diff --git "a/segmentation/upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.log" "b/segmentation/upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.log" new file mode 100644--- /dev/null +++ "b/segmentation/upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.log" @@ -0,0 +1,24475 @@ +2024-06-17 23:22:30,057 - mmseg - INFO - Multi-processing start method is `None` +2024-06-17 23:22:30,062 - mmseg - INFO - OpenCV num_threads is `128 +2024-06-17 23:22:30,229 - 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 +------------------------------------------------------------ + +2024-06-17 23:22:30,229 - mmseg - INFO - Distributed training: True +2024-06-17 23:22:31,291 - 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=192, + img_size=192, + 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_192_512_80k_ade20k_bs16_lr4e-5' +gpu_ids = range(0, 8) +auto_resume = True + +2024-06-17 23:22:36,836 - mmseg - INFO - Set random seed to 1990237461, deterministic: False +2024-06-17 23:23:39,379 - 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']) +2024-06-17 23:23:49,830 - 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']) +2024-06-17 23:24:49,365 - mmseg - INFO - initialize UPerHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} +2024-06-17 23:24:50,466 - 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, 145, 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 +2024-06-17 23:24:50,487 - 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'}} +) +2024-06-17 23:24:50,963 - mmseg - INFO - Loaded 20210 images +2024-06-17 23:24:52,021 - mmseg - INFO - {'num_layers': 48, 'layer_decay_rate': 0.95, 'skip_stride': 1.5} +2024-06-17 23:24:52,022 - mmseg - INFO - Build LayerDecayOptimizerConstructor 0.950000 - 50 +2024-06-17 23:24:52,031 - 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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23:25:41,300 - 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 + -------------------- +2024-06-17 23:25:41,300 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters +2024-06-17 23:25:41,326 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/PIIP/mmsegmentation/work_dirs/upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5 by HardDiskBackend. +2024-06-17 23:28:08,705 - mmseg - INFO - Iter [50/80000] lr: 1.306e-06, eta: 1 day, 11:31:25, time: 1.600, data_time: 0.013, memory: 70498, decode.loss_ce: 4.0372, decode.acc_seg: 0.7054, aux.loss_ce: 1.6111, aux.acc_seg: 0.6984, loss: 5.6483 +2024-06-17 23:29:15,838 - mmseg - INFO - Iter [100/80000] lr: 2.637e-06, eta: 1 day, 8:39:01, time: 1.343, data_time: 0.011, memory: 70498, decode.loss_ce: 3.9273, decode.acc_seg: 5.4339, aux.loss_ce: 1.5804, aux.acc_seg: 4.0444, loss: 5.5077 +2024-06-17 23:30:22,379 - mmseg - INFO - Iter [150/80000] lr: 3.966e-06, eta: 1 day, 7:35:33, time: 1.331, data_time: 0.011, memory: 70498, decode.loss_ce: 3.6156, decode.acc_seg: 20.6818, aux.loss_ce: 1.4993, aux.acc_seg: 14.2426, loss: 5.1149 +2024-06-17 23:31:29,017 - mmseg - INFO - Iter [200/80000] lr: 5.294e-06, eta: 1 day, 7:03:54, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 3.1588, decode.acc_seg: 31.2278, aux.loss_ce: 1.3956, aux.acc_seg: 24.2574, loss: 4.5544 +2024-06-17 23:32:35,313 - mmseg - INFO - Iter [250/80000] lr: 6.619e-06, eta: 1 day, 6:42:40, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 2.6074, decode.acc_seg: 41.3237, aux.loss_ce: 1.1948, aux.acc_seg: 35.4717, loss: 3.8022 +2024-06-17 23:33:41,513 - mmseg - INFO - Iter [300/80000] lr: 7.944e-06, eta: 1 day, 6:27:42, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 2.2730, decode.acc_seg: 45.1297, aux.loss_ce: 1.0173, aux.acc_seg: 41.6819, loss: 3.2903 +2024-06-17 23:34:47,964 - mmseg - INFO - Iter [350/80000] lr: 9.266e-06, eta: 1 day, 6:17:39, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 1.9568, decode.acc_seg: 51.2450, aux.loss_ce: 0.8679, aux.acc_seg: 48.4438, loss: 2.8248 +2024-06-17 23:35:54,671 - mmseg - INFO - Iter [400/80000] lr: 1.059e-05, eta: 1 day, 6:10:42, time: 1.334, data_time: 0.009, memory: 70498, decode.loss_ce: 1.7470, decode.acc_seg: 54.8958, aux.loss_ce: 0.7742, aux.acc_seg: 51.9837, loss: 2.5212 +2024-06-17 23:37:01,401 - mmseg - INFO - Iter [450/80000] lr: 1.191e-05, eta: 1 day, 6:05:06, time: 1.335, data_time: 0.009, memory: 70498, decode.loss_ce: 1.5412, decode.acc_seg: 58.3589, aux.loss_ce: 0.6816, aux.acc_seg: 56.5869, loss: 2.2228 +2024-06-17 23:38:07,914 - mmseg - INFO - Iter [500/80000] lr: 1.322e-05, eta: 1 day, 5:59:50, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 1.4240, decode.acc_seg: 61.0607, aux.loss_ce: 0.6251, aux.acc_seg: 59.5737, loss: 2.0491 +2024-06-17 23:39:14,885 - mmseg - INFO - Iter [550/80000] lr: 1.454e-05, eta: 1 day, 5:56:25, time: 1.339, data_time: 0.009, memory: 70498, decode.loss_ce: 1.3398, decode.acc_seg: 62.3666, aux.loss_ce: 0.5810, aux.acc_seg: 61.1871, loss: 1.9207 +2024-06-17 23:40:21,911 - mmseg - INFO - Iter [600/80000] lr: 1.585e-05, eta: 1 day, 5:53:30, time: 1.341, data_time: 0.009, memory: 70498, decode.loss_ce: 1.2614, decode.acc_seg: 63.9837, aux.loss_ce: 0.5470, aux.acc_seg: 62.9118, loss: 1.8084 +2024-06-17 23:41:28,769 - mmseg - INFO - Iter [650/80000] lr: 1.717e-05, eta: 1 day, 5:50:32, time: 1.337, data_time: 0.009, memory: 70498, decode.loss_ce: 1.2117, decode.acc_seg: 64.4956, aux.loss_ce: 0.5232, aux.acc_seg: 63.7487, loss: 1.7349 +2024-06-17 23:42:35,285 - mmseg - INFO - Iter [700/80000] lr: 1.848e-05, eta: 1 day, 5:47:10, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 1.1793, decode.acc_seg: 65.7385, aux.loss_ce: 0.5096, aux.acc_seg: 64.8930, loss: 1.6890 +2024-06-17 23:43:42,187 - mmseg - INFO - Iter [750/80000] lr: 1.979e-05, eta: 1 day, 5:44:48, time: 1.338, data_time: 0.009, memory: 70498, decode.loss_ce: 1.1308, decode.acc_seg: 66.3015, aux.loss_ce: 0.4785, aux.acc_seg: 66.2539, loss: 1.6092 +2024-06-17 23:44:48,627 - mmseg - INFO - Iter [800/80000] lr: 2.109e-05, eta: 1 day, 5:41:49, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 1.0572, decode.acc_seg: 68.7028, aux.loss_ce: 0.4516, aux.acc_seg: 68.2518, loss: 1.5088 +2024-06-17 23:45:55,123 - mmseg - INFO - Iter [850/80000] lr: 2.240e-05, eta: 1 day, 5:39:09, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 1.0636, decode.acc_seg: 67.4788, aux.loss_ce: 0.4440, aux.acc_seg: 67.6517, loss: 1.5076 +2024-06-17 23:47:01,957 - mmseg - INFO - Iter [900/80000] lr: 2.370e-05, eta: 1 day, 5:37:08, time: 1.337, data_time: 0.009, memory: 70498, decode.loss_ce: 1.0272, decode.acc_seg: 67.9175, aux.loss_ce: 0.4252, aux.acc_seg: 68.4821, loss: 1.4523 +2024-06-17 23:48:08,840 - mmseg - INFO - Iter [950/80000] lr: 2.501e-05, eta: 1 day, 5:35:18, time: 1.338, data_time: 0.009, memory: 70498, decode.loss_ce: 0.9544, decode.acc_seg: 68.7457, aux.loss_ce: 0.3949, aux.acc_seg: 69.1776, loss: 1.3493 +2024-06-17 23:49:15,414 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 23:49:15,415 - mmseg - INFO - Iter [1000/80000] lr: 2.631e-05, eta: 1 day, 5:33:07, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 1.0104, decode.acc_seg: 68.2336, aux.loss_ce: 0.4173, aux.acc_seg: 68.6646, loss: 1.4277 +2024-06-17 23:51:58,491 - mmseg - INFO - per class results: +2024-06-17 23:51:58,517 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 65.43 | 77.29 | +| building | 74.93 | 85.44 | +| sky | 89.27 | 94.19 | +| floor | 67.94 | 76.78 | +| tree | 66.48 | 85.54 | +| ceiling | 74.72 | 89.43 | +| road | 74.63 | 83.5 | +| bed | 75.51 | 95.78 | +| windowpane | 50.08 | 79.05 | +| grass | 57.9 | 71.95 | +| cabinet | 48.13 | 70.13 | +| sidewalk | 51.45 | 70.98 | +| person | 61.76 | 78.05 | +| earth | 28.52 | 44.8 | +| door | 35.94 | 46.98 | +| table | 42.15 | 66.51 | +| mountain | 48.01 | 80.18 | +| plant | 47.33 | 61.98 | +| curtain | 57.24 | 75.21 | +| chair | 41.42 | 66.27 | +| car | 62.48 | 90.12 | +| water | 42.71 | 90.41 | +| painting | 51.18 | 74.79 | +| sofa | 51.98 | 81.78 | +| shelf | 21.67 | 58.73 | +| house | 40.33 | 84.22 | +| sea | 31.18 | 35.83 | +| mirror | 49.99 | 87.0 | +| rug | 45.22 | 73.94 | +| field | 29.96 | 61.26 | +| armchair | 25.21 | 31.22 | +| seat | 48.27 | 85.0 | +| fence | 22.52 | 25.4 | +| desk | 22.58 | 34.35 | +| rock | 40.77 | 55.04 | +| wardrobe | 41.38 | 82.43 | +| lamp | 28.17 | 36.11 | +| bathtub | 9.68 | 9.81 | +| railing | 16.36 | 19.93 | +| cushion | 26.75 | 35.84 | +| base | 10.34 | 12.36 | +| box | 5.38 | 5.64 | +| column | 0.29 | 0.29 | +| signboard | 0.06 | 0.06 | +| chest of drawers | 37.47 | 50.68 | +| counter | 19.41 | 21.62 | +| sand | 12.63 | 12.7 | +| sink | 42.1 | 48.51 | +| skyscraper | 38.22 | 83.63 | +| fireplace | 52.03 | 90.56 | +| refrigerator | 50.55 | 80.31 | +| grandstand | 26.89 | 82.59 | +| path | 7.12 | 8.18 | +| stairs | 0.14 | 0.14 | +| runway | 51.6 | 66.94 | +| case | 14.95 | 17.28 | +| pool table | 77.41 | 89.87 | +| pillow | 10.87 | 11.24 | +| screen door | 50.9 | 78.28 | +| stairway | 20.51 | 44.81 | +| river | 0.01 | 0.01 | +| bridge | 36.06 | 42.45 | +| bookcase | 26.08 | 75.49 | +| blind | 0.0 | 0.0 | +| coffee table | 35.79 | 82.64 | +| toilet | 67.09 | 81.77 | +| flower | 5.85 | 5.98 | +| book | 0.02 | 0.02 | +| hill | 0.0 | 0.0 | +| bench | 0.0 | 0.0 | +| countertop | 22.24 | 25.13 | +| stove | 36.23 | 86.97 | +| palm | 3.25 | 3.26 | +| kitchen island | 9.94 | 10.21 | +| computer | 36.72 | 92.5 | +| swivel chair | 1.96 | 2.01 | +| boat | 4.77 | 4.99 | +| bar | 39.76 | 67.32 | +| arcade machine | 36.7 | 97.36 | +| hovel | 21.69 | 24.22 | +| bus | 53.55 | 83.0 | +| towel | 0.67 | 0.67 | +| light | 0.0 | 0.0 | +| truck | 10.48 | 11.36 | +| tower | 0.0 | 0.0 | +| chandelier | 0.0 | 0.0 | +| awning | 0.0 | 0.0 | +| streetlight | 0.0 | 0.0 | +| booth | 0.0 | 0.0 | +| television receiver | 0.0 | 0.0 | +| airplane | 17.08 | 18.11 | +| dirt track | 0.0 | 0.0 | +| apparel | 0.0 | 0.0 | +| pole | 0.0 | 0.0 | +| land | 0.0 | 0.0 | +| bannister | 0.0 | 0.0 | +| escalator | 0.0 | 0.0 | +| ottoman | 0.0 | 0.0 | +| bottle | 0.0 | 0.0 | +| buffet | 0.0 | 0.0 | +| poster | 0.0 | 0.0 | +| stage | 0.0 | 0.0 | +| van | 0.0 | 0.0 | +| ship | 0.0 | 0.0 | +| fountain | 0.0 | 0.0 | +| conveyer belt | 0.0 | 0.0 | +| canopy | 0.0 | 0.0 | +| washer | 15.67 | 15.67 | +| plaything | 0.0 | 0.0 | +| swimming pool | 0.0 | 0.0 | +| stool | 0.0 | 0.0 | +| barrel | 9.27 | 9.27 | +| basket | 0.0 | 0.0 | +| waterfall | 30.29 | 33.0 | +| tent | 88.43 | 95.31 | +| bag | 0.0 | 0.0 | +| minibike | 0.0 | 0.0 | +| cradle | 63.9 | 83.92 | +| oven | 0.0 | 0.0 | +| ball | 4.09 | 4.15 | +| food | 0.0 | 0.0 | +| step | 0.0 | 0.0 | +| tank | 0.0 | 0.0 | +| trade name | 0.0 | 0.0 | +| microwave | 0.0 | 0.0 | +| pot | 0.0 | 0.0 | +| animal | 0.0 | 0.0 | +| bicycle | 0.0 | 0.0 | +| lake | 0.0 | 0.0 | +| dishwasher | 0.0 | 0.0 | +| screen | 0.0 | 0.0 | +| 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 | ++---------------------+-------+-------+ +2024-06-17 23:51:58,517 - mmseg - INFO - Summary: +2024-06-17 23:51:58,518 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 73.28 | 20.49 | 30.1 | ++-------+-------+------+ +2024-06-17 23:51:58,519 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 23:51:58,519 - mmseg - INFO - Iter(val) [250] aAcc: 0.7328, mIoU: 0.2049, mAcc: 0.3010, IoU.wall: 0.6543, IoU.building: 0.7493, IoU.sky: 0.8927, IoU.floor: 0.6794, IoU.tree: 0.6648, IoU.ceiling: 0.7472, IoU.road: 0.7463, IoU.bed : 0.7551, IoU.windowpane: 0.5008, IoU.grass: 0.5790, IoU.cabinet: 0.4813, IoU.sidewalk: 0.5145, IoU.person: 0.6176, IoU.earth: 0.2852, IoU.door: 0.3594, IoU.table: 0.4215, IoU.mountain: 0.4801, IoU.plant: 0.4733, IoU.curtain: 0.5724, IoU.chair: 0.4142, IoU.car: 0.6248, IoU.water: 0.4271, IoU.painting: 0.5118, IoU.sofa: 0.5198, IoU.shelf: 0.2167, IoU.house: 0.4033, IoU.sea: 0.3118, IoU.mirror: 0.4999, IoU.rug: 0.4522, IoU.field: 0.2996, IoU.armchair: 0.2521, IoU.seat: 0.4827, IoU.fence: 0.2252, IoU.desk: 0.2258, IoU.rock: 0.4077, IoU.wardrobe: 0.4138, IoU.lamp: 0.2817, IoU.bathtub: 0.0968, IoU.railing: 0.1636, IoU.cushion: 0.2675, IoU.base: 0.1034, IoU.box: 0.0538, IoU.column: 0.0029, IoU.signboard: 0.0006, IoU.chest of drawers: 0.3747, IoU.counter: 0.1941, IoU.sand: 0.1263, IoU.sink: 0.4210, IoU.skyscraper: 0.3822, IoU.fireplace: 0.5203, IoU.refrigerator: 0.5055, IoU.grandstand: 0.2689, IoU.path: 0.0712, IoU.stairs: 0.0014, IoU.runway: 0.5160, IoU.case: 0.1495, IoU.pool table: 0.7741, IoU.pillow: 0.1087, IoU.screen door: 0.5090, IoU.stairway: 0.2051, IoU.river: 0.0001, IoU.bridge: 0.3606, IoU.bookcase: 0.2608, IoU.blind: 0.0000, IoU.coffee table: 0.3579, IoU.toilet: 0.6709, IoU.flower: 0.0585, IoU.book: 0.0002, IoU.hill: 0.0000, IoU.bench: 0.0000, IoU.countertop: 0.2224, IoU.stove: 0.3623, IoU.palm: 0.0325, IoU.kitchen island: 0.0994, IoU.computer: 0.3672, IoU.swivel chair: 0.0196, IoU.boat: 0.0477, IoU.bar: 0.3976, IoU.arcade machine: 0.3670, IoU.hovel: 0.2169, IoU.bus: 0.5355, IoU.towel: 0.0067, IoU.light: 0.0000, IoU.truck: 0.1048, IoU.tower: 0.0000, IoU.chandelier: 0.0000, IoU.awning: 0.0000, IoU.streetlight: 0.0000, IoU.booth: 0.0000, IoU.television receiver: 0.0000, IoU.airplane: 0.1708, IoU.dirt track: 0.0000, IoU.apparel: 0.0000, IoU.pole: 0.0000, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.0000, IoU.ottoman: 0.0000, IoU.bottle: 0.0000, IoU.buffet: 0.0000, IoU.poster: 0.0000, IoU.stage: 0.0000, IoU.van: 0.0000, IoU.ship: 0.0000, IoU.fountain: 0.0000, IoU.conveyer belt: 0.0000, IoU.canopy: 0.0000, IoU.washer: 0.1567, IoU.plaything: 0.0000, IoU.swimming pool: 0.0000, IoU.stool: 0.0000, IoU.barrel: 0.0927, IoU.basket: 0.0000, IoU.waterfall: 0.3029, IoU.tent: 0.8843, IoU.bag: 0.0000, IoU.minibike: 0.0000, IoU.cradle: 0.6390, IoU.oven: 0.0000, IoU.ball: 0.0409, IoU.food: 0.0000, IoU.step: 0.0000, IoU.tank: 0.0000, IoU.trade name: 0.0000, IoU.microwave: 0.0000, IoU.pot: 0.0000, IoU.animal: 0.0000, IoU.bicycle: 0.0000, IoU.lake: 0.0000, IoU.dishwasher: 0.0000, IoU.screen: 0.0000, 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: 0.7729, Acc.building: 0.8544, Acc.sky: 0.9419, Acc.floor: 0.7678, Acc.tree: 0.8554, Acc.ceiling: 0.8943, Acc.road: 0.8350, Acc.bed : 0.9578, Acc.windowpane: 0.7905, Acc.grass: 0.7195, Acc.cabinet: 0.7013, Acc.sidewalk: 0.7098, Acc.person: 0.7805, Acc.earth: 0.4480, Acc.door: 0.4698, Acc.table: 0.6651, Acc.mountain: 0.8018, Acc.plant: 0.6198, Acc.curtain: 0.7521, Acc.chair: 0.6627, Acc.car: 0.9012, Acc.water: 0.9041, Acc.painting: 0.7479, Acc.sofa: 0.8178, Acc.shelf: 0.5873, Acc.house: 0.8422, Acc.sea: 0.3583, Acc.mirror: 0.8700, Acc.rug: 0.7394, Acc.field: 0.6126, Acc.armchair: 0.3122, Acc.seat: 0.8500, Acc.fence: 0.2540, Acc.desk: 0.3435, Acc.rock: 0.5504, Acc.wardrobe: 0.8243, Acc.lamp: 0.3611, Acc.bathtub: 0.0981, Acc.railing: 0.1993, Acc.cushion: 0.3584, Acc.base: 0.1236, Acc.box: 0.0564, Acc.column: 0.0029, Acc.signboard: 0.0006, Acc.chest of drawers: 0.5068, Acc.counter: 0.2162, Acc.sand: 0.1270, Acc.sink: 0.4851, Acc.skyscraper: 0.8363, Acc.fireplace: 0.9056, Acc.refrigerator: 0.8031, Acc.grandstand: 0.8259, Acc.path: 0.0818, Acc.stairs: 0.0014, Acc.runway: 0.6694, Acc.case: 0.1728, Acc.pool table: 0.8987, Acc.pillow: 0.1124, Acc.screen door: 0.7828, Acc.stairway: 0.4481, Acc.river: 0.0001, Acc.bridge: 0.4245, Acc.bookcase: 0.7549, Acc.blind: 0.0000, Acc.coffee table: 0.8264, Acc.toilet: 0.8177, Acc.flower: 0.0598, Acc.book: 0.0002, Acc.hill: 0.0000, Acc.bench: 0.0000, Acc.countertop: 0.2513, Acc.stove: 0.8697, Acc.palm: 0.0326, Acc.kitchen island: 0.1021, Acc.computer: 0.9250, Acc.swivel chair: 0.0201, Acc.boat: 0.0499, Acc.bar: 0.6732, Acc.arcade machine: 0.9736, Acc.hovel: 0.2422, Acc.bus: 0.8300, Acc.towel: 0.0067, Acc.light: 0.0000, Acc.truck: 0.1136, Acc.tower: 0.0000, Acc.chandelier: 0.0000, Acc.awning: 0.0000, Acc.streetlight: 0.0000, Acc.booth: 0.0000, Acc.television receiver: 0.0000, Acc.airplane: 0.1811, Acc.dirt track: 0.0000, Acc.apparel: 0.0000, Acc.pole: 0.0000, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.0000, Acc.ottoman: 0.0000, Acc.bottle: 0.0000, Acc.buffet: 0.0000, Acc.poster: 0.0000, Acc.stage: 0.0000, Acc.van: 0.0000, Acc.ship: 0.0000, Acc.fountain: 0.0000, Acc.conveyer belt: 0.0000, Acc.canopy: 0.0000, Acc.washer: 0.1567, Acc.plaything: 0.0000, Acc.swimming pool: 0.0000, Acc.stool: 0.0000, Acc.barrel: 0.0927, Acc.basket: 0.0000, Acc.waterfall: 0.3300, Acc.tent: 0.9531, Acc.bag: 0.0000, Acc.minibike: 0.0000, Acc.cradle: 0.8392, Acc.oven: 0.0000, Acc.ball: 0.0415, Acc.food: 0.0000, Acc.step: 0.0000, Acc.tank: 0.0000, Acc.trade name: 0.0000, Acc.microwave: 0.0000, Acc.pot: 0.0000, Acc.animal: 0.0000, Acc.bicycle: 0.0000, Acc.lake: 0.0000, Acc.dishwasher: 0.0000, Acc.screen: 0.0000, 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 +2024-06-17 23:53:05,713 - mmseg - INFO - Iter [1050/80000] lr: 2.761e-05, eta: 1 day, 8:56:13, time: 4.606, data_time: 3.279, memory: 70498, decode.loss_ce: 0.9023, decode.acc_seg: 71.4421, aux.loss_ce: 0.3726, aux.acc_seg: 71.6607, loss: 1.2749 +2024-06-17 23:54:12,462 - mmseg - INFO - Iter [1100/80000] lr: 2.890e-05, eta: 1 day, 8:45:00, time: 1.335, data_time: 0.009, memory: 70498, decode.loss_ce: 0.9496, decode.acc_seg: 69.7879, aux.loss_ce: 0.3880, aux.acc_seg: 70.3473, loss: 1.3376 +2024-06-17 23:55:19,327 - mmseg - INFO - Iter [1150/80000] lr: 3.020e-05, eta: 1 day, 8:34:47, time: 1.337, data_time: 0.009, memory: 70498, decode.loss_ce: 0.9007, decode.acc_seg: 70.7450, aux.loss_ce: 0.3653, aux.acc_seg: 71.4369, loss: 1.2660 +2024-06-17 23:56:25,817 - mmseg - INFO - Iter [1200/80000] lr: 3.149e-05, eta: 1 day, 8:24:54, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.8935, decode.acc_seg: 70.8698, aux.loss_ce: 0.3654, aux.acc_seg: 71.2423, loss: 1.2589 +2024-06-17 23:57:32,185 - mmseg - INFO - Iter [1250/80000] lr: 3.279e-05, eta: 1 day, 8:15:37, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.8704, decode.acc_seg: 71.2788, aux.loss_ce: 0.3536, aux.acc_seg: 71.6371, loss: 1.2239 +2024-06-17 23:58:41,394 - mmseg - INFO - Iter [1300/80000] lr: 3.408e-05, eta: 1 day, 8:09:49, time: 1.384, data_time: 0.065, memory: 70498, decode.loss_ce: 0.8434, decode.acc_seg: 72.4812, aux.loss_ce: 0.3427, aux.acc_seg: 72.7111, loss: 1.1862 +2024-06-17 23:59:47,797 - mmseg - INFO - Iter [1350/80000] lr: 3.537e-05, eta: 1 day, 8:01:38, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.8326, decode.acc_seg: 71.8988, aux.loss_ce: 0.3376, aux.acc_seg: 72.2832, loss: 1.1702 +2024-06-18 00:00:54,093 - mmseg - INFO - Iter [1400/80000] lr: 3.665e-05, eta: 1 day, 7:53:52, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.8061, decode.acc_seg: 72.1258, aux.loss_ce: 0.3256, aux.acc_seg: 72.7801, loss: 1.1317 +2024-06-18 00:02:00,502 - mmseg - INFO - Iter [1450/80000] lr: 3.794e-05, eta: 1 day, 7:46:39, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.8004, decode.acc_seg: 72.7801, aux.loss_ce: 0.3218, aux.acc_seg: 73.2623, loss: 1.1221 +2024-06-18 00:03:06,958 - mmseg - INFO - Iter [1500/80000] lr: 3.922e-05, eta: 1 day, 7:39:53, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.8202, decode.acc_seg: 72.6040, aux.loss_ce: 0.3317, aux.acc_seg: 73.1038, loss: 1.1519 +2024-06-18 00:04:13,243 - mmseg - INFO - Iter [1550/80000] lr: 3.923e-05, eta: 1 day, 7:33:20, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7742, decode.acc_seg: 73.5879, aux.loss_ce: 0.3098, aux.acc_seg: 74.3771, loss: 1.0840 +2024-06-18 00:05:19,480 - mmseg - INFO - Iter [1600/80000] lr: 3.920e-05, eta: 1 day, 7:27:06, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7852, decode.acc_seg: 73.5277, aux.loss_ce: 0.3161, aux.acc_seg: 74.0257, loss: 1.1013 +2024-06-18 00:06:25,893 - mmseg - INFO - Iter [1650/80000] lr: 3.918e-05, eta: 1 day, 7:21:18, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7400, decode.acc_seg: 73.7670, aux.loss_ce: 0.2976, aux.acc_seg: 74.5263, loss: 1.0376 +2024-06-18 00:07:32,318 - mmseg - INFO - Iter [1700/80000] lr: 3.915e-05, eta: 1 day, 7:15:48, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7999, decode.acc_seg: 73.1069, aux.loss_ce: 0.3203, aux.acc_seg: 73.6110, loss: 1.1202 +2024-06-18 00:08:38,521 - mmseg - INFO - Iter [1750/80000] lr: 3.913e-05, eta: 1 day, 7:10:23, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7254, decode.acc_seg: 74.1419, aux.loss_ce: 0.2900, aux.acc_seg: 74.8386, loss: 1.0154 +2024-06-18 00:09:44,614 - mmseg - INFO - Iter [1800/80000] lr: 3.910e-05, eta: 1 day, 7:05:07, time: 1.322, data_time: 0.009, memory: 70498, decode.loss_ce: 0.8160, decode.acc_seg: 72.8131, aux.loss_ce: 0.3228, aux.acc_seg: 73.5143, loss: 1.1388 +2024-06-18 00:10:50,837 - mmseg - INFO - Iter [1850/80000] lr: 3.908e-05, eta: 1 day, 7:00:10, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7813, decode.acc_seg: 72.9785, aux.loss_ce: 0.3107, aux.acc_seg: 73.4335, loss: 1.0921 +2024-06-18 00:11:57,260 - mmseg - INFO - Iter [1900/80000] lr: 3.905e-05, eta: 1 day, 6:55:34, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7169, decode.acc_seg: 74.8473, aux.loss_ce: 0.2860, aux.acc_seg: 75.1731, loss: 1.0029 +2024-06-18 00:13:03,550 - mmseg - INFO - Iter [1950/80000] lr: 3.903e-05, eta: 1 day, 6:51:03, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7416, decode.acc_seg: 73.9653, aux.loss_ce: 0.2937, aux.acc_seg: 74.5186, loss: 1.0353 +2024-06-18 00:14:09,817 - mmseg - INFO - Saving checkpoint at 2000 iterations +2024-06-18 00:15:44,182 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 00:15:44,183 - mmseg - INFO - Iter [2000/80000] lr: 3.900e-05, eta: 1 day, 7:48:02, time: 3.213, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6978, decode.acc_seg: 75.2205, aux.loss_ce: 0.2779, aux.acc_seg: 75.7381, loss: 0.9757 +2024-06-18 00:17:21,475 - mmseg - INFO - per class results: +2024-06-18 00:17:21,481 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 71.0 | 85.6 | +| building | 80.52 | 91.65 | +| sky | 92.12 | 96.8 | +| floor | 75.06 | 88.02 | +| tree | 70.76 | 84.74 | +| ceiling | 77.44 | 92.85 | +| road | 79.38 | 88.16 | +| bed | 83.35 | 91.97 | +| windowpane | 55.78 | 75.4 | +| grass | 64.89 | 84.78 | +| cabinet | 53.66 | 61.46 | +| sidewalk | 55.9 | 74.73 | +| person | 69.85 | 85.21 | +| earth | 31.92 | 40.65 | +| door | 42.52 | 50.57 | +| table | 48.69 | 64.24 | +| mountain | 52.97 | 72.36 | +| plant | 50.0 | 59.89 | +| curtain | 65.9 | 76.34 | +| chair | 46.04 | 57.35 | +| car | 75.76 | 88.95 | +| water | 52.25 | 65.58 | +| painting | 62.13 | 77.25 | +| sofa | 62.6 | 85.54 | +| shelf | 37.44 | 65.98 | +| house | 49.23 | 66.51 | +| sea | 56.09 | 89.46 | +| mirror | 62.83 | 79.91 | +| rug | 50.85 | 57.32 | +| field | 37.22 | 66.94 | +| armchair | 38.97 | 59.73 | +| seat | 59.33 | 65.37 | +| fence | 36.75 | 47.43 | +| desk | 36.56 | 71.85 | +| rock | 36.43 | 43.29 | +| wardrobe | 50.09 | 78.59 | +| lamp | 38.85 | 43.02 | +| bathtub | 66.41 | 79.28 | +| railing | 33.14 | 54.57 | +| cushion | 44.8 | 59.09 | +| base | 6.18 | 6.97 | +| box | 13.41 | 17.82 | +| column | 38.85 | 57.84 | +| signboard | 17.98 | 19.52 | +| chest of drawers | 41.63 | 65.09 | +| counter | 49.36 | 69.26 | +| sand | 38.05 | 57.86 | +| sink | 49.81 | 55.06 | +| skyscraper | 46.02 | 54.19 | +| fireplace | 57.73 | 91.47 | +| refrigerator | 63.64 | 83.21 | +| grandstand | 55.82 | 66.7 | +| path | 20.74 | 30.84 | +| stairs | 28.43 | 35.75 | +| runway | 64.1 | 86.36 | +| case | 60.9 | 81.07 | +| pool table | 75.47 | 96.54 | +| pillow | 34.87 | 38.5 | +| screen door | 63.92 | 83.69 | +| stairway | 36.56 | 61.5 | +| river | 18.76 | 31.79 | +| bridge | 37.81 | 44.92 | +| bookcase | 25.73 | 44.34 | +| blind | 14.06 | 14.12 | +| coffee table | 45.1 | 76.92 | +| toilet | 76.72 | 86.14 | +| flower | 25.93 | 38.88 | +| book | 14.74 | 16.91 | +| hill | 0.13 | 0.13 | +| bench | 45.15 | 56.01 | +| countertop | 45.64 | 55.1 | +| stove | 54.36 | 81.77 | +| palm | 47.2 | 59.11 | +| kitchen island | 32.04 | 80.04 | +| computer | 63.98 | 81.49 | +| swivel chair | 36.59 | 65.47 | +| boat | 30.82 | 69.12 | +| bar | 51.98 | 55.07 | +| arcade machine | 70.57 | 77.29 | +| hovel | 40.3 | 52.37 | +| bus | 82.3 | 91.55 | +| towel | 46.53 | 53.05 | +| light | 0.08 | 0.08 | +| truck | 24.8 | 31.54 | +| tower | 20.05 | 32.71 | +| chandelier | 50.85 | 70.97 | +| awning | 27.35 | 39.53 | +| streetlight | 0.0 | 0.0 | +| booth | 32.2 | 35.61 | +| television receiver | 58.1 | 70.36 | +| airplane | 47.17 | 58.22 | +| dirt track | 0.0 | 0.0 | +| apparel | 22.39 | 30.29 | +| pole | 0.0 | 0.0 | +| land | 0.0 | 0.0 | +| bannister | 0.0 | 0.0 | +| escalator | 46.04 | 76.1 | +| ottoman | 30.71 | 37.72 | +| bottle | 32.21 | 45.0 | +| buffet | 32.31 | 40.29 | +| poster | 0.66 | 0.66 | +| stage | 10.82 | 25.16 | +| van | 22.86 | 27.04 | +| ship | 45.06 | 58.29 | +| fountain | 55.48 | 73.33 | +| conveyer belt | 47.31 | 48.44 | +| canopy | 37.24 | 40.71 | +| washer | 72.37 | 79.15 | +| plaything | 4.92 | 5.43 | +| swimming pool | 44.86 | 99.27 | +| stool | 9.89 | 11.26 | +| barrel | 4.22 | 63.42 | +| basket | 0.0 | 0.0 | +| waterfall | 59.39 | 80.76 | +| tent | 85.87 | 99.25 | +| bag | 0.0 | 0.0 | +| minibike | 45.83 | 49.45 | +| cradle | 65.32 | 97.3 | +| oven | 0.58 | 0.58 | +| ball | 27.7 | 64.23 | +| food | 26.33 | 28.41 | +| step | 0.0 | 0.0 | +| tank | 12.8 | 12.82 | +| trade name | 0.0 | 0.0 | +| microwave | 69.82 | 82.25 | +| pot | 12.46 | 12.8 | +| animal | 39.43 | 40.17 | +| bicycle | 23.72 | 25.77 | +| lake | 0.0 | 0.0 | +| dishwasher | 3.59 | 3.59 | +| screen | 56.14 | 74.76 | +| blanket | 0.0 | 0.0 | +| sculpture | 13.1 | 13.15 | +| hood | 47.22 | 59.02 | +| sconce | 0.0 | 0.0 | +| vase | 8.54 | 8.81 | +| traffic light | 0.0 | 0.0 | +| tray | 0.04 | 0.05 | +| ashcan | 0.0 | 0.0 | +| fan | 1.05 | 1.05 | +| pier | 0.82 | 0.82 | +| crt screen | 0.0 | 0.0 | +| plate | 17.67 | 19.83 | +| 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 00:17:21,481 - mmseg - INFO - Summary: +2024-06-18 00:17:21,481 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 79.21 | 36.15 | 47.56 | ++-------+-------+-------+ +2024-06-18 00:17:21,482 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 00:17:21,482 - mmseg - INFO - Iter(val) [250] aAcc: 0.7921, mIoU: 0.3615, mAcc: 0.4756, IoU.wall: 0.7100, IoU.building: 0.8052, IoU.sky: 0.9212, IoU.floor: 0.7506, IoU.tree: 0.7076, IoU.ceiling: 0.7744, IoU.road: 0.7938, IoU.bed : 0.8335, IoU.windowpane: 0.5578, IoU.grass: 0.6489, IoU.cabinet: 0.5366, IoU.sidewalk: 0.5590, IoU.person: 0.6985, IoU.earth: 0.3192, IoU.door: 0.4252, IoU.table: 0.4869, IoU.mountain: 0.5297, IoU.plant: 0.5000, IoU.curtain: 0.6590, IoU.chair: 0.4604, IoU.car: 0.7576, IoU.water: 0.5225, IoU.painting: 0.6213, IoU.sofa: 0.6260, IoU.shelf: 0.3744, IoU.house: 0.4923, IoU.sea: 0.5609, IoU.mirror: 0.6283, IoU.rug: 0.5085, IoU.field: 0.3722, IoU.armchair: 0.3897, IoU.seat: 0.5933, IoU.fence: 0.3675, IoU.desk: 0.3656, IoU.rock: 0.3643, IoU.wardrobe: 0.5009, IoU.lamp: 0.3885, IoU.bathtub: 0.6641, IoU.railing: 0.3314, IoU.cushion: 0.4480, IoU.base: 0.0618, IoU.box: 0.1341, IoU.column: 0.3885, IoU.signboard: 0.1798, IoU.chest of drawers: 0.4163, IoU.counter: 0.4936, IoU.sand: 0.3805, IoU.sink: 0.4981, IoU.skyscraper: 0.4602, IoU.fireplace: 0.5773, IoU.refrigerator: 0.6364, IoU.grandstand: 0.5582, IoU.path: 0.2074, IoU.stairs: 0.2843, IoU.runway: 0.6410, IoU.case: 0.6090, IoU.pool table: 0.7547, IoU.pillow: 0.3487, IoU.screen door: 0.6392, IoU.stairway: 0.3656, IoU.river: 0.1876, IoU.bridge: 0.3781, IoU.bookcase: 0.2573, IoU.blind: 0.1406, IoU.coffee table: 0.4510, IoU.toilet: 0.7672, IoU.flower: 0.2593, IoU.book: 0.1474, IoU.hill: 0.0013, IoU.bench: 0.4515, IoU.countertop: 0.4564, IoU.stove: 0.5436, IoU.palm: 0.4720, IoU.kitchen island: 0.3204, IoU.computer: 0.6398, IoU.swivel chair: 0.3659, IoU.boat: 0.3082, IoU.bar: 0.5198, IoU.arcade machine: 0.7057, IoU.hovel: 0.4030, IoU.bus: 0.8230, IoU.towel: 0.4653, IoU.light: 0.0008, IoU.truck: 0.2480, IoU.tower: 0.2005, IoU.chandelier: 0.5085, IoU.awning: 0.2735, IoU.streetlight: 0.0000, IoU.booth: 0.3220, IoU.television receiver: 0.5810, IoU.airplane: 0.4717, IoU.dirt track: 0.0000, IoU.apparel: 0.2239, IoU.pole: 0.0000, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.4604, IoU.ottoman: 0.3071, IoU.bottle: 0.3221, IoU.buffet: 0.3231, IoU.poster: 0.0066, IoU.stage: 0.1082, IoU.van: 0.2286, IoU.ship: 0.4506, IoU.fountain: 0.5548, IoU.conveyer belt: 0.4731, IoU.canopy: 0.3724, IoU.washer: 0.7237, IoU.plaything: 0.0492, IoU.swimming pool: 0.4486, IoU.stool: 0.0989, IoU.barrel: 0.0422, IoU.basket: 0.0000, IoU.waterfall: 0.5939, IoU.tent: 0.8587, IoU.bag: 0.0000, IoU.minibike: 0.4583, IoU.cradle: 0.6532, IoU.oven: 0.0058, IoU.ball: 0.2770, IoU.food: 0.2633, IoU.step: 0.0000, IoU.tank: 0.1280, IoU.trade name: 0.0000, IoU.microwave: 0.6982, IoU.pot: 0.1246, IoU.animal: 0.3943, IoU.bicycle: 0.2372, IoU.lake: 0.0000, IoU.dishwasher: 0.0359, IoU.screen: 0.5614, IoU.blanket: 0.0000, IoU.sculpture: 0.1310, IoU.hood: 0.4722, IoU.sconce: 0.0000, IoU.vase: 0.0854, IoU.traffic light: 0.0000, IoU.tray: 0.0004, IoU.ashcan: 0.0000, IoU.fan: 0.0105, IoU.pier: 0.0082, IoU.crt screen: 0.0000, IoU.plate: 0.1767, 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: 0.8560, Acc.building: 0.9165, Acc.sky: 0.9680, Acc.floor: 0.8802, Acc.tree: 0.8474, Acc.ceiling: 0.9285, Acc.road: 0.8816, Acc.bed : 0.9197, Acc.windowpane: 0.7540, Acc.grass: 0.8478, Acc.cabinet: 0.6146, Acc.sidewalk: 0.7473, Acc.person: 0.8521, Acc.earth: 0.4065, Acc.door: 0.5057, Acc.table: 0.6424, Acc.mountain: 0.7236, Acc.plant: 0.5989, Acc.curtain: 0.7634, Acc.chair: 0.5735, Acc.car: 0.8895, Acc.water: 0.6558, Acc.painting: 0.7725, Acc.sofa: 0.8554, Acc.shelf: 0.6598, Acc.house: 0.6651, Acc.sea: 0.8946, Acc.mirror: 0.7991, Acc.rug: 0.5732, Acc.field: 0.6694, Acc.armchair: 0.5973, Acc.seat: 0.6537, Acc.fence: 0.4743, Acc.desk: 0.7185, Acc.rock: 0.4329, Acc.wardrobe: 0.7859, Acc.lamp: 0.4302, Acc.bathtub: 0.7928, Acc.railing: 0.5457, Acc.cushion: 0.5909, Acc.base: 0.0697, Acc.box: 0.1782, Acc.column: 0.5784, Acc.signboard: 0.1952, Acc.chest of drawers: 0.6509, Acc.counter: 0.6926, Acc.sand: 0.5786, Acc.sink: 0.5506, Acc.skyscraper: 0.5419, Acc.fireplace: 0.9147, Acc.refrigerator: 0.8321, Acc.grandstand: 0.6670, Acc.path: 0.3084, Acc.stairs: 0.3575, Acc.runway: 0.8636, Acc.case: 0.8107, Acc.pool table: 0.9654, Acc.pillow: 0.3850, Acc.screen door: 0.8369, Acc.stairway: 0.6150, Acc.river: 0.3179, Acc.bridge: 0.4492, Acc.bookcase: 0.4434, Acc.blind: 0.1412, Acc.coffee table: 0.7692, Acc.toilet: 0.8614, Acc.flower: 0.3888, Acc.book: 0.1691, Acc.hill: 0.0013, Acc.bench: 0.5601, Acc.countertop: 0.5510, Acc.stove: 0.8177, Acc.palm: 0.5911, Acc.kitchen island: 0.8004, Acc.computer: 0.8149, Acc.swivel chair: 0.6547, Acc.boat: 0.6912, Acc.bar: 0.5507, Acc.arcade machine: 0.7729, Acc.hovel: 0.5237, Acc.bus: 0.9155, Acc.towel: 0.5305, Acc.light: 0.0008, Acc.truck: 0.3154, Acc.tower: 0.3271, Acc.chandelier: 0.7097, Acc.awning: 0.3953, Acc.streetlight: 0.0000, Acc.booth: 0.3561, Acc.television receiver: 0.7036, Acc.airplane: 0.5822, Acc.dirt track: 0.0000, Acc.apparel: 0.3029, Acc.pole: 0.0000, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.7610, Acc.ottoman: 0.3772, Acc.bottle: 0.4500, Acc.buffet: 0.4029, Acc.poster: 0.0066, Acc.stage: 0.2516, Acc.van: 0.2704, Acc.ship: 0.5829, Acc.fountain: 0.7333, Acc.conveyer belt: 0.4844, Acc.canopy: 0.4071, Acc.washer: 0.7915, Acc.plaything: 0.0543, Acc.swimming pool: 0.9927, Acc.stool: 0.1126, Acc.barrel: 0.6342, Acc.basket: 0.0000, Acc.waterfall: 0.8076, Acc.tent: 0.9925, Acc.bag: 0.0000, Acc.minibike: 0.4945, Acc.cradle: 0.9730, Acc.oven: 0.0058, Acc.ball: 0.6423, Acc.food: 0.2841, Acc.step: 0.0000, Acc.tank: 0.1282, Acc.trade name: 0.0000, Acc.microwave: 0.8225, Acc.pot: 0.1280, Acc.animal: 0.4017, Acc.bicycle: 0.2577, Acc.lake: 0.0000, Acc.dishwasher: 0.0359, Acc.screen: 0.7476, Acc.blanket: 0.0000, Acc.sculpture: 0.1315, Acc.hood: 0.5902, Acc.sconce: 0.0000, Acc.vase: 0.0881, Acc.traffic light: 0.0000, Acc.tray: 0.0005, Acc.ashcan: 0.0000, Acc.fan: 0.0105, Acc.pier: 0.0082, Acc.crt screen: 0.0000, Acc.plate: 0.1983, 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 00:18:28,436 - mmseg - INFO - Iter [2050/80000] lr: 3.898e-05, eta: 1 day, 8:44:24, time: 3.285, data_time: 1.963, memory: 70498, decode.loss_ce: 0.7720, decode.acc_seg: 73.3521, aux.loss_ce: 0.3088, aux.acc_seg: 73.8399, loss: 1.0808 +2024-06-18 00:19:34,746 - mmseg - INFO - Iter [2100/80000] lr: 3.895e-05, eta: 1 day, 8:37:23, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7486, decode.acc_seg: 73.3045, aux.loss_ce: 0.2965, aux.acc_seg: 73.6937, loss: 1.0451 +2024-06-18 00:20:41,090 - mmseg - INFO - Iter [2150/80000] lr: 3.893e-05, eta: 1 day, 8:30:41, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7614, decode.acc_seg: 73.3536, aux.loss_ce: 0.3026, aux.acc_seg: 73.7852, loss: 1.0640 +2024-06-18 00:21:47,194 - mmseg - INFO - Iter [2200/80000] lr: 3.890e-05, eta: 1 day, 8:24:05, time: 1.322, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7087, decode.acc_seg: 73.7075, aux.loss_ce: 0.2805, aux.acc_seg: 74.3672, loss: 0.9892 +2024-06-18 00:22:53,485 - mmseg - INFO - Iter [2250/80000] lr: 3.888e-05, eta: 1 day, 8:17:50, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.7161, decode.acc_seg: 73.9959, aux.loss_ce: 0.2858, aux.acc_seg: 74.3988, loss: 1.0019 +2024-06-18 00:23:59,985 - mmseg - INFO - Iter [2300/80000] lr: 3.885e-05, eta: 1 day, 8:11:56, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.7227, decode.acc_seg: 73.9097, aux.loss_ce: 0.2852, aux.acc_seg: 74.4169, loss: 1.0079 +2024-06-18 00:25:06,358 - mmseg - INFO - Iter [2350/80000] lr: 3.883e-05, eta: 1 day, 8:06:10, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.6956, decode.acc_seg: 75.0176, aux.loss_ce: 0.2756, aux.acc_seg: 75.3167, loss: 0.9711 +2024-06-18 00:26:12,909 - mmseg - INFO - Iter [2400/80000] lr: 3.880e-05, eta: 1 day, 8:00:41, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6694, decode.acc_seg: 75.6397, aux.loss_ce: 0.2662, aux.acc_seg: 75.9729, loss: 0.9356 +2024-06-18 00:27:19,305 - mmseg - INFO - Iter [2450/80000] lr: 3.878e-05, eta: 1 day, 7:55:18, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6979, decode.acc_seg: 74.6794, aux.loss_ce: 0.2762, aux.acc_seg: 75.2301, loss: 0.9741 +2024-06-18 00:28:25,913 - mmseg - INFO - Iter [2500/80000] lr: 3.875e-05, eta: 1 day, 7:50:12, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6902, decode.acc_seg: 75.3061, aux.loss_ce: 0.2731, aux.acc_seg: 75.8416, loss: 0.9633 +2024-06-18 00:29:34,257 - mmseg - INFO - Iter [2550/80000] lr: 3.873e-05, eta: 1 day, 7:46:08, time: 1.367, data_time: 0.051, memory: 70498, decode.loss_ce: 0.6843, decode.acc_seg: 75.7974, aux.loss_ce: 0.2708, aux.acc_seg: 76.1447, loss: 0.9552 +2024-06-18 00:30:40,511 - mmseg - INFO - Iter [2600/80000] lr: 3.870e-05, eta: 1 day, 7:41:08, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.6463, decode.acc_seg: 76.6543, aux.loss_ce: 0.2589, aux.acc_seg: 77.2164, loss: 0.9052 +2024-06-18 00:31:46,659 - mmseg - INFO - Iter [2650/80000] lr: 3.868e-05, eta: 1 day, 7:36:14, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.6381, decode.acc_seg: 76.4233, aux.loss_ce: 0.2536, aux.acc_seg: 76.6439, loss: 0.8917 +2024-06-18 00:32:53,113 - mmseg - INFO - Iter [2700/80000] lr: 3.865e-05, eta: 1 day, 7:31:38, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.6406, decode.acc_seg: 77.0102, aux.loss_ce: 0.2544, aux.acc_seg: 77.2329, loss: 0.8950 +2024-06-18 00:33:59,161 - mmseg - INFO - Iter [2750/80000] lr: 3.863e-05, eta: 1 day, 7:26:57, time: 1.321, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6456, decode.acc_seg: 76.1699, aux.loss_ce: 0.2567, aux.acc_seg: 76.6296, loss: 0.9023 +2024-06-18 00:35:05,484 - mmseg - INFO - Iter [2800/80000] lr: 3.860e-05, eta: 1 day, 7:22:32, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6665, decode.acc_seg: 76.0020, aux.loss_ce: 0.2661, aux.acc_seg: 76.2350, loss: 0.9326 +2024-06-18 00:36:11,671 - mmseg - INFO - Iter [2850/80000] lr: 3.858e-05, eta: 1 day, 7:18:11, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.6619, decode.acc_seg: 75.5255, aux.loss_ce: 0.2628, aux.acc_seg: 75.9481, loss: 0.9247 +2024-06-18 00:37:17,887 - mmseg - INFO - Iter [2900/80000] lr: 3.855e-05, eta: 1 day, 7:13:56, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6501, decode.acc_seg: 76.8993, aux.loss_ce: 0.2574, aux.acc_seg: 77.3330, loss: 0.9076 +2024-06-18 00:38:24,213 - mmseg - INFO - Iter [2950/80000] lr: 3.853e-05, eta: 1 day, 7:09:51, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5931, decode.acc_seg: 78.0207, aux.loss_ce: 0.2359, aux.acc_seg: 78.2450, loss: 0.8290 +2024-06-18 00:39:30,556 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 00:39:30,556 - mmseg - INFO - Iter [3000/80000] lr: 3.850e-05, eta: 1 day, 7:05:53, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6253, decode.acc_seg: 77.4665, aux.loss_ce: 0.2498, aux.acc_seg: 77.3228, loss: 0.8751 +2024-06-18 00:41:07,235 - mmseg - INFO - per class results: +2024-06-18 00:41:07,241 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 74.33 | 83.89 | +| building | 79.65 | 93.37 | +| sky | 93.03 | 95.65 | +| floor | 78.97 | 86.17 | +| tree | 73.29 | 86.11 | +| ceiling | 80.57 | 90.9 | +| road | 80.35 | 84.06 | +| bed | 84.34 | 96.28 | +| windowpane | 57.55 | 70.9 | +| grass | 61.8 | 76.63 | +| cabinet | 54.87 | 65.33 | +| sidewalk | 59.1 | 83.05 | +| person | 75.31 | 87.19 | +| earth | 35.4 | 47.12 | +| door | 45.31 | 63.65 | +| table | 51.39 | 65.54 | +| mountain | 56.01 | 80.25 | +| plant | 50.22 | 66.01 | +| curtain | 63.19 | 78.89 | +| chair | 48.73 | 61.65 | +| car | 78.76 | 92.39 | +| water | 58.01 | 79.28 | +| painting | 69.36 | 84.41 | +| sofa | 67.13 | 85.67 | +| shelf | 39.02 | 70.4 | +| house | 26.03 | 27.8 | +| sea | 59.33 | 71.63 | +| mirror | 61.24 | 77.13 | +| rug | 61.65 | 79.2 | +| field | 26.01 | 51.1 | +| armchair | 41.39 | 69.8 | +| seat | 62.25 | 83.46 | +| fence | 40.53 | 59.69 | +| desk | 37.65 | 71.97 | +| rock | 49.48 | 56.12 | +| wardrobe | 54.87 | 72.62 | +| lamp | 50.91 | 67.16 | +| bathtub | 70.81 | 82.8 | +| railing | 26.17 | 32.38 | +| cushion | 48.8 | 55.34 | +| base | 38.54 | 57.76 | +| box | 22.39 | 42.97 | +| column | 45.65 | 61.31 | +| signboard | 30.68 | 40.37 | +| chest of drawers | 31.51 | 72.92 | +| counter | 34.13 | 43.92 | +| sand | 40.54 | 61.48 | +| sink | 51.88 | 55.89 | +| skyscraper | 48.02 | 69.98 | +| fireplace | 53.26 | 95.73 | +| refrigerator | 62.85 | 86.68 | +| grandstand | 45.22 | 74.44 | +| path | 4.45 | 4.73 | +| stairs | 31.27 | 50.47 | +| runway | 66.47 | 94.06 | +| case | 48.19 | 52.56 | +| pool table | 87.47 | 95.61 | +| pillow | 44.78 | 52.67 | +| screen door | 63.97 | 75.26 | +| stairway | 33.26 | 49.4 | +| river | 28.11 | 40.48 | +| bridge | 59.08 | 89.27 | +| bookcase | 23.56 | 34.15 | +| blind | 44.65 | 67.18 | +| coffee table | 49.76 | 83.97 | +| toilet | 74.92 | 94.79 | +| flower | 30.27 | 38.79 | +| book | 41.34 | 71.08 | +| hill | 3.31 | 3.57 | +| bench | 53.43 | 62.21 | +| countertop | 51.0 | 66.66 | +| stove | 65.98 | 75.58 | +| palm | 49.31 | 63.77 | +| kitchen island | 30.38 | 84.51 | +| computer | 46.85 | 92.75 | +| swivel chair | 40.87 | 58.39 | +| boat | 57.92 | 63.95 | +| bar | 48.4 | 68.24 | +| arcade machine | 80.29 | 95.93 | +| hovel | 52.57 | 63.46 | +| bus | 82.69 | 91.59 | +| towel | 50.53 | 59.34 | +| light | 25.03 | 27.13 | +| truck | 31.57 | 38.33 | +| tower | 25.08 | 49.91 | +| chandelier | 57.37 | 74.76 | +| awning | 32.79 | 46.58 | +| streetlight | 9.68 | 11.85 | +| booth | 35.69 | 40.54 | +| television receiver | 56.44 | 81.36 | +| airplane | 44.59 | 59.55 | +| dirt track | 2.08 | 3.3 | +| apparel | 29.08 | 33.72 | +| pole | 12.63 | 14.99 | +| land | 0.0 | 0.0 | +| bannister | 4.22 | 5.7 | +| escalator | 55.83 | 81.1 | +| ottoman | 44.33 | 58.61 | +| bottle | 16.06 | 20.83 | +| buffet | 38.89 | 53.83 | +| poster | 30.14 | 38.01 | +| stage | 15.37 | 28.26 | +| van | 25.8 | 28.68 | +| ship | 8.06 | 9.7 | +| fountain | 33.88 | 36.78 | +| conveyer belt | 78.26 | 92.43 | +| canopy | 36.54 | 47.26 | +| washer | 69.36 | 74.4 | +| plaything | 4.2 | 4.69 | +| swimming pool | 46.09 | 98.86 | +| stool | 21.6 | 24.08 | +| barrel | 49.55 | 64.71 | +| basket | 10.56 | 11.36 | +| waterfall | 54.23 | 79.46 | +| tent | 87.25 | 99.43 | +| bag | 0.61 | 0.62 | +| minibike | 57.91 | 76.13 | +| cradle | 69.1 | 98.24 | +| oven | 21.71 | 36.12 | +| ball | 16.72 | 17.71 | +| food | 48.67 | 59.45 | +| step | 0.11 | 0.11 | +| tank | 57.93 | 66.51 | +| trade name | 0.41 | 0.41 | +| microwave | 74.13 | 90.98 | +| pot | 20.81 | 21.7 | +| animal | 56.96 | 59.68 | +| bicycle | 41.7 | 71.48 | +| lake | 0.0 | 0.0 | +| dishwasher | 41.11 | 43.61 | +| screen | 55.68 | 85.75 | +| blanket | 0.04 | 0.04 | +| sculpture | 40.67 | 61.12 | +| hood | 49.22 | 58.83 | +| sconce | 14.2 | 14.96 | +| vase | 15.25 | 18.93 | +| traffic light | 1.03 | 1.04 | +| tray | 0.0 | 0.0 | +| ashcan | 27.4 | 30.83 | +| fan | 39.75 | 45.73 | +| pier | 33.07 | 41.52 | +| crt screen | 8.32 | 8.86 | +| plate | 34.45 | 52.84 | +| monitor | 0.0 | 0.0 | +| bulletin board | 42.69 | 51.5 | +| shower | 0.0 | 0.0 | +| radiator | 39.81 | 42.88 | +| glass | 0.0 | 0.0 | +| clock | 0.0 | 0.0 | +| flag | 47.22 | 47.8 | ++---------------------+-------+-------+ +2024-06-18 00:41:07,241 - mmseg - INFO - Summary: +2024-06-18 00:41:07,241 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 80.46 | 42.21 | 55.24 | ++-------+-------+-------+ +2024-06-18 00:41:07,242 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 00:41:07,242 - mmseg - INFO - Iter(val) [250] aAcc: 0.8046, mIoU: 0.4221, mAcc: 0.5524, IoU.wall: 0.7433, IoU.building: 0.7965, IoU.sky: 0.9303, IoU.floor: 0.7897, IoU.tree: 0.7329, IoU.ceiling: 0.8057, IoU.road: 0.8035, IoU.bed : 0.8434, IoU.windowpane: 0.5755, IoU.grass: 0.6180, IoU.cabinet: 0.5487, IoU.sidewalk: 0.5910, IoU.person: 0.7531, IoU.earth: 0.3540, IoU.door: 0.4531, IoU.table: 0.5139, IoU.mountain: 0.5601, IoU.plant: 0.5022, IoU.curtain: 0.6319, IoU.chair: 0.4873, IoU.car: 0.7876, IoU.water: 0.5801, IoU.painting: 0.6936, IoU.sofa: 0.6713, IoU.shelf: 0.3902, IoU.house: 0.2603, IoU.sea: 0.5933, IoU.mirror: 0.6124, IoU.rug: 0.6165, IoU.field: 0.2601, IoU.armchair: 0.4139, IoU.seat: 0.6225, IoU.fence: 0.4053, IoU.desk: 0.3765, IoU.rock: 0.4948, IoU.wardrobe: 0.5487, IoU.lamp: 0.5091, IoU.bathtub: 0.7081, IoU.railing: 0.2617, IoU.cushion: 0.4880, IoU.base: 0.3854, IoU.box: 0.2239, IoU.column: 0.4565, IoU.signboard: 0.3068, IoU.chest of drawers: 0.3151, IoU.counter: 0.3413, IoU.sand: 0.4054, IoU.sink: 0.5188, IoU.skyscraper: 0.4802, IoU.fireplace: 0.5326, IoU.refrigerator: 0.6285, IoU.grandstand: 0.4522, IoU.path: 0.0445, IoU.stairs: 0.3127, IoU.runway: 0.6647, IoU.case: 0.4819, IoU.pool table: 0.8747, IoU.pillow: 0.4478, IoU.screen door: 0.6397, IoU.stairway: 0.3326, IoU.river: 0.2811, IoU.bridge: 0.5908, IoU.bookcase: 0.2356, IoU.blind: 0.4465, IoU.coffee table: 0.4976, IoU.toilet: 0.7492, IoU.flower: 0.3027, IoU.book: 0.4134, IoU.hill: 0.0331, IoU.bench: 0.5343, IoU.countertop: 0.5100, IoU.stove: 0.6598, IoU.palm: 0.4931, IoU.kitchen island: 0.3038, IoU.computer: 0.4685, IoU.swivel chair: 0.4087, IoU.boat: 0.5792, IoU.bar: 0.4840, IoU.arcade machine: 0.8029, IoU.hovel: 0.5257, IoU.bus: 0.8269, IoU.towel: 0.5053, IoU.light: 0.2503, IoU.truck: 0.3157, IoU.tower: 0.2508, IoU.chandelier: 0.5737, IoU.awning: 0.3279, IoU.streetlight: 0.0968, IoU.booth: 0.3569, IoU.television receiver: 0.5644, IoU.airplane: 0.4459, IoU.dirt track: 0.0208, IoU.apparel: 0.2908, IoU.pole: 0.1263, IoU.land: 0.0000, IoU.bannister: 0.0422, IoU.escalator: 0.5583, IoU.ottoman: 0.4433, IoU.bottle: 0.1606, IoU.buffet: 0.3889, IoU.poster: 0.3014, IoU.stage: 0.1537, IoU.van: 0.2580, IoU.ship: 0.0806, IoU.fountain: 0.3388, IoU.conveyer belt: 0.7826, IoU.canopy: 0.3654, IoU.washer: 0.6936, IoU.plaything: 0.0420, IoU.swimming pool: 0.4609, IoU.stool: 0.2160, IoU.barrel: 0.4955, IoU.basket: 0.1056, IoU.waterfall: 0.5423, IoU.tent: 0.8725, IoU.bag: 0.0061, IoU.minibike: 0.5791, IoU.cradle: 0.6910, IoU.oven: 0.2171, IoU.ball: 0.1672, IoU.food: 0.4867, IoU.step: 0.0011, IoU.tank: 0.5793, IoU.trade name: 0.0041, IoU.microwave: 0.7413, IoU.pot: 0.2081, IoU.animal: 0.5696, IoU.bicycle: 0.4170, IoU.lake: 0.0000, IoU.dishwasher: 0.4111, IoU.screen: 0.5568, IoU.blanket: 0.0004, IoU.sculpture: 0.4067, IoU.hood: 0.4922, IoU.sconce: 0.1420, IoU.vase: 0.1525, IoU.traffic light: 0.0103, IoU.tray: 0.0000, IoU.ashcan: 0.2740, IoU.fan: 0.3975, IoU.pier: 0.3307, IoU.crt screen: 0.0832, IoU.plate: 0.3445, IoU.monitor: 0.0000, IoU.bulletin board: 0.4269, IoU.shower: 0.0000, IoU.radiator: 0.3981, IoU.glass: 0.0000, IoU.clock: 0.0000, IoU.flag: 0.4722, Acc.wall: 0.8389, Acc.building: 0.9337, Acc.sky: 0.9565, Acc.floor: 0.8617, Acc.tree: 0.8611, Acc.ceiling: 0.9090, Acc.road: 0.8406, Acc.bed : 0.9628, Acc.windowpane: 0.7090, Acc.grass: 0.7663, Acc.cabinet: 0.6533, Acc.sidewalk: 0.8305, Acc.person: 0.8719, Acc.earth: 0.4712, Acc.door: 0.6365, Acc.table: 0.6554, Acc.mountain: 0.8025, Acc.plant: 0.6601, Acc.curtain: 0.7889, Acc.chair: 0.6165, Acc.car: 0.9239, Acc.water: 0.7928, Acc.painting: 0.8441, Acc.sofa: 0.8567, Acc.shelf: 0.7040, Acc.house: 0.2780, Acc.sea: 0.7163, Acc.mirror: 0.7713, Acc.rug: 0.7920, Acc.field: 0.5110, Acc.armchair: 0.6980, Acc.seat: 0.8346, Acc.fence: 0.5969, Acc.desk: 0.7197, Acc.rock: 0.5612, Acc.wardrobe: 0.7262, Acc.lamp: 0.6716, Acc.bathtub: 0.8280, Acc.railing: 0.3238, Acc.cushion: 0.5534, Acc.base: 0.5776, Acc.box: 0.4297, Acc.column: 0.6131, Acc.signboard: 0.4037, Acc.chest of drawers: 0.7292, Acc.counter: 0.4392, Acc.sand: 0.6148, Acc.sink: 0.5589, Acc.skyscraper: 0.6998, Acc.fireplace: 0.9573, Acc.refrigerator: 0.8668, Acc.grandstand: 0.7444, Acc.path: 0.0473, Acc.stairs: 0.5047, Acc.runway: 0.9406, Acc.case: 0.5256, Acc.pool table: 0.9561, Acc.pillow: 0.5267, Acc.screen door: 0.7526, Acc.stairway: 0.4940, Acc.river: 0.4048, Acc.bridge: 0.8927, Acc.bookcase: 0.3415, Acc.blind: 0.6718, Acc.coffee table: 0.8397, Acc.toilet: 0.9479, Acc.flower: 0.3879, Acc.book: 0.7108, Acc.hill: 0.0357, Acc.bench: 0.6221, Acc.countertop: 0.6666, Acc.stove: 0.7558, Acc.palm: 0.6377, Acc.kitchen island: 0.8451, Acc.computer: 0.9275, Acc.swivel chair: 0.5839, Acc.boat: 0.6395, Acc.bar: 0.6824, Acc.arcade machine: 0.9593, Acc.hovel: 0.6346, Acc.bus: 0.9159, Acc.towel: 0.5934, Acc.light: 0.2713, Acc.truck: 0.3833, Acc.tower: 0.4991, Acc.chandelier: 0.7476, Acc.awning: 0.4658, Acc.streetlight: 0.1185, Acc.booth: 0.4054, Acc.television receiver: 0.8136, Acc.airplane: 0.5955, Acc.dirt track: 0.0330, Acc.apparel: 0.3372, Acc.pole: 0.1499, Acc.land: 0.0000, Acc.bannister: 0.0570, Acc.escalator: 0.8110, Acc.ottoman: 0.5861, Acc.bottle: 0.2083, Acc.buffet: 0.5383, Acc.poster: 0.3801, Acc.stage: 0.2826, Acc.van: 0.2868, Acc.ship: 0.0970, Acc.fountain: 0.3678, Acc.conveyer belt: 0.9243, Acc.canopy: 0.4726, Acc.washer: 0.7440, Acc.plaything: 0.0469, Acc.swimming pool: 0.9886, Acc.stool: 0.2408, Acc.barrel: 0.6471, Acc.basket: 0.1136, Acc.waterfall: 0.7946, Acc.tent: 0.9943, Acc.bag: 0.0062, Acc.minibike: 0.7613, Acc.cradle: 0.9824, Acc.oven: 0.3612, Acc.ball: 0.1771, Acc.food: 0.5945, Acc.step: 0.0011, Acc.tank: 0.6651, Acc.trade name: 0.0041, Acc.microwave: 0.9098, Acc.pot: 0.2170, Acc.animal: 0.5968, Acc.bicycle: 0.7148, Acc.lake: 0.0000, Acc.dishwasher: 0.4361, Acc.screen: 0.8575, Acc.blanket: 0.0004, Acc.sculpture: 0.6112, Acc.hood: 0.5883, Acc.sconce: 0.1496, Acc.vase: 0.1893, Acc.traffic light: 0.0104, Acc.tray: 0.0000, Acc.ashcan: 0.3083, Acc.fan: 0.4573, Acc.pier: 0.4152, Acc.crt screen: 0.0886, Acc.plate: 0.5284, Acc.monitor: 0.0000, Acc.bulletin board: 0.5150, Acc.shower: 0.0000, Acc.radiator: 0.4288, Acc.glass: 0.0000, Acc.clock: 0.0000, Acc.flag: 0.4780 +2024-06-18 00:42:14,407 - mmseg - INFO - Iter [3050/80000] lr: 3.848e-05, eta: 1 day, 7:43:00, time: 3.277, data_time: 1.953, memory: 70498, decode.loss_ce: 0.6448, decode.acc_seg: 76.5980, aux.loss_ce: 0.2575, aux.acc_seg: 76.9947, loss: 0.9023 +2024-06-18 00:43:20,869 - mmseg - INFO - Iter [3100/80000] lr: 3.845e-05, eta: 1 day, 7:38:34, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6485, decode.acc_seg: 77.1208, aux.loss_ce: 0.2562, aux.acc_seg: 77.3943, loss: 0.9047 +2024-06-18 00:44:27,214 - mmseg - INFO - Iter [3150/80000] lr: 3.843e-05, eta: 1 day, 7:34:11, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6364, decode.acc_seg: 77.5176, aux.loss_ce: 0.2524, aux.acc_seg: 77.6672, loss: 0.8888 +2024-06-18 00:45:33,534 - mmseg - INFO - Iter [3200/80000] lr: 3.840e-05, eta: 1 day, 7:29:54, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5908, decode.acc_seg: 78.2851, aux.loss_ce: 0.2351, aux.acc_seg: 78.5704, loss: 0.8259 +2024-06-18 00:46:39,786 - mmseg - INFO - Iter [3250/80000] lr: 3.838e-05, eta: 1 day, 7:25:42, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.6190, decode.acc_seg: 77.0422, aux.loss_ce: 0.2453, aux.acc_seg: 77.2811, loss: 0.8643 +2024-06-18 00:47:46,055 - mmseg - INFO - Iter [3300/80000] lr: 3.835e-05, eta: 1 day, 7:21:35, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6366, decode.acc_seg: 77.4194, aux.loss_ce: 0.2537, aux.acc_seg: 77.7979, loss: 0.8904 +2024-06-18 00:48:52,183 - mmseg - INFO - Iter [3350/80000] lr: 3.833e-05, eta: 1 day, 7:17:31, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.6239, decode.acc_seg: 77.3987, aux.loss_ce: 0.2472, aux.acc_seg: 77.8169, loss: 0.8711 +2024-06-18 00:49:58,552 - mmseg - INFO - Iter [3400/80000] lr: 3.830e-05, eta: 1 day, 7:13:37, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6373, decode.acc_seg: 76.2388, aux.loss_ce: 0.2513, aux.acc_seg: 76.5175, loss: 0.8886 +2024-06-18 00:51:05,042 - mmseg - INFO - Iter [3450/80000] lr: 3.828e-05, eta: 1 day, 7:09:51, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6210, decode.acc_seg: 76.3898, aux.loss_ce: 0.2440, aux.acc_seg: 77.1419, loss: 0.8650 +2024-06-18 00:52:11,405 - mmseg - INFO - Iter [3500/80000] lr: 3.825e-05, eta: 1 day, 7:06:06, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5989, decode.acc_seg: 77.9294, aux.loss_ce: 0.2410, aux.acc_seg: 78.0119, loss: 0.8399 +2024-06-18 00:53:17,682 - mmseg - INFO - Iter [3550/80000] lr: 3.823e-05, eta: 1 day, 7:02:24, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6379, decode.acc_seg: 77.2608, aux.loss_ce: 0.2529, aux.acc_seg: 77.4020, loss: 0.8907 +2024-06-18 00:54:24,140 - mmseg - INFO - Iter [3600/80000] lr: 3.820e-05, eta: 1 day, 6:58:50, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6337, decode.acc_seg: 76.9252, aux.loss_ce: 0.2513, aux.acc_seg: 77.2361, loss: 0.8849 +2024-06-18 00:55:30,644 - mmseg - INFO - Iter [3650/80000] lr: 3.818e-05, eta: 1 day, 6:55:22, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5911, decode.acc_seg: 78.1678, aux.loss_ce: 0.2356, aux.acc_seg: 78.3528, loss: 0.8267 +2024-06-18 00:56:37,076 - mmseg - INFO - Iter [3700/80000] lr: 3.815e-05, eta: 1 day, 6:51:55, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6318, decode.acc_seg: 77.3589, aux.loss_ce: 0.2500, aux.acc_seg: 77.7384, loss: 0.8818 +2024-06-18 00:57:43,317 - mmseg - INFO - Iter [3750/80000] lr: 3.813e-05, eta: 1 day, 6:48:29, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6216, decode.acc_seg: 77.2623, aux.loss_ce: 0.2461, aux.acc_seg: 77.5620, loss: 0.8677 +2024-06-18 00:58:51,765 - mmseg - INFO - Iter [3800/80000] lr: 3.810e-05, eta: 1 day, 6:45:50, time: 1.369, data_time: 0.051, memory: 70498, decode.loss_ce: 0.6055, decode.acc_seg: 77.9103, aux.loss_ce: 0.2406, aux.acc_seg: 78.2799, loss: 0.8461 +2024-06-18 00:59:58,202 - mmseg - INFO - Iter [3850/80000] lr: 3.808e-05, eta: 1 day, 6:42:34, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6172, decode.acc_seg: 77.6913, aux.loss_ce: 0.2457, aux.acc_seg: 77.9178, loss: 0.8629 +2024-06-18 01:01:04,475 - mmseg - INFO - Iter [3900/80000] lr: 3.805e-05, eta: 1 day, 6:39:19, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5692, decode.acc_seg: 79.2761, aux.loss_ce: 0.2261, aux.acc_seg: 79.5346, loss: 0.7953 +2024-06-18 01:02:10,862 - mmseg - INFO - Iter [3950/80000] lr: 3.803e-05, eta: 1 day, 6:36:08, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5834, decode.acc_seg: 78.6194, aux.loss_ce: 0.2330, aux.acc_seg: 78.5134, loss: 0.8163 +2024-06-18 01:03:17,136 - mmseg - INFO - Saving checkpoint at 4000 iterations +2024-06-18 01:05:01,178 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 01:05:01,178 - mmseg - INFO - Iter [4000/80000] lr: 3.800e-05, eta: 1 day, 7:05:55, time: 3.406, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5985, decode.acc_seg: 77.8506, aux.loss_ce: 0.2358, aux.acc_seg: 78.1103, loss: 0.8343 +2024-06-18 01:06:37,339 - mmseg - INFO - per class results: +2024-06-18 01:06:37,345 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 76.42 | 86.99 | +| building | 81.08 | 93.08 | +| sky | 93.46 | 95.88 | +| floor | 77.78 | 83.69 | +| tree | 73.22 | 91.8 | +| ceiling | 81.93 | 91.0 | +| road | 80.81 | 90.72 | +| bed | 87.2 | 95.21 | +| windowpane | 59.1 | 80.03 | +| grass | 64.35 | 82.16 | +| cabinet | 54.12 | 60.74 | +| sidewalk | 57.56 | 76.72 | +| person | 76.53 | 86.77 | +| earth | 31.66 | 43.13 | +| door | 47.47 | 58.6 | +| table | 49.64 | 57.73 | +| mountain | 58.81 | 70.19 | +| plant | 49.56 | 55.64 | +| curtain | 68.67 | 85.74 | +| chair | 51.63 | 65.8 | +| car | 80.86 | 92.44 | +| water | 45.55 | 52.18 | +| painting | 70.15 | 83.8 | +| sofa | 67.71 | 83.56 | +| shelf | 32.22 | 41.03 | +| house | 12.81 | 13.16 | +| sea | 62.6 | 95.26 | +| mirror | 64.43 | 71.71 | +| rug | 58.26 | 83.44 | +| field | 30.42 | 66.86 | +| armchair | 46.37 | 64.55 | +| seat | 63.7 | 88.46 | +| fence | 38.36 | 48.54 | +| desk | 38.01 | 73.32 | +| rock | 43.74 | 84.91 | +| wardrobe | 51.1 | 69.57 | +| lamp | 54.94 | 68.01 | +| bathtub | 66.6 | 90.81 | +| railing | 30.78 | 40.21 | +| cushion | 49.06 | 69.38 | +| base | 29.76 | 53.17 | +| box | 20.79 | 42.05 | +| column | 48.93 | 56.77 | +| signboard | 30.48 | 35.37 | +| chest of drawers | 33.43 | 70.77 | +| counter | 35.19 | 39.39 | +| sand | 43.75 | 49.68 | +| sink | 61.09 | 78.66 | +| skyscraper | 55.04 | 79.04 | +| fireplace | 63.9 | 86.32 | +| refrigerator | 65.04 | 88.32 | +| grandstand | 43.62 | 85.02 | +| path | 20.84 | 25.0 | +| stairs | 23.92 | 30.19 | +| runway | 68.7 | 94.82 | +| case | 58.4 | 79.23 | +| pool table | 80.24 | 98.48 | +| pillow | 55.89 | 74.38 | +| screen door | 69.36 | 73.76 | +| stairway | 40.55 | 64.34 | +| river | 17.87 | 38.69 | +| bridge | 65.28 | 88.05 | +| bookcase | 23.27 | 44.58 | +| blind | 40.87 | 52.04 | +| coffee table | 46.37 | 90.2 | +| toilet | 80.75 | 94.59 | +| flower | 32.7 | 45.15 | +| book | 43.89 | 69.35 | +| hill | 8.04 | 11.41 | +| bench | 49.58 | 59.48 | +| countertop | 44.69 | 49.55 | +| stove | 60.52 | 90.94 | +| palm | 43.84 | 49.47 | +| kitchen island | 37.82 | 76.37 | +| computer | 63.65 | 91.09 | +| swivel chair | 41.08 | 74.15 | +| boat | 68.85 | 88.48 | +| bar | 47.47 | 68.66 | +| arcade machine | 69.85 | 81.23 | +| hovel | 33.95 | 37.04 | +| bus | 86.67 | 91.1 | +| towel | 51.14 | 62.36 | +| light | 32.77 | 37.62 | +| truck | 32.79 | 41.02 | +| tower | 27.91 | 47.05 | +| chandelier | 58.01 | 81.19 | +| awning | 26.77 | 43.8 | +| streetlight | 9.83 | 13.32 | +| booth | 12.89 | 13.13 | +| television receiver | 60.69 | 78.46 | +| airplane | 48.5 | 58.12 | +| dirt track | 0.68 | 0.99 | +| apparel | 15.55 | 18.18 | +| pole | 6.37 | 6.85 | +| land | 0.03 | 0.04 | +| bannister | 0.2 | 0.22 | +| escalator | 56.83 | 77.92 | +| ottoman | 44.49 | 65.49 | +| bottle | 32.74 | 39.72 | +| buffet | 48.24 | 85.8 | +| poster | 4.84 | 4.87 | +| stage | 4.21 | 4.71 | +| van | 26.6 | 30.07 | +| ship | 8.79 | 8.89 | +| fountain | 65.67 | 75.28 | +| conveyer belt | 69.85 | 92.69 | +| canopy | 4.92 | 5.02 | +| washer | 67.3 | 70.8 | +| plaything | 12.0 | 15.22 | +| swimming pool | 75.79 | 83.08 | +| stool | 37.7 | 56.28 | +| barrel | 58.18 | 64.6 | +| basket | 22.55 | 37.82 | +| waterfall | 67.71 | 92.79 | +| tent | 93.79 | 96.49 | +| bag | 4.85 | 4.98 | +| minibike | 57.26 | 70.02 | +| cradle | 71.57 | 96.74 | +| oven | 24.08 | 59.72 | +| ball | 35.95 | 63.81 | +| food | 30.2 | 34.08 | +| step | 2.92 | 2.95 | +| tank | 49.42 | 53.55 | +| trade name | 11.35 | 12.37 | +| microwave | 75.67 | 94.59 | +| pot | 39.19 | 44.34 | +| animal | 66.09 | 70.69 | +| bicycle | 41.15 | 50.89 | +| lake | 6.67 | 14.84 | +| dishwasher | 44.51 | 51.17 | +| screen | 54.67 | 88.54 | +| blanket | 1.69 | 2.0 | +| sculpture | 49.65 | 67.51 | +| hood | 53.48 | 67.81 | +| sconce | 34.9 | 44.95 | +| vase | 18.38 | 44.44 | +| traffic light | 6.92 | 7.53 | +| tray | 2.49 | 3.15 | +| ashcan | 32.03 | 43.58 | +| fan | 48.39 | 70.86 | +| pier | 36.5 | 40.73 | +| crt screen | 0.0 | 0.0 | +| plate | 32.87 | 76.48 | +| monitor | 65.01 | 70.74 | +| bulletin board | 43.55 | 52.15 | +| shower | 0.0 | 0.0 | +| radiator | 50.42 | 56.3 | +| glass | 0.0 | 0.0 | +| clock | 17.99 | 18.8 | +| flag | 56.76 | 60.81 | ++---------------------+-------+-------+ +2024-06-18 01:06:37,345 - mmseg - INFO - Summary: +2024-06-18 01:06:37,345 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 81.08 | 44.25 | 57.87 | ++-------+-------+-------+ +2024-06-18 01:06:37,346 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 01:06:37,346 - mmseg - INFO - Iter(val) [250] aAcc: 0.8108, mIoU: 0.4425, mAcc: 0.5787, IoU.wall: 0.7642, IoU.building: 0.8108, IoU.sky: 0.9346, IoU.floor: 0.7778, IoU.tree: 0.7322, IoU.ceiling: 0.8193, IoU.road: 0.8081, IoU.bed : 0.8720, IoU.windowpane: 0.5910, IoU.grass: 0.6435, IoU.cabinet: 0.5412, IoU.sidewalk: 0.5756, IoU.person: 0.7653, IoU.earth: 0.3166, IoU.door: 0.4747, IoU.table: 0.4964, IoU.mountain: 0.5881, IoU.plant: 0.4956, IoU.curtain: 0.6867, IoU.chair: 0.5163, IoU.car: 0.8086, IoU.water: 0.4555, IoU.painting: 0.7015, IoU.sofa: 0.6771, IoU.shelf: 0.3222, IoU.house: 0.1281, IoU.sea: 0.6260, IoU.mirror: 0.6443, IoU.rug: 0.5826, IoU.field: 0.3042, IoU.armchair: 0.4637, IoU.seat: 0.6370, IoU.fence: 0.3836, IoU.desk: 0.3801, IoU.rock: 0.4374, IoU.wardrobe: 0.5110, IoU.lamp: 0.5494, IoU.bathtub: 0.6660, IoU.railing: 0.3078, IoU.cushion: 0.4906, IoU.base: 0.2976, IoU.box: 0.2079, IoU.column: 0.4893, IoU.signboard: 0.3048, IoU.chest of drawers: 0.3343, IoU.counter: 0.3519, IoU.sand: 0.4375, IoU.sink: 0.6109, IoU.skyscraper: 0.5504, IoU.fireplace: 0.6390, IoU.refrigerator: 0.6504, IoU.grandstand: 0.4362, IoU.path: 0.2084, IoU.stairs: 0.2392, IoU.runway: 0.6870, IoU.case: 0.5840, IoU.pool table: 0.8024, IoU.pillow: 0.5589, IoU.screen door: 0.6936, IoU.stairway: 0.4055, IoU.river: 0.1787, IoU.bridge: 0.6528, IoU.bookcase: 0.2327, IoU.blind: 0.4087, IoU.coffee table: 0.4637, IoU.toilet: 0.8075, IoU.flower: 0.3270, IoU.book: 0.4389, IoU.hill: 0.0804, IoU.bench: 0.4958, IoU.countertop: 0.4469, IoU.stove: 0.6052, IoU.palm: 0.4384, IoU.kitchen island: 0.3782, IoU.computer: 0.6365, IoU.swivel chair: 0.4108, IoU.boat: 0.6885, IoU.bar: 0.4747, IoU.arcade machine: 0.6985, IoU.hovel: 0.3395, IoU.bus: 0.8667, IoU.towel: 0.5114, IoU.light: 0.3277, IoU.truck: 0.3279, IoU.tower: 0.2791, IoU.chandelier: 0.5801, IoU.awning: 0.2677, IoU.streetlight: 0.0983, IoU.booth: 0.1289, IoU.television receiver: 0.6069, IoU.airplane: 0.4850, IoU.dirt track: 0.0068, IoU.apparel: 0.1555, IoU.pole: 0.0637, IoU.land: 0.0003, IoU.bannister: 0.0020, IoU.escalator: 0.5683, IoU.ottoman: 0.4449, IoU.bottle: 0.3274, IoU.buffet: 0.4824, IoU.poster: 0.0484, IoU.stage: 0.0421, IoU.van: 0.2660, IoU.ship: 0.0879, IoU.fountain: 0.6567, IoU.conveyer belt: 0.6985, IoU.canopy: 0.0492, IoU.washer: 0.6730, IoU.plaything: 0.1200, IoU.swimming pool: 0.7579, IoU.stool: 0.3770, IoU.barrel: 0.5818, IoU.basket: 0.2255, IoU.waterfall: 0.6771, IoU.tent: 0.9379, IoU.bag: 0.0485, IoU.minibike: 0.5726, IoU.cradle: 0.7157, IoU.oven: 0.2408, IoU.ball: 0.3595, IoU.food: 0.3020, IoU.step: 0.0292, IoU.tank: 0.4942, IoU.trade name: 0.1135, IoU.microwave: 0.7567, IoU.pot: 0.3919, IoU.animal: 0.6609, IoU.bicycle: 0.4115, IoU.lake: 0.0667, IoU.dishwasher: 0.4451, IoU.screen: 0.5467, IoU.blanket: 0.0169, IoU.sculpture: 0.4965, IoU.hood: 0.5348, IoU.sconce: 0.3490, IoU.vase: 0.1838, IoU.traffic light: 0.0692, IoU.tray: 0.0249, IoU.ashcan: 0.3203, IoU.fan: 0.4839, IoU.pier: 0.3650, IoU.crt screen: 0.0000, IoU.plate: 0.3287, IoU.monitor: 0.6501, IoU.bulletin board: 0.4355, IoU.shower: 0.0000, IoU.radiator: 0.5042, IoU.glass: 0.0000, IoU.clock: 0.1799, IoU.flag: 0.5676, Acc.wall: 0.8699, Acc.building: 0.9308, Acc.sky: 0.9588, Acc.floor: 0.8369, Acc.tree: 0.9180, Acc.ceiling: 0.9100, Acc.road: 0.9072, Acc.bed : 0.9521, Acc.windowpane: 0.8003, Acc.grass: 0.8216, Acc.cabinet: 0.6074, Acc.sidewalk: 0.7672, Acc.person: 0.8677, Acc.earth: 0.4313, Acc.door: 0.5860, Acc.table: 0.5773, Acc.mountain: 0.7019, Acc.plant: 0.5564, Acc.curtain: 0.8574, Acc.chair: 0.6580, Acc.car: 0.9244, Acc.water: 0.5218, Acc.painting: 0.8380, Acc.sofa: 0.8356, Acc.shelf: 0.4103, Acc.house: 0.1316, Acc.sea: 0.9526, Acc.mirror: 0.7171, Acc.rug: 0.8344, Acc.field: 0.6686, Acc.armchair: 0.6455, Acc.seat: 0.8846, Acc.fence: 0.4854, Acc.desk: 0.7332, Acc.rock: 0.8491, Acc.wardrobe: 0.6957, Acc.lamp: 0.6801, Acc.bathtub: 0.9081, Acc.railing: 0.4021, Acc.cushion: 0.6938, Acc.base: 0.5317, Acc.box: 0.4205, Acc.column: 0.5677, Acc.signboard: 0.3537, Acc.chest of drawers: 0.7077, Acc.counter: 0.3939, Acc.sand: 0.4968, Acc.sink: 0.7866, Acc.skyscraper: 0.7904, Acc.fireplace: 0.8632, Acc.refrigerator: 0.8832, Acc.grandstand: 0.8502, Acc.path: 0.2500, Acc.stairs: 0.3019, Acc.runway: 0.9482, Acc.case: 0.7923, Acc.pool table: 0.9848, Acc.pillow: 0.7438, Acc.screen door: 0.7376, Acc.stairway: 0.6434, Acc.river: 0.3869, Acc.bridge: 0.8805, Acc.bookcase: 0.4458, Acc.blind: 0.5204, Acc.coffee table: 0.9020, Acc.toilet: 0.9459, Acc.flower: 0.4515, Acc.book: 0.6935, Acc.hill: 0.1141, Acc.bench: 0.5948, Acc.countertop: 0.4955, Acc.stove: 0.9094, Acc.palm: 0.4947, Acc.kitchen island: 0.7637, Acc.computer: 0.9109, Acc.swivel chair: 0.7415, Acc.boat: 0.8848, Acc.bar: 0.6866, Acc.arcade machine: 0.8123, Acc.hovel: 0.3704, Acc.bus: 0.9110, Acc.towel: 0.6236, Acc.light: 0.3762, Acc.truck: 0.4102, Acc.tower: 0.4705, Acc.chandelier: 0.8119, Acc.awning: 0.4380, Acc.streetlight: 0.1332, Acc.booth: 0.1313, Acc.television receiver: 0.7846, Acc.airplane: 0.5812, Acc.dirt track: 0.0099, Acc.apparel: 0.1818, Acc.pole: 0.0685, Acc.land: 0.0004, Acc.bannister: 0.0022, Acc.escalator: 0.7792, Acc.ottoman: 0.6549, Acc.bottle: 0.3972, Acc.buffet: 0.8580, Acc.poster: 0.0487, Acc.stage: 0.0471, Acc.van: 0.3007, Acc.ship: 0.0889, Acc.fountain: 0.7528, Acc.conveyer belt: 0.9269, Acc.canopy: 0.0502, Acc.washer: 0.7080, Acc.plaything: 0.1522, Acc.swimming pool: 0.8308, Acc.stool: 0.5628, Acc.barrel: 0.6460, Acc.basket: 0.3782, Acc.waterfall: 0.9279, Acc.tent: 0.9649, Acc.bag: 0.0498, Acc.minibike: 0.7002, Acc.cradle: 0.9674, Acc.oven: 0.5972, Acc.ball: 0.6381, Acc.food: 0.3408, Acc.step: 0.0295, Acc.tank: 0.5355, Acc.trade name: 0.1237, Acc.microwave: 0.9459, Acc.pot: 0.4434, Acc.animal: 0.7069, Acc.bicycle: 0.5089, Acc.lake: 0.1484, Acc.dishwasher: 0.5117, Acc.screen: 0.8854, Acc.blanket: 0.0200, Acc.sculpture: 0.6751, Acc.hood: 0.6781, Acc.sconce: 0.4495, Acc.vase: 0.4444, Acc.traffic light: 0.0753, Acc.tray: 0.0315, Acc.ashcan: 0.4358, Acc.fan: 0.7086, Acc.pier: 0.4073, Acc.crt screen: 0.0000, Acc.plate: 0.7648, Acc.monitor: 0.7074, Acc.bulletin board: 0.5215, Acc.shower: 0.0000, Acc.radiator: 0.5630, Acc.glass: 0.0000, Acc.clock: 0.1880, Acc.flag: 0.6081 +2024-06-18 01:07:45,541 - mmseg - INFO - Iter [4050/80000] lr: 3.798e-05, eta: 1 day, 7:33:03, time: 3.287, data_time: 1.970, memory: 70498, decode.loss_ce: 0.5470, decode.acc_seg: 79.5577, aux.loss_ce: 0.2179, aux.acc_seg: 79.7109, loss: 0.7649 +2024-06-18 01:08:51,916 - mmseg - INFO - Iter [4100/80000] lr: 3.795e-05, eta: 1 day, 7:29:13, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5909, decode.acc_seg: 78.5139, aux.loss_ce: 0.2343, aux.acc_seg: 78.9857, loss: 0.8252 +2024-06-18 01:09:58,176 - mmseg - INFO - Iter [4150/80000] lr: 3.793e-05, eta: 1 day, 7:25:24, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5805, decode.acc_seg: 78.8519, aux.loss_ce: 0.2310, aux.acc_seg: 79.0147, loss: 0.8115 +2024-06-18 01:11:04,532 - mmseg - INFO - Iter [4200/80000] lr: 3.790e-05, eta: 1 day, 7:21:41, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5701, decode.acc_seg: 79.5743, aux.loss_ce: 0.2257, aux.acc_seg: 79.8135, loss: 0.7958 +2024-06-18 01:12:10,763 - mmseg - INFO - Iter [4250/80000] lr: 3.788e-05, eta: 1 day, 7:18:00, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5734, decode.acc_seg: 77.9795, aux.loss_ce: 0.2264, aux.acc_seg: 78.6031, loss: 0.7998 +2024-06-18 01:13:17,017 - mmseg - INFO - Iter [4300/80000] lr: 3.785e-05, eta: 1 day, 7:14:23, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5849, decode.acc_seg: 78.4826, aux.loss_ce: 0.2304, aux.acc_seg: 78.7487, loss: 0.8153 +2024-06-18 01:14:23,310 - mmseg - INFO - Iter [4350/80000] lr: 3.783e-05, eta: 1 day, 7:10:49, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5671, decode.acc_seg: 78.0992, aux.loss_ce: 0.2259, aux.acc_seg: 78.3749, loss: 0.7930 +2024-06-18 01:15:29,739 - mmseg - INFO - Iter [4400/80000] lr: 3.780e-05, eta: 1 day, 7:07:22, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5571, decode.acc_seg: 79.5812, aux.loss_ce: 0.2206, aux.acc_seg: 79.9205, loss: 0.7777 +2024-06-18 01:16:35,838 - mmseg - INFO - Iter [4450/80000] lr: 3.778e-05, eta: 1 day, 7:03:52, time: 1.322, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5427, decode.acc_seg: 79.8639, aux.loss_ce: 0.2151, aux.acc_seg: 80.1781, loss: 0.7578 +2024-06-18 01:17:42,060 - mmseg - INFO - Iter [4500/80000] lr: 3.775e-05, eta: 1 day, 7:00:27, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5671, decode.acc_seg: 78.5125, aux.loss_ce: 0.2254, aux.acc_seg: 78.7090, loss: 0.7924 +2024-06-18 01:18:48,206 - mmseg - INFO - Iter [4550/80000] lr: 3.773e-05, eta: 1 day, 6:57:04, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5917, decode.acc_seg: 78.6150, aux.loss_ce: 0.2353, aux.acc_seg: 78.6066, loss: 0.8269 +2024-06-18 01:19:54,444 - mmseg - INFO - Iter [4600/80000] lr: 3.770e-05, eta: 1 day, 6:53:46, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5603, decode.acc_seg: 79.1448, aux.loss_ce: 0.2218, aux.acc_seg: 79.3697, loss: 0.7821 +2024-06-18 01:21:00,948 - mmseg - INFO - Iter [4650/80000] lr: 3.768e-05, eta: 1 day, 6:50:34, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.6057, decode.acc_seg: 77.6601, aux.loss_ce: 0.2388, aux.acc_seg: 78.0890, loss: 0.8445 +2024-06-18 01:22:07,253 - mmseg - INFO - Iter [4700/80000] lr: 3.765e-05, eta: 1 day, 6:47:23, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5659, decode.acc_seg: 78.3189, aux.loss_ce: 0.2243, aux.acc_seg: 78.7153, loss: 0.7902 +2024-06-18 01:23:13,803 - mmseg - INFO - Iter [4750/80000] lr: 3.763e-05, eta: 1 day, 6:44:17, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5580, decode.acc_seg: 79.5604, aux.loss_ce: 0.2218, aux.acc_seg: 79.7896, loss: 0.7797 +2024-06-18 01:24:20,079 - mmseg - INFO - Iter [4800/80000] lr: 3.760e-05, eta: 1 day, 6:41:10, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5731, decode.acc_seg: 78.6177, aux.loss_ce: 0.2293, aux.acc_seg: 78.9019, loss: 0.8024 +2024-06-18 01:25:26,327 - mmseg - INFO - Iter [4850/80000] lr: 3.758e-05, eta: 1 day, 6:38:05, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5752, decode.acc_seg: 78.7711, aux.loss_ce: 0.2282, aux.acc_seg: 79.0326, loss: 0.8035 +2024-06-18 01:26:32,795 - mmseg - INFO - Iter [4900/80000] lr: 3.755e-05, eta: 1 day, 6:35:06, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5917, decode.acc_seg: 78.2857, aux.loss_ce: 0.2337, aux.acc_seg: 78.6326, loss: 0.8254 +2024-06-18 01:27:39,151 - mmseg - INFO - Iter [4950/80000] lr: 3.753e-05, eta: 1 day, 6:32:07, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5870, decode.acc_seg: 78.9084, aux.loss_ce: 0.2311, aux.acc_seg: 79.1749, loss: 0.8181 +2024-06-18 01:28:45,659 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 01:28:45,659 - mmseg - INFO - Iter [5000/80000] lr: 3.750e-05, eta: 1 day, 6:29:13, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5630, decode.acc_seg: 78.5975, aux.loss_ce: 0.2202, aux.acc_seg: 79.2761, loss: 0.7832 +2024-06-18 01:30:21,510 - mmseg - INFO - per class results: +2024-06-18 01:30:21,516 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 76.28 | 87.14 | +| building | 82.98 | 92.59 | +| sky | 93.94 | 97.3 | +| floor | 81.7 | 90.36 | +| tree | 74.51 | 89.09 | +| ceiling | 79.7 | 84.09 | +| road | 82.63 | 92.43 | +| bed | 89.13 | 95.08 | +| windowpane | 59.2 | 77.7 | +| grass | 61.88 | 72.48 | +| cabinet | 58.31 | 66.68 | +| sidewalk | 61.07 | 74.21 | +| person | 79.35 | 89.5 | +| earth | 30.73 | 39.32 | +| door | 53.77 | 71.72 | +| table | 56.82 | 69.59 | +| mountain | 60.61 | 72.69 | +| plant | 50.68 | 62.05 | +| curtain | 66.95 | 72.9 | +| chair | 56.07 | 74.8 | +| car | 83.12 | 91.2 | +| water | 58.46 | 79.95 | +| painting | 70.66 | 84.83 | +| sofa | 63.64 | 67.53 | +| shelf | 22.11 | 25.37 | +| house | 57.55 | 81.04 | +| sea | 52.61 | 64.66 | +| mirror | 65.69 | 85.15 | +| rug | 55.48 | 58.38 | +| field | 21.47 | 56.56 | +| armchair | 46.1 | 71.86 | +| seat | 60.87 | 86.98 | +| fence | 40.54 | 67.75 | +| desk | 48.03 | 71.24 | +| rock | 59.21 | 77.94 | +| wardrobe | 53.25 | 73.2 | +| lamp | 57.31 | 73.75 | +| bathtub | 70.69 | 92.4 | +| railing | 30.91 | 41.01 | +| cushion | 50.49 | 75.68 | +| base | 30.25 | 75.72 | +| box | 22.81 | 32.01 | +| column | 45.55 | 50.15 | +| signboard | 31.34 | 39.6 | +| chest of drawers | 46.54 | 56.92 | +| counter | 38.72 | 42.43 | +| sand | 41.97 | 57.28 | +| sink | 63.95 | 76.97 | +| skyscraper | 49.73 | 65.88 | +| fireplace | 56.61 | 97.92 | +| refrigerator | 65.91 | 87.39 | +| grandstand | 37.68 | 58.86 | +| path | 19.21 | 30.2 | +| stairs | 25.33 | 29.15 | +| runway | 69.41 | 95.89 | +| case | 56.95 | 87.17 | +| pool table | 82.63 | 98.1 | +| pillow | 55.04 | 64.74 | +| screen door | 62.1 | 65.38 | +| stairway | 27.77 | 39.83 | +| river | 24.78 | 30.46 | +| bridge | 71.36 | 88.79 | +| bookcase | 16.09 | 44.73 | +| blind | 46.23 | 69.19 | +| coffee table | 54.73 | 86.24 | +| toilet | 81.64 | 92.54 | +| flower | 23.88 | 41.42 | +| book | 34.37 | 76.84 | +| hill | 3.46 | 4.99 | +| bench | 44.43 | 53.96 | +| countertop | 52.98 | 71.46 | +| stove | 70.91 | 90.42 | +| palm | 48.22 | 59.11 | +| kitchen island | 30.7 | 93.75 | +| computer | 60.83 | 89.12 | +| swivel chair | 37.84 | 44.61 | +| boat | 59.44 | 82.32 | +| bar | 59.46 | 70.24 | +| arcade machine | 71.27 | 79.93 | +| hovel | 27.04 | 30.54 | +| bus | 87.97 | 92.37 | +| towel | 59.37 | 69.61 | +| light | 17.59 | 17.9 | +| truck | 36.03 | 44.07 | +| tower | 4.04 | 4.7 | +| chandelier | 61.25 | 74.22 | +| awning | 33.54 | 43.44 | +| streetlight | 11.76 | 13.18 | +| booth | 28.38 | 77.99 | +| television receiver | 57.64 | 68.79 | +| airplane | 49.15 | 66.78 | +| dirt track | 3.97 | 31.3 | +| apparel | 43.82 | 59.96 | +| pole | 5.43 | 5.82 | +| land | 0.0 | 0.0 | +| bannister | 5.26 | 7.66 | +| escalator | 55.71 | 74.06 | +| ottoman | 41.46 | 64.98 | +| bottle | 35.42 | 54.6 | +| buffet | 45.93 | 72.48 | +| poster | 24.12 | 27.54 | +| stage | 11.75 | 14.93 | +| van | 25.04 | 28.83 | +| ship | 59.71 | 71.01 | +| fountain | 53.09 | 55.48 | +| conveyer belt | 75.6 | 92.18 | +| canopy | 45.65 | 65.15 | +| washer | 71.97 | 77.14 | +| plaything | 29.06 | 58.39 | +| swimming pool | 53.7 | 97.65 | +| stool | 34.44 | 43.61 | +| barrel | 4.54 | 65.12 | +| basket | 25.31 | 37.4 | +| waterfall | 51.05 | 98.7 | +| tent | 94.16 | 97.9 | +| bag | 2.47 | 2.5 | +| minibike | 61.64 | 80.28 | +| cradle | 74.23 | 96.5 | +| oven | 44.24 | 48.38 | +| ball | 38.55 | 62.89 | +| food | 44.33 | 51.65 | +| step | 2.06 | 2.08 | +| tank | 45.85 | 56.11 | +| trade name | 25.89 | 34.72 | +| microwave | 78.63 | 92.99 | +| pot | 32.2 | 34.91 | +| animal | 65.66 | 70.78 | +| bicycle | 45.65 | 65.46 | +| lake | 0.0 | 0.0 | +| dishwasher | 49.13 | 62.11 | +| screen | 54.89 | 94.25 | +| blanket | 29.15 | 39.12 | +| sculpture | 54.61 | 58.02 | +| hood | 51.74 | 74.41 | +| sconce | 19.06 | 20.24 | +| vase | 27.79 | 39.42 | +| traffic light | 15.67 | 43.8 | +| tray | 3.53 | 4.85 | +| ashcan | 36.93 | 49.98 | +| fan | 47.38 | 54.43 | +| pier | 33.45 | 38.24 | +| crt screen | 0.0 | 0.0 | +| plate | 38.16 | 71.22 | +| monitor | 7.77 | 8.33 | +| bulletin board | 39.66 | 71.52 | +| shower | 0.0 | 0.0 | +| radiator | 50.88 | 59.92 | +| glass | 0.35 | 0.35 | +| clock | 26.02 | 32.18 | +| flag | 40.23 | 42.37 | ++---------------------+-------+-------+ +2024-06-18 01:30:21,516 - mmseg - INFO - Summary: +2024-06-18 01:30:21,516 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 81.84 | 45.36 | 59.93 | ++-------+-------+-------+ +2024-06-18 01:30:21,517 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 01:30:21,517 - mmseg - INFO - Iter(val) [250] aAcc: 0.8184, mIoU: 0.4536, mAcc: 0.5993, IoU.wall: 0.7628, IoU.building: 0.8298, IoU.sky: 0.9394, IoU.floor: 0.8170, IoU.tree: 0.7451, IoU.ceiling: 0.7970, IoU.road: 0.8263, IoU.bed : 0.8913, IoU.windowpane: 0.5920, IoU.grass: 0.6188, IoU.cabinet: 0.5831, IoU.sidewalk: 0.6107, IoU.person: 0.7935, IoU.earth: 0.3073, IoU.door: 0.5377, IoU.table: 0.5682, IoU.mountain: 0.6061, IoU.plant: 0.5068, IoU.curtain: 0.6695, IoU.chair: 0.5607, IoU.car: 0.8312, IoU.water: 0.5846, IoU.painting: 0.7066, IoU.sofa: 0.6364, IoU.shelf: 0.2211, IoU.house: 0.5755, IoU.sea: 0.5261, IoU.mirror: 0.6569, IoU.rug: 0.5548, IoU.field: 0.2147, IoU.armchair: 0.4610, IoU.seat: 0.6087, IoU.fence: 0.4054, IoU.desk: 0.4803, IoU.rock: 0.5921, IoU.wardrobe: 0.5325, IoU.lamp: 0.5731, IoU.bathtub: 0.7069, IoU.railing: 0.3091, IoU.cushion: 0.5049, IoU.base: 0.3025, IoU.box: 0.2281, IoU.column: 0.4555, IoU.signboard: 0.3134, IoU.chest of drawers: 0.4654, IoU.counter: 0.3872, IoU.sand: 0.4197, IoU.sink: 0.6395, IoU.skyscraper: 0.4973, IoU.fireplace: 0.5661, IoU.refrigerator: 0.6591, IoU.grandstand: 0.3768, IoU.path: 0.1921, IoU.stairs: 0.2533, IoU.runway: 0.6941, IoU.case: 0.5695, IoU.pool table: 0.8263, IoU.pillow: 0.5504, IoU.screen door: 0.6210, IoU.stairway: 0.2777, IoU.river: 0.2478, IoU.bridge: 0.7136, IoU.bookcase: 0.1609, IoU.blind: 0.4623, IoU.coffee table: 0.5473, IoU.toilet: 0.8164, IoU.flower: 0.2388, IoU.book: 0.3437, IoU.hill: 0.0346, IoU.bench: 0.4443, IoU.countertop: 0.5298, IoU.stove: 0.7091, IoU.palm: 0.4822, IoU.kitchen island: 0.3070, IoU.computer: 0.6083, IoU.swivel chair: 0.3784, IoU.boat: 0.5944, IoU.bar: 0.5946, IoU.arcade machine: 0.7127, IoU.hovel: 0.2704, IoU.bus: 0.8797, IoU.towel: 0.5937, IoU.light: 0.1759, IoU.truck: 0.3603, IoU.tower: 0.0404, IoU.chandelier: 0.6125, IoU.awning: 0.3354, IoU.streetlight: 0.1176, IoU.booth: 0.2838, IoU.television receiver: 0.5764, IoU.airplane: 0.4915, IoU.dirt track: 0.0397, IoU.apparel: 0.4382, IoU.pole: 0.0543, IoU.land: 0.0000, IoU.bannister: 0.0526, IoU.escalator: 0.5571, IoU.ottoman: 0.4146, IoU.bottle: 0.3542, IoU.buffet: 0.4593, IoU.poster: 0.2412, IoU.stage: 0.1175, IoU.van: 0.2504, IoU.ship: 0.5971, IoU.fountain: 0.5309, IoU.conveyer belt: 0.7560, IoU.canopy: 0.4565, IoU.washer: 0.7197, IoU.plaything: 0.2906, IoU.swimming pool: 0.5370, IoU.stool: 0.3444, IoU.barrel: 0.0454, IoU.basket: 0.2531, IoU.waterfall: 0.5105, IoU.tent: 0.9416, IoU.bag: 0.0247, IoU.minibike: 0.6164, IoU.cradle: 0.7423, IoU.oven: 0.4424, IoU.ball: 0.3855, IoU.food: 0.4433, IoU.step: 0.0206, IoU.tank: 0.4585, IoU.trade name: 0.2589, IoU.microwave: 0.7863, IoU.pot: 0.3220, IoU.animal: 0.6566, IoU.bicycle: 0.4565, IoU.lake: 0.0000, IoU.dishwasher: 0.4913, IoU.screen: 0.5489, IoU.blanket: 0.2915, IoU.sculpture: 0.5461, IoU.hood: 0.5174, IoU.sconce: 0.1906, IoU.vase: 0.2779, IoU.traffic light: 0.1567, IoU.tray: 0.0353, IoU.ashcan: 0.3693, IoU.fan: 0.4738, IoU.pier: 0.3345, IoU.crt screen: 0.0000, IoU.plate: 0.3816, IoU.monitor: 0.0777, IoU.bulletin board: 0.3966, IoU.shower: 0.0000, IoU.radiator: 0.5088, IoU.glass: 0.0035, IoU.clock: 0.2602, IoU.flag: 0.4023, Acc.wall: 0.8714, Acc.building: 0.9259, Acc.sky: 0.9730, Acc.floor: 0.9036, Acc.tree: 0.8909, Acc.ceiling: 0.8409, Acc.road: 0.9243, Acc.bed : 0.9508, Acc.windowpane: 0.7770, Acc.grass: 0.7248, Acc.cabinet: 0.6668, Acc.sidewalk: 0.7421, Acc.person: 0.8950, Acc.earth: 0.3932, Acc.door: 0.7172, Acc.table: 0.6959, Acc.mountain: 0.7269, Acc.plant: 0.6205, Acc.curtain: 0.7290, Acc.chair: 0.7480, Acc.car: 0.9120, Acc.water: 0.7995, Acc.painting: 0.8483, Acc.sofa: 0.6753, Acc.shelf: 0.2537, Acc.house: 0.8104, Acc.sea: 0.6466, Acc.mirror: 0.8515, Acc.rug: 0.5838, Acc.field: 0.5656, Acc.armchair: 0.7186, Acc.seat: 0.8698, Acc.fence: 0.6775, Acc.desk: 0.7124, Acc.rock: 0.7794, Acc.wardrobe: 0.7320, Acc.lamp: 0.7375, Acc.bathtub: 0.9240, Acc.railing: 0.4101, Acc.cushion: 0.7568, Acc.base: 0.7572, Acc.box: 0.3201, Acc.column: 0.5015, Acc.signboard: 0.3960, Acc.chest of drawers: 0.5692, Acc.counter: 0.4243, Acc.sand: 0.5728, Acc.sink: 0.7697, Acc.skyscraper: 0.6588, Acc.fireplace: 0.9792, Acc.refrigerator: 0.8739, Acc.grandstand: 0.5886, Acc.path: 0.3020, Acc.stairs: 0.2915, Acc.runway: 0.9589, Acc.case: 0.8717, Acc.pool table: 0.9810, Acc.pillow: 0.6474, Acc.screen door: 0.6538, Acc.stairway: 0.3983, Acc.river: 0.3046, Acc.bridge: 0.8879, Acc.bookcase: 0.4473, Acc.blind: 0.6919, Acc.coffee table: 0.8624, Acc.toilet: 0.9254, Acc.flower: 0.4142, Acc.book: 0.7684, Acc.hill: 0.0499, Acc.bench: 0.5396, Acc.countertop: 0.7146, Acc.stove: 0.9042, Acc.palm: 0.5911, Acc.kitchen island: 0.9375, Acc.computer: 0.8912, Acc.swivel chair: 0.4461, Acc.boat: 0.8232, Acc.bar: 0.7024, Acc.arcade machine: 0.7993, Acc.hovel: 0.3054, Acc.bus: 0.9237, Acc.towel: 0.6961, Acc.light: 0.1790, Acc.truck: 0.4407, Acc.tower: 0.0470, Acc.chandelier: 0.7422, Acc.awning: 0.4344, Acc.streetlight: 0.1318, Acc.booth: 0.7799, Acc.television receiver: 0.6879, Acc.airplane: 0.6678, Acc.dirt track: 0.3130, Acc.apparel: 0.5996, Acc.pole: 0.0582, Acc.land: 0.0000, Acc.bannister: 0.0766, Acc.escalator: 0.7406, Acc.ottoman: 0.6498, Acc.bottle: 0.5460, Acc.buffet: 0.7248, Acc.poster: 0.2754, Acc.stage: 0.1493, Acc.van: 0.2883, Acc.ship: 0.7101, Acc.fountain: 0.5548, Acc.conveyer belt: 0.9218, Acc.canopy: 0.6515, Acc.washer: 0.7714, Acc.plaything: 0.5839, Acc.swimming pool: 0.9765, Acc.stool: 0.4361, Acc.barrel: 0.6512, Acc.basket: 0.3740, Acc.waterfall: 0.9870, Acc.tent: 0.9790, Acc.bag: 0.0250, Acc.minibike: 0.8028, Acc.cradle: 0.9650, Acc.oven: 0.4838, Acc.ball: 0.6289, Acc.food: 0.5165, Acc.step: 0.0208, Acc.tank: 0.5611, Acc.trade name: 0.3472, Acc.microwave: 0.9299, Acc.pot: 0.3491, Acc.animal: 0.7078, Acc.bicycle: 0.6546, Acc.lake: 0.0000, Acc.dishwasher: 0.6211, Acc.screen: 0.9425, Acc.blanket: 0.3912, Acc.sculpture: 0.5802, Acc.hood: 0.7441, Acc.sconce: 0.2024, Acc.vase: 0.3942, Acc.traffic light: 0.4380, Acc.tray: 0.0485, Acc.ashcan: 0.4998, Acc.fan: 0.5443, Acc.pier: 0.3824, Acc.crt screen: 0.0000, Acc.plate: 0.7122, Acc.monitor: 0.0833, Acc.bulletin board: 0.7152, Acc.shower: 0.0000, Acc.radiator: 0.5992, Acc.glass: 0.0035, Acc.clock: 0.3218, Acc.flag: 0.4237 +2024-06-18 01:31:28,259 - mmseg - INFO - Iter [5050/80000] lr: 3.748e-05, eta: 1 day, 6:50:07, time: 3.252, data_time: 1.933, memory: 70498, decode.loss_ce: 0.5989, decode.acc_seg: 78.0049, aux.loss_ce: 0.2360, aux.acc_seg: 78.4500, loss: 0.8350 +2024-06-18 01:32:37,265 - mmseg - INFO - Iter [5100/80000] lr: 3.745e-05, eta: 1 day, 6:47:39, time: 1.380, data_time: 0.059, memory: 70498, decode.loss_ce: 0.5111, decode.acc_seg: 80.5292, aux.loss_ce: 0.2044, aux.acc_seg: 80.8427, loss: 0.7154 +2024-06-18 01:33:43,663 - mmseg - INFO - Iter [5150/80000] lr: 3.743e-05, eta: 1 day, 6:44:34, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5193, decode.acc_seg: 80.4158, aux.loss_ce: 0.2054, aux.acc_seg: 80.8439, loss: 0.7247 +2024-06-18 01:34:50,154 - mmseg - INFO - Iter [5200/80000] lr: 3.740e-05, eta: 1 day, 6:41:33, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5659, decode.acc_seg: 79.4824, aux.loss_ce: 0.2229, aux.acc_seg: 79.5619, loss: 0.7888 +2024-06-18 01:35:56,396 - mmseg - INFO - Iter [5250/80000] lr: 3.738e-05, eta: 1 day, 6:38:31, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5378, decode.acc_seg: 79.1029, aux.loss_ce: 0.2117, aux.acc_seg: 79.7222, loss: 0.7495 +2024-06-18 01:37:02,943 - mmseg - INFO - Iter [5300/80000] lr: 3.735e-05, eta: 1 day, 6:35:35, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5308, decode.acc_seg: 79.8742, aux.loss_ce: 0.2111, aux.acc_seg: 80.1820, loss: 0.7420 +2024-06-18 01:38:09,012 - mmseg - INFO - Iter [5350/80000] lr: 3.733e-05, eta: 1 day, 6:32:35, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5206, decode.acc_seg: 80.8356, aux.loss_ce: 0.2084, aux.acc_seg: 80.8617, loss: 0.7289 +2024-06-18 01:39:15,364 - mmseg - INFO - Iter [5400/80000] lr: 3.730e-05, eta: 1 day, 6:29:40, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5356, decode.acc_seg: 80.3526, aux.loss_ce: 0.2113, aux.acc_seg: 80.7411, loss: 0.7469 +2024-06-18 01:40:21,642 - mmseg - INFO - Iter [5450/80000] lr: 3.728e-05, eta: 1 day, 6:26:47, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5193, decode.acc_seg: 80.0764, aux.loss_ce: 0.2051, aux.acc_seg: 80.5407, loss: 0.7244 +2024-06-18 01:41:27,821 - mmseg - INFO - Iter [5500/80000] lr: 3.725e-05, eta: 1 day, 6:23:54, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5377, decode.acc_seg: 79.7146, aux.loss_ce: 0.2140, aux.acc_seg: 79.7264, loss: 0.7517 +2024-06-18 01:42:34,261 - mmseg - INFO - Iter [5550/80000] lr: 3.723e-05, eta: 1 day, 6:21:07, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5115, decode.acc_seg: 81.0390, aux.loss_ce: 0.2038, aux.acc_seg: 80.9954, loss: 0.7154 +2024-06-18 01:43:40,361 - mmseg - INFO - Iter [5600/80000] lr: 3.720e-05, eta: 1 day, 6:18:16, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5694, decode.acc_seg: 78.1972, aux.loss_ce: 0.2246, aux.acc_seg: 78.4232, loss: 0.7940 +2024-06-18 01:44:46,865 - mmseg - INFO - Iter [5650/80000] lr: 3.718e-05, eta: 1 day, 6:15:33, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5103, decode.acc_seg: 80.6287, aux.loss_ce: 0.2041, aux.acc_seg: 80.8779, loss: 0.7144 +2024-06-18 01:45:53,286 - mmseg - INFO - Iter [5700/80000] lr: 3.715e-05, eta: 1 day, 6:12:51, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5423, decode.acc_seg: 79.5204, aux.loss_ce: 0.2134, aux.acc_seg: 79.9173, loss: 0.7557 +2024-06-18 01:46:59,909 - mmseg - INFO - Iter [5750/80000] lr: 3.713e-05, eta: 1 day, 6:10:13, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5451, decode.acc_seg: 79.2814, aux.loss_ce: 0.2157, aux.acc_seg: 79.6091, loss: 0.7608 +2024-06-18 01:48:06,295 - mmseg - INFO - Iter [5800/80000] lr: 3.710e-05, eta: 1 day, 6:07:33, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5360, decode.acc_seg: 79.9652, aux.loss_ce: 0.2129, aux.acc_seg: 80.1895, loss: 0.7488 +2024-06-18 01:49:12,742 - mmseg - INFO - Iter [5850/80000] lr: 3.708e-05, eta: 1 day, 6:04:56, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5679, decode.acc_seg: 78.9276, aux.loss_ce: 0.2245, aux.acc_seg: 79.2644, loss: 0.7924 +2024-06-18 01:50:19,277 - mmseg - INFO - Iter [5900/80000] lr: 3.705e-05, eta: 1 day, 6:02:22, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5644, decode.acc_seg: 78.1074, aux.loss_ce: 0.2240, aux.acc_seg: 78.3772, loss: 0.7884 +2024-06-18 01:51:25,554 - mmseg - INFO - Iter [5950/80000] lr: 3.703e-05, eta: 1 day, 5:59:45, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5261, decode.acc_seg: 79.8309, aux.loss_ce: 0.2088, aux.acc_seg: 80.1971, loss: 0.7349 +2024-06-18 01:52:31,652 - mmseg - INFO - Saving checkpoint at 6000 iterations +2024-06-18 01:54:16,835 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 01:54:16,835 - mmseg - INFO - Iter [6000/80000] lr: 3.700e-05, eta: 1 day, 6:18:46, time: 3.426, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5410, decode.acc_seg: 79.8921, aux.loss_ce: 0.2159, aux.acc_seg: 80.0237, loss: 0.7568 +2024-06-18 01:55:57,104 - mmseg - INFO - per class results: +2024-06-18 01:55:57,110 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 76.99 | 84.22 | +| building | 83.05 | 92.1 | +| sky | 94.0 | 96.74 | +| floor | 80.13 | 85.28 | +| tree | 74.42 | 87.48 | +| ceiling | 83.05 | 90.14 | +| road | 81.98 | 90.93 | +| bed | 88.05 | 95.67 | +| windowpane | 59.62 | 75.89 | +| grass | 70.31 | 85.49 | +| cabinet | 58.7 | 70.34 | +| sidewalk | 62.58 | 79.38 | +| person | 79.09 | 91.45 | +| earth | 35.07 | 45.75 | +| door | 53.65 | 73.83 | +| table | 59.09 | 74.11 | +| mountain | 57.51 | 73.09 | +| plant | 50.99 | 73.58 | +| curtain | 71.93 | 90.14 | +| chair | 54.48 | 68.04 | +| car | 83.51 | 91.3 | +| water | 63.66 | 84.53 | +| painting | 70.29 | 87.92 | +| sofa | 71.55 | 85.55 | +| shelf | 34.88 | 59.22 | +| house | 54.07 | 65.12 | +| sea | 58.75 | 68.65 | +| mirror | 63.65 | 87.1 | +| rug | 63.31 | 80.92 | +| field | 23.81 | 41.33 | +| armchair | 45.62 | 80.65 | +| seat | 53.53 | 89.52 | +| fence | 39.76 | 73.38 | +| desk | 47.64 | 63.6 | +| rock | 50.0 | 61.1 | +| wardrobe | 48.77 | 84.8 | +| lamp | 60.27 | 71.64 | +| bathtub | 72.1 | 82.84 | +| railing | 37.68 | 52.71 | +| cushion | 55.84 | 69.61 | +| base | 32.66 | 45.93 | +| box | 23.25 | 26.36 | +| column | 47.59 | 54.35 | +| signboard | 33.85 | 47.88 | +| chest of drawers | 41.88 | 53.56 | +| counter | 57.21 | 75.74 | +| sand | 48.18 | 52.04 | +| sink | 66.98 | 72.33 | +| skyscraper | 45.65 | 56.14 | +| fireplace | 65.78 | 94.03 | +| refrigerator | 64.02 | 89.04 | +| grandstand | 37.46 | 92.67 | +| path | 19.68 | 24.98 | +| stairs | 33.3 | 42.47 | +| runway | 67.36 | 88.01 | +| case | 54.92 | 84.63 | +| pool table | 83.2 | 98.47 | +| pillow | 60.1 | 71.92 | +| screen door | 47.22 | 93.88 | +| stairway | 32.28 | 48.11 | +| river | 27.86 | 48.09 | +| bridge | 72.06 | 89.95 | +| bookcase | 26.84 | 53.3 | +| blind | 41.5 | 46.47 | +| coffee table | 51.1 | 89.26 | +| toilet | 82.31 | 91.15 | +| flower | 36.45 | 49.96 | +| book | 46.19 | 73.0 | +| hill | 5.2 | 13.68 | +| bench | 42.63 | 55.52 | +| countertop | 54.4 | 67.21 | +| stove | 70.25 | 89.65 | +| palm | 45.49 | 58.34 | +| kitchen island | 33.34 | 53.35 | +| computer | 71.23 | 89.71 | +| swivel chair | 39.69 | 48.99 | +| boat | 50.02 | 86.58 | +| bar | 55.35 | 70.47 | +| arcade machine | 66.48 | 84.87 | +| hovel | 38.12 | 43.53 | +| bus | 81.96 | 95.6 | +| towel | 55.5 | 65.58 | +| light | 46.93 | 59.34 | +| truck | 38.91 | 51.0 | +| tower | 29.82 | 52.75 | +| chandelier | 61.68 | 86.14 | +| awning | 29.79 | 33.67 | +| streetlight | 15.7 | 19.83 | +| booth | 33.3 | 46.92 | +| television receiver | 63.36 | 73.89 | +| airplane | 55.1 | 62.18 | +| dirt track | 17.97 | 17.98 | +| apparel | 32.24 | 48.85 | +| pole | 7.4 | 8.05 | +| land | 0.3 | 0.43 | +| bannister | 3.46 | 3.84 | +| escalator | 55.59 | 81.37 | +| ottoman | 41.38 | 59.45 | +| bottle | 22.94 | 25.15 | +| buffet | 45.9 | 77.61 | +| poster | 23.84 | 26.99 | +| stage | 22.32 | 49.5 | +| van | 31.62 | 37.79 | +| ship | 15.85 | 17.93 | +| fountain | 43.04 | 43.93 | +| conveyer belt | 60.48 | 93.3 | +| canopy | 42.13 | 45.31 | +| washer | 65.03 | 68.24 | +| plaything | 27.6 | 54.63 | +| swimming pool | 62.3 | 95.98 | +| stool | 37.74 | 44.87 | +| barrel | 24.6 | 65.12 | +| basket | 19.44 | 22.91 | +| waterfall | 54.63 | 86.48 | +| tent | 83.46 | 99.59 | +| bag | 0.65 | 0.65 | +| minibike | 60.74 | 82.1 | +| cradle | 68.06 | 99.37 | +| oven | 29.28 | 30.26 | +| ball | 42.27 | 61.43 | +| food | 39.04 | 49.99 | +| step | 14.94 | 17.37 | +| tank | 56.55 | 65.83 | +| trade name | 25.9 | 50.03 | +| microwave | 81.77 | 92.91 | +| pot | 45.51 | 51.68 | +| animal | 63.79 | 72.16 | +| bicycle | 45.2 | 82.29 | +| lake | 0.0 | 0.0 | +| dishwasher | 50.96 | 65.11 | +| screen | 39.17 | 98.5 | +| blanket | 2.78 | 2.95 | +| sculpture | 54.44 | 66.45 | +| hood | 52.94 | 59.04 | +| sconce | 24.6 | 26.78 | +| vase | 27.01 | 35.16 | +| traffic light | 16.58 | 19.94 | +| tray | 3.39 | 3.92 | +| ashcan | 36.3 | 44.71 | +| fan | 50.58 | 74.72 | +| pier | 31.93 | 44.04 | +| crt screen | 0.25 | 0.73 | +| plate | 44.8 | 52.13 | +| monitor | 9.34 | 10.02 | +| bulletin board | 48.57 | 55.12 | +| shower | 0.0 | 0.0 | +| radiator | 48.85 | 58.26 | +| glass | 1.17 | 1.18 | +| clock | 23.27 | 42.68 | +| flag | 59.61 | 62.3 | ++---------------------+-------+-------+ +2024-06-18 01:55:57,110 - mmseg - INFO - Summary: +2024-06-18 01:55:57,110 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 82.39 | 46.31 | 60.82 | ++-------+-------+-------+ +2024-06-18 01:55:57,111 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 01:55:57,111 - mmseg - INFO - Iter(val) [250] aAcc: 0.8239, mIoU: 0.4631, mAcc: 0.6082, IoU.wall: 0.7699, IoU.building: 0.8305, IoU.sky: 0.9400, IoU.floor: 0.8013, IoU.tree: 0.7442, IoU.ceiling: 0.8305, IoU.road: 0.8198, IoU.bed : 0.8805, IoU.windowpane: 0.5962, IoU.grass: 0.7031, IoU.cabinet: 0.5870, IoU.sidewalk: 0.6258, IoU.person: 0.7909, IoU.earth: 0.3507, IoU.door: 0.5365, IoU.table: 0.5909, IoU.mountain: 0.5751, IoU.plant: 0.5099, IoU.curtain: 0.7193, IoU.chair: 0.5448, IoU.car: 0.8351, IoU.water: 0.6366, IoU.painting: 0.7029, IoU.sofa: 0.7155, IoU.shelf: 0.3488, IoU.house: 0.5407, IoU.sea: 0.5875, IoU.mirror: 0.6365, IoU.rug: 0.6331, IoU.field: 0.2381, IoU.armchair: 0.4562, IoU.seat: 0.5353, IoU.fence: 0.3976, IoU.desk: 0.4764, IoU.rock: 0.5000, IoU.wardrobe: 0.4877, IoU.lamp: 0.6027, IoU.bathtub: 0.7210, IoU.railing: 0.3768, IoU.cushion: 0.5584, IoU.base: 0.3266, IoU.box: 0.2325, IoU.column: 0.4759, IoU.signboard: 0.3385, IoU.chest of drawers: 0.4188, IoU.counter: 0.5721, IoU.sand: 0.4818, IoU.sink: 0.6698, IoU.skyscraper: 0.4565, IoU.fireplace: 0.6578, IoU.refrigerator: 0.6402, IoU.grandstand: 0.3746, IoU.path: 0.1968, IoU.stairs: 0.3330, IoU.runway: 0.6736, IoU.case: 0.5492, IoU.pool table: 0.8320, IoU.pillow: 0.6010, IoU.screen door: 0.4722, IoU.stairway: 0.3228, IoU.river: 0.2786, IoU.bridge: 0.7206, IoU.bookcase: 0.2684, IoU.blind: 0.4150, IoU.coffee table: 0.5110, IoU.toilet: 0.8231, IoU.flower: 0.3645, IoU.book: 0.4619, IoU.hill: 0.0520, IoU.bench: 0.4263, IoU.countertop: 0.5440, IoU.stove: 0.7025, IoU.palm: 0.4549, IoU.kitchen island: 0.3334, IoU.computer: 0.7123, IoU.swivel chair: 0.3969, IoU.boat: 0.5002, IoU.bar: 0.5535, IoU.arcade machine: 0.6648, IoU.hovel: 0.3812, IoU.bus: 0.8196, IoU.towel: 0.5550, IoU.light: 0.4693, IoU.truck: 0.3891, IoU.tower: 0.2982, IoU.chandelier: 0.6168, IoU.awning: 0.2979, IoU.streetlight: 0.1570, IoU.booth: 0.3330, IoU.television receiver: 0.6336, IoU.airplane: 0.5510, IoU.dirt track: 0.1797, IoU.apparel: 0.3224, IoU.pole: 0.0740, IoU.land: 0.0030, IoU.bannister: 0.0346, IoU.escalator: 0.5559, IoU.ottoman: 0.4138, IoU.bottle: 0.2294, IoU.buffet: 0.4590, IoU.poster: 0.2384, IoU.stage: 0.2232, IoU.van: 0.3162, IoU.ship: 0.1585, IoU.fountain: 0.4304, IoU.conveyer belt: 0.6048, IoU.canopy: 0.4213, IoU.washer: 0.6503, IoU.plaything: 0.2760, IoU.swimming pool: 0.6230, IoU.stool: 0.3774, IoU.barrel: 0.2460, IoU.basket: 0.1944, IoU.waterfall: 0.5463, IoU.tent: 0.8346, IoU.bag: 0.0065, IoU.minibike: 0.6074, IoU.cradle: 0.6806, IoU.oven: 0.2928, IoU.ball: 0.4227, IoU.food: 0.3904, IoU.step: 0.1494, IoU.tank: 0.5655, IoU.trade name: 0.2590, IoU.microwave: 0.8177, IoU.pot: 0.4551, IoU.animal: 0.6379, IoU.bicycle: 0.4520, IoU.lake: 0.0000, IoU.dishwasher: 0.5096, IoU.screen: 0.3917, IoU.blanket: 0.0278, IoU.sculpture: 0.5444, IoU.hood: 0.5294, IoU.sconce: 0.2460, IoU.vase: 0.2701, IoU.traffic light: 0.1658, IoU.tray: 0.0339, IoU.ashcan: 0.3630, IoU.fan: 0.5058, IoU.pier: 0.3193, IoU.crt screen: 0.0025, IoU.plate: 0.4480, IoU.monitor: 0.0934, IoU.bulletin board: 0.4857, IoU.shower: 0.0000, IoU.radiator: 0.4885, IoU.glass: 0.0117, IoU.clock: 0.2327, IoU.flag: 0.5961, Acc.wall: 0.8422, Acc.building: 0.9210, Acc.sky: 0.9674, Acc.floor: 0.8528, Acc.tree: 0.8748, Acc.ceiling: 0.9014, Acc.road: 0.9093, Acc.bed : 0.9567, Acc.windowpane: 0.7589, Acc.grass: 0.8549, Acc.cabinet: 0.7034, Acc.sidewalk: 0.7938, Acc.person: 0.9145, Acc.earth: 0.4575, Acc.door: 0.7383, Acc.table: 0.7411, Acc.mountain: 0.7309, Acc.plant: 0.7358, Acc.curtain: 0.9014, Acc.chair: 0.6804, Acc.car: 0.9130, Acc.water: 0.8453, Acc.painting: 0.8792, Acc.sofa: 0.8555, Acc.shelf: 0.5922, Acc.house: 0.6512, Acc.sea: 0.6865, Acc.mirror: 0.8710, Acc.rug: 0.8092, Acc.field: 0.4133, Acc.armchair: 0.8065, Acc.seat: 0.8952, Acc.fence: 0.7338, Acc.desk: 0.6360, Acc.rock: 0.6110, Acc.wardrobe: 0.8480, Acc.lamp: 0.7164, Acc.bathtub: 0.8284, Acc.railing: 0.5271, Acc.cushion: 0.6961, Acc.base: 0.4593, Acc.box: 0.2636, Acc.column: 0.5435, Acc.signboard: 0.4788, Acc.chest of drawers: 0.5356, Acc.counter: 0.7574, Acc.sand: 0.5204, Acc.sink: 0.7233, Acc.skyscraper: 0.5614, Acc.fireplace: 0.9403, Acc.refrigerator: 0.8904, Acc.grandstand: 0.9267, Acc.path: 0.2498, Acc.stairs: 0.4247, Acc.runway: 0.8801, Acc.case: 0.8463, Acc.pool table: 0.9847, Acc.pillow: 0.7192, Acc.screen door: 0.9388, Acc.stairway: 0.4811, Acc.river: 0.4809, Acc.bridge: 0.8995, Acc.bookcase: 0.5330, Acc.blind: 0.4647, Acc.coffee table: 0.8926, Acc.toilet: 0.9115, Acc.flower: 0.4996, Acc.book: 0.7300, Acc.hill: 0.1368, Acc.bench: 0.5552, Acc.countertop: 0.6721, Acc.stove: 0.8965, Acc.palm: 0.5834, Acc.kitchen island: 0.5335, Acc.computer: 0.8971, Acc.swivel chair: 0.4899, Acc.boat: 0.8658, Acc.bar: 0.7047, Acc.arcade machine: 0.8487, Acc.hovel: 0.4353, Acc.bus: 0.9560, Acc.towel: 0.6558, Acc.light: 0.5934, Acc.truck: 0.5100, Acc.tower: 0.5275, Acc.chandelier: 0.8614, Acc.awning: 0.3367, Acc.streetlight: 0.1983, Acc.booth: 0.4692, Acc.television receiver: 0.7389, Acc.airplane: 0.6218, Acc.dirt track: 0.1798, Acc.apparel: 0.4885, Acc.pole: 0.0805, Acc.land: 0.0043, Acc.bannister: 0.0384, Acc.escalator: 0.8137, Acc.ottoman: 0.5945, Acc.bottle: 0.2515, Acc.buffet: 0.7761, Acc.poster: 0.2699, Acc.stage: 0.4950, Acc.van: 0.3779, Acc.ship: 0.1793, Acc.fountain: 0.4393, Acc.conveyer belt: 0.9330, Acc.canopy: 0.4531, Acc.washer: 0.6824, Acc.plaything: 0.5463, Acc.swimming pool: 0.9598, Acc.stool: 0.4487, Acc.barrel: 0.6512, Acc.basket: 0.2291, Acc.waterfall: 0.8648, Acc.tent: 0.9959, Acc.bag: 0.0065, Acc.minibike: 0.8210, Acc.cradle: 0.9937, Acc.oven: 0.3026, Acc.ball: 0.6143, Acc.food: 0.4999, Acc.step: 0.1737, Acc.tank: 0.6583, Acc.trade name: 0.5003, Acc.microwave: 0.9291, Acc.pot: 0.5168, Acc.animal: 0.7216, Acc.bicycle: 0.8229, Acc.lake: 0.0000, Acc.dishwasher: 0.6511, Acc.screen: 0.9850, Acc.blanket: 0.0295, Acc.sculpture: 0.6645, Acc.hood: 0.5904, Acc.sconce: 0.2678, Acc.vase: 0.3516, Acc.traffic light: 0.1994, Acc.tray: 0.0392, Acc.ashcan: 0.4471, Acc.fan: 0.7472, Acc.pier: 0.4404, Acc.crt screen: 0.0073, Acc.plate: 0.5213, Acc.monitor: 0.1002, Acc.bulletin board: 0.5512, Acc.shower: 0.0000, Acc.radiator: 0.5826, Acc.glass: 0.0118, Acc.clock: 0.4268, Acc.flag: 0.6230 +2024-06-18 01:57:03,922 - mmseg - INFO - Iter [6050/80000] lr: 3.698e-05, eta: 1 day, 6:36:33, time: 3.342, data_time: 2.023, memory: 70498, decode.loss_ce: 0.5212, decode.acc_seg: 80.1932, aux.loss_ce: 0.2070, aux.acc_seg: 80.2926, loss: 0.7282 +2024-06-18 01:58:10,404 - mmseg - INFO - Iter [6100/80000] lr: 3.695e-05, eta: 1 day, 6:33:41, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5101, decode.acc_seg: 80.2932, aux.loss_ce: 0.2031, aux.acc_seg: 80.3305, loss: 0.7133 +2024-06-18 01:59:16,754 - mmseg - INFO - Iter [6150/80000] lr: 3.693e-05, eta: 1 day, 6:30:50, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5627, decode.acc_seg: 79.2475, aux.loss_ce: 0.2223, aux.acc_seg: 79.3735, loss: 0.7851 +2024-06-18 02:00:23,703 - mmseg - INFO - Iter [6200/80000] lr: 3.690e-05, eta: 1 day, 6:28:07, time: 1.339, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5136, decode.acc_seg: 80.8074, aux.loss_ce: 0.2026, aux.acc_seg: 81.1760, loss: 0.7163 +2024-06-18 02:01:30,126 - mmseg - INFO - Iter [6250/80000] lr: 3.688e-05, eta: 1 day, 6:25:19, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5595, decode.acc_seg: 79.4802, aux.loss_ce: 0.2210, aux.acc_seg: 79.8568, loss: 0.7805 +2024-06-18 02:02:36,340 - mmseg - INFO - Iter [6300/80000] lr: 3.685e-05, eta: 1 day, 6:22:31, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5184, decode.acc_seg: 80.1805, aux.loss_ce: 0.2055, aux.acc_seg: 80.5331, loss: 0.7239 +2024-06-18 02:03:44,961 - mmseg - INFO - Iter [6350/80000] lr: 3.683e-05, eta: 1 day, 6:20:12, time: 1.372, data_time: 0.051, memory: 70498, decode.loss_ce: 0.5070, decode.acc_seg: 81.0955, aux.loss_ce: 0.2027, aux.acc_seg: 81.2940, loss: 0.7097 +2024-06-18 02:04:51,456 - mmseg - INFO - Iter [6400/80000] lr: 3.680e-05, eta: 1 day, 6:17:30, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4909, decode.acc_seg: 81.1877, aux.loss_ce: 0.1955, aux.acc_seg: 81.3610, loss: 0.6864 +2024-06-18 02:05:57,813 - mmseg - INFO - Iter [6450/80000] lr: 3.678e-05, eta: 1 day, 6:14:48, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5134, decode.acc_seg: 80.8585, aux.loss_ce: 0.2050, aux.acc_seg: 80.8067, loss: 0.7184 +2024-06-18 02:07:04,177 - mmseg - INFO - Iter [6500/80000] lr: 3.675e-05, eta: 1 day, 6:12:08, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4898, decode.acc_seg: 81.1860, aux.loss_ce: 0.1955, aux.acc_seg: 81.1879, loss: 0.6853 +2024-06-18 02:08:10,790 - mmseg - INFO - Iter [6550/80000] lr: 3.673e-05, eta: 1 day, 6:09:31, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5137, decode.acc_seg: 80.4154, aux.loss_ce: 0.2030, aux.acc_seg: 80.7214, loss: 0.7167 +2024-06-18 02:09:17,387 - mmseg - INFO - Iter [6600/80000] lr: 3.670e-05, eta: 1 day, 6:06:56, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5027, decode.acc_seg: 81.2050, aux.loss_ce: 0.1997, aux.acc_seg: 81.2522, loss: 0.7024 +2024-06-18 02:10:23,999 - mmseg - INFO - Iter [6650/80000] lr: 3.668e-05, eta: 1 day, 6:04:22, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5103, decode.acc_seg: 81.0777, aux.loss_ce: 0.2019, aux.acc_seg: 81.2411, loss: 0.7122 +2024-06-18 02:11:30,536 - mmseg - INFO - Iter [6700/80000] lr: 3.665e-05, eta: 1 day, 6:01:49, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4878, decode.acc_seg: 81.4129, aux.loss_ce: 0.1943, aux.acc_seg: 81.4288, loss: 0.6821 +2024-06-18 02:12:37,010 - mmseg - INFO - Iter [6750/80000] lr: 3.663e-05, eta: 1 day, 5:59:16, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4789, decode.acc_seg: 81.7306, aux.loss_ce: 0.1916, aux.acc_seg: 81.8127, loss: 0.6705 +2024-06-18 02:13:43,601 - mmseg - INFO - Iter [6800/80000] lr: 3.660e-05, eta: 1 day, 5:56:46, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4945, decode.acc_seg: 80.4707, aux.loss_ce: 0.1965, aux.acc_seg: 80.6352, loss: 0.6910 +2024-06-18 02:14:50,243 - mmseg - INFO - Iter [6850/80000] lr: 3.658e-05, eta: 1 day, 5:54:18, time: 1.333, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5053, decode.acc_seg: 80.9304, aux.loss_ce: 0.2018, aux.acc_seg: 80.8614, loss: 0.7071 +2024-06-18 02:15:56,830 - mmseg - INFO - Iter [6900/80000] lr: 3.655e-05, eta: 1 day, 5:51:50, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5565, decode.acc_seg: 79.6006, aux.loss_ce: 0.2188, aux.acc_seg: 80.0363, loss: 0.7754 +2024-06-18 02:17:03,457 - mmseg - INFO - Iter [6950/80000] lr: 3.653e-05, eta: 1 day, 5:49:24, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5055, decode.acc_seg: 81.3422, aux.loss_ce: 0.2004, aux.acc_seg: 81.6678, loss: 0.7059 +2024-06-18 02:18:09,922 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 02:18:09,922 - mmseg - INFO - Iter [7000/80000] lr: 3.650e-05, eta: 1 day, 5:46:57, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5248, decode.acc_seg: 79.5260, aux.loss_ce: 0.2088, aux.acc_seg: 79.7454, loss: 0.7336 +2024-06-18 02:19:47,052 - mmseg - INFO - per class results: +2024-06-18 02:19:47,058 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 78.36 | 86.45 | +| building | 83.04 | 92.84 | +| sky | 94.0 | 96.78 | +| floor | 81.97 | 88.21 | +| tree | 71.52 | 91.86 | +| ceiling | 83.2 | 93.74 | +| road | 84.31 | 91.87 | +| bed | 89.18 | 96.02 | +| windowpane | 61.82 | 80.72 | +| grass | 66.21 | 76.47 | +| cabinet | 60.42 | 69.48 | +| sidewalk | 65.31 | 79.68 | +| person | 79.97 | 92.98 | +| earth | 37.05 | 49.19 | +| door | 53.31 | 72.38 | +| table | 59.27 | 76.64 | +| mountain | 61.63 | 76.95 | +| plant | 42.92 | 49.52 | +| curtain | 73.21 | 86.26 | +| chair | 54.3 | 73.63 | +| car | 80.63 | 87.15 | +| water | 51.86 | 58.72 | +| painting | 69.39 | 88.5 | +| sofa | 65.86 | 71.2 | +| shelf | 37.67 | 71.55 | +| house | 47.38 | 51.09 | +| sea | 70.57 | 89.69 | +| mirror | 59.39 | 62.51 | +| rug | 66.08 | 84.4 | +| field | 30.87 | 66.36 | +| armchair | 45.82 | 79.27 | +| seat | 70.86 | 82.47 | +| fence | 42.04 | 62.23 | +| desk | 43.68 | 77.74 | +| rock | 41.28 | 52.48 | +| wardrobe | 49.39 | 65.98 | +| lamp | 60.52 | 71.54 | +| bathtub | 77.32 | 80.54 | +| railing | 35.18 | 44.6 | +| cushion | 55.37 | 68.27 | +| base | 21.9 | 32.0 | +| box | 17.02 | 18.97 | +| column | 48.02 | 64.22 | +| signboard | 34.42 | 42.52 | +| chest of drawers | 43.63 | 69.75 | +| counter | 40.75 | 50.13 | +| sand | 43.88 | 59.37 | +| sink | 68.61 | 76.55 | +| skyscraper | 48.97 | 75.56 | +| fireplace | 65.37 | 93.73 | +| refrigerator | 66.33 | 93.26 | +| grandstand | 46.47 | 90.07 | +| path | 28.44 | 33.14 | +| stairs | 32.94 | 44.32 | +| runway | 68.71 | 93.95 | +| case | 49.12 | 59.38 | +| pool table | 92.81 | 97.29 | +| pillow | 59.03 | 69.45 | +| screen door | 69.11 | 81.68 | +| stairway | 35.22 | 36.97 | +| river | 16.4 | 62.15 | +| bridge | 55.1 | 69.36 | +| bookcase | 25.8 | 37.74 | +| blind | 41.24 | 44.98 | +| coffee table | 58.34 | 86.91 | +| toilet | 85.2 | 91.75 | +| flower | 29.69 | 43.88 | +| book | 44.85 | 72.37 | +| hill | 5.1 | 9.83 | +| bench | 53.09 | 64.57 | +| countertop | 58.6 | 77.58 | +| stove | 71.0 | 80.64 | +| palm | 42.98 | 55.39 | +| kitchen island | 28.46 | 41.26 | +| computer | 67.52 | 92.0 | +| swivel chair | 37.41 | 81.99 | +| boat | 35.35 | 82.82 | +| bar | 58.1 | 63.72 | +| arcade machine | 74.41 | 81.52 | +| hovel | 41.24 | 47.57 | +| bus | 86.53 | 92.83 | +| towel | 51.61 | 59.58 | +| light | 45.42 | 52.34 | +| truck | 20.5 | 71.27 | +| tower | 28.31 | 43.21 | +| chandelier | 59.87 | 69.94 | +| awning | 40.53 | 50.13 | +| streetlight | 20.19 | 31.99 | +| booth | 36.57 | 51.87 | +| television receiver | 63.04 | 79.16 | +| airplane | 52.48 | 62.77 | +| dirt track | 2.94 | 9.09 | +| apparel | 33.85 | 50.41 | +| pole | 13.41 | 16.12 | +| land | 0.0 | 0.0 | +| bannister | 0.89 | 1.0 | +| escalator | 44.35 | 60.73 | +| ottoman | 47.25 | 59.75 | +| bottle | 17.8 | 23.54 | +| buffet | 45.79 | 64.93 | +| poster | 23.78 | 34.48 | +| stage | 15.81 | 22.63 | +| van | 39.85 | 44.68 | +| ship | 0.0 | 0.0 | +| fountain | 30.44 | 31.2 | +| conveyer belt | 62.8 | 94.0 | +| canopy | 34.64 | 40.96 | +| washer | 64.25 | 75.06 | +| plaything | 10.94 | 13.39 | +| swimming pool | 50.49 | 95.46 | +| stool | 29.5 | 31.32 | +| barrel | 41.21 | 68.13 | +| basket | 19.68 | 31.3 | +| waterfall | 63.4 | 97.54 | +| tent | 94.18 | 98.3 | +| bag | 15.94 | 17.8 | +| minibike | 63.45 | 69.39 | +| cradle | 72.47 | 97.5 | +| oven | 36.33 | 55.66 | +| ball | 35.07 | 69.57 | +| food | 53.28 | 60.16 | +| step | 4.31 | 4.84 | +| tank | 54.55 | 69.37 | +| trade name | 24.78 | 27.9 | +| microwave | 77.86 | 93.74 | +| pot | 30.47 | 33.97 | +| animal | 64.85 | 69.46 | +| bicycle | 47.41 | 53.87 | +| lake | 0.0 | 0.0 | +| dishwasher | 51.96 | 53.05 | +| screen | 54.58 | 95.98 | +| blanket | 2.39 | 2.75 | +| sculpture | 58.35 | 62.64 | +| hood | 49.24 | 56.4 | +| sconce | 33.92 | 35.78 | +| vase | 30.03 | 37.57 | +| traffic light | 20.43 | 28.57 | +| tray | 1.37 | 1.52 | +| ashcan | 39.83 | 52.1 | +| fan | 48.59 | 54.66 | +| pier | 35.31 | 46.88 | +| crt screen | 1.73 | 5.06 | +| plate | 49.84 | 69.58 | +| monitor | 1.64 | 1.65 | +| bulletin board | 46.75 | 60.97 | +| shower | 0.0 | 0.0 | +| radiator | 47.14 | 49.83 | +| glass | 5.05 | 5.26 | +| clock | 29.06 | 32.59 | +| flag | 39.01 | 39.57 | ++---------------------+-------+-------+ +2024-06-18 02:19:47,058 - mmseg - INFO - Summary: +2024-06-18 02:19:47,059 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 82.64 | 46.08 | 59.02 | ++-------+-------+-------+ +2024-06-18 02:19:47,059 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 02:19:47,060 - mmseg - INFO - Iter(val) [250] aAcc: 0.8264, mIoU: 0.4608, mAcc: 0.5902, IoU.wall: 0.7836, IoU.building: 0.8304, IoU.sky: 0.9400, IoU.floor: 0.8197, IoU.tree: 0.7152, IoU.ceiling: 0.8320, IoU.road: 0.8431, IoU.bed : 0.8918, IoU.windowpane: 0.6182, IoU.grass: 0.6621, IoU.cabinet: 0.6042, IoU.sidewalk: 0.6531, IoU.person: 0.7997, IoU.earth: 0.3705, IoU.door: 0.5331, IoU.table: 0.5927, IoU.mountain: 0.6163, IoU.plant: 0.4292, IoU.curtain: 0.7321, IoU.chair: 0.5430, IoU.car: 0.8063, IoU.water: 0.5186, IoU.painting: 0.6939, IoU.sofa: 0.6586, IoU.shelf: 0.3767, IoU.house: 0.4738, IoU.sea: 0.7057, IoU.mirror: 0.5939, IoU.rug: 0.6608, IoU.field: 0.3087, IoU.armchair: 0.4582, IoU.seat: 0.7086, IoU.fence: 0.4204, IoU.desk: 0.4368, IoU.rock: 0.4128, IoU.wardrobe: 0.4939, IoU.lamp: 0.6052, IoU.bathtub: 0.7732, IoU.railing: 0.3518, IoU.cushion: 0.5537, IoU.base: 0.2190, IoU.box: 0.1702, IoU.column: 0.4802, IoU.signboard: 0.3442, IoU.chest of drawers: 0.4363, IoU.counter: 0.4075, IoU.sand: 0.4388, IoU.sink: 0.6861, IoU.skyscraper: 0.4897, IoU.fireplace: 0.6537, IoU.refrigerator: 0.6633, IoU.grandstand: 0.4647, IoU.path: 0.2844, IoU.stairs: 0.3294, IoU.runway: 0.6871, IoU.case: 0.4912, IoU.pool table: 0.9281, IoU.pillow: 0.5903, IoU.screen door: 0.6911, IoU.stairway: 0.3522, IoU.river: 0.1640, IoU.bridge: 0.5510, IoU.bookcase: 0.2580, IoU.blind: 0.4124, IoU.coffee table: 0.5834, IoU.toilet: 0.8520, IoU.flower: 0.2969, IoU.book: 0.4485, IoU.hill: 0.0510, IoU.bench: 0.5309, IoU.countertop: 0.5860, IoU.stove: 0.7100, IoU.palm: 0.4298, IoU.kitchen island: 0.2846, IoU.computer: 0.6752, IoU.swivel chair: 0.3741, IoU.boat: 0.3535, IoU.bar: 0.5810, IoU.arcade machine: 0.7441, IoU.hovel: 0.4124, IoU.bus: 0.8653, IoU.towel: 0.5161, IoU.light: 0.4542, IoU.truck: 0.2050, IoU.tower: 0.2831, IoU.chandelier: 0.5987, IoU.awning: 0.4053, IoU.streetlight: 0.2019, IoU.booth: 0.3657, IoU.television receiver: 0.6304, IoU.airplane: 0.5248, IoU.dirt track: 0.0294, IoU.apparel: 0.3385, IoU.pole: 0.1341, IoU.land: 0.0000, IoU.bannister: 0.0089, IoU.escalator: 0.4435, IoU.ottoman: 0.4725, IoU.bottle: 0.1780, IoU.buffet: 0.4579, IoU.poster: 0.2378, IoU.stage: 0.1581, IoU.van: 0.3985, IoU.ship: 0.0000, IoU.fountain: 0.3044, IoU.conveyer belt: 0.6280, IoU.canopy: 0.3464, IoU.washer: 0.6425, IoU.plaything: 0.1094, IoU.swimming pool: 0.5049, IoU.stool: 0.2950, IoU.barrel: 0.4121, IoU.basket: 0.1968, IoU.waterfall: 0.6340, IoU.tent: 0.9418, IoU.bag: 0.1594, IoU.minibike: 0.6345, IoU.cradle: 0.7247, IoU.oven: 0.3633, IoU.ball: 0.3507, IoU.food: 0.5328, IoU.step: 0.0431, IoU.tank: 0.5455, IoU.trade name: 0.2478, IoU.microwave: 0.7786, IoU.pot: 0.3047, IoU.animal: 0.6485, IoU.bicycle: 0.4741, IoU.lake: 0.0000, IoU.dishwasher: 0.5196, IoU.screen: 0.5458, IoU.blanket: 0.0239, IoU.sculpture: 0.5835, IoU.hood: 0.4924, IoU.sconce: 0.3392, IoU.vase: 0.3003, IoU.traffic light: 0.2043, IoU.tray: 0.0137, IoU.ashcan: 0.3983, IoU.fan: 0.4859, IoU.pier: 0.3531, IoU.crt screen: 0.0173, IoU.plate: 0.4984, IoU.monitor: 0.0164, IoU.bulletin board: 0.4675, IoU.shower: 0.0000, IoU.radiator: 0.4714, IoU.glass: 0.0505, IoU.clock: 0.2906, IoU.flag: 0.3901, Acc.wall: 0.8645, Acc.building: 0.9284, Acc.sky: 0.9678, Acc.floor: 0.8821, Acc.tree: 0.9186, Acc.ceiling: 0.9374, Acc.road: 0.9187, Acc.bed : 0.9602, Acc.windowpane: 0.8072, Acc.grass: 0.7647, Acc.cabinet: 0.6948, Acc.sidewalk: 0.7968, Acc.person: 0.9298, Acc.earth: 0.4919, Acc.door: 0.7238, Acc.table: 0.7664, Acc.mountain: 0.7695, Acc.plant: 0.4952, Acc.curtain: 0.8626, Acc.chair: 0.7363, Acc.car: 0.8715, Acc.water: 0.5872, Acc.painting: 0.8850, Acc.sofa: 0.7120, Acc.shelf: 0.7155, Acc.house: 0.5109, Acc.sea: 0.8969, Acc.mirror: 0.6251, Acc.rug: 0.8440, Acc.field: 0.6636, Acc.armchair: 0.7927, Acc.seat: 0.8247, Acc.fence: 0.6223, Acc.desk: 0.7774, Acc.rock: 0.5248, Acc.wardrobe: 0.6598, Acc.lamp: 0.7154, Acc.bathtub: 0.8054, Acc.railing: 0.4460, Acc.cushion: 0.6827, Acc.base: 0.3200, Acc.box: 0.1897, Acc.column: 0.6422, Acc.signboard: 0.4252, Acc.chest of drawers: 0.6975, Acc.counter: 0.5013, Acc.sand: 0.5937, Acc.sink: 0.7655, Acc.skyscraper: 0.7556, Acc.fireplace: 0.9373, Acc.refrigerator: 0.9326, Acc.grandstand: 0.9007, Acc.path: 0.3314, Acc.stairs: 0.4432, Acc.runway: 0.9395, Acc.case: 0.5938, Acc.pool table: 0.9729, Acc.pillow: 0.6945, Acc.screen door: 0.8168, Acc.stairway: 0.3697, Acc.river: 0.6215, Acc.bridge: 0.6936, Acc.bookcase: 0.3774, Acc.blind: 0.4498, Acc.coffee table: 0.8691, Acc.toilet: 0.9175, Acc.flower: 0.4388, Acc.book: 0.7237, Acc.hill: 0.0983, Acc.bench: 0.6457, Acc.countertop: 0.7758, Acc.stove: 0.8064, Acc.palm: 0.5539, Acc.kitchen island: 0.4126, Acc.computer: 0.9200, Acc.swivel chair: 0.8199, Acc.boat: 0.8282, Acc.bar: 0.6372, Acc.arcade machine: 0.8152, Acc.hovel: 0.4757, Acc.bus: 0.9283, Acc.towel: 0.5958, Acc.light: 0.5234, Acc.truck: 0.7127, Acc.tower: 0.4321, Acc.chandelier: 0.6994, Acc.awning: 0.5013, Acc.streetlight: 0.3199, Acc.booth: 0.5187, Acc.television receiver: 0.7916, Acc.airplane: 0.6277, Acc.dirt track: 0.0909, Acc.apparel: 0.5041, Acc.pole: 0.1612, Acc.land: 0.0000, Acc.bannister: 0.0100, Acc.escalator: 0.6073, Acc.ottoman: 0.5975, Acc.bottle: 0.2354, Acc.buffet: 0.6493, Acc.poster: 0.3448, Acc.stage: 0.2263, Acc.van: 0.4468, Acc.ship: 0.0000, Acc.fountain: 0.3120, Acc.conveyer belt: 0.9400, Acc.canopy: 0.4096, Acc.washer: 0.7506, Acc.plaything: 0.1339, Acc.swimming pool: 0.9546, Acc.stool: 0.3132, Acc.barrel: 0.6813, Acc.basket: 0.3130, Acc.waterfall: 0.9754, Acc.tent: 0.9830, Acc.bag: 0.1780, Acc.minibike: 0.6939, Acc.cradle: 0.9750, Acc.oven: 0.5566, Acc.ball: 0.6957, Acc.food: 0.6016, Acc.step: 0.0484, Acc.tank: 0.6937, Acc.trade name: 0.2790, Acc.microwave: 0.9374, Acc.pot: 0.3397, Acc.animal: 0.6946, Acc.bicycle: 0.5387, Acc.lake: 0.0000, Acc.dishwasher: 0.5305, Acc.screen: 0.9598, Acc.blanket: 0.0275, Acc.sculpture: 0.6264, Acc.hood: 0.5640, Acc.sconce: 0.3578, Acc.vase: 0.3757, Acc.traffic light: 0.2857, Acc.tray: 0.0152, Acc.ashcan: 0.5210, Acc.fan: 0.5466, Acc.pier: 0.4688, Acc.crt screen: 0.0506, Acc.plate: 0.6958, Acc.monitor: 0.0165, Acc.bulletin board: 0.6097, Acc.shower: 0.0000, Acc.radiator: 0.4983, Acc.glass: 0.0526, Acc.clock: 0.3259, Acc.flag: 0.3957 +2024-06-18 02:20:55,439 - mmseg - INFO - Iter [7050/80000] lr: 3.648e-05, eta: 1 day, 6:01:36, time: 3.310, data_time: 1.962, memory: 70498, decode.loss_ce: 0.5474, decode.acc_seg: 79.7804, aux.loss_ce: 0.2166, aux.acc_seg: 79.8290, loss: 0.7639 +2024-06-18 02:22:01,729 - mmseg - INFO - Iter [7100/80000] lr: 3.645e-05, eta: 1 day, 5:59:02, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4971, decode.acc_seg: 81.0488, aux.loss_ce: 0.1963, aux.acc_seg: 81.2825, loss: 0.6934 +2024-06-18 02:23:07,879 - mmseg - INFO - Iter [7150/80000] lr: 3.643e-05, eta: 1 day, 5:56:28, time: 1.323, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5117, decode.acc_seg: 80.4769, aux.loss_ce: 0.2038, aux.acc_seg: 80.7247, loss: 0.7155 +2024-06-18 02:24:14,515 - mmseg - INFO - Iter [7200/80000] lr: 3.640e-05, eta: 1 day, 5:54:00, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4903, decode.acc_seg: 81.1572, aux.loss_ce: 0.1950, aux.acc_seg: 81.4486, loss: 0.6853 +2024-06-18 02:25:20,934 - mmseg - INFO - Iter [7250/80000] lr: 3.638e-05, eta: 1 day, 5:51:30, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5044, decode.acc_seg: 80.5142, aux.loss_ce: 0.1998, aux.acc_seg: 80.7999, loss: 0.7042 +2024-06-18 02:26:27,497 - mmseg - INFO - Iter [7300/80000] lr: 3.635e-05, eta: 1 day, 5:49:04, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5037, decode.acc_seg: 81.1032, aux.loss_ce: 0.2011, aux.acc_seg: 81.2436, loss: 0.7048 +2024-06-18 02:27:33,951 - mmseg - INFO - Iter [7350/80000] lr: 3.633e-05, eta: 1 day, 5:46:37, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.5227, decode.acc_seg: 80.1631, aux.loss_ce: 0.2079, aux.acc_seg: 80.1855, loss: 0.7306 +2024-06-18 02:28:40,445 - mmseg - INFO - Iter [7400/80000] lr: 3.630e-05, eta: 1 day, 5:44:12, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5087, decode.acc_seg: 80.6927, aux.loss_ce: 0.2023, aux.acc_seg: 80.6683, loss: 0.7110 +2024-06-18 02:29:46,896 - mmseg - INFO - Iter [7450/80000] lr: 3.628e-05, eta: 1 day, 5:41:47, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4954, decode.acc_seg: 80.2765, aux.loss_ce: 0.1958, aux.acc_seg: 80.5785, loss: 0.6912 +2024-06-18 02:30:53,314 - mmseg - INFO - Iter [7500/80000] lr: 3.625e-05, eta: 1 day, 5:39:23, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4961, decode.acc_seg: 80.8938, aux.loss_ce: 0.1954, aux.acc_seg: 81.0201, loss: 0.6915 +2024-06-18 02:31:59,895 - mmseg - INFO - Iter [7550/80000] lr: 3.623e-05, eta: 1 day, 5:37:02, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4960, decode.acc_seg: 81.2263, aux.loss_ce: 0.1980, aux.acc_seg: 81.2610, loss: 0.6940 +2024-06-18 02:33:08,821 - mmseg - INFO - Iter [7600/80000] lr: 3.620e-05, eta: 1 day, 5:35:04, time: 1.379, data_time: 0.063, memory: 70498, decode.loss_ce: 0.5057, decode.acc_seg: 81.3426, aux.loss_ce: 0.2025, aux.acc_seg: 81.2619, loss: 0.7082 +2024-06-18 02:34:15,142 - mmseg - INFO - Iter [7650/80000] lr: 3.618e-05, eta: 1 day, 5:32:42, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4433, decode.acc_seg: 82.8963, aux.loss_ce: 0.1772, aux.acc_seg: 82.9020, loss: 0.6205 +2024-06-18 02:35:21,605 - mmseg - INFO - Iter [7700/80000] lr: 3.615e-05, eta: 1 day, 5:30:22, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4562, decode.acc_seg: 82.3310, aux.loss_ce: 0.1832, aux.acc_seg: 82.2425, loss: 0.6395 +2024-06-18 02:36:28,110 - mmseg - INFO - Iter [7750/80000] lr: 3.613e-05, eta: 1 day, 5:28:04, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4568, decode.acc_seg: 82.3247, aux.loss_ce: 0.1836, aux.acc_seg: 82.2442, loss: 0.6404 +2024-06-18 02:37:34,676 - mmseg - INFO - Iter [7800/80000] lr: 3.610e-05, eta: 1 day, 5:25:47, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4766, decode.acc_seg: 81.5561, aux.loss_ce: 0.1903, aux.acc_seg: 81.6285, loss: 0.6669 +2024-06-18 02:38:41,204 - mmseg - INFO - Iter [7850/80000] lr: 3.608e-05, eta: 1 day, 5:23:31, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4860, decode.acc_seg: 81.4645, aux.loss_ce: 0.1931, aux.acc_seg: 81.5901, loss: 0.6791 +2024-06-18 02:39:47,493 - mmseg - INFO - Iter [7900/80000] lr: 3.605e-05, eta: 1 day, 5:21:14, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4653, decode.acc_seg: 81.4980, aux.loss_ce: 0.1858, aux.acc_seg: 81.4959, loss: 0.6512 +2024-06-18 02:40:53,717 - mmseg - INFO - Iter [7950/80000] lr: 3.603e-05, eta: 1 day, 5:18:56, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4682, decode.acc_seg: 82.3555, aux.loss_ce: 0.1867, aux.acc_seg: 82.5799, loss: 0.6549 +2024-06-18 02:42:00,360 - mmseg - INFO - Saving checkpoint at 8000 iterations +2024-06-18 02:43:43,661 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 02:43:43,661 - mmseg - INFO - Iter [8000/80000] lr: 3.600e-05, eta: 1 day, 5:32:13, time: 3.399, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4785, decode.acc_seg: 81.1889, aux.loss_ce: 0.1902, aux.acc_seg: 81.4033, loss: 0.6686 +2024-06-18 02:45:20,533 - mmseg - INFO - per class results: +2024-06-18 02:45:20,540 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 78.52 | 88.18 | +| building | 81.71 | 90.24 | +| sky | 94.12 | 97.71 | +| floor | 75.98 | 81.66 | +| tree | 74.9 | 86.91 | +| ceiling | 83.62 | 89.99 | +| road | 82.14 | 85.24 | +| bed | 90.28 | 95.91 | +| windowpane | 60.54 | 73.51 | +| grass | 61.05 | 72.06 | +| cabinet | 61.65 | 74.47 | +| sidewalk | 64.64 | 79.9 | +| person | 81.89 | 91.12 | +| earth | 38.89 | 52.6 | +| door | 52.64 | 63.4 | +| table | 62.08 | 75.06 | +| mountain | 63.77 | 81.71 | +| plant | 49.95 | 58.21 | +| curtain | 75.89 | 88.7 | +| chair | 58.63 | 75.66 | +| car | 83.61 | 91.75 | +| water | 55.87 | 67.12 | +| painting | 73.74 | 89.44 | +| sofa | 73.35 | 88.08 | +| shelf | 37.67 | 48.69 | +| house | 48.5 | 90.62 | +| sea | 67.16 | 90.84 | +| mirror | 67.41 | 72.35 | +| rug | 42.17 | 93.91 | +| field | 28.8 | 74.66 | +| armchair | 49.76 | 60.54 | +| seat | 64.31 | 81.73 | +| fence | 45.7 | 61.02 | +| desk | 51.09 | 70.61 | +| rock | 55.87 | 75.44 | +| wardrobe | 51.7 | 58.46 | +| lamp | 62.31 | 82.22 | +| bathtub | 81.52 | 87.17 | +| railing | 36.7 | 52.66 | +| cushion | 57.15 | 80.25 | +| base | 39.63 | 48.08 | +| box | 28.24 | 39.83 | +| column | 46.96 | 52.33 | +| signboard | 34.7 | 51.72 | +| chest of drawers | 45.89 | 56.9 | +| counter | 36.59 | 38.37 | +| sand | 56.41 | 61.19 | +| sink | 66.11 | 74.05 | +| skyscraper | 46.02 | 66.06 | +| fireplace | 71.56 | 88.84 | +| refrigerator | 67.56 | 89.99 | +| grandstand | 41.61 | 83.86 | +| path | 22.22 | 40.65 | +| stairs | 31.41 | 42.14 | +| runway | 53.96 | 99.08 | +| case | 50.8 | 81.29 | +| pool table | 92.0 | 97.46 | +| pillow | 52.5 | 56.51 | +| screen door | 68.79 | 79.62 | +| stairway | 37.19 | 41.84 | +| river | 18.59 | 46.45 | +| bridge | 58.39 | 82.08 | +| bookcase | 29.94 | 55.55 | +| blind | 51.43 | 68.93 | +| coffee table | 62.3 | 84.43 | +| toilet | 81.79 | 95.42 | +| flower | 33.21 | 51.52 | +| book | 41.85 | 75.03 | +| hill | 1.15 | 1.23 | +| bench | 47.02 | 56.88 | +| countertop | 57.65 | 75.33 | +| stove | 77.38 | 86.74 | +| palm | 49.74 | 58.94 | +| kitchen island | 34.0 | 68.27 | +| computer | 66.69 | 91.01 | +| swivel chair | 43.4 | 73.88 | +| boat | 56.8 | 81.59 | +| bar | 58.2 | 71.31 | +| arcade machine | 77.89 | 88.29 | +| hovel | 11.63 | 12.69 | +| bus | 88.47 | 93.34 | +| towel | 63.03 | 78.28 | +| light | 49.89 | 66.81 | +| truck | 38.56 | 53.28 | +| tower | 30.19 | 39.67 | +| chandelier | 61.3 | 79.42 | +| awning | 35.73 | 46.27 | +| streetlight | 22.21 | 38.41 | +| booth | 27.26 | 81.33 | +| television receiver | 64.57 | 82.44 | +| airplane | 61.27 | 76.86 | +| dirt track | 15.36 | 17.75 | +| apparel | 33.3 | 50.99 | +| pole | 19.42 | 26.5 | +| land | 6.05 | 19.38 | +| bannister | 7.78 | 10.75 | +| escalator | 54.61 | 77.02 | +| ottoman | 45.09 | 57.39 | +| bottle | 36.89 | 62.33 | +| buffet | 39.98 | 61.38 | +| poster | 25.53 | 28.77 | +| stage | 15.85 | 30.44 | +| van | 40.27 | 53.75 | +| ship | 41.67 | 53.38 | +| fountain | 35.92 | 38.04 | +| conveyer belt | 82.2 | 94.7 | +| canopy | 41.71 | 72.16 | +| washer | 81.54 | 89.69 | +| plaything | 18.0 | 46.11 | +| swimming pool | 82.45 | 87.52 | +| stool | 50.04 | 68.9 | +| barrel | 53.29 | 63.19 | +| basket | 23.81 | 34.48 | +| waterfall | 60.44 | 83.69 | +| tent | 92.43 | 98.86 | +| bag | 12.43 | 13.32 | +| minibike | 57.12 | 84.71 | +| cradle | 81.37 | 97.87 | +| oven | 52.87 | 65.15 | +| ball | 33.35 | 46.0 | +| food | 52.69 | 67.39 | +| step | 10.2 | 14.03 | +| tank | 51.12 | 72.62 | +| trade name | 30.96 | 42.25 | +| microwave | 76.44 | 95.89 | +| pot | 51.42 | 65.47 | +| animal | 69.79 | 72.59 | +| bicycle | 48.27 | 76.96 | +| lake | 0.0 | 0.0 | +| dishwasher | 53.03 | 78.41 | +| screen | 64.81 | 90.86 | +| blanket | 17.59 | 21.25 | +| sculpture | 50.58 | 67.73 | +| hood | 63.53 | 77.27 | +| sconce | 43.12 | 50.31 | +| vase | 30.41 | 56.75 | +| traffic light | 22.71 | 29.27 | +| tray | 6.45 | 13.06 | +| ashcan | 36.94 | 58.32 | +| fan | 55.13 | 64.15 | +| pier | 33.47 | 41.22 | +| crt screen | 0.0 | 0.0 | +| plate | 46.58 | 79.63 | +| monitor | 54.6 | 79.41 | +| bulletin board | 34.07 | 73.23 | +| shower | 0.0 | 0.0 | +| radiator | 57.01 | 75.07 | +| glass | 11.83 | 12.82 | +| clock | 25.96 | 28.59 | +| flag | 63.41 | 76.46 | ++---------------------+-------+-------+ +2024-06-18 02:45:20,540 - mmseg - INFO - Summary: +2024-06-18 02:45:20,540 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 82.61 | 49.48 | 64.58 | ++-------+-------+-------+ +2024-06-18 02:45:20,541 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 02:45:20,541 - mmseg - INFO - Iter(val) [250] aAcc: 0.8261, mIoU: 0.4948, mAcc: 0.6458, IoU.wall: 0.7852, IoU.building: 0.8171, IoU.sky: 0.9412, IoU.floor: 0.7598, IoU.tree: 0.7490, IoU.ceiling: 0.8362, IoU.road: 0.8214, IoU.bed : 0.9028, IoU.windowpane: 0.6054, IoU.grass: 0.6105, IoU.cabinet: 0.6165, IoU.sidewalk: 0.6464, IoU.person: 0.8189, IoU.earth: 0.3889, IoU.door: 0.5264, IoU.table: 0.6208, IoU.mountain: 0.6377, IoU.plant: 0.4995, IoU.curtain: 0.7589, IoU.chair: 0.5863, IoU.car: 0.8361, IoU.water: 0.5587, IoU.painting: 0.7374, IoU.sofa: 0.7335, IoU.shelf: 0.3767, IoU.house: 0.4850, IoU.sea: 0.6716, IoU.mirror: 0.6741, IoU.rug: 0.4217, IoU.field: 0.2880, IoU.armchair: 0.4976, IoU.seat: 0.6431, IoU.fence: 0.4570, IoU.desk: 0.5109, IoU.rock: 0.5587, IoU.wardrobe: 0.5170, IoU.lamp: 0.6231, IoU.bathtub: 0.8152, IoU.railing: 0.3670, IoU.cushion: 0.5715, IoU.base: 0.3963, IoU.box: 0.2824, IoU.column: 0.4696, IoU.signboard: 0.3470, IoU.chest of drawers: 0.4589, IoU.counter: 0.3659, IoU.sand: 0.5641, IoU.sink: 0.6611, IoU.skyscraper: 0.4602, IoU.fireplace: 0.7156, IoU.refrigerator: 0.6756, IoU.grandstand: 0.4161, IoU.path: 0.2222, IoU.stairs: 0.3141, IoU.runway: 0.5396, IoU.case: 0.5080, IoU.pool table: 0.9200, IoU.pillow: 0.5250, IoU.screen door: 0.6879, IoU.stairway: 0.3719, IoU.river: 0.1859, IoU.bridge: 0.5839, IoU.bookcase: 0.2994, IoU.blind: 0.5143, IoU.coffee table: 0.6230, IoU.toilet: 0.8179, IoU.flower: 0.3321, IoU.book: 0.4185, IoU.hill: 0.0115, IoU.bench: 0.4702, IoU.countertop: 0.5765, IoU.stove: 0.7738, IoU.palm: 0.4974, IoU.kitchen island: 0.3400, IoU.computer: 0.6669, IoU.swivel chair: 0.4340, IoU.boat: 0.5680, IoU.bar: 0.5820, IoU.arcade machine: 0.7789, IoU.hovel: 0.1163, IoU.bus: 0.8847, IoU.towel: 0.6303, IoU.light: 0.4989, IoU.truck: 0.3856, IoU.tower: 0.3019, IoU.chandelier: 0.6130, IoU.awning: 0.3573, IoU.streetlight: 0.2221, IoU.booth: 0.2726, IoU.television receiver: 0.6457, IoU.airplane: 0.6127, IoU.dirt track: 0.1536, IoU.apparel: 0.3330, IoU.pole: 0.1942, IoU.land: 0.0605, IoU.bannister: 0.0778, IoU.escalator: 0.5461, IoU.ottoman: 0.4509, IoU.bottle: 0.3689, IoU.buffet: 