60.3 | 85.07 | +| base | 37.12 | 46.5 | +| box | 34.1 | 53.15 | +| column | 46.59 | 74.46 | +| signboard | 36.14 | 50.48 | +| chest of drawers | 41.92 | 71.89 | +| counter | 41.89 | 61.18 | +| sand | 39.12 | 52.14 | +| sink | 67.35 | 87.98 | +| skyscraper | 48.47 | 67.77 | +| fireplace | 70.38 | 93.54 | +| refrigerator | 77.21 | 87.85 | +| grandstand | 49.13 | 87.04 | +| path | 27.65 | 39.62 | +| stairs | 34.88 | 41.23 | +| runway | 73.35 | 96.98 | +| case | 57.62 | 70.02 | +| pool table | 88.15 | 98.73 | +| pillow | 62.54 | 70.9 | +| screen door | 45.45 | 47.75 | +| stairway | 38.92 | 66.58 | +| river | 23.91 | 28.02 | +| bridge | 67.57 | 83.21 | +| bookcase | 34.36 | 57.64 | +| blind | 40.71 | 49.58 | +| coffee table | 52.95 | 87.37 | +| toilet | 88.08 | 94.23 | +| flower | 37.86 | 55.18 | +| book | 47.87 | 73.43 | +| hill | 3.71 | 3.81 | +| bench | 49.43 | 57.92 | +| countertop | 45.82 | 49.42 | +| stove | 80.53 | 92.37 | +| palm | 49.49 | 81.12 | +| kitchen island | 37.08 | 87.32 | +| computer | 72.26 | 91.13 | +| swivel chair | 43.66 | 88.05 | +| boat | 64.74 | 86.12 | +| bar | 54.8 | 90.06 | +| arcade machine | 80.63 | 99.49 | +| hovel | 18.64 | 22.97 | +| bus | 88.34 | 96.65 | +| towel | 63.78 | 81.56 | +| light | 46.92 | 61.22 | +| truck | 43.79 | 56.89 | +| tower | 27.94 | 57.62 | +| chandelier | 64.64 | 86.09 | +| awning | 33.74 | 45.38 | +| streetlight | 22.31 | 30.37 | +| booth | 35.37 | 54.17 | +| television receiver | 76.18 | 85.19 | +| airplane | 69.78 | 96.23 | +| dirt track | 0.0 | 0.0 | +| apparel | 46.59 | 64.42 | +| pole | 19.0 | 24.33 | +| land | 0.0 | 0.0 | +| bannister | 7.77 | 14.74 | +| escalator | 53.48 | 69.15 | +| ottoman | 53.81 | 78.65 | +| bottle | 38.53 | 63.26 | +| buffet | 22.6 | 22.72 | +| poster | 34.87 | 45.03 | +| stage | 30.32 | 58.07 | +| van | 38.25 | 59.86 | +| ship | 83.14 | 89.07 | +| fountain | 36.4 | 40.61 | +| conveyer belt | 70.91 | 96.28 | +| canopy | 28.68 | 33.52 | +| washer | 82.19 | 94.32 | +| plaything | 28.8 | 43.12 | +| swimming pool | 56.83 | 85.3 | +| stool | 39.53 | 64.63 | +| barrel | 41.38 | 65.12 | +| basket | 35.75 | 56.1 | +| waterfall | 67.21 | 90.1 | +| tent | 93.05 | 99.24 | +| bag | 25.85 | 32.1 | +| minibike | 70.65 | 81.84 | +| cradle | 79.96 | 99.22 | +| oven | 56.42 | 68.64 | +| ball | 51.74 | 71.6 | +| food | 58.41 | 86.79 | +| step | 24.31 | 35.08 | +| tank | 83.78 | 94.74 | +| trade name | 30.33 | 39.99 | +| microwave | 85.18 | 94.6 | +| pot | 49.88 | 68.97 | +| animal | 63.4 | 69.22 | +| bicycle | 56.08 | 74.22 | +| lake | 0.0 | 0.0 | +| dishwasher | 64.27 | 84.48 | +| screen | 55.85 | 85.63 | +| blanket | 36.63 | 62.98 | +| sculpture | 62.2 | 77.48 | +| hood | 61.4 | 73.32 | +| sconce | 47.91 | 63.26 | +| vase | 38.25 | 48.81 | +| traffic light | 30.4 | 48.47 | +| tray | 13.26 | 32.57 | +| ashcan | 41.84 | 70.54 | +| fan | 59.94 | 79.83 | +| pier | 39.11 | 45.56 | +| crt screen | 0.33 | 0.39 | +| plate | 51.03 | 61.42 | +| monitor | 53.85 | 73.97 | +| bulletin board | 55.84 | 66.16 | +| shower | 0.0 | 0.0 | +| radiator | 62.08 | 76.14 | +| glass | 8.96 | 9.23 | +| clock | 46.28 | 53.92 | +| flag | 65.77 | 73.49 | ++---------------------+-------+-------+ +2024-06-18 06:38:44,779 - mmseg - INFO - Summary: +2024-06-18 06:38:44,779 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 82.94 | 51.9 | 67.46 | ++-------+------+-------+ +2024-06-18 06:38:44,780 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 06:38:44,780 - mmseg - INFO - Iter(val) [250] aAcc: 0.8294, mIoU: 0.5190, mAcc: 0.6746, IoU.wall: 0.7505, IoU.building: 0.8311, IoU.sky: 0.9378, IoU.floor: 0.8149, IoU.tree: 0.7419, IoU.ceiling: 0.8259, IoU.road: 0.8485, IoU.bed : 0.9018, IoU.windowpane: 0.6213, IoU.grass: 0.6315, IoU.cabinet: 0.6130, IoU.sidewalk: 0.6555, IoU.person: 0.8053, IoU.earth: 0.3342, IoU.door: 0.4553, IoU.table: 0.5973, IoU.mountain: 0.5467, IoU.plant: 0.5550, IoU.curtain: 0.7246, IoU.chair: 0.5864, IoU.car: 0.8261, IoU.water: 0.5752, IoU.painting: 0.7195, IoU.sofa: 0.7694, IoU.shelf: 0.3966, IoU.house: 0.4601, IoU.sea: 0.6562, IoU.mirror: 0.7113, IoU.rug: 0.6517, IoU.field: 0.3177, IoU.armchair: 0.5709, IoU.seat: 0.6411, IoU.fence: 0.4894, IoU.desk: 0.4773, IoU.rock: 0.5989, IoU.wardrobe: 0.5107, IoU.lamp: 0.6385, IoU.bathtub: 0.8692, IoU.railing: 0.3337, IoU.cushion: 0.6030, IoU.base: 0.3712, IoU.box: 0.3410, IoU.column: 0.4659, IoU.signboard: 0.3614, IoU.chest of drawers: 0.4192, IoU.counter: 0.4189, IoU.sand: 0.3912, IoU.sink: 0.6735, IoU.skyscraper: 0.4847, IoU.fireplace: 0.7038, IoU.refrigerator: 0.7721, IoU.grandstand: 0.4913, IoU.path: 0.2765, IoU.stairs: 0.3488, IoU.runway: 0.7335, IoU.case: 0.5762, IoU.pool table: 0.8815, IoU.pillow: 0.6254, IoU.screen door: 0.4545, IoU.stairway: 0.3892, IoU.river: 0.2391, IoU.bridge: 0.6757, IoU.bookcase: 0.3436, IoU.blind: 0.4071, IoU.coffee table: 0.5295, IoU.toilet: 0.8808, IoU.flower: 0.3786, IoU.book: 0.4787, IoU.hill: 0.0371, IoU.bench: 0.4943, IoU.countertop: 0.4582, IoU.stove: 0.8053, IoU.palm: 0.4949, IoU.kitchen island: 0.3708, IoU.computer: 0.7226, IoU.swivel chair: 0.4366, IoU.boat: 0.6474, IoU.bar: 0.5480, IoU.arcade machine: 0.8063, IoU.hovel: 0.1864, IoU.bus: 0.8834, IoU.towel: 0.6378, IoU.light: 0.4692, IoU.truck: 0.4379, IoU.tower: 0.2794, IoU.chandelier: 0.6464, IoU.awning: 0.3374, IoU.streetlight: 0.2231, IoU.booth: 0.3537, IoU.television receiver: 0.7618, IoU.airplane: 0.6978, IoU.dirt track: 0.0000, IoU.apparel: 0.4659, IoU.pole: 0.1900, IoU.land: 0.0000, IoU.bannister: 0.0777, IoU.escalator: 0.5348, IoU.ottoman: 0.5381, IoU.bottle: 0.3853, IoU.buffet: 0.2260, IoU.poster: 0.3487, IoU.stage: 0.3032, IoU.van: 0.3825, IoU.ship: 0.8314, IoU.fountain: 0.3640, IoU.conveyer belt: 0.7091, IoU.canopy: 0.2868, IoU.washer: 0.8219, IoU.plaything: 0.2880, IoU.swimming pool: 0.5683, IoU.stool: 0.3953, IoU.barrel: 0.4138, IoU.basket: 0.3575, IoU.waterfall: 0.6721, IoU.tent: 0.9305, IoU.bag: 0.2585, IoU.minibike: 0.7065, IoU.cradle: 0.7996, IoU.oven: 0.5642, IoU.ball: 0.5174, IoU.food: 0.5841, IoU.step: 0.2431, IoU.tank: 0.8378, IoU.trade name: 0.3033, IoU.microwave: 0.8518, IoU.pot: 0.4988, IoU.animal: 0.6340, IoU.bicycle: 0.5608, IoU.lake: 0.0000, IoU.dishwasher: 0.6427, IoU.screen: 0.5585, IoU.blanket: 0.3663, IoU.sculpture: 0.6220, IoU.hood: 0.6140, IoU.sconce: 0.4791, IoU.vase: 0.3825, IoU.traffic light: 0.3040, IoU.tray: 0.1326, IoU.ashcan: 0.4184, IoU.fan: 0.5994, IoU.pier: 0.3911, IoU.crt screen: 0.0033, IoU.plate: 0.5103, IoU.monitor: 0.5385, IoU.bulletin board: 0.5584, IoU.shower: 0.0000, IoU.radiator: 0.6208, IoU.glass: 0.0896, IoU.clock: 0.4628, IoU.flag: 0.6577, Acc.wall: 0.8116, Acc.building: 0.9172, Acc.sky: 0.9738, Acc.floor: 0.8719, Acc.tree: 0.8511, Acc.ceiling: 0.9470, Acc.road: 0.8941, Acc.bed : 0.9564, Acc.windowpane: 0.8039, Acc.grass: 0.8668, Acc.cabinet: 0.7418, Acc.sidewalk: 0.7971, Acc.person: 0.9164, Acc.earth: 0.4077, Acc.door: 0.8401, Acc.table: 0.7061, Acc.mountain: 0.7118, Acc.plant: 0.6608, Acc.curtain: 0.8912, Acc.chair: 0.7275, Acc.car: 0.9361, Acc.water: 0.7332, Acc.painting: 0.8553, Acc.sofa: 0.8571, Acc.shelf: 0.5123, Acc.house: 0.6612, Acc.sea: 0.9401, Acc.mirror: 0.8807, Acc.rug: 0.8776, Acc.field: 0.5885, Acc.armchair: 0.7549, Acc.seat: 0.8496, Acc.fence: 0.5710, Acc.desk: 0.7833, Acc.rock: 0.7347, Acc.wardrobe: 0.7393, Acc.lamp: 0.7938, Acc.bathtub: 0.9414, Acc.railing: 0.4550, Acc.cushion: 0.8507, Acc.base: 0.4650, Acc.box: 0.5315, Acc.column: 0.7446, Acc.signboard: 0.5048, Acc.chest of drawers: 0.7189, Acc.counter: 0.6118, Acc.sand: 0.5214, Acc.sink: 0.8798, Acc.skyscraper: 0.6777, Acc.fireplace: 0.9354, Acc.refrigerator: 0.8785, Acc.grandstand: 0.8704, Acc.path: 0.3962, Acc.stairs: 0.4123, Acc.runway: 0.9698, Acc.case: 0.7002, Acc.pool table: 0.9873, Acc.pillow: 0.7090, Acc.screen door: 0.4775, Acc.stairway: 0.6658, Acc.river: 0.2802, Acc.bridge: 0.8321, Acc.bookcase: 0.5764, Acc.blind: 0.4958, Acc.coffee table: 0.8737, Acc.toilet: 0.9423, Acc.flower: 0.5518, Acc.book: 0.7343, Acc.hill: 0.0381, Acc.bench: 0.5792, Acc.countertop: 0.4942, Acc.stove: 0.9237, Acc.palm: 0.8112, Acc.kitchen island: 0.8732, Acc.computer: 0.9113, Acc.swivel chair: 0.8805, Acc.boat: 0.8612, Acc.bar: 0.9006, Acc.arcade machine: 0.9949, Acc.hovel: 0.2297, Acc.bus: 0.9665, Acc.towel: 0.8156, Acc.light: 0.6122, Acc.truck: 0.5689, Acc.tower: 0.5762, Acc.chandelier: 0.8609, Acc.awning: 0.4538, Acc.streetlight: 0.3037, Acc.booth: 0.5417, Acc.television receiver: 0.8519, Acc.airplane: 0.9623, Acc.dirt track: 0.0000, Acc.apparel: 0.6442, Acc.pole: 0.2433, Acc.land: 0.0000, Acc.bannister: 0.1474, Acc.escalator: 0.6915, Acc.ottoman: 0.7865, Acc.bottle: 0.6326, Acc.buffet: 0.2272, Acc.poster: 0.4503, Acc.stage: 0.5807, Acc.van: 0.5986, Acc.ship: 0.8907, Acc.fountain: 0.4061, Acc.conveyer belt: 0.9628, Acc.canopy: 0.3352, Acc.washer: 0.9432, Acc.plaything: 0.4312, Acc.swimming pool: 0.8530, Acc.stool: 0.6463, Acc.barrel: 0.6512, Acc.basket: 0.5610, Acc.waterfall: 0.9010, Acc.tent: 0.9924, Acc.bag: 0.3210, Acc.minibike: 0.8184, Acc.cradle: 0.9922, Acc.oven: 0.6864, Acc.ball: 0.7160, Acc.food: 0.8679, Acc.step: 0.3508, Acc.tank: 0.9474, Acc.trade name: 0.3999, Acc.microwave: 0.9460, Acc.pot: 0.6897, Acc.animal: 0.6922, Acc.bicycle: 0.7422, Acc.lake: 0.0000, Acc.dishwasher: 0.8448, Acc.screen: 0.8563, Acc.blanket: 0.6298, Acc.sculpture: 0.7748, Acc.hood: 0.7332, Acc.sconce: 0.6326, Acc.vase: 0.4881, Acc.traffic light: 0.4847, Acc.tray: 0.3257, Acc.ashcan: 0.7054, Acc.fan: 0.7983, Acc.pier: 0.4556, Acc.crt screen: 0.0039, Acc.plate: 0.6142, Acc.monitor: 0.7397, Acc.bulletin board: 0.6616, Acc.shower: 0.0000, Acc.radiator: 0.7614, Acc.glass: 0.0923, Acc.clock: 0.5392, Acc.flag: 0.7349 +2024-06-18 06:40:24,740 - mmseg - INFO - Iter [7050/80000] lr: 3.648e-05, eta: 1 day, 19:19:36, time: 4.187, data_time: 2.203, memory: 72263, decode.loss_ce: 0.4819, decode.acc_seg: 80.8863, aux.loss_ce: 0.1888, aux.acc_seg: 81.2408, loss: 0.6708 +2024-06-18 06:42:03,688 - mmseg - INFO - Iter [7100/80000] lr: 3.645e-05, eta: 1 day, 19:16:27, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4452, decode.acc_seg: 82.7889, aux.loss_ce: 0.1767, aux.acc_seg: 82.9713, loss: 0.6219 +2024-06-18 06:43:42,817 - mmseg - INFO - Iter [7150/80000] lr: 3.643e-05, eta: 1 day, 19:13:22, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4374, decode.acc_seg: 83.2186, aux.loss_ce: 0.1737, aux.acc_seg: 83.5109, loss: 0.6111 +2024-06-18 06:45:21,800 - mmseg - INFO - Iter [7200/80000] lr: 3.640e-05, eta: 1 day, 19:10:16, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4760, decode.acc_seg: 80.9361, aux.loss_ce: 0.1879, aux.acc_seg: 81.3567, loss: 0.6639 +2024-06-18 06:47:00,882 - mmseg - INFO - Iter [7250/80000] lr: 3.638e-05, eta: 1 day, 19:07:12, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4516, decode.acc_seg: 82.6889, aux.loss_ce: 0.1774, aux.acc_seg: 83.2492, loss: 0.6290 +2024-06-18 06:48:39,772 - mmseg - INFO - Iter [7300/80000] lr: 3.635e-05, eta: 1 day, 19:04:08, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4825, decode.acc_seg: 81.5944, aux.loss_ce: 0.1892, aux.acc_seg: 81.8346, loss: 0.6718 +2024-06-18 06:50:18,794 - mmseg - INFO - Iter [7350/80000] lr: 3.633e-05, eta: 1 day, 19:01:06, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4350, decode.acc_seg: 82.9560, aux.loss_ce: 0.1727, aux.acc_seg: 82.9621, loss: 0.6077 +2024-06-18 06:51:57,896 - mmseg - INFO - Iter [7400/80000] lr: 3.630e-05, eta: 1 day, 18:58:06, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4403, decode.acc_seg: 82.7079, aux.loss_ce: 0.1743, aux.acc_seg: 83.0411, loss: 0.6146 +2024-06-18 06:53:36,886 - mmseg - INFO - Iter [7450/80000] lr: 3.628e-05, eta: 1 day, 18:55:06, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4573, decode.acc_seg: 82.4324, aux.loss_ce: 0.1822, aux.acc_seg: 82.6759, loss: 0.6395 +2024-06-18 06:55:15,922 - mmseg - INFO - Iter [7500/80000] lr: 3.625e-05, eta: 1 day, 18:52:08, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4533, decode.acc_seg: 82.7311, aux.loss_ce: 0.1785, aux.acc_seg: 83.0135, loss: 0.6319 +2024-06-18 06:56:54,911 - mmseg - INFO - Iter [7550/80000] lr: 3.623e-05, eta: 1 day, 18:49:10, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.5072, decode.acc_seg: 80.6272, aux.loss_ce: 0.2001, aux.acc_seg: 80.8184, loss: 0.7074 +2024-06-18 06:58:36,421 - mmseg - INFO - Iter [7600/80000] lr: 3.620e-05, eta: 1 day, 18:46:37, time: 2.030, data_time: 0.058, memory: 72263, decode.loss_ce: 0.4464, decode.acc_seg: 82.6722, aux.loss_ce: 0.1773, aux.acc_seg: 82.8714, loss: 0.6236 +2024-06-18 07:00:15,356 - mmseg - INFO - Iter [7650/80000] lr: 3.618e-05, eta: 1 day, 18:43:40, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4302, decode.acc_seg: 82.9656, aux.loss_ce: 0.1698, aux.acc_seg: 83.3945, loss: 0.6001 +2024-06-18 07:01:54,445 - mmseg - INFO - Iter [7700/80000] lr: 3.615e-05, eta: 1 day, 18:40:46, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4506, decode.acc_seg: 82.7106, aux.loss_ce: 0.1774, aux.acc_seg: 82.9636, loss: 0.6280 +2024-06-18 07:03:33,423 - mmseg - INFO - Iter [7750/80000] lr: 3.613e-05, eta: 1 day, 18:37:52, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4480, decode.acc_seg: 81.7621, aux.loss_ce: 0.1773, aux.acc_seg: 82.1355, loss: 0.6252 +2024-06-18 07:05:12,387 - mmseg - INFO - Iter [7800/80000] lr: 3.610e-05, eta: 1 day, 18:34:59, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4552, decode.acc_seg: 82.3745, aux.loss_ce: 0.1797, aux.acc_seg: 82.7070, loss: 0.6349 +2024-06-18 07:06:51,421 - mmseg - INFO - Iter [7850/80000] lr: 3.608e-05, eta: 1 day, 18:32:07, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4050, decode.acc_seg: 84.0819, aux.loss_ce: 0.1613, aux.acc_seg: 84.2222, loss: 0.5663 +2024-06-18 07:08:30,423 - mmseg - INFO - Iter [7900/80000] lr: 3.605e-05, eta: 1 day, 18:29:16, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4360, decode.acc_seg: 83.0494, aux.loss_ce: 0.1742, aux.acc_seg: 83.1330, loss: 0.6102 +2024-06-18 07:10:09,380 - mmseg - INFO - Iter [7950/80000] lr: 3.603e-05, eta: 1 day, 18:26:26, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4230, decode.acc_seg: 83.4221, aux.loss_ce: 0.1687, aux.acc_seg: 83.4351, loss: 0.5917 +2024-06-18 07:11:48,332 - mmseg - INFO - Saving checkpoint at 8000 iterations +2024-06-18 07:13:13,829 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 07:13:13,829 - mmseg - INFO - Iter [8000/80000] lr: 3.600e-05, eta: 1 day, 18:36:25, time: 3.689, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4319, decode.acc_seg: 82.7414, aux.loss_ce: 0.1716, aux.acc_seg: 82.8190, loss: 0.6034 +2024-06-18 07:15:02,671 - mmseg - INFO - per class results: +2024-06-18 07:15:02,678 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.68 | 87.67 | +| building | 83.78 | 92.39 | +| sky | 94.02 | 96.58 | +| floor | 82.49 | 88.52 | +| tree | 75.86 | 89.06 | +| ceiling | 85.19 | 92.02 | +| road | 85.39 | 93.19 | +| bed | 89.92 | 96.89 | +| windowpane | 64.27 | 78.27 | +| grass | 67.2 | 77.82 | +| cabinet | 63.26 | 71.47 | +| sidewalk | 66.36 | 82.53 | +| person | 81.43 | 94.04 | +| earth | 37.95 | 51.3 | +| door | 56.68 | 70.46 | +| table | 60.06 | 68.76 | +| mountain | 62.43 | 73.37 | +| plant | 55.72 | 72.72 | +| curtain | 76.25 | 86.5 | +| chair | 58.95 | 73.41 | +| car | 84.35 | 92.19 | +| water | 42.02 | 50.29 | +| painting | 75.24 | 89.87 | +| sofa | 69.63 | 92.13 | +| shelf | 40.0 | 58.3 | +| house | 44.06 | 48.77 | +| sea | 70.94 | 79.23 | +| mirror | 76.16 | 85.2 | +| rug | 68.7 | 83.47 | +| field | 28.85 | 49.04 | +| armchair | 42.92 | 47.6 | +| seat | 59.63 | 84.08 | +| fence | 49.7 | 65.17 | +| desk | 47.72 | 82.26 | +| rock | 55.54 | 82.64 | +| wardrobe | 55.72 | 77.32 | +| lamp | 63.46 | 80.55 | +| bathtub | 85.02 | 92.1 | +| railing | 35.62 | 48.82 | +| cushion | 61.69 | 76.06 | +| base | 32.73 | 41.95 | +| box | 33.67 | 63.69 | +| column | 49.83 | 75.36 | +| signboard | 34.98 | 46.26 | +| chest of drawers | 48.5 | 66.45 | +| counter | 52.4 | 64.84 | +| sand | 38.91 | 54.28 | +| sink | 75.12 | 88.48 | +| skyscraper | 43.33 | 46.07 | +| fireplace | 68.41 | 96.39 | +| refrigerator | 77.1 | 85.61 | +| grandstand | 44.24 | 90.22 | +| path | 24.58 | 29.5 | +| stairs | 38.03 | 48.17 | +| runway | 69.32 | 93.44 | +| case | 51.5 | 79.14 | +| pool table | 91.71 | 98.26 | +| pillow | 63.19 | 76.4 | +| screen door | 70.51 | 84.1 | +| stairway | 36.45 | 38.75 | +| river | 14.6 | 67.69 | +| bridge | 73.96 | 85.52 | +| bookcase | 35.92 | 61.99 | +| blind | 37.78 | 40.73 | +| coffee table | 56.0 | 85.87 | +| toilet | 87.52 | 95.75 | +| flower | 36.84 | 52.8 | +| book | 48.47 | 67.1 | +| hill | 9.87 | 26.37 | +| bench | 52.77 | 65.05 | +| countertop | 60.84 | 86.35 | +| stove | 81.03 | 89.01 | +| palm | 54.18 | 76.56 | +| kitchen island | 44.16 | 78.35 | +| computer | 71.54 | 92.06 | +| swivel chair | 46.4 | 88.39 | +| boat | 50.36 | 94.47 | +| bar | 59.04 | 74.4 | +| arcade machine | 84.69 | 98.64 | +| hovel | 25.55 | 25.88 | +| bus | 90.42 | 95.25 | +| towel | 67.55 | 80.23 | +| light | 45.97 | 52.18 | +| truck | 47.86 | 58.13 | +| tower | 28.88 | 64.25 | +| chandelier | 64.21 | 88.87 | +| awning | 38.3 | 61.53 | +| streetlight | 24.74 | 33.35 | +| booth | 38.46 | 57.24 | +| television receiver | 68.47 | 86.48 | +| airplane | 78.5 | 95.0 | +| dirt track | 5.74 | 17.93 | +| apparel | 58.53 | 71.41 | +| pole | 21.23 | 29.98 | +| land | 1.49 | 2.39 | +| bannister | 13.82 | 21.45 | +| escalator | 56.11 | 89.35 | +| ottoman | 54.74 | 75.96 | +| bottle | 40.33 | 67.02 | +| buffet | 47.53 | 79.75 | +| poster | 34.02 | 42.27 | +| stage | 9.75 | 11.53 | +| van | 46.29 | 70.08 | +| ship | 52.59 | 53.7 | +| fountain | 54.25 | 57.55 | +| conveyer belt | 65.78 | 98.75 | +| canopy | 51.64 | 70.43 | +| washer | 76.63 | 86.87 | +| plaything | 28.82 | 43.08 | +| swimming pool | 74.31 | 78.42 | +| stool | 48.58 | 61.95 | +| barrel | 46.33 | 65.57 | +| basket | 37.25 | 48.25 | +| waterfall | 62.1 | 92.58 | +| tent | 93.85 | 98.72 | +| bag | 28.23 | 35.66 | +| minibike | 67.2 | 89.45 | +| cradle | 78.56 | 99.17 | +| oven | 51.34 | 67.4 | +| ball | 0.0 | 0.0 | +| food | 58.94 | 87.52 | +| step | 14.87 | 19.83 | +| tank | 63.09 | 98.17 | +| trade name | 32.14 | 62.5 | +| microwave | 85.85 | 94.8 | +| pot | 50.57 | 61.35 | +| animal | 71.12 | 76.91 | +| bicycle | 57.23 | 84.78 | +| lake | 0.0 | 0.0 | +| dishwasher | 63.98 | 69.31 | +| screen | 59.43 | 76.04 | +| blanket | 8.93 | 9.74 | +| sculpture | 54.6 | 87.65 | +| hood | 67.2 | 81.09 | +| sconce | 51.7 | 72.87 | +| vase | 41.16 | 55.13 | +| traffic light | 25.0 | 64.85 | +| tray | 14.45 | 16.91 | +| ashcan | 43.46 | 72.87 | +| fan | 62.03 | 78.06 | +| pier | 35.69 | 39.57 | +| crt screen | 2.44 | 3.34 | +| plate | 58.16 | 75.38 | +| monitor | 62.26 | 68.71 | +| bulletin board | 57.98 | 69.67 | +| shower | 0.0 | 0.0 | +| radiator | 62.02 | 79.77 | +| glass | 15.18 | 16.89 | +| clock | 45.94 | 61.86 | +| flag | 65.46 | 78.82 | ++---------------------+-------+-------+ +2024-06-18 07:15:02,678 - mmseg - INFO - Summary: +2024-06-18 07:15:02,678 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.06 | 52.61 | 67.91 | ++-------+-------+-------+ +2024-06-18 07:15:02,679 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 07:15:02,679 - mmseg - INFO - Iter(val) [250] aAcc: 0.8406, mIoU: 0.5261, mAcc: 0.6791, IoU.wall: 0.7968, IoU.building: 0.8378, IoU.sky: 0.9402, IoU.floor: 0.8249, IoU.tree: 0.7586, IoU.ceiling: 0.8519, IoU.road: 0.8539, IoU.bed : 0.8992, IoU.windowpane: 0.6427, IoU.grass: 0.6720, IoU.cabinet: 0.6326, IoU.sidewalk: 0.6636, IoU.person: 0.8143, IoU.earth: 0.3795, IoU.door: 0.5668, IoU.table: 0.6006, IoU.mountain: 0.6243, IoU.plant: 0.5572, IoU.curtain: 0.7625, IoU.chair: 0.5895, IoU.car: 0.8435, IoU.water: 0.4202, IoU.painting: 0.7524, IoU.sofa: 0.6963, IoU.shelf: 0.4000, IoU.house: 0.4406, IoU.sea: 0.7094, IoU.mirror: 0.7616, IoU.rug: 0.6870, IoU.field: 0.2885, IoU.armchair: 0.4292, IoU.seat: 0.5963, IoU.fence: 0.4970, IoU.desk: 0.4772, IoU.rock: 0.5554, IoU.wardrobe: 0.5572, IoU.lamp: 0.6346, IoU.bathtub: 0.8502, IoU.railing: 0.3562, IoU.cushion: 0.6169, IoU.base: 0.3273, IoU.box: 0.3367, IoU.column: 0.4983, IoU.signboard: 0.3498, IoU.chest of drawers: 0.4850, IoU.counter: 0.5240, IoU.sand: 0.3891, IoU.sink: 0.7512, IoU.skyscraper: 0.4333, IoU.fireplace: 0.6841, IoU.refrigerator: 0.7710, IoU.grandstand: 0.4424, IoU.path: 0.2458, IoU.stairs: 0.3803, IoU.runway: 0.6932, IoU.case: 0.5150, IoU.pool table: 0.9171, IoU.pillow: 0.6319, IoU.screen door: 0.7051, IoU.stairway: 0.3645, IoU.river: 0.1460, IoU.bridge: 0.7396, IoU.bookcase: 0.3592, IoU.blind: 0.3778, IoU.coffee table: 0.5600, IoU.toilet: 0.8752, IoU.flower: 0.3684, IoU.book: 0.4847, IoU.hill: 0.0987, IoU.bench: 0.5277, IoU.countertop: 0.6084, IoU.stove: 0.8103, IoU.palm: 0.5418, IoU.kitchen island: 0.4416, IoU.computer: 0.7154, IoU.swivel chair: 0.4640, IoU.boat: 0.5036, IoU.bar: 0.5904, IoU.arcade machine: 0.8469, IoU.hovel: 0.2555, IoU.bus: 0.9042, IoU.towel: 0.6755, IoU.light: 0.4597, IoU.truck: 0.4786, IoU.tower: 0.2888, IoU.chandelier: 0.6421, IoU.awning: 0.3830, IoU.streetlight: 0.2474, IoU.booth: 0.3846, IoU.television receiver: 0.6847, IoU.airplane: 0.7850, IoU.dirt track: 0.0574, IoU.apparel: 0.5853, IoU.pole: 0.2123, IoU.land: 0.0149, IoU.bannister: 0.1382, IoU.escalator: 0.5611, IoU.ottoman: 0.5474, IoU.bottle: 0.4033, IoU.buffet: 0.4753, IoU.poster: 0.3402, IoU.stage: 0.0975, IoU.van: 0.4629, IoU.ship: 0.5259, IoU.fountain: 0.5425, IoU.conveyer belt: 0.6578, IoU.canopy: 0.5164, IoU.washer: 0.7663, IoU.plaything: 0.2882, IoU.swimming pool: 0.7431, IoU.stool: 0.4858, IoU.barrel: 0.4633, IoU.basket: 0.3725, IoU.waterfall: 0.6210, IoU.tent: 0.9385, IoU.bag: 0.2823, IoU.minibike: 0.6720, IoU.cradle: 0.7856, IoU.oven: 0.5134, IoU.ball: 0.0000, IoU.food: 0.5894, IoU.step: 0.1487, IoU.tank: 0.6309, IoU.trade name: 0.3214, IoU.microwave: 0.8585, IoU.pot: 0.5057, IoU.animal: 0.7112, IoU.bicycle: 0.5723, IoU.lake: 0.0000, IoU.dishwasher: 0.6398, IoU.screen: 0.5943, IoU.blanket: 0.0893, IoU.sculpture: 0.5460, IoU.hood: 0.6720, IoU.sconce: 0.5170, IoU.vase: 0.4116, IoU.traffic light: 0.2500, IoU.tray: 0.1445, IoU.ashcan: 0.4346, IoU.fan: 0.6203, IoU.pier: 0.3569, IoU.crt screen: 0.0244, IoU.plate: 0.5816, IoU.monitor: 0.6226, IoU.bulletin board: 0.5798, IoU.shower: 0.0000, IoU.radiator: 0.6202, IoU.glass: 0.1518, IoU.clock: 0.4594, IoU.flag: 0.6546, Acc.wall: 0.8767, Acc.building: 0.9239, Acc.sky: 0.9658, Acc.floor: 0.8852, Acc.tree: 0.8906, Acc.ceiling: 0.9202, Acc.road: 0.9319, Acc.bed : 0.9689, Acc.windowpane: 0.7827, Acc.grass: 0.7782, Acc.cabinet: 0.7147, Acc.sidewalk: 0.8253, Acc.person: 0.9404, Acc.earth: 0.5130, Acc.door: 0.7046, Acc.table: 0.6876, Acc.mountain: 0.7337, Acc.plant: 0.7272, Acc.curtain: 0.8650, Acc.chair: 0.7341, Acc.car: 0.9219, Acc.water: 0.5029, Acc.painting: 0.8987, Acc.sofa: 0.9213, Acc.shelf: 0.5830, Acc.house: 0.4877, Acc.sea: 0.7923, Acc.mirror: 0.8520, Acc.rug: 0.8347, Acc.field: 0.4904, Acc.armchair: 0.4760, Acc.seat: 0.8408, Acc.fence: 0.6517, Acc.desk: 0.8226, Acc.rock: 0.8264, Acc.wardrobe: 0.7732, Acc.lamp: 0.8055, Acc.bathtub: 0.9210, Acc.railing: 0.4882, Acc.cushion: 0.7606, Acc.base: 0.4195, Acc.box: 0.6369, Acc.column: 0.7536, Acc.signboard: 0.4626, Acc.chest of drawers: 0.6645, Acc.counter: 0.6484, Acc.sand: 0.5428, Acc.sink: 0.8848, Acc.skyscraper: 0.4607, Acc.fireplace: 0.9639, Acc.refrigerator: 0.8561, Acc.grandstand: 0.9022, Acc.path: 0.2950, Acc.stairs: 0.4817, Acc.runway: 0.9344, Acc.case: 0.7914, Acc.pool table: 0.9826, Acc.pillow: 0.7640, Acc.screen door: 0.8410, Acc.stairway: 0.3875, Acc.river: 0.6769, Acc.bridge: 0.8552, Acc.bookcase: 0.6199, Acc.blind: 0.4073, Acc.coffee table: 0.8587, Acc.toilet: 0.9575, Acc.flower: 0.5280, Acc.book: 0.6710, Acc.hill: 0.2637, Acc.bench: 0.6505, Acc.countertop: 0.8635, Acc.stove: 0.8901, Acc.palm: 0.7656, Acc.kitchen island: 0.7835, Acc.computer: 0.9206, Acc.swivel chair: 0.8839, Acc.boat: 0.9447, Acc.bar: 0.7440, Acc.arcade machine: 0.9864, Acc.hovel: 0.2588, Acc.bus: 0.9525, Acc.towel: 0.8023, Acc.light: 0.5218, Acc.truck: 0.5813, Acc.tower: 0.6425, Acc.chandelier: 0.8887, Acc.awning: 0.6153, Acc.streetlight: 0.3335, Acc.booth: 0.5724, Acc.television receiver: 0.8648, Acc.airplane: 0.9500, Acc.dirt track: 0.1793, Acc.apparel: 0.7141, Acc.pole: 0.2998, Acc.land: 0.0239, Acc.bannister: 0.2145, Acc.escalator: 0.8935, Acc.ottoman: 0.7596, Acc.bottle: 0.6702, Acc.buffet: 0.7975, Acc.poster: 0.4227, Acc.stage: 0.1153, Acc.van: 0.7008, Acc.ship: 0.5370, Acc.fountain: 0.5755, Acc.conveyer belt: 0.9875, Acc.canopy: 0.7043, Acc.washer: 0.8687, Acc.plaything: 0.4308, Acc.swimming pool: 0.7842, Acc.stool: 0.6195, Acc.barrel: 0.6557, Acc.basket: 0.4825, Acc.waterfall: 0.9258, Acc.tent: 0.9872, Acc.bag: 0.3566, Acc.minibike: 0.8945, Acc.cradle: 0.9917, Acc.oven: 0.6740, Acc.ball: 0.0000, Acc.food: 0.8752, Acc.step: 0.1983, Acc.tank: 0.9817, Acc.trade name: 0.6250, Acc.microwave: 0.9480, Acc.pot: 0.6135, Acc.animal: 0.7691, Acc.bicycle: 0.8478, Acc.lake: 0.0000, Acc.dishwasher: 0.6931, Acc.screen: 0.7604, Acc.blanket: 0.0974, Acc.sculpture: 0.8765, Acc.hood: 0.8109, Acc.sconce: 0.7287, Acc.vase: 0.5513, Acc.traffic light: 0.6485, Acc.tray: 0.1691, Acc.ashcan: 0.7287, Acc.fan: 0.7806, Acc.pier: 0.3957, Acc.crt screen: 0.0334, Acc.plate: 0.7538, Acc.monitor: 0.6871, Acc.bulletin board: 0.6967, Acc.shower: 0.0000, Acc.radiator: 0.7977, Acc.glass: 0.1689, Acc.clock: 0.6186, Acc.flag: 0.7882 +2024-06-18 07:16:42,119 - mmseg - INFO - Iter [8050/80000] lr: 3.598e-05, eta: 1 day, 18:49:48, time: 4.166, data_time: 2.194, memory: 72263, decode.loss_ce: 0.4746, decode.acc_seg: 81.4400, aux.loss_ce: 0.1878, aux.acc_seg: 81.6775, loss: 0.6624 +2024-06-18 07:18:21,032 - mmseg - INFO - Iter [8100/80000] lr: 3.595e-05, eta: 1 day, 18:46:48, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4598, decode.acc_seg: 82.4634, aux.loss_ce: 0.1818, aux.acc_seg: 82.5450, loss: 0.6416 +2024-06-18 07:20:00,128 - mmseg - INFO - Iter [8150/80000] lr: 3.593e-05, eta: 1 day, 18:43:51, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4264, decode.acc_seg: 83.5298, aux.loss_ce: 0.1690, aux.acc_seg: 83.5156, loss: 0.5954 +2024-06-18 07:21:39,080 - mmseg - INFO - Iter [8200/80000] lr: 3.590e-05, eta: 1 day, 18:40:53, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4509, decode.acc_seg: 81.8385, aux.loss_ce: 0.1786, aux.acc_seg: 82.2104, loss: 0.6295 +2024-06-18 07:23:18,090 - mmseg - INFO - Iter [8250/80000] lr: 3.588e-05, eta: 1 day, 18:37:56, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4184, decode.acc_seg: 83.7666, aux.loss_ce: 0.1661, aux.acc_seg: 83.8964, loss: 0.5845 +2024-06-18 07:24:57,221 - mmseg - INFO - Iter [8300/80000] lr: 3.585e-05, eta: 1 day, 18:35:02, time: 1.983, data_time: 0.011, memory: 72263, decode.loss_ce: 0.4304, decode.acc_seg: 83.3778, aux.loss_ce: 0.1708, aux.acc_seg: 83.4368, loss: 0.6012 +2024-06-18 07:26:36,299 - mmseg - INFO - Iter [8350/80000] lr: 3.583e-05, eta: 1 day, 18:32:08, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4409, decode.acc_seg: 83.0973, aux.loss_ce: 0.1744, aux.acc_seg: 83.2014, loss: 0.6153 +2024-06-18 07:28:15,353 - mmseg - INFO - Iter [8400/80000] lr: 3.580e-05, eta: 1 day, 18:29:14, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.4072, decode.acc_seg: 84.3943, aux.loss_ce: 0.1630, aux.acc_seg: 84.3092, loss: 0.5702 +2024-06-18 07:29:54,330 - mmseg - INFO - Iter [8450/80000] lr: 3.578e-05, eta: 1 day, 18:26:21, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4461, decode.acc_seg: 82.6550, aux.loss_ce: 0.1769, aux.acc_seg: 82.7965, loss: 0.6231 +2024-06-18 07:31:33,424 - mmseg - INFO - Iter [8500/80000] lr: 3.575e-05, eta: 1 day, 18:23:30, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4294, decode.acc_seg: 83.1024, aux.loss_ce: 0.1691, aux.acc_seg: 83.3660, loss: 0.5984 +2024-06-18 07:33:12,504 - mmseg - INFO - Iter [8550/80000] lr: 3.573e-05, eta: 1 day, 18:20:39, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4347, decode.acc_seg: 82.7676, aux.loss_ce: 0.1726, aux.acc_seg: 82.9984, loss: 0.6073 +2024-06-18 07:34:51,624 - mmseg - INFO - Iter [8600/80000] lr: 3.570e-05, eta: 1 day, 18:17:50, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.4308, decode.acc_seg: 82.7989, aux.loss_ce: 0.1705, aux.acc_seg: 83.0507, loss: 0.6014 +2024-06-18 07:36:30,712 - mmseg - INFO - Iter [8650/80000] lr: 3.568e-05, eta: 1 day, 18:15:01, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4531, decode.acc_seg: 82.4098, aux.loss_ce: 0.1799, aux.acc_seg: 82.6571, loss: 0.6330 +2024-06-18 07:38:09,729 - mmseg - INFO - Iter [8700/80000] lr: 3.565e-05, eta: 1 day, 18:12:12, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4450, decode.acc_seg: 82.3844, aux.loss_ce: 0.1781, aux.acc_seg: 82.2409, loss: 0.6231 +2024-06-18 07:39:48,714 - mmseg - INFO - Iter [8750/80000] lr: 3.563e-05, eta: 1 day, 18:09:24, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4172, decode.acc_seg: 83.2568, aux.loss_ce: 0.1657, aux.acc_seg: 83.4569, loss: 0.5829 +2024-06-18 07:41:27,828 - mmseg - INFO - Iter [8800/80000] lr: 3.560e-05, eta: 1 day, 18:06:38, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4335, decode.acc_seg: 83.0918, aux.loss_ce: 0.1709, aux.acc_seg: 83.3020, loss: 0.6044 +2024-06-18 07:43:08,989 - mmseg - INFO - Iter [8850/80000] lr: 3.558e-05, eta: 1 day, 18:04:09, time: 2.023, data_time: 0.054, memory: 72263, decode.loss_ce: 0.4246, decode.acc_seg: 83.6813, aux.loss_ce: 0.1697, aux.acc_seg: 83.8392, loss: 0.5943 +2024-06-18 07:44:48,043 - mmseg - INFO - Iter [8900/80000] lr: 3.555e-05, eta: 1 day, 18:01:23, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3950, decode.acc_seg: 84.5868, aux.loss_ce: 0.1565, aux.acc_seg: 84.5490, loss: 0.5515 +2024-06-18 07:46:27,010 - mmseg - INFO - Iter [8950/80000] lr: 3.553e-05, eta: 1 day, 17:58:38, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4093, decode.acc_seg: 83.5593, aux.loss_ce: 0.1626, aux.acc_seg: 83.6413, loss: 0.5718 +2024-06-18 07:48:06,169 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 07:48:06,169 - mmseg - INFO - Iter [9000/80000] lr: 3.550e-05, eta: 1 day, 17:55:55, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4205, decode.acc_seg: 83.4226, aux.loss_ce: 0.1667, aux.acc_seg: 83.4974, loss: 0.5871 +2024-06-18 07:50:03,153 - mmseg - INFO - per class results: +2024-06-18 07:50:03,160 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.44 | 88.7 | +| building | 83.38 | 92.26 | +| sky | 94.22 | 97.36 | +| floor | 82.06 | 89.72 | +| tree | 75.18 | 90.14 | +| ceiling | 85.26 | 93.61 | +| road | 84.96 | 94.14 | +| bed | 91.12 | 96.26 | +| windowpane | 64.54 | 79.11 | +| grass | 61.27 | 73.42 | +| cabinet | 62.51 | 71.29 | +| sidewalk | 67.33 | 77.68 | +| person | 82.43 | 89.72 | +| earth | 28.86 | 35.67 | +| door | 56.08 | 70.23 | +| table | 61.35 | 76.27 | +| mountain | 58.96 | 77.71 | +| plant | 56.5 | 71.38 | +| curtain | 76.24 | 86.66 | +| chair | 57.47 | 63.47 | +| car | 85.32 | 91.74 | +| water | 56.26 | 68.32 | +| painting | 77.92 | 88.15 | +| sofa | 71.35 | 91.33 | +| shelf | 38.42 | 45.62 | +| house | 38.25 | 40.84 | +| sea | 61.54 | 86.49 | +| mirror | 76.63 | 87.17 | +| rug | 66.19 | 82.61 | +| field | 31.37 | 64.22 | +| armchair | 57.75 | 77.11 | +| seat | 59.49 | 68.86 | +| fence | 52.98 | 69.39 | +| desk | 50.13 | 59.59 | +| rock | 48.22 | 65.24 | +| wardrobe | 51.8 | 88.15 | +| lamp | 66.6 | 77.19 | +| bathtub | 86.45 | 93.57 | +| railing | 38.93 | 52.63 | +| cushion | 52.62 | 85.62 | +| base | 33.7 | 81.99 | +| box | 32.13 | 37.56 | +| column | 49.22 | 64.56 | +| signboard | 37.77 | 47.94 | +| chest of drawers | 49.11 | 67.83 | +| counter | 30.62 | 38.26 | +| sand | 45.45 | 81.04 | +| sink | 73.42 | 81.07 | +| skyscraper | 51.78 | 68.46 | +| fireplace | 66.86 | 94.73 | +| refrigerator | 80.01 | 85.03 | +| grandstand | 47.13 | 86.22 | +| path | 28.02 | 36.04 | +| stairs | 28.6 | 32.76 | +| runway | 71.01 | 97.33 | +| case | 43.22 | 44.28 | +| pool table | 92.1 | 98.37 | +| pillow | 33.54 | 35.29 | +| screen door | 77.62 | 94.24 | +| stairway | 44.18 | 78.84 | +| river | 18.28 | 35.07 | +| bridge | 73.0 | 89.39 | +| bookcase | 34.94 | 58.23 | +| blind | 37.63 | 40.41 | +| coffee table | 53.57 | 88.89 | +| toilet | 86.98 | 95.88 | +| flower | 35.55 | 62.34 | +| book | 49.94 | 69.66 | +| hill | 5.78 | 9.22 | +| bench | 64.41 | 73.93 | +| countertop | 56.5 | 80.63 | +| stove | 79.8 | 90.4 | +| palm | 49.49 | 88.62 | +| kitchen island | 40.05 | 54.71 | +| computer | 75.7 | 85.7 | +| swivel chair | 46.4 | 81.91 | +| boat | 48.8 | 84.74 | +| bar | 60.31 | 86.89 | +| arcade machine | 86.78 | 98.65 | +| hovel | 43.68 | 89.91 | +| bus | 92.25 | 94.22 | +| towel | 67.57 | 75.66 | +| light | 46.35 | 50.84 | +| truck | 45.46 | 49.71 | +| tower | 30.97 | 71.83 | +| chandelier | 67.6 | 82.12 | +| awning | 34.54 | 50.22 | +| streetlight | 20.41 | 24.95 | +| booth | 36.5 | 38.57 | +| television receiver | 74.61 | 82.66 | +| airplane | 81.32 | 85.26 | +| dirt track | 0.0 | 0.0 | +| apparel | 42.27 | 47.95 | +| pole | 20.67 | 24.77 | +| land | 0.19 | 0.3 | +| bannister | 9.26 | 10.55 | +| escalator | 57.4 | 87.8 | +| ottoman | 51.17 | 81.75 | +| bottle | 40.26 | 71.99 | +| buffet | 49.42 | 68.27 | +| poster | 38.56 | 51.26 | +| stage | 23.34 | 50.09 | +| van | 46.15 | 71.22 | +| ship | 22.72 | 24.83 | +| fountain | 36.35 | 40.39 | +| conveyer belt | 85.98 | 95.58 | +| canopy | 32.18 | 45.85 | +| washer | 85.55 | 93.32 | +| plaything | 26.81 | 33.89 | +| swimming pool | 59.11 | 59.25 | +| stool | 36.39 | 67.72 | +| barrel | 34.09 | 83.88 | +| basket | 39.89 | 57.66 | +| waterfall | 68.9 | 83.72 | +| tent | 93.79 | 98.58 | +| bag | 23.37 | 26.92 | +| minibike | 71.72 | 83.32 | +| cradle | 83.71 | 97.79 | +| oven | 55.6 | 64.97 | +| ball | 53.29 | 69.86 | +| food | 60.85 | 76.98 | +| step | 6.54 | 7.51 | +| tank | 47.53 | 67.0 | +| trade name | 27.55 | 31.52 | +| microwave | 86.03 | 93.16 | +| pot | 54.48 | 62.0 | +| animal | 72.91 | 79.25 | +| bicycle | 58.6 | 78.61 | +| lake | 44.03 | 60.94 | +| dishwasher | 68.52 | 82.46 | +| screen | 60.79 | 93.32 | +| blanket | 24.21 | 28.6 | +| sculpture | 55.22 | 87.28 | +| hood | 63.48 | 69.7 | +| sconce | 53.84 | 71.06 | +| vase | 42.23 | 53.51 | +| traffic light | 27.08 | 62.14 | +| tray | 11.97 | 13.69 | +| ashcan | 46.9 | 61.69 | +| fan | 61.58 | 78.37 | +| pier | 32.15 | 40.31 | +| crt screen | 0.18 | 0.42 | +| plate | 56.29 | 78.7 | +| monitor | 19.67 | 25.45 | +| bulletin board | 56.35 | 59.0 | +| shower | 0.0 | 0.0 | +| radiator | 62.82 | 80.7 | +| glass | 19.22 | 23.04 | +| clock | 47.71 | 55.67 | +| flag | 51.45 | 54.74 | ++---------------------+-------+-------+ +2024-06-18 07:50:03,160 - mmseg - INFO - Summary: +2024-06-18 07:50:03,160 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 83.98 | 52.31 | 66.61 | ++-------+-------+-------+ +2024-06-18 07:50:03,160 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 07:50:03,161 - mmseg - INFO - Iter(val) [250] aAcc: 0.8398, mIoU: 0.5231, mAcc: 0.6661, IoU.wall: 0.7944, IoU.building: 0.8338, IoU.sky: 0.9422, IoU.floor: 0.8206, IoU.tree: 0.7518, IoU.ceiling: 0.8526, IoU.road: 0.8496, IoU.bed : 0.9112, IoU.windowpane: 0.6454, IoU.grass: 0.6127, IoU.cabinet: 0.6251, IoU.sidewalk: 0.6733, IoU.person: 0.8243, IoU.earth: 0.2886, IoU.door: 0.5608, IoU.table: 0.6135, IoU.mountain: 0.5896, IoU.plant: 0.5650, IoU.curtain: 0.7624, IoU.chair: 0.5747, IoU.car: 0.8532, IoU.water: 0.5626, IoU.painting: 0.7792, IoU.sofa: 0.7135, IoU.shelf: 0.3842, IoU.house: 0.3825, IoU.sea: 0.6154, IoU.mirror: 0.7663, IoU.rug: 0.6619, IoU.field: 0.3137, IoU.armchair: 0.5775, IoU.seat: 0.5949, IoU.fence: 0.5298, IoU.desk: 0.5013, IoU.rock: 0.4822, IoU.wardrobe: 0.5180, IoU.lamp: 0.6660, IoU.bathtub: 0.8645, IoU.railing: 0.3893, IoU.cushion: 0.5262, IoU.base: 0.3370, IoU.box: 0.3213, IoU.column: 0.4922, IoU.signboard: 0.3777, IoU.chest of drawers: 0.4911, IoU.counter: 0.3062, IoU.sand: 0.4545, IoU.sink: 0.7342, IoU.skyscraper: 0.5178, IoU.fireplace: 0.6686, IoU.refrigerator: 0.8001, IoU.grandstand: 0.4713, IoU.path: 0.2802, IoU.stairs: 0.2860, IoU.runway: 0.7101, IoU.case: 0.4322, IoU.pool table: 0.9210, IoU.pillow: 0.3354, IoU.screen door: 0.7762, IoU.stairway: 0.4418, IoU.river: 0.1828, IoU.bridge: 0.7300, IoU.bookcase: 0.3494, IoU.blind: 0.3763, IoU.coffee table: 0.5357, IoU.toilet: 0.8698, IoU.flower: 0.3555, IoU.book: 0.4994, IoU.hill: 0.0578, IoU.bench: 0.6441, IoU.countertop: 0.5650, IoU.stove: 0.7980, IoU.palm: 0.4949, IoU.kitchen island: 0.4005, IoU.computer: 0.7570, IoU.swivel chair: 0.4640, IoU.boat: 0.4880, IoU.bar: 0.6031, IoU.arcade machine: 0.8678, IoU.hovel: 0.4368, IoU.bus: 0.9225, IoU.towel: 0.6757, IoU.light: 0.4635, IoU.truck: 0.4546, IoU.tower: 0.3097, IoU.chandelier: 0.6760, IoU.awning: 0.3454, IoU.streetlight: 0.2041, IoU.booth: 0.3650, IoU.television receiver: 0.7461, IoU.airplane: 0.8132, IoU.dirt track: 0.0000, IoU.apparel: 0.4227, IoU.pole: 0.2067, IoU.land: 0.0019, IoU.bannister: 0.0926, IoU.escalator: 0.5740, IoU.ottoman: 0.5117, IoU.bottle: 0.4026, IoU.buffet: 0.4942, IoU.poster: 0.3856, IoU.stage: 0.2334, IoU.van: 0.4615, IoU.ship: 0.2272, IoU.fountain: 0.3635, IoU.conveyer belt: 0.8598, IoU.canopy: 0.3218, IoU.washer: 0.8555, IoU.plaything: 0.2681, IoU.swimming pool: 0.5911, IoU.stool: 0.3639, IoU.barrel: 0.3409, IoU.basket: 0.3989, IoU.waterfall: 0.6890, IoU.tent: 0.9379, IoU.bag: 0.2337, IoU.minibike: 0.7172, IoU.cradle: 0.8371, IoU.oven: 0.5560, IoU.ball: 0.5329, IoU.food: 0.6085, IoU.step: 0.0654, IoU.tank: 0.4753, IoU.trade name: 0.2755, IoU.microwave: 0.8603, IoU.pot: 0.5448, IoU.animal: 0.7291, IoU.bicycle: 0.5860, IoU.lake: 0.4403, IoU.dishwasher: 0.6852, IoU.screen: 0.6079, IoU.blanket: 0.2421, IoU.sculpture: 0.5522, IoU.hood: 0.6348, IoU.sconce: 0.5384, IoU.vase: 0.4223, IoU.traffic light: 0.2708, IoU.tray: 0.1197, IoU.ashcan: 0.4690, IoU.fan: 0.6158, IoU.pier: 0.3215, IoU.crt screen: 0.0018, IoU.plate: 0.5629, IoU.monitor: 0.1967, IoU.bulletin board: 0.5635, IoU.shower: 0.0000, IoU.radiator: 0.6282, IoU.glass: 0.1922, IoU.clock: 0.4771, IoU.flag: 0.5145, Acc.wall: 0.8870, Acc.building: 0.9226, Acc.sky: 0.9736, Acc.floor: 0.8972, Acc.tree: 0.9014, Acc.ceiling: 0.9361, Acc.road: 0.9414, Acc.bed : 0.9626, Acc.windowpane: 0.7911, Acc.grass: 0.7342, Acc.cabinet: 0.7129, Acc.sidewalk: 0.7768, Acc.person: 0.8972, Acc.earth: 0.3567, Acc.door: 0.7023, Acc.table: 0.7627, Acc.mountain: 0.7771, Acc.plant: 0.7138, Acc.curtain: 0.8666, Acc.chair: 0.6347, Acc.car: 0.9174, Acc.water: 0.6832, Acc.painting: 0.8815, Acc.sofa: 0.9133, Acc.shelf: 0.4562, Acc.house: 0.4084, Acc.sea: 0.8649, Acc.mirror: 0.8717, Acc.rug: 0.8261, Acc.field: 0.6422, Acc.armchair: 0.7711, Acc.seat: 0.6886, Acc.fence: 0.6939, Acc.desk: 0.5959, Acc.rock: 0.6524, Acc.wardrobe: 0.8815, Acc.lamp: 0.7719, Acc.bathtub: 0.9357, Acc.railing: 0.5263, Acc.cushion: 0.8562, Acc.base: 0.8199, Acc.box: 0.3756, Acc.column: 0.6456, Acc.signboard: 0.4794, Acc.chest of drawers: 0.6783, Acc.counter: 0.3826, Acc.sand: 0.8104, Acc.sink: 0.8107, Acc.skyscraper: 0.6846, Acc.fireplace: 0.9473, Acc.refrigerator: 0.8503, Acc.grandstand: 0.8622, Acc.path: 0.3604, Acc.stairs: 0.3276, Acc.runway: 0.9733, Acc.case: 0.4428, Acc.pool table: 0.9837, Acc.pillow: 0.3529, Acc.screen door: 0.9424, Acc.stairway: 0.7884, Acc.river: 0.3507, Acc.bridge: 0.8939, Acc.bookcase: 0.5823, Acc.blind: 0.4041, Acc.coffee table: 0.8889, Acc.toilet: 0.9588, Acc.flower: 0.6234, Acc.book: 0.6966, Acc.hill: 0.0922, Acc.bench: 0.7393, Acc.countertop: 0.8063, Acc.stove: 0.9040, Acc.palm: 0.8862, Acc.kitchen island: 0.5471, Acc.computer: 0.8570, Acc.swivel chair: 0.8191, Acc.boat: 0.8474, Acc.bar: 0.8689, Acc.arcade machine: 0.9865, Acc.hovel: 0.8991, Acc.bus: 0.9422, Acc.towel: 0.7566, Acc.light: 0.5084, Acc.truck: 0.4971, Acc.tower: 0.7183, Acc.chandelier: 0.8212, Acc.awning: 0.5022, Acc.streetlight: 0.2495, Acc.booth: 0.3857, Acc.television receiver: 0.8266, Acc.airplane: 0.8526, Acc.dirt track: 0.0000, Acc.apparel: 0.4795, Acc.pole: 0.2477, Acc.land: 0.0030, Acc.bannister: 0.1055, Acc.escalator: 0.8780, Acc.ottoman: 0.8175, Acc.bottle: 0.7199, Acc.buffet: 0.6827, Acc.poster: 0.5126, Acc.stage: 0.5009, Acc.van: 0.7122, Acc.ship: 0.2483, Acc.fountain: 0.4039, Acc.conveyer belt: 0.9558, Acc.canopy: 0.4585, Acc.washer: 0.9332, Acc.plaything: 0.3389, Acc.swimming pool: 0.5925, Acc.stool: 0.6772, Acc.barrel: 0.8388, Acc.basket: 0.5766, Acc.waterfall: 0.8372, Acc.tent: 0.9858, Acc.bag: 0.2692, Acc.minibike: 0.8332, Acc.cradle: 0.9779, Acc.oven: 0.6497, Acc.ball: 0.6986, Acc.food: 0.7698, Acc.step: 0.0751, Acc.tank: 0.6700, Acc.trade name: 0.3152, Acc.microwave: 0.9316, Acc.pot: 0.6200, Acc.animal: 0.7925, Acc.bicycle: 0.7861, Acc.lake: 0.6094, Acc.dishwasher: 0.8246, Acc.screen: 0.9332, Acc.blanket: 0.2860, Acc.sculpture: 0.8728, Acc.hood: 0.6970, Acc.sconce: 0.7106, Acc.vase: 0.5351, Acc.traffic light: 0.6214, Acc.tray: 0.1369, Acc.ashcan: 0.6169, Acc.fan: 0.7837, Acc.pier: 0.4031, Acc.crt screen: 0.0042, Acc.plate: 0.7870, Acc.monitor: 0.2545, Acc.bulletin board: 0.5900, Acc.shower: 0.0000, Acc.radiator: 0.8070, Acc.glass: 0.2304, Acc.clock: 0.5567, Acc.flag: 0.5474 +2024-06-18 07:51:42,685 - mmseg - INFO - Iter [9050/80000] lr: 3.548e-05, eta: 1 day, 18:08:33, time: 4.330, data_time: 2.359, memory: 72263, decode.loss_ce: 0.4292, decode.acc_seg: 82.8860, aux.loss_ce: 0.1709, aux.acc_seg: 83.1205, loss: 0.6001 +2024-06-18 07:53:21,722 - mmseg - INFO - Iter [9100/80000] lr: 3.545e-05, eta: 1 day, 18:05:44, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3897, decode.acc_seg: 84.0211, aux.loss_ce: 0.1550, aux.acc_seg: 84.1721, loss: 0.5447 +2024-06-18 07:55:00,705 - mmseg - INFO - Iter [9150/80000] lr: 3.543e-05, eta: 1 day, 18:02:56, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4023, decode.acc_seg: 84.1095, aux.loss_ce: 0.1610, aux.acc_seg: 84.0642, loss: 0.5633 +2024-06-18 07:56:39,678 - mmseg - INFO - Iter [9200/80000] lr: 3.540e-05, eta: 1 day, 18:00:09, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3712, decode.acc_seg: 85.3073, aux.loss_ce: 0.1480, aux.acc_seg: 85.3730, loss: 0.5192 +2024-06-18 07:58:18,836 - mmseg - INFO - Iter [9250/80000] lr: 3.538e-05, eta: 1 day, 17:57:24, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3973, decode.acc_seg: 84.4106, aux.loss_ce: 0.1589, aux.acc_seg: 84.5498, loss: 0.5562 +2024-06-18 07:59:57,840 - mmseg - INFO - Iter [9300/80000] lr: 3.535e-05, eta: 1 day, 17:54:38, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4022, decode.acc_seg: 84.1698, aux.loss_ce: 0.1608, aux.acc_seg: 84.2038, loss: 0.5630 +2024-06-18 08:01:36,866 - mmseg - INFO - Iter [9350/80000] lr: 3.533e-05, eta: 1 day, 17:51:54, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4350, decode.acc_seg: 83.6514, aux.loss_ce: 0.1718, aux.acc_seg: 83.7922, loss: 0.6068 +2024-06-18 08:03:15,790 - mmseg - INFO - Iter [9400/80000] lr: 3.530e-05, eta: 1 day, 17:49:09, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4131, decode.acc_seg: 83.8964, aux.loss_ce: 0.1629, aux.acc_seg: 84.0382, loss: 0.5760 +2024-06-18 08:04:54,865 - mmseg - INFO - Iter [9450/80000] lr: 3.528e-05, eta: 1 day, 17:46:26, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4374, decode.acc_seg: 83.3094, aux.loss_ce: 0.1744, aux.acc_seg: 83.3290, loss: 0.6118 +2024-06-18 08:06:33,832 - mmseg - INFO - Iter [9500/80000] lr: 3.525e-05, eta: 1 day, 17:43:43, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4191, decode.acc_seg: 84.0522, aux.loss_ce: 0.1662, aux.acc_seg: 84.1316, loss: 0.5854 +2024-06-18 08:08:12,857 - mmseg - INFO - Iter [9550/80000] lr: 3.523e-05, eta: 1 day, 17:41:01, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4148, decode.acc_seg: 84.3823, aux.loss_ce: 0.1648, aux.acc_seg: 84.5367, loss: 0.5796 +2024-06-18 08:09:51,902 - mmseg - INFO - Iter [9600/80000] lr: 3.520e-05, eta: 1 day, 17:38:20, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4064, decode.acc_seg: 84.8010, aux.loss_ce: 0.1612, aux.acc_seg: 85.0138, loss: 0.5675 +2024-06-18 08:11:30,907 - mmseg - INFO - Iter [9650/80000] lr: 3.518e-05, eta: 1 day, 17:35:39, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.4179, decode.acc_seg: 83.5097, aux.loss_ce: 0.1678, aux.acc_seg: 83.3618, loss: 0.5857 +2024-06-18 08:13:09,948 - mmseg - INFO - Iter [9700/80000] lr: 3.515e-05, eta: 1 day, 17:32:59, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4125, decode.acc_seg: 83.9285, aux.loss_ce: 0.1655, aux.acc_seg: 84.0142, loss: 0.5780 +2024-06-18 08:14:48,960 - mmseg - INFO - Iter [9750/80000] lr: 3.513e-05, eta: 1 day, 17:30:19, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4229, decode.acc_seg: 82.9790, aux.loss_ce: 0.1686, aux.acc_seg: 82.7961, loss: 0.5915 +2024-06-18 08:16:28,160 - mmseg - INFO - Iter [9800/80000] lr: 3.510e-05, eta: 1 day, 17:27:42, time: 1.984, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4312, decode.acc_seg: 83.0871, aux.loss_ce: 0.1714, aux.acc_seg: 82.9446, loss: 0.6026 +2024-06-18 08:18:07,095 - mmseg - INFO - Iter [9850/80000] lr: 3.508e-05, eta: 1 day, 17:25:03, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4289, decode.acc_seg: 83.4925, aux.loss_ce: 0.1703, aux.acc_seg: 83.5197, loss: 0.5992 +2024-06-18 08:19:46,179 - mmseg - INFO - Iter [9900/80000] lr: 3.505e-05, eta: 1 day, 17:22:26, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4153, decode.acc_seg: 82.9647, aux.loss_ce: 0.1657, aux.acc_seg: 83.0308, loss: 0.5810 +2024-06-18 08:21:25,170 - mmseg - INFO - Iter [9950/80000] lr: 3.503e-05, eta: 1 day, 17:19:49, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4100, decode.acc_seg: 83.9287, aux.loss_ce: 0.1635, aux.acc_seg: 83.9777, loss: 0.5735 +2024-06-18 08:23:04,104 - mmseg - INFO - Saving checkpoint at 10000 iterations +2024-06-18 08:24:28,663 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 08:24:28,663 - mmseg - INFO - Iter [10000/80000] lr: 3.500e-05, eta: 1 day, 17:27:03, time: 3.670, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4169, decode.acc_seg: 83.9720, aux.loss_ce: 0.1666, aux.acc_seg: 84.0705, loss: 0.5835 +2024-06-18 08:26:17,435 - mmseg - INFO - per class results: +2024-06-18 08:26:17,441 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.3 | 88.11 | +| building | 84.19 | 91.77 | +| sky | 94.26 | 96.91 | +| floor | 82.83 | 89.14 | +| tree | 75.2 | 90.72 | +| ceiling | 85.41 | 92.54 | +| road | 84.81 | 90.15 | +| bed | 90.6 | 93.96 | +| windowpane | 62.77 | 76.63 | +| grass | 65.52 | 90.39 | +| cabinet | 62.53 | 70.64 | +| sidewalk | 67.68 | 82.57 | +| person | 83.17 | 92.98 | +| earth | 33.27 | 39.85 | +| door | 57.64 | 74.58 | +| table | 61.88 | 76.46 | +| mountain | 66.26 | 82.15 | +| plant | 56.11 | 71.82 | +| curtain | 77.0 | 88.42 | +| chair | 59.84 | 68.05 | +| car | 84.57 | 93.14 | +| water | 45.22 | 52.18 | +| painting | 78.75 | 85.29 | +| sofa | 76.38 | 91.43 | +| shelf | 44.5 | 58.95 | +| house | 49.52 | 66.48 | +| sea | 69.91 | 75.65 | +| mirror | 75.28 | 86.29 | +| rug | 65.14 | 86.1 | +| field | 34.12 | 49.15 | +| armchair | 57.5 | 76.7 | +| seat | 58.18 | 76.8 | +| fence | 49.92 | 69.85 | +| desk | 42.59 | 79.99 | +| rock | 55.5 | 74.98 | +| wardrobe | 50.88 | 75.25 | +| lamp | 67.43 | 82.95 | +| bathtub | 81.74 | 84.54 | +| railing | 37.72 | 50.49 | +| cushion | 62.53 | 85.52 | +| base | 43.37 | 56.04 | +| box | 34.41 | 55.5 | +| column | 56.42 | 70.32 | +| signboard | 34.62 | 53.93 | +| chest of drawers | 53.28 | 69.83 | +| counter | 45.54 | 73.17 | +| sand | 48.22 | 74.6 | +| sink | 72.89 | 83.27 | +| skyscraper | 39.69 | 45.2 | +| fireplace | 70.83 | 92.76 | +| refrigerator | 78.92 | 90.74 | +| grandstand | 48.84 | 87.41 | +| path | 22.94 | 26.41 | +| stairs | 39.56 | 50.68 | +| runway | 70.28 | 92.43 | +| case | 43.3 | 67.38 | +| pool table | 93.13 | 98.09 | +| pillow | 60.66 | 70.5 | +| screen door | 80.39 | 95.6 | +| stairway | 53.19 | 68.36 | +| river | 12.12 | 69.06 | +| bridge | 67.01 | 86.42 | +| bookcase | 32.56 | 54.55 | +| blind | 49.93 | 65.37 | +| coffee table | 55.71 | 87.14 | +| toilet | 86.72 | 95.01 | +| flower | 40.53 | 54.11 | +| book | 53.04 | 78.45 | +| hill | 4.92 | 6.28 | +| bench | 56.13 | 62.54 | +| countertop | 58.11 | 77.03 | +| stove | 82.3 | 92.4 | +| palm | 53.53 | 74.03 | +| kitchen island | 41.03 | 89.39 | +| computer | 74.73 | 93.1 | +| swivel chair | 48.34 | 80.05 | +| boat | 72.45 | 88.74 | +| bar | 63.42 | 77.79 | +| arcade machine | 88.99 | 97.91 | +| hovel | 22.94 | 23.82 | +| bus | 91.03 | 95.57 | +| towel | 71.2 | 82.77 | +| light | 52.75 | 63.53 | +| truck | 47.17 | 56.62 | +| tower | 10.04 | 12.92 | +| chandelier | 64.51 | 90.38 | +| awning | 33.11 | 47.24 | +| streetlight | 28.83 | 48.23 | +| booth | 31.49 | 52.65 | +| television receiver | 70.37 | 75.78 | +| airplane | 79.83 | 93.69 | +| dirt track | 5.23 | 9.47 | +| apparel | 51.03 | 67.47 | +| pole | 21.18 | 25.83 | +| land | 0.25 | 0.3 | +| bannister | 17.91 | 23.78 | +| escalator | 53.74 | 84.67 | +| ottoman | 56.46 | 80.62 | +| bottle | 41.26 | 65.15 | +| buffet | 56.32 | 75.73 | +| poster | 26.74 | 31.33 | +| stage | 20.06 | 33.12 | +| van | 26.33 | 31.41 | +| ship | 67.26 | 92.69 | +| fountain | 45.61 | 51.23 | +| conveyer belt | 60.95 | 98.83 | +| canopy | 47.58 | 76.74 | +| washer | 77.78 | 84.1 | +| plaything | 41.28 | 63.97 | +| swimming pool | 52.62 | 76.21 | +| stool | 47.77 | 60.4 | +| barrel | 46.19 | 65.12 | +| basket | 40.41 | 51.97 | +| waterfall | 72.59 | 92.31 | +| tent | 90.83 | 99.09 | +| bag | 18.88 | 19.73 | +| minibike | 70.48 | 89.16 | +| cradle | 84.48 | 94.56 | +| oven | 62.43 | 70.24 | +| ball | 9.98 | 10.08 | +| food | 60.09 | 73.08 | +| step | 13.53 | 15.02 | +| tank | 55.51 | 66.13 | +| trade name | 8.88 | 9.32 | +| microwave | 87.6 | 95.71 | +| pot | 54.59 | 67.89 | +| animal | 64.88 | 68.08 | +| bicycle | 59.3 | 78.73 | +| lake | 0.0 | 0.0 | +| dishwasher | 66.26 | 76.36 | +| screen | 59.23 | 97.37 | +| blanket | 22.24 | 25.4 | +| sculpture | 57.47 | 87.33 | +| hood | 68.38 | 72.6 | +| sconce | 53.8 | 66.35 | +| vase | 40.19 | 70.27 | +| traffic light | 29.55 | 59.6 | +| tray | 14.45 | 18.31 | +| ashcan | 49.54 | 64.6 | +| fan | 66.19 | 80.21 | +| pier | 38.49 | 42.1 | +| crt screen | 0.33 | 0.39 | +| plate | 57.15 | 70.59 | +| monitor | 44.78 | 82.09 | +| bulletin board | 58.59 | 75.29 | +| shower | 0.0 | 0.0 | +| radiator | 61.85 | 73.03 | +| glass | 12.52 | 13.1 | +| clock | 48.28 | 60.47 | +| flag | 67.11 | 83.01 | ++---------------------+-------+-------+ +2024-06-18 08:26:17,441 - mmseg - INFO - Summary: +2024-06-18 08:26:17,442 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.44 | 53.26 | 67.75 | ++-------+-------+-------+ +2024-06-18 08:26:17,442 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 08:26:17,443 - mmseg - INFO - Iter(val) [250] aAcc: 0.8444, mIoU: 0.5326, mAcc: 0.6775, IoU.wall: 0.8030, IoU.building: 0.8419, IoU.sky: 0.9426, IoU.floor: 0.8283, IoU.tree: 0.7520, IoU.ceiling: 0.8541, IoU.road: 0.8481, IoU.bed : 0.9060, IoU.windowpane: 0.6277, IoU.grass: 0.6552, IoU.cabinet: 0.6253, IoU.sidewalk: 0.6768, IoU.person: 0.8317, IoU.earth: 0.3327, IoU.door: 0.5764, IoU.table: 0.6188, IoU.mountain: 0.6626, IoU.plant: 0.5611, IoU.curtain: 0.7700, IoU.chair: 0.5984, IoU.car: 0.8457, IoU.water: 0.4522, IoU.painting: 0.7875, IoU.sofa: 0.7638, IoU.shelf: 0.4450, IoU.house: 0.4952, IoU.sea: 0.6991, IoU.mirror: 0.7528, IoU.rug: 0.6514, IoU.field: 0.3412, IoU.armchair: 0.5750, IoU.seat: 0.5818, IoU.fence: 0.4992, IoU.desk: 0.4259, IoU.rock: 0.5550, IoU.wardrobe: 0.5088, IoU.lamp: 0.6743, IoU.bathtub: 0.8174, IoU.railing: 0.3772, IoU.cushion: 0.6253, IoU.base: 0.4337, IoU.box: 0.3441, IoU.column: 0.5642, IoU.signboard: 0.3462, IoU.chest of drawers: 0.5328, IoU.counter: 0.4554, IoU.sand: 0.4822, IoU.sink: 0.7289, IoU.skyscraper: 0.3969, IoU.fireplace: 0.7083, IoU.refrigerator: 0.7892, IoU.grandstand: 0.4884, IoU.path: 0.2294, IoU.stairs: 0.3956, IoU.runway: 0.7028, IoU.case: 0.4330, IoU.pool table: 0.9313, IoU.pillow: 0.6066, IoU.screen door: 0.8039, IoU.stairway: 0.5319, IoU.river: 0.1212, IoU.bridge: 0.6701, IoU.bookcase: 0.3256, IoU.blind: 0.4993, IoU.coffee table: 0.5571, IoU.toilet: 0.8672, IoU.flower: 0.4053, IoU.book: 0.5304, IoU.hill: 0.0492, IoU.bench: 0.5613, IoU.countertop: 0.5811, IoU.stove: 0.8230, IoU.palm: 0.5353, IoU.kitchen island: 0.4103, IoU.computer: 0.7473, IoU.swivel chair: 0.4834, IoU.boat: 0.7245, IoU.bar: 0.6342, IoU.arcade machine: 0.8899, IoU.hovel: 0.2294, IoU.bus: 0.9103, IoU.towel: 0.7120, IoU.light: 0.5275, IoU.truck: 0.4717, IoU.tower: 0.1004, IoU.chandelier: 0.6451, IoU.awning: 0.3311, IoU.streetlight: 0.2883, IoU.booth: 0.3149, IoU.television receiver: 0.7037, IoU.airplane: 0.7983, IoU.dirt track: 0.0523, IoU.apparel: 0.5103, IoU.pole: 0.2118, IoU.land: 0.0025, IoU.bannister: 0.1791, IoU.escalator: 0.5374, IoU.ottoman: 0.5646, IoU.bottle: 0.4126, IoU.buffet: 0.5632, IoU.poster: 0.2674, IoU.stage: 0.2006, IoU.van: 0.2633, IoU.ship: 0.6726, IoU.fountain: 0.4561, IoU.conveyer belt: 0.6095, IoU.canopy: 0.4758, IoU.washer: 0.7778, IoU.plaything: 0.4128, IoU.swimming pool: 0.5262, IoU.stool: 0.4777, IoU.barrel: 0.4619, IoU.basket: 0.4041, IoU.waterfall: 0.7259, IoU.tent: 0.9083, IoU.bag: 0.1888, IoU.minibike: 0.7048, IoU.cradle: 0.8448, IoU.oven: 0.6243, IoU.ball: 0.0998, IoU.food: 0.6009, IoU.step: 0.1353, IoU.tank: 0.5551, IoU.trade name: 0.0888, IoU.microwave: 0.8760, IoU.pot: 0.5459, IoU.animal: 0.6488, IoU.bicycle: 0.5930, IoU.lake: 0.0000, IoU.dishwasher: 0.6626, IoU.screen: 0.5923, IoU.blanket: 0.2224, IoU.sculpture: 0.5747, IoU.hood: 0.6838, IoU.sconce: 0.5380, IoU.vase: 0.4019, IoU.traffic light: 0.2955, IoU.tray: 0.1445, IoU.ashcan: 0.4954, IoU.fan: 0.6619, IoU.pier: 0.3849, IoU.crt screen: 0.0033, IoU.plate: 0.5715, IoU.monitor: 0.4478, IoU.bulletin board: 0.5859, IoU.shower: 0.0000, IoU.radiator: 0.6185, IoU.glass: 0.1252, IoU.clock: 0.4828, IoU.flag: 0.6711, Acc.wall: 0.8811, Acc.building: 0.9177, Acc.sky: 0.9691, Acc.floor: 0.8914, Acc.tree: 0.9072, Acc.ceiling: 0.9254, Acc.road: 0.9015, Acc.bed : 0.9396, Acc.windowpane: 0.7663, Acc.grass: 0.9039, Acc.cabinet: 0.7064, Acc.sidewalk: 0.8257, Acc.person: 0.9298, Acc.earth: 0.3985, Acc.door: 0.7458, Acc.table: 0.7646, Acc.mountain: 0.8215, Acc.plant: 0.7182, Acc.curtain: 0.8842, Acc.chair: 0.6805, Acc.car: 0.9314, Acc.water: 0.5218, Acc.painting: 0.8529, Acc.sofa: 0.9143, Acc.shelf: 0.5895, Acc.house: 0.6648, Acc.sea: 0.7565, Acc.mirror: 0.8629, Acc.rug: 0.8610, Acc.field: 0.4915, Acc.armchair: 0.7670, Acc.seat: 0.7680, Acc.fence: 0.6985, Acc.desk: 0.7999, Acc.rock: 0.7498, Acc.wardrobe: 0.7525, Acc.lamp: 0.8295, Acc.bathtub: 0.8454, Acc.railing: 0.5049, Acc.cushion: 0.8552, Acc.base: 0.5604, Acc.box: 0.5550, Acc.column: 0.7032, Acc.signboard: 0.5393, Acc.chest of drawers: 0.6983, Acc.counter: 0.7317, Acc.sand: 0.7460, Acc.sink: 0.8327, Acc.skyscraper: 0.4520, Acc.fireplace: 0.9276, Acc.refrigerator: 0.9074, Acc.grandstand: 0.8741, Acc.path: 0.2641, Acc.stairs: 0.5068, Acc.runway: 0.9243, Acc.case: 0.6738, Acc.pool table: 0.9809, Acc.pillow: 0.7050, Acc.screen door: 0.9560, Acc.stairway: 0.6836, Acc.river: 0.6906, Acc.bridge: 0.8642, Acc.bookcase: 0.5455, Acc.blind: 0.6537, Acc.coffee table: 0.8714, Acc.toilet: 0.9501, Acc.flower: 0.5411, Acc.book: 0.7845, Acc.hill: 0.0628, Acc.bench: 0.6254, Acc.countertop: 0.7703, Acc.stove: 0.9240, Acc.palm: 0.7403, Acc.kitchen island: 0.8939, Acc.computer: 0.9310, Acc.swivel chair: 0.8005, Acc.boat: 0.8874, Acc.bar: 0.7779, Acc.arcade machine: 0.9791, Acc.hovel: 0.2382, Acc.bus: 0.9557, Acc.towel: 0.8277, Acc.light: 0.6353, Acc.truck: 0.5662, Acc.tower: 0.1292, Acc.chandelier: 0.9038, Acc.awning: 0.4724, Acc.streetlight: 0.4823, Acc.booth: 0.5265, Acc.television receiver: 0.7578, Acc.airplane: 0.9369, Acc.dirt track: 0.0947, Acc.apparel: 0.6747, Acc.pole: 0.2583, Acc.land: 0.0030, Acc.bannister: 0.2378, Acc.escalator: 0.8467, Acc.ottoman: 0.8062, Acc.bottle: 0.6515, Acc.buffet: 0.7573, Acc.poster: 0.3133, Acc.stage: 0.3312, Acc.van: 0.3141, Acc.ship: 0.9269, Acc.fountain: 0.5123, Acc.conveyer belt: 0.9883, Acc.canopy: 0.7674, Acc.washer: 0.8410, Acc.plaything: 0.6397, Acc.swimming pool: 0.7621, Acc.stool: 0.6040, Acc.barrel: 0.6512, Acc.basket: 0.5197, Acc.waterfall: 0.9231, Acc.tent: 0.9909, Acc.bag: 0.1973, Acc.minibike: 0.8916, Acc.cradle: 0.9456, Acc.oven: 0.7024, Acc.ball: 0.1008, Acc.food: 0.7308, Acc.step: 0.1502, Acc.tank: 0.6613, Acc.trade name: 0.0932, Acc.microwave: 0.9571, Acc.pot: 0.6789, Acc.animal: 0.6808, Acc.bicycle: 0.7873, Acc.lake: 0.0000, Acc.dishwasher: 0.7636, Acc.screen: 0.9737, Acc.blanket: 0.2540, Acc.sculpture: 0.8733, Acc.hood: 0.7260, Acc.sconce: 0.6635, Acc.vase: 0.7027, Acc.traffic light: 0.5960, Acc.tray: 0.1831, Acc.ashcan: 0.6460, Acc.fan: 0.8021, Acc.pier: 0.4210, Acc.crt screen: 0.0039, Acc.plate: 0.7059, Acc.monitor: 0.8209, Acc.bulletin board: 0.7529, Acc.shower: 0.0000, Acc.radiator: 0.7303, Acc.glass: 0.1310, Acc.clock: 0.6047, Acc.flag: 0.8301 +2024-06-18 08:27:56,719 - mmseg - INFO - Iter [10050/80000] lr: 3.498e-05, eta: 1 day, 17:37:03, time: 4.161, data_time: 2.192, memory: 72263, decode.loss_ce: 0.4522, decode.acc_seg: 83.1081, aux.loss_ce: 0.1777, aux.acc_seg: 83.2336, loss: 0.6299 +2024-06-18 08:29:35,778 - mmseg - INFO - Iter [10100/80000] lr: 3.495e-05, eta: 1 day, 17:34:20, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4164, decode.acc_seg: 83.7942, aux.loss_ce: 0.1663, aux.acc_seg: 83.9621, loss: 0.5827 +2024-06-18 08:31:17,338 - mmseg - INFO - Iter [10150/80000] lr: 3.493e-05, eta: 1 day, 17:31:55, time: 2.031, data_time: 0.058, memory: 72263, decode.loss_ce: 0.3837, decode.acc_seg: 84.8785, aux.loss_ce: 0.1537, aux.acc_seg: 84.9806, loss: 0.5373 +2024-06-18 08:32:56,321 - mmseg - INFO - Iter [10200/80000] lr: 3.490e-05, eta: 1 day, 17:29:13, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4144, decode.acc_seg: 84.6139, aux.loss_ce: 0.1645, aux.acc_seg: 84.5862, loss: 0.5789 +2024-06-18 08:34:35,436 - mmseg - INFO - Iter [10250/80000] lr: 3.488e-05, eta: 1 day, 17:26:33, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3615, decode.acc_seg: 86.1979, aux.loss_ce: 0.1439, aux.acc_seg: 86.0312, loss: 0.5054 +2024-06-18 08:36:14,475 - mmseg - INFO - Iter [10300/80000] lr: 3.485e-05, eta: 1 day, 17:23:52, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3857, decode.acc_seg: 84.8325, aux.loss_ce: 0.1550, aux.acc_seg: 84.7254, loss: 0.5408 +2024-06-18 08:37:53,509 - mmseg - INFO - Iter [10350/80000] lr: 3.483e-05, eta: 1 day, 17:21:12, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4196, decode.acc_seg: 83.2645, aux.loss_ce: 0.1678, aux.acc_seg: 83.2742, loss: 0.5874 +2024-06-18 08:39:32,525 - mmseg - INFO - Iter [10400/80000] lr: 3.480e-05, eta: 1 day, 17:18:33, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3634, decode.acc_seg: 85.1432, aux.loss_ce: 0.1447, aux.acc_seg: 85.3039, loss: 0.5081 +2024-06-18 08:41:11,627 - mmseg - INFO - Iter [10450/80000] lr: 3.478e-05, eta: 1 day, 17:15:55, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3691, decode.acc_seg: 86.0837, aux.loss_ce: 0.1484, aux.acc_seg: 86.0326, loss: 0.5174 +2024-06-18 08:42:50,611 - mmseg - INFO - Iter [10500/80000] lr: 3.475e-05, eta: 1 day, 17:13:16, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3650, decode.acc_seg: 85.4077, aux.loss_ce: 0.1462, aux.acc_seg: 85.4228, loss: 0.5112 +2024-06-18 08:44:29,730 - mmseg - INFO - Iter [10550/80000] lr: 3.473e-05, eta: 1 day, 17:10:39, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3658, decode.acc_seg: 84.5945, aux.loss_ce: 0.1473, aux.acc_seg: 84.6643, loss: 0.5131 +2024-06-18 08:46:08,729 - mmseg - INFO - Iter [10600/80000] lr: 3.470e-05, eta: 1 day, 17:08:02, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3604, decode.acc_seg: 85.6662, aux.loss_ce: 0.1446, aux.acc_seg: 85.6145, loss: 0.5050 +2024-06-18 08:47:47,747 - mmseg - INFO - Iter [10650/80000] lr: 3.468e-05, eta: 1 day, 17:05:25, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3924, decode.acc_seg: 84.6154, aux.loss_ce: 0.1566, aux.acc_seg: 84.6683, loss: 0.5490 +2024-06-18 08:49:26,862 - mmseg - INFO - Iter [10700/80000] lr: 3.465e-05, eta: 1 day, 17:02:50, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4031, decode.acc_seg: 84.0023, aux.loss_ce: 0.1612, aux.acc_seg: 83.9938, loss: 0.5643 +2024-06-18 08:51:05,907 - mmseg - INFO - Iter [10750/80000] lr: 3.463e-05, eta: 1 day, 17:00:14, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4116, decode.acc_seg: 83.3078, aux.loss_ce: 0.1637, aux.acc_seg: 83.5025, loss: 0.5753 +2024-06-18 08:52:44,928 - mmseg - INFO - Iter [10800/80000] lr: 3.460e-05, eta: 1 day, 16:57:39, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3961, decode.acc_seg: 84.2674, aux.loss_ce: 0.1579, aux.acc_seg: 84.2945, loss: 0.5540 +2024-06-18 08:54:23,873 - mmseg - INFO - Iter [10850/80000] lr: 3.458e-05, eta: 1 day, 16:55:04, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3768, decode.acc_seg: 85.3742, aux.loss_ce: 0.1522, aux.acc_seg: 85.3495, loss: 0.5290 +2024-06-18 08:56:02,898 - mmseg - INFO - Iter [10900/80000] lr: 3.455e-05, eta: 1 day, 16:52:30, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3645, decode.acc_seg: 85.4910, aux.loss_ce: 0.1461, aux.acc_seg: 85.4959, loss: 0.5107 +2024-06-18 08:57:41,933 - mmseg - INFO - Iter [10950/80000] lr: 3.453e-05, eta: 1 day, 16:49:57, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3865, decode.acc_seg: 84.4730, aux.loss_ce: 0.1534, aux.acc_seg: 84.3235, loss: 0.5399 +2024-06-18 08:59:20,923 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 08:59:20,923 - mmseg - INFO - Iter [11000/80000] lr: 3.450e-05, eta: 1 day, 16:47:24, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3860, decode.acc_seg: 84.5139, aux.loss_ce: 0.1553, aux.acc_seg: 84.3887, loss: 0.5413 +2024-06-18 09:01:11,290 - mmseg - INFO - per class results: +2024-06-18 09:01:11,296 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.69 | 88.07 | +| building | 83.26 | 93.51 | +| sky | 94.21 | 97.82 | +| floor | 83.86 | 88.93 | +| tree | 74.8 | 87.31 | +| ceiling | 84.59 | 90.07 | +| road | 83.66 | 90.33 | +| bed | 90.85 | 96.24 | +| windowpane | 64.48 | 75.49 | +| grass | 64.89 | 72.05 | +| cabinet | 62.76 | 70.35 | +| sidewalk | 65.52 | 83.2 | +| person | 83.64 | 92.35 | +| earth | 34.84 | 48.42 | +| door | 57.43 | 78.07 | +| table | 60.32 | 72.8 | +| mountain | 61.32 | 77.95 | +| plant | 52.93 | 59.89 | +| curtain | 77.79 | 86.48 | +| chair | 63.61 | 78.32 | +| car | 84.65 | 92.3 | +| water | 61.42 | 79.56 | +| painting | 75.01 | 87.64 | +| sofa | 79.38 | 88.04 | +| shelf | 41.48 | 53.31 | +| house | 48.5 | 56.55 | +| sea | 70.64 | 88.74 | +| mirror | 75.09 | 85.11 | +| rug | 68.53 | 81.53 | +| field | 32.84 | 75.69 | +| armchair | 59.91 | 83.83 | +| seat | 63.58 | 85.28 | +| fence | 48.18 | 58.84 | +| desk | 49.93 | 79.8 | +| rock | 58.51 | 72.78 | +| wardrobe | 53.39 | 75.04 | +| lamp | 66.02 | 83.77 | +| bathtub | 80.6 | 82.73 | +| railing | 35.77 | 47.27 | +| cushion | 67.87 | 78.82 | +| base | 29.93 | 81.45 | +| box | 33.48 | 44.59 | +| column | 52.31 | 78.65 | +| signboard | 40.02 | 49.19 | +| chest of drawers | 48.72 | 78.57 | +| counter | 46.37 | 54.18 | +| sand | 7.74 | 9.69 | +| sink | 79.84 | 89.0 | +| skyscraper | 48.53 | 60.83 | +| fireplace | 66.97 | 89.58 | +| refrigerator | 73.74 | 91.34 | +| grandstand | 43.08 | 90.11 | +| path | 24.47 | 35.63 | +| stairs | 32.91 | 34.6 | +| runway | 67.9 | 93.6 | +| case | 58.81 | 75.1 | +| pool table | 93.36 | 98.21 | +| pillow | 60.5 | 65.21 | +| screen door | 77.53 | 83.88 | +| stairway | 39.99 | 51.25 | +| river | 20.33 | 23.25 | +| bridge | 46.77 | 54.48 | +| bookcase | 33.51 | 54.76 | +| blind | 50.47 | 63.36 | +| coffee table | 48.51 | 92.49 | +| toilet | 89.04 | 94.96 | +| flower | 37.81 | 64.69 | +| book | 49.62 | 80.26 | +| hill | 5.09 | 11.29 | +| bench | 51.95 | 61.28 | +| countertop | 63.08 | 76.96 | +| stove | 83.25 | 92.87 | +| palm | 49.49 | 84.32 | +| kitchen island | 38.64 | 83.83 | +| computer | 70.84 | 93.87 | +| swivel chair | 53.03 | 75.54 | +| boat | 61.02 | 84.38 | +| bar | 60.84 | 68.92 | +| arcade machine | 89.68 | 97.68 | +| hovel | 27.12 | 30.57 | +| bus | 90.21 | 97.18 | +| towel | 69.95 | 88.48 | +| light | 51.93 | 57.95 | +| truck | 44.89 | 58.18 | +| tower | 15.71 | 27.71 | +| chandelier | 65.91 | 90.12 | +| awning | 46.5 | 59.93 | +| streetlight | 27.24 | 33.7 | +| booth | 39.95 | 72.2 | +| television receiver | 75.07 | 86.9 | +| airplane | 79.07 | 93.84 | +| dirt track | 11.39 | 18.79 | +| apparel | 49.94 | 73.18 | +| pole | 24.65 | 33.37 | +| land | 0.42 | 2.19 | +| bannister | 16.67 | 27.49 | +| escalator | 54.4 | 79.91 | +| ottoman | 44.95 | 62.9 | +| bottle | 39.31 | 52.65 | +| buffet | 52.56 | 71.44 | +| poster | 40.3 | 56.33 | +| stage | 17.63 | 48.83 | +| van | 45.26 | 77.97 | +| ship | 77.25 | 98.3 | +| fountain | 39.67 | 40.64 | +| conveyer belt | 82.52 | 94.57 | +| canopy | 43.42 | 62.07 | +| washer | 78.34 | 84.49 | +| plaything | 22.02 | 24.61 | +| swimming pool | 57.92 | 89.25 | +| stool | 47.42 | 59.71 | +| barrel | 61.08 | 73.07 | +| basket | 41.01 | 54.47 | +| waterfall | 57.68 | 63.44 | +| tent | 93.47 | 98.87 | +| bag | 24.77 | 27.31 | +| minibike | 63.47 | 92.44 | +| cradle | 88.87 | 95.99 | +| oven | 56.9 | 66.73 | +| ball | 57.7 | 69.55 | +| food | 63.26 | 77.46 | +| step | 13.39 | 19.43 | +| tank | 55.69 | 66.92 | +| trade name | 29.03 | 31.89 | +| microwave | 89.24 | 94.23 | +| pot | 57.89 | 66.44 | +| animal | 70.27 | 72.99 | +| bicycle | 59.71 | 76.71 | +| lake | 0.0 | 0.0 | +| dishwasher | 68.43 | 71.31 | +| screen | 59.24 | 96.42 | +| blanket | 29.0 | 44.23 | +| sculpture | 64.79 | 84.11 | +| hood | 65.59 | 81.38 | +| sconce | 43.78 | 46.8 | +| vase | 44.41 | 59.69 | +| traffic light | 29.55 | 60.34 | +| tray | 16.04 | 21.51 | +| ashcan | 51.68 | 64.54 | +| fan | 65.86 | 85.35 | +| pier | 59.83 | 67.95 | +| crt screen | 0.32 | 1.09 | +| plate | 58.13 | 76.18 | +| monitor | 5.03 | 5.38 | +| bulletin board | 55.78 | 68.87 | +| shower | 0.0 | 0.0 | +| radiator | 60.1 | 75.41 | +| glass | 14.81 | 15.95 | +| clock | 44.83 | 61.6 | +| flag | 69.63 | 77.91 | ++---------------------+-------+-------+ +2024-06-18 09:01:11,296 - mmseg - INFO - Summary: +2024-06-18 09:01:11,297 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.19 | 53.63 | 67.65 | ++-------+-------+-------+ +2024-06-18 09:01:11,297 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 09:01:11,298 - mmseg - INFO - Iter(val) [250] aAcc: 0.8419, mIoU: 0.5363, mAcc: 0.6765, IoU.wall: 0.7969, IoU.building: 0.8326, IoU.sky: 0.9421, IoU.floor: 0.8386, IoU.tree: 0.7480, IoU.ceiling: 0.8459, IoU.road: 0.8366, IoU.bed : 0.9085, IoU.windowpane: 0.6448, IoU.grass: 0.6489, IoU.cabinet: 0.6276, IoU.sidewalk: 0.6552, IoU.person: 0.8364, IoU.earth: 0.3484, IoU.door: 0.5743, IoU.table: 0.6032, IoU.mountain: 0.6132, IoU.plant: 0.5293, IoU.curtain: 0.7779, IoU.chair: 0.6361, IoU.car: 0.8465, IoU.water: 0.6142, IoU.painting: 0.7501, IoU.sofa: 0.7938, IoU.shelf: 0.4148, IoU.house: 0.4850, IoU.sea: 0.7064, IoU.mirror: 0.7509, IoU.rug: 0.6853, IoU.field: 0.3284, IoU.armchair: 0.5991, IoU.seat: 0.6358, IoU.fence: 0.4818, IoU.desk: 0.4993, IoU.rock: 0.5851, IoU.wardrobe: 0.5339, IoU.lamp: 0.6602, IoU.bathtub: 0.8060, IoU.railing: 0.3577, IoU.cushion: 0.6787, IoU.base: 0.2993, IoU.box: 0.3348, IoU.column: 0.5231, IoU.signboard: 0.4002, IoU.chest of drawers: 0.4872, IoU.counter: 0.4637, IoU.sand: 0.0774, IoU.sink: 0.7984, IoU.skyscraper: 0.4853, IoU.fireplace: 0.6697, IoU.refrigerator: 0.7374, IoU.grandstand: 0.4308, IoU.path: 0.2447, IoU.stairs: 0.3291, IoU.runway: 0.6790, IoU.case: 0.5881, IoU.pool table: 0.9336, IoU.pillow: 0.6050, IoU.screen door: 0.7753, IoU.stairway: 0.3999, IoU.river: 0.2033, IoU.bridge: 0.4677, IoU.bookcase: 0.3351, IoU.blind: 0.5047, IoU.coffee table: 0.4851, IoU.toilet: 0.8904, IoU.flower: 0.3781, IoU.book: 0.4962, IoU.hill: 0.0509, IoU.bench: 0.5195, IoU.countertop: 0.6308, IoU.stove: 0.8325, IoU.palm: 0.4949, IoU.kitchen island: 0.3864, IoU.computer: 0.7084, IoU.swivel chair: 0.5303, IoU.boat: 0.6102, IoU.bar: 0.6084, IoU.arcade machine: 0.8968, IoU.hovel: 0.2712, IoU.bus: 0.9021, IoU.towel: 0.6995, IoU.light: 0.5193, IoU.truck: 0.4489, IoU.tower: 0.1571, IoU.chandelier: 0.6591, IoU.awning: 0.4650, IoU.streetlight: 0.2724, IoU.booth: 0.3995, IoU.television receiver: 0.7507, IoU.airplane: 0.7907, IoU.dirt track: 0.1139, IoU.apparel: 0.4994, IoU.pole: 0.2465, IoU.land: 0.0042, IoU.bannister: 0.1667, IoU.escalator: 0.5440, IoU.ottoman: 0.4495, IoU.bottle: 0.3931, IoU.buffet: 0.5256, IoU.poster: 0.4030, IoU.stage: 0.1763, IoU.van: 0.4526, IoU.ship: 0.7725, IoU.fountain: 0.3967, IoU.conveyer belt: 0.8252, IoU.canopy: 0.4342, IoU.washer: 0.7834, IoU.plaything: 0.2202, IoU.swimming pool: 0.5792, IoU.stool: 0.4742, IoU.barrel: 0.6108, IoU.basket: 0.4101, IoU.waterfall: 0.5768, IoU.tent: 0.9347, IoU.bag: 0.2477, IoU.minibike: 0.6347, IoU.cradle: 0.8887, IoU.oven: 0.5690, IoU.ball: 0.5770, IoU.food: 0.6326, IoU.step: 0.1339, IoU.tank: 0.5569, IoU.trade name: 0.2903, IoU.microwave: 0.8924, IoU.pot: 0.5789, IoU.animal: 0.7027, IoU.bicycle: 0.5971, IoU.lake: 0.0000, IoU.dishwasher: 0.6843, IoU.screen: 0.5924, IoU.blanket: 0.2900, IoU.sculpture: 0.6479, IoU.hood: 0.6559, IoU.sconce: 0.4378, IoU.vase: 0.4441, IoU.traffic light: 0.2955, IoU.tray: 0.1604, IoU.ashcan: 0.5168, IoU.fan: 0.6586, IoU.pier: 0.5983, IoU.crt screen: 0.0032, IoU.plate: 0.5813, IoU.monitor: 0.0503, IoU.bulletin board: 0.5578, IoU.shower: 0.0000, IoU.radiator: 0.6010, IoU.glass: 0.1481, IoU.clock: 0.4483, IoU.flag: 0.6963, Acc.wall: 0.8807, Acc.building: 0.9351, Acc.sky: 0.9782, Acc.floor: 0.8893, Acc.tree: 0.8731, Acc.ceiling: 0.9007, Acc.road: 0.9033, Acc.bed : 0.9624, Acc.windowpane: 0.7549, Acc.grass: 0.7205, Acc.cabinet: 0.7035, Acc.sidewalk: 0.8320, Acc.person: 0.9235, Acc.earth: 0.4842, Acc.door: 0.7807, Acc.table: 0.7280, Acc.mountain: 0.7795, Acc.plant: 0.5989, Acc.curtain: 0.8648, Acc.chair: 0.7832, Acc.car: 0.9230, Acc.water: 0.7956, Acc.painting: 0.8764, Acc.sofa: 0.8804, Acc.shelf: 0.5331, Acc.house: 0.5655, Acc.sea: 0.8874, Acc.mirror: 0.8511, Acc.rug: 0.8153, Acc.field: 0.7569, Acc.armchair: 0.8383, Acc.seat: 0.8528, Acc.fence: 0.5884, Acc.desk: 0.7980, Acc.rock: 0.7278, Acc.wardrobe: 0.7504, Acc.lamp: 0.8377, Acc.bathtub: 0.8273, Acc.railing: 0.4727, Acc.cushion: 0.7882, Acc.base: 0.8145, Acc.box: 0.4459, Acc.column: 0.7865, Acc.signboard: 0.4919, Acc.chest of drawers: 0.7857, Acc.counter: 0.5418, Acc.sand: 0.0969, Acc.sink: 0.8900, Acc.skyscraper: 0.6083, Acc.fireplace: 0.8958, Acc.refrigerator: 0.9134, Acc.grandstand: 0.9011, Acc.path: 0.3563, Acc.stairs: 0.3460, Acc.runway: 0.9360, Acc.case: 0.7510, Acc.pool table: 0.9821, Acc.pillow: 0.6521, Acc.screen door: 0.8388, Acc.stairway: 0.5125, Acc.river: 0.2325, Acc.bridge: 0.5448, Acc.bookcase: 0.5476, Acc.blind: 0.6336, Acc.coffee table: 0.9249, Acc.toilet: 0.9496, Acc.flower: 0.6469, Acc.book: 0.8026, Acc.hill: 0.1129, Acc.bench: 0.6128, Acc.countertop: 0.7696, Acc.stove: 0.9287, Acc.palm: 0.8432, Acc.kitchen island: 0.8383, Acc.computer: 0.9387, Acc.swivel chair: 0.7554, Acc.boat: 0.8438, Acc.bar: 0.6892, Acc.arcade machine: 0.9768, Acc.hovel: 0.3057, Acc.bus: 0.9718, Acc.towel: 0.8848, Acc.light: 0.5795, Acc.truck: 0.5818, Acc.tower: 0.2771, Acc.chandelier: 0.9012, Acc.awning: 0.5993, Acc.streetlight: 0.3370, Acc.booth: 0.7220, Acc.television receiver: 0.8690, Acc.airplane: 0.9384, Acc.dirt track: 0.1879, Acc.apparel: 0.7318, Acc.pole: 0.3337, Acc.land: 0.0219, Acc.bannister: 0.2749, Acc.escalator: 0.7991, Acc.ottoman: 0.6290, Acc.bottle: 0.5265, Acc.buffet: 0.7144, Acc.poster: 0.5633, Acc.stage: 0.4883, Acc.van: 0.7797, Acc.ship: 0.9830, Acc.fountain: 0.4064, Acc.conveyer belt: 0.9457, Acc.canopy: 0.6207, Acc.washer: 0.8449, Acc.plaything: 0.2461, Acc.swimming pool: 0.8925, Acc.stool: 0.5971, Acc.barrel: 0.7307, Acc.basket: 0.5447, Acc.waterfall: 0.6344, Acc.tent: 0.9887, Acc.bag: 0.2731, Acc.minibike: 0.9244, Acc.cradle: 0.9599, Acc.oven: 0.6673, Acc.ball: 0.6955, Acc.food: 0.7746, Acc.step: 0.1943, Acc.tank: 0.6692, Acc.trade name: 0.3189, Acc.microwave: 0.9423, Acc.pot: 0.6644, Acc.animal: 0.7299, Acc.bicycle: 0.7671, Acc.lake: 0.0000, Acc.dishwasher: 0.7131, Acc.screen: 0.9642, Acc.blanket: 0.4423, Acc.sculpture: 0.8411, Acc.hood: 0.8138, Acc.sconce: 0.4680, Acc.vase: 0.5969, Acc.traffic light: 0.6034, Acc.tray: 0.2151, Acc.ashcan: 0.6454, Acc.fan: 0.8535, Acc.pier: 0.6795, Acc.crt screen: 0.0109, Acc.plate: 0.7618, Acc.monitor: 0.0538, Acc.bulletin board: 0.6887, Acc.shower: 0.0000, Acc.radiator: 0.7541, Acc.glass: 0.1595, Acc.clock: 0.6160, Acc.flag: 0.7791 +2024-06-18 09:02:50,571 - mmseg - INFO - Iter [11050/80000] lr: 3.448e-05, eta: 1 day, 16:56:21, time: 4.193, data_time: 2.223, memory: 72263, decode.loss_ce: 0.3824, decode.acc_seg: 84.6815, aux.loss_ce: 0.1525, aux.acc_seg: 84.6462, loss: 0.5348 +2024-06-18 09:04:29,585 - mmseg - INFO - Iter [11100/80000] lr: 3.445e-05, eta: 1 day, 16:53:46, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4119, decode.acc_seg: 83.5887, aux.loss_ce: 0.1645, aux.acc_seg: 83.6426, loss: 0.5763 +2024-06-18 09:06:23,593 - mmseg - INFO - Iter [11150/80000] lr: 3.443e-05, eta: 1 day, 16:52:43, time: 2.280, data_time: 0.303, memory: 72263, decode.loss_ce: 0.4101, decode.acc_seg: 83.6791, aux.loss_ce: 0.1636, aux.acc_seg: 83.4995, loss: 0.5737 +2024-06-18 09:08:02,658 - mmseg - INFO - Iter [11200/80000] lr: 3.440e-05, eta: 1 day, 16:50:08, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4403, decode.acc_seg: 82.9548, aux.loss_ce: 0.1743, aux.acc_seg: 83.0448, loss: 0.6146 +2024-06-18 09:09:41,679 - mmseg - INFO - Iter [11250/80000] lr: 3.438e-05, eta: 1 day, 16:47:34, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.4110, decode.acc_seg: 84.1084, aux.loss_ce: 0.1643, aux.acc_seg: 84.1282, loss: 0.5753 +2024-06-18 09:11:20,752 - mmseg - INFO - Iter [11300/80000] lr: 3.435e-05, eta: 1 day, 16:45:00, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3927, decode.acc_seg: 85.1104, aux.loss_ce: 0.1570, aux.acc_seg: 85.1079, loss: 0.5497 +2024-06-18 09:12:59,755 - mmseg - INFO - Iter [11350/80000] lr: 3.433e-05, eta: 1 day, 16:42:26, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3964, decode.acc_seg: 83.9358, aux.loss_ce: 0.1583, aux.acc_seg: 84.0401, loss: 0.5547 +2024-06-18 09:14:40,922 - mmseg - INFO - Iter [11400/80000] lr: 3.430e-05, eta: 1 day, 16:40:06, time: 2.023, data_time: 0.052, memory: 72263, decode.loss_ce: 0.4070, decode.acc_seg: 84.1053, aux.loss_ce: 0.1634, aux.acc_seg: 84.0618, loss: 0.5704 +2024-06-18 09:16:19,832 - mmseg - INFO - Iter [11450/80000] lr: 3.428e-05, eta: 1 day, 16:37:33, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3675, decode.acc_seg: 85.4178, aux.loss_ce: 0.1471, aux.acc_seg: 85.4302, loss: 0.5146 +2024-06-18 09:17:58,772 - mmseg - INFO - Iter [11500/80000] lr: 3.425e-05, eta: 1 day, 16:35:00, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3864, decode.acc_seg: 84.3167, aux.loss_ce: 0.1559, aux.acc_seg: 84.3317, loss: 0.5423 +2024-06-18 09:19:37,742 - mmseg - INFO - Iter [11550/80000] lr: 3.423e-05, eta: 1 day, 16:32:28, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3602, decode.acc_seg: 85.7502, aux.loss_ce: 0.1434, aux.acc_seg: 85.7567, loss: 0.5036 +2024-06-18 09:21:16,672 - mmseg - INFO - Iter [11600/80000] lr: 3.420e-05, eta: 1 day, 16:29:56, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3719, decode.acc_seg: 85.5377, aux.loss_ce: 0.1485, aux.acc_seg: 85.4537, loss: 0.5204 +2024-06-18 09:22:55,738 - mmseg - INFO - Iter [11650/80000] lr: 3.418e-05, eta: 1 day, 16:27:25, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3590, decode.acc_seg: 86.0315, aux.loss_ce: 0.1453, aux.acc_seg: 85.7123, loss: 0.5043 +2024-06-18 09:24:34,608 - mmseg - INFO - Iter [11700/80000] lr: 3.415e-05, eta: 1 day, 16:24:54, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3473, decode.acc_seg: 86.3940, aux.loss_ce: 0.1401, aux.acc_seg: 86.3282, loss: 0.4874 +2024-06-18 09:26:13,600 - mmseg - INFO - Iter [11750/80000] lr: 3.413e-05, eta: 1 day, 16:22:24, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3793, decode.acc_seg: 84.4483, aux.loss_ce: 0.1507, aux.acc_seg: 84.5510, loss: 0.5300 +2024-06-18 09:27:52,668 - mmseg - INFO - Iter [11800/80000] lr: 3.410e-05, eta: 1 day, 16:19:54, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3595, decode.acc_seg: 85.8477, aux.loss_ce: 0.1434, aux.acc_seg: 85.8876, loss: 0.5029 +2024-06-18 09:29:31,608 - mmseg - INFO - Iter [11850/80000] lr: 3.408e-05, eta: 1 day, 16:17:25, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3673, decode.acc_seg: 85.5827, aux.loss_ce: 0.1484, aux.acc_seg: 85.3721, loss: 0.5158 +2024-06-18 09:31:10,673 - mmseg - INFO - Iter [11900/80000] lr: 3.405e-05, eta: 1 day, 16:14:56, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3780, decode.acc_seg: 84.9575, aux.loss_ce: 0.1511, aux.acc_seg: 85.0024, loss: 0.5292 +2024-06-18 09:32:49,677 - mmseg - INFO - Iter [11950/80000] lr: 3.403e-05, eta: 1 day, 16:12:28, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3641, decode.acc_seg: 85.4598, aux.loss_ce: 0.1466, aux.acc_seg: 85.5392, loss: 0.5108 +2024-06-18 09:34:28,648 - mmseg - INFO - Saving checkpoint at 12000 iterations +2024-06-18 09:35:55,484 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 09:35:55,484 - mmseg - INFO - Iter [12000/80000] lr: 3.400e-05, eta: 1 day, 16:18:12, time: 3.716, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3803, decode.acc_seg: 84.6864, aux.loss_ce: 0.1518, aux.acc_seg: 84.8066, loss: 0.5322 +2024-06-18 09:37:45,019 - mmseg - INFO - per class results: +2024-06-18 09:37:45,026 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.02 | 87.73 | +| building | 84.46 | 92.81 | +| sky | 94.25 | 97.83 | +| floor | 82.47 | 88.55 | +| tree | 75.41 | 83.46 | +| ceiling | 84.86 | 94.65 | +| road | 82.94 | 87.12 | +| bed | 90.5 | 96.53 | +| windowpane | 64.94 | 82.52 | +| grass | 66.83 | 82.32 | +| cabinet | 62.66 | 72.27 | +| sidewalk | 64.81 | 91.9 | +| person | 83.6 | 89.6 | +| earth | 36.32 | 50.1 | +| door | 56.06 | 74.14 | +| table | 64.85 | 81.91 | +| mountain | 64.34 | 72.07 | +| plant | 57.64 | 73.78 | +| curtain | 73.51 | 85.24 | +| chair | 62.46 | 72.26 | +| car | 84.8 | 95.73 | +| water | 43.66 | 47.99 | +| painting | 75.4 | 90.99 | +| sofa | 77.47 | 84.75 | +| shelf | 46.22 | 63.55 | +| house | 51.94 | 56.2 | +| sea | 73.41 | 88.89 | +| mirror | 72.82 | 89.03 | +| rug | 67.08 | 86.2 | +| field | 27.7 | 53.82 | +| armchair | 59.68 | 78.16 | +| seat | 59.24 | 87.73 | +| fence | 50.12 | 64.05 | +| desk | 49.57 | 82.2 | +| rock | 58.43 | 83.63 | +| wardrobe | 51.25 | 80.4 | +| lamp | 65.64 | 76.44 | +| bathtub | 85.22 | 93.09 | +| railing | 40.09 | 56.05 | +| cushion | 66.85 | 83.45 | +| base | 41.16 | 57.33 | +| box | 33.21 | 43.67 | +| column | 52.43 | 64.77 | +| signboard | 39.26 | 53.65 | +| chest of drawers | 46.68 | 65.7 | +| counter | 40.4 | 44.92 | +| sand | 46.64 | 76.34 | +| sink | 74.0 | 80.49 | +| skyscraper | 48.58 | 85.4 | +| fireplace | 69.42 | 91.41 | +| refrigerator | 82.92 | 90.86 | +| grandstand | 45.72 | 82.92 | +| path | 19.5 | 24.9 | +| stairs | 31.62 | 39.82 | +| runway | 67.26 | 88.39 | +| case | 58.92 | 80.27 | +| pool table | 91.26 | 99.04 | +| pillow | 63.58 | 69.16 | +| screen door | 79.19 | 87.33 | +| stairway | 47.61 | 55.1 | +| river | 13.59 | 63.89 | +| bridge | 43.07 | 49.07 | +| bookcase | 35.71 | 54.24 | +| blind | 30.6 | 30.99 | +| coffee table | 67.51 | 88.2 | +| toilet | 88.59 | 93.08 | +| flower | 41.19 | 52.79 | +| book | 52.7 | 78.81 | +| hill | 4.4 | 9.15 | +| bench | 49.86 | 59.06 | +| countertop | 64.43 | 80.54 | +| stove | 83.21 | 87.83 | +| palm | 53.2 | 80.23 | +| kitchen island | 37.54 | 78.71 | +| computer | 74.01 | 90.77 | +| swivel chair | 52.32 | 83.17 | +| boat | 72.98 | 89.06 | +| bar | 66.67 | 76.55 | +| arcade machine | 88.8 | 96.69 | +| hovel | 57.75 | 68.62 | +| bus | 86.06 | 97.95 | +| towel | 70.18 | 85.41 | +| light | 51.49 | 56.09 | +| truck | 41.25 | 46.61 | +| tower | 28.54 | 59.2 | +| chandelier | 67.17 | 89.07 | +| awning | 48.7 | 61.47 | +| streetlight | 29.5 | 36.61 | +| booth | 50.11 | 61.14 | +| television receiver | 74.14 | 86.65 | +| airplane | 86.73 | 93.28 | +| dirt track | 1.63 | 9.28 | +| apparel | 48.07 | 60.14 | +| pole | 20.78 | 24.89 | +| land | 0.0 | 0.0 | +| bannister | 12.27 | 17.05 | +| escalator | 58.85 | 83.15 | +| ottoman | 53.64 | 76.37 | +| bottle | 33.48 | 42.89 | +| buffet | 58.06 | 81.04 | +| poster | 39.27 | 46.86 | +| stage | 19.95 | 27.37 | +| van | 41.83 | 52.08 | +| ship | 67.83 | 98.59 | +| fountain | 57.3 | 67.58 | +| conveyer belt | 74.57 | 97.35 | +| canopy | 43.21 | 73.2 | +| washer | 79.43 | 81.55 | +| plaything | 29.44 | 44.67 | +| swimming pool | 51.75 | 74.34 | +| stool | 49.5 | 61.96 | +| barrel | 63.04 | 72.78 | +| basket | 41.37 | 61.08 | +| waterfall | 59.3 | 96.47 | +| tent | 96.02 | 97.59 | +| bag | 24.14 | 29.24 | +| minibike | 72.22 | 84.2 | +| cradle | 84.34 | 97.56 | +| oven | 51.82 | 55.07 | +| ball | 52.92 | 61.67 | +| food | 56.83 | 65.89 | +| step | 6.16 | 6.39 | +| tank | 60.52 | 68.84 | +| trade name | 28.91 | 33.98 | +| microwave | 87.47 | 92.08 | +| pot | 55.34 | 62.67 | +| animal | 52.67 | 53.16 | +| bicycle | 54.74 | 69.59 | +| lake | 0.0 | 0.0 | +| dishwasher | 62.58 | 69.77 | +| screen | 58.47 | 93.73 | +| blanket | 26.0 | 30.48 | +| sculpture | 63.67 | 88.04 | +| hood | 56.01 | 65.58 | +| sconce | 54.65 | 72.09 | +| vase | 45.13 | 59.04 | +| traffic light | 31.38 | 61.99 | +| tray | 17.98 | 23.03 | +| ashcan | 46.99 | 53.0 | +| fan | 65.1 | 78.05 | +| pier | 38.29 | 43.91 | +| crt screen | 0.2 | 0.56 | +| plate | 60.24 | 71.1 | +| monitor | 12.17 | 13.93 | +| bulletin board | 51.29 | 56.35 | +| shower | 0.9 | 0.92 | +| radiator | 61.55 | 72.67 | +| glass | 14.18 | 14.96 | +| clock | 47.61 | 68.54 | +| flag | 67.15 | 70.35 | ++---------------------+-------+-------+ +2024-06-18 09:37:45,026 - mmseg - INFO - Summary: +2024-06-18 09:37:45,026 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.39 | 54.01 | 67.35 | ++-------+-------+-------+ +2024-06-18 09:37:45,027 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 09:37:45,027 - mmseg - INFO - Iter(val) [250] aAcc: 0.8439, mIoU: 0.5401, mAcc: 0.6735, IoU.wall: 0.8002, IoU.building: 0.8446, IoU.sky: 0.9425, IoU.floor: 0.8247, IoU.tree: 0.7541, IoU.ceiling: 0.8486, IoU.road: 0.8294, IoU.bed : 0.9050, IoU.windowpane: 0.6494, IoU.grass: 0.6683, IoU.cabinet: 0.6266, IoU.sidewalk: 0.6481, IoU.person: 0.8360, IoU.earth: 0.3632, IoU.door: 0.5606, IoU.table: 0.6485, IoU.mountain: 0.6434, IoU.plant: 0.5764, IoU.curtain: 0.7351, IoU.chair: 0.6246, IoU.car: 0.8480, IoU.water: 0.4366, IoU.painting: 0.7540, IoU.sofa: 0.7747, IoU.shelf: 0.4622, IoU.house: 0.5194, IoU.sea: 0.7341, IoU.mirror: 0.7282, IoU.rug: 0.6708, IoU.field: 0.2770, IoU.armchair: 0.5968, IoU.seat: 0.5924, IoU.fence: 0.5012, IoU.desk: 0.4957, IoU.rock: 0.5843, IoU.wardrobe: 0.5125, IoU.lamp: 0.6564, IoU.bathtub: 0.8522, IoU.railing: 0.4009, IoU.cushion: 0.6685, IoU.base: 0.4116, IoU.box: 0.3321, IoU.column: 0.5243, IoU.signboard: 0.3926, IoU.chest of drawers: 0.4668, IoU.counter: 0.4040, IoU.sand: 0.4664, IoU.sink: 0.7400, IoU.skyscraper: 0.4858, IoU.fireplace: 0.6942, IoU.refrigerator: 0.8292, IoU.grandstand: 0.4572, IoU.path: 0.1950, IoU.stairs: 0.3162, IoU.runway: 0.6726, IoU.case: 0.5892, IoU.pool table: 0.9126, IoU.pillow: 0.6358, IoU.screen door: 0.7919, IoU.stairway: 0.4761, IoU.river: 0.1359, IoU.bridge: 0.4307, IoU.bookcase: 0.3571, IoU.blind: 0.3060, IoU.coffee table: 0.6751, IoU.toilet: 0.8859, IoU.flower: 0.4119, IoU.book: 0.5270, IoU.hill: 0.0440, IoU.bench: 0.4986, IoU.countertop: 0.6443, IoU.stove: 0.8321, IoU.palm: 0.5320, IoU.kitchen island: 0.3754, IoU.computer: 0.7401, IoU.swivel chair: 0.5232, IoU.boat: 0.7298, IoU.bar: 0.6667, IoU.arcade machine: 0.8880, IoU.hovel: 0.5775, IoU.bus: 0.8606, IoU.towel: 0.7018, IoU.light: 0.5149, IoU.truck: 0.4125, IoU.tower: 0.2854, IoU.chandelier: 0.6717, IoU.awning: 0.4870, IoU.streetlight: 0.2950, IoU.booth: 0.5011, IoU.television receiver: 0.7414, IoU.airplane: 0.8673, IoU.dirt track: 0.0163, IoU.apparel: 0.4807, IoU.pole: 0.2078, IoU.land: 0.0000, IoU.bannister: 0.1227, IoU.escalator: 0.5885, IoU.ottoman: 0.5364, IoU.bottle: 0.3348, IoU.buffet: 0.5806, IoU.poster: 0.3927, IoU.stage: 0.1995, IoU.van: 0.4183, IoU.ship: 0.6783, IoU.fountain: 0.5730, IoU.conveyer belt: 0.7457, IoU.canopy: 0.4321, IoU.washer: 0.7943, IoU.plaything: 0.2944, IoU.swimming pool: 0.5175, IoU.stool: 0.4950, IoU.barrel: 0.6304, IoU.basket: 0.4137, IoU.waterfall: 0.5930, IoU.tent: 0.9602, IoU.bag: 0.2414, IoU.minibike: 0.7222, IoU.cradle: 0.8434, IoU.oven: 0.5182, IoU.ball: 0.5292, IoU.food: 0.5683, IoU.step: 0.0616, IoU.tank: 0.6052, IoU.trade name: 0.2891, IoU.microwave: 0.8747, IoU.pot: 0.5534, IoU.animal: 0.5267, IoU.bicycle: 0.5474, IoU.lake: 0.0000, IoU.dishwasher: 0.6258, IoU.screen: 0.5847, IoU.blanket: 0.2600, IoU.sculpture: 0.6367, IoU.hood: 0.5601, IoU.sconce: 0.5465, IoU.vase: 0.4513, IoU.traffic light: 0.3138, IoU.tray: 0.1798, IoU.ashcan: 0.4699, IoU.fan: 0.6510, IoU.pier: 0.3829, IoU.crt screen: 0.0020, IoU.plate: 0.6024, IoU.monitor: 0.1217, IoU.bulletin board: 0.5129, IoU.shower: 0.0090, IoU.radiator: 0.6155, IoU.glass: 0.1418, IoU.clock: 0.4761, IoU.flag: 0.6715, Acc.wall: 0.8773, Acc.building: 0.9281, Acc.sky: 0.9783, Acc.floor: 0.8855, Acc.tree: 0.8346, Acc.ceiling: 0.9465, Acc.road: 0.8712, Acc.bed : 0.9653, Acc.windowpane: 0.8252, Acc.grass: 0.8232, Acc.cabinet: 0.7227, Acc.sidewalk: 0.9190, Acc.person: 0.8960, Acc.earth: 0.5010, Acc.door: 0.7414, Acc.table: 0.8191, Acc.mountain: 0.7207, Acc.plant: 0.7378, Acc.curtain: 0.8524, Acc.chair: 0.7226, Acc.car: 0.9573, Acc.water: 0.4799, Acc.painting: 0.9099, Acc.sofa: 0.8475, Acc.shelf: 0.6355, Acc.house: 0.5620, Acc.sea: 0.8889, Acc.mirror: 0.8903, Acc.rug: 0.8620, Acc.field: 0.5382, Acc.armchair: 0.7816, Acc.seat: 0.8773, Acc.fence: 0.6405, Acc.desk: 0.8220, Acc.rock: 0.8363, Acc.wardrobe: 0.8040, Acc.lamp: 0.7644, Acc.bathtub: 0.9309, Acc.railing: 0.5605, Acc.cushion: 0.8345, Acc.base: 0.5733, Acc.box: 0.4367, Acc.column: 0.6477, Acc.signboard: 0.5365, Acc.chest of drawers: 0.6570, Acc.counter: 0.4492, Acc.sand: 0.7634, Acc.sink: 0.8049, Acc.skyscraper: 0.8540, Acc.fireplace: 0.9141, Acc.refrigerator: 0.9086, Acc.grandstand: 0.8292, Acc.path: 0.2490, Acc.stairs: 0.3982, Acc.runway: 0.8839, Acc.case: 0.8027, Acc.pool table: 0.9904, Acc.pillow: 0.6916, Acc.screen door: 0.8733, Acc.stairway: 0.5510, Acc.river: 0.6389, Acc.bridge: 0.4907, Acc.bookcase: 0.5424, Acc.blind: 0.3099, Acc.coffee table: 0.8820, Acc.toilet: 0.9308, Acc.flower: 0.5279, Acc.book: 0.7881, Acc.hill: 0.0915, Acc.bench: 0.5906, Acc.countertop: 0.8054, Acc.stove: 0.8783, Acc.palm: 0.8023, Acc.kitchen island: 0.7871, Acc.computer: 0.9077, Acc.swivel chair: 0.8317, Acc.boat: 0.8906, Acc.bar: 0.7655, Acc.arcade machine: 0.9669, Acc.hovel: 0.6862, Acc.bus: 0.9795, Acc.towel: 0.8541, Acc.light: 0.5609, Acc.truck: 0.4661, Acc.tower: 0.5920, Acc.chandelier: 0.8907, Acc.awning: 0.6147, Acc.streetlight: 0.3661, Acc.booth: 0.6114, Acc.television receiver: 0.8665, Acc.airplane: 0.9328, Acc.dirt track: 0.0928, Acc.apparel: 0.6014, Acc.pole: 0.2489, Acc.land: 0.0000, Acc.bannister: 0.1705, Acc.escalator: 0.8315, Acc.ottoman: 0.7637, Acc.bottle: 0.4289, Acc.buffet: 0.8104, Acc.poster: 0.4686, Acc.stage: 0.2737, Acc.van: 0.5208, Acc.ship: 0.9859, Acc.fountain: 0.6758, Acc.conveyer belt: 0.9735, Acc.canopy: 0.7320, Acc.washer: 0.8155, Acc.plaything: 0.4467, Acc.swimming pool: 0.7434, Acc.stool: 0.6196, Acc.barrel: 0.7278, Acc.basket: 0.6108, Acc.waterfall: 0.9647, Acc.tent: 0.9759, Acc.bag: 0.2924, Acc.minibike: 0.8420, Acc.cradle: 0.9756, Acc.oven: 0.5507, Acc.ball: 0.6167, Acc.food: 0.6589, Acc.step: 0.0639, Acc.tank: 0.6884, Acc.trade name: 0.3398, Acc.microwave: 0.9208, Acc.pot: 0.6267, Acc.animal: 0.5316, Acc.bicycle: 0.6959, Acc.lake: 0.0000, Acc.dishwasher: 0.6977, Acc.screen: 0.9373, Acc.blanket: 0.3048, Acc.sculpture: 0.8804, Acc.hood: 0.6558, Acc.sconce: 0.7209, Acc.vase: 0.5904, Acc.traffic light: 0.6199, Acc.tray: 0.2303, Acc.ashcan: 0.5300, Acc.fan: 0.7805, Acc.pier: 0.4391, Acc.crt screen: 0.0056, Acc.plate: 0.7110, Acc.monitor: 0.1393, Acc.bulletin board: 0.5635, Acc.shower: 0.0092, Acc.radiator: 0.7267, Acc.glass: 0.1496, Acc.clock: 0.6854, Acc.flag: 0.7035 +2024-06-18 09:39:24,299 - mmseg - INFO - Iter [12050/80000] lr: 3.398e-05, eta: 1 day, 16:26:01, time: 4.176, data_time: 2.207, memory: 72263, decode.loss_ce: 0.3808, decode.acc_seg: 85.0063, aux.loss_ce: 0.1530, aux.acc_seg: 85.0401, loss: 0.5338 +2024-06-18 09:41:03,254 - mmseg - INFO - Iter [12100/80000] lr: 3.395e-05, eta: 1 day, 16:23:28, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3717, decode.acc_seg: 85.0459, aux.loss_ce: 0.1491, aux.acc_seg: 85.0508, loss: 0.5208 +2024-06-18 09:42:42,230 - mmseg - INFO - Iter [12150/80000] lr: 3.393e-05, eta: 1 day, 16:20:56, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3730, decode.acc_seg: 85.1017, aux.loss_ce: 0.1499, aux.acc_seg: 84.8738, loss: 0.5229 +2024-06-18 09:44:21,127 - mmseg - INFO - Iter [12200/80000] lr: 3.390e-05, eta: 1 day, 16:18:23, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3937, decode.acc_seg: 84.1782, aux.loss_ce: 0.1592, aux.acc_seg: 84.2019, loss: 0.5528 +2024-06-18 09:46:00,070 - mmseg - INFO - Iter [12250/80000] lr: 3.388e-05, eta: 1 day, 16:15:52, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3869, decode.acc_seg: 84.1408, aux.loss_ce: 0.1549, aux.acc_seg: 84.1992, loss: 0.5418 +2024-06-18 09:47:39,039 - mmseg - INFO - Iter [12300/80000] lr: 3.385e-05, eta: 1 day, 16:13:21, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3753, decode.acc_seg: 85.1531, aux.loss_ce: 0.1507, aux.acc_seg: 85.1796, loss: 0.5261 +2024-06-18 09:49:18,092 - mmseg - INFO - Iter [12350/80000] lr: 3.383e-05, eta: 1 day, 16:10:51, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3506, decode.acc_seg: 86.0209, aux.loss_ce: 0.1420, aux.acc_seg: 85.8630, loss: 0.4926 +2024-06-18 09:50:56,996 - mmseg - INFO - Iter [12400/80000] lr: 3.380e-05, eta: 1 day, 16:08:20, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3617, decode.acc_seg: 85.3986, aux.loss_ce: 0.1458, aux.acc_seg: 85.3771, loss: 0.5075 +2024-06-18 09:52:36,035 - mmseg - INFO - Iter [12450/80000] lr: 3.378e-05, eta: 1 day, 16:05:51, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3640, decode.acc_seg: 85.9578, aux.loss_ce: 0.1459, aux.acc_seg: 85.8772, loss: 0.5099 +2024-06-18 09:54:14,930 - mmseg - INFO - Iter [12500/80000] lr: 3.375e-05, eta: 1 day, 16:03:21, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3664, decode.acc_seg: 85.5228, aux.loss_ce: 0.1478, aux.acc_seg: 85.4015, loss: 0.5141 +2024-06-18 09:55:54,011 - mmseg - INFO - Iter [12550/80000] lr: 3.373e-05, eta: 1 day, 16:00:52, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3803, decode.acc_seg: 85.1095, aux.loss_ce: 0.1520, aux.acc_seg: 85.1504, loss: 0.5323 +2024-06-18 09:57:32,886 - mmseg - INFO - Iter [12600/80000] lr: 3.370e-05, eta: 1 day, 15:58:23, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3593, decode.acc_seg: 85.4097, aux.loss_ce: 0.1434, aux.acc_seg: 85.3331, loss: 0.5027 +2024-06-18 09:59:14,362 - mmseg - INFO - Iter [12650/80000] lr: 3.368e-05, eta: 1 day, 15:56:08, time: 2.030, data_time: 0.056, memory: 72263, decode.loss_ce: 0.3845, decode.acc_seg: 85.0681, aux.loss_ce: 0.1529, aux.acc_seg: 85.0516, loss: 0.5374 +2024-06-18 10:00:53,329 - mmseg - INFO - Iter [12700/80000] lr: 3.365e-05, eta: 1 day, 15:53:41, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3266, decode.acc_seg: 86.3830, aux.loss_ce: 0.1315, aux.acc_seg: 86.3767, loss: 0.4581 +2024-06-18 10:02:32,257 - mmseg - INFO - Iter [12750/80000] lr: 3.363e-05, eta: 1 day, 15:51:13, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3537, decode.acc_seg: 85.7191, aux.loss_ce: 0.1424, aux.acc_seg: 85.5093, loss: 0.4961 +2024-06-18 10:04:11,248 - mmseg - INFO - Iter [12800/80000] lr: 3.360e-05, eta: 1 day, 15:48:46, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3413, decode.acc_seg: 86.2128, aux.loss_ce: 0.1371, aux.acc_seg: 86.2055, loss: 0.4784 +2024-06-18 10:05:50,136 - mmseg - INFO - Iter [12850/80000] lr: 3.358e-05, eta: 1 day, 15:46:19, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3453, decode.acc_seg: 86.0325, aux.loss_ce: 0.1382, aux.acc_seg: 86.1181, loss: 0.4835 +2024-06-18 10:07:29,197 - mmseg - INFO - Iter [12900/80000] lr: 3.355e-05, eta: 1 day, 15:43:53, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3625, decode.acc_seg: 85.3377, aux.loss_ce: 0.1449, aux.acc_seg: 85.1934, loss: 0.5074 +2024-06-18 10:09:08,019 - mmseg - INFO - Iter [12950/80000] lr: 3.353e-05, eta: 1 day, 15:41:26, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3418, decode.acc_seg: 86.2658, aux.loss_ce: 0.1387, aux.acc_seg: 86.2486, loss: 0.4805 +2024-06-18 10:10:46,936 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 10:10:46,936 - mmseg - INFO - Iter [13000/80000] lr: 3.350e-05, eta: 1 day, 15:39:00, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3432, decode.acc_seg: 86.2751, aux.loss_ce: 0.1384, aux.acc_seg: 86.1940, loss: 0.4816 +2024-06-18 10:12:37,355 - mmseg - INFO - per class results: +2024-06-18 10:12:37,361 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.4 | 88.4 | +| building | 83.31 | 91.05 | +| sky | 94.48 | 96.64 | +| floor | 83.44 | 90.46 | +| tree | 75.52 | 91.96 | +| ceiling | 85.12 | 91.65 | +| road | 84.81 | 91.63 | +| bed | 91.31 | 96.86 | +| windowpane | 66.24 | 81.66 | +| grass | 62.86 | 81.61 | +| cabinet | 64.08 | 72.95 | +| sidewalk | 68.65 | 84.01 | +| person | 83.64 | 93.17 | +| earth | 33.65 | 40.87 | +| door | 58.3 | 70.64 | +| table | 62.61 | 77.99 | +| mountain | 59.99 | 72.11 | +| plant | 54.85 | 62.11 | +| curtain | 79.05 | 89.06 | +| chair | 60.11 | 66.46 | +| car | 85.81 | 94.37 | +| water | 59.18 | 69.19 | +| painting | 79.6 | 87.5 | +| sofa | 70.53 | 76.94 | +| shelf | 39.61 | 48.48 | +| house | 49.37 | 62.96 | +| sea | 65.95 | 81.47 | +| mirror | 73.42 | 78.85 | +| rug | 68.11 | 78.05 | +| field | 33.04 | 67.57 | +| armchair | 55.61 | 84.08 | +| seat | 63.12 | 89.5 | +| fence | 53.06 | 70.91 | +| desk | 48.74 | 80.98 | +| rock | 55.32 | 76.98 | +| wardrobe | 55.18 | 60.51 | +| lamp | 69.57 | 81.97 | +| bathtub | 85.7 | 89.78 | +| railing | 40.33 | 56.33 | +| cushion | 66.36 | 81.15 | +| base | 41.28 | 64.96 | +| box | 36.68 | 63.55 | +| column | 52.69 | 65.21 | +| signboard | 41.27 | 54.71 | +| chest of drawers | 48.63 | 76.27 | +| counter | 42.21 | 60.56 | +| sand | 47.58 | 64.76 | +| sink | 75.55 | 91.63 | +| skyscraper | 50.53 | 65.3 | +| fireplace | 67.85 | 93.78 | +| refrigerator | 82.39 | 90.17 | +| grandstand | 48.22 | 89.5 | +| path | 26.48 | 40.93 | +| stairs | 35.58 | 41.41 | +| runway | 64.05 | 82.73 | +| case | 60.58 | 81.96 | +| pool table | 93.89 | 98.01 | +| pillow | 63.0 | 71.04 | +| screen door | 75.77 | 95.47 | +| stairway | 53.57 | 82.87 | +| river | 10.8 | 20.55 | +| bridge | 54.04 | 67.35 | +| bookcase | 38.68 | 63.95 | +| blind | 40.27 | 42.62 | +| coffee table | 58.11 | 87.85 | +| toilet | 87.66 | 95.8 | +| flower | 37.16 | 55.97 | +| book | 50.29 | 79.37 | +| hill | 6.56 | 21.95 | +| bench | 58.86 | 70.88 | +| countertop | 58.48 | 83.06 | +| stove | 78.47 | 93.45 | +| palm | 45.3 | 92.51 | +| kitchen island | 36.07 | 88.53 | +| computer | 75.32 | 87.29 | +| swivel chair | 52.91 | 83.82 | +| boat | 58.96 | 90.46 | +| bar | 63.44 | 84.76 | +| arcade machine | 90.7 | 96.61 | +| hovel | 42.87 | 49.28 | +| bus | 91.21 | 96.12 | +| towel | 74.02 | 88.86 | +| light | 54.4 | 63.02 | +| truck | 44.76 | 58.72 | +| tower | 10.26 | 17.5 | +| chandelier | 68.54 | 86.8 | +| awning | 37.75 | 53.62 | +| streetlight | 29.91 | 49.48 | +| booth | 42.32 | 57.42 | +| television receiver | 71.91 | 84.19 | +| airplane | 84.45 | 94.74 | +| dirt track | 0.0 | 0.0 | +| apparel | 49.64 | 68.95 | +| pole | 22.73 | 29.29 | +| land | 0.0 | 0.0 | +| bannister | 18.01 | 29.31 | +| escalator | 60.82 | 87.99 | +| ottoman | 53.17 | 82.58 | +| bottle | 39.25 | 68.89 | +| buffet | 55.55 | 77.91 | +| poster | 44.01 | 66.49 | +| stage | 16.14 | 31.61 | +| van | 43.79 | 58.43 | +| ship | 75.83 | 88.24 | +| fountain | 55.52 | 61.19 | +| conveyer belt | 79.03 | 97.53 | +| canopy | 45.05 | 80.53 | +| washer | 79.96 | 86.44 | +| plaything | 26.96 | 35.48 | +| swimming pool | 52.75 | 78.03 | +| stool | 42.85 | 62.99 | +| barrel | 61.81 | 76.2 | +| basket | 40.11 | 56.05 | +| waterfall | 69.09 | 91.94 | +| tent | 87.38 | 98.63 | +| bag | 25.09 | 28.19 | +| minibike | 72.02 | 90.25 | +| cradle | 88.93 | 96.6 | +| oven | 52.55 | 68.62 | +| ball | 8.85 | 8.88 | +| food | 68.16 | 79.2 | +| step | 17.36 | 25.89 | +| tank | 50.8 | 79.49 | +| trade name | 22.85 | 25.32 | +| microwave | 85.49 | 91.33 | +| pot | 53.11 | 58.88 | +| animal | 66.49 | 71.4 | +| bicycle | 59.43 | 76.3 | +| lake | 43.08 | 89.36 | +| dishwasher | 63.14 | 83.96 | +| screen | 51.09 | 72.87 | +| blanket | 33.55 | 39.35 | +| sculpture | 66.28 | 85.9 | +| hood | 62.78 | 73.66 | +| sconce | 56.87 | 71.96 | +| vase | 45.48 | 61.71 | +| traffic light | 35.15 | 56.91 | +| tray | 14.15 | 20.98 | +| ashcan | 47.33 | 55.26 | +| fan | 66.59 | 80.92 | +| pier | 36.51 | 44.51 | +| crt screen | 8.76 | 33.18 | +| plate | 58.49 | 79.97 | +| monitor | 12.79 | 15.77 | +| bulletin board | 55.64 | 71.41 | +| shower | 0.15 | 0.15 | +| radiator | 62.11 | 77.54 | +| glass | 17.43 | 18.83 | +| clock | 47.78 | 55.11 | +| flag | 63.9 | 84.52 | ++---------------------+-------+-------+ +2024-06-18 10:12:37,361 - mmseg - INFO - Summary: +2024-06-18 10:12:37,361 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.58 | 54.35 | 69.46 | ++-------+-------+-------+ +2024-06-18 10:12:37,362 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 10:12:37,363 - mmseg - INFO - Iter(val) [250] aAcc: 0.8458, mIoU: 0.5435, mAcc: 0.6946, IoU.wall: 0.8040, IoU.building: 0.8331, IoU.sky: 0.9448, IoU.floor: 0.8344, IoU.tree: 0.7552, IoU.ceiling: 0.8512, IoU.road: 0.8481, IoU.bed : 0.9131, IoU.windowpane: 0.6624, IoU.grass: 0.6286, IoU.cabinet: 0.6408, IoU.sidewalk: 0.6865, IoU.person: 0.8364, IoU.earth: 0.3365, IoU.door: 0.5830, IoU.table: 0.6261, IoU.mountain: 0.5999, IoU.plant: 0.5485, IoU.curtain: 0.7905, IoU.chair: 0.6011, IoU.car: 0.8581, IoU.water: 0.5918, IoU.painting: 0.7960, IoU.sofa: 0.7053, IoU.shelf: 0.3961, IoU.house: 0.4937, IoU.sea: 0.6595, IoU.mirror: 0.7342, IoU.rug: 0.6811, IoU.field: 0.3304, IoU.armchair: 0.5561, IoU.seat: 0.6312, IoU.fence: 0.5306, IoU.desk: 0.4874, IoU.rock: 0.5532, IoU.wardrobe: 0.5518, IoU.lamp: 0.6957, IoU.bathtub: 0.8570, IoU.railing: 0.4033, IoU.cushion: 0.6636, IoU.base: 0.4128, IoU.box: 0.3668, IoU.column: 0.5269, IoU.signboard: 0.4127, IoU.chest of drawers: 0.4863, IoU.counter: 0.4221, IoU.sand: 0.4758, IoU.sink: 0.7555, IoU.skyscraper: 0.5053, IoU.fireplace: 0.6785, IoU.refrigerator: 0.8239, IoU.grandstand: 0.4822, IoU.path: 0.2648, IoU.stairs: 0.3558, IoU.runway: 0.6405, IoU.case: 0.6058, IoU.pool table: 0.9389, IoU.pillow: 0.6300, IoU.screen door: 0.7577, IoU.stairway: 0.5357, IoU.river: 0.1080, IoU.bridge: 0.5404, IoU.bookcase: 0.3868, IoU.blind: 0.4027, IoU.coffee table: 0.5811, IoU.toilet: 0.8766, IoU.flower: 0.3716, IoU.book: 0.5029, IoU.hill: 0.0656, IoU.bench: 0.5886, IoU.countertop: 0.5848, IoU.stove: 0.7847, IoU.palm: 0.4530, IoU.kitchen island: 0.3607, IoU.computer: 0.7532, IoU.swivel chair: 0.5291, IoU.boat: 0.5896, IoU.bar: 0.6344, IoU.arcade machine: 0.9070, IoU.hovel: 0.4287, IoU.bus: 0.9121, IoU.towel: 0.7402, IoU.light: 0.5440, IoU.truck: 0.4476, IoU.tower: 0.1026, IoU.chandelier: 0.6854, IoU.awning: 0.3775, IoU.streetlight: 0.2991, IoU.booth: 0.4232, IoU.television receiver: 0.7191, IoU.airplane: 0.8445, IoU.dirt track: 0.0000, IoU.apparel: 0.4964, IoU.pole: 0.2273, IoU.land: 0.0000, IoU.bannister: 0.1801, IoU.escalator: 0.6082, IoU.ottoman: 0.5317, IoU.bottle: 0.3925, IoU.buffet: 0.5555, IoU.poster: 0.4401, IoU.stage: 0.1614, IoU.van: 0.4379, IoU.ship: 0.7583, IoU.fountain: 0.5552, IoU.conveyer belt: 0.7903, IoU.canopy: 0.4505, IoU.washer: 0.7996, IoU.plaything: 0.2696, IoU.swimming pool: 0.5275, IoU.stool: 0.4285, IoU.barrel: 0.6181, IoU.basket: 0.4011, IoU.waterfall: 0.6909, IoU.tent: 0.8738, IoU.bag: 0.2509, IoU.minibike: 0.7202, IoU.cradle: 0.8893, IoU.oven: 0.5255, IoU.ball: 0.0885, IoU.food: 0.6816, IoU.step: 0.1736, IoU.tank: 0.5080, IoU.trade name: 0.2285, IoU.microwave: 0.8549, IoU.pot: 0.5311, IoU.animal: 0.6649, IoU.bicycle: 0.5943, IoU.lake: 0.4308, IoU.dishwasher: 0.6314, IoU.screen: 0.5109, IoU.blanket: 0.3355, IoU.sculpture: 0.6628, IoU.hood: 0.6278, IoU.sconce: 0.5687, IoU.vase: 0.4548, IoU.traffic light: 0.3515, IoU.tray: 0.1415, IoU.ashcan: 0.4733, IoU.fan: 0.6659, IoU.pier: 0.3651, IoU.crt screen: 0.0876, IoU.plate: 0.5849, IoU.monitor: 0.1279, IoU.bulletin board: 0.5564, IoU.shower: 0.0015, IoU.radiator: 0.6211, IoU.glass: 0.1743, IoU.clock: 0.4778, IoU.flag: 0.6390, Acc.wall: 0.8840, Acc.building: 0.9105, Acc.sky: 0.9664, Acc.floor: 0.9046, Acc.tree: 0.9196, Acc.ceiling: 0.9165, Acc.road: 0.9163, Acc.bed : 0.9686, Acc.windowpane: 0.8166, Acc.grass: 0.8161, Acc.cabinet: 0.7295, Acc.sidewalk: 0.8401, Acc.person: 0.9317, Acc.earth: 0.4087, Acc.door: 0.7064, Acc.table: 0.7799, Acc.mountain: 0.7211, Acc.plant: 0.6211, Acc.curtain: 0.8906, Acc.chair: 0.6646, Acc.car: 0.9437, Acc.water: 0.6919, Acc.painting: 0.8750, Acc.sofa: 0.7694, Acc.shelf: 0.4848, Acc.house: 0.6296, Acc.sea: 0.8147, Acc.mirror: 0.7885, Acc.rug: 0.7805, Acc.field: 0.6757, Acc.armchair: 0.8408, Acc.seat: 0.8950, Acc.fence: 0.7091, Acc.desk: 0.8098, Acc.rock: 0.7698, Acc.wardrobe: 0.6051, Acc.lamp: 0.8197, Acc.bathtub: 0.8978, Acc.railing: 0.5633, Acc.cushion: 0.8115, Acc.base: 0.6496, Acc.box: 0.6355, Acc.column: 0.6521, Acc.signboard: 0.5471, Acc.chest of drawers: 0.7627, Acc.counter: 0.6056, Acc.sand: 0.6476, Acc.sink: 0.9163, Acc.skyscraper: 0.6530, Acc.fireplace: 0.9378, Acc.refrigerator: 0.9017, Acc.grandstand: 0.8950, Acc.path: 0.4093, Acc.stairs: 0.4141, Acc.runway: 0.8273, Acc.case: 0.8196, Acc.pool table: 0.9801, Acc.pillow: 0.7104, Acc.screen door: 0.9547, Acc.stairway: 0.8287, Acc.river: 0.2055, Acc.bridge: 0.6735, Acc.bookcase: 0.6395, Acc.blind: 0.4262, Acc.coffee table: 0.8785, Acc.toilet: 0.9580, Acc.flower: 0.5597, Acc.book: 0.7937, Acc.hill: 0.2195, Acc.bench: 0.7088, Acc.countertop: 0.8306, Acc.stove: 0.9345, Acc.palm: 0.9251, Acc.kitchen island: 0.8853, Acc.computer: 0.8729, Acc.swivel chair: 0.8382, Acc.boat: 0.9046, Acc.bar: 0.8476, Acc.arcade machine: 0.9661, Acc.hovel: 0.4928, Acc.bus: 0.9612, Acc.towel: 0.8886, Acc.light: 0.6302, Acc.truck: 0.5872, Acc.tower: 0.1750, Acc.chandelier: 0.8680, Acc.awning: 0.5362, Acc.streetlight: 0.4948, Acc.booth: 0.5742, Acc.television receiver: 0.8419, Acc.airplane: 0.9474, Acc.dirt track: 0.0000, Acc.apparel: 0.6895, Acc.pole: 0.2929, Acc.land: 0.0000, Acc.bannister: 0.2931, Acc.escalator: 0.8799, Acc.ottoman: 0.8258, Acc.bottle: 0.6889, Acc.buffet: 0.7791, Acc.poster: 0.6649, Acc.stage: 0.3161, Acc.van: 0.5843, Acc.ship: 0.8824, Acc.fountain: 0.6119, Acc.conveyer belt: 0.9753, Acc.canopy: 0.8053, Acc.washer: 0.8644, Acc.plaything: 0.3548, Acc.swimming pool: 0.7803, Acc.stool: 0.6299, Acc.barrel: 0.7620, Acc.basket: 0.5605, Acc.waterfall: 0.9194, Acc.tent: 0.9863, Acc.bag: 0.2819, Acc.minibike: 0.9025, Acc.cradle: 0.9660, Acc.oven: 0.6862, Acc.ball: 0.0888, Acc.food: 0.7920, Acc.step: 0.2589, Acc.tank: 0.7949, Acc.trade name: 0.2532, Acc.microwave: 0.9133, Acc.pot: 0.5888, Acc.animal: 0.7140, Acc.bicycle: 0.7630, Acc.lake: 0.8936, Acc.dishwasher: 0.8396, Acc.screen: 0.7287, Acc.blanket: 0.3935, Acc.sculpture: 0.8590, Acc.hood: 0.7366, Acc.sconce: 0.7196, Acc.vase: 0.6171, Acc.traffic light: 0.5691, Acc.tray: 0.2098, Acc.ashcan: 0.5526, Acc.fan: 0.8092, Acc.pier: 0.4451, Acc.crt screen: 0.3318, Acc.plate: 0.7997, Acc.monitor: 0.1577, Acc.bulletin board: 0.7141, Acc.shower: 0.0015, Acc.radiator: 0.7754, Acc.glass: 0.1883, Acc.clock: 0.5511, Acc.flag: 0.8452 +2024-06-18 10:14:16,576 - mmseg - INFO - Iter [13050/80000] lr: 3.348e-05, eta: 1 day, 15:46:03, time: 4.193, data_time: 2.224, memory: 72263, decode.loss_ce: 0.3217, decode.acc_seg: 86.5663, aux.loss_ce: 0.1294, aux.acc_seg: 86.4089, loss: 0.4511 +2024-06-18 10:15:55,592 - mmseg - INFO - Iter [13100/80000] lr: 3.345e-05, eta: 1 day, 15:43:35, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3658, decode.acc_seg: 85.5417, aux.loss_ce: 0.1481, aux.acc_seg: 85.4571, loss: 0.5139 +2024-06-18 10:17:34,632 - mmseg - INFO - Iter [13150/80000] lr: 3.343e-05, eta: 1 day, 15:41:09, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3509, decode.acc_seg: 85.6827, aux.loss_ce: 0.1405, aux.acc_seg: 85.6895, loss: 0.4914 +2024-06-18 10:19:13,729 - mmseg - INFO - Iter [13200/80000] lr: 3.340e-05, eta: 1 day, 15:38:42, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3694, decode.acc_seg: 85.7951, aux.loss_ce: 0.1486, aux.acc_seg: 85.7759, loss: 0.5180 +2024-06-18 10:20:52,668 - mmseg - INFO - Iter [13250/80000] lr: 3.338e-05, eta: 1 day, 15:36:16, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3630, decode.acc_seg: 85.4480, aux.loss_ce: 0.1455, aux.acc_seg: 85.4061, loss: 0.5085 +2024-06-18 10:22:31,634 - mmseg - INFO - Iter [13300/80000] lr: 3.335e-05, eta: 1 day, 15:33:50, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3479, decode.acc_seg: 85.9073, aux.loss_ce: 0.1402, aux.acc_seg: 85.8724, loss: 0.4880 +2024-06-18 10:24:10,714 - mmseg - INFO - Iter [13350/80000] lr: 3.333e-05, eta: 1 day, 15:31:25, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3788, decode.acc_seg: 85.0262, aux.loss_ce: 0.1516, aux.acc_seg: 85.0685, loss: 0.5304 +2024-06-18 10:25:49,627 - mmseg - INFO - Iter [13400/80000] lr: 3.330e-05, eta: 1 day, 15:28:59, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3598, decode.acc_seg: 85.2544, aux.loss_ce: 0.1445, aux.acc_seg: 85.1877, loss: 0.5043 +2024-06-18 10:27:28,677 - mmseg - INFO - Iter [13450/80000] lr: 3.328e-05, eta: 1 day, 15:26:34, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3577, decode.acc_seg: 85.9109, aux.loss_ce: 0.1442, aux.acc_seg: 85.6917, loss: 0.5019 +2024-06-18 10:29:07,759 - mmseg - INFO - Iter [13500/80000] lr: 3.325e-05, eta: 1 day, 15:24:10, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3667, decode.acc_seg: 85.1867, aux.loss_ce: 0.1486, aux.acc_seg: 84.9426, loss: 0.5153 +2024-06-18 10:30:46,818 - mmseg - INFO - Iter [13550/80000] lr: 3.323e-05, eta: 1 day, 15:21:46, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3448, decode.acc_seg: 86.3756, aux.loss_ce: 0.1387, aux.acc_seg: 86.2782, loss: 0.4834 +2024-06-18 10:32:25,993 - mmseg - INFO - Iter [13600/80000] lr: 3.320e-05, eta: 1 day, 15:19:23, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3585, decode.acc_seg: 86.1902, aux.loss_ce: 0.1452, aux.acc_seg: 85.9278, loss: 0.5036 +2024-06-18 10:34:05,030 - mmseg - INFO - Iter [13650/80000] lr: 3.318e-05, eta: 1 day, 15:17:00, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3502, decode.acc_seg: 86.2150, aux.loss_ce: 0.1408, aux.acc_seg: 86.1820, loss: 0.4909 +2024-06-18 10:35:44,046 - mmseg - INFO - Iter [13700/80000] lr: 3.315e-05, eta: 1 day, 15:14:37, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3623, decode.acc_seg: 85.7729, aux.loss_ce: 0.1456, aux.acc_seg: 85.8367, loss: 0.5079 +2024-06-18 10:37:22,954 - mmseg - INFO - Iter [13750/80000] lr: 3.313e-05, eta: 1 day, 15:12:13, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3527, decode.acc_seg: 86.1118, aux.loss_ce: 0.1419, aux.acc_seg: 85.9133, loss: 0.4946 +2024-06-18 10:39:02,054 - mmseg - INFO - Iter [13800/80000] lr: 3.310e-05, eta: 1 day, 15:09:51, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3339, decode.acc_seg: 86.5677, aux.loss_ce: 0.1358, aux.acc_seg: 86.2736, loss: 0.4697 +2024-06-18 10:40:40,998 - mmseg - INFO - Iter [13850/80000] lr: 3.308e-05, eta: 1 day, 15:07:29, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3581, decode.acc_seg: 86.0292, aux.loss_ce: 0.1443, aux.acc_seg: 85.9407, loss: 0.5024 +2024-06-18 10:42:22,168 - mmseg - INFO - Iter [13900/80000] lr: 3.305e-05, eta: 1 day, 15:05:17, time: 2.023, data_time: 0.052, memory: 72263, decode.loss_ce: 0.3594, decode.acc_seg: 85.5950, aux.loss_ce: 0.1436, aux.acc_seg: 85.5582, loss: 0.5031 +2024-06-18 10:44:01,085 - mmseg - INFO - Iter [13950/80000] lr: 3.303e-05, eta: 1 day, 15:02:55, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3048, decode.acc_seg: 87.7668, aux.loss_ce: 0.1241, aux.acc_seg: 87.5852, loss: 0.4288 +2024-06-18 10:45:39,945 - mmseg - INFO - Saving checkpoint at 14000 iterations +2024-06-18 10:47:04,682 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 10:47:04,682 - mmseg - INFO - Iter [14000/80000] lr: 3.300e-05, eta: 1 day, 15:07:13, time: 3.672, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3236, decode.acc_seg: 87.3750, aux.loss_ce: 0.1311, aux.acc_seg: 87.1450, loss: 0.4547 +2024-06-18 10:48:54,913 - mmseg - INFO - per class results: +2024-06-18 10:48:54,919 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.03 | 88.32 | +| building | 84.07 | 94.26 | +| sky | 93.08 | 97.26 | +| floor | 84.14 | 90.02 | +| tree | 73.08 | 81.71 | +| ceiling | 85.88 | 91.86 | +| road | 85.13 | 91.19 | +| bed | 91.48 | 95.9 | +| windowpane | 65.06 | 79.95 | +| grass | 67.69 | 76.75 | +| cabinet | 64.82 | 75.86 | +| sidewalk | 67.08 | 80.65 | +| person | 84.21 | 93.67 | +| earth | 38.49 | 53.91 | +| door | 58.24 | 75.03 | +| table | 65.17 | 76.11 | +| mountain | 60.88 | 78.48 | +| plant | 57.56 | 69.39 | +| curtain | 76.21 | 91.59 | +| chair | 65.64 | 79.86 | +| car | 86.74 | 93.75 | +| water | 65.19 | 78.28 | +| painting | 77.36 | 85.59 | +| sofa | 76.18 | 93.33 | +| shelf | 47.96 | 74.43 | +| house | 56.09 | 84.69 | +| sea | 71.76 | 84.91 | +| mirror | 75.48 | 84.96 | +| rug | 70.86 | 82.85 | +| field | 30.98 | 51.93 | +| armchair | 58.94 | 67.88 | +| seat | 68.02 | 81.04 | +| fence | 49.94 | 66.85 | +| desk | 57.14 | 72.77 | +| rock | 59.99 | 83.79 | +| wardrobe | 55.34 | 77.39 | +| lamp | 70.51 | 82.69 | +| bathtub | 83.12 | 87.23 | +| railing | 38.84 | 50.73 | +| cushion | 62.89 | 76.01 | +| base | 38.11 | 54.4 | +| box | 33.79 | 43.55 | +| column | 54.67 | 71.03 | +| signboard | 37.72 | 44.87 | +| chest of drawers | 44.31 | 71.73 | +| counter | 33.74 | 41.64 | +| sand | 49.44 | 67.21 | +| sink | 76.55 | 84.26 | +| skyscraper | 51.21 | 55.86 | +| fireplace | 71.71 | 95.14 | +| refrigerator | 82.26 | 90.12 | +| grandstand | 48.43 | 88.78 | +| path | 26.68 | 48.59 | +| stairs | 35.32 | 49.75 | +| runway | 67.65 | 87.06 | +| case | 61.96 | 73.01 | +| pool table | 92.29 | 98.87 | +| pillow | 63.61 | 79.15 | +| screen door | 79.92 | 88.73 | +| stairway | 42.93 | 49.12 | +| river | 22.46 | 45.61 | +| bridge | 68.25 | 83.51 | +| bookcase | 45.46 | 60.74 | +| blind | 42.68 | 47.8 | +| coffee table | 58.43 | 89.3 | +| toilet | 89.0 | 93.72 | +| flower | 38.55 | 62.45 | +| book | 54.23 | 69.15 | +| hill | 4.74 | 8.64 | +| bench | 61.86 | 73.65 | +| countertop | 63.66 | 74.72 | +| stove | 82.21 | 87.56 | +| palm | 43.3 | 84.48 | +| kitchen island | 40.32 | 82.71 | +| computer | 73.83 | 92.46 | +| swivel chair | 50.78 | 91.45 | +| boat | 72.8 | 89.5 | +| bar | 58.16 | 87.19 | +| arcade machine | 87.14 | 99.21 | +| hovel | 26.41 | 32.08 | +| bus | 91.4 | 96.89 | +| towel | 74.74 | 81.02 | +| light | 57.98 | 73.54 | +| truck | 48.62 | 59.13 | +| tower | 19.57 | 27.27 | +| chandelier | 67.99 | 87.77 | +| awning | 28.73 | 31.44 | +| streetlight | 29.56 | 38.52 | +| booth | 55.88 | 75.21 | +| television receiver | 79.16 | 86.42 | +| airplane | 83.51 | 95.21 | +| dirt track | 1.13 | 2.48 | +| apparel | 51.83 | 78.4 | +| pole | 25.56 | 34.96 | +| land | 0.02 | 0.04 | +| bannister | 16.72 | 20.55 | +| escalator | 64.79 | 82.37 | +| ottoman | 53.84 | 72.19 | +| bottle | 41.01 | 71.47 | +| buffet | 49.05 | 56.99 | +| poster | 38.83 | 73.49 | +| stage | 17.12 | 23.38 | +| van | 46.03 | 56.42 | +| ship | 43.17 | 44.81 | +| fountain | 40.14 | 41.88 | +| conveyer belt | 64.5 | 98.66 | +| canopy | 50.07 | 80.57 | +| washer | 81.83 | 92.4 | +| plaything | 28.64 | 59.28 | +| swimming pool | 53.22 | 77.0 | +| stool | 50.73 | 59.03 | +| barrel | 51.55 | 65.74 | +| basket | 38.28 | 62.51 | +| waterfall | 70.26 | 88.97 | +| tent | 91.17 | 97.09 | +| bag | 27.33 | 30.75 | +| minibike | 75.52 | 85.64 | +| cradle | 82.66 | 98.74 | +| oven | 62.39 | 73.03 | +| ball | 50.53 | 75.96 | +| food | 57.63 | 60.22 | +| step | 20.39 | 34.85 | +| tank | 69.33 | 92.02 | +| trade name | 10.3 | 10.57 | +| microwave | 88.83 | 95.94 | +| pot | 52.8 | 67.24 | +| animal | 66.34 | 70.55 | +| bicycle | 58.42 | 80.75 | +| lake | 52.77 | 69.98 | +| dishwasher | 72.76 | 79.04 | +| screen | 62.23 | 96.02 | +| blanket | 26.43 | 29.48 | +| sculpture | 71.45 | 85.15 | +| hood | 65.01 | 74.79 | +| sconce | 56.75 | 71.5 | +| vase | 45.91 | 62.79 | +| traffic light | 30.34 | 60.06 | +| tray | 19.37 | 24.56 | +| ashcan | 49.03 | 64.98 | +| fan | 67.31 | 85.05 | +| pier | 38.24 | 43.72 | +| crt screen | 0.66 | 1.33 | +| plate | 60.47 | 75.02 | +| monitor | 33.8 | 41.35 | +| bulletin board | 63.32 | 79.29 | +| shower | 0.32 | 0.32 | +| radiator | 65.76 | 76.91 | +| glass | 18.02 | 19.85 | +| clock | 46.68 | 58.55 | +| flag | 66.35 | 75.16 | ++---------------------+-------+-------+ +2024-06-18 10:48:54,919 - mmseg - INFO - Summary: +2024-06-18 10:48:54,919 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.14 | 55.54 | 69.37 | ++-------+-------+-------+ +2024-06-18 10:48:54,920 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 10:48:54,920 - mmseg - INFO - Iter(val) [250] aAcc: 0.8514, mIoU: 0.5554, mAcc: 0.6937, IoU.wall: 0.8103, IoU.building: 0.8407, IoU.sky: 0.9308, IoU.floor: 0.8414, IoU.tree: 0.7308, IoU.ceiling: 0.8588, IoU.road: 0.8513, IoU.bed : 0.9148, IoU.windowpane: 0.6506, IoU.grass: 0.6769, IoU.cabinet: 0.6482, IoU.sidewalk: 0.6708, IoU.person: 0.8421, IoU.earth: 0.3849, IoU.door: 0.5824, IoU.table: 0.6517, IoU.mountain: 0.6088, IoU.plant: 0.5756, IoU.curtain: 0.7621, IoU.chair: 0.6564, IoU.car: 0.8674, IoU.water: 0.6519, IoU.painting: 0.7736, IoU.sofa: 0.7618, IoU.shelf: 0.4796, IoU.house: 0.5609, IoU.sea: 0.7176, IoU.mirror: 0.7548, IoU.rug: 0.7086, IoU.field: 0.3098, IoU.armchair: 0.5894, IoU.seat: 0.6802, IoU.fence: 0.4994, IoU.desk: 0.5714, IoU.rock: 0.5999, IoU.wardrobe: 0.5534, IoU.lamp: 0.7051, IoU.bathtub: 0.8312, IoU.railing: 0.3884, IoU.cushion: 0.6289, IoU.base: 0.3811, IoU.box: 0.3379, IoU.column: 0.5467, IoU.signboard: 0.3772, IoU.chest of drawers: 0.4431, IoU.counter: 0.3374, IoU.sand: 0.4944, IoU.sink: 0.7655, IoU.skyscraper: 0.5121, IoU.fireplace: 0.7171, IoU.refrigerator: 0.8226, IoU.grandstand: 0.4843, IoU.path: 0.2668, IoU.stairs: 0.3532, IoU.runway: 0.6765, IoU.case: 0.6196, IoU.pool table: 0.9229, IoU.pillow: 0.6361, IoU.screen door: 0.7992, IoU.stairway: 0.4293, IoU.river: 0.2246, IoU.bridge: 0.6825, IoU.bookcase: 0.4546, IoU.blind: 0.4268, IoU.coffee table: 0.5843, IoU.toilet: 0.8900, IoU.flower: 0.3855, IoU.book: 0.5423, IoU.hill: 0.0474, IoU.bench: 0.6186, IoU.countertop: 0.6366, IoU.stove: 0.8221, IoU.palm: 0.4330, IoU.kitchen island: 0.4032, IoU.computer: 0.7383, IoU.swivel chair: 0.5078, IoU.boat: 0.7280, IoU.bar: 0.5816, IoU.arcade machine: 0.8714, IoU.hovel: 0.2641, IoU.bus: 0.9140, IoU.towel: 0.7474, IoU.light: 0.5798, IoU.truck: 0.4862, IoU.tower: 0.1957, IoU.chandelier: 0.6799, IoU.awning: 0.2873, IoU.streetlight: 0.2956, IoU.booth: 0.5588, IoU.television receiver: 0.7916, IoU.airplane: 0.8351, IoU.dirt track: 0.0113, IoU.apparel: 0.5183, IoU.pole: 0.2556, IoU.land: 0.0002, IoU.bannister: 0.1672, IoU.escalator: 0.6479, IoU.ottoman: 0.5384, IoU.bottle: 0.4101, IoU.buffet: 0.4905, IoU.poster: 0.3883, IoU.stage: 0.1712, IoU.van: 0.4603, IoU.ship: 0.4317, IoU.fountain: 0.4014, IoU.conveyer belt: 0.6450, IoU.canopy: 0.5007, IoU.washer: 0.8183, IoU.plaything: 0.2864, IoU.swimming pool: 0.5322, IoU.stool: 0.5073, IoU.barrel: 0.5155, IoU.basket: 0.3828, IoU.waterfall: 0.7026, IoU.tent: 0.9117, IoU.bag: 0.2733, IoU.minibike: 0.7552, IoU.cradle: 0.8266, IoU.oven: 0.6239, IoU.ball: 0.5053, IoU.food: 0.5763, IoU.step: 0.2039, IoU.tank: 0.6933, IoU.trade name: 0.1030, IoU.microwave: 0.8883, IoU.pot: 0.5280, IoU.animal: 0.6634, IoU.bicycle: 0.5842, IoU.lake: 0.5277, IoU.dishwasher: 0.7276, IoU.screen: 0.6223, IoU.blanket: 0.2643, IoU.sculpture: 0.7145, IoU.hood: 0.6501, IoU.sconce: 0.5675, IoU.vase: 0.4591, IoU.traffic light: 0.3034, IoU.tray: 0.1937, IoU.ashcan: 0.4903, IoU.fan: 0.6731, IoU.pier: 0.3824, IoU.crt screen: 0.0066, IoU.plate: 0.6047, IoU.monitor: 0.3380, IoU.bulletin board: 0.6332, IoU.shower: 0.0032, IoU.radiator: 0.6576, IoU.glass: 0.1802, IoU.clock: 0.4668, IoU.flag: 0.6635, Acc.wall: 0.8832, Acc.building: 0.9426, Acc.sky: 0.9726, Acc.floor: 0.9002, Acc.tree: 0.8171, Acc.ceiling: 0.9186, Acc.road: 0.9119, Acc.bed : 0.9590, Acc.windowpane: 0.7995, Acc.grass: 0.7675, Acc.cabinet: 0.7586, Acc.sidewalk: 0.8065, Acc.person: 0.9367, Acc.earth: 0.5391, Acc.door: 0.7503, Acc.table: 0.7611, Acc.mountain: 0.7848, Acc.plant: 0.6939, Acc.curtain: 0.9159, Acc.chair: 0.7986, Acc.car: 0.9375, Acc.water: 0.7828, Acc.painting: 0.8559, Acc.sofa: 0.9333, Acc.shelf: 0.7443, Acc.house: 0.8469, Acc.sea: 0.8491, Acc.mirror: 0.8496, Acc.rug: 0.8285, Acc.field: 0.5193, Acc.armchair: 0.6788, Acc.seat: 0.8104, Acc.fence: 0.6685, Acc.desk: 0.7277, Acc.rock: 0.8379, Acc.wardrobe: 0.7739, Acc.lamp: 0.8269, Acc.bathtub: 0.8723, Acc.railing: 0.5073, Acc.cushion: 0.7601, Acc.base: 0.5440, Acc.box: 0.4355, Acc.column: 0.7103, Acc.signboard: 0.4487, Acc.chest of drawers: 0.7173, Acc.counter: 0.4164, Acc.sand: 0.6721, Acc.sink: 0.8426, Acc.skyscraper: 0.5586, Acc.fireplace: 0.9514, Acc.refrigerator: 0.9012, Acc.grandstand: 0.8878, Acc.path: 0.4859, Acc.stairs: 0.4975, Acc.runway: 0.8706, Acc.case: 0.7301, Acc.pool table: 0.9887, Acc.pillow: 0.7915, Acc.screen door: 0.8873, Acc.stairway: 0.4912, Acc.river: 0.4561, Acc.bridge: 0.8351, Acc.bookcase: 0.6074, Acc.blind: 0.4780, Acc.coffee table: 0.8930, Acc.toilet: 0.9372, Acc.flower: 0.6245, Acc.book: 0.6915, Acc.hill: 0.0864, Acc.bench: 0.7365, Acc.countertop: 0.7472, Acc.stove: 0.8756, Acc.palm: 0.8448, Acc.kitchen island: 0.8271, Acc.computer: 0.9246, Acc.swivel chair: 0.9145, Acc.boat: 0.8950, Acc.bar: 0.8719, Acc.arcade machine: 0.9921, Acc.hovel: 0.3208, Acc.bus: 0.9689, Acc.towel: 0.8102, Acc.light: 0.7354, Acc.truck: 0.5913, Acc.tower: 0.2727, Acc.chandelier: 0.8777, Acc.awning: 0.3144, Acc.streetlight: 0.3852, Acc.booth: 0.7521, Acc.television receiver: 0.8642, Acc.airplane: 0.9521, Acc.dirt track: 0.0248, Acc.apparel: 0.7840, Acc.pole: 0.3496, Acc.land: 0.0004, Acc.bannister: 0.2055, Acc.escalator: 0.8237, Acc.ottoman: 0.7219, Acc.bottle: 0.7147, Acc.buffet: 0.5699, Acc.poster: 0.7349, Acc.stage: 0.2338, Acc.van: 0.5642, Acc.ship: 0.4481, Acc.fountain: 0.4188, Acc.conveyer belt: 0.9866, Acc.canopy: 0.8057, Acc.washer: 0.9240, Acc.plaything: 0.5928, Acc.swimming pool: 0.7700, Acc.stool: 0.5903, Acc.barrel: 0.6574, Acc.basket: 0.6251, Acc.waterfall: 0.8897, Acc.tent: 0.9709, Acc.bag: 0.3075, Acc.minibike: 0.8564, Acc.cradle: 0.9874, Acc.oven: 0.7303, Acc.ball: 0.7596, Acc.food: 0.6022, Acc.step: 0.3485, Acc.tank: 0.9202, Acc.trade name: 0.1057, Acc.microwave: 0.9594, Acc.pot: 0.6724, Acc.animal: 0.7055, Acc.bicycle: 0.8075, Acc.lake: 0.6998, Acc.dishwasher: 0.7904, Acc.screen: 0.9602, Acc.blanket: 0.2948, Acc.sculpture: 0.8515, Acc.hood: 0.7479, Acc.sconce: 0.7150, Acc.vase: 0.6279, Acc.traffic light: 0.6006, Acc.tray: 0.2456, Acc.ashcan: 0.6498, Acc.fan: 0.8505, Acc.pier: 0.4372, Acc.crt screen: 0.0133, Acc.plate: 0.7502, Acc.monitor: 0.4135, Acc.bulletin board: 0.7929, Acc.shower: 0.0032, Acc.radiator: 0.7691, Acc.glass: 0.1985, Acc.clock: 0.5855, Acc.flag: 0.7516 +2024-06-18 10:50:34,192 - mmseg - INFO - Iter [14050/80000] lr: 3.298e-05, eta: 1 day, 15:13:29, time: 4.190, data_time: 2.221, memory: 72263, decode.loss_ce: 0.3449, decode.acc_seg: 86.2850, aux.loss_ce: 0.1386, aux.acc_seg: 86.0520, loss: 0.4835 +2024-06-18 10:52:13,179 - mmseg - INFO - Iter [14100/80000] lr: 3.295e-05, eta: 1 day, 15:11:04, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3505, decode.acc_seg: 86.3754, aux.loss_ce: 0.1409, aux.acc_seg: 86.1233, loss: 0.4914 +2024-06-18 10:53:52,266 - mmseg - INFO - Iter [14150/80000] lr: 3.293e-05, eta: 1 day, 15:08:40, time: 1.982, data_time: 0.012, memory: 72263, decode.loss_ce: 0.3354, decode.acc_seg: 86.5527, aux.loss_ce: 0.1356, aux.acc_seg: 86.4147, loss: 0.4710 +2024-06-18 10:55:31,208 - mmseg - INFO - Iter [14200/80000] lr: 3.290e-05, eta: 1 day, 15:06:15, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3209, decode.acc_seg: 87.0470, aux.loss_ce: 0.1292, aux.acc_seg: 87.1242, loss: 0.4501 +2024-06-18 10:57:10,226 - mmseg - INFO - Iter [14250/80000] lr: 3.288e-05, eta: 1 day, 15:03:52, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3304, decode.acc_seg: 86.8841, aux.loss_ce: 0.1344, aux.acc_seg: 86.6057, loss: 0.4648 +2024-06-18 10:58:49,134 - mmseg - INFO - Iter [14300/80000] lr: 3.285e-05, eta: 1 day, 15:01:28, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3256, decode.acc_seg: 87.2529, aux.loss_ce: 0.1326, aux.acc_seg: 87.1309, loss: 0.4582 +2024-06-18 11:00:28,175 - mmseg - INFO - Iter [14350/80000] lr: 3.283e-05, eta: 1 day, 14:59:05, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3227, decode.acc_seg: 87.0488, aux.loss_ce: 0.1309, aux.acc_seg: 86.9076, loss: 0.4537 +2024-06-18 11:02:07,002 - mmseg - INFO - Iter [14400/80000] lr: 3.280e-05, eta: 1 day, 14:56:41, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3655, decode.acc_seg: 84.9461, aux.loss_ce: 0.1463, aux.acc_seg: 85.0620, loss: 0.5118 +2024-06-18 11:03:46,063 - mmseg - INFO - Iter [14450/80000] lr: 3.278e-05, eta: 1 day, 14:54:19, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3309, decode.acc_seg: 86.5566, aux.loss_ce: 0.1332, aux.acc_seg: 86.5047, loss: 0.4641 +2024-06-18 11:05:24,899 - mmseg - INFO - Iter [14500/80000] lr: 3.275e-05, eta: 1 day, 14:51:56, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3428, decode.acc_seg: 86.4990, aux.loss_ce: 0.1381, aux.acc_seg: 86.3680, loss: 0.4810 +2024-06-18 11:07:03,895 - mmseg - INFO - Iter [14550/80000] lr: 3.273e-05, eta: 1 day, 14:49:34, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3597, decode.acc_seg: 85.9982, aux.loss_ce: 0.1445, aux.acc_seg: 85.9942, loss: 0.5042 +2024-06-18 11:08:42,789 - mmseg - INFO - Iter [14600/80000] lr: 3.270e-05, eta: 1 day, 14:47:12, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3702, decode.acc_seg: 85.1742, aux.loss_ce: 0.1493, aux.acc_seg: 85.0882, loss: 0.5195 +2024-06-18 11:10:21,698 - mmseg - INFO - Iter [14650/80000] lr: 3.268e-05, eta: 1 day, 14:44:50, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3351, decode.acc_seg: 86.7798, aux.loss_ce: 0.1354, aux.acc_seg: 86.5589, loss: 0.4705 +2024-06-18 11:12:00,764 - mmseg - INFO - Iter [14700/80000] lr: 3.265e-05, eta: 1 day, 14:42:30, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3309, decode.acc_seg: 86.8644, aux.loss_ce: 0.1339, aux.acc_seg: 86.6349, loss: 0.4648 +2024-06-18 11:13:39,639 - mmseg - INFO - Iter [14750/80000] lr: 3.263e-05, eta: 1 day, 14:40:08, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3444, decode.acc_seg: 86.3013, aux.loss_ce: 0.1390, aux.acc_seg: 86.1565, loss: 0.4834 +2024-06-18 11:15:18,610 - mmseg - INFO - Iter [14800/80000] lr: 3.260e-05, eta: 1 day, 14:37:48, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3138, decode.acc_seg: 86.9718, aux.loss_ce: 0.1270, aux.acc_seg: 86.8070, loss: 0.4408 +2024-06-18 11:16:57,532 - mmseg - INFO - Iter [14850/80000] lr: 3.258e-05, eta: 1 day, 14:35:27, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3546, decode.acc_seg: 86.0616, aux.loss_ce: 0.1424, aux.acc_seg: 85.9546, loss: 0.4970 +2024-06-18 11:18:36,675 - mmseg - INFO - Iter [14900/80000] lr: 3.255e-05, eta: 1 day, 14:33:08, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3394, decode.acc_seg: 86.4231, aux.loss_ce: 0.1367, aux.acc_seg: 86.3113, loss: 0.4761 +2024-06-18 11:20:15,670 - mmseg - INFO - Iter [14950/80000] lr: 3.253e-05, eta: 1 day, 14:30:48, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3308, decode.acc_seg: 86.5242, aux.loss_ce: 0.1324, aux.acc_seg: 86.4565, loss: 0.4633 +2024-06-18 11:21:54,637 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 11:21:54,637 - mmseg - INFO - Iter [15000/80000] lr: 3.250e-05, eta: 1 day, 14:28:29, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3413, decode.acc_seg: 86.3179, aux.loss_ce: 0.1379, aux.acc_seg: 86.1477, loss: 0.4792 +2024-06-18 11:23:44,594 - mmseg - INFO - per class results: +2024-06-18 11:23:44,600 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.04 | 88.79 | +| building | 84.61 | 94.38 | +| sky | 94.49 | 97.35 | +| floor | 83.51 | 90.26 | +| tree | 76.81 | 86.16 | +| ceiling | 85.24 | 91.13 | +| road | 84.83 | 90.87 | +| bed | 91.3 | 97.4 | +| windowpane | 66.1 | 78.16 | +| grass | 64.56 | 81.16 | +| cabinet | 64.33 | 71.61 | +| sidewalk | 68.14 | 84.04 | +| person | 84.97 | 92.25 | +| earth | 34.0 | 44.95 | +| door | 58.09 | 71.7 | +| table | 65.28 | 76.69 | +| mountain | 61.89 | 70.81 | +| plant | 57.46 | 67.86 | +| curtain | 79.15 | 89.82 | +| chair | 64.82 | 77.29 | +| car | 86.58 | 93.96 | +| water | 64.93 | 84.75 | +| painting | 76.19 | 90.56 | +| sofa | 77.84 | 90.09 | +| shelf | 44.04 | 56.72 | +| house | 53.86 | 66.04 | +| sea | 62.32 | 67.71 | +| mirror | 78.64 | 87.56 | +| rug | 62.43 | 70.69 | +| field | 31.6 | 74.32 | +| armchair | 57.75 | 75.35 | +| seat | 66.51 | 88.96 | +| fence | 51.7 | 75.54 | +| desk | 48.31 | 80.15 | +| rock | 53.51 | 86.51 | +| wardrobe | 58.95 | 84.37 | +| lamp | 71.19 | 83.19 | +| bathtub | 87.2 | 90.02 | +| railing | 39.93 | 57.28 | +| cushion | 65.02 | 83.29 | +| base | 40.12 | 59.94 | +| box | 37.49 | 54.47 | +| column | 54.83 | 71.76 | +| signboard | 42.5 | 58.88 | +| chest of drawers | 49.66 | 73.52 | +| counter | 39.85 | 59.34 | +| sand | 49.2 | 82.33 | +| sink | 76.19 | 85.19 | +| skyscraper | 60.9 | 77.9 | +| fireplace | 77.9 | 93.52 | +| refrigerator | 84.08 | 92.23 | +| grandstand | 47.47 | 87.84 | +| path | 26.62 | 35.86 | +| stairs | 40.94 | 46.72 | +| runway | 69.07 | 93.25 | +| case | 54.99 | 67.46 | +| pool table | 93.35 | 98.65 | +| pillow | 54.28 | 58.42 | +| screen door | 51.31 | 53.54 | +| stairway | 48.37 | 64.2 | +| river | 19.7 | 32.41 | +| bridge | 72.61 | 88.69 | +| bookcase | 39.67 | 60.08 | +| blind | 45.76 | 54.6 | +| coffee table | 57.53 | 87.38 | +| toilet | 87.41 | 95.7 | +| flower | 36.68 | 56.45 | +| book | 51.49 | 74.47 | +| hill | 4.33 | 7.59 | +| bench | 61.54 | 73.63 | +| countertop | 63.73 | 85.84 | +| stove | 83.65 | 91.28 | +| palm | 51.5 | 77.28 | +| kitchen island | 47.52 | 85.27 | +| computer | 71.19 | 84.29 | +| swivel chair | 50.26 | 69.84 | +| boat | 53.51 | 90.21 | +| bar | 60.37 | 79.48 | +| arcade machine | 88.41 | 98.88 | +| hovel | 19.91 | 21.19 | +| bus | 91.08 | 95.97 | +| towel | 72.35 | 87.7 | +| light | 57.72 | 66.4 | +| truck | 44.58 | 54.62 | +| tower | 4.49 | 4.88 | +| chandelier | 68.93 | 85.28 | +| awning | 46.37 | 58.55 | +| streetlight | 33.48 | 51.61 | +| booth | 59.09 | 77.86 | +| television receiver | 77.12 | 87.95 | +| airplane | 62.33 | 70.43 | +| dirt track | 0.04 | 0.1 | +| apparel | 57.66 | 65.61 | +| pole | 19.93 | 23.24 | +| land | 0.14 | 0.16 | +| bannister | 18.82 | 28.08 | +| escalator | 61.03 | 86.81 | +| ottoman | 55.09 | 77.89 | +| bottle | 42.4 | 62.04 | +| buffet | 56.67 | 81.47 | +| poster | 36.41 | 45.31 | +| stage | 22.37 | 48.26 | +| van | 36.96 | 45.44 | +| ship | 70.68 | 83.68 | +| fountain | 46.08 | 48.05 | +| conveyer belt | 70.73 | 98.2 | +| canopy | 57.5 | 67.41 | +| washer | 86.57 | 93.47 | +| plaything | 31.52 | 43.46 | +| swimming pool | 68.25 | 81.44 | +| stool | 47.78 | 58.31 | +| barrel | 61.92 | 75.95 | +| basket | 41.21 | 57.34 | +| waterfall | 60.15 | 74.56 | +| tent | 96.66 | 97.2 | +| bag | 30.74 | 36.54 | +| minibike | 67.77 | 93.32 | +| cradle | 84.93 | 97.44 | +| oven | 61.45 | 68.38 | +| ball | 8.53 | 8.55 | +| food | 67.78 | 80.07 | +| step | 21.0 | 28.02 | +| tank | 61.32 | 74.88 | +| trade name | 35.19 | 44.22 | +| microwave | 87.81 | 95.32 | +| pot | 54.15 | 63.77 | +| animal | 67.99 | 71.59 | +| bicycle | 60.7 | 78.0 | +| lake | 60.61 | 63.43 | +| dishwasher | 75.92 | 83.01 | +| screen | 62.19 | 74.82 | +| blanket | 21.9 | 25.45 | +| sculpture | 67.01 | 86.25 | +| hood | 65.61 | 78.8 | +| sconce | 57.52 | 71.62 | +| vase | 46.28 | 63.9 | +| traffic light | 33.24 | 66.91 | +| tray | 18.35 | 27.57 | +| ashcan | 50.67 | 57.78 | +| fan | 67.41 | 81.75 | +| pier | 34.28 | 38.63 | +| crt screen | 24.31 | 31.85 | +| plate | 61.76 | 73.72 | +| monitor | 54.95 | 88.5 | +| bulletin board | 54.01 | 73.37 | +| shower | 0.5 | 7.41 | +| radiator | 60.18 | 75.84 | +| glass | 16.05 | 17.2 | +| clock | 45.46 | 62.49 | +| flag | 66.76 | 74.38 | ++---------------------+-------+-------+ +2024-06-18 11:23:44,600 - mmseg - INFO - Summary: +2024-06-18 11:23:44,600 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.22 | 55.82 | 69.39 | ++-------+-------+-------+ +2024-06-18 11:23:44,601 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 11:23:44,601 - mmseg - INFO - Iter(val) [250] aAcc: 0.8522, mIoU: 0.5582, mAcc: 0.6939, IoU.wall: 0.8104, IoU.building: 0.8461, IoU.sky: 0.9449, IoU.floor: 0.8351, IoU.tree: 0.7681, IoU.ceiling: 0.8524, IoU.road: 0.8483, IoU.bed : 0.9130, IoU.windowpane: 0.6610, IoU.grass: 0.6456, IoU.cabinet: 0.6433, IoU.sidewalk: 0.6814, IoU.person: 0.8497, IoU.earth: 0.3400, IoU.door: 0.5809, IoU.table: 0.6528, IoU.mountain: 0.6189, IoU.plant: 0.5746, IoU.curtain: 0.7915, IoU.chair: 0.6482, IoU.car: 0.8658, IoU.water: 0.6493, IoU.painting: 0.7619, IoU.sofa: 0.7784, IoU.shelf: 0.4404, IoU.house: 0.5386, IoU.sea: 0.6232, IoU.mirror: 0.7864, IoU.rug: 0.6243, IoU.field: 0.3160, IoU.armchair: 0.5775, IoU.seat: 0.6651, IoU.fence: 0.5170, IoU.desk: 0.4831, IoU.rock: 0.5351, IoU.wardrobe: 0.5895, IoU.lamp: 0.7119, IoU.bathtub: 0.8720, IoU.railing: 0.3993, IoU.cushion: 0.6502, IoU.base: 0.4012, IoU.box: 0.3749, IoU.column: 0.5483, IoU.signboard: 0.4250, IoU.chest of drawers: 0.4966, IoU.counter: 0.3985, IoU.sand: 0.4920, IoU.sink: 0.7619, IoU.skyscraper: 0.6090, IoU.fireplace: 0.7790, IoU.refrigerator: 0.8408, IoU.grandstand: 0.4747, IoU.path: 0.2662, IoU.stairs: 0.4094, IoU.runway: 0.6907, IoU.case: 0.5499, IoU.pool table: 0.9335, IoU.pillow: 0.5428, IoU.screen door: 0.5131, IoU.stairway: 0.4837, IoU.river: 0.1970, IoU.bridge: 0.7261, IoU.bookcase: 0.3967, IoU.blind: 0.4576, IoU.coffee table: 0.5753, IoU.toilet: 0.8741, IoU.flower: 0.3668, IoU.book: 0.5149, IoU.hill: 0.0433, IoU.bench: 0.6154, IoU.countertop: 0.6373, IoU.stove: 0.8365, IoU.palm: 0.5150, IoU.kitchen island: 0.4752, IoU.computer: 0.7119, IoU.swivel chair: 0.5026, IoU.boat: 0.5351, IoU.bar: 0.6037, IoU.arcade machine: 0.8841, IoU.hovel: 0.1991, IoU.bus: 0.9108, IoU.towel: 0.7235, IoU.light: 0.5772, IoU.truck: 0.4458, IoU.tower: 0.0449, IoU.chandelier: 0.6893, IoU.awning: 0.4637, IoU.streetlight: 0.3348, IoU.booth: 0.5909, IoU.television receiver: 0.7712, IoU.airplane: 0.6233, IoU.dirt track: 0.0004, IoU.apparel: 0.5766, IoU.pole: 0.1993, IoU.land: 0.0014, IoU.bannister: 0.1882, IoU.escalator: 0.6103, IoU.ottoman: 0.5509, IoU.bottle: 0.4240, IoU.buffet: 0.5667, IoU.poster: 0.3641, IoU.stage: 0.2237, IoU.van: 0.3696, IoU.ship: 0.7068, IoU.fountain: 0.4608, IoU.conveyer belt: 0.7073, IoU.canopy: 0.5750, IoU.washer: 0.8657, IoU.plaything: 0.3152, IoU.swimming pool: 0.6825, IoU.stool: 0.4778, IoU.barrel: 0.6192, IoU.basket: 0.4121, IoU.waterfall: 0.6015, IoU.tent: 0.9666, IoU.bag: 0.3074, IoU.minibike: 0.6777, IoU.cradle: 0.8493, IoU.oven: 0.6145, IoU.ball: 0.0853, IoU.food: 0.6778, IoU.step: 0.2100, IoU.tank: 0.6132, IoU.trade name: 0.3519, IoU.microwave: 0.8781, IoU.pot: 0.5415, IoU.animal: 0.6799, IoU.bicycle: 0.6070, IoU.lake: 0.6061, IoU.dishwasher: 0.7592, IoU.screen: 0.6219, IoU.blanket: 0.2190, IoU.sculpture: 0.6701, IoU.hood: 0.6561, IoU.sconce: 0.5752, IoU.vase: 0.4628, IoU.traffic light: 0.3324, IoU.tray: 0.1835, IoU.ashcan: 0.5067, IoU.fan: 0.6741, IoU.pier: 0.3428, IoU.crt screen: 0.2431, IoU.plate: 0.6176, IoU.monitor: 0.5495, IoU.bulletin board: 0.5401, IoU.shower: 0.0050, IoU.radiator: 0.6018, IoU.glass: 0.1605, IoU.clock: 0.4546, IoU.flag: 0.6676, Acc.wall: 0.8879, Acc.building: 0.9438, Acc.sky: 0.9735, Acc.floor: 0.9026, Acc.tree: 0.8616, Acc.ceiling: 0.9113, Acc.road: 0.9087, Acc.bed : 0.9740, Acc.windowpane: 0.7816, Acc.grass: 0.8116, Acc.cabinet: 0.7161, Acc.sidewalk: 0.8404, Acc.person: 0.9225, Acc.earth: 0.4495, Acc.door: 0.7170, Acc.table: 0.7669, Acc.mountain: 0.7081, Acc.plant: 0.6786, Acc.curtain: 0.8982, Acc.chair: 0.7729, Acc.car: 0.9396, Acc.water: 0.8475, Acc.painting: 0.9056, Acc.sofa: 0.9009, Acc.shelf: 0.5672, Acc.house: 0.6604, Acc.sea: 0.6771, Acc.mirror: 0.8756, Acc.rug: 0.7069, Acc.field: 0.7432, Acc.armchair: 0.7535, Acc.seat: 0.8896, Acc.fence: 0.7554, Acc.desk: 0.8015, Acc.rock: 0.8651, Acc.wardrobe: 0.8437, Acc.lamp: 0.8319, Acc.bathtub: 0.9002, Acc.railing: 0.5728, Acc.cushion: 0.8329, Acc.base: 0.5994, Acc.box: 0.5447, Acc.column: 0.7176, Acc.signboard: 0.5888, Acc.chest of drawers: 0.7352, Acc.counter: 0.5934, Acc.sand: 0.8233, Acc.sink: 0.8519, Acc.skyscraper: 0.7790, Acc.fireplace: 0.9352, Acc.refrigerator: 0.9223, Acc.grandstand: 0.8784, Acc.path: 0.3586, Acc.stairs: 0.4672, Acc.runway: 0.9325, Acc.case: 0.6746, Acc.pool table: 0.9865, Acc.pillow: 0.5842, Acc.screen door: 0.5354, Acc.stairway: 0.6420, Acc.river: 0.3241, Acc.bridge: 0.8869, Acc.bookcase: 0.6008, Acc.blind: 0.5460, Acc.coffee table: 0.8738, Acc.toilet: 0.9570, Acc.flower: 0.5645, Acc.book: 0.7447, Acc.hill: 0.0759, Acc.bench: 0.7363, Acc.countertop: 0.8584, Acc.stove: 0.9128, Acc.palm: 0.7728, Acc.kitchen island: 0.8527, Acc.computer: 0.8429, Acc.swivel chair: 0.6984, Acc.boat: 0.9021, Acc.bar: 0.7948, Acc.arcade machine: 0.9888, Acc.hovel: 0.2119, Acc.bus: 0.9597, Acc.towel: 0.8770, Acc.light: 0.6640, Acc.truck: 0.5462, Acc.tower: 0.0488, Acc.chandelier: 0.8528, Acc.awning: 0.5855, Acc.streetlight: 0.5161, Acc.booth: 0.7786, Acc.television receiver: 0.8795, Acc.airplane: 0.7043, Acc.dirt track: 0.0010, Acc.apparel: 0.6561, Acc.pole: 0.2324, Acc.land: 0.0016, Acc.bannister: 0.2808, Acc.escalator: 0.8681, Acc.ottoman: 0.7789, Acc.bottle: 0.6204, Acc.buffet: 0.8147, Acc.poster: 0.4531, Acc.stage: 0.4826, Acc.van: 0.4544, Acc.ship: 0.8368, Acc.fountain: 0.4805, Acc.conveyer belt: 0.9820, Acc.canopy: 0.6741, Acc.washer: 0.9347, Acc.plaything: 0.4346, Acc.swimming pool: 0.8144, Acc.stool: 0.5831, Acc.barrel: 0.7595, Acc.basket: 0.5734, Acc.waterfall: 0.7456, Acc.tent: 0.9720, Acc.bag: 0.3654, Acc.minibike: 0.9332, Acc.cradle: 0.9744, Acc.oven: 0.6838, Acc.ball: 0.0855, Acc.food: 0.8007, Acc.step: 0.2802, Acc.tank: 0.7488, Acc.trade name: 0.4422, Acc.microwave: 0.9532, Acc.pot: 0.6377, Acc.animal: 0.7159, Acc.bicycle: 0.7800, Acc.lake: 0.6343, Acc.dishwasher: 0.8301, Acc.screen: 0.7482, Acc.blanket: 0.2545, Acc.sculpture: 0.8625, Acc.hood: 0.7880, Acc.sconce: 0.7162, Acc.vase: 0.6390, Acc.traffic light: 0.6691, Acc.tray: 0.2757, Acc.ashcan: 0.5778, Acc.fan: 0.8175, Acc.pier: 0.3863, Acc.crt screen: 0.3185, Acc.plate: 0.7372, Acc.monitor: 0.8850, Acc.bulletin board: 0.7337, Acc.shower: 0.0741, Acc.radiator: 0.7584, Acc.glass: 0.1720, Acc.clock: 0.6249, Acc.flag: 0.7438 +2024-06-18 11:25:24,086 - mmseg - INFO - Iter [15050/80000] lr: 3.248e-05, eta: 1 day, 14:34:06, time: 4.189, data_time: 2.215, memory: 72263, decode.loss_ce: 0.3382, decode.acc_seg: 86.3440, aux.loss_ce: 0.1364, aux.acc_seg: 86.1813, loss: 0.4746 +2024-06-18 11:27:02,988 - mmseg - INFO - Iter [15100/80000] lr: 3.245e-05, eta: 1 day, 14:31:45, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3586, decode.acc_seg: 85.9644, aux.loss_ce: 0.1442, aux.acc_seg: 85.7241, loss: 0.5027 +2024-06-18 11:28:42,019 - mmseg - INFO - Iter [15150/80000] lr: 3.243e-05, eta: 1 day, 14:29:25, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3467, decode.acc_seg: 86.3705, aux.loss_ce: 0.1401, aux.acc_seg: 86.3404, loss: 0.4868 +2024-06-18 11:30:23,198 - mmseg - INFO - Iter [15200/80000] lr: 3.240e-05, eta: 1 day, 14:27:14, time: 2.024, data_time: 0.053, memory: 72263, decode.loss_ce: 0.3238, decode.acc_seg: 87.0726, aux.loss_ce: 0.1321, aux.acc_seg: 86.8900, loss: 0.4559 +2024-06-18 11:32:02,105 - mmseg - INFO - Iter [15250/80000] lr: 3.238e-05, eta: 1 day, 14:24:53, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3308, decode.acc_seg: 86.5749, aux.loss_ce: 0.1329, aux.acc_seg: 86.4281, loss: 0.4637 +2024-06-18 11:33:41,071 - mmseg - INFO - Iter [15300/80000] lr: 3.235e-05, eta: 1 day, 14:22:33, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3123, decode.acc_seg: 86.9856, aux.loss_ce: 0.1257, aux.acc_seg: 86.9494, loss: 0.4380 +2024-06-18 11:35:19,949 - mmseg - INFO - Iter [15350/80000] lr: 3.233e-05, eta: 1 day, 14:20:13, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3270, decode.acc_seg: 86.9348, aux.loss_ce: 0.1328, aux.acc_seg: 86.7547, loss: 0.4598 +2024-06-18 11:36:58,854 - mmseg - INFO - Iter [15400/80000] lr: 3.230e-05, eta: 1 day, 14:17:54, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3381, decode.acc_seg: 86.7643, aux.loss_ce: 0.1374, aux.acc_seg: 86.5182, loss: 0.4754 +2024-06-18 11:38:37,893 - mmseg - INFO - Iter [15450/80000] lr: 3.228e-05, eta: 1 day, 14:15:35, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3172, decode.acc_seg: 86.8215, aux.loss_ce: 0.1295, aux.acc_seg: 86.7262, loss: 0.4467 +2024-06-18 11:40:16,904 - mmseg - INFO - Iter [15500/80000] lr: 3.225e-05, eta: 1 day, 14:13:16, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3361, decode.acc_seg: 86.8205, aux.loss_ce: 0.1358, aux.acc_seg: 86.6010, loss: 0.4719 +2024-06-18 11:41:55,794 - mmseg - INFO - Iter [15550/80000] lr: 3.223e-05, eta: 1 day, 14:10:57, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3237, decode.acc_seg: 87.1953, aux.loss_ce: 0.1308, aux.acc_seg: 86.9486, loss: 0.4545 +2024-06-18 11:43:34,761 - mmseg - INFO - Iter [15600/80000] lr: 3.220e-05, eta: 1 day, 14:08:39, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3027, decode.acc_seg: 87.8547, aux.loss_ce: 0.1238, aux.acc_seg: 87.5584, loss: 0.4265 +2024-06-18 11:45:13,637 - mmseg - INFO - Iter [15650/80000] lr: 3.218e-05, eta: 1 day, 14:06:21, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3212, decode.acc_seg: 86.9247, aux.loss_ce: 0.1305, aux.acc_seg: 86.7227, loss: 0.4517 +2024-06-18 11:46:52,544 - mmseg - INFO - Iter [15700/80000] lr: 3.215e-05, eta: 1 day, 14:04:03, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3332, decode.acc_seg: 86.8463, aux.loss_ce: 0.1353, aux.acc_seg: 86.5517, loss: 0.4684 +2024-06-18 11:48:31,441 - mmseg - INFO - Iter [15750/80000] lr: 3.213e-05, eta: 1 day, 14:01:45, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3263, decode.acc_seg: 86.4584, aux.loss_ce: 0.1313, aux.acc_seg: 86.3888, loss: 0.4576 +2024-06-18 11:50:10,402 - mmseg - INFO - Iter [15800/80000] lr: 3.210e-05, eta: 1 day, 13:59:27, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3257, decode.acc_seg: 86.6075, aux.loss_ce: 0.1328, aux.acc_seg: 86.3965, loss: 0.4584 +2024-06-18 11:51:49,319 - mmseg - INFO - Iter [15850/80000] lr: 3.208e-05, eta: 1 day, 13:57:10, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3191, decode.acc_seg: 86.9415, aux.loss_ce: 0.1289, aux.acc_seg: 86.9732, loss: 0.4480 +2024-06-18 11:53:28,282 - mmseg - INFO - Iter [15900/80000] lr: 3.205e-05, eta: 1 day, 13:54:53, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3263, decode.acc_seg: 87.1541, aux.loss_ce: 0.1323, aux.acc_seg: 86.9283, loss: 0.4585 +2024-06-18 11:55:07,199 - mmseg - INFO - Iter [15950/80000] lr: 3.203e-05, eta: 1 day, 13:52:36, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3094, decode.acc_seg: 87.7823, aux.loss_ce: 0.1255, aux.acc_seg: 87.6171, loss: 0.4348 +2024-06-18 11:56:46,111 - mmseg - INFO - Saving checkpoint at 16000 iterations +2024-06-18 11:58:10,584 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 11:58:10,584 - mmseg - INFO - Iter [16000/80000] lr: 3.200e-05, eta: 1 day, 13:55:58, time: 3.668, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3260, decode.acc_seg: 86.8761, aux.loss_ce: 0.1319, aux.acc_seg: 86.7270, loss: 0.4580 +2024-06-18 11:59:59,831 - mmseg - INFO - per class results: +2024-06-18 11:59:59,837 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.69 | 87.74 | +| building | 82.51 | 89.14 | +| sky | 94.56 | 97.43 | +| floor | 83.95 | 89.42 | +| tree | 76.9 | 89.99 | +| ceiling | 85.96 | 94.33 | +| road | 83.68 | 92.69 | +| bed | 91.35 | 96.01 | +| windowpane | 64.57 | 83.43 | +| grass | 68.23 | 81.88 | +| cabinet | 66.25 | 78.0 | +| sidewalk | 68.66 | 81.71 | +| person | 84.94 | 94.54 | +| earth | 35.98 | 46.59 | +| door | 56.96 | 68.59 | +| table | 66.48 | 82.88 | +| mountain | 62.62 | 74.97 | +| plant | 57.09 | 73.64 | +| curtain | 78.73 | 89.16 | +| chair | 63.78 | 75.64 | +| car | 87.12 | 93.39 | +| water | 65.53 | 83.46 | +| painting | 77.39 | 89.18 | +| sofa | 80.46 | 91.92 | +| shelf | 49.08 | 67.7 | +| house | 47.88 | 89.31 | +| sea | 69.32 | 77.08 | +| mirror | 75.85 | 81.81 | +| rug | 69.81 | 86.32 | +| field | 27.88 | 56.18 | +| armchair | 64.12 | 78.76 | +| seat | 66.71 | 89.3 | +| fence | 52.88 | 64.99 | +| desk | 50.7 | 82.33 | +| rock | 59.58 | 79.78 | +| wardrobe | 51.94 | 71.48 | +| lamp | 70.58 | 85.05 | +| bathtub | 86.36 | 90.17 | +| railing | 42.23 | 60.17 | +| cushion | 61.06 | 83.59 | +| base | 31.92 | 40.31 | +| box | 37.18 | 51.48 | +| column | 53.55 | 71.4 | +| signboard | 37.97 | 48.53 | +| chest of drawers | 48.9 | 69.44 | +| counter | 40.55 | 48.92 | +| sand | 39.83 | 56.11 | +| sink | 78.38 | 87.89 | +| skyscraper | 52.4 | 64.23 | +| fireplace | 74.42 | 94.0 | +| refrigerator | 81.48 | 90.95 | +| grandstand | 50.17 | 75.07 | +| path | 20.55 | 22.25 | +| stairs | 38.54 | 44.24 | +| runway | 68.54 | 89.45 | +| case | 58.99 | 92.85 | +| pool table | 93.1 | 98.56 | +| pillow | 52.18 | 56.22 | +| screen door | 78.92 | 85.79 | +| stairway | 49.37 | 66.2 | +| river | 18.42 | 32.43 | +| bridge | 78.06 | 85.1 | +| bookcase | 38.5 | 49.06 | +| blind | 37.79 | 39.73 | +| coffee table | 67.13 | 86.68 | +| toilet | 87.89 | 91.77 | +| flower | 41.08 | 53.89 | +| book | 53.71 | 77.82 | +| hill | 4.99 | 5.84 | +| bench | 61.28 | 70.39 | +| countertop | 65.94 | 85.44 | +| stove | 84.34 | 91.58 | +| palm | 49.42 | 83.06 | +| kitchen island | 21.68 | 26.05 | +| computer | 75.48 | 92.64 | +| swivel chair | 46.29 | 62.72 | +| boat | 49.62 | 94.05 | +| bar | 67.6 | 83.53 | +| arcade machine | 86.51 | 98.81 | +| hovel | 36.76 | 42.86 | +| bus | 90.84 | 97.05 | +| towel | 75.38 | 84.82 | +| light | 58.44 | 72.13 | +| truck | 47.4 | 62.91 | +| tower | 33.0 | 91.13 | +| chandelier | 69.15 | 86.91 | +| awning | 39.89 | 51.66 | +| streetlight | 31.41 | 42.3 | +| booth | 34.95 | 52.61 | +| television receiver | 78.98 | 85.69 | +| airplane | 81.38 | 90.52 | +| dirt track | 0.0 | 0.0 | +| apparel | 57.85 | 89.27 | +| pole | 22.57 | 27.11 | +| land | 2.18 | 3.5 | +| bannister | 19.17 | 22.81 | +| escalator | 62.73 | 87.13 | +| ottoman | 53.78 | 79.96 | +| bottle | 41.95 | 65.94 | +| buffet | 65.51 | 80.85 | +| poster | 37.57 | 68.84 | +| stage | 23.48 | 49.37 | +| van | 48.54 | 68.48 | +| ship | 81.16 | 84.74 | +| fountain | 23.45 | 23.51 | +| conveyer belt | 80.68 | 94.75 | +| canopy | 53.56 | 77.15 | +| washer | 84.53 | 89.67 | +| plaything | 25.02 | 53.64 | +| swimming pool | 56.91 | 83.88 | +| stool | 28.92 | 81.67 | +| barrel | 59.2 | 64.48 | +| basket | 39.94 | 59.88 | +| waterfall | 56.55 | 70.31 | +| tent | 96.77 | 98.12 | +| bag | 24.54 | 28.35 | +| minibike | 74.55 | 83.95 | +| cradle | 86.33 | 97.53 | +| oven | 59.74 | 72.88 | +| ball | 50.39 | 73.97 | +| food | 44.0 | 44.78 | +| step | 11.9 | 16.32 | +| tank | 54.56 | 66.08 | +| trade name | 17.45 | 18.51 | +| microwave | 88.49 | 95.79 | +| pot | 55.13 | 65.84 | +| animal | 61.62 | 63.37 | +| bicycle | 