0.3998, IoU.poster: 0.2553, IoU.stage: 0.1585, IoU.van: 0.4027, IoU.ship: 0.4167, IoU.fountain: 0.3592, IoU.conveyer belt: 0.8220, IoU.canopy: 0.4171, IoU.washer: 0.8154, IoU.plaything: 0.1800, IoU.swimming pool: 0.8245, IoU.stool: 0.5004, IoU.barrel: 0.5329, IoU.basket: 0.2381, IoU.waterfall: 0.6044, IoU.tent: 0.9243, IoU.bag: 0.1243, IoU.minibike: 0.5712, IoU.cradle: 0.8137, IoU.oven: 0.5287, IoU.ball: 0.3335, IoU.food: 0.5269, IoU.step: 0.1020, IoU.tank: 0.5112, IoU.trade name: 0.3096, IoU.microwave: 0.7644, IoU.pot: 0.5142, IoU.animal: 0.6979, IoU.bicycle: 0.4827, IoU.lake: 0.0000, IoU.dishwasher: 0.5303, IoU.screen: 0.6481, IoU.blanket: 0.1759, IoU.sculpture: 0.5058, IoU.hood: 0.6353, IoU.sconce: 0.4312, IoU.vase: 0.3041, IoU.traffic light: 0.2271, IoU.tray: 0.0645, IoU.ashcan: 0.3694, IoU.fan: 0.5513, IoU.pier: 0.3347, IoU.crt screen: 0.0000, IoU.plate: 0.4658, IoU.monitor: 0.5460, IoU.bulletin board: 0.3407, IoU.shower: 0.0000, IoU.radiator: 0.5701, IoU.glass: 0.1183, IoU.clock: 0.2596, IoU.flag: 0.6341, Acc.wall: 0.8818, Acc.building: 0.9024, Acc.sky: 0.9771, Acc.floor: 0.8166, Acc.tree: 0.8691, Acc.ceiling: 0.8999, Acc.road: 0.8524, Acc.bed : 0.9591, Acc.windowpane: 0.7351, Acc.grass: 0.7206, Acc.cabinet: 0.7447, Acc.sidewalk: 0.7990, Acc.person: 0.9112, Acc.earth: 0.5260, Acc.door: 0.6340, Acc.table: 0.7506, Acc.mountain: 0.8171, Acc.plant: 0.5821, Acc.curtain: 0.8870, Acc.chair: 0.7566, Acc.car: 0.9175, Acc.water: 0.6712, Acc.painting: 0.8944, Acc.sofa: 0.8808, Acc.shelf: 0.4869, Acc.house: 0.9062, Acc.sea: 0.9084, Acc.mirror: 0.7235, Acc.rug: 0.9391, Acc.field: 0.7466, Acc.armchair: 0.6054, Acc.seat: 0.8173, Acc.fence: 0.6102, Acc.desk: 0.7061, Acc.rock: 0.7544, Acc.wardrobe: 0.5846, Acc.lamp: 0.8222, Acc.bathtub: 0.8717, Acc.railing: 0.5266, Acc.cushion: 0.8025, Acc.base: 0.4808, Acc.box: 0.3983, Acc.column: 0.5233, Acc.signboard: 0.5172, Acc.chest of drawers: 0.5690, Acc.counter: 0.3837, Acc.sand: 0.6119, Acc.sink: 0.7405, Acc.skyscraper: 0.6606, Acc.fireplace: 0.8884, Acc.refrigerator: 0.8999, Acc.grandstand: 0.8386, Acc.path: 0.4065, Acc.stairs: 0.4214, Acc.runway: 0.9908, Acc.case: 0.8129, Acc.pool table: 0.9746, Acc.pillow: 0.5651, Acc.screen door: 0.7962, Acc.stairway: 0.4184, Acc.river: 0.4645, Acc.bridge: 0.8208, Acc.bookcase: 0.5555, Acc.blind: 0.6893, Acc.coffee table: 0.8443, Acc.toilet: 0.9542, Acc.flower: 0.5152, Acc.book: 0.7503, Acc.hill: 0.0123, Acc.bench: 0.5688, Acc.countertop: 0.7533, Acc.stove: 0.8674, Acc.palm: 0.5894, Acc.kitchen island: 0.6827, Acc.computer: 0.9101, Acc.swivel chair: 0.7388, Acc.boat: 0.8159, Acc.bar: 0.7131, Acc.arcade machine: 0.8829, Acc.hovel: 0.1269, Acc.bus: 0.9334, Acc.towel: 0.7828, Acc.light: 0.6681, Acc.truck: 0.5328, Acc.tower: 0.3967, Acc.chandelier: 0.7942, Acc.awning: 0.4627, Acc.streetlight: 0.3841, Acc.booth: 0.8133, Acc.television receiver: 0.8244, Acc.airplane: 0.7686, Acc.dirt track: 0.1775, Acc.apparel: 0.5099, Acc.pole: 0.2650, Acc.land: 0.1938, Acc.bannister: 0.1075, Acc.escalator: 0.7702, Acc.ottoman: 0.5739, Acc.bottle: 0.6233, Acc.buffet: 0.6138, Acc.poster: 0.2877, Acc.stage: 0.3044, Acc.van: 0.5375, Acc.ship: 0.5338, Acc.fountain: 0.3804, Acc.conveyer belt: 0.9470, Acc.canopy: 0.7216, Acc.washer: 0.8969, Acc.plaything: 0.4611, Acc.swimming pool: 0.8752, Acc.stool: 0.6890, Acc.barrel: 0.6319, Acc.basket: 0.3448, Acc.waterfall: 0.8369, Acc.tent: 0.9886, Acc.bag: 0.1332, Acc.minibike: 0.8471, Acc.cradle: 0.9787, Acc.oven: 0.6515, Acc.ball: 0.4600, Acc.food: 0.6739, Acc.step: 0.1403, Acc.tank: 0.7262, Acc.trade name: 0.4225, Acc.microwave: 0.9589, Acc.pot: 0.6547, Acc.animal: 0.7259, Acc.bicycle: 0.7696, Acc.lake: 0.0000, Acc.dishwasher: 0.7841, Acc.screen: 0.9086, Acc.blanket: 0.2125, Acc.sculpture: 0.6773, Acc.hood: 0.7727, Acc.sconce: 0.5031, Acc.vase: 0.5675, Acc.traffic light: 0.2927, Acc.tray: 0.1306, Acc.ashcan: 0.5832, Acc.fan: 0.6415, Acc.pier: 0.4122, Acc.crt screen: 0.0000, Acc.plate: 0.7963, Acc.monitor: 0.7941, Acc.bulletin board: 0.7323, Acc.shower: 0.0000, Acc.radiator: 0.7507, Acc.glass: 0.1282, Acc.clock: 0.2859, Acc.flag: 0.7646 +2024-06-18 02:46:27,558 - mmseg - INFO - Iter [8050/80000] lr: 3.598e-05, eta: 1 day, 5:44:24, time: 3.278, data_time: 1.956, memory: 70498, decode.loss_ce: 0.4864, decode.acc_seg: 81.5787, aux.loss_ce: 0.1936, aux.acc_seg: 81.7876, loss: 0.6800 +2024-06-18 02:47:34,034 - mmseg - INFO - Iter [8100/80000] lr: 3.595e-05, eta: 1 day, 5:42:00, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4767, decode.acc_seg: 82.2199, aux.loss_ce: 0.1887, aux.acc_seg: 82.4494, loss: 0.6654 +2024-06-18 02:48:40,531 - mmseg - INFO - Iter [8150/80000] lr: 3.593e-05, eta: 1 day, 5:39:36, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4807, decode.acc_seg: 81.3537, aux.loss_ce: 0.1899, aux.acc_seg: 81.5156, loss: 0.6707 +2024-06-18 02:49:47,037 - mmseg - INFO - Iter [8200/80000] lr: 3.590e-05, eta: 1 day, 5:37:13, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4588, decode.acc_seg: 81.9195, aux.loss_ce: 0.1817, aux.acc_seg: 82.0755, loss: 0.6405 +2024-06-18 02:50:53,400 - mmseg - INFO - Iter [8250/80000] lr: 3.588e-05, eta: 1 day, 5:34:51, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4611, decode.acc_seg: 81.2737, aux.loss_ce: 0.1837, aux.acc_seg: 81.3791, loss: 0.6448 +2024-06-18 02:51:59,799 - mmseg - INFO - Iter [8300/80000] lr: 3.585e-05, eta: 1 day, 5:32:29, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4699, decode.acc_seg: 82.1474, aux.loss_ce: 0.1872, aux.acc_seg: 82.3001, loss: 0.6571 +2024-06-18 02:53:06,536 - mmseg - INFO - Iter [8350/80000] lr: 3.583e-05, eta: 1 day, 5:30:11, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4661, decode.acc_seg: 82.0548, aux.loss_ce: 0.1861, aux.acc_seg: 82.1897, loss: 0.6522 +2024-06-18 02:54:13,021 - mmseg - INFO - Iter [8400/80000] lr: 3.580e-05, eta: 1 day, 5:27:52, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4605, decode.acc_seg: 82.1647, aux.loss_ce: 0.1834, aux.acc_seg: 82.1230, loss: 0.6439 +2024-06-18 02:55:19,379 - mmseg - INFO - Iter [8450/80000] lr: 3.578e-05, eta: 1 day, 5:25:32, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4710, decode.acc_seg: 82.3491, aux.loss_ce: 0.1880, aux.acc_seg: 82.3783, loss: 0.6590 +2024-06-18 02:56:26,559 - mmseg - INFO - Iter [8500/80000] lr: 3.575e-05, eta: 1 day, 5:23:21, time: 1.344, data_time: 0.031, memory: 70498, decode.loss_ce: 0.4916, decode.acc_seg: 81.1628, aux.loss_ce: 0.1962, aux.acc_seg: 81.4306, loss: 0.6879 +2024-06-18 02:57:32,758 - mmseg - INFO - Iter [8550/80000] lr: 3.573e-05, eta: 1 day, 5:21:02, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4723, decode.acc_seg: 82.5036, aux.loss_ce: 0.1882, aux.acc_seg: 82.5223, loss: 0.6605 +2024-06-18 02:58:38,874 - mmseg - INFO - Iter [8600/80000] lr: 3.570e-05, eta: 1 day, 5:18:43, time: 1.322, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4616, decode.acc_seg: 82.0146, aux.loss_ce: 0.1859, aux.acc_seg: 82.0299, loss: 0.6475 +2024-06-18 02:59:45,264 - mmseg - INFO - Iter [8650/80000] lr: 3.568e-05, eta: 1 day, 5:16:27, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4460, decode.acc_seg: 82.1397, aux.loss_ce: 0.1787, aux.acc_seg: 82.2315, loss: 0.6248 +2024-06-18 03:00:51,685 - mmseg - INFO - Iter [8700/80000] lr: 3.565e-05, eta: 1 day, 5:14:12, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4858, decode.acc_seg: 80.9876, aux.loss_ce: 0.1930, aux.acc_seg: 81.1764, loss: 0.6788 +2024-06-18 03:01:57,943 - mmseg - INFO - Iter [8750/80000] lr: 3.563e-05, eta: 1 day, 5:11:57, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4801, decode.acc_seg: 81.1529, aux.loss_ce: 0.1926, aux.acc_seg: 81.0506, loss: 0.6727 +2024-06-18 03:03:04,277 - mmseg - INFO - Iter [8800/80000] lr: 3.560e-05, eta: 1 day, 5:09:43, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.5011, decode.acc_seg: 80.4214, aux.loss_ce: 0.1983, aux.acc_seg: 80.9041, loss: 0.6994 +2024-06-18 03:04:12,626 - mmseg - INFO - Iter [8850/80000] lr: 3.558e-05, eta: 1 day, 5:07:46, time: 1.367, data_time: 0.051, memory: 70498, decode.loss_ce: 0.4556, decode.acc_seg: 82.7559, aux.loss_ce: 0.1819, aux.acc_seg: 82.7377, loss: 0.6375 +2024-06-18 03:05:18,989 - mmseg - INFO - Iter [8900/80000] lr: 3.555e-05, eta: 1 day, 5:05:34, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4460, decode.acc_seg: 83.0748, aux.loss_ce: 0.1777, aux.acc_seg: 83.1537, loss: 0.6237 +2024-06-18 03:06:25,173 - mmseg - INFO - Iter [8950/80000] lr: 3.553e-05, eta: 1 day, 5:03:21, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4247, decode.acc_seg: 83.5108, aux.loss_ce: 0.1688, aux.acc_seg: 83.7923, loss: 0.5935 +2024-06-18 03:07:31,732 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 03:07:31,732 - mmseg - INFO - Iter [9000/80000] lr: 3.550e-05, eta: 1 day, 5:01:12, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4408, decode.acc_seg: 82.2094, aux.loss_ce: 0.1757, aux.acc_seg: 82.3657, loss: 0.6165 +2024-06-18 03:09:08,455 - mmseg - INFO - per class results: +2024-06-18 03:09:08,461 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.07 | 87.63 | +| building | 83.38 | 92.45 | +| sky | 94.19 | 97.56 | +| floor | 83.31 | 88.09 | +| tree | 74.43 | 91.4 | +| ceiling | 84.83 | 91.66 | +| road | 83.55 | 88.79 | +| bed | 90.13 | 94.6 | +| windowpane | 62.11 | 75.82 | +| grass | 68.49 | 85.33 | +| cabinet | 60.98 | 77.0 | +| sidewalk | 67.31 | 87.58 | +| person | 81.51 | 93.81 | +| earth | 35.93 | 46.3 | +| door | 55.16 | 71.69 | +| table | 60.21 | 71.31 | +| mountain | 59.95 | 82.39 | +| plant | 55.38 | 66.49 | +| curtain | 76.59 | 90.16 | +| chair | 58.36 | 74.24 | +| car | 82.37 | 93.86 | +| water | 67.34 | 85.94 | +| painting | 73.32 | 89.13 | +| sofa | 73.18 | 82.89 | +| shelf | 42.61 | 58.71 | +| house | 53.08 | 65.27 | +| sea | 76.41 | 89.51 | +| mirror | 66.13 | 70.62 | +| rug | 68.51 | 81.67 | +| field | 29.52 | 44.71 | +| armchair | 49.27 | 78.97 | +| seat | 67.97 | 84.57 | +| fence | 45.67 | 62.56 | +| desk | 46.74 | 69.63 | +| rock | 51.46 | 61.05 | +| wardrobe | 54.29 | 81.78 | +| lamp | 64.68 | 78.27 | +| bathtub | 78.7 | 83.78 | +| railing | 36.2 | 48.22 | +| cushion | 59.64 | 78.24 | +| base | 34.15 | 43.66 | +| box | 28.48 | 38.27 | +| column | 50.43 | 66.6 | +| signboard | 36.5 | 48.4 | +| chest of drawers | 34.25 | 37.41 | +| counter | 51.07 | 71.78 | +| sand | 47.91 | 69.24 | +| sink | 67.87 | 75.03 | +| skyscraper | 49.51 | 62.29 | +| fireplace | 66.77 | 94.21 | +| refrigerator | 76.34 | 81.85 | +| grandstand | 37.84 | 84.94 | +| path | 21.2 | 24.83 | +| stairs | 27.21 | 31.17 | +| runway | 66.62 | 93.92 | +| case | 59.65 | 77.73 | +| pool table | 92.76 | 97.57 | +| pillow | 62.8 | 73.76 | +| screen door | 67.8 | 88.42 | +| stairway | 30.64 | 42.06 | +| river | 30.73 | 35.94 | +| bridge | 65.67 | 77.98 | +| bookcase | 35.35 | 52.78 | +| blind | 45.33 | 51.92 | +| coffee table | 55.56 | 90.07 | +| toilet | 86.05 | 92.98 | +| flower | 35.47 | 49.83 | +| book | 48.16 | 75.27 | +| hill | 3.61 | 4.89 | +| bench | 54.05 | 65.09 | +| countertop | 49.37 | 56.95 | +| stove | 75.69 | 81.23 | +| palm | 33.81 | 35.37 | +| kitchen island | 41.25 | 89.03 | +| computer | 66.35 | 93.95 | +| swivel chair | 48.04 | 77.98 | +| boat | 49.32 | 85.91 | +| bar | 59.16 | 70.92 | +| arcade machine | 72.78 | 92.93 | +| hovel | 21.41 | 24.11 | +| bus | 84.24 | 95.18 | +| towel | 64.49 | 75.31 | +| light | 45.23 | 50.59 | +| truck | 36.66 | 47.45 | +| tower | 25.59 | 48.0 | +| chandelier | 64.34 | 78.92 | +| awning | 35.77 | 39.64 | +| streetlight | 21.1 | 27.5 | +| booth | 37.53 | 48.6 | +| television receiver | 65.84 | 71.43 | +| airplane | 56.36 | 66.26 | +| dirt track | 8.95 | 68.6 | +| apparel | 40.63 | 73.8 | +| pole | 13.81 | 15.84 | +| land | 0.07 | 0.14 | +| bannister | 8.72 | 9.84 | +| escalator | 58.61 | 74.79 | +| ottoman | 41.53 | 61.39 | +| bottle | 35.49 | 63.68 | +| buffet | 48.99 | 88.29 | +| poster | 25.77 | 34.27 | +| stage | 9.4 | 10.34 | +| van | 22.05 | 24.53 | +| ship | 76.25 | 88.56 | +| fountain | 46.93 | 48.11 | +| conveyer belt | 73.08 | 93.2 | +| canopy | 49.42 | 65.62 | +| washer | 61.79 | 72.36 | +| plaything | 17.49 | 24.07 | +| swimming pool | 64.52 | 95.67 | +| stool | 43.47 | 63.2 | +| barrel | 52.11 | 64.94 | +| basket | 28.1 | 45.7 | +| waterfall | 54.33 | 82.43 | +| tent | 93.88 | 98.24 | +| bag | 18.26 | 20.64 | +| minibike | 64.49 | 75.79 | +| cradle | 79.71 | 97.3 | +| oven | 53.1 | 71.7 | +| ball | 47.11 | 62.95 | +| food | 48.84 | 57.27 | +| step | 2.89 | 3.14 | +| tank | 60.34 | 69.9 | +| trade name | 14.08 | 14.85 | +| microwave | 84.36 | 87.7 | +| pot | 47.96 | 57.36 | +| animal | 67.67 | 70.02 | +| bicycle | 50.45 | 60.89 | +| lake | 0.0 | 0.0 | +| dishwasher | 61.4 | 70.14 | +| screen | 49.24 | 82.65 | +| blanket | 21.28 | 24.76 | +| sculpture | 57.51 | 64.46 | +| hood | 59.12 | 70.5 | +| sconce | 40.9 | 48.19 | +| vase | 33.88 | 50.35 | +| traffic light | 24.76 | 44.75 | +| tray | 1.23 | 1.31 | +| ashcan | 45.47 | 60.74 | +| fan | 56.76 | 72.71 | +| pier | 37.71 | 42.82 | +| crt screen | 12.57 | 17.83 | +| plate | 50.7 | 66.77 | +| monitor | 41.5 | 76.36 | +| bulletin board | 48.11 | 62.54 | +| shower | 0.0 | 0.0 | +| radiator | 58.63 | 70.12 | +| glass | 9.09 | 9.32 | +| clock | 27.31 | 33.09 | +| flag | 67.04 | 73.4 | ++---------------------+-------+-------+ +2024-06-18 03:09:08,461 - mmseg - INFO - Summary: +2024-06-18 03:09:08,461 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 84.03 | 50.3 | 63.74 | ++-------+------+-------+ +2024-06-18 03:09:08,462 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 03:09:08,462 - mmseg - INFO - Iter(val) [250] aAcc: 0.8403, mIoU: 0.5030, mAcc: 0.6374, IoU.wall: 0.7907, IoU.building: 0.8338, IoU.sky: 0.9419, IoU.floor: 0.8331, IoU.tree: 0.7443, IoU.ceiling: 0.8483, IoU.road: 0.8355, IoU.bed : 0.9013, IoU.windowpane: 0.6211, IoU.grass: 0.6849, IoU.cabinet: 0.6098, IoU.sidewalk: 0.6731, IoU.person: 0.8151, IoU.earth: 0.3593, IoU.door: 0.5516, IoU.table: 0.6021, IoU.mountain: 0.5995, IoU.plant: 0.5538, IoU.curtain: 0.7659, IoU.chair: 0.5836, IoU.car: 0.8237, IoU.water: 0.6734, IoU.painting: 0.7332, IoU.sofa: 0.7318, IoU.shelf: 0.4261, IoU.house: 0.5308, IoU.sea: 0.7641, IoU.mirror: 0.6613, IoU.rug: 0.6851, IoU.field: 0.2952, IoU.armchair: 0.4927, IoU.seat: 0.6797, IoU.fence: 0.4567, IoU.desk: 0.4674, IoU.rock: 0.5146, IoU.wardrobe: 0.5429, IoU.lamp: 0.6468, IoU.bathtub: 0.7870, IoU.railing: 0.3620, IoU.cushion: 0.5964, IoU.base: 0.3415, IoU.box: 0.2848, IoU.column: 0.5043, IoU.signboard: 0.3650, IoU.chest of drawers: 0.3425, IoU.counter: 0.5107, IoU.sand: 0.4791, IoU.sink: 0.6787, IoU.skyscraper: 0.4951, IoU.fireplace: 0.6677, IoU.refrigerator: 0.7634, IoU.grandstand: 0.3784, IoU.path: 0.2120, IoU.stairs: 0.2721, IoU.runway: 0.6662, IoU.case: 0.5965, IoU.pool table: 0.9276, IoU.pillow: 0.6280, IoU.screen door: 0.6780, IoU.stairway: 0.3064, IoU.river: 0.3073, IoU.bridge: 0.6567, IoU.bookcase: 0.3535, IoU.blind: 0.4533, IoU.coffee table: 0.5556, IoU.toilet: 0.8605, IoU.flower: 0.3547, IoU.book: 0.4816, IoU.hill: 0.0361, IoU.bench: 0.5405, IoU.countertop: 0.4937, IoU.stove: 0.7569, IoU.palm: 0.3381, IoU.kitchen island: 0.4125, IoU.computer: 0.6635, IoU.swivel chair: 0.4804, IoU.boat: 0.4932, IoU.bar: 0.5916, IoU.arcade machine: 0.7278, IoU.hovel: 0.2141, IoU.bus: 0.8424, IoU.towel: 0.6449, IoU.light: 0.4523, IoU.truck: 0.3666, IoU.tower: 0.2559, IoU.chandelier: 0.6434, IoU.awning: 0.3577, IoU.streetlight: 0.2110, IoU.booth: 0.3753, IoU.television receiver: 0.6584, IoU.airplane: 0.5636, IoU.dirt track: 0.0895, IoU.apparel: 0.4063, IoU.pole: 0.1381, IoU.land: 0.0007, IoU.bannister: 0.0872, IoU.escalator: 0.5861, IoU.ottoman: 0.4153, IoU.bottle: 0.3549, IoU.buffet: 0.4899, IoU.poster: 0.2577, IoU.stage: 0.0940, IoU.van: 0.2205, IoU.ship: 0.7625, IoU.fountain: 0.4693, IoU.conveyer belt: 0.7308, IoU.canopy: 0.4942, IoU.washer: 0.6179, IoU.plaything: 0.1749, IoU.swimming pool: 0.6452, IoU.stool: 0.4347, IoU.barrel: 0.5211, IoU.basket: 0.2810, IoU.waterfall: 0.5433, IoU.tent: 0.9388, IoU.bag: 0.1826, IoU.minibike: 0.6449, IoU.cradle: 0.7971, IoU.oven: 0.5310, IoU.ball: 0.4711, IoU.food: 0.4884, IoU.step: 0.0289, IoU.tank: 0.6034, IoU.trade name: 0.1408, IoU.microwave: 0.8436, IoU.pot: 0.4796, IoU.animal: 0.6767, IoU.bicycle: 0.5045, IoU.lake: 0.0000, IoU.dishwasher: 0.6140, IoU.screen: 0.4924, IoU.blanket: 0.2128, IoU.sculpture: 0.5751, IoU.hood: 0.5912, IoU.sconce: 0.4090, IoU.vase: 0.3388, IoU.traffic light: 0.2476, IoU.tray: 0.0123, IoU.ashcan: 0.4547, IoU.fan: 0.5676, IoU.pier: 0.3771, IoU.crt screen: 0.1257, IoU.plate: 0.5070, IoU.monitor: 0.4150, IoU.bulletin board: 0.4811, IoU.shower: 0.0000, IoU.radiator: 0.5863, IoU.glass: 0.0909, IoU.clock: 0.2731, IoU.flag: 0.6704, Acc.wall: 0.8763, Acc.building: 0.9245, Acc.sky: 0.9756, Acc.floor: 0.8809, Acc.tree: 0.9140, Acc.ceiling: 0.9166, Acc.road: 0.8879, Acc.bed : 0.9460, Acc.windowpane: 0.7582, Acc.grass: 0.8533, Acc.cabinet: 0.7700, Acc.sidewalk: 0.8758, Acc.person: 0.9381, Acc.earth: 0.4630, Acc.door: 0.7169, Acc.table: 0.7131, Acc.mountain: 0.8239, Acc.plant: 0.6649, Acc.curtain: 0.9016, Acc.chair: 0.7424, Acc.car: 0.9386, Acc.water: 0.8594, Acc.painting: 0.8913, Acc.sofa: 0.8289, Acc.shelf: 0.5871, Acc.house: 0.6527, Acc.sea: 0.8951, Acc.mirror: 0.7062, Acc.rug: 0.8167, Acc.field: 0.4471, Acc.armchair: 0.7897, Acc.seat: 0.8457, Acc.fence: 0.6256, Acc.desk: 0.6963, Acc.rock: 0.6105, Acc.wardrobe: 0.8178, Acc.lamp: 0.7827, Acc.bathtub: 0.8378, Acc.railing: 0.4822, Acc.cushion: 0.7824, Acc.base: 0.4366, Acc.box: 0.3827, Acc.column: 0.6660, Acc.signboard: 0.4840, Acc.chest of drawers: 0.3741, Acc.counter: 0.7178, Acc.sand: 0.6924, Acc.sink: 0.7503, Acc.skyscraper: 0.6229, Acc.fireplace: 0.9421, Acc.refrigerator: 0.8185, Acc.grandstand: 0.8494, Acc.path: 0.2483, Acc.stairs: 0.3117, Acc.runway: 0.9392, Acc.case: 0.7773, Acc.pool table: 0.9757, Acc.pillow: 0.7376, Acc.screen door: 0.8842, Acc.stairway: 0.4206, Acc.river: 0.3594, Acc.bridge: 0.7798, Acc.bookcase: 0.5278, Acc.blind: 0.5192, Acc.coffee table: 0.9007, Acc.toilet: 0.9298, Acc.flower: 0.4983, Acc.book: 0.7527, Acc.hill: 0.0489, Acc.bench: 0.6509, Acc.countertop: 0.5695, Acc.stove: 0.8123, Acc.palm: 0.3537, Acc.kitchen island: 0.8903, Acc.computer: 0.9395, Acc.swivel chair: 0.7798, Acc.boat: 0.8591, Acc.bar: 0.7092, Acc.arcade machine: 0.9293, Acc.hovel: 0.2411, Acc.bus: 0.9518, Acc.towel: 0.7531, Acc.light: 0.5059, Acc.truck: 0.4745, Acc.tower: 0.4800, Acc.chandelier: 0.7892, Acc.awning: 0.3964, Acc.streetlight: 0.2750, Acc.booth: 0.4860, Acc.television receiver: 0.7143, Acc.airplane: 0.6626, Acc.dirt track: 0.6860, Acc.apparel: 0.7380, Acc.pole: 0.1584, Acc.land: 0.0014, Acc.bannister: 0.0984, Acc.escalator: 0.7479, Acc.ottoman: 0.6139, Acc.bottle: 0.6368, Acc.buffet: 0.8829, Acc.poster: 0.3427, Acc.stage: 0.1034, Acc.van: 0.2453, Acc.ship: 0.8856, Acc.fountain: 0.4811, Acc.conveyer belt: 0.9320, Acc.canopy: 0.6562, Acc.washer: 0.7236, Acc.plaything: 0.2407, Acc.swimming pool: 0.9567, Acc.stool: 0.6320, Acc.barrel: 0.6494, Acc.basket: 0.4570, Acc.waterfall: 0.8243, Acc.tent: 0.9824, Acc.bag: 0.2064, Acc.minibike: 0.7579, Acc.cradle: 0.9730, Acc.oven: 0.7170, Acc.ball: 0.6295, Acc.food: 0.5727, Acc.step: 0.0314, Acc.tank: 0.6990, Acc.trade name: 0.1485, Acc.microwave: 0.8770, Acc.pot: 0.5736, Acc.animal: 0.7002, Acc.bicycle: 0.6089, Acc.lake: 0.0000, Acc.dishwasher: 0.7014, Acc.screen: 0.8265, Acc.blanket: 0.2476, Acc.sculpture: 0.6446, Acc.hood: 0.7050, Acc.sconce: 0.4819, Acc.vase: 0.5035, Acc.traffic light: 0.4475, Acc.tray: 0.0131, Acc.ashcan: 0.6074, Acc.fan: 0.7271, Acc.pier: 0.4282, Acc.crt screen: 0.1783, Acc.plate: 0.6677, Acc.monitor: 0.7636, Acc.bulletin board: 0.6254, Acc.shower: 0.0000, Acc.radiator: 0.7012, Acc.glass: 0.0932, Acc.clock: 0.3309, Acc.flag: 0.7340 +2024-06-18 03:10:15,214 - mmseg - INFO - Iter [9050/80000] lr: 3.548e-05, eta: 1 day, 5:11:43, time: 3.270, data_time: 1.951, memory: 70498, decode.loss_ce: 0.4589, decode.acc_seg: 82.1945, aux.loss_ce: 0.1820, aux.acc_seg: 82.4940, loss: 0.6408 +2024-06-18 03:11:21,797 - mmseg - INFO - Iter [9100/80000] lr: 3.545e-05, eta: 1 day, 5:09:30, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4201, decode.acc_seg: 83.4681, aux.loss_ce: 0.1677, aux.acc_seg: 83.6103, loss: 0.5878 +2024-06-18 03:12:28,297 - mmseg - INFO - Iter [9150/80000] lr: 3.543e-05, eta: 1 day, 5:07:18, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4569, decode.acc_seg: 82.4786, aux.loss_ce: 0.1818, aux.acc_seg: 82.5809, loss: 0.6387 +2024-06-18 03:13:34,602 - mmseg - INFO - Iter [9200/80000] lr: 3.540e-05, eta: 1 day, 5:05:05, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4792, decode.acc_seg: 81.7200, aux.loss_ce: 0.1919, aux.acc_seg: 81.8597, loss: 0.6712 +2024-06-18 03:14:40,924 - mmseg - INFO - Iter [9250/80000] lr: 3.538e-05, eta: 1 day, 5:02:53, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4395, decode.acc_seg: 82.8178, aux.loss_ce: 0.1749, aux.acc_seg: 82.9249, loss: 0.6144 +2024-06-18 03:15:47,097 - mmseg - INFO - Iter [9300/80000] lr: 3.535e-05, eta: 1 day, 5:00:40, time: 1.323, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4517, decode.acc_seg: 82.9899, aux.loss_ce: 0.1827, aux.acc_seg: 82.6835, loss: 0.6344 +2024-06-18 03:16:53,700 - mmseg - INFO - Iter [9350/80000] lr: 3.533e-05, eta: 1 day, 4:58:31, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4504, decode.acc_seg: 82.7018, aux.loss_ce: 0.1798, aux.acc_seg: 82.7259, loss: 0.6302 +2024-06-18 03:18:00,035 - mmseg - INFO - Iter [9400/80000] lr: 3.530e-05, eta: 1 day, 4:56:21, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4726, decode.acc_seg: 82.7266, aux.loss_ce: 0.1894, aux.acc_seg: 82.7353, loss: 0.6620 +2024-06-18 03:19:06,497 - mmseg - INFO - Iter [9450/80000] lr: 3.528e-05, eta: 1 day, 4:54:13, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4399, decode.acc_seg: 82.8466, aux.loss_ce: 0.1767, aux.acc_seg: 82.8825, loss: 0.6166 +2024-06-18 03:20:12,650 - mmseg - INFO - Iter [9500/80000] lr: 3.525e-05, eta: 1 day, 4:52:03, time: 1.323, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4160, decode.acc_seg: 83.8887, aux.loss_ce: 0.1667, aux.acc_seg: 83.7479, loss: 0.5827 +2024-06-18 03:21:18,891 - mmseg - INFO - Iter [9550/80000] lr: 3.523e-05, eta: 1 day, 4:49:54, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4383, decode.acc_seg: 82.8055, aux.loss_ce: 0.1750, aux.acc_seg: 82.8492, loss: 0.6133 +2024-06-18 03:22:25,479 - mmseg - INFO - Iter [9600/80000] lr: 3.520e-05, eta: 1 day, 4:47:48, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4519, decode.acc_seg: 82.1074, aux.loss_ce: 0.1812, aux.acc_seg: 82.2322, loss: 0.6331 +2024-06-18 03:23:31,901 - mmseg - INFO - Iter [9650/80000] lr: 3.518e-05, eta: 1 day, 4:45:42, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4431, decode.acc_seg: 82.1170, aux.loss_ce: 0.1775, aux.acc_seg: 82.3392, loss: 0.6206 +2024-06-18 03:24:38,201 - mmseg - INFO - Iter [9700/80000] lr: 3.515e-05, eta: 1 day, 4:43:36, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4716, decode.acc_seg: 81.8984, aux.loss_ce: 0.1886, aux.acc_seg: 81.9891, loss: 0.6602 +2024-06-18 03:25:44,377 - mmseg - INFO - Iter [9750/80000] lr: 3.513e-05, eta: 1 day, 4:41:29, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4368, decode.acc_seg: 83.0230, aux.loss_ce: 0.1746, aux.acc_seg: 83.0104, loss: 0.6113 +2024-06-18 03:26:50,724 - mmseg - INFO - Iter [9800/80000] lr: 3.510e-05, eta: 1 day, 4:39:24, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4559, decode.acc_seg: 82.1066, aux.loss_ce: 0.1809, aux.acc_seg: 82.3975, loss: 0.6368 +2024-06-18 03:27:57,198 - mmseg - INFO - Iter [9850/80000] lr: 3.508e-05, eta: 1 day, 4:37:21, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4210, decode.acc_seg: 83.3768, aux.loss_ce: 0.1680, aux.acc_seg: 83.4419, loss: 0.5890 +2024-06-18 03:29:03,540 - mmseg - INFO - Iter [9900/80000] lr: 3.505e-05, eta: 1 day, 4:35:17, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4392, decode.acc_seg: 83.0106, aux.loss_ce: 0.1762, aux.acc_seg: 83.0328, loss: 0.6154 +2024-06-18 03:30:09,922 - mmseg - INFO - Iter [9950/80000] lr: 3.503e-05, eta: 1 day, 4:33:14, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4355, decode.acc_seg: 83.1298, aux.loss_ce: 0.1738, aux.acc_seg: 83.2009, loss: 0.6093 +2024-06-18 03:31:16,407 - mmseg - INFO - Saving checkpoint at 10000 iterations +2024-06-18 03:32:59,466 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 03:32:59,466 - mmseg - INFO - Iter [10000/80000] lr: 3.500e-05, eta: 1 day, 4:43:14, time: 3.391, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4213, decode.acc_seg: 83.5421, aux.loss_ce: 0.1681, aux.acc_seg: 83.6197, loss: 0.5894 +2024-06-18 03:34:35,436 - mmseg - INFO - per class results: +2024-06-18 03:34:35,442 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.65 | 89.33 | +| building | 83.0 | 90.62 | +| sky | 94.31 | 97.49 | +| floor | 82.27 | 90.51 | +| tree | 75.84 | 86.62 | +| ceiling | 83.91 | 89.04 | +| road | 85.1 | 91.89 | +| bed | 90.8 | 95.6 | +| windowpane | 63.34 | 79.71 | +| grass | 66.24 | 73.73 | +| cabinet | 61.08 | 76.41 | +| sidewalk | 66.14 | 82.22 | +| person | 82.85 | 90.96 | +| earth | 39.09 | 66.39 | +| door | 51.96 | 61.15 | +| table | 58.39 | 71.87 | +| mountain | 58.11 | 66.56 | +| plant | 54.21 | 61.64 | +| curtain | 75.07 | 89.37 | +| chair | 60.45 | 73.24 | +| car | 84.75 | 93.37 | +| water | 54.82 | 72.59 | +| painting | 72.35 | 89.11 | +| sofa | 71.43 | 88.88 | +| shelf | 34.94 | 47.1 | +| house | 55.8 | 82.97 | +| sea | 66.18 | 89.52 | +| mirror | 70.18 | 77.83 | +| rug | 55.5 | 57.96 | +| field | 34.2 | 50.09 | +| armchair | 50.95 | 63.28 | +| seat | 66.4 | 86.67 | +| fence | 46.46 | 60.63 | +| desk | 43.51 | 80.97 | +| rock | 48.4 | 81.88 | +| wardrobe | 50.42 | 55.81 | +| lamp | 62.42 | 87.05 | +| bathtub | 78.66 | 82.77 | +| railing | 39.1 | 54.83 | +| cushion | 59.84 | 72.65 | +| base | 39.44 | 47.35 | +| box | 27.5 | 44.43 | +| column | 53.72 | 66.45 | +| signboard | 38.85 | 49.68 | +| chest of drawers | 43.68 | 74.87 | +| counter | 48.57 | 60.01 | +| sand | 46.7 | 63.22 | +| sink | 69.29 | 81.32 | +| skyscraper | 45.51 | 73.39 | +| fireplace | 70.6 | 93.44 | +| refrigerator | 69.58 | 83.17 | +| grandstand | 54.7 | 78.48 | +| path | 24.6 | 34.84 | +| stairs | 16.84 | 17.99 | +| runway | 65.85 | 90.6 | +| case | 41.77 | 94.75 | +| pool table | 92.68 | 96.93 | +| pillow | 65.35 | 80.43 | +| screen door | 76.9 | 89.17 | +| stairway | 27.91 | 50.31 | +| river | 6.01 | 6.33 | +| bridge | 68.69 | 89.92 | +| bookcase | 34.95 | 52.66 | +| blind | 31.41 | 32.62 | +| coffee table | 52.26 | 87.63 | +| toilet | 86.78 | 94.67 | +| flower | 36.91 | 50.81 | +| book | 50.09 | 64.41 | +| hill | 5.12 | 9.28 | +| bench | 46.67 | 57.81 | +| countertop | 55.88 | 81.82 | +| stove | 76.69 | 84.99 | +| palm | 48.12 | 80.8 | +| kitchen island | 38.81 | 68.28 | +| computer | 71.33 | 95.05 | +| swivel chair | 44.62 | 60.11 | +| boat | 51.59 | 87.27 | +| bar | 62.25 | 67.88 | +| arcade machine | 65.57 | 85.0 | +| hovel | 61.73 | 80.18 | +| bus | 86.71 | 95.0 | +| towel | 66.05 | 82.17 | +| light | 50.82 | 67.61 | +| truck | 40.01 | 50.14 | +| tower | 33.4 | 58.14 | +| chandelier | 63.56 | 81.52 | +| awning | 40.81 | 62.55 | +| streetlight | 26.07 | 40.92 | +| booth | 34.54 | 40.17 | +| television receiver | 66.44 | 82.38 | +| airplane | 56.69 | 62.53 | +| dirt track | 17.5 | 39.05 | +| apparel | 37.31 | 52.01 | +| pole | 23.33 | 34.06 | +| land | 0.2 | 0.23 | +| bannister | 15.63 | 21.46 | +| escalator | 51.61 | 79.83 | +| ottoman | 45.02 | 67.16 | +| bottle | 37.21 | 55.31 | +| buffet | 44.57 | 65.64 | +| poster | 25.54 | 26.89 | +| stage | 5.2 | 16.4 | +| van | 25.94 | 27.99 | +| ship | 10.95 | 11.48 | +| fountain | 42.62 | 44.58 | +| conveyer belt | 44.95 | 98.26 | +| canopy | 52.43 | 60.22 | +| washer | 81.67 | 93.19 | +| plaything | 17.2 | 25.27 | +| swimming pool | 67.48 | 93.49 | +| stool | 40.88 | 67.95 | +| barrel | 54.74 | 64.77 | +| basket | 25.79 | 34.35 | +| waterfall | 61.46 | 92.19 | +| tent | 94.11 | 98.62 | +| bag | 11.26 | 12.94 | +| minibike | 64.81 | 79.6 | +| cradle | 78.07 | 98.89 | +| oven | 40.57 | 47.58 | +| ball | 47.6 | 64.69 | +| food | 37.56 | 41.35 | +| step | 9.61 | 11.35 | +| tank | 59.52 | 72.58 | +| trade name | 22.35 | 24.74 | +| microwave | 77.78 | 96.53 | +| pot | 50.05 | 62.28 | +| animal | 58.01 | 58.89 | +| bicycle | 53.75 | 71.8 | +| lake | 0.0 | 0.0 | +| dishwasher | 57.48 | 65.28 | +| screen | 52.75 | 95.88 | +| blanket | 14.16 | 14.95 | +| sculpture | 62.02 | 74.99 | +| hood | 55.26 | 67.33 | +| sconce | 47.96 | 60.59 | +| vase | 29.81 | 58.7 | +| traffic light | 23.42 | 55.58 | +| tray | 0.1 | 0.11 | +| ashcan | 35.55 | 51.27 | +| fan | 56.46 | 80.3 | +| pier | 35.71 | 42.42 | +| crt screen | 0.51 | 1.45 | +| plate | 37.53 | 78.86 | +| monitor | 4.7 | 5.28 | +| bulletin board | 48.99 | 70.75 | +| shower | 0.0 | 0.0 | +| radiator | 58.46 | 69.87 | +| glass | 11.25 | 11.83 | +| clock | 29.84 | 32.42 | +| flag | 66.14 | 71.18 | ++---------------------+-------+-------+ +2024-06-18 03:34:35,442 - mmseg - INFO - Summary: +2024-06-18 03:34:35,442 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 83.63 | 49.34 | 63.7 | ++-------+-------+------+ +2024-06-18 03:34:35,443 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 03:34:35,443 - mmseg - INFO - Iter(val) [250] aAcc: 0.8363, mIoU: 0.4934, mAcc: 0.6370, IoU.wall: 0.7965, IoU.building: 0.8300, IoU.sky: 0.9431, IoU.floor: 0.8227, IoU.tree: 0.7584, IoU.ceiling: 0.8391, IoU.road: 0.8510, IoU.bed : 0.9080, IoU.windowpane: 0.6334, IoU.grass: 0.6624, IoU.cabinet: 0.6108, IoU.sidewalk: 0.6614, IoU.person: 0.8285, IoU.earth: 0.3909, IoU.door: 0.5196, IoU.table: 0.5839, IoU.mountain: 0.5811, IoU.plant: 0.5421, IoU.curtain: 0.7507, IoU.chair: 0.6045, IoU.car: 0.8475, IoU.water: 0.5482, IoU.painting: 0.7235, IoU.sofa: 0.7143, IoU.shelf: 0.3494, IoU.house: 0.5580, IoU.sea: 0.6618, IoU.mirror: 0.7018, IoU.rug: 0.5550, IoU.field: 0.3420, IoU.armchair: 0.5095, IoU.seat: 0.6640, IoU.fence: 0.4646, IoU.desk: 0.4351, IoU.rock: 0.4840, IoU.wardrobe: 0.5042, IoU.lamp: 0.6242, IoU.bathtub: 0.7866, IoU.railing: 0.3910, IoU.cushion: 0.5984, IoU.base: 0.3944, IoU.box: 0.2750, IoU.column: 0.5372, IoU.signboard: 0.3885, IoU.chest of drawers: 0.4368, IoU.counter: 0.4857, IoU.sand: 0.4670, IoU.sink: 0.6929, IoU.skyscraper: 0.4551, IoU.fireplace: 0.7060, IoU.refrigerator: 0.6958, IoU.grandstand: 0.5470, IoU.path: 0.2460, IoU.stairs: 0.1684, IoU.runway: 0.6585, IoU.case: 0.4177, IoU.pool table: 0.9268, IoU.pillow: 0.6535, IoU.screen door: 0.7690, IoU.stairway: 0.2791, IoU.river: 0.0601, IoU.bridge: 0.6869, IoU.bookcase: 0.3495, IoU.blind: 0.3141, IoU.coffee table: 0.5226, IoU.toilet: 0.8678, IoU.flower: 0.3691, IoU.book: 0.5009, IoU.hill: 0.0512, IoU.bench: 0.4667, IoU.countertop: 0.5588, IoU.stove: 0.7669, IoU.palm: 0.4812, IoU.kitchen island: 0.3881, IoU.computer: 0.7133, IoU.swivel chair: 0.4462, IoU.boat: 0.5159, IoU.bar: 0.6225, IoU.arcade machine: 0.6557, IoU.hovel: 0.6173, IoU.bus: 0.8671, IoU.towel: 0.6605, IoU.light: 0.5082, IoU.truck: 0.4001, IoU.tower: 0.3340, IoU.chandelier: 0.6356, IoU.awning: 0.4081, IoU.streetlight: 0.2607, IoU.booth: 0.3454, IoU.television receiver: 0.6644, IoU.airplane: 0.5669, IoU.dirt track: 0.1750, IoU.apparel: 0.3731, IoU.pole: 0.2333, IoU.land: 0.0020, IoU.bannister: 0.1563, IoU.escalator: 0.5161, IoU.ottoman: 0.4502, IoU.bottle: 0.3721, IoU.buffet: 0.4457, IoU.poster: 0.2554, IoU.stage: 0.0520, IoU.van: 0.2594, IoU.ship: 0.1095, IoU.fountain: 0.4262, IoU.conveyer belt: 0.4495, IoU.canopy: 0.5243, IoU.washer: 0.8167, IoU.plaything: 0.1720, IoU.swimming pool: 0.6748, IoU.stool: 0.4088, IoU.barrel: 0.5474, IoU.basket: 0.2579, IoU.waterfall: 0.6146, IoU.tent: 0.9411, IoU.bag: 0.1126, IoU.minibike: 0.6481, IoU.cradle: 0.7807, IoU.oven: 0.4057, IoU.ball: 0.4760, IoU.food: 0.3756, IoU.step: 0.0961, IoU.tank: 0.5952, IoU.trade name: 0.2235, IoU.microwave: 0.7778, IoU.pot: 0.5005, IoU.animal: 0.5801, IoU.bicycle: 0.5375, IoU.lake: 0.0000, IoU.dishwasher: 0.5748, IoU.screen: 0.5275, IoU.blanket: 0.1416, IoU.sculpture: 0.6202, IoU.hood: 0.5526, IoU.sconce: 0.4796, IoU.vase: 0.2981, IoU.traffic light: 0.2342, IoU.tray: 0.0010, IoU.ashcan: 0.3555, IoU.fan: 0.5646, IoU.pier: 0.3571, IoU.crt screen: 0.0051, IoU.plate: 0.3753, IoU.monitor: 0.0470, IoU.bulletin board: 0.4899, IoU.shower: 0.0000, IoU.radiator: 0.5846, IoU.glass: 0.1125, IoU.clock: 0.2984, IoU.flag: 0.6614, Acc.wall: 0.8933, Acc.building: 0.9062, Acc.sky: 0.9749, Acc.floor: 0.9051, Acc.tree: 0.8662, Acc.ceiling: 0.8904, Acc.road: 0.9189, Acc.bed : 0.9560, Acc.windowpane: 0.7971, Acc.grass: 0.7373, Acc.cabinet: 0.7641, Acc.sidewalk: 0.8222, Acc.person: 0.9096, Acc.earth: 0.6639, Acc.door: 0.6115, Acc.table: 0.7187, Acc.mountain: 0.6656, Acc.plant: 0.6164, Acc.curtain: 0.8937, Acc.chair: 0.7324, Acc.car: 0.9337, Acc.water: 0.7259, Acc.painting: 0.8911, Acc.sofa: 0.8888, Acc.shelf: 0.4710, Acc.house: 0.8297, Acc.sea: 0.8952, Acc.mirror: 0.7783, Acc.rug: 0.5796, Acc.field: 0.5009, Acc.armchair: 0.6328, Acc.seat: 0.8667, Acc.fence: 0.6063, Acc.desk: 0.8097, Acc.rock: 0.8188, Acc.wardrobe: 0.5581, Acc.lamp: 0.8705, Acc.bathtub: 0.8277, Acc.railing: 0.5483, Acc.cushion: 0.7265, Acc.base: 0.4735, Acc.box: 0.4443, Acc.column: 0.6645, Acc.signboard: 0.4968, Acc.chest of drawers: 0.7487, Acc.counter: 0.6001, Acc.sand: 0.6322, Acc.sink: 0.8132, Acc.skyscraper: 0.7339, Acc.fireplace: 0.9344, Acc.refrigerator: 0.8317, Acc.grandstand: 0.7848, Acc.path: 0.3484, Acc.stairs: 0.1799, Acc.runway: 0.9060, Acc.case: 0.9475, Acc.pool table: 0.9693, Acc.pillow: 0.8043, Acc.screen door: 0.8917, Acc.stairway: 0.5031, Acc.river: 0.0633, Acc.bridge: 0.8992, Acc.bookcase: 0.5266, Acc.blind: 0.3262, Acc.coffee table: 0.8763, Acc.toilet: 0.9467, Acc.flower: 0.5081, Acc.book: 0.6441, Acc.hill: 0.0928, Acc.bench: 0.5781, Acc.countertop: 0.8182, Acc.stove: 0.8499, Acc.palm: 0.8080, Acc.kitchen island: 0.6828, Acc.computer: 0.9505, Acc.swivel chair: 0.6011, Acc.boat: 0.8727, Acc.bar: 0.6788, Acc.arcade machine: 0.8500, Acc.hovel: 0.8018, Acc.bus: 0.9500, Acc.towel: 0.8217, Acc.light: 0.6761, Acc.truck: 0.5014, Acc.tower: 0.5814, Acc.chandelier: 0.8152, Acc.awning: 0.6255, Acc.streetlight: 0.4092, Acc.booth: 0.4017, Acc.television receiver: 0.8238, Acc.airplane: 0.6253, Acc.dirt track: 0.3905, Acc.apparel: 0.5201, Acc.pole: 0.3406, Acc.land: 0.0023, Acc.bannister: 0.2146, Acc.escalator: 0.7983, Acc.ottoman: 0.6716, Acc.bottle: 0.5531, Acc.buffet: 0.6564, Acc.poster: 0.2689, Acc.stage: 0.1640, Acc.van: 0.2799, Acc.ship: 0.1148, Acc.fountain: 0.4458, Acc.conveyer belt: 0.9826, Acc.canopy: 0.6022, Acc.washer: 0.9319, Acc.plaything: 0.2527, Acc.swimming pool: 0.9349, Acc.stool: 0.6795, Acc.barrel: 0.6477, Acc.basket: 0.3435, Acc.waterfall: 0.9219, Acc.tent: 0.9862, Acc.bag: 0.1294, Acc.minibike: 0.7960, Acc.cradle: 0.9889, Acc.oven: 0.4758, Acc.ball: 0.6469, Acc.food: 0.4135, Acc.step: 0.1135, Acc.tank: 0.7258, Acc.trade name: 0.2474, Acc.microwave: 0.9653, Acc.pot: 0.6228, Acc.animal: 0.5889, Acc.bicycle: 0.7180, Acc.lake: 0.0000, Acc.dishwasher: 0.6528, Acc.screen: 0.9588, Acc.blanket: 0.1495, Acc.sculpture: 0.7499, Acc.hood: 0.6733, Acc.sconce: 0.6059, Acc.vase: 0.5870, Acc.traffic light: 0.5558, Acc.tray: 0.0011, Acc.ashcan: 0.5127, Acc.fan: 0.8030, Acc.pier: 0.4242, Acc.crt screen: 0.0145, Acc.plate: 0.7886, Acc.monitor: 0.0528, Acc.bulletin board: 0.7075, Acc.shower: 0.0000, Acc.radiator: 0.6987, Acc.glass: 0.1183, Acc.clock: 0.3242, Acc.flag: 0.7118 +2024-06-18 03:35:42,164 - mmseg - INFO - Iter [10050/80000] lr: 3.498e-05, eta: 1 day, 4:52:19, time: 3.254, data_time: 1.936, memory: 70498, decode.loss_ce: 0.4674, decode.acc_seg: 82.0403, aux.loss_ce: 0.1865, aux.acc_seg: 82.2695, loss: 0.6538 +2024-06-18 03:36:48,426 - mmseg - INFO - Iter [10100/80000] lr: 3.495e-05, eta: 1 day, 4:50:09, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4409, decode.acc_seg: 83.6012, aux.loss_ce: 0.1768, aux.acc_seg: 83.3989, loss: 0.6177 +2024-06-18 03:37:57,385 - mmseg - INFO - Iter [10150/80000] lr: 3.493e-05, eta: 1 day, 4:48:18, time: 1.379, data_time: 0.060, memory: 70498, decode.loss_ce: 0.4222, decode.acc_seg: 83.1351, aux.loss_ce: 0.1693, aux.acc_seg: 83.1183, loss: 0.5915 +2024-06-18 03:39:03,757 - mmseg - INFO - Iter [10200/80000] lr: 3.490e-05, eta: 1 day, 4:46:10, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4522, decode.acc_seg: 81.8982, aux.loss_ce: 0.1818, aux.acc_seg: 81.7617, loss: 0.6340 +2024-06-18 03:40:10,455 - mmseg - INFO - Iter [10250/80000] lr: 3.488e-05, eta: 1 day, 4:44:05, time: 1.334, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4139, decode.acc_seg: 83.9781, aux.loss_ce: 0.1652, aux.acc_seg: 84.1255, loss: 0.5791 +2024-06-18 03:41:16,619 - mmseg - INFO - Iter [10300/80000] lr: 3.485e-05, eta: 1 day, 4:41:57, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4604, decode.acc_seg: 82.6169, aux.loss_ce: 0.1840, aux.acc_seg: 82.6695, loss: 0.6444 +2024-06-18 03:42:22,978 - mmseg - INFO - Iter [10350/80000] lr: 3.483e-05, eta: 1 day, 4:39:50, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4489, decode.acc_seg: 82.5427, aux.loss_ce: 0.1801, aux.acc_seg: 82.6498, loss: 0.6290 +2024-06-18 03:43:29,148 - mmseg - INFO - Iter [10400/80000] lr: 3.480e-05, eta: 1 day, 4:37:43, time: 1.323, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4125, decode.acc_seg: 83.5517, aux.loss_ce: 0.1645, aux.acc_seg: 83.7367, loss: 0.5770 +2024-06-18 03:44:35,372 - mmseg - INFO - Iter [10450/80000] lr: 3.478e-05, eta: 1 day, 4:35:37, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4615, decode.acc_seg: 81.7986, aux.loss_ce: 0.1846, aux.acc_seg: 81.8311, loss: 0.6461 +2024-06-18 03:45:41,488 - mmseg - INFO - Iter [10500/80000] lr: 3.475e-05, eta: 1 day, 4:33:31, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4340, decode.acc_seg: 82.7425, aux.loss_ce: 0.1739, aux.acc_seg: 82.9290, loss: 0.6079 +2024-06-18 03:46:47,681 - mmseg - INFO - Iter [10550/80000] lr: 3.473e-05, eta: 1 day, 4:31:26, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4425, decode.acc_seg: 82.6485, aux.loss_ce: 0.1769, aux.acc_seg: 82.9166, loss: 0.6193 +2024-06-18 03:47:53,955 - mmseg - INFO - Iter [10600/80000] lr: 3.470e-05, eta: 1 day, 4:29:22, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4145, decode.acc_seg: 83.3645, aux.loss_ce: 0.1658, aux.acc_seg: 83.4177, loss: 0.5803 +2024-06-18 03:49:00,261 - mmseg - INFO - Iter [10650/80000] lr: 3.468e-05, eta: 1 day, 4:27:19, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4265, decode.acc_seg: 82.8168, aux.loss_ce: 0.1712, aux.acc_seg: 82.8339, loss: 0.5977 +2024-06-18 03:50:06,843 - mmseg - INFO - Iter [10700/80000] lr: 3.465e-05, eta: 1 day, 4:25:18, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4224, decode.acc_seg: 83.6408, aux.loss_ce: 0.1698, aux.acc_seg: 83.6113, loss: 0.5922 +2024-06-18 03:51:13,217 - mmseg - INFO - Iter [10750/80000] lr: 3.463e-05, eta: 1 day, 4:23:16, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4459, decode.acc_seg: 82.6585, aux.loss_ce: 0.1781, aux.acc_seg: 82.7032, loss: 0.6240 +2024-06-18 03:52:19,726 - mmseg - INFO - Iter [10800/80000] lr: 3.460e-05, eta: 1 day, 4:21:15, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4255, decode.acc_seg: 82.8249, aux.loss_ce: 0.1705, aux.acc_seg: 82.8578, loss: 0.5960 +2024-06-18 03:53:25,914 - mmseg - INFO - Iter [10850/80000] lr: 3.458e-05, eta: 1 day, 4:19:13, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4158, decode.acc_seg: 83.7555, aux.loss_ce: 0.1660, aux.acc_seg: 83.8777, loss: 0.5818 +2024-06-18 03:54:32,141 - mmseg - INFO - Iter [10900/80000] lr: 3.455e-05, eta: 1 day, 4:17:12, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4362, decode.acc_seg: 82.7935, aux.loss_ce: 0.1727, aux.acc_seg: 82.7992, loss: 0.6089 +2024-06-18 03:55:38,347 - mmseg - INFO - Iter [10950/80000] lr: 3.453e-05, eta: 1 day, 4:15:11, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4294, decode.acc_seg: 83.1770, aux.loss_ce: 0.1731, aux.acc_seg: 83.1798, loss: 0.6026 +2024-06-18 03:56:44,706 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 03:56:44,706 - mmseg - INFO - Iter [11000/80000] lr: 3.450e-05, eta: 1 day, 4:13:12, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4094, decode.acc_seg: 83.7296, aux.loss_ce: 0.1641, aux.acc_seg: 83.6677, loss: 0.5735 +2024-06-18 03:58:22,618 - mmseg - INFO - per class results: +2024-06-18 03:58:22,628 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.2 | 88.03 | +| building | 83.87 | 92.85 | +| sky | 93.79 | 95.3 | +| floor | 83.65 | 89.37 | +| tree | 75.8 | 88.85 | +| ceiling | 84.19 | 91.92 | +| road | 84.85 | 91.01 | +| bed | 90.39 | 97.28 | +| windowpane | 60.16 | 77.02 | +| grass | 68.31 | 87.09 | +| cabinet | 60.8 | 73.12 | +| sidewalk | 67.5 | 80.95 | +| person | 83.29 | 92.13 | +| earth | 32.16 | 42.9 | +| door | 56.26 | 76.04 | +| table | 61.87 | 72.45 | +| mountain | 57.56 | 67.42 | +| plant | 54.39 | 63.16 | +| curtain | 76.01 | 89.06 | +| chair | 59.54 | 71.86 | +| car | 82.82 | 93.86 | +| water | 58.12 | 72.69 | +| painting | 69.56 | 80.4 | +| sofa | 76.25 | 86.82 | +| shelf | 39.0 | 49.85 | +| house | 62.54 | 87.57 | +| sea | 71.02 | 92.08 | +| mirror | 68.73 | 77.82 | +| rug | 67.14 | 80.47 | +| field | 36.52 | 58.57 | +| armchair | 55.99 | 75.51 | +| seat | 62.17 | 88.55 | +| fence | 44.86 | 65.88 | +| desk | 50.23 | 61.05 | +| rock | 49.26 | 86.21 | +| wardrobe | 51.37 | 77.44 | +| lamp | 66.67 | 79.68 | +| bathtub | 80.34 | 84.16 | +| railing | 43.39 | 67.06 | +| cushion | 59.33 | 65.99 | +| base | 39.37 | 55.47 | +| box | 28.2 | 36.25 | +| column | 52.62 | 63.99 | +| signboard | 36.74 | 49.07 | +| chest of drawers | 40.86 | 74.56 | +| counter | 51.11 | 68.53 | +| sand | 42.85 | 63.34 | +| sink | 74.22 | 79.59 | +| skyscraper | 48.76 | 65.4 | +| fireplace | 69.73 | 92.8 | +| refrigerator | 81.23 | 90.07 | +| grandstand | 39.63 | 86.93 | +| path | 25.41 | 32.99 | +| stairs | 31.08 | 39.45 | +| runway | 70.44 | 91.93 | +| case | 39.62 | 46.11 | +| pool table | 93.73 | 97.06 | +| pillow | 57.72 | 68.8 | +| screen door | 71.28 | 82.23 | +| stairway | 48.33 | 58.96 | +| river | 28.9 | 36.33 | +| bridge | 36.52 | 41.68 | +| bookcase | 33.19 | 52.78 | +| blind | 5.31 | 5.32 | +| coffee table | 52.68 | 92.08 | +| toilet | 87.27 | 93.17 | +| flower | 35.65 | 49.26 | +| book | 44.81 | 71.89 | +| hill | 6.03 | 22.83 | +| bench | 48.83 | 64.77 | +| countertop | 60.39 | 74.46 | +| stove | 76.28 | 92.38 | +| palm | 53.13 | 69.73 | +| kitchen island | 38.34 | 86.27 | +| computer | 68.19 | 94.25 | +| swivel chair | 45.1 | 80.67 | +| boat | 61.4 | 78.99 | +| bar | 63.2 | 72.26 | +| arcade machine | 64.15 | 78.81 | +| hovel | 44.17 | 58.96 | +| bus | 90.97 | 93.97 | +| towel | 65.8 | 72.02 | +| light | 34.12 | 35.13 | +| truck | 40.89 | 56.84 | +| tower | 26.92 | 40.18 | +| chandelier | 65.14 | 78.25 | +| awning | 38.06 | 47.78 | +| streetlight | 24.73 | 35.8 | +| booth | 34.28 | 82.6 | +| television receiver | 67.19 | 83.02 | +| airplane | 63.27 | 69.17 | +| dirt track | 17.58 | 40.98 | +| apparel | 41.68 | 57.94 | +| pole | 21.86 | 29.23 | +| land | 0.0 | 0.0 | +| bannister | 14.57 | 24.86 | +| escalator | 51.35 | 81.72 | +| ottoman | 40.86 | 72.43 | +| bottle | 13.08 | 13.76 | +| buffet | 45.47 | 67.05 | +| poster | 25.19 | 55.91 | +| stage | 6.9 | 20.59 | +| van | 41.02 | 50.4 | +| ship | 46.33 | 48.4 | +| fountain | 53.51 | 56.47 | +| conveyer belt | 64.58 | 94.73 | +| canopy | 41.29 | 68.03 | +| washer | 70.15 | 72.49 | +| plaything | 24.8 | 29.87 | +| swimming pool | 71.3 | 90.66 | +| stool | 39.69 | 67.47 | +| barrel | 55.85 | 65.01 | +| basket | 31.68 | 45.73 | +| waterfall | 42.19 | 58.3 | +| tent | 93.46 | 97.46 | +| bag | 19.63 | 25.61 | +| minibike | 66.26 | 77.61 | +| cradle | 77.39 | 97.47 | +| oven | 51.31 | 69.72 | +| ball | 14.31 | 15.06 | +| food | 59.52 | 82.88 | +| step | 11.61 | 13.78 | +| tank | 57.25 | 69.95 | +| trade name | 2.48 | 2.53 | +| microwave | 85.04 | 94.01 | +| pot | 50.73 | 61.76 | +| animal | 70.32 | 75.41 | +| bicycle | 51.63 | 73.64 | +| lake | 0.0 | 0.0 | +| dishwasher | 56.74 | 64.24 | +| screen | 55.75 | 94.45 | +| blanket | 9.3 | 10.32 | +| sculpture | 55.75 | 69.52 | +| hood | 58.11 | 69.75 | +| sconce | 43.06 | 50.86 | +| vase | 34.37 | 55.66 | +| traffic light | 25.79 | 48.61 | +| tray | 9.68 | 17.96 | +| ashcan | 41.64 | 61.05 | +| fan | 60.59 | 73.86 | +| pier | 30.45 | 46.56 | +| crt screen | 0.8 | 2.01 | +| plate | 54.1 | 70.13 | +| monitor | 23.73 | 26.6 | +| bulletin board | 39.45 | 69.12 | +| shower | 0.0 | 0.0 | +| radiator | 56.01 | 66.54 | +| glass | 14.95 | 17.33 | +| clock | 28.76 | 33.91 | +| flag | 66.99 | 69.76 | ++---------------------+-------+-------+ +2024-06-18 03:58:22,628 - mmseg - INFO - Summary: +2024-06-18 03:58:22,628 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 83.8 | 49.96 | 64.01 | ++------+-------+-------+ +2024-06-18 03:58:22,629 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 03:58:22,629 - mmseg - INFO - Iter(val) [250] aAcc: 0.8380, mIoU: 0.4996, mAcc: 0.6401, IoU.wall: 0.7920, IoU.building: 0.8387, IoU.sky: 0.9379, IoU.floor: 0.8365, IoU.tree: 0.7580, IoU.ceiling: 0.8419, IoU.road: 0.8485, IoU.bed : 0.9039, IoU.windowpane: 0.6016, IoU.grass: 0.6831, IoU.cabinet: 0.6080, IoU.sidewalk: 0.6750, IoU.person: 0.8329, IoU.earth: 0.3216, IoU.door: 0.5626, IoU.table: 0.6187, IoU.mountain: 0.5756, IoU.plant: 0.5439, IoU.curtain: 0.7601, IoU.chair: 0.5954, IoU.car: 0.8282, IoU.water: 0.5812, IoU.painting: 0.6956, IoU.sofa: 0.7625, IoU.shelf: 0.3900, IoU.house: 0.6254, IoU.sea: 0.7102, IoU.mirror: 0.6873, IoU.rug: 0.6714, IoU.field: 0.3652, IoU.armchair: 0.5599, IoU.seat: 0.6217, IoU.fence: 0.4486, IoU.desk: 0.5023, IoU.rock: 0.4926, IoU.wardrobe: 0.5137, IoU.lamp: 0.6667, IoU.bathtub: 0.8034, IoU.railing: 0.4339, IoU.cushion: 0.5933, IoU.base: 0.3937, IoU.box: 0.2820, IoU.column: 0.5262, IoU.signboard: 0.3674, IoU.chest of drawers: 0.4086, IoU.counter: 0.5111, IoU.sand: 0.4285, IoU.sink: 0.7422, IoU.skyscraper: 0.4876, IoU.fireplace: 0.6973, IoU.refrigerator: 0.8123, IoU.grandstand: 0.3963, IoU.path: 0.2541, IoU.stairs: 0.3108, IoU.runway: 0.7044, IoU.case: 0.3962, IoU.pool table: 0.9373, IoU.pillow: 0.5772, IoU.screen door: 0.7128, IoU.stairway: 0.4833, IoU.river: 0.2890, IoU.bridge: 0.3652, IoU.bookcase: 0.3319, IoU.blind: 0.0531, IoU.coffee table: 0.5268, IoU.toilet: 0.8727, IoU.flower: 0.3565, IoU.book: 0.4481, IoU.hill: 0.0603, IoU.bench: 0.4883, IoU.countertop: 0.6039, IoU.stove: 0.7628, IoU.palm: 0.5313, IoU.kitchen island: 0.3834, IoU.computer: 0.6819, IoU.swivel chair: 0.4510, IoU.boat: 0.6140, IoU.bar: 0.6320, IoU.arcade machine: 0.6415, IoU.hovel: 0.4417, IoU.bus: 0.9097, IoU.towel: 0.6580, IoU.light: 0.3412, IoU.truck: 0.4089, IoU.tower: 0.2692, IoU.chandelier: 0.6514, IoU.awning: 0.3806, IoU.streetlight: 0.2473, IoU.booth: 0.3428, IoU.television receiver: 0.6719, IoU.airplane: 0.6327, IoU.dirt track: 0.1758, IoU.apparel: 0.4168, IoU.pole: 0.2186, IoU.land: 0.0000, IoU.bannister: 0.1457, IoU.escalator: 0.5135, IoU.ottoman: 0.4086, IoU.bottle: 0.1308, IoU.buffet: 0.4547, IoU.poster: 0.2519, IoU.stage: 0.0690, IoU.van: 0.4102, IoU.ship: 0.4633, IoU.fountain: 0.5351, IoU.conveyer belt: 0.6458, IoU.canopy: 0.4129, IoU.washer: 0.7015, IoU.plaything: 0.2480, IoU.swimming pool: 0.7130, IoU.stool: 0.3969, IoU.barrel: 0.5585, IoU.basket: 0.3168, IoU.waterfall: 0.4219, IoU.tent: 0.9346, IoU.bag: 0.1963, IoU.minibike: 0.6626, IoU.cradle: 0.7739, IoU.oven: 0.5131, IoU.ball: 0.1431, IoU.food: 0.5952, IoU.step: 0.1161, IoU.tank: 0.5725, IoU.trade name: 0.0248, IoU.microwave: 0.8504, IoU.pot: 0.5073, IoU.animal: 0.7032, IoU.bicycle: 0.5163, IoU.lake: 0.0000, IoU.dishwasher: 0.5674, IoU.screen: 0.5575, IoU.blanket: 0.0930, IoU.sculpture: 0.5575, IoU.hood: 0.5811, IoU.sconce: 0.4306, IoU.vase: 0.3437, IoU.traffic light: 0.2579, IoU.tray: 0.0968, IoU.ashcan: 0.4164, IoU.fan: 0.6059, IoU.pier: 0.3045, IoU.crt screen: 0.0080, IoU.plate: 0.5410, IoU.monitor: 0.2373, IoU.bulletin board: 0.3945, IoU.shower: 0.0000, IoU.radiator: 0.5601, IoU.glass: 0.1495, IoU.clock: 0.2876, IoU.flag: 0.6699, Acc.wall: 0.8803, Acc.building: 0.9285, Acc.sky: 0.9530, Acc.floor: 0.8937, Acc.tree: 0.8885, Acc.ceiling: 0.9192, Acc.road: 0.9101, Acc.bed : 0.9728, Acc.windowpane: 0.7702, Acc.grass: 0.8709, Acc.cabinet: 0.7312, Acc.sidewalk: 0.8095, Acc.person: 0.9213, Acc.earth: 0.4290, Acc.door: 0.7604, Acc.table: 0.7245, Acc.mountain: 0.6742, Acc.plant: 0.6316, Acc.curtain: 0.8906, Acc.chair: 0.7186, Acc.car: 0.9386, Acc.water: 0.7269, Acc.painting: 0.8040, Acc.sofa: 0.8682, Acc.shelf: 0.4985, Acc.house: 0.8757, Acc.sea: 0.9208, Acc.mirror: 0.7782, Acc.rug: 0.8047, Acc.field: 0.5857, Acc.armchair: 0.7551, Acc.seat: 0.8855, Acc.fence: 0.6588, Acc.desk: 0.6105, Acc.rock: 0.8621, Acc.wardrobe: 0.7744, Acc.lamp: 0.7968, Acc.bathtub: 0.8416, Acc.railing: 0.6706, Acc.cushion: 0.6599, Acc.base: 0.5547, Acc.box: 0.3625, Acc.column: 0.6399, Acc.signboard: 0.4907, Acc.chest of drawers: 0.7456, Acc.counter: 0.6853, Acc.sand: 0.6334, Acc.sink: 0.7959, Acc.skyscraper: 0.6540, Acc.fireplace: 0.9280, Acc.refrigerator: 0.9007, Acc.grandstand: 0.8693, Acc.path: 0.3299, Acc.stairs: 0.3945, Acc.runway: 0.9193, Acc.case: 0.4611, Acc.pool table: 0.9706, Acc.pillow: 0.6880, Acc.screen door: 0.8223, Acc.stairway: 0.5896, Acc.river: 0.3633, Acc.bridge: 0.4168, Acc.bookcase: 0.5278, Acc.blind: 0.0532, Acc.coffee table: 0.9208, Acc.toilet: 0.9317, Acc.flower: 0.4926, Acc.book: 0.7189, Acc.hill: 0.2283, Acc.bench: 0.6477, Acc.countertop: 0.7446, Acc.stove: 0.9238, Acc.palm: 0.6973, Acc.kitchen island: 0.8627, Acc.computer: 0.9425, Acc.swivel chair: 0.8067, Acc.boat: 0.7899, Acc.bar: 0.7226, Acc.arcade machine: 0.7881, Acc.hovel: 0.5896, Acc.bus: 0.9397, Acc.towel: 0.7202, Acc.light: 0.3513, Acc.truck: 0.5684, Acc.tower: 0.4018, Acc.chandelier: 0.7825, Acc.awning: 0.4778, Acc.streetlight: 0.3580, Acc.booth: 0.8260, Acc.television receiver: 0.8302, Acc.airplane: 0.6917, Acc.dirt track: 0.4098, Acc.apparel: 0.5794, Acc.pole: 0.2923, Acc.land: 0.0000, Acc.bannister: 0.2486, Acc.escalator: 0.8172, Acc.ottoman: 0.7243, Acc.bottle: 0.1376, Acc.buffet: 0.6705, Acc.poster: 0.5591, Acc.stage: 0.2059, Acc.van: 0.5040, Acc.ship: 0.4840, Acc.fountain: 0.5647, Acc.conveyer belt: 0.9473, Acc.canopy: 0.6803, Acc.washer: 0.7249, Acc.plaything: 0.2987, Acc.swimming pool: 0.9066, Acc.stool: 0.6747, Acc.barrel: 0.6501, Acc.basket: 0.4573, Acc.waterfall: 0.5830, Acc.tent: 0.9746, Acc.bag: 0.2561, Acc.minibike: 0.7761, Acc.cradle: 0.9747, Acc.oven: 0.6972, Acc.ball: 0.1506, Acc.food: 0.8288, Acc.step: 0.1378, Acc.tank: 0.6995, Acc.trade name: 0.0253, Acc.microwave: 0.9401, Acc.pot: 0.6176, Acc.animal: 0.7541, Acc.bicycle: 0.7364, Acc.lake: 0.0000, Acc.dishwasher: 0.6424, Acc.screen: 0.9445, Acc.blanket: 0.1032, Acc.sculpture: 0.6952, Acc.hood: 0.6975, Acc.sconce: 0.5086, Acc.vase: 0.5566, Acc.traffic light: 0.4861, Acc.tray: 0.1796, Acc.ashcan: 0.6105, Acc.fan: 0.7386, Acc.pier: 0.4656, Acc.crt screen: 0.0201, Acc.plate: 0.7013, Acc.monitor: 0.2660, Acc.bulletin board: 0.6912, Acc.shower: 0.0000, Acc.radiator: 0.6654, Acc.glass: 0.1733, Acc.clock: 0.3391, Acc.flag: 0.6976 +2024-06-18 03:59:29,376 - mmseg - INFO - Iter [11050/80000] lr: 3.448e-05, eta: 1 day, 4:21:26, time: 3.293, data_time: 1.975, memory: 70498, decode.loss_ce: 0.4205, decode.acc_seg: 84.0562, aux.loss_ce: 0.1686, aux.acc_seg: 84.0068, loss: 0.5890 +2024-06-18 04:00:35,670 - mmseg - INFO - Iter [11100/80000] lr: 3.445e-05, eta: 1 day, 4:19:24, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4214, decode.acc_seg: 83.8852, aux.loss_ce: 0.1703, aux.acc_seg: 83.7058, loss: 0.5917 +2024-06-18 04:01:42,495 - mmseg - INFO - Iter [11150/80000] lr: 3.443e-05, eta: 1 day, 4:17:26, time: 1.336, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4261, decode.acc_seg: 82.8127, aux.loss_ce: 0.1707, aux.acc_seg: 82.8966, loss: 0.5968 +2024-06-18 04:02:48,700 - mmseg - INFO - Iter [11200/80000] lr: 3.440e-05, eta: 1 day, 4:15:25, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4227, decode.acc_seg: 83.5142, aux.loss_ce: 0.1691, aux.acc_seg: 83.4730, loss: 0.5918 +2024-06-18 04:03:55,096 - mmseg - INFO - Iter [11250/80000] lr: 3.438e-05, eta: 1 day, 4:13:25, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4470, decode.acc_seg: 82.5626, aux.loss_ce: 0.1779, aux.acc_seg: 82.8319, loss: 0.6249 +2024-06-18 04:05:01,354 - mmseg - INFO - Iter [11300/80000] lr: 3.435e-05, eta: 1 day, 4:11:24, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4182, decode.acc_seg: 83.9137, aux.loss_ce: 0.1679, aux.acc_seg: 84.0626, loss: 0.5861 +2024-06-18 04:06:07,697 - mmseg - INFO - Iter [11350/80000] lr: 3.433e-05, eta: 1 day, 4:09:25, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4336, decode.acc_seg: 83.4656, aux.loss_ce: 0.1736, aux.acc_seg: 83.4785, loss: 0.6072 +2024-06-18 04:07:16,787 - mmseg - INFO - Iter [11400/80000] lr: 3.430e-05, eta: 1 day, 4:07:43, time: 1.382, data_time: 0.058, memory: 70498, decode.loss_ce: 0.3897, decode.acc_seg: 84.7739, aux.loss_ce: 0.1566, aux.acc_seg: 84.7627, loss: 0.5463 +2024-06-18 04:08:22,931 - mmseg - INFO - Iter [11450/80000] lr: 3.428e-05, eta: 1 day, 4:05:43, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4063, decode.acc_seg: 84.2589, aux.loss_ce: 0.1627, aux.acc_seg: 84.2300, loss: 0.5690 +2024-06-18 04:09:29,401 - mmseg - INFO - Iter [11500/80000] lr: 3.425e-05, eta: 1 day, 4:03:46, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3783, decode.acc_seg: 85.3329, aux.loss_ce: 0.1515, aux.acc_seg: 85.2499, loss: 0.5298 +2024-06-18 04:10:35,757 - mmseg - INFO - Iter [11550/80000] lr: 3.423e-05, eta: 1 day, 4:01:48, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3847, decode.acc_seg: 84.4139, aux.loss_ce: 0.1545, aux.acc_seg: 84.4196, loss: 0.5392 +2024-06-18 04:11:41,860 - mmseg - INFO - Iter [11600/80000] lr: 3.420e-05, eta: 1 day, 3:59:50, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3989, decode.acc_seg: 84.0489, aux.loss_ce: 0.1607, aux.acc_seg: 83.9903, loss: 0.5596 +2024-06-18 04:12:48,032 - mmseg - INFO - Iter [11650/80000] lr: 3.418e-05, eta: 1 day, 3:57:52, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4154, decode.acc_seg: 83.6175, aux.loss_ce: 0.1659, aux.acc_seg: 83.5293, loss: 0.5813 +2024-06-18 04:13:54,295 - mmseg - INFO - Iter [11700/80000] lr: 3.415e-05, eta: 1 day, 3:55:55, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4406, decode.acc_seg: 83.1928, aux.loss_ce: 0.1757, aux.acc_seg: 83.2542, loss: 0.6162 +2024-06-18 04:15:00,459 - mmseg - INFO - Iter [11750/80000] lr: 3.413e-05, eta: 1 day, 3:53:58, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4044, decode.acc_seg: 84.0103, aux.loss_ce: 0.1629, aux.acc_seg: 84.0528, loss: 0.5674 +2024-06-18 04:16:06,863 - mmseg - INFO - Iter [11800/80000] lr: 3.410e-05, eta: 1 day, 3:52:03, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4268, decode.acc_seg: 83.7470, aux.loss_ce: 0.1701, aux.acc_seg: 83.6535, loss: 0.5969 +2024-06-18 04:17:13,253 - mmseg - INFO - Iter [11850/80000] lr: 3.408e-05, eta: 1 day, 3:50:08, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4273, decode.acc_seg: 83.8261, aux.loss_ce: 0.1702, aux.acc_seg: 83.8954, loss: 0.5976 +2024-06-18 04:18:19,468 - mmseg - INFO - Iter [11900/80000] lr: 3.405e-05, eta: 1 day, 3:48:13, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3907, decode.acc_seg: 84.4161, aux.loss_ce: 0.1561, aux.acc_seg: 84.3358, loss: 0.5468 +2024-06-18 04:19:26,227 - mmseg - INFO - Iter [11950/80000] lr: 3.403e-05, eta: 1 day, 3:46:21, time: 1.335, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4012, decode.acc_seg: 84.5863, aux.loss_ce: 0.1611, aux.acc_seg: 84.4012, loss: 0.5623 +2024-06-18 04:20:32,503 - mmseg - INFO - Saving checkpoint at 12000 iterations +2024-06-18 04:22:16,867 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 04:22:16,867 - mmseg - INFO - Iter [12000/80000] lr: 3.400e-05, eta: 1 day, 3:54:18, time: 3.413, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4133, decode.acc_seg: 83.5520, aux.loss_ce: 0.1638, aux.acc_seg: 83.6291, loss: 0.5771 +2024-06-18 04:23:52,421 - mmseg - INFO - per class results: +2024-06-18 04:23:52,427 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.8 | 88.38 | +| building | 84.34 | 92.89 | +| sky | 94.4 | 97.64 | +| floor | 83.42 | 89.96 | +| tree | 76.66 | 87.04 | +| ceiling | 85.21 | 94.12 | +| road | 81.39 | 91.64 | +| bed | 90.4 | 96.29 | +| windowpane | 63.21 | 79.86 | +| grass | 67.06 | 75.98 | +| cabinet | 62.35 | 74.63 | +| sidewalk | 61.49 | 73.37 | +| person | 83.04 | 93.07 | +| earth | 35.85 | 50.69 | +| door | 52.69 | 63.51 | +| table | 61.8 | 78.13 | +| mountain | 61.51 | 70.59 | +| plant | 54.43 | 67.67 | +| curtain | 78.16 | 87.12 | +| chair | 62.79 | 75.81 | +| car | 84.46 | 92.77 | +| water | 54.1 | 63.67 | +| painting | 73.5 | 87.61 | +| sofa | 68.93 | 94.06 | +| shelf | 43.78 | 68.68 | +| house | 60.61 | 79.19 | +| sea | 69.42 | 89.06 | +| mirror | 72.2 | 83.56 | +| rug | 66.02 | 77.04 | +| field | 39.56 | 75.18 | +| armchair | 42.02 | 52.34 | +| seat | 63.58 | 88.59 | +| fence | 48.74 | 67.26 | +| desk | 48.74 | 69.01 | +| rock | 54.48 | 84.93 | +| wardrobe | 50.33 | 77.55 | +| lamp | 66.26 | 77.81 | +| bathtub | 79.86 | 86.12 | +| railing | 40.59 | 66.79 | +| cushion | 53.87 | 59.11 | +| base | 44.4 | 58.55 | +| box | 24.8 | 30.41 | +| column | 51.52 | 59.91 | +| signboard | 34.86 | 41.97 | +| chest of drawers | 42.4 | 65.06 | +| counter | 50.74 | 61.37 | +| sand | 51.92 | 87.34 | +| sink | 70.52 | 83.74 | +| skyscraper | 48.54 | 63.42 | +| fireplace | 68.16 | 95.33 | +| refrigerator | 74.91 | 87.31 | +| grandstand | 48.72 | 88.97 | +| path | 24.5 | 31.76 | +| stairs | 28.87 | 39.07 | +| runway | 67.86 | 92.59 | +| case | 53.07 | 77.27 | +| pool table | 94.03 | 97.27 | +| pillow | 63.15 | 81.72 | +| screen door | 77.52 | 86.49 | +| stairway | 43.52 | 57.57 | +| river | 18.22 | 60.34 | +| bridge | 70.71 | 88.75 | +| bookcase | 37.86 | 56.97 | +| blind | 35.56 | 36.69 | +| coffee table | 57.37 | 91.51 | +| toilet | 85.81 | 91.91 | +| flower | 27.6 | 29.76 | +| book | 46.44 | 67.27 | +| hill | 4.73 | 8.95 | +| bench | 51.79 | 54.82 | +| countertop | 58.96 | 66.91 | +| stove | 81.96 | 87.95 | +| palm | 56.06 | 78.08 | +| kitchen island | 35.33 | 91.45 | +| computer | 73.51 | 90.29 | +| swivel chair | 52.94 | 73.88 | +| boat | 62.51 | 88.56 | +| bar | 60.05 | 70.31 | +| arcade machine | 64.21 | 68.17 | +| hovel | 11.94 | 12.6 | +| bus | 88.8 | 96.06 | +| towel | 58.83 | 67.56 | +| light | 46.2 | 48.73 | +| truck | 32.16 | 35.13 | +| tower | 15.41 | 22.53 | +| chandelier | 67.34 | 82.89 | +| awning | 41.49 | 60.7 | +| streetlight | 23.46 | 31.48 | +| booth | 43.53 | 58.54 | +| television receiver | 73.51 | 81.46 | +| airplane | 62.27 | 68.64 | +| dirt track | 15.32 | 61.98 | +| apparel | 43.28 | 54.74 | +| pole | 19.47 | 24.16 | +| land | 0.16 | 0.29 | +| bannister | 5.16 | 6.17 | +| escalator | 51.63 | 73.29 | +| ottoman | 47.04 | 64.26 | +| bottle | 19.35 | 24.41 | +| buffet | 38.84 | 46.49 | +| poster | 17.31 | 18.65 | +| stage | 19.99 | 35.96 | +| van | 37.1 | 44.82 | +| ship | 20.87 | 20.94 | +| fountain | 21.94 | 22.56 | +| conveyer belt | 64.22 | 93.68 | +| canopy | 60.6 | 80.01 | +| washer | 65.52 | 73.73 | +| plaything | 15.21 | 18.5 | +| swimming pool | 66.24 | 90.76 | +| stool | 49.88 | 59.72 | +| barrel | 49.01 | 65.05 | +| basket | 28.85 | 48.19 | +| waterfall | 45.64 | 64.94 | +| tent | 92.26 | 97.54 | +| bag | 10.94 | 11.78 | +| minibike | 63.79 | 76.73 | +| cradle | 68.33 | 99.37 | +| oven | 57.47 | 65.53 | +| ball | 23.55 | 68.98 | +| food | 54.28 | 64.66 | +| step | 7.39 | 7.69 | +| tank | 66.86 | 96.87 | +| trade name | 30.52 | 37.33 | +| microwave | 87.23 | 91.92 | +| pot | 51.23 | 66.99 | +| animal | 63.88 | 65.82 | +| bicycle | 51.97 | 73.91 | +| lake | 0.0 | 0.0 | +| dishwasher | 60.64 | 76.09 | +| screen | 66.43 | 93.8 | +| blanket | 7.1 | 8.26 | +| sculpture | 56.58 | 62.54 | +| hood | 60.77 | 68.73 | +| sconce | 42.82 | 49.2 | +| vase | 37.44 | 51.71 | +| traffic light | 26.67 | 49.08 | +| tray | 7.01 | 10.02 | +| ashcan | 40.72 | 57.79 | +| fan | 55.81 | 65.87 | +| pier | 34.22 | 43.38 | +| crt screen | 1.31 | 4.06 | +| plate | 50.23 | 68.4 | +| monitor | 7.41 | 8.43 | +| bulletin board | 40.72 | 50.7 | +| shower | 0.43 | 0.76 | +| radiator | 61.19 | 71.39 | +| glass | 16.59 | 18.82 | +| clock | 30.18 | 31.98 | +| flag | 67.89 | 71.26 | ++---------------------+-------+-------+ +2024-06-18 04:23:52,427 - mmseg - INFO - Summary: +2024-06-18 04:23:52,427 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 84.1 | 50.11 | 63.37 | ++------+-------+-------+ +2024-06-18 04:23:52,428 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 04:23:52,428 - mmseg - INFO - Iter(val) [250] aAcc: 0.8410, mIoU: 0.5011, mAcc: 0.6337, IoU.wall: 0.7980, IoU.building: 0.8434, IoU.sky: 0.9440, IoU.floor: 0.8342, IoU.tree: 0.7666, IoU.ceiling: 0.8521, IoU.road: 0.8139, IoU.bed : 0.9040, IoU.windowpane: 0.6321, IoU.grass: 0.6706, IoU.cabinet: 0.6235, IoU.sidewalk: 0.6149, IoU.person: 0.8304, IoU.earth: 0.3585, IoU.door: 0.5269, IoU.table: 0.6180, IoU.mountain: 0.6151, IoU.plant: 0.5443, IoU.curtain: 0.7816, IoU.chair: 0.6279, IoU.car: 0.8446, IoU.water: 0.5410, IoU.painting: 0.7350, IoU.sofa: 0.6893, IoU.shelf: 0.4378, IoU.house: 0.6061, IoU.sea: 0.6942, IoU.mirror: 0.7220, IoU.rug: 0.6602, IoU.field: 0.3956, IoU.armchair: 0.4202, IoU.seat: 0.6358, IoU.fence: 0.4874, IoU.desk: 0.4874, IoU.rock: 0.5448, IoU.wardrobe: 0.5033, IoU.lamp: 0.6626, IoU.bathtub: 0.7986, IoU.railing: 0.4059, IoU.cushion: 0.5387, IoU.base: 0.4440, IoU.box: 0.2480, IoU.column: 0.5152, IoU.signboard: 0.3486, IoU.chest of drawers: 0.4240, IoU.counter: 0.5074, IoU.sand: 0.5192, IoU.sink: 0.7052, IoU.skyscraper: 0.4854, IoU.fireplace: 0.6816, IoU.refrigerator: 0.7491, IoU.grandstand: 0.4872, IoU.path: 0.2450, IoU.stairs: 0.2887, IoU.runway: 0.6786, IoU.case: 0.5307, IoU.pool table: 0.9403, IoU.pillow: 0.6315, IoU.screen door: 0.7752, IoU.stairway: 0.4352, IoU.river: 0.1822, IoU.bridge: 0.7071, IoU.bookcase: 0.3786, IoU.blind: 0.3556, IoU.coffee table: 0.5737, IoU.toilet: 0.8581, IoU.flower: 0.2760, IoU.book: 0.4644, IoU.hill: 0.0473, IoU.bench: 0.5179, IoU.countertop: 0.5896, IoU.stove: 0.8196, IoU.palm: 0.5606, IoU.kitchen island: 0.3533, IoU.computer: 0.7351, IoU.swivel chair: 0.5294, IoU.boat: 0.6251, IoU.bar: 0.6005, IoU.arcade machine: 0.6421, IoU.hovel: 0.1194, IoU.bus: 0.8880, IoU.towel: 0.5883, IoU.light: 0.4620, IoU.truck: 0.3216, IoU.tower: 0.1541, IoU.chandelier: 0.6734, IoU.awning: 0.4149, IoU.streetlight: 0.2346, IoU.booth: 0.4353, IoU.television receiver: 0.7351, IoU.airplane: 0.6227, IoU.dirt track: 0.1532, IoU.apparel: 0.4328, IoU.pole: 0.1947, IoU.land: 0.0016, IoU.bannister: 0.0516, IoU.escalator: 0.5163, IoU.ottoman: 0.4704, IoU.bottle: 0.1935, IoU.buffet: 0.3884, IoU.poster: 0.1731, IoU.stage: 0.1999, IoU.van: 0.3710, IoU.ship: 0.2087, IoU.fountain: 0.2194, IoU.conveyer belt: 0.6422, IoU.canopy: 0.6060, IoU.washer: 0.6552, IoU.plaything: 0.1521, IoU.swimming pool: 0.6624, IoU.stool: 0.4988, IoU.barrel: 0.4901, IoU.basket: 0.2885, IoU.waterfall: 0.4564, IoU.tent: 0.9226, IoU.bag: 0.1094, IoU.minibike: 0.6379, IoU.cradle: 0.6833, IoU.oven: 0.5747, IoU.ball: 0.2355, IoU.food: 0.5428, IoU.step: 0.0739, IoU.tank: 0.6686, IoU.trade name: 0.3052, IoU.microwave: 0.8723, IoU.pot: 0.5123, IoU.animal: 0.6388, IoU.bicycle: 0.5197, IoU.lake: 0.0000, IoU.dishwasher: 0.6064, IoU.screen: 0.6643, IoU.blanket: 0.0710, IoU.sculpture: 0.5658, IoU.hood: 0.6077, IoU.sconce: 0.4282, IoU.vase: 0.3744, IoU.traffic light: 0.2667, IoU.tray: 0.0701, IoU.ashcan: 0.4072, IoU.fan: 0.5581, IoU.pier: 0.3422, IoU.crt screen: 0.0131, IoU.plate: 0.5023, IoU.monitor: 0.0741, IoU.bulletin board: 0.4072, IoU.shower: 0.0043, IoU.radiator: 0.6119, IoU.glass: 0.1659, IoU.clock: 0.3018, IoU.flag: 0.6789, Acc.wall: 0.8838, Acc.building: 0.9289, Acc.sky: 0.9764, Acc.floor: 0.8996, Acc.tree: 0.8704, Acc.ceiling: 0.9412, Acc.road: 0.9164, Acc.bed : 0.9629, Acc.windowpane: 0.7986, Acc.grass: 0.7598, Acc.cabinet: 0.7463, Acc.sidewalk: 0.7337, Acc.person: 0.9307, Acc.earth: 0.5069, Acc.door: 0.6351, Acc.table: 0.7813, Acc.mountain: 0.7059, Acc.plant: 0.6767, Acc.curtain: 0.8712, Acc.chair: 0.7581, Acc.car: 0.9277, Acc.water: 0.6367, Acc.painting: 0.8761, Acc.sofa: 0.9406, Acc.shelf: 0.6868, Acc.house: 0.7919, Acc.sea: 0.8906, Acc.mirror: 0.8356, Acc.rug: 0.7704, Acc.field: 0.7518, Acc.armchair: 0.5234, Acc.seat: 0.8859, Acc.fence: 0.6726, Acc.desk: 0.6901, Acc.rock: 0.8493, Acc.wardrobe: 0.7755, Acc.lamp: 0.7781, Acc.bathtub: 0.8612, Acc.railing: 0.6679, Acc.cushion: 0.5911, Acc.base: 0.5855, Acc.box: 0.3041, Acc.column: 0.5991, Acc.signboard: 0.4197, Acc.chest of drawers: 0.6506, Acc.counter: 0.6137, Acc.sand: 0.8734, Acc.sink: 0.8374, Acc.skyscraper: 0.6342, Acc.fireplace: 0.9533, Acc.refrigerator: 0.8731, Acc.grandstand: 0.8897, Acc.path: 0.3176, Acc.stairs: 0.3907, Acc.runway: 0.9259, Acc.case: 0.7727, Acc.pool table: 0.9727, Acc.pillow: 0.8172, Acc.screen door: 0.8649, Acc.stairway: 0.5757, Acc.river: 0.6034, Acc.bridge: 0.8875, Acc.bookcase: 0.5697, Acc.blind: 0.3669, Acc.coffee table: 0.9151, Acc.toilet: 0.9191, Acc.flower: 0.2976, Acc.book: 0.6727, Acc.hill: 0.0895, Acc.bench: 0.5482, Acc.countertop: 0.6691, Acc.stove: 0.8795, Acc.palm: 0.7808, Acc.kitchen island: 0.9145, Acc.computer: 0.9029, Acc.swivel chair: 0.7388, Acc.boat: 0.8856, Acc.bar: 0.7031, Acc.arcade machine: 0.6817, Acc.hovel: 0.1260, Acc.bus: 0.9606, Acc.towel: 0.6756, Acc.light: 0.4873, Acc.truck: 0.3513, Acc.tower: 0.2253, Acc.chandelier: 0.8289, Acc.awning: 0.6070, Acc.streetlight: 0.3148, Acc.booth: 0.5854, Acc.television receiver: 0.8146, Acc.airplane: 0.6864, Acc.dirt track: 0.6198, Acc.apparel: 0.5474, Acc.pole: 0.2416, Acc.land: 0.0029, Acc.bannister: 0.0617, Acc.escalator: 0.7329, Acc.ottoman: 0.6426, Acc.bottle: 0.2441, Acc.buffet: 0.4649, Acc.poster: 0.1865, Acc.stage: 0.3596, Acc.van: 0.4482, Acc.ship: 0.2094, Acc.fountain: 0.2256, Acc.conveyer belt: 0.9368, Acc.canopy: 0.8001, Acc.washer: 0.7373, Acc.plaything: 0.1850, Acc.swimming pool: 0.9076, Acc.stool: 0.5972, Acc.barrel: 0.6505, Acc.basket: 0.4819, Acc.waterfall: 0.6494, Acc.tent: 0.9754, Acc.bag: 0.1178, Acc.minibike: 0.7673, Acc.cradle: 0.9937, Acc.oven: 0.6553, Acc.ball: 0.6898, Acc.food: 0.6466, Acc.step: 0.0769, Acc.tank: 0.9687, Acc.trade name: 0.3733, Acc.microwave: 0.9192, Acc.pot: 0.6699, Acc.animal: 0.6582, Acc.bicycle: 0.7391, Acc.lake: 0.0000, Acc.dishwasher: 0.7609, Acc.screen: 0.9380, Acc.blanket: 0.0826, Acc.sculpture: 0.6254, Acc.hood: 0.6873, Acc.sconce: 0.4920, Acc.vase: 0.5171, Acc.traffic light: 0.4908, Acc.tray: 0.1002, Acc.ashcan: 0.5779, Acc.fan: 0.6587, Acc.pier: 0.4338, Acc.crt screen: 0.0406, Acc.plate: 0.6840, Acc.monitor: 0.0843, Acc.bulletin board: 0.5070, Acc.shower: 0.0076, Acc.radiator: 0.7139, Acc.glass: 0.1882, Acc.clock: 0.3198, Acc.flag: 0.7126 +2024-06-18 04:24:59,071 - mmseg - INFO - Iter [12050/80000] lr: 3.398e-05, eta: 1 day, 4:01:23, time: 3.244, data_time: 1.927, memory: 70498, decode.loss_ce: 0.4042, decode.acc_seg: 83.9383, aux.loss_ce: 0.1631, aux.acc_seg: 83.8843, loss: 0.5673 +2024-06-18 04:26:05,485 - mmseg - INFO - Iter [12100/80000] lr: 3.395e-05, eta: 1 day, 3:59:25, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3937, decode.acc_seg: 84.1234, aux.loss_ce: 0.1578, aux.acc_seg: 84.2059, loss: 0.5516 +2024-06-18 04:27:12,071 - mmseg - INFO - Iter [12150/80000] lr: 3.393e-05, eta: 1 day, 3:57:28, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3874, decode.acc_seg: 84.5252, aux.loss_ce: 0.1558, aux.acc_seg: 84.3596, loss: 0.5432 +2024-06-18 04:28:18,500 - mmseg - INFO - Iter [12200/80000] lr: 3.390e-05, eta: 1 day, 3:55:31, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4256, decode.acc_seg: 83.6181, aux.loss_ce: 0.1711, aux.acc_seg: 83.5012, loss: 0.5967 +2024-06-18 04:29:24,732 - mmseg - INFO - Iter [12250/80000] lr: 3.388e-05, eta: 1 day, 3:53:33, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4036, decode.acc_seg: 84.1379, aux.loss_ce: 0.1621, aux.acc_seg: 84.1972, loss: 0.5657 +2024-06-18 04:30:30,974 - mmseg - INFO - Iter [12300/80000] lr: 3.385e-05, eta: 1 day, 3:51:35, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3800, decode.acc_seg: 85.1511, aux.loss_ce: 0.1518, aux.acc_seg: 85.0705, loss: 0.5318 +2024-06-18 04:31:37,199 - mmseg - INFO - Iter [12350/80000] lr: 3.383e-05, eta: 1 day, 3:49:38, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4100, decode.acc_seg: 84.5049, aux.loss_ce: 0.1654, aux.acc_seg: 84.2929, loss: 0.5754 +2024-06-18 04:32:43,360 - mmseg - INFO - Iter [12400/80000] lr: 3.380e-05, eta: 1 day, 3:47:41, time: 1.323, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4316, decode.acc_seg: 82.7266, aux.loss_ce: 0.1729, aux.acc_seg: 82.6695, loss: 0.6046 +2024-06-18 04:33:49,646 - mmseg - INFO - Iter [12450/80000] lr: 3.378e-05, eta: 1 day, 3:45:45, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4300, decode.acc_seg: 83.1623, aux.loss_ce: 0.1726, aux.acc_seg: 83.1177, loss: 0.6026 +2024-06-18 04:34:56,010 - mmseg - INFO - Iter [12500/80000] lr: 3.375e-05, eta: 1 day, 3:43:50, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4109, decode.acc_seg: 84.3725, aux.loss_ce: 0.1652, aux.acc_seg: 84.1530, loss: 0.5761 +2024-06-18 04:36:02,214 - mmseg - INFO - Iter [12550/80000] lr: 3.373e-05, eta: 1 day, 3:41:55, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4067, decode.acc_seg: 84.1629, aux.loss_ce: 0.1628, aux.acc_seg: 84.2002, loss: 0.5695 +2024-06-18 04:37:08,379 - mmseg - INFO - Iter [12600/80000] lr: 3.370e-05, eta: 1 day, 3:39:59, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4340, decode.acc_seg: 83.1602, aux.loss_ce: 0.1742, aux.acc_seg: 83.0405, loss: 0.6082 +2024-06-18 04:38:16,972 - mmseg - INFO - Iter [12650/80000] lr: 3.368e-05, eta: 1 day, 3:38:17, time: 1.372, data_time: 0.051, memory: 70498, decode.loss_ce: 0.4119, decode.acc_seg: 84.2141, aux.loss_ce: 0.1640, aux.acc_seg: 84.1305, loss: 0.5759 +2024-06-18 04:39:23,101 - mmseg - INFO - Iter [12700/80000] lr: 3.365e-05, eta: 1 day, 3:36:22, time: 1.323, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4088, decode.acc_seg: 84.5874, aux.loss_ce: 0.1631, aux.acc_seg: 84.6662, loss: 0.5720 +2024-06-18 04:40:29,176 - mmseg - INFO - Iter [12750/80000] lr: 3.363e-05, eta: 1 day, 3:34:28, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4074, decode.acc_seg: 84.0597, aux.loss_ce: 0.1625, aux.acc_seg: 84.0563, loss: 0.5699 +2024-06-18 04:41:35,262 - mmseg - INFO - Iter [12800/80000] lr: 3.360e-05, eta: 1 day, 3:32:33, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3889, decode.acc_seg: 85.0813, aux.loss_ce: 0.1557, aux.acc_seg: 85.1524, loss: 0.5447 +2024-06-18 04:42:41,440 - mmseg - INFO - Iter [12850/80000] lr: 3.358e-05, eta: 1 day, 3:30:40, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3911, decode.acc_seg: 84.5144, aux.loss_ce: 0.1566, aux.acc_seg: 84.5817, loss: 0.5477 +2024-06-18 04:43:47,439 - mmseg - INFO - Iter [12900/80000] lr: 3.355e-05, eta: 1 day, 3:28:46, time: 1.320, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3860, decode.acc_seg: 84.9171, aux.loss_ce: 0.1556, aux.acc_seg: 84.9022, loss: 0.5416 +2024-06-18 04:44:53,681 - mmseg - INFO - Iter [12950/80000] lr: 3.353e-05, eta: 1 day, 3:26:53, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4144, decode.acc_seg: 83.6910, aux.loss_ce: 0.1659, aux.acc_seg: 83.7111, loss: 0.5803 +2024-06-18 04:46:00,022 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 04:46:00,022 - mmseg - INFO - Iter [13000/80000] lr: 3.350e-05, eta: 1 day, 3:25:02, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4108, decode.acc_seg: 83.9744, aux.loss_ce: 0.1633, aux.acc_seg: 83.9186, loss: 0.5741 +2024-06-18 04:47:36,855 - mmseg - INFO - per class results: +2024-06-18 04:47:36,861 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.39 | 88.08 | +| building | 83.37 | 92.12 | +| sky | 93.95 | 97.98 | +| floor | 83.11 | 89.59 | +| tree | 74.6 | 86.12 | +| ceiling | 85.5 | 92.41 | +| road | 83.84 | 90.15 | +| bed | 91.42 | 95.96 | +| windowpane | 62.96 | 77.22 | +| grass | 69.43 | 81.63 | +| cabinet | 58.36 | 67.43 | +| sidewalk | 66.04 | 83.74 | +| person | 82.51 | 93.67 | +| earth | 37.37 | 51.38 | +| door | 57.15 | 78.46 | +| table | 60.3 | 70.95 | +| mountain | 60.64 | 66.69 | +| plant | 53.09 | 69.76 | +| curtain | 77.86 | 84.44 | +| chair | 62.44 | 80.54 | +| car | 84.61 | 91.78 | +| water | 57.57 | 71.55 | +| painting | 75.42 | 88.44 | +| sofa | 76.23 | 90.48 | +| shelf | 38.24 | 50.91 | +| house | 55.52 | 78.71 | +| sea | 66.54 | 88.8 | +| mirror | 71.86 | 81.98 | +| rug | 69.51 | 78.15 | +| field | 24.48 | 32.95 | +| armchair | 50.81 | 62.71 | +| seat | 63.28 | 87.49 | +| fence | 46.43 | 64.4 | +| desk | 46.84 | 77.26 | +| rock | 50.17 | 85.92 | +| wardrobe | 50.84 | 80.68 | +| lamp | 67.42 | 76.75 | +| bathtub | 76.75 | 84.82 | +| railing | 33.5 | 45.43 | +| cushion | 62.35 | 81.48 | +| base | 52.32 | 72.95 | +| box | 29.15 | 38.12 | +| column | 57.3 | 72.77 | +| signboard | 38.15 | 55.21 | +| chest of drawers | 44.12 | 76.32 | +| counter | 51.09 | 64.14 | +| sand | 56.57 | 84.28 | +| sink | 71.87 | 79.26 | +| skyscraper | 45.79 | 73.02 | +| fireplace | 65.32 | 95.92 | +| refrigerator | 68.45 | 89.75 | +| grandstand | 47.01 | 72.86 | +| path | 26.7 | 40.13 | +| stairs | 32.29 | 40.51 | +| runway | 69.32 | 89.3 | +| case | 39.74 | 89.01 | +| pool table | 89.56 | 98.02 | +| pillow | 64.44 | 73.26 | +| screen door | 66.67 | 92.42 | +| stairway | 31.79 | 50.15 | +| river | 21.16 | 28.55 | +| bridge | 65.07 | 91.58 | +| bookcase | 35.41 | 62.85 | +| blind | 45.09 | 50.38 | +| coffee table | 55.51 | 91.14 | +| toilet | 85.28 | 92.72 | +| flower | 39.0 | 47.14 | +| book | 48.01 | 69.9 | +| hill | 3.77 | 7.29 | +| bench | 45.67 | 54.83 | +| countertop | 61.4 | 73.39 | +| stove | 81.01 | 90.95 | +| palm | 54.82 | 67.67 | +| kitchen island | 36.66 | 97.45 | +| computer | 69.07 | 91.71 | +| swivel chair | 33.62 | 37.5 | +| boat | 61.24 | 87.33 | +| bar | 57.75 | 67.29 | +| arcade machine | 77.82 | 97.17 | +| hovel | 31.91 | 50.78 | +| bus | 90.33 | 95.6 | +| towel | 65.5 | 71.31 | +| light | 53.53 | 65.95 | +| truck | 42.1 | 57.77 | +| tower | 10.56 | 14.4 | +| chandelier | 66.62 | 82.76 | +| awning | 39.02 | 52.7 | +| streetlight | 23.92 | 30.11 | +| booth | 32.22 | 52.11 | +| television receiver | 66.38 | 82.24 | +| airplane | 64.26 | 69.27 | +| dirt track | 5.72 | 25.54 | +| apparel | 47.4 | 77.05 | +| pole | 20.84 | 25.86 | +| land | 0.2 | 0.38 | +| bannister | 8.27 | 9.73 | +| escalator | 20.16 | 20.82 | +| ottoman | 47.11 | 70.05 | +| bottle | 40.76 | 53.32 | +| buffet | 53.46 | 84.99 | +| poster | 37.04 | 44.54 | +| stage | 26.85 | 41.63 | +| van | 41.36 | 54.0 | +| ship | 29.62 | 30.04 | +| fountain | 59.03 | 71.37 | +| conveyer belt | 77.23 | 90.78 | +| canopy | 46.63 | 79.77 | +| washer | 69.93 | 76.67 | +| plaything | 18.69 | 23.77 | +| swimming pool | 56.77 | 92.89 | +| stool | 37.05 | 39.73 | +| barrel | 54.48 | 64.65 | +| basket | 31.29 | 47.02 | +| waterfall | 59.34 | 97.01 | +| tent | 90.34 | 98.83 | +| bag | 20.18 | 25.95 | +| minibike | 66.49 | 78.27 | +| cradle | 80.09 | 98.55 | +| oven | 61.01 | 68.89 | +| ball | 51.36 | 64.43 | +| food | 53.85 | 58.73 | +| step | 14.22 | 17.88 | +| tank | 63.18 | 75.81 | +| trade name | 14.48 | 15.22 | +| microwave | 83.97 | 93.87 | +| pot | 50.25 | 56.22 | +| animal | 64.47 | 66.36 | +| bicycle | 45.72 | 52.78 | +| lake | 9.64 | 11.02 | +| dishwasher | 52.53 | 75.93 | +| screen | 65.54 | 90.73 | +| blanket | 28.07 | 33.01 | +| sculpture | 57.13 | 67.59 | +| hood | 68.23 | 83.83 | +| sconce | 44.09 | 51.16 | +| vase | 38.68 | 56.51 | +| traffic light | 25.9 | 51.61 | +| tray | 8.44 | 10.65 | +| ashcan | 35.33 | 57.18 | +| fan | 57.59 | 67.83 | +| pier | 32.86 | 49.68 | +| crt screen | 4.24 | 11.85 | +| plate | 49.61 | 79.17 | +| monitor | 13.63 | 18.24 | +| bulletin board | 48.65 | 54.96 | +| shower | 0.0 | 0.0 | +| radiator | 55.47 | 57.62 | +| glass | 14.19 | 15.1 | +| clock | 30.3 | 43.68 | +| flag | 66.1 | 74.19 | ++---------------------+-------+-------+ +2024-06-18 04:47:36,862 - mmseg - INFO - Summary: +2024-06-18 04:47:36,862 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.13 | 51.06 | 65.15 | ++-------+-------+-------+ +2024-06-18 04:47:36,862 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 04:47:36,863 - mmseg - INFO - Iter(val) [250] aAcc: 0.8413, mIoU: 0.5106, mAcc: 0.6515, IoU.wall: 0.8039, IoU.building: 0.8337, IoU.sky: 0.9395, IoU.floor: 0.8311, IoU.tree: 0.7460, IoU.ceiling: 0.8550, IoU.road: 0.8384, IoU.bed : 0.9142, IoU.windowpane: 0.6296, IoU.grass: 0.6943, IoU.cabinet: 0.5836, IoU.sidewalk: 0.6604, IoU.person: 0.8251, IoU.earth: 0.3737, IoU.door: 0.5715, IoU.table: 0.6030, IoU.mountain: 0.6064, IoU.plant: 0.5309, IoU.curtain: 0.7786, IoU.chair: 0.6244, IoU.car: 0.8461, IoU.water: 0.5757, IoU.painting: 0.7542, IoU.sofa: 0.7623, IoU.shelf: 0.3824, IoU.house: 0.5552, IoU.sea: 0.6654, IoU.mirror: 0.7186, IoU.rug: 0.6951, IoU.field: 0.2448, IoU.armchair: 0.5081, IoU.seat: 0.6328, IoU.fence: 0.4643, IoU.desk: 0.4684, IoU.rock: 0.5017, IoU.wardrobe: 0.5084, IoU.lamp: 0.6742, IoU.bathtub: 0.7675, IoU.railing: 0.3350, IoU.cushion: 0.6235, IoU.base: 0.5232, IoU.box: 0.2915, IoU.column: 0.5730, IoU.signboard: 0.3815, IoU.chest of drawers: 0.4412, IoU.counter: 0.5109, IoU.sand: 0.5657, IoU.sink: 0.7187, IoU.skyscraper: 0.4579, IoU.fireplace: 0.6532, IoU.refrigerator: 0.6845, IoU.grandstand: 0.4701, IoU.path: 0.2670, IoU.stairs: 0.3229, IoU.runway: 0.6932, IoU.case: 0.3974, IoU.pool table: 0.8956, IoU.pillow: 0.6444, IoU.screen door: 0.6667, IoU.stairway: 0.3179, IoU.river: 0.2116, IoU.bridge: 0.6507, IoU.bookcase: 0.3541, IoU.blind: 0.4509, IoU.coffee table: 0.5551, IoU.toilet: 0.8528, IoU.flower: 0.3900, IoU.book: 0.4801, IoU.hill: 0.0377, IoU.bench: 0.4567, IoU.countertop: 0.6140, IoU.stove: 0.8101, IoU.palm: 0.5482, IoU.kitchen island: 0.3666, IoU.computer: 0.6907, IoU.swivel chair: 0.3362, IoU.boat: 0.6124, IoU.bar: 0.5775, IoU.arcade machine: 0.7782, IoU.hovel: 0.3191, IoU.bus: 0.9033, IoU.towel: 0.6550, IoU.light: 0.5353, IoU.truck: 0.4210, IoU.tower: 0.1056, IoU.chandelier: 0.6662, IoU.awning: 0.3902, IoU.streetlight: 0.2392, IoU.booth: 0.3222, IoU.television receiver: 0.6638, IoU.airplane: 0.6426, IoU.dirt track: 0.0572, IoU.apparel: 0.4740, IoU.pole: 0.2084, IoU.land: 0.0020, IoU.bannister: 0.0827, IoU.escalator: 0.2016, IoU.ottoman: 0.4711, IoU.bottle: 0.4076, IoU.buffet: 0.5346, IoU.poster: 0.3704, IoU.stage: 0.2685, IoU.van: 0.4136, IoU.ship: 0.2962, IoU.fountain: 0.5903, IoU.conveyer belt: 0.7723, IoU.canopy: 0.4663, IoU.washer: 0.6993, IoU.plaything: 0.1869, IoU.swimming pool: 0.5677, IoU.stool: 0.3705, IoU.barrel: 0.5448, IoU.basket: 0.3129, IoU.waterfall: 0.5934, IoU.tent: 0.9034, IoU.bag: 0.2018, IoU.minibike: 0.6649, IoU.cradle: 0.8009, IoU.oven: 0.6101, IoU.ball: 0.5136, IoU.food: 0.5385, IoU.step: 0.1422, IoU.tank: 0.6318, IoU.trade name: 0.1448, IoU.microwave: 0.8397, IoU.pot: 0.5025, IoU.animal: 0.6447, IoU.bicycle: 0.4572, IoU.lake: 0.0964, IoU.dishwasher: 0.5253, IoU.screen: 0.6554, IoU.blanket: 0.2807, IoU.sculpture: 0.5713, IoU.hood: 0.6823, IoU.sconce: 0.4409, IoU.vase: 0.3868, IoU.traffic light: 0.2590, IoU.tray: 0.0844, IoU.ashcan: 0.3533, IoU.fan: 0.5759, IoU.pier: 0.3286, IoU.crt screen: 0.0424, IoU.plate: 0.4961, IoU.monitor: 0.1363, IoU.bulletin board: 0.4865, IoU.shower: 0.0000, IoU.radiator: 0.5547, IoU.glass: 0.1419, IoU.clock: 0.3030, IoU.flag: 0.6610, Acc.wall: 0.8808, Acc.building: 0.9212, Acc.sky: 0.9798, Acc.floor: 0.8959, Acc.tree: 0.8612, Acc.ceiling: 0.9241, Acc.road: 0.9015, Acc.bed : 0.9596, Acc.windowpane: 0.7722, Acc.grass: 0.8163, Acc.cabinet: 0.6743, Acc.sidewalk: 0.8374, Acc.person: 0.9367, Acc.earth: 0.5138, Acc.door: 0.7846, Acc.table: 0.7095, Acc.mountain: 0.6669, Acc.plant: 0.6976, Acc.curtain: 0.8444, Acc.chair: 0.8054, Acc.car: 0.9178, Acc.water: 0.7155, Acc.painting: 0.8844, Acc.sofa: 0.9048, Acc.shelf: 0.5091, Acc.house: 0.7871, Acc.sea: 0.8880, Acc.mirror: 0.8198, Acc.rug: 0.7815, Acc.field: 0.3295, Acc.armchair: 0.6271, Acc.seat: 0.8749, Acc.fence: 0.6440, Acc.desk: 0.7726, Acc.rock: 0.8592, Acc.wardrobe: 0.8068, Acc.lamp: 0.7675, Acc.bathtub: 0.8482, Acc.railing: 0.4543, Acc.cushion: 0.8148, Acc.base: 0.7295, Acc.box: 0.3812, Acc.column: 0.7277, Acc.signboard: 0.5521, Acc.chest of drawers: 0.7632, Acc.counter: 0.6414, Acc.sand: 0.8428, Acc.sink: 0.7926, Acc.skyscraper: 0.7302, Acc.fireplace: 0.9592, Acc.refrigerator: 0.8975, Acc.grandstand: 0.7286, Acc.path: 0.4013, Acc.stairs: 0.4051, Acc.runway: 0.8930, Acc.case: 0.8901, Acc.pool table: 0.9802, Acc.pillow: 0.7326, Acc.screen door: 0.9242, Acc.stairway: 0.5015, Acc.river: 0.2855, Acc.bridge: 0.9158, Acc.bookcase: 0.6285, Acc.blind: 0.5038, Acc.coffee table: 0.9114, Acc.toilet: 0.9272, Acc.flower: 0.4714, Acc.book: 0.6990, Acc.hill: 0.0729, Acc.bench: 0.5483, Acc.countertop: 0.7339, Acc.stove: 0.9095, Acc.palm: 0.6767, Acc.kitchen island: 0.9745, Acc.computer: 0.9171, Acc.swivel chair: 0.3750, Acc.boat: 0.8733, Acc.bar: 0.6729, Acc.arcade machine: 0.9717, Acc.hovel: 0.5078, Acc.bus: 0.9560, Acc.towel: 0.7131, Acc.light: 0.6595, Acc.truck: 0.5777, Acc.tower: 0.1440, Acc.chandelier: 0.8276, Acc.awning: 0.5270, Acc.streetlight: 0.3011, Acc.booth: 0.5211, Acc.television receiver: 0.8224, Acc.airplane: 0.6927, Acc.dirt track: 0.2554, Acc.apparel: 0.7705, Acc.pole: 0.2586, Acc.land: 0.0038, Acc.bannister: 0.0973, Acc.escalator: 0.2082, Acc.ottoman: 0.7005, Acc.bottle: 0.5332, Acc.buffet: 0.8499, Acc.poster: 0.4454, Acc.stage: 0.4163, Acc.van: 0.5400, Acc.ship: 0.3004, Acc.fountain: 0.7137, Acc.conveyer belt: 0.9078, Acc.canopy: 0.7977, Acc.washer: 0.7667, Acc.plaything: 0.2377, Acc.swimming pool: 0.9289, Acc.stool: 0.3973, Acc.barrel: 0.6465, Acc.basket: 0.4702, Acc.waterfall: 0.9701, Acc.tent: 0.9883, Acc.bag: 0.2595, Acc.minibike: 0.7827, Acc.cradle: 0.9855, Acc.oven: 0.6889, Acc.ball: 0.6443, Acc.food: 0.5873, Acc.step: 0.1788, Acc.tank: 0.7581, Acc.trade name: 0.1522, Acc.microwave: 0.9387, Acc.pot: 0.5622, Acc.animal: 0.6636, Acc.bicycle: 0.5278, Acc.lake: 0.1102, Acc.dishwasher: 0.7593, Acc.screen: 0.9073, Acc.blanket: 0.3301, Acc.sculpture: 0.6759, Acc.hood: 0.8383, Acc.sconce: 0.5116, Acc.vase: 0.5651, Acc.traffic light: 0.5161, Acc.tray: 0.1065, Acc.ashcan: 0.5718, Acc.fan: 0.6783, Acc.pier: 0.4968, Acc.crt screen: 0.1185, Acc.plate: 0.7917, Acc.monitor: 0.1824, Acc.bulletin board: 0.5496, Acc.shower: 0.0000, Acc.radiator: 0.5762, Acc.glass: 0.1510, Acc.clock: 0.4368, Acc.flag: 0.7419 +2024-06-18 04:48:43,878 - mmseg - INFO - Iter [13050/80000] lr: 3.348e-05, eta: 1 day, 3:31:31, time: 3.277, data_time: 1.953, memory: 70498, decode.loss_ce: 0.3887, decode.acc_seg: 84.6864, aux.loss_ce: 0.1566, aux.acc_seg: 84.5860, loss: 0.5453 +2024-06-18 04:49:50,362 - mmseg - INFO - Iter [13100/80000] lr: 3.345e-05, eta: 1 day, 3:29:38, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3867, decode.acc_seg: 84.9086, aux.loss_ce: 0.1556, aux.acc_seg: 84.9298, loss: 0.5423 +2024-06-18 04:50:56,769 - mmseg - INFO - Iter [13150/80000] lr: 3.343e-05, eta: 1 day, 3:27:46, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4274, decode.acc_seg: 83.2807, aux.loss_ce: 0.1707, aux.acc_seg: 83.1926, loss: 0.5982 +2024-06-18 04:52:03,270 - mmseg - INFO - Iter [13200/80000] lr: 3.340e-05, eta: 1 day, 3:25:54, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3983, decode.acc_seg: 84.7784, aux.loss_ce: 0.1587, aux.acc_seg: 84.7472, loss: 0.5569 +2024-06-18 04:53:09,570 - mmseg - INFO - Iter [13250/80000] lr: 3.338e-05, eta: 1 day, 3:24:02, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3858, decode.acc_seg: 84.7840, aux.loss_ce: 0.1555, aux.acc_seg: 84.5433, loss: 0.5413 +2024-06-18 04:54:16,162 - mmseg - INFO - Iter [13300/80000] lr: 3.335e-05, eta: 1 day, 3:22:12, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4007, decode.acc_seg: 84.2787, aux.loss_ce: 0.1615, aux.acc_seg: 84.1583, loss: 0.5622 +2024-06-18 04:55:22,580 - mmseg - INFO - Iter [13350/80000] lr: 3.333e-05, eta: 1 day, 3:20:21, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4057, decode.acc_seg: 84.5748, aux.loss_ce: 0.1631, aux.acc_seg: 84.4407, loss: 0.5688 +2024-06-18 04:56:28,786 - mmseg - INFO - Iter [13400/80000] lr: 3.330e-05, eta: 1 day, 3:18:29, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4082, decode.acc_seg: 84.1073, aux.loss_ce: 0.1637, aux.acc_seg: 83.9225, loss: 0.5720 +2024-06-18 04:57:35,064 - mmseg - INFO - Iter [13450/80000] lr: 3.328e-05, eta: 1 day, 3:16:38, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3622, decode.acc_seg: 85.8242, aux.loss_ce: 0.1454, aux.acc_seg: 85.8598, loss: 0.5075 +2024-06-18 04:58:41,556 - mmseg - INFO - Iter [13500/80000] lr: 3.325e-05, eta: 1 day, 3:14:48, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3708, decode.acc_seg: 85.0228, aux.loss_ce: 0.1490, aux.acc_seg: 84.8760, loss: 0.5199 +2024-06-18 04:59:47,929 - mmseg - INFO - Iter [13550/80000] lr: 3.323e-05, eta: 1 day, 3:12:58, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3794, decode.acc_seg: 84.7437, aux.loss_ce: 0.1531, aux.acc_seg: 84.7156, loss: 0.5325 +2024-06-18 05:00:54,043 - mmseg - INFO - Iter [13600/80000] lr: 3.320e-05, eta: 1 day, 3:11:07, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3921, decode.acc_seg: 84.8361, aux.loss_ce: 0.1567, aux.acc_seg: 84.7464, loss: 0.5488 +2024-06-18 05:02:00,297 - mmseg - INFO - Iter [13650/80000] lr: 3.318e-05, eta: 1 day, 3:09:17, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3831, decode.acc_seg: 84.3571, aux.loss_ce: 0.1538, aux.acc_seg: 84.3270, loss: 0.5369 +2024-06-18 05:03:06,545 - mmseg - INFO - Iter [13700/80000] lr: 3.315e-05, eta: 1 day, 3:07:28, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4098, decode.acc_seg: 83.8675, aux.loss_ce: 0.1634, aux.acc_seg: 83.8393, loss: 0.5732 +2024-06-18 05:04:12,814 - mmseg - INFO - Iter [13750/80000] lr: 3.313e-05, eta: 1 day, 3:05:39, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3791, decode.acc_seg: 84.7569, aux.loss_ce: 0.1529, aux.acc_seg: 84.7511, loss: 0.5320 +2024-06-18 05:05:19,209 - mmseg - INFO - Iter [13800/80000] lr: 3.310e-05, eta: 1 day, 3:03:50, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3985, decode.acc_seg: 84.6923, aux.loss_ce: 0.1612, aux.acc_seg: 84.5821, loss: 0.5597 +2024-06-18 05:06:25,587 - mmseg - INFO - Iter [13850/80000] lr: 3.308e-05, eta: 1 day, 3:02:02, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4098, decode.acc_seg: 84.2503, aux.loss_ce: 0.1637, aux.acc_seg: 84.3033, loss: 0.5735 +2024-06-18 05:07:34,319 - mmseg - INFO - Iter [13900/80000] lr: 3.305e-05, eta: 1 day, 3:00:26, time: 1.375, data_time: 0.056, memory: 70498, decode.loss_ce: 0.3911, decode.acc_seg: 84.5332, aux.loss_ce: 0.1578, aux.acc_seg: 84.3034, loss: 0.5488 +2024-06-18 05:08:40,511 - mmseg - INFO - Iter [13950/80000] lr: 3.303e-05, eta: 1 day, 2:58:38, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3603, decode.acc_seg: 85.1834, aux.loss_ce: 0.1449, aux.acc_seg: 85.1632, loss: 0.5051 +2024-06-18 05:09:46,895 - mmseg - INFO - Saving checkpoint at 14000 iterations +2024-06-18 05:11:28,859 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 05:11:28,859 - mmseg - INFO - Iter [14000/80000] lr: 3.300e-05, eta: 1 day, 3:04:51, time: 3.367, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4071, decode.acc_seg: 84.1870, aux.loss_ce: 0.1618, aux.acc_seg: 84.1678, loss: 0.5689 +2024-06-18 05:13:04,217 - mmseg - INFO - per class results: +2024-06-18 05:13:04,223 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.86 | 87.66 | +| building | 83.13 | 94.33 | +| sky | 94.35 | 97.46 | +| floor | 83.88 | 89.73 | +| tree | 75.66 | 87.57 | +| ceiling | 84.59 | 91.74 | +| road | 83.56 | 93.9 | +| bed | 91.36 | 96.35 | +| windowpane | 63.44 | 78.15 | +| grass | 69.72 | 86.68 | +| cabinet | 64.87 | 75.46 | +| sidewalk | 66.09 | 76.84 | +| person | 83.63 | 94.21 | +| earth | 33.05 | 43.97 | +| door | 56.47 | 71.72 | +| table | 56.8 | 64.3 | +| mountain | 62.05 | 71.84 | +| plant | 53.42 | 66.53 | +| curtain | 75.83 | 87.16 | +| chair | 61.75 | 80.14 | +| car | 83.29 | 93.42 | +| water | 55.16 | 73.69 | +| painting | 76.4 | 89.48 | +| sofa | 79.37 | 88.7 | +| shelf | 40.83 | 57.84 | +| house | 21.94 | 22.45 | +| sea | 48.47 | 52.05 | +| mirror | 70.75 | 81.01 | +| rug | 68.35 | 83.51 | +| field | 31.32 | 39.83 | +| armchair | 54.32 | 74.13 | +| seat | 68.82 | 87.34 | +| fence | 48.39 | 60.91 | +| desk | 45.77 | 77.89 | +| rock | 48.52 | 79.56 | +| wardrobe | 54.33 | 70.08 | +| lamp | 66.31 | 83.83 | +| bathtub | 79.46 | 85.98 | +| railing | 37.02 | 47.24 | +| cushion | 59.6 | 88.05 | +| base | 33.74 | 67.38 | +| box | 28.02 | 36.78 | +| column | 54.98 | 73.02 | +| signboard | 38.94 | 55.84 | +| chest of drawers | 39.21 | 62.73 | +| counter | 50.09 | 62.97 | +| sand | 46.54 | 83.28 | +| sink | 71.74 | 81.74 | +| skyscraper | 50.39 | 63.62 | +| fireplace | 71.95 | 89.23 | +| refrigerator | 74.57 | 85.79 | +| grandstand | 51.7 | 86.84 | +| path | 25.9 | 31.83 | +| stairs | 34.05 | 41.48 | +| runway | 68.32 | 91.52 | +| case | 48.48 | 71.87 | +| pool table | 93.18 | 97.55 | +| pillow | 55.13 | 59.99 | +| screen door | 73.29 | 89.39 | +| stairway | 51.18 | 59.11 | +| river | 14.88 | 52.08 | +| bridge | 68.38 | 88.85 | +| bookcase | 35.48 | 52.45 | +| blind | 37.84 | 40.23 | +| coffee table | 60.78 | 86.56 | +| toilet | 85.34 | 91.31 | +| flower | 38.67 | 60.59 | +| book | 48.63 | 73.6 | +| hill | 3.05 | 3.43 | +| bench | 46.66 | 53.52 | +| countertop | 62.87 | 80.32 | +| stove | 82.16 | 88.79 | +| palm | 56.13 | 73.19 | +| kitchen island | 42.0 | 86.7 | +| computer | 74.98 | 91.87 | +| swivel chair | 39.35 | 46.44 | +| boat | 45.63 | 89.43 | +| bar | 65.74 | 77.59 | +| arcade machine | 75.87 | 97.48 | +| hovel | 41.56 | 53.06 | +| bus | 91.23 | 95.46 | +| towel | 67.4 | 76.64 | +| light | 53.09 | 61.29 | +| truck | 40.3 | 54.9 | +| tower | 24.0 | 34.87 | +| chandelier | 66.81 | 87.06 | +| awning | 40.9 | 67.79 | +| streetlight | 26.96 | 37.37 | +| booth | 30.4 | 57.93 | +| television receiver | 68.09 | 85.01 | +| airplane | 52.55 | 73.81 | +| dirt track | 10.26 | 74.15 | +| apparel | 43.71 | 56.65 | +| pole | 19.56 | 23.66 | +| land | 0.1 | 0.35 | +| bannister | 13.37 | 21.95 | +| escalator | 54.8 | 69.69 | +| ottoman | 45.61 | 62.92 | +| bottle | 34.48 | 43.67 | +| buffet | 46.35 | 65.29 | +| poster | 37.91 | 51.58 | +| stage | 12.84 | 20.37 | +| van | 32.32 | 43.04 | +| ship | 86.68 | 94.77 | +| fountain | 67.5 | 78.51 | +| conveyer belt | 73.2 | 92.82 | +| canopy | 46.23 | 69.46 | +| washer | 66.81 | 75.51 | +| plaything | 18.79 | 25.36 | +| swimming pool | 59.98 | 89.34 | +| stool | 46.38 | 72.85 | +| barrel | 41.94 | 65.08 | +| basket | 30.63 | 45.01 | +| waterfall | 51.97 | 74.46 | +| tent | 86.06 | 98.49 | +| bag | 8.9 | 10.29 | +| minibike | 65.98 | 80.36 | +| cradle | 72.01 | 98.21 | +| oven | 62.68 | 69.05 | +| ball | 25.75 | 28.64 | +| food | 56.28 | 73.51 | +| step | 10.99 | 13.11 | +| tank | 56.25 | 97.61 | +| trade name | 30.18 | 35.25 | +| microwave | 84.6 | 95.26 | +| pot | 48.49 | 57.64 | +| animal | 69.14 | 73.29 | +| bicycle | 52.73 | 71.89 | +| lake | 3.75 | 4.61 | +| dishwasher | 55.96 | 78.74 | +| screen | 53.79 | 96.32 | +| blanket | 22.64 | 31.66 | +| sculpture | 57.41 | 65.15 | +| hood | 62.27 | 71.52 | +| sconce | 49.31 | 61.55 | +| vase | 37.25 | 56.72 | +| traffic light | 28.21 | 58.24 | +| tray | 7.51 | 11.3 | +| ashcan | 40.32 | 57.99 | +| fan | 60.6 | 79.05 | +| pier | 36.61 | 46.35 | +| crt screen | 1.21 | 3.22 | +| plate | 52.86 | 70.78 | +| monitor | 11.31 | 13.6 | +| bulletin board | 45.82 | 61.72 | +| shower | 0.0 | 0.0 | +| radiator | 58.61 | 64.33 | +| glass | 13.02 | 13.68 | +| clock | 33.36 | 37.3 | +| flag | 67.23 | 76.74 | ++---------------------+-------+-------+ +2024-06-18 05:13:04,223 - mmseg - INFO - Summary: +2024-06-18 05:13:04,223 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.08 | 51.21 | 65.76 | ++-------+-------+-------+ +2024-06-18 05:13:04,224 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 05:13:04,224 - mmseg - INFO - Iter(val) [250] aAcc: 0.8408, mIoU: 0.5121, mAcc: 0.6576, IoU.wall: 0.7986, IoU.building: 0.8313, IoU.sky: 0.9435, IoU.floor: 0.8388, IoU.tree: 0.7566, IoU.ceiling: 0.8459, IoU.road: 0.8356, IoU.bed : 0.9136, IoU.windowpane: 0.6344, IoU.grass: 0.6972, IoU.cabinet: 0.6487, IoU.sidewalk: 0.6609, IoU.person: 0.8363, IoU.earth: 0.3305, IoU.door: 0.5647, IoU.table: 0.5680, IoU.mountain: 0.6205, IoU.plant: 0.5342, IoU.curtain: 0.7583, IoU.chair: 0.6175, IoU.car: 0.8329, IoU.water: 0.5516, IoU.painting: 0.7640, IoU.sofa: 0.7937, IoU.shelf: 0.4083, IoU.house: 0.2194, IoU.sea: 0.4847, IoU.mirror: 0.7075, IoU.rug: 0.6835, IoU.field: 0.3132, IoU.armchair: 0.5432, IoU.seat: 0.6882, IoU.fence: 0.4839, IoU.desk: 0.4577, IoU.rock: 0.4852, IoU.wardrobe: 0.5433, IoU.lamp: 0.6631, IoU.bathtub: 0.7946, IoU.railing: 0.3702, IoU.cushion: 0.5960, IoU.base: 0.3374, IoU.box: 0.2802, IoU.column: 0.5498, IoU.signboard: 0.3894, IoU.chest of drawers: 0.3921, IoU.counter: 0.5009, IoU.sand: 0.4654, IoU.sink: 0.7174, IoU.skyscraper: 0.5039, IoU.fireplace: 0.7195, IoU.refrigerator: 0.7457, IoU.grandstand: 0.5170, IoU.path: 0.2590, IoU.stairs: 0.3405, IoU.runway: 0.6832, IoU.case: 0.4848, IoU.pool table: 0.9318, IoU.pillow: 0.5513, IoU.screen door: 0.7329, IoU.stairway: 0.5118, IoU.river: 0.1488, IoU.bridge: 0.6838, IoU.bookcase: 0.3548, IoU.blind: 0.3784, IoU.coffee table: 0.6078, IoU.toilet: 0.8534, IoU.flower: 0.3867, IoU.book: 0.4863, IoU.hill: 0.0305, IoU.bench: 0.4666, IoU.countertop: 0.6287, IoU.stove: 0.8216, IoU.palm: 0.5613, IoU.kitchen island: 0.4200, IoU.computer: 0.7498, IoU.swivel chair: 0.3935, IoU.boat: 0.4563, IoU.bar: 0.6574, IoU.arcade machine: 0.7587, IoU.hovel: 0.4156, IoU.bus: 0.9123, IoU.towel: 0.6740, IoU.light: 0.5309, IoU.truck: 0.4030, IoU.tower: 0.2400, IoU.chandelier: 0.6681, IoU.awning: 0.4090, IoU.streetlight: 0.2696, IoU.booth: 0.3040, IoU.television receiver: 0.6809, IoU.airplane: 0.5255, IoU.dirt track: 0.1026, IoU.apparel: 0.4371, IoU.pole: 0.1956, IoU.land: 0.0010, IoU.bannister: 0.1337, IoU.escalator: 0.5480, IoU.ottoman: 0.4561, IoU.bottle: 0.3448, IoU.buffet: 0.4635, IoU.poster: 0.3791, IoU.stage: 0.1284, IoU.van: 0.3232, IoU.ship: 0.8668, IoU.fountain: 0.6750, IoU.conveyer belt: 0.7320, IoU.canopy: 0.4623, IoU.washer: 0.6681, IoU.plaything: 0.1879, IoU.swimming pool: 0.5998, IoU.stool: 0.4638, IoU.barrel: 0.4194, IoU.basket: 0.3063, IoU.waterfall: 0.5197, IoU.tent: 0.8606, IoU.bag: 0.0890, IoU.minibike: 0.6598, IoU.cradle: 0.7201, IoU.oven: 0.6268, IoU.ball: 0.2575, IoU.food: 0.5628, IoU.step: 0.1099, IoU.tank: 0.5625, IoU.trade name: 0.3018, IoU.microwave: 0.8460, IoU.pot: 0.4849, IoU.animal: 0.6914, IoU.bicycle: 0.5273, IoU.lake: 0.0375, IoU.dishwasher: 0.5596, IoU.screen: 0.5379, IoU.blanket: 0.2264, IoU.sculpture: 0.5741, IoU.hood: 0.6227, IoU.sconce: 0.4931, IoU.vase: 0.3725, IoU.traffic light: 0.2821, IoU.tray: 0.0751, IoU.ashcan: 0.4032, IoU.fan: 0.6060, IoU.pier: 0.3661, IoU.crt screen: 0.0121, IoU.plate: 0.5286, IoU.monitor: 0.1131, IoU.bulletin board: 0.4582, IoU.shower: 0.0000, IoU.radiator: 0.5861, IoU.glass: 0.1302, IoU.clock: 0.3336, IoU.flag: 0.6723, Acc.wall: 0.8766, Acc.building: 0.9433, Acc.sky: 0.9746, Acc.floor: 0.8973, Acc.tree: 0.8757, Acc.ceiling: 0.9174, Acc.road: 0.9390, Acc.bed : 0.9635, Acc.windowpane: 0.7815, Acc.grass: 0.8668, Acc.cabinet: 0.7546, Acc.sidewalk: 0.7684, Acc.person: 0.9421, Acc.earth: 0.4397, Acc.door: 0.7172, Acc.table: 0.6430, Acc.mountain: 0.7184, Acc.plant: 0.6653, Acc.curtain: 0.8716, Acc.chair: 0.8014, Acc.car: 0.9342, Acc.water: 0.7369, Acc.painting: 0.8948, Acc.sofa: 0.8870, Acc.shelf: 0.5784, Acc.house: 0.2245, Acc.sea: 0.5205, Acc.mirror: 0.8101, Acc.rug: 0.8351, Acc.field: 0.3983, Acc.armchair: 0.7413, Acc.seat: 0.8734, Acc.fence: 0.6091, Acc.desk: 0.7789, Acc.rock: 0.7956, Acc.wardrobe: 0.7008, Acc.lamp: 0.8383, Acc.bathtub: 0.8598, Acc.railing: 0.4724, Acc.cushion: 0.8805, Acc.base: 0.6738, Acc.box: 0.3678, Acc.column: 0.7302, Acc.signboard: 0.5584, Acc.chest of drawers: 0.6273, Acc.counter: 0.6297, Acc.sand: 0.8328, Acc.sink: 0.8174, Acc.skyscraper: 0.6362, Acc.fireplace: 0.8923, Acc.refrigerator: 0.8579, Acc.grandstand: 0.8684, Acc.path: 0.3183, Acc.stairs: 0.4148, Acc.runway: 0.9152, Acc.case: 0.7187, Acc.pool table: 0.9755, Acc.pillow: 0.5999, Acc.screen door: 0.8939, Acc.stairway: 0.5911, Acc.river: 0.5208, Acc.bridge: 0.8885, Acc.bookcase: 0.5245, Acc.blind: 0.4023, Acc.coffee table: 0.8656, Acc.toilet: 0.9131, Acc.flower: 0.6059, Acc.book: 0.7360, Acc.hill: 0.0343, Acc.bench: 0.5352, Acc.countertop: 0.8032, Acc.stove: 0.8879, Acc.palm: 0.7319, Acc.kitchen island: 0.8670, Acc.computer: 0.9187, Acc.swivel chair: 0.4644, Acc.boat: 0.8943, Acc.bar: 0.7759, Acc.arcade machine: 0.9748, Acc.hovel: 0.5306, Acc.bus: 0.9546, Acc.towel: 0.7664, Acc.light: 0.6129, Acc.truck: 0.5490, Acc.tower: 0.3487, Acc.chandelier: 0.8706, Acc.awning: 0.6779, Acc.streetlight: 0.3737, Acc.booth: 0.5793, Acc.television receiver: 0.8501, Acc.airplane: 0.7381, Acc.dirt track: 0.7415, Acc.apparel: 0.5665, Acc.pole: 0.2366, Acc.land: 0.0035, Acc.bannister: 0.2195, Acc.escalator: 0.6969, Acc.ottoman: 0.6292, Acc.bottle: 0.4367, Acc.buffet: 0.6529, Acc.poster: 0.5158, Acc.stage: 0.2037, Acc.van: 0.4304, Acc.ship: 0.9477, Acc.fountain: 0.7851, Acc.conveyer belt: 0.9282, Acc.canopy: 0.6946, Acc.washer: 0.7551, Acc.plaything: 0.2536, Acc.swimming pool: 0.8934, Acc.stool: 0.7285, Acc.barrel: 0.6508, Acc.basket: 0.4501, Acc.waterfall: 0.7446, Acc.tent: 0.9849, Acc.bag: 0.1029, Acc.minibike: 0.8036, Acc.cradle: 0.9821, Acc.oven: 0.6905, Acc.ball: 0.2864, Acc.food: 0.7351, Acc.step: 0.1311, Acc.tank: 0.9761, Acc.trade name: 0.3525, Acc.microwave: 0.9526, Acc.pot: 0.5764, Acc.animal: 0.7329, Acc.bicycle: 0.7189, Acc.lake: 0.0461, Acc.dishwasher: 0.7874, Acc.screen: 0.9632, Acc.blanket: 0.3166, Acc.sculpture: 0.6515, Acc.hood: 0.7152, Acc.sconce: 0.6155, Acc.vase: 0.5672, Acc.traffic light: 0.5824, Acc.tray: 0.1130, Acc.ashcan: 0.5799, Acc.fan: 0.7905, Acc.pier: 0.4635, Acc.crt screen: 0.0322, Acc.plate: 0.7078, Acc.monitor: 0.1360, Acc.bulletin board: 0.6172, Acc.shower: 0.0000, Acc.radiator: 0.6433, Acc.glass: 0.1368, Acc.clock: 0.3730, Acc.flag: 0.7674 +2024-06-18 05:14:10,904 - mmseg - INFO - Iter [14050/80000] lr: 3.298e-05, eta: 1 day, 3:10:31, time: 3.241, data_time: 1.924, memory: 70498, decode.loss_ce: 0.3831, decode.acc_seg: 84.7181, aux.loss_ce: 0.1532, aux.acc_seg: 84.7762, loss: 0.5363 +2024-06-18 05:15:17,401 - mmseg - INFO - Iter [14100/80000] lr: 3.295e-05, eta: 1 day, 3:08:41, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3528, decode.acc_seg: 85.7128, aux.loss_ce: 0.1432, aux.acc_seg: 85.5023, loss: 0.4960 +2024-06-18 05:16:23,759 - mmseg - INFO - Iter [14150/80000] lr: 3.293e-05, eta: 1 day, 3:06:51, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3714, decode.acc_seg: 85.1987, aux.loss_ce: 0.1490, aux.acc_seg: 85.0583, loss: 0.5205 +2024-06-18 05:17:30,281 - mmseg - INFO - Iter [14200/80000] lr: 3.290e-05, eta: 1 day, 3:05:01, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3783, decode.acc_seg: 84.4874, aux.loss_ce: 0.1537, aux.acc_seg: 84.2957, loss: 0.5320 +2024-06-18 05:18:36,711 - mmseg - INFO - Iter [14250/80000] lr: 3.288e-05, eta: 1 day, 3:03:12, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3631, decode.acc_seg: 85.6924, aux.loss_ce: 0.1459, aux.acc_seg: 85.6028, loss: 0.5090 +2024-06-18 05:19:43,100 - mmseg - INFO - Iter [14300/80000] lr: 3.285e-05, eta: 1 day, 3:01:23, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3817, decode.acc_seg: 84.9192, aux.loss_ce: 0.1542, aux.acc_seg: 84.8810, loss: 0.5360 +2024-06-18 05:20:49,486 - mmseg - INFO - Iter [14350/80000] lr: 3.283e-05, eta: 1 day, 2:59:34, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3664, decode.acc_seg: 85.3346, aux.loss_ce: 0.1475, aux.acc_seg: 85.1704, loss: 0.5139 +2024-06-18 05:21:55,698 - mmseg - INFO - Iter [14400/80000] lr: 3.280e-05, eta: 1 day, 2:57:44, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3742, decode.acc_seg: 85.3891, aux.loss_ce: 0.1505, aux.acc_seg: 85.3645, loss: 0.5247 +2024-06-18 05:23:02,007 - mmseg - INFO - Iter [14450/80000] lr: 3.278e-05, eta: 1 day, 2:55:55, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3744, decode.acc_seg: 85.4579, aux.loss_ce: 0.1515, aux.acc_seg: 85.4995, loss: 0.5258 +2024-06-18 05:24:08,426 - mmseg - INFO - Iter [14500/80000] lr: 3.275e-05, eta: 1 day, 2:54:07, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3605, decode.acc_seg: 86.1476, aux.loss_ce: 0.1462, aux.acc_seg: 86.0622, loss: 0.5066 +2024-06-18 05:25:14,924 - mmseg - INFO - Iter [14550/80000] lr: 3.273e-05, eta: 1 day, 2:52:20, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3703, decode.acc_seg: 85.5183, aux.loss_ce: 0.1477, aux.acc_seg: 85.5434, loss: 0.5180 +2024-06-18 05:26:21,307 - mmseg - INFO - Iter [14600/80000] lr: 3.270e-05, eta: 1 day, 2:50:32, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3597, decode.acc_seg: 85.6473, aux.loss_ce: 0.1442, aux.acc_seg: 85.5426, loss: 0.5039 +2024-06-18 05:27:27,827 - mmseg - INFO - Iter [14650/80000] lr: 3.268e-05, eta: 1 day, 2:48:46, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3568, decode.acc_seg: 85.3686, aux.loss_ce: 0.1442, aux.acc_seg: 85.1382, loss: 0.5010 +2024-06-18 05:28:34,284 - mmseg - INFO - Iter [14700/80000] lr: 3.265e-05, eta: 1 day, 2:46:59, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3832, decode.acc_seg: 84.8490, aux.loss_ce: 0.1547, aux.acc_seg: 84.6214, loss: 0.5379 +2024-06-18 05:29:40,692 - mmseg - INFO - Iter [14750/80000] lr: 3.263e-05, eta: 1 day, 2:45:12, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3790, decode.acc_seg: 85.6167, aux.loss_ce: 0.1516, aux.acc_seg: 85.5008, loss: 0.5306 +2024-06-18 05:30:47,059 - mmseg - INFO - Iter [14800/80000] lr: 3.260e-05, eta: 1 day, 2:43:26, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3864, decode.acc_seg: 84.7557, aux.loss_ce: 0.1546, aux.acc_seg: 84.6238, loss: 0.5410 +2024-06-18 05:31:53,201 - mmseg - INFO - Iter [14850/80000] lr: 3.258e-05, eta: 1 day, 2:41:38, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.4042, decode.acc_seg: 84.3343, aux.loss_ce: 0.1620, aux.acc_seg: 84.2690, loss: 0.5663 +2024-06-18 05:32:59,407 - mmseg - INFO - Iter [14900/80000] lr: 3.255e-05, eta: 1 day, 2:39:52, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3815, decode.acc_seg: 84.8242, aux.loss_ce: 0.1520, aux.acc_seg: 84.9174, loss: 0.5335 +2024-06-18 05:34:05,689 - mmseg - INFO - Iter [14950/80000] lr: 3.253e-05, eta: 1 day, 2:38:06, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3565, decode.acc_seg: 85.3725, aux.loss_ce: 0.1436, aux.acc_seg: 85.4467, loss: 0.5002 +2024-06-18 05:35:11,878 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 05:35:11,878 - mmseg - INFO - Iter [15000/80000] lr: 3.250e-05, eta: 1 day, 2:36:19, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3835, decode.acc_seg: 84.7828, aux.loss_ce: 0.1522, aux.acc_seg: 84.8237, loss: 0.5357 +2024-06-18 05:36:47,377 - mmseg - INFO - per class results: +2024-06-18 05:36:47,383 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.02 | 89.04 | +| building | 83.51 | 90.82 | +| sky | 94.31 | 98.06 | +| floor | 82.88 | 89.95 | +| tree | 76.26 | 88.57 | +| ceiling | 85.44 | 91.46 | +| road | 84.4 | 92.06 | +| bed | 91.31 | 96.63 | +| windowpane | 64.69 | 78.68 | +| grass | 63.54 | 72.28 | +| cabinet | 63.37 | 76.69 | +| sidewalk | 70.05 | 82.24 | +| person | 83.34 | 91.53 | +| earth | 37.77 | 50.5 | +| door | 50.93 | 59.02 | +| table | 61.39 | 71.14 | +| mountain | 63.31 | 71.87 | +| plant | 57.15 | 69.75 | +| curtain | 78.43 | 91.05 | +| chair | 63.43 | 76.71 | +| car | 82.57 | 94.38 | +| water | 46.2 | 53.66 | +| painting | 76.76 | 89.65 | +| sofa | 77.79 | 91.07 | +| shelf | 47.17 | 70.34 | +| house | 55.02 | 82.22 | +| sea | 65.19 | 90.93 | +| mirror | 69.83 | 80.38 | +| rug | 70.42 | 89.56 | +| field | 30.23 | 80.46 | +| armchair | 57.15 | 73.15 | +| seat | 67.34 | 85.77 | +| fence | 48.71 | 67.7 | +| desk | 49.65 | 78.26 | +| rock | 54.21 | 80.45 | +| wardrobe | 53.34 | 69.19 | +| lamp | 68.81 | 80.3 | +| bathtub | 80.74 | 84.2 | +| railing | 36.39 | 50.85 | +| cushion | 63.47 | 73.09 | +| base | 42.52 | 53.57 | +| box | 28.92 | 38.88 | +| column | 41.45 | 45.67 | +| signboard | 38.51 | 54.61 | +| chest of drawers | 43.46 | 55.5 | +| counter | 43.79 | 63.22 | +| sand | 42.81 | 59.37 | +| sink | 69.92 | 84.54 | +| skyscraper | 51.45 | 63.45 | +| fireplace | 70.54 | 93.82 | +| refrigerator | 76.02 | 84.67 | +| grandstand | 46.59 | 85.38 | +| path | 20.71 | 25.19 | +| stairs | 31.52 | 39.22 | +| runway | 66.2 | 83.42 | +| case | 54.68 | 70.19 | +| pool table | 93.52 | 97.53 | +| pillow | 62.75 | 75.43 | +| screen door | 79.59 | 83.25 | +| stairway | 52.3 | 63.27 | +| river | 19.55 | 54.53 | +| bridge | 70.88 | 89.81 | +| bookcase | 37.55 | 60.28 | +| blind | 51.02 | 73.57 | +| coffee table | 56.65 | 85.39 | +| toilet | 86.96 | 92.69 | +| flower | 37.48 | 45.16 | +| book | 38.53 | 44.78 | +| hill | 4.71 | 8.64 | +| bench | 52.32 | 58.5 | +| countertop | 62.98 | 80.28 | +| stove | 79.72 | 87.73 | +| palm | 52.88 | 74.28 | +| kitchen island | 41.08 | 78.54 | +| computer | 74.39 | 94.45 | +| swivel chair | 48.34 | 77.27 | +| boat | 55.38 | 83.75 | +| bar | 59.56 | 65.14 | +| arcade machine | 78.48 | 84.39 | +| hovel | 46.93 | 51.02 | +| bus | 91.64 | 94.27 | +| towel | 70.58 | 78.44 | +| light | 54.65 | 67.78 | +| truck | 37.97 | 52.26 | +| tower | 20.76 | 40.05 | +| chandelier | 68.61 | 86.0 | +| awning | 33.38 | 38.46 | +| streetlight | 25.81 | 34.45 | +| booth | 23.53 | 40.5 | +| television receiver | 74.74 | 87.81 | +| airplane | 57.87 | 68.1 | +| dirt track | 7.03 | 69.8 | +| apparel | 44.49 | 68.62 | +| pole | 22.26 | 28.45 | +| land | 0.1 | 0.18 | +| bannister | 14.32 | 34.51 | +| escalator | 39.66 | 53.42 | +| ottoman | 50.59 | 67.58 | +| bottle | 31.85 | 38.84 | +| buffet | 49.6 | 78.44 | +| poster | 31.55 | 33.07 | +| stage | 17.13 | 30.87 | +| van | 33.68 | 36.27 | +| ship | 19.57 | 19.59 | +| fountain | 37.01 | 40.99 | +| conveyer belt | 70.19 | 92.86 | +| canopy | 40.12 | 71.94 | +| washer | 61.79 | 72.04 | +| plaything | 32.03 | 67.18 | +| swimming pool | 59.38 | 95.42 | +| stool | 48.89 | 64.12 | +| barrel | 23.73 | 65.05 | +| basket | 34.86 | 44.47 | +| waterfall | 51.19 | 70.07 | +| tent | 84.98 | 98.23 | +| bag | 12.62 | 13.62 | +| minibike | 65.66 | 86.95 | +| cradle | 83.83 | 96.8 | +| oven | 52.91 | 65.41 | +| ball | 52.67 | 69.63 | +| food | 60.6 | 79.14 | +| step | 9.56 | 10.9 | +| tank | 68.01 | 98.23 | +| trade name | 31.43 | 38.48 | +| microwave | 79.99 | 95.58 | +| pot | 52.45 | 62.34 | +| animal | 64.75 | 68.05 | +| bicycle | 57.94 | 74.59 | +| lake | 54.42 | 58.59 | +| dishwasher | 58.47 | 66.07 | +| screen | 61.75 | 93.91 | +| blanket | 23.86 | 29.77 | +| sculpture | 57.54 | 64.92 | +| hood | 64.06 | 75.78 | +| sconce | 49.92 | 60.42 | +| vase | 37.47 | 55.55 | +| traffic light | 28.67 | 55.81 | +| tray | 10.05 | 12.33 | +| ashcan | 41.74 | 61.01 | +| fan | 62.41 | 80.7 | +| pier | 37.44 | 43.05 | +| crt screen | 8.69 | 17.21 | +| plate | 43.59 | 82.41 | +| monitor | 37.82 | 43.01 | +| bulletin board | 49.94 | 66.6 | +| shower | 0.0 | 0.0 | +| radiator | 60.95 | 73.14 | +| glass | 13.02 | 13.65 | +| clock | 32.8 | 36.25 | +| flag | 69.23 | 76.34 | ++---------------------+-------+-------+ +2024-06-18 05:36:47,383 - mmseg - INFO - Summary: +2024-06-18 05:36:47,383 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 84.33 | 52.07 | 66.4 | ++-------+-------+------+ +2024-06-18 05:36:47,384 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 05:36:47,384 - mmseg - INFO - Iter(val) [250] aAcc: 0.8433, mIoU: 0.5207, mAcc: 0.6640, IoU.wall: 0.8002, IoU.building: 0.8351, IoU.sky: 0.9431, IoU.floor: 0.8288, IoU.tree: 0.7626, IoU.ceiling: 0.8544, IoU.road: 0.8440, IoU.bed : 0.9131, IoU.windowpane: 0.6469, IoU.grass: 0.6354, IoU.cabinet: 0.6337, IoU.sidewalk: 0.7005, IoU.person: 0.8334, IoU.earth: 0.3777, IoU.door: 0.5093, IoU.table: 0.6139, IoU.mountain: 0.6331, IoU.plant: 0.5715, IoU.curtain: 0.7843, IoU.chair: 0.6343, IoU.car: 0.8257, IoU.water: 0.4620, IoU.painting: 0.7676, IoU.sofa: 0.7779, IoU.shelf: 0.4717, IoU.house: 0.5502, IoU.sea: 0.6519, IoU.mirror: 0.6983, IoU.rug: 0.7042, IoU.field: 0.3023, IoU.armchair: 0.5715, IoU.seat: 0.6734, IoU.fence: 0.4871, IoU.desk: 0.4965, IoU.rock: 0.5421, IoU.wardrobe: 0.5334, IoU.lamp: 0.6881, IoU.bathtub: 0.8074, IoU.railing: 0.3639, IoU.cushion: 0.6347, IoU.base: 0.4252, IoU.box: 0.2892, IoU.column: 0.4145, IoU.signboard: 0.3851, IoU.chest of drawers: 0.4346, IoU.counter: 0.4379, IoU.sand: 0.4281, IoU.sink: 0.6992, IoU.skyscraper: 0.5145, IoU.fireplace: 0.7054, IoU.refrigerator: 0.7602, IoU.grandstand: 0.4659, IoU.path: 0.2071, IoU.stairs: 0.3152, IoU.runway: 0.6620, IoU.case: 0.5468, IoU.pool table: 0.9352, IoU.pillow: 0.6275, IoU.screen door: 0.7959, IoU.stairway: 0.5230, IoU.river: 0.1955, IoU.bridge: 0.7088, IoU.bookcase: 0.3755, IoU.blind: 0.5102, IoU.coffee table: 0.5665, IoU.toilet: 0.8696, IoU.flower: 0.3748, IoU.book: 0.3853, IoU.hill: 0.0471, IoU.bench: 0.5232, IoU.countertop: 0.6298, IoU.stove: 0.7972, IoU.palm: 0.5288, IoU.kitchen island: 0.4108, IoU.computer: 0.7439, IoU.swivel chair: 0.4834, IoU.boat: 0.5538, IoU.bar: 0.5956, IoU.arcade machine: 0.7848, IoU.hovel: 0.4693, IoU.bus: 0.9164, IoU.towel: 0.7058, IoU.light: 0.5465, IoU.truck: 0.3797, IoU.tower: 0.2076, IoU.chandelier: 0.6861, IoU.awning: 0.3338, IoU.streetlight: 0.2581, IoU.booth: 0.2353, IoU.television receiver: 0.7474, IoU.airplane: 0.5787, IoU.dirt track: 0.0703, IoU.apparel: 0.4449, IoU.pole: 0.2226, IoU.land: 0.0010, IoU.bannister: 0.1432, IoU.escalator: 0.3966, IoU.ottoman: 0.5059, IoU.bottle: 0.3185, IoU.buffet: 0.4960, IoU.poster: 0.3155, IoU.stage: 0.1713, IoU.van: 0.3368, IoU.ship: 0.1957, IoU.fountain: 0.3701, IoU.conveyer belt: 0.7019, IoU.canopy: 0.4012, IoU.washer: 0.6179, IoU.plaything: 0.3203, IoU.swimming pool: 0.5938, IoU.stool: 0.4889, IoU.barrel: 0.2373, IoU.basket: 0.3486, IoU.waterfall: 0.5119, IoU.tent: 0.8498, IoU.bag: 0.1262, IoU.minibike: 0.6566, IoU.cradle: 0.8383, IoU.oven: 0.5291, IoU.ball: 0.5267, IoU.food: 0.6060, IoU.step: 0.0956, IoU.tank: 0.6801, IoU.trade name: 0.3143, IoU.microwave: 0.7999, IoU.pot: 0.5245, IoU.animal: 0.6475, IoU.bicycle: 0.5794, IoU.lake: 0.5442, IoU.dishwasher: 0.5847, IoU.screen: 0.6175, IoU.blanket: 0.2386, IoU.sculpture: 0.5754, IoU.hood: 0.6406, IoU.sconce: 0.4992, IoU.vase: 0.3747, IoU.traffic light: 0.2867, IoU.tray: 0.1005, IoU.ashcan: 0.4174, IoU.fan: 0.6241, IoU.pier: 0.3744, IoU.crt screen: 0.0869, IoU.plate: 0.4359, IoU.monitor: 0.3782, IoU.bulletin board: 0.4994, IoU.shower: 0.0000, IoU.radiator: 0.6095, IoU.glass: 0.1302, IoU.clock: 0.3280, IoU.flag: 0.6923, Acc.wall: 0.8904, Acc.building: 0.9082, Acc.sky: 0.9806, Acc.floor: 0.8995, Acc.tree: 0.8857, Acc.ceiling: 0.9146, Acc.road: 0.9206, Acc.bed : 0.9663, Acc.windowpane: 0.7868, Acc.grass: 0.7228, Acc.cabinet: 0.7669, Acc.sidewalk: 0.8224, Acc.person: 0.9153, Acc.earth: 0.5050, Acc.door: 0.5902, Acc.table: 0.7114, Acc.mountain: 0.7187, Acc.plant: 0.6975, Acc.curtain: 0.9105, Acc.chair: 0.7671, Acc.car: 0.9438, Acc.water: 0.5366, Acc.painting: 0.8965, Acc.sofa: 0.9107, Acc.shelf: 0.7034, Acc.house: 0.8222, Acc.sea: 0.9093, Acc.mirror: 0.8038, Acc.rug: 0.8956, Acc.field: 0.8046, Acc.armchair: 0.7315, Acc.seat: 0.8577, Acc.fence: 0.6770, Acc.desk: 0.7826, Acc.rock: 0.8045, Acc.wardrobe: 0.6919, Acc.lamp: 0.8030, Acc.bathtub: 0.8420, Acc.railing: 0.5085, Acc.cushion: 0.7309, Acc.base: 0.5357, Acc.box: 0.3888, Acc.column: 0.4567, Acc.signboard: 0.5461, Acc.chest of drawers: 0.5550, Acc.counter: 0.6322, Acc.sand: 0.5937, Acc.sink: 0.8454, Acc.skyscraper: 0.6345, Acc.fireplace: 0.9382, Acc.refrigerator: 0.8467, Acc.grandstand: 0.8538, Acc.path: 0.2519, Acc.stairs: 0.3922, Acc.runway: 0.8342, Acc.case: 0.7019, Acc.pool table: 0.9753, Acc.pillow: 0.7543, Acc.screen door: 0.8325, Acc.stairway: 0.6327, Acc.river: 0.5453, Acc.bridge: 0.8981, Acc.bookcase: 0.6028, Acc.blind: 0.7357, Acc.coffee table: 0.8539, Acc.toilet: 0.9269, Acc.flower: 0.4516, Acc.book: 0.4478, Acc.hill: 0.0864, Acc.bench: 0.5850, Acc.countertop: 0.8028, Acc.stove: 0.8773, Acc.palm: 0.7428, Acc.kitchen island: 0.7854, Acc.computer: 0.9445, Acc.swivel chair: 0.7727, Acc.boat: 0.8375, Acc.bar: 0.6514, Acc.arcade machine: 0.8439, Acc.hovel: 0.5102, Acc.bus: 0.9427, Acc.towel: 0.7844, Acc.light: 0.6778, Acc.truck: 0.5226, Acc.tower: 0.4005, Acc.chandelier: 0.8600, Acc.awning: 0.3846, Acc.streetlight: 0.3445, Acc.booth: 0.4050, Acc.television receiver: 0.8781, Acc.airplane: 0.6810, Acc.dirt track: 0.6980, Acc.apparel: 0.6862, Acc.pole: 0.2845, Acc.land: 0.0018, Acc.bannister: 0.3451, Acc.escalator: 0.5342, Acc.ottoman: 0.6758, Acc.bottle: 0.3884, Acc.buffet: 0.7844, Acc.poster: 0.3307, Acc.stage: 0.3087, Acc.van: 0.3627, Acc.ship: 0.1959, Acc.fountain: 0.4099, Acc.conveyer belt: 0.9286, Acc.canopy: 0.7194, Acc.washer: 0.7204, Acc.plaything: 0.6718, Acc.swimming pool: 0.9542, Acc.stool: 0.6412, Acc.barrel: 0.6505, Acc.basket: 0.4447, Acc.waterfall: 0.7007, Acc.tent: 0.9823, Acc.bag: 0.1362, Acc.minibike: 0.8695, Acc.cradle: 0.9680, Acc.oven: 0.6541, Acc.ball: 0.6963, Acc.food: 0.7914, Acc.step: 0.1090, Acc.tank: 0.9823, Acc.trade name: 0.3848, Acc.microwave: 0.9558, Acc.pot: 0.6234, Acc.animal: 0.6805, Acc.bicycle: 0.7459, Acc.lake: 0.5859, Acc.dishwasher: 0.6607, Acc.screen: 0.9391, Acc.blanket: 0.2977, Acc.sculpture: 0.6492, Acc.hood: 0.7578, Acc.sconce: 0.6042, Acc.vase: 0.5555, Acc.traffic light: 0.5581, Acc.tray: 0.1233, Acc.ashcan: 0.6101, Acc.fan: 0.8070, Acc.pier: 0.4305, Acc.crt screen: 0.1721, Acc.plate: 0.8241, Acc.monitor: 0.4301, Acc.bulletin board: 0.6660, Acc.shower: 0.0000, Acc.radiator: 0.7314, Acc.glass: 0.1365, Acc.clock: 0.3625, Acc.flag: 0.7634 +2024-06-18 05:37:54,161 - mmseg - INFO - Iter [15050/80000] lr: 3.248e-05, eta: 1 day, 2:41:28, time: 3.246, data_time: 1.926, memory: 70498, decode.loss_ce: 0.3832, decode.acc_seg: 85.1356, aux.loss_ce: 0.1529, aux.acc_seg: 85.0544, loss: 0.5362 +2024-06-18 05:39:00,547 - mmseg - INFO - Iter [15100/80000] lr: 3.245e-05, eta: 1 day, 2:39:41, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3609, decode.acc_seg: 86.0218, aux.loss_ce: 0.1462, aux.acc_seg: 85.8958, loss: 0.5071 +2024-06-18 05:40:06,802 - mmseg - INFO - Iter [15150/80000] lr: 3.243e-05, eta: 1 day, 2:37:55, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3725, decode.acc_seg: 85.4096, aux.loss_ce: 0.1503, aux.acc_seg: 85.2896, loss: 0.5228 +2024-06-18 05:41:15,333 - mmseg - INFO - Iter [15200/80000] lr: 3.240e-05, eta: 1 day, 2:36:18, time: 1.371, data_time: 0.052, memory: 70498, decode.loss_ce: 0.3447, decode.acc_seg: 86.8077, aux.loss_ce: 0.1402, aux.acc_seg: 86.6013, loss: 0.4850 +2024-06-18 05:42:21,917 - mmseg - INFO - Iter [15250/80000] lr: 3.238e-05, eta: 1 day, 2:34:33, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3603, decode.acc_seg: 85.9011, aux.loss_ce: 0.1441, aux.acc_seg: 85.8769, loss: 0.5044 +2024-06-18 05:43:28,791 - mmseg - INFO - Iter [15300/80000] lr: 3.235e-05, eta: 1 day, 2:32:49, time: 1.337, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3548, decode.acc_seg: 85.8913, aux.loss_ce: 0.1439, aux.acc_seg: 85.7519, loss: 0.4987 +2024-06-18 05:44:35,301 - mmseg - INFO - Iter [15350/80000] lr: 3.233e-05, eta: 1 day, 2:31:04, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3740, decode.acc_seg: 85.3483, aux.loss_ce: 0.1520, aux.acc_seg: 85.1345, loss: 0.5259 +2024-06-18 05:45:41,865 - mmseg - INFO - Iter [15400/80000] lr: 3.230e-05, eta: 1 day, 2:29:20, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3762, decode.acc_seg: 85.0975, aux.loss_ce: 0.1525, aux.acc_seg: 85.0604, loss: 0.5288 +2024-06-18 05:46:48,180 - mmseg - INFO - Iter [15450/80000] lr: 3.228e-05, eta: 1 day, 2:27:35, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3410, decode.acc_seg: 86.3627, aux.loss_ce: 0.1385, aux.acc_seg: 86.1087, loss: 0.4795 +2024-06-18 05:47:54,435 - mmseg - INFO - Iter [15500/80000] lr: 3.225e-05, eta: 1 day, 2:25:50, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3446, decode.acc_seg: 86.1848, aux.loss_ce: 0.1393, aux.acc_seg: 86.0179, loss: 0.4839 +2024-06-18 05:49:00,870 - mmseg - INFO - Iter [15550/80000] lr: 3.223e-05, eta: 1 day, 2:24:06, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3897, decode.acc_seg: 84.7728, aux.loss_ce: 0.1553, aux.acc_seg: 84.9480, loss: 0.5449 +2024-06-18 05:50:07,220 - mmseg - INFO - Iter [15600/80000] lr: 3.220e-05, eta: 1 day, 2:22:22, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3631, decode.acc_seg: 85.3886, aux.loss_ce: 0.1459, aux.acc_seg: 85.2152, loss: 0.5090 +2024-06-18 05:51:13,622 - mmseg - INFO - Iter [15650/80000] lr: 3.218e-05, eta: 1 day, 2:20:38, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3679, decode.acc_seg: 85.3049, aux.loss_ce: 0.1469, aux.acc_seg: 85.1885, loss: 0.5148 +2024-06-18 05:52:19,864 - mmseg - INFO - Iter [15700/80000] lr: 3.215e-05, eta: 1 day, 2:18:54, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3834, decode.acc_seg: 85.1238, aux.loss_ce: 0.1536, aux.acc_seg: 85.0573, loss: 0.5371 +2024-06-18 05:53:25,973 - mmseg - INFO - Iter [15750/80000] lr: 3.213e-05, eta: 1 day, 2:17:09, time: 1.322, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3915, decode.acc_seg: 84.4464, aux.loss_ce: 0.1571, aux.acc_seg: 84.4799, loss: 0.5486 +2024-06-18 05:54:32,234 - mmseg - INFO - Iter [15800/80000] lr: 3.210e-05, eta: 1 day, 2:15:25, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3622, decode.acc_seg: 85.1993, aux.loss_ce: 0.1451, aux.acc_seg: 85.0942, loss: 0.5072 +2024-06-18 05:55:38,530 - mmseg - INFO - Iter [15850/80000] lr: 3.208e-05, eta: 1 day, 2:13:42, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3540, decode.acc_seg: 85.8599, aux.loss_ce: 0.1438, aux.acc_seg: 85.6670, loss: 0.4978 +2024-06-18 05:56:44,779 - mmseg - INFO - Iter [15900/80000] lr: 3.205e-05, eta: 1 day, 2:11:59, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3971, decode.acc_seg: 84.3353, aux.loss_ce: 0.1597, aux.acc_seg: 84.2848, loss: 0.5568 +2024-06-18 05:57:51,348 - mmseg - INFO - Iter [15950/80000] lr: 3.203e-05, eta: 1 day, 2:10:17, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3812, decode.acc_seg: 85.1741, aux.loss_ce: 0.1534, aux.acc_seg: 85.0357, loss: 0.5346 +2024-06-18 05:58:57,580 - mmseg - INFO - Saving checkpoint at 16000 iterations +2024-06-18 06:00:40,372 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 06:00:40,373 - mmseg - INFO - Iter [16000/80000] lr: 3.200e-05, eta: 1 day, 2:15:26, time: 3.380, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3892, decode.acc_seg: 84.7255, aux.loss_ce: 0.1563, aux.acc_seg: 84.5722, loss: 0.5455 +2024-06-18 06:02:18,509 - mmseg - INFO - per class results: +2024-06-18 06:02:18,515 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.0 | 87.46 | +| building | 82.74 | 92.63 | +| sky | 94.49 | 97.26 | +| floor | 84.0 | 90.49 | +| tree | 76.55 | 89.39 | +| ceiling | 85.6 | 93.6 | +| road | 84.85 | 91.9 | +| bed | 91.8 | 96.19 | +| windowpane | 64.21 | 84.12 | +| grass | 66.63 | 75.73 | +| cabinet | 62.47 | 71.91 | +| sidewalk | 65.81 | 78.31 | +| person | 83.21 | 91.02 | +| earth | 35.22 | 45.94 | +| door | 55.74 | 73.76 | +| table | 63.8 | 77.3 | +| mountain | 60.94 | 69.82 | +| plant | 55.68 | 68.86 | +| curtain | 76.98 | 87.09 | +| chair | 58.33 | 65.55 | +| car | 84.4 | 92.94 | +| water | 62.62 | 78.93 | +| painting | 73.76 | 90.13 | +| sofa | 79.62 | 89.54 | +| shelf | 48.39 | 69.44 | +| house | 46.36 | 61.02 | +| sea | 70.81 | 76.39 | +| mirror | 71.14 | 76.82 | +| rug | 72.44 | 86.35 | +| field | 35.68 | 80.04 | +| armchair | 54.55 | 77.7 | +| seat | 64.59 | 89.18 | +| fence | 43.31 | 67.56 | +| desk | 51.48 | 77.95 | +| rock | 49.31 | 73.46 | +| wardrobe | 56.22 | 86.49 | +| lamp | 68.72 | 83.27 | +| bathtub | 74.74 | 86.66 | +| railing | 31.06 | 39.35 | +| cushion | 65.33 | 78.13 | +| base | 37.18 | 49.4 | +| box | 30.55 | 44.18 | +| column | 52.69 | 67.71 | +| signboard | 38.26 | 49.68 | +| chest of drawers | 46.05 | 74.77 | +| counter | 46.14 | 65.29 | +| sand | 49.29 | 77.11 | +| sink | 68.78 | 76.45 | +| skyscraper | 47.66 | 69.41 | +| fireplace | 70.86 | 91.45 | +| refrigerator | 74.43 | 89.4 | +| grandstand | 46.77 | 87.63 | +| path | 27.48 | 43.25 | +| stairs | 30.37 | 36.61 | +| runway | 73.73 | 97.53 | +| case | 52.79 | 56.5 | +| pool table | 90.16 | 98.12 | +| pillow | 68.48 | 83.66 | +| screen door | 60.13 | 63.82 | +| stairway | 39.18 | 51.21 | +| river | 17.2 | 30.9 | +| bridge | 67.56 | 90.8 | +| bookcase | 39.11 | 58.11 | +| blind | 39.47 | 41.11 | +| coffee table | 64.66 | 85.46 | +| toilet | 85.9 | 95.87 | +| flower | 35.15 | 43.27 | +| book | 46.15 | 59.61 | +| hill | 5.42 | 12.2 | +| bench | 47.7 | 62.61 | +| countertop | 61.04 | 70.22 | +| stove | 79.06 | 93.97 | +| palm | 53.25 | 69.81 | +| kitchen island | 41.57 | 88.38 | +| computer | 71.24 | 95.44 | +| swivel chair | 45.68 | 70.69 | +| boat | 58.8 | 87.04 | +| bar | 62.69 | 68.98 | +| arcade machine | 84.08 | 95.34 | +| hovel | 67.51 | 77.04 | +| bus | 91.35 | 94.6 | +| towel | 68.92 | 83.45 | +| light | 52.56 | 58.58 | +| truck | 41.91 | 59.63 | +| tower | 23.91 | 49.04 | +| chandelier | 68.07 | 86.7 | +| awning | 34.67 | 40.72 | +| streetlight | 24.97 | 32.0 | +| booth | 50.1 | 56.46 | +| television receiver | 69.11 | 80.95 | +| airplane | 58.08 | 75.8 | +| dirt track | 4.21 | 7.54 | +| apparel | 40.95 | 61.84 | +| pole | 18.69 | 22.39 | +| land | 0.05 | 0.08 | +| bannister | 12.32 | 17.94 | +| escalator | 52.32 | 80.78 | +| ottoman | 48.14 | 70.6 | +| bottle | 40.89 | 60.12 | +| buffet | 45.16 | 67.49 | +| poster | 27.49 | 32.17 | +| stage | 17.42 | 31.56 | +| van | 35.28 | 40.76 | +| ship | 84.33 | 93.1 | +| fountain | 21.55 | 21.91 | +| conveyer belt | 66.23 | 93.3 | +| canopy | 34.8 | 56.51 | +| washer | 61.24 | 70.82 | +| plaything | 19.29 | 36.41 | +| swimming pool | 61.6 | 93.0 | +| stool | 40.44 | 65.96 | +| barrel | 31.84 | 65.12 | +| basket | 31.68 | 49.48 | +| waterfall | 61.8 | 92.52 | +| tent | 90.36 | 98.69 | +| bag | 15.72 | 16.69 | +| minibike | 67.97 | 81.37 | +| cradle | 77.51 | 98.38 | +| oven | 38.73 | 42.48 | +| ball | 49.96 | 72.06 | +| food | 58.45 | 84.67 | +| step | 11.71 | 14.39 | +| tank | 49.58 | 56.91 | +| trade name | 15.31 | 16.52 | +| microwave | 83.05 | 94.45 | +| pot | 50.44 | 66.94 | +| animal | 64.87 | 66.57 | +| bicycle | 55.87 | 69.55 | +| lake | 41.52 | 48.72 | +| dishwasher | 52.07 | 64.96 | +| screen | 61.69 | 94.71 | +| blanket | 16.48 | 18.3 | +| sculpture | 58.88 | 66.2 | +| hood | 57.51 | 70.06 | +| sconce | 45.24 | 52.19 | +| vase | 38.13 | 54.39 | +| traffic light | 28.53 | 56.61 | +| tray | 12.37 | 16.16 | +| ashcan | 35.77 | 61.49 | +| fan | 60.97 | 70.01 | +| pier | 36.07 | 42.35 | +| crt screen | 1.48 | 2.63 | +| plate | 52.92 | 67.9 | +| monitor | 37.99 | 47.58 | +| bulletin board | 47.98 | 63.1 | +| shower | 0.07 | 0.75 | +| radiator | 60.65 | 69.96 | +| glass | 13.16 | 13.79 | +| clock | 30.99 | 39.84 | +| flag | 55.47 | 56.85 | ++---------------------+-------+-------+ +2024-06-18 06:02:18,515 - mmseg - INFO - Summary: +2024-06-18 06:02:18,515 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.41 | 51.83 | 65.78 | ++-------+-------+-------+ +2024-06-18 06:02:18,516 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 06:02:18,516 - mmseg - INFO - Iter(val) [250] aAcc: 0.8441, mIoU: 0.5183, mAcc: 0.6578, IoU.wall: 0.8000, IoU.building: 0.8274, IoU.sky: 0.9449, IoU.floor: 0.8400, IoU.tree: 0.7655, IoU.ceiling: 0.8560, IoU.road: 0.8485, IoU.bed : 0.9180, IoU.windowpane: 0.6421, IoU.grass: 0.6663, IoU.cabinet: 0.6247, IoU.sidewalk: 0.6581, IoU.person: 0.8321, IoU.earth: 0.3522, IoU.door: 0.5574, IoU.table: 0.6380, IoU.mountain: 0.6094, IoU.plant: 0.5568, IoU.curtain: 0.7698, IoU.chair: 0.5833, IoU.car: 0.8440, IoU.water: 0.6262, IoU.painting: 0.7376, IoU.sofa: 0.7962, IoU.shelf: 0.4839, IoU.house: 0.4636, IoU.sea: 0.7081, IoU.mirror: 0.7114, IoU.rug: 0.7244, IoU.field: 0.3568, IoU.armchair: 0.5455, IoU.seat: 0.6459, IoU.fence: 0.4331, IoU.desk: 0.5148, IoU.rock: 0.4931, IoU.wardrobe: 0.5622, IoU.lamp: 0.6872, IoU.bathtub: 0.7474, IoU.railing: 0.3106, IoU.cushion: 0.6533, IoU.base: 0.3718, IoU.box: 0.3055, IoU.column: 0.5269, IoU.signboard: 0.3826, IoU.chest of drawers: 0.4605, IoU.counter: 0.4614, IoU.sand: 0.4929, IoU.sink: 0.6878, IoU.skyscraper: 0.4766, IoU.fireplace: 0.7086, IoU.refrigerator: 0.7443, IoU.grandstand: 0.4677, IoU.path: 0.2748, IoU.stairs: 0.3037, IoU.runway: 0.7373, IoU.case: 0.5279, IoU.pool table: 0.9016, IoU.pillow: 0.6848, IoU.screen door: 0.6013, IoU.stairway: 0.3918, IoU.river: 0.1720, IoU.bridge: 0.6756, IoU.bookcase: 0.3911, IoU.blind: 0.3947, IoU.coffee table: 0.6466, IoU.toilet: 0.8590, IoU.flower: 0.3515, IoU.book: 0.4615, IoU.hill: 0.0542, IoU.bench: 0.4770, IoU.countertop: 0.6104, IoU.stove: 0.7906, IoU.palm: 0.5325, IoU.kitchen island: 0.4157, IoU.computer: 0.7124, IoU.swivel chair: 0.4568, IoU.boat: 0.5880, IoU.bar: 0.6269, IoU.arcade machine: 0.8408, IoU.hovel: 0.6751, IoU.bus: 0.9135, IoU.towel: 0.6892, IoU.light: 0.5256, IoU.truck: 0.4191, IoU.tower: 0.2391, IoU.chandelier: 0.6807, IoU.awning: 0.3467, IoU.streetlight: 0.2497, IoU.booth: 0.5010, IoU.television receiver: 0.6911, IoU.airplane: 0.5808, IoU.dirt track: 0.0421, IoU.apparel: 0.4095, IoU.pole: 0.1869, IoU.land: 0.0005, IoU.bannister: 0.1232, IoU.escalator: 0.5232, IoU.ottoman: 0.4814, IoU.bottle: 0.4089, IoU.buffet: 0.4516, IoU.poster: 0.2749, IoU.stage: 0.1742, IoU.van: 0.3528, IoU.ship: 0.8433, IoU.fountain: 0.2155, IoU.conveyer belt: 0.6623, IoU.canopy: 0.3480, IoU.washer: 0.6124, IoU.plaything: 0.1929, IoU.swimming pool: 0.6160, IoU.stool: 0.4044, IoU.barrel: 0.3184, IoU.basket: 0.3168, IoU.waterfall: 0.6180, IoU.tent: 0.9036, IoU.bag: 0.1572, IoU.minibike: 0.6797, IoU.cradle: 0.7751, IoU.oven: 0.3873, IoU.ball: 0.4996, IoU.food: 0.5845, IoU.step: 0.1171, IoU.tank: 0.4958, IoU.trade name: 0.1531, IoU.microwave: 0.8305, IoU.pot: 0.5044, IoU.animal: 0.6487, IoU.bicycle: 0.5587, IoU.lake: 0.4152, IoU.dishwasher: 0.5207, IoU.screen: 0.6169, IoU.blanket: 0.1648, IoU.sculpture: 0.5888, IoU.hood: 0.5751, IoU.sconce: 0.4524, IoU.vase: 0.3813, IoU.traffic light: 0.2853, IoU.tray: 0.1237, IoU.ashcan: 0.3577, IoU.fan: 0.6097, IoU.pier: 0.3607, IoU.crt screen: 0.0148, IoU.plate: 0.5292, IoU.monitor: 0.3799, IoU.bulletin board: 0.4798, IoU.shower: 0.0007, IoU.radiator: 0.6065, IoU.glass: 0.1316, IoU.clock: 0.3099, IoU.flag: 0.5547, Acc.wall: 0.8746, Acc.building: 0.9263, Acc.sky: 0.9726, Acc.floor: 0.9049, Acc.tree: 0.8939, Acc.ceiling: 0.9360, Acc.road: 0.9190, Acc.bed : 0.9619, Acc.windowpane: 0.8412, Acc.grass: 0.7573, Acc.cabinet: 0.7191, Acc.sidewalk: 0.7831, Acc.person: 0.9102, Acc.earth: 0.4594, Acc.door: 0.7376, Acc.table: 0.7730, Acc.mountain: 0.6982, Acc.plant: 0.6886, Acc.curtain: 0.8709, Acc.chair: 0.6555, Acc.car: 0.9294, Acc.water: 0.7893, Acc.painting: 0.9013, Acc.sofa: 0.8954, Acc.shelf: 0.6944, Acc.house: 0.6102, Acc.sea: 0.7639, Acc.mirror: 0.7682, Acc.rug: 0.8635, Acc.field: 0.8004, Acc.armchair: 0.7770, Acc.seat: 0.8918, Acc.fence: 0.6756, Acc.desk: 0.7795, Acc.rock: 0.7346, Acc.wardrobe: 0.8649, Acc.lamp: 0.8327, Acc.bathtub: 0.8666, Acc.railing: 0.3935, Acc.cushion: 0.7813, Acc.base: 0.4940, Acc.box: 0.4418, Acc.column: 0.6771, Acc.signboard: 0.4968, Acc.chest of drawers: 0.7477, Acc.counter: 0.6529, Acc.sand: 0.7711, Acc.sink: 0.7645, Acc.skyscraper: 0.6941, Acc.fireplace: 0.9145, Acc.refrigerator: 0.8940, Acc.grandstand: 0.8763, Acc.path: 0.4325, Acc.stairs: 0.3661, Acc.runway: 0.9753, Acc.case: 0.5650, Acc.pool table: 0.9812, Acc.pillow: 0.8366, Acc.screen door: 0.6382, Acc.stairway: 0.5121, Acc.river: 0.3090, Acc.bridge: 0.9080, Acc.bookcase: 0.5811, Acc.blind: 0.4111, Acc.coffee table: 0.8546, Acc.toilet: 0.9587, Acc.flower: 0.4327, Acc.book: 0.5961, Acc.hill: 0.1220, Acc.bench: 0.6261, Acc.countertop: 0.7022, Acc.stove: 0.9397, Acc.palm: 0.6981, Acc.kitchen island: 0.8838, Acc.computer: 0.9544, Acc.swivel chair: 0.7069, Acc.boat: 0.8704, Acc.bar: 0.6898, Acc.arcade machine: 0.9534, Acc.hovel: 0.7704, Acc.bus: 0.9460, Acc.towel: 0.8345, Acc.light: 0.5858, Acc.truck: 0.5963, Acc.tower: 0.4904, Acc.chandelier: 0.8670, Acc.awning: 0.4072, Acc.streetlight: 0.3200, Acc.booth: 0.5646, Acc.television receiver: 0.8095, Acc.airplane: 0.7580, Acc.dirt track: 0.0754, Acc.apparel: 0.6184, Acc.pole: 0.2239, Acc.land: 0.0008, Acc.bannister: 0.1794, Acc.escalator: 0.8078, Acc.ottoman: 0.7060, Acc.bottle: 0.6012, Acc.buffet: 0.6749, Acc.poster: 0.3217, Acc.stage: 0.3156, Acc.van: 0.4076, Acc.ship: 0.9310, Acc.fountain: 0.2191, Acc.conveyer belt: 0.9330, Acc.canopy: 0.5651, Acc.washer: 0.7082, Acc.plaything: 0.3641, Acc.swimming pool: 0.9300, Acc.stool: 0.6596, Acc.barrel: 0.6512, Acc.basket: 0.4948, Acc.waterfall: 0.9252, Acc.tent: 0.9869, Acc.bag: 0.1669, Acc.minibike: 0.8137, Acc.cradle: 0.9838, Acc.oven: 0.4248, Acc.ball: 0.7206, Acc.food: 0.8467, Acc.step: 0.1439, Acc.tank: 0.5691, Acc.trade name: 0.1652, Acc.microwave: 0.9445, Acc.pot: 0.6694, Acc.animal: 0.6657, Acc.bicycle: 0.6955, Acc.lake: 0.4872, Acc.dishwasher: 0.6496, Acc.screen: 0.9471, Acc.blanket: 0.1830, Acc.sculpture: 0.6620, Acc.hood: 0.7006, Acc.sconce: 0.5219, Acc.vase: 0.5439, Acc.traffic light: 0.5661, Acc.tray: 0.1616, Acc.ashcan: 0.6149, Acc.fan: 0.7001, Acc.pier: 0.4235, Acc.crt screen: 0.0263, Acc.plate: 0.6790, Acc.monitor: 0.4758, Acc.bulletin board: 0.6310, Acc.shower: 0.0075, Acc.radiator: 0.6996, Acc.glass: 0.1379, Acc.clock: 0.3984, Acc.flag: 0.5685 +2024-06-18 06:03:25,277 - mmseg - INFO - Iter [16050/80000] lr: 3.198e-05, eta: 1 day, 2:20:15, time: 3.298, data_time: 1.978, memory: 70498, decode.loss_ce: 0.3546, decode.acc_seg: 85.6730, aux.loss_ce: 0.1427, aux.acc_seg: 85.5674, loss: 0.4974 +2024-06-18 06:04:31,633 - mmseg - INFO - Iter [16100/80000] lr: 3.195e-05, eta: 1 day, 2:18:30, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3852, decode.acc_seg: 84.9516, aux.loss_ce: 0.1542, aux.acc_seg: 84.9652, loss: 0.5394 +2024-06-18 06:05:37,960 - mmseg - INFO - Iter [16150/80000] lr: 3.193e-05, eta: 1 day, 2:16:45, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3728, decode.acc_seg: 85.4860, aux.loss_ce: 0.1512, aux.acc_seg: 85.3317, loss: 0.5240 +2024-06-18 06:06:44,306 - mmseg - INFO - Iter [16200/80000] lr: 3.190e-05, eta: 1 day, 2:15:00, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3460, decode.acc_seg: 86.3728, aux.loss_ce: 0.1404, aux.acc_seg: 86.1568, loss: 0.4864 +2024-06-18 06:07:50,662 - mmseg - INFO - Iter [16250/80000] lr: 3.188e-05, eta: 1 day, 2:13:16, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3561, decode.acc_seg: 85.8895, aux.loss_ce: 0.1441, aux.acc_seg: 85.7345, loss: 0.5002 +2024-06-18 06:08:57,094 - mmseg - INFO - Iter [16300/80000] lr: 3.185e-05, eta: 1 day, 2:11:32, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3672, decode.acc_seg: 85.4944, aux.loss_ce: 0.1493, aux.acc_seg: 85.2426, loss: 0.5164 +2024-06-18 06:10:03,503 - mmseg - INFO - Iter [16350/80000] lr: 3.183e-05, eta: 1 day, 2:09:48, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.4061, decode.acc_seg: 83.8890, aux.loss_ce: 0.1627, aux.acc_seg: 83.8901, loss: 0.5688 +2024-06-18 06:11:10,069 - mmseg - INFO - Iter [16400/80000] lr: 3.180e-05, eta: 1 day, 2:08:06, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3683, decode.acc_seg: 85.3330, aux.loss_ce: 0.1480, aux.acc_seg: 85.1769, loss: 0.5163 +2024-06-18 06:12:18,447 - mmseg - INFO - Iter [16450/80000] lr: 3.178e-05, eta: 1 day, 2:06:30, time: 1.368, data_time: 0.052, memory: 70498, decode.loss_ce: 0.3703, decode.acc_seg: 84.7534, aux.loss_ce: 0.1491, aux.acc_seg: 84.8308, loss: 0.5193 +2024-06-18 06:13:24,823 - mmseg - INFO - Iter [16500/80000] lr: 3.175e-05, eta: 1 day, 2:04:47, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3388, decode.acc_seg: 86.4291, aux.loss_ce: 0.1376, aux.acc_seg: 86.2530, loss: 0.4764 +2024-06-18 06:14:31,152 - mmseg - INFO - Iter [16550/80000] lr: 3.173e-05, eta: 1 day, 2:03:04, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3435, decode.acc_seg: 86.6860, aux.loss_ce: 0.1389, aux.acc_seg: 86.4470, loss: 0.4824 +2024-06-18 06:15:37,751 - mmseg - INFO - Iter [16600/80000] lr: 3.170e-05, eta: 1 day, 2:01:22, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3422, decode.acc_seg: 86.4009, aux.loss_ce: 0.1377, aux.acc_seg: 86.3570, loss: 0.4799 +2024-06-18 06:16:44,357 - mmseg - INFO - Iter [16650/80000] lr: 3.168e-05, eta: 1 day, 1:59:41, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3381, decode.acc_seg: 86.2001, aux.loss_ce: 0.1374, aux.acc_seg: 86.0749, loss: 0.4755 +2024-06-18 06:17:50,631 - mmseg - INFO - Iter [16700/80000] lr: 3.165e-05, eta: 1 day, 1:57:58, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3462, decode.acc_seg: 86.2163, aux.loss_ce: 0.1399, aux.acc_seg: 86.0613, loss: 0.4861 +2024-06-18 06:18:57,192 - mmseg - INFO - Iter [16750/80000] lr: 3.163e-05, eta: 1 day, 1:56:17, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3745, decode.acc_seg: 85.5031, aux.loss_ce: 0.1500, aux.acc_seg: 85.4340, loss: 0.5245 +2024-06-18 06:20:03,344 - mmseg - INFO - Iter [16800/80000] lr: 3.160e-05, eta: 1 day, 1:54:34, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3619, decode.acc_seg: 85.6216, aux.loss_ce: 0.1458, aux.acc_seg: 85.5539, loss: 0.5077 +2024-06-18 06:21:09,813 - mmseg - INFO - Iter [16850/80000] lr: 3.158e-05, eta: 1 day, 1:52:53, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3565, decode.acc_seg: 85.8023, aux.loss_ce: 0.1443, aux.acc_seg: 85.7600, loss: 0.5008 +2024-06-18 06:22:16,212 - mmseg - INFO - Iter [16900/80000] lr: 3.155e-05, eta: 1 day, 1:51:11, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3392, decode.acc_seg: 86.4214, aux.loss_ce: 0.1382, aux.acc_seg: 86.1647, loss: 0.4774 +2024-06-18 06:23:22,590 - mmseg - INFO - Iter [16950/80000] lr: 3.153e-05, eta: 1 day, 1:49:30, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3343, decode.acc_seg: 86.9167, aux.loss_ce: 0.1357, aux.acc_seg: 86.7790, loss: 0.4700 +2024-06-18 06:24:28,886 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 06:24:28,886 - mmseg - INFO - Iter [17000/80000] lr: 3.150e-05, eta: 1 day, 1:47:49, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3656, decode.acc_seg: 85.7584, aux.loss_ce: 0.1472, aux.acc_seg: 85.6449, loss: 0.5128 +2024-06-18 06:26:05,919 - mmseg - INFO - per class results: +2024-06-18 06:26:05,925 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.22 | 88.24 | +| building | 83.73 | 91.56 | +| sky | 94.17 | 96.04 | +| floor | 84.49 | 90.46 | +| tree | 75.7 | 89.72 | +| ceiling | 84.13 | 88.68 | +| road | 85.54 | 90.65 | +| bed | 91.83 | 96.1 | +| windowpane | 64.21 | 78.47 | +| grass | 64.63 | 84.11 | +| cabinet | 61.11 | 68.05 | +| sidewalk | 69.66 | 84.01 | +| person | 83.22 | 95.3 | +| earth | 28.11 | 35.99 | +| door | 55.04 | 77.62 | +| table | 62.68 | 76.26 | +| mountain | 60.53 | 73.66 | +| plant | 53.43 | 60.76 | +| curtain | 78.26 | 89.8 | +| chair | 64.17 | 80.01 | +| car | 85.05 | 93.03 | +| water | 50.24 | 59.0 | +| painting | 73.51 | 92.15 | +| sofa | 78.65 | 90.36 | +| shelf | 44.95 | 61.0 | +| house | 49.48 | 69.56 | +| sea | 58.01 | 64.29 | +| mirror | 72.88 | 78.42 | +| rug | 71.86 | 84.5 | +| field | 34.99 | 64.12 | +| armchair | 53.21 | 64.72 | +| seat | 66.32 | 86.0 | +| fence | 47.63 | 74.98 | +| desk | 54.26 | 79.08 | +| rock | 49.98 | 75.13 | +| wardrobe | 54.43 | 84.28 | +| lamp | 68.61 | 83.44 | +| bathtub | 80.67 | 86.92 | +| railing | 38.91 | 54.84 | +| cushion | 61.61 | 83.51 | +| base | 43.81 | 62.32 | +| box | 29.5 | 37.6 | +| column | 50.54 | 64.6 | +| signboard | 36.0 | 61.22 | +| chest of drawers | 43.44 | 80.66 | +| counter | 45.99 | 57.72 | +| sand | 45.39 | 64.67 | +| sink | 72.98 | 78.95 | +| skyscraper | 52.07 | 83.8 | +| fireplace | 67.31 | 94.79 | +| refrigerator | 74.88 | 86.82 | +| grandstand | 49.65 | 84.95 | +| path | 25.27 | 35.96 | +| stairs | 27.86 | 43.44 | +| runway | 73.2 | 97.45 | +| case | 37.43 | 92.36 | +| pool table | 93.86 | 96.98 | +| pillow | 67.16 | 77.76 | +| screen door | 59.02 | 60.25 | +| stairway | 32.43 | 35.09 | +| river | 13.96 | 47.21 | +| bridge | 72.4 | 88.4 | +| bookcase | 37.49 | 53.09 | +| blind | 36.55 | 38.56 | +| coffee table | 58.04 | 89.49 | +| toilet | 77.88 | 92.7 | +| flower | 38.89 | 58.19 | +| book | 52.6 | 77.1 | +| hill | 4.03 | 15.32 | +| bench | 50.1 | 63.02 | +| countertop | 63.84 | 73.34 | +| stove | 79.71 | 94.33 | +| palm | 52.38 | 67.77 | +| kitchen island | 40.61 | 89.77 | +| computer | 76.9 | 92.57 | +| swivel chair | 51.26 | 70.27 | +| boat | 53.34 | 83.07 | +| bar | 64.06 | 71.5 | +| arcade machine | 68.41 | 82.98 | +| hovel | 30.3 | 32.41 | +| bus | 89.66 | 94.97 | +| towel | 67.13 | 81.41 | +| light | 56.96 | 74.16 | +| truck | 47.53 | 60.49 | +| tower | 25.55 | 52.49 | +| chandelier | 68.56 | 85.57 | +| awning | 23.86 | 25.75 | +| streetlight | 28.25 | 42.51 | +| booth | 19.71 | 32.94 | +| television receiver | 64.16 | 89.26 | +| airplane | 63.91 | 79.54 | +| dirt track | 3.37 | 7.96 | +| apparel | 47.75 | 65.11 | +| pole | 23.28 | 30.48 | +| land | 2.18 | 3.82 | +| bannister | 12.15 | 16.3 | +| escalator | 54.44 | 82.29 | +| ottoman | 50.46 | 68.2 | +| bottle | 22.84 | 25.88 | +| buffet | 46.42 | 85.11 | +| poster | 34.97 | 43.43 | +| stage | 21.77 | 40.36 | +| van | 38.75 | 44.48 | +| ship | 72.66 | 98.72 | +| fountain | 22.0 | 22.24 | +| conveyer belt | 78.49 | 92.79 | +| canopy | 46.03 | 64.31 | +| washer | 61.13 | 69.48 | +| plaything | 26.15 | 63.95 | +| swimming pool | 53.26 | 95.4 | +| stool | 49.19 | 63.49 | +| barrel | 53.77 | 64.4 | +| basket | 30.61 | 50.72 | +| waterfall | 60.23 | 93.87 | +| tent | 87.85 | 98.11 | +| bag | 16.18 | 19.1 | +| minibike | 64.62 | 87.14 | +| cradle | 79.52 | 95.19 | +| oven | 66.81 | 77.92 | +| ball | 46.28 | 70.97 | +| food | 55.42 | 67.96 | +| step | 12.4 | 17.26 | +| tank | 61.23 | 64.92 | +| trade name | 16.16 | 17.93 | +| microwave | 87.03 | 94.05 | +| pot | 51.31 | 61.66 | +| animal | 67.73 | 70.07 | +| bicycle | 54.8 | 66.71 | +| lake | 51.19 | 90.64 | +| dishwasher | 58.93 | 70.61 | +| screen | 60.96 | 75.57 | +| blanket | 23.57 | 27.3 | +| sculpture | 66.82 | 81.36 | +| hood | 67.14 | 75.24 | +| sconce | 49.7 | 59.13 | +| vase | 40.7 | 52.31 | +| traffic light | 29.15 | 57.14 | +| tray | 5.03 | 5.67 | +| ashcan | 39.81 | 54.25 | +| fan | 62.49 | 78.34 | +| pier | 30.69 | 43.5 | +| crt screen | 5.05 | 6.55 | +| plate | 53.0 | 65.25 | +| monitor | 64.43 | 76.31 | +| bulletin board | 54.43 | 60.1 | +| shower | 0.0 | 0.0 | +| radiator | 61.11 | 68.34 | +| glass | 12.93 | 13.94 | +| clock | 34.69 | 41.36 | +| flag | 69.29 | 76.93 | ++---------------------+-------+-------+ +2024-06-18 06:26:05,925 - mmseg - INFO - Summary: +2024-06-18 06:26:05,925 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.09 | 52.31 | 66.96 | ++-------+-------+-------+ +2024-06-18 06:26:05,926 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 06:26:05,926 - mmseg - INFO - Iter(val) [250] aAcc: 0.8409, mIoU: 0.5231, mAcc: 0.6696, IoU.wall: 0.8022, IoU.building: 0.8373, IoU.sky: 0.9417, IoU.floor: 0.8449, IoU.tree: 0.7570, IoU.ceiling: 0.8413, IoU.road: 0.8554, IoU.bed : 0.9183, IoU.windowpane: 0.6421, IoU.grass: 0.6463, IoU.cabinet: 0.6111, IoU.sidewalk: 0.6966, IoU.person: 0.8322, IoU.earth: 0.2811, IoU.door: 0.5504, IoU.table: 0.6268, IoU.mountain: 0.6053, IoU.plant: 0.5343, IoU.curtain: 0.7826, IoU.chair: 0.6417, IoU.car: 0.8505, IoU.water: 0.5024, IoU.painting: 0.7351, IoU.sofa: 0.7865, IoU.shelf: 0.4495, IoU.house: 0.4948, IoU.sea: 0.5801, IoU.mirror: 0.7288, IoU.rug: 0.7186, IoU.field: 0.3499, IoU.armchair: 0.5321, IoU.seat: 0.6632, IoU.fence: 0.4763, IoU.desk: 0.5426, IoU.rock: 0.4998, IoU.wardrobe: 0.5443, IoU.lamp: 0.6861, IoU.bathtub: 0.8067, IoU.railing: 0.3891, IoU.cushion: 0.6161, IoU.base: 0.4381, IoU.box: 0.2950, IoU.column: 0.5054, IoU.signboard: 0.3600, IoU.chest of drawers: 0.4344, IoU.counter: 0.4599, IoU.sand: 0.4539, IoU.sink: 0.7298, IoU.skyscraper: 0.5207, IoU.fireplace: 0.6731, IoU.refrigerator: 0.7488, IoU.grandstand: 0.4965, IoU.path: 0.2527, IoU.stairs: 0.2786, IoU.runway: 0.7320, IoU.case: 0.3743, IoU.pool table: 0.9386, IoU.pillow: 0.6716, IoU.screen door: 0.5902, IoU.stairway: 0.3243, IoU.river: 0.1396, IoU.bridge: 0.7240, IoU.bookcase: 0.3749, IoU.blind: 0.3655, IoU.coffee table: 0.5804, IoU.toilet: 0.7788, IoU.flower: 0.3889, IoU.book: 0.5260, IoU.hill: 0.0403, IoU.bench: 0.5010, IoU.countertop: 0.6384, IoU.stove: 0.7971, IoU.palm: 0.5238, IoU.kitchen island: 0.4061, IoU.computer: 0.7690, IoU.swivel chair: 0.5126, IoU.boat: 0.5334, IoU.bar: 0.6406, IoU.arcade machine: 0.6841, IoU.hovel: 0.3030, IoU.bus: 0.8966, IoU.towel: 0.6713, IoU.light: 0.5696, IoU.truck: 0.4753, IoU.tower: 0.2555, IoU.chandelier: 0.6856, IoU.awning: 0.2386, IoU.streetlight: 0.2825, IoU.booth: 0.1971, IoU.television receiver: 0.6416, IoU.airplane: 0.6391, IoU.dirt track: 0.0337, IoU.apparel: 0.4775, IoU.pole: 0.2328, IoU.land: 0.0218, IoU.bannister: 0.1215, IoU.escalator: 0.5444, IoU.ottoman: 0.5046, IoU.bottle: 0.2284, IoU.buffet: 0.4642, IoU.poster: 0.3497, IoU.stage: 0.2177, IoU.van: 0.3875, IoU.ship: 0.7266, IoU.fountain: 0.2200, IoU.conveyer belt: 0.7849, IoU.canopy: 0.4603, IoU.washer: 0.6113, IoU.plaything: 0.2615, IoU.swimming pool: 0.5326, IoU.stool: 0.4919, IoU.barrel: 0.5377, IoU.basket: 0.3061, IoU.waterfall: 0.6023, IoU.tent: 0.8785, IoU.bag: 0.1618, IoU.minibike: 0.6462, IoU.cradle: 0.7952, IoU.oven: 0.6681, IoU.ball: 0.4628, IoU.food: 0.5542, IoU.step: 0.1240, IoU.tank: 0.6123, IoU.trade name: 0.1616, IoU.microwave: 0.8703, IoU.pot: 0.5131, IoU.animal: 0.6773, IoU.bicycle: 0.5480, IoU.lake: 0.5119, IoU.dishwasher: 0.5893, IoU.screen: 0.6096, IoU.blanket: 0.2357, IoU.sculpture: 0.6682, IoU.hood: 0.6714, IoU.sconce: 0.4970, IoU.vase: 0.4070, IoU.traffic light: 0.2915, IoU.tray: 0.0503, IoU.ashcan: 0.3981, IoU.fan: 0.6249, IoU.pier: 0.3069, IoU.crt screen: 0.0505, IoU.plate: 0.5300, IoU.monitor: 0.6443, IoU.bulletin board: 0.5443, IoU.shower: 0.0000, IoU.radiator: 0.6111, IoU.glass: 0.1293, IoU.clock: 0.3469, IoU.flag: 0.6929, Acc.wall: 0.8824, Acc.building: 0.9156, Acc.sky: 0.9604, Acc.floor: 0.9046, Acc.tree: 0.8972, Acc.ceiling: 0.8868, Acc.road: 0.9065, Acc.bed : 0.9610, Acc.windowpane: 0.7847, Acc.grass: 0.8411, Acc.cabinet: 0.6805, Acc.sidewalk: 0.8401, Acc.person: 0.9530, Acc.earth: 0.3599, Acc.door: 0.7762, Acc.table: 0.7626, Acc.mountain: 0.7366, Acc.plant: 0.6076, Acc.curtain: 0.8980, Acc.chair: 0.8001, Acc.car: 0.9303, Acc.water: 0.5900, Acc.painting: 0.9215, Acc.sofa: 0.9036, Acc.shelf: 0.6100, Acc.house: 0.6956, Acc.sea: 0.6429, Acc.mirror: 0.7842, Acc.rug: 0.8450, Acc.field: 0.6412, Acc.armchair: 0.6472, Acc.seat: 0.8600, Acc.fence: 0.7498, Acc.desk: 0.7908, Acc.rock: 0.7513, Acc.wardrobe: 0.8428, Acc.lamp: 0.8344, Acc.bathtub: 0.8692, Acc.railing: 0.5484, Acc.cushion: 0.8351, Acc.base: 0.6232, Acc.box: 0.3760, Acc.column: 0.6460, Acc.signboard: 0.6122, Acc.chest of drawers: 0.8066, Acc.counter: 0.5772, Acc.sand: 0.6467, Acc.sink: 0.7895, Acc.skyscraper: 0.8380, Acc.fireplace: 0.9479, Acc.refrigerator: 0.8682, Acc.grandstand: 0.8495, Acc.path: 0.3596, Acc.stairs: 0.4344, Acc.runway: 0.9745, Acc.case: 0.9236, Acc.pool table: 0.9698, Acc.pillow: 0.7776, Acc.screen door: 0.6025, Acc.stairway: 0.3509, Acc.river: 0.4721, Acc.bridge: 0.8840, Acc.bookcase: 0.5309, Acc.blind: 0.3856, Acc.coffee table: 0.8949, Acc.toilet: 0.9270, Acc.flower: 0.5819, Acc.book: 0.7710, Acc.hill: 0.1532, Acc.bench: 0.6302, Acc.countertop: 0.7334, Acc.stove: 0.9433, Acc.palm: 0.6777, Acc.kitchen island: 0.8977, Acc.computer: 0.9257, Acc.swivel chair: 0.7027, Acc.boat: 0.8307, Acc.bar: 0.7150, Acc.arcade machine: 0.8298, Acc.hovel: 0.3241, Acc.bus: 0.9497, Acc.towel: 0.8141, Acc.light: 0.7416, Acc.truck: 0.6049, Acc.tower: 0.5249, Acc.chandelier: 0.8557, Acc.awning: 0.2575, Acc.streetlight: 0.4251, Acc.booth: 0.3294, Acc.television receiver: 0.8926, Acc.airplane: 0.7954, Acc.dirt track: 0.0796, Acc.apparel: 0.6511, Acc.pole: 0.3048, Acc.land: 0.0382, Acc.bannister: 0.1630, Acc.escalator: 0.8229, Acc.ottoman: 0.6820, Acc.bottle: 0.2588, Acc.buffet: 0.8511, Acc.poster: 0.4343, Acc.stage: 0.4036, Acc.van: 0.4448, Acc.ship: 0.9872, Acc.fountain: 0.2224, Acc.conveyer belt: 0.9279, Acc.canopy: 0.6431, Acc.washer: 0.6948, Acc.plaything: 0.6395, Acc.swimming pool: 0.9540, Acc.stool: 0.6349, Acc.barrel: 0.6440, Acc.basket: 0.5072, Acc.waterfall: 0.9387, Acc.tent: 0.9811, Acc.bag: 0.1910, Acc.minibike: 0.8714, Acc.cradle: 0.9519, Acc.oven: 0.7792, Acc.ball: 0.7097, Acc.food: 0.6796, Acc.step: 0.1726, Acc.tank: 0.6492, Acc.trade name: 0.1793, Acc.microwave: 0.9405, Acc.pot: 0.6166, Acc.animal: 0.7007, Acc.bicycle: 0.6671, Acc.lake: 0.9064, Acc.dishwasher: 0.7061, Acc.screen: 0.7557, Acc.blanket: 0.2730, Acc.sculpture: 0.8136, Acc.hood: 0.7524, Acc.sconce: 0.5913, Acc.vase: 0.5231, Acc.traffic light: 0.5714, Acc.tray: 0.0567, Acc.ashcan: 0.5425, Acc.fan: 0.7834, Acc.pier: 0.4350, Acc.crt screen: 0.0655, Acc.plate: 0.6525, Acc.monitor: 0.7631, Acc.bulletin board: 0.6010, Acc.shower: 0.0000, Acc.radiator: 0.6834, Acc.glass: 0.1394, Acc.clock: 0.4136, Acc.flag: 0.7693 +2024-06-18 06:27:12,523 - mmseg - INFO - Iter [17050/80000] lr: 3.148e-05, eta: 1 day, 1:52:07, time: 3.273, data_time: 1.958, memory: 70498, decode.loss_ce: 0.3523, decode.acc_seg: 86.0792, aux.loss_ce: 0.1420, aux.acc_seg: 86.0101, loss: 0.4943 +2024-06-18 06:28:18,699 - mmseg - INFO - Iter [17100/80000] lr: 3.145e-05, eta: 1 day, 1:50:25, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3693, decode.acc_seg: 85.2919, aux.loss_ce: 0.1500, aux.acc_seg: 84.9791, loss: 0.5193 +2024-06-18 06:29:25,011 - mmseg - INFO - Iter [17150/80000] lr: 3.143e-05, eta: 1 day, 1:48:43, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3612, decode.acc_seg: 85.5539, aux.loss_ce: 0.1457, aux.acc_seg: 85.4174, loss: 0.5069 +2024-06-18 06:30:31,522 - mmseg - INFO - Iter [17200/80000] lr: 3.140e-05, eta: 1 day, 1:47:02, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3606, decode.acc_seg: 86.2575, aux.loss_ce: 0.1454, aux.acc_seg: 86.1351, loss: 0.5060 +2024-06-18 06:31:37,710 - mmseg - INFO - Iter [17250/80000] lr: 3.138e-05, eta: 1 day, 1:45:20, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3528, decode.acc_seg: 86.0261, aux.loss_ce: 0.1419, aux.acc_seg: 86.0151, loss: 0.4947 +2024-06-18 06:32:43,910 - mmseg - INFO - Iter [17300/80000] lr: 3.135e-05, eta: 1 day, 1:43:38, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3660, decode.acc_seg: 85.0776, aux.loss_ce: 0.1471, aux.acc_seg: 84.9036, loss: 0.5131 +2024-06-18 06:33:50,122 - mmseg - INFO - Iter [17350/80000] lr: 3.133e-05, eta: 1 day, 1:41:57, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3522, decode.acc_seg: 86.0682, aux.loss_ce: 0.1419, aux.acc_seg: 85.8563, loss: 0.4941 +2024-06-18 06:34:56,611 - mmseg - INFO - Iter [17400/80000] lr: 3.130e-05, eta: 1 day, 1:40:16, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3576, decode.acc_seg: 86.2218, aux.loss_ce: 0.1443, aux.acc_seg: 86.1570, loss: 0.5019 +2024-06-18 06:36:02,994 - mmseg - INFO - Iter [17450/80000] lr: 3.128e-05, eta: 1 day, 1:38:36, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3388, decode.acc_seg: 86.3682, aux.loss_ce: 0.1364, aux.acc_seg: 86.3316, loss: 0.4752 +2024-06-18 06:37:09,395 - mmseg - INFO - Iter [17500/80000] lr: 3.125e-05, eta: 1 day, 1:36:56, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3473, decode.acc_seg: 86.2340, aux.loss_ce: 0.1400, aux.acc_seg: 86.1224, loss: 0.4873 +2024-06-18 06:38:15,858 - mmseg - INFO - Iter [17550/80000] lr: 3.123e-05, eta: 1 day, 1:35:16, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3546, decode.acc_seg: 86.2606, aux.loss_ce: 0.1425, aux.acc_seg: 86.2489, loss: 0.4972 +2024-06-18 06:39:22,206 - mmseg - INFO - Iter [17600/80000] lr: 3.120e-05, eta: 1 day, 1:33:36, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3717, decode.acc_seg: 85.1769, aux.loss_ce: 0.1494, aux.acc_seg: 85.2008, loss: 0.5211 +2024-06-18 06:40:28,653 - mmseg - INFO - Iter [17650/80000] lr: 3.118e-05, eta: 1 day, 1:31:56, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3837, decode.acc_seg: 84.7622, aux.loss_ce: 0.1546, aux.acc_seg: 84.6041, loss: 0.5384 +2024-06-18 06:41:38,026 - mmseg - INFO - Iter [17700/80000] lr: 3.115e-05, eta: 1 day, 1:30:27, time: 1.387, data_time: 0.063, memory: 70498, decode.loss_ce: 0.3339, decode.acc_seg: 86.6860, aux.loss_ce: 0.1340, aux.acc_seg: 86.5674, loss: 0.4679 +2024-06-18 06:42:44,431 - mmseg - INFO - Iter [17750/80000] lr: 3.113e-05, eta: 1 day, 1:28:48, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3332, decode.acc_seg: 86.7142, aux.loss_ce: 0.1352, aux.acc_seg: 86.5317, loss: 0.4684 +2024-06-18 06:43:50,583 - mmseg - INFO - Iter [17800/80000] lr: 3.110e-05, eta: 1 day, 1:27:08, time: 1.323, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3562, decode.acc_seg: 85.7941, aux.loss_ce: 0.1443, aux.acc_seg: 85.5116, loss: 0.5005 +2024-06-18 06:44:56,897 - mmseg - INFO - Iter [17850/80000] lr: 3.108e-05, eta: 1 day, 1:25:29, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3313, decode.acc_seg: 86.7612, aux.loss_ce: 0.1341, aux.acc_seg: 86.4570, loss: 0.4654 +2024-06-18 06:46:03,316 - mmseg - INFO - Iter [17900/80000] lr: 3.105e-05, eta: 1 day, 1:23:50, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3395, decode.acc_seg: 85.9792, aux.loss_ce: 0.1365, aux.acc_seg: 85.9458, loss: 0.4760 +2024-06-18 06:47:09,794 - mmseg - INFO - Iter [17950/80000] lr: 3.103e-05, eta: 1 day, 1:22:12, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3481, decode.acc_seg: 86.2128, aux.loss_ce: 0.1412, aux.acc_seg: 85.9291, loss: 0.4893 +2024-06-18 06:48:16,104 - mmseg - INFO - Saving checkpoint at 18000 iterations +2024-06-18 06:49:56,418 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 06:49:56,418 - mmseg - INFO - Iter [18000/80000] lr: 3.100e-05, eta: 1 day, 1:26:19, time: 3.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3386, decode.acc_seg: 86.5530, aux.loss_ce: 0.1373, aux.acc_seg: 86.4271, loss: 0.4759 +2024-06-18 06:51:32,695 - mmseg - INFO - per class results: +2024-06-18 06:51:32,701 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.66 | 88.64 | +| building | 84.79 | 94.73 | +| sky | 94.63 | 97.63 | +| floor | 84.59 | 90.25 | +| tree | 76.09 | 86.89 | +| ceiling | 85.73 | 92.85 | +| road | 85.53 | 92.12 | +| bed | 90.41 | 97.53 | +| windowpane | 63.64 | 81.48 | +| grass | 67.51 | 83.44 | +| cabinet | 60.47 | 74.27 | +| sidewalk | 69.88 | 81.51 | +| person | 83.64 | 91.66 | +| earth | 36.36 | 52.24 | +| door | 58.65 | 75.24 | +| table | 63.06 | 72.91 | +| mountain | 60.93 | 73.91 | +| plant | 54.85 | 63.82 | +| curtain | 78.6 | 90.13 | +| chair | 62.26 | 75.01 | +| car | 84.88 | 91.96 | +| water | 52.8 | 64.12 | +| painting | 74.69 | 89.0 | +| sofa | 77.98 | 86.64 | +| shelf | 45.46 | 66.61 | +| house | 58.52 | 67.48 | +| sea | 68.06 | 82.62 | +| mirror | 74.06 | 86.96 | +| rug | 68.86 | 84.24 | +| field | 41.39 | 64.4 | +| armchair | 56.6 | 71.83 | +| seat | 65.63 | 87.64 | +| fence | 48.07 | 59.46 | +| desk | 53.13 | 74.34 | +| rock | 48.18 | 69.26 | +| wardrobe | 52.75 | 75.86 | +| lamp | 69.67 | 77.79 | +| bathtub | 80.92 | 83.92 | +| railing | 38.87 | 53.63 | +| cushion | 59.31 | 66.18 | +| base | 30.99 | 49.68 | +| box | 30.36 | 42.88 | +| column | 51.1 | 63.18 | +| signboard | 37.79 | 50.05 | +| chest of drawers | 43.29 | 65.13 | +| counter | 44.49 | 59.82 | +| sand | 46.79 | 62.56 | +| sink | 74.21 | 79.38 | +| skyscraper | 46.83 | 62.29 | +| fireplace | 69.69 | 77.61 | +| refrigerator | 75.16 | 85.39 | +| grandstand | 43.93 | 87.54 | +| path | 28.31 | 37.67 | +| stairs | 19.45 | 22.78 | +| runway | 75.21 | 96.48 | +| case | 44.39 | 58.55 | +| pool table | 92.34 | 97.99 | +| pillow | 59.21 | 71.67 | +| screen door | 81.73 | 90.5 | +| stairway | 41.01 | 67.73 | +| river | 14.37 | 51.48 | +| bridge | 70.71 | 88.85 | +| bookcase | 39.42 | 61.52 | +| blind | 38.35 | 41.62 | +| coffee table | 57.6 | 91.63 | +| toilet | 87.92 | 93.45 | +| flower | 41.82 | 52.24 | +| book | 48.99 | 62.81 | +| hill | 6.66 | 14.83 | +| bench | 51.29 | 65.45 | +| countertop | 59.31 | 87.9 | +| stove | 83.81 | 91.53 | +| palm | 54.77 | 77.0 | +| kitchen island | 42.21 | 90.91 | +| computer | 77.33 | 88.56 | +| swivel chair | 46.86 | 77.69 | +| boat | 46.33 | 93.19 | +| bar | 61.39 | 69.71 | +| arcade machine | 71.35 | 84.43 | +| hovel | 40.89 | 49.0 | +| bus | 91.73 | 95.16 | +| towel | 69.94 | 77.71 | +| light | 57.02 | 67.71 | +| truck | 46.35 | 59.59 | +| tower | 12.74 | 19.34 | +| chandelier | 67.71 | 82.43 | +| awning | 47.12 | 56.06 | +| streetlight | 25.85 | 35.17 | +| booth | 26.66 | 41.6 | +| television receiver | 75.8 | 82.86 | +| airplane | 65.89 | 73.56 | +| dirt track | 10.85 | 16.72 | +| apparel | 36.24 | 43.34 | +| pole | 19.54 | 22.25 | +| land | 0.17 | 0.26 | +| bannister | 10.97 | 17.94 | +| escalator | 57.17 | 79.09 | +| ottoman | 43.65 | 70.31 | +| bottle | 35.63 | 45.6 | +| buffet | 45.23 | 63.13 | +| poster | 29.33 | 35.94 | +| stage | 21.01 | 29.29 | +| van | 42.66 | 48.77 | +| ship | 88.11 | 97.85 | +| fountain | 32.4 | 32.95 | +| conveyer belt | 79.8 | 92.64 | +| canopy | 45.21 | 67.36 | +| washer | 63.45 | 73.52 | +| plaything | 25.76 | 32.84 | +| swimming pool | 61.66 | 95.69 | +| stool | 44.76 | 58.91 | +| barrel | 28.37 | 64.97 | +| basket | 35.65 | 47.34 | +| waterfall | 56.29 | 74.65 | +| tent | 94.99 | 98.47 | +| bag | 6.42 | 6.53 | +| minibike | 66.57 | 83.11 | +| cradle | 82.76 | 97.96 | +| oven | 66.27 | 78.62 | +| ball | 47.11 | 63.0 | +| food | 62.93 | 72.28 | +| step | 8.78 | 10.21 | +| tank | 56.49 | 68.89 | +| trade name | 25.09 | 27.97 | +| microwave | 88.63 | 94.79 | +| pot | 53.46 | 67.78 | +| animal | 64.12 | 65.38 | +| bicycle | 53.66 | 68.58 | +| lake | 30.59 | 30.62 | +| dishwasher | 61.41 | 72.29 | +| screen | 64.07 | 92.77 | +| blanket | 20.13 | 22.86 | +| sculpture | 67.52 | 85.13 | +| hood | 58.54 | 70.84 | +| sconce | 47.5 | 60.13 | +| vase | 39.65 | 49.11 | +| traffic light | 30.59 | 46.36 | +| tray | 7.16 | 8.85 | +| ashcan | 42.19 | 55.1 | +| fan | 64.39 | 76.14 | +| pier | 61.76 | 80.92 | +| crt screen | 7.78 | 14.57 | +| plate | 54.67 | 70.03 | +| monitor | 50.38 | 60.25 | +| bulletin board | 54.33 | 55.58 | +| shower | 0.0 | 0.0 | +| radiator | 59.65 | 69.79 | +| glass | 15.85 | 16.68 | +| clock | 31.67 | 33.85 | +| flag | 65.45 | 70.79 | ++---------------------+-------+-------+ +2024-06-18 06:51:32,702 - mmseg - INFO - Summary: +2024-06-18 06:51:32,702 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.82 | 53.02 | 65.81 | ++-------+-------+-------+ +2024-06-18 06:51:32,703 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 06:51:32,703 - mmseg - INFO - Iter(val) [250] aAcc: 0.8482, mIoU: 0.5302, mAcc: 0.6581, IoU.wall: 0.8066, IoU.building: 0.8479, IoU.sky: 0.9463, IoU.floor: 0.8459, IoU.tree: 0.7609, IoU.ceiling: 0.8573, IoU.road: 0.8553, IoU.bed : 0.9041, IoU.windowpane: 0.6364, IoU.grass: 0.6751, IoU.cabinet: 0.6047, IoU.sidewalk: 0.6988, IoU.person: 0.8364, IoU.earth: 0.3636, IoU.door: 0.5865, IoU.table: 0.6306, IoU.mountain: 0.6093, IoU.plant: 0.5485, IoU.curtain: 0.7860, IoU.chair: 0.6226, IoU.car: 0.8488, IoU.water: 0.5280, IoU.painting: 0.7469, IoU.sofa: 0.7798, IoU.shelf: 0.4546, IoU.house: 0.5852, IoU.sea: 0.6806, IoU.mirror: 0.7406, IoU.rug: 0.6886, IoU.field: 0.4139, IoU.armchair: 0.5660, IoU.seat: 0.6563, IoU.fence: 0.4807, IoU.desk: 0.5313, IoU.rock: 0.4818, IoU.wardrobe: 0.5275, IoU.lamp: 0.6967, IoU.bathtub: 0.8092, IoU.railing: 0.3887, IoU.cushion: 0.5931, IoU.base: 0.3099, IoU.box: 0.3036, IoU.column: 0.5110, IoU.signboard: 0.3779, IoU.chest of drawers: 0.4329, IoU.counter: 0.4449, IoU.sand: 0.4679, IoU.sink: 0.7421, IoU.skyscraper: 0.4683, IoU.fireplace: 0.6969, IoU.refrigerator: 0.7516, IoU.grandstand: 0.4393, IoU.path: 0.2831, IoU.stairs: 0.1945, IoU.runway: 0.7521, IoU.case: 0.4439, IoU.pool table: 0.9234, IoU.pillow: 0.5921, IoU.screen door: 0.8173, IoU.stairway: 0.4101, IoU.river: 0.1437, IoU.bridge: 0.7071, IoU.bookcase: 0.3942, IoU.blind: 0.3835, IoU.coffee table: 0.5760, IoU.toilet: 0.8792, IoU.flower: 0.4182, IoU.book: 0.4899, IoU.hill: 0.0666, IoU.bench: 0.5129, IoU.countertop: 0.5931, IoU.stove: 0.8381, IoU.palm: 0.5477, IoU.kitchen island: 0.4221, IoU.computer: 0.7733, IoU.swivel chair: 0.4686, IoU.boat: 0.4633, IoU.bar: 0.6139, IoU.arcade machine: 0.7135, IoU.hovel: 0.4089, IoU.bus: 0.9173, IoU.towel: 0.6994, IoU.light: 0.5702, IoU.truck: 0.4635, IoU.tower: 0.1274, IoU.chandelier: 0.6771, IoU.awning: 0.4712, IoU.streetlight: 0.2585, IoU.booth: 0.2666, IoU.television receiver: 0.7580, IoU.airplane: 0.6589, IoU.dirt track: 0.1085, IoU.apparel: 0.3624, IoU.pole: 0.1954, IoU.land: 0.0017, IoU.bannister: 0.1097, IoU.escalator: 0.5717, IoU.ottoman: 0.4365, IoU.bottle: 0.3563, IoU.buffet: 0.4523, IoU.poster: 0.2933, IoU.stage: 0.2101, IoU.van: 0.4266, IoU.ship: 0.8811, IoU.fountain: 0.3240, IoU.conveyer belt: 0.7980, IoU.canopy: 0.4521, IoU.washer: 0.6345, IoU.plaything: 0.2576, IoU.swimming pool: 0.6166, IoU.stool: 0.4476, IoU.barrel: 0.2837, IoU.basket: 0.3565, IoU.waterfall: 0.5629, IoU.tent: 0.9499, IoU.bag: 0.0642, IoU.minibike: 0.6657, IoU.cradle: 0.8276, IoU.oven: 0.6627, IoU.ball: 0.4711, IoU.food: 0.6293, IoU.step: 0.0878, IoU.tank: 0.5649, IoU.trade name: 0.2509, IoU.microwave: 0.8863, IoU.pot: 0.5346, IoU.animal: 0.6412, IoU.bicycle: 0.5366, IoU.lake: 0.3059, IoU.dishwasher: 0.6141, IoU.screen: 0.6407, IoU.blanket: 0.2013, IoU.sculpture: 0.6752, IoU.hood: 0.5854, IoU.sconce: 0.4750, IoU.vase: 0.3965, IoU.traffic light: 0.3059, IoU.tray: 0.0716, IoU.ashcan: 0.4219, IoU.fan: 0.6439, IoU.pier: 0.6176, IoU.crt screen: 0.0778, IoU.plate: 0.5467, IoU.monitor: 0.5038, IoU.bulletin board: 0.5433, IoU.shower: 0.0000, IoU.radiator: 0.5965, IoU.glass: 0.1585, IoU.clock: 0.3167, IoU.flag: 0.6545, Acc.wall: 0.8864, Acc.building: 0.9473, Acc.sky: 0.9763, Acc.floor: 0.9025, Acc.tree: 0.8689, Acc.ceiling: 0.9285, Acc.road: 0.9212, Acc.bed : 0.9753, Acc.windowpane: 0.8148, Acc.grass: 0.8344, Acc.cabinet: 0.7427, Acc.sidewalk: 0.8151, Acc.person: 0.9166, Acc.earth: 0.5224, Acc.door: 0.7524, Acc.table: 0.7291, Acc.mountain: 0.7391, Acc.plant: 0.6382, Acc.curtain: 0.9013, Acc.chair: 0.7501, Acc.car: 0.9196, Acc.water: 0.6412, Acc.painting: 0.8900, Acc.sofa: 0.8664, Acc.shelf: 0.6661, Acc.house: 0.6748, Acc.sea: 0.8262, Acc.mirror: 0.8696, Acc.rug: 0.8424, Acc.field: 0.6440, Acc.armchair: 0.7183, Acc.seat: 0.8764, Acc.fence: 0.5946, Acc.desk: 0.7434, Acc.rock: 0.6926, Acc.wardrobe: 0.7586, Acc.lamp: 0.7779, Acc.bathtub: 0.8392, Acc.railing: 0.5363, Acc.cushion: 0.6618, Acc.base: 0.4968, Acc.box: 0.4288, Acc.column: 0.6318, Acc.signboard: 0.5005, Acc.chest of drawers: 0.6513, Acc.counter: 0.5982, Acc.sand: 0.6256, Acc.sink: 0.7938, Acc.skyscraper: 0.6229, Acc.fireplace: 0.7761, Acc.refrigerator: 0.8539, Acc.grandstand: 0.8754, Acc.path: 0.3767, Acc.stairs: 0.2278, Acc.runway: 0.9648, Acc.case: 0.5855, Acc.pool table: 0.9799, Acc.pillow: 0.7167, Acc.screen door: 0.9050, Acc.stairway: 0.6773, Acc.river: 0.5148, Acc.bridge: 0.8885, Acc.bookcase: 0.6152, Acc.blind: 0.4162, Acc.coffee table: 0.9163, Acc.toilet: 0.9345, Acc.flower: 0.5224, Acc.book: 0.6281, Acc.hill: 0.1483, Acc.bench: 0.6545, Acc.countertop: 0.8790, Acc.stove: 0.9153, Acc.palm: 0.7700, Acc.kitchen island: 0.9091, Acc.computer: 0.8856, Acc.swivel chair: 0.7769, Acc.boat: 0.9319, Acc.bar: 0.6971, Acc.arcade machine: 0.8443, Acc.hovel: 0.4900, Acc.bus: 0.9516, Acc.towel: 0.7771, Acc.light: 0.6771, Acc.truck: 0.5959, Acc.tower: 0.1934, Acc.chandelier: 0.8243, Acc.awning: 0.5606, Acc.streetlight: 0.3517, Acc.booth: 0.4160, Acc.television receiver: 0.8286, Acc.airplane: 0.7356, Acc.dirt track: 0.1672, Acc.apparel: 0.4334, Acc.pole: 0.2225, Acc.land: 0.0026, Acc.bannister: 0.1794, Acc.escalator: 0.7909, Acc.ottoman: 0.7031, Acc.bottle: 0.4560, Acc.buffet: 0.6313, Acc.poster: 0.3594, Acc.stage: 0.2929, Acc.van: 0.4877, Acc.ship: 0.9785, Acc.fountain: 0.3295, Acc.conveyer belt: 0.9264, Acc.canopy: 0.6736, Acc.washer: 0.7352, Acc.plaything: 0.3284, Acc.swimming pool: 0.9569, Acc.stool: 0.5891, Acc.barrel: 0.6497, Acc.basket: 0.4734, Acc.waterfall: 0.7465, Acc.tent: 0.9847, Acc.bag: 0.0653, Acc.minibike: 0.8311, Acc.cradle: 0.9796, Acc.oven: 0.7862, Acc.ball: 0.6300, Acc.food: 0.7228, Acc.step: 0.1021, Acc.tank: 0.6889, Acc.trade name: 0.2797, Acc.microwave: 0.9479, Acc.pot: 0.6778, Acc.animal: 0.6538, Acc.bicycle: 0.6858, Acc.lake: 0.3062, Acc.dishwasher: 0.7229, Acc.screen: 0.9277, Acc.blanket: 0.2286, Acc.sculpture: 0.8513, Acc.hood: 0.7084, Acc.sconce: 0.6013, Acc.vase: 0.4911, Acc.traffic light: 0.4636, Acc.tray: 0.0885, Acc.ashcan: 0.5510, Acc.fan: 0.7614, Acc.pier: 0.8092, Acc.crt screen: 0.1457, Acc.plate: 0.7003, Acc.monitor: 0.6025, Acc.bulletin board: 0.5558, Acc.shower: 0.0000, Acc.radiator: 0.6979, Acc.glass: 0.1668, Acc.clock: 0.3385, Acc.flag: 0.7079 +2024-06-18 06:52:39,618 - mmseg - INFO - Iter [18050/80000] lr: 3.098e-05, eta: 1 day, 1:30:12, time: 3.264, data_time: 1.941, memory: 70498, decode.loss_ce: 0.3292, decode.acc_seg: 87.1989, aux.loss_ce: 0.1322, aux.acc_seg: 87.0328, loss: 0.4615 +2024-06-18 06:53:46,056 - mmseg - INFO - Iter [18100/80000] lr: 3.095e-05, eta: 1 day, 1:28:31, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3338, decode.acc_seg: 86.8845, aux.loss_ce: 0.1350, aux.acc_seg: 86.7656, loss: 0.4688 +2024-06-18 06:54:52,419 - mmseg - INFO - Iter [18150/80000] lr: 3.093e-05, eta: 1 day, 1:26:51, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3187, decode.acc_seg: 87.0134, aux.loss_ce: 0.1296, aux.acc_seg: 86.8370, loss: 0.4483 +2024-06-18 06:55:58,700 - mmseg - INFO - Iter [18200/80000] lr: 3.090e-05, eta: 1 day, 1:25:10, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3259, decode.acc_seg: 86.9101, aux.loss_ce: 0.1320, aux.acc_seg: 86.8524, loss: 0.4579 +2024-06-18 06:57:05,101 - mmseg - INFO - Iter [18250/80000] lr: 3.088e-05, eta: 1 day, 1:23:31, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3358, decode.acc_seg: 86.4183, aux.loss_ce: 0.1352, aux.acc_seg: 86.1797, loss: 0.4710 +2024-06-18 06:58:11,397 - mmseg - INFO - Iter [18300/80000] lr: 3.085e-05, eta: 1 day, 1:21:51, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3382, decode.acc_seg: 86.2978, aux.loss_ce: 0.1365, aux.acc_seg: 86.1084, loss: 0.4747 +2024-06-18 06:59:17,686 - mmseg - INFO - Iter [18350/80000] lr: 3.083e-05, eta: 1 day, 1:20:11, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3375, decode.acc_seg: 86.6204, aux.loss_ce: 0.1368, aux.acc_seg: 86.4093, loss: 0.4743 +2024-06-18 07:00:24,232 - mmseg - INFO - Iter [18400/80000] lr: 3.080e-05, eta: 1 day, 1:18:32, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3321, decode.acc_seg: 86.5748, aux.loss_ce: 0.1332, aux.acc_seg: 86.4999, loss: 0.4653 +2024-06-18 07:01:30,713 - mmseg - INFO - Iter [18450/80000] lr: 3.078e-05, eta: 1 day, 1:16:53, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3349, decode.acc_seg: 86.9461, aux.loss_ce: 0.1369, aux.acc_seg: 86.5642, loss: 0.4718 +2024-06-18 07:02:37,092 - mmseg - INFO - Iter [18500/80000] lr: 3.075e-05, eta: 1 day, 1:15:14, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3197, decode.acc_seg: 86.8069, aux.loss_ce: 0.1293, aux.acc_seg: 86.7441, loss: 0.4490 +2024-06-18 07:03:43,630 - mmseg - INFO - Iter [18550/80000] lr: 3.073e-05, eta: 1 day, 1:13:36, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3436, decode.acc_seg: 86.5410, aux.loss_ce: 0.1396, aux.acc_seg: 86.3602, loss: 0.4832 +2024-06-18 07:04:49,979 - mmseg - INFO - Iter [18600/80000] lr: 3.070e-05, eta: 1 day, 1:11:57, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3403, decode.acc_seg: 86.3821, aux.loss_ce: 0.1385, aux.acc_seg: 86.3358, loss: 0.4788 +2024-06-18 07:05:56,161 - mmseg - INFO - Iter [18650/80000] lr: 3.068e-05, eta: 1 day, 1:10:18, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3430, decode.acc_seg: 86.6622, aux.loss_ce: 0.1398, aux.acc_seg: 86.3620, loss: 0.4828 +2024-06-18 07:07:02,653 - mmseg - INFO - Iter [18700/80000] lr: 3.065e-05, eta: 1 day, 1:08:40, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3266, decode.acc_seg: 86.5541, aux.loss_ce: 0.1327, aux.acc_seg: 86.4099, loss: 0.4593 +2024-06-18 07:08:09,238 - mmseg - INFO - Iter [18750/80000] lr: 3.063e-05, eta: 1 day, 1:07:02, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3363, decode.acc_seg: 86.7388, aux.loss_ce: 0.1362, aux.acc_seg: 86.5797, loss: 0.4725 +2024-06-18 07:09:15,875 - mmseg - INFO - Iter [18800/80000] lr: 3.060e-05, eta: 1 day, 1:05:25, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3478, decode.acc_seg: 85.8222, aux.loss_ce: 0.1400, aux.acc_seg: 85.9397, loss: 0.4878 +2024-06-18 07:10:22,268 - mmseg - INFO - Iter [18850/80000] lr: 3.058e-05, eta: 1 day, 1:03:47, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3477, decode.acc_seg: 86.3088, aux.loss_ce: 0.1405, aux.acc_seg: 86.3710, loss: 0.4882 +2024-06-18 07:11:28,780 - mmseg - INFO - Iter [18900/80000] lr: 3.055e-05, eta: 1 day, 1:02:10, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3430, decode.acc_seg: 86.0518, aux.loss_ce: 0.1387, aux.acc_seg: 85.8926, loss: 0.4817 +2024-06-18 07:12:37,344 - mmseg - INFO - Iter [18950/80000] lr: 3.053e-05, eta: 1 day, 1:00:39, time: 1.371, data_time: 0.051, memory: 70498, decode.loss_ce: 0.3536, decode.acc_seg: 86.1983, aux.loss_ce: 0.1419, aux.acc_seg: 86.1967, loss: 0.4955 +2024-06-18 07:13:43,621 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 07:13:43,621 - mmseg - INFO - Iter [19000/80000] lr: 3.050e-05, eta: 1 day, 0:59:02, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3437, decode.acc_seg: 86.0235, aux.loss_ce: 0.1377, aux.acc_seg: 86.0329, loss: 0.4814 +2024-06-18 07:15:22,173 - mmseg - INFO - per class results: +2024-06-18 07:15:22,179 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.86 | 87.72 | +| building | 81.78 | 93.4 | +| sky | 94.61 | 97.65 | +| floor | 84.24 | 90.52 | +| tree | 76.11 | 88.44 | +| ceiling | 84.79 | 91.06 | +| road | 86.3 | 90.73 | +| bed | 91.11 | 95.78 | +| windowpane | 64.43 | 79.87 | +| grass | 68.5 | 82.28 | +| cabinet | 60.56 | 70.12 | +| sidewalk | 69.68 | 85.16 | +| person | 84.19 | 92.78 | +| earth | 37.9 | 51.05 | +| door | 54.08 | 67.34 | +| table | 64.55 | 78.51 | +| mountain | 57.32 | 72.8 | +| plant | 57.9 | 72.48 | +| curtain | 79.57 | 87.72 | +| chair | 61.71 | 70.87 | +| car | 84.97 | 93.91 | +| water | 62.45 | 75.61 | +| painting | 74.42 | 90.57 | +| sofa | 79.54 | 89.3 | +| shelf | 41.91 | 53.26 | +| house | 64.14 | 88.64 | +| sea | 66.89 | 91.07 | +| mirror | 76.19 | 85.03 | +| rug | 71.94 | 81.48 | +| field | 39.11 | 62.54 | +| armchair | 56.38 | 75.28 | +| seat | 63.14 | 91.08 | +| fence | 44.94 | 59.14 | +| desk | 51.68 | 78.12 | +| rock | 54.73 | 78.54 | +| wardrobe | 51.63 | 79.42 | +| lamp | 69.84 | 80.35 | +| bathtub | 81.97 | 86.65 | +| railing | 34.49 | 48.72 | +| cushion | 63.37 | 73.06 | +| base | 43.68 | 63.39 | +| box | 29.49 | 41.19 | +| column | 51.96 | 56.26 | +| signboard | 38.14 | 47.85 | +| chest of drawers | 41.21 | 74.79 | +| counter | 44.03 | 56.13 | +| sand | 46.83 | 64.38 | +| sink | 74.37 | 80.42 | +| skyscraper | 52.41 | 71.22 | +| fireplace | 67.77 | 94.95 | +| refrigerator | 81.86 | 93.17 | +| grandstand | 55.42 | 77.05 | +| path | 29.59 | 42.02 | +| stairs | 28.4 | 34.2 | +| runway | 72.06 | 92.78 | +| case | 51.6 | 68.95 | +| pool table | 93.92 | 97.83 | +| pillow | 64.38 | 73.63 | +| screen door | 79.77 | 84.06 | +| stairway | 32.87 | 44.22 | +| river | 26.36 | 39.55 | +| bridge | 70.37 | 90.18 | +| bookcase | 35.55 | 58.47 | +| blind | 41.61 | 45.02 | +| coffee table | 63.32 | 87.76 | +| toilet | 88.1 | 93.75 | +| flower | 38.93 | 54.09 | +| book | 48.37 | 69.09 | +| hill | 7.49 | 10.86 | +| bench | 51.12 | 58.57 | +| countertop | 55.92 | 84.7 | +| stove | 77.52 | 94.59 | +| palm | 53.92 | 70.56 | +| kitchen island | 41.39 | 91.68 | +| computer | 76.46 | 89.53 | +| swivel chair | 49.26 | 75.27 | +| boat | 56.61 | 81.21 | +| bar | 59.5 | 76.59 | +| arcade machine | 82.44 | 91.89 | +| hovel | 45.03 | 60.43 | +| bus | 91.6 | 94.75 | +| towel | 69.88 | 84.01 | +| light | 56.59 | 63.88 | +| truck | 38.53 | 62.78 | +| tower | 26.06 | 42.92 | +| chandelier | 69.23 | 85.98 | +| awning | 47.64 | 62.82 | +| streetlight | 27.6 | 41.21 | +| booth | 30.71 | 41.6 | +| television receiver | 72.18 | 84.75 | +| airplane | 77.01 | 82.39 | +| dirt track | 13.22 | 19.14 | +| apparel | 50.41 | 72.73 | +| pole | 18.92 | 22.43 | +| land | 0.03 | 0.03 | +| bannister | 10.97 | 13.88 | +| escalator | 58.1 | 79.13 | +| ottoman | 51.05 | 68.59 | +| bottle | 37.37 | 66.54 | +| buffet | 53.23 | 61.55 | +| poster | 33.71 | 40.95 | +| stage | 15.19 | 35.37 | +| van | 39.57 | 52.44 | +| ship | 85.68 | 94.02 | +| fountain | 46.42 | 52.97 | +| conveyer belt | 74.38 | 93.4 | +| canopy | 47.26 | 75.89 | +| washer | 68.65 | 77.77 | +| plaything | 22.52 | 32.96 | +| swimming pool | 53.57 | 91.93 | +| stool | 42.13 | 61.42 | +| barrel | 35.8 | 64.98 | +| basket | 33.1 | 56.4 | +| waterfall | 70.02 | 87.48 | +| tent | 86.78 | 98.96 | +| bag | 18.45 | 21.61 | +| minibike | 66.13 | 83.8 | +| cradle | 70.09 | 98.98 | +| oven | 48.5 | 51.31 | +| ball | 50.58 | 73.98 | +| food | 59.79 | 76.01 | +| step | 10.66 | 13.52 | +| tank | 57.64 | 71.38 | +| trade name | 7.56 | 7.94 | +| microwave | 84.54 | 95.09 | +| pot | 50.79 | 60.13 | +| animal | 67.18 | 70.62 | +| bicycle | 55.21 | 72.13 | +| lake | 58.15 | 63.06 | +| dishwasher | 59.74 | 71.4 | +| screen | 63.06 | 93.78 | +| blanket | 15.53 | 17.59 | +| sculpture | 71.4 | 81.1 | +| hood | 61.74 | 76.25 | +| sconce | 52.87 | 61.29 | +| vase | 40.84 | 56.98 | +| traffic light | 33.17 | 45.09 | +| tray | 6.53 | 7.06 | +| ashcan | 41.16 | 58.53 | +| fan | 62.25 | 76.11 | +| pier | 47.04 | 56.12 | +| crt screen | 2.17 | 4.38 | +| plate | 54.24 | 71.58 | +| monitor | 34.38 | 43.86 | +| bulletin board | 42.98 | 65.93 | +| shower | 0.37 | 0.47 | +| radiator | 58.66 | 67.22 | +| glass | 10.2 | 10.36 | +| clock | 32.57 | 40.45 | +| flag | 69.39 | 73.5 | ++---------------------+-------+-------+ +2024-06-18 07:15:22,179 - mmseg - INFO - Summary: +2024-06-18 07:15:22,179 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.81 | 53.62 | 67.31 | ++-------+-------+-------+ +2024-06-18 07:15:22,180 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 07:15:22,180 - mmseg - INFO - Iter(val) [250] aAcc: 0.8481, mIoU: 0.5362, mAcc: 0.6731, IoU.wall: 0.7986, IoU.building: 0.8178, IoU.sky: 0.9461, IoU.floor: 0.8424, IoU.tree: 0.7611, IoU.ceiling: 0.8479, IoU.road: 0.8630, IoU.bed : 0.9111, IoU.windowpane: 0.6443, IoU.grass: 0.6850, IoU.cabinet: 0.6056, IoU.sidewalk: 0.6968, IoU.person: 0.8419, IoU.earth: 0.3790, IoU.door: 0.5408, IoU.table: 0.6455, IoU.mountain: 0.5732, IoU.plant: 0.5790, IoU.curtain: 0.7957, IoU.chair: 0.6171, IoU.car: 0.8497, IoU.water: 0.6245, IoU.painting: 0.7442, IoU.sofa: 0.7954, IoU.shelf: 0.4191, IoU.house: 0.6414, IoU.sea: 0.6689, IoU.mirror: 0.7619, IoU.rug: 0.7194, IoU.field: 0.3911, IoU.armchair: 0.5638, IoU.seat: 0.6314, IoU.fence: 0.4494, IoU.desk: 0.5168, IoU.rock: 0.5473, IoU.wardrobe: 0.5163, IoU.lamp: 0.6984, IoU.bathtub: 0.8197, IoU.railing: 0.3449, IoU.cushion: 0.6337, IoU.base: 0.4368, IoU.box: 0.2949, IoU.column: 0.5196, IoU.signboard: 0.3814, IoU.chest of drawers: 0.4121, IoU.counter: 0.4403, IoU.sand: 0.4683, IoU.sink: 0.7437, IoU.skyscraper: 0.5241, IoU.fireplace: 0.6777, IoU.refrigerator: 0.8186, IoU.grandstand: 0.5542, IoU.path: 0.2959, IoU.stairs: 0.2840, IoU.runway: 0.7206, IoU.case: 0.5160, IoU.pool table: 0.9392, IoU.pillow: 0.6438, IoU.screen door: 0.7977, IoU.stairway: 0.3287, IoU.river: 0.2636, IoU.bridge: 0.7037, IoU.bookcase: 0.3555, IoU.blind: 0.4161, IoU.coffee table: 0.6332, IoU.toilet: 0.8810, IoU.flower: 0.3893, IoU.book: 0.4837, IoU.hill: 0.0749, IoU.bench: 0.5112, IoU.countertop: 0.5592, IoU.stove: 0.7752, IoU.palm: 0.5392, IoU.kitchen island: 0.4139, IoU.computer: 0.7646, IoU.swivel chair: 0.4926, IoU.boat: 0.5661, IoU.bar: 0.5950, IoU.arcade machine: 0.8244, IoU.hovel: 0.4503, IoU.bus: 0.9160, IoU.towel: 0.6988, IoU.light: 0.5659, IoU.truck: 0.3853, IoU.tower: 0.2606, IoU.chandelier: 0.6923, IoU.awning: 0.4764, IoU.streetlight: 0.2760, IoU.booth: 0.3071, IoU.television receiver: 0.7218, IoU.airplane: 0.7701, IoU.dirt track: 0.1322, IoU.apparel: 0.5041, IoU.pole: 0.1892, IoU.land: 0.0003, IoU.bannister: 0.1097, IoU.escalator: 0.5810, IoU.ottoman: 0.5105, IoU.bottle: 0.3737, IoU.buffet: 0.5323, IoU.poster: 0.3371, IoU.stage: 0.1519, IoU.van: 0.3957, IoU.ship: 0.8568, IoU.fountain: 0.4642, IoU.conveyer belt: 0.7438, IoU.canopy: 0.4726, IoU.washer: 0.6865, IoU.plaything: 0.2252, IoU.swimming pool: 0.5357, IoU.stool: 0.4213, IoU.barrel: 0.3580, IoU.basket: 0.3310, IoU.waterfall: 0.7002, IoU.tent: 0.8678, IoU.bag: 0.1845, IoU.minibike: 0.6613, IoU.cradle: 0.7009, IoU.oven: 0.4850, IoU.ball: 0.5058, IoU.food: 0.5979, IoU.step: 0.1066, IoU.tank: 0.5764, IoU.trade name: 0.0756, IoU.microwave: 0.8454, IoU.pot: 0.5079, IoU.animal: 0.6718, IoU.bicycle: 0.5521, IoU.lake: 0.5815, IoU.dishwasher: 0.5974, IoU.screen: 0.6306, IoU.blanket: 0.1553, IoU.sculpture: 0.7140, IoU.hood: 0.6174, IoU.sconce: 0.5287, IoU.vase: 0.4084, IoU.traffic light: 0.3317, IoU.tray: 0.0653, IoU.ashcan: 0.4116, IoU.fan: 0.6225, IoU.pier: 0.4704, IoU.crt screen: 0.0217, IoU.plate: 0.5424, IoU.monitor: 0.3438, IoU.bulletin board: 0.4298, IoU.shower: 0.0037, IoU.radiator: 0.5866, IoU.glass: 0.1020, IoU.clock: 0.3257, IoU.flag: 0.6939, Acc.wall: 0.8772, Acc.building: 0.9340, Acc.sky: 0.9765, Acc.floor: 0.9052, Acc.tree: 0.8844, Acc.ceiling: 0.9106, Acc.road: 0.9073, Acc.bed : 0.9578, Acc.windowpane: 0.7987, Acc.grass: 0.8228, Acc.cabinet: 0.7012, Acc.sidewalk: 0.8516, Acc.person: 0.9278, Acc.earth: 0.5105, Acc.door: 0.6734, Acc.table: 0.7851, Acc.mountain: 0.7280, Acc.plant: 0.7248, Acc.curtain: 0.8772, Acc.chair: 0.7087, Acc.car: 0.9391, Acc.water: 0.7561, Acc.painting: 0.9057, Acc.sofa: 0.8930, Acc.shelf: 0.5326, Acc.house: 0.8864, Acc.sea: 0.9107, Acc.mirror: 0.8503, Acc.rug: 0.8148, Acc.field: 0.6254, Acc.armchair: 0.7528, Acc.seat: 0.9108, Acc.fence: 0.5914, Acc.desk: 0.7812, Acc.rock: 0.7854, Acc.wardrobe: 0.7942, Acc.lamp: 0.8035, Acc.bathtub: 0.8665, Acc.railing: 0.4872, Acc.cushion: 0.7306, Acc.base: 0.6339, Acc.box: 0.4119, Acc.column: 0.5626, Acc.signboard: 0.4785, Acc.chest of drawers: 0.7479, Acc.counter: 0.5613, Acc.sand: 0.6438, Acc.sink: 0.8042, Acc.skyscraper: 0.7122, Acc.fireplace: 0.9495, Acc.refrigerator: 0.9317, Acc.grandstand: 0.7705, Acc.path: 0.4202, Acc.stairs: 0.3420, Acc.runway: 0.9278, Acc.case: 0.6895, Acc.pool table: 0.9783, Acc.pillow: 0.7363, Acc.screen door: 0.8406, Acc.stairway: 0.4422, Acc.river: 0.3955, Acc.bridge: 0.9018, Acc.bookcase: 0.5847, Acc.blind: 0.4502, Acc.coffee table: 0.8776, Acc.toilet: 0.9375, Acc.flower: 0.5409, Acc.book: 0.6909, Acc.hill: 0.1086, Acc.bench: 0.5857, Acc.countertop: 0.8470, Acc.stove: 0.9459, Acc.palm: 0.7056, Acc.kitchen island: 0.9168, Acc.computer: 0.8953, Acc.swivel chair: 0.7527, Acc.boat: 0.8121, Acc.bar: 0.7659, Acc.arcade machine: 0.9189, Acc.hovel: 0.6043, Acc.bus: 0.9475, Acc.towel: 0.8401, Acc.light: 0.6388, Acc.truck: 0.6278, Acc.tower: 0.4292, Acc.chandelier: 0.8598, Acc.awning: 0.6282, Acc.streetlight: 0.4121, Acc.booth: 0.4160, Acc.television receiver: 0.8475, Acc.airplane: 0.8239, Acc.dirt track: 0.1914, Acc.apparel: 0.7273, Acc.pole: 0.2243, Acc.land: 0.0003, Acc.bannister: 0.1388, Acc.escalator: 0.7913, Acc.ottoman: 0.6859, Acc.bottle: 0.6654, Acc.buffet: 0.6155, Acc.poster: 0.4095, Acc.stage: 0.3537, Acc.van: 0.5244, Acc.ship: 0.9402, Acc.fountain: 0.5297, Acc.conveyer belt: 0.9340, Acc.canopy: 0.7589, Acc.washer: 0.7777, Acc.plaything: 0.3296, Acc.swimming pool: 0.9193, Acc.stool: 0.6142, Acc.barrel: 0.6498, Acc.basket: 0.5640, Acc.waterfall: 0.8748, Acc.tent: 0.9896, Acc.bag: 0.2161, Acc.minibike: 0.8380, Acc.cradle: 0.9898, Acc.oven: 0.5131, Acc.ball: 0.7398, Acc.food: 0.7601, Acc.step: 0.1352, Acc.tank: 0.7138, Acc.trade name: 0.0794, Acc.microwave: 0.9509, Acc.pot: 0.6013, Acc.animal: 0.7062, Acc.bicycle: 0.7213, Acc.lake: 0.6306, Acc.dishwasher: 0.7140, Acc.screen: 0.9378, Acc.blanket: 0.1759, Acc.sculpture: 0.8110, Acc.hood: 0.7625, Acc.sconce: 0.6129, Acc.vase: 0.5698, Acc.traffic light: 0.4509, Acc.tray: 0.0706, Acc.ashcan: 0.5853, Acc.fan: 0.7611, Acc.pier: 0.5612, Acc.crt screen: 0.0438, Acc.plate: 0.7158, Acc.monitor: 0.4386, Acc.bulletin board: 0.6593, Acc.shower: 0.0047, Acc.radiator: 0.6722, Acc.glass: 0.1036, Acc.clock: 0.4045, Acc.flag: 0.7350 +2024-06-18 07:16:28,833 - mmseg - INFO - Iter [19050/80000] lr: 3.048e-05, eta: 1 day, 1:02:41, time: 3.304, data_time: 1.987, memory: 70498, decode.loss_ce: 0.3089, decode.acc_seg: 87.9446, aux.loss_ce: 0.1253, aux.acc_seg: 87.7055, loss: 0.4342 +2024-06-18 07:17:35,016 - mmseg - INFO - Iter [19100/80000] lr: 3.045e-05, eta: 1 day, 1:01:02, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3284, decode.acc_seg: 86.6106, aux.loss_ce: 0.1339, aux.acc_seg: 86.3021, loss: 0.4623 +2024-06-18 07:18:41,389 - mmseg - INFO - Iter [19150/80000] lr: 3.043e-05, eta: 1 day, 0:59:24, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3310, decode.acc_seg: 86.7876, aux.loss_ce: 0.1335, aux.acc_seg: 86.7434, loss: 0.4645 +2024-06-18 07:19:47,889 - mmseg - INFO - Iter [19200/80000] lr: 3.040e-05, eta: 1 day, 0:57:46, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3291, decode.acc_seg: 87.1210, aux.loss_ce: 0.1342, aux.acc_seg: 86.7663, loss: 0.4634 +2024-06-18 07:20:54,236 - mmseg - INFO - Iter [19250/80000] lr: 3.038e-05, eta: 1 day, 0:56:09, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3229, decode.acc_seg: 87.3489, aux.loss_ce: 0.1316, aux.acc_seg: 87.0539, loss: 0.4545 +2024-06-18 07:22:00,561 - mmseg - INFO - Iter [19300/80000] lr: 3.035e-05, eta: 1 day, 0:54:31, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3331, decode.acc_seg: 86.8406, aux.loss_ce: 0.1344, aux.acc_seg: 86.6883, loss: 0.4675 +2024-06-18 07:23:07,018 - mmseg - INFO - Iter [19350/80000] lr: 3.033e-05, eta: 1 day, 0:52:54, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3324, decode.acc_seg: 86.3509, aux.loss_ce: 0.1351, aux.acc_seg: 86.1175, loss: 0.4675 +2024-06-18 07:24:13,407 - mmseg - INFO - Iter [19400/80000] lr: 3.030e-05, eta: 1 day, 0:51:17, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3193, decode.acc_seg: 87.0242, aux.loss_ce: 0.1287, aux.acc_seg: 86.9497, loss: 0.4480 +2024-06-18 07:25:19,768 - mmseg - INFO - Iter [19450/80000] lr: 3.028e-05, eta: 1 day, 0:49:40, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3175, decode.acc_seg: 87.0736, aux.loss_ce: 0.1292, aux.acc_seg: 86.8854, loss: 0.4467 +2024-06-18 07:26:25,856 - mmseg - INFO - Iter [19500/80000] lr: 3.025e-05, eta: 1 day, 0:48:02, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3293, decode.acc_seg: 86.7088, aux.loss_ce: 0.1333, aux.acc_seg: 86.3786, loss: 0.4625 +2024-06-18 07:27:32,221 - mmseg - INFO - Iter [19550/80000] lr: 3.023e-05, eta: 1 day, 0:46:25, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3570, decode.acc_seg: 86.2037, aux.loss_ce: 0.1417, aux.acc_seg: 86.1749, loss: 0.4987 +2024-06-18 07:28:38,782 - mmseg - INFO - Iter [19600/80000] lr: 3.020e-05, eta: 1 day, 0:44:49, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3426, decode.acc_seg: 86.5090, aux.loss_ce: 0.1388, aux.acc_seg: 86.3165, loss: 0.4813 +2024-06-18 07:29:45,365 - mmseg - INFO - Iter [19650/80000] lr: 3.018e-05, eta: 1 day, 0:43:13, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3321, decode.acc_seg: 86.6897, aux.loss_ce: 0.1345, aux.acc_seg: 86.6464, loss: 0.4666 +2024-06-18 07:30:51,843 - mmseg - INFO - Iter [19700/80000] lr: 3.015e-05, eta: 1 day, 0:41:38, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3416, decode.acc_seg: 86.2375, aux.loss_ce: 0.1384, aux.acc_seg: 86.0515, loss: 0.4800 +2024-06-18 07:31:58,136 - mmseg - INFO - Iter [19750/80000] lr: 3.013e-05, eta: 1 day, 0:40:01, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3212, decode.acc_seg: 87.3622, aux.loss_ce: 0.1310, aux.acc_seg: 87.1489, loss: 0.4522 +2024-06-18 07:33:04,602 - mmseg - INFO - Iter [19800/80000] lr: 3.010e-05, eta: 1 day, 0:38:25, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3236, decode.acc_seg: 86.9651, aux.loss_ce: 0.1317, aux.acc_seg: 86.8680, loss: 0.4552 +2024-06-18 07:34:10,784 - mmseg - INFO - Iter [19850/80000] lr: 3.008e-05, eta: 1 day, 0:36:49, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3240, decode.acc_seg: 87.4078, aux.loss_ce: 0.1315, aux.acc_seg: 87.3565, loss: 0.4555 +2024-06-18 07:35:17,278 - mmseg - INFO - Iter [19900/80000] lr: 3.005e-05, eta: 1 day, 0:35:14, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3209, decode.acc_seg: 87.0457, aux.loss_ce: 0.1302, aux.acc_seg: 86.9081, loss: 0.4511 +2024-06-18 07:36:23,727 - mmseg - INFO - Iter [19950/80000] lr: 3.003e-05, eta: 1 day, 0:33:39, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3320, decode.acc_seg: 86.6732, aux.loss_ce: 0.1348, aux.acc_seg: 86.5737, loss: 0.4668 +2024-06-18 07:37:30,206 - mmseg - INFO - Saving checkpoint at 20000 iterations +2024-06-18 07:39:12,379 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 07:39:12,379 - mmseg - INFO - Iter [20000/80000] lr: 3.000e-05, eta: 1 day, 0:37:10, time: 3.373, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3496, decode.acc_seg: 86.2928, aux.loss_ce: 0.1408, aux.acc_seg: 86.1299, loss: 0.4904 +2024-06-18 07:40:48,865 - mmseg - INFO - per class results: +2024-06-18 07:40:48,871 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.25 | 88.98 | +| building | 84.95 | 92.2 | +| sky | 94.69 | 96.94 | +| floor | 84.06 | 90.53 | +| tree | 76.81 | 89.39 | +| ceiling | 85.13 | 90.58 | +| road | 85.93 | 90.63 | +| bed | 90.38 | 95.68 | +| windowpane | 65.32 | 80.2 | +| grass | 66.63 | 81.14 | +| cabinet | 61.51 | 73.76 | +| sidewalk | 71.22 | 84.64 | +| person | 84.11 | 94.52 | +| earth | 34.98 | 49.79 | +| door | 55.94 | 74.78 | +| table | 63.63 | 77.24 | +| mountain | 61.92 | 75.14 | +| plant | 55.64 | 71.02 | +| curtain | 79.7 | 89.34 | +| chair | 62.03 | 71.81 | +| car | 84.34 | 94.2 | +| water | 62.34 | 75.84 | +| painting | 77.22 | 88.95 | +| sofa | 76.5 | 91.88 | +| shelf | 46.9 | 62.07 | +| house | 65.03 | 78.68 | +| sea | 64.49 | 88.06 | +| mirror | 72.21 | 78.89 | +| rug | 70.45 | 84.64 | +| field | 39.68 | 58.05 | +| armchair | 51.61 | 70.5 | +| seat | 62.56 | 89.2 | +| fence | 46.79 | 66.08 | +| desk | 51.37 | 76.42 | +| rock | 55.08 | 69.45 | +| wardrobe | 52.4 | 77.16 | +| lamp | 67.59 | 82.57 | +| bathtub | 82.68 | 85.65 | +| railing | 39.85 | 70.22 | +| cushion | 65.2 | 79.89 | +| base | 32.45 | 62.21 | +| box | 31.48 | 43.87 | +| column | 51.43 | 72.03 | +| signboard | 39.92 | 55.3 | +| chest of drawers | 39.33 | 64.0 | +| counter | 34.41 | 42.79 | +| sand | 47.43 | 69.9 | +| sink | 75.28 | 81.41 | +| skyscraper | 51.17 | 68.06 | +| fireplace | 73.74 | 91.04 | +| refrigerator | 79.15 | 87.27 | +| grandstand | 52.38 | 85.46 | +| path | 29.73 | 44.28 | +| stairs | 33.39 | 46.04 | +| runway | 67.78 | 87.79 | +| case | 56.36 | 72.08 | +| pool table | 93.06 | 97.98 | +| pillow | 65.72 | 74.54 | +| screen door | 78.1 | 81.56 | +| stairway | 30.78 | 31.86 | +| river | 26.86 | 30.96 | +| bridge | 72.72 | 87.29 | +| bookcase | 37.88 | 58.64 | +| blind | 50.0 | 59.2 | +| coffee table | 59.5 | 89.98 | +| toilet | 87.56 | 92.72 | +| flower | 40.2 | 49.48 | +| book | 51.86 | 75.45 | +| hill | 2.08 | 2.44 | +| bench | 50.65 | 60.27 | +| countertop | 58.41 | 70.94 | +| stove | 87.31 | 93.75 | +| palm | 55.67 | 77.52 | +| kitchen island | 48.5 | 78.29 | +| computer | 76.2 | 93.0 | +| swivel chair | 50.84 | 73.12 | +| boat | 65.58 | 87.06 | +| bar | 49.69 | 76.52 | +| arcade machine | 75.49 | 84.18 | +| hovel | 41.32 | 49.43 | +| bus | 90.81 | 95.65 | +| towel | 73.71 | 83.98 | +| light | 56.89 | 65.78 | +| truck | 41.95 | 62.64 | +| tower | 26.14 | 51.9 | +| chandelier | 65.24 | 77.51 | +| awning | 43.07 | 57.18 | +| streetlight | 29.99 | 39.95 | +| booth | 30.03 | 47.81 | +| television receiver | 74.27 | 82.6 | +| airplane | 65.2 | 70.77 | +| dirt track | 25.04 | 25.29 | +| apparel | 37.95 | 46.28 | +| pole | 21.59 | 28.19 | +| land | 2.53 | 7.81 | +| bannister | 12.73 | 16.9 | +| escalator | 45.34 | 60.71 | +| ottoman | 49.25 | 72.74 | +| bottle | 39.91 | 54.76 | +| buffet | 56.51 | 88.28 | +| poster | 31.97 | 43.23 | +| stage | 20.82 | 29.12 | +| van | 40.24 | 48.88 | +| ship | 29.96 | 31.83 | +| fountain | 29.32 | 30.3 | +| conveyer belt | 79.2 | 91.31 | +| canopy | 49.64 | 72.6 | +| washer | 67.22 | 77.85 | +| plaything | 18.49 | 20.09 | +| swimming pool | 63.67 | 92.11 | +| stool | 44.29 | 67.14 | +| barrel | 50.01 | 64.92 | +| basket | 33.89 | 48.75 | +| waterfall | 67.48 | 93.35 | +| tent | 93.09 | 98.85 | +| bag | 18.28 | 22.29 | +| minibike | 68.69 | 81.94 | +| cradle | 73.22 | 98.86 | +| oven | 56.73 | 63.29 | +| ball | 49.65 | 63.87 | +| food | 57.93 | 66.71 | +| step | 8.34 | 11.27 | +| tank | 52.44 | 58.05 | +| trade name | 25.74 | 28.5 | +| microwave | 85.29 | 95.49 | +| pot | 52.56 | 65.15 | +| animal | 62.56 | 64.16 | +| bicycle | 51.18 | 69.6 | +| lake | 42.62 | 46.64 | +| dishwasher | 67.0 | 78.04 | +| screen | 62.36 | 90.61 | +| blanket | 8.55 | 9.53 | +| sculpture | 72.41 | 83.27 | +| hood | 61.27 | 73.36 | +| sconce | 46.12 | 56.14 | +| vase | 42.46 | 57.12 | +| traffic light | 25.33 | 68.21 | +| tray | 15.81 | 22.89 | +| ashcan | 43.51 | 61.75 | +| fan | 55.8 | 61.34 | +| pier | 34.73 | 50.42 | +| crt screen | 0.75 | 0.83 | +| plate | 54.39 | 67.97 | +| monitor | 70.37 | 76.95 | +| bulletin board | 53.92 | 62.67 | +| shower | 0.0 | 0.01 | +| radiator | 57.92 | 69.46 | +| glass | 16.13 | 16.99 | +| clock | 33.6 | 40.89 | +| flag | 69.36 | 73.1 | ++---------------------+-------+-------+ +2024-06-18 07:40:48,872 - mmseg - INFO - Summary: +2024-06-18 07:40:48,872 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.96 | 53.57 | 66.54 | ++-------+-------+-------+ +2024-06-18 07:40:48,873 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 07:40:48,873 - mmseg - INFO - Iter(val) [250] aAcc: 0.8496, mIoU: 0.5357, mAcc: 0.6654, IoU.wall: 0.8025, IoU.building: 0.8495, IoU.sky: 0.9469, IoU.floor: 0.8406, IoU.tree: 0.7681, IoU.ceiling: 0.8513, IoU.road: 0.8593, IoU.bed : 0.9038, IoU.windowpane: 0.6532, IoU.grass: 0.6663, IoU.cabinet: 0.6151, IoU.sidewalk: 0.7122, IoU.person: 0.8411, IoU.earth: 0.3498, IoU.door: 0.5594, IoU.table: 0.6363, IoU.mountain: 0.6192, IoU.plant: 0.5564, IoU.curtain: 0.7970, IoU.chair: 0.6203, IoU.car: 0.8434, IoU.water: 0.6234, IoU.painting: 0.7722, IoU.sofa: 0.7650, IoU.shelf: 0.4690, IoU.house: 0.6503, IoU.sea: 0.6449, IoU.mirror: 0.7221, IoU.rug: 0.7045, IoU.field: 0.3968, IoU.armchair: 0.5161, IoU.seat: 0.6256, IoU.fence: 0.4679, IoU.desk: 0.5137, IoU.rock: 0.5508, IoU.wardrobe: 0.5240, IoU.lamp: 0.6759, IoU.bathtub: 0.8268, IoU.railing: 0.3985, IoU.cushion: 0.6520, IoU.base: 0.3245, IoU.box: 0.3148, IoU.column: 0.5143, IoU.signboard: 0.3992, IoU.chest of drawers: 0.3933, IoU.counter: 0.3441, IoU.sand: 0.4743, IoU.sink: 0.7528, IoU.skyscraper: 0.5117, IoU.fireplace: 0.7374, IoU.refrigerator: 0.7915, IoU.grandstand: 0.5238, IoU.path: 0.2973, IoU.stairs: 0.3339, IoU.runway: 0.6778, IoU.case: 0.5636, IoU.pool table: 0.9306, IoU.pillow: 0.6572, IoU.screen door: 0.7810, IoU.stairway: 0.3078, IoU.river: 0.2686, IoU.bridge: 0.7272, IoU.bookcase: 0.3788, IoU.blind: 0.5000, IoU.coffee table: 0.5950, IoU.toilet: 0.8756, IoU.flower: 0.4020, IoU.book: 0.5186, IoU.hill: 0.0208, IoU.bench: 0.5065, IoU.countertop: 0.5841, IoU.stove: 0.8731, IoU.palm: 0.5567, IoU.kitchen island: 0.4850, IoU.computer: 0.7620, IoU.swivel chair: 0.5084, IoU.boat: 0.6558, IoU.bar: 0.4969, IoU.arcade machine: 0.7549, IoU.hovel: 0.4132, IoU.bus: 0.9081, IoU.towel: 0.7371, IoU.light: 0.5689, IoU.truck: 0.4195, IoU.tower: 0.2614, IoU.chandelier: 0.6524, IoU.awning: 0.4307, IoU.streetlight: 0.2999, IoU.booth: 0.3003, IoU.television receiver: 0.7427, IoU.airplane: 0.6520, IoU.dirt track: 0.2504, IoU.apparel: 0.3795, IoU.pole: 0.2159, IoU.land: 0.0253, IoU.bannister: 0.1273, IoU.escalator: 0.4534, IoU.ottoman: 0.4925, IoU.bottle: 0.3991, IoU.buffet: 0.5651, IoU.poster: 0.3197, IoU.stage: 0.2082, IoU.van: 0.4024, IoU.ship: 0.2996, IoU.fountain: 0.2932, IoU.conveyer belt: 0.7920, IoU.canopy: 0.4964, IoU.washer: 0.6722, IoU.plaything: 0.1849, IoU.swimming pool: 0.6367, IoU.stool: 0.4429, IoU.barrel: 0.5001, IoU.basket: 0.3389, IoU.waterfall: 0.6748, IoU.tent: 0.9309, IoU.bag: 0.1828, IoU.minibike: 0.6869, IoU.cradle: 0.7322, IoU.oven: 0.5673, IoU.ball: 0.4965, IoU.food: 0.5793, IoU.step: 0.0834, IoU.tank: 0.5244, IoU.trade name: 0.2574, IoU.microwave: 0.8529, IoU.pot: 0.5256, IoU.animal: 0.6256, IoU.bicycle: 0.5118, IoU.lake: 0.4262, IoU.dishwasher: 0.6700, IoU.screen: 0.6236, IoU.blanket: 0.0855, IoU.sculpture: 0.7241, IoU.hood: 0.6127, IoU.sconce: 0.4612, IoU.vase: 0.4246, IoU.traffic light: 0.2533, IoU.tray: 0.1581, IoU.ashcan: 0.4351, IoU.fan: 0.5580, IoU.pier: 0.3473, IoU.crt screen: 0.0075, IoU.plate: 0.5439, IoU.monitor: 0.7037, IoU.bulletin board: 0.5392, IoU.shower: 0.0000, IoU.radiator: 0.5792, IoU.glass: 0.1613, IoU.clock: 0.3360, IoU.flag: 0.6936, Acc.wall: 0.8898, Acc.building: 0.9220, Acc.sky: 0.9694, Acc.floor: 0.9053, Acc.tree: 0.8939, Acc.ceiling: 0.9058, Acc.road: 0.9063, Acc.bed : 0.9568, Acc.windowpane: 0.8020, Acc.grass: 0.8114, Acc.cabinet: 0.7376, Acc.sidewalk: 0.8464, Acc.person: 0.9452, Acc.earth: 0.4979, Acc.door: 0.7478, Acc.table: 0.7724, Acc.mountain: 0.7514, Acc.plant: 0.7102, Acc.curtain: 0.8934, Acc.chair: 0.7181, Acc.car: 0.9420, Acc.water: 0.7584, Acc.painting: 0.8895, Acc.sofa: 0.9188, Acc.shelf: 0.6207, Acc.house: 0.7868, Acc.sea: 0.8806, Acc.mirror: 0.7889, Acc.rug: 0.8464, Acc.field: 0.5805, Acc.armchair: 0.7050, Acc.seat: 0.8920, Acc.fence: 0.6608, Acc.desk: 0.7642, Acc.rock: 0.6945, Acc.wardrobe: 0.7716, Acc.lamp: 0.8257, Acc.bathtub: 0.8565, Acc.railing: 0.7022, Acc.cushion: 0.7989, Acc.base: 0.6221, Acc.box: 0.4387, Acc.column: 0.7203, Acc.signboard: 0.5530, Acc.chest of drawers: 0.6400, Acc.counter: 0.4279, Acc.sand: 0.6990, Acc.sink: 0.8141, Acc.skyscraper: 0.6806, Acc.fireplace: 0.9104, Acc.refrigerator: 0.8727, Acc.grandstand: 0.8546, Acc.path: 0.4428, Acc.stairs: 0.4604, Acc.runway: 0.8779, Acc.case: 0.7208, Acc.pool table: 0.9798, Acc.pillow: 0.7454, Acc.screen door: 0.8156, Acc.stairway: 0.3186, Acc.river: 0.3096, Acc.bridge: 0.8729, Acc.bookcase: 0.5864, Acc.blind: 0.5920, Acc.coffee table: 0.8998, Acc.toilet: 0.9272, Acc.flower: 0.4948, Acc.book: 0.7545, Acc.hill: 0.0244, Acc.bench: 0.6027, Acc.countertop: 0.7094, Acc.stove: 0.9375, Acc.palm: 0.7752, Acc.kitchen island: 0.7829, Acc.computer: 0.9300, Acc.swivel chair: 0.7312, Acc.boat: 0.8706, Acc.bar: 0.7652, Acc.arcade machine: 0.8418, Acc.hovel: 0.4943, Acc.bus: 0.9565, Acc.towel: 0.8398, Acc.light: 0.6578, Acc.truck: 0.6264, Acc.tower: 0.5190, Acc.chandelier: 0.7751, Acc.awning: 0.5718, Acc.streetlight: 0.3995, Acc.booth: 0.4781, Acc.television receiver: 0.8260, Acc.airplane: 0.7077, Acc.dirt track: 0.2529, Acc.apparel: 0.4628, Acc.pole: 0.2819, Acc.land: 0.0781, Acc.bannister: 0.1690, Acc.escalator: 0.6071, Acc.ottoman: 0.7274, Acc.bottle: 0.5476, Acc.buffet: 0.8828, Acc.poster: 0.4323, Acc.stage: 0.2912, Acc.van: 0.4888, Acc.ship: 0.3183, Acc.fountain: 0.3030, Acc.conveyer belt: 0.9131, Acc.canopy: 0.7260, Acc.washer: 0.7785, Acc.plaything: 0.2009, Acc.swimming pool: 0.9211, Acc.stool: 0.6714, Acc.barrel: 0.6492, Acc.basket: 0.4875, Acc.waterfall: 0.9335, Acc.tent: 0.9885, Acc.bag: 0.2229, Acc.minibike: 0.8194, Acc.cradle: 0.9886, Acc.oven: 0.6329, Acc.ball: 0.6387, Acc.food: 0.6671, Acc.step: 0.1127, Acc.tank: 0.5805, Acc.trade name: 0.2850, Acc.microwave: 0.9549, Acc.pot: 0.6515, Acc.animal: 0.6416, Acc.bicycle: 0.6960, Acc.lake: 0.4664, Acc.dishwasher: 0.7804, Acc.screen: 0.9061, Acc.blanket: 0.0953, Acc.sculpture: 0.8327, Acc.hood: 0.7336, Acc.sconce: 0.5614, Acc.vase: 0.5712, Acc.traffic light: 0.6821, Acc.tray: 0.2289, Acc.ashcan: 0.6175, Acc.fan: 0.6134, Acc.pier: 0.5042, Acc.crt screen: 0.0083, Acc.plate: 0.6797, Acc.monitor: 0.7695, Acc.bulletin board: 0.6267, Acc.shower: 0.0001, Acc.radiator: 0.6946, Acc.glass: 0.1699, Acc.clock: 0.4089, Acc.flag: 0.7310 +2024-06-18 07:41:55,827 - mmseg - INFO - Iter [20050/80000] lr: 2.998e-05, eta: 1 day, 0:40:24, time: 3.269, data_time: 1.948, memory: 70498, decode.loss_ce: 0.3237, decode.acc_seg: 87.1692, aux.loss_ce: 0.1313, aux.acc_seg: 86.8860, loss: 0.4550 +2024-06-18 07:43:02,063 - mmseg - INFO - Iter [20100/80000] lr: 2.995e-05, eta: 1 day, 0:38:47, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3379, decode.acc_seg: 86.5873, aux.loss_ce: 0.1369, aux.acc_seg: 86.4855, loss: 0.4748 +2024-06-18 07:44:08,421 - mmseg - INFO - Iter [20150/80000] lr: 2.993e-05, eta: 1 day, 0:37:10, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3118, decode.acc_seg: 87.7030, aux.loss_ce: 0.1266, aux.acc_seg: 87.3740, loss: 0.4384 +2024-06-18 07:45:14,684 - mmseg - INFO - Iter [20200/80000] lr: 2.990e-05, eta: 1 day, 0:35:33, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3216, decode.acc_seg: 86.5762, aux.loss_ce: 0.1307, aux.acc_seg: 86.4095, loss: 0.4524 +2024-06-18 07:46:23,700 - mmseg - INFO - Iter [20250/80000] lr: 2.988e-05, eta: 1 day, 0:34:04, time: 1.380, data_time: 0.059, memory: 70498, decode.loss_ce: 0.3127, decode.acc_seg: 87.3831, aux.loss_ce: 0.1264, aux.acc_seg: 87.2439, loss: 0.4391 +2024-06-18 07:47:29,730 - mmseg - INFO - Iter [20300/80000] lr: 2.985e-05, eta: 1 day, 0:32:26, time: 1.321, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3061, decode.acc_seg: 87.3924, aux.loss_ce: 0.1246, aux.acc_seg: 87.1845, loss: 0.4307 +2024-06-18 07:48:36,054 - mmseg - INFO - Iter [20350/80000] lr: 2.983e-05, eta: 1 day, 0:30:50, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3256, decode.acc_seg: 86.9924, aux.loss_ce: 0.1318, aux.acc_seg: 86.8231, loss: 0.4574 +2024-06-18 07:49:42,076 - mmseg - INFO - Iter [20400/80000] lr: 2.980e-05, eta: 1 day, 0:29:13, time: 1.320, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3195, decode.acc_seg: 87.3176, aux.loss_ce: 0.1299, aux.acc_seg: 87.0885, loss: 0.4494 +2024-06-18 07:50:48,153 - mmseg - INFO - Iter [20450/80000] lr: 2.978e-05, eta: 1 day, 0:27:36, time: 1.322, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3453, decode.acc_seg: 86.3519, aux.loss_ce: 0.1403, aux.acc_seg: 86.2422, loss: 0.4855 +2024-06-18 07:51:54,531 - mmseg - INFO - Iter [20500/80000] lr: 2.975e-05, eta: 1 day, 0:26:00, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3197, decode.acc_seg: 86.9580, aux.loss_ce: 0.1300, aux.acc_seg: 86.7933, loss: 0.4497 +2024-06-18 07:53:00,474 - mmseg - INFO - Iter [20550/80000] lr: 2.973e-05, eta: 1 day, 0:24:23, time: 1.319, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3304, decode.acc_seg: 86.7968, aux.loss_ce: 0.1337, aux.acc_seg: 86.5954, loss: 0.4641 +2024-06-18 07:54:06,739 - mmseg - INFO - Iter [20600/80000] lr: 2.970e-05, eta: 1 day, 0:22:47, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3448, decode.acc_seg: 86.4144, aux.loss_ce: 0.1392, aux.acc_seg: 86.3068, loss: 0.4840 +2024-06-18 07:55:12,829 - mmseg - INFO - Iter [20650/80000] lr: 2.968e-05, eta: 1 day, 0:21:11, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3191, decode.acc_seg: 87.0441, aux.loss_ce: 0.1296, aux.acc_seg: 86.7911, loss: 0.4487 +2024-06-18 07:56:19,205 - mmseg - INFO - Iter [20700/80000] lr: 2.965e-05, eta: 1 day, 0:19:35, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3325, decode.acc_seg: 86.8824, aux.loss_ce: 0.1342, aux.acc_seg: 86.7673, loss: 0.4667 +2024-06-18 07:57:25,498 - mmseg - INFO - Iter [20750/80000] lr: 2.963e-05, eta: 1 day, 0:18:00, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3216, decode.acc_seg: 87.0843, aux.loss_ce: 0.1309, aux.acc_seg: 86.8651, loss: 0.4525 +2024-06-18 07:58:32,149 - mmseg - INFO - Iter [20800/80000] lr: 2.960e-05, eta: 1 day, 0:16:26, time: 1.333, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3299, decode.acc_seg: 86.7643, aux.loss_ce: 0.1334, aux.acc_seg: 86.4909, loss: 0.4633 +2024-06-18 07:59:38,192 - mmseg - INFO - Iter [20850/80000] lr: 2.958e-05, eta: 1 day, 0:14:50, time: 1.321, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3067, decode.acc_seg: 87.6165, aux.loss_ce: 0.1248, aux.acc_seg: 87.4490, loss: 0.4315 +2024-06-18 08:00:44,458 - mmseg - INFO - Iter [20900/80000] lr: 2.955e-05, eta: 1 day, 0:13:15, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3019, decode.acc_seg: 87.6033, aux.loss_ce: 0.1236, aux.acc_seg: 87.3525, loss: 0.4255 +2024-06-18 08:01:51,014 - mmseg - INFO - Iter [20950/80000] lr: 2.953e-05, eta: 1 day, 0:11:41, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3075, decode.acc_seg: 87.2884, aux.loss_ce: 0.1245, aux.acc_seg: 87.1834, loss: 0.4320 +2024-06-18 08:02:57,103 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 08:02:57,103 - mmseg - INFO - Iter [21000/80000] lr: 2.950e-05, eta: 1 day, 0:10:05, time: 1.322, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3264, decode.acc_seg: 86.9126, aux.loss_ce: 0.1319, aux.acc_seg: 86.6419, loss: 0.4583 +2024-06-18 08:04:33,915 - mmseg - INFO - per class results: +2024-06-18 08:04:33,921 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.15 | 89.13 | +| building | 85.12 | 93.91 | +| sky | 94.82 | 97.52 | +| floor | 84.66 | 92.47 | +| tree | 76.88 | 89.62 | +| ceiling | 86.66 | 91.65 | +| road | 84.59 | 91.81 | +| bed | 91.67 | 96.64 | +| windowpane | 64.62 | 82.08 | +| grass | 64.03 | 79.81 | +| cabinet | 60.36 | 75.06 | +| sidewalk | 69.29 | 83.53 | +| person | 83.94 | 93.23 | +| earth | 34.45 | 44.68 | +| door | 55.65 | 68.6 | +| table | 66.47 | 77.13 | +| mountain | 61.25 | 76.08 | +| plant | 54.51 | 65.37 | +| curtain | 79.06 | 86.73 | +| chair | 62.89 | 73.33 | +| car | 84.56 | 94.05 | +| water | 63.46 | 80.36 | +| painting | 78.78 | 89.63 | +| sofa | 78.55 | 90.79 | +| shelf | 46.15 | 61.82 | +| house | 57.89 | 69.61 | +| sea | 73.0 | 91.78 | +| mirror | 75.04 | 79.26 | +| rug | 72.48 | 78.55 | +| field | 30.81 | 52.44 | +| armchair | 53.29 | 76.39 | +| seat | 67.79 | 86.77 | +| fence | 46.98 | 64.18 | +| desk | 55.82 | 75.83 | +| rock | 47.53 | 64.75 | +| wardrobe | 47.21 | 68.99 | +| lamp | 69.56 | 79.56 | +| bathtub | 79.08 | 86.96 | +| railing | 38.6 | 54.02 | +| cushion | 66.55 | 76.1 | +| base | 34.66 | 53.77 | +| box | 32.7 | 43.94 | +| column | 52.18 | 63.71 | +| signboard | 38.83 | 49.63 | +| chest of drawers | 38.9 | 70.29 | +| counter | 42.76 | 52.25 | +| sand | 49.59 | 72.81 | +| sink | 73.51 | 80.77 | +| skyscraper | 52.24 | 64.33 | +| fireplace | 71.39 | 95.87 | +| refrigerator | 76.54 | 83.55 | +| grandstand | 59.19 | 83.78 | +| path | 29.23 | 38.56 | +| stairs | 35.89 | 47.2 | +| runway | 70.59 | 94.12 | +| case | 50.32 | 89.24 | +| pool table | 93.78 | 97.62 | +| pillow | 65.45 | 77.1 | +| screen door | 81.66 | 87.31 | +| stairway | 45.35 | 50.59 | +| river | 14.83 | 19.91 | +| bridge | 76.17 | 88.58 | +| bookcase | 33.8 | 49.04 | +| blind | 39.94 | 43.01 | +| coffee table | 63.87 | 89.09 | +| toilet | 87.56 | 91.44 | +| flower | 41.87 | 51.47 | +| book | 51.56 | 79.51 | +| hill | 7.18 | 17.01 | +| bench | 47.61 | 59.02 | +| countertop | 59.64 | 83.23 | +| stove | 82.76 | 93.5 | +| palm | 55.92 | 72.04 | +| kitchen island | 42.05 | 70.93 | +| computer | 73.85 | 93.83 | +| swivel chair | 50.42 | 81.64 | +| boat | 45.7 | 87.39 | +| bar | 58.1 | 72.47 | +| arcade machine | 73.12 | 81.33 | +| hovel | 41.63 | 45.37 | +| bus | 91.66 | 97.13 | +| towel | 69.4 | 74.46 | +| light | 57.69 | 68.62 | +| truck | 28.81 | 33.48 | +| tower | 13.89 | 21.83 | +| chandelier | 69.38 | 85.5 | +| awning | 32.78 | 36.28 | +| streetlight | 29.01 | 39.16 | +| booth | 50.91 | 70.62 | +| television receiver | 69.01 | 75.42 | +| airplane | 69.52 | 92.91 | +| dirt track | 12.64 | 12.64 | +| apparel | 36.61 | 81.85 | +| pole | 19.12 | 22.93 | +| land | 2.28 | 5.21 | +| bannister | 13.07 | 19.29 | +| escalator | 52.14 | 88.81 | +| ottoman | 51.12 | 60.42 | +| bottle | 24.87 | 31.31 | +| buffet | 50.99 | 56.95 | +| poster | 25.99 | 35.07 | +| stage | 22.86 | 33.72 | +| van | 32.28 | 38.08 | +| ship | 84.68 | 93.84 | +| fountain | 15.39 | 18.06 | +| conveyer belt | 72.67 | 93.0 | +| canopy | 58.9 | 65.75 | +| washer | 73.52 | 87.97 | +| plaything | 20.89 | 31.83 | +| swimming pool | 57.91 | 91.46 | +| stool | 51.57 | 70.27 | +| barrel | 49.59 | 64.49 | +| basket | 29.99 | 42.21 | +| waterfall | 64.78 | 90.7 | +| tent | 87.86 | 98.69 | +| bag | 14.92 | 16.36 | +| minibike | 70.83 | 84.71 | +| cradle | 68.38 | 97.87 | +| oven | 57.69 | 67.05 | +| ball | 55.92 | 65.88 | +| food | 47.59 | 54.55 | +| step | 8.9 | 12.25 | +| tank | 60.13 | 96.86 | +| trade name | 21.34 | 23.32 | +| microwave | 84.65 | 94.97 | +| pot | 54.11 | 63.23 | +| animal | 64.51 | 68.9 | +| bicycle | 54.35 | 74.91 | +| lake | 62.02 | 74.92 | +| dishwasher | 61.84 | 70.17 | +| screen | 57.08 | 94.53 | +| blanket | 24.27 | 27.47 | +| sculpture | 69.02 | 83.13 | +| hood | 59.85 | 69.85 | +| sconce | 49.78 | 54.72 | +| vase | 40.65 | 57.42 | +| traffic light | 30.4 | 55.04 | +| tray | 7.15 | 9.94 | +| ashcan | 39.93 | 53.47 | +| fan | 62.65 | 82.81 | +| pier | 49.68 | 78.43 | +| crt screen | 1.8 | 2.6 | +| plate | 51.71 | 74.41 | +| monitor | 56.4 | 68.23 | +| bulletin board | 45.54 | 64.43 | +| shower | 0.02 | 0.03 | +| radiator | 56.36 | 66.0 | +| glass | 16.13 | 17.43 | +| clock | 34.48 | 38.12 | +| flag | 70.03 | 77.8 | ++---------------------+-------+-------+ +2024-06-18 08:04:33,921 - mmseg - INFO - Summary: +2024-06-18 08:04:33,921 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.11 | 53.48 | 66.72 | ++-------+-------+-------+ +2024-06-18 08:04:33,922 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 08:04:33,922 - mmseg - INFO - Iter(val) [250] aAcc: 0.8511, mIoU: 0.5348, mAcc: 0.6672, IoU.wall: 0.8115, IoU.building: 0.8512, IoU.sky: 0.9482, IoU.floor: 0.8466, IoU.tree: 0.7688, IoU.ceiling: 0.8666, IoU.road: 0.8459, IoU.bed : 0.9167, IoU.windowpane: 0.6462, IoU.grass: 0.6403, IoU.cabinet: 0.6036, IoU.sidewalk: 0.6929, IoU.person: 0.8394, IoU.earth: 0.3445, IoU.door: 0.5565, IoU.table: 0.6647, IoU.mountain: 0.6125, IoU.plant: 0.5451, IoU.curtain: 0.7906, IoU.chair: 0.6289, IoU.car: 0.8456, IoU.water: 0.6346, IoU.painting: 0.7878, IoU.sofa: 0.7855, IoU.shelf: 0.4615, IoU.house: 0.5789, IoU.sea: 0.7300, IoU.mirror: 0.7504, IoU.rug: 0.7248, IoU.field: 0.3081, IoU.armchair: 0.5329, IoU.seat: 0.6779, IoU.fence: 0.4698, IoU.desk: 0.5582, IoU.rock: 0.4753, IoU.wardrobe: 0.4721, IoU.lamp: 0.6956, IoU.bathtub: 0.7908, IoU.railing: 0.3860, IoU.cushion: 0.6655, IoU.base: 0.3466, IoU.box: 0.3270, IoU.column: 0.5218, IoU.signboard: 0.3883, IoU.chest of drawers: 0.3890, IoU.counter: 0.4276, IoU.sand: 0.4959, IoU.sink: 0.7351, IoU.skyscraper: 0.5224, IoU.fireplace: 0.7139, IoU.refrigerator: 0.7654, IoU.grandstand: 0.5919, IoU.path: 0.2923, IoU.stairs: 0.3589, IoU.runway: 0.7059, IoU.case: 0.5032, IoU.pool table: 0.9378, IoU.pillow: 0.6545, IoU.screen door: 0.8166, IoU.stairway: 0.4535, IoU.river: 0.1483, IoU.bridge: 0.7617, IoU.bookcase: 0.3380, IoU.blind: 0.3994, IoU.coffee table: 0.6387, IoU.toilet: 0.8756, IoU.flower: 0.4187, IoU.book: 0.5156, IoU.hill: 0.0718, IoU.bench: 0.4761, IoU.countertop: 0.5964, IoU.stove: 0.8276, IoU.palm: 0.5592, IoU.kitchen island: 0.4205, IoU.computer: 0.7385, IoU.swivel chair: 0.5042, IoU.boat: 0.4570, IoU.bar: 0.5810, IoU.arcade machine: 0.7312, IoU.hovel: 0.4163, IoU.bus: 0.9166, IoU.towel: 0.6940, IoU.light: 0.5769, IoU.truck: 0.2881, IoU.tower: 0.1389, IoU.chandelier: 0.6938, IoU.awning: 0.3278, IoU.streetlight: 0.2901, IoU.booth: 0.5091, IoU.television receiver: 0.6901, IoU.airplane: 0.6952, IoU.dirt track: 0.1264, IoU.apparel: 0.3661, IoU.pole: 0.1912, IoU.land: 0.0228, IoU.bannister: 0.1307, IoU.escalator: 0.5214, IoU.ottoman: 0.5112, IoU.bottle: 0.2487, IoU.buffet: 0.5099, IoU.poster: 0.2599, IoU.stage: 0.2286, IoU.van: 0.3228, IoU.ship: 0.8468, IoU.fountain: 0.1539, IoU.conveyer belt: 0.7267, IoU.canopy: 0.5890, IoU.washer: 0.7352, IoU.plaything: 0.2089, IoU.swimming pool: 0.5791, IoU.stool: 0.5157, IoU.barrel: 0.4959, IoU.basket: 0.2999, IoU.waterfall: 0.6478, IoU.tent: 0.8786, IoU.bag: 0.1492, IoU.minibike: 0.7083, IoU.cradle: 0.6838, IoU.oven: 0.5769, IoU.ball: 0.5592, IoU.food: 0.4759, IoU.step: 0.0890, IoU.tank: 0.6013, IoU.trade name: 0.2134, IoU.microwave: 0.8465, IoU.pot: 0.5411, IoU.animal: 0.6451, IoU.bicycle: 0.5435, IoU.lake: 0.6202, IoU.dishwasher: 0.6184, IoU.screen: 0.5708, IoU.blanket: 0.2427, IoU.sculpture: 0.6902, IoU.hood: 0.5985, IoU.sconce: 0.4978, IoU.vase: 0.4065, IoU.traffic light: 0.3040, IoU.tray: 0.0715, IoU.ashcan: 0.3993, IoU.fan: 0.6265, IoU.pier: 0.4968, IoU.crt screen: 0.0180, IoU.plate: 0.5171, IoU.monitor: 0.5640, IoU.bulletin board: 0.4554, IoU.shower: 0.0002, IoU.radiator: 0.5636, IoU.glass: 0.1613, IoU.clock: 0.3448, IoU.flag: 0.7003, Acc.wall: 0.8913, Acc.building: 0.9391, Acc.sky: 0.9752, Acc.floor: 0.9247, Acc.tree: 0.8962, Acc.ceiling: 0.9165, Acc.road: 0.9181, Acc.bed : 0.9664, Acc.windowpane: 0.8208, Acc.grass: 0.7981, Acc.cabinet: 0.7506, Acc.sidewalk: 0.8353, Acc.person: 0.9323, Acc.earth: 0.4468, Acc.door: 0.6860, Acc.table: 0.7713, Acc.mountain: 0.7608, Acc.plant: 0.6537, Acc.curtain: 0.8673, Acc.chair: 0.7333, Acc.car: 0.9405, Acc.water: 0.8036, Acc.painting: 0.8963, Acc.sofa: 0.9079, Acc.shelf: 0.6182, Acc.house: 0.6961, Acc.sea: 0.9178, Acc.mirror: 0.7926, Acc.rug: 0.7855, Acc.field: 0.5244, Acc.armchair: 0.7639, Acc.seat: 0.8677, Acc.fence: 0.6418, Acc.desk: 0.7583, Acc.rock: 0.6475, Acc.wardrobe: 0.6899, Acc.lamp: 0.7956, Acc.bathtub: 0.8696, Acc.railing: 0.5402, Acc.cushion: 0.7610, Acc.base: 0.5377, Acc.box: 0.4394, Acc.column: 0.6371, Acc.signboard: 0.4963, Acc.chest of drawers: 0.7029, Acc.counter: 0.5225, Acc.sand: 0.7281, Acc.sink: 0.8077, Acc.skyscraper: 0.6433, Acc.fireplace: 0.9587, Acc.refrigerator: 0.8355, Acc.grandstand: 0.8378, Acc.path: 0.3856, Acc.stairs: 0.4720, Acc.runway: 0.9412, Acc.case: 0.8924, Acc.pool table: 0.9762, Acc.pillow: 0.7710, Acc.screen door: 0.8731, Acc.stairway: 0.5059, Acc.river: 0.1991, Acc.bridge: 0.8858, Acc.bookcase: 0.4904, Acc.blind: 0.4301, Acc.coffee table: 0.8909, Acc.toilet: 0.9144, Acc.flower: 0.5147, Acc.book: 0.7951, Acc.hill: 0.1701, Acc.bench: 0.5902, Acc.countertop: 0.8323, Acc.stove: 0.9350, Acc.palm: 0.7204, Acc.kitchen island: 0.7093, Acc.computer: 0.9383, Acc.swivel chair: 0.8164, Acc.boat: 0.8739, Acc.bar: 0.7247, Acc.arcade machine: 0.8133, Acc.hovel: 0.4537, Acc.bus: 0.9713, Acc.towel: 0.7446, Acc.light: 0.6862, Acc.truck: 0.3348, Acc.tower: 0.2183, Acc.chandelier: 0.8550, Acc.awning: 0.3628, Acc.streetlight: 0.3916, Acc.booth: 0.7062, Acc.television receiver: 0.7542, Acc.airplane: 0.9291, Acc.dirt track: 0.1264, Acc.apparel: 0.8185, Acc.pole: 0.2293, Acc.land: 0.0521, Acc.bannister: 0.1929, Acc.escalator: 0.8881, Acc.ottoman: 0.6042, Acc.bottle: 0.3131, Acc.buffet: 0.5695, Acc.poster: 0.3507, Acc.stage: 0.3372, Acc.van: 0.3808, Acc.ship: 0.9384, Acc.fountain: 0.1806, Acc.conveyer belt: 0.9300, Acc.canopy: 0.6575, Acc.washer: 0.8797, Acc.plaything: 0.3183, Acc.swimming pool: 0.9146, Acc.stool: 0.7027, Acc.barrel: 0.6449, Acc.basket: 0.4221, Acc.waterfall: 0.9070, Acc.tent: 0.9869, Acc.bag: 0.1636, Acc.minibike: 0.8471, Acc.cradle: 0.9787, Acc.oven: 0.6705, Acc.ball: 0.6588, Acc.food: 0.5455, Acc.step: 0.1225, Acc.tank: 0.9686, Acc.trade name: 0.2332, Acc.microwave: 0.9497, Acc.pot: 0.6323, Acc.animal: 0.6890, Acc.bicycle: 0.7491, Acc.lake: 0.7492, Acc.dishwasher: 0.7017, Acc.screen: 0.9453, Acc.blanket: 0.2747, Acc.sculpture: 0.8313, Acc.hood: 0.6985, Acc.sconce: 0.5472, Acc.vase: 0.5742, Acc.traffic light: 0.5504, Acc.tray: 0.0994, Acc.ashcan: 0.5347, Acc.fan: 0.8281, Acc.pier: 0.7843, Acc.crt screen: 0.0260, Acc.plate: 0.7441, Acc.monitor: 0.6823, Acc.bulletin board: 0.6443, Acc.shower: 0.0003, Acc.radiator: 0.6600, Acc.glass: 0.1743, Acc.clock: 0.3812, Acc.flag: 0.7780 +2024-06-18 08:05:40,743 - mmseg - INFO - Iter [21050/80000] lr: 2.948e-05, eta: 1 day, 0:13:03, time: 3.273, data_time: 1.952, memory: 70498, decode.loss_ce: 0.3132, decode.acc_seg: 87.3788, aux.loss_ce: 0.1277, aux.acc_seg: 87.0487, loss: 0.4409 +2024-06-18 08:06:46,957 - mmseg - INFO - Iter [21100/80000] lr: 2.945e-05, eta: 1 day, 0:11:28, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3225, decode.acc_seg: 86.9338, aux.loss_ce: 0.1295, aux.acc_seg: 86.9058, loss: 0.4520 +2024-06-18 08:07:53,374 - mmseg - INFO - Iter [21150/80000] lr: 2.943e-05, eta: 1 day, 0:09:53, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3303, decode.acc_seg: 86.8555, aux.loss_ce: 0.1327, aux.acc_seg: 86.7670, loss: 0.4629 +2024-06-18 08:08:59,929 - mmseg - INFO - Iter [21200/80000] lr: 2.940e-05, eta: 1 day, 0:08:19, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3199, decode.acc_seg: 87.2525, aux.loss_ce: 0.1296, aux.acc_seg: 87.1177, loss: 0.4495 +2024-06-18 08:10:06,488 - mmseg - INFO - Iter [21250/80000] lr: 2.938e-05, eta: 1 day, 0:06:45, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3208, decode.acc_seg: 87.2747, aux.loss_ce: 0.1297, aux.acc_seg: 87.1220, loss: 0.4506 +2024-06-18 08:11:12,819 - mmseg - INFO - Iter [21300/80000] lr: 2.935e-05, eta: 1 day, 0:05:10, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3114, decode.acc_seg: 87.3486, aux.loss_ce: 0.1272, aux.acc_seg: 87.0465, loss: 0.4386 +2024-06-18 08:12:19,252 - mmseg - INFO - Iter [21350/80000] lr: 2.933e-05, eta: 1 day, 0:03:36, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2908, decode.acc_seg: 88.2244, aux.loss_ce: 0.1187, aux.acc_seg: 87.8422, loss: 0.4095 +2024-06-18 08:13:25,760 - mmseg - INFO - Iter [21400/80000] lr: 2.930e-05, eta: 1 day, 0:02:02, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3211, decode.acc_seg: 86.9154, aux.loss_ce: 0.1299, aux.acc_seg: 86.7805, loss: 0.4510 +2024-06-18 08:14:32,103 - mmseg - INFO - Iter [21450/80000] lr: 2.928e-05, eta: 1 day, 0:00:28, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3137, decode.acc_seg: 87.2876, aux.loss_ce: 0.1278, aux.acc_seg: 87.0814, loss: 0.4415 +2024-06-18 08:15:41,137 - mmseg - INFO - Iter [21500/80000] lr: 2.925e-05, eta: 23:59:01, time: 1.381, data_time: 0.062, memory: 70498, decode.loss_ce: 0.3101, decode.acc_seg: 87.7628, aux.loss_ce: 0.1253, aux.acc_seg: 87.6401, loss: 0.4353 +2024-06-18 08:16:47,249 - mmseg - INFO - Iter [21550/80000] lr: 2.923e-05, eta: 23:57:26, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2997, decode.acc_seg: 87.6826, aux.loss_ce: 0.1221, aux.acc_seg: 87.4796, loss: 0.4218 +2024-06-18 08:17:53,855 - mmseg - INFO - Iter [21600/80000] lr: 2.920e-05, eta: 23:55:53, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3033, decode.acc_seg: 87.6646, aux.loss_ce: 0.1225, aux.acc_seg: 87.4258, loss: 0.4257 +2024-06-18 08:18:59,962 - mmseg - INFO - Iter [21650/80000] lr: 2.918e-05, eta: 23:54:19, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3157, decode.acc_seg: 86.9248, aux.loss_ce: 0.1293, aux.acc_seg: 86.6608, loss: 0.4450 +2024-06-18 08:20:06,339 - mmseg - INFO - Iter [21700/80000] lr: 2.915e-05, eta: 23:52:45, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3017, decode.acc_seg: 87.9983, aux.loss_ce: 0.1224, aux.acc_seg: 87.7852, loss: 0.4241 +2024-06-18 08:21:12,714 - mmseg - INFO - Iter [21750/80000] lr: 2.913e-05, eta: 23:51:12, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3100, decode.acc_seg: 87.4235, aux.loss_ce: 0.1259, aux.acc_seg: 87.2946, loss: 0.4358 +2024-06-18 08:22:19,118 - mmseg - INFO - Iter [21800/80000] lr: 2.910e-05, eta: 23:49:38, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3101, decode.acc_seg: 87.3719, aux.loss_ce: 0.1254, aux.acc_seg: 87.2249, loss: 0.4354 +2024-06-18 08:23:25,485 - mmseg - INFO - Iter [21850/80000] lr: 2.908e-05, eta: 23:48:05, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3128, decode.acc_seg: 87.4119, aux.loss_ce: 0.1267, aux.acc_seg: 87.2958, loss: 0.4395 +2024-06-18 08:24:31,809 - mmseg - INFO - Iter [21900/80000] lr: 2.905e-05, eta: 23:46:32, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3084, decode.acc_seg: 87.5036, aux.loss_ce: 0.1256, aux.acc_seg: 87.2874, loss: 0.4340 +2024-06-18 08:25:38,081 - mmseg - INFO - Iter [21950/80000] lr: 2.903e-05, eta: 23:44:59, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2947, decode.acc_seg: 87.3934, aux.loss_ce: 0.1206, aux.acc_seg: 87.1435, loss: 0.4153 +2024-06-18 08:26:44,134 - mmseg - INFO - Saving checkpoint at 22000 iterations +2024-06-18 08:28:24,850 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 08:28:24,850 - mmseg - INFO - Iter [22000/80000] lr: 2.900e-05, eta: 23:47:51, time: 3.335, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2996, decode.acc_seg: 87.5358, aux.loss_ce: 0.1214, aux.acc_seg: 87.4450, loss: 0.4210 +2024-06-18 08:30:00,970 - mmseg - INFO - per class results: +2024-06-18 08:30:00,976 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.73 | 88.28 | +| building | 85.07 | 93.11 | +| sky | 94.44 | 97.3 | +| floor | 83.96 | 89.27 | +| tree | 75.71 | 91.24 | +| ceiling | 83.93 | 88.9 | +| road | 84.85 | 91.32 | +| bed | 91.71 | 96.4 | +| windowpane | 64.77 | 82.23 | +| grass | 68.71 | 84.51 | +| cabinet | 62.75 | 71.76 | +| sidewalk | 69.07 | 82.7 | +| person | 84.39 | 93.0 | +| earth | 35.22 | 50.39 | +| door | 56.27 | 75.6 | +| table | 64.41 | 76.31 | +| mountain | 61.8 | 70.98 | +| plant | 56.06 | 66.93 | +| curtain | 77.24 | 90.89 | +| chair | 64.26 | 79.78 | +| car | 84.93 | 92.82 | +| water | 59.97 | 74.9 | +| painting | 73.68 | 91.56 | +| sofa | 78.86 | 91.76 | +| shelf | 46.76 | 61.91 | +| house | 59.52 | 77.39 | +| sea | 59.87 | 67.22 | +| mirror | 71.96 | 84.51 | +| rug | 70.03 | 85.08 | +| field | 39.69 | 61.45 | +| armchair | 55.78 | 69.43 | +| seat | 66.57 | 82.25 | +| fence | 45.57 | 55.71 | +| desk | 54.8 | 75.84 | +| rock | 59.01 | 77.98 | +| wardrobe | 53.1 | 80.18 | +| lamp | 71.03 | 82.64 | +| bathtub | 83.19 | 85.25 | +| railing | 36.41 | 52.74 | +| cushion | 65.41 | 77.76 | +| base | 35.35 | 47.85 | +| box | 31.57 | 39.59 | +| column | 52.87 | 59.59 | +| signboard | 39.84 | 50.42 | +| chest of drawers | 46.98 | 72.42 | +| counter | 40.03 | 48.23 | +| sand | 49.78 | 68.72 | +| sink | 71.74 | 80.59 | +| skyscraper | 57.92 | 71.71 | +| fireplace | 70.25 | 90.42 | +| refrigerator | 74.27 | 87.65 | +| grandstand | 54.25 | 86.29 | +| path | 25.91 | 43.31 | +| stairs | 22.22 | 27.53 | +| runway | 70.46 | 91.9 | +| case | 61.12 | 82.58 | +| pool table | 93.95 | 98.05 | +| pillow | 68.85 | 82.08 | +| screen door | 83.0 | 85.95 | +| stairway | 42.31 | 69.96 | +| river | 23.18 | 58.66 | +| bridge | 70.96 | 89.63 | +| bookcase | 36.4 | 61.24 | +| blind | 40.65 | 44.19 | +| coffee table | 60.01 | 86.94 | +| toilet | 88.31 | 94.6 | +| flower | 38.07 | 47.54 | +| book | 50.73 | 74.1 | +| hill | 5.61 | 10.85 | +| bench | 48.5 | 62.87 | +| countertop | 65.58 | 80.29 | +| stove | 84.06 | 91.67 | +| palm | 55.16 | 72.84 | +| kitchen island | 44.05 | 79.67 | +| computer | 77.17 | 90.87 | +| swivel chair | 52.44 | 67.76 | +| boat | 54.4 | 79.14 | +| bar | 50.45 | 76.22 | +| arcade machine | 76.24 | 84.27 | +| hovel | 14.85 | 15.95 | +| bus | 91.79 | 92.99 | +| towel | 69.43 | 86.08 | +| light | 52.41 | 57.08 | +| truck | 45.72 | 62.48 | +| tower | 23.51 | 42.56 | +| chandelier | 67.15 | 75.73 | +| awning | 46.35 | 59.9 | +| streetlight | 27.95 | 36.87 | +| booth | 41.18 | 73.44 | +| television receiver | 76.85 | 88.28 | +| airplane | 86.58 | 94.93 | +| dirt track | 12.39 | 43.08 | +| apparel | 49.72 | 67.93 | +| pole | 25.05 | 33.09 | +| land | 1.58 | 4.13 | +| bannister | 8.09 | 10.32 | +| escalator | 53.15 | 82.47 | +| ottoman | 44.04 | 64.46 | +| bottle | 36.13 | 47.88 | +| buffet | 51.05 | 80.48 | +| poster | 25.33 | 44.69 | +| stage | 14.89 | 28.29 | +| van | 44.56 | 54.81 | +| ship | 86.51 | 92.81 | +| fountain | 28.43 | 29.06 | +| conveyer belt | 81.03 | 90.86 | +| canopy | 51.01 | 67.89 | +| washer | 71.28 | 83.8 | +| plaything | 18.05 | 28.23 | +| swimming pool | 60.49 | 89.89 | +| stool | 50.83 | 59.02 | +| barrel | 51.91 | 64.59 | +| basket | 31.63 | 47.55 | +| waterfall | 52.71 | 78.67 | +| tent | 89.06 | 98.0 | +| bag | 19.71 | 25.48 | +| minibike | 70.89 | 81.3 | +| cradle | 85.15 | 97.12 | +| oven | 57.17 | 79.79 | +| ball | 56.0 | 65.89 | +| food | 63.45 | 73.58 | +| step | 12.0 | 15.24 | +| tank | 53.64 | 62.64 | +| trade name | 29.74 | 33.78 | +| microwave | 87.32 | 95.17 | +| pot | 51.56 | 57.42 | +| animal | 61.04 | 61.89 | +| bicycle | 52.6 | 70.62 | +| lake | 60.32 | 61.37 | +| dishwasher | 59.98 | 71.01 | +| screen | 58.66 | 92.14 | +| blanket | 22.11 | 24.6 | +| sculpture | 71.21 | 85.64 | +| hood | 59.49 | 74.02 | +| sconce | 52.02 | 59.8 | +| vase | 43.23 | 53.99 | +| traffic light | 35.36 | 54.59 | +| tray | 9.4 | 12.34 | +| ashcan | 42.58 | 59.14 | +| fan | 63.83 | 81.06 | +| pier | 61.09 | 81.47 | +| crt screen | 0.0 | 0.0 | +| plate | 55.31 | 67.84 | +| monitor | 55.33 | 88.89 | +| bulletin board | 40.97 | 64.54 | +| shower | 0.0 | 0.0 | +| radiator | 57.88 | 69.13 | +| glass | 15.16 | 15.78 | +| clock | 36.16 | 44.66 | +| flag | 51.67 | 56.06 | ++---------------------+-------+-------+ +2024-06-18 08:30:00,976 - mmseg - INFO - Summary: +2024-06-18 08:30:00,976 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 84.89 | 54.2 | 67.63 | ++-------+------+-------+ +2024-06-18 08:30:00,977 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 08:30:00,977 - mmseg - INFO - Iter(val) [250] aAcc: 0.8489, mIoU: 0.5420, mAcc: 0.6763, IoU.wall: 0.7973, IoU.building: 0.8507, IoU.sky: 0.9444, IoU.floor: 0.8396, IoU.tree: 0.7571, IoU.ceiling: 0.8393, IoU.road: 0.8485, IoU.bed : 0.9171, IoU.windowpane: 0.6477, IoU.grass: 0.6871, IoU.cabinet: 0.6275, IoU.sidewalk: 0.6907, IoU.person: 0.8439, IoU.earth: 0.3522, IoU.door: 0.5627, IoU.table: 0.6441, IoU.mountain: 0.6180, IoU.plant: 0.5606, IoU.curtain: 0.7724, IoU.chair: 0.6426, IoU.car: 0.8493, IoU.water: 0.5997, IoU.painting: 0.7368, IoU.sofa: 0.7886, IoU.shelf: 0.4676, IoU.house: 0.5952, IoU.sea: 0.5987, IoU.mirror: 0.7196, IoU.rug: 0.7003, IoU.field: 0.3969, IoU.armchair: 0.5578, IoU.seat: 0.6657, IoU.fence: 0.4557, IoU.desk: 0.5480, IoU.rock: 0.5901, IoU.wardrobe: 0.5310, IoU.lamp: 0.7103, IoU.bathtub: 0.8319, IoU.railing: 0.3641, IoU.cushion: 0.6541, IoU.base: 0.3535, IoU.box: 0.3157, IoU.column: 0.5287, IoU.signboard: 0.3984, IoU.chest of drawers: 0.4698, IoU.counter: 0.4003, IoU.sand: 0.4978, IoU.sink: 0.7174, IoU.skyscraper: 0.5792, IoU.fireplace: 0.7025, IoU.refrigerator: 0.7427, IoU.grandstand: 0.5425, IoU.path: 0.2591, IoU.stairs: 0.2222, IoU.runway: 0.7046, IoU.case: 0.6112, IoU.pool table: 0.9395, IoU.pillow: 0.6885, IoU.screen door: 0.8300, IoU.stairway: 0.4231, IoU.river: 0.2318, IoU.bridge: 0.7096, IoU.bookcase: 0.3640, IoU.blind: 0.4065, IoU.coffee table: 0.6001, IoU.toilet: 0.8831, IoU.flower: 0.3807, IoU.book: 0.5073, IoU.hill: 0.0561, IoU.bench: 0.4850, IoU.countertop: 0.6558, IoU.stove: 0.8406, IoU.palm: 0.5516, IoU.kitchen island: 0.4405, IoU.computer: 0.7717, IoU.swivel chair: 0.5244, IoU.boat: 0.5440, IoU.bar: 0.5045, IoU.arcade machine: 0.7624, IoU.hovel: 0.1485, IoU.bus: 0.9179, IoU.towel: 0.6943, IoU.light: 0.5241, IoU.truck: 0.4572, IoU.tower: 0.2351, IoU.chandelier: 0.6715, IoU.awning: 0.4635, IoU.streetlight: 0.2795, IoU.booth: 0.4118, IoU.television receiver: 0.7685, IoU.airplane: 0.8658, IoU.dirt track: 0.1239, IoU.apparel: 0.4972, IoU.pole: 0.2505, IoU.land: 0.0158, IoU.bannister: 0.0809, IoU.escalator: 0.5315, IoU.ottoman: 0.4404, IoU.bottle: 0.3613, IoU.buffet: 0.5105, IoU.poster: 0.2533, IoU.stage: 0.1489, IoU.van: 0.4456, IoU.ship: 0.8651, IoU.fountain: 0.2843, IoU.conveyer belt: 0.8103, IoU.canopy: 0.5101, IoU.washer: 0.7128, IoU.plaything: 0.1805, IoU.swimming pool: 0.6049, IoU.stool: 0.5083, IoU.barrel: 0.5191, IoU.basket: 0.3163, IoU.waterfall: 0.5271, IoU.tent: 0.8906, IoU.bag: 0.1971, IoU.minibike: 0.7089, IoU.cradle: 0.8515, IoU.oven: 0.5717, IoU.ball: 0.5600, IoU.food: 0.6345, IoU.step: 0.1200, IoU.tank: 0.5364, IoU.trade name: 0.2974, IoU.microwave: 0.8732, IoU.pot: 0.5156, IoU.animal: 0.6104, IoU.bicycle: 0.5260, IoU.lake: 0.6032, IoU.dishwasher: 0.5998, IoU.screen: 0.5866, IoU.blanket: 0.2211, IoU.sculpture: 0.7121, IoU.hood: 0.5949, IoU.sconce: 0.5202, IoU.vase: 0.4323, IoU.traffic light: 0.3536, IoU.tray: 0.0940, IoU.ashcan: 0.4258, IoU.fan: 0.6383, IoU.pier: 0.6109, IoU.crt screen: 0.0000, IoU.plate: 0.5531, IoU.monitor: 0.5533, IoU.bulletin board: 0.4097, IoU.shower: 0.0000, IoU.radiator: 0.5788, IoU.glass: 0.1516, IoU.clock: 0.3616, IoU.flag: 0.5167, Acc.wall: 0.8828, Acc.building: 0.9311, Acc.sky: 0.9730, Acc.floor: 0.8927, Acc.tree: 0.9124, Acc.ceiling: 0.8890, Acc.road: 0.9132, Acc.bed : 0.9640, Acc.windowpane: 0.8223, Acc.grass: 0.8451, Acc.cabinet: 0.7176, Acc.sidewalk: 0.8270, Acc.person: 0.9300, Acc.earth: 0.5039, Acc.door: 0.7560, Acc.table: 0.7631, Acc.mountain: 0.7098, Acc.plant: 0.6693, Acc.curtain: 0.9089, Acc.chair: 0.7978, Acc.car: 0.9282, Acc.water: 0.7490, Acc.painting: 0.9156, Acc.sofa: 0.9176, Acc.shelf: 0.6191, Acc.house: 0.7739, Acc.sea: 0.6722, Acc.mirror: 0.8451, Acc.rug: 0.8508, Acc.field: 0.6145, Acc.armchair: 0.6943, Acc.seat: 0.8225, Acc.fence: 0.5571, Acc.desk: 0.7584, Acc.rock: 0.7798, Acc.wardrobe: 0.8018, Acc.lamp: 0.8264, Acc.bathtub: 0.8525, Acc.railing: 0.5274, Acc.cushion: 0.7776, Acc.base: 0.4785, Acc.box: 0.3959, Acc.column: 0.5959, Acc.signboard: 0.5042, Acc.chest of drawers: 0.7242, Acc.counter: 0.4823, Acc.sand: 0.6872, Acc.sink: 0.8059, Acc.skyscraper: 0.7171, Acc.fireplace: 0.9042, Acc.refrigerator: 0.8765, Acc.grandstand: 0.8629, Acc.path: 0.4331, Acc.stairs: 0.2753, Acc.runway: 0.9190, Acc.case: 0.8258, Acc.pool table: 0.9805, Acc.pillow: 0.8208, Acc.screen door: 0.8595, Acc.stairway: 0.6996, Acc.river: 0.5866, Acc.bridge: 0.8963, Acc.bookcase: 0.6124, Acc.blind: 0.4419, Acc.coffee table: 0.8694, Acc.toilet: 0.9460, Acc.flower: 0.4754, Acc.book: 0.7410, Acc.hill: 0.1085, Acc.bench: 0.6287, Acc.countertop: 0.8029, Acc.stove: 0.9167, Acc.palm: 0.7284, Acc.kitchen island: 0.7967, Acc.computer: 0.9087, Acc.swivel chair: 0.6776, Acc.boat: 0.7914, Acc.bar: 0.7622, Acc.arcade machine: 0.8427, Acc.hovel: 0.1595, Acc.bus: 0.9299, Acc.towel: 0.8608, Acc.light: 0.5708, Acc.truck: 0.6248, Acc.tower: 0.4256, Acc.chandelier: 0.7573, Acc.awning: 0.5990, Acc.streetlight: 0.3687, Acc.booth: 0.7344, Acc.television receiver: 0.8828, Acc.airplane: 0.9493, Acc.dirt track: 0.4308, Acc.apparel: 0.6793, Acc.pole: 0.3309, Acc.land: 0.0413, Acc.bannister: 0.1032, Acc.escalator: 0.8247, Acc.ottoman: 0.6446, Acc.bottle: 0.4788, Acc.buffet: 0.8048, Acc.poster: 0.4469, Acc.stage: 0.2829, Acc.van: 0.5481, Acc.ship: 0.9281, Acc.fountain: 0.2906, Acc.conveyer belt: 0.9086, Acc.canopy: 0.6789, Acc.washer: 0.8380, Acc.plaything: 0.2823, Acc.swimming pool: 0.8989, Acc.stool: 0.5902, Acc.barrel: 0.6459, Acc.basket: 0.4755, Acc.waterfall: 0.7867, Acc.tent: 0.9800, Acc.bag: 0.2548, Acc.minibike: 0.8130, Acc.cradle: 0.9712, Acc.oven: 0.7979, Acc.ball: 0.6589, Acc.food: 0.7358, Acc.step: 0.1524, Acc.tank: 0.6264, Acc.trade name: 0.3378, Acc.microwave: 0.9517, Acc.pot: 0.5742, Acc.animal: 0.6189, Acc.bicycle: 0.7062, Acc.lake: 0.6137, Acc.dishwasher: 0.7101, Acc.screen: 0.9214, Acc.blanket: 0.2460, Acc.sculpture: 0.8564, Acc.hood: 0.7402, Acc.sconce: 0.5980, Acc.vase: 0.5399, Acc.traffic light: 0.5459, Acc.tray: 0.1234, Acc.ashcan: 0.5914, Acc.fan: 0.8106, Acc.pier: 0.8147, Acc.crt screen: 0.0000, Acc.plate: 0.6784, Acc.monitor: 0.8889, Acc.bulletin board: 0.6454, Acc.shower: 0.0000, Acc.radiator: 0.6913, Acc.glass: 0.1578, Acc.clock: 0.4466, Acc.flag: 0.5606 +2024-06-18 08:31:07,621 - mmseg - INFO - Iter [22050/80000] lr: 2.898e-05, eta: 23:50:30, time: 3.255, data_time: 1.938, memory: 70498, decode.loss_ce: 0.3071, decode.acc_seg: 87.3867, aux.loss_ce: 0.1252, aux.acc_seg: 87.2855, loss: 0.4324 +2024-06-18 08:32:13,864 - mmseg - INFO - Iter [22100/80000] lr: 2.895e-05, eta: 23:48:56, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3093, decode.acc_seg: 87.9455, aux.loss_ce: 0.1264, aux.acc_seg: 87.5602, loss: 0.4357 +2024-06-18 08:33:20,241 - mmseg - INFO - Iter [22150/80000] lr: 2.893e-05, eta: 23:47:22, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3088, decode.acc_seg: 87.3398, aux.loss_ce: 0.1259, aux.acc_seg: 87.1823, loss: 0.4348 +2024-06-18 08:34:26,620 - mmseg - INFO - Iter [22200/80000] lr: 2.890e-05, eta: 23:45:48, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2917, decode.acc_seg: 88.0084, aux.loss_ce: 0.1193, aux.acc_seg: 87.7720, loss: 0.4109 +2024-06-18 08:35:33,136 - mmseg - INFO - Iter [22250/80000] lr: 2.888e-05, eta: 23:44:15, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3126, decode.acc_seg: 87.5932, aux.loss_ce: 0.1276, aux.acc_seg: 87.3850, loss: 0.4402 +2024-06-18 08:36:39,369 - mmseg - INFO - Iter [22300/80000] lr: 2.885e-05, eta: 23:42:41, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3254, decode.acc_seg: 86.8026, aux.loss_ce: 0.1312, aux.acc_seg: 86.6790, loss: 0.4566 +2024-06-18 08:37:45,973 - mmseg - INFO - Iter [22350/80000] lr: 2.883e-05, eta: 23:41:08, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2962, decode.acc_seg: 87.7737, aux.loss_ce: 0.1217, aux.acc_seg: 87.4474, loss: 0.4179 +2024-06-18 08:38:52,337 - mmseg - INFO - Iter [22400/80000] lr: 2.880e-05, eta: 23:39:34, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3206, decode.acc_seg: 86.9816, aux.loss_ce: 0.1299, aux.acc_seg: 86.9057, loss: 0.4506 +2024-06-18 08:39:58,585 - mmseg - INFO - Iter [22450/80000] lr: 2.878e-05, eta: 23:38:00, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3323, decode.acc_seg: 86.8248, aux.loss_ce: 0.1353, aux.acc_seg: 86.4595, loss: 0.4677 +2024-06-18 08:41:04,994 - mmseg - INFO - Iter [22500/80000] lr: 2.875e-05, eta: 23:36:27, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3286, decode.acc_seg: 86.5675, aux.loss_ce: 0.1329, aux.acc_seg: 86.4204, loss: 0.4615 +2024-06-18 08:42:11,372 - mmseg - INFO - Iter [22550/80000] lr: 2.873e-05, eta: 23:34:54, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3133, decode.acc_seg: 87.4971, aux.loss_ce: 0.1256, aux.acc_seg: 87.3911, loss: 0.4388 +2024-06-18 08:43:17,774 - mmseg - INFO - Iter [22600/80000] lr: 2.870e-05, eta: 23:33:21, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3306, decode.acc_seg: 86.5398, aux.loss_ce: 0.1339, aux.acc_seg: 86.3547, loss: 0.4646 +2024-06-18 08:44:24,125 - mmseg - INFO - Iter [22650/80000] lr: 2.868e-05, eta: 23:31:48, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3231, decode.acc_seg: 86.5081, aux.loss_ce: 0.1307, aux.acc_seg: 86.4161, loss: 0.4538 +2024-06-18 08:45:30,274 - mmseg - INFO - Iter [22700/80000] lr: 2.865e-05, eta: 23:30:15, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3198, decode.acc_seg: 87.1627, aux.loss_ce: 0.1294, aux.acc_seg: 86.9668, loss: 0.4492 +2024-06-18 08:46:38,801 - mmseg - INFO - Iter [22750/80000] lr: 2.863e-05, eta: 23:28:48, time: 1.371, data_time: 0.052, memory: 70498, decode.loss_ce: 0.3236, decode.acc_seg: 87.4438, aux.loss_ce: 0.1299, aux.acc_seg: 87.2633, loss: 0.4535 +2024-06-18 08:47:45,183 - mmseg - INFO - Iter [22800/80000] lr: 2.860e-05, eta: 23:27:15, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3133, decode.acc_seg: 87.6126, aux.loss_ce: 0.1278, aux.acc_seg: 87.2859, loss: 0.4412 +2024-06-18 08:48:51,491 - mmseg - INFO - Iter [22850/80000] lr: 2.858e-05, eta: 23:25:43, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2958, decode.acc_seg: 88.2596, aux.loss_ce: 0.1195, aux.acc_seg: 87.9832, loss: 0.4153 +2024-06-18 08:49:58,385 - mmseg - INFO - Iter [22900/80000] lr: 2.855e-05, eta: 23:24:12, time: 1.338, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3038, decode.acc_seg: 87.8130, aux.loss_ce: 0.1233, aux.acc_seg: 87.5421, loss: 0.4271 +2024-06-18 08:51:04,813 - mmseg - INFO - Iter [22950/80000] lr: 2.853e-05, eta: 23:22:40, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2944, decode.acc_seg: 88.1122, aux.loss_ce: 0.1199, aux.acc_seg: 87.9588, loss: 0.4143 +2024-06-18 08:52:11,236 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 08:52:11,236 - mmseg - INFO - Iter [23000/80000] lr: 2.850e-05, eta: 23:21:08, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3092, decode.acc_seg: 88.1853, aux.loss_ce: 0.1252, aux.acc_seg: 88.0232, loss: 0.4345 +2024-06-18 08:53:50,066 - mmseg - INFO - per class results: +2024-06-18 08:53:50,072 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.59 | 86.92 | +| building | 85.16 | 94.3 | +| sky | 94.8 | 97.68 | +| floor | 83.42 | 90.09 | +| tree | 76.95 | 89.24 | +| ceiling | 85.85 | 94.06 | +| road | 84.78 | 91.48 | +| bed | 91.8 | 96.17 | +| windowpane | 63.64 | 84.4 | +| grass | 65.07 | 77.39 | +| cabinet | 62.06 | 71.04 | +| sidewalk | 70.11 | 84.62 | +| person | 84.55 | 93.92 | +| earth | 35.19 | 48.1 | +| door | 59.18 | 77.78 | +| table | 65.8 | 82.61 | +| mountain | 63.82 | 81.23 | +| plant | 58.82 | 69.69 | +| curtain | 79.11 | 90.66 | +| chair | 63.03 | 76.12 | +| car | 85.43 | 92.69 | +| water | 57.25 | 69.06 | +| painting | 72.69 | 91.42 | +| sofa | 77.88 | 90.24 | +| shelf | 47.08 | 60.6 | +| house | 62.16 | 69.97 | +| sea | 67.79 | 84.3 | +| mirror | 73.53 | 83.31 | +| rug | 66.72 | 79.95 | +| field | 35.85 | 59.39 | +| armchair | 52.43 | 67.97 | +| seat | 65.42 | 86.01 | +| fence | 47.01 | 55.89 | +| desk | 55.34 | 72.75 | +| rock | 41.65 | 57.02 | +| wardrobe | 52.93 | 69.88 | +| lamp | 67.69 | 84.0 | +| bathtub | 82.48 | 86.03 | +| railing | 32.02 | 43.54 | +| cushion | 65.55 | 77.76 | +| base | 33.66 | 61.22 | +| box | 29.37 | 41.02 | +| column | 55.84 | 81.47 | +| signboard | 40.23 | 53.99 | +| chest of drawers | 42.69 | 67.97 | +| counter | 36.82 | 42.64 | +| sand | 42.28 | 60.75 | +| sink | 70.54 | 83.8 | +| skyscraper | 56.39 | 71.51 | +| fireplace | 70.64 | 95.11 | +| refrigerator | 76.72 | 94.08 | +| grandstand | 62.56 | 86.19 | +| path | 28.59 | 43.19 | +| stairs | 29.96 | 38.14 | +| runway | 62.82 | 82.23 | +| case | 58.49 | 75.99 | +| pool table | 89.96 | 98.52 | +| pillow | 67.57 | 81.41 | +| screen door | 72.4 | 94.13 | +| stairway | 41.4 | 47.45 | +| river | 18.04 | 42.62 | +| bridge | 73.52 | 91.64 | +| bookcase | 42.74 | 60.21 | +| blind | 18.57 | 18.74 | +| coffee table | 59.83 | 89.83 | +| toilet | 89.07 | 94.16 | +| flower | 41.54 | 54.6 | +| book | 48.36 | 65.17 | +| hill | 6.3 | 14.1 | +| bench | 51.22 | 63.23 | +| countertop | 61.79 | 84.82 | +| stove | 82.3 | 96.59 | +| palm | 57.07 | 78.16 | +| kitchen island | 48.88 | 83.74 | +| computer | 74.26 | 95.36 | +| swivel chair | 47.61 | 79.73 | +| boat | 51.74 | 87.93 | +| bar | 53.13 | 77.01 | +| arcade machine | 85.52 | 91.37 | +| hovel | 42.56 | 50.46 | +| bus | 90.95 | 95.56 | +| towel | 69.45 | 80.05 | +| light | 57.23 | 79.32 | +| truck | 43.21 | 58.87 | +| tower | 21.11 | 39.86 | +| chandelier | 67.6 | 87.51 | +| awning | 45.09 | 55.54 | +| streetlight | 30.1 | 39.48 | +| booth | 46.72 | 65.31 | +| television receiver | 67.62 | 80.35 | +| airplane | 63.37 | 69.87 | +| dirt track | 22.06 | 24.1 | +| apparel | 41.32 | 55.66 | +| pole | 30.33 | 42.56 | +| land | 1.27 | 2.63 | +| bannister | 14.55 | 18.64 | +| escalator | 55.78 | 80.12 | +| ottoman | 51.3 | 75.36 | +| bottle | 36.79 | 46.24 | +| buffet | 52.88 | 89.42 | +| poster | 31.58 | 45.53 | +| stage | 14.59 | 24.18 | +| van | 43.11 | 52.88 | +| ship | 77.54 | 94.47 | +| fountain | 32.42 | 33.82 | +| conveyer belt | 75.39 | 90.19 | +| canopy | 52.05 | 71.32 | +| washer | 68.11 | 78.69 | +| plaything | 19.93 | 32.19 | +| swimming pool | 72.94 | 91.86 | +| stool | 46.73 | 73.74 | +| barrel | 49.91 | 64.85 | +| basket | 30.88 | 50.34 | +| waterfall | 67.0 | 93.69 | +| tent | 90.32 | 98.77 | +| bag | 17.17 | 22.35 | +| minibike | 70.7 | 82.75 | +| cradle | 71.06 | 97.76 | +| oven | 52.04 | 59.97 | +| ball | 53.87 | 74.3 | +| food | 66.55 | 80.41 | +| step | 11.95 | 18.02 | +| tank | 59.9 | 72.39 | +| trade name | 26.2 | 29.31 | +| microwave | 83.81 | 95.17 | +| pot | 51.45 | 61.06 | +| animal | 68.41 | 69.92 | +| bicycle | 55.18 | 72.2 | +| lake | 53.98 | 54.04 | +| dishwasher | 63.61 | 75.4 | +| screen | 55.72 | 94.2 | +| blanket | 24.05 | 26.52 | +| sculpture | 69.02 | 81.16 | +| hood | 56.19 | 66.66 | +| sconce | 38.29 | 42.57 | +| vase | 43.75 | 56.01 | +| traffic light | 30.99 | 40.51 | +| tray | 11.94 | 13.76 | +| ashcan | 41.6 | 59.5 | +| fan | 65.27 | 80.25 | +| pier | 35.51 | 47.33 | +| crt screen | 8.19 | 9.25 | +| plate | 58.08 | 73.12 | +| monitor | 62.13 | 79.39 | +| bulletin board | 56.25 | 67.53 | +| shower | 0.0 | 0.0 | +| radiator | 63.43 | 69.7 | +| glass | 15.08 | 15.7 | +| clock | 36.82 | 47.39 | +| flag | 65.77 | 74.68 | ++---------------------+-------+-------+ +2024-06-18 08:53:50,072 - mmseg - INFO - Summary: +2024-06-18 08:53:50,072 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.01 | 54.07 | 67.82 | ++-------+-------+-------+ +2024-06-18 08:53:50,073 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 08:53:50,073 - mmseg - INFO - Iter(val) [250] aAcc: 0.8501, mIoU: 0.5407, mAcc: 0.6782, IoU.wall: 0.8059, IoU.building: 0.8516, IoU.sky: 0.9480, IoU.floor: 0.8342, IoU.tree: 0.7695, IoU.ceiling: 0.8585, IoU.road: 0.8478, IoU.bed : 0.9180, IoU.windowpane: 0.6364, IoU.grass: 0.6507, IoU.cabinet: 0.6206, IoU.sidewalk: 0.7011, IoU.person: 0.8455, IoU.earth: 0.3519, IoU.door: 0.5918, IoU.table: 0.6580, IoU.mountain: 0.6382, IoU.plant: 0.5882, IoU.curtain: 0.7911, IoU.chair: 0.6303, IoU.car: 0.8543, IoU.water: 0.5725, IoU.painting: 0.7269, IoU.sofa: 0.7788, IoU.shelf: 0.4708, IoU.house: 0.6216, IoU.sea: 0.6779, IoU.mirror: 0.7353, IoU.rug: 0.6672, IoU.field: 0.3585, IoU.armchair: 0.5243, IoU.seat: 0.6542, IoU.fence: 0.4701, IoU.desk: 0.5534, IoU.rock: 0.4165, IoU.wardrobe: 0.5293, IoU.lamp: 0.6769, IoU.bathtub: 0.8248, IoU.railing: 0.3202, IoU.cushion: 0.6555, IoU.base: 0.3366, IoU.box: 0.2937, IoU.column: 0.5584, IoU.signboard: 0.4023, IoU.chest of drawers: 0.4269, IoU.counter: 0.3682, IoU.sand: 0.4228, IoU.sink: 0.7054, IoU.skyscraper: 0.5639, IoU.fireplace: 0.7064, IoU.refrigerator: 0.7672, IoU.grandstand: 0.6256, IoU.path: 0.2859, IoU.stairs: 0.2996, IoU.runway: 0.6282, IoU.case: 0.5849, IoU.pool table: 0.8996, IoU.pillow: 0.6757, IoU.screen door: 0.7240, IoU.stairway: 0.4140, IoU.river: 0.1804, IoU.bridge: 0.7352, IoU.bookcase: 0.4274, IoU.blind: 0.1857, IoU.coffee table: 0.5983, IoU.toilet: 0.8907, IoU.flower: 0.4154, IoU.book: 0.4836, IoU.hill: 0.0630, IoU.bench: 0.5122, IoU.countertop: 0.6179, IoU.stove: 0.8230, IoU.palm: 0.5707, IoU.kitchen island: 0.4888, IoU.computer: 0.7426, IoU.swivel chair: 0.4761, IoU.boat: 0.5174, IoU.bar: 0.5313, IoU.arcade machine: 0.8552, IoU.hovel: 0.4256, IoU.bus: 0.9095, IoU.towel: 0.6945, IoU.light: 0.5723, IoU.truck: 0.4321, IoU.tower: 0.2111, IoU.chandelier: 0.6760, IoU.awning: 0.4509, IoU.streetlight: 0.3010, IoU.booth: 0.4672, IoU.television receiver: 0.6762, IoU.airplane: 0.6337, IoU.dirt track: 0.2206, IoU.apparel: 0.4132, IoU.pole: 0.3033, IoU.land: 0.0127, IoU.bannister: 0.1455, IoU.escalator: 0.5578, IoU.ottoman: 0.5130, IoU.bottle: 0.3679, IoU.buffet: 0.5288, IoU.poster: 0.3158, IoU.stage: 0.1459, IoU.van: 0.4311, IoU.ship: 0.7754, IoU.fountain: 0.3242, IoU.conveyer belt: 0.7539, IoU.canopy: 0.5205, IoU.washer: 0.6811, IoU.plaything: 0.1993, IoU.swimming pool: 0.7294, IoU.stool: 0.4673, IoU.barrel: 0.4991, IoU.basket: 0.3088, IoU.waterfall: 0.6700, IoU.tent: 0.9032, IoU.bag: 0.1717, IoU.minibike: 0.7070, IoU.cradle: 0.7106, IoU.oven: 0.5204, IoU.ball: 0.5387, IoU.food: 0.6655, IoU.step: 0.1195, IoU.tank: 0.5990, IoU.trade name: 0.2620, IoU.microwave: 0.8381, IoU.pot: 0.5145, IoU.animal: 0.6841, IoU.bicycle: 0.5518, IoU.lake: 0.5398, IoU.dishwasher: 0.6361, IoU.screen: 0.5572, IoU.blanket: 0.2405, IoU.sculpture: 0.6902, IoU.hood: 0.5619, IoU.sconce: 0.3829, IoU.vase: 0.4375, IoU.traffic light: 0.3099, IoU.tray: 0.1194, IoU.ashcan: 0.4160, IoU.fan: 0.6527, IoU.pier: 0.3551, IoU.crt screen: 0.0819, IoU.plate: 0.5808, IoU.monitor: 0.6213, IoU.bulletin board: 0.5625, IoU.shower: 0.0000, IoU.radiator: 0.6343, IoU.glass: 0.1508, IoU.clock: 0.3682, IoU.flag: 0.6577, Acc.wall: 0.8692, Acc.building: 0.9430, Acc.sky: 0.9768, Acc.floor: 0.9009, Acc.tree: 0.8924, Acc.ceiling: 0.9406, Acc.road: 0.9148, Acc.bed : 0.9617, Acc.windowpane: 0.8440, Acc.grass: 0.7739, Acc.cabinet: 0.7104, Acc.sidewalk: 0.8462, Acc.person: 0.9392, Acc.earth: 0.4810, Acc.door: 0.7778, Acc.table: 0.8261, Acc.mountain: 0.8123, Acc.plant: 0.6969, Acc.curtain: 0.9066, Acc.chair: 0.7612, Acc.car: 0.9269, Acc.water: 0.6906, Acc.painting: 0.9142, Acc.sofa: 0.9024, Acc.shelf: 0.6060, Acc.house: 0.6997, Acc.sea: 0.8430, Acc.mirror: 0.8331, Acc.rug: 0.7995, Acc.field: 0.5939, Acc.armchair: 0.6797, Acc.seat: 0.8601, Acc.fence: 0.5589, Acc.desk: 0.7275, Acc.rock: 0.5702, Acc.wardrobe: 0.6988, Acc.lamp: 0.8400, Acc.bathtub: 0.8603, Acc.railing: 0.4354, Acc.cushion: 0.7776, Acc.base: 0.6122, Acc.box: 0.4102, Acc.column: 0.8147, Acc.signboard: 0.5399, Acc.chest of drawers: 0.6797, Acc.counter: 0.4264, Acc.sand: 0.6075, Acc.sink: 0.8380, Acc.skyscraper: 0.7151, Acc.fireplace: 0.9511, Acc.refrigerator: 0.9408, Acc.grandstand: 0.8619, Acc.path: 0.4319, Acc.stairs: 0.3814, Acc.runway: 0.8223, Acc.case: 0.7599, Acc.pool table: 0.9852, Acc.pillow: 0.8141, Acc.screen door: 0.9413, Acc.stairway: 0.4745, Acc.river: 0.4262, Acc.bridge: 0.9164, Acc.bookcase: 0.6021, Acc.blind: 0.1874, Acc.coffee table: 0.8983, Acc.toilet: 0.9416, Acc.flower: 0.5460, Acc.book: 0.6517, Acc.hill: 0.1410, Acc.bench: 0.6323, Acc.countertop: 0.8482, Acc.stove: 0.9659, Acc.palm: 0.7816, Acc.kitchen island: 0.8374, Acc.computer: 0.9536, Acc.swivel chair: 0.7973, Acc.boat: 0.8793, Acc.bar: 0.7701, Acc.arcade machine: 0.9137, Acc.hovel: 0.5046, Acc.bus: 0.9556, Acc.towel: 0.8005, Acc.light: 0.7932, Acc.truck: 0.5887, Acc.tower: 0.3986, Acc.chandelier: 0.8751, Acc.awning: 0.5554, Acc.streetlight: 0.3948, Acc.booth: 0.6531, Acc.television receiver: 0.8035, Acc.airplane: 0.6987, Acc.dirt track: 0.2410, Acc.apparel: 0.5566, Acc.pole: 0.4256, Acc.land: 0.0263, Acc.bannister: 0.1864, Acc.escalator: 0.8012, Acc.ottoman: 0.7536, Acc.bottle: 0.4624, Acc.buffet: 0.8942, Acc.poster: 0.4553, Acc.stage: 0.2418, Acc.van: 0.5288, Acc.ship: 0.9447, Acc.fountain: 0.3382, Acc.conveyer belt: 0.9019, Acc.canopy: 0.7132, Acc.washer: 0.7869, Acc.plaything: 0.3219, Acc.swimming pool: 0.9186, Acc.stool: 0.7374, Acc.barrel: 0.6485, Acc.basket: 0.5034, Acc.waterfall: 0.9369, Acc.tent: 0.9877, Acc.bag: 0.2235, Acc.minibike: 0.8275, Acc.cradle: 0.9776, Acc.oven: 0.5997, Acc.ball: 0.7430, Acc.food: 0.8041, Acc.step: 0.1802, Acc.tank: 0.7239, Acc.trade name: 0.2931, Acc.microwave: 0.9517, Acc.pot: 0.6106, Acc.animal: 0.6992, Acc.bicycle: 0.7220, Acc.lake: 0.5404, Acc.dishwasher: 0.7540, Acc.screen: 0.9420, Acc.blanket: 0.2652, Acc.sculpture: 0.8116, Acc.hood: 0.6666, Acc.sconce: 0.4257, Acc.vase: 0.5601, Acc.traffic light: 0.4051, Acc.tray: 0.1376, Acc.ashcan: 0.5950, Acc.fan: 0.8025, Acc.pier: 0.4733, Acc.crt screen: 0.0925, Acc.plate: 0.7312, Acc.monitor: 0.7939, Acc.bulletin board: 0.6753, Acc.shower: 0.0000, Acc.radiator: 0.6970, Acc.glass: 0.1570, Acc.clock: 0.4739, Acc.flag: 0.7468 +2024-06-18 08:54:56,956 - mmseg - INFO - Iter [23050/80000] lr: 2.848e-05, eta: 23:23:41, time: 3.314, data_time: 1.994, memory: 70498, decode.loss_ce: 0.3047, decode.acc_seg: 88.1312, aux.loss_ce: 0.1239, aux.acc_seg: 87.8777, loss: 0.4286 +2024-06-18 08:56:03,482 - mmseg - INFO - Iter [23100/80000] lr: 2.845e-05, eta: 23:22:09, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2932, decode.acc_seg: 88.0420, aux.loss_ce: 0.1201, aux.acc_seg: 87.7099, loss: 0.4133 +2024-06-18 08:57:09,855 - mmseg - INFO - Iter [23150/80000] lr: 2.843e-05, eta: 23:20:37, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2964, decode.acc_seg: 88.1662, aux.loss_ce: 0.1208, aux.acc_seg: 87.9834, loss: 0.4172 +2024-06-18 08:58:16,548 - mmseg - INFO - Iter [23200/80000] lr: 2.840e-05, eta: 23:19:05, time: 1.334, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3049, decode.acc_seg: 87.6811, aux.loss_ce: 0.1243, aux.acc_seg: 87.4944, loss: 0.4292 +2024-06-18 08:59:22,920 - mmseg - INFO - Iter [23250/80000] lr: 2.838e-05, eta: 23:17:33, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3111, decode.acc_seg: 87.1566, aux.loss_ce: 0.1259, aux.acc_seg: 86.9610, loss: 0.4370 +2024-06-18 09:00:29,299 - mmseg - INFO - Iter [23300/80000] lr: 2.835e-05, eta: 23:16:01, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3015, decode.acc_seg: 87.8433, aux.loss_ce: 0.1229, aux.acc_seg: 87.5943, loss: 0.4245 +2024-06-18 09:01:35,460 - mmseg - INFO - Iter [23350/80000] lr: 2.833e-05, eta: 23:14:28, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2945, decode.acc_seg: 88.0305, aux.loss_ce: 0.1203, aux.acc_seg: 87.7882, loss: 0.4148 +2024-06-18 09:02:41,800 - mmseg - INFO - Iter [23400/80000] lr: 2.830e-05, eta: 23:12:56, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3006, decode.acc_seg: 87.9808, aux.loss_ce: 0.1222, aux.acc_seg: 87.8245, loss: 0.4228 +2024-06-18 09:03:48,314 - mmseg - INFO - Iter [23450/80000] lr: 2.828e-05, eta: 23:11:25, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3057, decode.acc_seg: 87.3397, aux.loss_ce: 0.1238, aux.acc_seg: 87.3047, loss: 0.4294 +2024-06-18 09:05:15,896 - mmseg - INFO - Iter [23500/80000] lr: 2.825e-05, eta: 23:10:44, time: 1.752, data_time: 0.440, memory: 70498, decode.loss_ce: 0.3015, decode.acc_seg: 87.9854, aux.loss_ce: 0.1222, aux.acc_seg: 87.7529, loss: 0.4237 +2024-06-18 09:06:21,950 - mmseg - INFO - Iter [23550/80000] lr: 2.823e-05, eta: 23:09:11, time: 1.321, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3214, decode.acc_seg: 87.3007, aux.loss_ce: 0.1312, aux.acc_seg: 87.0105, loss: 0.4525 +2024-06-18 09:07:28,353 - mmseg - INFO - Iter [23600/80000] lr: 2.820e-05, eta: 23:07:40, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3021, decode.acc_seg: 87.5787, aux.loss_ce: 0.1217, aux.acc_seg: 87.4671, loss: 0.4238 +2024-06-18 09:08:34,848 - mmseg - INFO - Iter [23650/80000] lr: 2.818e-05, eta: 23:06:09, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3056, decode.acc_seg: 87.9542, aux.loss_ce: 0.1222, aux.acc_seg: 87.8264, loss: 0.4279 +2024-06-18 09:09:41,162 - mmseg - INFO - Iter [23700/80000] lr: 2.815e-05, eta: 23:04:37, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3050, decode.acc_seg: 87.7319, aux.loss_ce: 0.1243, aux.acc_seg: 87.5793, loss: 0.4293 +2024-06-18 09:10:47,558 - mmseg - INFO - Iter [23750/80000] lr: 2.813e-05, eta: 23:03:06, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2969, decode.acc_seg: 88.0032, aux.loss_ce: 0.1209, aux.acc_seg: 87.7710, loss: 0.4178 +2024-06-18 09:11:53,767 - mmseg - INFO - Iter [23800/80000] lr: 2.810e-05, eta: 23:01:34, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3124, decode.acc_seg: 87.7043, aux.loss_ce: 0.1272, aux.acc_seg: 87.5004, loss: 0.4396 +2024-06-18 09:13:00,110 - mmseg - INFO - Iter [23850/80000] lr: 2.808e-05, eta: 23:00:03, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3125, decode.acc_seg: 87.4994, aux.loss_ce: 0.1266, aux.acc_seg: 87.3492, loss: 0.4392 +2024-06-18 09:14:06,564 - mmseg - INFO - Iter [23900/80000] lr: 2.805e-05, eta: 22:58:32, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2904, decode.acc_seg: 87.7682, aux.loss_ce: 0.1183, aux.acc_seg: 87.5455, loss: 0.4087 +2024-06-18 09:15:12,765 - mmseg - INFO - Iter [23950/80000] lr: 2.803e-05, eta: 22:57:01, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3013, decode.acc_seg: 87.8410, aux.loss_ce: 0.1232, aux.acc_seg: 87.5577, loss: 0.4246 +2024-06-18 09:16:21,669 - mmseg - INFO - Saving checkpoint at 24000 iterations +2024-06-18 09:18:02,849 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 09:18:02,849 - mmseg - INFO - Iter [24000/80000] lr: 2.800e-05, eta: 22:59:32, time: 3.402, data_time: 0.058, memory: 70498, decode.loss_ce: 0.2994, decode.acc_seg: 87.7684, aux.loss_ce: 0.1220, aux.acc_seg: 87.5262, loss: 0.4214 +2024-06-18 09:19:38,469 - mmseg - INFO - per class results: +2024-06-18 09:19:38,475 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.11 | 88.94 | +| building | 85.66 | 93.5 | +| sky | 94.69 | 97.19 | +| floor | 84.12 | 91.86 | +| tree | 77.12 | 91.18 | +| ceiling | 86.96 | 93.93 | +| road | 86.34 | 93.12 | +| bed | 91.49 | 96.37 | +| windowpane | 65.31 | 78.43 | +| grass | 67.07 | 83.82 | +| cabinet | 62.88 | 73.1 | +| sidewalk | 69.86 | 83.11 | +| person | 84.67 | 92.55 | +| earth | 36.5 | 46.77 | +| door | 58.64 | 74.84 | +| table | 66.78 | 79.72 | +| mountain | 62.11 | 73.9 | +| plant | 53.41 | 62.34 | +| curtain | 79.44 | 90.75 | +| chair | 64.16 | 76.23 | +| car | 85.96 | 92.0 | +| water | 67.79 | 85.93 | +| painting | 76.15 | 89.22 | +| sofa | 78.97 | 91.74 | +| shelf | 47.77 | 62.89 | +| house | 52.67 | 60.88 | +| sea | 70.26 | 78.45 | +| mirror | 73.74 | 80.93 | +| rug | 66.95 | 77.92 | +| field | 38.07 | 68.56 | +| armchair | 55.7 | 69.98 | +| seat | 63.87 | 86.53 | +| fence | 44.01 | 58.15 | +| desk | 51.94 | 77.86 | +| rock | 51.46 | 71.87 | +| wardrobe | 55.89 | 83.19 | +| lamp | 69.89 | 80.14 | +| bathtub | 80.72 | 82.2 | +| railing | 39.5 | 55.88 | +| cushion | 65.26 | 76.75 | +| base | 35.99 | 58.88 | +| box | 30.85 | 45.12 | +| column | 54.1 | 65.04 | +| signboard | 37.9 | 57.03 | +| chest of drawers | 43.35 | 67.28 | +| counter | 39.83 | 47.51 | +| sand | 58.25 | 81.06 | +| sink | 74.77 | 80.3 | +| skyscraper | 58.34 | 74.31 | +| fireplace | 72.33 | 90.79 | +| refrigerator | 81.4 | 92.53 | +| grandstand | 43.66 | 91.31 | +| path | 29.13 | 42.09 | +| stairs | 33.62 | 43.58 | +| runway | 73.52 | 97.62 | +| case | 54.95 | 81.53 | +| pool table | 93.85 | 97.94 | +| pillow | 68.65 | 82.38 | +| screen door | 78.19 | 83.63 | +| stairway | 42.03 | 45.66 | +| river | 15.65 | 24.41 | +| bridge | 76.59 | 90.96 | +| bookcase | 37.55 | 53.14 | +| blind | 42.91 | 49.73 | +| coffee table | 64.69 | 84.08 | +| toilet | 87.71 | 93.6 | +| flower | 40.87 | 59.5 | +| book | 51.02 | 76.24 | +| hill | 6.38 | 12.73 | +| bench | 50.72 | 56.4 | +| countertop | 62.17 | 85.27 | +| stove | 83.46 | 88.92 | +| palm | 55.34 | 81.71 | +| kitchen island | 44.27 | 86.88 | +| computer | 74.96 | 95.13 | +| swivel chair | 47.24 | 74.89 | +| boat | 64.56 | 89.52 | +| bar | 57.02 | 74.72 | +| arcade machine | 72.91 | 79.6 | +| hovel | 44.37 | 53.34 | +| bus | 91.16 | 96.57 | +| towel | 67.38 | 85.2 | +| light | 57.74 | 73.74 | +| truck | 46.66 | 62.57 | +| tower | 18.7 | 23.41 | +| chandelier | 69.68 | 87.9 | +| awning | 45.38 | 62.19 | +| streetlight | 30.63 | 41.38 | +| booth | 34.21 | 39.2 | +| television receiver | 77.89 | 82.98 | +| airplane | 67.33 | 76.74 | +| dirt track | 24.07 | 41.65 | +| apparel | 50.9 | 75.91 | +| pole | 25.6 | 34.22 | +| land | 1.88 | 3.37 | +| bannister | 14.27 | 24.16 | +| escalator | 53.7 | 77.15 | +| ottoman | 49.57 | 65.47 | +| bottle | 41.41 | 52.94 | +| buffet | 48.87 | 55.07 | +| poster | 30.98 | 37.54 | +| stage | 20.33 | 29.55 | +| van | 46.1 | 68.94 | +| ship | 10.87 | 10.9 | +| fountain | 25.5 | 26.14 | +| conveyer belt | 74.41 | 92.4 | +| canopy | 61.79 | 78.05 | +| washer | 71.63 | 80.21 | +| plaything | 16.38 | 36.13 | +| swimming pool | 62.35 | 91.24 | +| stool | 53.96 | 63.21 | +| barrel | 41.84 | 64.87 | +| basket | 34.15 | 51.2 | +| waterfall | 63.33 | 93.18 | +| tent | 91.19 | 98.65 | +| bag | 14.24 | 16.51 | +| minibike | 71.7 | 86.2 | +| cradle | 83.63 | 97.42 | +| oven | 55.67 | 63.91 | +| ball | 46.22 | 70.51 | +| food | 59.01 | 76.62 | +| step | 9.78 | 10.73 | +| tank | 67.57 | 72.16 | +| trade name | 4.57 | 4.64 | +| microwave | 83.1 | 95.02 | +| pot | 53.23 | 62.58 | +| animal | 61.53 | 63.04 | +| bicycle | 59.54 | 79.1 | +| lake | 54.43 | 63.39 | +| dishwasher | 65.48 | 82.82 | +| screen | 51.75 | 94.03 | +| blanket | 28.91 | 33.73 | +| sculpture | 68.65 | 84.62 | +| hood | 61.14 | 74.87 | +| sconce | 54.46 | 64.44 | +| vase | 44.21 | 56.79 | +| traffic light | 30.7 | 57.54 | +| tray | 9.91 | 12.09 | +| ashcan | 41.44 | 61.41 | +| fan | 64.36 | 79.78 | +| pier | 35.66 | 45.13 | +| crt screen | 13.13 | 16.18 | +| plate | 54.96 | 80.12 | +| monitor | 67.93 | 76.9 | +| bulletin board | 47.9 | 70.76 | +| shower | 1.7 | 2.02 | +| radiator | 63.82 | 77.56 | +| glass | 19.56 | 22.13 | +| clock | 37.06 | 42.32 | +| flag | 68.79 | 71.95 | ++---------------------+-------+-------+ +2024-06-18 09:19:38,476 - mmseg - INFO - Summary: +2024-06-18 09:19:38,476 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.48 | 54.38 | 67.88 | ++-------+-------+-------+ +2024-06-18 09:19:38,477 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 09:19:38,477 - mmseg - INFO - Iter(val) [250] aAcc: 0.8548, mIoU: 0.5438, mAcc: 0.6788, IoU.wall: 0.8111, IoU.building: 0.8566, IoU.sky: 0.9469, IoU.floor: 0.8412, IoU.tree: 0.7712, IoU.ceiling: 0.8696, IoU.road: 0.8634, IoU.bed : 0.9149, IoU.windowpane: 0.6531, IoU.grass: 0.6707, IoU.cabinet: 0.6288, IoU.sidewalk: 0.6986, IoU.person: 0.8467, IoU.earth: 0.3650, IoU.door: 0.5864, IoU.table: 0.6678, IoU.mountain: 0.6211, IoU.plant: 0.5341, IoU.curtain: 0.7944, IoU.chair: 0.6416, IoU.car: 0.8596, IoU.water: 0.6779, IoU.painting: 0.7615, IoU.sofa: 0.7897, IoU.shelf: 0.4777, IoU.house: 0.5267, IoU.sea: 0.7026, IoU.mirror: 0.7374, IoU.rug: 0.6695, IoU.field: 0.3807, IoU.armchair: 0.5570, IoU.seat: 0.6387, IoU.fence: 0.4401, IoU.desk: 0.5194, IoU.rock: 0.5146, IoU.wardrobe: 0.5589, IoU.lamp: 0.6989, IoU.bathtub: 0.8072, IoU.railing: 0.3950, IoU.cushion: 0.6526, IoU.base: 0.3599, IoU.box: 0.3085, IoU.column: 0.5410, IoU.signboard: 0.3790, IoU.chest of drawers: 0.4335, IoU.counter: 0.3983, IoU.sand: 0.5825, IoU.sink: 0.7477, IoU.skyscraper: 0.5834, IoU.fireplace: 0.7233, IoU.refrigerator: 0.8140, IoU.grandstand: 0.4366, IoU.path: 0.2913, IoU.stairs: 0.3362, IoU.runway: 0.7352, IoU.case: 0.5495, IoU.pool table: 0.9385, IoU.pillow: 0.6865, IoU.screen door: 0.7819, IoU.stairway: 0.4203, IoU.river: 0.1565, IoU.bridge: 0.7659, IoU.bookcase: 0.3755, IoU.blind: 0.4291, IoU.coffee table: 0.6469, IoU.toilet: 0.8771, IoU.flower: 0.4087, IoU.book: 0.5102, IoU.hill: 0.0638, IoU.bench: 0.5072, IoU.countertop: 0.6217, IoU.stove: 0.8346, IoU.palm: 0.5534, IoU.kitchen island: 0.4427, IoU.computer: 0.7496, IoU.swivel chair: 0.4724, IoU.boat: 0.6456, IoU.bar: 0.5702, IoU.arcade machine: 0.7291, IoU.hovel: 0.4437, IoU.bus: 0.9116, IoU.towel: 0.6738, IoU.light: 0.5774, IoU.truck: 0.4666, IoU.tower: 0.1870, IoU.chandelier: 0.6968, IoU.awning: 0.4538, IoU.streetlight: 0.3063, IoU.booth: 0.3421, IoU.television receiver: 0.7789, IoU.airplane: 0.6733, IoU.dirt track: 0.2407, IoU.apparel: 0.5090, IoU.pole: 0.2560, IoU.land: 0.0188, IoU.bannister: 0.1427, IoU.escalator: 0.5370, IoU.ottoman: 0.4957, IoU.bottle: 0.4141, IoU.buffet: 0.4887, IoU.poster: 0.3098, IoU.stage: 0.2033, IoU.van: 0.4610, IoU.ship: 0.1087, IoU.fountain: 0.2550, IoU.conveyer belt: 0.7441, IoU.canopy: 0.6179, IoU.washer: 0.7163, IoU.plaything: 0.1638, IoU.swimming pool: 0.6235, IoU.stool: 0.5396, IoU.barrel: 0.4184, IoU.basket: 0.3415, IoU.waterfall: 0.6333, IoU.tent: 0.9119, IoU.bag: 0.1424, IoU.minibike: 0.7170, IoU.cradle: 0.8363, IoU.oven: 0.5567, IoU.ball: 0.4622, IoU.food: 0.5901, IoU.step: 0.0978, IoU.tank: 0.6757, IoU.trade name: 0.0457, IoU.microwave: 0.8310, IoU.pot: 0.5323, IoU.animal: 0.6153, IoU.bicycle: 0.5954, IoU.lake: 0.5443, IoU.dishwasher: 0.6548, IoU.screen: 0.5175, IoU.blanket: 0.2891, IoU.sculpture: 0.6865, IoU.hood: 0.6114, IoU.sconce: 0.5446, IoU.vase: 0.4421, IoU.traffic light: 0.3070, IoU.tray: 0.0991, IoU.ashcan: 0.4144, IoU.fan: 0.6436, IoU.pier: 0.3566, IoU.crt screen: 0.1313, IoU.plate: 0.5496, IoU.monitor: 0.6793, IoU.bulletin board: 0.4790, IoU.shower: 0.0170, IoU.radiator: 0.6382, IoU.glass: 0.1956, IoU.clock: 0.3706, IoU.flag: 0.6879, Acc.wall: 0.8894, Acc.building: 0.9350, Acc.sky: 0.9719, Acc.floor: 0.9186, Acc.tree: 0.9118, Acc.ceiling: 0.9393, Acc.road: 0.9312, Acc.bed : 0.9637, Acc.windowpane: 0.7843, Acc.grass: 0.8382, Acc.cabinet: 0.7310, Acc.sidewalk: 0.8311, Acc.person: 0.9255, Acc.earth: 0.4677, Acc.door: 0.7484, Acc.table: 0.7972, Acc.mountain: 0.7390, Acc.plant: 0.6234, Acc.curtain: 0.9075, Acc.chair: 0.7623, Acc.car: 0.9200, Acc.water: 0.8593, Acc.painting: 0.8922, Acc.sofa: 0.9174, Acc.shelf: 0.6289, Acc.house: 0.6088, Acc.sea: 0.7845, Acc.mirror: 0.8093, Acc.rug: 0.7792, Acc.field: 0.6856, Acc.armchair: 0.6998, Acc.seat: 0.8653, Acc.fence: 0.5815, Acc.desk: 0.7786, Acc.rock: 0.7187, Acc.wardrobe: 0.8319, Acc.lamp: 0.8014, Acc.bathtub: 0.8220, Acc.railing: 0.5588, Acc.cushion: 0.7675, Acc.base: 0.5888, Acc.box: 0.4512, Acc.column: 0.6504, Acc.signboard: 0.5703, Acc.chest of drawers: 0.6728, Acc.counter: 0.4751, Acc.sand: 0.8106, Acc.sink: 0.8030, Acc.skyscraper: 0.7431, Acc.fireplace: 0.9079, Acc.refrigerator: 0.9253, Acc.grandstand: 0.9131, Acc.path: 0.4209, Acc.stairs: 0.4358, Acc.runway: 0.9762, Acc.case: 0.8153, Acc.pool table: 0.9794, Acc.pillow: 0.8238, Acc.screen door: 0.8363, Acc.stairway: 0.4566, Acc.river: 0.2441, Acc.bridge: 0.9096, Acc.bookcase: 0.5314, Acc.blind: 0.4973, Acc.coffee table: 0.8408, Acc.toilet: 0.9360, Acc.flower: 0.5950, Acc.book: 0.7624, Acc.hill: 0.1273, Acc.bench: 0.5640, Acc.countertop: 0.8527, Acc.stove: 0.8892, Acc.palm: 0.8171, Acc.kitchen island: 0.8688, Acc.computer: 0.9513, Acc.swivel chair: 0.7489, Acc.boat: 0.8952, Acc.bar: 0.7472, Acc.arcade machine: 0.7960, Acc.hovel: 0.5334, Acc.bus: 0.9657, Acc.towel: 0.8520, Acc.light: 0.7374, Acc.truck: 0.6257, Acc.tower: 0.2341, Acc.chandelier: 0.8790, Acc.awning: 0.6219, Acc.streetlight: 0.4138, Acc.booth: 0.3920, Acc.television receiver: 0.8298, Acc.airplane: 0.7674, Acc.dirt track: 0.4165, Acc.apparel: 0.7591, Acc.pole: 0.3422, Acc.land: 0.0337, Acc.bannister: 0.2416, Acc.escalator: 0.7715, Acc.ottoman: 0.6547, Acc.bottle: 0.5294, Acc.buffet: 0.5507, Acc.poster: 0.3754, Acc.stage: 0.2955, Acc.van: 0.6894, Acc.ship: 0.1090, Acc.fountain: 0.2614, Acc.conveyer belt: 0.9240, Acc.canopy: 0.7805, Acc.washer: 0.8021, Acc.plaything: 0.3613, Acc.swimming pool: 0.9124, Acc.stool: 0.6321, Acc.barrel: 0.6487, Acc.basket: 0.5120, Acc.waterfall: 0.9318, Acc.tent: 0.9865, Acc.bag: 0.1651, Acc.minibike: 0.8620, Acc.cradle: 0.9742, Acc.oven: 0.6391, Acc.ball: 0.7051, Acc.food: 0.7662, Acc.step: 0.1073, Acc.tank: 0.7216, Acc.trade name: 0.0464, Acc.microwave: 0.9502, Acc.pot: 0.6258, Acc.animal: 0.6304, Acc.bicycle: 0.7910, Acc.lake: 0.6339, Acc.dishwasher: 0.8282, Acc.screen: 0.9403, Acc.blanket: 0.3373, Acc.sculpture: 0.8462, Acc.hood: 0.7487, Acc.sconce: 0.6444, Acc.vase: 0.5679, Acc.traffic light: 0.5754, Acc.tray: 0.1209, Acc.ashcan: 0.6141, Acc.fan: 0.7978, Acc.pier: 0.4513, Acc.crt screen: 0.1618, Acc.plate: 0.8012, Acc.monitor: 0.7690, Acc.bulletin board: 0.7076, Acc.shower: 0.0202, Acc.radiator: 0.7756, Acc.glass: 0.2213, Acc.clock: 0.4232, Acc.flag: 0.7195 +2024-06-18 09:20:44,990 - mmseg - INFO - Iter [24050/80000] lr: 2.798e-05, eta: 23:01:43, time: 3.243, data_time: 1.928, memory: 70498, decode.loss_ce: 0.2870, decode.acc_seg: 88.5722, aux.loss_ce: 0.1172, aux.acc_seg: 88.3383, loss: 0.4042 +2024-06-18 09:21:51,229 - mmseg - INFO - Iter [24100/80000] lr: 2.795e-05, eta: 23:00:11, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2726, decode.acc_seg: 88.6005, aux.loss_ce: 0.1113, aux.acc_seg: 88.4511, loss: 0.3840 +2024-06-18 09:22:57,396 - mmseg - INFO - Iter [24150/80000] lr: 2.793e-05, eta: 22:58:39, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2908, decode.acc_seg: 88.3387, aux.loss_ce: 0.1192, aux.acc_seg: 88.0915, loss: 0.4100 +2024-06-18 09:24:03,867 - mmseg - INFO - Iter [24200/80000] lr: 2.790e-05, eta: 22:57:07, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2917, decode.acc_seg: 88.4388, aux.loss_ce: 0.1196, aux.acc_seg: 88.1322, loss: 0.4113 +2024-06-18 09:25:10,138 - mmseg - INFO - Iter [24250/80000] lr: 2.788e-05, eta: 22:55:35, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3007, decode.acc_seg: 87.9421, aux.loss_ce: 0.1234, aux.acc_seg: 87.5737, loss: 0.4241 +2024-06-18 09:26:16,458 - mmseg - INFO - Iter [24300/80000] lr: 2.785e-05, eta: 22:54:04, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3079, decode.acc_seg: 87.6688, aux.loss_ce: 0.1246, aux.acc_seg: 87.4697, loss: 0.4325 +2024-06-18 09:27:22,878 - mmseg - INFO - Iter [24350/80000] lr: 2.783e-05, eta: 22:52:32, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3000, decode.acc_seg: 87.4626, aux.loss_ce: 0.1214, aux.acc_seg: 87.6480, loss: 0.4214 +2024-06-18 09:28:29,018 - mmseg - INFO - Iter [24400/80000] lr: 2.780e-05, eta: 22:51:00, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2630, decode.acc_seg: 89.3447, aux.loss_ce: 0.1080, aux.acc_seg: 89.0614, loss: 0.3710 +2024-06-18 09:29:35,341 - mmseg - INFO - Iter [24450/80000] lr: 2.778e-05, eta: 22:49:29, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2841, decode.acc_seg: 88.1664, aux.loss_ce: 0.1166, aux.acc_seg: 87.7969, loss: 0.4008 +2024-06-18 09:30:41,537 - mmseg - INFO - Iter [24500/80000] lr: 2.775e-05, eta: 22:47:57, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2816, decode.acc_seg: 88.5401, aux.loss_ce: 0.1158, aux.acc_seg: 88.2099, loss: 0.3975 +2024-06-18 09:31:47,858 - mmseg - INFO - Iter [24550/80000] lr: 2.773e-05, eta: 22:46:26, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2966, decode.acc_seg: 87.8367, aux.loss_ce: 0.1207, aux.acc_seg: 87.6025, loss: 0.4173 +2024-06-18 09:32:53,851 - mmseg - INFO - Iter [24600/80000] lr: 2.770e-05, eta: 22:44:55, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2821, decode.acc_seg: 88.6928, aux.loss_ce: 0.1151, aux.acc_seg: 88.4698, loss: 0.3972 +2024-06-18 09:34:00,020 - mmseg - INFO - Iter [24650/80000] lr: 2.768e-05, eta: 22:43:23, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2983, decode.acc_seg: 88.1010, aux.loss_ce: 0.1211, aux.acc_seg: 87.9422, loss: 0.4194 +2024-06-18 09:35:06,080 - mmseg - INFO - Iter [24700/80000] lr: 2.765e-05, eta: 22:41:52, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2974, decode.acc_seg: 87.9576, aux.loss_ce: 0.1196, aux.acc_seg: 87.8862, loss: 0.4171 +2024-06-18 09:36:12,040 - mmseg - INFO - Iter [24750/80000] lr: 2.763e-05, eta: 22:40:20, time: 1.319, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2943, decode.acc_seg: 88.3681, aux.loss_ce: 0.1198, aux.acc_seg: 88.2505, loss: 0.4141 +2024-06-18 09:37:18,371 - mmseg - INFO - Iter [24800/80000] lr: 2.760e-05, eta: 22:38:50, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2987, decode.acc_seg: 87.6746, aux.loss_ce: 0.1223, aux.acc_seg: 87.3555, loss: 0.4210 +2024-06-18 09:38:24,395 - mmseg - INFO - Iter [24850/80000] lr: 2.758e-05, eta: 22:37:18, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2966, decode.acc_seg: 88.1492, aux.loss_ce: 0.1209, aux.acc_seg: 87.9332, loss: 0.4175 +2024-06-18 09:39:30,830 - mmseg - INFO - Iter [24900/80000] lr: 2.755e-05, eta: 22:35:48, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2916, decode.acc_seg: 88.0422, aux.loss_ce: 0.1183, aux.acc_seg: 87.9512, loss: 0.4099 +2024-06-18 09:40:37,032 - mmseg - INFO - Iter [24950/80000] lr: 2.753e-05, eta: 22:34:18, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2832, decode.acc_seg: 88.6434, aux.loss_ce: 0.1155, aux.acc_seg: 88.4882, loss: 0.3987 +2024-06-18 09:41:43,295 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 09:41:43,296 - mmseg - INFO - Iter [25000/80000] lr: 2.750e-05, eta: 22:32:47, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2974, decode.acc_seg: 87.9609, aux.loss_ce: 0.1211, aux.acc_seg: 87.8235, loss: 0.4185 +2024-06-18 09:43:20,837 - mmseg - INFO - per class results: +2024-06-18 09:43:20,844 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.55 | 89.58 | +| building | 84.94 | 92.38 | +| sky | 94.89 | 97.73 | +| floor | 85.14 | 91.17 | +| tree | 76.37 | 91.13 | +| ceiling | 87.37 | 94.23 | +| road | 85.89 | 89.66 | +| bed | 91.9 | 96.48 | +| windowpane | 65.0 | 80.37 | +| grass | 67.24 | 83.99 | +| cabinet | 63.63 | 75.16 | +| sidewalk | 69.13 | 87.31 | +| person | 84.23 | 93.41 | +| earth | 38.52 | 51.85 | +| door | 55.96 | 70.33 | +| table | 64.98 | 80.97 | +| mountain | 64.97 | 71.84 | +| plant | 56.81 | 68.75 | +| curtain | 78.02 | 92.19 | +| chair | 64.53 | 76.46 | +| car | 86.52 | 92.41 | +| water | 64.72 | 78.84 | +| painting | 75.28 | 88.9 | +| sofa | 80.36 | 87.78 | +| shelf | 46.99 | 65.29 | +| house | 55.1 | 66.2 | +| sea | 67.0 | 83.05 | +| mirror | 74.84 | 79.66 | +| rug | 72.79 | 83.89 | +| field | 35.01 | 54.84 | +| armchair | 58.07 | 76.94 | +| seat | 66.61 | 86.68 | +| fence | 47.35 | 59.39 | +| desk | 54.55 | 79.08 | +| rock | 58.45 | 88.03 | +| wardrobe | 54.06 | 76.91 | +| lamp | 70.8 | 81.14 | +| bathtub | 78.87 | 80.64 | +| railing | 42.05 | 56.75 | +| cushion | 67.52 | 81.23 | +| base | 33.07 | 50.8 | +| box | 30.9 | 42.45 | +| column | 54.97 | 67.38 | +| signboard | 38.96 | 45.59 | +| chest of drawers | 46.75 | 67.6 | +| counter | 36.52 | 46.55 | +| sand | 46.84 | 63.64 | +| sink | 75.44 | 81.41 | +| skyscraper | 60.77 | 81.06 | +| fireplace | 70.85 | 90.45 | +| refrigerator | 79.59 | 91.75 | +| grandstand | 54.16 | 82.91 | +| path | 23.85 | 34.45 | +| stairs | 33.48 | 45.5 | +| runway | 72.6 | 94.71 | +| case | 56.35 | 81.22 | +| pool table | 93.81 | 97.37 | +| pillow | 67.78 | 78.18 | +| screen door | 77.75 | 85.59 | +| stairway | 49.89 | 60.71 | +| river | 17.6 | 30.24 | +| bridge | 77.72 | 90.82 | +| bookcase | 35.15 | 61.02 | +| blind | 42.52 | 45.91 | +| coffee table | 63.31 | 88.0 | +| toilet | 87.4 | 92.95 | +| flower | 40.96 | 50.66 | +| book | 52.75 | 67.99 | +| hill | 7.36 | 14.64 | +| bench | 53.39 | 61.61 | +| countertop | 64.88 | 83.74 | +| stove | 83.17 | 88.87 | +| palm | 49.83 | 89.21 | +| kitchen island | 45.94 | 80.28 | +| computer | 56.96 | 61.66 | +| swivel chair | 45.1 | 79.14 | +| boat | 54.02 | 87.03 | +| bar | 56.09 | 73.51 | +| arcade machine | 74.88 | 83.34 | +| hovel | 26.2 | 31.62 | +| bus | 90.38 | 95.72 | +| towel | 72.79 | 79.87 | +| light | 56.38 | 62.85 | +| truck | 42.9 | 58.77 | +| tower | 24.54 | 44.57 | +| chandelier | 71.14 | 85.39 | +| awning | 48.43 | 63.32 | +| streetlight | 32.52 | 46.15 | +| booth | 35.58 | 43.1 | +| television receiver | 77.68 | 88.72 | +| airplane | 69.01 | 73.84 | +| dirt track | 30.0 | 66.58 | +| apparel | 44.33 | 63.11 | +| pole | 24.5 | 32.91 | +| land | 1.68 | 2.59 | +| bannister | 15.69 | 24.8 | +| escalator | 60.21 | 78.77 | +| ottoman | 54.39 | 72.53 | +| bottle | 38.33 | 50.12 | +| buffet | 41.77 | 46.84 | +| poster | 33.4 | 46.41 | +| stage | 18.69 | 34.37 | +| van | 44.53 | 57.79 | +| ship | 81.49 | 82.29 | +| fountain | 21.89 | 24.57 | +| conveyer belt | 73.49 | 93.12 | +| canopy | 49.16 | 60.42 | +| washer | 88.08 | 96.02 | +| plaything | 20.21 | 42.83 | +| swimming pool | 59.7 | 92.28 | +| stool | 54.76 | 61.86 | +| barrel | 56.81 | 64.32 | +| basket | 31.93 | 57.24 | +| waterfall | 64.41 | 93.93 | +| tent | 93.37 | 97.75 | +| bag | 16.2 | 19.31 | +| minibike | 70.92 | 81.61 | +| cradle | 75.1 | 97.3 | +| oven | 61.7 | 77.59 | +| ball | 39.77 | 74.36 | +| food | 44.48 | 44.97 | +| step | 8.18 | 9.01 | +| tank | 61.7 | 84.39 | +| trade name | 30.1 | 33.62 | +| microwave | 87.99 | 94.29 | +| pot | 52.64 | 65.95 | +| animal | 65.98 | 67.75 | +| bicycle | 56.3 | 81.08 | +| lake | 52.78 | 63.43 | +| dishwasher | 68.85 | 80.59 | +| screen | 61.8 | 75.92 | +| blanket | 22.59 | 26.33 | +| sculpture | 70.4 | 84.68 | +| hood | 59.69 | 69.64 | +| sconce | 53.09 | 60.48 | +| vase | 42.74 | 55.69 | +| traffic light | 31.09 | 60.18 | +| tray | 10.31 | 15.03 | +| ashcan | 41.18 | 59.07 | +| fan | 63.39 | 78.77 | +| pier | 35.95 | 45.29 | +| crt screen | 15.43 | 19.81 | +| plate | 58.88 | 67.06 | +| monitor | 36.64 | 80.59 | +| bulletin board | 48.17 | 60.92 | +| shower | 0.86 | 1.02 | +| radiator | 64.93 | 77.21 | +| glass | 17.12 | 18.26 | +| clock | 37.84 | 45.51 | +| flag | 69.64 | 74.33 | ++---------------------+-------+-------+ +2024-06-18 09:43:20,844 - mmseg - INFO - Summary: +2024-06-18 09:43:20,844 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.54 | 54.77 | 68.07 | ++-------+-------+-------+ +2024-06-18 09:43:20,845 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 09:43:20,845 - mmseg - INFO - Iter(val) [250] aAcc: 0.8554, mIoU: 0.5477, mAcc: 0.6807, IoU.wall: 0.8155, IoU.building: 0.8494, IoU.sky: 0.9489, IoU.floor: 0.8514, IoU.tree: 0.7637, IoU.ceiling: 0.8737, IoU.road: 0.8589, IoU.bed : 0.9190, IoU.windowpane: 0.6500, IoU.grass: 0.6724, IoU.cabinet: 0.6363, IoU.sidewalk: 0.6913, IoU.person: 0.8423, IoU.earth: 0.3852, IoU.door: 0.5596, IoU.table: 0.6498, IoU.mountain: 0.6497, IoU.plant: 0.5681, IoU.curtain: 0.7802, IoU.chair: 0.6453, IoU.car: 0.8652, IoU.water: 0.6472, IoU.painting: 0.7528, IoU.sofa: 0.8036, IoU.shelf: 0.4699, IoU.house: 0.5510, IoU.sea: 0.6700, IoU.mirror: 0.7484, IoU.rug: 0.7279, IoU.field: 0.3501, IoU.armchair: 0.5807, IoU.seat: 0.6661, IoU.fence: 0.4735, IoU.desk: 0.5455, IoU.rock: 0.5845, IoU.wardrobe: 0.5406, IoU.lamp: 0.7080, IoU.bathtub: 0.7887, IoU.railing: 0.4205, IoU.cushion: 0.6752, IoU.base: 0.3307, IoU.box: 0.3090, IoU.column: 0.5497, IoU.signboard: 0.3896, IoU.chest of drawers: 0.4675, IoU.counter: 0.3652, IoU.sand: 0.4684, IoU.sink: 0.7544, IoU.skyscraper: 0.6077, IoU.fireplace: 0.7085, IoU.refrigerator: 0.7959, IoU.grandstand: 0.5416, IoU.path: 0.2385, IoU.stairs: 0.3348, IoU.runway: 0.7260, IoU.case: 0.5635, IoU.pool table: 0.9381, IoU.pillow: 0.6778, IoU.screen door: 0.7775, IoU.stairway: 0.4989, IoU.river: 0.1760, IoU.bridge: 0.7772, IoU.bookcase: 0.3515, IoU.blind: 0.4252, IoU.coffee table: 0.6331, IoU.toilet: 0.8740, IoU.flower: 0.4096, IoU.book: 0.5275, IoU.hill: 0.0736, IoU.bench: 0.5339, IoU.countertop: 0.6488, IoU.stove: 0.8317, IoU.palm: 0.4983, IoU.kitchen island: 0.4594, IoU.computer: 0.5696, IoU.swivel chair: 0.4510, IoU.boat: 0.5402, IoU.bar: 0.5609, IoU.arcade machine: 0.7488, IoU.hovel: 0.2620, IoU.bus: 0.9038, IoU.towel: 0.7279, IoU.light: 0.5638, IoU.truck: 0.4290, IoU.tower: 0.2454, IoU.chandelier: 0.7114, IoU.awning: 0.4843, IoU.streetlight: 0.3252, IoU.booth: 0.3558, IoU.television receiver: 0.7768, IoU.airplane: 0.6901, IoU.dirt track: 0.3000, IoU.apparel: 0.4433, IoU.pole: 0.2450, IoU.land: 0.0168, IoU.bannister: 0.1569, IoU.escalator: 0.6021, IoU.ottoman: 0.5439, IoU.bottle: 0.3833, IoU.buffet: 0.4177, IoU.poster: 0.3340, IoU.stage: 0.1869, IoU.van: 0.4453, IoU.ship: 0.8149, IoU.fountain: 0.2189, IoU.conveyer belt: 0.7349, IoU.canopy: 0.4916, IoU.washer: 0.8808, IoU.plaything: 0.2021, IoU.swimming pool: 0.5970, IoU.stool: 0.5476, IoU.barrel: 0.5681, IoU.basket: 0.3193, IoU.waterfall: 0.6441, IoU.tent: 0.9337, IoU.bag: 0.1620, IoU.minibike: 0.7092, IoU.cradle: 0.7510, IoU.oven: 0.6170, IoU.ball: 0.3977, IoU.food: 0.4448, IoU.step: 0.0818, IoU.tank: 0.6170, IoU.trade name: 0.3010, IoU.microwave: 0.8799, IoU.pot: 0.5264, IoU.animal: 0.6598, IoU.bicycle: 0.5630, IoU.lake: 0.5278, IoU.dishwasher: 0.6885, IoU.screen: 0.6180, IoU.blanket: 0.2259, IoU.sculpture: 0.7040, IoU.hood: 0.5969, IoU.sconce: 0.5309, IoU.vase: 0.4274, IoU.traffic light: 0.3109, IoU.tray: 0.1031, IoU.ashcan: 0.4118, IoU.fan: 0.6339, IoU.pier: 0.3595, IoU.crt screen: 0.1543, IoU.plate: 0.5888, IoU.monitor: 0.3664, IoU.bulletin board: 0.4817, IoU.shower: 0.0086, IoU.radiator: 0.6493, IoU.glass: 0.1712, IoU.clock: 0.3784, IoU.flag: 0.6964, Acc.wall: 0.8958, Acc.building: 0.9238, Acc.sky: 0.9773, Acc.floor: 0.9117, Acc.tree: 0.9113, Acc.ceiling: 0.9423, Acc.road: 0.8966, Acc.bed : 0.9648, Acc.windowpane: 0.8037, Acc.grass: 0.8399, Acc.cabinet: 0.7516, Acc.sidewalk: 0.8731, Acc.person: 0.9341, Acc.earth: 0.5185, Acc.door: 0.7033, Acc.table: 0.8097, Acc.mountain: 0.7184, Acc.plant: 0.6875, Acc.curtain: 0.9219, Acc.chair: 0.7646, Acc.car: 0.9241, Acc.water: 0.7884, Acc.painting: 0.8890, Acc.sofa: 0.8778, Acc.shelf: 0.6529, Acc.house: 0.6620, Acc.sea: 0.8305, Acc.mirror: 0.7966, Acc.rug: 0.8389, Acc.field: 0.5484, Acc.armchair: 0.7694, Acc.seat: 0.8668, Acc.fence: 0.5939, Acc.desk: 0.7908, Acc.rock: 0.8803, Acc.wardrobe: 0.7691, Acc.lamp: 0.8114, Acc.bathtub: 0.8064, Acc.railing: 0.5675, Acc.cushion: 0.8123, Acc.base: 0.5080, Acc.box: 0.4245, Acc.column: 0.6738, Acc.signboard: 0.4559, Acc.chest of drawers: 0.6760, Acc.counter: 0.4655, Acc.sand: 0.6364, Acc.sink: 0.8141, Acc.skyscraper: 0.8106, Acc.fireplace: 0.9045, Acc.refrigerator: 0.9175, Acc.grandstand: 0.8291, Acc.path: 0.3445, Acc.stairs: 0.4550, Acc.runway: 0.9471, Acc.case: 0.8122, Acc.pool table: 0.9737, Acc.pillow: 0.7818, Acc.screen door: 0.8559, Acc.stairway: 0.6071, Acc.river: 0.3024, Acc.bridge: 0.9082, Acc.bookcase: 0.6102, Acc.blind: 0.4591, Acc.coffee table: 0.8800, Acc.toilet: 0.9295, Acc.flower: 0.5066, Acc.book: 0.6799, Acc.hill: 0.1464, Acc.bench: 0.6161, Acc.countertop: 0.8374, Acc.stove: 0.8887, Acc.palm: 0.8921, Acc.kitchen island: 0.8028, Acc.computer: 0.6166, Acc.swivel chair: 0.7914, Acc.boat: 0.8703, Acc.bar: 0.7351, Acc.arcade machine: 0.8334, Acc.hovel: 0.3162, Acc.bus: 0.9572, Acc.towel: 0.7987, Acc.light: 0.6285, Acc.truck: 0.5877, Acc.tower: 0.4457, Acc.chandelier: 0.8539, Acc.awning: 0.6332, Acc.streetlight: 0.4615, Acc.booth: 0.4310, Acc.television receiver: 0.8872, Acc.airplane: 0.7384, Acc.dirt track: 0.6658, Acc.apparel: 0.6311, Acc.pole: 0.3291, Acc.land: 0.0259, Acc.bannister: 0.2480, Acc.escalator: 0.7877, Acc.ottoman: 0.7253, Acc.bottle: 0.5012, Acc.buffet: 0.4684, Acc.poster: 0.4641, Acc.stage: 0.3437, Acc.van: 0.5779, Acc.ship: 0.8229, Acc.fountain: 0.2457, Acc.conveyer belt: 0.9312, Acc.canopy: 0.6042, Acc.washer: 0.9602, Acc.plaything: 0.4283, Acc.swimming pool: 0.9228, Acc.stool: 0.6186, Acc.barrel: 0.6432, Acc.basket: 0.5724, Acc.waterfall: 0.9393, Acc.tent: 0.9775, Acc.bag: 0.1931, Acc.minibike: 0.8161, Acc.cradle: 0.9730, Acc.oven: 0.7759, Acc.ball: 0.7436, Acc.food: 0.4497, Acc.step: 0.0901, Acc.tank: 0.8439, Acc.trade name: 0.3362, Acc.microwave: 0.9429, Acc.pot: 0.6595, Acc.animal: 0.6775, Acc.bicycle: 0.8108, Acc.lake: 0.6343, Acc.dishwasher: 0.8059, Acc.screen: 0.7592, Acc.blanket: 0.2633, Acc.sculpture: 0.8468, Acc.hood: 0.6964, Acc.sconce: 0.6048, Acc.vase: 0.5569, Acc.traffic light: 0.6018, Acc.tray: 0.1503, Acc.ashcan: 0.5907, Acc.fan: 0.7877, Acc.pier: 0.4529, Acc.crt screen: 0.1981, Acc.plate: 0.6706, Acc.monitor: 0.8059, Acc.bulletin board: 0.6092, Acc.shower: 0.0102, Acc.radiator: 0.7721, Acc.glass: 0.1826, Acc.clock: 0.4551, Acc.flag: 0.7433 +2024-06-18 09:44:27,432 - mmseg - INFO - Iter [25050/80000] lr: 2.748e-05, eta: 22:34:52, time: 3.283, data_time: 1.966, memory: 70498, decode.loss_ce: 0.2909, decode.acc_seg: 88.0449, aux.loss_ce: 0.1184, aux.acc_seg: 88.0054, loss: 0.4093 +2024-06-18 09:45:33,551 - mmseg - INFO - Iter [25100/80000] lr: 2.745e-05, eta: 22:33:20, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2911, decode.acc_seg: 88.0787, aux.loss_ce: 0.1186, aux.acc_seg: 87.8135, loss: 0.4097 +2024-06-18 09:46:39,524 - mmseg - INFO - Iter [25150/80000] lr: 2.743e-05, eta: 22:31:49, time: 1.319, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2854, decode.acc_seg: 88.1301, aux.loss_ce: 0.1169, aux.acc_seg: 87.9898, loss: 0.4023 +2024-06-18 09:47:45,843 - mmseg - INFO - Iter [25200/80000] lr: 2.740e-05, eta: 22:30:19, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2817, decode.acc_seg: 88.3620, aux.loss_ce: 0.1148, aux.acc_seg: 88.1349, loss: 0.3966 +2024-06-18 09:48:51,919 - mmseg - INFO - Iter [25250/80000] lr: 2.738e-05, eta: 22:28:48, time: 1.321, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2866, decode.acc_seg: 88.4301, aux.loss_ce: 0.1176, aux.acc_seg: 88.2842, loss: 0.4042 +2024-06-18 09:50:00,517 - mmseg - INFO - Iter [25300/80000] lr: 2.735e-05, eta: 22:27:22, time: 1.372, data_time: 0.060, memory: 70498, decode.loss_ce: 0.2779, decode.acc_seg: 88.7289, aux.loss_ce: 0.1127, aux.acc_seg: 88.6236, loss: 0.3906 +2024-06-18 09:51:07,293 - mmseg - INFO - Iter [25350/80000] lr: 2.733e-05, eta: 22:25:53, time: 1.336, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2702, decode.acc_seg: 88.9104, aux.loss_ce: 0.1104, aux.acc_seg: 88.7569, loss: 0.3806 +2024-06-18 09:52:13,204 - mmseg - INFO - Iter [25400/80000] lr: 2.730e-05, eta: 22:24:22, time: 1.318, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2758, decode.acc_seg: 88.7885, aux.loss_ce: 0.1133, aux.acc_seg: 88.5283, loss: 0.3892 +2024-06-18 09:53:19,194 - mmseg - INFO - Iter [25450/80000] lr: 2.728e-05, eta: 22:22:51, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3101, decode.acc_seg: 87.7964, aux.loss_ce: 0.1266, aux.acc_seg: 87.6393, loss: 0.4367 +2024-06-18 09:54:25,243 - mmseg - INFO - Iter [25500/80000] lr: 2.725e-05, eta: 22:21:21, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2721, decode.acc_seg: 88.8880, aux.loss_ce: 0.1113, aux.acc_seg: 88.6466, loss: 0.3834 +2024-06-18 09:55:31,818 - mmseg - INFO - Iter [25550/80000] lr: 2.723e-05, eta: 22:19:51, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2782, decode.acc_seg: 88.6590, aux.loss_ce: 0.1135, aux.acc_seg: 88.4273, loss: 0.3918 +2024-06-18 09:56:37,989 - mmseg - INFO - Iter [25600/80000] lr: 2.720e-05, eta: 22:18:21, time: 1.323, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3058, decode.acc_seg: 87.6053, aux.loss_ce: 0.1245, aux.acc_seg: 87.4687, loss: 0.4304 +2024-06-18 09:57:44,057 - mmseg - INFO - Iter [25650/80000] lr: 2.718e-05, eta: 22:16:51, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2923, decode.acc_seg: 88.4229, aux.loss_ce: 0.1197, aux.acc_seg: 88.1608, loss: 0.4120 +2024-06-18 09:58:50,277 - mmseg - INFO - Iter [25700/80000] lr: 2.715e-05, eta: 22:15:21, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2770, decode.acc_seg: 88.6454, aux.loss_ce: 0.1137, aux.acc_seg: 88.3356, loss: 0.3907 +2024-06-18 09:59:56,746 - mmseg - INFO - Iter [25750/80000] lr: 2.713e-05, eta: 22:13:52, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2869, decode.acc_seg: 88.4634, aux.loss_ce: 0.1173, aux.acc_seg: 88.1769, loss: 0.4042 +2024-06-18 10:01:02,966 - mmseg - INFO - Iter [25800/80000] lr: 2.710e-05, eta: 22:12:22, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2714, decode.acc_seg: 89.0547, aux.loss_ce: 0.1113, aux.acc_seg: 88.8645, loss: 0.3827 +2024-06-18 10:02:09,069 - mmseg - INFO - Iter [25850/80000] lr: 2.708e-05, eta: 22:10:53, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2910, decode.acc_seg: 88.1887, aux.loss_ce: 0.1187, aux.acc_seg: 87.9799, loss: 0.4097 +2024-06-18 10:03:15,242 - mmseg - INFO - Iter [25900/80000] lr: 2.705e-05, eta: 22:09:23, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2753, decode.acc_seg: 88.6211, aux.loss_ce: 0.1123, aux.acc_seg: 88.5576, loss: 0.3876 +2024-06-18 10:04:21,610 - mmseg - INFO - Iter [25950/80000] lr: 2.703e-05, eta: 22:07:54, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2830, decode.acc_seg: 88.5150, aux.loss_ce: 0.1149, aux.acc_seg: 88.0968, loss: 0.3979 +2024-06-18 10:05:27,677 - mmseg - INFO - Saving checkpoint at 26000 iterations +2024-06-18 10:07:09,079 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 10:07:09,079 - mmseg - INFO - Iter [26000/80000] lr: 2.700e-05, eta: 22:09:55, time: 3.349, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2702, decode.acc_seg: 88.9743, aux.loss_ce: 0.1101, aux.acc_seg: 88.7615, loss: 0.3803 +2024-06-18 10:08:45,944 - mmseg - INFO - per class results: +2024-06-18 10:08:45,955 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.97 | 88.84 | +| building | 84.72 | 93.58 | +| sky | 94.81 | 97.47 | +| floor | 85.52 | 91.31 | +| tree | 77.41 | 89.12 | +| ceiling | 87.09 | 92.61 | +| road | 86.68 | 93.13 | +| bed | 92.01 | 96.31 | +| windowpane | 65.45 | 78.03 | +| grass | 63.49 | 73.44 | +| cabinet | 65.26 | 76.11 | +| sidewalk | 71.91 | 83.83 | +| person | 84.75 | 92.99 | +| earth | 39.17 | 57.1 | +| door | 58.35 | 77.07 | +| table | 65.9 | 78.38 | +| mountain | 59.69 | 72.19 | +| plant | 56.73 | 67.32 | +| curtain | 75.51 | 90.04 | +| chair | 64.66 | 76.07 | +| car | 85.79 | 93.63 | +| water | 56.77 | 68.8 | +| painting | 76.87 | 90.82 | +| sofa | 80.1 | 91.19 | +| shelf | 45.02 | 62.32 | +| house | 52.95 | 68.63 | +| sea | 64.54 | 80.03 | +| mirror | 76.86 | 85.57 | +| rug | 71.1 | 82.99 | +| field | 31.71 | 59.04 | +| armchair | 59.25 | 75.67 | +| seat | 66.57 | 88.3 | +| fence | 50.02 | 66.33 | +| desk | 55.45 | 75.17 | +| rock | 49.67 | 80.26 | +| wardrobe | 52.56 | 68.28 | +| lamp | 71.16 | 81.33 | +| bathtub | 81.91 | 85.25 | +| railing | 40.23 | 63.34 | +| cushion | 61.15 | 66.87 | +| base | 40.05 | 55.98 | +| box | 32.84 | 49.36 | +| column | 54.01 | 66.32 | +| signboard | 40.32 | 53.55 | +| chest of drawers | 43.64 | 63.81 | +| counter | 39.69 | 58.85 | +| sand | 43.13 | 58.22 | +| sink | 75.2 | 82.6 | +| skyscraper | 49.69 | 61.31 | +| fireplace | 70.81 | 95.34 | +| refrigerator | 79.03 | 86.63 | +| grandstand | 48.4 | 86.58 | +| path | 28.65 | 43.66 | +| stairs | 31.24 | 40.75 | +| runway | 63.92 | 82.45 | +| case | 50.74 | 67.96 | +| pool table | 94.59 | 97.44 | +| pillow | 67.99 | 84.74 | +| screen door | 79.32 | 82.69 | +| stairway | 43.72 | 52.18 | +| river | 13.6 | 51.74 | +| bridge | 75.24 | 90.94 | +| bookcase | 34.44 | 49.54 | +| blind | 48.68 | 60.64 | +| coffee table | 62.17 | 89.41 | +| toilet | 87.47 | 92.96 | +| flower | 37.87 | 42.33 | +| book | 52.18 | 70.02 | +| hill | 6.01 | 8.5 | +| bench | 52.78 | 61.91 | +| countertop | 62.68 | 83.17 | +| stove | 83.92 | 90.61 | +| palm | 56.42 | 76.61 | +| kitchen island | 44.12 | 90.54 | +| computer | 77.64 | 93.3 | +| swivel chair | 50.61 | 73.05 | +| boat | 57.26 | 87.06 | +| bar | 53.81 | 72.75 | +| arcade machine | 83.44 | 95.29 | +| hovel | 40.97 | 48.66 | +| bus | 92.86 | 95.83 | +| towel | 72.33 | 82.03 | +| light | 59.63 | 71.09 | +| truck | 38.18 | 50.15 | +| tower | 26.51 | 45.2 | +| chandelier | 70.7 | 87.86 | +| awning | 44.84 | 52.71 | +| streetlight | 29.83 | 38.85 | +| booth | 49.55 | 69.6 | +| television receiver | 66.72 | 89.8 | +| airplane | 65.67 | 73.57 | +| dirt track | 27.49 | 64.53 | +| apparel | 39.31 | 49.65 | +| pole | 19.79 | 23.3 | +| land | 0.0 | 0.0 | +| bannister | 15.39 | 24.8 | +| escalator | 55.08 | 79.77 | +| ottoman | 50.77 | 74.58 | +| bottle | 25.4 | 31.33 | +| buffet | 50.18 | 67.99 | +| poster | 28.65 | 48.2 | +| stage | 25.6 | 55.75 | +| van | 43.49 | 56.05 | +| ship | 89.02 | 97.69 | +| fountain | 20.61 | 20.92 | +| conveyer belt | 71.02 | 93.95 | +| canopy | 47.93 | 51.94 | +| washer | 68.38 | 78.08 | +| plaything | 18.55 | 25.74 | +| swimming pool | 48.14 | 48.69 | +| stool | 51.17 | 65.01 | +| barrel | 53.23 | 64.82 | +| basket | 36.0 | 54.91 | +| waterfall | 68.22 | 83.5 | +| tent | 94.81 | 98.22 | +| bag | 17.52 | 20.15 | +| minibike | 68.45 | 88.36 | +| cradle | 81.22 | 98.02 | +| oven | 59.74 | 81.17 | +| ball | 31.31 | 32.28 | +| food | 62.36 | 79.5 | +| step | 11.69 | 14.35 | +| tank | 62.41 | 64.7 | +| trade name | 36.57 | 45.4 | +| microwave | 89.98 | 93.71 | +| pot | 55.37 | 67.27 | +| animal | 62.66 | 64.3 | +| bicycle | 60.04 | 76.92 | +| lake | 0.04 | 0.04 | +| dishwasher | 60.93 | 81.7 | +| screen | 59.78 | 93.09 | +| blanket | 25.09 | 28.32 | +| sculpture | 71.12 | 78.72 | +| hood | 59.36 | 71.84 | +| sconce | 49.66 | 54.3 | +| vase | 42.96 | 52.94 | +| traffic light | 28.57 | 61.28 | +| tray | 9.47 | 11.9 | +| ashcan | 42.65 | 62.16 | +| fan | 64.49 | 76.73 | +| pier | 37.83 | 55.43 | +| crt screen | 4.94 | 12.71 | +| plate | 55.36 | 78.85 | +| monitor | 22.31 | 24.82 | +| bulletin board | 52.76 | 54.77 | +| shower | 0.0 | 0.0 | +| radiator | 61.2 | 81.65 | +| glass | 16.46 | 17.59 | +| clock | 37.76 | 45.27 | +| flag | 70.02 | 77.28 | ++---------------------+-------+-------+ +2024-06-18 10:08:45,955 - mmseg - INFO - Summary: +2024-06-18 10:08:45,955 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 85.23 | 53.9 | 67.08 | ++-------+------+-------+ +2024-06-18 10:08:45,956 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 10:08:45,957 - mmseg - INFO - Iter(val) [250] aAcc: 0.8523, mIoU: 0.5390, mAcc: 0.6708, IoU.wall: 0.8097, IoU.building: 0.8472, IoU.sky: 0.9481, IoU.floor: 0.8552, IoU.tree: 0.7741, IoU.ceiling: 0.8709, IoU.road: 0.8668, IoU.bed : 0.9201, IoU.windowpane: 0.6545, IoU.grass: 0.6349, IoU.cabinet: 0.6526, IoU.sidewalk: 0.7191, IoU.person: 0.8475, IoU.earth: 0.3917, IoU.door: 0.5835, IoU.table: 0.6590, IoU.mountain: 0.5969, IoU.plant: 0.5673, IoU.curtain: 0.7551, IoU.chair: 0.6466, IoU.car: 0.8579, IoU.water: 0.5677, IoU.painting: 0.7687, IoU.sofa: 0.8010, IoU.shelf: 0.4502, IoU.house: 0.5295, IoU.sea: 0.6454, IoU.mirror: 0.7686, IoU.rug: 0.7110, IoU.field: 0.3171, IoU.armchair: 0.5925, IoU.seat: 0.6657, IoU.fence: 0.5002, IoU.desk: 0.5545, IoU.rock: 0.4967, IoU.wardrobe: 0.5256, IoU.lamp: 0.7116, IoU.bathtub: 0.8191, IoU.railing: 0.4023, IoU.cushion: 0.6115, IoU.base: 0.4005, IoU.box: 0.3284, IoU.column: 0.5401, IoU.signboard: 0.4032, IoU.chest of drawers: 0.4364, IoU.counter: 0.3969, IoU.sand: 0.4313, IoU.sink: 0.7520, IoU.skyscraper: 0.4969, IoU.fireplace: 0.7081, IoU.refrigerator: 0.7903, IoU.grandstand: 0.4840, IoU.path: 0.2865, IoU.stairs: 0.3124, IoU.runway: 0.6392, IoU.case: 0.5074, IoU.pool table: 0.9459, IoU.pillow: 0.6799, IoU.screen door: 0.7932, IoU.stairway: 0.4372, IoU.river: 0.1360, IoU.bridge: 0.7524, IoU.bookcase: 0.3444, IoU.blind: 0.4868, IoU.coffee table: 0.6217, IoU.toilet: 0.8747, IoU.flower: 0.3787, IoU.book: 0.5218, IoU.hill: 0.0601, IoU.bench: 0.5278, IoU.countertop: 0.6268, IoU.stove: 0.8392, IoU.palm: 0.5642, IoU.kitchen island: 0.4412, IoU.computer: 0.7764, IoU.swivel chair: 0.5061, IoU.boat: 0.5726, IoU.bar: 0.5381, IoU.arcade machine: 0.8344, IoU.hovel: 0.4097, IoU.bus: 0.9286, IoU.towel: 0.7233, IoU.light: 0.5963, IoU.truck: 0.3818, IoU.tower: 0.2651, IoU.chandelier: 0.7070, IoU.awning: 0.4484, IoU.streetlight: 0.2983, IoU.booth: 0.4955, IoU.television receiver: 0.6672, IoU.airplane: 0.6567, IoU.dirt track: 0.2749, IoU.apparel: 0.3931, IoU.pole: 0.1979, IoU.land: 0.0000, IoU.bannister: 0.1539, IoU.escalator: 0.5508, IoU.ottoman: 0.5077, IoU.bottle: 0.2540, IoU.buffet: 0.5018, IoU.poster: 0.2865, IoU.stage: 0.2560, IoU.van: 0.4349, IoU.ship: 0.8902, IoU.fountain: 0.2061, IoU.conveyer belt: 0.7102, IoU.canopy: 0.4793, IoU.washer: 0.6838, IoU.plaything: 0.1855, IoU.swimming pool: 0.4814, IoU.stool: 0.5117, IoU.barrel: 0.5323, IoU.basket: 0.3600, IoU.waterfall: 0.6822, IoU.tent: 0.9481, IoU.bag: 0.1752, IoU.minibike: 0.6845, IoU.cradle: 0.8122, IoU.oven: 0.5974, IoU.ball: 0.3131, IoU.food: 0.6236, IoU.step: 0.1169, IoU.tank: 0.6241, IoU.trade name: 0.3657, IoU.microwave: 0.8998, IoU.pot: 0.5537, IoU.animal: 0.6266, IoU.bicycle: 0.6004, IoU.lake: 0.0004, IoU.dishwasher: 0.6093, IoU.screen: 0.5978, IoU.blanket: 0.2509, IoU.sculpture: 0.7112, IoU.hood: 0.5936, IoU.sconce: 0.4966, IoU.vase: 0.4296, IoU.traffic light: 0.2857, IoU.tray: 0.0947, IoU.ashcan: 0.4265, IoU.fan: 0.6449, IoU.pier: 0.3783, IoU.crt screen: 0.0494, IoU.plate: 0.5536, IoU.monitor: 0.2231, IoU.bulletin board: 0.5276, IoU.shower: 0.0000, IoU.radiator: 0.6120, IoU.glass: 0.1646, IoU.clock: 0.3776, IoU.flag: 0.7002, Acc.wall: 0.8884, Acc.building: 0.9358, Acc.sky: 0.9747, Acc.floor: 0.9131, Acc.tree: 0.8912, Acc.ceiling: 0.9261, Acc.road: 0.9313, Acc.bed : 0.9631, Acc.windowpane: 0.7803, Acc.grass: 0.7344, Acc.cabinet: 0.7611, Acc.sidewalk: 0.8383, Acc.person: 0.9299, Acc.earth: 0.5710, Acc.door: 0.7707, Acc.table: 0.7838, Acc.mountain: 0.7219, Acc.plant: 0.6732, Acc.curtain: 0.9004, Acc.chair: 0.7607, Acc.car: 0.9363, Acc.water: 0.6880, Acc.painting: 0.9082, Acc.sofa: 0.9119, Acc.shelf: 0.6232, Acc.house: 0.6863, Acc.sea: 0.8003, Acc.mirror: 0.8557, Acc.rug: 0.8299, Acc.field: 0.5904, Acc.armchair: 0.7567, Acc.seat: 0.8830, Acc.fence: 0.6633, Acc.desk: 0.7517, Acc.rock: 0.8026, Acc.wardrobe: 0.6828, Acc.lamp: 0.8133, Acc.bathtub: 0.8525, Acc.railing: 0.6334, Acc.cushion: 0.6687, Acc.base: 0.5598, Acc.box: 0.4936, Acc.column: 0.6632, Acc.signboard: 0.5355, Acc.chest of drawers: 0.6381, Acc.counter: 0.5885, Acc.sand: 0.5822, Acc.sink: 0.8260, Acc.skyscraper: 0.6131, Acc.fireplace: 0.9534, Acc.refrigerator: 0.8663, Acc.grandstand: 0.8658, Acc.path: 0.4366, Acc.stairs: 0.4075, Acc.runway: 0.8245, Acc.case: 0.6796, Acc.pool table: 0.9744, Acc.pillow: 0.8474, Acc.screen door: 0.8269, Acc.stairway: 0.5218, Acc.river: 0.5174, Acc.bridge: 0.9094, Acc.bookcase: 0.4954, Acc.blind: 0.6064, Acc.coffee table: 0.8941, Acc.toilet: 0.9296, Acc.flower: 0.4233, Acc.book: 0.7002, Acc.hill: 0.0850, Acc.bench: 0.6191, Acc.countertop: 0.8317, Acc.stove: 0.9061, Acc.palm: 0.7661, Acc.kitchen island: 0.9054, Acc.computer: 0.9330, Acc.swivel chair: 0.7305, Acc.boat: 0.8706, Acc.bar: 0.7275, Acc.arcade machine: 0.9529, Acc.hovel: 0.4866, Acc.bus: 0.9583, Acc.towel: 0.8203, Acc.light: 0.7109, Acc.truck: 0.5015, Acc.tower: 0.4520, Acc.chandelier: 0.8786, Acc.awning: 0.5271, Acc.streetlight: 0.3885, Acc.booth: 0.6960, Acc.television receiver: 0.8980, Acc.airplane: 0.7357, Acc.dirt track: 0.6453, Acc.apparel: 0.4965, Acc.pole: 0.2330, Acc.land: 0.0000, Acc.bannister: 0.2480, Acc.escalator: 0.7977, Acc.ottoman: 0.7458, Acc.bottle: 0.3133, Acc.buffet: 0.6799, Acc.poster: 0.4820, Acc.stage: 0.5575, Acc.van: 0.5605, Acc.ship: 0.9769, Acc.fountain: 0.2092, Acc.conveyer belt: 0.9395, Acc.canopy: 0.5194, Acc.washer: 0.7808, Acc.plaything: 0.2574, Acc.swimming pool: 0.4869, Acc.stool: 0.6501, Acc.barrel: 0.6482, Acc.basket: 0.5491, Acc.waterfall: 0.8350, Acc.tent: 0.9822, Acc.bag: 0.2015, Acc.minibike: 0.8836, Acc.cradle: 0.9802, Acc.oven: 0.8117, Acc.ball: 0.3228, Acc.food: 0.7950, Acc.step: 0.1435, Acc.tank: 0.6470, Acc.trade name: 0.4540, Acc.microwave: 0.9371, Acc.pot: 0.6727, Acc.animal: 0.6430, Acc.bicycle: 0.7692, Acc.lake: 0.0004, Acc.dishwasher: 0.8170, Acc.screen: 0.9309, Acc.blanket: 0.2832, Acc.sculpture: 0.7872, Acc.hood: 0.7184, Acc.sconce: 0.5430, Acc.vase: 0.5294, Acc.traffic light: 0.6128, Acc.tray: 0.1190, Acc.ashcan: 0.6216, Acc.fan: 0.7673, Acc.pier: 0.5543, Acc.crt screen: 0.1271, Acc.plate: 0.7885, Acc.monitor: 0.2482, Acc.bulletin board: 0.5477, Acc.shower: 0.0000, Acc.radiator: 0.8165, Acc.glass: 0.1759, Acc.clock: 0.4527, Acc.flag: 0.7728 +2024-06-18 10:09:52,763 - mmseg - INFO - Iter [26050/80000] lr: 2.698e-05, eta: 22:11:47, time: 3.274, data_time: 1.954, memory: 70498, decode.loss_ce: 0.2743, decode.acc_seg: 88.4642, aux.loss_ce: 0.1122, aux.acc_seg: 88.1582, loss: 0.3864 +2024-06-18 10:10:58,890 - mmseg - INFO - Iter [26100/80000] lr: 2.695e-05, eta: 22:10:17, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2843, decode.acc_seg: 88.1810, aux.loss_ce: 0.1160, aux.acc_seg: 87.9712, loss: 0.4004 +2024-06-18 10:12:05,135 - mmseg - INFO - Iter [26150/80000] lr: 2.693e-05, eta: 22:08:47, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2803, decode.acc_seg: 88.7199, aux.loss_ce: 0.1141, aux.acc_seg: 88.4132, loss: 0.3945 +2024-06-18 10:13:11,608 - mmseg - INFO - Iter [26200/80000] lr: 2.690e-05, eta: 22:07:17, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2880, decode.acc_seg: 88.5749, aux.loss_ce: 0.1178, aux.acc_seg: 88.2740, loss: 0.4059 +2024-06-18 10:14:17,860 - mmseg - INFO - Iter [26250/80000] lr: 2.688e-05, eta: 22:05:47, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2824, decode.acc_seg: 88.0960, aux.loss_ce: 0.1154, aux.acc_seg: 87.9331, loss: 0.3978 +2024-06-18 10:15:24,360 - mmseg - INFO - Iter [26300/80000] lr: 2.685e-05, eta: 22:04:18, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2730, decode.acc_seg: 88.7981, aux.loss_ce: 0.1121, aux.acc_seg: 88.4395, loss: 0.3851 +2024-06-18 10:16:30,411 - mmseg - INFO - Iter [26350/80000] lr: 2.683e-05, eta: 22:02:48, time: 1.321, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2854, decode.acc_seg: 88.1104, aux.loss_ce: 0.1162, aux.acc_seg: 87.8427, loss: 0.4016 +2024-06-18 10:17:36,669 - mmseg - INFO - Iter [26400/80000] lr: 2.680e-05, eta: 22:01:18, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.3009, decode.acc_seg: 87.7204, aux.loss_ce: 0.1229, aux.acc_seg: 87.4609, loss: 0.4238 +2024-06-18 10:18:42,861 - mmseg - INFO - Iter [26450/80000] lr: 2.678e-05, eta: 21:59:49, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.3051, decode.acc_seg: 88.0625, aux.loss_ce: 0.1252, aux.acc_seg: 87.6800, loss: 0.4303 +2024-06-18 10:19:49,153 - mmseg - INFO - Iter [26500/80000] lr: 2.675e-05, eta: 21:58:19, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2937, decode.acc_seg: 88.3928, aux.loss_ce: 0.1185, aux.acc_seg: 88.2284, loss: 0.4123 +2024-06-18 10:20:57,665 - mmseg - INFO - Iter [26550/80000] lr: 2.673e-05, eta: 21:56:54, time: 1.370, data_time: 0.051, memory: 70498, decode.loss_ce: 0.2954, decode.acc_seg: 87.8266, aux.loss_ce: 0.1200, aux.acc_seg: 87.5766, loss: 0.4155 +2024-06-18 10:22:03,701 - mmseg - INFO - Iter [26600/80000] lr: 2.670e-05, eta: 21:55:25, time: 1.321, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2737, decode.acc_seg: 88.8720, aux.loss_ce: 0.1112, aux.acc_seg: 88.6786, loss: 0.3849 +2024-06-18 10:23:10,063 - mmseg - INFO - Iter [26650/80000] lr: 2.668e-05, eta: 21:53:56, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2798, decode.acc_seg: 88.4796, aux.loss_ce: 0.1137, aux.acc_seg: 88.4160, loss: 0.3935 +2024-06-18 10:24:16,354 - mmseg - INFO - Iter [26700/80000] lr: 2.665e-05, eta: 21:52:27, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2770, decode.acc_seg: 89.0060, aux.loss_ce: 0.1131, aux.acc_seg: 88.7623, loss: 0.3901 +2024-06-18 10:25:22,907 - mmseg - INFO - Iter [26750/80000] lr: 2.663e-05, eta: 21:50:58, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2831, decode.acc_seg: 88.6462, aux.loss_ce: 0.1162, aux.acc_seg: 88.3310, loss: 0.3994 +2024-06-18 10:26:29,420 - mmseg - INFO - Iter [26800/80000] lr: 2.660e-05, eta: 21:49:30, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2842, decode.acc_seg: 88.7209, aux.loss_ce: 0.1169, aux.acc_seg: 88.4177, loss: 0.4010 +2024-06-18 10:27:35,830 - mmseg - INFO - Iter [26850/80000] lr: 2.658e-05, eta: 21:48:01, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2883, decode.acc_seg: 88.2489, aux.loss_ce: 0.1167, aux.acc_seg: 88.0958, loss: 0.4050 +2024-06-18 10:28:42,009 - mmseg - INFO - Iter [26900/80000] lr: 2.655e-05, eta: 21:46:32, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2676, decode.acc_seg: 88.7402, aux.loss_ce: 0.1095, aux.acc_seg: 88.4709, loss: 0.3771 +2024-06-18 10:29:48,314 - mmseg - INFO - Iter [26950/80000] lr: 2.653e-05, eta: 21:45:04, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2776, decode.acc_seg: 88.4409, aux.loss_ce: 0.1132, aux.acc_seg: 88.2506, loss: 0.3909 +2024-06-18 10:30:54,701 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 10:30:54,702 - mmseg - INFO - Iter [27000/80000] lr: 2.650e-05, eta: 21:43:35, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2810, decode.acc_seg: 88.9851, aux.loss_ce: 0.1150, aux.acc_seg: 88.7323, loss: 0.3960 +2024-06-18 10:32:34,760 - mmseg - INFO - per class results: +2024-06-18 10:32:34,766 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.94 | 87.78 | +| building | 84.07 | 92.21 | +| sky | 94.82 | 97.18 | +| floor | 84.76 | 90.27 | +| tree | 77.26 | 90.32 | +| ceiling | 85.29 | 91.81 | +| road | 86.36 | 90.12 | +| bed | 91.51 | 97.71 | +| windowpane | 65.36 | 78.61 | +| grass | 66.73 | 82.13 | +| cabinet | 64.0 | 73.04 | +| sidewalk | 70.89 | 89.05 | +| person | 84.63 | 92.95 | +| earth | 34.96 | 47.09 | +| door | 56.76 | 74.45 | +| table | 66.16 | 79.89 | +| mountain | 67.02 | 79.11 | +| plant | 56.4 | 66.69 | +| curtain | 78.63 | 91.64 | +| chair | 63.84 | 77.48 | +| car | 85.23 | 92.26 | +| water | 63.03 | 76.23 | +| painting | 78.36 | 88.95 | +| sofa | 78.01 | 93.53 | +| shelf | 45.08 | 69.45 | +| house | 55.6 | 76.27 | +| sea | 68.06 | 81.72 | +| mirror | 74.98 | 82.13 | +| rug | 71.56 | 83.36 | +| field | 38.17 | 58.99 | +| armchair | 56.17 | 68.64 | +| seat | 64.68 | 86.65 | +| fence | 45.95 | 60.78 | +| desk | 57.72 | 77.23 | +| rock | 60.92 | 87.56 | +| wardrobe | 54.43 | 79.04 | +| lamp | 71.32 | 85.6 | +| bathtub | 82.11 | 85.48 | +| railing | 36.71 | 48.79 | +| cushion | 66.24 | 78.92 | +| base | 28.83 | 79.43 | +| box | 30.12 | 37.69 | +| column | 54.5 | 67.95 | +| signboard | 40.4 | 55.13 | +| chest of drawers | 45.64 | 62.84 | +| counter | 54.27 | 68.99 | +| sand | 54.89 | 77.31 | +| sink | 70.32 | 86.65 | +| skyscraper | 48.26 | 66.75 | +| fireplace | 75.09 | 89.66 | +| refrigerator | 72.17 | 91.06 | +| grandstand | 51.63 | 82.98 | +| path | 28.92 | 40.27 | +| stairs | 33.52 | 45.39 | +| runway | 72.66 | 95.05 | +| case | 61.44 | 79.67 | +| pool table | 94.31 | 97.71 | +| pillow | 64.03 | 71.79 | +| screen door | 84.44 | 90.98 | +| stairway | 45.78 | 58.33 | +| river | 11.6 | 27.17 | +| bridge | 68.54 | 88.76 | +| bookcase | 41.88 | 61.05 | +| blind | 46.5 | 53.44 | +| coffee table | 60.27 | 90.25 | +| toilet | 88.09 | 92.86 | +| flower | 44.35 | 55.7 | +| book | 54.38 | 73.2 | +| hill | 4.74 | 13.73 | +| bench | 47.93 | 60.09 | +| countertop | 64.52 | 78.96 | +| stove | 82.84 | 87.86 | +| palm | 53.84 | 78.52 | +| kitchen island | 38.67 | 64.26 | +| computer | 75.78 | 95.26 | +| swivel chair | 46.0 | 77.75 | +| boat | 57.06 | 83.24 | +| bar | 63.85 | 72.43 | +| arcade machine | 76.03 | 87.99 | +| hovel | 10.93 | 11.15 | +| bus | 91.66 | 95.49 | +| towel | 70.04 | 76.97 | +| light | 60.21 | 71.17 | +| truck | 47.33 | 67.2 | +| tower | 19.23 | 39.03 | +| chandelier | 71.35 | 88.9 | +| awning | 43.76 | 60.44 | +| streetlight | 34.28 | 46.3 | +| booth | 52.23 | 72.0 | +| television receiver | 76.28 | 89.56 | +| airplane | 62.22 | 68.58 | +| dirt track | 28.91 | 31.05 | +| apparel | 50.62 | 71.38 | +| pole | 27.94 | 42.93 | +| land | 3.56 | 6.31 | +| bannister | 17.04 | 22.82 | +| escalator | 54.76 | 81.38 | +| ottoman | 52.84 | 72.22 | +| bottle | 38.84 | 53.78 | +| buffet | 56.66 | 84.47 | +| poster | 33.44 | 45.84 | +| stage | 24.21 | 42.54 | +| van | 43.04 | 60.55 | +| ship | 81.53 | 86.44 | +| fountain | 15.94 | 16.55 | +| conveyer belt | 77.92 | 92.27 | +| canopy | 42.64 | 74.82 | +| washer | 70.16 | 79.07 | +| plaything | 24.27 | 37.23 | +| swimming pool | 68.1 | 96.34 | +| stool | 55.17 | 70.02 | +| barrel | 49.83 | 64.81 | +| basket | 36.01 | 55.11 | +| waterfall | 73.87 | 93.37 | +| tent | 82.56 | 98.37 | +| bag | 8.77 | 9.31 | +| minibike | 71.41 | 82.62 | +| cradle | 81.45 | 98.2 | +| oven | 63.38 | 73.68 | +| ball | 49.95 | 70.13 | +| food | 66.43 | 79.77 | +| step | 12.03 | 15.19 | +| tank | 71.51 | 95.96 | +| trade name | 18.21 | 19.59 | +| microwave | 89.42 | 94.93 | +| pot | 51.74 | 60.22 | +| animal | 60.86 | 62.13 | +| bicycle | 54.24 | 69.99 | +| lake | 40.61 | 72.24 | +| dishwasher | 66.92 | 77.68 | +| screen | 59.61 | 93.39 | +| blanket | 23.99 | 27.27 | +| sculpture | 73.6 | 80.55 | +| hood | 60.47 | 73.86 | +| sconce | 53.27 | 63.62 | +| vase | 41.07 | 54.11 | +| traffic light | 33.34 | 58.41 | +| tray | 8.95 | 11.01 | +| ashcan | 42.73 | 62.02 | +| fan | 65.04 | 74.02 | +| pier | 34.48 | 42.34 | +| crt screen | 2.31 | 2.4 | +| plate | 54.95 | 79.84 | +| monitor | 69.67 | 83.06 | +| bulletin board | 47.77 | 68.56 | +| shower | 0.8 | 0.8 | +| radiator | 61.17 | 74.88 | +| glass | 17.26 | 18.43 | +| clock | 39.02 | 51.64 | +| flag | 70.26 | 75.88 | ++---------------------+-------+-------+ +2024-06-18 10:32:34,766 - mmseg - INFO - Summary: +2024-06-18 10:32:34,766 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.26 | 55.13 | 69.09 | ++-------+-------+-------+ +2024-06-18 10:32:34,767 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 10:32:34,767 - mmseg - INFO - Iter(val) [250] aAcc: 0.8526, mIoU: 0.5513, mAcc: 0.6909, IoU.wall: 0.8094, IoU.building: 0.8407, IoU.sky: 0.9482, IoU.floor: 0.8476, IoU.tree: 0.7726, IoU.ceiling: 0.8529, IoU.road: 0.8636, IoU.bed : 0.9151, IoU.windowpane: 0.6536, IoU.grass: 0.6673, IoU.cabinet: 0.6400, IoU.sidewalk: 0.7089, IoU.person: 0.8463, IoU.earth: 0.3496, IoU.door: 0.5676, IoU.table: 0.6616, IoU.mountain: 0.6702, IoU.plant: 0.5640, IoU.curtain: 0.7863, IoU.chair: 0.6384, IoU.car: 0.8523, IoU.water: 0.6303, IoU.painting: 0.7836, IoU.sofa: 0.7801, IoU.shelf: 0.4508, IoU.house: 0.5560, IoU.sea: 0.6806, IoU.mirror: 0.7498, IoU.rug: 0.7156, IoU.field: 0.3817, IoU.armchair: 0.5617, IoU.seat: 0.6468, IoU.fence: 0.4595, IoU.desk: 0.5772, IoU.rock: 0.6092, IoU.wardrobe: 0.5443, IoU.lamp: 0.7132, IoU.bathtub: 0.8211, IoU.railing: 0.3671, IoU.cushion: 0.6624, IoU.base: 0.2883, IoU.box: 0.3012, IoU.column: 0.5450, IoU.signboard: 0.4040, IoU.chest of drawers: 0.4564, IoU.counter: 0.5427, IoU.sand: 0.5489, IoU.sink: 0.7032, IoU.skyscraper: 0.4826, IoU.fireplace: 0.7509, IoU.refrigerator: 0.7217, IoU.grandstand: 0.5163, IoU.path: 0.2892, IoU.stairs: 0.3352, IoU.runway: 0.7266, IoU.case: 0.6144, IoU.pool table: 0.9431, IoU.pillow: 0.6403, IoU.screen door: 0.8444, IoU.stairway: 0.4578, IoU.river: 0.1160, IoU.bridge: 0.6854, IoU.bookcase: 0.4188, IoU.blind: 0.4650, IoU.coffee table: 0.6027, IoU.toilet: 0.8809, IoU.flower: 0.4435, IoU.book: 0.5438, IoU.hill: 0.0474, IoU.bench: 0.4793, IoU.countertop: 0.6452, IoU.stove: 0.8284, IoU.palm: 0.5384, IoU.kitchen island: 0.3867, IoU.computer: 0.7578, IoU.swivel chair: 0.4600, IoU.boat: 0.5706, IoU.bar: 0.6385, IoU.arcade machine: 0.7603, IoU.hovel: 0.1093, IoU.bus: 0.9166, IoU.towel: 0.7004, IoU.light: 0.6021, IoU.truck: 0.4733, IoU.tower: 0.1923, IoU.chandelier: 0.7135, IoU.awning: 0.4376, IoU.streetlight: 0.3428, IoU.booth: 0.5223, IoU.television receiver: 0.7628, IoU.airplane: 0.6222, IoU.dirt track: 0.2891, IoU.apparel: 0.5062, IoU.pole: 0.2794, IoU.land: 0.0356, IoU.bannister: 0.1704, IoU.escalator: 0.5476, IoU.ottoman: 0.5284, IoU.bottle: 0.3884, IoU.buffet: 0.5666, IoU.poster: 0.3344, IoU.stage: 0.2421, IoU.van: 0.4304, IoU.ship: 0.8153, IoU.fountain: 0.1594, IoU.conveyer belt: 0.7792, IoU.canopy: 0.4264, IoU.washer: 0.7016, IoU.plaything: 0.2427, IoU.swimming pool: 0.6810, IoU.stool: 0.5517, IoU.barrel: 0.4983, IoU.basket: 0.3601, IoU.waterfall: 0.7387, IoU.tent: 0.8256, IoU.bag: 0.0877, IoU.minibike: 0.7141, IoU.cradle: 0.8145, IoU.oven: 0.6338, IoU.ball: 0.4995, IoU.food: 0.6643, IoU.step: 0.1203, IoU.tank: 0.7151, IoU.trade name: 0.1821, IoU.microwave: 0.8942, IoU.pot: 0.5174, IoU.animal: 0.6086, IoU.bicycle: 0.5424, IoU.lake: 0.4061, IoU.dishwasher: 0.6692, IoU.screen: 0.5961, IoU.blanket: 0.2399, IoU.sculpture: 0.7360, IoU.hood: 0.6047, IoU.sconce: 0.5327, IoU.vase: 0.4107, IoU.traffic light: 0.3334, IoU.tray: 0.0895, IoU.ashcan: 0.4273, IoU.fan: 0.6504, IoU.pier: 0.3448, IoU.crt screen: 0.0231, IoU.plate: 0.5495, IoU.monitor: 0.6967, IoU.bulletin board: 0.4777, IoU.shower: 0.0080, IoU.radiator: 0.6117, IoU.glass: 0.1726, IoU.clock: 0.3902, IoU.flag: 0.7026, Acc.wall: 0.8778, Acc.building: 0.9221, Acc.sky: 0.9718, Acc.floor: 0.9027, Acc.tree: 0.9032, Acc.ceiling: 0.9181, Acc.road: 0.9012, Acc.bed : 0.9771, Acc.windowpane: 0.7861, Acc.grass: 0.8213, Acc.cabinet: 0.7304, Acc.sidewalk: 0.8905, Acc.person: 0.9295, Acc.earth: 0.4709, Acc.door: 0.7445, Acc.table: 0.7989, Acc.mountain: 0.7911, Acc.plant: 0.6669, Acc.curtain: 0.9164, Acc.chair: 0.7748, Acc.car: 0.9226, Acc.water: 0.7623, Acc.painting: 0.8895, Acc.sofa: 0.9353, Acc.shelf: 0.6945, Acc.house: 0.7627, Acc.sea: 0.8172, Acc.mirror: 0.8213, Acc.rug: 0.8336, Acc.field: 0.5899, Acc.armchair: 0.6864, Acc.seat: 0.8665, Acc.fence: 0.6078, Acc.desk: 0.7723, Acc.rock: 0.8756, Acc.wardrobe: 0.7904, Acc.lamp: 0.8560, Acc.bathtub: 0.8548, Acc.railing: 0.4879, Acc.cushion: 0.7892, Acc.base: 0.7943, Acc.box: 0.3769, Acc.column: 0.6795, Acc.signboard: 0.5513, Acc.chest of drawers: 0.6284, Acc.counter: 0.6899, Acc.sand: 0.7731, Acc.sink: 0.8665, Acc.skyscraper: 0.6675, Acc.fireplace: 0.8966, Acc.refrigerator: 0.9106, Acc.grandstand: 0.8298, Acc.path: 0.4027, Acc.stairs: 0.4539, Acc.runway: 0.9505, Acc.case: 0.7967, Acc.pool table: 0.9771, Acc.pillow: 0.7179, Acc.screen door: 0.9098, Acc.stairway: 0.5833, Acc.river: 0.2717, Acc.bridge: 0.8876, Acc.bookcase: 0.6105, Acc.blind: 0.5344, Acc.coffee table: 0.9025, Acc.toilet: 0.9286, Acc.flower: 0.5570, Acc.book: 0.7320, Acc.hill: 0.1373, Acc.bench: 0.6009, Acc.countertop: 0.7896, Acc.stove: 0.8786, Acc.palm: 0.7852, Acc.kitchen island: 0.6426, Acc.computer: 0.9526, Acc.swivel chair: 0.7775, Acc.boat: 0.8324, Acc.bar: 0.7243, Acc.arcade machine: 0.8799, Acc.hovel: 0.1115, Acc.bus: 0.9549, Acc.towel: 0.7697, Acc.light: 0.7117, Acc.truck: 0.6720, Acc.tower: 0.3903, Acc.chandelier: 0.8890, Acc.awning: 0.6044, Acc.streetlight: 0.4630, Acc.booth: 0.7200, Acc.television receiver: 0.8956, Acc.airplane: 0.6858, Acc.dirt track: 0.3105, Acc.apparel: 0.7138, Acc.pole: 0.4293, Acc.land: 0.0631, Acc.bannister: 0.2282, Acc.escalator: 0.8138, Acc.ottoman: 0.7222, Acc.bottle: 0.5378, Acc.buffet: 0.8447, Acc.poster: 0.4584, Acc.stage: 0.4254, Acc.van: 0.6055, Acc.ship: 0.8644, Acc.fountain: 0.1655, Acc.conveyer belt: 0.9227, Acc.canopy: 0.7482, Acc.washer: 0.7907, Acc.plaything: 0.3723, Acc.swimming pool: 0.9634, Acc.stool: 0.7002, Acc.barrel: 0.6481, Acc.basket: 0.5511, Acc.waterfall: 0.9337, Acc.tent: 0.9837, Acc.bag: 0.0931, Acc.minibike: 0.8262, Acc.cradle: 0.9820, Acc.oven: 0.7368, Acc.ball: 0.7013, Acc.food: 0.7977, Acc.step: 0.1519, Acc.tank: 0.9596, Acc.trade name: 0.1959, Acc.microwave: 0.9493, Acc.pot: 0.6022, Acc.animal: 0.6213, Acc.bicycle: 0.6999, Acc.lake: 0.7224, Acc.dishwasher: 0.7768, Acc.screen: 0.9339, Acc.blanket: 0.2727, Acc.sculpture: 0.8055, Acc.hood: 0.7386, Acc.sconce: 0.6362, Acc.vase: 0.5411, Acc.traffic light: 0.5841, Acc.tray: 0.1101, Acc.ashcan: 0.6202, Acc.fan: 0.7402, Acc.pier: 0.4234, Acc.crt screen: 0.0240, Acc.plate: 0.7984, Acc.monitor: 0.8306, Acc.bulletin board: 0.6856, Acc.shower: 0.0080, Acc.radiator: 0.7488, Acc.glass: 0.1843, Acc.clock: 0.5164, Acc.flag: 0.7588 +2024-06-18 10:33:41,280 - mmseg - INFO - Iter [27050/80000] lr: 2.648e-05, eta: 21:45:23, time: 3.332, data_time: 2.017, memory: 70498, decode.loss_ce: 0.2715, decode.acc_seg: 88.9213, aux.loss_ce: 0.1119, aux.acc_seg: 88.5555, loss: 0.3834 +2024-06-18 10:34:47,861 - mmseg - INFO - Iter [27100/80000] lr: 2.645e-05, eta: 21:43:55, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2654, decode.acc_seg: 88.8334, aux.loss_ce: 0.1087, aux.acc_seg: 88.5996, loss: 0.3741 +2024-06-18 10:35:53,940 - mmseg - INFO - Iter [27150/80000] lr: 2.643e-05, eta: 21:42:25, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2825, decode.acc_seg: 88.4154, aux.loss_ce: 0.1144, aux.acc_seg: 88.2183, loss: 0.3969 +2024-06-18 10:37:00,300 - mmseg - INFO - Iter [27200/80000] lr: 2.640e-05, eta: 21:40:57, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2847, decode.acc_seg: 88.1613, aux.loss_ce: 0.1172, aux.acc_seg: 87.8099, loss: 0.4018 +2024-06-18 10:38:06,563 - mmseg - INFO - Iter [27250/80000] lr: 2.638e-05, eta: 21:39:28, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2722, decode.acc_seg: 88.7452, aux.loss_ce: 0.1117, aux.acc_seg: 88.3867, loss: 0.3839 +2024-06-18 10:39:13,022 - mmseg - INFO - Iter [27300/80000] lr: 2.635e-05, eta: 21:38:00, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2668, decode.acc_seg: 88.9498, aux.loss_ce: 0.1092, aux.acc_seg: 88.6943, loss: 0.3760 +2024-06-18 10:40:19,293 - mmseg - INFO - Iter [27350/80000] lr: 2.633e-05, eta: 21:36:31, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2685, decode.acc_seg: 88.8315, aux.loss_ce: 0.1100, aux.acc_seg: 88.5219, loss: 0.3784 +2024-06-18 10:41:25,605 - mmseg - INFO - Iter [27400/80000] lr: 2.630e-05, eta: 21:35:03, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2751, decode.acc_seg: 88.9133, aux.loss_ce: 0.1137, aux.acc_seg: 88.5427, loss: 0.3888 +2024-06-18 10:42:32,031 - mmseg - INFO - Iter [27450/80000] lr: 2.628e-05, eta: 21:33:35, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2905, decode.acc_seg: 88.1950, aux.loss_ce: 0.1176, aux.acc_seg: 88.0121, loss: 0.4081 +2024-06-18 10:43:38,258 - mmseg - INFO - Iter [27500/80000] lr: 2.625e-05, eta: 21:32:06, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2718, decode.acc_seg: 88.6330, aux.loss_ce: 0.1104, aux.acc_seg: 88.4641, loss: 0.3823 +2024-06-18 10:44:44,516 - mmseg - INFO - Iter [27550/80000] lr: 2.623e-05, eta: 21:30:38, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2972, decode.acc_seg: 88.0468, aux.loss_ce: 0.1207, aux.acc_seg: 87.9069, loss: 0.4179 +2024-06-18 10:45:50,596 - mmseg - INFO - Iter [27600/80000] lr: 2.620e-05, eta: 21:29:10, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2791, decode.acc_seg: 88.5360, aux.loss_ce: 0.1131, aux.acc_seg: 88.4751, loss: 0.3923 +2024-06-18 10:46:56,616 - mmseg - INFO - Iter [27650/80000] lr: 2.618e-05, eta: 21:27:41, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2863, decode.acc_seg: 88.8596, aux.loss_ce: 0.1178, aux.acc_seg: 88.4233, loss: 0.4041 +2024-06-18 10:48:02,934 - mmseg - INFO - Iter [27700/80000] lr: 2.615e-05, eta: 21:26:13, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2718, decode.acc_seg: 89.2565, aux.loss_ce: 0.1117, aux.acc_seg: 89.0370, loss: 0.3835 +2024-06-18 10:49:09,028 - mmseg - INFO - Iter [27750/80000] lr: 2.613e-05, eta: 21:24:45, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2810, decode.acc_seg: 88.3223, aux.loss_ce: 0.1150, aux.acc_seg: 88.1184, loss: 0.3960 +2024-06-18 10:50:19,702 - mmseg - INFO - Iter [27800/80000] lr: 2.610e-05, eta: 21:23:25, time: 1.413, data_time: 0.100, memory: 70498, decode.loss_ce: 0.2552, decode.acc_seg: 89.5978, aux.loss_ce: 0.1048, aux.acc_seg: 89.2346, loss: 0.3600 +2024-06-18 10:51:25,942 - mmseg - INFO - Iter [27850/80000] lr: 2.608e-05, eta: 21:21:57, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2626, decode.acc_seg: 89.2838, aux.loss_ce: 0.1075, aux.acc_seg: 88.9703, loss: 0.3701 +2024-06-18 10:52:31,787 - mmseg - INFO - Iter [27900/80000] lr: 2.605e-05, eta: 21:20:29, time: 1.317, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2731, decode.acc_seg: 88.8261, aux.loss_ce: 0.1116, aux.acc_seg: 88.5932, loss: 0.3847 +2024-06-18 10:53:37,822 - mmseg - INFO - Iter [27950/80000] lr: 2.603e-05, eta: 21:19:01, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2823, decode.acc_seg: 88.6827, aux.loss_ce: 0.1167, aux.acc_seg: 88.4085, loss: 0.3989 +2024-06-18 10:54:44,281 - mmseg - INFO - Saving checkpoint at 28000 iterations +2024-06-18 10:56:25,917 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 10:56:25,917 - mmseg - INFO - Iter [28000/80000] lr: 2.600e-05, eta: 21:20:42, time: 3.362, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2630, decode.acc_seg: 89.1071, aux.loss_ce: 0.1081, aux.acc_seg: 88.7791, loss: 0.3710 +2024-06-18 10:58:02,921 - mmseg - INFO - per class results: +2024-06-18 10:58:02,927 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.94 | 89.57 | +| building | 85.31 | 92.93 | +| sky | 94.78 | 97.12 | +| floor | 84.4 | 90.95 | +| tree | 76.92 | 91.14 | +| ceiling | 85.98 | 91.57 | +| road | 86.4 | 91.03 | +| bed | 92.08 | 96.3 | +| windowpane | 65.87 | 77.26 | +| grass | 63.51 | 76.14 | +| cabinet | 64.9 | 75.97 | +| sidewalk | 71.02 | 85.76 | +| person | 85.05 | 93.08 | +| earth | 33.47 | 45.48 | +| door | 58.88 | 76.46 | +| table | 65.71 | 82.68 | +| mountain | 64.37 | 82.79 | +| plant | 55.56 | 66.55 | +| curtain | 76.99 | 90.17 | +| chair | 65.01 | 75.27 | +| car | 85.51 | 91.06 | +| water | 61.01 | 72.5 | +| painting | 76.36 | 90.77 | +| sofa | 80.61 | 88.07 | +| shelf | 45.73 | 62.75 | +| house | 61.68 | 81.0 | +| sea | 70.53 | 83.69 | +| mirror | 75.54 | 82.33 | +| rug | 68.19 | 83.9 | +| field | 38.42 | 75.52 | +| armchair | 58.17 | 76.21 | +| seat | 66.85 | 87.59 | +| fence | 50.47 | 70.12 | +| desk | 53.22 | 77.05 | +| rock | 54.59 | 69.6 | +| wardrobe | 57.65 | 78.7 | +| lamp | 71.07 | 81.4 | +| bathtub | 82.48 | 85.72 | +| railing | 39.53 | 61.31 | +| cushion | 66.96 | 77.83 | +| base | 35.89 | 46.62 | +| box | 37.0 | 48.81 | +| column | 53.61 | 66.4 | +| signboard | 39.73 | 48.82 | +| chest of drawers | 42.22 | 52.69 | +| counter | 31.83 | 36.66 | +| sand | 55.12 | 77.13 | +| sink | 70.41 | 82.25 | +| skyscraper | 53.05 | 64.81 | +| fireplace | 69.16 | 94.16 | +| refrigerator | 78.97 | 91.38 | +| grandstand | 51.51 | 79.27 | +| path | 31.88 | 43.87 | +| stairs | 38.36 | 52.37 | +| runway | 74.41 | 97.61 | +| case | 61.29 | 79.47 | +| pool table | 94.07 | 97.4 | +| pillow | 69.95 | 82.2 | +| screen door | 68.96 | 71.06 | +| stairway | 35.76 | 38.56 | +| river | 19.54 | 46.72 | +| bridge | 76.23 | 88.39 | +| bookcase | 41.93 | 61.66 | +| blind | 44.76 | 52.9 | +| coffee table | 67.84 | 81.94 | +| toilet | 88.41 | 91.65 | +| flower | 39.49 | 54.14 | +| book | 49.9 | 62.3 | +| hill | 3.05 | 5.73 | +| bench | 53.32 | 60.54 | +| countertop | 58.83 | 87.88 | +| stove | 83.78 | 94.75 | +| palm | 53.14 | 80.73 | +| kitchen island | 42.26 | 63.61 | +| computer | 79.37 | 93.95 | +| swivel chair | 49.42 | 77.31 | +| boat | 62.13 | 87.25 | +| bar | 54.23 | 75.67 | +| arcade machine | 71.58 | 76.81 | +| hovel | 46.74 | 54.0 | +| bus | 89.86 | 96.1 | +| towel | 73.04 | 85.53 | +| light | 59.9 | 70.15 | +| truck | 44.1 | 65.65 | +| tower | 21.86 | 37.11 | +| chandelier | 70.45 | 89.32 | +| awning | 46.4 | 65.99 | +| streetlight | 32.72 | 50.67 | +| booth | 50.03 | 68.0 | +| television receiver | 74.38 | 87.35 | +| airplane | 63.17 | 66.68 | +| dirt track | 23.54 | 44.7 | +| apparel | 50.01 | 72.14 | +| pole | 21.73 | 25.93 | +| land | 2.35 | 4.88 | +| bannister | 12.65 | 16.37 | +| escalator | 54.49 | 81.93 | +| ottoman | 52.27 | 63.51 | +| bottle | 39.67 | 61.15 | +| buffet | 52.99 | 65.38 | +| poster | 32.61 | 44.32 | +| stage | 23.75 | 45.69 | +| van | 43.46 | 68.94 | +| ship | 84.05 | 85.86 | +| fountain | 29.08 | 29.55 | +| conveyer belt | 79.82 | 93.26 | +| canopy | 49.35 | 71.26 | +| washer | 69.75 | 83.13 | +| plaything | 36.27 | 57.14 | +| swimming pool | 72.4 | 91.77 | +| stool | 52.29 | 63.89 | +| barrel | 55.52 | 64.33 | +| basket | 35.08 | 49.76 | +| waterfall | 66.34 | 93.1 | +| tent | 90.77 | 97.74 | +| bag | 15.93 | 18.26 | +| minibike | 70.64 | 88.87 | +| cradle | 81.75 | 97.64 | +| oven | 52.02 | 61.96 | +| ball | 46.9 | 49.62 | +| food | 65.19 | 77.54 | +| step | 16.4 | 20.42 | +| tank | 62.9 | 68.8 | +| trade name | 18.24 | 19.1 | +| microwave | 88.34 | 94.71 | +| pot | 56.06 | 72.54 | +| animal | 61.9 | 64.88 | +| bicycle | 58.2 | 76.8 | +| lake | 58.99 | 59.77 | +| dishwasher | 66.21 | 78.62 | +| screen | 60.2 | 94.01 | +| blanket | 29.81 | 34.48 | +| sculpture | 75.2 | 81.12 | +| hood | 61.05 | 73.93 | +| sconce | 51.83 | 70.11 | +| vase | 44.3 | 60.03 | +| traffic light | 32.08 | 59.01 | +| tray | 16.17 | 26.47 | +| ashcan | 41.78 | 60.28 | +| fan | 64.12 | 74.63 | +| pier | 37.64 | 48.43 | +| crt screen | 5.54 | 11.49 | +| plate | 58.98 | 66.83 | +| monitor | 30.21 | 35.84 | +| bulletin board | 56.17 | 57.77 | +| shower | 0.89 | 1.36 | +| radiator | 63.89 | 74.03 | +| glass | 16.43 | 17.22 | +| clock | 35.2 | 39.24 | +| flag | 71.74 | 77.64 | ++---------------------+-------+-------+ +2024-06-18 10:58:02,927 - mmseg - INFO - Summary: +2024-06-18 10:58:02,927 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.48 | 55.53 | 68.44 | ++-------+-------+-------+ +2024-06-18 10:58:02,928 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 10:58:02,928 - mmseg - INFO - Iter(val) [250] aAcc: 0.8548, mIoU: 0.5553, mAcc: 0.6844, IoU.wall: 0.8094, IoU.building: 0.8531, IoU.sky: 0.9478, IoU.floor: 0.8440, IoU.tree: 0.7692, IoU.ceiling: 0.8598, IoU.road: 0.8640, IoU.bed : 0.9208, IoU.windowpane: 0.6587, IoU.grass: 0.6351, IoU.cabinet: 0.6490, IoU.sidewalk: 0.7102, IoU.person: 0.8505, IoU.earth: 0.3347, IoU.door: 0.5888, IoU.table: 0.6571, IoU.mountain: 0.6437, IoU.plant: 0.5556, IoU.curtain: 0.7699, IoU.chair: 0.6501, IoU.car: 0.8551, IoU.water: 0.6101, IoU.painting: 0.7636, IoU.sofa: 0.8061, IoU.shelf: 0.4573, IoU.house: 0.6168, IoU.sea: 0.7053, IoU.mirror: 0.7554, IoU.rug: 0.6819, IoU.field: 0.3842, IoU.armchair: 0.5817, IoU.seat: 0.6685, IoU.fence: 0.5047, IoU.desk: 0.5322, IoU.rock: 0.5459, IoU.wardrobe: 0.5765, IoU.lamp: 0.7107, IoU.bathtub: 0.8248, IoU.railing: 0.3953, IoU.cushion: 0.6696, IoU.base: 0.3589, IoU.box: 0.3700, IoU.column: 0.5361, IoU.signboard: 0.3973, IoU.chest of drawers: 0.4222, IoU.counter: 0.3183, IoU.sand: 0.5512, IoU.sink: 0.7041, IoU.skyscraper: 0.5305, IoU.fireplace: 0.6916, IoU.refrigerator: 0.7897, IoU.grandstand: 0.5151, IoU.path: 0.3188, IoU.stairs: 0.3836, IoU.runway: 0.7441, IoU.case: 0.6129, IoU.pool table: 0.9407, IoU.pillow: 0.6995, IoU.screen door: 0.6896, IoU.stairway: 0.3576, IoU.river: 0.1954, IoU.bridge: 0.7623, IoU.bookcase: 0.4193, IoU.blind: 0.4476, IoU.coffee table: 0.6784, IoU.toilet: 0.8841, IoU.flower: 0.3949, IoU.book: 0.4990, IoU.hill: 0.0305, IoU.bench: 0.5332, IoU.countertop: 0.5883, IoU.stove: 0.8378, IoU.palm: 0.5314, IoU.kitchen island: 0.4226, IoU.computer: 0.7937, IoU.swivel chair: 0.4942, IoU.boat: 0.6213, IoU.bar: 0.5423, IoU.arcade machine: 0.7158, IoU.hovel: 0.4674, IoU.bus: 0.8986, IoU.towel: 0.7304, IoU.light: 0.5990, IoU.truck: 0.4410, IoU.tower: 0.2186, IoU.chandelier: 0.7045, IoU.awning: 0.4640, IoU.streetlight: 0.3272, IoU.booth: 0.5003, IoU.television receiver: 0.7438, IoU.airplane: 0.6317, IoU.dirt track: 0.2354, IoU.apparel: 0.5001, IoU.pole: 0.2173, IoU.land: 0.0235, IoU.bannister: 0.1265, IoU.escalator: 0.5449, IoU.ottoman: 0.5227, IoU.bottle: 0.3967, IoU.buffet: 0.5299, IoU.poster: 0.3261, IoU.stage: 0.2375, IoU.van: 0.4346, IoU.ship: 0.8405, IoU.fountain: 0.2908, IoU.conveyer belt: 0.7982, IoU.canopy: 0.4935, IoU.washer: 0.6975, IoU.plaything: 0.3627, IoU.swimming pool: 0.7240, IoU.stool: 0.5229, IoU.barrel: 0.5552, IoU.basket: 0.3508, IoU.waterfall: 0.6634, IoU.tent: 0.9077, IoU.bag: 0.1593, IoU.minibike: 0.7064, IoU.cradle: 0.8175, IoU.oven: 0.5202, IoU.ball: 0.4690, IoU.food: 0.6519, IoU.step: 0.1640, IoU.tank: 0.6290, IoU.trade name: 0.1824, IoU.microwave: 0.8834, IoU.pot: 0.5606, IoU.animal: 0.6190, IoU.bicycle: 0.5820, IoU.lake: 0.5899, IoU.dishwasher: 0.6621, IoU.screen: 0.6020, IoU.blanket: 0.2981, IoU.sculpture: 0.7520, IoU.hood: 0.6105, IoU.sconce: 0.5183, IoU.vase: 0.4430, IoU.traffic light: 0.3208, IoU.tray: 0.1617, IoU.ashcan: 0.4178, IoU.fan: 0.6412, IoU.pier: 0.3764, IoU.crt screen: 0.0554, IoU.plate: 0.5898, IoU.monitor: 0.3021, IoU.bulletin board: 0.5617, IoU.shower: 0.0089, IoU.radiator: 0.6389, IoU.glass: 0.1643, IoU.clock: 0.3520, IoU.flag: 0.7174, Acc.wall: 0.8957, Acc.building: 0.9293, Acc.sky: 0.9712, Acc.floor: 0.9095, Acc.tree: 0.9114, Acc.ceiling: 0.9157, Acc.road: 0.9103, Acc.bed : 0.9630, Acc.windowpane: 0.7726, Acc.grass: 0.7614, Acc.cabinet: 0.7597, Acc.sidewalk: 0.8576, Acc.person: 0.9308, Acc.earth: 0.4548, Acc.door: 0.7646, Acc.table: 0.8268, Acc.mountain: 0.8279, Acc.plant: 0.6655, Acc.curtain: 0.9017, Acc.chair: 0.7527, Acc.car: 0.9106, Acc.water: 0.7250, Acc.painting: 0.9077, Acc.sofa: 0.8807, Acc.shelf: 0.6275, Acc.house: 0.8100, Acc.sea: 0.8369, Acc.mirror: 0.8233, Acc.rug: 0.8390, Acc.field: 0.7552, Acc.armchair: 0.7621, Acc.seat: 0.8759, Acc.fence: 0.7012, Acc.desk: 0.7705, Acc.rock: 0.6960, Acc.wardrobe: 0.7870, Acc.lamp: 0.8140, Acc.bathtub: 0.8572, Acc.railing: 0.6131, Acc.cushion: 0.7783, Acc.base: 0.4662, Acc.box: 0.4881, Acc.column: 0.6640, Acc.signboard: 0.4882, Acc.chest of drawers: 0.5269, Acc.counter: 0.3666, Acc.sand: 0.7713, Acc.sink: 0.8225, Acc.skyscraper: 0.6481, Acc.fireplace: 0.9416, Acc.refrigerator: 0.9138, Acc.grandstand: 0.7927, Acc.path: 0.4387, Acc.stairs: 0.5237, Acc.runway: 0.9761, Acc.case: 0.7947, Acc.pool table: 0.9740, Acc.pillow: 0.8220, Acc.screen door: 0.7106, Acc.stairway: 0.3856, Acc.river: 0.4672, Acc.bridge: 0.8839, Acc.bookcase: 0.6166, Acc.blind: 0.5290, Acc.coffee table: 0.8194, Acc.toilet: 0.9165, Acc.flower: 0.5414, Acc.book: 0.6230, Acc.hill: 0.0573, Acc.bench: 0.6054, Acc.countertop: 0.8788, Acc.stove: 0.9475, Acc.palm: 0.8073, Acc.kitchen island: 0.6361, Acc.computer: 0.9395, Acc.swivel chair: 0.7731, Acc.boat: 0.8725, Acc.bar: 0.7567, Acc.arcade machine: 0.7681, Acc.hovel: 0.5400, Acc.bus: 0.9610, Acc.towel: 0.8553, Acc.light: 0.7015, Acc.truck: 0.6565, Acc.tower: 0.3711, Acc.chandelier: 0.8932, Acc.awning: 0.6599, Acc.streetlight: 0.5067, Acc.booth: 0.6800, Acc.television receiver: 0.8735, Acc.airplane: 0.6668, Acc.dirt track: 0.4470, Acc.apparel: 0.7214, Acc.pole: 0.2593, Acc.land: 0.0488, Acc.bannister: 0.1637, Acc.escalator: 0.8193, Acc.ottoman: 0.6351, Acc.bottle: 0.6115, Acc.buffet: 0.6538, Acc.poster: 0.4432, Acc.stage: 0.4569, Acc.van: 0.6894, Acc.ship: 0.8586, Acc.fountain: 0.2955, Acc.conveyer belt: 0.9326, Acc.canopy: 0.7126, Acc.washer: 0.8313, Acc.plaything: 0.5714, Acc.swimming pool: 0.9177, Acc.stool: 0.6389, Acc.barrel: 0.6433, Acc.basket: 0.4976, Acc.waterfall: 0.9310, Acc.tent: 0.9774, Acc.bag: 0.1826, Acc.minibike: 0.8887, Acc.cradle: 0.9764, Acc.oven: 0.6196, Acc.ball: 0.4962, Acc.food: 0.7754, Acc.step: 0.2042, Acc.tank: 0.6880, Acc.trade name: 0.1910, Acc.microwave: 0.9471, Acc.pot: 0.7254, Acc.animal: 0.6488, Acc.bicycle: 0.7680, Acc.lake: 0.5977, Acc.dishwasher: 0.7862, Acc.screen: 0.9401, Acc.blanket: 0.3448, Acc.sculpture: 0.8112, Acc.hood: 0.7393, Acc.sconce: 0.7011, Acc.vase: 0.6003, Acc.traffic light: 0.5901, Acc.tray: 0.2647, Acc.ashcan: 0.6028, Acc.fan: 0.7463, Acc.pier: 0.4843, Acc.crt screen: 0.1149, Acc.plate: 0.6683, Acc.monitor: 0.3584, Acc.bulletin board: 0.5777, Acc.shower: 0.0136, Acc.radiator: 0.7403, Acc.glass: 0.1722, Acc.clock: 0.3924, Acc.flag: 0.7764 +2024-06-18 10:59:09,927 - mmseg - INFO - Iter [28050/80000] lr: 2.598e-05, eta: 21:22:15, time: 3.280, data_time: 1.956, memory: 70498, decode.loss_ce: 0.2913, decode.acc_seg: 88.3976, aux.loss_ce: 0.1184, aux.acc_seg: 88.2846, loss: 0.4098 +2024-06-18 11:00:16,357 - mmseg - INFO - Iter [28100/80000] lr: 2.595e-05, eta: 21:20:47, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2691, decode.acc_seg: 88.6554, aux.loss_ce: 0.1100, aux.acc_seg: 88.5096, loss: 0.3791 +2024-06-18 11:01:22,534 - mmseg - INFO - Iter [28150/80000] lr: 2.593e-05, eta: 21:19:19, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2645, decode.acc_seg: 89.1149, aux.loss_ce: 0.1084, aux.acc_seg: 88.8422, loss: 0.3729 +2024-06-18 11:02:28,655 - mmseg - INFO - Iter [28200/80000] lr: 2.590e-05, eta: 21:17:50, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2591, decode.acc_seg: 88.8425, aux.loss_ce: 0.1065, aux.acc_seg: 88.5946, loss: 0.3656 +2024-06-18 11:03:35,133 - mmseg - INFO - Iter [28250/80000] lr: 2.588e-05, eta: 21:16:23, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2760, decode.acc_seg: 88.9254, aux.loss_ce: 0.1122, aux.acc_seg: 88.7324, loss: 0.3882 +2024-06-18 11:04:41,306 - mmseg - INFO - Iter [28300/80000] lr: 2.585e-05, eta: 21:14:54, time: 1.323, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2726, decode.acc_seg: 88.6456, aux.loss_ce: 0.1122, aux.acc_seg: 88.3900, loss: 0.3848 +2024-06-18 11:05:47,894 - mmseg - INFO - Iter [28350/80000] lr: 2.583e-05, eta: 21:13:27, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2684, decode.acc_seg: 89.0809, aux.loss_ce: 0.1100, aux.acc_seg: 88.7557, loss: 0.3785 +2024-06-18 11:06:54,210 - mmseg - INFO - Iter [28400/80000] lr: 2.580e-05, eta: 21:11:59, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2658, decode.acc_seg: 89.0517, aux.loss_ce: 0.1085, aux.acc_seg: 88.7754, loss: 0.3743 +2024-06-18 11:08:00,761 - mmseg - INFO - Iter [28450/80000] lr: 2.578e-05, eta: 21:10:32, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2839, decode.acc_seg: 88.4760, aux.loss_ce: 0.1157, aux.acc_seg: 88.2440, loss: 0.3995 +2024-06-18 11:09:07,173 - mmseg - INFO - Iter [28500/80000] lr: 2.575e-05, eta: 21:09:04, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2680, decode.acc_seg: 88.9918, aux.loss_ce: 0.1099, aux.acc_seg: 88.6264, loss: 0.3779 +2024-06-18 11:10:13,632 - mmseg - INFO - Iter [28550/80000] lr: 2.573e-05, eta: 21:07:37, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2790, decode.acc_seg: 88.7891, aux.loss_ce: 0.1142, aux.acc_seg: 88.5645, loss: 0.3932 +2024-06-18 11:11:19,902 - mmseg - INFO - Iter [28600/80000] lr: 2.570e-05, eta: 21:06:09, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2509, decode.acc_seg: 89.5770, aux.loss_ce: 0.1031, aux.acc_seg: 89.2440, loss: 0.3540 +2024-06-18 11:12:31,342 - mmseg - INFO - Iter [28650/80000] lr: 2.568e-05, eta: 21:04:51, time: 1.429, data_time: 0.115, memory: 70498, decode.loss_ce: 0.2638, decode.acc_seg: 88.8367, aux.loss_ce: 0.1078, aux.acc_seg: 88.5256, loss: 0.3716 +2024-06-18 11:13:38,051 - mmseg - INFO - Iter [28700/80000] lr: 2.565e-05, eta: 21:03:24, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2756, decode.acc_seg: 88.6428, aux.loss_ce: 0.1135, aux.acc_seg: 88.3449, loss: 0.3891 +2024-06-18 11:14:44,589 - mmseg - INFO - Iter [28750/80000] lr: 2.563e-05, eta: 21:01:57, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2630, decode.acc_seg: 88.9298, aux.loss_ce: 0.1077, aux.acc_seg: 88.6631, loss: 0.3707 +2024-06-18 11:15:50,795 - mmseg - INFO - Iter [28800/80000] lr: 2.560e-05, eta: 21:00:29, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2578, decode.acc_seg: 89.2186, aux.loss_ce: 0.1060, aux.acc_seg: 89.0353, loss: 0.3639 +2024-06-18 11:16:57,108 - mmseg - INFO - Iter [28850/80000] lr: 2.558e-05, eta: 20:59:02, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2768, decode.acc_seg: 88.5780, aux.loss_ce: 0.1131, aux.acc_seg: 88.3833, loss: 0.3900 +2024-06-18 11:18:03,627 - mmseg - INFO - Iter [28900/80000] lr: 2.555e-05, eta: 20:57:35, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2726, decode.acc_seg: 88.7776, aux.loss_ce: 0.1116, aux.acc_seg: 88.4833, loss: 0.3841 +2024-06-18 11:19:10,033 - mmseg - INFO - Iter [28950/80000] lr: 2.553e-05, eta: 20:56:09, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2500, decode.acc_seg: 89.8510, aux.loss_ce: 0.1027, aux.acc_seg: 89.6290, loss: 0.3527 +2024-06-18 11:20:16,237 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 11:20:16,237 - mmseg - INFO - Iter [29000/80000] lr: 2.550e-05, eta: 20:54:41, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2629, decode.acc_seg: 89.0829, aux.loss_ce: 0.1072, aux.acc_seg: 88.8521, loss: 0.3702 +2024-06-18 11:21:52,296 - mmseg - INFO - per class results: +2024-06-18 11:21:52,302 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.06 | 89.33 | +| building | 84.42 | 93.02 | +| sky | 94.86 | 97.55 | +| floor | 84.85 | 91.14 | +| tree | 76.99 | 89.76 | +| ceiling | 85.3 | 91.2 | +| road | 84.88 | 92.8 | +| bed | 92.22 | 97.11 | +| windowpane | 65.74 | 81.37 | +| grass | 65.81 | 75.46 | +| cabinet | 64.06 | 75.44 | +| sidewalk | 68.5 | 78.5 | +| person | 85.25 | 94.13 | +| earth | 35.86 | 49.96 | +| door | 56.61 | 69.64 | +| table | 66.13 | 78.87 | +| mountain | 62.06 | 69.28 | +| plant | 56.92 | 68.64 | +| curtain | 79.37 | 90.41 | +| chair | 64.17 | 74.99 | +| car | 85.72 | 92.89 | +| water | 52.84 | 63.28 | +| painting | 75.76 | 90.15 | +| sofa | 79.92 | 90.51 | +| shelf | 50.17 | 72.49 | +| house | 48.92 | 58.86 | +| sea | 66.17 | 83.92 | +| mirror | 76.21 | 86.3 | +| rug | 70.5 | 82.1 | +| field | 39.49 | 70.35 | +| armchair | 55.01 | 76.14 | +| seat | 67.11 | 88.67 | +| fence | 50.54 | 65.2 | +| desk | 58.57 | 69.84 | +| rock | 61.22 | 79.11 | +| wardrobe | 49.74 | 68.89 | +| lamp | 72.24 | 83.41 | +| bathtub | 82.81 | 85.29 | +| railing | 40.13 | 54.51 | +| cushion | 66.76 | 78.37 | +| base | 33.67 | 47.72 | +| box | 33.02 | 45.01 | +| column | 55.73 | 74.75 | +| signboard | 41.41 | 58.24 | +| chest of drawers | 40.85 | 69.97 | +| counter | 44.84 | 70.22 | +| sand | 41.91 | 64.66 | +| sink | 75.35 | 81.8 | +| skyscraper | 56.53 | 77.32 | +| fireplace | 75.72 | 91.87 | +| refrigerator | 76.12 | 89.88 | +| grandstand | 48.29 | 74.57 | +| path | 28.22 | 43.92 | +| stairs | 19.3 | 23.53 | +| runway | 75.46 | 98.35 | +| case | 58.38 | 80.37 | +| pool table | 94.4 | 97.7 | +| pillow | 68.53 | 80.61 | +| screen door | 59.02 | 60.85 | +| stairway | 35.82 | 51.17 | +| river | 12.54 | 38.71 | +| bridge | 72.72 | 86.2 | +| bookcase | 44.76 | 60.61 | +| blind | 48.44 | 55.01 | +| coffee table | 62.79 | 87.16 | +| toilet | 86.88 | 91.93 | +| flower | 41.19 | 55.28 | +| book | 55.14 | 67.72 | +| hill | 4.73 | 14.62 | +| bench | 48.77 | 64.05 | +| countertop | 64.78 | 83.63 | +| stove | 85.36 | 94.69 | +| palm | 51.34 | 86.11 | +| kitchen island | 47.88 | 91.13 | +| computer | 80.59 | 93.44 | +| swivel chair | 49.24 | 78.01 | +| boat | 53.97 | 86.89 | +| bar | 60.47 | 74.22 | +| arcade machine | 73.62 | 84.89 | +| hovel | 43.83 | 48.27 | +| bus | 92.35 | 95.4 | +| towel | 73.46 | 79.27 | +| light | 57.85 | 64.81 | +| truck | 44.1 | 62.87 | +| tower | 20.66 | 47.83 | +| chandelier | 72.83 | 86.08 | +| awning | 45.76 | 54.6 | +| streetlight | 31.58 | 49.28 | +| booth | 52.94 | 56.96 | +| television receiver | 78.97 | 88.74 | +| airplane | 65.38 | 71.27 | +| dirt track | 10.44 | 46.14 | +| apparel | 51.28 | 68.55 | +| pole | 25.2 | 37.02 | +| land | 2.23 | 5.64 | +| bannister | 11.28 | 15.47 | +| escalator | 57.61 | 78.05 | +| ottoman | 49.53 | 74.6 | +| bottle | 39.42 | 49.71 | +| buffet | 57.05 | 65.47 | +| poster | 32.17 | 47.2 | +| stage | 20.32 | 38.31 | +| van | 45.08 | 61.14 | +| ship | 87.8 | 89.17 | +| fountain | 22.76 | 23.14 | +| conveyer belt | 80.75 | 92.22 | +| canopy | 48.14 | 75.28 | +| washer | 71.17 | 80.92 | +| plaything | 30.48 | 36.82 | +| swimming pool | 57.67 | 89.19 | +| stool | 52.95 | 69.25 | +| barrel | 54.47 | 64.99 | +| basket | 41.67 | 60.72 | +| waterfall | 67.41 | 91.7 | +| tent | 90.59 | 98.34 | +| bag | 13.28 | 14.9 | +| minibike | 71.67 | 84.81 | +| cradle | 82.47 | 97.69 | +| oven | 51.41 | 56.56 | +| ball | 9.35 | 9.57 | +| food | 57.47 | 69.4 | +| step | 20.59 | 27.73 | +| tank | 61.58 | 70.3 | +| trade name | 18.35 | 19.48 | +| microwave | 86.67 | 95.74 | +| pot | 55.65 | 67.04 | +| animal | 65.53 | 67.61 | +| bicycle | 58.68 | 76.19 | +| lake | 53.3 | 63.73 | +| dishwasher | 66.23 | 75.94 | +| screen | 56.17 | 88.85 | +| blanket | 28.18 | 31.84 | +| sculpture | 70.22 | 85.66 | +| hood | 60.89 | 72.66 | +| sconce | 53.11 | 62.06 | +| vase | 44.12 | 62.97 | +| traffic light | 36.26 | 53.41 | +| tray | 12.6 | 14.3 | +| ashcan | 41.54 | 63.24 | +| fan | 63.58 | 73.99 | +| pier | 39.23 | 48.94 | +| crt screen | 24.26 | 30.2 | +| plate | 57.94 | 66.47 | +| monitor | 66.94 | 80.96 | +| bulletin board | 53.92 | 64.49 | +| shower | 0.3 | 3.32 | +| radiator | 62.25 | 73.22 | +| glass | 17.3 | 18.42 | +| clock | 39.67 | 50.61 | +| flag | 71.23 | 75.77 | ++---------------------+-------+-------+ +2024-06-18 11:21:52,302 - mmseg - INFO - Summary: +2024-06-18 11:21:52,302 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.17 | 55.15 | 68.32 | ++-------+-------+-------+ +2024-06-18 11:21:52,303 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 11:21:52,303 - mmseg - INFO - Iter(val) [250] aAcc: 0.8517, mIoU: 0.5515, mAcc: 0.6832, IoU.wall: 0.8106, IoU.building: 0.8442, IoU.sky: 0.9486, IoU.floor: 0.8485, IoU.tree: 0.7699, IoU.ceiling: 0.8530, IoU.road: 0.8488, IoU.bed : 0.9222, IoU.windowpane: 0.6574, IoU.grass: 0.6581, IoU.cabinet: 0.6406, IoU.sidewalk: 0.6850, IoU.person: 0.8525, IoU.earth: 0.3586, IoU.door: 0.5661, IoU.table: 0.6613, IoU.mountain: 0.6206, IoU.plant: 0.5692, IoU.curtain: 0.7937, IoU.chair: 0.6417, IoU.car: 0.8572, IoU.water: 0.5284, IoU.painting: 0.7576, IoU.sofa: 0.7992, IoU.shelf: 0.5017, IoU.house: 0.4892, IoU.sea: 0.6617, IoU.mirror: 0.7621, IoU.rug: 0.7050, IoU.field: 0.3949, IoU.armchair: 0.5501, IoU.seat: 0.6711, IoU.fence: 0.5054, IoU.desk: 0.5857, IoU.rock: 0.6122, IoU.wardrobe: 0.4974, IoU.lamp: 0.7224, IoU.bathtub: 0.8281, IoU.railing: 0.4013, IoU.cushion: 0.6676, IoU.base: 0.3367, IoU.box: 0.3302, IoU.column: 0.5573, IoU.signboard: 0.4141, IoU.chest of drawers: 0.4085, IoU.counter: 0.4484, IoU.sand: 0.4191, IoU.sink: 0.7535, IoU.skyscraper: 0.5653, IoU.fireplace: 0.7572, IoU.refrigerator: 0.7612, IoU.grandstand: 0.4829, IoU.path: 0.2822, IoU.stairs: 0.1930, IoU.runway: 0.7546, IoU.case: 0.5838, IoU.pool table: 0.9440, IoU.pillow: 0.6853, IoU.screen door: 0.5902, IoU.stairway: 0.3582, IoU.river: 0.1254, IoU.bridge: 0.7272, IoU.bookcase: 0.4476, IoU.blind: 0.4844, IoU.coffee table: 0.6279, IoU.toilet: 0.8688, IoU.flower: 0.4119, IoU.book: 0.5514, IoU.hill: 0.0473, IoU.bench: 0.4877, IoU.countertop: 0.6478, IoU.stove: 0.8536, IoU.palm: 0.5134, IoU.kitchen island: 0.4788, IoU.computer: 0.8059, IoU.swivel chair: 0.4924, IoU.boat: 0.5397, IoU.bar: 0.6047, IoU.arcade machine: 0.7362, IoU.hovel: 0.4383, IoU.bus: 0.9235, IoU.towel: 0.7346, IoU.light: 0.5785, IoU.truck: 0.4410, IoU.tower: 0.2066, IoU.chandelier: 0.7283, IoU.awning: 0.4576, IoU.streetlight: 0.3158, IoU.booth: 0.5294, IoU.television receiver: 0.7897, IoU.airplane: 0.6538, IoU.dirt track: 0.1044, IoU.apparel: 0.5128, IoU.pole: 0.2520, IoU.land: 0.0223, IoU.bannister: 0.1128, IoU.escalator: 0.5761, IoU.ottoman: 0.4953, IoU.bottle: 0.3942, IoU.buffet: 0.5705, IoU.poster: 0.3217, IoU.stage: 0.2032, IoU.van: 0.4508, IoU.ship: 0.8780, IoU.fountain: 0.2276, IoU.conveyer belt: 0.8075, IoU.canopy: 0.4814, IoU.washer: 0.7117, IoU.plaything: 0.3048, IoU.swimming pool: 0.5767, IoU.stool: 0.5295, IoU.barrel: 0.5447, IoU.basket: 0.4167, IoU.waterfall: 0.6741, IoU.tent: 0.9059, IoU.bag: 0.1328, IoU.minibike: 0.7167, IoU.cradle: 0.8247, IoU.oven: 0.5141, IoU.ball: 0.0935, IoU.food: 0.5747, IoU.step: 0.2059, IoU.tank: 0.6158, IoU.trade name: 0.1835, IoU.microwave: 0.8667, IoU.pot: 0.5565, IoU.animal: 0.6553, IoU.bicycle: 0.5868, IoU.lake: 0.5330, IoU.dishwasher: 0.6623, IoU.screen: 0.5617, IoU.blanket: 0.2818, IoU.sculpture: 0.7022, IoU.hood: 0.6089, IoU.sconce: 0.5311, IoU.vase: 0.4412, IoU.traffic light: 0.3626, IoU.tray: 0.1260, IoU.ashcan: 0.4154, IoU.fan: 0.6358, IoU.pier: 0.3923, IoU.crt screen: 0.2426, IoU.plate: 0.5794, IoU.monitor: 0.6694, IoU.bulletin board: 0.5392, IoU.shower: 0.0030, IoU.radiator: 0.6225, IoU.glass: 0.1730, IoU.clock: 0.3967, IoU.flag: 0.7123, Acc.wall: 0.8933, Acc.building: 0.9302, Acc.sky: 0.9755, Acc.floor: 0.9114, Acc.tree: 0.8976, Acc.ceiling: 0.9120, Acc.road: 0.9280, Acc.bed : 0.9711, Acc.windowpane: 0.8137, Acc.grass: 0.7546, Acc.cabinet: 0.7544, Acc.sidewalk: 0.7850, Acc.person: 0.9413, Acc.earth: 0.4996, Acc.door: 0.6964, Acc.table: 0.7887, Acc.mountain: 0.6928, Acc.plant: 0.6864, Acc.curtain: 0.9041, Acc.chair: 0.7499, Acc.car: 0.9289, Acc.water: 0.6328, Acc.painting: 0.9015, Acc.sofa: 0.9051, Acc.shelf: 0.7249, Acc.house: 0.5886, Acc.sea: 0.8392, Acc.mirror: 0.8630, Acc.rug: 0.8210, Acc.field: 0.7035, Acc.armchair: 0.7614, Acc.seat: 0.8867, Acc.fence: 0.6520, Acc.desk: 0.6984, Acc.rock: 0.7911, Acc.wardrobe: 0.6889, Acc.lamp: 0.8341, Acc.bathtub: 0.8529, Acc.railing: 0.5451, Acc.cushion: 0.7837, Acc.base: 0.4772, Acc.box: 0.4501, Acc.column: 0.7475, Acc.signboard: 0.5824, Acc.chest of drawers: 0.6997, Acc.counter: 0.7022, Acc.sand: 0.6466, Acc.sink: 0.8180, Acc.skyscraper: 0.7732, Acc.fireplace: 0.9187, Acc.refrigerator: 0.8988, Acc.grandstand: 0.7457, Acc.path: 0.4392, Acc.stairs: 0.2353, Acc.runway: 0.9835, Acc.case: 0.8037, Acc.pool table: 0.9770, Acc.pillow: 0.8061, Acc.screen door: 0.6085, Acc.stairway: 0.5117, Acc.river: 0.3871, Acc.bridge: 0.8620, Acc.bookcase: 0.6061, Acc.blind: 0.5501, Acc.coffee table: 0.8716, Acc.toilet: 0.9193, Acc.flower: 0.5528, Acc.book: 0.6772, Acc.hill: 0.1462, Acc.bench: 0.6405, Acc.countertop: 0.8363, Acc.stove: 0.9469, Acc.palm: 0.8611, Acc.kitchen island: 0.9113, Acc.computer: 0.9344, Acc.swivel chair: 0.7801, Acc.boat: 0.8689, Acc.bar: 0.7422, Acc.arcade machine: 0.8489, Acc.hovel: 0.4827, Acc.bus: 0.9540, Acc.towel: 0.7927, Acc.light: 0.6481, Acc.truck: 0.6287, Acc.tower: 0.4783, Acc.chandelier: 0.8608, Acc.awning: 0.5460, Acc.streetlight: 0.4928, Acc.booth: 0.5696, Acc.television receiver: 0.8874, Acc.airplane: 0.7127, Acc.dirt track: 0.4614, Acc.apparel: 0.6855, Acc.pole: 0.3702, Acc.land: 0.0564, Acc.bannister: 0.1547, Acc.escalator: 0.7805, Acc.ottoman: 0.7460, Acc.bottle: 0.4971, Acc.buffet: 0.6547, Acc.poster: 0.4720, Acc.stage: 0.3831, Acc.van: 0.6114, Acc.ship: 0.8917, Acc.fountain: 0.2314, Acc.conveyer belt: 0.9222, Acc.canopy: 0.7528, Acc.washer: 0.8092, Acc.plaything: 0.3682, Acc.swimming pool: 0.8919, Acc.stool: 0.6925, Acc.barrel: 0.6499, Acc.basket: 0.6072, Acc.waterfall: 0.9170, Acc.tent: 0.9834, Acc.bag: 0.1490, Acc.minibike: 0.8481, Acc.cradle: 0.9769, Acc.oven: 0.5656, Acc.ball: 0.0957, Acc.food: 0.6940, Acc.step: 0.2773, Acc.tank: 0.7030, Acc.trade name: 0.1948, Acc.microwave: 0.9574, Acc.pot: 0.6704, Acc.animal: 0.6761, Acc.bicycle: 0.7619, Acc.lake: 0.6373, Acc.dishwasher: 0.7594, Acc.screen: 0.8885, Acc.blanket: 0.3184, Acc.sculpture: 0.8566, Acc.hood: 0.7266, Acc.sconce: 0.6206, Acc.vase: 0.6297, Acc.traffic light: 0.5341, Acc.tray: 0.1430, Acc.ashcan: 0.6324, Acc.fan: 0.7399, Acc.pier: 0.4894, Acc.crt screen: 0.3020, Acc.plate: 0.6647, Acc.monitor: 0.8096, Acc.bulletin board: 0.6449, Acc.shower: 0.0332, Acc.radiator: 0.7322, Acc.glass: 0.1842, Acc.clock: 0.5061, Acc.flag: 0.7577 +2024-06-18 11:23:01,836 - mmseg - INFO - Iter [29050/80000] lr: 2.548e-05, eta: 20:56:08, time: 3.312, data_time: 1.991, memory: 70498, decode.loss_ce: 0.2633, decode.acc_seg: 89.2350, aux.loss_ce: 0.1084, aux.acc_seg: 88.9738, loss: 0.3717 +2024-06-18 11:24:08,108 - mmseg - INFO - Iter [29100/80000] lr: 2.545e-05, eta: 20:54:41, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2478, decode.acc_seg: 89.9003, aux.loss_ce: 0.1023, aux.acc_seg: 89.6292, loss: 0.3501 +2024-06-18 11:25:14,445 - mmseg - INFO - Iter [29150/80000] lr: 2.543e-05, eta: 20:53:14, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2778, decode.acc_seg: 88.8012, aux.loss_ce: 0.1133, aux.acc_seg: 88.5181, loss: 0.3911 +2024-06-18 11:26:20,884 - mmseg - INFO - Iter [29200/80000] lr: 2.540e-05, eta: 20:51:47, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2725, decode.acc_seg: 88.9160, aux.loss_ce: 0.1115, aux.acc_seg: 88.6003, loss: 0.3840 +2024-06-18 11:27:27,266 - mmseg - INFO - Iter [29250/80000] lr: 2.538e-05, eta: 20:50:20, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2524, decode.acc_seg: 89.3416, aux.loss_ce: 0.1037, aux.acc_seg: 89.1205, loss: 0.3561 +2024-06-18 11:28:33,700 - mmseg - INFO - Iter [29300/80000] lr: 2.535e-05, eta: 20:48:53, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2653, decode.acc_seg: 89.1376, aux.loss_ce: 0.1094, aux.acc_seg: 88.8323, loss: 0.3748 +2024-06-18 11:29:40,088 - mmseg - INFO - Iter [29350/80000] lr: 2.533e-05, eta: 20:47:26, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2536, decode.acc_seg: 89.5790, aux.loss_ce: 0.1043, aux.acc_seg: 89.3160, loss: 0.3579 +2024-06-18 11:30:46,521 - mmseg - INFO - Iter [29400/80000] lr: 2.530e-05, eta: 20:45:59, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2882, decode.acc_seg: 88.4410, aux.loss_ce: 0.1175, aux.acc_seg: 88.3128, loss: 0.4057 +2024-06-18 11:31:52,815 - mmseg - INFO - Iter [29450/80000] lr: 2.528e-05, eta: 20:44:33, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2542, decode.acc_seg: 89.6376, aux.loss_ce: 0.1044, aux.acc_seg: 89.3562, loss: 0.3586 +2024-06-18 11:32:58,934 - mmseg - INFO - Iter [29500/80000] lr: 2.525e-05, eta: 20:43:05, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2546, decode.acc_seg: 89.0897, aux.loss_ce: 0.1049, aux.acc_seg: 88.7583, loss: 0.3594 +2024-06-18 11:34:05,258 - mmseg - INFO - Iter [29550/80000] lr: 2.523e-05, eta: 20:41:39, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2803, decode.acc_seg: 87.9179, aux.loss_ce: 0.1138, aux.acc_seg: 87.8023, loss: 0.3941 +2024-06-18 11:35:11,658 - mmseg - INFO - Iter [29600/80000] lr: 2.520e-05, eta: 20:40:12, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2727, decode.acc_seg: 89.0476, aux.loss_ce: 0.1116, aux.acc_seg: 88.7628, loss: 0.3843 +2024-06-18 11:36:18,235 - mmseg - INFO - Iter [29650/80000] lr: 2.518e-05, eta: 20:38:46, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2724, decode.acc_seg: 89.0383, aux.loss_ce: 0.1116, aux.acc_seg: 88.8322, loss: 0.3841 +2024-06-18 11:37:24,318 - mmseg - INFO - Iter [29700/80000] lr: 2.515e-05, eta: 20:37:19, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2614, decode.acc_seg: 89.4087, aux.loss_ce: 0.1079, aux.acc_seg: 89.1260, loss: 0.3692 +2024-06-18 11:38:30,608 - mmseg - INFO - Iter [29750/80000] lr: 2.513e-05, eta: 20:35:53, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2588, decode.acc_seg: 89.2695, aux.loss_ce: 0.1062, aux.acc_seg: 89.0086, loss: 0.3650 +2024-06-18 11:39:36,841 - mmseg - INFO - Iter [29800/80000] lr: 2.510e-05, eta: 20:34:26, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2409, decode.acc_seg: 89.9765, aux.loss_ce: 0.0990, aux.acc_seg: 89.7872, loss: 0.3399 +2024-06-18 11:40:43,203 - mmseg - INFO - Iter [29850/80000] lr: 2.508e-05, eta: 20:33:00, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2481, decode.acc_seg: 89.5695, aux.loss_ce: 0.1016, aux.acc_seg: 89.2667, loss: 0.3497 +2024-06-18 11:41:49,380 - mmseg - INFO - Iter [29900/80000] lr: 2.505e-05, eta: 20:31:34, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2859, decode.acc_seg: 88.2547, aux.loss_ce: 0.1162, aux.acc_seg: 88.0298, loss: 0.4020 +2024-06-18 11:42:55,538 - mmseg - INFO - Iter [29950/80000] lr: 2.503e-05, eta: 20:30:07, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2691, decode.acc_seg: 88.9691, aux.loss_ce: 0.1098, aux.acc_seg: 88.7509, loss: 0.3789 +2024-06-18 11:44:02,030 - mmseg - INFO - Saving checkpoint at 30000 iterations +2024-06-18 11:45:42,735 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 11:45:42,735 - mmseg - INFO - Iter [30000/80000] lr: 2.500e-05, eta: 20:31:29, time: 3.344, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2726, decode.acc_seg: 88.6369, aux.loss_ce: 0.1114, aux.acc_seg: 88.3260, loss: 0.3840 +2024-06-18 11:47:18,552 - mmseg - INFO - per class results: +2024-06-18 11:47:18,558 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.92 | 88.67 | +| building | 84.72 | 92.95 | +| sky | 94.7 | 97.63 | +| floor | 84.85 | 90.97 | +| tree | 76.34 | 90.12 | +| ceiling | 86.05 | 92.54 | +| road | 86.35 | 92.4 | +| bed | 92.43 | 96.48 | +| windowpane | 65.26 | 79.15 | +| grass | 65.29 | 73.72 | +| cabinet | 63.96 | 74.45 | +| sidewalk | 70.39 | 86.58 | +| person | 84.76 | 92.98 | +| earth | 33.75 | 43.51 | +| door | 56.13 | 71.11 | +| table | 66.86 | 80.95 | +| mountain | 66.52 | 80.25 | +| plant | 52.41 | 61.45 | +| curtain | 76.41 | 89.27 | +| chair | 64.9 | 74.86 | +| car | 85.7 | 92.87 | +| water | 63.26 | 76.53 | +| painting | 75.68 | 89.0 | +| sofa | 77.84 | 93.62 | +| shelf | 46.51 | 60.48 | +| house | 55.01 | 82.31 | +| sea | 72.08 | 80.4 | +| mirror | 76.36 | 85.4 | +| rug | 71.01 | 79.92 | +| field | 31.71 | 73.72 | +| armchair | 56.29 | 73.52 | +| seat | 65.35 | 85.69 | +| fence | 50.78 | 63.24 | +| desk | 56.46 | 78.29 | +| rock | 59.9 | 75.16 | +| wardrobe | 51.42 | 77.74 | +| lamp | 71.06 | 83.0 | +| bathtub | 83.48 | 86.2 | +| railing | 38.58 | 55.24 | +| cushion | 65.02 | 75.67 | +| base | 35.6 | 50.48 | +| box | 36.03 | 46.76 | +| column | 56.5 | 69.11 | +| signboard | 38.28 | 53.07 | +| chest of drawers | 47.43 | 70.58 | +| counter | 52.16 | 69.02 | +| sand | 49.64 | 74.34 | +| sink | 72.18 | 77.28 | +| skyscraper | 45.62 | 60.49 | +| fireplace | 72.69 | 92.53 | +| refrigerator | 79.69 | 94.54 | +| grandstand | 48.48 | 87.05 | +| path | 28.51 | 37.07 | +| stairs | 31.83 | 36.22 | +| runway | 74.16 | 98.37 | +| case | 53.3 | 75.75 | +| pool table | 94.49 | 97.83 | +| pillow | 68.73 | 80.79 | +| screen door | 84.27 | 88.44 | +| stairway | 50.05 | 61.66 | +| river | 18.7 | 44.56 | +| bridge | 75.18 | 89.12 | +| bookcase | 42.53 | 61.36 | +| blind | 46.38 | 52.33 | +| coffee table | 64.97 | 87.96 | +| toilet | 87.34 | 93.32 | +| flower | 42.83 | 56.36 | +| book | 51.94 | 72.35 | +| hill | 5.6 | 15.47 | +| bench | 49.21 | 64.55 | +| countertop | 63.84 | 83.59 | +| stove | 83.05 | 95.25 | +| palm | 53.91 | 80.22 | +| kitchen island | 46.2 | 84.97 | +| computer | 79.96 | 90.69 | +| swivel chair | 49.57 | 78.32 | +| boat | 49.99 | 87.41 | +| bar | 61.92 | 77.42 | +| arcade machine | 76.72 | 84.04 | +| hovel | 9.2 | 9.77 | +| bus | 90.83 | 96.33 | +| towel | 68.97 | 80.71 | +| light | 60.01 | 72.9 | +| truck | 44.94 | 64.86 | +| tower | 21.26 | 40.31 | +| chandelier | 70.9 | 90.19 | +| awning | 46.09 | 60.66 | +| streetlight | 31.88 | 42.85 | +| booth | 62.5 | 78.6 | +| television receiver | 78.01 | 88.78 | +| airplane | 62.82 | 67.63 | +| dirt track | 5.93 | 5.99 | +| apparel | 43.31 | 56.7 | +| pole | 25.19 | 32.72 | +| land | 0.0 | 0.0 | +| bannister | 12.08 | 16.18 | +| escalator | 52.48 | 81.15 | +| ottoman | 52.24 | 73.53 | +| bottle | 37.04 | 64.23 | +| buffet | 54.54 | 84.5 | +| poster | 36.38 | 50.29 | +| stage | 17.63 | 33.02 | +| van | 45.11 | 63.96 | +| ship | 93.31 | 97.39 | +| fountain | 33.53 | 34.74 | +| conveyer belt | 71.73 | 90.68 | +| canopy | 43.05 | 68.22 | +| washer | 72.68 | 76.92 | +| plaything | 34.26 | 53.55 | +| swimming pool | 54.68 | 84.76 | +| stool | 52.8 | 71.73 | +| barrel | 44.85 | 64.87 | +| basket | 39.9 | 54.32 | +| waterfall | 49.69 | 70.53 | +| tent | 95.26 | 98.05 | +| bag | 17.13 | 22.51 | +| minibike | 72.7 | 86.8 | +| cradle | 81.53 | 97.93 | +| oven | 53.67 | 62.95 | +| ball | 54.27 | 69.86 | +| food | 57.88 | 69.5 | +| step | 13.56 | 16.29 | +| tank | 64.09 | 80.13 | +| trade name | 27.62 | 30.66 | +| microwave | 86.38 | 95.16 | +| pot | 51.4 | 57.24 | +| animal | 60.78 | 63.02 | +| bicycle | 55.67 | 74.42 | +| lake | 57.75 | 63.25 | +| dishwasher | 61.47 | 75.94 | +| screen | 57.17 | 89.64 | +| blanket | 33.57 | 39.84 | +| sculpture | 68.19 | 85.8 | +| hood | 62.83 | 77.01 | +| sconce | 54.43 | 66.97 | +| vase | 42.95 | 57.86 | +| traffic light | 32.51 | 60.64 | +| tray | 10.31 | 12.58 | +| ashcan | 48.57 | 61.75 | +| fan | 64.06 | 75.5 | +| pier | 32.41 | 48.63 | +| crt screen | 24.04 | 29.41 | +| plate | 55.23 | 77.85 | +| monitor | 70.37 | 82.89 | +| bulletin board | 42.29 | 42.75 | +| shower | 0.01 | 0.01 | +| radiator | 62.98 | 71.9 | +| glass | 19.32 | 21.96 | +| clock | 35.93 | 40.18 | +| flag | 70.15 | 74.8 | ++---------------------+-------+-------+ +2024-06-18 11:47:18,558 - mmseg - INFO - Summary: +2024-06-18 11:47:18,558 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.32 | 55.29 | 68.96 | ++-------+-------+-------+ +2024-06-18 11:47:18,559 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 11:47:18,559 - mmseg - INFO - Iter(val) [250] aAcc: 0.8532, mIoU: 0.5529, mAcc: 0.6896, IoU.wall: 0.8092, IoU.building: 0.8472, IoU.sky: 0.9470, IoU.floor: 0.8485, IoU.tree: 0.7634, IoU.ceiling: 0.8605, IoU.road: 0.8635, IoU.bed : 0.9243, IoU.windowpane: 0.6526, IoU.grass: 0.6529, IoU.cabinet: 0.6396, IoU.sidewalk: 0.7039, IoU.person: 0.8476, IoU.earth: 0.3375, IoU.door: 0.5613, IoU.table: 0.6686, IoU.mountain: 0.6652, IoU.plant: 0.5241, IoU.curtain: 0.7641, IoU.chair: 0.6490, IoU.car: 0.8570, IoU.water: 0.6326, IoU.painting: 0.7568, IoU.sofa: 0.7784, IoU.shelf: 0.4651, IoU.house: 0.5501, IoU.sea: 0.7208, IoU.mirror: 0.7636, IoU.rug: 0.7101, IoU.field: 0.3171, IoU.armchair: 0.5629, IoU.seat: 0.6535, IoU.fence: 0.5078, IoU.desk: 0.5646, IoU.rock: 0.5990, IoU.wardrobe: 0.5142, IoU.lamp: 0.7106, IoU.bathtub: 0.8348, IoU.railing: 0.3858, IoU.cushion: 0.6502, IoU.base: 0.3560, IoU.box: 0.3603, IoU.column: 0.5650, IoU.signboard: 0.3828, IoU.chest of drawers: 0.4743, IoU.counter: 0.5216, IoU.sand: 0.4964, IoU.sink: 0.7218, IoU.skyscraper: 0.4562, IoU.fireplace: 0.7269, IoU.refrigerator: 0.7969, IoU.grandstand: 0.4848, IoU.path: 0.2851, IoU.stairs: 0.3183, IoU.runway: 0.7416, IoU.case: 0.5330, IoU.pool table: 0.9449, IoU.pillow: 0.6873, IoU.screen door: 0.8427, IoU.stairway: 0.5005, IoU.river: 0.1870, IoU.bridge: 0.7518, IoU.bookcase: 0.4253, IoU.blind: 0.4638, IoU.coffee table: 0.6497, IoU.toilet: 0.8734, IoU.flower: 0.4283, IoU.book: 0.5194, IoU.hill: 0.0560, IoU.bench: 0.4921, IoU.countertop: 0.6384, IoU.stove: 0.8305, IoU.palm: 0.5391, IoU.kitchen island: 0.4620, IoU.computer: 0.7996, IoU.swivel chair: 0.4957, IoU.boat: 0.4999, IoU.bar: 0.6192, IoU.arcade machine: 0.7672, IoU.hovel: 0.0920, IoU.bus: 0.9083, IoU.towel: 0.6897, IoU.light: 0.6001, IoU.truck: 0.4494, IoU.tower: 0.2126, IoU.chandelier: 0.7090, IoU.awning: 0.4609, IoU.streetlight: 0.3188, IoU.booth: 0.6250, IoU.television receiver: 0.7801, IoU.airplane: 0.6282, IoU.dirt track: 0.0593, IoU.apparel: 0.4331, IoU.pole: 0.2519, IoU.land: 0.0000, IoU.bannister: 0.1208, IoU.escalator: 0.5248, IoU.ottoman: 0.5224, IoU.bottle: 0.3704, IoU.buffet: 0.5454, IoU.poster: 0.3638, IoU.stage: 0.1763, IoU.van: 0.4511, IoU.ship: 0.9331, IoU.fountain: 0.3353, IoU.conveyer belt: 0.7173, IoU.canopy: 0.4305, IoU.washer: 0.7268, IoU.plaything: 0.3426, IoU.swimming pool: 0.5468, IoU.stool: 0.5280, IoU.barrel: 0.4485, IoU.basket: 0.3990, IoU.waterfall: 0.4969, IoU.tent: 0.9526, IoU.bag: 0.1713, IoU.minibike: 0.7270, IoU.cradle: 0.8153, IoU.oven: 0.5367, IoU.ball: 0.5427, IoU.food: 0.5788, IoU.step: 0.1356, IoU.tank: 0.6409, IoU.trade name: 0.2762, IoU.microwave: 0.8638, IoU.pot: 0.5140, IoU.animal: 0.6078, IoU.bicycle: 0.5567, IoU.lake: 0.5775, IoU.dishwasher: 0.6147, IoU.screen: 0.5717, IoU.blanket: 0.3357, IoU.sculpture: 0.6819, IoU.hood: 0.6283, IoU.sconce: 0.5443, IoU.vase: 0.4295, IoU.traffic light: 0.3251, IoU.tray: 0.1031, IoU.ashcan: 0.4857, IoU.fan: 0.6406, IoU.pier: 0.3241, IoU.crt screen: 0.2404, IoU.plate: 0.5523, IoU.monitor: 0.7037, IoU.bulletin board: 0.4229, IoU.shower: 0.0001, IoU.radiator: 0.6298, IoU.glass: 0.1932, IoU.clock: 0.3593, IoU.flag: 0.7015, Acc.wall: 0.8867, Acc.building: 0.9295, Acc.sky: 0.9763, Acc.floor: 0.9097, Acc.tree: 0.9012, Acc.ceiling: 0.9254, Acc.road: 0.9240, Acc.bed : 0.9648, Acc.windowpane: 0.7915, Acc.grass: 0.7372, Acc.cabinet: 0.7445, Acc.sidewalk: 0.8658, Acc.person: 0.9298, Acc.earth: 0.4351, Acc.door: 0.7111, Acc.table: 0.8095, Acc.mountain: 0.8025, Acc.plant: 0.6145, Acc.curtain: 0.8927, Acc.chair: 0.7486, Acc.car: 0.9287, Acc.water: 0.7653, Acc.painting: 0.8900, Acc.sofa: 0.9362, Acc.shelf: 0.6048, Acc.house: 0.8231, Acc.sea: 0.8040, Acc.mirror: 0.8540, Acc.rug: 0.7992, Acc.field: 0.7372, Acc.armchair: 0.7352, Acc.seat: 0.8569, Acc.fence: 0.6324, Acc.desk: 0.7829, Acc.rock: 0.7516, Acc.wardrobe: 0.7774, Acc.lamp: 0.8300, Acc.bathtub: 0.8620, Acc.railing: 0.5524, Acc.cushion: 0.7567, Acc.base: 0.5048, Acc.box: 0.4676, Acc.column: 0.6911, Acc.signboard: 0.5307, Acc.chest of drawers: 0.7058, Acc.counter: 0.6902, Acc.sand: 0.7434, Acc.sink: 0.7728, Acc.skyscraper: 0.6049, Acc.fireplace: 0.9253, Acc.refrigerator: 0.9454, Acc.grandstand: 0.8705, Acc.path: 0.3707, Acc.stairs: 0.3622, Acc.runway: 0.9837, Acc.case: 0.7575, Acc.pool table: 0.9783, Acc.pillow: 0.8079, Acc.screen door: 0.8844, Acc.stairway: 0.6166, Acc.river: 0.4456, Acc.bridge: 0.8912, Acc.bookcase: 0.6136, Acc.blind: 0.5233, Acc.coffee table: 0.8796, Acc.toilet: 0.9332, Acc.flower: 0.5636, Acc.book: 0.7235, Acc.hill: 0.1547, Acc.bench: 0.6455, Acc.countertop: 0.8359, Acc.stove: 0.9525, Acc.palm: 0.8022, Acc.kitchen island: 0.8497, Acc.computer: 0.9069, Acc.swivel chair: 0.7832, Acc.boat: 0.8741, Acc.bar: 0.7742, Acc.arcade machine: 0.8404, Acc.hovel: 0.0977, Acc.bus: 0.9633, Acc.towel: 0.8071, Acc.light: 0.7290, Acc.truck: 0.6486, Acc.tower: 0.4031, Acc.chandelier: 0.9019, Acc.awning: 0.6066, Acc.streetlight: 0.4285, Acc.booth: 0.7860, Acc.television receiver: 0.8878, Acc.airplane: 0.6763, Acc.dirt track: 0.0599, Acc.apparel: 0.5670, Acc.pole: 0.3272, Acc.land: 0.0000, Acc.bannister: 0.1618, Acc.escalator: 0.8115, Acc.ottoman: 0.7353, Acc.bottle: 0.6423, Acc.buffet: 0.8450, Acc.poster: 0.5029, Acc.stage: 0.3302, Acc.van: 0.6396, Acc.ship: 0.9739, Acc.fountain: 0.3474, Acc.conveyer belt: 0.9068, Acc.canopy: 0.6822, Acc.washer: 0.7692, Acc.plaything: 0.5355, Acc.swimming pool: 0.8476, Acc.stool: 0.7173, Acc.barrel: 0.6487, Acc.basket: 0.5432, Acc.waterfall: 0.7053, Acc.tent: 0.9805, Acc.bag: 0.2251, Acc.minibike: 0.8680, Acc.cradle: 0.9793, Acc.oven: 0.6295, Acc.ball: 0.6986, Acc.food: 0.6950, Acc.step: 0.1629, Acc.tank: 0.8013, Acc.trade name: 0.3066, Acc.microwave: 0.9516, Acc.pot: 0.5724, Acc.animal: 0.6302, Acc.bicycle: 0.7442, Acc.lake: 0.6325, Acc.dishwasher: 0.7594, Acc.screen: 0.8964, Acc.blanket: 0.3984, Acc.sculpture: 0.8580, Acc.hood: 0.7701, Acc.sconce: 0.6697, Acc.vase: 0.5786, Acc.traffic light: 0.6064, Acc.tray: 0.1258, Acc.ashcan: 0.6175, Acc.fan: 0.7550, Acc.pier: 0.4863, Acc.crt screen: 0.2941, Acc.plate: 0.7785, Acc.monitor: 0.8289, Acc.bulletin board: 0.4275, Acc.shower: 0.0001, Acc.radiator: 0.7190, Acc.glass: 0.2196, Acc.clock: 0.4018, Acc.flag: 0.7480 +2024-06-18 11:48:25,514 - mmseg - INFO - Iter [30050/80000] lr: 2.498e-05, eta: 20:32:43, time: 3.256, data_time: 1.932, memory: 70498, decode.loss_ce: 0.2597, decode.acc_seg: 89.2722, aux.loss_ce: 0.1061, aux.acc_seg: 89.1115, loss: 0.3658 +2024-06-18 11:49:32,041 - mmseg - INFO - Iter [30100/80000] lr: 2.495e-05, eta: 20:31:17, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2600, decode.acc_seg: 89.1229, aux.loss_ce: 0.1068, aux.acc_seg: 88.8564, loss: 0.3669 +2024-06-18 11:50:38,145 - mmseg - INFO - Iter [30150/80000] lr: 2.493e-05, eta: 20:29:49, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2726, decode.acc_seg: 88.7551, aux.loss_ce: 0.1117, aux.acc_seg: 88.5128, loss: 0.3843 +2024-06-18 11:51:44,417 - mmseg - INFO - Iter [30200/80000] lr: 2.490e-05, eta: 20:28:23, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2562, decode.acc_seg: 89.2824, aux.loss_ce: 0.1044, aux.acc_seg: 89.1931, loss: 0.3607 +2024-06-18 11:52:50,537 - mmseg - INFO - Iter [30250/80000] lr: 2.488e-05, eta: 20:26:56, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2661, decode.acc_seg: 89.0800, aux.loss_ce: 0.1084, aux.acc_seg: 88.9784, loss: 0.3746 +2024-06-18 11:53:56,832 - mmseg - INFO - Iter [30300/80000] lr: 2.485e-05, eta: 20:25:29, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2658, decode.acc_seg: 89.3200, aux.loss_ce: 0.1083, aux.acc_seg: 89.0402, loss: 0.3741 +2024-06-18 11:55:05,362 - mmseg - INFO - Iter [30350/80000] lr: 2.483e-05, eta: 20:24:06, time: 1.371, data_time: 0.060, memory: 70498, decode.loss_ce: 0.2260, decode.acc_seg: 90.5466, aux.loss_ce: 0.0937, aux.acc_seg: 90.1370, loss: 0.3197 +2024-06-18 11:56:11,701 - mmseg - INFO - Iter [30400/80000] lr: 2.480e-05, eta: 20:22:40, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2679, decode.acc_seg: 89.3105, aux.loss_ce: 0.1102, aux.acc_seg: 88.9758, loss: 0.3781 +2024-06-18 11:57:18,075 - mmseg - INFO - Iter [30450/80000] lr: 2.478e-05, eta: 20:21:13, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2607, decode.acc_seg: 89.2666, aux.loss_ce: 0.1058, aux.acc_seg: 89.1375, loss: 0.3666 +2024-06-18 11:58:24,158 - mmseg - INFO - Iter [30500/80000] lr: 2.475e-05, eta: 20:19:47, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2452, decode.acc_seg: 89.8006, aux.loss_ce: 0.1010, aux.acc_seg: 89.5699, loss: 0.3461 +2024-06-18 11:59:30,553 - mmseg - INFO - Iter [30550/80000] lr: 2.473e-05, eta: 20:18:21, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2502, decode.acc_seg: 89.6932, aux.loss_ce: 0.1022, aux.acc_seg: 89.4833, loss: 0.3524 +2024-06-18 12:00:36,784 - mmseg - INFO - Iter [30600/80000] lr: 2.470e-05, eta: 20:16:54, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2454, decode.acc_seg: 90.1071, aux.loss_ce: 0.1009, aux.acc_seg: 89.7712, loss: 0.3463 +2024-06-18 12:01:43,324 - mmseg - INFO - Iter [30650/80000] lr: 2.468e-05, eta: 20:15:29, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2493, decode.acc_seg: 89.6228, aux.loss_ce: 0.1022, aux.acc_seg: 89.3045, loss: 0.3514 +2024-06-18 12:02:49,754 - mmseg - INFO - Iter [30700/80000] lr: 2.465e-05, eta: 20:14:03, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2585, decode.acc_seg: 89.1400, aux.loss_ce: 0.1071, aux.acc_seg: 88.8788, loss: 0.3655 +2024-06-18 12:03:56,225 - mmseg - INFO - Iter [30750/80000] lr: 2.463e-05, eta: 20:12:37, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2567, decode.acc_seg: 89.6771, aux.loss_ce: 0.1057, aux.acc_seg: 89.3798, loss: 0.3624 +2024-06-18 12:05:02,366 - mmseg - INFO - Iter [30800/80000] lr: 2.460e-05, eta: 20:11:11, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2657, decode.acc_seg: 89.0897, aux.loss_ce: 0.1085, aux.acc_seg: 88.8441, loss: 0.3742 +2024-06-18 12:06:08,937 - mmseg - INFO - Iter [30850/80000] lr: 2.458e-05, eta: 20:09:45, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2533, decode.acc_seg: 89.2627, aux.loss_ce: 0.1031, aux.acc_seg: 89.1489, loss: 0.3563 +2024-06-18 12:07:15,333 - mmseg - INFO - Iter [30900/80000] lr: 2.455e-05, eta: 20:08:20, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2548, decode.acc_seg: 89.3118, aux.loss_ce: 0.1058, aux.acc_seg: 88.8980, loss: 0.3606 +2024-06-18 12:08:21,729 - mmseg - INFO - Iter [30950/80000] lr: 2.453e-05, eta: 20:06:54, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2516, decode.acc_seg: 89.1806, aux.loss_ce: 0.1037, aux.acc_seg: 88.9002, loss: 0.3552 +2024-06-18 12:09:28,051 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 12:09:28,051 - mmseg - INFO - Iter [31000/80000] lr: 2.450e-05, eta: 20:05:28, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2477, decode.acc_seg: 89.5531, aux.loss_ce: 0.1016, aux.acc_seg: 89.2614, loss: 0.3493 +2024-06-18 12:11:05,735 - mmseg - INFO - per class results: +2024-06-18 12:11:05,741 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.27 | 88.61 | +| building | 84.69 | 93.09 | +| sky | 94.8 | 97.82 | +| floor | 85.05 | 91.66 | +| tree | 77.44 | 88.89 | +| ceiling | 86.16 | 94.55 | +| road | 86.67 | 91.36 | +| bed | 92.17 | 96.45 | +| windowpane | 65.81 | 82.66 | +| grass | 66.59 | 79.11 | +| cabinet | 64.74 | 76.29 | +| sidewalk | 72.15 | 86.67 | +| person | 84.67 | 93.99 | +| earth | 33.29 | 45.21 | +| door | 60.22 | 77.86 | +| table | 68.97 | 81.59 | +| mountain | 61.06 | 71.22 | +| plant | 57.1 | 68.92 | +| curtain | 78.98 | 89.32 | +| chair | 63.89 | 74.24 | +| car | 86.18 | 93.08 | +| water | 63.13 | 78.35 | +| painting | 76.23 | 89.2 | +| sofa | 81.55 | 89.52 | +| shelf | 47.6 | 61.75 | +| house | 51.22 | 67.37 | +| sea | 65.25 | 84.91 | +| mirror | 76.02 | 87.27 | +| rug | 71.3 | 80.56 | +| field | 34.18 | 62.64 | +| armchair | 57.06 | 78.23 | +| seat | 66.37 | 86.83 | +| fence | 50.51 | 61.24 | +| desk | 57.75 | 73.54 | +| rock | 53.24 | 87.88 | +| wardrobe | 50.9 | 66.47 | +| lamp | 70.99 | 81.89 | +| bathtub | 83.35 | 85.81 | +| railing | 40.73 | 57.66 | +| cushion | 67.13 | 79.49 | +| base | 38.49 | 63.18 | +| box | 38.52 | 55.15 | +| column | 56.1 | 68.31 | +| signboard | 40.92 | 53.95 | +| chest of drawers | 44.89 | 68.8 | +| counter | 52.7 | 61.65 | +| sand | 48.57 | 72.71 | +| sink | 73.19 | 80.98 | +| skyscraper | 47.97 | 65.35 | +| fireplace | 75.04 | 87.17 | +| refrigerator | 82.22 | 93.03 | +| grandstand | 52.0 | 75.1 | +| path | 31.86 | 50.46 | +| stairs | 31.67 | 40.92 | +| runway | 74.75 | 96.82 | +| case | 60.79 | 82.8 | +| pool table | 94.58 | 97.79 | +| pillow | 68.36 | 79.35 | +| screen door | 82.42 | 86.87 | +| stairway | 52.39 | 74.29 | +| river | 15.32 | 23.84 | +| bridge | 70.2 | 83.51 | +| bookcase | 43.79 | 62.98 | +| blind | 43.85 | 51.32 | +| coffee table | 66.13 | 87.78 | +| toilet | 89.05 | 91.93 | +| flower | 40.11 | 56.28 | +| book | 52.69 | 78.69 | +| hill | 4.64 | 10.49 | +| bench | 50.9 | 62.97 | +| countertop | 59.49 | 84.29 | +| stove | 85.88 | 90.47 | +| palm | 56.7 | 80.61 | +| kitchen island | 42.69 | 64.9 | +| computer | 80.36 | 93.93 | +| swivel chair | 49.23 | 75.5 | +| boat | 54.32 | 83.6 | +| bar | 59.92 | 76.79 | +| arcade machine | 70.94 | 84.43 | +| hovel | 14.09 | 15.82 | +| bus | 92.58 | 96.48 | +| towel | 77.91 | 86.98 | +| light | 54.46 | 59.02 | +| truck | 46.63 | 62.27 | +| tower | 6.24 | 8.0 | +| chandelier | 72.36 | 86.92 | +| awning | 48.38 | 63.87 | +| streetlight | 29.21 | 37.42 | +| booth | 44.6 | 47.11 | +| television receiver | 80.21 | 85.1 | +| airplane | 77.22 | 81.82 | +| dirt track | 13.47 | 30.67 | +| apparel | 42.3 | 76.15 | +| pole | 29.64 | 40.26 | +| land | 4.25 | 7.28 | +| bannister | 16.82 | 25.72 | +| escalator | 54.81 | 84.91 | +| ottoman | 46.36 | 69.04 | +| bottle | 41.85 | 70.52 | +| buffet | 45.77 | 48.27 | +| poster | 29.52 | 49.02 | +| stage | 24.0 | 45.07 | +| van | 40.78 | 54.65 | +| ship | 91.63 | 94.64 | +| fountain | 32.9 | 34.64 | +| conveyer belt | 82.53 | 90.52 | +| canopy | 45.5 | 68.86 | +| washer | 74.91 | 78.39 | +| plaything | 40.58 | 49.01 | +| swimming pool | 58.96 | 90.75 | +| stool | 44.33 | 75.45 | +| barrel | 54.76 | 64.46 | +| basket | 37.44 | 59.21 | +| waterfall | 55.32 | 80.16 | +| tent | 83.72 | 98.97 | +| bag | 18.45 | 22.03 | +| minibike | 74.52 | 84.81 | +| cradle | 83.41 | 97.95 | +| oven | 56.98 | 73.28 | +| ball | 56.37 | 63.62 | +| food | 60.18 | 72.87 | +| step | 18.9 | 25.98 | +| tank | 71.24 | 75.49 | +| trade name | 29.1 | 33.4 | +| microwave | 87.85 | 94.18 | +| pot | 52.57 | 59.33 | +| animal | 62.27 | 63.38 | +| bicycle | 58.6 | 69.96 | +| lake | 60.48 | 63.24 | +| dishwasher | 66.48 | 75.27 | +| screen | 65.71 | 93.01 | +| blanket | 33.91 | 40.33 | +| sculpture | 66.41 | 86.69 | +| hood | 61.29 | 74.05 | +| sconce | 55.22 | 66.36 | +| vase | 47.14 | 57.1 | +| traffic light | 37.9 | 49.17 | +| tray | 17.52 | 24.54 | +| ashcan | 43.04 | 56.17 | +| fan | 61.71 | 73.24 | +| pier | 35.74 | 50.15 | +| crt screen | 23.83 | 28.61 | +| plate | 59.26 | 70.99 | +| monitor | 70.86 | 83.46 | +| bulletin board | 52.58 | 65.78 | +| shower | 0.2 | 0.25 | +| radiator | 59.92 | 71.65 | +| glass | 17.87 | 19.26 | +| clock | 39.16 | 47.02 | +| flag | 70.45 | 79.43 | ++---------------------+-------+-------+ +2024-06-18 12:11:05,741 - mmseg - INFO - Summary: +2024-06-18 12:11:05,741 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.62 | 56.09 | 69.01 | ++-------+-------+-------+ +2024-06-18 12:11:05,742 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 12:11:05,742 - mmseg - INFO - Iter(val) [250] aAcc: 0.8562, mIoU: 0.5609, mAcc: 0.6901, IoU.wall: 0.8127, IoU.building: 0.8469, IoU.sky: 0.9480, IoU.floor: 0.8505, IoU.tree: 0.7744, IoU.ceiling: 0.8616, IoU.road: 0.8667, IoU.bed : 0.9217, IoU.windowpane: 0.6581, IoU.grass: 0.6659, IoU.cabinet: 0.6474, IoU.sidewalk: 0.7215, IoU.person: 0.8467, IoU.earth: 0.3329, IoU.door: 0.6022, IoU.table: 0.6897, IoU.mountain: 0.6106, IoU.plant: 0.5710, IoU.curtain: 0.7898, IoU.chair: 0.6389, IoU.car: 0.8618, IoU.water: 0.6313, IoU.painting: 0.7623, IoU.sofa: 0.8155, IoU.shelf: 0.4760, IoU.house: 0.5122, IoU.sea: 0.6525, IoU.mirror: 0.7602, IoU.rug: 0.7130, IoU.field: 0.3418, IoU.armchair: 0.5706, IoU.seat: 0.6637, IoU.fence: 0.5051, IoU.desk: 0.5775, IoU.rock: 0.5324, IoU.wardrobe: 0.5090, IoU.lamp: 0.7099, IoU.bathtub: 0.8335, IoU.railing: 0.4073, IoU.cushion: 0.6713, IoU.base: 0.3849, IoU.box: 0.3852, IoU.column: 0.5610, IoU.signboard: 0.4092, IoU.chest of drawers: 0.4489, IoU.counter: 0.5270, IoU.sand: 0.4857, IoU.sink: 0.7319, IoU.skyscraper: 0.4797, IoU.fireplace: 0.7504, IoU.refrigerator: 0.8222, IoU.grandstand: 0.5200, IoU.path: 0.3186, IoU.stairs: 0.3167, IoU.runway: 0.7475, IoU.case: 0.6079, IoU.pool table: 0.9458, IoU.pillow: 0.6836, IoU.screen door: 0.8242, IoU.stairway: 0.5239, IoU.river: 0.1532, IoU.bridge: 0.7020, IoU.bookcase: 0.4379, IoU.blind: 0.4385, IoU.coffee table: 0.6613, IoU.toilet: 0.8905, IoU.flower: 0.4011, IoU.book: 0.5269, IoU.hill: 0.0464, IoU.bench: 0.5090, IoU.countertop: 0.5949, IoU.stove: 0.8588, IoU.palm: 0.5670, IoU.kitchen island: 0.4269, IoU.computer: 0.8036, IoU.swivel chair: 0.4923, IoU.boat: 0.5432, IoU.bar: 0.5992, IoU.arcade machine: 0.7094, IoU.hovel: 0.1409, IoU.bus: 0.9258, IoU.towel: 0.7791, IoU.light: 0.5446, IoU.truck: 0.4663, IoU.tower: 0.0624, IoU.chandelier: 0.7236, IoU.awning: 0.4838, IoU.streetlight: 0.2921, IoU.booth: 0.4460, IoU.television receiver: 0.8021, IoU.airplane: 0.7722, IoU.dirt track: 0.1347, IoU.apparel: 0.4230, IoU.pole: 0.2964, IoU.land: 0.0425, IoU.bannister: 0.1682, IoU.escalator: 0.5481, IoU.ottoman: 0.4636, IoU.bottle: 0.4185, IoU.buffet: 0.4577, IoU.poster: 0.2952, IoU.stage: 0.2400, IoU.van: 0.4078, IoU.ship: 0.9163, IoU.fountain: 0.3290, IoU.conveyer belt: 0.8253, IoU.canopy: 0.4550, IoU.washer: 0.7491, IoU.plaything: 0.4058, IoU.swimming pool: 0.5896, IoU.stool: 0.4433, IoU.barrel: 0.5476, IoU.basket: 0.3744, IoU.waterfall: 0.5532, IoU.tent: 0.8372, IoU.bag: 0.1845, IoU.minibike: 0.7452, IoU.cradle: 0.8341, IoU.oven: 0.5698, IoU.ball: 0.5637, IoU.food: 0.6018, IoU.step: 0.1890, IoU.tank: 0.7124, IoU.trade name: 0.2910, IoU.microwave: 0.8785, IoU.pot: 0.5257, IoU.animal: 0.6227, IoU.bicycle: 0.5860, IoU.lake: 0.6048, IoU.dishwasher: 0.6648, IoU.screen: 0.6571, IoU.blanket: 0.3391, IoU.sculpture: 0.6641, IoU.hood: 0.6129, IoU.sconce: 0.5522, IoU.vase: 0.4714, IoU.traffic light: 0.3790, IoU.tray: 0.1752, IoU.ashcan: 0.4304, IoU.fan: 0.6171, IoU.pier: 0.3574, IoU.crt screen: 0.2383, IoU.plate: 0.5926, IoU.monitor: 0.7086, IoU.bulletin board: 0.5258, IoU.shower: 0.0020, IoU.radiator: 0.5992, IoU.glass: 0.1787, IoU.clock: 0.3916, IoU.flag: 0.7045, Acc.wall: 0.8861, Acc.building: 0.9309, Acc.sky: 0.9782, Acc.floor: 0.9166, Acc.tree: 0.8889, Acc.ceiling: 0.9455, Acc.road: 0.9136, Acc.bed : 0.9645, Acc.windowpane: 0.8266, Acc.grass: 0.7911, Acc.cabinet: 0.7629, Acc.sidewalk: 0.8667, Acc.person: 0.9399, Acc.earth: 0.4521, Acc.door: 0.7786, Acc.table: 0.8159, Acc.mountain: 0.7122, Acc.plant: 0.6892, Acc.curtain: 0.8932, Acc.chair: 0.7424, Acc.car: 0.9308, Acc.water: 0.7835, Acc.painting: 0.8920, Acc.sofa: 0.8952, Acc.shelf: 0.6175, Acc.house: 0.6737, Acc.sea: 0.8491, Acc.mirror: 0.8727, Acc.rug: 0.8056, Acc.field: 0.6264, Acc.armchair: 0.7823, Acc.seat: 0.8683, Acc.fence: 0.6124, Acc.desk: 0.7354, Acc.rock: 0.8788, Acc.wardrobe: 0.6647, Acc.lamp: 0.8189, Acc.bathtub: 0.8581, Acc.railing: 0.5766, Acc.cushion: 0.7949, Acc.base: 0.6318, Acc.box: 0.5515, Acc.column: 0.6831, Acc.signboard: 0.5395, Acc.chest of drawers: 0.6880, Acc.counter: 0.6165, Acc.sand: 0.7271, Acc.sink: 0.8098, Acc.skyscraper: 0.6535, Acc.fireplace: 0.8717, Acc.refrigerator: 0.9303, Acc.grandstand: 0.7510, Acc.path: 0.5046, Acc.stairs: 0.4092, Acc.runway: 0.9682, Acc.case: 0.8280, Acc.pool table: 0.9779, Acc.pillow: 0.7935, Acc.screen door: 0.8687, Acc.stairway: 0.7429, Acc.river: 0.2384, Acc.bridge: 0.8351, Acc.bookcase: 0.6298, Acc.blind: 0.5132, Acc.coffee table: 0.8778, Acc.toilet: 0.9193, Acc.flower: 0.5628, Acc.book: 0.7869, Acc.hill: 0.1049, Acc.bench: 0.6297, Acc.countertop: 0.8429, Acc.stove: 0.9047, Acc.palm: 0.8061, Acc.kitchen island: 0.6490, Acc.computer: 0.9393, Acc.swivel chair: 0.7550, Acc.boat: 0.8360, Acc.bar: 0.7679, Acc.arcade machine: 0.8443, Acc.hovel: 0.1582, Acc.bus: 0.9648, Acc.towel: 0.8698, Acc.light: 0.5902, Acc.truck: 0.6227, Acc.tower: 0.0800, Acc.chandelier: 0.8692, Acc.awning: 0.6387, Acc.streetlight: 0.3742, Acc.booth: 0.4711, Acc.television receiver: 0.8510, Acc.airplane: 0.8182, Acc.dirt track: 0.3067, Acc.apparel: 0.7615, Acc.pole: 0.4026, Acc.land: 0.0728, Acc.bannister: 0.2572, Acc.escalator: 0.8491, Acc.ottoman: 0.6904, Acc.bottle: 0.7052, Acc.buffet: 0.4827, Acc.poster: 0.4902, Acc.stage: 0.4507, Acc.van: 0.5465, Acc.ship: 0.9464, Acc.fountain: 0.3464, Acc.conveyer belt: 0.9052, Acc.canopy: 0.6886, Acc.washer: 0.7839, Acc.plaything: 0.4901, Acc.swimming pool: 0.9075, Acc.stool: 0.7545, Acc.barrel: 0.6446, Acc.basket: 0.5921, Acc.waterfall: 0.8016, Acc.tent: 0.9897, Acc.bag: 0.2203, Acc.minibike: 0.8481, Acc.cradle: 0.9795, Acc.oven: 0.7328, Acc.ball: 0.6362, Acc.food: 0.7287, Acc.step: 0.2598, Acc.tank: 0.7549, Acc.trade name: 0.3340, Acc.microwave: 0.9418, Acc.pot: 0.5933, Acc.animal: 0.6338, Acc.bicycle: 0.6996, Acc.lake: 0.6324, Acc.dishwasher: 0.7527, Acc.screen: 0.9301, Acc.blanket: 0.4033, Acc.sculpture: 0.8669, Acc.hood: 0.7405, Acc.sconce: 0.6636, Acc.vase: 0.5710, Acc.traffic light: 0.4917, Acc.tray: 0.2454, Acc.ashcan: 0.5617, Acc.fan: 0.7324, Acc.pier: 0.5015, Acc.crt screen: 0.2861, Acc.plate: 0.7099, Acc.monitor: 0.8346, Acc.bulletin board: 0.6578, Acc.shower: 0.0025, Acc.radiator: 0.7165, Acc.glass: 0.1926, Acc.clock: 0.4702, Acc.flag: 0.7943 +2024-06-18 12:12:12,677 - mmseg - INFO - Iter [31050/80000] lr: 2.448e-05, eta: 20:06:38, time: 3.293, data_time: 1.970, memory: 70498, decode.loss_ce: 0.2507, decode.acc_seg: 89.4456, aux.loss_ce: 0.1037, aux.acc_seg: 89.1245, loss: 0.3544 +2024-06-18 12:13:19,289 - mmseg - INFO - Iter [31100/80000] lr: 2.445e-05, eta: 20:05:12, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2665, decode.acc_seg: 89.0452, aux.loss_ce: 0.1097, aux.acc_seg: 88.7336, loss: 0.3762 +2024-06-18 12:14:25,863 - mmseg - INFO - Iter [31150/80000] lr: 2.443e-05, eta: 20:03:47, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2754, decode.acc_seg: 88.9595, aux.loss_ce: 0.1133, aux.acc_seg: 88.6911, loss: 0.3887 +2024-06-18 12:15:32,462 - mmseg - INFO - Iter [31200/80000] lr: 2.440e-05, eta: 20:02:21, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2509, decode.acc_seg: 89.6905, aux.loss_ce: 0.1030, aux.acc_seg: 89.4945, loss: 0.3539 +2024-06-18 12:16:38,988 - mmseg - INFO - Iter [31250/80000] lr: 2.438e-05, eta: 20:00:56, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2530, decode.acc_seg: 89.3694, aux.loss_ce: 0.1040, aux.acc_seg: 89.1488, loss: 0.3570 +2024-06-18 12:17:45,431 - mmseg - INFO - Iter [31300/80000] lr: 2.435e-05, eta: 19:59:30, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2556, decode.acc_seg: 