54.65 | 86.84 | +| lake | 58.12 | 59.86 | +| dishwasher | 68.53 | 83.5 | +| screen | 64.1 | 91.01 | +| blanket | 35.91 | 51.22 | +| sculpture | 71.57 | 86.7 | +| hood | 57.88 | 65.65 | +| sconce | 57.09 | 66.62 | +| vase | 48.03 | 67.7 | +| traffic light | 32.43 | 63.26 | +| tray | 12.34 | 16.89 | +| ashcan | 48.27 | 57.13 | +| fan | 67.09 | 78.48 | +| pier | 36.76 | 43.2 | +| crt screen | 0.08 | 0.08 | +| plate | 50.28 | 84.96 | +| monitor | 53.15 | 86.3 | +| bulletin board | 41.02 | 83.44 | +| shower | 9.93 | 10.71 | +| radiator | 68.32 | 77.21 | +| glass | 18.32 | 19.81 | +| clock | 49.31 | 57.81 | +| flag | 65.65 | 75.44 | ++---------------------+-------+-------+ +2024-06-18 11:59:59,837 - mmseg - INFO - Summary: +2024-06-18 11:59:59,837 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.12 | 55.53 | 69.74 | ++-------+-------+-------+ +2024-06-18 11:59:59,838 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 11:59:59,838 - mmseg - INFO - Iter(val) [250] aAcc: 0.8512, mIoU: 0.5553, mAcc: 0.6974, IoU.wall: 0.8069, IoU.building: 0.8251, IoU.sky: 0.9456, IoU.floor: 0.8395, IoU.tree: 0.7690, IoU.ceiling: 0.8596, IoU.road: 0.8368, IoU.bed : 0.9135, IoU.windowpane: 0.6457, IoU.grass: 0.6823, IoU.cabinet: 0.6625, IoU.sidewalk: 0.6866, IoU.person: 0.8494, IoU.earth: 0.3598, IoU.door: 0.5696, IoU.table: 0.6648, IoU.mountain: 0.6262, IoU.plant: 0.5709, IoU.curtain: 0.7873, IoU.chair: 0.6378, IoU.car: 0.8712, IoU.water: 0.6553, IoU.painting: 0.7739, IoU.sofa: 0.8046, IoU.shelf: 0.4908, IoU.house: 0.4788, IoU.sea: 0.6932, IoU.mirror: 0.7585, IoU.rug: 0.6981, IoU.field: 0.2788, IoU.armchair: 0.6412, IoU.seat: 0.6671, IoU.fence: 0.5288, IoU.desk: 0.5070, IoU.rock: 0.5958, IoU.wardrobe: 0.5194, IoU.lamp: 0.7058, IoU.bathtub: 0.8636, IoU.railing: 0.4223, IoU.cushion: 0.6106, IoU.base: 0.3192, IoU.box: 0.3718, IoU.column: 0.5355, IoU.signboard: 0.3797, IoU.chest of drawers: 0.4890, IoU.counter: 0.4055, IoU.sand: 0.3983, IoU.sink: 0.7838, IoU.skyscraper: 0.5240, IoU.fireplace: 0.7442, IoU.refrigerator: 0.8148, IoU.grandstand: 0.5017, IoU.path: 0.2055, IoU.stairs: 0.3854, IoU.runway: 0.6854, IoU.case: 0.5899, IoU.pool table: 0.9310, IoU.pillow: 0.5218, IoU.screen door: 0.7892, IoU.stairway: 0.4937, IoU.river: 0.1842, IoU.bridge: 0.7806, IoU.bookcase: 0.3850, IoU.blind: 0.3779, IoU.coffee table: 0.6713, IoU.toilet: 0.8789, IoU.flower: 0.4108, IoU.book: 0.5371, IoU.hill: 0.0499, IoU.bench: 0.6128, IoU.countertop: 0.6594, IoU.stove: 0.8434, IoU.palm: 0.4942, IoU.kitchen island: 0.2168, IoU.computer: 0.7548, IoU.swivel chair: 0.4629, IoU.boat: 0.4962, IoU.bar: 0.6760, IoU.arcade machine: 0.8651, IoU.hovel: 0.3676, IoU.bus: 0.9084, IoU.towel: 0.7538, IoU.light: 0.5844, IoU.truck: 0.4740, IoU.tower: 0.3300, IoU.chandelier: 0.6915, IoU.awning: 0.3989, IoU.streetlight: 0.3141, IoU.booth: 0.3495, IoU.television receiver: 0.7898, IoU.airplane: 0.8138, IoU.dirt track: 0.0000, IoU.apparel: 0.5785, IoU.pole: 0.2257, IoU.land: 0.0218, IoU.bannister: 0.1917, IoU.escalator: 0.6273, IoU.ottoman: 0.5378, IoU.bottle: 0.4195, IoU.buffet: 0.6551, IoU.poster: 0.3757, IoU.stage: 0.2348, IoU.van: 0.4854, IoU.ship: 0.8116, IoU.fountain: 0.2345, IoU.conveyer belt: 0.8068, IoU.canopy: 0.5356, IoU.washer: 0.8453, IoU.plaything: 0.2502, IoU.swimming pool: 0.5691, IoU.stool: 0.2892, IoU.barrel: 0.5920, IoU.basket: 0.3994, IoU.waterfall: 0.5655, IoU.tent: 0.9677, IoU.bag: 0.2454, IoU.minibike: 0.7455, IoU.cradle: 0.8633, IoU.oven: 0.5974, IoU.ball: 0.5039, IoU.food: 0.4400, IoU.step: 0.1190, IoU.tank: 0.5456, IoU.trade name: 0.1745, IoU.microwave: 0.8849, IoU.pot: 0.5513, IoU.animal: 0.6162, IoU.bicycle: 0.5465, IoU.lake: 0.5812, IoU.dishwasher: 0.6853, IoU.screen: 0.6410, IoU.blanket: 0.3591, IoU.sculpture: 0.7157, IoU.hood: 0.5788, IoU.sconce: 0.5709, IoU.vase: 0.4803, IoU.traffic light: 0.3243, IoU.tray: 0.1234, IoU.ashcan: 0.4827, IoU.fan: 0.6709, IoU.pier: 0.3676, IoU.crt screen: 0.0008, IoU.plate: 0.5028, IoU.monitor: 0.5315, IoU.bulletin board: 0.4102, IoU.shower: 0.0993, IoU.radiator: 0.6832, IoU.glass: 0.1832, IoU.clock: 0.4931, IoU.flag: 0.6565, Acc.wall: 0.8774, Acc.building: 0.8914, Acc.sky: 0.9743, Acc.floor: 0.8942, Acc.tree: 0.8999, Acc.ceiling: 0.9433, Acc.road: 0.9269, Acc.bed : 0.9601, Acc.windowpane: 0.8343, Acc.grass: 0.8188, Acc.cabinet: 0.7800, Acc.sidewalk: 0.8171, Acc.person: 0.9454, Acc.earth: 0.4659, Acc.door: 0.6859, Acc.table: 0.8288, Acc.mountain: 0.7497, Acc.plant: 0.7364, Acc.curtain: 0.8916, Acc.chair: 0.7564, Acc.car: 0.9339, Acc.water: 0.8346, Acc.painting: 0.8918, Acc.sofa: 0.9192, Acc.shelf: 0.6770, Acc.house: 0.8931, Acc.sea: 0.7708, Acc.mirror: 0.8181, Acc.rug: 0.8632, Acc.field: 0.5618, Acc.armchair: 0.7876, Acc.seat: 0.8930, Acc.fence: 0.6499, Acc.desk: 0.8233, Acc.rock: 0.7978, Acc.wardrobe: 0.7148, Acc.lamp: 0.8505, Acc.bathtub: 0.9017, Acc.railing: 0.6017, Acc.cushion: 0.8359, Acc.base: 0.4031, Acc.box: 0.5148, Acc.column: 0.7140, Acc.signboard: 0.4853, Acc.chest of drawers: 0.6944, Acc.counter: 0.4892, Acc.sand: 0.5611, Acc.sink: 0.8789, Acc.skyscraper: 0.6423, Acc.fireplace: 0.9400, Acc.refrigerator: 0.9095, Acc.grandstand: 0.7507, Acc.path: 0.2225, Acc.stairs: 0.4424, Acc.runway: 0.8945, Acc.case: 0.9285, Acc.pool table: 0.9856, Acc.pillow: 0.5622, Acc.screen door: 0.8579, Acc.stairway: 0.6620, Acc.river: 0.3243, Acc.bridge: 0.8510, Acc.bookcase: 0.4906, Acc.blind: 0.3973, Acc.coffee table: 0.8668, Acc.toilet: 0.9177, Acc.flower: 0.5389, Acc.book: 0.7782, Acc.hill: 0.0584, Acc.bench: 0.7039, Acc.countertop: 0.8544, Acc.stove: 0.9158, Acc.palm: 0.8306, Acc.kitchen island: 0.2605, Acc.computer: 0.9264, Acc.swivel chair: 0.6272, Acc.boat: 0.9405, Acc.bar: 0.8353, Acc.arcade machine: 0.9881, Acc.hovel: 0.4286, Acc.bus: 0.9705, Acc.towel: 0.8482, Acc.light: 0.7213, Acc.truck: 0.6291, Acc.tower: 0.9113, Acc.chandelier: 0.8691, Acc.awning: 0.5166, Acc.streetlight: 0.4230, Acc.booth: 0.5261, Acc.television receiver: 0.8569, Acc.airplane: 0.9052, Acc.dirt track: 0.0000, Acc.apparel: 0.8927, Acc.pole: 0.2711, Acc.land: 0.0350, Acc.bannister: 0.2281, Acc.escalator: 0.8713, Acc.ottoman: 0.7996, Acc.bottle: 0.6594, Acc.buffet: 0.8085, Acc.poster: 0.6884, Acc.stage: 0.4937, Acc.van: 0.6848, Acc.ship: 0.8474, Acc.fountain: 0.2351, Acc.conveyer belt: 0.9475, Acc.canopy: 0.7715, Acc.washer: 0.8967, Acc.plaything: 0.5364, Acc.swimming pool: 0.8388, Acc.stool: 0.8167, Acc.barrel: 0.6448, Acc.basket: 0.5988, Acc.waterfall: 0.7031, Acc.tent: 0.9812, Acc.bag: 0.2835, Acc.minibike: 0.8395, Acc.cradle: 0.9753, Acc.oven: 0.7288, Acc.ball: 0.7397, Acc.food: 0.4478, Acc.step: 0.1632, Acc.tank: 0.6608, Acc.trade name: 0.1851, Acc.microwave: 0.9579, Acc.pot: 0.6584, Acc.animal: 0.6337, Acc.bicycle: 0.8684, Acc.lake: 0.5986, Acc.dishwasher: 0.8350, Acc.screen: 0.9101, Acc.blanket: 0.5122, Acc.sculpture: 0.8670, Acc.hood: 0.6565, Acc.sconce: 0.6662, Acc.vase: 0.6770, Acc.traffic light: 0.6326, Acc.tray: 0.1689, Acc.ashcan: 0.5713, Acc.fan: 0.7848, Acc.pier: 0.4320, Acc.crt screen: 0.0008, Acc.plate: 0.8496, Acc.monitor: 0.8630, Acc.bulletin board: 0.8344, Acc.shower: 0.1071, Acc.radiator: 0.7721, Acc.glass: 0.1981, Acc.clock: 0.5781, Acc.flag: 0.7544 +2024-06-18 12:01:39,231 - mmseg - INFO - Iter [16050/80000] lr: 3.198e-05, eta: 1 day, 14:00:57, time: 4.173, data_time: 2.203, memory: 72263, decode.loss_ce: 0.3479, decode.acc_seg: 86.2035, aux.loss_ce: 0.1409, aux.acc_seg: 86.0057, loss: 0.4888 +2024-06-18 12:03:18,284 - mmseg - INFO - Iter [16100/80000] lr: 3.195e-05, eta: 1 day, 13:58:39, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3238, decode.acc_seg: 86.7658, aux.loss_ce: 0.1313, aux.acc_seg: 86.5084, loss: 0.4551 +2024-06-18 12:04:57,195 - mmseg - INFO - Iter [16150/80000] lr: 3.193e-05, eta: 1 day, 13:56:20, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3267, decode.acc_seg: 87.2623, aux.loss_ce: 0.1331, aux.acc_seg: 87.1323, loss: 0.4599 +2024-06-18 12:06:36,215 - mmseg - INFO - Iter [16200/80000] lr: 3.190e-05, eta: 1 day, 13:54:02, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3360, decode.acc_seg: 87.0182, aux.loss_ce: 0.1355, aux.acc_seg: 87.0433, loss: 0.4715 +2024-06-18 12:08:15,166 - mmseg - INFO - Iter [16250/80000] lr: 3.188e-05, eta: 1 day, 13:51:43, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3213, decode.acc_seg: 87.0545, aux.loss_ce: 0.1301, aux.acc_seg: 86.9672, loss: 0.4514 +2024-06-18 12:09:54,121 - mmseg - INFO - Iter [16300/80000] lr: 3.185e-05, eta: 1 day, 13:49:25, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3272, decode.acc_seg: 86.8739, aux.loss_ce: 0.1336, aux.acc_seg: 86.5304, loss: 0.4608 +2024-06-18 12:11:33,053 - mmseg - INFO - Iter [16350/80000] lr: 3.183e-05, eta: 1 day, 13:47:08, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3272, decode.acc_seg: 86.7223, aux.loss_ce: 0.1319, aux.acc_seg: 86.5500, loss: 0.4591 +2024-06-18 12:13:12,087 - mmseg - INFO - Iter [16400/80000] lr: 3.180e-05, eta: 1 day, 13:44:50, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3302, decode.acc_seg: 87.0639, aux.loss_ce: 0.1336, aux.acc_seg: 86.9270, loss: 0.4637 +2024-06-18 12:14:53,777 - mmseg - INFO - Iter [16450/80000] lr: 3.178e-05, eta: 1 day, 13:42:44, time: 2.034, data_time: 0.061, memory: 72263, decode.loss_ce: 0.3121, decode.acc_seg: 87.1127, aux.loss_ce: 0.1267, aux.acc_seg: 86.9898, loss: 0.4388 +2024-06-18 12:16:32,822 - mmseg - INFO - Iter [16500/80000] lr: 3.175e-05, eta: 1 day, 13:40:27, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3104, decode.acc_seg: 87.5577, aux.loss_ce: 0.1270, aux.acc_seg: 87.2052, loss: 0.4374 +2024-06-18 12:18:11,851 - mmseg - INFO - Iter [16550/80000] lr: 3.173e-05, eta: 1 day, 13:38:10, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3137, decode.acc_seg: 87.5584, aux.loss_ce: 0.1263, aux.acc_seg: 87.5744, loss: 0.4400 +2024-06-18 12:19:50,795 - mmseg - INFO - Iter [16600/80000] lr: 3.170e-05, eta: 1 day, 13:35:54, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2994, decode.acc_seg: 87.9929, aux.loss_ce: 0.1220, aux.acc_seg: 87.7283, loss: 0.4214 +2024-06-18 12:21:29,800 - mmseg - INFO - Iter [16650/80000] lr: 3.168e-05, eta: 1 day, 13:33:38, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3059, decode.acc_seg: 87.6978, aux.loss_ce: 0.1241, aux.acc_seg: 87.4390, loss: 0.4299 +2024-06-18 12:23:08,774 - mmseg - INFO - Iter [16700/80000] lr: 3.165e-05, eta: 1 day, 13:31:22, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3172, decode.acc_seg: 87.4018, aux.loss_ce: 0.1287, aux.acc_seg: 87.2693, loss: 0.4460 +2024-06-18 12:24:47,655 - mmseg - INFO - Iter [16750/80000] lr: 3.163e-05, eta: 1 day, 13:29:05, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3064, decode.acc_seg: 87.9614, aux.loss_ce: 0.1244, aux.acc_seg: 87.7247, loss: 0.4308 +2024-06-18 12:26:26,628 - mmseg - INFO - Iter [16800/80000] lr: 3.160e-05, eta: 1 day, 13:26:50, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3032, decode.acc_seg: 87.8537, aux.loss_ce: 0.1224, aux.acc_seg: 87.7861, loss: 0.4256 +2024-06-18 12:28:05,689 - mmseg - INFO - Iter [16850/80000] lr: 3.158e-05, eta: 1 day, 13:24:35, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3211, decode.acc_seg: 86.9822, aux.loss_ce: 0.1316, aux.acc_seg: 86.6961, loss: 0.4527 +2024-06-18 12:29:44,619 - mmseg - INFO - Iter [16900/80000] lr: 3.155e-05, eta: 1 day, 13:22:19, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3296, decode.acc_seg: 87.1752, aux.loss_ce: 0.1332, aux.acc_seg: 86.9777, loss: 0.4628 +2024-06-18 12:31:23,559 - mmseg - INFO - Iter [16950/80000] lr: 3.153e-05, eta: 1 day, 13:20:04, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3181, decode.acc_seg: 87.2672, aux.loss_ce: 0.1282, aux.acc_seg: 87.0439, loss: 0.4462 +2024-06-18 12:33:02,572 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 12:33:02,572 - mmseg - INFO - Iter [17000/80000] lr: 3.150e-05, eta: 1 day, 13:17:49, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3091, decode.acc_seg: 87.5335, aux.loss_ce: 0.1252, aux.acc_seg: 87.2656, loss: 0.4343 +2024-06-18 12:34:52,173 - mmseg - INFO - per class results: +2024-06-18 12:34:52,179 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.81 | 87.66 | +| building | 84.58 | 91.06 | +| sky | 94.58 | 96.9 | +| floor | 84.13 | 90.39 | +| tree | 76.78 | 88.05 | +| ceiling | 86.66 | 94.5 | +| road | 84.16 | 89.63 | +| bed | 91.82 | 97.66 | +| windowpane | 64.58 | 84.75 | +| grass | 68.55 | 85.23 | +| cabinet | 62.81 | 72.05 | +| sidewalk | 69.33 | 86.45 | +| person | 84.92 | 91.13 | +| earth | 37.12 | 48.62 | +| door | 56.5 | 71.17 | +| table | 65.26 | 77.31 | +| mountain | 62.91 | 79.59 | +| plant | 59.28 | 70.24 | +| curtain | 71.38 | 82.96 | +| chair | 66.39 | 78.56 | +| car | 86.84 | 93.12 | +| water | 68.25 | 83.38 | +| painting | 80.11 | 90.63 | +| sofa | 80.53 | 89.83 | +| shelf | 50.2 | 68.79 | +| house | 54.44 | 89.98 | +| sea | 74.11 | 83.78 | +| mirror | 78.46 | 86.73 | +| rug | 70.78 | 87.45 | +| field | 29.88 | 54.8 | +| armchair | 61.72 | 74.34 | +| seat | 65.48 | 89.02 | +| fence | 50.06 | 69.05 | +| desk | 53.25 | 82.74 | +| rock | 60.1 | 83.05 | +| wardrobe | 50.24 | 73.73 | +| lamp | 71.21 | 83.01 | +| bathtub | 86.25 | 92.12 | +| railing | 37.88 | 49.19 | +| cushion | 67.93 | 79.5 | +| base | 39.89 | 61.41 | +| box | 30.33 | 38.21 | +| column | 57.64 | 72.27 | +| signboard | 42.55 | 58.56 | +| chest of drawers | 43.1 | 81.66 | +| counter | 42.29 | 51.81 | +| sand | 47.74 | 66.13 | +| sink | 80.35 | 88.96 | +| skyscraper | 52.56 | 73.72 | +| fireplace | 74.72 | 93.45 | +| refrigerator | 82.76 | 93.86 | +| grandstand | 50.16 | 86.84 | +| path | 30.13 | 38.98 | +| stairs | 36.19 | 49.49 | +| runway | 65.33 | 85.52 | +| case | 74.9 | 81.53 | +| pool table | 93.56 | 98.57 | +| pillow | 59.6 | 69.29 | +| screen door | 64.18 | 94.02 | +| stairway | 34.71 | 38.87 | +| river | 25.98 | 46.89 | +| bridge | 68.38 | 85.76 | +| bookcase | 42.31 | 59.54 | +| blind | 37.86 | 39.36 | +| coffee table | 58.36 | 87.6 | +| toilet | 89.79 | 95.05 | +| flower | 43.77 | 56.99 | +| book | 52.64 | 79.01 | +| hill | 10.67 | 22.25 | +| bench | 58.4 | 72.01 | +| countertop | 66.75 | 81.78 | +| stove | 82.14 | 92.38 | +| palm | 43.95 | 87.66 | +| kitchen island | 42.52 | 84.19 | +| computer | 74.88 | 90.54 | +| swivel chair | 54.07 | 80.57 | +| boat | 66.78 | 89.85 | +| bar | 58.85 | 74.37 | +| arcade machine | 89.03 | 99.01 | +| hovel | 20.17 | 22.49 | +| bus | 91.41 | 97.34 | +| towel | 76.06 | 86.37 | +| light | 52.34 | 56.29 | +| truck | 43.45 | 66.03 | +| tower | 29.13 | 61.64 | +| chandelier | 71.64 | 84.35 | +| awning | 48.97 | 59.31 | +| streetlight | 28.14 | 34.16 | +| booth | 42.33 | 71.36 | +| television receiver | 77.76 | 88.44 | +| airplane | 73.36 | 82.73 | +| dirt track | 1.87 | 10.54 | +| apparel | 57.54 | 88.77 | +| pole | 29.47 | 39.25 | +| land | 0.0 | 0.0 | +| bannister | 18.23 | 24.73 | +| escalator | 56.07 | 89.17 | +| ottoman | 61.12 | 77.02 | +| bottle | 38.03 | 46.77 | +| buffet | 45.63 | 53.55 | +| poster | 40.91 | 53.46 | +| stage | 24.17 | 49.11 | +| van | 44.15 | 63.32 | +| ship | 85.87 | 92.15 | +| fountain | 33.45 | 34.07 | +| conveyer belt | 81.01 | 95.95 | +| canopy | 47.22 | 62.82 | +| washer | 87.52 | 93.12 | +| plaything | 32.06 | 46.18 | +| swimming pool | 54.25 | 81.01 | +| stool | 53.41 | 58.91 | +| barrel | 42.88 | 85.87 | +| basket | 41.35 | 57.91 | +| waterfall | 65.69 | 88.97 | +| tent | 96.3 | 98.26 | +| bag | 31.17 | 38.02 | +| minibike | 73.26 | 89.9 | +| cradle | 84.28 | 95.83 | +| oven | 49.96 | 60.8 | +| ball | 53.29 | 76.94 | +| food | 69.1 | 84.45 | +| step | 14.57 | 18.01 | +| tank | 53.5 | 70.69 | +| trade name | 27.17 | 31.97 | +| microwave | 88.15 | 94.0 | +| pot | 55.75 | 64.92 | +| animal | 64.23 | 66.44 | +| bicycle | 60.22 | 77.01 | +| lake | 56.1 | 64.2 | +| dishwasher | 70.74 | 76.7 | +| screen | 60.96 | 97.45 | +| blanket | 41.87 | 58.46 | +| sculpture | 70.84 | 85.33 | +| hood | 61.13 | 71.57 | +| sconce | 55.89 | 61.8 | +| vase | 45.22 | 63.33 | +| traffic light | 33.77 | 60.38 | +| tray | 11.38 | 13.49 | +| ashcan | 46.37 | 68.39 | +| fan | 68.1 | 78.09 | +| pier | 38.27 | 42.37 | +| crt screen | 1.76 | 3.49 | +| plate | 59.91 | 80.42 | +| monitor | 26.96 | 37.45 | +| bulletin board | 56.74 | 73.93 | +| shower | 3.83 | 3.83 | +| radiator | 64.73 | 77.06 | +| glass | 19.61 | 21.8 | +| clock | 44.54 | 55.68 | +| flag | 68.23 | 77.37 | ++---------------------+-------+-------+ +2024-06-18 12:34:52,179 - mmseg - INFO - Summary: +2024-06-18 12:34:52,180 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.39 | 56.16 | 70.39 | ++-------+-------+-------+ +2024-06-18 12:34:52,180 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 12:34:52,181 - mmseg - INFO - Iter(val) [250] aAcc: 0.8539, mIoU: 0.5616, mAcc: 0.7039, IoU.wall: 0.8081, IoU.building: 0.8458, IoU.sky: 0.9458, IoU.floor: 0.8413, IoU.tree: 0.7678, IoU.ceiling: 0.8666, IoU.road: 0.8416, IoU.bed : 0.9182, IoU.windowpane: 0.6458, IoU.grass: 0.6855, IoU.cabinet: 0.6281, IoU.sidewalk: 0.6933, IoU.person: 0.8492, IoU.earth: 0.3712, IoU.door: 0.5650, IoU.table: 0.6526, IoU.mountain: 0.6291, IoU.plant: 0.5928, IoU.curtain: 0.7138, IoU.chair: 0.6639, IoU.car: 0.8684, IoU.water: 0.6825, IoU.painting: 0.8011, IoU.sofa: 0.8053, IoU.shelf: 0.5020, IoU.house: 0.5444, IoU.sea: 0.7411, IoU.mirror: 0.7846, IoU.rug: 0.7078, IoU.field: 0.2988, IoU.armchair: 0.6172, IoU.seat: 0.6548, IoU.fence: 0.5006, IoU.desk: 0.5325, IoU.rock: 0.6010, IoU.wardrobe: 0.5024, IoU.lamp: 0.7121, IoU.bathtub: 0.8625, IoU.railing: 0.3788, IoU.cushion: 0.6793, IoU.base: 0.3989, IoU.box: 0.3033, IoU.column: 0.5764, IoU.signboard: 0.4255, IoU.chest of drawers: 0.4310, IoU.counter: 0.4229, IoU.sand: 0.4774, IoU.sink: 0.8035, IoU.skyscraper: 0.5256, IoU.fireplace: 0.7472, IoU.refrigerator: 0.8276, IoU.grandstand: 0.5016, IoU.path: 0.3013, IoU.stairs: 0.3619, IoU.runway: 0.6533, IoU.case: 0.7490, IoU.pool table: 0.9356, IoU.pillow: 0.5960, IoU.screen door: 0.6418, IoU.stairway: 0.3471, IoU.river: 0.2598, IoU.bridge: 0.6838, IoU.bookcase: 0.4231, IoU.blind: 0.3786, IoU.coffee table: 0.5836, IoU.toilet: 0.8979, IoU.flower: 0.4377, IoU.book: 0.5264, IoU.hill: 0.1067, IoU.bench: 0.5840, IoU.countertop: 0.6675, IoU.stove: 0.8214, IoU.palm: 0.4395, IoU.kitchen island: 0.4252, IoU.computer: 0.7488, IoU.swivel chair: 0.5407, IoU.boat: 0.6678, IoU.bar: 0.5885, IoU.arcade machine: 0.8903, IoU.hovel: 0.2017, IoU.bus: 0.9141, IoU.towel: 0.7606, IoU.light: 0.5234, IoU.truck: 0.4345, IoU.tower: 0.2913, IoU.chandelier: 0.7164, IoU.awning: 0.4897, IoU.streetlight: 0.2814, IoU.booth: 0.4233, IoU.television receiver: 0.7776, IoU.airplane: 0.7336, IoU.dirt track: 0.0187, IoU.apparel: 0.5754, IoU.pole: 0.2947, IoU.land: 0.0000, IoU.bannister: 0.1823, IoU.escalator: 0.5607, IoU.ottoman: 0.6112, IoU.bottle: 0.3803, IoU.buffet: 0.4563, IoU.poster: 0.4091, IoU.stage: 0.2417, IoU.van: 0.4415, IoU.ship: 0.8587, IoU.fountain: 0.3345, IoU.conveyer belt: 0.8101, IoU.canopy: 0.4722, IoU.washer: 0.8752, IoU.plaything: 0.3206, IoU.swimming pool: 0.5425, IoU.stool: 0.5341, IoU.barrel: 0.4288, IoU.basket: 0.4135, IoU.waterfall: 0.6569, IoU.tent: 0.9630, IoU.bag: 0.3117, IoU.minibike: 0.7326, IoU.cradle: 0.8428, IoU.oven: 0.4996, IoU.ball: 0.5329, IoU.food: 0.6910, IoU.step: 0.1457, IoU.tank: 0.5350, IoU.trade name: 0.2717, IoU.microwave: 0.8815, IoU.pot: 0.5575, IoU.animal: 0.6423, IoU.bicycle: 0.6022, IoU.lake: 0.5610, IoU.dishwasher: 0.7074, IoU.screen: 0.6096, IoU.blanket: 0.4187, IoU.sculpture: 0.7084, IoU.hood: 0.6113, IoU.sconce: 0.5589, IoU.vase: 0.4522, IoU.traffic light: 0.3377, IoU.tray: 0.1138, IoU.ashcan: 0.4637, IoU.fan: 0.6810, IoU.pier: 0.3827, IoU.crt screen: 0.0176, IoU.plate: 0.5991, IoU.monitor: 0.2696, IoU.bulletin board: 0.5674, IoU.shower: 0.0383, IoU.radiator: 0.6473, IoU.glass: 0.1961, IoU.clock: 0.4454, IoU.flag: 0.6823, Acc.wall: 0.8766, Acc.building: 0.9106, Acc.sky: 0.9690, Acc.floor: 0.9039, Acc.tree: 0.8805, Acc.ceiling: 0.9450, Acc.road: 0.8963, Acc.bed : 0.9766, Acc.windowpane: 0.8475, Acc.grass: 0.8523, Acc.cabinet: 0.7205, Acc.sidewalk: 0.8645, Acc.person: 0.9113, Acc.earth: 0.4862, Acc.door: 0.7117, Acc.table: 0.7731, Acc.mountain: 0.7959, Acc.plant: 0.7024, Acc.curtain: 0.8296, Acc.chair: 0.7856, Acc.car: 0.9312, Acc.water: 0.8338, Acc.painting: 0.9063, Acc.sofa: 0.8983, Acc.shelf: 0.6879, Acc.house: 0.8998, Acc.sea: 0.8378, Acc.mirror: 0.8673, Acc.rug: 0.8745, Acc.field: 0.5480, Acc.armchair: 0.7434, Acc.seat: 0.8902, Acc.fence: 0.6905, Acc.desk: 0.8274, Acc.rock: 0.8305, Acc.wardrobe: 0.7373, Acc.lamp: 0.8301, Acc.bathtub: 0.9212, Acc.railing: 0.4919, Acc.cushion: 0.7950, Acc.base: 0.6141, Acc.box: 0.3821, Acc.column: 0.7227, Acc.signboard: 0.5856, Acc.chest of drawers: 0.8166, Acc.counter: 0.5181, Acc.sand: 0.6613, Acc.sink: 0.8896, Acc.skyscraper: 0.7372, Acc.fireplace: 0.9345, Acc.refrigerator: 0.9386, Acc.grandstand: 0.8684, Acc.path: 0.3898, Acc.stairs: 0.4949, Acc.runway: 0.8552, Acc.case: 0.8153, Acc.pool table: 0.9857, Acc.pillow: 0.6929, Acc.screen door: 0.9402, Acc.stairway: 0.3887, Acc.river: 0.4689, Acc.bridge: 0.8576, Acc.bookcase: 0.5954, Acc.blind: 0.3936, Acc.coffee table: 0.8760, Acc.toilet: 0.9505, Acc.flower: 0.5699, Acc.book: 0.7901, Acc.hill: 0.2225, Acc.bench: 0.7201, Acc.countertop: 0.8178, Acc.stove: 0.9238, Acc.palm: 0.8766, Acc.kitchen island: 0.8419, Acc.computer: 0.9054, Acc.swivel chair: 0.8057, Acc.boat: 0.8985, Acc.bar: 0.7437, Acc.arcade machine: 0.9901, Acc.hovel: 0.2249, Acc.bus: 0.9734, Acc.towel: 0.8637, Acc.light: 0.5629, Acc.truck: 0.6603, Acc.tower: 0.6164, Acc.chandelier: 0.8435, Acc.awning: 0.5931, Acc.streetlight: 0.3416, Acc.booth: 0.7136, Acc.television receiver: 0.8844, Acc.airplane: 0.8273, Acc.dirt track: 0.1054, Acc.apparel: 0.8877, Acc.pole: 0.3925, Acc.land: 0.0000, Acc.bannister: 0.2473, Acc.escalator: 0.8917, Acc.ottoman: 0.7702, Acc.bottle: 0.4677, Acc.buffet: 0.5355, Acc.poster: 0.5346, Acc.stage: 0.4911, Acc.van: 0.6332, Acc.ship: 0.9215, Acc.fountain: 0.3407, Acc.conveyer belt: 0.9595, Acc.canopy: 0.6282, Acc.washer: 0.9312, Acc.plaything: 0.4618, Acc.swimming pool: 0.8101, Acc.stool: 0.5891, Acc.barrel: 0.8587, Acc.basket: 0.5791, Acc.waterfall: 0.8897, Acc.tent: 0.9826, Acc.bag: 0.3802, Acc.minibike: 0.8990, Acc.cradle: 0.9583, Acc.oven: 0.6080, Acc.ball: 0.7694, Acc.food: 0.8445, Acc.step: 0.1801, Acc.tank: 0.7069, Acc.trade name: 0.3197, Acc.microwave: 0.9400, Acc.pot: 0.6492, Acc.animal: 0.6644, Acc.bicycle: 0.7701, Acc.lake: 0.6420, Acc.dishwasher: 0.7670, Acc.screen: 0.9745, Acc.blanket: 0.5846, Acc.sculpture: 0.8533, Acc.hood: 0.7157, Acc.sconce: 0.6180, Acc.vase: 0.6333, Acc.traffic light: 0.6038, Acc.tray: 0.1349, Acc.ashcan: 0.6839, Acc.fan: 0.7809, Acc.pier: 0.4237, Acc.crt screen: 0.0349, Acc.plate: 0.8042, Acc.monitor: 0.3745, Acc.bulletin board: 0.7393, Acc.shower: 0.0383, Acc.radiator: 0.7706, Acc.glass: 0.2180, Acc.clock: 0.5568, Acc.flag: 0.7737 +2024-06-18 12:36:31,513 - mmseg - INFO - Iter [17050/80000] lr: 3.148e-05, eta: 1 day, 13:22:21, time: 4.179, data_time: 2.208, memory: 72263, decode.loss_ce: 0.3105, decode.acc_seg: 87.5203, aux.loss_ce: 0.1256, aux.acc_seg: 87.3392, loss: 0.4361 +2024-06-18 12:38:10,404 - mmseg - INFO - Iter [17100/80000] lr: 3.145e-05, eta: 1 day, 13:20:05, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3040, decode.acc_seg: 87.8794, aux.loss_ce: 0.1231, aux.acc_seg: 87.6390, loss: 0.4271 +2024-06-18 12:39:49,289 - mmseg - INFO - Iter [17150/80000] lr: 3.143e-05, eta: 1 day, 13:17:49, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3108, decode.acc_seg: 87.7592, aux.loss_ce: 0.1267, aux.acc_seg: 87.5143, loss: 0.4375 +2024-06-18 12:41:28,265 - mmseg - INFO - Iter [17200/80000] lr: 3.140e-05, eta: 1 day, 13:15:33, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3337, decode.acc_seg: 86.6165, aux.loss_ce: 0.1345, aux.acc_seg: 86.4652, loss: 0.4682 +2024-06-18 12:43:07,236 - mmseg - INFO - Iter [17250/80000] lr: 3.138e-05, eta: 1 day, 13:13:18, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3057, decode.acc_seg: 87.6233, aux.loss_ce: 0.1238, aux.acc_seg: 87.4414, loss: 0.4295 +2024-06-18 12:44:46,269 - mmseg - INFO - Iter [17300/80000] lr: 3.135e-05, eta: 1 day, 13:11:03, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3381, decode.acc_seg: 86.8735, aux.loss_ce: 0.1372, aux.acc_seg: 86.6251, loss: 0.4753 +2024-06-18 12:46:25,139 - mmseg - INFO - Iter [17350/80000] lr: 3.133e-05, eta: 1 day, 13:08:48, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3211, decode.acc_seg: 87.2192, aux.loss_ce: 0.1295, aux.acc_seg: 87.1331, loss: 0.4506 +2024-06-18 12:48:04,030 - mmseg - INFO - Iter [17400/80000] lr: 3.130e-05, eta: 1 day, 13:06:33, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3384, decode.acc_seg: 86.3611, aux.loss_ce: 0.1360, aux.acc_seg: 86.3037, loss: 0.4743 +2024-06-18 12:49:42,981 - mmseg - INFO - Iter [17450/80000] lr: 3.128e-05, eta: 1 day, 13:04:18, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3335, decode.acc_seg: 86.8691, aux.loss_ce: 0.1359, aux.acc_seg: 86.6734, loss: 0.4695 +2024-06-18 12:51:21,994 - mmseg - INFO - Iter [17500/80000] lr: 3.125e-05, eta: 1 day, 13:02:04, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3109, decode.acc_seg: 87.5042, aux.loss_ce: 0.1258, aux.acc_seg: 87.2911, loss: 0.4367 +2024-06-18 12:53:00,968 - mmseg - INFO - Iter [17550/80000] lr: 3.123e-05, eta: 1 day, 12:59:50, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3170, decode.acc_seg: 87.4814, aux.loss_ce: 0.1281, aux.acc_seg: 87.3862, loss: 0.4451 +2024-06-18 12:54:39,927 - mmseg - INFO - Iter [17600/80000] lr: 3.120e-05, eta: 1 day, 12:57:37, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3075, decode.acc_seg: 87.4539, aux.loss_ce: 0.1247, aux.acc_seg: 87.3575, loss: 0.4322 +2024-06-18 12:56:19,022 - mmseg - INFO - Iter [17650/80000] lr: 3.118e-05, eta: 1 day, 12:55:23, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2979, decode.acc_seg: 87.0619, aux.loss_ce: 0.1203, aux.acc_seg: 87.1096, loss: 0.4181 +2024-06-18 12:58:00,110 - mmseg - INFO - Iter [17700/80000] lr: 3.115e-05, eta: 1 day, 12:53:17, time: 2.022, data_time: 0.052, memory: 72263, decode.loss_ce: 0.3102, decode.acc_seg: 87.4013, aux.loss_ce: 0.1254, aux.acc_seg: 87.2511, loss: 0.4356 +2024-06-18 12:59:39,151 - mmseg - INFO - Iter [17750/80000] lr: 3.113e-05, eta: 1 day, 12:51:04, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3007, decode.acc_seg: 87.5396, aux.loss_ce: 0.1224, aux.acc_seg: 87.4406, loss: 0.4231 +2024-06-18 13:01:18,192 - mmseg - INFO - Iter [17800/80000] lr: 3.110e-05, eta: 1 day, 12:48:51, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2973, decode.acc_seg: 88.1702, aux.loss_ce: 0.1212, aux.acc_seg: 87.9391, loss: 0.4185 +2024-06-18 13:02:57,257 - mmseg - INFO - Iter [17850/80000] lr: 3.108e-05, eta: 1 day, 12:46:39, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2963, decode.acc_seg: 87.8897, aux.loss_ce: 0.1196, aux.acc_seg: 87.8239, loss: 0.4159 +2024-06-18 13:04:36,183 - mmseg - INFO - Iter [17900/80000] lr: 3.105e-05, eta: 1 day, 12:44:26, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2902, decode.acc_seg: 88.1834, aux.loss_ce: 0.1184, aux.acc_seg: 87.9836, loss: 0.4086 +2024-06-18 13:06:15,248 - mmseg - INFO - Iter [17950/80000] lr: 3.103e-05, eta: 1 day, 12:42:14, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2982, decode.acc_seg: 88.2173, aux.loss_ce: 0.1200, aux.acc_seg: 88.0658, loss: 0.4182 +2024-06-18 13:07:54,330 - mmseg - INFO - Saving checkpoint at 18000 iterations +2024-06-18 13:09:16,205 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 13:09:16,205 - mmseg - INFO - Iter [18000/80000] lr: 3.100e-05, eta: 1 day, 12:44:44, time: 3.619, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2929, decode.acc_seg: 88.3628, aux.loss_ce: 0.1188, aux.acc_seg: 88.1974, loss: 0.4117 +2024-06-18 13:11:05,618 - mmseg - INFO - per class results: +2024-06-18 13:11:05,624 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.51 | 86.64 | +| building | 84.58 | 92.56 | +| sky | 94.71 | 97.13 | +| floor | 83.53 | 88.37 | +| tree | 77.31 | 90.96 | +| ceiling | 85.97 | 95.51 | +| road | 85.1 | 91.97 | +| bed | 91.94 | 97.03 | +| windowpane | 65.74 | 82.22 | +| grass | 65.8 | 84.25 | +| cabinet | 65.37 | 75.9 | +| sidewalk | 69.79 | 85.45 | +| person | 85.29 | 93.83 | +| earth | 36.06 | 44.43 | +| door | 59.44 | 74.29 | +| table | 65.18 | 76.68 | +| mountain | 63.28 | 73.81 | +| plant | 56.78 | 68.75 | +| curtain | 78.02 | 91.75 | +| chair | 61.74 | 69.72 | +| car | 87.41 | 92.46 | +| water | 68.6 | 83.33 | +| painting | 76.35 | 88.33 | +| sofa | 77.1 | 83.31 | +| shelf | 48.01 | 69.67 | +| house | 53.46 | 62.97 | +| sea | 75.11 | 88.0 | +| mirror | 74.06 | 79.08 | +| rug | 67.75 | 89.79 | +| field | 32.35 | 61.29 | +| armchair | 51.85 | 89.64 | +| seat | 69.67 | 89.12 | +| fence | 53.67 | 68.34 | +| desk | 59.19 | 77.34 | +| rock | 61.43 | 85.22 | +| wardrobe | 54.67 | 84.8 | +| lamp | 70.96 | 82.97 | +| bathtub | 78.23 | 80.29 | +| railing | 41.61 | 56.89 | +| cushion | 67.5 | 85.96 | +| base | 37.57 | 63.12 | +| box | 32.46 | 44.33 | +| column | 55.36 | 84.46 | +| signboard | 42.63 | 55.71 | +| chest of drawers | 40.09 | 54.49 | +| counter | 35.13 | 37.98 | +| sand | 53.57 | 73.76 | +| sink | 83.41 | 90.67 | +| skyscraper | 51.84 | 67.8 | +| fireplace | 68.81 | 95.91 | +| refrigerator | 82.02 | 89.16 | +| grandstand | 55.08 | 82.72 | +| path | 26.48 | 35.11 | +| stairs | 37.93 | 50.16 | +| runway | 68.42 | 90.1 | +| case | 67.89 | 88.3 | +| pool table | 93.73 | 98.27 | +| pillow | 67.05 | 75.85 | +| screen door | 84.61 | 89.0 | +| stairway | 56.17 | 76.87 | +| river | 17.79 | 25.23 | +| bridge | 70.12 | 79.78 | +| bookcase | 42.88 | 59.52 | +| blind | 41.36 | 44.56 | +| coffee table | 59.54 | 85.43 | +| toilet | 89.51 | 95.17 | +| flower | 42.36 | 61.02 | +| book | 50.33 | 82.15 | +| hill | 8.18 | 20.2 | +| bench | 67.02 | 77.84 | +| countertop | 65.53 | 82.03 | +| stove | 76.65 | 80.75 | +| palm | 49.09 | 84.8 | +| kitchen island | 42.62 | 84.7 | +| computer | 77.79 | 90.01 | +| swivel chair | 53.12 | 78.97 | +| boat | 66.96 | 89.5 | +| bar | 61.68 | 85.57 | +| arcade machine | 87.48 | 94.97 | +| hovel | 43.4 | 49.16 | +| bus | 91.15 | 97.6 | +| towel | 75.31 | 90.0 | +| light | 56.32 | 62.44 | +| truck | 44.62 | 63.05 | +| tower | 24.91 | 65.58 | +| chandelier | 69.68 | 82.34 | +| awning | 51.55 | 66.57 | +| streetlight | 33.06 | 42.58 | +| booth | 53.66 | 66.88 | +| television receiver | 76.75 | 90.28 | +| airplane | 79.79 | 96.7 | +| dirt track | 4.74 | 7.88 | +| apparel | 61.0 | 84.34 | +| pole | 21.69 | 26.9 | +| land | 0.43 | 0.6 | +| bannister | 19.01 | 25.53 | +| escalator | 60.76 | 85.68 | +| ottoman | 55.55 | 78.67 | +| bottle | 30.95 | 40.14 | +| buffet | 55.26 | 71.53 | +| poster | 42.56 | 72.02 | +| stage | 20.49 | 44.79 | +| van | 51.53 | 76.38 | +| ship | 81.92 | 86.85 | +| fountain | 44.98 | 45.77 | +| conveyer belt | 81.14 | 95.7 | +| canopy | 49.13 | 68.91 | +| washer | 79.68 | 85.42 | +| plaything | 31.41 | 40.78 | +| swimming pool | 55.31 | 95.79 | +| stool | 52.58 | 63.44 | +| barrel | 59.95 | 70.79 | +| basket | 40.66 | 57.89 | +| waterfall | 54.68 | 79.08 | +| tent | 94.95 | 98.93 | +| bag | 25.19 | 30.15 | +| minibike | 74.56 | 89.89 | +| cradle | 78.11 | 98.94 | +| oven | 48.81 | 89.78 | +| ball | 55.19 | 80.43 | +| food | 62.46 | 76.42 | +| step | 10.1 | 17.13 | +| tank | 55.34 | 71.24 | +| trade name | 31.61 | 37.35 | +| microwave | 88.25 | 97.17 | +| pot | 55.63 | 63.91 | +| animal | 75.0 | 85.23 | +| bicycle | 60.74 | 79.75 | +| lake | 60.99 | 63.5 | +| dishwasher | 74.24 | 78.92 | +| screen | 58.1 | 95.97 | +| blanket | 27.45 | 31.64 | +| sculpture | 61.5 | 86.46 | +| hood | 69.91 | 84.39 | +| sconce | 58.67 | 66.58 | +| vase | 43.32 | 60.92 | +| traffic light | 33.89 | 64.99 | +| tray | 14.76 | 17.28 | +| ashcan | 51.46 | 57.68 | +| fan | 69.16 | 78.27 | +| pier | 38.69 | 40.91 | +| crt screen | 0.88 | 1.89 | +| plate | 58.49 | 83.81 | +| monitor | 25.98 | 36.77 | +| bulletin board | 61.49 | 71.26 | +| shower | 0.04 | 0.04 | +| radiator | 65.2 | 81.54 | +| glass | 20.55 | 22.99 | +| clock | 45.35 | 55.42 | +| flag | 69.26 | 77.4 | ++---------------------+-------+-------+ +2024-06-18 13:11:05,625 - mmseg - INFO - Summary: +2024-06-18 13:11:05,625 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.46 | 56.78 | 71.06 | ++-------+-------+-------+ +2024-06-18 13:11:05,626 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 13:11:05,626 - mmseg - INFO - Iter(val) [250] aAcc: 0.8546, mIoU: 0.5678, mAcc: 0.7106, IoU.wall: 0.8051, IoU.building: 0.8458, IoU.sky: 0.9471, IoU.floor: 0.8353, IoU.tree: 0.7731, IoU.ceiling: 0.8597, IoU.road: 0.8510, IoU.bed : 0.9194, IoU.windowpane: 0.6574, IoU.grass: 0.6580, IoU.cabinet: 0.6537, IoU.sidewalk: 0.6979, IoU.person: 0.8529, IoU.earth: 0.3606, IoU.door: 0.5944, IoU.table: 0.6518, IoU.mountain: 0.6328, IoU.plant: 0.5678, IoU.curtain: 0.7802, IoU.chair: 0.6174, IoU.car: 0.8741, IoU.water: 0.6860, IoU.painting: 0.7635, IoU.sofa: 0.7710, IoU.shelf: 0.4801, IoU.house: 0.5346, IoU.sea: 0.7511, IoU.mirror: 0.7406, IoU.rug: 0.6775, IoU.field: 0.3235, IoU.armchair: 0.5185, IoU.seat: 0.6967, IoU.fence: 0.5367, IoU.desk: 0.5919, IoU.rock: 0.6143, IoU.wardrobe: 0.5467, IoU.lamp: 0.7096, IoU.bathtub: 0.7823, IoU.railing: 0.4161, IoU.cushion: 0.6750, IoU.base: 0.3757, IoU.box: 0.3246, IoU.column: 0.5536, IoU.signboard: 0.4263, IoU.chest of drawers: 0.4009, IoU.counter: 0.3513, IoU.sand: 0.5357, IoU.sink: 0.8341, IoU.skyscraper: 0.5184, IoU.fireplace: 0.6881, IoU.refrigerator: 0.8202, IoU.grandstand: 0.5508, IoU.path: 0.2648, IoU.stairs: 0.3793, IoU.runway: 0.6842, IoU.case: 0.6789, IoU.pool table: 0.9373, IoU.pillow: 0.6705, IoU.screen door: 0.8461, IoU.stairway: 0.5617, IoU.river: 0.1779, IoU.bridge: 0.7012, IoU.bookcase: 0.4288, IoU.blind: 0.4136, IoU.coffee table: 0.5954, IoU.toilet: 0.8951, IoU.flower: 0.4236, IoU.book: 0.5033, IoU.hill: 0.0818, IoU.bench: 0.6702, IoU.countertop: 0.6553, IoU.stove: 0.7665, IoU.palm: 0.4909, IoU.kitchen island: 0.4262, IoU.computer: 0.7779, IoU.swivel chair: 0.5312, IoU.boat: 0.6696, IoU.bar: 0.6168, IoU.arcade machine: 0.8748, IoU.hovel: 0.4340, IoU.bus: 0.9115, IoU.towel: 0.7531, IoU.light: 0.5632, IoU.truck: 0.4462, IoU.tower: 0.2491, IoU.chandelier: 0.6968, IoU.awning: 0.5155, IoU.streetlight: 0.3306, IoU.booth: 0.5366, IoU.television receiver: 0.7675, IoU.airplane: 0.7979, IoU.dirt track: 0.0474, IoU.apparel: 0.6100, IoU.pole: 0.2169, IoU.land: 0.0043, IoU.bannister: 0.1901, IoU.escalator: 0.6076, IoU.ottoman: 0.5555, IoU.bottle: 0.3095, IoU.buffet: 0.5526, IoU.poster: 0.4256, IoU.stage: 0.2049, IoU.van: 0.5153, IoU.ship: 0.8192, IoU.fountain: 0.4498, IoU.conveyer belt: 0.8114, IoU.canopy: 0.4913, IoU.washer: 0.7968, IoU.plaything: 0.3141, IoU.swimming pool: 0.5531, IoU.stool: 0.5258, IoU.barrel: 0.5995, IoU.basket: 0.4066, IoU.waterfall: 0.5468, IoU.tent: 0.9495, IoU.bag: 0.2519, IoU.minibike: 0.7456, IoU.cradle: 0.7811, IoU.oven: 0.4881, IoU.ball: 0.5519, IoU.food: 0.6246, IoU.step: 0.1010, IoU.tank: 0.5534, IoU.trade name: 0.3161, IoU.microwave: 0.8825, IoU.pot: 0.5563, IoU.animal: 0.7500, IoU.bicycle: 0.6074, IoU.lake: 0.6099, IoU.dishwasher: 0.7424, IoU.screen: 0.5810, IoU.blanket: 0.2745, IoU.sculpture: 0.6150, IoU.hood: 0.6991, IoU.sconce: 0.5867, IoU.vase: 0.4332, IoU.traffic light: 0.3389, IoU.tray: 0.1476, IoU.ashcan: 0.5146, IoU.fan: 0.6916, IoU.pier: 0.3869, IoU.crt screen: 0.0088, IoU.plate: 0.5849, IoU.monitor: 0.2598, IoU.bulletin board: 0.6149, IoU.shower: 0.0004, IoU.radiator: 0.6520, IoU.glass: 0.2055, IoU.clock: 0.4535, IoU.flag: 0.6926, Acc.wall: 0.8664, Acc.building: 0.9256, Acc.sky: 0.9713, Acc.floor: 0.8837, Acc.tree: 0.9096, Acc.ceiling: 0.9551, Acc.road: 0.9197, Acc.bed : 0.9703, Acc.windowpane: 0.8222, Acc.grass: 0.8425, Acc.cabinet: 0.7590, Acc.sidewalk: 0.8545, Acc.person: 0.9383, Acc.earth: 0.4443, Acc.door: 0.7429, Acc.table: 0.7668, Acc.mountain: 0.7381, Acc.plant: 0.6875, Acc.curtain: 0.9175, Acc.chair: 0.6972, Acc.car: 0.9246, Acc.water: 0.8333, Acc.painting: 0.8833, Acc.sofa: 0.8331, Acc.shelf: 0.6967, Acc.house: 0.6297, Acc.sea: 0.8800, Acc.mirror: 0.7908, Acc.rug: 0.8979, Acc.field: 0.6129, Acc.armchair: 0.8964, Acc.seat: 0.8912, Acc.fence: 0.6834, Acc.desk: 0.7734, Acc.rock: 0.8522, Acc.wardrobe: 0.8480, Acc.lamp: 0.8297, Acc.bathtub: 0.8029, Acc.railing: 0.5689, Acc.cushion: 0.8596, Acc.base: 0.6312, Acc.box: 0.4433, Acc.column: 0.8446, Acc.signboard: 0.5571, Acc.chest of drawers: 0.5449, Acc.counter: 0.3798, Acc.sand: 0.7376, Acc.sink: 0.9067, Acc.skyscraper: 0.6780, Acc.fireplace: 0.9591, Acc.refrigerator: 0.8916, Acc.grandstand: 0.8272, Acc.path: 0.3511, Acc.stairs: 0.5016, Acc.runway: 0.9010, Acc.case: 0.8830, Acc.pool table: 0.9827, Acc.pillow: 0.7585, Acc.screen door: 0.8900, Acc.stairway: 0.7687, Acc.river: 0.2523, Acc.bridge: 0.7978, Acc.bookcase: 0.5952, Acc.blind: 0.4456, Acc.coffee table: 0.8543, Acc.toilet: 0.9517, Acc.flower: 0.6102, Acc.book: 0.8215, Acc.hill: 0.2020, Acc.bench: 0.7784, Acc.countertop: 0.8203, Acc.stove: 0.8075, Acc.palm: 0.8480, Acc.kitchen island: 0.8470, Acc.computer: 0.9001, Acc.swivel chair: 0.7897, Acc.boat: 0.8950, Acc.bar: 0.8557, Acc.arcade machine: 0.9497, Acc.hovel: 0.4916, Acc.bus: 0.9760, Acc.towel: 0.9000, Acc.light: 0.6244, Acc.truck: 0.6305, Acc.tower: 0.6558, Acc.chandelier: 0.8234, Acc.awning: 0.6657, Acc.streetlight: 0.4258, Acc.booth: 0.6688, Acc.television receiver: 0.9028, Acc.airplane: 0.9670, Acc.dirt track: 0.0788, Acc.apparel: 0.8434, Acc.pole: 0.2690, Acc.land: 0.0060, Acc.bannister: 0.2553, Acc.escalator: 0.8568, Acc.ottoman: 0.7867, Acc.bottle: 0.4014, Acc.buffet: 0.7153, Acc.poster: 0.7202, Acc.stage: 0.4479, Acc.van: 0.7638, Acc.ship: 0.8685, Acc.fountain: 0.4577, Acc.conveyer belt: 0.9570, Acc.canopy: 0.6891, Acc.washer: 0.8542, Acc.plaything: 0.4078, Acc.swimming pool: 0.9579, Acc.stool: 0.6344, Acc.barrel: 0.7079, Acc.basket: 0.5789, Acc.waterfall: 0.7908, Acc.tent: 0.9893, Acc.bag: 0.3015, Acc.minibike: 0.8989, Acc.cradle: 0.9894, Acc.oven: 0.8978, Acc.ball: 0.8043, Acc.food: 0.7642, Acc.step: 0.1713, Acc.tank: 0.7124, Acc.trade name: 0.3735, Acc.microwave: 0.9717, Acc.pot: 0.6391, Acc.animal: 0.8523, Acc.bicycle: 0.7975, Acc.lake: 0.6350, Acc.dishwasher: 0.7892, Acc.screen: 0.9597, Acc.blanket: 0.3164, Acc.sculpture: 0.8646, Acc.hood: 0.8439, Acc.sconce: 0.6658, Acc.vase: 0.6092, Acc.traffic light: 0.6499, Acc.tray: 0.1728, Acc.ashcan: 0.5768, Acc.fan: 0.7827, Acc.pier: 0.4091, Acc.crt screen: 0.0189, Acc.plate: 0.8381, Acc.monitor: 0.3677, Acc.bulletin board: 0.7126, Acc.shower: 0.0004, Acc.radiator: 0.8154, Acc.glass: 0.2299, Acc.clock: 0.5542, Acc.flag: 0.7740 +2024-06-18 13:12:44,856 - mmseg - INFO - Iter [18050/80000] lr: 3.098e-05, eta: 1 day, 12:48:47, time: 4.173, data_time: 2.205, memory: 72263, decode.loss_ce: 0.2925, decode.acc_seg: 87.8397, aux.loss_ce: 0.1196, aux.acc_seg: 87.7501, loss: 0.4121 +2024-06-18 13:14:23,803 - mmseg - INFO - Iter [18100/80000] lr: 3.095e-05, eta: 1 day, 12:46:33, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3044, decode.acc_seg: 87.9018, aux.loss_ce: 0.1250, aux.acc_seg: 87.6008, loss: 0.4294 +2024-06-18 13:16:02,792 - mmseg - INFO - Iter [18150/80000] lr: 3.093e-05, eta: 1 day, 12:44:19, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3137, decode.acc_seg: 87.6538, aux.loss_ce: 0.1269, aux.acc_seg: 87.5393, loss: 0.4406 +2024-06-18 13:17:41,775 - mmseg - INFO - Iter [18200/80000] lr: 3.090e-05, eta: 1 day, 12:42:05, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2856, decode.acc_seg: 88.3625, aux.loss_ce: 0.1162, aux.acc_seg: 88.1807, loss: 0.4018 +2024-06-18 13:19:20,671 - mmseg - INFO - Iter [18250/80000] lr: 3.088e-05, eta: 1 day, 12:39:51, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3156, decode.acc_seg: 87.6121, aux.loss_ce: 0.1280, aux.acc_seg: 87.3779, loss: 0.4437 +2024-06-18 13:20:59,748 - mmseg - INFO - Iter [18300/80000] lr: 3.085e-05, eta: 1 day, 12:37:38, time: 1.982, data_time: 0.013, memory: 72263, decode.loss_ce: 0.3235, decode.acc_seg: 87.1937, aux.loss_ce: 0.1302, aux.acc_seg: 87.1247, loss: 0.4537 +2024-06-18 13:22:38,635 - mmseg - INFO - Iter [18350/80000] lr: 3.083e-05, eta: 1 day, 12:35:24, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3067, decode.acc_seg: 87.2368, aux.loss_ce: 0.1244, aux.acc_seg: 87.0039, loss: 0.4311 +2024-06-18 13:24:17,738 - mmseg - INFO - Iter [18400/80000] lr: 3.080e-05, eta: 1 day, 12:33:11, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2892, decode.acc_seg: 88.3746, aux.loss_ce: 0.1175, aux.acc_seg: 88.1124, loss: 0.4067 +2024-06-18 13:25:56,801 - mmseg - INFO - Iter [18450/80000] lr: 3.078e-05, eta: 1 day, 12:30:59, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3112, decode.acc_seg: 87.9382, aux.loss_ce: 0.1258, aux.acc_seg: 87.7285, loss: 0.4370 +2024-06-18 13:27:35,706 - mmseg - INFO - Iter [18500/80000] lr: 3.075e-05, eta: 1 day, 12:28:46, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2964, decode.acc_seg: 87.9508, aux.loss_ce: 0.1204, aux.acc_seg: 87.8247, loss: 0.4168 +2024-06-18 13:29:14,682 - mmseg - INFO - Iter [18550/80000] lr: 3.073e-05, eta: 1 day, 12:26:33, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2950, decode.acc_seg: 87.9899, aux.loss_ce: 0.1189, aux.acc_seg: 87.8823, loss: 0.4138 +2024-06-18 13:30:53,752 - mmseg - INFO - Iter [18600/80000] lr: 3.070e-05, eta: 1 day, 12:24:21, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2999, decode.acc_seg: 87.8915, aux.loss_ce: 0.1219, aux.acc_seg: 87.6542, loss: 0.4218 +2024-06-18 13:32:32,903 - mmseg - INFO - Iter [18650/80000] lr: 3.068e-05, eta: 1 day, 12:22:09, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3019, decode.acc_seg: 87.6765, aux.loss_ce: 0.1221, aux.acc_seg: 87.4960, loss: 0.4241 +2024-06-18 13:34:11,862 - mmseg - INFO - Iter [18700/80000] lr: 3.065e-05, eta: 1 day, 12:19:57, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3235, decode.acc_seg: 86.5684, aux.loss_ce: 0.1308, aux.acc_seg: 86.5095, loss: 0.4542 +2024-06-18 13:35:50,773 - mmseg - INFO - Iter [18750/80000] lr: 3.063e-05, eta: 1 day, 12:17:45, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3076, decode.acc_seg: 87.3995, aux.loss_ce: 0.1242, aux.acc_seg: 87.3639, loss: 0.4319 +2024-06-18 13:37:29,788 - mmseg - INFO - Iter [18800/80000] lr: 3.060e-05, eta: 1 day, 12:15:34, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3342, decode.acc_seg: 86.9903, aux.loss_ce: 0.1341, aux.acc_seg: 86.9133, loss: 0.4682 +2024-06-18 13:39:08,653 - mmseg - INFO - Iter [18850/80000] lr: 3.058e-05, eta: 1 day, 12:13:22, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2894, decode.acc_seg: 88.0444, aux.loss_ce: 0.1184, aux.acc_seg: 87.7773, loss: 0.4078 +2024-06-18 13:40:47,773 - mmseg - INFO - Iter [18900/80000] lr: 3.055e-05, eta: 1 day, 12:11:11, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3056, decode.acc_seg: 87.4537, aux.loss_ce: 0.1251, aux.acc_seg: 87.2239, loss: 0.4307 +2024-06-18 13:42:29,335 - mmseg - INFO - Iter [18950/80000] lr: 3.053e-05, eta: 1 day, 12:09:08, time: 2.031, data_time: 0.061, memory: 72263, decode.loss_ce: 0.2876, decode.acc_seg: 88.4153, aux.loss_ce: 0.1171, aux.acc_seg: 88.1385, loss: 0.4047 +2024-06-18 13:44:08,459 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 13:44:08,460 - mmseg - INFO - Iter [19000/80000] lr: 3.050e-05, eta: 1 day, 12:06:58, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2660, decode.acc_seg: 88.9933, aux.loss_ce: 0.1088, aux.acc_seg: 88.6690, loss: 0.3748 +2024-06-18 13:45:58,560 - mmseg - INFO - per class results: +2024-06-18 13:45:58,566 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.27 | 88.92 | +| building | 84.74 | 94.2 | +| sky | 94.69 | 96.86 | +| floor | 84.08 | 91.21 | +| tree | 77.1 | 90.18 | +| ceiling | 86.81 | 93.1 | +| road | 84.01 | 90.3 | +| bed | 92.39 | 97.3 | +| windowpane | 66.02 | 79.03 | +| grass | 66.48 | 76.39 | +| cabinet | 66.23 | 75.05 | +| sidewalk | 69.31 | 85.66 | +| person | 85.19 | 94.32 | +| earth | 37.74 | 51.68 | +| door | 58.96 | 72.06 | +| table | 64.65 | 76.36 | +| mountain | 63.7 | 76.41 | +| plant | 57.55 | 68.57 | +| curtain | 79.15 | 90.82 | +| chair | 64.2 | 72.87 | +| car | 86.72 | 94.62 | +| water | 61.51 | 72.94 | +| painting | 75.58 | 87.84 | +| sofa | 80.17 | 90.56 | +| shelf | 49.01 | 66.25 | +| house | 53.01 | 65.48 | +| sea | 75.78 | 89.32 | +| mirror | 76.42 | 85.36 | +| rug | 69.49 | 80.6 | +| field | 32.09 | 62.31 | +| armchair | 60.5 | 82.37 | +| seat | 67.02 | 87.09 | +| fence | 43.06 | 51.07 | +| desk | 53.34 | 79.65 | +| rock | 60.01 | 78.79 | +| wardrobe | 58.38 | 81.47 | +| lamp | 70.6 | 80.21 | +| bathtub | 86.36 | 91.76 | +| railing | 38.57 | 56.11 | +| cushion | 65.57 | 80.78 | +| base | 43.46 | 67.85 | +| box | 26.28 | 29.9 | +| column | 54.02 | 65.85 | +| signboard | 40.92 | 53.97 | +| chest of drawers | 47.58 | 65.69 | +| counter | 42.44 | 52.07 | +| sand | 52.4 | 81.49 | +| sink | 79.61 | 86.68 | +| skyscraper | 48.05 | 63.29 | +| fireplace | 80.56 | 93.18 | +| refrigerator | 84.1 | 93.54 | +| grandstand | 53.46 | 84.52 | +| path | 29.68 | 40.31 | +| stairs | 36.16 | 44.73 | +| runway | 65.11 | 85.05 | +| case | 73.44 | 87.62 | +| pool table | 94.5 | 98.15 | +| pillow | 67.65 | 80.96 | +| screen door | 84.64 | 92.47 | +| stairway | 49.58 | 61.89 | +| river | 25.29 | 63.39 | +| bridge | 72.64 | 85.1 | +| bookcase | 39.02 | 63.35 | +| blind | 41.24 | 42.85 | +| coffee table | 60.36 | 89.16 | +| toilet | 89.95 | 94.72 | +| flower | 41.76 | 60.59 | +| book | 54.61 | 73.17 | +| hill | 4.44 | 10.36 | +| bench | 62.23 | 70.29 | +| countertop | 63.35 | 84.89 | +| stove | 83.45 | 91.72 | +| palm | 51.78 | 81.26 | +| kitchen island | 43.33 | 90.08 | +| computer | 77.75 | 91.04 | +| swivel chair | 52.69 | 93.41 | +| boat | 62.48 | 92.89 | +| bar | 61.39 | 85.64 | +| arcade machine | 91.83 | 98.08 | +| hovel | 14.92 | 15.87 | +| bus | 92.87 | 97.19 | +| towel | 77.18 | 88.91 | +| light | 55.83 | 81.37 | +| truck | 46.35 | 61.44 | +| tower | 11.69 | 14.76 | +| chandelier | 66.27 | 91.67 | +| awning | 42.0 | 52.21 | +| streetlight | 35.95 | 51.78 | +| booth | 39.81 | 70.72 | +| television receiver | 80.88 | 87.3 | +| airplane | 87.06 | 91.78 | +| dirt track | 2.98 | 13.19 | +| apparel | 60.73 | 74.17 | +| pole | 24.47 | 31.78 | +| land | 1.02 | 1.36 | +| bannister | 18.18 | 23.73 | +| escalator | 61.07 | 83.13 | +| ottoman | 59.3 | 78.94 | +| bottle | 29.35 | 34.66 | +| buffet | 49.99 | 60.37 | +| poster | 38.19 | 76.87 | +| stage | 23.21 | 49.38 | +| van | 48.39 | 63.85 | +| ship | 78.75 | 85.31 | +| fountain | 50.94 | 52.17 | +| conveyer belt | 80.99 | 96.72 | +| canopy | 55.87 | 80.44 | +| washer | 84.21 | 89.66 | +| plaything | 46.22 | 65.65 | +| swimming pool | 56.66 | 83.09 | +| stool | 58.79 | 71.9 | +| barrel | 51.57 | 70.19 | +| basket | 42.7 | 53.54 | +| waterfall | 58.86 | 72.13 | +| tent | 96.75 | 98.04 | +| bag | 20.33 | 22.31 | +| minibike | 73.43 | 90.12 | +| cradle | 86.05 | 97.42 | +| oven | 51.98 | 60.86 | +| ball | 36.49 | 38.04 | +| food | 64.09 | 80.53 | +| step | 7.76 | 8.46 | +| tank | 64.35 | 69.57 | +| trade name | 12.68 | 13.28 | +| microwave | 86.82 | 96.55 | +| pot | 57.21 | 69.03 | +| animal | 68.87 | 72.62 | +| bicycle | 60.85 | 79.17 | +| lake | 61.0 | 61.83 | +| dishwasher | 70.23 | 74.28 | +| screen | 57.91 | 96.88 | +| blanket | 32.85 | 43.36 | +| sculpture | 72.95 | 84.15 | +| hood | 70.11 | 75.28 | +| sconce | 58.1 | 76.42 | +| vase | 45.51 | 63.28 | +| traffic light | 31.67 | 65.85 | +| tray | 16.79 | 21.3 | +| ashcan | 50.95 | 66.84 | +| fan | 69.29 | 82.11 | +| pier | 38.35 | 40.33 | +| crt screen | 1.25 | 3.52 | +| plate | 59.83 | 82.75 | +| monitor | 11.37 | 12.73 | +| bulletin board | 53.87 | 69.38 | +| shower | 2.86 | 4.12 | +| radiator | 64.72 | 72.7 | +| glass | 21.0 | 23.5 | +| clock | 47.02 | 55.52 | +| flag | 65.97 | 73.68 | ++---------------------+-------+-------+ +2024-06-18 13:45:58,566 - mmseg - INFO - Summary: +2024-06-18 13:45:58,566 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.64 | 56.57 | 69.87 | ++-------+-------+-------+ +2024-06-18 13:45:58,567 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 13:45:58,567 - mmseg - INFO - Iter(val) [250] aAcc: 0.8564, mIoU: 0.5657, mAcc: 0.6987, IoU.wall: 0.8127, IoU.building: 0.8474, IoU.sky: 0.9469, IoU.floor: 0.8408, IoU.tree: 0.7710, IoU.ceiling: 0.8681, IoU.road: 0.8401, IoU.bed : 0.9239, IoU.windowpane: 0.6602, IoU.grass: 0.6648, IoU.cabinet: 0.6623, IoU.sidewalk: 0.6931, IoU.person: 0.8519, IoU.earth: 0.3774, IoU.door: 0.5896, IoU.table: 0.6465, IoU.mountain: 0.6370, IoU.plant: 0.5755, IoU.curtain: 0.7915, IoU.chair: 0.6420, IoU.car: 0.8672, IoU.water: 0.6151, IoU.painting: 0.7558, IoU.sofa: 0.8017, IoU.shelf: 0.4901, IoU.house: 0.5301, IoU.sea: 0.7578, IoU.mirror: 0.7642, IoU.rug: 0.6949, IoU.field: 0.3209, IoU.armchair: 0.6050, IoU.seat: 0.6702, IoU.fence: 0.4306, IoU.desk: 0.5334, IoU.rock: 0.6001, IoU.wardrobe: 0.5838, IoU.lamp: 0.7060, IoU.bathtub: 0.8636, IoU.railing: 0.3857, IoU.cushion: 0.6557, IoU.base: 0.4346, IoU.box: 0.2628, IoU.column: 0.5402, IoU.signboard: 0.4092, IoU.chest of drawers: 0.4758, IoU.counter: 0.4244, IoU.sand: 0.5240, IoU.sink: 0.7961, IoU.skyscraper: 0.4805, IoU.fireplace: 0.8056, IoU.refrigerator: 0.8410, IoU.grandstand: 0.5346, IoU.path: 0.2968, IoU.stairs: 0.3616, IoU.runway: 0.6511, IoU.case: 0.7344, IoU.pool table: 0.9450, IoU.pillow: 0.6765, IoU.screen door: 0.8464, IoU.stairway: 0.4958, IoU.river: 0.2529, IoU.bridge: 0.7264, IoU.bookcase: 0.3902, IoU.blind: 0.4124, IoU.coffee table: 0.6036, IoU.toilet: 0.8995, IoU.flower: 0.4176, IoU.book: 0.5461, IoU.hill: 0.0444, IoU.bench: 0.6223, IoU.countertop: 0.6335, IoU.stove: 0.8345, IoU.palm: 0.5178, IoU.kitchen island: 0.4333, IoU.computer: 0.7775, IoU.swivel chair: 0.5269, IoU.boat: 0.6248, IoU.bar: 0.6139, IoU.arcade machine: 0.9183, IoU.hovel: 0.1492, IoU.bus: 0.9287, IoU.towel: 0.7718, IoU.light: 0.5583, IoU.truck: 0.4635, IoU.tower: 0.1169, IoU.chandelier: 0.6627, IoU.awning: 0.4200, IoU.streetlight: 0.3595, IoU.booth: 0.3981, IoU.television receiver: 0.8088, IoU.airplane: 0.8706, IoU.dirt track: 0.0298, IoU.apparel: 0.6073, IoU.pole: 0.2447, IoU.land: 0.0102, IoU.bannister: 0.1818, IoU.escalator: 0.6107, IoU.ottoman: 0.5930, IoU.bottle: 0.2935, IoU.buffet: 0.4999, IoU.poster: 0.3819, IoU.stage: 0.2321, IoU.van: 0.4839, IoU.ship: 0.7875, IoU.fountain: 0.5094, IoU.conveyer belt: 0.8099, IoU.canopy: 0.5587, IoU.washer: 0.8421, IoU.plaything: 0.4622, IoU.swimming pool: 0.5666, IoU.stool: 0.5879, IoU.barrel: 0.5157, IoU.basket: 0.4270, IoU.waterfall: 0.5886, IoU.tent: 0.9675, IoU.bag: 0.2033, IoU.minibike: 0.7343, IoU.cradle: 0.8605, IoU.oven: 0.5198, IoU.ball: 0.3649, IoU.food: 0.6409, IoU.step: 0.0776, IoU.tank: 0.6435, IoU.trade name: 0.1268, IoU.microwave: 0.8682, IoU.pot: 0.5721, IoU.animal: 0.6887, IoU.bicycle: 0.6085, IoU.lake: 0.6100, IoU.dishwasher: 0.7023, IoU.screen: 0.5791, IoU.blanket: 0.3285, IoU.sculpture: 0.7295, IoU.hood: 0.7011, IoU.sconce: 0.5810, IoU.vase: 0.4551, IoU.traffic light: 0.3167, IoU.tray: 0.1679, IoU.ashcan: 0.5095, IoU.fan: 0.6929, IoU.pier: 0.3835, IoU.crt screen: 0.0125, IoU.plate: 0.5983, IoU.monitor: 0.1137, IoU.bulletin board: 0.5387, IoU.shower: 0.0286, IoU.radiator: 0.6472, IoU.glass: 0.2100, IoU.clock: 0.4702, IoU.flag: 0.6597, Acc.wall: 0.8892, Acc.building: 0.9420, Acc.sky: 0.9686, Acc.floor: 0.9121, Acc.tree: 0.9018, Acc.ceiling: 0.9310, Acc.road: 0.9030, Acc.bed : 0.9730, Acc.windowpane: 0.7903, Acc.grass: 0.7639, Acc.cabinet: 0.7505, Acc.sidewalk: 0.8566, Acc.person: 0.9432, Acc.earth: 0.5168, Acc.door: 0.7206, Acc.table: 0.7636, Acc.mountain: 0.7641, Acc.plant: 0.6857, Acc.curtain: 0.9082, Acc.chair: 0.7287, Acc.car: 0.9462, Acc.water: 0.7294, Acc.painting: 0.8784, Acc.sofa: 0.9056, Acc.shelf: 0.6625, Acc.house: 0.6548, Acc.sea: 0.8932, Acc.mirror: 0.8536, Acc.rug: 0.8060, Acc.field: 0.6231, Acc.armchair: 0.8237, Acc.seat: 0.8709, Acc.fence: 0.5107, Acc.desk: 0.7965, Acc.rock: 0.7879, Acc.wardrobe: 0.8147, Acc.lamp: 0.8021, Acc.bathtub: 0.9176, Acc.railing: 0.5611, Acc.cushion: 0.8078, Acc.base: 0.6785, Acc.box: 0.2990, Acc.column: 0.6585, Acc.signboard: 0.5397, Acc.chest of drawers: 0.6569, Acc.counter: 0.5207, Acc.sand: 0.8149, Acc.sink: 0.8668, Acc.skyscraper: 0.6329, Acc.fireplace: 0.9318, Acc.refrigerator: 0.9354, Acc.grandstand: 0.8452, Acc.path: 0.4031, Acc.stairs: 0.4473, Acc.runway: 0.8505, Acc.case: 0.8762, Acc.pool table: 0.9815, Acc.pillow: 0.8096, Acc.screen door: 0.9247, Acc.stairway: 0.6189, Acc.river: 0.6339, Acc.bridge: 0.8510, Acc.bookcase: 0.6335, Acc.blind: 0.4285, Acc.coffee table: 0.8916, Acc.toilet: 0.9472, Acc.flower: 0.6059, Acc.book: 0.7317, Acc.hill: 0.1036, Acc.bench: 0.7029, Acc.countertop: 0.8489, Acc.stove: 0.9172, Acc.palm: 0.8126, Acc.kitchen island: 0.9008, Acc.computer: 0.9104, Acc.swivel chair: 0.9341, Acc.boat: 0.9289, Acc.bar: 0.8564, Acc.arcade machine: 0.9808, Acc.hovel: 0.1587, Acc.bus: 0.9719, Acc.towel: 0.8891, Acc.light: 0.8137, Acc.truck: 0.6144, Acc.tower: 0.1476, Acc.chandelier: 0.9167, Acc.awning: 0.5221, Acc.streetlight: 0.5178, Acc.booth: 0.7072, Acc.television receiver: 0.8730, Acc.airplane: 0.9178, Acc.dirt track: 0.1319, Acc.apparel: 0.7417, Acc.pole: 0.3178, Acc.land: 0.0136, Acc.bannister: 0.2373, Acc.escalator: 0.8313, Acc.ottoman: 0.7894, Acc.bottle: 0.3466, Acc.buffet: 0.6037, Acc.poster: 0.7687, Acc.stage: 0.4938, Acc.van: 0.6385, Acc.ship: 0.8531, Acc.fountain: 0.5217, Acc.conveyer belt: 0.9672, Acc.canopy: 0.8044, Acc.washer: 0.8966, Acc.plaything: 0.6565, Acc.swimming pool: 0.8309, Acc.stool: 0.7190, Acc.barrel: 0.7019, Acc.basket: 0.5354, Acc.waterfall: 0.7213, Acc.tent: 0.9804, Acc.bag: 0.2231, Acc.minibike: 0.9012, Acc.cradle: 0.9742, Acc.oven: 0.6086, Acc.ball: 0.3804, Acc.food: 0.8053, Acc.step: 0.0846, Acc.tank: 0.6957, Acc.trade name: 0.1328, Acc.microwave: 0.9655, Acc.pot: 0.6903, Acc.animal: 0.7262, Acc.bicycle: 0.7917, Acc.lake: 0.6183, Acc.dishwasher: 0.7428, Acc.screen: 0.9688, Acc.blanket: 0.4336, Acc.sculpture: 0.8415, Acc.hood: 0.7528, Acc.sconce: 0.7642, Acc.vase: 0.6328, Acc.traffic light: 0.6585, Acc.tray: 0.2130, Acc.ashcan: 0.6684, Acc.fan: 0.8211, Acc.pier: 0.4033, Acc.crt screen: 0.0352, Acc.plate: 0.8275, Acc.monitor: 0.1273, Acc.bulletin board: 0.6938, Acc.shower: 0.0412, Acc.radiator: 0.7270, Acc.glass: 0.2350, Acc.clock: 0.5552, Acc.flag: 0.7368 +2024-06-18 13:47:37,878 - mmseg - INFO - Iter [19050/80000] lr: 3.048e-05, eta: 1 day, 12:10:40, time: 4.188, data_time: 2.221, memory: 72263, decode.loss_ce: 0.2823, decode.acc_seg: 88.3031, aux.loss_ce: 0.1152, aux.acc_seg: 88.1506, loss: 0.3975 +2024-06-18 13:49:16,891 - mmseg - INFO - Iter [19100/80000] lr: 3.045e-05, eta: 1 day, 12:08:28, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2762, decode.acc_seg: 88.7338, aux.loss_ce: 0.1129, aux.acc_seg: 88.5119, loss: 0.3891 +2024-06-18 13:50:55,916 - mmseg - INFO - Iter [19150/80000] lr: 3.043e-05, eta: 1 day, 12:06:17, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2781, decode.acc_seg: 88.4530, aux.loss_ce: 0.1141, aux.acc_seg: 88.1385, loss: 0.3922 +2024-06-18 13:52:34,779 - mmseg - INFO - Iter [19200/80000] lr: 3.040e-05, eta: 1 day, 12:04:05, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2847, decode.acc_seg: 88.1666, aux.loss_ce: 0.1161, aux.acc_seg: 88.0837, loss: 0.4008 +2024-06-18 13:54:13,864 - mmseg - INFO - Iter [19250/80000] lr: 3.038e-05, eta: 1 day, 12:01:54, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2843, decode.acc_seg: 88.0340, aux.loss_ce: 0.1164, aux.acc_seg: 87.8193, loss: 0.4007 +2024-06-18 13:55:52,864 - mmseg - INFO - Iter [19300/80000] lr: 3.035e-05, eta: 1 day, 11:59:42, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3172, decode.acc_seg: 87.6076, aux.loss_ce: 0.1276, aux.acc_seg: 87.5172, loss: 0.4448 +2024-06-18 13:57:31,857 - mmseg - INFO - Iter [19350/80000] lr: 3.033e-05, eta: 1 day, 11:57:31, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2943, decode.acc_seg: 88.5851, aux.loss_ce: 0.1205, aux.acc_seg: 88.2027, loss: 0.4148 +2024-06-18 13:59:10,749 - mmseg - INFO - Iter [19400/80000] lr: 3.030e-05, eta: 1 day, 11:55:20, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2839, decode.acc_seg: 88.3865, aux.loss_ce: 0.1158, aux.acc_seg: 88.1664, loss: 0.3997 +2024-06-18 14:00:49,854 - mmseg - INFO - Iter [19450/80000] lr: 3.028e-05, eta: 1 day, 11:53:10, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2914, decode.acc_seg: 88.3818, aux.loss_ce: 0.1181, aux.acc_seg: 88.1564, loss: 0.4095 +2024-06-18 14:02:28,791 - mmseg - INFO - Iter [19500/80000] lr: 3.025e-05, eta: 1 day, 11:50:59, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3151, decode.acc_seg: 87.0968, aux.loss_ce: 0.1280, aux.acc_seg: 86.9596, loss: 0.4432 +2024-06-18 14:04:07,788 - mmseg - INFO - Iter [19550/80000] lr: 3.023e-05, eta: 1 day, 11:48:49, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2940, decode.acc_seg: 88.1890, aux.loss_ce: 0.1193, aux.acc_seg: 88.0534, loss: 0.4133 +2024-06-18 14:05:46,684 - mmseg - INFO - Iter [19600/80000] lr: 3.020e-05, eta: 1 day, 11:46:38, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2841, decode.acc_seg: 88.3237, aux.loss_ce: 0.1151, aux.acc_seg: 87.9898, loss: 0.3992 +2024-06-18 14:07:25,619 - mmseg - INFO - Iter [19650/80000] lr: 3.018e-05, eta: 1 day, 11:44:28, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3038, decode.acc_seg: 87.8476, aux.loss_ce: 0.1225, aux.acc_seg: 87.7629, loss: 0.4263 +2024-06-18 14:09:04,652 - mmseg - INFO - Iter [19700/80000] lr: 3.015e-05, eta: 1 day, 11:42:18, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2978, decode.acc_seg: 88.1148, aux.loss_ce: 0.1221, aux.acc_seg: 87.8751, loss: 0.4199 +2024-06-18 14:10:43,575 - mmseg - INFO - Iter [19750/80000] lr: 3.013e-05, eta: 1 day, 11:40:08, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2952, decode.acc_seg: 87.9227, aux.loss_ce: 0.1196, aux.acc_seg: 87.6874, loss: 0.4148 +2024-06-18 14:12:22,543 - mmseg - INFO - Iter [19800/80000] lr: 3.010e-05, eta: 1 day, 11:37:59, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2755, decode.acc_seg: 89.0594, aux.loss_ce: 0.1121, aux.acc_seg: 88.9410, loss: 0.3875 +2024-06-18 14:14:01,563 - mmseg - INFO - Iter [19850/80000] lr: 3.008e-05, eta: 1 day, 11:35:49, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2859, decode.acc_seg: 88.4133, aux.loss_ce: 0.1164, aux.acc_seg: 88.1821, loss: 0.4023 +2024-06-18 14:15:40,523 - mmseg - INFO - Iter [19900/80000] lr: 3.005e-05, eta: 1 day, 11:33:40, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2771, decode.acc_seg: 88.4685, aux.loss_ce: 0.1129, aux.acc_seg: 88.2657, loss: 0.3900 +2024-06-18 14:17:19,533 - mmseg - INFO - Iter [19950/80000] lr: 3.003e-05, eta: 1 day, 11:31:31, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3149, decode.acc_seg: 87.4434, aux.loss_ce: 0.1266, aux.acc_seg: 87.3075, loss: 0.4415 +2024-06-18 14:18:58,633 - mmseg - INFO - Saving checkpoint at 20000 iterations +2024-06-18 14:20:25,066 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 14:20:25,067 - mmseg - INFO - Iter [20000/80000] lr: 3.000e-05, eta: 1 day, 11:33:42, time: 3.711, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2947, decode.acc_seg: 88.1572, aux.loss_ce: 0.1190, aux.acc_seg: 88.0000, loss: 0.4137 +2024-06-18 14:22:14,187 - mmseg - INFO - per class results: +2024-06-18 14:22:14,193 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.32 | 90.74 | +| building | 85.39 | 92.75 | +| sky | 94.86 | 97.79 | +| floor | 83.8 | 90.01 | +| tree | 77.3 | 88.7 | +| ceiling | 85.9 | 91.63 | +| road | 84.98 | 91.8 | +| bed | 92.77 | 95.98 | +| windowpane | 67.04 | 82.67 | +| grass | 65.93 | 88.97 | +| cabinet | 65.25 | 74.38 | +| sidewalk | 69.56 | 85.71 | +| person | 84.78 | 93.81 | +| earth | 33.57 | 40.67 | +| door | 58.72 | 70.71 | +| table | 66.68 | 76.58 | +| mountain | 52.97 | 59.12 | +| plant | 57.78 | 68.78 | +| curtain | 81.03 | 89.78 | +| chair | 65.9 | 76.19 | +| car | 87.4 | 93.02 | +| water | 57.46 | 70.53 | +| painting | 78.42 | 90.23 | +| sofa | 79.76 | 86.66 | +| shelf | 46.76 | 59.8 | +| house | 53.49 | 71.48 | +| sea | 64.09 | 91.53 | +| mirror | 77.65 | 89.96 | +| rug | 68.54 | 82.95 | +| field | 28.26 | 43.58 | +| armchair | 59.46 | 80.95 | +| seat | 69.44 | 88.43 | +| fence | 51.97 | 63.92 | +| desk | 52.85 | 77.74 | +| rock | 51.35 | 85.61 | +| wardrobe | 53.69 | 81.3 | +| lamp | 72.72 | 83.21 | +| bathtub | 89.14 | 92.63 | +| railing | 42.02 | 61.8 | +| cushion | 67.34 | 75.56 | +| base | 41.43 | 59.41 | +| box | 38.01 | 55.06 | +| column | 53.41 | 64.63 | +| signboard | 42.91 | 56.13 | +| chest of drawers | 49.34 | 75.47 | +| counter | 48.98 | 56.57 | +| sand | 53.71 | 78.26 | +| sink | 83.02 | 89.87 | +| skyscraper | 54.0 | 69.44 | +| fireplace | 73.23 | 95.26 | +| refrigerator | 82.53 | 96.44 | +| grandstand | 51.95 | 75.81 | +| path | 28.7 | 34.59 | +| stairs | 39.88 | 47.11 | +| runway | 64.84 | 84.04 | +| case | 66.62 | 87.21 | +| pool table | 94.15 | 98.61 | +| pillow | 69.69 | 85.42 | +| screen door | 87.41 | 90.61 | +| stairway | 52.15 | 69.14 | +| river | 17.91 | 25.63 | +| bridge | 72.26 | 81.28 | +| bookcase | 35.47 | 43.82 | +| blind | 42.3 | 45.69 | +| coffee table | 62.28 | 85.44 | +| toilet | 89.67 | 95.58 | +| flower | 41.32 | 60.33 | +| book | 53.59 | 83.51 | +| hill | 7.9 | 20.19 | +| bench | 66.76 | 76.38 | +| countertop | 64.53 | 78.31 | +| stove | 83.91 | 93.86 | +| palm | 48.65 | 84.18 | +| kitchen island | 46.19 | 87.03 | +| computer | 75.29 | 92.37 | +| swivel chair | 52.48 | 84.17 | +| boat | 56.93 | 93.73 | +| bar | 62.68 | 83.16 | +| arcade machine | 86.12 | 95.49 | +| hovel | 18.06 | 19.29 | +| bus | 93.1 | 95.67 | +| towel | 64.22 | 89.38 | +| light | 57.35 | 63.2 | +| truck | 46.39 | 60.4 | +| tower | 26.07 | 57.02 | +| chandelier | 70.29 | 87.53 | +| awning | 38.87 | 55.83 | +| streetlight | 33.65 | 49.83 | +| booth | 45.15 | 54.74 | +| television receiver | 70.55 | 88.52 | +| airplane | 82.91 | 96.85 | +| dirt track | 1.35 | 3.97 | +| apparel | 56.56 | 69.64 | +| pole | 27.18 | 33.18 | +| land | 0.08 | 0.11 | +| bannister | 13.91 | 20.15 | +| escalator | 62.45 | 86.51 | +| ottoman | 56.03 | 75.99 | +| bottle | 40.5 | 63.24 | +| buffet | 66.25 | 86.67 | +| poster | 43.27 | 46.09 | +| stage | 23.01 | 40.9 | +| van | 46.77 | 74.2 | +| ship | 75.71 | 80.55 | +| fountain | 34.5 | 34.68 | +| conveyer belt | 79.43 | 97.31 | +| canopy | 49.06 | 67.15 | +| washer | 80.59 | 86.26 | +| plaything | 37.13 | 45.47 | +| swimming pool | 69.94 | 79.24 | +| stool | 54.14 | 67.02 | +| barrel | 56.33 | 66.44 | +| basket | 40.17 | 62.18 | +| waterfall | 52.36 | 70.38 | +| tent | 92.53 | 98.33 | +| bag | 24.73 | 27.84 | +| minibike | 76.54 | 89.13 | +| cradle | 73.79 | 76.13 | +| oven | 50.63 | 59.48 | +| ball | 58.44 | 66.78 | +| food | 60.43 | 69.85 | +| step | 11.19 | 14.69 | +| tank | 57.7 | 71.96 | +| trade name | 23.14 | 24.73 | +| microwave | 85.2 | 97.14 | +| pot | 56.56 | 67.03 | +| animal | 55.3 | 56.88 | +| bicycle | 60.98 | 80.79 | +| lake | 58.08 | 63.37 | +| dishwasher | 72.93 | 77.68 | +| screen | 60.5 | 93.4 | +| blanket | 25.35 | 33.37 | +| sculpture | 71.72 | 81.65 | +| hood | 70.42 | 85.39 | +| sconce | 56.24 | 65.73 | +| vase | 43.59 | 70.04 | +| traffic light | 34.99 | 62.2 | +| tray | 21.01 | 33.58 | +| ashcan | 49.79 | 67.53 | +| fan | 65.35 | 75.29 | +| pier | 39.3 | 42.22 | +| crt screen | 1.18 | 2.31 | +| plate | 62.62 | 75.56 | +| monitor | 23.97 | 27.35 | +| bulletin board | 51.7 | 56.1 | +| shower | 1.78 | 2.33 | +| radiator | 65.97 | 79.72 | +| glass | 21.71 | 25.84 | +| clock | 47.15 | 53.28 | +| flag | 70.61 | 77.1 | ++---------------------+-------+-------+ +2024-06-18 14:22:14,193 - mmseg - INFO - Summary: +2024-06-18 14:22:14,193 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.62 | 56.44 | 69.49 | ++-------+-------+-------+ +2024-06-18 14:22:14,194 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 14:22:14,194 - mmseg - INFO - Iter(val) [250] aAcc: 0.8562, mIoU: 0.5644, mAcc: 0.6949, IoU.wall: 0.8132, IoU.building: 0.8539, IoU.sky: 0.9486, IoU.floor: 0.8380, IoU.tree: 0.7730, IoU.ceiling: 0.8590, IoU.road: 0.8498, IoU.bed : 0.9277, IoU.windowpane: 0.6704, IoU.grass: 0.6593, IoU.cabinet: 0.6525, IoU.sidewalk: 0.6956, IoU.person: 0.8478, IoU.earth: 0.3357, IoU.door: 0.5872, IoU.table: 0.6668, IoU.mountain: 0.5297, IoU.plant: 0.5778, IoU.curtain: 0.8103, IoU.chair: 0.6590, IoU.car: 0.8740, IoU.water: 0.5746, IoU.painting: 0.7842, IoU.sofa: 0.7976, IoU.shelf: 0.4676, IoU.house: 0.5349, IoU.sea: 0.6409, IoU.mirror: 0.7765, IoU.rug: 0.6854, IoU.field: 0.2826, IoU.armchair: 0.5946, IoU.seat: 0.6944, IoU.fence: 0.5197, IoU.desk: 0.5285, IoU.rock: 0.5135, IoU.wardrobe: 0.5369, IoU.lamp: 0.7272, IoU.bathtub: 0.8914, IoU.railing: 0.4202, IoU.cushion: 0.6734, IoU.base: 0.4143, IoU.box: 0.3801, IoU.column: 0.5341, IoU.signboard: 0.4291, IoU.chest of drawers: 0.4934, IoU.counter: 0.4898, IoU.sand: 0.5371, IoU.sink: 0.8302, IoU.skyscraper: 0.5400, IoU.fireplace: 0.7323, IoU.refrigerator: 0.8253, IoU.grandstand: 0.5195, IoU.path: 0.2870, IoU.stairs: 0.3988, IoU.runway: 0.6484, IoU.case: 0.6662, IoU.pool table: 0.9415, IoU.pillow: 0.6969, IoU.screen door: 0.8741, IoU.stairway: 0.5215, IoU.river: 0.1791, IoU.bridge: 0.7226, IoU.bookcase: 0.3547, IoU.blind: 0.4230, IoU.coffee table: 0.6228, IoU.toilet: 0.8967, IoU.flower: 0.4132, IoU.book: 0.5359, IoU.hill: 0.0790, IoU.bench: 0.6676, IoU.countertop: 0.6453, IoU.stove: 0.8391, IoU.palm: 0.4865, IoU.kitchen island: 0.4619, IoU.computer: 0.7529, IoU.swivel chair: 0.5248, IoU.boat: 0.5693, IoU.bar: 0.6268, IoU.arcade machine: 0.8612, IoU.hovel: 0.1806, IoU.bus: 0.9310, IoU.towel: 0.6422, IoU.light: 0.5735, IoU.truck: 0.4639, IoU.tower: 0.2607, IoU.chandelier: 0.7029, IoU.awning: 0.3887, IoU.streetlight: 0.3365, IoU.booth: 0.4515, IoU.television receiver: 0.7055, IoU.airplane: 0.8291, IoU.dirt track: 0.0135, IoU.apparel: 0.5656, IoU.pole: 0.2718, IoU.land: 0.0008, IoU.bannister: 0.1391, IoU.escalator: 0.6245, IoU.ottoman: 0.5603, IoU.bottle: 0.4050, IoU.buffet: 0.6625, IoU.poster: 0.4327, IoU.stage: 0.2301, IoU.van: 0.4677, IoU.ship: 0.7571, IoU.fountain: 0.3450, IoU.conveyer belt: 0.7943, IoU.canopy: 0.4906, IoU.washer: 0.8059, IoU.plaything: 0.3713, IoU.swimming pool: 0.6994, IoU.stool: 0.5414, IoU.barrel: 0.5633, IoU.basket: 0.4017, IoU.waterfall: 0.5236, IoU.tent: 0.9253, IoU.bag: 0.2473, IoU.minibike: 0.7654, IoU.cradle: 0.7379, IoU.oven: 0.5063, IoU.ball: 0.5844, IoU.food: 0.6043, IoU.step: 0.1119, IoU.tank: 0.5770, IoU.trade name: 0.2314, IoU.microwave: 0.8520, IoU.pot: 0.5656, IoU.animal: 0.5530, IoU.bicycle: 0.6098, IoU.lake: 0.5808, IoU.dishwasher: 0.7293, IoU.screen: 0.6050, IoU.blanket: 0.2535, IoU.sculpture: 0.7172, IoU.hood: 0.7042, IoU.sconce: 0.5624, IoU.vase: 0.4359, IoU.traffic light: 0.3499, IoU.tray: 0.2101, IoU.ashcan: 0.4979, IoU.fan: 0.6535, IoU.pier: 0.3930, IoU.crt screen: 0.0118, IoU.plate: 0.6262, IoU.monitor: 0.2397, IoU.bulletin board: 0.5170, IoU.shower: 0.0178, IoU.radiator: 0.6597, IoU.glass: 0.2171, IoU.clock: 0.4715, IoU.flag: 0.7061, Acc.wall: 0.9074, Acc.building: 0.9275, Acc.sky: 0.9779, Acc.floor: 0.9001, Acc.tree: 0.8870, Acc.ceiling: 0.9163, Acc.road: 0.9180, Acc.bed : 0.9598, Acc.windowpane: 0.8267, Acc.grass: 0.8897, Acc.cabinet: 0.7438, Acc.sidewalk: 0.8571, Acc.person: 0.9381, Acc.earth: 0.4067, Acc.door: 0.7071, Acc.table: 0.7658, Acc.mountain: 0.5912, Acc.plant: 0.6878, Acc.curtain: 0.8978, Acc.chair: 0.7619, Acc.car: 0.9302, Acc.water: 0.7053, Acc.painting: 0.9023, Acc.sofa: 0.8666, Acc.shelf: 0.5980, Acc.house: 0.7148, Acc.sea: 0.9153, Acc.mirror: 0.8996, Acc.rug: 0.8295, Acc.field: 0.4358, Acc.armchair: 0.8095, Acc.seat: 0.8843, Acc.fence: 0.6392, Acc.desk: 0.7774, Acc.rock: 0.8561, Acc.wardrobe: 0.8130, Acc.lamp: 0.8321, Acc.bathtub: 0.9263, Acc.railing: 0.6180, Acc.cushion: 0.7556, Acc.base: 0.5941, Acc.box: 0.5506, Acc.column: 0.6463, Acc.signboard: 0.5613, Acc.chest of drawers: 0.7547, Acc.counter: 0.5657, Acc.sand: 0.7826, Acc.sink: 0.8987, Acc.skyscraper: 0.6944, Acc.fireplace: 0.9526, Acc.refrigerator: 0.9644, Acc.grandstand: 0.7581, Acc.path: 0.3459, Acc.stairs: 0.4711, Acc.runway: 0.8404, Acc.case: 0.8721, Acc.pool table: 0.9861, Acc.pillow: 0.8542, Acc.screen door: 0.9061, Acc.stairway: 0.6914, Acc.river: 0.2563, Acc.bridge: 0.8128, Acc.bookcase: 0.4382, Acc.blind: 0.4569, Acc.coffee table: 0.8544, Acc.toilet: 0.9558, Acc.flower: 0.6033, Acc.book: 0.8351, Acc.hill: 0.2019, Acc.bench: 0.7638, Acc.countertop: 0.7831, Acc.stove: 0.9386, Acc.palm: 0.8418, Acc.kitchen island: 0.8703, Acc.computer: 0.9237, Acc.swivel chair: 0.8417, Acc.boat: 0.9373, Acc.bar: 0.8316, Acc.arcade machine: 0.9549, Acc.hovel: 0.1929, Acc.bus: 0.9567, Acc.towel: 0.8938, Acc.light: 0.6320, Acc.truck: 0.6040, Acc.tower: 0.5702, Acc.chandelier: 0.8753, Acc.awning: 0.5583, Acc.streetlight: 0.4983, Acc.booth: 0.5474, Acc.television receiver: 0.8852, Acc.airplane: 0.9685, Acc.dirt track: 0.0397, Acc.apparel: 0.6964, Acc.pole: 0.3318, Acc.land: 0.0011, Acc.bannister: 0.2015, Acc.escalator: 0.8651, Acc.ottoman: 0.7599, Acc.bottle: 0.6324, Acc.buffet: 0.8667, Acc.poster: 0.4609, Acc.stage: 0.4090, Acc.van: 0.7420, Acc.ship: 0.8055, Acc.fountain: 0.3468, Acc.conveyer belt: 0.9731, Acc.canopy: 0.6715, Acc.washer: 0.8626, Acc.plaything: 0.4547, Acc.swimming pool: 0.7924, Acc.stool: 0.6702, Acc.barrel: 0.6644, Acc.basket: 0.6218, Acc.waterfall: 0.7038, Acc.tent: 0.9833, Acc.bag: 0.2784, Acc.minibike: 0.8913, Acc.cradle: 0.7613, Acc.oven: 0.5948, Acc.ball: 0.6678, Acc.food: 0.6985, Acc.step: 0.1469, Acc.tank: 0.7196, Acc.trade name: 0.2473, Acc.microwave: 0.9714, Acc.pot: 0.6703, Acc.animal: 0.5688, Acc.bicycle: 0.8079, Acc.lake: 0.6337, Acc.dishwasher: 0.7768, Acc.screen: 0.9340, Acc.blanket: 0.3337, Acc.sculpture: 0.8165, Acc.hood: 0.8539, Acc.sconce: 0.6573, Acc.vase: 0.7004, Acc.traffic light: 0.6220, Acc.tray: 0.3358, Acc.ashcan: 0.6753, Acc.fan: 0.7529, Acc.pier: 0.4222, Acc.crt screen: 0.0231, Acc.plate: 0.7556, Acc.monitor: 0.2735, Acc.bulletin board: 0.5610, Acc.shower: 0.0233, Acc.radiator: 0.7972, Acc.glass: 0.2584, Acc.clock: 0.5328, Acc.flag: 0.7710 +2024-06-18 14:23:53,459 - mmseg - INFO - Iter [20050/80000] lr: 2.998e-05, eta: 1 day, 11:36:59, time: 4.168, data_time: 2.200, memory: 72263, decode.loss_ce: 0.2893, decode.acc_seg: 87.9179, aux.loss_ce: 0.1177, aux.acc_seg: 87.7480, loss: 0.4070 +2024-06-18 14:25:32,336 - mmseg - INFO - Iter [20100/80000] lr: 2.995e-05, eta: 1 day, 11:34:48, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2963, decode.acc_seg: 88.2196, aux.loss_ce: 0.1199, aux.acc_seg: 88.0274, loss: 0.4161 +2024-06-18 14:27:11,281 - mmseg - INFO - Iter [20150/80000] lr: 2.993e-05, eta: 1 day, 11:32:37, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3035, decode.acc_seg: 87.7500, aux.loss_ce: 0.1228, aux.acc_seg: 87.5505, loss: 0.4263 +2024-06-18 14:28:50,316 - mmseg - INFO - Iter [20200/80000] lr: 2.990e-05, eta: 1 day, 11:30:27, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.3331, decode.acc_seg: 87.1434, aux.loss_ce: 0.1344, aux.acc_seg: 86.9842, loss: 0.4675 +2024-06-18 14:30:33,494 - mmseg - INFO - Iter [20250/80000] lr: 2.988e-05, eta: 1 day, 11:28:29, time: 2.064, data_time: 0.094, memory: 72263, decode.loss_ce: 0.2799, decode.acc_seg: 88.2234, aux.loss_ce: 0.1135, aux.acc_seg: 88.0476, loss: 0.3934 +2024-06-18 14:32:12,630 - mmseg - INFO - Iter [20300/80000] lr: 2.985e-05, eta: 1 day, 11:26:20, time: 1.983, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3005, decode.acc_seg: 88.1572, aux.loss_ce: 0.1219, aux.acc_seg: 87.9456, loss: 0.4225 +2024-06-18 14:33:51,611 - mmseg - INFO - Iter [20350/80000] lr: 2.983e-05, eta: 1 day, 11:24:10, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2661, decode.acc_seg: 89.2944, aux.loss_ce: 0.1091, aux.acc_seg: 89.0288, loss: 0.3752 +2024-06-18 14:35:30,546 - mmseg - INFO - Iter [20400/80000] lr: 2.980e-05, eta: 1 day, 11:22:00, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2798, decode.acc_seg: 88.6484, aux.loss_ce: 0.1143, aux.acc_seg: 88.2793, loss: 0.3941 +2024-06-18 14:37:09,510 - mmseg - INFO - Iter [20450/80000] lr: 2.978e-05, eta: 1 day, 11:19:50, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2899, decode.acc_seg: 88.3245, aux.loss_ce: 0.1178, aux.acc_seg: 88.0552, loss: 0.4077 +2024-06-18 14:38:48,560 - mmseg - INFO - Iter [20500/80000] lr: 2.975e-05, eta: 1 day, 11:17:41, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2847, decode.acc_seg: 88.1085, aux.loss_ce: 0.1158, aux.acc_seg: 87.9909, loss: 0.4005 +2024-06-18 14:40:27,584 - mmseg - INFO - Iter [20550/80000] lr: 2.973e-05, eta: 1 day, 11:15:32, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2890, decode.acc_seg: 88.2784, aux.loss_ce: 0.1172, aux.acc_seg: 88.1595, loss: 0.4062 +2024-06-18 14:42:06,583 - mmseg - INFO - Iter [20600/80000] lr: 2.970e-05, eta: 1 day, 11:13:23, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2921, decode.acc_seg: 87.7209, aux.loss_ce: 0.1174, aux.acc_seg: 87.6742, loss: 0.4095 +2024-06-18 14:43:45,718 - mmseg - INFO - Iter [20650/80000] lr: 2.968e-05, eta: 1 day, 11:11:14, time: 1.983, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2907, decode.acc_seg: 88.2903, aux.loss_ce: 0.1189, aux.acc_seg: 88.0332, loss: 0.4096 +2024-06-18 14:45:24,611 - mmseg - INFO - Iter [20700/80000] lr: 2.965e-05, eta: 1 day, 11:09:05, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2682, decode.acc_seg: 89.2858, aux.loss_ce: 0.1090, aux.acc_seg: 89.0841, loss: 0.3772 +2024-06-18 14:47:03,468 - mmseg - INFO - Iter [20750/80000] lr: 2.963e-05, eta: 1 day, 11:06:56, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2644, decode.acc_seg: 89.3133, aux.loss_ce: 0.1089, aux.acc_seg: 88.9611, loss: 0.3733 +2024-06-18 14:48:42,462 - mmseg - INFO - Iter [20800/80000] lr: 2.960e-05, eta: 1 day, 11:04:47, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2950, decode.acc_seg: 87.8794, aux.loss_ce: 0.1198, aux.acc_seg: 87.7397, loss: 0.4148 +2024-06-18 14:50:21,461 - mmseg - INFO - Iter [20850/80000] lr: 2.958e-05, eta: 1 day, 11:02:39, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2649, decode.acc_seg: 89.2099, aux.loss_ce: 0.1081, aux.acc_seg: 88.9671, loss: 0.3730 +2024-06-18 14:52:00,378 - mmseg - INFO - Iter [20900/80000] lr: 2.955e-05, eta: 1 day, 11:00:30, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2836, decode.acc_seg: 88.9096, aux.loss_ce: 0.1162, aux.acc_seg: 88.6761, loss: 0.3998 +2024-06-18 14:53:39,262 - mmseg - INFO - Iter [20950/80000] lr: 2.953e-05, eta: 1 day, 10:58:22, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2986, decode.acc_seg: 87.9515, aux.loss_ce: 0.1201, aux.acc_seg: 87.7740, loss: 0.4187 +2024-06-18 14:55:18,401 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 14:55:18,401 - mmseg - INFO - Iter [21000/80000] lr: 2.950e-05, eta: 1 day, 10:56:14, time: 1.983, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3010, decode.acc_seg: 88.0890, aux.loss_ce: 0.1215, aux.acc_seg: 87.9634, loss: 0.4225 +2024-06-18 14:57:08,996 - mmseg - INFO - per class results: +2024-06-18 14:57:09,003 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.32 | 88.18 | +| building | 85.03 | 92.9 | +| sky | 94.46 | 97.78 | +| floor | 84.31 | 88.74 | +| tree | 77.61 | 90.02 | +| ceiling | 85.41 | 95.38 | +| road | 84.6 | 89.14 | +| bed | 92.24 | 97.03 | +| windowpane | 65.4 | 81.53 | +| grass | 68.88 | 80.63 | +| cabinet | 64.92 | 79.46 | +| sidewalk | 69.59 | 86.6 | +| person | 84.75 | 93.9 | +| earth | 39.13 | 50.06 | +| door | 56.9 | 71.63 | +| table | 64.59 | 80.1 | +| mountain | 63.28 | 75.84 | +| plant | 57.55 | 73.3 | +| curtain | 79.14 | 87.8 | +| chair | 65.98 | 80.24 | +| car | 85.07 | 95.71 | +| water | 55.65 | 69.74 | +| painting | 75.67 | 92.37 | +| sofa | 77.35 | 93.91 | +| shelf | 49.01 | 70.1 | +| house | 50.34 | 63.65 | +| sea | 65.31 | 84.34 | +| mirror | 76.55 | 86.0 | +| rug | 70.43 | 84.92 | +| field | 35.68 | 61.32 | +| armchair | 55.37 | 66.1 | +| seat | 63.43 | 89.44 | +| fence | 47.42 | 58.84 | +| desk | 51.26 | 80.47 | +| rock | 53.13 | 74.91 | +| wardrobe | 47.56 | 59.05 | +| lamp | 71.5 | 85.46 | +| bathtub | 87.01 | 90.46 | +| railing | 43.98 | 67.21 | +| cushion | 65.3 | 80.96 | +| base | 38.62 | 59.82 | +| box | 32.84 | 40.2 | +| column | 53.82 | 64.42 | +| signboard | 42.2 | 57.34 | +| chest of drawers | 41.67 | 67.89 | +| counter | 40.8 | 52.66 | +| sand | 57.48 | 88.56 | +| sink | 79.67 | 84.08 | +| skyscraper | 53.63 | 64.96 | +| fireplace | 78.78 | 93.11 | +| refrigerator | 81.35 | 88.35 | +| grandstand | 50.62 | 86.0 | +| path | 30.21 | 41.21 | +| stairs | 37.73 | 45.05 | +| runway | 68.97 | 90.62 | +| case | 64.85 | 72.96 | +| pool table | 93.52 | 98.97 | +| pillow | 59.0 | 63.33 | +| screen door | 86.46 | 95.7 | +| stairway | 52.82 | 70.91 | +| river | 14.98 | 31.75 | +| bridge | 71.38 | 86.46 | +| bookcase | 36.26 | 44.86 | +| blind | 38.76 | 42.12 | +| coffee table | 59.83 | 87.76 | +| toilet | 90.62 | 95.06 | +| flower | 44.95 | 57.9 | +| book | 54.46 | 82.67 | +| hill | 7.35 | 13.38 | +| bench | 51.42 | 57.59 | +| countertop | 64.78 | 81.2 | +| stove | 80.86 | 93.47 | +| palm | 53.09 | 81.78 | +| kitchen island | 46.4 | 76.76 | +| computer | 77.36 | 92.12 | +| swivel chair | 55.14 | 81.89 | +| boat | 58.43 | 92.17 | +| bar | 60.07 | 83.36 | +| arcade machine | 85.58 | 99.43 | +| hovel | 31.04 | 34.43 | +| bus | 92.45 | 96.81 | +| towel | 76.8 | 88.0 | +| light | 54.56 | 59.27 | +| truck | 47.41 | 61.81 | +| tower | 18.62 | 37.53 | +| chandelier | 71.19 | 85.68 | +| awning | 38.92 | 47.9 | +| streetlight | 36.23 | 53.31 | +| booth | 38.2 | 42.19 | +| television receiver | 76.36 | 90.48 | +| airplane | 87.47 | 95.28 | +| dirt track | 9.5 | 40.65 | +| apparel | 63.42 | 77.76 | +| pole | 26.37 | 34.63 | +| land | 0.11 | 0.13 | +| bannister | 18.73 | 24.02 | +| escalator | 61.08 | 88.11 | +| ottoman | 48.22 | 60.8 | +| bottle | 35.73 | 45.77 | +| buffet | 56.0 | 90.88 | +| poster | 38.05 | 54.4 | +| stage | 23.34 | 47.99 | +| van | 35.24 | 40.3 | +| ship | 81.52 | 96.19 | +| fountain | 23.0 | 23.39 | +| conveyer belt | 84.94 | 94.65 | +| canopy | 47.28 | 53.52 | +| washer | 87.36 | 92.87 | +| plaything | 45.81 | 67.07 | +| swimming pool | 54.77 | 80.04 | +| stool | 54.59 | 72.95 | +| barrel | 56.04 | 69.78 | +| basket | 41.86 | 54.62 | +| waterfall | 63.68 | 83.53 | +| tent | 92.52 | 98.41 | +| bag | 20.07 | 23.03 | +| minibike | 76.96 | 89.27 | +| cradle | 90.29 | 96.5 | +| oven | 52.21 | 81.44 | +| ball | 47.22 | 79.35 | +| food | 66.77 | 87.92 | +| step | 11.36 | 13.2 | +| tank | 55.93 | 74.22 | +| trade name | 20.82 | 22.03 | +| microwave | 89.28 | 96.34 | +| pot | 56.9 | 70.9 | +| animal | 58.83 | 60.5 | +| bicycle | 61.94 | 81.92 | +| lake | 58.55 | 62.81 | +| dishwasher | 30.37 | 30.69 | +| screen | 60.57 | 73.07 | +| blanket | 33.43 | 40.27 | +| sculpture | 70.16 | 87.82 | +| hood | 67.55 | 74.36 | +| sconce | 59.7 | 73.31 | +| vase | 46.99 | 58.9 | +| traffic light | 36.62 | 65.64 | +| tray | 20.92 | 25.45 | +| ashcan | 45.64 | 70.89 | +| fan | 68.36 | 81.54 | +| pier | 38.74 | 42.45 | +| crt screen | 9.95 | 33.04 | +| plate | 60.62 | 73.93 | +| monitor | 12.95 | 15.69 | +| bulletin board | 50.03 | 51.03 | +| shower | 1.88 | 1.88 | +| radiator | 67.32 | 80.22 | +| glass | 19.95 | 21.67 | +| clock | 46.57 | 59.05 | +| flag | 68.52 | 78.63 | ++---------------------+-------+-------+ +2024-06-18 14:57:09,003 - mmseg - INFO - Summary: +2024-06-18 14:57:09,003 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.51 | 55.94 | 69.38 | ++-------+-------+-------+ +2024-06-18 14:57:09,004 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 14:57:09,004 - mmseg - INFO - Iter(val) [250] aAcc: 0.8551, mIoU: 0.5594, mAcc: 0.6938, IoU.wall: 0.8132, IoU.building: 0.8503, IoU.sky: 0.9446, IoU.floor: 0.8431, IoU.tree: 0.7761, IoU.ceiling: 0.8541, IoU.road: 0.8460, IoU.bed : 0.9224, IoU.windowpane: 0.6540, IoU.grass: 0.6888, IoU.cabinet: 0.6492, IoU.sidewalk: 0.6959, IoU.person: 0.8475, IoU.earth: 0.3913, IoU.door: 0.5690, IoU.table: 0.6459, IoU.mountain: 0.6328, IoU.plant: 0.5755, IoU.curtain: 0.7914, IoU.chair: 0.6598, IoU.car: 0.8507, IoU.water: 0.5565, IoU.painting: 0.7567, IoU.sofa: 0.7735, IoU.shelf: 0.4901, IoU.house: 0.5034, IoU.sea: 0.6531, IoU.mirror: 0.7655, IoU.rug: 0.7043, IoU.field: 0.3568, IoU.armchair: 0.5537, IoU.seat: 0.6343, IoU.fence: 0.4742, IoU.desk: 0.5126, IoU.rock: 0.5313, IoU.wardrobe: 0.4756, IoU.lamp: 0.7150, IoU.bathtub: 0.8701, IoU.railing: 0.4398, IoU.cushion: 0.6530, IoU.base: 0.3862, IoU.box: 0.3284, IoU.column: 0.5382, IoU.signboard: 0.4220, IoU.chest of drawers: 0.4167, IoU.counter: 0.4080, IoU.sand: 0.5748, IoU.sink: 0.7967, IoU.skyscraper: 0.5363, IoU.fireplace: 0.7878, IoU.refrigerator: 0.8135, IoU.grandstand: 0.5062, IoU.path: 0.3021, IoU.stairs: 0.3773, IoU.runway: 0.6897, IoU.case: 0.6485, IoU.pool table: 0.9352, IoU.pillow: 0.5900, IoU.screen door: 0.8646, IoU.stairway: 0.5282, IoU.river: 0.1498, IoU.bridge: 0.7138, IoU.bookcase: 0.3626, IoU.blind: 0.3876, IoU.coffee table: 0.5983, IoU.toilet: 0.9062, IoU.flower: 0.4495, IoU.book: 0.5446, IoU.hill: 0.0735, IoU.bench: 0.5142, IoU.countertop: 0.6478, IoU.stove: 0.8086, IoU.palm: 0.5309, IoU.kitchen island: 0.4640, IoU.computer: 0.7736, IoU.swivel chair: 0.5514, IoU.boat: 0.5843, IoU.bar: 0.6007, IoU.arcade machine: 0.8558, IoU.hovel: 0.3104, IoU.bus: 0.9245, IoU.towel: 0.7680, IoU.light: 0.5456, IoU.truck: 0.4741, IoU.tower: 0.1862, IoU.chandelier: 0.7119, IoU.awning: 0.3892, IoU.streetlight: 0.3623, IoU.booth: 0.3820, IoU.television receiver: 0.7636, IoU.airplane: 0.8747, IoU.dirt track: 0.0950, IoU.apparel: 0.6342, IoU.pole: 0.2637, IoU.land: 0.0011, IoU.bannister: 0.1873, IoU.escalator: 0.6108, IoU.ottoman: 0.4822, IoU.bottle: 0.3573, IoU.buffet: 0.5600, IoU.poster: 0.3805, IoU.stage: 0.2334, IoU.van: 0.3524, IoU.ship: 0.8152, IoU.fountain: 0.2300, IoU.conveyer belt: 0.8494, IoU.canopy: 0.4728, IoU.washer: 0.8736, IoU.plaything: 0.4581, IoU.swimming pool: 0.5477, IoU.stool: 0.5459, IoU.barrel: 0.5604, IoU.basket: 0.4186, IoU.waterfall: 0.6368, IoU.tent: 0.9252, IoU.bag: 0.2007, IoU.minibike: 0.7696, IoU.cradle: 0.9029, IoU.oven: 0.5221, IoU.ball: 0.4722, IoU.food: 0.6677, IoU.step: 0.1136, IoU.tank: 0.5593, IoU.trade name: 0.2082, IoU.microwave: 0.8928, IoU.pot: 0.5690, IoU.animal: 0.5883, IoU.bicycle: 0.6194, IoU.lake: 0.5855, IoU.dishwasher: 0.3037, IoU.screen: 0.6057, IoU.blanket: 0.3343, IoU.sculpture: 0.7016, IoU.hood: 0.6755, IoU.sconce: 0.5970, IoU.vase: 0.4699, IoU.traffic light: 0.3662, IoU.tray: 0.2092, IoU.ashcan: 0.4564, IoU.fan: 0.6836, IoU.pier: 0.3874, IoU.crt screen: 0.0995, IoU.plate: 0.6062, IoU.monitor: 0.1295, IoU.bulletin board: 0.5003, IoU.shower: 0.0188, IoU.radiator: 0.6732, IoU.glass: 0.1995, IoU.clock: 0.4657, IoU.flag: 0.6852, Acc.wall: 0.8818, Acc.building: 0.9290, Acc.sky: 0.9778, Acc.floor: 0.8874, Acc.tree: 0.9002, Acc.ceiling: 0.9538, Acc.road: 0.8914, Acc.bed : 0.9703, Acc.windowpane: 0.8153, Acc.grass: 0.8063, Acc.cabinet: 0.7946, Acc.sidewalk: 0.8660, Acc.person: 0.9390, Acc.earth: 0.5006, Acc.door: 0.7163, Acc.table: 0.8010, Acc.mountain: 0.7584, Acc.plant: 0.7330, Acc.curtain: 0.8780, Acc.chair: 0.8024, Acc.car: 0.9571, Acc.water: 0.6974, Acc.painting: 0.9237, Acc.sofa: 0.9391, Acc.shelf: 0.7010, Acc.house: 0.6365, Acc.sea: 0.8434, Acc.mirror: 0.8600, Acc.rug: 0.8492, Acc.field: 0.6132, Acc.armchair: 0.6610, Acc.seat: 0.8944, Acc.fence: 0.5884, Acc.desk: 0.8047, Acc.rock: 0.7491, Acc.wardrobe: 0.5905, Acc.lamp: 0.8546, Acc.bathtub: 0.9046, Acc.railing: 0.6721, Acc.cushion: 0.8096, Acc.base: 0.5982, Acc.box: 0.4020, Acc.column: 0.6442, Acc.signboard: 0.5734, Acc.chest of drawers: 0.6789, Acc.counter: 0.5266, Acc.sand: 0.8856, Acc.sink: 0.8408, Acc.skyscraper: 0.6496, Acc.fireplace: 0.9311, Acc.refrigerator: 0.8835, Acc.grandstand: 0.8600, Acc.path: 0.4121, Acc.stairs: 0.4505, Acc.runway: 0.9062, Acc.case: 0.7296, Acc.pool table: 0.9897, Acc.pillow: 0.6333, Acc.screen door: 0.9570, Acc.stairway: 0.7091, Acc.river: 0.3175, Acc.bridge: 0.8646, Acc.bookcase: 0.4486, Acc.blind: 0.4212, Acc.coffee table: 0.8776, Acc.toilet: 0.9506, Acc.flower: 0.5790, Acc.book: 0.8267, Acc.hill: 0.1338, Acc.bench: 0.5759, Acc.countertop: 0.8120, Acc.stove: 0.9347, Acc.palm: 0.8178, Acc.kitchen island: 0.7676, Acc.computer: 0.9212, Acc.swivel chair: 0.8189, Acc.boat: 0.9217, Acc.bar: 0.8336, Acc.arcade machine: 0.9943, Acc.hovel: 0.3443, Acc.bus: 0.9681, Acc.towel: 0.8800, Acc.light: 0.5927, Acc.truck: 0.6181, Acc.tower: 0.3753, Acc.chandelier: 0.8568, Acc.awning: 0.4790, Acc.streetlight: 0.5331, Acc.booth: 0.4219, Acc.television receiver: 0.9048, Acc.airplane: 0.9528, Acc.dirt track: 0.4065, Acc.apparel: 0.7776, Acc.pole: 0.3463, Acc.land: 0.0013, Acc.bannister: 0.2402, Acc.escalator: 0.8811, Acc.ottoman: 0.6080, Acc.bottle: 0.4577, Acc.buffet: 0.9088, Acc.poster: 0.5440, Acc.stage: 0.4799, Acc.van: 0.4030, Acc.ship: 0.9619, Acc.fountain: 0.2339, Acc.conveyer belt: 0.9465, Acc.canopy: 0.5352, Acc.washer: 0.9287, Acc.plaything: 0.6707, Acc.swimming pool: 0.8004, Acc.stool: 0.7295, Acc.barrel: 0.6978, Acc.basket: 0.5462, Acc.waterfall: 0.8353, Acc.tent: 0.9841, Acc.bag: 0.2303, Acc.minibike: 0.8927, Acc.cradle: 0.9650, Acc.oven: 0.8144, Acc.ball: 0.7935, Acc.food: 0.8792, Acc.step: 0.1320, Acc.tank: 0.7422, Acc.trade name: 0.2203, Acc.microwave: 0.9634, Acc.pot: 0.7090, Acc.animal: 0.6050, Acc.bicycle: 0.8192, Acc.lake: 0.6281, Acc.dishwasher: 0.3069, Acc.screen: 0.7307, Acc.blanket: 0.4027, Acc.sculpture: 0.8782, Acc.hood: 0.7436, Acc.sconce: 0.7331, Acc.vase: 0.5890, Acc.traffic light: 0.6564, Acc.tray: 0.2545, Acc.ashcan: 0.7089, Acc.fan: 0.8154, Acc.pier: 0.4245, Acc.crt screen: 0.3304, Acc.plate: 0.7393, Acc.monitor: 0.1569, Acc.bulletin board: 0.5103, Acc.shower: 0.0188, Acc.radiator: 0.8022, Acc.glass: 0.2167, Acc.clock: 0.5905, Acc.flag: 0.7863 +2024-06-18 14:58:48,313 - mmseg - INFO - Iter [21050/80000] lr: 2.948e-05, eta: 1 day, 10:59:17, time: 4.198, data_time: 2.229, memory: 72263, decode.loss_ce: 0.2687, decode.acc_seg: 88.8384, aux.loss_ce: 0.1093, aux.acc_seg: 88.7147, loss: 0.3780 +2024-06-18 15:00:27,377 - mmseg - INFO - Iter [21100/80000] lr: 2.945e-05, eta: 1 day, 10:57:09, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2983, decode.acc_seg: 87.7458, aux.loss_ce: 0.1219, aux.acc_seg: 87.4687, loss: 0.4202 +2024-06-18 15:02:06,350 - mmseg - INFO - Iter [21150/80000] lr: 2.943e-05, eta: 1 day, 10:55:00, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2773, decode.acc_seg: 88.6819, aux.loss_ce: 0.1132, aux.acc_seg: 88.3917, loss: 0.3904 +2024-06-18 15:03:45,389 - mmseg - INFO - Iter [21200/80000] lr: 2.940e-05, eta: 1 day, 10:52:52, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2617, decode.acc_seg: 89.2159, aux.loss_ce: 0.1072, aux.acc_seg: 88.8664, loss: 0.3688 +2024-06-18 15:05:24,468 - mmseg - INFO - Iter [21250/80000] lr: 2.938e-05, eta: 1 day, 10:50:44, time: 1.982, data_time: 0.012, memory: 72263, decode.loss_ce: 0.3067, decode.acc_seg: 87.8136, aux.loss_ce: 0.1248, aux.acc_seg: 87.6031, loss: 0.4315 +2024-06-18 15:07:03,600 - mmseg - INFO - Iter [21300/80000] lr: 2.935e-05, eta: 1 day, 10:48:36, time: 1.983, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2870, decode.acc_seg: 88.6147, aux.loss_ce: 0.1169, aux.acc_seg: 88.5206, loss: 0.4039 +2024-06-18 15:08:42,513 - mmseg - INFO - Iter [21350/80000] lr: 2.933e-05, eta: 1 day, 10:46:28, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3037, decode.acc_seg: 87.6964, aux.loss_ce: 0.1234, aux.acc_seg: 87.5642, loss: 0.4271 +2024-06-18 15:10:21,597 - mmseg - INFO - Iter [21400/80000] lr: 2.930e-05, eta: 1 day, 10:44:20, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.3021, decode.acc_seg: 88.0011, aux.loss_ce: 0.1228, aux.acc_seg: 87.6784, loss: 0.4249 +2024-06-18 15:12:00,611 - mmseg - INFO - Iter [21450/80000] lr: 2.928e-05, eta: 1 day, 10:42:12, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2980, decode.acc_seg: 87.9340, aux.loss_ce: 0.1214, aux.acc_seg: 87.5567, loss: 0.4194 +2024-06-18 15:13:42,597 - mmseg - INFO - Iter [21500/80000] lr: 2.925e-05, eta: 1 day, 10:40:13, time: 2.040, data_time: 0.070, memory: 72263, decode.loss_ce: 0.2889, decode.acc_seg: 88.3093, aux.loss_ce: 0.1181, aux.acc_seg: 88.0875, loss: 0.4070 +2024-06-18 15:15:21,658 - mmseg - INFO - Iter [21550/80000] lr: 2.923e-05, eta: 1 day, 10:38:05, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2519, decode.acc_seg: 89.6388, aux.loss_ce: 0.1037, aux.acc_seg: 89.3289, loss: 0.3556 +2024-06-18 15:17:00,725 - mmseg - INFO - Iter [21600/80000] lr: 2.920e-05, eta: 1 day, 10:35:58, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2515, decode.acc_seg: 89.3893, aux.loss_ce: 0.1032, aux.acc_seg: 89.0093, loss: 0.3547 +2024-06-18 15:18:39,800 - mmseg - INFO - Iter [21650/80000] lr: 2.918e-05, eta: 1 day, 10:33:51, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2802, decode.acc_seg: 88.6777, aux.loss_ce: 0.1144, aux.acc_seg: 88.3649, loss: 0.3946 +2024-06-18 15:20:18,774 - mmseg - INFO - Iter [21700/80000] lr: 2.915e-05, eta: 1 day, 10:31:44, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2747, decode.acc_seg: 88.9427, aux.loss_ce: 0.1122, aux.acc_seg: 88.6941, loss: 0.3869 +2024-06-18 15:21:57,894 - mmseg - INFO - Iter [21750/80000] lr: 2.913e-05, eta: 1 day, 10:29:37, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2650, decode.acc_seg: 88.9791, aux.loss_ce: 0.1091, aux.acc_seg: 88.7106, loss: 0.3741 +2024-06-18 15:23:37,101 - mmseg - INFO - Iter [21800/80000] lr: 2.910e-05, eta: 1 day, 10:27:31, time: 1.984, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2806, decode.acc_seg: 88.4580, aux.loss_ce: 0.1136, aux.acc_seg: 88.2517, loss: 0.3942 +2024-06-18 15:25:16,097 - mmseg - INFO - Iter [21850/80000] lr: 2.908e-05, eta: 1 day, 10:25:24, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2632, decode.acc_seg: 89.1322, aux.loss_ce: 0.1079, aux.acc_seg: 88.9536, loss: 0.3711 +2024-06-18 15:26:55,154 - mmseg - INFO - Iter [21900/80000] lr: 2.905e-05, eta: 1 day, 10:23:18, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2710, decode.acc_seg: 88.8679, aux.loss_ce: 0.1110, aux.acc_seg: 88.5241, loss: 0.3821 +2024-06-18 15:28:34,207 - mmseg - INFO - Iter [21950/80000] lr: 2.903e-05, eta: 1 day, 10:21:11, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2839, decode.acc_seg: 88.8042, aux.loss_ce: 0.1160, aux.acc_seg: 88.4858, loss: 0.3999 +2024-06-18 15:30:13,216 - mmseg - INFO - Saving checkpoint at 22000 iterations +2024-06-18 15:31:38,684 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 15:31:38,685 - mmseg - INFO - Iter [22000/80000] lr: 2.900e-05, eta: 1 day, 10:22:50, time: 3.690, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2632, decode.acc_seg: 89.2836, aux.loss_ce: 0.1080, aux.acc_seg: 88.8975, loss: 0.3712 +2024-06-18 15:33:29,834 - mmseg - INFO - per class results: +2024-06-18 15:33:29,840 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.08 | 87.63 | +| building | 84.96 | 93.71 | +| sky | 94.52 | 96.5 | +| floor | 84.2 | 90.22 | +| tree | 77.45 | 90.58 | +| ceiling | 86.37 | 92.65 | +| road | 85.21 | 91.11 | +| bed | 92.52 | 96.93 | +| windowpane | 65.62 | 81.22 | +| grass | 68.33 | 78.85 | +| cabinet | 66.05 | 73.79 | +| sidewalk | 69.44 | 84.13 | +| person | 85.24 | 93.37 | +| earth | 43.11 | 56.13 | +| door | 59.3 | 74.52 | +| table | 66.87 | 76.78 | +| mountain | 63.4 | 83.91 | +| plant | 58.15 | 72.04 | +| curtain | 79.2 | 90.21 | +| chair | 66.01 | 76.35 | +| car | 86.86 | 93.47 | +| water | 55.24 | 67.31 | +| painting | 77.21 | 90.47 | +| sofa | 79.58 | 87.25 | +| shelf | 47.61 | 65.5 | +| house | 57.21 | 76.28 | +| sea | 65.27 | 86.75 | +| mirror | 75.31 | 81.9 | +| rug | 71.34 | 83.05 | +| field | 33.95 | 62.52 | +| armchair | 58.86 | 86.8 | +| seat | 69.98 | 87.1 | +| fence | 48.74 | 60.44 | +| desk | 52.7 | 84.58 | +| rock | 44.27 | 56.26 | +| wardrobe | 54.9 | 82.32 | +| lamp | 72.73 | 85.14 | +| bathtub | 86.33 | 89.3 | +| railing | 41.06 | 54.36 | +| cushion | 68.9 | 77.47 | +| base | 40.82 | 64.41 | +| box | 38.6 | 50.04 | +| column | 52.93 | 63.65 | +| signboard | 42.95 | 57.48 | +| chest of drawers | 42.53 | 82.0 | +| counter | 44.61 | 51.61 | +| sand | 48.3 | 78.29 | +| sink | 81.63 | 86.39 | +| skyscraper | 52.3 | 68.13 | +| fireplace | 67.87 | 96.55 | +| refrigerator | 86.47 | 95.35 | +| grandstand | 46.78 | 82.44 | +| path | 33.9 | 44.37 | +| stairs | 24.85 | 29.75 | +| runway | 67.06 | 88.19 | +| case | 65.4 | 91.86 | +| pool table | 93.85 | 98.66 | +| pillow | 68.26 | 80.08 | +| screen door | 84.66 | 93.1 | +| stairway | 43.99 | 71.99 | +| river | 14.43 | 31.37 | +| bridge | 43.38 | 51.08 | +| bookcase | 40.12 | 61.31 | +| blind | 45.47 | 52.14 | +| coffee table | 59.16 | 89.54 | +| toilet | 90.11 | 93.39 | +| flower | 40.98 | 52.0 | +| book | 56.32 | 75.4 | +| hill | 5.78 | 8.93 | +| bench | 66.31 | 79.89 | +| countertop | 65.23 | 82.74 | +| stove | 84.7 | 91.75 | +| palm | 53.91 | 84.8 | +| kitchen island | 47.13 | 86.05 | +| computer | 78.08 | 91.01 | +| swivel chair | 55.04 | 86.26 | +| boat | 58.16 | 92.94 | +| bar | 66.1 | 91.49 | +| arcade machine | 89.32 | 96.75 | +| hovel | 10.29 | 11.11 | +| bus | 91.25 | 97.1 | +| towel | 78.61 | 84.63 | +| light | 59.44 | 78.34 | +| truck | 45.95 | 62.38 | +| tower | 23.8 | 32.29 | +| chandelier | 69.42 | 89.59 | +| awning | 52.63 | 68.74 | +| streetlight | 33.34 | 43.3 | +| booth | 52.3 | 54.11 | +| television receiver | 78.83 | 82.76 | +| airplane | 83.49 | 97.72 | +| dirt track | 9.45 | 60.11 | +| apparel | 58.15 | 80.69 | +| pole | 27.39 | 35.21 | +| land | 1.53 | 2.26 | +| bannister | 20.66 | 29.41 | +| escalator | 58.44 | 88.63 | +| ottoman | 53.76 | 71.13 | +| bottle | 41.95 | 67.63 | +| buffet | 64.67 | 79.61 | +| poster | 33.23 | 59.31 | +| stage | 16.23 | 82.46 | +| van | 47.42 | 71.89 | +| ship | 76.38 | 78.88 | +| fountain | 31.18 | 34.72 | +| conveyer belt | 83.56 | 95.22 | +| canopy | 44.77 | 67.7 | +| washer | 86.27 | 92.52 | +| plaything | 52.28 | 63.05 | +| swimming pool | 56.42 | 82.18 | +| stool | 55.51 | 66.31 | +| barrel | 50.81 | 71.02 | +| basket | 43.04 | 55.98 | +| waterfall | 60.0 | 86.51 | +| tent | 87.39 | 99.18 | +| bag | 20.68 | 23.15 | +| minibike | 76.15 | 89.77 | +| cradle | 83.51 | 97.33 | +| oven | 65.01 | 76.98 | +| ball | 57.57 | 65.99 | +| food | 59.57 | 68.43 | +| step | 12.38 | 14.34 | +| tank | 51.55 | 71.52 | +| trade name | 15.78 | 17.2 | +| microwave | 89.13 | 97.12 | +| pot | 58.47 | 66.97 | +| animal | 61.79 | 63.67 | +| bicycle | 59.47 | 76.83 | +| lake | 33.65 | 38.76 | +| dishwasher | 74.82 | 78.2 | +| screen | 57.9 | 97.7 | +| blanket | 36.55 | 44.21 | +| sculpture | 74.7 | 85.36 | +| hood | 71.82 | 88.56 | +| sconce | 58.23 | 76.94 | +| vase | 48.27 | 64.32 | +| traffic light | 35.86 | 65.24 | +| tray | 21.77 | 26.4 | +| ashcan | 48.61 | 59.07 | +| fan | 70.58 | 81.01 | +| pier | 39.51 | 43.98 | +| crt screen | 1.18 | 3.2 | +| plate | 61.54 | 75.86 | +| monitor | 16.45 | 21.4 | +| bulletin board | 54.6 | 74.35 | +| shower | 5.51 | 8.79 | +| radiator | 66.36 | 80.58 | +| glass | 20.73 | 22.46 | +| clock | 46.94 | 55.28 | +| flag | 66.48 | 83.32 | ++---------------------+-------+-------+ +2024-06-18 15:33:29,840 - mmseg - INFO - Summary: +2024-06-18 15:33:29,840 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 85.61 | 56.5 | 70.93 | ++-------+------+-------+ +2024-06-18 15:33:29,841 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 15:33:29,841 - mmseg - INFO - Iter(val) [250] aAcc: 0.8561, mIoU: 0.5650, mAcc: 0.7093, IoU.wall: 0.8108, IoU.building: 0.8496, IoU.sky: 0.9452, IoU.floor: 0.8420, IoU.tree: 0.7745, IoU.ceiling: 0.8637, IoU.road: 0.8521, IoU.bed : 0.9252, IoU.windowpane: 0.6562, IoU.grass: 0.6833, IoU.cabinet: 0.6605, IoU.sidewalk: 0.6944, IoU.person: 0.8524, IoU.earth: 0.4311, IoU.door: 0.5930, IoU.table: 0.6687, IoU.mountain: 0.6340, IoU.plant: 0.5815, IoU.curtain: 0.7920, IoU.chair: 0.6601, IoU.car: 0.8686, IoU.water: 0.5524, IoU.painting: 0.7721, IoU.sofa: 0.7958, IoU.shelf: 0.4761, IoU.house: 0.5721, IoU.sea: 0.6527, IoU.mirror: 0.7531, IoU.rug: 0.7134, IoU.field: 0.3395, IoU.armchair: 0.5886, IoU.seat: 0.6998, IoU.fence: 0.4874, IoU.desk: 0.5270, IoU.rock: 0.4427, IoU.wardrobe: 0.5490, IoU.lamp: 0.7273, IoU.bathtub: 0.8633, IoU.railing: 0.4106, IoU.cushion: 0.6890, IoU.base: 0.4082, IoU.box: 0.3860, IoU.column: 0.5293, IoU.signboard: 0.4295, IoU.chest of drawers: 0.4253, IoU.counter: 0.4461, IoU.sand: 0.4830, IoU.sink: 0.8163, IoU.skyscraper: 0.5230, IoU.fireplace: 0.6787, IoU.refrigerator: 0.8647, IoU.grandstand: 0.4678, IoU.path: 0.3390, IoU.stairs: 0.2485, IoU.runway: 0.6706, IoU.case: 0.6540, IoU.pool table: 0.9385, IoU.pillow: 0.6826, IoU.screen door: 0.8466, IoU.stairway: 0.4399, IoU.river: 0.1443, IoU.bridge: 0.4338, IoU.bookcase: 0.4012, IoU.blind: 0.4547, IoU.coffee table: 0.5916, IoU.toilet: 0.9011, IoU.flower: 0.4098, IoU.book: 0.5632, IoU.hill: 0.0578, IoU.bench: 0.6631, IoU.countertop: 0.6523, IoU.stove: 0.8470, IoU.palm: 0.5391, IoU.kitchen island: 0.4713, IoU.computer: 0.7808, IoU.swivel chair: 0.5504, IoU.boat: 0.5816, IoU.bar: 0.6610, IoU.arcade machine: 0.8932, IoU.hovel: 0.1029, IoU.bus: 0.9125, IoU.towel: 0.7861, IoU.light: 0.5944, IoU.truck: 0.4595, IoU.tower: 0.2380, IoU.chandelier: 0.6942, IoU.awning: 0.5263, IoU.streetlight: 0.3334, IoU.booth: 0.5230, IoU.television receiver: 0.7883, IoU.airplane: 0.8349, IoU.dirt track: 0.0945, IoU.apparel: 0.5815, IoU.pole: 0.2739, IoU.land: 0.0153, IoU.bannister: 0.2066, IoU.escalator: 0.5844, IoU.ottoman: 0.5376, IoU.bottle: 0.4195, IoU.buffet: 0.6467, IoU.poster: 0.3323, IoU.stage: 0.1623, IoU.van: 0.4742, IoU.ship: 0.7638, IoU.fountain: 0.3118, IoU.conveyer belt: 0.8356, IoU.canopy: 0.4477, IoU.washer: 0.8627, IoU.plaything: 0.5228, IoU.swimming pool: 0.5642, IoU.stool: 0.5551, IoU.barrel: 0.5081, IoU.basket: 0.4304, IoU.waterfall: 0.6000, IoU.tent: 0.8739, IoU.bag: 0.2068, IoU.minibike: 0.7615, IoU.cradle: 0.8351, IoU.oven: 0.6501, IoU.ball: 0.5757, IoU.food: 0.5957, IoU.step: 0.1238, IoU.tank: 0.5155, IoU.trade name: 0.1578, IoU.microwave: 0.8913, IoU.pot: 0.5847, IoU.animal: 0.6179, IoU.bicycle: 0.5947, IoU.lake: 0.3365, IoU.dishwasher: 0.7482, IoU.screen: 0.5790, IoU.blanket: 0.3655, IoU.sculpture: 0.7470, IoU.hood: 0.7182, IoU.sconce: 0.5823, IoU.vase: 0.4827, IoU.traffic light: 0.3586, IoU.tray: 0.2177, IoU.ashcan: 0.4861, IoU.fan: 0.7058, IoU.pier: 0.3951, IoU.crt screen: 0.0118, IoU.plate: 0.6154, IoU.monitor: 0.1645, IoU.bulletin board: 0.5460, IoU.shower: 0.0551, IoU.radiator: 0.6636, IoU.glass: 0.2073, IoU.clock: 0.4694, IoU.flag: 0.6648, Acc.wall: 0.8763, Acc.building: 0.9371, Acc.sky: 0.9650, Acc.floor: 0.9022, Acc.tree: 0.9058, Acc.ceiling: 0.9265, Acc.road: 0.9111, Acc.bed : 0.9693, Acc.windowpane: 0.8122, Acc.grass: 0.7885, Acc.cabinet: 0.7379, Acc.sidewalk: 0.8413, Acc.person: 0.9337, Acc.earth: 0.5613, Acc.door: 0.7452, Acc.table: 0.7678, Acc.mountain: 0.8391, Acc.plant: 0.7204, Acc.curtain: 0.9021, Acc.chair: 0.7635, Acc.car: 0.9347, Acc.water: 0.6731, Acc.painting: 0.9047, Acc.sofa: 0.8725, Acc.shelf: 0.6550, Acc.house: 0.7628, Acc.sea: 0.8675, Acc.mirror: 0.8190, Acc.rug: 0.8305, Acc.field: 0.6252, Acc.armchair: 0.8680, Acc.seat: 0.8710, Acc.fence: 0.6044, Acc.desk: 0.8458, Acc.rock: 0.5626, Acc.wardrobe: 0.8232, Acc.lamp: 0.8514, Acc.bathtub: 0.8930, Acc.railing: 0.5436, Acc.cushion: 0.7747, Acc.base: 0.6441, Acc.box: 0.5004, Acc.column: 0.6365, Acc.signboard: 0.5748, Acc.chest of drawers: 0.8200, Acc.counter: 0.5161, Acc.sand: 0.7829, Acc.sink: 0.8639, Acc.skyscraper: 0.6813, Acc.fireplace: 0.9655, Acc.refrigerator: 0.9535, Acc.grandstand: 0.8244, Acc.path: 0.4437, Acc.stairs: 0.2975, Acc.runway: 0.8819, Acc.case: 0.9186, Acc.pool table: 0.9866, Acc.pillow: 0.8008, Acc.screen door: 0.9310, Acc.stairway: 0.7199, Acc.river: 0.3137, Acc.bridge: 0.5108, Acc.bookcase: 0.6131, Acc.blind: 0.5214, Acc.coffee table: 0.8954, Acc.toilet: 0.9339, Acc.flower: 0.5200, Acc.book: 0.7540, Acc.hill: 0.0893, Acc.bench: 0.7989, Acc.countertop: 0.8274, Acc.stove: 0.9175, Acc.palm: 0.8480, Acc.kitchen island: 0.8605, Acc.computer: 0.9101, Acc.swivel chair: 0.8626, Acc.boat: 0.9294, Acc.bar: 0.9149, Acc.arcade machine: 0.9675, Acc.hovel: 0.1111, Acc.bus: 0.9710, Acc.towel: 0.8463, Acc.light: 0.7834, Acc.truck: 0.6238, Acc.tower: 0.3229, Acc.chandelier: 0.8959, Acc.awning: 0.6874, Acc.streetlight: 0.4330, Acc.booth: 0.5411, Acc.television receiver: 0.8276, Acc.airplane: 0.9772, Acc.dirt track: 0.6011, Acc.apparel: 0.8069, Acc.pole: 0.3521, Acc.land: 0.0226, Acc.bannister: 0.2941, Acc.escalator: 0.8863, Acc.ottoman: 0.7113, Acc.bottle: 0.6763, Acc.buffet: 0.7961, Acc.poster: 0.5931, Acc.stage: 0.8246, Acc.van: 0.7189, Acc.ship: 0.7888, Acc.fountain: 0.3472, Acc.conveyer belt: 0.9522, Acc.canopy: 0.6770, Acc.washer: 0.9252, Acc.plaything: 0.6305, Acc.swimming pool: 0.8218, Acc.stool: 0.6631, Acc.barrel: 0.7102, Acc.basket: 0.5598, Acc.waterfall: 0.8651, Acc.tent: 0.9918, Acc.bag: 0.2315, Acc.minibike: 0.8977, Acc.cradle: 0.9733, Acc.oven: 0.7698, Acc.ball: 0.6599, Acc.food: 0.6843, Acc.step: 0.1434, Acc.tank: 0.7152, Acc.trade name: 0.1720, Acc.microwave: 0.9712, Acc.pot: 0.6697, Acc.animal: 0.6367, Acc.bicycle: 0.7683, Acc.lake: 0.3876, Acc.dishwasher: 0.7820, Acc.screen: 0.9770, Acc.blanket: 0.4421, Acc.sculpture: 0.8536, Acc.hood: 0.8856, Acc.sconce: 0.7694, Acc.vase: 0.6432, Acc.traffic light: 0.6524, Acc.tray: 0.2640, Acc.ashcan: 0.5907, Acc.fan: 0.8101, Acc.pier: 0.4398, Acc.crt screen: 0.0320, Acc.plate: 0.7586, Acc.monitor: 0.2140, Acc.bulletin board: 0.7435, Acc.shower: 0.0879, Acc.radiator: 0.8058, Acc.glass: 0.2246, Acc.clock: 0.5528, Acc.flag: 0.8332 +2024-06-18 15:35:09,193 - mmseg - INFO - Iter [22050/80000] lr: 2.898e-05, eta: 1 day, 10:25:37, time: 4.210, data_time: 2.241, memory: 72263, decode.loss_ce: 0.2566, decode.acc_seg: 89.3382, aux.loss_ce: 0.1046, aux.acc_seg: 89.2069, loss: 0.3612 +2024-06-18 15:36:48,124 - mmseg - INFO - Iter [22100/80000] lr: 2.895e-05, eta: 1 day, 10:23:29, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2493, decode.acc_seg: 89.6218, aux.loss_ce: 0.1023, aux.acc_seg: 89.4199, loss: 0.3516 +2024-06-18 15:38:27,188 - mmseg - INFO - Iter [22150/80000] lr: 2.893e-05, eta: 1 day, 10:21:21, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2691, decode.acc_seg: 88.9806, aux.loss_ce: 0.1102, aux.acc_seg: 88.7223, loss: 0.3793 +2024-06-18 15:40:06,227 - mmseg - INFO - Iter [22200/80000] lr: 2.890e-05, eta: 1 day, 10:19:14, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2735, decode.acc_seg: 88.9580, aux.loss_ce: 0.1129, aux.acc_seg: 88.7155, loss: 0.3863 +2024-06-18 15:41:45,154 - mmseg - INFO - Iter [22250/80000] lr: 2.888e-05, eta: 1 day, 10:17:06, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2692, decode.acc_seg: 88.9841, aux.loss_ce: 0.1099, aux.acc_seg: 88.7588, loss: 0.3792 +2024-06-18 15:43:24,334 - mmseg - INFO - Iter [22300/80000] lr: 2.885e-05, eta: 1 day, 10:15:00, time: 1.984, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2804, decode.acc_seg: 88.8184, aux.loss_ce: 0.1146, aux.acc_seg: 88.4994, loss: 0.3950 +2024-06-18 15:45:03,314 - mmseg - INFO - Iter [22350/80000] lr: 2.883e-05, eta: 1 day, 10:12:52, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2711, decode.acc_seg: 88.8760, aux.loss_ce: 0.1109, aux.acc_seg: 88.6756, loss: 0.3820 +2024-06-18 15:46:42,229 - mmseg - INFO - Iter [22400/80000] lr: 2.880e-05, eta: 1 day, 10:10:45, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2727, decode.acc_seg: 88.6457, aux.loss_ce: 0.1112, aux.acc_seg: 88.4906, loss: 0.3839 +2024-06-18 15:48:21,052 - mmseg - INFO - Iter [22450/80000] lr: 2.878e-05, eta: 1 day, 10:08:38, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2808, decode.acc_seg: 88.2535, aux.loss_ce: 0.1154, aux.acc_seg: 88.0308, loss: 0.3962 +2024-06-18 15:50:00,025 - mmseg - INFO - Iter [22500/80000] lr: 2.875e-05, eta: 1 day, 10:06:31, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2564, decode.acc_seg: 88.9493, aux.loss_ce: 0.1049, aux.acc_seg: 88.7562, loss: 0.3613 +2024-06-18 15:51:39,123 - mmseg - INFO - Iter [22550/80000] lr: 2.873e-05, eta: 1 day, 10:04:25, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2638, decode.acc_seg: 89.4331, aux.loss_ce: 0.1078, aux.acc_seg: 89.1449, loss: 0.3716 +2024-06-18 15:53:18,043 - mmseg - INFO - Iter [22600/80000] lr: 2.870e-05, eta: 1 day, 10:02:18, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2718, decode.acc_seg: 88.9074, aux.loss_ce: 0.1108, aux.acc_seg: 88.6202, loss: 0.3826 +2024-06-18 15:54:57,012 - mmseg - INFO - Iter [22650/80000] lr: 2.868e-05, eta: 1 day, 10:00:12, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2644, decode.acc_seg: 89.1316, aux.loss_ce: 0.1079, aux.acc_seg: 88.8392, loss: 0.3723 +2024-06-18 15:56:35,969 - mmseg - INFO - Iter [22700/80000] lr: 2.865e-05, eta: 1 day, 9:58:05, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2682, decode.acc_seg: 89.2449, aux.loss_ce: 0.1100, aux.acc_seg: 88.8995, loss: 0.3781 +2024-06-18 15:58:17,266 - mmseg - INFO - Iter [22750/80000] lr: 2.863e-05, eta: 1 day, 9:56:05, time: 2.026, data_time: 0.055, memory: 72263, decode.loss_ce: 0.2553, decode.acc_seg: 89.2874, aux.loss_ce: 0.1055, aux.acc_seg: 88.9402, loss: 0.3607 +2024-06-18 15:59:56,204 - mmseg - INFO - Iter [22800/80000] lr: 2.860e-05, eta: 1 day, 9:53:59, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2654, decode.acc_seg: 89.0692, aux.loss_ce: 0.1081, aux.acc_seg: 88.9071, loss: 0.3735 +2024-06-18 16:01:35,155 - mmseg - INFO - Iter [22850/80000] lr: 2.858e-05, eta: 1 day, 9:51:53, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2701, decode.acc_seg: 88.8653, aux.loss_ce: 0.1102, aux.acc_seg: 88.6199, loss: 0.3803 +2024-06-18 16:03:14,071 - mmseg - INFO - Iter [22900/80000] lr: 2.855e-05, eta: 1 day, 9:49:47, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2730, decode.acc_seg: 89.0003, aux.loss_ce: 0.1120, aux.acc_seg: 88.6443, loss: 0.3850 +2024-06-18 16:04:53,148 - mmseg - INFO - Iter [22950/80000] lr: 2.853e-05, eta: 1 day, 9:47:42, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2783, decode.acc_seg: 88.7658, aux.loss_ce: 0.1143, aux.acc_seg: 88.3552, loss: 0.3927 +2024-06-18 16:06:32,257 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 16:06:32,257 - mmseg - INFO - Iter [23000/80000] lr: 2.850e-05, eta: 1 day, 9:45:36, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2618, decode.acc_seg: 89.3448, aux.loss_ce: 0.1066, aux.acc_seg: 89.0776, loss: 0.3684 +2024-06-18 16:08:22,539 - mmseg - INFO - per class results: +2024-06-18 16:08:22,546 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.53 | 88.6 | +| building | 84.33 | 93.15 | +| sky | 94.91 | 97.32 | +| floor | 84.46 | 90.77 | +| tree | 78.08 | 90.9 | +| ceiling | 86.72 | 93.89 | +| road | 84.85 | 92.15 | +| bed | 92.21 | 96.82 | +| windowpane | 66.14 | 83.79 | +| grass | 67.91 | 78.64 | +| cabinet | 66.32 | 75.09 | +| sidewalk | 68.43 | 79.51 | +| person | 85.34 | 94.95 | +| earth | 39.4 | 57.65 | +| door | 55.17 | 74.96 | +| table | 66.15 | 79.58 | +| mountain | 58.21 | 64.19 | +| plant | 58.19 | 70.27 | +| curtain | 80.67 | 90.7 | +| chair | 64.7 | 73.54 | +| car | 85.47 | 92.37 | +| water | 65.16 | 79.22 | +| painting | 80.43 | 90.45 | +| sofa | 81.26 | 90.87 | +| shelf | 51.11 | 65.87 | +| house | 43.9 | 52.07 | +| sea | 77.53 | 88.4 | +| mirror | 78.07 | 85.18 | +| rug | 69.24 | 81.17 | +| field | 33.35 | 63.64 | +| armchair | 56.36 | 81.84 | +| seat | 72.33 | 86.47 | +| fence | 50.17 | 62.93 | +| desk | 54.97 | 77.11 | +| rock | 51.87 | 78.01 | +| wardrobe | 57.65 | 79.5 | +| lamp | 74.47 | 86.11 | +| bathtub | 88.81 | 93.97 | +| railing | 41.56 | 55.36 | +| cushion | 67.89 | 79.14 | +| base | 37.28 | 76.98 | +| box | 34.53 | 47.71 | +| column | 54.09 | 65.43 | +| signboard | 43.57 | 59.92 | +| chest of drawers | 49.28 | 77.18 | +| counter | 50.07 | 63.65 | +| sand | 49.51 | 77.12 | +| sink | 77.27 | 83.83 | +| skyscraper | 50.0 | 60.43 | +| fireplace | 75.98 | 93.71 | +| refrigerator | 86.35 | 95.28 | +| grandstand | 50.87 | 75.25 | +| path | 28.33 | 40.74 | +| stairs | 29.27 | 36.92 | +| runway | 66.29 | 83.08 | +| case | 64.45 | 89.06 | +| pool table | 94.6 | 98.46 | +| pillow | 66.13 | 74.98 | +| screen door | 61.23 | 61.9 | +| stairway | 48.8 | 73.18 | +| river | 13.57 | 19.89 | +| bridge | 65.94 | 75.37 | +| bookcase | 44.82 | 55.16 | +| blind | 40.98 | 43.59 | +| coffee table | 61.17 | 87.15 | +| toilet | 90.42 | 94.39 | +| flower | 47.17 | 57.83 | +| book | 54.58 | 70.81 | +| hill | 10.9 | 19.16 | +| bench | 66.26 | 79.71 | +| countertop | 66.42 | 84.6 | +| stove | 86.23 | 92.02 | +| palm | 50.86 | 84.35 | +| kitchen island | 42.06 | 91.36 | +| computer | 79.02 | 91.95 | +| swivel chair | 51.91 | 87.63 | +| boat | 66.07 | 90.51 | +| bar | 63.94 | 79.28 | +| arcade machine | 91.45 | 97.72 | +| hovel | 14.17 | 14.99 | +| bus | 92.56 | 97.8 | +| towel | 76.2 | 90.22 | +| light | 60.61 | 73.07 | +| truck | 33.78 | 80.98 | +| tower | 15.32 | 22.71 | +| chandelier | 72.43 | 82.96 | +| awning | 47.24 | 65.14 | +| streetlight | 35.7 | 48.23 | +| booth | 41.83 | 49.43 | +| television receiver | 79.58 | 88.23 | +| airplane | 87.6 | 96.84 | +| dirt track | 2.78 | 14.35 | +| apparel | 57.6 | 74.55 | +| pole | 26.3 | 32.96 | +| land | 0.33 | 0.48 | +| bannister | 21.16 | 28.64 | +| escalator | 58.55 | 87.5 | +| ottoman | 55.05 | 76.25 | +| bottle | 42.12 | 55.51 | +| buffet | 65.16 | 89.03 | +| poster | 32.89 | 36.64 | +| stage | 19.74 | 41.18 | +| van | 46.21 | 59.14 | +| ship | 12.12 | 12.73 | +| fountain | 33.96 | 39.58 | +| conveyer belt | 83.7 | 94.41 | +| canopy | 60.65 | 73.14 | +| washer | 81.6 | 85.97 | +| plaything | 30.21 | 40.66 | +| swimming pool | 53.27 | 76.68 | +| stool | 54.64 | 69.33 | +| barrel | 53.93 | 64.61 | +| basket | 44.6 | 54.07 | +| waterfall | 49.42 | 54.62 | +| tent | 95.45 | 98.76 | +| bag | 28.28 | 31.63 | +| minibike | 75.01 | 89.77 | +| cradle | 90.09 | 96.66 | +| oven | 67.59 | 84.0 | +| ball | 60.19 | 74.36 | +| food | 61.75 | 66.99 | +| step | 19.49 | 24.05 | +| tank | 57.64 | 79.59 | +| trade name | 23.28 | 26.3 | +| microwave | 90.16 | 96.24 | +| pot | 59.05 | 71.98 | +| animal | 66.47 | 69.18 | +| bicycle | 60.47 | 79.33 | +| lake | 40.67 | 79.88 | +| dishwasher | 68.07 | 77.14 | +| screen | 66.22 | 95.92 | +| blanket | 33.15 | 41.12 | +| sculpture | 75.38 | 87.66 | +| hood | 63.67 | 74.49 | +| sconce | 60.2 | 73.44 | +| vase | 46.19 | 68.82 | +| traffic light | 35.9 | 63.74 | +| tray | 24.65 | 35.53 | +| ashcan | 52.0 | 65.2 | +| fan | 69.66 | 77.23 | +| pier | 37.43 | 41.66 | +| crt screen | 6.03 | 13.06 | +| plate | 61.65 | 79.41 | +| monitor | 27.25 | 34.93 | +| bulletin board | 61.28 | 82.71 | +| shower | 0.26 | 0.26 | +| radiator | 68.29 | 79.06 | +| glass | 20.14 | 21.31 | +| clock | 47.0 | 65.67 | +| flag | 69.76 | 82.77 | ++---------------------+-------+-------+ +2024-06-18 16:08:22,546 - mmseg - INFO - Summary: +2024-06-18 16:08:22,546 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 85.68 | 56.6 | 69.84 | ++-------+------+-------+ +2024-06-18 16:08:22,547 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 16:08:22,547 - mmseg - INFO - Iter(val) [250] aAcc: 0.8568, mIoU: 0.5660, mAcc: 0.6984, IoU.wall: 0.8153, IoU.building: 0.8433, IoU.sky: 0.9491, IoU.floor: 0.8446, IoU.tree: 0.7808, IoU.ceiling: 0.8672, IoU.road: 0.8485, IoU.bed : 0.9221, IoU.windowpane: 0.6614, IoU.grass: 0.6791, IoU.cabinet: 0.6632, IoU.sidewalk: 0.6843, IoU.person: 0.8534, IoU.earth: 0.3940, IoU.door: 0.5517, IoU.table: 0.6615, IoU.mountain: 0.5821, IoU.plant: 0.5819, IoU.curtain: 0.8067, IoU.chair: 0.6470, IoU.car: 0.8547, IoU.water: 0.6516, IoU.painting: 0.8043, IoU.sofa: 0.8126, IoU.shelf: 0.5111, IoU.house: 0.4390, IoU.sea: 0.7753, IoU.mirror: 0.7807, IoU.rug: 0.6924, IoU.field: 0.3335, IoU.armchair: 0.5636, IoU.seat: 0.7233, IoU.fence: 0.5017, IoU.desk: 0.5497, IoU.rock: 0.5187, IoU.wardrobe: 0.5765, IoU.lamp: 0.7447, IoU.bathtub: 0.8881, IoU.railing: 0.4156, IoU.cushion: 0.6789, IoU.base: 0.3728, IoU.box: 0.3453, IoU.column: 0.5409, IoU.signboard: 0.4357, IoU.chest of drawers: 0.4928, IoU.counter: 0.5007, IoU.sand: 0.4951, IoU.sink: 0.7727, IoU.skyscraper: 0.5000, IoU.fireplace: 0.7598, IoU.refrigerator: 0.8635, IoU.grandstand: 0.5087, IoU.path: 0.2833, IoU.stairs: 0.2927, IoU.runway: 0.6629, IoU.case: 0.6445, IoU.pool table: 0.9460, IoU.pillow: 0.6613, IoU.screen door: 0.6123, IoU.stairway: 0.4880, IoU.river: 0.1357, IoU.bridge: 0.6594, IoU.bookcase: 0.4482, IoU.blind: 0.4098, IoU.coffee table: 0.6117, IoU.toilet: 0.9042, IoU.flower: 0.4717, IoU.book: 0.5458, IoU.hill: 0.1090, IoU.bench: 0.6626, IoU.countertop: 0.6642, IoU.stove: 0.8623, IoU.palm: 0.5086, IoU.kitchen island: 0.4206, IoU.computer: 0.7902, IoU.swivel chair: 0.5191, IoU.boat: 0.6607, IoU.bar: 0.6394, IoU.arcade machine: 0.9145, IoU.hovel: 0.1417, IoU.bus: 0.9256, IoU.towel: 0.7620, IoU.light: 0.6061, IoU.truck: 0.3378, IoU.tower: 0.1532, IoU.chandelier: 0.7243, IoU.awning: 0.4724, IoU.streetlight: 0.3570, IoU.booth: 0.4183, IoU.television receiver: 0.7958, IoU.airplane: 0.8760, IoU.dirt track: 0.0278, IoU.apparel: 0.5760, IoU.pole: 0.2630, IoU.land: 0.0033, IoU.bannister: 0.2116, IoU.escalator: 0.5855, IoU.ottoman: 0.5505, IoU.bottle: 0.4212, IoU.buffet: 0.6516, IoU.poster: 0.3289, IoU.stage: 0.1974, IoU.van: 0.4621, IoU.ship: 0.1212, IoU.fountain: 0.3396, IoU.conveyer belt: 0.8370, IoU.canopy: 0.6065, IoU.washer: 0.8160, IoU.plaything: 0.3021, IoU.swimming pool: 0.5327, IoU.stool: 0.5464, IoU.barrel: 0.5393, IoU.basket: 0.4460, IoU.waterfall: 0.4942, IoU.tent: 0.9545, IoU.bag: 0.2828, IoU.minibike: 0.7501, IoU.cradle: 0.9009, IoU.oven: 0.6759, IoU.ball: 0.6019, IoU.food: 0.6175, IoU.step: 0.1949, IoU.tank: 0.5764, IoU.trade name: 0.2328, IoU.microwave: 0.9016, IoU.pot: 0.5905, IoU.animal: 0.6647, IoU.bicycle: 0.6047, IoU.lake: 0.4067, IoU.dishwasher: 0.6807, IoU.screen: 0.6622, IoU.blanket: 0.3315, IoU.sculpture: 0.7538, IoU.hood: 0.6367, IoU.sconce: 0.6020, IoU.vase: 0.4619, IoU.traffic light: 0.3590, IoU.tray: 0.2465, IoU.ashcan: 0.5200, IoU.fan: 0.6966, IoU.pier: 0.3743, IoU.crt screen: 0.0603, IoU.plate: 0.6165, IoU.monitor: 0.2725, IoU.bulletin board: 0.6128, IoU.shower: 0.0026, IoU.radiator: 0.6829, IoU.glass: 0.2014, IoU.clock: 0.4700, IoU.flag: 0.6976, Acc.wall: 0.8860, Acc.building: 0.9315, Acc.sky: 0.9732, Acc.floor: 0.9077, Acc.tree: 0.9090, Acc.ceiling: 0.9389, Acc.road: 0.9215, Acc.bed : 0.9682, Acc.windowpane: 0.8379, Acc.grass: 0.7864, Acc.cabinet: 0.7509, Acc.sidewalk: 0.7951, Acc.person: 0.9495, Acc.earth: 0.5765, Acc.door: 0.7496, Acc.table: 0.7958, Acc.mountain: 0.6419, Acc.plant: 0.7027, Acc.curtain: 0.9070, Acc.chair: 0.7354, Acc.car: 0.9237, Acc.water: 0.7922, Acc.painting: 0.9045, Acc.sofa: 0.9087, Acc.shelf: 0.6587, Acc.house: 0.5207, Acc.sea: 0.8840, Acc.mirror: 0.8518, Acc.rug: 0.8117, Acc.field: 0.6364, Acc.armchair: 0.8184, Acc.seat: 0.8647, Acc.fence: 0.6293, Acc.desk: 0.7711, Acc.rock: 0.7801, Acc.wardrobe: 0.7950, Acc.lamp: 0.8611, Acc.bathtub: 0.9397, Acc.railing: 0.5536, Acc.cushion: 0.7914, Acc.base: 0.7698, Acc.box: 0.4771, Acc.column: 0.6543, Acc.signboard: 0.5992, Acc.chest of drawers: 0.7718, Acc.counter: 0.6365, Acc.sand: 0.7712, Acc.sink: 0.8383, Acc.skyscraper: 0.6043, Acc.fireplace: 0.9371, Acc.refrigerator: 0.9528, Acc.grandstand: 0.7525, Acc.path: 0.4074, Acc.stairs: 0.3692, Acc.runway: 0.8308, Acc.case: 0.8906, Acc.pool table: 0.9846, Acc.pillow: 0.7498, Acc.screen door: 0.6190, Acc.stairway: 0.7318, Acc.river: 0.1989, Acc.bridge: 0.7537, Acc.bookcase: 0.5516, Acc.blind: 0.4359, Acc.coffee table: 0.8715, Acc.toilet: 0.9439, Acc.flower: 0.5783, Acc.book: 0.7081, Acc.hill: 0.1916, Acc.bench: 0.7971, Acc.countertop: 0.8460, Acc.stove: 0.9202, Acc.palm: 0.8435, Acc.kitchen island: 0.9136, Acc.computer: 0.9195, Acc.swivel chair: 0.8763, Acc.boat: 0.9051, Acc.bar: 0.7928, Acc.arcade machine: 0.9772, Acc.hovel: 0.1499, Acc.bus: 0.9780, Acc.towel: 0.9022, Acc.light: 0.7307, Acc.truck: 0.8098, Acc.tower: 0.2271, Acc.chandelier: 0.8296, Acc.awning: 0.6514, Acc.streetlight: 0.4823, Acc.booth: 0.4943, Acc.television receiver: 0.8823, Acc.airplane: 0.9684, Acc.dirt track: 0.1435, Acc.apparel: 0.7455, Acc.pole: 0.3296, Acc.land: 0.0048, Acc.bannister: 0.2864, Acc.escalator: 0.8750, Acc.ottoman: 0.7625, Acc.bottle: 0.5551, Acc.buffet: 0.8903, Acc.poster: 0.3664, Acc.stage: 0.4118, Acc.van: 0.5914, Acc.ship: 0.1273, Acc.fountain: 0.3958, Acc.conveyer belt: 0.9441, Acc.canopy: 0.7314, Acc.washer: 0.8597, Acc.plaything: 0.4066, Acc.swimming pool: 0.7668, Acc.stool: 0.6933, Acc.barrel: 0.6461, Acc.basket: 0.5407, Acc.waterfall: 0.5462, Acc.tent: 0.9876, Acc.bag: 0.3163, Acc.minibike: 0.8977, Acc.cradle: 0.9666, Acc.oven: 0.8400, Acc.ball: 0.7436, Acc.food: 0.6699, Acc.step: 0.2405, Acc.tank: 0.7959, Acc.trade name: 0.2630, Acc.microwave: 0.9624, Acc.pot: 0.7198, Acc.animal: 0.6918, Acc.bicycle: 0.7933, Acc.lake: 0.7988, Acc.dishwasher: 0.7714, Acc.screen: 0.9592, Acc.blanket: 0.4112, Acc.sculpture: 0.8766, Acc.hood: 0.7449, Acc.sconce: 0.7344, Acc.vase: 0.6882, Acc.traffic light: 0.6374, Acc.tray: 0.3553, Acc.ashcan: 0.6520, Acc.fan: 0.7723, Acc.pier: 0.4166, Acc.crt screen: 0.1306, Acc.plate: 0.7941, Acc.monitor: 0.3493, Acc.bulletin board: 0.8271, Acc.shower: 0.0026, Acc.radiator: 0.7906, Acc.glass: 0.2131, Acc.clock: 0.6567, Acc.flag: 0.8277 +2024-06-18 16:10:01,793 - mmseg - INFO - Iter [23050/80000] lr: 2.848e-05, eta: 1 day, 9:48:04, time: 4.191, data_time: 2.222, memory: 72263, decode.loss_ce: 0.2567, decode.acc_seg: 89.8121, aux.loss_ce: 0.1044, aux.acc_seg: 89.6227, loss: 0.3611 +2024-06-18 16:11:40,662 - mmseg - INFO - Iter [23100/80000] lr: 2.845e-05, eta: 1 day, 9:45:58, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2670, decode.acc_seg: 89.0695, aux.loss_ce: 0.1086, aux.acc_seg: 88.8528, loss: 0.3756 +2024-06-18 16:13:19,640 - mmseg - INFO - Iter [23150/80000] lr: 2.843e-05, eta: 1 day, 9:43:51, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2676, decode.acc_seg: 89.3501, aux.loss_ce: 0.1097, aux.acc_seg: 89.1385, loss: 0.3772 +2024-06-18 16:14:58,604 - mmseg - INFO - Iter [23200/80000] lr: 2.840e-05, eta: 1 day, 9:41:45, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2541, decode.acc_seg: 89.7995, aux.loss_ce: 0.1035, aux.acc_seg: 89.5791, loss: 0.3576 +2024-06-18 16:16:37,566 - mmseg - INFO - Iter [23250/80000] lr: 2.838e-05, eta: 1 day, 9:39:40, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2659, decode.acc_seg: 89.2526, aux.loss_ce: 0.1096, aux.acc_seg: 88.8871, loss: 0.3755 +2024-06-18 16:18:16,578 - mmseg - INFO - Iter [23300/80000] lr: 2.835e-05, eta: 1 day, 9:37:34, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2600, decode.acc_seg: 89.1267, aux.loss_ce: 0.1063, aux.acc_seg: 88.8932, loss: 0.3663 +2024-06-18 16:19:55,547 - mmseg - INFO - Iter [23350/80000] lr: 2.833e-05, eta: 1 day, 9:35:28, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2766, decode.acc_seg: 88.9209, aux.loss_ce: 0.1119, aux.acc_seg: 88.6451, loss: 0.3885 +2024-06-18 16:21:34,548 - mmseg - INFO - Iter [23400/80000] lr: 2.830e-05, eta: 1 day, 9:33:23, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2572, decode.acc_seg: 89.4896, aux.loss_ce: 0.1055, aux.acc_seg: 89.2199, loss: 0.3627 +2024-06-18 16:23:13,562 - mmseg - INFO - Iter [23450/80000] lr: 2.828e-05, eta: 1 day, 9:31:18, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2576, decode.acc_seg: 89.0815, aux.loss_ce: 0.1052, aux.acc_seg: 88.9141, loss: 0.3628 +2024-06-18 16:24:52,553 - mmseg - INFO - Iter [23500/80000] lr: 2.825e-05, eta: 1 day, 9:29:12, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2814, decode.acc_seg: 88.4677, aux.loss_ce: 0.1139, aux.acc_seg: 88.3693, loss: 0.3953 +2024-06-18 16:26:31,710 - mmseg - INFO - Iter [23550/80000] lr: 2.823e-05, eta: 1 day, 9:27:08, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2663, decode.acc_seg: 88.8034, aux.loss_ce: 0.1088, aux.acc_seg: 88.5173, loss: 0.3752 +2024-06-18 16:28:10,677 - mmseg - INFO - Iter [23600/80000] lr: 2.820e-05, eta: 1 day, 9:25:03, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2534, decode.acc_seg: 89.6233, aux.loss_ce: 0.1044, aux.acc_seg: 89.3010, loss: 0.3578 +2024-06-18 16:29:49,644 - mmseg - INFO - Iter [23650/80000] lr: 2.818e-05, eta: 1 day, 9:22:58, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2670, decode.acc_seg: 88.7725, aux.loss_ce: 0.1098, aux.acc_seg: 88.4425, loss: 0.3768 +2024-06-18 16:31:28,524 - mmseg - INFO - Iter [23700/80000] lr: 2.815e-05, eta: 1 day, 9:20:53, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2750, decode.acc_seg: 88.5896, aux.loss_ce: 0.1118, aux.acc_seg: 88.4260, loss: 0.3869 +2024-06-18 16:33:07,647 - mmseg - INFO - Iter [23750/80000] lr: 2.813e-05, eta: 1 day, 9:18:48, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2531, decode.acc_seg: 89.4685, aux.loss_ce: 0.1031, aux.acc_seg: 89.2831, loss: 0.3562 +2024-06-18 16:34:46,603 - mmseg - INFO - Iter [23800/80000] lr: 2.810e-05, eta: 1 day, 9:16:44, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2625, decode.acc_seg: 89.2197, aux.loss_ce: 0.1078, aux.acc_seg: 88.9957, loss: 0.3704 +2024-06-18 16:36:25,661 - mmseg - INFO - Iter [23850/80000] lr: 2.808e-05, eta: 1 day, 9:14:39, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2526, decode.acc_seg: 89.3666, aux.loss_ce: 0.1038, aux.acc_seg: 89.0924, loss: 0.3564 +2024-06-18 16:38:04,577 - mmseg - INFO - Iter [23900/80000] lr: 2.805e-05, eta: 1 day, 9:12:35, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2630, decode.acc_seg: 88.7126, aux.loss_ce: 0.1078, aux.acc_seg: 88.5298, loss: 0.3708 +2024-06-18 16:39:43,486 - mmseg - INFO - Iter [23950/80000] lr: 2.803e-05, eta: 1 day, 9:10:30, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2545, decode.acc_seg: 89.3518, aux.loss_ce: 0.1039, aux.acc_seg: 89.1072, loss: 0.3584 +2024-06-18 16:41:24,762 - mmseg - INFO - Saving checkpoint at 24000 iterations +2024-06-18 16:42:51,050 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 16:42:51,050 - mmseg - INFO - Iter [24000/80000] lr: 2.800e-05, eta: 1 day, 9:11:53, time: 3.751, data_time: 0.054, memory: 72263, decode.loss_ce: 0.2647, decode.acc_seg: 89.1644, aux.loss_ce: 0.1091, aux.acc_seg: 88.8801, loss: 0.3738 +2024-06-18 16:44:41,549 - mmseg - INFO - per class results: +2024-06-18 16:44:41,555 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.82 | 87.46 | +| building | 84.15 | 93.1 | +| sky | 94.56 | 96.71 | +| floor | 84.02 | 89.78 | +| tree | 77.53 | 91.41 | +| ceiling | 85.48 | 92.7 | +| road | 85.69 | 92.26 | +| bed | 92.44 | 96.49 | +| windowpane | 65.43 | 85.74 | +| grass | 68.77 | 80.9 | +| cabinet | 65.3 | 75.65 | +| sidewalk | 70.57 | 80.4 | +| person | 85.42 | 93.87 | +| earth | 38.89 | 51.26 | +| door | 58.71 | 71.28 | +| table | 64.83 | 74.02 | +| mountain | 63.54 | 72.77 | +| plant | 58.61 | 68.13 | +| curtain | 78.11 | 91.12 | +| chair | 65.67 | 80.49 | +| car | 87.28 | 93.86 | +| water | 64.77 | 80.33 | +| painting | 74.66 | 91.65 | +| sofa | 80.27 | 86.82 | +| shelf | 50.2 | 68.28 | +| house | 47.01 | 54.21 | +| sea | 82.51 | 87.88 | +| mirror | 80.85 | 89.63 | +| rug | 69.69 | 86.41 | +| field | 31.71 | 72.02 | +| armchair | 59.34 | 84.81 | +| seat | 69.83 | 87.1 | +| fence | 48.26 | 59.89 | +| desk | 49.48 | 84.88 | +| rock | 61.85 | 81.33 | +| wardrobe | 54.69 | 81.73 | +| lamp | 73.79 | 84.29 | +| bathtub | 89.66 | 93.37 | +| railing | 39.9 | 58.04 | +| cushion | 66.54 | 78.23 | +| base | 38.75 | 64.94 | +| box | 33.5 | 42.19 | +| column | 56.84 | 68.53 | +| signboard | 43.69 | 55.62 | +| chest of drawers | 48.11 | 68.22 | +| counter | 39.51 | 56.75 | +| sand | 50.64 | 79.95 | +| sink | 82.42 | 88.16 | +| skyscraper | 46.93 | 57.72 | +| fireplace | 71.38 | 96.5 | +| refrigerator | 87.33 | 92.58 | +| grandstand | 47.11 | 82.95 | +| path | 28.12 | 44.52 | +| stairs | 34.52 | 39.14 | +| runway | 68.33 | 88.12 | +| case | 55.97 | 77.78 | +| pool table | 94.55 | 98.45 | +| pillow | 64.59 | 75.78 | +| screen door | 86.43 | 93.84 | +| stairway | 44.91 | 64.2 | +| river | 12.04 | 17.64 | +| bridge | 68.37 | 85.65 | +| bookcase | 45.43 | 56.62 | +| blind | 41.68 | 44.93 | +| coffee table | 58.6 | 87.43 | +| toilet | 90.11 | 94.28 | +| flower | 41.86 | 52.95 | +| book | 56.82 | 73.44 | +| hill | 7.22 | 13.67 | +| bench | 64.73 | 73.92 | +| countertop | 64.78 | 83.41 | +| stove | 82.85 | 91.31 | +| palm | 50.96 | 84.91 | +| kitchen island | 41.31 | 88.23 | +| computer | 77.59 | 92.84 | +| swivel chair | 45.74 | 59.9 | +| boat | 56.93 | 94.65 | +| bar | 61.42 | 83.27 | +| arcade machine | 86.42 | 92.11 | +| hovel | 47.07 | 57.82 | +| bus | 91.86 | 96.89 | +| towel | 81.03 | 91.51 | +| light | 57.03 | 64.4 | +| truck | 49.22 | 68.36 | +| tower | 29.93 | 67.46 | +| chandelier | 72.27 | 88.31 | +| awning | 42.55 | 56.26 | +| streetlight | 34.81 | 45.54 | +| booth | 51.47 | 73.59 | +| television receiver | 76.31 | 89.37 | +| airplane | 84.2 | 98.4 | +| dirt track | 6.99 | 29.51 | +| apparel | 63.72 | 77.51 | +| pole | 26.61 | 35.26 | +| land | 4.61 | 5.8 | +| bannister | 16.65 | 26.16 | +| escalator | 57.66 | 87.73 | +| ottoman | 51.72 | 75.09 | +| bottle | 40.53 | 69.28 | +| buffet | 66.38 | 90.14 | +| poster | 35.98 | 57.57 | +| stage | 23.01 | 53.01 | +| van | 52.56 | 74.99 | +| ship | 69.98 | 73.0 | +| fountain | 38.12 | 38.5 | +| conveyer belt | 77.84 | 94.97 | +| canopy | 48.08 | 69.83 | +| washer | 88.49 | 95.34 | +| plaything | 47.83 | 69.17 | +| swimming pool | 54.85 | 79.1 | +| stool | 55.5 | 64.97 | +| barrel | 33.04 | 65.07 | +| basket | 37.67 | 49.71 | +| waterfall | 46.03 | 54.83 | +| tent | 94.32 | 98.9 | +| bag | 22.84 | 26.33 | +| minibike | 75.61 | 89.21 | +| cradle | 81.27 | 98.94 | +| oven | 61.38 | 79.22 | +| ball | 54.39 | 59.81 | +| food | 54.88 | 61.39 | +| step | 10.45 | 11.39 | +| tank | 60.18 | 69.51 | +| trade name | 35.96 | 44.06 | +| microwave | 89.29 | 96.48 | +| pot | 59.42 | 69.34 | +| animal | 67.38 | 70.3 | +| bicycle | 59.5 | 74.39 | +| lake | 57.31 | 69.28 | +| dishwasher | 76.48 | 85.39 | +| screen | 61.11 | 91.08 | +| blanket | 30.89 | 36.82 | +| sculpture | 65.29 | 88.37 | +| hood | 62.44 | 71.69 | +| sconce | 56.57 | 76.48 | +| vase | 45.74 | 59.28 | +| traffic light | 33.99 | 69.66 | +| tray | 22.37 | 26.36 | +| ashcan | 50.48 | 64.36 | +| fan | 67.19 | 75.28 | +| pier | 39.16 | 45.13 | +| crt screen | 7.74 | 8.44 | +| plate | 58.97 | 83.84 | +| monitor | 62.97 | 80.26 | +| bulletin board | 62.81 | 71.83 | +| shower | 0.74 | 0.74 | +| radiator | 65.37 | 78.81 | +| glass | 21.67 | 23.94 | +| clock | 49.57 | 63.88 | +| flag | 71.04 | 80.28 | ++---------------------+-------+-------+ +2024-06-18 16:44:41,555 - mmseg - INFO - Summary: +2024-06-18 16:44:41,555 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.64 | 57.25 | 71.42 | ++-------+-------+-------+ +2024-06-18 16:44:41,556 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 16:44:41,556 - mmseg - INFO - Iter(val) [250] aAcc: 0.8564, mIoU: 0.5725, mAcc: 0.7142, IoU.wall: 0.8082, IoU.building: 0.8415, IoU.sky: 0.9456, IoU.floor: 0.8402, IoU.tree: 0.7753, IoU.ceiling: 0.8548, IoU.road: 0.8569, IoU.bed : 0.9244, IoU.windowpane: 0.6543, IoU.grass: 0.6877, IoU.cabinet: 0.6530, IoU.sidewalk: 0.7057, IoU.person: 0.8542, IoU.earth: 0.3889, IoU.door: 0.5871, IoU.table: 0.6483, IoU.mountain: 0.6354, IoU.plant: 0.5861, IoU.curtain: 0.7811, IoU.chair: 0.6567, IoU.car: 0.8728, IoU.water: 0.6477, IoU.painting: 0.7466, IoU.sofa: 0.8027, IoU.shelf: 0.5020, IoU.house: 0.4701, IoU.sea: 0.8251, IoU.mirror: 0.8085, IoU.rug: 0.6969, IoU.field: 0.3171, IoU.armchair: 0.5934, IoU.seat: 0.6983, IoU.fence: 0.4826, IoU.desk: 0.4948, IoU.rock: 0.6185, IoU.wardrobe: 0.5469, IoU.lamp: 0.7379, IoU.bathtub: 0.8966, IoU.railing: 0.3990, IoU.cushion: 0.6654, IoU.base: 0.3875, IoU.box: 0.3350, IoU.column: 0.5684, IoU.signboard: 0.4369, IoU.chest of drawers: 0.4811, IoU.counter: 0.3951, IoU.sand: 0.5064, IoU.sink: 0.8242, IoU.skyscraper: 0.4693, IoU.fireplace: 0.7138, IoU.refrigerator: 0.8733, IoU.grandstand: 0.4711, IoU.path: 0.2812, IoU.stairs: 0.3452, IoU.runway: 0.6833, IoU.case: 0.5597, IoU.pool table: 0.9455, IoU.pillow: 0.6459, IoU.screen door: 0.8643, IoU.stairway: 0.4491, IoU.river: 0.1204, IoU.bridge: 0.6837, IoU.bookcase: 0.4543, IoU.blind: 0.4168, IoU.coffee table: 0.5860, IoU.toilet: 0.9011, IoU.flower: 0.4186, IoU.book: 0.5682, IoU.hill: 0.0722, IoU.bench: 0.6473, IoU.countertop: 0.6478, IoU.stove: 0.8285, IoU.palm: 0.5096, IoU.kitchen island: 0.4131, IoU.computer: 0.7759, IoU.swivel chair: 0.4574, IoU.boat: 0.5693, IoU.bar: 0.6142, IoU.arcade machine: 0.8642, IoU.hovel: 0.4707, IoU.bus: 0.9186, IoU.towel: 0.8103, IoU.light: 0.5703, IoU.truck: 0.4922, IoU.tower: 0.2993, IoU.chandelier: 0.7227, IoU.awning: 0.4255, IoU.streetlight: 0.3481, IoU.booth: 0.5147, IoU.television receiver: 0.7631, IoU.airplane: 0.8420, IoU.dirt track: 0.0699, IoU.apparel: 0.6372, IoU.pole: 0.2661, IoU.land: 0.0461, IoU.bannister: 0.1665, IoU.escalator: 0.5766, IoU.ottoman: 0.5172, IoU.bottle: 0.4053, IoU.buffet: 0.6638, IoU.poster: 0.3598, IoU.stage: 0.2301, IoU.van: 0.5256, IoU.ship: 0.6998, IoU.fountain: 0.3812, IoU.conveyer belt: 0.7784, IoU.canopy: 0.4808, IoU.washer: 0.8849, IoU.plaything: 0.4783, IoU.swimming pool: 0.5485, IoU.stool: 0.5550, IoU.barrel: 0.3304, IoU.basket: 0.3767, IoU.waterfall: 0.4603, IoU.tent: 0.9432, IoU.bag: 0.2284, IoU.minibike: 0.7561, IoU.cradle: 0.8127, IoU.oven: 0.6138, IoU.ball: 0.5439, IoU.food: 0.5488, IoU.step: 0.1045, IoU.tank: 0.6018, IoU.trade name: 0.3596, IoU.microwave: 0.8929, IoU.pot: 0.5942, IoU.animal: 0.6738, IoU.bicycle: 0.5950, IoU.lake: 0.5731, IoU.dishwasher: 0.7648, IoU.screen: 0.6111, IoU.blanket: 0.3089, IoU.sculpture: 0.6529, IoU.hood: 0.6244, IoU.sconce: 0.5657, IoU.vase: 0.4574, IoU.traffic light: 0.3399, IoU.tray: 0.2237, IoU.ashcan: 0.5048, IoU.fan: 0.6719, IoU.pier: 0.3916, IoU.crt screen: 0.0774, IoU.plate: 0.5897, IoU.monitor: 0.6297, IoU.bulletin board: 0.6281, IoU.shower: 0.0074, IoU.radiator: 0.6537, IoU.glass: 0.2167, IoU.clock: 0.4957, IoU.flag: 0.7104, Acc.wall: 0.8746, Acc.building: 0.9310, Acc.sky: 0.9671, Acc.floor: 0.8978, Acc.tree: 0.9141, Acc.ceiling: 0.9270, Acc.road: 0.9226, Acc.bed : 0.9649, Acc.windowpane: 0.8574, Acc.grass: 0.8090, Acc.cabinet: 0.7565, Acc.sidewalk: 0.8040, Acc.person: 0.9387, Acc.earth: 0.5126, Acc.door: 0.7128, Acc.table: 0.7402, Acc.mountain: 0.7277, Acc.plant: 0.6813, Acc.curtain: 0.9112, Acc.chair: 0.8049, Acc.car: 0.9386, Acc.water: 0.8033, Acc.painting: 0.9165, Acc.sofa: 0.8682, Acc.shelf: 0.6828, Acc.house: 0.5421, Acc.sea: 0.8788, Acc.mirror: 0.8963, Acc.rug: 0.8641, Acc.field: 0.7202, Acc.armchair: 0.8481, Acc.seat: 0.8710, Acc.fence: 0.5989, Acc.desk: 0.8488, Acc.rock: 0.8133, Acc.wardrobe: 0.8173, Acc.lamp: 0.8429, Acc.bathtub: 0.9337, Acc.railing: 0.5804, Acc.cushion: 0.7823, Acc.base: 0.6494, Acc.box: 0.4219, Acc.column: 0.6853, Acc.signboard: 0.5562, Acc.chest of drawers: 0.6822, Acc.counter: 0.5675, Acc.sand: 0.7995, Acc.sink: 0.8816, Acc.skyscraper: 0.5772, Acc.fireplace: 0.9650, Acc.refrigerator: 0.9258, Acc.grandstand: 0.8295, Acc.path: 0.4452, Acc.stairs: 0.3914, Acc.runway: 0.8812, Acc.case: 0.7778, Acc.pool table: 0.9845, Acc.pillow: 0.7578, Acc.screen door: 0.9384, Acc.stairway: 0.6420, Acc.river: 0.1764, Acc.bridge: 0.8565, Acc.bookcase: 0.5662, Acc.blind: 0.4493, Acc.coffee table: 0.8743, Acc.toilet: 0.9428, Acc.flower: 0.5295, Acc.book: 0.7344, Acc.hill: 0.1367, Acc.bench: 0.7392, Acc.countertop: 0.8341, Acc.stove: 0.9131, Acc.palm: 0.8491, Acc.kitchen island: 0.8823, Acc.computer: 0.9284, Acc.swivel chair: 0.5990, Acc.boat: 0.9465, Acc.bar: 0.8327, Acc.arcade machine: 0.9211, Acc.hovel: 0.5782, Acc.bus: 0.9689, Acc.towel: 0.9151, Acc.light: 0.6440, Acc.truck: 0.6836, Acc.tower: 0.6746, Acc.chandelier: 0.8831, Acc.awning: 0.5626, Acc.streetlight: 0.4554, Acc.booth: 0.7359, Acc.television receiver: 0.8937, Acc.airplane: 0.9840, Acc.dirt track: 0.2951, Acc.apparel: 0.7751, Acc.pole: 0.3526, Acc.land: 0.0580, Acc.bannister: 0.2616, Acc.escalator: 0.8773, Acc.ottoman: 0.7509, Acc.bottle: 0.6928, Acc.buffet: 0.9014, Acc.poster: 0.5757, Acc.stage: 0.5301, Acc.van: 0.7499, Acc.ship: 0.7300, Acc.fountain: 0.3850, Acc.conveyer belt: 0.9497, Acc.canopy: 0.6983, Acc.washer: 0.9534, Acc.plaything: 0.6917, Acc.swimming pool: 0.7910, Acc.stool: 0.6497, Acc.barrel: 0.6507, Acc.basket: 0.4971, Acc.waterfall: 0.5483, Acc.tent: 0.9890, Acc.bag: 0.2633, Acc.minibike: 0.8921, Acc.cradle: 0.9894, Acc.oven: 0.7922, Acc.ball: 0.5981, Acc.food: 0.6139, Acc.step: 0.1139, Acc.tank: 0.6951, Acc.trade name: 0.4406, Acc.microwave: 0.9648, Acc.pot: 0.6934, Acc.animal: 0.7030, Acc.bicycle: 0.7439, Acc.lake: 0.6928, Acc.dishwasher: 0.8539, Acc.screen: 0.9108, Acc.blanket: 0.3682, Acc.sculpture: 0.8837, Acc.hood: 0.7169, Acc.sconce: 0.7648, Acc.vase: 0.5928, Acc.traffic light: 0.6966, Acc.tray: 0.2636, Acc.ashcan: 0.6436, Acc.fan: 0.7528, Acc.pier: 0.4513, Acc.crt screen: 0.0844, Acc.plate: 0.8384, Acc.monitor: 0.8026, Acc.bulletin board: 0.7183, Acc.shower: 0.0074, Acc.radiator: 0.7881, Acc.glass: 0.2394, Acc.clock: 0.6388, Acc.flag: 0.8028 +2024-06-18 16:46:20,987 - mmseg - INFO - Iter [24050/80000] lr: 2.798e-05, eta: 1 day, 9:14:06, time: 4.199, data_time: 2.227, memory: 72263, decode.loss_ce: 0.2539, decode.acc_seg: 89.8551, aux.loss_ce: 0.1044, aux.acc_seg: 89.5760, loss: 0.3582 +2024-06-18 16:47:59,929 - mmseg - INFO - Iter [24100/80000] lr: 2.795e-05, eta: 1 day, 9:12:01, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2474, decode.acc_seg: 89.9607, aux.loss_ce: 0.1015, aux.acc_seg: 89.6760, loss: 0.3489 +2024-06-18 16:49:38,984 - mmseg - INFO - Iter [24150/80000] lr: 2.793e-05, eta: 1 day, 9:09:56, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2562, decode.acc_seg: 89.5915, aux.loss_ce: 0.1054, aux.acc_seg: 89.3135, loss: 0.3616 +2024-06-18 16:51:17,928 - mmseg - INFO - Iter [24200/80000] lr: 2.790e-05, eta: 1 day, 9:07:50, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2363, decode.acc_seg: 90.2897, aux.loss_ce: 0.0967, aux.acc_seg: 90.0840, loss: 0.3330 +2024-06-18 16:52:56,852 - mmseg - INFO - Iter [24250/80000] lr: 2.788e-05, eta: 1 day, 9:05:45, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2545, decode.acc_seg: 89.4471, aux.loss_ce: 0.1037, aux.acc_seg: 89.3021, loss: 0.3582 +2024-06-18 16:54:35,891 - mmseg - INFO - Iter [24300/80000] lr: 2.785e-05, eta: 1 day, 9:03:41, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2631, decode.acc_seg: 88.9601, aux.loss_ce: 0.1081, aux.acc_seg: 88.7924, loss: 0.3713 +2024-06-18 16:56:14,842 - mmseg - INFO - Iter [24350/80000] lr: 2.783e-05, eta: 1 day, 9:01:36, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2701, decode.acc_seg: 89.1538, aux.loss_ce: 0.1101, aux.acc_seg: 88.8968, loss: 0.3802 +2024-06-18 16:57:53,808 - mmseg - INFO - Iter [24400/80000] lr: 2.780e-05, eta: 1 day, 8:59:31, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2577, decode.acc_seg: 89.4360, aux.loss_ce: 0.1054, aux.acc_seg: 89.2246, loss: 0.3631 +2024-06-18 16:59:32,777 - mmseg - INFO - Iter [24450/80000] lr: 2.778e-05, eta: 1 day, 8:57:26, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2517, decode.acc_seg: 89.4750, aux.loss_ce: 0.1027, aux.acc_seg: 89.1976, loss: 0.3545 +2024-06-18 17:01:11,839 - mmseg - INFO - Iter [24500/80000] lr: 2.775e-05, eta: 1 day, 8:55:22, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2599, decode.acc_seg: 89.1862, aux.loss_ce: 0.1068, aux.acc_seg: 88.8611, loss: 0.3667 +2024-06-18 17:02:50,789 - mmseg - INFO - Iter [24550/80000] lr: 2.773e-05, eta: 1 day, 8:53:18, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2559, decode.acc_seg: 89.4335, aux.loss_ce: 0.1050, aux.acc_seg: 89.0808, loss: 0.3609 +2024-06-18 17:04:29,643 - mmseg - INFO - Iter [24600/80000] lr: 2.770e-05, eta: 1 day, 8:51:13, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2395, decode.acc_seg: 89.9222, aux.loss_ce: 0.0987, aux.acc_seg: 89.5815, loss: 0.3383 +2024-06-18 17:06:08,628 - mmseg - INFO - Iter [24650/80000] lr: 2.768e-05, eta: 1 day, 8:49:09, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2591, decode.acc_seg: 89.2440, aux.loss_ce: 0.1055, aux.acc_seg: 89.1258, loss: 0.3646 +2024-06-18 17:07:47,655 - mmseg - INFO - Iter [24700/80000] lr: 2.765e-05, eta: 1 day, 8:47:05, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2600, decode.acc_seg: 89.7450, aux.loss_ce: 0.1057, aux.acc_seg: 89.5660, loss: 0.3657 +2024-06-18 17:09:26,644 - mmseg - INFO - Iter [24750/80000] lr: 2.763e-05, eta: 1 day, 8:45:01, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2614, decode.acc_seg: 89.4129, aux.loss_ce: 0.1071, aux.acc_seg: 89.1571, loss: 0.3685 +2024-06-18 17:11:05,565 - mmseg - INFO - Iter [24800/80000] lr: 2.760e-05, eta: 1 day, 8:42:57, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2331, decode.acc_seg: 90.1298, aux.loss_ce: 0.0951, aux.acc_seg: 89.9720, loss: 0.3282 +2024-06-18 17:12:44,483 - mmseg - INFO - Iter [24850/80000] lr: 2.758e-05, eta: 1 day, 8:40:53, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2570, decode.acc_seg: 89.3127, aux.loss_ce: 0.1056, aux.acc_seg: 89.0562, loss: 0.3626 +2024-06-18 17:14:23,377 - mmseg - INFO - Iter [24900/80000] lr: 2.755e-05, eta: 1 day, 8:38:49, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2414, decode.acc_seg: 90.2039, aux.loss_ce: 0.0997, aux.acc_seg: 89.8312, loss: 0.3411 +2024-06-18 17:16:02,339 - mmseg - INFO - Iter [24950/80000] lr: 2.753e-05, eta: 1 day, 8:36:45, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2566, decode.acc_seg: 89.5544, aux.loss_ce: 0.1055, aux.acc_seg: 89.1170, loss: 0.3621 +2024-06-18 17:17:41,286 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 17:17:41,286 - mmseg - INFO - Iter [25000/80000] lr: 2.750e-05, eta: 1 day, 8:34:42, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2564, decode.acc_seg: 89.5413, aux.loss_ce: 0.1055, aux.acc_seg: 89.3422, loss: 0.3619 +2024-06-18 17:19:31,739 - mmseg - INFO - per class results: +2024-06-18 17:19:31,745 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.37 | 89.34 | +| building | 84.76 | 93.61 | +| sky | 94.7 | 97.48 | +| floor | 84.47 | 91.35 | +| tree | 77.69 | 87.8 | +| ceiling | 86.21 | 93.03 | +| road | 85.03 | 90.52 | +| bed | 92.74 | 97.18 | +| windowpane | 67.44 | 81.95 | +| grass | 66.75 | 86.61 | +| cabinet | 66.73 | 72.94 | +| sidewalk | 69.95 | 88.2 | +| person | 85.69 | 92.04 | +| earth | 35.94 | 46.16 | +| door | 58.77 | 71.69 | +| table | 67.41 | 79.26 | +| mountain | 61.46 | 72.59 | +| plant | 57.08 | 68.7 | +| curtain | 79.49 | 85.43 | +| chair | 66.48 | 75.92 | +| car | 87.62 | 93.75 | +| water | 62.58 | 75.25 | +| painting | 75.51 | 92.37 | +| sofa | 80.95 | 93.56 | +| shelf | 51.4 | 74.79 | +| house | 56.58 | 76.07 | +| sea | 71.48 | 82.22 | +| mirror | 78.25 | 84.32 | +| rug | 68.33 | 76.28 | +| field | 25.6 | 39.58 | +| armchair | 60.81 | 75.86 | +| seat | 68.95 | 90.68 | +| fence | 44.65 | 57.02 | +| desk | 58.14 | 80.13 | +| rock | 58.63 | 88.28 | +| wardrobe | 57.81 | 87.8 | +| lamp | 75.12 | 85.44 | +| bathtub | 87.84 | 92.57 | +| railing | 41.69 | 55.18 | +| cushion | 70.32 | 78.7 | +| base | 40.36 | 57.27 | +| box | 35.76 | 45.38 | +| column | 57.2 | 75.14 | +| signboard | 44.39 | 57.89 | +| chest of drawers | 48.7 | 80.45 | +| counter | 48.59 | 58.44 | +| sand | 44.6 | 67.37 | +| sink | 81.8 | 87.95 | +| skyscraper | 45.56 | 58.54 | +| fireplace | 74.94 | 96.67 | +| refrigerator | 80.28 | 91.56 | +| grandstand | 46.79 | 85.19 | +| path | 33.37 | 46.71 | +| stairs | 33.09 | 42.22 | +| runway | 66.1 | 87.22 | +| case | 61.17 | 88.83 | +| pool table | 95.0 | 98.06 | +| pillow | 69.12 | 83.22 | +| screen door | 80.81 | 83.38 | +| stairway | 43.66 | 55.8 | +| river | 9.4 | 20.89 | +| bridge | 52.7 | 60.09 | +| bookcase | 43.49 | 51.45 | +| blind | 45.33 | 52.8 | +| coffee table | 59.64 | 87.26 | +| toilet | 89.89 | 94.48 | +| flower | 41.46 | 53.4 | +| book | 55.7 | 75.29 | +| hill | 8.42 | 20.46 | +| bench | 68.89 | 77.24 | +| countertop | 61.58 | 80.02 | +| stove | 85.84 | 90.02 | +| palm | 52.06 | 79.91 | +| kitchen island | 43.71 | 92.95 | +| computer | 80.4 | 91.33 | +| swivel chair | 54.38 | 76.9 | +| boat | 64.67 | 91.47 | +| bar | 63.21 | 90.42 | +| arcade machine | 83.26 | 87.4 | +| hovel | 20.73 | 23.75 | +| bus | 92.58 | 96.8 | +| towel | 76.19 | 79.72 | +| light | 60.51 | 72.1 | +| truck | 51.26 | 60.34 | +| tower | 27.55 | 47.96 | +| chandelier | 73.7 | 88.26 | +| awning | 41.36 | 70.95 | +| streetlight | 33.31 | 43.53 | +| booth | 58.23 | 67.99 | +| television receiver | 80.51 | 86.08 | +| airplane | 86.62 | 96.79 | +| dirt track | 12.13 | 41.56 | +| apparel | 62.61 | 79.32 | +| pole | 28.02 | 41.18 | +| land | 0.06 | 0.1 | +| bannister | 19.82 | 25.67 | +| escalator | 59.69 | 88.22 | +| ottoman | 56.13 | 78.44 | +| bottle | 30.84 | 37.97 | +| buffet | 59.72 | 76.59 | +| poster | 36.36 | 43.68 | +| stage | 21.3 | 47.72 | +| van | 49.2 | 71.94 | +| ship | 24.2 | 25.67 | +| fountain | 39.42 | 41.47 | +| conveyer belt | 80.92 | 95.9 | +| canopy | 68.27 | 78.23 | +| washer | 85.43 | 87.71 | +| plaything | 34.57 | 73.58 | +| swimming pool | 55.6 | 82.02 | +| stool | 55.16 | 73.91 | +| barrel | 69.66 | 83.43 | +| basket | 42.19 | 57.49 | +| waterfall | 57.56 | 83.82 | +| tent | 90.36 | 98.6 | +| bag | 23.6 | 27.36 | +| minibike | 77.28 | 86.8 | +| cradle | 83.49 | 98.68 | +| oven | 60.87 | 72.85 | +| ball | 13.21 | 13.31 | +| food | 58.44 | 67.0 | +| step | 13.96 | 20.67 | +| tank | 56.16 | 70.04 | +| trade name | 32.86 | 37.07 | +| microwave | 90.66 | 95.78 | +| pot | 59.77 | 67.22 | +| animal | 67.68 | 70.39 | +| bicycle | 60.74 | 73.83 | +| lake | 49.98 | 69.79 | +| dishwasher | 71.52 | 76.97 | +| screen | 33.45 | 39.13 | +| blanket | 31.75 | 39.29 | +| sculpture | 75.79 | 87.08 | +| hood | 69.19 | 82.73 | +| sconce | 60.97 | 73.87 | +| vase | 48.29 | 66.83 | +| traffic light | 39.4 | 61.26 | +| tray | 16.11 | 19.7 | +| ashcan | 49.53 | 67.83 | +| fan | 71.19 | 85.87 | +| pier | 38.92 | 42.72 | +| crt screen | 19.6 | 58.84 | +| plate | 63.98 | 78.5 | +| monitor | 53.47 | 75.59 | +| bulletin board | 55.85 | 75.35 | +| shower | 0.61 | 1.11 | +| radiator | 64.61 | 84.32 | +| glass | 19.36 | 20.26 | +| clock | 54.05 | 65.32 | +| flag | 66.64 | 80.04 | ++---------------------+-------+-------+ +2024-06-18 17:19:31,745 - mmseg - INFO - Summary: +2024-06-18 17:19:31,745 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.79 | 56.95 | 70.43 | ++-------+-------+-------+ +2024-06-18 17:19:31,746 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 17:19:31,746 - mmseg - INFO - Iter(val) [250] aAcc: 0.8579, mIoU: 0.5695, mAcc: 0.7043, IoU.wall: 0.8137, IoU.building: 0.8476, IoU.sky: 0.9470, IoU.floor: 0.8447, IoU.tree: 0.7769, IoU.ceiling: 0.8621, IoU.road: 0.8503, IoU.bed : 0.9274, IoU.windowpane: 0.6744, IoU.grass: 0.6675, IoU.cabinet: 0.6673, IoU.sidewalk: 0.6995, IoU.person: 0.8569, IoU.earth: 0.3594, IoU.door: 0.5877, IoU.table: 0.6741, IoU.mountain: 0.6146, IoU.plant: 0.5708, IoU.curtain: 0.7949, IoU.chair: 0.6648, IoU.car: 0.8762, IoU.water: 0.6258, IoU.painting: 0.7551, IoU.sofa: 0.8095, IoU.shelf: 0.5140, IoU.house: 0.5658, IoU.sea: 0.7148, IoU.mirror: 0.7825, IoU.rug: 0.6833, IoU.field: 0.2560, IoU.armchair: 0.6081, IoU.seat: 0.6895, IoU.fence: 0.4465, IoU.desk: 0.5814, IoU.rock: 0.5863, IoU.wardrobe: 0.5781, IoU.lamp: 0.7512, IoU.bathtub: 0.8784, IoU.railing: 0.4169, IoU.cushion: 0.7032, IoU.base: 0.4036, IoU.box: 0.3576, IoU.column: 0.5720, IoU.signboard: 0.4439, IoU.chest of drawers: 0.4870, IoU.counter: 0.4859, IoU.sand: 0.4460, IoU.sink: 0.8180, IoU.skyscraper: 0.4556, IoU.fireplace: 0.7494, IoU.refrigerator: 0.8028, IoU.grandstand: 0.4679, IoU.path: 0.3337, IoU.stairs: 0.3309, IoU.runway: 0.6610, IoU.case: 0.6117, IoU.pool table: 0.9500, IoU.pillow: 0.6912, IoU.screen door: 0.8081, IoU.stairway: 0.4366, IoU.river: 0.0940, IoU.bridge: 0.5270, IoU.bookcase: 0.4349, IoU.blind: 0.4533, IoU.coffee table: 0.5964, IoU.toilet: 0.8989, IoU.flower: 0.4146, IoU.book: 0.5570, IoU.hill: 0.0842, IoU.bench: 0.6889, IoU.countertop: 0.6158, IoU.stove: 0.8584, IoU.palm: 0.5206, IoU.kitchen island: 