89.2299, aux.loss_ce: 0.1039, aux.acc_seg: 89.0134, loss: 0.3595 +2024-06-18 12:18:52,002 - mmseg - INFO - Iter [31350/80000] lr: 2.433e-05, eta: 19:58:05, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2612, decode.acc_seg: 89.3084, aux.loss_ce: 0.1072, aux.acc_seg: 89.0973, loss: 0.3685 +2024-06-18 12:19:58,465 - mmseg - INFO - Iter [31400/80000] lr: 2.430e-05, eta: 19:56:40, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2336, decode.acc_seg: 90.1242, aux.loss_ce: 0.0953, aux.acc_seg: 90.0318, loss: 0.3289 +2024-06-18 12:21:04,839 - mmseg - INFO - Iter [31450/80000] lr: 2.428e-05, eta: 19:55:14, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2528, decode.acc_seg: 89.5727, aux.loss_ce: 0.1036, aux.acc_seg: 89.3160, loss: 0.3564 +2024-06-18 12:22:11,223 - mmseg - INFO - Iter [31500/80000] lr: 2.425e-05, eta: 19:53:49, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2526, decode.acc_seg: 89.4684, aux.loss_ce: 0.1036, aux.acc_seg: 89.2292, loss: 0.3562 +2024-06-18 12:23:17,773 - mmseg - INFO - Iter [31550/80000] lr: 2.423e-05, eta: 19:52:24, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2689, decode.acc_seg: 89.2692, aux.loss_ce: 0.1097, aux.acc_seg: 89.0873, loss: 0.3786 +2024-06-18 12:24:26,133 - mmseg - INFO - Iter [31600/80000] lr: 2.420e-05, eta: 19:51:02, time: 1.367, data_time: 0.052, memory: 70498, decode.loss_ce: 0.2643, decode.acc_seg: 89.7814, aux.loss_ce: 0.1080, aux.acc_seg: 89.5597, loss: 0.3723 +2024-06-18 12:25:32,445 - mmseg - INFO - Iter [31650/80000] lr: 2.418e-05, eta: 19:49:36, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2426, decode.acc_seg: 90.0580, aux.loss_ce: 0.0994, aux.acc_seg: 89.7407, loss: 0.3420 +2024-06-18 12:26:38,986 - mmseg - INFO - Iter [31700/80000] lr: 2.415e-05, eta: 19:48:12, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2566, decode.acc_seg: 89.5585, aux.loss_ce: 0.1059, aux.acc_seg: 89.2053, loss: 0.3625 +2024-06-18 12:27:45,735 - mmseg - INFO - Iter [31750/80000] lr: 2.413e-05, eta: 19:46:47, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2549, decode.acc_seg: 89.6248, aux.loss_ce: 0.1039, aux.acc_seg: 89.3149, loss: 0.3588 +2024-06-18 12:28:51,979 - mmseg - INFO - Iter [31800/80000] lr: 2.410e-05, eta: 19:45:22, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2567, decode.acc_seg: 89.6487, aux.loss_ce: 0.1052, aux.acc_seg: 89.2731, loss: 0.3619 +2024-06-18 12:29:58,230 - mmseg - INFO - Iter [31850/80000] lr: 2.408e-05, eta: 19:43:57, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2507, decode.acc_seg: 89.6312, aux.loss_ce: 0.1033, aux.acc_seg: 89.4360, loss: 0.3540 +2024-06-18 12:31:04,521 - mmseg - INFO - Iter [31900/80000] lr: 2.405e-05, eta: 19:42:32, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2538, decode.acc_seg: 89.5287, aux.loss_ce: 0.1051, aux.acc_seg: 89.2802, loss: 0.3589 +2024-06-18 12:32:11,135 - mmseg - INFO - Iter [31950/80000] lr: 2.403e-05, eta: 19:41:07, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2657, decode.acc_seg: 89.0702, aux.loss_ce: 0.1085, aux.acc_seg: 88.7639, loss: 0.3741 +2024-06-18 12:33:17,347 - mmseg - INFO - Saving checkpoint at 32000 iterations +2024-06-18 12:35:01,157 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 12:35:01,157 - mmseg - INFO - Iter [32000/80000] lr: 2.400e-05, eta: 19:42:18, time: 3.400, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2536, decode.acc_seg: 89.6711, aux.loss_ce: 0.1033, aux.acc_seg: 89.4481, loss: 0.3570 +2024-06-18 12:36:37,776 - mmseg - INFO - per class results: +2024-06-18 12:36:37,782 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.38 | 89.69 | +| building | 85.33 | 93.44 | +| sky | 94.91 | 97.42 | +| floor | 84.29 | 89.88 | +| tree | 78.12 | 88.71 | +| ceiling | 86.31 | 93.23 | +| road | 85.81 | 89.82 | +| bed | 92.29 | 97.28 | +| windowpane | 65.26 | 79.99 | +| grass | 68.83 | 89.78 | +| cabinet | 64.17 | 72.94 | +| sidewalk | 70.98 | 84.72 | +| person | 85.2 | 92.9 | +| earth | 34.98 | 46.23 | +| door | 58.89 | 77.57 | +| table | 67.46 | 77.97 | +| mountain | 63.58 | 77.24 | +| plant | 57.26 | 68.67 | +| curtain | 78.33 | 91.02 | +| chair | 66.39 | 79.08 | +| car | 86.93 | 94.51 | +| water | 60.17 | 71.66 | +| painting | 76.17 | 91.04 | +| sofa | 76.86 | 82.72 | +| shelf | 48.28 | 69.56 | +| house | 62.71 | 83.76 | +| sea | 63.45 | 83.1 | +| mirror | 78.64 | 88.69 | +| rug | 69.4 | 87.09 | +| field | 42.43 | 55.81 | +| armchair | 54.16 | 81.15 | +| seat | 68.21 | 85.27 | +| fence | 51.15 | 64.5 | +| desk | 53.08 | 77.24 | +| rock | 58.06 | 85.35 | +| wardrobe | 51.53 | 68.38 | +| lamp | 71.16 | 82.15 | +| bathtub | 82.55 | 85.37 | +| railing | 39.4 | 54.04 | +| cushion | 65.94 | 73.8 | +| base | 38.47 | 51.0 | +| box | 36.64 | 48.98 | +| column | 51.1 | 59.58 | +| signboard | 42.75 | 55.48 | +| chest of drawers | 46.43 | 69.92 | +| counter | 50.36 | 62.34 | +| sand | 47.08 | 67.63 | +| sink | 74.41 | 83.1 | +| skyscraper | 46.69 | 59.97 | +| fireplace | 74.03 | 91.87 | +| refrigerator | 81.77 | 92.13 | +| grandstand | 48.94 | 87.23 | +| path | 26.21 | 47.99 | +| stairs | 24.5 | 30.57 | +| runway | 67.99 | 90.11 | +| case | 60.95 | 80.2 | +| pool table | 94.45 | 97.72 | +| pillow | 69.53 | 81.28 | +| screen door | 79.37 | 85.11 | +| stairway | 51.96 | 64.86 | +| river | 18.55 | 38.81 | +| bridge | 65.09 | 73.77 | +| bookcase | 39.28 | 59.61 | +| blind | 42.49 | 47.2 | +| coffee table | 65.93 | 88.02 | +| toilet | 87.03 | 93.44 | +| flower | 46.19 | 66.2 | +| book | 53.74 | 69.13 | +| hill | 3.7 | 6.75 | +| bench | 53.22 | 65.98 | +| countertop | 63.09 | 80.19 | +| stove | 82.53 | 88.57 | +| palm | 58.1 | 81.45 | +| kitchen island | 43.39 | 74.94 | +| computer | 77.53 | 92.45 | +| swivel chair | 49.22 | 72.9 | +| boat | 51.3 | 85.44 | +| bar | 58.13 | 65.81 | +| arcade machine | 69.82 | 84.65 | +| hovel | 14.3 | 16.3 | +| bus | 92.77 | 95.37 | +| towel | 75.91 | 83.62 | +| light | 58.5 | 67.1 | +| truck | 47.93 | 62.02 | +| tower | 6.05 | 7.87 | +| chandelier | 68.66 | 84.07 | +| awning | 47.7 | 60.57 | +| streetlight | 28.61 | 38.21 | +| booth | 69.2 | 71.1 | +| television receiver | 79.27 | 84.31 | +| airplane | 81.69 | 86.64 | +| dirt track | 8.29 | 36.78 | +| apparel | 48.51 | 67.56 | +| pole | 26.35 | 35.95 | +| land | 1.78 | 5.94 | +| bannister | 14.31 | 19.07 | +| escalator | 54.54 | 84.86 | +| ottoman | 49.34 | 69.87 | +| bottle | 41.55 | 67.83 | +| buffet | 56.98 | 64.54 | +| poster | 33.16 | 48.48 | +| stage | 17.97 | 31.81 | +| van | 43.52 | 50.14 | +| ship | 93.82 | 97.42 | +| fountain | 31.19 | 32.39 | +| conveyer belt | 75.77 | 93.5 | +| canopy | 49.95 | 77.78 | +| washer | 81.76 | 86.64 | +| plaything | 34.09 | 46.88 | +| swimming pool | 58.86 | 89.56 | +| stool | 50.94 | 65.37 | +| barrel | 25.25 | 64.88 | +| basket | 38.43 | 55.71 | +| waterfall | 56.47 | 82.1 | +| tent | 90.47 | 98.67 | +| bag | 18.76 | 20.86 | +| minibike | 72.86 | 85.86 | +| cradle | 74.07 | 98.7 | +| oven | 56.77 | 79.55 | +| ball | 36.84 | 38.07 | +| food | 62.93 | 80.31 | +| step | 12.98 | 18.05 | +| tank | 61.84 | 65.99 | +| trade name | 33.95 | 42.72 | +| microwave | 88.83 | 95.67 | +| pot | 56.55 | 68.18 | +| animal | 61.77 | 62.87 | +| bicycle | 57.26 | 75.28 | +| lake | 57.04 | 63.43 | +| dishwasher | 66.93 | 85.52 | +| screen | 53.53 | 82.15 | +| blanket | 22.25 | 24.34 | +| sculpture | 73.7 | 84.33 | +| hood | 60.74 | 69.86 | +| sconce | 52.06 | 60.27 | +| vase | 44.5 | 58.19 | +| traffic light | 39.02 | 57.39 | +| tray | 14.94 | 19.89 | +| ashcan | 39.31 | 68.81 | +| fan | 62.19 | 75.53 | +| pier | 32.45 | 47.04 | +| crt screen | 10.9 | 34.09 | +| plate | 60.23 | 75.01 | +| monitor | 16.72 | 17.81 | +| bulletin board | 61.62 | 67.88 | +| shower | 0.0 | 0.0 | +| radiator | 60.4 | 72.76 | +| glass | 17.58 | 18.88 | +| clock | 40.74 | 46.75 | +| flag | 68.53 | 81.03 | ++---------------------+-------+-------+ +2024-06-18 12:36:37,782 - mmseg - INFO - Summary: +2024-06-18 12:36:37,782 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.67 | 55.18 | 68.39 | ++-------+-------+-------+ +2024-06-18 12:36:37,783 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 12:36:37,783 - mmseg - INFO - Iter(val) [250] aAcc: 0.8567, mIoU: 0.5518, mAcc: 0.6839, IoU.wall: 0.8138, IoU.building: 0.8533, IoU.sky: 0.9491, IoU.floor: 0.8429, IoU.tree: 0.7812, IoU.ceiling: 0.8631, IoU.road: 0.8581, IoU.bed : 0.9229, IoU.windowpane: 0.6526, IoU.grass: 0.6883, IoU.cabinet: 0.6417, IoU.sidewalk: 0.7098, IoU.person: 0.8520, IoU.earth: 0.3498, IoU.door: 0.5889, IoU.table: 0.6746, IoU.mountain: 0.6358, IoU.plant: 0.5726, IoU.curtain: 0.7833, IoU.chair: 0.6639, IoU.car: 0.8693, IoU.water: 0.6017, IoU.painting: 0.7617, IoU.sofa: 0.7686, IoU.shelf: 0.4828, IoU.house: 0.6271, IoU.sea: 0.6345, IoU.mirror: 0.7864, IoU.rug: 0.6940, IoU.field: 0.4243, IoU.armchair: 0.5416, IoU.seat: 0.6821, IoU.fence: 0.5115, IoU.desk: 0.5308, IoU.rock: 0.5806, IoU.wardrobe: 0.5153, IoU.lamp: 0.7116, IoU.bathtub: 0.8255, IoU.railing: 0.3940, IoU.cushion: 0.6594, IoU.base: 0.3847, IoU.box: 0.3664, IoU.column: 0.5110, IoU.signboard: 0.4275, IoU.chest of drawers: 0.4643, IoU.counter: 0.5036, IoU.sand: 0.4708, IoU.sink: 0.7441, IoU.skyscraper: 0.4669, IoU.fireplace: 0.7403, IoU.refrigerator: 0.8177, IoU.grandstand: 0.4894, IoU.path: 0.2621, IoU.stairs: 0.2450, IoU.runway: 0.6799, IoU.case: 0.6095, IoU.pool table: 0.9445, IoU.pillow: 0.6953, IoU.screen door: 0.7937, IoU.stairway: 0.5196, IoU.river: 0.1855, IoU.bridge: 0.6509, IoU.bookcase: 0.3928, IoU.blind: 0.4249, IoU.coffee table: 0.6593, IoU.toilet: 0.8703, IoU.flower: 0.4619, IoU.book: 0.5374, IoU.hill: 0.0370, IoU.bench: 0.5322, IoU.countertop: 0.6309, IoU.stove: 0.8253, IoU.palm: 0.5810, IoU.kitchen island: 0.4339, IoU.computer: 0.7753, IoU.swivel chair: 0.4922, IoU.boat: 0.5130, IoU.bar: 0.5813, IoU.arcade machine: 0.6982, IoU.hovel: 0.1430, IoU.bus: 0.9277, IoU.towel: 0.7591, IoU.light: 0.5850, IoU.truck: 0.4793, IoU.tower: 0.0605, IoU.chandelier: 0.6866, IoU.awning: 0.4770, IoU.streetlight: 0.2861, IoU.booth: 0.6920, IoU.television receiver: 0.7927, IoU.airplane: 0.8169, IoU.dirt track: 0.0829, IoU.apparel: 0.4851, IoU.pole: 0.2635, IoU.land: 0.0178, IoU.bannister: 0.1431, IoU.escalator: 0.5454, IoU.ottoman: 0.4934, IoU.bottle: 0.4155, IoU.buffet: 0.5698, IoU.poster: 0.3316, IoU.stage: 0.1797, IoU.van: 0.4352, IoU.ship: 0.9382, IoU.fountain: 0.3119, IoU.conveyer belt: 0.7577, IoU.canopy: 0.4995, IoU.washer: 0.8176, IoU.plaything: 0.3409, IoU.swimming pool: 0.5886, IoU.stool: 0.5094, IoU.barrel: 0.2525, IoU.basket: 0.3843, IoU.waterfall: 0.5647, IoU.tent: 0.9047, IoU.bag: 0.1876, IoU.minibike: 0.7286, IoU.cradle: 0.7407, IoU.oven: 0.5677, IoU.ball: 0.3684, IoU.food: 0.6293, IoU.step: 0.1298, IoU.tank: 0.6184, IoU.trade name: 0.3395, IoU.microwave: 0.8883, IoU.pot: 0.5655, IoU.animal: 0.6177, IoU.bicycle: 0.5726, IoU.lake: 0.5704, IoU.dishwasher: 0.6693, IoU.screen: 0.5353, IoU.blanket: 0.2225, IoU.sculpture: 0.7370, IoU.hood: 0.6074, IoU.sconce: 0.5206, IoU.vase: 0.4450, IoU.traffic light: 0.3902, IoU.tray: 0.1494, IoU.ashcan: 0.3931, IoU.fan: 0.6219, IoU.pier: 0.3245, IoU.crt screen: 0.1090, IoU.plate: 0.6023, IoU.monitor: 0.1672, IoU.bulletin board: 0.6162, IoU.shower: 0.0000, IoU.radiator: 0.6040, IoU.glass: 0.1758, IoU.clock: 0.4074, IoU.flag: 0.6853, Acc.wall: 0.8969, Acc.building: 0.9344, Acc.sky: 0.9742, Acc.floor: 0.8988, Acc.tree: 0.8871, Acc.ceiling: 0.9323, Acc.road: 0.8982, Acc.bed : 0.9728, Acc.windowpane: 0.7999, Acc.grass: 0.8978, Acc.cabinet: 0.7294, Acc.sidewalk: 0.8472, Acc.person: 0.9290, Acc.earth: 0.4623, Acc.door: 0.7757, Acc.table: 0.7797, Acc.mountain: 0.7724, Acc.plant: 0.6867, Acc.curtain: 0.9102, Acc.chair: 0.7908, Acc.car: 0.9451, Acc.water: 0.7166, Acc.painting: 0.9104, Acc.sofa: 0.8272, Acc.shelf: 0.6956, Acc.house: 0.8376, Acc.sea: 0.8310, Acc.mirror: 0.8869, Acc.rug: 0.8709, Acc.field: 0.5581, Acc.armchair: 0.8115, Acc.seat: 0.8527, Acc.fence: 0.6450, Acc.desk: 0.7724, Acc.rock: 0.8535, Acc.wardrobe: 0.6838, Acc.lamp: 0.8215, Acc.bathtub: 0.8537, Acc.railing: 0.5404, Acc.cushion: 0.7380, Acc.base: 0.5100, Acc.box: 0.4898, Acc.column: 0.5958, Acc.signboard: 0.5548, Acc.chest of drawers: 0.6992, Acc.counter: 0.6234, Acc.sand: 0.6763, Acc.sink: 0.8310, Acc.skyscraper: 0.5997, Acc.fireplace: 0.9187, Acc.refrigerator: 0.9213, Acc.grandstand: 0.8723, Acc.path: 0.4799, Acc.stairs: 0.3057, Acc.runway: 0.9011, Acc.case: 0.8020, Acc.pool table: 0.9772, Acc.pillow: 0.8128, Acc.screen door: 0.8511, Acc.stairway: 0.6486, Acc.river: 0.3881, Acc.bridge: 0.7377, Acc.bookcase: 0.5961, Acc.blind: 0.4720, Acc.coffee table: 0.8802, Acc.toilet: 0.9344, Acc.flower: 0.6620, Acc.book: 0.6913, Acc.hill: 0.0675, Acc.bench: 0.6598, Acc.countertop: 0.8019, Acc.stove: 0.8857, Acc.palm: 0.8145, Acc.kitchen island: 0.7494, Acc.computer: 0.9245, Acc.swivel chair: 0.7290, Acc.boat: 0.8544, Acc.bar: 0.6581, Acc.arcade machine: 0.8465, Acc.hovel: 0.1630, Acc.bus: 0.9537, Acc.towel: 0.8362, Acc.light: 0.6710, Acc.truck: 0.6202, Acc.tower: 0.0787, Acc.chandelier: 0.8407, Acc.awning: 0.6057, Acc.streetlight: 0.3821, Acc.booth: 0.7110, Acc.television receiver: 0.8431, Acc.airplane: 0.8664, Acc.dirt track: 0.3678, Acc.apparel: 0.6756, Acc.pole: 0.3595, Acc.land: 0.0594, Acc.bannister: 0.1907, Acc.escalator: 0.8486, Acc.ottoman: 0.6987, Acc.bottle: 0.6783, Acc.buffet: 0.6454, Acc.poster: 0.4848, Acc.stage: 0.3181, Acc.van: 0.5014, Acc.ship: 0.9742, Acc.fountain: 0.3239, Acc.conveyer belt: 0.9350, Acc.canopy: 0.7778, Acc.washer: 0.8664, Acc.plaything: 0.4688, Acc.swimming pool: 0.8956, Acc.stool: 0.6537, Acc.barrel: 0.6488, Acc.basket: 0.5571, Acc.waterfall: 0.8210, Acc.tent: 0.9867, Acc.bag: 0.2086, Acc.minibike: 0.8586, Acc.cradle: 0.9870, Acc.oven: 0.7955, Acc.ball: 0.3807, Acc.food: 0.8031, Acc.step: 0.1805, Acc.tank: 0.6599, Acc.trade name: 0.4272, Acc.microwave: 0.9567, Acc.pot: 0.6818, Acc.animal: 0.6287, Acc.bicycle: 0.7528, Acc.lake: 0.6343, Acc.dishwasher: 0.8552, Acc.screen: 0.8215, Acc.blanket: 0.2434, Acc.sculpture: 0.8433, Acc.hood: 0.6986, Acc.sconce: 0.6027, Acc.vase: 0.5819, Acc.traffic light: 0.5739, Acc.tray: 0.1989, Acc.ashcan: 0.6881, Acc.fan: 0.7553, Acc.pier: 0.4704, Acc.crt screen: 0.3409, Acc.plate: 0.7501, Acc.monitor: 0.1781, Acc.bulletin board: 0.6788, Acc.shower: 0.0000, Acc.radiator: 0.7276, Acc.glass: 0.1888, Acc.clock: 0.4675, Acc.flag: 0.8103 +2024-06-18 12:37:46,488 - mmseg - INFO - Iter [32050/80000] lr: 2.398e-05, eta: 19:43:21, time: 3.307, data_time: 1.990, memory: 70498, decode.loss_ce: 0.2394, decode.acc_seg: 90.2119, aux.loss_ce: 0.0988, aux.acc_seg: 89.9582, loss: 0.3383 +2024-06-18 12:38:52,891 - mmseg - INFO - Iter [32100/80000] lr: 2.395e-05, eta: 19:41:55, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2559, decode.acc_seg: 89.3458, aux.loss_ce: 0.1050, aux.acc_seg: 89.1034, loss: 0.3609 +2024-06-18 12:39:59,209 - mmseg - INFO - Iter [32150/80000] lr: 2.393e-05, eta: 19:40:30, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2510, decode.acc_seg: 89.3523, aux.loss_ce: 0.1030, aux.acc_seg: 89.1029, loss: 0.3540 +2024-06-18 12:41:05,598 - mmseg - INFO - Iter [32200/80000] lr: 2.390e-05, eta: 19:39:04, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2484, decode.acc_seg: 89.4379, aux.loss_ce: 0.1019, aux.acc_seg: 89.1852, loss: 0.3503 +2024-06-18 12:42:12,070 - mmseg - INFO - Iter [32250/80000] lr: 2.388e-05, eta: 19:37:39, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2601, decode.acc_seg: 89.1229, aux.loss_ce: 0.1067, aux.acc_seg: 88.8279, loss: 0.3668 +2024-06-18 12:43:18,237 - mmseg - INFO - Iter [32300/80000] lr: 2.385e-05, eta: 19:36:14, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2520, decode.acc_seg: 89.4098, aux.loss_ce: 0.1039, aux.acc_seg: 89.1203, loss: 0.3559 +2024-06-18 12:44:24,589 - mmseg - INFO - Iter [32350/80000] lr: 2.383e-05, eta: 19:34:49, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2754, decode.acc_seg: 88.8248, aux.loss_ce: 0.1128, aux.acc_seg: 88.5936, loss: 0.3882 +2024-06-18 12:45:31,022 - mmseg - INFO - Iter [32400/80000] lr: 2.380e-05, eta: 19:33:24, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2426, decode.acc_seg: 90.3406, aux.loss_ce: 0.0993, aux.acc_seg: 90.1013, loss: 0.3419 +2024-06-18 12:46:37,382 - mmseg - INFO - Iter [32450/80000] lr: 2.378e-05, eta: 19:31:58, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2506, decode.acc_seg: 90.0775, aux.loss_ce: 0.1029, aux.acc_seg: 89.8057, loss: 0.3535 +2024-06-18 12:47:43,679 - mmseg - INFO - Iter [32500/80000] lr: 2.375e-05, eta: 19:30:33, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2535, decode.acc_seg: 89.4467, aux.loss_ce: 0.1042, aux.acc_seg: 89.1814, loss: 0.3577 +2024-06-18 12:48:50,066 - mmseg - INFO - Iter [32550/80000] lr: 2.373e-05, eta: 19:29:08, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2659, decode.acc_seg: 89.1999, aux.loss_ce: 0.1088, aux.acc_seg: 88.9171, loss: 0.3747 +2024-06-18 12:49:56,356 - mmseg - INFO - Iter [32600/80000] lr: 2.370e-05, eta: 19:27:43, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2522, decode.acc_seg: 89.5938, aux.loss_ce: 0.1040, aux.acc_seg: 89.2523, loss: 0.3562 +2024-06-18 12:51:02,437 - mmseg - INFO - Iter [32650/80000] lr: 2.368e-05, eta: 19:26:18, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2535, decode.acc_seg: 89.4975, aux.loss_ce: 0.1053, aux.acc_seg: 89.1423, loss: 0.3588 +2024-06-18 12:52:08,955 - mmseg - INFO - Iter [32700/80000] lr: 2.365e-05, eta: 19:24:54, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2807, decode.acc_seg: 88.6364, aux.loss_ce: 0.1151, aux.acc_seg: 88.2951, loss: 0.3958 +2024-06-18 12:53:15,396 - mmseg - INFO - Iter [32750/80000] lr: 2.363e-05, eta: 19:23:29, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2757, decode.acc_seg: 88.6728, aux.loss_ce: 0.1113, aux.acc_seg: 88.3970, loss: 0.3869 +2024-06-18 12:54:21,554 - mmseg - INFO - Iter [32800/80000] lr: 2.360e-05, eta: 19:22:04, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2613, decode.acc_seg: 88.9464, aux.loss_ce: 0.1069, aux.acc_seg: 88.7622, loss: 0.3682 +2024-06-18 12:55:30,425 - mmseg - INFO - Iter [32850/80000] lr: 2.358e-05, eta: 19:20:43, time: 1.377, data_time: 0.057, memory: 70498, decode.loss_ce: 0.2582, decode.acc_seg: 89.3738, aux.loss_ce: 0.1060, aux.acc_seg: 89.0973, loss: 0.3642 +2024-06-18 12:56:36,719 - mmseg - INFO - Iter [32900/80000] lr: 2.355e-05, eta: 19:19:18, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2512, decode.acc_seg: 89.6285, aux.loss_ce: 0.1041, aux.acc_seg: 89.2152, loss: 0.3553 +2024-06-18 12:57:43,075 - mmseg - INFO - Iter [32950/80000] lr: 2.353e-05, eta: 19:17:54, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2490, decode.acc_seg: 89.9507, aux.loss_ce: 0.1023, aux.acc_seg: 89.6727, loss: 0.3513 +2024-06-18 12:58:49,522 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 12:58:49,522 - mmseg - INFO - Iter [33000/80000] lr: 2.350e-05, eta: 19:16:29, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2395, decode.acc_seg: 90.1019, aux.loss_ce: 0.0990, aux.acc_seg: 89.7910, loss: 0.3385 +2024-06-18 13:00:26,936 - mmseg - INFO - per class results: +2024-06-18 13:00:26,942 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.14 | 89.42 | +| building | 84.31 | 93.54 | +| sky | 94.87 | 97.37 | +| floor | 84.99 | 91.15 | +| tree | 78.06 | 89.41 | +| ceiling | 85.99 | 91.95 | +| road | 86.5 | 91.28 | +| bed | 92.31 | 97.03 | +| windowpane | 65.14 | 80.88 | +| grass | 67.28 | 81.37 | +| cabinet | 65.15 | 79.61 | +| sidewalk | 72.79 | 87.34 | +| person | 85.43 | 94.06 | +| earth | 35.18 | 48.14 | +| door | 59.12 | 73.1 | +| table | 67.49 | 82.06 | +| mountain | 63.99 | 75.96 | +| plant | 58.69 | 71.87 | +| curtain | 79.04 | 89.66 | +| chair | 64.83 | 75.56 | +| car | 86.99 | 93.31 | +| water | 58.47 | 70.58 | +| painting | 77.02 | 90.23 | +| sofa | 79.01 | 89.92 | +| shelf | 52.79 | 69.59 | +| house | 51.06 | 60.37 | +| sea | 61.43 | 78.48 | +| mirror | 75.38 | 84.62 | +| rug | 71.41 | 79.3 | +| field | 39.55 | 70.94 | +| armchair | 58.06 | 77.47 | +| seat | 66.42 | 85.75 | +| fence | 51.44 | 65.34 | +| desk | 52.92 | 73.9 | +| rock | 55.39 | 71.52 | +| wardrobe | 56.67 | 74.74 | +| lamp | 72.22 | 83.96 | +| bathtub | 83.62 | 85.98 | +| railing | 40.71 | 54.34 | +| cushion | 65.13 | 71.48 | +| base | 39.66 | 61.15 | +| box | 33.77 | 41.13 | +| column | 52.81 | 65.17 | +| signboard | 41.72 | 57.72 | +| chest of drawers | 43.4 | 59.26 | +| counter | 49.61 | 63.9 | +| sand | 39.35 | 56.3 | +| sink | 75.81 | 82.88 | +| skyscraper | 48.96 | 63.57 | +| fireplace | 69.82 | 89.21 | +| refrigerator | 80.17 | 90.96 | +| grandstand | 53.08 | 86.11 | +| path | 34.25 | 46.77 | +| stairs | 34.71 | 43.37 | +| runway | 74.86 | 97.84 | +| case | 61.85 | 83.21 | +| pool table | 94.4 | 98.14 | +| pillow | 69.63 | 83.48 | +| screen door | 71.18 | 74.69 | +| stairway | 58.51 | 65.15 | +| river | 17.63 | 39.23 | +| bridge | 74.6 | 83.75 | +| bookcase | 42.8 | 59.94 | +| blind | 47.03 | 56.27 | +| coffee table | 66.51 | 84.94 | +| toilet | 89.03 | 93.94 | +| flower | 43.92 | 56.81 | +| book | 53.45 | 73.71 | +| hill | 7.83 | 15.94 | +| bench | 53.46 | 61.51 | +| countertop | 64.35 | 86.44 | +| stove | 84.67 | 90.69 | +| palm | 57.19 | 77.79 | +| kitchen island | 44.85 | 70.01 | +| computer | 79.03 | 91.11 | +| swivel chair | 47.95 | 78.61 | +| boat | 56.34 | 85.77 | +| bar | 62.85 | 72.22 | +| arcade machine | 76.16 | 87.21 | +| hovel | 44.21 | 49.07 | +| bus | 92.97 | 95.89 | +| towel | 77.24 | 86.89 | +| light | 60.19 | 75.42 | +| truck | 45.86 | 62.56 | +| tower | 6.1 | 8.25 | +| chandelier | 71.22 | 90.73 | +| awning | 46.19 | 65.23 | +| streetlight | 31.36 | 42.53 | +| booth | 53.76 | 55.36 | +| television receiver | 75.38 | 83.23 | +| airplane | 72.41 | 76.72 | +| dirt track | 15.51 | 40.71 | +| apparel | 46.84 | 66.28 | +| pole | 27.84 | 37.63 | +| land | 2.97 | 8.19 | +| bannister | 17.96 | 26.35 | +| escalator | 60.86 | 77.24 | +| ottoman | 45.71 | 63.77 | +| bottle | 33.7 | 42.36 | +| buffet | 50.35 | 68.29 | +| poster | 33.58 | 47.25 | +| stage | 20.78 | 36.12 | +| van | 45.31 | 59.43 | +| ship | 91.06 | 95.03 | +| fountain | 31.44 | 32.01 | +| conveyer belt | 80.63 | 92.33 | +| canopy | 48.96 | 68.51 | +| washer | 74.5 | 77.08 | +| plaything | 35.88 | 44.98 | +| swimming pool | 60.91 | 92.65 | +| stool | 49.35 | 74.71 | +| barrel | 44.74 | 64.76 | +| basket | 40.97 | 60.23 | +| waterfall | 62.23 | 91.74 | +| tent | 93.79 | 98.35 | +| bag | 17.63 | 20.69 | +| minibike | 73.72 | 88.26 | +| cradle | 83.38 | 98.28 | +| oven | 66.1 | 77.4 | +| ball | 55.09 | 75.25 | +| food | 55.87 | 70.25 | +| step | 10.97 | 13.93 | +| tank | 66.17 | 75.46 | +| trade name | 21.99 | 24.39 | +| microwave | 89.97 | 94.74 | +| pot | 56.61 | 64.95 | +| animal | 63.7 | 65.55 | +| bicycle | 57.68 | 79.29 | +| lake | 47.5 | 63.77 | +| dishwasher | 71.24 | 79.68 | +| screen | 61.8 | 92.37 | +| blanket | 33.04 | 39.98 | +| sculpture | 68.51 | 86.45 | +| hood | 62.08 | 74.22 | +| sconce | 54.8 | 67.17 | +| vase | 44.86 | 58.09 | +| traffic light | 38.15 | 58.17 | +| tray | 8.01 | 9.12 | +| ashcan | 45.37 | 59.98 | +| fan | 64.85 | 78.65 | +| pier | 33.73 | 46.63 | +| crt screen | 20.11 | 30.97 | +| plate | 57.32 | 72.4 | +| monitor | 58.29 | 65.02 | +| bulletin board | 57.08 | 73.04 | +| shower | 0.39 | 2.41 | +| radiator | 59.28 | 69.52 | +| glass | 17.89 | 19.28 | +| clock | 32.52 | 36.84 | +| flag | 71.08 | 80.13 | ++---------------------+-------+-------+ +2024-06-18 13:00:26,942 - mmseg - INFO - Summary: +2024-06-18 13:00:26,942 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 85.73 | 56.3 | 69.06 | ++-------+------+-------+ +2024-06-18 13:00:26,943 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 13:00:26,943 - mmseg - INFO - Iter(val) [250] aAcc: 0.8573, mIoU: 0.5630, mAcc: 0.6906, IoU.wall: 0.8114, IoU.building: 0.8431, IoU.sky: 0.9487, IoU.floor: 0.8499, IoU.tree: 0.7806, IoU.ceiling: 0.8599, IoU.road: 0.8650, IoU.bed : 0.9231, IoU.windowpane: 0.6514, IoU.grass: 0.6728, IoU.cabinet: 0.6515, IoU.sidewalk: 0.7279, IoU.person: 0.8543, IoU.earth: 0.3518, IoU.door: 0.5912, IoU.table: 0.6749, IoU.mountain: 0.6399, IoU.plant: 0.5869, IoU.curtain: 0.7904, IoU.chair: 0.6483, IoU.car: 0.8699, IoU.water: 0.5847, IoU.painting: 0.7702, IoU.sofa: 0.7901, IoU.shelf: 0.5279, IoU.house: 0.5106, IoU.sea: 0.6143, IoU.mirror: 0.7538, IoU.rug: 0.7141, IoU.field: 0.3955, IoU.armchair: 0.5806, IoU.seat: 0.6642, IoU.fence: 0.5144, IoU.desk: 0.5292, IoU.rock: 0.5539, IoU.wardrobe: 0.5667, IoU.lamp: 0.7222, IoU.bathtub: 0.8362, IoU.railing: 0.4071, IoU.cushion: 0.6513, IoU.base: 0.3966, IoU.box: 0.3377, IoU.column: 0.5281, IoU.signboard: 0.4172, IoU.chest of drawers: 0.4340, IoU.counter: 0.4961, IoU.sand: 0.3935, IoU.sink: 0.7581, IoU.skyscraper: 0.4896, IoU.fireplace: 0.6982, IoU.refrigerator: 0.8017, IoU.grandstand: 0.5308, IoU.path: 0.3425, IoU.stairs: 0.3471, IoU.runway: 0.7486, IoU.case: 0.6185, IoU.pool table: 0.9440, IoU.pillow: 0.6963, IoU.screen door: 0.7118, IoU.stairway: 0.5851, IoU.river: 0.1763, IoU.bridge: 0.7460, IoU.bookcase: 0.4280, IoU.blind: 0.4703, IoU.coffee table: 0.6651, IoU.toilet: 0.8903, IoU.flower: 0.4392, IoU.book: 0.5345, IoU.hill: 0.0783, IoU.bench: 0.5346, IoU.countertop: 0.6435, IoU.stove: 0.8467, IoU.palm: 0.5719, IoU.kitchen island: 0.4485, IoU.computer: 0.7903, IoU.swivel chair: 0.4795, IoU.boat: 0.5634, IoU.bar: 0.6285, IoU.arcade machine: 0.7616, IoU.hovel: 0.4421, IoU.bus: 0.9297, IoU.towel: 0.7724, IoU.light: 0.6019, IoU.truck: 0.4586, IoU.tower: 0.0610, IoU.chandelier: 0.7122, IoU.awning: 0.4619, IoU.streetlight: 0.3136, IoU.booth: 0.5376, IoU.television receiver: 0.7538, IoU.airplane: 0.7241, IoU.dirt track: 0.1551, IoU.apparel: 0.4684, IoU.pole: 0.2784, IoU.land: 0.0297, IoU.bannister: 0.1796, IoU.escalator: 0.6086, IoU.ottoman: 0.4571, IoU.bottle: 0.3370, IoU.buffet: 0.5035, IoU.poster: 0.3358, IoU.stage: 0.2078, IoU.van: 0.4531, IoU.ship: 0.9106, IoU.fountain: 0.3144, IoU.conveyer belt: 0.8063, IoU.canopy: 0.4896, IoU.washer: 0.7450, IoU.plaything: 0.3588, IoU.swimming pool: 0.6091, IoU.stool: 0.4935, IoU.barrel: 0.4474, IoU.basket: 0.4097, IoU.waterfall: 0.6223, IoU.tent: 0.9379, IoU.bag: 0.1763, IoU.minibike: 0.7372, IoU.cradle: 0.8338, IoU.oven: 0.6610, IoU.ball: 0.5509, IoU.food: 0.5587, IoU.step: 0.1097, IoU.tank: 0.6617, IoU.trade name: 0.2199, IoU.microwave: 0.8997, IoU.pot: 0.5661, IoU.animal: 0.6370, IoU.bicycle: 0.5768, IoU.lake: 0.4750, IoU.dishwasher: 0.7124, IoU.screen: 0.6180, IoU.blanket: 0.3304, IoU.sculpture: 0.6851, IoU.hood: 0.6208, IoU.sconce: 0.5480, IoU.vase: 0.4486, IoU.traffic light: 0.3815, IoU.tray: 0.0801, IoU.ashcan: 0.4537, IoU.fan: 0.6485, IoU.pier: 0.3373, IoU.crt screen: 0.2011, IoU.plate: 0.5732, IoU.monitor: 0.5829, IoU.bulletin board: 0.5708, IoU.shower: 0.0039, IoU.radiator: 0.5928, IoU.glass: 0.1789, IoU.clock: 0.3252, IoU.flag: 0.7108, Acc.wall: 0.8942, Acc.building: 0.9354, Acc.sky: 0.9737, Acc.floor: 0.9115, Acc.tree: 0.8941, Acc.ceiling: 0.9195, Acc.road: 0.9128, Acc.bed : 0.9703, Acc.windowpane: 0.8088, Acc.grass: 0.8137, Acc.cabinet: 0.7961, Acc.sidewalk: 0.8734, Acc.person: 0.9406, Acc.earth: 0.4814, Acc.door: 0.7310, Acc.table: 0.8206, Acc.mountain: 0.7596, Acc.plant: 0.7187, Acc.curtain: 0.8966, Acc.chair: 0.7556, Acc.car: 0.9331, Acc.water: 0.7058, Acc.painting: 0.9023, Acc.sofa: 0.8992, Acc.shelf: 0.6959, Acc.house: 0.6037, Acc.sea: 0.7848, Acc.mirror: 0.8462, Acc.rug: 0.7930, Acc.field: 0.7094, Acc.armchair: 0.7747, Acc.seat: 0.8575, Acc.fence: 0.6534, Acc.desk: 0.7390, Acc.rock: 0.7152, Acc.wardrobe: 0.7474, Acc.lamp: 0.8396, Acc.bathtub: 0.8598, Acc.railing: 0.5434, Acc.cushion: 0.7148, Acc.base: 0.6115, Acc.box: 0.4113, Acc.column: 0.6517, Acc.signboard: 0.5772, Acc.chest of drawers: 0.5926, Acc.counter: 0.6390, Acc.sand: 0.5630, Acc.sink: 0.8288, Acc.skyscraper: 0.6357, Acc.fireplace: 0.8921, Acc.refrigerator: 0.9096, Acc.grandstand: 0.8611, Acc.path: 0.4677, Acc.stairs: 0.4337, Acc.runway: 0.9784, Acc.case: 0.8321, Acc.pool table: 0.9814, Acc.pillow: 0.8348, Acc.screen door: 0.7469, Acc.stairway: 0.6515, Acc.river: 0.3923, Acc.bridge: 0.8375, Acc.bookcase: 0.5994, Acc.blind: 0.5627, Acc.coffee table: 0.8494, Acc.toilet: 0.9394, Acc.flower: 0.5681, Acc.book: 0.7371, Acc.hill: 0.1594, Acc.bench: 0.6151, Acc.countertop: 0.8644, Acc.stove: 0.9069, Acc.palm: 0.7779, Acc.kitchen island: 0.7001, Acc.computer: 0.9111, Acc.swivel chair: 0.7861, Acc.boat: 0.8577, Acc.bar: 0.7222, Acc.arcade machine: 0.8721, Acc.hovel: 0.4907, Acc.bus: 0.9589, Acc.towel: 0.8689, Acc.light: 0.7542, Acc.truck: 0.6256, Acc.tower: 0.0825, Acc.chandelier: 0.9073, Acc.awning: 0.6523, Acc.streetlight: 0.4253, Acc.booth: 0.5536, Acc.television receiver: 0.8323, Acc.airplane: 0.7672, Acc.dirt track: 0.4071, Acc.apparel: 0.6628, Acc.pole: 0.3763, Acc.land: 0.0819, Acc.bannister: 0.2635, Acc.escalator: 0.7724, Acc.ottoman: 0.6377, Acc.bottle: 0.4236, Acc.buffet: 0.6829, Acc.poster: 0.4725, Acc.stage: 0.3612, Acc.van: 0.5943, Acc.ship: 0.9503, Acc.fountain: 0.3201, Acc.conveyer belt: 0.9233, Acc.canopy: 0.6851, Acc.washer: 0.7708, Acc.plaything: 0.4498, Acc.swimming pool: 0.9265, Acc.stool: 0.7471, Acc.barrel: 0.6476, Acc.basket: 0.6023, Acc.waterfall: 0.9174, Acc.tent: 0.9835, Acc.bag: 0.2069, Acc.minibike: 0.8826, Acc.cradle: 0.9828, Acc.oven: 0.7740, Acc.ball: 0.7525, Acc.food: 0.7025, Acc.step: 0.1393, Acc.tank: 0.7546, Acc.trade name: 0.2439, Acc.microwave: 0.9474, Acc.pot: 0.6495, Acc.animal: 0.6555, Acc.bicycle: 0.7929, Acc.lake: 0.6377, Acc.dishwasher: 0.7968, Acc.screen: 0.9237, Acc.blanket: 0.3998, Acc.sculpture: 0.8645, Acc.hood: 0.7422, Acc.sconce: 0.6717, Acc.vase: 0.5809, Acc.traffic light: 0.5817, Acc.tray: 0.0912, Acc.ashcan: 0.5998, Acc.fan: 0.7865, Acc.pier: 0.4663, Acc.crt screen: 0.3097, Acc.plate: 0.7240, Acc.monitor: 0.6502, Acc.bulletin board: 0.7304, Acc.shower: 0.0241, Acc.radiator: 0.6952, Acc.glass: 0.1928, Acc.clock: 0.3684, Acc.flag: 0.8013 +2024-06-18 13:01:33,975 - mmseg - INFO - Iter [33050/80000] lr: 2.348e-05, eta: 19:17:24, time: 3.289, data_time: 1.965, memory: 70498, decode.loss_ce: 0.2267, decode.acc_seg: 90.3815, aux.loss_ce: 0.0940, aux.acc_seg: 90.0728, loss: 0.3208 +2024-06-18 13:02:40,399 - mmseg - INFO - Iter [33100/80000] lr: 2.345e-05, eta: 19:16:00, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2541, decode.acc_seg: 89.8965, aux.loss_ce: 0.1048, aux.acc_seg: 89.5657, loss: 0.3590 +2024-06-18 13:03:46,694 - mmseg - INFO - Iter [33150/80000] lr: 2.343e-05, eta: 19:14:35, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2480, decode.acc_seg: 89.8530, aux.loss_ce: 0.1015, aux.acc_seg: 89.5416, loss: 0.3495 +2024-06-18 13:04:53,232 - mmseg - INFO - Iter [33200/80000] lr: 2.340e-05, eta: 19:13:11, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2499, decode.acc_seg: 89.6979, aux.loss_ce: 0.1031, aux.acc_seg: 89.3479, loss: 0.3530 +2024-06-18 13:05:59,472 - mmseg - INFO - Iter [33250/80000] lr: 2.338e-05, eta: 19:11:46, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2558, decode.acc_seg: 89.4464, aux.loss_ce: 0.1045, aux.acc_seg: 89.1477, loss: 0.3604 +2024-06-18 13:07:05,851 - mmseg - INFO - Iter [33300/80000] lr: 2.335e-05, eta: 19:10:21, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2647, decode.acc_seg: 89.3738, aux.loss_ce: 0.1079, aux.acc_seg: 89.1262, loss: 0.3726 +2024-06-18 13:08:12,178 - mmseg - INFO - Iter [33350/80000] lr: 2.333e-05, eta: 19:08:57, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2554, decode.acc_seg: 89.5689, aux.loss_ce: 0.1056, aux.acc_seg: 89.1420, loss: 0.3610 +2024-06-18 13:09:18,571 - mmseg - INFO - Iter [33400/80000] lr: 2.330e-05, eta: 19:07:33, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2300, decode.acc_seg: 90.5355, aux.loss_ce: 0.0959, aux.acc_seg: 90.1015, loss: 0.3260 +2024-06-18 13:10:24,979 - mmseg - INFO - Iter [33450/80000] lr: 2.328e-05, eta: 19:06:08, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2374, decode.acc_seg: 90.3971, aux.loss_ce: 0.0980, aux.acc_seg: 90.0609, loss: 0.3355 +2024-06-18 13:11:31,244 - mmseg - INFO - Iter [33500/80000] lr: 2.325e-05, eta: 19:04:44, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2385, decode.acc_seg: 89.9389, aux.loss_ce: 0.0989, aux.acc_seg: 89.6104, loss: 0.3374 +2024-06-18 13:12:37,707 - mmseg - INFO - Iter [33550/80000] lr: 2.323e-05, eta: 19:03:20, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2480, decode.acc_seg: 90.0830, aux.loss_ce: 0.1023, aux.acc_seg: 89.7175, loss: 0.3503 +2024-06-18 13:13:43,977 - mmseg - INFO - Iter [33600/80000] lr: 2.320e-05, eta: 19:01:55, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2494, decode.acc_seg: 89.9217, aux.loss_ce: 0.1025, aux.acc_seg: 89.6429, loss: 0.3519 +2024-06-18 13:14:50,155 - mmseg - INFO - Iter [33650/80000] lr: 2.318e-05, eta: 19:00:31, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2582, decode.acc_seg: 89.3688, aux.loss_ce: 0.1062, aux.acc_seg: 89.0483, loss: 0.3644 +2024-06-18 13:15:56,425 - mmseg - INFO - Iter [33700/80000] lr: 2.315e-05, eta: 18:59:07, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2396, decode.acc_seg: 90.1730, aux.loss_ce: 0.0983, aux.acc_seg: 89.9351, loss: 0.3379 +2024-06-18 13:17:02,905 - mmseg - INFO - Iter [33750/80000] lr: 2.313e-05, eta: 18:57:43, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2497, decode.acc_seg: 89.6518, aux.loss_ce: 0.1018, aux.acc_seg: 89.4504, loss: 0.3515 +2024-06-18 13:18:09,046 - mmseg - INFO - Iter [33800/80000] lr: 2.310e-05, eta: 18:56:19, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2595, decode.acc_seg: 89.4760, aux.loss_ce: 0.1064, aux.acc_seg: 89.2772, loss: 0.3659 +2024-06-18 13:19:15,221 - mmseg - INFO - Iter [33850/80000] lr: 2.308e-05, eta: 18:54:55, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2595, decode.acc_seg: 89.8531, aux.loss_ce: 0.1059, aux.acc_seg: 89.7057, loss: 0.3654 +2024-06-18 13:20:21,130 - mmseg - INFO - Iter [33900/80000] lr: 2.305e-05, eta: 18:53:30, time: 1.318, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2339, decode.acc_seg: 90.3409, aux.loss_ce: 0.0959, aux.acc_seg: 90.1233, loss: 0.3297 +2024-06-18 13:21:27,638 - mmseg - INFO - Iter [33950/80000] lr: 2.303e-05, eta: 18:52:07, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2311, decode.acc_seg: 90.4386, aux.loss_ce: 0.0956, aux.acc_seg: 90.1048, loss: 0.3267 +2024-06-18 13:22:33,982 - mmseg - INFO - Saving checkpoint at 34000 iterations +2024-06-18 13:24:13,558 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 13:24:13,558 - mmseg - INFO - Iter [34000/80000] lr: 2.300e-05, eta: 18:52:57, time: 3.318, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2259, decode.acc_seg: 90.0765, aux.loss_ce: 0.0931, aux.acc_seg: 89.8832, loss: 0.3190 +2024-06-18 13:25:49,210 - mmseg - INFO - per class results: +2024-06-18 13:25:49,217 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.17 | 89.46 | +| building | 84.17 | 93.53 | +| sky | 94.92 | 97.44 | +| floor | 85.25 | 90.97 | +| tree | 77.0 | 89.88 | +| ceiling | 85.44 | 90.33 | +| road | 85.73 | 93.83 | +| bed | 92.46 | 97.21 | +| windowpane | 66.25 | 82.27 | +| grass | 65.97 | 82.04 | +| cabinet | 65.24 | 78.31 | +| sidewalk | 69.85 | 82.16 | +| person | 85.5 | 92.38 | +| earth | 32.78 | 44.67 | +| door | 57.4 | 70.08 | +| table | 68.79 | 81.81 | +| mountain | 60.74 | 76.1 | +| plant | 53.91 | 62.88 | +| curtain | 78.69 | 91.06 | +| chair | 66.25 | 79.35 | +| car | 87.08 | 93.94 | +| water | 65.41 | 78.46 | +| painting | 74.37 | 90.93 | +| sofa | 79.16 | 92.11 | +| shelf | 48.04 | 72.8 | +| house | 52.95 | 65.9 | +| sea | 64.83 | 80.62 | +| mirror | 76.08 | 81.97 | +| rug | 72.79 | 83.41 | +| field | 33.2 | 51.45 | +| armchair | 56.46 | 74.62 | +| seat | 69.0 | 88.61 | +| fence | 51.58 | 60.6 | +| desk | 57.0 | 73.03 | +| rock | 50.65 | 71.59 | +| wardrobe | 52.48 | 70.12 | +| lamp | 72.06 | 83.16 | +| bathtub | 83.94 | 86.83 | +| railing | 41.72 | 62.17 | +| cushion | 65.93 | 77.07 | +| base | 32.53 | 52.57 | +| box | 37.2 | 47.51 | +| column | 52.83 | 70.72 | +| signboard | 42.72 | 55.34 | +| chest of drawers | 45.05 | 70.25 | +| counter | 46.48 | 56.11 | +| sand | 42.77 | 60.03 | +| sink | 74.74 | 84.35 | +| skyscraper | 47.67 | 60.69 | +| fireplace | 74.42 | 93.45 | +| refrigerator | 81.36 | 90.15 | +| grandstand | 53.89 | 85.83 | +| path | 30.94 | 38.52 | +| stairs | 24.69 | 28.25 | +| runway | 70.22 | 91.97 | +| case | 53.88 | 70.43 | +| pool table | 94.31 | 97.89 | +| pillow | 66.77 | 74.34 | +| screen door | 86.9 | 90.51 | +| stairway | 43.75 | 65.47 | +| river | 16.52 | 29.3 | +| bridge | 76.15 | 88.1 | +| bookcase | 48.99 | 64.35 | +| blind | 40.91 | 42.27 | +| coffee table | 65.93 | 88.94 | +| toilet | 89.05 | 94.37 | +| flower | 45.04 | 64.96 | +| book | 53.45 | 70.82 | +| hill | 4.48 | 11.16 | +| bench | 52.76 | 58.76 | +| countertop | 65.2 | 80.78 | +| stove | 85.02 | 93.32 | +| palm | 56.74 | 79.74 | +| kitchen island | 49.34 | 79.22 | +| computer | 75.7 | 95.82 | +| swivel chair | 52.86 | 69.42 | +| boat | 63.96 | 84.97 | +| bar | 62.41 | 73.78 | +| arcade machine | 75.29 | 84.18 | +| hovel | 40.81 | 50.07 | +| bus | 93.77 | 96.04 | +| towel | 70.75 | 86.76 | +| light | 59.59 | 68.45 | +| truck | 49.05 | 60.33 | +| tower | 9.34 | 17.49 | +| chandelier | 72.01 | 88.2 | +| awning | 44.91 | 58.6 | +| streetlight | 29.8 | 39.31 | +| booth | 56.34 | 69.94 | +| television receiver | 79.4 | 85.22 | +| airplane | 68.05 | 73.52 | +| dirt track | 23.08 | 36.47 | +| apparel | 43.97 | 55.12 | +| pole | 25.46 | 34.48 | +| land | 2.08 | 3.96 | +| bannister | 14.78 | 19.16 | +| escalator | 49.13 | 80.07 | +| ottoman | 44.1 | 64.35 | +| bottle | 38.34 | 53.89 | +| buffet | 57.68 | 63.81 | +| poster | 33.02 | 47.34 | +| stage | 21.64 | 33.81 | +| van | 44.1 | 55.87 | +| ship | 85.25 | 95.11 | +| fountain | 39.7 | 43.02 | +| conveyer belt | 81.0 | 93.02 | +| canopy | 51.03 | 85.79 | +| washer | 68.07 | 70.23 | +| plaything | 33.77 | 59.02 | +| swimming pool | 52.88 | 95.65 | +| stool | 49.94 | 73.49 | +| barrel | 57.77 | 64.24 | +| basket | 39.16 | 58.08 | +| waterfall | 49.96 | 71.91 | +| tent | 90.93 | 98.74 | +| bag | 17.35 | 20.76 | +| minibike | 72.13 | 86.27 | +| cradle | 73.1 | 98.41 | +| oven | 66.88 | 77.5 | +| ball | 47.73 | 51.06 | +| food | 58.15 | 67.32 | +| step | 15.14 | 20.1 | +| tank | 64.03 | 71.9 | +| trade name | 18.3 | 19.38 | +| microwave | 89.33 | 94.65 | +| pot | 55.51 | 68.32 | +| animal | 65.48 | 67.21 | +| bicycle | 57.53 | 70.11 | +| lake | 48.85 | 77.93 | +| dishwasher | 62.03 | 72.32 | +| screen | 60.9 | 92.0 | +| blanket | 16.63 | 17.91 | +| sculpture | 74.93 | 83.87 | +| hood | 63.09 | 73.86 | +| sconce | 52.34 | 61.54 | +| vase | 47.63 | 62.31 | +| traffic light | 37.62 | 59.09 | +| tray | 12.43 | 16.89 | +| ashcan | 43.45 | 60.36 | +| fan | 64.2 | 78.28 | +| pier | 32.76 | 47.39 | +| crt screen | 15.09 | 15.48 | +| plate | 56.03 | 70.92 | +| monitor | 68.3 | 80.63 | +| bulletin board | 58.23 | 68.57 | +| shower | 1.46 | 1.74 | +| radiator | 61.62 | 73.55 | +| glass | 17.5 | 18.81 | +| clock | 31.83 | 36.18 | +| flag | 70.32 | 78.0 | ++---------------------+-------+-------+ +2024-06-18 13:25:49,217 - mmseg - INFO - Summary: +2024-06-18 13:25:49,217 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.48 | 55.76 | 68.37 | ++-------+-------+-------+ +2024-06-18 13:25:49,218 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 13:25:49,218 - mmseg - INFO - Iter(val) [250] aAcc: 0.8548, mIoU: 0.5576, mAcc: 0.6837, IoU.wall: 0.8117, IoU.building: 0.8417, IoU.sky: 0.9492, IoU.floor: 0.8525, IoU.tree: 0.7700, IoU.ceiling: 0.8544, IoU.road: 0.8573, IoU.bed : 0.9246, IoU.windowpane: 0.6625, IoU.grass: 0.6597, IoU.cabinet: 0.6524, IoU.sidewalk: 0.6985, IoU.person: 0.8550, IoU.earth: 0.3278, IoU.door: 0.5740, IoU.table: 0.6879, IoU.mountain: 0.6074, IoU.plant: 0.5391, IoU.curtain: 0.7869, IoU.chair: 0.6625, IoU.car: 0.8708, IoU.water: 0.6541, IoU.painting: 0.7437, IoU.sofa: 0.7916, IoU.shelf: 0.4804, IoU.house: 0.5295, IoU.sea: 0.6483, IoU.mirror: 0.7608, IoU.rug: 0.7279, IoU.field: 0.3320, IoU.armchair: 0.5646, IoU.seat: 0.6900, IoU.fence: 0.5158, IoU.desk: 0.5700, IoU.rock: 0.5065, IoU.wardrobe: 0.5248, IoU.lamp: 0.7206, IoU.bathtub: 0.8394, IoU.railing: 0.4172, IoU.cushion: 0.6593, IoU.base: 0.3253, IoU.box: 0.3720, IoU.column: 0.5283, IoU.signboard: 0.4272, IoU.chest of drawers: 0.4505, IoU.counter: 0.4648, IoU.sand: 0.4277, IoU.sink: 0.7474, IoU.skyscraper: 0.4767, IoU.fireplace: 0.7442, IoU.refrigerator: 0.8136, IoU.grandstand: 0.5389, IoU.path: 0.3094, IoU.stairs: 0.2469, IoU.runway: 0.7022, IoU.case: 0.5388, IoU.pool table: 0.9431, IoU.pillow: 0.6677, IoU.screen door: 0.8690, IoU.stairway: 0.4375, IoU.river: 0.1652, IoU.bridge: 0.7615, IoU.bookcase: 0.4899, IoU.blind: 0.4091, IoU.coffee table: 0.6593, IoU.toilet: 0.8905, IoU.flower: 0.4504, IoU.book: 0.5345, IoU.hill: 0.0448, IoU.bench: 0.5276, IoU.countertop: 0.6520, IoU.stove: 0.8502, IoU.palm: 0.5674, IoU.kitchen island: 0.4934, IoU.computer: 0.7570, IoU.swivel chair: 0.5286, IoU.boat: 0.6396, IoU.bar: 0.6241, IoU.arcade machine: 0.7529, IoU.hovel: 0.4081, IoU.bus: 0.9377, IoU.towel: 0.7075, IoU.light: 0.5959, IoU.truck: 0.4905, IoU.tower: 0.0934, IoU.chandelier: 0.7201, IoU.awning: 0.4491, IoU.streetlight: 0.2980, IoU.booth: 0.5634, IoU.television receiver: 0.7940, IoU.airplane: 0.6805, IoU.dirt track: 0.2308, IoU.apparel: 0.4397, IoU.pole: 0.2546, IoU.land: 0.0208, IoU.bannister: 0.1478, IoU.escalator: 0.4913, IoU.ottoman: 0.4410, IoU.bottle: 0.3834, IoU.buffet: 0.5768, IoU.poster: 0.3302, IoU.stage: 0.2164, IoU.van: 0.4410, IoU.ship: 0.8525, IoU.fountain: 0.3970, IoU.conveyer belt: 0.8100, IoU.canopy: 0.5103, IoU.washer: 0.6807, IoU.plaything: 0.3377, IoU.swimming pool: 0.5288, IoU.stool: 0.4994, IoU.barrel: 0.5777, IoU.basket: 0.3916, IoU.waterfall: 0.4996, IoU.tent: 0.9093, IoU.bag: 0.1735, IoU.minibike: 0.7213, IoU.cradle: 0.7310, IoU.oven: 0.6688, IoU.ball: 0.4773, IoU.food: 0.5815, IoU.step: 0.1514, IoU.tank: 0.6403, IoU.trade name: 0.1830, IoU.microwave: 0.8933, IoU.pot: 0.5551, IoU.animal: 0.6548, IoU.bicycle: 0.5753, IoU.lake: 0.4885, IoU.dishwasher: 0.6203, IoU.screen: 0.6090, IoU.blanket: 0.1663, IoU.sculpture: 0.7493, IoU.hood: 0.6309, IoU.sconce: 0.5234, IoU.vase: 0.4763, IoU.traffic light: 0.3762, IoU.tray: 0.1243, IoU.ashcan: 0.4345, IoU.fan: 0.6420, IoU.pier: 0.3276, IoU.crt screen: 0.1509, IoU.plate: 0.5603, IoU.monitor: 0.6830, IoU.bulletin board: 0.5823, IoU.shower: 0.0146, IoU.radiator: 0.6162, IoU.glass: 0.1750, IoU.clock: 0.3183, IoU.flag: 0.7032, Acc.wall: 0.8946, Acc.building: 0.9353, Acc.sky: 0.9744, Acc.floor: 0.9097, Acc.tree: 0.8988, Acc.ceiling: 0.9033, Acc.road: 0.9383, Acc.bed : 0.9721, Acc.windowpane: 0.8227, Acc.grass: 0.8204, Acc.cabinet: 0.7831, Acc.sidewalk: 0.8216, Acc.person: 0.9238, Acc.earth: 0.4467, Acc.door: 0.7008, Acc.table: 0.8181, Acc.mountain: 0.7610, Acc.plant: 0.6288, Acc.curtain: 0.9106, Acc.chair: 0.7935, Acc.car: 0.9394, Acc.water: 0.7846, Acc.painting: 0.9093, Acc.sofa: 0.9211, Acc.shelf: 0.7280, Acc.house: 0.6590, Acc.sea: 0.8062, Acc.mirror: 0.8197, Acc.rug: 0.8341, Acc.field: 0.5145, Acc.armchair: 0.7462, Acc.seat: 0.8861, Acc.fence: 0.6060, Acc.desk: 0.7303, Acc.rock: 0.7159, Acc.wardrobe: 0.7012, Acc.lamp: 0.8316, Acc.bathtub: 0.8683, Acc.railing: 0.6217, Acc.cushion: 0.7707, Acc.base: 0.5257, Acc.box: 0.4751, Acc.column: 0.7072, Acc.signboard: 0.5534, Acc.chest of drawers: 0.7025, Acc.counter: 0.5611, Acc.sand: 0.6003, Acc.sink: 0.8435, Acc.skyscraper: 0.6069, Acc.fireplace: 0.9345, Acc.refrigerator: 0.9015, Acc.grandstand: 0.8583, Acc.path: 0.3852, Acc.stairs: 0.2825, Acc.runway: 0.9197, Acc.case: 0.7043, Acc.pool table: 0.9789, Acc.pillow: 0.7434, Acc.screen door: 0.9051, Acc.stairway: 0.6547, Acc.river: 0.2930, Acc.bridge: 0.8810, Acc.bookcase: 0.6435, Acc.blind: 0.4227, Acc.coffee table: 0.8894, Acc.toilet: 0.9437, Acc.flower: 0.6496, Acc.book: 0.7082, Acc.hill: 0.1116, Acc.bench: 0.5876, Acc.countertop: 0.8078, Acc.stove: 0.9332, Acc.palm: 0.7974, Acc.kitchen island: 0.7922, Acc.computer: 0.9582, Acc.swivel chair: 0.6942, Acc.boat: 0.8497, Acc.bar: 0.7378, Acc.arcade machine: 0.8418, Acc.hovel: 0.5007, Acc.bus: 0.9604, Acc.towel: 0.8676, Acc.light: 0.6845, Acc.truck: 0.6033, Acc.tower: 0.1749, Acc.chandelier: 0.8820, Acc.awning: 0.5860, Acc.streetlight: 0.3931, Acc.booth: 0.6994, Acc.television receiver: 0.8522, Acc.airplane: 0.7352, Acc.dirt track: 0.3647, Acc.apparel: 0.5512, Acc.pole: 0.3448, Acc.land: 0.0396, Acc.bannister: 0.1916, Acc.escalator: 0.8007, Acc.ottoman: 0.6435, Acc.bottle: 0.5389, Acc.buffet: 0.6381, Acc.poster: 0.4734, Acc.stage: 0.3381, Acc.van: 0.5587, Acc.ship: 0.9511, Acc.fountain: 0.4302, Acc.conveyer belt: 0.9302, Acc.canopy: 0.8579, Acc.washer: 0.7023, Acc.plaything: 0.5902, Acc.swimming pool: 0.9565, Acc.stool: 0.7349, Acc.barrel: 0.6424, Acc.basket: 0.5808, Acc.waterfall: 0.7191, Acc.tent: 0.9874, Acc.bag: 0.2076, Acc.minibike: 0.8627, Acc.cradle: 0.9841, Acc.oven: 0.7750, Acc.ball: 0.5106, Acc.food: 0.6732, Acc.step: 0.2010, Acc.tank: 0.7190, Acc.trade name: 0.1938, Acc.microwave: 0.9465, Acc.pot: 0.6832, Acc.animal: 0.6721, Acc.bicycle: 0.7011, Acc.lake: 0.7793, Acc.dishwasher: 0.7232, Acc.screen: 0.9200, Acc.blanket: 0.1791, Acc.sculpture: 0.8387, Acc.hood: 0.7386, Acc.sconce: 0.6154, Acc.vase: 0.6231, Acc.traffic light: 0.5909, Acc.tray: 0.1689, Acc.ashcan: 0.6036, Acc.fan: 0.7828, Acc.pier: 0.4739, Acc.crt screen: 0.1548, Acc.plate: 0.7092, Acc.monitor: 0.8063, Acc.bulletin board: 0.6857, Acc.shower: 0.0174, Acc.radiator: 0.7355, Acc.glass: 0.1881, Acc.clock: 0.3618, Acc.flag: 0.7800 +2024-06-18 13:26:57,517 - mmseg - INFO - Iter [34050/80000] lr: 2.298e-05, eta: 18:53:45, time: 3.279, data_time: 1.957, memory: 70498, decode.loss_ce: 0.2330, decode.acc_seg: 90.4229, aux.loss_ce: 0.0961, aux.acc_seg: 90.1264, loss: 0.3292 +2024-06-18 13:28:03,586 - mmseg - INFO - Iter [34100/80000] lr: 2.295e-05, eta: 18:52:20, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2249, decode.acc_seg: 90.6386, aux.loss_ce: 0.0928, aux.acc_seg: 90.4037, loss: 0.3177 +2024-06-18 13:29:12,119 - mmseg - INFO - Iter [34150/80000] lr: 2.293e-05, eta: 18:50:59, time: 1.371, data_time: 0.053, memory: 70498, decode.loss_ce: 0.2334, decode.acc_seg: 90.2619, aux.loss_ce: 0.0974, aux.acc_seg: 89.8637, loss: 0.3309 +2024-06-18 13:30:18,284 - mmseg - INFO - Iter [34200/80000] lr: 2.290e-05, eta: 18:49:35, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2324, decode.acc_seg: 90.5750, aux.loss_ce: 0.0955, aux.acc_seg: 90.3389, loss: 0.3279 +2024-06-18 13:31:24,486 - mmseg - INFO - Iter [34250/80000] lr: 2.288e-05, eta: 18:48:10, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2369, decode.acc_seg: 90.2327, aux.loss_ce: 0.0980, aux.acc_seg: 89.8916, loss: 0.3349 +2024-06-18 13:32:30,799 - mmseg - INFO - Iter [34300/80000] lr: 2.285e-05, eta: 18:46:46, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2387, decode.acc_seg: 90.0985, aux.loss_ce: 0.0975, aux.acc_seg: 89.9586, loss: 0.3362 +2024-06-18 13:33:37,187 - mmseg - INFO - Iter [34350/80000] lr: 2.283e-05, eta: 18:45:22, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2288, decode.acc_seg: 90.4647, aux.loss_ce: 0.0945, aux.acc_seg: 90.1661, loss: 0.3233 +2024-06-18 13:34:43,453 - mmseg - INFO - Iter [34400/80000] lr: 2.280e-05, eta: 18:43:58, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2422, decode.acc_seg: 90.0533, aux.loss_ce: 0.0995, aux.acc_seg: 89.7697, loss: 0.3418 +2024-06-18 13:35:49,493 - mmseg - INFO - Iter [34450/80000] lr: 2.278e-05, eta: 18:42:33, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2589, decode.acc_seg: 89.4709, aux.loss_ce: 0.1058, aux.acc_seg: 89.3177, loss: 0.3647 +2024-06-18 13:36:55,898 - mmseg - INFO - Iter [34500/80000] lr: 2.275e-05, eta: 18:41:09, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2489, decode.acc_seg: 89.6683, aux.loss_ce: 0.1019, aux.acc_seg: 89.4390, loss: 0.3508 +2024-06-18 13:38:02,248 - mmseg - INFO - Iter [34550/80000] lr: 2.273e-05, eta: 18:39:46, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2450, decode.acc_seg: 89.8335, aux.loss_ce: 0.1004, aux.acc_seg: 89.6294, loss: 0.3454 +2024-06-18 13:39:08,772 - mmseg - INFO - Iter [34600/80000] lr: 2.270e-05, eta: 18:38:22, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2344, decode.acc_seg: 89.9579, aux.loss_ce: 0.0970, aux.acc_seg: 89.6862, loss: 0.3314 +2024-06-18 13:40:14,856 - mmseg - INFO - Iter [34650/80000] lr: 2.268e-05, eta: 18:36:58, time: 1.322, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2332, decode.acc_seg: 90.1218, aux.loss_ce: 0.0967, aux.acc_seg: 89.7729, loss: 0.3299 +2024-06-18 13:41:20,989 - mmseg - INFO - Iter [34700/80000] lr: 2.265e-05, eta: 18:35:34, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2329, decode.acc_seg: 90.2392, aux.loss_ce: 0.0967, aux.acc_seg: 89.9736, loss: 0.3295 +2024-06-18 13:42:27,677 - mmseg - INFO - Iter [34750/80000] lr: 2.263e-05, eta: 18:34:11, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2349, decode.acc_seg: 90.2920, aux.loss_ce: 0.0972, aux.acc_seg: 89.9783, loss: 0.3321 +2024-06-18 13:43:33,669 - mmseg - INFO - Iter [34800/80000] lr: 2.260e-05, eta: 18:32:47, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2253, decode.acc_seg: 90.5048, aux.loss_ce: 0.0925, aux.acc_seg: 90.2712, loss: 0.3178 +2024-06-18 13:44:39,674 - mmseg - INFO - Iter [34850/80000] lr: 2.258e-05, eta: 18:31:22, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2328, decode.acc_seg: 90.3471, aux.loss_ce: 0.0964, aux.acc_seg: 89.9805, loss: 0.3293 +2024-06-18 13:45:45,749 - mmseg - INFO - Iter [34900/80000] lr: 2.255e-05, eta: 18:29:59, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2373, decode.acc_seg: 89.8964, aux.loss_ce: 0.0984, aux.acc_seg: 89.5657, loss: 0.3357 +2024-06-18 13:46:51,992 - mmseg - INFO - Iter [34950/80000] lr: 2.253e-05, eta: 18:28:35, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2353, decode.acc_seg: 90.1964, aux.loss_ce: 0.0969, aux.acc_seg: 89.9136, loss: 0.3322 +2024-06-18 13:47:57,938 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 13:47:57,938 - mmseg - INFO - Iter [35000/80000] lr: 2.250e-05, eta: 18:27:11, time: 1.319, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2320, decode.acc_seg: 90.0920, aux.loss_ce: 0.0953, aux.acc_seg: 89.8127, loss: 0.3273 +2024-06-18 13:49:34,639 - mmseg - INFO - per class results: +2024-06-18 13:49:34,646 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.96 | 89.55 | +| building | 84.07 | 92.72 | +| sky | 94.76 | 98.05 | +| floor | 84.99 | 91.67 | +| tree | 76.88 | 89.52 | +| ceiling | 87.44 | 92.99 | +| road | 86.22 | 90.53 | +| bed | 92.02 | 97.06 | +| windowpane | 65.56 | 80.88 | +| grass | 65.47 | 80.2 | +| cabinet | 64.31 | 72.7 | +| sidewalk | 72.55 | 87.56 | +| person | 84.99 | 94.63 | +| earth | 36.71 | 47.33 | +| door | 61.22 | 79.53 | +| table | 68.37 | 81.24 | +| mountain | 61.47 | 78.55 | +| plant | 58.42 | 72.73 | +| curtain | 78.63 | 88.16 | +| chair | 65.7 | 75.83 | +| car | 86.7 | 93.53 | +| water | 63.24 | 83.71 | +| painting | 76.77 | 91.51 | +| sofa | 82.59 | 90.37 | +| shelf | 49.01 | 63.37 | +| house | 39.73 | 44.66 | +| sea | 56.22 | 62.95 | +| mirror | 76.02 | 83.8 | +| rug | 68.58 | 77.63 | +| field | 35.58 | 59.47 | +| armchair | 58.9 | 82.39 | +| seat | 67.99 | 86.79 | +| fence | 49.39 | 60.69 | +| desk | 51.01 | 76.58 | +| rock | 55.31 | 74.81 | +| wardrobe | 55.02 | 74.43 | +| lamp | 71.61 | 83.95 | +| bathtub | 83.72 | 86.97 | +| railing | 41.18 | 55.7 | +| cushion | 68.03 | 81.33 | +| base | 38.93 | 58.71 | +| box | 36.46 | 52.02 | +| column | 53.01 | 69.52 | +| signboard | 40.54 | 56.28 | +| chest of drawers | 48.7 | 78.56 | +| counter | 39.12 | 49.23 | +| sand | 50.17 | 67.23 | +| sink | 75.15 | 81.62 | +| skyscraper | 46.8 | 60.14 | +| fireplace | 73.26 | 94.85 | +| refrigerator | 81.09 | 91.3 | +| grandstand | 52.96 | 85.1 | +| path | 25.61 | 39.14 | +| stairs | 30.79 | 39.69 | +| runway | 72.18 | 95.43 | +| case | 61.34 | 78.25 | +| pool table | 94.66 | 97.46 | +| pillow | 66.66 | 74.6 | +| screen door | 72.11 | 75.04 | +| stairway | 44.03 | 60.02 | +| river | 15.01 | 28.03 | +| bridge | 75.91 | 89.43 | +| bookcase | 40.81 | 59.42 | +| blind | 44.52 | 47.83 | +| coffee table | 66.2 | 87.66 | +| toilet | 89.41 | 93.94 | +| flower | 45.21 | 55.4 | +| book | 53.43 | 69.52 | +| hill | 6.66 | 13.87 | +| bench | 54.44 | 67.03 | +| countertop | 62.41 | 83.98 | +| stove | 82.83 | 90.88 | +| palm | 57.32 | 82.61 | +| kitchen island | 49.3 | 86.28 | +| computer | 78.92 | 89.81 | +| swivel chair | 51.29 | 74.74 | +| boat | 52.06 | 86.6 | +| bar | 54.78 | 76.06 | +| arcade machine | 72.0 | 80.46 | +| hovel | 44.18 | 51.57 | +| bus | 92.65 | 96.37 | +| towel | 74.44 | 82.06 | +| light | 61.33 | 72.38 | +| truck | 43.06 | 60.76 | +| tower | 6.1 | 11.5 | +| chandelier | 72.59 | 87.5 | +| awning | 50.84 | 63.62 | +| streetlight | 31.37 | 43.76 | +| booth | 59.93 | 74.87 | +| television receiver | 81.11 | 89.15 | +| airplane | 69.59 | 82.73 | +| dirt track | 10.83 | 51.45 | +| apparel | 47.49 | 66.03 | +| pole | 22.23 | 28.17 | +| land | 2.55 | 5.77 | +| bannister | 13.6 | 18.84 | +| escalator | 53.19 | 84.21 | +| ottoman | 45.54 | 69.49 | +| bottle | 41.95 | 65.42 | +| buffet | 50.82 | 54.07 | +| poster | 34.31 | 50.4 | +| stage | 29.99 | 52.12 | +| van | 48.01 | 64.29 | +| ship | 89.82 | 95.76 | +| fountain | 33.8 | 35.51 | +| conveyer belt | 78.87 | 93.32 | +| canopy | 54.39 | 68.23 | +| washer | 72.02 | 73.93 | +| plaything | 34.24 | 55.8 | +| swimming pool | 61.24 | 75.56 | +| stool | 51.44 | 73.02 | +| barrel | 56.4 | 64.54 | +| basket | 38.8 | 56.6 | +| waterfall | 47.76 | 64.78 | +| tent | 92.63 | 98.37 | +| bag | 19.91 | 25.24 | +| minibike | 71.87 | 90.69 | +| cradle | 84.8 | 98.22 | +| oven | 56.69 | 70.92 | +| ball | 42.75 | 44.38 | +| food | 60.77 | 76.2 | +| step | 17.23 | 22.77 | +| tank | 69.74 | 76.83 | +| trade name | 35.1 | 47.39 | +| microwave | 88.12 | 94.79 | +| pot | 54.56 | 64.39 | +| animal | 67.42 | 69.98 | +| bicycle | 60.67 | 79.49 | +| lake | 57.9 | 73.37 | +| dishwasher | 63.53 | 79.49 | +| screen | 61.64 | 92.36 | +| blanket | 28.56 | 32.22 | +| sculpture | 76.94 | 86.3 | +| hood | 62.25 | 75.76 | +| sconce | 49.19 | 57.2 | +| vase | 46.91 | 62.14 | +| traffic light | 31.24 | 64.61 | +| tray | 10.97 | 15.31 | +| ashcan | 41.54 | 64.84 | +| fan | 66.81 | 82.03 | +| pier | 30.35 | 52.52 | +| crt screen | 18.71 | 21.85 | +| plate | 58.23 | 75.94 | +| monitor | 61.9 | 85.02 | +| bulletin board | 58.6 | 67.82 | +| shower | 1.73 | 2.26 | +| radiator | 64.43 | 71.76 | +| glass | 17.42 | 18.64 | +| clock | 36.99 | 50.71 | +| flag | 70.22 | 81.8 | ++---------------------+-------+-------+ +2024-06-18 13:49:34,646 - mmseg - INFO - Summary: +2024-06-18 13:49:34,646 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.67 | 56.06 | 69.47 | ++-------+-------+-------+ +2024-06-18 13:49:34,647 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 13:49:34,647 - mmseg - INFO - Iter(val) [250] aAcc: 0.8567, mIoU: 0.5606, mAcc: 0.6947, IoU.wall: 0.8196, IoU.building: 0.8407, IoU.sky: 0.9476, IoU.floor: 0.8499, IoU.tree: 0.7688, IoU.ceiling: 0.8744, IoU.road: 0.8622, IoU.bed : 0.9202, IoU.windowpane: 0.6556, IoU.grass: 0.6547, IoU.cabinet: 0.6431, IoU.sidewalk: 0.7255, IoU.person: 0.8499, IoU.earth: 0.3671, IoU.door: 0.6122, IoU.table: 0.6837, IoU.mountain: 0.6147, IoU.plant: 0.5842, IoU.curtain: 0.7863, IoU.chair: 0.6570, IoU.car: 0.8670, IoU.water: 0.6324, IoU.painting: 0.7677, IoU.sofa: 0.8259, IoU.shelf: 0.4901, IoU.house: 0.3973, IoU.sea: 0.5622, IoU.mirror: 0.7602, IoU.rug: 0.6858, IoU.field: 0.3558, IoU.armchair: 0.5890, IoU.seat: 0.6799, IoU.fence: 0.4939, IoU.desk: 0.5101, IoU.rock: 0.5531, IoU.wardrobe: 0.5502, IoU.lamp: 0.7161, IoU.bathtub: 0.8372, IoU.railing: 0.4118, IoU.cushion: 0.6803, IoU.base: 0.3893, IoU.box: 0.3646, IoU.column: 0.5301, IoU.signboard: 0.4054, IoU.chest of drawers: 0.4870, IoU.counter: 0.3912, IoU.sand: 0.5017, IoU.sink: 0.7515, IoU.skyscraper: 0.4680, IoU.fireplace: 0.7326, IoU.refrigerator: 0.8109, IoU.grandstand: 0.5296, IoU.path: 0.2561, IoU.stairs: 0.3079, IoU.runway: 0.7218, IoU.case: 0.6134, IoU.pool table: 0.9466, IoU.pillow: 0.6666, IoU.screen door: 0.7211, IoU.stairway: 0.4403, IoU.river: 0.1501, IoU.bridge: 0.7591, IoU.bookcase: 0.4081, IoU.blind: 0.4452, IoU.coffee table: 0.6620, IoU.toilet: 0.8941, IoU.flower: 0.4521, IoU.book: 0.5343, IoU.hill: 0.0666, IoU.bench: 0.5444, IoU.countertop: 0.6241, IoU.stove: 0.8283, IoU.palm: 0.5732, IoU.kitchen island: 0.4930, IoU.computer: 0.7892, IoU.swivel chair: 0.5129, IoU.boat: 0.5206, IoU.bar: 0.5478, IoU.arcade machine: 0.7200, IoU.hovel: 0.4418, IoU.bus: 0.9265, IoU.towel: 0.7444, IoU.light: 0.6133, IoU.truck: 0.4306, IoU.tower: 0.0610, IoU.chandelier: 0.7259, IoU.awning: 0.5084, IoU.streetlight: 0.3137, IoU.booth: 0.5993, IoU.television receiver: 0.8111, IoU.airplane: 0.6959, IoU.dirt track: 0.1083, IoU.apparel: 0.4749, IoU.pole: 0.2223, IoU.land: 0.0255, IoU.bannister: 0.1360, IoU.escalator: 0.5319, IoU.ottoman: 0.4554, IoU.bottle: 0.4195, IoU.buffet: 0.5082, IoU.poster: 0.3431, IoU.stage: 0.2999, IoU.van: 0.4801, IoU.ship: 0.8982, IoU.fountain: 0.3380, IoU.conveyer belt: 0.7887, IoU.canopy: 0.5439, IoU.washer: 0.7202, IoU.plaything: 0.3424, IoU.swimming pool: 0.6124, IoU.stool: 0.5144, IoU.barrel: 0.5640, IoU.basket: 0.3880, IoU.waterfall: 0.4776, IoU.tent: 0.9263, IoU.bag: 0.1991, IoU.minibike: 0.7187, IoU.cradle: 0.8480, IoU.oven: 0.5669, IoU.ball: 0.4275, IoU.food: 0.6077, IoU.step: 0.1723, IoU.tank: 0.6974, IoU.trade name: 0.3510, IoU.microwave: 0.8812, IoU.pot: 0.5456, IoU.animal: 0.6742, IoU.bicycle: 0.6067, IoU.lake: 0.5790, IoU.dishwasher: 0.6353, IoU.screen: 0.6164, IoU.blanket: 0.2856, IoU.sculpture: 0.7694, IoU.hood: 0.6225, IoU.sconce: 0.4919, IoU.vase: 0.4691, IoU.traffic light: 0.3124, IoU.tray: 0.1097, IoU.ashcan: 0.4154, IoU.fan: 0.6681, IoU.pier: 0.3035, IoU.crt screen: 0.1871, IoU.plate: 0.5823, IoU.monitor: 0.6190, IoU.bulletin board: 0.5860, IoU.shower: 0.0173, IoU.radiator: 0.6443, IoU.glass: 0.1742, IoU.clock: 0.3699, IoU.flag: 0.7022, Acc.wall: 0.8955, Acc.building: 0.9272, Acc.sky: 0.9805, Acc.floor: 0.9167, Acc.tree: 0.8952, Acc.ceiling: 0.9299, Acc.road: 0.9053, Acc.bed : 0.9706, Acc.windowpane: 0.8088, Acc.grass: 0.8020, Acc.cabinet: 0.7270, Acc.sidewalk: 0.8756, Acc.person: 0.9463, Acc.earth: 0.4733, Acc.door: 0.7953, Acc.table: 0.8124, Acc.mountain: 0.7855, Acc.plant: 0.7273, Acc.curtain: 0.8816, Acc.chair: 0.7583, Acc.car: 0.9353, Acc.water: 0.8371, Acc.painting: 0.9151, Acc.sofa: 0.9037, Acc.shelf: 0.6337, Acc.house: 0.4466, Acc.sea: 0.6295, Acc.mirror: 0.8380, Acc.rug: 0.7763, Acc.field: 0.5947, Acc.armchair: 0.8239, Acc.seat: 0.8679, Acc.fence: 0.6069, Acc.desk: 0.7658, Acc.rock: 0.7481, Acc.wardrobe: 0.7443, Acc.lamp: 0.8395, Acc.bathtub: 0.8697, Acc.railing: 0.5570, Acc.cushion: 0.8133, Acc.base: 0.5871, Acc.box: 0.5202, Acc.column: 0.6952, Acc.signboard: 0.5628, Acc.chest of drawers: 0.7856, Acc.counter: 0.4923, Acc.sand: 0.6723, Acc.sink: 0.8162, Acc.skyscraper: 0.6014, Acc.fireplace: 0.9485, Acc.refrigerator: 0.9130, Acc.grandstand: 0.8510, Acc.path: 0.3914, Acc.stairs: 0.3969, Acc.runway: 0.9543, Acc.case: 0.7825, Acc.pool table: 0.9746, Acc.pillow: 0.7460, Acc.screen door: 0.7504, Acc.stairway: 0.6002, Acc.river: 0.2803, Acc.bridge: 0.8943, Acc.bookcase: 0.5942, Acc.blind: 0.4783, Acc.coffee table: 0.8766, Acc.toilet: 0.9394, Acc.flower: 0.5540, Acc.book: 0.6952, Acc.hill: 0.1387, Acc.bench: 0.6703, Acc.countertop: 0.8398, Acc.stove: 0.9088, Acc.palm: 0.8261, Acc.kitchen island: 0.8628, Acc.computer: 0.8981, Acc.swivel chair: 0.7474, Acc.boat: 0.8660, Acc.bar: 0.7606, Acc.arcade machine: 0.8046, Acc.hovel: 0.5157, Acc.bus: 0.9637, Acc.towel: 0.8206, Acc.light: 0.7238, Acc.truck: 0.6076, Acc.tower: 0.1150, Acc.chandelier: 0.8750, Acc.awning: 0.6362, Acc.streetlight: 0.4376, Acc.booth: 0.7487, Acc.television receiver: 0.8915, Acc.airplane: 0.8273, Acc.dirt track: 0.5145, Acc.apparel: 0.6603, Acc.pole: 0.2817, Acc.land: 0.0577, Acc.bannister: 0.1884, Acc.escalator: 0.8421, Acc.ottoman: 0.6949, Acc.bottle: 0.6542, Acc.buffet: 0.5407, Acc.poster: 0.5040, Acc.stage: 0.5212, Acc.van: 0.6429, Acc.ship: 0.9576, Acc.fountain: 0.3551, Acc.conveyer belt: 0.9332, Acc.canopy: 0.6823, Acc.washer: 0.7393, Acc.plaything: 0.5580, Acc.swimming pool: 0.7556, Acc.stool: 0.7302, Acc.barrel: 0.6454, Acc.basket: 0.5660, Acc.waterfall: 0.6478, Acc.tent: 0.9837, Acc.bag: 0.2524, Acc.minibike: 0.9069, Acc.cradle: 0.9822, Acc.oven: 0.7092, Acc.ball: 0.4438, Acc.food: 0.7620, Acc.step: 0.2277, Acc.tank: 0.7683, Acc.trade name: 0.4739, Acc.microwave: 0.9479, Acc.pot: 0.6439, Acc.animal: 0.6998, Acc.bicycle: 0.7949, Acc.lake: 0.7337, Acc.dishwasher: 0.7949, Acc.screen: 0.9236, Acc.blanket: 0.3222, Acc.sculpture: 0.8630, Acc.hood: 0.7576, Acc.sconce: 0.5720, Acc.vase: 0.6214, Acc.traffic light: 0.6461, Acc.tray: 0.1531, Acc.ashcan: 0.6484, Acc.fan: 0.8203, Acc.pier: 0.5252, Acc.crt screen: 0.2185, Acc.plate: 0.7594, Acc.monitor: 0.8502, Acc.bulletin board: 0.6782, Acc.shower: 0.0226, Acc.radiator: 0.7176, Acc.glass: 0.1864, Acc.clock: 0.5071, Acc.flag: 0.8180 +2024-06-18 13:50:41,368 - mmseg - INFO - Iter [35050/80000] lr: 2.248e-05, eta: 18:27:52, time: 3.269, data_time: 1.950, memory: 70498, decode.loss_ce: 0.2399, decode.acc_seg: 90.1565, aux.loss_ce: 0.0989, aux.acc_seg: 89.9277, loss: 0.3388 +2024-06-18 13:51:47,775 - mmseg - INFO - Iter [35100/80000] lr: 2.245e-05, eta: 18:26:29, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2393, decode.acc_seg: 90.3038, aux.loss_ce: 0.0995, aux.acc_seg: 89.9587, loss: 0.3388 +2024-06-18 13:52:54,209 - mmseg - INFO - Iter [35150/80000] lr: 2.243e-05, eta: 18:25:05, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2314, decode.acc_seg: 90.3767, aux.loss_ce: 0.0959, aux.acc_seg: 90.0916, loss: 0.3273 +2024-06-18 13:54:00,280 - mmseg - INFO - Iter [35200/80000] lr: 2.240e-05, eta: 18:23:41, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2299, decode.acc_seg: 90.4677, aux.loss_ce: 0.0949, aux.acc_seg: 90.2053, loss: 0.3248 +2024-06-18 13:55:06,319 - mmseg - INFO - Iter [35250/80000] lr: 2.238e-05, eta: 18:22:17, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2465, decode.acc_seg: 90.0492, aux.loss_ce: 0.1017, aux.acc_seg: 89.7663, loss: 0.3482 +2024-06-18 13:56:12,861 - mmseg - INFO - Iter [35300/80000] lr: 2.235e-05, eta: 18:20:54, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2480, decode.acc_seg: 89.5304, aux.loss_ce: 0.1027, aux.acc_seg: 89.2029, loss: 0.3507 +2024-06-18 13:57:19,165 - mmseg - INFO - Iter [35350/80000] lr: 2.233e-05, eta: 18:19:31, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2403, decode.acc_seg: 90.2264, aux.loss_ce: 0.0994, aux.acc_seg: 89.9194, loss: 0.3397 +2024-06-18 13:58:27,573 - mmseg - INFO - Iter [35400/80000] lr: 2.230e-05, eta: 18:18:10, time: 1.368, data_time: 0.051, memory: 70498, decode.loss_ce: 0.2340, decode.acc_seg: 90.0718, aux.loss_ce: 0.0964, aux.acc_seg: 89.8250, loss: 0.3303 +2024-06-18 13:59:33,679 - mmseg - INFO - Iter [35450/80000] lr: 2.228e-05, eta: 18:16:46, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2306, decode.acc_seg: 90.6991, aux.loss_ce: 0.0953, aux.acc_seg: 90.3667, loss: 0.3259 +2024-06-18 14:00:39,722 - mmseg - INFO - Iter [35500/80000] lr: 2.225e-05, eta: 18:15:23, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2315, decode.acc_seg: 90.4276, aux.loss_ce: 0.0946, aux.acc_seg: 90.1645, loss: 0.3260 +2024-06-18 14:01:45,878 - mmseg - INFO - Iter [35550/80000] lr: 2.223e-05, eta: 18:13:59, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2368, decode.acc_seg: 89.8851, aux.loss_ce: 0.0979, aux.acc_seg: 89.4311, loss: 0.3346 +2024-06-18 14:02:52,196 - mmseg - INFO - Iter [35600/80000] lr: 2.220e-05, eta: 18:12:36, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2472, decode.acc_seg: 89.9027, aux.loss_ce: 0.1016, aux.acc_seg: 89.6263, loss: 0.3487 +2024-06-18 14:03:58,405 - mmseg - INFO - Iter [35650/80000] lr: 2.218e-05, eta: 18:11:13, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2324, decode.acc_seg: 89.9789, aux.loss_ce: 0.0958, aux.acc_seg: 89.6367, loss: 0.3282 +2024-06-18 14:05:04,629 - mmseg - INFO - Iter [35700/80000] lr: 2.215e-05, eta: 18:09:49, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2317, decode.acc_seg: 90.3635, aux.loss_ce: 0.0953, aux.acc_seg: 90.0524, loss: 0.3269 +2024-06-18 14:06:11,012 - mmseg - INFO - Iter [35750/80000] lr: 2.213e-05, eta: 18:08:26, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2336, decode.acc_seg: 90.2849, aux.loss_ce: 0.0972, aux.acc_seg: 89.8923, loss: 0.3308 +2024-06-18 14:07:17,174 - mmseg - INFO - Iter [35800/80000] lr: 2.210e-05, eta: 18:07:03, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2382, decode.acc_seg: 90.0538, aux.loss_ce: 0.0994, aux.acc_seg: 89.6296, loss: 0.3376 +2024-06-18 14:08:23,428 - mmseg - INFO - Iter [35850/80000] lr: 2.208e-05, eta: 18:05:40, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2188, decode.acc_seg: 91.0167, aux.loss_ce: 0.0909, aux.acc_seg: 90.6285, loss: 0.3097 +2024-06-18 14:09:29,781 - mmseg - INFO - Iter [35900/80000] lr: 2.205e-05, eta: 18:04:17, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2272, decode.acc_seg: 90.1019, aux.loss_ce: 0.0931, aux.acc_seg: 89.9125, loss: 0.3203 +2024-06-18 14:10:36,220 - mmseg - INFO - Iter [35950/80000] lr: 2.203e-05, eta: 18:02:54, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2473, decode.acc_seg: 89.8024, aux.loss_ce: 0.1018, aux.acc_seg: 89.5482, loss: 0.3491 +2024-06-18 14:11:42,374 - mmseg - INFO - Saving checkpoint at 36000 iterations +2024-06-18 14:13:28,157 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 14:13:28,157 - mmseg - INFO - Iter [36000/80000] lr: 2.200e-05, eta: 18:03:41, time: 3.439, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2365, decode.acc_seg: 90.3815, aux.loss_ce: 0.0976, aux.acc_seg: 90.0700, loss: 0.3341 +2024-06-18 14:15:05,143 - mmseg - INFO - per class results: +2024-06-18 14:15:05,149 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.66 | 89.95 | +| building | 84.39 | 92.48 | +| sky | 94.68 | 97.5 | +| floor | 85.23 | 91.82 | +| tree | 77.3 | 88.99 | +| ceiling | 86.55 | 93.93 | +| road | 85.61 | 89.74 | +| bed | 92.2 | 96.59 | +| windowpane | 65.34 | 80.11 | +| grass | 66.78 | 80.92 | +| cabinet | 63.78 | 77.22 | +| sidewalk | 71.26 | 88.09 | +| person | 85.6 | 92.17 | +| earth | 39.41 | 53.5 | +| door | 57.44 | 71.96 | +| table | 67.8 | 80.07 | +| mountain | 62.15 | 76.96 | +| plant | 55.72 | 66.49 | +| curtain | 79.56 | 86.88 | +| chair | 65.31 | 75.83 | +| car | 87.08 | 92.88 | +| water | 60.35 | 74.78 | +| painting | 75.12 | 87.78 | +| sofa | 80.81 | 90.99 | +| shelf | 48.85 | 70.08 | +| house | 61.35 | 78.12 | +| sea | 70.26 | 81.68 | +| mirror | 76.57 | 84.07 | +| rug | 71.0 | 81.27 | +| field | 34.85 | 59.55 | +| armchair | 58.98 | 77.78 | +| seat | 69.24 | 89.01 | +| fence | 50.3 | 68.27 | +| desk | 56.62 | 70.67 | +| rock | 55.4 | 69.52 | +| wardrobe | 55.25 | 79.08 | +| lamp | 71.52 | 84.52 | +| bathtub | 83.5 | 86.64 | +| railing | 40.06 | 60.03 | +| cushion | 66.7 | 77.93 | +| base | 34.54 | 45.44 | +| box | 35.11 | 44.45 | +| column | 51.79 | 68.03 | +| signboard | 38.77 | 48.93 | +| chest of drawers | 48.18 | 66.26 | +| counter | 48.23 | 64.39 | +| sand | 58.23 | 80.42 | +| sink | 74.11 | 84.33 | +| skyscraper | 45.43 | 58.95 | +| fireplace | 70.12 | 95.05 | +| refrigerator | 82.8 | 93.3 | +| grandstand | 53.89 | 84.35 | +| path | 27.66 | 50.74 | +| stairs | 21.74 | 26.6 | +| runway | 73.01 | 94.66 | +| case | 59.8 | 78.09 | +| pool table | 94.57 | 97.08 | +| pillow | 67.37 | 76.97 | +| screen door | 84.1 | 87.44 | +| stairway | 41.11 | 58.81 | +| river | 10.75 | 31.72 | +| bridge | 76.55 | 85.04 | +| bookcase | 39.29 | 54.19 | +| blind | 42.69 | 47.8 | +| coffee table | 63.87 | 88.06 | +| toilet | 89.1 | 92.25 | +| flower | 41.32 | 49.78 | +| book | 49.98 | 80.99 | +| hill | 6.63 | 10.09 | +| bench | 54.32 | 60.16 | +| countertop | 64.0 | 79.46 | +| stove | 83.23 | 87.38 | +| palm | 58.76 | 78.43 | +| kitchen island | 37.48 | 56.78 | +| computer | 79.51 | 93.75 | +| swivel chair | 49.84 | 75.3 | +| boat | 55.86 | 85.5 | +| bar | 60.82 | 64.84 | +| arcade machine | 70.59 | 74.45 | +| hovel | 39.77 | 50.25 | +| bus | 90.25 | 96.11 | +| towel | 74.19 | 85.6 | +| light | 61.23 | 73.03 | +| truck | 44.29 | 57.64 | +| tower | 22.56 | 52.7 | +| chandelier | 71.6 | 91.3 | +| awning | 49.72 | 65.47 | +| streetlight | 30.88 | 41.83 | +| booth | 50.97 | 70.49 | +| television receiver | 78.01 | 85.65 | +| airplane | 78.87 | 87.51 | +| dirt track | 12.97 | 51.43 | +| apparel | 44.62 | 58.55 | +| pole | 23.93 | 30.88 | +| land | 2.92 | 8.13 | +| bannister | 14.85 | 18.18 | +| escalator | 54.41 | 80.02 | +| ottoman | 46.73 | 64.54 | +| bottle | 33.95 | 40.24 | +| buffet | 39.75 | 42.55 | +| poster | 30.79 | 52.78 | +| stage | 27.45 | 45.32 | +| van | 43.29 | 57.12 | +| ship | 71.58 | 72.79 | +| fountain | 41.09 | 42.15 | +| conveyer belt | 78.42 | 93.15 | +| canopy | 48.29 | 70.87 | +| washer | 70.01 | 72.08 | +| plaything | 28.13 | 34.82 | +| swimming pool | 59.83 | 81.5 | +| stool | 49.51 | 72.6 | +| barrel | 57.5 | 64.35 | +| basket | 37.31 | 59.02 | +| waterfall | 44.3 | 60.85 | +| tent | 90.79 | 97.94 | +| bag | 16.46 | 18.35 | +| minibike | 74.12 | 82.88 | +| cradle | 82.88 | 97.4 | +| oven | 55.09 | 72.01 | +| ball | 56.03 | 63.3 | +| food | 55.6 | 66.59 | +| step | 5.99 | 6.65 | +| tank | 66.57 | 81.35 | +| trade name | 24.92 | 27.02 | +| microwave | 88.48 | 94.8 | +| pot | 55.89 | 64.5 | +| animal | 65.35 | 66.55 | +| bicycle | 55.94 | 81.65 | +| lake | 0.1 | 0.11 | +| dishwasher | 69.04 | 81.1 | +| screen | 59.67 | 92.77 | +| blanket | 31.87 | 37.65 | +| sculpture | 75.42 | 85.61 | +| hood | 62.66 | 78.05 | +| sconce | 50.9 | 58.49 | +| vase | 45.17 | 62.94 | +| traffic light | 31.4 | 59.05 | +| tray | 14.34 | 16.97 | +| ashcan | 39.94 | 56.92 | +| fan | 63.51 | 85.23 | +| pier | 30.57 | 54.41 | +| crt screen | 14.52 | 16.49 | +| plate | 56.64 | 64.06 | +| monitor | 68.22 | 84.67 | +| bulletin board | 59.95 | 64.2 | +| shower | 0.2 | 0.2 | +| radiator | 62.55 | 71.7 | +| glass | 14.65 | 15.21 | +| clock | 37.21 | 49.81 | +| flag | 70.26 | 77.27 | ++---------------------+-------+-------+ +2024-06-18 14:15:05,149 - mmseg - INFO - Summary: +2024-06-18 14:15:05,149 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.62 | 55.29 | 68.01 | ++-------+-------+-------+ +2024-06-18 14:15:05,150 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 14:15:05,150 - mmseg - INFO - Iter(val) [250] aAcc: 0.8562, mIoU: 0.5529, mAcc: 0.6801, IoU.wall: 0.8166, IoU.building: 0.8439, IoU.sky: 0.9468, IoU.floor: 0.8523, IoU.tree: 0.7730, IoU.ceiling: 0.8655, IoU.road: 0.8561, IoU.bed : 0.9220, IoU.windowpane: 0.6534, IoU.grass: 0.6678, IoU.cabinet: 0.6378, IoU.sidewalk: 0.7126, IoU.person: 0.8560, IoU.earth: 0.3941, IoU.door: 0.5744, IoU.table: 0.6780, IoU.mountain: 0.6215, IoU.plant: 0.5572, IoU.curtain: 0.7956, IoU.chair: 0.6531, IoU.car: 0.8708, IoU.water: 0.6035, IoU.painting: 0.7512, IoU.sofa: 0.8081, IoU.shelf: 0.4885, IoU.house: 0.6135, IoU.sea: 0.7026, IoU.mirror: 0.7657, IoU.rug: 0.7100, IoU.field: 0.3485, IoU.armchair: 0.5898, IoU.seat: 0.6924, IoU.fence: 0.5030, IoU.desk: 0.5662, IoU.rock: 0.5540, IoU.wardrobe: 0.5525, IoU.lamp: 0.7152, IoU.bathtub: 0.8350, IoU.railing: 0.4006, IoU.cushion: 0.6670, IoU.base: 0.3454, IoU.box: 0.3511, IoU.column: 0.5179, IoU.signboard: 0.3877, IoU.chest of drawers: 0.4818, IoU.counter: 0.4823, IoU.sand: 0.5823, IoU.sink: 0.7411, IoU.skyscraper: 0.4543, IoU.fireplace: 0.7012, IoU.refrigerator: 0.8280, IoU.grandstand: 0.5389, IoU.path: 0.2766, IoU.stairs: 0.2174, IoU.runway: 0.7301, IoU.case: 0.5980, IoU.pool table: 0.9457, IoU.pillow: 0.6737, IoU.screen door: 0.8410, IoU.stairway: 0.4111, IoU.river: 0.1075, IoU.bridge: 0.7655, IoU.bookcase: 0.3929, IoU.blind: 0.4269, IoU.coffee table: 0.6387, IoU.toilet: 0.8910, IoU.flower: 0.4132, IoU.book: 0.4998, IoU.hill: 0.0663, IoU.bench: 0.5432, IoU.countertop: 0.6400, IoU.stove: 0.8323, IoU.palm: 0.5876, IoU.kitchen island: 0.3748, IoU.computer: 0.7951, IoU.swivel chair: 0.4984, IoU.boat: 0.5586, IoU.bar: 0.6082, IoU.arcade machine: 0.7059, IoU.hovel: 0.3977, IoU.bus: 0.9025, IoU.towel: 0.7419, IoU.light: 0.6123, IoU.truck: 0.4429, IoU.tower: 0.2256, IoU.chandelier: 0.7160, IoU.awning: 0.4972, IoU.streetlight: 0.3088, IoU.booth: 0.5097, IoU.television receiver: 0.7801, IoU.airplane: 0.7887, IoU.dirt track: 0.1297, IoU.apparel: 0.4462, IoU.pole: 0.2393, IoU.land: 0.0292, IoU.bannister: 0.1485, IoU.escalator: 0.5441, IoU.ottoman: 0.4673, IoU.bottle: 0.3395, IoU.buffet: 0.3975, IoU.poster: 0.3079, IoU.stage: 0.2745, IoU.van: 0.4329, IoU.ship: 0.7158, IoU.fountain: 0.4109, IoU.conveyer belt: 0.7842, IoU.canopy: 0.4829, IoU.washer: 0.7001, IoU.plaything: 0.2813, IoU.swimming pool: 0.5983, IoU.stool: 0.4951, IoU.barrel: 0.5750, IoU.basket: 0.3731, IoU.waterfall: 0.4430, IoU.tent: 0.9079, IoU.bag: 0.1646, IoU.minibike: 0.7412, IoU.cradle: 0.8288, IoU.oven: 0.5509, IoU.ball: 0.5603, IoU.food: 0.5560, IoU.step: 0.0599, IoU.tank: 0.6657, IoU.trade name: 0.2492, IoU.microwave: 0.8848, IoU.pot: 0.5589, IoU.animal: 0.6535, IoU.bicycle: 0.5594, IoU.lake: 0.0010, IoU.dishwasher: 0.6904, IoU.screen: 0.5967, IoU.blanket: 0.3187, IoU.sculpture: 0.7542, IoU.hood: 0.6266, IoU.sconce: 0.5090, IoU.vase: 0.4517, IoU.traffic light: 0.3140, IoU.tray: 0.1434, IoU.ashcan: 0.3994, IoU.fan: 0.6351, IoU.pier: 0.3057, IoU.crt screen: 0.1452, IoU.plate: 0.5664, IoU.monitor: 0.6822, IoU.bulletin board: 0.5995, IoU.shower: 0.0020, IoU.radiator: 0.6255, IoU.glass: 0.1465, IoU.clock: 0.3721, IoU.flag: 0.7026, Acc.wall: 0.8995, Acc.building: 0.9248, Acc.sky: 0.9750, Acc.floor: 0.9182, Acc.tree: 0.8899, Acc.ceiling: 0.9393, Acc.road: 0.8974, Acc.bed : 0.9659, Acc.windowpane: 0.8011, Acc.grass: 0.8092, Acc.cabinet: 0.7722, Acc.sidewalk: 0.8809, Acc.person: 0.9217, Acc.earth: 0.5350, Acc.door: 0.7196, Acc.table: 0.8007, Acc.mountain: 0.7696, Acc.plant: 0.6649, Acc.curtain: 0.8688, Acc.chair: 0.7583, Acc.car: 0.9288, Acc.water: 0.7478, Acc.painting: 0.8778, Acc.sofa: 0.9099, Acc.shelf: 0.7008, Acc.house: 0.7812, Acc.sea: 0.8168, Acc.mirror: 0.8407, Acc.rug: 0.8127, Acc.field: 0.5955, Acc.armchair: 0.7778, Acc.seat: 0.8901, Acc.fence: 0.6827, Acc.desk: 0.7067, Acc.rock: 0.6952, Acc.wardrobe: 0.7908, Acc.lamp: 0.8452, Acc.bathtub: 0.8664, Acc.railing: 0.6003, Acc.cushion: 0.7793, Acc.base: 0.4544, Acc.box: 0.4445, Acc.column: 0.6803, Acc.signboard: 0.4893, Acc.chest of drawers: 0.6626, Acc.counter: 0.6439, Acc.sand: 0.8042, Acc.sink: 0.8433, Acc.skyscraper: 0.5895, Acc.fireplace: 0.9505, Acc.refrigerator: 0.9330, Acc.grandstand: 0.8435, Acc.path: 0.5074, Acc.stairs: 0.2660, Acc.runway: 0.9466, Acc.case: 0.7809, Acc.pool table: 0.9708, Acc.pillow: 0.7697, Acc.screen door: 0.8744, Acc.stairway: 0.5881, Acc.river: 0.3172, Acc.bridge: 0.8504, Acc.bookcase: 0.5419, Acc.blind: 0.4780, Acc.coffee table: 0.8806, Acc.toilet: 0.9225, Acc.flower: 0.4978, Acc.book: 0.8099, Acc.hill: 0.1009, Acc.bench: 0.6016, Acc.countertop: 0.7946, Acc.stove: 0.8738, Acc.palm: 0.7843, Acc.kitchen island: 0.5678, Acc.computer: 0.9375, Acc.swivel chair: 0.7530, Acc.boat: 0.8550, Acc.bar: 0.6484, Acc.arcade machine: 0.7445, Acc.hovel: 0.5025, Acc.bus: 0.9611, Acc.towel: 0.8560, Acc.light: 0.7303, Acc.truck: 0.5764, Acc.tower: 0.5270, Acc.chandelier: 0.9130, Acc.awning: 0.6547, Acc.streetlight: 0.4183, Acc.booth: 0.7049, Acc.television receiver: 0.8565, Acc.airplane: 0.8751, Acc.dirt track: 0.5143, Acc.apparel: 0.5855, Acc.pole: 0.3088, Acc.land: 0.0813, Acc.bannister: 0.1818, Acc.escalator: 0.8002, Acc.ottoman: 0.6454, Acc.bottle: 0.4024, Acc.buffet: 0.4255, Acc.poster: 0.5278, Acc.stage: 0.4532, Acc.van: 0.5712, Acc.ship: 0.7279, Acc.fountain: 0.4215, Acc.conveyer belt: 0.9315, Acc.canopy: 0.7087, Acc.washer: 0.7208, Acc.plaything: 0.3482, Acc.swimming pool: 0.8150, Acc.stool: 0.7260, Acc.barrel: 0.6435, Acc.basket: 0.5902, Acc.waterfall: 0.6085, Acc.tent: 0.9794, Acc.bag: 0.1835, Acc.minibike: 0.8288, Acc.cradle: 0.9740, Acc.oven: 0.7201, Acc.ball: 0.6330, Acc.food: 0.6659, Acc.step: 0.0665, Acc.tank: 0.8135, Acc.trade name: 0.2702, Acc.microwave: 0.9480, Acc.pot: 0.6450, Acc.animal: 0.6655, Acc.bicycle: 0.8165, Acc.lake: 0.0011, Acc.dishwasher: 0.8110, Acc.screen: 0.9277, Acc.blanket: 0.3765, Acc.sculpture: 0.8561, Acc.hood: 0.7805, Acc.sconce: 0.5849, Acc.vase: 0.6294, Acc.traffic light: 0.5905, Acc.tray: 0.1697, Acc.ashcan: 0.5692, Acc.fan: 0.8523, Acc.pier: 0.5441, Acc.crt screen: 0.1649, Acc.plate: 0.6406, Acc.monitor: 0.8467, Acc.bulletin board: 0.6420, Acc.shower: 0.0020, Acc.radiator: 0.7170, Acc.glass: 0.1521, Acc.clock: 0.4981, Acc.flag: 0.7727 +2024-06-18 14:16:11,692 - mmseg - INFO - Iter [36050/80000] lr: 2.198e-05, eta: 18:04:16, time: 3.271, data_time: 1.957, memory: 70498, decode.loss_ce: 0.2406, decode.acc_seg: 89.8579, aux.loss_ce: 0.1001, aux.acc_seg: 89.4904, loss: 0.3407 +2024-06-18 14:17:18,006 - mmseg - INFO - Iter [36100/80000] lr: 2.195e-05, eta: 18:02:53, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2436, decode.acc_seg: 89.3850, aux.loss_ce: 0.1006, aux.acc_seg: 89.1544, loss: 0.3442 +2024-06-18 14:18:24,379 - mmseg - INFO - Iter [36150/80000] lr: 2.193e-05, eta: 18:01:29, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2450, decode.acc_seg: 90.1737, aux.loss_ce: 0.1010, aux.acc_seg: 89.8334, loss: 0.3460 +2024-06-18 14:19:30,720 - mmseg - INFO - Iter [36200/80000] lr: 2.190e-05, eta: 18:00:06, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2285, decode.acc_seg: 90.3316, aux.loss_ce: 0.0948, aux.acc_seg: 90.0320, loss: 0.3233 +2024-06-18 14:20:36,684 - mmseg - INFO - Iter [36250/80000] lr: 2.188e-05, eta: 17:58:43, time: 1.319, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2304, decode.acc_seg: 90.1973, aux.loss_ce: 0.0950, aux.acc_seg: 89.8114, loss: 0.3253 +2024-06-18 14:21:43,116 - mmseg - INFO - Iter [36300/80000] lr: 2.185e-05, eta: 17:57:20, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2321, decode.acc_seg: 90.1699, aux.loss_ce: 0.0955, aux.acc_seg: 89.9478, loss: 0.3276 +2024-06-18 14:22:49,485 - mmseg - INFO - Iter [36350/80000] lr: 2.183e-05, eta: 17:55:56, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2231, decode.acc_seg: 90.5196, aux.loss_ce: 0.0919, aux.acc_seg: 90.2579, loss: 0.3150 +2024-06-18 14:23:55,596 - mmseg - INFO - Iter [36400/80000] lr: 2.180e-05, eta: 17:54:33, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2167, decode.acc_seg: 90.8050, aux.loss_ce: 0.0896, aux.acc_seg: 90.4109, loss: 0.3063 +2024-06-18 14:25:01,886 - mmseg - INFO - Iter [36450/80000] lr: 2.178e-05, eta: 17:53:10, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2202, decode.acc_seg: 90.8633, aux.loss_ce: 0.0912, aux.acc_seg: 90.5008, loss: 0.3114 +2024-06-18 14:26:08,243 - mmseg - INFO - Iter [36500/80000] lr: 2.175e-05, eta: 17:51:47, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2305, decode.acc_seg: 90.3429, aux.loss_ce: 0.0949, aux.acc_seg: 90.0617, loss: 0.3254 +2024-06-18 14:27:14,472 - mmseg - INFO - Iter [36550/80000] lr: 2.173e-05, eta: 17:50:24, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2413, decode.acc_seg: 89.8709, aux.loss_ce: 0.0996, aux.acc_seg: 89.5244, loss: 0.3410 +2024-06-18 14:28:20,887 - mmseg - INFO - Iter [36600/80000] lr: 2.170e-05, eta: 17:49:01, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2390, decode.acc_seg: 89.8948, aux.loss_ce: 0.0990, aux.acc_seg: 89.5927, loss: 0.3381 +2024-06-18 14:29:30,882 - mmseg - INFO - Iter [36650/80000] lr: 2.168e-05, eta: 17:47:43, time: 1.400, data_time: 0.085, memory: 70498, decode.loss_ce: 0.2164, decode.acc_seg: 91.0023, aux.loss_ce: 0.0899, aux.acc_seg: 90.6744, loss: 0.3063 +2024-06-18 14:30:36,949 - mmseg - INFO - Iter [36700/80000] lr: 2.165e-05, eta: 17:46:20, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2366, decode.acc_seg: 90.4412, aux.loss_ce: 0.0984, aux.acc_seg: 90.0513, loss: 0.3350 +2024-06-18 14:31:43,276 - mmseg - INFO - Iter [36750/80000] lr: 2.163e-05, eta: 17:44:57, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2174, decode.acc_seg: 91.1085, aux.loss_ce: 0.0905, aux.acc_seg: 90.6696, loss: 0.3079 +2024-06-18 14:32:49,601 - mmseg - INFO - Iter [36800/80000] lr: 2.160e-05, eta: 17:43:34, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2280, decode.acc_seg: 90.4515, aux.loss_ce: 0.0949, aux.acc_seg: 90.0495, loss: 0.3228 +2024-06-18 14:33:55,745 - mmseg - INFO - Iter [36850/80000] lr: 2.158e-05, eta: 17:42:11, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2277, decode.acc_seg: 90.7953, aux.loss_ce: 0.0950, aux.acc_seg: 90.4377, loss: 0.3227 +2024-06-18 14:35:02,271 - mmseg - INFO - Iter [36900/80000] lr: 2.155e-05, eta: 17:40:49, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2188, decode.acc_seg: 90.7321, aux.loss_ce: 0.0912, aux.acc_seg: 90.4342, loss: 0.3100 +2024-06-18 14:36:08,594 - mmseg - INFO - Iter [36950/80000] lr: 2.153e-05, eta: 17:39:26, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2235, decode.acc_seg: 90.6093, aux.loss_ce: 0.0929, aux.acc_seg: 90.3182, loss: 0.3164 +2024-06-18 14:37:14,812 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 14:37:14,812 - mmseg - INFO - Iter [37000/80000] lr: 2.150e-05, eta: 17:38:04, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2150, decode.acc_seg: 90.9057, aux.loss_ce: 0.0894, aux.acc_seg: 90.5788, loss: 0.3044 +2024-06-18 14:38:54,508 - mmseg - INFO - per class results: +2024-06-18 14:38:54,514 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.31 | 88.64 | +| building | 85.12 | 93.4 | +| sky | 94.83 | 97.92 | +| floor | 85.35 | 91.91 | +| tree | 76.99 | 89.62 | +| ceiling | 86.69 | 93.67 | +| road | 85.86 | 91.81 | +| bed | 92.13 | 97.05 | +| windowpane | 65.47 | 79.97 | +| grass | 65.43 | 83.35 | +| cabinet | 63.64 | 76.6 | +| sidewalk | 71.4 | 82.85 | +| person | 84.95 | 94.57 | +| earth | 37.08 | 47.77 | +| door | 59.31 | 80.22 | +| table | 68.73 | 80.73 | +| mountain | 58.72 | 72.09 | +| plant | 55.05 | 65.34 | +| curtain | 78.77 | 86.84 | +| chair | 65.75 | 78.92 | +| car | 87.32 | 93.82 | +| water | 60.26 | 74.37 | +| painting | 76.76 | 89.83 | +| sofa | 79.92 | 89.7 | +| shelf | 49.34 | 62.04 | +| house | 63.74 | 74.95 | +| sea | 63.7 | 75.97 | +| mirror | 75.51 | 83.93 | +| rug | 70.1 | 75.43 | +| field | 33.75 | 52.91 | +| armchair | 57.74 | 76.64 | +| seat | 67.92 | 86.75 | +| fence | 51.89 | 67.61 | +| desk | 58.58 | 75.16 | +| rock | 49.74 | 76.09 | +| wardrobe | 54.08 | 69.62 | +| lamp | 71.84 | 81.65 | +| bathtub | 84.13 | 86.7 | +| railing | 40.52 | 65.56 | +| cushion | 66.75 | 79.98 | +| base | 36.11 | 56.29 | +| box | 37.87 | 52.1 | +| column | 52.59 | 67.55 | +| signboard | 40.06 | 58.43 | +| chest of drawers | 41.45 | 69.87 | +| counter | 40.45 | 49.47 | +| sand | 53.92 | 75.75 | +| sink | 75.08 | 85.11 | +| skyscraper | 47.9 | 57.13 | +| fireplace | 76.86 | 90.8 | +| refrigerator | 80.71 | 91.68 | +| grandstand | 52.14 | 82.4 | +| path | 32.38 | 50.38 | +| stairs | 22.88 | 30.66 | +| runway | 67.47 | 89.34 | +| case | 56.41 | 75.37 | +| pool table | 94.48 | 97.62 | +| pillow | 65.24 | 73.25 | +| screen door | 80.99 | 83.86 | +| stairway | 39.15 | 56.35 | +| river | 18.04 | 46.49 | +| bridge | 77.46 | 88.85 | +| bookcase | 42.95 | 57.89 | +| blind | 44.85 | 51.16 | +| coffee table | 69.07 | 84.95 | +| toilet | 88.42 | 93.3 | +| flower | 46.69 | 62.41 | +| book | 51.88 | 69.96 | +| hill | 5.96 | 9.59 | +| bench | 53.91 | 63.86 | +| countertop | 64.37 | 81.85 | +| stove | 84.02 | 90.84 | +| palm | 56.69 | 77.66 | +| kitchen island | 41.94 | 90.09 | +| computer | 76.8 | 95.32 | +| swivel chair | 51.72 | 75.94 | +| boat | 51.68 | 88.16 | +| bar | 61.49 | 83.63 | +| arcade machine | 72.61 | 78.34 | +| hovel | 44.18 | 49.05 | +| bus | 91.18 | 96.34 | +| towel | 76.91 | 86.34 | +| light | 59.74 | 67.64 | +| truck | 44.62 | 58.74 | +| tower | 9.81 | 17.51 | +| chandelier | 70.3 | 90.02 | +| awning | 43.56 | 55.49 | +| streetlight | 34.37 | 46.81 | +| booth | 47.25 | 49.63 | +| television receiver | 72.57 | 87.88 | +| airplane | 61.0 | 67.74 | +| dirt track | 8.43 | 41.63 | +| apparel | 44.58 | 65.52 | +| pole | 24.93 | 33.06 | +| land | 2.18 | 5.49 | +| bannister | 17.06 | 22.64 | +| escalator | 54.74 | 81.03 | +| ottoman | 39.78 | 51.84 | +| bottle | 41.74 | 66.47 | +| buffet | 42.58 | 48.26 | +| poster | 37.25 | 40.75 | +| stage | 22.39 | 39.47 | +| van | 43.39 | 59.76 | +| ship | 85.69 | 86.43 | +| fountain | 33.39 | 34.51 | +| conveyer belt | 73.33 | 93.49 | +| canopy | 47.19 | 74.28 | +| washer | 76.22 | 79.18 | +| plaything | 33.31 | 65.24 | +| swimming pool | 56.64 | 90.34 | +| stool | 49.24 | 64.38 | +| barrel | 55.78 | 64.63 | +| basket | 37.25 | 55.51 | +| waterfall | 53.32 | 74.62 | +| tent | 90.1 | 98.56 | +| bag | 19.41 | 22.46 | +| minibike | 72.16 | 90.37 | +| cradle | 84.56 | 98.44 | +| oven | 51.08 | 65.31 | +| ball | 47.82 | 50.13 | +| food | 61.43 | 79.66 | +| step | 18.48 | 26.16 | +| tank | 67.6 | 81.06 | +| trade name | 19.92 | 21.67 | +| microwave | 85.69 | 95.44 | +| pot | 57.82 | 67.26 | +| animal | 65.46 | 67.11 | +| bicycle | 59.93 | 76.66 | +| lake | 40.11 | 42.55 | +| dishwasher | 67.89 | 81.85 | +| screen | 59.08 | 92.66 | +| blanket | 29.95 | 33.1 | +| sculpture | 71.97 | 85.96 | +| hood | 64.34 | 79.14 | +| sconce | 54.1 | 62.58 | +| vase | 47.83 | 60.52 | +| traffic light | 36.95 | 59.19 | +| tray | 14.25 | 21.59 | +| ashcan | 44.32 | 64.76 | +| fan | 63.32 | 74.19 | +| pier | 31.67 | 60.3 | +| crt screen | 3.59 | 3.65 | +| plate | 56.26 | 76.19 | +| monitor | 67.42 | 77.2 | +| bulletin board | 45.58 | 49.82 | +| shower | 0.0 | 0.0 | +| radiator | 63.03 | 76.74 | +| glass | 18.13 | 19.82 | +| clock | 42.33 | 49.01 | +| flag | 71.27 | 76.4 | ++---------------------+-------+-------+ +2024-06-18 14:38:54,514 - mmseg - INFO - Summary: +2024-06-18 14:38:54,514 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 85.6 | 55.41 | 68.63 | ++------+-------+-------+ +2024-06-18 14:38:54,515 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 14:38:54,515 - mmseg - INFO - Iter(val) [250] aAcc: 0.8560, mIoU: 0.5541, mAcc: 0.6863, IoU.wall: 0.8131, IoU.building: 0.8512, IoU.sky: 0.9483, IoU.floor: 0.8535, IoU.tree: 0.7699, IoU.ceiling: 0.8669, IoU.road: 0.8586, IoU.bed : 0.9213, IoU.windowpane: 0.6547, IoU.grass: 0.6543, IoU.cabinet: 0.6364, IoU.sidewalk: 0.7140, IoU.person: 0.8495, IoU.earth: 0.3708, IoU.door: 0.5931, IoU.table: 0.6873, IoU.mountain: 0.5872, IoU.plant: 0.5505, IoU.curtain: 0.7877, IoU.chair: 0.6575, IoU.car: 0.8732, IoU.water: 0.6026, IoU.painting: 0.7676, IoU.sofa: 0.7992, IoU.shelf: 0.4934, IoU.house: 0.6374, IoU.sea: 0.6370, IoU.mirror: 0.7551, IoU.rug: 0.7010, IoU.field: 0.3375, IoU.armchair: 0.5774, IoU.seat: 0.6792, IoU.fence: 0.5189, IoU.desk: 0.5858, IoU.rock: 0.4974, IoU.wardrobe: 0.5408, IoU.lamp: 0.7184, IoU.bathtub: 0.8413, IoU.railing: 0.4052, IoU.cushion: 0.6675, IoU.base: 0.3611, IoU.box: 0.3787, IoU.column: 0.5259, IoU.signboard: 0.4006, IoU.chest of drawers: 0.4145, IoU.counter: 0.4045, IoU.sand: 0.5392, IoU.sink: 0.7508, IoU.skyscraper: 0.4790, IoU.fireplace: 0.7686, IoU.refrigerator: 0.8071, IoU.grandstand: 0.5214, IoU.path: 0.3238, IoU.stairs: 0.2288, IoU.runway: 0.6747, IoU.case: 0.5641, IoU.pool table: 0.9448, IoU.pillow: 0.6524, IoU.screen door: 0.8099, IoU.stairway: 0.3915, IoU.river: 0.1804, IoU.bridge: 0.7746, IoU.bookcase: 0.4295, IoU.blind: 0.4485, IoU.coffee table: 0.6907, IoU.toilet: 0.8842, IoU.flower: 0.4669, IoU.book: 0.5188, IoU.hill: 0.0596, IoU.bench: 0.5391, IoU.countertop: 0.6437, IoU.stove: 0.8402, IoU.palm: 0.5669, IoU.kitchen island: 0.4194, IoU.computer: 0.7680, IoU.swivel chair: 0.5172, IoU.boat: 0.5168, IoU.bar: 0.6149, IoU.arcade machine: 0.7261, IoU.hovel: 0.4418, IoU.bus: 0.9118, IoU.towel: 0.7691, IoU.light: 0.5974, IoU.truck: 0.4462, IoU.tower: 0.0981, IoU.chandelier: 0.7030, IoU.awning: 0.4356, IoU.streetlight: 0.3437, IoU.booth: 0.4725, IoU.television receiver: 0.7257, IoU.airplane: 0.6100, IoU.dirt track: 0.0843, IoU.apparel: 0.4458, IoU.pole: 0.2493, IoU.land: 0.0218, IoU.bannister: 0.1706, IoU.escalator: 0.5474, IoU.ottoman: 0.3978, IoU.bottle: 0.4174, IoU.buffet: 0.4258, IoU.poster: 0.3725, IoU.stage: 0.2239, IoU.van: 0.4339, IoU.ship: 0.8569, IoU.fountain: 0.3339, IoU.conveyer belt: 0.7333, IoU.canopy: 0.4719, IoU.washer: 0.7622, IoU.plaything: 0.3331, IoU.swimming pool: 0.5664, IoU.stool: 0.4924, IoU.barrel: 0.5578, IoU.basket: 0.3725, IoU.waterfall: 0.5332, IoU.tent: 0.9010, IoU.bag: 0.1941, IoU.minibike: 0.7216, IoU.cradle: 0.8456, IoU.oven: 0.5108, IoU.ball: 0.4782, IoU.food: 0.6143, IoU.step: 0.1848, IoU.tank: 0.6760, IoU.trade name: 0.1992, IoU.microwave: 0.8569, IoU.pot: 0.5782, IoU.animal: 0.6546, IoU.bicycle: 0.5993, IoU.lake: 0.4011, IoU.dishwasher: 0.6789, IoU.screen: 0.5908, IoU.blanket: 0.2995, IoU.sculpture: 0.7197, IoU.hood: 0.6434, IoU.sconce: 0.5410, IoU.vase: 0.4783, IoU.traffic light: 0.3695, IoU.tray: 0.1425, IoU.ashcan: 0.4432, IoU.fan: 0.6332, IoU.pier: 0.3167, IoU.crt screen: 0.0359, IoU.plate: 0.5626, IoU.monitor: 0.6742, IoU.bulletin board: 0.4558, IoU.shower: 0.0000, IoU.radiator: 0.6303, IoU.glass: 0.1813, IoU.clock: 0.4233, IoU.flag: 0.7127, Acc.wall: 0.8864, Acc.building: 0.9340, Acc.sky: 0.9792, Acc.floor: 0.9191, Acc.tree: 0.8962, Acc.ceiling: 0.9367, Acc.road: 0.9181, Acc.bed : 0.9705, Acc.windowpane: 0.7997, Acc.grass: 0.8335, Acc.cabinet: 0.7660, Acc.sidewalk: 0.8285, Acc.person: 0.9457, Acc.earth: 0.4777, Acc.door: 0.8022, Acc.table: 0.8073, Acc.mountain: 0.7209, Acc.plant: 0.6534, Acc.curtain: 0.8684, Acc.chair: 0.7892, Acc.car: 0.9382, Acc.water: 0.7437, Acc.painting: 0.8983, Acc.sofa: 0.8970, Acc.shelf: 0.6204, Acc.house: 0.7495, Acc.sea: 0.7597, Acc.mirror: 0.8393, Acc.rug: 0.7543, Acc.field: 0.5291, Acc.armchair: 0.7664, Acc.seat: 0.8675, Acc.fence: 0.6761, Acc.desk: 0.7516, Acc.rock: 0.7609, Acc.wardrobe: 0.6962, Acc.lamp: 0.8165, Acc.bathtub: 0.8670, Acc.railing: 0.6556, Acc.cushion: 0.7998, Acc.base: 0.5629, Acc.box: 0.5210, Acc.column: 0.6755, Acc.signboard: 0.5843, Acc.chest of drawers: 0.6987, Acc.counter: 0.4947, Acc.sand: 0.7575, Acc.sink: 0.8511, Acc.skyscraper: 0.5713, Acc.fireplace: 0.9080, Acc.refrigerator: 0.9168, Acc.grandstand: 0.8240, Acc.path: 0.5038, Acc.stairs: 0.3066, Acc.runway: 0.8934, Acc.case: 0.7537, Acc.pool table: 0.9762, Acc.pillow: 0.7325, Acc.screen door: 0.8386, Acc.stairway: 0.5635, Acc.river: 0.4649, Acc.bridge: 0.8885, Acc.bookcase: 0.5789, Acc.blind: 0.5116, Acc.coffee table: 0.8495, Acc.toilet: 0.9330, Acc.flower: 0.6241, Acc.book: 0.6996, Acc.hill: 0.0959, Acc.bench: 0.6386, Acc.countertop: 0.8185, Acc.stove: 0.9084, Acc.palm: 0.7766, Acc.kitchen island: 0.9009, Acc.computer: 0.9532, Acc.swivel chair: 0.7594, Acc.boat: 0.8816, Acc.bar: 0.8363, Acc.arcade machine: 0.7834, Acc.hovel: 0.4905, Acc.bus: 0.9634, Acc.towel: 0.8634, Acc.light: 0.6764, Acc.truck: 0.5874, Acc.tower: 0.1751, Acc.chandelier: 0.9002, Acc.awning: 0.5549, Acc.streetlight: 0.4681, Acc.booth: 0.4963, Acc.television receiver: 0.8788, Acc.airplane: 0.6774, Acc.dirt track: 0.4163, Acc.apparel: 0.6552, Acc.pole: 0.3306, Acc.land: 0.0549, Acc.bannister: 0.2264, Acc.escalator: 0.8103, Acc.ottoman: 0.5184, Acc.bottle: 0.6647, Acc.buffet: 0.4826, Acc.poster: 0.4075, Acc.stage: 0.3947, Acc.van: 0.5976, Acc.ship: 0.8643, Acc.fountain: 0.3451, Acc.conveyer belt: 0.9349, Acc.canopy: 0.7428, Acc.washer: 0.7918, Acc.plaything: 0.6524, Acc.swimming pool: 0.9034, Acc.stool: 0.6438, Acc.barrel: 0.6463, Acc.basket: 0.5551, Acc.waterfall: 0.7462, Acc.tent: 0.9856, Acc.bag: 0.2246, Acc.minibike: 0.9037, Acc.cradle: 0.9844, Acc.oven: 0.6531, Acc.ball: 0.5013, Acc.food: 0.7966, Acc.step: 0.2616, Acc.tank: 0.8106, Acc.trade name: 0.2167, Acc.microwave: 0.9544, Acc.pot: 0.6726, Acc.animal: 0.6711, Acc.bicycle: 0.7666, Acc.lake: 0.4255, Acc.dishwasher: 0.8185, Acc.screen: 0.9266, Acc.blanket: 0.3310, Acc.sculpture: 0.8596, Acc.hood: 0.7914, Acc.sconce: 0.6258, Acc.vase: 0.6052, Acc.traffic light: 0.5919, Acc.tray: 0.2159, Acc.ashcan: 0.6476, Acc.fan: 0.7419, Acc.pier: 0.6030, Acc.crt screen: 0.0365, Acc.plate: 0.7619, Acc.monitor: 0.7720, Acc.bulletin board: 0.4982, Acc.shower: 0.0000, Acc.radiator: 0.7674, Acc.glass: 0.1982, Acc.clock: 0.4901, Acc.flag: 0.7640 +2024-06-18 14:40:01,449 - mmseg - INFO - Iter [37050/80000] lr: 2.148e-05, eta: 17:38:37, time: 3.333, data_time: 2.011, memory: 70498, decode.loss_ce: 0.2342, decode.acc_seg: 90.2690, aux.loss_ce: 0.0972, aux.acc_seg: 89.8521, loss: 0.3314 +2024-06-18 14:41:07,530 - mmseg - INFO - Iter [37100/80000] lr: 2.145e-05, eta: 17:37:14, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2258, decode.acc_seg: 90.7745, aux.loss_ce: 0.0935, aux.acc_seg: 90.4340, loss: 0.3193 +2024-06-18 14:42:13,747 - mmseg - INFO - Iter [37150/80000] lr: 2.143e-05, eta: 17:35:51, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2258, decode.acc_seg: 90.6538, aux.loss_ce: 0.0940, aux.acc_seg: 90.2538, loss: 0.3198 +2024-06-18 14:43:19,977 - mmseg - INFO - Iter [37200/80000] lr: 2.140e-05, eta: 17:34:29, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2287, decode.acc_seg: 90.7093, aux.loss_ce: 0.0948, aux.acc_seg: 90.4213, loss: 0.3235 +2024-06-18 14:44:26,353 - mmseg - INFO - Iter [37250/80000] lr: 2.138e-05, eta: 17:33:06, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2108, decode.acc_seg: 91.1973, aux.loss_ce: 0.0865, aux.acc_seg: 90.9576, loss: 0.2973 +2024-06-18 14:45:32,832 - mmseg - INFO - Iter [37300/80000] lr: 2.135e-05, eta: 17:31:44, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2299, decode.acc_seg: 90.4620, aux.loss_ce: 0.0957, aux.acc_seg: 90.1212, loss: 0.3256 +2024-06-18 14:46:38,937 - mmseg - INFO - Iter [37350/80000] lr: 2.133e-05, eta: 17:30:21, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2461, decode.acc_seg: 89.8768, aux.loss_ce: 0.1009, aux.acc_seg: 89.6192, loss: 0.3470 +2024-06-18 14:47:45,456 - mmseg - INFO - Iter [37400/80000] lr: 2.130e-05, eta: 17:28:59, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2491, decode.acc_seg: 89.9290, aux.loss_ce: 0.1019, aux.acc_seg: 89.6417, loss: 0.3510 +2024-06-18 14:48:51,814 - mmseg - INFO - Iter [37450/80000] lr: 2.128e-05, eta: 17:27:36, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2205, decode.acc_seg: 90.5704, aux.loss_ce: 0.0917, aux.acc_seg: 90.2399, loss: 0.3122 +2024-06-18 14:49:58,539 - mmseg - INFO - Iter [37500/80000] lr: 2.125e-05, eta: 17:26:14, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2336, decode.acc_seg: 90.3734, aux.loss_ce: 0.0958, aux.acc_seg: 90.1483, loss: 0.3294 +2024-06-18 14:51:04,762 - mmseg - INFO - Iter [37550/80000] lr: 2.123e-05, eta: 17:24:52, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2141, decode.acc_seg: 91.0007, aux.loss_ce: 0.0898, aux.acc_seg: 90.5584, loss: 0.3039 +2024-06-18 14:52:11,186 - mmseg - INFO - Iter [37600/80000] lr: 2.120e-05, eta: 17:23:30, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2440, decode.acc_seg: 89.7697, aux.loss_ce: 0.0996, aux.acc_seg: 89.5228, loss: 0.3436 +2024-06-18 14:53:17,555 - mmseg - INFO - Iter [37650/80000] lr: 2.118e-05, eta: 17:22:07, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2165, decode.acc_seg: 90.7827, aux.loss_ce: 0.0894, aux.acc_seg: 90.5739, loss: 0.3058 +2024-06-18 14:54:23,725 - mmseg - INFO - Iter [37700/80000] lr: 2.115e-05, eta: 17:20:45, time: 1.323, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2194, decode.acc_seg: 90.7393, aux.loss_ce: 0.0910, aux.acc_seg: 90.4163, loss: 0.3104 +2024-06-18 14:55:29,938 - mmseg - INFO - Iter [37750/80000] lr: 2.113e-05, eta: 17:19:23, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2271, decode.acc_seg: 90.4489, aux.loss_ce: 0.0937, aux.acc_seg: 90.1360, loss: 0.3208 +2024-06-18 14:56:36,356 - mmseg - INFO - Iter [37800/80000] lr: 2.110e-05, eta: 17:18:01, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2285, decode.acc_seg: 90.1312, aux.loss_ce: 0.0949, aux.acc_seg: 89.7277, loss: 0.3235 +2024-06-18 14:57:42,636 - mmseg - INFO - Iter [37850/80000] lr: 2.108e-05, eta: 17:16:38, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2414, decode.acc_seg: 89.8987, aux.loss_ce: 0.0991, aux.acc_seg: 89.5768, loss: 0.3405 +2024-06-18 14:58:51,880 - mmseg - INFO - Iter [37900/80000] lr: 2.105e-05, eta: 17:15:20, time: 1.385, data_time: 0.065, memory: 70498, decode.loss_ce: 0.2157, decode.acc_seg: 90.8527, aux.loss_ce: 0.0889, aux.acc_seg: 90.5789, loss: 0.3046 +2024-06-18 14:59:58,526 - mmseg - INFO - Iter [37950/80000] lr: 2.103e-05, eta: 17:13:58, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2057, decode.acc_seg: 91.2291, aux.loss_ce: 0.0855, aux.acc_seg: 90.9231, loss: 0.2912 +2024-06-18 15:01:05,050 - mmseg - INFO - Saving checkpoint at 38000 iterations +2024-06-18 15:02:49,645 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 15:02:49,645 - mmseg - INFO - Iter [38000/80000] lr: 2.100e-05, eta: 17:14:32, time: 3.422, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2162, decode.acc_seg: 91.0596, aux.loss_ce: 0.0903, aux.acc_seg: 90.7013, loss: 0.3065 +2024-06-18 15:04:27,644 - mmseg - INFO - per class results: +2024-06-18 15:04:27,650 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.13 | 89.17 | +| building | 84.46 | 93.86 | +| sky | 94.54 | 98.2 | +| floor | 84.77 | 91.45 | +| tree | 75.71 | 84.49 | +| ceiling | 85.78 | 92.86 | +| road | 87.59 | 93.02 | +| bed | 92.4 | 96.71 | +| windowpane | 67.2 | 80.76 | +| grass | 66.22 | 78.89 | +| cabinet | 65.86 | 77.19 | +| sidewalk | 72.29 | 83.41 | +| person | 85.02 | 93.12 | +| earth | 37.29 | 46.93 | +| door | 61.09 | 76.42 | +| table | 67.58 | 78.86 | +| mountain | 62.84 | 76.67 | +| plant | 55.42 | 73.48 | +| curtain | 78.98 | 88.59 | +| chair | 64.15 | 72.14 | +| car | 86.51 | 94.89 | +| water | 65.41 | 81.93 | +| painting | 77.22 | 89.25 | +| sofa | 78.54 | 86.44 | +| shelf | 48.85 | 74.21 | +| house | 56.25 | 74.96 | +| sea | 70.65 | 78.67 | +| mirror | 76.87 | 83.93 | +| rug | 69.63 | 86.03 | +| field | 39.81 | 69.24 | +| armchair | 57.25 | 82.93 | +| seat | 66.31 | 87.96 | +| fence | 52.75 | 62.78 | +| desk | 55.38 | 70.08 | +| rock | 53.65 | 77.38 | +| wardrobe | 57.8 | 76.67 | +| lamp | 71.85 | 79.18 | +| bathtub | 84.15 | 87.08 | +| railing | 40.04 | 55.82 | +| cushion | 68.78 | 80.78 | +| base | 40.59 | 53.45 | +| box | 35.06 | 46.42 | +| column | 55.32 | 66.51 | +| signboard | 40.9 | 51.15 | +| chest of drawers | 48.91 | 63.72 | +| counter | 45.55 | 68.09 | +| sand | 54.1 | 82.44 | +| sink | 75.7 | 82.31 | +| skyscraper | 46.33 | 55.97 | +| fireplace | 68.67 | 96.36 | +| refrigerator | 79.47 | 92.46 | +| grandstand | 46.24 | 81.03 | +| path | 31.32 | 47.26 | +| stairs | 31.75 | 44.67 | +| runway | 74.63 | 98.0 | +| case | 53.29 | 75.69 | +| pool table | 94.61 | 97.1 | +| pillow | 70.14 | 83.12 | +| screen door | 83.96 | 87.47 | +| stairway | 45.3 | 48.17 | +| river | 15.72 | 30.19 | +| bridge | 76.63 | 88.73 | +| bookcase | 43.93 | 59.04 | +| blind | 42.15 | 47.08 | +| coffee table | 63.88 | 88.02 | +| toilet | 89.08 | 92.65 | +| flower | 46.52 | 60.16 | +| book | 52.72 | 79.0 | +| hill | 6.56 | 11.68 | +| bench | 52.24 | 61.13 | +| countertop | 64.95 | 81.06 | +| stove | 84.61 | 92.06 | +| palm | 56.07 | 79.34 | +| kitchen island | 45.04 | 89.59 | +| computer | 76.83 | 95.3 | +| swivel chair | 52.8 | 78.11 | +| boat | 50.28 | 86.1 | +| bar | 60.97 | 67.11 | +| arcade machine | 72.84 | 78.38 | +| hovel | 44.62 | 49.36 | +| bus | 92.93 | 96.19 | +| towel | 73.55 | 82.65 | +| light | 58.15 | 65.79 | +| truck | 46.49 | 61.45 | +| tower | 19.39 | 40.2 | +| chandelier | 71.58 | 87.48 | +| awning | 34.87 | 37.28 | +| streetlight | 30.56 | 39.63 | +| booth | 39.59 | 73.75 | +| television receiver | 78.11 | 87.24 | +| airplane | 69.28 | 73.42 | +| dirt track | 23.53 | 43.05 | +| apparel | 41.05 | 52.41 | +| pole | 23.3 | 30.68 | +| land | 2.86 | 9.36 | +| bannister | 16.26 | 21.08 | +| escalator | 56.66 | 81.7 | +| ottoman | 51.26 | 71.95 | +| bottle | 32.65 | 38.05 | +| buffet | 54.31 | 64.21 | +| poster | 35.14 | 42.44 | +| stage | 24.09 | 44.36 | +| van | 42.56 | 50.17 | +| ship | 92.26 | 97.25 | +| fountain | 32.19 | 33.09 | +| conveyer belt | 73.71 | 93.37 | +| canopy | 47.51 | 76.5 | +| washer | 78.64 | 81.35 | +| plaything | 35.41 | 51.35 | +| swimming pool | 56.88 | 81.7 | +| stool | 48.08 | 65.88 | +| barrel | 55.83 | 64.66 | +| basket | 38.25 | 56.78 | +| waterfall | 48.73 | 69.24 | +| tent | 84.33 | 98.73 | +| bag | 20.41 | 24.94 | +| minibike | 72.95 | 86.84 | +| cradle | 79.82 | 97.62 | +| oven | 46.19 | 60.97 | +| ball | 54.75 | 77.2 | +| food | 50.59 | 60.48 | +| step | 13.05 | 18.75 | +| tank | 51.4 | 57.75 | +| trade name | 26.53 | 28.68 | +| microwave | 86.88 | 95.72 | +| pot | 54.06 | 60.26 | +| animal | 65.47 | 67.48 | +| bicycle | 57.19 | 76.37 | +| lake | 51.77 | 63.77 | +| dishwasher | 66.71 | 73.72 | +| screen | 58.53 | 85.35 | +| blanket | 28.91 | 32.26 | +| sculpture | 77.13 | 87.45 | +| hood | 60.67 | 71.21 | +| sconce | 55.16 | 63.09 | +| vase | 46.76 | 62.08 | +| traffic light | 34.8 | 54.15 | +| tray | 8.26 | 10.33 | +| ashcan | 42.5 | 62.25 | +| fan | 63.14 | 75.65 | +| pier | 47.69 | 83.02 | +| crt screen | 14.54 | 17.33 | +| plate | 57.04 | 72.96 | +| monitor | 70.88 | 82.08 | +| bulletin board | 56.54 | 61.89 | +| shower | 0.13 | 0.13 | +| radiator | 64.33 | 72.28 | +| glass | 18.53 | 19.99 | +| clock | 36.93 | 40.59 | +| flag | 69.85 | 76.54 | ++---------------------+-------+-------+ +2024-06-18 15:04:27,650 - mmseg - INFO - Summary: +2024-06-18 15:04:27,651 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.72 | 55.92 | 68.85 | ++-------+-------+-------+ +2024-06-18 15:04:27,651 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 15:04:27,652 - mmseg - INFO - Iter(val) [250] aAcc: 0.8572, mIoU: 0.5592, mAcc: 0.6885, IoU.wall: 0.8113, IoU.building: 0.8446, IoU.sky: 0.9454, IoU.floor: 0.8477, IoU.tree: 0.7571, IoU.ceiling: 0.8578, IoU.road: 0.8759, IoU.bed : 0.9240, IoU.windowpane: 0.6720, IoU.grass: 0.6622, IoU.cabinet: 0.6586, IoU.sidewalk: 0.7229, IoU.person: 0.8502, IoU.earth: 0.3729, IoU.door: 0.6109, IoU.table: 0.6758, IoU.mountain: 0.6284, IoU.plant: 0.5542, IoU.curtain: 0.7898, IoU.chair: 0.6415, IoU.car: 0.8651, IoU.water: 0.6541, IoU.painting: 0.7722, IoU.sofa: 0.7854, IoU.shelf: 0.4885, IoU.house: 0.5625, IoU.sea: 0.7065, IoU.mirror: 0.7687, IoU.rug: 0.6963, IoU.field: 0.3981, IoU.armchair: 0.5725, IoU.seat: 0.6631, IoU.fence: 0.5275, IoU.desk: 0.5538, IoU.rock: 0.5365, IoU.wardrobe: 0.5780, IoU.lamp: 0.7185, IoU.bathtub: 0.8415, IoU.railing: 0.4004, IoU.cushion: 0.6878, IoU.base: 0.4059, IoU.box: 0.3506, IoU.column: 0.5532, IoU.signboard: 0.4090, IoU.chest of drawers: 0.4891, IoU.counter: 0.4555, IoU.sand: 0.5410, IoU.sink: 0.7570, IoU.skyscraper: 0.4633, IoU.fireplace: 0.6867, IoU.refrigerator: 0.7947, IoU.grandstand: 0.4624, IoU.path: 0.3132, IoU.stairs: 0.3175, IoU.runway: 0.7463, IoU.case: 0.5329, IoU.pool table: 0.9461, IoU.pillow: 0.7014, IoU.screen door: 0.8396, IoU.stairway: 0.4530, IoU.river: 0.1572, IoU.bridge: 0.7663, IoU.bookcase: 0.4393, IoU.blind: 0.4215, IoU.coffee table: 0.6388, IoU.toilet: 0.8908, IoU.flower: 0.4652, IoU.book: 0.5272, IoU.hill: 0.0656, IoU.bench: 0.5224, IoU.countertop: 0.6495, IoU.stove: 0.8461, IoU.palm: 0.5607, IoU.kitchen island: 0.4504, IoU.computer: 0.7683, IoU.swivel chair: 0.5280, IoU.boat: 0.5028, IoU.bar: 0.6097, IoU.arcade machine: 0.7284, IoU.hovel: 0.4462, IoU.bus: 0.9293, IoU.towel: 0.7355, IoU.light: 0.5815, IoU.truck: 0.4649, IoU.tower: 0.1939, IoU.chandelier: 0.7158, IoU.awning: 0.3487, IoU.streetlight: 0.3056, IoU.booth: 0.3959, IoU.television receiver: 0.7811, IoU.airplane: 0.6928, IoU.dirt track: 0.2353, IoU.apparel: 0.4105, IoU.pole: 0.2330, IoU.land: 0.0286, IoU.bannister: 0.1626, IoU.escalator: 0.5666, IoU.ottoman: 0.5126, IoU.bottle: 0.3265, IoU.buffet: 0.5431, IoU.poster: 0.3514, IoU.stage: 0.2409, IoU.van: 0.4256, IoU.ship: 0.9226, IoU.fountain: 0.3219, IoU.conveyer belt: 0.7371, IoU.canopy: 0.4751, IoU.washer: 0.7864, IoU.plaything: 0.3541, IoU.swimming pool: 0.5688, IoU.stool: 0.4808, IoU.barrel: 0.5583, IoU.basket: 0.3825, IoU.waterfall: 0.4873, IoU.tent: 0.8433, IoU.bag: 0.2041, IoU.minibike: 0.7295, IoU.cradle: 0.7982, IoU.oven: 0.4619, IoU.ball: 0.5475, IoU.food: 0.5059, IoU.step: 0.1305, IoU.tank: 0.5140, IoU.trade name: 0.2653, IoU.microwave: 0.8688, IoU.pot: 0.5406, IoU.animal: 0.6547, IoU.bicycle: 0.5719, IoU.lake: 0.5177, IoU.dishwasher: 0.6671, IoU.screen: 0.5853, IoU.blanket: 0.2891, IoU.sculpture: 0.7713, IoU.hood: 0.6067, IoU.sconce: 0.5516, IoU.vase: 0.4676, IoU.traffic light: 0.3480, IoU.tray: 0.0826, IoU.ashcan: 0.4250, IoU.fan: 0.6314, IoU.pier: 0.4769, IoU.crt screen: 0.1454, IoU.plate: 0.5704, IoU.monitor: 0.7088, IoU.bulletin board: 0.5654, IoU.shower: 0.0013, IoU.radiator: 0.6433, IoU.glass: 0.1853, IoU.clock: 0.3693, IoU.flag: 0.6985, Acc.wall: 0.8917, Acc.building: 0.9386, Acc.sky: 0.9820, Acc.floor: 0.9145, Acc.tree: 0.8449, Acc.ceiling: 0.9286, Acc.road: 0.9302, Acc.bed : 0.9671, Acc.windowpane: 0.8076, Acc.grass: 0.7889, Acc.cabinet: 0.7719, Acc.sidewalk: 0.8341, Acc.person: 0.9312, Acc.earth: 0.4693, Acc.door: 0.7642, Acc.table: 0.7886, Acc.mountain: 0.7667, Acc.plant: 0.7348, Acc.curtain: 0.8859, Acc.chair: 0.7214, Acc.car: 0.9489, Acc.water: 0.8193, Acc.painting: 0.8925, Acc.sofa: 0.8644, Acc.shelf: 0.7421, Acc.house: 0.7496, Acc.sea: 0.7867, Acc.mirror: 0.8393, Acc.rug: 0.8603, Acc.field: 0.6924, Acc.armchair: 0.8293, Acc.seat: 0.8796, Acc.fence: 0.6278, Acc.desk: 0.7008, Acc.rock: 0.7738, Acc.wardrobe: 0.7667, Acc.lamp: 0.7918, Acc.bathtub: 0.8708, Acc.railing: 0.5582, Acc.cushion: 0.8078, Acc.base: 0.5345, Acc.box: 0.4642, Acc.column: 0.6651, Acc.signboard: 0.5115, Acc.chest of drawers: 0.6372, Acc.counter: 0.6809, Acc.sand: 0.8244, Acc.sink: 0.8231, Acc.skyscraper: 0.5597, Acc.fireplace: 0.9636, Acc.refrigerator: 0.9246, Acc.grandstand: 0.8103, Acc.path: 0.4726, Acc.stairs: 0.4467, Acc.runway: 0.9800, Acc.case: 0.7569, Acc.pool table: 0.9710, Acc.pillow: 0.8312, Acc.screen door: 0.8747, Acc.stairway: 0.4817, Acc.river: 0.3019, Acc.bridge: 0.8873, Acc.bookcase: 0.5904, Acc.blind: 0.4708, Acc.coffee table: 0.8802, Acc.toilet: 0.9265, Acc.flower: 0.6016, Acc.book: 0.7900, Acc.hill: 0.1168, Acc.bench: 0.6113, Acc.countertop: 0.8106, Acc.stove: 0.9206, Acc.palm: 0.7934, Acc.kitchen island: 0.8959, Acc.computer: 0.9530, Acc.swivel chair: 0.7811, Acc.boat: 0.8610, Acc.bar: 0.6711, Acc.arcade machine: 0.7838, Acc.hovel: 0.4936, Acc.bus: 0.9619, Acc.towel: 0.8265, Acc.light: 0.6579, Acc.truck: 0.6145, Acc.tower: 0.4020, Acc.chandelier: 0.8748, Acc.awning: 0.3728, Acc.streetlight: 0.3963, Acc.booth: 0.7375, Acc.television receiver: 0.8724, Acc.airplane: 0.7342, Acc.dirt track: 0.4305, Acc.apparel: 0.5241, Acc.pole: 0.3068, Acc.land: 0.0936, Acc.bannister: 0.2108, Acc.escalator: 0.8170, Acc.ottoman: 0.7195, Acc.bottle: 0.3805, Acc.buffet: 0.6421, Acc.poster: 0.4244, Acc.stage: 0.4436, Acc.van: 0.5017, Acc.ship: 0.9725, Acc.fountain: 0.3309, Acc.conveyer belt: 0.9337, Acc.canopy: 0.7650, Acc.washer: 0.8135, Acc.plaything: 0.5135, Acc.swimming pool: 0.8170, Acc.stool: 0.6588, Acc.barrel: 0.6466, Acc.basket: 0.5678, Acc.waterfall: 0.6924, Acc.tent: 0.9873, Acc.bag: 0.2494, Acc.minibike: 0.8684, Acc.cradle: 0.9762, Acc.oven: 0.6097, Acc.ball: 0.7720, Acc.food: 0.6048, Acc.step: 0.1875, Acc.tank: 0.5775, Acc.trade name: 0.2868, Acc.microwave: 0.9572, Acc.pot: 0.6026, Acc.animal: 0.6748, Acc.bicycle: 0.7637, Acc.lake: 0.6377, Acc.dishwasher: 0.7372, Acc.screen: 0.8535, Acc.blanket: 0.3226, Acc.sculpture: 0.8745, Acc.hood: 0.7121, Acc.sconce: 0.6309, Acc.vase: 0.6208, Acc.traffic light: 0.5415, Acc.tray: 0.1033, Acc.ashcan: 0.6225, Acc.fan: 0.7565, Acc.pier: 0.8302, Acc.crt screen: 0.1733, Acc.plate: 0.7296, Acc.monitor: 0.8208, Acc.bulletin board: 0.6189, Acc.shower: 0.0013, Acc.radiator: 0.7228, Acc.glass: 0.1999, Acc.clock: 0.4059, Acc.flag: 0.7654 +2024-06-18 15:05:34,654 - mmseg - INFO - Iter [38050/80000] lr: 2.098e-05, eta: 17:14:58, time: 3.300, data_time: 1.979, memory: 70498, decode.loss_ce: 0.2027, decode.acc_seg: 91.4397, aux.loss_ce: 0.0841, aux.acc_seg: 91.0936, loss: 0.2868 +2024-06-18 15:06:40,836 - mmseg - INFO - Iter [38100/80000] lr: 2.095e-05, eta: 17:13:36, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2235, decode.acc_seg: 90.6030, aux.loss_ce: 0.0927, aux.acc_seg: 90.2664, loss: 0.3162 +2024-06-18 15:07:47,169 - mmseg - INFO - Iter [38150/80000] lr: 2.093e-05, eta: 17:12:13, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2240, decode.acc_seg: 90.7285, aux.loss_ce: 0.0930, aux.acc_seg: 90.3252, loss: 0.3170 +2024-06-18 15:08:53,245 - mmseg - INFO - Iter [38200/80000] lr: 2.090e-05, eta: 17:10:51, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2300, decode.acc_seg: 90.6036, aux.loss_ce: 0.0961, aux.acc_seg: 90.2092, loss: 0.3261 +2024-06-18 15:09:59,786 - mmseg - INFO - Iter [38250/80000] lr: 2.088e-05, eta: 17:09:29, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2199, decode.acc_seg: 90.7050, aux.loss_ce: 0.0921, aux.acc_seg: 90.2745, loss: 0.3120 +2024-06-18 15:11:05,898 - mmseg - INFO - Iter [38300/80000] lr: 2.085e-05, eta: 17:08:06, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2215, decode.acc_seg: 90.5683, aux.loss_ce: 0.0920, aux.acc_seg: 90.2542, loss: 0.3135 +2024-06-18 15:12:12,363 - mmseg - INFO - Iter [38350/80000] lr: 2.083e-05, eta: 17:06:44, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2118, decode.acc_seg: 91.1199, aux.loss_ce: 0.0888, aux.acc_seg: 90.7532, loss: 0.3005 +2024-06-18 15:13:18,774 - mmseg - INFO - Iter [38400/80000] lr: 2.080e-05, eta: 17:05:22, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2369, decode.acc_seg: 90.1471, aux.loss_ce: 0.0973, aux.acc_seg: 89.9340, loss: 0.3342 +2024-06-18 15:14:24,936 - mmseg - INFO - Iter [38450/80000] lr: 2.078e-05, eta: 17:03:59, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2132, decode.acc_seg: 91.0629, aux.loss_ce: 0.0881, aux.acc_seg: 90.7867, loss: 0.3013 +2024-06-18 15:15:31,161 - mmseg - INFO - Iter [38500/80000] lr: 2.075e-05, eta: 17:02:37, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2377, decode.acc_seg: 90.4286, aux.loss_ce: 0.0985, aux.acc_seg: 90.0592, loss: 0.3363 +2024-06-18 15:16:37,552 - mmseg - INFO - Iter [38550/80000] lr: 2.073e-05, eta: 17:01:15, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2241, decode.acc_seg: 90.5258, aux.loss_ce: 0.0932, aux.acc_seg: 90.1970, loss: 0.3172 +2024-06-18 15:17:43,885 - mmseg - INFO - Iter [38600/80000] lr: 2.070e-05, eta: 16:59:53, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2234, decode.acc_seg: 90.4225, aux.loss_ce: 0.0925, aux.acc_seg: 90.0787, loss: 0.3159 +2024-06-18 15:18:50,106 - mmseg - INFO - Iter [38650/80000] lr: 2.068e-05, eta: 16:58:31, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2149, decode.acc_seg: 91.0297, aux.loss_ce: 0.0893, aux.acc_seg: 90.7402, loss: 0.3042 +2024-06-18 15:19:56,473 - mmseg - INFO - Iter [38700/80000] lr: 2.065e-05, eta: 16:57:09, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2246, decode.acc_seg: 90.6916, aux.loss_ce: 0.0935, aux.acc_seg: 90.3945, loss: 0.3181 +2024-06-18 15:21:03,000 - mmseg - INFO - Iter [38750/80000] lr: 2.063e-05, eta: 16:55:47, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2212, decode.acc_seg: 90.6620, aux.loss_ce: 0.0909, aux.acc_seg: 90.3987, loss: 0.3121 +2024-06-18 15:22:09,379 - mmseg - INFO - Iter [38800/80000] lr: 2.060e-05, eta: 16:54:25, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2296, decode.acc_seg: 90.2537, aux.loss_ce: 0.0951, aux.acc_seg: 89.8861, loss: 0.3247 +2024-06-18 15:23:15,800 - mmseg - INFO - Iter [38850/80000] lr: 2.058e-05, eta: 16:53:04, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2321, decode.acc_seg: 90.5869, aux.loss_ce: 0.0964, aux.acc_seg: 90.2288, loss: 0.3285 +2024-06-18 15:24:21,998 - mmseg - INFO - Iter [38900/80000] lr: 2.055e-05, eta: 16:51:42, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2209, decode.acc_seg: 90.8431, aux.loss_ce: 0.0913, aux.acc_seg: 90.5623, loss: 0.3122 +2024-06-18 15:25:28,292 - mmseg - INFO - Iter [38950/80000] lr: 2.053e-05, eta: 16:50:20, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2374, decode.acc_seg: 90.0932, aux.loss_ce: 0.0981, aux.acc_seg: 89.9123, loss: 0.3356 +2024-06-18 15:26:34,456 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 15:26:34,456 - mmseg - INFO - Iter [39000/80000] lr: 2.050e-05, eta: 16:48:58, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2266, decode.acc_seg: 90.7329, aux.loss_ce: 0.0925, aux.acc_seg: 90.5073, loss: 0.3191 +2024-06-18 15:28:12,598 - mmseg - INFO - per class results: +2024-06-18 15:28:12,604 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.33 | 89.45 | +| building | 85.26 | 93.25 | +| sky | 94.78 | 97.05 | +| floor | 85.26 | 90.65 | +| tree | 77.04 | 90.54 | +| ceiling | 86.62 | 93.12 | +| road | 87.28 | 92.24 | +| bed | 92.01 | 96.84 | +| windowpane | 65.74 | 78.55 | +| grass | 66.23 | 78.66 | +| cabinet | 63.91 | 74.35 | +| sidewalk | 71.11 | 84.26 | +| person | 85.33 | 92.96 | +| earth | 36.39 | 50.43 | +| door | 59.17 | 74.32 | +| table | 67.57 | 80.48 | +| mountain | 61.0 | 73.95 | +| plant | 55.3 | 65.02 | +| curtain | 78.38 | 90.09 | +| chair | 66.5 | 77.82 | +| car | 85.33 | 92.66 | +| water | 62.61 | 76.34 | +| painting | 75.15 | 92.46 | +| sofa | 78.28 | 91.88 | +| shelf | 49.34 | 72.55 | +| house | 55.48 | 71.97 | +| sea | 71.29 | 88.26 | +| mirror | 75.73 | 81.62 | +| rug | 69.1 | 85.63 | +| field | 33.52 | 60.21 | +| armchair | 59.5 | 77.23 | +| seat | 66.24 | 86.68 | +| fence | 50.87 | 72.3 | +| desk | 53.21 | 75.37 | +| rock | 54.73 | 76.7 | +| wardrobe | 54.59 | 75.53 | +| lamp | 72.05 | 84.41 | +| bathtub | 83.61 | 85.45 | +| railing | 39.51 | 60.13 | +| cushion | 68.79 | 79.27 | +| base | 38.54 | 49.07 | +| box | 36.04 | 48.74 | +| column | 54.89 | 71.8 | +| signboard | 41.97 | 54.64 | +| chest of drawers | 48.28 | 67.77 | +| counter | 38.78 | 47.38 | +| sand | 58.27 | 86.96 | +| sink | 74.67 | 81.06 | +| skyscraper | 47.68 | 58.31 | +| fireplace | 70.99 | 95.91 | +| refrigerator | 76.43 | 84.35 | +| grandstand | 49.53 | 79.72 | +| path | 31.05 | 43.85 | +| stairs | 20.9 | 26.6 | +| runway | 72.64 | 94.61 | +| case | 57.68 | 78.54 | +| pool table | 94.3 | 97.86 | +| pillow | 70.53 | 83.11 | +| screen door | 87.09 | 92.26 | +| stairway | 38.96 | 57.83 | +| river | 19.77 | 32.48 | +| bridge | 76.12 | 86.85 | +| bookcase | 42.03 | 55.31 | +| blind | 45.71 | 50.7 | +| coffee table | 62.05 | 89.56 | +| toilet | 87.5 | 93.39 | +| flower | 39.35 | 50.38 | +| book | 53.26 | 73.03 | +| hill | 8.3 | 13.78 | +| bench | 52.12 | 58.56 | +| countertop | 64.31 | 84.11 | +| stove | 83.42 | 89.7 | +| palm | 59.26 | 78.77 | +| kitchen island | 48.56 | 90.09 | +| computer | 77.69 | 94.51 | +| swivel chair | 53.4 | 71.87 | +| boat | 56.61 | 82.9 | +| bar | 55.33 | 77.75 | +| arcade machine | 69.42 | 72.77 | +| hovel | 44.22 | 49.76 | +| bus | 93.07 | 95.91 | +| towel | 73.66 | 81.71 | +| light | 61.58 | 69.99 | +| truck | 42.63 | 63.61 | +| tower | 25.5 | 57.41 | +| chandelier | 72.41 | 89.35 | +| awning | 47.1 | 54.96 | +| streetlight | 33.15 | 46.99 | +| booth | 36.81 | 58.23 | +| television receiver | 76.1 | 88.4 | +| airplane | 66.7 | 72.02 | +| dirt track | 24.68 | 34.73 | +| apparel | 50.1 | 73.22 | +| pole | 27.8 | 40.86 | +| land | 3.03 | 6.61 | +| bannister | 18.56 | 27.19 | +| escalator | 57.92 | 84.39 | +| ottoman | 46.34 | 59.8 | +| bottle | 45.31 | 59.9 | +| buffet | 51.32 | 57.36 | +| poster | 30.06 | 43.09 | +| stage | 22.68 | 42.51 | +| van | 19.91 | 22.47 | +| ship | 89.93 | 94.3 | +| fountain | 30.44 | 31.17 | +| conveyer belt | 83.79 | 92.32 | +| canopy | 49.22 | 63.63 | +| washer | 73.1 | 83.37 | +| plaything | 23.42 | 33.98 | +| swimming pool | 57.73 | 91.56 | +| stool | 51.83 | 64.57 | +| barrel | 38.08 | 64.93 | +| basket | 38.69 | 57.38 | +| waterfall | 54.61 | 77.99 | +| tent | 92.13 | 98.24 | +| bag | 21.25 | 24.9 | +| minibike | 74.37 | 83.16 | +| cradle | 84.74 | 97.02 | +| oven | 53.36 | 65.73 | +| ball | 52.73 | 57.12 | +| food | 62.55 | 82.63 | +| step | 8.39 | 9.28 | +| tank | 65.95 | 81.44 | +| trade name | 36.06 | 43.65 | +| microwave | 86.08 | 95.79 | +| pot | 53.1 | 59.93 | +| animal | 64.51 | 66.26 | +| bicycle | 58.18 | 77.29 | +| lake | 50.97 | 63.68 | +| dishwasher | 60.95 | 75.68 | +| screen | 49.65 | 76.64 | +| blanket | 31.77 | 35.96 | +| sculpture | 74.66 | 84.98 | +| hood | 62.56 | 73.99 | +| sconce | 52.68 | 60.37 | +| vase | 45.67 | 61.98 | +| traffic light | 35.51 | 58.18 | +| tray | 7.08 | 9.0 | +| ashcan | 40.08 | 62.35 | +| fan | 64.11 | 77.7 | +| pier | 33.88 | 46.8 | +| crt screen | 6.6 | 9.55 | +| plate | 56.24 | 73.18 | +| monitor | 69.23 | 83.57 | +| bulletin board | 57.38 | 70.6 | +| shower | 0.14 | 0.14 | +| radiator | 62.73 | 73.33 | +| glass | 18.74 | 20.5 | +| clock | 34.38 | 44.86 | +| flag | 70.7 | 76.9 | ++---------------------+-------+-------+ +2024-06-18 15:28:12,604 - mmseg - INFO - Summary: +2024-06-18 15:28:12,605 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.68 | 55.66 | 68.77 | ++-------+-------+-------+ +2024-06-18 15:28:12,605 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 15:28:12,606 - mmseg - INFO - Iter(val) [250] aAcc: 0.8568, mIoU: 0.5566, mAcc: 0.6877, IoU.wall: 0.8133, IoU.building: 0.8526, IoU.sky: 0.9478, IoU.floor: 0.8526, IoU.tree: 0.7704, IoU.ceiling: 0.8662, IoU.road: 0.8728, IoU.bed : 0.9201, IoU.windowpane: 0.6574, IoU.grass: 0.6623, IoU.cabinet: 0.6391, IoU.sidewalk: 0.7111, IoU.person: 0.8533, IoU.earth: 0.3639, IoU.door: 0.5917, IoU.table: 0.6757, IoU.mountain: 0.6100, IoU.plant: 0.5530, IoU.curtain: 0.7838, IoU.chair: 0.6650, IoU.car: 0.8533, IoU.water: 0.6261, IoU.painting: 0.7515, IoU.sofa: 0.7828, IoU.shelf: 0.4934, IoU.house: 0.5548, IoU.sea: 0.7129, IoU.mirror: 0.7573, IoU.rug: 0.6910, IoU.field: 0.3352, IoU.armchair: 0.5950, IoU.seat: 0.6624, IoU.fence: 0.5087, IoU.desk: 0.5321, IoU.rock: 0.5473, IoU.wardrobe: 0.5459, IoU.lamp: 0.7205, IoU.bathtub: 0.8361, IoU.railing: 0.3951, IoU.cushion: 0.6879, IoU.base: 0.3854, IoU.box: 0.3604, IoU.column: 0.5489, IoU.signboard: 0.4197, IoU.chest of drawers: 0.4828, IoU.counter: 0.3878, IoU.sand: 0.5827, IoU.sink: 0.7467, IoU.skyscraper: 0.4768, IoU.fireplace: 0.7099, IoU.refrigerator: 0.7643, IoU.grandstand: 0.4953, IoU.path: 0.3105, IoU.stairs: 0.2090, IoU.runway: 0.7264, IoU.case: 0.5768, IoU.pool table: 0.9430, IoU.pillow: 0.7053, IoU.screen door: 0.8709, IoU.stairway: 0.3896, IoU.river: 0.1977, IoU.bridge: 0.7612, IoU.bookcase: 0.4203, IoU.blind: 0.4571, IoU.coffee table: 0.6205, IoU.toilet: 0.8750, IoU.flower: 0.3935, IoU.book: 0.5326, IoU.hill: 0.0830, IoU.bench: 0.5212, IoU.countertop: 0.6431, IoU.stove: 0.8342, IoU.palm: 0.5926, IoU.kitchen island: 0.4856, IoU.computer: 0.7769, IoU.swivel chair: 0.5340, IoU.boat: 0.5661, IoU.bar: 0.5533, IoU.arcade machine: 0.6942, IoU.hovel: 0.4422, IoU.bus: 0.9307, IoU.towel: 0.7366, IoU.light: 0.6158, IoU.truck: 0.4263, IoU.tower: 0.2550, IoU.chandelier: 0.7241, IoU.awning: 0.4710, IoU.streetlight: 0.3315, IoU.booth: 0.3681, IoU.television receiver: 0.7610, IoU.airplane: 0.6670, IoU.dirt track: 0.2468, IoU.apparel: 0.5010, IoU.pole: 0.2780, IoU.land: 0.0303, IoU.bannister: 0.1856, IoU.escalator: 0.5792, IoU.ottoman: 0.4634, IoU.bottle: 0.4531, IoU.buffet: 0.5132, IoU.poster: 0.3006, IoU.stage: 0.2268, IoU.van: 0.1991, IoU.ship: 0.8993, IoU.fountain: 0.3044, IoU.conveyer belt: 0.8379, IoU.canopy: 0.4922, IoU.washer: 0.7310, IoU.plaything: 0.2342, IoU.swimming pool: 0.5773, IoU.stool: 0.5183, IoU.barrel: 0.3808, IoU.basket: 0.3869, IoU.waterfall: 0.5461, IoU.tent: 0.9213, IoU.bag: 0.2125, IoU.minibike: 0.7437, IoU.cradle: 0.8474, IoU.oven: 0.5336, IoU.ball: 0.5273, IoU.food: 0.6255, IoU.step: 0.0839, IoU.tank: 0.6595, IoU.trade name: 0.3606, IoU.microwave: 0.8608, IoU.pot: 0.5310, IoU.animal: 0.6451, IoU.bicycle: 0.5818, IoU.lake: 0.5097, IoU.dishwasher: 0.6095, IoU.screen: 0.4965, IoU.blanket: 0.3177, IoU.sculpture: 0.7466, IoU.hood: 0.6256, IoU.sconce: 0.5268, IoU.vase: 0.4567, IoU.traffic light: 0.3551, IoU.tray: 0.0708, IoU.ashcan: 0.4008, IoU.fan: 0.6411, IoU.pier: 0.3388, IoU.crt screen: 0.0660, IoU.plate: 0.5624, IoU.monitor: 0.6923, IoU.bulletin board: 0.5738, IoU.shower: 0.0014, IoU.radiator: 0.6273, IoU.glass: 0.1874, IoU.clock: 0.3438, IoU.flag: 0.7070, Acc.wall: 0.8945, Acc.building: 0.9325, Acc.sky: 0.9705, Acc.floor: 0.9065, Acc.tree: 0.9054, Acc.ceiling: 0.9312, Acc.road: 0.9224, Acc.bed : 0.9684, Acc.windowpane: 0.7855, Acc.grass: 0.7866, Acc.cabinet: 0.7435, Acc.sidewalk: 0.8426, Acc.person: 0.9296, Acc.earth: 0.5043, Acc.door: 0.7432, Acc.table: 0.8048, Acc.mountain: 0.7395, Acc.plant: 0.6502, Acc.curtain: 0.9009, Acc.chair: 0.7782, Acc.car: 0.9266, Acc.water: 0.7634, Acc.painting: 0.9246, Acc.sofa: 0.9188, Acc.shelf: 0.7255, Acc.house: 0.7197, Acc.sea: 0.8826, Acc.mirror: 0.8162, Acc.rug: 0.8563, Acc.field: 0.6021, Acc.armchair: 0.7723, Acc.seat: 0.8668, Acc.fence: 0.7230, Acc.desk: 0.7537, Acc.rock: 0.7670, Acc.wardrobe: 0.7553, Acc.lamp: 0.8441, Acc.bathtub: 0.8545, Acc.railing: 0.6013, Acc.cushion: 0.7927, Acc.base: 0.4907, Acc.box: 0.4874, Acc.column: 0.7180, Acc.signboard: 0.5464, Acc.chest of drawers: 0.6777, Acc.counter: 0.4738, Acc.sand: 0.8696, Acc.sink: 0.8106, Acc.skyscraper: 0.5831, Acc.fireplace: 0.9591, Acc.refrigerator: 0.8435, Acc.grandstand: 0.7972, Acc.path: 0.4385, Acc.stairs: 0.2660, Acc.runway: 0.9461, Acc.case: 0.7854, Acc.pool table: 0.9786, Acc.pillow: 0.8311, Acc.screen door: 0.9226, Acc.stairway: 0.5783, Acc.river: 0.3248, Acc.bridge: 0.8685, Acc.bookcase: 0.5531, Acc.blind: 0.5070, Acc.coffee table: 0.8956, Acc.toilet: 0.9339, Acc.flower: 0.5038, Acc.book: 0.7303, Acc.hill: 0.1378, Acc.bench: 0.5856, Acc.countertop: 0.8411, Acc.stove: 0.8970, Acc.palm: 0.7877, Acc.kitchen island: 0.9009, Acc.computer: 0.9451, Acc.swivel chair: 0.7187, Acc.boat: 0.8290, Acc.bar: 0.7775, Acc.arcade machine: 0.7277, Acc.hovel: 0.4976, Acc.bus: 0.9591, Acc.towel: 0.8171, Acc.light: 0.6999, Acc.truck: 0.6361, Acc.tower: 0.5741, Acc.chandelier: 0.8935, Acc.awning: 0.5496, Acc.streetlight: 0.4699, Acc.booth: 0.5823, Acc.television receiver: 0.8840, Acc.airplane: 0.7202, Acc.dirt track: 0.3473, Acc.apparel: 0.7322, Acc.pole: 0.4086, Acc.land: 0.0661, Acc.bannister: 0.2719, Acc.escalator: 0.8439, Acc.ottoman: 0.5980, Acc.bottle: 0.5990, Acc.buffet: 0.5736, Acc.poster: 0.4309, Acc.stage: 0.4251, Acc.van: 0.2247, Acc.ship: 0.9430, Acc.fountain: 0.3117, Acc.conveyer belt: 0.9232, Acc.canopy: 0.6363, Acc.washer: 0.8337, Acc.plaything: 0.3398, Acc.swimming pool: 0.9156, Acc.stool: 0.6457, Acc.barrel: 0.6493, Acc.basket: 0.5738, Acc.waterfall: 0.7799, Acc.tent: 0.9824, Acc.bag: 0.2490, Acc.minibike: 0.8316, Acc.cradle: 0.9702, Acc.oven: 0.6573, Acc.ball: 0.5712, Acc.food: 0.8263, Acc.step: 0.0928, Acc.tank: 0.8144, Acc.trade name: 0.4365, Acc.microwave: 0.9579, Acc.pot: 0.5993, Acc.animal: 0.6626, Acc.bicycle: 0.7729, Acc.lake: 0.6368, Acc.dishwasher: 0.7568, Acc.screen: 0.7664, Acc.blanket: 0.3596, Acc.sculpture: 0.8498, Acc.hood: 0.7399, Acc.sconce: 0.6037, Acc.vase: 0.6198, Acc.traffic light: 0.5818, Acc.tray: 0.0900, Acc.ashcan: 0.6235, Acc.fan: 0.7770, Acc.pier: 0.4680, Acc.crt screen: 0.0955, Acc.plate: 0.7318, Acc.monitor: 0.8357, Acc.bulletin board: 0.7060, Acc.shower: 0.0014, Acc.radiator: 0.7333, Acc.glass: 0.2050, Acc.clock: 0.4486, Acc.flag: 0.7690 +2024-06-18 15:29:19,386 - mmseg - INFO - Iter [39050/80000] lr: 2.048e-05, eta: 16:49:20, time: 3.299, data_time: 1.979, memory: 70498, decode.loss_ce: 0.2248, decode.acc_seg: 90.4686, aux.loss_ce: 0.0926, aux.acc_seg: 90.1914, loss: 0.3174 +2024-06-18 15:30:25,695 - mmseg - INFO - Iter [39100/80000] lr: 2.045e-05, eta: 16:47:58, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2350, decode.acc_seg: 89.9727, aux.loss_ce: 0.0971, aux.acc_seg: 89.6909, loss: 0.3321 +2024-06-18 15:31:32,194 - mmseg - INFO - Iter [39150/80000] lr: 2.043e-05, eta: 16:46:36, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2292, decode.acc_seg: 90.2088, aux.loss_ce: 0.0956, aux.acc_seg: 89.8539, loss: 0.3248 +2024-06-18 15:32:41,987 - mmseg - INFO - Iter [39200/80000] lr: 2.040e-05, eta: 16:45:18, time: 1.396, data_time: 0.078, memory: 70498, decode.loss_ce: 0.2151, decode.acc_seg: 91.1048, aux.loss_ce: 0.0894, aux.acc_seg: 90.7898, loss: 0.3045 +2024-06-18 15:33:48,205 - mmseg - INFO - Iter [39250/80000] lr: 2.038e-05, eta: 16:43:56, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2393, decode.acc_seg: 90.1718, aux.loss_ce: 0.0996, aux.acc_seg: 89.7461, loss: 0.3389 +2024-06-18 15:34:54,704 - mmseg - INFO - Iter [39300/80000] lr: 2.035e-05, eta: 16:42:34, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2250, decode.acc_seg: 90.7619, aux.loss_ce: 0.0932, aux.acc_seg: 90.4350, loss: 0.3181 +2024-06-18 15:36:01,217 - mmseg - INFO - Iter [39350/80000] lr: 2.033e-05, eta: 16:41:13, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2126, decode.acc_seg: 90.9203, aux.loss_ce: 0.0885, aux.acc_seg: 90.6523, loss: 0.3011 +2024-06-18 15:37:07,773 - mmseg - INFO - Iter [39400/80000] lr: 2.030e-05, eta: 16:39:51, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2221, decode.acc_seg: 90.8392, aux.loss_ce: 0.0920, aux.acc_seg: 90.5808, loss: 0.3141 +2024-06-18 15:38:14,089 - mmseg - INFO - Iter [39450/80000] lr: 2.028e-05, eta: 16:38:30, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2277, decode.acc_seg: 90.5585, aux.loss_ce: 0.0947, aux.acc_seg: 90.0897, loss: 0.3224 +2024-06-18 15:39:20,575 - mmseg - INFO - Iter [39500/80000] lr: 2.025e-05, eta: 16:37:08, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2098, decode.acc_seg: 91.0733, aux.loss_ce: 0.0877, aux.acc_seg: 90.6831, loss: 0.2975 +2024-06-18 15:40:26,954 - mmseg - INFO - Iter [39550/80000] lr: 2.023e-05, eta: 16:35:47, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2222, decode.acc_seg: 90.7407, aux.loss_ce: 0.0919, aux.acc_seg: 90.4544, loss: 0.3140 +2024-06-18 15:41:33,472 - mmseg - INFO - Iter [39600/80000] lr: 2.020e-05, eta: 16:34:25, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2300, decode.acc_seg: 90.3935, aux.loss_ce: 0.0948, aux.acc_seg: 90.1368, loss: 0.3248 +2024-06-18 15:42:39,868 - mmseg - INFO - Iter [39650/80000] lr: 2.018e-05, eta: 16:33:04, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2186, decode.acc_seg: 90.8686, aux.loss_ce: 0.0904, aux.acc_seg: 90.5718, loss: 0.3090 +2024-06-18 15:43:46,197 - mmseg - INFO - Iter [39700/80000] lr: 2.015e-05, eta: 16:31:43, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2115, decode.acc_seg: 91.2172, aux.loss_ce: 0.0883, aux.acc_seg: 90.8961, loss: 0.2999 +2024-06-18 15:44:52,529 - mmseg - INFO - Iter [39750/80000] lr: 2.013e-05, eta: 16:30:21, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2136, decode.acc_seg: 90.7556, aux.loss_ce: 0.0891, aux.acc_seg: 90.4671, loss: 0.3027 +2024-06-18 15:45:58,549 - mmseg - INFO - Iter [39800/80000] lr: 2.010e-05, eta: 16:28:59, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2262, decode.acc_seg: 90.5926, aux.loss_ce: 0.0940, aux.acc_seg: 90.2471, loss: 0.3203 +2024-06-18 15:47:05,083 - mmseg - INFO - Iter [39850/80000] lr: 2.008e-05, eta: 16:27:38, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2113, decode.acc_seg: 90.9218, aux.loss_ce: 0.0876, aux.acc_seg: 90.6021, loss: 0.2990 +2024-06-18 15:48:11,497 - mmseg - INFO - Iter [39900/80000] lr: 2.005e-05, eta: 16:26:17, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2121, decode.acc_seg: 91.0223, aux.loss_ce: 0.0887, aux.acc_seg: 90.6438, loss: 0.3008 +2024-06-18 15:49:18,207 - mmseg - INFO - Iter [39950/80000] lr: 2.003e-05, eta: 16:24:56, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2149, decode.acc_seg: 90.9596, aux.loss_ce: 0.0893, aux.acc_seg: 90.6355, loss: 0.3042 +2024-06-18 15:50:24,467 - mmseg - INFO - Saving checkpoint at 40000 iterations +2024-06-18 15:52:57,966 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 15:52:57,966 - mmseg - INFO - Iter [40000/80000] lr: 2.000e-05, eta: 16:26:08, time: 4.395, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2250, decode.acc_seg: 90.1305, aux.loss_ce: 0.0931, aux.acc_seg: 89.9312, loss: 0.3181 +2024-06-18 15:54:34,636 - mmseg - INFO - per class results: +2024-06-18 15:54:34,642 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.36 | 89.12 | +| building | 84.04 | 93.31 | +| sky | 94.85 | 97.48 | +| floor | 85.17 | 91.38 | +| tree | 77.66 | 89.96 | +| ceiling | 87.08 | 94.58 | +| road | 86.56 | 91.14 | +| bed | 92.42 | 96.36 | +| windowpane | 65.63 | 82.4 | +| grass | 67.86 | 81.68 | +| cabinet | 67.76 | 79.89 | +| sidewalk | 71.94 | 85.85 | +| person | 85.77 | 93.04 | +| earth | 35.08 | 46.94 | +| door | 59.89 | 74.19 | +| table | 67.7 | 80.02 | +| mountain | 60.11 | 78.99 | +| plant | 56.49 | 67.64 | +| curtain | 79.07 | 89.14 | +| chair | 65.16 | 73.81 | +| car | 85.71 | 93.01 | +| water | 59.99 | 73.4 | +| painting | 77.4 | 89.18 | +| sofa | 81.33 | 89.85 | +| shelf | 50.35 | 70.54 | +| house | 55.33 | 71.21 | +| sea | 65.67 | 88.49 | +| mirror | 76.2 | 84.48 | +| rug | 68.7 | 82.47 | +| field | 39.02 | 66.1 | +| armchair | 58.3 | 78.15 | +| seat | 65.47 | 87.81 | +| fence | 49.49 | 60.63 | +| desk | 58.7 | 71.74 | +| rock | 52.17 | 64.29 | +| wardrobe | 59.93 | 73.03 | +| lamp | 69.45 | 80.77 | +| bathtub | 84.24 | 86.33 | +| railing | 40.28 | 59.33 | +| cushion | 69.8 | 79.53 | +| base | 41.06 | 56.16 | +| box | 37.09 | 52.79 | +| column | 51.86 | 66.77 | +| signboard | 39.01 | 48.54 | +| chest of drawers | 49.53 | 67.43 | +| counter | 40.69 | 53.64 | +| sand | 54.32 | 77.08 | +| sink | 76.73 | 84.23 | +| skyscraper | 48.74 | 62.44 | +| fireplace | 73.23 | 94.46 | +| refrigerator | 83.24 | 92.62 | +| grandstand | 50.61 | 79.2 | +| path | 30.61 | 42.0 | +| stairs | 28.29 | 40.08 | +| runway | 69.8 | 90.0 | +| case | 58.76 | 73.2 | +| pool table | 94.36 | 97.54 | +| pillow | 70.2 | 82.99 | +| screen door | 72.88 | 76.05 | +| stairway | 43.76 | 55.21 | +| river | 17.48 | 29.81 | +| bridge | 65.64 | 74.13 | +| bookcase | 41.16 | 50.36 | +| blind | 46.96 | 55.94 | +| coffee table | 63.09 | 88.53 | +| toilet | 88.98 | 93.02 | +| flower | 41.4 | 51.42 | +| book | 50.47 | 79.31 | +| hill | 7.43 | 11.69 | +| bench | 52.89 | 60.71 | +| countertop | 60.34 | 86.58 | +| stove | 83.53 | 92.98 | +| palm | 57.26 | 78.34 | +| kitchen island | 48.16 | 76.97 | +| computer | 80.68 | 92.32 | +| swivel chair | 51.17 | 82.81 | +| boat | 56.29 | 86.53 | +| bar | 58.41 | 73.13 | +| arcade machine | 79.08 | 84.03 | +| hovel | 45.66 | 50.98 | +| bus | 92.31 | 95.59 | +| towel | 70.53 | 79.08 | +| light | 60.55 | 69.62 | +| truck | 42.69 | 62.28 | +| tower | 19.92 | 38.16 | +| chandelier | 70.18 | 80.5 | +| awning | 43.97 | 63.75 | +| streetlight | 31.27 | 42.52 | +| booth | 32.56 | 50.15 | +| television receiver | 73.78 | 91.76 | +| airplane | 62.65 | 68.4 | +| dirt track | 8.97 | 33.7 | +| apparel | 45.72 | 61.38 | +| pole | 24.49 | 31.13 | +| land | 8.79 | 19.94 | +| bannister | 16.95 | 26.44 | +| escalator | 60.89 | 84.45 | +| ottoman | 49.35 | 72.55 | +| bottle | 41.76 | 51.37 | +| buffet | 55.59 | 63.21 | +| poster | 34.74 | 50.68 | +| stage | 26.46 | 47.52 | +| van | 40.79 | 58.81 | +| ship | 93.67 | 98.54 | +| fountain | 31.53 | 32.11 | +| conveyer belt | 83.7 | 90.45 | +| canopy | 56.9 | 74.47 | +| washer | 71.98 | 78.66 | +| plaything | 36.7 | 52.24 | +| swimming pool | 64.16 | 96.26 | +| stool | 47.9 | 74.46 | +| barrel | 47.51 | 64.87 | +| basket | 39.32 | 57.94 | +| waterfall | 50.31 | 68.34 | +| tent | 89.41 | 98.63 | +| bag | 21.71 | 25.88 | +| minibike | 71.22 | 88.81 | +| cradle | 79.6 | 98.21 | +| oven | 54.31 | 67.31 | +| ball | 55.68 | 75.75 | +| food | 60.38 | 74.51 | +| step | 11.39 | 14.09 | +| tank | 52.24 | 55.9 | +| trade name | 20.33 | 21.6 | +| microwave | 86.81 | 96.31 | +| pot | 57.39 | 67.33 | +| animal | 65.89 | 67.75 | +| bicycle | 58.79 | 75.92 | +| lake | 53.63 | 60.92 | +| dishwasher | 66.23 | 74.46 | +| screen | 59.55 | 95.54 | +| blanket | 33.75 | 39.57 | +| sculpture | 72.1 | 84.71 | +| hood | 60.76 | 71.2 | +| sconce | 47.73 | 52.21 | +| vase | 46.69 | 59.02 | +| traffic light | 31.85 | 63.3 | +| tray | 12.13 | 14.45 | +| ashcan | 41.51 | 58.43 | +| fan | 61.53 | 70.43 | +| pier | 32.07 | 51.73 | +| crt screen | 4.52 | 5.1 | +| plate | 57.75 | 68.59 | +| monitor | 69.35 | 80.19 | +| bulletin board | 62.11 | 65.76 | +| shower | 0.53 | 3.08 | +| radiator | 63.57 | 70.69 | +| glass | 16.93 | 17.8 | +| clock | 31.26 | 36.37 | +| flag | 71.19 | 78.36 | ++---------------------+-------+-------+ +2024-06-18 15:54:34,642 - mmseg - INFO - Summary: +2024-06-18 15:54:34,643 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 85.77 | 55.8 | 68.67 | ++-------+------+-------+ +2024-06-18 15:54:34,644 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 15:54:34,644 - mmseg - INFO - Iter(val) [250] aAcc: 0.8577, mIoU: 0.5580, mAcc: 0.6867, IoU.wall: 0.8136, IoU.building: 0.8404, IoU.sky: 0.9485, IoU.floor: 0.8517, IoU.tree: 0.7766, IoU.ceiling: 0.8708, IoU.road: 0.8656, IoU.bed : 0.9242, IoU.windowpane: 0.6563, IoU.grass: 0.6786, IoU.cabinet: 0.6776, IoU.sidewalk: 0.7194, IoU.person: 0.8577, IoU.earth: 0.3508, IoU.door: 0.5989, IoU.table: 0.6770, IoU.mountain: 0.6011, IoU.plant: 0.5649, IoU.curtain: 0.7907, IoU.chair: 0.6516, IoU.car: 0.8571, IoU.water: 0.5999, IoU.painting: 0.7740, IoU.sofa: 0.8133, IoU.shelf: 0.5035, IoU.house: 0.5533, IoU.sea: 0.6567, IoU.mirror: 0.7620, IoU.rug: 0.6870, IoU.field: 0.3902, IoU.armchair: 0.5830, IoU.seat: 0.6547, IoU.fence: 0.4949, IoU.desk: 0.5870, IoU.rock: 0.5217, IoU.wardrobe: 0.5993, IoU.lamp: 0.6945, IoU.bathtub: 0.8424, IoU.railing: 0.4028, IoU.cushion: 0.6980, IoU.base: 0.4106, IoU.box: 0.3709, IoU.column: 0.5186, IoU.signboard: 0.3901, IoU.chest of drawers: 0.4953, IoU.counter: 0.4069, IoU.sand: 0.5432, IoU.sink: 0.7673, IoU.skyscraper: 0.4874, IoU.fireplace: 0.7323, IoU.refrigerator: 0.8324, IoU.grandstand: 0.5061, IoU.path: 0.3061, IoU.stairs: 0.2829, IoU.runway: 0.6980, IoU.case: 0.5876, IoU.pool table: 0.9436, IoU.pillow: 0.7020, IoU.screen door: 0.7288, IoU.stairway: 0.4376, IoU.river: 0.1748, IoU.bridge: 0.6564, IoU.bookcase: 0.4116, IoU.blind: 0.4696, IoU.coffee table: 0.6309, IoU.toilet: 0.8898, IoU.flower: 0.4140, IoU.book: 0.5047, IoU.hill: 0.0743, IoU.bench: 0.5289, IoU.countertop: 0.6034, IoU.stove: 0.8353, IoU.palm: 0.5726, IoU.kitchen island: 0.4816, IoU.computer: 0.8068, IoU.swivel chair: 0.5117, IoU.boat: 0.5629, IoU.bar: 0.5841, IoU.arcade machine: 0.7908, IoU.hovel: 0.4566, IoU.bus: 0.9231, IoU.towel: 0.7053, IoU.light: 0.6055, IoU.truck: 0.4269, IoU.tower: 0.1992, IoU.chandelier: 0.7018, IoU.awning: 0.4397, IoU.streetlight: 0.3127, IoU.booth: 0.3256, IoU.television receiver: 0.7378, IoU.airplane: 0.6265, IoU.dirt track: 0.0897, IoU.apparel: 0.4572, IoU.pole: 0.2449, IoU.land: 0.0879, IoU.bannister: 0.1695, IoU.escalator: 0.6089, IoU.ottoman: 0.4935, IoU.bottle: 0.4176, IoU.buffet: 0.5559, IoU.poster: 0.3474, IoU.stage: 0.2646, IoU.van: 0.4079, IoU.ship: 0.9367, IoU.fountain: 0.3153, IoU.conveyer belt: 0.8370, IoU.canopy: 0.5690, IoU.washer: 0.7198, IoU.plaything: 0.3670, IoU.swimming pool: 0.6416, IoU.stool: 0.4790, IoU.barrel: 0.4751, IoU.basket: 0.3932, IoU.waterfall: 0.5031, IoU.tent: 0.8941, IoU.bag: 0.2171, IoU.minibike: 0.7122, IoU.cradle: 0.7960, IoU.oven: 0.5431, IoU.ball: 0.5568, IoU.food: 0.6038, IoU.step: 0.1139, IoU.tank: 0.5224, IoU.trade name: 0.2033, IoU.microwave: 0.8681, IoU.pot: 0.5739, IoU.animal: 0.6589, IoU.bicycle: 0.5879, IoU.lake: 0.5363, IoU.dishwasher: 0.6623, IoU.screen: 0.5955, IoU.blanket: 0.3375, IoU.sculpture: 0.7210, IoU.hood: 0.6076, IoU.sconce: 0.4773, IoU.vase: 0.4669, IoU.traffic light: 0.3185, IoU.tray: 0.1213, IoU.ashcan: 0.4151, IoU.fan: 0.6153, IoU.pier: 0.3207, IoU.crt screen: 0.0452, IoU.plate: 0.5775, IoU.monitor: 0.6935, IoU.bulletin board: 0.6211, IoU.shower: 0.0053, IoU.radiator: 0.6357, IoU.glass: 0.1693, IoU.clock: 0.3126, IoU.flag: 0.7119, Acc.wall: 0.8912, Acc.building: 0.9331, Acc.sky: 0.9748, Acc.floor: 0.9138, Acc.tree: 0.8996, Acc.ceiling: 0.9458, Acc.road: 0.9114, Acc.bed : 0.9636, Acc.windowpane: 0.8240, Acc.grass: 0.8168, Acc.cabinet: 0.7989, Acc.sidewalk: 0.8585, Acc.person: 0.9304, Acc.earth: 0.4694, Acc.door: 0.7419, Acc.table: 0.8002, Acc.mountain: 0.7899, Acc.plant: 0.6764, Acc.curtain: 0.8914, Acc.chair: 0.7381, Acc.car: 0.9301, Acc.water: 0.7340, Acc.painting: 0.8918, Acc.sofa: 0.8985, Acc.shelf: 0.7054, Acc.house: 0.7121, Acc.sea: 0.8849, Acc.mirror: 0.8448, Acc.rug: 0.8247, Acc.field: 0.6610, Acc.armchair: 0.7815, Acc.seat: 0.8781, Acc.fence: 0.6063, Acc.desk: 0.7174, Acc.rock: 0.6429, Acc.wardrobe: 0.7303, Acc.lamp: 0.8077, Acc.bathtub: 0.8633, Acc.railing: 0.5933, Acc.cushion: 0.7953, Acc.base: 0.5616, Acc.box: 0.5279, Acc.column: 0.6677, Acc.signboard: 0.4854, Acc.chest of drawers: 0.6743, Acc.counter: 0.5364, Acc.sand: 0.7708, Acc.sink: 0.8423, Acc.skyscraper: 0.6244, Acc.fireplace: 0.9446, Acc.refrigerator: 0.9262, Acc.grandstand: 0.7920, Acc.path: 0.4200, Acc.stairs: 0.4008, Acc.runway: 0.9000, Acc.case: 0.7320, Acc.pool table: 0.9754, Acc.pillow: 0.8299, Acc.screen door: 0.7605, Acc.stairway: 0.5521, Acc.river: 0.2981, Acc.bridge: 0.7413, Acc.bookcase: 0.5036, Acc.blind: 0.5594, Acc.coffee table: 0.8853, Acc.toilet: 0.9302, Acc.flower: 0.5142, Acc.book: 0.7931, Acc.hill: 0.1169, Acc.bench: 0.6071, Acc.countertop: 0.8658, Acc.stove: 0.9298, Acc.palm: 0.7834, Acc.kitchen island: 0.7697, Acc.computer: 0.9232, Acc.swivel chair: 0.8281, Acc.boat: 0.8653, Acc.bar: 0.7313, Acc.arcade machine: 0.8403, Acc.hovel: 0.5098, Acc.bus: 0.9559, Acc.towel: 0.7908, Acc.light: 0.6962, Acc.truck: 0.6228, Acc.tower: 0.3816, Acc.chandelier: 0.8050, Acc.awning: 0.6375, Acc.streetlight: 0.4252, Acc.booth: 0.5015, Acc.television receiver: 0.9176, Acc.airplane: 0.6840, Acc.dirt track: 0.3370, Acc.apparel: 0.6138, Acc.pole: 0.3113, Acc.land: 0.1994, Acc.bannister: 0.2644, Acc.escalator: 0.8445, Acc.ottoman: 0.7255, Acc.bottle: 0.5137, Acc.buffet: 0.6321, Acc.poster: 0.5068, Acc.stage: 0.4752, Acc.van: 0.5881, Acc.ship: 0.9854, Acc.fountain: 0.3211, Acc.conveyer belt: 0.9045, Acc.canopy: 0.7447, Acc.washer: 0.7866, Acc.plaything: 0.5224, Acc.swimming pool: 0.9626, Acc.stool: 0.7446, Acc.barrel: 0.6487, Acc.basket: 0.5794, Acc.waterfall: 0.6834, Acc.tent: 0.9863, Acc.bag: 0.2588, Acc.minibike: 0.8881, Acc.cradle: 0.9821, Acc.oven: 0.6731, Acc.ball: 0.7575, Acc.food: 0.7451, Acc.step: 0.1409, Acc.tank: 0.5590, Acc.trade name: 0.2160, Acc.microwave: 0.9631, Acc.pot: 0.6733, Acc.animal: 0.6775, Acc.bicycle: 0.7592, Acc.lake: 0.6092, Acc.dishwasher: 0.7446, Acc.screen: 0.9554, Acc.blanket: 0.3957, Acc.sculpture: 0.8471, Acc.hood: 0.7120, Acc.sconce: 0.5221, Acc.vase: 0.5902, Acc.traffic light: 0.6330, Acc.tray: 0.1445, Acc.ashcan: 0.5843, Acc.fan: 0.7043, Acc.pier: 0.5173, Acc.crt screen: 0.0510, Acc.plate: 0.6859, Acc.monitor: 0.8019, Acc.bulletin board: 0.6576, Acc.shower: 0.0308, Acc.radiator: 0.7069, Acc.glass: 0.1780, Acc.clock: 0.3637, Acc.flag: 0.7836 +2024-06-18 15:55:41,473 - mmseg - INFO - Iter [40050/80000] lr: 1.998e-05, eta: 16:26:24, time: 3.270, data_time: 1.951, memory: 70498, decode.loss_ce: 0.2271, decode.acc_seg: 90.5563, aux.loss_ce: 0.0938, aux.acc_seg: 90.1992, loss: 0.3210 +2024-06-18 15:56:47,999 - mmseg - INFO - Iter [40100/80000] lr: 1.995e-05, eta: 16:25:02, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2283, decode.acc_seg: 90.4908, aux.loss_ce: 0.0947, aux.acc_seg: 90.1412, loss: 0.3231 +2024-06-18 15:57:54,306 - mmseg - INFO - Iter [40150/80000] lr: 1.993e-05, eta: 16:23:40, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2187, decode.acc_seg: 90.6650, aux.loss_ce: 0.0911, aux.acc_seg: 90.3259, loss: 0.3099 +2024-06-18 15:59:00,702 - mmseg - INFO - Iter [40200/80000] lr: 1.990e-05, eta: 16:22:19, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2262, decode.acc_seg: 90.5247, aux.loss_ce: 0.0940, aux.acc_seg: 90.1144, loss: 0.3202 +2024-06-18 16:00:06,946 - mmseg - INFO - Iter [40250/80000] lr: 1.988e-05, eta: 16:20:57, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2109, decode.acc_seg: 91.0344, aux.loss_ce: 0.0880, aux.acc_seg: 90.7453, loss: 0.2990 +2024-06-18 16:01:13,212 - mmseg - INFO - Iter [40300/80000] lr: 1.985e-05, eta: 16:19:35, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2317, decode.acc_seg: 90.2224, aux.loss_ce: 0.0955, aux.acc_seg: 89.9474, loss: 0.3272 +2024-06-18 16:02:19,458 - mmseg - INFO - Iter [40350/80000] lr: 1.983e-05, eta: 16:18:14, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2237, decode.acc_seg: 90.6421, aux.loss_ce: 0.0915, aux.acc_seg: 90.4676, loss: 0.3152 +2024-06-18 16:03:25,858 - mmseg - INFO - Iter [40400/80000] lr: 1.980e-05, eta: 16:16:52, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2194, decode.acc_seg: 90.7880, aux.loss_ce: 0.0910, aux.acc_seg: 90.4891, loss: 0.3104 +2024-06-18 16:04:34,571 - mmseg - INFO - Iter [40450/80000] lr: 1.978e-05, eta: 16:15:33, time: 1.374, data_time: 0.064, memory: 70498, decode.loss_ce: 0.2160, decode.acc_seg: 90.9814, aux.loss_ce: 0.0902, aux.acc_seg: 90.6050, loss: 0.3062 +2024-06-18 16:05:40,658 - mmseg - INFO - Iter [40500/80000] lr: 1.975e-05, eta: 16:14:11, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2089, decode.acc_seg: 91.1939, aux.loss_ce: 0.0868, aux.acc_seg: 90.8421, loss: 0.2957 +2024-06-18 16:06:46,924 - mmseg - INFO - Iter [40550/80000] lr: 1.973e-05, eta: 16:12:50, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1954, decode.acc_seg: 91.8309, aux.loss_ce: 0.0819, aux.acc_seg: 91.4177, loss: 0.2772 +2024-06-18 16:07:53,421 - mmseg - INFO - Iter [40600/80000] lr: 1.970e-05, eta: 16:11:29, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2136, decode.acc_seg: 91.0107, aux.loss_ce: 0.0890, aux.acc_seg: 90.5888, loss: 0.3026 +2024-06-18 16:08:59,915 - mmseg - INFO - Iter [40650/80000] lr: 1.968e-05, eta: 16:10:07, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2120, decode.acc_seg: 91.2086, aux.loss_ce: 0.0891, aux.acc_seg: 90.7784, loss: 0.3011 +2024-06-18 16:10:06,195 - mmseg - INFO - Iter [40700/80000] lr: 1.965e-05, eta: 16:08:46, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2069, decode.acc_seg: 91.1256, aux.loss_ce: 0.0862, aux.acc_seg: 90.8589, loss: 0.2931 +2024-06-18 16:11:12,415 - mmseg - INFO - Iter [40750/80000] lr: 1.963e-05, eta: 16:07:25, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2219, decode.acc_seg: 90.7435, aux.loss_ce: 0.0920, aux.acc_seg: 90.5147, loss: 0.3139 +2024-06-18 16:12:18,561 - mmseg - INFO - Iter [40800/80000] lr: 1.960e-05, eta: 16:06:03, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2077, decode.acc_seg: 91.3318, aux.loss_ce: 0.0861, aux.acc_seg: 91.0487, loss: 0.2939 +2024-06-18 16:13:24,827 - mmseg - INFO - Iter [40850/80000] lr: 1.958e-05, eta: 16:04:42, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2075, decode.acc_seg: 91.2094, aux.loss_ce: 0.0872, aux.acc_seg: 90.7411, loss: 0.2947 +2024-06-18 16:14:30,864 - mmseg - INFO - Iter [40900/80000] lr: 1.955e-05, eta: 16:03:20, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2208, decode.acc_seg: 90.4918, aux.loss_ce: 0.0918, aux.acc_seg: 90.1251, loss: 0.3126 +2024-06-18 16:15:37,273 - mmseg - INFO - Iter [40950/80000] lr: 1.953e-05, eta: 16:01:59, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2191, decode.acc_seg: 90.7998, aux.loss_ce: 0.0910, aux.acc_seg: 90.4706, loss: 0.3101 +2024-06-18 16:16:43,634 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 16:16:43,634 - mmseg - INFO - Iter [41000/80000] lr: 1.950e-05, eta: 16:00:38, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2212, decode.acc_seg: 90.6265, aux.loss_ce: 0.0912, aux.acc_seg: 90.3525, loss: 0.3124 +2024-06-18 16:18:20,412 - mmseg - INFO - per class results: +2024-06-18 16:18:20,418 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.47 | 89.24 | +| building | 84.92 | 93.2 | +| sky | 94.71 | 97.46 | +| floor | 84.63 | 91.52 | +| tree | 76.69 | 91.42 | +| ceiling | 86.98 | 94.44 | +| road | 86.7 | 91.72 | +| bed | 92.16 | 96.66 | +| windowpane | 64.12 | 81.24 | +| grass | 66.76 | 82.38 | +| cabinet | 64.85 | 75.33 | +| sidewalk | 71.06 | 86.57 | +| person | 85.71 | 93.66 | +| earth | 37.68 | 48.45 | +| door | 57.98 | 72.5 | +| table | 69.18 | 81.4 | +| mountain | 59.59 | 73.83 | +| plant | 54.73 | 64.74 | +| curtain | 75.65 | 83.4 | +| chair | 67.3 | 80.28 | +| car | 87.09 | 94.02 | +| water | 58.18 | 81.07 | +| painting | 75.54 | 91.02 | +| sofa | 80.37 | 89.72 | +| shelf | 48.46 | 66.84 | +| house | 57.36 | 71.08 | +| sea | 47.31 | 51.26 | +| mirror | 76.99 | 84.75 | +| rug | 69.22 | 83.87 | +| field | 35.91 | 65.17 | +| armchair | 58.0 | 77.23 | +| seat | 67.51 | 87.66 | +| fence | 51.72 | 65.6 | +| desk | 56.66 | 72.78 | +| rock | 50.95 | 74.09 | +| wardrobe | 53.53 | 69.0 | +| lamp | 70.51 | 81.09 | +| bathtub | 84.57 | 87.15 | +| railing | 43.42 | 62.15 | +| cushion | 70.19 | 80.06 | +| base | 40.51 | 54.28 | +| box | 36.73 | 50.08 | +| column | 55.4 | 69.93 | +| signboard | 40.43 | 51.41 | +| chest of drawers | 49.95 | 73.71 | +| counter | 45.43 | 56.17 | +| sand | 53.63 | 79.22 | +| sink | 76.59 | 83.4 | +| skyscraper | 47.62 | 55.06 | +| fireplace | 74.24 | 87.25 | +| refrigerator | 80.41 | 87.03 | +| grandstand | 53.41 | 77.26 | +| path | 30.05 | 38.08 | +| stairs | 24.06 | 30.96 | +| runway | 68.55 | 91.94 | +| case | 58.39 | 84.81 | +| pool table | 94.96 | 97.58 | +| pillow | 69.38 | 80.6 | +| screen door | 56.77 | 57.67 | +| stairway | 47.62 | 67.61 | +| river | 10.12 | 23.22 | +| bridge | 73.14 | 87.11 | +| bookcase | 39.53 | 60.84 | +| blind | 44.36 | 55.42 | +| coffee table | 64.37 | 87.79 | +| toilet | 89.52 | 94.5 | +| flower | 38.04 | 43.78 | +| book | 53.67 | 71.87 | +| hill | 7.09 | 11.25 | +| bench | 52.21 | 62.01 | +| countertop | 63.62 | 83.22 | +| stove | 84.14 | 91.35 | +| palm | 54.92 | 72.54 | +| kitchen island | 51.81 | 84.04 | +| computer | 79.34 | 91.93 | +| swivel chair | 53.29 | 72.99 | +| boat | 61.88 | 85.53 | +| bar | 62.07 | 79.32 | +| arcade machine | 73.47 | 76.93 | +| hovel | 44.96 | 49.43 | +| bus | 91.89 | 95.88 | +| towel | 72.71 | 81.2 | +| light | 59.02 | 66.9 | +| truck | 45.45 | 58.89 | +| tower | 6.43 | 10.3 | +| chandelier | 70.95 | 88.93 | +| awning | 46.92 | 62.54 | +| streetlight | 31.33 | 45.36 | +| booth | 32.27 | 49.81 | +| television receiver | 77.77 | 81.98 | +| airplane | 75.05 | 80.01 | +| dirt track | 16.12 | 31.42 | +| apparel | 45.11 | 62.1 | +| pole | 25.17 | 34.73 | +| land | 8.82 | 11.42 | +| bannister | 18.9 | 25.99 | +| escalator | 57.08 | 87.16 | +| ottoman | 46.49 | 62.25 | +| bottle | 42.1 | 54.24 | +| buffet | 49.8 | 62.27 | +| poster | 32.37 | 50.4 | +| stage | 22.27 | 41.33 | +| van | 44.08 | 57.38 | +| ship | 33.22 | 33.99 | +| fountain | 30.79 | 31.81 | +| conveyer belt | 77.31 | 92.66 | +| canopy | 51.52 | 68.36 | +| washer | 82.2 | 86.29 | +| plaything | 27.98 | 44.12 | +| swimming pool | 58.68 | 84.21 | +| stool | 55.79 | 65.15 | +| barrel | 55.01 | 64.79 | +| basket | 38.28 | 55.99 | +| waterfall | 70.94 | 87.32 | +| tent | 89.79 | 97.72 | +| bag | 23.05 | 27.41 | +| minibike | 72.92 | 87.1 | +| cradle | 80.41 | 97.73 | +| oven | 54.71 | 67.85 | +| ball | 37.11 | 38.09 | +| food | 56.47 | 69.81 | +| step | 16.41 | 21.22 | +| tank | 68.32 | 80.63 | +| trade name | 32.13 | 37.61 | +| microwave | 87.57 | 95.15 | +| pot | 57.38 | 69.54 | +| animal | 65.3 | 67.48 | +| bicycle | 58.44 | 79.18 | +| lake | 20.42 | 22.85 | +| dishwasher | 62.51 | 76.8 | +| screen | 52.64 | 89.42 | +| blanket | 28.8 | 32.87 | +| sculpture | 72.44 | 88.75 | +| hood | 60.87 | 72.71 | +| sconce | 54.21 | 65.28 | +| vase | 46.96 | 61.21 | +| traffic light | 36.83 | 49.46 | +| tray | 7.68 | 8.84 | +| ashcan | 42.04 | 65.64 | +| fan | 64.07 | 75.77 | +| pier | 34.11 | 45.52 | +| crt screen | 6.77 | 10.37 | +| plate | 58.26 | 79.02 | +| monitor | 62.64 | 77.59 | +| bulletin board | 49.42 | 70.84 | +| shower | 0.39 | 1.6 | +| radiator | 64.02 | 75.66 | +| glass | 17.97 | 19.31 | +| clock | 43.54 | 51.6 | +| flag | 71.45 | 75.41 | ++---------------------+-------+-------+ +2024-06-18 16:18:20,418 - mmseg - INFO - Summary: +2024-06-18 16:18:20,418 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.59 | 55.13 | 67.56 | ++-------+-------+-------+ +2024-06-18 16:18:20,419 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 16:18:20,419 - mmseg - INFO - Iter(val) [250] aAcc: 0.8559, mIoU: 0.5513, mAcc: 0.6756, IoU.wall: 0.8147, IoU.building: 0.8492, IoU.sky: 0.9471, IoU.floor: 0.8463, IoU.tree: 0.7669, IoU.ceiling: 0.8698, IoU.road: 0.8670, IoU.bed : 0.9216, IoU.windowpane: 0.6412, IoU.grass: 0.6676, IoU.cabinet: 0.6485, IoU.sidewalk: 0.7106, IoU.person: 0.8571, IoU.earth: 0.3768, IoU.door: 0.5798, IoU.table: 0.6918, IoU.mountain: 0.5959, IoU.plant: 0.5473, IoU.curtain: 0.7565, IoU.chair: 0.6730, IoU.car: 0.8709, IoU.water: 0.5818, IoU.painting: 0.7554, IoU.sofa: 0.8037, IoU.shelf: 0.4846, IoU.house: 0.5736, IoU.sea: 0.4731, IoU.mirror: 0.7699, IoU.rug: 0.6922, IoU.field: 0.3591, IoU.armchair: 0.5800, IoU.seat: 0.6751, IoU.fence: 0.5172, IoU.desk: 0.5666, IoU.rock: 0.5095, IoU.wardrobe: 0.5353, IoU.lamp: 0.7051, IoU.bathtub: 0.8457, IoU.railing: 0.4342, IoU.cushion: 0.7019, IoU.base: 0.4051, IoU.box: 0.3673, IoU.column: 0.5540, IoU.signboard: 0.4043, IoU.chest of drawers: 0.4995, IoU.counter: 0.4543, IoU.sand: 0.5363, IoU.sink: 0.7659, IoU.skyscraper: 0.4762, IoU.fireplace: 0.7424, IoU.refrigerator: 0.8041, IoU.grandstand: 0.5341, IoU.path: 0.3005, IoU.stairs: 0.2406, IoU.runway: 0.6855, IoU.case: 0.5839, IoU.pool table: 0.9496, IoU.pillow: 0.6938, IoU.screen door: 0.5677, IoU.stairway: 0.4762, IoU.river: 0.1012, IoU.bridge: 0.7314, IoU.bookcase: 0.3953, IoU.blind: 0.4436, IoU.coffee table: 0.6437, IoU.toilet: 0.8952, IoU.flower: 0.3804, IoU.book: 0.5367, IoU.hill: 0.0709, IoU.bench: 0.5221, IoU.countertop: 0.6362, IoU.stove: 0.8414, IoU.palm: 0.5492, IoU.kitchen island: 0.5181, IoU.computer: 0.7934, IoU.swivel chair: 0.5329, IoU.boat: 0.6188, IoU.bar: 0.6207, IoU.arcade machine: 0.7347, IoU.hovel: 0.4496, IoU.bus: 0.9189, IoU.towel: 0.7271, IoU.light: 0.5902, IoU.truck: 0.4545, IoU.tower: 0.0643, IoU.chandelier: 0.7095, IoU.awning: 0.4692, IoU.streetlight: 0.3133, IoU.booth: 0.3227, IoU.television receiver: 0.7777, IoU.airplane: 0.7505, IoU.dirt track: 0.1612, IoU.apparel: 0.4511, IoU.pole: 0.2517, IoU.land: 0.0882, IoU.bannister: 0.1890, IoU.escalator: 0.5708, IoU.ottoman: 0.4649, IoU.bottle: 0.4210, IoU.buffet: 0.4980, IoU.poster: 0.3237, IoU.stage: 0.2227, IoU.van: 0.4408, IoU.ship: 0.3322, IoU.fountain: 0.3079, IoU.conveyer belt: 0.7731, IoU.canopy: 0.5152, IoU.washer: 0.8220, IoU.plaything: 0.2798, IoU.swimming pool: 0.5868, IoU.stool: 0.5579, IoU.barrel: 0.5501, IoU.basket: 0.3828, IoU.waterfall: 0.7094, IoU.tent: 0.8979, IoU.bag: 0.2305, IoU.minibike: 0.7292, IoU.cradle: 0.8041, IoU.oven: 0.5471, IoU.ball: 0.3711, IoU.food: 0.5647, IoU.step: 0.1641, IoU.tank: 0.6832, IoU.trade name: 0.3213, IoU.microwave: 0.8757, IoU.pot: 0.5738, IoU.animal: 0.6530, IoU.bicycle: 0.5844, IoU.lake: 0.2042, IoU.dishwasher: 0.6251, IoU.screen: 0.5264, IoU.blanket: 0.2880, IoU.sculpture: 0.7244, IoU.hood: 0.6087, IoU.sconce: 0.5421, IoU.vase: 0.4696, IoU.traffic light: 0.3683, IoU.tray: 0.0768, IoU.ashcan: 0.4204, IoU.fan: 0.6407, IoU.pier: 0.3411, IoU.crt screen: 0.0677, IoU.plate: 0.5826, IoU.monitor: 0.6264, IoU.bulletin board: 0.4942, IoU.shower: 0.0039, IoU.radiator: 0.6402, IoU.glass: 0.1797, IoU.clock: 0.4354, IoU.flag: 0.7145, Acc.wall: 0.8924, Acc.building: 0.9320, Acc.sky: 0.9746, Acc.floor: 0.9152, Acc.tree: 0.9142, Acc.ceiling: 0.9444, Acc.road: 0.9172, Acc.bed : 0.9666, Acc.windowpane: 0.8124, Acc.grass: 0.8238, Acc.cabinet: 0.7533, Acc.sidewalk: 0.8657, Acc.person: 0.9366, Acc.earth: 0.4845, Acc.door: 0.7250, Acc.table: 0.8140, Acc.mountain: 0.7383, Acc.plant: 0.6474, Acc.curtain: 0.8340, Acc.chair: 0.8028, Acc.car: 0.9402, Acc.water: 0.8107, Acc.painting: 0.9102, Acc.sofa: 0.8972, Acc.shelf: 0.6684, Acc.house: 0.7108, Acc.sea: 0.5126, Acc.mirror: 0.8475, Acc.rug: 0.8387, Acc.field: 0.6517, Acc.armchair: 0.7723, Acc.seat: 0.8766, Acc.fence: 0.6560, Acc.desk: 0.7278, Acc.rock: 0.7409, Acc.wardrobe: 0.6900, Acc.lamp: 0.8109, Acc.bathtub: 0.8715, Acc.railing: 0.6215, Acc.cushion: 0.8006, Acc.base: 0.5428, Acc.box: 0.5008, Acc.column: 0.6993, Acc.signboard: 0.5141, Acc.chest of drawers: 0.7371, Acc.counter: 0.5617, Acc.sand: 0.7922, Acc.sink: 0.8340, Acc.skyscraper: 0.5506, Acc.fireplace: 0.8725, Acc.refrigerator: 0.8703, Acc.grandstand: 0.7726, Acc.path: 0.3808, Acc.stairs: 0.3096, Acc.runway: 0.9194, Acc.case: 0.8481, Acc.pool table: 0.9758, Acc.pillow: 0.8060, Acc.screen door: 0.5767, Acc.stairway: 0.6761, Acc.river: 0.2322, Acc.bridge: 0.8711, Acc.bookcase: 0.6084, Acc.blind: 0.5542, Acc.coffee table: 0.8779, Acc.toilet: 0.9450, Acc.flower: 0.4378, Acc.book: 0.7187, Acc.hill: 0.1125, Acc.bench: 0.6201, Acc.countertop: 0.8322, Acc.stove: 0.9135, Acc.palm: 0.7254, Acc.kitchen island: 0.8404, Acc.computer: 0.9193, Acc.swivel chair: 0.7299, Acc.boat: 0.8553, Acc.bar: 0.7932, Acc.arcade machine: 0.7693, Acc.hovel: 0.4943, Acc.bus: 0.9588, Acc.towel: 0.8120, Acc.light: 0.6690, Acc.truck: 0.5889, Acc.tower: 0.1030, Acc.chandelier: 0.8893, Acc.awning: 0.6254, Acc.streetlight: 0.4536, Acc.booth: 0.4981, Acc.television receiver: 0.8198, Acc.airplane: 0.8001, Acc.dirt track: 0.3142, Acc.apparel: 0.6210, Acc.pole: 0.3473, Acc.land: 0.1142, Acc.bannister: 0.2599, Acc.escalator: 0.8716, Acc.ottoman: 0.6225, Acc.bottle: 0.5424, Acc.buffet: 0.6227, Acc.poster: 0.5040, Acc.stage: 0.4133, Acc.van: 0.5738, Acc.ship: 0.3399, Acc.fountain: 0.3181, Acc.conveyer belt: 0.9266, Acc.canopy: 0.6836, Acc.washer: 0.8629, Acc.plaything: 0.4412, Acc.swimming pool: 0.8421, Acc.stool: 0.6515, Acc.barrel: 0.6479, Acc.basket: 0.5599, Acc.waterfall: 0.8732, Acc.tent: 0.9772, Acc.bag: 0.2741, Acc.minibike: 0.8710, Acc.cradle: 0.9773, Acc.oven: 0.6785, Acc.ball: 0.3809, Acc.food: 0.6981, Acc.step: 0.2122, Acc.tank: 0.8063, Acc.trade name: 0.3761, Acc.microwave: 0.9515, Acc.pot: 0.6954, Acc.animal: 0.6748, Acc.bicycle: 0.7918, Acc.lake: 0.2285, Acc.dishwasher: 0.7680, Acc.screen: 0.8942, Acc.blanket: 0.3287, Acc.sculpture: 0.8875, Acc.hood: 0.7271, Acc.sconce: 0.6528, Acc.vase: 0.6121, Acc.traffic light: 0.4946, Acc.tray: 0.0884, Acc.ashcan: 0.6564, Acc.fan: 0.7577, Acc.pier: 0.4552, Acc.crt screen: 0.1037, Acc.plate: 0.7902, Acc.monitor: 0.7759, Acc.bulletin board: 0.7084, Acc.shower: 0.0160, Acc.radiator: 0.7566, Acc.glass: 0.1931, Acc.clock: 0.5160, Acc.flag: 0.7541 +2024-06-18 16:19:26,775 - mmseg - INFO - Iter [41050/80000] lr: 1.948e-05, eta: 16:00:49, time: 3.263, data_time: 1.952, memory: 70498, decode.loss_ce: 0.2156, decode.acc_seg: 91.1145, aux.loss_ce: 0.0887, aux.acc_seg: 90.8797, loss: 0.3043 +2024-06-18 16:20:33,072 - mmseg - INFO - Iter [41100/80000] lr: 1.945e-05, eta: 15:59:28, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2159, decode.acc_seg: 90.9579, aux.loss_ce: 0.0892, aux.acc_seg: 90.6371, loss: 0.3051 +2024-06-18 16:21:39,198 - mmseg - INFO - Iter [41150/80000] lr: 1.943e-05, eta: 15:58:06, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2095, decode.acc_seg: 91.1595, aux.loss_ce: 0.0878, aux.acc_seg: 90.7434, loss: 0.2973 +2024-06-18 16:22:45,636 - mmseg - INFO - Iter [41200/80000] lr: 1.940e-05, eta: 15:56:45, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2219, decode.acc_seg: 90.8225, aux.loss_ce: 0.0922, aux.acc_seg: 90.4743, loss: 0.3141 +2024-06-18 16:23:51,937 - mmseg - INFO - Iter [41250/80000] lr: 1.938e-05, eta: 15:55:24, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2229, decode.acc_seg: 90.4882, aux.loss_ce: 0.0930, aux.acc_seg: 90.0509, loss: 0.3160 +2024-06-18 16:24:58,255 - mmseg - INFO - Iter [41300/80000] lr: 1.935e-05, eta: 15:54:03, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2155, decode.acc_seg: 90.6662, aux.loss_ce: 0.0895, aux.acc_seg: 90.2957, loss: 0.3050 +2024-06-18 16:26:04,567 - mmseg - INFO - Iter [41350/80000] lr: 1.933e-05, eta: 15:52:42, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2115, decode.acc_seg: 90.9104, aux.loss_ce: 0.0885, aux.acc_seg: 90.5431, loss: 0.3001 +2024-06-18 16:27:10,871 - mmseg - INFO - Iter [41400/80000] lr: 1.930e-05, eta: 15:51:21, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2151, decode.acc_seg: 90.4988, aux.loss_ce: 0.0888, aux.acc_seg: 90.1962, loss: 0.3038 +2024-06-18 16:28:17,213 - mmseg - INFO - Iter [41450/80000] lr: 1.928e-05, eta: 15:50:00, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2079, decode.acc_seg: 91.1796, aux.loss_ce: 0.0869, aux.acc_seg: 90.7151, loss: 0.2948 +2024-06-18 16:29:23,504 - mmseg - INFO - Iter [41500/80000] lr: 1.925e-05, eta: 15:48:39, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2163, decode.acc_seg: 90.9382, aux.loss_ce: 0.0903, aux.acc_seg: 90.5955, loss: 0.3066 +2024-06-18 16:30:29,895 - mmseg - INFO - Iter [41550/80000] lr: 1.923e-05, eta: 15:47:18, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2143, decode.acc_seg: 90.8659, aux.loss_ce: 0.0887, aux.acc_seg: 90.6030, loss: 0.3030 +2024-06-18 16:31:36,220 - mmseg - INFO - Iter [41600/80000] lr: 1.920e-05, eta: 15:45:57, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2216, decode.acc_seg: 90.7409, aux.loss_ce: 0.0922, aux.acc_seg: 90.3944, loss: 0.3137 +2024-06-18 16:32:42,704 - mmseg - INFO - Iter [41650/80000] lr: 1.918e-05, eta: 15:44:36, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2116, decode.acc_seg: 91.0606, aux.loss_ce: 0.0879, aux.acc_seg: 90.7354, loss: 0.2995 +2024-06-18 16:33:51,470 - mmseg - INFO - Iter [41700/80000] lr: 1.915e-05, eta: 15:43:18, time: 1.375, data_time: 0.061, memory: 70498, decode.loss_ce: 0.2172, decode.acc_seg: 90.7479, aux.loss_ce: 0.0902, aux.acc_seg: 90.3887, loss: 0.3074 +2024-06-18 16:34:57,574 - mmseg - INFO - Iter [41750/80000] lr: 1.913e-05, eta: 15:41:57, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2111, decode.acc_seg: 90.7749, aux.loss_ce: 0.0876, aux.acc_seg: 90.4086, loss: 0.2987 +2024-06-18 16:36:03,679 - mmseg - INFO - Iter [41800/80000] lr: 1.910e-05, eta: 15:40:36, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2232, decode.acc_seg: 90.8094, aux.loss_ce: 0.0936, aux.acc_seg: 90.4201, loss: 0.3168 +2024-06-18 16:37:10,085 - mmseg - INFO - Iter [41850/80000] lr: 1.908e-05, eta: 15:39:15, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2043, decode.acc_seg: 91.1247, aux.loss_ce: 0.0849, aux.acc_seg: 90.7074, loss: 0.2892 +2024-06-18 16:38:16,569 - mmseg - INFO - Iter [41900/80000] lr: 1.905e-05, eta: 15:37:54, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2018, decode.acc_seg: 91.3330, aux.loss_ce: 0.0837, aux.acc_seg: 91.0440, loss: 0.2855 +2024-06-18 16:39:23,065 - mmseg - INFO - Iter [41950/80000] lr: 1.903e-05, eta: 15:36:34, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2058, decode.acc_seg: 91.3117, aux.loss_ce: 0.0853, aux.acc_seg: 91.0378, loss: 0.2911 +2024-06-18 16:40:29,265 - mmseg - INFO - Saving checkpoint at 42000 iterations +2024-06-18 16:42:14,244 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 16:42:14,244 - mmseg - INFO - Iter [42000/80000] lr: 1.900e-05, eta: 15:36:48, time: 3.424, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2048, decode.acc_seg: 91.1776, aux.loss_ce: 0.0854, aux.acc_seg: 90.8127, loss: 0.2902 +2024-06-18 16:43:51,182 - mmseg - INFO - per class results: +2024-06-18 16:43:51,188 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.6 | 89.0 | +| building | 84.84 | 92.74 | +| sky | 94.89 | 97.08 | +| floor | 85.35 | 90.9 | +| tree | 76.83 | 91.29 | +| ceiling | 86.73 | 93.76 | +| road | 87.48 | 93.15 | +| bed | 92.42 | 96.77 | +| windowpane | 64.97 | 81.38 | +| grass | 66.75 | 80.9 | +| cabinet | 65.28 | 74.51 | +| sidewalk | 71.36 | 81.52 | +| person | 85.95 | 94.22 | +| earth | 37.39 | 50.95 | +| door | 58.73 | 71.71 | +| table | 69.57 | 82.57 | +| mountain | 61.85 | 76.35 | +| plant | 56.37 | 69.01 | +| curtain | 76.64 | 87.57 | +| chair | 66.64 | 81.05 | +| car | 86.2 | 93.08 | +| water | 61.41 | 70.24 | +| painting | 78.39 | 91.46 | +| sofa | 81.33 | 88.22 | +| shelf | 48.84 | 66.79 | +| house | 59.08 | 71.25 | +| sea | 69.18 | 82.4 | +| mirror | 77.73 | 83.72 | +| rug | 70.77 | 83.44 | +| field | 32.56 | 55.52 | +| armchair | 60.24 | 81.59 | +| seat | 65.58 | 89.18 | +| fence | 50.35 | 62.14 | +| desk | 57.54 | 75.36 | +| rock | 56.48 | 77.4 | +| wardrobe | 53.76 | 67.4 | +| lamp | 69.65 | 82.96 | +| bathtub | 84.25 | 86.51 | +| railing | 41.41 | 65.62 | +| cushion | 69.23 | 80.35 | +| base | 32.82 | 55.54 | +| box | 37.68 | 49.94 | +| column | 54.78 | 70.66 | +| signboard | 41.79 | 56.75 | +| chest of drawers | 45.32 | 71.4 | +| counter | 41.4 | 52.18 | +| sand | 52.82 | 72.56 | +| sink | 71.88 | 84.94 | +| skyscraper | 48.79 | 63.92 | +| fireplace | 72.77 | 90.91 | +| refrigerator | 80.25 | 92.22 | +| grandstand | 52.08 | 77.24 | +| path | 27.44 | 48.66 | +| stairs | 29.04 | 38.12 | +| runway | 72.46 | 94.13 | +| case | 52.83 | 66.52 | +| pool table | 94.28 | 98.0 | +| pillow | 70.81 | 85.68 | +| screen door | 83.44 | 86.65 | +| stairway | 50.69 | 69.85 | +| river | 19.48 | 52.43 | +| bridge | 75.61 | 87.54 | +| bookcase | 42.32 | 58.67 | +| blind | 46.58 | 54.6 | +| coffee table | 65.03 | 85.59 | +| toilet | 88.66 | 93.48 | +| flower | 41.43 | 49.78 | +| book | 55.52 | 74.78 | +| hill | 8.49 | 13.0 | +| bench | 53.3 | 62.49 | +| countertop | 62.03 | 85.54 | +| stove | 81.98 | 90.51 | +| palm | 56.6 | 79.08 | +| kitchen island | 50.6 | 84.57 | +| computer | 81.1 | 93.45 | +| swivel chair | 51.28 | 69.21 | +| boat | 55.91 | 86.54 | +| bar | 61.06 | 74.76 | +| arcade machine | 77.94 | 84.06 | +| hovel | 43.85 | 49.43 | +| bus | 91.44 | 96.31 | +| towel | 71.63 | 81.16 | +| light | 60.79 | 75.11 | +| truck | 41.62 | 59.01 | +| tower | 10.27 | 17.23 | +| chandelier | 71.55 | 90.21 | +| awning | 42.58 | 60.4 | +| streetlight | 32.41 | 45.39 | +| booth | 45.81 | 63.32 | +| television receiver | 75.79 | 88.72 | +| airplane | 81.99 | 94.3 | +| dirt track | 11.12 | 55.14 | +| apparel | 52.34 | 69.68 | +| pole | 25.53 | 34.87 | +| land | 3.04 | 4.17 | +| bannister | 17.37 | 25.63 | +| escalator | 58.57 | 78.6 | +| ottoman | 52.04 | 72.67 | +| bottle | 44.38 | 62.56 | +| buffet | 54.49 | 67.0 | +| poster | 36.71 | 48.45 | +| stage | 20.71 | 34.47 | +| van | 43.02 | 68.51 | +| ship | 90.65 | 93.91 | +| fountain | 31.08 | 31.9 | +| conveyer belt | 77.59 | 92.39 | +| canopy | 48.72 | 76.11 | +| washer | 74.85 | 76.99 | +| plaything | 37.97 | 52.64 | +| swimming pool | 67.73 | 90.39 | +| stool | 56.3 | 70.87 | +| barrel | 37.04 | 64.99 | +| basket | 39.54 | 54.18 | +| waterfall | 68.73 | 94.28 | +| tent | 88.76 | 98.76 | +| bag | 20.57 | 26.15 | +| minibike | 72.82 | 89.89 | +| cradle | 78.36 | 98.67 | +| oven | 61.09 | 71.17 | +| ball | 54.23 | 57.59 | +| food | 62.95 | 82.63 | +| step | 16.12 | 21.14 | +| tank | 71.25 | 96.75 | +| trade name | 29.75 | 34.52 | +| microwave | 87.96 | 95.26 | +| pot | 57.33 | 65.08 | +| animal | 68.75 | 71.82 | +| bicycle | 59.18 | 73.93 | +| lake | 47.06 | 63.63 | +| dishwasher | 60.1 | 78.97 | +| screen | 58.32 | 92.93 | +| blanket | 41.62 | 47.8 | +| sculpture | 71.95 | 83.97 | +| hood | 59.52 | 68.22 | +| sconce | 56.51 | 71.61 | +| vase | 49.65 | 65.37 | +| traffic light | 38.82 | 57.06 | +| tray | 15.61 | 27.62 | +| ashcan | 43.27 | 67.8 | +| fan | 64.95 | 78.74 | +| pier | 35.97 | 49.01 | +| crt screen | 10.95 | 12.13 | +| plate | 57.41 | 78.5 | +| monitor | 65.44 | 80.37 | +| bulletin board | 58.63 | 67.0 | +| shower | 1.05 | 1.4 | +| radiator | 63.83 | 74.93 | +| glass | 19.81 | 22.74 | +| clock | 39.63 | 47.44 | +| flag | 69.79 | 78.64 | ++---------------------+-------+-------+ +2024-06-18 16:43:51,188 - mmseg - INFO - Summary: +2024-06-18 16:43:51,188 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.84 | 56.75 | 70.52 | ++-------+-------+-------+ +2024-06-18 16:43:51,189 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 16:43:51,189 - mmseg - INFO - Iter(val) [250] aAcc: 0.8584, mIoU: 0.5675, mAcc: 0.7052, IoU.wall: 0.8160, IoU.building: 0.8484, IoU.sky: 0.9489, IoU.floor: 0.8535, IoU.tree: 0.7683, IoU.ceiling: 0.8673, IoU.road: 0.8748, IoU.bed : 0.9242, IoU.windowpane: 0.6497, IoU.grass: 0.6675, IoU.cabinet: 0.6528, IoU.sidewalk: 0.7136, IoU.person: 0.8595, IoU.earth: 0.3739, IoU.door: 0.5873, IoU.table: 0.6957, IoU.mountain: 0.6185, IoU.plant: 0.5637, IoU.curtain: 0.7664, IoU.chair: 0.6664, IoU.car: 0.8620, IoU.water: 0.6141, IoU.painting: 0.7839, IoU.sofa: 0.8133, IoU.shelf: 0.4884, IoU.house: 0.5908, IoU.sea: 0.6918, IoU.mirror: 0.7773, IoU.rug: 0.7077, IoU.field: 0.3256, IoU.armchair: 0.6024, IoU.seat: 0.6558, IoU.fence: 0.5035, IoU.desk: 0.5754, IoU.rock: 0.5648, IoU.wardrobe: 0.5376, IoU.lamp: 0.6965, IoU.bathtub: 0.8425, IoU.railing: 0.4141, IoU.cushion: 0.6923, IoU.base: 0.3282, IoU.box: 0.3768, IoU.column: 0.5478, IoU.signboard: 0.4179, IoU.chest of drawers: 0.4532, IoU.counter: 0.4140, IoU.sand: 0.5282, IoU.sink: 0.7188, IoU.skyscraper: 0.4879, IoU.fireplace: 0.7277, IoU.refrigerator: 0.8025, IoU.grandstand: 0.5208, IoU.path: 0.2744, IoU.stairs: 0.2904, IoU.runway: 0.7246, IoU.case: 0.5283, IoU.pool table: 0.9428, IoU.pillow: 0.7081, IoU.screen door: 0.8344, IoU.stairway: 0.5069, IoU.river: 0.1948, IoU.bridge: 0.7561, IoU.bookcase: 0.4232, IoU.blind: 0.4658, IoU.coffee table: 0.6503, IoU.toilet: 0.8866, IoU.flower: 0.4143, IoU.book: 0.5552, IoU.hill: 0.0849, IoU.bench: 0.5330, IoU.countertop: 0.6203, IoU.stove: 0.8198, IoU.palm: 0.5660, IoU.kitchen island: 0.5060, IoU.computer: 0.8110, IoU.swivel chair: 0.5128, IoU.boat: 0.5591, IoU.bar: 0.6106, IoU.arcade machine: 0.7794, IoU.hovel: 0.4385, IoU.bus: 0.9144, IoU.towel: 0.7163, IoU.light: 0.6079, IoU.truck: 0.4162, IoU.tower: 0.1027, IoU.chandelier: 0.7155, IoU.awning: 0.4258, IoU.streetlight: 0.3241, IoU.booth: 0.4581, IoU.television receiver: 0.7579, IoU.airplane: 0.8199, IoU.dirt track: 0.1112, IoU.apparel: 0.5234, IoU.pole: 0.2553, IoU.land: 0.0304, IoU.bannister: 0.1737, IoU.escalator: 0.5857, IoU.ottoman: 0.5204, IoU.bottle: 0.4438, IoU.buffet: 0.5449, IoU.poster: 0.3671, IoU.stage: 0.2071, IoU.van: 0.4302, IoU.ship: 0.9065, IoU.fountain: 0.3108, IoU.conveyer belt: 0.7759, IoU.canopy: 0.4872, IoU.washer: 0.7485, IoU.plaything: 0.3797, IoU.swimming pool: 0.6773, IoU.stool: 0.5630, IoU.barrel: 0.3704, IoU.basket: 0.3954, IoU.waterfall: 0.6873, IoU.tent: 0.8876, IoU.bag: 0.2057, IoU.minibike: 0.7282, IoU.cradle: 0.7836, IoU.oven: 0.6109, IoU.ball: 0.5423, IoU.food: 0.6295, IoU.step: 0.1612, IoU.tank: 0.7125, IoU.trade name: 0.2975, IoU.microwave: 0.8796, IoU.pot: 0.5733, IoU.animal: 0.6875, IoU.bicycle: 0.5918, IoU.lake: 0.4706, IoU.dishwasher: 0.6010, IoU.screen: 0.5832, IoU.blanket: 0.4162, IoU.sculpture: 0.7195, IoU.hood: 0.5952, IoU.sconce: 0.5651, IoU.vase: 0.4965, IoU.traffic light: 0.3882, IoU.tray: 0.1561, IoU.ashcan: 0.4327, IoU.fan: 0.6495, IoU.pier: 0.3597, IoU.crt screen: 0.1095, IoU.plate: 0.5741, IoU.monitor: 0.6544, IoU.bulletin board: 0.5863, IoU.shower: 0.0105, IoU.radiator: 0.6383, IoU.glass: 0.1981, IoU.clock: 0.3963, IoU.flag: 0.6979, Acc.wall: 0.8900, Acc.building: 0.9274, Acc.sky: 0.9708, Acc.floor: 0.9090, Acc.tree: 0.9129, Acc.ceiling: 0.9376, Acc.road: 0.9315, Acc.bed : 0.9677, Acc.windowpane: 0.8138, Acc.grass: 0.8090, Acc.cabinet: 0.7451, Acc.sidewalk: 0.8152, Acc.person: 0.9422, Acc.earth: 0.5095, Acc.door: 0.7171, Acc.table: 0.8257, Acc.mountain: 0.7635, Acc.plant: 0.6901, Acc.curtain: 0.8757, Acc.chair: 0.8105, Acc.car: 0.9308, Acc.water: 0.7024, Acc.painting: 0.9146, Acc.sofa: 0.8822, Acc.shelf: 0.6679, Acc.house: 0.7125, Acc.sea: 0.8240, Acc.mirror: 0.8372, Acc.rug: 0.8344, Acc.field: 0.5552, Acc.armchair: 0.8159, Acc.seat: 0.8918, Acc.fence: 0.6214, Acc.desk: 0.7536, Acc.rock: 0.7740, Acc.wardrobe: 0.6740, Acc.lamp: 0.8296, Acc.bathtub: 0.8651, Acc.railing: 0.6562, Acc.cushion: 0.8035, Acc.base: 0.5554, Acc.box: 0.4994, Acc.column: 0.7066, Acc.signboard: 0.5675, Acc.chest of drawers: 0.7140, Acc.counter: 0.5218, Acc.sand: 0.7256, Acc.sink: 0.8494, Acc.skyscraper: 0.6392, Acc.fireplace: 0.9091, Acc.refrigerator: 0.9222, Acc.grandstand: 0.7724, Acc.path: 0.4866, Acc.stairs: 0.3812, Acc.runway: 0.9413, Acc.case: 0.6652, Acc.pool table: 0.9800, Acc.pillow: 0.8568, Acc.screen door: 0.8665, Acc.stairway: 0.6985, Acc.river: 0.5243, Acc.bridge: 0.8754, Acc.bookcase: 0.5867, Acc.blind: 0.5460, Acc.coffee table: 0.8559, Acc.toilet: 0.9348, Acc.flower: 0.4978, Acc.book: 0.7478, Acc.hill: 0.1300, Acc.bench: 0.6249, Acc.countertop: 0.8554, Acc.stove: 0.9051, Acc.palm: 0.7908, Acc.kitchen island: 0.8457, Acc.computer: 0.9345, Acc.swivel chair: 0.6921, Acc.boat: 0.8654, Acc.bar: 0.7476, Acc.arcade machine: 0.8406, Acc.hovel: 0.4943, Acc.bus: 0.9631, Acc.towel: 0.8116, Acc.light: 0.7511, Acc.truck: 0.5901, Acc.tower: 0.1723, Acc.chandelier: 0.9021, Acc.awning: 0.6040, Acc.streetlight: 0.4539, Acc.booth: 0.6332, Acc.television receiver: 0.8872, Acc.airplane: 0.9430, Acc.dirt track: 0.5514, Acc.apparel: 0.6968, Acc.pole: 0.3487, Acc.land: 0.0417, Acc.bannister: 0.2563, Acc.escalator: 0.7860, Acc.ottoman: 0.7267, Acc.bottle: 0.6256, Acc.buffet: 0.6700, Acc.poster: 0.4845, Acc.stage: 0.3447, Acc.van: 0.6851, Acc.ship: 0.9391, Acc.fountain: 0.3190, Acc.conveyer belt: 0.9239, Acc.canopy: 0.7611, Acc.washer: 0.7699, Acc.plaything: 0.5264, Acc.swimming pool: 0.9039, Acc.stool: 0.7087, Acc.barrel: 0.6499, Acc.basket: 0.5418, Acc.waterfall: 0.9428, Acc.tent: 0.9876, Acc.bag: 0.2615, Acc.minibike: 0.8989, Acc.cradle: 0.9867, Acc.oven: 0.7117, Acc.ball: 0.5759, Acc.food: 0.8263, Acc.step: 0.2114, Acc.tank: 0.9675, Acc.trade name: 0.3452, Acc.microwave: 0.9526, Acc.pot: 0.6508, Acc.animal: 0.7182, Acc.bicycle: 0.7393, Acc.lake: 0.6363, Acc.dishwasher: 0.7897, Acc.screen: 0.9293, Acc.blanket: 0.4780, Acc.sculpture: 0.8397, Acc.hood: 0.6822, Acc.sconce: 0.7161, Acc.vase: 0.6537, Acc.traffic light: 0.5706, Acc.tray: 0.2762, Acc.ashcan: 0.6780, Acc.fan: 0.7874, Acc.pier: 0.4901, Acc.crt screen: 0.1213, Acc.plate: 0.7850, Acc.monitor: 0.8037, Acc.bulletin board: 0.6700, Acc.shower: 0.0140, Acc.radiator: 0.7493, Acc.glass: 0.2274, Acc.clock: 0.4744, Acc.flag: 0.7864 +2024-06-18 16:44:57,915 - mmseg - INFO - Iter [42050/80000] lr: 1.898e-05, eta: 15:36:55, time: 3.273, data_time: 1.957, memory: 70498, decode.loss_ce: 0.2017, decode.acc_seg: 91.2223, aux.loss_ce: 0.0844, aux.acc_seg: 90.8897, loss: 0.2861 +2024-06-18 16:46:04,401 - mmseg - INFO - Iter [42100/80000] lr: 1.895e-05, eta: 15:35:34, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1961, decode.acc_seg: 91.7441, aux.loss_ce: 0.0819, aux.acc_seg: 91.4000, loss: 0.2780 +2024-06-18 16:47:10,705 - mmseg - INFO - Iter [42150/80000] lr: 1.893e-05, eta: 15:34:13, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2035, decode.acc_seg: 91.4456, aux.loss_ce: 0.0846, aux.acc_seg: 91.0477, loss: 0.2881 +2024-06-18 16:48:17,169 - mmseg - INFO - Iter [42200/80000] lr: 1.890e-05, eta: 15:32:52, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2259, decode.acc_seg: 90.6409, aux.loss_ce: 0.0940, aux.acc_seg: 90.2498, loss: 0.3199 +2024-06-18 16:49:23,701 - mmseg - INFO - Iter [42250/80000] lr: 1.888e-05, eta: 15:31:32, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2235, decode.acc_seg: 91.0098, aux.loss_ce: 0.0923, aux.acc_seg: 90.7574, loss: 0.3158 +2024-06-18 16:50:30,134 - mmseg - INFO - Iter [42300/80000] lr: 1.885e-05, eta: 15:30:11, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2245, decode.acc_seg: 90.9675, aux.loss_ce: 0.0934, aux.acc_seg: 90.5079, loss: 0.3180 +2024-06-18 16:51:36,431 - mmseg - INFO - Iter [42350/80000] lr: 1.883e-05, eta: 15:28:50, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2259, decode.acc_seg: 90.7024, aux.loss_ce: 0.0938, aux.acc_seg: 90.3148, loss: 0.3197 +2024-06-18 16:52:42,966 - mmseg - INFO - Iter [42400/80000] lr: 1.880e-05, eta: 15:27:29, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2160, decode.acc_seg: 90.8365, aux.loss_ce: 0.0900, aux.acc_seg: 90.4338, loss: 0.3059 +2024-06-18 16:53:49,544 - mmseg - INFO - Iter [42450/80000] lr: 1.878e-05, eta: 15:26:09, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2231, decode.acc_seg: 90.8569, aux.loss_ce: 0.0927, aux.acc_seg: 90.4500, loss: 0.3158 +2024-06-18 16:54:55,879 - mmseg - INFO - Iter [42500/80000] lr: 1.875e-05, eta: 15:24:48, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2082, decode.acc_seg: 91.0924, aux.loss_ce: 0.0871, aux.acc_seg: 90.6663, loss: 0.2953 +2024-06-18 16:56:01,899 - mmseg - INFO - Iter [42550/80000] lr: 1.873e-05, eta: 15:23:27, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2086, decode.acc_seg: 91.4437, aux.loss_ce: 0.0863, aux.acc_seg: 91.1586, loss: 0.2949 +2024-06-18 16:57:07,818 - mmseg - INFO - Iter [42600/80000] lr: 1.870e-05, eta: 15:22:06, time: 1.318, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2003, decode.acc_seg: 91.5706, aux.loss_ce: 0.0840, aux.acc_seg: 91.1792, loss: 0.2843 +2024-06-18 16:58:13,711 - mmseg - INFO - Iter [42650/80000] lr: 1.868e-05, eta: 15:20:45, time: 1.318, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2247, decode.acc_seg: 90.4031, aux.loss_ce: 0.0934, aux.acc_seg: 90.1649, loss: 0.3181 +2024-06-18 16:59:19,708 - mmseg - INFO - Iter [42700/80000] lr: 1.865e-05, eta: 15:19:24, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2151, decode.acc_seg: 91.0165, aux.loss_ce: 0.0899, aux.acc_seg: 90.6435, loss: 0.3050 +2024-06-18 17:00:25,897 - mmseg - INFO - Iter [42750/80000] lr: 1.863e-05, eta: 15:18:03, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2190, decode.acc_seg: 90.7274, aux.loss_ce: 0.0911, aux.acc_seg: 90.3877, loss: 0.3101 +2024-06-18 17:01:32,306 - mmseg - INFO - Iter [42800/80000] lr: 1.860e-05, eta: 15:16:43, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2162, decode.acc_seg: 90.9565, aux.loss_ce: 0.0898, aux.acc_seg: 90.6876, loss: 0.3061 +2024-06-18 17:02:38,607 - mmseg - INFO - Iter [42850/80000] lr: 1.858e-05, eta: 15:15:22, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2264, decode.acc_seg: 90.3050, aux.loss_ce: 0.0941, aux.acc_seg: 89.9505, loss: 0.3204 +2024-06-18 17:03:44,861 - mmseg - INFO - Iter [42900/80000] lr: 1.855e-05, eta: 15:14:02, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2009, decode.acc_seg: 91.3983, aux.loss_ce: 0.0844, aux.acc_seg: 91.0688, loss: 0.2854 +2024-06-18 17:04:53,266 - mmseg - INFO - Iter [42950/80000] lr: 1.853e-05, eta: 15:12:43, time: 1.368, data_time: 0.054, memory: 70498, decode.loss_ce: 0.2055, decode.acc_seg: 91.3137, aux.loss_ce: 0.0855, aux.acc_seg: 90.9995, loss: 0.2909 +2024-06-18 17:05:59,816 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 17:05:59,816 - mmseg - INFO - Iter [43000/80000] lr: 1.850e-05, eta: 15:11:23, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2018, decode.acc_seg: 91.4838, aux.loss_ce: 0.0849, aux.acc_seg: 91.1252, loss: 0.2867 +2024-06-18 17:07:37,701 - mmseg - INFO - per class results: +2024-06-18 17:07:37,707 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.72 | 88.94 | +| building | 85.49 | 93.62 | +| sky | 94.89 | 97.44 | +| floor | 85.04 | 90.86 | +| tree | 77.75 | 90.27 | +| ceiling | 86.96 | 93.65 | +| road | 86.38 | 91.47 | +| bed | 92.33 | 97.04 | +| windowpane | 65.94 | 81.65 | +| grass | 65.84 | 80.57 | +| cabinet | 65.13 | 75.84 | +| sidewalk | 73.1 | 86.83 | +| person | 85.7 | 92.45 | +| earth | 33.9 | 43.37 | +| door | 58.74 | 77.6 | +| table | 67.03 | 79.96 | +| mountain | 59.92 | 75.83 | +| plant | 56.13 | 66.51 | +| curtain | 77.4 | 88.34 | +| chair | 66.16 | 75.9 | +| car | 86.84 | 93.93 | +| water | 57.04 | 65.12 | +| painting | 79.28 | 91.42 | +| sofa | 80.7 | 92.94 | +| shelf | 47.0 | 65.58 | +| house | 59.65 | 70.93 | +| sea | 64.98 | 84.14 | +| mirror | 77.7 | 82.71 | +| rug | 68.48 | 83.8 | +| field | 30.79 | 59.45 | +| armchair | 58.2 | 76.12 | +| seat | 66.47 | 87.51 | +| fence | 50.12 | 64.63 | +| desk | 56.39 | 78.04 | +| rock | 52.2 | 77.39 | +| wardrobe | 55.4 | 77.38 | +| lamp | 71.3 | 83.77 | +| bathtub | 84.27 | 87.05 | +| railing | 41.37 | 58.19 | +| cushion | 70.06 | 80.81 | +| base | 40.18 | 65.37 | +| box | 37.26 | 48.02 | +| column | 58.04 | 74.15 | +| signboard | 42.07 | 57.77 | +| chest of drawers | 49.19 | 69.11 | +| counter | 40.2 | 55.72 | +| sand | 51.25 | 78.1 | +| sink | 74.97 | 85.32 | +| skyscraper | 48.88 | 62.92 | +| fireplace | 75.34 | 92.23 | +| refrigerator | 82.25 | 92.7 | +| grandstand | 49.43 | 88.46 | +| path | 26.85 | 41.68 | +| stairs | 23.02 | 27.92 | +| runway | 75.25 | 97.14 | +| case | 54.09 | 74.94 | +| pool table | 94.55 | 98.51 | +| pillow | 69.89 | 80.98 | +| screen door | 69.02 | 72.1 | +| stairway | 42.75 | 67.54 | +| river | 12.88 | 39.9 | +| bridge | 76.41 | 91.04 | +| bookcase | 40.75 | 56.63 | +| blind | 44.19 | 50.42 | +| coffee table | 64.38 | 88.3 | +| toilet | 88.89 | 93.18 | +| flower | 43.78 | 54.12 | +| book | 51.88 | 81.27 | +| hill | 8.26 | 18.28 | +| bench | 49.31 | 56.23 | +| countertop | 63.76 | 82.5 | +| stove | 85.91 | 92.66 | +| palm | 56.21 | 80.22 | +| kitchen island | 46.36 | 88.19 | +| computer | 78.53 | 93.12 | +| swivel chair | 54.65 | 79.33 | +| boat | 62.27 | 87.26 | +| bar | 59.6 | 76.44 | +| arcade machine | 74.82 | 79.68 | +| hovel | 44.69 | 49.21 | +| bus | 91.9 | 97.32 | +| towel | 69.95 | 84.34 | +| light | 60.63 | 71.59 | +| truck | 44.73 | 58.59 | +| tower | 17.83 | 25.93 | +| chandelier | 70.91 | 83.47 | +| awning | 39.82 | 52.13 | +| streetlight | 34.06 | 45.71 | +| booth | 50.81 | 74.91 | +| television receiver | 77.59 | 87.14 | +| airplane | 71.84 | 76.12 | +| dirt track | 11.89 | 59.29 | +| apparel | 46.16 | 56.41 | +| pole | 27.01 | 38.99 | +| land | 3.61 | 6.23 | +| bannister | 19.21 | 25.91 | +| escalator | 57.18 | 76.78 | +| ottoman | 53.3 | 75.03 | +| bottle | 44.56 | 66.03 | +| buffet | 56.36 | 64.58 | +| poster | 35.75 | 49.34 | +| stage | 22.25 | 47.59 | +| van | 45.1 | 62.72 | +| ship | 80.68 | 84.68 | +| fountain | 25.56 | 26.02 | +| conveyer belt | 77.44 | 92.52 | +| canopy | 50.93 | 73.03 | +| washer | 74.7 | 77.63 | +| plaything | 41.5 | 51.47 | +| swimming pool | 57.81 | 92.28 | +| stool | 53.41 | 65.52 | +| barrel | 56.57 | 64.55 | +| basket | 39.41 | 55.22 | +| waterfall | 77.61 | 92.5 | +| tent | 94.77 | 97.05 | +| bag | 16.74 | 19.15 | +| minibike | 74.72 | 84.33 | +| cradle | 83.06 | 97.44 | +| oven | 52.72 | 70.6 | +| ball | 57.04 | 72.54 | +| food | 58.19 | 68.65 | +| step | 7.63 | 8.94 | +| tank | 60.26 | 67.2 | +| trade name | 25.09 | 27.87 | +| microwave | 87.8 | 95.44 | +| pot | 59.93 | 72.7 | +| animal | 66.18 | 67.07 | +| bicycle | 58.66 | 75.0 | +| lake | 2.03 | 3.67 | +| dishwasher | 69.21 | 82.6 | +| screen | 60.52 | 91.69 | +| blanket | 26.05 | 29.0 | +| sculpture | 70.57 | 86.52 | +| hood | 62.57 | 73.13 | +| sconce | 53.85 | 61.07 | +| vase | 46.95 | 63.97 | +| traffic light | 33.06 | 65.57 | +| tray | 13.13 | 17.59 | +| ashcan | 42.54 | 66.97 | +| fan | 65.95 | 79.05 | +| pier | 32.12 | 54.25 | +| crt screen | 14.56 | 17.01 | +| plate | 58.67 | 75.37 | +| monitor | 69.79 | 81.15 | +| bulletin board | 54.18 | 69.7 | +| shower | 2.78 | 2.81 | +| radiator | 65.11 | 73.58 | +| glass | 17.83 | 19.18 | +| clock | 37.43 | 45.27 | +| flag | 71.35 | 77.82 | ++---------------------+-------+-------+ +2024-06-18 17:07:37,707 - mmseg - INFO - Summary: +2024-06-18 17:07:37,707 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.64 | 55.98 | 69.47 | ++-------+-------+-------+ +2024-06-18 17:07:37,708 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 17:07:37,708 - mmseg - INFO - Iter(val) [250] aAcc: 0.8564, mIoU: 0.5598, mAcc: 0.6947, IoU.wall: 0.8172, IoU.building: 0.8549, IoU.sky: 0.9489, IoU.floor: 0.8504, IoU.tree: 0.7775, IoU.ceiling: 0.8696, IoU.road: 0.8638, IoU.bed : 0.9233, IoU.windowpane: 0.6594, IoU.grass: 0.6584, IoU.cabinet: 0.6513, IoU.sidewalk: 0.7310, IoU.person: 0.8570, IoU.earth: 0.3390, IoU.door: 0.5874, IoU.table: 0.6703, IoU.mountain: 0.5992, IoU.plant: 0.5613, IoU.curtain: 0.7740, IoU.chair: 0.6616, IoU.car: 0.8684, IoU.water: 0.5704, IoU.painting: 0.7928, IoU.sofa: 0.8070, IoU.shelf: 0.4700, IoU.house: 0.5965, IoU.sea: 0.6498, IoU.mirror: 0.7770, IoU.rug: 0.6848, IoU.field: 0.3079, IoU.armchair: 0.5820, IoU.seat: 0.6647, IoU.fence: 0.5012, IoU.desk: 0.5639, IoU.rock: 0.5220, IoU.wardrobe: 0.5540, IoU.lamp: 0.7130, IoU.bathtub: 0.8427, IoU.railing: 0.4137, IoU.cushion: 0.7006, IoU.base: 0.4018, IoU.box: 0.3726, IoU.column: 0.5804, IoU.signboard: 0.4207, IoU.chest of drawers: 0.4919, IoU.counter: 0.4020, IoU.sand: 0.5125, IoU.sink: 0.7497, IoU.skyscraper: 0.4888, IoU.fireplace: 0.7534, IoU.refrigerator: 0.8225, IoU.grandstand: 0.4943, IoU.path: 0.2685, IoU.stairs: 0.2302, IoU.runway: 0.7525, IoU.case: 0.5409, IoU.pool table: 0.9455, IoU.pillow: 0.6989, IoU.screen door: 0.6902, IoU.stairway: 0.4275, IoU.river: 0.1288, IoU.bridge: 0.7641, IoU.bookcase: 0.4075, IoU.blind: 0.4419, IoU.coffee table: 0.6438, IoU.toilet: 0.8889, IoU.flower: 0.4378, IoU.book: 0.5188, IoU.hill: 0.0826, IoU.bench: 0.4931, IoU.countertop: 0.6376, IoU.stove: 0.8591, IoU.palm: 0.5621, IoU.kitchen island: 0.4636, IoU.computer: 0.7853, IoU.swivel chair: 0.5465, IoU.boat: 0.6227, IoU.bar: 0.5960, IoU.arcade machine: 0.7482, IoU.hovel: 0.4469, IoU.bus: 0.9190, IoU.towel: 0.6995, IoU.light: 0.6063, IoU.truck: 0.4473, IoU.tower: 0.1783, IoU.chandelier: 0.7091, IoU.awning: 0.3982, IoU.streetlight: 0.3406, IoU.booth: 0.5081, IoU.television receiver: 0.7759, IoU.airplane: 0.7184, IoU.dirt track: 0.1189, IoU.apparel: 0.4616, IoU.pole: 0.2701, IoU.land: 0.0361, IoU.bannister: 0.1921, IoU.escalator: 0.5718, IoU.ottoman: 0.5330, IoU.bottle: 0.4456, IoU.buffet: 0.5636, IoU.poster: 0.3575, IoU.stage: 0.2225, IoU.van: 0.4510, IoU.ship: 0.8068, IoU.fountain: 0.2556, IoU.conveyer belt: 0.7744, IoU.canopy: 0.5093, IoU.washer: 0.7470, IoU.plaything: 0.4150, IoU.swimming pool: 0.5781, IoU.stool: 0.5341, IoU.barrel: 0.5657, IoU.basket: 0.3941, IoU.waterfall: 0.7761, IoU.tent: 0.9477, IoU.bag: 0.1674, IoU.minibike: 0.7472, IoU.cradle: 0.8306, IoU.oven: 0.5272, IoU.ball: 0.5704, IoU.food: 0.5819, IoU.step: 0.0763, IoU.tank: 0.6026, IoU.trade name: 0.2509, IoU.microwave: 0.8780, IoU.pot: 0.5993, IoU.animal: 0.6618, IoU.bicycle: 0.5866, IoU.lake: 0.0203, IoU.dishwasher: 0.6921, IoU.screen: 0.6052, IoU.blanket: 0.2605, IoU.sculpture: 0.7057, IoU.hood: 0.6257, IoU.sconce: 0.5385, IoU.vase: 0.4695, IoU.traffic light: 0.3306, IoU.tray: 0.1313, IoU.ashcan: 0.4254, IoU.fan: 0.6595, IoU.pier: 0.3212, IoU.crt screen: 0.1456, IoU.plate: 0.5867, IoU.monitor: 0.6979, IoU.bulletin board: 0.5418, IoU.shower: 0.0278, IoU.radiator: 0.6511, IoU.glass: 0.1783, IoU.clock: 0.3743, IoU.flag: 0.7135, Acc.wall: 0.8894, Acc.building: 0.9362, Acc.sky: 0.9744, Acc.floor: 0.9086, Acc.tree: 0.9027, Acc.ceiling: 0.9365, Acc.road: 0.9147, Acc.bed : 0.9704, Acc.windowpane: 0.8165, Acc.grass: 0.8057, Acc.cabinet: 0.7584, Acc.sidewalk: 0.8683, Acc.person: 0.9245, Acc.earth: 0.4337, Acc.door: 0.7760, Acc.table: 0.7996, Acc.mountain: 0.7583, Acc.plant: 0.6651, Acc.curtain: 0.8834, Acc.chair: 0.7590, Acc.car: 0.9393, Acc.water: 0.6512, Acc.painting: 0.9142, Acc.sofa: 0.9294, Acc.shelf: 0.6558, Acc.house: 0.7093, Acc.sea: 0.8414, Acc.mirror: 0.8271, Acc.rug: 0.8380, Acc.field: 0.5945, Acc.armchair: 0.7612, Acc.seat: 0.8751, Acc.fence: 0.6463, Acc.desk: 0.7804, Acc.rock: 0.7739, Acc.wardrobe: 0.7738, Acc.lamp: 0.8377, Acc.bathtub: 0.8705, Acc.railing: 0.5819, Acc.cushion: 0.8081, Acc.base: 0.6537, Acc.box: 0.4802, Acc.column: 0.7415, Acc.signboard: 0.5777, Acc.chest of drawers: 0.6911, Acc.counter: 0.5572, Acc.sand: 0.7810, Acc.sink: 0.8532, Acc.skyscraper: 0.6292, Acc.fireplace: 0.9223, Acc.refrigerator: 0.9270, Acc.grandstand: 0.8846, Acc.path: 0.4168, Acc.stairs: 0.2792, Acc.runway: 0.9714, Acc.case: 0.7494, Acc.pool table: 0.9851, Acc.pillow: 0.8098, Acc.screen door: 0.7210, Acc.stairway: 0.6754, Acc.river: 0.3990, Acc.bridge: 0.9104, Acc.bookcase: 0.5663, Acc.blind: 0.5042, Acc.coffee table: 0.8830, Acc.toilet: 0.9318, Acc.flower: 0.5412, Acc.book: 0.8127, Acc.hill: 0.1828, Acc.bench: 0.5623, Acc.countertop: 0.8250, Acc.stove: 0.9266, Acc.palm: 0.8022, Acc.kitchen island: 0.8819, Acc.computer: 0.9312, Acc.swivel chair: 0.7933, Acc.boat: 0.8726, Acc.bar: 0.7644, Acc.arcade machine: 0.7968, Acc.hovel: 0.4921, Acc.bus: 0.9732, Acc.towel: 0.8434, Acc.light: 0.7159, Acc.truck: 0.5859, Acc.tower: 0.2593, Acc.chandelier: 0.8347, Acc.awning: 0.5213, Acc.streetlight: 0.4571, Acc.booth: 0.7491, Acc.television receiver: 0.8714, Acc.airplane: 0.7612, Acc.dirt track: 0.5929, Acc.apparel: 0.5641, Acc.pole: 0.3899, Acc.land: 0.0623, Acc.bannister: 0.2591, Acc.escalator: 0.7678, Acc.ottoman: 0.7503, Acc.bottle: 0.6603, Acc.buffet: 0.6458, Acc.poster: 0.4934, Acc.stage: 0.4759, Acc.van: 0.6272, Acc.ship: 0.8468, Acc.fountain: 0.2602, Acc.conveyer belt: 0.9252, Acc.canopy: 0.7303, Acc.washer: 0.7763, Acc.plaything: 0.5147, Acc.swimming pool: 0.9228, Acc.stool: 0.6552, Acc.barrel: 0.6455, Acc.basket: 0.5522, Acc.waterfall: 0.9250, Acc.tent: 0.9705, Acc.bag: 0.1915, Acc.minibike: 0.8433, Acc.cradle: 0.9744, Acc.oven: 0.7060, Acc.ball: 0.7254, Acc.food: 0.6865, Acc.step: 0.0894, Acc.tank: 0.6720, Acc.trade name: 0.2787, Acc.microwave: 0.9544, Acc.pot: 0.7270, Acc.animal: 0.6707, Acc.bicycle: 0.7500, Acc.lake: 0.0367, Acc.dishwasher: 0.8260, Acc.screen: 0.9169, Acc.blanket: 0.2900, Acc.sculpture: 0.8652, Acc.hood: 0.7313, Acc.sconce: 0.6107, Acc.vase: 0.6397, Acc.traffic light: 0.6557, Acc.tray: 0.1759, Acc.ashcan: 0.6697, Acc.fan: 0.7905, Acc.pier: 0.5425, Acc.crt screen: 0.1701, Acc.plate: 0.7537, Acc.monitor: 0.8115, Acc.bulletin board: 0.6970, Acc.shower: 0.0281, Acc.radiator: 0.7358, Acc.glass: 0.1918, Acc.clock: 0.4527, Acc.flag: 0.7782 +2024-06-18 17:08:44,603 - mmseg - INFO - Iter [43050/80000] lr: 1.848e-05, eta: 15:11:27, time: 3.296, data_time: 1.975, memory: 70498, decode.loss_ce: 0.2022, decode.acc_seg: 91.5003, aux.loss_ce: 0.0841, aux.acc_seg: 91.2121, loss: 0.2862 +2024-06-18 17:09:50,796 - mmseg - INFO - Iter [43100/80000] lr: 1.845e-05, eta: 15:10:06, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2081, decode.acc_seg: 91.3447, aux.loss_ce: 0.0868, aux.acc_seg: 90.9621, loss: 0.2950 +2024-06-18 17:10:56,794 - mmseg - INFO - Iter [43150/80000] lr: 1.843e-05, eta: 15:08:45, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2128, decode.acc_seg: 91.6082, aux.loss_ce: 0.0875, aux.acc_seg: 91.4065, loss: 0.3003 +2024-06-18 17:12:03,140 - mmseg - INFO - Iter [43200/80000] lr: 1.840e-05, eta: 15:07:25, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1998, decode.acc_seg: 91.2389, aux.loss_ce: 0.0840, aux.acc_seg: 91.0026, loss: 0.2838 +2024-06-18 17:13:09,347 - mmseg - INFO - Iter [43250/80000] lr: 1.838e-05, eta: 15:06:04, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1996, decode.acc_seg: 91.6198, aux.loss_ce: 0.0834, aux.acc_seg: 91.2802, loss: 0.2830 +2024-06-18 17:14:15,534 - mmseg - INFO - Iter [43300/80000] lr: 1.835e-05, eta: 15:04:44, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2079, decode.acc_seg: 91.1602, aux.loss_ce: 0.0864, aux.acc_seg: 90.8249, loss: 0.2943 +2024-06-18 17:15:21,905 - mmseg - INFO - Iter [43350/80000] lr: 1.833e-05, eta: 15:03:23, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2137, decode.acc_seg: 91.0691, aux.loss_ce: 0.0894, aux.acc_seg: 90.7163, loss: 0.3031 +2024-06-18 17:16:28,296 - mmseg - INFO - Iter [43400/80000] lr: 1.830e-05, eta: 15:02:03, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2142, decode.acc_seg: 91.1775, aux.loss_ce: 0.0890, aux.acc_seg: 90.9094, loss: 0.3032 +2024-06-18 17:17:34,641 - mmseg - INFO - Iter [43450/80000] lr: 1.828e-05, eta: 15:00:43, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2018, decode.acc_seg: 91.2110, aux.loss_ce: 0.0841, aux.acc_seg: 90.8905, loss: 0.2859 +2024-06-18 17:18:40,980 - mmseg - INFO - Iter [43500/80000] lr: 1.825e-05, eta: 14:59:22, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2098, decode.acc_seg: 91.1364, aux.loss_ce: 0.0877, aux.acc_seg: 90.8242, loss: 0.2975 +2024-06-18 17:19:47,264 - mmseg - INFO - Iter [43550/80000] lr: 1.823e-05, eta: 14:58:02, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1926, decode.acc_seg: 92.0728, aux.loss_ce: 0.0804, aux.acc_seg: 91.7296, loss: 0.2730 +2024-06-18 17:20:53,416 - mmseg - INFO - Iter [43600/80000] lr: 1.820e-05, eta: 14:56:42, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1961, decode.acc_seg: 91.6597, aux.loss_ce: 0.0823, aux.acc_seg: 91.3351, loss: 0.2784 +2024-06-18 17:21:59,720 - mmseg - INFO - Iter [43650/80000] lr: 1.818e-05, eta: 14:55:21, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2086, decode.acc_seg: 91.4062, aux.loss_ce: 0.0868, aux.acc_seg: 91.0535, loss: 0.2954 +2024-06-18 17:23:06,129 - mmseg - INFO - Iter [43700/80000] lr: 1.815e-05, eta: 14:54:01, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2172, decode.acc_seg: 91.0692, aux.loss_ce: 0.0897, aux.acc_seg: 90.7533, loss: 0.3069 +2024-06-18 17:24:12,425 - mmseg - INFO - Iter [43750/80000] lr: 1.813e-05, eta: 14:52:41, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2102, decode.acc_seg: 91.0666, aux.loss_ce: 0.0879, aux.acc_seg: 90.7187, loss: 0.2981 +2024-06-18 17:25:18,655 - mmseg - INFO - Iter [43800/80000] lr: 1.810e-05, eta: 14:51:21, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2069, decode.acc_seg: 91.2932, aux.loss_ce: 0.0865, aux.acc_seg: 90.8839, loss: 0.2934 +2024-06-18 17:26:24,891 - mmseg - INFO - Iter [43850/80000] lr: 1.808e-05, eta: 14:50:01, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2033, decode.acc_seg: 91.6257, aux.loss_ce: 0.0849, aux.acc_seg: 91.2196, loss: 0.2882 +2024-06-18 17:27:31,217 - mmseg - INFO - Iter [43900/80000] lr: 1.805e-05, eta: 14:48:41, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2061, decode.acc_seg: 91.0048, aux.loss_ce: 0.0863, aux.acc_seg: 90.6868, loss: 0.2924 +2024-06-18 17:28:37,434 - mmseg - INFO - Iter [43950/80000] lr: 1.803e-05, eta: 14:47:21, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2073, decode.acc_seg: 91.2058, aux.loss_ce: 0.0859, aux.acc_seg: 90.8438, loss: 0.2932 +2024-06-18 17:29:43,875 - mmseg - INFO - Saving checkpoint at 44000 iterations +2024-06-18 17:31:29,344 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 17:31:29,344 - mmseg - INFO - Iter [44000/80000] lr: 1.800e-05, eta: 14:47:27, time: 3.438, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1976, decode.acc_seg: 91.5017, aux.loss_ce: 0.0832, aux.acc_seg: 91.1199, loss: 0.2808 +2024-06-18 17:33:08,857 - mmseg - INFO - per class results: +2024-06-18 17:33:08,863 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.71 | 89.47 | +| building | 85.02 | 92.92 | +| sky | 94.86 | 96.97 | +| floor | 84.47 | 91.62 | +| tree | 77.38 | 90.65 | +| ceiling | 86.72 | 92.78 | +| road | 86.57 | 90.87 | +| bed | 92.22 | 97.17 | +| windowpane | 65.96 | 83.14 | +| grass | 64.82 | 76.06 | +| cabinet | 65.82 | 78.51 | +| sidewalk | 71.65 | 86.0 | +| person | 84.86 | 91.5 | +| earth | 36.01 | 51.05 | +| door | 57.54 | 71.49 | +| table | 69.84 | 81.65 | +| mountain | 60.79 | 74.42 | +| plant | 57.61 | 69.98 | +| curtain | 78.77 | 87.46 | +| chair | 66.94 | 78.03 | +| car | 86.71 | 93.27 | +| water | 60.33 | 71.91 | +| painting | 79.14 | 91.57 | +| sofa | 81.79 | 90.88 | +| shelf | 48.77 | 65.06 | +| house | 62.26 | 78.99 | +| sea | 65.72 | 82.55 | +| mirror | 77.66 | 84.03 | +| rug | 67.86 | 79.55 | +| field | 33.03 | 57.05 | +| armchair | 60.62 | 76.66 | +| seat | 66.66 | 86.94 | +| fence | 51.23 | 73.46 | +| desk | 58.83 | 77.66 | +| rock | 53.25 | 82.75 | +| wardrobe | 51.15 | 66.34 | +| lamp | 72.93 | 83.17 | +| bathtub | 83.94 | 86.58 | +| railing | 39.69 | 58.29 | +| cushion | 70.4 | 81.71 | +| base | 45.4 | 63.39 | +| box | 38.17 | 51.27 | +| column | 55.0 | 66.02 | +| signboard | 40.18 | 56.18 | +| chest of drawers | 45.02 | 66.61 | +| counter | 36.4 | 46.04 | +| sand | 53.71 | 78.12 | +| sink | 77.64 | 84.08 | +| skyscraper | 49.57 | 62.24 | +| fireplace | 74.45 | 93.51 | +| refrigerator | 82.19 | 92.72 | +| grandstand | 46.36 | 84.04 | +| path | 31.56 | 50.15 | +| stairs | 23.14 | 29.49 | +| runway | 72.27 | 94.81 | +| case | 58.2 | 77.01 | +| pool table | 94.74 | 98.13 | +| pillow | 66.26 | 74.97 | +| screen door | 76.86 | 79.55 | +| stairway | 44.23 | 63.64 | +| river | 13.44 | 32.09 | +| bridge | 75.43 | 88.89 | +| bookcase | 40.6 | 62.0 | +| blind | 42.59 | 48.74 | +| coffee table | 68.2 | 87.84 | +| toilet | 88.82 | 92.15 | +| flower | 45.47 | 58.62 | +| book | 52.63 | 71.1 | +| hill | 12.68 | 16.96 | +| bench | 49.48 | 60.7 | +| countertop | 62.63 | 83.23 | +| stove | 86.99 | 92.77 | +| palm | 56.65 | 77.19 | +| kitchen island | 47.99 | 85.56 | +| computer | 80.84 | 91.5 | +| swivel chair | 52.27 | 75.34 | +| boat | 53.15 | 86.27 | +| bar | 56.15 | 75.81 | +| arcade machine | 70.79 | 74.82 | +| hovel | 31.05 | 49.98 | +| bus | 91.32 | 97.11 | +| towel | 71.5 | 77.87 | +| light | 60.55 | 70.32 | +| truck | 43.07 | 59.94 | +| tower | 7.5 | 12.1 | +| chandelier | 71.9 | 87.37 | +| awning | 46.44 | 73.57 | +| streetlight | 33.72 | 44.16 | +| booth | 36.63 | 74.35 | +| television receiver | 76.92 | 86.62 | +| airplane | 72.73 | 78.0 | +| dirt track | 19.37 | 41.53 | +| apparel | 53.62 | 69.17 | +| pole | 25.68 | 35.83 | +| land | 3.67 | 6.49 | +| bannister | 15.58 | 22.22 | +| escalator | 58.66 | 78.28 | +| ottoman | 50.68 | 71.15 | +| bottle | 43.12 | 54.05 | +| buffet | 50.75 | 56.65 | +| poster | 37.6 | 53.47 | +| stage | 23.61 | 45.72 | +| van | 42.13 | 59.44 | +| ship | 72.06 | 73.05 | +| fountain | 21.72 | 22.05 | +| conveyer belt | 75.11 | 92.57 | +| canopy | 53.45 | 80.15 | +| washer | 86.12 | 89.5 | +| plaything | 38.37 | 50.56 | +| swimming pool | 58.05 | 85.47 | +| stool | 52.85 | 66.62 | +| barrel | 39.22 | 64.74 | +| basket | 35.6 | 48.5 | +| waterfall | 73.3 | 87.45 | +| tent | 90.34 | 98.81 | +| bag | 19.77 | 24.92 | +| minibike | 74.11 | 87.15 | +| cradle | 81.64 | 97.6 | +| oven | 60.64 | 72.08 | +| ball | 56.02 | 71.74 | +| food | 64.22 | 86.99 | +| step | 13.68 | 17.21 | +| tank | 66.58 | 77.89 | +| trade name | 33.96 | 40.26 | +| microwave | 88.86 | 94.98 | +| pot | 54.82 | 66.03 | +| animal | 54.49 | 56.71 | +| bicycle | 58.2 | 74.1 | +| lake | 50.74 | 63.75 | +| dishwasher | 69.72 | 79.81 | +| screen | 59.03 | 94.9 | +| blanket | 29.24 | 32.16 | +| sculpture | 72.54 | 80.42 | +| hood | 59.64 | 70.4 | +| sconce | 55.89 | 65.81 | +| vase | 48.64 | 58.05 | +| traffic light | 33.88 | 61.94 | +| tray | 13.43 | 18.74 | +| ashcan | 45.78 | 59.18 | +| fan | 66.31 | 79.21 | +| pier | 33.47 | 52.62 | +| crt screen | 14.13 | 16.72 | +| plate | 57.67 | 64.62 | +| monitor | 65.26 | 81.0 | +| bulletin board | 48.14 | 49.3 | +| shower | 1.1 | 1.12 | +| radiator | 63.13 | 75.89 | +| glass | 16.68 | 17.36 | +| clock | 34.79 | 42.62 | +| flag | 71.31 | 77.49 | ++---------------------+-------+-------+ +2024-06-18 17:33:08,863 - mmseg - INFO - Summary: +2024-06-18 17:33:08,863 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.72 | 56.07 | 69.26 | ++-------+-------+-------+ +2024-06-18 17:33:08,864 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 17:33:08,864 - mmseg - INFO - Iter(val) [250] aAcc: 0.8572, mIoU: 0.5607, mAcc: 0.6926, IoU.wall: 0.8171, IoU.building: 0.8502, IoU.sky: 0.9486, IoU.floor: 0.8447, IoU.tree: 0.7738, IoU.ceiling: 0.8672, IoU.road: 0.8657, IoU.bed : 0.9222, IoU.windowpane: 0.6596, IoU.grass: 0.6482, IoU.cabinet: 0.6582, IoU.sidewalk: 0.7165, IoU.person: 0.8486, IoU.earth: 0.3601, IoU.door: 0.5754, IoU.table: 0.6984, IoU.mountain: 0.6079, IoU.plant: 0.5761, IoU.curtain: 0.7877, IoU.chair: 0.6694, IoU.car: 0.8671, IoU.water: 0.6033, IoU.painting: 0.7914, IoU.sofa: 0.8179, IoU.shelf: 0.4877, IoU.house: 0.6226, IoU.sea: 0.6572, IoU.mirror: 0.7766, IoU.rug: 0.6786, IoU.field: 0.3303, IoU.armchair: 0.6062, IoU.seat: 0.6666, IoU.fence: 0.5123, IoU.desk: 0.5883, IoU.rock: 0.5325, IoU.wardrobe: 0.5115, IoU.lamp: 0.7293, IoU.bathtub: 0.8394, IoU.railing: 0.3969, IoU.cushion: 0.7040, IoU.base: 0.4540, IoU.box: 0.3817, IoU.column: 0.5500, IoU.signboard: 0.4018, IoU.chest of drawers: 0.4502, IoU.counter: 0.3640, IoU.sand: 0.5371, IoU.sink: 0.7764, IoU.skyscraper: 0.4957, IoU.fireplace: 0.7445, IoU.refrigerator: 0.8219, IoU.grandstand: 0.4636, IoU.path: 0.3156, IoU.stairs: 0.2314, IoU.runway: 0.7227, IoU.case: 0.5820, IoU.pool table: 0.9474, IoU.pillow: 0.6626, IoU.screen door: 0.7686, IoU.stairway: 0.4423, IoU.river: 0.1344, IoU.bridge: 0.7543, IoU.bookcase: 0.4060, IoU.blind: 0.4259, IoU.coffee table: 0.6820, IoU.toilet: 0.8882, IoU.flower: 0.4547, IoU.book: 0.5263, IoU.hill: 0.1268, IoU.bench: 0.4948, IoU.countertop: 0.6263, IoU.stove: 0.8699, IoU.palm: 0.5665, IoU.kitchen island: 0.4799, IoU.computer: 0.8084, IoU.swivel chair: 0.5227, IoU.boat: 0.5315, IoU.bar: 0.5615, IoU.arcade machine: 0.7079, IoU.hovel: 0.3105, IoU.bus: 0.9132, IoU.towel: 0.7150, IoU.light: 0.6055, IoU.truck: 0.4307, IoU.tower: 0.0750, IoU.chandelier: 0.7190, IoU.awning: 0.4644, IoU.streetlight: 0.3372, IoU.booth: 0.3663, IoU.television receiver: 0.7692, IoU.airplane: 0.7273, IoU.dirt track: 0.1937, IoU.apparel: 0.5362, IoU.pole: 0.2568, IoU.land: 0.0367, IoU.bannister: 0.1558, IoU.escalator: 0.5866, IoU.ottoman: 0.5068, IoU.bottle: 0.4312, IoU.buffet: 0.5075, IoU.poster: 0.3760, IoU.stage: 0.2361, IoU.van: 0.4213, IoU.ship: 0.7206, IoU.fountain: 0.2172, IoU.conveyer belt: 0.7511, IoU.canopy: 0.5345, IoU.washer: 0.8612, IoU.plaything: 0.3837, IoU.swimming pool: 0.5805, IoU.stool: 0.5285, IoU.barrel: 0.3922, IoU.basket: 0.3560, IoU.waterfall: 0.7330, IoU.tent: 0.9034, IoU.bag: 0.1977, IoU.minibike: 0.7411, IoU.cradle: 0.8164, IoU.oven: 0.6064, IoU.ball: 0.5602, IoU.food: 0.6422, IoU.step: 0.1368, IoU.tank: 0.6658, IoU.trade name: 0.3396, IoU.microwave: 0.8886, IoU.pot: 0.5482, IoU.animal: 0.5449, IoU.bicycle: 0.5820, IoU.lake: 0.5074, IoU.dishwasher: 0.6972, IoU.screen: 0.5903, IoU.blanket: 0.2924, IoU.sculpture: 0.7254, IoU.hood: 0.5964, IoU.sconce: 0.5589, IoU.vase: 0.4864, IoU.traffic light: 0.3388, IoU.tray: 0.1343, IoU.ashcan: 0.4578, IoU.fan: 0.6631, IoU.pier: 0.3347, IoU.crt screen: 0.1413, IoU.plate: 0.5767, IoU.monitor: 0.6526, IoU.bulletin board: 0.4814, IoU.shower: 0.0110, IoU.radiator: 0.6313, IoU.glass: 0.1668, IoU.clock: 0.3479, IoU.flag: 0.7131, Acc.wall: 0.8947, Acc.building: 0.9292, Acc.sky: 0.9697, Acc.floor: 0.9162, Acc.tree: 0.9065, Acc.ceiling: 0.9278, Acc.road: 0.9087, Acc.bed : 0.9717, Acc.windowpane: 0.8314, Acc.grass: 0.7606, Acc.cabinet: 0.7851, Acc.sidewalk: 0.8600, Acc.person: 0.9150, Acc.earth: 0.5105, Acc.door: 0.7149, Acc.table: 0.8165, Acc.mountain: 0.7442, Acc.plant: 0.6998, Acc.curtain: 0.8746, Acc.chair: 0.7803, Acc.car: 0.9327, Acc.water: 0.7191, Acc.painting: 0.9157, Acc.sofa: 0.9088, Acc.shelf: 0.6506, Acc.house: 0.7899, Acc.sea: 0.8255, Acc.mirror: 0.8403, Acc.rug: 0.7955, Acc.field: 0.5705, Acc.armchair: 0.7666, Acc.seat: 0.8694, Acc.fence: 0.7346, Acc.desk: 0.7766, Acc.rock: 0.8275, Acc.wardrobe: 0.6634, Acc.lamp: 0.8317, Acc.bathtub: 0.8658, Acc.railing: 0.5829, Acc.cushion: 0.8171, Acc.base: 0.6339, Acc.box: 0.5127, Acc.column: 0.6602, Acc.signboard: 0.5618, Acc.chest of drawers: 0.6661, Acc.counter: 0.4604, Acc.sand: 0.7812, Acc.sink: 0.8408, Acc.skyscraper: 0.6224, Acc.fireplace: 0.9351, Acc.refrigerator: 0.9272, Acc.grandstand: 0.8404, Acc.path: 0.5015, Acc.stairs: 0.2949, Acc.runway: 0.9481, Acc.case: 0.7701, Acc.pool table: 0.9813, Acc.pillow: 0.7497, Acc.screen door: 0.7955, Acc.stairway: 0.6364, Acc.river: 0.3209, Acc.bridge: 0.8889, Acc.bookcase: 0.6200, Acc.blind: 0.4874, Acc.coffee table: 0.8784, Acc.toilet: 0.9215, Acc.flower: 0.5862, Acc.book: 0.7110, Acc.hill: 0.1696, Acc.bench: 0.6070, Acc.countertop: 0.8323, Acc.stove: 0.9277, Acc.palm: 0.7719, Acc.kitchen island: 0.8556, Acc.computer: 0.9150, Acc.swivel chair: 0.7534, Acc.boat: 0.8627, Acc.bar: 0.7581, Acc.arcade machine: 0.7482, Acc.hovel: 0.4998, Acc.bus: 0.9711, Acc.towel: 0.7787, Acc.light: 0.7032, Acc.truck: 0.5994, Acc.tower: 0.1210, Acc.chandelier: 0.8737, Acc.awning: 0.7357, Acc.streetlight: 0.4416, Acc.booth: 0.7435, Acc.television receiver: 0.8662, Acc.airplane: 0.7800, Acc.dirt track: 0.4153, Acc.apparel: 0.6917, Acc.pole: 0.3583, Acc.land: 0.0649, Acc.bannister: 0.2222, Acc.escalator: 0.7828, Acc.ottoman: 0.7115, Acc.bottle: 0.5405, Acc.buffet: 0.5665, Acc.poster: 0.5347, Acc.stage: 0.4572, Acc.van: 0.5944, Acc.ship: 0.7305, Acc.fountain: 0.2205, Acc.conveyer belt: 0.9257, Acc.canopy: 0.8015, Acc.washer: 0.8950, Acc.plaything: 0.5056, Acc.swimming pool: 0.8547, Acc.stool: 0.6662, Acc.barrel: 0.6474, Acc.basket: 0.4850, Acc.waterfall: 0.8745, Acc.tent: 0.9881, Acc.bag: 0.2492, Acc.minibike: 0.8715, Acc.cradle: 0.9760, Acc.oven: 0.7208, Acc.ball: 0.7174, Acc.food: 0.8699, Acc.step: 0.1721, Acc.tank: 0.7789, Acc.trade name: 0.4026, Acc.microwave: 0.9498, Acc.pot: 0.6603, Acc.animal: 0.5671, Acc.bicycle: 0.7410, Acc.lake: 0.6375, Acc.dishwasher: 0.7981, Acc.screen: 0.9490, Acc.blanket: 0.3216, Acc.sculpture: 0.8042, Acc.hood: 0.7040, Acc.sconce: 0.6581, Acc.vase: 0.5805, Acc.traffic light: 0.6194, Acc.tray: 0.1874, Acc.ashcan: 0.5918, Acc.fan: 0.7921, Acc.pier: 0.5262, Acc.crt screen: 0.1672, Acc.plate: 0.6462, Acc.monitor: 0.8100, Acc.bulletin board: 0.4930, Acc.shower: 0.0112, Acc.radiator: 0.7589, Acc.glass: 0.1736, Acc.clock: 0.4262, Acc.flag: 0.7749 +2024-06-18 17:34:15,610 - mmseg - INFO - Iter [44050/80000] lr: 1.798e-05, eta: 14:47:28, time: 3.325, data_time: 2.011, memory: 70498, decode.loss_ce: 0.2173, decode.acc_seg: 91.1396, aux.loss_ce: 0.0897, aux.acc_seg: 90.8481, loss: 0.3070 +2024-06-18 17:35:21,678 - mmseg - INFO - Iter [44100/80000] lr: 1.795e-05, eta: 14:46:08, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2152, decode.acc_seg: 91.4024, aux.loss_ce: 0.0895, aux.acc_seg: 91.0660, loss: 0.3047 +2024-06-18 17:36:28,032 - mmseg - INFO - Iter [44150/80000] lr: 1.793e-05, eta: 14:44:48, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2136, decode.acc_seg: 91.0705, aux.loss_ce: 0.0890, aux.acc_seg: 90.7774, loss: 0.3026 +2024-06-18 17:37:34,142 - mmseg - INFO - Iter [44200/80000] lr: 1.790e-05, eta: 14:43:27, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2252, decode.acc_seg: 90.9520, aux.loss_ce: 0.0941, aux.acc_seg: 90.5082, loss: 0.3193 +2024-06-18 17:38:42,884 - mmseg - INFO - Iter [44250/80000] lr: 1.788e-05, eta: 14:42:09, time: 1.375, data_time: 0.052, memory: 70498, decode.loss_ce: 0.1997, decode.acc_seg: 91.5791, aux.loss_ce: 0.0829, aux.acc_seg: 91.1742, loss: 0.2827 +2024-06-18 17:39:49,175 - mmseg - INFO - Iter [44300/80000] lr: 1.785e-05, eta: 14:40:48, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2026, decode.acc_seg: 91.2384, aux.loss_ce: 0.0850, aux.acc_seg: 90.7932, loss: 0.2876 +2024-06-18 17:40:55,254 - mmseg - INFO - Iter [44350/80000] lr: 1.783e-05, eta: 14:39:28, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2064, decode.acc_seg: 91.3431, aux.loss_ce: 0.0865, aux.acc_seg: 91.0012, loss: 0.2930 +2024-06-18 17:42:01,816 - mmseg - INFO - Iter [44400/80000] lr: 1.780e-05, eta: 14:38:08, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1989, decode.acc_seg: 91.6982, aux.loss_ce: 0.0836, aux.acc_seg: 91.2833, loss: 0.2825 +2024-06-18 17:43:08,032 - mmseg - INFO - Iter [44450/80000] lr: 1.778e-05, eta: 14:36:48, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1967, decode.acc_seg: 91.7675, aux.loss_ce: 0.0818, aux.acc_seg: 91.5060, loss: 0.2785 +2024-06-18 17:44:14,472 - mmseg - INFO - Iter [44500/80000] lr: 1.775e-05, eta: 14:35:28, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2028, decode.acc_seg: 91.4147, aux.loss_ce: 0.0852, aux.acc_seg: 90.9798, loss: 0.2880 +2024-06-18 17:45:20,662 - mmseg - INFO - Iter [44550/80000] lr: 1.773e-05, eta: 14:34:08, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2036, decode.acc_seg: 91.4155, aux.loss_ce: 0.0849, aux.acc_seg: 90.9890, loss: 0.2885 +2024-06-18 17:46:27,159 - mmseg - INFO - Iter [44600/80000] lr: 1.770e-05, eta: 14:32:48, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2034, decode.acc_seg: 91.3758, aux.loss_ce: 0.0851, aux.acc_seg: 90.9750, loss: 0.2885 +2024-06-18 17:47:33,530 - mmseg - INFO - Iter [44650/80000] lr: 1.768e-05, eta: 14:31:28, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1982, decode.acc_seg: 91.3979, aux.loss_ce: 0.0829, aux.acc_seg: 91.0365, loss: 0.2811 +2024-06-18 17:48:39,682 - mmseg - INFO - Iter [44700/80000] lr: 1.765e-05, eta: 14:30:08, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1992, decode.acc_seg: 91.4633, aux.loss_ce: 0.0833, aux.acc_seg: 91.0759, loss: 0.2825 +2024-06-18 17:49:46,039 - mmseg - INFO - Iter [44750/80000] lr: 1.763e-05, eta: 14:28:48, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1984, decode.acc_seg: 91.6060, aux.loss_ce: 0.0824, aux.acc_seg: 91.2835, loss: 0.2808 +2024-06-18 17:50:52,467 - mmseg - INFO - Iter [44800/80000] lr: 1.760e-05, eta: 14:27:28, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2001, decode.acc_seg: 91.3580, aux.loss_ce: 0.0836, aux.acc_seg: 90.9750, loss: 0.2836 +2024-06-18 17:51:58,727 - mmseg - INFO - Iter [44850/80000] lr: 1.758e-05, eta: 14:26:08, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2058, decode.acc_seg: 91.2854, aux.loss_ce: 0.0854, aux.acc_seg: 90.9236, loss: 0.2912 +2024-06-18 17:53:05,122 - mmseg - INFO - Iter [44900/80000] lr: 1.755e-05, eta: 14:24:48, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2070, decode.acc_seg: 91.2115, aux.loss_ce: 0.0862, aux.acc_seg: 90.8794, loss: 0.2932 +2024-06-18 17:54:11,635 - mmseg - INFO - Iter [44950/80000] lr: 1.753e-05, eta: 14:23:28, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2050, decode.acc_seg: 91.3313, aux.loss_ce: 0.0858, aux.acc_seg: 90.8975, loss: 0.2908 +2024-06-18 17:55:18,127 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 17:55:18,127 - mmseg - INFO - Iter [45000/80000] lr: 1.750e-05, eta: 14:22:09, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1910, decode.acc_seg: 92.0404, aux.loss_ce: 0.0801, aux.acc_seg: 91.6563, loss: 0.2712 +2024-06-18 17:56:54,430 - mmseg - INFO - per class results: +2024-06-18 17:56:54,436 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.63 | 89.02 | +| building | 84.32 | 92.44 | +| sky | 94.95 | 97.83 | +| floor | 84.14 | 90.24 | +| tree | 76.95 | 91.04 | +| ceiling | 86.75 | 92.56 | +| road | 87.3 | 92.42 | +| bed | 92.5 | 97.03 | +| windowpane | 65.39 | 82.99 | +| grass | 66.63 | 81.46 | +| cabinet | 66.34 | 78.57 | +| sidewalk | 72.22 | 84.05 | +| person | 85.68 | 93.63 | +| earth | 37.35 | 49.31 | +| door | 59.24 | 73.55 | +| table | 68.88 | 81.54 | +| mountain | 62.57 | 73.75 | +| plant | 55.08 | 66.67 | +| curtain | 79.17 | 89.14 | +| chair | 66.91 | 78.65 | +| car | 87.14 | 94.09 | +| water | 62.0 | 73.09 | +| painting | 77.84 | 90.34 | +| sofa | 78.61 | 84.93 | +| shelf | 52.08 | 72.78 | +| house | 59.34 | 78.21 | +| sea | 63.62 | 84.81 | +| mirror | 78.35 | 85.17 | +| rug | 68.32 | 83.63 | +| field | 33.09 | 60.29 | +| armchair | 58.32 | 78.37 | +| seat | 66.32 | 88.04 | +| fence | 53.75 | 67.81 | +| desk | 55.06 | 77.31 | +| rock | 54.37 | 79.64 | +| wardrobe | 55.11 | 72.07 | +| lamp | 72.5 | 83.41 | +| bathtub | 84.22 | 85.8 | +| railing | 41.34 | 62.3 | +| cushion | 70.97 | 82.54 | +| base | 44.57 | 64.43 | +| box | 34.44 | 42.18 | +| column | 54.01 | 67.2 | +| signboard | 41.14 | 54.57 | +| chest of drawers | 46.84 | 63.67 | +| counter | 41.92 | 51.29 | +| sand | 49.68 | 75.48 | +| sink | 76.25 | 84.94 | +| skyscraper | 48.2 | 61.2 | +| fireplace | 74.82 | 94.77 | +| refrigerator | 80.47 | 91.68 | +| grandstand | 50.72 | 80.98 | +| path | 31.6 | 39.32 | +| stairs | 23.91 | 29.49 | +| runway | 72.11 | 94.4 | +| case | 53.64 | 83.61 | +| pool table | 94.68 | 97.83 | +| pillow | 70.32 | 83.74 | +| screen door | 84.46 | 91.84 | +| stairway | 46.36 | 65.63 | +| river | 13.33 | 25.57 | +| bridge | 75.78 | 91.54 | +| bookcase | 48.01 | 64.16 | +| blind | 43.22 | 48.89 | +| coffee table | 63.62 | 89.8 | +| toilet | 88.81 | 93.04 | +| flower | 41.35 | 53.54 | +| book | 55.87 | 69.23 | +| hill | 7.55 | 13.51 | +| bench | 49.83 | 55.86 | +| countertop | 63.05 | 79.63 | +| stove | 87.04 | 92.42 | +| palm | 55.94 | 82.1 | +| kitchen island | 48.87 | 84.27 | +| computer | 77.16 | 92.26 | +| swivel chair | 50.75 | 80.39 | +| boat | 56.93 | 86.89 | +| bar | 60.36 | 76.62 | +| arcade machine | 77.83 | 83.42 | +| hovel | 45.09 | 49.86 | +| bus | 93.59 | 96.62 | +| towel | 75.15 | 84.23 | +| light | 59.3 | 65.86 | +| truck | 45.18 | 57.0 | +| tower | 17.06 | 33.36 | +| chandelier | 72.57 | 87.35 | +| awning | 44.69 | 67.5 | +| streetlight | 32.73 | 43.4 | +| booth | 59.73 | 67.29 | +| television receiver | 76.6 | 85.3 | +| airplane | 65.74 | 70.82 | +| dirt track | 23.02 | 44.86 | +| apparel | 48.74 | 61.2 | +| pole | 24.53 | 31.98 | +| land | 2.74 | 4.18 | +| bannister | 17.96 | 27.3 | +| escalator | 57.66 | 78.46 | +| ottoman | 48.46 | 64.81 | +| bottle | 42.28 | 56.96 | +| buffet | 56.38 | 65.46 | +| poster | 33.96 | 53.89 | +| stage | 22.97 | 43.42 | +| van | 44.18 | 59.68 | +| ship | 73.1 | 98.6 | +| fountain | 21.61 | 22.01 | +| conveyer belt | 82.6 | 92.51 | +| canopy | 60.53 | 75.9 | +| washer | 79.59 | 82.53 | +| plaything | 38.75 | 56.36 | +| swimming pool | 57.55 | 88.75 | +| stool | 52.07 | 73.31 | +| barrel | 47.02 | 64.43 | +| basket | 38.58 | 59.12 | +| waterfall | 68.92 | 94.99 | +| tent | 89.1 | 98.53 | +| bag | 19.81 | 22.65 | +| minibike | 70.4 | 88.56 | +| cradle | 79.75 | 98.09 | +| oven | 51.66 | 60.53 | +| ball | 53.66 | 66.48 | +| food | 51.94 | 65.97 | +| step | 14.16 | 18.5 | +| tank | 66.77 | 76.51 | +| trade name | 29.82 | 33.95 | +| microwave | 86.42 | 95.44 | +| pot | 57.92 | 67.79 | +| animal | 61.88 | 63.4 | +| bicycle | 57.77 | 75.35 | +| lake | 41.21 | 63.73 | +| dishwasher | 70.26 | 82.24 | +| screen | 59.57 | 94.39 | +| blanket | 35.68 | 43.48 | +| sculpture | 72.61 | 87.78 | +| hood | 62.89 | 75.43 | +| sconce | 53.72 | 62.8 | +| vase | 45.91 | 55.9 | +| traffic light | 38.77 | 55.66 | +| tray | 6.74 | 8.75 | +| ashcan | 45.54 | 66.2 | +| fan | 65.22 | 83.04 | +| pier | 38.89 | 61.18 | +| crt screen | 17.84 | 20.4 | +| plate | 58.32 | 79.36 | +| monitor | 66.98 | 77.43 | +| bulletin board | 62.39 | 68.91 | +| shower | 1.92 | 1.94 | +| radiator | 64.47 | 75.11 | +| glass | 17.36 | 18.48 | +| clock | 37.15 | 44.96 | +| flag | 69.38 | 79.47 | ++---------------------+-------+-------+ +2024-06-18 17:56:54,437 - mmseg - INFO - Summary: +2024-06-18 17:56:54,437 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.83 | 56.58 | 69.93 | ++-------+-------+-------+ +2024-06-18 17:56:54,438 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 17:56:54,438 - mmseg - INFO - Iter(val) [250] aAcc: 0.8583, mIoU: 0.5658, mAcc: 0.6993, IoU.wall: 0.8163, IoU.building: 0.8432, IoU.sky: 0.9495, IoU.floor: 0.8414, IoU.tree: 0.7695, IoU.ceiling: 0.8675, IoU.road: 0.8730, IoU.bed : 0.9250, IoU.windowpane: 0.6539, IoU.grass: 0.6663, IoU.cabinet: 0.6634, IoU.sidewalk: 0.7222, IoU.person: 0.8568, IoU.earth: 0.3735, IoU.door: 0.5924, IoU.table: 0.6888, IoU.mountain: 0.6257, IoU.plant: 0.5508, IoU.curtain: 0.7917, IoU.chair: 0.6691, IoU.car: 0.8714, IoU.water: 0.6200, IoU.painting: 0.7784, IoU.sofa: 0.7861, IoU.shelf: 0.5208, IoU.house: 0.5934, IoU.sea: 0.6362, IoU.mirror: 0.7835, IoU.rug: 0.6832, IoU.field: 0.3309, IoU.armchair: 0.5832, IoU.seat: 0.6632, IoU.fence: 0.5375, IoU.desk: 0.5506, IoU.rock: 0.5437, IoU.wardrobe: 0.5511, IoU.lamp: 0.7250, IoU.bathtub: 0.8422, IoU.railing: 0.4134, IoU.cushion: 0.7097, IoU.base: 0.4457, IoU.box: 0.3444, IoU.column: 0.5401, IoU.signboard: 0.4114, IoU.chest of drawers: 0.4684, IoU.counter: 0.4192, IoU.sand: 0.4968, IoU.sink: 0.7625, IoU.skyscraper: 0.4820, IoU.fireplace: 0.7482, IoU.refrigerator: 0.8047, IoU.grandstand: 0.5072, IoU.path: 0.3160, IoU.stairs: 0.2391, IoU.runway: 0.7211, IoU.case: 0.5364, IoU.pool table: 0.9468, IoU.pillow: 0.7032, IoU.screen door: 0.8446, IoU.stairway: 0.4636, IoU.river: 0.1333, IoU.bridge: 0.7578, IoU.bookcase: 0.4801, IoU.blind: 0.4322, IoU.coffee table: 0.6362, IoU.toilet: 0.8881, IoU.flower: 0.4135, IoU.book: 0.5587, IoU.hill: 0.0755, IoU.bench: 0.4983, IoU.countertop: 0.6305, IoU.stove: 0.8704, IoU.palm: 0.5594, IoU.kitchen island: 0.4887, IoU.computer: 0.7716, IoU.swivel chair: 0.5075, IoU.boat: 0.5693, IoU.bar: 0.6036, IoU.arcade machine: 0.7783, IoU.hovel: 0.4509, IoU.bus: 0.9359, IoU.towel: 0.7515, IoU.light: 0.5930, IoU.truck: 0.4518, IoU.tower: 0.1706, IoU.chandelier: 0.7257, IoU.awning: 0.4469, IoU.streetlight: 0.3273, IoU.booth: 0.5973, IoU.television receiver: 0.7660, IoU.airplane: 0.6574, IoU.dirt track: 0.2302, IoU.apparel: 0.4874, IoU.pole: 0.2453, IoU.land: 0.0274, IoU.bannister: 0.1796, IoU.escalator: 0.5766, IoU.ottoman: 0.4846, IoU.bottle: 0.4228, IoU.buffet: 0.5638, IoU.poster: 0.3396, IoU.stage: 0.2297, IoU.van: 0.4418, IoU.ship: 0.7310, IoU.fountain: 0.2161, IoU.conveyer belt: 0.8260, IoU.canopy: 0.6053, IoU.washer: 0.7959, IoU.plaything: 0.3875, IoU.swimming pool: 0.5755, IoU.stool: 0.5207, IoU.barrel: 0.4702, IoU.basket: 0.3858, IoU.waterfall: 0.6892, IoU.tent: 0.8910, IoU.bag: 0.1981, IoU.minibike: 0.7040, IoU.cradle: 0.7975, IoU.oven: 0.5166, IoU.ball: 0.5366, IoU.food: 0.5194, IoU.step: 0.1416, IoU.tank: 0.6677, IoU.trade name: 0.2982, IoU.microwave: 0.8642, IoU.pot: 0.5792, IoU.animal: 0.6188, IoU.bicycle: 0.5777, IoU.lake: 0.4121, IoU.dishwasher: 0.7026, IoU.screen: 0.5957, IoU.blanket: 0.3568, IoU.sculpture: 0.7261, IoU.hood: 0.6289, IoU.sconce: 0.5372, IoU.vase: 0.4591, IoU.traffic light: 0.3877, IoU.tray: 0.0674, IoU.ashcan: 0.4554, IoU.fan: 0.6522, IoU.pier: 0.3889, IoU.crt screen: 0.1784, IoU.plate: 0.5832, IoU.monitor: 0.6698, IoU.bulletin board: 0.6239, IoU.shower: 0.0192, IoU.radiator: 0.6447, IoU.glass: 0.1736, IoU.clock: 0.3715, IoU.flag: 0.6938, Acc.wall: 0.8902, Acc.building: 0.9244, Acc.sky: 0.9783, Acc.floor: 0.9024, Acc.tree: 0.9104, Acc.ceiling: 0.9256, Acc.road: 0.9242, Acc.bed : 0.9703, Acc.windowpane: 0.8299, Acc.grass: 0.8146, Acc.cabinet: 0.7857, Acc.sidewalk: 0.8405, Acc.person: 0.9363, Acc.earth: 0.4931, Acc.door: 0.7355, Acc.table: 0.8154, Acc.mountain: 0.7375, Acc.plant: 0.6667, Acc.curtain: 0.8914, Acc.chair: 0.7865, Acc.car: 0.9409, Acc.water: 0.7309, Acc.painting: 0.9034, Acc.sofa: 0.8493, Acc.shelf: 0.7278, Acc.house: 0.7821, Acc.sea: 0.8481, Acc.mirror: 0.8517, Acc.rug: 0.8363, Acc.field: 0.6029, Acc.armchair: 0.7837, Acc.seat: 0.8804, Acc.fence: 0.6781, Acc.desk: 0.7731, Acc.rock: 0.7964, Acc.wardrobe: 0.7207, Acc.lamp: 0.8341, Acc.bathtub: 0.8580, Acc.railing: 0.6230, Acc.cushion: 0.8254, Acc.base: 0.6443, Acc.box: 0.4218, Acc.column: 0.6720, Acc.signboard: 0.5457, Acc.chest of drawers: 0.6367, Acc.counter: 0.5129, Acc.sand: 0.7548, Acc.sink: 0.8494, Acc.skyscraper: 0.6120, Acc.fireplace: 0.9477, Acc.refrigerator: 0.9168, Acc.grandstand: 0.8098, Acc.path: 0.3932, Acc.stairs: 0.2949, Acc.runway: 0.9440, Acc.case: 0.8361, Acc.pool table: 0.9783, Acc.pillow: 0.8374, Acc.screen door: 0.9184, Acc.stairway: 0.6563, Acc.river: 0.2557, Acc.bridge: 0.9154, Acc.bookcase: 0.6416, Acc.blind: 0.4889, Acc.coffee table: 0.8980, Acc.toilet: 0.9304, Acc.flower: 0.5354, Acc.book: 0.6923, Acc.hill: 0.1351, Acc.bench: 0.5586, Acc.countertop: 0.7963, Acc.stove: 0.9242, Acc.palm: 0.8210, Acc.kitchen island: 0.8427, Acc.computer: 0.9226, Acc.swivel chair: 0.8039, Acc.boat: 0.8689, Acc.bar: 0.7662, Acc.arcade machine: 0.8342, Acc.hovel: 0.4986, Acc.bus: 0.9662, Acc.towel: 0.8423, Acc.light: 0.6586, Acc.truck: 0.5700, Acc.tower: 0.3336, Acc.chandelier: 0.8735, Acc.awning: 0.6750, Acc.streetlight: 0.4340, Acc.booth: 0.6729, Acc.television receiver: 0.8530, Acc.airplane: 0.7082, Acc.dirt track: 0.4486, Acc.apparel: 0.6120, Acc.pole: 0.3198, Acc.land: 0.0418, Acc.bannister: 0.2730, Acc.escalator: 0.7846, Acc.ottoman: 0.6481, Acc.bottle: 0.5696, Acc.buffet: 0.6546, Acc.poster: 0.5389, Acc.stage: 0.4342, Acc.van: 0.5968, Acc.ship: 0.9860, Acc.fountain: 0.2201, Acc.conveyer belt: 0.9251, Acc.canopy: 0.7590, Acc.washer: 0.8253, Acc.plaything: 0.5636, Acc.swimming pool: 0.8875, Acc.stool: 0.7331, Acc.barrel: 0.6443, Acc.basket: 0.5912, Acc.waterfall: 0.9499, Acc.tent: 0.9853, Acc.bag: 0.2265, Acc.minibike: 0.8856, Acc.cradle: 0.9809, Acc.oven: 0.6053, Acc.ball: 0.6648, Acc.food: 0.6597, Acc.step: 0.1850, Acc.tank: 0.7651, Acc.trade name: 0.3395, Acc.microwave: 0.9544, Acc.pot: 0.6779, Acc.animal: 0.6340, Acc.bicycle: 0.7535, Acc.lake: 0.6373, Acc.dishwasher: 0.8224, Acc.screen: 0.9439, Acc.blanket: 0.4348, Acc.sculpture: 0.8778, Acc.hood: 0.7543, Acc.sconce: 0.6280, Acc.vase: 0.5590, Acc.traffic light: 0.5566, Acc.tray: 0.0875, Acc.ashcan: 0.6620, Acc.fan: 0.8304, Acc.pier: 0.6118, Acc.crt screen: 0.2040, Acc.plate: 0.7936, Acc.monitor: 0.7743, Acc.bulletin board: 0.6891, Acc.shower: 0.0194, Acc.radiator: 0.7511, Acc.glass: 0.1848, Acc.clock: 0.4496, Acc.flag: 0.7947 +2024-06-18 17:58:01,116 - mmseg - INFO - Iter [45050/80000] lr: 1.748e-05, eta: 14:22:04, time: 3.260, data_time: 1.944, memory: 70498, decode.loss_ce: 0.1949, decode.acc_seg: 91.6656, aux.loss_ce: 0.0821, aux.acc_seg: 91.2663, loss: 0.2770 +2024-06-18 17:59:07,512 - mmseg - INFO - Iter [45100/80000] lr: 1.745e-05, eta: 14:20:44, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2043, decode.acc_seg: 91.4853, aux.loss_ce: 0.0848, aux.acc_seg: 91.1318, loss: 0.2891 +2024-06-18 18:00:14,028 - mmseg - INFO - Iter [45150/80000] lr: 1.743e-05, eta: 14:19:24, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2002, decode.acc_seg: 91.7280, aux.loss_ce: 0.0843, aux.acc_seg: 91.3172, loss: 0.2845 +2024-06-18 18:01:20,441 - mmseg - INFO - Iter [45200/80000] lr: 1.740e-05, eta: 14:18:05, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1979, decode.acc_seg: 91.7468, aux.loss_ce: 0.0828, aux.acc_seg: 91.3919, loss: 0.2807 +2024-06-18 18:02:27,176 - mmseg - INFO - Iter [45250/80000] lr: 1.738e-05, eta: 14:16:45, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2151, decode.acc_seg: 90.6918, aux.loss_ce: 0.0900, aux.acc_seg: 90.3737, loss: 0.3050 +2024-06-18 18:03:33,523 - mmseg - INFO - Iter [45300/80000] lr: 1.735e-05, eta: 14:15:25, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2060, decode.acc_seg: 91.0094, aux.loss_ce: 0.0863, aux.acc_seg: 90.6673, loss: 0.2923 +2024-06-18 18:04:39,921 - mmseg - INFO - Iter [45350/80000] lr: 1.733e-05, eta: 14:14:05, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1948, decode.acc_seg: 92.0292, aux.loss_ce: 0.0817, aux.acc_seg: 91.6515, loss: 0.2765 +2024-06-18 18:05:46,289 - mmseg - INFO - Iter [45400/80000] lr: 1.730e-05, eta: 14:12:46, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2012, decode.acc_seg: 91.1726, aux.loss_ce: 0.0838, aux.acc_seg: 90.8077, loss: 0.2849 +2024-06-18 18:06:52,579 - mmseg - INFO - Iter [45450/80000] lr: 1.728e-05, eta: 14:11:26, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2018, decode.acc_seg: 91.5164, aux.loss_ce: 0.0847, aux.acc_seg: 91.0788, loss: 0.2865 +2024-06-18 18:08:01,969 - mmseg - INFO - Iter [45500/80000] lr: 1.725e-05, eta: 14:10:09, time: 1.388, data_time: 0.068, memory: 70498, decode.loss_ce: 0.1999, decode.acc_seg: 91.7587, aux.loss_ce: 0.0831, aux.acc_seg: 91.4144, loss: 0.2830 +2024-06-18 18:09:08,354 - mmseg - INFO - Iter [45550/80000] lr: 1.723e-05, eta: 14:08:49, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2007, decode.acc_seg: 91.4185, aux.loss_ce: 0.0847, aux.acc_seg: 90.9400, loss: 0.2854 +2024-06-18 18:10:14,661 - mmseg - INFO - Iter [45600/80000] lr: 1.720e-05, eta: 14:07:29, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1949, decode.acc_seg: 91.9436, aux.loss_ce: 0.0817, aux.acc_seg: 91.5833, loss: 0.2765 +2024-06-18 18:11:21,058 - mmseg - INFO - Iter [45650/80000] lr: 1.718e-05, eta: 14:06:10, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1831, decode.acc_seg: 92.2093, aux.loss_ce: 0.0769, aux.acc_seg: 91.8455, loss: 0.2600 +2024-06-18 18:12:27,536 - mmseg - INFO - Iter [45700/80000] lr: 1.715e-05, eta: 14:04:50, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1927, decode.acc_seg: 91.5840, aux.loss_ce: 0.0806, aux.acc_seg: 91.3014, loss: 0.2733 +2024-06-18 18:13:33,828 - mmseg - INFO - Iter [45750/80000] lr: 1.713e-05, eta: 14:03:31, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1963, decode.acc_seg: 91.8399, aux.loss_ce: 0.0822, aux.acc_seg: 91.4347, loss: 0.2786 +2024-06-18 18:14:40,310 - mmseg - INFO - Iter [45800/80000] lr: 1.710e-05, eta: 14:02:11, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1914, decode.acc_seg: 91.8436, aux.loss_ce: 0.0799, aux.acc_seg: 91.4901, loss: 0.2713 +2024-06-18 18:15:46,576 - mmseg - INFO - Iter [45850/80000] lr: 1.708e-05, eta: 14:00:52, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1977, decode.acc_seg: 91.6316, aux.loss_ce: 0.0823, aux.acc_seg: 91.3386, loss: 0.2799 +2024-06-18 18:16:53,173 - mmseg - INFO - Iter [45900/80000] lr: 1.705e-05, eta: 13:59:32, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1982, decode.acc_seg: 91.6149, aux.loss_ce: 0.0830, aux.acc_seg: 91.2812, loss: 0.2811 +2024-06-18 18:17:59,529 - mmseg - INFO - Iter [45950/80000] lr: 1.703e-05, eta: 13:58:13, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1939, decode.acc_seg: 91.4964, aux.loss_ce: 0.0820, aux.acc_seg: 91.0041, loss: 0.2759 +2024-06-18 18:19:05,804 - mmseg - INFO - Saving checkpoint at 46000 iterations +2024-06-18 18:20:45,818 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 18:20:45,818 - mmseg - INFO - Iter [46000/80000] lr: 1.700e-05, eta: 13:58:08, time: 3.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1890, decode.acc_seg: 91.9581, aux.loss_ce: 0.0793, aux.acc_seg: 91.5868, loss: 0.2683 +2024-06-18 18:22:21,985 - mmseg - INFO - per class results: +2024-06-18 18:22:21,991 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.04 | 89.74 | +| building | 84.23 | 91.8 | +| sky | 94.95 | 97.85 | +| floor | 85.15 | 92.75 | +| tree | 77.56 | 89.69 | +| ceiling | 87.57 | 93.95 | +| road | 87.24 | 91.83 | +| bed | 92.32 | 96.66 | +| windowpane | 66.24 | 82.9 | +| grass | 66.48 | 79.44 | +| cabinet | 64.83 | 77.78 | +| sidewalk | 72.59 | 85.38 | +| person | 85.55 | 94.3 | +| earth | 37.18 | 49.74 | +| door | 58.96 | 77.62 | +| table | 68.99 | 79.95 | +| mountain | 62.09 | 71.89 | +| plant | 56.07 | 71.61 | +| curtain | 79.19 | 87.85 | +| chair | 66.43 | 75.72 | +| car | 87.09 | 93.62 | +| water | 61.09 | 73.29 | +| painting | 75.66 | 89.91 | +| sofa | 79.57 | 89.74 | +| shelf | 53.08 | 68.44 | +| house | 53.3 | 74.12 | +| sea | 67.99 | 89.61 | +| mirror | 76.3 | 82.38 | +| rug | 70.78 | 78.34 | +| field | 27.92 | 50.44 | +| armchair | 57.46 | 77.48 | +| seat | 65.06 | 87.85 | +| fence | 51.6 | 65.82 | +| desk | 59.66 | 72.96 | +| rock | 55.38 | 83.11 | +| wardrobe | 55.42 | 75.61 | +| lamp | 72.27 | 82.99 | +| bathtub | 84.75 | 86.25 | +| railing | 42.04 | 55.27 | +| cushion | 70.61 | 84.27 | +| base | 45.66 | 62.86 | +| box | 36.24 | 52.81 | +| column | 53.1 | 69.64 | +| signboard | 40.19 | 59.3 | +| chest of drawers | 44.73 | 71.43 | +| counter | 40.63 | 48.77 | +| sand | 53.23 | 80.33 | +| sink | 77.95 | 83.62 | +| skyscraper | 47.94 | 59.94 | +| fireplace | 75.57 | 93.24 | +| refrigerator | 80.02 | 89.92 | +| grandstand | 53.31 | 84.06 | +| path | 34.1 | 48.51 | +| stairs | 26.75 | 33.85 | +| runway | 73.69 | 97.2 | +| case | 57.56 | 80.79 | +| pool table | 94.99 | 97.46 | +| pillow | 71.01 | 83.39 | +| screen door | 70.52 | 71.94 | +| stairway | 39.37 | 49.92 | +| river | 26.95 | 42.35 | +| bridge | 67.93 | 77.03 | +| bookcase | 43.19 | 48.95 | +| blind | 42.53 | 43.98 | +| coffee table | 66.9 | 86.23 | +| toilet | 89.04 | 93.06 | +| flower | 44.59 | 55.52 | +| book | 51.67 | 82.6 | +| hill | 11.65 | 19.59 | +| bench | 52.75 | 60.59 | +| countertop | 63.84 | 84.1 | +| stove | 87.61 | 94.26 | +| palm | 55.79 | 79.79 | +| kitchen island | 45.9 | 72.52 | +| computer | 79.1 | 92.82 | +| swivel chair | 53.73 | 71.38 | +| boat | 60.49 | 83.32 | +| bar | 59.3 | 73.07 | +| arcade machine | 69.61 | 84.54 | +| hovel | 49.57 | 61.48 | +| bus | 93.19 | 96.19 | +| towel | 75.57 | 83.03 | +| light | 60.68 | 72.77 | +| truck | 44.48 | 56.69 | +| tower | 29.55 | 55.5 | +| chandelier | 71.85 | 87.8 | +| awning | 34.33 | 41.1 | +| streetlight | 33.75 | 48.77 | +| booth | 50.56 | 53.12 | +| television receiver | 75.31 | 89.78 | +| airplane | 64.11 | 69.38 | +| dirt track | 11.63 | 45.31 | +| apparel | 50.23 | 76.02 | +| pole | 22.72 | 30.41 | +| land | 3.64 | 7.54 | +| bannister | 18.83 | 23.18 | +| escalator | 60.07 | 82.32 | +| ottoman | 48.62 | 71.21 | +| bottle | 42.67 | 61.03 | +| buffet | 39.13 | 42.89 | +| poster | 38.4 | 51.26 | +| stage | 23.89 | 47.39 | +| van | 44.22 | 61.98 | +| ship | 90.96 | 99.03 | +| fountain | 27.45 | 28.14 | +| conveyer belt | 74.6 | 93.07 | +| canopy | 53.36 | 75.32 | +| washer | 84.73 | 88.62 | +| plaything | 26.89 | 39.91 | +| swimming pool | 56.47 | 86.22 | +| stool | 55.46 | 66.42 | +| barrel | 42.22 | 64.63 | +| basket | 38.3 | 51.13 | +| waterfall | 62.07 | 80.3 | +| tent | 89.81 | 98.54 | +| bag | 21.98 | 27.13 | +| minibike | 73.45 | 88.57 | +| cradle | 86.92 | 98.03 | +| oven | 47.9 | 57.73 | +| ball | 38.67 | 39.53 | +| food | 57.11 | 74.67 | +| step | 13.62 | 15.85 | +| tank | 56.6 | 64.88 | +| trade name | 19.89 | 20.99 | +| microwave | 84.97 | 95.79 | +| pot | 56.85 | 67.77 | +| animal | 63.71 | 64.74 | +| bicycle | 56.97 | 75.74 | +| lake | 56.58 | 63.74 | +| dishwasher | 69.88 | 83.37 | +| screen | 48.15 | 74.46 | +| blanket | 28.46 | 32.86 | +| sculpture | 75.69 | 86.56 | +| hood | 60.85 | 72.94 | +| sconce | 54.07 | 64.91 | +| vase | 48.45 | 61.04 | +| traffic light | 38.87 | 62.16 | +| tray | 9.34 | 11.67 | +| ashcan | 45.58 | 60.03 | +| fan | 65.62 | 80.22 | +| pier | 36.7 | 53.37 | +| crt screen | 17.85 | 30.26 | +| plate | 59.48 | 73.93 | +| monitor | 65.33 | 85.99 | +| bulletin board | 56.47 | 61.56 | +| shower | 1.4 | 1.95 | +| radiator | 63.59 | 73.37 | +| glass | 18.83 | 20.29 | +| clock | 35.67 | 41.73 | +| flag | 71.3 | 76.35 | ++---------------------+-------+-------+ +2024-06-18 18:22:21,991 - mmseg - INFO - Summary: +2024-06-18 18:22:21,991 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.91 | 56.19 | 69.04 | ++-------+-------+-------+ +2024-06-18 18:22:21,992 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 18:22:21,992 - mmseg - INFO - Iter(val) [250] aAcc: 0.8591, mIoU: 0.5619, mAcc: 0.6904, IoU.wall: 0.8204, IoU.building: 0.8423, IoU.sky: 0.9495, IoU.floor: 0.8515, IoU.tree: 0.7756, IoU.ceiling: 0.8757, IoU.road: 0.8724, IoU.bed : 0.9232, IoU.windowpane: 0.6624, IoU.grass: 0.6648, IoU.cabinet: 0.6483, IoU.sidewalk: 0.7259, IoU.person: 0.8555, IoU.earth: 0.3718, IoU.door: 0.5896, IoU.table: 0.6899, IoU.mountain: 0.6209, IoU.plant: 0.5607, IoU.curtain: 0.7919, IoU.chair: 0.6643, IoU.car: 0.8709, IoU.water: 0.6109, IoU.painting: 0.7566, IoU.sofa: 0.7957, IoU.shelf: 0.5308, IoU.house: 0.5330, IoU.sea: 0.6799, IoU.mirror: 0.7630, IoU.rug: 0.7078, IoU.field: 0.2792, IoU.armchair: 0.5746, IoU.seat: 0.6506, IoU.fence: 0.5160, IoU.desk: 0.5966, IoU.rock: 0.5538, IoU.wardrobe: 0.5542, IoU.lamp: 0.7227, IoU.bathtub: 0.8475, IoU.railing: 0.4204, IoU.cushion: 0.7061, IoU.base: 0.4566, IoU.box: 0.3624, IoU.column: 0.5310, IoU.signboard: 0.4019, IoU.chest of drawers: 0.4473, IoU.counter: 0.4063, IoU.sand: 0.5323, IoU.sink: 0.7795, IoU.skyscraper: 0.4794, IoU.fireplace: 0.7557, IoU.refrigerator: 0.8002, IoU.grandstand: 0.5331, IoU.path: 0.3410, IoU.stairs: 0.2675, IoU.runway: 0.7369, IoU.case: 0.5756, IoU.pool table: 0.9499, IoU.pillow: 0.7101, IoU.screen door: 0.7052, IoU.stairway: 0.3937, IoU.river: 0.2695, IoU.bridge: 0.6793, IoU.bookcase: 0.4319, IoU.blind: 0.4253, IoU.coffee table: 0.6690, IoU.toilet: 0.8904, IoU.flower: 0.4459, IoU.book: 0.5167, IoU.hill: 0.1165, IoU.bench: 0.5275, IoU.countertop: 0.6384, IoU.stove: 0.8761, IoU.palm: 0.5579, IoU.kitchen island: 0.4590, IoU.computer: 0.7910, IoU.swivel chair: 0.5373, IoU.boat: 0.6049, IoU.bar: 0.5930, IoU.arcade machine: 0.6961, IoU.hovel: 0.4957, IoU.bus: 0.9319, IoU.towel: 0.7557, IoU.light: 0.6068, IoU.truck: 0.4448, IoU.tower: 0.2955, IoU.chandelier: 0.7185, IoU.awning: 0.3433, IoU.streetlight: 0.3375, IoU.booth: 0.5056, IoU.television receiver: 0.7531, IoU.airplane: 0.6411, IoU.dirt track: 0.1163, IoU.apparel: 0.5023, IoU.pole: 0.2272, IoU.land: 0.0364, IoU.bannister: 0.1883, IoU.escalator: 0.6007, IoU.ottoman: 0.4862, IoU.bottle: 0.4267, IoU.buffet: 0.3913, IoU.poster: 0.3840, IoU.stage: 0.2389, IoU.van: 0.4422, IoU.ship: 0.9096, IoU.fountain: 0.2745, IoU.conveyer belt: 0.7460, IoU.canopy: 0.5336, IoU.washer: 0.8473, IoU.plaything: 0.2689, IoU.swimming pool: 0.5647, IoU.stool: 0.5546, IoU.barrel: 0.4222, IoU.basket: 0.3830, IoU.waterfall: 0.6207, IoU.tent: 0.8981, IoU.bag: 0.2198, IoU.minibike: 0.7345, IoU.cradle: 0.8692, IoU.oven: 0.4790, IoU.ball: 0.3867, IoU.food: 0.5711, IoU.step: 0.1362, IoU.tank: 0.5660, IoU.trade name: 0.1989, IoU.microwave: 0.8497, IoU.pot: 0.5685, IoU.animal: 0.6371, IoU.bicycle: 0.5697, IoU.lake: 0.5658, IoU.dishwasher: 0.6988, IoU.screen: 0.4815, IoU.blanket: 0.2846, IoU.sculpture: 0.7569, IoU.hood: 0.6085, IoU.sconce: 0.5407, IoU.vase: 0.4845, IoU.traffic light: 0.3887, IoU.tray: 0.0934, IoU.ashcan: 0.4558, IoU.fan: 0.6562, IoU.pier: 0.3670, IoU.crt screen: 0.1785, IoU.plate: 0.5948, IoU.monitor: 0.6533, IoU.bulletin board: 0.5647, IoU.shower: 0.0140, IoU.radiator: 0.6359, IoU.glass: 0.1883, IoU.clock: 0.3567, IoU.flag: 0.7130, Acc.wall: 0.8974, Acc.building: 0.9180, Acc.sky: 0.9785, Acc.floor: 0.9275, Acc.tree: 0.8969, Acc.ceiling: 0.9395, Acc.road: 0.9183, Acc.bed : 0.9666, Acc.windowpane: 0.8290, Acc.grass: 0.7944, Acc.cabinet: 0.7778, Acc.sidewalk: 0.8538, Acc.person: 0.9430, Acc.earth: 0.4974, Acc.door: 0.7762, Acc.table: 0.7995, Acc.mountain: 0.7189, Acc.plant: 0.7161, Acc.curtain: 0.8785, Acc.chair: 0.7572, Acc.car: 0.9362, Acc.water: 0.7329, Acc.painting: 0.8991, Acc.sofa: 0.8974, Acc.shelf: 0.6844, Acc.house: 0.7412, Acc.sea: 0.8961, Acc.mirror: 0.8238, Acc.rug: 0.7834, Acc.field: 0.5044, Acc.armchair: 0.7748, Acc.seat: 0.8785, Acc.fence: 0.6582, Acc.desk: 0.7296, Acc.rock: 0.8311, Acc.wardrobe: 0.7561, Acc.lamp: 0.8299, Acc.bathtub: 0.8625, Acc.railing: 0.5527, Acc.cushion: 0.8427, Acc.base: 0.6286, Acc.box: 0.5281, Acc.column: 0.6964, Acc.signboard: 0.5930, Acc.chest of drawers: 0.7143, Acc.counter: 0.4877, Acc.sand: 0.8033, Acc.sink: 0.8362, Acc.skyscraper: 0.5994, Acc.fireplace: 0.9324, Acc.refrigerator: 0.8992, Acc.grandstand: 0.8406, Acc.path: 0.4851, Acc.stairs: 0.3385, Acc.runway: 0.9720, Acc.case: 0.8079, Acc.pool table: 0.9746, Acc.pillow: 0.8339, Acc.screen door: 0.7194, Acc.stairway: 0.4992, Acc.river: 0.4235, Acc.bridge: 0.7703, Acc.bookcase: 0.4895, Acc.blind: 0.4398, Acc.coffee table: 0.8623, Acc.toilet: 0.9306, Acc.flower: 0.5552, Acc.book: 0.8260, Acc.hill: 0.1959, Acc.bench: 0.6059, Acc.countertop: 0.8410, Acc.stove: 0.9426, Acc.palm: 0.7979, Acc.kitchen island: 0.7252, Acc.computer: 0.9282, Acc.swivel chair: 0.7138, Acc.boat: 0.8332, Acc.bar: 0.7307, Acc.arcade machine: 0.8454, Acc.hovel: 0.6148, Acc.bus: 0.9619, Acc.towel: 0.8303, Acc.light: 0.7277, Acc.truck: 0.5669, Acc.tower: 0.5550, Acc.chandelier: 0.8780, Acc.awning: 0.4110, Acc.streetlight: 0.4877, Acc.booth: 0.5312, Acc.television receiver: 0.8978, Acc.airplane: 0.6938, Acc.dirt track: 0.4531, Acc.apparel: 0.7602, Acc.pole: 0.3041, Acc.land: 0.0754, Acc.bannister: 0.2318, Acc.escalator: 0.8232, Acc.ottoman: 0.7121, Acc.bottle: 0.6103, Acc.buffet: 0.4289, Acc.poster: 0.5126, Acc.stage: 0.4739, Acc.van: 0.6198, Acc.ship: 0.9903, Acc.fountain: 0.2814, Acc.conveyer belt: 0.9307, Acc.canopy: 0.7532, Acc.washer: 0.8862, Acc.plaything: 0.3991, Acc.swimming pool: 0.8622, Acc.stool: 0.6642, Acc.barrel: 0.6463, Acc.basket: 0.5113, Acc.waterfall: 0.8030, Acc.tent: 0.9854, Acc.bag: 0.2713, Acc.minibike: 0.8857, Acc.cradle: 0.9803, Acc.oven: 0.5773, Acc.ball: 0.3953, Acc.food: 0.7467, Acc.step: 0.1585, Acc.tank: 0.6488, Acc.trade name: 0.2099, Acc.microwave: 0.9579, Acc.pot: 0.6777, Acc.animal: 0.6474, Acc.bicycle: 0.7574, Acc.lake: 0.6374, Acc.dishwasher: 0.8337, Acc.screen: 0.7446, Acc.blanket: 0.3286, Acc.sculpture: 0.8656, Acc.hood: 0.7294, Acc.sconce: 0.6491, Acc.vase: 0.6104, Acc.traffic light: 0.6216, Acc.tray: 0.1167, Acc.ashcan: 0.6003, Acc.fan: 0.8022, Acc.pier: 0.5337, Acc.crt screen: 0.3026, Acc.plate: 0.7393, Acc.monitor: 0.8599, Acc.bulletin board: 0.6156, Acc.shower: 0.0195, Acc.radiator: 0.7337, Acc.glass: 0.2029, Acc.clock: 0.4173, Acc.flag: 0.7635 +2024-06-18 18:23:28,793 - mmseg - INFO - Iter [46050/80000] lr: 1.698e-05, eta: 13:57:59, time: 3.259, data_time: 1.940, memory: 70498, decode.loss_ce: 0.1970, decode.acc_seg: 91.6568, aux.loss_ce: 0.0819, aux.acc_seg: 91.2498, loss: 0.2789 +2024-06-18 18:24:35,195 - mmseg - INFO - Iter [46100/80000] lr: 1.695e-05, eta: 13:56:40, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1969, decode.acc_seg: 91.9061, aux.loss_ce: 0.0825, aux.acc_seg: 91.5518, loss: 0.2795 +2024-06-18 18:25:41,576 - mmseg - INFO - Iter [46150/80000] lr: 1.693e-05, eta: 13:55:20, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1923, decode.acc_seg: 92.1251, aux.loss_ce: 0.0801, aux.acc_seg: 91.8156, loss: 0.2725 +2024-06-18 18:26:48,243 - mmseg - INFO - Iter [46200/80000] lr: 1.690e-05, eta: 13:54:00, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1924, decode.acc_seg: 91.7233, aux.loss_ce: 0.0804, aux.acc_seg: 91.4178, loss: 0.2728 +2024-06-18 18:27:54,575 - mmseg - INFO - Iter [46250/80000] lr: 1.688e-05, eta: 13:52:41, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2020, decode.acc_seg: 91.4967, aux.loss_ce: 0.0841, aux.acc_seg: 91.1273, loss: 0.2861 +2024-06-18 18:29:00,823 - mmseg - INFO - Iter [46300/80000] lr: 1.685e-05, eta: 13:51:21, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1940, decode.acc_seg: 91.6285, aux.loss_ce: 0.0812, aux.acc_seg: 91.2167, loss: 0.2752 +2024-06-18 18:30:07,319 - mmseg - INFO - Iter [46350/80000] lr: 1.683e-05, eta: 13:50:02, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2006, decode.acc_seg: 91.5307, aux.loss_ce: 0.0844, aux.acc_seg: 91.1349, loss: 0.2850 +2024-06-18 18:31:13,484 - mmseg - INFO - Iter [46400/80000] lr: 1.680e-05, eta: 13:48:42, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2092, decode.acc_seg: 91.2641, aux.loss_ce: 0.0870, aux.acc_seg: 90.8978, loss: 0.2962 +2024-06-18 18:32:19,937 - mmseg - INFO - Iter [46450/80000] lr: 1.678e-05, eta: 13:47:23, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2034, decode.acc_seg: 91.3764, aux.loss_ce: 0.0852, aux.acc_seg: 91.0320, loss: 0.2887 +2024-06-18 18:33:26,292 - mmseg - INFO - Iter [46500/80000] lr: 1.675e-05, eta: 13:46:03, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2046, decode.acc_seg: 91.2365, aux.loss_ce: 0.0849, aux.acc_seg: 90.9043, loss: 0.2895 +2024-06-18 18:34:32,867 - mmseg - INFO - Iter [46550/80000] lr: 1.673e-05, eta: 13:44:44, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2047, decode.acc_seg: 91.3435, aux.loss_ce: 0.0858, aux.acc_seg: 90.9688, loss: 0.2904 +2024-06-18 18:35:39,464 - mmseg - INFO - Iter [46600/80000] lr: 1.670e-05, eta: 13:43:25, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1986, decode.acc_seg: 91.8289, aux.loss_ce: 0.0834, aux.acc_seg: 91.4800, loss: 0.2820 +2024-06-18 18:36:45,868 - mmseg - INFO - Iter [46650/80000] lr: 1.668e-05, eta: 13:42:05, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1996, decode.acc_seg: 91.5241, aux.loss_ce: 0.0830, aux.acc_seg: 91.1349, loss: 0.2826 +2024-06-18 18:37:52,326 - mmseg - INFO - Iter [46700/80000] lr: 1.665e-05, eta: 13:40:46, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2002, decode.acc_seg: 91.1986, aux.loss_ce: 0.0838, aux.acc_seg: 90.8703, loss: 0.2840 +2024-06-18 18:39:00,857 - mmseg - INFO - Iter [46750/80000] lr: 1.663e-05, eta: 13:39:28, time: 1.371, data_time: 0.052, memory: 70498, decode.loss_ce: 0.1924, decode.acc_seg: 91.8524, aux.loss_ce: 0.0814, aux.acc_seg: 91.4152, loss: 0.2738 +2024-06-18 18:40:07,336 - mmseg - INFO - Iter [46800/80000] lr: 1.660e-05, eta: 13:38:09, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1855, decode.acc_seg: 92.0859, aux.loss_ce: 0.0782, aux.acc_seg: 91.6897, loss: 0.2637 +2024-06-18 18:41:13,754 - mmseg - INFO - Iter [46850/80000] lr: 1.658e-05, eta: 13:36:50, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1945, decode.acc_seg: 91.6074, aux.loss_ce: 0.0813, aux.acc_seg: 91.3519, loss: 0.2757 +2024-06-18 18:42:20,365 - mmseg - INFO - Iter [46900/80000] lr: 1.655e-05, eta: 13:35:31, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1940, decode.acc_seg: 91.7709, aux.loss_ce: 0.0812, aux.acc_seg: 91.3321, loss: 0.2752 +2024-06-18 18:43:26,850 - mmseg - INFO - Iter [46950/80000] lr: 1.653e-05, eta: 13:34:11, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1935, decode.acc_seg: 91.9429, aux.loss_ce: 0.0812, aux.acc_seg: 91.6202, loss: 0.2746 +2024-06-18 18:44:33,107 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 18:44:33,107 - mmseg - INFO - Iter [47000/80000] lr: 1.650e-05, eta: 13:32:52, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1967, decode.acc_seg: 91.5852, aux.loss_ce: 0.0823, aux.acc_seg: 91.1799, loss: 0.2790 +2024-06-18 18:46:11,851 - mmseg - INFO - per class results: +2024-06-18 18:46:11,858 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.97 | 89.63 | +| building | 85.08 | 93.5 | +| sky | 94.91 | 97.86 | +| floor | 85.13 | 91.53 | +| tree | 77.84 | 89.03 | +| ceiling | 87.43 | 94.33 | +| road | 86.26 | 90.63 | +| bed | 92.59 | 96.76 | +| windowpane | 66.44 | 81.23 | +| grass | 64.33 | 76.95 | +| cabinet | 63.8 | 77.27 | +| sidewalk | 71.25 | 86.75 | +| person | 85.5 | 92.96 | +| earth | 38.65 | 51.58 | +| door | 57.5 | 73.95 | +| table | 68.91 | 83.96 | +| mountain | 62.02 | 74.89 | +| plant | 55.91 | 66.67 | +| curtain | 78.17 | 87.54 | +| chair | 66.54 | 79.12 | +| car | 86.02 | 91.4 | +| water | 63.39 | 77.09 | +| painting | 77.0 | 90.2 | +| sofa | 82.74 | 90.74 | +| shelf | 50.47 | 68.6 | +| house | 55.94 | 70.31 | +| sea | 69.65 | 82.68 | +| mirror | 77.03 | 83.3 | +| rug | 70.86 | 84.01 | +| field | 33.79 | 66.89 | +| armchair | 60.76 | 76.11 | +| seat | 66.41 | 87.89 | +| fence | 52.22 | 71.93 | +| desk | 58.85 | 74.58 | +| rock | 54.24 | 80.18 | +| wardrobe | 52.44 | 70.5 | +| lamp | 71.74 | 80.22 | +| bathtub | 84.5 | 87.05 | +| railing | 41.67 | 58.44 | +| cushion | 70.86 | 82.55 | +| base | 45.69 | 61.05 | +| box | 34.27 | 45.31 | +| column | 53.05 | 67.8 | +| signboard | 41.14 | 55.42 | +| chest of drawers | 43.48 | 63.91 | +| counter | 38.55 | 59.26 | +| sand | 55.44 | 86.13 | +| sink | 76.39 | 82.64 | +| skyscraper | 57.94 | 77.21 | +| fireplace | 74.26 | 94.12 | +| refrigerator | 79.3 | 86.68 | +| grandstand | 49.83 | 85.85 | +| path | 29.13 | 43.1 | +| stairs | 28.16 | 34.34 | +| runway | 71.22 | 92.24 | +| case | 57.04 | 76.76 | +| pool table | 94.65 | 98.04 | +| pillow | 70.65 | 82.91 | +| screen door | 60.53 | 61.81 | +| stairway | 51.55 | 66.23 | +| river | 14.51 | 38.39 | +| bridge | 74.23 | 88.93 | +| bookcase | 41.99 | 56.29 | +| blind | 44.11 | 52.45 | +| coffee table | 67.13 | 84.85 | +| toilet | 89.32 | 92.52 | +| flower | 47.26 | 65.8 | +| book | 53.78 | 71.57 | +| hill | 8.06 | 11.75 | +| bench | 50.42 | 59.82 | +| countertop | 63.69 | 80.61 | +| stove | 84.49 | 92.38 | +| palm | 51.32 | 83.74 | +| kitchen island | 46.72 | 81.7 | +| computer | 81.14 | 92.6 | +| swivel chair | 47.21 | 59.91 | +| boat | 54.58 | 87.77 | +| bar | 33.72 | 34.64 | +| arcade machine | 78.15 | 84.58 | +| hovel | 44.16 | 49.42 | +| bus | 93.04 | 96.3 | +| towel | 76.44 | 87.51 | +| light | 61.25 | 72.17 | +| truck | 43.83 | 60.3 | +| tower | 11.79 | 15.86 | +| chandelier | 71.82 | 85.13 | +| awning | 46.18 | 61.42 | +| streetlight | 28.57 | 36.54 | +| booth | 55.39 | 64.11 | +| television receiver | 71.58 | 79.23 | +| airplane | 68.66 | 74.19 | +| dirt track | 7.79 | 35.41 | +| apparel | 48.13 | 64.85 | +| pole | 24.74 | 33.41 | +| land | 3.08 | 8.24 | +| bannister | 17.78 | 22.54 | +| escalator | 62.69 | 83.02 | +| ottoman | 49.53 | 69.31 | +| bottle | 43.83 | 60.74 | +| buffet | 30.49 | 32.24 | +| poster | 34.71 | 43.66 | +| stage | 24.15 | 47.45 | +| van | 40.53 | 70.3 | +| ship | 92.32 | 95.28 | +| fountain | 25.83 | 26.62 | +| conveyer belt | 75.17 | 93.73 | +| canopy | 56.38 | 70.04 | +| washer | 84.99 | 87.86 | +| plaything | 35.1 | 49.92 | +| swimming pool | 61.06 | 93.71 | +| stool | 56.4 | 63.25 | +| barrel | 51.72 | 64.77 | +| basket | 39.06 | 58.63 | +| waterfall | 65.61 | 86.05 | +| tent | 90.22 | 98.48 | +| bag | 15.33 | 16.85 | +| minibike | 74.7 | 85.98 | +| cradle | 79.09 | 98.14 | +| oven | 52.14 | 65.35 | +| ball | 41.76 | 43.5 | +| food | 56.86 | 69.54 | +| step | 10.86 | 13.04 | +| tank | 65.75 | 75.11 | +| trade name | 28.99 | 32.88 | +| microwave | 85.86 | 96.3 | +| pot | 55.69 | 64.04 | +| animal | 69.74 | 71.81 | +| bicycle | 57.48 | 79.91 | +| lake | 0.4 | 0.42 | +| dishwasher | 69.1 | 85.97 | +| screen | 49.11 | 77.14 | +| blanket | 30.24 | 35.57 | +| sculpture | 73.6 | 88.16 | +| hood | 59.26 | 69.07 | +| sconce | 51.82 | 57.63 | +| vase | 48.04 | 62.52 | +| traffic light | 39.05 | 56.44 | +| tray | 13.19 | 20.57 | +| ashcan | 45.85 | 60.53 | +| fan | 65.32 | 74.73 | +| pier | 31.02 | 48.15 | +| crt screen | 14.49 | 28.46 | +| plate | 56.3 | 68.58 | +| monitor | 54.42 | 61.99 | +| bulletin board | 59.44 | 66.11 | +| shower | 0.0 | 0.0 | +| radiator | 61.95 | 74.95 | +| glass | 18.74 | 19.9 | +| clock | 35.97 | 41.25 | +| flag | 71.66 | 77.71 | ++---------------------+-------+-------+ +2024-06-18 18:46:11,858 - mmseg - INFO - Summary: +2024-06-18 18:46:11,858 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.82 | 55.55 | 68.15 | ++-------+-------+-------+ +2024-06-18 18:46:11,859 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 18:46:11,859 - mmseg - INFO - Iter(val) [250] aAcc: 0.8582, mIoU: 0.5555, mAcc: 0.6815, IoU.wall: 0.8197, IoU.building: 0.8508, IoU.sky: 0.9491, IoU.floor: 0.8513, IoU.tree: 0.7784, IoU.ceiling: 0.8743, IoU.road: 0.8626, IoU.bed : 0.9259, IoU.windowpane: 0.6644, IoU.grass: 0.6433, IoU.cabinet: 0.6380, IoU.sidewalk: 0.7125, IoU.person: 0.8550, IoU.earth: 0.3865, IoU.door: 0.5750, IoU.table: 0.6891, IoU.mountain: 0.6202, IoU.plant: 0.5591, IoU.curtain: 0.7817, IoU.chair: 0.6654, IoU.car: 0.8602, IoU.water: 0.6339, IoU.painting: 0.7700, IoU.sofa: 0.8274, IoU.shelf: 0.5047, IoU.house: 0.5594, IoU.sea: 0.6965, IoU.mirror: 0.7703, IoU.rug: 0.7086, IoU.field: 0.3379, IoU.armchair: 0.6076, IoU.seat: 0.6641, IoU.fence: 0.5222, IoU.desk: 0.5885, IoU.rock: 0.5424, IoU.wardrobe: 0.5244, IoU.lamp: 0.7174, IoU.bathtub: 0.8450, IoU.railing: 0.4167, IoU.cushion: 0.7086, IoU.base: 0.4569, IoU.box: 0.3427, IoU.column: 0.5305, IoU.signboard: 0.4114, IoU.chest of drawers: 0.4348, IoU.counter: 0.3855, IoU.sand: 0.5544, IoU.sink: 0.7639, IoU.skyscraper: 0.5794, IoU.fireplace: 0.7426, IoU.refrigerator: 0.7930, IoU.grandstand: 0.4983, IoU.path: 0.2913, IoU.stairs: 0.2816, IoU.runway: 0.7122, IoU.case: 0.5704, IoU.pool table: 0.9465, IoU.pillow: 0.7065, IoU.screen door: 0.6053, IoU.stairway: 0.5155, IoU.river: 0.1451, IoU.bridge: 0.7423, IoU.bookcase: 0.4199, IoU.blind: 0.4411, IoU.coffee table: 0.6713, IoU.toilet: 0.8932, IoU.flower: 0.4726, IoU.book: 0.5378, IoU.hill: 0.0806, IoU.bench: 0.5042, IoU.countertop: 0.6369, IoU.stove: 0.8449, IoU.palm: 0.5132, IoU.kitchen island: 0.4672, IoU.computer: 0.8114, IoU.swivel chair: 0.4721, IoU.boat: 0.5458, IoU.bar: 0.3372, IoU.arcade machine: 0.7815, IoU.hovel: 0.4416, IoU.bus: 0.9304, IoU.towel: 0.7644, IoU.light: 0.6125, IoU.truck: 0.4383, IoU.tower: 0.1179, IoU.chandelier: 0.7182, IoU.awning: 0.4618, IoU.streetlight: 0.2857, IoU.booth: 0.5539, IoU.television receiver: 0.7158, IoU.airplane: 