0.4371, IoU.computer: 0.8040, IoU.swivel chair: 0.5438, IoU.boat: 0.6467, IoU.bar: 0.6321, IoU.arcade machine: 0.8326, IoU.hovel: 0.2073, IoU.bus: 0.9258, IoU.towel: 0.7619, IoU.light: 0.6051, IoU.truck: 0.5126, IoU.tower: 0.2755, IoU.chandelier: 0.7370, IoU.awning: 0.4136, IoU.streetlight: 0.3331, IoU.booth: 0.5823, IoU.television receiver: 0.8051, IoU.airplane: 0.8662, IoU.dirt track: 0.1213, IoU.apparel: 0.6261, IoU.pole: 0.2802, IoU.land: 0.0006, IoU.bannister: 0.1982, IoU.escalator: 0.5969, IoU.ottoman: 0.5613, IoU.bottle: 0.3084, IoU.buffet: 0.5972, IoU.poster: 0.3636, IoU.stage: 0.2130, IoU.van: 0.4920, IoU.ship: 0.2420, IoU.fountain: 0.3942, IoU.conveyer belt: 0.8092, IoU.canopy: 0.6827, IoU.washer: 0.8543, IoU.plaything: 0.3457, IoU.swimming pool: 0.5560, IoU.stool: 0.5516, IoU.barrel: 0.6966, IoU.basket: 0.4219, IoU.waterfall: 0.5756, IoU.tent: 0.9036, IoU.bag: 0.2360, IoU.minibike: 0.7728, IoU.cradle: 0.8349, IoU.oven: 0.6087, IoU.ball: 0.1321, IoU.food: 0.5844, IoU.step: 0.1396, IoU.tank: 0.5616, IoU.trade name: 0.3286, IoU.microwave: 0.9066, IoU.pot: 0.5977, IoU.animal: 0.6768, IoU.bicycle: 0.6074, IoU.lake: 0.4998, IoU.dishwasher: 0.7152, IoU.screen: 0.3345, IoU.blanket: 0.3175, IoU.sculpture: 0.7579, IoU.hood: 0.6919, IoU.sconce: 0.6097, IoU.vase: 0.4829, IoU.traffic light: 0.3940, IoU.tray: 0.1611, IoU.ashcan: 0.4953, IoU.fan: 0.7119, IoU.pier: 0.3892, IoU.crt screen: 0.1960, IoU.plate: 0.6398, IoU.monitor: 0.5347, IoU.bulletin board: 0.5585, IoU.shower: 0.0061, IoU.radiator: 0.6461, IoU.glass: 0.1936, IoU.clock: 0.5405, IoU.flag: 0.6664, Acc.wall: 0.8934, Acc.building: 0.9361, Acc.sky: 0.9748, Acc.floor: 0.9135, Acc.tree: 0.8780, Acc.ceiling: 0.9303, Acc.road: 0.9052, Acc.bed : 0.9718, Acc.windowpane: 0.8195, Acc.grass: 0.8661, Acc.cabinet: 0.7294, Acc.sidewalk: 0.8820, Acc.person: 0.9204, Acc.earth: 0.4616, Acc.door: 0.7169, Acc.table: 0.7926, Acc.mountain: 0.7259, Acc.plant: 0.6870, Acc.curtain: 0.8543, Acc.chair: 0.7592, Acc.car: 0.9375, Acc.water: 0.7525, Acc.painting: 0.9237, Acc.sofa: 0.9356, Acc.shelf: 0.7479, Acc.house: 0.7607, Acc.sea: 0.8222, Acc.mirror: 0.8432, Acc.rug: 0.7628, Acc.field: 0.3958, Acc.armchair: 0.7586, Acc.seat: 0.9068, Acc.fence: 0.5702, Acc.desk: 0.8013, Acc.rock: 0.8828, Acc.wardrobe: 0.8780, Acc.lamp: 0.8544, Acc.bathtub: 0.9257, Acc.railing: 0.5518, Acc.cushion: 0.7870, Acc.base: 0.5727, Acc.box: 0.4538, Acc.column: 0.7514, Acc.signboard: 0.5789, Acc.chest of drawers: 0.8045, Acc.counter: 0.5844, Acc.sand: 0.6737, Acc.sink: 0.8795, Acc.skyscraper: 0.5854, Acc.fireplace: 0.9667, Acc.refrigerator: 0.9156, Acc.grandstand: 0.8519, Acc.path: 0.4671, Acc.stairs: 0.4222, Acc.runway: 0.8722, Acc.case: 0.8883, Acc.pool table: 0.9806, Acc.pillow: 0.8322, Acc.screen door: 0.8338, Acc.stairway: 0.5580, Acc.river: 0.2089, Acc.bridge: 0.6009, Acc.bookcase: 0.5145, Acc.blind: 0.5280, Acc.coffee table: 0.8726, Acc.toilet: 0.9448, Acc.flower: 0.5340, Acc.book: 0.7529, Acc.hill: 0.2046, Acc.bench: 0.7724, Acc.countertop: 0.8002, Acc.stove: 0.9002, Acc.palm: 0.7991, Acc.kitchen island: 0.9295, Acc.computer: 0.9133, Acc.swivel chair: 0.7690, Acc.boat: 0.9147, Acc.bar: 0.9042, Acc.arcade machine: 0.8740, Acc.hovel: 0.2375, Acc.bus: 0.9680, Acc.towel: 0.7972, Acc.light: 0.7210, Acc.truck: 0.6034, Acc.tower: 0.4796, Acc.chandelier: 0.8826, Acc.awning: 0.7095, Acc.streetlight: 0.4353, Acc.booth: 0.6799, Acc.television receiver: 0.8608, Acc.airplane: 0.9679, Acc.dirt track: 0.4156, Acc.apparel: 0.7932, Acc.pole: 0.4118, Acc.land: 0.0010, Acc.bannister: 0.2567, Acc.escalator: 0.8822, Acc.ottoman: 0.7844, Acc.bottle: 0.3797, Acc.buffet: 0.7659, Acc.poster: 0.4368, Acc.stage: 0.4772, Acc.van: 0.7194, Acc.ship: 0.2567, Acc.fountain: 0.4147, Acc.conveyer belt: 0.9590, Acc.canopy: 0.7823, Acc.washer: 0.8771, Acc.plaything: 0.7358, Acc.swimming pool: 0.8202, Acc.stool: 0.7391, Acc.barrel: 0.8343, Acc.basket: 0.5749, Acc.waterfall: 0.8382, Acc.tent: 0.9860, Acc.bag: 0.2736, Acc.minibike: 0.8680, Acc.cradle: 0.9868, Acc.oven: 0.7285, Acc.ball: 0.1331, Acc.food: 0.6700, Acc.step: 0.2067, Acc.tank: 0.7004, Acc.trade name: 0.3707, Acc.microwave: 0.9578, Acc.pot: 0.6722, Acc.animal: 0.7039, Acc.bicycle: 0.7383, Acc.lake: 0.6979, Acc.dishwasher: 0.7697, Acc.screen: 0.3913, Acc.blanket: 0.3929, Acc.sculpture: 0.8708, Acc.hood: 0.8273, Acc.sconce: 0.7387, Acc.vase: 0.6683, Acc.traffic light: 0.6126, Acc.tray: 0.1970, Acc.ashcan: 0.6783, Acc.fan: 0.8587, Acc.pier: 0.4272, Acc.crt screen: 0.5884, Acc.plate: 0.7850, Acc.monitor: 0.7559, Acc.bulletin board: 0.7535, Acc.shower: 0.0111, Acc.radiator: 0.8432, Acc.glass: 0.2026, Acc.clock: 0.6532, Acc.flag: 0.8004 +2024-06-18 17:21:11,192 - mmseg - INFO - Iter [25050/80000] lr: 2.748e-05, eta: 1 day, 8:36:42, time: 4.198, data_time: 2.228, memory: 72263, decode.loss_ce: 0.2468, decode.acc_seg: 89.6538, aux.loss_ce: 0.1014, aux.acc_seg: 89.3040, loss: 0.3482 +2024-06-18 17:22:50,165 - mmseg - INFO - Iter [25100/80000] lr: 2.745e-05, eta: 1 day, 8:34:38, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2648, decode.acc_seg: 89.1252, aux.loss_ce: 0.1083, aux.acc_seg: 88.8446, loss: 0.3731 +2024-06-18 17:24:29,190 - mmseg - INFO - Iter [25150/80000] lr: 2.743e-05, eta: 1 day, 8:32:34, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2512, decode.acc_seg: 89.7923, aux.loss_ce: 0.1032, aux.acc_seg: 89.5686, loss: 0.3544 +2024-06-18 17:26:08,126 - mmseg - INFO - Iter [25200/80000] lr: 2.740e-05, eta: 1 day, 8:30:30, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2588, decode.acc_seg: 89.4180, aux.loss_ce: 0.1043, aux.acc_seg: 89.3763, loss: 0.3632 +2024-06-18 17:27:46,963 - mmseg - INFO - Iter [25250/80000] lr: 2.738e-05, eta: 1 day, 8:28:26, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2607, decode.acc_seg: 89.4710, aux.loss_ce: 0.1066, aux.acc_seg: 89.1490, loss: 0.3673 +2024-06-18 17:29:28,895 - mmseg - INFO - Iter [25300/80000] lr: 2.735e-05, eta: 1 day, 8:26:29, time: 2.039, data_time: 0.069, memory: 72263, decode.loss_ce: 0.2419, decode.acc_seg: 90.3636, aux.loss_ce: 0.0987, aux.acc_seg: 90.0992, loss: 0.3406 +2024-06-18 17:31:07,875 - mmseg - INFO - Iter [25350/80000] lr: 2.733e-05, eta: 1 day, 8:24:25, time: 1.980, data_time: 0.012, memory: 72263, decode.loss_ce: 0.2347, decode.acc_seg: 90.3436, aux.loss_ce: 0.0968, aux.acc_seg: 90.0270, loss: 0.3316 +2024-06-18 17:32:46,979 - mmseg - INFO - Iter [25400/80000] lr: 2.730e-05, eta: 1 day, 8:22:22, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2347, decode.acc_seg: 90.6791, aux.loss_ce: 0.0967, aux.acc_seg: 90.3192, loss: 0.3314 +2024-06-18 17:34:25,978 - mmseg - INFO - Iter [25450/80000] lr: 2.728e-05, eta: 1 day, 8:20:19, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2504, decode.acc_seg: 89.4286, aux.loss_ce: 0.1028, aux.acc_seg: 89.0681, loss: 0.3532 +2024-06-18 17:36:04,960 - mmseg - INFO - Iter [25500/80000] lr: 2.725e-05, eta: 1 day, 8:18:16, time: 1.980, data_time: 0.012, memory: 72263, decode.loss_ce: 0.2301, decode.acc_seg: 90.4062, aux.loss_ce: 0.0942, aux.acc_seg: 90.1699, loss: 0.3243 +2024-06-18 17:37:44,129 - mmseg - INFO - Iter [25550/80000] lr: 2.723e-05, eta: 1 day, 8:16:13, time: 1.983, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2350, decode.acc_seg: 90.2813, aux.loss_ce: 0.0968, aux.acc_seg: 90.0175, loss: 0.3317 +2024-06-18 17:39:23,084 - mmseg - INFO - Iter [25600/80000] lr: 2.720e-05, eta: 1 day, 8:14:10, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2333, decode.acc_seg: 90.4032, aux.loss_ce: 0.0966, aux.acc_seg: 90.1301, loss: 0.3299 +2024-06-18 17:41:02,050 - mmseg - INFO - Iter [25650/80000] lr: 2.718e-05, eta: 1 day, 8:12:07, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2564, decode.acc_seg: 89.5838, aux.loss_ce: 0.1050, aux.acc_seg: 89.2502, loss: 0.3614 +2024-06-18 17:42:41,054 - mmseg - INFO - Iter [25700/80000] lr: 2.715e-05, eta: 1 day, 8:10:04, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2627, decode.acc_seg: 89.5144, aux.loss_ce: 0.1068, aux.acc_seg: 89.1638, loss: 0.3695 +2024-06-18 17:44:20,097 - mmseg - INFO - Iter [25750/80000] lr: 2.713e-05, eta: 1 day, 8:08:02, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2476, decode.acc_seg: 89.8447, aux.loss_ce: 0.1011, aux.acc_seg: 89.5443, loss: 0.3487 +2024-06-18 17:45:59,075 - mmseg - INFO - Iter [25800/80000] lr: 2.710e-05, eta: 1 day, 8:05:59, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2393, decode.acc_seg: 90.0177, aux.loss_ce: 0.0980, aux.acc_seg: 89.7553, loss: 0.3372 +2024-06-18 17:47:37,982 - mmseg - INFO - Iter [25850/80000] lr: 2.708e-05, eta: 1 day, 8:03:56, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2439, decode.acc_seg: 89.5904, aux.loss_ce: 0.1000, aux.acc_seg: 89.4235, loss: 0.3439 +2024-06-18 17:49:17,007 - mmseg - INFO - Iter [25900/80000] lr: 2.705e-05, eta: 1 day, 8:01:54, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2470, decode.acc_seg: 90.0225, aux.loss_ce: 0.1009, aux.acc_seg: 89.7615, loss: 0.3479 +2024-06-18 17:50:56,002 - mmseg - INFO - Iter [25950/80000] lr: 2.703e-05, eta: 1 day, 7:59:51, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2411, decode.acc_seg: 90.0940, aux.loss_ce: 0.0994, aux.acc_seg: 89.7804, loss: 0.3405 +2024-06-18 17:52:35,003 - mmseg - INFO - Saving checkpoint at 26000 iterations +2024-06-18 17:53:59,009 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 17:53:59,009 - mmseg - INFO - Iter [26000/80000] lr: 2.700e-05, eta: 1 day, 8:00:44, time: 3.660, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2494, decode.acc_seg: 89.9182, aux.loss_ce: 0.1028, aux.acc_seg: 89.6302, loss: 0.3522 +2024-06-18 17:55:47,311 - mmseg - INFO - per class results: +2024-06-18 17:55:47,317 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.02 | 91.16 | +| building | 84.71 | 91.94 | +| sky | 94.77 | 97.74 | +| floor | 84.81 | 91.87 | +| tree | 75.24 | 92.48 | +| ceiling | 87.31 | 93.98 | +| road | 85.75 | 91.21 | +| bed | 92.65 | 96.88 | +| windowpane | 68.29 | 80.02 | +| grass | 68.22 | 78.87 | +| cabinet | 67.93 | 77.74 | +| sidewalk | 68.48 | 84.39 | +| person | 85.82 | 93.57 | +| earth | 38.33 | 50.91 | +| door | 57.08 | 67.74 | +| table | 68.05 | 78.74 | +| mountain | 64.8 | 72.67 | +| plant | 52.05 | 61.11 | +| curtain | 79.63 | 89.6 | +| chair | 67.96 | 79.24 | +| car | 87.66 | 93.71 | +| water | 58.34 | 73.48 | +| painting | 78.13 | 89.96 | +| sofa | 80.67 | 86.75 | +| shelf | 50.63 | 63.98 | +| house | 46.09 | 53.67 | +| sea | 75.46 | 90.02 | +| mirror | 77.55 | 86.37 | +| rug | 69.13 | 81.91 | +| field | 26.11 | 49.04 | +| armchair | 60.88 | 77.42 | +| seat | 73.96 | 87.25 | +| fence | 51.34 | 62.6 | +| desk | 58.77 | 74.16 | +| rock | 59.65 | 79.15 | +| wardrobe | 53.16 | 72.73 | +| lamp | 73.8 | 85.13 | +| bathtub | 88.95 | 90.95 | +| railing | 42.51 | 60.97 | +| cushion | 69.84 | 84.3 | +| base | 42.72 | 61.46 | +| box | 32.34 | 36.66 | +| column | 56.64 | 68.18 | +| signboard | 44.22 | 57.31 | +| chest of drawers | 48.1 | 67.77 | +| counter | 45.67 | 55.54 | +| sand | 54.21 | 85.33 | +| sink | 82.03 | 86.34 | +| skyscraper | 45.81 | 57.47 | +| fireplace | 73.71 | 95.18 | +| refrigerator | 85.93 | 94.22 | +| grandstand | 49.05 | 83.32 | +| path | 28.87 | 47.77 | +| stairs | 38.0 | 45.79 | +| runway | 64.21 | 85.05 | +| case | 58.74 | 75.67 | +| pool table | 94.93 | 98.22 | +| pillow | 68.79 | 81.63 | +| screen door | 78.99 | 83.0 | +| stairway | 49.77 | 59.76 | +| river | 11.11 | 27.64 | +| bridge | 72.26 | 83.13 | +| bookcase | 44.36 | 63.04 | +| blind | 49.22 | 64.22 | +| coffee table | 60.67 | 90.37 | +| toilet | 90.97 | 94.25 | +| flower | 41.04 | 53.45 | +| book | 55.76 | 76.61 | +| hill | 11.37 | 27.33 | +| bench | 71.7 | 81.77 | +| countertop | 64.52 | 75.09 | +| stove | 86.32 | 91.53 | +| palm | 51.61 | 84.91 | +| kitchen island | 49.18 | 85.87 | +| computer | 78.55 | 92.45 | +| swivel chair | 54.44 | 81.53 | +| boat | 78.59 | 91.32 | +| bar | 65.44 | 83.63 | +| arcade machine | 90.27 | 99.05 | +| hovel | 52.31 | 60.35 | +| bus | 93.75 | 96.07 | +| towel | 72.3 | 91.11 | +| light | 60.88 | 71.57 | +| truck | 50.42 | 67.85 | +| tower | 29.63 | 49.68 | +| chandelier | 73.99 | 88.62 | +| awning | 41.93 | 52.87 | +| streetlight | 34.72 | 48.77 | +| booth | 44.59 | 77.91 | +| television receiver | 76.34 | 84.07 | +| airplane | 88.68 | 95.7 | +| dirt track | 12.39 | 57.99 | +| apparel | 53.24 | 70.12 | +| pole | 25.01 | 34.73 | +| land | 3.16 | 6.09 | +| bannister | 18.28 | 23.12 | +| escalator | 64.26 | 84.47 | +| ottoman | 54.81 | 72.64 | +| bottle | 39.92 | 59.0 | +| buffet | 59.15 | 66.33 | +| poster | 37.86 | 49.85 | +| stage | 18.67 | 41.99 | +| van | 51.35 | 68.31 | +| ship | 26.05 | 26.62 | +| fountain | 23.91 | 24.18 | +| conveyer belt | 81.15 | 95.6 | +| canopy | 39.41 | 46.37 | +| washer | 86.83 | 92.08 | +| plaything | 40.03 | 52.43 | +| swimming pool | 58.49 | 84.45 | +| stool | 56.51 | 70.17 | +| barrel | 54.81 | 64.46 | +| basket | 45.82 | 58.21 | +| waterfall | 54.82 | 70.68 | +| tent | 91.54 | 98.77 | +| bag | 23.52 | 25.41 | +| minibike | 75.84 | 89.76 | +| cradle | 87.28 | 98.32 | +| oven | 61.07 | 72.76 | +| ball | 46.24 | 79.37 | +| food | 60.57 | 78.69 | +| step | 11.48 | 14.11 | +| tank | 56.49 | 64.4 | +| trade name | 31.04 | 36.08 | +| microwave | 88.81 | 96.72 | +| pot | 57.34 | 70.11 | +| animal | 63.48 | 66.83 | +| bicycle | 62.12 | 80.29 | +| lake | 52.27 | 63.19 | +| dishwasher | 74.54 | 79.63 | +| screen | 62.27 | 90.4 | +| blanket | 27.31 | 33.75 | +| sculpture | 72.43 | 88.45 | +| hood | 66.66 | 80.85 | +| sconce | 59.56 | 71.9 | +| vase | 49.48 | 63.15 | +| traffic light | 40.94 | 61.09 | +| tray | 18.44 | 21.6 | +| ashcan | 50.54 | 67.97 | +| fan | 70.19 | 79.07 | +| pier | 38.9 | 43.86 | +| crt screen | 15.37 | 26.22 | +| plate | 63.81 | 81.49 | +| monitor | 41.95 | 51.09 | +| bulletin board | 55.68 | 69.25 | +| shower | 4.48 | 4.63 | +| radiator | 67.3 | 78.72 | +| glass | 18.69 | 19.59 | +| clock | 52.66 | 65.71 | +| flag | 68.13 | 82.05 | ++---------------------+-------+-------+ +2024-06-18 17:55:47,317 - mmseg - INFO - Summary: +2024-06-18 17:55:47,317 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 85.96 | 57.6 | 70.52 | ++-------+------+-------+ +2024-06-18 17:55:47,318 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 17:55:47,318 - mmseg - INFO - Iter(val) [250] aAcc: 0.8596, mIoU: 0.5760, mAcc: 0.7052, IoU.wall: 0.8202, IoU.building: 0.8471, IoU.sky: 0.9477, IoU.floor: 0.8481, IoU.tree: 0.7524, IoU.ceiling: 0.8731, IoU.road: 0.8575, IoU.bed : 0.9265, IoU.windowpane: 0.6829, IoU.grass: 0.6822, IoU.cabinet: 0.6793, IoU.sidewalk: 0.6848, IoU.person: 0.8582, IoU.earth: 0.3833, IoU.door: 0.5708, IoU.table: 0.6805, IoU.mountain: 0.6480, IoU.plant: 0.5205, IoU.curtain: 0.7963, IoU.chair: 0.6796, IoU.car: 0.8766, IoU.water: 0.5834, IoU.painting: 0.7813, IoU.sofa: 0.8067, IoU.shelf: 0.5063, IoU.house: 0.4609, IoU.sea: 0.7546, IoU.mirror: 0.7755, IoU.rug: 0.6913, IoU.field: 0.2611, IoU.armchair: 0.6088, IoU.seat: 0.7396, IoU.fence: 0.5134, IoU.desk: 0.5877, IoU.rock: 0.5965, IoU.wardrobe: 0.5316, IoU.lamp: 0.7380, IoU.bathtub: 0.8895, IoU.railing: 0.4251, IoU.cushion: 0.6984, IoU.base: 0.4272, IoU.box: 0.3234, IoU.column: 0.5664, IoU.signboard: 0.4422, IoU.chest of drawers: 0.4810, IoU.counter: 0.4567, IoU.sand: 0.5421, IoU.sink: 0.8203, IoU.skyscraper: 0.4581, IoU.fireplace: 0.7371, IoU.refrigerator: 0.8593, IoU.grandstand: 0.4905, IoU.path: 0.2887, IoU.stairs: 0.3800, IoU.runway: 0.6421, IoU.case: 0.5874, IoU.pool table: 0.9493, IoU.pillow: 0.6879, IoU.screen door: 0.7899, IoU.stairway: 0.4977, IoU.river: 0.1111, IoU.bridge: 0.7226, IoU.bookcase: 0.4436, IoU.blind: 0.4922, IoU.coffee table: 0.6067, IoU.toilet: 0.9097, IoU.flower: 0.4104, IoU.book: 0.5576, IoU.hill: 0.1137, IoU.bench: 0.7170, IoU.countertop: 0.6452, IoU.stove: 0.8632, IoU.palm: 0.5161, IoU.kitchen island: 0.4918, IoU.computer: 0.7855, IoU.swivel chair: 0.5444, IoU.boat: 0.7859, IoU.bar: 0.6544, IoU.arcade machine: 0.9027, IoU.hovel: 0.5231, IoU.bus: 0.9375, IoU.towel: 0.7230, IoU.light: 0.6088, IoU.truck: 0.5042, IoU.tower: 0.2963, IoU.chandelier: 0.7399, IoU.awning: 0.4193, IoU.streetlight: 0.3472, IoU.booth: 0.4459, IoU.television receiver: 0.7634, IoU.airplane: 0.8868, IoU.dirt track: 0.1239, IoU.apparel: 0.5324, IoU.pole: 0.2501, IoU.land: 0.0316, IoU.bannister: 0.1828, IoU.escalator: 0.6426, IoU.ottoman: 0.5481, IoU.bottle: 0.3992, IoU.buffet: 0.5915, IoU.poster: 0.3786, IoU.stage: 0.1867, IoU.van: 0.5135, IoU.ship: 0.2605, IoU.fountain: 0.2391, IoU.conveyer belt: 0.8115, IoU.canopy: 0.3941, IoU.washer: 0.8683, IoU.plaything: 0.4003, IoU.swimming pool: 0.5849, IoU.stool: 0.5651, IoU.barrel: 0.5481, IoU.basket: 0.4582, IoU.waterfall: 0.5482, IoU.tent: 0.9154, IoU.bag: 0.2352, IoU.minibike: 0.7584, IoU.cradle: 0.8728, IoU.oven: 0.6107, IoU.ball: 0.4624, IoU.food: 0.6057, IoU.step: 0.1148, IoU.tank: 0.5649, IoU.trade name: 0.3104, IoU.microwave: 0.8881, IoU.pot: 0.5734, IoU.animal: 0.6348, IoU.bicycle: 0.6212, IoU.lake: 0.5227, IoU.dishwasher: 0.7454, IoU.screen: 0.6227, IoU.blanket: 0.2731, IoU.sculpture: 0.7243, IoU.hood: 0.6666, IoU.sconce: 0.5956, IoU.vase: 0.4948, IoU.traffic light: 0.4094, IoU.tray: 0.1844, IoU.ashcan: 0.5054, IoU.fan: 0.7019, IoU.pier: 0.3890, IoU.crt screen: 0.1537, IoU.plate: 0.6381, IoU.monitor: 0.4195, IoU.bulletin board: 0.5568, IoU.shower: 0.0448, IoU.radiator: 0.6730, IoU.glass: 0.1869, IoU.clock: 0.5266, IoU.flag: 0.6813, Acc.wall: 0.9116, Acc.building: 0.9194, Acc.sky: 0.9774, Acc.floor: 0.9187, Acc.tree: 0.9248, Acc.ceiling: 0.9398, Acc.road: 0.9121, Acc.bed : 0.9688, Acc.windowpane: 0.8002, Acc.grass: 0.7887, Acc.cabinet: 0.7774, Acc.sidewalk: 0.8439, Acc.person: 0.9357, Acc.earth: 0.5091, Acc.door: 0.6774, Acc.table: 0.7874, Acc.mountain: 0.7267, Acc.plant: 0.6111, Acc.curtain: 0.8960, Acc.chair: 0.7924, Acc.car: 0.9371, Acc.water: 0.7348, Acc.painting: 0.8996, Acc.sofa: 0.8675, Acc.shelf: 0.6398, Acc.house: 0.5367, Acc.sea: 0.9002, Acc.mirror: 0.8637, Acc.rug: 0.8191, Acc.field: 0.4904, Acc.armchair: 0.7742, Acc.seat: 0.8725, Acc.fence: 0.6260, Acc.desk: 0.7416, Acc.rock: 0.7915, Acc.wardrobe: 0.7273, Acc.lamp: 0.8513, Acc.bathtub: 0.9095, Acc.railing: 0.6097, Acc.cushion: 0.8430, Acc.base: 0.6146, Acc.box: 0.3666, Acc.column: 0.6818, Acc.signboard: 0.5731, Acc.chest of drawers: 0.6777, Acc.counter: 0.5554, Acc.sand: 0.8533, Acc.sink: 0.8634, Acc.skyscraper: 0.5747, Acc.fireplace: 0.9518, Acc.refrigerator: 0.9422, Acc.grandstand: 0.8332, Acc.path: 0.4777, Acc.stairs: 0.4579, Acc.runway: 0.8505, Acc.case: 0.7567, Acc.pool table: 0.9822, Acc.pillow: 0.8163, Acc.screen door: 0.8300, Acc.stairway: 0.5976, Acc.river: 0.2764, Acc.bridge: 0.8313, Acc.bookcase: 0.6304, Acc.blind: 0.6422, Acc.coffee table: 0.9037, Acc.toilet: 0.9425, Acc.flower: 0.5345, Acc.book: 0.7661, Acc.hill: 0.2733, Acc.bench: 0.8177, Acc.countertop: 0.7509, Acc.stove: 0.9153, Acc.palm: 0.8491, Acc.kitchen island: 0.8587, Acc.computer: 0.9245, Acc.swivel chair: 0.8153, Acc.boat: 0.9132, Acc.bar: 0.8363, Acc.arcade machine: 0.9905, Acc.hovel: 0.6035, Acc.bus: 0.9607, Acc.towel: 0.9111, Acc.light: 0.7157, Acc.truck: 0.6785, Acc.tower: 0.4968, Acc.chandelier: 0.8862, Acc.awning: 0.5287, Acc.streetlight: 0.4877, Acc.booth: 0.7791, Acc.television receiver: 0.8407, Acc.airplane: 0.9570, Acc.dirt track: 0.5799, Acc.apparel: 0.7012, Acc.pole: 0.3473, Acc.land: 0.0609, Acc.bannister: 0.2312, Acc.escalator: 0.8447, Acc.ottoman: 0.7264, Acc.bottle: 0.5900, Acc.buffet: 0.6633, Acc.poster: 0.4985, Acc.stage: 0.4199, Acc.van: 0.6831, Acc.ship: 0.2662, Acc.fountain: 0.2418, Acc.conveyer belt: 0.9560, Acc.canopy: 0.4637, Acc.washer: 0.9208, Acc.plaything: 0.5243, Acc.swimming pool: 0.8445, Acc.stool: 0.7017, Acc.barrel: 0.6446, Acc.basket: 0.5821, Acc.waterfall: 0.7068, Acc.tent: 0.9877, Acc.bag: 0.2541, Acc.minibike: 0.8976, Acc.cradle: 0.9832, Acc.oven: 0.7276, Acc.ball: 0.7937, Acc.food: 0.7869, Acc.step: 0.1411, Acc.tank: 0.6440, Acc.trade name: 0.3608, Acc.microwave: 0.9672, Acc.pot: 0.7011, Acc.animal: 0.6683, Acc.bicycle: 0.8029, Acc.lake: 0.6319, Acc.dishwasher: 0.7963, Acc.screen: 0.9040, Acc.blanket: 0.3375, Acc.sculpture: 0.8845, Acc.hood: 0.8085, Acc.sconce: 0.7190, Acc.vase: 0.6315, Acc.traffic light: 0.6109, Acc.tray: 0.2160, Acc.ashcan: 0.6797, Acc.fan: 0.7907, Acc.pier: 0.4386, Acc.crt screen: 0.2622, Acc.plate: 0.8149, Acc.monitor: 0.5109, Acc.bulletin board: 0.6925, Acc.shower: 0.0463, Acc.radiator: 0.7872, Acc.glass: 0.1959, Acc.clock: 0.6571, Acc.flag: 0.8205 +2024-06-18 17:57:26,701 - mmseg - INFO - Iter [26050/80000] lr: 2.698e-05, eta: 1 day, 8:02:26, time: 4.154, data_time: 2.183, memory: 72263, decode.loss_ce: 0.2445, decode.acc_seg: 89.8815, aux.loss_ce: 0.1001, aux.acc_seg: 89.7132, loss: 0.3446 +2024-06-18 17:59:05,701 - mmseg - INFO - Iter [26100/80000] lr: 2.695e-05, eta: 1 day, 8:00:23, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2716, decode.acc_seg: 89.2206, aux.loss_ce: 0.1109, aux.acc_seg: 88.9564, loss: 0.3824 +2024-06-18 18:00:44,623 - mmseg - INFO - Iter [26150/80000] lr: 2.693e-05, eta: 1 day, 7:58:20, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2759, decode.acc_seg: 88.6433, aux.loss_ce: 0.1124, aux.acc_seg: 88.2925, loss: 0.3883 +2024-06-18 18:02:23,779 - mmseg - INFO - Iter [26200/80000] lr: 2.690e-05, eta: 1 day, 7:56:17, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2654, decode.acc_seg: 89.4327, aux.loss_ce: 0.1083, aux.acc_seg: 89.2619, loss: 0.3737 +2024-06-18 18:04:02,600 - mmseg - INFO - Iter [26250/80000] lr: 2.688e-05, eta: 1 day, 7:54:13, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2436, decode.acc_seg: 90.0842, aux.loss_ce: 0.0992, aux.acc_seg: 89.7809, loss: 0.3428 +2024-06-18 18:05:41,563 - mmseg - INFO - Iter [26300/80000] lr: 2.685e-05, eta: 1 day, 7:52:11, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2558, decode.acc_seg: 89.1575, aux.loss_ce: 0.1049, aux.acc_seg: 88.9003, loss: 0.3607 +2024-06-18 18:07:20,497 - mmseg - INFO - Iter [26350/80000] lr: 2.683e-05, eta: 1 day, 7:50:08, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2403, decode.acc_seg: 90.0305, aux.loss_ce: 0.0991, aux.acc_seg: 89.7508, loss: 0.3394 +2024-06-18 18:08:59,426 - mmseg - INFO - Iter [26400/80000] lr: 2.680e-05, eta: 1 day, 7:48:05, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2560, decode.acc_seg: 89.5845, aux.loss_ce: 0.1047, aux.acc_seg: 89.2576, loss: 0.3607 +2024-06-18 18:10:38,337 - mmseg - INFO - Iter [26450/80000] lr: 2.678e-05, eta: 1 day, 7:46:02, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2569, decode.acc_seg: 89.3395, aux.loss_ce: 0.1058, aux.acc_seg: 89.0868, loss: 0.3626 +2024-06-18 18:12:17,230 - mmseg - INFO - Iter [26500/80000] lr: 2.675e-05, eta: 1 day, 7:43:59, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2448, decode.acc_seg: 89.6668, aux.loss_ce: 0.1001, aux.acc_seg: 89.4826, loss: 0.3449 +2024-06-18 18:13:58,340 - mmseg - INFO - Iter [26550/80000] lr: 2.673e-05, eta: 1 day, 7:42:01, time: 2.022, data_time: 0.052, memory: 72263, decode.loss_ce: 0.2379, decode.acc_seg: 90.3902, aux.loss_ce: 0.0980, aux.acc_seg: 90.1078, loss: 0.3360 +2024-06-18 18:15:37,166 - mmseg - INFO - Iter [26600/80000] lr: 2.670e-05, eta: 1 day, 7:39:59, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2490, decode.acc_seg: 89.8821, aux.loss_ce: 0.1025, aux.acc_seg: 89.5106, loss: 0.3514 +2024-06-18 18:17:16,052 - mmseg - INFO - Iter [26650/80000] lr: 2.668e-05, eta: 1 day, 7:37:56, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2655, decode.acc_seg: 89.3041, aux.loss_ce: 0.1091, aux.acc_seg: 88.9357, loss: 0.3745 +2024-06-18 18:18:54,891 - mmseg - INFO - Iter [26700/80000] lr: 2.665e-05, eta: 1 day, 7:35:54, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2280, decode.acc_seg: 90.7288, aux.loss_ce: 0.0929, aux.acc_seg: 90.5226, loss: 0.3209 +2024-06-18 18:20:33,910 - mmseg - INFO - Iter [26750/80000] lr: 2.663e-05, eta: 1 day, 7:33:52, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2547, decode.acc_seg: 89.5562, aux.loss_ce: 0.1056, aux.acc_seg: 89.1274, loss: 0.3604 +2024-06-18 18:22:12,810 - mmseg - INFO - Iter [26800/80000] lr: 2.660e-05, eta: 1 day, 7:31:49, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2508, decode.acc_seg: 89.8802, aux.loss_ce: 0.1039, aux.acc_seg: 89.5289, loss: 0.3547 +2024-06-18 18:23:51,643 - mmseg - INFO - Iter [26850/80000] lr: 2.658e-05, eta: 1 day, 7:29:47, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2418, decode.acc_seg: 90.2355, aux.loss_ce: 0.0993, aux.acc_seg: 90.0119, loss: 0.3411 +2024-06-18 18:25:30,452 - mmseg - INFO - Iter [26900/80000] lr: 2.655e-05, eta: 1 day, 7:27:45, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2374, decode.acc_seg: 89.8851, aux.loss_ce: 0.0976, aux.acc_seg: 89.6074, loss: 0.3350 +2024-06-18 18:27:09,328 - mmseg - INFO - Iter [26950/80000] lr: 2.653e-05, eta: 1 day, 7:25:43, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2460, decode.acc_seg: 90.0182, aux.loss_ce: 0.1007, aux.acc_seg: 89.7284, loss: 0.3467 +2024-06-18 18:28:48,225 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 18:28:48,225 - mmseg - INFO - Iter [27000/80000] lr: 2.650e-05, eta: 1 day, 7:23:41, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2277, decode.acc_seg: 90.5255, aux.loss_ce: 0.0939, aux.acc_seg: 90.1469, loss: 0.3216 +2024-06-18 18:30:38,670 - mmseg - INFO - per class results: +2024-06-18 18:30:38,676 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.98 | 89.79 | +| building | 85.17 | 93.54 | +| sky | 94.83 | 97.54 | +| floor | 84.84 | 91.56 | +| tree | 77.98 | 89.02 | +| ceiling | 86.91 | 92.78 | +| road | 84.82 | 91.14 | +| bed | 92.27 | 96.4 | +| windowpane | 67.18 | 78.83 | +| grass | 66.52 | 82.91 | +| cabinet | 68.13 | 75.11 | +| sidewalk | 69.2 | 84.46 | +| person | 85.36 | 93.34 | +| earth | 41.65 | 55.94 | +| door | 59.48 | 73.61 | +| table | 68.1 | 80.47 | +| mountain | 59.63 | 69.51 | +| plant | 59.11 | 66.81 | +| curtain | 79.5 | 89.29 | +| chair | 66.67 | 76.77 | +| car | 87.43 | 94.29 | +| water | 65.84 | 83.2 | +| painting | 78.06 | 89.73 | +| sofa | 81.91 | 93.32 | +| shelf | 47.32 | 59.91 | +| house | 59.25 | 74.72 | +| sea | 75.45 | 82.92 | +| mirror | 75.76 | 84.28 | +| rug | 71.14 | 85.57 | +| field | 21.79 | 37.92 | +| armchair | 62.38 | 77.62 | +| seat | 69.84 | 89.15 | +| fence | 51.53 | 69.94 | +| desk | 55.84 | 75.94 | +| rock | 53.38 | 88.37 | +| wardrobe | 55.22 | 76.63 | +| lamp | 73.33 | 85.8 | +| bathtub | 86.65 | 91.03 | +| railing | 42.09 | 56.61 | +| cushion | 68.47 | 84.05 | +| base | 42.1 | 60.24 | +| box | 38.57 | 51.4 | +| column | 54.03 | 60.7 | +| signboard | 41.21 | 60.78 | +| chest of drawers | 51.74 | 74.86 | +| counter | 45.05 | 52.71 | +| sand | 51.68 | 74.59 | +| sink | 80.47 | 85.98 | +| skyscraper | 44.11 | 60.1 | +| fireplace | 75.13 | 91.38 | +| refrigerator | 81.04 | 97.12 | +| grandstand | 43.04 | 88.72 | +| path | 34.78 | 44.78 | +| stairs | 26.82 | 33.67 | +| runway | 68.03 | 88.03 | +| case | 63.87 | 88.86 | +| pool table | 93.83 | 99.06 | +| pillow | 66.16 | 76.91 | +| screen door | 82.49 | 85.1 | +| stairway | 41.81 | 57.23 | +| river | 12.99 | 26.81 | +| bridge | 56.66 | 62.85 | +| bookcase | 39.48 | 63.86 | +| blind | 43.34 | 55.23 | +| coffee table | 59.15 | 89.69 | +| toilet | 89.53 | 93.14 | +| flower | 40.95 | 45.92 | +| book | 54.12 | 74.41 | +| hill | 13.48 | 25.43 | +| bench | 66.89 | 74.83 | +| countertop | 64.56 | 83.52 | +| stove | 83.98 | 93.8 | +| palm | 49.84 | 85.56 | +| kitchen island | 43.88 | 73.54 | +| computer | 79.31 | 93.95 | +| swivel chair | 50.77 | 75.31 | +| boat | 67.2 | 90.53 | +| bar | 63.3 | 89.69 | +| arcade machine | 87.14 | 99.21 | +| hovel | 45.5 | 50.63 | +| bus | 93.71 | 96.82 | +| towel | 79.48 | 86.62 | +| light | 61.53 | 71.97 | +| truck | 50.54 | 65.71 | +| tower | 30.47 | 55.49 | +| chandelier | 73.32 | 88.57 | +| awning | 43.42 | 51.37 | +| streetlight | 33.61 | 44.2 | +| booth | 43.56 | 59.06 | +| television receiver | 79.04 | 90.68 | +| airplane | 87.9 | 96.07 | +| dirt track | 3.74 | 15.03 | +| apparel | 64.48 | 79.46 | +| pole | 29.77 | 45.38 | +| land | 3.75 | 4.51 | +| bannister | 20.1 | 25.65 | +| escalator | 61.98 | 86.8 | +| ottoman | 58.09 | 79.0 | +| bottle | 43.82 | 70.91 | +| buffet | 66.36 | 87.94 | +| poster | 34.94 | 51.76 | +| stage | 17.27 | 42.23 | +| van | 49.62 | 64.42 | +| ship | 70.6 | 77.95 | +| fountain | 24.44 | 24.8 | +| conveyer belt | 82.21 | 96.71 | +| canopy | 58.45 | 79.92 | +| washer | 88.31 | 94.98 | +| plaything | 38.44 | 56.29 | +| swimming pool | 54.83 | 79.37 | +| stool | 56.84 | 68.18 | +| barrel | 44.31 | 66.9 | +| basket | 41.87 | 64.53 | +| waterfall | 52.88 | 67.68 | +| tent | 73.37 | 99.13 | +| bag | 23.48 | 26.78 | +| minibike | 76.79 | 88.26 | +| cradle | 89.12 | 97.93 | +| oven | 55.26 | 62.28 | +| ball | 62.9 | 78.26 | +| food | 58.95 | 69.2 | +| step | 11.07 | 12.85 | +| tank | 55.28 | 69.85 | +| trade name | 19.49 | 22.14 | +| microwave | 87.05 | 97.1 | +| pot | 59.17 | 69.6 | +| animal | 62.61 | 64.48 | +| bicycle | 60.63 | 73.94 | +| lake | 52.85 | 63.64 | +| dishwasher | 70.42 | 77.39 | +| screen | 58.14 | 95.51 | +| blanket | 29.58 | 36.02 | +| sculpture | 76.03 | 86.19 | +| hood | 67.66 | 81.48 | +| sconce | 57.94 | 66.53 | +| vase | 49.19 | 64.7 | +| traffic light | 36.9 | 69.74 | +| tray | 19.3 | 23.3 | +| ashcan | 50.51 | 62.39 | +| fan | 70.52 | 84.24 | +| pier | 39.37 | 43.97 | +| crt screen | 5.74 | 11.21 | +| plate | 64.83 | 76.64 | +| monitor | 34.72 | 44.03 | +| bulletin board | 59.79 | 67.37 | +| shower | 7.93 | 8.05 | +| radiator | 65.2 | 79.94 | +| glass | 20.67 | 22.36 | +| clock | 48.95 | 54.51 | +| flag | 67.13 | 76.12 | ++---------------------+-------+-------+ +2024-06-18 18:30:38,677 - mmseg - INFO - Summary: +2024-06-18 18:30:38,677 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.06 | 57.28 | 70.63 | ++-------+-------+-------+ +2024-06-18 18:30:38,677 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 18:30:38,678 - mmseg - INFO - Iter(val) [250] aAcc: 0.8606, mIoU: 0.5728, mAcc: 0.7063, IoU.wall: 0.8198, IoU.building: 0.8517, IoU.sky: 0.9483, IoU.floor: 0.8484, IoU.tree: 0.7798, IoU.ceiling: 0.8691, IoU.road: 0.8482, IoU.bed : 0.9227, IoU.windowpane: 0.6718, IoU.grass: 0.6652, IoU.cabinet: 0.6813, IoU.sidewalk: 0.6920, IoU.person: 0.8536, IoU.earth: 0.4165, IoU.door: 0.5948, IoU.table: 0.6810, IoU.mountain: 0.5963, IoU.plant: 0.5911, IoU.curtain: 0.7950, IoU.chair: 0.6667, IoU.car: 0.8743, IoU.water: 0.6584, IoU.painting: 0.7806, IoU.sofa: 0.8191, IoU.shelf: 0.4732, IoU.house: 0.5925, IoU.sea: 0.7545, IoU.mirror: 0.7576, IoU.rug: 0.7114, IoU.field: 0.2179, IoU.armchair: 0.6238, IoU.seat: 0.6984, IoU.fence: 0.5153, IoU.desk: 0.5584, IoU.rock: 0.5338, IoU.wardrobe: 0.5522, IoU.lamp: 0.7333, IoU.bathtub: 0.8665, IoU.railing: 0.4209, IoU.cushion: 0.6847, IoU.base: 0.4210, IoU.box: 0.3857, IoU.column: 0.5403, IoU.signboard: 0.4121, IoU.chest of drawers: 0.5174, IoU.counter: 0.4505, IoU.sand: 0.5168, IoU.sink: 0.8047, IoU.skyscraper: 0.4411, IoU.fireplace: 0.7513, IoU.refrigerator: 0.8104, IoU.grandstand: 0.4304, IoU.path: 0.3478, IoU.stairs: 0.2682, IoU.runway: 0.6803, IoU.case: 0.6387, IoU.pool table: 0.9383, IoU.pillow: 0.6616, IoU.screen door: 0.8249, IoU.stairway: 0.4181, IoU.river: 0.1299, IoU.bridge: 0.5666, IoU.bookcase: 0.3948, IoU.blind: 0.4334, IoU.coffee table: 0.5915, IoU.toilet: 0.8953, IoU.flower: 0.4095, IoU.book: 0.5412, IoU.hill: 0.1348, IoU.bench: 0.6689, IoU.countertop: 0.6456, IoU.stove: 0.8398, IoU.palm: 0.4984, IoU.kitchen island: 0.4388, IoU.computer: 0.7931, IoU.swivel chair: 0.5077, IoU.boat: 0.6720, IoU.bar: 0.6330, IoU.arcade machine: 0.8714, IoU.hovel: 0.4550, IoU.bus: 0.9371, IoU.towel: 0.7948, IoU.light: 0.6153, IoU.truck: 0.5054, IoU.tower: 0.3047, IoU.chandelier: 0.7332, IoU.awning: 0.4342, IoU.streetlight: 0.3361, IoU.booth: 0.4356, IoU.television receiver: 0.7904, IoU.airplane: 0.8790, IoU.dirt track: 0.0374, IoU.apparel: 0.6448, IoU.pole: 0.2977, IoU.land: 0.0375, IoU.bannister: 0.2010, IoU.escalator: 0.6198, IoU.ottoman: 0.5809, IoU.bottle: 0.4382, IoU.buffet: 0.6636, IoU.poster: 0.3494, IoU.stage: 0.1727, IoU.van: 0.4962, IoU.ship: 0.7060, IoU.fountain: 0.2444, IoU.conveyer belt: 0.8221, IoU.canopy: 0.5845, IoU.washer: 0.8831, IoU.plaything: 0.3844, IoU.swimming pool: 0.5483, IoU.stool: 0.5684, IoU.barrel: 0.4431, IoU.basket: 0.4187, IoU.waterfall: 0.5288, IoU.tent: 0.7337, IoU.bag: 0.2348, IoU.minibike: 0.7679, IoU.cradle: 0.8912, IoU.oven: 0.5526, IoU.ball: 0.6290, IoU.food: 0.5895, IoU.step: 0.1107, IoU.tank: 0.5528, IoU.trade name: 0.1949, IoU.microwave: 0.8705, IoU.pot: 0.5917, IoU.animal: 0.6261, IoU.bicycle: 0.6063, IoU.lake: 0.5285, IoU.dishwasher: 0.7042, IoU.screen: 0.5814, IoU.blanket: 0.2958, IoU.sculpture: 0.7603, IoU.hood: 0.6766, IoU.sconce: 0.5794, IoU.vase: 0.4919, IoU.traffic light: 0.3690, IoU.tray: 0.1930, IoU.ashcan: 0.5051, IoU.fan: 0.7052, IoU.pier: 0.3937, IoU.crt screen: 0.0574, IoU.plate: 0.6483, IoU.monitor: 0.3472, IoU.bulletin board: 0.5979, IoU.shower: 0.0793, IoU.radiator: 0.6520, IoU.glass: 0.2067, IoU.clock: 0.4895, IoU.flag: 0.6713, Acc.wall: 0.8979, Acc.building: 0.9354, Acc.sky: 0.9754, Acc.floor: 0.9156, Acc.tree: 0.8902, Acc.ceiling: 0.9278, Acc.road: 0.9114, Acc.bed : 0.9640, Acc.windowpane: 0.7883, Acc.grass: 0.8291, Acc.cabinet: 0.7511, Acc.sidewalk: 0.8446, Acc.person: 0.9334, Acc.earth: 0.5594, Acc.door: 0.7361, Acc.table: 0.8047, Acc.mountain: 0.6951, Acc.plant: 0.6681, Acc.curtain: 0.8929, Acc.chair: 0.7677, Acc.car: 0.9429, Acc.water: 0.8320, Acc.painting: 0.8973, Acc.sofa: 0.9332, Acc.shelf: 0.5991, Acc.house: 0.7472, Acc.sea: 0.8292, Acc.mirror: 0.8428, Acc.rug: 0.8557, Acc.field: 0.3792, Acc.armchair: 0.7762, Acc.seat: 0.8915, Acc.fence: 0.6994, Acc.desk: 0.7594, Acc.rock: 0.8837, Acc.wardrobe: 0.7663, Acc.lamp: 0.8580, Acc.bathtub: 0.9103, Acc.railing: 0.5661, Acc.cushion: 0.8405, Acc.base: 0.6024, Acc.box: 0.5140, Acc.column: 0.6070, Acc.signboard: 0.6078, Acc.chest of drawers: 0.7486, Acc.counter: 0.5271, Acc.sand: 0.7459, Acc.sink: 0.8598, Acc.skyscraper: 0.6010, Acc.fireplace: 0.9138, Acc.refrigerator: 0.9712, Acc.grandstand: 0.8872, Acc.path: 0.4478, Acc.stairs: 0.3367, Acc.runway: 0.8803, Acc.case: 0.8886, Acc.pool table: 0.9906, Acc.pillow: 0.7691, Acc.screen door: 0.8510, Acc.stairway: 0.5723, Acc.river: 0.2681, Acc.bridge: 0.6285, Acc.bookcase: 0.6386, Acc.blind: 0.5523, Acc.coffee table: 0.8969, Acc.toilet: 0.9314, Acc.flower: 0.4592, Acc.book: 0.7441, Acc.hill: 0.2543, Acc.bench: 0.7483, Acc.countertop: 0.8352, Acc.stove: 0.9380, Acc.palm: 0.8556, Acc.kitchen island: 0.7354, Acc.computer: 0.9395, Acc.swivel chair: 0.7531, Acc.boat: 0.9053, Acc.bar: 0.8969, Acc.arcade machine: 0.9921, Acc.hovel: 0.5063, Acc.bus: 0.9682, Acc.towel: 0.8662, Acc.light: 0.7197, Acc.truck: 0.6571, Acc.tower: 0.5549, Acc.chandelier: 0.8857, Acc.awning: 0.5137, Acc.streetlight: 0.4420, Acc.booth: 0.5906, Acc.television receiver: 0.9068, Acc.airplane: 0.9607, Acc.dirt track: 0.1503, Acc.apparel: 0.7946, Acc.pole: 0.4538, Acc.land: 0.0451, Acc.bannister: 0.2565, Acc.escalator: 0.8680, Acc.ottoman: 0.7900, Acc.bottle: 0.7091, Acc.buffet: 0.8794, Acc.poster: 0.5176, Acc.stage: 0.4223, Acc.van: 0.6442, Acc.ship: 0.7795, Acc.fountain: 0.2480, Acc.conveyer belt: 0.9671, Acc.canopy: 0.7992, Acc.washer: 0.9498, Acc.plaything: 0.5629, Acc.swimming pool: 0.7937, Acc.stool: 0.6818, Acc.barrel: 0.6690, Acc.basket: 0.6453, Acc.waterfall: 0.6768, Acc.tent: 0.9913, Acc.bag: 0.2678, Acc.minibike: 0.8826, Acc.cradle: 0.9793, Acc.oven: 0.6228, Acc.ball: 0.7826, Acc.food: 0.6920, Acc.step: 0.1285, Acc.tank: 0.6985, Acc.trade name: 0.2214, Acc.microwave: 0.9710, Acc.pot: 0.6960, Acc.animal: 0.6448, Acc.bicycle: 0.7394, Acc.lake: 0.6364, Acc.dishwasher: 0.7739, Acc.screen: 0.9551, Acc.blanket: 0.3602, Acc.sculpture: 0.8619, Acc.hood: 0.8148, Acc.sconce: 0.6653, Acc.vase: 0.6470, Acc.traffic light: 0.6974, Acc.tray: 0.2330, Acc.ashcan: 0.6239, Acc.fan: 0.8424, Acc.pier: 0.4397, Acc.crt screen: 0.1121, Acc.plate: 0.7664, Acc.monitor: 0.4403, Acc.bulletin board: 0.6737, Acc.shower: 0.0805, Acc.radiator: 0.7994, Acc.glass: 0.2236, Acc.clock: 0.5451, Acc.flag: 0.7612 +2024-06-18 18:32:17,970 - mmseg - INFO - Iter [27050/80000] lr: 2.648e-05, eta: 1 day, 7:25:16, time: 4.195, data_time: 2.225, memory: 72263, decode.loss_ce: 0.2450, decode.acc_seg: 89.9708, aux.loss_ce: 0.1005, aux.acc_seg: 89.7037, loss: 0.3454 +2024-06-18 18:33:56,923 - mmseg - INFO - Iter [27100/80000] lr: 2.645e-05, eta: 1 day, 7:23:14, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2277, decode.acc_seg: 90.5382, aux.loss_ce: 0.0939, aux.acc_seg: 90.2491, loss: 0.3216 +2024-06-18 18:35:35,873 - mmseg - INFO - Iter [27150/80000] lr: 2.643e-05, eta: 1 day, 7:21:12, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2441, decode.acc_seg: 90.0669, aux.loss_ce: 0.1003, aux.acc_seg: 89.8804, loss: 0.3444 +2024-06-18 18:37:14,884 - mmseg - INFO - Iter [27200/80000] lr: 2.640e-05, eta: 1 day, 7:19:10, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2471, decode.acc_seg: 89.6634, aux.loss_ce: 0.1011, aux.acc_seg: 89.3596, loss: 0.3482 +2024-06-18 18:38:53,895 - mmseg - INFO - Iter [27250/80000] lr: 2.638e-05, eta: 1 day, 7:17:09, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2355, decode.acc_seg: 90.3636, aux.loss_ce: 0.0970, aux.acc_seg: 90.0393, loss: 0.3325 +2024-06-18 18:40:32,724 - mmseg - INFO - Iter [27300/80000] lr: 2.635e-05, eta: 1 day, 7:15:06, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2664, decode.acc_seg: 89.1870, aux.loss_ce: 0.1076, aux.acc_seg: 89.0381, loss: 0.3739 +2024-06-18 18:42:11,806 - mmseg - INFO - Iter [27350/80000] lr: 2.633e-05, eta: 1 day, 7:13:05, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2458, decode.acc_seg: 89.3542, aux.loss_ce: 0.1010, aux.acc_seg: 89.0662, loss: 0.3468 +2024-06-18 18:43:50,708 - mmseg - INFO - Iter [27400/80000] lr: 2.630e-05, eta: 1 day, 7:11:03, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2591, decode.acc_seg: 89.3971, aux.loss_ce: 0.1060, aux.acc_seg: 89.1044, loss: 0.3651 +2024-06-18 18:45:29,663 - mmseg - INFO - Iter [27450/80000] lr: 2.628e-05, eta: 1 day, 7:09:02, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2454, decode.acc_seg: 89.5365, aux.loss_ce: 0.1007, aux.acc_seg: 89.2745, loss: 0.3460 +2024-06-18 18:47:08,602 - mmseg - INFO - Iter [27500/80000] lr: 2.625e-05, eta: 1 day, 7:07:00, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2497, decode.acc_seg: 90.2692, aux.loss_ce: 0.1026, aux.acc_seg: 90.0448, loss: 0.3523 +2024-06-18 18:48:47,629 - mmseg - INFO - Iter [27550/80000] lr: 2.623e-05, eta: 1 day, 7:04:59, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2399, decode.acc_seg: 90.1741, aux.loss_ce: 0.0992, aux.acc_seg: 89.8040, loss: 0.3391 +2024-06-18 18:50:26,604 - mmseg - INFO - Iter [27600/80000] lr: 2.620e-05, eta: 1 day, 7:02:58, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2549, decode.acc_seg: 89.6242, aux.loss_ce: 0.1047, aux.acc_seg: 89.3212, loss: 0.3597 +2024-06-18 18:52:05,513 - mmseg - INFO - Iter [27650/80000] lr: 2.618e-05, eta: 1 day, 7:00:56, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2346, decode.acc_seg: 90.1690, aux.loss_ce: 0.0970, aux.acc_seg: 89.8644, loss: 0.3316 +2024-06-18 18:53:44,408 - mmseg - INFO - Iter [27700/80000] lr: 2.615e-05, eta: 1 day, 6:58:55, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2329, decode.acc_seg: 90.3148, aux.loss_ce: 0.0958, aux.acc_seg: 90.0599, loss: 0.3287 +2024-06-18 18:55:23,447 - mmseg - INFO - Iter [27750/80000] lr: 2.613e-05, eta: 1 day, 6:56:54, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2577, decode.acc_seg: 89.1947, aux.loss_ce: 0.1058, aux.acc_seg: 88.9711, loss: 0.3635 +2024-06-18 18:57:04,632 - mmseg - INFO - Iter [27800/80000] lr: 2.610e-05, eta: 1 day, 6:54:57, time: 2.024, data_time: 0.052, memory: 72263, decode.loss_ce: 0.2339, decode.acc_seg: 90.3184, aux.loss_ce: 0.0961, aux.acc_seg: 90.0062, loss: 0.3300 +2024-06-18 18:58:43,543 - mmseg - INFO - Iter [27850/80000] lr: 2.608e-05, eta: 1 day, 6:52:56, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2368, decode.acc_seg: 90.0999, aux.loss_ce: 0.0972, aux.acc_seg: 89.8460, loss: 0.3339 +2024-06-18 19:00:22,604 - mmseg - INFO - Iter [27900/80000] lr: 2.605e-05, eta: 1 day, 6:50:56, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2187, decode.acc_seg: 90.6565, aux.loss_ce: 0.0904, aux.acc_seg: 90.3890, loss: 0.3091 +2024-06-18 19:02:01,609 - mmseg - INFO - Iter [27950/80000] lr: 2.603e-05, eta: 1 day, 6:48:55, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2098, decode.acc_seg: 91.0411, aux.loss_ce: 0.0871, aux.acc_seg: 90.7452, loss: 0.2969 +2024-06-18 19:03:40,491 - mmseg - INFO - Saving checkpoint at 28000 iterations +2024-06-18 19:05:04,222 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 19:05:04,222 - mmseg - INFO - Iter [28000/80000] lr: 2.600e-05, eta: 1 day, 6:49:30, time: 3.652, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2376, decode.acc_seg: 90.1728, aux.loss_ce: 0.0980, aux.acc_seg: 89.9068, loss: 0.3356 +2024-06-18 19:06:52,852 - mmseg - INFO - per class results: +2024-06-18 19:06:52,858 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.22 | 89.2 | +| building | 85.16 | 93.81 | +| sky | 94.73 | 98.04 | +| floor | 84.57 | 90.5 | +| tree | 76.79 | 86.6 | +| ceiling | 86.94 | 94.18 | +| road | 85.48 | 90.97 | +| bed | 92.76 | 97.0 | +| windowpane | 66.92 | 78.64 | +| grass | 66.93 | 79.58 | +| cabinet | 67.94 | 76.47 | +| sidewalk | 67.88 | 80.71 | +| person | 85.74 | 94.82 | +| earth | 36.63 | 46.51 | +| door | 60.82 | 74.95 | +| table | 68.05 | 78.21 | +| mountain | 60.34 | 71.58 | +| plant | 56.64 | 72.94 | +| curtain | 80.4 | 89.76 | +| chair | 66.39 | 80.19 | +| car | 86.82 | 94.69 | +| water | 63.48 | 76.72 | +| painting | 76.41 | 91.08 | +| sofa | 81.13 | 90.06 | +| shelf | 49.1 | 64.8 | +| house | 58.56 | 77.33 | +| sea | 76.29 | 89.49 | +| mirror | 79.6 | 91.28 | +| rug | 69.1 | 83.59 | +| field | 33.9 | 65.62 | +| armchair | 59.84 | 80.92 | +| seat | 71.63 | 87.8 | +| fence | 49.8 | 66.98 | +| desk | 55.88 | 82.34 | +| rock | 50.08 | 75.2 | +| wardrobe | 55.83 | 81.47 | +| lamp | 72.98 | 86.61 | +| bathtub | 86.99 | 89.21 | +| railing | 42.25 | 55.13 | +| cushion | 67.51 | 80.67 | +| base | 43.93 | 64.35 | +| box | 40.39 | 53.54 | +| column | 54.95 | 71.44 | +| signboard | 43.56 | 56.82 | +| chest of drawers | 44.91 | 63.35 | +| counter | 43.81 | 58.7 | +| sand | 44.78 | 71.91 | +| sink | 79.48 | 85.94 | +| skyscraper | 46.32 | 55.71 | +| fireplace | 76.93 | 93.22 | +| refrigerator | 83.68 | 96.2 | +| grandstand | 50.8 | 79.4 | +| path | 28.57 | 49.75 | +| stairs | 29.01 | 32.42 | +| runway | 73.96 | 95.74 | +| case | 58.14 | 78.06 | +| pool table | 94.81 | 98.28 | +| pillow | 63.77 | 71.76 | +| screen door | 86.88 | 92.7 | +| stairway | 37.37 | 60.49 | +| river | 10.75 | 18.16 | +| bridge | 46.69 | 53.8 | +| bookcase | 45.37 | 65.12 | +| blind | 40.65 | 48.64 | +| coffee table | 62.61 | 85.75 | +| toilet | 90.44 | 95.46 | +| flower | 40.18 | 58.52 | +| book | 54.02 | 73.03 | +| hill | 5.19 | 9.62 | +| bench | 66.91 | 86.68 | +| countertop | 63.31 | 79.89 | +| stove | 84.95 | 93.3 | +| palm | 50.49 | 81.28 | +| kitchen island | 47.72 | 87.74 | +| computer | 79.43 | 92.42 | +| swivel chair | 52.7 | 87.49 | +| boat | 66.72 | 92.64 | +| bar | 61.47 | 85.12 | +| arcade machine | 89.1 | 97.98 | +| hovel | 42.21 | 53.14 | +| bus | 92.07 | 97.74 | +| towel | 78.54 | 91.2 | +| light | 60.4 | 78.28 | +| truck | 50.53 | 58.97 | +| tower | 27.83 | 40.41 | +| chandelier | 71.83 | 87.36 | +| awning | 38.31 | 48.22 | +| streetlight | 34.74 | 46.68 | +| booth | 53.28 | 59.61 | +| television receiver | 79.32 | 89.22 | +| airplane | 82.88 | 97.2 | +| dirt track | 6.22 | 26.88 | +| apparel | 62.29 | 81.89 | +| pole | 29.45 | 40.96 | +| land | 3.0 | 4.39 | +| bannister | 19.83 | 25.32 | +| escalator | 63.23 | 86.43 | +| ottoman | 47.58 | 61.7 | +| bottle | 42.1 | 78.21 | +| buffet | 62.4 | 71.19 | +| poster | 34.94 | 44.31 | +| stage | 12.74 | 32.46 | +| van | 50.26 | 73.55 | +| ship | 79.02 | 84.04 | +| fountain | 43.47 | 49.83 | +| conveyer belt | 84.04 | 96.7 | +| canopy | 56.6 | 83.01 | +| washer | 83.93 | 89.35 | +| plaything | 42.01 | 74.06 | +| swimming pool | 58.14 | 83.4 | +| stool | 53.01 | 75.2 | +| barrel | 50.05 | 68.65 | +| basket | 44.79 | 62.61 | +| waterfall | 58.8 | 77.28 | +| tent | 93.02 | 98.75 | +| bag | 28.36 | 33.23 | +| minibike | 75.9 | 91.12 | +| cradle | 85.71 | 98.27 | +| oven | 53.66 | 67.16 | +| ball | 61.75 | 72.48 | +| food | 55.2 | 61.29 | +| step | 17.73 | 24.39 | +| tank | 57.42 | 70.9 | +| trade name | 34.01 | 40.71 | +| microwave | 86.67 | 97.36 | +| pot | 57.44 | 65.09 | +| animal | 62.52 | 63.95 | +| bicycle | 60.69 | 83.92 | +| lake | 57.66 | 69.84 | +| dishwasher | 70.05 | 85.91 | +| screen | 61.53 | 95.71 | +| blanket | 33.97 | 41.51 | +| sculpture | 73.96 | 85.64 | +| hood | 66.66 | 80.89 | +| sconce | 58.17 | 77.59 | +| vase | 46.33 | 67.11 | +| traffic light | 39.98 | 62.5 | +| tray | 24.26 | 37.26 | +| ashcan | 52.87 | 63.83 | +| fan | 70.98 | 82.39 | +| pier | 41.71 | 44.83 | +| crt screen | 1.56 | 2.5 | +| plate | 65.28 | 75.7 | +| monitor | 44.64 | 58.52 | +| bulletin board | 58.25 | 76.86 | +| shower | 5.13 | 5.23 | +| radiator | 63.32 | 84.55 | +| glass | 21.55 | 23.75 | +| clock | 50.47 | 62.53 | +| flag | 67.77 | 75.73 | ++---------------------+-------+-------+ +2024-06-18 19:06:52,859 - mmseg - INFO - Summary: +2024-06-18 19:06:52,859 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.99 | 57.68 | 71.64 | ++-------+-------+-------+ +2024-06-18 19:06:52,860 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 19:06:52,860 - mmseg - INFO - Iter(val) [250] aAcc: 0.8599, mIoU: 0.5768, mAcc: 0.7164, IoU.wall: 0.8222, IoU.building: 0.8516, IoU.sky: 0.9473, IoU.floor: 0.8457, IoU.tree: 0.7679, IoU.ceiling: 0.8694, IoU.road: 0.8548, IoU.bed : 0.9276, IoU.windowpane: 0.6692, IoU.grass: 0.6693, IoU.cabinet: 0.6794, IoU.sidewalk: 0.6788, IoU.person: 0.8574, IoU.earth: 0.3663, IoU.door: 0.6082, IoU.table: 0.6805, IoU.mountain: 0.6034, IoU.plant: 0.5664, IoU.curtain: 0.8040, IoU.chair: 0.6639, IoU.car: 0.8682, IoU.water: 0.6348, IoU.painting: 0.7641, IoU.sofa: 0.8113, IoU.shelf: 0.4910, IoU.house: 0.5856, IoU.sea: 0.7629, IoU.mirror: 0.7960, IoU.rug: 0.6910, IoU.field: 0.3390, IoU.armchair: 0.5984, IoU.seat: 0.7163, IoU.fence: 0.4980, IoU.desk: 0.5588, IoU.rock: 0.5008, IoU.wardrobe: 0.5583, IoU.lamp: 0.7298, IoU.bathtub: 0.8699, IoU.railing: 0.4225, IoU.cushion: 0.6751, IoU.base: 0.4393, IoU.box: 0.4039, IoU.column: 0.5495, IoU.signboard: 0.4356, IoU.chest of drawers: 0.4491, IoU.counter: 0.4381, IoU.sand: 0.4478, IoU.sink: 0.7948, IoU.skyscraper: 0.4632, IoU.fireplace: 0.7693, IoU.refrigerator: 0.8368, IoU.grandstand: 0.5080, IoU.path: 0.2857, IoU.stairs: 0.2901, IoU.runway: 0.7396, IoU.case: 0.5814, IoU.pool table: 0.9481, IoU.pillow: 0.6377, IoU.screen door: 0.8688, IoU.stairway: 0.3737, IoU.river: 0.1075, IoU.bridge: 0.4669, IoU.bookcase: 0.4537, IoU.blind: 0.4065, IoU.coffee table: 0.6261, IoU.toilet: 0.9044, IoU.flower: 0.4018, IoU.book: 0.5402, IoU.hill: 0.0519, IoU.bench: 0.6691, IoU.countertop: 0.6331, IoU.stove: 0.8495, IoU.palm: 0.5049, IoU.kitchen island: 0.4772, IoU.computer: 0.7943, IoU.swivel chair: 0.5270, IoU.boat: 0.6672, IoU.bar: 0.6147, IoU.arcade machine: 0.8910, IoU.hovel: 0.4221, IoU.bus: 0.9207, IoU.towel: 0.7854, IoU.light: 0.6040, IoU.truck: 0.5053, IoU.tower: 0.2783, IoU.chandelier: 0.7183, IoU.awning: 0.3831, IoU.streetlight: 0.3474, IoU.booth: 0.5328, IoU.television receiver: 0.7932, IoU.airplane: 0.8288, IoU.dirt track: 0.0622, IoU.apparel: 0.6229, IoU.pole: 0.2945, IoU.land: 0.0300, IoU.bannister: 0.1983, IoU.escalator: 0.6323, IoU.ottoman: 0.4758, IoU.bottle: 0.4210, IoU.buffet: 0.6240, IoU.poster: 0.3494, IoU.stage: 0.1274, IoU.van: 0.5026, IoU.ship: 0.7902, IoU.fountain: 0.4347, IoU.conveyer belt: 0.8404, IoU.canopy: 0.5660, IoU.washer: 0.8393, IoU.plaything: 0.4201, IoU.swimming pool: 0.5814, IoU.stool: 0.5301, IoU.barrel: 0.5005, IoU.basket: 0.4479, IoU.waterfall: 0.5880, IoU.tent: 0.9302, IoU.bag: 0.2836, IoU.minibike: 0.7590, IoU.cradle: 0.8571, IoU.oven: 0.5366, IoU.ball: 0.6175, IoU.food: 0.5520, IoU.step: 0.1773, IoU.tank: 0.5742, IoU.trade name: 0.3401, IoU.microwave: 0.8667, IoU.pot: 0.5744, IoU.animal: 0.6252, IoU.bicycle: 0.6069, IoU.lake: 0.5766, IoU.dishwasher: 0.7005, IoU.screen: 0.6153, IoU.blanket: 0.3397, IoU.sculpture: 0.7396, IoU.hood: 0.6666, IoU.sconce: 0.5817, IoU.vase: 0.4633, IoU.traffic light: 0.3998, IoU.tray: 0.2426, IoU.ashcan: 0.5287, IoU.fan: 0.7098, IoU.pier: 0.4171, IoU.crt screen: 0.0156, IoU.plate: 0.6528, IoU.monitor: 0.4464, IoU.bulletin board: 0.5825, IoU.shower: 0.0513, IoU.radiator: 0.6332, IoU.glass: 0.2155, IoU.clock: 0.5047, IoU.flag: 0.6777, Acc.wall: 0.8920, Acc.building: 0.9381, Acc.sky: 0.9804, Acc.floor: 0.9050, Acc.tree: 0.8660, Acc.ceiling: 0.9418, Acc.road: 0.9097, Acc.bed : 0.9700, Acc.windowpane: 0.7864, Acc.grass: 0.7958, Acc.cabinet: 0.7647, Acc.sidewalk: 0.8071, Acc.person: 0.9482, Acc.earth: 0.4651, Acc.door: 0.7495, Acc.table: 0.7821, Acc.mountain: 0.7158, Acc.plant: 0.7294, Acc.curtain: 0.8976, Acc.chair: 0.8019, Acc.car: 0.9469, Acc.water: 0.7672, Acc.painting: 0.9108, Acc.sofa: 0.9006, Acc.shelf: 0.6480, Acc.house: 0.7733, Acc.sea: 0.8949, Acc.mirror: 0.9128, Acc.rug: 0.8359, Acc.field: 0.6562, Acc.armchair: 0.8092, Acc.seat: 0.8780, Acc.fence: 0.6698, Acc.desk: 0.8234, Acc.rock: 0.7520, Acc.wardrobe: 0.8147, Acc.lamp: 0.8661, Acc.bathtub: 0.8921, Acc.railing: 0.5513, Acc.cushion: 0.8067, Acc.base: 0.6435, Acc.box: 0.5354, Acc.column: 0.7144, Acc.signboard: 0.5682, Acc.chest of drawers: 0.6335, Acc.counter: 0.5870, Acc.sand: 0.7191, Acc.sink: 0.8594, Acc.skyscraper: 0.5571, Acc.fireplace: 0.9322, Acc.refrigerator: 0.9620, Acc.grandstand: 0.7940, Acc.path: 0.4975, Acc.stairs: 0.3242, Acc.runway: 0.9574, Acc.case: 0.7806, Acc.pool table: 0.9828, Acc.pillow: 0.7176, Acc.screen door: 0.9270, Acc.stairway: 0.6049, Acc.river: 0.1816, Acc.bridge: 0.5380, Acc.bookcase: 0.6512, Acc.blind: 0.4864, Acc.coffee table: 0.8575, Acc.toilet: 0.9546, Acc.flower: 0.5852, Acc.book: 0.7303, Acc.hill: 0.0962, Acc.bench: 0.8668, Acc.countertop: 0.7989, Acc.stove: 0.9330, Acc.palm: 0.8128, Acc.kitchen island: 0.8774, Acc.computer: 0.9242, Acc.swivel chair: 0.8749, Acc.boat: 0.9264, Acc.bar: 0.8512, Acc.arcade machine: 0.9798, Acc.hovel: 0.5314, Acc.bus: 0.9774, Acc.towel: 0.9120, Acc.light: 0.7828, Acc.truck: 0.5897, Acc.tower: 0.4041, Acc.chandelier: 0.8736, Acc.awning: 0.4822, Acc.streetlight: 0.4668, Acc.booth: 0.5961, Acc.television receiver: 0.8922, Acc.airplane: 0.9720, Acc.dirt track: 0.2688, Acc.apparel: 0.8189, Acc.pole: 0.4096, Acc.land: 0.0439, Acc.bannister: 0.2532, Acc.escalator: 0.8643, Acc.ottoman: 0.6170, Acc.bottle: 0.7821, Acc.buffet: 0.7119, Acc.poster: 0.4431, Acc.stage: 0.3246, Acc.van: 0.7355, Acc.ship: 0.8404, Acc.fountain: 0.4983, Acc.conveyer belt: 0.9670, Acc.canopy: 0.8301, Acc.washer: 0.8935, Acc.plaything: 0.7406, Acc.swimming pool: 0.8340, Acc.stool: 0.7520, Acc.barrel: 0.6865, Acc.basket: 0.6261, Acc.waterfall: 0.7728, Acc.tent: 0.9875, Acc.bag: 0.3323, Acc.minibike: 0.9112, Acc.cradle: 0.9827, Acc.oven: 0.6716, Acc.ball: 0.7248, Acc.food: 0.6129, Acc.step: 0.2439, Acc.tank: 0.7090, Acc.trade name: 0.4071, Acc.microwave: 0.9736, Acc.pot: 0.6509, Acc.animal: 0.6395, Acc.bicycle: 0.8392, Acc.lake: 0.6984, Acc.dishwasher: 0.8591, Acc.screen: 0.9571, Acc.blanket: 0.4151, Acc.sculpture: 0.8564, Acc.hood: 0.8089, Acc.sconce: 0.7759, Acc.vase: 0.6711, Acc.traffic light: 0.6250, Acc.tray: 0.3726, Acc.ashcan: 0.6383, Acc.fan: 0.8239, Acc.pier: 0.4483, Acc.crt screen: 0.0250, Acc.plate: 0.7570, Acc.monitor: 0.5852, Acc.bulletin board: 0.7686, Acc.shower: 0.0523, Acc.radiator: 0.8455, Acc.glass: 0.2375, Acc.clock: 0.6253, Acc.flag: 0.7573 +2024-06-18 19:08:32,238 - mmseg - INFO - Iter [28050/80000] lr: 2.598e-05, eta: 1 day, 6:50:50, time: 4.160, data_time: 2.191, memory: 72263, decode.loss_ce: 0.2078, decode.acc_seg: 91.5201, aux.loss_ce: 0.0863, aux.acc_seg: 91.2138, loss: 0.2941 +2024-06-18 19:10:11,227 - mmseg - INFO - Iter [28100/80000] lr: 2.595e-05, eta: 1 day, 6:48:49, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2276, decode.acc_seg: 90.5323, aux.loss_ce: 0.0937, aux.acc_seg: 90.2909, loss: 0.3213 +2024-06-18 19:11:50,093 - mmseg - INFO - Iter [28150/80000] lr: 2.593e-05, eta: 1 day, 6:46:47, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2325, decode.acc_seg: 90.6784, aux.loss_ce: 0.0959, aux.acc_seg: 90.3432, loss: 0.3285 +2024-06-18 19:13:29,110 - mmseg - INFO - Iter [28200/80000] lr: 2.590e-05, eta: 1 day, 6:44:46, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2120, decode.acc_seg: 90.8109, aux.loss_ce: 0.0874, aux.acc_seg: 90.5720, loss: 0.2995 +2024-06-18 19:15:08,009 - mmseg - INFO - Iter [28250/80000] lr: 2.588e-05, eta: 1 day, 6:42:45, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2454, decode.acc_seg: 90.0939, aux.loss_ce: 0.0996, aux.acc_seg: 89.8769, loss: 0.3451 +2024-06-18 19:16:46,918 - mmseg - INFO - Iter [28300/80000] lr: 2.585e-05, eta: 1 day, 6:40:43, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2371, decode.acc_seg: 90.3003, aux.loss_ce: 0.0981, aux.acc_seg: 90.0387, loss: 0.3352 +2024-06-18 19:18:25,761 - mmseg - INFO - Iter [28350/80000] lr: 2.583e-05, eta: 1 day, 6:38:42, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2475, decode.acc_seg: 89.7738, aux.loss_ce: 0.1021, aux.acc_seg: 89.5416, loss: 0.3496 +2024-06-18 19:20:04,606 - mmseg - INFO - Iter [28400/80000] lr: 2.580e-05, eta: 1 day, 6:36:41, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2379, decode.acc_seg: 90.3311, aux.loss_ce: 0.0975, aux.acc_seg: 90.1191, loss: 0.3354 +2024-06-18 19:21:43,620 - mmseg - INFO - Iter [28450/80000] lr: 2.578e-05, eta: 1 day, 6:34:40, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2355, decode.acc_seg: 89.9935, aux.loss_ce: 0.0969, aux.acc_seg: 89.6605, loss: 0.3324 +2024-06-18 19:23:22,650 - mmseg - INFO - Iter [28500/80000] lr: 2.575e-05, eta: 1 day, 6:32:39, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2301, decode.acc_seg: 90.3401, aux.loss_ce: 0.0942, aux.acc_seg: 90.1106, loss: 0.3243 +2024-06-18 19:25:01,685 - mmseg - INFO - Iter [28550/80000] lr: 2.573e-05, eta: 1 day, 6:30:39, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2196, decode.acc_seg: 90.7678, aux.loss_ce: 0.0912, aux.acc_seg: 90.5727, loss: 0.3108 +2024-06-18 19:26:40,553 - mmseg - INFO - Iter [28600/80000] lr: 2.570e-05, eta: 1 day, 6:28:38, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2284, decode.acc_seg: 90.4845, aux.loss_ce: 0.0943, aux.acc_seg: 90.1917, loss: 0.3226 +2024-06-18 19:28:19,454 - mmseg - INFO - Iter [28650/80000] lr: 2.568e-05, eta: 1 day, 6:26:37, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2231, decode.acc_seg: 90.5017, aux.loss_ce: 0.0918, aux.acc_seg: 90.1894, loss: 0.3149 +2024-06-18 19:29:58,491 - mmseg - INFO - Iter [28700/80000] lr: 2.565e-05, eta: 1 day, 6:24:36, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2421, decode.acc_seg: 89.9294, aux.loss_ce: 0.0992, aux.acc_seg: 89.7407, loss: 0.3412 +2024-06-18 19:31:37,375 - mmseg - INFO - Iter [28750/80000] lr: 2.563e-05, eta: 1 day, 6:22:36, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2320, decode.acc_seg: 90.4254, aux.loss_ce: 0.0957, aux.acc_seg: 90.0640, loss: 0.3276 +2024-06-18 19:33:16,391 - mmseg - INFO - Iter [28800/80000] lr: 2.560e-05, eta: 1 day, 6:20:36, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2263, decode.acc_seg: 90.3926, aux.loss_ce: 0.0936, aux.acc_seg: 90.1155, loss: 0.3199 +2024-06-18 19:34:55,260 - mmseg - INFO - Iter [28850/80000] lr: 2.558e-05, eta: 1 day, 6:18:35, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2356, decode.acc_seg: 90.4460, aux.loss_ce: 0.0978, aux.acc_seg: 90.2089, loss: 0.3333 +2024-06-18 19:36:34,103 - mmseg - INFO - Iter [28900/80000] lr: 2.555e-05, eta: 1 day, 6:16:34, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2280, decode.acc_seg: 90.0553, aux.loss_ce: 0.0942, aux.acc_seg: 89.7307, loss: 0.3222 +2024-06-18 19:38:12,986 - mmseg - INFO - Iter [28950/80000] lr: 2.553e-05, eta: 1 day, 6:14:34, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2341, decode.acc_seg: 90.3422, aux.loss_ce: 0.0970, aux.acc_seg: 90.0649, loss: 0.3312 +2024-06-18 19:39:51,978 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 19:39:51,978 - mmseg - INFO - Iter [29000/80000] lr: 2.550e-05, eta: 1 day, 6:12:34, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2346, decode.acc_seg: 89.8490, aux.loss_ce: 0.0962, aux.acc_seg: 89.5696, loss: 0.3308 +2024-06-18 19:41:42,035 - mmseg - INFO - per class results: +2024-06-18 19:41:42,042 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.19 | 89.06 | +| building | 84.25 | 93.33 | +| sky | 94.83 | 97.24 | +| floor | 85.2 | 90.38 | +| tree | 76.78 | 90.47 | +| ceiling | 87.01 | 93.94 | +| road | 85.21 | 89.09 | +| bed | 92.67 | 96.01 | +| windowpane | 65.47 | 84.8 | +| grass | 68.75 | 83.66 | +| cabinet | 68.16 | 74.78 | +| sidewalk | 69.23 | 84.78 | +| person | 85.82 | 93.86 | +| earth | 40.2 | 53.52 | +| door | 59.23 | 74.75 | +| table | 68.1 | 79.55 | +| mountain | 60.33 | 71.31 | +| plant | 52.27 | 61.67 | +| curtain | 75.26 | 80.08 | +| chair | 65.67 | 77.21 | +| car | 86.86 | 93.51 | +| water | 61.01 | 75.66 | +| painting | 77.61 | 91.41 | +| sofa | 80.91 | 89.2 | +| shelf | 46.8 | 58.55 | +| house | 54.65 | 71.78 | +| sea | 66.86 | 81.92 | +| mirror | 77.13 | 82.53 | +| rug | 71.46 | 82.36 | +| field | 33.54 | 54.19 | +| armchair | 60.07 | 79.14 | +| seat | 69.12 | 89.83 | +| fence | 50.28 | 66.5 | +| desk | 57.87 | 69.42 | +| rock | 56.9 | 85.89 | +| wardrobe | 58.27 | 81.25 | +| lamp | 74.6 | 86.33 | +| bathtub | 83.67 | 85.03 | +| railing | 41.34 | 59.89 | +| cushion | 67.21 | 86.41 | +| base | 44.05 | 66.11 | +| box | 40.07 | 54.17 | +| column | 52.98 | 68.75 | +| signboard | 40.09 | 61.38 | +| chest of drawers | 47.54 | 80.84 | +| counter | 40.18 | 49.59 | +| sand | 57.72 | 87.2 | +| sink | 81.75 | 86.01 | +| skyscraper | 46.31 | 57.84 | +| fireplace | 73.78 | 88.25 | +| refrigerator | 80.62 | 92.02 | +| grandstand | 46.53 | 84.26 | +| path | 30.54 | 49.89 | +| stairs | 31.76 | 41.3 | +| runway | 71.06 | 88.81 | +| case | 66.1 | 81.99 | +| pool table | 94.4 | 98.41 | +| pillow | 64.13 | 75.23 | +| screen door | 66.67 | 71.61 | +| stairway | 43.6 | 61.44 | +| river | 9.92 | 18.72 | +| bridge | 53.55 | 62.22 | +| bookcase | 40.63 | 61.0 | +| blind | 39.9 | 42.37 | +| coffee table | 61.18 | 87.08 | +| toilet | 90.5 | 94.08 | +| flower | 39.0 | 61.73 | +| book | 53.97 | 79.05 | +| hill | 10.96 | 20.85 | +| bench | 69.76 | 81.11 | +| countertop | 64.16 | 80.75 | +| stove | 83.82 | 91.89 | +| palm | 50.58 | 84.3 | +| kitchen island | 46.78 | 89.71 | +| computer | 77.16 | 92.35 | +| swivel chair | 52.67 | 92.45 | +| boat | 69.93 | 92.72 | +| bar | 66.0 | 80.53 | +| arcade machine | 89.04 | 96.11 | +| hovel | 20.0 | 20.44 | +| bus | 94.31 | 97.41 | +| towel | 79.72 | 90.27 | +| light | 61.75 | 69.55 | +| truck | 47.78 | 62.49 | +| tower | 28.23 | 57.96 | +| chandelier | 73.33 | 86.98 | +| awning | 38.83 | 48.48 | +| streetlight | 33.66 | 42.24 | +| booth | 58.99 | 64.45 | +| television receiver | 79.25 | 89.2 | +| airplane | 88.65 | 95.63 | +| dirt track | 10.13 | 22.61 | +| apparel | 60.47 | 75.83 | +| pole | 28.7 | 41.63 | +| land | 2.31 | 4.04 | +| bannister | 22.27 | 29.13 | +| escalator | 55.75 | 89.55 | +| ottoman | 57.31 | 77.57 | +| bottle | 43.5 | 73.4 | +| buffet | 61.34 | 77.38 | +| poster | 27.9 | 32.69 | +| stage | 18.51 | 43.81 | +| van | 45.36 | 73.48 | +| ship | 70.15 | 76.96 | +| fountain | 39.2 | 40.57 | +| conveyer belt | 80.34 | 97.79 | +| canopy | 55.4 | 80.79 | +| washer | 82.63 | 88.4 | +| plaything | 47.22 | 72.17 | +| swimming pool | 60.51 | 88.35 | +| stool | 49.06 | 78.74 | +| barrel | 63.48 | 80.03 | +| basket | 39.91 | 61.6 | +| waterfall | 58.64 | 71.82 | +| tent | 93.48 | 97.87 | +| bag | 23.48 | 26.32 | +| minibike | 74.55 | 90.39 | +| cradle | 83.81 | 98.37 | +| oven | 64.47 | 79.3 | +| ball | 62.11 | 77.4 | +| food | 68.38 | 79.94 | +| step | 17.94 | 20.73 | +| tank | 59.9 | 67.71 | +| trade name | 17.05 | 19.09 | +| microwave | 91.05 | 96.56 | +| pot | 57.93 | 65.86 | +| animal | 59.57 | 61.21 | +| bicycle | 59.1 | 87.14 | +| lake | 61.01 | 63.43 | +| dishwasher | 70.74 | 79.19 | +| screen | 62.68 | 91.43 | +| blanket | 42.95 | 62.82 | +| sculpture | 71.43 | 88.48 | +| hood | 66.93 | 80.66 | +| sconce | 60.29 | 71.67 | +| vase | 48.98 | 66.76 | +| traffic light | 36.52 | 68.52 | +| tray | 23.06 | 30.99 | +| ashcan | 44.37 | 69.14 | +| fan | 69.93 | 85.93 | +| pier | 39.46 | 43.2 | +| crt screen | 7.04 | 13.9 | +| plate | 63.29 | 82.49 | +| monitor | 36.31 | 42.45 | +| bulletin board | 57.95 | 71.62 | +| shower | 1.33 | 8.98 | +| radiator | 65.34 | 81.92 | +| glass | 19.86 | 21.0 | +| clock | 46.99 | 53.63 | +| flag | 68.61 | 79.57 | ++---------------------+-------+-------+ +2024-06-18 19:41:42,042 - mmseg - INFO - Summary: +2024-06-18 19:41:42,042 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.74 | 57.42 | 71.42 | ++-------+-------+-------+ +2024-06-18 19:41:42,043 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 19:41:42,043 - mmseg - INFO - Iter(val) [250] aAcc: 0.8574, mIoU: 0.5742, mAcc: 0.7142, IoU.wall: 0.8119, IoU.building: 0.8425, IoU.sky: 0.9483, IoU.floor: 0.8520, IoU.tree: 0.7678, IoU.ceiling: 0.8701, IoU.road: 0.8521, IoU.bed : 0.9267, IoU.windowpane: 0.6547, IoU.grass: 0.6875, IoU.cabinet: 0.6816, IoU.sidewalk: 0.6923, IoU.person: 0.8582, IoU.earth: 0.4020, IoU.door: 0.5923, IoU.table: 0.6810, IoU.mountain: 0.6033, IoU.plant: 0.5227, IoU.curtain: 0.7526, IoU.chair: 0.6567, IoU.car: 0.8686, IoU.water: 0.6101, IoU.painting: 0.7761, IoU.sofa: 0.8091, IoU.shelf: 0.4680, IoU.house: 0.5465, IoU.sea: 0.6686, IoU.mirror: 0.7713, IoU.rug: 0.7146, IoU.field: 0.3354, IoU.armchair: 0.6007, IoU.seat: 0.6912, IoU.fence: 0.5028, IoU.desk: 0.5787, IoU.rock: 0.5690, IoU.wardrobe: 0.5827, IoU.lamp: 0.7460, IoU.bathtub: 0.8367, IoU.railing: 0.4134, IoU.cushion: 0.6721, IoU.base: 0.4405, IoU.box: 0.4007, IoU.column: 0.5298, IoU.signboard: 0.4009, IoU.chest of drawers: 0.4754, IoU.counter: 0.4018, IoU.sand: 0.5772, IoU.sink: 0.8175, IoU.skyscraper: 0.4631, IoU.fireplace: 0.7378, IoU.refrigerator: 0.8062, IoU.grandstand: 0.4653, IoU.path: 0.3054, IoU.stairs: 0.3176, IoU.runway: 0.7106, IoU.case: 0.6610, IoU.pool table: 0.9440, IoU.pillow: 0.6413, IoU.screen door: 0.6667, IoU.stairway: 0.4360, IoU.river: 0.0992, IoU.bridge: 0.5355, IoU.bookcase: 0.4063, IoU.blind: 0.3990, IoU.coffee table: 0.6118, IoU.toilet: 0.9050, IoU.flower: 0.3900, IoU.book: 0.5397, IoU.hill: 0.1096, IoU.bench: 0.6976, IoU.countertop: 0.6416, IoU.stove: 0.8382, IoU.palm: 0.5058, IoU.kitchen island: 0.4678, IoU.computer: 0.7716, IoU.swivel chair: 0.5267, IoU.boat: 0.6993, IoU.bar: 0.6600, IoU.arcade machine: 0.8904, IoU.hovel: 0.2000, IoU.bus: 0.9431, IoU.towel: 0.7972, IoU.light: 0.6175, IoU.truck: 0.4778, IoU.tower: 0.2823, IoU.chandelier: 0.7333, IoU.awning: 0.3883, IoU.streetlight: 0.3366, IoU.booth: 0.5899, IoU.television receiver: 0.7925, IoU.airplane: 0.8865, IoU.dirt track: 0.1013, IoU.apparel: 0.6047, IoU.pole: 0.2870, IoU.land: 0.0231, IoU.bannister: 0.2227, IoU.escalator: 0.5575, IoU.ottoman: 0.5731, IoU.bottle: 0.4350, IoU.buffet: 0.6134, IoU.poster: 0.2790, IoU.stage: 0.1851, IoU.van: 0.4536, IoU.ship: 0.7015, IoU.fountain: 0.3920, IoU.conveyer belt: 0.8034, IoU.canopy: 0.5540, IoU.washer: 0.8263, IoU.plaything: 0.4722, IoU.swimming pool: 0.6051, IoU.stool: 0.4906, IoU.barrel: 0.6348, IoU.basket: 0.3991, IoU.waterfall: 0.5864, IoU.tent: 0.9348, IoU.bag: 0.2348, IoU.minibike: 0.7455, IoU.cradle: 0.8381, IoU.oven: 0.6447, IoU.ball: 0.6211, IoU.food: 0.6838, IoU.step: 0.1794, IoU.tank: 0.5990, IoU.trade name: 0.1705, IoU.microwave: 0.9105, IoU.pot: 0.5793, IoU.animal: 0.5957, IoU.bicycle: 0.5910, IoU.lake: 0.6101, IoU.dishwasher: 0.7074, IoU.screen: 0.6268, IoU.blanket: 0.4295, IoU.sculpture: 0.7143, IoU.hood: 0.6693, IoU.sconce: 0.6029, IoU.vase: 0.4898, IoU.traffic light: 0.3652, IoU.tray: 0.2306, IoU.ashcan: 0.4437, IoU.fan: 0.6993, IoU.pier: 0.3946, IoU.crt screen: 0.0704, IoU.plate: 0.6329, IoU.monitor: 0.3631, IoU.bulletin board: 0.5795, IoU.shower: 0.0133, IoU.radiator: 0.6534, IoU.glass: 0.1986, IoU.clock: 0.4699, IoU.flag: 0.6861, Acc.wall: 0.8906, Acc.building: 0.9333, Acc.sky: 0.9724, Acc.floor: 0.9038, Acc.tree: 0.9047, Acc.ceiling: 0.9394, Acc.road: 0.8909, Acc.bed : 0.9601, Acc.windowpane: 0.8480, Acc.grass: 0.8366, Acc.cabinet: 0.7478, Acc.sidewalk: 0.8478, Acc.person: 0.9386, Acc.earth: 0.5352, Acc.door: 0.7475, Acc.table: 0.7955, Acc.mountain: 0.7131, Acc.plant: 0.6167, Acc.curtain: 0.8008, Acc.chair: 0.7721, Acc.car: 0.9351, Acc.water: 0.7566, Acc.painting: 0.9141, Acc.sofa: 0.8920, Acc.shelf: 0.5855, Acc.house: 0.7178, Acc.sea: 0.8192, Acc.mirror: 0.8253, Acc.rug: 0.8236, Acc.field: 0.5419, Acc.armchair: 0.7914, Acc.seat: 0.8983, Acc.fence: 0.6650, Acc.desk: 0.6942, Acc.rock: 0.8589, Acc.wardrobe: 0.8125, Acc.lamp: 0.8633, Acc.bathtub: 0.8503, Acc.railing: 0.5989, Acc.cushion: 0.8641, Acc.base: 0.6611, Acc.box: 0.5417, Acc.column: 0.6875, Acc.signboard: 0.6138, Acc.chest of drawers: 0.8084, Acc.counter: 0.4959, Acc.sand: 0.8720, Acc.sink: 0.8601, Acc.skyscraper: 0.5784, Acc.fireplace: 0.8825, Acc.refrigerator: 0.9202, Acc.grandstand: 0.8426, Acc.path: 0.4989, Acc.stairs: 0.4130, Acc.runway: 0.8881, Acc.case: 0.8199, Acc.pool table: 0.9841, Acc.pillow: 0.7523, Acc.screen door: 0.7161, Acc.stairway: 0.6144, Acc.river: 0.1872, Acc.bridge: 0.6222, Acc.bookcase: 0.6100, Acc.blind: 0.4237, Acc.coffee table: 0.8708, Acc.toilet: 0.9408, Acc.flower: 0.6173, Acc.book: 0.7905, Acc.hill: 0.2085, Acc.bench: 0.8111, Acc.countertop: 0.8075, Acc.stove: 0.9189, Acc.palm: 0.8430, Acc.kitchen island: 0.8971, Acc.computer: 0.9235, Acc.swivel chair: 0.9245, Acc.boat: 0.9272, Acc.bar: 0.8053, Acc.arcade machine: 0.9611, Acc.hovel: 0.2044, Acc.bus: 0.9741, Acc.towel: 0.9027, Acc.light: 0.6955, Acc.truck: 0.6249, Acc.tower: 0.5796, Acc.chandelier: 0.8698, Acc.awning: 0.4848, Acc.streetlight: 0.4224, Acc.booth: 0.6445, Acc.television receiver: 0.8920, Acc.airplane: 0.9563, Acc.dirt track: 0.2261, Acc.apparel: 0.7583, Acc.pole: 0.4163, Acc.land: 0.0404, Acc.bannister: 0.2913, Acc.escalator: 0.8955, Acc.ottoman: 0.7757, Acc.bottle: 0.7340, Acc.buffet: 0.7738, Acc.poster: 0.3269, Acc.stage: 0.4381, Acc.van: 0.7348, Acc.ship: 0.7696, Acc.fountain: 0.4057, Acc.conveyer belt: 0.9779, Acc.canopy: 0.8079, Acc.washer: 0.8840, Acc.plaything: 0.7217, Acc.swimming pool: 0.8835, Acc.stool: 0.7874, Acc.barrel: 0.8003, Acc.basket: 0.6160, Acc.waterfall: 0.7182, Acc.tent: 0.9787, Acc.bag: 0.2632, Acc.minibike: 0.9039, Acc.cradle: 0.9837, Acc.oven: 0.7930, Acc.ball: 0.7740, Acc.food: 0.7994, Acc.step: 0.2073, Acc.tank: 0.6771, Acc.trade name: 0.1909, Acc.microwave: 0.9656, Acc.pot: 0.6586, Acc.animal: 0.6121, Acc.bicycle: 0.8714, Acc.lake: 0.6343, Acc.dishwasher: 0.7919, Acc.screen: 0.9143, Acc.blanket: 0.6282, Acc.sculpture: 0.8848, Acc.hood: 0.8066, Acc.sconce: 0.7167, Acc.vase: 0.6676, Acc.traffic light: 0.6852, Acc.tray: 0.3099, Acc.ashcan: 0.6914, Acc.fan: 0.8593, Acc.pier: 0.4320, Acc.crt screen: 0.1390, Acc.plate: 0.8249, Acc.monitor: 0.4245, Acc.bulletin board: 0.7162, Acc.shower: 0.0898, Acc.radiator: 0.8192, Acc.glass: 0.2100, Acc.clock: 0.5363, Acc.flag: 0.7957 +2024-06-18 19:43:23,505 - mmseg - INFO - Iter [29050/80000] lr: 2.548e-05, eta: 1 day, 6:13:51, time: 4.231, data_time: 2.260, memory: 72263, decode.loss_ce: 0.2275, decode.acc_seg: 90.4268, aux.loss_ce: 0.0937, aux.acc_seg: 90.1280, loss: 0.3212 +2024-06-18 19:45:02,503 - mmseg - INFO - Iter [29100/80000] lr: 2.545e-05, eta: 1 day, 6:11:51, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2185, decode.acc_seg: 90.7605, aux.loss_ce: 0.0906, aux.acc_seg: 90.4385, loss: 0.3090 +2024-06-18 19:46:41,385 - mmseg - INFO - Iter [29150/80000] lr: 2.543e-05, eta: 1 day, 6:09:50, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2158, decode.acc_seg: 91.0269, aux.loss_ce: 0.0889, aux.acc_seg: 90.6528, loss: 0.3047 +2024-06-18 19:48:20,290 - mmseg - INFO - Iter [29200/80000] lr: 2.540e-05, eta: 1 day, 6:07:50, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2130, decode.acc_seg: 91.0354, aux.loss_ce: 0.0886, aux.acc_seg: 90.6646, loss: 0.3016 +2024-06-18 19:49:59,135 - mmseg - INFO - Iter [29250/80000] lr: 2.538e-05, eta: 1 day, 6:05:49, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2268, decode.acc_seg: 90.4970, aux.loss_ce: 0.0932, aux.acc_seg: 90.1294, loss: 0.3200 +2024-06-18 19:51:38,064 - mmseg - INFO - Iter [29300/80000] lr: 2.535e-05, eta: 1 day, 6:03:49, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2254, decode.acc_seg: 90.8679, aux.loss_ce: 0.0932, aux.acc_seg: 90.5009, loss: 0.3187 +2024-06-18 19:53:16,887 - mmseg - INFO - Iter [29350/80000] lr: 2.533e-05, eta: 1 day, 6:01:49, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2376, decode.acc_seg: 89.9334, aux.loss_ce: 0.0970, aux.acc_seg: 89.7868, loss: 0.3346 +2024-06-18 19:54:55,951 - mmseg - INFO - Iter [29400/80000] lr: 2.530e-05, eta: 1 day, 5:59:49, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2166, decode.acc_seg: 90.8876, aux.loss_ce: 0.0886, aux.acc_seg: 90.7164, loss: 0.3052 +2024-06-18 19:56:34,878 - mmseg - INFO - Iter [29450/80000] lr: 2.528e-05, eta: 1 day, 5:57:49, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2286, decode.acc_seg: 90.3355, aux.loss_ce: 0.0948, aux.acc_seg: 89.9818, loss: 0.3234 +2024-06-18 19:58:13,802 - mmseg - INFO - Iter [29500/80000] lr: 2.525e-05, eta: 1 day, 5:55:49, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2272, decode.acc_seg: 90.4978, aux.loss_ce: 0.0936, aux.acc_seg: 90.1861, loss: 0.3208 +2024-06-18 19:59:52,614 - mmseg - INFO - Iter [29550/80000] lr: 2.523e-05, eta: 1 day, 5:53:49, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2266, decode.acc_seg: 90.5525, aux.loss_ce: 0.0937, aux.acc_seg: 90.2429, loss: 0.3203 +2024-06-18 20:01:31,576 - mmseg - INFO - Iter [29600/80000] lr: 2.520e-05, eta: 1 day, 5:51:49, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2253, decode.acc_seg: 90.6663, aux.loss_ce: 0.0920, aux.acc_seg: 90.4794, loss: 0.3173 +2024-06-18 20:03:10,419 - mmseg - INFO - Iter [29650/80000] lr: 2.518e-05, eta: 1 day, 5:49:49, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2388, decode.acc_seg: 90.3626, aux.loss_ce: 0.0988, aux.acc_seg: 90.0629, loss: 0.3376 +2024-06-18 20:04:49,273 - mmseg - INFO - Iter [29700/80000] lr: 2.515e-05, eta: 1 day, 5:47:49, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2438, decode.acc_seg: 89.6602, aux.loss_ce: 0.1008, aux.acc_seg: 89.2975, loss: 0.3446 +2024-06-18 20:06:28,171 - mmseg - INFO - Iter [29750/80000] lr: 2.513e-05, eta: 1 day, 5:45:49, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2240, decode.acc_seg: 90.4716, aux.loss_ce: 0.0921, aux.acc_seg: 90.2661, loss: 0.3161 +2024-06-18 20:08:06,982 - mmseg - INFO - Iter [29800/80000] lr: 2.510e-05, eta: 1 day, 5:43:50, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2198, decode.acc_seg: 90.9452, aux.loss_ce: 0.0908, aux.acc_seg: 90.6980, loss: 0.3106 +2024-06-18 20:09:45,805 - mmseg - INFO - Iter [29850/80000] lr: 2.508e-05, eta: 1 day, 5:41:50, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2248, decode.acc_seg: 90.4195, aux.loss_ce: 0.0921, aux.acc_seg: 90.2727, loss: 0.3169 +2024-06-18 20:11:24,768 - mmseg - INFO - Iter [29900/80000] lr: 2.505e-05, eta: 1 day, 5:39:51, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2245, decode.acc_seg: 90.3460, aux.loss_ce: 0.0928, aux.acc_seg: 90.1068, loss: 0.3174 +2024-06-18 20:13:03,667 - mmseg - INFO - Iter [29950/80000] lr: 2.503e-05, eta: 1 day, 5:37:51, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2245, decode.acc_seg: 90.7108, aux.loss_ce: 0.0926, aux.acc_seg: 90.4106, loss: 0.3171 +2024-06-18 20:14:42,513 - mmseg - INFO - Saving checkpoint at 30000 iterations +2024-06-18 20:16:08,475 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 20:16:08,476 - mmseg - INFO - Iter [30000/80000] lr: 2.500e-05, eta: 1 day, 5:38:15, time: 3.696, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2254, decode.acc_seg: 90.4509, aux.loss_ce: 0.0932, aux.acc_seg: 90.1279, loss: 0.3186 +2024-06-18 20:17:57,796 - mmseg - INFO - per class results: +2024-06-18 20:17:57,802 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.24 | 89.85 | +| building | 84.36 | 92.07 | +| sky | 94.95 | 97.24 | +| floor | 85.1 | 91.17 | +| tree | 78.12 | 89.95 | +| ceiling | 87.09 | 94.02 | +| road | 85.4 | 89.48 | +| bed | 92.54 | 97.18 | +| windowpane | 67.25 | 80.42 | +| grass | 66.15 | 81.11 | +| cabinet | 67.15 | 75.41 | +| sidewalk | 68.72 | 83.48 | +| person | 86.06 | 94.07 | +| earth | 39.3 | 54.27 | +| door | 60.41 | 74.84 | +| table | 67.18 | 78.93 | +| mountain | 60.73 | 71.46 | +| plant | 57.43 | 69.41 | +| curtain | 78.57 | 87.39 | +| chair | 65.61 | 76.03 | +| car | 87.55 | 93.89 | +| water | 59.65 | 71.35 | +| painting | 79.19 | 90.43 | +| sofa | 81.93 | 90.94 | +| shelf | 47.39 | 59.58 | +| house | 53.56 | 72.08 | +| sea | 67.54 | 84.95 | +| mirror | 79.57 | 86.38 | +| rug | 69.52 | 83.4 | +| field | 29.59 | 54.39 | +| armchair | 61.77 | 80.44 | +| seat | 65.08 | 86.12 | +| fence | 49.61 | 60.62 | +| desk | 55.74 | 74.77 | +| rock | 57.83 | 86.44 | +| wardrobe | 56.27 | 80.13 | +| lamp | 73.89 | 88.38 | +| bathtub | 90.41 | 94.2 | +| railing | 41.83 | 59.06 | +| cushion | 68.2 | 80.79 | +| base | 46.3 | 64.45 | +| box | 38.36 | 46.15 | +| column | 56.9 | 67.75 | +| signboard | 41.69 | 60.58 | +| chest of drawers | 53.29 | 77.39 | +| counter | 45.59 | 60.77 | +| sand | 57.15 | 86.88 | +| sink | 78.33 | 85.02 | +| skyscraper | 46.64 | 62.23 | +| fireplace | 70.33 | 96.91 | +| refrigerator | 83.56 | 94.63 | +| grandstand | 50.2 | 82.51 | +| path | 35.04 | 48.4 | +| stairs | 29.09 | 33.05 | +| runway | 74.31 | 96.24 | +| case | 62.75 | 73.15 | +| pool table | 94.39 | 98.64 | +| pillow | 65.08 | 74.92 | +| screen door | 82.15 | 85.96 | +| stairway | 40.72 | 55.45 | +| river | 16.2 | 31.56 | +| bridge | 70.42 | 81.07 | +| bookcase | 38.79 | 48.47 | +| blind | 47.36 | 61.17 | +| coffee table | 58.4 | 90.38 | +| toilet | 90.6 | 94.42 | +| flower | 46.35 | 67.84 | +| book | 53.36 | 82.68 | +| hill | 12.0 | 28.23 | +| bench | 59.66 | 67.73 | +| countertop | 66.59 | 83.22 | +| stove | 85.22 | 93.84 | +| palm | 49.92 | 83.73 | +| kitchen island | 45.82 | 75.68 | +| computer | 76.56 | 92.27 | +| swivel chair | 52.64 | 88.86 | +| boat | 70.15 | 94.77 | +| bar | 59.37 | 75.59 | +| arcade machine | 91.47 | 98.65 | +| hovel | 38.98 | 41.93 | +| bus | 92.51 | 97.32 | +| towel | 79.22 | 91.7 | +| light | 61.0 | 70.53 | +| truck | 46.42 | 60.75 | +| tower | 24.75 | 55.05 | +| chandelier | 74.19 | 89.47 | +| awning | 47.81 | 72.54 | +| streetlight | 37.62 | 53.11 | +| booth | 53.23 | 55.69 | +| television receiver | 79.51 | 88.38 | +| airplane | 82.52 | 98.15 | +| dirt track | 12.91 | 13.77 | +| apparel | 60.53 | 71.22 | +| pole | 30.61 | 39.87 | +| land | 3.31 | 4.35 | +| bannister | 20.45 | 27.2 | +| escalator | 60.35 | 88.28 | +| ottoman | 54.69 | 79.9 | +| bottle | 43.72 | 70.15 | +| buffet | 60.86 | 77.61 | +| poster | 33.37 | 40.46 | +| stage | 23.2 | 49.82 | +| van | 44.23 | 62.03 | +| ship | 78.82 | 79.73 | +| fountain | 42.16 | 47.47 | +| conveyer belt | 80.27 | 96.98 | +| canopy | 54.6 | 73.53 | +| washer | 84.5 | 89.34 | +| plaything | 45.47 | 65.53 | +| swimming pool | 56.4 | 78.81 | +| stool | 56.35 | 72.14 | +| barrel | 61.3 | 88.61 | +| basket | 45.9 | 60.96 | +| waterfall | 54.62 | 66.84 | +| tent | 96.7 | 98.05 | +| bag | 34.95 | 46.35 | +| minibike | 75.9 | 89.19 | +| cradle | 87.9 | 98.23 | +| oven | 67.39 | 81.9 | +| ball | 61.62 | 69.04 | +| food | 67.89 | 79.82 | +| step | 17.13 | 27.1 | +| tank | 81.48 | 93.13 | +| trade name | 16.85 | 18.27 | +| microwave | 91.44 | 96.08 | +| pot | 58.84 | 70.77 | +| animal | 56.61 | 57.66 | +| bicycle | 59.11 | 79.27 | +| lake | 44.38 | 70.14 | +| dishwasher | 74.09 | 82.01 | +| screen | 54.25 | 70.99 | +| blanket | 37.0 | 44.48 | +| sculpture | 76.61 | 87.02 | +| hood | 59.32 | 69.33 | +| sconce | 59.79 | 69.55 | +| vase | 49.09 | 64.12 | +| traffic light | 36.59 | 68.09 | +| tray | 24.68 | 33.12 | +| ashcan | 46.06 | 72.89 | +| fan | 71.09 | 85.42 | +| pier | 47.65 | 54.04 | +| crt screen | 7.52 | 22.18 | +| plate | 63.6 | 83.2 | +| monitor | 27.88 | 34.14 | +| bulletin board | 53.97 | 81.4 | +| shower | 1.61 | 1.61 | +| radiator | 65.39 | 83.17 | +| glass | 21.11 | 22.73 | +| clock | 52.06 | 68.03 | +| flag | 65.84 | 81.55 | ++---------------------+-------+-------+ +2024-06-18 20:17:57,802 - mmseg - INFO - Summary: +2024-06-18 20:17:57,802 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 86.0 | 58.22 | 72.07 | ++------+-------+-------+ +2024-06-18 20:17:57,803 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 20:17:57,803 - mmseg - INFO - Iter(val) [250] aAcc: 0.8600, mIoU: 0.5822, mAcc: 0.7207, IoU.wall: 0.8224, IoU.building: 0.8436, IoU.sky: 0.9495, IoU.floor: 0.8510, IoU.tree: 0.7812, IoU.ceiling: 0.8709, IoU.road: 0.8540, IoU.bed : 0.9254, IoU.windowpane: 0.6725, IoU.grass: 0.6615, IoU.cabinet: 0.6715, IoU.sidewalk: 0.6872, IoU.person: 0.8606, IoU.earth: 0.3930, IoU.door: 0.6041, IoU.table: 0.6718, IoU.mountain: 0.6073, IoU.plant: 0.5743, IoU.curtain: 0.7857, IoU.chair: 0.6561, IoU.car: 0.8755, IoU.water: 0.5965, IoU.painting: 0.7919, IoU.sofa: 0.8193, IoU.shelf: 0.4739, IoU.house: 0.5356, IoU.sea: 0.6754, IoU.mirror: 0.7957, IoU.rug: 0.6952, IoU.field: 0.2959, IoU.armchair: 0.6177, IoU.seat: 0.6508, IoU.fence: 0.4961, IoU.desk: 0.5574, IoU.rock: 0.5783, IoU.wardrobe: 0.5627, IoU.lamp: 0.7389, IoU.bathtub: 0.9041, IoU.railing: 0.4183, IoU.cushion: 0.6820, IoU.base: 0.4630, IoU.box: 0.3836, IoU.column: 0.5690, IoU.signboard: 0.4169, IoU.chest of drawers: 0.5329, IoU.counter: 0.4559, IoU.sand: 0.5715, IoU.sink: 0.7833, IoU.skyscraper: 0.4664, IoU.fireplace: 0.7033, IoU.refrigerator: 0.8356, IoU.grandstand: 0.5020, IoU.path: 0.3504, IoU.stairs: 0.2909, IoU.runway: 0.7431, IoU.case: 0.6275, IoU.pool table: 0.9439, IoU.pillow: 0.6508, IoU.screen door: 0.8215, IoU.stairway: 0.4072, IoU.river: 0.1620, IoU.bridge: 0.7042, IoU.bookcase: 0.3879, IoU.blind: 0.4736, IoU.coffee table: 0.5840, IoU.toilet: 0.9060, IoU.flower: 0.4635, IoU.book: 0.5336, IoU.hill: 0.1200, IoU.bench: 0.5966, IoU.countertop: 0.6659, IoU.stove: 0.8522, IoU.palm: 0.4992, IoU.kitchen island: 0.4582, IoU.computer: 0.7656, IoU.swivel chair: 0.5264, IoU.boat: 0.7015, IoU.bar: 0.5937, IoU.arcade machine: 0.9147, IoU.hovel: 0.3898, IoU.bus: 0.9251, IoU.towel: 0.7922, IoU.light: 0.6100, IoU.truck: 0.4642, IoU.tower: 0.2475, IoU.chandelier: 0.7419, IoU.awning: 0.4781, IoU.streetlight: 0.3762, IoU.booth: 0.5323, IoU.television receiver: 0.7951, IoU.airplane: 0.8252, IoU.dirt track: 0.1291, IoU.apparel: 0.6053, IoU.pole: 0.3061, IoU.land: 0.0331, IoU.bannister: 0.2045, IoU.escalator: 0.6035, IoU.ottoman: 0.5469, IoU.bottle: 0.4372, IoU.buffet: 0.6086, IoU.poster: 0.3337, IoU.stage: 0.2320, IoU.van: 0.4423, IoU.ship: 0.7882, IoU.fountain: 0.4216, IoU.conveyer belt: 0.8027, IoU.canopy: 0.5460, IoU.washer: 0.8450, IoU.plaything: 0.4547, IoU.swimming pool: 0.5640, IoU.stool: 0.5635, IoU.barrel: 0.6130, IoU.basket: 0.4590, IoU.waterfall: 0.5462, IoU.tent: 0.9670, IoU.bag: 0.3495, IoU.minibike: 0.7590, IoU.cradle: 0.8790, IoU.oven: 0.6739, IoU.ball: 0.6162, IoU.food: 0.6789, IoU.step: 0.1713, IoU.tank: 0.8148, IoU.trade name: 0.1685, IoU.microwave: 0.9144, IoU.pot: 0.5884, IoU.animal: 0.5661, IoU.bicycle: 0.5911, IoU.lake: 0.4438, IoU.dishwasher: 0.7409, IoU.screen: 0.5425, IoU.blanket: 0.3700, IoU.sculpture: 0.7661, IoU.hood: 0.5932, IoU.sconce: 0.5979, IoU.vase: 0.4909, IoU.traffic light: 0.3659, IoU.tray: 0.2468, IoU.ashcan: 0.4606, IoU.fan: 0.7109, IoU.pier: 0.4765, IoU.crt screen: 0.0752, IoU.plate: 0.6360, IoU.monitor: 0.2788, IoU.bulletin board: 0.5397, IoU.shower: 0.0161, IoU.radiator: 0.6539, IoU.glass: 0.2111, IoU.clock: 0.5206, IoU.flag: 0.6584, Acc.wall: 0.8985, Acc.building: 0.9207, Acc.sky: 0.9724, Acc.floor: 0.9117, Acc.tree: 0.8995, Acc.ceiling: 0.9402, Acc.road: 0.8948, Acc.bed : 0.9718, Acc.windowpane: 0.8042, Acc.grass: 0.8111, Acc.cabinet: 0.7541, Acc.sidewalk: 0.8348, Acc.person: 0.9407, Acc.earth: 0.5427, Acc.door: 0.7484, Acc.table: 0.7893, Acc.mountain: 0.7146, Acc.plant: 0.6941, Acc.curtain: 0.8739, Acc.chair: 0.7603, Acc.car: 0.9389, Acc.water: 0.7135, Acc.painting: 0.9043, Acc.sofa: 0.9094, Acc.shelf: 0.5958, Acc.house: 0.7208, Acc.sea: 0.8495, Acc.mirror: 0.8638, Acc.rug: 0.8340, Acc.field: 0.5439, Acc.armchair: 0.8044, Acc.seat: 0.8612, Acc.fence: 0.6062, Acc.desk: 0.7477, Acc.rock: 0.8644, Acc.wardrobe: 0.8013, Acc.lamp: 0.8838, Acc.bathtub: 0.9420, Acc.railing: 0.5906, Acc.cushion: 0.8079, Acc.base: 0.6445, Acc.box: 0.4615, Acc.column: 0.6775, Acc.signboard: 0.6058, Acc.chest of drawers: 0.7739, Acc.counter: 0.6077, Acc.sand: 0.8688, Acc.sink: 0.8502, Acc.skyscraper: 0.6223, Acc.fireplace: 0.9691, Acc.refrigerator: 0.9463, Acc.grandstand: 0.8251, Acc.path: 0.4840, Acc.stairs: 0.3305, Acc.runway: 0.9624, Acc.case: 0.7315, Acc.pool table: 0.9864, Acc.pillow: 0.7492, Acc.screen door: 0.8596, Acc.stairway: 0.5545, Acc.river: 0.3156, Acc.bridge: 0.8107, Acc.bookcase: 0.4847, Acc.blind: 0.6117, Acc.coffee table: 0.9038, Acc.toilet: 0.9442, Acc.flower: 0.6784, Acc.book: 0.8268, Acc.hill: 0.2823, Acc.bench: 0.6773, Acc.countertop: 0.8322, Acc.stove: 0.9384, Acc.palm: 0.8373, Acc.kitchen island: 0.7568, Acc.computer: 0.9227, Acc.swivel chair: 0.8886, Acc.boat: 0.9477, Acc.bar: 0.7559, Acc.arcade machine: 0.9865, Acc.hovel: 0.4193, Acc.bus: 0.9732, Acc.towel: 0.9170, Acc.light: 0.7053, Acc.truck: 0.6075, Acc.tower: 0.5505, Acc.chandelier: 0.8947, Acc.awning: 0.7254, Acc.streetlight: 0.5311, Acc.booth: 0.5569, Acc.television receiver: 0.8838, Acc.airplane: 0.9815, Acc.dirt track: 0.1377, Acc.apparel: 0.7122, Acc.pole: 0.3987, Acc.land: 0.0435, Acc.bannister: 0.2720, Acc.escalator: 0.8828, Acc.ottoman: 0.7990, Acc.bottle: 0.7015, Acc.buffet: 0.7761, Acc.poster: 0.4046, Acc.stage: 0.4982, Acc.van: 0.6203, Acc.ship: 0.7973, Acc.fountain: 0.4747, Acc.conveyer belt: 0.9698, Acc.canopy: 0.7353, Acc.washer: 0.8934, Acc.plaything: 0.6553, Acc.swimming pool: 0.7881, Acc.stool: 0.7214, Acc.barrel: 0.8861, Acc.basket: 0.6096, Acc.waterfall: 0.6684, Acc.tent: 0.9805, Acc.bag: 0.4635, Acc.minibike: 0.8919, Acc.cradle: 0.9823, Acc.oven: 0.8190, Acc.ball: 0.6904, Acc.food: 0.7982, Acc.step: 0.2710, Acc.tank: 0.9313, Acc.trade name: 0.1827, Acc.microwave: 0.9608, Acc.pot: 0.7077, Acc.animal: 0.5766, Acc.bicycle: 0.7927, Acc.lake: 0.7014, Acc.dishwasher: 0.8201, Acc.screen: 0.7099, Acc.blanket: 0.4448, Acc.sculpture: 0.8702, Acc.hood: 0.6933, Acc.sconce: 0.6955, Acc.vase: 0.6412, Acc.traffic light: 0.6809, Acc.tray: 0.3312, Acc.ashcan: 0.7289, Acc.fan: 0.8542, Acc.pier: 0.5404, Acc.crt screen: 0.2218, Acc.plate: 0.8320, Acc.monitor: 0.3414, Acc.bulletin board: 0.8140, Acc.shower: 0.0161, Acc.radiator: 0.8317, Acc.glass: 0.2273, Acc.clock: 0.6803, Acc.flag: 0.8155 +2024-06-18 20:19:37,067 - mmseg - INFO - Iter [30050/80000] lr: 2.498e-05, eta: 1 day, 5:39:18, time: 4.172, data_time: 2.203, memory: 72263, decode.loss_ce: 0.2229, decode.acc_seg: 91.0150, aux.loss_ce: 0.0922, aux.acc_seg: 90.7118, loss: 0.3150 +2024-06-18 20:21:16,078 - mmseg - INFO - Iter [30100/80000] lr: 2.495e-05, eta: 1 day, 5:37:18, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2279, decode.acc_seg: 90.5091, aux.loss_ce: 0.0941, aux.acc_seg: 90.2737, loss: 0.3220 +2024-06-18 20:22:55,003 - mmseg - INFO - Iter [30150/80000] lr: 2.493e-05, eta: 1 day, 5:35:18, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2226, decode.acc_seg: 90.7029, aux.loss_ce: 0.0923, aux.acc_seg: 90.3589, loss: 0.3150 +2024-06-18 20:24:33,966 - mmseg - INFO - Iter [30200/80000] lr: 2.490e-05, eta: 1 day, 5:33:18, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2440, decode.acc_seg: 89.8670, aux.loss_ce: 0.1004, aux.acc_seg: 89.6614, loss: 0.3445 +2024-06-18 20:26:12,892 - mmseg - INFO - Iter [30250/80000] lr: 2.488e-05, eta: 1 day, 5:31:18, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2444, decode.acc_seg: 90.1419, aux.loss_ce: 0.1001, aux.acc_seg: 89.8842, loss: 0.3445 +2024-06-18 20:27:51,867 - mmseg - INFO - Iter [30300/80000] lr: 2.485e-05, eta: 1 day, 5:29:19, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2252, decode.acc_seg: 90.6797, aux.loss_ce: 0.0923, aux.acc_seg: 90.3808, loss: 0.3176 +2024-06-18 20:29:33,410 - mmseg - INFO - Iter [30350/80000] lr: 2.483e-05, eta: 1 day, 5:27:23, time: 2.031, data_time: 0.060, memory: 72263, decode.loss_ce: 0.2264, decode.acc_seg: 90.5722, aux.loss_ce: 0.0932, aux.acc_seg: 90.2668, loss: 0.3196 +2024-06-18 20:31:12,271 - mmseg - INFO - Iter [30400/80000] lr: 2.480e-05, eta: 1 day, 5:25:23, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2270, decode.acc_seg: 90.5957, aux.loss_ce: 0.0927, aux.acc_seg: 90.3970, loss: 0.3196 +2024-06-18 20:32:51,155 - mmseg - INFO - Iter [30450/80000] lr: 2.478e-05, eta: 1 day, 5:23:24, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2230, decode.acc_seg: 90.7297, aux.loss_ce: 0.0923, aux.acc_seg: 90.4863, loss: 0.3153 +2024-06-18 20:34:30,047 - mmseg - INFO - Iter [30500/80000] lr: 2.475e-05, eta: 1 day, 5:21:24, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2294, decode.acc_seg: 90.7049, aux.loss_ce: 0.0936, aux.acc_seg: 90.4507, loss: 0.3230 +2024-06-18 20:36:08,964 - mmseg - INFO - Iter [30550/80000] lr: 2.473e-05, eta: 1 day, 5:19:25, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2247, decode.acc_seg: 90.6701, aux.loss_ce: 0.0927, aux.acc_seg: 90.2932, loss: 0.3174 +2024-06-18 20:37:47,840 - mmseg - INFO - Iter [30600/80000] lr: 2.470e-05, eta: 1 day, 5:17:25, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2135, decode.acc_seg: 91.1040, aux.loss_ce: 0.0886, aux.acc_seg: 90.7664, loss: 0.3022 +2024-06-18 20:39:26,747 - mmseg - INFO - Iter [30650/80000] lr: 2.468e-05, eta: 1 day, 5:15:26, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2164, decode.acc_seg: 90.7066, aux.loss_ce: 0.0898, aux.acc_seg: 90.3470, loss: 0.3062 +2024-06-18 20:41:05,623 - mmseg - INFO - Iter [30700/80000] lr: 2.465e-05, eta: 1 day, 5:13:27, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2170, decode.acc_seg: 91.0359, aux.loss_ce: 0.0897, aux.acc_seg: 90.7896, loss: 0.3067 +2024-06-18 20:42:44,619 - mmseg - INFO - Iter [30750/80000] lr: 2.463e-05, eta: 1 day, 5:11:28, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2235, decode.acc_seg: 90.5338, aux.loss_ce: 0.0937, aux.acc_seg: 90.1789, loss: 0.3172 +2024-06-18 20:44:23,519 - mmseg - INFO - Iter [30800/80000] lr: 2.460e-05, eta: 1 day, 5:09:29, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2226, decode.acc_seg: 90.6720, aux.loss_ce: 0.0911, aux.acc_seg: 90.5287, loss: 0.3137 +2024-06-18 20:46:02,386 - mmseg - INFO - Iter [30850/80000] lr: 2.458e-05, eta: 1 day, 5:07:29, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2234, decode.acc_seg: 90.4648, aux.loss_ce: 0.0920, aux.acc_seg: 90.2082, loss: 0.3154 +2024-06-18 20:47:41,467 - mmseg - INFO - Iter [30900/80000] lr: 2.455e-05, eta: 1 day, 5:05:31, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2021, decode.acc_seg: 91.6939, aux.loss_ce: 0.0844, aux.acc_seg: 91.2412, loss: 0.2865 +2024-06-18 20:49:20,474 - mmseg - INFO - Iter [30950/80000] lr: 2.453e-05, eta: 1 day, 5:03:32, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2075, decode.acc_seg: 91.2461, aux.loss_ce: 0.0860, aux.acc_seg: 91.0143, loss: 0.2936 +2024-06-18 20:50:59,306 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 20:50:59,306 - mmseg - INFO - Iter [31000/80000] lr: 2.450e-05, eta: 1 day, 5:01:33, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2136, decode.acc_seg: 91.1557, aux.loss_ce: 0.0882, aux.acc_seg: 90.8401, loss: 0.3018 +2024-06-18 20:52:49,751 - mmseg - INFO - per class results: +2024-06-18 20:52:49,758 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.55 | 88.75 | +| building | 84.94 | 92.77 | +| sky | 94.71 | 97.45 | +| floor | 85.58 | 92.02 | +| tree | 77.37 | 89.5 | +| ceiling | 86.32 | 92.68 | +| road | 86.33 | 91.76 | +| bed | 93.02 | 96.9 | +| windowpane | 67.47 | 80.72 | +| grass | 68.81 | 84.4 | +| cabinet | 68.15 | 77.93 | +| sidewalk | 69.89 | 85.2 | +| person | 85.81 | 93.68 | +| earth | 40.45 | 52.25 | +| door | 59.19 | 78.77 | +| table | 67.84 | 79.24 | +| mountain | 56.84 | 63.19 | +| plant | 55.69 | 67.79 | +| curtain | 76.76 | 90.42 | +| chair | 66.9 | 76.44 | +| car | 86.36 | 94.79 | +| water | 62.87 | 75.23 | +| painting | 77.02 | 90.88 | +| sofa | 80.8 | 91.96 | +| shelf | 50.22 | 67.84 | +| house | 54.8 | 82.8 | +| sea | 70.29 | 84.47 | +| mirror | 80.24 | 86.51 | +| rug | 71.74 | 83.91 | +| field | 34.07 | 52.74 | +| armchair | 62.07 | 80.78 | +| seat | 67.9 | 89.8 | +| fence | 55.77 | 74.8 | +| desk | 60.23 | 77.96 | +| rock | 59.23 | 84.88 | +| wardrobe | 57.09 | 82.68 | +| lamp | 73.69 | 84.76 | +| bathtub | 88.59 | 91.55 | +| railing | 41.42 | 58.46 | +| cushion | 68.02 | 85.91 | +| base | 45.23 | 61.13 | +| box | 39.88 | 47.77 | +| column | 55.37 | 65.32 | +| signboard | 41.7 | 53.83 | +| chest of drawers | 45.88 | 60.2 | +| counter | 44.0 | 56.83 | +| sand | 52.75 | 72.51 | +| sink | 83.13 | 90.33 | +| skyscraper | 47.71 | 63.2 | +| fireplace | 74.9 | 94.96 | +| refrigerator | 85.1 | 93.09 | +| grandstand | 45.03 | 87.4 | +| path | 29.22 | 38.47 | +| stairs | 40.72 | 50.7 | +| runway | 71.81 | 91.85 | +| case | 61.78 | 75.93 | +| pool table | 94.15 | 98.17 | +| pillow | 63.89 | 72.18 | +| screen door | 78.18 | 79.85 | +| stairway | 47.25 | 59.49 | +| river | 14.6 | 32.23 | +| bridge | 69.68 | 78.12 | +| bookcase | 45.08 | 66.93 | +| blind | 42.94 | 46.87 | +| coffee table | 60.22 | 89.14 | +| toilet | 90.41 | 93.57 | +| flower | 44.0 | 64.37 | +| book | 55.12 | 75.19 | +| hill | 11.63 | 25.85 | +| bench | 62.29 | 69.61 | +| countertop | 65.52 | 84.09 | +| stove | 87.5 | 94.68 | +| palm | 52.79 | 83.37 | +| kitchen island | 43.66 | 76.39 | +| computer | 78.76 | 92.71 | +| swivel chair | 52.12 | 77.98 | +| boat | 78.97 | 93.26 | +| bar | 66.13 | 86.19 | +| arcade machine | 76.27 | 97.76 | +| hovel | 18.69 | 20.85 | +| bus | 93.13 | 97.22 | +| towel | 78.94 | 91.41 | +| light | 54.79 | 84.01 | +| truck | 47.78 | 66.49 | +| tower | 14.55 | 23.65 | +| chandelier | 69.16 | 91.88 | +| awning | 53.82 | 72.87 | +| streetlight | 33.83 | 42.91 | +| booth | 55.05 | 60.83 | +| television receiver | 74.99 | 90.25 | +| airplane | 85.43 | 97.31 | +| dirt track | 13.04 | 18.52 | +| apparel | 59.84 | 90.69 | +| pole | 27.83 | 36.6 | +| land | 3.53 | 5.63 | +| bannister | 21.02 | 27.63 | +| escalator | 64.14 | 87.87 | +| ottoman | 56.5 | 77.95 | +| bottle | 42.77 | 65.72 | +| buffet | 64.73 | 72.63 | +| poster | 38.93 | 53.27 | +| stage | 23.22 | 49.92 | +| van | 41.73 | 54.84 | +| ship | 84.64 | 86.47 | +| fountain | 38.42 | 38.93 | +| conveyer belt | 83.07 | 96.11 | +| canopy | 56.43 | 79.11 | +| washer | 88.61 | 95.99 | +| plaything | 43.15 | 69.44 | +| swimming pool | 53.6 | 76.85 | +| stool | 54.03 | 74.46 | +| barrel | 48.93 | 70.76 | +| basket | 44.12 | 58.12 | +| waterfall | 49.38 | 58.08 | +| tent | 90.32 | 98.79 | +| bag | 31.09 | 35.3 | +| minibike | 75.01 | 89.83 | +| cradle | 85.23 | 97.55 | +| oven | 60.97 | 68.5 | +| ball | 61.05 | 72.74 | +| food | 58.91 | 65.67 | +| step | 19.43 | 26.08 | +| tank | 61.39 | 66.25 | +| trade name | 33.03 | 40.27 | +| microwave | 87.85 | 96.86 | +| pot | 59.22 | 70.67 | +| animal | 61.57 | 63.21 | +| bicycle | 60.73 | 75.91 | +| lake | 43.53 | 65.85 | +| dishwasher | 73.46 | 84.2 | +| screen | 59.11 | 91.9 | +| blanket | 34.11 | 43.19 | +| sculpture | 77.12 | 85.25 | +| hood | 65.63 | 78.93 | +| sconce | 59.18 | 69.55 | +| vase | 47.78 | 69.1 | +| traffic light | 36.3 | 69.21 | +| tray | 27.09 | 34.48 | +| ashcan | 48.46 | 72.0 | +| fan | 70.26 | 89.88 | +| pier | 40.37 | 44.64 | +| crt screen | 1.37 | 1.72 | +| plate | 63.31 | 81.83 | +| monitor | 55.44 | 70.54 | +| bulletin board | 64.01 | 78.43 | +| shower | 2.04 | 11.2 | +| radiator | 63.99 | 80.51 | +| glass | 21.5 | 23.21 | +| clock | 51.95 | 67.57 | +| flag | 68.39 | 78.24 | ++---------------------+-------+-------+ +2024-06-18 20:52:49,758 - mmseg - INFO - Summary: +2024-06-18 20:52:49,758 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.14 | 58.16 | 71.87 | ++-------+-------+-------+ +2024-06-18 20:52:49,758 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 20:52:49,759 - mmseg - INFO - Iter(val) [250] aAcc: 0.8614, mIoU: 0.5816, mAcc: 0.7187, IoU.wall: 0.8155, IoU.building: 0.8494, IoU.sky: 0.9471, IoU.floor: 0.8558, IoU.tree: 0.7737, IoU.ceiling: 0.8632, IoU.road: 0.8633, IoU.bed : 0.9302, IoU.windowpane: 0.6747, IoU.grass: 0.6881, IoU.cabinet: 0.6815, IoU.sidewalk: 0.6989, IoU.person: 0.8581, IoU.earth: 0.4045, IoU.door: 0.5919, IoU.table: 0.6784, IoU.mountain: 0.5684, IoU.plant: 0.5569, IoU.curtain: 0.7676, IoU.chair: 0.6690, IoU.car: 0.8636, IoU.water: 0.6287, IoU.painting: 0.7702, IoU.sofa: 0.8080, IoU.shelf: 0.5022, IoU.house: 0.5480, IoU.sea: 0.7029, IoU.mirror: 0.8024, IoU.rug: 0.7174, IoU.field: 0.3407, IoU.armchair: 0.6207, IoU.seat: 0.6790, IoU.fence: 0.5577, IoU.desk: 0.6023, IoU.rock: 0.5923, IoU.wardrobe: 0.5709, IoU.lamp: 0.7369, IoU.bathtub: 0.8859, IoU.railing: 0.4142, IoU.cushion: 0.6802, IoU.base: 0.4523, IoU.box: 0.3988, IoU.column: 0.5537, IoU.signboard: 0.4170, IoU.chest of drawers: 0.4588, IoU.counter: 0.4400, IoU.sand: 0.5275, IoU.sink: 0.8313, IoU.skyscraper: 0.4771, IoU.fireplace: 0.7490, IoU.refrigerator: 0.8510, IoU.grandstand: 0.4503, IoU.path: 0.2922, IoU.stairs: 0.4072, IoU.runway: 0.7181, IoU.case: 0.6178, IoU.pool table: 0.9415, IoU.pillow: 0.6389, IoU.screen door: 0.7818, IoU.stairway: 0.4725, IoU.river: 0.1460, IoU.bridge: 0.6968, IoU.bookcase: 0.4508, IoU.blind: 0.4294, IoU.coffee table: 0.6022, IoU.toilet: 0.9041, IoU.flower: 0.4400, IoU.book: 0.5512, IoU.hill: 0.1163, IoU.bench: 0.6229, IoU.countertop: 0.6552, IoU.stove: 0.8750, IoU.palm: 0.5279, IoU.kitchen island: 0.4366, IoU.computer: 0.7876, IoU.swivel chair: 0.5212, IoU.boat: 0.7897, IoU.bar: 0.6613, IoU.arcade machine: 0.7627, IoU.hovel: 0.1869, IoU.bus: 0.9313, IoU.towel: 0.7894, IoU.light: 0.5479, IoU.truck: 0.4778, IoU.tower: 0.1455, IoU.chandelier: 0.6916, IoU.awning: 0.5382, IoU.streetlight: 0.3383, IoU.booth: 0.5505, IoU.television receiver: 0.7499, IoU.airplane: 0.8543, IoU.dirt track: 0.1304, IoU.apparel: 0.5984, IoU.pole: 0.2783, IoU.land: 0.0353, IoU.bannister: 0.2102, IoU.escalator: 0.6414, IoU.ottoman: 0.5650, IoU.bottle: 0.4277, IoU.buffet: 0.6473, IoU.poster: 0.3893, IoU.stage: 0.2322, IoU.van: 0.4173, IoU.ship: 0.8464, IoU.fountain: 0.3842, IoU.conveyer belt: 0.8307, IoU.canopy: 0.5643, IoU.washer: 0.8861, IoU.plaything: 0.4315, IoU.swimming pool: 0.5360, IoU.stool: 0.5403, IoU.barrel: 0.4893, IoU.basket: 0.4412, IoU.waterfall: 0.4938, IoU.tent: 0.9032, IoU.bag: 0.3109, IoU.minibike: 0.7501, IoU.cradle: 0.8523, IoU.oven: 0.6097, IoU.ball: 0.6105, IoU.food: 0.5891, IoU.step: 0.1943, IoU.tank: 0.6139, IoU.trade name: 0.3303, IoU.microwave: 0.8785, IoU.pot: 0.5922, IoU.animal: 0.6157, IoU.bicycle: 0.6073, IoU.lake: 0.4353, IoU.dishwasher: 0.7346, IoU.screen: 0.5911, IoU.blanket: 0.3411, IoU.sculpture: 0.7712, IoU.hood: 0.6563, IoU.sconce: 0.5918, IoU.vase: 0.4778, IoU.traffic light: 0.3630, IoU.tray: 0.2709, IoU.ashcan: 0.4846, IoU.fan: 0.7026, IoU.pier: 0.4037, IoU.crt screen: 0.0137, IoU.plate: 0.6331, IoU.monitor: 0.5544, IoU.bulletin board: 0.6401, IoU.shower: 0.0204, IoU.radiator: 0.6399, IoU.glass: 0.2150, IoU.clock: 0.5195, IoU.flag: 0.6839, Acc.wall: 0.8875, Acc.building: 0.9277, Acc.sky: 0.9745, Acc.floor: 0.9202, Acc.tree: 0.8950, Acc.ceiling: 0.9268, Acc.road: 0.9176, Acc.bed : 0.9690, Acc.windowpane: 0.8072, Acc.grass: 0.8440, Acc.cabinet: 0.7793, Acc.sidewalk: 0.8520, Acc.person: 0.9368, Acc.earth: 0.5225, Acc.door: 0.7877, Acc.table: 0.7924, Acc.mountain: 0.6319, Acc.plant: 0.6779, Acc.curtain: 0.9042, Acc.chair: 0.7644, Acc.car: 0.9479, Acc.water: 0.7523, Acc.painting: 0.9088, Acc.sofa: 0.9196, Acc.shelf: 0.6784, Acc.house: 0.8280, Acc.sea: 0.8447, Acc.mirror: 0.8651, Acc.rug: 0.8391, Acc.field: 0.5274, Acc.armchair: 0.8078, Acc.seat: 0.8980, Acc.fence: 0.7480, Acc.desk: 0.7796, Acc.rock: 0.8488, Acc.wardrobe: 0.8268, Acc.lamp: 0.8476, Acc.bathtub: 0.9155, Acc.railing: 0.5846, Acc.cushion: 0.8591, Acc.base: 0.6113, Acc.box: 0.4777, Acc.column: 0.6532, Acc.signboard: 0.5383, Acc.chest of drawers: 0.6020, Acc.counter: 0.5683, Acc.sand: 0.7251, Acc.sink: 0.9033, Acc.skyscraper: 0.6320, Acc.fireplace: 0.9496, Acc.refrigerator: 0.9309, Acc.grandstand: 0.8740, Acc.path: 0.3847, Acc.stairs: 0.5070, Acc.runway: 0.9185, Acc.case: 0.7593, Acc.pool table: 0.9817, Acc.pillow: 0.7218, Acc.screen door: 0.7985, Acc.stairway: 0.5949, Acc.river: 0.3223, Acc.bridge: 0.7812, Acc.bookcase: 0.6693, Acc.blind: 0.4687, Acc.coffee table: 0.8914, Acc.toilet: 0.9357, Acc.flower: 0.6437, Acc.book: 0.7519, Acc.hill: 0.2585, Acc.bench: 0.6961, Acc.countertop: 0.8409, Acc.stove: 0.9468, Acc.palm: 0.8337, Acc.kitchen island: 0.7639, Acc.computer: 0.9271, Acc.swivel chair: 0.7798, Acc.boat: 0.9326, Acc.bar: 0.8619, Acc.arcade machine: 0.9776, Acc.hovel: 0.2085, Acc.bus: 0.9722, Acc.towel: 0.9141, Acc.light: 0.8401, Acc.truck: 0.6649, Acc.tower: 0.2365, Acc.chandelier: 0.9188, Acc.awning: 0.7287, Acc.streetlight: 0.4291, Acc.booth: 0.6083, Acc.television receiver: 0.9025, Acc.airplane: 0.9731, Acc.dirt track: 0.1852, Acc.apparel: 0.9069, Acc.pole: 0.3660, Acc.land: 0.0563, Acc.bannister: 0.2763, Acc.escalator: 0.8787, Acc.ottoman: 0.7795, Acc.bottle: 0.6572, Acc.buffet: 0.7263, Acc.poster: 0.5327, Acc.stage: 0.4992, Acc.van: 0.5484, Acc.ship: 0.8647, Acc.fountain: 0.3893, Acc.conveyer belt: 0.9611, Acc.canopy: 0.7911, Acc.washer: 0.9599, Acc.plaything: 0.6944, Acc.swimming pool: 0.7685, Acc.stool: 0.7446, Acc.barrel: 0.7076, Acc.basket: 0.5812, Acc.waterfall: 0.5808, Acc.tent: 0.9879, Acc.bag: 0.3530, Acc.minibike: 0.8983, Acc.cradle: 0.9755, Acc.oven: 0.6850, Acc.ball: 0.7274, Acc.food: 0.6567, Acc.step: 0.2608, Acc.tank: 0.6625, Acc.trade name: 0.4027, Acc.microwave: 0.9686, Acc.pot: 0.7067, Acc.animal: 0.6321, Acc.bicycle: 0.7591, Acc.lake: 0.6585, Acc.dishwasher: 0.8420, Acc.screen: 0.9190, Acc.blanket: 0.4319, Acc.sculpture: 0.8525, Acc.hood: 0.7893, Acc.sconce: 0.6955, Acc.vase: 0.6910, Acc.traffic light: 0.6921, Acc.tray: 0.3448, Acc.ashcan: 0.7200, Acc.fan: 0.8988, Acc.pier: 0.4464, Acc.crt screen: 0.0172, Acc.plate: 0.8183, Acc.monitor: 0.7054, Acc.bulletin board: 0.7843, Acc.shower: 0.1120, Acc.radiator: 0.8051, Acc.glass: 0.2321, Acc.clock: 0.6757, Acc.flag: 0.7824 +2024-06-18 20:54:28,989 - mmseg - INFO - Iter [31050/80000] lr: 2.448e-05, eta: 1 day, 5:02:29, time: 4.194, data_time: 2.225, memory: 72263, decode.loss_ce: 0.2148, decode.acc_seg: 90.9957, aux.loss_ce: 0.0888, aux.acc_seg: 90.7197, loss: 0.3037 +2024-06-18 20:56:07,896 - mmseg - INFO - Iter [31100/80000] lr: 2.445e-05, eta: 1 day, 5:00:30, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2190, decode.acc_seg: 90.9050, aux.loss_ce: 0.0909, aux.acc_seg: 90.5962, loss: 0.3099 +2024-06-18 20:57:46,735 - mmseg - INFO - Iter [31150/80000] lr: 2.443e-05, eta: 1 day, 4:58:30, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2253, decode.acc_seg: 90.3431, aux.loss_ce: 0.0928, aux.acc_seg: 90.0057, loss: 0.3181 +2024-06-18 20:59:25,623 - mmseg - INFO - Iter [31200/80000] lr: 2.440e-05, eta: 1 day, 4:56:31, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2152, decode.acc_seg: 91.1592, aux.loss_ce: 0.0884, aux.acc_seg: 90.9098, loss: 0.3036 +2024-06-18 21:01:04,608 - mmseg - INFO - Iter [31250/80000] lr: 2.438e-05, eta: 1 day, 4:54:32, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2448, decode.acc_seg: 89.8904, aux.loss_ce: 0.1010, aux.acc_seg: 89.5647, loss: 0.3458 +2024-06-18 21:02:43,579 - mmseg - INFO - Iter [31300/80000] lr: 2.435e-05, eta: 1 day, 4:52:34, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2316, decode.acc_seg: 90.2040, aux.loss_ce: 0.0955, aux.acc_seg: 90.0146, loss: 0.3271 +2024-06-18 21:04:22,500 - mmseg - INFO - Iter [31350/80000] lr: 2.433e-05, eta: 1 day, 4:50:35, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2190, decode.acc_seg: 91.0179, aux.loss_ce: 0.0899, aux.acc_seg: 90.7357, loss: 0.3089 +2024-06-18 21:06:01,385 - mmseg - INFO - Iter [31400/80000] lr: 2.430e-05, eta: 1 day, 4:48:36, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2375, decode.acc_seg: 89.9476, aux.loss_ce: 0.0972, aux.acc_seg: 89.6536, loss: 0.3347 +2024-06-18 21:07:40,363 - mmseg - INFO - Iter [31450/80000] lr: 2.428e-05, eta: 1 day, 4:46:37, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2248, decode.acc_seg: 90.4966, aux.loss_ce: 0.0930, aux.acc_seg: 90.1366, loss: 0.3179 +2024-06-18 21:09:19,228 - mmseg - INFO - Iter [31500/80000] lr: 2.425e-05, eta: 1 day, 4:44:39, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2159, decode.acc_seg: 91.1389, aux.loss_ce: 0.0889, aux.acc_seg: 90.9143, loss: 0.3048 +2024-06-18 21:10:58,274 - mmseg - INFO - Iter [31550/80000] lr: 2.423e-05, eta: 1 day, 4:42:40, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2282, decode.acc_seg: 90.6219, aux.loss_ce: 0.0948, aux.acc_seg: 90.2743, loss: 0.3230 +2024-06-18 21:12:39,248 - mmseg - INFO - Iter [31600/80000] lr: 2.420e-05, eta: 1 day, 4:40:45, time: 2.019, data_time: 0.052, memory: 72263, decode.loss_ce: 0.2160, decode.acc_seg: 90.8574, aux.loss_ce: 0.0892, aux.acc_seg: 90.5558, loss: 0.3052 +2024-06-18 21:14:18,187 - mmseg - INFO - Iter [31650/80000] lr: 2.418e-05, eta: 1 day, 4:38:46, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2051, decode.acc_seg: 91.3810, aux.loss_ce: 0.0853, aux.acc_seg: 91.0330, loss: 0.2903 +2024-06-18 21:15:57,122 - mmseg - INFO - Iter [31700/80000] lr: 2.415e-05, eta: 1 day, 4:36:48, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2169, decode.acc_seg: 91.2070, aux.loss_ce: 0.0898, aux.acc_seg: 90.9746, loss: 0.3067 +2024-06-18 21:17:35,852 - mmseg - INFO - Iter [31750/80000] lr: 2.413e-05, eta: 1 day, 4:34:49, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2149, decode.acc_seg: 91.1700, aux.loss_ce: 0.0888, aux.acc_seg: 90.8587, loss: 0.3037 +2024-06-18 21:19:14,816 - mmseg - INFO - Iter [31800/80000] lr: 2.410e-05, eta: 1 day, 4:32:51, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2287, decode.acc_seg: 90.7838, aux.loss_ce: 0.0943, aux.acc_seg: 90.4628, loss: 0.3231 +2024-06-18 21:20:53,678 - mmseg - INFO - Iter [31850/80000] lr: 2.408e-05, eta: 1 day, 4:30:53, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2094, decode.acc_seg: 91.1326, aux.loss_ce: 0.0867, aux.acc_seg: 90.8887, loss: 0.2960 +2024-06-18 21:22:32,705 - mmseg - INFO - Iter [31900/80000] lr: 2.405e-05, eta: 1 day, 4:28:55, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2117, decode.acc_seg: 90.9608, aux.loss_ce: 0.0876, aux.acc_seg: 90.6426, loss: 0.2993 +2024-06-18 21:24:11,531 - mmseg - INFO - Iter [31950/80000] lr: 2.403e-05, eta: 1 day, 4:26:56, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2059, decode.acc_seg: 91.0082, aux.loss_ce: 0.0853, aux.acc_seg: 90.8530, loss: 0.2911 +2024-06-18 21:25:50,446 - mmseg - INFO - Saving checkpoint at 32000 iterations +2024-06-18 21:27:13,164 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 21:27:13,164 - mmseg - INFO - Iter [32000/80000] lr: 2.400e-05, eta: 1 day, 4:27:03, time: 3.633, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2125, decode.acc_seg: 91.2608, aux.loss_ce: 0.0881, aux.acc_seg: 90.9501, loss: 0.3006 +2024-06-18 21:29:02,722 - mmseg - INFO - per class results: +2024-06-18 21:29:02,728 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.37 | 89.14 | +| building | 85.43 | 94.03 | +| sky | 94.85 | 98.01 | +| floor | 84.82 | 91.21 | +| tree | 76.9 | 89.37 | +| ceiling | 86.53 | 93.01 | +| road | 84.92 | 90.44 | +| bed | 92.96 | 97.54 | +| windowpane | 66.97 | 79.43 | +| grass | 67.78 | 81.19 | +| cabinet | 66.82 | 77.33 | +| sidewalk | 67.64 | 83.61 | +| person | 85.76 | 94.23 | +| earth | 36.98 | 50.75 | +| door | 58.73 | 80.75 | +| table | 69.69 | 81.29 | +| mountain | 63.6 | 71.33 | +| plant | 53.39 | 60.97 | +| curtain | 78.34 | 89.18 | +| chair | 66.31 | 75.4 | +| car | 87.31 | 93.16 | +| water | 64.49 | 78.88 | +| painting | 76.45 | 92.04 | +| sofa | 80.16 | 86.63 | +| shelf | 45.53 | 56.68 | +| house | 52.02 | 59.54 | +| sea | 76.6 | 90.52 | +| mirror | 79.03 | 87.61 | +| rug | 70.28 | 84.0 | +| field | 33.4 | 62.69 | +| armchair | 59.1 | 83.31 | +| seat | 66.23 | 89.99 | +| fence | 51.3 | 67.07 | +| desk | 59.79 | 78.86 | +| rock | 57.97 | 83.13 | +| wardrobe | 53.4 | 72.78 | +| lamp | 74.46 | 87.06 | +| bathtub | 87.16 | 89.95 | +| railing | 41.81 | 62.85 | +| cushion | 69.18 | 83.39 | +| base | 45.37 | 64.88 | +| box | 40.85 | 54.52 | +| column | 54.99 | 75.47 | +| signboard | 41.4 | 55.69 | +| chest of drawers | 45.03 | 81.46 | +| counter | 42.65 | 60.16 | +| sand | 38.68 | 59.51 | +| sink | 82.45 | 89.7 | +| skyscraper | 49.85 | 61.43 | +| fireplace | 75.62 | 94.8 | +| refrigerator | 84.54 | 94.01 | +| grandstand | 50.6 | 81.76 | +| path | 28.94 | 37.39 | +| stairs | 27.44 | 32.09 | +| runway | 70.33 | 89.97 | +| case | 61.7 | 76.22 | +| pool table | 93.3 | 98.96 | +| pillow | 62.99 | 70.91 | +| screen door | 71.53 | 73.26 | +| stairway | 43.31 | 66.0 | +| river | 19.36 | 30.32 | +| bridge | 66.84 | 90.02 | +| bookcase | 37.5 | 57.36 | +| blind | 47.13 | 53.48 | +| coffee table | 66.03 | 87.93 | +| toilet | 90.29 | 95.03 | +| flower | 42.43 | 70.64 | +| book | 53.57 | 80.8 | +| hill | 5.76 | 10.63 | +| bench | 59.22 | 68.48 | +| countertop | 63.65 | 86.84 | +| stove | 86.86 | 95.0 | +| palm | 53.64 | 80.82 | +| kitchen island | 50.7 | 78.07 | +| computer | 79.81 | 91.51 | +| swivel chair | 52.72 | 77.33 | +| boat | 78.49 | 91.31 | +| bar | 66.02 | 83.37 | +| arcade machine | 81.54 | 89.76 | +| hovel | 51.16 | 59.83 | +| bus | 92.95 | 97.12 | +| towel | 78.3 | 82.7 | +| light | 62.19 | 74.44 | +| truck | 45.73 | 65.7 | +| tower | 35.15 | 61.33 | +| chandelier | 71.59 | 90.41 | +| awning | 39.87 | 52.65 | +| streetlight | 33.9 | 44.42 | +| booth | 42.14 | 50.36 | +| television receiver | 74.2 | 90.17 | +| airplane | 86.17 | 97.35 | +| dirt track | 6.2 | 21.07 | +| apparel | 58.61 | 92.14 | +| pole | 24.64 | 37.31 | +| land | 2.93 | 4.62 | +| bannister | 22.77 | 29.16 | +| escalator | 64.23 | 84.83 | +| ottoman | 57.36 | 73.59 | +| bottle | 42.69 | 70.6 | +| buffet | 57.6 | 63.75 | +| poster | 38.59 | 53.29 | +| stage | 24.69 | 43.34 | +| van | 47.1 | 75.72 | +| ship | 25.92 | 26.29 | +| fountain | 22.12 | 22.19 | +| conveyer belt | 84.47 | 93.91 | +| canopy | 46.59 | 69.49 | +| washer | 88.64 | 94.86 | +| plaything | 29.4 | 70.77 | +| swimming pool | 59.65 | 91.07 | +| stool | 54.88 | 70.01 | +| barrel | 55.08 | 70.35 | +| basket | 43.15 | 61.58 | +| waterfall | 57.19 | 65.82 | +| tent | 91.84 | 98.59 | +| bag | 19.52 | 21.33 | +| minibike | 73.69 | 89.51 | +| cradle | 83.32 | 98.47 | +| oven | 61.78 | 72.94 | +| ball | 41.83 | 43.42 | +| food | 63.0 | 74.27 | +| step | 13.54 | 17.26 | +| tank | 82.12 | 94.07 | +| trade name | 21.34 | 23.08 | +| microwave | 88.34 | 96.62 | +| pot | 58.49 | 67.99 | +| animal | 63.74 | 65.55 | +| bicycle | 62.29 | 81.52 | +| lake | 48.14 | 63.98 | +| dishwasher | 72.8 | 83.62 | +| screen | 62.79 | 92.68 | +| blanket | 37.02 | 46.91 | +| sculpture | 78.31 | 86.63 | +| hood | 64.39 | 79.47 | +| sconce | 59.93 | 71.94 | +| vase | 46.68 | 69.24 | +| traffic light | 38.32 | 62.47 | +| tray | 24.47 | 29.43 | +| ashcan | 52.67 | 66.19 | +| fan | 71.19 | 84.15 | +| pier | 47.19 | 60.53 | +| crt screen | 1.85 | 1.89 | +| plate | 62.3 | 81.03 | +| monitor | 64.73 | 85.67 | +| bulletin board | 54.7 | 67.45 | +| shower | 8.58 | 10.01 | +| radiator | 68.48 | 81.36 | +| glass | 20.78 | 22.33 | +| clock | 52.97 | 65.4 | +| flag | 68.79 | 77.78 | ++---------------------+-------+-------+ +2024-06-18 21:29:02,728 - mmseg - INFO - Summary: +2024-06-18 21:29:02,729 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.01 | 57.53 | 71.22 | ++-------+-------+-------+ +2024-06-18 21:29:02,729 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 21:29:02,730 - mmseg - INFO - Iter(val) [250] aAcc: 0.8601, mIoU: 0.5753, mAcc: 0.7122, IoU.wall: 0.8237, IoU.building: 0.8543, IoU.sky: 0.9485, IoU.floor: 0.8482, IoU.tree: 0.7690, IoU.ceiling: 0.8653, IoU.road: 0.8492, IoU.bed : 0.9296, IoU.windowpane: 0.6697, IoU.grass: 0.6778, IoU.cabinet: 0.6682, IoU.sidewalk: 0.6764, IoU.person: 0.8576, IoU.earth: 0.3698, IoU.door: 0.5873, IoU.table: 0.6969, IoU.mountain: 0.6360, IoU.plant: 0.5339, IoU.curtain: 0.7834, IoU.chair: 0.6631, IoU.car: 0.8731, IoU.water: 0.6449, IoU.painting: 0.7645, IoU.sofa: 0.8016, IoU.shelf: 0.4553, IoU.house: 0.5202, IoU.sea: 0.7660, IoU.mirror: 0.7903, IoU.rug: 0.7028, IoU.field: 0.3340, IoU.armchair: 0.5910, IoU.seat: 0.6623, IoU.fence: 0.5130, IoU.desk: 0.5979, IoU.rock: 0.5797, IoU.wardrobe: 0.5340, IoU.lamp: 0.7446, IoU.bathtub: 0.8716, IoU.railing: 0.4181, IoU.cushion: 0.6918, IoU.base: 0.4537, IoU.box: 0.4085, IoU.column: 0.5499, IoU.signboard: 0.4140, IoU.chest of drawers: 0.4503, IoU.counter: 0.4265, IoU.sand: 0.3868, IoU.sink: 0.8245, IoU.skyscraper: 0.4985, IoU.fireplace: 0.7562, IoU.refrigerator: 0.8454, IoU.grandstand: 0.5060, IoU.path: 0.2894, IoU.stairs: 0.2744, IoU.runway: 0.7033, IoU.case: 0.6170, IoU.pool table: 0.9330, IoU.pillow: 0.6299, IoU.screen door: 0.7153, IoU.stairway: 0.4331, IoU.river: 0.1936, IoU.bridge: 0.6684, IoU.bookcase: 0.3750, IoU.blind: 0.4713, IoU.coffee table: 0.6603, IoU.toilet: 0.9029, IoU.flower: 0.4243, IoU.book: 0.5357, IoU.hill: 0.0576, IoU.bench: 0.5922, IoU.countertop: 0.6365, IoU.stove: 0.8686, IoU.palm: 0.5364, IoU.kitchen island: 0.5070, IoU.computer: 0.7981, IoU.swivel chair: 0.5272, IoU.boat: 0.7849, IoU.bar: 0.6602, IoU.arcade machine: 0.8154, IoU.hovel: 0.5116, IoU.bus: 0.9295, IoU.towel: 0.7830, IoU.light: 0.6219, IoU.truck: 0.4573, IoU.tower: 0.3515, IoU.chandelier: 0.7159, IoU.awning: 0.3987, IoU.streetlight: 0.3390, IoU.booth: 0.4214, IoU.television receiver: 0.7420, IoU.airplane: 0.8617, IoU.dirt track: 0.0620, IoU.apparel: 0.5861, IoU.pole: 0.2464, IoU.land: 0.0293, IoU.bannister: 0.2277, IoU.escalator: 0.6423, IoU.ottoman: 0.5736, IoU.bottle: 0.4269, IoU.buffet: 0.5760, IoU.poster: 0.3859, IoU.stage: 0.2469, IoU.van: 0.4710, IoU.ship: 0.2592, IoU.fountain: 0.2212, IoU.conveyer belt: 0.8447, IoU.canopy: 0.4659, IoU.washer: 0.8864, IoU.plaything: 0.2940, IoU.swimming pool: 0.5965, IoU.stool: 0.5488, IoU.barrel: 0.5508, IoU.basket: 0.4315, IoU.waterfall: 0.5719, IoU.tent: 0.9184, IoU.bag: 0.1952, IoU.minibike: 0.7369, IoU.cradle: 0.8332, IoU.oven: 0.6178, IoU.ball: 0.4183, IoU.food: 0.6300, IoU.step: 0.1354, IoU.tank: 0.8212, IoU.trade name: 0.2134, IoU.microwave: 0.8834, IoU.pot: 0.5849, IoU.animal: 0.6374, IoU.bicycle: 0.6229, IoU.lake: 0.4814, IoU.dishwasher: 0.7280, IoU.screen: 0.6279, IoU.blanket: 0.3702, IoU.sculpture: 0.7831, IoU.hood: 0.6439, IoU.sconce: 0.5993, IoU.vase: 0.4668, IoU.traffic light: 0.3832, IoU.tray: 0.2447, IoU.ashcan: 0.5267, IoU.fan: 0.7119, IoU.pier: 0.4719, IoU.crt screen: 0.0185, IoU.plate: 0.6230, IoU.monitor: 0.6473, IoU.bulletin board: 0.5470, IoU.shower: 0.0858, IoU.radiator: 0.6848, IoU.glass: 0.2078, IoU.clock: 0.5297, IoU.flag: 0.6879, Acc.wall: 0.8914, Acc.building: 0.9403, Acc.sky: 0.9801, Acc.floor: 0.9121, Acc.tree: 0.8937, Acc.ceiling: 0.9301, Acc.road: 0.9044, Acc.bed : 0.9754, Acc.windowpane: 0.7943, Acc.grass: 0.8119, Acc.cabinet: 0.7733, Acc.sidewalk: 0.8361, Acc.person: 0.9423, Acc.earth: 0.5075, Acc.door: 0.8075, Acc.table: 0.8129, Acc.mountain: 0.7133, Acc.plant: 0.6097, Acc.curtain: 0.8918, Acc.chair: 0.7540, Acc.car: 0.9316, Acc.water: 0.7888, Acc.painting: 0.9204, Acc.sofa: 0.8663, Acc.shelf: 0.5668, Acc.house: 0.5954, Acc.sea: 0.9052, Acc.mirror: 0.8761, Acc.rug: 0.8400, Acc.field: 0.6269, Acc.armchair: 0.8331, Acc.seat: 0.8999, Acc.fence: 0.6707, Acc.desk: 0.7886, Acc.rock: 0.8313, Acc.wardrobe: 0.7278, Acc.lamp: 0.8706, Acc.bathtub: 0.8995, Acc.railing: 0.6285, Acc.cushion: 0.8339, Acc.base: 0.6488, Acc.box: 0.5452, Acc.column: 0.7547, Acc.signboard: 0.5569, Acc.chest of drawers: 0.8146, Acc.counter: 0.6016, Acc.sand: 0.5951, Acc.sink: 0.8970, Acc.skyscraper: 0.6143, Acc.fireplace: 0.9480, Acc.refrigerator: 0.9401, Acc.grandstand: 0.8176, Acc.path: 0.3739, Acc.stairs: 0.3209, Acc.runway: 0.8997, Acc.case: 0.7622, Acc.pool table: 0.9896, Acc.pillow: 0.7091, Acc.screen door: 0.7326, Acc.stairway: 0.6600, Acc.river: 0.3032, Acc.bridge: 0.9002, Acc.bookcase: 0.5736, Acc.blind: 0.5348, Acc.coffee table: 0.8793, Acc.toilet: 0.9503, Acc.flower: 0.7064, Acc.book: 0.8080, Acc.hill: 0.1063, Acc.bench: 0.6848, Acc.countertop: 0.8684, Acc.stove: 0.9500, Acc.palm: 0.8082, Acc.kitchen island: 0.7807, Acc.computer: 0.9151, Acc.swivel chair: 0.7733, Acc.boat: 0.9131, Acc.bar: 0.8337, Acc.arcade machine: 0.8976, Acc.hovel: 0.5983, Acc.bus: 0.9712, Acc.towel: 0.8270, Acc.light: 0.7444, Acc.truck: 0.6570, Acc.tower: 0.6133, Acc.chandelier: 0.9041, Acc.awning: 0.5265, Acc.streetlight: 0.4442, Acc.booth: 0.5036, Acc.television receiver: 0.9017, Acc.airplane: 0.9735, Acc.dirt track: 0.2107, Acc.apparel: 0.9214, Acc.pole: 0.3731, Acc.land: 0.0462, Acc.bannister: 0.2916, Acc.escalator: 0.8483, Acc.ottoman: 0.7359, Acc.bottle: 0.7060, Acc.buffet: 0.6375, Acc.poster: 0.5329, Acc.stage: 0.4334, Acc.van: 0.7572, Acc.ship: 0.2629, Acc.fountain: 0.2219, Acc.conveyer belt: 0.9391, Acc.canopy: 0.6949, Acc.washer: 0.9486, Acc.plaything: 0.7077, Acc.swimming pool: 0.9107, Acc.stool: 0.7001, Acc.barrel: 0.7035, Acc.basket: 0.6158, Acc.waterfall: 0.6582, Acc.tent: 0.9859, Acc.bag: 0.2133, Acc.minibike: 0.8951, Acc.cradle: 0.9847, Acc.oven: 0.7294, Acc.ball: 0.4342, Acc.food: 0.7427, Acc.step: 0.1726, Acc.tank: 0.9407, Acc.trade name: 0.2308, Acc.microwave: 0.9662, Acc.pot: 0.6799, Acc.animal: 0.6555, Acc.bicycle: 0.8152, Acc.lake: 0.6398, Acc.dishwasher: 0.8362, Acc.screen: 0.9268, Acc.blanket: 0.4691, Acc.sculpture: 0.8663, Acc.hood: 0.7947, Acc.sconce: 0.7194, Acc.vase: 0.6924, Acc.traffic light: 0.6247, Acc.tray: 0.2943, Acc.ashcan: 0.6619, Acc.fan: 0.8415, Acc.pier: 0.6053, Acc.crt screen: 0.0189, Acc.plate: 0.8103, Acc.monitor: 0.8567, Acc.bulletin board: 0.6745, Acc.shower: 0.1001, Acc.radiator: 0.8136, Acc.glass: 0.2233, Acc.clock: 0.6540, Acc.flag: 0.7778 +2024-06-18 21:30:42,002 - mmseg - INFO - Iter [32050/80000] lr: 2.398e-05, eta: 1 day, 4:27:49, time: 4.177, data_time: 2.209, memory: 72263, decode.loss_ce: 0.2221, decode.acc_seg: 90.7142, aux.loss_ce: 0.0921, aux.acc_seg: 90.3505, loss: 0.3141 +2024-06-18 21:32:20,931 - mmseg - INFO - Iter [32100/80000] lr: 2.395e-05, eta: 1 day, 4:25:50, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2096, decode.acc_seg: 91.2100, aux.loss_ce: 0.0870, aux.acc_seg: 90.9306, loss: 0.2966 +2024-06-18 21:33:59,793 - mmseg - INFO - Iter [32150/80000] lr: 2.393e-05, eta: 1 day, 4:23:51, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2134, decode.acc_seg: 91.1449, aux.loss_ce: 0.0882, aux.acc_seg: 90.8150, loss: 0.3016 +2024-06-18 21:35:38,729 - mmseg - INFO - Iter [32200/80000] lr: 2.390e-05, eta: 1 day, 4:21:53, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2156, decode.acc_seg: 91.0492, aux.loss_ce: 0.0883, aux.acc_seg: 90.7327, loss: 0.3039 +2024-06-18 21:37:17,682 - mmseg - INFO - Iter [32250/80000] lr: 2.388e-05, eta: 1 day, 4:19:54, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2144, decode.acc_seg: 91.0903, aux.loss_ce: 0.0883, aux.acc_seg: 90.7714, loss: 0.3028 +2024-06-18 21:38:56,513 - mmseg - INFO - Iter [32300/80000] lr: 2.385e-05, eta: 1 day, 4:17:56, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2116, decode.acc_seg: 90.9678, aux.loss_ce: 0.0872, aux.acc_seg: 90.6821, loss: 0.2988 +2024-06-18 21:40:35,465 - mmseg - INFO - Iter [32350/80000] lr: 2.383e-05, eta: 1 day, 4:15:57, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2132, decode.acc_seg: 91.1385, aux.loss_ce: 0.0884, aux.acc_seg: 90.7081, loss: 0.3016 +2024-06-18 21:42:14,344 - mmseg - INFO - Iter [32400/80000] lr: 2.380e-05, eta: 1 day, 4:13:59, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2245, decode.acc_seg: 90.6006, aux.loss_ce: 0.0935, aux.acc_seg: 90.1912, loss: 0.3179 +2024-06-18 21:43:53,437 - mmseg - INFO - Iter [32450/80000] lr: 2.378e-05, eta: 1 day, 4:12:01, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2302, decode.acc_seg: 90.2643, aux.loss_ce: 0.0940, aux.acc_seg: 89.9773, loss: 0.3242 +2024-06-18 21:45:32,260 - mmseg - INFO - Iter [32500/80000] lr: 2.375e-05, eta: 1 day, 4:10:03, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2256, decode.acc_seg: 90.4561, aux.loss_ce: 0.0930, aux.acc_seg: 90.1667, loss: 0.3186 +2024-06-18 21:47:11,176 - mmseg - INFO - Iter [32550/80000] lr: 2.373e-05, eta: 1 day, 4:08:04, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2236, decode.acc_seg: 90.9391, aux.loss_ce: 0.0917, aux.acc_seg: 90.7081, loss: 0.3152 +2024-06-18 21:48:50,081 - mmseg - INFO - Iter [32600/80000] lr: 2.370e-05, eta: 1 day, 4:06:06, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2143, decode.acc_seg: 91.0682, aux.loss_ce: 0.0886, aux.acc_seg: 90.7774, loss: 0.3029 +2024-06-18 21:50:28,969 - mmseg - INFO - Iter [32650/80000] lr: 2.368e-05, eta: 1 day, 4:04:08, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2177, decode.acc_seg: 91.0153, aux.loss_ce: 0.0897, aux.acc_seg: 90.6482, loss: 0.3075 +2024-06-18 21:52:07,861 - mmseg - INFO - Iter [32700/80000] lr: 2.365e-05, eta: 1 day, 4:02:10, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2176, decode.acc_seg: 90.7845, aux.loss_ce: 0.0902, aux.acc_seg: 90.4678, loss: 0.3078 +2024-06-18 21:53:46,842 - mmseg - INFO - Iter [32750/80000] lr: 2.363e-05, eta: 1 day, 4:00:12, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2058, decode.acc_seg: 91.4099, aux.loss_ce: 0.0854, aux.acc_seg: 91.0017, loss: 0.2912 +2024-06-18 21:55:25,810 - mmseg - INFO - Iter [32800/80000] lr: 2.360e-05, eta: 1 day, 3:58:15, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2296, decode.acc_seg: 90.4735, aux.loss_ce: 0.0941, aux.acc_seg: 90.1754, loss: 0.3237 +2024-06-18 21:57:07,233 - mmseg - INFO - Iter [32850/80000] lr: 2.358e-05, eta: 1 day, 3:56:21, time: 2.028, data_time: 0.059, memory: 72263, decode.loss_ce: 0.2226, decode.acc_seg: 90.7207, aux.loss_ce: 0.0920, aux.acc_seg: 90.3936, loss: 0.3146 +2024-06-18 21:58:46,161 - mmseg - INFO - Iter [32900/80000] lr: 2.355e-05, eta: 1 day, 3:54:23, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2209, decode.acc_seg: 90.9909, aux.loss_ce: 0.0913, aux.acc_seg: 90.6906, loss: 0.3122 +2024-06-18 22:00:25,066 - mmseg - INFO - Iter [32950/80000] lr: 2.353e-05, eta: 1 day, 3:52:25, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1976, decode.acc_seg: 91.4923, aux.loss_ce: 0.0819, aux.acc_seg: 91.1762, loss: 0.2794 +2024-06-18 22:02:04,130 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 22:02:04,130 - mmseg - INFO - Iter [33000/80000] lr: 2.350e-05, eta: 1 day, 3:50:28, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2020, decode.acc_seg: 91.2937, aux.loss_ce: 0.0842, aux.acc_seg: 90.9451, loss: 0.2862 +2024-06-18 22:03:54,922 - mmseg - INFO - per class results: +2024-06-18 22:03:54,928 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.1 | 89.04 | +| building | 84.52 | 92.36 | +| sky | 94.67 | 96.74 | +| floor | 85.61 | 90.71 | +| tree | 77.37 | 90.93 | +| ceiling | 86.54 | 92.66 | +| road | 86.03 | 89.61 | +| bed | 91.65 | 97.61 | +| windowpane | 67.0 | 80.51 | +| grass | 67.16 | 83.92 | +| cabinet | 65.57 | 73.19 | +| sidewalk | 69.68 | 88.02 | +| person | 85.46 | 95.28 | +| earth | 41.37 | 55.27 | +| door | 59.81 | 77.97 | +| table | 68.52 | 79.03 | +| mountain | 63.46 | 73.77 | +| plant | 53.71 | 66.71 | +| curtain | 78.67 | 91.5 | +| chair | 66.47 | 78.08 | +| car | 87.15 | 95.1 | +| water | 60.94 | 75.94 | +| painting | 80.21 | 90.72 | +| sofa | 80.07 | 86.59 | +| shelf | 48.62 | 63.24 | +| house | 53.39 | 72.87 | +| sea | 73.26 | 90.9 | +| mirror | 80.04 | 88.67 | +| rug | 74.11 | 81.7 | +| field | 33.11 | 53.67 | +| armchair | 57.09 | 82.1 | +| seat | 68.83 | 92.43 | +| fence | 51.84 | 62.48 | +| desk | 58.51 | 77.62 | +| rock | 53.23 | 84.33 | +| wardrobe | 54.6 | 80.34 | +| lamp | 74.45 | 87.04 | +| bathtub | 89.33 | 91.52 | +| railing | 41.56 | 60.85 | +| cushion | 69.36 | 80.98 | +| base | 38.72 | 59.89 | +| box | 39.46 | 52.42 | +| column | 54.77 | 68.2 | +| signboard | 41.28 | 56.33 | +| chest of drawers | 42.22 | 86.19 | +| counter | 44.39 | 58.0 | +| sand | 41.57 | 57.76 | +| sink | 84.69 | 91.78 | +| skyscraper | 56.04 | 74.45 | +| fireplace | 74.02 | 94.96 | +| refrigerator | 84.29 | 94.01 | +| grandstand | 58.34 | 84.09 | +| path | 33.97 | 40.17 | +| stairs | 28.14 | 32.56 | +| runway | 67.86 | 88.0 | +| case | 55.22 | 70.87 | +| pool table | 94.55 | 98.22 | +| pillow | 56.86 | 62.74 | +| screen door | 76.74 | 78.76 | +| stairway | 33.76 | 57.58 | +| river | 17.69 | 39.07 | +| bridge | 68.81 | 80.88 | +| bookcase | 40.77 | 61.75 | +| blind | 41.97 | 49.65 | +| coffee table | 65.91 | 88.99 | +| toilet | 89.41 | 94.2 | +| flower | 40.61 | 59.1 | +| book | 54.55 | 77.53 | +| hill | 10.12 | 21.46 | +| bench | 65.16 | 76.47 | +| countertop | 66.71 | 83.2 | +| stove | 87.21 | 94.04 | +| palm | 54.36 | 85.53 | +| kitchen island | 46.06 | 77.86 | +| computer | 79.35 | 92.26 | +| swivel chair | 53.09 | 81.32 | +| boat | 74.42 | 93.53 | +| bar | 59.77 | 73.05 | +| arcade machine | 91.57 | 95.79 | +| hovel | 53.27 | 60.75 | +| bus | 94.31 | 96.58 | +| towel | 79.38 | 86.57 | +| light | 61.53 | 76.73 | +| truck | 49.12 | 67.72 | +| tower | 31.47 | 61.36 | +| chandelier | 72.04 | 90.78 | +| awning | 52.18 | 67.81 | +| streetlight | 35.96 | 54.31 | +| booth | 38.75 | 56.18 | +| television receiver | 77.47 | 86.43 | +| airplane | 86.23 | 97.37 | +| dirt track | 13.24 | 21.61 | +| apparel | 64.04 | 86.37 | +| pole | 24.23 | 30.59 | +| land | 4.03 | 5.82 | +| bannister | 18.89 | 33.71 | +| escalator | 58.25 | 83.19 | +| ottoman | 55.58 | 76.94 | +| bottle | 41.71 | 52.25 | +| buffet | 63.47 | 79.73 | +| poster | 43.95 | 54.76 | +| stage | 20.69 | 47.33 | +| van | 45.76 | 55.06 | +| ship | 11.34 | 11.53 | +| fountain | 35.53 | 37.31 | +| conveyer belt | 83.88 | 95.42 | +| canopy | 25.38 | 37.49 | +| washer | 83.65 | 88.98 | +| plaything | 40.75 | 60.19 | +| swimming pool | 58.34 | 90.35 | +| stool | 57.55 | 75.01 | +| barrel | 68.62 | 86.02 | +| basket | 45.18 | 61.6 | +| waterfall | 51.71 | 55.97 | +| tent | 79.44 | 99.04 | +| bag | 32.58 | 38.42 | +| minibike | 78.2 | 88.61 | +| cradle | 90.0 | 96.64 | +| oven | 61.79 | 71.24 | +| ball | 54.45 | 72.01 | +| food | 60.47 | 72.95 | +| step | 15.23 | 18.38 | +| tank | 71.38 | 80.63 | +| trade name | 26.37 | 30.11 | +| microwave | 88.23 | 96.84 | +| pot | 57.78 | 68.21 | +| animal | 66.06 | 68.2 | +| bicycle | 61.28 | 76.2 | +| lake | 0.0 | 0.0 | +| dishwasher | 73.52 | 81.06 | +| screen | 66.52 | 93.38 | +| blanket | 34.46 | 45.51 | +| sculpture | 66.77 | 87.88 | +| hood | 67.71 | 82.51 | +| sconce | 61.01 | 74.82 | +| vase | 49.06 | 68.67 | +| traffic light | 36.29 | 69.25 | +| tray | 27.03 | 40.0 | +| ashcan | 50.27 | 61.93 | +| fan | 72.04 | 87.93 | +| pier | 39.3 | 46.67 | +| crt screen | 1.77 | 3.55 | +| plate | 61.63 | 85.02 | +| monitor | 38.3 | 44.63 | +| bulletin board | 56.53 | 66.47 | +| shower | 12.51 | 15.25 | +| radiator | 68.21 | 80.13 | +| glass | 23.29 | 25.96 | +| clock | 55.42 | 63.93 | +| flag | 68.45 | 81.27 | ++---------------------+-------+-------+ +2024-06-18 22:03:54,928 - mmseg - INFO - Summary: +2024-06-18 22:03:54,928 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.92 | 57.34 | 70.85 | ++-------+-------+-------+ +2024-06-18 22:03:54,929 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 22:03:54,929 - mmseg - INFO - Iter(val) [250] aAcc: 0.8592, mIoU: 0.5734, mAcc: 0.7085, IoU.wall: 0.8210, IoU.building: 0.8452, IoU.sky: 0.9467, IoU.floor: 0.8561, IoU.tree: 0.7737, IoU.ceiling: 0.8654, IoU.road: 0.8603, IoU.bed : 0.9165, IoU.windowpane: 0.6700, IoU.grass: 0.6716, IoU.cabinet: 0.6557, IoU.sidewalk: 0.6968, IoU.person: 0.8546, IoU.earth: 0.4137, IoU.door: 0.5981, IoU.table: 0.6852, IoU.mountain: 0.6346, IoU.plant: 0.5371, IoU.curtain: 0.7867, IoU.chair: 0.6647, IoU.car: 0.8715, IoU.water: 0.6094, IoU.painting: 0.8021, IoU.sofa: 0.8007, IoU.shelf: 0.4862, IoU.house: 0.5339, IoU.sea: 0.7326, IoU.mirror: 0.8004, IoU.rug: 0.7411, IoU.field: 0.3311, IoU.armchair: 0.5709, IoU.seat: 0.6883, IoU.fence: 0.5184, IoU.desk: 0.5851, IoU.rock: 0.5323, IoU.wardrobe: 0.5460, IoU.lamp: 0.7445, IoU.bathtub: 0.8933, IoU.railing: 0.4156, IoU.cushion: 0.6936, IoU.base: 0.3872, IoU.box: 0.3946, IoU.column: 0.5477, IoU.signboard: 0.4128, IoU.chest of drawers: 0.4222, IoU.counter: 0.4439, IoU.sand: 0.4157, IoU.sink: 0.8469, IoU.skyscraper: 0.5604, IoU.fireplace: 0.7402, IoU.refrigerator: 0.8429, IoU.grandstand: 0.5834, IoU.path: 0.3397, IoU.stairs: 0.2814, IoU.runway: 0.6786, IoU.case: 0.5522, IoU.pool table: 0.9455, IoU.pillow: 0.5686, IoU.screen door: 0.7674, IoU.stairway: 0.3376, IoU.river: 0.1769, IoU.bridge: 0.6881, IoU.bookcase: 0.4077, IoU.blind: 0.4197, IoU.coffee table: 0.6591, IoU.toilet: 0.8941, IoU.flower: 0.4061, IoU.book: 0.5455, IoU.hill: 0.1012, IoU.bench: 0.6516, IoU.countertop: 0.6671, IoU.stove: 0.8721, IoU.palm: 0.5436, IoU.kitchen island: 0.4606, IoU.computer: 0.7935, IoU.swivel chair: 0.5309, IoU.boat: 0.7442, IoU.bar: 0.5977, IoU.arcade machine: 0.9157, IoU.hovel: 0.5327, IoU.bus: 0.9431, IoU.towel: 0.7938, IoU.light: 0.6153, IoU.truck: 0.4912, IoU.tower: 0.3147, IoU.chandelier: 0.7204, IoU.awning: 0.5218, IoU.streetlight: 0.3596, IoU.booth: 0.3875, IoU.television receiver: 0.7747, IoU.airplane: 0.8623, IoU.dirt track: 0.1324, IoU.apparel: 0.6404, IoU.pole: 0.2423, IoU.land: 0.0403, IoU.bannister: 0.1889, IoU.escalator: 0.5825, IoU.ottoman: 0.5558, IoU.bottle: 0.4171, IoU.buffet: 0.6347, IoU.poster: 0.4395, IoU.stage: 0.2069, IoU.van: 0.4576, IoU.ship: 0.1134, IoU.fountain: 0.3553, IoU.conveyer belt: 0.8388, IoU.canopy: 0.2538, IoU.washer: 0.8365, IoU.plaything: 0.4075, IoU.swimming pool: 0.5834, IoU.stool: 0.5755, IoU.barrel: 0.6862, IoU.basket: 0.4518, IoU.waterfall: 0.5171, IoU.tent: 0.7944, IoU.bag: 0.3258, IoU.minibike: 0.7820, IoU.cradle: 0.9000, IoU.oven: 0.6179, IoU.ball: 0.5445, IoU.food: 0.6047, IoU.step: 0.1523, IoU.tank: 0.7138, IoU.trade name: 0.2637, IoU.microwave: 0.8823, IoU.pot: 0.5778, IoU.animal: 0.6606, IoU.bicycle: 0.6128, IoU.lake: 0.0000, IoU.dishwasher: 0.7352, IoU.screen: 0.6652, IoU.blanket: 0.3446, IoU.sculpture: 0.6677, IoU.hood: 0.6771, IoU.sconce: 0.6101, IoU.vase: 0.4906, IoU.traffic light: 0.3629, IoU.tray: 0.2703, IoU.ashcan: 0.5027, IoU.fan: 0.7204, IoU.pier: 0.3930, IoU.crt screen: 0.0177, IoU.plate: 0.6163, IoU.monitor: 0.3830, IoU.bulletin board: 0.5653, IoU.shower: 0.1251, IoU.radiator: 0.6821, IoU.glass: 0.2329, IoU.clock: 0.5542, IoU.flag: 0.6845, Acc.wall: 0.8904, Acc.building: 0.9236, Acc.sky: 0.9674, Acc.floor: 0.9071, Acc.tree: 0.9093, Acc.ceiling: 0.9266, Acc.road: 0.8961, Acc.bed : 0.9761, Acc.windowpane: 0.8051, Acc.grass: 0.8392, Acc.cabinet: 0.7319, Acc.sidewalk: 0.8802, Acc.person: 0.9528, Acc.earth: 0.5527, Acc.door: 0.7797, Acc.table: 0.7903, Acc.mountain: 0.7377, Acc.plant: 0.6671, Acc.curtain: 0.9150, Acc.chair: 0.7808, Acc.car: 0.9510, Acc.water: 0.7594, Acc.painting: 0.9072, Acc.sofa: 0.8659, Acc.shelf: 0.6324, Acc.house: 0.7287, Acc.sea: 0.9090, Acc.mirror: 0.8867, Acc.rug: 0.8170, Acc.field: 0.5367, Acc.armchair: 0.8210, Acc.seat: 0.9243, Acc.fence: 0.6248, Acc.desk: 0.7762, Acc.rock: 0.8433, Acc.wardrobe: 0.8034, Acc.lamp: 0.8704, Acc.bathtub: 0.9152, Acc.railing: 0.6085, Acc.cushion: 0.8098, Acc.base: 0.5989, Acc.box: 0.5242, Acc.column: 0.6820, Acc.signboard: 0.5633, Acc.chest of drawers: 0.8619, Acc.counter: 0.5800, Acc.sand: 0.5776, Acc.sink: 0.9178, Acc.skyscraper: 0.7445, Acc.fireplace: 0.9496, Acc.refrigerator: 0.9401, Acc.grandstand: 0.8409, Acc.path: 0.4017, Acc.stairs: 0.3256, Acc.runway: 0.8800, Acc.case: 0.7087, Acc.pool table: 0.9822, Acc.pillow: 0.6274, Acc.screen door: 0.7876, Acc.stairway: 0.5758, Acc.river: 0.3907, Acc.bridge: 0.8088, Acc.bookcase: 0.6175, Acc.blind: 0.4965, Acc.coffee table: 0.8899, Acc.toilet: 0.9420, Acc.flower: 0.5910, Acc.book: 0.7753, Acc.hill: 0.2146, Acc.bench: 0.7647, Acc.countertop: 0.8320, Acc.stove: 0.9404, Acc.palm: 0.8553, Acc.kitchen island: 0.7786, Acc.computer: 0.9226, Acc.swivel chair: 0.8132, Acc.boat: 0.9353, Acc.bar: 0.7305, Acc.arcade machine: 0.9579, Acc.hovel: 0.6075, Acc.bus: 0.9658, Acc.towel: 0.8657, Acc.light: 0.7673, Acc.truck: 0.6772, Acc.tower: 0.6136, Acc.chandelier: 0.9078, Acc.awning: 0.6781, Acc.streetlight: 0.5431, Acc.booth: 0.5618, Acc.television receiver: 0.8643, Acc.airplane: 0.9737, Acc.dirt track: 0.2161, Acc.apparel: 0.8637, Acc.pole: 0.3059, Acc.land: 0.0582, Acc.bannister: 0.3371, Acc.escalator: 0.8319, Acc.ottoman: 0.7694, Acc.bottle: 0.5225, Acc.buffet: 0.7973, Acc.poster: 0.5476, Acc.stage: 0.4733, Acc.van: 0.5506, Acc.ship: 0.1153, Acc.fountain: 0.3731, Acc.conveyer belt: 0.9542, Acc.canopy: 0.3749, Acc.washer: 0.8898, Acc.plaything: 0.6019, Acc.swimming pool: 0.9035, Acc.stool: 0.7501, Acc.barrel: 0.8602, Acc.basket: 0.6160, Acc.waterfall: 0.5597, Acc.tent: 0.9904, Acc.bag: 0.3842, Acc.minibike: 0.8861, Acc.cradle: 0.9664, Acc.oven: 0.7124, Acc.ball: 0.7201, Acc.food: 0.7295, Acc.step: 0.1838, Acc.tank: 0.8063, Acc.trade name: 0.3011, Acc.microwave: 0.9684, Acc.pot: 0.6821, Acc.animal: 0.6820, Acc.bicycle: 0.7620, Acc.lake: 0.0000, Acc.dishwasher: 0.8106, Acc.screen: 0.9338, Acc.blanket: 0.4551, Acc.sculpture: 0.8788, Acc.hood: 0.8251, Acc.sconce: 0.7482, Acc.vase: 0.6867, Acc.traffic light: 0.6925, Acc.tray: 0.4000, Acc.ashcan: 0.6193, Acc.fan: 0.8793, Acc.pier: 0.4667, Acc.crt screen: 0.0355, Acc.plate: 0.8502, Acc.monitor: 0.4463, Acc.bulletin board: 0.6647, Acc.shower: 0.1525, Acc.radiator: 0.8013, Acc.glass: 0.2596, Acc.clock: 0.6393, Acc.flag: 0.8127 +2024-06-18 22:05:34,201 - mmseg - INFO - Iter [33050/80000] lr: 2.348e-05, eta: 1 day, 3:51:08, time: 4.201, data_time: 2.234, memory: 72263, decode.loss_ce: 0.2064, decode.acc_seg: 91.3669, aux.loss_ce: 0.0860, aux.acc_seg: 91.0610, loss: 0.2924 +2024-06-18 22:07:13,088 - mmseg - INFO - Iter [33100/80000] lr: 2.345e-05, eta: 1 day, 3:49:10, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2007, decode.acc_seg: 91.3882, aux.loss_ce: 0.0834, aux.acc_seg: 91.0973, loss: 0.2841 +2024-06-18 22:08:52,076 - mmseg - INFO - Iter [33150/80000] lr: 2.343e-05, eta: 1 day, 3:47:12, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2089, decode.acc_seg: 90.9970, aux.loss_ce: 0.0871, aux.acc_seg: 90.6724, loss: 0.2960 +2024-06-18 22:10:30,944 - mmseg - INFO - Iter [33200/80000] lr: 2.340e-05, eta: 1 day, 3:45:14, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1974, decode.acc_seg: 91.6460, aux.loss_ce: 0.0815, aux.acc_seg: 91.3503, loss: 0.2788 +2024-06-18 22:12:09,822 - mmseg - INFO - Iter [33250/80000] lr: 2.338e-05, eta: 1 day, 3:43:17, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2135, decode.acc_seg: 91.1303, aux.loss_ce: 0.0882, aux.acc_seg: 90.7442, loss: 0.3016 +2024-06-18 22:13:48,586 - mmseg - INFO - Iter [33300/80000] lr: 2.335e-05, eta: 1 day, 3:41:19, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2093, decode.acc_seg: 90.9327, aux.loss_ce: 0.0859, aux.acc_seg: 90.6983, loss: 0.2952 +2024-06-18 22:15:27,440 - mmseg - INFO - Iter [33350/80000] lr: 2.333e-05, eta: 1 day, 3:39:21, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2037, decode.acc_seg: 91.1477, aux.loss_ce: 0.0843, aux.acc_seg: 90.8335, loss: 0.2879 +2024-06-18 22:17:06,291 - mmseg - INFO - Iter [33400/80000] lr: 2.330e-05, eta: 1 day, 3:37:23, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2056, decode.acc_seg: 91.2694, aux.loss_ce: 0.0859, aux.acc_seg: 90.9840, loss: 0.2915 +2024-06-18 22:18:45,110 - mmseg - INFO - Iter [33450/80000] lr: 2.328e-05, eta: 1 day, 3:35:26, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2263, decode.acc_seg: 90.4401, aux.loss_ce: 0.0936, aux.acc_seg: 90.1493, loss: 0.3199 +2024-06-18 22:20:24,012 - mmseg - INFO - Iter [33500/80000] lr: 2.325e-05, eta: 1 day, 3:33:28, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2056, decode.acc_seg: 91.5467, aux.loss_ce: 0.0854, aux.acc_seg: 91.2124, loss: 0.2910 +2024-06-18 22:22:02,809 - mmseg - INFO - Iter [33550/80000] lr: 2.323e-05, eta: 1 day, 3:31:30, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2189, decode.acc_seg: 90.8686, aux.loss_ce: 0.0896, aux.acc_seg: 90.6085, loss: 0.3085 +2024-06-18 22:23:41,654 - mmseg - INFO - Iter [33600/80000] lr: 2.320e-05, eta: 1 day, 3:29:33, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2110, decode.acc_seg: 91.0933, aux.loss_ce: 0.0863, aux.acc_seg: 90.8791, loss: 0.2973 +2024-06-18 22:25:20,595 - mmseg - INFO - Iter [33650/80000] lr: 2.318e-05, eta: 1 day, 3:27:36, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2232, decode.acc_seg: 90.8396, aux.loss_ce: 0.0913, aux.acc_seg: 90.5811, loss: 0.3146 +2024-06-18 22:26:59,496 - mmseg - INFO - Iter [33700/80000] lr: 2.315e-05, eta: 1 day, 3:25:38, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2346, decode.acc_seg: 90.3846, aux.loss_ce: 0.0948, aux.acc_seg: 90.1867, loss: 0.3295 +2024-06-18 22:28:38,289 - mmseg - INFO - Iter [33750/80000] lr: 2.313e-05, eta: 1 day, 3:23:41, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2126, decode.acc_seg: 91.0546, aux.loss_ce: 0.0882, aux.acc_seg: 90.6270, loss: 0.3008 +2024-06-18 22:30:17,204 - mmseg - INFO - Iter [33800/80000] lr: 2.310e-05, eta: 1 day, 3:21:44, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2157, decode.acc_seg: 91.0300, aux.loss_ce: 0.0899, aux.acc_seg: 90.7143, loss: 0.3056 +2024-06-18 22:31:56,203 - mmseg - INFO - Iter [33850/80000] lr: 2.308e-05, eta: 1 day, 3:19:47, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2062, decode.acc_seg: 91.6384, aux.loss_ce: 0.0859, aux.acc_seg: 91.2147, loss: 0.2921 +2024-06-18 22:33:35,041 - mmseg - INFO - Iter [33900/80000] lr: 2.305e-05, eta: 1 day, 3:17:50, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2320, decode.acc_seg: 90.4189, aux.loss_ce: 0.0945, aux.acc_seg: 90.2234, loss: 0.3265 +2024-06-18 22:35:13,862 - mmseg - INFO - Iter [33950/80000] lr: 2.303e-05, eta: 1 day, 3:15:53, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2124, decode.acc_seg: 91.1782, aux.loss_ce: 0.0878, aux.acc_seg: 90.8837, loss: 0.3002 +2024-06-18 22:36:52,858 - mmseg - INFO - Saving checkpoint at 34000 iterations +2024-06-18 22:38:14,388 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 22:38:14,388 - mmseg - INFO - Iter [34000/80000] lr: 2.300e-05, eta: 1 day, 3:15:46, time: 3.610, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2091, decode.acc_seg: 91.1374, aux.loss_ce: 0.0868, aux.acc_seg: 90.7942, loss: 0.2959 +2024-06-18 22:40:04,545 - mmseg - INFO - per class results: +2024-06-18 22:40:04,552 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.63 | 88.73 | +| building | 84.43 | 93.32 | +| sky | 94.9 | 97.04 | +| floor | 85.14 | 92.04 | +| tree | 77.93 | 90.69 | +| ceiling | 86.96 | 94.27 | +| road | 85.47 | 89.61 | +| bed | 92.68 | 96.63 | +| windowpane | 66.39 | 79.98 | +| grass | 65.43 | 76.6 | +| cabinet | 67.47 | 76.49 | +| sidewalk | 70.75 | 85.77 | +| person | 85.78 | 94.56 | +| earth | 38.48 | 54.23 | +| door | 58.09 | 78.42 | +| table | 67.84 | 78.67 | +| mountain | 60.28 | 69.44 | +| plant | 54.27 | 68.72 | +| curtain | 78.93 | 85.76 | +| chair | 66.73 | 75.45 | +| car | 87.64 | 94.57 | +| water | 65.82 | 81.44 | +| painting | 78.67 | 89.43 | +| sofa | 82.02 | 93.56 | +| shelf | 46.54 | 60.44 | +| house | 54.41 | 71.49 | +| sea | 72.43 | 80.61 | +| mirror | 80.54 | 88.04 | +| rug | 71.5 | 83.83 | +| field | 30.62 | 58.99 | +| armchair | 61.41 | 74.36 | +| seat | 70.93 | 88.53 | +| fence | 54.27 | 72.0 | +| desk | 56.25 | 78.06 | +| rock | 59.21 | 84.31 | +| wardrobe | 59.43 | 79.17 | +| lamp | 75.55 | 85.19 | +| bathtub | 88.41 | 91.1 | +| railing | 43.15 | 63.71 | +| cushion | 70.67 | 83.9 | +| base | 38.99 | 56.93 | +| box | 37.68 | 50.66 | +| column | 56.32 | 71.89 | +| signboard | 41.91 | 56.55 | +| chest of drawers | 52.67 | 74.25 | +| counter | 39.49 | 45.38 | +| sand | 47.65 | 68.24 | +| sink | 84.74 | 90.57 | +| skyscraper | 47.33 | 64.58 | +| fireplace | 73.68 | 96.46 | +| refrigerator | 85.07 | 96.79 | +| grandstand | 56.93 | 77.62 | +| path | 27.48 | 34.96 | +| stairs | 28.63 | 35.07 | +| runway | 69.55 | 88.19 | +| case | 54.89 | 77.16 | +| pool table | 94.24 | 98.74 | +| pillow | 66.94 | 77.32 | +| screen door | 67.53 | 77.32 | +| stairway | 33.55 | 56.84 | +| river | 15.43 | 31.71 | +| bridge | 43.63 | 52.74 | +| bookcase | 39.92 | 61.52 | +| blind | 40.97 | 49.43 | +| coffee table | 60.46 | 85.73 | +| toilet | 89.86 | 93.28 | +| flower | 42.23 | 55.1 | +| book | 54.9 | 79.47 | +| hill | 11.59 | 27.99 | +| bench | 66.28 | 74.06 | +| countertop | 63.98 | 82.46 | +| stove | 87.17 | 92.55 | +| palm | 55.49 | 82.86 | +| kitchen island | 44.89 | 74.97 | +| computer | 80.27 | 89.88 | +| swivel chair | 52.7 | 83.69 | +| boat | 78.91 | 90.96 | +| bar | 60.18 | 83.65 | +| arcade machine | 89.09 | 92.36 | +| hovel | 19.27 | 21.0 | +| bus | 94.17 | 96.91 | +| towel | 80.62 | 88.92 | +| light | 62.12 | 74.32 | +| truck | 47.26 | 64.7 | +| tower | 15.47 | 22.52 | +| chandelier | 72.76 | 89.88 | +| awning | 37.66 | 44.55 | +| streetlight | 36.67 | 47.9 | +| booth | 42.41 | 53.45 | +| television receiver | 79.92 | 88.93 | +| airplane | 88.85 | 96.71 | +| dirt track | 6.59 | 45.72 | +| apparel | 64.17 | 79.78 | +| pole | 28.07 | 37.11 | +| land | 3.82 | 6.43 | +| bannister | 21.79 | 29.3 | +| escalator | 61.8 | 88.36 | +| ottoman | 57.47 | 82.03 | +| bottle | 44.53 | 69.69 | +| buffet | 46.88 | 53.4 | +| poster | 41.33 | 56.04 | +| stage | 21.17 | 48.1 | +| van | 50.94 | 70.75 | +| ship | 90.79 | 95.42 | +| fountain | 21.84 | 21.88 | +| conveyer belt | 81.16 | 96.96 | +| canopy | 47.3 | 62.66 | +| washer | 81.05 | 86.11 | +| plaything | 36.3 | 62.98 | +| swimming pool | 56.46 | 84.29 | +| stool | 58.15 | 67.38 | +| barrel | 71.38 | 86.71 | +| basket | 43.84 | 60.69 | +| waterfall | 52.88 | 63.86 | +| tent | 78.38 | 98.23 | +| bag | 29.11 | 33.37 | +| minibike | 73.33 | 91.94 | +| cradle | 83.42 | 97.32 | +| oven | 63.64 | 77.48 | +| ball | 63.15 | 75.08 | +| food | 57.03 | 67.89 | +| step | 10.87 | 13.3 | +| tank | 70.19 | 79.17 | +| trade name | 24.56 | 28.75 | +| microwave | 89.33 | 96.69 | +| pot | 59.8 | 71.09 | +| animal | 64.77 | 67.03 | +| bicycle | 60.81 | 83.34 | +| lake | 47.44 | 63.59 | +| dishwasher | 68.84 | 82.0 | +| screen | 62.2 | 93.37 | +| blanket | 32.61 | 45.94 | +| sculpture | 73.12 | 86.45 | +| hood | 64.3 | 75.83 | +| sconce | 59.42 | 77.01 | +| vase | 48.88 | 65.92 | +| traffic light | 37.78 | 64.81 | +| tray | 27.63 | 38.14 | +| ashcan | 50.9 | 63.72 | +| fan | 71.41 | 84.79 | +| pier | 37.29 | 51.17 | +| crt screen | 1.79 | 3.65 | +| plate | 64.13 | 81.24 | +| monitor | 31.47 | 36.71 | +| bulletin board | 56.34 | 72.69 | +| shower | 12.21 | 16.23 | +| radiator | 68.12 | 82.63 | +| glass | 23.65 | 26.11 | +| clock | 50.67 | 65.13 | +| flag | 69.21 | 81.21 | ++---------------------+-------+-------+ +2024-06-18 22:40:04,552 - mmseg - INFO - Summary: +2024-06-18 22:40:04,552 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.83 | 57.45 | 70.89 | ++-------+-------+-------+ +2024-06-18 22:40:04,553 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 22:40:04,553 - mmseg - INFO - Iter(val) [250] aAcc: 0.8583, mIoU: 0.5745, mAcc: 0.7089, IoU.wall: 0.8163, IoU.building: 0.8443, IoU.sky: 0.9490, IoU.floor: 0.8514, IoU.tree: 0.7793, IoU.ceiling: 0.8696, IoU.road: 0.8547, IoU.bed : 0.9268, IoU.windowpane: 0.6639, IoU.grass: 0.6543, IoU.cabinet: 0.6747, IoU.sidewalk: 0.7075, IoU.person: 0.8578, IoU.earth: 0.3848, IoU.door: 0.5809, IoU.table: 0.6784, IoU.mountain: 0.6028, IoU.plant: 0.5427, IoU.curtain: 0.7893, IoU.chair: 0.6673, IoU.car: 0.8764, IoU.water: 0.6582, IoU.painting: 0.7867, IoU.sofa: 0.8202, IoU.shelf: 0.4654, IoU.house: 0.5441, IoU.sea: 0.7243, IoU.mirror: 0.8054, IoU.rug: 0.7150, IoU.field: 0.3062, IoU.armchair: 0.6141, IoU.seat: 0.7093, IoU.fence: 0.5427, IoU.desk: 0.5625, IoU.rock: 0.5921, IoU.wardrobe: 0.5943, IoU.lamp: 0.7555, IoU.bathtub: 0.8841, IoU.railing: 0.4315, IoU.cushion: 0.7067, IoU.base: 0.3899, IoU.box: 0.3768, IoU.column: 0.5632, IoU.signboard: 0.4191, IoU.chest of drawers: 0.5267, IoU.counter: 0.3949, IoU.sand: 0.4765, IoU.sink: 0.8474, IoU.skyscraper: 0.4733, IoU.fireplace: 0.7368, IoU.refrigerator: 0.8507, IoU.grandstand: 0.5693, IoU.path: 0.2748, IoU.stairs: 0.2863, IoU.runway: 0.6955, IoU.case: 0.5489, IoU.pool table: 0.9424, IoU.pillow: 0.6694, IoU.screen door: 0.6753, IoU.stairway: 0.3355, IoU.river: 0.1543, IoU.bridge: 0.4363, IoU.bookcase: 0.3992, IoU.blind: 0.4097, IoU.coffee table: 0.6046, IoU.toilet: 0.8986, IoU.flower: 0.4223, IoU.book: 0.5490, IoU.hill: 0.1159, IoU.bench: 0.6628, IoU.countertop: 0.6398, IoU.stove: 0.8717, IoU.palm: 0.5549, IoU.kitchen island: 0.4489, IoU.computer: 0.8027, IoU.swivel chair: 0.5270, IoU.boat: 0.7891, IoU.bar: 0.6018, IoU.arcade machine: 0.8909, IoU.hovel: 0.1927, IoU.bus: 0.9417, IoU.towel: 0.8062, IoU.light: 0.6212, IoU.truck: 0.4726, IoU.tower: 0.1547, IoU.chandelier: 0.7276, IoU.awning: 0.3766, IoU.streetlight: 0.3667, IoU.booth: 0.4241, IoU.television receiver: 0.7992, IoU.airplane: 0.8885, IoU.dirt track: 0.0659, IoU.apparel: 0.6417, IoU.pole: 0.2807, IoU.land: 0.0382, IoU.bannister: 0.2179, IoU.escalator: 0.6180, IoU.ottoman: 0.5747, IoU.bottle: 0.4453, IoU.buffet: 0.4688, IoU.poster: 0.4133, IoU.stage: 0.2117, IoU.van: 0.5094, IoU.ship: 0.9079, IoU.fountain: 0.2184, IoU.conveyer belt: 0.8116, IoU.canopy: 0.4730, IoU.washer: 0.8105, IoU.plaything: 0.3630, IoU.swimming pool: 0.5646, IoU.stool: 0.5815, IoU.barrel: 0.7138, IoU.basket: 0.4384, IoU.waterfall: 0.5288, IoU.tent: 0.7838, IoU.bag: 0.2911, IoU.minibike: 0.7333, IoU.cradle: 0.8342, IoU.oven: 0.6364, IoU.ball: 0.6315, IoU.food: 0.5703, IoU.step: 0.1087, IoU.tank: 0.7019, IoU.trade name: 0.2456, IoU.microwave: 0.8933, IoU.pot: 0.5980, IoU.animal: 0.6477, IoU.bicycle: 0.6081, IoU.lake: 0.4744, IoU.dishwasher: 0.6884, IoU.screen: 0.6220, IoU.blanket: 0.3261, IoU.sculpture: 0.7312, IoU.hood: 0.6430, IoU.sconce: 0.5942, IoU.vase: 0.4888, IoU.traffic light: 0.3778, IoU.tray: 0.2763, IoU.ashcan: 0.5090, IoU.fan: 0.7141, IoU.pier: 0.3729, IoU.crt screen: 0.0179, IoU.plate: 0.6413, IoU.monitor: 0.3147, IoU.bulletin board: 0.5634, IoU.shower: 0.1221, IoU.radiator: 0.6812, IoU.glass: 0.2365, IoU.clock: 0.5067, IoU.flag: 0.6921, Acc.wall: 0.8873, Acc.building: 0.9332, Acc.sky: 0.9704, Acc.floor: 0.9204, Acc.tree: 0.9069, Acc.ceiling: 0.9427, Acc.road: 0.8961, Acc.bed : 0.9663, Acc.windowpane: 0.7998, Acc.grass: 0.7660, Acc.cabinet: 0.7649, Acc.sidewalk: 0.8577, Acc.person: 0.9456, Acc.earth: 0.5423, Acc.door: 0.7842, Acc.table: 0.7867, Acc.mountain: 0.6944, Acc.plant: 0.6872, Acc.curtain: 0.8576, Acc.chair: 0.7545, Acc.car: 0.9457, Acc.water: 0.8144, Acc.painting: 0.8943, Acc.sofa: 0.9356, Acc.shelf: 0.6044, Acc.house: 0.7149, Acc.sea: 0.8061, Acc.mirror: 0.8804, Acc.rug: 0.8383, Acc.field: 0.5899, Acc.armchair: 0.7436, Acc.seat: 0.8853, Acc.fence: 0.7200, Acc.desk: 0.7806, Acc.rock: 0.8431, Acc.wardrobe: 0.7917, Acc.lamp: 0.8519, Acc.bathtub: 0.9110, Acc.railing: 0.6371, Acc.cushion: 0.8390, Acc.base: 0.5693, Acc.box: 0.5066, Acc.column: 0.7189, Acc.signboard: 0.5655, Acc.chest of drawers: 0.7425, Acc.counter: 0.4538, Acc.sand: 0.6824, Acc.sink: 0.9057, Acc.skyscraper: 0.6458, Acc.fireplace: 0.9646, Acc.refrigerator: 0.9679, Acc.grandstand: 0.7762, Acc.path: 0.3496, Acc.stairs: 0.3507, Acc.runway: 0.8819, Acc.case: 0.7716, Acc.pool table: 0.9874, Acc.pillow: 0.7732, Acc.screen door: 0.7732, Acc.stairway: 0.5684, Acc.river: 0.3171, Acc.bridge: 0.5274, Acc.bookcase: 0.6152, Acc.blind: 0.4943, Acc.coffee table: 0.8573, Acc.toilet: 0.9328, Acc.flower: 0.5510, Acc.book: 0.7947, Acc.hill: 0.2799, Acc.bench: 0.7406, Acc.countertop: 0.8246, Acc.stove: 0.9255, Acc.palm: 0.8286, Acc.kitchen island: 0.7497, Acc.computer: 0.8988, Acc.swivel chair: 0.8369, Acc.boat: 0.9096, Acc.bar: 0.8365, Acc.arcade machine: 0.9236, Acc.hovel: 0.2100, Acc.bus: 0.9691, Acc.towel: 0.8892, Acc.light: 0.7432, Acc.truck: 0.6470, Acc.tower: 0.2252, Acc.chandelier: 0.8988, Acc.awning: 0.4455, Acc.streetlight: 0.4790, Acc.booth: 0.5345, Acc.television receiver: 0.8893, Acc.airplane: 0.9671, Acc.dirt track: 0.4572, Acc.apparel: 0.7978, Acc.pole: 0.3711, Acc.land: 0.0643, Acc.bannister: 0.2930, Acc.escalator: 0.8836, Acc.ottoman: 0.8203, Acc.bottle: 0.6969, Acc.buffet: 0.5340, Acc.poster: 0.5604, Acc.stage: 0.4810, Acc.van: 0.7075, Acc.ship: 0.9542, Acc.fountain: 0.2188, Acc.conveyer belt: 0.9696, Acc.canopy: 0.6266, Acc.washer: 0.8611, Acc.plaything: 0.6298, Acc.swimming pool: 0.8429, Acc.stool: 0.6738, Acc.barrel: 0.8671, Acc.basket: 0.6069, Acc.waterfall: 0.6386, Acc.tent: 0.9823, Acc.bag: 0.3337, Acc.minibike: 0.9194, Acc.cradle: 0.9732, Acc.oven: 0.7748, Acc.ball: 0.7508, Acc.food: 0.6789, Acc.step: 0.1330, Acc.tank: 0.7917, Acc.trade name: 0.2875, Acc.microwave: 0.9669, Acc.pot: 0.7109, Acc.animal: 0.6703, Acc.bicycle: 0.8334, Acc.lake: 0.6359, Acc.dishwasher: 0.8200, Acc.screen: 0.9337, Acc.blanket: 0.4594, Acc.sculpture: 0.8645, Acc.hood: 0.7583, Acc.sconce: 0.7701, Acc.vase: 0.6592, Acc.traffic light: 0.6481, Acc.tray: 0.3814, Acc.ashcan: 0.6372, Acc.fan: 0.8479, Acc.pier: 0.5117, Acc.crt screen: 0.0365, Acc.plate: 0.8124, Acc.monitor: 0.3671, Acc.bulletin board: 0.7269, Acc.shower: 0.1623, Acc.radiator: 0.8263, Acc.glass: 0.2611, Acc.clock: 0.6513, Acc.flag: 0.8121 +2024-06-18 22:41:43,818 - mmseg - INFO - Iter [34050/80000] lr: 2.298e-05, eta: 1 day, 3:16:18, time: 4.189, data_time: 2.220, memory: 72263, decode.loss_ce: 0.2273, decode.acc_seg: 90.1970, aux.loss_ce: 0.0938, aux.acc_seg: 89.9598, loss: 0.3211 +2024-06-18 22:43:22,807 - mmseg - INFO - Iter [34100/80000] lr: 2.295e-05, eta: 1 day, 3:14:21, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2337, decode.acc_seg: 90.5476, aux.loss_ce: 0.0961, aux.acc_seg: 90.2149, loss: 0.3298 +2024-06-18 22:45:03,795 - mmseg - INFO - Iter [34150/80000] lr: 2.293e-05, eta: 1 day, 3:12:26, time: 2.020, data_time: 0.052, memory: 72263, decode.loss_ce: 0.2036, decode.acc_seg: 91.3244, aux.loss_ce: 0.0844, aux.acc_seg: 91.0203, loss: 0.2880 +2024-06-18 22:46:42,753 - mmseg - INFO - Iter [34200/80000] lr: 2.290e-05, eta: 1 day, 3:10:29, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2065, decode.acc_seg: 91.0858, aux.loss_ce: 0.0856, aux.acc_seg: 90.8275, loss: 0.2922 +2024-06-18 22:48:21,728 - mmseg - INFO - Iter [34250/80000] lr: 2.288e-05, eta: 1 day, 3:08:32, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2022, decode.acc_seg: 91.6221, aux.loss_ce: 0.0840, aux.acc_seg: 91.3253, loss: 0.2862 +2024-06-18 22:50:00,560 - mmseg - INFO - Iter [34300/80000] lr: 2.285e-05, eta: 1 day, 3:06:34, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2123, decode.acc_seg: 91.1165, aux.loss_ce: 0.0886, aux.acc_seg: 90.6767, loss: 0.3009 +2024-06-18 22:51:39,356 - mmseg - INFO - Iter [34350/80000] lr: 2.283e-05, eta: 1 day, 3:04:37, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2096, decode.acc_seg: 91.4584, aux.loss_ce: 0.0861, aux.acc_seg: 91.1426, loss: 0.2957 +2024-06-18 22:53:18,215 - mmseg - INFO - Iter [34400/80000] lr: 2.280e-05, eta: 1 day, 3:02:40, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2009, decode.acc_seg: 91.6419, aux.loss_ce: 0.0836, aux.acc_seg: 91.3588, loss: 0.2845 +2024-06-18 22:54:57,170 - mmseg - INFO - Iter [34450/80000] lr: 2.278e-05, eta: 1 day, 3:00:42, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2123, decode.acc_seg: 91.0653, aux.loss_ce: 0.0883, aux.acc_seg: 90.6785, loss: 0.3005 +2024-06-18 22:56:36,032 - mmseg - INFO - Iter [34500/80000] lr: 2.275e-05, eta: 1 day, 2:58:45, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2137, decode.acc_seg: 90.9942, aux.loss_ce: 0.0889, aux.acc_seg: 90.7177, loss: 0.3026 +2024-06-18 22:58:14,880 - mmseg - INFO - Iter [34550/80000] lr: 2.273e-05, eta: 1 day, 2:56:48, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2219, decode.acc_seg: 90.5232, aux.loss_ce: 0.0923, aux.acc_seg: 90.1918, loss: 0.3143 +2024-06-18 22:59:53,909 - mmseg - INFO - Iter [34600/80000] lr: 2.270e-05, eta: 1 day, 2:54:51, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2104, decode.acc_seg: 91.2718, aux.loss_ce: 0.0876, aux.acc_seg: 90.9537, loss: 0.2981 +2024-06-18 23:01:32,805 - mmseg - INFO - Iter [34650/80000] lr: 2.268e-05, eta: 1 day, 2:52:54, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2128, decode.acc_seg: 91.0047, aux.loss_ce: 0.0886, aux.acc_seg: 90.6719, loss: 0.3014 +2024-06-18 23:03:11,658 - mmseg - INFO - Iter [34700/80000] lr: 2.265e-05, eta: 1 day, 2:50:58, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2036, decode.acc_seg: 91.3500, aux.loss_ce: 0.0833, aux.acc_seg: 91.1138, loss: 0.2869 +2024-06-18 23:04:50,584 - mmseg - INFO - Iter [34750/80000] lr: 2.263e-05, eta: 1 day, 2:49:01, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2081, decode.acc_seg: 91.1982, aux.loss_ce: 0.0861, aux.acc_seg: 90.9054, loss: 0.2942 +2024-06-18 23:06:29,422 - mmseg - INFO - Iter [34800/80000] lr: 2.260e-05, eta: 1 day, 2:47:04, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2089, decode.acc_seg: 91.4628, aux.loss_ce: 0.0864, aux.acc_seg: 91.1773, loss: 0.2953 +2024-06-18 23:08:08,273 - mmseg - INFO - Iter [34850/80000] lr: 2.258e-05, eta: 1 day, 2:45:07, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2007, decode.acc_seg: 91.5528, aux.loss_ce: 0.0832, aux.acc_seg: 91.2819, loss: 0.2839 +2024-06-18 23:09:47,175 - mmseg - INFO - Iter [34900/80000] lr: 2.255e-05, eta: 1 day, 2:43:10, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2085, decode.acc_seg: 91.4532, aux.loss_ce: 0.0865, aux.acc_seg: 91.1307, loss: 0.2950 +2024-06-18 23:11:25,997 - mmseg - INFO - Iter [34950/80000] lr: 2.253e-05, eta: 1 day, 2:41:14, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.2006, decode.acc_seg: 91.4077, aux.loss_ce: 0.0836, aux.acc_seg: 91.0535, loss: 0.2843 +2024-06-18 23:13:04,889 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 23:13:04,889 - mmseg - INFO - Iter [35000/80000] lr: 2.250e-05, eta: 1 day, 2:39:17, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2151, decode.acc_seg: 90.8163, aux.loss_ce: 0.0883, aux.acc_seg: 90.5920, loss: 0.3035 +2024-06-18 23:14:56,225 - mmseg - INFO - per class results: +2024-06-18 23:14:56,231 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.81 | 89.16 | +| building | 84.74 | 94.46 | +| sky | 94.89 | 97.8 | +| floor | 85.33 | 91.6 | +| tree | 77.31 | 87.08 | +| ceiling | 87.01 | 92.83 | +| road | 85.48 | 88.64 | +| bed | 92.43 | 97.58 | +| windowpane | 66.91 | 81.09 | +| grass | 66.6 | 76.85 | +| cabinet | 67.1 | 78.55 | +| sidewalk | 71.2 | 86.86 | +| person | 85.54 | 95.02 | +| earth | 40.51 | 57.97 | +| door | 59.57 | 74.54 | +| table | 67.08 | 80.57 | +| mountain | 62.74 | 73.16 | +| plant | 57.23 | 70.33 | +| curtain | 78.9 | 89.93 | +| chair | 67.74 | 78.52 | +| car | 88.0 | 93.47 | +| water | 63.58 | 78.72 | +| painting | 80.38 | 92.17 | +| sofa | 83.53 | 93.53 | +| shelf | 46.26 | 58.6 | +| house | 45.28 | 54.49 | +| sea | 70.43 | 80.31 | +| mirror | 81.5 | 88.15 | +| rug | 69.11 | 75.44 | +| field | 33.96 | 60.68 | +| armchair | 61.23 | 81.47 | +| seat | 70.4 | 84.89 | +| fence | 52.68 | 70.86 | +| desk | 54.73 | 80.84 | +| rock | 59.1 | 81.14 | +| wardrobe | 56.21 | 75.54 | +| lamp | 74.78 | 87.67 | +| bathtub | 89.17 | 92.46 | +| railing | 41.22 | 59.04 | +| cushion | 70.44 | 80.46 | +| base | 45.85 | 60.35 | +| box | 38.75 | 51.04 | +| column | 53.99 | 67.64 | +| signboard | 40.9 | 50.37 | +| chest of drawers | 46.24 | 65.12 | +| counter | 42.84 | 56.27 | +| sand | 49.65 | 84.6 | +| sink | 78.84 | 84.85 | +| skyscraper | 46.88 | 60.92 | +| fireplace | 73.85 | 94.5 | +| refrigerator | 83.34 | 95.97 | +| grandstand | 49.16 | 82.68 | +| path | 36.15 | 49.43 | +| stairs | 33.58 | 40.41 | +| runway | 71.91 | 92.96 | +| case | 50.16 | 82.56 | +| pool table | 94.69 | 98.59 | +| pillow | 66.53 | 75.92 | +| screen door | 80.44 | 89.34 | +| stairway | 45.28 | 69.85 | +| river | 12.72 | 28.69 | +| bridge | 61.17 | 79.6 | +| bookcase | 41.25 | 58.98 | +| blind | 41.23 | 47.13 | +| coffee table | 61.4 | 86.8 | +| toilet | 90.66 | 94.91 | +| flower | 40.27 | 56.31 | +| book | 55.27 | 79.26 | +| hill | 5.5 | 8.18 | +| bench | 61.92 | 69.15 | +| countertop | 64.74 | 89.69 | +| stove | 86.8 | 91.99 | +| palm | 52.08 | 87.31 | +| kitchen island | 41.78 | 69.98 | +| computer | 78.1 | 91.66 | +| swivel chair | 51.41 | 81.76 | +| boat | 67.11 | 92.11 | +| bar | 63.96 | 85.18 | +| arcade machine | 85.86 | 91.23 | +| hovel | 18.32 | 19.96 | +| bus | 93.91 | 96.99 | +| towel | 76.86 | 90.1 | +| light | 61.73 | 71.33 | +| truck | 49.07 | 72.01 | +| tower | 18.76 | 32.18 | +| chandelier | 72.35 | 82.12 | +| awning | 49.95 | 67.89 | +| streetlight | 36.57 | 50.8 | +| booth | 43.76 | 54.89 | +| television receiver | 78.7 | 90.02 | +| airplane | 89.42 | 95.0 | +| dirt track | 20.98 | 43.45 | +| apparel | 61.38 | 84.53 | +| pole | 28.56 | 40.52 | +| land | 3.56 | 6.57 | +| bannister | 18.27 | 22.5 | +| escalator | 63.21 | 87.39 | +| ottoman | 54.93 | 76.83 | +| bottle | 46.51 | 69.39 | +| buffet | 68.88 | 84.54 | +| poster | 39.24 | 43.14 | +| stage | 21.03 | 44.53 | +| van | 50.78 | 71.29 | +| ship | 78.37 | 82.56 | +| fountain | 30.38 | 34.39 | +| conveyer belt | 80.9 | 97.49 | +| canopy | 46.4 | 60.45 | +| washer | 83.93 | 89.46 | +| plaything | 39.61 | 53.34 | +| swimming pool | 65.01 | 83.61 | +| stool | 60.43 | 74.28 | +| barrel | 77.85 | 89.17 | +| basket | 40.96 | 59.36 | +| waterfall | 52.46 | 74.21 | +| tent | 90.37 | 98.45 | +| bag | 26.13 | 30.24 | +| minibike | 76.32 | 90.69 | +| cradle | 90.28 | 96.85 | +| oven | 56.95 | 70.23 | +| ball | 57.53 | 77.24 | +| food | 52.64 | 57.8 | +| step | 14.46 | 18.22 | +| tank | 59.82 | 66.68 | +| trade name | 30.33 | 37.8 | +| microwave | 87.64 | 96.7 | +| pot | 58.55 | 70.01 | +| animal | 62.68 | 64.61 | +| bicycle | 60.84 | 75.43 | +| lake | 41.15 | 63.74 | +| dishwasher | 75.15 | 83.9 | +| screen | 58.84 | 92.95 | +| blanket | 27.04 | 32.84 | +| sculpture | 67.11 | 82.64 | +| hood | 64.4 | 74.34 | +| sconce | 59.68 | 71.42 | +| vase | 46.27 | 71.14 | +| traffic light | 39.35 | 67.44 | +| tray | 24.45 | 28.94 | +| ashcan | 50.27 | 67.76 | +| fan | 70.49 | 83.16 | +| pier | 39.35 | 47.38 | +| crt screen | 1.86 | 4.78 | +| plate | 61.89 | 82.39 | +| monitor | 22.92 | 27.13 | +| bulletin board | 57.52 | 68.29 | +| shower | 6.63 | 14.91 | +| radiator | 65.69 | 85.29 | +| glass | 21.78 | 23.91 | +| clock | 53.3 | 59.42 | +| flag | 68.91 | 77.86 | ++---------------------+-------+-------+ +2024-06-18 23:14:56,231 - mmseg - INFO - Summary: +2024-06-18 23:14:56,232 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.01 | 57.57 | 71.03 | ++-------+-------+-------+ +2024-06-18 23:14:56,232 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 23:14:56,233 - mmseg - INFO - Iter(val) [250] aAcc: 0.8601, mIoU: 0.5757, mAcc: 0.7103, IoU.wall: 0.8181, IoU.building: 0.8474, IoU.sky: 0.9489, IoU.floor: 0.8533, IoU.tree: 0.7731, IoU.ceiling: 0.8701, IoU.road: 0.8548, IoU.bed : 0.9243, IoU.windowpane: 0.6691, IoU.grass: 0.6660, IoU.cabinet: 0.6710, IoU.sidewalk: 0.7120, IoU.person: 0.8554, IoU.earth: 0.4051, IoU.door: 0.5957, IoU.table: 0.6708, IoU.mountain: 0.6274, IoU.plant: 0.5723, IoU.curtain: 0.7890, IoU.chair: 0.6774, IoU.car: 0.8800, IoU.water: 0.6358, IoU.painting: 0.8038, IoU.sofa: 0.8353, IoU.shelf: 0.4626, IoU.house: 0.4528, IoU.sea: 0.7043, IoU.mirror: 0.8150, IoU.rug: 0.6911, IoU.field: 0.3396, IoU.armchair: 0.6123, IoU.seat: 0.7040, IoU.fence: 0.5268, IoU.desk: 0.5473, IoU.rock: 0.5910, IoU.wardrobe: 0.5621, IoU.lamp: 0.7478, IoU.bathtub: 0.8917, IoU.railing: 0.4122, IoU.cushion: 0.7044, IoU.base: 0.4585, IoU.box: 0.3875, IoU.column: 0.5399, IoU.signboard: 0.4090, IoU.chest of drawers: 0.4624, IoU.counter: 0.4284, IoU.sand: 0.4965, IoU.sink: 0.7884, IoU.skyscraper: 0.4688, IoU.fireplace: 0.7385, IoU.refrigerator: 0.8334, IoU.grandstand: 0.4916, IoU.path: 0.3615, IoU.stairs: 0.3358, IoU.runway: 0.7191, IoU.case: 0.5016, IoU.pool table: 0.9469, IoU.pillow: 0.6653, IoU.screen door: 0.8044, IoU.stairway: 0.4528, IoU.river: 0.1272, IoU.bridge: 0.6117, IoU.bookcase: 0.4125, IoU.blind: 0.4123, IoU.coffee table: 0.6140, IoU.toilet: 0.9066, IoU.flower: 0.4027, IoU.book: 0.5527, IoU.hill: 0.0550, IoU.bench: 0.6192, IoU.countertop: 0.6474, IoU.stove: 0.8680, IoU.palm: 0.5208, IoU.kitchen island: 0.4178, IoU.computer: 0.7810, IoU.swivel chair: 0.5141, IoU.boat: 0.6711, IoU.bar: 0.6396, IoU.arcade machine: 0.8586, IoU.hovel: 0.1832, IoU.bus: 0.9391, IoU.towel: 0.7686, IoU.light: 0.6173, IoU.truck: 0.4907, IoU.tower: 0.1876, IoU.chandelier: 0.7235, IoU.awning: 0.4995, IoU.streetlight: 0.3657, IoU.booth: 0.4376, IoU.television receiver: 0.7870, IoU.airplane: 0.8942, IoU.dirt track: 0.2098, IoU.apparel: 0.6138, IoU.pole: 0.2856, IoU.land: 0.0356, IoU.bannister: 0.1827, IoU.escalator: 0.6321, IoU.ottoman: 0.5493, IoU.bottle: 0.4651, IoU.buffet: 0.6888, IoU.poster: 0.3924, IoU.stage: 0.2103, IoU.van: 0.5078, IoU.ship: 0.7837, IoU.fountain: 0.3038, IoU.conveyer belt: 0.8090, IoU.canopy: 0.4640, IoU.washer: 0.8393, IoU.plaything: 0.3961, IoU.swimming pool: 0.6501, IoU.stool: 0.6043, IoU.barrel: 0.7785, IoU.basket: 0.4096, IoU.waterfall: 0.5246, IoU.tent: 0.9037, IoU.bag: 0.2613, IoU.minibike: 0.7632, IoU.cradle: 0.9028, IoU.oven: 0.5695, IoU.ball: 0.5753, IoU.food: 0.5264, IoU.step: 0.1446, IoU.tank: 0.5982, IoU.trade name: 0.3033, IoU.microwave: 0.8764, IoU.pot: 0.5855, IoU.animal: 0.6268, IoU.bicycle: 0.6084, IoU.lake: 0.4115, IoU.dishwasher: 0.7515, IoU.screen: 0.5884, IoU.blanket: 0.2704, IoU.sculpture: 0.6711, IoU.hood: 0.6440, IoU.sconce: 0.5968, IoU.vase: 0.4627, IoU.traffic light: 0.3935, IoU.tray: 0.2445, IoU.ashcan: 0.5027, IoU.fan: 0.7049, IoU.pier: 0.3935, IoU.crt screen: 0.0186, IoU.plate: 0.6189, IoU.monitor: 0.2292, IoU.bulletin board: 0.5752, IoU.shower: 0.0663, IoU.radiator: 0.6569, IoU.glass: 0.2178, IoU.clock: 0.5330, IoU.flag: 0.6891, Acc.wall: 0.8916, Acc.building: 0.9446, Acc.sky: 0.9780, Acc.floor: 0.9160, Acc.tree: 0.8708, Acc.ceiling: 0.9283, Acc.road: 0.8864, Acc.bed : 0.9758, Acc.windowpane: 0.8109, Acc.grass: 0.7685, Acc.cabinet: 0.7855, Acc.sidewalk: 0.8686, Acc.person: 0.9502, Acc.earth: 0.5797, Acc.door: 0.7454, Acc.table: 0.8057, Acc.mountain: 0.7316, Acc.plant: 0.7033, Acc.curtain: 0.8993, Acc.chair: 0.7852, Acc.car: 0.9347, Acc.water: 0.7872, Acc.painting: 0.9217, Acc.sofa: 0.9353, Acc.shelf: 0.5860, Acc.house: 0.5449, Acc.sea: 0.8031, Acc.mirror: 0.8815, Acc.rug: 0.7544, Acc.field: 0.6068, Acc.armchair: 0.8147, Acc.seat: 0.8489, Acc.fence: 0.7086, Acc.desk: 0.8084, Acc.rock: 0.8114, Acc.wardrobe: 0.7554, Acc.lamp: 0.8767, Acc.bathtub: 0.9246, Acc.railing: 0.5904, Acc.cushion: 0.8046, Acc.base: 0.6035, Acc.box: 0.5104, Acc.column: 0.6764, Acc.signboard: 0.5037, Acc.chest of drawers: 0.6512, Acc.counter: 0.5627, Acc.sand: 0.8460, Acc.sink: 0.8485, Acc.skyscraper: 0.6092, Acc.fireplace: 0.9450, Acc.refrigerator: 0.9597, Acc.grandstand: 0.8268, Acc.path: 0.4943, Acc.stairs: 0.4041, Acc.runway: 0.9296, Acc.case: 0.8256, Acc.pool table: 0.9859, Acc.pillow: 0.7592, Acc.screen door: 0.8934, Acc.stairway: 0.6985, Acc.river: 0.2869, Acc.bridge: 0.7960, Acc.bookcase: 0.5898, Acc.blind: 0.4713, Acc.coffee table: 0.8680, Acc.toilet: 0.9491, Acc.flower: 0.5631, Acc.book: 0.7926, Acc.hill: 0.0818, Acc.bench: 0.6915, Acc.countertop: 0.8969, Acc.stove: 0.9199, Acc.palm: 0.8731, Acc.kitchen island: 0.6998, Acc.computer: 0.9166, Acc.swivel chair: 0.8176, Acc.boat: 0.9211, Acc.bar: 0.8518, Acc.arcade machine: 0.9123, Acc.hovel: 0.1996, Acc.bus: 0.9699, Acc.towel: 0.9010, Acc.light: 0.7133, Acc.truck: 0.7201, Acc.tower: 0.3218, Acc.chandelier: 0.8212, Acc.awning: 0.6789, Acc.streetlight: 0.5080, Acc.booth: 0.5489, Acc.television receiver: 0.9002, Acc.airplane: 0.9500, Acc.dirt track: 0.4345, Acc.apparel: 0.8453, Acc.pole: 0.4052, Acc.land: 0.0657, Acc.bannister: 0.2250, Acc.escalator: 0.8739, Acc.ottoman: 0.7683, Acc.bottle: 0.6939, Acc.buffet: 0.8454, Acc.poster: 0.4314, Acc.stage: 0.4453, Acc.van: 0.7129, Acc.ship: 0.8256, Acc.fountain: 0.3439, Acc.conveyer belt: 0.9749, Acc.canopy: 0.6045, Acc.washer: 0.8946, Acc.plaything: 0.5334, Acc.swimming pool: 0.8361, Acc.stool: 0.7428, Acc.barrel: 0.8917, Acc.basket: 0.5936, Acc.waterfall: 0.7421, Acc.tent: 0.9845, Acc.bag: 0.3024, Acc.minibike: 0.9069, Acc.cradle: 0.9685, Acc.oven: 0.7023, Acc.ball: 0.7724, Acc.food: 0.5780, Acc.step: 0.1822, Acc.tank: 0.6668, Acc.trade name: 0.3780, Acc.microwave: 0.9670, Acc.pot: 0.7001, Acc.animal: 0.6461, Acc.bicycle: 0.7543, Acc.lake: 0.6374, Acc.dishwasher: 0.8390, Acc.screen: 0.9295, Acc.blanket: 0.3284, Acc.sculpture: 0.8264, Acc.hood: 0.7434, Acc.sconce: 0.7142, Acc.vase: 0.7114, Acc.traffic light: 0.6744, Acc.tray: 0.2894, Acc.ashcan: 0.6776, Acc.fan: 0.8316, Acc.pier: 0.4738, Acc.crt screen: 0.0478, Acc.plate: 0.8239, Acc.monitor: 0.2713, Acc.bulletin board: 0.6829, Acc.shower: 0.1491, Acc.radiator: 0.8529, Acc.glass: 0.2391, Acc.clock: 0.5942, Acc.flag: 0.7786 +2024-06-18 23:16:35,413 - mmseg - INFO - Iter [35050/80000] lr: 2.248e-05, eta: 1 day, 2:39:44, time: 4.210, data_time: 2.243, memory: 72263, decode.loss_ce: 0.2075, decode.acc_seg: 91.3589, aux.loss_ce: 0.0861, aux.acc_seg: 91.1135, loss: 0.2936 +2024-06-18 23:18:14,305 - mmseg - INFO - Iter [35100/80000] lr: 2.245e-05, eta: 1 day, 2:37:47, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2156, decode.acc_seg: 90.9059, aux.loss_ce: 0.0896, aux.acc_seg: 90.5506, loss: 0.3051 +2024-06-18 23:19:53,197 - mmseg - INFO - Iter [35150/80000] lr: 2.243e-05, eta: 1 day, 2:35:50, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2004, decode.acc_seg: 91.7166, aux.loss_ce: 0.0830, aux.acc_seg: 91.4186, loss: 0.2834 +2024-06-18 23:21:32,105 - mmseg - INFO - Iter [35200/80000] lr: 2.240e-05, eta: 1 day, 2:33:53, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1947, decode.acc_seg: 91.9626, aux.loss_ce: 0.0812, aux.acc_seg: 91.5475, loss: 0.2758 +2024-06-18 23:23:10,927 - mmseg - INFO - Iter [35250/80000] lr: 2.238e-05, eta: 1 day, 2:31:57, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2078, decode.acc_seg: 91.0994, aux.loss_ce: 0.0858, aux.acc_seg: 90.7736, loss: 0.2936 +2024-06-18 23:24:49,945 - mmseg - INFO - Iter [35300/80000] lr: 2.235e-05, eta: 1 day, 2:30:00, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2246, decode.acc_seg: 90.6090, aux.loss_ce: 0.0933, aux.acc_seg: 90.2660, loss: 0.3178 +2024-06-18 23:26:28,748 - mmseg - INFO - Iter [35350/80000] lr: 2.233e-05, eta: 1 day, 2:28:03, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2124, decode.acc_seg: 91.4457, aux.loss_ce: 0.0878, aux.acc_seg: 91.0958, loss: 0.3002 +2024-06-18 23:28:10,269 - mmseg - INFO - Iter [35400/80000] lr: 2.230e-05, eta: 1 day, 2:26:10, time: 2.030, data_time: 0.062, memory: 72263, decode.loss_ce: 0.2143, decode.acc_seg: 91.0902, aux.loss_ce: 0.0890, aux.acc_seg: 90.7486, loss: 0.3032 +2024-06-18 23:29:49,178 - mmseg - INFO - Iter [35450/80000] lr: 2.228e-05, eta: 1 day, 2:24:14, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2001, decode.acc_seg: 91.3487, aux.loss_ce: 0.0823, aux.acc_seg: 91.0543, loss: 0.2824 +2024-06-18 23:31:27,994 - mmseg - INFO - Iter [35500/80000] lr: 2.225e-05, eta: 1 day, 2:22:17, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1994, decode.acc_seg: 91.5835, aux.loss_ce: 0.0828, aux.acc_seg: 91.2389, loss: 0.2822 +2024-06-18 23:33:06,942 - mmseg - INFO - Iter [35550/80000] lr: 2.223e-05, eta: 1 day, 2:20:21, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1941, decode.acc_seg: 91.6533, aux.loss_ce: 0.0804, aux.acc_seg: 91.3601, loss: 0.2745 +2024-06-18 23:34:45,766 - mmseg - INFO - Iter [35600/80000] lr: 2.220e-05, eta: 1 day, 2:18:24, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2032, decode.acc_seg: 91.5888, aux.loss_ce: 0.0839, aux.acc_seg: 91.3521, loss: 0.2871 +2024-06-18 23:36:24,903 - mmseg - INFO - Iter [35650/80000] lr: 2.218e-05, eta: 1 day, 2:16:28, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1969, decode.acc_seg: 91.9510, aux.loss_ce: 0.0817, aux.acc_seg: 91.6141, loss: 0.2785 +2024-06-18 23:38:03,791 - mmseg - INFO - Iter [35700/80000] lr: 2.215e-05, eta: 1 day, 2:14:32, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2080, decode.acc_seg: 91.2394, aux.loss_ce: 0.0869, aux.acc_seg: 90.8167, loss: 0.2949 +2024-06-18 23:39:42,739 - mmseg - INFO - Iter [35750/80000] lr: 2.213e-05, eta: 1 day, 2:12:36, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2147, decode.acc_seg: 90.8083, aux.loss_ce: 0.0896, aux.acc_seg: 90.4796, loss: 0.3044 +2024-06-18 23:41:21,668 - mmseg - INFO - Iter [35800/80000] lr: 2.210e-05, eta: 1 day, 2:10:40, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1997, decode.acc_seg: 91.4308, aux.loss_ce: 0.0834, aux.acc_seg: 91.0873, loss: 0.2831 +2024-06-18 23:43:00,675 - mmseg - INFO - Iter [35850/80000] lr: 2.208e-05, eta: 1 day, 2:08:44, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1963, decode.acc_seg: 91.4766, aux.loss_ce: 0.0819, aux.acc_seg: 91.1415, loss: 0.2782 +2024-06-18 23:44:39,646 - mmseg - INFO - Iter [35900/80000] lr: 2.205e-05, eta: 1 day, 2:06:48, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2056, decode.acc_seg: 91.3964, aux.loss_ce: 0.0853, aux.acc_seg: 91.0909, loss: 0.2908 +2024-06-18 23:46:18,443 - mmseg - INFO - Iter [35950/80000] lr: 2.203e-05, eta: 1 day, 2:04:52, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2019, decode.acc_seg: 91.7773, aux.loss_ce: 0.0837, aux.acc_seg: 91.4348, loss: 0.2856 +2024-06-18 23:47:57,333 - mmseg - INFO - Saving checkpoint at 36000 iterations +2024-06-18 23:49:24,037 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 23:49:24,037 - mmseg - INFO - Iter [36000/80000] lr: 2.200e-05, eta: 1 day, 2:04:42, time: 3.712, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1990, decode.acc_seg: 91.6008, aux.loss_ce: 0.0828, aux.acc_seg: 91.2641, loss: 0.2818 +2024-06-18 23:51:15,450 - mmseg - INFO - per class results: +2024-06-18 23:51:15,457 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.09 | 89.04 | +| building | 84.87 | 93.07 | +| sky | 95.0 | 97.22 | +| floor | 85.21 | 91.82 | +| tree | 77.41 | 89.89 | +| ceiling | 86.78 | 93.55 | +| road | 84.53 | 91.28 | +| bed | 92.82 | 96.5 | +| windowpane | 66.5 | 78.65 | +| grass | 67.52 | 83.41 | +| cabinet | 67.49 | 75.47 | +| sidewalk | 68.31 | 83.22 | +| person | 86.15 | 94.09 | +| earth | 37.71 | 49.25 | +| door | 60.68 | 75.96 | +| table | 67.81 | 80.47 | +| mountain | 61.1 | 70.95 | +| plant | 55.53 | 66.29 | +| curtain | 77.62 | 91.62 | +| chair | 65.86 | 74.75 | +| car | 87.84 | 93.86 | +| water | 55.46 | 67.9 | +| painting | 77.0 | 91.06 | +| sofa | 81.33 | 90.93 | +| shelf | 49.68 | 68.54 | +| house | 54.99 | 77.16 | +| sea | 67.86 | 87.4 | +| mirror | 78.94 | 90.22 | +| rug | 70.01 | 78.67 | +| field | 29.74 | 50.94 | +| armchair | 60.43 | 83.84 | +| seat | 70.11 | 90.03 | +| fence | 51.83 | 68.57 | +| desk | 58.98 | 78.75 | +| rock | 56.48 | 87.62 | +| wardrobe | 53.23 | 71.13 | +| lamp | 75.46 | 86.69 | +| bathtub | 87.94 | 89.89 | +| railing | 42.88 | 62.68 | +| cushion | 70.69 | 80.84 | +| base | 41.87 | 53.58 | +| box | 38.17 | 51.0 | +| column | 56.49 | 73.54 | +| signboard | 41.13 | 56.7 | +| chest of drawers | 44.16 | 81.58 | +| counter | 49.43 | 67.64 | +| sand | 46.99 | 75.38 | +| sink | 80.93 | 85.74 | +| skyscraper | 44.32 | 58.93 | +| fireplace | 75.02 | 89.97 | +| refrigerator | 84.07 | 94.69 | +| grandstand | 52.03 | 84.48 | +| path | 28.6 | 37.84 | +| stairs | 36.1 | 48.76 | +| runway | 67.23 | 84.23 | +| case | 57.85 | 75.98 | +| pool table | 93.94 | 98.63 | +| pillow | 69.63 | 83.81 | +| screen door | 78.01 | 83.87 | +| stairway | 46.91 | 66.19 | +| river | 15.7 | 40.25 | +| bridge | 77.42 | 88.11 | +| bookcase | 43.97 | 59.35 | +| blind | 54.04 | 71.01 | +| coffee table | 60.94 | 81.95 | +| toilet | 90.45 | 93.6 | +| flower | 41.17 | 55.95 | +| book | 56.29 | 79.46 | +| hill | 9.47 | 17.85 | +| bench | 66.53 | 77.87 | +| countertop | 64.14 | 81.69 | +| stove | 87.18 | 92.54 | +| palm | 51.88 | 85.58 | +| kitchen island | 42.55 | 84.75 | +| computer | 75.92 | 92.48 | +| swivel chair | 50.2 | 75.31 | +| boat | 79.77 | 92.97 | +| bar | 64.69 | 86.77 | +| arcade machine | 84.83 | 88.73 | +| hovel | 20.77 | 22.07 | +| bus | 93.82 | 97.15 | +| towel | 80.33 | 89.73 | +| light | 61.85 | 71.2 | +| truck | 45.31 | 65.85 | +| tower | 24.39 | 42.81 | +| chandelier | 73.31 | 88.49 | +| awning | 41.96 | 52.59 | +| streetlight | 36.67 | 49.52 | +| booth | 52.61 | 70.55 | +| television receiver | 75.09 | 90.1 | +| airplane | 87.49 | 97.48 | +| dirt track | 0.67 | 0.85 | +| apparel | 62.03 | 80.75 | +| pole | 26.07 | 35.05 | +| land | 4.38 | 8.34 | +| bannister | 20.31 | 25.36 | +| escalator | 65.48 | 85.18 | +| ottoman | 60.6 | 79.71 | +| bottle | 43.67 | 67.67 | +| buffet | 49.76 | 55.31 | +| poster | 36.73 | 45.59 | +| stage | 18.3 | 31.29 | +| van | 51.07 | 76.33 | +| ship | 59.15 | 61.85 | +| fountain | 23.53 | 24.75 | +| conveyer belt | 84.59 | 95.59 | +| canopy | 55.82 | 68.29 | +| washer | 82.02 | 87.1 | +| plaything | 35.14 | 52.33 | +| swimming pool | 55.94 | 82.29 | +| stool | 55.51 | 72.39 | +| barrel | 75.58 | 89.87 | +| basket | 41.93 | 62.85 | +| waterfall | 58.14 | 69.33 | +| tent | 91.23 | 98.71 | +| bag | 24.56 | 27.6 | +| minibike | 73.87 | 90.93 | +| cradle | 83.91 | 97.73 | +| oven | 60.82 | 75.96 | +| ball | 56.84 | 62.86 | +| food | 59.07 | 74.77 | +| step | 20.67 | 24.54 | +| tank | 85.24 | 94.07 | +| trade name | 22.64 | 25.32 | +| microwave | 89.34 | 96.6 | +| pot | 57.95 | 66.62 | +| animal | 61.24 | 62.95 | +| bicycle | 62.43 | 79.08 | +| lake | 1.82 | 1.87 | +| dishwasher | 67.97 | 83.61 | +| screen | 62.23 | 95.26 | +| blanket | 27.79 | 32.24 | +| sculpture | 72.75 | 87.12 | +| hood | 78.06 | 91.79 | +| sconce | 57.68 | 72.83 | +| vase | 49.02 | 60.79 | +| traffic light | 36.86 | 70.42 | +| tray | 22.31 | 26.02 | +| ashcan | 50.86 | 64.9 | +| fan | 67.64 | 74.01 | +| pier | 40.26 | 43.66 | +| crt screen | 6.45 | 14.24 | +| plate | 64.55 | 75.57 | +| monitor | 25.97 | 31.6 | +| bulletin board | 49.34 | 66.19 | +| shower | 2.74 | 13.38 | +| radiator | 67.74 | 81.61 | +| glass | 20.74 | 21.83 | +| clock | 53.0 | 59.77 | +| flag | 70.91 | 78.53 | ++---------------------+-------+-------+ +2024-06-18 23:51:15,457 - mmseg - INFO - Summary: +2024-06-18 23:51:15,457 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.92 | 57.39 | 70.55 | ++-------+-------+-------+ +2024-06-18 23:51:15,458 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 23:51:15,458 - mmseg - INFO - Iter(val) [250] aAcc: 0.8592, mIoU: 0.5739, mAcc: 0.7055, IoU.wall: 0.8209, IoU.building: 0.8487, IoU.sky: 0.9500, IoU.floor: 0.8521, IoU.tree: 0.7741, IoU.ceiling: 0.8678, IoU.road: 0.8453, IoU.bed : 0.9282, IoU.windowpane: 0.6650, IoU.grass: 0.6752, IoU.cabinet: 0.6749, IoU.sidewalk: 0.6831, IoU.person: 0.8615, IoU.earth: 0.3771, IoU.door: 0.6068, IoU.table: 0.6781, IoU.mountain: 0.6110, IoU.plant: 0.5553, IoU.curtain: 0.7762, IoU.chair: 0.6586, IoU.car: 0.8784, IoU.water: 0.5546, IoU.painting: 0.7700, IoU.sofa: 0.8133, IoU.shelf: 0.4968, IoU.house: 0.5499, IoU.sea: 0.6786, IoU.mirror: 0.7894, IoU.rug: 0.7001, IoU.field: 0.2974, IoU.armchair: 0.6043, IoU.seat: 0.7011, IoU.fence: 0.5183, IoU.desk: 0.5898, IoU.rock: 0.5648, IoU.wardrobe: 0.5323, IoU.lamp: 0.7546, IoU.bathtub: 0.8794, IoU.railing: 0.4288, IoU.cushion: 0.7069, IoU.base: 0.4187, IoU.box: 0.3817, IoU.column: 0.5649, IoU.signboard: 0.4113, IoU.chest of drawers: 0.4416, IoU.counter: 0.4943, IoU.sand: 0.4699, IoU.sink: 0.8093, IoU.skyscraper: 0.4432, IoU.fireplace: 0.7502, IoU.refrigerator: 0.8407, IoU.grandstand: 0.5203, IoU.path: 0.2860, IoU.stairs: 0.3610, IoU.runway: 0.6723, IoU.case: 0.5785, IoU.pool table: 0.9394, IoU.pillow: 0.6963, IoU.screen door: 0.7801, IoU.stairway: 0.4691, IoU.river: 0.1570, IoU.bridge: 0.7742, IoU.bookcase: 0.4397, IoU.blind: 0.5404, IoU.coffee table: 0.6094, IoU.toilet: 0.9045, IoU.flower: 0.4117, IoU.book: 0.5629, IoU.hill: 0.0947, IoU.bench: 0.6653, IoU.countertop: 0.6414, IoU.stove: 0.8718, IoU.palm: 0.5188, IoU.kitchen island: 0.4255, IoU.computer: 0.7592, IoU.swivel chair: 0.5020, IoU.boat: 0.7977, IoU.bar: 0.6469, IoU.arcade machine: 0.8483, IoU.hovel: 0.2077, IoU.bus: 0.9382, IoU.towel: 0.8033, IoU.light: 0.6185, IoU.truck: 0.4531, IoU.tower: 0.2439, IoU.chandelier: 0.7331, IoU.awning: 0.4196, IoU.streetlight: 0.3667, IoU.booth: 0.5261, IoU.television receiver: 0.7509, IoU.airplane: 0.8749, IoU.dirt track: 0.0067, IoU.apparel: 0.6203, IoU.pole: 0.2607, IoU.land: 0.0438, IoU.bannister: 0.2031, IoU.escalator: 0.6548, IoU.ottoman: 0.6060, IoU.bottle: 0.4367, IoU.buffet: 0.4976, IoU.poster: 0.3673, IoU.stage: 0.1830, IoU.van: 0.5107, IoU.ship: 0.5915, IoU.fountain: 0.2353, IoU.conveyer belt: 0.8459, IoU.canopy: 0.5582, IoU.washer: 0.8202, IoU.plaything: 0.3514, IoU.swimming pool: 0.5594, IoU.stool: 0.5551, IoU.barrel: 0.7558, IoU.basket: 0.4193, IoU.waterfall: 0.5814, IoU.tent: 0.9123, IoU.bag: 0.2456, IoU.minibike: 0.7387, IoU.cradle: 0.8391, IoU.oven: 0.6082, IoU.ball: 0.5684, IoU.food: 0.5907, IoU.step: 0.2067, IoU.tank: 0.8524, IoU.trade name: 0.2264, IoU.microwave: 0.8934, IoU.pot: 0.5795, IoU.animal: 0.6124, IoU.bicycle: 0.6243, IoU.lake: 0.0182, IoU.dishwasher: 0.6797, IoU.screen: 0.6223, IoU.blanket: 0.2779, IoU.sculpture: 0.7275, IoU.hood: 0.7806, IoU.sconce: 0.5768, IoU.vase: 0.4902, IoU.traffic light: 0.3686, IoU.tray: 0.2231, IoU.ashcan: 0.5086, IoU.fan: 0.6764, IoU.pier: 0.4026, IoU.crt screen: 0.0645, IoU.plate: 0.6455, IoU.monitor: 0.2597, IoU.bulletin board: 0.4934, IoU.shower: 0.0274, IoU.radiator: 0.6774, IoU.glass: 0.2074, IoU.clock: 0.5300, IoU.flag: 0.7091, Acc.wall: 0.8904, Acc.building: 0.9307, Acc.sky: 0.9722, Acc.floor: 0.9182, Acc.tree: 0.8989, Acc.ceiling: 0.9355, Acc.road: 0.9128, Acc.bed : 0.9650, Acc.windowpane: 0.7865, Acc.grass: 0.8341, Acc.cabinet: 0.7547, Acc.sidewalk: 0.8322, Acc.person: 0.9409, Acc.earth: 0.4925, Acc.door: 0.7596, Acc.table: 0.8047, Acc.mountain: 0.7095, Acc.plant: 0.6629, Acc.curtain: 0.9162, Acc.chair: 0.7475, Acc.car: 0.9386, Acc.water: 0.6790, Acc.painting: 0.9106, Acc.sofa: 0.9093, Acc.shelf: 0.6854, Acc.house: 0.7716, Acc.sea: 0.8740, Acc.mirror: 0.9022, Acc.rug: 0.7867, Acc.field: 0.5094, Acc.armchair: 0.8384, Acc.seat: 0.9003, Acc.fence: 0.6857, Acc.desk: 0.7875, Acc.rock: 0.8762, Acc.wardrobe: 0.7113, Acc.lamp: 0.8669, Acc.bathtub: 0.8989, Acc.railing: 0.6268, Acc.cushion: 0.8084, Acc.base: 0.5358, Acc.box: 0.5100, Acc.column: 0.7354, Acc.signboard: 0.5670, Acc.chest of drawers: 0.8158, Acc.counter: 0.6764, Acc.sand: 0.7538, Acc.sink: 0.8574, Acc.skyscraper: 0.5893, Acc.fireplace: 0.8997, Acc.refrigerator: 0.9469, Acc.grandstand: 0.8448, Acc.path: 0.3784, Acc.stairs: 0.4876, Acc.runway: 0.8423, Acc.case: 0.7598, Acc.pool table: 0.9863, Acc.pillow: 0.8381, Acc.screen door: 0.8387, Acc.stairway: 0.6619, Acc.river: 0.4025, Acc.bridge: 0.8811, Acc.bookcase: 0.5935, Acc.blind: 0.7101, Acc.coffee table: 0.8195, Acc.toilet: 0.9360, Acc.flower: 0.5595, Acc.book: 0.7946, Acc.hill: 0.1785, Acc.bench: 0.7787, Acc.countertop: 0.8169, Acc.stove: 0.9254, Acc.palm: 0.8558, Acc.kitchen island: 0.8475, Acc.computer: 0.9248, Acc.swivel chair: 0.7531, Acc.boat: 0.9297, Acc.bar: 0.8677, Acc.arcade machine: 0.8873, Acc.hovel: 0.2207, Acc.bus: 0.9715, Acc.towel: 0.8973, Acc.light: 0.7120, Acc.truck: 0.6585, Acc.tower: 0.4281, Acc.chandelier: 0.8849, Acc.awning: 0.5259, Acc.streetlight: 0.4952, Acc.booth: 0.7055, Acc.television receiver: 0.9010, Acc.airplane: 0.9748, Acc.dirt track: 0.0085, Acc.apparel: 0.8075, Acc.pole: 0.3505, Acc.land: 0.0834, Acc.bannister: 0.2536, Acc.escalator: 0.8518, Acc.ottoman: 0.7971, Acc.bottle: 0.6767, Acc.buffet: 0.5531, Acc.poster: 0.4559, Acc.stage: 0.3129, Acc.van: 0.7633, Acc.ship: 0.6185, Acc.fountain: 0.2475, Acc.conveyer belt: 0.9559, Acc.canopy: 0.6829, Acc.washer: 0.8710, Acc.plaything: 0.5233, Acc.swimming pool: 0.8229, Acc.stool: 0.7239, Acc.barrel: 0.8987, Acc.basket: 0.6285, Acc.waterfall: 0.6933, Acc.tent: 0.9871, Acc.bag: 0.2760, Acc.minibike: 0.9093, Acc.cradle: 0.9773, Acc.oven: 0.7596, Acc.ball: 0.6286, Acc.food: 0.7477, Acc.step: 0.2454, Acc.tank: 0.9407, Acc.trade name: 0.2532, Acc.microwave: 0.9660, Acc.pot: 0.6662, Acc.animal: 0.6295, Acc.bicycle: 0.7908, Acc.lake: 0.0187, Acc.dishwasher: 0.8361, Acc.screen: 0.9526, Acc.blanket: 0.3224, Acc.sculpture: 0.8712, Acc.hood: 0.9179, Acc.sconce: 0.7283, Acc.vase: 0.6079, Acc.traffic light: 0.7042, Acc.tray: 0.2602, Acc.ashcan: 0.6490, Acc.fan: 0.7401, Acc.pier: 0.4366, Acc.crt screen: 0.1424, Acc.plate: 0.7557, Acc.monitor: 0.3160, Acc.bulletin board: 0.6619, Acc.shower: 0.1338, Acc.radiator: 0.8161, Acc.glass: 0.2183, Acc.clock: 0.5977, Acc.flag: 0.7853 +2024-06-18 23:52:54,619 - mmseg - INFO - Iter [36050/80000] lr: 2.198e-05, eta: 1 day, 2:05:02, time: 4.212, data_time: 2.245, memory: 72263, decode.loss_ce: 0.2231, decode.acc_seg: 91.2867, aux.loss_ce: 0.0918, aux.acc_seg: 90.9889, loss: 0.3149 +2024-06-18 23:54:33,481 - mmseg - INFO - Iter [36100/80000] lr: 2.195e-05, eta: 1 day, 2:03:05, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2204, decode.acc_seg: 91.0990, aux.loss_ce: 0.0907, aux.acc_seg: 90.8150, loss: 0.3111 +2024-06-18 23:56:12,447 - mmseg - INFO - Iter [36150/80000] lr: 2.193e-05, eta: 1 day, 2:01:09, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1908, decode.acc_seg: 91.8657, aux.loss_ce: 0.0793, aux.acc_seg: 91.5330, loss: 0.2701 +2024-06-18 23:57:51,326 - mmseg - INFO - Iter [36200/80000] lr: 2.190e-05, eta: 1 day, 1:59:13, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1950, decode.acc_seg: 91.7404, aux.loss_ce: 0.0810, aux.acc_seg: 91.4376, loss: 0.2760 +2024-06-18 23:59:30,180 - mmseg - INFO - Iter [36250/80000] lr: 2.188e-05, eta: 1 day, 1:57:16, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2145, decode.acc_seg: 91.0026, aux.loss_ce: 0.0889, aux.acc_seg: 90.6797, loss: 0.3034 +2024-06-19 00:01:08,993 - mmseg - INFO - Iter [36300/80000] lr: 2.185e-05, eta: 1 day, 1:55:20, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1967, decode.acc_seg: 91.5020, aux.loss_ce: 0.0814, aux.acc_seg: 91.1766, loss: 0.2781 +2024-06-19 00:02:47,860 - mmseg - INFO - Iter [36350/80000] lr: 2.183e-05, eta: 1 day, 1:53:24, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2207, decode.acc_seg: 90.6415, aux.loss_ce: 0.0913, aux.acc_seg: 90.4302, loss: 0.3119 +2024-06-19 00:04:26,704 - mmseg - INFO - Iter [36400/80000] lr: 2.180e-05, eta: 1 day, 1:51:27, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2108, decode.acc_seg: 91.2397, aux.loss_ce: 0.0871, aux.acc_seg: 90.9428, loss: 0.2979 +2024-06-19 00:06:05,581 - mmseg - INFO - Iter [36450/80000] lr: 2.178e-05, eta: 1 day, 1:49:31, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2002, decode.acc_seg: 91.6584, aux.loss_ce: 0.0831, aux.acc_seg: 91.3699, loss: 0.2833 +2024-06-19 00:07:44,410 - mmseg - INFO - Iter [36500/80000] lr: 2.175e-05, eta: 1 day, 1:47:35, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2066, decode.acc_seg: 91.3772, aux.loss_ce: 0.0857, aux.acc_seg: 90.9819, loss: 0.2923 +2024-06-19 00:09:23,233 - mmseg - INFO - Iter [36550/80000] lr: 2.173e-05, eta: 1 day, 1:45:39, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2097, decode.acc_seg: 91.0895, aux.loss_ce: 0.0862, aux.acc_seg: 90.8720, loss: 0.2959 +2024-06-19 00:11:02,064 - mmseg - INFO - Iter [36600/80000] lr: 2.170e-05, eta: 1 day, 1:43:43, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2096, decode.acc_seg: 91.5677, aux.loss_ce: 0.0869, aux.acc_seg: 91.2965, loss: 0.2965 +2024-06-19 00:12:42,997 - mmseg - INFO - Iter [36650/80000] lr: 2.168e-05, eta: 1 day, 1:41:49, time: 2.019, data_time: 0.053, memory: 72263, decode.loss_ce: 0.1986, decode.acc_seg: 91.4185, aux.loss_ce: 0.0823, aux.acc_seg: 91.1544, loss: 0.2808 +2024-06-19 00:14:21,786 - mmseg - INFO - Iter [36700/80000] lr: 2.165e-05, eta: 1 day, 1:39:53, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1925, decode.acc_seg: 91.6653, aux.loss_ce: 0.0807, aux.acc_seg: 91.3006, loss: 0.2732 +2024-06-19 00:16:00,627 - mmseg - INFO - Iter [36750/80000] lr: 2.163e-05, eta: 1 day, 1:37:57, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1937, decode.acc_seg: 91.9799, aux.loss_ce: 0.0798, aux.acc_seg: 91.6931, loss: 0.2734 +2024-06-19 00:17:39,528 - mmseg - INFO - Iter [36800/80000] lr: 2.160e-05, eta: 1 day, 1:36:01, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2072, decode.acc_seg: 91.0210, aux.loss_ce: 0.0852, aux.acc_seg: 90.8200, loss: 0.2923 +2024-06-19 00:19:18,398 - mmseg - INFO - Iter [36850/80000] lr: 2.158e-05, eta: 1 day, 1:34:06, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2042, decode.acc_seg: 91.4450, aux.loss_ce: 0.0850, aux.acc_seg: 91.1449, loss: 0.2892 +2024-06-19 00:20:57,349 - mmseg - INFO - Iter [36900/80000] lr: 2.155e-05, eta: 1 day, 1:32:10, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1983, decode.acc_seg: 91.4188, aux.loss_ce: 0.0824, aux.acc_seg: 91.0738, loss: 0.2807 +2024-06-19 00:22:36,196 - mmseg - INFO - Iter [36950/80000] lr: 2.153e-05, eta: 1 day, 1:30:14, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2024, decode.acc_seg: 91.6383, aux.loss_ce: 0.0849, aux.acc_seg: 91.2239, loss: 0.2872 +2024-06-19 00:24:15,129 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 00:24:15,129 - mmseg - INFO - Iter [37000/80000] lr: 2.150e-05, eta: 1 day, 1:28:19, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2025, decode.acc_seg: 91.4473, aux.loss_ce: 0.0841, aux.acc_seg: 91.1420, loss: 0.2866 +2024-06-19 00:26:05,043 - mmseg - INFO - per class results: +2024-06-19 00:26:05,049 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.48 | 90.34 | +| building | 84.93 | 91.3 | +| sky | 94.84 | 97.95 | +| floor | 85.5 | 92.28 | +| tree | 77.67 | 88.93 | +| ceiling | 87.59 | 93.8 | +| road | 86.25 | 89.16 | +| bed | 92.75 | 97.41 | +| windowpane | 67.25 | 80.99 | +| grass | 68.96 | 82.84 | +| cabinet | 67.96 | 76.62 | +| sidewalk | 71.19 | 89.75 | +| person | 85.73 | 92.8 | +| earth | 42.58 | 60.63 | +| door | 58.91 | 72.18 | +| table | 68.25 | 80.27 | +| mountain | 61.54 | 70.4 | +| plant | 55.9 | 63.85 | +| curtain | 79.29 | 88.15 | +| chair | 67.87 | 79.78 | +| car | 88.28 | 93.53 | +| water | 59.11 | 70.99 | +| painting | 81.47 | 91.06 | +| sofa | 81.01 | 88.76 | +| shelf | 49.01 | 63.65 | +| house | 53.0 | 84.93 | +| sea | 65.08 | 84.51 | +| mirror | 80.22 | 85.52 | +| rug | 67.1 | 71.57 | +| field | 31.5 | 55.91 | +| armchair | 60.3 | 74.25 | +| seat | 66.6 | 90.14 | +| fence | 50.7 | 69.2 | +| desk | 58.8 | 74.49 | +| rock | 55.94 | 83.45 | +| wardrobe | 55.41 | 79.29 | +| lamp | 75.55 | 86.83 | +| bathtub | 88.48 | 92.15 | +| railing | 41.61 | 58.88 | +| cushion | 67.48 | 87.03 | +| base | 40.99 | 68.88 | +| box | 38.24 | 50.96 | +| column | 57.59 | 73.44 | +| signboard | 42.89 | 59.96 | +| chest of drawers | 45.26 | 69.9 | +| counter | 38.49 | 49.33 | +| sand | 49.98 | 79.15 | +| sink | 83.81 | 88.24 | +| skyscraper | 49.37 | 62.28 | +| fireplace | 74.48 | 92.36 | +| refrigerator | 83.13 | 91.67 | +| grandstand | 43.6 | 87.18 | +| path | 35.33 | 47.46 | +| stairs | 28.87 | 44.48 | +| runway | 73.13 | 93.77 | +| case | 62.72 | 74.56 | +| pool table | 95.12 | 97.86 | +| pillow | 63.76 | 70.99 | +| screen door | 78.79 | 85.23 | +| stairway | 29.02 | 36.37 | +| river | 18.03 | 36.35 | +| bridge | 77.02 | 87.54 | +| bookcase | 41.54 | 70.17 | +| blind | 46.38 | 56.16 | +| coffee table | 61.26 | 88.02 | +| toilet | 90.58 | 94.11 | +| flower | 40.86 | 55.96 | +| book | 50.56 | 60.56 | +| hill | 6.95 | 12.42 | +| bench | 63.95 | 80.78 | +| countertop | 62.89 | 81.25 | +| stove | 86.73 | 93.51 | +| palm | 51.41 | 79.45 | +| kitchen island | 45.97 | 76.76 | +| computer | 78.78 | 91.03 | +| swivel chair | 53.38 | 80.35 | +| boat | 71.22 | 92.86 | +| bar | 65.09 | 85.63 | +| arcade machine | 93.07 | 97.68 | +| hovel | 19.21 | 21.27 | +| bus | 93.67 | 96.26 | +| towel | 76.34 | 90.56 | +| light | 62.11 | 72.08 | +| truck | 50.58 | 63.27 | +| tower | 27.47 | 60.06 | +| chandelier | 72.13 | 82.42 | +| awning | 56.29 | 71.24 | +| streetlight | 37.8 | 53.21 | +| booth | 42.9 | 71.15 | +| television receiver | 80.66 | 87.86 | +| airplane | 88.22 | 96.27 | +| dirt track | 0.0 | 0.0 | +| apparel | 63.41 | 77.93 | +| pole | 23.54 | 30.37 | +| land | 2.97 | 4.59 | +| bannister | 17.89 | 24.61 | +| escalator | 63.7 | 87.31 | +| ottoman | 58.35 | 74.16 | +| bottle | 42.62 | 59.74 | +| buffet | 70.25 | 85.3 | +| poster | 43.45 | 51.85 | +| stage | 15.3 | 25.97 | +| van | 52.85 | 76.2 | +| ship | 82.08 | 91.35 | +| fountain | 32.19 | 33.28 | +| conveyer belt | 84.62 | 95.23 | +| canopy | 52.12 | 68.82 | +| washer | 83.23 | 88.87 | +| plaything | 41.6 | 59.13 | +| swimming pool | 54.95 | 79.72 | +| stool | 60.98 | 72.27 | +| barrel | 67.42 | 77.07 | +| basket | 44.2 | 62.96 | +| waterfall | 67.76 | 78.57 | +| tent | 95.79 | 98.76 | +| bag | 23.92 | 26.86 | +| minibike | 75.9 | 87.06 | +| cradle | 86.15 | 97.96 | +| oven | 57.7 | 70.24 | +| ball | 55.54 | 61.15 | +| food | 64.44 | 76.96 | +| step | 14.93 | 19.31 | +| tank | 84.9 | 92.29 | +| trade name | 20.58 | 23.8 | +| microwave | 88.3 | 96.29 | +| pot | 57.38 | 66.55 | +| animal | 58.8 | 59.71 | +| bicycle | 62.91 | 77.98 | +| lake | 47.98 | 63.84 | +| dishwasher | 76.23 | 82.4 | +| screen | 62.85 | 91.4 | +| blanket | 29.95 | 40.97 | +| sculpture | 74.75 | 81.02 | +| hood | 74.42 | 85.47 | +| sconce | 59.29 | 69.57 | +| vase | 50.51 | 66.85 | +| traffic light | 37.3 | 66.17 | +| tray | 25.15 | 30.17 | +| ashcan | 52.62 | 63.97 | +| fan | 71.46 | 80.08 | +| pier | 39.18 | 44.78 | +| crt screen | 4.18 | 9.58 | +| plate | 66.38 | 77.22 | +| monitor | 22.38 | 25.07 | +| bulletin board | 61.12 | 77.92 | +| shower | 2.97 | 13.84 | +| radiator | 69.13 | 81.38 | +| glass | 20.46 | 21.98 | +| clock | 53.87 | 64.96 | +| flag | 69.4 | 79.35 | ++---------------------+-------+-------+ +2024-06-19 00:26:05,049 - mmseg - INFO - Summary: +2024-06-19 00:26:05,050 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.18 | 58.24 | 71.03 | ++-------+-------+-------+ +2024-06-19 00:26:05,050 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 00:26:05,051 - mmseg - INFO - Iter(val) [250] aAcc: 0.8618, mIoU: 0.5824, mAcc: 0.7103, IoU.wall: 0.8248, IoU.building: 0.8493, IoU.sky: 0.9484, IoU.floor: 0.8550, IoU.tree: 0.7767, IoU.ceiling: 0.8759, IoU.road: 0.8625, IoU.bed : 0.9275, IoU.windowpane: 0.6725, IoU.grass: 0.6896, IoU.cabinet: 0.6796, IoU.sidewalk: 0.7119, IoU.person: 0.8573, IoU.earth: 0.4258, IoU.door: 0.5891, IoU.table: 0.6825, IoU.mountain: 0.6154, IoU.plant: 0.5590, IoU.curtain: 0.7929, IoU.chair: 0.6787, IoU.car: 0.8828, IoU.water: 0.5911, IoU.painting: 0.8147, IoU.sofa: 0.8101, IoU.shelf: 0.4901, IoU.house: 0.5300, IoU.sea: 0.6508, IoU.mirror: 0.8022, IoU.rug: 0.6710, IoU.field: 0.3150, IoU.armchair: 0.6030, IoU.seat: 0.6660, IoU.fence: 0.5070, IoU.desk: 0.5880, IoU.rock: 0.5594, IoU.wardrobe: 0.5541, IoU.lamp: 0.7555, IoU.bathtub: 0.8848, IoU.railing: 0.4161, IoU.cushion: 0.6748, IoU.base: 0.4099, IoU.box: 0.3824, IoU.column: 0.5759, IoU.signboard: 0.4289, IoU.chest of drawers: 0.4526, IoU.counter: 0.3849, IoU.sand: 0.4998, IoU.sink: 0.8381, IoU.skyscraper: 0.4937, IoU.fireplace: 0.7448, IoU.refrigerator: 0.8313, IoU.grandstand: 0.4360, IoU.path: 0.3533, IoU.stairs: 0.2887, IoU.runway: 0.7313, IoU.case: 0.6272, IoU.pool table: 0.9512, IoU.pillow: 0.6376, IoU.screen door: 0.7879, IoU.stairway: 0.2902, IoU.river: 0.1803, IoU.bridge: 0.7702, IoU.bookcase: 0.4154, IoU.blind: 0.4638, IoU.coffee table: 0.6126, IoU.toilet: 0.9058, IoU.flower: 0.4086, IoU.book: 0.5056, IoU.hill: 0.0695, IoU.bench: 0.6395, IoU.countertop: 0.6289, IoU.stove: 0.8673, IoU.palm: 0.5141, IoU.kitchen island: 0.4597, IoU.computer: 0.7878, IoU.swivel chair: 0.5338, IoU.boat: 0.7122, IoU.bar: 0.6509, IoU.arcade machine: 0.9307, IoU.hovel: 0.1921, IoU.bus: 0.9367, IoU.towel: 0.7634, IoU.light: 0.6211, IoU.truck: 0.5058, IoU.tower: 0.2747, IoU.chandelier: 0.7213, IoU.awning: 0.5629, IoU.streetlight: 0.3780, IoU.booth: 0.4290, IoU.television receiver: 0.8066, IoU.airplane: 0.8822, IoU.dirt track: 0.0000, IoU.apparel: 0.6341, IoU.pole: 0.2354, IoU.land: 0.0297, IoU.bannister: 0.1789, IoU.escalator: 0.6370, IoU.ottoman: 0.5835, IoU.bottle: 0.4262, IoU.buffet: 0.7025, IoU.poster: 0.4345, IoU.stage: 0.1530, IoU.van: 0.5285, IoU.ship: 0.8208, IoU.fountain: 0.3219, IoU.conveyer belt: 0.8462, IoU.canopy: 0.5212, IoU.washer: 0.8323, IoU.plaything: 0.4160, IoU.swimming pool: 0.5495, IoU.stool: 0.6098, IoU.barrel: 0.6742, IoU.basket: 0.4420, IoU.waterfall: 0.6776, IoU.tent: 0.9579, IoU.bag: 0.2392, IoU.minibike: 0.7590, IoU.cradle: 0.8615, IoU.oven: 0.5770, IoU.ball: 0.5554, IoU.food: 0.6444, IoU.step: 0.1493, IoU.tank: 0.8490, IoU.trade name: 0.2058, IoU.microwave: 0.8830, IoU.pot: 0.5738, IoU.animal: 0.5880, IoU.bicycle: 0.6291, IoU.lake: 0.4798, IoU.dishwasher: 0.7623, IoU.screen: 0.6285, IoU.blanket: 0.2995, IoU.sculpture: 0.7475, IoU.hood: 0.7442, IoU.sconce: 0.5929, IoU.vase: 0.5051, IoU.traffic light: 0.3730, IoU.tray: 0.2515, IoU.ashcan: 0.5262, IoU.fan: 0.7146, IoU.pier: 0.3918, IoU.crt screen: 0.0418, IoU.plate: 0.6638, IoU.monitor: 0.2238, IoU.bulletin board: 0.6112, IoU.shower: 0.0297, IoU.radiator: 0.6913, IoU.glass: 0.2046, IoU.clock: 0.5387, IoU.flag: 0.6940, Acc.wall: 0.9034, Acc.building: 0.9130, Acc.sky: 0.9795, Acc.floor: 0.9228, Acc.tree: 0.8893, Acc.ceiling: 0.9380, Acc.road: 0.8916, Acc.bed : 0.9741, Acc.windowpane: 0.8099, Acc.grass: 0.8284, Acc.cabinet: 0.7662, Acc.sidewalk: 0.8975, Acc.person: 0.9280, Acc.earth: 0.6063, Acc.door: 0.7218, Acc.table: 0.8027, Acc.mountain: 0.7040, Acc.plant: 0.6385, Acc.curtain: 0.8815, Acc.chair: 0.7978, Acc.car: 0.9353, Acc.water: 0.7099, Acc.painting: 0.9106, Acc.sofa: 0.8876, Acc.shelf: 0.6365, Acc.house: 0.8493, Acc.sea: 0.8451, Acc.mirror: 0.8552, Acc.rug: 0.7157, Acc.field: 0.5591, Acc.armchair: 0.7425, Acc.seat: 0.9014, Acc.fence: 0.6920, Acc.desk: 0.7449, Acc.rock: 0.8345, Acc.wardrobe: 0.7929, Acc.lamp: 0.8683, Acc.bathtub: 0.9215, Acc.railing: 0.5888, Acc.cushion: 0.8703, Acc.base: 0.6888, Acc.box: 0.5096, Acc.column: 0.7344, Acc.signboard: 0.5996, Acc.chest of drawers: 0.6990, Acc.counter: 0.4933, Acc.sand: 0.7915, Acc.sink: 0.8824, Acc.skyscraper: 0.6228, Acc.fireplace: 0.9236, Acc.refrigerator: 0.9167, Acc.grandstand: 0.8718, Acc.path: 0.4746, Acc.stairs: 0.4448, Acc.runway: 0.9377, Acc.case: 0.7456, Acc.pool table: 0.9786, Acc.pillow: 0.7099, Acc.screen door: 0.8523, Acc.stairway: 0.3637, Acc.river: 0.3635, Acc.bridge: 0.8754, Acc.bookcase: 0.7017, Acc.blind: 0.5616, Acc.coffee table: 0.8802, Acc.toilet: 0.9411, Acc.flower: 0.5596, Acc.book: 0.6056, Acc.hill: 0.1242, Acc.bench: 0.8078, Acc.countertop: 0.8125, Acc.stove: 0.9351, Acc.palm: 0.7945, Acc.kitchen island: 0.7676, Acc.computer: 0.9103, Acc.swivel chair: 0.8035, Acc.boat: 0.9286, Acc.bar: 0.8563, Acc.arcade machine: 0.9768, Acc.hovel: 0.2127, Acc.bus: 0.9626, Acc.towel: 0.9056, Acc.light: 0.7208, Acc.truck: 0.6327, Acc.tower: 0.6006, Acc.chandelier: 0.8242, Acc.awning: 0.7124, Acc.streetlight: 0.5321, Acc.booth: 0.7115, Acc.television receiver: 0.8786, Acc.airplane: 0.9627, Acc.dirt track: 0.0000, Acc.apparel: 0.7793, Acc.pole: 0.3037, Acc.land: 0.0459, Acc.bannister: 0.2461, Acc.escalator: 0.8731, Acc.ottoman: 0.7416, Acc.bottle: 0.5974, Acc.buffet: 0.8530, Acc.poster: 0.5185, Acc.stage: 0.2597, Acc.van: 0.7620, Acc.ship: 0.9135, Acc.fountain: 0.3328, Acc.conveyer belt: 0.9523, Acc.canopy: 0.6882, Acc.washer: 0.8887, Acc.plaything: 0.5913, Acc.swimming pool: 0.7972, Acc.stool: 0.7227, Acc.barrel: 0.7707, Acc.basket: 0.6296, Acc.waterfall: 0.7857, Acc.tent: 0.9876, Acc.bag: 0.2686, Acc.minibike: 0.8706, Acc.cradle: 0.9796, Acc.oven: 0.7024, Acc.ball: 0.6115, Acc.food: 0.7696, Acc.step: 0.1931, Acc.tank: 0.9229, Acc.trade name: 0.2380, Acc.microwave: 0.9629, Acc.pot: 0.6655, Acc.animal: 0.5971, Acc.bicycle: 0.7798, Acc.lake: 0.6384, Acc.dishwasher: 0.8240, Acc.screen: 0.9140, Acc.blanket: 0.4097, Acc.sculpture: 0.8102, Acc.hood: 0.8547, Acc.sconce: 0.6957, Acc.vase: 0.6685, Acc.traffic light: 0.6617, Acc.tray: 0.3017, Acc.ashcan: 0.6397, Acc.fan: 0.8008, Acc.pier: 0.4478, Acc.crt screen: 0.0958, Acc.plate: 0.7722, Acc.monitor: 0.2507, Acc.bulletin board: 0.7792, Acc.shower: 0.1384, Acc.radiator: 0.8138, Acc.glass: 0.2198, Acc.clock: 0.6496, Acc.flag: 0.7935 +2024-06-19 00:27:44,284 - mmseg - INFO - Iter [37050/80000] lr: 2.148e-05, eta: 1 day, 1:28:31, time: 4.183, data_time: 2.216, memory: 72263, decode.loss_ce: 0.1964, decode.acc_seg: 91.5558, aux.loss_ce: 0.0813, aux.acc_seg: 91.3120, loss: 0.2777 +2024-06-19 00:29:23,065 - mmseg - INFO - Iter [37100/80000] lr: 2.145e-05, eta: 1 day, 1:26:35, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1961, decode.acc_seg: 91.9059, aux.loss_ce: 0.0816, aux.acc_seg: 91.5853, loss: 0.2777 +2024-06-19 00:31:01,851 - mmseg - INFO - Iter [37150/80000] lr: 2.143e-05, eta: 1 day, 1:24:39, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1892, decode.acc_seg: 91.6615, aux.loss_ce: 0.0788, aux.acc_seg: 91.3616, loss: 0.2679 +2024-06-19 00:32:40,724 - mmseg - INFO - Iter [37200/80000] lr: 2.140e-05, eta: 1 day, 1:22:43, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1972, decode.acc_seg: 91.7325, aux.loss_ce: 0.0821, aux.acc_seg: 91.4623, loss: 0.2793 +2024-06-19 00:34:19,696 - mmseg - INFO - Iter [37250/80000] lr: 2.138e-05, eta: 1 day, 1:20:48, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1875, decode.acc_seg: 92.0077, aux.loss_ce: 0.0782, aux.acc_seg: 91.5868, loss: 0.2657 +2024-06-19 00:35:58,579 - mmseg - INFO - Iter [37300/80000] lr: 2.135e-05, eta: 1 day, 1:18:52, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1966, decode.acc_seg: 91.7070, aux.loss_ce: 0.0820, aux.acc_seg: 91.3643, loss: 0.2786 +2024-06-19 00:37:37,413 - mmseg - INFO - Iter [37350/80000] lr: 2.133e-05, eta: 1 day, 1:16:56, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2005, decode.acc_seg: 91.3831, aux.loss_ce: 0.0838, aux.acc_seg: 90.9845, loss: 0.2843 +2024-06-19 00:39:16,397 - mmseg - INFO - Iter [37400/80000] lr: 2.130e-05, eta: 1 day, 1:15:01, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2026, decode.acc_seg: 91.1982, aux.loss_ce: 0.0841, aux.acc_seg: 90.8812, loss: 0.2867 +2024-06-19 00:40:55,350 - mmseg - INFO - Iter [37450/80000] lr: 2.128e-05, eta: 1 day, 1:13:05, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1891, decode.acc_seg: 92.0615, aux.loss_ce: 0.0784, aux.acc_seg: 91.7765, loss: 0.2675 +2024-06-19 00:42:34,218 - mmseg - INFO - Iter [37500/80000] lr: 2.125e-05, eta: 1 day, 1:11:10, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1926, decode.acc_seg: 91.7942, aux.loss_ce: 0.0805, aux.acc_seg: 91.4706, loss: 0.2730 +2024-06-19 00:44:13,142 - mmseg - INFO - Iter [37550/80000] lr: 2.123e-05, eta: 1 day, 1:09:14, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1920, decode.acc_seg: 91.7062, aux.loss_ce: 0.0791, aux.acc_seg: 91.4775, loss: 0.2711 +2024-06-19 00:45:52,043 - mmseg - INFO - Iter [37600/80000] lr: 2.120e-05, eta: 1 day, 1:07:19, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1874, decode.acc_seg: 91.8216, aux.loss_ce: 0.0772, aux.acc_seg: 91.6365, loss: 0.2646 +2024-06-19 00:47:30,805 - mmseg - INFO - Iter [37650/80000] lr: 2.118e-05, eta: 1 day, 1:05:23, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2036, decode.acc_seg: 91.4865, aux.loss_ce: 0.0850, aux.acc_seg: 91.1249, loss: 0.2886 +2024-06-19 00:49:09,664 - mmseg - INFO - Iter [37700/80000] lr: 2.115e-05, eta: 1 day, 1:03:28, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2101, decode.acc_seg: 91.0581, aux.loss_ce: 0.0859, aux.acc_seg: 90.9032, loss: 0.2961 +2024-06-19 00:50:48,580 - mmseg - INFO - Iter [37750/80000] lr: 2.113e-05, eta: 1 day, 1:01:33, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1954, decode.acc_seg: 92.0247, aux.loss_ce: 0.0810, aux.acc_seg: 91.7141, loss: 0.2764 +2024-06-19 00:52:27,528 - mmseg - INFO - Iter [37800/80000] lr: 2.110e-05, eta: 1 day, 0:59:37, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2059, decode.acc_seg: 91.2631, aux.loss_ce: 0.0858, aux.acc_seg: 90.9885, loss: 0.2918 +2024-06-19 00:54:06,410 - mmseg - INFO - Iter [37850/80000] lr: 2.108e-05, eta: 1 day, 0:57:42, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2009, decode.acc_seg: 91.5845, aux.loss_ce: 0.0833, aux.acc_seg: 91.2945, loss: 0.2842 +2024-06-19 00:55:48,108 - mmseg - INFO - Iter [37900/80000] lr: 2.105e-05, eta: 1 day, 0:55:50, time: 2.034, data_time: 0.065, memory: 72263, decode.loss_ce: 0.2079, decode.acc_seg: 91.3000, aux.loss_ce: 0.0856, aux.acc_seg: 91.1182, loss: 0.2936 +2024-06-19 00:57:27,041 - mmseg - INFO - Iter [37950/80000] lr: 2.103e-05, eta: 1 day, 0:53:55, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1845, decode.acc_seg: 92.2436, aux.loss_ce: 0.0772, aux.acc_seg: 91.9157, loss: 0.2617 +2024-06-19 00:59:05,800 - mmseg - INFO - Saving checkpoint at 38000 iterations +2024-06-19 01:00:28,725 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 01:00:28,725 - mmseg - INFO - Iter [38000/80000] lr: 2.100e-05, eta: 1 day, 0:53:31, time: 3.634, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1957, decode.acc_seg: 91.8160, aux.loss_ce: 0.0811, aux.acc_seg: 91.5209, loss: 0.2768 +2024-06-19 01:02:18,282 - mmseg - INFO - per class results: +2024-06-19 01:02:18,288 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.09 | 91.56 | +| building | 84.79 | 92.88 | +| sky | 94.6 | 97.17 | +| floor | 85.37 | 92.29 | +| tree | 77.51 | 89.9 | +| ceiling | 86.28 | 92.46 | +| road | 86.27 | 91.28 | +| bed | 91.83 | 96.37 | +| windowpane | 65.37 | 80.94 | +| grass | 67.79 | 83.86 | +| cabinet | 65.78 | 71.6 | +| sidewalk | 68.76 | 81.39 | +| person | 86.03 | 94.06 | +| earth | 40.45 | 51.56 | +| door | 56.88 | 72.91 | +| table | 68.82 | 80.15 | +| mountain | 65.09 | 79.66 | +| plant | 56.56 | 65.38 | +| curtain | 73.96 | 79.4 | +| chair | 64.58 | 73.38 | +| car | 88.27 | 94.46 | +| water | 62.65 | 76.25 | +| painting | 77.55 | 90.29 | +| sofa | 79.97 | 86.33 | +| shelf | 49.36 | 68.31 | +| house | 52.05 | 70.51 | +| sea | 65.93 | 76.23 | +| mirror | 78.04 | 83.91 | +| rug | 70.08 | 78.14 | +| field | 31.17 | 55.79 | +| armchair | 58.15 | 81.09 | +| seat | 70.94 | 90.08 | +| fence | 54.67 | 71.34 | +| desk | 57.27 | 75.95 | +| rock | 58.75 | 86.61 | +| wardrobe | 44.83 | 59.98 | +| lamp | 76.0 | 83.03 | +| bathtub | 86.73 | 89.43 | +| railing | 42.4 | 57.29 | +| cushion | 68.29 | 78.71 | +| base | 46.48 | 62.91 | +| box | 38.63 | 49.5 | +| column | 56.62 | 62.69 | +| signboard | 40.75 | 52.05 | +| chest of drawers | 46.18 | 79.32 | +| counter | 43.84 | 49.59 | +| sand | 47.76 | 81.22 | +| sink | 81.21 | 86.21 | +| skyscraper | 47.76 | 65.83 | +| fireplace | 73.13 | 92.33 | +| refrigerator | 80.91 | 89.88 | +| grandstand | 59.6 | 83.66 | +| path | 34.07 | 52.68 | +| stairs | 33.33 | 40.27 | +| runway | 70.37 | 96.52 | +| case | 64.14 | 81.2 | +| pool table | 95.16 | 98.0 | +| pillow | 63.75 | 70.54 | +| screen door | 59.22 | 60.93 | +| stairway | 43.13 | 73.02 | +| river | 13.29 | 28.45 | +| bridge | 57.21 | 62.19 | +| bookcase | 42.86 | 58.67 | +| blind | 48.0 | 60.78 | +| coffee table | 60.79 | 85.4 | +| toilet | 90.34 | 93.73 | +| flower | 42.73 | 57.5 | +| book | 55.24 | 79.66 | +| hill | 5.54 | 10.11 | +| bench | 69.78 | 80.59 | +| countertop | 64.34 | 79.61 | +| stove | 86.75 | 93.83 | +| palm | 54.31 | 84.41 | +| kitchen island | 51.42 | 78.04 | +| computer | 77.56 | 91.58 | +| swivel chair | 52.58 | 93.4 | +| boat | 80.07 | 89.85 | +| bar | 65.7 | 86.69 | +| arcade machine | 90.33 | 95.54 | +| hovel | 15.77 | 18.36 | +| bus | 93.62 | 96.83 | +| towel | 78.21 | 84.51 | +| light | 62.64 | 72.32 | +| truck | 51.42 | 65.17 | +| tower | 15.35 | 25.04 | +| chandelier | 74.26 | 82.65 | +| awning | 43.87 | 62.39 | +| streetlight | 36.47 | 47.19 | +| booth | 45.12 | 48.5 | +| television receiver | 79.99 | 85.18 | +| airplane | 72.96 | 80.9 | +| dirt track | 2.63 | 8.8 | +| apparel | 64.01 | 87.69 | +| pole | 25.91 | 33.07 | +| land | 3.16 | 5.13 | +| bannister | 19.9 | 24.88 | +| escalator | 64.95 | 85.86 | +| ottoman | 50.6 | 66.03 | +| bottle | 43.3 | 66.85 | +| buffet | 62.59 | 76.08 | +| poster | 41.58 | 55.39 | +| stage | 12.97 | 22.33 | +| van | 53.43 | 72.13 | +| ship | 83.64 | 86.26 | +| fountain | 29.67 | 32.07 | +| conveyer belt | 82.94 | 95.35 | +| canopy | 40.08 | 51.18 | +| washer | 83.62 | 88.94 | +| plaything | 34.94 | 47.46 | +| swimming pool | 53.86 | 76.13 | +| stool | 46.99 | 75.25 | +| barrel | 54.68 | 69.61 | +| basket | 47.49 | 60.28 | +| waterfall | 57.24 | 77.48 | +| tent | 95.59 | 98.97 | +| bag | 26.03 | 29.58 | +| minibike | 76.4 | 85.87 | +| cradle | 85.53 | 96.61 | +| oven | 63.1 | 73.09 | +| ball | 57.54 | 62.93 | +| food | 64.19 | 78.73 | +| step | 12.27 | 13.69 | +| tank | 84.99 | 91.87 | +| trade name | 22.93 | 26.44 | +| microwave | 89.93 | 96.2 | +| pot | 59.13 | 66.89 | +| animal | 60.94 | 63.38 | +| bicycle | 58.56 | 79.14 | +| lake | 48.52 | 66.24 | +| dishwasher | 77.23 | 83.91 | +| screen | 46.36 | 60.37 | +| blanket | 31.86 | 37.37 | +| sculpture | 71.28 | 86.17 | +| hood | 71.57 | 84.29 | +| sconce | 58.61 | 67.27 | +| vase | 46.83 | 68.99 | +| traffic light | 34.81 | 71.69 | +| tray | 27.85 | 35.61 | +| ashcan | 49.1 | 67.73 | +| fan | 71.5 | 82.87 | +| pier | 37.63 | 40.72 | +| crt screen | 17.13 | 51.26 | +| plate | 64.87 | 77.42 | +| monitor | 36.26 | 43.64 | +| bulletin board | 59.74 | 66.88 | +| shower | 2.17 | 11.45 | +| radiator | 69.09 | 82.93 | +| glass | 21.07 | 22.68 | +| clock | 50.94 | 64.82 | +| flag | 69.72 | 78.87 | ++---------------------+-------+-------+ +2024-06-19 01:02:18,288 - mmseg - INFO - Summary: +2024-06-19 01:02:18,288 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 85.96 | 57.52 | 70.0 | ++-------+-------+------+ +2024-06-19 01:02:18,289 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 01:02:18,289 - mmseg - INFO - Iter(val) [250] aAcc: 0.8596, mIoU: 0.5752, mAcc: 0.7000, IoU.wall: 0.8209, IoU.building: 0.8479, IoU.sky: 0.9460, IoU.floor: 0.8537, IoU.tree: 0.7751, IoU.ceiling: 0.8628, IoU.road: 0.8627, IoU.bed : 0.9183, IoU.windowpane: 0.6537, IoU.grass: 0.6779, IoU.cabinet: 0.6578, IoU.sidewalk: 0.6876, IoU.person: 0.8603, IoU.earth: 0.4045, IoU.door: 0.5688, IoU.table: 0.6882, IoU.mountain: 0.6509, IoU.plant: 0.5656, IoU.curtain: 0.7396, IoU.chair: 0.6458, IoU.car: 0.8827, IoU.water: 0.6265, IoU.painting: 0.7755, IoU.sofa: 0.7997, IoU.shelf: 0.4936, IoU.house: 0.5205, IoU.sea: 0.6593, IoU.mirror: 0.7804, IoU.rug: 0.7008, IoU.field: 0.3117, IoU.armchair: 0.5815, IoU.seat: 0.7094, IoU.fence: 0.5467, IoU.desk: 0.5727, IoU.rock: 0.5875, IoU.wardrobe: 0.4483, IoU.lamp: 0.7600, IoU.bathtub: 0.8673, IoU.railing: 0.4240, IoU.cushion: 0.6829, IoU.base: 0.4648, IoU.box: 0.3863, IoU.column: 0.5662, IoU.signboard: 0.4075, IoU.chest of drawers: 0.4618, IoU.counter: 0.4384, IoU.sand: 0.4776, IoU.sink: 0.8121, IoU.skyscraper: 0.4776, IoU.fireplace: 0.7313, IoU.refrigerator: 0.8091, IoU.grandstand: 0.5960, IoU.path: 0.3407, IoU.stairs: 0.3333, IoU.runway: 0.7037, IoU.case: 0.6414, IoU.pool table: 0.9516, IoU.pillow: 0.6375, IoU.screen door: 0.5922, IoU.stairway: 0.4313, IoU.river: 0.1329, IoU.bridge: 0.5721, IoU.bookcase: 0.4286, IoU.blind: 0.4800, IoU.coffee table: 0.6079, IoU.toilet: 0.9034, IoU.flower: 0.4273, IoU.book: 0.5524, IoU.hill: 0.0554, IoU.bench: 0.6978, IoU.countertop: 0.6434, IoU.stove: 0.8675, IoU.palm: 0.5431, IoU.kitchen island: 0.5142, IoU.computer: 0.7756, IoU.swivel chair: 0.5258, IoU.boat: 0.8007, IoU.bar: 0.6570, IoU.arcade machine: 0.9033, IoU.hovel: 0.1577, IoU.bus: 0.9362, IoU.towel: 0.7821, IoU.light: 0.6264, IoU.truck: 0.5142, IoU.tower: 0.1535, IoU.chandelier: 0.7426, IoU.awning: 0.4387, IoU.streetlight: 0.3647, IoU.booth: 0.4512, IoU.television receiver: 0.7999, IoU.airplane: 0.7296, IoU.dirt track: 0.0263, IoU.apparel: 0.6401, IoU.pole: 0.2591, IoU.land: 0.0316, IoU.bannister: 0.1990, IoU.escalator: 0.6495, IoU.ottoman: 0.5060, IoU.bottle: 0.4330, IoU.buffet: 0.6259, IoU.poster: 0.4158, IoU.stage: 0.1297, IoU.van: 0.5343, IoU.ship: 0.8364, IoU.fountain: 0.2967, IoU.conveyer belt: 0.8294, IoU.canopy: 0.4008, IoU.washer: 0.8362, IoU.plaything: 0.3494, IoU.swimming pool: 0.5386, IoU.stool: 0.4699, IoU.barrel: 0.5468, IoU.basket: 0.4749, IoU.waterfall: 0.5724, IoU.tent: 0.9559, IoU.bag: 0.2603, IoU.minibike: 0.7640, IoU.cradle: 0.8553, IoU.oven: 0.6310, IoU.ball: 0.5754, IoU.food: 0.6419, IoU.step: 0.1227, IoU.tank: 0.8499, IoU.trade name: 0.2293, IoU.microwave: 0.8993, IoU.pot: 0.5913, IoU.animal: 0.6094, IoU.bicycle: 0.5856, IoU.lake: 0.4852, IoU.dishwasher: 0.7723, IoU.screen: 0.4636, IoU.blanket: 0.3186, IoU.sculpture: 0.7128, IoU.hood: 0.7157, IoU.sconce: 0.5861, IoU.vase: 0.4683, IoU.traffic light: 0.3481, IoU.tray: 0.2785, IoU.ashcan: 0.4910, IoU.fan: 0.7150, IoU.pier: 0.3763, IoU.crt screen: 0.1713, IoU.plate: 0.6487, IoU.monitor: 0.3626, IoU.bulletin board: 0.5974, IoU.shower: 0.0217, IoU.radiator: 0.6909, IoU.glass: 0.2107, IoU.clock: 0.5094, IoU.flag: 0.6972, Acc.wall: 0.9156, Acc.building: 0.9288, Acc.sky: 0.9717, Acc.floor: 0.9229, Acc.tree: 0.8990, Acc.ceiling: 0.9246, Acc.road: 0.9128, Acc.bed : 0.9637, Acc.windowpane: 0.8094, Acc.grass: 0.8386, Acc.cabinet: 0.7160, Acc.sidewalk: 0.8139, Acc.person: 0.9406, Acc.earth: 0.5156, Acc.door: 0.7291, Acc.table: 0.8015, Acc.mountain: 0.7966, Acc.plant: 0.6538, Acc.curtain: 0.7940, Acc.chair: 0.7338, Acc.car: 0.9446, Acc.water: 0.7625, Acc.painting: 0.9029, Acc.sofa: 0.8633, Acc.shelf: 0.6831, Acc.house: 0.7051, Acc.sea: 0.7623, Acc.mirror: 0.8391, Acc.rug: 0.7814, Acc.field: 0.5579, Acc.armchair: 0.8109, Acc.seat: 0.9008, Acc.fence: 0.7134, Acc.desk: 0.7595, Acc.rock: 0.8661, Acc.wardrobe: 0.5998, Acc.lamp: 0.8303, Acc.bathtub: 0.8943, Acc.railing: 0.5729, Acc.cushion: 0.7871, Acc.base: 0.6291, Acc.box: 0.4950, Acc.column: 0.6269, Acc.signboard: 0.5205, Acc.chest of drawers: 0.7932, Acc.counter: 0.4959, Acc.sand: 0.8122, Acc.sink: 0.8621, Acc.skyscraper: 0.6583, Acc.fireplace: 0.9233, Acc.refrigerator: 0.8988, Acc.grandstand: 0.8366, Acc.path: 0.5268, Acc.stairs: 0.4027, Acc.runway: 0.9652, Acc.case: 0.8120, Acc.pool table: 0.9800, Acc.pillow: 0.7054, Acc.screen door: 0.6093, Acc.stairway: 0.7302, Acc.river: 0.2845, Acc.bridge: 0.6219, Acc.bookcase: 0.5867, Acc.blind: 0.6078, Acc.coffee table: 0.8540, Acc.toilet: 0.9373, Acc.flower: 0.5750, Acc.book: 0.7966, Acc.hill: 0.1011, Acc.bench: 0.8059, Acc.countertop: 0.7961, Acc.stove: 0.9383, Acc.palm: 0.8441, Acc.kitchen island: 0.7804, Acc.computer: 0.9158, Acc.swivel chair: 0.9340, Acc.boat: 0.8985, Acc.bar: 0.8669, Acc.arcade machine: 0.9554, Acc.hovel: 0.1836, Acc.bus: 0.9683, Acc.towel: 0.8451, Acc.light: 0.7232, Acc.truck: 0.6517, Acc.tower: 0.2504, Acc.chandelier: 0.8265, Acc.awning: 0.6239, Acc.streetlight: 0.4719, Acc.booth: 0.4850, Acc.television receiver: 0.8518, Acc.airplane: 0.8090, Acc.dirt track: 0.0880, Acc.apparel: 0.8769, Acc.pole: 0.3307, Acc.land: 0.0513, Acc.bannister: 0.2488, Acc.escalator: 0.8586, Acc.ottoman: 0.6603, Acc.bottle: 0.6685, Acc.buffet: 0.7608, Acc.poster: 0.5539, Acc.stage: 0.2233, Acc.van: 0.7213, Acc.ship: 0.8626, Acc.fountain: 0.3207, Acc.conveyer belt: 0.9535, Acc.canopy: 0.5118, Acc.washer: 0.8894, Acc.plaything: 0.4746, Acc.swimming pool: 0.7613, Acc.stool: 0.7525, Acc.barrel: 0.6961, Acc.basket: 0.6028, Acc.waterfall: 0.7748, Acc.tent: 0.9897, Acc.bag: 0.2958, Acc.minibike: 0.8587, Acc.cradle: 0.9661, Acc.oven: 0.7309, Acc.ball: 0.6293, Acc.food: 0.7873, Acc.step: 0.1369, Acc.tank: 0.9187, Acc.trade name: 0.2644, Acc.microwave: 0.9620, Acc.pot: 0.6689, Acc.animal: 0.6338, Acc.bicycle: 0.7914, Acc.lake: 0.6624, Acc.dishwasher: 0.8391, Acc.screen: 0.6037, Acc.blanket: 0.3737, Acc.sculpture: 0.8617, Acc.hood: 0.8429, Acc.sconce: 0.6727, Acc.vase: 0.6899, Acc.traffic light: 0.7169, Acc.tray: 0.3561, Acc.ashcan: 0.6773, Acc.fan: 0.8287, Acc.pier: 0.4072, Acc.crt screen: 0.5126, Acc.plate: 0.7742, Acc.monitor: 0.4364, Acc.bulletin board: 0.6688, Acc.shower: 0.1145, Acc.radiator: 0.8293, Acc.glass: 0.2268, Acc.clock: 0.6482, Acc.flag: 0.7887 +2024-06-19 01:03:57,585 - mmseg - INFO - Iter [38050/80000] lr: 2.098e-05, eta: 1 day, 0:53:37, time: 4.177, data_time: 2.209, memory: 72263, decode.loss_ce: 0.1974, decode.acc_seg: 91.5925, aux.loss_ce: 0.0824, aux.acc_seg: 91.1761, loss: 0.2798 +2024-06-19 01:05:36,522 - mmseg - INFO - Iter [38100/80000] lr: 2.095e-05, eta: 1 day, 0:51:42, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1990, decode.acc_seg: 91.4843, aux.loss_ce: 0.0826, aux.acc_seg: 91.1941, loss: 0.2816 +2024-06-19 01:07:15,304 - mmseg - INFO - Iter [38150/80000] lr: 2.093e-05, eta: 1 day, 0:49:46, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1803, decode.acc_seg: 92.2194, aux.loss_ce: 0.0749, aux.acc_seg: 91.9230, loss: 0.2552 +2024-06-19 01:08:54,117 - mmseg - INFO - Iter [38200/80000] lr: 2.090e-05, eta: 1 day, 0:47:51, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2019, decode.acc_seg: 91.4814, aux.loss_ce: 0.0839, aux.acc_seg: 91.1054, loss: 0.2858 +2024-06-19 01:10:33,090 - mmseg - INFO - Iter [38250/80000] lr: 2.088e-05, eta: 1 day, 0:45:56, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1848, decode.acc_seg: 92.0857, aux.loss_ce: 0.0773, aux.acc_seg: 91.7657, loss: 0.2621 +2024-06-19 01:12:11,902 - mmseg - INFO - Iter [38300/80000] lr: 2.085e-05, eta: 1 day, 0:44:00, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1794, decode.acc_seg: 92.3106, aux.loss_ce: 0.0751, aux.acc_seg: 92.0043, loss: 0.2545 +2024-06-19 01:13:50,757 - mmseg - INFO - Iter [38350/80000] lr: 2.083e-05, eta: 1 day, 0:42:05, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1850, decode.acc_seg: 91.9285, aux.loss_ce: 0.0773, aux.acc_seg: 91.6232, loss: 0.2623 +2024-06-19 01:15:29,720 - mmseg - INFO - Iter [38400/80000] lr: 2.080e-05, eta: 1 day, 0:40:10, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1906, decode.acc_seg: 92.0368, aux.loss_ce: 0.0791, aux.acc_seg: 91.6348, loss: 0.2696 +2024-06-19 01:17:08,697 - mmseg - INFO - Iter [38450/80000] lr: 2.078e-05, eta: 1 day, 0:38:14, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1976, decode.acc_seg: 91.6323, aux.loss_ce: 0.0821, aux.acc_seg: 91.2641, loss: 0.2797 +2024-06-19 01:18:47,542 - mmseg - INFO - Iter [38500/80000] lr: 2.075e-05, eta: 1 day, 0:36:19, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1885, decode.acc_seg: 92.1859, aux.loss_ce: 0.0785, aux.acc_seg: 91.8724, loss: 0.2670 +2024-06-19 01:20:26,406 - mmseg - INFO - Iter [38550/80000] lr: 2.073e-05, eta: 1 day, 0:34:24, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1862, decode.acc_seg: 91.9692, aux.loss_ce: 0.0775, aux.acc_seg: 91.6571, loss: 0.2637 +2024-06-19 01:22:05,350 - mmseg - INFO - Iter [38600/80000] lr: 2.070e-05, eta: 1 day, 0:32:29, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1841, decode.acc_seg: 92.0823, aux.loss_ce: 0.0764, aux.acc_seg: 91.8081, loss: 0.2605 +2024-06-19 01:23:44,230 - mmseg - INFO - Iter [38650/80000] lr: 2.068e-05, eta: 1 day, 0:30:34, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1880, decode.acc_seg: 91.9325, aux.loss_ce: 0.0776, aux.acc_seg: 91.6412, loss: 0.2656 +2024-06-19 01:25:23,053 - mmseg - INFO - Iter [38700/80000] lr: 2.065e-05, eta: 1 day, 0:28:39, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1917, decode.acc_seg: 91.9666, aux.loss_ce: 0.0801, aux.acc_seg: 91.5863, loss: 0.2718 +2024-06-19 01:27:01,838 - mmseg - INFO - Iter [38750/80000] lr: 2.063e-05, eta: 1 day, 0:26:44, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1947, decode.acc_seg: 91.6627, aux.loss_ce: 0.0809, aux.acc_seg: 91.4067, loss: 0.2757 +2024-06-19 01:28:40,687 - mmseg - INFO - Iter [38800/80000] lr: 2.060e-05, eta: 1 day, 0:24:49, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1987, decode.acc_seg: 91.6824, aux.loss_ce: 0.0831, aux.acc_seg: 91.3675, loss: 0.2818 +2024-06-19 01:30:19,593 - mmseg - INFO - Iter [38850/80000] lr: 2.058e-05, eta: 1 day, 0:22:54, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1919, decode.acc_seg: 91.7195, aux.loss_ce: 0.0796, aux.acc_seg: 91.3686, loss: 0.2716 +2024-06-19 01:31:58,413 - mmseg - INFO - Iter [38900/80000] lr: 2.055e-05, eta: 1 day, 0:20:59, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1855, decode.acc_seg: 92.2247, aux.loss_ce: 0.0778, aux.acc_seg: 91.8280, loss: 0.2633 +2024-06-19 01:33:37,238 - mmseg - INFO - Iter [38950/80000] lr: 2.053e-05, eta: 1 day, 0:19:04, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2034, decode.acc_seg: 91.3537, aux.loss_ce: 0.0846, aux.acc_seg: 91.0006, loss: 0.2880 +2024-06-19 01:35:16,061 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 01:35:16,062 - mmseg - INFO - Iter [39000/80000] lr: 2.050e-05, eta: 1 day, 0:17:09, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2070, decode.acc_seg: 91.6124, aux.loss_ce: 0.0863, aux.acc_seg: 91.2481, loss: 0.2933 +2024-06-19 01:37:05,680 - mmseg - INFO - per class results: +2024-06-19 01:37:05,686 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.01 | 89.24 | +| building | 84.43 | 92.81 | +| sky | 94.93 | 97.56 | +| floor | 84.97 | 91.03 | +| tree | 78.43 | 91.17 | +| ceiling | 87.38 | 94.16 | +| road | 85.78 | 89.85 | +| bed | 92.81 | 97.35 | +| windowpane | 66.39 | 81.0 | +| grass | 68.11 | 83.85 | +| cabinet | 67.26 | 76.36 | +| sidewalk | 69.77 | 87.02 | +| person | 85.51 | 94.74 | +| earth | 43.21 | 55.88 | +| door | 57.41 | 72.29 | +| table | 67.91 | 79.99 | +| mountain | 62.1 | 71.12 | +| plant | 57.32 | 66.5 | +| curtain | 77.96 | 90.52 | +| chair | 66.21 | 76.49 | +| car | 87.4 | 95.19 | +| water | 50.71 | 60.36 | +| painting | 80.86 | 91.92 | +| sofa | 80.74 | 89.6 | +| shelf | 49.0 | 65.18 | +| house | 51.23 | 64.95 | +| sea | 67.57 | 82.66 | +| mirror | 78.73 | 85.47 | +| rug | 71.74 | 78.65 | +| field | 33.24 | 63.81 | +| armchair | 59.67 | 81.03 | +| seat | 73.6 | 87.27 | +| fence | 49.82 | 61.43 | +| desk | 57.11 | 73.85 | +| rock | 57.08 | 87.11 | +| wardrobe | 53.31 | 76.38 | +| lamp | 75.18 | 87.59 | +| bathtub | 86.9 | 89.68 | +| railing | 42.91 | 59.86 | +| cushion | 68.68 | 82.15 | +| base | 42.23 | 54.09 | +| box | 38.24 | 50.45 | +| column | 57.21 | 71.92 | +| signboard | 43.9 | 59.12 | +| chest of drawers | 48.68 | 71.76 | +| counter | 35.9 | 42.13 | +| sand | 45.87 | 77.78 | +| sink | 85.18 | 90.84 | +| skyscraper | 47.83 | 63.64 | +| fireplace | 73.55 | 95.57 | +| refrigerator | 80.23 | 88.18 | +| grandstand | 50.88 | 79.71 | +| path | 34.93 | 46.17 | +| stairs | 37.36 | 49.47 | +| runway | 69.88 | 90.23 | +| case | 67.42 | 87.47 | +| pool table | 95.11 | 98.49 | +| pillow | 65.74 | 77.37 | +| screen door | 84.94 | 88.73 | +| stairway | 38.57 | 40.98 | +| river | 11.54 | 36.67 | +| bridge | 76.27 | 87.09 | +| bookcase | 41.58 | 54.34 | +| blind | 47.19 | 57.55 | +| coffee table | 61.2 | 89.18 | +| toilet | 90.54 | 94.25 | +| flower | 45.89 | 56.93 | +| book | 56.14 | 78.93 | +| hill | 6.36 | 13.01 | +| bench | 67.79 | 79.72 | +| countertop | 59.34 | 76.76 | +| stove | 87.88 | 92.82 | +| palm | 51.94 | 86.05 | +| kitchen island | 47.12 | 87.22 | +| computer | 76.11 | 91.9 | +| swivel chair | 49.32 | 73.54 | +| boat | 63.99 | 91.43 | +| bar | 57.23 | 91.77 | +| arcade machine | 84.89 | 88.97 | +| hovel | 48.1 | 57.33 | +| bus | 92.75 | 97.09 | +| towel | 80.56 | 84.54 | +| light | 62.41 | 71.85 | +| truck | 49.63 | 61.6 | +| tower | 29.02 | 47.95 | +| chandelier | 73.08 | 84.15 | +| awning | 47.66 | 79.11 | +| streetlight | 34.05 | 43.87 | +| booth | 42.72 | 50.9 | +| television receiver | 82.49 | 87.14 | +| airplane | 83.62 | 97.12 | +| dirt track | 6.95 | 26.46 | +| apparel | 60.45 | 83.62 | +| pole | 28.56 | 40.25 | +| land | 4.57 | 6.93 | +| bannister | 20.22 | 26.9 | +| escalator | 63.52 | 88.17 | +| ottoman | 52.42 | 67.2 | +| bottle | 43.61 | 76.05 | +| buffet | 59.15 | 71.43 | +| poster | 39.75 | 44.02 | +| stage | 14.81 | 24.52 | +| van | 47.77 | 60.55 | +| ship | 65.05 | 70.93 | +| fountain | 29.51 | 29.75 | +| conveyer belt | 75.37 | 98.82 | +| canopy | 47.65 | 59.97 | +| washer | 85.36 | 91.77 | +| plaything | 33.75 | 56.49 | +| swimming pool | 54.87 | 82.64 | +| stool | 50.42 | 74.14 | +| barrel | 64.47 | 85.86 | +| basket | 45.74 | 60.34 | +| waterfall | 50.09 | 56.08 | +| tent | 94.06 | 98.81 | +| bag | 27.83 | 33.15 | +| minibike | 76.4 | 89.83 | +| cradle | 88.99 | 98.29 | +| oven | 59.5 | 71.35 | +| ball | 57.18 | 77.83 | +| food | 61.76 | 77.85 | +| step | 14.09 | 18.41 | +| tank | 83.99 | 93.3 | +| trade name | 34.25 | 45.03 | +| microwave | 87.66 | 96.54 | +| pot | 59.86 | 70.75 | +| animal | 61.61 | 63.5 | +| bicycle | 59.99 | 76.39 | +| lake | 44.08 | 63.56 | +| dishwasher | 75.94 | 81.87 | +| screen | 60.38 | 83.32 | +| blanket | 25.03 | 31.07 | +| sculpture | 73.29 | 80.12 | +| hood | 76.14 | 92.16 | +| sconce | 58.72 | 70.35 | +| vase | 49.81 | 60.24 | +| traffic light | 40.64 | 67.82 | +| tray | 17.32 | 19.41 | +| ashcan | 48.63 | 66.41 | +| fan | 72.83 | 86.05 | +| pier | 37.86 | 42.53 | +| crt screen | 12.23 | 26.97 | +| plate | 64.48 | 82.11 | +| monitor | 34.14 | 40.88 | +| bulletin board | 65.14 | 76.54 | +| shower | 2.92 | 2.92 | +| radiator | 68.03 | 84.66 | +| glass | 23.62 | 26.13 | +| clock | 52.32 | 60.34 | +| flag | 69.4 | 79.17 | ++---------------------+-------+-------+ +2024-06-19 01:37:05,686 - mmseg - INFO - Summary: +2024-06-19 01:37:05,686 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.04 | 57.94 | 71.18 | ++-------+-------+-------+ +2024-06-19 01:37:05,687 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 01:37:05,687 - mmseg - INFO - Iter(val) [250] aAcc: 0.8604, mIoU: 0.5794, mAcc: 0.7118, IoU.wall: 0.8201, IoU.building: 0.8443, IoU.sky: 0.9493, IoU.floor: 0.8497, IoU.tree: 0.7843, IoU.ceiling: 0.8738, IoU.road: 0.8578, IoU.bed : 0.9281, IoU.windowpane: 0.6639, IoU.grass: 0.6811, IoU.cabinet: 0.6726, IoU.sidewalk: 0.6977, IoU.person: 0.8551, IoU.earth: 0.4321, IoU.door: 0.5741, IoU.table: 0.6791, IoU.mountain: 0.6210, IoU.plant: 0.5732, IoU.curtain: 0.7796, IoU.chair: 0.6621, IoU.car: 0.8740, IoU.water: 0.5071, IoU.painting: 0.8086, IoU.sofa: 0.8074, IoU.shelf: 0.4900, IoU.house: 0.5123, IoU.sea: 0.6757, IoU.mirror: 0.7873, IoU.rug: 0.7174, IoU.field: 0.3324, IoU.armchair: 0.5967, IoU.seat: 0.7360, IoU.fence: 0.4982, IoU.desk: 0.5711, IoU.rock: 0.5708, IoU.wardrobe: 0.5331, IoU.lamp: 0.7518, IoU.bathtub: 0.8690, IoU.railing: 0.4291, IoU.cushion: 0.6868, IoU.base: 0.4223, IoU.box: 0.3824, IoU.column: 0.5721, IoU.signboard: 0.4390, IoU.chest of drawers: 0.4868, IoU.counter: 0.3590, IoU.sand: 0.4587, IoU.sink: 0.8518, IoU.skyscraper: 0.4783, IoU.fireplace: 0.7355, IoU.refrigerator: 0.8023, IoU.grandstand: 0.5088, IoU.path: 0.3493, IoU.stairs: 0.3736, IoU.runway: 0.6988, IoU.case: 0.6742, IoU.pool table: 0.9511, IoU.pillow: 0.6574, IoU.screen door: 0.8494, IoU.stairway: 0.3857, IoU.river: 0.1154, IoU.bridge: 0.7627, IoU.bookcase: 0.4158, IoU.blind: 0.4719, IoU.coffee table: 0.6120, IoU.toilet: 0.9054, IoU.flower: 0.4589, IoU.book: 0.5614, IoU.hill: 0.0636, IoU.bench: 0.6779, IoU.countertop: 0.5934, IoU.stove: 0.8788, IoU.palm: 0.5194, IoU.kitchen island: 0.4712, IoU.computer: 0.7611, IoU.swivel chair: 0.4932, IoU.boat: 0.6399, IoU.bar: 0.5723, IoU.arcade machine: 0.8489, IoU.hovel: 0.4810, IoU.bus: 0.9275, IoU.towel: 0.8056, IoU.light: 0.6241, IoU.truck: 0.4963, IoU.tower: 0.2902, IoU.chandelier: 0.7308, IoU.awning: 0.4766, IoU.streetlight: 0.3405, IoU.booth: 0.4272, IoU.television receiver: 0.8249, IoU.airplane: 0.8362, IoU.dirt track: 0.0695, IoU.apparel: 0.6045, IoU.pole: 0.2856, IoU.land: 0.0457, IoU.bannister: 0.2022, IoU.escalator: 0.6352, IoU.ottoman: 0.5242, IoU.bottle: 0.4361, IoU.buffet: 0.5915, IoU.poster: 0.3975, IoU.stage: 0.1481, IoU.van: 0.4777, IoU.ship: 0.6505, IoU.fountain: 0.2951, IoU.conveyer belt: 0.7537, IoU.canopy: 0.4765, IoU.washer: 0.8536, IoU.plaything: 0.3375, IoU.swimming pool: 0.5487, IoU.stool: 0.5042, IoU.barrel: 0.6447, IoU.basket: 0.4574, IoU.waterfall: 0.5009, IoU.tent: 0.9406, IoU.bag: 0.2783, IoU.minibike: 0.7640, IoU.cradle: 0.8899, IoU.oven: 0.5950, IoU.ball: 0.5718, IoU.food: 0.6176, IoU.step: 0.1409, IoU.tank: 0.8399, IoU.trade name: 0.3425, IoU.microwave: 0.8766, IoU.pot: 0.5986, IoU.animal: 0.6161, IoU.bicycle: 0.5999, IoU.lake: 0.4408, IoU.dishwasher: 0.7594, IoU.screen: 0.6038, IoU.blanket: 0.2503, IoU.sculpture: 0.7329, IoU.hood: 0.7614, IoU.sconce: 0.5872, IoU.vase: 0.4981, IoU.traffic light: 0.4064, IoU.tray: 0.1732, IoU.ashcan: 0.4863, IoU.fan: 0.7283, IoU.pier: 0.3786, IoU.crt screen: 0.1223, IoU.plate: 0.6448, IoU.monitor: 0.3414, IoU.bulletin board: 0.6514, IoU.shower: 0.0292, IoU.radiator: 0.6803, IoU.glass: 0.2362, IoU.clock: 0.5232, IoU.flag: 0.6940, Acc.wall: 0.8924, Acc.building: 0.9281, Acc.sky: 0.9756, Acc.floor: 0.9103, Acc.tree: 0.9117, Acc.ceiling: 0.9416, Acc.road: 0.8985, Acc.bed : 0.9735, Acc.windowpane: 0.8100, Acc.grass: 0.8385, Acc.cabinet: 0.7636, Acc.sidewalk: 0.8702, Acc.person: 0.9474, Acc.earth: 0.5588, Acc.door: 0.7229, Acc.table: 0.7999, Acc.mountain: 0.7112, Acc.plant: 0.6650, Acc.curtain: 0.9052, Acc.chair: 0.7649, Acc.car: 0.9519, Acc.water: 0.6036, Acc.painting: 0.9192, Acc.sofa: 0.8960, Acc.shelf: 0.6518, Acc.house: 0.6495, Acc.sea: 0.8266, Acc.mirror: 0.8547, Acc.rug: 0.7865, Acc.field: 0.6381, Acc.armchair: 0.8103, Acc.seat: 0.8727, Acc.fence: 0.6143, Acc.desk: 0.7385, Acc.rock: 0.8711, Acc.wardrobe: 0.7638, Acc.lamp: 0.8759, Acc.bathtub: 0.8968, Acc.railing: 0.5986, Acc.cushion: 0.8215, Acc.base: 0.5409, Acc.box: 0.5045, Acc.column: 0.7192, Acc.signboard: 0.5912, Acc.chest of drawers: 0.7176, Acc.counter: 0.4213, Acc.sand: 0.7778, Acc.sink: 0.9084, Acc.skyscraper: 0.6364, Acc.fireplace: 0.9557, Acc.refrigerator: 0.8818, Acc.grandstand: 0.7971, Acc.path: 0.4617, Acc.stairs: 0.4947, Acc.runway: 0.9023, Acc.case: 0.8747, Acc.pool table: 0.9849, Acc.pillow: 0.7737, Acc.screen door: 0.8873, Acc.stairway: 0.4098, Acc.river: 0.3667, Acc.bridge: 0.8709, Acc.bookcase: 0.5434, Acc.blind: 0.5755, Acc.coffee table: 0.8918, Acc.toilet: 0.9425, Acc.flower: 0.5693, Acc.book: 0.7893, Acc.hill: 0.1301, Acc.bench: 0.7972, Acc.countertop: 0.7676, Acc.stove: 0.9282, Acc.palm: 0.8605, Acc.kitchen island: 0.8722, Acc.computer: 0.9190, Acc.swivel chair: 0.7354, Acc.boat: 0.9143, Acc.bar: 0.9177, Acc.arcade machine: 0.8897, Acc.hovel: 0.5733, Acc.bus: 0.9709, Acc.towel: 0.8454, Acc.light: 0.7185, Acc.truck: 0.6160, Acc.tower: 0.4795, Acc.chandelier: 0.8415, Acc.awning: 0.7911, Acc.streetlight: 0.4387, Acc.booth: 0.5090, Acc.television receiver: 0.8714, Acc.airplane: 0.9712, Acc.dirt track: 0.2646, Acc.apparel: 0.8362, Acc.pole: 0.4025, Acc.land: 0.0693, Acc.bannister: 0.2690, Acc.escalator: 0.8817, Acc.ottoman: 0.6720, Acc.bottle: 0.7605, Acc.buffet: 0.7143, Acc.poster: 0.4402, Acc.stage: 0.2452, Acc.van: 0.6055, Acc.ship: 0.7093, Acc.fountain: 0.2975, Acc.conveyer belt: 0.9882, Acc.canopy: 0.5997, Acc.washer: 0.9177, Acc.plaything: 0.5649, Acc.swimming pool: 0.8264, Acc.stool: 0.7414, Acc.barrel: 0.8586, Acc.basket: 0.6034, Acc.waterfall: 0.5608, Acc.tent: 0.9881, Acc.bag: 0.3315, Acc.minibike: 0.8983, Acc.cradle: 0.9829, Acc.oven: 0.7135, Acc.ball: 0.7783, Acc.food: 0.7785, Acc.step: 0.1841, Acc.tank: 0.9330, Acc.trade name: 0.4503, Acc.microwave: 0.9654, Acc.pot: 0.7075, Acc.animal: 0.6350, Acc.bicycle: 0.7639, Acc.lake: 0.6356, Acc.dishwasher: 0.8187, Acc.screen: 0.8332, Acc.blanket: 0.3107, Acc.sculpture: 0.8012, Acc.hood: 0.9216, Acc.sconce: 0.7035, Acc.vase: 0.6024, Acc.traffic light: 0.6782, Acc.tray: 0.1941, Acc.ashcan: 0.6641, Acc.fan: 0.8605, Acc.pier: 0.4253, Acc.crt screen: 0.2697, Acc.plate: 0.8211, Acc.monitor: 0.4088, Acc.bulletin board: 0.7654, Acc.shower: 0.0292, Acc.radiator: 0.8466, Acc.glass: 0.2613, Acc.clock: 0.6034, Acc.flag: 0.7917 +2024-06-19 01:38:45,060 - mmseg - INFO - Iter [39050/80000] lr: 2.048e-05, eta: 1 day, 0:17:10, time: 4.180, data_time: 2.209, memory: 72263, decode.loss_ce: 0.1927, decode.acc_seg: 91.7695, aux.loss_ce: 0.0798, aux.acc_seg: 91.4631, loss: 0.2724 +2024-06-19 01:40:23,952 - mmseg - INFO - Iter [39100/80000] lr: 2.045e-05, eta: 1 day, 0:15:15, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2026, decode.acc_seg: 91.4090, aux.loss_ce: 0.0839, aux.acc_seg: 90.9767, loss: 0.2865 +2024-06-19 01:42:02,839 - mmseg - INFO - Iter [39150/80000] lr: 2.043e-05, eta: 1 day, 0:13:20, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2045, decode.acc_seg: 91.3563, aux.loss_ce: 0.0852, aux.acc_seg: 91.0089, loss: 0.2898 +2024-06-19 01:43:44,120 - mmseg - INFO - Iter [39200/80000] lr: 2.040e-05, eta: 1 day, 0:11:28, time: 2.026, data_time: 0.055, memory: 72263, decode.loss_ce: 0.1890, decode.acc_seg: 92.0172, aux.loss_ce: 0.0792, aux.acc_seg: 91.6235, loss: 0.2682 +2024-06-19 01:45:23,095 - mmseg - INFO - Iter [39250/80000] lr: 2.038e-05, eta: 1 day, 0:09:33, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1923, decode.acc_seg: 91.7884, aux.loss_ce: 0.0802, aux.acc_seg: 91.4671, loss: 0.2725 +2024-06-19 01:47:01,970 - mmseg - INFO - Iter [39300/80000] lr: 2.035e-05, eta: 1 day, 0:07:38, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1966, decode.acc_seg: 91.5634, aux.loss_ce: 0.0815, aux.acc_seg: 91.2030, loss: 0.2782 +2024-06-19 01:48:40,736 - mmseg - INFO - Iter [39350/80000] lr: 2.033e-05, eta: 1 day, 0:05:43, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1768, decode.acc_seg: 92.3819, aux.loss_ce: 0.0742, aux.acc_seg: 92.0077, loss: 0.2510 +2024-06-19 01:50:19,653 - mmseg - INFO - Iter [39400/80000] lr: 2.030e-05, eta: 1 day, 0:03:48, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1796, decode.acc_seg: 92.4238, aux.loss_ce: 0.0750, aux.acc_seg: 92.0446, loss: 0.2546 +2024-06-19 01:51:58,572 - mmseg - INFO - Iter [39450/80000] lr: 2.028e-05, eta: 1 day, 0:01:54, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1860, decode.acc_seg: 92.3018, aux.loss_ce: 0.0774, aux.acc_seg: 91.9804, loss: 0.2634 +2024-06-19 01:53:37,307 - mmseg - INFO - Iter [39500/80000] lr: 2.025e-05, eta: 23:59:59, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1972, decode.acc_seg: 91.7128, aux.loss_ce: 0.0820, aux.acc_seg: 91.3822, loss: 0.2792 +2024-06-19 01:55:16,189 - mmseg - INFO - Iter [39550/80000] lr: 2.023e-05, eta: 23:58:04, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1787, decode.acc_seg: 92.2994, aux.loss_ce: 0.0746, aux.acc_seg: 91.9814, loss: 0.2533 +2024-06-19 01:56:55,061 - mmseg - INFO - Iter [39600/80000] lr: 2.020e-05, eta: 23:56:10, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1919, decode.acc_seg: 92.1092, aux.loss_ce: 0.0803, aux.acc_seg: 91.7908, loss: 0.2722 +2024-06-19 01:58:33,923 - mmseg - INFO - Iter [39650/80000] lr: 2.018e-05, eta: 23:54:15, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1850, decode.acc_seg: 92.2131, aux.loss_ce: 0.0767, aux.acc_seg: 91.9263, loss: 0.2618 +2024-06-19 02:00:12,769 - mmseg - INFO - Iter [39700/80000] lr: 2.015e-05, eta: 23:52:21, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1868, decode.acc_seg: 92.2282, aux.loss_ce: 0.0784, aux.acc_seg: 91.8065, loss: 0.2652 +2024-06-19 02:01:51,701 - mmseg - INFO - Iter [39750/80000] lr: 2.013e-05, eta: 23:50:26, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1924, decode.acc_seg: 91.6926, aux.loss_ce: 0.0807, aux.acc_seg: 91.3134, loss: 0.2731 +2024-06-19 02:03:30,520 - mmseg - INFO - Iter [39800/80000] lr: 2.010e-05, eta: 23:48:32, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1976, decode.acc_seg: 91.5413, aux.loss_ce: 0.0819, aux.acc_seg: 91.2020, loss: 0.2795 +2024-06-19 02:05:09,397 - mmseg - INFO - Iter [39850/80000] lr: 2.008e-05, eta: 23:46:37, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1949, decode.acc_seg: 91.8834, aux.loss_ce: 0.0816, aux.acc_seg: 91.5534, loss: 0.2765 +2024-06-19 02:06:48,280 - mmseg - INFO - Iter [39900/80000] lr: 2.005e-05, eta: 23:44:43, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1809, decode.acc_seg: 92.3983, aux.loss_ce: 0.0754, aux.acc_seg: 92.0732, loss: 0.2563 +2024-06-19 02:08:27,210 - mmseg - INFO - Iter [39950/80000] lr: 2.003e-05, eta: 23:42:49, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1775, decode.acc_seg: 92.2529, aux.loss_ce: 0.0745, aux.acc_seg: 91.9070, loss: 0.2520 +2024-06-19 02:10:06,073 - mmseg - INFO - Saving checkpoint at 40000 iterations +2024-06-19 02:11:32,700 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 02:11:32,700 - mmseg - INFO - Iter [40000/80000] lr: 2.000e-05, eta: 23:42:21, time: 3.710, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1927, decode.acc_seg: 91.7802, aux.loss_ce: 0.0800, aux.acc_seg: 91.4518, loss: 0.2727 +2024-06-19 02:13:23,386 - mmseg - INFO - per class results: +2024-06-19 02:13:23,392 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.68 | 88.53 | +| building | 84.88 | 92.97 | +| sky | 94.8 | 97.47 | +| floor | 85.03 | 91.4 | +| tree | 77.99 | 90.59 | +| ceiling | 86.93 | 94.83 | +| road | 86.24 | 92.16 | +| bed | 92.33 | 96.55 | +| windowpane | 64.62 | 86.03 | +| grass | 66.54 | 79.47 | +| cabinet | 66.3 | 77.46 | +| sidewalk | 70.44 | 82.19 | +| person | 86.09 | 94.27 | +| earth | 41.71 | 56.91 | +| door | 57.96 | 76.13 | +| table | 67.72 | 79.61 | +| mountain | 64.15 | 70.77 | +| plant | 58.1 | 69.6 | +| curtain | 78.02 | 85.35 | +| chair | 66.88 | 79.66 | +| car | 88.29 | 95.23 | +| water | 55.8 | 66.78 | +| painting | 80.07 | 91.01 | +| sofa | 79.42 | 86.89 | +| shelf | 49.02 | 63.62 | +| house | 54.15 | 66.17 | +| sea | 67.97 | 84.64 | +| mirror | 79.71 | 87.8 | +| rug | 66.88 | 78.23 | +| field | 34.42 | 61.3 | +| armchair | 59.48 | 80.37 | +| seat | 68.51 | 89.38 | +| fence | 53.26 | 67.45 | +| desk | 59.1 | 77.15 | +| rock | 57.52 | 88.72 | +| wardrobe | 49.54 | 65.31 | +| lamp | 75.19 | 85.98 | +| bathtub | 86.58 | 90.35 | +| railing | 40.53 | 64.89 | +| cushion | 69.73 | 77.01 | +| base | 42.58 | 54.81 | +| box | 38.19 | 49.32 | +| column | 59.37 | 75.98 | +| signboard | 42.72 | 54.81 | +| chest of drawers | 46.03 | 77.25 | +| counter | 46.88 | 56.62 | +| sand | 45.56 | 79.32 | +| sink | 81.88 | 87.01 | +| skyscraper | 45.9 | 59.28 | +| fireplace | 75.57 | 94.86 | +| refrigerator | 81.09 | 90.16 | +| grandstand | 49.99 | 79.2 | +| path | 34.57 | 52.79 | +| stairs | 39.18 | 48.58 | +| runway | 66.24 | 84.28 | +| case | 64.73 | 79.32 | +| pool table | 94.99 | 98.33 | +| pillow | 69.73 | 83.18 | +| screen door | 74.53 | 77.25 | +| stairway | 46.49 | 55.65 | +| river | 12.45 | 31.15 | +| bridge | 76.48 | 88.75 | +| bookcase | 39.27 | 59.79 | +| blind | 41.91 | 46.5 | +| coffee table | 61.65 | 88.75 | +| toilet | 90.63 | 93.79 | +| flower | 48.2 | 58.39 | +| book | 54.12 | 69.16 | +| hill | 6.84 | 12.25 | +| bench | 64.11 | 70.89 | +| countertop | 65.11 | 79.49 | +| stove | 87.49 | 94.1 | +| palm | 50.85 | 84.89 | +| kitchen island | 43.27 | 74.54 | +| computer | 77.33 | 92.19 | +| swivel chair | 52.84 | 82.22 | +| boat | 80.9 | 92.29 | +| bar | 63.45 | 89.95 | +| arcade machine | 90.32 | 97.88 | +| hovel | 43.03 | 49.3 | +| bus | 94.29 | 97.37 | +| towel | 80.27 | 86.66 | +| light | 62.19 | 73.02 | +| truck | 51.9 | 62.66 | +| tower | 23.32 | 39.19 | +| chandelier | 74.68 | 86.5 | +| awning | 52.41 | 68.56 | +| streetlight | 36.36 | 50.09 | +| booth | 42.7 | 68.5 | +| television receiver | 83.03 | 91.5 | +| airplane | 88.52 | 96.75 | +| dirt track | 10.35 | 28.42 | +| apparel | 65.15 | 86.96 | +| pole | 24.62 | 31.33 | +| land | 4.24 | 6.5 | +| bannister | 16.81 | 20.43 | +| escalator | 65.18 | 84.18 | +| ottoman | 34.99 | 42.07 | +| bottle | 45.7 | 64.09 | +| buffet | 31.52 | 33.84 | +| poster | 36.42 | 55.37 | +| stage | 21.02 | 40.5 | +| van | 56.1 | 74.11 | +| ship | 37.08 | 42.44 | +| fountain | 24.92 | 25.61 | +| conveyer belt | 85.03 | 96.57 | +| canopy | 50.38 | 64.0 | +| washer | 87.21 | 93.16 | +| plaything | 42.96 | 63.43 | +| swimming pool | 49.49 | 91.73 | +| stool | 45.44 | 79.28 | +| barrel | 78.04 | 91.36 | +| basket | 46.49 | 59.4 | +| waterfall | 57.06 | 67.08 | +| tent | 96.32 | 98.14 | +| bag | 28.11 | 33.71 | +| minibike | 77.06 | 90.19 | +| cradle | 79.13 | 97.72 | +| oven | 68.28 | 81.29 | +| ball | 36.93 | 38.85 | +| food | 60.43 | 72.52 | +| step | 14.73 | 16.25 | +| tank | 61.54 | 65.86 | +| trade name | 14.27 | 15.11 | +| microwave | 90.92 | 96.84 | +| pot | 59.57 | 70.47 | +| animal | 61.1 | 62.35 | +| bicycle | 60.59 | 81.4 | +| lake | 43.99 | 68.94 | +| dishwasher | 72.16 | 84.32 | +| screen | 61.57 | 89.3 | +| blanket | 30.6 | 40.18 | +| sculpture | 76.13 | 84.48 | +| hood | 65.61 | 75.69 | +| sconce | 60.67 | 79.23 | +| vase | 51.09 | 64.89 | +| traffic light | 36.57 | 70.67 | +| tray | 22.25 | 26.37 | +| ashcan | 46.56 | 70.13 | +| fan | 72.32 | 83.63 | +| pier | 37.14 | 43.24 | +| crt screen | 15.15 | 30.18 | +| plate | 65.28 | 80.84 | +| monitor | 34.62 | 40.98 | +| bulletin board | 57.56 | 82.13 | +| shower | 16.78 | 19.58 | +| radiator | 65.58 | 88.61 | +| glass | 22.14 | 23.75 | +| clock | 47.96 | 51.61 | +| flag | 70.93 | 78.79 | ++---------------------+-------+-------+ +2024-06-19 02:13:23,393 - mmseg - INFO - Summary: +2024-06-19 02:13:23,393 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 85.96 | 57.58 | 70.7 | ++-------+-------+------+ +2024-06-19 02:13:23,394 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 02:13:23,394 - mmseg - INFO - Iter(val) [250] aAcc: 0.8596, mIoU: 0.5758, mAcc: 0.7070, IoU.wall: 0.8168, IoU.building: 0.8488, IoU.sky: 0.9480, IoU.floor: 0.8503, IoU.tree: 0.7799, IoU.ceiling: 0.8693, IoU.road: 0.8624, IoU.bed : 0.9233, IoU.windowpane: 0.6462, IoU.grass: 0.6654, IoU.cabinet: 0.6630, IoU.sidewalk: 0.7044, IoU.person: 0.8609, IoU.earth: 0.4171, IoU.door: 0.5796, IoU.table: 0.6772, IoU.mountain: 0.6415, IoU.plant: 0.5810, IoU.curtain: 0.7802, IoU.chair: 0.6688, IoU.car: 0.8829, IoU.water: 0.5580, IoU.painting: 0.8007, IoU.sofa: 0.7942, IoU.shelf: 0.4902, IoU.house: 0.5415, IoU.sea: 0.6797, IoU.mirror: 0.7971, IoU.rug: 0.6688, IoU.field: 0.3442, IoU.armchair: 0.5948, IoU.seat: 0.6851, IoU.fence: 0.5326, IoU.desk: 0.5910, IoU.rock: 0.5752, IoU.wardrobe: 0.4954, IoU.lamp: 0.7519, IoU.bathtub: 0.8658, IoU.railing: 0.4053, IoU.cushion: 0.6973, IoU.base: 0.4258, IoU.box: 0.3819, IoU.column: 0.5937, IoU.signboard: 0.4272, IoU.chest of drawers: 0.4603, IoU.counter: 0.4688, IoU.sand: 0.4556, IoU.sink: 0.8188, IoU.skyscraper: 0.4590, IoU.fireplace: 0.7557, IoU.refrigerator: 0.8109, IoU.grandstand: 0.4999, IoU.path: 0.3457, IoU.stairs: 0.3918, IoU.runway: 0.6624, IoU.case: 0.6473, IoU.pool table: 0.9499, IoU.pillow: 0.6973, IoU.screen door: 0.7453, IoU.stairway: 0.4649, IoU.river: 0.1245, IoU.bridge: 0.7648, IoU.bookcase: 0.3927, IoU.blind: 0.4191, IoU.coffee table: 0.6165, IoU.toilet: 0.9063, IoU.flower: 0.4820, IoU.book: 0.5412, IoU.hill: 0.0684, IoU.bench: 0.6411, IoU.countertop: 0.6511, IoU.stove: 0.8749, IoU.palm: 0.5085, IoU.kitchen island: 0.4327, IoU.computer: 0.7733, IoU.swivel chair: 0.5284, IoU.boat: 0.8090, IoU.bar: 0.6345, IoU.arcade machine: 0.9032, IoU.hovel: 0.4303, IoU.bus: 0.9429, IoU.towel: 0.8027, IoU.light: 0.6219, IoU.truck: 0.5190, IoU.tower: 0.2332, IoU.chandelier: 0.7468, IoU.awning: 0.5241, IoU.streetlight: 0.3636, IoU.booth: 0.4270, IoU.television receiver: 0.8303, IoU.airplane: 0.8852, IoU.dirt track: 0.1035, IoU.apparel: 0.6515, IoU.pole: 0.2462, IoU.land: 0.0424, IoU.bannister: 0.1681, IoU.escalator: 0.6518, IoU.ottoman: 0.3499, IoU.bottle: 0.4570, IoU.buffet: 0.3152, IoU.poster: 0.3642, IoU.stage: 0.2102, IoU.van: 0.5610, IoU.ship: 0.3708, IoU.fountain: 0.2492, IoU.conveyer belt: 0.8503, IoU.canopy: 0.5038, IoU.washer: 0.8721, IoU.plaything: 0.4296, IoU.swimming pool: 0.4949, IoU.stool: 0.4544, IoU.barrel: 0.7804, IoU.basket: 0.4649, IoU.waterfall: 0.5706, IoU.tent: 0.9632, IoU.bag: 0.2811, IoU.minibike: 0.7706, IoU.cradle: 0.7913, IoU.oven: 0.6828, IoU.ball: 0.3693, IoU.food: 0.6043, IoU.step: 0.1473, IoU.tank: 0.6154, IoU.trade name: 0.1427, IoU.microwave: 0.9092, IoU.pot: 0.5957, IoU.animal: 0.6110, IoU.bicycle: 0.6059, IoU.lake: 0.4399, IoU.dishwasher: 0.7216, IoU.screen: 0.6157, IoU.blanket: 0.3060, IoU.sculpture: 0.7613, IoU.hood: 0.6561, IoU.sconce: 0.6067, IoU.vase: 0.5109, IoU.traffic light: 0.3657, IoU.tray: 0.2225, IoU.ashcan: 0.4656, IoU.fan: 0.7232, IoU.pier: 0.3714, IoU.crt screen: 0.1515, IoU.plate: 0.6528, IoU.monitor: 0.3462, IoU.bulletin board: 0.5756, IoU.shower: 0.1678, IoU.radiator: 0.6558, IoU.glass: 0.2214, IoU.clock: 0.4796, IoU.flag: 0.7093, Acc.wall: 0.8853, Acc.building: 0.9297, Acc.sky: 0.9747, Acc.floor: 0.9140, Acc.tree: 0.9059, Acc.ceiling: 0.9483, Acc.road: 0.9216, Acc.bed : 0.9655, Acc.windowpane: 0.8603, Acc.grass: 0.7947, Acc.cabinet: 0.7746, Acc.sidewalk: 0.8219, Acc.person: 0.9427, Acc.earth: 0.5691, Acc.door: 0.7613, Acc.table: 0.7961, Acc.mountain: 0.7077, Acc.plant: 0.6960, Acc.curtain: 0.8535, Acc.chair: 0.7966, Acc.car: 0.9523, Acc.water: 0.6678, Acc.painting: 0.9101, Acc.sofa: 0.8689, Acc.shelf: 0.6362, Acc.house: 0.6617, Acc.sea: 0.8464, Acc.mirror: 0.8780, Acc.rug: 0.7823, Acc.field: 0.6130, Acc.armchair: 0.8037, Acc.seat: 0.8938, Acc.fence: 0.6745, Acc.desk: 0.7715, Acc.rock: 0.8872, Acc.wardrobe: 0.6531, Acc.lamp: 0.8598, Acc.bathtub: 0.9035, Acc.railing: 0.6489, Acc.cushion: 0.7701, Acc.base: 0.5481, Acc.box: 0.4932, Acc.column: 0.7598, Acc.signboard: 0.5481, Acc.chest of drawers: 0.7725, Acc.counter: 0.5662, Acc.sand: 0.7932, Acc.sink: 0.8701, Acc.skyscraper: 0.5928, Acc.fireplace: 0.9486, Acc.refrigerator: 0.9016, Acc.grandstand: 0.7920, Acc.path: 0.5279, Acc.stairs: 0.4858, Acc.runway: 0.8428, Acc.case: 0.7932, Acc.pool table: 0.9833, Acc.pillow: 0.8318, Acc.screen door: 0.7725, Acc.stairway: 0.5565, Acc.river: 0.3115, Acc.bridge: 0.8875, Acc.bookcase: 0.5979, Acc.blind: 0.4650, Acc.coffee table: 0.8875, Acc.toilet: 0.9379, Acc.flower: 0.5839, Acc.book: 0.6916, Acc.hill: 0.1225, Acc.bench: 0.7089, Acc.countertop: 0.7949, Acc.stove: 0.9410, Acc.palm: 0.8489, Acc.kitchen island: 0.7454, Acc.computer: 0.9219, Acc.swivel chair: 0.8222, Acc.boat: 0.9229, Acc.bar: 0.8995, Acc.arcade machine: 0.9788, Acc.hovel: 0.4930, Acc.bus: 0.9737, Acc.towel: 0.8666, Acc.light: 0.7302, Acc.truck: 0.6266, Acc.tower: 0.3919, Acc.chandelier: 0.8650, Acc.awning: 0.6856, Acc.streetlight: 0.5009, Acc.booth: 0.6850, Acc.television receiver: 0.9150, Acc.airplane: 0.9675, Acc.dirt track: 0.2842, Acc.apparel: 0.8696, Acc.pole: 0.3133, Acc.land: 0.0650, Acc.bannister: 0.2043, Acc.escalator: 0.8418, Acc.ottoman: 0.4207, Acc.bottle: 0.6409, Acc.buffet: 0.3384, Acc.poster: 0.5537, Acc.stage: 0.4050, Acc.van: 0.7411, Acc.ship: 0.4244, Acc.fountain: 0.2561, Acc.conveyer belt: 0.9657, Acc.canopy: 0.6400, Acc.washer: 0.9316, Acc.plaything: 0.6343, Acc.swimming pool: 0.9173, Acc.stool: 0.7928, Acc.barrel: 0.9136, Acc.basket: 0.5940, Acc.waterfall: 0.6708, Acc.tent: 0.9814, Acc.bag: 0.3371, Acc.minibike: 0.9019, Acc.cradle: 0.9772, Acc.oven: 0.8129, Acc.ball: 0.3885, Acc.food: 0.7252, Acc.step: 0.1625, Acc.tank: 0.6586, Acc.trade name: 0.1511, Acc.microwave: 0.9684, Acc.pot: 0.7047, Acc.animal: 0.6235, Acc.bicycle: 0.8140, Acc.lake: 0.6894, Acc.dishwasher: 0.8432, Acc.screen: 0.8930, Acc.blanket: 0.4018, Acc.sculpture: 0.8448, Acc.hood: 0.7569, Acc.sconce: 0.7923, Acc.vase: 0.6489, Acc.traffic light: 0.7067, Acc.tray: 0.2637, Acc.ashcan: 0.7013, Acc.fan: 0.8363, Acc.pier: 0.4324, Acc.crt screen: 0.3018, Acc.plate: 0.8084, Acc.monitor: 0.4098, Acc.bulletin board: 0.8213, Acc.shower: 0.1958, Acc.radiator: 0.8861, Acc.glass: 0.2375, Acc.clock: 0.5161, Acc.flag: 0.7879 +2024-06-19 02:15:02,632 - mmseg - INFO - Iter [40050/80000] lr: 1.998e-05, eta: 23:42:17, time: 4.199, data_time: 2.231, memory: 72263, decode.loss_ce: 0.1912, decode.acc_seg: 91.7710, aux.loss_ce: 0.0798, aux.acc_seg: 91.4405, loss: 0.2710 +2024-06-19 02:16:41,547 - mmseg - INFO - Iter [40100/80000] lr: 1.995e-05, eta: 23:40:23, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1954, decode.acc_seg: 91.6077, aux.loss_ce: 0.0810, aux.acc_seg: 91.2964, loss: 0.2764 +2024-06-19 02:18:20,445 - mmseg - INFO - Iter [40150/80000] lr: 1.993e-05, eta: 23:38:28, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1863, decode.acc_seg: 92.0118, aux.loss_ce: 0.0779, aux.acc_seg: 91.6243, loss: 0.2642 +2024-06-19 02:19:59,370 - mmseg - INFO - Iter [40200/80000] lr: 1.990e-05, eta: 23:36:33, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1877, decode.acc_seg: 92.0333, aux.loss_ce: 0.0786, aux.acc_seg: 91.6705, loss: 0.2662 +2024-06-19 02:21:38,238 - mmseg - INFO - Iter [40250/80000] lr: 1.988e-05, eta: 23:34:39, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1908, decode.acc_seg: 91.8155, aux.loss_ce: 0.0796, aux.acc_seg: 91.5783, loss: 0.2704 +2024-06-19 02:23:17,143 - mmseg - INFO - Iter [40300/80000] lr: 1.985e-05, eta: 23:32:44, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1950, decode.acc_seg: 92.0087, aux.loss_ce: 0.0810, aux.acc_seg: 91.5763, loss: 0.2760 +2024-06-19 02:24:56,211 - mmseg - INFO - Iter [40350/80000] lr: 1.983e-05, eta: 23:30:50, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1876, decode.acc_seg: 91.9425, aux.loss_ce: 0.0786, aux.acc_seg: 91.6397, loss: 0.2662 +2024-06-19 02:26:35,029 - mmseg - INFO - Iter [40400/80000] lr: 1.980e-05, eta: 23:28:56, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1848, decode.acc_seg: 92.1475, aux.loss_ce: 0.0774, aux.acc_seg: 91.7903, loss: 0.2622 +2024-06-19 02:28:16,605 - mmseg - INFO - Iter [40450/80000] lr: 1.978e-05, eta: 23:27:04, time: 2.031, data_time: 0.061, memory: 72263, decode.loss_ce: 0.1865, decode.acc_seg: 92.2064, aux.loss_ce: 0.0772, aux.acc_seg: 91.8395, loss: 0.2637 +2024-06-19 02:29:55,569 - mmseg - INFO - Iter [40500/80000] lr: 1.975e-05, eta: 23:25:09, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1725, decode.acc_seg: 92.4033, aux.loss_ce: 0.0720, aux.acc_seg: 92.0271, loss: 0.2445 +2024-06-19 02:31:34,418 - mmseg - INFO - Iter [40550/80000] lr: 1.973e-05, eta: 23:23:15, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1808, decode.acc_seg: 92.0877, aux.loss_ce: 0.0752, aux.acc_seg: 91.8118, loss: 0.2560 +2024-06-19 02:33:13,230 - mmseg - INFO - Iter [40600/80000] lr: 1.970e-05, eta: 23:21:21, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1912, decode.acc_seg: 92.2175, aux.loss_ce: 0.0800, aux.acc_seg: 91.8457, loss: 0.2712 +2024-06-19 02:34:52,404 - mmseg - INFO - Iter [40650/80000] lr: 1.968e-05, eta: 23:19:27, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1943, decode.acc_seg: 91.5647, aux.loss_ce: 0.0812, aux.acc_seg: 91.2079, loss: 0.2755 +2024-06-19 02:36:31,267 - mmseg - INFO - Iter [40700/80000] lr: 1.965e-05, eta: 23:17:32, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1808, decode.acc_seg: 92.3432, aux.loss_ce: 0.0760, aux.acc_seg: 91.8950, loss: 0.2567 +2024-06-19 02:38:10,135 - mmseg - INFO - Iter [40750/80000] lr: 1.963e-05, eta: 23:15:38, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1760, decode.acc_seg: 92.2887, aux.loss_ce: 0.0737, aux.acc_seg: 91.9333, loss: 0.2497 +2024-06-19 02:39:48,928 - mmseg - INFO - Iter [40800/80000] lr: 1.960e-05, eta: 23:13:44, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2043, decode.acc_seg: 91.5098, aux.loss_ce: 0.0845, aux.acc_seg: 91.2072, loss: 0.2887 +2024-06-19 02:41:28,018 - mmseg - INFO - Iter [40850/80000] lr: 1.958e-05, eta: 23:11:50, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1939, decode.acc_seg: 91.7754, aux.loss_ce: 0.0810, aux.acc_seg: 91.4445, loss: 0.2749 +2024-06-19 02:43:06,919 - mmseg - INFO - Iter [40900/80000] lr: 1.955e-05, eta: 23:09:56, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1928, decode.acc_seg: 92.0985, aux.loss_ce: 0.0804, aux.acc_seg: 91.7608, loss: 0.2732 +2024-06-19 02:44:45,913 - mmseg - INFO - Iter [40950/80000] lr: 1.953e-05, eta: 23:08:02, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1864, decode.acc_seg: 92.0370, aux.loss_ce: 0.0772, aux.acc_seg: 91.7234, loss: 0.2635 +2024-06-19 02:46:24,774 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 02:46:24,774 - mmseg - INFO - Iter [41000/80000] lr: 1.950e-05, eta: 23:06:08, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1899, decode.acc_seg: 92.2592, aux.loss_ce: 0.0788, aux.acc_seg: 91.8838, loss: 0.2687 +2024-06-19 02:48:15,139 - mmseg - INFO - per class results: +2024-06-19 02:48:15,145 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.9 | 90.09 | +| building | 84.74 | 92.84 | +| sky | 94.75 | 97.72 | +| floor | 84.66 | 90.29 | +| tree | 77.56 | 88.9 | +| ceiling | 87.51 | 93.98 | +| road | 85.52 | 91.89 | +| bed | 92.49 | 96.22 | +| windowpane | 66.65 | 80.05 | +| grass | 70.05 | 88.66 | +| cabinet | 67.45 | 76.18 | +| sidewalk | 70.11 | 82.47 | +| person | 85.69 | 93.96 | +| earth | 40.47 | 53.54 | +| door | 58.33 | 75.31 | +| table | 67.8 | 82.05 | +| mountain | 63.06 | 72.75 | +| plant | 57.06 | 65.96 | +| curtain | 79.25 | 89.5 | +| chair | 66.57 | 78.58 | +| car | 87.59 | 94.66 | +| water | 63.17 | 79.51 | +| painting | 79.17 | 91.34 | +| sofa | 81.67 | 89.2 | +| shelf | 49.5 | 66.0 | +| house | 52.35 | 67.92 | +| sea | 74.98 | 86.23 | +| mirror | 79.71 | 86.77 | +| rug | 67.21 | 81.01 | +| field | 32.75 | 38.54 | +| armchair | 60.8 | 80.52 | +| seat | 68.44 | 88.74 | +| fence | 47.93 | 65.86 | +| desk | 59.55 | 77.97 | +| rock | 57.24 | 88.46 | +| wardrobe | 54.92 | 71.15 | +| lamp | 76.09 | 87.73 | +| bathtub | 84.95 | 88.65 | +| railing | 43.78 | 60.5 | +| cushion | 69.08 | 85.82 | +| base | 40.34 | 59.3 | +| box | 37.74 | 52.99 | +| column | 58.35 | 70.12 | +| signboard | 41.28 | 54.81 | +| chest of drawers | 44.58 | 63.07 | +| counter | 42.87 | 54.58 | +| sand | 46.86 | 78.97 | +| sink | 84.28 | 91.51 | +| skyscraper | 49.52 | 64.94 | +| fireplace | 73.02 | 94.03 | +| refrigerator | 82.27 | 93.2 | +| grandstand | 49.43 | 85.59 | +| path | 33.04 | 42.7 | +| stairs | 30.09 | 35.11 | +| runway | 67.79 | 88.32 | +| case | 57.02 | 66.4 | +| pool table | 94.86 | 98.52 | +| pillow | 64.19 | 74.11 | +| screen door | 82.82 | 85.88 | +| stairway | 48.38 | 71.76 | +| river | 13.37 | 20.52 | +| bridge | 63.86 | 70.84 | +| bookcase | 46.14 | 65.81 | +| blind | 46.81 | 53.21 | +| coffee table | 61.4 | 91.71 | +| toilet | 90.79 | 95.79 | +| flower | 49.52 | 70.17 | +| book | 57.13 | 79.56 | +| hill | 7.9 | 13.11 | +| bench | 61.11 | 68.59 | +| countertop | 64.0 | 84.13 | +| stove | 86.54 | 93.79 | +| palm | 51.27 | 83.47 | +| kitchen island | 43.46 | 93.91 | +| computer | 76.8 | 92.85 | +| swivel chair | 52.2 | 82.48 | +| boat | 64.82 | 94.11 | +| bar | 61.81 | 78.76 | +| arcade machine | 91.37 | 98.11 | +| hovel | 43.12 | 49.39 | +| bus | 93.79 | 96.95 | +| towel | 74.9 | 92.26 | +| light | 62.01 | 70.65 | +| truck | 52.69 | 62.37 | +| tower | 27.45 | 49.26 | +| chandelier | 75.42 | 85.7 | +| awning | 49.81 | 64.4 | +| streetlight | 37.57 | 51.42 | +| booth | 34.3 | 50.58 | +| television receiver | 82.14 | 90.13 | +| airplane | 82.67 | 96.83 | +| dirt track | 6.57 | 23.13 | +| apparel | 63.11 | 78.49 | +| pole | 25.86 | 34.92 | +| land | 4.1 | 7.38 | +| bannister | 21.31 | 30.21 | +| escalator | 65.32 | 82.38 | +| ottoman | 57.07 | 69.97 | +| bottle | 44.32 | 71.44 | +| buffet | 61.12 | 71.63 | +| poster | 33.72 | 37.74 | +| stage | 24.8 | 45.87 | +| van | 49.84 | 63.54 | +| ship | 77.58 | 90.53 | +| fountain | 52.66 | 52.86 | +| conveyer belt | 78.46 | 97.37 | +| canopy | 48.04 | 61.31 | +| washer | 79.17 | 83.24 | +| plaything | 39.65 | 59.17 | +| swimming pool | 53.82 | 83.92 | +| stool | 53.16 | 78.53 | +| barrel | 65.74 | 94.17 | +| basket | 41.7 | 61.5 | +| waterfall | 49.32 | 70.52 | +| tent | 95.36 | 98.98 | +| bag | 23.37 | 26.76 | +| minibike | 77.08 | 88.98 | +| cradle | 89.26 | 96.85 | +| oven | 59.27 | 68.79 | +| ball | 47.08 | 49.8 | +| food | 66.21 | 77.74 | +| step | 13.59 | 17.52 | +| tank | 73.27 | 84.13 | +| trade name | 33.29 | 40.58 | +| microwave | 88.57 | 95.79 | +| pot | 60.83 | 70.36 | +| animal | 61.27 | 62.9 | +| bicycle | 60.34 | 81.76 | +| lake | 53.47 | 63.74 | +| dishwasher | 76.67 | 82.06 | +| screen | 61.59 | 88.42 | +| blanket | 26.6 | 33.63 | +| sculpture | 69.26 | 86.25 | +| hood | 64.49 | 75.18 | +| sconce | 61.85 | 72.82 | +| vase | 50.69 | 66.52 | +| traffic light | 36.47 | 68.2 | +| tray | 20.6 | 24.11 | +| ashcan | 52.55 | 66.75 | +| fan | 71.01 | 79.02 | +| pier | 43.1 | 48.51 | +| crt screen | 10.22 | 22.5 | +| plate | 63.54 | 78.05 | +| monitor | 32.04 | 42.12 | +| bulletin board | 57.81 | 64.78 | +| shower | 15.26 | 15.74 | +| radiator | 68.91 | 83.44 | +| glass | 23.02 | 25.16 | +| clock | 55.57 | 62.93 | +| flag | 71.97 | 82.56 | ++---------------------+-------+-------+ +2024-06-19 02:48:15,145 - mmseg - INFO - Summary: +2024-06-19 02:48:15,146 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.22 | 58.26 | 71.45 | ++-------+-------+-------+ +2024-06-19 02:48:15,146 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 02:48:15,147 - mmseg - INFO - Iter(val) [250] aAcc: 0.8622, mIoU: 0.5826, mAcc: 0.7145, IoU.wall: 0.8190, IoU.building: 0.8474, IoU.sky: 0.9475, IoU.floor: 0.8466, IoU.tree: 0.7756, IoU.ceiling: 0.8751, IoU.road: 0.8552, IoU.bed : 0.9249, IoU.windowpane: 0.6665, IoU.grass: 0.7005, IoU.cabinet: 0.6745, IoU.sidewalk: 0.7011, IoU.person: 0.8569, IoU.earth: 0.4047, IoU.door: 0.5833, IoU.table: 0.6780, IoU.mountain: 0.6306, IoU.plant: 0.5706, IoU.curtain: 0.7925, IoU.chair: 0.6657, IoU.car: 0.8759, IoU.water: 0.6317, IoU.painting: 0.7917, IoU.sofa: 0.8167, IoU.shelf: 0.4950, IoU.house: 0.5235, IoU.sea: 0.7498, IoU.mirror: 0.7971, IoU.rug: 0.6721, IoU.field: 0.3275, IoU.armchair: 0.6080, IoU.seat: 0.6844, IoU.fence: 0.4793, IoU.desk: 0.5955, IoU.rock: 0.5724, IoU.wardrobe: 0.5492, IoU.lamp: 0.7609, IoU.bathtub: 0.8495, IoU.railing: 0.4378, IoU.cushion: 0.6908, IoU.base: 0.4034, IoU.box: 0.3774, IoU.column: 0.5835, IoU.signboard: 0.4128, IoU.chest of drawers: 0.4458, IoU.counter: 0.4287, IoU.sand: 0.4686, IoU.sink: 0.8428, IoU.skyscraper: 0.4952, IoU.fireplace: 0.7302, IoU.refrigerator: 0.8227, IoU.grandstand: 0.4943, IoU.path: 0.3304, IoU.stairs: 0.3009, IoU.runway: 0.6779, IoU.case: 0.5702, IoU.pool table: 0.9486, IoU.pillow: 0.6419, IoU.screen door: 0.8282, IoU.stairway: 0.4838, IoU.river: 0.1337, IoU.bridge: 0.6386, IoU.bookcase: 0.4614, IoU.blind: 0.4681, IoU.coffee table: 0.6140, IoU.toilet: 0.9079, IoU.flower: 0.4952, IoU.book: 0.5713, IoU.hill: 0.0790, IoU.bench: 0.6111, IoU.countertop: 0.6400, IoU.stove: 0.8654, IoU.palm: 0.5127, IoU.kitchen island: 0.4346, IoU.computer: 0.7680, IoU.swivel chair: 0.5220, IoU.boat: 0.6482, IoU.bar: 0.6181, IoU.arcade machine: 0.9137, IoU.hovel: 0.4312, IoU.bus: 0.9379, IoU.towel: 0.7490, IoU.light: 0.6201, IoU.truck: 0.5269, IoU.tower: 0.2745, IoU.chandelier: 0.7542, IoU.awning: 0.4981, IoU.streetlight: 0.3757, IoU.booth: 0.3430, IoU.television receiver: 0.8214, IoU.airplane: 0.8267, IoU.dirt track: 0.0657, IoU.apparel: 0.6311, IoU.pole: 0.2586, IoU.land: 0.0410, IoU.bannister: 0.2131, IoU.escalator: 0.6532, IoU.ottoman: 0.5707, IoU.bottle: 0.4432, IoU.buffet: 0.6112, IoU.poster: 0.3372, IoU.stage: 0.2480, IoU.van: 0.4984, IoU.ship: 0.7758, IoU.fountain: 0.5266, IoU.conveyer belt: 0.7846, IoU.canopy: 0.4804, IoU.washer: 0.7917, IoU.plaything: 0.3965, IoU.swimming pool: 0.5382, IoU.stool: 0.5316, IoU.barrel: 0.6574, IoU.basket: 0.4170, IoU.waterfall: 0.4932, IoU.tent: 0.9536, IoU.bag: 0.2337, IoU.minibike: 0.7708, IoU.cradle: 0.8926, IoU.oven: 0.5927, IoU.ball: 0.4708, IoU.food: 0.6621, IoU.step: 0.1359, IoU.tank: 0.7327, IoU.trade name: 0.3329, IoU.microwave: 0.8857, IoU.pot: 0.6083, IoU.animal: 0.6127, IoU.bicycle: 0.6034, IoU.lake: 0.5347, IoU.dishwasher: 0.7667, IoU.screen: 0.6159, IoU.blanket: 0.2660, IoU.sculpture: 0.6926, IoU.hood: 0.6449, IoU.sconce: 0.6185, IoU.vase: 0.5069, IoU.traffic light: 0.3647, IoU.tray: 0.2060, IoU.ashcan: 0.5255, IoU.fan: 0.7101, IoU.pier: 0.4310, IoU.crt screen: 0.1022, IoU.plate: 0.6354, IoU.monitor: 0.3204, IoU.bulletin board: 0.5781, IoU.shower: 0.1526, IoU.radiator: 0.6891, IoU.glass: 0.2302, IoU.clock: 0.5557, IoU.flag: 0.7197, Acc.wall: 0.9009, Acc.building: 0.9284, Acc.sky: 0.9772, Acc.floor: 0.9029, Acc.tree: 0.8890, Acc.ceiling: 0.9398, Acc.road: 0.9189, Acc.bed : 0.9622, Acc.windowpane: 0.8005, Acc.grass: 0.8866, Acc.cabinet: 0.7618, Acc.sidewalk: 0.8247, Acc.person: 0.9396, Acc.earth: 0.5354, Acc.door: 0.7531, Acc.table: 0.8205, Acc.mountain: 0.7275, Acc.plant: 0.6596, Acc.curtain: 0.8950, Acc.chair: 0.7858, Acc.car: 0.9466, Acc.water: 0.7951, Acc.painting: 0.9134, Acc.sofa: 0.8920, Acc.shelf: 0.6600, Acc.house: 0.6792, Acc.sea: 0.8623, Acc.mirror: 0.8677, Acc.rug: 0.8101, Acc.field: 0.3854, Acc.armchair: 0.8052, Acc.seat: 0.8874, Acc.fence: 0.6586, Acc.desk: 0.7797, Acc.rock: 0.8846, Acc.wardrobe: 0.7115, Acc.lamp: 0.8773, Acc.bathtub: 0.8865, Acc.railing: 0.6050, Acc.cushion: 0.8582, Acc.base: 0.5930, Acc.box: 0.5299, Acc.column: 0.7012, Acc.signboard: 0.5481, Acc.chest of drawers: 0.6307, Acc.counter: 0.5458, Acc.sand: 0.7897, Acc.sink: 0.9151, Acc.skyscraper: 0.6494, Acc.fireplace: 0.9403, Acc.refrigerator: 0.9320, Acc.grandstand: 0.8559, Acc.path: 0.4270, Acc.stairs: 0.3511, Acc.runway: 0.8832, Acc.case: 0.6640, Acc.pool table: 0.9852, Acc.pillow: 0.7411, Acc.screen door: 0.8588, Acc.stairway: 0.7176, Acc.river: 0.2052, Acc.bridge: 0.7084, Acc.bookcase: 0.6581, Acc.blind: 0.5321, Acc.coffee table: 0.9171, Acc.toilet: 0.9579, Acc.flower: 0.7017, Acc.book: 0.7956, Acc.hill: 0.1311, Acc.bench: 0.6859, Acc.countertop: 0.8413, Acc.stove: 0.9379, Acc.palm: 0.8347, Acc.kitchen island: 0.9391, Acc.computer: 0.9285, Acc.swivel chair: 0.8248, Acc.boat: 0.9411, Acc.bar: 0.7876, Acc.arcade machine: 0.9811, Acc.hovel: 0.4939, Acc.bus: 0.9695, Acc.towel: 0.9226, Acc.light: 0.7065, Acc.truck: 0.6237, Acc.tower: 0.4926, Acc.chandelier: 0.8570, Acc.awning: 0.6440, Acc.streetlight: 0.5142, Acc.booth: 0.5058, Acc.television receiver: 0.9013, Acc.airplane: 0.9683, Acc.dirt track: 0.2313, Acc.apparel: 0.7849, Acc.pole: 0.3492, Acc.land: 0.0738, Acc.bannister: 0.3021, Acc.escalator: 0.8238, Acc.ottoman: 0.6997, Acc.bottle: 0.7144, Acc.buffet: 0.7163, Acc.poster: 0.3774, Acc.stage: 0.4587, Acc.van: 0.6354, Acc.ship: 0.9053, Acc.fountain: 0.5286, Acc.conveyer belt: 0.9737, Acc.canopy: 0.6131, Acc.washer: 0.8324, Acc.plaything: 0.5917, Acc.swimming pool: 0.8392, Acc.stool: 0.7853, Acc.barrel: 0.9417, Acc.basket: 0.6150, Acc.waterfall: 0.7052, Acc.tent: 0.9898, Acc.bag: 0.2676, Acc.minibike: 0.8898, Acc.cradle: 0.9685, Acc.oven: 0.6879, Acc.ball: 0.4980, Acc.food: 0.7774, Acc.step: 0.1752, Acc.tank: 0.8413, Acc.trade name: 0.4058, Acc.microwave: 0.9579, Acc.pot: 0.7036, Acc.animal: 0.6290, Acc.bicycle: 0.8176, Acc.lake: 0.6374, Acc.dishwasher: 0.8206, Acc.screen: 0.8842, Acc.blanket: 0.3363, Acc.sculpture: 0.8625, Acc.hood: 0.7518, Acc.sconce: 0.7282, Acc.vase: 0.6652, Acc.traffic light: 0.6820, Acc.tray: 0.2411, Acc.ashcan: 0.6675, Acc.fan: 0.7902, Acc.pier: 0.4851, Acc.crt screen: 0.2250, Acc.plate: 0.7805, Acc.monitor: 0.4212, Acc.bulletin board: 0.6478, Acc.shower: 0.1574, Acc.radiator: 0.8344, Acc.glass: 0.2516, Acc.clock: 0.6293, Acc.flag: 0.8256 +2024-06-19 02:49:54,439 - mmseg - INFO - Iter [41050/80000] lr: 1.948e-05, eta: 23:05:59, time: 4.193, data_time: 2.224, memory: 72263, decode.loss_ce: 0.1871, decode.acc_seg: 92.0701, aux.loss_ce: 0.0780, aux.acc_seg: 91.6990, loss: 0.2651 +2024-06-19 02:51:33,322 - mmseg - INFO - Iter [41100/80000] lr: 1.945e-05, eta: 23:04:05, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1897, decode.acc_seg: 92.0592, aux.loss_ce: 0.0788, aux.acc_seg: 91.7004, loss: 0.2685 +2024-06-19 02:53:12,242 - mmseg - INFO - Iter [41150/80000] lr: 1.943e-05, eta: 23:02:11, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2098, decode.acc_seg: 91.4201, aux.loss_ce: 0.0870, aux.acc_seg: 91.0943, loss: 0.2968 +2024-06-19 02:54:51,303 - mmseg - INFO - Iter [41200/80000] lr: 1.940e-05, eta: 23:00:17, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1963, decode.acc_seg: 91.2936, aux.loss_ce: 0.0818, aux.acc_seg: 90.9334, loss: 0.2781 +2024-06-19 02:56:30,109 - mmseg - INFO - Iter [41250/80000] lr: 1.938e-05, eta: 22:58:23, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1893, decode.acc_seg: 92.2429, aux.loss_ce: 0.0782, aux.acc_seg: 91.9421, loss: 0.2675 +2024-06-19 02:58:09,034 - mmseg - INFO - Iter [41300/80000] lr: 1.935e-05, eta: 22:56:29, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1839, decode.acc_seg: 92.3960, aux.loss_ce: 0.0765, aux.acc_seg: 92.0199, loss: 0.2604 +2024-06-19 02:59:47,917 - mmseg - INFO - Iter [41350/80000] lr: 1.933e-05, eta: 22:54:35, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1912, decode.acc_seg: 91.9830, aux.loss_ce: 0.0795, aux.acc_seg: 91.6739, loss: 0.2708 +2024-06-19 03:01:26,912 - mmseg - INFO - Iter [41400/80000] lr: 1.930e-05, eta: 22:52:41, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1805, decode.acc_seg: 92.1989, aux.loss_ce: 0.0754, aux.acc_seg: 91.9214, loss: 0.2559 +2024-06-19 03:03:05,833 - mmseg - INFO - Iter [41450/80000] lr: 1.928e-05, eta: 22:50:47, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1928, decode.acc_seg: 92.0211, aux.loss_ce: 0.0803, aux.acc_seg: 91.6988, loss: 0.2731 +2024-06-19 03:04:44,677 - mmseg - INFO - Iter [41500/80000] lr: 1.925e-05, eta: 22:48:53, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1895, decode.acc_seg: 92.0204, aux.loss_ce: 0.0793, aux.acc_seg: 91.7061, loss: 0.2688 +2024-06-19 03:06:23,603 - mmseg - INFO - Iter [41550/80000] lr: 1.923e-05, eta: 22:46:59, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1720, decode.acc_seg: 92.4566, aux.loss_ce: 0.0716, aux.acc_seg: 92.1801, loss: 0.2436 +2024-06-19 03:08:02,731 - mmseg - INFO - Iter [41600/80000] lr: 1.920e-05, eta: 22:45:05, time: 1.983, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1977, decode.acc_seg: 91.8071, aux.loss_ce: 0.0822, aux.acc_seg: 91.4572, loss: 0.2800 +2024-06-19 03:09:41,611 - mmseg - INFO - Iter [41650/80000] lr: 1.918e-05, eta: 22:43:12, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1757, decode.acc_seg: 92.3170, aux.loss_ce: 0.0729, aux.acc_seg: 92.0663, loss: 0.2486 +2024-06-19 03:11:23,061 - mmseg - INFO - Iter [41700/80000] lr: 1.915e-05, eta: 22:41:20, time: 2.029, data_time: 0.060, memory: 72263, decode.loss_ce: 0.1857, decode.acc_seg: 92.0235, aux.loss_ce: 0.0778, aux.acc_seg: 91.7164, loss: 0.2634 +2024-06-19 03:13:02,030 - mmseg - INFO - Iter [41750/80000] lr: 1.913e-05, eta: 22:39:27, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1661, decode.acc_seg: 92.9465, aux.loss_ce: 0.0701, aux.acc_seg: 92.6350, loss: 0.2362 +2024-06-19 03:14:40,969 - mmseg - INFO - Iter [41800/80000] lr: 1.910e-05, eta: 22:37:33, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1843, decode.acc_seg: 92.2763, aux.loss_ce: 0.0773, aux.acc_seg: 91.9012, loss: 0.2615 +2024-06-19 03:16:19,916 - mmseg - INFO - Iter [41850/80000] lr: 1.908e-05, eta: 22:35:39, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1789, decode.acc_seg: 92.1194, aux.loss_ce: 0.0749, aux.acc_seg: 91.7950, loss: 0.2538 +2024-06-19 03:17:58,699 - mmseg - INFO - Iter [41900/80000] lr: 1.905e-05, eta: 22:33:46, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1788, decode.acc_seg: 92.2086, aux.loss_ce: 0.0752, aux.acc_seg: 91.8735, loss: 0.2540 +2024-06-19 03:19:37,537 - mmseg - INFO - Iter [41950/80000] lr: 1.903e-05, eta: 22:31:52, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1921, decode.acc_seg: 91.9198, aux.loss_ce: 0.0803, aux.acc_seg: 91.5647, loss: 0.2723 +2024-06-19 03:21:16,486 - mmseg - INFO - Saving checkpoint at 42000 iterations +2024-06-19 03:22:41,254 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 03:22:41,254 - mmseg - INFO - Iter [42000/80000] lr: 1.900e-05, eta: 22:31:15, time: 3.674, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1778, decode.acc_seg: 92.5336, aux.loss_ce: 0.0747, aux.acc_seg: 92.1387, loss: 0.2525 +2024-06-19 03:24:30,338 - mmseg - INFO - per class results: +2024-06-19 03:24:30,345 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.61 | 89.93 | +| building | 85.2 | 93.11 | +| sky | 94.85 | 97.87 | +| floor | 84.44 | 91.46 | +| tree | 78.22 | 89.11 | +| ceiling | 86.98 | 94.06 | +| road | 83.4 | 92.21 | +| bed | 92.85 | 97.34 | +| windowpane | 65.98 | 81.23 | +| grass | 69.58 | 86.04 | +| cabinet | 67.25 | 74.95 | +| sidewalk | 67.75 | 78.14 | +| person | 86.2 | 94.03 | +| earth | 41.31 | 52.67 | +| door | 57.92 | 76.93 | +| table | 69.8 | 82.33 | +| mountain | 63.86 | 72.29 | +| plant | 57.72 | 70.16 | +| curtain | 78.28 | 87.42 | +| chair | 67.3 | 78.56 | +| car | 88.35 | 94.01 | +| water | 64.69 | 81.08 | +| painting | 80.45 | 91.37 | +| sofa | 82.71 | 89.26 | +| shelf | 51.14 | 72.43 | +| house | 56.18 | 71.5 | +| sea | 77.03 | 88.93 | +| mirror | 79.61 | 87.6 | +| rug | 66.51 | 80.14 | +| field | 29.98 | 46.06 | +| armchair | 59.71 | 76.89 | +| seat | 69.58 | 87.94 | +| fence | 51.99 | 71.88 | +| desk | 57.68 | 78.62 | +| rock | 58.78 | 86.72 | +| wardrobe | 56.58 | 75.18 | +| lamp | 76.29 | 84.38 | +| bathtub | 86.63 | 89.4 | +| railing | 42.68 | 63.87 | +| cushion | 71.23 | 83.5 | +| base | 37.75 | 66.73 | +| box | 39.27 | 52.47 | +| column | 58.47 | 70.88 | +| signboard | 41.9 | 58.22 | +| chest of drawers | 46.04 | 69.06 | +| counter | 37.55 | 40.92 | +| sand | 55.61 | 75.9 | +| sink | 84.23 | 89.72 | +| skyscraper | 46.77 | 59.38 | +| fireplace | 72.55 | 91.42 | +| refrigerator | 81.16 | 90.12 | +| grandstand | 52.88 | 86.58 | +| path | 29.12 | 41.54 | +| stairs | 36.29 | 42.85 | +| runway | 68.38 | 88.6 | +| case | 61.51 | 75.56 | +| pool table | 95.16 | 97.78 | +| pillow | 65.18 | 73.57 | +| screen door | 76.62 | 79.71 | +| stairway | 50.41 | 62.79 | +| river | 14.29 | 22.78 | +| bridge | 62.37 | 85.45 | +| bookcase | 38.77 | 46.79 | +| blind | 44.19 | 50.26 | +| coffee table | 62.02 | 86.46 | +| toilet | 91.2 | 94.77 | +| flower | 50.96 | 61.23 | +| book | 55.38 | 81.18 | +| hill | 10.97 | 22.05 | +| bench | 66.99 | 76.85 | +| countertop | 61.53 | 85.94 | +| stove | 87.87 | 93.33 | +| palm | 50.58 | 84.83 | +| kitchen island | 51.76 | 85.73 | +| computer | 77.39 | 90.84 | +| swivel chair | 51.98 | 77.6 | +| boat | 73.87 | 90.12 | +| bar | 65.95 | 81.35 | +| arcade machine | 92.13 | 97.2 | +| hovel | 45.74 | 53.92 | +| bus | 93.47 | 96.43 | +| towel | 80.27 | 83.8 | +| light | 62.22 | 72.35 | +| truck | 52.41 | 66.4 | +| tower | 31.27 | 52.09 | +| chandelier | 74.87 | 84.63 | +| awning | 50.86 | 68.74 | +| streetlight | 37.0 | 49.25 | +| booth | 40.39 | 54.53 | +| television receiver | 80.21 | 86.8 | +| airplane | 89.24 | 94.43 | +| dirt track | 5.53 | 21.88 | +| apparel | 64.4 | 89.12 | +| pole | 26.26 | 35.6 | +| land | 4.65 | 6.34 | +| bannister | 19.88 | 24.44 | +| escalator | 62.47 | 86.95 | +| ottoman | 57.04 | 69.4 | +| bottle | 48.63 | 66.99 | +| buffet | 56.16 | 62.55 | +| poster | 34.36 | 44.15 | +| stage | 24.3 | 42.24 | +| van | 52.85 | 70.97 | +| ship | 76.81 | 88.68 | +| fountain | 33.48 | 33.61 | +| conveyer belt | 78.72 | 95.91 | +| canopy | 28.09 | 37.62 | +| washer | 85.64 | 91.14 | +| plaything | 36.3 | 55.46 | +| swimming pool | 61.59 | 88.33 | +| stool | 53.98 | 76.54 | +| barrel | 71.72 | 85.02 | +| basket | 44.05 | 58.42 | +| waterfall | 50.67 | 66.02 | +| tent | 86.47 | 98.81 | +| bag | 24.6 | 27.35 | +| minibike | 77.98 | 87.06 | +| cradle | 85.56 | 97.43 | +| oven | 70.87 | 81.17 | +| ball | 57.65 | 63.43 | +| food | 61.05 | 70.55 | +| step | 18.97 | 22.38 | +| tank | 76.58 | 84.16 | +| trade name | 19.27 | 21.4 | +| microwave | 91.26 | 95.57 | +| pot | 60.24 | 70.82 | +| animal | 59.88 | 61.23 | +| bicycle | 61.43 | 76.15 | +| lake | 59.93 | 63.25 | +| dishwasher | 76.21 | 82.24 | +| screen | 64.76 | 92.7 | +| blanket | 29.39 | 40.89 | +| sculpture | 69.25 | 82.6 | +| hood | 72.25 | 92.44 | +| sconce | 60.13 | 68.5 | +| vase | 52.11 | 64.76 | +| traffic light | 37.83 | 69.47 | +| tray | 26.55 | 34.4 | +| ashcan | 50.58 | 69.71 | +| fan | 69.9 | 82.18 | +| pier | 40.62 | 43.98 | +| crt screen | 6.32 | 16.68 | +| plate | 64.99 | 82.7 | +| monitor | 13.05 | 15.61 | +| bulletin board | 53.77 | 68.46 | +| shower | 9.41 | 14.83 | +| radiator | 68.1 | 83.35 | +| glass | 21.39 | 22.67 | +| clock | 52.91 | 59.86 | +| flag | 73.9 | 81.26 | ++---------------------+-------+-------+ +2024-06-19 03:24:30,345 - mmseg - INFO - Summary: +2024-06-19 03:24:30,345 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.33 | 58.51 | 71.07 | ++-------+-------+-------+ +2024-06-19 03:24:30,346 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 03:24:30,346 - mmseg - INFO - Iter(val) [250] aAcc: 0.8633, mIoU: 0.5851, mAcc: 0.7107, IoU.wall: 0.8261, IoU.building: 0.8520, IoU.sky: 0.9485, IoU.floor: 0.8444, IoU.tree: 0.7822, IoU.ceiling: 0.8698, IoU.road: 0.8340, IoU.bed : 0.9285, IoU.windowpane: 0.6598, IoU.grass: 0.6958, IoU.cabinet: 0.6725, IoU.sidewalk: 0.6775, IoU.person: 0.8620, IoU.earth: 0.4131, IoU.door: 0.5792, IoU.table: 0.6980, IoU.mountain: 0.6386, IoU.plant: 0.5772, IoU.curtain: 0.7828, IoU.chair: 0.6730, IoU.car: 0.8835, IoU.water: 0.6469, IoU.painting: 0.8045, IoU.sofa: 0.8271, IoU.shelf: 0.5114, IoU.house: 0.5618, IoU.sea: 0.7703, IoU.mirror: 0.7961, IoU.rug: 0.6651, IoU.field: 0.2998, IoU.armchair: 0.5971, IoU.seat: 0.6958, IoU.fence: 0.5199, IoU.desk: 0.5768, IoU.rock: 0.5878, IoU.wardrobe: 0.5658, IoU.lamp: 0.7629, IoU.bathtub: 0.8663, IoU.railing: 0.4268, IoU.cushion: 0.7123, IoU.base: 0.3775, IoU.box: 0.3927, IoU.column: 0.5847, IoU.signboard: 0.4190, IoU.chest of drawers: 0.4604, IoU.counter: 0.3755, IoU.sand: 0.5561, IoU.sink: 0.8423, IoU.skyscraper: 0.4677, IoU.fireplace: 0.7255, IoU.refrigerator: 0.8116, IoU.grandstand: 0.5288, IoU.path: 0.2912, IoU.stairs: 0.3629, IoU.runway: 0.6838, IoU.case: 0.6151, IoU.pool table: 0.9516, IoU.pillow: 0.6518, IoU.screen door: 0.7662, IoU.stairway: 0.5041, IoU.river: 0.1429, IoU.bridge: 0.6237, IoU.bookcase: 0.3877, IoU.blind: 0.4419, IoU.coffee table: 0.6202, IoU.toilet: 0.9120, IoU.flower: 0.5096, IoU.book: 0.5538, IoU.hill: 0.1097, IoU.bench: 0.6699, IoU.countertop: 0.6153, IoU.stove: 0.8787, IoU.palm: 0.5058, IoU.kitchen island: 0.5176, IoU.computer: 0.7739, IoU.swivel chair: 0.5198, IoU.boat: 0.7387, IoU.bar: 0.6595, IoU.arcade machine: 0.9213, IoU.hovel: 0.4574, IoU.bus: 0.9347, IoU.towel: 0.8027, IoU.light: 0.6222, IoU.truck: 0.5241, IoU.tower: 0.3127, IoU.chandelier: 0.7487, IoU.awning: 0.5086, IoU.streetlight: 0.3700, IoU.booth: 0.4039, IoU.television receiver: 0.8021, IoU.airplane: 0.8924, IoU.dirt track: 0.0553, IoU.apparel: 0.6440, IoU.pole: 0.2626, IoU.land: 0.0465, IoU.bannister: 0.1988, IoU.escalator: 0.6247, IoU.ottoman: 0.5704, IoU.bottle: 0.4863, IoU.buffet: 0.5616, IoU.poster: 0.3436, IoU.stage: 0.2430, IoU.van: 0.5285, IoU.ship: 0.7681, IoU.fountain: 0.3348, IoU.conveyer belt: 0.7872, IoU.canopy: 0.2809, IoU.washer: 0.8564, IoU.plaything: 0.3630, IoU.swimming pool: 0.6159, IoU.stool: 0.5398, IoU.barrel: 0.7172, IoU.basket: 0.4405, IoU.waterfall: 0.5067, IoU.tent: 0.8647, IoU.bag: 0.2460, IoU.minibike: 0.7798, IoU.cradle: 0.8556, IoU.oven: 0.7087, IoU.ball: 0.5765, IoU.food: 0.6105, IoU.step: 0.1897, IoU.tank: 0.7658, IoU.trade name: 0.1927, IoU.microwave: 0.9126, IoU.pot: 0.6024, IoU.animal: 0.5988, IoU.bicycle: 0.6143, IoU.lake: 0.5993, IoU.dishwasher: 0.7621, IoU.screen: 0.6476, IoU.blanket: 0.2939, IoU.sculpture: 0.6925, IoU.hood: 0.7225, IoU.sconce: 0.6013, IoU.vase: 0.5211, IoU.traffic light: 0.3783, IoU.tray: 0.2655, IoU.ashcan: 0.5058, IoU.fan: 0.6990, IoU.pier: 0.4062, IoU.crt screen: 0.0632, IoU.plate: 0.6499, IoU.monitor: 0.1305, IoU.bulletin board: 0.5377, IoU.shower: 0.0941, IoU.radiator: 0.6810, IoU.glass: 0.2139, IoU.clock: 0.5291, IoU.flag: 0.7390, Acc.wall: 0.8993, Acc.building: 0.9311, Acc.sky: 0.9787, Acc.floor: 0.9146, Acc.tree: 0.8911, Acc.ceiling: 0.9406, Acc.road: 0.9221, Acc.bed : 0.9734, Acc.windowpane: 0.8123, Acc.grass: 0.8604, Acc.cabinet: 0.7495, Acc.sidewalk: 0.7814, Acc.person: 0.9403, Acc.earth: 0.5267, Acc.door: 0.7693, Acc.table: 0.8233, Acc.mountain: 0.7229, Acc.plant: 0.7016, Acc.curtain: 0.8742, Acc.chair: 0.7856, Acc.car: 0.9401, Acc.water: 0.8108, Acc.painting: 0.9137, Acc.sofa: 0.8926, Acc.shelf: 0.7243, Acc.house: 0.7150, Acc.sea: 0.8893, Acc.mirror: 0.8760, Acc.rug: 0.8014, Acc.field: 0.4606, Acc.armchair: 0.7689, Acc.seat: 0.8794, Acc.fence: 0.7188, Acc.desk: 0.7862, Acc.rock: 0.8672, Acc.wardrobe: 0.7518, Acc.lamp: 0.8438, Acc.bathtub: 0.8940, Acc.railing: 0.6387, Acc.cushion: 0.8350, Acc.base: 0.6673, Acc.box: 0.5247, Acc.column: 0.7088, Acc.signboard: 0.5822, Acc.chest of drawers: 0.6906, Acc.counter: 0.4092, Acc.sand: 0.7590, Acc.sink: 0.8972, Acc.skyscraper: 0.5938, Acc.fireplace: 0.9142, Acc.refrigerator: 0.9012, Acc.grandstand: 0.8658, Acc.path: 0.4154, Acc.stairs: 0.4285, Acc.runway: 0.8860, Acc.case: 0.7556, Acc.pool table: 0.9778, Acc.pillow: 0.7357, Acc.screen door: 0.7971, Acc.stairway: 0.6279, Acc.river: 0.2278, Acc.bridge: 0.8545, Acc.bookcase: 0.4679, Acc.blind: 0.5026, Acc.coffee table: 0.8646, Acc.toilet: 0.9477, Acc.flower: 0.6123, Acc.book: 0.8118, Acc.hill: 0.2205, Acc.bench: 0.7685, Acc.countertop: 0.8594, Acc.stove: 0.9333, Acc.palm: 0.8483, Acc.kitchen island: 0.8573, Acc.computer: 0.9084, Acc.swivel chair: 0.7760, Acc.boat: 0.9012, Acc.bar: 0.8135, Acc.arcade machine: 0.9720, Acc.hovel: 0.5392, Acc.bus: 0.9643, Acc.towel: 0.8380, Acc.light: 0.7235, Acc.truck: 0.6640, Acc.tower: 0.5209, Acc.chandelier: 0.8463, Acc.awning: 0.6874, Acc.streetlight: 0.4925, Acc.booth: 0.5453, Acc.television receiver: 0.8680, Acc.airplane: 0.9443, Acc.dirt track: 0.2188, Acc.apparel: 0.8912, Acc.pole: 0.3560, Acc.land: 0.0634, Acc.bannister: 0.2444, Acc.escalator: 0.8695, Acc.ottoman: 0.6940, Acc.bottle: 0.6699, Acc.buffet: 0.6255, Acc.poster: 0.4415, Acc.stage: 0.4224, Acc.van: 0.7097, Acc.ship: 0.8868, Acc.fountain: 0.3361, Acc.conveyer belt: 0.9591, Acc.canopy: 0.3762, Acc.washer: 0.9114, Acc.plaything: 0.5546, Acc.swimming pool: 0.8833, Acc.stool: 0.7654, Acc.barrel: 0.8502, Acc.basket: 0.5842, Acc.waterfall: 0.6602, Acc.tent: 0.9881, Acc.bag: 0.2735, Acc.minibike: 0.8706, Acc.cradle: 0.9743, Acc.oven: 0.8117, Acc.ball: 0.6343, Acc.food: 0.7055, Acc.step: 0.2238, Acc.tank: 0.8416, Acc.trade name: 0.2140, Acc.microwave: 0.9557, Acc.pot: 0.7082, Acc.animal: 0.6123, Acc.bicycle: 0.7615, Acc.lake: 0.6325, Acc.dishwasher: 0.8224, Acc.screen: 0.9270, Acc.blanket: 0.4089, Acc.sculpture: 0.8260, Acc.hood: 0.9244, Acc.sconce: 0.6850, Acc.vase: 0.6476, Acc.traffic light: 0.6947, Acc.tray: 0.3440, Acc.ashcan: 0.6971, Acc.fan: 0.8218, Acc.pier: 0.4398, Acc.crt screen: 0.1668, Acc.plate: 0.8270, Acc.monitor: 0.1561, Acc.bulletin board: 0.6846, Acc.shower: 0.1483, Acc.radiator: 0.8335, Acc.glass: 0.2267, Acc.clock: 0.5986, Acc.flag: 0.8126 +2024-06-19 03:26:09,525 - mmseg - INFO - Iter [42050/80000] lr: 1.898e-05, eta: 22:31:00, time: 4.165, data_time: 2.198, memory: 72263, decode.loss_ce: 0.1782, decode.acc_seg: 92.3754, aux.loss_ce: 0.0748, aux.acc_seg: 91.9895, loss: 0.2530 +2024-06-19 03:27:48,377 - mmseg - INFO - Iter [42100/80000] lr: 1.895e-05, eta: 22:29:06, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1857, decode.acc_seg: 92.3207, aux.loss_ce: 0.0776, aux.acc_seg: 91.9556, loss: 0.2633 +2024-06-19 03:29:27,319 - mmseg - INFO - Iter [42150/80000] lr: 1.893e-05, eta: 22:27:12, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1774, decode.acc_seg: 92.3023, aux.loss_ce: 0.0741, aux.acc_seg: 91.9938, loss: 0.2516 +2024-06-19 03:31:06,114 - mmseg - INFO - Iter [42200/80000] lr: 1.890e-05, eta: 22:25:19, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1863, decode.acc_seg: 92.0121, aux.loss_ce: 0.0776, aux.acc_seg: 91.7117, loss: 0.2640 +2024-06-19 03:32:44,989 - mmseg - INFO - Iter [42250/80000] lr: 1.888e-05, eta: 22:23:25, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1882, decode.acc_seg: 91.8767, aux.loss_ce: 0.0782, aux.acc_seg: 91.5768, loss: 0.2664 +2024-06-19 03:34:23,746 - mmseg - INFO - Iter [42300/80000] lr: 1.885e-05, eta: 22:21:31, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1876, decode.acc_seg: 92.0095, aux.loss_ce: 0.0778, aux.acc_seg: 91.7094, loss: 0.2654 +2024-06-19 03:36:02,577 - mmseg - INFO - Iter [42350/80000] lr: 1.883e-05, eta: 22:19:37, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1808, decode.acc_seg: 92.0517, aux.loss_ce: 0.0758, aux.acc_seg: 91.6588, loss: 0.2567 +2024-06-19 03:37:41,423 - mmseg - INFO - Iter [42400/80000] lr: 1.880e-05, eta: 22:17:43, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1831, decode.acc_seg: 91.9637, aux.loss_ce: 0.0768, aux.acc_seg: 91.6938, loss: 0.2599 +2024-06-19 03:39:20,342 - mmseg - INFO - Iter [42450/80000] lr: 1.878e-05, eta: 22:15:50, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1839, decode.acc_seg: 92.1453, aux.loss_ce: 0.0766, aux.acc_seg: 91.7927, loss: 0.2606 +2024-06-19 03:40:59,216 - mmseg - INFO - Iter [42500/80000] lr: 1.875e-05, eta: 22:13:56, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1824, decode.acc_seg: 92.0468, aux.loss_ce: 0.0755, aux.acc_seg: 91.7569, loss: 0.2579 +2024-06-19 03:42:38,084 - mmseg - INFO - Iter [42550/80000] lr: 1.873e-05, eta: 22:12:02, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1803, decode.acc_seg: 92.2026, aux.loss_ce: 0.0750, aux.acc_seg: 91.8683, loss: 0.2553 +2024-06-19 03:44:16,948 - mmseg - INFO - Iter [42600/80000] lr: 1.870e-05, eta: 22:10:09, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1948, decode.acc_seg: 91.9915, aux.loss_ce: 0.0808, aux.acc_seg: 91.6482, loss: 0.2756 +2024-06-19 03:45:55,703 - mmseg - INFO - Iter [42650/80000] lr: 1.868e-05, eta: 22:08:15, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1877, decode.acc_seg: 92.2051, aux.loss_ce: 0.0783, aux.acc_seg: 91.8484, loss: 0.2660 +2024-06-19 03:47:34,483 - mmseg - INFO - Iter [42700/80000] lr: 1.865e-05, eta: 22:06:21, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1812, decode.acc_seg: 92.3645, aux.loss_ce: 0.0757, aux.acc_seg: 92.0341, loss: 0.2569 +2024-06-19 03:49:13,373 - mmseg - INFO - Iter [42750/80000] lr: 1.863e-05, eta: 22:04:28, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1877, decode.acc_seg: 91.9601, aux.loss_ce: 0.0783, aux.acc_seg: 91.6378, loss: 0.2661 +2024-06-19 03:50:52,174 - mmseg - INFO - Iter [42800/80000] lr: 1.860e-05, eta: 22:02:35, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1789, decode.acc_seg: 92.2031, aux.loss_ce: 0.0748, aux.acc_seg: 91.8258, loss: 0.2538 +2024-06-19 03:52:31,127 - mmseg - INFO - Iter [42850/80000] lr: 1.858e-05, eta: 22:00:41, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1883, decode.acc_seg: 91.9135, aux.loss_ce: 0.0788, aux.acc_seg: 91.5719, loss: 0.2670 +2024-06-19 03:54:09,924 - mmseg - INFO - Iter [42900/80000] lr: 1.855e-05, eta: 21:58:48, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1774, decode.acc_seg: 92.3198, aux.loss_ce: 0.0733, aux.acc_seg: 92.0594, loss: 0.2507 +2024-06-19 03:55:51,412 - mmseg - INFO - Iter [42950/80000] lr: 1.853e-05, eta: 21:56:57, time: 2.030, data_time: 0.058, memory: 72263, decode.loss_ce: 0.1804, decode.acc_seg: 92.3739, aux.loss_ce: 0.0750, aux.acc_seg: 92.0538, loss: 0.2554 +2024-06-19 03:57:30,349 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 03:57:30,349 - mmseg - INFO - Iter [43000/80000] lr: 1.850e-05, eta: 21:55:03, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1748, decode.acc_seg: 92.3323, aux.loss_ce: 0.0740, aux.acc_seg: 91.9284, loss: 0.2488 +2024-06-19 03:59:20,469 - mmseg - INFO - per class results: +2024-06-19 03:59:20,475 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.37 | 90.51 | +| building | 85.29 | 94.1 | +| sky | 94.86 | 97.47 | +| floor | 84.86 | 90.89 | +| tree | 77.68 | 88.07 | +| ceiling | 86.69 | 93.76 | +| road | 85.23 | 90.87 | +| bed | 92.38 | 97.25 | +| windowpane | 66.08 | 81.19 | +| grass | 68.96 | 79.82 | +| cabinet | 67.48 | 76.72 | +| sidewalk | 69.82 | 86.22 | +| person | 85.68 | 94.82 | +| earth | 40.52 | 56.28 | +| door | 60.93 | 73.27 | +| table | 69.33 | 80.99 | +| mountain | 63.18 | 73.04 | +| plant | 56.47 | 65.74 | +| curtain | 80.12 | 89.49 | +| chair | 68.25 | 82.45 | +| car | 88.16 | 94.51 | +| water | 62.76 | 76.52 | +| painting | 81.22 | 90.13 | +| sofa | 82.1 | 88.88 | +| shelf | 49.09 | 69.21 | +| house | 53.29 | 66.08 | +| sea | 73.37 | 86.15 | +| mirror | 78.07 | 86.28 | +| rug | 65.99 | 75.98 | +| field | 29.74 | 52.14 | +| armchair | 60.81 | 75.54 | +| seat | 69.22 | 87.45 | +| fence | 51.86 | 65.68 | +| desk | 56.9 | 78.34 | +| rock | 60.07 | 87.65 | +| wardrobe | 57.75 | 75.6 | +| lamp | 75.79 | 87.89 | +| bathtub | 88.81 | 92.29 | +| railing | 44.97 | 64.38 | +| cushion | 72.35 | 84.26 | +| base | 38.84 | 64.59 | +| box | 38.29 | 51.34 | +| column | 59.11 | 70.73 | +| signboard | 42.75 | 58.03 | +| chest of drawers | 47.73 | 68.14 | +| counter | 47.37 | 54.57 | +| sand | 49.27 | 78.21 | +| sink | 84.93 | 91.59 | +| skyscraper | 45.5 | 56.33 | +| fireplace | 71.37 | 84.09 | +| refrigerator | 79.91 | 84.42 | +| grandstand | 53.88 | 83.55 | +| path | 29.97 | 43.83 | +| stairs | 26.6 | 31.59 | +| runway | 68.48 | 88.85 | +| case | 61.72 | 76.44 | +| pool table | 95.09 | 98.43 | +| pillow | 66.29 | 74.88 | +| screen door | 86.37 | 89.42 | +| stairway | 39.93 | 58.07 | +| river | 11.55 | 21.52 | +| bridge | 75.9 | 83.83 | +| bookcase | 41.38 | 60.35 | +| blind | 43.59 | 50.55 | +| coffee table | 61.68 | 86.34 | +| toilet | 91.28 | 94.81 | +| flower | 47.08 | 65.39 | +| book | 57.2 | 75.75 | +| hill | 8.97 | 14.43 | +| bench | 65.27 | 77.29 | +| countertop | 64.46 | 82.77 | +| stove | 86.26 | 93.37 | +| palm | 50.47 | 84.56 | +| kitchen island | 43.81 | 80.03 | +| computer | 75.74 | 86.26 | +| swivel chair | 48.39 | 69.15 | +| boat | 54.21 | 92.55 | +| bar | 63.25 | 76.5 | +| arcade machine | 92.31 | 95.52 | +| hovel | 48.93 | 55.11 | +| bus | 93.6 | 96.86 | +| towel | 83.04 | 88.96 | +| light | 62.82 | 75.14 | +| truck | 53.15 | 64.04 | +| tower | 17.65 | 27.1 | +| chandelier | 73.99 | 84.75 | +| awning | 41.87 | 49.39 | +| streetlight | 34.79 | 46.23 | +| booth | 45.69 | 72.72 | +| television receiver | 80.97 | 87.77 | +| airplane | 88.52 | 95.37 | +| dirt track | 7.27 | 30.02 | +| apparel | 63.7 | 78.07 | +| pole | 32.2 | 47.18 | +| land | 5.15 | 7.92 | +| bannister | 20.8 | 27.14 | +| escalator | 64.37 | 87.45 | +| ottoman | 57.6 | 77.13 | +| bottle | 45.8 | 74.28 | +| buffet | 58.08 | 65.36 | +| poster | 36.99 | 45.23 | +| stage | 25.88 | 43.48 | +| van | 44.47 | 56.36 | +| ship | 71.59 | 84.41 | +| fountain | 43.51 | 45.14 | +| conveyer belt | 80.48 | 97.16 | +| canopy | 56.32 | 65.44 | +| washer | 87.88 | 93.24 | +| plaything | 33.91 | 50.78 | +| swimming pool | 59.7 | 91.19 | +| stool | 59.24 | 72.13 | +| barrel | 66.93 | 91.19 | +| basket | 44.5 | 59.56 | +| waterfall | 55.11 | 74.29 | +| tent | 93.02 | 98.76 | +| bag | 29.73 | 33.79 | +| minibike | 79.02 | 90.02 | +| cradle | 88.2 | 97.85 | +| oven | 69.49 | 81.32 | +| ball | 60.54 | 72.78 | +| food | 61.98 | 68.49 | +| step | 19.96 | 25.98 | +| tank | 78.74 | 94.07 | +| trade name | 17.74 | 19.57 | +| microwave | 91.59 | 96.28 | +| pot | 58.86 | 67.44 | +| animal | 62.26 | 64.42 | +| bicycle | 62.15 | 80.01 | +| lake | 50.36 | 66.64 | +| dishwasher | 72.79 | 81.49 | +| screen | 58.83 | 79.84 | +| blanket | 30.64 | 37.91 | +| sculpture | 70.44 | 83.52 | +| hood | 60.81 | 70.8 | +| sconce | 62.34 | 74.81 | +| vase | 51.32 | 69.39 | +| traffic light | 43.12 | 57.48 | +| tray | 26.37 | 32.85 | +| ashcan | 48.17 | 64.7 | +| fan | 72.94 | 85.7 | +| pier | 38.58 | 43.37 | +| crt screen | 8.82 | 21.63 | +| plate | 62.99 | 81.77 | +| monitor | 24.38 | 34.62 | +| bulletin board | 54.55 | 64.77 | +| shower | 17.93 | 21.82 | +| radiator | 69.83 | 79.61 | +| glass | 24.03 | 26.88 | +| clock | 50.34 | 59.67 | +| flag | 70.07 | 72.78 | ++---------------------+-------+-------+ +2024-06-19 03:59:20,475 - mmseg - INFO - Summary: +2024-06-19 03:59:20,475 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.29 | 58.63 | 71.18 | ++-------+-------+-------+ +2024-06-19 03:59:20,476 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 03:59:20,476 - mmseg - INFO - Iter(val) [250] aAcc: 0.8629, mIoU: 0.5863, mAcc: 0.7118, IoU.wall: 0.8237, IoU.building: 0.8529, IoU.sky: 0.9486, IoU.floor: 0.8486, IoU.tree: 0.7768, IoU.ceiling: 0.8669, IoU.road: 0.8523, IoU.bed : 0.9238, IoU.windowpane: 0.6608, IoU.grass: 0.6896, IoU.cabinet: 0.6748, IoU.sidewalk: 0.6982, IoU.person: 0.8568, IoU.earth: 0.4052, IoU.door: 0.6093, IoU.table: 0.6933, IoU.mountain: 0.6318, IoU.plant: 0.5647, IoU.curtain: 0.8012, IoU.chair: 0.6825, IoU.car: 0.8816, IoU.water: 0.6276, IoU.painting: 0.8122, IoU.sofa: 0.8210, IoU.shelf: 0.4909, IoU.house: 0.5329, IoU.sea: 0.7337, IoU.mirror: 0.7807, IoU.rug: 0.6599, IoU.field: 0.2974, IoU.armchair: 0.6081, IoU.seat: 0.6922, IoU.fence: 0.5186, IoU.desk: 0.5690, IoU.rock: 0.6007, IoU.wardrobe: 0.5775, IoU.lamp: 0.7579, IoU.bathtub: 0.8881, IoU.railing: 0.4497, IoU.cushion: 0.7235, IoU.base: 0.3884, IoU.box: 0.3829, IoU.column: 0.5911, IoU.signboard: 0.4275, IoU.chest of drawers: 0.4773, IoU.counter: 0.4737, IoU.sand: 0.4927, IoU.sink: 0.8493, IoU.skyscraper: 0.4550, IoU.fireplace: 0.7137, IoU.refrigerator: 0.7991, IoU.grandstand: 0.5388, IoU.path: 0.2997, IoU.stairs: 0.2660, IoU.runway: 0.6848, IoU.case: 0.6172, IoU.pool table: 0.9509, IoU.pillow: 0.6629, IoU.screen door: 0.8637, IoU.stairway: 0.3993, IoU.river: 0.1155, IoU.bridge: 0.7590, IoU.bookcase: 0.4138, IoU.blind: 0.4359, IoU.coffee table: 0.6168, IoU.toilet: 0.9128, IoU.flower: 0.4708, IoU.book: 0.5720, IoU.hill: 0.0897, IoU.bench: 0.6527, IoU.countertop: 0.6446, IoU.stove: 0.8626, IoU.palm: 0.5047, IoU.kitchen island: 0.4381, IoU.computer: 0.7574, IoU.swivel chair: 0.4839, IoU.boat: 0.5421, IoU.bar: 0.6325, IoU.arcade machine: 0.9231, IoU.hovel: 0.4893, IoU.bus: 0.9360, IoU.towel: 0.8304, IoU.light: 0.6282, IoU.truck: 0.5315, IoU.tower: 0.1765, IoU.chandelier: 0.7399, IoU.awning: 0.4187, IoU.streetlight: 0.3479, IoU.booth: 0.4569, IoU.television receiver: 0.8097, IoU.airplane: 0.8852, IoU.dirt track: 0.0727, IoU.apparel: 0.6370, IoU.pole: 0.3220, IoU.land: 0.0515, IoU.bannister: 0.2080, IoU.escalator: 0.6437, IoU.ottoman: 0.5760, IoU.bottle: 0.4580, IoU.buffet: 0.5808, IoU.poster: 0.3699, IoU.stage: 0.2588, IoU.van: 0.4447, IoU.ship: 0.7159, IoU.fountain: 0.4351, IoU.conveyer belt: 0.8048, IoU.canopy: 0.5632, IoU.washer: 0.8788, IoU.plaything: 0.3391, IoU.swimming pool: 0.5970, IoU.stool: 0.5924, IoU.barrel: 0.6693, IoU.basket: 0.4450, IoU.waterfall: 0.5511, IoU.tent: 0.9302, IoU.bag: 0.2973, IoU.minibike: 0.7902, IoU.cradle: 0.8820, IoU.oven: 0.6949, IoU.ball: 0.6054, IoU.food: 0.6198, IoU.step: 0.1996, IoU.tank: 0.7874, IoU.trade name: 0.1774, IoU.microwave: 0.9159, IoU.pot: 0.5886, IoU.animal: 0.6226, IoU.bicycle: 0.6215, IoU.lake: 0.5036, IoU.dishwasher: 0.7279, IoU.screen: 0.5883, IoU.blanket: 0.3064, IoU.sculpture: 0.7044, IoU.hood: 0.6081, IoU.sconce: 0.6234, IoU.vase: 0.5132, IoU.traffic light: 0.4312, IoU.tray: 0.2637, IoU.ashcan: 0.4817, IoU.fan: 0.7294, IoU.pier: 0.3858, IoU.crt screen: 0.0882, IoU.plate: 0.6299, IoU.monitor: 0.2438, IoU.bulletin board: 0.5455, IoU.shower: 0.1793, IoU.radiator: 0.6983, IoU.glass: 0.2403, IoU.clock: 0.5034, IoU.flag: 0.7007, Acc.wall: 0.9051, Acc.building: 0.9410, Acc.sky: 0.9747, Acc.floor: 0.9089, Acc.tree: 0.8807, Acc.ceiling: 0.9376, Acc.road: 0.9087, Acc.bed : 0.9725, Acc.windowpane: 0.8119, Acc.grass: 0.7982, Acc.cabinet: 0.7672, Acc.sidewalk: 0.8622, Acc.person: 0.9482, Acc.earth: 0.5628, Acc.door: 0.7327, Acc.table: 0.8099, Acc.mountain: 0.7304, Acc.plant: 0.6574, Acc.curtain: 0.8949, Acc.chair: 0.8245, Acc.car: 0.9451, Acc.water: 0.7652, Acc.painting: 0.9013, Acc.sofa: 0.8888, Acc.shelf: 0.6921, Acc.house: 0.6608, Acc.sea: 0.8615, Acc.mirror: 0.8628, Acc.rug: 0.7598, Acc.field: 0.5214, Acc.armchair: 0.7554, Acc.seat: 0.8745, Acc.fence: 0.6568, Acc.desk: 0.7834, Acc.rock: 0.8765, Acc.wardrobe: 0.7560, Acc.lamp: 0.8789, Acc.bathtub: 0.9229, Acc.railing: 0.6438, Acc.cushion: 0.8426, Acc.base: 0.6459, Acc.box: 0.5134, Acc.column: 0.7073, Acc.signboard: 0.5803, Acc.chest of drawers: 0.6814, Acc.counter: 0.5457, Acc.sand: 0.7821, Acc.sink: 0.9159, Acc.skyscraper: 0.5633, Acc.fireplace: 0.8409, Acc.refrigerator: 0.8442, Acc.grandstand: 0.8355, Acc.path: 0.4383, Acc.stairs: 0.3159, Acc.runway: 0.8885, Acc.case: 0.7644, Acc.pool table: 0.9843, Acc.pillow: 0.7488, Acc.screen door: 0.8942, Acc.stairway: 0.5807, Acc.river: 0.2152, Acc.bridge: 0.8383, Acc.bookcase: 0.6035, Acc.blind: 0.5055, Acc.coffee table: 0.8634, Acc.toilet: 0.9481, Acc.flower: 0.6539, Acc.book: 0.7575, Acc.hill: 0.1443, Acc.bench: 0.7729, Acc.countertop: 0.8277, Acc.stove: 0.9337, Acc.palm: 0.8456, Acc.kitchen island: 0.8003, Acc.computer: 0.8626, Acc.swivel chair: 0.6915, Acc.boat: 0.9255, Acc.bar: 0.7650, Acc.arcade machine: 0.9552, Acc.hovel: 0.5511, Acc.bus: 0.9686, Acc.towel: 0.8896, Acc.light: 0.7514, Acc.truck: 0.6404, Acc.tower: 0.2710, Acc.chandelier: 0.8475, Acc.awning: 0.4939, Acc.streetlight: 0.4623, Acc.booth: 0.7272, Acc.television receiver: 0.8777, Acc.airplane: 0.9537, Acc.dirt track: 0.3002, Acc.apparel: 0.7807, Acc.pole: 0.4718, Acc.land: 0.0792, Acc.bannister: 0.2714, Acc.escalator: 0.8745, Acc.ottoman: 0.7713, Acc.bottle: 0.7428, Acc.buffet: 0.6536, Acc.poster: 0.4523, Acc.stage: 0.4348, Acc.van: 0.5636, Acc.ship: 0.8441, Acc.fountain: 0.4514, Acc.conveyer belt: 0.9716, Acc.canopy: 0.6544, Acc.washer: 0.9324, Acc.plaything: 0.5078, Acc.swimming pool: 0.9119, Acc.stool: 0.7213, Acc.barrel: 0.9119, Acc.basket: 0.5956, Acc.waterfall: 0.7429, Acc.tent: 0.9876, Acc.bag: 0.3379, Acc.minibike: 0.9002, Acc.cradle: 0.9785, Acc.oven: 0.8132, Acc.ball: 0.7278, Acc.food: 0.6849, Acc.step: 0.2598, Acc.tank: 0.9407, Acc.trade name: 0.1957, Acc.microwave: 0.9628, Acc.pot: 0.6744, Acc.animal: 0.6442, Acc.bicycle: 0.8001, Acc.lake: 0.6664, Acc.dishwasher: 0.8149, Acc.screen: 0.7984, Acc.blanket: 0.3791, Acc.sculpture: 0.8352, Acc.hood: 0.7080, Acc.sconce: 0.7481, Acc.vase: 0.6939, Acc.traffic light: 0.5748, Acc.tray: 0.3285, Acc.ashcan: 0.6470, Acc.fan: 0.8570, Acc.pier: 0.4337, Acc.crt screen: 0.2163, Acc.plate: 0.8177, Acc.monitor: 0.3462, Acc.bulletin board: 0.6477, Acc.shower: 0.2182, Acc.radiator: 0.7961, Acc.glass: 0.2688, Acc.clock: 0.5967, Acc.flag: 0.7278 +2024-06-19 04:00:59,921 - mmseg - INFO - Iter [43050/80000] lr: 1.848e-05, eta: 21:54:45, time: 4.191, data_time: 2.219, memory: 72263, decode.loss_ce: 0.1788, decode.acc_seg: 92.3308, aux.loss_ce: 0.0752, aux.acc_seg: 91.9649, loss: 0.2541 +2024-06-19 04:02:38,828 - mmseg - INFO - Iter [43100/80000] lr: 1.845e-05, eta: 21:52:52, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1781, decode.acc_seg: 92.3075, aux.loss_ce: 0.0750, aux.acc_seg: 91.9556, loss: 0.2530 +2024-06-19 04:04:17,738 - mmseg - INFO - Iter [43150/80000] lr: 1.843e-05, eta: 21:50:58, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1826, decode.acc_seg: 91.8971, aux.loss_ce: 0.0762, aux.acc_seg: 91.6125, loss: 0.2588 +2024-06-19 04:05:56,573 - mmseg - INFO - Iter [43200/80000] lr: 1.840e-05, eta: 21:49:05, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1820, decode.acc_seg: 92.3857, aux.loss_ce: 0.0757, aux.acc_seg: 92.0439, loss: 0.2578 +2024-06-19 04:07:35,579 - mmseg - INFO - Iter [43250/80000] lr: 1.838e-05, eta: 21:47:12, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1811, decode.acc_seg: 92.4150, aux.loss_ce: 0.0755, aux.acc_seg: 92.0894, loss: 0.2566 +2024-06-19 04:09:14,417 - mmseg - INFO - Iter [43300/80000] lr: 1.835e-05, eta: 21:45:18, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1607, decode.acc_seg: 93.0520, aux.loss_ce: 0.0678, aux.acc_seg: 92.6713, loss: 0.2285 +2024-06-19 04:10:53,319 - mmseg - INFO - Iter [43350/80000] lr: 1.833e-05, eta: 21:43:25, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1710, decode.acc_seg: 92.4690, aux.loss_ce: 0.0720, aux.acc_seg: 92.0889, loss: 0.2430 +2024-06-19 04:12:32,230 - mmseg - INFO - Iter [43400/80000] lr: 1.830e-05, eta: 21:41:32, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1834, decode.acc_seg: 92.2468, aux.loss_ce: 0.0764, aux.acc_seg: 91.9471, loss: 0.2598 +2024-06-19 04:14:11,083 - mmseg - INFO - Iter [43450/80000] lr: 1.828e-05, eta: 21:39:38, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1766, decode.acc_seg: 92.3956, aux.loss_ce: 0.0746, aux.acc_seg: 91.9358, loss: 0.2512 +2024-06-19 04:15:49,988 - mmseg - INFO - Iter [43500/80000] lr: 1.825e-05, eta: 21:37:45, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1800, decode.acc_seg: 92.4113, aux.loss_ce: 0.0748, aux.acc_seg: 92.0763, loss: 0.2548 +2024-06-19 04:17:28,833 - mmseg - INFO - Iter [43550/80000] lr: 1.823e-05, eta: 21:35:52, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1703, decode.acc_seg: 92.7148, aux.loss_ce: 0.0714, aux.acc_seg: 92.4018, loss: 0.2417 +2024-06-19 04:19:07,807 - mmseg - INFO - Iter [43600/80000] lr: 1.820e-05, eta: 21:33:59, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1769, decode.acc_seg: 92.3502, aux.loss_ce: 0.0742, aux.acc_seg: 91.9028, loss: 0.2511 +2024-06-19 04:20:46,719 - mmseg - INFO - Iter [43650/80000] lr: 1.818e-05, eta: 21:32:06, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1776, decode.acc_seg: 92.4676, aux.loss_ce: 0.0744, aux.acc_seg: 92.1168, loss: 0.2520 +2024-06-19 04:22:25,659 - mmseg - INFO - Iter [43700/80000] lr: 1.815e-05, eta: 21:30:13, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1761, decode.acc_seg: 92.5541, aux.loss_ce: 0.0736, aux.acc_seg: 92.2626, loss: 0.2497 +2024-06-19 04:24:04,621 - mmseg - INFO - Iter [43750/80000] lr: 1.813e-05, eta: 21:28:20, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1760, decode.acc_seg: 92.4628, aux.loss_ce: 0.0742, aux.acc_seg: 92.0502, loss: 0.2502 +2024-06-19 04:25:43,541 - mmseg - INFO - Iter [43800/80000] lr: 1.810e-05, eta: 21:26:27, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1865, decode.acc_seg: 92.1487, aux.loss_ce: 0.0775, aux.acc_seg: 91.8010, loss: 0.2640 +2024-06-19 04:27:22,330 - mmseg - INFO - Iter [43850/80000] lr: 1.808e-05, eta: 21:24:34, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1703, decode.acc_seg: 92.7205, aux.loss_ce: 0.0710, aux.acc_seg: 92.3664, loss: 0.2413 +2024-06-19 04:29:01,261 - mmseg - INFO - Iter [43900/80000] lr: 1.805e-05, eta: 21:22:41, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1767, decode.acc_seg: 92.7256, aux.loss_ce: 0.0734, aux.acc_seg: 92.4171, loss: 0.2502 +2024-06-19 04:30:40,118 - mmseg - INFO - Iter [43950/80000] lr: 1.803e-05, eta: 21:20:48, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1681, decode.acc_seg: 92.6643, aux.loss_ce: 0.0703, aux.acc_seg: 92.3133, loss: 0.2383 +2024-06-19 04:32:19,019 - mmseg - INFO - Saving checkpoint at 44000 iterations +2024-06-19 04:33:43,632 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 04:33:43,633 - mmseg - INFO - Iter [44000/80000] lr: 1.800e-05, eta: 21:20:04, time: 3.670, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1757, decode.acc_seg: 92.4732, aux.loss_ce: 0.0739, aux.acc_seg: 92.0880, loss: 0.2496 +2024-06-19 04:35:32,885 - mmseg - INFO - per class results: +2024-06-19 04:35:32,891 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.57 | 90.2 | +| building | 85.35 | 93.75 | +| sky | 95.15 | 97.65 | +| floor | 85.01 | 91.59 | +| tree | 78.74 | 90.24 | +| ceiling | 87.65 | 93.79 | +| road | 85.59 | 91.19 | +| bed | 93.07 | 96.93 | +| windowpane | 66.35 | 82.96 | +| grass | 66.6 | 78.28 | +| cabinet | 67.92 | 76.38 | +| sidewalk | 71.17 | 85.77 | +| person | 86.05 | 94.64 | +| earth | 40.08 | 55.78 | +| door | 59.77 | 71.27 | +| table | 66.52 | 82.33 | +| mountain | 64.7 | 76.56 | +| plant | 58.69 | 68.97 | +| curtain | 79.35 | 89.67 | +| chair | 68.13 | 77.94 | +| car | 88.43 | 94.28 | +| water | 61.49 | 75.29 | +| painting | 79.77 | 90.95 | +| sofa | 81.39 | 88.16 | +| shelf | 46.13 | 58.88 | +| house | 55.1 | 64.11 | +| sea | 75.31 | 90.97 | +| mirror | 78.72 | 88.66 | +| rug | 68.12 | 79.15 | +| field | 27.92 | 54.31 | +| armchair | 61.69 | 82.17 | +| seat | 69.75 | 87.09 | +| fence | 50.85 | 63.19 | +| desk | 58.5 | 81.58 | +| rock | 60.96 | 78.23 | +| wardrobe | 55.76 | 77.71 | +| lamp | 76.59 | 87.72 | +| bathtub | 86.68 | 91.09 | +| railing | 43.33 | 60.79 | +| cushion | 71.05 | 82.44 | +| base | 46.09 | 64.4 | +| box | 38.44 | 48.62 | +| column | 57.09 | 68.51 | +| signboard | 42.12 | 53.97 | +| chest of drawers | 51.49 | 71.78 | +| counter | 44.65 | 49.04 | +| sand | 50.52 | 79.51 | +| sink | 85.19 | 89.0 | +| skyscraper | 48.41 | 62.27 | +| fireplace | 74.51 | 93.36 | +| refrigerator | 79.68 | 90.57 | +| grandstand | 49.21 | 84.58 | +| path | 31.04 | 40.17 | +| stairs | 31.3 | 40.57 | +| runway | 65.92 | 84.7 | +| case | 60.98 | 79.91 | +| pool table | 95.28 | 98.24 | +| pillow | 67.71 | 77.29 | +| screen door | 87.55 | 92.03 | +| stairway | 48.51 | 61.3 | +| river | 9.96 | 18.46 | +| bridge | 75.66 | 84.53 | +| bookcase | 37.66 | 72.34 | +| blind | 45.35 | 53.41 | +| coffee table | 61.43 | 86.94 | +| toilet | 90.98 | 94.55 | +| flower | 44.13 | 58.29 | +| book | 50.43 | 70.51 | +| hill | 15.07 | 26.34 | +| bench | 70.54 | 81.24 | +| countertop | 63.33 | 84.13 | +| stove | 87.16 | 91.22 | +| palm | 51.58 | 83.45 | +| kitchen island | 48.56 | 91.87 | +| computer | 78.25 | 91.24 | +| swivel chair | 50.83 | 83.78 | +| boat | 66.87 | 91.13 | +| bar | 62.64 | 74.36 | +| arcade machine | 88.09 | 91.89 | +| hovel | 48.47 | 54.63 | +| bus | 93.89 | 97.34 | +| towel | 79.89 | 85.25 | +| light | 62.14 | 72.95 | +| truck | 52.24 | 69.5 | +| tower | 33.26 | 61.19 | +| chandelier | 75.23 | 87.97 | +| awning | 53.91 | 69.35 | +| streetlight | 36.38 | 50.32 | +| booth | 49.68 | 73.88 | +| television receiver | 81.76 | 85.72 | +| airplane | 85.77 | 95.29 | +| dirt track | 2.94 | 9.31 | +| apparel | 60.59 | 77.09 | +| pole | 28.41 | 37.94 | +| land | 5.6 | 8.64 | +| bannister | 24.59 | 30.83 | +| escalator | 65.54 | 85.74 | +| ottoman | 59.42 | 76.52 | +| bottle | 44.88 | 72.57 | +| buffet | 61.48 | 77.09 | +| poster | 35.01 | 42.2 | +| stage | 25.39 | 42.07 | +| van | 51.75 | 67.31 | +| ship | 71.56 | 77.68 | +| fountain | 38.64 | 39.36 | +| conveyer belt | 83.2 | 94.66 | +| canopy | 48.59 | 64.16 | +| washer | 88.72 | 94.92 | +| plaything | 48.66 | 67.29 | +| swimming pool | 56.3 | 80.74 | +| stool | 57.7 | 70.29 | +| barrel | 67.05 | 94.78 | +| basket | 42.25 | 58.23 | +| waterfall | 58.83 | 79.44 | +| tent | 92.02 | 98.9 | +| bag | 25.11 | 27.62 | +| minibike | 79.41 | 86.99 | +| cradle | 78.43 | 97.17 | +| oven | 66.56 | 80.46 | +| ball | 57.2 | 64.85 | +| food | 49.59 | 54.99 | +| step | 10.99 | 13.16 | +| tank | 61.79 | 67.74 | +| trade name | 20.73 | 22.99 | +| microwave | 91.36 | 96.25 | +| pot | 59.07 | 69.94 | +| animal | 61.05 | 62.14 | +| bicycle | 58.25 | 75.15 | +| lake | 54.33 | 63.84 | +| dishwasher | 72.33 | 78.78 | +| screen | 62.13 | 86.95 | +| blanket | 33.93 | 48.13 | +| sculpture | 76.93 | 86.32 | +| hood | 65.22 | 75.49 | +| sconce | 59.78 | 69.83 | +| vase | 52.09 | 68.12 | +| traffic light | 39.42 | 55.42 | +| tray | 25.38 | 32.3 | +| ashcan | 47.96 | 63.69 | +| fan | 72.85 | 83.71 | +| pier | 39.97 | 42.59 | +| crt screen | 15.68 | 21.12 | +| plate | 65.54 | 79.74 | +| monitor | 62.15 | 74.77 | +| bulletin board | 52.13 | 85.72 | +| shower | 17.02 | 17.96 | +| radiator | 68.43 | 82.41 | +| glass | 21.56 | 22.9 | +| clock | 53.18 | 61.86 | +| flag | 70.47 | 79.55 | ++---------------------+-------+-------+ +2024-06-19 04:35:32,891 - mmseg - INFO - Summary: +2024-06-19 04:35:32,891 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.43 | 59.07 | 71.83 | ++-------+-------+-------+ +2024-06-19 04:35:32,892 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 04:35:32,892 - mmseg - INFO - Iter(val) [250] aAcc: 0.8643, mIoU: 0.5907, mAcc: 0.7183, IoU.wall: 0.8257, IoU.building: 0.8535, IoU.sky: 0.9515, IoU.floor: 0.8501, IoU.tree: 0.7874, IoU.ceiling: 0.8765, IoU.road: 0.8559, IoU.bed : 0.9307, IoU.windowpane: 0.6635, IoU.grass: 0.6660, IoU.cabinet: 0.6792, IoU.sidewalk: 0.7117, IoU.person: 0.8605, IoU.earth: 0.4008, IoU.door: 0.5977, IoU.table: 0.6652, IoU.mountain: 0.6470, IoU.plant: 0.5869, IoU.curtain: 0.7935, IoU.chair: 0.6813, IoU.car: 0.8843, IoU.water: 0.6149, IoU.painting: 0.7977, IoU.sofa: 0.8139, IoU.shelf: 0.4613, IoU.house: 0.5510, IoU.sea: 0.7531, IoU.mirror: 0.7872, IoU.rug: 0.6812, IoU.field: 0.2792, IoU.armchair: 0.6169, IoU.seat: 0.6975, IoU.fence: 0.5085, IoU.desk: 0.5850, IoU.rock: 0.6096, IoU.wardrobe: 0.5576, IoU.lamp: 0.7659, IoU.bathtub: 0.8668, IoU.railing: 0.4333, IoU.cushion: 0.7105, IoU.base: 0.4609, IoU.box: 0.3844, IoU.column: 0.5709, IoU.signboard: 0.4212, IoU.chest of drawers: 0.5149, IoU.counter: 0.4465, IoU.sand: 0.5052, IoU.sink: 0.8519, IoU.skyscraper: 0.4841, IoU.fireplace: 0.7451, IoU.refrigerator: 0.7968, IoU.grandstand: 0.4921, IoU.path: 0.3104, IoU.stairs: 0.3130, IoU.runway: 0.6592, IoU.case: 0.6098, IoU.pool table: 0.9528, IoU.pillow: 0.6771, IoU.screen door: 0.8755, IoU.stairway: 0.4851, IoU.river: 0.0996, IoU.bridge: 0.7566, IoU.bookcase: 0.3766, IoU.blind: 0.4535, IoU.coffee table: 0.6143, IoU.toilet: 0.9098, IoU.flower: 0.4413, IoU.book: 0.5043, IoU.hill: 0.1507, IoU.bench: 0.7054, IoU.countertop: 0.6333, IoU.stove: 0.8716, IoU.palm: 0.5158, IoU.kitchen island: 0.4856, IoU.computer: 0.7825, IoU.swivel chair: 0.5083, IoU.boat: 0.6687, IoU.bar: 0.6264, IoU.arcade machine: 0.8809, IoU.hovel: 0.4847, IoU.bus: 0.9389, IoU.towel: 0.7989, IoU.light: 0.6214, IoU.truck: 0.5224, IoU.tower: 0.3326, IoU.chandelier: 0.7523, IoU.awning: 0.5391, IoU.streetlight: 0.3638, IoU.booth: 0.4968, IoU.television receiver: 0.8176, IoU.airplane: 0.8577, IoU.dirt track: 0.0294, IoU.apparel: 0.6059, IoU.pole: 0.2841, IoU.land: 0.0560, IoU.bannister: 0.2459, IoU.escalator: 0.6554, IoU.ottoman: 0.5942, IoU.bottle: 0.4488, IoU.buffet: 0.6148, IoU.poster: 0.3501, IoU.stage: 0.2539, IoU.van: 0.5175, IoU.ship: 0.7156, IoU.fountain: 0.3864, IoU.conveyer belt: 0.8320, IoU.canopy: 0.4859, IoU.washer: 0.8872, IoU.plaything: 0.4866, IoU.swimming pool: 0.5630, IoU.stool: 0.5770, IoU.barrel: 0.6705, IoU.basket: 0.4225, IoU.waterfall: 0.5883, IoU.tent: 0.9202, IoU.bag: 0.2511, IoU.minibike: 0.7941, IoU.cradle: 0.7843, IoU.oven: 0.6656, IoU.ball: 0.5720, IoU.food: 0.4959, IoU.step: 0.1099, IoU.tank: 0.6179, IoU.trade name: 0.2073, IoU.microwave: 0.9136, IoU.pot: 0.5907, IoU.animal: 0.6105, IoU.bicycle: 0.5825, IoU.lake: 0.5433, IoU.dishwasher: 0.7233, IoU.screen: 0.6213, IoU.blanket: 0.3393, IoU.sculpture: 0.7693, IoU.hood: 0.6522, IoU.sconce: 0.5978, IoU.vase: 0.5209, IoU.traffic light: 0.3942, IoU.tray: 0.2538, IoU.ashcan: 0.4796, IoU.fan: 0.7285, IoU.pier: 0.3997, IoU.crt screen: 0.1568, IoU.plate: 0.6554, IoU.monitor: 0.6215, IoU.bulletin board: 0.5213, IoU.shower: 0.1702, IoU.radiator: 0.6843, IoU.glass: 0.2156, IoU.clock: 0.5318, IoU.flag: 0.7047, Acc.wall: 0.9020, Acc.building: 0.9375, Acc.sky: 0.9765, Acc.floor: 0.9159, Acc.tree: 0.9024, Acc.ceiling: 0.9379, Acc.road: 0.9119, Acc.bed : 0.9693, Acc.windowpane: 0.8296, Acc.grass: 0.7828, Acc.cabinet: 0.7638, Acc.sidewalk: 0.8577, Acc.person: 0.9464, Acc.earth: 0.5578, Acc.door: 0.7127, Acc.table: 0.8233, Acc.mountain: 0.7656, Acc.plant: 0.6897, Acc.curtain: 0.8967, Acc.chair: 0.7794, Acc.car: 0.9428, Acc.water: 0.7529, Acc.painting: 0.9095, Acc.sofa: 0.8816, Acc.shelf: 0.5888, Acc.house: 0.6411, Acc.sea: 0.9097, Acc.mirror: 0.8866, Acc.rug: 0.7915, Acc.field: 0.5431, Acc.armchair: 0.8217, Acc.seat: 0.8709, Acc.fence: 0.6319, Acc.desk: 0.8158, Acc.rock: 0.7823, Acc.wardrobe: 0.7771, Acc.lamp: 0.8772, Acc.bathtub: 0.9109, Acc.railing: 0.6079, Acc.cushion: 0.8244, Acc.base: 0.6440, Acc.box: 0.4862, Acc.column: 0.6851, Acc.signboard: 0.5397, Acc.chest of drawers: 0.7178, Acc.counter: 0.4904, Acc.sand: 0.7951, Acc.sink: 0.8900, Acc.skyscraper: 0.6227, Acc.fireplace: 0.9336, Acc.refrigerator: 0.9057, Acc.grandstand: 0.8458, Acc.path: 0.4017, Acc.stairs: 0.4057, Acc.runway: 0.8470, Acc.case: 0.7991, Acc.pool table: 0.9824, Acc.pillow: 0.7729, Acc.screen door: 0.9203, Acc.stairway: 0.6130, Acc.river: 0.1846, Acc.bridge: 0.8453, Acc.bookcase: 0.7234, Acc.blind: 0.5341, Acc.coffee table: 0.8694, Acc.toilet: 0.9455, Acc.flower: 0.5829, Acc.book: 0.7051, Acc.hill: 0.2634, Acc.bench: 0.8124, Acc.countertop: 0.8413, Acc.stove: 0.9122, Acc.palm: 0.8345, Acc.kitchen island: 0.9187, Acc.computer: 0.9124, Acc.swivel chair: 0.8378, Acc.boat: 0.9113, Acc.bar: 0.7436, Acc.arcade machine: 0.9189, Acc.hovel: 0.5463, Acc.bus: 0.9734, Acc.towel: 0.8525, Acc.light: 0.7295, Acc.truck: 0.6950, Acc.tower: 0.6119, Acc.chandelier: 0.8797, Acc.awning: 0.6935, Acc.streetlight: 0.5032, Acc.booth: 0.7388, Acc.television receiver: 0.8572, Acc.airplane: 0.9529, Acc.dirt track: 0.0931, Acc.apparel: 0.7709, Acc.pole: 0.3794, Acc.land: 0.0864, Acc.bannister: 0.3083, Acc.escalator: 0.8574, Acc.ottoman: 0.7652, Acc.bottle: 0.7257, Acc.buffet: 0.7709, Acc.poster: 0.4220, Acc.stage: 0.4207, Acc.van: 0.6731, Acc.ship: 0.7768, Acc.fountain: 0.3936, Acc.conveyer belt: 0.9466, Acc.canopy: 0.6416, Acc.washer: 0.9492, Acc.plaything: 0.6729, Acc.swimming pool: 0.8074, Acc.stool: 0.7029, Acc.barrel: 0.9478, Acc.basket: 0.5823, Acc.waterfall: 0.7944, Acc.tent: 0.9890, Acc.bag: 0.2762, Acc.minibike: 0.8699, Acc.cradle: 0.9717, Acc.oven: 0.8046, Acc.ball: 0.6485, Acc.food: 0.5499, Acc.step: 0.1316, Acc.tank: 0.6774, Acc.trade name: 0.2299, Acc.microwave: 0.9625, Acc.pot: 0.6994, Acc.animal: 0.6214, Acc.bicycle: 0.7515, Acc.lake: 0.6384, Acc.dishwasher: 0.7878, Acc.screen: 0.8695, Acc.blanket: 0.4813, Acc.sculpture: 0.8632, Acc.hood: 0.7549, Acc.sconce: 0.6983, Acc.vase: 0.6812, Acc.traffic light: 0.5542, Acc.tray: 0.3230, Acc.ashcan: 0.6369, Acc.fan: 0.8371, Acc.pier: 0.4259, Acc.crt screen: 0.2112, Acc.plate: 0.7974, Acc.monitor: 0.7477, Acc.bulletin board: 0.8572, Acc.shower: 0.1796, Acc.radiator: 0.8241, Acc.glass: 0.2290, Acc.clock: 0.6186, Acc.flag: 0.7955 +2024-06-19 04:37:12,112 - mmseg - INFO - Iter [44050/80000] lr: 1.798e-05, eta: 21:19:41, time: 4.170, data_time: 2.202, memory: 72263, decode.loss_ce: 0.1823, decode.acc_seg: 92.2022, aux.loss_ce: 0.0762, aux.acc_seg: 91.9109, loss: 0.2585 +2024-06-19 04:38:50,940 - mmseg - INFO - Iter [44100/80000] lr: 1.795e-05, eta: 21:17:47, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1611, decode.acc_seg: 92.9942, aux.loss_ce: 0.0674, aux.acc_seg: 92.6746, loss: 0.2285 +2024-06-19 04:40:29,892 - mmseg - INFO - Iter [44150/80000] lr: 1.793e-05, eta: 21:15:54, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1747, decode.acc_seg: 92.5574, aux.loss_ce: 0.0728, aux.acc_seg: 92.2855, loss: 0.2475 +2024-06-19 04:42:08,683 - mmseg - INFO - Iter [44200/80000] lr: 1.790e-05, eta: 21:14:01, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1813, decode.acc_seg: 92.4604, aux.loss_ce: 0.0759, aux.acc_seg: 92.1184, loss: 0.2572 +2024-06-19 04:43:50,379 - mmseg - INFO - Iter [44250/80000] lr: 1.788e-05, eta: 21:12:10, time: 2.034, data_time: 0.060, memory: 72263, decode.loss_ce: 0.1672, decode.acc_seg: 92.5641, aux.loss_ce: 0.0698, aux.acc_seg: 92.2166, loss: 0.2370 +2024-06-19 04:45:29,154 - mmseg - INFO - Iter [44300/80000] lr: 1.785e-05, eta: 21:10:17, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1829, decode.acc_seg: 92.2325, aux.loss_ce: 0.0760, aux.acc_seg: 91.9166, loss: 0.2589 +2024-06-19 04:47:08,090 - mmseg - INFO - Iter [44350/80000] lr: 1.783e-05, eta: 21:08:24, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1826, decode.acc_seg: 92.2679, aux.loss_ce: 0.0763, aux.acc_seg: 91.9776, loss: 0.2590 +2024-06-19 04:48:46,906 - mmseg - INFO - Iter [44400/80000] lr: 1.780e-05, eta: 21:06:31, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1778, decode.acc_seg: 92.5673, aux.loss_ce: 0.0736, aux.acc_seg: 92.2449, loss: 0.2514 +2024-06-19 04:50:25,825 - mmseg - INFO - Iter [44450/80000] lr: 1.778e-05, eta: 21:04:38, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1834, decode.acc_seg: 92.5403, aux.loss_ce: 0.0760, aux.acc_seg: 92.2047, loss: 0.2595 +2024-06-19 04:52:04,756 - mmseg - INFO - Iter [44500/80000] lr: 1.775e-05, eta: 21:02:45, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1649, decode.acc_seg: 92.8541, aux.loss_ce: 0.0693, aux.acc_seg: 92.4706, loss: 0.2342 +2024-06-19 04:53:43,779 - mmseg - INFO - Iter [44550/80000] lr: 1.773e-05, eta: 21:00:52, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1667, decode.acc_seg: 92.6277, aux.loss_ce: 0.0701, aux.acc_seg: 92.2753, loss: 0.2368 +2024-06-19 04:55:22,633 - mmseg - INFO - Iter [44600/80000] lr: 1.770e-05, eta: 20:58:59, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1736, decode.acc_seg: 92.3222, aux.loss_ce: 0.0728, aux.acc_seg: 91.9357, loss: 0.2464 +2024-06-19 04:57:01,465 - mmseg - INFO - Iter [44650/80000] lr: 1.768e-05, eta: 20:57:06, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1744, decode.acc_seg: 92.2494, aux.loss_ce: 0.0729, aux.acc_seg: 91.9343, loss: 0.2473 +2024-06-19 04:58:40,423 - mmseg - INFO - Iter [44700/80000] lr: 1.765e-05, eta: 20:55:13, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1778, decode.acc_seg: 92.4542, aux.loss_ce: 0.0744, aux.acc_seg: 92.1328, loss: 0.2521 +2024-06-19 05:00:19,318 - mmseg - INFO - Iter [44750/80000] lr: 1.763e-05, eta: 20:53:21, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1659, decode.acc_seg: 92.9329, aux.loss_ce: 0.0699, aux.acc_seg: 92.5932, loss: 0.2358 +2024-06-19 05:01:58,371 - mmseg - INFO - Iter [44800/80000] lr: 1.760e-05, eta: 20:51:28, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1817, decode.acc_seg: 92.2273, aux.loss_ce: 0.0760, aux.acc_seg: 91.8851, loss: 0.2577 +2024-06-19 05:03:37,275 - mmseg - INFO - Iter [44850/80000] lr: 1.758e-05, eta: 20:49:35, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1687, decode.acc_seg: 92.9716, aux.loss_ce: 0.0710, aux.acc_seg: 92.6279, loss: 0.2397 +2024-06-19 05:05:16,158 - mmseg - INFO - Iter [44900/80000] lr: 1.755e-05, eta: 20:47:42, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1741, decode.acc_seg: 92.3879, aux.loss_ce: 0.0731, aux.acc_seg: 91.9919, loss: 0.2472 +2024-06-19 05:06:55,095 - mmseg - INFO - Iter [44950/80000] lr: 1.753e-05, eta: 20:45:50, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1823, decode.acc_seg: 92.2628, aux.loss_ce: 0.0756, aux.acc_seg: 92.0041, loss: 0.2579 +2024-06-19 05:08:34,045 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 05:08:34,045 - mmseg - INFO - Iter [45000/80000] lr: 1.750e-05, eta: 20:43:57, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1809, decode.acc_seg: 92.3261, aux.loss_ce: 0.0754, aux.acc_seg: 91.9953, loss: 0.2563 +2024-06-19 05:10:25,175 - mmseg - INFO - per class results: +2024-06-19 05:10:25,181 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.6 | 90.48 | +| building | 85.39 | 93.44 | +| sky | 94.97 | 97.04 | +| floor | 84.78 | 92.96 | +| tree | 78.11 | 90.83 | +| ceiling | 87.53 | 93.64 | +| road | 85.26 | 93.21 | +| bed | 92.66 | 96.91 | +| windowpane | 66.31 | 80.89 | +| grass | 67.19 | 80.12 | +| cabinet | 67.32 | 74.8 | +| sidewalk | 69.91 | 80.29 | +| person | 86.75 | 94.15 | +| earth | 39.84 | 53.18 | +| door | 57.87 | 71.97 | +| table | 69.08 | 79.72 | +| mountain | 62.5 | 71.42 | +| plant | 57.24 | 67.97 | +| curtain | 78.01 | 87.62 | +| chair | 67.12 | 78.64 | +| car | 88.45 | 94.2 | +| water | 61.11 | 76.52 | +| painting | 81.08 | 90.68 | +| sofa | 81.2 | 88.62 | +| shelf | 50.15 | 65.45 | +| house | 57.66 | 73.03 | +| sea | 75.21 | 90.35 | +| mirror | 78.59 | 85.74 | +| rug | 64.88 | 72.22 | +| field | 29.26 | 52.23 | +| armchair | 60.31 | 76.66 | +| seat | 65.08 | 90.38 | +| fence | 54.07 | 75.85 | +| desk | 59.82 | 77.43 | +| rock | 58.26 | 82.13 | +| wardrobe | 54.57 | 77.16 | +| lamp | 75.12 | 88.22 | +| bathtub | 85.45 | 89.4 | +| railing | 45.74 | 62.87 | +| cushion | 71.29 | 82.2 | +| base | 43.5 | 73.51 | +| box | 32.7 | 38.07 | +| column | 59.11 | 73.55 | +| signboard | 39.32 | 54.18 | +| chest of drawers | 50.05 | 76.69 | +| counter | 43.93 | 57.8 | +| sand | 53.37 | 76.76 | +| sink | 84.22 | 91.44 | +| skyscraper | 47.29 | 60.89 | +| fireplace | 73.76 | 92.01 | +| refrigerator | 83.79 | 88.97 | +| grandstand | 65.14 | 71.04 | +| path | 29.86 | 40.15 | +| stairs | 37.12 | 60.09 | +| runway | 72.9 | 94.81 | +| case | 66.03 | 82.03 | +| pool table | 95.12 | 98.3 | +| pillow | 68.59 | 80.19 | +| screen door | 87.77 | 91.31 | +| stairway | 38.87 | 50.31 | +| river | 12.56 | 22.26 | +| bridge | 77.74 | 86.96 | +| bookcase | 43.85 | 59.09 | +| blind | 44.47 | 51.67 | +| coffee table | 61.86 | 85.74 | +| toilet | 89.96 | 94.28 | +| flower | 40.63 | 50.61 | +| book | 56.51 | 79.9 | +| hill | 12.11 | 26.31 | +| bench | 67.2 | 76.81 | +| countertop | 63.05 | 79.13 | +| stove | 86.16 | 91.85 | +| palm | 52.94 | 82.12 | +| kitchen island | 49.04 | 93.4 | +| computer | 77.56 | 90.91 | +| swivel chair | 49.97 | 77.78 | +| boat | 75.73 | 90.47 | +| bar | 60.57 | 87.26 | +| arcade machine | 91.57 | 97.41 | +| hovel | 33.85 | 36.45 | +| bus | 94.56 | 97.15 | +| towel | 76.15 | 79.51 | +| light | 61.34 | 71.26 | +| truck | 52.14 | 67.99 | +| tower | 26.7 | 46.91 | +| chandelier | 73.77 | 86.83 | +| awning | 43.07 | 54.85 | +| streetlight | 37.77 | 50.88 | +| booth | 47.18 | 49.31 | +| television receiver | 77.53 | 88.81 | +| airplane | 86.82 | 97.1 | +| dirt track | 3.3 | 10.23 | +| apparel | 65.2 | 86.51 | +| pole | 27.26 | 35.49 | +| land | 3.38 | 4.31 | +| bannister | 21.5 | 27.14 | +| escalator | 63.61 | 86.34 | +| ottoman | 53.98 | 70.11 | +| bottle | 43.77 | 70.84 | +| buffet | 64.07 | 74.6 | +| poster | 41.1 | 46.48 | +| stage | 16.03 | 21.34 | +| van | 51.76 | 68.11 | +| ship | 76.91 | 91.31 | +| fountain | 30.74 | 31.01 | +| conveyer belt | 85.35 | 95.03 | +| canopy | 46.87 | 57.44 | +| washer | 86.51 | 91.95 | +| plaything | 47.89 | 68.57 | +| swimming pool | 54.62 | 74.55 | +| stool | 56.18 | 74.35 | +| barrel | 60.4 | 87.59 | +| basket | 42.18 | 54.12 | +| waterfall | 46.52 | 56.14 | +| tent | 95.6 | 99.18 | +| bag | 23.49 | 26.01 | +| minibike | 76.93 | 90.44 | +| cradle | 88.66 | 97.38 | +| oven | 66.04 | 80.26 | +| ball | 57.51 | 75.38 | +| food | 63.16 | 79.06 | +| step | 9.29 | 10.32 | +| tank | 62.04 | 66.77 | +| trade name | 19.54 | 21.91 | +| microwave | 90.96 | 96.5 | +| pot | 58.44 | 65.91 | +| animal | 60.17 | 61.96 | +| bicycle | 61.5 | 78.22 | +| lake | 52.8 | 63.69 | +| dishwasher | 77.6 | 83.52 | +| screen | 65.43 | 93.86 | +| blanket | 34.4 | 46.54 | +| sculpture | 71.71 | 79.5 | +| hood | 76.49 | 92.9 | +| sconce | 61.07 | 72.44 | +| vase | 52.63 | 61.97 | +| traffic light | 39.51 | 67.02 | +| tray | 26.99 | 36.04 | +| ashcan | 53.06 | 65.49 | +| fan | 71.62 | 83.75 | +| pier | 41.01 | 46.0 | +| crt screen | 10.57 | 19.97 | +| plate | 64.82 | 78.36 | +| monitor | 41.54 | 47.57 | +| bulletin board | 53.73 | 67.24 | +| shower | 15.14 | 15.25 | +| radiator | 67.11 | 80.7 | +| glass | 21.56 | 22.98 | +| clock | 52.94 | 59.17 | +| flag | 70.39 | 77.55 | ++---------------------+-------+-------+ +2024-06-19 05:10:25,181 - mmseg - INFO - Summary: +2024-06-19 05:10:25,181 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.36 | 58.79 | 71.05 | ++-------+-------+-------+ +2024-06-19 05:10:25,182 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 05:10:25,182 - mmseg - INFO - Iter(val) [250] aAcc: 0.8636, mIoU: 0.5879, mAcc: 0.7105, IoU.wall: 0.8260, IoU.building: 0.8539, IoU.sky: 0.9497, IoU.floor: 0.8478, IoU.tree: 0.7811, IoU.ceiling: 0.8753, IoU.road: 0.8526, IoU.bed : 0.9266, IoU.windowpane: 0.6631, IoU.grass: 0.6719, IoU.cabinet: 0.6732, IoU.sidewalk: 0.6991, IoU.person: 0.8675, IoU.earth: 0.3984, IoU.door: 0.5787, IoU.table: 0.6908, IoU.mountain: 0.6250, IoU.plant: 0.5724, IoU.curtain: 0.7801, IoU.chair: 0.6712, IoU.car: 0.8845, IoU.water: 0.6111, IoU.painting: 0.8108, IoU.sofa: 0.8120, IoU.shelf: 0.5015, IoU.house: 0.5766, IoU.sea: 0.7521, IoU.mirror: 0.7859, IoU.rug: 0.6488, IoU.field: 0.2926, IoU.armchair: 0.6031, IoU.seat: 0.6508, IoU.fence: 0.5407, IoU.desk: 0.5982, IoU.rock: 0.5826, IoU.wardrobe: 0.5457, IoU.lamp: 0.7512, IoU.bathtub: 0.8545, IoU.railing: 0.4574, IoU.cushion: 0.7129, IoU.base: 0.4350, IoU.box: 0.3270, IoU.column: 0.5911, IoU.signboard: 0.3932, IoU.chest of drawers: 0.5005, IoU.counter: 0.4393, IoU.sand: 0.5337, IoU.sink: 0.8422, IoU.skyscraper: 0.4729, IoU.fireplace: 0.7376, IoU.refrigerator: 0.8379, IoU.grandstand: 0.6514, IoU.path: 0.2986, IoU.stairs: 0.3712, IoU.runway: 0.7290, IoU.case: 0.6603, IoU.pool table: 0.9512, IoU.pillow: 0.6859, IoU.screen door: 0.8777, IoU.stairway: 0.3887, IoU.river: 0.1256, IoU.bridge: 0.7774, IoU.bookcase: 0.4385, IoU.blind: 0.4447, IoU.coffee table: 0.6186, IoU.toilet: 0.8996, IoU.flower: 0.4063, IoU.book: 0.5651, IoU.hill: 0.1211, IoU.bench: 0.6720, IoU.countertop: 0.6305, IoU.stove: 0.8616, IoU.palm: 0.5294, IoU.kitchen island: 0.4904, IoU.computer: 0.7756, IoU.swivel chair: 0.4997, IoU.boat: 0.7573, IoU.bar: 0.6057, IoU.arcade machine: 0.9157, IoU.hovel: 0.3385, IoU.bus: 0.9456, IoU.towel: 0.7615, IoU.light: 0.6134, IoU.truck: 0.5214, IoU.tower: 0.2670, IoU.chandelier: 0.7377, IoU.awning: 0.4307, IoU.streetlight: 0.3777, IoU.booth: 0.4718, IoU.television receiver: 0.7753, IoU.airplane: 0.8682, IoU.dirt track: 0.0330, IoU.apparel: 0.6520, IoU.pole: 0.2726, IoU.land: 0.0338, IoU.bannister: 0.2150, IoU.escalator: 0.6361, IoU.ottoman: 0.5398, IoU.bottle: 0.4377, IoU.buffet: 0.6407, IoU.poster: 0.4110, IoU.stage: 0.1603, IoU.van: 0.5176, IoU.ship: 0.7691, IoU.fountain: 0.3074, IoU.conveyer belt: 0.8535, IoU.canopy: 0.4687, IoU.washer: 0.8651, IoU.plaything: 0.4789, IoU.swimming pool: 0.5462, IoU.stool: 0.5618, IoU.barrel: 0.6040, IoU.basket: 0.4218, IoU.waterfall: 0.4652, IoU.tent: 0.9560, IoU.bag: 0.2349, IoU.minibike: 0.7693, IoU.cradle: 0.8866, IoU.oven: 0.6604, IoU.ball: 0.5751, IoU.food: 0.6316, IoU.step: 0.0929, IoU.tank: 0.6204, IoU.trade name: 0.1954, IoU.microwave: 0.9096, IoU.pot: 0.5844, IoU.animal: 0.6017, IoU.bicycle: 0.6150, IoU.lake: 0.5280, IoU.dishwasher: 0.7760, IoU.screen: 0.6543, IoU.blanket: 0.3440, IoU.sculpture: 0.7171, IoU.hood: 0.7649, IoU.sconce: 0.6107, IoU.vase: 0.5263, IoU.traffic light: 0.3951, IoU.tray: 0.2699, IoU.ashcan: 0.5306, IoU.fan: 0.7162, IoU.pier: 0.4101, IoU.crt screen: 0.1057, IoU.plate: 0.6482, IoU.monitor: 0.4154, IoU.bulletin board: 0.5373, IoU.shower: 0.1514, IoU.radiator: 0.6711, IoU.glass: 0.2156, IoU.clock: 0.5294, IoU.flag: 0.7039, Acc.wall: 0.9048, Acc.building: 0.9344, Acc.sky: 0.9704, Acc.floor: 0.9296, Acc.tree: 0.9083, Acc.ceiling: 0.9364, Acc.road: 0.9321, Acc.bed : 0.9691, Acc.windowpane: 0.8089, Acc.grass: 0.8012, Acc.cabinet: 0.7480, Acc.sidewalk: 0.8029, Acc.person: 0.9415, Acc.earth: 0.5318, Acc.door: 0.7197, Acc.table: 0.7972, Acc.mountain: 0.7142, Acc.plant: 0.6797, Acc.curtain: 0.8762, Acc.chair: 0.7864, Acc.car: 0.9420, Acc.water: 0.7652, Acc.painting: 0.9068, Acc.sofa: 0.8862, Acc.shelf: 0.6545, Acc.house: 0.7303, Acc.sea: 0.9035, Acc.mirror: 0.8574, Acc.rug: 0.7222, Acc.field: 0.5223, Acc.armchair: 0.7666, Acc.seat: 0.9038, Acc.fence: 0.7585, Acc.desk: 0.7743, Acc.rock: 0.8213, Acc.wardrobe: 0.7716, Acc.lamp: 0.8822, Acc.bathtub: 0.8940, Acc.railing: 0.6287, Acc.cushion: 0.8220, Acc.base: 0.7351, Acc.box: 0.3807, Acc.column: 0.7355, Acc.signboard: 0.5418, Acc.chest of drawers: 0.7669, Acc.counter: 0.5780, Acc.sand: 0.7676, Acc.sink: 0.9144, Acc.skyscraper: 0.6089, Acc.fireplace: 0.9201, Acc.refrigerator: 0.8897, Acc.grandstand: 0.7104, Acc.path: 0.4015, Acc.stairs: 0.6009, Acc.runway: 0.9481, Acc.case: 0.8203, Acc.pool table: 0.9830, Acc.pillow: 0.8019, Acc.screen door: 0.9131, Acc.stairway: 0.5031, Acc.river: 0.2226, Acc.bridge: 0.8696, Acc.bookcase: 0.5909, Acc.blind: 0.5167, Acc.coffee table: 0.8574, Acc.toilet: 0.9428, Acc.flower: 0.5061, Acc.book: 0.7990, Acc.hill: 0.2631, Acc.bench: 0.7681, Acc.countertop: 0.7913, Acc.stove: 0.9185, Acc.palm: 0.8212, Acc.kitchen island: 0.9340, Acc.computer: 0.9091, Acc.swivel chair: 0.7778, Acc.boat: 0.9047, Acc.bar: 0.8726, Acc.arcade machine: 0.9741, Acc.hovel: 0.3645, Acc.bus: 0.9715, Acc.towel: 0.7951, Acc.light: 0.7126, Acc.truck: 0.6799, Acc.tower: 0.4691, Acc.chandelier: 0.8683, Acc.awning: 0.5485, Acc.streetlight: 0.5088, Acc.booth: 0.4931, Acc.television receiver: 0.8881, Acc.airplane: 0.9710, Acc.dirt track: 0.1023, Acc.apparel: 0.8651, Acc.pole: 0.3549, Acc.land: 0.0431, Acc.bannister: 0.2714, Acc.escalator: 0.8634, Acc.ottoman: 0.7011, Acc.bottle: 0.7084, Acc.buffet: 0.7460, Acc.poster: 0.4648, Acc.stage: 0.2134, Acc.van: 0.6811, Acc.ship: 0.9131, Acc.fountain: 0.3101, Acc.conveyer belt: 0.9503, Acc.canopy: 0.5744, Acc.washer: 0.9195, Acc.plaything: 0.6857, Acc.swimming pool: 0.7455, Acc.stool: 0.7435, Acc.barrel: 0.8759, Acc.basket: 0.5412, Acc.waterfall: 0.5614, Acc.tent: 0.9918, Acc.bag: 0.2601, Acc.minibike: 0.9044, Acc.cradle: 0.9738, Acc.oven: 0.8026, Acc.ball: 0.7538, Acc.food: 0.7906, Acc.step: 0.1032, Acc.tank: 0.6677, Acc.trade name: 0.2191, Acc.microwave: 0.9650, Acc.pot: 0.6591, Acc.animal: 0.6196, Acc.bicycle: 0.7822, Acc.lake: 0.6369, Acc.dishwasher: 0.8352, Acc.screen: 0.9386, Acc.blanket: 0.4654, Acc.sculpture: 0.7950, Acc.hood: 0.9290, Acc.sconce: 0.7244, Acc.vase: 0.6197, Acc.traffic light: 0.6702, Acc.tray: 0.3604, Acc.ashcan: 0.6549, Acc.fan: 0.8375, Acc.pier: 0.4600, Acc.crt screen: 0.1997, Acc.plate: 0.7836, Acc.monitor: 0.4757, Acc.bulletin board: 0.6724, Acc.shower: 0.1525, Acc.radiator: 0.8070, Acc.glass: 0.2298, Acc.clock: 0.5917, Acc.flag: 0.7755 +2024-06-19 05:12:04,314 - mmseg - INFO - Iter [45050/80000] lr: 1.748e-05, eta: 20:43:31, time: 4.205, data_time: 2.240, memory: 72263, decode.loss_ce: 0.1839, decode.acc_seg: 92.3363, aux.loss_ce: 0.0769, aux.acc_seg: 91.9385, loss: 0.2607 +2024-06-19 05:13:43,240 - mmseg - INFO - Iter [45100/80000] lr: 1.745e-05, eta: 20:41:38, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.2015, decode.acc_seg: 91.3582, aux.loss_ce: 0.0833, aux.acc_seg: 91.0213, loss: 0.2848 +2024-06-19 05:15:22,051 - mmseg - INFO - Iter [45150/80000] lr: 1.743e-05, eta: 20:39:45, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1693, decode.acc_seg: 92.6127, aux.loss_ce: 0.0710, aux.acc_seg: 92.3200, loss: 0.2402 +2024-06-19 05:17:00,865 - mmseg - INFO - Iter [45200/80000] lr: 1.740e-05, eta: 20:37:53, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1846, decode.acc_seg: 91.8394, aux.loss_ce: 0.0769, aux.acc_seg: 91.4649, loss: 0.2615 +2024-06-19 05:18:39,801 - mmseg - INFO - Iter [45250/80000] lr: 1.738e-05, eta: 20:36:00, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1727, decode.acc_seg: 92.7005, aux.loss_ce: 0.0728, aux.acc_seg: 92.3547, loss: 0.2456 +2024-06-19 05:20:18,609 - mmseg - INFO - Iter [45300/80000] lr: 1.735e-05, eta: 20:34:07, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1805, decode.acc_seg: 92.4297, aux.loss_ce: 0.0754, aux.acc_seg: 92.1265, loss: 0.2560 +2024-06-19 05:21:57,585 - mmseg - INFO - Iter [45350/80000] lr: 1.733e-05, eta: 20:32:15, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1812, decode.acc_seg: 92.1556, aux.loss_ce: 0.0754, aux.acc_seg: 91.8406, loss: 0.2566 +2024-06-19 05:23:36,408 - mmseg - INFO - Iter [45400/80000] lr: 1.730e-05, eta: 20:30:22, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1715, decode.acc_seg: 92.5450, aux.loss_ce: 0.0722, aux.acc_seg: 92.1335, loss: 0.2437 +2024-06-19 05:25:15,261 - mmseg - INFO - Iter [45450/80000] lr: 1.728e-05, eta: 20:28:29, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1762, decode.acc_seg: 92.1639, aux.loss_ce: 0.0735, aux.acc_seg: 91.8905, loss: 0.2497 +2024-06-19 05:26:57,010 - mmseg - INFO - Iter [45500/80000] lr: 1.725e-05, eta: 20:26:39, time: 2.035, data_time: 0.065, memory: 72263, decode.loss_ce: 0.1693, decode.acc_seg: 92.7180, aux.loss_ce: 0.0708, aux.acc_seg: 92.3074, loss: 0.2401 +2024-06-19 05:28:35,886 - mmseg - INFO - Iter [45550/80000] lr: 1.723e-05, eta: 20:24:46, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1748, decode.acc_seg: 92.3941, aux.loss_ce: 0.0733, aux.acc_seg: 92.0306, loss: 0.2481 +2024-06-19 05:30:14,959 - mmseg - INFO - Iter [45600/80000] lr: 1.720e-05, eta: 20:22:54, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1691, decode.acc_seg: 92.6945, aux.loss_ce: 0.0714, aux.acc_seg: 92.3321, loss: 0.2405 +2024-06-19 05:31:53,848 - mmseg - INFO - Iter [45650/80000] lr: 1.718e-05, eta: 20:21:01, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1703, decode.acc_seg: 92.6033, aux.loss_ce: 0.0717, aux.acc_seg: 92.3043, loss: 0.2420 +2024-06-19 05:33:32,821 - mmseg - INFO - Iter [45700/80000] lr: 1.715e-05, eta: 20:19:09, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1712, decode.acc_seg: 92.5338, aux.loss_ce: 0.0720, aux.acc_seg: 92.1969, loss: 0.2433 +2024-06-19 05:35:11,657 - mmseg - INFO - Iter [45750/80000] lr: 1.713e-05, eta: 20:17:17, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1738, decode.acc_seg: 92.7415, aux.loss_ce: 0.0726, aux.acc_seg: 92.4052, loss: 0.2464 +2024-06-19 05:36:50,520 - mmseg - INFO - Iter [45800/80000] lr: 1.710e-05, eta: 20:15:24, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1683, decode.acc_seg: 92.5079, aux.loss_ce: 0.0701, aux.acc_seg: 92.1906, loss: 0.2384 +2024-06-19 05:38:29,520 - mmseg - INFO - Iter [45850/80000] lr: 1.708e-05, eta: 20:13:32, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1724, decode.acc_seg: 92.4070, aux.loss_ce: 0.0729, aux.acc_seg: 92.0241, loss: 0.2453 +2024-06-19 05:40:08,378 - mmseg - INFO - Iter [45900/80000] lr: 1.705e-05, eta: 20:11:39, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1619, decode.acc_seg: 92.9548, aux.loss_ce: 0.0687, aux.acc_seg: 92.5515, loss: 0.2306 +2024-06-19 05:41:47,184 - mmseg - INFO - Iter [45950/80000] lr: 1.703e-05, eta: 20:09:47, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1712, decode.acc_seg: 92.6888, aux.loss_ce: 0.0713, aux.acc_seg: 92.3640, loss: 0.2424 +2024-06-19 05:43:26,077 - mmseg - INFO - Saving checkpoint at 46000 iterations +2024-06-19 05:44:51,898 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 05:44:51,899 - mmseg - INFO - Iter [46000/80000] lr: 1.700e-05, eta: 20:08:58, time: 3.694, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1579, decode.acc_seg: 93.1072, aux.loss_ce: 0.0669, aux.acc_seg: 92.6988, loss: 0.2248 +2024-06-19 05:46:41,802 - mmseg - INFO - per class results: +2024-06-19 05:46:41,808 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.58 | 89.5 | +| building | 85.74 | 93.74 | +| sky | 95.09 | 97.76 | +| floor | 84.98 | 91.63 | +| tree | 78.46 | 89.28 | +| ceiling | 86.96 | 94.99 | +| road | 84.8 | 91.4 | +| bed | 92.76 | 96.77 | +| windowpane | 65.62 | 82.8 | +| grass | 68.8 | 81.96 | +| cabinet | 68.67 | 78.14 | +| sidewalk | 70.55 | 83.36 | +| person | 86.11 | 94.92 | +| earth | 40.95 | 55.12 | +| door | 58.57 | 74.69 | +| table | 69.37 | 80.71 | +| mountain | 62.55 | 75.53 | +| plant | 58.55 | 68.73 | +| curtain | 80.57 | 89.78 | +| chair | 68.1 | 76.79 | +| car | 87.42 | 94.28 | +| water | 64.54 | 81.63 | +| painting | 80.14 | 91.31 | +| sofa | 82.4 | 90.44 | +| shelf | 46.51 | 57.69 | +| house | 58.63 | 72.33 | +| sea | 74.18 | 90.73 | +| mirror | 80.39 | 87.75 | +| rug | 67.16 | 79.31 | +| field | 29.22 | 49.78 | +| armchair | 63.64 | 81.43 | +| seat | 67.53 | 90.15 | +| fence | 54.0 | 66.28 | +| desk | 59.49 | 79.05 | +| rock | 56.35 | 83.11 | +| wardrobe | 58.44 | 79.75 | +| lamp | 75.32 | 86.9 | +| bathtub | 88.7 | 92.55 | +| railing | 43.88 | 61.11 | +| cushion | 70.64 | 86.42 | +| base | 42.74 | 66.67 | +| box | 39.43 | 52.47 | +| column | 58.37 | 73.68 | +| signboard | 41.69 | 57.55 | +| chest of drawers | 47.52 | 63.91 | +| counter | 49.33 | 61.48 | +| sand | 51.7 | 78.72 | +| sink | 81.33 | 86.1 | +| skyscraper | 49.51 | 64.96 | +| fireplace | 74.7 | 93.05 | +| refrigerator | 85.19 | 91.73 | +| grandstand | 64.25 | 82.29 | +| path | 31.22 | 42.52 | +| stairs | 40.64 | 45.95 | +| runway | 65.63 | 84.04 | +| case | 69.95 | 84.32 | +| pool table | 95.05 | 98.37 | +| pillow | 68.28 | 81.91 | +| screen door | 82.4 | 85.57 | +| stairway | 45.2 | 69.2 | +| river | 10.89 | 15.76 | +| bridge | 67.67 | 86.65 | +| bookcase | 41.43 | 59.66 | +| blind | 40.13 | 42.67 | +| coffee table | 60.34 | 88.1 | +| toilet | 90.05 | 93.28 | +| flower | 44.35 | 56.63 | +| book | 55.86 | 82.31 | +| hill | 8.8 | 15.26 | +| bench | 61.38 | 75.21 | +| countertop | 64.43 | 85.47 | +| stove | 86.68 | 92.73 | +| palm | 55.4 | 81.85 | +| kitchen island | 50.78 | 86.5 | +| computer | 76.26 | 91.4 | +| swivel chair | 50.34 | 80.52 | +| boat | 73.89 | 91.56 | +| bar | 65.9 | 81.85 | +| arcade machine | 86.11 | 90.23 | +| hovel | 50.07 | 60.35 | +| bus | 93.97 | 97.32 | +| towel | 83.01 | 90.71 | +| light | 62.31 | 75.63 | +| truck | 50.86 | 64.23 | +| tower | 29.62 | 43.67 | +| chandelier | 72.5 | 85.97 | +| awning | 47.77 | 60.4 | +| streetlight | 36.96 | 52.57 | +| booth | 61.5 | 67.19 | +| television receiver | 80.19 | 86.26 | +| airplane | 86.39 | 96.81 | +| dirt track | 2.62 | 9.47 | +| apparel | 66.45 | 83.32 | +| pole | 27.73 | 38.56 | +| land | 3.34 | 5.06 | +| bannister | 19.7 | 23.71 | +| escalator | 66.13 | 84.57 | +| ottoman | 59.26 | 79.79 | +| bottle | 46.32 | 72.57 | +| buffet | 56.18 | 69.56 | +| poster | 36.6 | 43.31 | +| stage | 18.51 | 30.94 | +| van | 45.7 | 67.57 | +| ship | 68.55 | 73.99 | +| fountain | 37.91 | 40.63 | +| conveyer belt | 84.82 | 96.66 | +| canopy | 47.49 | 60.12 | +| washer | 85.37 | 90.82 | +| plaything | 35.32 | 46.69 | +| swimming pool | 72.44 | 85.84 | +| stool | 59.09 | 73.03 | +| barrel | 65.41 | 92.42 | +| basket | 42.69 | 54.48 | +| waterfall | 56.22 | 72.73 | +| tent | 92.26 | 99.03 | +| bag | 28.63 | 36.63 | +| minibike | 75.63 | 91.15 | +| cradle | 83.34 | 97.52 | +| oven | 67.49 | 83.23 | +| ball | 61.78 | 70.57 | +| food | 59.13 | 65.05 | +| step | 12.4 | 15.91 | +| tank | 72.87 | 78.41 | +| trade name | 18.66 | 20.7 | +| microwave | 90.83 | 96.15 | +| pot | 58.61 | 71.34 | +| animal | 60.66 | 61.9 | +| bicycle | 61.59 | 80.95 | +| lake | 55.92 | 63.79 | +| dishwasher | 75.17 | 84.6 | +| screen | 63.7 | 93.57 | +| blanket | 32.9 | 44.23 | +| sculpture | 74.37 | 82.19 | +| hood | 70.72 | 81.71 | +| sconce | 60.24 | 70.86 | +| vase | 51.76 | 68.39 | +| traffic light | 37.99 | 72.29 | +| tray | 17.76 | 19.63 | +| ashcan | 52.47 | 66.02 | +| fan | 72.2 | 88.2 | +| pier | 38.44 | 40.66 | +| crt screen | 8.73 | 14.59 | +| plate | 64.09 | 82.75 | +| monitor | 48.85 | 57.5 | +| bulletin board | 53.87 | 68.85 | +| shower | 19.36 | 22.42 | +| radiator | 67.38 | 81.41 | +| glass | 21.6 | 23.07 | +| clock | 54.38 | 61.69 | +| flag | 70.95 | 82.05 | ++---------------------+-------+-------+ +2024-06-19 05:46:41,808 - mmseg - INFO - Summary: +2024-06-19 05:46:41,808 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.57 | 59.26 | 71.78 | ++-------+-------+-------+ +2024-06-19 05:46:41,809 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 05:46:41,809 - mmseg - INFO - Iter(val) [250] aAcc: 0.8657, mIoU: 0.5926, mAcc: 0.7178, IoU.wall: 0.8258, IoU.building: 0.8574, IoU.sky: 0.9509, IoU.floor: 0.8498, IoU.tree: 0.7846, IoU.ceiling: 0.8696, IoU.road: 0.8480, IoU.bed : 0.9276, IoU.windowpane: 0.6562, IoU.grass: 0.6880, IoU.cabinet: 0.6867, IoU.sidewalk: 0.7055, IoU.person: 0.8611, IoU.earth: 0.4095, IoU.door: 0.5857, IoU.table: 0.6937, IoU.mountain: 0.6255, IoU.plant: 0.5855, IoU.curtain: 0.8057, IoU.chair: 0.6810, IoU.car: 0.8742, IoU.water: 0.6454, IoU.painting: 0.8014, IoU.sofa: 0.8240, IoU.shelf: 0.4651, IoU.house: 0.5863, IoU.sea: 0.7418, IoU.mirror: 0.8039, IoU.rug: 0.6716, IoU.field: 0.2922, IoU.armchair: 0.6364, IoU.seat: 0.6753, IoU.fence: 0.5400, IoU.desk: 0.5949, IoU.rock: 0.5635, IoU.wardrobe: 0.5844, IoU.lamp: 0.7532, IoU.bathtub: 0.8870, IoU.railing: 0.4388, IoU.cushion: 0.7064, IoU.base: 0.4274, IoU.box: 0.3943, IoU.column: 0.5837, IoU.signboard: 0.4169, IoU.chest of drawers: 0.4752, IoU.counter: 0.4933, IoU.sand: 0.5170, IoU.sink: 0.8133, IoU.skyscraper: 0.4951, IoU.fireplace: 0.7470, IoU.refrigerator: 0.8519, IoU.grandstand: 0.6425, IoU.path: 0.3122, IoU.stairs: 0.4064, IoU.runway: 0.6563, IoU.case: 0.6995, IoU.pool table: 0.9505, IoU.pillow: 0.6828, IoU.screen door: 0.8240, IoU.stairway: 0.4520, IoU.river: 0.1089, IoU.bridge: 0.6767, IoU.bookcase: 0.4143, IoU.blind: 0.4013, IoU.coffee table: 0.6034, IoU.toilet: 0.9005, IoU.flower: 0.4435, IoU.book: 0.5586, IoU.hill: 0.0880, IoU.bench: 0.6138, IoU.countertop: 0.6443, IoU.stove: 0.8668, IoU.palm: 0.5540, IoU.kitchen island: 0.5078, IoU.computer: 0.7626, IoU.swivel chair: 0.5034, IoU.boat: 0.7389, IoU.bar: 0.6590, IoU.arcade machine: 0.8611, IoU.hovel: 0.5007, IoU.bus: 0.9397, IoU.towel: 0.8301, IoU.light: 0.6231, IoU.truck: 0.5086, IoU.tower: 0.2962, IoU.chandelier: 0.7250, IoU.awning: 0.4777, IoU.streetlight: 0.3696, IoU.booth: 0.6150, IoU.television receiver: 0.8019, IoU.airplane: 0.8639, IoU.dirt track: 0.0262, IoU.apparel: 0.6645, IoU.pole: 0.2773, IoU.land: 0.0334, IoU.bannister: 0.1970, IoU.escalator: 0.6613, IoU.ottoman: 0.5926, IoU.bottle: 0.4632, IoU.buffet: 0.5618, IoU.poster: 0.3660, IoU.stage: 0.1851, IoU.van: 0.4570, IoU.ship: 0.6855, IoU.fountain: 0.3791, IoU.conveyer belt: 0.8482, IoU.canopy: 0.4749, IoU.washer: 0.8537, IoU.plaything: 0.3532, IoU.swimming pool: 0.7244, IoU.stool: 0.5909, IoU.barrel: 0.6541, IoU.basket: 0.4269, IoU.waterfall: 0.5622, IoU.tent: 0.9226, IoU.bag: 0.2863, IoU.minibike: 0.7563, IoU.cradle: 0.8334, IoU.oven: 0.6749, IoU.ball: 0.6178, IoU.food: 0.5913, IoU.step: 0.1240, IoU.tank: 0.7287, IoU.trade name: 0.1866, IoU.microwave: 0.9083, IoU.pot: 0.5861, IoU.animal: 0.6066, IoU.bicycle: 0.6159, IoU.lake: 0.5592, IoU.dishwasher: 0.7517, IoU.screen: 0.6370, IoU.blanket: 0.3290, IoU.sculpture: 0.7437, IoU.hood: 0.7072, IoU.sconce: 0.6024, IoU.vase: 0.5176, IoU.traffic light: 0.3799, IoU.tray: 0.1776, IoU.ashcan: 0.5247, IoU.fan: 0.7220, IoU.pier: 0.3844, IoU.crt screen: 0.0873, IoU.plate: 0.6409, IoU.monitor: 0.4885, IoU.bulletin board: 0.5387, IoU.shower: 0.1936, IoU.radiator: 0.6738, IoU.glass: 0.2160, IoU.clock: 0.5438, IoU.flag: 0.7095, Acc.wall: 0.8950, Acc.building: 0.9374, Acc.sky: 0.9776, Acc.floor: 0.9163, Acc.tree: 0.8928, Acc.ceiling: 0.9499, Acc.road: 0.9140, Acc.bed : 0.9677, Acc.windowpane: 0.8280, Acc.grass: 0.8196, Acc.cabinet: 0.7814, Acc.sidewalk: 0.8336, Acc.person: 0.9492, Acc.earth: 0.5512, Acc.door: 0.7469, Acc.table: 0.8071, Acc.mountain: 0.7553, Acc.plant: 0.6873, Acc.curtain: 0.8978, Acc.chair: 0.7679, Acc.car: 0.9428, Acc.water: 0.8163, Acc.painting: 0.9131, Acc.sofa: 0.9044, Acc.shelf: 0.5769, Acc.house: 0.7233, Acc.sea: 0.9073, Acc.mirror: 0.8775, Acc.rug: 0.7931, Acc.field: 0.4978, Acc.armchair: 0.8143, Acc.seat: 0.9015, Acc.fence: 0.6628, Acc.desk: 0.7905, Acc.rock: 0.8311, Acc.wardrobe: 0.7975, Acc.lamp: 0.8690, Acc.bathtub: 0.9255, Acc.railing: 0.6111, Acc.cushion: 0.8642, Acc.base: 0.6667, Acc.box: 0.5247, Acc.column: 0.7368, Acc.signboard: 0.5755, Acc.chest of drawers: 0.6391, Acc.counter: 0.6148, Acc.sand: 0.7872, Acc.sink: 0.8610, Acc.skyscraper: 0.6496, Acc.fireplace: 0.9305, Acc.refrigerator: 0.9173, Acc.grandstand: 0.8229, Acc.path: 0.4252, Acc.stairs: 0.4595, Acc.runway: 0.8404, Acc.case: 0.8432, Acc.pool table: 0.9837, Acc.pillow: 0.8191, Acc.screen door: 0.8557, Acc.stairway: 0.6920, Acc.river: 0.1576, Acc.bridge: 0.8665, Acc.bookcase: 0.5966, Acc.blind: 0.4267, Acc.coffee table: 0.8810, Acc.toilet: 0.9328, Acc.flower: 0.5663, Acc.book: 0.8231, Acc.hill: 0.1526, Acc.bench: 0.7521, Acc.countertop: 0.8547, Acc.stove: 0.9273, Acc.palm: 0.8185, Acc.kitchen island: 0.8650, Acc.computer: 0.9140, Acc.swivel chair: 0.8052, Acc.boat: 0.9156, Acc.bar: 0.8185, Acc.arcade machine: 0.9023, Acc.hovel: 0.6035, Acc.bus: 0.9732, Acc.towel: 0.9071, Acc.light: 0.7563, Acc.truck: 0.6423, Acc.tower: 0.4367, Acc.chandelier: 0.8597, Acc.awning: 0.6040, Acc.streetlight: 0.5257, Acc.booth: 0.6719, Acc.television receiver: 0.8626, Acc.airplane: 0.9681, Acc.dirt track: 0.0947, Acc.apparel: 0.8332, Acc.pole: 0.3856, Acc.land: 0.0506, Acc.bannister: 0.2371, Acc.escalator: 0.8457, Acc.ottoman: 0.7979, Acc.bottle: 0.7257, Acc.buffet: 0.6956, Acc.poster: 0.4331, Acc.stage: 0.3094, Acc.van: 0.6757, Acc.ship: 0.7399, Acc.fountain: 0.4063, Acc.conveyer belt: 0.9666, Acc.canopy: 0.6012, Acc.washer: 0.9082, Acc.plaything: 0.4669, Acc.swimming pool: 0.8584, Acc.stool: 0.7303, Acc.barrel: 0.9242, Acc.basket: 0.5448, Acc.waterfall: 0.7273, Acc.tent: 0.9903, Acc.bag: 0.3663, Acc.minibike: 0.9115, Acc.cradle: 0.9752, Acc.oven: 0.8323, Acc.ball: 0.7057, Acc.food: 0.6505, Acc.step: 0.1591, Acc.tank: 0.7841, Acc.trade name: 0.2070, Acc.microwave: 0.9615, Acc.pot: 0.7134, Acc.animal: 0.6190, Acc.bicycle: 0.8095, Acc.lake: 0.6379, Acc.dishwasher: 0.8460, Acc.screen: 0.9357, Acc.blanket: 0.4423, Acc.sculpture: 0.8219, Acc.hood: 0.8171, Acc.sconce: 0.7086, Acc.vase: 0.6839, Acc.traffic light: 0.7229, Acc.tray: 0.1963, Acc.ashcan: 0.6602, Acc.fan: 0.8820, Acc.pier: 0.4066, Acc.crt screen: 0.1459, Acc.plate: 0.8275, Acc.monitor: 0.5750, Acc.bulletin board: 0.6885, Acc.shower: 0.2242, Acc.radiator: 0.8141, Acc.glass: 0.2307, Acc.clock: 0.6169, Acc.flag: 0.8205 +2024-06-19 05:48:20,889 - mmseg - INFO - Iter [46050/80000] lr: 1.698e-05, eta: 20:08:27, time: 4.180, data_time: 2.215, memory: 72263, decode.loss_ce: 0.1676, decode.acc_seg: 92.8361, aux.loss_ce: 0.0707, aux.acc_seg: 92.4504, loss: 0.2383 +2024-06-19 05:49:59,777 - mmseg - INFO - Iter [46100/80000] lr: 1.695e-05, eta: 20:06:34, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1773, decode.acc_seg: 92.5692, aux.loss_ce: 0.0748, aux.acc_seg: 92.1729, loss: 0.2520 +2024-06-19 05:51:38,788 - mmseg - INFO - Iter [46150/80000] lr: 1.693e-05, eta: 20:04:42, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1723, decode.acc_seg: 92.6885, aux.loss_ce: 0.0722, aux.acc_seg: 92.3508, loss: 0.2445 +2024-06-19 05:53:17,717 - mmseg - INFO - Iter [46200/80000] lr: 1.690e-05, eta: 20:02:49, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1642, decode.acc_seg: 92.9221, aux.loss_ce: 0.0691, aux.acc_seg: 92.5123, loss: 0.2333 +2024-06-19 05:54:56,604 - mmseg - INFO - Iter [46250/80000] lr: 1.688e-05, eta: 20:00:57, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1694, decode.acc_seg: 92.7146, aux.loss_ce: 0.0712, aux.acc_seg: 92.3687, loss: 0.2406 +2024-06-19 05:56:35,432 - mmseg - INFO - Iter [46300/80000] lr: 1.685e-05, eta: 19:59:04, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1708, decode.acc_seg: 92.5886, aux.loss_ce: 0.0719, aux.acc_seg: 92.2144, loss: 0.2426 +2024-06-19 05:58:14,436 - mmseg - INFO - Iter [46350/80000] lr: 1.683e-05, eta: 19:57:12, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1780, decode.acc_seg: 92.3736, aux.loss_ce: 0.0742, aux.acc_seg: 92.0513, loss: 0.2523 +2024-06-19 05:59:53,298 - mmseg - INFO - Iter [46400/80000] lr: 1.680e-05, eta: 19:55:20, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1712, decode.acc_seg: 92.7016, aux.loss_ce: 0.0718, aux.acc_seg: 92.3193, loss: 0.2430 +2024-06-19 06:01:32,252 - mmseg - INFO - Iter [46450/80000] lr: 1.678e-05, eta: 19:53:27, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1749, decode.acc_seg: 92.5385, aux.loss_ce: 0.0725, aux.acc_seg: 92.2404, loss: 0.2474 +2024-06-19 06:03:11,138 - mmseg - INFO - Iter [46500/80000] lr: 1.675e-05, eta: 19:51:35, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1726, decode.acc_seg: 92.5841, aux.loss_ce: 0.0723, aux.acc_seg: 92.2809, loss: 0.2449 +2024-06-19 06:04:50,123 - mmseg - INFO - Iter [46550/80000] lr: 1.673e-05, eta: 19:49:43, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1662, decode.acc_seg: 92.9416, aux.loss_ce: 0.0698, aux.acc_seg: 92.6446, loss: 0.2361 +2024-06-19 06:06:28,975 - mmseg - INFO - Iter [46600/80000] lr: 1.670e-05, eta: 19:47:50, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1630, decode.acc_seg: 93.0870, aux.loss_ce: 0.0677, aux.acc_seg: 92.7658, loss: 0.2307 +2024-06-19 06:08:07,984 - mmseg - INFO - Iter [46650/80000] lr: 1.668e-05, eta: 19:45:58, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1712, decode.acc_seg: 92.6199, aux.loss_ce: 0.0718, aux.acc_seg: 92.3016, loss: 0.2430 +2024-06-19 06:09:46,829 - mmseg - INFO - Iter [46700/80000] lr: 1.665e-05, eta: 19:44:06, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1756, decode.acc_seg: 92.3098, aux.loss_ce: 0.0732, aux.acc_seg: 92.0064, loss: 0.2488 +2024-06-19 06:11:27,993 - mmseg - INFO - Iter [46750/80000] lr: 1.663e-05, eta: 19:42:15, time: 2.023, data_time: 0.051, memory: 72263, decode.loss_ce: 0.1768, decode.acc_seg: 92.3416, aux.loss_ce: 0.0744, aux.acc_seg: 91.9564, loss: 0.2512 +2024-06-19 06:13:06,842 - mmseg - INFO - Iter [46800/80000] lr: 1.660e-05, eta: 19:40:23, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1719, decode.acc_seg: 92.7621, aux.loss_ce: 0.0721, aux.acc_seg: 92.4110, loss: 0.2440 +2024-06-19 06:14:45,589 - mmseg - INFO - Iter [46850/80000] lr: 1.658e-05, eta: 19:38:31, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1684, decode.acc_seg: 92.8474, aux.loss_ce: 0.0707, aux.acc_seg: 92.4788, loss: 0.2391 +2024-06-19 06:16:24,422 - mmseg - INFO - Iter [46900/80000] lr: 1.655e-05, eta: 19:36:39, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1620, decode.acc_seg: 93.0948, aux.loss_ce: 0.0686, aux.acc_seg: 92.6747, loss: 0.2306 +2024-06-19 06:18:03,269 - mmseg - INFO - Iter [46950/80000] lr: 1.653e-05, eta: 19:34:46, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1590, decode.acc_seg: 93.2188, aux.loss_ce: 0.0666, aux.acc_seg: 92.9290, loss: 0.2256 +2024-06-19 06:19:42,155 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 06:19:42,156 - mmseg - INFO - Iter [47000/80000] lr: 1.650e-05, eta: 19:32:54, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1664, decode.acc_seg: 92.8550, aux.loss_ce: 0.0698, aux.acc_seg: 92.4576, loss: 0.2361 +2024-06-19 06:21:33,082 - mmseg - INFO - per class results: +2024-06-19 06:21:33,089 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.47 | 89.26 | +| building | 84.94 | 93.05 | +| sky | 95.04 | 97.63 | +| floor | 84.95 | 91.93 | +| tree | 78.95 | 89.9 | +| ceiling | 87.35 | 94.91 | +| road | 85.0 | 89.51 | +| bed | 92.91 | 96.53 | +| windowpane | 65.82 | 82.59 | +| grass | 67.96 | 85.31 | +| cabinet | 68.35 | 79.21 | +| sidewalk | 70.95 | 88.65 | +| person | 86.35 | 94.34 | +| earth | 38.87 | 51.39 | +| door | 60.53 | 77.95 | +| table | 69.94 | 80.32 | +| mountain | 62.12 | 69.74 | +| plant | 59.27 | 69.83 | +| curtain | 79.48 | 89.03 | +| chair | 68.44 | 79.71 | +| car | 88.1 | 94.4 | +| water | 62.44 | 78.93 | +| painting | 80.62 | 90.57 | +| sofa | 82.01 | 89.05 | +| shelf | 49.97 | 65.08 | +| house | 54.72 | 66.52 | +| sea | 73.41 | 90.01 | +| mirror | 79.26 | 85.92 | +| rug | 67.04 | 74.43 | +| field | 31.63 | 51.03 | +| armchair | 59.63 | 79.02 | +| seat | 69.3 | 89.2 | +| fence | 49.95 | 59.23 | +| desk | 60.06 | 82.61 | +| rock | 56.27 | 86.72 | +| wardrobe | 56.21 | 75.67 | +| lamp | 75.68 | 86.99 | +| bathtub | 88.06 | 90.83 | +| railing | 43.17 | 62.12 | +| cushion | 69.27 | 85.05 | +| base | 49.47 | 72.27 | +| box | 40.07 | 53.86 | +| column | 58.02 | 76.98 | +| signboard | 42.58 | 57.13 | +| chest of drawers | 44.7 | 66.79 | +| counter | 49.45 | 58.31 | +| sand | 50.29 | 79.27 | +| sink | 85.08 | 90.06 | +| skyscraper | 50.05 | 66.68 | +| fireplace | 75.99 | 94.43 | +| refrigerator | 87.12 | 95.96 | +| grandstand | 63.44 | 84.5 | +| path | 32.93 | 42.53 | +| stairs | 41.39 | 53.96 | +| runway | 72.04 | 92.75 | +| case | 65.26 | 75.49 | +| pool table | 95.19 | 98.4 | +| pillow | 65.56 | 76.57 | +| screen door | 84.1 | 86.76 | +| stairway | 47.18 | 60.61 | +| river | 11.64 | 18.26 | +| bridge | 75.15 | 82.71 | +| bookcase | 43.44 | 67.36 | +| blind | 39.97 | 40.98 | +| coffee table | 60.81 | 86.04 | +| toilet | 90.42 | 94.25 | +| flower | 45.25 | 61.19 | +| book | 54.71 | 79.65 | +| hill | 12.1 | 23.62 | +| bench | 67.5 | 82.01 | +| countertop | 64.54 | 84.03 | +| stove | 88.26 | 94.28 | +| palm | 51.66 | 88.19 | +| kitchen island | 55.22 | 89.41 | +| computer | 76.42 | 92.13 | +| swivel chair | 50.23 | 79.34 | +| boat | 66.24 | 93.28 | +| bar | 67.82 | 83.98 | +| arcade machine | 86.24 | 90.81 | +| hovel | 47.2 | 56.02 | +| bus | 93.73 | 95.83 | +| towel | 81.37 | 88.97 | +| light | 62.73 | 75.78 | +| truck | 52.16 | 64.27 | +| tower | 29.55 | 62.37 | +| chandelier | 72.53 | 86.42 | +| awning | 51.19 | 66.28 | +| streetlight | 38.33 | 51.98 | +| booth | 61.37 | 69.83 | +| television receiver | 80.86 | 85.57 | +| airplane | 89.22 | 95.79 | +| dirt track | 6.61 | 21.37 | +| apparel | 63.52 | 83.78 | +| pole | 29.96 | 42.31 | +| land | 4.0 | 5.31 | +| bannister | 20.55 | 25.77 | +| escalator | 66.71 | 85.33 | +| ottoman | 52.48 | 68.53 | +| bottle | 46.14 | 75.21 | +| buffet | 61.61 | 72.41 | +| poster | 34.97 | 39.57 | +| stage | 23.99 | 38.74 | +| van | 53.12 | 67.18 | +| ship | 74.04 | 87.51 | +| fountain | 29.93 | 30.28 | +| conveyer belt | 85.0 | 96.27 | +| canopy | 66.02 | 82.22 | +| washer | 86.61 | 91.81 | +| plaything | 36.9 | 49.02 | +| swimming pool | 53.98 | 77.34 | +| stool | 60.65 | 73.11 | +| barrel | 64.42 | 91.9 | +| basket | 45.49 | 60.51 | +| waterfall | 47.1 | 58.86 | +| tent | 96.23 | 98.0 | +| bag | 24.18 | 27.08 | +| minibike | 77.63 | 87.99 | +| cradle | 85.69 | 96.47 | +| oven | 69.85 | 81.91 | +| ball | 50.0 | 51.98 | +| food | 62.28 | 79.97 | +| step | 11.23 | 13.3 | +| tank | 69.56 | 75.05 | +| trade name | 34.03 | 41.64 | +| microwave | 90.43 | 96.07 | +| pot | 59.87 | 71.09 | +| animal | 59.74 | 61.08 | +| bicycle | 60.53 | 77.52 | +| lake | 57.48 | 63.79 | +| dishwasher | 69.59 | 73.33 | +| screen | 61.01 | 96.07 | +| blanket | 24.49 | 26.3 | +| sculpture | 74.18 | 85.61 | +| hood | 69.59 | 80.95 | +| sconce | 59.1 | 79.39 | +| vase | 51.39 | 72.1 | +| traffic light | 39.32 | 66.83 | +| tray | 29.09 | 38.84 | +| ashcan | 50.58 | 66.8 | +| fan | 72.35 | 85.37 | +| pier | 40.47 | 43.15 | +| crt screen | 2.69 | 5.68 | +| plate | 65.11 | 79.95 | +| monitor | 28.18 | 32.5 | +| bulletin board | 59.0 | 74.11 | +| shower | 10.49 | 12.77 | +| radiator | 69.21 | 86.08 | +| glass | 23.08 | 25.14 | +| clock | 52.2 | 62.73 | +| flag | 70.2 | 75.84 | ++---------------------+-------+-------+ +2024-06-19 06:21:33,089 - mmseg - INFO - Summary: +2024-06-19 06:21:33,089 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.51 | 59.32 | 72.03 | ++-------+-------+-------+ +2024-06-19 06:21:33,090 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 06:21:33,090 - mmseg - INFO - Iter(val) [250] aAcc: 0.8651, mIoU: 0.5932, mAcc: 0.7203, IoU.wall: 0.8247, IoU.building: 0.8494, IoU.sky: 0.9504, IoU.floor: 0.8495, IoU.tree: 0.7895, IoU.ceiling: 0.8735, IoU.road: 0.8500, IoU.bed : 0.9291, IoU.windowpane: 0.6582, IoU.grass: 0.6796, IoU.cabinet: 0.6835, IoU.sidewalk: 0.7095, IoU.person: 0.8635, IoU.earth: 0.3887, IoU.door: 0.6053, IoU.table: 0.6994, IoU.mountain: 0.6212, IoU.plant: 0.5927, IoU.curtain: 0.7948, IoU.chair: 0.6844, IoU.car: 0.8810, IoU.water: 0.6244, IoU.painting: 0.8062, IoU.sofa: 0.8201, IoU.shelf: 0.4997, IoU.house: 0.5472, IoU.sea: 0.7341, IoU.mirror: 0.7926, IoU.rug: 0.6704, IoU.field: 0.3163, IoU.armchair: 0.5963, IoU.seat: 0.6930, IoU.fence: 0.4995, IoU.desk: 0.6006, IoU.rock: 0.5627, IoU.wardrobe: 0.5621, IoU.lamp: 0.7568, IoU.bathtub: 0.8806, IoU.railing: 0.4317, IoU.cushion: 0.6927, IoU.base: 0.4947, IoU.box: 0.4007, IoU.column: 0.5802, IoU.signboard: 0.4258, IoU.chest of drawers: 0.4470, IoU.counter: 0.4945, IoU.sand: 0.5029, IoU.sink: 0.8508, IoU.skyscraper: 0.5005, IoU.fireplace: 0.7599, IoU.refrigerator: 0.8712, IoU.grandstand: 0.6344, IoU.path: 0.3293, IoU.stairs: 0.4139, IoU.runway: 0.7204, IoU.case: 0.6526, IoU.pool table: 0.9519, IoU.pillow: 0.6556, IoU.screen door: 0.8410, IoU.stairway: 0.4718, IoU.river: 0.1164, IoU.bridge: 0.7515, IoU.bookcase: 0.4344, IoU.blind: 0.3997, IoU.coffee table: 0.6081, IoU.toilet: 0.9042, IoU.flower: 0.4525, IoU.book: 0.5471, IoU.hill: 0.1210, IoU.bench: 0.6750, IoU.countertop: 0.6454, IoU.stove: 0.8826, IoU.palm: 0.5166, IoU.kitchen island: 0.5522, IoU.computer: 0.7642, IoU.swivel chair: 0.5023, IoU.boat: 0.6624, IoU.bar: 0.6782, IoU.arcade machine: 0.8624, IoU.hovel: 0.4720, IoU.bus: 0.9373, IoU.towel: 0.8137, IoU.light: 0.6273, IoU.truck: 0.5216, IoU.tower: 0.2955, IoU.chandelier: 0.7253, IoU.awning: 0.5119, IoU.streetlight: 0.3833, IoU.booth: 0.6137, IoU.television receiver: 0.8086, IoU.airplane: 0.8922, IoU.dirt track: 0.0661, IoU.apparel: 0.6352, IoU.pole: 0.2996, IoU.land: 0.0400, IoU.bannister: 0.2055, IoU.escalator: 0.6671, IoU.ottoman: 0.5248, IoU.bottle: 0.4614, IoU.buffet: 0.6161, IoU.poster: 0.3497, IoU.stage: 0.2399, IoU.van: 0.5312, IoU.ship: 0.7404, IoU.fountain: 0.2993, IoU.conveyer belt: 0.8500, IoU.canopy: 0.6602, IoU.washer: 0.8661, IoU.plaything: 0.3690, IoU.swimming pool: 0.5398, IoU.stool: 0.6065, IoU.barrel: 0.6442, IoU.basket: 0.4549, IoU.waterfall: 0.4710, IoU.tent: 0.9623, IoU.bag: 0.2418, IoU.minibike: 0.7763, IoU.cradle: 0.8569, IoU.oven: 0.6985, IoU.ball: 0.5000, IoU.food: 0.6228, IoU.step: 0.1123, IoU.tank: 0.6956, IoU.trade name: 0.3403, IoU.microwave: 0.9043, IoU.pot: 0.5987, IoU.animal: 0.5974, IoU.bicycle: 0.6053, IoU.lake: 0.5748, IoU.dishwasher: 0.6959, IoU.screen: 0.6101, IoU.blanket: 0.2449, IoU.sculpture: 0.7418, IoU.hood: 0.6959, IoU.sconce: 0.5910, IoU.vase: 0.5139, IoU.traffic light: 0.3932, IoU.tray: 0.2909, IoU.ashcan: 0.5058, IoU.fan: 0.7235, IoU.pier: 0.4047, IoU.crt screen: 0.0269, IoU.plate: 0.6511, IoU.monitor: 0.2818, IoU.bulletin board: 0.5900, IoU.shower: 0.1049, IoU.radiator: 0.6921, IoU.glass: 0.2308, IoU.clock: 0.5220, IoU.flag: 0.7020, Acc.wall: 0.8926, Acc.building: 0.9305, Acc.sky: 0.9763, Acc.floor: 0.9193, Acc.tree: 0.8990, Acc.ceiling: 0.9491, Acc.road: 0.8951, Acc.bed : 0.9653, Acc.windowpane: 0.8259, Acc.grass: 0.8531, Acc.cabinet: 0.7921, Acc.sidewalk: 0.8865, Acc.person: 0.9434, Acc.earth: 0.5139, Acc.door: 0.7795, Acc.table: 0.8032, Acc.mountain: 0.6974, Acc.plant: 0.6983, Acc.curtain: 0.8903, Acc.chair: 0.7971, Acc.car: 0.9440, Acc.water: 0.7893, Acc.painting: 0.9057, Acc.sofa: 0.8905, Acc.shelf: 0.6508, Acc.house: 0.6652, Acc.sea: 0.9001, Acc.mirror: 0.8592, Acc.rug: 0.7443, Acc.field: 0.5103, Acc.armchair: 0.7902, Acc.seat: 0.8920, Acc.fence: 0.5923, Acc.desk: 0.8261, Acc.rock: 0.8672, Acc.wardrobe: 0.7567, Acc.lamp: 0.8699, Acc.bathtub: 0.9083, Acc.railing: 0.6212, Acc.cushion: 0.8505, Acc.base: 0.7227, Acc.box: 0.5386, Acc.column: 0.7698, Acc.signboard: 0.5713, Acc.chest of drawers: 0.6679, Acc.counter: 0.5831, Acc.sand: 0.7927, Acc.sink: 0.9006, Acc.skyscraper: 0.6668, Acc.fireplace: 0.9443, Acc.refrigerator: 0.9596, Acc.grandstand: 0.8450, Acc.path: 0.4253, Acc.stairs: 0.5396, Acc.runway: 0.9275, Acc.case: 0.7549, Acc.pool table: 0.9840, Acc.pillow: 0.7657, Acc.screen door: 0.8676, Acc.stairway: 0.6061, Acc.river: 0.1826, Acc.bridge: 0.8271, Acc.bookcase: 0.6736, Acc.blind: 0.4098, Acc.coffee table: 0.8604, Acc.toilet: 0.9425, Acc.flower: 0.6119, Acc.book: 0.7965, Acc.hill: 0.2362, Acc.bench: 0.8201, Acc.countertop: 0.8403, Acc.stove: 0.9428, Acc.palm: 0.8819, Acc.kitchen island: 0.8941, Acc.computer: 0.9213, Acc.swivel chair: 0.7934, Acc.boat: 0.9328, Acc.bar: 0.8398, Acc.arcade machine: 0.9081, Acc.hovel: 0.5602, Acc.bus: 0.9583, Acc.towel: 0.8897, Acc.light: 0.7578, Acc.truck: 0.6427, Acc.tower: 0.6237, Acc.chandelier: 0.8642, Acc.awning: 0.6628, Acc.streetlight: 0.5198, Acc.booth: 0.6983, Acc.television receiver: 0.8557, Acc.airplane: 0.9579, Acc.dirt track: 0.2137, Acc.apparel: 0.8378, Acc.pole: 0.4231, Acc.land: 0.0531, Acc.bannister: 0.2577, Acc.escalator: 0.8533, Acc.ottoman: 0.6853, Acc.bottle: 0.7521, Acc.buffet: 0.7241, Acc.poster: 0.3957, Acc.stage: 0.3874, Acc.van: 0.6718, Acc.ship: 0.8751, Acc.fountain: 0.3028, Acc.conveyer belt: 0.9627, Acc.canopy: 0.8222, Acc.washer: 0.9181, Acc.plaything: 0.4902, Acc.swimming pool: 0.7734, Acc.stool: 0.7311, Acc.barrel: 0.9190, Acc.basket: 0.6051, Acc.waterfall: 0.5886, Acc.tent: 0.9800, Acc.bag: 0.2708, Acc.minibike: 0.8799, Acc.cradle: 0.9647, Acc.oven: 0.8191, Acc.ball: 0.5198, Acc.food: 0.7997, Acc.step: 0.1330, Acc.tank: 0.7505, Acc.trade name: 0.4164, Acc.microwave: 0.9607, Acc.pot: 0.7109, Acc.animal: 0.6108, Acc.bicycle: 0.7752, Acc.lake: 0.6379, Acc.dishwasher: 0.7333, Acc.screen: 0.9607, Acc.blanket: 0.2630, Acc.sculpture: 0.8561, Acc.hood: 0.8095, Acc.sconce: 0.7939, Acc.vase: 0.7210, Acc.traffic light: 0.6683, Acc.tray: 0.3884, Acc.ashcan: 0.6680, Acc.fan: 0.8537, Acc.pier: 0.4315, Acc.crt screen: 0.0568, Acc.plate: 0.7995, Acc.monitor: 0.3250, Acc.bulletin board: 0.7411, Acc.shower: 0.1277, Acc.radiator: 0.8608, Acc.glass: 0.2514, Acc.clock: 0.6273, Acc.flag: 0.7584 +2024-06-19 06:23:12,253 - mmseg - INFO - Iter [47050/80000] lr: 1.648e-05, eta: 19:32:20, time: 4.202, data_time: 2.235, memory: 72263, decode.loss_ce: 0.1737, decode.acc_seg: 92.5539, aux.loss_ce: 0.0722, aux.acc_seg: 92.2490, loss: 0.2459 +2024-06-19 06:24:51,160 - mmseg - INFO - Iter [47100/80000] lr: 1.645e-05, eta: 19:30:28, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1583, decode.acc_seg: 93.2002, aux.loss_ce: 0.0670, aux.acc_seg: 92.7752, loss: 0.2252 +2024-06-19 06:26:30,058 - mmseg - INFO - Iter [47150/80000] lr: 1.643e-05, eta: 19:28:36, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1683, decode.acc_seg: 93.0928, aux.loss_ce: 0.0709, aux.acc_seg: 92.6562, loss: 0.2392 +2024-06-19 06:28:09,156 - mmseg - INFO - Iter [47200/80000] lr: 1.640e-05, eta: 19:26:44, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1674, decode.acc_seg: 92.7631, aux.loss_ce: 0.0704, aux.acc_seg: 92.3970, loss: 0.2379 +2024-06-19 06:29:47,968 - mmseg - INFO - Iter [47250/80000] lr: 1.638e-05, eta: 19:24:52, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1666, decode.acc_seg: 92.7584, aux.loss_ce: 0.0693, aux.acc_seg: 92.3971, loss: 0.2358 +2024-06-19 06:31:26,869 - mmseg - INFO - Iter [47300/80000] lr: 1.635e-05, eta: 19:22:59, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1616, decode.acc_seg: 92.8589, aux.loss_ce: 0.0677, aux.acc_seg: 92.5331, loss: 0.2293 +2024-06-19 06:33:05,780 - mmseg - INFO - Iter [47350/80000] lr: 1.633e-05, eta: 19:21:07, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1744, decode.acc_seg: 92.5854, aux.loss_ce: 0.0731, aux.acc_seg: 92.2633, loss: 0.2475 +2024-06-19 06:34:44,643 - mmseg - INFO - Iter [47400/80000] lr: 1.630e-05, eta: 19:19:15, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1698, decode.acc_seg: 92.7978, aux.loss_ce: 0.0711, aux.acc_seg: 92.4513, loss: 0.2409 +2024-06-19 06:36:23,488 - mmseg - INFO - Iter [47450/80000] lr: 1.628e-05, eta: 19:17:23, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1768, decode.acc_seg: 92.5207, aux.loss_ce: 0.0746, aux.acc_seg: 92.1734, loss: 0.2514 +2024-06-19 06:38:02,362 - mmseg - INFO - Iter [47500/80000] lr: 1.625e-05, eta: 19:15:31, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1693, decode.acc_seg: 92.6788, aux.loss_ce: 0.0711, aux.acc_seg: 92.2666, loss: 0.2405 +2024-06-19 06:39:41,140 - mmseg - INFO - Iter [47550/80000] lr: 1.623e-05, eta: 19:13:39, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1721, decode.acc_seg: 92.8114, aux.loss_ce: 0.0725, aux.acc_seg: 92.4484, loss: 0.2446 +2024-06-19 06:41:20,100 - mmseg - INFO - Iter [47600/80000] lr: 1.620e-05, eta: 19:11:47, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1668, decode.acc_seg: 92.8696, aux.loss_ce: 0.0704, aux.acc_seg: 92.4729, loss: 0.2373 +2024-06-19 06:42:58,978 - mmseg - INFO - Iter [47650/80000] lr: 1.618e-05, eta: 19:09:55, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1635, decode.acc_seg: 92.7953, aux.loss_ce: 0.0690, aux.acc_seg: 92.4014, loss: 0.2325 +2024-06-19 06:44:37,859 - mmseg - INFO - Iter [47700/80000] lr: 1.615e-05, eta: 19:08:04, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1708, decode.acc_seg: 92.6589, aux.loss_ce: 0.0715, aux.acc_seg: 92.3692, loss: 0.2423 +2024-06-19 06:46:16,838 - mmseg - INFO - Iter [47750/80000] lr: 1.613e-05, eta: 19:06:12, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1756, decode.acc_seg: 92.6151, aux.loss_ce: 0.0739, aux.acc_seg: 92.2381, loss: 0.2495 +2024-06-19 06:47:55,629 - mmseg - INFO - Iter [47800/80000] lr: 1.610e-05, eta: 19:04:20, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1581, decode.acc_seg: 92.7949, aux.loss_ce: 0.0663, aux.acc_seg: 92.5499, loss: 0.2244 +2024-06-19 06:49:34,379 - mmseg - INFO - Iter [47850/80000] lr: 1.608e-05, eta: 19:02:28, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1686, decode.acc_seg: 92.7610, aux.loss_ce: 0.0704, aux.acc_seg: 92.4502, loss: 0.2391 +2024-06-19 06:51:13,250 - mmseg - INFO - Iter [47900/80000] lr: 1.605e-05, eta: 19:00:36, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1679, decode.acc_seg: 92.6407, aux.loss_ce: 0.0705, aux.acc_seg: 92.2945, loss: 0.2384 +2024-06-19 06:52:52,099 - mmseg - INFO - Iter [47950/80000] lr: 1.603e-05, eta: 18:58:44, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1754, decode.acc_seg: 92.3776, aux.loss_ce: 0.0731, aux.acc_seg: 91.9871, loss: 0.2485 +2024-06-19 06:54:33,160 - mmseg - INFO - Saving checkpoint at 48000 iterations +2024-06-19 06:55:54,331 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 06:55:54,331 - mmseg - INFO - Iter [48000/80000] lr: 1.600e-05, eta: 18:57:48, time: 3.645, data_time: 0.052, memory: 72263, decode.loss_ce: 0.1684, decode.acc_seg: 92.5920, aux.loss_ce: 0.0708, aux.acc_seg: 92.2223, loss: 0.2392 +2024-06-19 06:57:43,519 - mmseg - INFO - per class results: +2024-06-19 06:57:43,526 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.5 | 89.87 | +| building | 85.15 | 92.68 | +| sky | 94.85 | 97.62 | +| floor | 84.48 | 91.91 | +| tree | 77.94 | 89.68 | +| ceiling | 87.1 | 94.27 | +| road | 86.85 | 90.95 | +| bed | 93.29 | 96.88 | +| windowpane | 66.58 | 80.95 | +| grass | 64.9 | 79.21 | +| cabinet | 66.1 | 74.18 | +| sidewalk | 71.21 | 86.37 | +| person | 86.29 | 94.73 | +| earth | 41.08 | 56.28 | +| door | 60.76 | 77.39 | +| table | 69.75 | 82.31 | +| mountain | 60.79 | 71.15 | +| plant | 55.49 | 64.11 | +| curtain | 79.72 | 89.1 | +| chair | 69.14 | 80.19 | +| car | 87.87 | 94.89 | +| water | 64.52 | 81.14 | +| painting | 80.18 | 92.92 | +| sofa | 80.47 | 91.84 | +| shelf | 49.75 | 63.01 | +| house | 51.45 | 67.45 | +| sea | 75.67 | 83.82 | +| mirror | 77.89 | 82.89 | +| rug | 65.21 | 74.97 | +| field | 26.36 | 49.88 | +| armchair | 60.63 | 76.76 | +| seat | 66.75 | 85.02 | +| fence | 50.5 | 60.62 | +| desk | 60.58 | 75.68 | +| rock | 56.33 | 83.77 | +| wardrobe | 54.66 | 79.11 | +| lamp | 75.0 | 84.58 | +| bathtub | 86.74 | 91.29 | +| railing | 41.64 | 59.2 | +| cushion | 69.68 | 85.92 | +| base | 43.55 | 64.22 | +| box | 37.24 | 48.09 | +| column | 57.18 | 68.64 | +| signboard | 40.59 | 55.32 | +| chest of drawers | 43.53 | 75.88 | +| counter | 46.82 | 50.72 | +| sand | 53.17 | 78.06 | +| sink | 83.45 | 88.57 | +| skyscraper | 50.14 | 63.97 | +| fireplace | 75.04 | 95.18 | +| refrigerator | 84.06 | 92.75 | +| grandstand | 56.52 | 80.87 | +| path | 32.32 | 44.03 | +| stairs | 38.74 | 46.15 | +| runway | 74.22 | 97.35 | +| case | 66.29 | 89.93 | +| pool table | 95.34 | 98.11 | +| pillow | 65.22 | 74.91 | +| screen door | 81.46 | 92.34 | +| stairway | 50.78 | 72.63 | +| river | 12.96 | 28.18 | +| bridge | 77.76 | 88.51 | +| bookcase | 44.04 | 68.84 | +| blind | 49.64 | 60.31 | +| coffee table | 62.59 | 86.25 | +| toilet | 89.97 | 93.49 | +| flower | 44.35 | 59.56 | +| book | 55.62 | 78.74 | +| hill | 13.04 | 31.25 | +| bench | 68.4 | 78.74 | +| countertop | 64.94 | 86.33 | +| stove | 87.9 | 92.86 | +| palm | 53.68 | 86.26 | +| kitchen island | 51.34 | 79.7 | +| computer | 75.91 | 91.88 | +| swivel chair | 49.67 | 77.51 | +| boat | 52.54 | 92.6 | +| bar | 66.45 | 81.54 | +| arcade machine | 89.95 | 94.62 | +| hovel | 47.11 | 57.42 | +| bus | 93.51 | 97.2 | +| towel | 80.16 | 87.72 | +| light | 59.57 | 65.61 | +| truck | 51.47 | 68.26 | +| tower | 27.14 | 44.83 | +| chandelier | 72.02 | 89.75 | +| awning | 39.07 | 47.97 | +| streetlight | 36.92 | 48.97 | +| booth | 58.03 | 69.39 | +| television receiver | 81.17 | 87.29 | +| airplane | 87.22 | 96.67 | +| dirt track | 5.44 | 27.18 | +| apparel | 59.31 | 68.87 | +| pole | 28.42 | 36.0 | +| land | 4.8 | 6.46 | +| bannister | 17.02 | 21.14 | +| escalator | 65.77 | 86.76 | +| ottoman | 54.46 | 71.09 | +| bottle | 45.59 | 74.71 | +| buffet | 61.46 | 82.15 | +| poster | 33.28 | 37.47 | +| stage | 19.7 | 35.46 | +| van | 48.6 | 62.14 | +| ship | 83.55 | 85.38 | +| fountain | 34.23 | 34.86 | +| conveyer belt | 84.98 | 96.27 | +| canopy | 53.48 | 70.49 | +| washer | 86.26 | 92.88 | +| plaything | 34.53 | 47.83 | +| swimming pool | 56.15 | 79.37 | +| stool | 59.25 | 74.58 | +| barrel | 62.68 | 93.89 | +| basket | 44.67 | 57.25 | +| waterfall | 45.91 | 49.08 | +| tent | 91.29 | 98.8 | +| bag | 28.56 | 33.02 | +| minibike | 74.91 | 92.39 | +| cradle | 78.73 | 97.55 | +| oven | 69.83 | 82.94 | +| ball | 60.85 | 70.11 | +| food | 52.2 | 62.11 | +| step | 10.88 | 11.46 | +| tank | 65.8 | 70.27 | +| trade name | 15.19 | 16.44 | +| microwave | 90.38 | 96.68 | +| pot | 59.52 | 68.73 | +| animal | 62.14 | 63.68 | +| bicycle | 60.08 | 79.79 | +| lake | 60.24 | 63.59 | +| dishwasher | 68.91 | 84.74 | +| screen | 53.71 | 66.81 | +| blanket | 34.4 | 40.5 | +| sculpture | 75.06 | 82.09 | +| hood | 64.15 | 71.49 | +| sconce | 61.74 | 74.32 | +| vase | 50.31 | 68.73 | +| traffic light | 37.56 | 61.67 | +| tray | 25.64 | 34.85 | +| ashcan | 50.28 | 65.6 | +| fan | 69.49 | 76.77 | +| pier | 39.9 | 43.44 | +| crt screen | 20.22 | 43.36 | +| plate | 64.01 | 76.43 | +| monitor | 58.64 | 74.27 | +| bulletin board | 60.25 | 74.26 | +| shower | 16.25 | 17.64 | +| radiator | 66.55 | 79.14 | +| glass | 23.25 | 25.43 | +| clock | 55.23 | 66.69 | +| flag | 68.39 | 83.33 | ++---------------------+-------+-------+ +2024-06-19 06:57:43,526 - mmseg - INFO - Summary: +2024-06-19 06:57:43,526 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.27 | 58.8 | 71.67 | ++-------+------+-------+ +2024-06-19 06:57:43,527 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 06:57:43,527 - mmseg - INFO - Iter(val) [250] aAcc: 0.8627, mIoU: 0.5880, mAcc: 0.7167, IoU.wall: 0.8250, IoU.building: 0.8515, IoU.sky: 0.9485, IoU.floor: 0.8448, IoU.tree: 0.7794, IoU.ceiling: 0.8710, IoU.road: 0.8685, IoU.bed : 0.9329, IoU.windowpane: 0.6658, IoU.grass: 0.6490, IoU.cabinet: 0.6610, IoU.sidewalk: 0.7121, IoU.person: 0.8629, IoU.earth: 0.4108, IoU.door: 0.6076, IoU.table: 0.6975, IoU.mountain: 0.6079, IoU.plant: 0.5549, IoU.curtain: 0.7972, IoU.chair: 0.6914, IoU.car: 0.8787, IoU.water: 0.6452, IoU.painting: 0.8018, IoU.sofa: 0.8047, IoU.shelf: 0.4975, IoU.house: 0.5145, IoU.sea: 0.7567, IoU.mirror: 0.7789, IoU.rug: 0.6521, IoU.field: 0.2636, IoU.armchair: 0.6063, IoU.seat: 0.6675, IoU.fence: 0.5050, IoU.desk: 0.6058, IoU.rock: 0.5633, IoU.wardrobe: 0.5466, IoU.lamp: 0.7500, IoU.bathtub: 0.8674, IoU.railing: 0.4164, IoU.cushion: 0.6968, IoU.base: 0.4355, IoU.box: 0.3724, IoU.column: 0.5718, IoU.signboard: 0.4059, IoU.chest of drawers: 0.4353, IoU.counter: 0.4682, IoU.sand: 0.5317, IoU.sink: 0.8345, IoU.skyscraper: 0.5014, IoU.fireplace: 0.7504, IoU.refrigerator: 0.8406, IoU.grandstand: 0.5652, IoU.path: 0.3232, IoU.stairs: 0.3874, IoU.runway: 0.7422, IoU.case: 0.6629, IoU.pool table: 0.9534, IoU.pillow: 0.6522, IoU.screen door: 0.8146, IoU.stairway: 0.5078, IoU.river: 0.1296, IoU.bridge: 0.7776, IoU.bookcase: 0.4404, IoU.blind: 0.4964, IoU.coffee table: 0.6259, IoU.toilet: 0.8997, IoU.flower: 0.4435, IoU.book: 0.5562, IoU.hill: 0.1304, IoU.bench: 0.6840, IoU.countertop: 0.6494, IoU.stove: 0.8790, IoU.palm: 0.5368, IoU.kitchen island: 0.5134, IoU.computer: 0.7591, IoU.swivel chair: 0.4967, IoU.boat: 0.5254, IoU.bar: 0.6645, IoU.arcade machine: 0.8995, IoU.hovel: 0.4711, IoU.bus: 0.9351, IoU.towel: 0.8016, IoU.light: 0.5957, IoU.truck: 0.5147, IoU.tower: 0.2714, IoU.chandelier: 0.7202, IoU.awning: 0.3907, IoU.streetlight: 0.3692, IoU.booth: 0.5803, IoU.television receiver: 0.8117, IoU.airplane: 0.8722, IoU.dirt track: 0.0544, IoU.apparel: 0.5931, IoU.pole: 0.2842, IoU.land: 0.0480, IoU.bannister: 0.1702, IoU.escalator: 0.6577, IoU.ottoman: 0.5446, IoU.bottle: 0.4559, IoU.buffet: 0.6146, IoU.poster: 0.3328, IoU.stage: 0.1970, IoU.van: 0.4860, IoU.ship: 0.8355, IoU.fountain: 0.3423, IoU.conveyer belt: 0.8498, IoU.canopy: 0.5348, IoU.washer: 0.8626, IoU.plaything: 0.3453, IoU.swimming pool: 0.5615, IoU.stool: 0.5925, IoU.barrel: 0.6268, IoU.basket: 0.4467, IoU.waterfall: 0.4591, IoU.tent: 0.9129, IoU.bag: 0.2856, IoU.minibike: 0.7491, IoU.cradle: 0.7873, IoU.oven: 0.6983, IoU.ball: 0.6085, IoU.food: 0.5220, IoU.step: 0.1088, IoU.tank: 0.6580, IoU.trade name: 0.1519, IoU.microwave: 0.9038, IoU.pot: 0.5952, IoU.animal: 0.6214, IoU.bicycle: 0.6008, IoU.lake: 0.6024, IoU.dishwasher: 0.6891, IoU.screen: 0.5371, IoU.blanket: 0.3440, IoU.sculpture: 0.7506, IoU.hood: 0.6415, IoU.sconce: 0.6174, IoU.vase: 0.5031, IoU.traffic light: 0.3756, IoU.tray: 0.2564, IoU.ashcan: 0.5028, IoU.fan: 0.6949, IoU.pier: 0.3990, IoU.crt screen: 0.2022, IoU.plate: 0.6401, IoU.monitor: 0.5864, IoU.bulletin board: 0.6025, IoU.shower: 0.1625, IoU.radiator: 0.6655, IoU.glass: 0.2325, IoU.clock: 0.5523, IoU.flag: 0.6839, Acc.wall: 0.8987, Acc.building: 0.9268, Acc.sky: 0.9762, Acc.floor: 0.9191, Acc.tree: 0.8968, Acc.ceiling: 0.9427, Acc.road: 0.9095, Acc.bed : 0.9688, Acc.windowpane: 0.8095, Acc.grass: 0.7921, Acc.cabinet: 0.7418, Acc.sidewalk: 0.8637, Acc.person: 0.9473, Acc.earth: 0.5628, Acc.door: 0.7739, Acc.table: 0.8231, Acc.mountain: 0.7115, Acc.plant: 0.6411, Acc.curtain: 0.8910, Acc.chair: 0.8019, Acc.car: 0.9489, Acc.water: 0.8114, Acc.painting: 0.9292, Acc.sofa: 0.9184, Acc.shelf: 0.6301, Acc.house: 0.6745, Acc.sea: 0.8382, Acc.mirror: 0.8289, Acc.rug: 0.7497, Acc.field: 0.4988, Acc.armchair: 0.7676, Acc.seat: 0.8502, Acc.fence: 0.6062, Acc.desk: 0.7568, Acc.rock: 0.8377, Acc.wardrobe: 0.7911, Acc.lamp: 0.8458, Acc.bathtub: 0.9129, Acc.railing: 0.5920, Acc.cushion: 0.8592, Acc.base: 0.6422, Acc.box: 0.4809, Acc.column: 0.6864, Acc.signboard: 0.5532, Acc.chest of drawers: 0.7588, Acc.counter: 0.5072, Acc.sand: 0.7806, Acc.sink: 0.8857, Acc.skyscraper: 0.6397, Acc.fireplace: 0.9518, Acc.refrigerator: 0.9275, Acc.grandstand: 0.8087, Acc.path: 0.4403, Acc.stairs: 0.4615, Acc.runway: 0.9735, Acc.case: 0.8993, Acc.pool table: 0.9811, Acc.pillow: 0.7491, Acc.screen door: 0.9234, Acc.stairway: 0.7263, Acc.river: 0.2818, Acc.bridge: 0.8851, Acc.bookcase: 0.6884, Acc.blind: 0.6031, Acc.coffee table: 0.8625, Acc.toilet: 0.9349, Acc.flower: 0.5956, Acc.book: 0.7874, Acc.hill: 0.3125, Acc.bench: 0.7874, Acc.countertop: 0.8633, Acc.stove: 0.9286, Acc.palm: 0.8626, Acc.kitchen island: 0.7970, Acc.computer: 0.9188, Acc.swivel chair: 0.7751, Acc.boat: 0.9260, Acc.bar: 0.8154, Acc.arcade machine: 0.9462, Acc.hovel: 0.5742, Acc.bus: 0.9720, Acc.towel: 0.8772, Acc.light: 0.6561, Acc.truck: 0.6826, Acc.tower: 0.4483, Acc.chandelier: 0.8975, Acc.awning: 0.4797, Acc.streetlight: 0.4897, Acc.booth: 0.6939, Acc.television receiver: 0.8729, Acc.airplane: 0.9667, Acc.dirt track: 0.2718, Acc.apparel: 0.6887, Acc.pole: 0.3600, Acc.land: 0.0646, Acc.bannister: 0.2114, Acc.escalator: 0.8676, Acc.ottoman: 0.7109, Acc.bottle: 0.7471, Acc.buffet: 0.8215, Acc.poster: 0.3747, Acc.stage: 0.3546, Acc.van: 0.6214, Acc.ship: 0.8538, Acc.fountain: 0.3486, Acc.conveyer belt: 0.9627, Acc.canopy: 0.7049, Acc.washer: 0.9288, Acc.plaything: 0.4783, Acc.swimming pool: 0.7937, Acc.stool: 0.7458, Acc.barrel: 0.9389, Acc.basket: 0.5725, Acc.waterfall: 0.4908, Acc.tent: 0.9880, Acc.bag: 0.3302, Acc.minibike: 0.9239, Acc.cradle: 0.9755, Acc.oven: 0.8294, Acc.ball: 0.7011, Acc.food: 0.6211, Acc.step: 0.1146, Acc.tank: 0.7027, Acc.trade name: 0.1644, Acc.microwave: 0.9668, Acc.pot: 0.6873, Acc.animal: 0.6368, Acc.bicycle: 0.7979, Acc.lake: 0.6359, Acc.dishwasher: 0.8474, Acc.screen: 0.6681, Acc.blanket: 0.4050, Acc.sculpture: 0.8209, Acc.hood: 0.7149, Acc.sconce: 0.7432, Acc.vase: 0.6873, Acc.traffic light: 0.6167, Acc.tray: 0.3485, Acc.ashcan: 0.6560, Acc.fan: 0.7677, Acc.pier: 0.4344, Acc.crt screen: 0.4336, Acc.plate: 0.7643, Acc.monitor: 0.7427, Acc.bulletin board: 0.7426, Acc.shower: 0.1764, Acc.radiator: 0.7914, Acc.glass: 0.2543, Acc.clock: 0.6669, Acc.flag: 0.8333 +2024-06-19 06:59:22,922 - mmseg - INFO - Iter [48050/80000] lr: 1.598e-05, eta: 18:57:09, time: 4.172, data_time: 2.201, memory: 72263, decode.loss_ce: 0.1648, decode.acc_seg: 93.1032, aux.loss_ce: 0.0694, aux.acc_seg: 92.7444, loss: 0.2342 +2024-06-19 07:01:01,894 - mmseg - INFO - Iter [48100/80000] lr: 1.595e-05, eta: 18:55:17, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1660, decode.acc_seg: 92.9641, aux.loss_ce: 0.0700, aux.acc_seg: 92.6299, loss: 0.2360 +2024-06-19 07:02:40,832 - mmseg - INFO - Iter [48150/80000] lr: 1.593e-05, eta: 18:53:25, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1726, decode.acc_seg: 92.5355, aux.loss_ce: 0.0719, aux.acc_seg: 92.2262, loss: 0.2445 +2024-06-19 07:04:19,689 - mmseg - INFO - Iter [48200/80000] lr: 1.590e-05, eta: 18:51:33, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1698, decode.acc_seg: 92.7389, aux.loss_ce: 0.0717, aux.acc_seg: 92.2641, loss: 0.2415 +2024-06-19 07:05:58,633 - mmseg - INFO - Iter [48250/80000] lr: 1.588e-05, eta: 18:49:42, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1605, decode.acc_seg: 93.0517, aux.loss_ce: 0.0682, aux.acc_seg: 92.6754, loss: 0.2287 +2024-06-19 07:07:37,670 - mmseg - INFO - Iter [48300/80000] lr: 1.585e-05, eta: 18:47:50, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1628, decode.acc_seg: 92.9963, aux.loss_ce: 0.0694, aux.acc_seg: 92.5563, loss: 0.2322 +2024-06-19 07:09:16,589 - mmseg - INFO - Iter [48350/80000] lr: 1.583e-05, eta: 18:45:58, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1572, decode.acc_seg: 93.0335, aux.loss_ce: 0.0662, aux.acc_seg: 92.6397, loss: 0.2234 +2024-06-19 07:10:55,534 - mmseg - INFO - Iter [48400/80000] lr: 1.580e-05, eta: 18:44:06, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1680, decode.acc_seg: 92.6288, aux.loss_ce: 0.0707, aux.acc_seg: 92.2766, loss: 0.2386 +2024-06-19 07:12:34,387 - mmseg - INFO - Iter [48450/80000] lr: 1.578e-05, eta: 18:42:14, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1664, decode.acc_seg: 92.7096, aux.loss_ce: 0.0699, aux.acc_seg: 92.3659, loss: 0.2363 +2024-06-19 07:14:13,302 - mmseg - INFO - Iter [48500/80000] lr: 1.575e-05, eta: 18:40:22, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1683, decode.acc_seg: 93.0502, aux.loss_ce: 0.0710, aux.acc_seg: 92.5880, loss: 0.2393 +2024-06-19 07:15:52,147 - mmseg - INFO - Iter [48550/80000] lr: 1.573e-05, eta: 18:38:31, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1637, decode.acc_seg: 92.9141, aux.loss_ce: 0.0687, aux.acc_seg: 92.5460, loss: 0.2323 +2024-06-19 07:17:31,067 - mmseg - INFO - Iter [48600/80000] lr: 1.570e-05, eta: 18:36:39, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1576, decode.acc_seg: 92.9879, aux.loss_ce: 0.0666, aux.acc_seg: 92.5862, loss: 0.2242 +2024-06-19 07:19:10,054 - mmseg - INFO - Iter [48650/80000] lr: 1.568e-05, eta: 18:34:47, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1654, decode.acc_seg: 92.6416, aux.loss_ce: 0.0688, aux.acc_seg: 92.3539, loss: 0.2341 +2024-06-19 07:20:48,987 - mmseg - INFO - Iter [48700/80000] lr: 1.565e-05, eta: 18:32:56, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1780, decode.acc_seg: 92.6415, aux.loss_ce: 0.0747, aux.acc_seg: 92.2695, loss: 0.2528 +2024-06-19 07:22:27,788 - mmseg - INFO - Iter [48750/80000] lr: 1.563e-05, eta: 18:31:04, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1634, decode.acc_seg: 92.7882, aux.loss_ce: 0.0685, aux.acc_seg: 92.3880, loss: 0.2319 +2024-06-19 07:24:06,720 - mmseg - INFO - Iter [48800/80000] lr: 1.560e-05, eta: 18:29:12, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1626, decode.acc_seg: 92.9310, aux.loss_ce: 0.0681, aux.acc_seg: 92.5579, loss: 0.2307 +2024-06-19 07:25:45,641 - mmseg - INFO - Iter [48850/80000] lr: 1.558e-05, eta: 18:27:21, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1692, decode.acc_seg: 92.6798, aux.loss_ce: 0.0707, aux.acc_seg: 92.3018, loss: 0.2399 +2024-06-19 07:27:24,582 - mmseg - INFO - Iter [48900/80000] lr: 1.555e-05, eta: 18:25:29, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1713, decode.acc_seg: 92.8346, aux.loss_ce: 0.0715, aux.acc_seg: 92.5044, loss: 0.2428 +2024-06-19 07:29:03,434 - mmseg - INFO - Iter [48950/80000] lr: 1.553e-05, eta: 18:23:38, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1599, decode.acc_seg: 93.0080, aux.loss_ce: 0.0677, aux.acc_seg: 92.6617, loss: 0.2276 +2024-06-19 07:30:42,464 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 07:30:42,464 - mmseg - INFO - Iter [49000/80000] lr: 1.550e-05, eta: 18:21:46, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1747, decode.acc_seg: 92.5863, aux.loss_ce: 0.0730, aux.acc_seg: 92.2872, loss: 0.2477 +2024-06-19 07:32:35,244 - mmseg - INFO - per class results: +2024-06-19 07:32:35,250 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.48 | 89.98 | +| building | 85.35 | 93.21 | +| sky | 94.94 | 97.81 | +| floor | 84.89 | 90.74 | +| tree | 77.73 | 88.99 | +| ceiling | 87.66 | 95.57 | +| road | 86.44 | 90.6 | +| bed | 92.97 | 96.96 | +| windowpane | 66.31 | 79.74 | +| grass | 67.13 | 80.96 | +| cabinet | 67.43 | 75.06 | +| sidewalk | 71.16 | 87.79 | +| person | 86.51 | 94.12 | +| earth | 39.8 | 55.19 | +| door | 58.1 | 73.18 | +| table | 70.85 | 81.03 | +| mountain | 59.98 | 74.3 | +| plant | 56.59 | 68.41 | +| curtain | 80.66 | 89.29 | +| chair | 68.53 | 79.87 | +| car | 88.07 | 93.75 | +| water | 64.98 | 78.77 | +| painting | 81.08 | 90.42 | +| sofa | 82.31 | 89.54 | +| shelf | 50.27 | 66.58 | +| house | 53.58 | 66.2 | +| sea | 73.2 | 82.76 | +| mirror | 79.71 | 88.03 | +| rug | 65.68 | 76.21 | +| field | 27.36 | 47.11 | +| armchair | 61.3 | 80.25 | +| seat | 66.92 | 89.6 | +| fence | 53.4 | 66.31 | +| desk | 58.41 | 79.52 | +| rock | 55.79 | 79.69 | +| wardrobe | 54.65 | 82.95 | +| lamp | 75.77 | 87.05 | +| bathtub | 90.62 | 93.6 | +| railing | 43.55 | 63.26 | +| cushion | 71.16 | 84.53 | +| base | 42.87 | 69.37 | +| box | 37.13 | 47.62 | +| column | 59.87 | 75.24 | +| signboard | 40.59 | 57.43 | +| chest of drawers | 48.76 | 71.06 | +| counter | 56.01 | 66.63 | +| sand | 50.68 | 79.12 | +| sink | 82.7 | 87.27 | +| skyscraper | 47.81 | 66.59 | +| fireplace | 72.82 | 91.22 | +| refrigerator | 87.66 | 95.21 | +| grandstand | 63.05 | 82.86 | +| path | 31.26 | 41.09 | +| stairs | 44.79 | 56.49 | +| runway | 72.86 | 93.71 | +| case | 65.38 | 81.85 | +| pool table | 95.25 | 98.18 | +| pillow | 67.11 | 77.38 | +| screen door | 74.82 | 78.39 | +| stairway | 49.87 | 63.21 | +| river | 10.22 | 22.83 | +| bridge | 76.19 | 90.12 | +| bookcase | 46.41 | 68.03 | +| blind | 46.03 | 50.99 | +| coffee table | 62.78 | 89.34 | +| toilet | 90.19 | 93.38 | +| flower | 47.79 | 62.41 | +| book | 57.27 | 76.91 | +| hill | 12.75 | 20.17 | +| bench | 62.98 | 69.61 | +| countertop | 64.33 | 84.62 | +| stove | 87.55 | 92.37 | +| palm | 51.97 | 82.57 | +| kitchen island | 54.91 | 85.57 | +| computer | 77.36 | 91.06 | +| swivel chair | 49.87 | 76.86 | +| boat | 64.71 | 92.07 | +| bar | 71.01 | 85.87 | +| arcade machine | 82.43 | 86.07 | +| hovel | 45.87 | 51.21 | +| bus | 94.43 | 97.0 | +| towel | 81.83 | 89.65 | +| light | 61.56 | 67.88 | +| truck | 51.59 | 62.7 | +| tower | 34.14 | 62.71 | +| chandelier | 73.27 | 84.97 | +| awning | 39.35 | 47.84 | +| streetlight | 36.87 | 47.67 | +| booth | 52.51 | 78.33 | +| television receiver | 80.4 | 88.5 | +| airplane | 83.64 | 95.64 | +| dirt track | 5.9 | 7.82 | +| apparel | 62.27 | 91.85 | +| pole | 29.53 | 38.98 | +| land | 4.89 | 6.86 | +| bannister | 22.68 | 29.68 | +| escalator | 67.83 | 86.27 | +| ottoman | 52.15 | 70.33 | +| bottle | 45.32 | 67.17 | +| buffet | 63.67 | 75.52 | +| poster | 34.94 | 46.36 | +| stage | 19.7 | 41.16 | +| van | 51.47 | 77.22 | +| ship | 76.12 | 76.66 | +| fountain | 27.91 | 28.13 | +| conveyer belt | 82.41 | 96.43 | +| canopy | 59.95 | 75.35 | +| washer | 82.7 | 87.73 | +| plaything | 37.75 | 51.18 | +| swimming pool | 61.26 | 87.82 | +| stool | 57.72 | 72.97 | +| barrel | 73.7 | 94.85 | +| basket | 42.72 | 58.56 | +| waterfall | 49.56 | 65.78 | +| tent | 94.12 | 98.68 | +| bag | 29.29 | 34.64 | +| minibike | 76.35 | 89.56 | +| cradle | 90.51 | 97.18 | +| oven | 68.57 | 79.41 | +| ball | 57.76 | 67.77 | +| food | 61.13 | 75.35 | +| step | 12.41 | 13.6 | +| tank | 65.95 | 72.63 | +| trade name | 22.32 | 25.68 | +| microwave | 90.16 | 96.33 | +| pot | 58.4 | 68.6 | +| animal | 61.93 | 63.67 | +| bicycle | 59.01 | 73.83 | +| lake | 42.23 | 68.74 | +| dishwasher | 75.05 | 83.47 | +| screen | 65.61 | 88.47 | +| blanket | 36.06 | 44.31 | +| sculpture | 62.77 | 83.82 | +| hood | 64.49 | 73.09 | +| sconce | 61.9 | 75.14 | +| vase | 51.44 | 64.93 | +| traffic light | 39.21 | 66.46 | +| tray | 28.24 | 37.26 | +| ashcan | 51.82 | 64.89 | +| fan | 71.83 | 82.26 | +| pier | 37.91 | 40.38 | +| crt screen | 18.96 | 21.31 | +| plate | 64.34 | 76.99 | +| monitor | 62.73 | 82.57 | +| bulletin board | 54.98 | 70.82 | +| shower | 18.34 | 18.54 | +| radiator | 69.39 | 80.39 | +| glass | 22.13 | 23.51 | +| clock | 52.77 | 60.53 | +| flag | 70.76 | 81.07 | ++---------------------+-------+-------+ +2024-06-19 07:32:35,250 - mmseg - INFO - Summary: +2024-06-19 07:32:35,251 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.44 | 59.4 | 72.16 | ++-------+------+-------+ +2024-06-19 07:32:35,251 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 07:32:35,252 - mmseg - INFO - Iter(val) [250] aAcc: 0.8644, mIoU: 0.5940, mAcc: 0.7216, IoU.wall: 0.8248, IoU.building: 0.8535, IoU.sky: 0.9494, IoU.floor: 0.8489, IoU.tree: 0.7773, IoU.ceiling: 0.8766, IoU.road: 0.8644, IoU.bed : 0.9297, IoU.windowpane: 0.6631, IoU.grass: 0.6713, IoU.cabinet: 0.6743, IoU.sidewalk: 0.7116, IoU.person: 0.8651, IoU.earth: 0.3980, IoU.door: 0.5810, IoU.table: 0.7085, IoU.mountain: 0.5998, IoU.plant: 0.5659, IoU.curtain: 0.8066, IoU.chair: 0.6853, IoU.car: 0.8807, IoU.water: 0.6498, IoU.painting: 0.8108, IoU.sofa: 0.8231, IoU.shelf: 0.5027, IoU.house: 0.5358, IoU.sea: 0.7320, IoU.mirror: 0.7971, IoU.rug: 0.6568, IoU.field: 0.2736, IoU.armchair: 0.6130, IoU.seat: 0.6692, IoU.fence: 0.5340, IoU.desk: 0.5841, IoU.rock: 0.5579, IoU.wardrobe: 0.5465, IoU.lamp: 0.7577, IoU.bathtub: 0.9062, IoU.railing: 0.4355, IoU.cushion: 0.7116, IoU.base: 0.4287, IoU.box: 0.3713, IoU.column: 0.5987, IoU.signboard: 0.4059, IoU.chest of drawers: 0.4876, IoU.counter: 0.5601, IoU.sand: 0.5068, IoU.sink: 0.8270, IoU.skyscraper: 0.4781, IoU.fireplace: 0.7282, IoU.refrigerator: 0.8766, IoU.grandstand: 0.6305, IoU.path: 0.3126, IoU.stairs: 0.4479, IoU.runway: 0.7286, IoU.case: 0.6538, IoU.pool table: 0.9525, IoU.pillow: 0.6711, IoU.screen door: 0.7482, IoU.stairway: 0.4987, IoU.river: 0.1022, IoU.bridge: 0.7619, IoU.bookcase: 0.4641, IoU.blind: 0.4603, IoU.coffee table: 0.6278, IoU.toilet: 0.9019, IoU.flower: 0.4779, IoU.book: 0.5727, IoU.hill: 0.1275, IoU.bench: 0.6298, IoU.countertop: 0.6433, IoU.stove: 0.8755, IoU.palm: 0.5197, IoU.kitchen island: 0.5491, IoU.computer: 0.7736, IoU.swivel chair: 0.4987, IoU.boat: 0.6471, IoU.bar: 0.7101, IoU.arcade machine: 0.8243, IoU.hovel: 0.4587, IoU.bus: 0.9443, IoU.towel: 0.8183, IoU.light: 0.6156, IoU.truck: 0.5159, IoU.tower: 0.3414, IoU.chandelier: 0.7327, IoU.awning: 0.3935, IoU.streetlight: 0.3687, IoU.booth: 0.5251, IoU.television receiver: 0.8040, IoU.airplane: 0.8364, IoU.dirt track: 0.0590, IoU.apparel: 0.6227, IoU.pole: 0.2953, IoU.land: 0.0489, IoU.bannister: 0.2268, IoU.escalator: 0.6783, IoU.ottoman: 0.5215, IoU.bottle: 0.4532, IoU.buffet: 0.6367, IoU.poster: 0.3494, IoU.stage: 0.1970, IoU.van: 0.5147, IoU.ship: 0.7612, IoU.fountain: 0.2791, IoU.conveyer belt: 0.8241, IoU.canopy: 0.5995, IoU.washer: 0.8270, IoU.plaything: 0.3775, IoU.swimming pool: 0.6126, IoU.stool: 0.5772, IoU.barrel: 0.7370, IoU.basket: 0.4272, IoU.waterfall: 0.4956, IoU.tent: 0.9412, IoU.bag: 0.2929, IoU.minibike: 0.7635, IoU.cradle: 0.9051, IoU.oven: 0.6857, IoU.ball: 0.5776, IoU.food: 0.6113, IoU.step: 0.1241, IoU.tank: 0.6595, IoU.trade name: 0.2232, IoU.microwave: 0.9016, IoU.pot: 0.5840, IoU.animal: 0.6193, IoU.bicycle: 0.5901, IoU.lake: 0.4223, IoU.dishwasher: 0.7505, IoU.screen: 0.6561, IoU.blanket: 0.3606, IoU.sculpture: 0.6277, IoU.hood: 0.6449, IoU.sconce: 0.6190, IoU.vase: 0.5144, IoU.traffic light: 0.3921, IoU.tray: 0.2824, IoU.ashcan: 0.5182, IoU.fan: 0.7183, IoU.pier: 0.3791, IoU.crt screen: 0.1896, IoU.plate: 0.6434, IoU.monitor: 0.6273, IoU.bulletin board: 0.5498, IoU.shower: 0.1834, IoU.radiator: 0.6939, IoU.glass: 0.2213, IoU.clock: 0.5277, IoU.flag: 0.7076, Acc.wall: 0.8998, Acc.building: 0.9321, Acc.sky: 0.9781, Acc.floor: 0.9074, Acc.tree: 0.8899, Acc.ceiling: 0.9557, Acc.road: 0.9060, Acc.bed : 0.9696, Acc.windowpane: 0.7974, Acc.grass: 0.8096, Acc.cabinet: 0.7506, Acc.sidewalk: 0.8779, Acc.person: 0.9412, Acc.earth: 0.5519, Acc.door: 0.7318, Acc.table: 0.8103, Acc.mountain: 0.7430, Acc.plant: 0.6841, Acc.curtain: 0.8929, Acc.chair: 0.7987, Acc.car: 0.9375, Acc.water: 0.7877, Acc.painting: 0.9042, Acc.sofa: 0.8954, Acc.shelf: 0.6658, Acc.house: 0.6620, Acc.sea: 0.8276, Acc.mirror: 0.8803, Acc.rug: 0.7621, Acc.field: 0.4711, Acc.armchair: 0.8025, Acc.seat: 0.8960, Acc.fence: 0.6631, Acc.desk: 0.7952, Acc.rock: 0.7969, Acc.wardrobe: 0.8295, Acc.lamp: 0.8705, Acc.bathtub: 0.9360, Acc.railing: 0.6326, Acc.cushion: 0.8453, Acc.base: 0.6937, Acc.box: 0.4762, Acc.column: 0.7524, Acc.signboard: 0.5743, Acc.chest of drawers: 0.7106, Acc.counter: 0.6663, Acc.sand: 0.7912, Acc.sink: 0.8727, Acc.skyscraper: 0.6659, Acc.fireplace: 0.9122, Acc.refrigerator: 0.9521, Acc.grandstand: 0.8286, Acc.path: 0.4109, Acc.stairs: 0.5649, Acc.runway: 0.9371, Acc.case: 0.8185, Acc.pool table: 0.9818, Acc.pillow: 0.7738, Acc.screen door: 0.7839, Acc.stairway: 0.6321, Acc.river: 0.2283, Acc.bridge: 0.9012, Acc.bookcase: 0.6803, Acc.blind: 0.5099, Acc.coffee table: 0.8934, Acc.toilet: 0.9338, Acc.flower: 0.6241, Acc.book: 0.7691, Acc.hill: 0.2017, Acc.bench: 0.6961, Acc.countertop: 0.8462, Acc.stove: 0.9237, Acc.palm: 0.8257, Acc.kitchen island: 0.8557, Acc.computer: 0.9106, Acc.swivel chair: 0.7686, Acc.boat: 0.9207, Acc.bar: 0.8587, Acc.arcade machine: 0.8607, Acc.hovel: 0.5121, Acc.bus: 0.9700, Acc.towel: 0.8965, Acc.light: 0.6788, Acc.truck: 0.6270, Acc.tower: 0.6271, Acc.chandelier: 0.8497, Acc.awning: 0.4784, Acc.streetlight: 0.4767, Acc.booth: 0.7833, Acc.television receiver: 0.8850, Acc.airplane: 0.9564, Acc.dirt track: 0.0782, Acc.apparel: 0.9185, Acc.pole: 0.3898, Acc.land: 0.0686, Acc.bannister: 0.2968, Acc.escalator: 0.8627, Acc.ottoman: 0.7033, Acc.bottle: 0.6717, Acc.buffet: 0.7552, Acc.poster: 0.4636, Acc.stage: 0.4116, Acc.van: 0.7722, Acc.ship: 0.7666, Acc.fountain: 0.2813, Acc.conveyer belt: 0.9643, Acc.canopy: 0.7535, Acc.washer: 0.8773, Acc.plaything: 0.5118, Acc.swimming pool: 0.8782, Acc.stool: 0.7297, Acc.barrel: 0.9485, Acc.basket: 0.5856, Acc.waterfall: 0.6578, Acc.tent: 0.9868, Acc.bag: 0.3464, Acc.minibike: 0.8956, Acc.cradle: 0.9718, Acc.oven: 0.7941, Acc.ball: 0.6777, Acc.food: 0.7535, Acc.step: 0.1360, Acc.tank: 0.7263, Acc.trade name: 0.2568, Acc.microwave: 0.9633, Acc.pot: 0.6860, Acc.animal: 0.6367, Acc.bicycle: 0.7383, Acc.lake: 0.6874, Acc.dishwasher: 0.8347, Acc.screen: 0.8847, Acc.blanket: 0.4431, Acc.sculpture: 0.8382, Acc.hood: 0.7309, Acc.sconce: 0.7514, Acc.vase: 0.6493, Acc.traffic light: 0.6646, Acc.tray: 0.3726, Acc.ashcan: 0.6489, Acc.fan: 0.8226, Acc.pier: 0.4038, Acc.crt screen: 0.2131, Acc.plate: 0.7699, Acc.monitor: 0.8257, Acc.bulletin board: 0.7082, Acc.shower: 0.1854, Acc.radiator: 0.8039, Acc.glass: 0.2351, Acc.clock: 0.6053, Acc.flag: 0.8107 +2024-06-19 07:34:14,540 - mmseg - INFO - Iter [49050/80000] lr: 1.548e-05, eta: 18:21:06, time: 4.242, data_time: 2.272, memory: 72263, decode.loss_ce: 0.1628, decode.acc_seg: 92.9862, aux.loss_ce: 0.0690, aux.acc_seg: 92.6067, loss: 0.2318 +2024-06-19 07:35:53,412 - mmseg - INFO - Iter [49100/80000] lr: 1.545e-05, eta: 18:19:14, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1680, decode.acc_seg: 92.7861, aux.loss_ce: 0.0702, aux.acc_seg: 92.4320, loss: 0.2382 +2024-06-19 07:37:32,340 - mmseg - INFO - Iter [49150/80000] lr: 1.543e-05, eta: 18:17:23, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1542, decode.acc_seg: 93.3500, aux.loss_ce: 0.0659, aux.acc_seg: 92.9371, loss: 0.2201 +2024-06-19 07:39:11,303 - mmseg - INFO - Iter [49200/80000] lr: 1.540e-05, eta: 18:15:31, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1653, decode.acc_seg: 92.9823, aux.loss_ce: 0.0695, aux.acc_seg: 92.5800, loss: 0.2347 +2024-06-19 07:40:50,271 - mmseg - INFO - Iter [49250/80000] lr: 1.538e-05, eta: 18:13:40, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1687, decode.acc_seg: 92.5817, aux.loss_ce: 0.0710, aux.acc_seg: 92.2789, loss: 0.2397 +2024-06-19 07:42:31,271 - mmseg - INFO - Iter [49300/80000] lr: 1.535e-05, eta: 18:11:49, time: 2.020, data_time: 0.051, memory: 72263, decode.loss_ce: 0.1610, decode.acc_seg: 93.0799, aux.loss_ce: 0.0677, aux.acc_seg: 92.7868, loss: 0.2286 +2024-06-19 07:44:10,227 - mmseg - INFO - Iter [49350/80000] lr: 1.533e-05, eta: 18:09:58, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1645, decode.acc_seg: 92.8095, aux.loss_ce: 0.0683, aux.acc_seg: 92.4796, loss: 0.2328 +2024-06-19 07:45:49,123 - mmseg - INFO - Iter [49400/80000] lr: 1.530e-05, eta: 18:08:06, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1567, decode.acc_seg: 93.1931, aux.loss_ce: 0.0663, aux.acc_seg: 92.8273, loss: 0.2231 +2024-06-19 07:47:28,052 - mmseg - INFO - Iter [49450/80000] lr: 1.528e-05, eta: 18:06:15, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1625, decode.acc_seg: 92.9237, aux.loss_ce: 0.0684, aux.acc_seg: 92.4995, loss: 0.2309 +2024-06-19 07:49:06,922 - mmseg - INFO - Iter [49500/80000] lr: 1.525e-05, eta: 18:04:23, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1619, decode.acc_seg: 93.0287, aux.loss_ce: 0.0681, aux.acc_seg: 92.6816, loss: 0.2300 +2024-06-19 07:50:45,874 - mmseg - INFO - Iter [49550/80000] lr: 1.523e-05, eta: 18:02:32, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1656, decode.acc_seg: 92.8928, aux.loss_ce: 0.0698, aux.acc_seg: 92.5002, loss: 0.2354 +2024-06-19 07:52:24,759 - mmseg - INFO - Iter [49600/80000] lr: 1.520e-05, eta: 18:00:41, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1655, decode.acc_seg: 92.8897, aux.loss_ce: 0.0693, aux.acc_seg: 92.5413, loss: 0.2348 +2024-06-19 07:54:03,618 - mmseg - INFO - Iter [49650/80000] lr: 1.518e-05, eta: 17:58:49, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1655, decode.acc_seg: 93.1212, aux.loss_ce: 0.0697, aux.acc_seg: 92.7697, loss: 0.2352 +2024-06-19 07:55:42,412 - mmseg - INFO - Iter [49700/80000] lr: 1.515e-05, eta: 17:56:58, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1642, decode.acc_seg: 92.8422, aux.loss_ce: 0.0693, aux.acc_seg: 92.4310, loss: 0.2334 +2024-06-19 07:57:21,445 - mmseg - INFO - Iter [49750/80000] lr: 1.513e-05, eta: 17:55:07, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1611, decode.acc_seg: 93.0699, aux.loss_ce: 0.0682, aux.acc_seg: 92.6207, loss: 0.2294 +2024-06-19 07:59:00,377 - mmseg - INFO - Iter [49800/80000] lr: 1.510e-05, eta: 17:53:15, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1592, decode.acc_seg: 93.0505, aux.loss_ce: 0.0672, aux.acc_seg: 92.6623, loss: 0.2264 +2024-06-19 08:00:39,248 - mmseg - INFO - Iter [49850/80000] lr: 1.508e-05, eta: 17:51:24, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1554, decode.acc_seg: 93.2126, aux.loss_ce: 0.0660, aux.acc_seg: 92.8505, loss: 0.2214 +2024-06-19 08:02:18,266 - mmseg - INFO - Iter [49900/80000] lr: 1.505e-05, eta: 17:49:33, time: 1.980, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1646, decode.acc_seg: 92.8431, aux.loss_ce: 0.0695, aux.acc_seg: 92.4572, loss: 0.2341 +2024-06-19 08:03:57,260 - mmseg - INFO - Iter [49950/80000] lr: 1.503e-05, eta: 17:47:42, time: 1.980, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1577, decode.acc_seg: 93.0856, aux.loss_ce: 0.0667, aux.acc_seg: 92.7125, loss: 0.2244 +2024-06-19 08:05:36,161 - mmseg - INFO - Saving checkpoint at 50000 iterations +2024-06-19 08:07:00,596 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 08:07:00,596 - mmseg - INFO - Iter [50000/80000] lr: 1.500e-05, eta: 17:46:41, time: 3.667, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1450, decode.acc_seg: 93.6974, aux.loss_ce: 0.0617, aux.acc_seg: 93.3165, loss: 0.2066 +2024-06-19 08:08:49,894 - mmseg - INFO - per class results: +2024-06-19 08:08:49,900 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.79 | 91.02 | +| building | 85.12 | 93.27 | +| sky | 94.86 | 97.5 | +| floor | 85.09 | 92.25 | +| tree | 78.28 | 90.09 | +| ceiling | 86.97 | 93.66 | +| road | 85.61 | 90.85 | +| bed | 93.33 | 96.77 | +| windowpane | 66.03 | 82.07 | +| grass | 66.85 | 82.73 | +| cabinet | 67.32 | 77.12 | +| sidewalk | 70.76 | 85.9 | +| person | 86.76 | 93.87 | +| earth | 40.07 | 51.84 | +| door | 59.46 | 72.64 | +| table | 70.77 | 81.55 | +| mountain | 62.88 | 72.62 | +| plant | 55.6 | 65.21 | +| curtain | 79.95 | 90.04 | +| chair | 67.03 | 76.42 | +| car | 88.47 | 94.31 | +| water | 64.49 | 80.06 | +| painting | 82.52 | 90.8 | +| sofa | 81.56 | 90.12 | +| shelf | 49.75 | 62.82 | +| house | 51.67 | 66.96 | +| sea | 73.59 | 84.84 | +| mirror | 78.48 | 85.25 | +| rug | 65.09 | 69.51 | +| field | 29.64 | 53.56 | +| armchair | 58.39 | 78.84 | +| seat | 67.99 | 89.45 | +| fence | 52.62 | 65.52 | +| desk | 59.65 | 80.55 | +| rock | 55.6 | 82.22 | +| wardrobe | 55.34 | 72.25 | +| lamp | 75.95 | 86.04 | +| bathtub | 91.19 | 93.82 | +| railing | 44.0 | 60.39 | +| cushion | 68.76 | 82.58 | +| base | 39.71 | 58.47 | +| box | 40.01 | 54.72 | +| column | 59.14 | 70.31 | +| signboard | 39.96 | 54.38 | +| chest of drawers | 42.15 | 70.46 | +| counter | 56.55 | 67.41 | +| sand | 52.97 | 77.53 | +| sink | 83.35 | 88.85 | +| skyscraper | 47.9 | 59.19 | +| fireplace | 72.31 | 88.21 | +| refrigerator | 86.84 | 92.51 | +| grandstand | 60.09 | 87.14 | +| path | 31.69 | 40.91 | +| stairs | 44.47 | 54.32 | +| runway | 72.09 | 92.8 | +| case | 67.17 | 82.92 | +| pool table | 95.4 | 97.67 | +| pillow | 66.23 | 77.79 | +| screen door | 88.88 | 91.36 | +| stairway | 51.97 | 65.59 | +| river | 12.31 | 24.71 | +| bridge | 77.09 | 87.06 | +| bookcase | 41.74 | 60.27 | +| blind | 44.9 | 48.13 | +| coffee table | 62.87 | 87.59 | +| toilet | 90.71 | 93.53 | +| flower | 43.68 | 62.24 | +| book | 55.7 | 80.28 | +| hill | 15.23 | 26.44 | +| bench | 69.08 | 77.97 | +| countertop | 63.84 | 81.36 | +| stove | 85.26 | 94.15 | +| palm | 51.79 | 81.31 | +| kitchen island | 48.06 | 80.51 | +| computer | 76.96 | 91.82 | +| swivel chair | 49.78 | 79.12 | +| boat | 64.78 | 92.39 | +| bar | 71.98 | 84.94 | +| arcade machine | 81.01 | 84.15 | +| hovel | 46.17 | 51.44 | +| bus | 93.83 | 96.81 | +| towel | 80.71 | 86.75 | +| light | 63.06 | 73.78 | +| truck | 53.73 | 64.08 | +| tower | 26.04 | 43.77 | +| chandelier | 72.72 | 81.38 | +| awning | 38.96 | 49.31 | +| streetlight | 33.66 | 42.83 | +| booth | 61.62 | 63.48 | +| television receiver | 80.84 | 86.13 | +| airplane | 87.66 | 96.6 | +| dirt track | 7.73 | 32.88 | +| apparel | 68.02 | 88.22 | +| pole | 29.69 | 37.77 | +| land | 5.08 | 9.1 | +| bannister | 22.43 | 30.65 | +| escalator | 67.32 | 86.42 | +| ottoman | 56.36 | 77.01 | +| bottle | 45.06 | 71.04 | +| buffet | 54.58 | 63.74 | +| poster | 37.72 | 43.25 | +| stage | 22.2 | 47.19 | +| van | 54.73 | 78.64 | +| ship | 86.42 | 91.5 | +| fountain | 29.3 | 30.68 | +| conveyer belt | 84.66 | 96.01 | +| canopy | 57.01 | 72.47 | +| washer | 83.55 | 88.66 | +| plaything | 32.41 | 59.98 | +| swimming pool | 57.07 | 79.59 | +| stool | 53.18 | 71.99 | +| barrel | 71.18 | 87.61 | +| basket | 41.77 | 61.72 | +| waterfall | 51.68 | 67.8 | +| tent | 96.93 | 98.33 | +| bag | 27.55 | 31.94 | +| minibike | 75.78 | 90.38 | +| cradle | 88.95 | 97.34 | +| oven | 67.57 | 80.84 | +| ball | 36.71 | 38.31 | +| food | 63.68 | 79.38 | +| step | 12.54 | 14.11 | +| tank | 65.05 | 70.6 | +| trade name | 19.74 | 22.36 | +| microwave | 89.43 | 96.77 | +| pot | 59.1 | 71.71 | +| animal | 61.24 | 62.92 | +| bicycle | 59.07 | 74.58 | +| lake | 52.42 | 63.73 | +| dishwasher | 72.83 | 81.46 | +| screen | 62.53 | 93.17 | +| blanket | 37.44 | 44.48 | +| sculpture | 70.66 | 81.76 | +| hood | 66.03 | 76.94 | +| sconce | 62.17 | 71.56 | +| vase | 51.09 | 67.37 | +| traffic light | 39.82 | 67.99 | +| tray | 29.22 | 39.37 | +| ashcan | 51.26 | 68.0 | +| fan | 72.46 | 84.18 | +| pier | 38.78 | 40.85 | +| crt screen | 10.25 | 12.18 | +| plate | 63.69 | 80.21 | +| monitor | 67.58 | 83.04 | +| bulletin board | 54.18 | 62.63 | +| shower | 12.88 | 13.18 | +| radiator | 68.95 | 83.9 | +| glass | 20.96 | 22.0 | +| clock | 53.51 | 62.9 | +| flag | 68.99 | 81.5 | ++---------------------+-------+-------+ +2024-06-19 08:08:49,900 - mmseg - INFO - Summary: +2024-06-19 08:08:49,900 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.49 | 59.3 | 71.73 | ++-------+------+-------+ +2024-06-19 08:08:49,901 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 08:08:49,901 - mmseg - INFO - Iter(val) [250] aAcc: 0.8649, mIoU: 0.5930, mAcc: 0.7173, IoU.wall: 0.8279, IoU.building: 0.8512, IoU.sky: 0.9486, IoU.floor: 0.8509, IoU.tree: 0.7828, IoU.ceiling: 0.8697, IoU.road: 0.8561, IoU.bed : 0.9333, IoU.windowpane: 0.6603, IoU.grass: 0.6685, IoU.cabinet: 0.6732, IoU.sidewalk: 0.7076, IoU.person: 0.8676, IoU.earth: 0.4007, IoU.door: 0.5946, IoU.table: 0.7077, IoU.mountain: 0.6288, IoU.plant: 0.5560, IoU.curtain: 0.7995, IoU.chair: 0.6703, IoU.car: 0.8847, IoU.water: 0.6449, IoU.painting: 0.8252, IoU.sofa: 0.8156, IoU.shelf: 0.4975, IoU.house: 0.5167, IoU.sea: 0.7359, IoU.mirror: 0.7848, IoU.rug: 0.6509, IoU.field: 0.2964, IoU.armchair: 0.5839, IoU.seat: 0.6799, IoU.fence: 0.5262, IoU.desk: 0.5965, IoU.rock: 0.5560, IoU.wardrobe: 0.5534, IoU.lamp: 0.7595, IoU.bathtub: 0.9119, IoU.railing: 0.4400, IoU.cushion: 0.6876, IoU.base: 0.3971, IoU.box: 0.4001, IoU.column: 0.5914, IoU.signboard: 0.3996, IoU.chest of drawers: 0.4215, IoU.counter: 0.5655, IoU.sand: 0.5297, IoU.sink: 0.8335, IoU.skyscraper: 0.4790, IoU.fireplace: 0.7231, IoU.refrigerator: 0.8684, IoU.grandstand: 0.6009, IoU.path: 0.3169, IoU.stairs: 0.4447, IoU.runway: 0.7209, IoU.case: 0.6717, IoU.pool table: 0.9540, IoU.pillow: 0.6623, IoU.screen door: 0.8888, IoU.stairway: 0.5197, IoU.river: 0.1231, IoU.bridge: 0.7709, IoU.bookcase: 0.4174, IoU.blind: 0.4490, IoU.coffee table: 0.6287, IoU.toilet: 0.9071, IoU.flower: 0.4368, IoU.book: 0.5570, IoU.hill: 0.1523, IoU.bench: 0.6908, IoU.countertop: 0.6384, IoU.stove: 0.8526, IoU.palm: 0.5179, IoU.kitchen island: 0.4806, IoU.computer: 0.7696, IoU.swivel chair: 0.4978, IoU.boat: 0.6478, IoU.bar: 0.7198, IoU.arcade machine: 0.8101, IoU.hovel: 0.4617, IoU.bus: 0.9383, IoU.towel: 0.8071, IoU.light: 0.6306, IoU.truck: 0.5373, IoU.tower: 0.2604, IoU.chandelier: 0.7272, IoU.awning: 0.3896, IoU.streetlight: 0.3366, IoU.booth: 0.6162, IoU.television receiver: 0.8084, IoU.airplane: 0.8766, IoU.dirt track: 0.0773, IoU.apparel: 0.6802, IoU.pole: 0.2969, IoU.land: 0.0508, IoU.bannister: 0.2243, IoU.escalator: 0.6732, IoU.ottoman: 0.5636, IoU.bottle: 0.4506, IoU.buffet: 0.5458, IoU.poster: 0.3772, IoU.stage: 0.2220, IoU.van: 0.5473, IoU.ship: 0.8642, IoU.fountain: 0.2930, IoU.conveyer belt: 0.8466, IoU.canopy: 0.5701, IoU.washer: 0.8355, IoU.plaything: 0.3241, IoU.swimming pool: 0.5707, IoU.stool: 0.5318, IoU.barrel: 0.7118, IoU.basket: 0.4177, IoU.waterfall: 0.5168, IoU.tent: 0.9693, IoU.bag: 0.2755, IoU.minibike: 0.7578, IoU.cradle: 0.8895, IoU.oven: 0.6757, IoU.ball: 0.3671, IoU.food: 0.6368, IoU.step: 0.1254, IoU.tank: 0.6505, IoU.trade name: 0.1974, IoU.microwave: 0.8943, IoU.pot: 0.5910, IoU.animal: 0.6124, IoU.bicycle: 0.5907, IoU.lake: 0.5242, IoU.dishwasher: 0.7283, IoU.screen: 0.6253, IoU.blanket: 0.3744, IoU.sculpture: 0.7066, IoU.hood: 0.6603, IoU.sconce: 0.6217, IoU.vase: 0.5109, IoU.traffic light: 0.3982, IoU.tray: 0.2922, IoU.ashcan: 0.5126, IoU.fan: 0.7246, IoU.pier: 0.3878, IoU.crt screen: 0.1025, IoU.plate: 0.6369, IoU.monitor: 0.6758, IoU.bulletin board: 0.5418, IoU.shower: 0.1288, IoU.radiator: 0.6895, IoU.glass: 0.2096, IoU.clock: 0.5351, IoU.flag: 0.6899, Acc.wall: 0.9102, Acc.building: 0.9327, Acc.sky: 0.9750, Acc.floor: 0.9225, Acc.tree: 0.9009, Acc.ceiling: 0.9366, Acc.road: 0.9085, Acc.bed : 0.9677, Acc.windowpane: 0.8207, Acc.grass: 0.8273, Acc.cabinet: 0.7712, Acc.sidewalk: 0.8590, Acc.person: 0.9387, Acc.earth: 0.5184, Acc.door: 0.7264, Acc.table: 0.8155, Acc.mountain: 0.7262, Acc.plant: 0.6521, Acc.curtain: 0.9004, Acc.chair: 0.7642, Acc.car: 0.9431, Acc.water: 0.8006, Acc.painting: 0.9080, Acc.sofa: 0.9012, Acc.shelf: 0.6282, Acc.house: 0.6696, Acc.sea: 0.8484, Acc.mirror: 0.8525, Acc.rug: 0.6951, Acc.field: 0.5356, Acc.armchair: 0.7884, Acc.seat: 0.8945, Acc.fence: 0.6552, Acc.desk: 0.8055, Acc.rock: 0.8222, Acc.wardrobe: 0.7225, Acc.lamp: 0.8604, Acc.bathtub: 0.9382, Acc.railing: 0.6039, Acc.cushion: 0.8258, Acc.base: 0.5847, Acc.box: 0.5472, Acc.column: 0.7031, Acc.signboard: 0.5438, Acc.chest of drawers: 0.7046, Acc.counter: 0.6741, Acc.sand: 0.7753, Acc.sink: 0.8885, Acc.skyscraper: 0.5919, Acc.fireplace: 0.8821, Acc.refrigerator: 0.9251, Acc.grandstand: 0.8714, Acc.path: 0.4091, Acc.stairs: 0.5432, Acc.runway: 0.9280, Acc.case: 0.8292, Acc.pool table: 0.9767, Acc.pillow: 0.7779, Acc.screen door: 0.9136, Acc.stairway: 0.6559, Acc.river: 0.2471, Acc.bridge: 0.8706, Acc.bookcase: 0.6027, Acc.blind: 0.4813, Acc.coffee table: 0.8759, Acc.toilet: 0.9353, Acc.flower: 0.6224, Acc.book: 0.8028, Acc.hill: 0.2644, Acc.bench: 0.7797, Acc.countertop: 0.8136, Acc.stove: 0.9415, Acc.palm: 0.8131, Acc.kitchen island: 0.8051, Acc.computer: 0.9182, Acc.swivel chair: 0.7912, Acc.boat: 0.9239, Acc.bar: 0.8494, Acc.arcade machine: 0.8415, Acc.hovel: 0.5144, Acc.bus: 0.9681, Acc.towel: 0.8675, Acc.light: 0.7378, Acc.truck: 0.6408, Acc.tower: 0.4377, Acc.chandelier: 0.8138, Acc.awning: 0.4931, Acc.streetlight: 0.4283, Acc.booth: 0.6348, Acc.television receiver: 0.8613, Acc.airplane: 0.9660, Acc.dirt track: 0.3288, Acc.apparel: 0.8822, Acc.pole: 0.3777, Acc.land: 0.0910, Acc.bannister: 0.3065, Acc.escalator: 0.8642, Acc.ottoman: 0.7701, Acc.bottle: 0.7104, Acc.buffet: 0.6374, Acc.poster: 0.4325, Acc.stage: 0.4719, Acc.van: 0.7864, Acc.ship: 0.9150, Acc.fountain: 0.3068, Acc.conveyer belt: 0.9601, Acc.canopy: 0.7247, Acc.washer: 0.8866, Acc.plaything: 0.5998, Acc.swimming pool: 0.7959, Acc.stool: 0.7199, Acc.barrel: 0.8761, Acc.basket: 0.6172, Acc.waterfall: 0.6780, Acc.tent: 0.9833, Acc.bag: 0.3194, Acc.minibike: 0.9038, Acc.cradle: 0.9734, Acc.oven: 0.8084, Acc.ball: 0.3831, Acc.food: 0.7938, Acc.step: 0.1411, Acc.tank: 0.7060, Acc.trade name: 0.2236, Acc.microwave: 0.9677, Acc.pot: 0.7171, Acc.animal: 0.6292, Acc.bicycle: 0.7458, Acc.lake: 0.6373, Acc.dishwasher: 0.8146, Acc.screen: 0.9317, Acc.blanket: 0.4448, Acc.sculpture: 0.8176, Acc.hood: 0.7694, Acc.sconce: 0.7156, Acc.vase: 0.6737, Acc.traffic light: 0.6799, Acc.tray: 0.3937, Acc.ashcan: 0.6800, Acc.fan: 0.8418, Acc.pier: 0.4085, Acc.crt screen: 0.1218, Acc.plate: 0.8021, Acc.monitor: 0.8304, Acc.bulletin board: 0.6263, Acc.shower: 0.1318, Acc.radiator: 0.8390, Acc.glass: 0.2200, Acc.clock: 0.6290, Acc.flag: 0.8150 +2024-06-19 08:10:29,138 - mmseg - INFO - Iter [50050/80000] lr: 1.498e-05, eta: 17:45:55, time: 4.171, data_time: 2.203, memory: 72263, decode.loss_ce: 0.1602, decode.acc_seg: 93.1610, aux.loss_ce: 0.0671, aux.acc_seg: 92.8554, loss: 0.2273 +2024-06-19 08:12:08,033 - mmseg - INFO - Iter [50100/80000] lr: 1.495e-05, eta: 17:44:04, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1666, decode.acc_seg: 92.7942, aux.loss_ce: 0.0701, aux.acc_seg: 92.4317, loss: 0.2367 +2024-06-19 08:13:46,888 - mmseg - INFO - Iter [50150/80000] lr: 1.493e-05, eta: 17:42:12, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1655, decode.acc_seg: 92.8804, aux.loss_ce: 0.0691, aux.acc_seg: 92.6258, loss: 0.2346 +2024-06-19 08:15:25,857 - mmseg - INFO - Iter [50200/80000] lr: 1.490e-05, eta: 17:40:21, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1584, decode.acc_seg: 93.1186, aux.loss_ce: 0.0665, aux.acc_seg: 92.7767, loss: 0.2249 +2024-06-19 08:17:04,752 - mmseg - INFO - Iter [50250/80000] lr: 1.488e-05, eta: 17:38:30, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1627, decode.acc_seg: 93.1611, aux.loss_ce: 0.0687, aux.acc_seg: 92.7955, loss: 0.2313 +2024-06-19 08:18:43,690 - mmseg - INFO - Iter [50300/80000] lr: 1.485e-05, eta: 17:36:38, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1535, decode.acc_seg: 93.3338, aux.loss_ce: 0.0644, aux.acc_seg: 93.0139, loss: 0.2179 +2024-06-19 08:20:22,676 - mmseg - INFO - Iter [50350/80000] lr: 1.483e-05, eta: 17:34:47, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1574, decode.acc_seg: 93.2310, aux.loss_ce: 0.0667, aux.acc_seg: 92.8630, loss: 0.2240 +2024-06-19 08:22:01,636 - mmseg - INFO - Iter [50400/80000] lr: 1.480e-05, eta: 17:32:56, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1486, decode.acc_seg: 93.3273, aux.loss_ce: 0.0635, aux.acc_seg: 92.9614, loss: 0.2120 +2024-06-19 08:23:40,554 - mmseg - INFO - Iter [50450/80000] lr: 1.478e-05, eta: 17:31:04, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1624, decode.acc_seg: 93.1994, aux.loss_ce: 0.0690, aux.acc_seg: 92.7876, loss: 0.2313 +2024-06-19 08:25:19,528 - mmseg - INFO - Iter [50500/80000] lr: 1.475e-05, eta: 17:29:13, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1667, decode.acc_seg: 92.7837, aux.loss_ce: 0.0705, aux.acc_seg: 92.3901, loss: 0.2372 +2024-06-19 08:27:00,678 - mmseg - INFO - Iter [50550/80000] lr: 1.473e-05, eta: 17:27:23, time: 2.023, data_time: 0.055, memory: 72263, decode.loss_ce: 0.1544, decode.acc_seg: 93.2841, aux.loss_ce: 0.0654, aux.acc_seg: 92.9037, loss: 0.2198 +2024-06-19 08:28:39,566 - mmseg - INFO - Iter [50600/80000] lr: 1.470e-05, eta: 17:25:32, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1553, decode.acc_seg: 93.3156, aux.loss_ce: 0.0662, aux.acc_seg: 92.8629, loss: 0.2216 +2024-06-19 08:30:18,478 - mmseg - INFO - Iter [50650/80000] lr: 1.468e-05, eta: 17:23:41, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1489, decode.acc_seg: 93.3473, aux.loss_ce: 0.0629, aux.acc_seg: 92.9970, loss: 0.2118 +2024-06-19 08:31:57,441 - mmseg - INFO - Iter [50700/80000] lr: 1.465e-05, eta: 17:21:50, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1547, decode.acc_seg: 93.2904, aux.loss_ce: 0.0654, aux.acc_seg: 92.9231, loss: 0.2201 +2024-06-19 08:33:36,398 - mmseg - INFO - Iter [50750/80000] lr: 1.463e-05, eta: 17:19:59, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1627, decode.acc_seg: 93.0506, aux.loss_ce: 0.0690, aux.acc_seg: 92.6713, loss: 0.2317 +2024-06-19 08:35:15,368 - mmseg - INFO - Iter [50800/80000] lr: 1.460e-05, eta: 17:18:08, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1597, decode.acc_seg: 92.9917, aux.loss_ce: 0.0677, aux.acc_seg: 92.6347, loss: 0.2273 +2024-06-19 08:36:54,240 - mmseg - INFO - Iter [50850/80000] lr: 1.458e-05, eta: 17:16:16, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1577, decode.acc_seg: 93.1127, aux.loss_ce: 0.0665, aux.acc_seg: 92.7397, loss: 0.2242 +2024-06-19 08:38:33,147 - mmseg - INFO - Iter [50900/80000] lr: 1.455e-05, eta: 17:14:25, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1611, decode.acc_seg: 93.1005, aux.loss_ce: 0.0683, aux.acc_seg: 92.6606, loss: 0.2294 +2024-06-19 08:40:12,308 - mmseg - INFO - Iter [50950/80000] lr: 1.453e-05, eta: 17:12:34, time: 1.983, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1572, decode.acc_seg: 93.3405, aux.loss_ce: 0.0656, aux.acc_seg: 93.0552, loss: 0.2228 +2024-06-19 08:41:51,205 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 08:41:51,205 - mmseg - INFO - Iter [51000/80000] lr: 1.450e-05, eta: 17:10:43, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1639, decode.acc_seg: 92.9439, aux.loss_ce: 0.0690, aux.acc_seg: 92.5574, loss: 0.2329 +2024-06-19 08:43:41,357 - mmseg - INFO - per class results: +2024-06-19 08:43:41,364 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.42 | 89.88 | +| building | 85.23 | 92.72 | +| sky | 94.96 | 97.67 | +| floor | 84.79 | 91.78 | +| tree | 78.31 | 89.6 | +| ceiling | 87.33 | 93.86 | +| road | 85.12 | 90.92 | +| bed | 92.9 | 96.97 | +| windowpane | 66.79 | 82.72 | +| grass | 67.88 | 81.32 | +| cabinet | 67.76 | 77.06 | +| sidewalk | 70.09 | 84.7 | +| person | 86.8 | 94.09 | +| earth | 39.58 | 58.24 | +| door | 60.43 | 78.83 | +| table | 70.82 | 81.57 | +| mountain | 64.91 | 75.66 | +| plant | 56.73 | 68.79 | +| curtain | 80.65 | 88.99 | +| chair | 69.34 | 80.03 | +| car | 88.58 | 94.15 | +| water | 60.7 | 74.56 | +| painting | 80.27 | 91.23 | +| sofa | 82.36 | 91.52 | +| shelf | 50.3 | 67.02 | +| house | 53.82 | 73.09 | +| sea | 71.56 | 84.7 | +| mirror | 79.14 | 88.97 | +| rug | 66.87 | 78.84 | +| field | 24.62 | 34.46 | +| armchair | 60.7 | 81.72 | +| seat | 66.66 | 89.42 | +| fence | 51.62 | 63.47 | +| desk | 61.25 | 79.41 | +| rock | 56.13 | 83.25 | +| wardrobe | 56.2 | 77.33 | +| lamp | 75.67 | 87.04 | +| bathtub | 84.78 | 88.23 | +| railing | 44.45 | 62.53 | +| cushion | 71.22 | 81.41 | +| base | 39.79 | 59.16 | +| box | 35.37 | 41.64 | +| column | 59.13 | 79.22 | +| signboard | 40.32 | 53.3 | +| chest of drawers | 45.28 | 67.76 | +| counter | 56.01 | 65.03 | +| sand | 51.32 | 78.05 | +| sink | 82.5 | 88.18 | +| skyscraper | 47.66 | 58.79 | +| fireplace | 72.63 | 93.84 | +| refrigerator | 84.36 | 92.23 | +| grandstand | 53.91 | 85.98 | +| path | 28.07 | 35.82 | +| stairs | 32.33 | 38.46 | +| runway | 72.68 | 93.5 | +| case | 65.39 | 85.73 | +| pool table | 94.94 | 98.69 | +| pillow | 69.95 | 82.45 | +| screen door | 73.56 | 75.86 | +| stairway | 44.81 | 58.95 | +| river | 11.68 | 31.25 | +| bridge | 55.33 | 62.17 | +| bookcase | 46.88 | 67.65 | +| blind | 42.07 | 45.74 | +| coffee table | 61.02 | 88.64 | +| toilet | 90.21 | 93.05 | +| flower | 46.25 | 60.81 | +| book | 56.92 | 74.08 | +| hill | 10.4 | 16.48 | +| bench | 60.21 | 67.99 | +| countertop | 64.55 | 82.17 | +| stove | 87.52 | 95.07 | +| palm | 53.24 | 81.71 | +| kitchen island | 49.44 | 79.06 | +| computer | 78.04 | 90.77 | +| swivel chair | 53.26 | 73.94 | +| boat | 67.05 | 92.24 | +| bar | 71.03 | 87.86 | +| arcade machine | 82.76 | 86.6 | +| hovel | 45.97 | 54.97 | +| bus | 92.37 | 97.45 | +| towel | 79.55 | 85.52 | +| light | 63.1 | 75.2 | +| truck | 51.38 | 65.96 | +| tower | 24.46 | 40.98 | +| chandelier | 73.18 | 85.35 | +| awning | 39.49 | 53.7 | +| streetlight | 38.91 | 55.93 | +| booth | 61.03 | 77.33 | +| television receiver | 83.22 | 91.7 | +| airplane | 84.04 | 97.55 | +| dirt track | 27.66 | 33.14 | +| apparel | 68.08 | 81.59 | +| pole | 30.0 | 39.66 | +| land | 5.56 | 9.0 | +| bannister | 21.65 | 25.13 | +| escalator | 66.04 | 86.37 | +| ottoman | 53.89 | 69.28 | +| bottle | 45.96 | 71.64 | +| buffet | 56.76 | 64.07 | +| poster | 34.52 | 40.27 | +| stage | 23.24 | 48.61 | +| van | 57.98 | 76.11 | +| ship | 81.83 | 87.15 | +| fountain | 34.93 | 35.89 | +| conveyer belt | 86.26 | 95.47 | +| canopy | 56.46 | 77.93 | +| washer | 88.91 | 95.02 | +| plaything | 30.95 | 43.48 | +| swimming pool | 57.34 | 84.45 | +| stool | 56.6 | 68.73 | +| barrel | 69.91 | 94.72 | +| basket | 42.6 | 56.22 | +| waterfall | 52.18 | 71.92 | +| tent | 95.74 | 98.81 | +| bag | 26.35 | 28.83 | +| minibike | 77.48 | 89.54 | +| cradle | 85.16 | 98.09 | +| oven | 72.46 | 81.87 | +| ball | 60.59 | 70.52 | +| food | 59.12 | 72.54 | +| step | 13.34 | 14.84 | +| tank | 62.77 | 67.96 | +| trade name | 26.88 | 32.33 | +| microwave | 89.01 | 96.97 | +| pot | 57.64 | 65.11 | +| animal | 62.25 | 64.3 | +| bicycle | 61.97 | 74.41 | +| lake | 40.42 | 43.29 | +| dishwasher | 73.68 | 84.62 | +| screen | 62.17 | 87.52 | +| blanket | 35.49 | 43.29 | +| sculpture | 74.06 | 80.61 | +| hood | 65.54 | 76.54 | +| sconce | 56.96 | 65.45 | +| vase | 50.39 | 68.59 | +| traffic light | 42.39 | 62.51 | +| tray | 27.42 | 36.29 | +| ashcan | 52.1 | 67.59 | +| fan | 71.34 | 86.13 | +| pier | 39.97 | 43.6 | +| crt screen | 9.64 | 21.59 | +| plate | 63.46 | 81.75 | +| monitor | 34.36 | 39.36 | +| bulletin board | 55.82 | 63.31 | +| shower | 18.28 | 19.75 | +| radiator | 69.65 | 83.43 | +| glass | 22.18 | 23.57 | +| clock | 58.31 | 67.14 | +| flag | 69.37 | 79.03 | ++---------------------+-------+-------+ +2024-06-19 08:43:41,364 - mmseg - INFO - Summary: +2024-06-19 08:43:41,364 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.36 | 58.98 | 71.31 | ++-------+-------+-------+ +2024-06-19 08:43:41,364 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 08:43:41,365 - mmseg - INFO - Iter(val) [250] aAcc: 0.8636, mIoU: 0.5898, mAcc: 0.7131, IoU.wall: 0.8242, IoU.building: 0.8523, IoU.sky: 0.9496, IoU.floor: 0.8479, IoU.tree: 0.7831, IoU.ceiling: 0.8733, IoU.road: 0.8512, IoU.bed : 0.9290, IoU.windowpane: 0.6679, IoU.grass: 0.6788, IoU.cabinet: 0.6776, IoU.sidewalk: 0.7009, IoU.person: 0.8680, IoU.earth: 0.3958, IoU.door: 0.6043, IoU.table: 0.7082, IoU.mountain: 0.6491, IoU.plant: 0.5673, IoU.curtain: 0.8065, IoU.chair: 0.6934, IoU.car: 0.8858, IoU.water: 0.6070, IoU.painting: 0.8027, IoU.sofa: 0.8236, IoU.shelf: 0.5030, IoU.house: 0.5382, IoU.sea: 0.7156, IoU.mirror: 0.7914, IoU.rug: 0.6687, IoU.field: 0.2462, IoU.armchair: 0.6070, IoU.seat: 0.6666, IoU.fence: 0.5162, IoU.desk: 0.6125, IoU.rock: 0.5613, IoU.wardrobe: 0.5620, IoU.lamp: 0.7567, IoU.bathtub: 0.8478, IoU.railing: 0.4445, IoU.cushion: 0.7122, IoU.base: 0.3979, IoU.box: 0.3537, IoU.column: 0.5913, IoU.signboard: 0.4032, IoU.chest of drawers: 0.4528, IoU.counter: 0.5601, IoU.sand: 0.5132, IoU.sink: 0.8250, IoU.skyscraper: 0.4766, IoU.fireplace: 0.7263, IoU.refrigerator: 0.8436, IoU.grandstand: 0.5391, IoU.path: 0.2807, IoU.stairs: 0.3233, IoU.runway: 0.7268, IoU.case: 0.6539, IoU.pool table: 0.9494, IoU.pillow: 0.6995, IoU.screen door: 0.7356, IoU.stairway: 0.4481, IoU.river: 0.1168, IoU.bridge: 0.5533, IoU.bookcase: 0.4688, IoU.blind: 0.4207, IoU.coffee table: 0.6102, IoU.toilet: 0.9021, IoU.flower: 0.4625, IoU.book: 0.5692, IoU.hill: 0.1040, IoU.bench: 0.6021, IoU.countertop: 0.6455, IoU.stove: 0.8752, IoU.palm: 0.5324, IoU.kitchen island: 0.4944, IoU.computer: 0.7804, IoU.swivel chair: 0.5326, IoU.boat: 0.6705, IoU.bar: 0.7103, IoU.arcade machine: 0.8276, IoU.hovel: 0.4597, IoU.bus: 0.9237, IoU.towel: 0.7955, IoU.light: 0.6310, IoU.truck: 0.5138, IoU.tower: 0.2446, IoU.chandelier: 0.7318, IoU.awning: 0.3949, IoU.streetlight: 0.3891, IoU.booth: 0.6103, IoU.television receiver: 0.8322, IoU.airplane: 0.8404, IoU.dirt track: 0.2766, IoU.apparel: 0.6808, IoU.pole: 0.3000, IoU.land: 0.0556, IoU.bannister: 0.2165, IoU.escalator: 0.6604, IoU.ottoman: 0.5389, IoU.bottle: 0.4596, IoU.buffet: 0.5676, IoU.poster: 0.3452, IoU.stage: 0.2324, IoU.van: 0.5798, IoU.ship: 0.8183, IoU.fountain: 0.3493, IoU.conveyer belt: 0.8626, IoU.canopy: 0.5646, IoU.washer: 0.8891, IoU.plaything: 0.3095, IoU.swimming pool: 0.5734, IoU.stool: 0.5660, IoU.barrel: 0.6991, IoU.basket: 0.4260, IoU.waterfall: 0.5218, IoU.tent: 0.9574, IoU.bag: 0.2635, IoU.minibike: 0.7748, IoU.cradle: 0.8516, IoU.oven: 0.7246, IoU.ball: 0.6059, IoU.food: 0.5912, IoU.step: 0.1334, IoU.tank: 0.6277, IoU.trade name: 0.2688, IoU.microwave: 0.8901, IoU.pot: 0.5764, IoU.animal: 0.6225, IoU.bicycle: 0.6197, IoU.lake: 0.4042, IoU.dishwasher: 0.7368, IoU.screen: 0.6217, IoU.blanket: 0.3549, IoU.sculpture: 0.7406, IoU.hood: 0.6554, IoU.sconce: 0.5696, IoU.vase: 0.5039, IoU.traffic light: 0.4239, IoU.tray: 0.2742, IoU.ashcan: 0.5210, IoU.fan: 0.7134, IoU.pier: 0.3997, IoU.crt screen: 0.0964, IoU.plate: 0.6346, IoU.monitor: 0.3436, IoU.bulletin board: 0.5582, IoU.shower: 0.1828, IoU.radiator: 0.6965, IoU.glass: 0.2218, IoU.clock: 0.5831, IoU.flag: 0.6937, Acc.wall: 0.8988, Acc.building: 0.9272, Acc.sky: 0.9767, Acc.floor: 0.9178, Acc.tree: 0.8960, Acc.ceiling: 0.9386, Acc.road: 0.9092, Acc.bed : 0.9697, Acc.windowpane: 0.8272, Acc.grass: 0.8132, Acc.cabinet: 0.7706, Acc.sidewalk: 0.8470, Acc.person: 0.9409, Acc.earth: 0.5824, Acc.door: 0.7883, Acc.table: 0.8157, Acc.mountain: 0.7566, Acc.plant: 0.6879, Acc.curtain: 0.8899, Acc.chair: 0.8003, Acc.car: 0.9415, Acc.water: 0.7456, Acc.painting: 0.9123, Acc.sofa: 0.9152, Acc.shelf: 0.6702, Acc.house: 0.7309, Acc.sea: 0.8470, Acc.mirror: 0.8897, Acc.rug: 0.7884, Acc.field: 0.3446, Acc.armchair: 0.8172, Acc.seat: 0.8942, Acc.fence: 0.6347, Acc.desk: 0.7941, Acc.rock: 0.8325, Acc.wardrobe: 0.7733, Acc.lamp: 0.8704, Acc.bathtub: 0.8823, Acc.railing: 0.6253, Acc.cushion: 0.8141, Acc.base: 0.5916, Acc.box: 0.4164, Acc.column: 0.7922, Acc.signboard: 0.5330, Acc.chest of drawers: 0.6776, Acc.counter: 0.6503, Acc.sand: 0.7805, Acc.sink: 0.8818, Acc.skyscraper: 0.5879, Acc.fireplace: 0.9384, Acc.refrigerator: 0.9223, Acc.grandstand: 0.8598, Acc.path: 0.3582, Acc.stairs: 0.3846, Acc.runway: 0.9350, Acc.case: 0.8573, Acc.pool table: 0.9869, Acc.pillow: 0.8245, Acc.screen door: 0.7586, Acc.stairway: 0.5895, Acc.river: 0.3125, Acc.bridge: 0.6217, Acc.bookcase: 0.6765, Acc.blind: 0.4574, Acc.coffee table: 0.8864, Acc.toilet: 0.9305, Acc.flower: 0.6081, Acc.book: 0.7408, Acc.hill: 0.1648, Acc.bench: 0.6799, Acc.countertop: 0.8217, Acc.stove: 0.9507, Acc.palm: 0.8171, Acc.kitchen island: 0.7906, Acc.computer: 0.9077, Acc.swivel chair: 0.7394, Acc.boat: 0.9224, Acc.bar: 0.8786, Acc.arcade machine: 0.8660, Acc.hovel: 0.5497, Acc.bus: 0.9745, Acc.towel: 0.8552, Acc.light: 0.7520, Acc.truck: 0.6596, Acc.tower: 0.4098, Acc.chandelier: 0.8535, Acc.awning: 0.5370, Acc.streetlight: 0.5593, Acc.booth: 0.7733, Acc.television receiver: 0.9170, Acc.airplane: 0.9755, Acc.dirt track: 0.3314, Acc.apparel: 0.8159, Acc.pole: 0.3966, Acc.land: 0.0900, Acc.bannister: 0.2513, Acc.escalator: 0.8637, Acc.ottoman: 0.6928, Acc.bottle: 0.7164, Acc.buffet: 0.6407, Acc.poster: 0.4027, Acc.stage: 0.4861, Acc.van: 0.7611, Acc.ship: 0.8715, Acc.fountain: 0.3589, Acc.conveyer belt: 0.9547, Acc.canopy: 0.7793, Acc.washer: 0.9502, Acc.plaything: 0.4348, Acc.swimming pool: 0.8445, Acc.stool: 0.6873, Acc.barrel: 0.9472, Acc.basket: 0.5622, Acc.waterfall: 0.7192, Acc.tent: 0.9881, Acc.bag: 0.2883, Acc.minibike: 0.8954, Acc.cradle: 0.9809, Acc.oven: 0.8187, Acc.ball: 0.7052, Acc.food: 0.7254, Acc.step: 0.1484, Acc.tank: 0.6796, Acc.trade name: 0.3233, Acc.microwave: 0.9697, Acc.pot: 0.6511, Acc.animal: 0.6430, Acc.bicycle: 0.7441, Acc.lake: 0.4329, Acc.dishwasher: 0.8462, Acc.screen: 0.8752, Acc.blanket: 0.4329, Acc.sculpture: 0.8061, Acc.hood: 0.7654, Acc.sconce: 0.6545, Acc.vase: 0.6859, Acc.traffic light: 0.6251, Acc.tray: 0.3629, Acc.ashcan: 0.6759, Acc.fan: 0.8613, Acc.pier: 0.4360, Acc.crt screen: 0.2159, Acc.plate: 0.8175, Acc.monitor: 0.3936, Acc.bulletin board: 0.6331, Acc.shower: 0.1975, Acc.radiator: 0.8343, Acc.glass: 0.2357, Acc.clock: 0.6714, Acc.flag: 0.7903 +2024-06-19 08:45:20,697 - mmseg - INFO - Iter [51050/80000] lr: 1.448e-05, eta: 17:09:55, time: 4.190, data_time: 2.220, memory: 72263, decode.loss_ce: 0.1589, decode.acc_seg: 93.1067, aux.loss_ce: 0.0670, aux.acc_seg: 92.7364, loss: 0.2259 +2024-06-19 08:46:59,584 - mmseg - INFO - Iter [51100/80000] lr: 1.445e-05, eta: 17:08:04, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1647, decode.acc_seg: 92.9421, aux.loss_ce: 0.0696, aux.acc_seg: 92.6380, loss: 0.2343 +2024-06-19 08:48:38,577 - mmseg - INFO - Iter [51150/80000] lr: 1.443e-05, eta: 17:06:13, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1786, decode.acc_seg: 92.2788, aux.loss_ce: 0.0746, aux.acc_seg: 91.8758, loss: 0.2532 +2024-06-19 08:50:17,464 - mmseg - INFO - Iter [51200/80000] lr: 1.440e-05, eta: 17:04:22, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1586, decode.acc_seg: 93.1549, aux.loss_ce: 0.0667, aux.acc_seg: 92.8101, loss: 0.2253 +2024-06-19 08:51:56,471 - mmseg - INFO - Iter [51250/80000] lr: 1.438e-05, eta: 17:02:31, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1618, decode.acc_seg: 92.9942, aux.loss_ce: 0.0679, aux.acc_seg: 92.6202, loss: 0.2298 +2024-06-19 08:53:35,343 - mmseg - INFO - Iter [51300/80000] lr: 1.435e-05, eta: 17:00:40, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1645, decode.acc_seg: 92.9991, aux.loss_ce: 0.0691, aux.acc_seg: 92.6620, loss: 0.2336 +2024-06-19 08:55:14,175 - mmseg - INFO - Iter [51350/80000] lr: 1.433e-05, eta: 16:58:49, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1536, decode.acc_seg: 93.2872, aux.loss_ce: 0.0645, aux.acc_seg: 92.9265, loss: 0.2181 +2024-06-19 08:56:53,076 - mmseg - INFO - Iter [51400/80000] lr: 1.430e-05, eta: 16:56:58, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1646, decode.acc_seg: 92.7151, aux.loss_ce: 0.0694, aux.acc_seg: 92.3841, loss: 0.2340 +2024-06-19 08:58:32,012 - mmseg - INFO - Iter [51450/80000] lr: 1.428e-05, eta: 16:55:07, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1612, decode.acc_seg: 93.1981, aux.loss_ce: 0.0673, aux.acc_seg: 92.8505, loss: 0.2285 +2024-06-19 09:00:11,019 - mmseg - INFO - Iter [51500/80000] lr: 1.425e-05, eta: 16:53:16, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1525, decode.acc_seg: 93.5872, aux.loss_ce: 0.0644, aux.acc_seg: 93.1820, loss: 0.2169 +2024-06-19 09:01:49,901 - mmseg - INFO - Iter [51550/80000] lr: 1.423e-05, eta: 16:51:25, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1544, decode.acc_seg: 93.1112, aux.loss_ce: 0.0652, aux.acc_seg: 92.7558, loss: 0.2196 +2024-06-19 09:03:28,749 - mmseg - INFO - Iter [51600/80000] lr: 1.420e-05, eta: 16:49:34, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1589, decode.acc_seg: 93.1885, aux.loss_ce: 0.0672, aux.acc_seg: 92.8728, loss: 0.2261 +2024-06-19 09:05:07,567 - mmseg - INFO - Iter [51650/80000] lr: 1.418e-05, eta: 16:47:43, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1627, decode.acc_seg: 93.0024, aux.loss_ce: 0.0692, aux.acc_seg: 92.5864, loss: 0.2319 +2024-06-19 09:06:46,313 - mmseg - INFO - Iter [51700/80000] lr: 1.415e-05, eta: 16:45:52, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1623, decode.acc_seg: 92.9356, aux.loss_ce: 0.0681, aux.acc_seg: 92.5660, loss: 0.2304 +2024-06-19 09:08:25,196 - mmseg - INFO - Iter [51750/80000] lr: 1.413e-05, eta: 16:44:01, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1667, decode.acc_seg: 92.6325, aux.loss_ce: 0.0705, aux.acc_seg: 92.2824, loss: 0.2372 +2024-06-19 09:10:06,862 - mmseg - INFO - Iter [51800/80000] lr: 1.410e-05, eta: 16:42:12, time: 2.033, data_time: 0.065, memory: 72263, decode.loss_ce: 0.1514, decode.acc_seg: 93.4558, aux.loss_ce: 0.0639, aux.acc_seg: 93.1741, loss: 0.2153 +2024-06-19 09:11:45,707 - mmseg - INFO - Iter [51850/80000] lr: 1.408e-05, eta: 16:40:21, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1567, decode.acc_seg: 93.2552, aux.loss_ce: 0.0664, aux.acc_seg: 92.8796, loss: 0.2231 +2024-06-19 09:13:24,511 - mmseg - INFO - Iter [51900/80000] lr: 1.405e-05, eta: 16:38:30, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1516, decode.acc_seg: 93.4973, aux.loss_ce: 0.0640, aux.acc_seg: 93.1036, loss: 0.2156 +2024-06-19 09:15:03,539 - mmseg - INFO - Iter [51950/80000] lr: 1.403e-05, eta: 16:36:39, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1591, decode.acc_seg: 93.2123, aux.loss_ce: 0.0669, aux.acc_seg: 92.8629, loss: 0.2260 +2024-06-19 09:16:42,394 - mmseg - INFO - Saving checkpoint at 52000 iterations +2024-06-19 09:18:07,021 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 09:18:07,021 - mmseg - INFO - Iter [52000/80000] lr: 1.400e-05, eta: 16:35:34, time: 3.670, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1558, decode.acc_seg: 93.1122, aux.loss_ce: 0.0658, aux.acc_seg: 92.7715, loss: 0.2216 +2024-06-19 09:19:57,420 - mmseg - INFO - per class results: +2024-06-19 09:19:57,426 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.37 | 89.08 | +| building | 85.27 | 94.15 | +| sky | 95.08 | 97.98 | +| floor | 84.83 | 91.67 | +| tree | 77.97 | 88.86 | +| ceiling | 87.59 | 94.71 | +| road | 85.23 | 92.39 | +| bed | 92.65 | 96.93 | +| windowpane | 66.42 | 83.2 | +| grass | 66.29 | 85.45 | +| cabinet | 67.14 | 76.89 | +| sidewalk | 68.43 | 81.58 | +| person | 86.66 | 93.77 | +| earth | 40.61 | 50.81 | +| door | 58.6 | 75.27 | +| table | 69.05 | 78.98 | +| mountain | 65.51 | 78.29 | +| plant | 55.95 | 65.98 | +| curtain | 78.6 | 88.63 | +| chair | 68.57 | 78.96 | +| car | 88.01 | 93.89 | +| water | 58.7 | 72.27 | +| painting | 79.41 | 91.78 | +| sofa | 83.16 | 91.23 | +| shelf | 50.31 | 66.53 | +| house | 59.44 | 75.05 | +| sea | 67.12 | 84.02 | +| mirror | 78.11 | 85.76 | +| rug | 65.91 | 75.99 | +| field | 28.25 | 44.47 | +| armchair | 62.58 | 77.84 | +| seat | 67.48 | 88.99 | +| fence | 49.4 | 62.74 | +| desk | 57.35 | 81.27 | +| rock | 58.25 | 85.53 | +| wardrobe | 52.16 | 73.53 | +| lamp | 76.05 | 86.68 | +| bathtub | 85.92 | 90.03 | +| railing | 42.79 | 58.38 | +| cushion | 70.13 | 78.25 | +| base | 42.5 | 59.77 | +| box | 39.3 | 50.59 | +| column | 57.44 | 74.9 | +| signboard | 40.42 | 54.39 | +| chest of drawers | 43.73 | 66.68 | +| counter | 52.17 | 66.82 | +| sand | 51.91 | 77.45 | +| sink | 83.27 | 88.77 | +| skyscraper | 36.97 | 43.03 | +| fireplace | 72.13 | 91.4 | +| refrigerator | 84.81 | 92.65 | +| grandstand | 56.03 | 84.84 | +| path | 27.03 | 48.16 | +| stairs | 39.56 | 49.52 | +| runway | 70.5 | 91.96 | +| case | 66.38 | 85.17 | +| pool table | 95.09 | 98.49 | +| pillow | 70.3 | 84.28 | +| screen door | 88.71 | 93.01 | +| stairway | 43.79 | 59.79 | +| river | 13.48 | 31.18 | +| bridge | 65.85 | 75.92 | +| bookcase | 44.71 | 70.4 | +| blind | 41.45 | 43.48 | +| coffee table | 61.21 | 87.9 | +| toilet | 90.67 | 93.27 | +| flower | 46.7 | 58.96 | +| book | 56.71 | 72.38 | +| hill | 11.07 | 16.78 | +| bench | 62.91 | 72.55 | +| countertop | 64.93 | 84.86 | +| stove | 86.76 | 92.84 | +| palm | 51.87 | 81.71 | +| kitchen island | 48.75 | 88.83 | +| computer | 76.79 | 91.61 | +| swivel chair | 50.35 | 80.15 | +| boat | 71.25 | 92.12 | +| bar | 71.25 | 81.87 | +| arcade machine | 81.19 | 87.14 | +| hovel | 40.05 | 45.26 | +| bus | 93.6 | 96.39 | +| towel | 78.57 | 84.67 | +| light | 62.47 | 74.79 | +| truck | 51.81 | 62.22 | +| tower | 17.79 | 26.89 | +| chandelier | 73.41 | 85.62 | +| awning | 44.4 | 56.54 | +| streetlight | 35.09 | 48.28 | +| booth | 63.09 | 79.15 | +| television receiver | 80.41 | 87.66 | +| airplane | 87.42 | 94.92 | +| dirt track | 7.44 | 20.29 | +| apparel | 65.08 | 88.55 | +| pole | 29.47 | 39.17 | +| land | 5.1 | 7.33 | +| bannister | 20.71 | 26.94 | +| escalator | 65.68 | 87.85 | +| ottoman | 53.56 | 74.35 | +| bottle | 45.87 | 70.54 | +| buffet | 63.14 | 71.59 | +| poster | 33.17 | 38.48 | +| stage | 23.27 | 38.72 | +| van | 46.7 | 60.11 | +| ship | 75.96 | 87.25 | +| fountain | 39.89 | 42.06 | +| conveyer belt | 81.87 | 97.39 | +| canopy | 53.79 | 72.8 | +| washer | 87.75 | 94.22 | +| plaything | 33.97 | 52.27 | +| swimming pool | 59.62 | 91.24 | +| stool | 52.0 | 75.69 | +| barrel | 72.82 | 98.26 | +| basket | 42.89 | 58.68 | +| waterfall | 52.33 | 65.81 | +| tent | 93.89 | 98.94 | +| bag | 28.6 | 33.07 | +| minibike | 75.17 | 90.23 | +| cradle | 88.15 | 97.73 | +| oven | 67.09 | 78.03 | +| ball | 52.77 | 59.89 | +| food | 62.77 | 74.03 | +| step | 14.17 | 18.53 | +| tank | 63.63 | 68.06 | +| trade name | 23.15 | 26.41 | +| microwave | 89.14 | 96.25 | +| pot | 59.19 | 67.8 | +| animal | 60.74 | 62.12 | +| bicycle | 59.25 | 75.64 | +| lake | 44.49 | 57.44 | +| dishwasher | 76.39 | 82.99 | +| screen | 53.29 | 78.04 | +| blanket | 33.56 | 37.15 | +| sculpture | 71.45 | 82.01 | +| hood | 80.1 | 93.8 | +| sconce | 61.63 | 76.32 | +| vase | 51.83 | 67.62 | +| traffic light | 40.33 | 64.83 | +| tray | 25.8 | 34.92 | +| ashcan | 50.7 | 68.93 | +| fan | 71.58 | 86.0 | +| pier | 40.6 | 42.85 | +| crt screen | 9.69 | 20.48 | +| plate | 64.97 | 78.39 | +| monitor | 39.44 | 48.86 | +| bulletin board | 48.02 | 59.89 | +| shower | 12.19 | 12.23 | +| radiator | 71.67 | 80.76 | +| glass | 23.7 | 26.26 | +| clock | 55.09 | 65.54 | +| flag | 69.83 | 77.02 | ++---------------------+-------+-------+ +2024-06-19 09:19:57,426 - mmseg - INFO - Summary: +2024-06-19 09:19:57,426 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 86.3 | 58.65 | 71.36 | ++------+-------+-------+ +2024-06-19 09:19:57,427 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 09:19:57,427 - mmseg - INFO - Iter(val) [250] aAcc: 0.8630, mIoU: 0.5865, mAcc: 0.7136, IoU.wall: 0.8237, IoU.building: 0.8527, IoU.sky: 0.9508, IoU.floor: 0.8483, IoU.tree: 0.7797, IoU.ceiling: 0.8759, IoU.road: 0.8523, IoU.bed : 0.9265, IoU.windowpane: 0.6642, IoU.grass: 0.6629, IoU.cabinet: 0.6714, IoU.sidewalk: 0.6843, IoU.person: 0.8666, IoU.earth: 0.4061, IoU.door: 0.5860, IoU.table: 0.6905, IoU.mountain: 0.6551, IoU.plant: 0.5595, IoU.curtain: 0.7860, IoU.chair: 0.6857, IoU.car: 0.8801, IoU.water: 0.5870, IoU.painting: 0.7941, IoU.sofa: 0.8316, IoU.shelf: 0.5031, IoU.house: 0.5944, IoU.sea: 0.6712, IoU.mirror: 0.7811, IoU.rug: 0.6591, IoU.field: 0.2825, IoU.armchair: 0.6258, IoU.seat: 0.6748, IoU.fence: 0.4940, IoU.desk: 0.5735, IoU.rock: 0.5825, IoU.wardrobe: 0.5216, IoU.lamp: 0.7605, IoU.bathtub: 0.8592, IoU.railing: 0.4279, IoU.cushion: 0.7013, IoU.base: 0.4250, IoU.box: 0.3930, IoU.column: 0.5744, IoU.signboard: 0.4042, IoU.chest of drawers: 0.4373, IoU.counter: 0.5217, IoU.sand: 0.5191, IoU.sink: 0.8327, IoU.skyscraper: 0.3697, IoU.fireplace: 0.7213, IoU.refrigerator: 0.8481, IoU.grandstand: 0.5603, IoU.path: 0.2703, IoU.stairs: 0.3956, IoU.runway: 0.7050, IoU.case: 0.6638, IoU.pool table: 0.9509, IoU.pillow: 0.7030, IoU.screen door: 0.8871, IoU.stairway: 0.4379, IoU.river: 0.1348, IoU.bridge: 0.6585, IoU.bookcase: 0.4471, IoU.blind: 0.4145, IoU.coffee table: 0.6121, IoU.toilet: 0.9067, IoU.flower: 0.4670, IoU.book: 0.5671, IoU.hill: 0.1107, IoU.bench: 0.6291, IoU.countertop: 0.6493, IoU.stove: 0.8676, IoU.palm: 0.5187, IoU.kitchen island: 0.4875, IoU.computer: 0.7679, IoU.swivel chair: 0.5035, IoU.boat: 0.7125, IoU.bar: 0.7125, IoU.arcade machine: 0.8119, IoU.hovel: 0.4005, IoU.bus: 0.9360, IoU.towel: 0.7857, IoU.light: 0.6247, IoU.truck: 0.5181, IoU.tower: 0.1779, IoU.chandelier: 0.7341, IoU.awning: 0.4440, IoU.streetlight: 0.3509, IoU.booth: 0.6309, IoU.television receiver: 0.8041, IoU.airplane: 0.8742, IoU.dirt track: 0.0744, IoU.apparel: 0.6508, IoU.pole: 0.2947, IoU.land: 0.0510, IoU.bannister: 0.2071, IoU.escalator: 0.6568, IoU.ottoman: 0.5356, IoU.bottle: 0.4587, IoU.buffet: 0.6314, IoU.poster: 0.3317, IoU.stage: 0.2327, IoU.van: 0.4670, IoU.ship: 0.7596, IoU.fountain: 0.3989, IoU.conveyer belt: 0.8187, IoU.canopy: 0.5379, IoU.washer: 0.8775, IoU.plaything: 0.3397, IoU.swimming pool: 0.5962, IoU.stool: 0.5200, IoU.barrel: 0.7282, IoU.basket: 0.4289, IoU.waterfall: 0.5233, IoU.tent: 0.9389, IoU.bag: 0.2860, IoU.minibike: 0.7517, IoU.cradle: 0.8815, IoU.oven: 0.6709, IoU.ball: 0.5277, IoU.food: 0.6277, IoU.step: 0.1417, IoU.tank: 0.6363, IoU.trade name: 0.2315, IoU.microwave: 0.8914, IoU.pot: 0.5919, IoU.animal: 0.6074, IoU.bicycle: 0.5925, IoU.lake: 0.4449, IoU.dishwasher: 0.7639, IoU.screen: 0.5329, IoU.blanket: 0.3356, IoU.sculpture: 0.7145, IoU.hood: 0.8010, IoU.sconce: 0.6163, IoU.vase: 0.5183, IoU.traffic light: 0.4033, IoU.tray: 0.2580, IoU.ashcan: 0.5070, IoU.fan: 0.7158, IoU.pier: 0.4060, IoU.crt screen: 0.0969, IoU.plate: 0.6497, IoU.monitor: 0.3944, IoU.bulletin board: 0.4802, IoU.shower: 0.1219, IoU.radiator: 0.7167, IoU.glass: 0.2370, IoU.clock: 0.5509, IoU.flag: 0.6983, Acc.wall: 0.8908, Acc.building: 0.9415, Acc.sky: 0.9798, Acc.floor: 0.9167, Acc.tree: 0.8886, Acc.ceiling: 0.9471, Acc.road: 0.9239, Acc.bed : 0.9693, Acc.windowpane: 0.8320, Acc.grass: 0.8545, Acc.cabinet: 0.7689, Acc.sidewalk: 0.8158, Acc.person: 0.9377, Acc.earth: 0.5081, Acc.door: 0.7527, Acc.table: 0.7898, Acc.mountain: 0.7829, Acc.plant: 0.6598, Acc.curtain: 0.8863, Acc.chair: 0.7896, Acc.car: 0.9389, Acc.water: 0.7227, Acc.painting: 0.9178, Acc.sofa: 0.9123, Acc.shelf: 0.6653, Acc.house: 0.7505, Acc.sea: 0.8402, Acc.mirror: 0.8576, Acc.rug: 0.7599, Acc.field: 0.4447, Acc.armchair: 0.7784, Acc.seat: 0.8899, Acc.fence: 0.6274, Acc.desk: 0.8127, Acc.rock: 0.8553, Acc.wardrobe: 0.7353, Acc.lamp: 0.8668, Acc.bathtub: 0.9003, Acc.railing: 0.5838, Acc.cushion: 0.7825, Acc.base: 0.5977, Acc.box: 0.5059, Acc.column: 0.7490, Acc.signboard: 0.5439, Acc.chest of drawers: 0.6668, Acc.counter: 0.6682, Acc.sand: 0.7745, Acc.sink: 0.8877, Acc.skyscraper: 0.4303, Acc.fireplace: 0.9140, Acc.refrigerator: 0.9265, Acc.grandstand: 0.8484, Acc.path: 0.4816, Acc.stairs: 0.4952, Acc.runway: 0.9196, Acc.case: 0.8517, Acc.pool table: 0.9849, Acc.pillow: 0.8428, Acc.screen door: 0.9301, Acc.stairway: 0.5979, Acc.river: 0.3118, Acc.bridge: 0.7592, Acc.bookcase: 0.7040, Acc.blind: 0.4348, Acc.coffee table: 0.8790, Acc.toilet: 0.9327, Acc.flower: 0.5896, Acc.book: 0.7238, Acc.hill: 0.1678, Acc.bench: 0.7255, Acc.countertop: 0.8486, Acc.stove: 0.9284, Acc.palm: 0.8171, Acc.kitchen island: 0.8883, Acc.computer: 0.9161, Acc.swivel chair: 0.8015, Acc.boat: 0.9212, Acc.bar: 0.8187, Acc.arcade machine: 0.8714, Acc.hovel: 0.4526, Acc.bus: 0.9639, Acc.towel: 0.8467, Acc.light: 0.7479, Acc.truck: 0.6222, Acc.tower: 0.2689, Acc.chandelier: 0.8562, Acc.awning: 0.5654, Acc.streetlight: 0.4828, Acc.booth: 0.7915, Acc.television receiver: 0.8766, Acc.airplane: 0.9492, Acc.dirt track: 0.2029, Acc.apparel: 0.8855, Acc.pole: 0.3917, Acc.land: 0.0733, Acc.bannister: 0.2694, Acc.escalator: 0.8785, Acc.ottoman: 0.7435, Acc.bottle: 0.7054, Acc.buffet: 0.7159, Acc.poster: 0.3848, Acc.stage: 0.3872, Acc.van: 0.6011, Acc.ship: 0.8725, Acc.fountain: 0.4206, Acc.conveyer belt: 0.9739, Acc.canopy: 0.7280, Acc.washer: 0.9422, Acc.plaything: 0.5227, Acc.swimming pool: 0.9124, Acc.stool: 0.7569, Acc.barrel: 0.9826, Acc.basket: 0.5868, Acc.waterfall: 0.6581, Acc.tent: 0.9894, Acc.bag: 0.3307, Acc.minibike: 0.9023, Acc.cradle: 0.9773, Acc.oven: 0.7803, Acc.ball: 0.5989, Acc.food: 0.7403, Acc.step: 0.1853, Acc.tank: 0.6806, Acc.trade name: 0.2641, Acc.microwave: 0.9625, Acc.pot: 0.6780, Acc.animal: 0.6212, Acc.bicycle: 0.7564, Acc.lake: 0.5744, Acc.dishwasher: 0.8299, Acc.screen: 0.7804, Acc.blanket: 0.3715, Acc.sculpture: 0.8201, Acc.hood: 0.9380, Acc.sconce: 0.7632, Acc.vase: 0.6762, Acc.traffic light: 0.6483, Acc.tray: 0.3492, Acc.ashcan: 0.6893, Acc.fan: 0.8600, Acc.pier: 0.4285, Acc.crt screen: 0.2048, Acc.plate: 0.7839, Acc.monitor: 0.4886, Acc.bulletin board: 0.5989, Acc.shower: 0.1223, Acc.radiator: 0.8076, Acc.glass: 0.2626, Acc.clock: 0.6554, Acc.flag: 0.7702 +2024-06-19 09:21:36,643 - mmseg - INFO - Iter [52050/80000] lr: 1.398e-05, eta: 16:34:43, time: 4.192, data_time: 2.226, memory: 72263, decode.loss_ce: 0.1512, decode.acc_seg: 93.3851, aux.loss_ce: 0.0640, aux.acc_seg: 92.9763, loss: 0.2153 +2024-06-19 09:23:15,691 - mmseg - INFO - Iter [52100/80000] lr: 1.395e-05, eta: 16:32:52, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1557, decode.acc_seg: 93.1221, aux.loss_ce: 0.0658, aux.acc_seg: 92.7724, loss: 0.2215 +2024-06-19 09:24:54,628 - mmseg - INFO - Iter [52150/80000] lr: 1.393e-05, eta: 16:31:01, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1644, decode.acc_seg: 93.0489, aux.loss_ce: 0.0690, aux.acc_seg: 92.6439, loss: 0.2334 +2024-06-19 09:26:33,626 - mmseg - INFO - Iter [52200/80000] lr: 1.390e-05, eta: 16:29:10, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1658, decode.acc_seg: 92.8186, aux.loss_ce: 0.0687, aux.acc_seg: 92.4926, loss: 0.2345 +2024-06-19 09:28:12,606 - mmseg - INFO - Iter [52250/80000] lr: 1.388e-05, eta: 16:27:19, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1608, decode.acc_seg: 92.8900, aux.loss_ce: 0.0677, aux.acc_seg: 92.5032, loss: 0.2285 +2024-06-19 09:29:51,628 - mmseg - INFO - Iter [52300/80000] lr: 1.385e-05, eta: 16:25:28, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1502, decode.acc_seg: 93.5088, aux.loss_ce: 0.0637, aux.acc_seg: 93.1504, loss: 0.2139 +2024-06-19 09:31:30,588 - mmseg - INFO - Iter [52350/80000] lr: 1.383e-05, eta: 16:23:37, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1507, decode.acc_seg: 93.2620, aux.loss_ce: 0.0645, aux.acc_seg: 92.7929, loss: 0.2152 +2024-06-19 09:33:09,472 - mmseg - INFO - Iter [52400/80000] lr: 1.380e-05, eta: 16:21:47, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1457, decode.acc_seg: 93.6399, aux.loss_ce: 0.0616, aux.acc_seg: 93.2678, loss: 0.2073 +2024-06-19 09:34:48,360 - mmseg - INFO - Iter [52450/80000] lr: 1.378e-05, eta: 16:19:56, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1554, decode.acc_seg: 93.1529, aux.loss_ce: 0.0659, aux.acc_seg: 92.7939, loss: 0.2213 +2024-06-19 09:36:27,301 - mmseg - INFO - Iter [52500/80000] lr: 1.375e-05, eta: 16:18:05, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1601, decode.acc_seg: 92.9268, aux.loss_ce: 0.0681, aux.acc_seg: 92.5000, loss: 0.2281 +2024-06-19 09:38:06,166 - mmseg - INFO - Iter [52550/80000] lr: 1.373e-05, eta: 16:16:14, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1519, decode.acc_seg: 93.3014, aux.loss_ce: 0.0641, aux.acc_seg: 92.9533, loss: 0.2160 +2024-06-19 09:39:45,085 - mmseg - INFO - Iter [52600/80000] lr: 1.370e-05, eta: 16:14:23, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1509, decode.acc_seg: 93.4075, aux.loss_ce: 0.0640, aux.acc_seg: 93.0243, loss: 0.2148 +2024-06-19 09:41:24,129 - mmseg - INFO - Iter [52650/80000] lr: 1.368e-05, eta: 16:12:33, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1560, decode.acc_seg: 93.1064, aux.loss_ce: 0.0661, aux.acc_seg: 92.6863, loss: 0.2221 +2024-06-19 09:43:03,045 - mmseg - INFO - Iter [52700/80000] lr: 1.365e-05, eta: 16:10:42, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1498, decode.acc_seg: 93.6156, aux.loss_ce: 0.0633, aux.acc_seg: 93.2382, loss: 0.2131 +2024-06-19 09:44:41,909 - mmseg - INFO - Iter [52750/80000] lr: 1.363e-05, eta: 16:08:51, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1609, decode.acc_seg: 93.0524, aux.loss_ce: 0.0680, aux.acc_seg: 92.6283, loss: 0.2289 +2024-06-19 09:46:20,755 - mmseg - INFO - Iter [52800/80000] lr: 1.360e-05, eta: 16:07:01, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1622, decode.acc_seg: 93.1056, aux.loss_ce: 0.0687, aux.acc_seg: 92.7126, loss: 0.2309 +2024-06-19 09:47:59,730 - mmseg - INFO - Iter [52850/80000] lr: 1.358e-05, eta: 16:05:10, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1630, decode.acc_seg: 93.1199, aux.loss_ce: 0.0687, aux.acc_seg: 92.7415, loss: 0.2317 +2024-06-19 09:49:38,665 - mmseg - INFO - Iter [52900/80000] lr: 1.355e-05, eta: 16:03:20, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1529, decode.acc_seg: 93.2513, aux.loss_ce: 0.0643, aux.acc_seg: 92.9744, loss: 0.2172 +2024-06-19 09:51:17,564 - mmseg - INFO - Iter [52950/80000] lr: 1.353e-05, eta: 16:01:29, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1559, decode.acc_seg: 93.1212, aux.loss_ce: 0.0659, aux.acc_seg: 92.7661, loss: 0.2218 +2024-06-19 09:52:56,459 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 09:52:56,459 - mmseg - INFO - Iter [53000/80000] lr: 1.350e-05, eta: 15:59:38, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1587, decode.acc_seg: 93.1704, aux.loss_ce: 0.0667, aux.acc_seg: 92.8182, loss: 0.2254 +2024-06-19 09:54:47,366 - mmseg - INFO - per class results: +2024-06-19 09:54:47,372 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.83 | 89.61 | +| building | 85.44 | 93.51 | +| sky | 94.99 | 97.83 | +| floor | 84.52 | 91.67 | +| tree | 77.74 | 88.69 | +| ceiling | 87.45 | 94.25 | +| road | 85.54 | 90.89 | +| bed | 92.98 | 97.15 | +| windowpane | 67.53 | 81.14 | +| grass | 67.3 | 83.25 | +| cabinet | 67.86 | 76.84 | +| sidewalk | 69.85 | 85.58 | +| person | 86.68 | 94.04 | +| earth | 40.62 | 51.62 | +| door | 58.58 | 76.75 | +| table | 70.7 | 81.64 | +| mountain | 65.15 | 77.45 | +| plant | 55.25 | 68.62 | +| curtain | 79.34 | 86.98 | +| chair | 69.3 | 80.87 | +| car | 88.02 | 95.11 | +| water | 55.54 | 67.71 | +| painting | 82.15 | 91.67 | +| sofa | 82.8 | 91.59 | +| shelf | 51.06 | 68.01 | +| house | 58.08 | 69.65 | +| sea | 65.82 | 85.11 | +| mirror | 78.28 | 86.73 | +| rug | 63.5 | 74.1 | +| field | 28.35 | 51.47 | +| armchair | 61.23 | 77.1 | +| seat | 65.96 | 89.56 | +| fence | 51.5 | 63.74 | +| desk | 60.51 | 81.42 | +| rock | 56.35 | 85.16 | +| wardrobe | 57.72 | 77.85 | +| lamp | 76.27 | 87.68 | +| bathtub | 86.49 | 89.13 | +| railing | 43.23 | 61.61 | +| cushion | 70.56 | 81.42 | +| base | 42.64 | 52.12 | +| box | 40.52 | 50.92 | +| column | 59.05 | 77.96 | +| signboard | 42.62 | 58.59 | +| chest of drawers | 45.04 | 69.29 | +| counter | 52.95 | 63.3 | +| sand | 52.26 | 77.72 | +| sink | 83.41 | 88.23 | +| skyscraper | 46.77 | 57.25 | +| fireplace | 72.61 | 92.85 | +| refrigerator | 84.74 | 94.41 | +| grandstand | 57.56 | 80.67 | +| path | 31.87 | 43.91 | +| stairs | 39.91 | 48.39 | +| runway | 72.49 | 93.26 | +| case | 64.45 | 87.54 | +| pool table | 95.24 | 98.45 | +| pillow | 68.41 | 80.82 | +| screen door | 75.56 | 77.6 | +| stairway | 52.16 | 71.43 | +| river | 10.67 | 25.8 | +| bridge | 66.66 | 74.37 | +| bookcase | 42.51 | 62.86 | +| blind | 48.57 | 59.11 | +| coffee table | 61.94 | 87.38 | +| toilet | 90.4 | 93.17 | +| flower | 44.13 | 59.47 | +| book | 56.12 | 77.81 | +| hill | 15.02 | 23.44 | +| bench | 60.52 | 68.88 | +| countertop | 66.29 | 85.56 | +| stove | 87.27 | 94.71 | +| palm | 50.91 | 83.45 | +| kitchen island | 50.29 | 81.32 | +| computer | 76.75 | 91.94 | +| swivel chair | 49.53 | 76.24 | +| boat | 71.26 | 91.64 | +| bar | 72.01 | 86.8 | +| arcade machine | 91.23 | 97.22 | +| hovel | 48.19 | 55.49 | +| bus | 93.03 | 97.4 | +| towel | 79.62 | 85.94 | +| light | 62.6 | 79.83 | +| truck | 52.07 | 62.44 | +| tower | 28.43 | 48.56 | +| chandelier | 74.39 | 89.21 | +| awning | 47.32 | 59.32 | +| streetlight | 39.65 | 56.57 | +| booth | 55.37 | 72.83 | +| television receiver | 80.96 | 89.46 | +| airplane | 87.25 | 97.09 | +| dirt track | 14.3 | 48.62 | +| apparel | 60.25 | 79.59 | +| pole | 28.1 | 38.04 | +| land | 6.27 | 11.16 | +| bannister | 22.25 | 29.15 | +| escalator | 65.09 | 88.5 | +| ottoman | 52.14 | 71.22 | +| bottle | 45.09 | 70.59 | +| buffet | 59.15 | 68.83 | +| poster | 34.01 | 40.33 | +| stage | 21.51 | 41.14 | +| van | 50.76 | 64.12 | +| ship | 72.61 | 86.76 | +| fountain | 40.33 | 40.85 | +| conveyer belt | 83.38 | 97.0 | +| canopy | 56.76 | 73.48 | +| washer | 89.71 | 96.47 | +| plaything | 35.07 | 46.09 | +| swimming pool | 51.2 | 72.99 | +| stool | 55.74 | 71.37 | +| barrel | 76.82 | 96.51 | +| basket | 42.92 | 60.8 | +| waterfall | 66.45 | 84.32 | +| tent | 96.36 | 98.54 | +| bag | 25.37 | 27.95 | +| minibike | 74.42 | 90.59 | +| cradle | 89.76 | 97.3 | +| oven | 58.13 | 67.74 | +| ball | 56.13 | 69.69 | +| food | 59.39 | 68.02 | +| step | 12.57 | 15.61 | +| tank | 62.79 | 70.58 | +| trade name | 24.89 | 29.47 | +| microwave | 86.52 | 96.65 | +| pot | 59.77 | 68.45 | +| animal | 59.16 | 60.46 | +| bicycle | 60.91 | 75.4 | +| lake | 51.74 | 63.78 | +| dishwasher | 73.24 | 80.18 | +| screen | 65.54 | 93.06 | +| blanket | 36.02 | 42.48 | +| sculpture | 74.49 | 88.13 | +| hood | 69.25 | 81.16 | +| sconce | 60.69 | 74.36 | +| vase | 50.16 | 67.48 | +| traffic light | 38.77 | 71.26 | +| tray | 30.32 | 42.39 | +| ashcan | 51.11 | 67.91 | +| fan | 73.16 | 84.24 | +| pier | 38.59 | 43.97 | +| crt screen | 11.61 | 19.6 | +| plate | 64.69 | 81.2 | +| monitor | 41.38 | 50.72 | +| bulletin board | 55.02 | 73.67 | +| shower | 13.78 | 13.93 | +| radiator | 67.69 | 84.93 | +| glass | 24.86 | 29.1 | +| clock | 55.03 | 67.99 | +| flag | 71.82 | 78.66 | ++---------------------+-------+-------+ +2024-06-19 09:54:47,372 - mmseg - INFO - Summary: +2024-06-19 09:54:47,372 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.43 | 59.26 | 72.39 | ++-------+-------+-------+ +2024-06-19 09:54:47,373 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 09:54:47,373 - mmseg - INFO - Iter(val) [250] aAcc: 0.8643, mIoU: 0.5926, mAcc: 0.7239, IoU.wall: 0.8283, IoU.building: 0.8544, IoU.sky: 0.9499, IoU.floor: 0.8452, IoU.tree: 0.7774, IoU.ceiling: 0.8745, IoU.road: 0.8554, IoU.bed : 0.9298, IoU.windowpane: 0.6753, IoU.grass: 0.6730, IoU.cabinet: 0.6786, IoU.sidewalk: 0.6985, IoU.person: 0.8668, IoU.earth: 0.4062, IoU.door: 0.5858, IoU.table: 0.7070, IoU.mountain: 0.6515, IoU.plant: 0.5525, IoU.curtain: 0.7934, IoU.chair: 0.6930, IoU.car: 0.8802, IoU.water: 0.5554, IoU.painting: 0.8215, IoU.sofa: 0.8280, IoU.shelf: 0.5106, IoU.house: 0.5808, IoU.sea: 0.6582, IoU.mirror: 0.7828, IoU.rug: 0.6350, IoU.field: 0.2835, IoU.armchair: 0.6123, IoU.seat: 0.6596, IoU.fence: 0.5150, IoU.desk: 0.6051, IoU.rock: 0.5635, IoU.wardrobe: 0.5772, IoU.lamp: 0.7627, IoU.bathtub: 0.8649, IoU.railing: 0.4323, IoU.cushion: 0.7056, IoU.base: 0.4264, IoU.box: 0.4052, IoU.column: 0.5905, IoU.signboard: 0.4262, IoU.chest of drawers: 0.4504, IoU.counter: 0.5295, IoU.sand: 0.5226, IoU.sink: 0.8341, IoU.skyscraper: 0.4677, IoU.fireplace: 0.7261, IoU.refrigerator: 0.8474, IoU.grandstand: 0.5756, IoU.path: 0.3187, IoU.stairs: 0.3991, IoU.runway: 0.7249, IoU.case: 0.6445, IoU.pool table: 0.9524, IoU.pillow: 0.6841, IoU.screen door: 0.7556, IoU.stairway: 0.5216, IoU.river: 0.1067, IoU.bridge: 0.6666, IoU.bookcase: 0.4251, IoU.blind: 0.4857, IoU.coffee table: 0.6194, IoU.toilet: 0.9040, IoU.flower: 0.4413, IoU.book: 0.5612, IoU.hill: 0.1502, IoU.bench: 0.6052, IoU.countertop: 0.6629, IoU.stove: 0.8727, IoU.palm: 0.5091, IoU.kitchen island: 0.5029, IoU.computer: 0.7675, IoU.swivel chair: 0.4953, IoU.boat: 0.7126, IoU.bar: 0.7201, IoU.arcade machine: 0.9123, IoU.hovel: 0.4819, IoU.bus: 0.9303, IoU.towel: 0.7962, IoU.light: 0.6260, IoU.truck: 0.5207, IoU.tower: 0.2843, IoU.chandelier: 0.7439, IoU.awning: 0.4732, IoU.streetlight: 0.3965, IoU.booth: 0.5537, IoU.television receiver: 0.8096, IoU.airplane: 0.8725, IoU.dirt track: 0.1430, IoU.apparel: 0.6025, IoU.pole: 0.2810, IoU.land: 0.0627, IoU.bannister: 0.2225, IoU.escalator: 0.6509, IoU.ottoman: 0.5214, IoU.bottle: 0.4509, IoU.buffet: 0.5915, IoU.poster: 0.3401, IoU.stage: 0.2151, IoU.van: 0.5076, IoU.ship: 0.7261, IoU.fountain: 0.4033, IoU.conveyer belt: 0.8338, IoU.canopy: 0.5676, IoU.washer: 0.8971, IoU.plaything: 0.3507, IoU.swimming pool: 0.5120, IoU.stool: 0.5574, IoU.barrel: 0.7682, IoU.basket: 0.4292, IoU.waterfall: 0.6645, IoU.tent: 0.9636, IoU.bag: 0.2537, IoU.minibike: 0.7442, IoU.cradle: 0.8976, IoU.oven: 0.5813, IoU.ball: 0.5613, IoU.food: 0.5939, IoU.step: 0.1257, IoU.tank: 0.6279, IoU.trade name: 0.2489, IoU.microwave: 0.8652, IoU.pot: 0.5977, IoU.animal: 0.5916, IoU.bicycle: 0.6091, IoU.lake: 0.5174, IoU.dishwasher: 0.7324, IoU.screen: 0.6554, IoU.blanket: 0.3602, IoU.sculpture: 0.7449, IoU.hood: 0.6925, IoU.sconce: 0.6069, IoU.vase: 0.5016, IoU.traffic light: 0.3877, IoU.tray: 0.3032, IoU.ashcan: 0.5111, IoU.fan: 0.7316, IoU.pier: 0.3859, IoU.crt screen: 0.1161, IoU.plate: 0.6469, IoU.monitor: 0.4138, IoU.bulletin board: 0.5502, IoU.shower: 0.1378, IoU.radiator: 0.6769, IoU.glass: 0.2486, IoU.clock: 0.5503, IoU.flag: 0.7182, Acc.wall: 0.8961, Acc.building: 0.9351, Acc.sky: 0.9783, Acc.floor: 0.9167, Acc.tree: 0.8869, Acc.ceiling: 0.9425, Acc.road: 0.9089, Acc.bed : 0.9715, Acc.windowpane: 0.8114, Acc.grass: 0.8325, Acc.cabinet: 0.7684, Acc.sidewalk: 0.8558, Acc.person: 0.9404, Acc.earth: 0.5162, Acc.door: 0.7675, Acc.table: 0.8164, Acc.mountain: 0.7745, Acc.plant: 0.6862, Acc.curtain: 0.8698, Acc.chair: 0.8087, Acc.car: 0.9511, Acc.water: 0.6771, Acc.painting: 0.9167, Acc.sofa: 0.9159, Acc.shelf: 0.6801, Acc.house: 0.6965, Acc.sea: 0.8511, Acc.mirror: 0.8673, Acc.rug: 0.7410, Acc.field: 0.5147, Acc.armchair: 0.7710, Acc.seat: 0.8956, Acc.fence: 0.6374, Acc.desk: 0.8142, Acc.rock: 0.8516, Acc.wardrobe: 0.7785, Acc.lamp: 0.8768, Acc.bathtub: 0.8913, Acc.railing: 0.6161, Acc.cushion: 0.8142, Acc.base: 0.5212, Acc.box: 0.5092, Acc.column: 0.7796, Acc.signboard: 0.5859, Acc.chest of drawers: 0.6929, Acc.counter: 0.6330, Acc.sand: 0.7772, Acc.sink: 0.8823, Acc.skyscraper: 0.5725, Acc.fireplace: 0.9285, Acc.refrigerator: 0.9441, Acc.grandstand: 0.8067, Acc.path: 0.4391, Acc.stairs: 0.4839, Acc.runway: 0.9326, Acc.case: 0.8754, Acc.pool table: 0.9845, Acc.pillow: 0.8082, Acc.screen door: 0.7760, Acc.stairway: 0.7143, Acc.river: 0.2580, Acc.bridge: 0.7437, Acc.bookcase: 0.6286, Acc.blind: 0.5911, Acc.coffee table: 0.8738, Acc.toilet: 0.9317, Acc.flower: 0.5947, Acc.book: 0.7781, Acc.hill: 0.2344, Acc.bench: 0.6888, Acc.countertop: 0.8556, Acc.stove: 0.9471, Acc.palm: 0.8345, Acc.kitchen island: 0.8132, Acc.computer: 0.9194, Acc.swivel chair: 0.7624, Acc.boat: 0.9164, Acc.bar: 0.8680, Acc.arcade machine: 0.9722, Acc.hovel: 0.5549, Acc.bus: 0.9740, Acc.towel: 0.8594, Acc.light: 0.7983, Acc.truck: 0.6244, Acc.tower: 0.4856, Acc.chandelier: 0.8921, Acc.awning: 0.5932, Acc.streetlight: 0.5657, Acc.booth: 0.7283, Acc.television receiver: 0.8946, Acc.airplane: 0.9709, Acc.dirt track: 0.4862, Acc.apparel: 0.7959, Acc.pole: 0.3804, Acc.land: 0.1116, Acc.bannister: 0.2915, Acc.escalator: 0.8850, Acc.ottoman: 0.7122, Acc.bottle: 0.7059, Acc.buffet: 0.6883, Acc.poster: 0.4033, Acc.stage: 0.4114, Acc.van: 0.6412, Acc.ship: 0.8676, Acc.fountain: 0.4085, Acc.conveyer belt: 0.9700, Acc.canopy: 0.7348, Acc.washer: 0.9647, Acc.plaything: 0.4609, Acc.swimming pool: 0.7299, Acc.stool: 0.7137, Acc.barrel: 0.9651, Acc.basket: 0.6080, Acc.waterfall: 0.8432, Acc.tent: 0.9854, Acc.bag: 0.2795, Acc.minibike: 0.9059, Acc.cradle: 0.9730, Acc.oven: 0.6774, Acc.ball: 0.6969, Acc.food: 0.6802, Acc.step: 0.1561, Acc.tank: 0.7058, Acc.trade name: 0.2947, Acc.microwave: 0.9665, Acc.pot: 0.6845, Acc.animal: 0.6046, Acc.bicycle: 0.7540, Acc.lake: 0.6378, Acc.dishwasher: 0.8018, Acc.screen: 0.9306, Acc.blanket: 0.4248, Acc.sculpture: 0.8813, Acc.hood: 0.8116, Acc.sconce: 0.7436, Acc.vase: 0.6748, Acc.traffic light: 0.7126, Acc.tray: 0.4239, Acc.ashcan: 0.6791, Acc.fan: 0.8424, Acc.pier: 0.4397, Acc.crt screen: 0.1960, Acc.plate: 0.8120, Acc.monitor: 0.5072, Acc.bulletin board: 0.7367, Acc.shower: 0.1393, Acc.radiator: 0.8493, Acc.glass: 0.2910, Acc.clock: 0.6799, Acc.flag: 0.7866 +2024-06-19 09:56:29,934 - mmseg - INFO - Iter [53050/80000] lr: 1.348e-05, eta: 15:58:46, time: 4.269, data_time: 2.298, memory: 72263, decode.loss_ce: 0.1512, decode.acc_seg: 93.3431, aux.loss_ce: 0.0636, aux.acc_seg: 93.0297, loss: 0.2149 +2024-06-19 09:58:08,848 - mmseg - INFO - Iter [53100/80000] lr: 1.345e-05, eta: 15:56:55, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1513, decode.acc_seg: 93.3834, aux.loss_ce: 0.0644, aux.acc_seg: 92.9918, loss: 0.2156 +2024-06-19 09:59:47,846 - mmseg - INFO - Iter [53150/80000] lr: 1.343e-05, eta: 15:55:05, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1436, decode.acc_seg: 93.8172, aux.loss_ce: 0.0612, aux.acc_seg: 93.3951, loss: 0.2048 +2024-06-19 10:01:26,645 - mmseg - INFO - Iter [53200/80000] lr: 1.340e-05, eta: 15:53:14, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1527, decode.acc_seg: 93.2778, aux.loss_ce: 0.0647, aux.acc_seg: 92.8870, loss: 0.2174 +2024-06-19 10:03:05,547 - mmseg - INFO - Iter [53250/80000] lr: 1.338e-05, eta: 15:51:23, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1466, decode.acc_seg: 93.6053, aux.loss_ce: 0.0625, aux.acc_seg: 93.1898, loss: 0.2091 +2024-06-19 10:04:44,426 - mmseg - INFO - Iter [53300/80000] lr: 1.335e-05, eta: 15:49:33, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1547, decode.acc_seg: 93.4261, aux.loss_ce: 0.0653, aux.acc_seg: 93.0447, loss: 0.2200 +2024-06-19 10:06:23,431 - mmseg - INFO - Iter [53350/80000] lr: 1.333e-05, eta: 15:47:42, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1511, decode.acc_seg: 93.3111, aux.loss_ce: 0.0643, aux.acc_seg: 92.8650, loss: 0.2154 +2024-06-19 10:08:02,537 - mmseg - INFO - Iter [53400/80000] lr: 1.330e-05, eta: 15:45:52, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1488, decode.acc_seg: 93.4076, aux.loss_ce: 0.0633, aux.acc_seg: 93.0185, loss: 0.2121 +2024-06-19 10:09:41,481 - mmseg - INFO - Iter [53450/80000] lr: 1.328e-05, eta: 15:44:01, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1463, decode.acc_seg: 93.5102, aux.loss_ce: 0.0620, aux.acc_seg: 93.1511, loss: 0.2084 +2024-06-19 10:11:20,534 - mmseg - INFO - Iter [53500/80000] lr: 1.325e-05, eta: 15:42:11, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1532, decode.acc_seg: 93.3282, aux.loss_ce: 0.0646, aux.acc_seg: 92.9597, loss: 0.2179 +2024-06-19 10:12:59,499 - mmseg - INFO - Iter [53550/80000] lr: 1.323e-05, eta: 15:40:20, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1498, decode.acc_seg: 93.3909, aux.loss_ce: 0.0636, aux.acc_seg: 92.9894, loss: 0.2134 +2024-06-19 10:14:38,507 - mmseg - INFO - Iter [53600/80000] lr: 1.320e-05, eta: 15:38:30, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1562, decode.acc_seg: 93.0792, aux.loss_ce: 0.0665, aux.acc_seg: 92.6212, loss: 0.2228 +2024-06-19 10:16:17,448 - mmseg - INFO - Iter [53650/80000] lr: 1.318e-05, eta: 15:36:39, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1580, decode.acc_seg: 92.9423, aux.loss_ce: 0.0671, aux.acc_seg: 92.5260, loss: 0.2250 +2024-06-19 10:17:56,516 - mmseg - INFO - Iter [53700/80000] lr: 1.315e-05, eta: 15:34:49, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1508, decode.acc_seg: 93.3175, aux.loss_ce: 0.0632, aux.acc_seg: 93.0213, loss: 0.2139 +2024-06-19 10:19:35,448 - mmseg - INFO - Iter [53750/80000] lr: 1.313e-05, eta: 15:32:59, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1586, decode.acc_seg: 92.9690, aux.loss_ce: 0.0669, aux.acc_seg: 92.6345, loss: 0.2255 +2024-06-19 10:21:14,553 - mmseg - INFO - Iter [53800/80000] lr: 1.310e-05, eta: 15:31:08, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1669, decode.acc_seg: 92.9129, aux.loss_ce: 0.0702, aux.acc_seg: 92.5115, loss: 0.2370 +2024-06-19 10:22:53,511 - mmseg - INFO - Iter [53850/80000] lr: 1.308e-05, eta: 15:29:18, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1535, decode.acc_seg: 93.4249, aux.loss_ce: 0.0652, aux.acc_seg: 93.0584, loss: 0.2187 +2024-06-19 10:24:32,494 - mmseg - INFO - Iter [53900/80000] lr: 1.305e-05, eta: 15:27:28, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1667, decode.acc_seg: 92.7849, aux.loss_ce: 0.0702, aux.acc_seg: 92.4738, loss: 0.2370 +2024-06-19 10:26:11,416 - mmseg - INFO - Iter [53950/80000] lr: 1.303e-05, eta: 15:25:37, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1621, decode.acc_seg: 93.1389, aux.loss_ce: 0.0679, aux.acc_seg: 92.8177, loss: 0.2300 +2024-06-19 10:27:50,355 - mmseg - INFO - Saving checkpoint at 54000 iterations +2024-06-19 10:29:14,069 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 10:29:14,070 - mmseg - INFO - Iter [54000/80000] lr: 1.300e-05, eta: 15:24:27, time: 3.653, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1540, decode.acc_seg: 93.3208, aux.loss_ce: 0.0651, aux.acc_seg: 92.9025, loss: 0.2191 +2024-06-19 10:31:04,813 - mmseg - INFO - per class results: +2024-06-19 10:31:04,820 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.9 | 90.84 | +| building | 85.29 | 93.32 | +| sky | 94.97 | 97.53 | +| floor | 84.88 | 92.16 | +| tree | 77.1 | 90.19 | +| ceiling | 87.42 | 94.38 | +| road | 86.39 | 91.82 | +| bed | 93.07 | 97.08 | +| windowpane | 67.43 | 80.23 | +| grass | 70.16 | 80.69 | +| cabinet | 68.2 | 75.74 | +| sidewalk | 70.1 | 84.43 | +| person | 86.76 | 94.09 | +| earth | 39.02 | 51.41 | +| door | 60.09 | 75.4 | +| table | 70.66 | 82.78 | +| mountain | 63.52 | 73.2 | +| plant | 55.62 | 64.86 | +| curtain | 79.38 | 88.75 | +| chair | 68.36 | 81.2 | +| car | 87.95 | 94.66 | +| water | 61.47 | 76.39 | +| painting | 80.79 | 90.9 | +| sofa | 82.73 | 91.66 | +| shelf | 51.76 | 67.27 | +| house | 52.24 | 64.44 | +| sea | 69.17 | 84.63 | +| mirror | 79.17 | 93.41 | +| rug | 60.57 | 67.28 | +| field | 30.02 | 56.62 | +| armchair | 60.04 | 73.73 | +| seat | 65.64 | 89.83 | +| fence | 50.54 | 65.73 | +| desk | 58.88 | 83.44 | +| rock | 54.82 | 80.89 | +| wardrobe | 57.58 | 79.5 | +| lamp | 75.33 | 87.29 | +| bathtub | 87.33 | 90.09 | +| railing | 43.85 | 64.65 | +| cushion | 69.13 | 82.18 | +| base | 37.47 | 46.95 | +| box | 38.2 | 47.46 | +| column | 58.53 | 73.32 | +| signboard | 41.83 | 57.49 | +| chest of drawers | 49.05 | 67.3 | +| counter | 54.11 | 66.17 | +| sand | 54.0 | 78.06 | +| sink | 82.24 | 86.94 | +| skyscraper | 48.77 | 63.1 | +| fireplace | 72.28 | 89.54 | +| refrigerator | 84.15 | 91.15 | +| grandstand | 53.69 | 82.36 | +| path | 32.2 | 45.86 | +| stairs | 28.46 | 31.79 | +| runway | 72.76 | 93.7 | +| case | 60.5 | 77.68 | +| pool table | 95.05 | 98.69 | +| pillow | 67.03 | 78.02 | +| screen door | 77.09 | 80.13 | +| stairway | 43.19 | 71.05 | +| river | 12.9 | 25.62 | +| bridge | 71.97 | 81.11 | +| bookcase | 43.6 | 61.92 | +| blind | 47.83 | 57.72 | +| coffee table | 61.5 | 88.8 | +| toilet | 90.52 | 93.66 | +| flower | 45.25 | 67.26 | +| book | 56.93 | 79.03 | +| hill | 14.47 | 23.7 | +| bench | 60.04 | 66.93 | +| countertop | 65.05 | 82.44 | +| stove | 86.66 | 93.55 | +| palm | 52.75 | 76.83 | +| kitchen island | 52.63 | 77.17 | +| computer | 76.62 | 92.31 | +| swivel chair | 51.25 | 81.55 | +| boat | 71.48 | 90.74 | +| bar | 73.06 | 84.19 | +| arcade machine | 85.68 | 90.04 | +| hovel | 48.19 | 55.05 | +| bus | 93.49 | 97.28 | +| towel | 80.98 | 89.9 | +| light | 62.9 | 75.81 | +| truck | 50.93 | 63.83 | +| tower | 20.76 | 34.96 | +| chandelier | 72.97 | 81.88 | +| awning | 39.6 | 51.93 | +| streetlight | 38.28 | 52.75 | +| booth | 58.76 | 72.92 | +| television receiver | 82.58 | 86.65 | +| airplane | 86.88 | 97.37 | +| dirt track | 3.5 | 8.45 | +| apparel | 68.61 | 89.84 | +| pole | 28.41 | 36.68 | +| land | 4.91 | 8.73 | +| bannister | 21.08 | 28.23 | +| escalator | 65.8 | 88.41 | +| ottoman | 51.56 | 64.78 | +| bottle | 43.86 | 65.77 | +| buffet | 63.74 | 74.98 | +| poster | 34.37 | 40.84 | +| stage | 27.8 | 45.19 | +| van | 50.08 | 64.15 | +| ship | 78.96 | 88.1 | +| fountain | 29.9 | 30.18 | +| conveyer belt | 85.15 | 96.3 | +| canopy | 57.6 | 71.9 | +| washer | 86.88 | 92.01 | +| plaything | 32.81 | 47.77 | +| swimming pool | 57.6 | 84.89 | +| stool | 57.32 | 72.93 | +| barrel | 79.66 | 97.95 | +| basket | 44.17 | 62.62 | +| waterfall | 70.14 | 88.99 | +| tent | 94.23 | 98.9 | +| bag | 34.15 | 40.51 | +| minibike | 77.91 | 90.5 | +| cradle | 87.43 | 97.93 | +| oven | 66.69 | 76.92 | +| ball | 47.68 | 61.57 | +| food | 64.4 | 79.81 | +| step | 10.97 | 12.47 | +| tank | 63.59 | 70.53 | +| trade name | 23.34 | 27.17 | +| microwave | 89.28 | 96.16 | +| pot | 59.59 | 66.99 | +| animal | 60.91 | 62.84 | +| bicycle | 61.07 | 81.49 | +| lake | 50.92 | 63.74 | +| dishwasher | 76.51 | 85.69 | +| screen | 61.86 | 96.66 | +| blanket | 39.18 | 47.48 | +| sculpture | 73.43 | 87.32 | +| hood | 69.87 | 76.4 | +| sconce | 61.26 | 75.07 | +| vase | 50.19 | 64.73 | +| traffic light | 40.81 | 67.24 | +| tray | 25.94 | 34.17 | +| ashcan | 49.83 | 65.44 | +| fan | 72.28 | 86.25 | +| pier | 39.43 | 40.79 | +| crt screen | 9.02 | 16.03 | +| plate | 66.72 | 79.91 | +| monitor | 39.38 | 46.57 | +| bulletin board | 54.99 | 64.78 | +| shower | 18.18 | 19.94 | +| radiator | 68.68 | 83.14 | +| glass | 21.81 | 23.44 | +| clock | 55.74 | 65.77 | +| flag | 67.43 | 79.69 | ++---------------------+-------+-------+ +2024-06-19 10:31:04,820 - mmseg - INFO - Summary: +2024-06-19 10:31:04,820 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.49 | 59.17 | 71.67 | ++-------+-------+-------+ +2024-06-19 10:31:04,821 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 10:31:04,821 - mmseg - INFO - Iter(val) [250] aAcc: 0.8649, mIoU: 0.5917, mAcc: 0.7167, IoU.wall: 0.8290, IoU.building: 0.8529, IoU.sky: 0.9497, IoU.floor: 0.8488, IoU.tree: 0.7710, IoU.ceiling: 0.8742, IoU.road: 0.8639, IoU.bed : 0.9307, IoU.windowpane: 0.6743, IoU.grass: 0.7016, IoU.cabinet: 0.6820, IoU.sidewalk: 0.7010, IoU.person: 0.8676, IoU.earth: 0.3902, IoU.door: 0.6009, IoU.table: 0.7066, IoU.mountain: 0.6352, IoU.plant: 0.5562, IoU.curtain: 0.7938, IoU.chair: 0.6836, IoU.car: 0.8795, IoU.water: 0.6147, IoU.painting: 0.8079, IoU.sofa: 0.8273, IoU.shelf: 0.5176, IoU.house: 0.5224, IoU.sea: 0.6917, IoU.mirror: 0.7917, IoU.rug: 0.6057, IoU.field: 0.3002, IoU.armchair: 0.6004, IoU.seat: 0.6564, IoU.fence: 0.5054, IoU.desk: 0.5888, IoU.rock: 0.5482, IoU.wardrobe: 0.5758, IoU.lamp: 0.7533, IoU.bathtub: 0.8733, IoU.railing: 0.4385, IoU.cushion: 0.6913, IoU.base: 0.3747, IoU.box: 0.3820, IoU.column: 0.5853, IoU.signboard: 0.4183, IoU.chest of drawers: 0.4905, IoU.counter: 0.5411, IoU.sand: 0.5400, IoU.sink: 0.8224, IoU.skyscraper: 0.4877, IoU.fireplace: 0.7228, IoU.refrigerator: 0.8415, IoU.grandstand: 0.5369, IoU.path: 0.3220, IoU.stairs: 0.2846, IoU.runway: 0.7276, IoU.case: 0.6050, IoU.pool table: 0.9505, IoU.pillow: 0.6703, IoU.screen door: 0.7709, IoU.stairway: 0.4319, IoU.river: 0.1290, IoU.bridge: 0.7197, IoU.bookcase: 0.4360, IoU.blind: 0.4783, IoU.coffee table: 0.6150, IoU.toilet: 0.9052, IoU.flower: 0.4525, IoU.book: 0.5693, IoU.hill: 0.1447, IoU.bench: 0.6004, IoU.countertop: 0.6505, IoU.stove: 0.8666, IoU.palm: 0.5275, IoU.kitchen island: 0.5263, IoU.computer: 0.7662, IoU.swivel chair: 0.5125, IoU.boat: 0.7148, IoU.bar: 0.7306, IoU.arcade machine: 0.8568, IoU.hovel: 0.4819, IoU.bus: 0.9349, IoU.towel: 0.8098, IoU.light: 0.6290, IoU.truck: 0.5093, IoU.tower: 0.2076, IoU.chandelier: 0.7297, IoU.awning: 0.3960, IoU.streetlight: 0.3828, IoU.booth: 0.5876, IoU.television receiver: 0.8258, IoU.airplane: 0.8688, IoU.dirt track: 0.0350, IoU.apparel: 0.6861, IoU.pole: 0.2841, IoU.land: 0.0491, IoU.bannister: 0.2108, IoU.escalator: 0.6580, IoU.ottoman: 0.5156, IoU.bottle: 0.4386, IoU.buffet: 0.6374, IoU.poster: 0.3437, IoU.stage: 0.2780, IoU.van: 0.5008, IoU.ship: 0.7896, IoU.fountain: 0.2990, IoU.conveyer belt: 0.8515, IoU.canopy: 0.5760, IoU.washer: 0.8688, IoU.plaything: 0.3281, IoU.swimming pool: 0.5760, IoU.stool: 0.5732, IoU.barrel: 0.7966, IoU.basket: 0.4417, IoU.waterfall: 0.7014, IoU.tent: 0.9423, IoU.bag: 0.3415, IoU.minibike: 0.7791, IoU.cradle: 0.8743, IoU.oven: 0.6669, IoU.ball: 0.4768, IoU.food: 0.6440, IoU.step: 0.1097, IoU.tank: 0.6359, IoU.trade name: 0.2334, IoU.microwave: 0.8928, IoU.pot: 0.5959, IoU.animal: 0.6091, IoU.bicycle: 0.6107, IoU.lake: 0.5092, IoU.dishwasher: 0.7651, IoU.screen: 0.6186, IoU.blanket: 0.3918, IoU.sculpture: 0.7343, IoU.hood: 0.6987, IoU.sconce: 0.6126, IoU.vase: 0.5019, IoU.traffic light: 0.4081, IoU.tray: 0.2594, IoU.ashcan: 0.4983, IoU.fan: 0.7228, IoU.pier: 0.3943, IoU.crt screen: 0.0902, IoU.plate: 0.6672, IoU.monitor: 0.3938, IoU.bulletin board: 0.5499, IoU.shower: 0.1818, IoU.radiator: 0.6868, IoU.glass: 0.2181, IoU.clock: 0.5574, IoU.flag: 0.6743, Acc.wall: 0.9084, Acc.building: 0.9332, Acc.sky: 0.9753, Acc.floor: 0.9216, Acc.tree: 0.9019, Acc.ceiling: 0.9438, Acc.road: 0.9182, Acc.bed : 0.9708, Acc.windowpane: 0.8023, Acc.grass: 0.8069, Acc.cabinet: 0.7574, Acc.sidewalk: 0.8443, Acc.person: 0.9409, Acc.earth: 0.5141, Acc.door: 0.7540, Acc.table: 0.8278, Acc.mountain: 0.7320, Acc.plant: 0.6486, Acc.curtain: 0.8875, Acc.chair: 0.8120, Acc.car: 0.9466, Acc.water: 0.7639, Acc.painting: 0.9090, Acc.sofa: 0.9166, Acc.shelf: 0.6727, Acc.house: 0.6444, Acc.sea: 0.8463, Acc.mirror: 0.9341, Acc.rug: 0.6728, Acc.field: 0.5662, Acc.armchair: 0.7373, Acc.seat: 0.8983, Acc.fence: 0.6573, Acc.desk: 0.8344, Acc.rock: 0.8089, Acc.wardrobe: 0.7950, Acc.lamp: 0.8729, Acc.bathtub: 0.9009, Acc.railing: 0.6465, Acc.cushion: 0.8218, Acc.base: 0.4695, Acc.box: 0.4746, Acc.column: 0.7332, Acc.signboard: 0.5749, Acc.chest of drawers: 0.6730, Acc.counter: 0.6617, Acc.sand: 0.7806, Acc.sink: 0.8694, Acc.skyscraper: 0.6310, Acc.fireplace: 0.8954, Acc.refrigerator: 0.9115, Acc.grandstand: 0.8236, Acc.path: 0.4586, Acc.stairs: 0.3179, Acc.runway: 0.9370, Acc.case: 0.7768, Acc.pool table: 0.9869, Acc.pillow: 0.7802, Acc.screen door: 0.8013, Acc.stairway: 0.7105, Acc.river: 0.2562, Acc.bridge: 0.8111, Acc.bookcase: 0.6192, Acc.blind: 0.5772, Acc.coffee table: 0.8880, Acc.toilet: 0.9366, Acc.flower: 0.6726, Acc.book: 0.7903, Acc.hill: 0.2370, Acc.bench: 0.6693, Acc.countertop: 0.8244, Acc.stove: 0.9355, Acc.palm: 0.7683, Acc.kitchen island: 0.7717, Acc.computer: 0.9231, Acc.swivel chair: 0.8155, Acc.boat: 0.9074, Acc.bar: 0.8419, Acc.arcade machine: 0.9004, Acc.hovel: 0.5505, Acc.bus: 0.9728, Acc.towel: 0.8990, Acc.light: 0.7581, Acc.truck: 0.6383, Acc.tower: 0.3496, Acc.chandelier: 0.8188, Acc.awning: 0.5193, Acc.streetlight: 0.5275, Acc.booth: 0.7292, Acc.television receiver: 0.8665, Acc.airplane: 0.9737, Acc.dirt track: 0.0845, Acc.apparel: 0.8984, Acc.pole: 0.3668, Acc.land: 0.0873, Acc.bannister: 0.2823, Acc.escalator: 0.8841, Acc.ottoman: 0.6478, Acc.bottle: 0.6577, Acc.buffet: 0.7498, Acc.poster: 0.4084, Acc.stage: 0.4519, Acc.van: 0.6415, Acc.ship: 0.8810, Acc.fountain: 0.3018, Acc.conveyer belt: 0.9630, Acc.canopy: 0.7190, Acc.washer: 0.9201, Acc.plaything: 0.4777, Acc.swimming pool: 0.8489, Acc.stool: 0.7293, Acc.barrel: 0.9795, Acc.basket: 0.6262, Acc.waterfall: 0.8899, Acc.tent: 0.9890, Acc.bag: 0.4051, Acc.minibike: 0.9050, Acc.cradle: 0.9793, Acc.oven: 0.7692, Acc.ball: 0.6157, Acc.food: 0.7981, Acc.step: 0.1247, Acc.tank: 0.7053, Acc.trade name: 0.2717, Acc.microwave: 0.9616, Acc.pot: 0.6699, Acc.animal: 0.6284, Acc.bicycle: 0.8149, Acc.lake: 0.6374, Acc.dishwasher: 0.8569, Acc.screen: 0.9666, Acc.blanket: 0.4748, Acc.sculpture: 0.8732, Acc.hood: 0.7640, Acc.sconce: 0.7507, Acc.vase: 0.6473, Acc.traffic light: 0.6724, Acc.tray: 0.3417, Acc.ashcan: 0.6544, Acc.fan: 0.8625, Acc.pier: 0.4079, Acc.crt screen: 0.1603, Acc.plate: 0.7991, Acc.monitor: 0.4657, Acc.bulletin board: 0.6478, Acc.shower: 0.1994, Acc.radiator: 0.8314, Acc.glass: 0.2344, Acc.clock: 0.6577, Acc.flag: 0.7969 +2024-06-19 10:32:44,235 - mmseg - INFO - Iter [54050/80000] lr: 1.298e-05, eta: 15:23:31, time: 4.203, data_time: 2.232, memory: 72263, decode.loss_ce: 0.1519, decode.acc_seg: 93.2325, aux.loss_ce: 0.0640, aux.acc_seg: 92.9497, loss: 0.2159 +2024-06-19 10:34:23,100 - mmseg - INFO - Iter [54100/80000] lr: 1.295e-05, eta: 15:21:40, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1524, decode.acc_seg: 93.3333, aux.loss_ce: 0.0646, aux.acc_seg: 92.9665, loss: 0.2170 +2024-06-19 10:36:02,110 - mmseg - INFO - Iter [54150/80000] lr: 1.293e-05, eta: 15:19:49, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1543, decode.acc_seg: 93.2434, aux.loss_ce: 0.0650, aux.acc_seg: 92.8653, loss: 0.2193 +2024-06-19 10:37:40,979 - mmseg - INFO - Iter [54200/80000] lr: 1.290e-05, eta: 15:17:59, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1552, decode.acc_seg: 93.1982, aux.loss_ce: 0.0655, aux.acc_seg: 92.8484, loss: 0.2207 +2024-06-19 10:39:19,889 - mmseg - INFO - Iter [54250/80000] lr: 1.288e-05, eta: 15:16:09, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1543, decode.acc_seg: 93.2127, aux.loss_ce: 0.0653, aux.acc_seg: 92.8095, loss: 0.2195 +2024-06-19 10:40:58,936 - mmseg - INFO - Iter [54300/80000] lr: 1.285e-05, eta: 15:14:18, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1551, decode.acc_seg: 93.2233, aux.loss_ce: 0.0659, aux.acc_seg: 92.8229, loss: 0.2211 +2024-06-19 10:42:39,903 - mmseg - INFO - Iter [54350/80000] lr: 1.283e-05, eta: 15:12:29, time: 2.019, data_time: 0.053, memory: 72263, decode.loss_ce: 0.1564, decode.acc_seg: 93.3124, aux.loss_ce: 0.0664, aux.acc_seg: 92.9010, loss: 0.2227 +2024-06-19 10:44:18,878 - mmseg - INFO - Iter [54400/80000] lr: 1.280e-05, eta: 15:10:38, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1619, decode.acc_seg: 93.0606, aux.loss_ce: 0.0682, aux.acc_seg: 92.7531, loss: 0.2302 +2024-06-19 10:45:57,761 - mmseg - INFO - Iter [54450/80000] lr: 1.278e-05, eta: 15:08:48, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1520, decode.acc_seg: 93.3741, aux.loss_ce: 0.0635, aux.acc_seg: 93.0098, loss: 0.2155 +2024-06-19 10:47:36,741 - mmseg - INFO - Iter [54500/80000] lr: 1.275e-05, eta: 15:06:58, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1531, decode.acc_seg: 93.3220, aux.loss_ce: 0.0645, aux.acc_seg: 92.8978, loss: 0.2177 +2024-06-19 10:49:15,601 - mmseg - INFO - Iter [54550/80000] lr: 1.273e-05, eta: 15:05:07, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1510, decode.acc_seg: 93.3876, aux.loss_ce: 0.0637, aux.acc_seg: 92.9558, loss: 0.2147 +2024-06-19 10:50:54,425 - mmseg - INFO - Iter [54600/80000] lr: 1.270e-05, eta: 15:03:17, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1493, decode.acc_seg: 93.3172, aux.loss_ce: 0.0627, aux.acc_seg: 93.0734, loss: 0.2120 +2024-06-19 10:52:33,448 - mmseg - INFO - Iter [54650/80000] lr: 1.268e-05, eta: 15:01:27, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1549, decode.acc_seg: 93.3974, aux.loss_ce: 0.0654, aux.acc_seg: 92.9957, loss: 0.2202 +2024-06-19 10:54:12,367 - mmseg - INFO - Iter [54700/80000] lr: 1.265e-05, eta: 14:59:36, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1522, decode.acc_seg: 93.1708, aux.loss_ce: 0.0649, aux.acc_seg: 92.7911, loss: 0.2171 +2024-06-19 10:55:51,347 - mmseg - INFO - Iter [54750/80000] lr: 1.263e-05, eta: 14:57:46, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1519, decode.acc_seg: 93.3854, aux.loss_ce: 0.0639, aux.acc_seg: 93.0818, loss: 0.2158 +2024-06-19 10:57:30,197 - mmseg - INFO - Iter [54800/80000] lr: 1.260e-05, eta: 14:55:56, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1616, decode.acc_seg: 92.9702, aux.loss_ce: 0.0684, aux.acc_seg: 92.5480, loss: 0.2299 +2024-06-19 10:59:09,098 - mmseg - INFO - Iter [54850/80000] lr: 1.258e-05, eta: 14:54:06, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1699, decode.acc_seg: 92.6371, aux.loss_ce: 0.0717, aux.acc_seg: 92.2202, loss: 0.2416 +2024-06-19 11:00:48,098 - mmseg - INFO - Iter [54900/80000] lr: 1.255e-05, eta: 14:52:16, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1537, decode.acc_seg: 93.3109, aux.loss_ce: 0.0650, aux.acc_seg: 92.9609, loss: 0.2187 +2024-06-19 11:02:26,965 - mmseg - INFO - Iter [54950/80000] lr: 1.253e-05, eta: 14:50:25, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1520, decode.acc_seg: 93.3277, aux.loss_ce: 0.0643, aux.acc_seg: 92.9809, loss: 0.2163 +2024-06-19 11:04:05,937 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 11:04:05,937 - mmseg - INFO - Iter [55000/80000] lr: 1.250e-05, eta: 14:48:35, time: 1.979, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1451, decode.acc_seg: 93.7295, aux.loss_ce: 0.0614, aux.acc_seg: 93.3926, loss: 0.2065 +2024-06-19 11:06:01,546 - mmseg - INFO - per class results: +2024-06-19 11:06:01,552 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.73 | 90.35 | +| building | 85.0 | 92.56 | +| sky | 95.09 | 97.81 | +| floor | 85.03 | 91.52 | +| tree | 77.8 | 88.61 | +| ceiling | 87.54 | 94.82 | +| road | 85.33 | 91.96 | +| bed | 92.83 | 96.98 | +| windowpane | 67.77 | 79.97 | +| grass | 67.26 | 80.3 | +| cabinet | 68.35 | 77.52 | +| sidewalk | 69.84 | 82.56 | +| person | 86.51 | 94.89 | +| earth | 39.9 | 52.93 | +| door | 59.98 | 77.2 | +| table | 71.4 | 81.9 | +| mountain | 62.45 | 74.06 | +| plant | 57.34 | 68.57 | +| curtain | 77.78 | 88.66 | +| chair | 68.55 | 78.22 | +| car | 88.26 | 93.95 | +| water | 62.34 | 78.51 | +| painting | 82.17 | 91.37 | +| sofa | 82.46 | 90.28 | +| shelf | 53.15 | 67.52 | +| house | 50.09 | 67.47 | +| sea | 67.23 | 81.82 | +| mirror | 79.17 | 87.12 | +| rug | 64.35 | 76.66 | +| field | 29.29 | 57.35 | +| armchair | 62.5 | 84.24 | +| seat | 62.56 | 90.71 | +| fence | 53.04 | 67.97 | +| desk | 60.3 | 77.89 | +| rock | 57.75 | 79.87 | +| wardrobe | 53.25 | 68.67 | +| lamp | 73.81 | 88.24 | +| bathtub | 87.64 | 91.67 | +| railing | 44.18 | 60.47 | +| cushion | 68.81 | 82.47 | +| base | 44.43 | 58.7 | +| box | 42.22 | 53.16 | +| column | 55.8 | 72.35 | +| signboard | 40.34 | 55.78 | +| chest of drawers | 43.01 | 69.69 | +| counter | 51.84 | 67.39 | +| sand | 56.51 | 76.36 | +| sink | 83.3 | 87.57 | +| skyscraper | 49.34 | 67.56 | +| fireplace | 72.92 | 93.19 | +| refrigerator | 87.12 | 92.56 | +| grandstand | 57.14 | 81.63 | +| path | 29.12 | 43.87 | +| stairs | 27.38 | 31.69 | +| runway | 72.31 | 93.16 | +| case | 59.3 | 75.96 | +| pool table | 95.29 | 98.31 | +| pillow | 64.13 | 73.16 | +| screen door | 83.75 | 86.04 | +| stairway | 41.95 | 72.39 | +| river | 11.18 | 19.82 | +| bridge | 63.69 | 84.76 | +| bookcase | 47.75 | 57.57 | +| blind | 51.89 | 66.2 | +| coffee table | 61.22 | 88.33 | +| toilet | 90.71 | 93.99 | +| flower | 43.05 | 58.59 | +| book | 55.74 | 76.81 | +| hill | 12.99 | 24.03 | +| bench | 59.37 | 66.0 | +| countertop | 66.03 | 85.63 | +| stove | 86.58 | 92.73 | +| palm | 51.08 | 84.67 | +| kitchen island | 53.39 | 79.09 | +| computer | 77.17 | 92.07 | +| swivel chair | 53.62 | 77.54 | +| boat | 80.43 | 91.1 | +| bar | 72.01 | 82.17 | +| arcade machine | 78.37 | 80.84 | +| hovel | 47.18 | 53.6 | +| bus | 93.52 | 97.04 | +| towel | 79.88 | 86.85 | +| light | 62.76 | 71.45 | +| truck | 51.59 | 64.49 | +| tower | 30.18 | 65.88 | +| chandelier | 73.69 | 85.68 | +| awning | 38.13 | 46.39 | +| streetlight | 36.61 | 48.76 | +| booth | 56.99 | 73.3 | +| television receiver | 78.86 | 89.87 | +| airplane | 87.94 | 96.19 | +| dirt track | 3.78 | 9.44 | +| apparel | 68.25 | 87.46 | +| pole | 28.81 | 38.51 | +| land | 5.7 | 8.53 | +| bannister | 21.31 | 26.73 | +| escalator | 66.17 | 87.33 | +| ottoman | 55.16 | 69.72 | +| bottle | 43.83 | 61.56 | +| buffet | 58.44 | 67.04 | +| poster | 35.83 | 41.95 | +| stage | 20.73 | 34.65 | +| van | 53.28 | 69.15 | +| ship | 83.08 | 90.11 | +| fountain | 31.63 | 32.15 | +| conveyer belt | 84.8 | 96.39 | +| canopy | 55.81 | 73.55 | +| washer | 86.41 | 92.17 | +| plaything | 34.72 | 48.34 | +| swimming pool | 53.53 | 77.12 | +| stool | 49.09 | 78.3 | +| barrel | 68.86 | 97.55 | +| basket | 44.17 | 60.84 | +| waterfall | 66.42 | 88.21 | +| tent | 93.44 | 98.64 | +| bag | 28.77 | 33.89 | +| minibike | 76.53 | 90.56 | +| cradle | 86.62 | 97.19 | +| oven | 59.56 | 72.6 | +| ball | 62.29 | 74.67 | +| food | 62.37 | 72.61 | +| step | 12.61 | 15.29 | +| tank | 62.97 | 69.93 | +| trade name | 19.13 | 22.15 | +| microwave | 88.19 | 96.96 | +| pot | 60.55 | 71.23 | +| animal | 61.12 | 62.67 | +| bicycle | 63.18 | 81.02 | +| lake | 51.16 | 63.79 | +| dishwasher | 78.9 | 84.96 | +| screen | 63.03 | 93.96 | +| blanket | 33.45 | 39.22 | +| sculpture | 74.8 | 85.81 | +| hood | 69.05 | 77.0 | +| sconce | 59.0 | 66.68 | +| vase | 50.97 | 69.05 | +| traffic light | 42.39 | 66.56 | +| tray | 29.82 | 40.65 | +| ashcan | 52.3 | 69.42 | +| fan | 72.96 | 84.33 | +| pier | 39.07 | 46.38 | +| crt screen | 8.2 | 16.61 | +| plate | 66.16 | 78.71 | +| monitor | 32.62 | 36.44 | +| bulletin board | 49.31 | 59.2 | +| shower | 19.69 | 22.56 | +| radiator | 69.51 | 84.63 | +| glass | 23.23 | 25.68 | +| clock | 54.74 | 63.75 | +| flag | 70.72 | 78.82 | ++---------------------+-------+-------+ +2024-06-19 11:06:01,552 - mmseg - INFO - Summary: +2024-06-19 11:06:01,552 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.38 | 59.0 | 71.65 | ++-------+------+-------+ +2024-06-19 11:06:01,553 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 11:06:01,553 - mmseg - INFO - Iter(val) [250] aAcc: 0.8638, mIoU: 0.5900, mAcc: 0.7165, IoU.wall: 0.8273, IoU.building: 0.8500, IoU.sky: 0.9509, IoU.floor: 0.8503, IoU.tree: 0.7780, IoU.ceiling: 0.8754, IoU.road: 0.8533, IoU.bed : 0.9283, IoU.windowpane: 0.6777, IoU.grass: 0.6726, IoU.cabinet: 0.6835, IoU.sidewalk: 0.6984, IoU.person: 0.8651, IoU.earth: 0.3990, IoU.door: 0.5998, IoU.table: 0.7140, IoU.mountain: 0.6245, IoU.plant: 0.5734, IoU.curtain: 0.7778, IoU.chair: 0.6855, IoU.car: 0.8826, IoU.water: 0.6234, IoU.painting: 0.8217, IoU.sofa: 0.8246, IoU.shelf: 0.5315, IoU.house: 0.5009, IoU.sea: 0.6723, IoU.mirror: 0.7917, IoU.rug: 0.6435, IoU.field: 0.2929, IoU.armchair: 0.6250, IoU.seat: 0.6256, IoU.fence: 0.5304, IoU.desk: 0.6030, IoU.rock: 0.5775, IoU.wardrobe: 0.5325, IoU.lamp: 0.7381, IoU.bathtub: 0.8764, IoU.railing: 0.4418, IoU.cushion: 0.6881, IoU.base: 0.4443, IoU.box: 0.4222, IoU.column: 0.5580, IoU.signboard: 0.4034, IoU.chest of drawers: 0.4301, IoU.counter: 0.5184, IoU.sand: 0.5651, IoU.sink: 0.8330, IoU.skyscraper: 0.4934, IoU.fireplace: 0.7292, IoU.refrigerator: 0.8712, IoU.grandstand: 0.5714, IoU.path: 0.2912, IoU.stairs: 0.2738, IoU.runway: 0.7231, IoU.case: 0.5930, IoU.pool table: 0.9529, IoU.pillow: 0.6413, IoU.screen door: 0.8375, IoU.stairway: 0.4195, IoU.river: 0.1118, IoU.bridge: 0.6369, IoU.bookcase: 0.4775, IoU.blind: 0.5189, IoU.coffee table: 0.6122, IoU.toilet: 0.9071, IoU.flower: 0.4305, IoU.book: 0.5574, IoU.hill: 0.1299, IoU.bench: 0.5937, IoU.countertop: 0.6603, IoU.stove: 0.8658, IoU.palm: 0.5108, IoU.kitchen island: 0.5339, IoU.computer: 0.7717, IoU.swivel chair: 0.5362, IoU.boat: 0.8043, IoU.bar: 0.7201, IoU.arcade machine: 0.7837, IoU.hovel: 0.4718, IoU.bus: 0.9352, IoU.towel: 0.7988, IoU.light: 0.6276, IoU.truck: 0.5159, IoU.tower: 0.3018, IoU.chandelier: 0.7369, IoU.awning: 0.3813, IoU.streetlight: 0.3661, IoU.booth: 0.5699, IoU.television receiver: 0.7886, IoU.airplane: 0.8794, IoU.dirt track: 0.0378, IoU.apparel: 0.6825, IoU.pole: 0.2881, IoU.land: 0.0570, IoU.bannister: 0.2131, IoU.escalator: 0.6617, IoU.ottoman: 0.5516, IoU.bottle: 0.4383, IoU.buffet: 0.5844, IoU.poster: 0.3583, IoU.stage: 0.2073, IoU.van: 0.5328, IoU.ship: 0.8308, IoU.fountain: 0.3163, IoU.conveyer belt: 0.8480, IoU.canopy: 0.5581, IoU.washer: 0.8641, IoU.plaything: 0.3472, IoU.swimming pool: 0.5353, IoU.stool: 0.4909, IoU.barrel: 0.6886, IoU.basket: 0.4417, IoU.waterfall: 0.6642, IoU.tent: 0.9344, IoU.bag: 0.2877, IoU.minibike: 0.7653, IoU.cradle: 0.8662, IoU.oven: 0.5956, IoU.ball: 0.6229, IoU.food: 0.6237, IoU.step: 0.1261, IoU.tank: 0.6297, IoU.trade name: 0.1913, IoU.microwave: 0.8819, IoU.pot: 0.6055, IoU.animal: 0.6112, IoU.bicycle: 0.6318, IoU.lake: 0.5116, IoU.dishwasher: 0.7890, IoU.screen: 0.6303, IoU.blanket: 0.3345, IoU.sculpture: 0.7480, IoU.hood: 0.6905, IoU.sconce: 0.5900, IoU.vase: 0.5097, IoU.traffic light: 0.4239, IoU.tray: 0.2982, IoU.ashcan: 0.5230, IoU.fan: 0.7296, IoU.pier: 0.3907, IoU.crt screen: 0.0820, IoU.plate: 0.6616, IoU.monitor: 0.3262, IoU.bulletin board: 0.4931, IoU.shower: 0.1969, IoU.radiator: 0.6951, IoU.glass: 0.2323, IoU.clock: 0.5474, IoU.flag: 0.7072, Acc.wall: 0.9035, Acc.building: 0.9256, Acc.sky: 0.9781, Acc.floor: 0.9152, Acc.tree: 0.8861, Acc.ceiling: 0.9482, Acc.road: 0.9196, Acc.bed : 0.9698, Acc.windowpane: 0.7997, Acc.grass: 0.8030, Acc.cabinet: 0.7752, Acc.sidewalk: 0.8256, Acc.person: 0.9489, Acc.earth: 0.5293, Acc.door: 0.7720, Acc.table: 0.8190, Acc.mountain: 0.7406, Acc.plant: 0.6857, Acc.curtain: 0.8866, Acc.chair: 0.7822, Acc.car: 0.9395, Acc.water: 0.7851, Acc.painting: 0.9137, Acc.sofa: 0.9028, Acc.shelf: 0.6752, Acc.house: 0.6747, Acc.sea: 0.8182, Acc.mirror: 0.8712, Acc.rug: 0.7666, Acc.field: 0.5735, Acc.armchair: 0.8424, Acc.seat: 0.9071, Acc.fence: 0.6797, Acc.desk: 0.7789, Acc.rock: 0.7987, Acc.wardrobe: 0.6867, Acc.lamp: 0.8824, Acc.bathtub: 0.9167, Acc.railing: 0.6047, Acc.cushion: 0.8247, Acc.base: 0.5870, Acc.box: 0.5316, Acc.column: 0.7235, Acc.signboard: 0.5578, Acc.chest of drawers: 0.6969, Acc.counter: 0.6739, Acc.sand: 0.7636, Acc.sink: 0.8757, Acc.skyscraper: 0.6756, Acc.fireplace: 0.9319, Acc.refrigerator: 0.9256, Acc.grandstand: 0.8163, Acc.path: 0.4387, Acc.stairs: 0.3169, Acc.runway: 0.9316, Acc.case: 0.7596, Acc.pool table: 0.9831, Acc.pillow: 0.7316, Acc.screen door: 0.8604, Acc.stairway: 0.7239, Acc.river: 0.1982, Acc.bridge: 0.8476, Acc.bookcase: 0.5757, Acc.blind: 0.6620, Acc.coffee table: 0.8833, Acc.toilet: 0.9399, Acc.flower: 0.5859, Acc.book: 0.7681, Acc.hill: 0.2403, Acc.bench: 0.6600, Acc.countertop: 0.8563, Acc.stove: 0.9273, Acc.palm: 0.8467, Acc.kitchen island: 0.7909, Acc.computer: 0.9207, Acc.swivel chair: 0.7754, Acc.boat: 0.9110, Acc.bar: 0.8217, Acc.arcade machine: 0.8084, Acc.hovel: 0.5360, Acc.bus: 0.9704, Acc.towel: 0.8685, Acc.light: 0.7145, Acc.truck: 0.6449, Acc.tower: 0.6588, Acc.chandelier: 0.8568, Acc.awning: 0.4639, Acc.streetlight: 0.4876, Acc.booth: 0.7330, Acc.television receiver: 0.8987, Acc.airplane: 0.9619, Acc.dirt track: 0.0944, Acc.apparel: 0.8746, Acc.pole: 0.3851, Acc.land: 0.0853, Acc.bannister: 0.2673, Acc.escalator: 0.8733, Acc.ottoman: 0.6972, Acc.bottle: 0.6156, Acc.buffet: 0.6704, Acc.poster: 0.4195, Acc.stage: 0.3465, Acc.van: 0.6915, Acc.ship: 0.9011, Acc.fountain: 0.3215, Acc.conveyer belt: 0.9639, Acc.canopy: 0.7355, Acc.washer: 0.9217, Acc.plaything: 0.4834, Acc.swimming pool: 0.7712, Acc.stool: 0.7830, Acc.barrel: 0.9755, Acc.basket: 0.6084, Acc.waterfall: 0.8821, Acc.tent: 0.9864, Acc.bag: 0.3389, Acc.minibike: 0.9056, Acc.cradle: 0.9719, Acc.oven: 0.7260, Acc.ball: 0.7467, Acc.food: 0.7261, Acc.step: 0.1529, Acc.tank: 0.6993, Acc.trade name: 0.2215, Acc.microwave: 0.9696, Acc.pot: 0.7123, Acc.animal: 0.6267, Acc.bicycle: 0.8102, Acc.lake: 0.6379, Acc.dishwasher: 0.8496, Acc.screen: 0.9396, Acc.blanket: 0.3922, Acc.sculpture: 0.8581, Acc.hood: 0.7700, Acc.sconce: 0.6668, Acc.vase: 0.6905, Acc.traffic light: 0.6656, Acc.tray: 0.4065, Acc.ashcan: 0.6942, Acc.fan: 0.8433, Acc.pier: 0.4638, Acc.crt screen: 0.1661, Acc.plate: 0.7871, Acc.monitor: 0.3644, Acc.bulletin board: 0.5920, Acc.shower: 0.2256, Acc.radiator: 0.8463, Acc.glass: 0.2568, Acc.clock: 0.6375, Acc.flag: 0.7882 +2024-06-19 11:07:40,803 - mmseg - INFO - Iter [55050/80000] lr: 1.248e-05, eta: 14:47:38, time: 4.297, data_time: 2.331, memory: 72263, decode.loss_ce: 0.1423, decode.acc_seg: 93.8257, aux.loss_ce: 0.0610, aux.acc_seg: 93.3601, loss: 0.2032 +2024-06-19 11:09:19,644 - mmseg - INFO - Iter [55100/80000] lr: 1.245e-05, eta: 14:45:47, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1502, decode.acc_seg: 93.4561, aux.loss_ce: 0.0636, aux.acc_seg: 93.0789, loss: 0.2138 +2024-06-19 11:10:58,511 - mmseg - INFO - Iter [55150/80000] lr: 1.243e-05, eta: 14:43:57, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1456, decode.acc_seg: 93.5598, aux.loss_ce: 0.0623, aux.acc_seg: 93.1084, loss: 0.2080 +2024-06-19 11:12:37,362 - mmseg - INFO - Iter [55200/80000] lr: 1.240e-05, eta: 14:42:07, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1496, decode.acc_seg: 93.3945, aux.loss_ce: 0.0633, aux.acc_seg: 92.9809, loss: 0.2129 +2024-06-19 11:14:16,366 - mmseg - INFO - Iter [55250/80000] lr: 1.238e-05, eta: 14:40:17, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1447, decode.acc_seg: 93.6884, aux.loss_ce: 0.0617, aux.acc_seg: 93.2779, loss: 0.2064 +2024-06-19 11:15:55,327 - mmseg - INFO - Iter [55300/80000] lr: 1.235e-05, eta: 14:38:27, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1449, decode.acc_seg: 93.6029, aux.loss_ce: 0.0612, aux.acc_seg: 93.2350, loss: 0.2060 +2024-06-19 11:17:34,147 - mmseg - INFO - Iter [55350/80000] lr: 1.233e-05, eta: 14:36:36, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1513, decode.acc_seg: 93.3680, aux.loss_ce: 0.0645, aux.acc_seg: 92.9336, loss: 0.2158 +2024-06-19 11:19:13,064 - mmseg - INFO - Iter [55400/80000] lr: 1.230e-05, eta: 14:34:46, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1521, decode.acc_seg: 93.3507, aux.loss_ce: 0.0649, aux.acc_seg: 92.9458, loss: 0.2170 +2024-06-19 11:20:51,998 - mmseg - INFO - Iter [55450/80000] lr: 1.228e-05, eta: 14:32:56, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1504, decode.acc_seg: 93.4307, aux.loss_ce: 0.0642, aux.acc_seg: 92.9680, loss: 0.2146 +2024-06-19 11:22:31,004 - mmseg - INFO - Iter [55500/80000] lr: 1.225e-05, eta: 14:31:06, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1430, decode.acc_seg: 93.7136, aux.loss_ce: 0.0612, aux.acc_seg: 93.2809, loss: 0.2043 +2024-06-19 11:24:09,878 - mmseg - INFO - Iter [55550/80000] lr: 1.223e-05, eta: 14:29:16, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1555, decode.acc_seg: 93.2758, aux.loss_ce: 0.0652, aux.acc_seg: 92.9727, loss: 0.2207 +2024-06-19 11:25:52,235 - mmseg - INFO - Iter [55600/80000] lr: 1.220e-05, eta: 14:27:27, time: 2.047, data_time: 0.078, memory: 72263, decode.loss_ce: 0.1409, decode.acc_seg: 93.6530, aux.loss_ce: 0.0600, aux.acc_seg: 93.2768, loss: 0.2008 +2024-06-19 11:27:31,295 - mmseg - INFO - Iter [55650/80000] lr: 1.218e-05, eta: 14:25:37, time: 1.981, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1511, decode.acc_seg: 93.4178, aux.loss_ce: 0.0640, aux.acc_seg: 93.0126, loss: 0.2151 +2024-06-19 11:29:10,251 - mmseg - INFO - Iter [55700/80000] lr: 1.215e-05, eta: 14:23:47, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1531, decode.acc_seg: 93.4172, aux.loss_ce: 0.0646, aux.acc_seg: 93.0556, loss: 0.2177 +2024-06-19 11:30:49,291 - mmseg - INFO - Iter [55750/80000] lr: 1.213e-05, eta: 14:21:57, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1461, decode.acc_seg: 93.6553, aux.loss_ce: 0.0618, aux.acc_seg: 93.3513, loss: 0.2079 +2024-06-19 11:32:28,149 - mmseg - INFO - Iter [55800/80000] lr: 1.210e-05, eta: 14:20:07, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1467, decode.acc_seg: 93.4161, aux.loss_ce: 0.0624, aux.acc_seg: 93.0594, loss: 0.2091 +2024-06-19 11:34:07,162 - mmseg - INFO - Iter [55850/80000] lr: 1.208e-05, eta: 14:18:18, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1579, decode.acc_seg: 93.1932, aux.loss_ce: 0.0672, aux.acc_seg: 92.7564, loss: 0.2251 +2024-06-19 11:35:46,103 - mmseg - INFO - Iter [55900/80000] lr: 1.205e-05, eta: 14:16:28, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1467, decode.acc_seg: 93.2464, aux.loss_ce: 0.0622, aux.acc_seg: 92.8546, loss: 0.2089 +2024-06-19 11:37:24,924 - mmseg - INFO - Iter [55950/80000] lr: 1.203e-05, eta: 14:14:38, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1430, decode.acc_seg: 93.7388, aux.loss_ce: 0.0613, aux.acc_seg: 93.2364, loss: 0.2043 +2024-06-19 11:39:04,067 - mmseg - INFO - Saving checkpoint at 56000 iterations +2024-06-19 11:40:30,509 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 11:40:30,509 - mmseg - INFO - Iter [56000/80000] lr: 1.200e-05, eta: 14:13:25, time: 3.712, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1476, decode.acc_seg: 93.2793, aux.loss_ce: 0.0626, aux.acc_seg: 92.8871, loss: 0.2103 +2024-06-19 11:42:20,679 - mmseg - INFO - per class results: +2024-06-19 11:42:20,685 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.82 | 90.21 | +| building | 85.13 | 93.33 | +| sky | 95.0 | 97.66 | +| floor | 84.79 | 92.53 | +| tree | 78.01 | 89.24 | +| ceiling | 87.54 | 94.73 | +| road | 84.75 | 92.38 | +| bed | 93.1 | 96.92 | +| windowpane | 67.27 | 80.35 | +| grass | 69.39 | 82.35 | +| cabinet | 67.66 | 78.67 | +| sidewalk | 68.72 | 79.94 | +| person | 86.77 | 94.66 | +| earth | 38.33 | 49.73 | +| door | 59.01 | 76.34 | +| table | 71.1 | 82.25 | +| mountain | 62.51 | 72.98 | +| plant | 57.4 | 69.08 | +| curtain | 79.0 | 88.54 | +| chair | 69.3 | 78.22 | +| car | 87.99 | 94.73 | +| water | 61.32 | 75.17 | +| painting | 80.77 | 91.88 | +| sofa | 82.48 | 89.43 | +| shelf | 50.04 | 62.2 | +| house | 50.11 | 61.45 | +| sea | 69.02 | 88.14 | +| mirror | 80.89 | 88.73 | +| rug | 63.56 | 68.19 | +| field | 30.69 | 54.91 | +| armchair | 62.42 | 81.97 | +| seat | 65.19 | 88.78 | +| fence | 51.87 | 65.83 | +| desk | 61.68 | 79.02 | +| rock | 56.53 | 89.38 | +| wardrobe | 55.16 | 72.54 | +| lamp | 76.76 | 86.95 | +| bathtub | 85.29 | 88.43 | +| railing | 43.99 | 61.1 | +| cushion | 70.55 | 83.97 | +| base | 45.92 | 61.56 | +| box | 41.39 | 55.55 | +| column | 58.36 | 65.98 | +| signboard | 40.67 | 55.62 | +| chest of drawers | 42.93 | 64.29 | +| counter | 53.87 | 64.02 | +| sand | 52.87 | 77.34 | +| sink | 82.33 | 86.66 | +| skyscraper | 48.51 | 64.89 | +| fireplace | 72.07 | 90.36 | +| refrigerator | 85.79 | 94.9 | +| grandstand | 51.2 | 83.16 | +| path | 28.6 | 44.42 | +| stairs | 26.12 | 29.26 | +| runway | 72.27 | 94.16 | +| case | 62.87 | 82.74 | +| pool table | 95.36 | 98.29 | +| pillow | 69.05 | 81.61 | +| screen door | 75.54 | 77.51 | +| stairway | 42.65 | 74.04 | +| river | 13.19 | 23.53 | +| bridge | 65.76 | 88.07 | +| bookcase | 42.42 | 63.14 | +| blind | 46.45 | 55.79 | +| coffee table | 61.67 | 88.73 | +| toilet | 91.02 | 94.28 | +| flower | 42.61 | 63.38 | +| book | 56.58 | 75.0 | +| hill | 12.58 | 26.9 | +| bench | 59.21 | 66.88 | +| countertop | 67.0 | 83.73 | +| stove | 87.35 | 93.7 | +| palm | 53.56 | 82.6 | +| kitchen island | 54.94 | 85.12 | +| computer | 76.64 | 91.92 | +| swivel chair | 52.78 | 75.87 | +| boat | 70.86 | 92.58 | +| bar | 67.59 | 77.88 | +| arcade machine | 78.11 | 81.03 | +| hovel | 51.78 | 60.44 | +| bus | 93.33 | 97.08 | +| towel | 79.65 | 86.6 | +| light | 63.19 | 73.23 | +| truck | 50.36 | 62.96 | +| tower | 29.44 | 54.15 | +| chandelier | 72.64 | 86.6 | +| awning | 38.63 | 47.92 | +| streetlight | 37.07 | 53.82 | +| booth | 59.91 | 68.6 | +| television receiver | 79.95 | 88.07 | +| airplane | 88.41 | 96.38 | +| dirt track | 9.81 | 17.55 | +| apparel | 68.61 | 88.41 | +| pole | 30.03 | 42.28 | +| land | 5.88 | 9.26 | +| bannister | 21.06 | 25.57 | +| escalator | 64.38 | 88.99 | +| ottoman | 61.73 | 79.07 | +| bottle | 45.01 | 71.07 | +| buffet | 63.2 | 73.55 | +| poster | 32.55 | 40.91 | +| stage | 21.97 | 40.03 | +| van | 50.46 | 64.49 | +| ship | 83.1 | 85.03 | +| fountain | 36.53 | 38.52 | +| conveyer belt | 83.79 | 96.99 | +| canopy | 54.36 | 69.12 | +| washer | 85.82 | 90.78 | +| plaything | 32.43 | 45.14 | +| swimming pool | 51.98 | 74.46 | +| stool | 57.24 | 74.82 | +| barrel | 80.68 | 97.76 | +| basket | 42.52 | 63.19 | +| waterfall | 65.05 | 72.79 | +| tent | 93.91 | 98.68 | +| bag | 30.11 | 34.65 | +| minibike | 77.19 | 89.19 | +| cradle | 88.04 | 97.73 | +| oven | 68.14 | 78.98 | +| ball | 62.25 | 76.51 | +| food | 63.31 | 73.91 | +| step | 11.65 | 14.06 | +| tank | 63.59 | 69.15 | +| trade name | 24.4 | 29.25 | +| microwave | 89.29 | 96.82 | +| pot | 61.08 | 70.96 | +| animal | 60.72 | 62.54 | +| bicycle | 61.82 | 79.5 | +| lake | 55.62 | 63.46 | +| dishwasher | 77.59 | 81.19 | +| screen | 60.7 | 90.25 | +| blanket | 34.59 | 40.82 | +| sculpture | 72.34 | 83.7 | +| hood | 71.87 | 79.43 | +| sconce | 62.09 | 74.4 | +| vase | 50.69 | 68.17 | +| traffic light | 42.37 | 66.09 | +| tray | 26.99 | 35.6 | +| ashcan | 50.45 | 68.54 | +| fan | 73.3 | 84.5 | +| pier | 41.09 | 45.15 | +| crt screen | 7.32 | 19.01 | +| plate | 64.73 | 80.44 | +| monitor | 22.34 | 24.88 | +| bulletin board | 53.84 | 67.74 | +| shower | 21.33 | 24.43 | +| radiator | 69.09 | 84.36 | +| glass | 23.77 | 26.1 | +| clock | 54.13 | 64.99 | +| flag | 72.44 | 79.5 | ++---------------------+-------+-------+ +2024-06-19 11:42:20,685 - mmseg - INFO - Summary: +2024-06-19 11:42:20,686 - mmseg - INFO - ++------+------+-------+ +| aAcc | mIoU | mAcc | ++------+------+-------+ +| 86.4 | 59.2 | 71.73 | ++------+------+-------+ +2024-06-19 11:42:20,686 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 11:42:20,687 - mmseg - INFO - Iter(val) [250] aAcc: 0.8640, mIoU: 0.5920, mAcc: 0.7173, IoU.wall: 0.8282, IoU.building: 0.8513, IoU.sky: 0.9500, IoU.floor: 0.8479, IoU.tree: 0.7801, IoU.ceiling: 0.8754, IoU.road: 0.8475, IoU.bed : 0.9310, IoU.windowpane: 0.6727, IoU.grass: 0.6939, IoU.cabinet: 0.6766, IoU.sidewalk: 0.6872, IoU.person: 0.8677, IoU.earth: 0.3833, IoU.door: 0.5901, IoU.table: 0.7110, IoU.mountain: 0.6251, IoU.plant: 0.5740, IoU.curtain: 0.7900, IoU.chair: 0.6930, IoU.car: 0.8799, IoU.water: 0.6132, IoU.painting: 0.8077, IoU.sofa: 0.8248, IoU.shelf: 0.5004, IoU.house: 0.5011, IoU.sea: 0.6902, IoU.mirror: 0.8089, IoU.rug: 0.6356, IoU.field: 0.3069, IoU.armchair: 0.6242, IoU.seat: 0.6519, IoU.fence: 0.5187, IoU.desk: 0.6168, IoU.rock: 0.5653, IoU.wardrobe: 0.5516, IoU.lamp: 0.7676, IoU.bathtub: 0.8529, IoU.railing: 0.4399, IoU.cushion: 0.7055, IoU.base: 0.4592, IoU.box: 0.4139, IoU.column: 0.5836, IoU.signboard: 0.4067, IoU.chest of drawers: 0.4293, IoU.counter: 0.5387, IoU.sand: 0.5287, IoU.sink: 0.8233, IoU.skyscraper: 0.4851, IoU.fireplace: 0.7207, IoU.refrigerator: 0.8579, IoU.grandstand: 0.5120, IoU.path: 0.2860, IoU.stairs: 0.2612, IoU.runway: 0.7227, IoU.case: 0.6287, IoU.pool table: 0.9536, IoU.pillow: 0.6905, IoU.screen door: 0.7554, IoU.stairway: 0.4265, IoU.river: 0.1319, IoU.bridge: 0.6576, IoU.bookcase: 0.4242, IoU.blind: 0.4645, IoU.coffee table: 0.6167, IoU.toilet: 0.9102, IoU.flower: 0.4261, IoU.book: 0.5658, IoU.hill: 0.1258, IoU.bench: 0.5921, IoU.countertop: 0.6700, IoU.stove: 0.8735, IoU.palm: 0.5356, IoU.kitchen island: 0.5494, IoU.computer: 0.7664, IoU.swivel chair: 0.5278, IoU.boat: 0.7086, IoU.bar: 0.6759, IoU.arcade machine: 0.7811, IoU.hovel: 0.5178, IoU.bus: 0.9333, IoU.towel: 0.7965, IoU.light: 0.6319, IoU.truck: 0.5036, IoU.tower: 0.2944, IoU.chandelier: 0.7264, IoU.awning: 0.3863, IoU.streetlight: 0.3707, IoU.booth: 0.5991, IoU.television receiver: 0.7995, IoU.airplane: 0.8841, IoU.dirt track: 0.0981, IoU.apparel: 0.6861, IoU.pole: 0.3003, IoU.land: 0.0588, IoU.bannister: 0.2106, IoU.escalator: 0.6438, IoU.ottoman: 0.6173, IoU.bottle: 0.4501, IoU.buffet: 0.6320, IoU.poster: 0.3255, IoU.stage: 0.2197, IoU.van: 0.5046, IoU.ship: 0.8310, IoU.fountain: 0.3653, IoU.conveyer belt: 0.8379, IoU.canopy: 0.5436, IoU.washer: 0.8582, IoU.plaything: 0.3243, IoU.swimming pool: 0.5198, IoU.stool: 0.5724, IoU.barrel: 0.8068, IoU.basket: 0.4252, IoU.waterfall: 0.6505, IoU.tent: 0.9391, IoU.bag: 0.3011, IoU.minibike: 0.7719, IoU.cradle: 0.8804, IoU.oven: 0.6814, IoU.ball: 0.6225, IoU.food: 0.6331, IoU.step: 0.1165, IoU.tank: 0.6359, IoU.trade name: 0.2440, IoU.microwave: 0.8929, IoU.pot: 0.6108, IoU.animal: 0.6072, IoU.bicycle: 0.6182, IoU.lake: 0.5562, IoU.dishwasher: 0.7759, IoU.screen: 0.6070, IoU.blanket: 0.3459, IoU.sculpture: 0.7234, IoU.hood: 0.7187, IoU.sconce: 0.6209, IoU.vase: 0.5069, IoU.traffic light: 0.4237, IoU.tray: 0.2699, IoU.ashcan: 0.5045, IoU.fan: 0.7330, IoU.pier: 0.4109, IoU.crt screen: 0.0732, IoU.plate: 0.6473, IoU.monitor: 0.2234, IoU.bulletin board: 0.5384, IoU.shower: 0.2133, IoU.radiator: 0.6909, IoU.glass: 0.2377, IoU.clock: 0.5413, IoU.flag: 0.7244, Acc.wall: 0.9021, Acc.building: 0.9333, Acc.sky: 0.9766, Acc.floor: 0.9253, Acc.tree: 0.8924, Acc.ceiling: 0.9473, Acc.road: 0.9238, Acc.bed : 0.9692, Acc.windowpane: 0.8035, Acc.grass: 0.8235, Acc.cabinet: 0.7867, Acc.sidewalk: 0.7994, Acc.person: 0.9466, Acc.earth: 0.4973, Acc.door: 0.7634, Acc.table: 0.8225, Acc.mountain: 0.7298, Acc.plant: 0.6908, Acc.curtain: 0.8854, Acc.chair: 0.7822, Acc.car: 0.9473, Acc.water: 0.7517, Acc.painting: 0.9188, Acc.sofa: 0.8943, Acc.shelf: 0.6220, Acc.house: 0.6145, Acc.sea: 0.8814, Acc.mirror: 0.8873, Acc.rug: 0.6819, Acc.field: 0.5491, Acc.armchair: 0.8197, Acc.seat: 0.8878, Acc.fence: 0.6583, Acc.desk: 0.7902, Acc.rock: 0.8938, Acc.wardrobe: 0.7254, Acc.lamp: 0.8695, Acc.bathtub: 0.8843, Acc.railing: 0.6110, Acc.cushion: 0.8397, Acc.base: 0.6156, Acc.box: 0.5555, Acc.column: 0.6598, Acc.signboard: 0.5562, Acc.chest of drawers: 0.6429, Acc.counter: 0.6402, Acc.sand: 0.7734, Acc.sink: 0.8666, Acc.skyscraper: 0.6489, Acc.fireplace: 0.9036, Acc.refrigerator: 0.9490, Acc.grandstand: 0.8316, Acc.path: 0.4442, Acc.stairs: 0.2926, Acc.runway: 0.9416, Acc.case: 0.8274, Acc.pool table: 0.9829, Acc.pillow: 0.8161, Acc.screen door: 0.7751, Acc.stairway: 0.7404, Acc.river: 0.2353, Acc.bridge: 0.8807, Acc.bookcase: 0.6314, Acc.blind: 0.5579, Acc.coffee table: 0.8873, Acc.toilet: 0.9428, Acc.flower: 0.6338, Acc.book: 0.7500, Acc.hill: 0.2690, Acc.bench: 0.6688, Acc.countertop: 0.8373, Acc.stove: 0.9370, Acc.palm: 0.8260, Acc.kitchen island: 0.8512, Acc.computer: 0.9192, Acc.swivel chair: 0.7587, Acc.boat: 0.9258, Acc.bar: 0.7788, Acc.arcade machine: 0.8103, Acc.hovel: 0.6044, Acc.bus: 0.9708, Acc.towel: 0.8660, Acc.light: 0.7323, Acc.truck: 0.6296, Acc.tower: 0.5415, Acc.chandelier: 0.8660, Acc.awning: 0.4792, Acc.streetlight: 0.5382, Acc.booth: 0.6860, Acc.television receiver: 0.8807, Acc.airplane: 0.9638, Acc.dirt track: 0.1755, Acc.apparel: 0.8841, Acc.pole: 0.4228, Acc.land: 0.0926, Acc.bannister: 0.2557, Acc.escalator: 0.8899, Acc.ottoman: 0.7907, Acc.bottle: 0.7107, Acc.buffet: 0.7355, Acc.poster: 0.4091, Acc.stage: 0.4003, Acc.van: 0.6449, Acc.ship: 0.8503, Acc.fountain: 0.3852, Acc.conveyer belt: 0.9699, Acc.canopy: 0.6912, Acc.washer: 0.9078, Acc.plaything: 0.4514, Acc.swimming pool: 0.7446, Acc.stool: 0.7482, Acc.barrel: 0.9776, Acc.basket: 0.6319, Acc.waterfall: 0.7279, Acc.tent: 0.9868, Acc.bag: 0.3465, Acc.minibike: 0.8919, Acc.cradle: 0.9773, Acc.oven: 0.7898, Acc.ball: 0.7651, Acc.food: 0.7391, Acc.step: 0.1406, Acc.tank: 0.6915, Acc.trade name: 0.2925, Acc.microwave: 0.9682, Acc.pot: 0.7096, Acc.animal: 0.6254, Acc.bicycle: 0.7950, Acc.lake: 0.6346, Acc.dishwasher: 0.8119, Acc.screen: 0.9025, Acc.blanket: 0.4082, Acc.sculpture: 0.8370, Acc.hood: 0.7943, Acc.sconce: 0.7440, Acc.vase: 0.6817, Acc.traffic light: 0.6609, Acc.tray: 0.3560, Acc.ashcan: 0.6854, Acc.fan: 0.8450, Acc.pier: 0.4515, Acc.crt screen: 0.1901, Acc.plate: 0.8044, Acc.monitor: 0.2488, Acc.bulletin board: 0.6774, Acc.shower: 0.2443, Acc.radiator: 0.8436, Acc.glass: 0.2610, Acc.clock: 0.6499, Acc.flag: 0.7950 +2024-06-19 11:44:00,009 - mmseg - INFO - Iter [56050/80000] lr: 1.198e-05, eta: 14:12:22, time: 4.190, data_time: 2.221, memory: 72263, decode.loss_ce: 0.1454, decode.acc_seg: 93.4810, aux.loss_ce: 0.0618, aux.acc_seg: 93.0612, loss: 0.2071 +2024-06-19 11:45:39,032 - mmseg - INFO - Iter [56100/80000] lr: 1.195e-05, eta: 14:10:32, time: 1.980, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1516, decode.acc_seg: 93.4018, aux.loss_ce: 0.0644, aux.acc_seg: 93.0359, loss: 0.2160 +2024-06-19 11:47:17,823 - mmseg - INFO - Iter [56150/80000] lr: 1.193e-05, eta: 14:08:42, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1485, decode.acc_seg: 93.5119, aux.loss_ce: 0.0631, aux.acc_seg: 93.1428, loss: 0.2116 +2024-06-19 11:48:56,700 - mmseg - INFO - Iter [56200/80000] lr: 1.190e-05, eta: 14:06:52, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1436, decode.acc_seg: 93.6406, aux.loss_ce: 0.0611, aux.acc_seg: 93.2311, loss: 0.2047 +2024-06-19 11:50:35,543 - mmseg - INFO - Iter [56250/80000] lr: 1.188e-05, eta: 14:05:02, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1503, decode.acc_seg: 93.4682, aux.loss_ce: 0.0636, aux.acc_seg: 93.1054, loss: 0.2140 +2024-06-19 11:52:14,470 - mmseg - INFO - Iter [56300/80000] lr: 1.185e-05, eta: 14:03:12, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1488, decode.acc_seg: 93.5400, aux.loss_ce: 0.0633, aux.acc_seg: 93.1161, loss: 0.2121 +2024-06-19 11:53:53,339 - mmseg - INFO - Iter [56350/80000] lr: 1.183e-05, eta: 14:01:22, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1413, decode.acc_seg: 93.7175, aux.loss_ce: 0.0601, aux.acc_seg: 93.3087, loss: 0.2014 +2024-06-19 11:55:32,186 - mmseg - INFO - Iter [56400/80000] lr: 1.180e-05, eta: 13:59:32, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1407, decode.acc_seg: 93.6276, aux.loss_ce: 0.0598, aux.acc_seg: 93.2224, loss: 0.2004 +2024-06-19 11:57:11,190 - mmseg - INFO - Iter [56450/80000] lr: 1.178e-05, eta: 13:57:42, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1489, decode.acc_seg: 93.4543, aux.loss_ce: 0.0636, aux.acc_seg: 93.0639, loss: 0.2125 +2024-06-19 11:58:50,006 - mmseg - INFO - Iter [56500/80000] lr: 1.175e-05, eta: 13:55:52, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1563, decode.acc_seg: 93.4671, aux.loss_ce: 0.0662, aux.acc_seg: 93.1125, loss: 0.2224 +2024-06-19 12:00:28,881 - mmseg - INFO - Iter [56550/80000] lr: 1.173e-05, eta: 13:54:02, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1596, decode.acc_seg: 93.1460, aux.loss_ce: 0.0674, aux.acc_seg: 92.7463, loss: 0.2271 +2024-06-19 12:02:07,798 - mmseg - INFO - Iter [56600/80000] lr: 1.170e-05, eta: 13:52:12, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1474, decode.acc_seg: 93.7086, aux.loss_ce: 0.0625, aux.acc_seg: 93.3574, loss: 0.2099 +2024-06-19 12:03:46,842 - mmseg - INFO - Iter [56650/80000] lr: 1.168e-05, eta: 13:50:22, time: 1.981, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1488, decode.acc_seg: 93.4660, aux.loss_ce: 0.0636, aux.acc_seg: 93.0345, loss: 0.2124 +2024-06-19 12:05:25,849 - mmseg - INFO - Iter [56700/80000] lr: 1.165e-05, eta: 13:48:32, time: 1.980, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1515, decode.acc_seg: 93.6026, aux.loss_ce: 0.0643, aux.acc_seg: 93.2100, loss: 0.2157 +2024-06-19 12:07:04,879 - mmseg - INFO - Iter [56750/80000] lr: 1.163e-05, eta: 13:46:42, time: 1.981, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1495, decode.acc_seg: 93.4326, aux.loss_ce: 0.0634, aux.acc_seg: 92.9822, loss: 0.2130 +2024-06-19 12:08:43,811 - mmseg - INFO - Iter [56800/80000] lr: 1.160e-05, eta: 13:44:52, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1453, decode.acc_seg: 93.5017, aux.loss_ce: 0.0611, aux.acc_seg: 93.2021, loss: 0.2065 +2024-06-19 12:10:26,349 - mmseg - INFO - Iter [56850/80000] lr: 1.158e-05, eta: 13:43:04, time: 2.051, data_time: 0.081, memory: 72263, decode.loss_ce: 0.1500, decode.acc_seg: 93.4852, aux.loss_ce: 0.0635, aux.acc_seg: 93.0815, loss: 0.2134 +2024-06-19 12:12:05,337 - mmseg - INFO - Iter [56900/80000] lr: 1.155e-05, eta: 13:41:14, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1542, decode.acc_seg: 93.7443, aux.loss_ce: 0.0652, aux.acc_seg: 93.3459, loss: 0.2194 +2024-06-19 12:13:44,247 - mmseg - INFO - Iter [56950/80000] lr: 1.153e-05, eta: 13:39:25, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1469, decode.acc_seg: 93.3970, aux.loss_ce: 0.0624, aux.acc_seg: 93.0520, loss: 0.2093 +2024-06-19 12:15:23,159 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 12:15:23,159 - mmseg - INFO - Iter [57000/80000] lr: 1.150e-05, eta: 13:37:35, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1510, decode.acc_seg: 93.4386, aux.loss_ce: 0.0643, aux.acc_seg: 93.0566, loss: 0.2153 +2024-06-19 12:17:15,402 - mmseg - INFO - per class results: +2024-06-19 12:17:15,408 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.79 | 90.27 | +| building | 85.14 | 93.24 | +| sky | 94.85 | 98.14 | +| floor | 84.91 | 91.19 | +| tree | 77.46 | 89.24 | +| ceiling | 87.64 | 94.78 | +| road | 86.34 | 91.48 | +| bed | 93.12 | 97.15 | +| windowpane | 66.89 | 82.83 | +| grass | 69.18 | 80.15 | +| cabinet | 67.4 | 76.19 | +| sidewalk | 70.32 | 85.04 | +| person | 86.66 | 94.37 | +| earth | 39.45 | 55.17 | +| door | 58.53 | 71.79 | +| table | 71.12 | 82.44 | +| mountain | 64.4 | 73.84 | +| plant | 55.69 | 67.48 | +| curtain | 77.86 | 85.76 | +| chair | 67.98 | 77.24 | +| car | 88.4 | 94.4 | +| water | 62.55 | 76.11 | +| painting | 81.05 | 92.31 | +| sofa | 80.83 | 88.65 | +| shelf | 52.74 | 69.98 | +| house | 53.29 | 65.11 | +| sea | 72.71 | 84.33 | +| mirror | 79.93 | 89.18 | +| rug | 67.86 | 81.86 | +| field | 29.23 | 52.61 | +| armchair | 61.51 | 81.37 | +| seat | 65.85 | 89.89 | +| fence | 48.19 | 58.79 | +| desk | 60.02 | 80.04 | +| rock | 58.34 | 88.11 | +| wardrobe | 53.59 | 71.03 | +| lamp | 76.77 | 88.02 | +| bathtub | 87.38 | 91.16 | +| railing | 44.16 | 64.08 | +| cushion | 69.49 | 80.7 | +| base | 41.78 | 62.9 | +| box | 40.26 | 52.4 | +| column | 59.6 | 73.47 | +| signboard | 41.77 | 57.02 | +| chest of drawers | 46.8 | 79.62 | +| counter | 48.39 | 66.29 | +| sand | 54.14 | 77.58 | +| sink | 83.96 | 88.87 | +| skyscraper | 49.4 | 61.77 | +| fireplace | 74.0 | 91.44 | +| refrigerator | 85.07 | 90.69 | +| grandstand | 53.43 | 84.3 | +| path | 30.84 | 42.11 | +| stairs | 29.89 | 35.4 | +| runway | 72.85 | 93.73 | +| case | 65.51 | 83.56 | +| pool table | 95.32 | 98.37 | +| pillow | 66.85 | 77.89 | +| screen door | 76.82 | 78.95 | +| stairway | 47.2 | 70.72 | +| river | 12.72 | 29.02 | +| bridge | 77.46 | 85.66 | +| bookcase | 48.06 | 59.59 | +| blind | 45.21 | 51.19 | +| coffee table | 62.28 | 87.68 | +| toilet | 90.96 | 94.41 | +| flower | 42.97 | 57.44 | +| book | 56.21 | 77.85 | +| hill | 14.2 | 23.66 | +| bench | 58.14 | 67.86 | +| countertop | 65.7 | 84.75 | +| stove | 86.84 | 92.79 | +| palm | 50.73 | 79.89 | +| kitchen island | 50.75 | 84.69 | +| computer | 76.52 | 91.77 | +| swivel chair | 51.14 | 81.12 | +| boat | 84.07 | 92.18 | +| bar | 58.16 | 65.88 | +| arcade machine | 81.58 | 85.63 | +| hovel | 51.67 | 58.74 | +| bus | 94.33 | 96.5 | +| towel | 80.76 | 87.06 | +| light | 63.15 | 73.13 | +| truck | 53.19 | 66.17 | +| tower | 35.64 | 69.24 | +| chandelier | 73.55 | 85.03 | +| awning | 38.38 | 49.64 | +| streetlight | 35.18 | 48.1 | +| booth | 60.07 | 68.96 | +| television receiver | 81.11 | 85.97 | +| airplane | 86.94 | 96.27 | +| dirt track | 10.44 | 20.1 | +| apparel | 67.84 | 85.38 | +| pole | 28.47 | 37.14 | +| land | 5.43 | 8.66 | +| bannister | 21.86 | 25.8 | +| escalator | 68.05 | 86.48 | +| ottoman | 57.13 | 70.43 | +| bottle | 45.44 | 67.65 | +| buffet | 55.82 | 63.39 | +| poster | 38.28 | 54.45 | +| stage | 21.02 | 37.52 | +| van | 51.63 | 69.59 | +| ship | 75.59 | 90.7 | +| fountain | 40.44 | 41.23 | +| conveyer belt | 84.72 | 96.75 | +| canopy | 59.92 | 74.48 | +| washer | 88.98 | 95.38 | +| plaything | 34.25 | 56.73 | +| swimming pool | 51.72 | 74.33 | +| stool | 54.55 | 72.13 | +| barrel | 75.42 | 97.75 | +| basket | 43.06 | 60.46 | +| waterfall | 66.8 | 77.93 | +| tent | 94.3 | 98.64 | +| bag | 24.96 | 28.01 | +| minibike | 77.73 | 89.86 | +| cradle | 89.01 | 97.19 | +| oven | 65.62 | 75.33 | +| ball | 58.01 | 66.87 | +| food | 62.12 | 75.69 | +| step | 12.28 | 15.67 | +| tank | 64.87 | 70.15 | +| trade name | 25.45 | 30.27 | +| microwave | 88.75 | 96.88 | +| pot | 63.12 | 74.16 | +| animal | 60.18 | 61.54 | +| bicycle | 59.34 | 69.75 | +| lake | 52.15 | 63.77 | +| dishwasher | 78.82 | 84.9 | +| screen | 59.68 | 86.99 | +| blanket | 29.68 | 34.88 | +| sculpture | 71.53 | 88.53 | +| hood | 72.14 | 82.08 | +| sconce | 61.3 | 74.23 | +| vase | 51.24 | 69.1 | +| traffic light | 42.78 | 65.5 | +| tray | 27.35 | 37.1 | +| ashcan | 52.63 | 67.35 | +| fan | 73.04 | 85.42 | +| pier | 39.86 | 43.04 | +| crt screen | 9.28 | 16.73 | +| plate | 66.16 | 77.64 | +| monitor | 49.0 | 58.08 | +| bulletin board | 54.06 | 62.77 | +| shower | 21.62 | 22.4 | +| radiator | 68.87 | 82.99 | +| glass | 20.79 | 21.78 | +| clock | 57.24 | 68.07 | +| flag | 71.33 | 78.97 | ++---------------------+-------+-------+ +2024-06-19 12:17:15,408 - mmseg - INFO - Summary: +2024-06-19 12:17:15,409 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.46 | 59.58 | 71.98 | ++-------+-------+-------+ +2024-06-19 12:17:15,409 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 12:17:15,409 - mmseg - INFO - Iter(val) [250] aAcc: 0.8646, mIoU: 0.5958, mAcc: 0.7198, IoU.wall: 0.8279, IoU.building: 0.8514, IoU.sky: 0.9485, IoU.floor: 0.8491, IoU.tree: 0.7746, IoU.ceiling: 0.8764, IoU.road: 0.8634, IoU.bed : 0.9312, IoU.windowpane: 0.6689, IoU.grass: 0.6918, IoU.cabinet: 0.6740, IoU.sidewalk: 0.7032, IoU.person: 0.8666, IoU.earth: 0.3945, IoU.door: 0.5853, IoU.table: 0.7112, IoU.mountain: 0.6440, IoU.plant: 0.5569, IoU.curtain: 0.7786, IoU.chair: 0.6798, IoU.car: 0.8840, IoU.water: 0.6255, IoU.painting: 0.8105, IoU.sofa: 0.8083, IoU.shelf: 0.5274, IoU.house: 0.5329, IoU.sea: 0.7271, IoU.mirror: 0.7993, IoU.rug: 0.6786, IoU.field: 0.2923, IoU.armchair: 0.6151, IoU.seat: 0.6585, IoU.fence: 0.4819, IoU.desk: 0.6002, IoU.rock: 0.5834, IoU.wardrobe: 0.5359, IoU.lamp: 0.7677, IoU.bathtub: 0.8738, IoU.railing: 0.4416, IoU.cushion: 0.6949, IoU.base: 0.4178, IoU.box: 0.4026, IoU.column: 0.5960, IoU.signboard: 0.4177, IoU.chest of drawers: 0.4680, IoU.counter: 0.4839, IoU.sand: 0.5414, IoU.sink: 0.8396, IoU.skyscraper: 0.4940, IoU.fireplace: 0.7400, IoU.refrigerator: 0.8507, IoU.grandstand: 0.5343, IoU.path: 0.3084, IoU.stairs: 0.2989, IoU.runway: 0.7285, IoU.case: 0.6551, IoU.pool table: 0.9532, IoU.pillow: 0.6685, IoU.screen door: 0.7682, IoU.stairway: 0.4720, IoU.river: 0.1272, IoU.bridge: 0.7746, IoU.bookcase: 0.4806, IoU.blind: 0.4521, IoU.coffee table: 0.6228, IoU.toilet: 0.9096, IoU.flower: 0.4297, IoU.book: 0.5621, IoU.hill: 0.1420, IoU.bench: 0.5814, IoU.countertop: 0.6570, IoU.stove: 0.8684, IoU.palm: 0.5073, IoU.kitchen island: 0.5075, IoU.computer: 0.7652, IoU.swivel chair: 0.5114, IoU.boat: 0.8407, IoU.bar: 0.5816, IoU.arcade machine: 0.8158, IoU.hovel: 0.5167, IoU.bus: 0.9433, IoU.towel: 0.8076, IoU.light: 0.6315, IoU.truck: 0.5319, IoU.tower: 0.3564, IoU.chandelier: 0.7355, IoU.awning: 0.3838, IoU.streetlight: 0.3518, IoU.booth: 0.6007, IoU.television receiver: 0.8111, IoU.airplane: 0.8694, IoU.dirt track: 0.1044, IoU.apparel: 0.6784, IoU.pole: 0.2847, IoU.land: 0.0543, IoU.bannister: 0.2186, IoU.escalator: 0.6805, IoU.ottoman: 0.5713, IoU.bottle: 0.4544, IoU.buffet: 0.5582, IoU.poster: 0.3828, IoU.stage: 0.2102, IoU.van: 0.5163, IoU.ship: 0.7559, IoU.fountain: 0.4044, IoU.conveyer belt: 0.8472, IoU.canopy: 0.5992, IoU.washer: 0.8898, IoU.plaything: 0.3425, IoU.swimming pool: 0.5172, IoU.stool: 0.5455, IoU.barrel: 0.7542, IoU.basket: 0.4306, IoU.waterfall: 0.6680, IoU.tent: 0.9430, IoU.bag: 0.2496, IoU.minibike: 0.7773, IoU.cradle: 0.8901, IoU.oven: 0.6562, IoU.ball: 0.5801, IoU.food: 0.6212, IoU.step: 0.1228, IoU.tank: 0.6487, IoU.trade name: 0.2545, IoU.microwave: 0.8875, IoU.pot: 0.6312, IoU.animal: 0.6018, IoU.bicycle: 0.5934, IoU.lake: 0.5215, IoU.dishwasher: 0.7882, IoU.screen: 0.5968, IoU.blanket: 0.2968, IoU.sculpture: 0.7153, IoU.hood: 0.7214, IoU.sconce: 0.6130, IoU.vase: 0.5124, IoU.traffic light: 0.4278, IoU.tray: 0.2735, IoU.ashcan: 0.5263, IoU.fan: 0.7304, IoU.pier: 0.3986, IoU.crt screen: 0.0928, IoU.plate: 0.6616, IoU.monitor: 0.4900, IoU.bulletin board: 0.5406, IoU.shower: 0.2162, IoU.radiator: 0.6887, IoU.glass: 0.2079, IoU.clock: 0.5724, IoU.flag: 0.7133, Acc.wall: 0.9027, Acc.building: 0.9324, Acc.sky: 0.9814, Acc.floor: 0.9119, Acc.tree: 0.8924, Acc.ceiling: 0.9478, Acc.road: 0.9148, Acc.bed : 0.9715, Acc.windowpane: 0.8283, Acc.grass: 0.8015, Acc.cabinet: 0.7619, Acc.sidewalk: 0.8504, Acc.person: 0.9437, Acc.earth: 0.5517, Acc.door: 0.7179, Acc.table: 0.8244, Acc.mountain: 0.7384, Acc.plant: 0.6748, Acc.curtain: 0.8576, Acc.chair: 0.7724, Acc.car: 0.9440, Acc.water: 0.7611, Acc.painting: 0.9231, Acc.sofa: 0.8865, Acc.shelf: 0.6998, Acc.house: 0.6511, Acc.sea: 0.8433, Acc.mirror: 0.8918, Acc.rug: 0.8186, Acc.field: 0.5261, Acc.armchair: 0.8137, Acc.seat: 0.8989, Acc.fence: 0.5879, Acc.desk: 0.8004, Acc.rock: 0.8811, Acc.wardrobe: 0.7103, Acc.lamp: 0.8802, Acc.bathtub: 0.9116, Acc.railing: 0.6408, Acc.cushion: 0.8070, Acc.base: 0.6290, Acc.box: 0.5240, Acc.column: 0.7347, Acc.signboard: 0.5702, Acc.chest of drawers: 0.7962, Acc.counter: 0.6629, Acc.sand: 0.7758, Acc.sink: 0.8887, Acc.skyscraper: 0.6177, Acc.fireplace: 0.9144, Acc.refrigerator: 0.9069, Acc.grandstand: 0.8430, Acc.path: 0.4211, Acc.stairs: 0.3540, Acc.runway: 0.9373, Acc.case: 0.8356, Acc.pool table: 0.9837, Acc.pillow: 0.7789, Acc.screen door: 0.7895, Acc.stairway: 0.7072, Acc.river: 0.2902, Acc.bridge: 0.8566, Acc.bookcase: 0.5959, Acc.blind: 0.5119, Acc.coffee table: 0.8768, Acc.toilet: 0.9441, Acc.flower: 0.5744, Acc.book: 0.7785, Acc.hill: 0.2366, Acc.bench: 0.6786, Acc.countertop: 0.8475, Acc.stove: 0.9279, Acc.palm: 0.7989, Acc.kitchen island: 0.8469, Acc.computer: 0.9177, Acc.swivel chair: 0.8112, Acc.boat: 0.9218, Acc.bar: 0.6588, Acc.arcade machine: 0.8563, Acc.hovel: 0.5874, Acc.bus: 0.9650, Acc.towel: 0.8706, Acc.light: 0.7313, Acc.truck: 0.6617, Acc.tower: 0.6924, Acc.chandelier: 0.8503, Acc.awning: 0.4964, Acc.streetlight: 0.4810, Acc.booth: 0.6896, Acc.television receiver: 0.8597, Acc.airplane: 0.9627, Acc.dirt track: 0.2010, Acc.apparel: 0.8538, Acc.pole: 0.3714, Acc.land: 0.0866, Acc.bannister: 0.2580, Acc.escalator: 0.8648, Acc.ottoman: 0.7043, Acc.bottle: 0.6765, Acc.buffet: 0.6339, Acc.poster: 0.5445, Acc.stage: 0.3752, Acc.van: 0.6959, Acc.ship: 0.9070, Acc.fountain: 0.4123, Acc.conveyer belt: 0.9675, Acc.canopy: 0.7448, Acc.washer: 0.9538, Acc.plaything: 0.5673, Acc.swimming pool: 0.7433, Acc.stool: 0.7213, Acc.barrel: 0.9775, Acc.basket: 0.6046, Acc.waterfall: 0.7793, Acc.tent: 0.9864, Acc.bag: 0.2801, Acc.minibike: 0.8986, Acc.cradle: 0.9719, Acc.oven: 0.7533, Acc.ball: 0.6687, Acc.food: 0.7569, Acc.step: 0.1567, Acc.tank: 0.7015, Acc.trade name: 0.3027, Acc.microwave: 0.9688, Acc.pot: 0.7416, Acc.animal: 0.6154, Acc.bicycle: 0.6975, Acc.lake: 0.6377, Acc.dishwasher: 0.8490, Acc.screen: 0.8699, Acc.blanket: 0.3488, Acc.sculpture: 0.8853, Acc.hood: 0.8208, Acc.sconce: 0.7423, Acc.vase: 0.6910, Acc.traffic light: 0.6550, Acc.tray: 0.3710, Acc.ashcan: 0.6735, Acc.fan: 0.8542, Acc.pier: 0.4304, Acc.crt screen: 0.1673, Acc.plate: 0.7764, Acc.monitor: 0.5808, Acc.bulletin board: 0.6277, Acc.shower: 0.2240, Acc.radiator: 0.8299, Acc.glass: 0.2178, Acc.clock: 0.6807, Acc.flag: 0.7897 +2024-06-19 12:18:54,659 - mmseg - INFO - Iter [57050/80000] lr: 1.148e-05, eta: 13:36:30, time: 4.230, data_time: 2.262, memory: 72263, decode.loss_ce: 0.1537, decode.acc_seg: 93.1102, aux.loss_ce: 0.0652, aux.acc_seg: 92.7063, loss: 0.2189 +2024-06-19 12:20:33,556 - mmseg - INFO - Iter [57100/80000] lr: 1.145e-05, eta: 13:34:40, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1455, decode.acc_seg: 93.5482, aux.loss_ce: 0.0617, aux.acc_seg: 93.1767, loss: 0.2072 +2024-06-19 12:22:12,521 - mmseg - INFO - Iter [57150/80000] lr: 1.143e-05, eta: 13:32:51, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1435, decode.acc_seg: 93.6648, aux.loss_ce: 0.0613, aux.acc_seg: 93.2276, loss: 0.2048 +2024-06-19 12:23:51,366 - mmseg - INFO - Iter [57200/80000] lr: 1.140e-05, eta: 13:31:01, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1539, decode.acc_seg: 93.3202, aux.loss_ce: 0.0657, aux.acc_seg: 92.8674, loss: 0.2196 +2024-06-19 12:25:30,369 - mmseg - INFO - Iter [57250/80000] lr: 1.138e-05, eta: 13:29:11, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1420, decode.acc_seg: 93.8189, aux.loss_ce: 0.0602, aux.acc_seg: 93.4972, loss: 0.2021 +2024-06-19 12:27:09,360 - mmseg - INFO - Iter [57300/80000] lr: 1.135e-05, eta: 13:27:21, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1389, decode.acc_seg: 94.0121, aux.loss_ce: 0.0591, aux.acc_seg: 93.6304, loss: 0.1980 +2024-06-19 12:28:48,292 - mmseg - INFO - Iter [57350/80000] lr: 1.133e-05, eta: 13:25:31, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1447, decode.acc_seg: 93.7196, aux.loss_ce: 0.0614, aux.acc_seg: 93.3556, loss: 0.2061 +2024-06-19 12:30:27,332 - mmseg - INFO - Iter [57400/80000] lr: 1.130e-05, eta: 13:23:42, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1442, decode.acc_seg: 93.6502, aux.loss_ce: 0.0616, aux.acc_seg: 93.2411, loss: 0.2058 +2024-06-19 12:32:06,218 - mmseg - INFO - Iter [57450/80000] lr: 1.128e-05, eta: 13:21:52, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1467, decode.acc_seg: 93.6231, aux.loss_ce: 0.0630, aux.acc_seg: 93.1580, loss: 0.2097 +2024-06-19 12:33:45,129 - mmseg - INFO - Iter [57500/80000] lr: 1.125e-05, eta: 13:20:02, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1509, decode.acc_seg: 93.3790, aux.loss_ce: 0.0643, aux.acc_seg: 92.9745, loss: 0.2152 +2024-06-19 12:35:23,989 - mmseg - INFO - Iter [57550/80000] lr: 1.123e-05, eta: 13:18:13, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1455, decode.acc_seg: 93.7813, aux.loss_ce: 0.0616, aux.acc_seg: 93.4052, loss: 0.2071 +2024-06-19 12:37:02,938 - mmseg - INFO - Iter [57600/80000] lr: 1.120e-05, eta: 13:16:23, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1536, decode.acc_seg: 93.2781, aux.loss_ce: 0.0655, aux.acc_seg: 92.8655, loss: 0.2192 +2024-06-19 12:38:41,882 - mmseg - INFO - Iter [57650/80000] lr: 1.118e-05, eta: 13:14:33, time: 1.979, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1515, decode.acc_seg: 93.3406, aux.loss_ce: 0.0645, aux.acc_seg: 92.9258, loss: 0.2160 +2024-06-19 12:40:20,917 - mmseg - INFO - Iter [57700/80000] lr: 1.115e-05, eta: 13:12:44, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1405, decode.acc_seg: 93.9216, aux.loss_ce: 0.0594, aux.acc_seg: 93.6262, loss: 0.1999 +2024-06-19 12:41:59,921 - mmseg - INFO - Iter [57750/80000] lr: 1.113e-05, eta: 13:10:54, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1452, decode.acc_seg: 93.6091, aux.loss_ce: 0.0612, aux.acc_seg: 93.2949, loss: 0.2064 +2024-06-19 12:43:38,887 - mmseg - INFO - Iter [57800/80000] lr: 1.110e-05, eta: 13:09:04, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1493, decode.acc_seg: 93.4311, aux.loss_ce: 0.0632, aux.acc_seg: 93.0463, loss: 0.2126 +2024-06-19 12:45:17,774 - mmseg - INFO - Iter [57850/80000] lr: 1.108e-05, eta: 13:07:15, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1529, decode.acc_seg: 93.4942, aux.loss_ce: 0.0646, aux.acc_seg: 93.1194, loss: 0.2175 +2024-06-19 12:46:56,715 - mmseg - INFO - Iter [57900/80000] lr: 1.105e-05, eta: 13:05:25, time: 1.979, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1429, decode.acc_seg: 93.6268, aux.loss_ce: 0.0604, aux.acc_seg: 93.2528, loss: 0.2033 +2024-06-19 12:48:35,746 - mmseg - INFO - Iter [57950/80000] lr: 1.103e-05, eta: 13:03:36, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1537, decode.acc_seg: 93.3902, aux.loss_ce: 0.0658, aux.acc_seg: 92.9628, loss: 0.2195 +2024-06-19 12:50:14,748 - mmseg - INFO - Saving checkpoint at 58000 iterations +2024-06-19 12:51:39,028 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 12:51:39,029 - mmseg - INFO - Iter [58000/80000] lr: 1.100e-05, eta: 13:02:18, time: 3.666, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1482, decode.acc_seg: 93.5439, aux.loss_ce: 0.0631, aux.acc_seg: 93.1809, loss: 0.2114 +2024-06-19 12:53:30,335 - mmseg - INFO - per class results: +2024-06-19 12:53:30,341 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.95 | 89.68 | +| building | 85.2 | 92.97 | +| sky | 94.99 | 97.83 | +| floor | 85.11 | 91.94 | +| tree | 77.86 | 90.2 | +| ceiling | 87.53 | 94.93 | +| road | 86.23 | 92.71 | +| bed | 93.27 | 97.3 | +| windowpane | 66.9 | 81.67 | +| grass | 68.36 | 83.66 | +| cabinet | 67.52 | 78.43 | +| sidewalk | 69.94 | 83.27 | +| person | 86.76 | 95.02 | +| earth | 39.81 | 51.64 | +| door | 59.98 | 76.43 | +| table | 71.38 | 81.31 | +| mountain | 62.85 | 75.64 | +| plant | 56.24 | 66.65 | +| curtain | 79.69 | 88.87 | +| chair | 68.6 | 81.0 | +| car | 87.81 | 94.65 | +| water | 65.3 | 80.25 | +| painting | 81.28 | 93.04 | +| sofa | 82.11 | 88.3 | +| shelf | 49.98 | 64.57 | +| house | 53.04 | 64.33 | +| sea | 73.51 | 82.8 | +| mirror | 78.47 | 85.99 | +| rug | 66.48 | 76.03 | +| field | 28.11 | 48.25 | +| armchair | 61.39 | 81.81 | +| seat | 68.36 | 88.0 | +| fence | 51.79 | 63.14 | +| desk | 59.61 | 78.92 | +| rock | 56.52 | 83.35 | +| wardrobe | 55.07 | 72.69 | +| lamp | 77.47 | 87.62 | +| bathtub | 85.06 | 88.66 | +| railing | 42.49 | 61.82 | +| cushion | 66.73 | 85.99 | +| base | 43.77 | 57.74 | +| box | 39.32 | 52.85 | +| column | 59.37 | 74.19 | +| signboard | 42.07 | 58.32 | +| chest of drawers | 46.22 | 67.55 | +| counter | 53.83 | 66.62 | +| sand | 53.37 | 82.08 | +| sink | 82.97 | 88.33 | +| skyscraper | 47.12 | 61.84 | +| fireplace | 74.66 | 94.77 | +| refrigerator | 86.26 | 93.41 | +| grandstand | 54.56 | 81.65 | +| path | 28.75 | 42.43 | +| stairs | 31.51 | 40.09 | +| runway | 72.2 | 93.52 | +| case | 62.76 | 84.81 | +| pool table | 95.25 | 98.42 | +| pillow | 64.27 | 74.41 | +| screen door | 83.83 | 88.44 | +| stairway | 46.37 | 68.44 | +| river | 14.16 | 29.63 | +| bridge | 76.46 | 85.45 | +| bookcase | 42.59 | 54.15 | +| blind | 43.67 | 47.19 | +| coffee table | 62.58 | 87.88 | +| toilet | 91.17 | 94.44 | +| flower | 44.77 | 58.23 | +| book | 56.27 | 80.77 | +| hill | 13.75 | 25.95 | +| bench | 64.66 | 74.38 | +| countertop | 65.12 | 88.43 | +| stove | 88.65 | 93.75 | +| palm | 53.66 | 85.29 | +| kitchen island | 46.62 | 77.5 | +| computer | 76.7 | 92.23 | +| swivel chair | 47.92 | 68.94 | +| boat | 79.48 | 91.94 | +| bar | 77.27 | 88.02 | +| arcade machine | 82.76 | 87.15 | +| hovel | 55.19 | 61.84 | +| bus | 94.04 | 96.88 | +| towel | 80.32 | 86.3 | +| light | 63.38 | 75.19 | +| truck | 51.78 | 64.01 | +| tower | 34.59 | 67.35 | +| chandelier | 74.78 | 89.07 | +| awning | 50.02 | 66.93 | +| streetlight | 36.07 | 48.8 | +| booth | 57.92 | 64.13 | +| television receiver | 81.31 | 86.53 | +| airplane | 88.81 | 95.69 | +| dirt track | 10.44 | 22.89 | +| apparel | 64.72 | 77.68 | +| pole | 31.01 | 41.46 | +| land | 5.37 | 7.49 | +| bannister | 22.39 | 29.71 | +| escalator | 68.47 | 85.21 | +| ottoman | 58.12 | 76.41 | +| bottle | 46.33 | 73.16 | +| buffet | 59.64 | 67.44 | +| poster | 33.29 | 45.43 | +| stage | 24.2 | 39.32 | +| van | 48.06 | 62.22 | +| ship | 76.0 | 91.81 | +| fountain | 38.09 | 38.59 | +| conveyer belt | 83.3 | 96.12 | +| canopy | 59.22 | 73.04 | +| washer | 88.88 | 94.84 | +| plaything | 31.85 | 40.87 | +| swimming pool | 53.72 | 77.59 | +| stool | 61.34 | 69.45 | +| barrel | 68.14 | 97.66 | +| basket | 43.68 | 58.9 | +| waterfall | 58.82 | 70.68 | +| tent | 96.38 | 98.74 | +| bag | 30.02 | 33.9 | +| minibike | 76.91 | 90.4 | +| cradle | 86.45 | 97.79 | +| oven | 65.04 | 74.07 | +| ball | 58.38 | 69.8 | +| food | 57.8 | 69.57 | +| step | 11.03 | 13.98 | +| tank | 64.98 | 72.53 | +| trade name | 21.23 | 24.62 | +| microwave | 89.28 | 96.21 | +| pot | 62.38 | 73.31 | +| animal | 59.41 | 60.72 | +| bicycle | 61.53 | 78.07 | +| lake | 52.12 | 63.68 | +| dishwasher | 76.2 | 84.23 | +| screen | 61.04 | 87.09 | +| blanket | 36.18 | 42.47 | +| sculpture | 74.76 | 86.49 | +| hood | 72.75 | 84.37 | +| sconce | 62.9 | 74.81 | +| vase | 52.69 | 69.11 | +| traffic light | 42.23 | 64.02 | +| tray | 26.36 | 33.97 | +| ashcan | 51.08 | 66.16 | +| fan | 73.06 | 84.2 | +| pier | 38.74 | 47.14 | +| crt screen | 8.61 | 16.2 | +| plate | 64.51 | 81.29 | +| monitor | 48.49 | 57.42 | +| bulletin board | 55.45 | 69.89 | +| shower | 21.13 | 21.73 | +| radiator | 68.2 | 81.89 | +| glass | 22.24 | 23.9 | +| clock | 56.61 | 65.74 | +| flag | 72.32 | 80.18 | ++---------------------+-------+-------+ +2024-06-19 12:53:30,341 - mmseg - INFO - Summary: +2024-06-19 12:53:30,341 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.58 | 59.73 | 72.15 | ++-------+-------+-------+ +2024-06-19 12:53:30,342 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 12:53:30,342 - mmseg - INFO - Iter(val) [250] aAcc: 0.8658, mIoU: 0.5973, mAcc: 0.7215, IoU.wall: 0.8295, IoU.building: 0.8520, IoU.sky: 0.9499, IoU.floor: 0.8511, IoU.tree: 0.7786, IoU.ceiling: 0.8753, IoU.road: 0.8623, IoU.bed : 0.9327, IoU.windowpane: 0.6690, IoU.grass: 0.6836, IoU.cabinet: 0.6752, IoU.sidewalk: 0.6994, IoU.person: 0.8676, IoU.earth: 0.3981, IoU.door: 0.5998, IoU.table: 0.7138, IoU.mountain: 0.6285, IoU.plant: 0.5624, IoU.curtain: 0.7969, IoU.chair: 0.6860, IoU.car: 0.8781, IoU.water: 0.6530, IoU.painting: 0.8128, IoU.sofa: 0.8211, IoU.shelf: 0.4998, IoU.house: 0.5304, IoU.sea: 0.7351, IoU.mirror: 0.7847, IoU.rug: 0.6648, IoU.field: 0.2811, IoU.armchair: 0.6139, IoU.seat: 0.6836, IoU.fence: 0.5179, IoU.desk: 0.5961, IoU.rock: 0.5652, IoU.wardrobe: 0.5507, IoU.lamp: 0.7747, IoU.bathtub: 0.8506, IoU.railing: 0.4249, IoU.cushion: 0.6673, IoU.base: 0.4377, IoU.box: 0.3932, IoU.column: 0.5937, IoU.signboard: 0.4207, IoU.chest of drawers: 0.4622, IoU.counter: 0.5383, IoU.sand: 0.5337, IoU.sink: 0.8297, IoU.skyscraper: 0.4712, IoU.fireplace: 0.7466, IoU.refrigerator: 0.8626, IoU.grandstand: 0.5456, IoU.path: 0.2875, IoU.stairs: 0.3151, IoU.runway: 0.7220, IoU.case: 0.6276, IoU.pool table: 0.9525, IoU.pillow: 0.6427, IoU.screen door: 0.8383, IoU.stairway: 0.4637, IoU.river: 0.1416, IoU.bridge: 0.7646, IoU.bookcase: 0.4259, IoU.blind: 0.4367, IoU.coffee table: 0.6258, IoU.toilet: 0.9117, IoU.flower: 0.4477, IoU.book: 0.5627, IoU.hill: 0.1375, IoU.bench: 0.6466, IoU.countertop: 0.6512, IoU.stove: 0.8865, IoU.palm: 0.5366, IoU.kitchen island: 0.4662, IoU.computer: 0.7670, IoU.swivel chair: 0.4792, IoU.boat: 0.7948, IoU.bar: 0.7727, IoU.arcade machine: 0.8276, IoU.hovel: 0.5519, IoU.bus: 0.9404, IoU.towel: 0.8032, IoU.light: 0.6338, IoU.truck: 0.5178, IoU.tower: 0.3459, IoU.chandelier: 0.7478, IoU.awning: 0.5002, IoU.streetlight: 0.3607, IoU.booth: 0.5792, IoU.television receiver: 0.8131, IoU.airplane: 0.8881, IoU.dirt track: 0.1044, IoU.apparel: 0.6472, IoU.pole: 0.3101, IoU.land: 0.0537, IoU.bannister: 0.2239, IoU.escalator: 0.6847, IoU.ottoman: 0.5812, IoU.bottle: 0.4633, IoU.buffet: 0.5964, IoU.poster: 0.3329, IoU.stage: 0.2420, IoU.van: 0.4806, IoU.ship: 0.7600, IoU.fountain: 0.3809, IoU.conveyer belt: 0.8330, IoU.canopy: 0.5922, IoU.washer: 0.8888, IoU.plaything: 0.3185, IoU.swimming pool: 0.5372, IoU.stool: 0.6134, IoU.barrel: 0.6814, IoU.basket: 0.4368, IoU.waterfall: 0.5882, IoU.tent: 0.9638, IoU.bag: 0.3002, IoU.minibike: 0.7691, IoU.cradle: 0.8645, IoU.oven: 0.6504, IoU.ball: 0.5838, IoU.food: 0.5780, IoU.step: 0.1103, IoU.tank: 0.6498, IoU.trade name: 0.2123, IoU.microwave: 0.8928, IoU.pot: 0.6238, IoU.animal: 0.5941, IoU.bicycle: 0.6153, IoU.lake: 0.5212, IoU.dishwasher: 0.7620, IoU.screen: 0.6104, IoU.blanket: 0.3618, IoU.sculpture: 0.7476, IoU.hood: 0.7275, IoU.sconce: 0.6290, IoU.vase: 0.5269, IoU.traffic light: 0.4223, IoU.tray: 0.2636, IoU.ashcan: 0.5108, IoU.fan: 0.7306, IoU.pier: 0.3874, IoU.crt screen: 0.0861, IoU.plate: 0.6451, IoU.monitor: 0.4849, IoU.bulletin board: 0.5545, IoU.shower: 0.2113, IoU.radiator: 0.6820, IoU.glass: 0.2224, IoU.clock: 0.5661, IoU.flag: 0.7232, Acc.wall: 0.8968, Acc.building: 0.9297, Acc.sky: 0.9783, Acc.floor: 0.9194, Acc.tree: 0.9020, Acc.ceiling: 0.9493, Acc.road: 0.9271, Acc.bed : 0.9730, Acc.windowpane: 0.8167, Acc.grass: 0.8366, Acc.cabinet: 0.7843, Acc.sidewalk: 0.8327, Acc.person: 0.9502, Acc.earth: 0.5164, Acc.door: 0.7643, Acc.table: 0.8131, Acc.mountain: 0.7564, Acc.plant: 0.6665, Acc.curtain: 0.8887, Acc.chair: 0.8100, Acc.car: 0.9465, Acc.water: 0.8025, Acc.painting: 0.9304, Acc.sofa: 0.8830, Acc.shelf: 0.6457, Acc.house: 0.6433, Acc.sea: 0.8280, Acc.mirror: 0.8599, Acc.rug: 0.7603, Acc.field: 0.4825, Acc.armchair: 0.8181, Acc.seat: 0.8800, Acc.fence: 0.6314, Acc.desk: 0.7892, Acc.rock: 0.8335, Acc.wardrobe: 0.7269, Acc.lamp: 0.8762, Acc.bathtub: 0.8866, Acc.railing: 0.6182, Acc.cushion: 0.8599, Acc.base: 0.5774, Acc.box: 0.5285, Acc.column: 0.7419, Acc.signboard: 0.5832, Acc.chest of drawers: 0.6755, Acc.counter: 0.6662, Acc.sand: 0.8208, Acc.sink: 0.8833, Acc.skyscraper: 0.6184, Acc.fireplace: 0.9477, Acc.refrigerator: 0.9341, Acc.grandstand: 0.8165, Acc.path: 0.4243, Acc.stairs: 0.4009, Acc.runway: 0.9352, Acc.case: 0.8481, Acc.pool table: 0.9842, Acc.pillow: 0.7441, Acc.screen door: 0.8844, Acc.stairway: 0.6844, Acc.river: 0.2963, Acc.bridge: 0.8545, Acc.bookcase: 0.5415, Acc.blind: 0.4719, Acc.coffee table: 0.8788, Acc.toilet: 0.9444, Acc.flower: 0.5823, Acc.book: 0.8077, Acc.hill: 0.2595, Acc.bench: 0.7438, Acc.countertop: 0.8843, Acc.stove: 0.9375, Acc.palm: 0.8529, Acc.kitchen island: 0.7750, Acc.computer: 0.9223, Acc.swivel chair: 0.6894, Acc.boat: 0.9194, Acc.bar: 0.8802, Acc.arcade machine: 0.8715, Acc.hovel: 0.6184, Acc.bus: 0.9688, Acc.towel: 0.8630, Acc.light: 0.7519, Acc.truck: 0.6401, Acc.tower: 0.6735, Acc.chandelier: 0.8907, Acc.awning: 0.6693, Acc.streetlight: 0.4880, Acc.booth: 0.6413, Acc.television receiver: 0.8653, Acc.airplane: 0.9569, Acc.dirt track: 0.2289, Acc.apparel: 0.7768, Acc.pole: 0.4146, Acc.land: 0.0749, Acc.bannister: 0.2971, Acc.escalator: 0.8521, Acc.ottoman: 0.7641, Acc.bottle: 0.7316, Acc.buffet: 0.6744, Acc.poster: 0.4543, Acc.stage: 0.3932, Acc.van: 0.6222, Acc.ship: 0.9181, Acc.fountain: 0.3859, Acc.conveyer belt: 0.9612, Acc.canopy: 0.7304, Acc.washer: 0.9484, Acc.plaything: 0.4087, Acc.swimming pool: 0.7759, Acc.stool: 0.6945, Acc.barrel: 0.9766, Acc.basket: 0.5890, Acc.waterfall: 0.7068, Acc.tent: 0.9874, Acc.bag: 0.3390, Acc.minibike: 0.9040, Acc.cradle: 0.9779, Acc.oven: 0.7407, Acc.ball: 0.6980, Acc.food: 0.6957, Acc.step: 0.1398, Acc.tank: 0.7253, Acc.trade name: 0.2462, Acc.microwave: 0.9621, Acc.pot: 0.7331, Acc.animal: 0.6072, Acc.bicycle: 0.7807, Acc.lake: 0.6368, Acc.dishwasher: 0.8423, Acc.screen: 0.8709, Acc.blanket: 0.4247, Acc.sculpture: 0.8649, Acc.hood: 0.8437, Acc.sconce: 0.7481, Acc.vase: 0.6911, Acc.traffic light: 0.6402, Acc.tray: 0.3397, Acc.ashcan: 0.6616, Acc.fan: 0.8420, Acc.pier: 0.4714, Acc.crt screen: 0.1620, Acc.plate: 0.8129, Acc.monitor: 0.5742, Acc.bulletin board: 0.6989, Acc.shower: 0.2173, Acc.radiator: 0.8189, Acc.glass: 0.2390, Acc.clock: 0.6574, Acc.flag: 0.8018 +2024-06-19 12:55:09,703 - mmseg - INFO - Iter [58050/80000] lr: 1.098e-05, eta: 13:01:11, time: 4.214, data_time: 2.243, memory: 72263, decode.loss_ce: 0.1465, decode.acc_seg: 93.5771, aux.loss_ce: 0.0619, aux.acc_seg: 93.2413, loss: 0.2084 +2024-06-19 12:56:51,297 - mmseg - INFO - Iter [58100/80000] lr: 1.095e-05, eta: 12:59:22, time: 2.032, data_time: 0.061, memory: 72263, decode.loss_ce: 0.1506, decode.acc_seg: 93.2820, aux.loss_ce: 0.0639, aux.acc_seg: 92.9675, loss: 0.2145 +2024-06-19 12:58:30,256 - mmseg - INFO - Iter [58150/80000] lr: 1.093e-05, eta: 12:57:32, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1492, decode.acc_seg: 93.4700, aux.loss_ce: 0.0630, aux.acc_seg: 93.1348, loss: 0.2122 +2024-06-19 13:00:09,165 - mmseg - INFO - Iter [58200/80000] lr: 1.090e-05, eta: 12:55:43, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1439, decode.acc_seg: 93.6970, aux.loss_ce: 0.0612, aux.acc_seg: 93.3025, loss: 0.2051 +2024-06-19 13:01:48,027 - mmseg - INFO - Iter [58250/80000] lr: 1.088e-05, eta: 12:53:53, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1466, decode.acc_seg: 93.6821, aux.loss_ce: 0.0626, aux.acc_seg: 93.2859, loss: 0.2092 +2024-06-19 13:03:27,011 - mmseg - INFO - Iter [58300/80000] lr: 1.085e-05, eta: 12:52:03, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1405, decode.acc_seg: 93.7857, aux.loss_ce: 0.0599, aux.acc_seg: 93.4688, loss: 0.2004 +2024-06-19 13:05:05,900 - mmseg - INFO - Iter [58350/80000] lr: 1.083e-05, eta: 12:50:14, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1426, decode.acc_seg: 93.7283, aux.loss_ce: 0.0606, aux.acc_seg: 93.4130, loss: 0.2031 +2024-06-19 13:06:44,816 - mmseg - INFO - Iter [58400/80000] lr: 1.080e-05, eta: 12:48:24, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1374, decode.acc_seg: 93.8913, aux.loss_ce: 0.0590, aux.acc_seg: 93.4200, loss: 0.1965 +2024-06-19 13:08:23,645 - mmseg - INFO - Iter [58450/80000] lr: 1.078e-05, eta: 12:46:35, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1365, decode.acc_seg: 93.9719, aux.loss_ce: 0.0580, aux.acc_seg: 93.5812, loss: 0.1945 +2024-06-19 13:10:02,557 - mmseg - INFO - Iter [58500/80000] lr: 1.075e-05, eta: 12:44:45, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1437, decode.acc_seg: 93.7033, aux.loss_ce: 0.0608, aux.acc_seg: 93.3875, loss: 0.2045 +2024-06-19 13:11:41,458 - mmseg - INFO - Iter [58550/80000] lr: 1.073e-05, eta: 12:42:55, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1364, decode.acc_seg: 94.0287, aux.loss_ce: 0.0586, aux.acc_seg: 93.6342, loss: 0.1950 +2024-06-19 13:13:20,474 - mmseg - INFO - Iter [58600/80000] lr: 1.070e-05, eta: 12:41:06, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1384, decode.acc_seg: 93.9971, aux.loss_ce: 0.0587, aux.acc_seg: 93.6342, loss: 0.1971 +2024-06-19 13:14:59,358 - mmseg - INFO - Iter [58650/80000] lr: 1.068e-05, eta: 12:39:16, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1462, decode.acc_seg: 93.4021, aux.loss_ce: 0.0619, aux.acc_seg: 93.0140, loss: 0.2081 +2024-06-19 13:16:38,344 - mmseg - INFO - Iter [58700/80000] lr: 1.065e-05, eta: 12:37:27, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1486, decode.acc_seg: 93.4553, aux.loss_ce: 0.0631, aux.acc_seg: 93.1451, loss: 0.2117 +2024-06-19 13:18:17,156 - mmseg - INFO - Iter [58750/80000] lr: 1.063e-05, eta: 12:35:37, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1521, decode.acc_seg: 93.4673, aux.loss_ce: 0.0643, aux.acc_seg: 93.1120, loss: 0.2163 +2024-06-19 13:19:56,248 - mmseg - INFO - Iter [58800/80000] lr: 1.060e-05, eta: 12:33:48, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1372, decode.acc_seg: 93.9288, aux.loss_ce: 0.0589, aux.acc_seg: 93.4803, loss: 0.1961 +2024-06-19 13:21:35,107 - mmseg - INFO - Iter [58850/80000] lr: 1.058e-05, eta: 12:31:58, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1460, decode.acc_seg: 93.4983, aux.loss_ce: 0.0617, aux.acc_seg: 93.1944, loss: 0.2077 +2024-06-19 13:23:14,106 - mmseg - INFO - Iter [58900/80000] lr: 1.055e-05, eta: 12:30:09, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1380, decode.acc_seg: 93.7947, aux.loss_ce: 0.0587, aux.acc_seg: 93.4636, loss: 0.1968 +2024-06-19 13:24:52,999 - mmseg - INFO - Iter [58950/80000] lr: 1.053e-05, eta: 12:28:20, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1386, decode.acc_seg: 93.7599, aux.loss_ce: 0.0594, aux.acc_seg: 93.3647, loss: 0.1980 +2024-06-19 13:26:31,992 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 13:26:31,993 - mmseg - INFO - Iter [59000/80000] lr: 1.050e-05, eta: 12:26:30, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1484, decode.acc_seg: 93.4010, aux.loss_ce: 0.0633, aux.acc_seg: 93.0316, loss: 0.2116 +2024-06-19 13:28:24,150 - mmseg - INFO - per class results: +2024-06-19 13:28:24,156 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.78 | 89.87 | +| building | 84.97 | 93.25 | +| sky | 95.04 | 97.65 | +| floor | 84.44 | 91.36 | +| tree | 78.01 | 89.63 | +| ceiling | 87.62 | 94.65 | +| road | 85.92 | 92.84 | +| bed | 93.33 | 97.17 | +| windowpane | 66.8 | 82.86 | +| grass | 67.6 | 83.98 | +| cabinet | 67.94 | 77.2 | +| sidewalk | 70.65 | 83.39 | +| person | 86.93 | 94.44 | +| earth | 38.21 | 49.4 | +| door | 58.5 | 70.81 | +| table | 70.46 | 82.37 | +| mountain | 61.55 | 72.08 | +| plant | 57.11 | 68.13 | +| curtain | 78.54 | 87.84 | +| chair | 68.36 | 78.12 | +| car | 88.6 | 94.05 | +| water | 64.49 | 79.38 | +| painting | 81.12 | 92.11 | +| sofa | 82.69 | 91.15 | +| shelf | 51.21 | 68.4 | +| house | 53.17 | 68.86 | +| sea | 71.68 | 82.49 | +| mirror | 79.16 | 87.4 | +| rug | 65.08 | 79.38 | +| field | 27.49 | 51.63 | +| armchair | 63.56 | 79.68 | +| seat | 66.45 | 89.67 | +| fence | 53.82 | 67.09 | +| desk | 60.39 | 82.63 | +| rock | 56.68 | 87.13 | +| wardrobe | 55.21 | 74.66 | +| lamp | 76.82 | 87.8 | +| bathtub | 85.86 | 89.11 | +| railing | 42.25 | 59.0 | +| cushion | 68.66 | 83.58 | +| base | 46.47 | 62.1 | +| box | 41.51 | 53.18 | +| column | 59.26 | 67.11 | +| signboard | 42.79 | 54.84 | +| chest of drawers | 47.53 | 72.84 | +| counter | 52.18 | 59.25 | +| sand | 52.18 | 80.52 | +| sink | 84.28 | 89.07 | +| skyscraper | 46.64 | 58.7 | +| fireplace | 74.87 | 92.7 | +| refrigerator | 87.63 | 93.63 | +| grandstand | 59.43 | 83.82 | +| path | 28.73 | 39.86 | +| stairs | 34.5 | 45.81 | +| runway | 68.96 | 89.12 | +| case | 63.72 | 85.9 | +| pool table | 95.49 | 98.24 | +| pillow | 64.06 | 73.92 | +| screen door | 86.2 | 90.92 | +| stairway | 38.27 | 56.56 | +| river | 12.15 | 25.2 | +| bridge | 75.49 | 84.85 | +| bookcase | 47.41 | 64.93 | +| blind | 42.88 | 49.03 | +| coffee table | 61.64 | 89.29 | +| toilet | 90.9 | 94.34 | +| flower | 46.94 | 58.91 | +| book | 57.9 | 78.68 | +| hill | 14.7 | 24.6 | +| bench | 62.45 | 70.35 | +| countertop | 65.22 | 84.2 | +| stove | 86.53 | 93.63 | +| palm | 53.13 | 80.99 | +| kitchen island | 49.27 | 77.75 | +| computer | 76.54 | 91.95 | +| swivel chair | 49.68 | 73.6 | +| boat | 81.38 | 93.53 | +| bar | 72.16 | 90.97 | +| arcade machine | 84.03 | 88.37 | +| hovel | 26.62 | 29.15 | +| bus | 93.21 | 97.4 | +| towel | 78.48 | 88.64 | +| light | 63.48 | 72.85 | +| truck | 51.58 | 64.28 | +| tower | 27.0 | 55.97 | +| chandelier | 73.06 | 84.55 | +| awning | 39.65 | 50.42 | +| streetlight | 36.6 | 46.88 | +| booth | 52.18 | 69.1 | +| television receiver | 82.6 | 87.77 | +| airplane | 89.34 | 96.88 | +| dirt track | 8.21 | 14.13 | +| apparel | 65.09 | 84.69 | +| pole | 28.53 | 36.53 | +| land | 5.28 | 7.9 | +| bannister | 22.53 | 27.55 | +| escalator | 68.17 | 84.84 | +| ottoman | 53.83 | 67.98 | +| bottle | 47.41 | 69.43 | +| buffet | 63.26 | 77.76 | +| poster | 33.98 | 44.33 | +| stage | 23.28 | 46.72 | +| van | 52.13 | 74.02 | +| ship | 75.05 | 88.79 | +| fountain | 41.73 | 42.5 | +| conveyer belt | 82.73 | 96.68 | +| canopy | 59.55 | 76.31 | +| washer | 85.09 | 90.64 | +| plaything | 35.75 | 46.84 | +| swimming pool | 53.02 | 77.0 | +| stool | 54.38 | 75.66 | +| barrel | 72.79 | 98.51 | +| basket | 43.96 | 61.07 | +| waterfall | 57.58 | 77.53 | +| tent | 93.59 | 98.99 | +| bag | 31.49 | 36.53 | +| minibike | 76.75 | 90.09 | +| cradle | 90.52 | 96.97 | +| oven | 66.87 | 79.8 | +| ball | 59.04 | 70.42 | +| food | 52.51 | 61.22 | +| step | 12.0 | 15.53 | +| tank | 66.78 | 73.46 | +| trade name | 28.4 | 35.13 | +| microwave | 89.31 | 96.84 | +| pot | 63.18 | 74.28 | +| animal | 60.06 | 61.88 | +| bicycle | 59.52 | 79.46 | +| lake | 51.0 | 63.72 | +| dishwasher | 73.99 | 81.89 | +| screen | 48.41 | 70.49 | +| blanket | 41.48 | 51.45 | +| sculpture | 74.73 | 87.3 | +| hood | 65.04 | 75.27 | +| sconce | 62.47 | 75.18 | +| vase | 52.55 | 65.46 | +| traffic light | 39.5 | 67.13 | +| tray | 27.9 | 37.55 | +| ashcan | 50.41 | 66.33 | +| fan | 72.83 | 83.11 | +| pier | 40.7 | 44.98 | +| crt screen | 8.08 | 20.7 | +| plate | 64.81 | 80.9 | +| monitor | 40.17 | 46.1 | +| bulletin board | 59.45 | 73.39 | +| shower | 17.68 | 18.15 | +| radiator | 68.46 | 82.11 | +| glass | 22.28 | 23.63 | +| clock | 57.29 | 69.39 | +| flag | 70.67 | 82.27 | ++---------------------+-------+-------+ +2024-06-19 13:28:24,156 - mmseg - INFO - Summary: +2024-06-19 13:28:24,156 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.43 | 59.27 | 71.89 | ++-------+-------+-------+ +2024-06-19 13:28:24,157 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 13:28:24,157 - mmseg - INFO - Iter(val) [250] aAcc: 0.8643, mIoU: 0.5927, mAcc: 0.7189, IoU.wall: 0.8278, IoU.building: 0.8497, IoU.sky: 0.9504, IoU.floor: 0.8444, IoU.tree: 0.7801, IoU.ceiling: 0.8762, IoU.road: 0.8592, IoU.bed : 0.9333, IoU.windowpane: 0.6680, IoU.grass: 0.6760, IoU.cabinet: 0.6794, IoU.sidewalk: 0.7065, IoU.person: 0.8693, IoU.earth: 0.3821, IoU.door: 0.5850, IoU.table: 0.7046, IoU.mountain: 0.6155, IoU.plant: 0.5711, IoU.curtain: 0.7854, IoU.chair: 0.6836, IoU.car: 0.8860, IoU.water: 0.6449, IoU.painting: 0.8112, IoU.sofa: 0.8269, IoU.shelf: 0.5121, IoU.house: 0.5317, IoU.sea: 0.7168, IoU.mirror: 0.7916, IoU.rug: 0.6508, IoU.field: 0.2749, IoU.armchair: 0.6356, IoU.seat: 0.6645, IoU.fence: 0.5382, IoU.desk: 0.6039, IoU.rock: 0.5668, IoU.wardrobe: 0.5521, IoU.lamp: 0.7682, IoU.bathtub: 0.8586, IoU.railing: 0.4225, IoU.cushion: 0.6866, IoU.base: 0.4647, IoU.box: 0.4151, IoU.column: 0.5926, IoU.signboard: 0.4279, IoU.chest of drawers: 0.4753, IoU.counter: 0.5218, IoU.sand: 0.5218, IoU.sink: 0.8428, IoU.skyscraper: 0.4664, IoU.fireplace: 0.7487, IoU.refrigerator: 0.8763, IoU.grandstand: 0.5943, IoU.path: 0.2873, IoU.stairs: 0.3450, IoU.runway: 0.6896, IoU.case: 0.6372, IoU.pool table: 0.9549, IoU.pillow: 0.6406, IoU.screen door: 0.8620, IoU.stairway: 0.3827, IoU.river: 0.1215, IoU.bridge: 0.7549, IoU.bookcase: 0.4741, IoU.blind: 0.4288, IoU.coffee table: 0.6164, IoU.toilet: 0.9090, IoU.flower: 0.4694, IoU.book: 0.5790, IoU.hill: 0.1470, IoU.bench: 0.6245, IoU.countertop: 0.6522, IoU.stove: 0.8653, IoU.palm: 0.5313, IoU.kitchen island: 0.4927, IoU.computer: 0.7654, IoU.swivel chair: 0.4968, IoU.boat: 0.8138, IoU.bar: 0.7216, IoU.arcade machine: 0.8403, IoU.hovel: 0.2662, IoU.bus: 0.9321, IoU.towel: 0.7848, IoU.light: 0.6348, IoU.truck: 0.5158, IoU.tower: 0.2700, IoU.chandelier: 0.7306, IoU.awning: 0.3965, IoU.streetlight: 0.3660, IoU.booth: 0.5218, IoU.television receiver: 0.8260, IoU.airplane: 0.8934, IoU.dirt track: 0.0821, IoU.apparel: 0.6509, IoU.pole: 0.2853, IoU.land: 0.0528, IoU.bannister: 0.2253, IoU.escalator: 0.6817, IoU.ottoman: 0.5383, IoU.bottle: 0.4741, IoU.buffet: 0.6326, IoU.poster: 0.3398, IoU.stage: 0.2328, IoU.van: 0.5213, IoU.ship: 0.7505, IoU.fountain: 0.4173, IoU.conveyer belt: 0.8273, IoU.canopy: 0.5955, IoU.washer: 0.8509, IoU.plaything: 0.3575, IoU.swimming pool: 0.5302, IoU.stool: 0.5438, IoU.barrel: 0.7279, IoU.basket: 0.4396, IoU.waterfall: 0.5758, IoU.tent: 0.9359, IoU.bag: 0.3149, IoU.minibike: 0.7675, IoU.cradle: 0.9052, IoU.oven: 0.6687, IoU.ball: 0.5904, IoU.food: 0.5251, IoU.step: 0.1200, IoU.tank: 0.6678, IoU.trade name: 0.2840, IoU.microwave: 0.8931, IoU.pot: 0.6318, IoU.animal: 0.6006, IoU.bicycle: 0.5952, IoU.lake: 0.5100, IoU.dishwasher: 0.7399, IoU.screen: 0.4841, IoU.blanket: 0.4148, IoU.sculpture: 0.7473, IoU.hood: 0.6504, IoU.sconce: 0.6247, IoU.vase: 0.5255, IoU.traffic light: 0.3950, IoU.tray: 0.2790, IoU.ashcan: 0.5041, IoU.fan: 0.7283, IoU.pier: 0.4070, IoU.crt screen: 0.0808, IoU.plate: 0.6481, IoU.monitor: 0.4017, IoU.bulletin board: 0.5945, IoU.shower: 0.1768, IoU.radiator: 0.6846, IoU.glass: 0.2228, IoU.clock: 0.5729, IoU.flag: 0.7067, Acc.wall: 0.8987, Acc.building: 0.9325, Acc.sky: 0.9765, Acc.floor: 0.9136, Acc.tree: 0.8963, Acc.ceiling: 0.9465, Acc.road: 0.9284, Acc.bed : 0.9717, Acc.windowpane: 0.8286, Acc.grass: 0.8398, Acc.cabinet: 0.7720, Acc.sidewalk: 0.8339, Acc.person: 0.9444, Acc.earth: 0.4940, Acc.door: 0.7081, Acc.table: 0.8237, Acc.mountain: 0.7208, Acc.plant: 0.6813, Acc.curtain: 0.8784, Acc.chair: 0.7812, Acc.car: 0.9405, Acc.water: 0.7938, Acc.painting: 0.9211, Acc.sofa: 0.9115, Acc.shelf: 0.6840, Acc.house: 0.6886, Acc.sea: 0.8249, Acc.mirror: 0.8740, Acc.rug: 0.7938, Acc.field: 0.5163, Acc.armchair: 0.7968, Acc.seat: 0.8967, Acc.fence: 0.6709, Acc.desk: 0.8263, Acc.rock: 0.8713, Acc.wardrobe: 0.7466, Acc.lamp: 0.8780, Acc.bathtub: 0.8911, Acc.railing: 0.5900, Acc.cushion: 0.8358, Acc.base: 0.6210, Acc.box: 0.5318, Acc.column: 0.6711, Acc.signboard: 0.5484, Acc.chest of drawers: 0.7284, Acc.counter: 0.5925, Acc.sand: 0.8052, Acc.sink: 0.8907, Acc.skyscraper: 0.5870, Acc.fireplace: 0.9270, Acc.refrigerator: 0.9363, Acc.grandstand: 0.8382, Acc.path: 0.3986, Acc.stairs: 0.4581, Acc.runway: 0.8912, Acc.case: 0.8590, Acc.pool table: 0.9824, Acc.pillow: 0.7392, Acc.screen door: 0.9092, Acc.stairway: 0.5656, Acc.river: 0.2520, Acc.bridge: 0.8485, Acc.bookcase: 0.6493, Acc.blind: 0.4903, Acc.coffee table: 0.8929, Acc.toilet: 0.9434, Acc.flower: 0.5891, Acc.book: 0.7868, Acc.hill: 0.2460, Acc.bench: 0.7035, Acc.countertop: 0.8420, Acc.stove: 0.9363, Acc.palm: 0.8099, Acc.kitchen island: 0.7775, Acc.computer: 0.9195, Acc.swivel chair: 0.7360, Acc.boat: 0.9353, Acc.bar: 0.9097, Acc.arcade machine: 0.8837, Acc.hovel: 0.2915, Acc.bus: 0.9740, Acc.towel: 0.8864, Acc.light: 0.7285, Acc.truck: 0.6428, Acc.tower: 0.5597, Acc.chandelier: 0.8455, Acc.awning: 0.5042, Acc.streetlight: 0.4688, Acc.booth: 0.6910, Acc.television receiver: 0.8777, Acc.airplane: 0.9688, Acc.dirt track: 0.1413, Acc.apparel: 0.8469, Acc.pole: 0.3653, Acc.land: 0.0790, Acc.bannister: 0.2755, Acc.escalator: 0.8484, Acc.ottoman: 0.6798, Acc.bottle: 0.6943, Acc.buffet: 0.7776, Acc.poster: 0.4433, Acc.stage: 0.4672, Acc.van: 0.7402, Acc.ship: 0.8879, Acc.fountain: 0.4250, Acc.conveyer belt: 0.9668, Acc.canopy: 0.7631, Acc.washer: 0.9064, Acc.plaything: 0.4684, Acc.swimming pool: 0.7700, Acc.stool: 0.7566, Acc.barrel: 0.9851, Acc.basket: 0.6107, Acc.waterfall: 0.7753, Acc.tent: 0.9899, Acc.bag: 0.3653, Acc.minibike: 0.9009, Acc.cradle: 0.9697, Acc.oven: 0.7980, Acc.ball: 0.7042, Acc.food: 0.6122, Acc.step: 0.1553, Acc.tank: 0.7346, Acc.trade name: 0.3513, Acc.microwave: 0.9684, Acc.pot: 0.7428, Acc.animal: 0.6188, Acc.bicycle: 0.7946, Acc.lake: 0.6372, Acc.dishwasher: 0.8189, Acc.screen: 0.7049, Acc.blanket: 0.5145, Acc.sculpture: 0.8730, Acc.hood: 0.7527, Acc.sconce: 0.7518, Acc.vase: 0.6546, Acc.traffic light: 0.6713, Acc.tray: 0.3755, Acc.ashcan: 0.6633, Acc.fan: 0.8311, Acc.pier: 0.4498, Acc.crt screen: 0.2070, Acc.plate: 0.8090, Acc.monitor: 0.4610, Acc.bulletin board: 0.7339, Acc.shower: 0.1815, Acc.radiator: 0.8211, Acc.glass: 0.2363, Acc.clock: 0.6939, Acc.flag: 0.8227 +2024-06-19 13:30:03,319 - mmseg - INFO - Iter [59050/80000] lr: 1.048e-05, eta: 12:25:21, time: 4.227, data_time: 2.259, memory: 72263, decode.loss_ce: 0.1403, decode.acc_seg: 93.5688, aux.loss_ce: 0.0600, aux.acc_seg: 93.1941, loss: 0.2003 +2024-06-19 13:31:42,186 - mmseg - INFO - Iter [59100/80000] lr: 1.045e-05, eta: 12:23:31, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1386, decode.acc_seg: 93.8959, aux.loss_ce: 0.0591, aux.acc_seg: 93.4798, loss: 0.1977 +2024-06-19 13:33:21,151 - mmseg - INFO - Iter [59150/80000] lr: 1.043e-05, eta: 12:21:42, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1488, decode.acc_seg: 93.5095, aux.loss_ce: 0.0631, aux.acc_seg: 93.1595, loss: 0.2119 +2024-06-19 13:35:00,186 - mmseg - INFO - Iter [59200/80000] lr: 1.040e-05, eta: 12:19:52, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1463, decode.acc_seg: 93.4838, aux.loss_ce: 0.0622, aux.acc_seg: 93.1694, loss: 0.2085 +2024-06-19 13:36:39,219 - mmseg - INFO - Iter [59250/80000] lr: 1.038e-05, eta: 12:18:03, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1445, decode.acc_seg: 93.6717, aux.loss_ce: 0.0615, aux.acc_seg: 93.3069, loss: 0.2060 +2024-06-19 13:38:18,146 - mmseg - INFO - Iter [59300/80000] lr: 1.035e-05, eta: 12:16:13, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1541, decode.acc_seg: 93.2018, aux.loss_ce: 0.0656, aux.acc_seg: 92.7676, loss: 0.2197 +2024-06-19 13:39:57,429 - mmseg - INFO - Iter [59350/80000] lr: 1.033e-05, eta: 12:14:24, time: 1.986, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1484, decode.acc_seg: 93.6132, aux.loss_ce: 0.0633, aux.acc_seg: 93.2060, loss: 0.2117 +2024-06-19 13:41:39,834 - mmseg - INFO - Iter [59400/80000] lr: 1.030e-05, eta: 12:12:36, time: 2.048, data_time: 0.077, memory: 72263, decode.loss_ce: 0.1350, decode.acc_seg: 93.8751, aux.loss_ce: 0.0580, aux.acc_seg: 93.4343, loss: 0.1930 +2024-06-19 13:43:18,824 - mmseg - INFO - Iter [59450/80000] lr: 1.028e-05, eta: 12:10:47, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1419, decode.acc_seg: 93.7781, aux.loss_ce: 0.0608, aux.acc_seg: 93.3437, loss: 0.2026 +2024-06-19 13:44:57,741 - mmseg - INFO - Iter [59500/80000] lr: 1.025e-05, eta: 12:08:57, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1479, decode.acc_seg: 93.5230, aux.loss_ce: 0.0630, aux.acc_seg: 93.1237, loss: 0.2109 +2024-06-19 13:46:36,800 - mmseg - INFO - Iter [59550/80000] lr: 1.023e-05, eta: 12:07:08, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1423, decode.acc_seg: 93.7545, aux.loss_ce: 0.0606, aux.acc_seg: 93.3605, loss: 0.2029 +2024-06-19 13:48:15,709 - mmseg - INFO - Iter [59600/80000] lr: 1.020e-05, eta: 12:05:19, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1463, decode.acc_seg: 93.6161, aux.loss_ce: 0.0626, aux.acc_seg: 93.2331, loss: 0.2089 +2024-06-19 13:49:54,646 - mmseg - INFO - Iter [59650/80000] lr: 1.018e-05, eta: 12:03:29, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1410, decode.acc_seg: 93.7840, aux.loss_ce: 0.0601, aux.acc_seg: 93.4166, loss: 0.2011 +2024-06-19 13:51:33,543 - mmseg - INFO - Iter [59700/80000] lr: 1.015e-05, eta: 12:01:40, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1373, decode.acc_seg: 93.9914, aux.loss_ce: 0.0590, aux.acc_seg: 93.5523, loss: 0.1963 +2024-06-19 13:53:12,435 - mmseg - INFO - Iter [59750/80000] lr: 1.013e-05, eta: 11:59:51, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1288, decode.acc_seg: 94.3099, aux.loss_ce: 0.0554, aux.acc_seg: 93.9052, loss: 0.1842 +2024-06-19 13:54:51,404 - mmseg - INFO - Iter [59800/80000] lr: 1.010e-05, eta: 11:58:02, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1428, decode.acc_seg: 93.7119, aux.loss_ce: 0.0609, aux.acc_seg: 93.3206, loss: 0.2037 +2024-06-19 13:56:30,290 - mmseg - INFO - Iter [59850/80000] lr: 1.008e-05, eta: 11:56:12, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1384, decode.acc_seg: 93.9414, aux.loss_ce: 0.0590, aux.acc_seg: 93.5322, loss: 0.1974 +2024-06-19 13:58:09,299 - mmseg - INFO - Iter [59900/80000] lr: 1.005e-05, eta: 11:54:23, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1444, decode.acc_seg: 93.6034, aux.loss_ce: 0.0616, aux.acc_seg: 93.1791, loss: 0.2060 +2024-06-19 13:59:48,287 - mmseg - INFO - Iter [59950/80000] lr: 1.003e-05, eta: 11:52:34, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1488, decode.acc_seg: 93.3497, aux.loss_ce: 0.0633, aux.acc_seg: 92.9332, loss: 0.2122 +2024-06-19 14:01:27,390 - mmseg - INFO - Saving checkpoint at 60000 iterations +2024-06-19 14:02:54,093 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 14:02:54,094 - mmseg - INFO - Iter [60000/80000] lr: 1.000e-05, eta: 11:51:14, time: 3.716, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1483, decode.acc_seg: 93.2688, aux.loss_ce: 0.0630, aux.acc_seg: 92.8552, loss: 0.2113 +2024-06-19 14:04:50,543 - mmseg - INFO - per class results: +2024-06-19 14:04:50,549 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.42 | 89.53 | +| building | 85.13 | 92.96 | +| sky | 95.01 | 97.48 | +| floor | 85.11 | 92.3 | +| tree | 78.52 | 90.67 | +| ceiling | 87.82 | 95.02 | +| road | 86.32 | 91.3 | +| bed | 93.29 | 97.27 | +| windowpane | 67.53 | 82.57 | +| grass | 68.35 | 83.09 | +| cabinet | 67.58 | 77.1 | +| sidewalk | 71.37 | 86.76 | +| person | 86.62 | 95.25 | +| earth | 40.78 | 54.43 | +| door | 60.37 | 78.09 | +| table | 70.95 | 81.39 | +| mountain | 63.65 | 74.25 | +| plant | 57.29 | 68.33 | +| curtain | 77.66 | 87.64 | +| chair | 69.52 | 79.53 | +| car | 88.57 | 94.53 | +| water | 64.73 | 81.05 | +| painting | 81.24 | 91.72 | +| sofa | 81.15 | 89.41 | +| shelf | 49.81 | 65.33 | +| house | 51.81 | 63.62 | +| sea | 74.72 | 84.2 | +| mirror | 80.28 | 87.91 | +| rug | 63.66 | 73.05 | +| field | 29.23 | 48.31 | +| armchair | 62.42 | 83.11 | +| seat | 71.7 | 87.68 | +| fence | 53.63 | 67.23 | +| desk | 59.94 | 80.34 | +| rock | 54.41 | 85.52 | +| wardrobe | 53.16 | 72.78 | +| lamp | 76.77 | 88.81 | +| bathtub | 85.51 | 88.46 | +| railing | 43.64 | 62.0 | +| cushion | 68.62 | 84.54 | +| base | 44.63 | 58.49 | +| box | 42.36 | 54.01 | +| column | 58.04 | 69.78 | +| signboard | 41.59 | 59.31 | +| chest of drawers | 49.4 | 75.53 | +| counter | 55.16 | 68.84 | +| sand | 53.58 | 77.48 | +| sink | 82.57 | 86.71 | +| skyscraper | 46.3 | 60.18 | +| fireplace | 74.77 | 95.18 | +| refrigerator | 88.07 | 94.77 | +| grandstand | 61.45 | 81.24 | +| path | 31.32 | 42.59 | +| stairs | 36.78 | 48.77 | +| runway | 72.48 | 93.39 | +| case | 63.26 | 84.98 | +| pool table | 95.4 | 98.3 | +| pillow | 62.56 | 70.37 | +| screen door | 86.55 | 91.35 | +| stairway | 42.92 | 61.8 | +| river | 11.83 | 23.12 | +| bridge | 63.63 | 71.29 | +| bookcase | 45.8 | 62.01 | +| blind | 47.02 | 55.7 | +| coffee table | 61.56 | 88.41 | +| toilet | 91.0 | 94.08 | +| flower | 45.2 | 57.96 | +| book | 57.67 | 79.75 | +| hill | 14.24 | 23.55 | +| bench | 66.17 | 72.92 | +| countertop | 64.98 | 84.17 | +| stove | 87.75 | 93.11 | +| palm | 53.42 | 77.78 | +| kitchen island | 52.5 | 75.77 | +| computer | 77.09 | 90.97 | +| swivel chair | 53.82 | 77.69 | +| boat | 71.77 | 93.09 | +| bar | 72.67 | 85.47 | +| arcade machine | 83.41 | 86.29 | +| hovel | 45.19 | 51.22 | +| bus | 93.29 | 97.42 | +| towel | 81.38 | 86.94 | +| light | 62.93 | 72.2 | +| truck | 55.2 | 65.9 | +| tower | 14.74 | 24.18 | +| chandelier | 73.54 | 85.8 | +| awning | 50.96 | 70.53 | +| streetlight | 37.59 | 50.54 | +| booth | 49.49 | 78.23 | +| television receiver | 80.88 | 87.99 | +| airplane | 89.46 | 96.19 | +| dirt track | 14.14 | 22.97 | +| apparel | 64.63 | 80.97 | +| pole | 28.05 | 36.08 | +| land | 5.57 | 8.45 | +| bannister | 23.48 | 28.08 | +| escalator | 66.44 | 85.72 | +| ottoman | 56.74 | 71.18 | +| bottle | 45.82 | 73.87 | +| buffet | 62.47 | 72.16 | +| poster | 33.61 | 48.32 | +| stage | 25.23 | 50.02 | +| van | 54.42 | 71.23 | +| ship | 69.44 | 80.42 | +| fountain | 37.86 | 39.44 | +| conveyer belt | 88.32 | 95.42 | +| canopy | 57.29 | 73.14 | +| washer | 86.53 | 92.24 | +| plaything | 36.84 | 51.04 | +| swimming pool | 52.81 | 76.86 | +| stool | 54.74 | 72.44 | +| barrel | 72.73 | 97.72 | +| basket | 43.11 | 60.8 | +| waterfall | 53.07 | 68.9 | +| tent | 96.57 | 98.88 | +| bag | 31.61 | 35.58 | +| minibike | 78.0 | 90.78 | +| cradle | 82.49 | 98.04 | +| oven | 67.22 | 78.01 | +| ball | 60.36 | 77.67 | +| food | 50.31 | 56.0 | +| step | 12.99 | 17.46 | +| tank | 62.81 | 67.44 | +| trade name | 26.86 | 32.71 | +| microwave | 89.73 | 96.75 | +| pot | 61.3 | 69.45 | +| animal | 58.04 | 59.19 | +| bicycle | 62.45 | 78.98 | +| lake | 52.34 | 63.71 | +| dishwasher | 76.98 | 84.71 | +| screen | 60.58 | 96.24 | +| blanket | 36.36 | 43.33 | +| sculpture | 71.99 | 89.26 | +| hood | 71.14 | 75.72 | +| sconce | 61.55 | 71.98 | +| vase | 51.68 | 69.02 | +| traffic light | 37.31 | 72.96 | +| tray | 24.79 | 34.16 | +| ashcan | 51.23 | 65.84 | +| fan | 71.16 | 80.46 | +| pier | 40.15 | 43.89 | +| crt screen | 2.03 | 3.88 | +| plate | 65.66 | 79.42 | +| monitor | 41.55 | 47.67 | +| bulletin board | 45.49 | 55.36 | +| shower | 20.77 | 21.43 | +| radiator | 68.07 | 82.29 | +| glass | 22.54 | 23.93 | +| clock | 57.08 | 64.18 | +| flag | 69.36 | 81.59 | ++---------------------+-------+-------+ +2024-06-19 14:04:50,549 - mmseg - INFO - Summary: +2024-06-19 14:04:50,550 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.58 | 59.4 | 71.82 | ++-------+------+-------+ +2024-06-19 14:04:50,550 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 14:04:50,551 - mmseg - INFO - Iter(val) [250] aAcc: 0.8658, mIoU: 0.5940, mAcc: 0.7182, IoU.wall: 0.8242, IoU.building: 0.8513, IoU.sky: 0.9501, IoU.floor: 0.8511, IoU.tree: 0.7852, IoU.ceiling: 0.8782, IoU.road: 0.8632, IoU.bed : 0.9329, IoU.windowpane: 0.6753, IoU.grass: 0.6835, IoU.cabinet: 0.6758, IoU.sidewalk: 0.7137, IoU.person: 0.8662, IoU.earth: 0.4078, IoU.door: 0.6037, IoU.table: 0.7095, IoU.mountain: 0.6365, IoU.plant: 0.5729, IoU.curtain: 0.7766, IoU.chair: 0.6952, IoU.car: 0.8857, IoU.water: 0.6473, IoU.painting: 0.8124, IoU.sofa: 0.8115, IoU.shelf: 0.4981, IoU.house: 0.5181, IoU.sea: 0.7472, IoU.mirror: 0.8028, IoU.rug: 0.6366, IoU.field: 0.2923, IoU.armchair: 0.6242, IoU.seat: 0.7170, IoU.fence: 0.5363, IoU.desk: 0.5994, IoU.rock: 0.5441, IoU.wardrobe: 0.5316, IoU.lamp: 0.7677, IoU.bathtub: 0.8551, IoU.railing: 0.4364, IoU.cushion: 0.6862, IoU.base: 0.4463, IoU.box: 0.4236, IoU.column: 0.5804, IoU.signboard: 0.4159, IoU.chest of drawers: 0.4940, IoU.counter: 0.5516, IoU.sand: 0.5358, IoU.sink: 0.8257, IoU.skyscraper: 0.4630, IoU.fireplace: 0.7477, IoU.refrigerator: 0.8807, IoU.grandstand: 0.6145, IoU.path: 0.3132, IoU.stairs: 0.3678, IoU.runway: 0.7248, IoU.case: 0.6326, IoU.pool table: 0.9540, IoU.pillow: 0.6256, IoU.screen door: 0.8655, IoU.stairway: 0.4292, IoU.river: 0.1183, IoU.bridge: 0.6363, IoU.bookcase: 0.4580, IoU.blind: 0.4702, IoU.coffee table: 0.6156, IoU.toilet: 0.9100, IoU.flower: 0.4520, IoU.book: 0.5767, IoU.hill: 0.1424, IoU.bench: 0.6617, IoU.countertop: 0.6498, IoU.stove: 0.8775, IoU.palm: 0.5342, IoU.kitchen island: 0.5250, IoU.computer: 0.7709, IoU.swivel chair: 0.5382, IoU.boat: 0.7177, IoU.bar: 0.7267, IoU.arcade machine: 0.8341, IoU.hovel: 0.4519, IoU.bus: 0.9329, IoU.towel: 0.8138, IoU.light: 0.6293, IoU.truck: 0.5520, IoU.tower: 0.1474, IoU.chandelier: 0.7354, IoU.awning: 0.5096, IoU.streetlight: 0.3759, IoU.booth: 0.4949, IoU.television receiver: 0.8088, IoU.airplane: 0.8946, IoU.dirt track: 0.1414, IoU.apparel: 0.6463, IoU.pole: 0.2805, IoU.land: 0.0557, IoU.bannister: 0.2348, IoU.escalator: 0.6644, IoU.ottoman: 0.5674, IoU.bottle: 0.4582, IoU.buffet: 0.6247, IoU.poster: 0.3361, IoU.stage: 0.2523, IoU.van: 0.5442, IoU.ship: 0.6944, IoU.fountain: 0.3786, IoU.conveyer belt: 0.8832, IoU.canopy: 0.5729, IoU.washer: 0.8653, IoU.plaything: 0.3684, IoU.swimming pool: 0.5281, IoU.stool: 0.5474, IoU.barrel: 0.7273, IoU.basket: 0.4311, IoU.waterfall: 0.5307, IoU.tent: 0.9657, IoU.bag: 0.3161, IoU.minibike: 0.7800, IoU.cradle: 0.8249, IoU.oven: 0.6722, IoU.ball: 0.6036, IoU.food: 0.5031, IoU.step: 0.1299, IoU.tank: 0.6281, IoU.trade name: 0.2686, IoU.microwave: 0.8973, IoU.pot: 0.6130, IoU.animal: 0.5804, IoU.bicycle: 0.6245, IoU.lake: 0.5234, IoU.dishwasher: 0.7698, IoU.screen: 0.6058, IoU.blanket: 0.3636, IoU.sculpture: 0.7199, IoU.hood: 0.7114, IoU.sconce: 0.6155, IoU.vase: 0.5168, IoU.traffic light: 0.3731, IoU.tray: 0.2479, IoU.ashcan: 0.5123, IoU.fan: 0.7116, IoU.pier: 0.4015, IoU.crt screen: 0.0203, IoU.plate: 0.6566, IoU.monitor: 0.4155, IoU.bulletin board: 0.4549, IoU.shower: 0.2077, IoU.radiator: 0.6807, IoU.glass: 0.2254, IoU.clock: 0.5708, IoU.flag: 0.6936, Acc.wall: 0.8953, Acc.building: 0.9296, Acc.sky: 0.9748, Acc.floor: 0.9230, Acc.tree: 0.9067, Acc.ceiling: 0.9502, Acc.road: 0.9130, Acc.bed : 0.9727, Acc.windowpane: 0.8257, Acc.grass: 0.8309, Acc.cabinet: 0.7710, Acc.sidewalk: 0.8676, Acc.person: 0.9525, Acc.earth: 0.5443, Acc.door: 0.7809, Acc.table: 0.8139, Acc.mountain: 0.7425, Acc.plant: 0.6833, Acc.curtain: 0.8764, Acc.chair: 0.7953, Acc.car: 0.9453, Acc.water: 0.8105, Acc.painting: 0.9172, Acc.sofa: 0.8941, Acc.shelf: 0.6533, Acc.house: 0.6362, Acc.sea: 0.8420, Acc.mirror: 0.8791, Acc.rug: 0.7305, Acc.field: 0.4831, Acc.armchair: 0.8311, Acc.seat: 0.8768, Acc.fence: 0.6723, Acc.desk: 0.8034, Acc.rock: 0.8552, Acc.wardrobe: 0.7278, Acc.lamp: 0.8881, Acc.bathtub: 0.8846, Acc.railing: 0.6200, Acc.cushion: 0.8454, Acc.base: 0.5849, Acc.box: 0.5401, Acc.column: 0.6978, Acc.signboard: 0.5931, Acc.chest of drawers: 0.7553, Acc.counter: 0.6884, Acc.sand: 0.7748, Acc.sink: 0.8671, Acc.skyscraper: 0.6018, Acc.fireplace: 0.9518, Acc.refrigerator: 0.9477, Acc.grandstand: 0.8124, Acc.path: 0.4259, Acc.stairs: 0.4877, Acc.runway: 0.9339, Acc.case: 0.8498, Acc.pool table: 0.9830, Acc.pillow: 0.7037, Acc.screen door: 0.9135, Acc.stairway: 0.6180, Acc.river: 0.2312, Acc.bridge: 0.7129, Acc.bookcase: 0.6201, Acc.blind: 0.5570, Acc.coffee table: 0.8841, Acc.toilet: 0.9408, Acc.flower: 0.5796, Acc.book: 0.7975, Acc.hill: 0.2355, Acc.bench: 0.7292, Acc.countertop: 0.8417, Acc.stove: 0.9311, Acc.palm: 0.7778, Acc.kitchen island: 0.7577, Acc.computer: 0.9097, Acc.swivel chair: 0.7769, Acc.boat: 0.9309, Acc.bar: 0.8547, Acc.arcade machine: 0.8629, Acc.hovel: 0.5122, Acc.bus: 0.9742, Acc.towel: 0.8694, Acc.light: 0.7220, Acc.truck: 0.6590, Acc.tower: 0.2418, Acc.chandelier: 0.8580, Acc.awning: 0.7053, Acc.streetlight: 0.5054, Acc.booth: 0.7823, Acc.television receiver: 0.8799, Acc.airplane: 0.9619, Acc.dirt track: 0.2297, Acc.apparel: 0.8097, Acc.pole: 0.3608, Acc.land: 0.0845, Acc.bannister: 0.2808, Acc.escalator: 0.8572, Acc.ottoman: 0.7118, Acc.bottle: 0.7387, Acc.buffet: 0.7216, Acc.poster: 0.4832, Acc.stage: 0.5002, Acc.van: 0.7123, Acc.ship: 0.8042, Acc.fountain: 0.3944, Acc.conveyer belt: 0.9542, Acc.canopy: 0.7314, Acc.washer: 0.9224, Acc.plaything: 0.5104, Acc.swimming pool: 0.7686, Acc.stool: 0.7244, Acc.barrel: 0.9772, Acc.basket: 0.6080, Acc.waterfall: 0.6890, Acc.tent: 0.9888, Acc.bag: 0.3558, Acc.minibike: 0.9078, Acc.cradle: 0.9804, Acc.oven: 0.7801, Acc.ball: 0.7767, Acc.food: 0.5600, Acc.step: 0.1746, Acc.tank: 0.6744, Acc.trade name: 0.3271, Acc.microwave: 0.9675, Acc.pot: 0.6945, Acc.animal: 0.5919, Acc.bicycle: 0.7898, Acc.lake: 0.6371, Acc.dishwasher: 0.8471, Acc.screen: 0.9624, Acc.blanket: 0.4333, Acc.sculpture: 0.8926, Acc.hood: 0.7572, Acc.sconce: 0.7198, Acc.vase: 0.6902, Acc.traffic light: 0.7296, Acc.tray: 0.3416, Acc.ashcan: 0.6584, Acc.fan: 0.8046, Acc.pier: 0.4389, Acc.crt screen: 0.0388, Acc.plate: 0.7942, Acc.monitor: 0.4767, Acc.bulletin board: 0.5536, Acc.shower: 0.2143, Acc.radiator: 0.8229, Acc.glass: 0.2393, Acc.clock: 0.6418, Acc.flag: 0.8159 +2024-06-19 14:06:29,765 - mmseg - INFO - Iter [60050/80000] lr: 9.975e-06, eta: 11:50:03, time: 4.313, data_time: 2.346, memory: 72263, decode.loss_ce: 0.1429, decode.acc_seg: 93.7536, aux.loss_ce: 0.0601, aux.acc_seg: 93.4333, loss: 0.2030 +2024-06-19 14:08:08,687 - mmseg - INFO - Iter [60100/80000] lr: 9.951e-06, eta: 11:48:14, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1343, decode.acc_seg: 94.0333, aux.loss_ce: 0.0577, aux.acc_seg: 93.5745, loss: 0.1919 +2024-06-19 14:09:47,716 - mmseg - INFO - Iter [60150/80000] lr: 9.926e-06, eta: 11:46:25, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1408, decode.acc_seg: 93.8102, aux.loss_ce: 0.0601, aux.acc_seg: 93.4648, loss: 0.2009 +2024-06-19 14:11:26,577 - mmseg - INFO - Iter [60200/80000] lr: 9.901e-06, eta: 11:44:35, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1412, decode.acc_seg: 93.7671, aux.loss_ce: 0.0600, aux.acc_seg: 93.4075, loss: 0.2012 +2024-06-19 14:13:05,586 - mmseg - INFO - Iter [60250/80000] lr: 9.876e-06, eta: 11:42:46, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1404, decode.acc_seg: 93.6766, aux.loss_ce: 0.0597, aux.acc_seg: 93.2817, loss: 0.2001 +2024-06-19 14:14:44,410 - mmseg - INFO - Iter [60300/80000] lr: 9.851e-06, eta: 11:40:57, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1403, decode.acc_seg: 93.8659, aux.loss_ce: 0.0597, aux.acc_seg: 93.4415, loss: 0.2000 +2024-06-19 14:16:23,375 - mmseg - INFO - Iter [60350/80000] lr: 9.825e-06, eta: 11:39:07, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1494, decode.acc_seg: 93.5352, aux.loss_ce: 0.0637, aux.acc_seg: 93.0860, loss: 0.2131 +2024-06-19 14:18:02,296 - mmseg - INFO - Iter [60400/80000] lr: 9.800e-06, eta: 11:37:18, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1439, decode.acc_seg: 93.8603, aux.loss_ce: 0.0611, aux.acc_seg: 93.4594, loss: 0.2050 +2024-06-19 14:19:41,243 - mmseg - INFO - Iter [60450/80000] lr: 9.775e-06, eta: 11:35:29, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1416, decode.acc_seg: 93.9130, aux.loss_ce: 0.0607, aux.acc_seg: 93.5225, loss: 0.2023 +2024-06-19 14:21:20,235 - mmseg - INFO - Iter [60500/80000] lr: 9.751e-06, eta: 11:33:40, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1395, decode.acc_seg: 93.9361, aux.loss_ce: 0.0596, aux.acc_seg: 93.5560, loss: 0.1990 +2024-06-19 14:22:59,178 - mmseg - INFO - Iter [60550/80000] lr: 9.726e-06, eta: 11:31:50, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1427, decode.acc_seg: 93.8204, aux.loss_ce: 0.0601, aux.acc_seg: 93.4641, loss: 0.2028 +2024-06-19 14:24:38,076 - mmseg - INFO - Iter [60600/80000] lr: 9.701e-06, eta: 11:30:01, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1398, decode.acc_seg: 93.8109, aux.loss_ce: 0.0595, aux.acc_seg: 93.4268, loss: 0.1993 +2024-06-19 14:26:20,442 - mmseg - INFO - Iter [60650/80000] lr: 9.676e-06, eta: 11:28:13, time: 2.047, data_time: 0.078, memory: 72263, decode.loss_ce: 0.1477, decode.acc_seg: 93.5650, aux.loss_ce: 0.0632, aux.acc_seg: 93.1556, loss: 0.2109 +2024-06-19 14:27:59,451 - mmseg - INFO - Iter [60700/80000] lr: 9.651e-06, eta: 11:26:24, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1453, decode.acc_seg: 93.5782, aux.loss_ce: 0.0621, aux.acc_seg: 93.1539, loss: 0.2074 +2024-06-19 14:29:38,369 - mmseg - INFO - Iter [60750/80000] lr: 9.625e-06, eta: 11:24:35, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1474, decode.acc_seg: 93.6836, aux.loss_ce: 0.0627, aux.acc_seg: 93.2696, loss: 0.2101 +2024-06-19 14:31:17,340 - mmseg - INFO - Iter [60800/80000] lr: 9.600e-06, eta: 11:22:46, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1303, decode.acc_seg: 94.1650, aux.loss_ce: 0.0559, aux.acc_seg: 93.8042, loss: 0.1862 +2024-06-19 14:32:56,337 - mmseg - INFO - Iter [60850/80000] lr: 9.576e-06, eta: 11:20:56, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1365, decode.acc_seg: 94.0592, aux.loss_ce: 0.0584, aux.acc_seg: 93.6449, loss: 0.1949 +2024-06-19 14:34:35,422 - mmseg - INFO - Iter [60900/80000] lr: 9.551e-06, eta: 11:19:07, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1416, decode.acc_seg: 93.6322, aux.loss_ce: 0.0602, aux.acc_seg: 93.1898, loss: 0.2019 +2024-06-19 14:36:14,384 - mmseg - INFO - Iter [60950/80000] lr: 9.526e-06, eta: 11:17:18, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1478, decode.acc_seg: 93.6184, aux.loss_ce: 0.0629, aux.acc_seg: 93.2472, loss: 0.2107 +2024-06-19 14:37:53,325 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 14:37:53,326 - mmseg - INFO - Iter [61000/80000] lr: 9.501e-06, eta: 11:15:29, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1337, decode.acc_seg: 94.0843, aux.loss_ce: 0.0573, aux.acc_seg: 93.6719, loss: 0.1911 +2024-06-19 14:39:45,487 - mmseg - INFO - per class results: +2024-06-19 14:39:45,493 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.84 | 91.03 | +| building | 85.63 | 93.09 | +| sky | 94.91 | 97.68 | +| floor | 85.05 | 91.57 | +| tree | 77.91 | 89.97 | +| ceiling | 87.69 | 93.97 | +| road | 86.98 | 91.18 | +| bed | 93.43 | 97.01 | +| windowpane | 66.85 | 81.51 | +| grass | 67.79 | 83.09 | +| cabinet | 67.79 | 77.31 | +| sidewalk | 71.78 | 86.97 | +| person | 86.86 | 94.57 | +| earth | 41.45 | 54.09 | +| door | 60.56 | 74.84 | +| table | 71.38 | 82.0 | +| mountain | 63.35 | 74.82 | +| plant | 56.71 | 67.92 | +| curtain | 78.59 | 87.8 | +| chair | 69.19 | 81.21 | +| car | 88.81 | 94.14 | +| water | 62.12 | 76.16 | +| painting | 81.9 | 91.42 | +| sofa | 82.74 | 91.23 | +| shelf | 50.51 | 68.6 | +| house | 53.08 | 61.89 | +| sea | 71.12 | 83.95 | +| mirror | 79.21 | 86.36 | +| rug | 64.58 | 76.67 | +| field | 28.67 | 49.05 | +| armchair | 61.41 | 75.96 | +| seat | 69.37 | 87.55 | +| fence | 52.79 | 68.32 | +| desk | 59.14 | 79.2 | +| rock | 57.97 | 86.67 | +| wardrobe | 54.35 | 73.06 | +| lamp | 77.41 | 88.48 | +| bathtub | 88.18 | 91.05 | +| railing | 43.95 | 63.96 | +| cushion | 68.63 | 85.63 | +| base | 42.6 | 54.05 | +| box | 40.26 | 50.28 | +| column | 57.22 | 67.65 | +| signboard | 41.63 | 58.44 | +| chest of drawers | 45.09 | 62.94 | +| counter | 50.12 | 60.15 | +| sand | 51.95 | 79.1 | +| sink | 81.94 | 86.85 | +| skyscraper | 45.79 | 58.67 | +| fireplace | 73.79 | 93.32 | +| refrigerator | 87.6 | 94.6 | +| grandstand | 59.24 | 81.89 | +| path | 32.2 | 46.15 | +| stairs | 39.83 | 50.32 | +| runway | 73.23 | 94.2 | +| case | 65.15 | 81.78 | +| pool table | 95.41 | 98.3 | +| pillow | 65.34 | 76.96 | +| screen door | 86.73 | 90.37 | +| stairway | 40.9 | 51.38 | +| river | 11.92 | 27.24 | +| bridge | 66.16 | 73.95 | +| bookcase | 46.33 | 60.09 | +| blind | 42.19 | 44.41 | +| coffee table | 61.97 | 87.79 | +| toilet | 90.58 | 93.45 | +| flower | 45.28 | 63.98 | +| book | 57.11 | 82.29 | +| hill | 13.25 | 21.26 | +| bench | 58.52 | 66.86 | +| countertop | 64.97 | 85.33 | +| stove | 86.34 | 91.99 | +| palm | 53.74 | 80.6 | +| kitchen island | 52.13 | 85.91 | +| computer | 76.72 | 91.84 | +| swivel chair | 51.23 | 79.97 | +| boat | 73.19 | 94.23 | +| bar | 70.45 | 89.71 | +| arcade machine | 83.16 | 85.84 | +| hovel | 51.82 | 59.0 | +| bus | 93.29 | 97.36 | +| towel | 79.88 | 90.61 | +| light | 63.6 | 76.54 | +| truck | 54.66 | 66.65 | +| tower | 23.89 | 43.09 | +| chandelier | 73.46 | 87.64 | +| awning | 42.45 | 53.68 | +| streetlight | 38.89 | 54.36 | +| booth | 50.78 | 72.62 | +| television receiver | 81.83 | 87.06 | +| airplane | 90.08 | 96.45 | +| dirt track | 5.72 | 17.75 | +| apparel | 68.97 | 86.13 | +| pole | 28.41 | 38.5 | +| land | 4.79 | 7.56 | +| bannister | 21.24 | 26.07 | +| escalator | 66.27 | 86.34 | +| ottoman | 55.83 | 70.21 | +| bottle | 45.63 | 72.88 | +| buffet | 55.88 | 63.32 | +| poster | 36.54 | 48.06 | +| stage | 20.66 | 35.19 | +| van | 54.81 | 72.3 | +| ship | 69.46 | 79.7 | +| fountain | 30.1 | 30.58 | +| conveyer belt | 87.15 | 95.69 | +| canopy | 59.32 | 74.74 | +| washer | 87.36 | 92.81 | +| plaything | 37.04 | 53.69 | +| swimming pool | 53.64 | 77.48 | +| stool | 56.68 | 73.72 | +| barrel | 73.68 | 97.99 | +| basket | 41.52 | 64.05 | +| waterfall | 52.4 | 65.83 | +| tent | 96.24 | 98.95 | +| bag | 32.95 | 38.61 | +| minibike | 77.99 | 91.76 | +| cradle | 87.81 | 97.47 | +| oven | 65.46 | 78.33 | +| ball | 55.69 | 63.47 | +| food | 64.26 | 74.34 | +| step | 12.89 | 14.09 | +| tank | 64.29 | 68.25 | +| trade name | 24.24 | 28.55 | +| microwave | 89.24 | 96.97 | +| pot | 60.96 | 69.67 | +| animal | 61.91 | 63.43 | +| bicycle | 62.73 | 79.03 | +| lake | 46.48 | 63.73 | +| dishwasher | 76.88 | 83.4 | +| screen | 61.19 | 90.25 | +| blanket | 38.21 | 46.06 | +| sculpture | 78.96 | 87.32 | +| hood | 69.81 | 75.06 | +| sconce | 62.32 | 76.63 | +| vase | 49.97 | 74.01 | +| traffic light | 37.75 | 71.29 | +| tray | 27.41 | 39.07 | +| ashcan | 49.66 | 69.26 | +| fan | 73.31 | 86.23 | +| pier | 41.21 | 45.13 | +| crt screen | 5.41 | 11.46 | +| plate | 65.04 | 79.76 | +| monitor | 35.98 | 41.03 | +| bulletin board | 49.92 | 59.24 | +| shower | 19.37 | 22.31 | +| radiator | 69.28 | 82.06 | +| glass | 24.33 | 26.84 | +| clock | 56.8 | 66.4 | +| flag | 70.56 | 83.79 | ++---------------------+-------+-------+ +2024-06-19 14:39:45,493 - mmseg - INFO - Summary: +2024-06-19 14:39:45,493 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.63 | 59.32 | 71.83 | ++-------+-------+-------+ +2024-06-19 14:39:45,494 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 14:39:45,494 - mmseg - INFO - Iter(val) [250] aAcc: 0.8663, mIoU: 0.5932, mAcc: 0.7183, IoU.wall: 0.8284, IoU.building: 0.8563, IoU.sky: 0.9491, IoU.floor: 0.8505, IoU.tree: 0.7791, IoU.ceiling: 0.8769, IoU.road: 0.8698, IoU.bed : 0.9343, IoU.windowpane: 0.6685, IoU.grass: 0.6779, IoU.cabinet: 0.6779, IoU.sidewalk: 0.7178, IoU.person: 0.8686, IoU.earth: 0.4145, IoU.door: 0.6056, IoU.table: 0.7138, IoU.mountain: 0.6335, IoU.plant: 0.5671, IoU.curtain: 0.7859, IoU.chair: 0.6919, IoU.car: 0.8881, IoU.water: 0.6212, IoU.painting: 0.8190, IoU.sofa: 0.8274, IoU.shelf: 0.5051, IoU.house: 0.5308, IoU.sea: 0.7112, IoU.mirror: 0.7921, IoU.rug: 0.6458, IoU.field: 0.2867, IoU.armchair: 0.6141, IoU.seat: 0.6937, IoU.fence: 0.5279, IoU.desk: 0.5914, IoU.rock: 0.5797, IoU.wardrobe: 0.5435, IoU.lamp: 0.7741, IoU.bathtub: 0.8818, IoU.railing: 0.4395, IoU.cushion: 0.6863, IoU.base: 0.4260, IoU.box: 0.4026, IoU.column: 0.5722, IoU.signboard: 0.4163, IoU.chest of drawers: 0.4509, IoU.counter: 0.5012, IoU.sand: 0.5195, IoU.sink: 0.8194, IoU.skyscraper: 0.4579, IoU.fireplace: 0.7379, IoU.refrigerator: 0.8760, IoU.grandstand: 0.5924, IoU.path: 0.3220, IoU.stairs: 0.3983, IoU.runway: 0.7323, IoU.case: 0.6515, IoU.pool table: 0.9541, IoU.pillow: 0.6534, IoU.screen door: 0.8673, IoU.stairway: 0.4090, IoU.river: 0.1192, IoU.bridge: 0.6616, IoU.bookcase: 0.4633, IoU.blind: 0.4219, IoU.coffee table: 0.6197, IoU.toilet: 0.9058, IoU.flower: 0.4528, IoU.book: 0.5711, IoU.hill: 0.1325, IoU.bench: 0.5852, IoU.countertop: 0.6497, IoU.stove: 0.8634, IoU.palm: 0.5374, IoU.kitchen island: 0.5213, IoU.computer: 0.7672, IoU.swivel chair: 0.5123, IoU.boat: 0.7319, IoU.bar: 0.7045, IoU.arcade machine: 0.8316, IoU.hovel: 0.5182, IoU.bus: 0.9329, IoU.towel: 0.7988, IoU.light: 0.6360, IoU.truck: 0.5466, IoU.tower: 0.2389, IoU.chandelier: 0.7346, IoU.awning: 0.4245, IoU.streetlight: 0.3889, IoU.booth: 0.5078, IoU.television receiver: 0.8183, IoU.airplane: 0.9008, IoU.dirt track: 0.0572, IoU.apparel: 0.6897, IoU.pole: 0.2841, IoU.land: 0.0479, IoU.bannister: 0.2124, IoU.escalator: 0.6627, IoU.ottoman: 0.5583, IoU.bottle: 0.4563, IoU.buffet: 0.5588, IoU.poster: 0.3654, IoU.stage: 0.2066, IoU.van: 0.5481, IoU.ship: 0.6946, IoU.fountain: 0.3010, IoU.conveyer belt: 0.8715, IoU.canopy: 0.5932, IoU.washer: 0.8736, IoU.plaything: 0.3704, IoU.swimming pool: 0.5364, IoU.stool: 0.5668, IoU.barrel: 0.7368, IoU.basket: 0.4152, IoU.waterfall: 0.5240, IoU.tent: 0.9624, IoU.bag: 0.3295, IoU.minibike: 0.7799, IoU.cradle: 0.8781, IoU.oven: 0.6546, IoU.ball: 0.5569, IoU.food: 0.6426, IoU.step: 0.1289, IoU.tank: 0.6429, IoU.trade name: 0.2424, IoU.microwave: 0.8924, IoU.pot: 0.6096, IoU.animal: 0.6191, IoU.bicycle: 0.6273, IoU.lake: 0.4648, IoU.dishwasher: 0.7688, IoU.screen: 0.6119, IoU.blanket: 0.3821, IoU.sculpture: 0.7896, IoU.hood: 0.6981, IoU.sconce: 0.6232, IoU.vase: 0.4997, IoU.traffic light: 0.3775, IoU.tray: 0.2741, IoU.ashcan: 0.4966, IoU.fan: 0.7331, IoU.pier: 0.4121, IoU.crt screen: 0.0541, IoU.plate: 0.6504, IoU.monitor: 0.3598, IoU.bulletin board: 0.4992, IoU.shower: 0.1937, IoU.radiator: 0.6928, IoU.glass: 0.2433, IoU.clock: 0.5680, IoU.flag: 0.7056, Acc.wall: 0.9103, Acc.building: 0.9309, Acc.sky: 0.9768, Acc.floor: 0.9157, Acc.tree: 0.8997, Acc.ceiling: 0.9397, Acc.road: 0.9118, Acc.bed : 0.9701, Acc.windowpane: 0.8151, Acc.grass: 0.8309, Acc.cabinet: 0.7731, Acc.sidewalk: 0.8697, Acc.person: 0.9457, Acc.earth: 0.5409, Acc.door: 0.7484, Acc.table: 0.8200, Acc.mountain: 0.7482, Acc.plant: 0.6792, Acc.curtain: 0.8780, Acc.chair: 0.8121, Acc.car: 0.9414, Acc.water: 0.7616, Acc.painting: 0.9142, Acc.sofa: 0.9123, Acc.shelf: 0.6860, Acc.house: 0.6189, Acc.sea: 0.8395, Acc.mirror: 0.8636, Acc.rug: 0.7667, Acc.field: 0.4905, Acc.armchair: 0.7596, Acc.seat: 0.8755, Acc.fence: 0.6832, Acc.desk: 0.7920, Acc.rock: 0.8667, Acc.wardrobe: 0.7306, Acc.lamp: 0.8848, Acc.bathtub: 0.9105, Acc.railing: 0.6396, Acc.cushion: 0.8563, Acc.base: 0.5405, Acc.box: 0.5028, Acc.column: 0.6765, Acc.signboard: 0.5844, Acc.chest of drawers: 0.6294, Acc.counter: 0.6015, Acc.sand: 0.7910, Acc.sink: 0.8685, Acc.skyscraper: 0.5867, Acc.fireplace: 0.9332, Acc.refrigerator: 0.9460, Acc.grandstand: 0.8189, Acc.path: 0.4615, Acc.stairs: 0.5032, Acc.runway: 0.9420, Acc.case: 0.8178, Acc.pool table: 0.9830, Acc.pillow: 0.7696, Acc.screen door: 0.9037, Acc.stairway: 0.5138, Acc.river: 0.2724, Acc.bridge: 0.7395, Acc.bookcase: 0.6009, Acc.blind: 0.4441, Acc.coffee table: 0.8779, Acc.toilet: 0.9345, Acc.flower: 0.6398, Acc.book: 0.8229, Acc.hill: 0.2126, Acc.bench: 0.6686, Acc.countertop: 0.8533, Acc.stove: 0.9199, Acc.palm: 0.8060, Acc.kitchen island: 0.8591, Acc.computer: 0.9184, Acc.swivel chair: 0.7997, Acc.boat: 0.9423, Acc.bar: 0.8971, Acc.arcade machine: 0.8584, Acc.hovel: 0.5900, Acc.bus: 0.9736, Acc.towel: 0.9061, Acc.light: 0.7654, Acc.truck: 0.6665, Acc.tower: 0.4309, Acc.chandelier: 0.8764, Acc.awning: 0.5368, Acc.streetlight: 0.5436, Acc.booth: 0.7262, Acc.television receiver: 0.8706, Acc.airplane: 0.9645, Acc.dirt track: 0.1775, Acc.apparel: 0.8613, Acc.pole: 0.3850, Acc.land: 0.0756, Acc.bannister: 0.2607, Acc.escalator: 0.8634, Acc.ottoman: 0.7021, Acc.bottle: 0.7288, Acc.buffet: 0.6332, Acc.poster: 0.4806, Acc.stage: 0.3519, Acc.van: 0.7230, Acc.ship: 0.7970, Acc.fountain: 0.3058, Acc.conveyer belt: 0.9569, Acc.canopy: 0.7474, Acc.washer: 0.9281, Acc.plaything: 0.5369, Acc.swimming pool: 0.7748, Acc.stool: 0.7372, Acc.barrel: 0.9799, Acc.basket: 0.6405, Acc.waterfall: 0.6583, Acc.tent: 0.9895, Acc.bag: 0.3861, Acc.minibike: 0.9176, Acc.cradle: 0.9747, Acc.oven: 0.7833, Acc.ball: 0.6347, Acc.food: 0.7434, Acc.step: 0.1409, Acc.tank: 0.6825, Acc.trade name: 0.2855, Acc.microwave: 0.9697, Acc.pot: 0.6967, Acc.animal: 0.6343, Acc.bicycle: 0.7903, Acc.lake: 0.6373, Acc.dishwasher: 0.8340, Acc.screen: 0.9025, Acc.blanket: 0.4606, Acc.sculpture: 0.8732, Acc.hood: 0.7506, Acc.sconce: 0.7663, Acc.vase: 0.7401, Acc.traffic light: 0.7129, Acc.tray: 0.3907, Acc.ashcan: 0.6926, Acc.fan: 0.8623, Acc.pier: 0.4513, Acc.crt screen: 0.1146, Acc.plate: 0.7976, Acc.monitor: 0.4103, Acc.bulletin board: 0.5924, Acc.shower: 0.2231, Acc.radiator: 0.8206, Acc.glass: 0.2684, Acc.clock: 0.6640, Acc.flag: 0.8379 +2024-06-19 14:41:25,843 - mmseg - INFO - Iter [61050/80000] lr: 9.476e-06, eta: 11:14:15, time: 4.250, data_time: 2.260, memory: 72263, decode.loss_ce: 0.1401, decode.acc_seg: 93.8787, aux.loss_ce: 0.0598, aux.acc_seg: 93.5147, loss: 0.1999 +2024-06-19 14:43:04,848 - mmseg - INFO - Iter [61100/80000] lr: 9.451e-06, eta: 11:12:26, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1398, decode.acc_seg: 93.8034, aux.loss_ce: 0.0599, aux.acc_seg: 93.3485, loss: 0.1997 +2024-06-19 14:44:43,834 - mmseg - INFO - Iter [61150/80000] lr: 9.426e-06, eta: 11:10:37, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1404, decode.acc_seg: 93.8565, aux.loss_ce: 0.0601, aux.acc_seg: 93.4379, loss: 0.2004 +2024-06-19 14:46:22,783 - mmseg - INFO - Iter [61200/80000] lr: 9.400e-06, eta: 11:08:48, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1495, decode.acc_seg: 93.5354, aux.loss_ce: 0.0630, aux.acc_seg: 93.1378, loss: 0.2125 +2024-06-19 14:48:01,741 - mmseg - INFO - Iter [61250/80000] lr: 9.376e-06, eta: 11:06:59, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1437, decode.acc_seg: 93.4129, aux.loss_ce: 0.0614, aux.acc_seg: 92.9890, loss: 0.2052 +2024-06-19 14:49:40,717 - mmseg - INFO - Iter [61300/80000] lr: 9.350e-06, eta: 11:05:10, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1361, decode.acc_seg: 94.0805, aux.loss_ce: 0.0579, aux.acc_seg: 93.7152, loss: 0.1940 +2024-06-19 14:51:19,595 - mmseg - INFO - Iter [61350/80000] lr: 9.326e-06, eta: 11:03:21, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1358, decode.acc_seg: 93.8952, aux.loss_ce: 0.0576, aux.acc_seg: 93.5663, loss: 0.1934 +2024-06-19 14:52:58,510 - mmseg - INFO - Iter [61400/80000] lr: 9.301e-06, eta: 11:01:32, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1418, decode.acc_seg: 93.9494, aux.loss_ce: 0.0602, aux.acc_seg: 93.5907, loss: 0.2020 +2024-06-19 14:54:37,485 - mmseg - INFO - Iter [61450/80000] lr: 9.276e-06, eta: 10:59:43, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1271, decode.acc_seg: 94.3188, aux.loss_ce: 0.0543, aux.acc_seg: 93.9506, loss: 0.1814 +2024-06-19 14:56:16,314 - mmseg - INFO - Iter [61500/80000] lr: 9.251e-06, eta: 10:57:54, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1468, decode.acc_seg: 93.5307, aux.loss_ce: 0.0627, aux.acc_seg: 93.0488, loss: 0.2094 +2024-06-19 14:57:55,211 - mmseg - INFO - Iter [61550/80000] lr: 9.226e-06, eta: 10:56:05, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1447, decode.acc_seg: 93.7680, aux.loss_ce: 0.0619, aux.acc_seg: 93.3444, loss: 0.2066 +2024-06-19 14:59:34,225 - mmseg - INFO - Iter [61600/80000] lr: 9.200e-06, eta: 10:54:16, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1502, decode.acc_seg: 93.7518, aux.loss_ce: 0.0635, aux.acc_seg: 93.3750, loss: 0.2137 +2024-06-19 15:01:13,312 - mmseg - INFO - Iter [61650/80000] lr: 9.175e-06, eta: 10:52:27, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1324, decode.acc_seg: 94.0218, aux.loss_ce: 0.0566, aux.acc_seg: 93.6244, loss: 0.1890 +2024-06-19 15:02:52,203 - mmseg - INFO - Iter [61700/80000] lr: 9.150e-06, eta: 10:50:38, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1417, decode.acc_seg: 93.8001, aux.loss_ce: 0.0602, aux.acc_seg: 93.3763, loss: 0.2019 +2024-06-19 15:04:31,079 - mmseg - INFO - Iter [61750/80000] lr: 9.126e-06, eta: 10:48:49, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1462, decode.acc_seg: 93.7610, aux.loss_ce: 0.0623, aux.acc_seg: 93.3514, loss: 0.2085 +2024-06-19 15:06:10,179 - mmseg - INFO - Iter [61800/80000] lr: 9.101e-06, eta: 10:47:00, time: 1.982, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1365, decode.acc_seg: 94.0649, aux.loss_ce: 0.0584, aux.acc_seg: 93.6591, loss: 0.1949 +2024-06-19 15:07:49,078 - mmseg - INFO - Iter [61850/80000] lr: 9.076e-06, eta: 10:45:11, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1501, decode.acc_seg: 93.4682, aux.loss_ce: 0.0643, aux.acc_seg: 93.0003, loss: 0.2144 +2024-06-19 15:09:30,364 - mmseg - INFO - Iter [61900/80000] lr: 9.051e-06, eta: 10:43:23, time: 2.026, data_time: 0.054, memory: 72263, decode.loss_ce: 0.1334, decode.acc_seg: 93.9088, aux.loss_ce: 0.0570, aux.acc_seg: 93.5142, loss: 0.1904 +2024-06-19 15:11:09,225 - mmseg - INFO - Iter [61950/80000] lr: 9.026e-06, eta: 10:41:34, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1364, decode.acc_seg: 93.9021, aux.loss_ce: 0.0585, aux.acc_seg: 93.4644, loss: 0.1948 +2024-06-19 15:12:48,108 - mmseg - INFO - Saving checkpoint at 62000 iterations +2024-06-19 15:14:14,918 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 15:14:14,918 - mmseg - INFO - Iter [62000/80000] lr: 9.000e-06, eta: 10:40:10, time: 3.714, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1360, decode.acc_seg: 93.8297, aux.loss_ce: 0.0577, aux.acc_seg: 93.4880, loss: 0.1936 +2024-06-19 15:16:05,472 - mmseg - INFO - per class results: +2024-06-19 15:16:05,479 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.96 | 90.22 | +| building | 85.4 | 93.05 | +| sky | 94.98 | 97.59 | +| floor | 84.51 | 91.63 | +| tree | 77.94 | 89.98 | +| ceiling | 87.44 | 94.4 | +| road | 87.24 | 91.53 | +| bed | 93.29 | 97.36 | +| windowpane | 67.36 | 81.63 | +| grass | 67.47 | 82.96 | +| cabinet | 67.55 | 76.43 | +| sidewalk | 72.08 | 87.99 | +| person | 86.57 | 94.22 | +| earth | 41.79 | 55.28 | +| door | 61.47 | 77.09 | +| table | 70.99 | 82.05 | +| mountain | 63.51 | 75.52 | +| plant | 56.3 | 66.36 | +| curtain | 78.37 | 88.13 | +| chair | 68.83 | 80.29 | +| car | 88.92 | 94.46 | +| water | 65.95 | 81.61 | +| painting | 81.48 | 91.71 | +| sofa | 82.77 | 91.59 | +| shelf | 48.88 | 64.86 | +| house | 51.8 | 63.39 | +| sea | 72.64 | 83.18 | +| mirror | 80.04 | 87.05 | +| rug | 64.64 | 76.51 | +| field | 29.23 | 48.33 | +| armchair | 61.73 | 78.48 | +| seat | 67.02 | 89.41 | +| fence | 54.83 | 73.25 | +| desk | 61.01 | 80.9 | +| rock | 54.49 | 83.47 | +| wardrobe | 54.13 | 73.62 | +| lamp | 76.98 | 88.41 | +| bathtub | 88.07 | 90.78 | +| railing | 43.61 | 59.98 | +| cushion | 68.95 | 82.15 | +| base | 43.98 | 58.96 | +| box | 41.85 | 52.06 | +| column | 56.25 | 71.09 | +| signboard | 41.93 | 56.44 | +| chest of drawers | 45.08 | 70.55 | +| counter | 49.75 | 55.68 | +| sand | 54.3 | 80.7 | +| sink | 83.21 | 88.52 | +| skyscraper | 44.7 | 59.88 | +| fireplace | 73.65 | 94.66 | +| refrigerator | 87.24 | 94.46 | +| grandstand | 59.31 | 84.97 | +| path | 30.9 | 40.64 | +| stairs | 33.45 | 41.63 | +| runway | 73.26 | 94.42 | +| case | 64.18 | 83.83 | +| pool table | 95.35 | 98.38 | +| pillow | 64.89 | 74.62 | +| screen door | 87.7 | 91.49 | +| stairway | 38.79 | 56.58 | +| river | 14.47 | 26.72 | +| bridge | 68.13 | 76.92 | +| bookcase | 45.52 | 64.62 | +| blind | 46.1 | 50.46 | +| coffee table | 61.94 | 87.07 | +| toilet | 90.21 | 93.84 | +| flower | 47.05 | 58.78 | +| book | 58.28 | 80.27 | +| hill | 14.41 | 22.79 | +| bench | 58.52 | 66.92 | +| countertop | 64.43 | 84.79 | +| stove | 87.57 | 92.89 | +| palm | 52.43 | 82.83 | +| kitchen island | 55.02 | 80.31 | +| computer | 76.97 | 91.93 | +| swivel chair | 47.52 | 69.3 | +| boat | 73.99 | 93.71 | +| bar | 69.46 | 90.09 | +| arcade machine | 82.68 | 85.99 | +| hovel | 50.09 | 59.26 | +| bus | 93.43 | 97.57 | +| towel | 80.82 | 89.15 | +| light | 63.6 | 76.82 | +| truck | 53.02 | 65.14 | +| tower | 27.61 | 49.94 | +| chandelier | 74.01 | 85.22 | +| awning | 54.09 | 72.57 | +| streetlight | 37.67 | 51.59 | +| booth | 50.31 | 73.69 | +| television receiver | 81.33 | 87.77 | +| airplane | 89.71 | 96.67 | +| dirt track | 6.64 | 20.44 | +| apparel | 64.48 | 87.47 | +| pole | 30.18 | 41.02 | +| land | 5.43 | 7.62 | +| bannister | 20.19 | 25.17 | +| escalator | 66.81 | 86.39 | +| ottoman | 54.95 | 73.69 | +| bottle | 45.66 | 74.61 | +| buffet | 62.13 | 71.35 | +| poster | 35.3 | 43.42 | +| stage | 21.49 | 43.13 | +| van | 55.46 | 74.93 | +| ship | 62.82 | 75.43 | +| fountain | 30.77 | 31.24 | +| conveyer belt | 86.26 | 95.92 | +| canopy | 58.33 | 72.58 | +| washer | 89.88 | 95.81 | +| plaything | 35.84 | 49.11 | +| swimming pool | 55.26 | 80.21 | +| stool | 54.09 | 72.5 | +| barrel | 72.63 | 98.99 | +| basket | 41.86 | 61.87 | +| waterfall | 53.98 | 70.81 | +| tent | 92.09 | 98.97 | +| bag | 28.54 | 32.07 | +| minibike | 78.44 | 89.69 | +| cradle | 87.22 | 98.0 | +| oven | 64.03 | 75.68 | +| ball | 59.18 | 71.15 | +| food | 58.99 | 68.05 | +| step | 12.0 | 13.7 | +| tank | 71.86 | 76.96 | +| trade name | 26.07 | 31.78 | +| microwave | 88.58 | 96.94 | +| pot | 61.75 | 73.28 | +| animal | 60.45 | 62.06 | +| bicycle | 63.19 | 81.74 | +| lake | 57.08 | 63.64 | +| dishwasher | 75.71 | 84.24 | +| screen | 61.66 | 95.43 | +| blanket | 33.74 | 39.61 | +| sculpture | 70.67 | 89.73 | +| hood | 70.21 | 82.8 | +| sconce | 61.76 | 77.61 | +| vase | 51.82 | 69.7 | +| traffic light | 41.99 | 67.42 | +| tray | 25.19 | 33.58 | +| ashcan | 50.05 | 66.89 | +| fan | 73.07 | 85.85 | +| pier | 40.27 | 45.58 | +| crt screen | 4.07 | 9.19 | +| plate | 63.8 | 84.13 | +| monitor | 26.57 | 30.67 | +| bulletin board | 59.57 | 72.02 | +| shower | 20.74 | 21.21 | +| radiator | 69.59 | 82.33 | +| glass | 23.32 | 25.21 | +| clock | 57.96 | 68.24 | +| flag | 71.52 | 82.06 | ++---------------------+-------+-------+ +2024-06-19 15:16:05,479 - mmseg - INFO - Summary: +2024-06-19 15:16:05,479 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.64 | 59.38 | 72.22 | ++-------+-------+-------+ +2024-06-19 15:16:05,480 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 15:16:05,481 - mmseg - INFO - Iter(val) [250] aAcc: 0.8664, mIoU: 0.5938, mAcc: 0.7222, IoU.wall: 0.8296, IoU.building: 0.8540, IoU.sky: 0.9498, IoU.floor: 0.8451, IoU.tree: 0.7794, IoU.ceiling: 0.8744, IoU.road: 0.8724, IoU.bed : 0.9329, IoU.windowpane: 0.6736, IoU.grass: 0.6747, IoU.cabinet: 0.6755, IoU.sidewalk: 0.7208, IoU.person: 0.8657, IoU.earth: 0.4179, IoU.door: 0.6147, IoU.table: 0.7099, IoU.mountain: 0.6351, IoU.plant: 0.5630, IoU.curtain: 0.7837, IoU.chair: 0.6883, IoU.car: 0.8892, IoU.water: 0.6595, IoU.painting: 0.8148, IoU.sofa: 0.8277, IoU.shelf: 0.4888, IoU.house: 0.5180, IoU.sea: 0.7264, IoU.mirror: 0.8004, IoU.rug: 0.6464, IoU.field: 0.2923, IoU.armchair: 0.6173, IoU.seat: 0.6702, IoU.fence: 0.5483, IoU.desk: 0.6101, IoU.rock: 0.5449, IoU.wardrobe: 0.5413, IoU.lamp: 0.7698, IoU.bathtub: 0.8807, IoU.railing: 0.4361, IoU.cushion: 0.6895, IoU.base: 0.4398, IoU.box: 0.4185, IoU.column: 0.5625, IoU.signboard: 0.4193, IoU.chest of drawers: 0.4508, IoU.counter: 0.4975, IoU.sand: 0.5430, IoU.sink: 0.8321, IoU.skyscraper: 0.4470, IoU.fireplace: 0.7365, IoU.refrigerator: 0.8724, IoU.grandstand: 0.5931, IoU.path: 0.3090, IoU.stairs: 0.3345, IoU.runway: 0.7326, IoU.case: 0.6418, IoU.pool table: 0.9535, IoU.pillow: 0.6489, IoU.screen door: 0.8770, IoU.stairway: 0.3879, IoU.river: 0.1447, IoU.bridge: 0.6813, IoU.bookcase: 0.4552, IoU.blind: 0.4610, IoU.coffee table: 0.6194, IoU.toilet: 0.9021, IoU.flower: 0.4705, IoU.book: 0.5828, IoU.hill: 0.1441, IoU.bench: 0.5852, IoU.countertop: 0.6443, IoU.stove: 0.8757, IoU.palm: 0.5243, IoU.kitchen island: 0.5502, IoU.computer: 0.7697, IoU.swivel chair: 0.4752, IoU.boat: 0.7399, IoU.bar: 0.6946, IoU.arcade machine: 0.8268, IoU.hovel: 0.5009, IoU.bus: 0.9343, IoU.towel: 0.8082, IoU.light: 0.6360, IoU.truck: 0.5302, IoU.tower: 0.2761, IoU.chandelier: 0.7401, IoU.awning: 0.5409, IoU.streetlight: 0.3767, IoU.booth: 0.5031, IoU.television receiver: 0.8133, IoU.airplane: 0.8971, IoU.dirt track: 0.0664, IoU.apparel: 0.6448, IoU.pole: 0.3018, IoU.land: 0.0543, IoU.bannister: 0.2019, IoU.escalator: 0.6681, IoU.ottoman: 0.5495, IoU.bottle: 0.4566, IoU.buffet: 0.6213, IoU.poster: 0.3530, IoU.stage: 0.2149, IoU.van: 0.5546, IoU.ship: 0.6282, IoU.fountain: 0.3077, IoU.conveyer belt: 0.8626, IoU.canopy: 0.5833, IoU.washer: 0.8988, IoU.plaything: 0.3584, IoU.swimming pool: 0.5526, IoU.stool: 0.5409, IoU.barrel: 0.7263, IoU.basket: 0.4186, IoU.waterfall: 0.5398, IoU.tent: 0.9209, IoU.bag: 0.2854, IoU.minibike: 0.7844, IoU.cradle: 0.8722, IoU.oven: 0.6403, IoU.ball: 0.5918, IoU.food: 0.5899, IoU.step: 0.1200, IoU.tank: 0.7186, IoU.trade name: 0.2607, IoU.microwave: 0.8858, IoU.pot: 0.6175, IoU.animal: 0.6045, IoU.bicycle: 0.6319, IoU.lake: 0.5708, IoU.dishwasher: 0.7571, IoU.screen: 0.6166, IoU.blanket: 0.3374, IoU.sculpture: 0.7067, IoU.hood: 0.7021, IoU.sconce: 0.6176, IoU.vase: 0.5182, IoU.traffic light: 0.4199, IoU.tray: 0.2519, IoU.ashcan: 0.5005, IoU.fan: 0.7307, IoU.pier: 0.4027, IoU.crt screen: 0.0407, IoU.plate: 0.6380, IoU.monitor: 0.2657, IoU.bulletin board: 0.5957, IoU.shower: 0.2074, IoU.radiator: 0.6959, IoU.glass: 0.2332, IoU.clock: 0.5796, IoU.flag: 0.7152, Acc.wall: 0.9022, Acc.building: 0.9305, Acc.sky: 0.9759, Acc.floor: 0.9163, Acc.tree: 0.8998, Acc.ceiling: 0.9440, Acc.road: 0.9153, Acc.bed : 0.9736, Acc.windowpane: 0.8163, Acc.grass: 0.8296, Acc.cabinet: 0.7643, Acc.sidewalk: 0.8799, Acc.person: 0.9422, Acc.earth: 0.5528, Acc.door: 0.7709, Acc.table: 0.8205, Acc.mountain: 0.7552, Acc.plant: 0.6636, Acc.curtain: 0.8813, Acc.chair: 0.8029, Acc.car: 0.9446, Acc.water: 0.8161, Acc.painting: 0.9171, Acc.sofa: 0.9159, Acc.shelf: 0.6486, Acc.house: 0.6339, Acc.sea: 0.8318, Acc.mirror: 0.8705, Acc.rug: 0.7651, Acc.field: 0.4833, Acc.armchair: 0.7848, Acc.seat: 0.8941, Acc.fence: 0.7325, Acc.desk: 0.8090, Acc.rock: 0.8347, Acc.wardrobe: 0.7362, Acc.lamp: 0.8841, Acc.bathtub: 0.9078, Acc.railing: 0.5998, Acc.cushion: 0.8215, Acc.base: 0.5896, Acc.box: 0.5206, Acc.column: 0.7109, Acc.signboard: 0.5644, Acc.chest of drawers: 0.7055, Acc.counter: 0.5568, Acc.sand: 0.8070, Acc.sink: 0.8852, Acc.skyscraper: 0.5988, Acc.fireplace: 0.9466, Acc.refrigerator: 0.9446, Acc.grandstand: 0.8497, Acc.path: 0.4064, Acc.stairs: 0.4163, Acc.runway: 0.9442, Acc.case: 0.8383, Acc.pool table: 0.9838, Acc.pillow: 0.7462, Acc.screen door: 0.9149, Acc.stairway: 0.5658, Acc.river: 0.2672, Acc.bridge: 0.7692, Acc.bookcase: 0.6462, Acc.blind: 0.5046, Acc.coffee table: 0.8707, Acc.toilet: 0.9384, Acc.flower: 0.5878, Acc.book: 0.8027, Acc.hill: 0.2279, Acc.bench: 0.6692, Acc.countertop: 0.8479, Acc.stove: 0.9289, Acc.palm: 0.8283, Acc.kitchen island: 0.8031, Acc.computer: 0.9193, Acc.swivel chair: 0.6930, Acc.boat: 0.9371, Acc.bar: 0.9009, Acc.arcade machine: 0.8599, Acc.hovel: 0.5926, Acc.bus: 0.9757, Acc.towel: 0.8915, Acc.light: 0.7682, Acc.truck: 0.6514, Acc.tower: 0.4994, Acc.chandelier: 0.8522, Acc.awning: 0.7257, Acc.streetlight: 0.5159, Acc.booth: 0.7369, Acc.television receiver: 0.8777, Acc.airplane: 0.9667, Acc.dirt track: 0.2044, Acc.apparel: 0.8747, Acc.pole: 0.4102, Acc.land: 0.0762, Acc.bannister: 0.2517, Acc.escalator: 0.8639, Acc.ottoman: 0.7369, Acc.bottle: 0.7461, Acc.buffet: 0.7135, Acc.poster: 0.4342, Acc.stage: 0.4313, Acc.van: 0.7493, Acc.ship: 0.7543, Acc.fountain: 0.3124, Acc.conveyer belt: 0.9592, Acc.canopy: 0.7258, Acc.washer: 0.9581, Acc.plaything: 0.4911, Acc.swimming pool: 0.8021, Acc.stool: 0.7250, Acc.barrel: 0.9899, Acc.basket: 0.6187, Acc.waterfall: 0.7081, Acc.tent: 0.9897, Acc.bag: 0.3207, Acc.minibike: 0.8969, Acc.cradle: 0.9800, Acc.oven: 0.7568, Acc.ball: 0.7115, Acc.food: 0.6805, Acc.step: 0.1370, Acc.tank: 0.7696, Acc.trade name: 0.3178, Acc.microwave: 0.9694, Acc.pot: 0.7328, Acc.animal: 0.6206, Acc.bicycle: 0.8174, Acc.lake: 0.6364, Acc.dishwasher: 0.8424, Acc.screen: 0.9543, Acc.blanket: 0.3961, Acc.sculpture: 0.8973, Acc.hood: 0.8280, Acc.sconce: 0.7761, Acc.vase: 0.6970, Acc.traffic light: 0.6742, Acc.tray: 0.3358, Acc.ashcan: 0.6689, Acc.fan: 0.8585, Acc.pier: 0.4558, Acc.crt screen: 0.0919, Acc.plate: 0.8413, Acc.monitor: 0.3067, Acc.bulletin board: 0.7202, Acc.shower: 0.2121, Acc.radiator: 0.8233, Acc.glass: 0.2521, Acc.clock: 0.6824, Acc.flag: 0.8206 +2024-06-19 15:17:44,629 - mmseg - INFO - Iter [62050/80000] lr: 8.975e-06, eta: 10:38:53, time: 4.194, data_time: 2.228, memory: 72263, decode.loss_ce: 0.1340, decode.acc_seg: 94.0248, aux.loss_ce: 0.0575, aux.acc_seg: 93.5914, loss: 0.1915 +2024-06-19 15:19:23,489 - mmseg - INFO - Iter [62100/80000] lr: 8.951e-06, eta: 10:37:04, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1448, decode.acc_seg: 93.7362, aux.loss_ce: 0.0623, aux.acc_seg: 93.2758, loss: 0.2071 +2024-06-19 15:21:02,462 - mmseg - INFO - Iter [62150/80000] lr: 8.925e-06, eta: 10:35:15, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1402, decode.acc_seg: 93.7069, aux.loss_ce: 0.0601, aux.acc_seg: 93.2716, loss: 0.2003 +2024-06-19 15:22:41,308 - mmseg - INFO - Iter [62200/80000] lr: 8.901e-06, eta: 10:33:26, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1431, decode.acc_seg: 93.7017, aux.loss_ce: 0.0615, aux.acc_seg: 93.2474, loss: 0.2046 +2024-06-19 15:24:20,276 - mmseg - INFO - Iter [62250/80000] lr: 8.876e-06, eta: 10:31:37, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1409, decode.acc_seg: 93.7637, aux.loss_ce: 0.0601, aux.acc_seg: 93.3492, loss: 0.2010 +2024-06-19 15:25:59,216 - mmseg - INFO - Iter [62300/80000] lr: 8.851e-06, eta: 10:29:48, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1433, decode.acc_seg: 93.6820, aux.loss_ce: 0.0614, aux.acc_seg: 93.2200, loss: 0.2046 +2024-06-19 15:27:38,227 - mmseg - INFO - Iter [62350/80000] lr: 8.826e-06, eta: 10:27:59, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1387, decode.acc_seg: 93.9573, aux.loss_ce: 0.0592, aux.acc_seg: 93.5395, loss: 0.1979 +2024-06-19 15:29:17,160 - mmseg - INFO - Iter [62400/80000] lr: 8.801e-06, eta: 10:26:10, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1395, decode.acc_seg: 93.8440, aux.loss_ce: 0.0592, aux.acc_seg: 93.4724, loss: 0.1986 +2024-06-19 15:30:56,005 - mmseg - INFO - Iter [62450/80000] lr: 8.775e-06, eta: 10:24:21, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1354, decode.acc_seg: 93.9337, aux.loss_ce: 0.0581, aux.acc_seg: 93.5160, loss: 0.1935 +2024-06-19 15:32:34,961 - mmseg - INFO - Iter [62500/80000] lr: 8.751e-06, eta: 10:22:32, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1501, decode.acc_seg: 93.4399, aux.loss_ce: 0.0643, aux.acc_seg: 92.9590, loss: 0.2143 +2024-06-19 15:34:13,938 - mmseg - INFO - Iter [62550/80000] lr: 8.725e-06, eta: 10:20:44, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1330, decode.acc_seg: 94.1227, aux.loss_ce: 0.0567, aux.acc_seg: 93.7508, loss: 0.1898 +2024-06-19 15:35:52,906 - mmseg - INFO - Iter [62600/80000] lr: 8.701e-06, eta: 10:18:55, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1335, decode.acc_seg: 94.0022, aux.loss_ce: 0.0572, aux.acc_seg: 93.6045, loss: 0.1907 +2024-06-19 15:37:31,835 - mmseg - INFO - Iter [62650/80000] lr: 8.676e-06, eta: 10:17:06, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1428, decode.acc_seg: 93.6498, aux.loss_ce: 0.0608, aux.acc_seg: 93.2773, loss: 0.2036 +2024-06-19 15:39:10,804 - mmseg - INFO - Iter [62700/80000] lr: 8.651e-06, eta: 10:15:17, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1347, decode.acc_seg: 94.0504, aux.loss_ce: 0.0577, aux.acc_seg: 93.6351, loss: 0.1924 +2024-06-19 15:40:49,608 - mmseg - INFO - Iter [62750/80000] lr: 8.626e-06, eta: 10:13:28, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1399, decode.acc_seg: 93.9198, aux.loss_ce: 0.0599, aux.acc_seg: 93.5239, loss: 0.1998 +2024-06-19 15:42:28,578 - mmseg - INFO - Iter [62800/80000] lr: 8.601e-06, eta: 10:11:39, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1352, decode.acc_seg: 94.0177, aux.loss_ce: 0.0579, aux.acc_seg: 93.5963, loss: 0.1931 +2024-06-19 15:44:07,332 - mmseg - INFO - Iter [62850/80000] lr: 8.575e-06, eta: 10:09:51, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1384, decode.acc_seg: 93.8226, aux.loss_ce: 0.0592, aux.acc_seg: 93.3945, loss: 0.1976 +2024-06-19 15:45:46,314 - mmseg - INFO - Iter [62900/80000] lr: 8.550e-06, eta: 10:08:02, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1401, decode.acc_seg: 93.7337, aux.loss_ce: 0.0594, aux.acc_seg: 93.3590, loss: 0.1995 +2024-06-19 15:47:25,242 - mmseg - INFO - Iter [62950/80000] lr: 8.525e-06, eta: 10:06:13, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1323, decode.acc_seg: 94.0367, aux.loss_ce: 0.0566, aux.acc_seg: 93.5952, loss: 0.1889 +2024-06-19 15:49:04,150 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 15:49:04,150 - mmseg - INFO - Iter [63000/80000] lr: 8.501e-06, eta: 10:04:24, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1423, decode.acc_seg: 93.8140, aux.loss_ce: 0.0604, aux.acc_seg: 93.4630, loss: 0.2027 +2024-06-19 15:50:55,122 - mmseg - INFO - per class results: +2024-06-19 15:50:55,128 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.91 | 90.04 | +| building | 84.94 | 93.92 | +| sky | 95.05 | 97.83 | +| floor | 84.55 | 91.34 | +| tree | 77.9 | 89.3 | +| ceiling | 87.64 | 95.05 | +| road | 87.01 | 92.31 | +| bed | 93.13 | 97.38 | +| windowpane | 66.93 | 82.1 | +| grass | 68.58 | 81.96 | +| cabinet | 68.15 | 78.22 | +| sidewalk | 71.59 | 85.77 | +| person | 86.72 | 95.28 | +| earth | 39.79 | 53.09 | +| door | 59.41 | 76.16 | +| table | 71.53 | 82.61 | +| mountain | 62.97 | 73.21 | +| plant | 57.02 | 66.79 | +| curtain | 77.88 | 85.66 | +| chair | 69.09 | 81.97 | +| car | 88.84 | 94.12 | +| water | 66.88 | 83.39 | +| painting | 81.85 | 90.16 | +| sofa | 83.23 | 91.88 | +| shelf | 50.74 | 67.2 | +| house | 46.75 | 52.55 | +| sea | 78.02 | 89.4 | +| mirror | 80.0 | 86.69 | +| rug | 64.58 | 75.94 | +| field | 30.01 | 56.64 | +| armchair | 63.92 | 78.17 | +| seat | 70.97 | 88.85 | +| fence | 53.91 | 69.56 | +| desk | 61.22 | 80.02 | +| rock | 57.33 | 87.17 | +| wardrobe | 54.41 | 74.27 | +| lamp | 76.94 | 87.17 | +| bathtub | 88.37 | 92.47 | +| railing | 43.5 | 63.57 | +| cushion | 68.76 | 83.4 | +| base | 44.55 | 55.35 | +| box | 40.21 | 50.58 | +| column | 57.75 | 69.75 | +| signboard | 40.55 | 54.52 | +| chest of drawers | 46.07 | 67.6 | +| counter | 52.71 | 61.36 | +| sand | 54.22 | 82.75 | +| sink | 85.41 | 91.04 | +| skyscraper | 44.89 | 59.38 | +| fireplace | 74.48 | 94.59 | +| refrigerator | 85.95 | 92.1 | +| grandstand | 57.11 | 81.08 | +| path | 26.22 | 34.63 | +| stairs | 28.26 | 34.45 | +| runway | 73.04 | 94.2 | +| case | 62.65 | 84.9 | +| pool table | 95.34 | 98.53 | +| pillow | 65.36 | 75.48 | +| screen door | 80.8 | 83.19 | +| stairway | 38.09 | 55.31 | +| river | 18.09 | 25.18 | +| bridge | 70.28 | 78.56 | +| bookcase | 50.26 | 71.53 | +| blind | 45.52 | 51.21 | +| coffee table | 62.52 | 89.1 | +| toilet | 91.09 | 94.21 | +| flower | 45.59 | 59.08 | +| book | 58.26 | 74.04 | +| hill | 15.42 | 24.75 | +| bench | 65.61 | 75.81 | +| countertop | 63.89 | 84.16 | +| stove | 87.65 | 92.61 | +| palm | 52.85 | 89.14 | +| kitchen island | 56.36 | 78.24 | +| computer | 77.74 | 90.78 | +| swivel chair | 47.99 | 67.98 | +| boat | 71.75 | 93.12 | +| bar | 70.8 | 88.59 | +| arcade machine | 82.52 | 85.25 | +| hovel | 51.97 | 57.82 | +| bus | 94.36 | 97.17 | +| towel | 79.74 | 87.93 | +| light | 63.24 | 72.89 | +| truck | 52.68 | 65.57 | +| tower | 28.02 | 49.9 | +| chandelier | 73.11 | 83.62 | +| awning | 49.15 | 62.98 | +| streetlight | 37.84 | 50.16 | +| booth | 50.29 | 73.06 | +| television receiver | 80.99 | 86.9 | +| airplane | 88.91 | 96.92 | +| dirt track | 8.09 | 17.07 | +| apparel | 66.91 | 82.91 | +| pole | 27.34 | 37.26 | +| land | 5.56 | 8.03 | +| bannister | 21.51 | 26.14 | +| escalator | 66.67 | 85.71 | +| ottoman | 54.51 | 68.4 | +| bottle | 46.45 | 71.55 | +| buffet | 60.51 | 68.76 | +| poster | 38.53 | 51.96 | +| stage | 21.16 | 34.87 | +| van | 55.94 | 74.34 | +| ship | 68.4 | 81.5 | +| fountain | 30.79 | 31.34 | +| conveyer belt | 84.28 | 96.3 | +| canopy | 62.06 | 77.66 | +| washer | 89.74 | 95.69 | +| plaything | 34.81 | 48.06 | +| swimming pool | 52.36 | 75.38 | +| stool | 49.94 | 72.27 | +| barrel | 76.81 | 98.01 | +| basket | 42.26 | 61.27 | +| waterfall | 54.36 | 68.68 | +| tent | 92.09 | 98.95 | +| bag | 27.32 | 30.82 | +| minibike | 76.89 | 92.54 | +| cradle | 89.4 | 97.76 | +| oven | 69.85 | 80.76 | +| ball | 60.97 | 75.57 | +| food | 62.0 | 72.18 | +| step | 12.54 | 14.55 | +| tank | 71.48 | 76.25 | +| trade name | 22.49 | 26.04 | +| microwave | 90.48 | 96.48 | +| pot | 61.11 | 71.0 | +| animal | 61.79 | 63.88 | +| bicycle | 61.69 | 79.5 | +| lake | 52.5 | 63.71 | +| dishwasher | 75.4 | 84.36 | +| screen | 58.97 | 90.18 | +| blanket | 35.04 | 41.49 | +| sculpture | 75.6 | 86.95 | +| hood | 66.48 | 75.19 | +| sconce | 60.69 | 74.95 | +| vase | 51.2 | 65.71 | +| traffic light | 37.51 | 70.23 | +| tray | 25.52 | 35.25 | +| ashcan | 50.57 | 68.17 | +| fan | 73.19 | 83.25 | +| pier | 41.38 | 45.21 | +| crt screen | 3.02 | 7.14 | +| plate | 65.51 | 82.11 | +| monitor | 28.42 | 33.37 | +| bulletin board | 57.06 | 65.86 | +| shower | 20.63 | 21.27 | +| radiator | 69.73 | 82.18 | +| glass | 22.92 | 24.38 | +| clock | 57.61 | 68.73 | +| flag | 70.92 | 81.68 | ++---------------------+-------+-------+ +2024-06-19 15:50:55,128 - mmseg - INFO - Summary: +2024-06-19 15:50:55,128 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.63 | 59.47 | 71.75 | ++-------+-------+-------+ +2024-06-19 15:50:55,129 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 15:50:55,129 - mmseg - INFO - Iter(val) [250] aAcc: 0.8663, mIoU: 0.5947, mAcc: 0.7175, IoU.wall: 0.8291, IoU.building: 0.8494, IoU.sky: 0.9505, IoU.floor: 0.8455, IoU.tree: 0.7790, IoU.ceiling: 0.8764, IoU.road: 0.8701, IoU.bed : 0.9313, IoU.windowpane: 0.6693, IoU.grass: 0.6858, IoU.cabinet: 0.6815, IoU.sidewalk: 0.7159, IoU.person: 0.8672, IoU.earth: 0.3979, IoU.door: 0.5941, IoU.table: 0.7153, IoU.mountain: 0.6297, IoU.plant: 0.5702, IoU.curtain: 0.7788, IoU.chair: 0.6909, IoU.car: 0.8884, IoU.water: 0.6688, IoU.painting: 0.8185, IoU.sofa: 0.8323, IoU.shelf: 0.5074, IoU.house: 0.4675, IoU.sea: 0.7802, IoU.mirror: 0.8000, IoU.rug: 0.6458, IoU.field: 0.3001, IoU.armchair: 0.6392, IoU.seat: 0.7097, IoU.fence: 0.5391, IoU.desk: 0.6122, IoU.rock: 0.5733, IoU.wardrobe: 0.5441, IoU.lamp: 0.7694, IoU.bathtub: 0.8837, IoU.railing: 0.4350, IoU.cushion: 0.6876, IoU.base: 0.4455, IoU.box: 0.4021, IoU.column: 0.5775, IoU.signboard: 0.4055, IoU.chest of drawers: 0.4607, IoU.counter: 0.5271, IoU.sand: 0.5422, IoU.sink: 0.8541, IoU.skyscraper: 0.4489, IoU.fireplace: 0.7448, IoU.refrigerator: 0.8595, IoU.grandstand: 0.5711, IoU.path: 0.2622, IoU.stairs: 0.2826, IoU.runway: 0.7304, IoU.case: 0.6265, IoU.pool table: 0.9534, IoU.pillow: 0.6536, IoU.screen door: 0.8080, IoU.stairway: 0.3809, IoU.river: 0.1809, IoU.bridge: 0.7028, IoU.bookcase: 0.5026, IoU.blind: 0.4552, IoU.coffee table: 0.6252, IoU.toilet: 0.9109, IoU.flower: 0.4559, IoU.book: 0.5826, IoU.hill: 0.1542, IoU.bench: 0.6561, IoU.countertop: 0.6389, IoU.stove: 0.8765, IoU.palm: 0.5285, IoU.kitchen island: 0.5636, IoU.computer: 0.7774, IoU.swivel chair: 0.4799, IoU.boat: 0.7175, IoU.bar: 0.7080, IoU.arcade machine: 0.8252, IoU.hovel: 0.5197, IoU.bus: 0.9436, IoU.towel: 0.7974, IoU.light: 0.6324, IoU.truck: 0.5268, IoU.tower: 0.2802, IoU.chandelier: 0.7311, IoU.awning: 0.4915, IoU.streetlight: 0.3784, IoU.booth: 0.5029, IoU.television receiver: 0.8099, IoU.airplane: 0.8891, IoU.dirt track: 0.0809, IoU.apparel: 0.6691, IoU.pole: 0.2734, IoU.land: 0.0556, IoU.bannister: 0.2151, IoU.escalator: 0.6667, IoU.ottoman: 0.5451, IoU.bottle: 0.4645, IoU.buffet: 0.6051, IoU.poster: 0.3853, IoU.stage: 0.2116, IoU.van: 0.5594, IoU.ship: 0.6840, IoU.fountain: 0.3079, IoU.conveyer belt: 0.8428, IoU.canopy: 0.6206, IoU.washer: 0.8974, IoU.plaything: 0.3481, IoU.swimming pool: 0.5236, IoU.stool: 0.4994, IoU.barrel: 0.7681, IoU.basket: 0.4226, IoU.waterfall: 0.5436, IoU.tent: 0.9209, IoU.bag: 0.2732, IoU.minibike: 0.7689, IoU.cradle: 0.8940, IoU.oven: 0.6985, IoU.ball: 0.6097, IoU.food: 0.6200, IoU.step: 0.1254, IoU.tank: 0.7148, IoU.trade name: 0.2249, IoU.microwave: 0.9048, IoU.pot: 0.6111, IoU.animal: 0.6179, IoU.bicycle: 0.6169, IoU.lake: 0.5250, IoU.dishwasher: 0.7540, IoU.screen: 0.5897, IoU.blanket: 0.3504, IoU.sculpture: 0.7560, IoU.hood: 0.6648, IoU.sconce: 0.6069, IoU.vase: 0.5120, IoU.traffic light: 0.3751, IoU.tray: 0.2552, IoU.ashcan: 0.5057, IoU.fan: 0.7319, IoU.pier: 0.4138, IoU.crt screen: 0.0302, IoU.plate: 0.6551, IoU.monitor: 0.2842, IoU.bulletin board: 0.5706, IoU.shower: 0.2063, IoU.radiator: 0.6973, IoU.glass: 0.2292, IoU.clock: 0.5761, IoU.flag: 0.7092, Acc.wall: 0.9004, Acc.building: 0.9392, Acc.sky: 0.9783, Acc.floor: 0.9134, Acc.tree: 0.8930, Acc.ceiling: 0.9505, Acc.road: 0.9231, Acc.bed : 0.9738, Acc.windowpane: 0.8210, Acc.grass: 0.8196, Acc.cabinet: 0.7822, Acc.sidewalk: 0.8577, Acc.person: 0.9528, Acc.earth: 0.5309, Acc.door: 0.7616, Acc.table: 0.8261, Acc.mountain: 0.7321, Acc.plant: 0.6679, Acc.curtain: 0.8566, Acc.chair: 0.8197, Acc.car: 0.9412, Acc.water: 0.8339, Acc.painting: 0.9016, Acc.sofa: 0.9188, Acc.shelf: 0.6720, Acc.house: 0.5255, Acc.sea: 0.8940, Acc.mirror: 0.8669, Acc.rug: 0.7594, Acc.field: 0.5664, Acc.armchair: 0.7817, Acc.seat: 0.8885, Acc.fence: 0.6956, Acc.desk: 0.8002, Acc.rock: 0.8717, Acc.wardrobe: 0.7427, Acc.lamp: 0.8717, Acc.bathtub: 0.9247, Acc.railing: 0.6357, Acc.cushion: 0.8340, Acc.base: 0.5535, Acc.box: 0.5058, Acc.column: 0.6975, Acc.signboard: 0.5452, Acc.chest of drawers: 0.6760, Acc.counter: 0.6136, Acc.sand: 0.8275, Acc.sink: 0.9104, Acc.skyscraper: 0.5938, Acc.fireplace: 0.9459, Acc.refrigerator: 0.9210, Acc.grandstand: 0.8108, Acc.path: 0.3463, Acc.stairs: 0.3445, Acc.runway: 0.9420, Acc.case: 0.8490, Acc.pool table: 0.9853, Acc.pillow: 0.7548, Acc.screen door: 0.8319, Acc.stairway: 0.5531, Acc.river: 0.2518, Acc.bridge: 0.7856, Acc.bookcase: 0.7153, Acc.blind: 0.5121, Acc.coffee table: 0.8910, Acc.toilet: 0.9421, Acc.flower: 0.5908, Acc.book: 0.7404, Acc.hill: 0.2475, Acc.bench: 0.7581, Acc.countertop: 0.8416, Acc.stove: 0.9261, Acc.palm: 0.8914, Acc.kitchen island: 0.7824, Acc.computer: 0.9078, Acc.swivel chair: 0.6798, Acc.boat: 0.9312, Acc.bar: 0.8859, Acc.arcade machine: 0.8525, Acc.hovel: 0.5782, Acc.bus: 0.9717, Acc.towel: 0.8793, Acc.light: 0.7289, Acc.truck: 0.6557, Acc.tower: 0.4990, Acc.chandelier: 0.8362, Acc.awning: 0.6298, Acc.streetlight: 0.5016, Acc.booth: 0.7306, Acc.television receiver: 0.8690, Acc.airplane: 0.9692, Acc.dirt track: 0.1707, Acc.apparel: 0.8291, Acc.pole: 0.3726, Acc.land: 0.0803, Acc.bannister: 0.2614, Acc.escalator: 0.8571, Acc.ottoman: 0.6840, Acc.bottle: 0.7155, Acc.buffet: 0.6876, Acc.poster: 0.5196, Acc.stage: 0.3487, Acc.van: 0.7434, Acc.ship: 0.8150, Acc.fountain: 0.3134, Acc.conveyer belt: 0.9630, Acc.canopy: 0.7766, Acc.washer: 0.9569, Acc.plaything: 0.4806, Acc.swimming pool: 0.7538, Acc.stool: 0.7227, Acc.barrel: 0.9801, Acc.basket: 0.6127, Acc.waterfall: 0.6868, Acc.tent: 0.9895, Acc.bag: 0.3082, Acc.minibike: 0.9254, Acc.cradle: 0.9776, Acc.oven: 0.8076, Acc.ball: 0.7557, Acc.food: 0.7218, Acc.step: 0.1455, Acc.tank: 0.7625, Acc.trade name: 0.2604, Acc.microwave: 0.9648, Acc.pot: 0.7100, Acc.animal: 0.6388, Acc.bicycle: 0.7950, Acc.lake: 0.6371, Acc.dishwasher: 0.8436, Acc.screen: 0.9018, Acc.blanket: 0.4149, Acc.sculpture: 0.8695, Acc.hood: 0.7519, Acc.sconce: 0.7495, Acc.vase: 0.6571, Acc.traffic light: 0.7023, Acc.tray: 0.3525, Acc.ashcan: 0.6817, Acc.fan: 0.8325, Acc.pier: 0.4521, Acc.crt screen: 0.0714, Acc.plate: 0.8211, Acc.monitor: 0.3337, Acc.bulletin board: 0.6586, Acc.shower: 0.2127, Acc.radiator: 0.8218, Acc.glass: 0.2438, Acc.clock: 0.6873, Acc.flag: 0.8168 +2024-06-19 15:52:34,382 - mmseg - INFO - Iter [63050/80000] lr: 8.476e-06, eta: 10:03:05, time: 4.205, data_time: 2.236, memory: 72263, decode.loss_ce: 0.1265, decode.acc_seg: 94.3016, aux.loss_ce: 0.0543, aux.acc_seg: 93.8741, loss: 0.1808 +2024-06-19 15:54:13,232 - mmseg - INFO - Iter [63100/80000] lr: 8.451e-06, eta: 10:01:17, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1431, decode.acc_seg: 93.6848, aux.loss_ce: 0.0615, aux.acc_seg: 93.2677, loss: 0.2047 +2024-06-19 15:55:52,088 - mmseg - INFO - Iter [63150/80000] lr: 8.426e-06, eta: 9:59:28, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1306, decode.acc_seg: 94.2238, aux.loss_ce: 0.0558, aux.acc_seg: 93.8035, loss: 0.1865 +2024-06-19 15:57:33,896 - mmseg - INFO - Iter [63200/80000] lr: 8.401e-06, eta: 9:57:40, time: 2.036, data_time: 0.068, memory: 72263, decode.loss_ce: 0.1386, decode.acc_seg: 94.0791, aux.loss_ce: 0.0596, aux.acc_seg: 93.6774, loss: 0.1982 +2024-06-19 15:59:12,965 - mmseg - INFO - Iter [63250/80000] lr: 8.375e-06, eta: 9:55:51, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1276, decode.acc_seg: 94.3376, aux.loss_ce: 0.0547, aux.acc_seg: 93.9493, loss: 0.1823 +2024-06-19 16:00:51,845 - mmseg - INFO - Iter [63300/80000] lr: 8.350e-06, eta: 9:54:02, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1374, decode.acc_seg: 93.8771, aux.loss_ce: 0.0582, aux.acc_seg: 93.5335, loss: 0.1957 +2024-06-19 16:02:30,803 - mmseg - INFO - Iter [63350/80000] lr: 8.326e-06, eta: 9:52:13, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1312, decode.acc_seg: 94.0529, aux.loss_ce: 0.0561, aux.acc_seg: 93.7054, loss: 0.1873 +2024-06-19 16:04:09,695 - mmseg - INFO - Iter [63400/80000] lr: 8.300e-06, eta: 9:50:25, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1400, decode.acc_seg: 93.9087, aux.loss_ce: 0.0599, aux.acc_seg: 93.4754, loss: 0.2000 +2024-06-19 16:05:48,713 - mmseg - INFO - Iter [63450/80000] lr: 8.276e-06, eta: 9:48:36, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1338, decode.acc_seg: 94.1269, aux.loss_ce: 0.0577, aux.acc_seg: 93.6800, loss: 0.1915 +2024-06-19 16:07:27,581 - mmseg - INFO - Iter [63500/80000] lr: 8.251e-06, eta: 9:46:47, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1371, decode.acc_seg: 93.9348, aux.loss_ce: 0.0587, aux.acc_seg: 93.5214, loss: 0.1958 +2024-06-19 16:09:06,511 - mmseg - INFO - Iter [63550/80000] lr: 8.226e-06, eta: 9:44:58, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1298, decode.acc_seg: 94.1471, aux.loss_ce: 0.0555, aux.acc_seg: 93.7111, loss: 0.1853 +2024-06-19 16:10:45,539 - mmseg - INFO - Iter [63600/80000] lr: 8.201e-06, eta: 9:43:10, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1333, decode.acc_seg: 94.0947, aux.loss_ce: 0.0572, aux.acc_seg: 93.7126, loss: 0.1905 +2024-06-19 16:12:24,426 - mmseg - INFO - Iter [63650/80000] lr: 8.176e-06, eta: 9:41:21, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1322, decode.acc_seg: 94.0222, aux.loss_ce: 0.0564, aux.acc_seg: 93.6663, loss: 0.1886 +2024-06-19 16:14:03,491 - mmseg - INFO - Iter [63700/80000] lr: 8.150e-06, eta: 9:39:33, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1433, decode.acc_seg: 93.8442, aux.loss_ce: 0.0611, aux.acc_seg: 93.4822, loss: 0.2044 +2024-06-19 16:15:42,322 - mmseg - INFO - Iter [63750/80000] lr: 8.125e-06, eta: 9:37:44, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1357, decode.acc_seg: 93.9281, aux.loss_ce: 0.0584, aux.acc_seg: 93.5352, loss: 0.1941 +2024-06-19 16:17:21,306 - mmseg - INFO - Iter [63800/80000] lr: 8.100e-06, eta: 9:35:55, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1378, decode.acc_seg: 93.9491, aux.loss_ce: 0.0591, aux.acc_seg: 93.5384, loss: 0.1968 +2024-06-19 16:19:00,197 - mmseg - INFO - Iter [63850/80000] lr: 8.076e-06, eta: 9:34:07, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1283, decode.acc_seg: 94.1795, aux.loss_ce: 0.0543, aux.acc_seg: 93.8587, loss: 0.1826 +2024-06-19 16:20:39,132 - mmseg - INFO - Iter [63900/80000] lr: 8.051e-06, eta: 9:32:18, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1410, decode.acc_seg: 93.6817, aux.loss_ce: 0.0601, aux.acc_seg: 93.2526, loss: 0.2011 +2024-06-19 16:22:18,142 - mmseg - INFO - Iter [63950/80000] lr: 8.026e-06, eta: 9:30:29, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1312, decode.acc_seg: 94.2975, aux.loss_ce: 0.0562, aux.acc_seg: 93.9373, loss: 0.1875 +2024-06-19 16:23:56,999 - mmseg - INFO - Saving checkpoint at 64000 iterations +2024-06-19 16:25:19,326 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 16:25:19,326 - mmseg - INFO - Iter [64000/80000] lr: 8.001e-06, eta: 9:29:01, time: 3.624, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1423, decode.acc_seg: 93.6210, aux.loss_ce: 0.0613, aux.acc_seg: 93.1575, loss: 0.2035 +2024-06-19 16:27:09,293 - mmseg - INFO - per class results: +2024-06-19 16:27:09,301 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.84 | 90.79 | +| building | 85.38 | 92.45 | +| sky | 95.07 | 97.64 | +| floor | 84.75 | 91.57 | +| tree | 78.1 | 90.05 | +| ceiling | 87.72 | 94.59 | +| road | 86.95 | 92.68 | +| bed | 92.85 | 96.52 | +| windowpane | 67.18 | 81.56 | +| grass | 68.09 | 83.64 | +| cabinet | 67.92 | 78.69 | +| sidewalk | 71.06 | 83.27 | +| person | 86.76 | 94.64 | +| earth | 40.9 | 52.64 | +| door | 60.1 | 72.99 | +| table | 70.74 | 82.69 | +| mountain | 63.82 | 75.41 | +| plant | 57.53 | 67.33 | +| curtain | 78.51 | 89.11 | +| chair | 69.34 | 80.75 | +| car | 88.77 | 94.45 | +| water | 66.12 | 81.21 | +| painting | 79.3 | 91.88 | +| sofa | 83.14 | 90.51 | +| shelf | 52.53 | 69.8 | +| house | 54.09 | 76.48 | +| sea | 77.91 | 89.79 | +| mirror | 78.85 | 85.48 | +| rug | 63.85 | 75.17 | +| field | 27.9 | 51.01 | +| armchair | 64.48 | 82.48 | +| seat | 67.64 | 89.09 | +| fence | 54.83 | 71.64 | +| desk | 60.17 | 78.65 | +| rock | 56.04 | 79.73 | +| wardrobe | 53.39 | 70.55 | +| lamp | 76.34 | 87.22 | +| bathtub | 87.5 | 90.05 | +| railing | 43.75 | 60.76 | +| cushion | 70.4 | 83.83 | +| base | 46.02 | 57.7 | +| box | 41.39 | 53.84 | +| column | 57.34 | 67.73 | +| signboard | 42.89 | 62.32 | +| chest of drawers | 45.54 | 71.54 | +| counter | 53.35 | 61.93 | +| sand | 51.32 | 78.53 | +| sink | 85.5 | 90.1 | +| skyscraper | 45.76 | 60.05 | +| fireplace | 73.83 | 88.12 | +| refrigerator | 84.59 | 89.02 | +| grandstand | 62.88 | 83.1 | +| path | 29.65 | 37.26 | +| stairs | 29.85 | 38.94 | +| runway | 72.96 | 94.42 | +| case | 65.33 | 84.11 | +| pool table | 95.38 | 98.41 | +| pillow | 68.16 | 79.92 | +| screen door | 86.38 | 88.69 | +| stairway | 33.94 | 52.21 | +| river | 15.21 | 23.92 | +| bridge | 63.21 | 72.07 | +| bookcase | 48.74 | 64.13 | +| blind | 41.88 | 46.21 | +| coffee table | 61.61 | 88.18 | +| toilet | 90.63 | 93.49 | +| flower | 46.19 | 60.63 | +| book | 58.15 | 79.07 | +| hill | 15.53 | 24.78 | +| bench | 60.54 | 68.94 | +| countertop | 66.68 | 83.47 | +| stove | 86.25 | 93.41 | +| palm | 53.78 | 78.69 | +| kitchen island | 59.08 | 76.66 | +| computer | 77.03 | 92.37 | +| swivel chair | 50.22 | 80.36 | +| boat | 70.54 | 93.99 | +| bar | 73.25 | 83.22 | +| arcade machine | 80.19 | 82.98 | +| hovel | 28.98 | 34.03 | +| bus | 93.61 | 97.43 | +| towel | 81.39 | 89.25 | +| light | 61.58 | 71.81 | +| truck | 51.02 | 62.52 | +| tower | 25.12 | 45.85 | +| chandelier | 72.85 | 85.83 | +| awning | 44.86 | 57.52 | +| streetlight | 38.5 | 55.43 | +| booth | 53.47 | 72.48 | +| television receiver | 80.79 | 87.19 | +| airplane | 86.88 | 97.47 | +| dirt track | 11.76 | 28.45 | +| apparel | 64.26 | 90.18 | +| pole | 27.04 | 37.02 | +| land | 5.35 | 8.46 | +| bannister | 19.9 | 27.03 | +| escalator | 66.22 | 86.1 | +| ottoman | 58.78 | 74.84 | +| bottle | 43.74 | 61.99 | +| buffet | 45.89 | 51.9 | +| poster | 32.96 | 42.74 | +| stage | 20.46 | 35.32 | +| van | 55.45 | 76.2 | +| ship | 76.59 | 89.72 | +| fountain | 30.74 | 31.21 | +| conveyer belt | 83.97 | 96.73 | +| canopy | 59.75 | 74.77 | +| washer | 89.42 | 95.14 | +| plaything | 33.32 | 50.22 | +| swimming pool | 54.26 | 78.41 | +| stool | 58.12 | 74.77 | +| barrel | 74.78 | 98.13 | +| basket | 43.53 | 62.91 | +| waterfall | 60.24 | 77.19 | +| tent | 92.36 | 99.09 | +| bag | 29.12 | 34.14 | +| minibike | 77.69 | 91.44 | +| cradle | 88.18 | 97.96 | +| oven | 69.15 | 77.03 | +| ball | 50.15 | 53.32 | +| food | 60.6 | 72.52 | +| step | 12.95 | 14.72 | +| tank | 64.9 | 70.18 | +| trade name | 23.89 | 27.75 | +| microwave | 90.19 | 96.26 | +| pot | 61.46 | 75.67 | +| animal | 61.51 | 63.4 | +| bicycle | 61.75 | 78.41 | +| lake | 48.25 | 64.78 | +| dishwasher | 74.43 | 83.02 | +| screen | 59.85 | 93.51 | +| blanket | 40.04 | 47.79 | +| sculpture | 75.78 | 88.41 | +| hood | 69.52 | 72.39 | +| sconce | 62.75 | 77.26 | +| vase | 50.26 | 68.44 | +| traffic light | 41.43 | 68.52 | +| tray | 26.05 | 33.65 | +| ashcan | 49.29 | 66.87 | +| fan | 72.73 | 83.27 | +| pier | 42.96 | 48.17 | +| crt screen | 3.35 | 7.77 | +| plate | 64.95 | 82.34 | +| monitor | 29.85 | 33.36 | +| bulletin board | 55.98 | 68.01 | +| shower | 21.32 | 23.28 | +| radiator | 69.43 | 83.06 | +| glass | 23.74 | 25.77 | +| clock | 57.58 | 68.56 | +| flag | 70.21 | 83.38 | ++---------------------+-------+-------+ +2024-06-19 16:27:09,301 - mmseg - INFO - Summary: +2024-06-19 16:27:09,301 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.64 | 59.22 | 71.62 | ++-------+-------+-------+ +2024-06-19 16:27:09,302 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 16:27:09,302 - mmseg - INFO - Iter(val) [250] aAcc: 0.8664, mIoU: 0.5922, mAcc: 0.7162, IoU.wall: 0.8284, IoU.building: 0.8538, IoU.sky: 0.9507, IoU.floor: 0.8475, IoU.tree: 0.7810, IoU.ceiling: 0.8772, IoU.road: 0.8695, IoU.bed : 0.9285, IoU.windowpane: 0.6718, IoU.grass: 0.6809, IoU.cabinet: 0.6792, IoU.sidewalk: 0.7106, IoU.person: 0.8676, IoU.earth: 0.4090, IoU.door: 0.6010, IoU.table: 0.7074, IoU.mountain: 0.6382, IoU.plant: 0.5753, IoU.curtain: 0.7851, IoU.chair: 0.6934, IoU.car: 0.8877, IoU.water: 0.6612, IoU.painting: 0.7930, IoU.sofa: 0.8314, IoU.shelf: 0.5253, IoU.house: 0.5409, IoU.sea: 0.7791, IoU.mirror: 0.7885, IoU.rug: 0.6385, IoU.field: 0.2790, IoU.armchair: 0.6448, IoU.seat: 0.6764, IoU.fence: 0.5483, IoU.desk: 0.6017, IoU.rock: 0.5604, IoU.wardrobe: 0.5339, IoU.lamp: 0.7634, IoU.bathtub: 0.8750, IoU.railing: 0.4375, IoU.cushion: 0.7040, IoU.base: 0.4602, IoU.box: 0.4139, IoU.column: 0.5734, IoU.signboard: 0.4289, IoU.chest of drawers: 0.4554, IoU.counter: 0.5335, IoU.sand: 0.5132, IoU.sink: 0.8550, IoU.skyscraper: 0.4576, IoU.fireplace: 0.7383, IoU.refrigerator: 0.8459, IoU.grandstand: 0.6288, IoU.path: 0.2965, IoU.stairs: 0.2985, IoU.runway: 0.7296, IoU.case: 0.6533, IoU.pool table: 0.9538, IoU.pillow: 0.6816, IoU.screen door: 0.8638, IoU.stairway: 0.3394, IoU.river: 0.1521, IoU.bridge: 0.6321, IoU.bookcase: 0.4874, IoU.blind: 0.4188, IoU.coffee table: 0.6161, IoU.toilet: 0.9063, IoU.flower: 0.4619, IoU.book: 0.5815, IoU.hill: 0.1553, IoU.bench: 0.6054, IoU.countertop: 0.6668, IoU.stove: 0.8625, IoU.palm: 0.5378, IoU.kitchen island: 0.5908, IoU.computer: 0.7703, IoU.swivel chair: 0.5022, IoU.boat: 0.7054, IoU.bar: 0.7325, IoU.arcade machine: 0.8019, IoU.hovel: 0.2898, IoU.bus: 0.9361, IoU.towel: 0.8139, IoU.light: 0.6158, IoU.truck: 0.5102, IoU.tower: 0.2512, IoU.chandelier: 0.7285, IoU.awning: 0.4486, IoU.streetlight: 0.3850, IoU.booth: 0.5347, IoU.television receiver: 0.8079, IoU.airplane: 0.8688, IoU.dirt track: 0.1176, IoU.apparel: 0.6426, IoU.pole: 0.2704, IoU.land: 0.0535, IoU.bannister: 0.1990, IoU.escalator: 0.6622, IoU.ottoman: 0.5878, IoU.bottle: 0.4374, IoU.buffet: 0.4589, IoU.poster: 0.3296, IoU.stage: 0.2046, IoU.van: 0.5545, IoU.ship: 0.7659, IoU.fountain: 0.3074, IoU.conveyer belt: 0.8397, IoU.canopy: 0.5975, IoU.washer: 0.8942, IoU.plaything: 0.3332, IoU.swimming pool: 0.5426, IoU.stool: 0.5812, IoU.barrel: 0.7478, IoU.basket: 0.4353, IoU.waterfall: 0.6024, IoU.tent: 0.9236, IoU.bag: 0.2912, IoU.minibike: 0.7769, IoU.cradle: 0.8818, IoU.oven: 0.6915, IoU.ball: 0.5015, IoU.food: 0.6060, IoU.step: 0.1295, IoU.tank: 0.6490, IoU.trade name: 0.2389, IoU.microwave: 0.9019, IoU.pot: 0.6146, IoU.animal: 0.6151, IoU.bicycle: 0.6175, IoU.lake: 0.4825, IoU.dishwasher: 0.7443, IoU.screen: 0.5985, IoU.blanket: 0.4004, IoU.sculpture: 0.7578, IoU.hood: 0.6952, IoU.sconce: 0.6275, IoU.vase: 0.5026, IoU.traffic light: 0.4143, IoU.tray: 0.2605, IoU.ashcan: 0.4929, IoU.fan: 0.7273, IoU.pier: 0.4296, IoU.crt screen: 0.0335, IoU.plate: 0.6495, IoU.monitor: 0.2985, IoU.bulletin board: 0.5598, IoU.shower: 0.2132, IoU.radiator: 0.6943, IoU.glass: 0.2374, IoU.clock: 0.5758, IoU.flag: 0.7021, Acc.wall: 0.9079, Acc.building: 0.9245, Acc.sky: 0.9764, Acc.floor: 0.9157, Acc.tree: 0.9005, Acc.ceiling: 0.9459, Acc.road: 0.9268, Acc.bed : 0.9652, Acc.windowpane: 0.8156, Acc.grass: 0.8364, Acc.cabinet: 0.7869, Acc.sidewalk: 0.8327, Acc.person: 0.9464, Acc.earth: 0.5264, Acc.door: 0.7299, Acc.table: 0.8269, Acc.mountain: 0.7541, Acc.plant: 0.6733, Acc.curtain: 0.8911, Acc.chair: 0.8075, Acc.car: 0.9445, Acc.water: 0.8121, Acc.painting: 0.9188, Acc.sofa: 0.9051, Acc.shelf: 0.6980, Acc.house: 0.7648, Acc.sea: 0.8979, Acc.mirror: 0.8548, Acc.rug: 0.7517, Acc.field: 0.5101, Acc.armchair: 0.8248, Acc.seat: 0.8909, Acc.fence: 0.7164, Acc.desk: 0.7865, Acc.rock: 0.7973, Acc.wardrobe: 0.7055, Acc.lamp: 0.8722, Acc.bathtub: 0.9005, Acc.railing: 0.6076, Acc.cushion: 0.8383, Acc.base: 0.5770, Acc.box: 0.5384, Acc.column: 0.6773, Acc.signboard: 0.6232, Acc.chest of drawers: 0.7154, Acc.counter: 0.6193, Acc.sand: 0.7853, Acc.sink: 0.9010, Acc.skyscraper: 0.6005, Acc.fireplace: 0.8812, Acc.refrigerator: 0.8902, Acc.grandstand: 0.8310, Acc.path: 0.3726, Acc.stairs: 0.3894, Acc.runway: 0.9442, Acc.case: 0.8411, Acc.pool table: 0.9841, Acc.pillow: 0.7992, Acc.screen door: 0.8869, Acc.stairway: 0.5221, Acc.river: 0.2392, Acc.bridge: 0.7207, Acc.bookcase: 0.6413, Acc.blind: 0.4621, Acc.coffee table: 0.8818, Acc.toilet: 0.9349, Acc.flower: 0.6063, Acc.book: 0.7907, Acc.hill: 0.2478, Acc.bench: 0.6894, Acc.countertop: 0.8347, Acc.stove: 0.9341, Acc.palm: 0.7869, Acc.kitchen island: 0.7666, Acc.computer: 0.9237, Acc.swivel chair: 0.8036, Acc.boat: 0.9399, Acc.bar: 0.8322, Acc.arcade machine: 0.8298, Acc.hovel: 0.3403, Acc.bus: 0.9743, Acc.towel: 0.8925, Acc.light: 0.7181, Acc.truck: 0.6252, Acc.tower: 0.4585, Acc.chandelier: 0.8583, Acc.awning: 0.5752, Acc.streetlight: 0.5543, Acc.booth: 0.7248, Acc.television receiver: 0.8719, Acc.airplane: 0.9747, Acc.dirt track: 0.2845, Acc.apparel: 0.9018, Acc.pole: 0.3702, Acc.land: 0.0846, Acc.bannister: 0.2703, Acc.escalator: 0.8610, Acc.ottoman: 0.7484, Acc.bottle: 0.6199, Acc.buffet: 0.5190, Acc.poster: 0.4274, Acc.stage: 0.3532, Acc.van: 0.7620, Acc.ship: 0.8972, Acc.fountain: 0.3121, Acc.conveyer belt: 0.9673, Acc.canopy: 0.7477, Acc.washer: 0.9514, Acc.plaything: 0.5022, Acc.swimming pool: 0.7841, Acc.stool: 0.7477, Acc.barrel: 0.9813, Acc.basket: 0.6291, Acc.waterfall: 0.7719, Acc.tent: 0.9909, Acc.bag: 0.3414, Acc.minibike: 0.9144, Acc.cradle: 0.9796, Acc.oven: 0.7703, Acc.ball: 0.5332, Acc.food: 0.7252, Acc.step: 0.1472, Acc.tank: 0.7018, Acc.trade name: 0.2775, Acc.microwave: 0.9626, Acc.pot: 0.7567, Acc.animal: 0.6340, Acc.bicycle: 0.7841, Acc.lake: 0.6478, Acc.dishwasher: 0.8302, Acc.screen: 0.9351, Acc.blanket: 0.4779, Acc.sculpture: 0.8841, Acc.hood: 0.7239, Acc.sconce: 0.7726, Acc.vase: 0.6844, Acc.traffic light: 0.6852, Acc.tray: 0.3365, Acc.ashcan: 0.6687, Acc.fan: 0.8327, Acc.pier: 0.4817, Acc.crt screen: 0.0777, Acc.plate: 0.8234, Acc.monitor: 0.3336, Acc.bulletin board: 0.6801, Acc.shower: 0.2328, Acc.radiator: 0.8306, Acc.glass: 0.2577, Acc.clock: 0.6856, Acc.flag: 0.8338 +2024-06-19 16:28:48,501 - mmseg - INFO - Iter [64050/80000] lr: 7.976e-06, eta: 9:27:40, time: 4.184, data_time: 2.216, memory: 72263, decode.loss_ce: 0.1411, decode.acc_seg: 93.7994, aux.loss_ce: 0.0601, aux.acc_seg: 93.4755, loss: 0.2013 +2024-06-19 16:30:27,480 - mmseg - INFO - Iter [64100/80000] lr: 7.950e-06, eta: 9:25:52, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1376, decode.acc_seg: 93.9328, aux.loss_ce: 0.0585, aux.acc_seg: 93.5634, loss: 0.1962 +2024-06-19 16:32:06,337 - mmseg - INFO - Iter [64150/80000] lr: 7.925e-06, eta: 9:24:03, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1400, decode.acc_seg: 93.8567, aux.loss_ce: 0.0593, aux.acc_seg: 93.5066, loss: 0.1993 +2024-06-19 16:33:45,171 - mmseg - INFO - Iter [64200/80000] lr: 7.900e-06, eta: 9:22:14, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1330, decode.acc_seg: 94.0492, aux.loss_ce: 0.0569, aux.acc_seg: 93.6774, loss: 0.1899 +2024-06-19 16:35:24,061 - mmseg - INFO - Iter [64250/80000] lr: 7.876e-06, eta: 9:20:25, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1310, decode.acc_seg: 94.2208, aux.loss_ce: 0.0562, aux.acc_seg: 93.7982, loss: 0.1873 +2024-06-19 16:37:02,929 - mmseg - INFO - Iter [64300/80000] lr: 7.851e-06, eta: 9:18:37, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1318, decode.acc_seg: 94.1371, aux.loss_ce: 0.0563, aux.acc_seg: 93.7261, loss: 0.1881 +2024-06-19 16:38:41,747 - mmseg - INFO - Iter [64350/80000] lr: 7.826e-06, eta: 9:16:48, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1392, decode.acc_seg: 93.7249, aux.loss_ce: 0.0597, aux.acc_seg: 93.3624, loss: 0.1989 +2024-06-19 16:40:20,598 - mmseg - INFO - Iter [64400/80000] lr: 7.801e-06, eta: 9:14:59, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1465, decode.acc_seg: 93.5295, aux.loss_ce: 0.0624, aux.acc_seg: 93.1905, loss: 0.2089 +2024-06-19 16:42:02,721 - mmseg - INFO - Iter [64450/80000] lr: 7.776e-06, eta: 9:13:12, time: 2.042, data_time: 0.072, memory: 72263, decode.loss_ce: 0.1406, decode.acc_seg: 93.9822, aux.loss_ce: 0.0604, aux.acc_seg: 93.5322, loss: 0.2010 +2024-06-19 16:43:41,572 - mmseg - INFO - Iter [64500/80000] lr: 7.750e-06, eta: 9:11:23, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1294, decode.acc_seg: 94.1979, aux.loss_ce: 0.0560, aux.acc_seg: 93.7727, loss: 0.1854 +2024-06-19 16:45:20,406 - mmseg - INFO - Iter [64550/80000] lr: 7.725e-06, eta: 9:09:34, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1362, decode.acc_seg: 93.7997, aux.loss_ce: 0.0585, aux.acc_seg: 93.3356, loss: 0.1947 +2024-06-19 16:46:59,275 - mmseg - INFO - Iter [64600/80000] lr: 7.701e-06, eta: 9:07:46, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1295, decode.acc_seg: 94.3416, aux.loss_ce: 0.0550, aux.acc_seg: 93.9766, loss: 0.1845 +2024-06-19 16:48:38,200 - mmseg - INFO - Iter [64650/80000] lr: 7.675e-06, eta: 9:05:57, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1387, decode.acc_seg: 93.7941, aux.loss_ce: 0.0599, aux.acc_seg: 93.3761, loss: 0.1986 +2024-06-19 16:50:17,177 - mmseg - INFO - Iter [64700/80000] lr: 7.651e-06, eta: 9:04:09, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1394, decode.acc_seg: 93.7045, aux.loss_ce: 0.0597, aux.acc_seg: 93.3737, loss: 0.1991 +2024-06-19 16:51:56,147 - mmseg - INFO - Iter [64750/80000] lr: 7.626e-06, eta: 9:02:20, time: 1.979, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1303, decode.acc_seg: 94.2440, aux.loss_ce: 0.0560, aux.acc_seg: 93.8482, loss: 0.1863 +2024-06-19 16:53:35,003 - mmseg - INFO - Iter [64800/80000] lr: 7.601e-06, eta: 9:00:32, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1384, decode.acc_seg: 93.6407, aux.loss_ce: 0.0589, aux.acc_seg: 93.3030, loss: 0.1973 +2024-06-19 16:55:13,963 - mmseg - INFO - Iter [64850/80000] lr: 7.576e-06, eta: 8:58:43, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1313, decode.acc_seg: 94.0780, aux.loss_ce: 0.0560, aux.acc_seg: 93.7318, loss: 0.1873 +2024-06-19 16:56:52,853 - mmseg - INFO - Iter [64900/80000] lr: 7.551e-06, eta: 8:56:55, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1256, decode.acc_seg: 94.3834, aux.loss_ce: 0.0538, aux.acc_seg: 94.0001, loss: 0.1794 +2024-06-19 16:58:31,751 - mmseg - INFO - Iter [64950/80000] lr: 7.525e-06, eta: 8:55:06, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1366, decode.acc_seg: 93.9658, aux.loss_ce: 0.0583, aux.acc_seg: 93.5450, loss: 0.1950 +2024-06-19 17:00:10,699 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 17:00:10,699 - mmseg - INFO - Iter [65000/80000] lr: 7.500e-06, eta: 8:53:18, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1288, decode.acc_seg: 94.2836, aux.loss_ce: 0.0555, aux.acc_seg: 93.8507, loss: 0.1844 +2024-06-19 17:02:00,821 - mmseg - INFO - per class results: +2024-06-19 17:02:00,827 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.9 | 90.89 | +| building | 85.43 | 93.36 | +| sky | 95.06 | 97.74 | +| floor | 85.01 | 92.46 | +| tree | 78.08 | 89.39 | +| ceiling | 87.57 | 93.61 | +| road | 88.28 | 92.58 | +| bed | 93.33 | 97.2 | +| windowpane | 66.16 | 80.07 | +| grass | 67.71 | 82.88 | +| cabinet | 67.91 | 77.02 | +| sidewalk | 73.37 | 86.4 | +| person | 86.69 | 93.98 | +| earth | 42.41 | 54.94 | +| door | 59.67 | 74.11 | +| table | 71.21 | 81.93 | +| mountain | 63.66 | 73.84 | +| plant | 57.04 | 67.53 | +| curtain | 78.97 | 88.85 | +| chair | 67.93 | 78.87 | +| car | 88.74 | 94.84 | +| water | 63.37 | 78.15 | +| painting | 80.09 | 90.96 | +| sofa | 82.57 | 89.41 | +| shelf | 50.5 | 67.22 | +| house | 51.42 | 64.49 | +| sea | 75.11 | 89.46 | +| mirror | 79.41 | 88.04 | +| rug | 64.43 | 76.82 | +| field | 28.44 | 49.97 | +| armchair | 64.12 | 80.9 | +| seat | 66.7 | 90.3 | +| fence | 48.25 | 60.02 | +| desk | 59.62 | 80.06 | +| rock | 57.53 | 88.44 | +| wardrobe | 53.1 | 70.22 | +| lamp | 76.97 | 88.27 | +| bathtub | 87.3 | 89.05 | +| railing | 42.14 | 61.25 | +| cushion | 68.85 | 83.89 | +| base | 45.57 | 63.39 | +| box | 39.96 | 51.53 | +| column | 56.89 | 71.77 | +| signboard | 42.45 | 57.58 | +| chest of drawers | 46.11 | 69.43 | +| counter | 50.68 | 60.01 | +| sand | 55.73 | 83.82 | +| sink | 84.21 | 88.18 | +| skyscraper | 44.83 | 60.13 | +| fireplace | 74.48 | 94.98 | +| refrigerator | 86.26 | 94.25 | +| grandstand | 60.98 | 82.14 | +| path | 32.0 | 43.67 | +| stairs | 31.59 | 40.26 | +| runway | 72.25 | 93.07 | +| case | 63.9 | 84.24 | +| pool table | 95.32 | 98.48 | +| pillow | 66.01 | 76.87 | +| screen door | 86.83 | 89.21 | +| stairway | 36.55 | 53.13 | +| river | 15.21 | 26.66 | +| bridge | 63.2 | 70.08 | +| bookcase | 43.4 | 58.06 | +| blind | 41.1 | 46.25 | +| coffee table | 62.25 | 89.04 | +| toilet | 90.15 | 93.48 | +| flower | 46.78 | 63.29 | +| book | 58.12 | 81.42 | +| hill | 15.9 | 27.25 | +| bench | 59.27 | 66.76 | +| countertop | 65.32 | 85.47 | +| stove | 88.07 | 92.92 | +| palm | 53.67 | 82.11 | +| kitchen island | 55.84 | 81.38 | +| computer | 75.9 | 89.12 | +| swivel chair | 45.88 | 66.65 | +| boat | 81.34 | 93.28 | +| bar | 71.43 | 85.09 | +| arcade machine | 83.12 | 86.43 | +| hovel | 43.56 | 48.53 | +| bus | 93.96 | 97.11 | +| towel | 81.45 | 92.1 | +| light | 63.14 | 75.03 | +| truck | 52.49 | 63.38 | +| tower | 30.46 | 60.65 | +| chandelier | 73.3 | 86.11 | +| awning | 43.53 | 55.38 | +| streetlight | 38.06 | 51.27 | +| booth | 50.38 | 71.95 | +| television receiver | 82.65 | 86.92 | +| airplane | 90.43 | 96.16 | +| dirt track | 4.16 | 6.95 | +| apparel | 65.02 | 82.33 | +| pole | 27.43 | 37.05 | +| land | 5.71 | 7.92 | +| bannister | 22.56 | 28.44 | +| escalator | 66.1 | 86.61 | +| ottoman | 58.23 | 74.37 | +| bottle | 46.32 | 68.28 | +| buffet | 56.47 | 64.69 | +| poster | 34.02 | 42.22 | +| stage | 22.07 | 40.73 | +| van | 56.77 | 75.14 | +| ship | 69.02 | 76.25 | +| fountain | 31.07 | 31.55 | +| conveyer belt | 84.54 | 96.8 | +| canopy | 59.88 | 76.52 | +| washer | 83.14 | 88.04 | +| plaything | 34.14 | 50.15 | +| swimming pool | 53.92 | 78.82 | +| stool | 55.23 | 74.25 | +| barrel | 71.69 | 97.9 | +| basket | 44.34 | 59.92 | +| waterfall | 55.86 | 67.58 | +| tent | 92.68 | 98.78 | +| bag | 28.22 | 32.8 | +| minibike | 77.51 | 90.08 | +| cradle | 82.74 | 97.86 | +| oven | 69.11 | 79.97 | +| ball | 57.0 | 65.17 | +| food | 62.86 | 74.63 | +| step | 12.15 | 14.66 | +| tank | 63.94 | 68.77 | +| trade name | 25.5 | 30.09 | +| microwave | 89.98 | 96.75 | +| pot | 63.8 | 74.59 | +| animal | 61.24 | 62.89 | +| bicycle | 61.84 | 76.78 | +| lake | 53.2 | 63.52 | +| dishwasher | 73.99 | 82.24 | +| screen | 61.39 | 95.3 | +| blanket | 34.83 | 40.96 | +| sculpture | 77.28 | 87.82 | +| hood | 64.64 | 74.69 | +| sconce | 61.63 | 72.29 | +| vase | 50.85 | 68.39 | +| traffic light | 39.31 | 67.22 | +| tray | 26.22 | 34.77 | +| ashcan | 52.2 | 68.3 | +| fan | 73.3 | 83.55 | +| pier | 42.47 | 46.53 | +| crt screen | 4.96 | 9.16 | +| plate | 64.59 | 82.41 | +| monitor | 44.5 | 55.64 | +| bulletin board | 60.29 | 71.88 | +| shower | 21.48 | 22.44 | +| radiator | 68.63 | 83.07 | +| glass | 22.7 | 24.3 | +| clock | 57.41 | 68.36 | +| flag | 73.07 | 80.71 | ++---------------------+-------+-------+ +2024-06-19 17:02:00,827 - mmseg - INFO - Summary: +2024-06-19 17:02:00,827 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 86.67 | 59.36 | 71.7 | ++-------+-------+------+ +2024-06-19 17:02:00,828 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 17:02:00,828 - mmseg - INFO - Iter(val) [250] aAcc: 0.8667, mIoU: 0.5936, mAcc: 0.7170, IoU.wall: 0.8290, IoU.building: 0.8543, IoU.sky: 0.9506, IoU.floor: 0.8501, IoU.tree: 0.7808, IoU.ceiling: 0.8757, IoU.road: 0.8828, IoU.bed : 0.9333, IoU.windowpane: 0.6616, IoU.grass: 0.6771, IoU.cabinet: 0.6791, IoU.sidewalk: 0.7337, IoU.person: 0.8669, IoU.earth: 0.4241, IoU.door: 0.5967, IoU.table: 0.7121, IoU.mountain: 0.6366, IoU.plant: 0.5704, IoU.curtain: 0.7897, IoU.chair: 0.6793, IoU.car: 0.8874, IoU.water: 0.6337, IoU.painting: 0.8009, IoU.sofa: 0.8257, IoU.shelf: 0.5050, IoU.house: 0.5142, IoU.sea: 0.7511, IoU.mirror: 0.7941, IoU.rug: 0.6443, IoU.field: 0.2844, IoU.armchair: 0.6412, IoU.seat: 0.6670, IoU.fence: 0.4825, IoU.desk: 0.5962, IoU.rock: 0.5753, IoU.wardrobe: 0.5310, IoU.lamp: 0.7697, IoU.bathtub: 0.8730, IoU.railing: 0.4214, IoU.cushion: 0.6885, IoU.base: 0.4557, IoU.box: 0.3996, IoU.column: 0.5689, IoU.signboard: 0.4245, IoU.chest of drawers: 0.4611, IoU.counter: 0.5068, IoU.sand: 0.5573, IoU.sink: 0.8421, IoU.skyscraper: 0.4483, IoU.fireplace: 0.7448, IoU.refrigerator: 0.8626, IoU.grandstand: 0.6098, IoU.path: 0.3200, IoU.stairs: 0.3159, IoU.runway: 0.7225, IoU.case: 0.6390, IoU.pool table: 0.9532, IoU.pillow: 0.6601, IoU.screen door: 0.8683, IoU.stairway: 0.3655, IoU.river: 0.1521, IoU.bridge: 0.6320, IoU.bookcase: 0.4340, IoU.blind: 0.4110, IoU.coffee table: 0.6225, IoU.toilet: 0.9015, IoU.flower: 0.4678, IoU.book: 0.5812, IoU.hill: 0.1590, IoU.bench: 0.5927, IoU.countertop: 0.6532, IoU.stove: 0.8807, IoU.palm: 0.5367, IoU.kitchen island: 0.5584, IoU.computer: 0.7590, IoU.swivel chair: 0.4588, IoU.boat: 0.8134, IoU.bar: 0.7143, IoU.arcade machine: 0.8312, IoU.hovel: 0.4356, IoU.bus: 0.9396, IoU.towel: 0.8145, IoU.light: 0.6314, IoU.truck: 0.5249, IoU.tower: 0.3046, IoU.chandelier: 0.7330, IoU.awning: 0.4353, IoU.streetlight: 0.3806, IoU.booth: 0.5038, IoU.television receiver: 0.8265, IoU.airplane: 0.9043, IoU.dirt track: 0.0416, IoU.apparel: 0.6502, IoU.pole: 0.2743, IoU.land: 0.0571, IoU.bannister: 0.2256, IoU.escalator: 0.6610, IoU.ottoman: 0.5823, IoU.bottle: 0.4632, IoU.buffet: 0.5647, IoU.poster: 0.3402, IoU.stage: 0.2207, IoU.van: 0.5677, IoU.ship: 0.6902, IoU.fountain: 0.3107, IoU.conveyer belt: 0.8454, IoU.canopy: 0.5988, IoU.washer: 0.8314, IoU.plaything: 0.3414, IoU.swimming pool: 0.5392, IoU.stool: 0.5523, IoU.barrel: 0.7169, IoU.basket: 0.4434, IoU.waterfall: 0.5586, IoU.tent: 0.9268, IoU.bag: 0.2822, IoU.minibike: 0.7751, IoU.cradle: 0.8274, IoU.oven: 0.6911, IoU.ball: 0.5700, IoU.food: 0.6286, IoU.step: 0.1215, IoU.tank: 0.6394, IoU.trade name: 0.2550, IoU.microwave: 0.8998, IoU.pot: 0.6380, IoU.animal: 0.6124, IoU.bicycle: 0.6184, IoU.lake: 0.5320, IoU.dishwasher: 0.7399, IoU.screen: 0.6139, IoU.blanket: 0.3483, IoU.sculpture: 0.7728, IoU.hood: 0.6464, IoU.sconce: 0.6163, IoU.vase: 0.5085, IoU.traffic light: 0.3931, IoU.tray: 0.2622, IoU.ashcan: 0.5220, IoU.fan: 0.7330, IoU.pier: 0.4247, IoU.crt screen: 0.0496, IoU.plate: 0.6459, IoU.monitor: 0.4450, IoU.bulletin board: 0.6029, IoU.shower: 0.2148, IoU.radiator: 0.6863, IoU.glass: 0.2270, IoU.clock: 0.5741, IoU.flag: 0.7307, Acc.wall: 0.9089, Acc.building: 0.9336, Acc.sky: 0.9774, Acc.floor: 0.9246, Acc.tree: 0.8939, Acc.ceiling: 0.9361, Acc.road: 0.9258, Acc.bed : 0.9720, Acc.windowpane: 0.8007, Acc.grass: 0.8288, Acc.cabinet: 0.7702, Acc.sidewalk: 0.8640, Acc.person: 0.9398, Acc.earth: 0.5494, Acc.door: 0.7411, Acc.table: 0.8193, Acc.mountain: 0.7384, Acc.plant: 0.6753, Acc.curtain: 0.8885, Acc.chair: 0.7887, Acc.car: 0.9484, Acc.water: 0.7815, Acc.painting: 0.9096, Acc.sofa: 0.8941, Acc.shelf: 0.6722, Acc.house: 0.6449, Acc.sea: 0.8946, Acc.mirror: 0.8804, Acc.rug: 0.7682, Acc.field: 0.4997, Acc.armchair: 0.8090, Acc.seat: 0.9030, Acc.fence: 0.6002, Acc.desk: 0.8006, Acc.rock: 0.8844, Acc.wardrobe: 0.7022, Acc.lamp: 0.8827, Acc.bathtub: 0.8905, Acc.railing: 0.6125, Acc.cushion: 0.8389, Acc.base: 0.6339, Acc.box: 0.5153, Acc.column: 0.7177, Acc.signboard: 0.5758, Acc.chest of drawers: 0.6943, Acc.counter: 0.6001, Acc.sand: 0.8382, Acc.sink: 0.8818, Acc.skyscraper: 0.6013, Acc.fireplace: 0.9498, Acc.refrigerator: 0.9425, Acc.grandstand: 0.8214, Acc.path: 0.4367, Acc.stairs: 0.4026, Acc.runway: 0.9307, Acc.case: 0.8424, Acc.pool table: 0.9848, Acc.pillow: 0.7687, Acc.screen door: 0.8921, Acc.stairway: 0.5313, Acc.river: 0.2666, Acc.bridge: 0.7008, Acc.bookcase: 0.5806, Acc.blind: 0.4625, Acc.coffee table: 0.8904, Acc.toilet: 0.9348, Acc.flower: 0.6329, Acc.book: 0.8142, Acc.hill: 0.2725, Acc.bench: 0.6676, Acc.countertop: 0.8547, Acc.stove: 0.9292, Acc.palm: 0.8211, Acc.kitchen island: 0.8138, Acc.computer: 0.8912, Acc.swivel chair: 0.6665, Acc.boat: 0.9328, Acc.bar: 0.8509, Acc.arcade machine: 0.8643, Acc.hovel: 0.4853, Acc.bus: 0.9711, Acc.towel: 0.9210, Acc.light: 0.7503, Acc.truck: 0.6338, Acc.tower: 0.6065, Acc.chandelier: 0.8611, Acc.awning: 0.5538, Acc.streetlight: 0.5127, Acc.booth: 0.7195, Acc.television receiver: 0.8692, Acc.airplane: 0.9616, Acc.dirt track: 0.0695, Acc.apparel: 0.8233, Acc.pole: 0.3705, Acc.land: 0.0792, Acc.bannister: 0.2844, Acc.escalator: 0.8661, Acc.ottoman: 0.7437, Acc.bottle: 0.6828, Acc.buffet: 0.6469, Acc.poster: 0.4222, Acc.stage: 0.4073, Acc.van: 0.7514, Acc.ship: 0.7625, Acc.fountain: 0.3155, Acc.conveyer belt: 0.9680, Acc.canopy: 0.7652, Acc.washer: 0.8804, Acc.plaything: 0.5015, Acc.swimming pool: 0.7882, Acc.stool: 0.7425, Acc.barrel: 0.9790, Acc.basket: 0.5992, Acc.waterfall: 0.6758, Acc.tent: 0.9878, Acc.bag: 0.3280, Acc.minibike: 0.9008, Acc.cradle: 0.9786, Acc.oven: 0.7997, Acc.ball: 0.6517, Acc.food: 0.7463, Acc.step: 0.1466, Acc.tank: 0.6877, Acc.trade name: 0.3009, Acc.microwave: 0.9675, Acc.pot: 0.7459, Acc.animal: 0.6289, Acc.bicycle: 0.7678, Acc.lake: 0.6352, Acc.dishwasher: 0.8224, Acc.screen: 0.9530, Acc.blanket: 0.4096, Acc.sculpture: 0.8782, Acc.hood: 0.7469, Acc.sconce: 0.7229, Acc.vase: 0.6839, Acc.traffic light: 0.6722, Acc.tray: 0.3477, Acc.ashcan: 0.6830, Acc.fan: 0.8355, Acc.pier: 0.4653, Acc.crt screen: 0.0916, Acc.plate: 0.8241, Acc.monitor: 0.5564, Acc.bulletin board: 0.7188, Acc.shower: 0.2244, Acc.radiator: 0.8307, Acc.glass: 0.2430, Acc.clock: 0.6836, Acc.flag: 0.8071 +2024-06-19 17:03:40,021 - mmseg - INFO - Iter [65050/80000] lr: 7.475e-06, eta: 8:51:55, time: 4.186, data_time: 2.219, memory: 72263, decode.loss_ce: 0.1382, decode.acc_seg: 93.8380, aux.loss_ce: 0.0589, aux.acc_seg: 93.4600, loss: 0.1971 +2024-06-19 17:05:18,915 - mmseg - INFO - Iter [65100/80000] lr: 7.451e-06, eta: 8:50:06, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1314, decode.acc_seg: 94.1215, aux.loss_ce: 0.0567, aux.acc_seg: 93.6576, loss: 0.1881 +2024-06-19 17:06:57,842 - mmseg - INFO - Iter [65150/80000] lr: 7.426e-06, eta: 8:48:18, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1410, decode.acc_seg: 93.6874, aux.loss_ce: 0.0603, aux.acc_seg: 93.3239, loss: 0.2013 +2024-06-19 17:08:36,801 - mmseg - INFO - Iter [65200/80000] lr: 7.401e-06, eta: 8:46:29, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1388, decode.acc_seg: 94.0559, aux.loss_ce: 0.0589, aux.acc_seg: 93.7019, loss: 0.1978 +2024-06-19 17:10:15,725 - mmseg - INFO - Iter [65250/80000] lr: 7.376e-06, eta: 8:44:41, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1360, decode.acc_seg: 94.1479, aux.loss_ce: 0.0580, aux.acc_seg: 93.7154, loss: 0.1940 +2024-06-19 17:11:54,731 - mmseg - INFO - Iter [65300/80000] lr: 7.351e-06, eta: 8:42:52, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1390, decode.acc_seg: 93.8375, aux.loss_ce: 0.0593, aux.acc_seg: 93.4752, loss: 0.1982 +2024-06-19 17:13:33,735 - mmseg - INFO - Iter [65350/80000] lr: 7.325e-06, eta: 8:41:04, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1311, decode.acc_seg: 94.1999, aux.loss_ce: 0.0566, aux.acc_seg: 93.7565, loss: 0.1877 +2024-06-19 17:15:12,605 - mmseg - INFO - Iter [65400/80000] lr: 7.300e-06, eta: 8:39:15, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1318, decode.acc_seg: 94.0270, aux.loss_ce: 0.0565, aux.acc_seg: 93.5922, loss: 0.1883 +2024-06-19 17:16:51,456 - mmseg - INFO - Iter [65450/80000] lr: 7.276e-06, eta: 8:37:27, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1385, decode.acc_seg: 94.0089, aux.loss_ce: 0.0595, aux.acc_seg: 93.5789, loss: 0.1981 +2024-06-19 17:18:30,424 - mmseg - INFO - Iter [65500/80000] lr: 7.251e-06, eta: 8:35:39, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1313, decode.acc_seg: 94.1651, aux.loss_ce: 0.0564, aux.acc_seg: 93.7543, loss: 0.1877 +2024-06-19 17:20:09,337 - mmseg - INFO - Iter [65550/80000] lr: 7.226e-06, eta: 8:33:50, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1259, decode.acc_seg: 94.4003, aux.loss_ce: 0.0538, aux.acc_seg: 93.9975, loss: 0.1797 +2024-06-19 17:21:48,223 - mmseg - INFO - Iter [65600/80000] lr: 7.201e-06, eta: 8:32:02, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1345, decode.acc_seg: 93.9842, aux.loss_ce: 0.0578, aux.acc_seg: 93.5848, loss: 0.1922 +2024-06-19 17:23:27,004 - mmseg - INFO - Iter [65650/80000] lr: 7.176e-06, eta: 8:30:13, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1451, decode.acc_seg: 93.7084, aux.loss_ce: 0.0613, aux.acc_seg: 93.3374, loss: 0.2064 +2024-06-19 17:25:08,093 - mmseg - INFO - Iter [65700/80000] lr: 7.151e-06, eta: 8:28:25, time: 2.022, data_time: 0.052, memory: 72263, decode.loss_ce: 0.1365, decode.acc_seg: 93.9459, aux.loss_ce: 0.0582, aux.acc_seg: 93.5768, loss: 0.1947 +2024-06-19 17:26:46,905 - mmseg - INFO - Iter [65750/80000] lr: 7.125e-06, eta: 8:26:37, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1331, decode.acc_seg: 94.0664, aux.loss_ce: 0.0571, aux.acc_seg: 93.6586, loss: 0.1902 +2024-06-19 17:28:25,850 - mmseg - INFO - Iter [65800/80000] lr: 7.100e-06, eta: 8:24:49, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1241, decode.acc_seg: 94.4472, aux.loss_ce: 0.0534, aux.acc_seg: 94.0463, loss: 0.1775 +2024-06-19 17:30:04,681 - mmseg - INFO - Iter [65850/80000] lr: 7.075e-06, eta: 8:23:00, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1356, decode.acc_seg: 94.0858, aux.loss_ce: 0.0583, aux.acc_seg: 93.7024, loss: 0.1939 +2024-06-19 17:31:43,659 - mmseg - INFO - Iter [65900/80000] lr: 7.051e-06, eta: 8:21:12, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1331, decode.acc_seg: 94.0181, aux.loss_ce: 0.0567, aux.acc_seg: 93.6074, loss: 0.1897 +2024-06-19 17:33:22,596 - mmseg - INFO - Iter [65950/80000] lr: 7.026e-06, eta: 8:19:24, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1331, decode.acc_seg: 94.2152, aux.loss_ce: 0.0571, aux.acc_seg: 93.7868, loss: 0.1901 +2024-06-19 17:35:01,480 - mmseg - INFO - Saving checkpoint at 66000 iterations +2024-06-19 17:36:27,192 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 17:36:27,192 - mmseg - INFO - Iter [66000/80000] lr: 7.001e-06, eta: 8:17:54, time: 3.692, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1319, decode.acc_seg: 94.0999, aux.loss_ce: 0.0574, aux.acc_seg: 93.6044, loss: 0.1893 +2024-06-19 17:38:16,973 - mmseg - INFO - per class results: +2024-06-19 17:38:16,979 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.8 | 90.11 | +| building | 85.31 | 93.37 | +| sky | 95.0 | 97.5 | +| floor | 84.86 | 92.06 | +| tree | 78.41 | 90.22 | +| ceiling | 87.89 | 94.69 | +| road | 86.69 | 91.73 | +| bed | 93.23 | 97.1 | +| windowpane | 67.03 | 80.29 | +| grass | 67.99 | 83.19 | +| cabinet | 68.31 | 78.75 | +| sidewalk | 71.98 | 86.06 | +| person | 86.58 | 94.76 | +| earth | 39.35 | 51.49 | +| door | 60.25 | 76.52 | +| table | 71.26 | 81.0 | +| mountain | 63.94 | 75.28 | +| plant | 57.62 | 67.91 | +| curtain | 78.04 | 87.84 | +| chair | 69.29 | 80.81 | +| car | 88.78 | 94.64 | +| water | 61.39 | 75.38 | +| painting | 79.47 | 91.78 | +| sofa | 82.88 | 90.53 | +| shelf | 50.02 | 65.13 | +| house | 55.99 | 69.01 | +| sea | 74.25 | 87.14 | +| mirror | 79.43 | 87.87 | +| rug | 63.54 | 75.83 | +| field | 30.32 | 59.15 | +| armchair | 63.55 | 79.19 | +| seat | 70.18 | 89.11 | +| fence | 54.92 | 66.44 | +| desk | 59.64 | 78.88 | +| rock | 57.95 | 86.37 | +| wardrobe | 54.16 | 73.58 | +| lamp | 77.1 | 87.86 | +| bathtub | 87.1 | 90.93 | +| railing | 43.93 | 62.99 | +| cushion | 69.05 | 84.1 | +| base | 45.66 | 58.07 | +| box | 41.65 | 53.21 | +| column | 57.8 | 72.86 | +| signboard | 41.37 | 58.09 | +| chest of drawers | 47.96 | 68.33 | +| counter | 51.54 | 64.11 | +| sand | 56.76 | 77.99 | +| sink | 83.72 | 88.57 | +| skyscraper | 39.7 | 49.19 | +| fireplace | 75.61 | 92.52 | +| refrigerator | 86.7 | 93.92 | +| grandstand | 56.32 | 79.64 | +| path | 31.3 | 42.65 | +| stairs | 36.86 | 46.23 | +| runway | 72.06 | 93.24 | +| case | 61.61 | 78.95 | +| pool table | 94.62 | 98.45 | +| pillow | 66.29 | 77.15 | +| screen door | 86.15 | 89.75 | +| stairway | 41.77 | 53.0 | +| river | 13.79 | 29.91 | +| bridge | 60.46 | 68.83 | +| bookcase | 49.5 | 69.01 | +| blind | 45.12 | 56.84 | +| coffee table | 62.47 | 87.97 | +| toilet | 90.57 | 93.72 | +| flower | 44.22 | 59.35 | +| book | 57.96 | 78.6 | +| hill | 17.02 | 26.18 | +| bench | 61.49 | 69.64 | +| countertop | 65.27 | 86.46 | +| stove | 87.25 | 91.94 | +| palm | 52.66 | 82.31 | +| kitchen island | 56.0 | 79.7 | +| computer | 76.94 | 91.21 | +| swivel chair | 46.28 | 69.59 | +| boat | 81.3 | 93.2 | +| bar | 70.33 | 87.26 | +| arcade machine | 83.41 | 87.57 | +| hovel | 41.08 | 45.79 | +| bus | 94.08 | 97.27 | +| towel | 80.6 | 87.4 | +| light | 63.65 | 73.2 | +| truck | 51.86 | 65.05 | +| tower | 30.22 | 60.92 | +| chandelier | 73.79 | 83.56 | +| awning | 41.67 | 51.2 | +| streetlight | 38.2 | 48.97 | +| booth | 50.99 | 72.86 | +| television receiver | 80.31 | 89.08 | +| airplane | 89.95 | 96.12 | +| dirt track | 5.42 | 5.61 | +| apparel | 65.53 | 83.9 | +| pole | 28.4 | 38.32 | +| land | 5.71 | 7.91 | +| bannister | 22.29 | 28.57 | +| escalator | 66.38 | 86.79 | +| ottoman | 56.8 | 72.01 | +| bottle | 46.71 | 69.61 | +| buffet | 56.57 | 64.4 | +| poster | 32.97 | 43.96 | +| stage | 22.47 | 46.78 | +| van | 56.56 | 73.23 | +| ship | 65.74 | 71.99 | +| fountain | 30.93 | 31.39 | +| conveyer belt | 81.72 | 97.44 | +| canopy | 61.83 | 78.87 | +| washer | 85.43 | 90.74 | +| plaything | 33.96 | 49.85 | +| swimming pool | 57.0 | 82.56 | +| stool | 54.18 | 74.92 | +| barrel | 65.12 | 98.26 | +| basket | 43.48 | 61.64 | +| waterfall | 56.07 | 66.62 | +| tent | 92.99 | 98.81 | +| bag | 28.5 | 33.25 | +| minibike | 77.68 | 91.33 | +| cradle | 86.75 | 97.83 | +| oven | 66.42 | 78.0 | +| ball | 55.72 | 60.81 | +| food | 62.09 | 72.18 | +| step | 12.31 | 13.88 | +| tank | 64.56 | 69.82 | +| trade name | 22.95 | 27.05 | +| microwave | 89.66 | 96.88 | +| pot | 61.59 | 71.0 | +| animal | 60.37 | 61.81 | +| bicycle | 61.84 | 78.49 | +| lake | 52.32 | 63.7 | +| dishwasher | 74.6 | 83.35 | +| screen | 56.55 | 86.73 | +| blanket | 37.07 | 43.75 | +| sculpture | 77.12 | 87.66 | +| hood | 66.57 | 75.48 | +| sconce | 59.5 | 67.34 | +| vase | 51.49 | 68.82 | +| traffic light | 39.03 | 65.8 | +| tray | 27.31 | 37.1 | +| ashcan | 53.1 | 66.84 | +| fan | 72.88 | 84.08 | +| pier | 41.19 | 46.43 | +| crt screen | 6.33 | 13.13 | +| plate | 65.38 | 81.37 | +| monitor | 41.53 | 48.6 | +| bulletin board | 50.38 | 58.55 | +| shower | 21.57 | 22.73 | +| radiator | 69.92 | 81.06 | +| glass | 23.1 | 24.72 | +| clock | 55.49 | 67.59 | +| flag | 70.73 | 80.29 | ++---------------------+-------+-------+ +2024-06-19 17:38:16,979 - mmseg - INFO - Summary: +2024-06-19 17:38:16,979 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.63 | 59.27 | 71.57 | ++-------+-------+-------+ +2024-06-19 17:38:16,980 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 17:38:16,980 - mmseg - INFO - Iter(val) [250] aAcc: 0.8663, mIoU: 0.5927, mAcc: 0.7157, IoU.wall: 0.8280, IoU.building: 0.8531, IoU.sky: 0.9500, IoU.floor: 0.8486, IoU.tree: 0.7841, IoU.ceiling: 0.8789, IoU.road: 0.8669, IoU.bed : 0.9323, IoU.windowpane: 0.6703, IoU.grass: 0.6799, IoU.cabinet: 0.6831, IoU.sidewalk: 0.7198, IoU.person: 0.8658, IoU.earth: 0.3935, IoU.door: 0.6025, IoU.table: 0.7126, IoU.mountain: 0.6394, IoU.plant: 0.5762, IoU.curtain: 0.7804, IoU.chair: 0.6929, IoU.car: 0.8878, IoU.water: 0.6139, IoU.painting: 0.7947, IoU.sofa: 0.8288, IoU.shelf: 0.5002, IoU.house: 0.5599, IoU.sea: 0.7425, IoU.mirror: 0.7943, IoU.rug: 0.6354, IoU.field: 0.3032, IoU.armchair: 0.6355, IoU.seat: 0.7018, IoU.fence: 0.5492, IoU.desk: 0.5964, IoU.rock: 0.5795, IoU.wardrobe: 0.5416, IoU.lamp: 0.7710, IoU.bathtub: 0.8710, IoU.railing: 0.4393, IoU.cushion: 0.6905, IoU.base: 0.4566, IoU.box: 0.4165, IoU.column: 0.5780, IoU.signboard: 0.4137, IoU.chest of drawers: 0.4796, IoU.counter: 0.5154, IoU.sand: 0.5676, IoU.sink: 0.8372, IoU.skyscraper: 0.3970, IoU.fireplace: 0.7561, IoU.refrigerator: 0.8670, IoU.grandstand: 0.5632, IoU.path: 0.3130, IoU.stairs: 0.3686, IoU.runway: 0.7206, IoU.case: 0.6161, IoU.pool table: 0.9462, IoU.pillow: 0.6629, IoU.screen door: 0.8615, IoU.stairway: 0.4177, IoU.river: 0.1379, IoU.bridge: 0.6046, IoU.bookcase: 0.4950, IoU.blind: 0.4512, IoU.coffee table: 0.6247, IoU.toilet: 0.9057, IoU.flower: 0.4422, IoU.book: 0.5796, IoU.hill: 0.1702, IoU.bench: 0.6149, IoU.countertop: 0.6527, IoU.stove: 0.8725, IoU.palm: 0.5266, IoU.kitchen island: 0.5600, IoU.computer: 0.7694, IoU.swivel chair: 0.4628, IoU.boat: 0.8130, IoU.bar: 0.7033, IoU.arcade machine: 0.8341, IoU.hovel: 0.4108, IoU.bus: 0.9408, IoU.towel: 0.8060, IoU.light: 0.6365, IoU.truck: 0.5186, IoU.tower: 0.3022, IoU.chandelier: 0.7379, IoU.awning: 0.4167, IoU.streetlight: 0.3820, IoU.booth: 0.5099, IoU.television receiver: 0.8031, IoU.airplane: 0.8995, IoU.dirt track: 0.0542, IoU.apparel: 0.6553, IoU.pole: 0.2840, IoU.land: 0.0571, IoU.bannister: 0.2229, IoU.escalator: 0.6638, IoU.ottoman: 0.5680, IoU.bottle: 0.4671, IoU.buffet: 0.5657, IoU.poster: 0.3297, IoU.stage: 0.2247, IoU.van: 0.5656, IoU.ship: 0.6574, IoU.fountain: 0.3093, IoU.conveyer belt: 0.8172, IoU.canopy: 0.6183, IoU.washer: 0.8543, IoU.plaything: 0.3396, IoU.swimming pool: 0.5700, IoU.stool: 0.5418, IoU.barrel: 0.6512, IoU.basket: 0.4348, IoU.waterfall: 0.5607, IoU.tent: 0.9299, IoU.bag: 0.2850, IoU.minibike: 0.7768, IoU.cradle: 0.8675, IoU.oven: 0.6642, IoU.ball: 0.5572, IoU.food: 0.6209, IoU.step: 0.1231, IoU.tank: 0.6456, IoU.trade name: 0.2295, IoU.microwave: 0.8966, IoU.pot: 0.6159, IoU.animal: 0.6037, IoU.bicycle: 0.6184, IoU.lake: 0.5232, IoU.dishwasher: 0.7460, IoU.screen: 0.5655, IoU.blanket: 0.3707, IoU.sculpture: 0.7712, IoU.hood: 0.6657, IoU.sconce: 0.5950, IoU.vase: 0.5149, IoU.traffic light: 0.3903, IoU.tray: 0.2731, IoU.ashcan: 0.5310, IoU.fan: 0.7288, IoU.pier: 0.4119, IoU.crt screen: 0.0633, IoU.plate: 0.6538, IoU.monitor: 0.4153, IoU.bulletin board: 0.5038, IoU.shower: 0.2157, IoU.radiator: 0.6992, IoU.glass: 0.2310, IoU.clock: 0.5549, IoU.flag: 0.7073, Acc.wall: 0.9011, Acc.building: 0.9337, Acc.sky: 0.9750, Acc.floor: 0.9206, Acc.tree: 0.9022, Acc.ceiling: 0.9469, Acc.road: 0.9173, Acc.bed : 0.9710, Acc.windowpane: 0.8029, Acc.grass: 0.8319, Acc.cabinet: 0.7875, Acc.sidewalk: 0.8606, Acc.person: 0.9476, Acc.earth: 0.5149, Acc.door: 0.7652, Acc.table: 0.8100, Acc.mountain: 0.7528, Acc.plant: 0.6791, Acc.curtain: 0.8784, Acc.chair: 0.8081, Acc.car: 0.9464, Acc.water: 0.7538, Acc.painting: 0.9178, Acc.sofa: 0.9053, Acc.shelf: 0.6513, Acc.house: 0.6901, Acc.sea: 0.8714, Acc.mirror: 0.8787, Acc.rug: 0.7583, Acc.field: 0.5915, Acc.armchair: 0.7919, Acc.seat: 0.8911, Acc.fence: 0.6644, Acc.desk: 0.7888, Acc.rock: 0.8637, Acc.wardrobe: 0.7358, Acc.lamp: 0.8786, Acc.bathtub: 0.9093, Acc.railing: 0.6299, Acc.cushion: 0.8410, Acc.base: 0.5807, Acc.box: 0.5321, Acc.column: 0.7286, Acc.signboard: 0.5809, Acc.chest of drawers: 0.6833, Acc.counter: 0.6411, Acc.sand: 0.7799, Acc.sink: 0.8857, Acc.skyscraper: 0.4919, Acc.fireplace: 0.9252, Acc.refrigerator: 0.9392, Acc.grandstand: 0.7964, Acc.path: 0.4265, Acc.stairs: 0.4623, Acc.runway: 0.9324, Acc.case: 0.7895, Acc.pool table: 0.9845, Acc.pillow: 0.7715, Acc.screen door: 0.8975, Acc.stairway: 0.5300, Acc.river: 0.2991, Acc.bridge: 0.6883, Acc.bookcase: 0.6901, Acc.blind: 0.5684, Acc.coffee table: 0.8797, Acc.toilet: 0.9372, Acc.flower: 0.5935, Acc.book: 0.7860, Acc.hill: 0.2618, Acc.bench: 0.6964, Acc.countertop: 0.8646, Acc.stove: 0.9194, Acc.palm: 0.8231, Acc.kitchen island: 0.7970, Acc.computer: 0.9121, Acc.swivel chair: 0.6959, Acc.boat: 0.9320, Acc.bar: 0.8726, Acc.arcade machine: 0.8757, Acc.hovel: 0.4579, Acc.bus: 0.9727, Acc.towel: 0.8740, Acc.light: 0.7320, Acc.truck: 0.6505, Acc.tower: 0.6092, Acc.chandelier: 0.8356, Acc.awning: 0.5120, Acc.streetlight: 0.4897, Acc.booth: 0.7286, Acc.television receiver: 0.8908, Acc.airplane: 0.9612, Acc.dirt track: 0.0561, Acc.apparel: 0.8390, Acc.pole: 0.3832, Acc.land: 0.0791, Acc.bannister: 0.2857, Acc.escalator: 0.8679, Acc.ottoman: 0.7201, Acc.bottle: 0.6961, Acc.buffet: 0.6440, Acc.poster: 0.4396, Acc.stage: 0.4678, Acc.van: 0.7323, Acc.ship: 0.7199, Acc.fountain: 0.3139, Acc.conveyer belt: 0.9744, Acc.canopy: 0.7887, Acc.washer: 0.9074, Acc.plaything: 0.4985, Acc.swimming pool: 0.8256, Acc.stool: 0.7492, Acc.barrel: 0.9826, Acc.basket: 0.6164, Acc.waterfall: 0.6662, Acc.tent: 0.9881, Acc.bag: 0.3325, Acc.minibike: 0.9133, Acc.cradle: 0.9783, Acc.oven: 0.7800, Acc.ball: 0.6081, Acc.food: 0.7218, Acc.step: 0.1388, Acc.tank: 0.6982, Acc.trade name: 0.2705, Acc.microwave: 0.9688, Acc.pot: 0.7100, Acc.animal: 0.6181, Acc.bicycle: 0.7849, Acc.lake: 0.6370, Acc.dishwasher: 0.8335, Acc.screen: 0.8673, Acc.blanket: 0.4375, Acc.sculpture: 0.8766, Acc.hood: 0.7548, Acc.sconce: 0.6734, Acc.vase: 0.6882, Acc.traffic light: 0.6580, Acc.tray: 0.3710, Acc.ashcan: 0.6684, Acc.fan: 0.8408, Acc.pier: 0.4643, Acc.crt screen: 0.1313, Acc.plate: 0.8137, Acc.monitor: 0.4860, Acc.bulletin board: 0.5855, Acc.shower: 0.2273, Acc.radiator: 0.8106, Acc.glass: 0.2472, Acc.clock: 0.6759, Acc.flag: 0.8029 +2024-06-19 17:39:56,209 - mmseg - INFO - Iter [66050/80000] lr: 6.976e-06, eta: 8:16:29, time: 4.180, data_time: 2.212, memory: 72263, decode.loss_ce: 0.1328, decode.acc_seg: 94.0793, aux.loss_ce: 0.0568, aux.acc_seg: 93.6731, loss: 0.1896 +2024-06-19 17:41:35,078 - mmseg - INFO - Iter [66100/80000] lr: 6.951e-06, eta: 8:14:40, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1435, decode.acc_seg: 93.7557, aux.loss_ce: 0.0607, aux.acc_seg: 93.3727, loss: 0.2041 +2024-06-19 17:43:14,113 - mmseg - INFO - Iter [66150/80000] lr: 6.926e-06, eta: 8:12:52, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1304, decode.acc_seg: 94.2347, aux.loss_ce: 0.0562, aux.acc_seg: 93.8378, loss: 0.1866 +2024-06-19 17:44:52,958 - mmseg - INFO - Iter [66200/80000] lr: 6.900e-06, eta: 8:11:03, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1252, decode.acc_seg: 94.2797, aux.loss_ce: 0.0536, aux.acc_seg: 93.8674, loss: 0.1788 +2024-06-19 17:46:31,867 - mmseg - INFO - Iter [66250/80000] lr: 6.875e-06, eta: 8:09:15, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1342, decode.acc_seg: 94.1037, aux.loss_ce: 0.0576, aux.acc_seg: 93.7162, loss: 0.1918 +2024-06-19 17:48:10,706 - mmseg - INFO - Iter [66300/80000] lr: 6.850e-06, eta: 8:07:27, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1453, decode.acc_seg: 93.7301, aux.loss_ce: 0.0621, aux.acc_seg: 93.3538, loss: 0.2074 +2024-06-19 17:49:49,618 - mmseg - INFO - Iter [66350/80000] lr: 6.826e-06, eta: 8:05:38, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1329, decode.acc_seg: 94.1979, aux.loss_ce: 0.0568, aux.acc_seg: 93.8126, loss: 0.1897 +2024-06-19 17:51:28,420 - mmseg - INFO - Iter [66400/80000] lr: 6.801e-06, eta: 8:03:50, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1281, decode.acc_seg: 94.3985, aux.loss_ce: 0.0554, aux.acc_seg: 93.9923, loss: 0.1835 +2024-06-19 17:53:07,279 - mmseg - INFO - Iter [66450/80000] lr: 6.776e-06, eta: 8:02:02, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1392, decode.acc_seg: 93.9437, aux.loss_ce: 0.0595, aux.acc_seg: 93.5230, loss: 0.1986 +2024-06-19 17:54:46,160 - mmseg - INFO - Iter [66500/80000] lr: 6.751e-06, eta: 8:00:13, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1299, decode.acc_seg: 94.1540, aux.loss_ce: 0.0557, aux.acc_seg: 93.7583, loss: 0.1856 +2024-06-19 17:56:25,098 - mmseg - INFO - Iter [66550/80000] lr: 6.726e-06, eta: 7:58:25, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1387, decode.acc_seg: 93.8973, aux.loss_ce: 0.0597, aux.acc_seg: 93.4555, loss: 0.1984 +2024-06-19 17:58:03,926 - mmseg - INFO - Iter [66600/80000] lr: 6.700e-06, eta: 7:56:37, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1301, decode.acc_seg: 94.1999, aux.loss_ce: 0.0561, aux.acc_seg: 93.7870, loss: 0.1862 +2024-06-19 17:59:42,771 - mmseg - INFO - Iter [66650/80000] lr: 6.675e-06, eta: 7:54:48, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1313, decode.acc_seg: 94.1807, aux.loss_ce: 0.0563, aux.acc_seg: 93.7906, loss: 0.1876 +2024-06-19 18:01:21,671 - mmseg - INFO - Iter [66700/80000] lr: 6.651e-06, eta: 7:53:00, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1382, decode.acc_seg: 93.6102, aux.loss_ce: 0.0589, aux.acc_seg: 93.2013, loss: 0.1970 +2024-06-19 18:03:00,561 - mmseg - INFO - Iter [66750/80000] lr: 6.626e-06, eta: 7:51:12, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1273, decode.acc_seg: 94.2957, aux.loss_ce: 0.0545, aux.acc_seg: 93.9149, loss: 0.1818 +2024-06-19 18:04:39,540 - mmseg - INFO - Iter [66800/80000] lr: 6.601e-06, eta: 7:49:24, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1361, decode.acc_seg: 94.0309, aux.loss_ce: 0.0578, aux.acc_seg: 93.6434, loss: 0.1939 +2024-06-19 18:06:18,402 - mmseg - INFO - Iter [66850/80000] lr: 6.576e-06, eta: 7:47:36, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1335, decode.acc_seg: 94.0897, aux.loss_ce: 0.0565, aux.acc_seg: 93.7207, loss: 0.1900 +2024-06-19 18:07:57,203 - mmseg - INFO - Iter [66900/80000] lr: 6.551e-06, eta: 7:45:47, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1294, decode.acc_seg: 94.1344, aux.loss_ce: 0.0558, aux.acc_seg: 93.7109, loss: 0.1851 +2024-06-19 18:09:38,451 - mmseg - INFO - Iter [66950/80000] lr: 6.526e-06, eta: 7:44:00, time: 2.025, data_time: 0.053, memory: 72263, decode.loss_ce: 0.1281, decode.acc_seg: 94.2113, aux.loss_ce: 0.0551, aux.acc_seg: 93.7795, loss: 0.1831 +2024-06-19 18:11:17,396 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 18:11:17,396 - mmseg - INFO - Iter [67000/80000] lr: 6.500e-06, eta: 7:42:11, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1277, decode.acc_seg: 94.2834, aux.loss_ce: 0.0552, aux.acc_seg: 93.8553, loss: 0.1829 +2024-06-19 18:13:07,822 - mmseg - INFO - per class results: +2024-06-19 18:13:07,828 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.86 | 90.64 | +| building | 85.39 | 93.43 | +| sky | 95.02 | 97.6 | +| floor | 85.01 | 91.73 | +| tree | 78.15 | 90.59 | +| ceiling | 87.82 | 94.38 | +| road | 87.18 | 91.86 | +| bed | 93.35 | 97.45 | +| windowpane | 67.39 | 81.2 | +| grass | 68.23 | 81.7 | +| cabinet | 67.71 | 77.06 | +| sidewalk | 70.75 | 86.18 | +| person | 86.62 | 94.71 | +| earth | 38.81 | 51.61 | +| door | 60.07 | 75.1 | +| table | 71.03 | 82.03 | +| mountain | 63.56 | 73.27 | +| plant | 56.83 | 66.26 | +| curtain | 78.46 | 88.6 | +| chair | 68.34 | 78.75 | +| car | 88.8 | 94.72 | +| water | 61.78 | 76.15 | +| painting | 79.67 | 92.07 | +| sofa | 81.44 | 86.98 | +| shelf | 50.39 | 64.55 | +| house | 51.84 | 63.68 | +| sea | 69.91 | 85.49 | +| mirror | 78.54 | 86.7 | +| rug | 67.09 | 80.28 | +| field | 28.28 | 52.97 | +| armchair | 61.31 | 82.75 | +| seat | 68.63 | 90.21 | +| fence | 49.97 | 61.24 | +| desk | 58.69 | 80.18 | +| rock | 55.58 | 88.51 | +| wardrobe | 52.03 | 70.37 | +| lamp | 77.22 | 86.89 | +| bathtub | 88.76 | 92.14 | +| railing | 42.22 | 60.34 | +| cushion | 68.87 | 82.55 | +| base | 46.54 | 59.61 | +| box | 41.52 | 52.13 | +| column | 56.28 | 71.94 | +| signboard | 41.48 | 57.99 | +| chest of drawers | 46.91 | 71.19 | +| counter | 53.66 | 63.89 | +| sand | 55.1 | 81.16 | +| sink | 84.84 | 88.66 | +| skyscraper | 46.14 | 58.01 | +| fireplace | 73.87 | 92.87 | +| refrigerator | 86.36 | 94.84 | +| grandstand | 60.02 | 80.32 | +| path | 30.65 | 41.61 | +| stairs | 30.69 | 38.43 | +| runway | 73.05 | 94.11 | +| case | 63.97 | 83.86 | +| pool table | 95.48 | 98.17 | +| pillow | 65.16 | 75.6 | +| screen door | 86.93 | 90.54 | +| stairway | 38.33 | 58.93 | +| river | 13.37 | 25.09 | +| bridge | 57.98 | 68.35 | +| bookcase | 46.04 | 64.54 | +| blind | 44.03 | 49.0 | +| coffee table | 62.33 | 87.72 | +| toilet | 91.12 | 94.75 | +| flower | 45.12 | 60.67 | +| book | 58.22 | 79.18 | +| hill | 15.19 | 23.41 | +| bench | 62.0 | 69.27 | +| countertop | 64.96 | 83.56 | +| stove | 87.92 | 93.06 | +| palm | 53.73 | 82.54 | +| kitchen island | 53.99 | 83.27 | +| computer | 77.08 | 91.56 | +| swivel chair | 50.56 | 77.31 | +| boat | 82.14 | 93.52 | +| bar | 73.96 | 85.52 | +| arcade machine | 83.95 | 87.26 | +| hovel | 39.48 | 43.13 | +| bus | 93.92 | 97.04 | +| towel | 81.43 | 87.37 | +| light | 62.6 | 72.21 | +| truck | 51.34 | 62.58 | +| tower | 27.17 | 48.89 | +| chandelier | 73.16 | 85.01 | +| awning | 42.43 | 52.74 | +| streetlight | 37.86 | 49.41 | +| booth | 48.78 | 72.06 | +| television receiver | 79.27 | 89.15 | +| airplane | 88.03 | 96.99 | +| dirt track | 8.67 | 24.13 | +| apparel | 65.33 | 84.59 | +| pole | 28.56 | 39.74 | +| land | 5.8 | 8.74 | +| bannister | 20.73 | 27.53 | +| escalator | 65.94 | 86.49 | +| ottoman | 59.06 | 77.79 | +| bottle | 46.26 | 71.58 | +| buffet | 53.38 | 60.13 | +| poster | 33.63 | 42.48 | +| stage | 20.94 | 35.51 | +| van | 55.3 | 75.18 | +| ship | 63.62 | 72.34 | +| fountain | 30.28 | 30.75 | +| conveyer belt | 82.65 | 97.36 | +| canopy | 56.44 | 71.18 | +| washer | 88.34 | 94.21 | +| plaything | 34.42 | 45.85 | +| swimming pool | 55.28 | 79.79 | +| stool | 54.84 | 71.56 | +| barrel | 73.22 | 97.88 | +| basket | 43.45 | 62.5 | +| waterfall | 61.71 | 81.22 | +| tent | 93.49 | 98.79 | +| bag | 27.79 | 32.24 | +| minibike | 78.47 | 90.53 | +| cradle | 87.32 | 97.71 | +| oven | 63.91 | 71.63 | +| ball | 60.1 | 68.3 | +| food | 58.69 | 68.09 | +| step | 11.9 | 14.29 | +| tank | 63.69 | 68.71 | +| trade name | 25.63 | 31.18 | +| microwave | 90.27 | 96.82 | +| pot | 61.43 | 71.68 | +| animal | 60.09 | 61.67 | +| bicycle | 61.36 | 73.87 | +| lake | 52.44 | 63.7 | +| dishwasher | 74.07 | 81.58 | +| screen | 56.35 | 86.39 | +| blanket | 41.77 | 50.46 | +| sculpture | 71.72 | 88.77 | +| hood | 64.78 | 74.26 | +| sconce | 61.43 | 72.63 | +| vase | 51.16 | 68.34 | +| traffic light | 38.33 | 68.47 | +| tray | 26.62 | 33.79 | +| ashcan | 50.78 | 68.38 | +| fan | 73.15 | 84.45 | +| pier | 41.09 | 44.86 | +| crt screen | 7.0 | 10.11 | +| plate | 64.78 | 82.06 | +| monitor | 62.14 | 75.18 | +| bulletin board | 57.84 | 68.79 | +| shower | 22.31 | 24.47 | +| radiator | 69.44 | 82.15 | +| glass | 23.06 | 24.69 | +| clock | 56.51 | 64.78 | +| flag | 70.18 | 82.65 | ++---------------------+-------+-------+ +2024-06-19 18:13:07,829 - mmseg - INFO - Summary: +2024-06-19 18:13:07,829 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.55 | 59.26 | 71.72 | ++-------+-------+-------+ +2024-06-19 18:13:07,829 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 18:13:07,830 - mmseg - INFO - Iter(val) [250] aAcc: 0.8655, mIoU: 0.5926, mAcc: 0.7172, IoU.wall: 0.8286, IoU.building: 0.8539, IoU.sky: 0.9502, IoU.floor: 0.8501, IoU.tree: 0.7815, IoU.ceiling: 0.8782, IoU.road: 0.8718, IoU.bed : 0.9335, IoU.windowpane: 0.6739, IoU.grass: 0.6823, IoU.cabinet: 0.6771, IoU.sidewalk: 0.7075, IoU.person: 0.8662, IoU.earth: 0.3881, IoU.door: 0.6007, IoU.table: 0.7103, IoU.mountain: 0.6356, IoU.plant: 0.5683, IoU.curtain: 0.7846, IoU.chair: 0.6834, IoU.car: 0.8880, IoU.water: 0.6178, IoU.painting: 0.7967, IoU.sofa: 0.8144, IoU.shelf: 0.5039, IoU.house: 0.5184, IoU.sea: 0.6991, IoU.mirror: 0.7854, IoU.rug: 0.6709, IoU.field: 0.2828, IoU.armchair: 0.6131, IoU.seat: 0.6863, IoU.fence: 0.4997, IoU.desk: 0.5869, IoU.rock: 0.5558, IoU.wardrobe: 0.5203, IoU.lamp: 0.7722, IoU.bathtub: 0.8876, IoU.railing: 0.4222, IoU.cushion: 0.6887, IoU.base: 0.4654, IoU.box: 0.4152, IoU.column: 0.5628, IoU.signboard: 0.4148, IoU.chest of drawers: 0.4691, IoU.counter: 0.5366, IoU.sand: 0.5510, IoU.sink: 0.8484, IoU.skyscraper: 0.4614, IoU.fireplace: 0.7387, IoU.refrigerator: 0.8636, IoU.grandstand: 0.6002, IoU.path: 0.3065, IoU.stairs: 0.3069, IoU.runway: 0.7305, IoU.case: 0.6397, IoU.pool table: 0.9548, IoU.pillow: 0.6516, IoU.screen door: 0.8693, IoU.stairway: 0.3833, IoU.river: 0.1337, IoU.bridge: 0.5798, IoU.bookcase: 0.4604, IoU.blind: 0.4403, IoU.coffee table: 0.6233, IoU.toilet: 0.9112, IoU.flower: 0.4512, IoU.book: 0.5822, IoU.hill: 0.1519, IoU.bench: 0.6200, IoU.countertop: 0.6496, IoU.stove: 0.8792, IoU.palm: 0.5373, IoU.kitchen island: 0.5399, IoU.computer: 0.7708, IoU.swivel chair: 0.5056, IoU.boat: 0.8214, IoU.bar: 0.7396, IoU.arcade machine: 0.8395, IoU.hovel: 0.3948, IoU.bus: 0.9392, IoU.towel: 0.8143, IoU.light: 0.6260, IoU.truck: 0.5134, IoU.tower: 0.2717, IoU.chandelier: 0.7316, IoU.awning: 0.4243, IoU.streetlight: 0.3786, IoU.booth: 0.4878, IoU.television receiver: 0.7927, IoU.airplane: 0.8803, IoU.dirt track: 0.0867, IoU.apparel: 0.6533, IoU.pole: 0.2856, IoU.land: 0.0580, IoU.bannister: 0.2073, IoU.escalator: 0.6594, IoU.ottoman: 0.5906, IoU.bottle: 0.4626, IoU.buffet: 0.5338, IoU.poster: 0.3363, IoU.stage: 0.2094, IoU.van: 0.5530, IoU.ship: 0.6362, IoU.fountain: 0.3028, IoU.conveyer belt: 0.8265, IoU.canopy: 0.5644, IoU.washer: 0.8834, IoU.plaything: 0.3442, IoU.swimming pool: 0.5528, IoU.stool: 0.5484, IoU.barrel: 0.7322, IoU.basket: 0.4345, IoU.waterfall: 0.6171, IoU.tent: 0.9349, IoU.bag: 0.2779, IoU.minibike: 0.7847, IoU.cradle: 0.8732, IoU.oven: 0.6391, IoU.ball: 0.6010, IoU.food: 0.5869, IoU.step: 0.1190, IoU.tank: 0.6369, IoU.trade name: 0.2563, IoU.microwave: 0.9027, IoU.pot: 0.6143, IoU.animal: 0.6009, IoU.bicycle: 0.6136, IoU.lake: 0.5244, IoU.dishwasher: 0.7407, IoU.screen: 0.5635, IoU.blanket: 0.4177, IoU.sculpture: 0.7172, IoU.hood: 0.6478, IoU.sconce: 0.6143, IoU.vase: 0.5116, IoU.traffic light: 0.3833, IoU.tray: 0.2662, IoU.ashcan: 0.5078, IoU.fan: 0.7315, IoU.pier: 0.4109, IoU.crt screen: 0.0700, IoU.plate: 0.6478, IoU.monitor: 0.6214, IoU.bulletin board: 0.5784, IoU.shower: 0.2231, IoU.radiator: 0.6944, IoU.glass: 0.2306, IoU.clock: 0.5651, IoU.flag: 0.7018, Acc.wall: 0.9064, Acc.building: 0.9343, Acc.sky: 0.9760, Acc.floor: 0.9173, Acc.tree: 0.9059, Acc.ceiling: 0.9438, Acc.road: 0.9186, Acc.bed : 0.9745, Acc.windowpane: 0.8120, Acc.grass: 0.8170, Acc.cabinet: 0.7706, Acc.sidewalk: 0.8618, Acc.person: 0.9471, Acc.earth: 0.5161, Acc.door: 0.7510, Acc.table: 0.8203, Acc.mountain: 0.7327, Acc.plant: 0.6626, Acc.curtain: 0.8860, Acc.chair: 0.7875, Acc.car: 0.9472, Acc.water: 0.7615, Acc.painting: 0.9207, Acc.sofa: 0.8698, Acc.shelf: 0.6455, Acc.house: 0.6368, Acc.sea: 0.8549, Acc.mirror: 0.8670, Acc.rug: 0.8028, Acc.field: 0.5297, Acc.armchair: 0.8275, Acc.seat: 0.9021, Acc.fence: 0.6124, Acc.desk: 0.8018, Acc.rock: 0.8851, Acc.wardrobe: 0.7037, Acc.lamp: 0.8689, Acc.bathtub: 0.9214, Acc.railing: 0.6034, Acc.cushion: 0.8255, Acc.base: 0.5961, Acc.box: 0.5213, Acc.column: 0.7194, Acc.signboard: 0.5799, Acc.chest of drawers: 0.7119, Acc.counter: 0.6389, Acc.sand: 0.8116, Acc.sink: 0.8866, Acc.skyscraper: 0.5801, Acc.fireplace: 0.9287, Acc.refrigerator: 0.9484, Acc.grandstand: 0.8032, Acc.path: 0.4161, Acc.stairs: 0.3843, Acc.runway: 0.9411, Acc.case: 0.8386, Acc.pool table: 0.9817, Acc.pillow: 0.7560, Acc.screen door: 0.9054, Acc.stairway: 0.5893, Acc.river: 0.2509, Acc.bridge: 0.6835, Acc.bookcase: 0.6454, Acc.blind: 0.4900, Acc.coffee table: 0.8772, Acc.toilet: 0.9475, Acc.flower: 0.6067, Acc.book: 0.7918, Acc.hill: 0.2341, Acc.bench: 0.6927, Acc.countertop: 0.8356, Acc.stove: 0.9306, Acc.palm: 0.8254, Acc.kitchen island: 0.8327, Acc.computer: 0.9156, Acc.swivel chair: 0.7731, Acc.boat: 0.9352, Acc.bar: 0.8552, Acc.arcade machine: 0.8726, Acc.hovel: 0.4313, Acc.bus: 0.9704, Acc.towel: 0.8737, Acc.light: 0.7221, Acc.truck: 0.6258, Acc.tower: 0.4889, Acc.chandelier: 0.8501, Acc.awning: 0.5274, Acc.streetlight: 0.4941, Acc.booth: 0.7206, Acc.television receiver: 0.8915, Acc.airplane: 0.9699, Acc.dirt track: 0.2413, Acc.apparel: 0.8459, Acc.pole: 0.3974, Acc.land: 0.0874, Acc.bannister: 0.2753, Acc.escalator: 0.8649, Acc.ottoman: 0.7779, Acc.bottle: 0.7158, Acc.buffet: 0.6013, Acc.poster: 0.4248, Acc.stage: 0.3551, Acc.van: 0.7518, Acc.ship: 0.7234, Acc.fountain: 0.3075, Acc.conveyer belt: 0.9736, Acc.canopy: 0.7118, Acc.washer: 0.9421, Acc.plaything: 0.4585, Acc.swimming pool: 0.7979, Acc.stool: 0.7156, Acc.barrel: 0.9788, Acc.basket: 0.6250, Acc.waterfall: 0.8122, Acc.tent: 0.9879, Acc.bag: 0.3224, Acc.minibike: 0.9053, Acc.cradle: 0.9771, Acc.oven: 0.7163, Acc.ball: 0.6830, Acc.food: 0.6809, Acc.step: 0.1429, Acc.tank: 0.6871, Acc.trade name: 0.3118, Acc.microwave: 0.9682, Acc.pot: 0.7168, Acc.animal: 0.6167, Acc.bicycle: 0.7387, Acc.lake: 0.6370, Acc.dishwasher: 0.8158, Acc.screen: 0.8639, Acc.blanket: 0.5046, Acc.sculpture: 0.8877, Acc.hood: 0.7426, Acc.sconce: 0.7263, Acc.vase: 0.6834, Acc.traffic light: 0.6847, Acc.tray: 0.3379, Acc.ashcan: 0.6838, Acc.fan: 0.8445, Acc.pier: 0.4486, Acc.crt screen: 0.1011, Acc.plate: 0.8206, Acc.monitor: 0.7518, Acc.bulletin board: 0.6879, Acc.shower: 0.2447, Acc.radiator: 0.8215, Acc.glass: 0.2469, Acc.clock: 0.6478, Acc.flag: 0.8265 +2024-06-19 18:14:47,115 - mmseg - INFO - Iter [67050/80000] lr: 6.475e-06, eta: 7:40:45, time: 4.194, data_time: 2.225, memory: 72263, decode.loss_ce: 0.1374, decode.acc_seg: 94.0067, aux.loss_ce: 0.0584, aux.acc_seg: 93.6034, loss: 0.1957 +2024-06-19 18:16:26,133 - mmseg - INFO - Iter [67100/80000] lr: 6.450e-06, eta: 7:38:56, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1374, decode.acc_seg: 93.9166, aux.loss_ce: 0.0587, aux.acc_seg: 93.4547, loss: 0.1961 +2024-06-19 18:18:04,957 - mmseg - INFO - Iter [67150/80000] lr: 6.425e-06, eta: 7:37:08, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1186, decode.acc_seg: 94.6247, aux.loss_ce: 0.0508, aux.acc_seg: 94.2557, loss: 0.1694 +2024-06-19 18:19:43,744 - mmseg - INFO - Iter [67200/80000] lr: 6.401e-06, eta: 7:35:20, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1272, decode.acc_seg: 94.4048, aux.loss_ce: 0.0542, aux.acc_seg: 94.0489, loss: 0.1814 +2024-06-19 18:21:22,770 - mmseg - INFO - Iter [67250/80000] lr: 6.376e-06, eta: 7:33:32, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1352, decode.acc_seg: 93.9274, aux.loss_ce: 0.0579, aux.acc_seg: 93.4842, loss: 0.1931 +2024-06-19 18:23:01,666 - mmseg - INFO - Iter [67300/80000] lr: 6.351e-06, eta: 7:31:44, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1219, decode.acc_seg: 94.4669, aux.loss_ce: 0.0528, aux.acc_seg: 94.0610, loss: 0.1747 +2024-06-19 18:24:40,509 - mmseg - INFO - Iter [67350/80000] lr: 6.326e-06, eta: 7:29:55, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1384, decode.acc_seg: 93.7854, aux.loss_ce: 0.0592, aux.acc_seg: 93.3975, loss: 0.1976 +2024-06-19 18:26:19,313 - mmseg - INFO - Iter [67400/80000] lr: 6.301e-06, eta: 7:28:07, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1260, decode.acc_seg: 94.3763, aux.loss_ce: 0.0542, aux.acc_seg: 93.9323, loss: 0.1802 +2024-06-19 18:27:58,186 - mmseg - INFO - Iter [67450/80000] lr: 6.275e-06, eta: 7:26:19, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1269, decode.acc_seg: 94.4604, aux.loss_ce: 0.0547, aux.acc_seg: 94.0836, loss: 0.1815 +2024-06-19 18:29:37,134 - mmseg - INFO - Iter [67500/80000] lr: 6.250e-06, eta: 7:24:31, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1358, decode.acc_seg: 94.0379, aux.loss_ce: 0.0579, aux.acc_seg: 93.6389, loss: 0.1937 +2024-06-19 18:31:16,107 - mmseg - INFO - Iter [67550/80000] lr: 6.225e-06, eta: 7:22:43, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1343, decode.acc_seg: 94.0238, aux.loss_ce: 0.0573, aux.acc_seg: 93.6615, loss: 0.1916 +2024-06-19 18:32:54,865 - mmseg - INFO - Iter [67600/80000] lr: 6.201e-06, eta: 7:20:55, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1291, decode.acc_seg: 94.1630, aux.loss_ce: 0.0552, aux.acc_seg: 93.7431, loss: 0.1843 +2024-06-19 18:34:33,711 - mmseg - INFO - Iter [67650/80000] lr: 6.176e-06, eta: 7:19:07, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1255, decode.acc_seg: 94.4917, aux.loss_ce: 0.0544, aux.acc_seg: 94.0702, loss: 0.1799 +2024-06-19 18:36:12,536 - mmseg - INFO - Iter [67700/80000] lr: 6.151e-06, eta: 7:17:18, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1315, decode.acc_seg: 94.1803, aux.loss_ce: 0.0559, aux.acc_seg: 93.8195, loss: 0.1874 +2024-06-19 18:37:51,468 - mmseg - INFO - Iter [67750/80000] lr: 6.126e-06, eta: 7:15:30, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1291, decode.acc_seg: 94.2606, aux.loss_ce: 0.0554, aux.acc_seg: 93.8490, loss: 0.1845 +2024-06-19 18:39:30,339 - mmseg - INFO - Iter [67800/80000] lr: 6.101e-06, eta: 7:13:42, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1319, decode.acc_seg: 94.1269, aux.loss_ce: 0.0557, aux.acc_seg: 93.7689, loss: 0.1876 +2024-06-19 18:41:09,255 - mmseg - INFO - Iter [67850/80000] lr: 6.075e-06, eta: 7:11:54, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1261, decode.acc_seg: 94.4195, aux.loss_ce: 0.0545, aux.acc_seg: 93.9887, loss: 0.1807 +2024-06-19 18:42:48,199 - mmseg - INFO - Iter [67900/80000] lr: 6.050e-06, eta: 7:10:06, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1279, decode.acc_seg: 94.1573, aux.loss_ce: 0.0550, aux.acc_seg: 93.7731, loss: 0.1829 +2024-06-19 18:44:27,145 - mmseg - INFO - Iter [67950/80000] lr: 6.025e-06, eta: 7:08:18, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1235, decode.acc_seg: 94.3650, aux.loss_ce: 0.0531, aux.acc_seg: 93.8712, loss: 0.1766 +2024-06-19 18:46:06,002 - mmseg - INFO - Saving checkpoint at 68000 iterations +2024-06-19 18:47:28,730 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 18:47:28,730 - mmseg - INFO - Iter [68000/80000] lr: 6.001e-06, eta: 7:06:45, time: 3.632, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1291, decode.acc_seg: 94.1916, aux.loss_ce: 0.0557, aux.acc_seg: 93.7938, loss: 0.1848 +2024-06-19 18:49:18,379 - mmseg - INFO - per class results: +2024-06-19 18:49:18,385 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.96 | 90.27 | +| building | 85.31 | 92.99 | +| sky | 94.94 | 97.71 | +| floor | 84.83 | 92.26 | +| tree | 78.02 | 89.82 | +| ceiling | 87.88 | 95.0 | +| road | 87.09 | 91.54 | +| bed | 93.02 | 97.07 | +| windowpane | 67.22 | 82.13 | +| grass | 68.64 | 82.16 | +| cabinet | 67.76 | 78.15 | +| sidewalk | 71.57 | 86.16 | +| person | 86.92 | 94.84 | +| earth | 39.88 | 52.67 | +| door | 60.27 | 76.98 | +| table | 71.21 | 82.56 | +| mountain | 65.15 | 77.0 | +| plant | 56.67 | 66.45 | +| curtain | 78.92 | 88.21 | +| chair | 69.38 | 80.35 | +| car | 88.86 | 94.12 | +| water | 62.89 | 78.2 | +| painting | 79.99 | 92.29 | +| sofa | 82.16 | 88.62 | +| shelf | 49.58 | 63.86 | +| house | 47.68 | 56.67 | +| sea | 68.33 | 83.48 | +| mirror | 79.89 | 86.97 | +| rug | 66.22 | 77.14 | +| field | 28.73 | 55.12 | +| armchair | 62.86 | 79.85 | +| seat | 69.38 | 90.49 | +| fence | 51.67 | 63.53 | +| desk | 58.99 | 79.93 | +| rock | 55.83 | 86.43 | +| wardrobe | 54.48 | 75.32 | +| lamp | 76.93 | 89.01 | +| bathtub | 88.14 | 91.32 | +| railing | 43.66 | 60.43 | +| cushion | 68.85 | 84.09 | +| base | 46.99 | 61.11 | +| box | 41.83 | 53.22 | +| column | 56.85 | 67.52 | +| signboard | 41.95 | 56.46 | +| chest of drawers | 47.76 | 68.18 | +| counter | 54.01 | 67.25 | +| sand | 55.28 | 80.7 | +| sink | 84.19 | 88.4 | +| skyscraper | 46.41 | 59.27 | +| fireplace | 74.94 | 94.22 | +| refrigerator | 88.43 | 94.71 | +| grandstand | 55.07 | 79.81 | +| path | 30.77 | 44.62 | +| stairs | 33.52 | 43.39 | +| runway | 72.48 | 93.67 | +| case | 61.92 | 79.33 | +| pool table | 95.29 | 98.32 | +| pillow | 64.97 | 75.91 | +| screen door | 77.9 | 79.73 | +| stairway | 44.06 | 59.98 | +| river | 13.89 | 25.35 | +| bridge | 65.9 | 74.68 | +| bookcase | 45.77 | 63.93 | +| blind | 43.64 | 49.24 | +| coffee table | 62.63 | 86.97 | +| toilet | 90.93 | 94.06 | +| flower | 45.9 | 61.28 | +| book | 58.53 | 78.84 | +| hill | 14.57 | 23.77 | +| bench | 60.79 | 68.29 | +| countertop | 65.37 | 84.98 | +| stove | 88.18 | 92.84 | +| palm | 53.54 | 81.44 | +| kitchen island | 56.69 | 81.58 | +| computer | 77.27 | 90.78 | +| swivel chair | 50.12 | 76.76 | +| boat | 83.55 | 93.23 | +| bar | 74.18 | 84.27 | +| arcade machine | 82.63 | 86.18 | +| hovel | 46.98 | 53.59 | +| bus | 93.72 | 97.2 | +| towel | 81.92 | 88.64 | +| light | 63.32 | 72.83 | +| truck | 51.53 | 64.11 | +| tower | 31.29 | 73.09 | +| chandelier | 73.03 | 84.5 | +| awning | 44.27 | 57.89 | +| streetlight | 39.54 | 53.57 | +| booth | 54.45 | 71.21 | +| television receiver | 79.59 | 87.56 | +| airplane | 88.33 | 96.77 | +| dirt track | 8.75 | 43.31 | +| apparel | 63.62 | 79.99 | +| pole | 25.74 | 33.54 | +| land | 5.9 | 8.33 | +| bannister | 21.61 | 25.51 | +| escalator | 66.42 | 87.24 | +| ottoman | 56.76 | 72.2 | +| bottle | 46.6 | 71.8 | +| buffet | 54.81 | 62.49 | +| poster | 32.36 | 40.38 | +| stage | 20.14 | 40.39 | +| van | 54.98 | 76.79 | +| ship | 42.61 | 48.89 | +| fountain | 30.14 | 30.61 | +| conveyer belt | 84.79 | 96.89 | +| canopy | 56.53 | 69.99 | +| washer | 86.19 | 91.57 | +| plaything | 35.13 | 46.02 | +| swimming pool | 53.76 | 77.11 | +| stool | 54.29 | 74.22 | +| barrel | 76.34 | 96.86 | +| basket | 43.19 | 62.41 | +| waterfall | 55.1 | 67.2 | +| tent | 94.0 | 98.6 | +| bag | 27.46 | 31.51 | +| minibike | 77.71 | 91.12 | +| cradle | 83.27 | 97.74 | +| oven | 67.21 | 77.33 | +| ball | 54.44 | 59.63 | +| food | 61.92 | 71.42 | +| step | 12.16 | 13.72 | +| tank | 62.93 | 67.16 | +| trade name | 29.13 | 36.63 | +| microwave | 89.65 | 96.77 | +| pot | 61.57 | 72.03 | +| animal | 60.88 | 62.37 | +| bicycle | 62.49 | 80.01 | +| lake | 52.36 | 63.68 | +| dishwasher | 74.84 | 81.97 | +| screen | 59.56 | 95.49 | +| blanket | 37.6 | 44.02 | +| sculpture | 77.11 | 86.82 | +| hood | 64.0 | 73.52 | +| sconce | 61.92 | 72.85 | +| vase | 50.65 | 70.57 | +| traffic light | 39.23 | 67.14 | +| tray | 27.53 | 36.11 | +| ashcan | 51.71 | 67.41 | +| fan | 73.26 | 84.79 | +| pier | 41.09 | 44.58 | +| crt screen | 2.38 | 4.28 | +| plate | 64.83 | 81.58 | +| monitor | 42.22 | 50.95 | +| bulletin board | 56.16 | 67.98 | +| shower | 22.63 | 23.4 | +| radiator | 68.76 | 83.64 | +| glass | 24.35 | 26.74 | +| clock | 56.66 | 65.79 | +| flag | 70.37 | 82.04 | ++---------------------+-------+-------+ +2024-06-19 18:49:18,385 - mmseg - INFO - Summary: +2024-06-19 18:49:18,386 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.58 | 59.19 | 71.68 | ++-------+-------+-------+ +2024-06-19 18:49:18,387 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 18:49:18,387 - mmseg - INFO - Iter(val) [250] aAcc: 0.8658, mIoU: 0.5919, mAcc: 0.7168, IoU.wall: 0.8296, IoU.building: 0.8531, IoU.sky: 0.9494, IoU.floor: 0.8483, IoU.tree: 0.7802, IoU.ceiling: 0.8788, IoU.road: 0.8709, IoU.bed : 0.9302, IoU.windowpane: 0.6722, IoU.grass: 0.6864, IoU.cabinet: 0.6776, IoU.sidewalk: 0.7157, IoU.person: 0.8692, IoU.earth: 0.3988, IoU.door: 0.6027, IoU.table: 0.7121, IoU.mountain: 0.6515, IoU.plant: 0.5667, IoU.curtain: 0.7892, IoU.chair: 0.6938, IoU.car: 0.8886, IoU.water: 0.6289, IoU.painting: 0.7999, IoU.sofa: 0.8216, IoU.shelf: 0.4958, IoU.house: 0.4768, IoU.sea: 0.6833, IoU.mirror: 0.7989, IoU.rug: 0.6622, IoU.field: 0.2873, IoU.armchair: 0.6286, IoU.seat: 0.6938, IoU.fence: 0.5167, IoU.desk: 0.5899, IoU.rock: 0.5583, IoU.wardrobe: 0.5448, IoU.lamp: 0.7693, IoU.bathtub: 0.8814, IoU.railing: 0.4366, IoU.cushion: 0.6885, IoU.base: 0.4699, IoU.box: 0.4183, IoU.column: 0.5685, IoU.signboard: 0.4195, IoU.chest of drawers: 0.4776, IoU.counter: 0.5401, IoU.sand: 0.5528, IoU.sink: 0.8419, IoU.skyscraper: 0.4641, IoU.fireplace: 0.7494, IoU.refrigerator: 0.8843, IoU.grandstand: 0.5507, IoU.path: 0.3077, IoU.stairs: 0.3352, IoU.runway: 0.7248, IoU.case: 0.6192, IoU.pool table: 0.9529, IoU.pillow: 0.6497, IoU.screen door: 0.7790, IoU.stairway: 0.4406, IoU.river: 0.1389, IoU.bridge: 0.6590, IoU.bookcase: 0.4577, IoU.blind: 0.4364, IoU.coffee table: 0.6263, IoU.toilet: 0.9093, IoU.flower: 0.4590, IoU.book: 0.5853, IoU.hill: 0.1457, IoU.bench: 0.6079, IoU.countertop: 0.6537, IoU.stove: 0.8818, IoU.palm: 0.5354, IoU.kitchen island: 0.5669, IoU.computer: 0.7727, IoU.swivel chair: 0.5012, IoU.boat: 0.8355, IoU.bar: 0.7418, IoU.arcade machine: 0.8263, IoU.hovel: 0.4698, IoU.bus: 0.9372, IoU.towel: 0.8192, IoU.light: 0.6332, IoU.truck: 0.5153, IoU.tower: 0.3129, IoU.chandelier: 0.7303, IoU.awning: 0.4427, IoU.streetlight: 0.3954, IoU.booth: 0.5445, IoU.television receiver: 0.7959, IoU.airplane: 0.8833, IoU.dirt track: 0.0875, IoU.apparel: 0.6362, IoU.pole: 0.2574, IoU.land: 0.0590, IoU.bannister: 0.2161, IoU.escalator: 0.6642, IoU.ottoman: 0.5676, IoU.bottle: 0.4660, IoU.buffet: 0.5481, IoU.poster: 0.3236, IoU.stage: 0.2014, IoU.van: 0.5498, IoU.ship: 0.4261, IoU.fountain: 0.3014, IoU.conveyer belt: 0.8479, IoU.canopy: 0.5653, IoU.washer: 0.8619, IoU.plaything: 0.3513, IoU.swimming pool: 0.5376, IoU.stool: 0.5429, IoU.barrel: 0.7634, IoU.basket: 0.4319, IoU.waterfall: 0.5510, IoU.tent: 0.9400, IoU.bag: 0.2746, IoU.minibike: 0.7771, IoU.cradle: 0.8327, IoU.oven: 0.6721, IoU.ball: 0.5444, IoU.food: 0.6192, IoU.step: 0.1216, IoU.tank: 0.6293, IoU.trade name: 0.2913, IoU.microwave: 0.8965, IoU.pot: 0.6157, IoU.animal: 0.6088, IoU.bicycle: 0.6249, IoU.lake: 0.5236, IoU.dishwasher: 0.7484, IoU.screen: 0.5956, IoU.blanket: 0.3760, IoU.sculpture: 0.7711, IoU.hood: 0.6400, IoU.sconce: 0.6192, IoU.vase: 0.5065, IoU.traffic light: 0.3923, IoU.tray: 0.2753, IoU.ashcan: 0.5171, IoU.fan: 0.7326, IoU.pier: 0.4109, IoU.crt screen: 0.0238, IoU.plate: 0.6483, IoU.monitor: 0.4222, IoU.bulletin board: 0.5616, IoU.shower: 0.2263, IoU.radiator: 0.6876, IoU.glass: 0.2435, IoU.clock: 0.5666, IoU.flag: 0.7037, Acc.wall: 0.9027, Acc.building: 0.9299, Acc.sky: 0.9771, Acc.floor: 0.9226, Acc.tree: 0.8982, Acc.ceiling: 0.9500, Acc.road: 0.9154, Acc.bed : 0.9707, Acc.windowpane: 0.8213, Acc.grass: 0.8216, Acc.cabinet: 0.7815, Acc.sidewalk: 0.8616, Acc.person: 0.9484, Acc.earth: 0.5267, Acc.door: 0.7698, Acc.table: 0.8256, Acc.mountain: 0.7700, Acc.plant: 0.6645, Acc.curtain: 0.8821, Acc.chair: 0.8035, Acc.car: 0.9412, Acc.water: 0.7820, Acc.painting: 0.9229, Acc.sofa: 0.8862, Acc.shelf: 0.6386, Acc.house: 0.5667, Acc.sea: 0.8348, Acc.mirror: 0.8697, Acc.rug: 0.7714, Acc.field: 0.5512, Acc.armchair: 0.7985, Acc.seat: 0.9049, Acc.fence: 0.6353, Acc.desk: 0.7993, Acc.rock: 0.8643, Acc.wardrobe: 0.7532, Acc.lamp: 0.8901, Acc.bathtub: 0.9132, Acc.railing: 0.6043, Acc.cushion: 0.8409, Acc.base: 0.6111, Acc.box: 0.5322, Acc.column: 0.6752, Acc.signboard: 0.5646, Acc.chest of drawers: 0.6818, Acc.counter: 0.6725, Acc.sand: 0.8070, Acc.sink: 0.8840, Acc.skyscraper: 0.5927, Acc.fireplace: 0.9422, Acc.refrigerator: 0.9471, Acc.grandstand: 0.7981, Acc.path: 0.4462, Acc.stairs: 0.4339, Acc.runway: 0.9367, Acc.case: 0.7933, Acc.pool table: 0.9832, Acc.pillow: 0.7591, Acc.screen door: 0.7973, Acc.stairway: 0.5998, Acc.river: 0.2535, Acc.bridge: 0.7468, Acc.bookcase: 0.6393, Acc.blind: 0.4924, Acc.coffee table: 0.8697, Acc.toilet: 0.9406, Acc.flower: 0.6128, Acc.book: 0.7884, Acc.hill: 0.2377, Acc.bench: 0.6829, Acc.countertop: 0.8498, Acc.stove: 0.9284, Acc.palm: 0.8144, Acc.kitchen island: 0.8158, Acc.computer: 0.9078, Acc.swivel chair: 0.7676, Acc.boat: 0.9323, Acc.bar: 0.8427, Acc.arcade machine: 0.8618, Acc.hovel: 0.5359, Acc.bus: 0.9720, Acc.towel: 0.8864, Acc.light: 0.7283, Acc.truck: 0.6411, Acc.tower: 0.7309, Acc.chandelier: 0.8450, Acc.awning: 0.5789, Acc.streetlight: 0.5357, Acc.booth: 0.7121, Acc.television receiver: 0.8756, Acc.airplane: 0.9677, Acc.dirt track: 0.4331, Acc.apparel: 0.7999, Acc.pole: 0.3354, Acc.land: 0.0833, Acc.bannister: 0.2551, Acc.escalator: 0.8724, Acc.ottoman: 0.7220, Acc.bottle: 0.7180, Acc.buffet: 0.6249, Acc.poster: 0.4038, Acc.stage: 0.4039, Acc.van: 0.7679, Acc.ship: 0.4889, Acc.fountain: 0.3061, Acc.conveyer belt: 0.9689, Acc.canopy: 0.6999, Acc.washer: 0.9157, Acc.plaything: 0.4602, Acc.swimming pool: 0.7711, Acc.stool: 0.7422, Acc.barrel: 0.9686, Acc.basket: 0.6241, Acc.waterfall: 0.6720, Acc.tent: 0.9860, Acc.bag: 0.3151, Acc.minibike: 0.9112, Acc.cradle: 0.9774, Acc.oven: 0.7733, Acc.ball: 0.5963, Acc.food: 0.7142, Acc.step: 0.1372, Acc.tank: 0.6716, Acc.trade name: 0.3663, Acc.microwave: 0.9677, Acc.pot: 0.7203, Acc.animal: 0.6237, Acc.bicycle: 0.8001, Acc.lake: 0.6368, Acc.dishwasher: 0.8197, Acc.screen: 0.9549, Acc.blanket: 0.4402, Acc.sculpture: 0.8682, Acc.hood: 0.7352, Acc.sconce: 0.7285, Acc.vase: 0.7057, Acc.traffic light: 0.6714, Acc.tray: 0.3611, Acc.ashcan: 0.6741, Acc.fan: 0.8479, Acc.pier: 0.4458, Acc.crt screen: 0.0428, Acc.plate: 0.8158, Acc.monitor: 0.5095, Acc.bulletin board: 0.6798, Acc.shower: 0.2340, Acc.radiator: 0.8364, Acc.glass: 0.2674, Acc.clock: 0.6579, Acc.flag: 0.8204 +2024-06-19 18:50:57,701 - mmseg - INFO - Iter [68050/80000] lr: 5.976e-06, eta: 7:05:16, time: 4.179, data_time: 2.210, memory: 72263, decode.loss_ce: 0.1400, decode.acc_seg: 93.6981, aux.loss_ce: 0.0598, aux.acc_seg: 93.3180, loss: 0.1998 +2024-06-19 18:52:36,529 - mmseg - INFO - Iter [68100/80000] lr: 5.951e-06, eta: 7:03:28, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1333, decode.acc_seg: 93.9228, aux.loss_ce: 0.0572, aux.acc_seg: 93.5339, loss: 0.1906 +2024-06-19 18:54:15,522 - mmseg - INFO - Iter [68150/80000] lr: 5.926e-06, eta: 7:01:40, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1374, decode.acc_seg: 93.9185, aux.loss_ce: 0.0588, aux.acc_seg: 93.5241, loss: 0.1962 +2024-06-19 18:55:54,305 - mmseg - INFO - Iter [68200/80000] lr: 5.901e-06, eta: 6:59:52, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1306, decode.acc_seg: 94.3126, aux.loss_ce: 0.0561, aux.acc_seg: 93.8858, loss: 0.1867 +2024-06-19 18:57:36,568 - mmseg - INFO - Iter [68250/80000] lr: 5.875e-06, eta: 6:58:04, time: 2.045, data_time: 0.075, memory: 72263, decode.loss_ce: 0.1271, decode.acc_seg: 94.2288, aux.loss_ce: 0.0548, aux.acc_seg: 93.8254, loss: 0.1820 +2024-06-19 18:59:15,582 - mmseg - INFO - Iter [68300/80000] lr: 5.850e-06, eta: 6:56:16, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1270, decode.acc_seg: 94.3327, aux.loss_ce: 0.0549, aux.acc_seg: 93.8777, loss: 0.1819 +2024-06-19 19:00:54,587 - mmseg - INFO - Iter [68350/80000] lr: 5.825e-06, eta: 6:54:28, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1292, decode.acc_seg: 94.1321, aux.loss_ce: 0.0553, aux.acc_seg: 93.7664, loss: 0.1845 +2024-06-19 19:02:33,549 - mmseg - INFO - Iter [68400/80000] lr: 5.800e-06, eta: 6:52:40, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1264, decode.acc_seg: 94.3367, aux.loss_ce: 0.0542, aux.acc_seg: 93.8788, loss: 0.1806 +2024-06-19 19:04:12,492 - mmseg - INFO - Iter [68450/80000] lr: 5.776e-06, eta: 6:50:52, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1313, decode.acc_seg: 94.0055, aux.loss_ce: 0.0566, aux.acc_seg: 93.5560, loss: 0.1880 +2024-06-19 19:05:51,400 - mmseg - INFO - Iter [68500/80000] lr: 5.751e-06, eta: 6:49:04, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1251, decode.acc_seg: 94.2964, aux.loss_ce: 0.0539, aux.acc_seg: 93.8601, loss: 0.1790 +2024-06-19 19:07:30,319 - mmseg - INFO - Iter [68550/80000] lr: 5.726e-06, eta: 6:47:16, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1331, decode.acc_seg: 94.0129, aux.loss_ce: 0.0573, aux.acc_seg: 93.6033, loss: 0.1904 +2024-06-19 19:09:09,266 - mmseg - INFO - Iter [68600/80000] lr: 5.701e-06, eta: 6:45:28, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1322, decode.acc_seg: 94.3088, aux.loss_ce: 0.0573, aux.acc_seg: 93.8364, loss: 0.1894 +2024-06-19 19:10:48,243 - mmseg - INFO - Iter [68650/80000] lr: 5.676e-06, eta: 6:43:40, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1291, decode.acc_seg: 94.1301, aux.loss_ce: 0.0554, aux.acc_seg: 93.7389, loss: 0.1845 +2024-06-19 19:12:27,135 - mmseg - INFO - Iter [68700/80000] lr: 5.650e-06, eta: 6:41:52, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1295, decode.acc_seg: 94.1242, aux.loss_ce: 0.0558, aux.acc_seg: 93.6975, loss: 0.1853 +2024-06-19 19:14:05,980 - mmseg - INFO - Iter [68750/80000] lr: 5.625e-06, eta: 6:40:04, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1273, decode.acc_seg: 94.2860, aux.loss_ce: 0.0550, aux.acc_seg: 93.8871, loss: 0.1823 +2024-06-19 19:15:45,075 - mmseg - INFO - Iter [68800/80000] lr: 5.600e-06, eta: 6:38:16, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1319, decode.acc_seg: 94.3063, aux.loss_ce: 0.0568, aux.acc_seg: 93.8636, loss: 0.1887 +2024-06-19 19:17:24,033 - mmseg - INFO - Iter [68850/80000] lr: 5.576e-06, eta: 6:36:28, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1238, decode.acc_seg: 94.5697, aux.loss_ce: 0.0533, aux.acc_seg: 94.1682, loss: 0.1771 +2024-06-19 19:19:02,943 - mmseg - INFO - Iter [68900/80000] lr: 5.551e-06, eta: 6:34:40, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1330, decode.acc_seg: 94.0930, aux.loss_ce: 0.0566, aux.acc_seg: 93.7077, loss: 0.1896 +2024-06-19 19:20:41,937 - mmseg - INFO - Iter [68950/80000] lr: 5.526e-06, eta: 6:32:52, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1318, decode.acc_seg: 94.3329, aux.loss_ce: 0.0571, aux.acc_seg: 93.8655, loss: 0.1889 +2024-06-19 19:22:20,830 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 19:22:20,830 - mmseg - INFO - Iter [69000/80000] lr: 5.501e-06, eta: 6:31:04, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1309, decode.acc_seg: 94.2402, aux.loss_ce: 0.0562, aux.acc_seg: 93.8036, loss: 0.1870 +2024-06-19 19:24:11,582 - mmseg - INFO - per class results: +2024-06-19 19:24:11,588 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.0 | 90.81 | +| building | 85.37 | 92.64 | +| sky | 95.01 | 97.48 | +| floor | 85.13 | 92.47 | +| tree | 78.22 | 91.33 | +| ceiling | 87.77 | 94.9 | +| road | 87.61 | 91.93 | +| bed | 93.04 | 97.12 | +| windowpane | 66.93 | 80.63 | +| grass | 68.03 | 83.66 | +| cabinet | 67.9 | 77.23 | +| sidewalk | 72.66 | 86.13 | +| person | 86.88 | 94.12 | +| earth | 40.15 | 52.09 | +| door | 60.23 | 74.31 | +| table | 71.18 | 81.53 | +| mountain | 63.15 | 73.55 | +| plant | 56.12 | 67.93 | +| curtain | 79.36 | 88.05 | +| chair | 68.95 | 79.7 | +| car | 88.97 | 94.14 | +| water | 63.63 | 80.14 | +| painting | 80.08 | 91.24 | +| sofa | 81.84 | 91.88 | +| shelf | 50.75 | 66.64 | +| house | 51.5 | 64.07 | +| sea | 69.61 | 84.89 | +| mirror | 79.03 | 87.57 | +| rug | 64.73 | 77.03 | +| field | 29.1 | 52.16 | +| armchair | 63.49 | 79.21 | +| seat | 65.24 | 85.19 | +| fence | 49.58 | 60.21 | +| desk | 60.09 | 78.39 | +| rock | 57.09 | 85.38 | +| wardrobe | 52.17 | 72.28 | +| lamp | 77.31 | 88.8 | +| bathtub | 88.04 | 90.82 | +| railing | 43.47 | 61.86 | +| cushion | 69.48 | 82.35 | +| base | 45.96 | 66.0 | +| box | 41.28 | 52.27 | +| column | 57.01 | 73.66 | +| signboard | 41.85 | 58.05 | +| chest of drawers | 46.21 | 70.83 | +| counter | 50.63 | 60.08 | +| sand | 55.16 | 77.64 | +| sink | 83.61 | 87.41 | +| skyscraper | 45.97 | 60.08 | +| fireplace | 73.91 | 92.35 | +| refrigerator | 86.12 | 94.01 | +| grandstand | 56.09 | 82.05 | +| path | 32.51 | 43.33 | +| stairs | 35.67 | 44.09 | +| runway | 72.32 | 93.36 | +| case | 63.71 | 81.96 | +| pool table | 95.65 | 98.02 | +| pillow | 66.2 | 77.07 | +| screen door | 87.1 | 90.51 | +| stairway | 46.68 | 63.09 | +| river | 13.89 | 22.64 | +| bridge | 64.19 | 75.72 | +| bookcase | 45.78 | 62.77 | +| blind | 43.17 | 50.9 | +| coffee table | 61.04 | 88.99 | +| toilet | 91.26 | 94.42 | +| flower | 45.02 | 60.44 | +| book | 58.41 | 78.49 | +| hill | 12.99 | 20.88 | +| bench | 58.86 | 65.96 | +| countertop | 64.77 | 84.64 | +| stove | 88.29 | 92.33 | +| palm | 54.68 | 82.41 | +| kitchen island | 56.45 | 86.51 | +| computer | 76.51 | 91.53 | +| swivel chair | 47.29 | 68.38 | +| boat | 80.55 | 92.73 | +| bar | 69.08 | 84.54 | +| arcade machine | 81.44 | 85.69 | +| hovel | 45.74 | 52.21 | +| bus | 93.53 | 97.04 | +| towel | 80.9 | 87.7 | +| light | 62.53 | 70.57 | +| truck | 51.78 | 62.51 | +| tower | 27.75 | 55.49 | +| chandelier | 73.61 | 84.68 | +| awning | 47.67 | 62.16 | +| streetlight | 38.07 | 51.59 | +| booth | 51.33 | 71.49 | +| television receiver | 79.5 | 86.76 | +| airplane | 85.38 | 96.47 | +| dirt track | 10.25 | 18.51 | +| apparel | 63.02 | 81.01 | +| pole | 31.18 | 44.27 | +| land | 5.31 | 7.76 | +| bannister | 20.95 | 25.05 | +| escalator | 67.03 | 86.46 | +| ottoman | 55.58 | 68.93 | +| bottle | 44.92 | 66.21 | +| buffet | 59.17 | 67.68 | +| poster | 34.82 | 42.18 | +| stage | 21.98 | 40.72 | +| van | 55.51 | 77.22 | +| ship | 65.27 | 74.37 | +| fountain | 31.9 | 32.44 | +| conveyer belt | 85.72 | 96.86 | +| canopy | 58.04 | 73.94 | +| washer | 85.81 | 90.84 | +| plaything | 35.82 | 44.65 | +| swimming pool | 56.2 | 81.54 | +| stool | 53.8 | 71.5 | +| barrel | 79.1 | 96.88 | +| basket | 42.86 | 61.27 | +| waterfall | 56.73 | 72.68 | +| tent | 94.63 | 98.75 | +| bag | 26.06 | 28.93 | +| minibike | 77.52 | 89.98 | +| cradle | 87.73 | 97.07 | +| oven | 63.39 | 72.76 | +| ball | 60.0 | 72.06 | +| food | 61.91 | 70.17 | +| step | 11.69 | 13.38 | +| tank | 69.07 | 76.97 | +| trade name | 23.41 | 27.51 | +| microwave | 89.76 | 96.94 | +| pot | 61.07 | 71.23 | +| animal | 61.13 | 62.73 | +| bicycle | 61.2 | 73.86 | +| lake | 55.35 | 63.73 | +| dishwasher | 73.15 | 80.71 | +| screen | 59.28 | 90.08 | +| blanket | 37.97 | 45.38 | +| sculpture | 76.04 | 88.55 | +| hood | 67.04 | 77.81 | +| sconce | 61.11 | 72.19 | +| vase | 51.18 | 69.3 | +| traffic light | 41.47 | 66.29 | +| tray | 27.31 | 35.48 | +| ashcan | 50.32 | 67.98 | +| fan | 72.66 | 82.55 | +| pier | 41.34 | 44.72 | +| crt screen | 8.47 | 14.13 | +| plate | 65.28 | 79.96 | +| monitor | 54.87 | 64.3 | +| bulletin board | 56.82 | 67.66 | +| shower | 23.33 | 24.9 | +| radiator | 69.36 | 80.12 | +| glass | 23.02 | 24.72 | +| clock | 56.37 | 65.46 | +| flag | 72.63 | 80.65 | ++---------------------+-------+-------+ +2024-06-19 19:24:11,588 - mmseg - INFO - Summary: +2024-06-19 19:24:11,588 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.67 | 59.54 | 71.72 | ++-------+-------+-------+ +2024-06-19 19:24:11,589 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 19:24:11,589 - mmseg - INFO - Iter(val) [250] aAcc: 0.8667, mIoU: 0.5954, mAcc: 0.7172, IoU.wall: 0.8300, IoU.building: 0.8537, IoU.sky: 0.9501, IoU.floor: 0.8513, IoU.tree: 0.7822, IoU.ceiling: 0.8777, IoU.road: 0.8761, IoU.bed : 0.9304, IoU.windowpane: 0.6693, IoU.grass: 0.6803, IoU.cabinet: 0.6790, IoU.sidewalk: 0.7266, IoU.person: 0.8688, IoU.earth: 0.4015, IoU.door: 0.6023, IoU.table: 0.7118, IoU.mountain: 0.6315, IoU.plant: 0.5612, IoU.curtain: 0.7936, IoU.chair: 0.6895, IoU.car: 0.8897, IoU.water: 0.6363, IoU.painting: 0.8008, IoU.sofa: 0.8184, IoU.shelf: 0.5075, IoU.house: 0.5150, IoU.sea: 0.6961, IoU.mirror: 0.7903, IoU.rug: 0.6473, IoU.field: 0.2910, IoU.armchair: 0.6349, IoU.seat: 0.6524, IoU.fence: 0.4958, IoU.desk: 0.6009, IoU.rock: 0.5709, IoU.wardrobe: 0.5217, IoU.lamp: 0.7731, IoU.bathtub: 0.8804, IoU.railing: 0.4347, IoU.cushion: 0.6948, IoU.base: 0.4596, IoU.box: 0.4128, IoU.column: 0.5701, IoU.signboard: 0.4185, IoU.chest of drawers: 0.4621, IoU.counter: 0.5063, IoU.sand: 0.5516, IoU.sink: 0.8361, IoU.skyscraper: 0.4597, IoU.fireplace: 0.7391, IoU.refrigerator: 0.8612, IoU.grandstand: 0.5609, IoU.path: 0.3251, IoU.stairs: 0.3567, IoU.runway: 0.7232, IoU.case: 0.6371, IoU.pool table: 0.9565, IoU.pillow: 0.6620, IoU.screen door: 0.8710, IoU.stairway: 0.4668, IoU.river: 0.1389, IoU.bridge: 0.6419, IoU.bookcase: 0.4578, IoU.blind: 0.4317, IoU.coffee table: 0.6104, IoU.toilet: 0.9126, IoU.flower: 0.4502, IoU.book: 0.5841, IoU.hill: 0.1299, IoU.bench: 0.5886, IoU.countertop: 0.6477, IoU.stove: 0.8829, IoU.palm: 0.5468, IoU.kitchen island: 0.5645, IoU.computer: 0.7651, IoU.swivel chair: 0.4729, IoU.boat: 0.8055, IoU.bar: 0.6908, IoU.arcade machine: 0.8144, IoU.hovel: 0.4574, IoU.bus: 0.9353, IoU.towel: 0.8090, IoU.light: 0.6253, IoU.truck: 0.5178, IoU.tower: 0.2775, IoU.chandelier: 0.7361, IoU.awning: 0.4767, IoU.streetlight: 0.3807, IoU.booth: 0.5133, IoU.television receiver: 0.7950, IoU.airplane: 0.8538, IoU.dirt track: 0.1025, IoU.apparel: 0.6302, IoU.pole: 0.3118, IoU.land: 0.0531, IoU.bannister: 0.2095, IoU.escalator: 0.6703, IoU.ottoman: 0.5558, IoU.bottle: 0.4492, IoU.buffet: 0.5917, IoU.poster: 0.3482, IoU.stage: 0.2198, IoU.van: 0.5551, IoU.ship: 0.6527, IoU.fountain: 0.3190, IoU.conveyer belt: 0.8572, IoU.canopy: 0.5804, IoU.washer: 0.8581, IoU.plaything: 0.3582, IoU.swimming pool: 0.5620, IoU.stool: 0.5380, IoU.barrel: 0.7910, IoU.basket: 0.4286, IoU.waterfall: 0.5673, IoU.tent: 0.9463, IoU.bag: 0.2606, IoU.minibike: 0.7752, IoU.cradle: 0.8773, IoU.oven: 0.6339, IoU.ball: 0.6000, IoU.food: 0.6191, IoU.step: 0.1169, IoU.tank: 0.6907, IoU.trade name: 0.2341, IoU.microwave: 0.8976, IoU.pot: 0.6107, IoU.animal: 0.6113, IoU.bicycle: 0.6120, IoU.lake: 0.5535, IoU.dishwasher: 0.7315, IoU.screen: 0.5928, IoU.blanket: 0.3797, IoU.sculpture: 0.7604, IoU.hood: 0.6704, IoU.sconce: 0.6111, IoU.vase: 0.5118, IoU.traffic light: 0.4147, IoU.tray: 0.2731, IoU.ashcan: 0.5032, IoU.fan: 0.7266, IoU.pier: 0.4134, IoU.crt screen: 0.0847, IoU.plate: 0.6528, IoU.monitor: 0.5487, IoU.bulletin board: 0.5682, IoU.shower: 0.2333, IoU.radiator: 0.6936, IoU.glass: 0.2302, IoU.clock: 0.5637, IoU.flag: 0.7263, Acc.wall: 0.9081, Acc.building: 0.9264, Acc.sky: 0.9748, Acc.floor: 0.9247, Acc.tree: 0.9133, Acc.ceiling: 0.9490, Acc.road: 0.9193, Acc.bed : 0.9712, Acc.windowpane: 0.8063, Acc.grass: 0.8366, Acc.cabinet: 0.7723, Acc.sidewalk: 0.8613, Acc.person: 0.9412, Acc.earth: 0.5209, Acc.door: 0.7431, Acc.table: 0.8153, Acc.mountain: 0.7355, Acc.plant: 0.6793, Acc.curtain: 0.8805, Acc.chair: 0.7970, Acc.car: 0.9414, Acc.water: 0.8014, Acc.painting: 0.9124, Acc.sofa: 0.9188, Acc.shelf: 0.6664, Acc.house: 0.6407, Acc.sea: 0.8489, Acc.mirror: 0.8757, Acc.rug: 0.7703, Acc.field: 0.5216, Acc.armchair: 0.7921, Acc.seat: 0.8519, Acc.fence: 0.6021, Acc.desk: 0.7839, Acc.rock: 0.8538, Acc.wardrobe: 0.7228, Acc.lamp: 0.8880, Acc.bathtub: 0.9082, Acc.railing: 0.6186, Acc.cushion: 0.8235, Acc.base: 0.6600, Acc.box: 0.5227, Acc.column: 0.7366, Acc.signboard: 0.5805, Acc.chest of drawers: 0.7083, Acc.counter: 0.6008, Acc.sand: 0.7764, Acc.sink: 0.8741, Acc.skyscraper: 0.6008, Acc.fireplace: 0.9235, Acc.refrigerator: 0.9401, Acc.grandstand: 0.8205, Acc.path: 0.4333, Acc.stairs: 0.4409, Acc.runway: 0.9336, Acc.case: 0.8196, Acc.pool table: 0.9802, Acc.pillow: 0.7707, Acc.screen door: 0.9051, Acc.stairway: 0.6309, Acc.river: 0.2264, Acc.bridge: 0.7572, Acc.bookcase: 0.6277, Acc.blind: 0.5090, Acc.coffee table: 0.8899, Acc.toilet: 0.9442, Acc.flower: 0.6044, Acc.book: 0.7849, Acc.hill: 0.2088, Acc.bench: 0.6596, Acc.countertop: 0.8464, Acc.stove: 0.9233, Acc.palm: 0.8241, Acc.kitchen island: 0.8651, Acc.computer: 0.9153, Acc.swivel chair: 0.6838, Acc.boat: 0.9273, Acc.bar: 0.8454, Acc.arcade machine: 0.8569, Acc.hovel: 0.5221, Acc.bus: 0.9704, Acc.towel: 0.8770, Acc.light: 0.7057, Acc.truck: 0.6251, Acc.tower: 0.5549, Acc.chandelier: 0.8468, Acc.awning: 0.6216, Acc.streetlight: 0.5159, Acc.booth: 0.7149, Acc.television receiver: 0.8676, Acc.airplane: 0.9647, Acc.dirt track: 0.1851, Acc.apparel: 0.8101, Acc.pole: 0.4427, Acc.land: 0.0776, Acc.bannister: 0.2505, Acc.escalator: 0.8646, Acc.ottoman: 0.6893, Acc.bottle: 0.6621, Acc.buffet: 0.6768, Acc.poster: 0.4218, Acc.stage: 0.4072, Acc.van: 0.7722, Acc.ship: 0.7437, Acc.fountain: 0.3244, Acc.conveyer belt: 0.9686, Acc.canopy: 0.7394, Acc.washer: 0.9084, Acc.plaything: 0.4465, Acc.swimming pool: 0.8154, Acc.stool: 0.7150, Acc.barrel: 0.9688, Acc.basket: 0.6127, Acc.waterfall: 0.7268, Acc.tent: 0.9875, Acc.bag: 0.2893, Acc.minibike: 0.8998, Acc.cradle: 0.9707, Acc.oven: 0.7276, Acc.ball: 0.7206, Acc.food: 0.7017, Acc.step: 0.1338, Acc.tank: 0.7697, Acc.trade name: 0.2751, Acc.microwave: 0.9694, Acc.pot: 0.7123, Acc.animal: 0.6273, Acc.bicycle: 0.7386, Acc.lake: 0.6373, Acc.dishwasher: 0.8071, Acc.screen: 0.9008, Acc.blanket: 0.4538, Acc.sculpture: 0.8855, Acc.hood: 0.7781, Acc.sconce: 0.7219, Acc.vase: 0.6930, Acc.traffic light: 0.6629, Acc.tray: 0.3548, Acc.ashcan: 0.6798, Acc.fan: 0.8255, Acc.pier: 0.4472, Acc.crt screen: 0.1413, Acc.plate: 0.7996, Acc.monitor: 0.6430, Acc.bulletin board: 0.6766, Acc.shower: 0.2490, Acc.radiator: 0.8012, Acc.glass: 0.2472, Acc.clock: 0.6546, Acc.flag: 0.8065 +2024-06-19 19:25:50,897 - mmseg - INFO - Iter [69050/80000] lr: 5.476e-06, eta: 6:29:34, time: 4.201, data_time: 2.233, memory: 72263, decode.loss_ce: 0.1301, decode.acc_seg: 94.1535, aux.loss_ce: 0.0559, aux.acc_seg: 93.7754, loss: 0.1860 +2024-06-19 19:27:29,725 - mmseg - INFO - Iter [69100/80000] lr: 5.450e-06, eta: 6:27:46, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1275, decode.acc_seg: 94.4137, aux.loss_ce: 0.0547, aux.acc_seg: 94.0188, loss: 0.1822 +2024-06-19 19:29:08,513 - mmseg - INFO - Iter [69150/80000] lr: 5.425e-06, eta: 6:25:58, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1339, decode.acc_seg: 94.0922, aux.loss_ce: 0.0580, aux.acc_seg: 93.6410, loss: 0.1918 +2024-06-19 19:30:47,382 - mmseg - INFO - Iter [69200/80000] lr: 5.400e-06, eta: 6:24:10, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1237, decode.acc_seg: 94.3676, aux.loss_ce: 0.0532, aux.acc_seg: 94.0081, loss: 0.1768 +2024-06-19 19:32:26,313 - mmseg - INFO - Iter [69250/80000] lr: 5.376e-06, eta: 6:22:22, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1342, decode.acc_seg: 94.2221, aux.loss_ce: 0.0578, aux.acc_seg: 93.8209, loss: 0.1920 +2024-06-19 19:34:05,180 - mmseg - INFO - Iter [69300/80000] lr: 5.351e-06, eta: 6:20:34, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1329, decode.acc_seg: 93.9778, aux.loss_ce: 0.0574, aux.acc_seg: 93.5560, loss: 0.1902 +2024-06-19 19:35:43,983 - mmseg - INFO - Iter [69350/80000] lr: 5.326e-06, eta: 6:18:47, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1375, decode.acc_seg: 93.9286, aux.loss_ce: 0.0590, aux.acc_seg: 93.5084, loss: 0.1965 +2024-06-19 19:37:22,803 - mmseg - INFO - Iter [69400/80000] lr: 5.301e-06, eta: 6:16:59, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1275, decode.acc_seg: 94.3282, aux.loss_ce: 0.0548, aux.acc_seg: 93.9587, loss: 0.1823 +2024-06-19 19:39:01,665 - mmseg - INFO - Iter [69450/80000] lr: 5.276e-06, eta: 6:15:11, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1260, decode.acc_seg: 94.1995, aux.loss_ce: 0.0543, aux.acc_seg: 93.7681, loss: 0.1804 +2024-06-19 19:40:42,782 - mmseg - INFO - Iter [69500/80000] lr: 5.250e-06, eta: 6:13:23, time: 2.022, data_time: 0.053, memory: 72263, decode.loss_ce: 0.1357, decode.acc_seg: 94.1374, aux.loss_ce: 0.0585, aux.acc_seg: 93.6945, loss: 0.1941 +2024-06-19 19:42:21,631 - mmseg - INFO - Iter [69550/80000] lr: 5.225e-06, eta: 6:11:35, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1350, decode.acc_seg: 94.0395, aux.loss_ce: 0.0577, aux.acc_seg: 93.6666, loss: 0.1927 +2024-06-19 19:44:00,614 - mmseg - INFO - Iter [69600/80000] lr: 5.200e-06, eta: 6:09:48, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1371, decode.acc_seg: 93.8469, aux.loss_ce: 0.0589, aux.acc_seg: 93.4355, loss: 0.1960 +2024-06-19 19:45:39,519 - mmseg - INFO - Iter [69650/80000] lr: 5.175e-06, eta: 6:08:00, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1342, decode.acc_seg: 94.1130, aux.loss_ce: 0.0576, aux.acc_seg: 93.7133, loss: 0.1918 +2024-06-19 19:47:18,349 - mmseg - INFO - Iter [69700/80000] lr: 5.151e-06, eta: 6:06:12, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1219, decode.acc_seg: 94.3541, aux.loss_ce: 0.0527, aux.acc_seg: 93.9135, loss: 0.1746 +2024-06-19 19:48:57,293 - mmseg - INFO - Iter [69750/80000] lr: 5.126e-06, eta: 6:04:24, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1328, decode.acc_seg: 94.0120, aux.loss_ce: 0.0575, aux.acc_seg: 93.5619, loss: 0.1902 +2024-06-19 19:50:36,282 - mmseg - INFO - Iter [69800/80000] lr: 5.101e-06, eta: 6:02:36, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1234, decode.acc_seg: 94.4249, aux.loss_ce: 0.0532, aux.acc_seg: 93.9999, loss: 0.1766 +2024-06-19 19:52:15,235 - mmseg - INFO - Iter [69850/80000] lr: 5.076e-06, eta: 6:00:49, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1273, decode.acc_seg: 94.3110, aux.loss_ce: 0.0546, aux.acc_seg: 93.9014, loss: 0.1819 +2024-06-19 19:53:54,239 - mmseg - INFO - Iter [69900/80000] lr: 5.051e-06, eta: 5:59:01, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1302, decode.acc_seg: 94.0967, aux.loss_ce: 0.0560, aux.acc_seg: 93.7402, loss: 0.1862 +2024-06-19 19:55:33,060 - mmseg - INFO - Iter [69950/80000] lr: 5.025e-06, eta: 5:57:13, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1292, decode.acc_seg: 94.2766, aux.loss_ce: 0.0558, aux.acc_seg: 93.8229, loss: 0.1850 +2024-06-19 19:57:12,077 - mmseg - INFO - Saving checkpoint at 70000 iterations +2024-06-19 19:58:36,192 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 19:58:36,192 - mmseg - INFO - Iter [70000/80000] lr: 5.000e-06, eta: 5:55:37, time: 3.663, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1301, decode.acc_seg: 94.1271, aux.loss_ce: 0.0558, aux.acc_seg: 93.7292, loss: 0.1859 +2024-06-19 20:00:26,072 - mmseg - INFO - per class results: +2024-06-19 20:00:26,078 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.06 | 90.43 | +| building | 85.8 | 92.68 | +| sky | 95.0 | 97.89 | +| floor | 85.21 | 92.14 | +| tree | 78.14 | 89.85 | +| ceiling | 87.84 | 95.13 | +| road | 86.86 | 91.49 | +| bed | 93.03 | 97.34 | +| windowpane | 66.91 | 81.94 | +| grass | 69.3 | 82.92 | +| cabinet | 67.9 | 77.24 | +| sidewalk | 71.78 | 86.84 | +| person | 86.88 | 94.24 | +| earth | 38.95 | 49.73 | +| door | 61.09 | 76.03 | +| table | 71.51 | 81.34 | +| mountain | 63.37 | 74.15 | +| plant | 56.46 | 67.96 | +| curtain | 78.81 | 88.45 | +| chair | 69.18 | 80.64 | +| car | 88.61 | 94.73 | +| water | 63.2 | 79.08 | +| painting | 79.53 | 91.68 | +| sofa | 83.18 | 90.68 | +| shelf | 51.03 | 66.42 | +| house | 57.74 | 73.86 | +| sea | 68.61 | 83.98 | +| mirror | 77.52 | 85.64 | +| rug | 65.73 | 78.23 | +| field | 29.93 | 56.75 | +| armchair | 63.62 | 80.7 | +| seat | 68.52 | 89.79 | +| fence | 50.43 | 62.28 | +| desk | 58.24 | 81.24 | +| rock | 56.93 | 86.28 | +| wardrobe | 51.73 | 70.04 | +| lamp | 77.67 | 87.19 | +| bathtub | 88.86 | 92.28 | +| railing | 42.79 | 60.89 | +| cushion | 68.7 | 84.37 | +| base | 46.29 | 59.34 | +| box | 41.56 | 52.52 | +| column | 58.17 | 70.23 | +| signboard | 40.95 | 56.33 | +| chest of drawers | 46.53 | 73.16 | +| counter | 47.57 | 56.1 | +| sand | 54.06 | 84.06 | +| sink | 83.82 | 88.47 | +| skyscraper | 44.71 | 60.1 | +| fireplace | 73.54 | 93.68 | +| refrigerator | 87.05 | 94.63 | +| grandstand | 56.82 | 80.75 | +| path | 31.06 | 44.75 | +| stairs | 36.01 | 45.38 | +| runway | 72.79 | 93.97 | +| case | 64.35 | 83.43 | +| pool table | 95.42 | 98.17 | +| pillow | 64.32 | 73.6 | +| screen door | 89.94 | 92.8 | +| stairway | 45.6 | 61.39 | +| river | 13.33 | 23.81 | +| bridge | 64.79 | 74.09 | +| bookcase | 45.29 | 61.91 | +| blind | 43.04 | 50.23 | +| coffee table | 61.93 | 87.58 | +| toilet | 91.48 | 95.11 | +| flower | 44.64 | 58.71 | +| book | 58.35 | 80.54 | +| hill | 11.16 | 18.28 | +| bench | 59.92 | 67.92 | +| countertop | 64.73 | 85.66 | +| stove | 88.44 | 92.97 | +| palm | 53.6 | 81.7 | +| kitchen island | 54.22 | 86.57 | +| computer | 76.44 | 91.53 | +| swivel chair | 47.59 | 68.88 | +| boat | 82.3 | 91.99 | +| bar | 69.13 | 86.73 | +| arcade machine | 81.03 | 84.68 | +| hovel | 45.62 | 54.59 | +| bus | 93.39 | 97.15 | +| towel | 81.2 | 88.83 | +| light | 62.75 | 70.58 | +| truck | 52.49 | 64.78 | +| tower | 31.14 | 68.91 | +| chandelier | 73.55 | 82.83 | +| awning | 43.27 | 53.32 | +| streetlight | 36.8 | 48.76 | +| booth | 54.22 | 73.23 | +| television receiver | 79.97 | 85.44 | +| airplane | 87.05 | 96.58 | +| dirt track | 8.37 | 14.8 | +| apparel | 64.93 | 87.7 | +| pole | 31.79 | 44.71 | +| land | 4.68 | 9.29 | +| bannister | 22.93 | 28.06 | +| escalator | 66.99 | 86.07 | +| ottoman | 59.18 | 76.85 | +| bottle | 46.29 | 71.41 | +| buffet | 59.55 | 68.16 | +| poster | 34.03 | 44.02 | +| stage | 20.67 | 40.51 | +| van | 55.57 | 74.14 | +| ship | 69.85 | 79.16 | +| fountain | 31.69 | 32.34 | +| conveyer belt | 85.83 | 96.58 | +| canopy | 53.78 | 67.03 | +| washer | 85.73 | 91.06 | +| plaything | 35.24 | 47.23 | +| swimming pool | 54.95 | 79.46 | +| stool | 51.2 | 74.83 | +| barrel | 77.62 | 97.97 | +| basket | 43.14 | 60.99 | +| waterfall | 52.28 | 66.04 | +| tent | 93.92 | 98.64 | +| bag | 27.27 | 31.74 | +| minibike | 77.61 | 90.16 | +| cradle | 84.65 | 97.74 | +| oven | 67.71 | 77.24 | +| ball | 59.49 | 66.08 | +| food | 64.17 | 74.21 | +| step | 12.64 | 15.07 | +| tank | 68.64 | 73.77 | +| trade name | 25.15 | 29.88 | +| microwave | 90.28 | 96.42 | +| pot | 61.0 | 71.37 | +| animal | 61.47 | 63.17 | +| bicycle | 61.92 | 77.49 | +| lake | 53.47 | 63.73 | +| dishwasher | 72.65 | 80.2 | +| screen | 60.03 | 94.19 | +| blanket | 36.3 | 44.01 | +| sculpture | 73.19 | 87.81 | +| hood | 65.47 | 74.17 | +| sconce | 61.62 | 73.2 | +| vase | 51.12 | 69.17 | +| traffic light | 42.65 | 60.94 | +| tray | 25.37 | 33.76 | +| ashcan | 49.67 | 67.92 | +| fan | 72.77 | 81.97 | +| pier | 41.86 | 45.87 | +| crt screen | 3.85 | 7.34 | +| plate | 64.67 | 79.42 | +| monitor | 42.8 | 50.11 | +| bulletin board | 54.94 | 66.38 | +| shower | 21.88 | 24.93 | +| radiator | 68.71 | 81.75 | +| glass | 23.79 | 25.81 | +| clock | 56.56 | 66.32 | +| flag | 71.22 | 79.98 | ++---------------------+-------+-------+ +2024-06-19 20:00:26,078 - mmseg - INFO - Summary: +2024-06-19 20:00:26,078 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.67 | 59.4 | 71.88 | ++-------+------+-------+ +2024-06-19 20:00:26,079 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 20:00:26,080 - mmseg - INFO - Iter(val) [250] aAcc: 0.8667, mIoU: 0.5940, mAcc: 0.7188, IoU.wall: 0.8306, IoU.building: 0.8580, IoU.sky: 0.9500, IoU.floor: 0.8521, IoU.tree: 0.7814, IoU.ceiling: 0.8784, IoU.road: 0.8686, IoU.bed : 0.9303, IoU.windowpane: 0.6691, IoU.grass: 0.6930, IoU.cabinet: 0.6790, IoU.sidewalk: 0.7178, IoU.person: 0.8688, IoU.earth: 0.3895, IoU.door: 0.6109, IoU.table: 0.7151, IoU.mountain: 0.6337, IoU.plant: 0.5646, IoU.curtain: 0.7881, IoU.chair: 0.6918, IoU.car: 0.8861, IoU.water: 0.6320, IoU.painting: 0.7953, IoU.sofa: 0.8318, IoU.shelf: 0.5103, IoU.house: 0.5774, IoU.sea: 0.6861, IoU.mirror: 0.7752, IoU.rug: 0.6573, IoU.field: 0.2993, IoU.armchair: 0.6362, IoU.seat: 0.6852, IoU.fence: 0.5043, IoU.desk: 0.5824, IoU.rock: 0.5693, IoU.wardrobe: 0.5173, IoU.lamp: 0.7767, IoU.bathtub: 0.8886, IoU.railing: 0.4279, IoU.cushion: 0.6870, IoU.base: 0.4629, IoU.box: 0.4156, IoU.column: 0.5817, IoU.signboard: 0.4095, IoU.chest of drawers: 0.4653, IoU.counter: 0.4757, IoU.sand: 0.5406, IoU.sink: 0.8382, IoU.skyscraper: 0.4471, IoU.fireplace: 0.7354, IoU.refrigerator: 0.8705, IoU.grandstand: 0.5682, IoU.path: 0.3106, IoU.stairs: 0.3601, IoU.runway: 0.7279, IoU.case: 0.6435, IoU.pool table: 0.9542, IoU.pillow: 0.6432, IoU.screen door: 0.8994, IoU.stairway: 0.4560, IoU.river: 0.1333, IoU.bridge: 0.6479, IoU.bookcase: 0.4529, IoU.blind: 0.4304, IoU.coffee table: 0.6193, IoU.toilet: 0.9148, IoU.flower: 0.4464, IoU.book: 0.5835, IoU.hill: 0.1116, IoU.bench: 0.5992, IoU.countertop: 0.6473, IoU.stove: 0.8844, IoU.palm: 0.5360, IoU.kitchen island: 0.5422, IoU.computer: 0.7644, IoU.swivel chair: 0.4759, IoU.boat: 0.8230, IoU.bar: 0.6913, IoU.arcade machine: 0.8103, IoU.hovel: 0.4562, IoU.bus: 0.9339, IoU.towel: 0.8120, IoU.light: 0.6275, IoU.truck: 0.5249, IoU.tower: 0.3114, IoU.chandelier: 0.7355, IoU.awning: 0.4327, IoU.streetlight: 0.3680, IoU.booth: 0.5422, IoU.television receiver: 0.7997, IoU.airplane: 0.8705, IoU.dirt track: 0.0837, IoU.apparel: 0.6493, IoU.pole: 0.3179, IoU.land: 0.0468, IoU.bannister: 0.2293, IoU.escalator: 0.6699, IoU.ottoman: 0.5918, IoU.bottle: 0.4629, IoU.buffet: 0.5955, IoU.poster: 0.3403, IoU.stage: 0.2067, IoU.van: 0.5557, IoU.ship: 0.6985, IoU.fountain: 0.3169, IoU.conveyer belt: 0.8583, IoU.canopy: 0.5378, IoU.washer: 0.8573, IoU.plaything: 0.3524, IoU.swimming pool: 0.5495, IoU.stool: 0.5120, IoU.barrel: 0.7762, IoU.basket: 0.4314, IoU.waterfall: 0.5228, IoU.tent: 0.9392, IoU.bag: 0.2727, IoU.minibike: 0.7761, IoU.cradle: 0.8465, IoU.oven: 0.6771, IoU.ball: 0.5949, IoU.food: 0.6417, IoU.step: 0.1264, IoU.tank: 0.6864, IoU.trade name: 0.2515, IoU.microwave: 0.9028, IoU.pot: 0.6100, IoU.animal: 0.6147, IoU.bicycle: 0.6192, IoU.lake: 0.5347, IoU.dishwasher: 0.7265, IoU.screen: 0.6003, IoU.blanket: 0.3630, IoU.sculpture: 0.7319, IoU.hood: 0.6547, IoU.sconce: 0.6162, IoU.vase: 0.5112, IoU.traffic light: 0.4265, IoU.tray: 0.2537, IoU.ashcan: 0.4967, IoU.fan: 0.7277, IoU.pier: 0.4186, IoU.crt screen: 0.0385, IoU.plate: 0.6467, IoU.monitor: 0.4280, IoU.bulletin board: 0.5494, IoU.shower: 0.2188, IoU.radiator: 0.6871, IoU.glass: 0.2379, IoU.clock: 0.5656, IoU.flag: 0.7122, Acc.wall: 0.9043, Acc.building: 0.9268, Acc.sky: 0.9789, Acc.floor: 0.9214, Acc.tree: 0.8985, Acc.ceiling: 0.9513, Acc.road: 0.9149, Acc.bed : 0.9734, Acc.windowpane: 0.8194, Acc.grass: 0.8292, Acc.cabinet: 0.7724, Acc.sidewalk: 0.8684, Acc.person: 0.9424, Acc.earth: 0.4973, Acc.door: 0.7603, Acc.table: 0.8134, Acc.mountain: 0.7415, Acc.plant: 0.6796, Acc.curtain: 0.8845, Acc.chair: 0.8064, Acc.car: 0.9473, Acc.water: 0.7908, Acc.painting: 0.9168, Acc.sofa: 0.9068, Acc.shelf: 0.6642, Acc.house: 0.7386, Acc.sea: 0.8398, Acc.mirror: 0.8564, Acc.rug: 0.7823, Acc.field: 0.5675, Acc.armchair: 0.8070, Acc.seat: 0.8979, Acc.fence: 0.6228, Acc.desk: 0.8124, Acc.rock: 0.8628, Acc.wardrobe: 0.7004, Acc.lamp: 0.8719, Acc.bathtub: 0.9228, Acc.railing: 0.6089, Acc.cushion: 0.8437, Acc.base: 0.5934, Acc.box: 0.5252, Acc.column: 0.7023, Acc.signboard: 0.5633, Acc.chest of drawers: 0.7316, Acc.counter: 0.5610, Acc.sand: 0.8406, Acc.sink: 0.8847, Acc.skyscraper: 0.6010, Acc.fireplace: 0.9368, Acc.refrigerator: 0.9463, Acc.grandstand: 0.8075, Acc.path: 0.4475, Acc.stairs: 0.4538, Acc.runway: 0.9397, Acc.case: 0.8343, Acc.pool table: 0.9817, Acc.pillow: 0.7360, Acc.screen door: 0.9280, Acc.stairway: 0.6139, Acc.river: 0.2381, Acc.bridge: 0.7409, Acc.bookcase: 0.6191, Acc.blind: 0.5023, Acc.coffee table: 0.8758, Acc.toilet: 0.9511, Acc.flower: 0.5871, Acc.book: 0.8054, Acc.hill: 0.1828, Acc.bench: 0.6792, Acc.countertop: 0.8566, Acc.stove: 0.9297, Acc.palm: 0.8170, Acc.kitchen island: 0.8657, Acc.computer: 0.9153, Acc.swivel chair: 0.6888, Acc.boat: 0.9199, Acc.bar: 0.8673, Acc.arcade machine: 0.8468, Acc.hovel: 0.5459, Acc.bus: 0.9715, Acc.towel: 0.8883, Acc.light: 0.7058, Acc.truck: 0.6478, Acc.tower: 0.6891, Acc.chandelier: 0.8283, Acc.awning: 0.5332, Acc.streetlight: 0.4876, Acc.booth: 0.7323, Acc.television receiver: 0.8544, Acc.airplane: 0.9658, Acc.dirt track: 0.1480, Acc.apparel: 0.8770, Acc.pole: 0.4471, Acc.land: 0.0929, Acc.bannister: 0.2806, Acc.escalator: 0.8607, Acc.ottoman: 0.7685, Acc.bottle: 0.7141, Acc.buffet: 0.6816, Acc.poster: 0.4402, Acc.stage: 0.4051, Acc.van: 0.7414, Acc.ship: 0.7916, Acc.fountain: 0.3234, Acc.conveyer belt: 0.9658, Acc.canopy: 0.6703, Acc.washer: 0.9106, Acc.plaything: 0.4723, Acc.swimming pool: 0.7946, Acc.stool: 0.7483, Acc.barrel: 0.9797, Acc.basket: 0.6099, Acc.waterfall: 0.6604, Acc.tent: 0.9864, Acc.bag: 0.3174, Acc.minibike: 0.9016, Acc.cradle: 0.9774, Acc.oven: 0.7724, Acc.ball: 0.6608, Acc.food: 0.7421, Acc.step: 0.1507, Acc.tank: 0.7377, Acc.trade name: 0.2988, Acc.microwave: 0.9642, Acc.pot: 0.7137, Acc.animal: 0.6317, Acc.bicycle: 0.7749, Acc.lake: 0.6373, Acc.dishwasher: 0.8020, Acc.screen: 0.9419, Acc.blanket: 0.4401, Acc.sculpture: 0.8781, Acc.hood: 0.7417, Acc.sconce: 0.7320, Acc.vase: 0.6917, Acc.traffic light: 0.6094, Acc.tray: 0.3376, Acc.ashcan: 0.6792, Acc.fan: 0.8197, Acc.pier: 0.4587, Acc.crt screen: 0.0734, Acc.plate: 0.7942, Acc.monitor: 0.5011, Acc.bulletin board: 0.6638, Acc.shower: 0.2493, Acc.radiator: 0.8175, Acc.glass: 0.2581, Acc.clock: 0.6632, Acc.flag: 0.7998 +2024-06-19 20:02:05,263 - mmseg - INFO - Iter [70050/80000] lr: 4.976e-06, eta: 5:54:05, time: 4.181, data_time: 2.215, memory: 72263, decode.loss_ce: 0.1332, decode.acc_seg: 94.0654, aux.loss_ce: 0.0572, aux.acc_seg: 93.6312, loss: 0.1904 +2024-06-19 20:03:44,098 - mmseg - INFO - Iter [70100/80000] lr: 4.951e-06, eta: 5:52:17, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1199, decode.acc_seg: 94.5799, aux.loss_ce: 0.0515, aux.acc_seg: 94.1962, loss: 0.1714 +2024-06-19 20:05:23,123 - mmseg - INFO - Iter [70150/80000] lr: 4.926e-06, eta: 5:50:29, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1358, decode.acc_seg: 93.9729, aux.loss_ce: 0.0584, aux.acc_seg: 93.5593, loss: 0.1942 +2024-06-19 20:07:02,000 - mmseg - INFO - Iter [70200/80000] lr: 4.901e-06, eta: 5:48:42, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1245, decode.acc_seg: 94.5210, aux.loss_ce: 0.0540, aux.acc_seg: 94.0539, loss: 0.1785 +2024-06-19 20:08:41,055 - mmseg - INFO - Iter [70250/80000] lr: 4.876e-06, eta: 5:46:54, time: 1.981, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1338, decode.acc_seg: 94.2903, aux.loss_ce: 0.0571, aux.acc_seg: 93.8525, loss: 0.1909 +2024-06-19 20:10:20,030 - mmseg - INFO - Iter [70300/80000] lr: 4.851e-06, eta: 5:45:06, time: 1.979, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1314, decode.acc_seg: 94.3196, aux.loss_ce: 0.0563, aux.acc_seg: 93.8972, loss: 0.1877 +2024-06-19 20:11:58,960 - mmseg - INFO - Iter [70350/80000] lr: 4.825e-06, eta: 5:43:18, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1286, decode.acc_seg: 94.1769, aux.loss_ce: 0.0554, aux.acc_seg: 93.7678, loss: 0.1839 +2024-06-19 20:13:37,804 - mmseg - INFO - Iter [70400/80000] lr: 4.800e-06, eta: 5:41:30, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1278, decode.acc_seg: 94.3123, aux.loss_ce: 0.0551, aux.acc_seg: 93.8546, loss: 0.1829 +2024-06-19 20:15:16,689 - mmseg - INFO - Iter [70450/80000] lr: 4.775e-06, eta: 5:39:43, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1289, decode.acc_seg: 94.2529, aux.loss_ce: 0.0553, aux.acc_seg: 93.8825, loss: 0.1842 +2024-06-19 20:16:55,499 - mmseg - INFO - Iter [70500/80000] lr: 4.751e-06, eta: 5:37:55, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1360, decode.acc_seg: 93.9805, aux.loss_ce: 0.0587, aux.acc_seg: 93.5356, loss: 0.1947 +2024-06-19 20:18:34,440 - mmseg - INFO - Iter [70550/80000] lr: 4.726e-06, eta: 5:36:07, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1262, decode.acc_seg: 94.2847, aux.loss_ce: 0.0546, aux.acc_seg: 93.8136, loss: 0.1808 +2024-06-19 20:20:13,191 - mmseg - INFO - Iter [70600/80000] lr: 4.701e-06, eta: 5:34:19, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1330, decode.acc_seg: 93.9662, aux.loss_ce: 0.0568, aux.acc_seg: 93.5639, loss: 0.1899 +2024-06-19 20:21:52,135 - mmseg - INFO - Iter [70650/80000] lr: 4.676e-06, eta: 5:32:32, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1178, decode.acc_seg: 94.6604, aux.loss_ce: 0.0512, aux.acc_seg: 94.2097, loss: 0.1689 +2024-06-19 20:23:30,952 - mmseg - INFO - Iter [70700/80000] lr: 4.651e-06, eta: 5:30:44, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1247, decode.acc_seg: 94.4435, aux.loss_ce: 0.0537, aux.acc_seg: 94.0244, loss: 0.1784 +2024-06-19 20:25:12,892 - mmseg - INFO - Iter [70750/80000] lr: 4.625e-06, eta: 5:28:57, time: 2.039, data_time: 0.071, memory: 72263, decode.loss_ce: 0.1274, decode.acc_seg: 94.2780, aux.loss_ce: 0.0549, aux.acc_seg: 93.8514, loss: 0.1823 +2024-06-19 20:26:51,836 - mmseg - INFO - Iter [70800/80000] lr: 4.600e-06, eta: 5:27:09, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1233, decode.acc_seg: 94.5240, aux.loss_ce: 0.0535, aux.acc_seg: 94.0724, loss: 0.1768 +2024-06-19 20:28:30,782 - mmseg - INFO - Iter [70850/80000] lr: 4.575e-06, eta: 5:25:21, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1269, decode.acc_seg: 94.4125, aux.loss_ce: 0.0549, aux.acc_seg: 93.9653, loss: 0.1818 +2024-06-19 20:30:09,742 - mmseg - INFO - Iter [70900/80000] lr: 4.550e-06, eta: 5:23:33, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1201, decode.acc_seg: 94.4756, aux.loss_ce: 0.0517, aux.acc_seg: 94.1086, loss: 0.1718 +2024-06-19 20:31:48,687 - mmseg - INFO - Iter [70950/80000] lr: 4.526e-06, eta: 5:21:46, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1328, decode.acc_seg: 94.0564, aux.loss_ce: 0.0569, aux.acc_seg: 93.6801, loss: 0.1897 +2024-06-19 20:33:27,558 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 20:33:27,558 - mmseg - INFO - Iter [71000/80000] lr: 4.501e-06, eta: 5:19:58, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1267, decode.acc_seg: 94.0711, aux.loss_ce: 0.0544, aux.acc_seg: 93.6877, loss: 0.1811 +2024-06-19 20:35:18,599 - mmseg - INFO - per class results: +2024-06-19 20:35:18,605 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.91 | 90.45 | +| building | 85.35 | 93.04 | +| sky | 95.06 | 97.74 | +| floor | 84.68 | 92.02 | +| tree | 78.21 | 89.96 | +| ceiling | 87.83 | 94.76 | +| road | 86.87 | 92.93 | +| bed | 93.07 | 97.33 | +| windowpane | 67.04 | 78.99 | +| grass | 68.43 | 82.31 | +| cabinet | 68.16 | 78.32 | +| sidewalk | 70.1 | 83.76 | +| person | 86.97 | 94.57 | +| earth | 39.78 | 52.37 | +| door | 59.1 | 75.6 | +| table | 71.71 | 82.82 | +| mountain | 62.9 | 74.21 | +| plant | 56.55 | 67.23 | +| curtain | 78.78 | 89.23 | +| chair | 68.74 | 78.6 | +| car | 88.72 | 94.54 | +| water | 63.87 | 79.69 | +| painting | 79.61 | 91.55 | +| sofa | 82.58 | 90.27 | +| shelf | 50.67 | 64.7 | +| house | 47.99 | 58.41 | +| sea | 69.21 | 82.86 | +| mirror | 77.97 | 85.98 | +| rug | 62.65 | 73.79 | +| field | 28.96 | 52.8 | +| armchair | 62.55 | 81.16 | +| seat | 69.15 | 89.15 | +| fence | 49.53 | 60.93 | +| desk | 58.22 | 79.44 | +| rock | 57.31 | 87.1 | +| wardrobe | 53.3 | 71.66 | +| lamp | 77.56 | 88.4 | +| bathtub | 88.07 | 91.88 | +| railing | 42.73 | 61.67 | +| cushion | 69.52 | 82.85 | +| base | 47.16 | 62.64 | +| box | 41.46 | 52.36 | +| column | 58.54 | 75.66 | +| signboard | 41.26 | 54.72 | +| chest of drawers | 48.6 | 68.99 | +| counter | 49.37 | 60.77 | +| sand | 56.53 | 81.74 | +| sink | 84.0 | 88.57 | +| skyscraper | 45.53 | 59.47 | +| fireplace | 73.9 | 92.78 | +| refrigerator | 86.43 | 95.87 | +| grandstand | 62.2 | 81.37 | +| path | 30.3 | 41.15 | +| stairs | 39.3 | 49.89 | +| runway | 71.86 | 92.88 | +| case | 64.88 | 80.36 | +| pool table | 95.29 | 98.54 | +| pillow | 65.44 | 76.23 | +| screen door | 87.39 | 89.42 | +| stairway | 42.82 | 59.37 | +| river | 13.4 | 23.65 | +| bridge | 69.0 | 78.38 | +| bookcase | 45.31 | 62.03 | +| blind | 45.81 | 56.81 | +| coffee table | 61.56 | 87.12 | +| toilet | 91.4 | 94.67 | +| flower | 45.32 | 58.62 | +| book | 58.11 | 79.01 | +| hill | 12.59 | 21.74 | +| bench | 61.1 | 69.91 | +| countertop | 65.43 | 84.97 | +| stove | 88.37 | 93.36 | +| palm | 53.41 | 81.06 | +| kitchen island | 54.36 | 81.23 | +| computer | 76.37 | 91.9 | +| swivel chair | 48.61 | 77.32 | +| boat | 80.34 | 93.15 | +| bar | 69.86 | 83.51 | +| arcade machine | 83.08 | 87.26 | +| hovel | 44.27 | 57.85 | +| bus | 93.77 | 96.81 | +| towel | 80.08 | 85.84 | +| light | 63.25 | 72.6 | +| truck | 52.58 | 63.15 | +| tower | 31.02 | 66.08 | +| chandelier | 73.75 | 84.9 | +| awning | 42.35 | 54.15 | +| streetlight | 38.06 | 52.22 | +| booth | 52.21 | 74.42 | +| television receiver | 80.5 | 86.89 | +| airplane | 89.05 | 96.67 | +| dirt track | 14.3 | 22.57 | +| apparel | 65.26 | 82.71 | +| pole | 28.93 | 40.87 | +| land | 5.77 | 8.15 | +| bannister | 21.32 | 26.71 | +| escalator | 66.68 | 85.96 | +| ottoman | 60.01 | 74.96 | +| bottle | 46.22 | 70.17 | +| buffet | 56.47 | 64.19 | +| poster | 35.95 | 46.98 | +| stage | 19.77 | 32.48 | +| van | 54.94 | 74.27 | +| ship | 68.95 | 74.94 | +| fountain | 32.82 | 33.43 | +| conveyer belt | 86.29 | 96.44 | +| canopy | 58.82 | 75.59 | +| washer | 87.95 | 93.7 | +| plaything | 37.0 | 53.13 | +| swimming pool | 54.21 | 78.13 | +| stool | 52.23 | 76.04 | +| barrel | 70.63 | 98.85 | +| basket | 42.64 | 60.99 | +| waterfall | 54.31 | 69.99 | +| tent | 93.23 | 98.86 | +| bag | 24.8 | 28.41 | +| minibike | 77.6 | 90.19 | +| cradle | 85.29 | 97.62 | +| oven | 67.71 | 76.86 | +| ball | 60.11 | 67.44 | +| food | 64.67 | 75.69 | +| step | 11.72 | 13.96 | +| tank | 63.39 | 69.51 | +| trade name | 24.55 | 28.66 | +| microwave | 90.29 | 96.4 | +| pot | 61.06 | 70.73 | +| animal | 61.48 | 63.08 | +| bicycle | 62.12 | 77.09 | +| lake | 52.3 | 63.74 | +| dishwasher | 74.27 | 82.68 | +| screen | 49.73 | 76.01 | +| blanket | 34.45 | 40.32 | +| sculpture | 76.85 | 87.92 | +| hood | 67.83 | 78.26 | +| sconce | 61.85 | 73.49 | +| vase | 51.13 | 68.42 | +| traffic light | 40.09 | 66.25 | +| tray | 25.92 | 34.06 | +| ashcan | 49.02 | 67.97 | +| fan | 73.03 | 83.67 | +| pier | 41.76 | 45.48 | +| crt screen | 7.04 | 15.32 | +| plate | 65.25 | 78.57 | +| monitor | 48.3 | 57.71 | +| bulletin board | 61.03 | 74.92 | +| shower | 21.72 | 25.59 | +| radiator | 69.15 | 81.84 | +| glass | 22.9 | 24.45 | +| clock | 58.59 | 67.37 | +| flag | 69.28 | 80.79 | ++---------------------+-------+-------+ +2024-06-19 20:35:18,605 - mmseg - INFO - Summary: +2024-06-19 20:35:18,605 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.57 | 59.47 | 71.91 | ++-------+-------+-------+ +2024-06-19 20:35:18,606 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 20:35:18,606 - mmseg - INFO - Iter(val) [250] aAcc: 0.8657, mIoU: 0.5947, mAcc: 0.7191, IoU.wall: 0.8291, IoU.building: 0.8535, IoU.sky: 0.9506, IoU.floor: 0.8468, IoU.tree: 0.7821, IoU.ceiling: 0.8783, IoU.road: 0.8687, IoU.bed : 0.9307, IoU.windowpane: 0.6704, IoU.grass: 0.6843, IoU.cabinet: 0.6816, IoU.sidewalk: 0.7010, IoU.person: 0.8697, IoU.earth: 0.3978, IoU.door: 0.5910, IoU.table: 0.7171, IoU.mountain: 0.6290, IoU.plant: 0.5655, IoU.curtain: 0.7878, IoU.chair: 0.6874, IoU.car: 0.8872, IoU.water: 0.6387, IoU.painting: 0.7961, IoU.sofa: 0.8258, IoU.shelf: 0.5067, IoU.house: 0.4799, IoU.sea: 0.6921, IoU.mirror: 0.7797, IoU.rug: 0.6265, IoU.field: 0.2896, IoU.armchair: 0.6255, IoU.seat: 0.6915, IoU.fence: 0.4953, IoU.desk: 0.5822, IoU.rock: 0.5731, IoU.wardrobe: 0.5330, IoU.lamp: 0.7756, IoU.bathtub: 0.8807, IoU.railing: 0.4273, IoU.cushion: 0.6952, IoU.base: 0.4716, IoU.box: 0.4146, IoU.column: 0.5854, IoU.signboard: 0.4126, IoU.chest of drawers: 0.4860, IoU.counter: 0.4937, IoU.sand: 0.5653, IoU.sink: 0.8400, IoU.skyscraper: 0.4553, IoU.fireplace: 0.7390, IoU.refrigerator: 0.8643, IoU.grandstand: 0.6220, IoU.path: 0.3030, IoU.stairs: 0.3930, IoU.runway: 0.7186, IoU.case: 0.6488, IoU.pool table: 0.9529, IoU.pillow: 0.6544, IoU.screen door: 0.8739, IoU.stairway: 0.4282, IoU.river: 0.1340, IoU.bridge: 0.6900, IoU.bookcase: 0.4531, IoU.blind: 0.4581, IoU.coffee table: 0.6156, IoU.toilet: 0.9140, IoU.flower: 0.4532, IoU.book: 0.5811, IoU.hill: 0.1259, IoU.bench: 0.6110, IoU.countertop: 0.6543, IoU.stove: 0.8837, IoU.palm: 0.5341, IoU.kitchen island: 0.5436, IoU.computer: 0.7637, IoU.swivel chair: 0.4861, IoU.boat: 0.8034, IoU.bar: 0.6986, IoU.arcade machine: 0.8308, IoU.hovel: 0.4427, IoU.bus: 0.9377, IoU.towel: 0.8008, IoU.light: 0.6325, IoU.truck: 0.5258, IoU.tower: 0.3102, IoU.chandelier: 0.7375, IoU.awning: 0.4235, IoU.streetlight: 0.3806, IoU.booth: 0.5221, IoU.television receiver: 0.8050, IoU.airplane: 0.8905, IoU.dirt track: 0.1430, IoU.apparel: 0.6526, IoU.pole: 0.2893, IoU.land: 0.0577, IoU.bannister: 0.2132, IoU.escalator: 0.6668, IoU.ottoman: 0.6001, IoU.bottle: 0.4622, IoU.buffet: 0.5647, IoU.poster: 0.3595, IoU.stage: 0.1977, IoU.van: 0.5494, IoU.ship: 0.6895, IoU.fountain: 0.3282, IoU.conveyer belt: 0.8629, IoU.canopy: 0.5882, IoU.washer: 0.8795, IoU.plaything: 0.3700, IoU.swimming pool: 0.5421, IoU.stool: 0.5223, IoU.barrel: 0.7063, IoU.basket: 0.4264, IoU.waterfall: 0.5431, IoU.tent: 0.9323, IoU.bag: 0.2480, IoU.minibike: 0.7760, IoU.cradle: 0.8529, IoU.oven: 0.6771, IoU.ball: 0.6011, IoU.food: 0.6467, IoU.step: 0.1172, IoU.tank: 0.6339, IoU.trade name: 0.2455, IoU.microwave: 0.9029, IoU.pot: 0.6106, IoU.animal: 0.6148, IoU.bicycle: 0.6212, IoU.lake: 0.5230, IoU.dishwasher: 0.7427, IoU.screen: 0.4973, IoU.blanket: 0.3445, IoU.sculpture: 0.7685, IoU.hood: 0.6783, IoU.sconce: 0.6185, IoU.vase: 0.5113, IoU.traffic light: 0.4009, IoU.tray: 0.2592, IoU.ashcan: 0.4902, IoU.fan: 0.7303, IoU.pier: 0.4176, IoU.crt screen: 0.0704, IoU.plate: 0.6525, IoU.monitor: 0.4830, IoU.bulletin board: 0.6103, IoU.shower: 0.2172, IoU.radiator: 0.6915, IoU.glass: 0.2290, IoU.clock: 0.5859, IoU.flag: 0.6928, Acc.wall: 0.9045, Acc.building: 0.9304, Acc.sky: 0.9774, Acc.floor: 0.9202, Acc.tree: 0.8996, Acc.ceiling: 0.9476, Acc.road: 0.9293, Acc.bed : 0.9733, Acc.windowpane: 0.7899, Acc.grass: 0.8231, Acc.cabinet: 0.7832, Acc.sidewalk: 0.8376, Acc.person: 0.9457, Acc.earth: 0.5237, Acc.door: 0.7560, Acc.table: 0.8282, Acc.mountain: 0.7421, Acc.plant: 0.6723, Acc.curtain: 0.8923, Acc.chair: 0.7860, Acc.car: 0.9454, Acc.water: 0.7969, Acc.painting: 0.9155, Acc.sofa: 0.9027, Acc.shelf: 0.6470, Acc.house: 0.5841, Acc.sea: 0.8286, Acc.mirror: 0.8598, Acc.rug: 0.7379, Acc.field: 0.5280, Acc.armchair: 0.8116, Acc.seat: 0.8915, Acc.fence: 0.6093, Acc.desk: 0.7944, Acc.rock: 0.8710, Acc.wardrobe: 0.7166, Acc.lamp: 0.8840, Acc.bathtub: 0.9188, Acc.railing: 0.6167, Acc.cushion: 0.8285, Acc.base: 0.6264, Acc.box: 0.5236, Acc.column: 0.7566, Acc.signboard: 0.5472, Acc.chest of drawers: 0.6899, Acc.counter: 0.6077, Acc.sand: 0.8174, Acc.sink: 0.8857, Acc.skyscraper: 0.5947, Acc.fireplace: 0.9278, Acc.refrigerator: 0.9587, Acc.grandstand: 0.8137, Acc.path: 0.4115, Acc.stairs: 0.4989, Acc.runway: 0.9288, Acc.case: 0.8036, Acc.pool table: 0.9854, Acc.pillow: 0.7623, Acc.screen door: 0.8942, Acc.stairway: 0.5937, Acc.river: 0.2365, Acc.bridge: 0.7838, Acc.bookcase: 0.6203, Acc.blind: 0.5681, Acc.coffee table: 0.8712, Acc.toilet: 0.9467, Acc.flower: 0.5862, Acc.book: 0.7901, Acc.hill: 0.2174, Acc.bench: 0.6991, Acc.countertop: 0.8497, Acc.stove: 0.9336, Acc.palm: 0.8106, Acc.kitchen island: 0.8123, Acc.computer: 0.9190, Acc.swivel chair: 0.7732, Acc.boat: 0.9315, Acc.bar: 0.8351, Acc.arcade machine: 0.8726, Acc.hovel: 0.5785, Acc.bus: 0.9681, Acc.towel: 0.8584, Acc.light: 0.7260, Acc.truck: 0.6315, Acc.tower: 0.6608, Acc.chandelier: 0.8490, Acc.awning: 0.5415, Acc.streetlight: 0.5222, Acc.booth: 0.7442, Acc.television receiver: 0.8689, Acc.airplane: 0.9667, Acc.dirt track: 0.2257, Acc.apparel: 0.8271, Acc.pole: 0.4087, Acc.land: 0.0815, Acc.bannister: 0.2671, Acc.escalator: 0.8596, Acc.ottoman: 0.7496, Acc.bottle: 0.7017, Acc.buffet: 0.6419, Acc.poster: 0.4698, Acc.stage: 0.3248, Acc.van: 0.7427, Acc.ship: 0.7494, Acc.fountain: 0.3343, Acc.conveyer belt: 0.9644, Acc.canopy: 0.7559, Acc.washer: 0.9370, Acc.plaything: 0.5313, Acc.swimming pool: 0.7813, Acc.stool: 0.7604, Acc.barrel: 0.9885, Acc.basket: 0.6099, Acc.waterfall: 0.6999, Acc.tent: 0.9886, Acc.bag: 0.2841, Acc.minibike: 0.9019, Acc.cradle: 0.9762, Acc.oven: 0.7686, Acc.ball: 0.6744, Acc.food: 0.7569, Acc.step: 0.1396, Acc.tank: 0.6951, Acc.trade name: 0.2866, Acc.microwave: 0.9640, Acc.pot: 0.7073, Acc.animal: 0.6308, Acc.bicycle: 0.7709, Acc.lake: 0.6374, Acc.dishwasher: 0.8268, Acc.screen: 0.7601, Acc.blanket: 0.4032, Acc.sculpture: 0.8792, Acc.hood: 0.7826, Acc.sconce: 0.7349, Acc.vase: 0.6842, Acc.traffic light: 0.6625, Acc.tray: 0.3406, Acc.ashcan: 0.6797, Acc.fan: 0.8367, Acc.pier: 0.4548, Acc.crt screen: 0.1532, Acc.plate: 0.7857, Acc.monitor: 0.5771, Acc.bulletin board: 0.7492, Acc.shower: 0.2559, Acc.radiator: 0.8184, Acc.glass: 0.2445, Acc.clock: 0.6737, Acc.flag: 0.8079 +2024-06-19 20:36:57,961 - mmseg - INFO - Iter [71050/80000] lr: 4.476e-06, eta: 5:18:25, time: 4.208, data_time: 2.239, memory: 72263, decode.loss_ce: 0.1354, decode.acc_seg: 93.9559, aux.loss_ce: 0.0584, aux.acc_seg: 93.4352, loss: 0.1938 +2024-06-19 20:38:36,898 - mmseg - INFO - Iter [71100/80000] lr: 4.451e-06, eta: 5:16:37, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1358, decode.acc_seg: 93.9031, aux.loss_ce: 0.0586, aux.acc_seg: 93.4411, loss: 0.1944 +2024-06-19 20:40:15,799 - mmseg - INFO - Iter [71150/80000] lr: 4.426e-06, eta: 5:14:49, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1272, decode.acc_seg: 94.3593, aux.loss_ce: 0.0549, aux.acc_seg: 93.9717, loss: 0.1820 +2024-06-19 20:41:54,672 - mmseg - INFO - Iter [71200/80000] lr: 4.400e-06, eta: 5:13:01, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1316, decode.acc_seg: 94.0221, aux.loss_ce: 0.0572, aux.acc_seg: 93.5285, loss: 0.1887 +2024-06-19 20:43:33,495 - mmseg - INFO - Iter [71250/80000] lr: 4.375e-06, eta: 5:11:14, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1281, decode.acc_seg: 94.3976, aux.loss_ce: 0.0546, aux.acc_seg: 93.9917, loss: 0.1827 +2024-06-19 20:45:12,268 - mmseg - INFO - Iter [71300/80000] lr: 4.351e-06, eta: 5:09:26, time: 1.975, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1363, decode.acc_seg: 93.8556, aux.loss_ce: 0.0581, aux.acc_seg: 93.4569, loss: 0.1944 +2024-06-19 20:46:51,161 - mmseg - INFO - Iter [71350/80000] lr: 4.326e-06, eta: 5:07:38, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1292, decode.acc_seg: 94.1661, aux.loss_ce: 0.0554, aux.acc_seg: 93.7994, loss: 0.1846 +2024-06-19 20:48:30,058 - mmseg - INFO - Iter [71400/80000] lr: 4.301e-06, eta: 5:05:51, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1238, decode.acc_seg: 94.4170, aux.loss_ce: 0.0538, aux.acc_seg: 93.9836, loss: 0.1775 +2024-06-19 20:50:08,895 - mmseg - INFO - Iter [71450/80000] lr: 4.276e-06, eta: 5:04:03, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1215, decode.acc_seg: 94.4817, aux.loss_ce: 0.0527, aux.acc_seg: 94.0800, loss: 0.1743 +2024-06-19 20:51:47,718 - mmseg - INFO - Iter [71500/80000] lr: 4.251e-06, eta: 5:02:16, time: 1.976, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1297, decode.acc_seg: 94.3222, aux.loss_ce: 0.0565, aux.acc_seg: 93.8418, loss: 0.1862 +2024-06-19 20:53:26,546 - mmseg - INFO - Iter [71550/80000] lr: 4.226e-06, eta: 5:00:28, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1210, decode.acc_seg: 94.4970, aux.loss_ce: 0.0523, aux.acc_seg: 94.0960, loss: 0.1732 +2024-06-19 20:55:05,520 - mmseg - INFO - Iter [71600/80000] lr: 4.200e-06, eta: 4:58:40, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1196, decode.acc_seg: 94.6048, aux.loss_ce: 0.0517, aux.acc_seg: 94.1674, loss: 0.1713 +2024-06-19 20:56:44,392 - mmseg - INFO - Iter [71650/80000] lr: 4.175e-06, eta: 4:56:53, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1285, decode.acc_seg: 94.3343, aux.loss_ce: 0.0557, aux.acc_seg: 93.8702, loss: 0.1842 +2024-06-19 20:58:23,282 - mmseg - INFO - Iter [71700/80000] lr: 4.150e-06, eta: 4:55:05, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1279, decode.acc_seg: 94.3976, aux.loss_ce: 0.0556, aux.acc_seg: 93.9598, loss: 0.1835 +2024-06-19 21:00:02,085 - mmseg - INFO - Iter [71750/80000] lr: 4.125e-06, eta: 4:53:18, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1205, decode.acc_seg: 94.3892, aux.loss_ce: 0.0521, aux.acc_seg: 93.9627, loss: 0.1726 +2024-06-19 21:01:40,936 - mmseg - INFO - Iter [71800/80000] lr: 4.101e-06, eta: 4:51:30, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1224, decode.acc_seg: 94.4519, aux.loss_ce: 0.0530, aux.acc_seg: 94.0468, loss: 0.1753 +2024-06-19 21:03:19,875 - mmseg - INFO - Iter [71850/80000] lr: 4.076e-06, eta: 4:49:43, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1292, decode.acc_seg: 94.1216, aux.loss_ce: 0.0560, aux.acc_seg: 93.6631, loss: 0.1852 +2024-06-19 21:04:58,843 - mmseg - INFO - Iter [71900/80000] lr: 4.051e-06, eta: 4:47:55, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1304, decode.acc_seg: 94.3004, aux.loss_ce: 0.0559, aux.acc_seg: 93.8922, loss: 0.1863 +2024-06-19 21:06:37,720 - mmseg - INFO - Iter [71950/80000] lr: 4.026e-06, eta: 4:46:08, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1281, decode.acc_seg: 94.2840, aux.loss_ce: 0.0550, aux.acc_seg: 93.8869, loss: 0.1831 +2024-06-19 21:08:19,472 - mmseg - INFO - Saving checkpoint at 72000 iterations +2024-06-19 21:09:46,732 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 21:09:46,733 - mmseg - INFO - Iter [72000/80000] lr: 4.000e-06, eta: 4:44:30, time: 3.780, data_time: 0.065, memory: 72263, decode.loss_ce: 0.1321, decode.acc_seg: 94.1490, aux.loss_ce: 0.0568, aux.acc_seg: 93.7317, loss: 0.1889 +2024-06-19 21:11:37,156 - mmseg - INFO - per class results: +2024-06-19 21:11:37,162 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.86 | 90.21 | +| building | 85.28 | 93.29 | +| sky | 94.95 | 97.52 | +| floor | 85.04 | 91.76 | +| tree | 78.17 | 89.24 | +| ceiling | 87.62 | 94.93 | +| road | 87.29 | 92.15 | +| bed | 93.26 | 97.09 | +| windowpane | 66.67 | 81.17 | +| grass | 68.47 | 82.44 | +| cabinet | 67.87 | 77.62 | +| sidewalk | 71.17 | 86.17 | +| person | 86.98 | 94.77 | +| earth | 41.83 | 55.28 | +| door | 59.86 | 75.18 | +| table | 71.72 | 82.68 | +| mountain | 63.24 | 74.13 | +| plant | 56.11 | 66.53 | +| curtain | 78.95 | 89.03 | +| chair | 69.27 | 79.91 | +| car | 88.89 | 94.5 | +| water | 66.36 | 83.52 | +| painting | 79.85 | 91.95 | +| sofa | 82.98 | 90.01 | +| shelf | 50.87 | 65.9 | +| house | 48.56 | 59.22 | +| sea | 72.12 | 82.45 | +| mirror | 77.56 | 86.24 | +| rug | 65.15 | 80.11 | +| field | 31.35 | 55.36 | +| armchair | 62.61 | 80.13 | +| seat | 69.16 | 89.17 | +| fence | 49.47 | 61.84 | +| desk | 59.44 | 78.05 | +| rock | 57.22 | 87.73 | +| wardrobe | 51.95 | 70.43 | +| lamp | 77.19 | 88.33 | +| bathtub | 88.15 | 91.76 | +| railing | 42.67 | 61.68 | +| cushion | 68.92 | 83.79 | +| base | 46.34 | 61.72 | +| box | 41.49 | 52.89 | +| column | 58.68 | 74.23 | +| signboard | 41.73 | 56.71 | +| chest of drawers | 45.12 | 70.71 | +| counter | 49.48 | 60.01 | +| sand | 57.87 | 81.07 | +| sink | 84.03 | 89.81 | +| skyscraper | 45.27 | 59.67 | +| fireplace | 75.09 | 94.07 | +| refrigerator | 87.69 | 94.6 | +| grandstand | 62.82 | 81.7 | +| path | 30.68 | 41.89 | +| stairs | 38.28 | 48.89 | +| runway | 73.05 | 94.54 | +| case | 63.57 | 83.36 | +| pool table | 95.42 | 98.26 | +| pillow | 64.59 | 74.82 | +| screen door | 89.68 | 92.14 | +| stairway | 42.24 | 58.45 | +| river | 14.1 | 24.06 | +| bridge | 69.02 | 79.3 | +| bookcase | 45.27 | 62.33 | +| blind | 43.06 | 49.12 | +| coffee table | 61.94 | 86.98 | +| toilet | 91.29 | 94.45 | +| flower | 44.9 | 56.67 | +| book | 58.62 | 78.45 | +| hill | 13.73 | 22.94 | +| bench | 59.07 | 67.45 | +| countertop | 65.68 | 85.1 | +| stove | 88.52 | 93.28 | +| palm | 54.08 | 79.44 | +| kitchen island | 57.15 | 87.13 | +| computer | 76.76 | 92.05 | +| swivel chair | 49.7 | 78.1 | +| boat | 78.46 | 93.23 | +| bar | 69.49 | 86.23 | +| arcade machine | 82.99 | 86.62 | +| hovel | 44.85 | 53.24 | +| bus | 93.48 | 97.39 | +| towel | 79.83 | 86.12 | +| light | 63.18 | 73.13 | +| truck | 52.57 | 63.64 | +| tower | 31.74 | 66.45 | +| chandelier | 73.38 | 85.26 | +| awning | 42.67 | 54.8 | +| streetlight | 38.24 | 52.35 | +| booth | 50.7 | 73.47 | +| television receiver | 80.67 | 87.35 | +| airplane | 87.25 | 97.15 | +| dirt track | 11.52 | 15.39 | +| apparel | 66.71 | 81.8 | +| pole | 26.66 | 35.01 | +| land | 5.8 | 8.7 | +| bannister | 22.25 | 28.71 | +| escalator | 66.4 | 86.0 | +| ottoman | 58.01 | 74.03 | +| bottle | 47.04 | 69.95 | +| buffet | 62.4 | 71.77 | +| poster | 36.39 | 42.87 | +| stage | 19.24 | 36.8 | +| van | 55.15 | 76.87 | +| ship | 73.96 | 84.59 | +| fountain | 33.59 | 34.62 | +| conveyer belt | 86.2 | 96.92 | +| canopy | 59.58 | 75.15 | +| washer | 85.45 | 90.92 | +| plaything | 34.96 | 49.02 | +| swimming pool | 53.28 | 76.65 | +| stool | 52.08 | 73.34 | +| barrel | 75.2 | 98.03 | +| basket | 43.06 | 61.99 | +| waterfall | 49.33 | 59.02 | +| tent | 94.07 | 98.76 | +| bag | 24.37 | 27.04 | +| minibike | 77.54 | 91.46 | +| cradle | 82.03 | 97.82 | +| oven | 66.87 | 77.82 | +| ball | 61.41 | 70.31 | +| food | 62.6 | 73.55 | +| step | 11.89 | 13.74 | +| tank | 63.69 | 68.99 | +| trade name | 28.13 | 34.19 | +| microwave | 89.59 | 96.64 | +| pot | 60.91 | 71.28 | +| animal | 61.63 | 63.43 | +| bicycle | 61.89 | 77.08 | +| lake | 53.05 | 63.75 | +| dishwasher | 75.52 | 83.24 | +| screen | 50.99 | 77.9 | +| blanket | 37.87 | 45.25 | +| sculpture | 77.0 | 86.92 | +| hood | 67.95 | 78.69 | +| sconce | 62.71 | 76.67 | +| vase | 51.28 | 67.91 | +| traffic light | 38.87 | 68.64 | +| tray | 25.19 | 32.73 | +| ashcan | 49.62 | 66.89 | +| fan | 73.3 | 84.37 | +| pier | 41.16 | 46.52 | +| crt screen | 7.8 | 16.32 | +| plate | 64.88 | 81.3 | +| monitor | 45.85 | 54.33 | +| bulletin board | 61.28 | 72.68 | +| shower | 20.18 | 24.2 | +| radiator | 68.37 | 82.6 | +| glass | 23.43 | 25.23 | +| clock | 59.02 | 70.31 | +| flag | 69.45 | 79.78 | ++---------------------+-------+-------+ +2024-06-19 21:11:37,162 - mmseg - INFO - Summary: +2024-06-19 21:11:37,162 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.66 | 59.59 | 72.08 | ++-------+-------+-------+ +2024-06-19 21:11:37,163 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 21:11:37,163 - mmseg - INFO - Iter(val) [250] aAcc: 0.8666, mIoU: 0.5959, mAcc: 0.7208, IoU.wall: 0.8286, IoU.building: 0.8528, IoU.sky: 0.9495, IoU.floor: 0.8504, IoU.tree: 0.7817, IoU.ceiling: 0.8762, IoU.road: 0.8729, IoU.bed : 0.9326, IoU.windowpane: 0.6667, IoU.grass: 0.6847, IoU.cabinet: 0.6787, IoU.sidewalk: 0.7117, IoU.person: 0.8698, IoU.earth: 0.4183, IoU.door: 0.5986, IoU.table: 0.7172, IoU.mountain: 0.6324, IoU.plant: 0.5611, IoU.curtain: 0.7895, IoU.chair: 0.6927, IoU.car: 0.8889, IoU.water: 0.6636, IoU.painting: 0.7985, IoU.sofa: 0.8298, IoU.shelf: 0.5087, IoU.house: 0.4856, IoU.sea: 0.7212, IoU.mirror: 0.7756, IoU.rug: 0.6515, IoU.field: 0.3135, IoU.armchair: 0.6261, IoU.seat: 0.6916, IoU.fence: 0.4947, IoU.desk: 0.5944, IoU.rock: 0.5722, IoU.wardrobe: 0.5195, IoU.lamp: 0.7719, IoU.bathtub: 0.8815, IoU.railing: 0.4267, IoU.cushion: 0.6892, IoU.base: 0.4634, IoU.box: 0.4149, IoU.column: 0.5868, IoU.signboard: 0.4173, IoU.chest of drawers: 0.4512, IoU.counter: 0.4948, IoU.sand: 0.5787, IoU.sink: 0.8403, IoU.skyscraper: 0.4527, IoU.fireplace: 0.7509, IoU.refrigerator: 0.8769, IoU.grandstand: 0.6282, IoU.path: 0.3068, IoU.stairs: 0.3828, IoU.runway: 0.7305, IoU.case: 0.6357, IoU.pool table: 0.9542, IoU.pillow: 0.6459, IoU.screen door: 0.8968, IoU.stairway: 0.4224, IoU.river: 0.1410, IoU.bridge: 0.6902, IoU.bookcase: 0.4527, IoU.blind: 0.4306, IoU.coffee table: 0.6194, IoU.toilet: 0.9129, IoU.flower: 0.4490, IoU.book: 0.5862, IoU.hill: 0.1373, IoU.bench: 0.5907, IoU.countertop: 0.6568, IoU.stove: 0.8852, IoU.palm: 0.5408, IoU.kitchen island: 0.5715, IoU.computer: 0.7676, IoU.swivel chair: 0.4970, IoU.boat: 0.7846, IoU.bar: 0.6949, IoU.arcade machine: 0.8299, IoU.hovel: 0.4485, IoU.bus: 0.9348, IoU.towel: 0.7983, IoU.light: 0.6318, IoU.truck: 0.5257, IoU.tower: 0.3174, IoU.chandelier: 0.7338, IoU.awning: 0.4267, IoU.streetlight: 0.3824, IoU.booth: 0.5070, IoU.television receiver: 0.8067, IoU.airplane: 0.8725, IoU.dirt track: 0.1152, IoU.apparel: 0.6671, IoU.pole: 0.2666, IoU.land: 0.0580, IoU.bannister: 0.2225, IoU.escalator: 0.6640, IoU.ottoman: 0.5801, IoU.bottle: 0.4704, IoU.buffet: 0.6240, IoU.poster: 0.3639, IoU.stage: 0.1924, IoU.van: 0.5515, IoU.ship: 0.7396, IoU.fountain: 0.3359, IoU.conveyer belt: 0.8620, IoU.canopy: 0.5958, IoU.washer: 0.8545, IoU.plaything: 0.3496, IoU.swimming pool: 0.5328, IoU.stool: 0.5208, IoU.barrel: 0.7520, IoU.basket: 0.4306, IoU.waterfall: 0.4933, IoU.tent: 0.9407, IoU.bag: 0.2437, IoU.minibike: 0.7754, IoU.cradle: 0.8203, IoU.oven: 0.6687, IoU.ball: 0.6141, IoU.food: 0.6260, IoU.step: 0.1189, IoU.tank: 0.6369, IoU.trade name: 0.2813, IoU.microwave: 0.8959, IoU.pot: 0.6091, IoU.animal: 0.6163, IoU.bicycle: 0.6189, IoU.lake: 0.5305, IoU.dishwasher: 0.7552, IoU.screen: 0.5099, IoU.blanket: 0.3787, IoU.sculpture: 0.7700, IoU.hood: 0.6795, IoU.sconce: 0.6271, IoU.vase: 0.5128, IoU.traffic light: 0.3887, IoU.tray: 0.2519, IoU.ashcan: 0.4962, IoU.fan: 0.7330, IoU.pier: 0.4116, IoU.crt screen: 0.0780, IoU.plate: 0.6488, IoU.monitor: 0.4585, IoU.bulletin board: 0.6128, IoU.shower: 0.2018, IoU.radiator: 0.6837, IoU.glass: 0.2343, IoU.clock: 0.5902, IoU.flag: 0.6945, Acc.wall: 0.9021, Acc.building: 0.9329, Acc.sky: 0.9752, Acc.floor: 0.9176, Acc.tree: 0.8924, Acc.ceiling: 0.9493, Acc.road: 0.9215, Acc.bed : 0.9709, Acc.windowpane: 0.8117, Acc.grass: 0.8244, Acc.cabinet: 0.7762, Acc.sidewalk: 0.8617, Acc.person: 0.9477, Acc.earth: 0.5528, Acc.door: 0.7518, Acc.table: 0.8268, Acc.mountain: 0.7413, Acc.plant: 0.6653, Acc.curtain: 0.8903, Acc.chair: 0.7991, Acc.car: 0.9450, Acc.water: 0.8352, Acc.painting: 0.9195, Acc.sofa: 0.9001, Acc.shelf: 0.6590, Acc.house: 0.5922, Acc.sea: 0.8245, Acc.mirror: 0.8624, Acc.rug: 0.8011, Acc.field: 0.5536, Acc.armchair: 0.8013, Acc.seat: 0.8917, Acc.fence: 0.6184, Acc.desk: 0.7805, Acc.rock: 0.8773, Acc.wardrobe: 0.7043, Acc.lamp: 0.8833, Acc.bathtub: 0.9176, Acc.railing: 0.6168, Acc.cushion: 0.8379, Acc.base: 0.6172, Acc.box: 0.5289, Acc.column: 0.7423, Acc.signboard: 0.5671, Acc.chest of drawers: 0.7071, Acc.counter: 0.6001, Acc.sand: 0.8107, Acc.sink: 0.8981, Acc.skyscraper: 0.5967, Acc.fireplace: 0.9407, Acc.refrigerator: 0.9460, Acc.grandstand: 0.8170, Acc.path: 0.4189, Acc.stairs: 0.4889, Acc.runway: 0.9454, Acc.case: 0.8336, Acc.pool table: 0.9826, Acc.pillow: 0.7482, Acc.screen door: 0.9214, Acc.stairway: 0.5845, Acc.river: 0.2406, Acc.bridge: 0.7930, Acc.bookcase: 0.6233, Acc.blind: 0.4912, Acc.coffee table: 0.8698, Acc.toilet: 0.9445, Acc.flower: 0.5667, Acc.book: 0.7845, Acc.hill: 0.2294, Acc.bench: 0.6745, Acc.countertop: 0.8510, Acc.stove: 0.9328, Acc.palm: 0.7944, Acc.kitchen island: 0.8713, Acc.computer: 0.9205, Acc.swivel chair: 0.7810, Acc.boat: 0.9323, Acc.bar: 0.8623, Acc.arcade machine: 0.8662, Acc.hovel: 0.5324, Acc.bus: 0.9739, Acc.towel: 0.8612, Acc.light: 0.7313, Acc.truck: 0.6364, Acc.tower: 0.6645, Acc.chandelier: 0.8526, Acc.awning: 0.5480, Acc.streetlight: 0.5235, Acc.booth: 0.7347, Acc.television receiver: 0.8735, Acc.airplane: 0.9715, Acc.dirt track: 0.1539, Acc.apparel: 0.8180, Acc.pole: 0.3501, Acc.land: 0.0870, Acc.bannister: 0.2871, Acc.escalator: 0.8600, Acc.ottoman: 0.7403, Acc.bottle: 0.6995, Acc.buffet: 0.7177, Acc.poster: 0.4287, Acc.stage: 0.3680, Acc.van: 0.7687, Acc.ship: 0.8459, Acc.fountain: 0.3462, Acc.conveyer belt: 0.9692, Acc.canopy: 0.7515, Acc.washer: 0.9092, Acc.plaything: 0.4902, Acc.swimming pool: 0.7665, Acc.stool: 0.7334, Acc.barrel: 0.9803, Acc.basket: 0.6199, Acc.waterfall: 0.5902, Acc.tent: 0.9876, Acc.bag: 0.2704, Acc.minibike: 0.9146, Acc.cradle: 0.9782, Acc.oven: 0.7782, Acc.ball: 0.7031, Acc.food: 0.7355, Acc.step: 0.1374, Acc.tank: 0.6899, Acc.trade name: 0.3419, Acc.microwave: 0.9664, Acc.pot: 0.7128, Acc.animal: 0.6343, Acc.bicycle: 0.7708, Acc.lake: 0.6375, Acc.dishwasher: 0.8324, Acc.screen: 0.7790, Acc.blanket: 0.4525, Acc.sculpture: 0.8692, Acc.hood: 0.7869, Acc.sconce: 0.7667, Acc.vase: 0.6791, Acc.traffic light: 0.6864, Acc.tray: 0.3273, Acc.ashcan: 0.6689, Acc.fan: 0.8437, Acc.pier: 0.4652, Acc.crt screen: 0.1632, Acc.plate: 0.8130, Acc.monitor: 0.5433, Acc.bulletin board: 0.7268, Acc.shower: 0.2420, Acc.radiator: 0.8260, Acc.glass: 0.2523, Acc.clock: 0.7031, Acc.flag: 0.7978 +2024-06-19 21:13:16,405 - mmseg - INFO - Iter [72050/80000] lr: 3.975e-06, eta: 4:42:55, time: 4.193, data_time: 2.227, memory: 72263, decode.loss_ce: 0.1186, decode.acc_seg: 94.5575, aux.loss_ce: 0.0509, aux.acc_seg: 94.1771, loss: 0.1695 +2024-06-19 21:14:55,366 - mmseg - INFO - Iter [72100/80000] lr: 3.950e-06, eta: 4:41:07, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1196, decode.acc_seg: 94.7340, aux.loss_ce: 0.0518, aux.acc_seg: 94.3029, loss: 0.1715 +2024-06-19 21:16:34,205 - mmseg - INFO - Iter [72150/80000] lr: 3.925e-06, eta: 4:39:20, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1264, decode.acc_seg: 94.2062, aux.loss_ce: 0.0545, aux.acc_seg: 93.7775, loss: 0.1809 +2024-06-19 21:18:13,053 - mmseg - INFO - Iter [72200/80000] lr: 3.901e-06, eta: 4:37:32, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1273, decode.acc_seg: 94.3057, aux.loss_ce: 0.0549, aux.acc_seg: 93.8534, loss: 0.1822 +2024-06-19 21:19:51,992 - mmseg - INFO - Iter [72250/80000] lr: 3.876e-06, eta: 4:35:44, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1268, decode.acc_seg: 94.3156, aux.loss_ce: 0.0547, aux.acc_seg: 93.8975, loss: 0.1814 +2024-06-19 21:21:30,818 - mmseg - INFO - Iter [72300/80000] lr: 3.851e-06, eta: 4:33:57, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1160, decode.acc_seg: 94.8345, aux.loss_ce: 0.0502, aux.acc_seg: 94.4146, loss: 0.1663 +2024-06-19 21:23:09,766 - mmseg - INFO - Iter [72350/80000] lr: 3.826e-06, eta: 4:32:09, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1215, decode.acc_seg: 94.5818, aux.loss_ce: 0.0534, aux.acc_seg: 94.0979, loss: 0.1748 +2024-06-19 21:24:48,650 - mmseg - INFO - Iter [72400/80000] lr: 3.801e-06, eta: 4:30:22, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1254, decode.acc_seg: 94.3743, aux.loss_ce: 0.0543, aux.acc_seg: 93.9510, loss: 0.1796 +2024-06-19 21:26:27,848 - mmseg - INFO - Iter [72450/80000] lr: 3.775e-06, eta: 4:28:34, time: 1.984, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1233, decode.acc_seg: 94.3656, aux.loss_ce: 0.0531, aux.acc_seg: 93.9641, loss: 0.1765 +2024-06-19 21:28:06,721 - mmseg - INFO - Iter [72500/80000] lr: 3.750e-06, eta: 4:26:47, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1214, decode.acc_seg: 94.5976, aux.loss_ce: 0.0526, aux.acc_seg: 94.1718, loss: 0.1739 +2024-06-19 21:29:45,667 - mmseg - INFO - Iter [72550/80000] lr: 3.725e-06, eta: 4:24:59, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1253, decode.acc_seg: 94.4313, aux.loss_ce: 0.0543, aux.acc_seg: 93.9291, loss: 0.1795 +2024-06-19 21:31:24,661 - mmseg - INFO - Iter [72600/80000] lr: 3.701e-06, eta: 4:23:12, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1257, decode.acc_seg: 94.3396, aux.loss_ce: 0.0544, aux.acc_seg: 93.9137, loss: 0.1801 +2024-06-19 21:33:03,552 - mmseg - INFO - Iter [72650/80000] lr: 3.676e-06, eta: 4:21:24, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1235, decode.acc_seg: 94.5959, aux.loss_ce: 0.0529, aux.acc_seg: 94.1416, loss: 0.1764 +2024-06-19 21:34:42,482 - mmseg - INFO - Iter [72700/80000] lr: 3.651e-06, eta: 4:19:37, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1310, decode.acc_seg: 94.2532, aux.loss_ce: 0.0568, aux.acc_seg: 93.7996, loss: 0.1878 +2024-06-19 21:36:21,436 - mmseg - INFO - Iter [72750/80000] lr: 3.626e-06, eta: 4:17:49, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1219, decode.acc_seg: 94.5729, aux.loss_ce: 0.0524, aux.acc_seg: 94.1774, loss: 0.1743 +2024-06-19 21:38:00,430 - mmseg - INFO - Iter [72800/80000] lr: 3.601e-06, eta: 4:16:02, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1352, decode.acc_seg: 94.0920, aux.loss_ce: 0.0579, aux.acc_seg: 93.6589, loss: 0.1931 +2024-06-19 21:39:39,435 - mmseg - INFO - Iter [72850/80000] lr: 3.575e-06, eta: 4:14:14, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1196, decode.acc_seg: 94.5639, aux.loss_ce: 0.0517, aux.acc_seg: 94.1674, loss: 0.1713 +2024-06-19 21:41:18,410 - mmseg - INFO - Iter [72900/80000] lr: 3.550e-06, eta: 4:12:27, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1239, decode.acc_seg: 94.3879, aux.loss_ce: 0.0533, aux.acc_seg: 93.9895, loss: 0.1772 +2024-06-19 21:42:57,402 - mmseg - INFO - Iter [72950/80000] lr: 3.525e-06, eta: 4:10:39, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1178, decode.acc_seg: 94.4847, aux.loss_ce: 0.0508, aux.acc_seg: 94.0958, loss: 0.1686 +2024-06-19 21:44:36,438 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 21:44:36,438 - mmseg - INFO - Iter [73000/80000] lr: 3.501e-06, eta: 4:08:52, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1288, decode.acc_seg: 94.1782, aux.loss_ce: 0.0558, aux.acc_seg: 93.7490, loss: 0.1847 +2024-06-19 21:46:27,527 - mmseg - INFO - per class results: +2024-06-19 21:46:27,533 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.89 | 90.21 | +| building | 85.45 | 92.95 | +| sky | 94.96 | 97.44 | +| floor | 85.05 | 92.69 | +| tree | 78.17 | 90.05 | +| ceiling | 87.65 | 94.73 | +| road | 87.14 | 92.71 | +| bed | 93.18 | 97.23 | +| windowpane | 66.39 | 81.21 | +| grass | 68.69 | 82.82 | +| cabinet | 68.7 | 78.68 | +| sidewalk | 70.9 | 84.37 | +| person | 86.89 | 94.38 | +| earth | 40.91 | 51.55 | +| door | 60.44 | 78.0 | +| table | 71.78 | 82.5 | +| mountain | 62.85 | 73.9 | +| plant | 55.86 | 67.96 | +| curtain | 79.98 | 87.99 | +| chair | 69.34 | 79.95 | +| car | 88.75 | 94.78 | +| water | 62.74 | 78.57 | +| painting | 79.93 | 92.0 | +| sofa | 81.77 | 88.9 | +| shelf | 51.25 | 68.41 | +| house | 50.47 | 61.82 | +| sea | 68.13 | 83.19 | +| mirror | 78.48 | 86.65 | +| rug | 63.36 | 75.11 | +| field | 30.54 | 57.1 | +| armchair | 62.22 | 81.05 | +| seat | 69.2 | 89.31 | +| fence | 49.63 | 60.96 | +| desk | 59.78 | 78.11 | +| rock | 57.85 | 86.65 | +| wardrobe | 52.93 | 68.69 | +| lamp | 77.67 | 88.23 | +| bathtub | 87.86 | 90.59 | +| railing | 43.33 | 62.53 | +| cushion | 69.86 | 81.9 | +| base | 45.05 | 57.31 | +| box | 41.81 | 53.15 | +| column | 58.23 | 76.1 | +| signboard | 41.61 | 57.58 | +| chest of drawers | 46.2 | 67.11 | +| counter | 52.2 | 69.03 | +| sand | 57.37 | 84.15 | +| sink | 83.66 | 87.97 | +| skyscraper | 45.47 | 59.6 | +| fireplace | 75.19 | 92.7 | +| refrigerator | 86.27 | 93.15 | +| grandstand | 63.6 | 82.51 | +| path | 31.68 | 44.69 | +| stairs | 34.87 | 42.67 | +| runway | 72.42 | 93.48 | +| case | 62.16 | 77.41 | +| pool table | 95.5 | 98.24 | +| pillow | 68.2 | 80.22 | +| screen door | 86.93 | 89.32 | +| stairway | 42.3 | 64.11 | +| river | 13.11 | 24.15 | +| bridge | 70.47 | 79.47 | +| bookcase | 44.92 | 61.31 | +| blind | 42.98 | 49.4 | +| coffee table | 60.74 | 86.01 | +| toilet | 91.39 | 94.27 | +| flower | 45.42 | 59.82 | +| book | 59.22 | 79.39 | +| hill | 13.1 | 23.38 | +| bench | 60.32 | 69.59 | +| countertop | 65.82 | 84.8 | +| stove | 88.57 | 92.78 | +| palm | 53.41 | 82.22 | +| kitchen island | 56.7 | 85.68 | +| computer | 77.19 | 90.84 | +| swivel chair | 50.99 | 75.66 | +| boat | 79.51 | 93.07 | +| bar | 69.3 | 79.01 | +| arcade machine | 83.05 | 86.45 | +| hovel | 46.53 | 53.72 | +| bus | 94.28 | 97.0 | +| towel | 80.35 | 86.93 | +| light | 63.86 | 74.09 | +| truck | 53.25 | 63.74 | +| tower | 32.08 | 73.68 | +| chandelier | 73.3 | 86.07 | +| awning | 42.78 | 53.56 | +| streetlight | 39.02 | 53.57 | +| booth | 52.79 | 74.33 | +| television receiver | 80.82 | 88.23 | +| airplane | 87.85 | 96.62 | +| dirt track | 10.38 | 15.85 | +| apparel | 67.23 | 87.89 | +| pole | 29.64 | 40.43 | +| land | 5.73 | 8.42 | +| bannister | 21.36 | 26.28 | +| escalator | 65.86 | 86.31 | +| ottoman | 53.49 | 65.14 | +| bottle | 46.94 | 69.39 | +| buffet | 60.74 | 69.12 | +| poster | 36.87 | 44.13 | +| stage | 20.43 | 37.19 | +| van | 54.96 | 73.36 | +| ship | 75.11 | 88.12 | +| fountain | 31.83 | 33.04 | +| conveyer belt | 86.33 | 96.61 | +| canopy | 60.64 | 77.12 | +| washer | 86.44 | 91.93 | +| plaything | 34.61 | 47.96 | +| swimming pool | 52.21 | 74.74 | +| stool | 56.25 | 72.5 | +| barrel | 74.03 | 98.16 | +| basket | 43.11 | 59.8 | +| waterfall | 52.76 | 65.5 | +| tent | 93.47 | 98.77 | +| bag | 26.45 | 29.86 | +| minibike | 77.3 | 90.96 | +| cradle | 84.79 | 97.48 | +| oven | 68.16 | 78.54 | +| ball | 62.26 | 71.8 | +| food | 63.32 | 72.36 | +| step | 11.45 | 13.92 | +| tank | 64.16 | 69.51 | +| trade name | 23.17 | 26.84 | +| microwave | 90.18 | 96.33 | +| pot | 61.11 | 71.39 | +| animal | 60.46 | 61.95 | +| bicycle | 61.89 | 75.85 | +| lake | 52.3 | 63.74 | +| dishwasher | 76.04 | 82.41 | +| screen | 53.14 | 80.44 | +| blanket | 36.01 | 42.43 | +| sculpture | 72.01 | 88.11 | +| hood | 67.46 | 77.01 | +| sconce | 62.42 | 75.55 | +| vase | 50.33 | 68.04 | +| traffic light | 41.92 | 63.83 | +| tray | 26.71 | 34.04 | +| ashcan | 51.31 | 67.34 | +| fan | 73.54 | 84.7 | +| pier | 41.25 | 46.78 | +| crt screen | 6.97 | 15.88 | +| plate | 64.88 | 81.03 | +| monitor | 37.36 | 43.46 | +| bulletin board | 59.96 | 72.06 | +| shower | 21.33 | 24.1 | +| radiator | 68.5 | 81.41 | +| glass | 23.58 | 25.47 | +| clock | 57.57 | 66.91 | +| flag | 70.73 | 80.1 | ++---------------------+-------+-------+ +2024-06-19 21:46:27,533 - mmseg - INFO - Summary: +2024-06-19 21:46:27,533 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 86.64 | 59.58 | 71.9 | ++-------+-------+------+ +2024-06-19 21:46:27,534 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 21:46:27,534 - mmseg - INFO - Iter(val) [250] aAcc: 0.8664, mIoU: 0.5958, mAcc: 0.7190, IoU.wall: 0.8289, IoU.building: 0.8545, IoU.sky: 0.9496, IoU.floor: 0.8505, IoU.tree: 0.7817, IoU.ceiling: 0.8765, IoU.road: 0.8714, IoU.bed : 0.9318, IoU.windowpane: 0.6639, IoU.grass: 0.6869, IoU.cabinet: 0.6870, IoU.sidewalk: 0.7090, IoU.person: 0.8689, IoU.earth: 0.4091, IoU.door: 0.6044, IoU.table: 0.7178, IoU.mountain: 0.6285, IoU.plant: 0.5586, IoU.curtain: 0.7998, IoU.chair: 0.6934, IoU.car: 0.8875, IoU.water: 0.6274, IoU.painting: 0.7993, IoU.sofa: 0.8177, IoU.shelf: 0.5125, IoU.house: 0.5047, IoU.sea: 0.6813, IoU.mirror: 0.7848, IoU.rug: 0.6336, IoU.field: 0.3054, IoU.armchair: 0.6222, IoU.seat: 0.6920, IoU.fence: 0.4963, IoU.desk: 0.5978, IoU.rock: 0.5785, IoU.wardrobe: 0.5293, IoU.lamp: 0.7767, IoU.bathtub: 0.8786, IoU.railing: 0.4333, IoU.cushion: 0.6986, IoU.base: 0.4505, IoU.box: 0.4181, IoU.column: 0.5823, IoU.signboard: 0.4161, IoU.chest of drawers: 0.4620, IoU.counter: 0.5220, IoU.sand: 0.5737, IoU.sink: 0.8366, IoU.skyscraper: 0.4547, IoU.fireplace: 0.7519, IoU.refrigerator: 0.8627, IoU.grandstand: 0.6360, IoU.path: 0.3168, IoU.stairs: 0.3487, IoU.runway: 0.7242, IoU.case: 0.6216, IoU.pool table: 0.9550, IoU.pillow: 0.6820, IoU.screen door: 0.8693, IoU.stairway: 0.4230, IoU.river: 0.1311, IoU.bridge: 0.7047, IoU.bookcase: 0.4492, IoU.blind: 0.4298, IoU.coffee table: 0.6074, IoU.toilet: 0.9139, IoU.flower: 0.4542, IoU.book: 0.5922, IoU.hill: 0.1310, IoU.bench: 0.6032, IoU.countertop: 0.6582, IoU.stove: 0.8857, IoU.palm: 0.5341, IoU.kitchen island: 0.5670, IoU.computer: 0.7719, IoU.swivel chair: 0.5099, IoU.boat: 0.7951, IoU.bar: 0.6930, IoU.arcade machine: 0.8305, IoU.hovel: 0.4653, IoU.bus: 0.9428, IoU.towel: 0.8035, IoU.light: 0.6386, IoU.truck: 0.5325, IoU.tower: 0.3208, IoU.chandelier: 0.7330, IoU.awning: 0.4278, IoU.streetlight: 0.3902, IoU.booth: 0.5279, IoU.television receiver: 0.8082, IoU.airplane: 0.8785, IoU.dirt track: 0.1038, IoU.apparel: 0.6723, IoU.pole: 0.2964, IoU.land: 0.0573, IoU.bannister: 0.2136, IoU.escalator: 0.6586, IoU.ottoman: 0.5349, IoU.bottle: 0.4694, IoU.buffet: 0.6074, IoU.poster: 0.3687, IoU.stage: 0.2043, IoU.van: 0.5496, IoU.ship: 0.7511, IoU.fountain: 0.3183, IoU.conveyer belt: 0.8633, IoU.canopy: 0.6064, IoU.washer: 0.8644, IoU.plaything: 0.3461, IoU.swimming pool: 0.5221, IoU.stool: 0.5625, IoU.barrel: 0.7403, IoU.basket: 0.4311, IoU.waterfall: 0.5276, IoU.tent: 0.9347, IoU.bag: 0.2645, IoU.minibike: 0.7730, IoU.cradle: 0.8479, IoU.oven: 0.6816, IoU.ball: 0.6226, IoU.food: 0.6332, IoU.step: 0.1145, IoU.tank: 0.6416, IoU.trade name: 0.2317, IoU.microwave: 0.9018, IoU.pot: 0.6111, IoU.animal: 0.6046, IoU.bicycle: 0.6189, IoU.lake: 0.5230, IoU.dishwasher: 0.7604, IoU.screen: 0.5314, IoU.blanket: 0.3601, IoU.sculpture: 0.7201, IoU.hood: 0.6746, IoU.sconce: 0.6242, IoU.vase: 0.5033, IoU.traffic light: 0.4192, IoU.tray: 0.2671, IoU.ashcan: 0.5131, IoU.fan: 0.7354, IoU.pier: 0.4125, IoU.crt screen: 0.0697, IoU.plate: 0.6488, IoU.monitor: 0.3736, IoU.bulletin board: 0.5996, IoU.shower: 0.2133, IoU.radiator: 0.6850, IoU.glass: 0.2358, IoU.clock: 0.5757, IoU.flag: 0.7073, Acc.wall: 0.9021, Acc.building: 0.9295, Acc.sky: 0.9744, Acc.floor: 0.9269, Acc.tree: 0.9005, Acc.ceiling: 0.9473, Acc.road: 0.9271, Acc.bed : 0.9723, Acc.windowpane: 0.8121, Acc.grass: 0.8282, Acc.cabinet: 0.7868, Acc.sidewalk: 0.8437, Acc.person: 0.9438, Acc.earth: 0.5155, Acc.door: 0.7800, Acc.table: 0.8250, Acc.mountain: 0.7390, Acc.plant: 0.6796, Acc.curtain: 0.8799, Acc.chair: 0.7995, Acc.car: 0.9478, Acc.water: 0.7857, Acc.painting: 0.9200, Acc.sofa: 0.8890, Acc.shelf: 0.6841, Acc.house: 0.6182, Acc.sea: 0.8319, Acc.mirror: 0.8665, Acc.rug: 0.7511, Acc.field: 0.5710, Acc.armchair: 0.8105, Acc.seat: 0.8931, Acc.fence: 0.6096, Acc.desk: 0.7811, Acc.rock: 0.8665, Acc.wardrobe: 0.6869, Acc.lamp: 0.8823, Acc.bathtub: 0.9059, Acc.railing: 0.6253, Acc.cushion: 0.8190, Acc.base: 0.5731, Acc.box: 0.5315, Acc.column: 0.7610, Acc.signboard: 0.5758, Acc.chest of drawers: 0.6711, Acc.counter: 0.6903, Acc.sand: 0.8415, Acc.sink: 0.8797, Acc.skyscraper: 0.5960, Acc.fireplace: 0.9270, Acc.refrigerator: 0.9315, Acc.grandstand: 0.8251, Acc.path: 0.4469, Acc.stairs: 0.4267, Acc.runway: 0.9348, Acc.case: 0.7741, Acc.pool table: 0.9824, Acc.pillow: 0.8022, Acc.screen door: 0.8932, Acc.stairway: 0.6411, Acc.river: 0.2415, Acc.bridge: 0.7947, Acc.bookcase: 0.6131, Acc.blind: 0.4940, Acc.coffee table: 0.8601, Acc.toilet: 0.9427, Acc.flower: 0.5982, Acc.book: 0.7939, Acc.hill: 0.2338, Acc.bench: 0.6959, Acc.countertop: 0.8480, Acc.stove: 0.9278, Acc.palm: 0.8222, Acc.kitchen island: 0.8568, Acc.computer: 0.9084, Acc.swivel chair: 0.7566, Acc.boat: 0.9307, Acc.bar: 0.7901, Acc.arcade machine: 0.8645, Acc.hovel: 0.5372, Acc.bus: 0.9700, Acc.towel: 0.8693, Acc.light: 0.7409, Acc.truck: 0.6374, Acc.tower: 0.7368, Acc.chandelier: 0.8607, Acc.awning: 0.5356, Acc.streetlight: 0.5357, Acc.booth: 0.7433, Acc.television receiver: 0.8823, Acc.airplane: 0.9662, Acc.dirt track: 0.1585, Acc.apparel: 0.8789, Acc.pole: 0.4043, Acc.land: 0.0842, Acc.bannister: 0.2628, Acc.escalator: 0.8631, Acc.ottoman: 0.6514, Acc.bottle: 0.6939, Acc.buffet: 0.6912, Acc.poster: 0.4413, Acc.stage: 0.3719, Acc.van: 0.7336, Acc.ship: 0.8812, Acc.fountain: 0.3304, Acc.conveyer belt: 0.9661, Acc.canopy: 0.7712, Acc.washer: 0.9193, Acc.plaything: 0.4796, Acc.swimming pool: 0.7474, Acc.stool: 0.7250, Acc.barrel: 0.9816, Acc.basket: 0.5980, Acc.waterfall: 0.6550, Acc.tent: 0.9877, Acc.bag: 0.2986, Acc.minibike: 0.9096, Acc.cradle: 0.9748, Acc.oven: 0.7854, Acc.ball: 0.7180, Acc.food: 0.7236, Acc.step: 0.1392, Acc.tank: 0.6951, Acc.trade name: 0.2684, Acc.microwave: 0.9633, Acc.pot: 0.7139, Acc.animal: 0.6195, Acc.bicycle: 0.7585, Acc.lake: 0.6374, Acc.dishwasher: 0.8241, Acc.screen: 0.8044, Acc.blanket: 0.4243, Acc.sculpture: 0.8811, Acc.hood: 0.7701, Acc.sconce: 0.7555, Acc.vase: 0.6804, Acc.traffic light: 0.6383, Acc.tray: 0.3404, Acc.ashcan: 0.6734, Acc.fan: 0.8470, Acc.pier: 0.4678, Acc.crt screen: 0.1588, Acc.plate: 0.8103, Acc.monitor: 0.4346, Acc.bulletin board: 0.7206, Acc.shower: 0.2410, Acc.radiator: 0.8141, Acc.glass: 0.2547, Acc.clock: 0.6691, Acc.flag: 0.8010 +2024-06-19 21:48:06,826 - mmseg - INFO - Iter [73050/80000] lr: 3.476e-06, eta: 4:07:15, time: 4.208, data_time: 2.239, memory: 72263, decode.loss_ce: 0.1290, decode.acc_seg: 94.0436, aux.loss_ce: 0.0558, aux.acc_seg: 93.5889, loss: 0.1847 +2024-06-19 21:49:45,789 - mmseg - INFO - Iter [73100/80000] lr: 3.451e-06, eta: 4:05:28, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1307, decode.acc_seg: 94.2679, aux.loss_ce: 0.0560, aux.acc_seg: 93.8629, loss: 0.1867 +2024-06-19 21:51:24,703 - mmseg - INFO - Iter [73150/80000] lr: 3.426e-06, eta: 4:03:40, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1291, decode.acc_seg: 94.2894, aux.loss_ce: 0.0556, aux.acc_seg: 93.8776, loss: 0.1848 +2024-06-19 21:53:03,533 - mmseg - INFO - Iter [73200/80000] lr: 3.401e-06, eta: 4:01:53, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1211, decode.acc_seg: 94.6342, aux.loss_ce: 0.0522, aux.acc_seg: 94.2654, loss: 0.1733 +2024-06-19 21:54:42,453 - mmseg - INFO - Iter [73250/80000] lr: 3.375e-06, eta: 4:00:05, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1238, decode.acc_seg: 94.4154, aux.loss_ce: 0.0534, aux.acc_seg: 93.9982, loss: 0.1772 +2024-06-19 21:56:24,858 - mmseg - INFO - Iter [73300/80000] lr: 3.350e-06, eta: 3:58:18, time: 2.048, data_time: 0.077, memory: 72263, decode.loss_ce: 0.1242, decode.acc_seg: 94.4000, aux.loss_ce: 0.0538, aux.acc_seg: 93.9632, loss: 0.1781 +2024-06-19 21:58:03,732 - mmseg - INFO - Iter [73350/80000] lr: 3.325e-06, eta: 3:56:31, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1222, decode.acc_seg: 94.5048, aux.loss_ce: 0.0529, aux.acc_seg: 94.0398, loss: 0.1751 +2024-06-19 21:59:42,672 - mmseg - INFO - Iter [73400/80000] lr: 3.300e-06, eta: 3:54:44, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1236, decode.acc_seg: 94.4485, aux.loss_ce: 0.0532, aux.acc_seg: 94.0373, loss: 0.1767 +2024-06-19 22:01:21,698 - mmseg - INFO - Iter [73450/80000] lr: 3.276e-06, eta: 3:52:56, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1262, decode.acc_seg: 94.3444, aux.loss_ce: 0.0549, aux.acc_seg: 93.8675, loss: 0.1810 +2024-06-19 22:03:00,645 - mmseg - INFO - Iter [73500/80000] lr: 3.251e-06, eta: 3:51:09, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1230, decode.acc_seg: 94.6003, aux.loss_ce: 0.0535, aux.acc_seg: 94.1420, loss: 0.1765 +2024-06-19 22:04:39,558 - mmseg - INFO - Iter [73550/80000] lr: 3.226e-06, eta: 3:49:21, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1276, decode.acc_seg: 94.2450, aux.loss_ce: 0.0547, aux.acc_seg: 93.8038, loss: 0.1823 +2024-06-19 22:06:18,581 - mmseg - INFO - Iter [73600/80000] lr: 3.201e-06, eta: 3:47:34, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1266, decode.acc_seg: 94.1990, aux.loss_ce: 0.0548, aux.acc_seg: 93.7342, loss: 0.1814 +2024-06-19 22:07:57,527 - mmseg - INFO - Iter [73650/80000] lr: 3.176e-06, eta: 3:45:47, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1219, decode.acc_seg: 94.4805, aux.loss_ce: 0.0525, aux.acc_seg: 94.0854, loss: 0.1744 +2024-06-19 22:09:36,505 - mmseg - INFO - Iter [73700/80000] lr: 3.150e-06, eta: 3:43:59, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1225, decode.acc_seg: 94.4658, aux.loss_ce: 0.0535, aux.acc_seg: 94.0383, loss: 0.1760 +2024-06-19 22:11:15,420 - mmseg - INFO - Iter [73750/80000] lr: 3.125e-06, eta: 3:42:12, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1202, decode.acc_seg: 94.6483, aux.loss_ce: 0.0518, aux.acc_seg: 94.2872, loss: 0.1720 +2024-06-19 22:12:54,451 - mmseg - INFO - Iter [73800/80000] lr: 3.100e-06, eta: 3:40:25, time: 1.981, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1191, decode.acc_seg: 94.5047, aux.loss_ce: 0.0515, aux.acc_seg: 94.0820, loss: 0.1706 +2024-06-19 22:14:33,384 - mmseg - INFO - Iter [73850/80000] lr: 3.076e-06, eta: 3:38:37, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1249, decode.acc_seg: 94.3540, aux.loss_ce: 0.0538, aux.acc_seg: 93.9476, loss: 0.1787 +2024-06-19 22:16:12,309 - mmseg - INFO - Iter [73900/80000] lr: 3.051e-06, eta: 3:36:50, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1204, decode.acc_seg: 94.5351, aux.loss_ce: 0.0522, aux.acc_seg: 94.1347, loss: 0.1726 +2024-06-19 22:17:51,231 - mmseg - INFO - Iter [73950/80000] lr: 3.026e-06, eta: 3:35:03, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1282, decode.acc_seg: 94.2037, aux.loss_ce: 0.0559, aux.acc_seg: 93.7152, loss: 0.1841 +2024-06-19 22:19:30,146 - mmseg - INFO - Saving checkpoint at 74000 iterations +2024-06-19 22:20:57,900 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 22:20:57,900 - mmseg - INFO - Iter [74000/80000] lr: 3.001e-06, eta: 3:33:23, time: 3.733, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1238, decode.acc_seg: 94.3702, aux.loss_ce: 0.0533, aux.acc_seg: 93.9661, loss: 0.1771 +2024-06-19 22:22:48,815 - mmseg - INFO - per class results: +2024-06-19 22:22:48,822 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.88 | 90.46 | +| building | 85.58 | 92.95 | +| sky | 94.95 | 97.63 | +| floor | 85.17 | 92.1 | +| tree | 78.36 | 90.0 | +| ceiling | 87.65 | 94.82 | +| road | 87.68 | 91.92 | +| bed | 93.23 | 97.2 | +| windowpane | 66.47 | 81.34 | +| grass | 68.74 | 81.54 | +| cabinet | 67.76 | 76.41 | +| sidewalk | 71.47 | 85.81 | +| person | 86.89 | 94.9 | +| earth | 41.28 | 54.46 | +| door | 60.12 | 76.14 | +| table | 71.5 | 82.36 | +| mountain | 63.28 | 73.61 | +| plant | 56.56 | 66.62 | +| curtain | 79.7 | 88.12 | +| chair | 69.57 | 80.47 | +| car | 88.9 | 94.75 | +| water | 63.52 | 79.62 | +| painting | 80.38 | 91.59 | +| sofa | 82.32 | 89.57 | +| shelf | 51.9 | 67.81 | +| house | 52.34 | 63.96 | +| sea | 69.24 | 83.4 | +| mirror | 77.95 | 85.89 | +| rug | 65.49 | 78.44 | +| field | 31.08 | 58.56 | +| armchair | 63.21 | 81.02 | +| seat | 69.1 | 89.32 | +| fence | 49.87 | 61.99 | +| desk | 58.83 | 80.56 | +| rock | 57.76 | 88.46 | +| wardrobe | 52.77 | 71.45 | +| lamp | 77.83 | 89.45 | +| bathtub | 87.86 | 91.31 | +| railing | 42.62 | 61.14 | +| cushion | 68.3 | 84.77 | +| base | 45.75 | 60.98 | +| box | 41.32 | 52.36 | +| column | 58.47 | 73.89 | +| signboard | 41.72 | 57.25 | +| chest of drawers | 46.39 | 70.75 | +| counter | 47.59 | 54.68 | +| sand | 58.43 | 86.54 | +| sink | 84.22 | 89.21 | +| skyscraper | 48.07 | 63.8 | +| fireplace | 73.82 | 94.12 | +| refrigerator | 86.72 | 94.02 | +| grandstand | 62.21 | 82.5 | +| path | 30.89 | 42.0 | +| stairs | 34.45 | 42.26 | +| runway | 73.03 | 94.33 | +| case | 63.79 | 84.04 | +| pool table | 95.43 | 98.39 | +| pillow | 65.07 | 74.94 | +| screen door | 83.06 | 85.07 | +| stairway | 42.62 | 64.02 | +| river | 12.86 | 23.46 | +| bridge | 72.14 | 82.66 | +| bookcase | 46.21 | 58.91 | +| blind | 44.47 | 50.65 | +| coffee table | 61.38 | 86.46 | +| toilet | 91.18 | 93.91 | +| flower | 50.05 | 68.59 | +| book | 58.77 | 80.31 | +| hill | 12.98 | 21.5 | +| bench | 59.02 | 66.86 | +| countertop | 65.63 | 84.95 | +| stove | 88.58 | 93.21 | +| palm | 53.13 | 81.26 | +| kitchen island | 57.7 | 87.78 | +| computer | 76.9 | 91.48 | +| swivel chair | 50.48 | 76.73 | +| boat | 75.25 | 93.5 | +| bar | 71.1 | 86.84 | +| arcade machine | 83.01 | 86.5 | +| hovel | 48.53 | 56.4 | +| bus | 93.94 | 97.23 | +| towel | 80.56 | 87.76 | +| light | 63.28 | 71.73 | +| truck | 53.64 | 64.7 | +| tower | 32.36 | 70.75 | +| chandelier | 74.23 | 84.0 | +| awning | 45.04 | 58.08 | +| streetlight | 38.91 | 54.55 | +| booth | 53.57 | 75.23 | +| television receiver | 80.5 | 87.38 | +| airplane | 86.83 | 96.43 | +| dirt track | 7.95 | 13.4 | +| apparel | 65.18 | 82.71 | +| pole | 27.39 | 36.81 | +| land | 5.82 | 8.62 | +| bannister | 21.92 | 26.0 | +| escalator | 66.77 | 85.81 | +| ottoman | 55.14 | 68.61 | +| bottle | 46.41 | 67.53 | +| buffet | 61.39 | 69.88 | +| poster | 36.77 | 43.64 | +| stage | 20.92 | 37.29 | +| van | 54.09 | 73.64 | +| ship | 75.98 | 90.02 | +| fountain | 31.0 | 31.57 | +| conveyer belt | 86.16 | 96.07 | +| canopy | 59.4 | 75.4 | +| washer | 86.68 | 92.29 | +| plaything | 35.02 | 49.59 | +| swimming pool | 53.97 | 77.89 | +| stool | 53.56 | 73.48 | +| barrel | 75.73 | 97.92 | +| basket | 43.2 | 60.43 | +| waterfall | 53.69 | 64.09 | +| tent | 92.95 | 98.86 | +| bag | 26.9 | 31.47 | +| minibike | 77.73 | 90.7 | +| cradle | 82.7 | 97.69 | +| oven | 67.45 | 77.57 | +| ball | 62.38 | 71.34 | +| food | 62.72 | 73.03 | +| step | 11.38 | 13.65 | +| tank | 63.43 | 68.01 | +| trade name | 23.87 | 27.86 | +| microwave | 89.52 | 96.66 | +| pot | 60.82 | 70.9 | +| animal | 61.12 | 62.78 | +| bicycle | 63.09 | 79.04 | +| lake | 52.23 | 63.76 | +| dishwasher | 76.42 | 83.77 | +| screen | 56.43 | 88.3 | +| blanket | 38.26 | 45.33 | +| sculpture | 72.06 | 86.74 | +| hood | 69.76 | 80.15 | +| sconce | 62.45 | 74.9 | +| vase | 51.46 | 70.25 | +| traffic light | 40.43 | 68.28 | +| tray | 25.0 | 32.24 | +| ashcan | 50.44 | 66.94 | +| fan | 72.32 | 81.37 | +| pier | 40.93 | 46.78 | +| crt screen | 3.1 | 6.03 | +| plate | 64.98 | 80.49 | +| monitor | 44.81 | 52.36 | +| bulletin board | 59.95 | 72.09 | +| shower | 20.32 | 24.32 | +| radiator | 68.52 | 80.76 | +| glass | 23.92 | 26.11 | +| clock | 57.47 | 66.62 | +| flag | 70.43 | 81.71 | ++---------------------+-------+-------+ +2024-06-19 22:22:48,822 - mmseg - INFO - Summary: +2024-06-19 22:22:48,822 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.71 | 59.65 | 72.19 | ++-------+-------+-------+ +2024-06-19 22:22:48,823 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 22:22:48,823 - mmseg - INFO - Iter(val) [250] aAcc: 0.8671, mIoU: 0.5965, mAcc: 0.7219, IoU.wall: 0.8288, IoU.building: 0.8558, IoU.sky: 0.9495, IoU.floor: 0.8517, IoU.tree: 0.7836, IoU.ceiling: 0.8765, IoU.road: 0.8768, IoU.bed : 0.9323, IoU.windowpane: 0.6647, IoU.grass: 0.6874, IoU.cabinet: 0.6776, IoU.sidewalk: 0.7147, IoU.person: 0.8689, IoU.earth: 0.4128, IoU.door: 0.6012, IoU.table: 0.7150, IoU.mountain: 0.6328, IoU.plant: 0.5656, IoU.curtain: 0.7970, IoU.chair: 0.6957, IoU.car: 0.8890, IoU.water: 0.6352, IoU.painting: 0.8038, IoU.sofa: 0.8232, IoU.shelf: 0.5190, IoU.house: 0.5234, IoU.sea: 0.6924, IoU.mirror: 0.7795, IoU.rug: 0.6549, IoU.field: 0.3108, IoU.armchair: 0.6321, IoU.seat: 0.6910, IoU.fence: 0.4987, IoU.desk: 0.5883, IoU.rock: 0.5776, IoU.wardrobe: 0.5277, IoU.lamp: 0.7783, IoU.bathtub: 0.8786, IoU.railing: 0.4262, IoU.cushion: 0.6830, IoU.base: 0.4575, IoU.box: 0.4132, IoU.column: 0.5847, IoU.signboard: 0.4172, IoU.chest of drawers: 0.4639, IoU.counter: 0.4759, IoU.sand: 0.5843, IoU.sink: 0.8422, IoU.skyscraper: 0.4807, IoU.fireplace: 0.7382, IoU.refrigerator: 0.8672, IoU.grandstand: 0.6221, IoU.path: 0.3089, IoU.stairs: 0.3445, IoU.runway: 0.7303, IoU.case: 0.6379, IoU.pool table: 0.9543, IoU.pillow: 0.6507, IoU.screen door: 0.8306, IoU.stairway: 0.4262, IoU.river: 0.1286, IoU.bridge: 0.7214, IoU.bookcase: 0.4621, IoU.blind: 0.4447, IoU.coffee table: 0.6138, IoU.toilet: 0.9118, IoU.flower: 0.5005, IoU.book: 0.5877, IoU.hill: 0.1298, IoU.bench: 0.5902, IoU.countertop: 0.6563, IoU.stove: 0.8858, IoU.palm: 0.5313, IoU.kitchen island: 0.5770, IoU.computer: 0.7690, IoU.swivel chair: 0.5048, IoU.boat: 0.7525, IoU.bar: 0.7110, IoU.arcade machine: 0.8301, IoU.hovel: 0.4853, IoU.bus: 0.9394, IoU.towel: 0.8056, IoU.light: 0.6328, IoU.truck: 0.5364, IoU.tower: 0.3236, IoU.chandelier: 0.7423, IoU.awning: 0.4504, IoU.streetlight: 0.3891, IoU.booth: 0.5357, IoU.television receiver: 0.8050, IoU.airplane: 0.8683, IoU.dirt track: 0.0795, IoU.apparel: 0.6518, IoU.pole: 0.2739, IoU.land: 0.0582, IoU.bannister: 0.2192, IoU.escalator: 0.6677, IoU.ottoman: 0.5514, IoU.bottle: 0.4641, IoU.buffet: 0.6139, IoU.poster: 0.3677, IoU.stage: 0.2092, IoU.van: 0.5409, IoU.ship: 0.7598, IoU.fountain: 0.3100, IoU.conveyer belt: 0.8616, IoU.canopy: 0.5940, IoU.washer: 0.8668, IoU.plaything: 0.3502, IoU.swimming pool: 0.5397, IoU.stool: 0.5356, IoU.barrel: 0.7573, IoU.basket: 0.4320, IoU.waterfall: 0.5369, IoU.tent: 0.9295, IoU.bag: 0.2690, IoU.minibike: 0.7773, IoU.cradle: 0.8270, IoU.oven: 0.6745, IoU.ball: 0.6238, IoU.food: 0.6272, IoU.step: 0.1138, IoU.tank: 0.6343, IoU.trade name: 0.2387, IoU.microwave: 0.8952, IoU.pot: 0.6082, IoU.animal: 0.6112, IoU.bicycle: 0.6309, IoU.lake: 0.5223, IoU.dishwasher: 0.7642, IoU.screen: 0.5643, IoU.blanket: 0.3826, IoU.sculpture: 0.7206, IoU.hood: 0.6976, IoU.sconce: 0.6245, IoU.vase: 0.5146, IoU.traffic light: 0.4043, IoU.tray: 0.2500, IoU.ashcan: 0.5044, IoU.fan: 0.7232, IoU.pier: 0.4093, IoU.crt screen: 0.0310, IoU.plate: 0.6498, IoU.monitor: 0.4481, IoU.bulletin board: 0.5995, IoU.shower: 0.2032, IoU.radiator: 0.6852, IoU.glass: 0.2392, IoU.clock: 0.5747, IoU.flag: 0.7043, Acc.wall: 0.9046, Acc.building: 0.9295, Acc.sky: 0.9763, Acc.floor: 0.9210, Acc.tree: 0.9000, Acc.ceiling: 0.9482, Acc.road: 0.9192, Acc.bed : 0.9720, Acc.windowpane: 0.8134, Acc.grass: 0.8154, Acc.cabinet: 0.7641, Acc.sidewalk: 0.8581, Acc.person: 0.9490, Acc.earth: 0.5446, Acc.door: 0.7614, Acc.table: 0.8236, Acc.mountain: 0.7361, Acc.plant: 0.6662, Acc.curtain: 0.8812, Acc.chair: 0.8047, Acc.car: 0.9475, Acc.water: 0.7962, Acc.painting: 0.9159, Acc.sofa: 0.8957, Acc.shelf: 0.6781, Acc.house: 0.6396, Acc.sea: 0.8340, Acc.mirror: 0.8589, Acc.rug: 0.7844, Acc.field: 0.5856, Acc.armchair: 0.8102, Acc.seat: 0.8932, Acc.fence: 0.6199, Acc.desk: 0.8056, Acc.rock: 0.8846, Acc.wardrobe: 0.7145, Acc.lamp: 0.8945, Acc.bathtub: 0.9131, Acc.railing: 0.6114, Acc.cushion: 0.8477, Acc.base: 0.6098, Acc.box: 0.5236, Acc.column: 0.7389, Acc.signboard: 0.5725, Acc.chest of drawers: 0.7075, Acc.counter: 0.5468, Acc.sand: 0.8654, Acc.sink: 0.8921, Acc.skyscraper: 0.6380, Acc.fireplace: 0.9412, Acc.refrigerator: 0.9402, Acc.grandstand: 0.8250, Acc.path: 0.4200, Acc.stairs: 0.4226, Acc.runway: 0.9433, Acc.case: 0.8404, Acc.pool table: 0.9839, Acc.pillow: 0.7494, Acc.screen door: 0.8507, Acc.stairway: 0.6402, Acc.river: 0.2346, Acc.bridge: 0.8266, Acc.bookcase: 0.5891, Acc.blind: 0.5065, Acc.coffee table: 0.8646, Acc.toilet: 0.9391, Acc.flower: 0.6859, Acc.book: 0.8031, Acc.hill: 0.2150, Acc.bench: 0.6686, Acc.countertop: 0.8495, Acc.stove: 0.9321, Acc.palm: 0.8126, Acc.kitchen island: 0.8778, Acc.computer: 0.9148, Acc.swivel chair: 0.7673, Acc.boat: 0.9350, Acc.bar: 0.8684, Acc.arcade machine: 0.8650, Acc.hovel: 0.5640, Acc.bus: 0.9723, Acc.towel: 0.8776, Acc.light: 0.7173, Acc.truck: 0.6470, Acc.tower: 0.7075, Acc.chandelier: 0.8400, Acc.awning: 0.5808, Acc.streetlight: 0.5455, Acc.booth: 0.7523, Acc.television receiver: 0.8738, Acc.airplane: 0.9643, Acc.dirt track: 0.1340, Acc.apparel: 0.8271, Acc.pole: 0.3681, Acc.land: 0.0862, Acc.bannister: 0.2600, Acc.escalator: 0.8581, Acc.ottoman: 0.6861, Acc.bottle: 0.6753, Acc.buffet: 0.6988, Acc.poster: 0.4364, Acc.stage: 0.3729, Acc.van: 0.7364, Acc.ship: 0.9002, Acc.fountain: 0.3157, Acc.conveyer belt: 0.9607, Acc.canopy: 0.7540, Acc.washer: 0.9229, Acc.plaything: 0.4959, Acc.swimming pool: 0.7789, Acc.stool: 0.7348, Acc.barrel: 0.9792, Acc.basket: 0.6043, Acc.waterfall: 0.6409, Acc.tent: 0.9886, Acc.bag: 0.3147, Acc.minibike: 0.9070, Acc.cradle: 0.9769, Acc.oven: 0.7757, Acc.ball: 0.7134, Acc.food: 0.7303, Acc.step: 0.1365, Acc.tank: 0.6801, Acc.trade name: 0.2786, Acc.microwave: 0.9666, Acc.pot: 0.7090, Acc.animal: 0.6278, Acc.bicycle: 0.7904, Acc.lake: 0.6376, Acc.dishwasher: 0.8377, Acc.screen: 0.8830, Acc.blanket: 0.4533, Acc.sculpture: 0.8674, Acc.hood: 0.8015, Acc.sconce: 0.7490, Acc.vase: 0.7025, Acc.traffic light: 0.6828, Acc.tray: 0.3224, Acc.ashcan: 0.6694, Acc.fan: 0.8137, Acc.pier: 0.4678, Acc.crt screen: 0.0603, Acc.plate: 0.8049, Acc.monitor: 0.5236, Acc.bulletin board: 0.7209, Acc.shower: 0.2432, Acc.radiator: 0.8076, Acc.glass: 0.2611, Acc.clock: 0.6662, Acc.flag: 0.8171 +2024-06-19 22:24:28,129 - mmseg - INFO - Iter [74050/80000] lr: 2.976e-06, eta: 3:31:44, time: 4.205, data_time: 2.236, memory: 72263, decode.loss_ce: 0.1182, decode.acc_seg: 94.7420, aux.loss_ce: 0.0514, aux.acc_seg: 94.2781, loss: 0.1696 +2024-06-19 22:26:07,023 - mmseg - INFO - Iter [74100/80000] lr: 2.950e-06, eta: 3:29:57, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1226, decode.acc_seg: 94.3544, aux.loss_ce: 0.0530, aux.acc_seg: 93.9798, loss: 0.1756 +2024-06-19 22:27:46,084 - mmseg - INFO - Iter [74150/80000] lr: 2.925e-06, eta: 3:28:10, time: 1.981, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1238, decode.acc_seg: 94.4723, aux.loss_ce: 0.0535, aux.acc_seg: 94.0864, loss: 0.1772 +2024-06-19 22:29:24,931 - mmseg - INFO - Iter [74200/80000] lr: 2.900e-06, eta: 3:26:22, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1278, decode.acc_seg: 94.1387, aux.loss_ce: 0.0555, aux.acc_seg: 93.6729, loss: 0.1833 +2024-06-19 22:31:03,986 - mmseg - INFO - Iter [74250/80000] lr: 2.875e-06, eta: 3:24:35, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1221, decode.acc_seg: 94.4247, aux.loss_ce: 0.0526, aux.acc_seg: 94.0359, loss: 0.1746 +2024-06-19 22:32:42,884 - mmseg - INFO - Iter [74300/80000] lr: 2.851e-06, eta: 3:22:48, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1220, decode.acc_seg: 94.5614, aux.loss_ce: 0.0528, aux.acc_seg: 94.1252, loss: 0.1748 +2024-06-19 22:34:21,830 - mmseg - INFO - Iter [74350/80000] lr: 2.826e-06, eta: 3:21:00, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1235, decode.acc_seg: 94.5987, aux.loss_ce: 0.0533, aux.acc_seg: 94.1133, loss: 0.1768 +2024-06-19 22:36:00,745 - mmseg - INFO - Iter [74400/80000] lr: 2.801e-06, eta: 3:19:13, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1220, decode.acc_seg: 94.6091, aux.loss_ce: 0.0528, aux.acc_seg: 94.1737, loss: 0.1748 +2024-06-19 22:37:39,736 - mmseg - INFO - Iter [74450/80000] lr: 2.776e-06, eta: 3:17:26, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1217, decode.acc_seg: 94.4171, aux.loss_ce: 0.0529, aux.acc_seg: 93.9667, loss: 0.1747 +2024-06-19 22:39:18,678 - mmseg - INFO - Iter [74500/80000] lr: 2.750e-06, eta: 3:15:38, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1302, decode.acc_seg: 94.0934, aux.loss_ce: 0.0564, aux.acc_seg: 93.6668, loss: 0.1866 +2024-06-19 22:40:59,676 - mmseg - INFO - Iter [74550/80000] lr: 2.725e-06, eta: 3:13:51, time: 2.020, data_time: 0.053, memory: 72263, decode.loss_ce: 0.1224, decode.acc_seg: 94.4617, aux.loss_ce: 0.0535, aux.acc_seg: 93.9735, loss: 0.1760 +2024-06-19 22:42:38,549 - mmseg - INFO - Iter [74600/80000] lr: 2.700e-06, eta: 3:12:04, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1204, decode.acc_seg: 94.5481, aux.loss_ce: 0.0522, aux.acc_seg: 94.1232, loss: 0.1726 +2024-06-19 22:44:17,323 - mmseg - INFO - Iter [74650/80000] lr: 2.675e-06, eta: 3:10:17, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1292, decode.acc_seg: 94.2344, aux.loss_ce: 0.0560, aux.acc_seg: 93.8033, loss: 0.1851 +2024-06-19 22:45:56,243 - mmseg - INFO - Iter [74700/80000] lr: 2.651e-06, eta: 3:08:29, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1228, decode.acc_seg: 94.4110, aux.loss_ce: 0.0531, aux.acc_seg: 93.9832, loss: 0.1759 +2024-06-19 22:47:35,070 - mmseg - INFO - Iter [74750/80000] lr: 2.626e-06, eta: 3:06:42, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1226, decode.acc_seg: 94.4122, aux.loss_ce: 0.0527, aux.acc_seg: 94.0232, loss: 0.1753 +2024-06-19 22:49:14,169 - mmseg - INFO - Iter [74800/80000] lr: 2.601e-06, eta: 3:04:55, time: 1.982, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1222, decode.acc_seg: 94.3113, aux.loss_ce: 0.0530, aux.acc_seg: 93.8469, loss: 0.1752 +2024-06-19 22:50:53,011 - mmseg - INFO - Iter [74850/80000] lr: 2.576e-06, eta: 3:03:08, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1224, decode.acc_seg: 94.4845, aux.loss_ce: 0.0527, aux.acc_seg: 94.0767, loss: 0.1751 +2024-06-19 22:52:31,809 - mmseg - INFO - Iter [74900/80000] lr: 2.551e-06, eta: 3:01:20, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1240, decode.acc_seg: 94.3521, aux.loss_ce: 0.0540, aux.acc_seg: 93.8943, loss: 0.1780 +2024-06-19 22:54:10,615 - mmseg - INFO - Iter [74950/80000] lr: 2.525e-06, eta: 2:59:33, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1168, decode.acc_seg: 94.8332, aux.loss_ce: 0.0505, aux.acc_seg: 94.4530, loss: 0.1673 +2024-06-19 22:55:49,597 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 22:55:49,597 - mmseg - INFO - Iter [75000/80000] lr: 2.500e-06, eta: 2:57:46, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1248, decode.acc_seg: 94.4981, aux.loss_ce: 0.0542, aux.acc_seg: 94.0250, loss: 0.1790 +2024-06-19 22:57:38,795 - mmseg - INFO - per class results: +2024-06-19 22:57:38,801 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.97 | 90.48 | +| building | 85.81 | 92.82 | +| sky | 94.95 | 97.52 | +| floor | 85.38 | 92.51 | +| tree | 78.24 | 90.4 | +| ceiling | 87.65 | 94.89 | +| road | 87.33 | 92.01 | +| bed | 93.38 | 97.21 | +| windowpane | 66.84 | 80.65 | +| grass | 69.1 | 82.27 | +| cabinet | 67.89 | 77.72 | +| sidewalk | 71.63 | 84.77 | +| person | 86.87 | 94.82 | +| earth | 41.62 | 53.81 | +| door | 60.6 | 76.91 | +| table | 71.59 | 82.71 | +| mountain | 63.1 | 74.25 | +| plant | 56.79 | 68.47 | +| curtain | 79.48 | 88.89 | +| chair | 69.38 | 79.9 | +| car | 88.87 | 94.75 | +| water | 62.74 | 78.25 | +| painting | 80.39 | 92.06 | +| sofa | 83.2 | 91.59 | +| shelf | 51.52 | 67.3 | +| house | 53.96 | 66.11 | +| sea | 71.92 | 83.39 | +| mirror | 78.2 | 84.55 | +| rug | 64.33 | 75.72 | +| field | 31.08 | 57.71 | +| armchair | 63.75 | 80.08 | +| seat | 68.43 | 89.38 | +| fence | 49.95 | 62.75 | +| desk | 58.78 | 79.84 | +| rock | 56.88 | 85.85 | +| wardrobe | 53.0 | 71.44 | +| lamp | 77.74 | 88.79 | +| bathtub | 88.11 | 90.89 | +| railing | 42.93 | 62.4 | +| cushion | 68.87 | 82.83 | +| base | 45.62 | 61.83 | +| box | 40.07 | 50.13 | +| column | 58.61 | 71.76 | +| signboard | 42.39 | 58.16 | +| chest of drawers | 45.71 | 70.81 | +| counter | 53.2 | 63.3 | +| sand | 58.14 | 85.33 | +| sink | 83.84 | 88.68 | +| skyscraper | 48.59 | 62.12 | +| fireplace | 74.79 | 92.35 | +| refrigerator | 86.58 | 93.67 | +| grandstand | 61.05 | 81.56 | +| path | 30.65 | 43.5 | +| stairs | 32.97 | 40.51 | +| runway | 72.84 | 93.8 | +| case | 63.22 | 81.51 | +| pool table | 95.48 | 98.24 | +| pillow | 65.6 | 75.81 | +| screen door | 82.89 | 84.97 | +| stairway | 41.04 | 62.53 | +| river | 12.0 | 25.55 | +| bridge | 72.29 | 80.25 | +| bookcase | 44.9 | 57.29 | +| blind | 45.59 | 52.87 | +| coffee table | 61.62 | 87.08 | +| toilet | 91.38 | 94.7 | +| flower | 50.44 | 67.97 | +| book | 58.37 | 81.19 | +| hill | 11.89 | 19.0 | +| bench | 58.77 | 67.73 | +| countertop | 65.6 | 85.03 | +| stove | 88.54 | 93.23 | +| palm | 52.74 | 84.09 | +| kitchen island | 58.63 | 85.83 | +| computer | 76.63 | 91.86 | +| swivel chair | 49.94 | 77.27 | +| boat | 79.88 | 93.02 | +| bar | 72.06 | 81.79 | +| arcade machine | 81.47 | 84.46 | +| hovel | 48.41 | 57.01 | +| bus | 94.06 | 97.21 | +| towel | 80.58 | 88.22 | +| light | 63.4 | 73.0 | +| truck | 53.16 | 62.03 | +| tower | 31.82 | 69.89 | +| chandelier | 73.63 | 85.34 | +| awning | 42.52 | 52.64 | +| streetlight | 39.04 | 52.91 | +| booth | 55.17 | 73.15 | +| television receiver | 80.92 | 86.76 | +| airplane | 88.31 | 96.56 | +| dirt track | 8.52 | 26.7 | +| apparel | 67.28 | 88.33 | +| pole | 29.68 | 41.72 | +| land | 5.86 | 8.44 | +| bannister | 22.44 | 27.07 | +| escalator | 67.04 | 86.05 | +| ottoman | 59.73 | 76.79 | +| bottle | 46.9 | 70.14 | +| buffet | 61.45 | 69.85 | +| poster | 37.67 | 44.64 | +| stage | 21.79 | 37.34 | +| van | 53.52 | 72.36 | +| ship | 74.88 | 86.7 | +| fountain | 30.48 | 31.39 | +| conveyer belt | 85.61 | 97.05 | +| canopy | 59.0 | 75.53 | +| washer | 86.12 | 91.44 | +| plaything | 34.68 | 47.11 | +| swimming pool | 54.3 | 78.43 | +| stool | 55.46 | 74.54 | +| barrel | 78.76 | 97.66 | +| basket | 43.67 | 62.33 | +| waterfall | 52.82 | 63.65 | +| tent | 93.87 | 98.84 | +| bag | 27.44 | 31.2 | +| minibike | 77.36 | 91.21 | +| cradle | 85.85 | 97.61 | +| oven | 68.77 | 80.09 | +| ball | 61.22 | 69.18 | +| food | 63.07 | 73.59 | +| step | 11.74 | 14.33 | +| tank | 62.67 | 66.97 | +| trade name | 24.67 | 28.77 | +| microwave | 90.37 | 96.49 | +| pot | 60.89 | 71.71 | +| animal | 60.27 | 61.84 | +| bicycle | 63.17 | 79.14 | +| lake | 52.46 | 63.7 | +| dishwasher | 75.99 | 83.64 | +| screen | 55.71 | 86.41 | +| blanket | 39.91 | 47.98 | +| sculpture | 71.54 | 87.99 | +| hood | 66.9 | 77.04 | +| sconce | 62.71 | 74.64 | +| vase | 51.25 | 69.95 | +| traffic light | 40.97 | 67.64 | +| tray | 26.84 | 37.18 | +| ashcan | 50.22 | 68.45 | +| fan | 73.84 | 86.04 | +| pier | 41.5 | 46.67 | +| crt screen | 4.97 | 10.2 | +| plate | 65.12 | 81.22 | +| monitor | 42.85 | 50.36 | +| bulletin board | 60.23 | 70.83 | +| shower | 21.33 | 24.57 | +| radiator | 69.03 | 81.39 | +| glass | 23.3 | 25.09 | +| clock | 57.99 | 68.16 | +| flag | 70.91 | 82.14 | ++---------------------+-------+-------+ +2024-06-19 22:57:38,801 - mmseg - INFO - Summary: +2024-06-19 22:57:38,801 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.76 | 59.85 | 72.34 | ++-------+-------+-------+ +2024-06-19 22:57:38,802 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 22:57:38,802 - mmseg - INFO - Iter(val) [250] aAcc: 0.8676, mIoU: 0.5985, mAcc: 0.7234, IoU.wall: 0.8297, IoU.building: 0.8581, IoU.sky: 0.9495, IoU.floor: 0.8538, IoU.tree: 0.7824, IoU.ceiling: 0.8765, IoU.road: 0.8733, IoU.bed : 0.9338, IoU.windowpane: 0.6684, IoU.grass: 0.6910, IoU.cabinet: 0.6789, IoU.sidewalk: 0.7163, IoU.person: 0.8687, IoU.earth: 0.4162, IoU.door: 0.6060, IoU.table: 0.7159, IoU.mountain: 0.6310, IoU.plant: 0.5679, IoU.curtain: 0.7948, IoU.chair: 0.6938, IoU.car: 0.8887, IoU.water: 0.6274, IoU.painting: 0.8039, IoU.sofa: 0.8320, IoU.shelf: 0.5152, IoU.house: 0.5396, IoU.sea: 0.7192, IoU.mirror: 0.7820, IoU.rug: 0.6433, IoU.field: 0.3108, IoU.armchair: 0.6375, IoU.seat: 0.6843, IoU.fence: 0.4995, IoU.desk: 0.5878, IoU.rock: 0.5688, IoU.wardrobe: 0.5300, IoU.lamp: 0.7774, IoU.bathtub: 0.8811, IoU.railing: 0.4293, IoU.cushion: 0.6887, IoU.base: 0.4562, IoU.box: 0.4007, IoU.column: 0.5861, IoU.signboard: 0.4239, IoU.chest of drawers: 0.4571, IoU.counter: 0.5320, IoU.sand: 0.5814, IoU.sink: 0.8384, IoU.skyscraper: 0.4859, IoU.fireplace: 0.7479, IoU.refrigerator: 0.8658, IoU.grandstand: 0.6105, IoU.path: 0.3065, IoU.stairs: 0.3297, IoU.runway: 0.7284, IoU.case: 0.6322, IoU.pool table: 0.9548, IoU.pillow: 0.6560, IoU.screen door: 0.8289, IoU.stairway: 0.4104, IoU.river: 0.1200, IoU.bridge: 0.7229, IoU.bookcase: 0.4490, IoU.blind: 0.4559, IoU.coffee table: 0.6162, IoU.toilet: 0.9138, IoU.flower: 0.5044, IoU.book: 0.5837, IoU.hill: 0.1189, IoU.bench: 0.5877, IoU.countertop: 0.6560, IoU.stove: 0.8854, IoU.palm: 0.5274, IoU.kitchen island: 0.5863, IoU.computer: 0.7663, IoU.swivel chair: 0.4994, IoU.boat: 0.7988, IoU.bar: 0.7206, IoU.arcade machine: 0.8147, IoU.hovel: 0.4841, IoU.bus: 0.9406, IoU.towel: 0.8058, IoU.light: 0.6340, IoU.truck: 0.5316, IoU.tower: 0.3182, IoU.chandelier: 0.7363, IoU.awning: 0.4252, IoU.streetlight: 0.3904, IoU.booth: 0.5517, IoU.television receiver: 0.8092, IoU.airplane: 0.8831, IoU.dirt track: 0.0852, IoU.apparel: 0.6728, IoU.pole: 0.2968, IoU.land: 0.0586, IoU.bannister: 0.2244, IoU.escalator: 0.6704, IoU.ottoman: 0.5973, IoU.bottle: 0.4690, IoU.buffet: 0.6145, IoU.poster: 0.3767, IoU.stage: 0.2179, IoU.van: 0.5352, IoU.ship: 0.7488, IoU.fountain: 0.3048, IoU.conveyer belt: 0.8561, IoU.canopy: 0.5900, IoU.washer: 0.8612, IoU.plaything: 0.3468, IoU.swimming pool: 0.5430, IoU.stool: 0.5546, IoU.barrel: 0.7876, IoU.basket: 0.4367, IoU.waterfall: 0.5282, IoU.tent: 0.9387, IoU.bag: 0.2744, IoU.minibike: 0.7736, IoU.cradle: 0.8585, IoU.oven: 0.6877, IoU.ball: 0.6122, IoU.food: 0.6307, IoU.step: 0.1174, IoU.tank: 0.6267, IoU.trade name: 0.2467, IoU.microwave: 0.9037, IoU.pot: 0.6089, IoU.animal: 0.6027, IoU.bicycle: 0.6317, IoU.lake: 0.5246, IoU.dishwasher: 0.7599, IoU.screen: 0.5571, IoU.blanket: 0.3991, IoU.sculpture: 0.7154, IoU.hood: 0.6690, IoU.sconce: 0.6271, IoU.vase: 0.5125, IoU.traffic light: 0.4097, IoU.tray: 0.2684, IoU.ashcan: 0.5022, IoU.fan: 0.7384, IoU.pier: 0.4150, IoU.crt screen: 0.0497, IoU.plate: 0.6512, IoU.monitor: 0.4285, IoU.bulletin board: 0.6023, IoU.shower: 0.2133, IoU.radiator: 0.6903, IoU.glass: 0.2330, IoU.clock: 0.5799, IoU.flag: 0.7091, Acc.wall: 0.9048, Acc.building: 0.9282, Acc.sky: 0.9752, Acc.floor: 0.9251, Acc.tree: 0.9040, Acc.ceiling: 0.9489, Acc.road: 0.9201, Acc.bed : 0.9721, Acc.windowpane: 0.8065, Acc.grass: 0.8227, Acc.cabinet: 0.7772, Acc.sidewalk: 0.8477, Acc.person: 0.9482, Acc.earth: 0.5381, Acc.door: 0.7691, Acc.table: 0.8271, Acc.mountain: 0.7425, Acc.plant: 0.6847, Acc.curtain: 0.8889, Acc.chair: 0.7990, Acc.car: 0.9475, Acc.water: 0.7825, Acc.painting: 0.9206, Acc.sofa: 0.9159, Acc.shelf: 0.6730, Acc.house: 0.6611, Acc.sea: 0.8339, Acc.mirror: 0.8455, Acc.rug: 0.7572, Acc.field: 0.5771, Acc.armchair: 0.8008, Acc.seat: 0.8938, Acc.fence: 0.6275, Acc.desk: 0.7984, Acc.rock: 0.8585, Acc.wardrobe: 0.7144, Acc.lamp: 0.8879, Acc.bathtub: 0.9089, Acc.railing: 0.6240, Acc.cushion: 0.8283, Acc.base: 0.6183, Acc.box: 0.5013, Acc.column: 0.7176, Acc.signboard: 0.5816, Acc.chest of drawers: 0.7081, Acc.counter: 0.6330, Acc.sand: 0.8533, Acc.sink: 0.8868, Acc.skyscraper: 0.6212, Acc.fireplace: 0.9235, Acc.refrigerator: 0.9367, Acc.grandstand: 0.8156, Acc.path: 0.4350, Acc.stairs: 0.4051, Acc.runway: 0.9380, Acc.case: 0.8151, Acc.pool table: 0.9824, Acc.pillow: 0.7581, Acc.screen door: 0.8497, Acc.stairway: 0.6253, Acc.river: 0.2555, Acc.bridge: 0.8025, Acc.bookcase: 0.5729, Acc.blind: 0.5287, Acc.coffee table: 0.8708, Acc.toilet: 0.9470, Acc.flower: 0.6797, Acc.book: 0.8119, Acc.hill: 0.1900, Acc.bench: 0.6773, Acc.countertop: 0.8503, Acc.stove: 0.9323, Acc.palm: 0.8409, Acc.kitchen island: 0.8583, Acc.computer: 0.9186, Acc.swivel chair: 0.7727, Acc.boat: 0.9302, Acc.bar: 0.8179, Acc.arcade machine: 0.8446, Acc.hovel: 0.5701, Acc.bus: 0.9721, Acc.towel: 0.8822, Acc.light: 0.7300, Acc.truck: 0.6203, Acc.tower: 0.6989, Acc.chandelier: 0.8534, Acc.awning: 0.5264, Acc.streetlight: 0.5291, Acc.booth: 0.7315, Acc.television receiver: 0.8676, Acc.airplane: 0.9656, Acc.dirt track: 0.2670, Acc.apparel: 0.8833, Acc.pole: 0.4172, Acc.land: 0.0844, Acc.bannister: 0.2707, Acc.escalator: 0.8605, Acc.ottoman: 0.7679, Acc.bottle: 0.7014, Acc.buffet: 0.6985, Acc.poster: 0.4464, Acc.stage: 0.3734, Acc.van: 0.7236, Acc.ship: 0.8670, Acc.fountain: 0.3139, Acc.conveyer belt: 0.9705, Acc.canopy: 0.7553, Acc.washer: 0.9144, Acc.plaything: 0.4711, Acc.swimming pool: 0.7843, Acc.stool: 0.7454, Acc.barrel: 0.9766, Acc.basket: 0.6233, Acc.waterfall: 0.6365, Acc.tent: 0.9884, Acc.bag: 0.3120, Acc.minibike: 0.9121, Acc.cradle: 0.9761, Acc.oven: 0.8009, Acc.ball: 0.6918, Acc.food: 0.7359, Acc.step: 0.1433, Acc.tank: 0.6697, Acc.trade name: 0.2877, Acc.microwave: 0.9649, Acc.pot: 0.7171, Acc.animal: 0.6184, Acc.bicycle: 0.7914, Acc.lake: 0.6370, Acc.dishwasher: 0.8364, Acc.screen: 0.8641, Acc.blanket: 0.4798, Acc.sculpture: 0.8799, Acc.hood: 0.7704, Acc.sconce: 0.7464, Acc.vase: 0.6995, Acc.traffic light: 0.6764, Acc.tray: 0.3718, Acc.ashcan: 0.6845, Acc.fan: 0.8604, Acc.pier: 0.4667, Acc.crt screen: 0.1020, Acc.plate: 0.8122, Acc.monitor: 0.5036, Acc.bulletin board: 0.7083, Acc.shower: 0.2457, Acc.radiator: 0.8139, Acc.glass: 0.2509, Acc.clock: 0.6816, Acc.flag: 0.8214 +2024-06-19 22:59:18,052 - mmseg - INFO - Iter [75050/80000] lr: 2.475e-06, eta: 2:56:06, time: 4.169, data_time: 2.201, memory: 72263, decode.loss_ce: 0.1175, decode.acc_seg: 94.7737, aux.loss_ce: 0.0511, aux.acc_seg: 94.3308, loss: 0.1686 +2024-06-19 23:00:57,342 - mmseg - INFO - Iter [75100/80000] lr: 2.451e-06, eta: 2:54:19, time: 1.986, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1205, decode.acc_seg: 94.6935, aux.loss_ce: 0.0521, aux.acc_seg: 94.2947, loss: 0.1725 +2024-06-19 23:02:36,251 - mmseg - INFO - Iter [75150/80000] lr: 2.426e-06, eta: 2:52:32, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1346, decode.acc_seg: 94.0957, aux.loss_ce: 0.0581, aux.acc_seg: 93.6485, loss: 0.1927 +2024-06-19 23:04:15,013 - mmseg - INFO - Iter [75200/80000] lr: 2.401e-06, eta: 2:50:44, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1239, decode.acc_seg: 94.4042, aux.loss_ce: 0.0535, aux.acc_seg: 93.9353, loss: 0.1773 +2024-06-19 23:05:53,940 - mmseg - INFO - Iter [75250/80000] lr: 2.376e-06, eta: 2:48:57, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1199, decode.acc_seg: 94.6812, aux.loss_ce: 0.0517, aux.acc_seg: 94.2402, loss: 0.1716 +2024-06-19 23:07:32,729 - mmseg - INFO - Iter [75300/80000] lr: 2.351e-06, eta: 2:47:10, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1264, decode.acc_seg: 94.3193, aux.loss_ce: 0.0544, aux.acc_seg: 93.8990, loss: 0.1808 +2024-06-19 23:09:11,610 - mmseg - INFO - Iter [75350/80000] lr: 2.325e-06, eta: 2:45:23, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1217, decode.acc_seg: 94.5509, aux.loss_ce: 0.0525, aux.acc_seg: 94.1224, loss: 0.1742 +2024-06-19 23:10:50,382 - mmseg - INFO - Iter [75400/80000] lr: 2.300e-06, eta: 2:43:36, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1310, decode.acc_seg: 94.2066, aux.loss_ce: 0.0566, aux.acc_seg: 93.7292, loss: 0.1876 +2024-06-19 23:12:29,320 - mmseg - INFO - Iter [75450/80000] lr: 2.275e-06, eta: 2:41:48, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1269, decode.acc_seg: 94.2796, aux.loss_ce: 0.0548, aux.acc_seg: 93.8915, loss: 0.1818 +2024-06-19 23:14:08,257 - mmseg - INFO - Iter [75500/80000] lr: 2.250e-06, eta: 2:40:01, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1207, decode.acc_seg: 94.7032, aux.loss_ce: 0.0521, aux.acc_seg: 94.2814, loss: 0.1728 +2024-06-19 23:15:47,135 - mmseg - INFO - Iter [75550/80000] lr: 2.226e-06, eta: 2:38:14, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1173, decode.acc_seg: 94.6053, aux.loss_ce: 0.0504, aux.acc_seg: 94.2222, loss: 0.1677 +2024-06-19 23:17:25,949 - mmseg - INFO - Iter [75600/80000] lr: 2.201e-06, eta: 2:36:27, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1229, decode.acc_seg: 94.5206, aux.loss_ce: 0.0531, aux.acc_seg: 94.1172, loss: 0.1760 +2024-06-19 23:19:04,808 - mmseg - INFO - Iter [75650/80000] lr: 2.176e-06, eta: 2:34:40, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1248, decode.acc_seg: 94.2228, aux.loss_ce: 0.0541, aux.acc_seg: 93.7420, loss: 0.1789 +2024-06-19 23:20:43,799 - mmseg - INFO - Iter [75700/80000] lr: 2.151e-06, eta: 2:32:53, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1240, decode.acc_seg: 94.2597, aux.loss_ce: 0.0534, aux.acc_seg: 93.8808, loss: 0.1774 +2024-06-19 23:22:22,625 - mmseg - INFO - Iter [75750/80000] lr: 2.125e-06, eta: 2:31:06, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1222, decode.acc_seg: 94.4788, aux.loss_ce: 0.0530, aux.acc_seg: 94.0411, loss: 0.1753 +2024-06-19 23:24:03,663 - mmseg - INFO - Iter [75800/80000] lr: 2.100e-06, eta: 2:29:19, time: 2.021, data_time: 0.052, memory: 72263, decode.loss_ce: 0.1285, decode.acc_seg: 94.2017, aux.loss_ce: 0.0557, aux.acc_seg: 93.6763, loss: 0.1841 +2024-06-19 23:25:42,478 - mmseg - INFO - Iter [75850/80000] lr: 2.075e-06, eta: 2:27:32, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1237, decode.acc_seg: 94.6254, aux.loss_ce: 0.0539, aux.acc_seg: 94.1856, loss: 0.1776 +2024-06-19 23:27:21,347 - mmseg - INFO - Iter [75900/80000] lr: 2.050e-06, eta: 2:25:45, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1175, decode.acc_seg: 94.8119, aux.loss_ce: 0.0508, aux.acc_seg: 94.4162, loss: 0.1683 +2024-06-19 23:29:00,178 - mmseg - INFO - Iter [75950/80000] lr: 2.026e-06, eta: 2:23:57, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1252, decode.acc_seg: 94.4117, aux.loss_ce: 0.0543, aux.acc_seg: 93.9632, loss: 0.1795 +2024-06-19 23:30:38,931 - mmseg - INFO - Saving checkpoint at 76000 iterations +2024-06-19 23:32:04,309 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 23:32:04,309 - mmseg - INFO - Iter [76000/80000] lr: 2.001e-06, eta: 2:22:15, time: 3.683, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1229, decode.acc_seg: 94.4584, aux.loss_ce: 0.0529, aux.acc_seg: 94.0438, loss: 0.1758 +2024-06-19 23:33:53,461 - mmseg - INFO - per class results: +2024-06-19 23:33:53,468 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.96 | 90.58 | +| building | 85.6 | 93.14 | +| sky | 94.93 | 97.66 | +| floor | 85.17 | 92.49 | +| tree | 78.15 | 90.06 | +| ceiling | 87.71 | 94.89 | +| road | 87.25 | 91.56 | +| bed | 93.3 | 97.25 | +| windowpane | 66.63 | 80.85 | +| grass | 68.87 | 81.88 | +| cabinet | 68.17 | 77.67 | +| sidewalk | 71.97 | 86.51 | +| person | 86.95 | 94.75 | +| earth | 40.69 | 53.05 | +| door | 60.62 | 75.06 | +| table | 71.54 | 82.14 | +| mountain | 62.37 | 72.94 | +| plant | 56.78 | 68.48 | +| curtain | 79.69 | 88.73 | +| chair | 69.13 | 79.67 | +| car | 88.78 | 94.76 | +| water | 65.25 | 81.65 | +| painting | 80.62 | 91.71 | +| sofa | 83.23 | 91.24 | +| shelf | 50.96 | 66.53 | +| house | 52.56 | 63.87 | +| sea | 73.19 | 83.06 | +| mirror | 78.19 | 86.22 | +| rug | 63.99 | 75.6 | +| field | 30.57 | 58.16 | +| armchair | 63.38 | 80.68 | +| seat | 68.84 | 89.57 | +| fence | 49.48 | 60.53 | +| desk | 59.47 | 80.89 | +| rock | 56.95 | 84.7 | +| wardrobe | 51.9 | 68.84 | +| lamp | 77.82 | 87.95 | +| bathtub | 88.02 | 91.2 | +| railing | 42.77 | 60.6 | +| cushion | 68.95 | 82.92 | +| base | 45.45 | 60.23 | +| box | 40.93 | 52.19 | +| column | 58.38 | 72.19 | +| signboard | 41.93 | 57.91 | +| chest of drawers | 45.45 | 69.25 | +| counter | 52.62 | 62.69 | +| sand | 58.52 | 85.63 | +| sink | 84.28 | 88.7 | +| skyscraper | 49.23 | 63.11 | +| fireplace | 75.26 | 92.83 | +| refrigerator | 86.76 | 94.58 | +| grandstand | 58.13 | 82.61 | +| path | 30.9 | 41.9 | +| stairs | 36.12 | 44.67 | +| runway | 72.88 | 93.96 | +| case | 63.29 | 83.13 | +| pool table | 95.47 | 98.3 | +| pillow | 65.26 | 75.29 | +| screen door | 89.92 | 92.71 | +| stairway | 42.32 | 56.24 | +| river | 12.68 | 24.0 | +| bridge | 69.88 | 76.74 | +| bookcase | 44.64 | 61.74 | +| blind | 45.58 | 53.94 | +| coffee table | 61.93 | 87.94 | +| toilet | 91.36 | 94.46 | +| flower | 48.05 | 62.0 | +| book | 58.53 | 79.84 | +| hill | 12.95 | 22.42 | +| bench | 58.75 | 67.1 | +| countertop | 65.33 | 84.15 | +| stove | 88.47 | 92.9 | +| palm | 52.78 | 81.83 | +| kitchen island | 58.98 | 85.46 | +| computer | 77.29 | 90.96 | +| swivel chair | 49.44 | 77.46 | +| boat | 80.5 | 92.61 | +| bar | 72.66 | 82.98 | +| arcade machine | 82.21 | 85.53 | +| hovel | 47.92 | 56.53 | +| bus | 94.01 | 97.27 | +| towel | 80.12 | 86.38 | +| light | 63.46 | 72.7 | +| truck | 52.87 | 65.13 | +| tower | 32.04 | 66.55 | +| chandelier | 73.54 | 84.8 | +| awning | 43.83 | 55.07 | +| streetlight | 37.33 | 49.09 | +| booth | 51.53 | 74.07 | +| television receiver | 79.77 | 87.15 | +| airplane | 86.89 | 96.57 | +| dirt track | 6.34 | 19.68 | +| apparel | 66.97 | 87.39 | +| pole | 27.85 | 37.83 | +| land | 5.81 | 8.22 | +| bannister | 22.48 | 27.19 | +| escalator | 66.89 | 85.91 | +| ottoman | 59.26 | 74.97 | +| bottle | 46.79 | 69.0 | +| buffet | 60.84 | 69.52 | +| poster | 36.72 | 43.96 | +| stage | 22.47 | 39.03 | +| van | 53.85 | 72.27 | +| ship | 73.55 | 87.41 | +| fountain | 30.74 | 31.37 | +| conveyer belt | 85.67 | 96.79 | +| canopy | 58.54 | 73.24 | +| washer | 86.17 | 91.67 | +| plaything | 34.5 | 46.0 | +| swimming pool | 54.03 | 77.93 | +| stool | 53.55 | 74.94 | +| barrel | 76.56 | 98.04 | +| basket | 43.41 | 61.51 | +| waterfall | 52.18 | 65.11 | +| tent | 92.8 | 98.82 | +| bag | 27.14 | 31.07 | +| minibike | 77.78 | 90.6 | +| cradle | 84.91 | 97.61 | +| oven | 68.09 | 77.93 | +| ball | 59.95 | 67.66 | +| food | 61.09 | 70.98 | +| step | 11.78 | 14.21 | +| tank | 63.12 | 67.64 | +| trade name | 27.09 | 32.55 | +| microwave | 90.0 | 96.69 | +| pot | 61.42 | 72.39 | +| animal | 59.93 | 61.15 | +| bicycle | 62.68 | 78.34 | +| lake | 52.22 | 63.75 | +| dishwasher | 75.86 | 84.26 | +| screen | 47.28 | 72.1 | +| blanket | 38.62 | 45.9 | +| sculpture | 73.85 | 87.34 | +| hood | 66.62 | 75.76 | +| sconce | 62.1 | 73.63 | +| vase | 51.58 | 67.67 | +| traffic light | 39.41 | 66.17 | +| tray | 26.27 | 35.15 | +| ashcan | 50.16 | 67.94 | +| fan | 73.59 | 84.97 | +| pier | 41.88 | 45.5 | +| crt screen | 7.01 | 16.53 | +| plate | 65.54 | 80.42 | +| monitor | 43.38 | 51.32 | +| bulletin board | 59.42 | 70.43 | +| shower | 19.68 | 24.65 | +| radiator | 69.33 | 81.2 | +| glass | 23.08 | 24.89 | +| clock | 57.88 | 68.09 | +| flag | 71.76 | 80.35 | ++---------------------+-------+-------+ +2024-06-19 23:33:53,468 - mmseg - INFO - Summary: +2024-06-19 23:33:53,468 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.73 | 59.68 | 71.99 | ++-------+-------+-------+ +2024-06-19 23:33:53,469 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 23:33:53,469 - mmseg - INFO - Iter(val) [250] aAcc: 0.8673, mIoU: 0.5968, mAcc: 0.7199, IoU.wall: 0.8296, IoU.building: 0.8560, IoU.sky: 0.9493, IoU.floor: 0.8517, IoU.tree: 0.7815, IoU.ceiling: 0.8771, IoU.road: 0.8725, IoU.bed : 0.9330, IoU.windowpane: 0.6663, IoU.grass: 0.6887, IoU.cabinet: 0.6817, IoU.sidewalk: 0.7197, IoU.person: 0.8695, IoU.earth: 0.4069, IoU.door: 0.6062, IoU.table: 0.7154, IoU.mountain: 0.6237, IoU.plant: 0.5678, IoU.curtain: 0.7969, IoU.chair: 0.6913, IoU.car: 0.8878, IoU.water: 0.6525, IoU.painting: 0.8062, IoU.sofa: 0.8323, IoU.shelf: 0.5096, IoU.house: 0.5256, IoU.sea: 0.7319, IoU.mirror: 0.7819, IoU.rug: 0.6399, IoU.field: 0.3057, IoU.armchair: 0.6338, IoU.seat: 0.6884, IoU.fence: 0.4948, IoU.desk: 0.5947, IoU.rock: 0.5695, IoU.wardrobe: 0.5190, IoU.lamp: 0.7782, IoU.bathtub: 0.8802, IoU.railing: 0.4277, IoU.cushion: 0.6895, IoU.base: 0.4545, IoU.box: 0.4093, IoU.column: 0.5838, IoU.signboard: 0.4193, IoU.chest of drawers: 0.4545, IoU.counter: 0.5262, IoU.sand: 0.5852, IoU.sink: 0.8428, IoU.skyscraper: 0.4923, IoU.fireplace: 0.7526, IoU.refrigerator: 0.8676, IoU.grandstand: 0.5813, IoU.path: 0.3090, IoU.stairs: 0.3612, IoU.runway: 0.7288, IoU.case: 0.6329, IoU.pool table: 0.9547, IoU.pillow: 0.6526, IoU.screen door: 0.8992, IoU.stairway: 0.4232, IoU.river: 0.1268, IoU.bridge: 0.6988, IoU.bookcase: 0.4464, IoU.blind: 0.4558, IoU.coffee table: 0.6193, IoU.toilet: 0.9136, IoU.flower: 0.4805, IoU.book: 0.5853, IoU.hill: 0.1295, IoU.bench: 0.5875, IoU.countertop: 0.6533, IoU.stove: 0.8847, IoU.palm: 0.5278, IoU.kitchen island: 0.5898, IoU.computer: 0.7729, IoU.swivel chair: 0.4944, IoU.boat: 0.8050, IoU.bar: 0.7266, IoU.arcade machine: 0.8221, IoU.hovel: 0.4792, IoU.bus: 0.9401, IoU.towel: 0.8012, IoU.light: 0.6346, IoU.truck: 0.5287, IoU.tower: 0.3204, IoU.chandelier: 0.7354, IoU.awning: 0.4383, IoU.streetlight: 0.3733, IoU.booth: 0.5153, IoU.television receiver: 0.7977, IoU.airplane: 0.8689, IoU.dirt track: 0.0634, IoU.apparel: 0.6697, IoU.pole: 0.2785, IoU.land: 0.0581, IoU.bannister: 0.2248, IoU.escalator: 0.6689, IoU.ottoman: 0.5926, IoU.bottle: 0.4679, IoU.buffet: 0.6084, IoU.poster: 0.3672, IoU.stage: 0.2247, IoU.van: 0.5385, IoU.ship: 0.7355, IoU.fountain: 0.3074, IoU.conveyer belt: 0.8567, IoU.canopy: 0.5854, IoU.washer: 0.8617, IoU.plaything: 0.3450, IoU.swimming pool: 0.5403, IoU.stool: 0.5355, IoU.barrel: 0.7656, IoU.basket: 0.4341, IoU.waterfall: 0.5218, IoU.tent: 0.9280, IoU.bag: 0.2714, IoU.minibike: 0.7778, IoU.cradle: 0.8491, IoU.oven: 0.6809, IoU.ball: 0.5995, IoU.food: 0.6109, IoU.step: 0.1178, IoU.tank: 0.6312, IoU.trade name: 0.2709, IoU.microwave: 0.9000, IoU.pot: 0.6142, IoU.animal: 0.5993, IoU.bicycle: 0.6268, IoU.lake: 0.5222, IoU.dishwasher: 0.7586, IoU.screen: 0.4728, IoU.blanket: 0.3862, IoU.sculpture: 0.7385, IoU.hood: 0.6662, IoU.sconce: 0.6210, IoU.vase: 0.5158, IoU.traffic light: 0.3941, IoU.tray: 0.2627, IoU.ashcan: 0.5016, IoU.fan: 0.7359, IoU.pier: 0.4188, IoU.crt screen: 0.0701, IoU.plate: 0.6554, IoU.monitor: 0.4338, IoU.bulletin board: 0.5942, IoU.shower: 0.1968, IoU.radiator: 0.6933, IoU.glass: 0.2308, IoU.clock: 0.5788, IoU.flag: 0.7176, Acc.wall: 0.9058, Acc.building: 0.9314, Acc.sky: 0.9766, Acc.floor: 0.9249, Acc.tree: 0.9006, Acc.ceiling: 0.9489, Acc.road: 0.9156, Acc.bed : 0.9725, Acc.windowpane: 0.8085, Acc.grass: 0.8188, Acc.cabinet: 0.7767, Acc.sidewalk: 0.8651, Acc.person: 0.9475, Acc.earth: 0.5305, Acc.door: 0.7506, Acc.table: 0.8214, Acc.mountain: 0.7294, Acc.plant: 0.6848, Acc.curtain: 0.8873, Acc.chair: 0.7967, Acc.car: 0.9476, Acc.water: 0.8165, Acc.painting: 0.9171, Acc.sofa: 0.9124, Acc.shelf: 0.6653, Acc.house: 0.6387, Acc.sea: 0.8306, Acc.mirror: 0.8622, Acc.rug: 0.7560, Acc.field: 0.5816, Acc.armchair: 0.8068, Acc.seat: 0.8957, Acc.fence: 0.6053, Acc.desk: 0.8089, Acc.rock: 0.8470, Acc.wardrobe: 0.6884, Acc.lamp: 0.8795, Acc.bathtub: 0.9120, Acc.railing: 0.6060, Acc.cushion: 0.8292, Acc.base: 0.6023, Acc.box: 0.5219, Acc.column: 0.7219, Acc.signboard: 0.5791, Acc.chest of drawers: 0.6925, Acc.counter: 0.6269, Acc.sand: 0.8563, Acc.sink: 0.8870, Acc.skyscraper: 0.6311, Acc.fireplace: 0.9283, Acc.refrigerator: 0.9458, Acc.grandstand: 0.8261, Acc.path: 0.4190, Acc.stairs: 0.4467, Acc.runway: 0.9396, Acc.case: 0.8313, Acc.pool table: 0.9830, Acc.pillow: 0.7529, Acc.screen door: 0.9271, Acc.stairway: 0.5624, Acc.river: 0.2400, Acc.bridge: 0.7674, Acc.bookcase: 0.6174, Acc.blind: 0.5394, Acc.coffee table: 0.8794, Acc.toilet: 0.9446, Acc.flower: 0.6200, Acc.book: 0.7984, Acc.hill: 0.2242, Acc.bench: 0.6710, Acc.countertop: 0.8415, Acc.stove: 0.9290, Acc.palm: 0.8183, Acc.kitchen island: 0.8546, Acc.computer: 0.9096, Acc.swivel chair: 0.7746, Acc.boat: 0.9261, Acc.bar: 0.8298, Acc.arcade machine: 0.8553, Acc.hovel: 0.5653, Acc.bus: 0.9727, Acc.towel: 0.8638, Acc.light: 0.7270, Acc.truck: 0.6513, Acc.tower: 0.6655, Acc.chandelier: 0.8480, Acc.awning: 0.5507, Acc.streetlight: 0.4909, Acc.booth: 0.7407, Acc.television receiver: 0.8715, Acc.airplane: 0.9657, Acc.dirt track: 0.1968, Acc.apparel: 0.8739, Acc.pole: 0.3783, Acc.land: 0.0822, Acc.bannister: 0.2719, Acc.escalator: 0.8591, Acc.ottoman: 0.7497, Acc.bottle: 0.6900, Acc.buffet: 0.6952, Acc.poster: 0.4396, Acc.stage: 0.3903, Acc.van: 0.7227, Acc.ship: 0.8741, Acc.fountain: 0.3137, Acc.conveyer belt: 0.9679, Acc.canopy: 0.7324, Acc.washer: 0.9167, Acc.plaything: 0.4600, Acc.swimming pool: 0.7793, Acc.stool: 0.7494, Acc.barrel: 0.9804, Acc.basket: 0.6151, Acc.waterfall: 0.6511, Acc.tent: 0.9882, Acc.bag: 0.3107, Acc.minibike: 0.9060, Acc.cradle: 0.9761, Acc.oven: 0.7793, Acc.ball: 0.6766, Acc.food: 0.7098, Acc.step: 0.1421, Acc.tank: 0.6764, Acc.trade name: 0.3255, Acc.microwave: 0.9669, Acc.pot: 0.7239, Acc.animal: 0.6115, Acc.bicycle: 0.7834, Acc.lake: 0.6375, Acc.dishwasher: 0.8426, Acc.screen: 0.7210, Acc.blanket: 0.4590, Acc.sculpture: 0.8734, Acc.hood: 0.7576, Acc.sconce: 0.7363, Acc.vase: 0.6767, Acc.traffic light: 0.6617, Acc.tray: 0.3515, Acc.ashcan: 0.6794, Acc.fan: 0.8497, Acc.pier: 0.4550, Acc.crt screen: 0.1653, Acc.plate: 0.8042, Acc.monitor: 0.5132, Acc.bulletin board: 0.7043, Acc.shower: 0.2465, Acc.radiator: 0.8120, Acc.glass: 0.2489, Acc.clock: 0.6809, Acc.flag: 0.8035 +2024-06-19 23:35:32,766 - mmseg - INFO - Iter [76050/80000] lr: 1.976e-06, eta: 2:20:34, time: 4.169, data_time: 2.199, memory: 72263, decode.loss_ce: 0.1310, decode.acc_seg: 94.4319, aux.loss_ce: 0.0563, aux.acc_seg: 94.0043, loss: 0.1873 +2024-06-19 23:37:11,614 - mmseg - INFO - Iter [76100/80000] lr: 1.951e-06, eta: 2:18:46, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1154, decode.acc_seg: 94.7366, aux.loss_ce: 0.0503, aux.acc_seg: 94.3083, loss: 0.1657 +2024-06-19 23:38:50,598 - mmseg - INFO - Iter [76150/80000] lr: 1.926e-06, eta: 2:16:59, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1265, decode.acc_seg: 94.3699, aux.loss_ce: 0.0550, aux.acc_seg: 93.9043, loss: 0.1815 +2024-06-19 23:40:29,420 - mmseg - INFO - Iter [76200/80000] lr: 1.900e-06, eta: 2:15:12, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1256, decode.acc_seg: 94.4559, aux.loss_ce: 0.0540, aux.acc_seg: 94.0754, loss: 0.1796 +2024-06-19 23:42:08,390 - mmseg - INFO - Iter [76250/80000] lr: 1.875e-06, eta: 2:13:25, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1297, decode.acc_seg: 94.4255, aux.loss_ce: 0.0562, aux.acc_seg: 93.9619, loss: 0.1859 +2024-06-19 23:43:47,308 - mmseg - INFO - Iter [76300/80000] lr: 1.850e-06, eta: 2:11:38, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1256, decode.acc_seg: 94.4487, aux.loss_ce: 0.0547, aux.acc_seg: 93.9350, loss: 0.1803 +2024-06-19 23:45:26,252 - mmseg - INFO - Iter [76350/80000] lr: 1.826e-06, eta: 2:09:51, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1290, decode.acc_seg: 94.0949, aux.loss_ce: 0.0559, aux.acc_seg: 93.5759, loss: 0.1849 +2024-06-19 23:47:05,247 - mmseg - INFO - Iter [76400/80000] lr: 1.801e-06, eta: 2:08:04, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1271, decode.acc_seg: 94.3487, aux.loss_ce: 0.0556, aux.acc_seg: 93.8575, loss: 0.1826 +2024-06-19 23:48:44,112 - mmseg - INFO - Iter [76450/80000] lr: 1.776e-06, eta: 2:06:17, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1233, decode.acc_seg: 94.6341, aux.loss_ce: 0.0532, aux.acc_seg: 94.2108, loss: 0.1765 +2024-06-19 23:50:22,945 - mmseg - INFO - Iter [76500/80000] lr: 1.751e-06, eta: 2:04:29, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1240, decode.acc_seg: 94.2473, aux.loss_ce: 0.0539, aux.acc_seg: 93.8030, loss: 0.1779 +2024-06-19 23:52:01,930 - mmseg - INFO - Iter [76550/80000] lr: 1.726e-06, eta: 2:02:42, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1195, decode.acc_seg: 94.6715, aux.loss_ce: 0.0519, aux.acc_seg: 94.2363, loss: 0.1715 +2024-06-19 23:53:40,767 - mmseg - INFO - Iter [76600/80000] lr: 1.700e-06, eta: 2:00:55, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1252, decode.acc_seg: 94.4576, aux.loss_ce: 0.0542, aux.acc_seg: 94.0403, loss: 0.1794 +2024-06-19 23:55:19,649 - mmseg - INFO - Iter [76650/80000] lr: 1.675e-06, eta: 1:59:08, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1293, decode.acc_seg: 94.2258, aux.loss_ce: 0.0558, aux.acc_seg: 93.7660, loss: 0.1851 +2024-06-19 23:56:58,548 - mmseg - INFO - Iter [76700/80000] lr: 1.650e-06, eta: 1:57:21, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1170, decode.acc_seg: 94.7500, aux.loss_ce: 0.0506, aux.acc_seg: 94.3532, loss: 0.1676 +2024-06-19 23:58:37,308 - mmseg - INFO - Iter [76750/80000] lr: 1.625e-06, eta: 1:55:34, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1176, decode.acc_seg: 94.6475, aux.loss_ce: 0.0508, aux.acc_seg: 94.1889, loss: 0.1684 +2024-06-20 00:00:16,237 - mmseg - INFO - Iter [76800/80000] lr: 1.601e-06, eta: 1:53:47, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1233, decode.acc_seg: 94.5027, aux.loss_ce: 0.0531, aux.acc_seg: 94.1082, loss: 0.1764 +2024-06-20 00:01:55,101 - mmseg - INFO - Iter [76850/80000] lr: 1.576e-06, eta: 1:52:00, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1288, decode.acc_seg: 94.1996, aux.loss_ce: 0.0560, aux.acc_seg: 93.7418, loss: 0.1848 +2024-06-20 00:03:34,122 - mmseg - INFO - Iter [76900/80000] lr: 1.551e-06, eta: 1:50:13, time: 1.980, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1204, decode.acc_seg: 94.4066, aux.loss_ce: 0.0523, aux.acc_seg: 93.9990, loss: 0.1727 +2024-06-20 00:05:13,058 - mmseg - INFO - Iter [76950/80000] lr: 1.526e-06, eta: 1:48:26, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1260, decode.acc_seg: 94.4063, aux.loss_ce: 0.0545, aux.acc_seg: 93.9232, loss: 0.1805 +2024-06-20 00:06:52,027 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-20 00:06:52,028 - mmseg - INFO - Iter [77000/80000] lr: 1.500e-06, eta: 1:46:39, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1268, decode.acc_seg: 94.3944, aux.loss_ce: 0.0546, aux.acc_seg: 93.9712, loss: 0.1814 +2024-06-20 00:08:44,045 - mmseg - INFO - per class results: +2024-06-20 00:08:44,051 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.9 | 90.5 | +| building | 85.45 | 93.17 | +| sky | 94.97 | 97.75 | +| floor | 85.14 | 92.52 | +| tree | 78.14 | 89.52 | +| ceiling | 87.64 | 94.79 | +| road | 86.81 | 92.28 | +| bed | 93.33 | 97.23 | +| windowpane | 66.6 | 81.52 | +| grass | 68.18 | 82.67 | +| cabinet | 68.18 | 77.28 | +| sidewalk | 71.62 | 86.23 | +| person | 86.92 | 94.6 | +| earth | 39.81 | 50.95 | +| door | 59.76 | 74.38 | +| table | 71.52 | 82.36 | +| mountain | 62.72 | 74.37 | +| plant | 56.18 | 66.9 | +| curtain | 79.73 | 88.32 | +| chair | 69.11 | 79.77 | +| car | 88.89 | 94.69 | +| water | 65.05 | 81.21 | +| painting | 80.48 | 91.74 | +| sofa | 82.74 | 89.69 | +| shelf | 51.05 | 67.69 | +| house | 53.13 | 64.78 | +| sea | 73.32 | 83.2 | +| mirror | 78.93 | 86.46 | +| rug | 63.2 | 74.1 | +| field | 30.31 | 57.01 | +| armchair | 63.6 | 81.21 | +| seat | 67.65 | 89.55 | +| fence | 48.59 | 58.67 | +| desk | 59.6 | 80.61 | +| rock | 56.77 | 84.8 | +| wardrobe | 54.22 | 73.09 | +| lamp | 77.81 | 88.28 | +| bathtub | 87.87 | 91.08 | +| railing | 42.36 | 61.49 | +| cushion | 68.7 | 83.51 | +| base | 45.46 | 60.02 | +| box | 40.98 | 52.83 | +| column | 57.77 | 70.34 | +| signboard | 41.8 | 58.22 | +| chest of drawers | 47.07 | 69.03 | +| counter | 53.58 | 64.47 | +| sand | 58.9 | 82.4 | +| sink | 83.97 | 89.22 | +| skyscraper | 48.83 | 63.65 | +| fireplace | 75.12 | 93.4 | +| refrigerator | 86.02 | 92.9 | +| grandstand | 58.42 | 82.33 | +| path | 31.06 | 41.86 | +| stairs | 34.52 | 42.24 | +| runway | 72.6 | 94.13 | +| case | 63.26 | 82.6 | +| pool table | 95.51 | 98.27 | +| pillow | 65.19 | 75.2 | +| screen door | 87.9 | 90.34 | +| stairway | 41.9 | 59.35 | +| river | 12.95 | 25.22 | +| bridge | 70.75 | 77.68 | +| bookcase | 44.71 | 62.9 | +| blind | 43.96 | 50.45 | +| coffee table | 61.58 | 87.23 | +| toilet | 91.41 | 94.29 | +| flower | 50.0 | 65.81 | +| book | 59.03 | 79.22 | +| hill | 13.13 | 22.44 | +| bench | 58.88 | 67.05 | +| countertop | 65.39 | 86.14 | +| stove | 88.61 | 93.07 | +| palm | 53.07 | 80.43 | +| kitchen island | 58.43 | 84.83 | +| computer | 77.14 | 90.76 | +| swivel chair | 48.19 | 72.82 | +| boat | 80.01 | 92.91 | +| bar | 72.41 | 83.31 | +| arcade machine | 82.68 | 85.96 | +| hovel | 48.22 | 56.42 | +| bus | 93.99 | 97.27 | +| towel | 80.32 | 86.29 | +| light | 63.4 | 72.66 | +| truck | 53.07 | 65.2 | +| tower | 32.58 | 67.23 | +| chandelier | 73.63 | 84.45 | +| awning | 43.2 | 55.26 | +| streetlight | 37.31 | 49.01 | +| booth | 54.21 | 73.1 | +| television receiver | 79.7 | 87.37 | +| airplane | 87.52 | 96.35 | +| dirt track | 8.51 | 14.38 | +| apparel | 66.84 | 84.84 | +| pole | 28.36 | 38.81 | +| land | 5.79 | 8.51 | +| bannister | 22.4 | 27.35 | +| escalator | 67.22 | 86.23 | +| ottoman | 58.74 | 75.96 | +| bottle | 47.08 | 69.89 | +| buffet | 57.17 | 64.65 | +| poster | 37.03 | 43.44 | +| stage | 22.88 | 40.43 | +| van | 53.33 | 73.11 | +| ship | 75.05 | 91.0 | +| fountain | 30.39 | 30.92 | +| conveyer belt | 85.7 | 96.78 | +| canopy | 59.57 | 75.39 | +| washer | 85.44 | 90.78 | +| plaything | 35.22 | 47.69 | +| swimming pool | 54.16 | 78.09 | +| stool | 55.9 | 73.53 | +| barrel | 77.98 | 97.96 | +| basket | 43.24 | 61.47 | +| waterfall | 52.12 | 64.53 | +| tent | 92.63 | 98.83 | +| bag | 27.37 | 31.37 | +| minibike | 77.52 | 91.66 | +| cradle | 86.15 | 97.64 | +| oven | 68.38 | 77.94 | +| ball | 59.22 | 66.83 | +| food | 62.12 | 73.14 | +| step | 11.85 | 14.21 | +| tank | 63.46 | 67.92 | +| trade name | 25.79 | 30.7 | +| microwave | 89.81 | 96.72 | +| pot | 61.62 | 72.49 | +| animal | 59.88 | 61.13 | +| bicycle | 62.76 | 78.25 | +| lake | 52.49 | 63.73 | +| dishwasher | 75.77 | 84.1 | +| screen | 48.84 | 74.17 | +| blanket | 37.11 | 43.51 | +| sculpture | 72.24 | 88.09 | +| hood | 68.15 | 76.65 | +| sconce | 62.58 | 75.09 | +| vase | 51.79 | 67.82 | +| traffic light | 39.88 | 67.17 | +| tray | 27.22 | 37.0 | +| ashcan | 50.72 | 66.08 | +| fan | 73.47 | 84.34 | +| pier | 41.15 | 46.01 | +| crt screen | 7.09 | 16.72 | +| plate | 65.58 | 80.18 | +| monitor | 40.61 | 48.26 | +| bulletin board | 58.31 | 68.85 | +| shower | 19.59 | 24.1 | +| radiator | 69.61 | 81.81 | +| glass | 23.04 | 24.76 | +| clock | 58.36 | 68.5 | +| flag | 72.27 | 79.89 | ++---------------------+-------+-------+ +2024-06-20 00:08:44,052 - mmseg - INFO - Summary: +2024-06-20 00:08:44,052 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.67 | 59.69 | 71.92 | ++-------+-------+-------+ +2024-06-20 00:08:44,053 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-20 00:08:44,053 - mmseg - INFO - Iter(val) [250] aAcc: 0.8667, mIoU: 0.5969, mAcc: 0.7192, IoU.wall: 0.8290, IoU.building: 0.8545, IoU.sky: 0.9497, IoU.floor: 0.8514, IoU.tree: 0.7814, IoU.ceiling: 0.8764, IoU.road: 0.8681, IoU.bed : 0.9333, IoU.windowpane: 0.6660, IoU.grass: 0.6818, IoU.cabinet: 0.6818, IoU.sidewalk: 0.7162, IoU.person: 0.8692, IoU.earth: 0.3981, IoU.door: 0.5976, IoU.table: 0.7152, IoU.mountain: 0.6272, IoU.plant: 0.5618, IoU.curtain: 0.7973, IoU.chair: 0.6911, IoU.car: 0.8889, IoU.water: 0.6505, IoU.painting: 0.8048, IoU.sofa: 0.8274, IoU.shelf: 0.5105, IoU.house: 0.5313, IoU.sea: 0.7332, IoU.mirror: 0.7893, IoU.rug: 0.6320, IoU.field: 0.3031, IoU.armchair: 0.6360, IoU.seat: 0.6765, IoU.fence: 0.4859, IoU.desk: 0.5960, IoU.rock: 0.5677, IoU.wardrobe: 0.5422, IoU.lamp: 0.7781, IoU.bathtub: 0.8787, IoU.railing: 0.4236, IoU.cushion: 0.6870, IoU.base: 0.4546, IoU.box: 0.4098, IoU.column: 0.5777, IoU.signboard: 0.4180, IoU.chest of drawers: 0.4707, IoU.counter: 0.5358, IoU.sand: 0.5890, IoU.sink: 0.8397, IoU.skyscraper: 0.4883, IoU.fireplace: 0.7512, IoU.refrigerator: 0.8602, IoU.grandstand: 0.5842, IoU.path: 0.3106, IoU.stairs: 0.3452, IoU.runway: 0.7260, IoU.case: 0.6326, IoU.pool table: 0.9551, IoU.pillow: 0.6519, IoU.screen door: 0.8790, IoU.stairway: 0.4190, IoU.river: 0.1295, IoU.bridge: 0.7075, IoU.bookcase: 0.4471, IoU.blind: 0.4396, IoU.coffee table: 0.6158, IoU.toilet: 0.9141, IoU.flower: 0.5000, IoU.book: 0.5903, IoU.hill: 0.1313, IoU.bench: 0.5888, IoU.countertop: 0.6539, IoU.stove: 0.8861, IoU.palm: 0.5307, IoU.kitchen island: 0.5843, IoU.computer: 0.7714, IoU.swivel chair: 0.4819, IoU.boat: 0.8001, IoU.bar: 0.7241, IoU.arcade machine: 0.8268, IoU.hovel: 0.4822, IoU.bus: 0.9399, IoU.towel: 0.8032, IoU.light: 0.6340, IoU.truck: 0.5307, IoU.tower: 0.3258, IoU.chandelier: 0.7363, IoU.awning: 0.4320, IoU.streetlight: 0.3731, IoU.booth: 0.5421, IoU.television receiver: 0.7970, IoU.airplane: 0.8752, IoU.dirt track: 0.0851, IoU.apparel: 0.6684, IoU.pole: 0.2836, IoU.land: 0.0579, IoU.bannister: 0.2240, IoU.escalator: 0.6722, IoU.ottoman: 0.5874, IoU.bottle: 0.4708, IoU.buffet: 0.5717, IoU.poster: 0.3703, IoU.stage: 0.2288, IoU.van: 0.5333, IoU.ship: 0.7505, IoU.fountain: 0.3039, IoU.conveyer belt: 0.8570, IoU.canopy: 0.5957, IoU.washer: 0.8544, IoU.plaything: 0.3522, IoU.swimming pool: 0.5416, IoU.stool: 0.5590, IoU.barrel: 0.7798, IoU.basket: 0.4324, IoU.waterfall: 0.5212, IoU.tent: 0.9263, IoU.bag: 0.2737, IoU.minibike: 0.7752, IoU.cradle: 0.8615, IoU.oven: 0.6838, IoU.ball: 0.5922, IoU.food: 0.6212, IoU.step: 0.1185, IoU.tank: 0.6346, IoU.trade name: 0.2579, IoU.microwave: 0.8981, IoU.pot: 0.6162, IoU.animal: 0.5988, IoU.bicycle: 0.6276, IoU.lake: 0.5249, IoU.dishwasher: 0.7577, IoU.screen: 0.4884, IoU.blanket: 0.3711, IoU.sculpture: 0.7224, IoU.hood: 0.6815, IoU.sconce: 0.6258, IoU.vase: 0.5179, IoU.traffic light: 0.3988, IoU.tray: 0.2722, IoU.ashcan: 0.5072, IoU.fan: 0.7347, IoU.pier: 0.4115, IoU.crt screen: 0.0709, IoU.plate: 0.6558, IoU.monitor: 0.4061, IoU.bulletin board: 0.5831, IoU.shower: 0.1959, IoU.radiator: 0.6961, IoU.glass: 0.2304, IoU.clock: 0.5836, IoU.flag: 0.7227, Acc.wall: 0.9050, Acc.building: 0.9317, Acc.sky: 0.9775, Acc.floor: 0.9252, Acc.tree: 0.8952, Acc.ceiling: 0.9479, Acc.road: 0.9228, Acc.bed : 0.9723, Acc.windowpane: 0.8152, Acc.grass: 0.8267, Acc.cabinet: 0.7728, Acc.sidewalk: 0.8623, Acc.person: 0.9460, Acc.earth: 0.5095, Acc.door: 0.7438, Acc.table: 0.8236, Acc.mountain: 0.7437, Acc.plant: 0.6690, Acc.curtain: 0.8832, Acc.chair: 0.7977, Acc.car: 0.9469, Acc.water: 0.8121, Acc.painting: 0.9174, Acc.sofa: 0.8969, Acc.shelf: 0.6769, Acc.house: 0.6478, Acc.sea: 0.8320, Acc.mirror: 0.8646, Acc.rug: 0.7410, Acc.field: 0.5701, Acc.armchair: 0.8121, Acc.seat: 0.8955, Acc.fence: 0.5867, Acc.desk: 0.8061, Acc.rock: 0.8480, Acc.wardrobe: 0.7309, Acc.lamp: 0.8828, Acc.bathtub: 0.9108, Acc.railing: 0.6149, Acc.cushion: 0.8351, Acc.base: 0.6002, Acc.box: 0.5283, Acc.column: 0.7034, Acc.signboard: 0.5822, Acc.chest of drawers: 0.6903, Acc.counter: 0.6447, Acc.sand: 0.8240, Acc.sink: 0.8922, Acc.skyscraper: 0.6365, Acc.fireplace: 0.9340, Acc.refrigerator: 0.9290, Acc.grandstand: 0.8233, Acc.path: 0.4186, Acc.stairs: 0.4224, Acc.runway: 0.9413, Acc.case: 0.8260, Acc.pool table: 0.9827, Acc.pillow: 0.7520, Acc.screen door: 0.9034, Acc.stairway: 0.5935, Acc.river: 0.2522, Acc.bridge: 0.7768, Acc.bookcase: 0.6290, Acc.blind: 0.5045, Acc.coffee table: 0.8723, Acc.toilet: 0.9429, Acc.flower: 0.6581, Acc.book: 0.7922, Acc.hill: 0.2244, Acc.bench: 0.6705, Acc.countertop: 0.8614, Acc.stove: 0.9307, Acc.palm: 0.8043, Acc.kitchen island: 0.8483, Acc.computer: 0.9076, Acc.swivel chair: 0.7282, Acc.boat: 0.9291, Acc.bar: 0.8331, Acc.arcade machine: 0.8596, Acc.hovel: 0.5642, Acc.bus: 0.9727, Acc.towel: 0.8629, Acc.light: 0.7266, Acc.truck: 0.6520, Acc.tower: 0.6723, Acc.chandelier: 0.8445, Acc.awning: 0.5526, Acc.streetlight: 0.4901, Acc.booth: 0.7310, Acc.television receiver: 0.8737, Acc.airplane: 0.9635, Acc.dirt track: 0.1438, Acc.apparel: 0.8484, Acc.pole: 0.3881, Acc.land: 0.0851, Acc.bannister: 0.2735, Acc.escalator: 0.8623, Acc.ottoman: 0.7596, Acc.bottle: 0.6989, Acc.buffet: 0.6465, Acc.poster: 0.4344, Acc.stage: 0.4043, Acc.van: 0.7311, Acc.ship: 0.9100, Acc.fountain: 0.3092, Acc.conveyer belt: 0.9678, Acc.canopy: 0.7539, Acc.washer: 0.9078, Acc.plaything: 0.4769, Acc.swimming pool: 0.7809, Acc.stool: 0.7353, Acc.barrel: 0.9796, Acc.basket: 0.6147, Acc.waterfall: 0.6453, Acc.tent: 0.9883, Acc.bag: 0.3137, Acc.minibike: 0.9166, Acc.cradle: 0.9764, Acc.oven: 0.7794, Acc.ball: 0.6683, Acc.food: 0.7314, Acc.step: 0.1421, Acc.tank: 0.6792, Acc.trade name: 0.3070, Acc.microwave: 0.9672, Acc.pot: 0.7249, Acc.animal: 0.6113, Acc.bicycle: 0.7825, Acc.lake: 0.6373, Acc.dishwasher: 0.8410, Acc.screen: 0.7417, Acc.blanket: 0.4351, Acc.sculpture: 0.8809, Acc.hood: 0.7665, Acc.sconce: 0.7509, Acc.vase: 0.6782, Acc.traffic light: 0.6717, Acc.tray: 0.3700, Acc.ashcan: 0.6608, Acc.fan: 0.8434, Acc.pier: 0.4601, Acc.crt screen: 0.1672, Acc.plate: 0.8018, Acc.monitor: 0.4826, Acc.bulletin board: 0.6885, Acc.shower: 0.2410, Acc.radiator: 0.8181, Acc.glass: 0.2476, Acc.clock: 0.6850, Acc.flag: 0.7989 +2024-06-20 00:10:26,453 - mmseg - INFO - Iter [77050/80000] lr: 1.475e-06, eta: 1:44:57, time: 4.289, data_time: 2.319, memory: 72263, decode.loss_ce: 0.1165, decode.acc_seg: 94.6626, aux.loss_ce: 0.0506, aux.acc_seg: 94.2163, loss: 0.1671 +2024-06-20 00:12:05,251 - mmseg - INFO - Iter [77100/80000] lr: 1.450e-06, eta: 1:43:10, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1221, decode.acc_seg: 94.5780, aux.loss_ce: 0.0529, aux.acc_seg: 94.1047, loss: 0.1750 +2024-06-20 00:13:44,177 - mmseg - INFO - Iter [77150/80000] lr: 1.425e-06, eta: 1:41:23, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1228, decode.acc_seg: 94.5355, aux.loss_ce: 0.0537, aux.acc_seg: 94.0899, loss: 0.1765 +2024-06-20 00:15:23,065 - mmseg - INFO - Iter [77200/80000] lr: 1.401e-06, eta: 1:39:36, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1196, decode.acc_seg: 94.4785, aux.loss_ce: 0.0519, aux.acc_seg: 94.0241, loss: 0.1715 +2024-06-20 00:17:01,960 - mmseg - INFO - Iter [77250/80000] lr: 1.376e-06, eta: 1:37:49, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1172, decode.acc_seg: 94.7702, aux.loss_ce: 0.0510, aux.acc_seg: 94.3345, loss: 0.1682 +2024-06-20 00:18:40,887 - mmseg - INFO - Iter [77300/80000] lr: 1.351e-06, eta: 1:36:02, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1220, decode.acc_seg: 94.5156, aux.loss_ce: 0.0529, aux.acc_seg: 94.0861, loss: 0.1749 +2024-06-20 00:20:19,790 - mmseg - INFO - Iter [77350/80000] lr: 1.326e-06, eta: 1:34:15, time: 1.978, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1174, decode.acc_seg: 94.6954, aux.loss_ce: 0.0513, aux.acc_seg: 94.2131, loss: 0.1687 +2024-06-20 00:21:58,658 - mmseg - INFO - Iter [77400/80000] lr: 1.301e-06, eta: 1:32:28, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1197, decode.acc_seg: 94.5454, aux.loss_ce: 0.0520, aux.acc_seg: 94.1147, loss: 0.1716 +2024-06-20 00:23:37,690 - mmseg - INFO - Iter [77450/80000] lr: 1.275e-06, eta: 1:30:41, time: 1.981, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1218, decode.acc_seg: 94.4579, aux.loss_ce: 0.0527, aux.acc_seg: 94.0040, loss: 0.1745 +2024-06-20 00:25:16,493 - mmseg - INFO - Iter [77500/80000] lr: 1.250e-06, eta: 1:28:54, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1161, decode.acc_seg: 94.7943, aux.loss_ce: 0.0505, aux.acc_seg: 94.3436, loss: 0.1666 +2024-06-20 00:26:55,481 - mmseg - INFO - Iter [77550/80000] lr: 1.225e-06, eta: 1:27:07, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1179, decode.acc_seg: 94.6135, aux.loss_ce: 0.0512, aux.acc_seg: 94.1873, loss: 0.1691 +2024-06-20 00:28:34,352 - mmseg - INFO - Iter [77600/80000] lr: 1.200e-06, eta: 1:25:20, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1223, decode.acc_seg: 94.5644, aux.loss_ce: 0.0531, aux.acc_seg: 94.0889, loss: 0.1754 +2024-06-20 00:30:13,357 - mmseg - INFO - Iter [77650/80000] lr: 1.176e-06, eta: 1:23:33, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1243, decode.acc_seg: 94.3987, aux.loss_ce: 0.0540, aux.acc_seg: 93.9614, loss: 0.1783 +2024-06-20 00:31:52,212 - mmseg - INFO - Iter [77700/80000] lr: 1.151e-06, eta: 1:21:46, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1189, decode.acc_seg: 94.7073, aux.loss_ce: 0.0515, aux.acc_seg: 94.2729, loss: 0.1704 +2024-06-20 00:33:31,135 - mmseg - INFO - Iter [77750/80000] lr: 1.126e-06, eta: 1:19:59, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1214, decode.acc_seg: 94.5779, aux.loss_ce: 0.0523, aux.acc_seg: 94.2061, loss: 0.1737 +2024-06-20 00:35:10,039 - mmseg - INFO - Iter [77800/80000] lr: 1.101e-06, eta: 1:18:12, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1192, decode.acc_seg: 94.6243, aux.loss_ce: 0.0519, aux.acc_seg: 94.1926, loss: 0.1711 +2024-06-20 00:36:48,941 - mmseg - INFO - Iter [77850/80000] lr: 1.075e-06, eta: 1:16:26, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1181, decode.acc_seg: 94.7176, aux.loss_ce: 0.0513, aux.acc_seg: 94.2758, loss: 0.1695 +2024-06-20 00:38:27,697 - mmseg - INFO - Iter [77900/80000] lr: 1.050e-06, eta: 1:14:39, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1176, decode.acc_seg: 94.6331, aux.loss_ce: 0.0509, aux.acc_seg: 94.2162, loss: 0.1685 +2024-06-20 00:40:06,543 - mmseg - INFO - Iter [77950/80000] lr: 1.025e-06, eta: 1:12:52, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1147, decode.acc_seg: 94.7071, aux.loss_ce: 0.0497, aux.acc_seg: 94.2988, loss: 0.1645 +2024-06-20 00:41:45,476 - mmseg - INFO - Saving checkpoint at 78000 iterations +2024-06-20 00:43:11,308 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-20 00:43:11,308 - mmseg - INFO - Iter [78000/80000] lr: 1.000e-06, eta: 1:11:07, time: 3.695, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1241, decode.acc_seg: 94.5562, aux.loss_ce: 0.0536, aux.acc_seg: 94.1365, loss: 0.1777 +2024-06-20 00:45:02,229 - mmseg - INFO - per class results: +2024-06-20 00:45:02,235 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.01 | 90.59 | +| building | 85.67 | 93.2 | +| sky | 94.97 | 97.73 | +| floor | 85.13 | 92.58 | +| tree | 78.14 | 89.82 | +| ceiling | 87.7 | 94.62 | +| road | 86.9 | 92.33 | +| bed | 93.39 | 97.32 | +| windowpane | 66.63 | 81.58 | +| grass | 68.09 | 82.55 | +| cabinet | 67.8 | 76.95 | +| sidewalk | 71.59 | 85.84 | +| person | 87.03 | 94.67 | +| earth | 39.86 | 51.21 | +| door | 59.99 | 75.1 | +| table | 71.54 | 82.29 | +| mountain | 62.86 | 74.21 | +| plant | 56.38 | 67.35 | +| curtain | 79.47 | 88.41 | +| chair | 69.42 | 80.06 | +| car | 88.9 | 94.53 | +| water | 63.2 | 78.59 | +| painting | 80.38 | 92.23 | +| sofa | 83.34 | 90.72 | +| shelf | 50.51 | 65.79 | +| house | 54.08 | 66.11 | +| sea | 71.02 | 84.48 | +| mirror | 78.44 | 85.53 | +| rug | 63.39 | 74.64 | +| field | 30.21 | 56.92 | +| armchair | 63.85 | 81.06 | +| seat | 68.7 | 89.17 | +| fence | 49.47 | 60.83 | +| desk | 59.48 | 80.3 | +| rock | 56.88 | 85.56 | +| wardrobe | 53.53 | 71.91 | +| lamp | 77.86 | 88.29 | +| bathtub | 87.84 | 90.87 | +| railing | 42.55 | 61.15 | +| cushion | 69.33 | 82.65 | +| base | 45.28 | 60.31 | +| box | 40.73 | 51.62 | +| column | 57.8 | 70.85 | +| signboard | 41.85 | 57.62 | +| chest of drawers | 46.46 | 72.45 | +| counter | 53.48 | 63.18 | +| sand | 58.99 | 84.21 | +| sink | 84.1 | 89.29 | +| skyscraper | 48.77 | 63.15 | +| fireplace | 75.07 | 93.08 | +| refrigerator | 86.65 | 94.02 | +| grandstand | 58.82 | 81.5 | +| path | 31.36 | 41.64 | +| stairs | 36.11 | 45.08 | +| runway | 72.57 | 93.83 | +| case | 63.05 | 82.99 | +| pool table | 95.53 | 98.23 | +| pillow | 65.68 | 76.22 | +| screen door | 87.63 | 90.16 | +| stairway | 42.24 | 57.94 | +| river | 13.37 | 25.6 | +| bridge | 72.26 | 80.13 | +| bookcase | 45.15 | 63.7 | +| blind | 42.77 | 48.53 | +| coffee table | 61.15 | 86.39 | +| toilet | 91.42 | 94.49 | +| flower | 49.74 | 66.97 | +| book | 59.02 | 79.62 | +| hill | 13.06 | 20.42 | +| bench | 58.78 | 67.11 | +| countertop | 65.61 | 85.02 | +| stove | 88.53 | 93.01 | +| palm | 52.71 | 80.98 | +| kitchen island | 58.73 | 86.87 | +| computer | 76.9 | 91.44 | +| swivel chair | 49.66 | 76.94 | +| boat | 80.65 | 92.68 | +| bar | 72.51 | 83.14 | +| arcade machine | 81.71 | 85.07 | +| hovel | 48.22 | 56.44 | +| bus | 94.02 | 97.09 | +| towel | 80.47 | 87.78 | +| light | 63.4 | 72.58 | +| truck | 52.65 | 65.0 | +| tower | 33.12 | 64.03 | +| chandelier | 73.66 | 84.26 | +| awning | 43.74 | 56.19 | +| streetlight | 37.95 | 50.8 | +| booth | 57.68 | 72.26 | +| television receiver | 79.85 | 87.04 | +| airplane | 88.0 | 95.97 | +| dirt track | 7.98 | 13.58 | +| apparel | 66.59 | 87.62 | +| pole | 28.98 | 39.42 | +| land | 5.68 | 8.39 | +| bannister | 21.99 | 26.41 | +| escalator | 67.33 | 85.71 | +| ottoman | 58.64 | 75.06 | +| bottle | 46.7 | 71.62 | +| buffet | 56.35 | 63.85 | +| poster | 37.39 | 44.14 | +| stage | 22.43 | 39.75 | +| van | 53.8 | 74.45 | +| ship | 75.43 | 90.53 | +| fountain | 30.94 | 31.49 | +| conveyer belt | 86.08 | 96.59 | +| canopy | 58.28 | 73.3 | +| washer | 86.17 | 91.51 | +| plaything | 35.0 | 47.24 | +| swimming pool | 54.95 | 79.34 | +| stool | 55.24 | 73.4 | +| barrel | 78.45 | 97.83 | +| basket | 43.53 | 62.11 | +| waterfall | 52.61 | 64.83 | +| tent | 92.6 | 98.84 | +| bag | 27.61 | 31.67 | +| minibike | 77.89 | 91.0 | +| cradle | 86.61 | 97.38 | +| oven | 69.89 | 80.1 | +| ball | 56.66 | 61.41 | +| food | 61.5 | 71.32 | +| step | 11.82 | 14.09 | +| tank | 63.46 | 67.91 | +| trade name | 26.54 | 31.18 | +| microwave | 90.4 | 96.61 | +| pot | 60.99 | 71.33 | +| animal | 59.88 | 61.14 | +| bicycle | 62.76 | 78.49 | +| lake | 52.25 | 63.73 | +| dishwasher | 75.44 | 83.02 | +| screen | 47.1 | 70.97 | +| blanket | 38.52 | 45.33 | +| sculpture | 71.98 | 88.38 | +| hood | 67.37 | 75.72 | +| sconce | 62.29 | 74.88 | +| vase | 51.38 | 69.92 | +| traffic light | 40.43 | 65.85 | +| tray | 26.15 | 34.94 | +| ashcan | 50.69 | 67.07 | +| fan | 73.26 | 83.62 | +| pier | 41.2 | 46.05 | +| crt screen | 7.55 | 18.42 | +| plate | 65.06 | 81.08 | +| monitor | 40.5 | 47.59 | +| bulletin board | 58.43 | 68.43 | +| shower | 20.58 | 24.41 | +| radiator | 69.2 | 82.09 | +| glass | 23.29 | 25.13 | +| clock | 58.03 | 67.98 | +| flag | 72.31 | 80.18 | ++---------------------+-------+-------+ +2024-06-20 00:45:02,235 - mmseg - INFO - Summary: +2024-06-20 00:45:02,235 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 86.7 | 59.73 | 71.93 | ++------+-------+-------+ +2024-06-20 00:45:02,236 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-20 00:45:02,236 - mmseg - INFO - Iter(val) [250] aAcc: 0.8670, mIoU: 0.5973, mAcc: 0.7193, IoU.wall: 0.8301, IoU.building: 0.8567, IoU.sky: 0.9497, IoU.floor: 0.8513, IoU.tree: 0.7814, IoU.ceiling: 0.8770, IoU.road: 0.8690, IoU.bed : 0.9339, IoU.windowpane: 0.6663, IoU.grass: 0.6809, IoU.cabinet: 0.6780, IoU.sidewalk: 0.7159, IoU.person: 0.8703, IoU.earth: 0.3986, IoU.door: 0.5999, IoU.table: 0.7154, IoU.mountain: 0.6286, IoU.plant: 0.5638, IoU.curtain: 0.7947, IoU.chair: 0.6942, IoU.car: 0.8890, IoU.water: 0.6320, IoU.painting: 0.8038, IoU.sofa: 0.8334, IoU.shelf: 0.5051, IoU.house: 0.5408, IoU.sea: 0.7102, IoU.mirror: 0.7844, IoU.rug: 0.6339, IoU.field: 0.3021, IoU.armchair: 0.6385, IoU.seat: 0.6870, IoU.fence: 0.4947, IoU.desk: 0.5948, IoU.rock: 0.5688, IoU.wardrobe: 0.5353, IoU.lamp: 0.7786, IoU.bathtub: 0.8784, IoU.railing: 0.4255, IoU.cushion: 0.6933, IoU.base: 0.4528, IoU.box: 0.4073, IoU.column: 0.5780, IoU.signboard: 0.4185, IoU.chest of drawers: 0.4646, IoU.counter: 0.5348, IoU.sand: 0.5899, IoU.sink: 0.8410, IoU.skyscraper: 0.4877, IoU.fireplace: 0.7507, IoU.refrigerator: 0.8665, IoU.grandstand: 0.5882, IoU.path: 0.3136, IoU.stairs: 0.3611, IoU.runway: 0.7257, IoU.case: 0.6305, IoU.pool table: 0.9553, IoU.pillow: 0.6568, IoU.screen door: 0.8763, IoU.stairway: 0.4224, IoU.river: 0.1337, IoU.bridge: 0.7226, IoU.bookcase: 0.4515, IoU.blind: 0.4277, IoU.coffee table: 0.6115, IoU.toilet: 0.9142, IoU.flower: 0.4974, IoU.book: 0.5902, IoU.hill: 0.1306, IoU.bench: 0.5878, IoU.countertop: 0.6561, IoU.stove: 0.8853, IoU.palm: 0.5271, IoU.kitchen island: 0.5873, IoU.computer: 0.7690, IoU.swivel chair: 0.4966, IoU.boat: 0.8065, IoU.bar: 0.7251, IoU.arcade machine: 0.8171, IoU.hovel: 0.4822, IoU.bus: 0.9402, IoU.towel: 0.8047, IoU.light: 0.6340, IoU.truck: 0.5265, IoU.tower: 0.3312, IoU.chandelier: 0.7366, IoU.awning: 0.4374, IoU.streetlight: 0.3795, IoU.booth: 0.5768, IoU.television receiver: 0.7985, IoU.airplane: 0.8800, IoU.dirt track: 0.0798, IoU.apparel: 0.6659, IoU.pole: 0.2898, IoU.land: 0.0568, IoU.bannister: 0.2199, IoU.escalator: 0.6733, IoU.ottoman: 0.5864, IoU.bottle: 0.4670, IoU.buffet: 0.5635, IoU.poster: 0.3739, IoU.stage: 0.2243, IoU.van: 0.5380, IoU.ship: 0.7543, IoU.fountain: 0.3094, IoU.conveyer belt: 0.8608, IoU.canopy: 0.5828, IoU.washer: 0.8617, IoU.plaything: 0.3500, IoU.swimming pool: 0.5495, IoU.stool: 0.5524, IoU.barrel: 0.7845, IoU.basket: 0.4353, IoU.waterfall: 0.5261, IoU.tent: 0.9260, IoU.bag: 0.2761, IoU.minibike: 0.7789, IoU.cradle: 0.8661, IoU.oven: 0.6989, IoU.ball: 0.5666, IoU.food: 0.6150, IoU.step: 0.1182, IoU.tank: 0.6346, IoU.trade name: 0.2654, IoU.microwave: 0.9040, IoU.pot: 0.6099, IoU.animal: 0.5988, IoU.bicycle: 0.6276, IoU.lake: 0.5225, IoU.dishwasher: 0.7544, IoU.screen: 0.4710, IoU.blanket: 0.3852, IoU.sculpture: 0.7198, IoU.hood: 0.6737, IoU.sconce: 0.6229, IoU.vase: 0.5138, IoU.traffic light: 0.4043, IoU.tray: 0.2615, IoU.ashcan: 0.5069, IoU.fan: 0.7326, IoU.pier: 0.4120, IoU.crt screen: 0.0755, IoU.plate: 0.6506, IoU.monitor: 0.4050, IoU.bulletin board: 0.5843, IoU.shower: 0.2058, IoU.radiator: 0.6920, IoU.glass: 0.2329, IoU.clock: 0.5803, IoU.flag: 0.7231, Acc.wall: 0.9059, Acc.building: 0.9320, Acc.sky: 0.9773, Acc.floor: 0.9258, Acc.tree: 0.8982, Acc.ceiling: 0.9462, Acc.road: 0.9233, Acc.bed : 0.9732, Acc.windowpane: 0.8158, Acc.grass: 0.8255, Acc.cabinet: 0.7695, Acc.sidewalk: 0.8584, Acc.person: 0.9467, Acc.earth: 0.5121, Acc.door: 0.7510, Acc.table: 0.8229, Acc.mountain: 0.7421, Acc.plant: 0.6735, Acc.curtain: 0.8841, Acc.chair: 0.8006, Acc.car: 0.9453, Acc.water: 0.7859, Acc.painting: 0.9223, Acc.sofa: 0.9072, Acc.shelf: 0.6579, Acc.house: 0.6611, Acc.sea: 0.8448, Acc.mirror: 0.8553, Acc.rug: 0.7464, Acc.field: 0.5692, Acc.armchair: 0.8106, Acc.seat: 0.8917, Acc.fence: 0.6083, Acc.desk: 0.8030, Acc.rock: 0.8556, Acc.wardrobe: 0.7191, Acc.lamp: 0.8829, Acc.bathtub: 0.9087, Acc.railing: 0.6115, Acc.cushion: 0.8265, Acc.base: 0.6031, Acc.box: 0.5162, Acc.column: 0.7085, Acc.signboard: 0.5762, Acc.chest of drawers: 0.7245, Acc.counter: 0.6318, Acc.sand: 0.8421, Acc.sink: 0.8929, Acc.skyscraper: 0.6315, Acc.fireplace: 0.9308, Acc.refrigerator: 0.9402, Acc.grandstand: 0.8150, Acc.path: 0.4164, Acc.stairs: 0.4508, Acc.runway: 0.9383, Acc.case: 0.8299, Acc.pool table: 0.9823, Acc.pillow: 0.7622, Acc.screen door: 0.9016, Acc.stairway: 0.5794, Acc.river: 0.2560, Acc.bridge: 0.8013, Acc.bookcase: 0.6370, Acc.blind: 0.4853, Acc.coffee table: 0.8639, Acc.toilet: 0.9449, Acc.flower: 0.6697, Acc.book: 0.7962, Acc.hill: 0.2042, Acc.bench: 0.6711, Acc.countertop: 0.8502, Acc.stove: 0.9301, Acc.palm: 0.8098, Acc.kitchen island: 0.8687, Acc.computer: 0.9144, Acc.swivel chair: 0.7694, Acc.boat: 0.9268, Acc.bar: 0.8314, Acc.arcade machine: 0.8507, Acc.hovel: 0.5644, Acc.bus: 0.9709, Acc.towel: 0.8778, Acc.light: 0.7258, Acc.truck: 0.6500, Acc.tower: 0.6403, Acc.chandelier: 0.8426, Acc.awning: 0.5619, Acc.streetlight: 0.5080, Acc.booth: 0.7226, Acc.television receiver: 0.8704, Acc.airplane: 0.9597, Acc.dirt track: 0.1358, Acc.apparel: 0.8762, Acc.pole: 0.3942, Acc.land: 0.0839, Acc.bannister: 0.2641, Acc.escalator: 0.8571, Acc.ottoman: 0.7506, Acc.bottle: 0.7162, Acc.buffet: 0.6385, Acc.poster: 0.4414, Acc.stage: 0.3975, Acc.van: 0.7445, Acc.ship: 0.9053, Acc.fountain: 0.3149, Acc.conveyer belt: 0.9659, Acc.canopy: 0.7330, Acc.washer: 0.9151, Acc.plaything: 0.4724, Acc.swimming pool: 0.7934, Acc.stool: 0.7340, Acc.barrel: 0.9783, Acc.basket: 0.6211, Acc.waterfall: 0.6483, Acc.tent: 0.9884, Acc.bag: 0.3167, Acc.minibike: 0.9100, Acc.cradle: 0.9738, Acc.oven: 0.8010, Acc.ball: 0.6141, Acc.food: 0.7132, Acc.step: 0.1409, Acc.tank: 0.6791, Acc.trade name: 0.3118, Acc.microwave: 0.9661, Acc.pot: 0.7133, Acc.animal: 0.6114, Acc.bicycle: 0.7849, Acc.lake: 0.6373, Acc.dishwasher: 0.8302, Acc.screen: 0.7097, Acc.blanket: 0.4533, Acc.sculpture: 0.8838, Acc.hood: 0.7572, Acc.sconce: 0.7488, Acc.vase: 0.6992, Acc.traffic light: 0.6585, Acc.tray: 0.3494, Acc.ashcan: 0.6707, Acc.fan: 0.8362, Acc.pier: 0.4605, Acc.crt screen: 0.1842, Acc.plate: 0.8108, Acc.monitor: 0.4759, Acc.bulletin board: 0.6843, Acc.shower: 0.2441, Acc.radiator: 0.8209, Acc.glass: 0.2513, Acc.clock: 0.6798, Acc.flag: 0.8018 +2024-06-20 00:46:41,452 - mmseg - INFO - Iter [78050/80000] lr: 9.755e-07, eta: 1:09:23, time: 4.203, data_time: 2.237, memory: 72263, decode.loss_ce: 0.1173, decode.acc_seg: 94.7958, aux.loss_ce: 0.0513, aux.acc_seg: 94.3235, loss: 0.1686 +2024-06-20 00:48:20,640 - mmseg - INFO - Iter [78100/80000] lr: 9.505e-07, eta: 1:07:36, time: 1.984, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1200, decode.acc_seg: 94.5919, aux.loss_ce: 0.0522, aux.acc_seg: 94.1583, loss: 0.1723 +2024-06-20 00:49:59,713 - mmseg - INFO - Iter [78150/80000] lr: 9.255e-07, eta: 1:05:49, time: 1.981, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1238, decode.acc_seg: 94.5920, aux.loss_ce: 0.0533, aux.acc_seg: 94.1629, loss: 0.1771 +2024-06-20 00:51:38,692 - mmseg - INFO - Iter [78200/80000] lr: 9.005e-07, eta: 1:04:02, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1269, decode.acc_seg: 94.3659, aux.loss_ce: 0.0546, aux.acc_seg: 93.9286, loss: 0.1815 +2024-06-20 00:53:17,634 - mmseg - INFO - Iter [78250/80000] lr: 8.755e-07, eta: 1:02:15, time: 1.979, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1304, decode.acc_seg: 94.2537, aux.loss_ce: 0.0563, aux.acc_seg: 93.8023, loss: 0.1867 +2024-06-20 00:54:56,595 - mmseg - INFO - Iter [78300/80000] lr: 8.505e-07, eta: 1:00:28, time: 1.979, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1265, decode.acc_seg: 94.3568, aux.loss_ce: 0.0547, aux.acc_seg: 93.8817, loss: 0.1812 +2024-06-20 00:56:37,951 - mmseg - INFO - Iter [78350/80000] lr: 8.255e-07, eta: 0:58:42, time: 2.027, data_time: 0.057, memory: 72263, decode.loss_ce: 0.1188, decode.acc_seg: 94.7023, aux.loss_ce: 0.0519, aux.acc_seg: 94.2366, loss: 0.1708 +2024-06-20 00:58:16,876 - mmseg - INFO - Iter [78400/80000] lr: 8.005e-07, eta: 0:56:55, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1206, decode.acc_seg: 94.5071, aux.loss_ce: 0.0523, aux.acc_seg: 94.0606, loss: 0.1728 +2024-06-20 00:59:55,926 - mmseg - INFO - Iter [78450/80000] lr: 7.755e-07, eta: 0:55:08, time: 1.981, data_time: 0.012, memory: 72263, decode.loss_ce: 0.1162, decode.acc_seg: 94.6911, aux.loss_ce: 0.0505, aux.acc_seg: 94.2407, loss: 0.1667 +2024-06-20 01:01:34,759 - mmseg - INFO - Iter [78500/80000] lr: 7.505e-07, eta: 0:53:21, time: 1.977, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1191, decode.acc_seg: 94.6506, aux.loss_ce: 0.0512, aux.acc_seg: 94.2690, loss: 0.1703 +2024-06-20 01:03:13,748 - mmseg - INFO - Iter [78550/80000] lr: 7.255e-07, eta: 0:51:34, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1253, decode.acc_seg: 94.2626, aux.loss_ce: 0.0549, aux.acc_seg: 93.7672, loss: 0.1802 +2024-06-20 01:04:52,718 - mmseg - INFO - Iter [78600/80000] lr: 7.005e-07, eta: 0:49:47, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1163, decode.acc_seg: 94.7531, aux.loss_ce: 0.0505, aux.acc_seg: 94.3708, loss: 0.1668 +2024-06-20 01:06:31,646 - mmseg - INFO - Iter [78650/80000] lr: 6.755e-07, eta: 0:48:00, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1162, decode.acc_seg: 94.7397, aux.loss_ce: 0.0506, aux.acc_seg: 94.2809, loss: 0.1668 +2024-06-20 01:08:10,535 - mmseg - INFO - Iter [78700/80000] lr: 6.505e-07, eta: 0:46:14, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1178, decode.acc_seg: 94.7215, aux.loss_ce: 0.0515, aux.acc_seg: 94.2670, loss: 0.1694 +2024-06-20 01:09:49,528 - mmseg - INFO - Iter [78750/80000] lr: 6.255e-07, eta: 0:44:27, time: 1.980, data_time: 0.011, memory: 72263, decode.loss_ce: 0.1224, decode.acc_seg: 94.4700, aux.loss_ce: 0.0533, aux.acc_seg: 93.9826, loss: 0.1757 +2024-06-20 01:11:28,491 - mmseg - INFO - Iter [78800/80000] lr: 6.005e-07, eta: 0:42:40, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1178, decode.acc_seg: 94.8539, aux.loss_ce: 0.0512, aux.acc_seg: 94.3981, loss: 0.1690 +2024-06-20 01:13:07,352 - mmseg - INFO - Iter [78850/80000] lr: 5.755e-07, eta: 0:40:53, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1220, decode.acc_seg: 94.4819, aux.loss_ce: 0.0527, aux.acc_seg: 94.0718, loss: 0.1747 +2024-06-20 01:14:46,213 - mmseg - INFO - Iter [78900/80000] lr: 5.505e-07, eta: 0:39:06, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1234, decode.acc_seg: 94.3957, aux.loss_ce: 0.0533, aux.acc_seg: 93.9998, loss: 0.1767 +2024-06-20 01:16:25,113 - mmseg - INFO - Iter [78950/80000] lr: 5.255e-07, eta: 0:37:20, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1190, decode.acc_seg: 94.5753, aux.loss_ce: 0.0515, aux.acc_seg: 94.1535, loss: 0.1705 +2024-06-20 01:18:04,055 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-20 01:18:04,055 - mmseg - INFO - Iter [79000/80000] lr: 5.005e-07, eta: 0:35:33, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1202, decode.acc_seg: 94.5898, aux.loss_ce: 0.0521, aux.acc_seg: 94.1783, loss: 0.1723 +2024-06-20 01:19:53,736 - mmseg - INFO - per class results: +2024-06-20 01:19:53,743 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.01 | 90.5 | +| building | 85.61 | 93.26 | +| sky | 94.99 | 97.61 | +| floor | 85.07 | 92.46 | +| tree | 78.23 | 90.11 | +| ceiling | 87.65 | 94.98 | +| road | 87.14 | 92.08 | +| bed | 93.34 | 97.35 | +| windowpane | 66.82 | 81.06 | +| grass | 68.32 | 82.22 | +| cabinet | 67.93 | 77.0 | +| sidewalk | 71.55 | 86.37 | +| person | 86.95 | 94.77 | +| earth | 40.29 | 51.8 | +| door | 60.1 | 75.37 | +| table | 71.48 | 82.27 | +| mountain | 62.86 | 74.34 | +| plant | 56.36 | 67.48 | +| curtain | 79.18 | 88.55 | +| chair | 69.43 | 80.22 | +| car | 88.82 | 94.6 | +| water | 64.64 | 80.5 | +| painting | 80.43 | 92.01 | +| sofa | 83.36 | 90.65 | +| shelf | 51.08 | 66.96 | +| house | 52.75 | 64.6 | +| sea | 72.76 | 84.41 | +| mirror | 78.86 | 86.52 | +| rug | 63.22 | 74.37 | +| field | 30.37 | 57.16 | +| armchair | 63.76 | 80.11 | +| seat | 68.5 | 89.39 | +| fence | 49.25 | 60.67 | +| desk | 59.85 | 80.36 | +| rock | 56.94 | 84.93 | +| wardrobe | 53.98 | 73.21 | +| lamp | 77.82 | 88.28 | +| bathtub | 87.58 | 90.65 | +| railing | 42.36 | 60.2 | +| cushion | 69.61 | 83.22 | +| base | 45.4 | 59.53 | +| box | 40.55 | 50.88 | +| column | 58.08 | 71.74 | +| signboard | 41.75 | 57.02 | +| chest of drawers | 47.02 | 72.05 | +| counter | 53.82 | 63.95 | +| sand | 57.69 | 85.45 | +| sink | 84.15 | 88.71 | +| skyscraper | 49.1 | 63.28 | +| fireplace | 75.19 | 93.71 | +| refrigerator | 86.6 | 93.58 | +| grandstand | 57.75 | 82.43 | +| path | 31.13 | 42.15 | +| stairs | 35.55 | 43.98 | +| runway | 72.79 | 94.13 | +| case | 62.69 | 82.74 | +| pool table | 95.5 | 98.3 | +| pillow | 65.87 | 76.41 | +| screen door | 87.16 | 89.39 | +| stairway | 42.51 | 58.26 | +| river | 13.64 | 25.49 | +| bridge | 71.09 | 78.66 | +| bookcase | 45.16 | 62.0 | +| blind | 43.71 | 50.52 | +| coffee table | 61.15 | 86.58 | +| toilet | 91.39 | 94.49 | +| flower | 49.51 | 65.11 | +| book | 59.1 | 79.95 | +| hill | 12.71 | 20.05 | +| bench | 58.71 | 67.24 | +| countertop | 65.42 | 85.29 | +| stove | 88.66 | 93.16 | +| palm | 53.13 | 81.63 | +| kitchen island | 58.43 | 86.18 | +| computer | 77.04 | 91.36 | +| swivel chair | 49.87 | 76.87 | +| boat | 79.66 | 92.97 | +| bar | 71.7 | 83.95 | +| arcade machine | 82.1 | 85.5 | +| hovel | 49.54 | 58.77 | +| bus | 94.06 | 97.18 | +| towel | 80.45 | 87.18 | +| light | 63.65 | 73.41 | +| truck | 52.65 | 65.0 | +| tower | 32.99 | 63.44 | +| chandelier | 73.67 | 84.04 | +| awning | 42.98 | 53.68 | +| streetlight | 37.81 | 50.13 | +| booth | 58.74 | 72.44 | +| television receiver | 79.56 | 87.02 | +| airplane | 87.54 | 96.18 | +| dirt track | 7.07 | 13.25 | +| apparel | 65.85 | 83.45 | +| pole | 28.89 | 39.47 | +| land | 5.63 | 8.08 | +| bannister | 22.1 | 26.7 | +| escalator | 67.31 | 85.95 | +| ottoman | 58.51 | 74.87 | +| bottle | 46.61 | 70.03 | +| buffet | 57.47 | 65.29 | +| poster | 36.95 | 44.37 | +| stage | 21.99 | 38.51 | +| van | 54.23 | 73.46 | +| ship | 75.9 | 90.07 | +| fountain | 30.96 | 31.5 | +| conveyer belt | 85.73 | 96.67 | +| canopy | 58.64 | 73.52 | +| washer | 86.56 | 91.91 | +| plaything | 34.89 | 47.53 | +| swimming pool | 54.77 | 79.04 | +| stool | 54.62 | 73.44 | +| barrel | 77.57 | 97.91 | +| basket | 43.42 | 61.52 | +| waterfall | 52.95 | 65.76 | +| tent | 92.57 | 98.87 | +| bag | 27.52 | 31.68 | +| minibike | 77.86 | 90.36 | +| cradle | 85.44 | 97.63 | +| oven | 70.27 | 79.58 | +| ball | 57.74 | 63.25 | +| food | 60.88 | 70.89 | +| step | 11.74 | 14.34 | +| tank | 63.75 | 68.38 | +| trade name | 27.63 | 32.83 | +| microwave | 90.45 | 96.54 | +| pot | 61.08 | 71.57 | +| animal | 60.15 | 61.49 | +| bicycle | 62.67 | 77.59 | +| lake | 52.19 | 63.74 | +| dishwasher | 75.73 | 82.67 | +| screen | 49.7 | 75.5 | +| blanket | 37.9 | 44.59 | +| sculpture | 72.77 | 87.91 | +| hood | 67.39 | 74.92 | +| sconce | 62.25 | 74.32 | +| vase | 51.63 | 68.53 | +| traffic light | 40.15 | 66.63 | +| tray | 25.93 | 34.71 | +| ashcan | 50.51 | 66.7 | +| fan | 73.34 | 84.16 | +| pier | 41.24 | 46.7 | +| crt screen | 7.25 | 16.75 | +| plate | 64.78 | 81.24 | +| monitor | 41.57 | 48.94 | +| bulletin board | 58.94 | 68.45 | +| shower | 19.81 | 24.51 | +| radiator | 68.97 | 82.31 | +| glass | 23.28 | 25.2 | +| clock | 58.27 | 67.4 | +| flag | 72.02 | 80.25 | ++---------------------+-------+-------+ +2024-06-20 01:19:53,743 - mmseg - INFO - Summary: +2024-06-20 01:19:53,743 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.73 | 59.75 | 71.93 | ++-------+-------+-------+ +2024-06-20 01:19:53,744 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-20 01:19:53,744 - mmseg - INFO - Iter(val) [250] aAcc: 0.8673, mIoU: 0.5975, mAcc: 0.7193, IoU.wall: 0.8301, IoU.building: 0.8561, IoU.sky: 0.9499, IoU.floor: 0.8507, IoU.tree: 0.7823, IoU.ceiling: 0.8765, IoU.road: 0.8714, IoU.bed : 0.9334, IoU.windowpane: 0.6682, IoU.grass: 0.6832, IoU.cabinet: 0.6793, IoU.sidewalk: 0.7155, IoU.person: 0.8695, IoU.earth: 0.4029, IoU.door: 0.6010, IoU.table: 0.7148, IoU.mountain: 0.6286, IoU.plant: 0.5636, IoU.curtain: 0.7918, IoU.chair: 0.6943, IoU.car: 0.8882, IoU.water: 0.6464, IoU.painting: 0.8043, IoU.sofa: 0.8336, IoU.shelf: 0.5108, IoU.house: 0.5275, IoU.sea: 0.7276, IoU.mirror: 0.7886, IoU.rug: 0.6322, IoU.field: 0.3037, IoU.armchair: 0.6376, IoU.seat: 0.6850, IoU.fence: 0.4925, IoU.desk: 0.5985, IoU.rock: 0.5694, IoU.wardrobe: 0.5398, IoU.lamp: 0.7782, IoU.bathtub: 0.8758, IoU.railing: 0.4236, IoU.cushion: 0.6961, IoU.base: 0.4540, IoU.box: 0.4055, IoU.column: 0.5808, IoU.signboard: 0.4175, IoU.chest of drawers: 0.4702, IoU.counter: 0.5382, IoU.sand: 0.5769, IoU.sink: 0.8415, IoU.skyscraper: 0.4910, IoU.fireplace: 0.7519, IoU.refrigerator: 0.8660, IoU.grandstand: 0.5775, IoU.path: 0.3113, IoU.stairs: 0.3555, IoU.runway: 0.7279, IoU.case: 0.6269, IoU.pool table: 0.9550, IoU.pillow: 0.6587, IoU.screen door: 0.8716, IoU.stairway: 0.4251, IoU.river: 0.1364, IoU.bridge: 0.7109, IoU.bookcase: 0.4516, IoU.blind: 0.4371, IoU.coffee table: 0.6115, IoU.toilet: 0.9139, IoU.flower: 0.4951, IoU.book: 0.5910, IoU.hill: 0.1271, IoU.bench: 0.5871, IoU.countertop: 0.6542, IoU.stove: 0.8866, IoU.palm: 0.5313, IoU.kitchen island: 0.5843, IoU.computer: 0.7704, IoU.swivel chair: 0.4987, IoU.boat: 0.7966, IoU.bar: 0.7170, IoU.arcade machine: 0.8210, IoU.hovel: 0.4954, IoU.bus: 0.9406, IoU.towel: 0.8045, IoU.light: 0.6365, IoU.truck: 0.5265, IoU.tower: 0.3299, IoU.chandelier: 0.7367, IoU.awning: 0.4298, IoU.streetlight: 0.3781, IoU.booth: 0.5874, IoU.television receiver: 0.7956, IoU.airplane: 0.8754, IoU.dirt track: 0.0707, IoU.apparel: 0.6585, IoU.pole: 0.2889, IoU.land: 0.0563, IoU.bannister: 0.2210, IoU.escalator: 0.6731, IoU.ottoman: 0.5851, IoU.bottle: 0.4661, IoU.buffet: 0.5747, IoU.poster: 0.3695, IoU.stage: 0.2199, IoU.van: 0.5423, IoU.ship: 0.7590, IoU.fountain: 0.3096, IoU.conveyer belt: 0.8573, IoU.canopy: 0.5864, IoU.washer: 0.8656, IoU.plaything: 0.3489, IoU.swimming pool: 0.5477, IoU.stool: 0.5462, IoU.barrel: 0.7757, IoU.basket: 0.4342, IoU.waterfall: 0.5295, IoU.tent: 0.9257, IoU.bag: 0.2752, IoU.minibike: 0.7786, IoU.cradle: 0.8544, IoU.oven: 0.7027, IoU.ball: 0.5774, IoU.food: 0.6088, IoU.step: 0.1174, IoU.tank: 0.6375, IoU.trade name: 0.2763, IoU.microwave: 0.9045, IoU.pot: 0.6108, IoU.animal: 0.6015, IoU.bicycle: 0.6267, IoU.lake: 0.5219, IoU.dishwasher: 0.7573, IoU.screen: 0.4970, IoU.blanket: 0.3790, IoU.sculpture: 0.7277, IoU.hood: 0.6739, IoU.sconce: 0.6225, IoU.vase: 0.5163, IoU.traffic light: 0.4015, IoU.tray: 0.2593, IoU.ashcan: 0.5051, IoU.fan: 0.7334, IoU.pier: 0.4124, IoU.crt screen: 0.0725, IoU.plate: 0.6478, IoU.monitor: 0.4157, IoU.bulletin board: 0.5894, IoU.shower: 0.1981, IoU.radiator: 0.6897, IoU.glass: 0.2328, IoU.clock: 0.5827, IoU.flag: 0.7202, Acc.wall: 0.9050, Acc.building: 0.9326, Acc.sky: 0.9761, Acc.floor: 0.9246, Acc.tree: 0.9011, Acc.ceiling: 0.9498, Acc.road: 0.9208, Acc.bed : 0.9735, Acc.windowpane: 0.8106, Acc.grass: 0.8222, Acc.cabinet: 0.7700, Acc.sidewalk: 0.8637, Acc.person: 0.9477, Acc.earth: 0.5180, Acc.door: 0.7537, Acc.table: 0.8227, Acc.mountain: 0.7434, Acc.plant: 0.6748, Acc.curtain: 0.8855, Acc.chair: 0.8022, Acc.car: 0.9460, Acc.water: 0.8050, Acc.painting: 0.9201, Acc.sofa: 0.9065, Acc.shelf: 0.6696, Acc.house: 0.6460, Acc.sea: 0.8441, Acc.mirror: 0.8652, Acc.rug: 0.7437, Acc.field: 0.5716, Acc.armchair: 0.8011, Acc.seat: 0.8939, Acc.fence: 0.6067, Acc.desk: 0.8036, Acc.rock: 0.8493, Acc.wardrobe: 0.7321, Acc.lamp: 0.8828, Acc.bathtub: 0.9065, Acc.railing: 0.6020, Acc.cushion: 0.8322, Acc.base: 0.5953, Acc.box: 0.5088, Acc.column: 0.7174, Acc.signboard: 0.5702, Acc.chest of drawers: 0.7205, Acc.counter: 0.6395, Acc.sand: 0.8545, Acc.sink: 0.8871, Acc.skyscraper: 0.6328, Acc.fireplace: 0.9371, Acc.refrigerator: 0.9358, Acc.grandstand: 0.8243, Acc.path: 0.4215, Acc.stairs: 0.4398, Acc.runway: 0.9413, Acc.case: 0.8274, Acc.pool table: 0.9830, Acc.pillow: 0.7641, Acc.screen door: 0.8939, Acc.stairway: 0.5826, Acc.river: 0.2549, Acc.bridge: 0.7866, Acc.bookcase: 0.6200, Acc.blind: 0.5052, Acc.coffee table: 0.8658, Acc.toilet: 0.9449, Acc.flower: 0.6511, Acc.book: 0.7995, Acc.hill: 0.2005, Acc.bench: 0.6724, Acc.countertop: 0.8529, Acc.stove: 0.9316, Acc.palm: 0.8163, Acc.kitchen island: 0.8618, Acc.computer: 0.9136, Acc.swivel chair: 0.7687, Acc.boat: 0.9297, Acc.bar: 0.8395, Acc.arcade machine: 0.8550, Acc.hovel: 0.5877, Acc.bus: 0.9718, Acc.towel: 0.8718, Acc.light: 0.7341, Acc.truck: 0.6500, Acc.tower: 0.6344, Acc.chandelier: 0.8404, Acc.awning: 0.5368, Acc.streetlight: 0.5013, Acc.booth: 0.7244, Acc.television receiver: 0.8702, Acc.airplane: 0.9618, Acc.dirt track: 0.1325, Acc.apparel: 0.8345, Acc.pole: 0.3947, Acc.land: 0.0808, Acc.bannister: 0.2670, Acc.escalator: 0.8595, Acc.ottoman: 0.7487, Acc.bottle: 0.7003, Acc.buffet: 0.6529, Acc.poster: 0.4437, Acc.stage: 0.3851, Acc.van: 0.7346, Acc.ship: 0.9007, Acc.fountain: 0.3150, Acc.conveyer belt: 0.9667, Acc.canopy: 0.7352, Acc.washer: 0.9191, Acc.plaything: 0.4753, Acc.swimming pool: 0.7904, Acc.stool: 0.7344, Acc.barrel: 0.9791, Acc.basket: 0.6152, Acc.waterfall: 0.6576, Acc.tent: 0.9887, Acc.bag: 0.3168, Acc.minibike: 0.9036, Acc.cradle: 0.9763, Acc.oven: 0.7958, Acc.ball: 0.6325, Acc.food: 0.7089, Acc.step: 0.1434, Acc.tank: 0.6838, Acc.trade name: 0.3283, Acc.microwave: 0.9654, Acc.pot: 0.7157, Acc.animal: 0.6149, Acc.bicycle: 0.7759, Acc.lake: 0.6374, Acc.dishwasher: 0.8267, Acc.screen: 0.7550, Acc.blanket: 0.4459, Acc.sculpture: 0.8791, Acc.hood: 0.7492, Acc.sconce: 0.7432, Acc.vase: 0.6853, Acc.traffic light: 0.6663, Acc.tray: 0.3471, Acc.ashcan: 0.6670, Acc.fan: 0.8416, Acc.pier: 0.4670, Acc.crt screen: 0.1675, Acc.plate: 0.8124, Acc.monitor: 0.4894, Acc.bulletin board: 0.6845, Acc.shower: 0.2451, Acc.radiator: 0.8231, Acc.glass: 0.2520, Acc.clock: 0.6740, Acc.flag: 0.8025 +2024-06-20 01:21:33,008 - mmseg - INFO - Iter [79050/80000] lr: 4.755e-07, eta: 0:33:47, time: 4.179, data_time: 2.210, memory: 72263, decode.loss_ce: 0.1212, decode.acc_seg: 94.5363, aux.loss_ce: 0.0528, aux.acc_seg: 94.0707, loss: 0.1740 +2024-06-20 01:23:11,931 - mmseg - INFO - Iter [79100/80000] lr: 4.505e-07, eta: 0:32:01, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1207, decode.acc_seg: 94.5991, aux.loss_ce: 0.0525, aux.acc_seg: 94.1828, loss: 0.1732 +2024-06-20 01:24:50,808 - mmseg - INFO - Iter [79150/80000] lr: 4.255e-07, eta: 0:30:14, time: 1.978, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1173, decode.acc_seg: 94.6532, aux.loss_ce: 0.0509, aux.acc_seg: 94.2409, loss: 0.1682 +2024-06-20 01:26:29,616 - mmseg - INFO - Iter [79200/80000] lr: 4.005e-07, eta: 0:28:27, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1193, decode.acc_seg: 94.5578, aux.loss_ce: 0.0516, aux.acc_seg: 94.1294, loss: 0.1709 +2024-06-20 01:28:08,400 - mmseg - INFO - Iter [79250/80000] lr: 3.755e-07, eta: 0:26:40, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1171, decode.acc_seg: 94.7379, aux.loss_ce: 0.0512, aux.acc_seg: 94.2628, loss: 0.1683 +2024-06-20 01:29:47,351 - mmseg - INFO - Iter [79300/80000] lr: 3.505e-07, eta: 0:24:53, time: 1.979, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1252, decode.acc_seg: 94.3770, aux.loss_ce: 0.0545, aux.acc_seg: 93.9155, loss: 0.1797 +2024-06-20 01:31:26,159 - mmseg - INFO - Iter [79350/80000] lr: 3.255e-07, eta: 0:23:07, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1234, decode.acc_seg: 94.5523, aux.loss_ce: 0.0535, aux.acc_seg: 94.1238, loss: 0.1768 +2024-06-20 01:33:05,015 - mmseg - INFO - Iter [79400/80000] lr: 3.005e-07, eta: 0:21:20, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1190, decode.acc_seg: 94.6244, aux.loss_ce: 0.0516, aux.acc_seg: 94.1905, loss: 0.1706 +2024-06-20 01:34:43,802 - mmseg - INFO - Iter [79450/80000] lr: 2.755e-07, eta: 0:19:33, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1191, decode.acc_seg: 94.5355, aux.loss_ce: 0.0517, aux.acc_seg: 94.0876, loss: 0.1709 +2024-06-20 01:36:22,635 - mmseg - INFO - Iter [79500/80000] lr: 2.505e-07, eta: 0:17:46, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1240, decode.acc_seg: 94.4183, aux.loss_ce: 0.0545, aux.acc_seg: 93.9051, loss: 0.1785 +2024-06-20 01:38:01,438 - mmseg - INFO - Iter [79550/80000] lr: 2.255e-07, eta: 0:16:00, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1164, decode.acc_seg: 94.7749, aux.loss_ce: 0.0507, aux.acc_seg: 94.3175, loss: 0.1672 +2024-06-20 01:39:42,406 - mmseg - INFO - Iter [79600/80000] lr: 2.005e-07, eta: 0:14:13, time: 2.019, data_time: 0.052, memory: 72263, decode.loss_ce: 0.1182, decode.acc_seg: 94.6725, aux.loss_ce: 0.0514, aux.acc_seg: 94.2663, loss: 0.1697 +2024-06-20 01:41:21,254 - mmseg - INFO - Iter [79650/80000] lr: 1.755e-07, eta: 0:12:26, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1215, decode.acc_seg: 94.6162, aux.loss_ce: 0.0533, aux.acc_seg: 94.1533, loss: 0.1749 +2024-06-20 01:43:00,000 - mmseg - INFO - Iter [79700/80000] lr: 1.505e-07, eta: 0:10:40, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1191, decode.acc_seg: 94.6480, aux.loss_ce: 0.0518, aux.acc_seg: 94.1269, loss: 0.1709 +2024-06-20 01:44:38,860 - mmseg - INFO - Iter [79750/80000] lr: 1.255e-07, eta: 0:08:53, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1170, decode.acc_seg: 94.7842, aux.loss_ce: 0.0508, aux.acc_seg: 94.3617, loss: 0.1678 +2024-06-20 01:46:17,628 - mmseg - INFO - Iter [79800/80000] lr: 1.005e-07, eta: 0:07:06, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1239, decode.acc_seg: 94.5498, aux.loss_ce: 0.0539, aux.acc_seg: 94.1402, loss: 0.1778 +2024-06-20 01:47:56,495 - mmseg - INFO - Iter [79850/80000] lr: 7.550e-08, eta: 0:05:19, time: 1.977, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1196, decode.acc_seg: 94.5495, aux.loss_ce: 0.0516, aux.acc_seg: 94.1325, loss: 0.1712 +2024-06-20 01:49:35,271 - mmseg - INFO - Iter [79900/80000] lr: 5.050e-08, eta: 0:03:33, time: 1.975, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1225, decode.acc_seg: 94.4604, aux.loss_ce: 0.0533, aux.acc_seg: 93.9709, loss: 0.1757 +2024-06-20 01:51:14,087 - mmseg - INFO - Iter [79950/80000] lr: 2.550e-08, eta: 0:01:46, time: 1.976, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1208, decode.acc_seg: 94.4774, aux.loss_ce: 0.0521, aux.acc_seg: 94.1066, loss: 0.1729 +2024-06-20 01:52:53,011 - mmseg - INFO - Saving checkpoint at 80000 iterations +2024-06-20 01:54:21,003 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-20 01:54:21,003 - mmseg - INFO - Iter [80000/80000] lr: 5.000e-10, eta: 0:00:00, time: 3.738, data_time: 0.010, memory: 72263, decode.loss_ce: 0.1159, decode.acc_seg: 94.8002, aux.loss_ce: 0.0504, aux.acc_seg: 94.3720, loss: 0.1662 +2024-06-20 01:56:09,742 - mmseg - INFO - per class results: +2024-06-20 01:56:09,748 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.05 | 90.5 | +| building | 85.65 | 93.13 | +| sky | 94.99 | 97.67 | +| floor | 85.1 | 92.32 | +| tree | 78.19 | 90.11 | +| ceiling | 87.68 | 94.7 | +| road | 87.25 | 91.99 | +| bed | 93.38 | 97.29 | +| windowpane | 66.73 | 81.52 | +| grass | 68.21 | 81.93 | +| cabinet | 67.92 | 76.82 | +| sidewalk | 71.68 | 86.42 | +| person | 86.98 | 94.74 | +| earth | 40.45 | 52.56 | +| door | 60.07 | 75.04 | +| table | 71.37 | 82.5 | +| mountain | 62.62 | 73.87 | +| plant | 56.41 | 67.15 | +| curtain | 79.38 | 88.49 | +| chair | 69.42 | 80.54 | +| car | 88.85 | 94.6 | +| water | 65.1 | 81.1 | +| painting | 80.51 | 92.01 | +| sofa | 83.32 | 90.74 | +| shelf | 51.33 | 67.81 | +| house | 52.94 | 64.78 | +| sea | 73.1 | 84.45 | +| mirror | 78.75 | 86.54 | +| rug | 63.09 | 74.03 | +| field | 30.14 | 57.26 | +| armchair | 63.7 | 80.4 | +| seat | 68.12 | 89.6 | +| fence | 49.07 | 60.17 | +| desk | 59.33 | 81.36 | +| rock | 56.85 | 84.42 | +| wardrobe | 53.69 | 72.78 | +| lamp | 77.77 | 88.72 | +| bathtub | 87.73 | 90.91 | +| railing | 42.11 | 60.61 | +| cushion | 69.35 | 83.71 | +| base | 45.57 | 60.55 | +| box | 40.51 | 50.9 | +| column | 58.0 | 71.69 | +| signboard | 41.95 | 57.61 | +| chest of drawers | 47.12 | 73.23 | +| counter | 53.04 | 62.66 | +| sand | 58.51 | 85.05 | +| sink | 84.15 | 89.04 | +| skyscraper | 48.86 | 63.35 | +| fireplace | 75.17 | 93.87 | +| refrigerator | 86.66 | 93.96 | +| grandstand | 57.83 | 82.15 | +| path | 31.14 | 42.19 | +| stairs | 35.52 | 44.01 | +| runway | 72.85 | 94.16 | +| case | 62.71 | 82.78 | +| pool table | 95.46 | 98.38 | +| pillow | 65.78 | 76.53 | +| screen door | 87.24 | 89.43 | +| stairway | 42.48 | 58.75 | +| river | 13.75 | 25.13 | +| bridge | 70.6 | 78.34 | +| bookcase | 45.1 | 60.95 | +| blind | 43.54 | 50.36 | +| coffee table | 61.09 | 86.69 | +| toilet | 91.35 | 94.63 | +| flower | 49.48 | 65.6 | +| book | 59.15 | 80.22 | +| hill | 12.95 | 21.32 | +| bench | 58.76 | 67.22 | +| countertop | 65.56 | 85.41 | +| stove | 88.7 | 93.41 | +| palm | 53.04 | 81.66 | +| kitchen island | 58.33 | 86.13 | +| computer | 76.94 | 91.58 | +| swivel chair | 49.82 | 77.15 | +| boat | 79.16 | 93.19 | +| bar | 72.34 | 83.27 | +| arcade machine | 81.98 | 85.46 | +| hovel | 48.19 | 57.5 | +| bus | 93.82 | 97.29 | +| towel | 80.52 | 87.87 | +| light | 63.8 | 74.27 | +| truck | 52.28 | 65.28 | +| tower | 33.15 | 64.82 | +| chandelier | 73.79 | 84.62 | +| awning | 43.35 | 54.38 | +| streetlight | 37.8 | 50.29 | +| booth | 57.82 | 72.76 | +| television receiver | 79.8 | 87.28 | +| airplane | 88.01 | 96.38 | +| dirt track | 7.04 | 13.86 | +| apparel | 66.1 | 83.06 | +| pole | 28.59 | 38.58 | +| land | 5.67 | 8.18 | +| bannister | 22.08 | 26.68 | +| escalator | 67.25 | 86.16 | +| ottoman | 57.74 | 74.09 | +| bottle | 46.69 | 69.77 | +| buffet | 58.52 | 66.63 | +| poster | 37.01 | 44.31 | +| stage | 22.03 | 38.69 | +| van | 54.16 | 74.59 | +| ship | 75.17 | 90.55 | +| fountain | 30.93 | 31.47 | +| conveyer belt | 85.65 | 96.74 | +| canopy | 59.06 | 74.39 | +| washer | 86.04 | 91.33 | +| plaything | 35.27 | 48.91 | +| swimming pool | 54.9 | 79.38 | +| stool | 55.48 | 73.52 | +| barrel | 78.41 | 97.95 | +| basket | 43.58 | 62.21 | +| waterfall | 52.76 | 64.67 | +| tent | 92.77 | 98.87 | +| bag | 27.67 | 32.07 | +| minibike | 77.79 | 91.05 | +| cradle | 85.92 | 97.67 | +| oven | 70.34 | 80.1 | +| ball | 58.17 | 63.92 | +| food | 60.97 | 71.06 | +| step | 11.75 | 14.27 | +| tank | 63.66 | 68.29 | +| trade name | 27.35 | 32.62 | +| microwave | 90.37 | 96.66 | +| pot | 61.17 | 71.85 | +| animal | 60.33 | 61.73 | +| bicycle | 62.78 | 77.98 | +| lake | 52.12 | 63.75 | +| dishwasher | 75.68 | 83.02 | +| screen | 48.37 | 73.56 | +| blanket | 38.28 | 45.15 | +| sculpture | 72.9 | 88.29 | +| hood | 67.45 | 75.44 | +| sconce | 62.3 | 74.56 | +| vase | 51.59 | 69.17 | +| traffic light | 40.14 | 66.8 | +| tray | 26.31 | 35.72 | +| ashcan | 50.35 | 67.48 | +| fan | 73.44 | 84.88 | +| pier | 41.29 | 46.68 | +| crt screen | 7.01 | 17.07 | +| plate | 64.84 | 81.59 | +| monitor | 40.33 | 47.61 | +| bulletin board | 59.05 | 69.38 | +| shower | 19.94 | 25.13 | +| radiator | 68.94 | 82.47 | +| glass | 23.06 | 24.8 | +| clock | 58.46 | 68.17 | +| flag | 72.11 | 80.35 | ++---------------------+-------+-------+ +2024-06-20 01:56:09,748 - mmseg - INFO - Summary: +2024-06-20 01:56:09,749 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.72 | 59.74 | 72.07 | ++-------+-------+-------+ +2024-06-20 01:56:09,750 - mmseg - INFO - Exp name: upernet_internvit_h6b_512_512_80k_ade20k_bs16_lr4e-5.py +2024-06-20 01:56:09,750 - mmseg - INFO - Iter(val) [250] aAcc: 0.8672, mIoU: 0.5974, mAcc: 0.7207, IoU.wall: 0.8305, IoU.building: 0.8565, IoU.sky: 0.9499, IoU.floor: 0.8510, IoU.tree: 0.7819, IoU.ceiling: 0.8768, IoU.road: 0.8725, IoU.bed : 0.9338, IoU.windowpane: 0.6673, IoU.grass: 0.6821, IoU.cabinet: 0.6792, IoU.sidewalk: 0.7168, IoU.person: 0.8698, IoU.earth: 0.4045, IoU.door: 0.6007, IoU.table: 0.7137, IoU.mountain: 0.6262, IoU.plant: 0.5641, IoU.curtain: 0.7938, IoU.chair: 0.6942, IoU.car: 0.8885, IoU.water: 0.6510, IoU.painting: 0.8051, IoU.sofa: 0.8332, IoU.shelf: 0.5133, IoU.house: 0.5294, IoU.sea: 0.7310, IoU.mirror: 0.7875, IoU.rug: 0.6309, IoU.field: 0.3014, IoU.armchair: 0.6370, IoU.seat: 0.6812, IoU.fence: 0.4907, IoU.desk: 0.5933, IoU.rock: 0.5685, IoU.wardrobe: 0.5369, IoU.lamp: 0.7777, IoU.bathtub: 0.8773, IoU.railing: 0.4211, IoU.cushion: 0.6935, IoU.base: 0.4557, IoU.box: 0.4051, IoU.column: 0.5800, IoU.signboard: 0.4195, IoU.chest of drawers: 0.4712, IoU.counter: 0.5304, IoU.sand: 0.5851, IoU.sink: 0.8415, IoU.skyscraper: 0.4886, IoU.fireplace: 0.7517, IoU.refrigerator: 0.8666, IoU.grandstand: 0.5783, IoU.path: 0.3114, IoU.stairs: 0.3552, IoU.runway: 0.7285, IoU.case: 0.6271, IoU.pool table: 0.9546, IoU.pillow: 0.6578, IoU.screen door: 0.8724, IoU.stairway: 0.4248, IoU.river: 0.1375, IoU.bridge: 0.7060, IoU.bookcase: 0.4510, IoU.blind: 0.4354, IoU.coffee table: 0.6109, IoU.toilet: 0.9135, IoU.flower: 0.4948, IoU.book: 0.5915, IoU.hill: 0.1295, IoU.bench: 0.5876, IoU.countertop: 0.6556, IoU.stove: 0.8870, IoU.palm: 0.5304, IoU.kitchen island: 0.5833, IoU.computer: 0.7694, IoU.swivel chair: 0.4982, IoU.boat: 0.7916, IoU.bar: 0.7234, IoU.arcade machine: 0.8198, IoU.hovel: 0.4819, IoU.bus: 0.9382, IoU.towel: 0.8052, IoU.light: 0.6380, IoU.truck: 0.5228, IoU.tower: 0.3315, IoU.chandelier: 0.7379, IoU.awning: 0.4335, IoU.streetlight: 0.3780, IoU.booth: 0.5782, IoU.television receiver: 0.7980, IoU.airplane: 0.8801, IoU.dirt track: 0.0704, IoU.apparel: 0.6610, IoU.pole: 0.2859, IoU.land: 0.0567, IoU.bannister: 0.2208, IoU.escalator: 0.6725, IoU.ottoman: 0.5774, IoU.bottle: 0.4669, IoU.buffet: 0.5852, IoU.poster: 0.3701, IoU.stage: 0.2203, IoU.van: 0.5416, IoU.ship: 0.7517, IoU.fountain: 0.3093, IoU.conveyer belt: 0.8565, IoU.canopy: 0.5906, IoU.washer: 0.8604, IoU.plaything: 0.3527, IoU.swimming pool: 0.5490, IoU.stool: 0.5548, IoU.barrel: 0.7841, IoU.basket: 0.4358, IoU.waterfall: 0.5276, IoU.tent: 0.9277, IoU.bag: 0.2767, IoU.minibike: 0.7779, IoU.cradle: 0.8592, IoU.oven: 0.7034, IoU.ball: 0.5817, IoU.food: 0.6097, IoU.step: 0.1175, IoU.tank: 0.6366, IoU.trade name: 0.2735, IoU.microwave: 0.9037, IoU.pot: 0.6117, IoU.animal: 0.6033, IoU.bicycle: 0.6278, IoU.lake: 0.5212, IoU.dishwasher: 0.7568, IoU.screen: 0.4837, IoU.blanket: 0.3828, IoU.sculpture: 0.7290, IoU.hood: 0.6745, IoU.sconce: 0.6230, IoU.vase: 0.5159, IoU.traffic light: 0.4014, IoU.tray: 0.2631, IoU.ashcan: 0.5035, IoU.fan: 0.7344, IoU.pier: 0.4129, IoU.crt screen: 0.0701, IoU.plate: 0.6484, IoU.monitor: 0.4033, IoU.bulletin board: 0.5905, IoU.shower: 0.1994, IoU.radiator: 0.6894, IoU.glass: 0.2306, IoU.clock: 0.5846, IoU.flag: 0.7211, Acc.wall: 0.9050, Acc.building: 0.9313, Acc.sky: 0.9767, Acc.floor: 0.9232, Acc.tree: 0.9011, Acc.ceiling: 0.9470, Acc.road: 0.9199, Acc.bed : 0.9729, Acc.windowpane: 0.8152, Acc.grass: 0.8193, Acc.cabinet: 0.7682, Acc.sidewalk: 0.8642, Acc.person: 0.9474, Acc.earth: 0.5256, Acc.door: 0.7504, Acc.table: 0.8250, Acc.mountain: 0.7387, Acc.plant: 0.6715, Acc.curtain: 0.8849, Acc.chair: 0.8054, Acc.car: 0.9460, Acc.water: 0.8110, Acc.painting: 0.9201, Acc.sofa: 0.9074, Acc.shelf: 0.6781, Acc.house: 0.6478, Acc.sea: 0.8445, Acc.mirror: 0.8654, Acc.rug: 0.7403, Acc.field: 0.5726, Acc.armchair: 0.8040, Acc.seat: 0.8960, Acc.fence: 0.6017, Acc.desk: 0.8136, Acc.rock: 0.8442, Acc.wardrobe: 0.7278, Acc.lamp: 0.8872, Acc.bathtub: 0.9091, Acc.railing: 0.6061, Acc.cushion: 0.8371, Acc.base: 0.6055, Acc.box: 0.5090, Acc.column: 0.7169, Acc.signboard: 0.5761, Acc.chest of drawers: 0.7323, Acc.counter: 0.6266, Acc.sand: 0.8505, Acc.sink: 0.8904, Acc.skyscraper: 0.6335, Acc.fireplace: 0.9387, Acc.refrigerator: 0.9396, Acc.grandstand: 0.8215, Acc.path: 0.4219, Acc.stairs: 0.4401, Acc.runway: 0.9416, Acc.case: 0.8278, Acc.pool table: 0.9838, Acc.pillow: 0.7653, Acc.screen door: 0.8943, Acc.stairway: 0.5875, Acc.river: 0.2513, Acc.bridge: 0.7834, Acc.bookcase: 0.6095, Acc.blind: 0.5036, Acc.coffee table: 0.8669, Acc.toilet: 0.9463, Acc.flower: 0.6560, Acc.book: 0.8022, Acc.hill: 0.2132, Acc.bench: 0.6722, Acc.countertop: 0.8541, Acc.stove: 0.9341, Acc.palm: 0.8166, Acc.kitchen island: 0.8613, Acc.computer: 0.9158, Acc.swivel chair: 0.7715, Acc.boat: 0.9319, Acc.bar: 0.8327, Acc.arcade machine: 0.8546, Acc.hovel: 0.5750, Acc.bus: 0.9729, Acc.towel: 0.8787, Acc.light: 0.7427, Acc.truck: 0.6528, Acc.tower: 0.6482, Acc.chandelier: 0.8462, Acc.awning: 0.5438, Acc.streetlight: 0.5029, Acc.booth: 0.7276, Acc.television receiver: 0.8728, Acc.airplane: 0.9638, Acc.dirt track: 0.1386, Acc.apparel: 0.8306, Acc.pole: 0.3858, Acc.land: 0.0818, Acc.bannister: 0.2668, Acc.escalator: 0.8616, Acc.ottoman: 0.7409, Acc.bottle: 0.6977, Acc.buffet: 0.6663, Acc.poster: 0.4431, Acc.stage: 0.3869, Acc.van: 0.7459, Acc.ship: 0.9055, Acc.fountain: 0.3147, Acc.conveyer belt: 0.9674, Acc.canopy: 0.7439, Acc.washer: 0.9133, Acc.plaything: 0.4891, Acc.swimming pool: 0.7938, Acc.stool: 0.7352, Acc.barrel: 0.9795, Acc.basket: 0.6221, Acc.waterfall: 0.6467, Acc.tent: 0.9887, Acc.bag: 0.3207, Acc.minibike: 0.9105, Acc.cradle: 0.9767, Acc.oven: 0.8010, Acc.ball: 0.6392, Acc.food: 0.7106, Acc.step: 0.1427, Acc.tank: 0.6829, Acc.trade name: 0.3262, Acc.microwave: 0.9666, Acc.pot: 0.7185, Acc.animal: 0.6173, Acc.bicycle: 0.7798, Acc.lake: 0.6375, Acc.dishwasher: 0.8302, Acc.screen: 0.7356, Acc.blanket: 0.4515, Acc.sculpture: 0.8829, Acc.hood: 0.7544, Acc.sconce: 0.7456, Acc.vase: 0.6917, Acc.traffic light: 0.6680, Acc.tray: 0.3572, Acc.ashcan: 0.6748, Acc.fan: 0.8488, Acc.pier: 0.4668, Acc.crt screen: 0.1707, Acc.plate: 0.8159, Acc.monitor: 0.4761, Acc.bulletin board: 0.6938, Acc.shower: 0.2513, Acc.radiator: 0.8247, Acc.glass: 0.2480, Acc.clock: 0.6817, Acc.flag: 0.8035