0.6866, IoU.dirt track: 0.0779, IoU.apparel: 0.4813, IoU.pole: 0.2474, IoU.land: 0.0308, IoU.bannister: 0.1778, IoU.escalator: 0.6269, IoU.ottoman: 0.4953, IoU.bottle: 0.4383, IoU.buffet: 0.3049, IoU.poster: 0.3471, IoU.stage: 0.2415, IoU.van: 0.4053, IoU.ship: 0.9232, IoU.fountain: 0.2583, IoU.conveyer belt: 0.7517, IoU.canopy: 0.5638, IoU.washer: 0.8499, IoU.plaything: 0.3510, IoU.swimming pool: 0.6106, IoU.stool: 0.5640, IoU.barrel: 0.5172, IoU.basket: 0.3906, IoU.waterfall: 0.6561, IoU.tent: 0.9022, IoU.bag: 0.1533, IoU.minibike: 0.7470, IoU.cradle: 0.7909, IoU.oven: 0.5214, IoU.ball: 0.4176, IoU.food: 0.5686, IoU.step: 0.1086, IoU.tank: 0.6575, IoU.trade name: 0.2899, IoU.microwave: 0.8586, IoU.pot: 0.5569, IoU.animal: 0.6974, IoU.bicycle: 0.5748, IoU.lake: 0.0040, IoU.dishwasher: 0.6910, IoU.screen: 0.4911, IoU.blanket: 0.3024, IoU.sculpture: 0.7360, IoU.hood: 0.5926, IoU.sconce: 0.5182, IoU.vase: 0.4804, IoU.traffic light: 0.3905, IoU.tray: 0.1319, IoU.ashcan: 0.4585, IoU.fan: 0.6532, IoU.pier: 0.3102, IoU.crt screen: 0.1449, IoU.plate: 0.5630, IoU.monitor: 0.5442, IoU.bulletin board: 0.5944, IoU.shower: 0.0000, IoU.radiator: 0.6195, IoU.glass: 0.1874, IoU.clock: 0.3597, IoU.flag: 0.7166, Acc.wall: 0.8963, Acc.building: 0.9350, Acc.sky: 0.9786, Acc.floor: 0.9153, Acc.tree: 0.8903, Acc.ceiling: 0.9433, Acc.road: 0.9063, Acc.bed : 0.9676, Acc.windowpane: 0.8123, Acc.grass: 0.7695, Acc.cabinet: 0.7727, Acc.sidewalk: 0.8675, Acc.person: 0.9296, Acc.earth: 0.5158, Acc.door: 0.7395, Acc.table: 0.8396, Acc.mountain: 0.7489, Acc.plant: 0.6667, Acc.curtain: 0.8754, Acc.chair: 0.7912, Acc.car: 0.9140, Acc.water: 0.7709, Acc.painting: 0.9020, Acc.sofa: 0.9074, Acc.shelf: 0.6860, Acc.house: 0.7031, Acc.sea: 0.8268, Acc.mirror: 0.8330, Acc.rug: 0.8401, Acc.field: 0.6689, Acc.armchair: 0.7611, Acc.seat: 0.8789, Acc.fence: 0.7193, Acc.desk: 0.7458, Acc.rock: 0.8018, Acc.wardrobe: 0.7050, Acc.lamp: 0.8022, Acc.bathtub: 0.8705, Acc.railing: 0.5844, Acc.cushion: 0.8255, Acc.base: 0.6105, Acc.box: 0.4531, Acc.column: 0.6780, Acc.signboard: 0.5542, Acc.chest of drawers: 0.6391, Acc.counter: 0.5926, Acc.sand: 0.8613, Acc.sink: 0.8264, Acc.skyscraper: 0.7721, Acc.fireplace: 0.9412, Acc.refrigerator: 0.8668, Acc.grandstand: 0.8585, Acc.path: 0.4310, Acc.stairs: 0.3434, Acc.runway: 0.9224, Acc.case: 0.7676, Acc.pool table: 0.9804, Acc.pillow: 0.8291, Acc.screen door: 0.6181, Acc.stairway: 0.6623, Acc.river: 0.3839, Acc.bridge: 0.8893, Acc.bookcase: 0.5629, Acc.blind: 0.5245, Acc.coffee table: 0.8485, Acc.toilet: 0.9252, Acc.flower: 0.6580, Acc.book: 0.7157, Acc.hill: 0.1175, Acc.bench: 0.5982, Acc.countertop: 0.8061, Acc.stove: 0.9238, Acc.palm: 0.8374, Acc.kitchen island: 0.8170, Acc.computer: 0.9260, Acc.swivel chair: 0.5991, Acc.boat: 0.8777, Acc.bar: 0.3464, Acc.arcade machine: 0.8458, Acc.hovel: 0.4942, Acc.bus: 0.9630, Acc.towel: 0.8751, Acc.light: 0.7217, Acc.truck: 0.6030, Acc.tower: 0.1586, Acc.chandelier: 0.8513, Acc.awning: 0.6142, Acc.streetlight: 0.3654, Acc.booth: 0.6411, Acc.television receiver: 0.7923, Acc.airplane: 0.7419, Acc.dirt track: 0.3541, Acc.apparel: 0.6485, Acc.pole: 0.3341, Acc.land: 0.0824, Acc.bannister: 0.2254, Acc.escalator: 0.8302, Acc.ottoman: 0.6931, Acc.bottle: 0.6074, Acc.buffet: 0.3224, Acc.poster: 0.4366, Acc.stage: 0.4745, Acc.van: 0.7030, Acc.ship: 0.9528, Acc.fountain: 0.2662, Acc.conveyer belt: 0.9373, Acc.canopy: 0.7004, Acc.washer: 0.8786, Acc.plaything: 0.4992, Acc.swimming pool: 0.9371, Acc.stool: 0.6325, Acc.barrel: 0.6477, Acc.basket: 0.5863, Acc.waterfall: 0.8605, Acc.tent: 0.9848, Acc.bag: 0.1685, Acc.minibike: 0.8598, Acc.cradle: 0.9814, Acc.oven: 0.6535, Acc.ball: 0.4350, Acc.food: 0.6954, Acc.step: 0.1304, Acc.tank: 0.7511, Acc.trade name: 0.3288, Acc.microwave: 0.9630, Acc.pot: 0.6404, Acc.animal: 0.7181, Acc.bicycle: 0.7991, Acc.lake: 0.0042, Acc.dishwasher: 0.8597, Acc.screen: 0.7714, Acc.blanket: 0.3557, Acc.sculpture: 0.8816, Acc.hood: 0.6907, Acc.sconce: 0.5763, Acc.vase: 0.6252, Acc.traffic light: 0.5644, Acc.tray: 0.2057, Acc.ashcan: 0.6053, Acc.fan: 0.7473, Acc.pier: 0.4815, Acc.crt screen: 0.2846, Acc.plate: 0.6858, Acc.monitor: 0.6199, Acc.bulletin board: 0.6611, Acc.shower: 0.0000, Acc.radiator: 0.7495, Acc.glass: 0.1990, Acc.clock: 0.4125, Acc.flag: 0.7771 +2024-06-18 18:47:18,807 - mmseg - INFO - Iter [47050/80000] lr: 1.648e-05, eta: 13:32:43, time: 3.314, data_time: 1.992, memory: 70498, decode.loss_ce: 0.1957, decode.acc_seg: 91.7096, aux.loss_ce: 0.0822, aux.acc_seg: 91.3510, loss: 0.2779 +2024-06-18 18:48:25,359 - mmseg - INFO - Iter [47100/80000] lr: 1.645e-05, eta: 13:31:23, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2107, decode.acc_seg: 91.4250, aux.loss_ce: 0.0873, aux.acc_seg: 91.0858, loss: 0.2979 +2024-06-18 18:49:31,865 - mmseg - INFO - Iter [47150/80000] lr: 1.643e-05, eta: 13:30:04, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1914, decode.acc_seg: 91.5965, aux.loss_ce: 0.0793, aux.acc_seg: 91.4053, loss: 0.2707 +2024-06-18 18:50:38,261 - mmseg - INFO - Iter [47200/80000] lr: 1.640e-05, eta: 13:28:45, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1953, decode.acc_seg: 91.6712, aux.loss_ce: 0.0821, aux.acc_seg: 91.2439, loss: 0.2774 +2024-06-18 18:51:44,810 - mmseg - INFO - Iter [47250/80000] lr: 1.638e-05, eta: 13:27:26, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1849, decode.acc_seg: 91.8726, aux.loss_ce: 0.0777, aux.acc_seg: 91.4739, loss: 0.2626 +2024-06-18 18:52:51,282 - mmseg - INFO - Iter [47300/80000] lr: 1.635e-05, eta: 13:26:07, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1902, decode.acc_seg: 91.9583, aux.loss_ce: 0.0801, aux.acc_seg: 91.5807, loss: 0.2704 +2024-06-18 18:53:57,697 - mmseg - INFO - Iter [47350/80000] lr: 1.633e-05, eta: 13:24:47, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1879, decode.acc_seg: 91.9097, aux.loss_ce: 0.0784, aux.acc_seg: 91.6134, loss: 0.2663 +2024-06-18 18:55:04,259 - mmseg - INFO - Iter [47400/80000] lr: 1.630e-05, eta: 13:23:28, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1932, decode.acc_seg: 91.7600, aux.loss_ce: 0.0817, aux.acc_seg: 91.3434, loss: 0.2750 +2024-06-18 18:56:10,822 - mmseg - INFO - Iter [47450/80000] lr: 1.628e-05, eta: 13:22:09, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2006, decode.acc_seg: 91.5419, aux.loss_ce: 0.0841, aux.acc_seg: 91.1827, loss: 0.2847 +2024-06-18 18:57:17,423 - mmseg - INFO - Iter [47500/80000] lr: 1.625e-05, eta: 13:20:50, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2014, decode.acc_seg: 91.5957, aux.loss_ce: 0.0847, aux.acc_seg: 91.1828, loss: 0.2862 +2024-06-18 18:58:23,961 - mmseg - INFO - Iter [47550/80000] lr: 1.623e-05, eta: 13:19:32, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1939, decode.acc_seg: 91.8735, aux.loss_ce: 0.0810, aux.acc_seg: 91.5006, loss: 0.2749 +2024-06-18 18:59:30,425 - mmseg - INFO - Iter [47600/80000] lr: 1.620e-05, eta: 13:18:13, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2023, decode.acc_seg: 91.4330, aux.loss_ce: 0.0846, aux.acc_seg: 91.1076, loss: 0.2870 +2024-06-18 19:00:36,833 - mmseg - INFO - Iter [47650/80000] lr: 1.618e-05, eta: 13:16:54, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1854, decode.acc_seg: 91.9445, aux.loss_ce: 0.0775, aux.acc_seg: 91.5307, loss: 0.2628 +2024-06-18 19:01:43,605 - mmseg - INFO - Iter [47700/80000] lr: 1.615e-05, eta: 13:15:35, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1924, decode.acc_seg: 91.5649, aux.loss_ce: 0.0805, aux.acc_seg: 91.1934, loss: 0.2729 +2024-06-18 19:02:50,071 - mmseg - INFO - Iter [47750/80000] lr: 1.613e-05, eta: 13:14:16, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2180, decode.acc_seg: 91.0935, aux.loss_ce: 0.0895, aux.acc_seg: 90.7839, loss: 0.3075 +2024-06-18 19:03:56,555 - mmseg - INFO - Iter [47800/80000] lr: 1.610e-05, eta: 13:12:57, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1958, decode.acc_seg: 91.5531, aux.loss_ce: 0.0816, aux.acc_seg: 91.2773, loss: 0.2774 +2024-06-18 19:05:02,998 - mmseg - INFO - Iter [47850/80000] lr: 1.608e-05, eta: 13:11:38, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2083, decode.acc_seg: 91.1002, aux.loss_ce: 0.0871, aux.acc_seg: 90.7152, loss: 0.2954 +2024-06-18 19:06:09,686 - mmseg - INFO - Iter [47900/80000] lr: 1.605e-05, eta: 13:10:19, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1964, decode.acc_seg: 91.5706, aux.loss_ce: 0.0819, aux.acc_seg: 91.2336, loss: 0.2782 +2024-06-18 19:07:16,239 - mmseg - INFO - Iter [47950/80000] lr: 1.603e-05, eta: 13:09:01, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1949, decode.acc_seg: 91.4630, aux.loss_ce: 0.0813, aux.acc_seg: 91.1095, loss: 0.2762 +2024-06-18 19:08:24,935 - mmseg - INFO - Saving checkpoint at 48000 iterations +2024-06-18 19:10:07,994 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 19:10:07,994 - mmseg - INFO - Iter [48000/80000] lr: 1.600e-05, eta: 13:08:52, time: 3.435, data_time: 0.052, memory: 70498, decode.loss_ce: 0.2021, decode.acc_seg: 91.4776, aux.loss_ce: 0.0845, aux.acc_seg: 91.0907, loss: 0.2866 +2024-06-18 19:11:44,417 - mmseg - INFO - per class results: +2024-06-18 19:11:44,423 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.62 | 89.13 | +| building | 85.02 | 93.3 | +| sky | 94.9 | 97.56 | +| floor | 84.86 | 90.93 | +| tree | 77.79 | 89.98 | +| ceiling | 87.23 | 93.37 | +| road | 85.95 | 92.59 | +| bed | 92.5 | 96.75 | +| windowpane | 65.6 | 83.63 | +| grass | 66.12 | 78.76 | +| cabinet | 64.37 | 72.74 | +| sidewalk | 70.26 | 81.97 | +| person | 85.1 | 94.77 | +| earth | 37.5 | 50.0 | +| door | 59.82 | 74.18 | +| table | 69.26 | 81.01 | +| mountain | 62.28 | 74.81 | +| plant | 57.23 | 68.41 | +| curtain | 76.45 | 83.29 | +| chair | 65.98 | 76.41 | +| car | 87.43 | 94.04 | +| water | 58.31 | 71.7 | +| painting | 74.97 | 90.81 | +| sofa | 80.41 | 89.75 | +| shelf | 47.74 | 65.63 | +| house | 57.92 | 82.02 | +| sea | 60.01 | 76.66 | +| mirror | 77.79 | 82.51 | +| rug | 69.64 | 81.72 | +| field | 34.87 | 65.93 | +| armchair | 60.29 | 79.62 | +| seat | 63.39 | 88.64 | +| fence | 52.99 | 69.79 | +| desk | 54.25 | 76.26 | +| rock | 53.89 | 83.05 | +| wardrobe | 55.58 | 73.24 | +| lamp | 73.35 | 82.78 | +| bathtub | 84.04 | 85.79 | +| railing | 41.06 | 59.48 | +| cushion | 68.34 | 75.28 | +| base | 44.71 | 66.23 | +| box | 37.2 | 49.42 | +| column | 53.09 | 65.17 | +| signboard | 39.13 | 49.72 | +| chest of drawers | 45.87 | 70.17 | +| counter | 39.5 | 48.93 | +| sand | 53.54 | 77.74 | +| sink | 76.13 | 84.54 | +| skyscraper | 48.0 | 58.21 | +| fireplace | 74.85 | 91.52 | +| refrigerator | 78.0 | 89.21 | +| grandstand | 52.22 | 82.14 | +| path | 28.66 | 38.89 | +| stairs | 20.62 | 26.22 | +| runway | 71.59 | 93.65 | +| case | 56.91 | 82.97 | +| pool table | 95.16 | 97.66 | +| pillow | 70.41 | 84.74 | +| screen door | 78.8 | 83.75 | +| stairway | 37.6 | 60.96 | +| river | 15.65 | 42.04 | +| bridge | 73.93 | 85.88 | +| bookcase | 41.26 | 56.87 | +| blind | 43.29 | 45.62 | +| coffee table | 66.1 | 85.21 | +| toilet | 89.01 | 92.98 | +| flower | 46.89 | 60.11 | +| book | 54.1 | 74.17 | +| hill | 5.53 | 10.29 | +| bench | 46.84 | 57.45 | +| countertop | 64.45 | 82.25 | +| stove | 84.93 | 94.02 | +| palm | 57.39 | 75.79 | +| kitchen island | 47.43 | 88.39 | +| computer | 79.52 | 94.33 | +| swivel chair | 48.96 | 80.44 | +| boat | 58.41 | 88.13 | +| bar | 57.37 | 75.49 | +| arcade machine | 77.19 | 81.41 | +| hovel | 42.21 | 49.17 | +| bus | 93.17 | 95.72 | +| towel | 78.54 | 88.85 | +| light | 61.09 | 71.57 | +| truck | 44.51 | 57.14 | +| tower | 6.33 | 8.34 | +| chandelier | 72.47 | 87.53 | +| awning | 43.55 | 65.22 | +| streetlight | 32.43 | 43.39 | +| booth | 41.92 | 77.33 | +| television receiver | 73.78 | 85.65 | +| airplane | 81.09 | 92.73 | +| dirt track | 7.86 | 45.85 | +| apparel | 48.29 | 66.44 | +| pole | 24.07 | 30.66 | +| land | 4.96 | 8.53 | +| bannister | 17.01 | 21.2 | +| escalator | 61.16 | 79.94 | +| ottoman | 49.82 | 70.06 | +| bottle | 42.66 | 69.68 | +| buffet | 50.56 | 69.47 | +| poster | 35.33 | 44.18 | +| stage | 24.34 | 47.03 | +| van | 46.05 | 60.62 | +| ship | 87.23 | 89.37 | +| fountain | 28.49 | 29.04 | +| conveyer belt | 78.37 | 93.42 | +| canopy | 54.92 | 79.2 | +| washer | 84.39 | 87.69 | +| plaything | 34.34 | 49.72 | +| swimming pool | 61.97 | 91.27 | +| stool | 49.74 | 70.06 | +| barrel | 55.71 | 64.52 | +| basket | 39.64 | 60.46 | +| waterfall | 55.0 | 71.34 | +| tent | 89.49 | 98.6 | +| bag | 19.66 | 22.1 | +| minibike | 75.57 | 87.95 | +| cradle | 77.06 | 98.06 | +| oven | 50.18 | 71.17 | +| ball | 55.88 | 70.09 | +| food | 57.13 | 71.01 | +| step | 8.35 | 10.29 | +| tank | 66.59 | 80.65 | +| trade name | 31.43 | 36.08 | +| microwave | 85.81 | 95.71 | +| pot | 56.49 | 66.4 | +| animal | 66.6 | 68.43 | +| bicycle | 58.16 | 77.89 | +| lake | 19.81 | 20.97 | +| dishwasher | 68.58 | 87.21 | +| screen | 61.72 | 93.68 | +| blanket | 27.73 | 31.15 | +| sculpture | 74.12 | 86.98 | +| hood | 60.02 | 72.28 | +| sconce | 55.05 | 63.82 | +| vase | 48.13 | 63.43 | +| traffic light | 36.48 | 62.35 | +| tray | 9.99 | 16.04 | +| ashcan | 44.86 | 62.66 | +| fan | 66.18 | 81.34 | +| pier | 36.07 | 45.23 | +| crt screen | 24.28 | 29.12 | +| plate | 58.62 | 75.15 | +| monitor | 65.16 | 78.95 | +| bulletin board | 48.1 | 70.16 | +| shower | 5.74 | 8.47 | +| radiator | 60.93 | 78.85 | +| glass | 19.84 | 22.5 | +| clock | 41.27 | 50.32 | +| flag | 71.16 | 78.29 | ++---------------------+-------+-------+ +2024-06-18 19:11:44,423 - mmseg - INFO - Summary: +2024-06-18 19:11:44,423 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.69 | 56.01 | 69.78 | ++-------+-------+-------+ +2024-06-18 19:11:44,424 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 19:11:44,424 - mmseg - INFO - Iter(val) [250] aAcc: 0.8569, mIoU: 0.5601, mAcc: 0.6978, IoU.wall: 0.8162, IoU.building: 0.8502, IoU.sky: 0.9490, IoU.floor: 0.8486, IoU.tree: 0.7779, IoU.ceiling: 0.8723, IoU.road: 0.8595, IoU.bed : 0.9250, IoU.windowpane: 0.6560, IoU.grass: 0.6612, IoU.cabinet: 0.6437, IoU.sidewalk: 0.7026, IoU.person: 0.8510, IoU.earth: 0.3750, IoU.door: 0.5982, IoU.table: 0.6926, IoU.mountain: 0.6228, IoU.plant: 0.5723, IoU.curtain: 0.7645, IoU.chair: 0.6598, IoU.car: 0.8743, IoU.water: 0.5831, IoU.painting: 0.7497, IoU.sofa: 0.8041, IoU.shelf: 0.4774, IoU.house: 0.5792, IoU.sea: 0.6001, IoU.mirror: 0.7779, IoU.rug: 0.6964, IoU.field: 0.3487, IoU.armchair: 0.6029, IoU.seat: 0.6339, IoU.fence: 0.5299, IoU.desk: 0.5425, IoU.rock: 0.5389, IoU.wardrobe: 0.5558, IoU.lamp: 0.7335, IoU.bathtub: 0.8404, IoU.railing: 0.4106, IoU.cushion: 0.6834, IoU.base: 0.4471, IoU.box: 0.3720, IoU.column: 0.5309, IoU.signboard: 0.3913, IoU.chest of drawers: 0.4587, IoU.counter: 0.3950, IoU.sand: 0.5354, IoU.sink: 0.7613, IoU.skyscraper: 0.4800, IoU.fireplace: 0.7485, IoU.refrigerator: 0.7800, IoU.grandstand: 0.5222, IoU.path: 0.2866, IoU.stairs: 0.2062, IoU.runway: 0.7159, IoU.case: 0.5691, IoU.pool table: 0.9516, IoU.pillow: 0.7041, IoU.screen door: 0.7880, IoU.stairway: 0.3760, IoU.river: 0.1565, IoU.bridge: 0.7393, IoU.bookcase: 0.4126, IoU.blind: 0.4329, IoU.coffee table: 0.6610, IoU.toilet: 0.8901, IoU.flower: 0.4689, IoU.book: 0.5410, IoU.hill: 0.0553, IoU.bench: 0.4684, IoU.countertop: 0.6445, IoU.stove: 0.8493, IoU.palm: 0.5739, IoU.kitchen island: 0.4743, IoU.computer: 0.7952, IoU.swivel chair: 0.4896, IoU.boat: 0.5841, IoU.bar: 0.5737, IoU.arcade machine: 0.7719, IoU.hovel: 0.4221, IoU.bus: 0.9317, IoU.towel: 0.7854, IoU.light: 0.6109, IoU.truck: 0.4451, IoU.tower: 0.0633, IoU.chandelier: 0.7247, IoU.awning: 0.4355, IoU.streetlight: 0.3243, IoU.booth: 0.4192, IoU.television receiver: 0.7378, IoU.airplane: 0.8109, IoU.dirt track: 0.0786, IoU.apparel: 0.4829, IoU.pole: 0.2407, IoU.land: 0.0496, IoU.bannister: 0.1701, IoU.escalator: 0.6116, IoU.ottoman: 0.4982, IoU.bottle: 0.4266, IoU.buffet: 0.5056, IoU.poster: 0.3533, IoU.stage: 0.2434, IoU.van: 0.4605, IoU.ship: 0.8723, IoU.fountain: 0.2849, IoU.conveyer belt: 0.7837, IoU.canopy: 0.5492, IoU.washer: 0.8439, IoU.plaything: 0.3434, IoU.swimming pool: 0.6197, IoU.stool: 0.4974, IoU.barrel: 0.5571, IoU.basket: 0.3964, IoU.waterfall: 0.5500, IoU.tent: 0.8949, IoU.bag: 0.1966, IoU.minibike: 0.7557, IoU.cradle: 0.7706, IoU.oven: 0.5018, IoU.ball: 0.5588, IoU.food: 0.5713, IoU.step: 0.0835, IoU.tank: 0.6659, IoU.trade name: 0.3143, IoU.microwave: 0.8581, IoU.pot: 0.5649, IoU.animal: 0.6660, IoU.bicycle: 0.5816, IoU.lake: 0.1981, IoU.dishwasher: 0.6858, IoU.screen: 0.6172, IoU.blanket: 0.2773, IoU.sculpture: 0.7412, IoU.hood: 0.6002, IoU.sconce: 0.5505, IoU.vase: 0.4813, IoU.traffic light: 0.3648, IoU.tray: 0.0999, IoU.ashcan: 0.4486, IoU.fan: 0.6618, IoU.pier: 0.3607, IoU.crt screen: 0.2428, IoU.plate: 0.5862, IoU.monitor: 0.6516, IoU.bulletin board: 0.4810, IoU.shower: 0.0574, IoU.radiator: 0.6093, IoU.glass: 0.1984, IoU.clock: 0.4127, IoU.flag: 0.7116, Acc.wall: 0.8913, Acc.building: 0.9330, Acc.sky: 0.9756, Acc.floor: 0.9093, Acc.tree: 0.8998, Acc.ceiling: 0.9337, Acc.road: 0.9259, Acc.bed : 0.9675, Acc.windowpane: 0.8363, Acc.grass: 0.7876, Acc.cabinet: 0.7274, Acc.sidewalk: 0.8197, Acc.person: 0.9477, Acc.earth: 0.5000, Acc.door: 0.7418, Acc.table: 0.8101, Acc.mountain: 0.7481, Acc.plant: 0.6841, Acc.curtain: 0.8329, Acc.chair: 0.7641, Acc.car: 0.9404, Acc.water: 0.7170, Acc.painting: 0.9081, Acc.sofa: 0.8975, Acc.shelf: 0.6563, Acc.house: 0.8202, Acc.sea: 0.7666, Acc.mirror: 0.8251, Acc.rug: 0.8172, Acc.field: 0.6593, Acc.armchair: 0.7962, Acc.seat: 0.8864, Acc.fence: 0.6979, Acc.desk: 0.7626, Acc.rock: 0.8305, Acc.wardrobe: 0.7324, Acc.lamp: 0.8278, Acc.bathtub: 0.8579, Acc.railing: 0.5948, Acc.cushion: 0.7528, Acc.base: 0.6623, Acc.box: 0.4942, Acc.column: 0.6517, Acc.signboard: 0.4972, Acc.chest of drawers: 0.7017, Acc.counter: 0.4893, Acc.sand: 0.7774, Acc.sink: 0.8454, Acc.skyscraper: 0.5821, Acc.fireplace: 0.9152, Acc.refrigerator: 0.8921, Acc.grandstand: 0.8214, Acc.path: 0.3889, Acc.stairs: 0.2622, Acc.runway: 0.9365, Acc.case: 0.8297, Acc.pool table: 0.9766, Acc.pillow: 0.8474, Acc.screen door: 0.8375, Acc.stairway: 0.6096, Acc.river: 0.4204, Acc.bridge: 0.8588, Acc.bookcase: 0.5687, Acc.blind: 0.4562, Acc.coffee table: 0.8521, Acc.toilet: 0.9298, Acc.flower: 0.6011, Acc.book: 0.7417, Acc.hill: 0.1029, Acc.bench: 0.5745, Acc.countertop: 0.8225, Acc.stove: 0.9402, Acc.palm: 0.7579, Acc.kitchen island: 0.8839, Acc.computer: 0.9433, Acc.swivel chair: 0.8044, Acc.boat: 0.8813, Acc.bar: 0.7549, Acc.arcade machine: 0.8141, Acc.hovel: 0.4917, Acc.bus: 0.9572, Acc.towel: 0.8885, Acc.light: 0.7157, Acc.truck: 0.5714, Acc.tower: 0.0834, Acc.chandelier: 0.8753, Acc.awning: 0.6522, Acc.streetlight: 0.4339, Acc.booth: 0.7733, Acc.television receiver: 0.8565, Acc.airplane: 0.9273, Acc.dirt track: 0.4585, Acc.apparel: 0.6644, Acc.pole: 0.3066, Acc.land: 0.0853, Acc.bannister: 0.2120, Acc.escalator: 0.7994, Acc.ottoman: 0.7006, Acc.bottle: 0.6968, Acc.buffet: 0.6947, Acc.poster: 0.4418, Acc.stage: 0.4703, Acc.van: 0.6062, Acc.ship: 0.8937, Acc.fountain: 0.2904, Acc.conveyer belt: 0.9342, Acc.canopy: 0.7920, Acc.washer: 0.8769, Acc.plaything: 0.4972, Acc.swimming pool: 0.9127, Acc.stool: 0.7006, Acc.barrel: 0.6452, Acc.basket: 0.6046, Acc.waterfall: 0.7134, Acc.tent: 0.9860, Acc.bag: 0.2210, Acc.minibike: 0.8795, Acc.cradle: 0.9806, Acc.oven: 0.7117, Acc.ball: 0.7009, Acc.food: 0.7101, Acc.step: 0.1029, Acc.tank: 0.8065, Acc.trade name: 0.3608, Acc.microwave: 0.9571, Acc.pot: 0.6640, Acc.animal: 0.6843, Acc.bicycle: 0.7789, Acc.lake: 0.2097, Acc.dishwasher: 0.8721, Acc.screen: 0.9368, Acc.blanket: 0.3115, Acc.sculpture: 0.8698, Acc.hood: 0.7228, Acc.sconce: 0.6382, Acc.vase: 0.6343, Acc.traffic light: 0.6235, Acc.tray: 0.1604, Acc.ashcan: 0.6266, Acc.fan: 0.8134, Acc.pier: 0.4523, Acc.crt screen: 0.2912, Acc.plate: 0.7515, Acc.monitor: 0.7895, Acc.bulletin board: 0.7016, Acc.shower: 0.0847, Acc.radiator: 0.7885, Acc.glass: 0.2250, Acc.clock: 0.5032, Acc.flag: 0.7829 +2024-06-18 19:12:51,530 - mmseg - INFO - Iter [48050/80000] lr: 1.598e-05, eta: 13:08:38, time: 3.271, data_time: 1.944, memory: 70498, decode.loss_ce: 0.1982, decode.acc_seg: 91.6350, aux.loss_ce: 0.0833, aux.acc_seg: 91.2605, loss: 0.2816 +2024-06-18 19:13:58,143 - mmseg - INFO - Iter [48100/80000] lr: 1.595e-05, eta: 13:07:19, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1877, decode.acc_seg: 91.9782, aux.loss_ce: 0.0788, aux.acc_seg: 91.5955, loss: 0.2664 +2024-06-18 19:15:04,542 - mmseg - INFO - Iter [48150/80000] lr: 1.593e-05, eta: 13:06:00, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1858, decode.acc_seg: 92.1874, aux.loss_ce: 0.0780, aux.acc_seg: 91.8296, loss: 0.2638 +2024-06-18 19:16:11,211 - mmseg - INFO - Iter [48200/80000] lr: 1.590e-05, eta: 13:04:41, time: 1.333, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1799, decode.acc_seg: 92.2733, aux.loss_ce: 0.0758, aux.acc_seg: 91.8042, loss: 0.2557 +2024-06-18 19:17:17,745 - mmseg - INFO - Iter [48250/80000] lr: 1.588e-05, eta: 13:03:22, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1880, decode.acc_seg: 91.9304, aux.loss_ce: 0.0792, aux.acc_seg: 91.5266, loss: 0.2672 +2024-06-18 19:18:24,032 - mmseg - INFO - Iter [48300/80000] lr: 1.585e-05, eta: 13:02:03, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2085, decode.acc_seg: 91.3370, aux.loss_ce: 0.0874, aux.acc_seg: 90.9441, loss: 0.2959 +2024-06-18 19:19:30,518 - mmseg - INFO - Iter [48350/80000] lr: 1.583e-05, eta: 13:00:44, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1994, decode.acc_seg: 91.3859, aux.loss_ce: 0.0842, aux.acc_seg: 91.0043, loss: 0.2836 +2024-06-18 19:20:36,916 - mmseg - INFO - Iter [48400/80000] lr: 1.580e-05, eta: 12:59:25, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1995, decode.acc_seg: 91.5533, aux.loss_ce: 0.0829, aux.acc_seg: 91.2693, loss: 0.2824 +2024-06-18 19:21:43,600 - mmseg - INFO - Iter [48450/80000] lr: 1.578e-05, eta: 12:58:06, time: 1.334, data_time: 0.009, memory: 70498, decode.loss_ce: 0.2005, decode.acc_seg: 91.9591, aux.loss_ce: 0.0847, aux.acc_seg: 91.5231, loss: 0.2852 +2024-06-18 19:22:49,946 - mmseg - INFO - Iter [48500/80000] lr: 1.575e-05, eta: 12:56:47, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1842, decode.acc_seg: 92.1749, aux.loss_ce: 0.0774, aux.acc_seg: 91.7606, loss: 0.2617 +2024-06-18 19:23:56,522 - mmseg - INFO - Iter [48550/80000] lr: 1.573e-05, eta: 12:55:28, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1999, decode.acc_seg: 91.5262, aux.loss_ce: 0.0844, aux.acc_seg: 91.0947, loss: 0.2843 +2024-06-18 19:25:03,041 - mmseg - INFO - Iter [48600/80000] lr: 1.570e-05, eta: 12:54:09, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1982, decode.acc_seg: 91.3124, aux.loss_ce: 0.0831, aux.acc_seg: 90.9446, loss: 0.2814 +2024-06-18 19:26:09,433 - mmseg - INFO - Iter [48650/80000] lr: 1.568e-05, eta: 12:52:51, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1994, decode.acc_seg: 91.7502, aux.loss_ce: 0.0828, aux.acc_seg: 91.4191, loss: 0.2821 +2024-06-18 19:27:15,970 - mmseg - INFO - Iter [48700/80000] lr: 1.565e-05, eta: 12:51:32, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1866, decode.acc_seg: 92.0996, aux.loss_ce: 0.0791, aux.acc_seg: 91.6856, loss: 0.2656 +2024-06-18 19:28:22,377 - mmseg - INFO - Iter [48750/80000] lr: 1.563e-05, eta: 12:50:13, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1998, decode.acc_seg: 91.5370, aux.loss_ce: 0.0840, aux.acc_seg: 91.0916, loss: 0.2838 +2024-06-18 19:29:28,774 - mmseg - INFO - Iter [48800/80000] lr: 1.560e-05, eta: 12:48:54, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1942, decode.acc_seg: 91.7904, aux.loss_ce: 0.0815, aux.acc_seg: 91.3740, loss: 0.2758 +2024-06-18 19:30:35,040 - mmseg - INFO - Iter [48850/80000] lr: 1.558e-05, eta: 12:47:36, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2057, decode.acc_seg: 91.7111, aux.loss_ce: 0.0859, aux.acc_seg: 91.3456, loss: 0.2916 +2024-06-18 19:31:41,704 - mmseg - INFO - Iter [48900/80000] lr: 1.555e-05, eta: 12:46:17, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2051, decode.acc_seg: 91.4669, aux.loss_ce: 0.0859, aux.acc_seg: 91.0773, loss: 0.2910 +2024-06-18 19:32:48,153 - mmseg - INFO - Iter [48950/80000] lr: 1.553e-05, eta: 12:44:58, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1965, decode.acc_seg: 91.5500, aux.loss_ce: 0.0827, aux.acc_seg: 91.2029, loss: 0.2791 +2024-06-18 19:33:54,445 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 19:33:54,445 - mmseg - INFO - Iter [49000/80000] lr: 1.550e-05, eta: 12:43:40, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1932, decode.acc_seg: 91.8332, aux.loss_ce: 0.0802, aux.acc_seg: 91.5491, loss: 0.2734 +2024-06-18 19:35:31,055 - mmseg - INFO - per class results: +2024-06-18 19:35:31,061 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.54 | 88.47 | +| building | 84.28 | 94.5 | +| sky | 94.98 | 97.61 | +| floor | 84.43 | 90.06 | +| tree | 76.85 | 89.54 | +| ceiling | 87.12 | 94.01 | +| road | 86.46 | 91.56 | +| bed | 92.31 | 96.89 | +| windowpane | 66.01 | 83.98 | +| grass | 65.83 | 78.04 | +| cabinet | 65.85 | 76.24 | +| sidewalk | 71.83 | 84.97 | +| person | 85.61 | 93.79 | +| earth | 38.66 | 52.32 | +| door | 59.91 | 72.84 | +| table | 69.49 | 82.16 | +| mountain | 61.5 | 76.74 | +| plant | 54.91 | 64.0 | +| curtain | 79.26 | 86.4 | +| chair | 65.96 | 78.92 | +| car | 87.35 | 94.1 | +| water | 63.53 | 77.43 | +| painting | 76.36 | 92.2 | +| sofa | 82.57 | 92.4 | +| shelf | 48.52 | 66.4 | +| house | 45.19 | 56.27 | +| sea | 66.76 | 81.89 | +| mirror | 76.14 | 81.23 | +| rug | 69.82 | 83.46 | +| field | 33.87 | 67.44 | +| armchair | 61.31 | 74.76 | +| seat | 64.21 | 88.33 | +| fence | 52.76 | 66.12 | +| desk | 59.3 | 75.89 | +| rock | 45.93 | 68.61 | +| wardrobe | 53.71 | 77.39 | +| lamp | 74.42 | 86.04 | +| bathtub | 83.9 | 85.94 | +| railing | 35.85 | 49.99 | +| cushion | 69.46 | 84.78 | +| base | 38.77 | 53.72 | +| box | 38.42 | 51.11 | +| column | 53.86 | 67.98 | +| signboard | 41.3 | 56.73 | +| chest of drawers | 47.29 | 69.66 | +| counter | 41.13 | 52.48 | +| sand | 50.99 | 70.79 | +| sink | 77.83 | 83.37 | +| skyscraper | 44.99 | 53.87 | +| fireplace | 71.71 | 94.04 | +| refrigerator | 81.38 | 89.08 | +| grandstand | 51.16 | 76.52 | +| path | 27.17 | 39.7 | +| stairs | 23.2 | 31.49 | +| runway | 67.84 | 88.54 | +| case | 59.07 | 78.73 | +| pool table | 94.23 | 97.96 | +| pillow | 65.91 | 73.05 | +| screen door | 82.1 | 91.04 | +| stairway | 39.42 | 59.09 | +| river | 20.6 | 40.52 | +| bridge | 75.76 | 86.32 | +| bookcase | 45.72 | 65.34 | +| blind | 44.01 | 52.14 | +| coffee table | 66.31 | 89.14 | +| toilet | 89.64 | 94.0 | +| flower | 40.12 | 57.07 | +| book | 55.76 | 74.53 | +| hill | 6.42 | 9.42 | +| bench | 50.32 | 62.31 | +| countertop | 63.45 | 83.67 | +| stove | 85.13 | 95.86 | +| palm | 57.96 | 74.66 | +| kitchen island | 47.65 | 80.85 | +| computer | 79.31 | 93.53 | +| swivel chair | 48.75 | 79.91 | +| boat | 54.49 | 86.46 | +| bar | 59.03 | 75.57 | +| arcade machine | 79.19 | 83.97 | +| hovel | 43.76 | 50.26 | +| bus | 92.9 | 96.42 | +| towel | 75.43 | 83.52 | +| light | 57.6 | 63.29 | +| truck | 45.4 | 63.11 | +| tower | 7.42 | 14.38 | +| chandelier | 72.54 | 85.67 | +| awning | 45.56 | 65.31 | +| streetlight | 32.78 | 46.51 | +| booth | 53.02 | 77.99 | +| television receiver | 73.85 | 89.01 | +| airplane | 74.1 | 79.66 | +| dirt track | 14.06 | 60.06 | +| apparel | 41.98 | 54.25 | +| pole | 23.44 | 31.19 | +| land | 2.44 | 3.97 | +| bannister | 17.07 | 25.2 | +| escalator | 58.58 | 79.97 | +| ottoman | 50.05 | 69.16 | +| bottle | 44.35 | 58.04 | +| buffet | 53.71 | 68.48 | +| poster | 35.02 | 45.72 | +| stage | 21.39 | 45.1 | +| van | 45.79 | 55.83 | +| ship | 78.84 | 99.23 | +| fountain | 23.03 | 24.07 | +| conveyer belt | 80.4 | 93.32 | +| canopy | 54.57 | 74.67 | +| washer | 90.32 | 93.89 | +| plaything | 37.13 | 50.94 | +| swimming pool | 62.59 | 92.42 | +| stool | 46.47 | 70.12 | +| barrel | 56.98 | 67.65 | +| basket | 40.81 | 57.2 | +| waterfall | 65.53 | 86.1 | +| tent | 90.55 | 98.52 | +| bag | 19.8 | 23.33 | +| minibike | 75.91 | 86.3 | +| cradle | 79.63 | 98.64 | +| oven | 56.66 | 69.71 | +| ball | 44.12 | 49.12 | +| food | 61.5 | 82.43 | +| step | 12.92 | 19.02 | +| tank | 68.3 | 74.71 | +| trade name | 31.89 | 37.16 | +| microwave | 87.3 | 95.87 | +| pot | 56.59 | 66.89 | +| animal | 67.73 | 70.7 | +| bicycle | 57.39 | 77.85 | +| lake | 45.05 | 63.64 | +| dishwasher | 73.77 | 78.97 | +| screen | 54.97 | 76.09 | +| blanket | 31.44 | 37.01 | +| sculpture | 76.12 | 88.18 | +| hood | 62.72 | 76.43 | +| sconce | 52.67 | 59.78 | +| vase | 48.55 | 59.55 | +| traffic light | 40.88 | 58.43 | +| tray | 10.56 | 16.19 | +| ashcan | 45.12 | 54.28 | +| fan | 66.29 | 79.32 | +| pier | 56.45 | 86.36 | +| crt screen | 20.06 | 30.58 | +| plate | 59.09 | 71.14 | +| monitor | 70.76 | 83.2 | +| bulletin board | 49.57 | 50.46 | +| shower | 2.26 | 2.47 | +| radiator | 62.33 | 77.59 | +| glass | 18.93 | 20.47 | +| clock | 32.14 | 38.48 | +| flag | 70.47 | 78.46 | ++---------------------+-------+-------+ +2024-06-18 19:35:31,061 - mmseg - INFO - Summary: +2024-06-18 19:35:31,061 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.68 | 56.47 | 69.65 | ++-------+-------+-------+ +2024-06-18 19:35:31,062 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 19:35:31,062 - mmseg - INFO - Iter(val) [250] aAcc: 0.8568, mIoU: 0.5647, mAcc: 0.6965, IoU.wall: 0.8154, IoU.building: 0.8428, IoU.sky: 0.9498, IoU.floor: 0.8443, IoU.tree: 0.7685, IoU.ceiling: 0.8712, IoU.road: 0.8646, IoU.bed : 0.9231, IoU.windowpane: 0.6601, IoU.grass: 0.6583, IoU.cabinet: 0.6585, IoU.sidewalk: 0.7183, IoU.person: 0.8561, IoU.earth: 0.3866, IoU.door: 0.5991, IoU.table: 0.6949, IoU.mountain: 0.6150, IoU.plant: 0.5491, IoU.curtain: 0.7926, IoU.chair: 0.6596, IoU.car: 0.8735, IoU.water: 0.6353, IoU.painting: 0.7636, IoU.sofa: 0.8257, IoU.shelf: 0.4852, IoU.house: 0.4519, IoU.sea: 0.6676, IoU.mirror: 0.7614, IoU.rug: 0.6982, IoU.field: 0.3387, IoU.armchair: 0.6131, IoU.seat: 0.6421, IoU.fence: 0.5276, IoU.desk: 0.5930, IoU.rock: 0.4593, IoU.wardrobe: 0.5371, IoU.lamp: 0.7442, IoU.bathtub: 0.8390, IoU.railing: 0.3585, IoU.cushion: 0.6946, IoU.base: 0.3877, IoU.box: 0.3842, IoU.column: 0.5386, IoU.signboard: 0.4130, IoU.chest of drawers: 0.4729, IoU.counter: 0.4113, IoU.sand: 0.5099, IoU.sink: 0.7783, IoU.skyscraper: 0.4499, IoU.fireplace: 0.7171, IoU.refrigerator: 0.8138, IoU.grandstand: 0.5116, IoU.path: 0.2717, IoU.stairs: 0.2320, IoU.runway: 0.6784, IoU.case: 0.5907, IoU.pool table: 0.9423, IoU.pillow: 0.6591, IoU.screen door: 0.8210, IoU.stairway: 0.3942, IoU.river: 0.2060, IoU.bridge: 0.7576, IoU.bookcase: 0.4572, IoU.blind: 0.4401, IoU.coffee table: 0.6631, IoU.toilet: 0.8964, IoU.flower: 0.4012, IoU.book: 0.5576, IoU.hill: 0.0642, IoU.bench: 0.5032, IoU.countertop: 0.6345, IoU.stove: 0.8513, IoU.palm: 0.5796, IoU.kitchen island: 0.4765, IoU.computer: 0.7931, IoU.swivel chair: 0.4875, IoU.boat: 0.5449, IoU.bar: 0.5903, IoU.arcade machine: 0.7919, IoU.hovel: 0.4376, IoU.bus: 0.9290, IoU.towel: 0.7543, IoU.light: 0.5760, IoU.truck: 0.4540, IoU.tower: 0.0742, IoU.chandelier: 0.7254, IoU.awning: 0.4556, IoU.streetlight: 0.3278, IoU.booth: 0.5302, IoU.television receiver: 0.7385, IoU.airplane: 0.7410, IoU.dirt track: 0.1406, IoU.apparel: 0.4198, IoU.pole: 0.2344, IoU.land: 0.0244, IoU.bannister: 0.1707, IoU.escalator: 0.5858, IoU.ottoman: 0.5005, IoU.bottle: 0.4435, IoU.buffet: 0.5371, IoU.poster: 0.3502, IoU.stage: 0.2139, IoU.van: 0.4579, IoU.ship: 0.7884, IoU.fountain: 0.2303, IoU.conveyer belt: 0.8040, IoU.canopy: 0.5457, IoU.washer: 0.9032, IoU.plaything: 0.3713, IoU.swimming pool: 0.6259, IoU.stool: 0.4647, IoU.barrel: 0.5698, IoU.basket: 0.4081, IoU.waterfall: 0.6553, IoU.tent: 0.9055, IoU.bag: 0.1980, IoU.minibike: 0.7591, IoU.cradle: 0.7963, IoU.oven: 0.5666, IoU.ball: 0.4412, IoU.food: 0.6150, IoU.step: 0.1292, IoU.tank: 0.6830, IoU.trade name: 0.3189, IoU.microwave: 0.8730, IoU.pot: 0.5659, IoU.animal: 0.6773, IoU.bicycle: 0.5739, IoU.lake: 0.4505, IoU.dishwasher: 0.7377, IoU.screen: 0.5497, IoU.blanket: 0.3144, IoU.sculpture: 0.7612, IoU.hood: 0.6272, IoU.sconce: 0.5267, IoU.vase: 0.4855, IoU.traffic light: 0.4088, IoU.tray: 0.1056, IoU.ashcan: 0.4512, IoU.fan: 0.6629, IoU.pier: 0.5645, IoU.crt screen: 0.2006, IoU.plate: 0.5909, IoU.monitor: 0.7076, IoU.bulletin board: 0.4957, IoU.shower: 0.0226, IoU.radiator: 0.6233, IoU.glass: 0.1893, IoU.clock: 0.3214, IoU.flag: 0.7047, Acc.wall: 0.8847, Acc.building: 0.9450, Acc.sky: 0.9761, Acc.floor: 0.9006, Acc.tree: 0.8954, Acc.ceiling: 0.9401, Acc.road: 0.9156, Acc.bed : 0.9689, Acc.windowpane: 0.8398, Acc.grass: 0.7804, Acc.cabinet: 0.7624, Acc.sidewalk: 0.8497, Acc.person: 0.9379, Acc.earth: 0.5232, Acc.door: 0.7284, Acc.table: 0.8216, Acc.mountain: 0.7674, Acc.plant: 0.6400, Acc.curtain: 0.8640, Acc.chair: 0.7892, Acc.car: 0.9410, Acc.water: 0.7743, Acc.painting: 0.9220, Acc.sofa: 0.9240, Acc.shelf: 0.6640, Acc.house: 0.5627, Acc.sea: 0.8189, Acc.mirror: 0.8123, Acc.rug: 0.8346, Acc.field: 0.6744, Acc.armchair: 0.7476, Acc.seat: 0.8833, Acc.fence: 0.6612, Acc.desk: 0.7589, Acc.rock: 0.6861, Acc.wardrobe: 0.7739, Acc.lamp: 0.8604, Acc.bathtub: 0.8594, Acc.railing: 0.4999, Acc.cushion: 0.8478, Acc.base: 0.5372, Acc.box: 0.5111, Acc.column: 0.6798, Acc.signboard: 0.5673, Acc.chest of drawers: 0.6966, Acc.counter: 0.5248, Acc.sand: 0.7079, Acc.sink: 0.8337, Acc.skyscraper: 0.5387, Acc.fireplace: 0.9404, Acc.refrigerator: 0.8908, Acc.grandstand: 0.7652, Acc.path: 0.3970, Acc.stairs: 0.3149, Acc.runway: 0.8854, Acc.case: 0.7873, Acc.pool table: 0.9796, Acc.pillow: 0.7305, Acc.screen door: 0.9104, Acc.stairway: 0.5909, Acc.river: 0.4052, Acc.bridge: 0.8632, Acc.bookcase: 0.6534, Acc.blind: 0.5214, Acc.coffee table: 0.8914, Acc.toilet: 0.9400, Acc.flower: 0.5707, Acc.book: 0.7453, Acc.hill: 0.0942, Acc.bench: 0.6231, Acc.countertop: 0.8367, Acc.stove: 0.9586, Acc.palm: 0.7466, Acc.kitchen island: 0.8085, Acc.computer: 0.9353, Acc.swivel chair: 0.7991, Acc.boat: 0.8646, Acc.bar: 0.7557, Acc.arcade machine: 0.8397, Acc.hovel: 0.5026, Acc.bus: 0.9642, Acc.towel: 0.8352, Acc.light: 0.6329, Acc.truck: 0.6311, Acc.tower: 0.1438, Acc.chandelier: 0.8567, Acc.awning: 0.6531, Acc.streetlight: 0.4651, Acc.booth: 0.7799, Acc.television receiver: 0.8901, Acc.airplane: 0.7966, Acc.dirt track: 0.6006, Acc.apparel: 0.5425, Acc.pole: 0.3119, Acc.land: 0.0397, Acc.bannister: 0.2520, Acc.escalator: 0.7997, Acc.ottoman: 0.6916, Acc.bottle: 0.5804, Acc.buffet: 0.6848, Acc.poster: 0.4572, Acc.stage: 0.4510, Acc.van: 0.5583, Acc.ship: 0.9923, Acc.fountain: 0.2407, Acc.conveyer belt: 0.9332, Acc.canopy: 0.7467, Acc.washer: 0.9389, Acc.plaything: 0.5094, Acc.swimming pool: 0.9242, Acc.stool: 0.7012, Acc.barrel: 0.6765, Acc.basket: 0.5720, Acc.waterfall: 0.8610, Acc.tent: 0.9852, Acc.bag: 0.2333, Acc.minibike: 0.8630, Acc.cradle: 0.9864, Acc.oven: 0.6971, Acc.ball: 0.4912, Acc.food: 0.8243, Acc.step: 0.1902, Acc.tank: 0.7471, Acc.trade name: 0.3716, Acc.microwave: 0.9587, Acc.pot: 0.6689, Acc.animal: 0.7070, Acc.bicycle: 0.7785, Acc.lake: 0.6364, Acc.dishwasher: 0.7897, Acc.screen: 0.7609, Acc.blanket: 0.3701, Acc.sculpture: 0.8818, Acc.hood: 0.7643, Acc.sconce: 0.5978, Acc.vase: 0.5955, Acc.traffic light: 0.5843, Acc.tray: 0.1619, Acc.ashcan: 0.5428, Acc.fan: 0.7932, Acc.pier: 0.8636, Acc.crt screen: 0.3058, Acc.plate: 0.7114, Acc.monitor: 0.8320, Acc.bulletin board: 0.5046, Acc.shower: 0.0247, Acc.radiator: 0.7759, Acc.glass: 0.2047, Acc.clock: 0.3848, Acc.flag: 0.7846 +2024-06-18 19:36:37,691 - mmseg - INFO - Iter [49050/80000] lr: 1.548e-05, eta: 12:43:22, time: 3.265, data_time: 1.949, memory: 70498, decode.loss_ce: 0.1900, decode.acc_seg: 91.8683, aux.loss_ce: 0.0800, aux.acc_seg: 91.4801, loss: 0.2700 +2024-06-18 19:37:44,014 - mmseg - INFO - Iter [49100/80000] lr: 1.545e-05, eta: 12:42:03, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1947, decode.acc_seg: 91.4947, aux.loss_ce: 0.0816, aux.acc_seg: 91.1169, loss: 0.2763 +2024-06-18 19:38:50,374 - mmseg - INFO - Iter [49150/80000] lr: 1.543e-05, eta: 12:40:44, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1802, decode.acc_seg: 92.1418, aux.loss_ce: 0.0754, aux.acc_seg: 91.8003, loss: 0.2556 +2024-06-18 19:39:56,990 - mmseg - INFO - Iter [49200/80000] lr: 1.540e-05, eta: 12:39:26, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1970, decode.acc_seg: 91.6375, aux.loss_ce: 0.0828, aux.acc_seg: 91.2508, loss: 0.2798 +2024-06-18 19:41:03,275 - mmseg - INFO - Iter [49250/80000] lr: 1.538e-05, eta: 12:38:07, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1959, decode.acc_seg: 91.5256, aux.loss_ce: 0.0822, aux.acc_seg: 91.1572, loss: 0.2781 +2024-06-18 19:42:11,749 - mmseg - INFO - Iter [49300/80000] lr: 1.535e-05, eta: 12:36:50, time: 1.369, data_time: 0.052, memory: 70498, decode.loss_ce: 0.1871, decode.acc_seg: 92.0777, aux.loss_ce: 0.0779, aux.acc_seg: 91.7486, loss: 0.2650 +2024-06-18 19:43:18,080 - mmseg - INFO - Iter [49350/80000] lr: 1.533e-05, eta: 12:35:31, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1884, decode.acc_seg: 91.8424, aux.loss_ce: 0.0791, aux.acc_seg: 91.4666, loss: 0.2675 +2024-06-18 19:44:24,754 - mmseg - INFO - Iter [49400/80000] lr: 1.530e-05, eta: 12:34:13, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1793, decode.acc_seg: 92.3827, aux.loss_ce: 0.0756, aux.acc_seg: 91.9354, loss: 0.2549 +2024-06-18 19:45:31,147 - mmseg - INFO - Iter [49450/80000] lr: 1.528e-05, eta: 12:32:54, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1861, decode.acc_seg: 92.0493, aux.loss_ce: 0.0784, aux.acc_seg: 91.6413, loss: 0.2645 +2024-06-18 19:46:37,378 - mmseg - INFO - Iter [49500/80000] lr: 1.525e-05, eta: 12:31:35, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1934, decode.acc_seg: 91.8494, aux.loss_ce: 0.0817, aux.acc_seg: 91.4523, loss: 0.2751 +2024-06-18 19:47:43,664 - mmseg - INFO - Iter [49550/80000] lr: 1.523e-05, eta: 12:30:17, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1869, decode.acc_seg: 92.0975, aux.loss_ce: 0.0790, aux.acc_seg: 91.7074, loss: 0.2659 +2024-06-18 19:48:50,016 - mmseg - INFO - Iter [49600/80000] lr: 1.520e-05, eta: 12:28:58, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1922, decode.acc_seg: 91.7153, aux.loss_ce: 0.0807, aux.acc_seg: 91.4123, loss: 0.2729 +2024-06-18 19:49:56,653 - mmseg - INFO - Iter [49650/80000] lr: 1.518e-05, eta: 12:27:40, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1850, decode.acc_seg: 92.0223, aux.loss_ce: 0.0781, aux.acc_seg: 91.6028, loss: 0.2631 +2024-06-18 19:51:03,071 - mmseg - INFO - Iter [49700/80000] lr: 1.515e-05, eta: 12:26:21, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1940, decode.acc_seg: 91.8583, aux.loss_ce: 0.0813, aux.acc_seg: 91.4025, loss: 0.2753 +2024-06-18 19:52:09,531 - mmseg - INFO - Iter [49750/80000] lr: 1.513e-05, eta: 12:25:03, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1863, decode.acc_seg: 92.0746, aux.loss_ce: 0.0781, aux.acc_seg: 91.6834, loss: 0.2644 +2024-06-18 19:53:16,075 - mmseg - INFO - Iter [49800/80000] lr: 1.510e-05, eta: 12:23:45, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1928, decode.acc_seg: 91.7489, aux.loss_ce: 0.0812, aux.acc_seg: 91.3099, loss: 0.2740 +2024-06-18 19:54:22,151 - mmseg - INFO - Iter [49850/80000] lr: 1.508e-05, eta: 12:22:26, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1833, decode.acc_seg: 92.0892, aux.loss_ce: 0.0768, aux.acc_seg: 91.7196, loss: 0.2601 +2024-06-18 19:55:28,140 - mmseg - INFO - Iter [49900/80000] lr: 1.505e-05, eta: 12:21:07, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1973, decode.acc_seg: 91.7147, aux.loss_ce: 0.0818, aux.acc_seg: 91.3843, loss: 0.2791 +2024-06-18 19:56:34,299 - mmseg - INFO - Iter [49950/80000] lr: 1.503e-05, eta: 12:19:49, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1891, decode.acc_seg: 91.8788, aux.loss_ce: 0.0797, aux.acc_seg: 91.4799, loss: 0.2688 +2024-06-18 19:57:40,579 - mmseg - INFO - Saving checkpoint at 50000 iterations +2024-06-18 19:59:20,882 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 19:59:20,882 - mmseg - INFO - Iter [50000/80000] lr: 1.500e-05, eta: 12:19:31, time: 3.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1985, decode.acc_seg: 91.5260, aux.loss_ce: 0.0835, aux.acc_seg: 91.0783, loss: 0.2820 +2024-06-18 20:00:57,713 - mmseg - INFO - per class results: +2024-06-18 20:00:57,719 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.91 | 90.3 | +| building | 85.19 | 93.58 | +| sky | 94.94 | 97.75 | +| floor | 85.23 | 92.51 | +| tree | 77.6 | 89.91 | +| ceiling | 87.21 | 92.43 | +| road | 86.29 | 91.89 | +| bed | 92.62 | 96.85 | +| windowpane | 67.04 | 81.27 | +| grass | 66.01 | 81.23 | +| cabinet | 63.58 | 72.03 | +| sidewalk | 71.76 | 86.17 | +| person | 85.77 | 93.54 | +| earth | 35.37 | 45.1 | +| door | 59.28 | 76.22 | +| table | 69.5 | 82.23 | +| mountain | 57.07 | 64.27 | +| plant | 55.43 | 66.14 | +| curtain | 78.05 | 89.38 | +| chair | 66.12 | 76.57 | +| car | 87.57 | 94.48 | +| water | 63.3 | 78.32 | +| painting | 76.97 | 87.08 | +| sofa | 82.1 | 90.36 | +| shelf | 51.45 | 70.29 | +| house | 53.66 | 74.35 | +| sea | 68.18 | 81.97 | +| mirror | 74.85 | 79.51 | +| rug | 72.55 | 78.54 | +| field | 30.89 | 60.68 | +| armchair | 60.03 | 77.81 | +| seat | 68.47 | 87.86 | +| fence | 50.8 | 68.35 | +| desk | 55.72 | 79.3 | +| rock | 56.5 | 83.98 | +| wardrobe | 54.44 | 72.93 | +| lamp | 72.71 | 82.8 | +| bathtub | 84.12 | 85.76 | +| railing | 39.25 | 60.36 | +| cushion | 69.98 | 83.13 | +| base | 39.81 | 60.79 | +| box | 34.97 | 44.81 | +| column | 53.66 | 64.76 | +| signboard | 41.42 | 55.28 | +| chest of drawers | 43.17 | 74.76 | +| counter | 37.4 | 48.13 | +| sand | 51.4 | 81.09 | +| sink | 77.57 | 83.64 | +| skyscraper | 46.25 | 55.81 | +| fireplace | 73.18 | 93.32 | +| refrigerator | 77.06 | 83.48 | +| grandstand | 49.48 | 81.03 | +| path | 22.23 | 30.21 | +| stairs | 25.23 | 32.82 | +| runway | 72.11 | 95.03 | +| case | 57.05 | 82.61 | +| pool table | 94.64 | 97.42 | +| pillow | 70.42 | 81.51 | +| screen door | 79.5 | 82.43 | +| stairway | 39.73 | 49.93 | +| river | 11.24 | 19.77 | +| bridge | 74.94 | 85.02 | +| bookcase | 48.77 | 62.43 | +| blind | 44.87 | 55.11 | +| coffee table | 63.77 | 88.5 | +| toilet | 89.43 | 92.94 | +| flower | 43.59 | 60.73 | +| book | 56.47 | 75.12 | +| hill | 6.9 | 18.81 | +| bench | 53.78 | 61.37 | +| countertop | 63.53 | 81.98 | +| stove | 86.73 | 91.62 | +| palm | 56.69 | 78.03 | +| kitchen island | 43.96 | 78.51 | +| computer | 79.47 | 89.3 | +| swivel chair | 47.32 | 80.75 | +| boat | 61.48 | 86.55 | +| bar | 57.68 | 75.19 | +| arcade machine | 79.26 | 84.15 | +| hovel | 42.52 | 49.7 | +| bus | 93.58 | 96.44 | +| towel | 75.86 | 84.06 | +| light | 60.62 | 69.25 | +| truck | 44.6 | 57.23 | +| tower | 11.43 | 15.65 | +| chandelier | 71.98 | 87.43 | +| awning | 45.48 | 67.19 | +| streetlight | 33.02 | 44.3 | +| booth | 51.41 | 54.18 | +| television receiver | 77.39 | 84.65 | +| airplane | 78.89 | 85.15 | +| dirt track | 11.8 | 57.11 | +| apparel | 43.32 | 59.19 | +| pole | 25.38 | 33.81 | +| land | 3.11 | 4.97 | +| bannister | 18.86 | 25.85 | +| escalator | 55.42 | 76.23 | +| ottoman | 49.41 | 62.84 | +| bottle | 41.12 | 48.65 | +| buffet | 51.91 | 68.21 | +| poster | 36.82 | 47.58 | +| stage | 25.47 | 48.61 | +| van | 47.79 | 61.56 | +| ship | 43.69 | 45.18 | +| fountain | 50.59 | 52.43 | +| conveyer belt | 82.75 | 92.7 | +| canopy | 45.46 | 61.88 | +| washer | 81.88 | 84.29 | +| plaything | 39.45 | 49.01 | +| swimming pool | 68.26 | 91.5 | +| stool | 52.31 | 78.95 | +| barrel | 55.48 | 64.79 | +| basket | 39.32 | 58.53 | +| waterfall | 62.76 | 88.86 | +| tent | 91.67 | 98.14 | +| bag | 14.0 | 15.47 | +| minibike | 75.31 | 88.12 | +| cradle | 85.71 | 96.74 | +| oven | 50.66 | 59.11 | +| ball | 53.38 | 64.07 | +| food | 63.46 | 79.54 | +| step | 7.92 | 9.55 | +| tank | 65.27 | 75.28 | +| trade name | 25.31 | 27.44 | +| microwave | 86.38 | 95.42 | +| pot | 58.91 | 70.56 | +| animal | 63.38 | 65.09 | +| bicycle | 59.09 | 81.42 | +| lake | 50.08 | 69.48 | +| dishwasher | 71.75 | 83.78 | +| screen | 56.78 | 79.73 | +| blanket | 31.95 | 36.4 | +| sculpture | 72.42 | 86.7 | +| hood | 62.82 | 68.99 | +| sconce | 49.18 | 53.45 | +| vase | 48.88 | 57.27 | +| traffic light | 40.08 | 63.18 | +| tray | 15.15 | 18.99 | +| ashcan | 45.13 | 61.1 | +| fan | 67.48 | 82.78 | +| pier | 37.0 | 50.79 | +| crt screen | 20.57 | 29.51 | +| plate | 60.84 | 71.62 | +| monitor | 65.89 | 85.54 | +| bulletin board | 55.62 | 67.56 | +| shower | 0.09 | 0.09 | +| radiator | 64.5 | 75.11 | +| glass | 19.41 | 21.06 | +| clock | 43.0 | 48.04 | +| flag | 71.22 | 76.62 | ++---------------------+-------+-------+ +2024-06-18 20:00:57,719 - mmseg - INFO - Summary: +2024-06-18 20:00:57,719 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.82 | 56.43 | 69.02 | ++-------+-------+-------+ +2024-06-18 20:00:57,720 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 20:00:57,720 - mmseg - INFO - Iter(val) [250] aAcc: 0.8582, mIoU: 0.5643, mAcc: 0.6902, IoU.wall: 0.8191, IoU.building: 0.8519, IoU.sky: 0.9494, IoU.floor: 0.8523, IoU.tree: 0.7760, IoU.ceiling: 0.8721, IoU.road: 0.8629, IoU.bed : 0.9262, IoU.windowpane: 0.6704, IoU.grass: 0.6601, IoU.cabinet: 0.6358, IoU.sidewalk: 0.7176, IoU.person: 0.8577, IoU.earth: 0.3537, IoU.door: 0.5928, IoU.table: 0.6950, IoU.mountain: 0.5707, IoU.plant: 0.5543, IoU.curtain: 0.7805, IoU.chair: 0.6612, IoU.car: 0.8757, IoU.water: 0.6330, IoU.painting: 0.7697, IoU.sofa: 0.8210, IoU.shelf: 0.5145, IoU.house: 0.5366, IoU.sea: 0.6818, IoU.mirror: 0.7485, IoU.rug: 0.7255, IoU.field: 0.3089, IoU.armchair: 0.6003, IoU.seat: 0.6847, IoU.fence: 0.5080, IoU.desk: 0.5572, IoU.rock: 0.5650, IoU.wardrobe: 0.5444, IoU.lamp: 0.7271, IoU.bathtub: 0.8412, IoU.railing: 0.3925, IoU.cushion: 0.6998, IoU.base: 0.3981, IoU.box: 0.3497, IoU.column: 0.5366, IoU.signboard: 0.4142, IoU.chest of drawers: 0.4317, IoU.counter: 0.3740, IoU.sand: 0.5140, IoU.sink: 0.7757, IoU.skyscraper: 0.4625, IoU.fireplace: 0.7318, IoU.refrigerator: 0.7706, IoU.grandstand: 0.4948, IoU.path: 0.2223, IoU.stairs: 0.2523, IoU.runway: 0.7211, IoU.case: 0.5705, IoU.pool table: 0.9464, IoU.pillow: 0.7042, IoU.screen door: 0.7950, IoU.stairway: 0.3973, IoU.river: 0.1124, IoU.bridge: 0.7494, IoU.bookcase: 0.4877, IoU.blind: 0.4487, IoU.coffee table: 0.6377, IoU.toilet: 0.8943, IoU.flower: 0.4359, IoU.book: 0.5647, IoU.hill: 0.0690, IoU.bench: 0.5378, IoU.countertop: 0.6353, IoU.stove: 0.8673, IoU.palm: 0.5669, IoU.kitchen island: 0.4396, IoU.computer: 0.7947, IoU.swivel chair: 0.4732, IoU.boat: 0.6148, IoU.bar: 0.5768, IoU.arcade machine: 0.7926, IoU.hovel: 0.4252, IoU.bus: 0.9358, IoU.towel: 0.7586, IoU.light: 0.6062, IoU.truck: 0.4460, IoU.tower: 0.1143, IoU.chandelier: 0.7198, IoU.awning: 0.4548, IoU.streetlight: 0.3302, IoU.booth: 0.5141, IoU.television receiver: 0.7739, IoU.airplane: 0.7889, IoU.dirt track: 0.1180, IoU.apparel: 0.4332, IoU.pole: 0.2538, IoU.land: 0.0311, IoU.bannister: 0.1886, IoU.escalator: 0.5542, IoU.ottoman: 0.4941, IoU.bottle: 0.4112, IoU.buffet: 0.5191, IoU.poster: 0.3682, IoU.stage: 0.2547, IoU.van: 0.4779, IoU.ship: 0.4369, IoU.fountain: 0.5059, IoU.conveyer belt: 0.8275, IoU.canopy: 0.4546, IoU.washer: 0.8188, IoU.plaything: 0.3945, IoU.swimming pool: 0.6826, IoU.stool: 0.5231, IoU.barrel: 0.5548, IoU.basket: 0.3932, IoU.waterfall: 0.6276, IoU.tent: 0.9167, IoU.bag: 0.1400, IoU.minibike: 0.7531, IoU.cradle: 0.8571, IoU.oven: 0.5066, IoU.ball: 0.5338, IoU.food: 0.6346, IoU.step: 0.0792, IoU.tank: 0.6527, IoU.trade name: 0.2531, IoU.microwave: 0.8638, IoU.pot: 0.5891, IoU.animal: 0.6338, IoU.bicycle: 0.5909, IoU.lake: 0.5008, IoU.dishwasher: 0.7175, IoU.screen: 0.5678, IoU.blanket: 0.3195, IoU.sculpture: 0.7242, IoU.hood: 0.6282, IoU.sconce: 0.4918, IoU.vase: 0.4888, IoU.traffic light: 0.4008, IoU.tray: 0.1515, IoU.ashcan: 0.4513, IoU.fan: 0.6748, IoU.pier: 0.3700, IoU.crt screen: 0.2057, IoU.plate: 0.6084, IoU.monitor: 0.6589, IoU.bulletin board: 0.5562, IoU.shower: 0.0009, IoU.radiator: 0.6450, IoU.glass: 0.1941, IoU.clock: 0.4300, IoU.flag: 0.7122, Acc.wall: 0.9030, Acc.building: 0.9358, Acc.sky: 0.9775, Acc.floor: 0.9251, Acc.tree: 0.8991, Acc.ceiling: 0.9243, Acc.road: 0.9189, Acc.bed : 0.9685, Acc.windowpane: 0.8127, Acc.grass: 0.8123, Acc.cabinet: 0.7203, Acc.sidewalk: 0.8617, Acc.person: 0.9354, Acc.earth: 0.4510, Acc.door: 0.7622, Acc.table: 0.8223, Acc.mountain: 0.6427, Acc.plant: 0.6614, Acc.curtain: 0.8938, Acc.chair: 0.7657, Acc.car: 0.9448, Acc.water: 0.7832, Acc.painting: 0.8708, Acc.sofa: 0.9036, Acc.shelf: 0.7029, Acc.house: 0.7435, Acc.sea: 0.8197, Acc.mirror: 0.7951, Acc.rug: 0.7854, Acc.field: 0.6068, Acc.armchair: 0.7781, Acc.seat: 0.8786, Acc.fence: 0.6835, Acc.desk: 0.7930, Acc.rock: 0.8398, Acc.wardrobe: 0.7293, Acc.lamp: 0.8280, Acc.bathtub: 0.8576, Acc.railing: 0.6036, Acc.cushion: 0.8313, Acc.base: 0.6079, Acc.box: 0.4481, Acc.column: 0.6476, Acc.signboard: 0.5528, Acc.chest of drawers: 0.7476, Acc.counter: 0.4813, Acc.sand: 0.8109, Acc.sink: 0.8364, Acc.skyscraper: 0.5581, Acc.fireplace: 0.9332, Acc.refrigerator: 0.8348, Acc.grandstand: 0.8103, Acc.path: 0.3021, Acc.stairs: 0.3282, Acc.runway: 0.9503, Acc.case: 0.8261, Acc.pool table: 0.9742, Acc.pillow: 0.8151, Acc.screen door: 0.8243, Acc.stairway: 0.4993, Acc.river: 0.1977, Acc.bridge: 0.8502, Acc.bookcase: 0.6243, Acc.blind: 0.5511, Acc.coffee table: 0.8850, Acc.toilet: 0.9294, Acc.flower: 0.6073, Acc.book: 0.7512, Acc.hill: 0.1881, Acc.bench: 0.6137, Acc.countertop: 0.8198, Acc.stove: 0.9162, Acc.palm: 0.7803, Acc.kitchen island: 0.7851, Acc.computer: 0.8930, Acc.swivel chair: 0.8075, Acc.boat: 0.8655, Acc.bar: 0.7519, Acc.arcade machine: 0.8415, Acc.hovel: 0.4970, Acc.bus: 0.9644, Acc.towel: 0.8406, Acc.light: 0.6925, Acc.truck: 0.5723, Acc.tower: 0.1565, Acc.chandelier: 0.8743, Acc.awning: 0.6719, Acc.streetlight: 0.4430, Acc.booth: 0.5418, Acc.television receiver: 0.8465, Acc.airplane: 0.8515, Acc.dirt track: 0.5711, Acc.apparel: 0.5919, Acc.pole: 0.3381, Acc.land: 0.0497, Acc.bannister: 0.2585, Acc.escalator: 0.7623, Acc.ottoman: 0.6284, Acc.bottle: 0.4865, Acc.buffet: 0.6821, Acc.poster: 0.4758, Acc.stage: 0.4861, Acc.van: 0.6156, Acc.ship: 0.4518, Acc.fountain: 0.5243, Acc.conveyer belt: 0.9270, Acc.canopy: 0.6188, Acc.washer: 0.8429, Acc.plaything: 0.4901, Acc.swimming pool: 0.9150, Acc.stool: 0.7895, Acc.barrel: 0.6479, Acc.basket: 0.5853, Acc.waterfall: 0.8886, Acc.tent: 0.9814, Acc.bag: 0.1547, Acc.minibike: 0.8812, Acc.cradle: 0.9674, Acc.oven: 0.5911, Acc.ball: 0.6407, Acc.food: 0.7954, Acc.step: 0.0955, Acc.tank: 0.7528, Acc.trade name: 0.2744, Acc.microwave: 0.9542, Acc.pot: 0.7056, Acc.animal: 0.6509, Acc.bicycle: 0.8142, Acc.lake: 0.6948, Acc.dishwasher: 0.8378, Acc.screen: 0.7973, Acc.blanket: 0.3640, Acc.sculpture: 0.8670, Acc.hood: 0.6899, Acc.sconce: 0.5345, Acc.vase: 0.5727, Acc.traffic light: 0.6318, Acc.tray: 0.1899, Acc.ashcan: 0.6110, Acc.fan: 0.8278, Acc.pier: 0.5079, Acc.crt screen: 0.2951, Acc.plate: 0.7162, Acc.monitor: 0.8554, Acc.bulletin board: 0.6756, Acc.shower: 0.0009, Acc.radiator: 0.7511, Acc.glass: 0.2106, Acc.clock: 0.4804, Acc.flag: 0.7662 +2024-06-18 20:02:04,538 - mmseg - INFO - Iter [50050/80000] lr: 1.498e-05, eta: 12:19:10, time: 3.273, data_time: 1.954, memory: 70498, decode.loss_ce: 0.1916, decode.acc_seg: 91.9576, aux.loss_ce: 0.0798, aux.acc_seg: 91.6209, loss: 0.2714 +2024-06-18 20:03:10,709 - mmseg - INFO - Iter [50100/80000] lr: 1.495e-05, eta: 12:17:52, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1962, decode.acc_seg: 91.4140, aux.loss_ce: 0.0823, aux.acc_seg: 90.9765, loss: 0.2785 +2024-06-18 20:04:17,073 - mmseg - INFO - Iter [50150/80000] lr: 1.493e-05, eta: 12:16:33, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1831, decode.acc_seg: 92.3258, aux.loss_ce: 0.0769, aux.acc_seg: 91.9242, loss: 0.2601 +2024-06-18 20:05:23,286 - mmseg - INFO - Iter [50200/80000] lr: 1.490e-05, eta: 12:15:14, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1968, decode.acc_seg: 91.6430, aux.loss_ce: 0.0823, aux.acc_seg: 91.3592, loss: 0.2791 +2024-06-18 20:06:29,632 - mmseg - INFO - Iter [50250/80000] lr: 1.488e-05, eta: 12:13:56, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1982, decode.acc_seg: 91.7360, aux.loss_ce: 0.0825, aux.acc_seg: 91.3604, loss: 0.2807 +2024-06-18 20:07:36,048 - mmseg - INFO - Iter [50300/80000] lr: 1.485e-05, eta: 12:12:37, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1913, decode.acc_seg: 91.9349, aux.loss_ce: 0.0806, aux.acc_seg: 91.5215, loss: 0.2719 +2024-06-18 20:08:42,495 - mmseg - INFO - Iter [50350/80000] lr: 1.483e-05, eta: 12:11:19, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1953, decode.acc_seg: 91.7711, aux.loss_ce: 0.0819, aux.acc_seg: 91.4151, loss: 0.2772 +2024-06-18 20:09:48,907 - mmseg - INFO - Iter [50400/80000] lr: 1.480e-05, eta: 12:10:00, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1947, decode.acc_seg: 91.8763, aux.loss_ce: 0.0820, aux.acc_seg: 91.5202, loss: 0.2766 +2024-06-18 20:10:55,290 - mmseg - INFO - Iter [50450/80000] lr: 1.478e-05, eta: 12:08:42, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1909, decode.acc_seg: 91.7235, aux.loss_ce: 0.0803, aux.acc_seg: 91.3700, loss: 0.2712 +2024-06-18 20:12:01,652 - mmseg - INFO - Iter [50500/80000] lr: 1.475e-05, eta: 12:07:23, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1868, decode.acc_seg: 92.0827, aux.loss_ce: 0.0784, aux.acc_seg: 91.7053, loss: 0.2652 +2024-06-18 20:13:10,680 - mmseg - INFO - Iter [50550/80000] lr: 1.473e-05, eta: 12:06:07, time: 1.381, data_time: 0.066, memory: 70498, decode.loss_ce: 0.1784, decode.acc_seg: 92.5100, aux.loss_ce: 0.0747, aux.acc_seg: 92.1016, loss: 0.2531 +2024-06-18 20:14:17,181 - mmseg - INFO - Iter [50600/80000] lr: 1.470e-05, eta: 12:04:48, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1875, decode.acc_seg: 92.2305, aux.loss_ce: 0.0795, aux.acc_seg: 91.7595, loss: 0.2670 +2024-06-18 20:15:23,400 - mmseg - INFO - Iter [50650/80000] lr: 1.468e-05, eta: 12:03:30, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1882, decode.acc_seg: 91.7812, aux.loss_ce: 0.0795, aux.acc_seg: 91.4217, loss: 0.2677 +2024-06-18 20:16:29,725 - mmseg - INFO - Iter [50700/80000] lr: 1.465e-05, eta: 12:02:11, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1773, decode.acc_seg: 92.1655, aux.loss_ce: 0.0749, aux.acc_seg: 91.7352, loss: 0.2522 +2024-06-18 20:17:36,281 - mmseg - INFO - Iter [50750/80000] lr: 1.463e-05, eta: 12:00:53, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1907, decode.acc_seg: 91.7525, aux.loss_ce: 0.0796, aux.acc_seg: 91.4299, loss: 0.2703 +2024-06-18 20:18:42,503 - mmseg - INFO - Iter [50800/80000] lr: 1.460e-05, eta: 11:59:35, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1806, decode.acc_seg: 92.3447, aux.loss_ce: 0.0760, aux.acc_seg: 91.9257, loss: 0.2566 +2024-06-18 20:19:48,808 - mmseg - INFO - Iter [50850/80000] lr: 1.458e-05, eta: 11:58:17, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1885, decode.acc_seg: 92.0697, aux.loss_ce: 0.0798, aux.acc_seg: 91.6132, loss: 0.2683 +2024-06-18 20:20:55,096 - mmseg - INFO - Iter [50900/80000] lr: 1.455e-05, eta: 11:56:58, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1847, decode.acc_seg: 92.2166, aux.loss_ce: 0.0771, aux.acc_seg: 91.8380, loss: 0.2618 +2024-06-18 20:22:01,416 - mmseg - INFO - Iter [50950/80000] lr: 1.453e-05, eta: 11:55:40, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1925, decode.acc_seg: 91.9583, aux.loss_ce: 0.0804, aux.acc_seg: 91.5686, loss: 0.2729 +2024-06-18 20:23:07,763 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 20:23:07,763 - mmseg - INFO - Iter [51000/80000] lr: 1.450e-05, eta: 11:54:22, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.2010, decode.acc_seg: 91.7807, aux.loss_ce: 0.0848, aux.acc_seg: 91.4095, loss: 0.2859 +2024-06-18 20:24:45,558 - mmseg - INFO - per class results: +2024-06-18 20:24:45,564 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.16 | 87.79 | +| building | 83.5 | 93.89 | +| sky | 94.97 | 97.85 | +| floor | 85.32 | 91.11 | +| tree | 77.59 | 89.57 | +| ceiling | 86.92 | 94.29 | +| road | 85.79 | 91.79 | +| bed | 92.53 | 96.53 | +| windowpane | 67.02 | 79.56 | +| grass | 67.34 | 80.96 | +| cabinet | 65.04 | 75.99 | +| sidewalk | 71.79 | 85.68 | +| person | 85.66 | 94.65 | +| earth | 37.37 | 49.71 | +| door | 60.02 | 75.45 | +| table | 69.83 | 81.37 | +| mountain | 59.56 | 73.53 | +| plant | 55.73 | 68.29 | +| curtain | 78.23 | 90.12 | +| chair | 67.02 | 79.68 | +| car | 87.1 | 93.68 | +| water | 63.46 | 75.44 | +| painting | 77.08 | 91.92 | +| sofa | 82.01 | 93.23 | +| shelf | 50.58 | 68.38 | +| house | 48.35 | 58.0 | +| sea | 66.77 | 82.98 | +| mirror | 80.06 | 86.55 | +| rug | 70.28 | 84.5 | +| field | 32.34 | 56.28 | +| armchair | 59.12 | 74.3 | +| seat | 65.53 | 88.58 | +| fence | 51.29 | 64.5 | +| desk | 58.44 | 79.94 | +| rock | 53.9 | 77.41 | +| wardrobe | 54.26 | 74.73 | +| lamp | 72.89 | 82.74 | +| bathtub | 84.6 | 86.62 | +| railing | 39.71 | 58.81 | +| cushion | 71.32 | 83.68 | +| base | 41.69 | 56.69 | +| box | 36.78 | 53.91 | +| column | 54.17 | 67.71 | +| signboard | 42.09 | 57.08 | +| chest of drawers | 42.27 | 72.42 | +| counter | 40.36 | 52.62 | +| sand | 52.77 | 76.89 | +| sink | 77.57 | 82.95 | +| skyscraper | 48.72 | 61.13 | +| fireplace | 75.61 | 91.59 | +| refrigerator | 80.59 | 92.59 | +| grandstand | 49.53 | 78.14 | +| path | 27.54 | 40.8 | +| stairs | 26.81 | 37.39 | +| runway | 64.64 | 84.4 | +| case | 62.34 | 82.55 | +| pool table | 94.59 | 97.44 | +| pillow | 71.34 | 83.59 | +| screen door | 65.96 | 67.26 | +| stairway | 44.2 | 59.26 | +| river | 18.31 | 48.07 | +| bridge | 75.67 | 88.88 | +| bookcase | 42.09 | 54.13 | +| blind | 49.53 | 70.46 | +| coffee table | 67.49 | 86.52 | +| toilet | 89.4 | 93.18 | +| flower | 43.58 | 61.86 | +| book | 55.14 | 79.73 | +| hill | 5.28 | 7.63 | +| bench | 51.54 | 61.54 | +| countertop | 62.97 | 86.82 | +| stove | 87.11 | 94.23 | +| palm | 57.1 | 80.5 | +| kitchen island | 43.11 | 75.98 | +| computer | 80.33 | 94.36 | +| swivel chair | 52.34 | 74.95 | +| boat | 64.6 | 87.47 | +| bar | 54.07 | 75.97 | +| arcade machine | 79.37 | 84.48 | +| hovel | 45.38 | 55.43 | +| bus | 93.0 | 96.22 | +| towel | 74.67 | 84.81 | +| light | 57.15 | 62.15 | +| truck | 43.83 | 56.16 | +| tower | 10.06 | 13.96 | +| chandelier | 72.59 | 84.31 | +| awning | 44.12 | 57.94 | +| streetlight | 34.53 | 47.79 | +| booth | 48.88 | 70.01 | +| television receiver | 81.43 | 88.24 | +| airplane | 80.9 | 88.67 | +| dirt track | 11.43 | 52.5 | +| apparel | 38.81 | 50.6 | +| pole | 23.42 | 30.99 | +| land | 3.49 | 5.04 | +| bannister | 15.91 | 26.46 | +| escalator | 59.35 | 78.55 | +| ottoman | 49.12 | 63.16 | +| bottle | 43.29 | 65.72 | +| buffet | 54.86 | 65.86 | +| poster | 40.28 | 49.5 | +| stage | 24.21 | 46.23 | +| van | 43.63 | 66.49 | +| ship | 91.47 | 98.61 | +| fountain | 26.13 | 26.68 | +| conveyer belt | 82.43 | 92.92 | +| canopy | 45.32 | 65.15 | +| washer | 88.34 | 91.69 | +| plaything | 34.74 | 47.58 | +| swimming pool | 67.93 | 90.96 | +| stool | 57.71 | 68.81 | +| barrel | 54.42 | 64.76 | +| basket | 39.16 | 50.99 | +| waterfall | 63.87 | 95.45 | +| tent | 90.91 | 98.66 | +| bag | 17.91 | 20.31 | +| minibike | 74.51 | 89.27 | +| cradle | 87.21 | 97.92 | +| oven | 62.92 | 73.25 | +| ball | 55.5 | 76.16 | +| food | 62.66 | 77.46 | +| step | 4.01 | 4.51 | +| tank | 65.75 | 72.59 | +| trade name | 25.35 | 27.92 | +| microwave | 88.28 | 96.16 | +| pot | 58.47 | 68.26 | +| animal | 65.81 | 68.23 | +| bicycle | 58.9 | 73.43 | +| lake | 19.11 | 19.62 | +| dishwasher | 74.11 | 81.87 | +| screen | 50.45 | 78.53 | +| blanket | 32.03 | 37.53 | +| sculpture | 71.75 | 89.73 | +| hood | 62.36 | 73.88 | +| sconce | 50.87 | 58.3 | +| vase | 48.01 | 59.63 | +| traffic light | 38.01 | 65.24 | +| tray | 11.21 | 13.58 | +| ashcan | 47.69 | 61.97 | +| fan | 62.46 | 71.79 | +| pier | 46.08 | 64.97 | +| crt screen | 13.82 | 20.99 | +| plate | 58.23 | 77.05 | +| monitor | 64.9 | 79.72 | +| bulletin board | 44.62 | 55.86 | +| shower | 0.54 | 2.41 | +| radiator | 63.18 | 73.87 | +| glass | 18.99 | 20.7 | +| clock | 43.19 | 47.82 | +| flag | 70.07 | 79.27 | ++---------------------+-------+-------+ +2024-06-18 20:24:45,564 - mmseg - INFO - Summary: +2024-06-18 20:24:45,564 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.72 | 56.51 | 69.48 | ++-------+-------+-------+ +2024-06-18 20:24:45,565 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 20:24:45,565 - mmseg - INFO - Iter(val) [250] aAcc: 0.8572, mIoU: 0.5651, mAcc: 0.6948, IoU.wall: 0.8116, IoU.building: 0.8350, IoU.sky: 0.9497, IoU.floor: 0.8532, IoU.tree: 0.7759, IoU.ceiling: 0.8692, IoU.road: 0.8579, IoU.bed : 0.9253, IoU.windowpane: 0.6702, IoU.grass: 0.6734, IoU.cabinet: 0.6504, IoU.sidewalk: 0.7179, IoU.person: 0.8566, IoU.earth: 0.3737, IoU.door: 0.6002, IoU.table: 0.6983, IoU.mountain: 0.5956, IoU.plant: 0.5573, IoU.curtain: 0.7823, IoU.chair: 0.6702, IoU.car: 0.8710, IoU.water: 0.6346, IoU.painting: 0.7708, IoU.sofa: 0.8201, IoU.shelf: 0.5058, IoU.house: 0.4835, IoU.sea: 0.6677, IoU.mirror: 0.8006, IoU.rug: 0.7028, IoU.field: 0.3234, IoU.armchair: 0.5912, IoU.seat: 0.6553, IoU.fence: 0.5129, IoU.desk: 0.5844, IoU.rock: 0.5390, IoU.wardrobe: 0.5426, IoU.lamp: 0.7289, IoU.bathtub: 0.8460, IoU.railing: 0.3971, IoU.cushion: 0.7132, IoU.base: 0.4169, IoU.box: 0.3678, IoU.column: 0.5417, IoU.signboard: 0.4209, IoU.chest of drawers: 0.4227, IoU.counter: 0.4036, IoU.sand: 0.5277, IoU.sink: 0.7757, IoU.skyscraper: 0.4872, IoU.fireplace: 0.7561, IoU.refrigerator: 0.8059, IoU.grandstand: 0.4953, IoU.path: 0.2754, IoU.stairs: 0.2681, IoU.runway: 0.6464, IoU.case: 0.6234, IoU.pool table: 0.9459, IoU.pillow: 0.7134, IoU.screen door: 0.6596, IoU.stairway: 0.4420, IoU.river: 0.1831, IoU.bridge: 0.7567, IoU.bookcase: 0.4209, IoU.blind: 0.4953, IoU.coffee table: 0.6749, IoU.toilet: 0.8940, IoU.flower: 0.4358, IoU.book: 0.5514, IoU.hill: 0.0528, IoU.bench: 0.5154, IoU.countertop: 0.6297, IoU.stove: 0.8711, IoU.palm: 0.5710, IoU.kitchen island: 0.4311, IoU.computer: 0.8033, IoU.swivel chair: 0.5234, IoU.boat: 0.6460, IoU.bar: 0.5407, IoU.arcade machine: 0.7937, IoU.hovel: 0.4538, IoU.bus: 0.9300, IoU.towel: 0.7467, IoU.light: 0.5715, IoU.truck: 0.4383, IoU.tower: 0.1006, IoU.chandelier: 0.7259, IoU.awning: 0.4412, IoU.streetlight: 0.3453, IoU.booth: 0.4888, IoU.television receiver: 0.8143, IoU.airplane: 0.8090, IoU.dirt track: 0.1143, IoU.apparel: 0.3881, IoU.pole: 0.2342, IoU.land: 0.0349, IoU.bannister: 0.1591, IoU.escalator: 0.5935, IoU.ottoman: 0.4912, IoU.bottle: 0.4329, IoU.buffet: 0.5486, IoU.poster: 0.4028, IoU.stage: 0.2421, IoU.van: 0.4363, IoU.ship: 0.9147, IoU.fountain: 0.2613, IoU.conveyer belt: 0.8243, IoU.canopy: 0.4532, IoU.washer: 0.8834, IoU.plaything: 0.3474, IoU.swimming pool: 0.6793, IoU.stool: 0.5771, IoU.barrel: 0.5442, IoU.basket: 0.3916, IoU.waterfall: 0.6387, IoU.tent: 0.9091, IoU.bag: 0.1791, IoU.minibike: 0.7451, IoU.cradle: 0.8721, IoU.oven: 0.6292, IoU.ball: 0.5550, IoU.food: 0.6266, IoU.step: 0.0401, IoU.tank: 0.6575, IoU.trade name: 0.2535, IoU.microwave: 0.8828, IoU.pot: 0.5847, IoU.animal: 0.6581, IoU.bicycle: 0.5890, IoU.lake: 0.1911, IoU.dishwasher: 0.7411, IoU.screen: 0.5045, IoU.blanket: 0.3203, IoU.sculpture: 0.7175, IoU.hood: 0.6236, IoU.sconce: 0.5087, IoU.vase: 0.4801, IoU.traffic light: 0.3801, IoU.tray: 0.1121, IoU.ashcan: 0.4769, IoU.fan: 0.6246, IoU.pier: 0.4608, IoU.crt screen: 0.1382, IoU.plate: 0.5823, IoU.monitor: 0.6490, IoU.bulletin board: 0.4462, IoU.shower: 0.0054, IoU.radiator: 0.6318, IoU.glass: 0.1899, IoU.clock: 0.4319, IoU.flag: 0.7007, Acc.wall: 0.8779, Acc.building: 0.9389, Acc.sky: 0.9785, Acc.floor: 0.9111, Acc.tree: 0.8957, Acc.ceiling: 0.9429, Acc.road: 0.9179, Acc.bed : 0.9653, Acc.windowpane: 0.7956, Acc.grass: 0.8096, Acc.cabinet: 0.7599, Acc.sidewalk: 0.8568, Acc.person: 0.9465, Acc.earth: 0.4971, Acc.door: 0.7545, Acc.table: 0.8137, Acc.mountain: 0.7353, Acc.plant: 0.6829, Acc.curtain: 0.9012, Acc.chair: 0.7968, Acc.car: 0.9368, Acc.water: 0.7544, Acc.painting: 0.9192, Acc.sofa: 0.9323, Acc.shelf: 0.6838, Acc.house: 0.5800, Acc.sea: 0.8298, Acc.mirror: 0.8655, Acc.rug: 0.8450, Acc.field: 0.5628, Acc.armchair: 0.7430, Acc.seat: 0.8858, Acc.fence: 0.6450, Acc.desk: 0.7994, Acc.rock: 0.7741, Acc.wardrobe: 0.7473, Acc.lamp: 0.8274, Acc.bathtub: 0.8662, Acc.railing: 0.5881, Acc.cushion: 0.8368, Acc.base: 0.5669, Acc.box: 0.5391, Acc.column: 0.6771, Acc.signboard: 0.5708, Acc.chest of drawers: 0.7242, Acc.counter: 0.5262, Acc.sand: 0.7689, Acc.sink: 0.8295, Acc.skyscraper: 0.6113, Acc.fireplace: 0.9159, Acc.refrigerator: 0.9259, Acc.grandstand: 0.7814, Acc.path: 0.4080, Acc.stairs: 0.3739, Acc.runway: 0.8440, Acc.case: 0.8255, Acc.pool table: 0.9744, Acc.pillow: 0.8359, Acc.screen door: 0.6726, Acc.stairway: 0.5926, Acc.river: 0.4807, Acc.bridge: 0.8888, Acc.bookcase: 0.5413, Acc.blind: 0.7046, Acc.coffee table: 0.8652, Acc.toilet: 0.9318, Acc.flower: 0.6186, Acc.book: 0.7973, Acc.hill: 0.0763, Acc.bench: 0.6154, Acc.countertop: 0.8682, Acc.stove: 0.9423, Acc.palm: 0.8050, Acc.kitchen island: 0.7598, Acc.computer: 0.9436, Acc.swivel chair: 0.7495, Acc.boat: 0.8747, Acc.bar: 0.7597, Acc.arcade machine: 0.8448, Acc.hovel: 0.5543, Acc.bus: 0.9622, Acc.towel: 0.8481, Acc.light: 0.6215, Acc.truck: 0.5616, Acc.tower: 0.1396, Acc.chandelier: 0.8431, Acc.awning: 0.5794, Acc.streetlight: 0.4779, Acc.booth: 0.7001, Acc.television receiver: 0.8824, Acc.airplane: 0.8867, Acc.dirt track: 0.5250, Acc.apparel: 0.5060, Acc.pole: 0.3099, Acc.land: 0.0504, Acc.bannister: 0.2646, Acc.escalator: 0.7855, Acc.ottoman: 0.6316, Acc.bottle: 0.6572, Acc.buffet: 0.6586, Acc.poster: 0.4950, Acc.stage: 0.4623, Acc.van: 0.6649, Acc.ship: 0.9861, Acc.fountain: 0.2668, Acc.conveyer belt: 0.9292, Acc.canopy: 0.6515, Acc.washer: 0.9169, Acc.plaything: 0.4758, Acc.swimming pool: 0.9096, Acc.stool: 0.6881, Acc.barrel: 0.6476, Acc.basket: 0.5099, Acc.waterfall: 0.9545, Acc.tent: 0.9866, Acc.bag: 0.2031, Acc.minibike: 0.8927, Acc.cradle: 0.9792, Acc.oven: 0.7325, Acc.ball: 0.7616, Acc.food: 0.7746, Acc.step: 0.0451, Acc.tank: 0.7259, Acc.trade name: 0.2792, Acc.microwave: 0.9616, Acc.pot: 0.6826, Acc.animal: 0.6823, Acc.bicycle: 0.7343, Acc.lake: 0.1962, Acc.dishwasher: 0.8187, Acc.screen: 0.7853, Acc.blanket: 0.3753, Acc.sculpture: 0.8973, Acc.hood: 0.7388, Acc.sconce: 0.5830, Acc.vase: 0.5963, Acc.traffic light: 0.6524, Acc.tray: 0.1358, Acc.ashcan: 0.6197, Acc.fan: 0.7179, Acc.pier: 0.6497, Acc.crt screen: 0.2099, Acc.plate: 0.7705, Acc.monitor: 0.7972, Acc.bulletin board: 0.5586, Acc.shower: 0.0241, Acc.radiator: 0.7387, Acc.glass: 0.2070, Acc.clock: 0.4782, Acc.flag: 0.7927 +2024-06-18 20:25:52,455 - mmseg - INFO - Iter [51050/80000] lr: 1.448e-05, eta: 11:53:59, time: 3.294, data_time: 1.972, memory: 70498, decode.loss_ce: 0.1908, decode.acc_seg: 91.6792, aux.loss_ce: 0.0798, aux.acc_seg: 91.4027, loss: 0.2705 +2024-06-18 20:26:58,858 - mmseg - INFO - Iter [51100/80000] lr: 1.445e-05, eta: 11:52:41, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1809, decode.acc_seg: 92.3508, aux.loss_ce: 0.0757, aux.acc_seg: 92.0390, loss: 0.2566 +2024-06-18 20:28:05,279 - mmseg - INFO - Iter [51150/80000] lr: 1.443e-05, eta: 11:51:23, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1855, decode.acc_seg: 92.1512, aux.loss_ce: 0.0776, aux.acc_seg: 91.8238, loss: 0.2631 +2024-06-18 20:29:11,558 - mmseg - INFO - Iter [51200/80000] lr: 1.440e-05, eta: 11:50:05, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1835, decode.acc_seg: 92.1060, aux.loss_ce: 0.0775, aux.acc_seg: 91.6387, loss: 0.2610 +2024-06-18 20:30:18,168 - mmseg - INFO - Iter [51250/80000] lr: 1.438e-05, eta: 11:48:47, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1810, decode.acc_seg: 92.1815, aux.loss_ce: 0.0766, aux.acc_seg: 91.7096, loss: 0.2576 +2024-06-18 20:31:24,495 - mmseg - INFO - Iter [51300/80000] lr: 1.435e-05, eta: 11:47:28, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1868, decode.acc_seg: 92.0699, aux.loss_ce: 0.0785, aux.acc_seg: 91.7238, loss: 0.2653 +2024-06-18 20:32:30,720 - mmseg - INFO - Iter [51350/80000] lr: 1.433e-05, eta: 11:46:10, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1885, decode.acc_seg: 91.9016, aux.loss_ce: 0.0794, aux.acc_seg: 91.4444, loss: 0.2679 +2024-06-18 20:33:37,182 - mmseg - INFO - Iter [51400/80000] lr: 1.430e-05, eta: 11:44:52, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1899, decode.acc_seg: 91.9772, aux.loss_ce: 0.0801, aux.acc_seg: 91.5513, loss: 0.2701 +2024-06-18 20:34:43,805 - mmseg - INFO - Iter [51450/80000] lr: 1.428e-05, eta: 11:43:34, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1934, decode.acc_seg: 91.7935, aux.loss_ce: 0.0814, aux.acc_seg: 91.3558, loss: 0.2748 +2024-06-18 20:35:50,432 - mmseg - INFO - Iter [51500/80000] lr: 1.425e-05, eta: 11:42:16, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1833, decode.acc_seg: 92.3722, aux.loss_ce: 0.0775, aux.acc_seg: 91.9580, loss: 0.2608 +2024-06-18 20:36:56,824 - mmseg - INFO - Iter [51550/80000] lr: 1.423e-05, eta: 11:40:58, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1744, decode.acc_seg: 92.5081, aux.loss_ce: 0.0736, aux.acc_seg: 92.1030, loss: 0.2480 +2024-06-18 20:38:03,420 - mmseg - INFO - Iter [51600/80000] lr: 1.420e-05, eta: 11:39:40, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1812, decode.acc_seg: 92.3118, aux.loss_ce: 0.0773, aux.acc_seg: 91.8282, loss: 0.2585 +2024-06-18 20:39:09,919 - mmseg - INFO - Iter [51650/80000] lr: 1.418e-05, eta: 11:38:22, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1851, decode.acc_seg: 92.2694, aux.loss_ce: 0.0781, aux.acc_seg: 91.8384, loss: 0.2633 +2024-06-18 20:40:16,671 - mmseg - INFO - Iter [51700/80000] lr: 1.415e-05, eta: 11:37:04, time: 1.335, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1942, decode.acc_seg: 91.7163, aux.loss_ce: 0.0815, aux.acc_seg: 91.2807, loss: 0.2757 +2024-06-18 20:41:23,270 - mmseg - INFO - Iter [51750/80000] lr: 1.413e-05, eta: 11:35:46, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1846, decode.acc_seg: 92.1499, aux.loss_ce: 0.0784, aux.acc_seg: 91.6737, loss: 0.2630 +2024-06-18 20:42:32,305 - mmseg - INFO - Iter [51800/80000] lr: 1.410e-05, eta: 11:34:30, time: 1.381, data_time: 0.060, memory: 70498, decode.loss_ce: 0.1991, decode.acc_seg: 91.4823, aux.loss_ce: 0.0838, aux.acc_seg: 91.1467, loss: 0.2829 +2024-06-18 20:43:38,666 - mmseg - INFO - Iter [51850/80000] lr: 1.408e-05, eta: 11:33:12, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1873, decode.acc_seg: 92.1087, aux.loss_ce: 0.0780, aux.acc_seg: 91.7838, loss: 0.2653 +2024-06-18 20:44:44,947 - mmseg - INFO - Iter [51900/80000] lr: 1.405e-05, eta: 11:31:54, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1765, decode.acc_seg: 92.5128, aux.loss_ce: 0.0740, aux.acc_seg: 92.1322, loss: 0.2505 +2024-06-18 20:45:51,497 - mmseg - INFO - Iter [51950/80000] lr: 1.403e-05, eta: 11:30:36, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1806, decode.acc_seg: 92.0962, aux.loss_ce: 0.0755, aux.acc_seg: 91.7686, loss: 0.2562 +2024-06-18 20:46:57,987 - mmseg - INFO - Saving checkpoint at 52000 iterations +2024-06-18 20:48:40,039 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 20:48:40,040 - mmseg - INFO - Iter [52000/80000] lr: 1.400e-05, eta: 11:30:13, time: 3.371, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1801, decode.acc_seg: 92.2897, aux.loss_ce: 0.0760, aux.acc_seg: 91.8860, loss: 0.2561 +2024-06-18 20:50:16,995 - mmseg - INFO - per class results: +2024-06-18 20:50:17,001 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.1 | 89.39 | +| building | 84.35 | 93.87 | +| sky | 94.78 | 97.18 | +| floor | 84.81 | 92.11 | +| tree | 77.09 | 89.37 | +| ceiling | 87.14 | 94.33 | +| road | 86.56 | 91.35 | +| bed | 92.25 | 96.79 | +| windowpane | 67.18 | 81.89 | +| grass | 66.82 | 81.94 | +| cabinet | 65.3 | 75.15 | +| sidewalk | 71.75 | 86.21 | +| person | 85.28 | 94.36 | +| earth | 36.76 | 51.69 | +| door | 61.09 | 78.41 | +| table | 68.08 | 83.51 | +| mountain | 60.39 | 72.89 | +| plant | 56.03 | 67.06 | +| curtain | 76.98 | 88.12 | +| chair | 67.71 | 80.24 | +| car | 87.68 | 94.04 | +| water | 65.53 | 81.11 | +| painting | 78.64 | 90.93 | +| sofa | 79.02 | 92.12 | +| shelf | 49.33 | 65.59 | +| house | 47.48 | 58.8 | +| sea | 79.11 | 88.09 | +| mirror | 77.24 | 82.42 | +| rug | 70.81 | 80.74 | +| field | 27.52 | 35.52 | +| armchair | 55.12 | 67.9 | +| seat | 64.58 | 88.15 | +| fence | 52.57 | 65.69 | +| desk | 57.29 | 76.1 | +| rock | 50.87 | 70.36 | +| wardrobe | 57.19 | 73.23 | +| lamp | 72.93 | 84.4 | +| bathtub | 84.25 | 85.7 | +| railing | 40.14 | 56.73 | +| cushion | 70.65 | 84.33 | +| base | 38.04 | 51.71 | +| box | 37.71 | 47.42 | +| column | 54.23 | 67.86 | +| signboard | 41.16 | 56.91 | +| chest of drawers | 46.67 | 69.8 | +| counter | 36.29 | 47.86 | +| sand | 52.65 | 78.94 | +| sink | 75.56 | 84.94 | +| skyscraper | 50.48 | 59.78 | +| fireplace | 71.61 | 93.93 | +| refrigerator | 76.25 | 91.3 | +| grandstand | 49.12 | 79.36 | +| path | 25.03 | 40.47 | +| stairs | 20.21 | 26.65 | +| runway | 73.89 | 97.78 | +| case | 60.29 | 77.46 | +| pool table | 94.81 | 97.41 | +| pillow | 69.96 | 79.01 | +| screen door | 65.38 | 67.66 | +| stairway | 40.37 | 58.44 | +| river | 22.8 | 40.42 | +| bridge | 74.65 | 86.65 | +| bookcase | 42.71 | 56.9 | +| blind | 44.15 | 49.65 | +| coffee table | 66.03 | 87.99 | +| toilet | 89.3 | 93.36 | +| flower | 42.12 | 55.62 | +| book | 52.01 | 76.45 | +| hill | 8.71 | 13.55 | +| bench | 49.47 | 61.42 | +| countertop | 62.74 | 79.91 | +| stove | 86.0 | 91.55 | +| palm | 53.33 | 87.46 | +| kitchen island | 38.69 | 64.99 | +| computer | 80.98 | 91.14 | +| swivel chair | 52.29 | 70.28 | +| boat | 65.75 | 88.84 | +| bar | 54.89 | 75.23 | +| arcade machine | 78.64 | 84.31 | +| hovel | 46.57 | 54.41 | +| bus | 93.05 | 96.63 | +| towel | 74.67 | 82.53 | +| light | 61.84 | 72.79 | +| truck | 47.12 | 65.94 | +| tower | 16.38 | 27.97 | +| chandelier | 72.73 | 85.6 | +| awning | 38.41 | 46.0 | +| streetlight | 34.87 | 50.23 | +| booth | 55.55 | 65.01 | +| television receiver | 78.75 | 89.31 | +| airplane | 80.9 | 88.74 | +| dirt track | 9.74 | 52.6 | +| apparel | 46.35 | 66.27 | +| pole | 25.08 | 33.01 | +| land | 1.9 | 4.05 | +| bannister | 18.22 | 24.42 | +| escalator | 58.1 | 82.06 | +| ottoman | 51.77 | 70.13 | +| bottle | 41.86 | 64.88 | +| buffet | 51.61 | 69.57 | +| poster | 38.47 | 49.94 | +| stage | 16.05 | 29.96 | +| van | 47.25 | 60.4 | +| ship | 80.88 | 85.39 | +| fountain | 26.54 | 27.04 | +| conveyer belt | 84.06 | 92.3 | +| canopy | 54.15 | 75.32 | +| washer | 89.9 | 92.85 | +| plaything | 40.35 | 64.85 | +| swimming pool | 76.02 | 91.96 | +| stool | 56.36 | 74.03 | +| barrel | 54.23 | 64.76 | +| basket | 42.77 | 61.02 | +| waterfall | 53.73 | 75.05 | +| tent | 89.34 | 98.99 | +| bag | 20.45 | 25.08 | +| minibike | 73.85 | 91.06 | +| cradle | 85.45 | 97.85 | +| oven | 49.58 | 60.92 | +| ball | 34.43 | 35.79 | +| food | 64.06 | 76.1 | +| step | 5.54 | 6.71 | +| tank | 68.78 | 80.21 | +| trade name | 32.12 | 36.21 | +| microwave | 86.59 | 95.87 | +| pot | 58.57 | 71.07 | +| animal | 57.74 | 58.85 | +| bicycle | 58.86 | 76.34 | +| lake | 61.4 | 62.96 | +| dishwasher | 72.22 | 83.61 | +| screen | 49.34 | 78.2 | +| blanket | 29.4 | 35.72 | +| sculpture | 67.84 | 89.33 | +| hood | 64.3 | 78.29 | +| sconce | 51.68 | 59.03 | +| vase | 48.43 | 60.1 | +| traffic light | 39.67 | 61.99 | +| tray | 15.68 | 22.33 | +| ashcan | 47.45 | 62.16 | +| fan | 66.29 | 79.34 | +| pier | 53.78 | 77.23 | +| crt screen | 16.84 | 26.42 | +| plate | 59.02 | 79.5 | +| monitor | 62.43 | 87.08 | +| bulletin board | 54.45 | 58.8 | +| shower | 0.53 | 1.44 | +| radiator | 65.67 | 76.78 | +| glass | 18.0 | 19.01 | +| clock | 39.89 | 50.04 | +| flag | 69.71 | 77.63 | ++---------------------+-------+-------+ +2024-06-18 20:50:17,001 - mmseg - INFO - Summary: +2024-06-18 20:50:17,001 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.88 | 56.61 | 69.42 | ++-------+-------+-------+ +2024-06-18 20:50:17,002 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 20:50:17,002 - mmseg - INFO - Iter(val) [250] aAcc: 0.8588, mIoU: 0.5661, mAcc: 0.6942, IoU.wall: 0.8210, IoU.building: 0.8435, IoU.sky: 0.9478, IoU.floor: 0.8481, IoU.tree: 0.7709, IoU.ceiling: 0.8714, IoU.road: 0.8656, IoU.bed : 0.9225, IoU.windowpane: 0.6718, IoU.grass: 0.6682, IoU.cabinet: 0.6530, IoU.sidewalk: 0.7175, IoU.person: 0.8528, IoU.earth: 0.3676, IoU.door: 0.6109, IoU.table: 0.6808, IoU.mountain: 0.6039, IoU.plant: 0.5603, IoU.curtain: 0.7698, IoU.chair: 0.6771, IoU.car: 0.8768, IoU.water: 0.6553, IoU.painting: 0.7864, IoU.sofa: 0.7902, IoU.shelf: 0.4933, IoU.house: 0.4748, IoU.sea: 0.7911, IoU.mirror: 0.7724, IoU.rug: 0.7081, IoU.field: 0.2752, IoU.armchair: 0.5512, IoU.seat: 0.6458, IoU.fence: 0.5257, IoU.desk: 0.5729, IoU.rock: 0.5087, IoU.wardrobe: 0.5719, IoU.lamp: 0.7293, IoU.bathtub: 0.8425, IoU.railing: 0.4014, IoU.cushion: 0.7065, IoU.base: 0.3804, IoU.box: 0.3771, IoU.column: 0.5423, IoU.signboard: 0.4116, IoU.chest of drawers: 0.4667, IoU.counter: 0.3629, IoU.sand: 0.5265, IoU.sink: 0.7556, IoU.skyscraper: 0.5048, IoU.fireplace: 0.7161, IoU.refrigerator: 0.7625, IoU.grandstand: 0.4912, IoU.path: 0.2503, IoU.stairs: 0.2021, IoU.runway: 0.7389, IoU.case: 0.6029, IoU.pool table: 0.9481, IoU.pillow: 0.6996, IoU.screen door: 0.6538, IoU.stairway: 0.4037, IoU.river: 0.2280, IoU.bridge: 0.7465, IoU.bookcase: 0.4271, IoU.blind: 0.4415, IoU.coffee table: 0.6603, IoU.toilet: 0.8930, IoU.flower: 0.4212, IoU.book: 0.5201, IoU.hill: 0.0871, IoU.bench: 0.4947, IoU.countertop: 0.6274, IoU.stove: 0.8600, IoU.palm: 0.5333, IoU.kitchen island: 0.3869, IoU.computer: 0.8098, IoU.swivel chair: 0.5229, IoU.boat: 0.6575, IoU.bar: 0.5489, IoU.arcade machine: 0.7864, IoU.hovel: 0.4657, IoU.bus: 0.9305, IoU.towel: 0.7467, IoU.light: 0.6184, IoU.truck: 0.4712, IoU.tower: 0.1638, IoU.chandelier: 0.7273, IoU.awning: 0.3841, IoU.streetlight: 0.3487, IoU.booth: 0.5555, IoU.television receiver: 0.7875, IoU.airplane: 0.8090, IoU.dirt track: 0.0974, IoU.apparel: 0.4635, IoU.pole: 0.2508, IoU.land: 0.0190, IoU.bannister: 0.1822, IoU.escalator: 0.5810, IoU.ottoman: 0.5177, IoU.bottle: 0.4186, IoU.buffet: 0.5161, IoU.poster: 0.3847, IoU.stage: 0.1605, IoU.van: 0.4725, IoU.ship: 0.8088, IoU.fountain: 0.2654, IoU.conveyer belt: 0.8406, IoU.canopy: 0.5415, IoU.washer: 0.8990, IoU.plaything: 0.4035, IoU.swimming pool: 0.7602, IoU.stool: 0.5636, IoU.barrel: 0.5423, IoU.basket: 0.4277, IoU.waterfall: 0.5373, IoU.tent: 0.8934, IoU.bag: 0.2045, IoU.minibike: 0.7385, IoU.cradle: 0.8545, IoU.oven: 0.4958, IoU.ball: 0.3443, IoU.food: 0.6406, IoU.step: 0.0554, IoU.tank: 0.6878, IoU.trade name: 0.3212, IoU.microwave: 0.8659, IoU.pot: 0.5857, IoU.animal: 0.5774, IoU.bicycle: 0.5886, IoU.lake: 0.6140, IoU.dishwasher: 0.7222, IoU.screen: 0.4934, IoU.blanket: 0.2940, IoU.sculpture: 0.6784, IoU.hood: 0.6430, IoU.sconce: 0.5168, IoU.vase: 0.4843, IoU.traffic light: 0.3967, IoU.tray: 0.1568, IoU.ashcan: 0.4745, IoU.fan: 0.6629, IoU.pier: 0.5378, IoU.crt screen: 0.1684, IoU.plate: 0.5902, IoU.monitor: 0.6243, IoU.bulletin board: 0.5445, IoU.shower: 0.0053, IoU.radiator: 0.6567, IoU.glass: 0.1800, IoU.clock: 0.3989, IoU.flag: 0.6971, Acc.wall: 0.8939, Acc.building: 0.9387, Acc.sky: 0.9718, Acc.floor: 0.9211, Acc.tree: 0.8937, Acc.ceiling: 0.9433, Acc.road: 0.9135, Acc.bed : 0.9679, Acc.windowpane: 0.8189, Acc.grass: 0.8194, Acc.cabinet: 0.7515, Acc.sidewalk: 0.8621, Acc.person: 0.9436, Acc.earth: 0.5169, Acc.door: 0.7841, Acc.table: 0.8351, Acc.mountain: 0.7289, Acc.plant: 0.6706, Acc.curtain: 0.8812, Acc.chair: 0.8024, Acc.car: 0.9404, Acc.water: 0.8111, Acc.painting: 0.9093, Acc.sofa: 0.9212, Acc.shelf: 0.6559, Acc.house: 0.5880, Acc.sea: 0.8809, Acc.mirror: 0.8242, Acc.rug: 0.8074, Acc.field: 0.3552, Acc.armchair: 0.6790, Acc.seat: 0.8815, Acc.fence: 0.6569, Acc.desk: 0.7610, Acc.rock: 0.7036, Acc.wardrobe: 0.7323, Acc.lamp: 0.8440, Acc.bathtub: 0.8570, Acc.railing: 0.5673, Acc.cushion: 0.8433, Acc.base: 0.5171, Acc.box: 0.4742, Acc.column: 0.6786, Acc.signboard: 0.5691, Acc.chest of drawers: 0.6980, Acc.counter: 0.4786, Acc.sand: 0.7894, Acc.sink: 0.8494, Acc.skyscraper: 0.5978, Acc.fireplace: 0.9393, Acc.refrigerator: 0.9130, Acc.grandstand: 0.7936, Acc.path: 0.4047, Acc.stairs: 0.2665, Acc.runway: 0.9778, Acc.case: 0.7746, Acc.pool table: 0.9741, Acc.pillow: 0.7901, Acc.screen door: 0.6766, Acc.stairway: 0.5844, Acc.river: 0.4042, Acc.bridge: 0.8665, Acc.bookcase: 0.5690, Acc.blind: 0.4965, Acc.coffee table: 0.8799, Acc.toilet: 0.9336, Acc.flower: 0.5562, Acc.book: 0.7645, Acc.hill: 0.1355, Acc.bench: 0.6142, Acc.countertop: 0.7991, Acc.stove: 0.9155, Acc.palm: 0.8746, Acc.kitchen island: 0.6499, Acc.computer: 0.9114, Acc.swivel chair: 0.7028, Acc.boat: 0.8884, Acc.bar: 0.7523, Acc.arcade machine: 0.8431, Acc.hovel: 0.5441, Acc.bus: 0.9663, Acc.towel: 0.8253, Acc.light: 0.7279, Acc.truck: 0.6594, Acc.tower: 0.2797, Acc.chandelier: 0.8560, Acc.awning: 0.4600, Acc.streetlight: 0.5023, Acc.booth: 0.6501, Acc.television receiver: 0.8931, Acc.airplane: 0.8874, Acc.dirt track: 0.5260, Acc.apparel: 0.6627, Acc.pole: 0.3301, Acc.land: 0.0405, Acc.bannister: 0.2442, Acc.escalator: 0.8206, Acc.ottoman: 0.7013, Acc.bottle: 0.6488, Acc.buffet: 0.6957, Acc.poster: 0.4994, Acc.stage: 0.2996, Acc.van: 0.6040, Acc.ship: 0.8539, Acc.fountain: 0.2704, Acc.conveyer belt: 0.9230, Acc.canopy: 0.7532, Acc.washer: 0.9285, Acc.plaything: 0.6485, Acc.swimming pool: 0.9196, Acc.stool: 0.7403, Acc.barrel: 0.6476, Acc.basket: 0.6102, Acc.waterfall: 0.7505, Acc.tent: 0.9899, Acc.bag: 0.2508, Acc.minibike: 0.9106, Acc.cradle: 0.9785, Acc.oven: 0.6092, Acc.ball: 0.3579, Acc.food: 0.7610, Acc.step: 0.0671, Acc.tank: 0.8021, Acc.trade name: 0.3621, Acc.microwave: 0.9587, Acc.pot: 0.7107, Acc.animal: 0.5885, Acc.bicycle: 0.7634, Acc.lake: 0.6296, Acc.dishwasher: 0.8361, Acc.screen: 0.7820, Acc.blanket: 0.3572, Acc.sculpture: 0.8933, Acc.hood: 0.7829, Acc.sconce: 0.5903, Acc.vase: 0.6010, Acc.traffic light: 0.6199, Acc.tray: 0.2233, Acc.ashcan: 0.6216, Acc.fan: 0.7934, Acc.pier: 0.7723, Acc.crt screen: 0.2642, Acc.plate: 0.7950, Acc.monitor: 0.8708, Acc.bulletin board: 0.5880, Acc.shower: 0.0144, Acc.radiator: 0.7678, Acc.glass: 0.1901, Acc.clock: 0.5004, Acc.flag: 0.7763 +2024-06-18 20:51:23,798 - mmseg - INFO - Iter [52050/80000] lr: 1.398e-05, eta: 11:29:47, time: 3.275, data_time: 1.956, memory: 70498, decode.loss_ce: 0.1850, decode.acc_seg: 92.2332, aux.loss_ce: 0.0775, aux.acc_seg: 91.8654, loss: 0.2625 +2024-06-18 20:52:30,381 - mmseg - INFO - Iter [52100/80000] lr: 1.395e-05, eta: 11:28:29, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1773, decode.acc_seg: 92.5049, aux.loss_ce: 0.0745, aux.acc_seg: 92.0829, loss: 0.2518 +2024-06-18 20:53:36,651 - mmseg - INFO - Iter [52150/80000] lr: 1.393e-05, eta: 11:27:11, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1982, decode.acc_seg: 91.6462, aux.loss_ce: 0.0831, aux.acc_seg: 91.2544, loss: 0.2813 +2024-06-18 20:54:43,148 - mmseg - INFO - Iter [52200/80000] lr: 1.390e-05, eta: 11:25:53, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1924, decode.acc_seg: 91.7605, aux.loss_ce: 0.0811, aux.acc_seg: 91.3770, loss: 0.2735 +2024-06-18 20:55:49,364 - mmseg - INFO - Iter [52250/80000] lr: 1.388e-05, eta: 11:24:35, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1788, decode.acc_seg: 92.2082, aux.loss_ce: 0.0749, aux.acc_seg: 91.7872, loss: 0.2536 +2024-06-18 20:56:55,586 - mmseg - INFO - Iter [52300/80000] lr: 1.385e-05, eta: 11:23:17, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1819, decode.acc_seg: 92.2772, aux.loss_ce: 0.0770, aux.acc_seg: 91.8777, loss: 0.2588 +2024-06-18 20:58:01,745 - mmseg - INFO - Iter [52350/80000] lr: 1.383e-05, eta: 11:21:59, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1898, decode.acc_seg: 91.7699, aux.loss_ce: 0.0805, aux.acc_seg: 91.3279, loss: 0.2704 +2024-06-18 20:59:08,000 - mmseg - INFO - Iter [52400/80000] lr: 1.380e-05, eta: 11:20:41, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1836, decode.acc_seg: 92.3225, aux.loss_ce: 0.0766, aux.acc_seg: 92.0349, loss: 0.2602 +2024-06-18 21:00:14,478 - mmseg - INFO - Iter [52450/80000] lr: 1.378e-05, eta: 11:19:23, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1998, decode.acc_seg: 91.4235, aux.loss_ce: 0.0834, aux.acc_seg: 91.0884, loss: 0.2832 +2024-06-18 21:01:20,637 - mmseg - INFO - Iter [52500/80000] lr: 1.375e-05, eta: 11:18:05, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1862, decode.acc_seg: 92.1388, aux.loss_ce: 0.0783, aux.acc_seg: 91.8098, loss: 0.2645 +2024-06-18 21:02:26,875 - mmseg - INFO - Iter [52550/80000] lr: 1.373e-05, eta: 11:16:47, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1854, decode.acc_seg: 91.9874, aux.loss_ce: 0.0779, aux.acc_seg: 91.6235, loss: 0.2633 +2024-06-18 21:03:33,313 - mmseg - INFO - Iter [52600/80000] lr: 1.370e-05, eta: 11:15:29, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1917, decode.acc_seg: 91.7169, aux.loss_ce: 0.0814, aux.acc_seg: 91.2523, loss: 0.2732 +2024-06-18 21:04:40,027 - mmseg - INFO - Iter [52650/80000] lr: 1.368e-05, eta: 11:14:11, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1837, decode.acc_seg: 92.1698, aux.loss_ce: 0.0781, aux.acc_seg: 91.7201, loss: 0.2617 +2024-06-18 21:05:46,402 - mmseg - INFO - Iter [52700/80000] lr: 1.365e-05, eta: 11:12:53, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1742, decode.acc_seg: 92.3554, aux.loss_ce: 0.0732, aux.acc_seg: 92.0451, loss: 0.2474 +2024-06-18 21:06:52,797 - mmseg - INFO - Iter [52750/80000] lr: 1.363e-05, eta: 11:11:35, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1815, decode.acc_seg: 92.3738, aux.loss_ce: 0.0765, aux.acc_seg: 92.0472, loss: 0.2580 +2024-06-18 21:07:59,153 - mmseg - INFO - Iter [52800/80000] lr: 1.360e-05, eta: 11:10:17, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1780, decode.acc_seg: 92.3201, aux.loss_ce: 0.0749, aux.acc_seg: 91.9871, loss: 0.2530 +2024-06-18 21:09:05,948 - mmseg - INFO - Iter [52850/80000] lr: 1.358e-05, eta: 11:09:00, time: 1.336, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1740, decode.acc_seg: 92.5806, aux.loss_ce: 0.0737, aux.acc_seg: 92.1705, loss: 0.2476 +2024-06-18 21:10:12,215 - mmseg - INFO - Iter [52900/80000] lr: 1.355e-05, eta: 11:07:42, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1869, decode.acc_seg: 91.9973, aux.loss_ce: 0.0792, aux.acc_seg: 91.5443, loss: 0.2661 +2024-06-18 21:11:18,337 - mmseg - INFO - Iter [52950/80000] lr: 1.353e-05, eta: 11:06:24, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1877, decode.acc_seg: 91.7376, aux.loss_ce: 0.0789, aux.acc_seg: 91.3892, loss: 0.2666 +2024-06-18 21:12:24,851 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 21:12:24,851 - mmseg - INFO - Iter [53000/80000] lr: 1.350e-05, eta: 11:05:06, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1877, decode.acc_seg: 91.9890, aux.loss_ce: 0.0789, aux.acc_seg: 91.5842, loss: 0.2666 +2024-06-18 21:14:02,135 - mmseg - INFO - per class results: +2024-06-18 21:14:02,141 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.53 | 90.09 | +| building | 85.26 | 93.34 | +| sky | 94.89 | 97.54 | +| floor | 84.53 | 91.36 | +| tree | 77.76 | 91.0 | +| ceiling | 86.97 | 94.05 | +| road | 86.9 | 92.47 | +| bed | 92.03 | 96.63 | +| windowpane | 66.33 | 80.78 | +| grass | 64.28 | 77.98 | +| cabinet | 66.39 | 75.65 | +| sidewalk | 72.52 | 84.53 | +| person | 85.48 | 93.11 | +| earth | 38.3 | 50.79 | +| door | 60.68 | 75.61 | +| table | 68.05 | 79.67 | +| mountain | 61.02 | 71.12 | +| plant | 56.28 | 68.75 | +| curtain | 76.46 | 84.88 | +| chair | 67.4 | 78.07 | +| car | 86.41 | 91.8 | +| water | 66.33 | 81.37 | +| painting | 78.14 | 91.71 | +| sofa | 81.35 | 91.0 | +| shelf | 49.48 | 69.18 | +| house | 53.01 | 65.48 | +| sea | 74.7 | 89.95 | +| mirror | 78.78 | 85.34 | +| rug | 69.61 | 84.0 | +| field | 34.94 | 64.42 | +| armchair | 60.42 | 75.23 | +| seat | 63.48 | 88.61 | +| fence | 51.49 | 63.02 | +| desk | 57.21 | 79.36 | +| rock | 52.89 | 80.43 | +| wardrobe | 58.87 | 74.58 | +| lamp | 73.06 | 83.87 | +| bathtub | 84.22 | 86.55 | +| railing | 41.19 | 62.92 | +| cushion | 70.35 | 85.2 | +| base | 39.4 | 56.63 | +| box | 37.51 | 46.67 | +| column | 54.42 | 72.22 | +| signboard | 41.02 | 55.43 | +| chest of drawers | 46.25 | 69.51 | +| counter | 37.77 | 47.21 | +| sand | 53.56 | 78.46 | +| sink | 76.93 | 84.35 | +| skyscraper | 59.67 | 81.29 | +| fireplace | 72.28 | 96.17 | +| refrigerator | 79.27 | 91.22 | +| grandstand | 48.72 | 83.1 | +| path | 29.35 | 43.24 | +| stairs | 21.48 | 27.29 | +| runway | 71.26 | 92.89 | +| case | 59.44 | 78.93 | +| pool table | 94.85 | 98.25 | +| pillow | 71.15 | 82.26 | +| screen door | 78.11 | 81.45 | +| stairway | 37.9 | 57.47 | +| river | 19.7 | 31.77 | +| bridge | 65.65 | 78.45 | +| bookcase | 41.12 | 55.09 | +| blind | 45.94 | 51.88 | +| coffee table | 59.49 | 88.01 | +| toilet | 89.41 | 92.75 | +| flower | 43.51 | 65.53 | +| book | 56.61 | 76.91 | +| hill | 8.45 | 17.72 | +| bench | 49.11 | 55.96 | +| countertop | 63.88 | 84.8 | +| stove | 87.7 | 93.7 | +| palm | 56.62 | 80.84 | +| kitchen island | 44.72 | 75.28 | +| computer | 81.61 | 92.6 | +| swivel chair | 51.93 | 76.8 | +| boat | 66.9 | 85.19 | +| bar | 55.49 | 74.94 | +| arcade machine | 78.02 | 83.09 | +| hovel | 44.37 | 49.29 | +| bus | 93.24 | 95.8 | +| towel | 76.53 | 86.55 | +| light | 59.41 | 66.44 | +| truck | 44.99 | 64.54 | +| tower | 10.67 | 14.54 | +| chandelier | 72.67 | 85.13 | +| awning | 38.46 | 46.32 | +| streetlight | 31.15 | 39.86 | +| booth | 55.55 | 69.68 | +| television receiver | 77.23 | 84.53 | +| airplane | 83.02 | 87.47 | +| dirt track | 1.75 | 6.22 | +| apparel | 44.98 | 65.47 | +| pole | 27.49 | 37.57 | +| land | 3.59 | 5.36 | +| bannister | 15.77 | 23.05 | +| escalator | 57.0 | 78.61 | +| ottoman | 50.0 | 71.08 | +| bottle | 40.39 | 62.24 | +| buffet | 51.7 | 65.97 | +| poster | 33.67 | 45.12 | +| stage | 22.1 | 44.08 | +| van | 43.29 | 62.74 | +| ship | 72.15 | 74.68 | +| fountain | 20.65 | 21.77 | +| conveyer belt | 78.89 | 93.64 | +| canopy | 50.31 | 68.19 | +| washer | 83.37 | 89.76 | +| plaything | 45.01 | 60.96 | +| swimming pool | 63.61 | 91.39 | +| stool | 54.47 | 62.58 | +| barrel | 54.54 | 64.69 | +| basket | 40.35 | 58.19 | +| waterfall | 45.12 | 54.63 | +| tent | 92.47 | 98.62 | +| bag | 20.93 | 25.08 | +| minibike | 75.83 | 88.29 | +| cradle | 81.72 | 98.03 | +| oven | 58.88 | 68.46 | +| ball | 58.62 | 72.13 | +| food | 60.45 | 73.85 | +| step | 11.05 | 13.22 | +| tank | 63.66 | 71.99 | +| trade name | 24.68 | 26.62 | +| microwave | 87.55 | 95.32 | +| pot | 58.55 | 67.61 | +| animal | 60.06 | 61.77 | +| bicycle | 58.13 | 71.74 | +| lake | 53.64 | 63.74 | +| dishwasher | 73.14 | 82.92 | +| screen | 41.76 | 59.39 | +| blanket | 27.13 | 31.0 | +| sculpture | 74.46 | 87.93 | +| hood | 60.39 | 71.06 | +| sconce | 55.76 | 66.54 | +| vase | 48.69 | 64.89 | +| traffic light | 41.08 | 60.34 | +| tray | 14.03 | 17.49 | +| ashcan | 48.41 | 61.59 | +| fan | 63.52 | 73.69 | +| pier | 49.21 | 81.99 | +| crt screen | 20.76 | 41.97 | +| plate | 56.64 | 76.64 | +| monitor | 67.57 | 82.75 | +| bulletin board | 49.76 | 52.82 | +| shower | 3.52 | 3.66 | +| radiator | 65.73 | 75.21 | +| glass | 20.16 | 22.42 | +| clock | 40.14 | 46.59 | +| flag | 71.31 | 76.27 | ++---------------------+-------+-------+ +2024-06-18 21:14:02,141 - mmseg - INFO - Summary: +2024-06-18 21:14:02,142 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.04 | 56.55 | 69.19 | ++-------+-------+-------+ +2024-06-18 21:14:02,142 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 21:14:02,143 - mmseg - INFO - Iter(val) [250] aAcc: 0.8604, mIoU: 0.5655, mAcc: 0.6919, IoU.wall: 0.8253, IoU.building: 0.8526, IoU.sky: 0.9489, IoU.floor: 0.8453, IoU.tree: 0.7776, IoU.ceiling: 0.8697, IoU.road: 0.8690, IoU.bed : 0.9203, IoU.windowpane: 0.6633, IoU.grass: 0.6428, IoU.cabinet: 0.6639, IoU.sidewalk: 0.7252, IoU.person: 0.8548, IoU.earth: 0.3830, IoU.door: 0.6068, IoU.table: 0.6805, IoU.mountain: 0.6102, IoU.plant: 0.5628, IoU.curtain: 0.7646, IoU.chair: 0.6740, IoU.car: 0.8641, IoU.water: 0.6633, IoU.painting: 0.7814, IoU.sofa: 0.8135, IoU.shelf: 0.4948, IoU.house: 0.5301, IoU.sea: 0.7470, IoU.mirror: 0.7878, IoU.rug: 0.6961, IoU.field: 0.3494, IoU.armchair: 0.6042, IoU.seat: 0.6348, IoU.fence: 0.5149, IoU.desk: 0.5721, IoU.rock: 0.5289, IoU.wardrobe: 0.5887, IoU.lamp: 0.7306, IoU.bathtub: 0.8422, IoU.railing: 0.4119, IoU.cushion: 0.7035, IoU.base: 0.3940, IoU.box: 0.3751, IoU.column: 0.5442, IoU.signboard: 0.4102, IoU.chest of drawers: 0.4625, IoU.counter: 0.3777, IoU.sand: 0.5356, IoU.sink: 0.7693, IoU.skyscraper: 0.5967, IoU.fireplace: 0.7228, IoU.refrigerator: 0.7927, IoU.grandstand: 0.4872, IoU.path: 0.2935, IoU.stairs: 0.2148, IoU.runway: 0.7126, IoU.case: 0.5944, IoU.pool table: 0.9485, IoU.pillow: 0.7115, IoU.screen door: 0.7811, IoU.stairway: 0.3790, IoU.river: 0.1970, IoU.bridge: 0.6565, IoU.bookcase: 0.4112, IoU.blind: 0.4594, IoU.coffee table: 0.5949, IoU.toilet: 0.8941, IoU.flower: 0.4351, IoU.book: 0.5661, IoU.hill: 0.0845, IoU.bench: 0.4911, IoU.countertop: 0.6388, IoU.stove: 0.8770, IoU.palm: 0.5662, IoU.kitchen island: 0.4472, IoU.computer: 0.8161, IoU.swivel chair: 0.5193, IoU.boat: 0.6690, IoU.bar: 0.5549, IoU.arcade machine: 0.7802, IoU.hovel: 0.4437, IoU.bus: 0.9324, IoU.towel: 0.7653, IoU.light: 0.5941, IoU.truck: 0.4499, IoU.tower: 0.1067, IoU.chandelier: 0.7267, IoU.awning: 0.3846, IoU.streetlight: 0.3115, IoU.booth: 0.5555, IoU.television receiver: 0.7723, IoU.airplane: 0.8302, IoU.dirt track: 0.0175, IoU.apparel: 0.4498, IoU.pole: 0.2749, IoU.land: 0.0359, IoU.bannister: 0.1577, IoU.escalator: 0.5700, IoU.ottoman: 0.5000, IoU.bottle: 0.4039, IoU.buffet: 0.5170, IoU.poster: 0.3367, IoU.stage: 0.2210, IoU.van: 0.4329, IoU.ship: 0.7215, IoU.fountain: 0.2065, IoU.conveyer belt: 0.7889, IoU.canopy: 0.5031, IoU.washer: 0.8337, IoU.plaything: 0.4501, IoU.swimming pool: 0.6361, IoU.stool: 0.5447, IoU.barrel: 0.5454, IoU.basket: 0.4035, IoU.waterfall: 0.4512, IoU.tent: 0.9247, IoU.bag: 0.2093, IoU.minibike: 0.7583, IoU.cradle: 0.8172, IoU.oven: 0.5888, IoU.ball: 0.5862, IoU.food: 0.6045, IoU.step: 0.1105, IoU.tank: 0.6366, IoU.trade name: 0.2468, IoU.microwave: 0.8755, IoU.pot: 0.5855, IoU.animal: 0.6006, IoU.bicycle: 0.5813, IoU.lake: 0.5364, IoU.dishwasher: 0.7314, IoU.screen: 0.4176, IoU.blanket: 0.2713, IoU.sculpture: 0.7446, IoU.hood: 0.6039, IoU.sconce: 0.5576, IoU.vase: 0.4869, IoU.traffic light: 0.4108, IoU.tray: 0.1403, IoU.ashcan: 0.4841, IoU.fan: 0.6352, IoU.pier: 0.4921, IoU.crt screen: 0.2076, IoU.plate: 0.5664, IoU.monitor: 0.6757, IoU.bulletin board: 0.4976, IoU.shower: 0.0352, IoU.radiator: 0.6573, IoU.glass: 0.2016, IoU.clock: 0.4014, IoU.flag: 0.7131, Acc.wall: 0.9009, Acc.building: 0.9334, Acc.sky: 0.9754, Acc.floor: 0.9136, Acc.tree: 0.9100, Acc.ceiling: 0.9405, Acc.road: 0.9247, Acc.bed : 0.9663, Acc.windowpane: 0.8078, Acc.grass: 0.7798, Acc.cabinet: 0.7565, Acc.sidewalk: 0.8453, Acc.person: 0.9311, Acc.earth: 0.5079, Acc.door: 0.7561, Acc.table: 0.7967, Acc.mountain: 0.7112, Acc.plant: 0.6875, Acc.curtain: 0.8488, Acc.chair: 0.7807, Acc.car: 0.9180, Acc.water: 0.8137, Acc.painting: 0.9171, Acc.sofa: 0.9100, Acc.shelf: 0.6918, Acc.house: 0.6548, Acc.sea: 0.8995, Acc.mirror: 0.8534, Acc.rug: 0.8400, Acc.field: 0.6442, Acc.armchair: 0.7523, Acc.seat: 0.8861, Acc.fence: 0.6302, Acc.desk: 0.7936, Acc.rock: 0.8043, Acc.wardrobe: 0.7458, Acc.lamp: 0.8387, Acc.bathtub: 0.8655, Acc.railing: 0.6292, Acc.cushion: 0.8520, Acc.base: 0.5663, Acc.box: 0.4667, Acc.column: 0.7222, Acc.signboard: 0.5543, Acc.chest of drawers: 0.6951, Acc.counter: 0.4721, Acc.sand: 0.7846, Acc.sink: 0.8435, Acc.skyscraper: 0.8129, Acc.fireplace: 0.9617, Acc.refrigerator: 0.9122, Acc.grandstand: 0.8310, Acc.path: 0.4324, Acc.stairs: 0.2729, Acc.runway: 0.9289, Acc.case: 0.7893, Acc.pool table: 0.9825, Acc.pillow: 0.8226, Acc.screen door: 0.8145, Acc.stairway: 0.5747, Acc.river: 0.3177, Acc.bridge: 0.7845, Acc.bookcase: 0.5509, Acc.blind: 0.5188, Acc.coffee table: 0.8801, Acc.toilet: 0.9275, Acc.flower: 0.6553, Acc.book: 0.7691, Acc.hill: 0.1772, Acc.bench: 0.5596, Acc.countertop: 0.8480, Acc.stove: 0.9370, Acc.palm: 0.8084, Acc.kitchen island: 0.7528, Acc.computer: 0.9260, Acc.swivel chair: 0.7680, Acc.boat: 0.8519, Acc.bar: 0.7494, Acc.arcade machine: 0.8309, Acc.hovel: 0.4929, Acc.bus: 0.9580, Acc.towel: 0.8655, Acc.light: 0.6644, Acc.truck: 0.6454, Acc.tower: 0.1454, Acc.chandelier: 0.8513, Acc.awning: 0.4632, Acc.streetlight: 0.3986, Acc.booth: 0.6968, Acc.television receiver: 0.8453, Acc.airplane: 0.8747, Acc.dirt track: 0.0622, Acc.apparel: 0.6547, Acc.pole: 0.3757, Acc.land: 0.0536, Acc.bannister: 0.2305, Acc.escalator: 0.7861, Acc.ottoman: 0.7108, Acc.bottle: 0.6224, Acc.buffet: 0.6597, Acc.poster: 0.4512, Acc.stage: 0.4408, Acc.van: 0.6274, Acc.ship: 0.7468, Acc.fountain: 0.2177, Acc.conveyer belt: 0.9364, Acc.canopy: 0.6819, Acc.washer: 0.8976, Acc.plaything: 0.6096, Acc.swimming pool: 0.9139, Acc.stool: 0.6258, Acc.barrel: 0.6469, Acc.basket: 0.5819, Acc.waterfall: 0.5463, Acc.tent: 0.9862, Acc.bag: 0.2508, Acc.minibike: 0.8829, Acc.cradle: 0.9803, Acc.oven: 0.6846, Acc.ball: 0.7213, Acc.food: 0.7385, Acc.step: 0.1322, Acc.tank: 0.7199, Acc.trade name: 0.2662, Acc.microwave: 0.9532, Acc.pot: 0.6761, Acc.animal: 0.6177, Acc.bicycle: 0.7174, Acc.lake: 0.6374, Acc.dishwasher: 0.8292, Acc.screen: 0.5939, Acc.blanket: 0.3100, Acc.sculpture: 0.8793, Acc.hood: 0.7106, Acc.sconce: 0.6654, Acc.vase: 0.6489, Acc.traffic light: 0.6034, Acc.tray: 0.1749, Acc.ashcan: 0.6159, Acc.fan: 0.7369, Acc.pier: 0.8199, Acc.crt screen: 0.4197, Acc.plate: 0.7664, Acc.monitor: 0.8275, Acc.bulletin board: 0.5282, Acc.shower: 0.0366, Acc.radiator: 0.7521, Acc.glass: 0.2242, Acc.clock: 0.4659, Acc.flag: 0.7627 +2024-06-18 21:15:11,321 - mmseg - INFO - Iter [53050/80000] lr: 1.348e-05, eta: 11:04:40, time: 3.329, data_time: 2.013, memory: 70498, decode.loss_ce: 0.1768, decode.acc_seg: 92.6268, aux.loss_ce: 0.0750, aux.acc_seg: 92.2029, loss: 0.2518 +2024-06-18 21:16:17,628 - mmseg - INFO - Iter [53100/80000] lr: 1.345e-05, eta: 11:03:22, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1832, decode.acc_seg: 92.2764, aux.loss_ce: 0.0778, aux.acc_seg: 91.8507, loss: 0.2609 +2024-06-18 21:17:23,860 - mmseg - INFO - Iter [53150/80000] lr: 1.343e-05, eta: 11:02:04, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1847, decode.acc_seg: 92.0899, aux.loss_ce: 0.0778, aux.acc_seg: 91.5902, loss: 0.2625 +2024-06-18 21:18:30,341 - mmseg - INFO - Iter [53200/80000] lr: 1.340e-05, eta: 11:00:46, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1785, decode.acc_seg: 92.5605, aux.loss_ce: 0.0755, aux.acc_seg: 92.0656, loss: 0.2540 +2024-06-18 21:19:36,677 - mmseg - INFO - Iter [53250/80000] lr: 1.338e-05, eta: 10:59:28, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1710, decode.acc_seg: 92.6379, aux.loss_ce: 0.0720, aux.acc_seg: 92.2929, loss: 0.2429 +2024-06-18 21:20:42,954 - mmseg - INFO - Iter [53300/80000] lr: 1.335e-05, eta: 10:58:10, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1782, decode.acc_seg: 92.2188, aux.loss_ce: 0.0755, aux.acc_seg: 91.8648, loss: 0.2537 +2024-06-18 21:21:49,555 - mmseg - INFO - Iter [53350/80000] lr: 1.333e-05, eta: 10:56:53, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1766, decode.acc_seg: 92.6621, aux.loss_ce: 0.0750, aux.acc_seg: 92.2825, loss: 0.2516 +2024-06-18 21:22:56,408 - mmseg - INFO - Iter [53400/80000] lr: 1.330e-05, eta: 10:55:35, time: 1.337, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1809, decode.acc_seg: 92.4561, aux.loss_ce: 0.0760, aux.acc_seg: 92.0290, loss: 0.2569 +2024-06-18 21:24:02,799 - mmseg - INFO - Iter [53450/80000] lr: 1.328e-05, eta: 10:54:18, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1835, decode.acc_seg: 92.3230, aux.loss_ce: 0.0776, aux.acc_seg: 91.9614, loss: 0.2611 +2024-06-18 21:25:09,038 - mmseg - INFO - Iter [53500/80000] lr: 1.325e-05, eta: 10:53:00, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1722, decode.acc_seg: 92.5273, aux.loss_ce: 0.0728, aux.acc_seg: 92.1082, loss: 0.2450 +2024-06-18 21:26:15,661 - mmseg - INFO - Iter [53550/80000] lr: 1.323e-05, eta: 10:51:42, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1859, decode.acc_seg: 92.2339, aux.loss_ce: 0.0797, aux.acc_seg: 91.6854, loss: 0.2656 +2024-06-18 21:27:22,184 - mmseg - INFO - Iter [53600/80000] lr: 1.320e-05, eta: 10:50:25, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1786, decode.acc_seg: 92.2004, aux.loss_ce: 0.0754, aux.acc_seg: 91.7866, loss: 0.2540 +2024-06-18 21:28:28,523 - mmseg - INFO - Iter [53650/80000] lr: 1.318e-05, eta: 10:49:07, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1709, decode.acc_seg: 92.4162, aux.loss_ce: 0.0725, aux.acc_seg: 91.9684, loss: 0.2434 +2024-06-18 21:29:35,025 - mmseg - INFO - Iter [53700/80000] lr: 1.315e-05, eta: 10:47:50, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1751, decode.acc_seg: 92.5473, aux.loss_ce: 0.0744, aux.acc_seg: 92.1117, loss: 0.2495 +2024-06-18 21:30:41,321 - mmseg - INFO - Iter [53750/80000] lr: 1.313e-05, eta: 10:46:32, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1824, decode.acc_seg: 92.1795, aux.loss_ce: 0.0762, aux.acc_seg: 91.8230, loss: 0.2586 +2024-06-18 21:31:47,791 - mmseg - INFO - Iter [53800/80000] lr: 1.310e-05, eta: 10:45:14, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1783, decode.acc_seg: 92.1080, aux.loss_ce: 0.0751, aux.acc_seg: 91.6592, loss: 0.2534 +2024-06-18 21:32:54,070 - mmseg - INFO - Iter [53850/80000] lr: 1.308e-05, eta: 10:43:57, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1725, decode.acc_seg: 92.6010, aux.loss_ce: 0.0725, aux.acc_seg: 92.2126, loss: 0.2449 +2024-06-18 21:34:00,832 - mmseg - INFO - Iter [53900/80000] lr: 1.305e-05, eta: 10:42:40, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1759, decode.acc_seg: 92.3669, aux.loss_ce: 0.0745, aux.acc_seg: 91.8892, loss: 0.2504 +2024-06-18 21:35:07,148 - mmseg - INFO - Iter [53950/80000] lr: 1.303e-05, eta: 10:41:22, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1707, decode.acc_seg: 92.6691, aux.loss_ce: 0.0716, aux.acc_seg: 92.3885, loss: 0.2424 +2024-06-18 21:36:13,768 - mmseg - INFO - Saving checkpoint at 54000 iterations +2024-06-18 21:38:02,970 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 21:38:02,970 - mmseg - INFO - Iter [54000/80000] lr: 1.300e-05, eta: 10:40:57, time: 3.516, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1790, decode.acc_seg: 92.2450, aux.loss_ce: 0.0751, aux.acc_seg: 91.8858, loss: 0.2541 +2024-06-18 21:39:41,269 - mmseg - INFO - per class results: +2024-06-18 21:39:41,275 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.0 | 89.84 | +| building | 84.96 | 93.9 | +| sky | 94.71 | 97.72 | +| floor | 85.27 | 91.29 | +| tree | 77.68 | 89.15 | +| ceiling | 86.9 | 93.79 | +| road | 86.76 | 91.29 | +| bed | 92.04 | 97.07 | +| windowpane | 66.48 | 79.17 | +| grass | 65.77 | 79.96 | +| cabinet | 64.01 | 73.34 | +| sidewalk | 72.14 | 86.99 | +| person | 86.02 | 93.96 | +| earth | 38.82 | 52.25 | +| door | 59.48 | 75.48 | +| table | 69.74 | 81.86 | +| mountain | 59.4 | 64.18 | +| plant | 55.42 | 67.34 | +| curtain | 78.15 | 88.86 | +| chair | 67.18 | 77.3 | +| car | 86.47 | 94.66 | +| water | 62.09 | 77.66 | +| painting | 76.22 | 89.78 | +| sofa | 81.55 | 91.7 | +| shelf | 51.04 | 69.18 | +| house | 55.15 | 64.5 | +| sea | 67.16 | 83.18 | +| mirror | 78.36 | 85.14 | +| rug | 67.82 | 82.24 | +| field | 34.82 | 63.35 | +| armchair | 59.03 | 75.87 | +| seat | 63.43 | 89.08 | +| fence | 50.8 | 66.05 | +| desk | 56.39 | 77.98 | +| rock | 55.48 | 85.12 | +| wardrobe | 54.76 | 75.26 | +| lamp | 72.44 | 84.43 | +| bathtub | 84.21 | 86.72 | +| railing | 41.66 | 59.49 | +| cushion | 69.27 | 79.0 | +| base | 39.53 | 53.7 | +| box | 34.49 | 40.7 | +| column | 55.33 | 73.57 | +| signboard | 41.28 | 53.58 | +| chest of drawers | 43.98 | 72.63 | +| counter | 41.4 | 50.75 | +| sand | 51.15 | 79.61 | +| sink | 77.22 | 85.72 | +| skyscraper | 50.43 | 65.58 | +| fireplace | 76.25 | 90.43 | +| refrigerator | 76.56 | 93.21 | +| grandstand | 51.11 | 81.28 | +| path | 25.99 | 33.91 | +| stairs | 21.57 | 27.29 | +| runway | 73.18 | 96.78 | +| case | 59.86 | 75.8 | +| pool table | 94.67 | 97.43 | +| pillow | 70.09 | 85.83 | +| screen door | 84.42 | 88.83 | +| stairway | 40.17 | 57.58 | +| river | 17.0 | 36.32 | +| bridge | 45.56 | 50.48 | +| bookcase | 51.79 | 62.15 | +| blind | 43.09 | 52.7 | +| coffee table | 63.95 | 88.99 | +| toilet | 89.57 | 93.18 | +| flower | 39.77 | 53.51 | +| book | 58.22 | 69.63 | +| hill | 8.74 | 22.99 | +| bench | 50.01 | 60.02 | +| countertop | 60.46 | 83.98 | +| stove | 83.86 | 93.33 | +| palm | 57.01 | 83.94 | +| kitchen island | 50.57 | 88.75 | +| computer | 81.46 | 93.56 | +| swivel chair | 49.92 | 79.39 | +| boat | 60.57 | 85.78 | +| bar | 56.5 | 72.14 | +| arcade machine | 79.45 | 83.39 | +| hovel | 45.67 | 52.26 | +| bus | 93.15 | 96.22 | +| towel | 74.22 | 87.5 | +| light | 59.52 | 69.0 | +| truck | 45.63 | 57.31 | +| tower | 11.53 | 16.34 | +| chandelier | 71.73 | 87.32 | +| awning | 43.32 | 53.83 | +| streetlight | 31.78 | 43.57 | +| booth | 42.55 | 56.62 | +| television receiver | 78.19 | 86.08 | +| airplane | 85.88 | 91.48 | +| dirt track | 6.04 | 33.19 | +| apparel | 41.14 | 55.77 | +| pole | 25.5 | 34.64 | +| land | 5.2 | 9.01 | +| bannister | 17.08 | 22.81 | +| escalator | 56.39 | 81.13 | +| ottoman | 51.0 | 69.83 | +| bottle | 42.53 | 69.97 | +| buffet | 53.26 | 67.88 | +| poster | 38.15 | 52.37 | +| stage | 20.48 | 38.47 | +| van | 43.76 | 60.26 | +| ship | 91.97 | 95.24 | +| fountain | 23.77 | 24.5 | +| conveyer belt | 73.68 | 93.72 | +| canopy | 53.67 | 71.45 | +| washer | 88.5 | 92.02 | +| plaything | 40.17 | 61.94 | +| swimming pool | 71.92 | 94.58 | +| stool | 53.17 | 69.58 | +| barrel | 56.56 | 64.31 | +| basket | 38.45 | 63.33 | +| waterfall | 46.68 | 64.16 | +| tent | 91.65 | 98.48 | +| bag | 22.95 | 27.27 | +| minibike | 74.41 | 89.98 | +| cradle | 77.75 | 98.35 | +| oven | 56.77 | 66.29 | +| ball | 56.81 | 73.29 | +| food | 63.43 | 75.38 | +| step | 11.53 | 13.81 | +| tank | 57.32 | 61.7 | +| trade name | 31.93 | 36.03 | +| microwave | 88.63 | 95.67 | +| pot | 58.67 | 68.03 | +| animal | 61.02 | 62.59 | +| bicycle | 59.57 | 75.58 | +| lake | 55.31 | 61.94 | +| dishwasher | 70.06 | 76.14 | +| screen | 61.59 | 91.7 | +| blanket | 24.36 | 26.86 | +| sculpture | 79.74 | 87.59 | +| hood | 62.69 | 76.27 | +| sconce | 56.35 | 65.74 | +| vase | 49.05 | 62.4 | +| traffic light | 39.75 | 65.31 | +| tray | 13.57 | 17.19 | +| ashcan | 46.11 | 61.67 | +| fan | 66.74 | 81.49 | +| pier | 45.78 | 68.76 | +| crt screen | 14.05 | 31.6 | +| plate | 59.03 | 74.61 | +| monitor | 28.41 | 34.48 | +| bulletin board | 54.73 | 65.92 | +| shower | 4.59 | 6.96 | +| radiator | 65.64 | 75.33 | +| glass | 19.51 | 21.45 | +| clock | 40.37 | 45.47 | +| flag | 71.11 | 76.83 | ++---------------------+-------+-------+ +2024-06-18 21:39:41,276 - mmseg - INFO - Summary: +2024-06-18 21:39:41,276 - mmseg - INFO - ++-------+------+------+ +| aAcc | mIoU | mAcc | ++-------+------+------+ +| 85.86 | 56.4 | 69.4 | ++-------+------+------+ +2024-06-18 21:39:41,276 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 21:39:41,277 - mmseg - INFO - Iter(val) [250] aAcc: 0.8586, mIoU: 0.5640, mAcc: 0.6940, IoU.wall: 0.8200, IoU.building: 0.8496, IoU.sky: 0.9471, IoU.floor: 0.8527, IoU.tree: 0.7768, IoU.ceiling: 0.8690, IoU.road: 0.8676, IoU.bed : 0.9204, IoU.windowpane: 0.6648, IoU.grass: 0.6577, IoU.cabinet: 0.6401, IoU.sidewalk: 0.7214, IoU.person: 0.8602, IoU.earth: 0.3882, IoU.door: 0.5948, IoU.table: 0.6974, IoU.mountain: 0.5940, IoU.plant: 0.5542, IoU.curtain: 0.7815, IoU.chair: 0.6718, IoU.car: 0.8647, IoU.water: 0.6209, IoU.painting: 0.7622, IoU.sofa: 0.8155, IoU.shelf: 0.5104, IoU.house: 0.5515, IoU.sea: 0.6716, IoU.mirror: 0.7836, IoU.rug: 0.6782, IoU.field: 0.3482, IoU.armchair: 0.5903, IoU.seat: 0.6343, IoU.fence: 0.5080, IoU.desk: 0.5639, IoU.rock: 0.5548, IoU.wardrobe: 0.5476, IoU.lamp: 0.7244, IoU.bathtub: 0.8421, IoU.railing: 0.4166, IoU.cushion: 0.6927, IoU.base: 0.3953, IoU.box: 0.3449, IoU.column: 0.5533, IoU.signboard: 0.4128, IoU.chest of drawers: 0.4398, IoU.counter: 0.4140, IoU.sand: 0.5115, IoU.sink: 0.7722, IoU.skyscraper: 0.5043, IoU.fireplace: 0.7625, IoU.refrigerator: 0.7656, IoU.grandstand: 0.5111, IoU.path: 0.2599, IoU.stairs: 0.2157, IoU.runway: 0.7318, IoU.case: 0.5986, IoU.pool table: 0.9467, IoU.pillow: 0.7009, IoU.screen door: 0.8442, IoU.stairway: 0.4017, IoU.river: 0.1700, IoU.bridge: 0.4556, IoU.bookcase: 0.5179, IoU.blind: 0.4309, IoU.coffee table: 0.6395, IoU.toilet: 0.8957, IoU.flower: 0.3977, IoU.book: 0.5822, IoU.hill: 0.0874, IoU.bench: 0.5001, IoU.countertop: 0.6046, IoU.stove: 0.8386, IoU.palm: 0.5701, IoU.kitchen island: 0.5057, IoU.computer: 0.8146, IoU.swivel chair: 0.4992, IoU.boat: 0.6057, IoU.bar: 0.5650, IoU.arcade machine: 0.7945, IoU.hovel: 0.4567, IoU.bus: 0.9315, IoU.towel: 0.7422, IoU.light: 0.5952, IoU.truck: 0.4563, IoU.tower: 0.1153, IoU.chandelier: 0.7173, IoU.awning: 0.4332, IoU.streetlight: 0.3178, IoU.booth: 0.4255, IoU.television receiver: 0.7819, IoU.airplane: 0.8588, IoU.dirt track: 0.0604, IoU.apparel: 0.4114, IoU.pole: 0.2550, IoU.land: 0.0520, IoU.bannister: 0.1708, IoU.escalator: 0.5639, IoU.ottoman: 0.5100, IoU.bottle: 0.4253, IoU.buffet: 0.5326, IoU.poster: 0.3815, IoU.stage: 0.2048, IoU.van: 0.4376, IoU.ship: 0.9197, IoU.fountain: 0.2377, IoU.conveyer belt: 0.7368, IoU.canopy: 0.5367, IoU.washer: 0.8850, IoU.plaything: 0.4017, IoU.swimming pool: 0.7192, IoU.stool: 0.5317, IoU.barrel: 0.5656, IoU.basket: 0.3845, IoU.waterfall: 0.4668, IoU.tent: 0.9165, IoU.bag: 0.2295, IoU.minibike: 0.7441, IoU.cradle: 0.7775, IoU.oven: 0.5677, IoU.ball: 0.5681, IoU.food: 0.6343, IoU.step: 0.1153, IoU.tank: 0.5732, IoU.trade name: 0.3193, IoU.microwave: 0.8863, IoU.pot: 0.5867, IoU.animal: 0.6102, IoU.bicycle: 0.5957, IoU.lake: 0.5531, IoU.dishwasher: 0.7006, IoU.screen: 0.6159, IoU.blanket: 0.2436, IoU.sculpture: 0.7974, IoU.hood: 0.6269, IoU.sconce: 0.5635, IoU.vase: 0.4905, IoU.traffic light: 0.3975, IoU.tray: 0.1357, IoU.ashcan: 0.4611, IoU.fan: 0.6674, IoU.pier: 0.4578, IoU.crt screen: 0.1405, IoU.plate: 0.5903, IoU.monitor: 0.2841, IoU.bulletin board: 0.5473, IoU.shower: 0.0459, IoU.radiator: 0.6564, IoU.glass: 0.1951, IoU.clock: 0.4037, IoU.flag: 0.7111, Acc.wall: 0.8984, Acc.building: 0.9390, Acc.sky: 0.9772, Acc.floor: 0.9129, Acc.tree: 0.8915, Acc.ceiling: 0.9379, Acc.road: 0.9129, Acc.bed : 0.9707, Acc.windowpane: 0.7917, Acc.grass: 0.7996, Acc.cabinet: 0.7334, Acc.sidewalk: 0.8699, Acc.person: 0.9396, Acc.earth: 0.5225, Acc.door: 0.7548, Acc.table: 0.8186, Acc.mountain: 0.6418, Acc.plant: 0.6734, Acc.curtain: 0.8886, Acc.chair: 0.7730, Acc.car: 0.9466, Acc.water: 0.7766, Acc.painting: 0.8978, Acc.sofa: 0.9170, Acc.shelf: 0.6918, Acc.house: 0.6450, Acc.sea: 0.8318, Acc.mirror: 0.8514, Acc.rug: 0.8224, Acc.field: 0.6335, Acc.armchair: 0.7587, Acc.seat: 0.8908, Acc.fence: 0.6605, Acc.desk: 0.7798, Acc.rock: 0.8512, Acc.wardrobe: 0.7526, Acc.lamp: 0.8443, Acc.bathtub: 0.8672, Acc.railing: 0.5949, Acc.cushion: 0.7900, Acc.base: 0.5370, Acc.box: 0.4070, Acc.column: 0.7357, Acc.signboard: 0.5358, Acc.chest of drawers: 0.7263, Acc.counter: 0.5075, Acc.sand: 0.7961, Acc.sink: 0.8572, Acc.skyscraper: 0.6558, Acc.fireplace: 0.9043, Acc.refrigerator: 0.9321, Acc.grandstand: 0.8128, Acc.path: 0.3391, Acc.stairs: 0.2729, Acc.runway: 0.9678, Acc.case: 0.7580, Acc.pool table: 0.9743, Acc.pillow: 0.8583, Acc.screen door: 0.8883, Acc.stairway: 0.5758, Acc.river: 0.3632, Acc.bridge: 0.5048, Acc.bookcase: 0.6215, Acc.blind: 0.5270, Acc.coffee table: 0.8899, Acc.toilet: 0.9318, Acc.flower: 0.5351, Acc.book: 0.6963, Acc.hill: 0.2299, Acc.bench: 0.6002, Acc.countertop: 0.8398, Acc.stove: 0.9333, Acc.palm: 0.8394, Acc.kitchen island: 0.8875, Acc.computer: 0.9356, Acc.swivel chair: 0.7939, Acc.boat: 0.8578, Acc.bar: 0.7214, Acc.arcade machine: 0.8339, Acc.hovel: 0.5226, Acc.bus: 0.9622, Acc.towel: 0.8750, Acc.light: 0.6900, Acc.truck: 0.5731, Acc.tower: 0.1634, Acc.chandelier: 0.8732, Acc.awning: 0.5383, Acc.streetlight: 0.4357, Acc.booth: 0.5662, Acc.television receiver: 0.8608, Acc.airplane: 0.9148, Acc.dirt track: 0.3319, Acc.apparel: 0.5577, Acc.pole: 0.3464, Acc.land: 0.0901, Acc.bannister: 0.2281, Acc.escalator: 0.8113, Acc.ottoman: 0.6983, Acc.bottle: 0.6997, Acc.buffet: 0.6788, Acc.poster: 0.5237, Acc.stage: 0.3847, Acc.van: 0.6026, Acc.ship: 0.9524, Acc.fountain: 0.2450, Acc.conveyer belt: 0.9372, Acc.canopy: 0.7145, Acc.washer: 0.9202, Acc.plaything: 0.6194, Acc.swimming pool: 0.9458, Acc.stool: 0.6958, Acc.barrel: 0.6431, Acc.basket: 0.6333, Acc.waterfall: 0.6416, Acc.tent: 0.9848, Acc.bag: 0.2727, Acc.minibike: 0.8998, Acc.cradle: 0.9835, Acc.oven: 0.6629, Acc.ball: 0.7329, Acc.food: 0.7538, Acc.step: 0.1381, Acc.tank: 0.6170, Acc.trade name: 0.3603, Acc.microwave: 0.9567, Acc.pot: 0.6803, Acc.animal: 0.6259, Acc.bicycle: 0.7558, Acc.lake: 0.6194, Acc.dishwasher: 0.7614, Acc.screen: 0.9170, Acc.blanket: 0.2686, Acc.sculpture: 0.8759, Acc.hood: 0.7627, Acc.sconce: 0.6574, Acc.vase: 0.6240, Acc.traffic light: 0.6531, Acc.tray: 0.1719, Acc.ashcan: 0.6167, Acc.fan: 0.8149, Acc.pier: 0.6876, Acc.crt screen: 0.3160, Acc.plate: 0.7461, Acc.monitor: 0.3448, Acc.bulletin board: 0.6592, Acc.shower: 0.0696, Acc.radiator: 0.7533, Acc.glass: 0.2145, Acc.clock: 0.4547, Acc.flag: 0.7683 +2024-06-18 21:40:48,140 - mmseg - INFO - Iter [54050/80000] lr: 1.298e-05, eta: 10:40:27, time: 3.303, data_time: 1.984, memory: 70498, decode.loss_ce: 0.1874, decode.acc_seg: 91.8455, aux.loss_ce: 0.0781, aux.acc_seg: 91.5040, loss: 0.2655 +2024-06-18 21:41:54,619 - mmseg - INFO - Iter [54100/80000] lr: 1.295e-05, eta: 10:39:09, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1830, decode.acc_seg: 92.0609, aux.loss_ce: 0.0772, aux.acc_seg: 91.6105, loss: 0.2602 +2024-06-18 21:43:01,454 - mmseg - INFO - Iter [54150/80000] lr: 1.293e-05, eta: 10:37:52, time: 1.337, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1645, decode.acc_seg: 93.0521, aux.loss_ce: 0.0702, aux.acc_seg: 92.5869, loss: 0.2347 +2024-06-18 21:44:07,707 - mmseg - INFO - Iter [54200/80000] lr: 1.290e-05, eta: 10:36:34, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1774, decode.acc_seg: 92.3795, aux.loss_ce: 0.0751, aux.acc_seg: 91.9438, loss: 0.2525 +2024-06-18 21:45:14,192 - mmseg - INFO - Iter [54250/80000] lr: 1.288e-05, eta: 10:35:17, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1713, decode.acc_seg: 92.5441, aux.loss_ce: 0.0726, aux.acc_seg: 92.1155, loss: 0.2439 +2024-06-18 21:46:20,578 - mmseg - INFO - Iter [54300/80000] lr: 1.285e-05, eta: 10:33:59, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1795, decode.acc_seg: 92.2050, aux.loss_ce: 0.0759, aux.acc_seg: 91.8279, loss: 0.2553 +2024-06-18 21:47:29,366 - mmseg - INFO - Iter [54350/80000] lr: 1.283e-05, eta: 10:32:43, time: 1.376, data_time: 0.052, memory: 70498, decode.loss_ce: 0.1889, decode.acc_seg: 92.0026, aux.loss_ce: 0.0799, aux.acc_seg: 91.5607, loss: 0.2688 +2024-06-18 21:48:35,613 - mmseg - INFO - Iter [54400/80000] lr: 1.280e-05, eta: 10:31:25, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1671, decode.acc_seg: 92.7179, aux.loss_ce: 0.0708, aux.acc_seg: 92.3300, loss: 0.2380 +2024-06-18 21:49:42,291 - mmseg - INFO - Iter [54450/80000] lr: 1.278e-05, eta: 10:30:07, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1838, decode.acc_seg: 92.2089, aux.loss_ce: 0.0776, aux.acc_seg: 91.7696, loss: 0.2613 +2024-06-18 21:50:48,970 - mmseg - INFO - Iter [54500/80000] lr: 1.275e-05, eta: 10:28:50, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1722, decode.acc_seg: 92.7551, aux.loss_ce: 0.0731, aux.acc_seg: 92.3665, loss: 0.2453 +2024-06-18 21:51:55,471 - mmseg - INFO - Iter [54550/80000] lr: 1.273e-05, eta: 10:27:33, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1764, decode.acc_seg: 92.3539, aux.loss_ce: 0.0748, aux.acc_seg: 91.9055, loss: 0.2512 +2024-06-18 21:53:01,750 - mmseg - INFO - Iter [54600/80000] lr: 1.270e-05, eta: 10:26:15, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1723, decode.acc_seg: 92.6899, aux.loss_ce: 0.0731, aux.acc_seg: 92.1820, loss: 0.2454 +2024-06-18 21:54:08,243 - mmseg - INFO - Iter [54650/80000] lr: 1.268e-05, eta: 10:24:58, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1687, decode.acc_seg: 92.6985, aux.loss_ce: 0.0714, aux.acc_seg: 92.3164, loss: 0.2402 +2024-06-18 21:55:14,687 - mmseg - INFO - Iter [54700/80000] lr: 1.265e-05, eta: 10:23:40, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1828, decode.acc_seg: 91.9207, aux.loss_ce: 0.0772, aux.acc_seg: 91.6113, loss: 0.2600 +2024-06-18 21:56:21,066 - mmseg - INFO - Iter [54750/80000] lr: 1.263e-05, eta: 10:22:23, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1902, decode.acc_seg: 91.8149, aux.loss_ce: 0.0806, aux.acc_seg: 91.3804, loss: 0.2707 +2024-06-18 21:57:27,665 - mmseg - INFO - Iter [54800/80000] lr: 1.260e-05, eta: 10:21:05, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1701, decode.acc_seg: 92.6379, aux.loss_ce: 0.0720, aux.acc_seg: 92.2252, loss: 0.2421 +2024-06-18 21:58:34,200 - mmseg - INFO - Iter [54850/80000] lr: 1.258e-05, eta: 10:19:48, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1816, decode.acc_seg: 92.4591, aux.loss_ce: 0.0774, aux.acc_seg: 91.9246, loss: 0.2591 +2024-06-18 21:59:40,521 - mmseg - INFO - Iter [54900/80000] lr: 1.255e-05, eta: 10:18:31, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1837, decode.acc_seg: 92.2789, aux.loss_ce: 0.0766, aux.acc_seg: 91.9038, loss: 0.2604 +2024-06-18 22:00:46,749 - mmseg - INFO - Iter [54950/80000] lr: 1.253e-05, eta: 10:17:13, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1809, decode.acc_seg: 92.1815, aux.loss_ce: 0.0769, aux.acc_seg: 91.7753, loss: 0.2578 +2024-06-18 22:01:53,264 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 22:01:53,264 - mmseg - INFO - Iter [55000/80000] lr: 1.250e-05, eta: 10:15:56, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1753, decode.acc_seg: 92.4988, aux.loss_ce: 0.0739, aux.acc_seg: 92.1478, loss: 0.2492 +2024-06-18 22:03:32,344 - mmseg - INFO - per class results: +2024-06-18 22:03:32,350 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.61 | 88.74 | +| building | 84.65 | 94.02 | +| sky | 94.83 | 97.57 | +| floor | 85.0 | 91.94 | +| tree | 77.76 | 90.04 | +| ceiling | 87.14 | 94.12 | +| road | 87.38 | 91.42 | +| bed | 92.43 | 97.31 | +| windowpane | 66.2 | 80.29 | +| grass | 65.83 | 81.0 | +| cabinet | 65.93 | 76.26 | +| sidewalk | 72.72 | 86.65 | +| person | 85.99 | 93.71 | +| earth | 39.03 | 49.81 | +| door | 58.56 | 75.88 | +| table | 70.15 | 82.51 | +| mountain | 60.57 | 72.8 | +| plant | 54.56 | 64.72 | +| curtain | 78.03 | 87.92 | +| chair | 66.74 | 76.2 | +| car | 87.3 | 94.31 | +| water | 64.37 | 78.9 | +| painting | 77.28 | 92.05 | +| sofa | 82.35 | 90.86 | +| shelf | 49.48 | 69.26 | +| house | 51.38 | 58.95 | +| sea | 70.11 | 84.99 | +| mirror | 78.59 | 84.09 | +| rug | 71.81 | 81.27 | +| field | 38.79 | 68.53 | +| armchair | 61.24 | 80.15 | +| seat | 65.12 | 87.06 | +| fence | 52.84 | 73.72 | +| desk | 58.94 | 78.64 | +| rock | 52.78 | 82.49 | +| wardrobe | 53.21 | 69.09 | +| lamp | 73.31 | 87.91 | +| bathtub | 84.75 | 87.34 | +| railing | 42.45 | 60.56 | +| cushion | 70.83 | 83.34 | +| base | 42.34 | 54.42 | +| box | 38.42 | 47.61 | +| column | 54.48 | 67.43 | +| signboard | 41.4 | 59.65 | +| chest of drawers | 44.64 | 69.87 | +| counter | 37.17 | 46.84 | +| sand | 51.79 | 75.08 | +| sink | 75.83 | 83.79 | +| skyscraper | 52.35 | 62.25 | +| fireplace | 73.88 | 93.59 | +| refrigerator | 81.19 | 94.34 | +| grandstand | 49.32 | 80.12 | +| path | 28.86 | 40.7 | +| stairs | 28.93 | 39.37 | +| runway | 73.85 | 97.22 | +| case | 58.05 | 82.27 | +| pool table | 94.83 | 98.01 | +| pillow | 69.97 | 78.96 | +| screen door | 82.79 | 86.08 | +| stairway | 47.79 | 62.27 | +| river | 19.84 | 35.48 | +| bridge | 73.25 | 89.29 | +| bookcase | 48.66 | 68.03 | +| blind | 44.49 | 50.88 | +| coffee table | 66.97 | 87.79 | +| toilet | 89.2 | 93.5 | +| flower | 42.25 | 58.02 | +| book | 58.23 | 76.37 | +| hill | 7.61 | 14.3 | +| bench | 52.77 | 61.88 | +| countertop | 63.32 | 84.43 | +| stove | 86.26 | 95.34 | +| palm | 56.04 | 80.78 | +| kitchen island | 51.23 | 86.19 | +| computer | 81.12 | 93.46 | +| swivel chair | 52.28 | 79.33 | +| boat | 61.98 | 87.29 | +| bar | 56.42 | 75.59 | +| arcade machine | 79.12 | 84.23 | +| hovel | 44.55 | 50.65 | +| bus | 93.33 | 96.88 | +| towel | 78.74 | 90.2 | +| light | 61.57 | 72.52 | +| truck | 41.82 | 54.16 | +| tower | 10.85 | 15.3 | +| chandelier | 71.9 | 86.14 | +| awning | 46.7 | 59.11 | +| streetlight | 34.7 | 48.71 | +| booth | 43.4 | 65.28 | +| television receiver | 77.83 | 88.29 | +| airplane | 81.69 | 89.5 | +| dirt track | 3.75 | 16.83 | +| apparel | 43.82 | 63.63 | +| pole | 28.33 | 41.55 | +| land | 4.23 | 6.26 | +| bannister | 16.89 | 24.41 | +| escalator | 59.18 | 80.42 | +| ottoman | 50.6 | 75.58 | +| bottle | 42.4 | 60.02 | +| buffet | 54.92 | 61.79 | +| poster | 35.94 | 45.61 | +| stage | 23.32 | 43.3 | +| van | 43.97 | 64.21 | +| ship | 59.05 | 65.21 | +| fountain | 23.0 | 24.37 | +| conveyer belt | 79.9 | 92.74 | +| canopy | 56.92 | 73.19 | +| washer | 86.24 | 89.05 | +| plaything | 38.78 | 65.39 | +| swimming pool | 67.12 | 77.84 | +| stool | 47.23 | 74.01 | +| barrel | 54.41 | 66.0 | +| basket | 40.24 | 64.88 | +| waterfall | 65.27 | 87.03 | +| tent | 89.15 | 99.03 | +| bag | 20.62 | 24.86 | +| minibike | 76.02 | 88.14 | +| cradle | 85.79 | 97.82 | +| oven | 52.62 | 60.5 | +| ball | 52.88 | 56.92 | +| food | 55.82 | 70.59 | +| step | 11.04 | 12.96 | +| tank | 62.85 | 74.26 | +| trade name | 29.34 | 34.03 | +| microwave | 86.22 | 95.85 | +| pot | 57.64 | 68.0 | +| animal | 63.44 | 65.67 | +| bicycle | 59.23 | 78.76 | +| lake | 55.61 | 63.6 | +| dishwasher | 66.76 | 74.84 | +| screen | 62.49 | 93.85 | +| blanket | 33.5 | 39.44 | +| sculpture | 75.17 | 88.01 | +| hood | 62.42 | 74.66 | +| sconce | 56.53 | 66.42 | +| vase | 48.88 | 63.02 | +| traffic light | 40.91 | 65.27 | +| tray | 15.62 | 20.25 | +| ashcan | 47.84 | 65.36 | +| fan | 66.16 | 79.88 | +| pier | 36.06 | 52.8 | +| crt screen | 14.79 | 29.86 | +| plate | 59.45 | 76.81 | +| monitor | 37.86 | 43.52 | +| bulletin board | 51.83 | 63.63 | +| shower | 6.67 | 7.04 | +| radiator | 64.75 | 76.04 | +| glass | 18.8 | 20.24 | +| clock | 42.66 | 49.59 | +| flag | 71.74 | 80.04 | ++---------------------+-------+-------+ +2024-06-18 22:03:32,351 - mmseg - INFO - Summary: +2024-06-18 22:03:32,351 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.03 | 56.88 | 69.93 | ++-------+-------+-------+ +2024-06-18 22:03:32,352 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 22:03:32,352 - mmseg - INFO - Iter(val) [250] aAcc: 0.8603, mIoU: 0.5688, mAcc: 0.6993, IoU.wall: 0.8161, IoU.building: 0.8465, IoU.sky: 0.9483, IoU.floor: 0.8500, IoU.tree: 0.7776, IoU.ceiling: 0.8714, IoU.road: 0.8738, IoU.bed : 0.9243, IoU.windowpane: 0.6620, IoU.grass: 0.6583, IoU.cabinet: 0.6593, IoU.sidewalk: 0.7272, IoU.person: 0.8599, IoU.earth: 0.3903, IoU.door: 0.5856, IoU.table: 0.7015, IoU.mountain: 0.6057, IoU.plant: 0.5456, IoU.curtain: 0.7803, IoU.chair: 0.6674, IoU.car: 0.8730, IoU.water: 0.6437, IoU.painting: 0.7728, IoU.sofa: 0.8235, IoU.shelf: 0.4948, IoU.house: 0.5138, IoU.sea: 0.7011, IoU.mirror: 0.7859, IoU.rug: 0.7181, IoU.field: 0.3879, IoU.armchair: 0.6124, IoU.seat: 0.6512, IoU.fence: 0.5284, IoU.desk: 0.5894, IoU.rock: 0.5278, IoU.wardrobe: 0.5321, IoU.lamp: 0.7331, IoU.bathtub: 0.8475, IoU.railing: 0.4245, IoU.cushion: 0.7083, IoU.base: 0.4234, IoU.box: 0.3842, IoU.column: 0.5448, IoU.signboard: 0.4140, IoU.chest of drawers: 0.4464, IoU.counter: 0.3717, IoU.sand: 0.5179, IoU.sink: 0.7583, IoU.skyscraper: 0.5235, IoU.fireplace: 0.7388, IoU.refrigerator: 0.8119, IoU.grandstand: 0.4932, IoU.path: 0.2886, IoU.stairs: 0.2893, IoU.runway: 0.7385, IoU.case: 0.5805, IoU.pool table: 0.9483, IoU.pillow: 0.6997, IoU.screen door: 0.8279, IoU.stairway: 0.4779, IoU.river: 0.1984, IoU.bridge: 0.7325, IoU.bookcase: 0.4866, IoU.blind: 0.4449, IoU.coffee table: 0.6697, IoU.toilet: 0.8920, IoU.flower: 0.4225, IoU.book: 0.5823, IoU.hill: 0.0761, IoU.bench: 0.5277, IoU.countertop: 0.6332, IoU.stove: 0.8626, IoU.palm: 0.5604, IoU.kitchen island: 0.5123, IoU.computer: 0.8112, IoU.swivel chair: 0.5228, IoU.boat: 0.6198, IoU.bar: 0.5642, IoU.arcade machine: 0.7912, IoU.hovel: 0.4455, IoU.bus: 0.9333, IoU.towel: 0.7874, IoU.light: 0.6157, IoU.truck: 0.4182, IoU.tower: 0.1085, IoU.chandelier: 0.7190, IoU.awning: 0.4670, IoU.streetlight: 0.3470, IoU.booth: 0.4340, IoU.television receiver: 0.7783, IoU.airplane: 0.8169, IoU.dirt track: 0.0375, IoU.apparel: 0.4382, IoU.pole: 0.2833, IoU.land: 0.0423, IoU.bannister: 0.1689, IoU.escalator: 0.5918, IoU.ottoman: 0.5060, IoU.bottle: 0.4240, IoU.buffet: 0.5492, IoU.poster: 0.3594, IoU.stage: 0.2332, IoU.van: 0.4397, IoU.ship: 0.5905, IoU.fountain: 0.2300, IoU.conveyer belt: 0.7990, IoU.canopy: 0.5692, IoU.washer: 0.8624, IoU.plaything: 0.3878, IoU.swimming pool: 0.6712, IoU.stool: 0.4723, IoU.barrel: 0.5441, IoU.basket: 0.4024, IoU.waterfall: 0.6527, IoU.tent: 0.8915, IoU.bag: 0.2062, IoU.minibike: 0.7602, IoU.cradle: 0.8579, IoU.oven: 0.5262, IoU.ball: 0.5288, IoU.food: 0.5582, IoU.step: 0.1104, IoU.tank: 0.6285, IoU.trade name: 0.2934, IoU.microwave: 0.8622, IoU.pot: 0.5764, IoU.animal: 0.6344, IoU.bicycle: 0.5923, IoU.lake: 0.5561, IoU.dishwasher: 0.6676, IoU.screen: 0.6249, IoU.blanket: 0.3350, IoU.sculpture: 0.7517, IoU.hood: 0.6242, IoU.sconce: 0.5653, IoU.vase: 0.4888, IoU.traffic light: 0.4091, IoU.tray: 0.1562, IoU.ashcan: 0.4784, IoU.fan: 0.6616, IoU.pier: 0.3606, IoU.crt screen: 0.1479, IoU.plate: 0.5945, IoU.monitor: 0.3786, IoU.bulletin board: 0.5183, IoU.shower: 0.0667, IoU.radiator: 0.6475, IoU.glass: 0.1880, IoU.clock: 0.4266, IoU.flag: 0.7174, Acc.wall: 0.8874, Acc.building: 0.9402, Acc.sky: 0.9757, Acc.floor: 0.9194, Acc.tree: 0.9004, Acc.ceiling: 0.9412, Acc.road: 0.9142, Acc.bed : 0.9731, Acc.windowpane: 0.8029, Acc.grass: 0.8100, Acc.cabinet: 0.7626, Acc.sidewalk: 0.8665, Acc.person: 0.9371, Acc.earth: 0.4981, Acc.door: 0.7588, Acc.table: 0.8251, Acc.mountain: 0.7280, Acc.plant: 0.6472, Acc.curtain: 0.8792, Acc.chair: 0.7620, Acc.car: 0.9431, Acc.water: 0.7890, Acc.painting: 0.9205, Acc.sofa: 0.9086, Acc.shelf: 0.6926, Acc.house: 0.5895, Acc.sea: 0.8499, Acc.mirror: 0.8409, Acc.rug: 0.8127, Acc.field: 0.6853, Acc.armchair: 0.8015, Acc.seat: 0.8706, Acc.fence: 0.7372, Acc.desk: 0.7864, Acc.rock: 0.8249, Acc.wardrobe: 0.6909, Acc.lamp: 0.8791, Acc.bathtub: 0.8734, Acc.railing: 0.6056, Acc.cushion: 0.8334, Acc.base: 0.5442, Acc.box: 0.4761, Acc.column: 0.6743, Acc.signboard: 0.5965, Acc.chest of drawers: 0.6987, Acc.counter: 0.4684, Acc.sand: 0.7508, Acc.sink: 0.8379, Acc.skyscraper: 0.6225, Acc.fireplace: 0.9359, Acc.refrigerator: 0.9434, Acc.grandstand: 0.8012, Acc.path: 0.4070, Acc.stairs: 0.3937, Acc.runway: 0.9722, Acc.case: 0.8227, Acc.pool table: 0.9801, Acc.pillow: 0.7896, Acc.screen door: 0.8608, Acc.stairway: 0.6227, Acc.river: 0.3548, Acc.bridge: 0.8929, Acc.bookcase: 0.6803, Acc.blind: 0.5088, Acc.coffee table: 0.8779, Acc.toilet: 0.9350, Acc.flower: 0.5802, Acc.book: 0.7637, Acc.hill: 0.1430, Acc.bench: 0.6188, Acc.countertop: 0.8443, Acc.stove: 0.9534, Acc.palm: 0.8078, Acc.kitchen island: 0.8619, Acc.computer: 0.9346, Acc.swivel chair: 0.7933, Acc.boat: 0.8729, Acc.bar: 0.7559, Acc.arcade machine: 0.8423, Acc.hovel: 0.5065, Acc.bus: 0.9688, Acc.towel: 0.9020, Acc.light: 0.7252, Acc.truck: 0.5416, Acc.tower: 0.1530, Acc.chandelier: 0.8614, Acc.awning: 0.5911, Acc.streetlight: 0.4871, Acc.booth: 0.6528, Acc.television receiver: 0.8829, Acc.airplane: 0.8950, Acc.dirt track: 0.1683, Acc.apparel: 0.6363, Acc.pole: 0.4155, Acc.land: 0.0626, Acc.bannister: 0.2441, Acc.escalator: 0.8042, Acc.ottoman: 0.7558, Acc.bottle: 0.6002, Acc.buffet: 0.6179, Acc.poster: 0.4561, Acc.stage: 0.4330, Acc.van: 0.6421, Acc.ship: 0.6521, Acc.fountain: 0.2437, Acc.conveyer belt: 0.9274, Acc.canopy: 0.7319, Acc.washer: 0.8905, Acc.plaything: 0.6539, Acc.swimming pool: 0.7784, Acc.stool: 0.7401, Acc.barrel: 0.6600, Acc.basket: 0.6488, Acc.waterfall: 0.8703, Acc.tent: 0.9903, Acc.bag: 0.2486, Acc.minibike: 0.8814, Acc.cradle: 0.9782, Acc.oven: 0.6050, Acc.ball: 0.5692, Acc.food: 0.7059, Acc.step: 0.1296, Acc.tank: 0.7426, Acc.trade name: 0.3403, Acc.microwave: 0.9585, Acc.pot: 0.6800, Acc.animal: 0.6567, Acc.bicycle: 0.7876, Acc.lake: 0.6360, Acc.dishwasher: 0.7484, Acc.screen: 0.9385, Acc.blanket: 0.3944, Acc.sculpture: 0.8801, Acc.hood: 0.7466, Acc.sconce: 0.6642, Acc.vase: 0.6302, Acc.traffic light: 0.6527, Acc.tray: 0.2025, Acc.ashcan: 0.6536, Acc.fan: 0.7988, Acc.pier: 0.5280, Acc.crt screen: 0.2986, Acc.plate: 0.7681, Acc.monitor: 0.4352, Acc.bulletin board: 0.6363, Acc.shower: 0.0704, Acc.radiator: 0.7604, Acc.glass: 0.2024, Acc.clock: 0.4959, Acc.flag: 0.8004 +2024-06-18 22:04:39,074 - mmseg - INFO - Iter [55050/80000] lr: 1.248e-05, eta: 10:15:24, time: 3.316, data_time: 2.000, memory: 70498, decode.loss_ce: 0.1696, decode.acc_seg: 92.8707, aux.loss_ce: 0.0716, aux.acc_seg: 92.4119, loss: 0.2412 +2024-06-18 22:05:45,562 - mmseg - INFO - Iter [55100/80000] lr: 1.245e-05, eta: 10:14:06, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1790, decode.acc_seg: 92.1843, aux.loss_ce: 0.0754, aux.acc_seg: 91.8417, loss: 0.2544 +2024-06-18 22:06:51,955 - mmseg - INFO - Iter [55150/80000] lr: 1.243e-05, eta: 10:12:49, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1709, decode.acc_seg: 92.5373, aux.loss_ce: 0.0724, aux.acc_seg: 92.0954, loss: 0.2432 +2024-06-18 22:07:58,311 - mmseg - INFO - Iter [55200/80000] lr: 1.240e-05, eta: 10:11:31, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1778, decode.acc_seg: 92.3475, aux.loss_ce: 0.0746, aux.acc_seg: 92.0064, loss: 0.2524 +2024-06-18 22:09:04,838 - mmseg - INFO - Iter [55250/80000] lr: 1.238e-05, eta: 10:10:14, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1837, decode.acc_seg: 92.3214, aux.loss_ce: 0.0774, aux.acc_seg: 91.9537, loss: 0.2611 +2024-06-18 22:10:11,269 - mmseg - INFO - Iter [55300/80000] lr: 1.235e-05, eta: 10:08:57, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1743, decode.acc_seg: 92.5536, aux.loss_ce: 0.0737, aux.acc_seg: 92.0749, loss: 0.2481 +2024-06-18 22:11:17,989 - mmseg - INFO - Iter [55350/80000] lr: 1.233e-05, eta: 10:07:40, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1785, decode.acc_seg: 91.9592, aux.loss_ce: 0.0755, aux.acc_seg: 91.5806, loss: 0.2540 +2024-06-18 22:12:24,211 - mmseg - INFO - Iter [55400/80000] lr: 1.230e-05, eta: 10:06:22, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1784, decode.acc_seg: 92.3068, aux.loss_ce: 0.0756, aux.acc_seg: 91.8651, loss: 0.2540 +2024-06-18 22:13:30,712 - mmseg - INFO - Iter [55450/80000] lr: 1.228e-05, eta: 10:05:05, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1634, decode.acc_seg: 92.8356, aux.loss_ce: 0.0696, aux.acc_seg: 92.4239, loss: 0.2330 +2024-06-18 22:14:37,132 - mmseg - INFO - Iter [55500/80000] lr: 1.225e-05, eta: 10:03:48, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1863, decode.acc_seg: 92.0952, aux.loss_ce: 0.0783, aux.acc_seg: 91.7169, loss: 0.2646 +2024-06-18 22:15:43,547 - mmseg - INFO - Iter [55550/80000] lr: 1.223e-05, eta: 10:02:30, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1749, decode.acc_seg: 92.2334, aux.loss_ce: 0.0737, aux.acc_seg: 91.9149, loss: 0.2486 +2024-06-18 22:16:53,175 - mmseg - INFO - Iter [55600/80000] lr: 1.220e-05, eta: 10:01:15, time: 1.393, data_time: 0.068, memory: 70498, decode.loss_ce: 0.1817, decode.acc_seg: 92.2570, aux.loss_ce: 0.0767, aux.acc_seg: 91.8530, loss: 0.2584 +2024-06-18 22:17:59,603 - mmseg - INFO - Iter [55650/80000] lr: 1.218e-05, eta: 9:59:57, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1634, decode.acc_seg: 92.8202, aux.loss_ce: 0.0693, aux.acc_seg: 92.4435, loss: 0.2326 +2024-06-18 22:19:06,151 - mmseg - INFO - Iter [55700/80000] lr: 1.215e-05, eta: 9:58:40, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1776, decode.acc_seg: 92.6033, aux.loss_ce: 0.0746, aux.acc_seg: 92.3087, loss: 0.2521 +2024-06-18 22:20:12,647 - mmseg - INFO - Iter [55750/80000] lr: 1.213e-05, eta: 9:57:23, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1779, decode.acc_seg: 92.1768, aux.loss_ce: 0.0750, aux.acc_seg: 91.9419, loss: 0.2529 +2024-06-18 22:21:19,187 - mmseg - INFO - Iter [55800/80000] lr: 1.210e-05, eta: 9:56:06, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1741, decode.acc_seg: 92.4810, aux.loss_ce: 0.0733, aux.acc_seg: 92.0633, loss: 0.2474 +2024-06-18 22:22:25,590 - mmseg - INFO - Iter [55850/80000] lr: 1.208e-05, eta: 9:54:49, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1750, decode.acc_seg: 92.4943, aux.loss_ce: 0.0737, aux.acc_seg: 92.0945, loss: 0.2487 +2024-06-18 22:23:32,258 - mmseg - INFO - Iter [55900/80000] lr: 1.205e-05, eta: 9:53:32, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1749, decode.acc_seg: 92.6920, aux.loss_ce: 0.0744, aux.acc_seg: 92.2498, loss: 0.2493 +2024-06-18 22:24:38,793 - mmseg - INFO - Iter [55950/80000] lr: 1.203e-05, eta: 9:52:15, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1732, decode.acc_seg: 92.5080, aux.loss_ce: 0.0735, aux.acc_seg: 92.0779, loss: 0.2467 +2024-06-18 22:25:45,123 - mmseg - INFO - Saving checkpoint at 56000 iterations +2024-06-18 22:27:28,134 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 22:27:28,135 - mmseg - INFO - Iter [56000/80000] lr: 1.200e-05, eta: 9:51:42, time: 3.387, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1669, decode.acc_seg: 92.8813, aux.loss_ce: 0.0707, aux.acc_seg: 92.3738, loss: 0.2376 +2024-06-18 22:29:04,392 - mmseg - INFO - per class results: +2024-06-18 22:29:04,398 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.2 | 89.91 | +| building | 85.42 | 94.06 | +| sky | 94.9 | 97.51 | +| floor | 85.02 | 92.29 | +| tree | 77.55 | 89.38 | +| ceiling | 87.26 | 94.26 | +| road | 86.21 | 92.54 | +| bed | 92.3 | 96.99 | +| windowpane | 66.63 | 82.16 | +| grass | 67.36 | 80.35 | +| cabinet | 66.09 | 75.93 | +| sidewalk | 71.94 | 84.27 | +| person | 85.47 | 93.88 | +| earth | 37.46 | 49.74 | +| door | 59.92 | 74.57 | +| table | 69.78 | 79.45 | +| mountain | 60.62 | 73.64 | +| plant | 54.36 | 67.76 | +| curtain | 77.52 | 89.29 | +| chair | 66.7 | 78.41 | +| car | 87.12 | 94.77 | +| water | 62.74 | 75.4 | +| painting | 76.99 | 90.46 | +| sofa | 83.1 | 93.55 | +| shelf | 50.23 | 67.58 | +| house | 52.02 | 59.4 | +| sea | 67.22 | 83.12 | +| mirror | 79.22 | 85.25 | +| rug | 71.72 | 80.87 | +| field | 38.91 | 65.87 | +| armchair | 61.24 | 76.55 | +| seat | 66.11 | 88.06 | +| fence | 50.31 | 63.39 | +| desk | 54.22 | 76.14 | +| rock | 55.18 | 82.57 | +| wardrobe | 56.16 | 74.2 | +| lamp | 74.2 | 84.59 | +| bathtub | 84.19 | 86.85 | +| railing | 39.91 | 52.58 | +| cushion | 68.74 | 78.43 | +| base | 44.49 | 62.11 | +| box | 38.69 | 50.6 | +| column | 56.51 | 70.29 | +| signboard | 41.32 | 55.01 | +| chest of drawers | 44.3 | 69.5 | +| counter | 36.94 | 42.26 | +| sand | 52.75 | 77.37 | +| sink | 77.76 | 84.17 | +| skyscraper | 50.45 | 64.99 | +| fireplace | 74.39 | 94.58 | +| refrigerator | 80.2 | 92.56 | +| grandstand | 49.44 | 83.83 | +| path | 29.2 | 45.28 | +| stairs | 29.83 | 38.65 | +| runway | 68.51 | 89.33 | +| case | 57.2 | 73.78 | +| pool table | 94.73 | 98.26 | +| pillow | 69.88 | 81.43 | +| screen door | 84.04 | 87.13 | +| stairway | 44.59 | 55.36 | +| river | 20.62 | 42.73 | +| bridge | 76.41 | 86.27 | +| bookcase | 45.65 | 67.75 | +| blind | 43.44 | 47.54 | +| coffee table | 65.69 | 86.26 | +| toilet | 89.56 | 93.21 | +| flower | 41.72 | 50.75 | +| book | 54.48 | 79.24 | +| hill | 7.54 | 12.4 | +| bench | 51.21 | 57.56 | +| countertop | 64.75 | 84.15 | +| stove | 87.95 | 95.13 | +| palm | 57.01 | 81.86 | +| kitchen island | 48.91 | 79.19 | +| computer | 79.02 | 93.58 | +| swivel chair | 50.76 | 81.33 | +| boat | 65.82 | 87.53 | +| bar | 56.18 | 75.14 | +| arcade machine | 78.01 | 84.44 | +| hovel | 43.8 | 49.56 | +| bus | 92.57 | 96.79 | +| towel | 75.64 | 86.64 | +| light | 60.47 | 68.76 | +| truck | 43.54 | 58.63 | +| tower | 9.75 | 15.74 | +| chandelier | 72.6 | 86.69 | +| awning | 45.26 | 62.34 | +| streetlight | 30.55 | 40.14 | +| booth | 39.74 | 58.9 | +| television receiver | 79.09 | 84.73 | +| airplane | 76.83 | 83.37 | +| dirt track | 6.28 | 31.21 | +| apparel | 44.85 | 62.2 | +| pole | 26.99 | 36.29 | +| land | 3.49 | 5.7 | +| bannister | 17.3 | 25.61 | +| escalator | 55.56 | 81.18 | +| ottoman | 52.36 | 68.68 | +| bottle | 42.31 | 60.28 | +| buffet | 52.04 | 65.5 | +| poster | 34.97 | 53.89 | +| stage | 26.08 | 43.8 | +| van | 45.36 | 58.6 | +| ship | 85.78 | 89.52 | +| fountain | 27.08 | 29.13 | +| conveyer belt | 80.33 | 93.5 | +| canopy | 60.43 | 77.53 | +| washer | 86.41 | 90.81 | +| plaything | 34.15 | 41.73 | +| swimming pool | 72.77 | 89.79 | +| stool | 49.16 | 71.39 | +| barrel | 57.04 | 64.48 | +| basket | 42.14 | 59.45 | +| waterfall | 71.23 | 85.01 | +| tent | 90.69 | 98.72 | +| bag | 17.38 | 19.25 | +| minibike | 75.98 | 86.97 | +| cradle | 84.58 | 97.27 | +| oven | 52.1 | 58.67 | +| ball | 53.72 | 59.59 | +| food | 61.12 | 74.55 | +| step | 11.91 | 14.3 | +| tank | 68.45 | 77.43 | +| trade name | 22.71 | 24.99 | +| microwave | 85.11 | 96.34 | +| pot | 59.37 | 71.71 | +| animal | 64.2 | 65.59 | +| bicycle | 57.88 | 72.07 | +| lake | 52.43 | 63.58 | +| dishwasher | 72.35 | 82.5 | +| screen | 52.89 | 78.34 | +| blanket | 25.94 | 29.1 | +| sculpture | 79.04 | 85.9 | +| hood | 61.81 | 74.1 | +| sconce | 53.75 | 59.72 | +| vase | 48.5 | 60.02 | +| traffic light | 41.62 | 59.11 | +| tray | 15.11 | 20.12 | +| ashcan | 47.11 | 58.6 | +| fan | 66.88 | 78.75 | +| pier | 35.72 | 45.95 | +| crt screen | 13.86 | 26.32 | +| plate | 59.2 | 72.0 | +| monitor | 58.52 | 72.15 | +| bulletin board | 54.05 | 74.79 | +| shower | 5.31 | 6.74 | +| radiator | 61.38 | 69.44 | +| glass | 19.7 | 21.35 | +| clock | 42.5 | 47.63 | +| flag | 70.72 | 79.35 | ++---------------------+-------+-------+ +2024-06-18 22:29:04,398 - mmseg - INFO - Summary: +2024-06-18 22:29:04,398 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.12 | 57.01 | 69.33 | ++-------+-------+-------+ +2024-06-18 22:29:04,399 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 22:29:04,399 - mmseg - INFO - Iter(val) [250] aAcc: 0.8612, mIoU: 0.5701, mAcc: 0.6933, IoU.wall: 0.8220, IoU.building: 0.8542, IoU.sky: 0.9490, IoU.floor: 0.8502, IoU.tree: 0.7755, IoU.ceiling: 0.8726, IoU.road: 0.8621, IoU.bed : 0.9230, IoU.windowpane: 0.6663, IoU.grass: 0.6736, IoU.cabinet: 0.6609, IoU.sidewalk: 0.7194, IoU.person: 0.8547, IoU.earth: 0.3746, IoU.door: 0.5992, IoU.table: 0.6978, IoU.mountain: 0.6062, IoU.plant: 0.5436, IoU.curtain: 0.7752, IoU.chair: 0.6670, IoU.car: 0.8712, IoU.water: 0.6274, IoU.painting: 0.7699, IoU.sofa: 0.8310, IoU.shelf: 0.5023, IoU.house: 0.5202, IoU.sea: 0.6722, IoU.mirror: 0.7922, IoU.rug: 0.7172, IoU.field: 0.3891, IoU.armchair: 0.6124, IoU.seat: 0.6611, IoU.fence: 0.5031, IoU.desk: 0.5422, IoU.rock: 0.5518, IoU.wardrobe: 0.5616, IoU.lamp: 0.7420, IoU.bathtub: 0.8419, IoU.railing: 0.3991, IoU.cushion: 0.6874, IoU.base: 0.4449, IoU.box: 0.3869, IoU.column: 0.5651, IoU.signboard: 0.4132, IoU.chest of drawers: 0.4430, IoU.counter: 0.3694, IoU.sand: 0.5275, IoU.sink: 0.7776, IoU.skyscraper: 0.5045, IoU.fireplace: 0.7439, IoU.refrigerator: 0.8020, IoU.grandstand: 0.4944, IoU.path: 0.2920, IoU.stairs: 0.2983, IoU.runway: 0.6851, IoU.case: 0.5720, IoU.pool table: 0.9473, IoU.pillow: 0.6988, IoU.screen door: 0.8404, IoU.stairway: 0.4459, IoU.river: 0.2062, IoU.bridge: 0.7641, IoU.bookcase: 0.4565, IoU.blind: 0.4344, IoU.coffee table: 0.6569, IoU.toilet: 0.8956, IoU.flower: 0.4172, IoU.book: 0.5448, IoU.hill: 0.0754, IoU.bench: 0.5121, IoU.countertop: 0.6475, IoU.stove: 0.8795, IoU.palm: 0.5701, IoU.kitchen island: 0.4891, IoU.computer: 0.7902, IoU.swivel chair: 0.5076, IoU.boat: 0.6582, IoU.bar: 0.5618, IoU.arcade machine: 0.7801, IoU.hovel: 0.4380, IoU.bus: 0.9257, IoU.towel: 0.7564, IoU.light: 0.6047, IoU.truck: 0.4354, IoU.tower: 0.0975, IoU.chandelier: 0.7260, IoU.awning: 0.4526, IoU.streetlight: 0.3055, IoU.booth: 0.3974, IoU.television receiver: 0.7909, IoU.airplane: 0.7683, IoU.dirt track: 0.0628, IoU.apparel: 0.4485, IoU.pole: 0.2699, IoU.land: 0.0349, IoU.bannister: 0.1730, IoU.escalator: 0.5556, IoU.ottoman: 0.5236, IoU.bottle: 0.4231, IoU.buffet: 0.5204, IoU.poster: 0.3497, IoU.stage: 0.2608, IoU.van: 0.4536, IoU.ship: 0.8578, IoU.fountain: 0.2708, IoU.conveyer belt: 0.8033, IoU.canopy: 0.6043, IoU.washer: 0.8641, IoU.plaything: 0.3415, IoU.swimming pool: 0.7277, IoU.stool: 0.4916, IoU.barrel: 0.5704, IoU.basket: 0.4214, IoU.waterfall: 0.7123, IoU.tent: 0.9069, IoU.bag: 0.1738, IoU.minibike: 0.7598, IoU.cradle: 0.8458, IoU.oven: 0.5210, IoU.ball: 0.5372, IoU.food: 0.6112, IoU.step: 0.1191, IoU.tank: 0.6845, IoU.trade name: 0.2271, IoU.microwave: 0.8511, IoU.pot: 0.5937, IoU.animal: 0.6420, IoU.bicycle: 0.5788, IoU.lake: 0.5243, IoU.dishwasher: 0.7235, IoU.screen: 0.5289, IoU.blanket: 0.2594, IoU.sculpture: 0.7904, IoU.hood: 0.6181, IoU.sconce: 0.5375, IoU.vase: 0.4850, IoU.traffic light: 0.4162, IoU.tray: 0.1511, IoU.ashcan: 0.4711, IoU.fan: 0.6688, IoU.pier: 0.3572, IoU.crt screen: 0.1386, IoU.plate: 0.5920, IoU.monitor: 0.5852, IoU.bulletin board: 0.5405, IoU.shower: 0.0531, IoU.radiator: 0.6138, IoU.glass: 0.1970, IoU.clock: 0.4250, IoU.flag: 0.7072, Acc.wall: 0.8991, Acc.building: 0.9406, Acc.sky: 0.9751, Acc.floor: 0.9229, Acc.tree: 0.8938, Acc.ceiling: 0.9426, Acc.road: 0.9254, Acc.bed : 0.9699, Acc.windowpane: 0.8216, Acc.grass: 0.8035, Acc.cabinet: 0.7593, Acc.sidewalk: 0.8427, Acc.person: 0.9388, Acc.earth: 0.4974, Acc.door: 0.7457, Acc.table: 0.7945, Acc.mountain: 0.7364, Acc.plant: 0.6776, Acc.curtain: 0.8929, Acc.chair: 0.7841, Acc.car: 0.9477, Acc.water: 0.7540, Acc.painting: 0.9046, Acc.sofa: 0.9355, Acc.shelf: 0.6758, Acc.house: 0.5940, Acc.sea: 0.8312, Acc.mirror: 0.8525, Acc.rug: 0.8087, Acc.field: 0.6587, Acc.armchair: 0.7655, Acc.seat: 0.8806, Acc.fence: 0.6339, Acc.desk: 0.7614, Acc.rock: 0.8257, Acc.wardrobe: 0.7420, Acc.lamp: 0.8459, Acc.bathtub: 0.8685, Acc.railing: 0.5258, Acc.cushion: 0.7843, Acc.base: 0.6211, Acc.box: 0.5060, Acc.column: 0.7029, Acc.signboard: 0.5501, Acc.chest of drawers: 0.6950, Acc.counter: 0.4226, Acc.sand: 0.7737, Acc.sink: 0.8417, Acc.skyscraper: 0.6499, Acc.fireplace: 0.9458, Acc.refrigerator: 0.9256, Acc.grandstand: 0.8383, Acc.path: 0.4528, Acc.stairs: 0.3865, Acc.runway: 0.8933, Acc.case: 0.7378, Acc.pool table: 0.9826, Acc.pillow: 0.8143, Acc.screen door: 0.8713, Acc.stairway: 0.5536, Acc.river: 0.4273, Acc.bridge: 0.8627, Acc.bookcase: 0.6775, Acc.blind: 0.4754, Acc.coffee table: 0.8626, Acc.toilet: 0.9321, Acc.flower: 0.5075, Acc.book: 0.7924, Acc.hill: 0.1240, Acc.bench: 0.5756, Acc.countertop: 0.8415, Acc.stove: 0.9513, Acc.palm: 0.8186, Acc.kitchen island: 0.7919, Acc.computer: 0.9358, Acc.swivel chair: 0.8133, Acc.boat: 0.8753, Acc.bar: 0.7514, Acc.arcade machine: 0.8444, Acc.hovel: 0.4956, Acc.bus: 0.9679, Acc.towel: 0.8664, Acc.light: 0.6876, Acc.truck: 0.5863, Acc.tower: 0.1574, Acc.chandelier: 0.8669, Acc.awning: 0.6234, Acc.streetlight: 0.4014, Acc.booth: 0.5890, Acc.television receiver: 0.8473, Acc.airplane: 0.8337, Acc.dirt track: 0.3121, Acc.apparel: 0.6220, Acc.pole: 0.3629, Acc.land: 0.0570, Acc.bannister: 0.2561, Acc.escalator: 0.8118, Acc.ottoman: 0.6868, Acc.bottle: 0.6028, Acc.buffet: 0.6550, Acc.poster: 0.5389, Acc.stage: 0.4380, Acc.van: 0.5860, Acc.ship: 0.8952, Acc.fountain: 0.2913, Acc.conveyer belt: 0.9350, Acc.canopy: 0.7753, Acc.washer: 0.9081, Acc.plaything: 0.4173, Acc.swimming pool: 0.8979, Acc.stool: 0.7139, Acc.barrel: 0.6448, Acc.basket: 0.5945, Acc.waterfall: 0.8501, Acc.tent: 0.9872, Acc.bag: 0.1925, Acc.minibike: 0.8697, Acc.cradle: 0.9727, Acc.oven: 0.5867, Acc.ball: 0.5959, Acc.food: 0.7455, Acc.step: 0.1430, Acc.tank: 0.7743, Acc.trade name: 0.2499, Acc.microwave: 0.9634, Acc.pot: 0.7171, Acc.animal: 0.6559, Acc.bicycle: 0.7207, Acc.lake: 0.6358, Acc.dishwasher: 0.8250, Acc.screen: 0.7834, Acc.blanket: 0.2910, Acc.sculpture: 0.8590, Acc.hood: 0.7410, Acc.sconce: 0.5972, Acc.vase: 0.6002, Acc.traffic light: 0.5911, Acc.tray: 0.2012, Acc.ashcan: 0.5860, Acc.fan: 0.7875, Acc.pier: 0.4595, Acc.crt screen: 0.2632, Acc.plate: 0.7200, Acc.monitor: 0.7215, Acc.bulletin board: 0.7479, Acc.shower: 0.0674, Acc.radiator: 0.6944, Acc.glass: 0.2135, Acc.clock: 0.4763, Acc.flag: 0.7935 +2024-06-18 22:30:11,173 - mmseg - INFO - Iter [56050/80000] lr: 1.198e-05, eta: 9:51:06, time: 3.261, data_time: 1.943, memory: 70498, decode.loss_ce: 0.1704, decode.acc_seg: 92.4939, aux.loss_ce: 0.0724, aux.acc_seg: 92.0278, loss: 0.2428 +2024-06-18 22:31:17,636 - mmseg - INFO - Iter [56100/80000] lr: 1.195e-05, eta: 9:49:49, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1821, decode.acc_seg: 92.2019, aux.loss_ce: 0.0765, aux.acc_seg: 91.8045, loss: 0.2586 +2024-06-18 22:32:24,135 - mmseg - INFO - Iter [56150/80000] lr: 1.193e-05, eta: 9:48:32, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1812, decode.acc_seg: 92.2736, aux.loss_ce: 0.0768, aux.acc_seg: 91.8689, loss: 0.2580 +2024-06-18 22:33:30,463 - mmseg - INFO - Iter [56200/80000] lr: 1.190e-05, eta: 9:47:14, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1706, decode.acc_seg: 92.7907, aux.loss_ce: 0.0726, aux.acc_seg: 92.3717, loss: 0.2431 +2024-06-18 22:34:36,789 - mmseg - INFO - Iter [56250/80000] lr: 1.188e-05, eta: 9:45:57, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1890, decode.acc_seg: 91.9590, aux.loss_ce: 0.0793, aux.acc_seg: 91.5786, loss: 0.2683 +2024-06-18 22:35:43,139 - mmseg - INFO - Iter [56300/80000] lr: 1.185e-05, eta: 9:44:40, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1861, decode.acc_seg: 92.0028, aux.loss_ce: 0.0786, aux.acc_seg: 91.6036, loss: 0.2647 +2024-06-18 22:36:49,683 - mmseg - INFO - Iter [56350/80000] lr: 1.183e-05, eta: 9:43:23, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1815, decode.acc_seg: 92.3529, aux.loss_ce: 0.0766, aux.acc_seg: 91.9564, loss: 0.2581 +2024-06-18 22:37:55,958 - mmseg - INFO - Iter [56400/80000] lr: 1.180e-05, eta: 9:42:05, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1790, decode.acc_seg: 92.3454, aux.loss_ce: 0.0754, aux.acc_seg: 91.9240, loss: 0.2544 +2024-06-18 22:39:02,470 - mmseg - INFO - Iter [56450/80000] lr: 1.178e-05, eta: 9:40:48, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1754, decode.acc_seg: 92.5327, aux.loss_ce: 0.0747, aux.acc_seg: 92.0871, loss: 0.2500 +2024-06-18 22:40:09,064 - mmseg - INFO - Iter [56500/80000] lr: 1.175e-05, eta: 9:39:31, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1764, decode.acc_seg: 92.3294, aux.loss_ce: 0.0750, aux.acc_seg: 91.9069, loss: 0.2514 +2024-06-18 22:41:15,352 - mmseg - INFO - Iter [56550/80000] lr: 1.173e-05, eta: 9:38:14, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1763, decode.acc_seg: 92.5613, aux.loss_ce: 0.0743, aux.acc_seg: 92.1849, loss: 0.2506 +2024-06-18 22:42:21,681 - mmseg - INFO - Iter [56600/80000] lr: 1.170e-05, eta: 9:36:57, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1798, decode.acc_seg: 92.1275, aux.loss_ce: 0.0761, aux.acc_seg: 91.7480, loss: 0.2559 +2024-06-18 22:43:28,038 - mmseg - INFO - Iter [56650/80000] lr: 1.168e-05, eta: 9:35:40, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1742, decode.acc_seg: 92.6279, aux.loss_ce: 0.0736, aux.acc_seg: 92.2207, loss: 0.2478 +2024-06-18 22:44:34,621 - mmseg - INFO - Iter [56700/80000] lr: 1.165e-05, eta: 9:34:23, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1649, decode.acc_seg: 92.6556, aux.loss_ce: 0.0699, aux.acc_seg: 92.2468, loss: 0.2349 +2024-06-18 22:45:40,827 - mmseg - INFO - Iter [56750/80000] lr: 1.163e-05, eta: 9:33:06, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1733, decode.acc_seg: 92.4062, aux.loss_ce: 0.0738, aux.acc_seg: 91.8916, loss: 0.2471 +2024-06-18 22:46:47,170 - mmseg - INFO - Iter [56800/80000] lr: 1.160e-05, eta: 9:31:49, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1869, decode.acc_seg: 92.0598, aux.loss_ce: 0.0787, aux.acc_seg: 91.5935, loss: 0.2656 +2024-06-18 22:47:56,528 - mmseg - INFO - Iter [56850/80000] lr: 1.158e-05, eta: 9:30:33, time: 1.387, data_time: 0.062, memory: 70498, decode.loss_ce: 0.1664, decode.acc_seg: 92.7119, aux.loss_ce: 0.0709, aux.acc_seg: 92.2447, loss: 0.2373 +2024-06-18 22:49:02,778 - mmseg - INFO - Iter [56900/80000] lr: 1.155e-05, eta: 9:29:16, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1653, decode.acc_seg: 92.8660, aux.loss_ce: 0.0702, aux.acc_seg: 92.4762, loss: 0.2355 +2024-06-18 22:50:09,013 - mmseg - INFO - Iter [56950/80000] lr: 1.153e-05, eta: 9:27:59, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1630, decode.acc_seg: 93.0340, aux.loss_ce: 0.0694, aux.acc_seg: 92.6795, loss: 0.2324 +2024-06-18 22:51:15,365 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 22:51:15,366 - mmseg - INFO - Iter [57000/80000] lr: 1.150e-05, eta: 9:26:42, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1620, decode.acc_seg: 92.8736, aux.loss_ce: 0.0692, aux.acc_seg: 92.4448, loss: 0.2312 +2024-06-18 22:52:53,867 - mmseg - INFO - per class results: +2024-06-18 22:52:53,873 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.82 | 89.47 | +| building | 85.33 | 93.77 | +| sky | 94.93 | 97.2 | +| floor | 85.01 | 91.98 | +| tree | 77.77 | 90.39 | +| ceiling | 86.99 | 93.35 | +| road | 86.67 | 91.95 | +| bed | 92.4 | 96.43 | +| windowpane | 66.76 | 81.66 | +| grass | 66.61 | 80.33 | +| cabinet | 66.69 | 75.83 | +| sidewalk | 71.92 | 86.17 | +| person | 85.5 | 93.64 | +| earth | 38.15 | 48.81 | +| door | 58.29 | 73.34 | +| table | 68.7 | 81.39 | +| mountain | 63.43 | 78.63 | +| plant | 56.2 | 70.37 | +| curtain | 77.84 | 87.72 | +| chair | 67.51 | 78.97 | +| car | 87.37 | 94.13 | +| water | 62.53 | 77.39 | +| painting | 77.94 | 90.11 | +| sofa | 81.68 | 93.34 | +| shelf | 52.78 | 72.6 | +| house | 57.56 | 74.97 | +| sea | 67.59 | 82.0 | +| mirror | 77.92 | 84.35 | +| rug | 70.88 | 84.14 | +| field | 37.98 | 71.76 | +| armchair | 59.98 | 72.34 | +| seat | 66.74 | 87.74 | +| fence | 51.24 | 61.54 | +| desk | 57.36 | 74.31 | +| rock | 57.49 | 76.33 | +| wardrobe | 54.44 | 73.81 | +| lamp | 74.09 | 84.52 | +| bathtub | 84.16 | 86.39 | +| railing | 39.14 | 56.7 | +| cushion | 69.11 | 79.4 | +| base | 45.81 | 62.8 | +| box | 39.68 | 54.1 | +| column | 55.02 | 71.12 | +| signboard | 39.74 | 53.63 | +| chest of drawers | 47.48 | 68.02 | +| counter | 40.88 | 51.55 | +| sand | 52.93 | 75.49 | +| sink | 76.2 | 82.63 | +| skyscraper | 50.83 | 60.53 | +| fireplace | 75.18 | 92.3 | +| refrigerator | 79.72 | 90.28 | +| grandstand | 48.17 | 79.53 | +| path | 26.06 | 35.87 | +| stairs | 25.99 | 34.98 | +| runway | 72.87 | 97.53 | +| case | 57.94 | 81.9 | +| pool table | 95.05 | 97.7 | +| pillow | 70.38 | 83.6 | +| screen door | 82.26 | 86.94 | +| stairway | 44.8 | 61.35 | +| river | 17.1 | 32.7 | +| bridge | 77.72 | 89.87 | +| bookcase | 49.37 | 59.39 | +| blind | 47.9 | 51.07 | +| coffee table | 63.54 | 90.15 | +| toilet | 89.61 | 92.87 | +| flower | 40.48 | 51.4 | +| book | 56.94 | 75.49 | +| hill | 8.34 | 12.61 | +| bench | 53.4 | 63.13 | +| countertop | 64.31 | 83.91 | +| stove | 87.34 | 94.73 | +| palm | 56.84 | 76.86 | +| kitchen island | 49.19 | 86.75 | +| computer | 80.93 | 91.24 | +| swivel chair | 53.7 | 73.42 | +| boat | 61.67 | 86.82 | +| bar | 54.92 | 77.79 | +| arcade machine | 77.13 | 81.2 | +| hovel | 44.21 | 50.06 | +| bus | 93.27 | 96.32 | +| towel | 77.52 | 88.67 | +| light | 60.91 | 71.13 | +| truck | 47.6 | 55.57 | +| tower | 5.54 | 9.5 | +| chandelier | 70.99 | 83.51 | +| awning | 41.69 | 54.06 | +| streetlight | 31.49 | 41.1 | +| booth | 51.36 | 71.23 | +| television receiver | 79.42 | 84.04 | +| airplane | 79.54 | 83.81 | +| dirt track | 5.41 | 16.47 | +| apparel | 42.89 | 62.74 | +| pole | 27.01 | 37.25 | +| land | 4.59 | 7.55 | +| bannister | 14.7 | 21.39 | +| escalator | 53.21 | 82.19 | +| ottoman | 43.64 | 59.0 | +| bottle | 41.41 | 56.1 | +| buffet | 50.6 | 69.26 | +| poster | 42.17 | 50.82 | +| stage | 27.19 | 48.1 | +| van | 45.17 | 57.27 | +| ship | 79.97 | 82.57 | +| fountain | 24.5 | 26.17 | +| conveyer belt | 79.94 | 93.38 | +| canopy | 57.21 | 73.23 | +| washer | 85.39 | 91.93 | +| plaything | 35.67 | 49.65 | +| swimming pool | 61.44 | 90.18 | +| stool | 56.59 | 68.12 | +| barrel | 56.63 | 64.57 | +| basket | 42.64 | 60.15 | +| waterfall | 65.3 | 89.21 | +| tent | 93.69 | 98.73 | +| bag | 19.03 | 21.26 | +| minibike | 75.71 | 87.25 | +| cradle | 85.42 | 97.63 | +| oven | 62.01 | 72.87 | +| ball | 54.81 | 60.14 | +| food | 60.62 | 71.82 | +| step | 11.57 | 14.14 | +| tank | 67.5 | 80.49 | +| trade name | 23.79 | 26.09 | +| microwave | 90.26 | 94.98 | +| pot | 58.65 | 66.98 | +| animal | 60.88 | 61.9 | +| bicycle | 58.64 | 76.24 | +| lake | 48.48 | 63.72 | +| dishwasher | 72.34 | 82.39 | +| screen | 48.02 | 72.51 | +| blanket | 31.76 | 36.33 | +| sculpture | 78.72 | 87.93 | +| hood | 60.41 | 72.72 | +| sconce | 55.8 | 65.36 | +| vase | 49.15 | 59.52 | +| traffic light | 41.06 | 61.25 | +| tray | 12.48 | 15.21 | +| ashcan | 48.23 | 59.52 | +| fan | 67.23 | 81.65 | +| pier | 32.94 | 47.74 | +| crt screen | 14.55 | 25.9 | +| plate | 59.22 | 75.87 | +| monitor | 67.46 | 85.51 | +| bulletin board | 55.15 | 63.06 | +| shower | 3.29 | 4.99 | +| radiator | 62.06 | 71.33 | +| glass | 18.69 | 19.98 | +| clock | 47.2 | 53.7 | +| flag | 72.13 | 78.84 | ++---------------------+-------+-------+ +2024-06-18 22:52:53,873 - mmseg - INFO - Summary: +2024-06-18 22:52:53,873 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.16 | 57.2 | 69.43 | ++-------+------+-------+ +2024-06-18 22:52:53,874 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 22:52:53,874 - mmseg - INFO - Iter(val) [250] aAcc: 0.8616, mIoU: 0.5720, mAcc: 0.6943, IoU.wall: 0.8182, IoU.building: 0.8533, IoU.sky: 0.9493, IoU.floor: 0.8501, IoU.tree: 0.7777, IoU.ceiling: 0.8699, IoU.road: 0.8667, IoU.bed : 0.9240, IoU.windowpane: 0.6676, IoU.grass: 0.6661, IoU.cabinet: 0.6669, IoU.sidewalk: 0.7192, IoU.person: 0.8550, IoU.earth: 0.3815, IoU.door: 0.5829, IoU.table: 0.6870, IoU.mountain: 0.6343, IoU.plant: 0.5620, IoU.curtain: 0.7784, IoU.chair: 0.6751, IoU.car: 0.8737, IoU.water: 0.6253, IoU.painting: 0.7794, IoU.sofa: 0.8168, IoU.shelf: 0.5278, IoU.house: 0.5756, IoU.sea: 0.6759, IoU.mirror: 0.7792, IoU.rug: 0.7088, IoU.field: 0.3798, IoU.armchair: 0.5998, IoU.seat: 0.6674, IoU.fence: 0.5124, IoU.desk: 0.5736, IoU.rock: 0.5749, IoU.wardrobe: 0.5444, IoU.lamp: 0.7409, IoU.bathtub: 0.8416, IoU.railing: 0.3914, IoU.cushion: 0.6911, IoU.base: 0.4581, IoU.box: 0.3968, IoU.column: 0.5502, IoU.signboard: 0.3974, IoU.chest of drawers: 0.4748, IoU.counter: 0.4088, IoU.sand: 0.5293, IoU.sink: 0.7620, IoU.skyscraper: 0.5083, IoU.fireplace: 0.7518, IoU.refrigerator: 0.7972, IoU.grandstand: 0.4817, IoU.path: 0.2606, IoU.stairs: 0.2599, IoU.runway: 0.7287, IoU.case: 0.5794, IoU.pool table: 0.9505, IoU.pillow: 0.7038, IoU.screen door: 0.8226, IoU.stairway: 0.4480, IoU.river: 0.1710, IoU.bridge: 0.7772, IoU.bookcase: 0.4937, IoU.blind: 0.4790, IoU.coffee table: 0.6354, IoU.toilet: 0.8961, IoU.flower: 0.4048, IoU.book: 0.5694, IoU.hill: 0.0834, IoU.bench: 0.5340, IoU.countertop: 0.6431, IoU.stove: 0.8734, IoU.palm: 0.5684, IoU.kitchen island: 0.4919, IoU.computer: 0.8093, IoU.swivel chair: 0.5370, IoU.boat: 0.6167, IoU.bar: 0.5492, IoU.arcade machine: 0.7713, IoU.hovel: 0.4421, IoU.bus: 0.9327, IoU.towel: 0.7752, IoU.light: 0.6091, IoU.truck: 0.4760, IoU.tower: 0.0554, IoU.chandelier: 0.7099, IoU.awning: 0.4169, IoU.streetlight: 0.3149, IoU.booth: 0.5136, IoU.television receiver: 0.7942, IoU.airplane: 0.7954, IoU.dirt track: 0.0541, IoU.apparel: 0.4289, IoU.pole: 0.2701, IoU.land: 0.0459, IoU.bannister: 0.1470, IoU.escalator: 0.5321, IoU.ottoman: 0.4364, IoU.bottle: 0.4141, IoU.buffet: 0.5060, IoU.poster: 0.4217, IoU.stage: 0.2719, IoU.van: 0.4517, IoU.ship: 0.7997, IoU.fountain: 0.2450, IoU.conveyer belt: 0.7994, IoU.canopy: 0.5721, IoU.washer: 0.8539, IoU.plaything: 0.3567, IoU.swimming pool: 0.6144, IoU.stool: 0.5659, IoU.barrel: 0.5663, IoU.basket: 0.4264, IoU.waterfall: 0.6530, IoU.tent: 0.9369, IoU.bag: 0.1903, IoU.minibike: 0.7571, IoU.cradle: 0.8542, IoU.oven: 0.6201, IoU.ball: 0.5481, IoU.food: 0.6062, IoU.step: 0.1157, IoU.tank: 0.6750, IoU.trade name: 0.2379, IoU.microwave: 0.9026, IoU.pot: 0.5865, IoU.animal: 0.6088, IoU.bicycle: 0.5864, IoU.lake: 0.4848, IoU.dishwasher: 0.7234, IoU.screen: 0.4802, IoU.blanket: 0.3176, IoU.sculpture: 0.7872, IoU.hood: 0.6041, IoU.sconce: 0.5580, IoU.vase: 0.4915, IoU.traffic light: 0.4106, IoU.tray: 0.1248, IoU.ashcan: 0.4823, IoU.fan: 0.6723, IoU.pier: 0.3294, IoU.crt screen: 0.1455, IoU.plate: 0.5922, IoU.monitor: 0.6746, IoU.bulletin board: 0.5515, IoU.shower: 0.0329, IoU.radiator: 0.6206, IoU.glass: 0.1869, IoU.clock: 0.4720, IoU.flag: 0.7213, Acc.wall: 0.8947, Acc.building: 0.9377, Acc.sky: 0.9720, Acc.floor: 0.9198, Acc.tree: 0.9039, Acc.ceiling: 0.9335, Acc.road: 0.9195, Acc.bed : 0.9643, Acc.windowpane: 0.8166, Acc.grass: 0.8033, Acc.cabinet: 0.7583, Acc.sidewalk: 0.8617, Acc.person: 0.9364, Acc.earth: 0.4881, Acc.door: 0.7334, Acc.table: 0.8139, Acc.mountain: 0.7863, Acc.plant: 0.7037, Acc.curtain: 0.8772, Acc.chair: 0.7897, Acc.car: 0.9413, Acc.water: 0.7739, Acc.painting: 0.9011, Acc.sofa: 0.9334, Acc.shelf: 0.7260, Acc.house: 0.7497, Acc.sea: 0.8200, Acc.mirror: 0.8435, Acc.rug: 0.8414, Acc.field: 0.7176, Acc.armchair: 0.7234, Acc.seat: 0.8774, Acc.fence: 0.6154, Acc.desk: 0.7431, Acc.rock: 0.7633, Acc.wardrobe: 0.7381, Acc.lamp: 0.8452, Acc.bathtub: 0.8639, Acc.railing: 0.5670, Acc.cushion: 0.7940, Acc.base: 0.6280, Acc.box: 0.5410, Acc.column: 0.7112, Acc.signboard: 0.5363, Acc.chest of drawers: 0.6802, Acc.counter: 0.5155, Acc.sand: 0.7549, Acc.sink: 0.8263, Acc.skyscraper: 0.6053, Acc.fireplace: 0.9230, Acc.refrigerator: 0.9028, Acc.grandstand: 0.7953, Acc.path: 0.3587, Acc.stairs: 0.3498, Acc.runway: 0.9753, Acc.case: 0.8190, Acc.pool table: 0.9770, Acc.pillow: 0.8360, Acc.screen door: 0.8694, Acc.stairway: 0.6135, Acc.river: 0.3270, Acc.bridge: 0.8987, Acc.bookcase: 0.5939, Acc.blind: 0.5107, Acc.coffee table: 0.9015, Acc.toilet: 0.9287, Acc.flower: 0.5140, Acc.book: 0.7549, Acc.hill: 0.1261, Acc.bench: 0.6313, Acc.countertop: 0.8391, Acc.stove: 0.9473, Acc.palm: 0.7686, Acc.kitchen island: 0.8675, Acc.computer: 0.9124, Acc.swivel chair: 0.7342, Acc.boat: 0.8682, Acc.bar: 0.7779, Acc.arcade machine: 0.8120, Acc.hovel: 0.5006, Acc.bus: 0.9632, Acc.towel: 0.8867, Acc.light: 0.7113, Acc.truck: 0.5557, Acc.tower: 0.0950, Acc.chandelier: 0.8351, Acc.awning: 0.5406, Acc.streetlight: 0.4110, Acc.booth: 0.7123, Acc.television receiver: 0.8404, Acc.airplane: 0.8381, Acc.dirt track: 0.1647, Acc.apparel: 0.6274, Acc.pole: 0.3725, Acc.land: 0.0755, Acc.bannister: 0.2139, Acc.escalator: 0.8219, Acc.ottoman: 0.5900, Acc.bottle: 0.5610, Acc.buffet: 0.6926, Acc.poster: 0.5082, Acc.stage: 0.4810, Acc.van: 0.5727, Acc.ship: 0.8257, Acc.fountain: 0.2617, Acc.conveyer belt: 0.9338, Acc.canopy: 0.7323, Acc.washer: 0.9193, Acc.plaything: 0.4965, Acc.swimming pool: 0.9018, Acc.stool: 0.6812, Acc.barrel: 0.6457, Acc.basket: 0.6015, Acc.waterfall: 0.8921, Acc.tent: 0.9873, Acc.bag: 0.2126, Acc.minibike: 0.8725, Acc.cradle: 0.9763, Acc.oven: 0.7287, Acc.ball: 0.6014, Acc.food: 0.7182, Acc.step: 0.1414, Acc.tank: 0.8049, Acc.trade name: 0.2609, Acc.microwave: 0.9498, Acc.pot: 0.6698, Acc.animal: 0.6190, Acc.bicycle: 0.7624, Acc.lake: 0.6372, Acc.dishwasher: 0.8239, Acc.screen: 0.7251, Acc.blanket: 0.3633, Acc.sculpture: 0.8793, Acc.hood: 0.7272, Acc.sconce: 0.6536, Acc.vase: 0.5952, Acc.traffic light: 0.6125, Acc.tray: 0.1521, Acc.ashcan: 0.5952, Acc.fan: 0.8165, Acc.pier: 0.4774, Acc.crt screen: 0.2590, Acc.plate: 0.7587, Acc.monitor: 0.8551, Acc.bulletin board: 0.6306, Acc.shower: 0.0499, Acc.radiator: 0.7133, Acc.glass: 0.1998, Acc.clock: 0.5370, Acc.flag: 0.7884 +2024-06-18 22:54:01,293 - mmseg - INFO - Iter [57050/80000] lr: 1.148e-05, eta: 9:26:05, time: 3.319, data_time: 1.992, memory: 70498, decode.loss_ce: 0.1741, decode.acc_seg: 92.3386, aux.loss_ce: 0.0739, aux.acc_seg: 91.8445, loss: 0.2479 +2024-06-18 22:55:07,641 - mmseg - INFO - Iter [57100/80000] lr: 1.145e-05, eta: 9:24:48, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1839, decode.acc_seg: 92.3022, aux.loss_ce: 0.0770, aux.acc_seg: 91.8606, loss: 0.2610 +2024-06-18 22:56:14,201 - mmseg - INFO - Iter [57150/80000] lr: 1.143e-05, eta: 9:23:31, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1767, decode.acc_seg: 92.2324, aux.loss_ce: 0.0745, aux.acc_seg: 91.8969, loss: 0.2512 +2024-06-18 22:57:20,504 - mmseg - INFO - Iter [57200/80000] lr: 1.140e-05, eta: 9:22:14, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1823, decode.acc_seg: 91.9773, aux.loss_ce: 0.0773, aux.acc_seg: 91.5161, loss: 0.2596 +2024-06-18 22:58:26,756 - mmseg - INFO - Iter [57250/80000] lr: 1.138e-05, eta: 9:20:57, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1766, decode.acc_seg: 92.4176, aux.loss_ce: 0.0745, aux.acc_seg: 92.0687, loss: 0.2511 +2024-06-18 22:59:32,826 - mmseg - INFO - Iter [57300/80000] lr: 1.135e-05, eta: 9:19:40, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1621, decode.acc_seg: 92.8252, aux.loss_ce: 0.0689, aux.acc_seg: 92.3523, loss: 0.2309 +2024-06-18 23:00:38,907 - mmseg - INFO - Iter [57350/80000] lr: 1.133e-05, eta: 9:18:22, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1737, decode.acc_seg: 92.6474, aux.loss_ce: 0.0737, aux.acc_seg: 92.2289, loss: 0.2474 +2024-06-18 23:01:45,265 - mmseg - INFO - Iter [57400/80000] lr: 1.130e-05, eta: 9:17:05, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1711, decode.acc_seg: 92.6998, aux.loss_ce: 0.0731, aux.acc_seg: 92.2449, loss: 0.2442 +2024-06-18 23:02:51,482 - mmseg - INFO - Iter [57450/80000] lr: 1.128e-05, eta: 9:15:49, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1645, decode.acc_seg: 92.9702, aux.loss_ce: 0.0705, aux.acc_seg: 92.4601, loss: 0.2350 +2024-06-18 23:03:58,169 - mmseg - INFO - Iter [57500/80000] lr: 1.125e-05, eta: 9:14:32, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1721, decode.acc_seg: 92.6467, aux.loss_ce: 0.0730, aux.acc_seg: 92.2561, loss: 0.2451 +2024-06-18 23:05:04,538 - mmseg - INFO - Iter [57550/80000] lr: 1.123e-05, eta: 9:13:15, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1658, decode.acc_seg: 92.9024, aux.loss_ce: 0.0709, aux.acc_seg: 92.4437, loss: 0.2367 +2024-06-18 23:06:10,870 - mmseg - INFO - Iter [57600/80000] lr: 1.120e-05, eta: 9:11:58, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1654, decode.acc_seg: 92.8435, aux.loss_ce: 0.0702, aux.acc_seg: 92.4222, loss: 0.2356 +2024-06-18 23:07:17,130 - mmseg - INFO - Iter [57650/80000] lr: 1.118e-05, eta: 9:10:41, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1655, decode.acc_seg: 92.6852, aux.loss_ce: 0.0706, aux.acc_seg: 92.2224, loss: 0.2360 +2024-06-18 23:08:23,869 - mmseg - INFO - Iter [57700/80000] lr: 1.115e-05, eta: 9:09:24, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1621, decode.acc_seg: 93.0577, aux.loss_ce: 0.0691, aux.acc_seg: 92.6058, loss: 0.2312 +2024-06-18 23:09:30,242 - mmseg - INFO - Iter [57750/80000] lr: 1.113e-05, eta: 9:08:08, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1770, decode.acc_seg: 92.4507, aux.loss_ce: 0.0743, aux.acc_seg: 92.0640, loss: 0.2513 +2024-06-18 23:10:36,483 - mmseg - INFO - Iter [57800/80000] lr: 1.110e-05, eta: 9:06:51, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1723, decode.acc_seg: 92.6410, aux.loss_ce: 0.0730, aux.acc_seg: 92.1881, loss: 0.2453 +2024-06-18 23:11:43,184 - mmseg - INFO - Iter [57850/80000] lr: 1.108e-05, eta: 9:05:34, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1671, decode.acc_seg: 92.6371, aux.loss_ce: 0.0710, aux.acc_seg: 92.2053, loss: 0.2381 +2024-06-18 23:12:49,627 - mmseg - INFO - Iter [57900/80000] lr: 1.105e-05, eta: 9:04:17, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1656, decode.acc_seg: 92.7937, aux.loss_ce: 0.0701, aux.acc_seg: 92.4395, loss: 0.2357 +2024-06-18 23:13:55,938 - mmseg - INFO - Iter [57950/80000] lr: 1.103e-05, eta: 9:03:01, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1671, decode.acc_seg: 92.9060, aux.loss_ce: 0.0716, aux.acc_seg: 92.4566, loss: 0.2387 +2024-06-18 23:15:02,400 - mmseg - INFO - Saving checkpoint at 58000 iterations +2024-06-18 23:16:43,636 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 23:16:43,636 - mmseg - INFO - Iter [58000/80000] lr: 1.100e-05, eta: 9:02:22, time: 3.354, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1724, decode.acc_seg: 92.5101, aux.loss_ce: 0.0736, aux.acc_seg: 92.0401, loss: 0.2460 +2024-06-18 23:18:21,357 - mmseg - INFO - per class results: +2024-06-18 23:18:21,363 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.18 | 89.76 | +| building | 84.71 | 94.01 | +| sky | 94.98 | 97.72 | +| floor | 85.18 | 91.62 | +| tree | 77.2 | 90.59 | +| ceiling | 87.64 | 93.96 | +| road | 85.89 | 91.19 | +| bed | 92.19 | 96.92 | +| windowpane | 66.76 | 81.74 | +| grass | 65.04 | 79.76 | +| cabinet | 65.69 | 75.16 | +| sidewalk | 70.23 | 84.98 | +| person | 85.15 | 94.58 | +| earth | 39.1 | 52.67 | +| door | 61.74 | 77.92 | +| table | 69.88 | 81.66 | +| mountain | 59.77 | 70.95 | +| plant | 53.9 | 62.51 | +| curtain | 77.91 | 89.32 | +| chair | 67.74 | 77.48 | +| car | 87.12 | 94.66 | +| water | 64.04 | 79.4 | +| painting | 77.6 | 90.62 | +| sofa | 82.25 | 92.07 | +| shelf | 49.48 | 68.14 | +| house | 47.71 | 54.01 | +| sea | 68.02 | 82.57 | +| mirror | 77.64 | 84.45 | +| rug | 68.38 | 81.2 | +| field | 33.38 | 64.58 | +| armchair | 61.37 | 78.27 | +| seat | 65.84 | 87.69 | +| fence | 51.63 | 64.44 | +| desk | 53.76 | 82.65 | +| rock | 54.94 | 81.35 | +| wardrobe | 53.65 | 67.53 | +| lamp | 72.95 | 84.65 | +| bathtub | 84.25 | 86.07 | +| railing | 41.12 | 63.05 | +| cushion | 68.78 | 78.24 | +| base | 43.69 | 55.93 | +| box | 38.76 | 49.33 | +| column | 54.79 | 69.27 | +| signboard | 41.54 | 55.08 | +| chest of drawers | 46.12 | 70.75 | +| counter | 37.53 | 46.97 | +| sand | 51.45 | 76.05 | +| sink | 75.88 | 83.7 | +| skyscraper | 48.49 | 59.74 | +| fireplace | 74.23 | 95.22 | +| refrigerator | 79.31 | 91.56 | +| grandstand | 48.92 | 76.29 | +| path | 25.02 | 33.52 | +| stairs | 23.33 | 28.6 | +| runway | 73.62 | 96.71 | +| case | 59.41 | 84.01 | +| pool table | 95.0 | 97.63 | +| pillow | 69.92 | 80.88 | +| screen door | 75.44 | 77.99 | +| stairway | 43.13 | 66.71 | +| river | 15.18 | 29.04 | +| bridge | 75.9 | 86.08 | +| bookcase | 47.19 | 66.34 | +| blind | 44.19 | 46.76 | +| coffee table | 66.87 | 87.68 | +| toilet | 89.33 | 93.51 | +| flower | 41.08 | 56.41 | +| book | 58.56 | 73.38 | +| hill | 10.25 | 14.63 | +| bench | 54.34 | 61.41 | +| countertop | 62.68 | 85.21 | +| stove | 87.76 | 94.19 | +| palm | 56.8 | 80.11 | +| kitchen island | 41.17 | 66.69 | +| computer | 80.09 | 94.54 | +| swivel chair | 53.06 | 73.19 | +| boat | 67.76 | 89.79 | +| bar | 55.79 | 74.78 | +| arcade machine | 78.87 | 83.67 | +| hovel | 43.7 | 49.58 | +| bus | 93.49 | 96.57 | +| towel | 75.51 | 83.94 | +| light | 60.55 | 74.11 | +| truck | 48.98 | 65.43 | +| tower | 5.62 | 8.77 | +| chandelier | 69.92 | 79.17 | +| awning | 39.66 | 51.45 | +| streetlight | 33.64 | 43.76 | +| booth | 50.27 | 64.58 | +| television receiver | 81.77 | 86.61 | +| airplane | 80.4 | 89.57 | +| dirt track | 8.91 | 40.62 | +| apparel | 45.34 | 63.83 | +| pole | 29.06 | 40.19 | +| land | 4.57 | 8.52 | +| bannister | 17.15 | 24.23 | +| escalator | 59.56 | 78.98 | +| ottoman | 50.46 | 68.51 | +| bottle | 42.95 | 68.55 | +| buffet | 54.0 | 73.72 | +| poster | 39.8 | 50.02 | +| stage | 27.98 | 44.39 | +| van | 45.89 | 56.25 | +| ship | 58.01 | 59.14 | +| fountain | 23.5 | 23.89 | +| conveyer belt | 82.69 | 92.45 | +| canopy | 58.52 | 76.08 | +| washer | 84.31 | 87.54 | +| plaything | 37.15 | 47.37 | +| swimming pool | 67.84 | 91.84 | +| stool | 54.94 | 65.69 | +| barrel | 54.73 | 64.75 | +| basket | 42.89 | 56.29 | +| waterfall | 69.18 | 81.31 | +| tent | 94.34 | 98.28 | +| bag | 21.86 | 24.75 | +| minibike | 76.08 | 87.37 | +| cradle | 85.21 | 97.85 | +| oven | 51.97 | 60.43 | +| ball | 55.85 | 65.7 | +| food | 56.87 | 68.39 | +| step | 14.41 | 16.88 | +| tank | 65.6 | 77.59 | +| trade name | 22.91 | 24.37 | +| microwave | 86.35 | 96.76 | +| pot | 59.76 | 70.11 | +| animal | 60.75 | 61.89 | +| bicycle | 58.83 | 76.5 | +| lake | 55.03 | 63.67 | +| dishwasher | 70.56 | 83.51 | +| screen | 52.43 | 73.45 | +| blanket | 28.97 | 34.3 | +| sculpture | 76.1 | 88.92 | +| hood | 62.01 | 73.14 | +| sconce | 53.75 | 60.55 | +| vase | 46.92 | 65.33 | +| traffic light | 40.14 | 62.02 | +| tray | 14.41 | 18.48 | +| ashcan | 47.88 | 68.34 | +| fan | 66.16 | 76.93 | +| pier | 34.92 | 45.0 | +| crt screen | 12.92 | 25.01 | +| plate | 58.79 | 75.27 | +| monitor | 57.48 | 69.7 | +| bulletin board | 57.37 | 61.34 | +| shower | 2.1 | 4.25 | +| radiator | 63.37 | 75.83 | +| glass | 19.04 | 20.67 | +| clock | 46.7 | 54.84 | +| flag | 71.65 | 77.93 | ++---------------------+-------+-------+ +2024-06-18 23:18:21,363 - mmseg - INFO - Summary: +2024-06-18 23:18:21,363 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.96 | 56.81 | 69.04 | ++-------+-------+-------+ +2024-06-18 23:18:21,363 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 23:18:21,364 - mmseg - INFO - Iter(val) [250] aAcc: 0.8596, mIoU: 0.5681, mAcc: 0.6904, IoU.wall: 0.8218, IoU.building: 0.8471, IoU.sky: 0.9498, IoU.floor: 0.8518, IoU.tree: 0.7720, IoU.ceiling: 0.8764, IoU.road: 0.8589, IoU.bed : 0.9219, IoU.windowpane: 0.6676, IoU.grass: 0.6504, IoU.cabinet: 0.6569, IoU.sidewalk: 0.7023, IoU.person: 0.8515, IoU.earth: 0.3910, IoU.door: 0.6174, IoU.table: 0.6988, IoU.mountain: 0.5977, IoU.plant: 0.5390, IoU.curtain: 0.7791, IoU.chair: 0.6774, IoU.car: 0.8712, IoU.water: 0.6404, IoU.painting: 0.7760, IoU.sofa: 0.8225, IoU.shelf: 0.4948, IoU.house: 0.4771, IoU.sea: 0.6802, IoU.mirror: 0.7764, IoU.rug: 0.6838, IoU.field: 0.3338, IoU.armchair: 0.6137, IoU.seat: 0.6584, IoU.fence: 0.5163, IoU.desk: 0.5376, IoU.rock: 0.5494, IoU.wardrobe: 0.5365, IoU.lamp: 0.7295, IoU.bathtub: 0.8425, IoU.railing: 0.4112, IoU.cushion: 0.6878, IoU.base: 0.4369, IoU.box: 0.3876, IoU.column: 0.5479, IoU.signboard: 0.4154, IoU.chest of drawers: 0.4612, IoU.counter: 0.3753, IoU.sand: 0.5145, IoU.sink: 0.7588, IoU.skyscraper: 0.4849, IoU.fireplace: 0.7423, IoU.refrigerator: 0.7931, IoU.grandstand: 0.4892, IoU.path: 0.2502, IoU.stairs: 0.2333, IoU.runway: 0.7362, IoU.case: 0.5941, IoU.pool table: 0.9500, IoU.pillow: 0.6992, IoU.screen door: 0.7544, IoU.stairway: 0.4313, IoU.river: 0.1518, IoU.bridge: 0.7590, IoU.bookcase: 0.4719, IoU.blind: 0.4419, IoU.coffee table: 0.6687, IoU.toilet: 0.8933, IoU.flower: 0.4108, IoU.book: 0.5856, IoU.hill: 0.1025, IoU.bench: 0.5434, IoU.countertop: 0.6268, IoU.stove: 0.8776, IoU.palm: 0.5680, IoU.kitchen island: 0.4117, IoU.computer: 0.8009, IoU.swivel chair: 0.5306, IoU.boat: 0.6776, IoU.bar: 0.5579, IoU.arcade machine: 0.7887, IoU.hovel: 0.4370, IoU.bus: 0.9349, IoU.towel: 0.7551, IoU.light: 0.6055, IoU.truck: 0.4898, IoU.tower: 0.0562, IoU.chandelier: 0.6992, IoU.awning: 0.3966, IoU.streetlight: 0.3364, IoU.booth: 0.5027, IoU.television receiver: 0.8177, IoU.airplane: 0.8040, IoU.dirt track: 0.0891, IoU.apparel: 0.4534, IoU.pole: 0.2906, IoU.land: 0.0457, IoU.bannister: 0.1715, IoU.escalator: 0.5956, IoU.ottoman: 0.5046, IoU.bottle: 0.4295, IoU.buffet: 0.5400, IoU.poster: 0.3980, IoU.stage: 0.2798, IoU.van: 0.4589, IoU.ship: 0.5801, IoU.fountain: 0.2350, IoU.conveyer belt: 0.8269, IoU.canopy: 0.5852, IoU.washer: 0.8431, IoU.plaything: 0.3715, IoU.swimming pool: 0.6784, IoU.stool: 0.5494, IoU.barrel: 0.5473, IoU.basket: 0.4289, IoU.waterfall: 0.6918, IoU.tent: 0.9434, IoU.bag: 0.2186, IoU.minibike: 0.7608, IoU.cradle: 0.8521, IoU.oven: 0.5197, IoU.ball: 0.5585, IoU.food: 0.5687, IoU.step: 0.1441, IoU.tank: 0.6560, IoU.trade name: 0.2291, IoU.microwave: 0.8635, IoU.pot: 0.5976, IoU.animal: 0.6075, IoU.bicycle: 0.5883, IoU.lake: 0.5503, IoU.dishwasher: 0.7056, IoU.screen: 0.5243, IoU.blanket: 0.2897, IoU.sculpture: 0.7610, IoU.hood: 0.6201, IoU.sconce: 0.5375, IoU.vase: 0.4692, IoU.traffic light: 0.4014, IoU.tray: 0.1441, IoU.ashcan: 0.4788, IoU.fan: 0.6616, IoU.pier: 0.3492, IoU.crt screen: 0.1292, IoU.plate: 0.5879, IoU.monitor: 0.5748, IoU.bulletin board: 0.5737, IoU.shower: 0.0210, IoU.radiator: 0.6337, IoU.glass: 0.1904, IoU.clock: 0.4670, IoU.flag: 0.7165, Acc.wall: 0.8976, Acc.building: 0.9401, Acc.sky: 0.9772, Acc.floor: 0.9162, Acc.tree: 0.9059, Acc.ceiling: 0.9396, Acc.road: 0.9119, Acc.bed : 0.9692, Acc.windowpane: 0.8174, Acc.grass: 0.7976, Acc.cabinet: 0.7516, Acc.sidewalk: 0.8498, Acc.person: 0.9458, Acc.earth: 0.5267, Acc.door: 0.7792, Acc.table: 0.8166, Acc.mountain: 0.7095, Acc.plant: 0.6251, Acc.curtain: 0.8932, Acc.chair: 0.7748, Acc.car: 0.9466, Acc.water: 0.7940, Acc.painting: 0.9062, Acc.sofa: 0.9207, Acc.shelf: 0.6814, Acc.house: 0.5401, Acc.sea: 0.8257, Acc.mirror: 0.8445, Acc.rug: 0.8120, Acc.field: 0.6458, Acc.armchair: 0.7827, Acc.seat: 0.8769, Acc.fence: 0.6444, Acc.desk: 0.8265, Acc.rock: 0.8135, Acc.wardrobe: 0.6753, Acc.lamp: 0.8465, Acc.bathtub: 0.8607, Acc.railing: 0.6305, Acc.cushion: 0.7824, Acc.base: 0.5593, Acc.box: 0.4933, Acc.column: 0.6927, Acc.signboard: 0.5508, Acc.chest of drawers: 0.7075, Acc.counter: 0.4697, Acc.sand: 0.7605, Acc.sink: 0.8370, Acc.skyscraper: 0.5974, Acc.fireplace: 0.9522, Acc.refrigerator: 0.9156, Acc.grandstand: 0.7629, Acc.path: 0.3352, Acc.stairs: 0.2860, Acc.runway: 0.9671, Acc.case: 0.8401, Acc.pool table: 0.9763, Acc.pillow: 0.8088, Acc.screen door: 0.7799, Acc.stairway: 0.6671, Acc.river: 0.2904, Acc.bridge: 0.8608, Acc.bookcase: 0.6634, Acc.blind: 0.4676, Acc.coffee table: 0.8768, Acc.toilet: 0.9351, Acc.flower: 0.5641, Acc.book: 0.7338, Acc.hill: 0.1463, Acc.bench: 0.6141, Acc.countertop: 0.8521, Acc.stove: 0.9419, Acc.palm: 0.8011, Acc.kitchen island: 0.6669, Acc.computer: 0.9454, Acc.swivel chair: 0.7319, Acc.boat: 0.8979, Acc.bar: 0.7478, Acc.arcade machine: 0.8367, Acc.hovel: 0.4958, Acc.bus: 0.9657, Acc.towel: 0.8394, Acc.light: 0.7411, Acc.truck: 0.6543, Acc.tower: 0.0877, Acc.chandelier: 0.7917, Acc.awning: 0.5145, Acc.streetlight: 0.4376, Acc.booth: 0.6458, Acc.television receiver: 0.8661, Acc.airplane: 0.8957, Acc.dirt track: 0.4062, Acc.apparel: 0.6383, Acc.pole: 0.4019, Acc.land: 0.0852, Acc.bannister: 0.2423, Acc.escalator: 0.7898, Acc.ottoman: 0.6851, Acc.bottle: 0.6855, Acc.buffet: 0.7372, Acc.poster: 0.5002, Acc.stage: 0.4439, Acc.van: 0.5625, Acc.ship: 0.5914, Acc.fountain: 0.2389, Acc.conveyer belt: 0.9245, Acc.canopy: 0.7608, Acc.washer: 0.8754, Acc.plaything: 0.4737, Acc.swimming pool: 0.9184, Acc.stool: 0.6569, Acc.barrel: 0.6475, Acc.basket: 0.5629, Acc.waterfall: 0.8131, Acc.tent: 0.9828, Acc.bag: 0.2475, Acc.minibike: 0.8737, Acc.cradle: 0.9785, Acc.oven: 0.6043, Acc.ball: 0.6570, Acc.food: 0.6839, Acc.step: 0.1688, Acc.tank: 0.7759, Acc.trade name: 0.2437, Acc.microwave: 0.9676, Acc.pot: 0.7011, Acc.animal: 0.6189, Acc.bicycle: 0.7650, Acc.lake: 0.6367, Acc.dishwasher: 0.8351, Acc.screen: 0.7345, Acc.blanket: 0.3430, Acc.sculpture: 0.8892, Acc.hood: 0.7314, Acc.sconce: 0.6055, Acc.vase: 0.6533, Acc.traffic light: 0.6202, Acc.tray: 0.1848, Acc.ashcan: 0.6834, Acc.fan: 0.7693, Acc.pier: 0.4500, Acc.crt screen: 0.2501, Acc.plate: 0.7527, Acc.monitor: 0.6970, Acc.bulletin board: 0.6134, Acc.shower: 0.0425, Acc.radiator: 0.7583, Acc.glass: 0.2067, Acc.clock: 0.5484, Acc.flag: 0.7793 +2024-06-18 23:19:28,231 - mmseg - INFO - Iter [58050/80000] lr: 1.098e-05, eta: 9:01:43, time: 3.292, data_time: 1.972, memory: 70498, decode.loss_ce: 0.1746, decode.acc_seg: 92.5178, aux.loss_ce: 0.0739, aux.acc_seg: 92.1529, loss: 0.2485 +2024-06-18 23:20:37,162 - mmseg - INFO - Iter [58100/80000] lr: 1.095e-05, eta: 9:00:27, time: 1.379, data_time: 0.061, memory: 70498, decode.loss_ce: 0.1794, decode.acc_seg: 92.2205, aux.loss_ce: 0.0757, aux.acc_seg: 91.7801, loss: 0.2551 +2024-06-18 23:21:43,440 - mmseg - INFO - Iter [58150/80000] lr: 1.093e-05, eta: 8:59:10, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1824, decode.acc_seg: 92.1240, aux.loss_ce: 0.0772, aux.acc_seg: 91.7111, loss: 0.2596 +2024-06-18 23:22:49,591 - mmseg - INFO - Iter [58200/80000] lr: 1.090e-05, eta: 8:57:53, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1690, decode.acc_seg: 92.9658, aux.loss_ce: 0.0721, aux.acc_seg: 92.4502, loss: 0.2411 +2024-06-18 23:23:55,906 - mmseg - INFO - Iter [58250/80000] lr: 1.088e-05, eta: 8:56:36, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1733, decode.acc_seg: 92.3969, aux.loss_ce: 0.0731, aux.acc_seg: 92.0258, loss: 0.2464 +2024-06-18 23:25:02,303 - mmseg - INFO - Iter [58300/80000] lr: 1.085e-05, eta: 8:55:19, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1806, decode.acc_seg: 92.3163, aux.loss_ce: 0.0760, aux.acc_seg: 91.9017, loss: 0.2566 +2024-06-18 23:26:08,639 - mmseg - INFO - Iter [58350/80000] lr: 1.083e-05, eta: 8:54:02, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1774, decode.acc_seg: 92.3717, aux.loss_ce: 0.0751, aux.acc_seg: 91.9822, loss: 0.2524 +2024-06-18 23:27:15,055 - mmseg - INFO - Iter [58400/80000] lr: 1.080e-05, eta: 8:52:45, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1746, decode.acc_seg: 92.5180, aux.loss_ce: 0.0741, aux.acc_seg: 92.0490, loss: 0.2487 +2024-06-18 23:28:21,423 - mmseg - INFO - Iter [58450/80000] lr: 1.078e-05, eta: 8:51:28, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1688, decode.acc_seg: 92.6663, aux.loss_ce: 0.0721, aux.acc_seg: 92.2046, loss: 0.2409 +2024-06-18 23:29:27,876 - mmseg - INFO - Iter [58500/80000] lr: 1.075e-05, eta: 8:50:12, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1642, decode.acc_seg: 92.9872, aux.loss_ce: 0.0694, aux.acc_seg: 92.6064, loss: 0.2336 +2024-06-18 23:30:34,069 - mmseg - INFO - Iter [58550/80000] lr: 1.073e-05, eta: 8:48:55, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1644, decode.acc_seg: 93.0084, aux.loss_ce: 0.0696, aux.acc_seg: 92.5676, loss: 0.2340 +2024-06-18 23:31:40,578 - mmseg - INFO - Iter [58600/80000] lr: 1.070e-05, eta: 8:47:38, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1617, decode.acc_seg: 92.8915, aux.loss_ce: 0.0682, aux.acc_seg: 92.4983, loss: 0.2299 +2024-06-18 23:32:46,989 - mmseg - INFO - Iter [58650/80000] lr: 1.068e-05, eta: 8:46:21, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1644, decode.acc_seg: 92.9935, aux.loss_ce: 0.0698, aux.acc_seg: 92.6085, loss: 0.2342 +2024-06-18 23:33:53,326 - mmseg - INFO - Iter [58700/80000] lr: 1.065e-05, eta: 8:45:05, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1775, decode.acc_seg: 92.4539, aux.loss_ce: 0.0757, aux.acc_seg: 91.9482, loss: 0.2532 +2024-06-18 23:34:59,892 - mmseg - INFO - Iter [58750/80000] lr: 1.063e-05, eta: 8:43:48, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1631, decode.acc_seg: 92.9426, aux.loss_ce: 0.0696, aux.acc_seg: 92.5121, loss: 0.2327 +2024-06-18 23:36:06,421 - mmseg - INFO - Iter [58800/80000] lr: 1.060e-05, eta: 8:42:31, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1643, decode.acc_seg: 92.7519, aux.loss_ce: 0.0705, aux.acc_seg: 92.3327, loss: 0.2348 +2024-06-18 23:37:13,217 - mmseg - INFO - Iter [58850/80000] lr: 1.058e-05, eta: 8:41:15, time: 1.336, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1635, decode.acc_seg: 92.9982, aux.loss_ce: 0.0692, aux.acc_seg: 92.5928, loss: 0.2327 +2024-06-18 23:38:19,664 - mmseg - INFO - Iter [58900/80000] lr: 1.055e-05, eta: 8:39:58, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1624, decode.acc_seg: 92.8109, aux.loss_ce: 0.0694, aux.acc_seg: 92.3577, loss: 0.2318 +2024-06-18 23:39:26,070 - mmseg - INFO - Iter [58950/80000] lr: 1.053e-05, eta: 8:38:42, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1753, decode.acc_seg: 92.3121, aux.loss_ce: 0.0739, aux.acc_seg: 92.0180, loss: 0.2493 +2024-06-18 23:40:32,681 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 23:40:32,682 - mmseg - INFO - Iter [59000/80000] lr: 1.050e-05, eta: 8:37:25, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1664, decode.acc_seg: 92.7030, aux.loss_ce: 0.0705, aux.acc_seg: 92.3043, loss: 0.2369 +2024-06-18 23:42:22,275 - mmseg - INFO - per class results: +2024-06-18 23:42:22,281 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.81 | 89.43 | +| building | 85.28 | 94.02 | +| sky | 95.08 | 97.43 | +| floor | 85.31 | 91.75 | +| tree | 78.22 | 89.41 | +| ceiling | 87.14 | 93.04 | +| road | 86.67 | 91.87 | +| bed | 92.3 | 96.62 | +| windowpane | 65.04 | 81.91 | +| grass | 64.85 | 78.61 | +| cabinet | 65.62 | 76.04 | +| sidewalk | 71.38 | 85.27 | +| person | 85.66 | 94.4 | +| earth | 39.9 | 53.45 | +| door | 61.01 | 77.53 | +| table | 68.7 | 80.78 | +| mountain | 61.47 | 73.43 | +| plant | 57.18 | 69.72 | +| curtain | 76.95 | 87.21 | +| chair | 67.01 | 76.77 | +| car | 87.22 | 94.59 | +| water | 61.03 | 74.04 | +| painting | 76.76 | 91.92 | +| sofa | 82.55 | 90.9 | +| shelf | 52.55 | 72.73 | +| house | 57.23 | 67.94 | +| sea | 65.64 | 83.92 | +| mirror | 77.56 | 84.02 | +| rug | 69.11 | 82.87 | +| field | 32.13 | 63.08 | +| armchair | 60.18 | 79.91 | +| seat | 63.25 | 89.06 | +| fence | 52.36 | 62.59 | +| desk | 59.73 | 73.69 | +| rock | 58.77 | 82.73 | +| wardrobe | 53.56 | 72.24 | +| lamp | 74.54 | 86.21 | +| bathtub | 84.25 | 86.92 | +| railing | 41.87 | 60.0 | +| cushion | 69.17 | 81.73 | +| base | 41.91 | 56.27 | +| box | 37.69 | 47.5 | +| column | 55.15 | 67.14 | +| signboard | 41.65 | 57.98 | +| chest of drawers | 47.62 | 69.38 | +| counter | 40.31 | 51.43 | +| sand | 51.53 | 78.04 | +| sink | 76.17 | 83.7 | +| skyscraper | 49.41 | 61.8 | +| fireplace | 75.57 | 94.78 | +| refrigerator | 79.82 | 90.26 | +| grandstand | 48.78 | 78.66 | +| path | 27.95 | 36.89 | +| stairs | 23.43 | 30.4 | +| runway | 73.32 | 97.86 | +| case | 60.99 | 83.11 | +| pool table | 94.62 | 97.87 | +| pillow | 70.67 | 80.81 | +| screen door | 74.69 | 78.03 | +| stairway | 38.98 | 57.79 | +| river | 10.63 | 20.27 | +| bridge | 75.8 | 88.97 | +| bookcase | 52.26 | 72.69 | +| blind | 43.88 | 46.28 | +| coffee table | 63.06 | 88.59 | +| toilet | 89.67 | 93.46 | +| flower | 44.6 | 59.63 | +| book | 57.73 | 72.69 | +| hill | 9.53 | 16.83 | +| bench | 52.54 | 61.18 | +| countertop | 64.78 | 86.89 | +| stove | 87.57 | 93.7 | +| palm | 57.14 | 80.26 | +| kitchen island | 41.17 | 69.26 | +| computer | 80.2 | 93.34 | +| swivel chair | 51.87 | 70.99 | +| boat | 73.25 | 85.33 | +| bar | 55.51 | 74.75 | +| arcade machine | 74.48 | 77.77 | +| hovel | 44.61 | 49.27 | +| bus | 93.53 | 96.07 | +| towel | 77.24 | 89.07 | +| light | 60.41 | 69.42 | +| truck | 45.81 | 60.62 | +| tower | 8.77 | 15.31 | +| chandelier | 73.26 | 89.37 | +| awning | 37.65 | 45.47 | +| streetlight | 33.78 | 47.44 | +| booth | 41.46 | 52.75 | +| television receiver | 81.17 | 89.34 | +| airplane | 83.05 | 91.93 | +| dirt track | 7.13 | 33.68 | +| apparel | 45.08 | 65.45 | +| pole | 25.57 | 34.53 | +| land | 3.91 | 5.78 | +| bannister | 16.94 | 25.46 | +| escalator | 54.49 | 81.0 | +| ottoman | 43.83 | 63.04 | +| bottle | 42.6 | 58.7 | +| buffet | 53.54 | 67.22 | +| poster | 36.37 | 50.55 | +| stage | 25.47 | 46.5 | +| van | 45.87 | 59.29 | +| ship | 83.7 | 86.5 | +| fountain | 29.02 | 30.58 | +| conveyer belt | 81.19 | 92.5 | +| canopy | 59.21 | 79.91 | +| washer | 81.32 | 84.84 | +| plaything | 34.77 | 47.71 | +| swimming pool | 72.94 | 92.47 | +| stool | 51.74 | 68.65 | +| barrel | 56.66 | 66.08 | +| basket | 41.46 | 57.68 | +| waterfall | 67.4 | 90.85 | +| tent | 92.11 | 98.85 | +| bag | 21.9 | 25.63 | +| minibike | 75.95 | 88.68 | +| cradle | 85.51 | 97.34 | +| oven | 62.26 | 71.74 | +| ball | 57.86 | 69.74 | +| food | 61.21 | 74.33 | +| step | 14.48 | 17.77 | +| tank | 64.81 | 74.57 | +| trade name | 29.3 | 33.61 | +| microwave | 90.62 | 94.05 | +| pot | 59.04 | 67.94 | +| animal | 60.46 | 61.66 | +| bicycle | 59.3 | 79.39 | +| lake | 49.75 | 63.74 | +| dishwasher | 68.35 | 83.47 | +| screen | 50.81 | 74.09 | +| blanket | 37.24 | 43.93 | +| sculpture | 75.03 | 89.09 | +| hood | 61.35 | 72.22 | +| sconce | 56.09 | 64.3 | +| vase | 48.29 | 58.71 | +| traffic light | 42.57 | 58.06 | +| tray | 13.1 | 15.85 | +| ashcan | 47.07 | 63.58 | +| fan | 67.93 | 82.75 | +| pier | 32.52 | 45.43 | +| crt screen | 17.02 | 30.13 | +| plate | 59.04 | 79.47 | +| monitor | 68.39 | 81.36 | +| bulletin board | 50.08 | 51.81 | +| shower | 1.83 | 1.84 | +| radiator | 63.45 | 73.37 | +| glass | 19.46 | 21.58 | +| clock | 41.29 | 47.69 | +| flag | 72.06 | 80.4 | ++---------------------+-------+-------+ +2024-06-18 23:42:22,281 - mmseg - INFO - Summary: +2024-06-18 23:42:22,281 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.04 | 57.14 | 69.57 | ++-------+-------+-------+ +2024-06-18 23:42:22,282 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 23:42:22,282 - mmseg - INFO - Iter(val) [250] aAcc: 0.8604, mIoU: 0.5714, mAcc: 0.6957, IoU.wall: 0.8181, IoU.building: 0.8528, IoU.sky: 0.9508, IoU.floor: 0.8531, IoU.tree: 0.7822, IoU.ceiling: 0.8714, IoU.road: 0.8667, IoU.bed : 0.9230, IoU.windowpane: 0.6504, IoU.grass: 0.6485, IoU.cabinet: 0.6562, IoU.sidewalk: 0.7138, IoU.person: 0.8566, IoU.earth: 0.3990, IoU.door: 0.6101, IoU.table: 0.6870, IoU.mountain: 0.6147, IoU.plant: 0.5718, IoU.curtain: 0.7695, IoU.chair: 0.6701, IoU.car: 0.8722, IoU.water: 0.6103, IoU.painting: 0.7676, IoU.sofa: 0.8255, IoU.shelf: 0.5255, IoU.house: 0.5723, IoU.sea: 0.6564, IoU.mirror: 0.7756, IoU.rug: 0.6911, IoU.field: 0.3213, IoU.armchair: 0.6018, IoU.seat: 0.6325, IoU.fence: 0.5236, IoU.desk: 0.5973, IoU.rock: 0.5877, IoU.wardrobe: 0.5356, IoU.lamp: 0.7454, IoU.bathtub: 0.8425, IoU.railing: 0.4187, IoU.cushion: 0.6917, IoU.base: 0.4191, IoU.box: 0.3769, IoU.column: 0.5515, IoU.signboard: 0.4165, IoU.chest of drawers: 0.4762, IoU.counter: 0.4031, IoU.sand: 0.5153, IoU.sink: 0.7617, IoU.skyscraper: 0.4941, IoU.fireplace: 0.7557, IoU.refrigerator: 0.7982, IoU.grandstand: 0.4878, IoU.path: 0.2795, IoU.stairs: 0.2343, IoU.runway: 0.7332, IoU.case: 0.6099, IoU.pool table: 0.9462, IoU.pillow: 0.7067, IoU.screen door: 0.7469, IoU.stairway: 0.3898, IoU.river: 0.1063, IoU.bridge: 0.7580, IoU.bookcase: 0.5226, IoU.blind: 0.4388, IoU.coffee table: 0.6306, IoU.toilet: 0.8967, IoU.flower: 0.4460, IoU.book: 0.5773, IoU.hill: 0.0953, IoU.bench: 0.5254, IoU.countertop: 0.6478, IoU.stove: 0.8757, IoU.palm: 0.5714, IoU.kitchen island: 0.4117, IoU.computer: 0.8020, IoU.swivel chair: 0.5187, IoU.boat: 0.7325, IoU.bar: 0.5551, IoU.arcade machine: 0.7448, IoU.hovel: 0.4461, IoU.bus: 0.9353, IoU.towel: 0.7724, IoU.light: 0.6041, IoU.truck: 0.4581, IoU.tower: 0.0877, IoU.chandelier: 0.7326, IoU.awning: 0.3765, IoU.streetlight: 0.3378, IoU.booth: 0.4146, IoU.television receiver: 0.8117, IoU.airplane: 0.8305, IoU.dirt track: 0.0713, IoU.apparel: 0.4508, IoU.pole: 0.2557, IoU.land: 0.0391, IoU.bannister: 0.1694, IoU.escalator: 0.5449, IoU.ottoman: 0.4383, IoU.bottle: 0.4260, IoU.buffet: 0.5354, IoU.poster: 0.3637, IoU.stage: 0.2547, IoU.van: 0.4587, IoU.ship: 0.8370, IoU.fountain: 0.2902, IoU.conveyer belt: 0.8119, IoU.canopy: 0.5921, IoU.washer: 0.8132, IoU.plaything: 0.3477, IoU.swimming pool: 0.7294, IoU.stool: 0.5174, IoU.barrel: 0.5666, IoU.basket: 0.4146, IoU.waterfall: 0.6740, IoU.tent: 0.9211, IoU.bag: 0.2190, IoU.minibike: 0.7595, IoU.cradle: 0.8551, IoU.oven: 0.6226, IoU.ball: 0.5786, IoU.food: 0.6121, IoU.step: 0.1448, IoU.tank: 0.6481, IoU.trade name: 0.2930, IoU.microwave: 0.9062, IoU.pot: 0.5904, IoU.animal: 0.6046, IoU.bicycle: 0.5930, IoU.lake: 0.4975, IoU.dishwasher: 0.6835, IoU.screen: 0.5081, IoU.blanket: 0.3724, IoU.sculpture: 0.7503, IoU.hood: 0.6135, IoU.sconce: 0.5609, IoU.vase: 0.4829, IoU.traffic light: 0.4257, IoU.tray: 0.1310, IoU.ashcan: 0.4707, IoU.fan: 0.6793, IoU.pier: 0.3252, IoU.crt screen: 0.1702, IoU.plate: 0.5904, IoU.monitor: 0.6839, IoU.bulletin board: 0.5008, IoU.shower: 0.0183, IoU.radiator: 0.6345, IoU.glass: 0.1946, IoU.clock: 0.4129, IoU.flag: 0.7206, Acc.wall: 0.8943, Acc.building: 0.9402, Acc.sky: 0.9743, Acc.floor: 0.9175, Acc.tree: 0.8941, Acc.ceiling: 0.9304, Acc.road: 0.9187, Acc.bed : 0.9662, Acc.windowpane: 0.8191, Acc.grass: 0.7861, Acc.cabinet: 0.7604, Acc.sidewalk: 0.8527, Acc.person: 0.9440, Acc.earth: 0.5345, Acc.door: 0.7753, Acc.table: 0.8078, Acc.mountain: 0.7343, Acc.plant: 0.6972, Acc.curtain: 0.8721, Acc.chair: 0.7677, Acc.car: 0.9459, Acc.water: 0.7404, Acc.painting: 0.9192, Acc.sofa: 0.9090, Acc.shelf: 0.7273, Acc.house: 0.6794, Acc.sea: 0.8392, Acc.mirror: 0.8402, Acc.rug: 0.8287, Acc.field: 0.6308, Acc.armchair: 0.7991, Acc.seat: 0.8906, Acc.fence: 0.6259, Acc.desk: 0.7369, Acc.rock: 0.8273, Acc.wardrobe: 0.7224, Acc.lamp: 0.8621, Acc.bathtub: 0.8692, Acc.railing: 0.6000, Acc.cushion: 0.8173, Acc.base: 0.5627, Acc.box: 0.4750, Acc.column: 0.6714, Acc.signboard: 0.5798, Acc.chest of drawers: 0.6938, Acc.counter: 0.5143, Acc.sand: 0.7804, Acc.sink: 0.8370, Acc.skyscraper: 0.6180, Acc.fireplace: 0.9478, Acc.refrigerator: 0.9026, Acc.grandstand: 0.7866, Acc.path: 0.3689, Acc.stairs: 0.3040, Acc.runway: 0.9786, Acc.case: 0.8311, Acc.pool table: 0.9787, Acc.pillow: 0.8081, Acc.screen door: 0.7803, Acc.stairway: 0.5779, Acc.river: 0.2027, Acc.bridge: 0.8897, Acc.bookcase: 0.7269, Acc.blind: 0.4628, Acc.coffee table: 0.8859, Acc.toilet: 0.9346, Acc.flower: 0.5963, Acc.book: 0.7269, Acc.hill: 0.1683, Acc.bench: 0.6118, Acc.countertop: 0.8689, Acc.stove: 0.9370, Acc.palm: 0.8026, Acc.kitchen island: 0.6926, Acc.computer: 0.9334, Acc.swivel chair: 0.7099, Acc.boat: 0.8533, Acc.bar: 0.7475, Acc.arcade machine: 0.7777, Acc.hovel: 0.4927, Acc.bus: 0.9607, Acc.towel: 0.8907, Acc.light: 0.6942, Acc.truck: 0.6062, Acc.tower: 0.1531, Acc.chandelier: 0.8937, Acc.awning: 0.4547, Acc.streetlight: 0.4744, Acc.booth: 0.5275, Acc.television receiver: 0.8934, Acc.airplane: 0.9193, Acc.dirt track: 0.3368, Acc.apparel: 0.6545, Acc.pole: 0.3453, Acc.land: 0.0578, Acc.bannister: 0.2546, Acc.escalator: 0.8100, Acc.ottoman: 0.6304, Acc.bottle: 0.5870, Acc.buffet: 0.6722, Acc.poster: 0.5055, Acc.stage: 0.4650, Acc.van: 0.5929, Acc.ship: 0.8650, Acc.fountain: 0.3058, Acc.conveyer belt: 0.9250, Acc.canopy: 0.7991, Acc.washer: 0.8484, Acc.plaything: 0.4771, Acc.swimming pool: 0.9247, Acc.stool: 0.6865, Acc.barrel: 0.6608, Acc.basket: 0.5768, Acc.waterfall: 0.9085, Acc.tent: 0.9885, Acc.bag: 0.2563, Acc.minibike: 0.8868, Acc.cradle: 0.9734, Acc.oven: 0.7174, Acc.ball: 0.6974, Acc.food: 0.7433, Acc.step: 0.1777, Acc.tank: 0.7457, Acc.trade name: 0.3361, Acc.microwave: 0.9405, Acc.pot: 0.6794, Acc.animal: 0.6166, Acc.bicycle: 0.7939, Acc.lake: 0.6374, Acc.dishwasher: 0.8347, Acc.screen: 0.7409, Acc.blanket: 0.4393, Acc.sculpture: 0.8909, Acc.hood: 0.7222, Acc.sconce: 0.6430, Acc.vase: 0.5871, Acc.traffic light: 0.5806, Acc.tray: 0.1585, Acc.ashcan: 0.6358, Acc.fan: 0.8275, Acc.pier: 0.4543, Acc.crt screen: 0.3013, Acc.plate: 0.7947, Acc.monitor: 0.8136, Acc.bulletin board: 0.5181, Acc.shower: 0.0184, Acc.radiator: 0.7337, Acc.glass: 0.2158, Acc.clock: 0.4769, Acc.flag: 0.8040 +2024-06-18 23:43:29,559 - mmseg - INFO - Iter [59050/80000] lr: 1.048e-05, eta: 8:36:48, time: 3.538, data_time: 2.211, memory: 70498, decode.loss_ce: 0.1649, decode.acc_seg: 92.8278, aux.loss_ce: 0.0701, aux.acc_seg: 92.4179, loss: 0.2351 +2024-06-18 23:44:36,042 - mmseg - INFO - Iter [59100/80000] lr: 1.045e-05, eta: 8:35:31, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1765, decode.acc_seg: 92.6834, aux.loss_ce: 0.0749, aux.acc_seg: 92.1899, loss: 0.2514 +2024-06-18 23:45:42,306 - mmseg - INFO - Iter [59150/80000] lr: 1.043e-05, eta: 8:34:14, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1716, decode.acc_seg: 92.5760, aux.loss_ce: 0.0725, aux.acc_seg: 92.1448, loss: 0.2441 +2024-06-18 23:46:48,903 - mmseg - INFO - Iter [59200/80000] lr: 1.040e-05, eta: 8:32:58, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1775, decode.acc_seg: 92.4249, aux.loss_ce: 0.0756, aux.acc_seg: 91.9967, loss: 0.2532 +2024-06-18 23:47:55,397 - mmseg - INFO - Iter [59250/80000] lr: 1.038e-05, eta: 8:31:41, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1605, decode.acc_seg: 93.1011, aux.loss_ce: 0.0682, aux.acc_seg: 92.6453, loss: 0.2288 +2024-06-18 23:49:01,741 - mmseg - INFO - Iter [59300/80000] lr: 1.035e-05, eta: 8:30:25, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1866, decode.acc_seg: 92.2442, aux.loss_ce: 0.0791, aux.acc_seg: 91.8000, loss: 0.2657 +2024-06-18 23:50:08,251 - mmseg - INFO - Iter [59350/80000] lr: 1.033e-05, eta: 8:29:08, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1693, decode.acc_seg: 92.7424, aux.loss_ce: 0.0718, aux.acc_seg: 92.3607, loss: 0.2411 +2024-06-18 23:51:17,019 - mmseg - INFO - Iter [59400/80000] lr: 1.030e-05, eta: 8:27:52, time: 1.375, data_time: 0.060, memory: 70498, decode.loss_ce: 0.1743, decode.acc_seg: 92.5255, aux.loss_ce: 0.0741, aux.acc_seg: 92.0404, loss: 0.2484 +2024-06-18 23:52:23,454 - mmseg - INFO - Iter [59450/80000] lr: 1.028e-05, eta: 8:26:36, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1703, decode.acc_seg: 92.8297, aux.loss_ce: 0.0721, aux.acc_seg: 92.4078, loss: 0.2424 +2024-06-18 23:53:30,092 - mmseg - INFO - Iter [59500/80000] lr: 1.025e-05, eta: 8:25:19, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1718, decode.acc_seg: 92.6662, aux.loss_ce: 0.0731, aux.acc_seg: 92.1786, loss: 0.2449 +2024-06-18 23:54:36,814 - mmseg - INFO - Iter [59550/80000] lr: 1.023e-05, eta: 8:24:03, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1668, decode.acc_seg: 92.4946, aux.loss_ce: 0.0705, aux.acc_seg: 92.1441, loss: 0.2374 +2024-06-18 23:55:43,211 - mmseg - INFO - Iter [59600/80000] lr: 1.020e-05, eta: 8:22:46, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1683, decode.acc_seg: 92.6379, aux.loss_ce: 0.0716, aux.acc_seg: 92.1381, loss: 0.2399 +2024-06-18 23:56:49,840 - mmseg - INFO - Iter [59650/80000] lr: 1.018e-05, eta: 8:21:30, time: 1.333, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1631, decode.acc_seg: 93.0499, aux.loss_ce: 0.0698, aux.acc_seg: 92.5702, loss: 0.2330 +2024-06-18 23:57:56,503 - mmseg - INFO - Iter [59700/80000] lr: 1.015e-05, eta: 8:20:13, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1629, decode.acc_seg: 92.9605, aux.loss_ce: 0.0697, aux.acc_seg: 92.5357, loss: 0.2326 +2024-06-18 23:59:02,838 - mmseg - INFO - Iter [59750/80000] lr: 1.013e-05, eta: 8:18:57, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1608, decode.acc_seg: 93.0490, aux.loss_ce: 0.0689, aux.acc_seg: 92.5811, loss: 0.2297 +2024-06-19 00:00:09,260 - mmseg - INFO - Iter [59800/80000] lr: 1.010e-05, eta: 8:17:40, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1660, decode.acc_seg: 92.8324, aux.loss_ce: 0.0707, aux.acc_seg: 92.4452, loss: 0.2367 +2024-06-19 00:01:15,591 - mmseg - INFO - Iter [59850/80000] lr: 1.008e-05, eta: 8:16:24, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1664, decode.acc_seg: 92.7379, aux.loss_ce: 0.0704, aux.acc_seg: 92.3894, loss: 0.2368 +2024-06-19 00:02:21,984 - mmseg - INFO - Iter [59900/80000] lr: 1.005e-05, eta: 8:15:08, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1637, decode.acc_seg: 92.9400, aux.loss_ce: 0.0698, aux.acc_seg: 92.4130, loss: 0.2335 +2024-06-19 00:03:28,217 - mmseg - INFO - Iter [59950/80000] lr: 1.003e-05, eta: 8:13:51, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1688, decode.acc_seg: 92.7902, aux.loss_ce: 0.0721, aux.acc_seg: 92.3353, loss: 0.2409 +2024-06-19 00:04:34,784 - mmseg - INFO - Saving checkpoint at 60000 iterations +2024-06-19 00:06:17,405 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 00:06:17,405 - mmseg - INFO - Iter [60000/80000] lr: 1.000e-05, eta: 8:13:09, time: 3.384, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1629, decode.acc_seg: 92.7514, aux.loss_ce: 0.0690, aux.acc_seg: 92.3251, loss: 0.2319 +2024-06-19 00:07:53,399 - mmseg - INFO - per class results: +2024-06-19 00:07:53,405 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.07 | 90.01 | +| building | 85.35 | 93.1 | +| sky | 94.98 | 97.7 | +| floor | 85.28 | 91.61 | +| tree | 77.02 | 90.77 | +| ceiling | 87.28 | 93.33 | +| road | 86.59 | 91.63 | +| bed | 92.4 | 96.83 | +| windowpane | 65.91 | 82.34 | +| grass | 66.85 | 79.75 | +| cabinet | 65.54 | 75.99 | +| sidewalk | 71.54 | 86.16 | +| person | 85.56 | 94.61 | +| earth | 38.38 | 50.62 | +| door | 59.31 | 72.24 | +| table | 69.27 | 80.45 | +| mountain | 60.25 | 73.63 | +| plant | 56.15 | 66.97 | +| curtain | 76.53 | 85.61 | +| chair | 67.58 | 80.08 | +| car | 87.26 | 94.22 | +| water | 62.57 | 76.54 | +| painting | 78.03 | 89.95 | +| sofa | 81.83 | 92.18 | +| shelf | 52.15 | 69.77 | +| house | 56.04 | 72.38 | +| sea | 69.89 | 88.25 | +| mirror | 77.55 | 83.76 | +| rug | 69.34 | 81.76 | +| field | 35.7 | 69.12 | +| armchair | 61.45 | 77.24 | +| seat | 66.47 | 87.71 | +| fence | 51.47 | 64.71 | +| desk | 59.58 | 77.53 | +| rock | 55.03 | 77.58 | +| wardrobe | 52.52 | 72.21 | +| lamp | 73.63 | 83.86 | +| bathtub | 84.24 | 87.2 | +| railing | 39.61 | 60.23 | +| cushion | 69.35 | 77.99 | +| base | 41.09 | 56.29 | +| box | 37.7 | 48.67 | +| column | 55.27 | 66.88 | +| signboard | 41.54 | 59.33 | +| chest of drawers | 47.59 | 70.3 | +| counter | 32.95 | 39.4 | +| sand | 51.95 | 76.36 | +| sink | 76.33 | 83.38 | +| skyscraper | 48.12 | 62.91 | +| fireplace | 76.48 | 91.86 | +| refrigerator | 79.06 | 90.46 | +| grandstand | 48.2 | 75.92 | +| path | 27.29 | 37.19 | +| stairs | 25.25 | 32.78 | +| runway | 73.04 | 97.93 | +| case | 60.45 | 78.62 | +| pool table | 94.73 | 97.62 | +| pillow | 68.79 | 78.46 | +| screen door | 85.99 | 91.05 | +| stairway | 47.91 | 63.67 | +| river | 19.22 | 29.99 | +| bridge | 77.23 | 89.33 | +| bookcase | 50.32 | 66.17 | +| blind | 48.93 | 52.2 | +| coffee table | 66.42 | 85.77 | +| toilet | 89.83 | 92.86 | +| flower | 42.6 | 56.58 | +| book | 57.68 | 76.03 | +| hill | 8.01 | 11.7 | +| bench | 51.01 | 62.06 | +| countertop | 63.58 | 85.54 | +| stove | 87.93 | 94.75 | +| palm | 57.54 | 80.58 | +| kitchen island | 40.17 | 72.03 | +| computer | 80.54 | 93.24 | +| swivel chair | 51.96 | 76.8 | +| boat | 60.72 | 87.05 | +| bar | 54.21 | 73.65 | +| arcade machine | 72.84 | 76.76 | +| hovel | 43.93 | 49.26 | +| bus | 93.14 | 96.43 | +| towel | 77.12 | 87.82 | +| light | 59.56 | 66.85 | +| truck | 45.6 | 57.66 | +| tower | 9.13 | 17.44 | +| chandelier | 71.9 | 86.23 | +| awning | 44.39 | 59.23 | +| streetlight | 32.74 | 43.0 | +| booth | 48.42 | 61.34 | +| television receiver | 82.14 | 88.83 | +| airplane | 80.5 | 90.79 | +| dirt track | 6.45 | 21.43 | +| apparel | 45.8 | 69.2 | +| pole | 27.91 | 37.67 | +| land | 3.04 | 6.49 | +| bannister | 16.0 | 22.72 | +| escalator | 56.19 | 80.81 | +| ottoman | 47.14 | 66.05 | +| bottle | 40.87 | 60.03 | +| buffet | 43.31 | 53.99 | +| poster | 39.7 | 49.56 | +| stage | 25.52 | 47.93 | +| van | 44.2 | 58.11 | +| ship | 84.96 | 88.55 | +| fountain | 21.45 | 22.19 | +| conveyer belt | 78.07 | 92.74 | +| canopy | 59.7 | 82.37 | +| washer | 81.59 | 84.01 | +| plaything | 38.28 | 58.25 | +| swimming pool | 64.07 | 91.69 | +| stool | 54.97 | 70.59 | +| barrel | 55.19 | 64.83 | +| basket | 42.05 | 58.44 | +| waterfall | 63.41 | 86.92 | +| tent | 89.52 | 99.08 | +| bag | 20.79 | 24.96 | +| minibike | 75.68 | 88.97 | +| cradle | 85.38 | 97.4 | +| oven | 59.56 | 69.67 | +| ball | 52.71 | 56.77 | +| food | 60.03 | 75.91 | +| step | 13.44 | 15.8 | +| tank | 69.13 | 76.74 | +| trade name | 23.3 | 25.73 | +| microwave | 90.1 | 96.3 | +| pot | 57.97 | 67.03 | +| animal | 62.11 | 63.53 | +| bicycle | 60.51 | 81.73 | +| lake | 52.33 | 63.6 | +| dishwasher | 71.59 | 80.46 | +| screen | 53.62 | 79.74 | +| blanket | 31.48 | 35.31 | +| sculpture | 72.85 | 88.59 | +| hood | 62.14 | 74.7 | +| sconce | 56.79 | 65.47 | +| vase | 48.03 | 63.19 | +| traffic light | 40.65 | 64.37 | +| tray | 16.42 | 21.16 | +| ashcan | 49.1 | 63.17 | +| fan | 67.33 | 80.1 | +| pier | 31.39 | 43.56 | +| crt screen | 14.13 | 29.7 | +| plate | 59.01 | 78.28 | +| monitor | 55.34 | 69.88 | +| bulletin board | 48.08 | 51.31 | +| shower | 5.02 | 6.19 | +| radiator | 62.2 | 69.72 | +| glass | 19.76 | 21.67 | +| clock | 45.69 | 51.86 | +| flag | 71.71 | 80.76 | ++---------------------+-------+-------+ +2024-06-19 00:07:53,406 - mmseg - INFO - Summary: +2024-06-19 00:07:53,406 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.07 | 56.94 | 69.48 | ++-------+-------+-------+ +2024-06-19 00:07:53,407 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 00:07:53,407 - mmseg - INFO - Iter(val) [250] aAcc: 0.8607, mIoU: 0.5694, mAcc: 0.6948, IoU.wall: 0.8207, IoU.building: 0.8535, IoU.sky: 0.9498, IoU.floor: 0.8528, IoU.tree: 0.7702, IoU.ceiling: 0.8728, IoU.road: 0.8659, IoU.bed : 0.9240, IoU.windowpane: 0.6591, IoU.grass: 0.6685, IoU.cabinet: 0.6554, IoU.sidewalk: 0.7154, IoU.person: 0.8556, IoU.earth: 0.3838, IoU.door: 0.5931, IoU.table: 0.6927, IoU.mountain: 0.6025, IoU.plant: 0.5615, IoU.curtain: 0.7653, IoU.chair: 0.6758, IoU.car: 0.8726, IoU.water: 0.6257, IoU.painting: 0.7803, IoU.sofa: 0.8183, IoU.shelf: 0.5215, IoU.house: 0.5604, IoU.sea: 0.6989, IoU.mirror: 0.7755, IoU.rug: 0.6934, IoU.field: 0.3570, IoU.armchair: 0.6145, IoU.seat: 0.6647, IoU.fence: 0.5147, IoU.desk: 0.5958, IoU.rock: 0.5503, IoU.wardrobe: 0.5252, IoU.lamp: 0.7363, IoU.bathtub: 0.8424, IoU.railing: 0.3961, IoU.cushion: 0.6935, IoU.base: 0.4109, IoU.box: 0.3770, IoU.column: 0.5527, IoU.signboard: 0.4154, IoU.chest of drawers: 0.4759, IoU.counter: 0.3295, IoU.sand: 0.5195, IoU.sink: 0.7633, IoU.skyscraper: 0.4812, IoU.fireplace: 0.7648, IoU.refrigerator: 0.7906, IoU.grandstand: 0.4820, IoU.path: 0.2729, IoU.stairs: 0.2525, IoU.runway: 0.7304, IoU.case: 0.6045, IoU.pool table: 0.9473, IoU.pillow: 0.6879, IoU.screen door: 0.8599, IoU.stairway: 0.4791, IoU.river: 0.1922, IoU.bridge: 0.7723, IoU.bookcase: 0.5032, IoU.blind: 0.4893, IoU.coffee table: 0.6642, IoU.toilet: 0.8983, IoU.flower: 0.4260, IoU.book: 0.5768, IoU.hill: 0.0801, IoU.bench: 0.5101, IoU.countertop: 0.6358, IoU.stove: 0.8793, IoU.palm: 0.5754, IoU.kitchen island: 0.4017, IoU.computer: 0.8054, IoU.swivel chair: 0.5196, IoU.boat: 0.6072, IoU.bar: 0.5421, IoU.arcade machine: 0.7284, IoU.hovel: 0.4393, IoU.bus: 0.9314, IoU.towel: 0.7712, IoU.light: 0.5956, IoU.truck: 0.4560, IoU.tower: 0.0913, IoU.chandelier: 0.7190, IoU.awning: 0.4439, IoU.streetlight: 0.3274, IoU.booth: 0.4842, IoU.television receiver: 0.8214, IoU.airplane: 0.8050, IoU.dirt track: 0.0645, IoU.apparel: 0.4580, IoU.pole: 0.2791, IoU.land: 0.0304, IoU.bannister: 0.1600, IoU.escalator: 0.5619, IoU.ottoman: 0.4714, IoU.bottle: 0.4087, IoU.buffet: 0.4331, IoU.poster: 0.3970, IoU.stage: 0.2552, IoU.van: 0.4420, IoU.ship: 0.8496, IoU.fountain: 0.2145, IoU.conveyer belt: 0.7807, IoU.canopy: 0.5970, IoU.washer: 0.8159, IoU.plaything: 0.3828, IoU.swimming pool: 0.6407, IoU.stool: 0.5497, IoU.barrel: 0.5519, IoU.basket: 0.4205, IoU.waterfall: 0.6341, IoU.tent: 0.8952, IoU.bag: 0.2079, IoU.minibike: 0.7568, IoU.cradle: 0.8538, IoU.oven: 0.5956, IoU.ball: 0.5271, IoU.food: 0.6003, IoU.step: 0.1344, IoU.tank: 0.6913, IoU.trade name: 0.2330, IoU.microwave: 0.9010, IoU.pot: 0.5797, IoU.animal: 0.6211, IoU.bicycle: 0.6051, IoU.lake: 0.5233, IoU.dishwasher: 0.7159, IoU.screen: 0.5362, IoU.blanket: 0.3148, IoU.sculpture: 0.7285, IoU.hood: 0.6214, IoU.sconce: 0.5679, IoU.vase: 0.4803, IoU.traffic light: 0.4065, IoU.tray: 0.1642, IoU.ashcan: 0.4910, IoU.fan: 0.6733, IoU.pier: 0.3139, IoU.crt screen: 0.1413, IoU.plate: 0.5901, IoU.monitor: 0.5534, IoU.bulletin board: 0.4808, IoU.shower: 0.0502, IoU.radiator: 0.6220, IoU.glass: 0.1976, IoU.clock: 0.4569, IoU.flag: 0.7171, Acc.wall: 0.9001, Acc.building: 0.9310, Acc.sky: 0.9770, Acc.floor: 0.9161, Acc.tree: 0.9077, Acc.ceiling: 0.9333, Acc.road: 0.9163, Acc.bed : 0.9683, Acc.windowpane: 0.8234, Acc.grass: 0.7975, Acc.cabinet: 0.7599, Acc.sidewalk: 0.8616, Acc.person: 0.9461, Acc.earth: 0.5062, Acc.door: 0.7224, Acc.table: 0.8045, Acc.mountain: 0.7363, Acc.plant: 0.6697, Acc.curtain: 0.8561, Acc.chair: 0.8008, Acc.car: 0.9422, Acc.water: 0.7654, Acc.painting: 0.8995, Acc.sofa: 0.9218, Acc.shelf: 0.6977, Acc.house: 0.7238, Acc.sea: 0.8825, Acc.mirror: 0.8376, Acc.rug: 0.8176, Acc.field: 0.6912, Acc.armchair: 0.7724, Acc.seat: 0.8771, Acc.fence: 0.6471, Acc.desk: 0.7753, Acc.rock: 0.7758, Acc.wardrobe: 0.7221, Acc.lamp: 0.8386, Acc.bathtub: 0.8720, Acc.railing: 0.6023, Acc.cushion: 0.7799, Acc.base: 0.5629, Acc.box: 0.4867, Acc.column: 0.6688, Acc.signboard: 0.5933, Acc.chest of drawers: 0.7030, Acc.counter: 0.3940, Acc.sand: 0.7636, Acc.sink: 0.8338, Acc.skyscraper: 0.6291, Acc.fireplace: 0.9186, Acc.refrigerator: 0.9046, Acc.grandstand: 0.7592, Acc.path: 0.3719, Acc.stairs: 0.3278, Acc.runway: 0.9793, Acc.case: 0.7862, Acc.pool table: 0.9762, Acc.pillow: 0.7846, Acc.screen door: 0.9105, Acc.stairway: 0.6367, Acc.river: 0.2999, Acc.bridge: 0.8933, Acc.bookcase: 0.6617, Acc.blind: 0.5220, Acc.coffee table: 0.8577, Acc.toilet: 0.9286, Acc.flower: 0.5658, Acc.book: 0.7603, Acc.hill: 0.1170, Acc.bench: 0.6206, Acc.countertop: 0.8554, Acc.stove: 0.9475, Acc.palm: 0.8058, Acc.kitchen island: 0.7203, Acc.computer: 0.9324, Acc.swivel chair: 0.7680, Acc.boat: 0.8705, Acc.bar: 0.7365, Acc.arcade machine: 0.7676, Acc.hovel: 0.4926, Acc.bus: 0.9643, Acc.towel: 0.8782, Acc.light: 0.6685, Acc.truck: 0.5766, Acc.tower: 0.1744, Acc.chandelier: 0.8623, Acc.awning: 0.5923, Acc.streetlight: 0.4300, Acc.booth: 0.6134, Acc.television receiver: 0.8883, Acc.airplane: 0.9079, Acc.dirt track: 0.2143, Acc.apparel: 0.6920, Acc.pole: 0.3767, Acc.land: 0.0649, Acc.bannister: 0.2272, Acc.escalator: 0.8081, Acc.ottoman: 0.6605, Acc.bottle: 0.6003, Acc.buffet: 0.5399, Acc.poster: 0.4956, Acc.stage: 0.4793, Acc.van: 0.5811, Acc.ship: 0.8855, Acc.fountain: 0.2219, Acc.conveyer belt: 0.9274, Acc.canopy: 0.8237, Acc.washer: 0.8401, Acc.plaything: 0.5825, Acc.swimming pool: 0.9169, Acc.stool: 0.7059, Acc.barrel: 0.6483, Acc.basket: 0.5844, Acc.waterfall: 0.8692, Acc.tent: 0.9908, Acc.bag: 0.2496, Acc.minibike: 0.8897, Acc.cradle: 0.9740, Acc.oven: 0.6967, Acc.ball: 0.5677, Acc.food: 0.7591, Acc.step: 0.1580, Acc.tank: 0.7674, Acc.trade name: 0.2573, Acc.microwave: 0.9630, Acc.pot: 0.6703, Acc.animal: 0.6353, Acc.bicycle: 0.8173, Acc.lake: 0.6360, Acc.dishwasher: 0.8046, Acc.screen: 0.7974, Acc.blanket: 0.3531, Acc.sculpture: 0.8859, Acc.hood: 0.7470, Acc.sconce: 0.6547, Acc.vase: 0.6319, Acc.traffic light: 0.6437, Acc.tray: 0.2116, Acc.ashcan: 0.6317, Acc.fan: 0.8010, Acc.pier: 0.4356, Acc.crt screen: 0.2970, Acc.plate: 0.7828, Acc.monitor: 0.6988, Acc.bulletin board: 0.5131, Acc.shower: 0.0619, Acc.radiator: 0.6972, Acc.glass: 0.2167, Acc.clock: 0.5186, Acc.flag: 0.8076 +2024-06-19 00:09:00,183 - mmseg - INFO - Iter [60050/80000] lr: 9.975e-06, eta: 8:12:24, time: 3.256, data_time: 1.937, memory: 70498, decode.loss_ce: 0.1735, decode.acc_seg: 92.4761, aux.loss_ce: 0.0742, aux.acc_seg: 91.9791, loss: 0.2477 +2024-06-19 00:10:06,746 - mmseg - INFO - Iter [60100/80000] lr: 9.951e-06, eta: 8:11:08, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1613, decode.acc_seg: 92.8735, aux.loss_ce: 0.0688, aux.acc_seg: 92.4323, loss: 0.2301 +2024-06-19 00:11:13,092 - mmseg - INFO - Iter [60150/80000] lr: 9.926e-06, eta: 8:09:51, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1730, decode.acc_seg: 92.6148, aux.loss_ce: 0.0735, aux.acc_seg: 92.1502, loss: 0.2466 +2024-06-19 00:12:19,548 - mmseg - INFO - Iter [60200/80000] lr: 9.901e-06, eta: 8:08:35, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1519, decode.acc_seg: 93.2929, aux.loss_ce: 0.0653, aux.acc_seg: 92.8009, loss: 0.2172 +2024-06-19 00:13:26,137 - mmseg - INFO - Iter [60250/80000] lr: 9.876e-06, eta: 8:07:18, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1760, decode.acc_seg: 92.4074, aux.loss_ce: 0.0748, aux.acc_seg: 91.9976, loss: 0.2508 +2024-06-19 00:14:32,559 - mmseg - INFO - Iter [60300/80000] lr: 9.851e-06, eta: 8:06:02, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1596, decode.acc_seg: 93.0001, aux.loss_ce: 0.0679, aux.acc_seg: 92.5883, loss: 0.2275 +2024-06-19 00:15:38,983 - mmseg - INFO - Iter [60350/80000] lr: 9.825e-06, eta: 8:04:45, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1660, decode.acc_seg: 92.7133, aux.loss_ce: 0.0705, aux.acc_seg: 92.2459, loss: 0.2365 +2024-06-19 00:16:45,568 - mmseg - INFO - Iter [60400/80000] lr: 9.800e-06, eta: 8:03:29, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1636, decode.acc_seg: 92.8988, aux.loss_ce: 0.0693, aux.acc_seg: 92.5331, loss: 0.2329 +2024-06-19 00:17:52,257 - mmseg - INFO - Iter [60450/80000] lr: 9.775e-06, eta: 8:02:13, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1775, decode.acc_seg: 92.5899, aux.loss_ce: 0.0744, aux.acc_seg: 92.1884, loss: 0.2519 +2024-06-19 00:18:58,619 - mmseg - INFO - Iter [60500/80000] lr: 9.751e-06, eta: 8:00:56, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1676, decode.acc_seg: 92.8298, aux.loss_ce: 0.0715, aux.acc_seg: 92.3540, loss: 0.2391 +2024-06-19 00:20:05,442 - mmseg - INFO - Iter [60550/80000] lr: 9.726e-06, eta: 7:59:40, time: 1.336, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1716, decode.acc_seg: 92.4296, aux.loss_ce: 0.0723, aux.acc_seg: 92.1111, loss: 0.2439 +2024-06-19 00:21:11,804 - mmseg - INFO - Iter [60600/80000] lr: 9.701e-06, eta: 7:58:23, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1652, decode.acc_seg: 92.6281, aux.loss_ce: 0.0708, aux.acc_seg: 92.1267, loss: 0.2360 +2024-06-19 00:22:20,993 - mmseg - INFO - Iter [60650/80000] lr: 9.676e-06, eta: 7:57:08, time: 1.384, data_time: 0.066, memory: 70498, decode.loss_ce: 0.1607, decode.acc_seg: 93.0102, aux.loss_ce: 0.0681, aux.acc_seg: 92.6020, loss: 0.2288 +2024-06-19 00:23:27,378 - mmseg - INFO - Iter [60700/80000] lr: 9.651e-06, eta: 7:55:51, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1651, decode.acc_seg: 92.8196, aux.loss_ce: 0.0702, aux.acc_seg: 92.3705, loss: 0.2353 +2024-06-19 00:24:33,815 - mmseg - INFO - Iter [60750/80000] lr: 9.625e-06, eta: 7:54:35, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1695, decode.acc_seg: 92.6076, aux.loss_ce: 0.0718, aux.acc_seg: 92.2049, loss: 0.2413 +2024-06-19 00:25:40,377 - mmseg - INFO - Iter [60800/80000] lr: 9.600e-06, eta: 7:53:19, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1617, decode.acc_seg: 92.7217, aux.loss_ce: 0.0692, aux.acc_seg: 92.2318, loss: 0.2309 +2024-06-19 00:26:46,733 - mmseg - INFO - Iter [60850/80000] lr: 9.576e-06, eta: 7:52:02, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1675, decode.acc_seg: 92.7188, aux.loss_ce: 0.0714, aux.acc_seg: 92.3467, loss: 0.2389 +2024-06-19 00:27:53,515 - mmseg - INFO - Iter [60900/80000] lr: 9.551e-06, eta: 7:50:46, time: 1.336, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1505, decode.acc_seg: 93.4120, aux.loss_ce: 0.0649, aux.acc_seg: 92.9750, loss: 0.2154 +2024-06-19 00:29:00,208 - mmseg - INFO - Iter [60950/80000] lr: 9.526e-06, eta: 7:49:30, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1713, decode.acc_seg: 92.6679, aux.loss_ce: 0.0727, aux.acc_seg: 92.2155, loss: 0.2440 +2024-06-19 00:30:06,558 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 00:30:06,558 - mmseg - INFO - Iter [61000/80000] lr: 9.501e-06, eta: 7:48:14, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1606, decode.acc_seg: 92.9025, aux.loss_ce: 0.0683, aux.acc_seg: 92.5154, loss: 0.2289 +2024-06-19 00:31:42,981 - mmseg - INFO - per class results: +2024-06-19 00:31:42,987 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.15 | 90.67 | +| building | 85.86 | 93.15 | +| sky | 94.95 | 97.9 | +| floor | 84.82 | 91.99 | +| tree | 77.81 | 89.09 | +| ceiling | 87.06 | 93.27 | +| road | 85.81 | 91.92 | +| bed | 92.22 | 97.06 | +| windowpane | 65.51 | 83.36 | +| grass | 66.52 | 80.38 | +| cabinet | 65.27 | 76.56 | +| sidewalk | 71.44 | 84.92 | +| person | 85.8 | 93.91 | +| earth | 38.39 | 50.68 | +| door | 59.16 | 70.95 | +| table | 69.43 | 82.04 | +| mountain | 59.53 | 73.4 | +| plant | 56.82 | 68.79 | +| curtain | 75.51 | 85.68 | +| chair | 66.78 | 77.24 | +| car | 87.49 | 93.75 | +| water | 64.12 | 77.63 | +| painting | 77.82 | 90.83 | +| sofa | 81.5 | 89.82 | +| shelf | 52.39 | 69.05 | +| house | 60.82 | 76.05 | +| sea | 69.3 | 86.48 | +| mirror | 76.5 | 81.85 | +| rug | 67.23 | 77.6 | +| field | 32.89 | 61.63 | +| armchair | 60.97 | 79.68 | +| seat | 63.63 | 88.48 | +| fence | 48.84 | 62.1 | +| desk | 59.58 | 78.32 | +| rock | 54.72 | 78.45 | +| wardrobe | 52.07 | 68.98 | +| lamp | 73.75 | 85.11 | +| bathtub | 84.16 | 86.34 | +| railing | 39.34 | 57.23 | +| cushion | 69.95 | 82.65 | +| base | 40.03 | 62.15 | +| box | 38.16 | 51.44 | +| column | 53.31 | 65.29 | +| signboard | 41.84 | 57.54 | +| chest of drawers | 45.49 | 63.67 | +| counter | 37.89 | 48.58 | +| sand | 53.29 | 76.76 | +| sink | 78.2 | 83.65 | +| skyscraper | 49.12 | 60.98 | +| fireplace | 77.72 | 92.2 | +| refrigerator | 82.5 | 92.37 | +| grandstand | 47.23 | 82.7 | +| path | 25.51 | 33.42 | +| stairs | 27.55 | 37.12 | +| runway | 73.91 | 98.19 | +| case | 59.72 | 78.39 | +| pool table | 94.81 | 97.74 | +| pillow | 70.56 | 79.94 | +| screen door | 85.79 | 89.44 | +| stairway | 46.58 | 59.19 | +| river | 18.18 | 31.78 | +| bridge | 76.13 | 87.85 | +| bookcase | 50.56 | 63.78 | +| blind | 46.98 | 49.69 | +| coffee table | 65.08 | 88.09 | +| toilet | 89.36 | 94.07 | +| flower | 42.32 | 56.77 | +| book | 58.64 | 73.58 | +| hill | 7.82 | 10.75 | +| bench | 50.11 | 58.08 | +| countertop | 63.01 | 84.29 | +| stove | 86.45 | 93.22 | +| palm | 56.2 | 85.09 | +| kitchen island | 37.72 | 63.23 | +| computer | 79.34 | 91.98 | +| swivel chair | 50.67 | 77.15 | +| boat | 68.25 | 87.27 | +| bar | 55.28 | 74.84 | +| arcade machine | 74.94 | 78.81 | +| hovel | 43.16 | 49.56 | +| bus | 92.98 | 96.02 | +| towel | 72.81 | 82.12 | +| light | 59.62 | 66.97 | +| truck | 46.56 | 61.2 | +| tower | 6.23 | 8.78 | +| chandelier | 71.17 | 84.74 | +| awning | 44.72 | 59.47 | +| streetlight | 33.59 | 45.01 | +| booth | 52.77 | 58.21 | +| television receiver | 81.93 | 87.34 | +| airplane | 83.09 | 89.34 | +| dirt track | 9.4 | 44.16 | +| apparel | 45.15 | 58.6 | +| pole | 26.35 | 35.07 | +| land | 3.19 | 5.57 | +| bannister | 16.45 | 21.34 | +| escalator | 59.95 | 80.66 | +| ottoman | 45.45 | 64.77 | +| bottle | 42.54 | 60.08 | +| buffet | 42.72 | 54.31 | +| poster | 36.36 | 52.0 | +| stage | 25.44 | 47.08 | +| van | 43.88 | 60.19 | +| ship | 51.69 | 52.83 | +| fountain | 24.87 | 26.13 | +| conveyer belt | 80.82 | 93.0 | +| canopy | 63.14 | 80.54 | +| washer | 76.05 | 78.36 | +| plaything | 39.4 | 50.83 | +| swimming pool | 70.58 | 87.52 | +| stool | 45.81 | 71.64 | +| barrel | 55.12 | 64.76 | +| basket | 41.88 | 58.11 | +| waterfall | 71.76 | 86.4 | +| tent | 89.02 | 99.02 | +| bag | 21.21 | 25.23 | +| minibike | 75.67 | 88.89 | +| cradle | 85.25 | 97.46 | +| oven | 60.81 | 73.49 | +| ball | 53.16 | 57.35 | +| food | 60.64 | 75.51 | +| step | 12.64 | 15.5 | +| tank | 60.9 | 65.88 | +| trade name | 29.18 | 33.14 | +| microwave | 88.69 | 96.03 | +| pot | 58.83 | 67.19 | +| animal | 61.39 | 62.55 | +| bicycle | 59.65 | 80.92 | +| lake | 57.54 | 61.04 | +| dishwasher | 71.41 | 83.66 | +| screen | 62.67 | 93.27 | +| blanket | 30.98 | 34.81 | +| sculpture | 77.51 | 87.3 | +| hood | 62.84 | 75.97 | +| sconce | 55.1 | 62.15 | +| vase | 48.33 | 58.39 | +| traffic light | 38.32 | 67.54 | +| tray | 14.53 | 18.24 | +| ashcan | 45.05 | 67.97 | +| fan | 66.46 | 79.64 | +| pier | 30.59 | 47.79 | +| crt screen | 20.86 | 27.14 | +| plate | 58.64 | 76.68 | +| monitor | 67.75 | 84.02 | +| bulletin board | 59.02 | 68.55 | +| shower | 4.08 | 5.37 | +| radiator | 64.15 | 74.57 | +| glass | 18.76 | 20.03 | +| clock | 41.22 | 46.04 | +| flag | 71.52 | 80.07 | ++---------------------+-------+-------+ +2024-06-19 00:31:42,987 - mmseg - INFO - Summary: +2024-06-19 00:31:42,987 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.09 | 56.97 | 69.25 | ++-------+-------+-------+ +2024-06-19 00:31:42,988 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 00:31:42,988 - mmseg - INFO - Iter(val) [250] aAcc: 0.8609, mIoU: 0.5697, mAcc: 0.6925, IoU.wall: 0.8215, IoU.building: 0.8586, IoU.sky: 0.9495, IoU.floor: 0.8482, IoU.tree: 0.7781, IoU.ceiling: 0.8706, IoU.road: 0.8581, IoU.bed : 0.9222, IoU.windowpane: 0.6551, IoU.grass: 0.6652, IoU.cabinet: 0.6527, IoU.sidewalk: 0.7144, IoU.person: 0.8580, IoU.earth: 0.3839, IoU.door: 0.5916, IoU.table: 0.6943, IoU.mountain: 0.5953, IoU.plant: 0.5682, IoU.curtain: 0.7551, IoU.chair: 0.6678, IoU.car: 0.8749, IoU.water: 0.6412, IoU.painting: 0.7782, IoU.sofa: 0.8150, IoU.shelf: 0.5239, IoU.house: 0.6082, IoU.sea: 0.6930, IoU.mirror: 0.7650, IoU.rug: 0.6723, IoU.field: 0.3289, IoU.armchair: 0.6097, IoU.seat: 0.6363, IoU.fence: 0.4884, IoU.desk: 0.5958, IoU.rock: 0.5472, IoU.wardrobe: 0.5207, IoU.lamp: 0.7375, IoU.bathtub: 0.8416, IoU.railing: 0.3934, IoU.cushion: 0.6995, IoU.base: 0.4003, IoU.box: 0.3816, IoU.column: 0.5331, IoU.signboard: 0.4184, IoU.chest of drawers: 0.4549, IoU.counter: 0.3789, IoU.sand: 0.5329, IoU.sink: 0.7820, IoU.skyscraper: 0.4912, IoU.fireplace: 0.7772, IoU.refrigerator: 0.8250, IoU.grandstand: 0.4723, IoU.path: 0.2551, IoU.stairs: 0.2755, IoU.runway: 0.7391, IoU.case: 0.5972, IoU.pool table: 0.9481, IoU.pillow: 0.7056, IoU.screen door: 0.8579, IoU.stairway: 0.4658, IoU.river: 0.1818, IoU.bridge: 0.7613, IoU.bookcase: 0.5056, IoU.blind: 0.4698, IoU.coffee table: 0.6508, IoU.toilet: 0.8936, IoU.flower: 0.4232, IoU.book: 0.5864, IoU.hill: 0.0782, IoU.bench: 0.5011, IoU.countertop: 0.6301, IoU.stove: 0.8645, IoU.palm: 0.5620, IoU.kitchen island: 0.3772, IoU.computer: 0.7934, IoU.swivel chair: 0.5067, IoU.boat: 0.6825, IoU.bar: 0.5528, IoU.arcade machine: 0.7494, IoU.hovel: 0.4316, IoU.bus: 0.9298, IoU.towel: 0.7281, IoU.light: 0.5962, IoU.truck: 0.4656, IoU.tower: 0.0623, IoU.chandelier: 0.7117, IoU.awning: 0.4472, IoU.streetlight: 0.3359, IoU.booth: 0.5277, IoU.television receiver: 0.8193, IoU.airplane: 0.8309, IoU.dirt track: 0.0940, IoU.apparel: 0.4515, IoU.pole: 0.2635, IoU.land: 0.0319, IoU.bannister: 0.1645, IoU.escalator: 0.5995, IoU.ottoman: 0.4545, IoU.bottle: 0.4254, IoU.buffet: 0.4272, IoU.poster: 0.3636, IoU.stage: 0.2544, IoU.van: 0.4388, IoU.ship: 0.5169, IoU.fountain: 0.2487, IoU.conveyer belt: 0.8082, IoU.canopy: 0.6314, IoU.washer: 0.7605, IoU.plaything: 0.3940, IoU.swimming pool: 0.7058, IoU.stool: 0.4581, IoU.barrel: 0.5512, IoU.basket: 0.4188, IoU.waterfall: 0.7176, IoU.tent: 0.8902, IoU.bag: 0.2121, IoU.minibike: 0.7567, IoU.cradle: 0.8525, IoU.oven: 0.6081, IoU.ball: 0.5316, IoU.food: 0.6064, IoU.step: 0.1264, IoU.tank: 0.6090, IoU.trade name: 0.2918, IoU.microwave: 0.8869, IoU.pot: 0.5883, IoU.animal: 0.6139, IoU.bicycle: 0.5965, IoU.lake: 0.5754, IoU.dishwasher: 0.7141, IoU.screen: 0.6267, IoU.blanket: 0.3098, IoU.sculpture: 0.7751, IoU.hood: 0.6284, IoU.sconce: 0.5510, IoU.vase: 0.4833, IoU.traffic light: 0.3832, IoU.tray: 0.1453, IoU.ashcan: 0.4505, IoU.fan: 0.6646, IoU.pier: 0.3059, IoU.crt screen: 0.2086, IoU.plate: 0.5864, IoU.monitor: 0.6775, IoU.bulletin board: 0.5902, IoU.shower: 0.0408, IoU.radiator: 0.6415, IoU.glass: 0.1876, IoU.clock: 0.4122, IoU.flag: 0.7152, Acc.wall: 0.9067, Acc.building: 0.9315, Acc.sky: 0.9790, Acc.floor: 0.9199, Acc.tree: 0.8909, Acc.ceiling: 0.9327, Acc.road: 0.9192, Acc.bed : 0.9706, Acc.windowpane: 0.8336, Acc.grass: 0.8038, Acc.cabinet: 0.7656, Acc.sidewalk: 0.8492, Acc.person: 0.9391, Acc.earth: 0.5068, Acc.door: 0.7095, Acc.table: 0.8204, Acc.mountain: 0.7340, Acc.plant: 0.6879, Acc.curtain: 0.8568, Acc.chair: 0.7724, Acc.car: 0.9375, Acc.water: 0.7763, Acc.painting: 0.9083, Acc.sofa: 0.8982, Acc.shelf: 0.6905, Acc.house: 0.7605, Acc.sea: 0.8648, Acc.mirror: 0.8185, Acc.rug: 0.7760, Acc.field: 0.6163, Acc.armchair: 0.7968, Acc.seat: 0.8848, Acc.fence: 0.6210, Acc.desk: 0.7832, Acc.rock: 0.7845, Acc.wardrobe: 0.6898, Acc.lamp: 0.8511, Acc.bathtub: 0.8634, Acc.railing: 0.5723, Acc.cushion: 0.8265, Acc.base: 0.6215, Acc.box: 0.5144, Acc.column: 0.6529, Acc.signboard: 0.5754, Acc.chest of drawers: 0.6367, Acc.counter: 0.4858, Acc.sand: 0.7676, Acc.sink: 0.8365, Acc.skyscraper: 0.6098, Acc.fireplace: 0.9220, Acc.refrigerator: 0.9237, Acc.grandstand: 0.8270, Acc.path: 0.3342, Acc.stairs: 0.3712, Acc.runway: 0.9819, Acc.case: 0.7839, Acc.pool table: 0.9774, Acc.pillow: 0.7994, Acc.screen door: 0.8944, Acc.stairway: 0.5919, Acc.river: 0.3178, Acc.bridge: 0.8785, Acc.bookcase: 0.6378, Acc.blind: 0.4969, Acc.coffee table: 0.8809, Acc.toilet: 0.9407, Acc.flower: 0.5677, Acc.book: 0.7358, Acc.hill: 0.1075, Acc.bench: 0.5808, Acc.countertop: 0.8429, Acc.stove: 0.9322, Acc.palm: 0.8509, Acc.kitchen island: 0.6323, Acc.computer: 0.9198, Acc.swivel chair: 0.7715, Acc.boat: 0.8727, Acc.bar: 0.7484, Acc.arcade machine: 0.7881, Acc.hovel: 0.4956, Acc.bus: 0.9602, Acc.towel: 0.8212, Acc.light: 0.6697, Acc.truck: 0.6120, Acc.tower: 0.0878, Acc.chandelier: 0.8474, Acc.awning: 0.5947, Acc.streetlight: 0.4501, Acc.booth: 0.5821, Acc.television receiver: 0.8734, Acc.airplane: 0.8934, Acc.dirt track: 0.4416, Acc.apparel: 0.5860, Acc.pole: 0.3507, Acc.land: 0.0557, Acc.bannister: 0.2134, Acc.escalator: 0.8066, Acc.ottoman: 0.6477, Acc.bottle: 0.6008, Acc.buffet: 0.5431, Acc.poster: 0.5200, Acc.stage: 0.4708, Acc.van: 0.6019, Acc.ship: 0.5283, Acc.fountain: 0.2613, Acc.conveyer belt: 0.9300, Acc.canopy: 0.8054, Acc.washer: 0.7836, Acc.plaything: 0.5083, Acc.swimming pool: 0.8752, Acc.stool: 0.7164, Acc.barrel: 0.6476, Acc.basket: 0.5811, Acc.waterfall: 0.8640, Acc.tent: 0.9902, Acc.bag: 0.2523, Acc.minibike: 0.8889, Acc.cradle: 0.9746, Acc.oven: 0.7349, Acc.ball: 0.5735, Acc.food: 0.7551, Acc.step: 0.1550, Acc.tank: 0.6588, Acc.trade name: 0.3314, Acc.microwave: 0.9603, Acc.pot: 0.6719, Acc.animal: 0.6255, Acc.bicycle: 0.8092, Acc.lake: 0.6104, Acc.dishwasher: 0.8366, Acc.screen: 0.9327, Acc.blanket: 0.3481, Acc.sculpture: 0.8730, Acc.hood: 0.7597, Acc.sconce: 0.6215, Acc.vase: 0.5839, Acc.traffic light: 0.6754, Acc.tray: 0.1824, Acc.ashcan: 0.6797, Acc.fan: 0.7964, Acc.pier: 0.4779, Acc.crt screen: 0.2714, Acc.plate: 0.7668, Acc.monitor: 0.8402, Acc.bulletin board: 0.6855, Acc.shower: 0.0537, Acc.radiator: 0.7457, Acc.glass: 0.2003, Acc.clock: 0.4604, Acc.flag: 0.8007 +2024-06-19 00:32:49,731 - mmseg - INFO - Iter [61050/80000] lr: 9.476e-06, eta: 7:47:28, time: 3.263, data_time: 1.945, memory: 70498, decode.loss_ce: 0.1606, decode.acc_seg: 93.0129, aux.loss_ce: 0.0685, aux.acc_seg: 92.5888, loss: 0.2290 +2024-06-19 00:33:56,130 - mmseg - INFO - Iter [61100/80000] lr: 9.451e-06, eta: 7:46:11, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1543, decode.acc_seg: 93.1805, aux.loss_ce: 0.0661, aux.acc_seg: 92.7723, loss: 0.2203 +2024-06-19 00:35:02,657 - mmseg - INFO - Iter [61150/80000] lr: 9.426e-06, eta: 7:44:55, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1615, decode.acc_seg: 92.8910, aux.loss_ce: 0.0689, aux.acc_seg: 92.4035, loss: 0.2304 +2024-06-19 00:36:09,100 - mmseg - INFO - Iter [61200/80000] lr: 9.400e-06, eta: 7:43:39, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1678, decode.acc_seg: 92.6965, aux.loss_ce: 0.0720, aux.acc_seg: 92.2135, loss: 0.2398 +2024-06-19 00:37:15,441 - mmseg - INFO - Iter [61250/80000] lr: 9.376e-06, eta: 7:42:22, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1615, decode.acc_seg: 92.8381, aux.loss_ce: 0.0691, aux.acc_seg: 92.3872, loss: 0.2306 +2024-06-19 00:38:21,859 - mmseg - INFO - Iter [61300/80000] lr: 9.350e-06, eta: 7:41:06, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1542, decode.acc_seg: 93.2010, aux.loss_ce: 0.0655, aux.acc_seg: 92.8021, loss: 0.2197 +2024-06-19 00:39:28,235 - mmseg - INFO - Iter [61350/80000] lr: 9.326e-06, eta: 7:39:50, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1623, decode.acc_seg: 92.9828, aux.loss_ce: 0.0690, aux.acc_seg: 92.5601, loss: 0.2313 +2024-06-19 00:40:34,727 - mmseg - INFO - Iter [61400/80000] lr: 9.301e-06, eta: 7:38:33, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1663, decode.acc_seg: 92.8027, aux.loss_ce: 0.0706, aux.acc_seg: 92.3907, loss: 0.2369 +2024-06-19 00:41:41,108 - mmseg - INFO - Iter [61450/80000] lr: 9.276e-06, eta: 7:37:17, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1604, decode.acc_seg: 93.0092, aux.loss_ce: 0.0689, aux.acc_seg: 92.5029, loss: 0.2293 +2024-06-19 00:42:47,315 - mmseg - INFO - Iter [61500/80000] lr: 9.251e-06, eta: 7:36:01, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1663, decode.acc_seg: 92.8872, aux.loss_ce: 0.0703, aux.acc_seg: 92.4726, loss: 0.2366 +2024-06-19 00:43:53,887 - mmseg - INFO - Iter [61550/80000] lr: 9.226e-06, eta: 7:34:45, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1684, decode.acc_seg: 92.5889, aux.loss_ce: 0.0716, aux.acc_seg: 92.2123, loss: 0.2400 +2024-06-19 00:45:00,263 - mmseg - INFO - Iter [61600/80000] lr: 9.200e-06, eta: 7:33:29, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1806, decode.acc_seg: 92.5125, aux.loss_ce: 0.0768, aux.acc_seg: 92.0623, loss: 0.2574 +2024-06-19 00:46:07,178 - mmseg - INFO - Iter [61650/80000] lr: 9.175e-06, eta: 7:32:12, time: 1.338, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1833, decode.acc_seg: 92.1321, aux.loss_ce: 0.0768, aux.acc_seg: 91.7755, loss: 0.2602 +2024-06-19 00:47:13,776 - mmseg - INFO - Iter [61700/80000] lr: 9.150e-06, eta: 7:30:56, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1676, decode.acc_seg: 92.8670, aux.loss_ce: 0.0717, aux.acc_seg: 92.3877, loss: 0.2393 +2024-06-19 00:48:20,498 - mmseg - INFO - Iter [61750/80000] lr: 9.126e-06, eta: 7:29:40, time: 1.334, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1641, decode.acc_seg: 92.8468, aux.loss_ce: 0.0696, aux.acc_seg: 92.4028, loss: 0.2337 +2024-06-19 00:49:26,889 - mmseg - INFO - Iter [61800/80000] lr: 9.101e-06, eta: 7:28:24, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1647, decode.acc_seg: 93.1323, aux.loss_ce: 0.0701, aux.acc_seg: 92.6444, loss: 0.2348 +2024-06-19 00:50:33,374 - mmseg - INFO - Iter [61850/80000] lr: 9.076e-06, eta: 7:27:08, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1649, decode.acc_seg: 93.1063, aux.loss_ce: 0.0699, aux.acc_seg: 92.6475, loss: 0.2348 +2024-06-19 00:51:42,028 - mmseg - INFO - Iter [61900/80000] lr: 9.051e-06, eta: 7:25:53, time: 1.373, data_time: 0.056, memory: 70498, decode.loss_ce: 0.1588, decode.acc_seg: 92.9130, aux.loss_ce: 0.0680, aux.acc_seg: 92.4306, loss: 0.2268 +2024-06-19 00:52:48,287 - mmseg - INFO - Iter [61950/80000] lr: 9.026e-06, eta: 7:24:37, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1602, decode.acc_seg: 93.0931, aux.loss_ce: 0.0685, aux.acc_seg: 92.6028, loss: 0.2287 +2024-06-19 00:53:54,626 - mmseg - INFO - Saving checkpoint at 62000 iterations +2024-06-19 00:55:39,658 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 00:55:39,659 - mmseg - INFO - Iter [62000/80000] lr: 9.000e-06, eta: 7:23:51, time: 3.427, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1529, decode.acc_seg: 93.3351, aux.loss_ce: 0.0655, aux.acc_seg: 92.9142, loss: 0.2184 +2024-06-19 00:57:16,741 - mmseg - INFO - per class results: +2024-06-19 00:57:16,747 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.25 | 90.36 | +| building | 84.86 | 93.49 | +| sky | 94.95 | 97.86 | +| floor | 85.39 | 92.15 | +| tree | 77.55 | 89.53 | +| ceiling | 86.96 | 93.77 | +| road | 86.79 | 91.34 | +| bed | 92.48 | 96.58 | +| windowpane | 66.38 | 81.15 | +| grass | 66.44 | 80.87 | +| cabinet | 64.09 | 75.2 | +| sidewalk | 71.34 | 87.45 | +| person | 85.59 | 94.07 | +| earth | 38.93 | 51.62 | +| door | 60.72 | 76.04 | +| table | 69.51 | 81.58 | +| mountain | 59.68 | 71.16 | +| plant | 56.65 | 67.57 | +| curtain | 78.32 | 86.94 | +| chair | 67.71 | 77.96 | +| car | 87.56 | 94.37 | +| water | 61.82 | 75.75 | +| painting | 78.39 | 90.82 | +| sofa | 81.83 | 90.22 | +| shelf | 51.76 | 69.79 | +| house | 45.51 | 54.05 | +| sea | 67.61 | 88.46 | +| mirror | 76.55 | 82.47 | +| rug | 70.22 | 79.2 | +| field | 30.76 | 58.39 | +| armchair | 60.17 | 78.29 | +| seat | 66.19 | 88.12 | +| fence | 47.83 | 61.72 | +| desk | 58.65 | 76.52 | +| rock | 54.5 | 77.88 | +| wardrobe | 51.05 | 72.09 | +| lamp | 74.06 | 85.0 | +| bathtub | 84.56 | 86.53 | +| railing | 39.56 | 57.63 | +| cushion | 70.67 | 83.95 | +| base | 41.93 | 53.49 | +| box | 36.71 | 49.14 | +| column | 55.22 | 69.45 | +| signboard | 42.47 | 58.87 | +| chest of drawers | 44.37 | 68.7 | +| counter | 40.97 | 50.76 | +| sand | 50.62 | 75.24 | +| sink | 78.4 | 85.08 | +| skyscraper | 47.85 | 62.03 | +| fireplace | 78.79 | 93.45 | +| refrigerator | 80.49 | 89.57 | +| grandstand | 48.67 | 81.8 | +| path | 25.56 | 33.92 | +| stairs | 30.47 | 41.35 | +| runway | 73.82 | 98.03 | +| case | 59.57 | 77.38 | +| pool table | 95.1 | 98.01 | +| pillow | 71.25 | 82.0 | +| screen door | 84.61 | 87.59 | +| stairway | 48.54 | 58.22 | +| river | 20.11 | 34.67 | +| bridge | 77.07 | 89.2 | +| bookcase | 45.79 | 55.25 | +| blind | 46.36 | 49.43 | +| coffee table | 65.39 | 87.04 | +| toilet | 89.57 | 93.5 | +| flower | 40.51 | 51.48 | +| book | 56.28 | 74.5 | +| hill | 9.56 | 14.66 | +| bench | 54.86 | 65.45 | +| countertop | 63.21 | 84.04 | +| stove | 87.76 | 93.72 | +| palm | 55.96 | 79.21 | +| kitchen island | 40.59 | 67.5 | +| computer | 79.95 | 91.36 | +| swivel chair | 52.06 | 77.0 | +| boat | 65.11 | 87.01 | +| bar | 56.36 | 75.09 | +| arcade machine | 78.63 | 83.99 | +| hovel | 44.98 | 49.65 | +| bus | 91.98 | 96.87 | +| towel | 75.97 | 85.87 | +| light | 61.83 | 72.29 | +| truck | 46.48 | 57.22 | +| tower | 18.49 | 33.49 | +| chandelier | 71.76 | 85.21 | +| awning | 41.34 | 52.81 | +| streetlight | 32.57 | 45.21 | +| booth | 44.85 | 60.63 | +| television receiver | 81.78 | 86.96 | +| airplane | 83.0 | 91.06 | +| dirt track | 14.5 | 25.92 | +| apparel | 45.97 | 62.37 | +| pole | 28.19 | 38.28 | +| land | 3.17 | 7.27 | +| bannister | 18.55 | 27.06 | +| escalator | 56.62 | 80.83 | +| ottoman | 49.59 | 71.8 | +| bottle | 43.09 | 60.94 | +| buffet | 52.08 | 66.39 | +| poster | 35.61 | 46.97 | +| stage | 24.43 | 43.79 | +| van | 44.74 | 62.47 | +| ship | 81.24 | 83.74 | +| fountain | 28.31 | 29.72 | +| conveyer belt | 74.24 | 93.42 | +| canopy | 62.21 | 79.72 | +| washer | 81.88 | 86.82 | +| plaything | 37.32 | 51.76 | +| swimming pool | 64.1 | 85.83 | +| stool | 49.64 | 72.24 | +| barrel | 56.64 | 66.09 | +| basket | 44.82 | 56.85 | +| waterfall | 56.78 | 68.02 | +| tent | 91.61 | 98.85 | +| bag | 20.78 | 24.27 | +| minibike | 75.27 | 89.58 | +| cradle | 85.43 | 97.45 | +| oven | 60.92 | 72.3 | +| ball | 41.7 | 43.59 | +| food | 58.57 | 75.35 | +| step | 10.68 | 13.37 | +| tank | 64.15 | 68.46 | +| trade name | 21.48 | 22.89 | +| microwave | 90.53 | 93.26 | +| pot | 59.6 | 69.22 | +| animal | 62.76 | 64.04 | +| bicycle | 59.8 | 80.54 | +| lake | 54.51 | 57.52 | +| dishwasher | 73.95 | 83.17 | +| screen | 59.28 | 94.01 | +| blanket | 30.3 | 35.06 | +| sculpture | 75.17 | 87.48 | +| hood | 60.92 | 74.4 | +| sconce | 54.8 | 60.81 | +| vase | 49.42 | 59.42 | +| traffic light | 42.93 | 60.4 | +| tray | 18.02 | 24.23 | +| ashcan | 48.44 | 62.05 | +| fan | 66.46 | 78.68 | +| pier | 32.0 | 44.05 | +| crt screen | 11.89 | 15.86 | +| plate | 59.29 | 74.87 | +| monitor | 62.41 | 78.56 | +| bulletin board | 54.62 | 66.25 | +| shower | 5.07 | 6.43 | +| radiator | 63.19 | 71.62 | +| glass | 19.16 | 20.74 | +| clock | 45.43 | 52.48 | +| flag | 71.76 | 82.14 | ++---------------------+-------+-------+ +2024-06-19 00:57:16,747 - mmseg - INFO - Summary: +2024-06-19 00:57:16,747 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.05 | 57.12 | 69.23 | ++-------+-------+-------+ +2024-06-19 00:57:16,748 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 00:57:16,748 - mmseg - INFO - Iter(val) [250] aAcc: 0.8605, mIoU: 0.5712, mAcc: 0.6923, IoU.wall: 0.8225, IoU.building: 0.8486, IoU.sky: 0.9495, IoU.floor: 0.8539, IoU.tree: 0.7755, IoU.ceiling: 0.8696, IoU.road: 0.8679, IoU.bed : 0.9248, IoU.windowpane: 0.6638, IoU.grass: 0.6644, IoU.cabinet: 0.6409, IoU.sidewalk: 0.7134, IoU.person: 0.8559, IoU.earth: 0.3893, IoU.door: 0.6072, IoU.table: 0.6951, IoU.mountain: 0.5968, IoU.plant: 0.5665, IoU.curtain: 0.7832, IoU.chair: 0.6771, IoU.car: 0.8756, IoU.water: 0.6182, IoU.painting: 0.7839, IoU.sofa: 0.8183, IoU.shelf: 0.5176, IoU.house: 0.4551, IoU.sea: 0.6761, IoU.mirror: 0.7655, IoU.rug: 0.7022, IoU.field: 0.3076, IoU.armchair: 0.6017, IoU.seat: 0.6619, IoU.fence: 0.4783, IoU.desk: 0.5865, IoU.rock: 0.5450, IoU.wardrobe: 0.5105, IoU.lamp: 0.7406, IoU.bathtub: 0.8456, IoU.railing: 0.3956, IoU.cushion: 0.7067, IoU.base: 0.4193, IoU.box: 0.3671, IoU.column: 0.5522, IoU.signboard: 0.4247, IoU.chest of drawers: 0.4437, IoU.counter: 0.4097, IoU.sand: 0.5062, IoU.sink: 0.7840, IoU.skyscraper: 0.4785, IoU.fireplace: 0.7879, IoU.refrigerator: 0.8049, IoU.grandstand: 0.4867, IoU.path: 0.2556, IoU.stairs: 0.3047, IoU.runway: 0.7382, IoU.case: 0.5957, IoU.pool table: 0.9510, IoU.pillow: 0.7125, IoU.screen door: 0.8461, IoU.stairway: 0.4854, IoU.river: 0.2011, IoU.bridge: 0.7707, IoU.bookcase: 0.4579, IoU.blind: 0.4636, IoU.coffee table: 0.6539, IoU.toilet: 0.8957, IoU.flower: 0.4051, IoU.book: 0.5628, IoU.hill: 0.0956, IoU.bench: 0.5486, IoU.countertop: 0.6321, IoU.stove: 0.8776, IoU.palm: 0.5596, IoU.kitchen island: 0.4059, IoU.computer: 0.7995, IoU.swivel chair: 0.5206, IoU.boat: 0.6511, IoU.bar: 0.5636, IoU.arcade machine: 0.7863, IoU.hovel: 0.4498, IoU.bus: 0.9198, IoU.towel: 0.7597, IoU.light: 0.6183, IoU.truck: 0.4648, IoU.tower: 0.1849, IoU.chandelier: 0.7176, IoU.awning: 0.4134, IoU.streetlight: 0.3257, IoU.booth: 0.4485, IoU.television receiver: 0.8178, IoU.airplane: 0.8300, IoU.dirt track: 0.1450, IoU.apparel: 0.4597, IoU.pole: 0.2819, IoU.land: 0.0317, IoU.bannister: 0.1855, IoU.escalator: 0.5662, IoU.ottoman: 0.4959, IoU.bottle: 0.4309, IoU.buffet: 0.5208, IoU.poster: 0.3561, IoU.stage: 0.2443, IoU.van: 0.4474, IoU.ship: 0.8124, IoU.fountain: 0.2831, IoU.conveyer belt: 0.7424, IoU.canopy: 0.6221, IoU.washer: 0.8188, IoU.plaything: 0.3732, IoU.swimming pool: 0.6410, IoU.stool: 0.4964, IoU.barrel: 0.5664, IoU.basket: 0.4482, IoU.waterfall: 0.5678, IoU.tent: 0.9161, IoU.bag: 0.2078, IoU.minibike: 0.7527, IoU.cradle: 0.8543, IoU.oven: 0.6092, IoU.ball: 0.4170, IoU.food: 0.5857, IoU.step: 0.1068, IoU.tank: 0.6415, IoU.trade name: 0.2148, IoU.microwave: 0.9053, IoU.pot: 0.5960, IoU.animal: 0.6276, IoU.bicycle: 0.5980, IoU.lake: 0.5451, IoU.dishwasher: 0.7395, IoU.screen: 0.5928, IoU.blanket: 0.3030, IoU.sculpture: 0.7517, IoU.hood: 0.6092, IoU.sconce: 0.5480, IoU.vase: 0.4942, IoU.traffic light: 0.4293, IoU.tray: 0.1802, IoU.ashcan: 0.4844, IoU.fan: 0.6646, IoU.pier: 0.3200, IoU.crt screen: 0.1189, IoU.plate: 0.5929, IoU.monitor: 0.6241, IoU.bulletin board: 0.5462, IoU.shower: 0.0507, IoU.radiator: 0.6319, IoU.glass: 0.1916, IoU.clock: 0.4543, IoU.flag: 0.7176, Acc.wall: 0.9036, Acc.building: 0.9349, Acc.sky: 0.9786, Acc.floor: 0.9215, Acc.tree: 0.8953, Acc.ceiling: 0.9377, Acc.road: 0.9134, Acc.bed : 0.9658, Acc.windowpane: 0.8115, Acc.grass: 0.8087, Acc.cabinet: 0.7520, Acc.sidewalk: 0.8745, Acc.person: 0.9407, Acc.earth: 0.5162, Acc.door: 0.7604, Acc.table: 0.8158, Acc.mountain: 0.7116, Acc.plant: 0.6757, Acc.curtain: 0.8694, Acc.chair: 0.7796, Acc.car: 0.9437, Acc.water: 0.7575, Acc.painting: 0.9082, Acc.sofa: 0.9022, Acc.shelf: 0.6979, Acc.house: 0.5405, Acc.sea: 0.8846, Acc.mirror: 0.8247, Acc.rug: 0.7920, Acc.field: 0.5839, Acc.armchair: 0.7829, Acc.seat: 0.8812, Acc.fence: 0.6172, Acc.desk: 0.7652, Acc.rock: 0.7788, Acc.wardrobe: 0.7209, Acc.lamp: 0.8500, Acc.bathtub: 0.8653, Acc.railing: 0.5763, Acc.cushion: 0.8395, Acc.base: 0.5349, Acc.box: 0.4914, Acc.column: 0.6945, Acc.signboard: 0.5887, Acc.chest of drawers: 0.6870, Acc.counter: 0.5076, Acc.sand: 0.7524, Acc.sink: 0.8508, Acc.skyscraper: 0.6203, Acc.fireplace: 0.9345, Acc.refrigerator: 0.8957, Acc.grandstand: 0.8180, Acc.path: 0.3392, Acc.stairs: 0.4135, Acc.runway: 0.9803, Acc.case: 0.7738, Acc.pool table: 0.9801, Acc.pillow: 0.8200, Acc.screen door: 0.8759, Acc.stairway: 0.5822, Acc.river: 0.3467, Acc.bridge: 0.8920, Acc.bookcase: 0.5525, Acc.blind: 0.4943, Acc.coffee table: 0.8704, Acc.toilet: 0.9350, Acc.flower: 0.5148, Acc.book: 0.7450, Acc.hill: 0.1466, Acc.bench: 0.6545, Acc.countertop: 0.8404, Acc.stove: 0.9372, Acc.palm: 0.7921, Acc.kitchen island: 0.6750, Acc.computer: 0.9136, Acc.swivel chair: 0.7700, Acc.boat: 0.8701, Acc.bar: 0.7509, Acc.arcade machine: 0.8399, Acc.hovel: 0.4965, Acc.bus: 0.9687, Acc.towel: 0.8587, Acc.light: 0.7229, Acc.truck: 0.5722, Acc.tower: 0.3349, Acc.chandelier: 0.8521, Acc.awning: 0.5281, Acc.streetlight: 0.4521, Acc.booth: 0.6063, Acc.television receiver: 0.8696, Acc.airplane: 0.9106, Acc.dirt track: 0.2592, Acc.apparel: 0.6237, Acc.pole: 0.3828, Acc.land: 0.0727, Acc.bannister: 0.2706, Acc.escalator: 0.8083, Acc.ottoman: 0.7180, Acc.bottle: 0.6094, Acc.buffet: 0.6639, Acc.poster: 0.4697, Acc.stage: 0.4379, Acc.van: 0.6247, Acc.ship: 0.8374, Acc.fountain: 0.2972, Acc.conveyer belt: 0.9342, Acc.canopy: 0.7972, Acc.washer: 0.8682, Acc.plaything: 0.5176, Acc.swimming pool: 0.8583, Acc.stool: 0.7224, Acc.barrel: 0.6609, Acc.basket: 0.5685, Acc.waterfall: 0.6802, Acc.tent: 0.9885, Acc.bag: 0.2427, Acc.minibike: 0.8958, Acc.cradle: 0.9745, Acc.oven: 0.7230, Acc.ball: 0.4359, Acc.food: 0.7535, Acc.step: 0.1337, Acc.tank: 0.6846, Acc.trade name: 0.2289, Acc.microwave: 0.9326, Acc.pot: 0.6922, Acc.animal: 0.6404, Acc.bicycle: 0.8054, Acc.lake: 0.5752, Acc.dishwasher: 0.8317, Acc.screen: 0.9401, Acc.blanket: 0.3506, Acc.sculpture: 0.8748, Acc.hood: 0.7440, Acc.sconce: 0.6081, Acc.vase: 0.5942, Acc.traffic light: 0.6040, Acc.tray: 0.2423, Acc.ashcan: 0.6205, Acc.fan: 0.7868, Acc.pier: 0.4405, Acc.crt screen: 0.1586, Acc.plate: 0.7487, Acc.monitor: 0.7856, Acc.bulletin board: 0.6625, Acc.shower: 0.0643, Acc.radiator: 0.7162, Acc.glass: 0.2074, Acc.clock: 0.5248, Acc.flag: 0.8214 +2024-06-19 00:58:23,738 - mmseg - INFO - Iter [62050/80000] lr: 8.975e-06, eta: 7:23:03, time: 3.282, data_time: 1.958, memory: 70498, decode.loss_ce: 0.1559, decode.acc_seg: 92.9575, aux.loss_ce: 0.0663, aux.acc_seg: 92.5248, loss: 0.2222 +2024-06-19 00:59:30,004 - mmseg - INFO - Iter [62100/80000] lr: 8.951e-06, eta: 7:21:47, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1688, decode.acc_seg: 92.5216, aux.loss_ce: 0.0717, aux.acc_seg: 92.0908, loss: 0.2405 +2024-06-19 01:00:36,380 - mmseg - INFO - Iter [62150/80000] lr: 8.925e-06, eta: 7:20:30, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1676, decode.acc_seg: 92.6650, aux.loss_ce: 0.0712, aux.acc_seg: 92.2306, loss: 0.2388 +2024-06-19 01:01:42,500 - mmseg - INFO - Iter [62200/80000] lr: 8.901e-06, eta: 7:19:14, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1550, decode.acc_seg: 93.2349, aux.loss_ce: 0.0662, aux.acc_seg: 92.7810, loss: 0.2212 +2024-06-19 01:02:49,070 - mmseg - INFO - Iter [62250/80000] lr: 8.876e-06, eta: 7:17:58, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1718, decode.acc_seg: 92.5656, aux.loss_ce: 0.0726, aux.acc_seg: 92.1673, loss: 0.2444 +2024-06-19 01:03:55,433 - mmseg - INFO - Iter [62300/80000] lr: 8.851e-06, eta: 7:16:42, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1669, decode.acc_seg: 92.6542, aux.loss_ce: 0.0711, aux.acc_seg: 92.1469, loss: 0.2380 +2024-06-19 01:05:01,870 - mmseg - INFO - Iter [62350/80000] lr: 8.826e-06, eta: 7:15:26, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1631, decode.acc_seg: 93.1676, aux.loss_ce: 0.0696, aux.acc_seg: 92.7302, loss: 0.2327 +2024-06-19 01:06:08,355 - mmseg - INFO - Iter [62400/80000] lr: 8.801e-06, eta: 7:14:10, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1594, decode.acc_seg: 93.1613, aux.loss_ce: 0.0675, aux.acc_seg: 92.7832, loss: 0.2270 +2024-06-19 01:07:14,724 - mmseg - INFO - Iter [62450/80000] lr: 8.775e-06, eta: 7:12:53, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1646, decode.acc_seg: 92.9842, aux.loss_ce: 0.0705, aux.acc_seg: 92.5290, loss: 0.2350 +2024-06-19 01:08:21,074 - mmseg - INFO - Iter [62500/80000] lr: 8.751e-06, eta: 7:11:37, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1612, decode.acc_seg: 92.9636, aux.loss_ce: 0.0694, aux.acc_seg: 92.4550, loss: 0.2307 +2024-06-19 01:09:27,396 - mmseg - INFO - Iter [62550/80000] lr: 8.725e-06, eta: 7:10:21, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1648, decode.acc_seg: 92.8794, aux.loss_ce: 0.0707, aux.acc_seg: 92.4478, loss: 0.2355 +2024-06-19 01:10:33,623 - mmseg - INFO - Iter [62600/80000] lr: 8.701e-06, eta: 7:09:05, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1696, decode.acc_seg: 92.8499, aux.loss_ce: 0.0720, aux.acc_seg: 92.3804, loss: 0.2415 +2024-06-19 01:11:39,972 - mmseg - INFO - Iter [62650/80000] lr: 8.676e-06, eta: 7:07:49, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1713, decode.acc_seg: 92.6414, aux.loss_ce: 0.0735, aux.acc_seg: 92.0940, loss: 0.2448 +2024-06-19 01:12:46,180 - mmseg - INFO - Iter [62700/80000] lr: 8.651e-06, eta: 7:06:33, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1613, decode.acc_seg: 93.0190, aux.loss_ce: 0.0687, aux.acc_seg: 92.5352, loss: 0.2300 +2024-06-19 01:13:52,637 - mmseg - INFO - Iter [62750/80000] lr: 8.626e-06, eta: 7:05:17, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1630, decode.acc_seg: 93.1410, aux.loss_ce: 0.0689, aux.acc_seg: 92.8020, loss: 0.2320 +2024-06-19 01:14:58,859 - mmseg - INFO - Iter [62800/80000] lr: 8.601e-06, eta: 7:04:01, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1663, decode.acc_seg: 92.8807, aux.loss_ce: 0.0710, aux.acc_seg: 92.4774, loss: 0.2373 +2024-06-19 01:16:05,306 - mmseg - INFO - Iter [62850/80000] lr: 8.575e-06, eta: 7:02:45, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1637, decode.acc_seg: 93.0185, aux.loss_ce: 0.0700, aux.acc_seg: 92.5261, loss: 0.2336 +2024-06-19 01:17:11,648 - mmseg - INFO - Iter [62900/80000] lr: 8.550e-06, eta: 7:01:29, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1619, decode.acc_seg: 92.8956, aux.loss_ce: 0.0689, aux.acc_seg: 92.4442, loss: 0.2309 +2024-06-19 01:18:18,195 - mmseg - INFO - Iter [62950/80000] lr: 8.525e-06, eta: 7:00:13, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1650, decode.acc_seg: 92.7450, aux.loss_ce: 0.0703, aux.acc_seg: 92.2994, loss: 0.2353 +2024-06-19 01:19:24,528 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 01:19:24,528 - mmseg - INFO - Iter [63000/80000] lr: 8.501e-06, eta: 6:58:57, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1582, decode.acc_seg: 93.1734, aux.loss_ce: 0.0671, aux.acc_seg: 92.7669, loss: 0.2253 +2024-06-19 01:21:02,822 - mmseg - INFO - per class results: +2024-06-19 01:21:02,828 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.17 | 89.62 | +| building | 85.28 | 93.66 | +| sky | 94.96 | 97.47 | +| floor | 84.74 | 91.6 | +| tree | 77.7 | 89.67 | +| ceiling | 86.71 | 93.08 | +| road | 86.97 | 91.08 | +| bed | 92.33 | 96.79 | +| windowpane | 66.29 | 80.45 | +| grass | 66.97 | 80.62 | +| cabinet | 64.86 | 76.38 | +| sidewalk | 71.9 | 87.41 | +| person | 85.34 | 95.07 | +| earth | 38.73 | 51.31 | +| door | 61.51 | 80.51 | +| table | 69.79 | 81.63 | +| mountain | 59.79 | 71.15 | +| plant | 56.15 | 66.42 | +| curtain | 77.23 | 87.04 | +| chair | 67.78 | 78.28 | +| car | 87.02 | 94.12 | +| water | 61.68 | 76.43 | +| painting | 77.87 | 90.58 | +| sofa | 82.02 | 91.6 | +| shelf | 52.2 | 68.03 | +| house | 54.88 | 70.09 | +| sea | 68.17 | 87.28 | +| mirror | 77.55 | 83.23 | +| rug | 68.11 | 81.05 | +| field | 35.57 | 66.73 | +| armchair | 60.34 | 78.0 | +| seat | 63.56 | 88.39 | +| fence | 49.79 | 61.84 | +| desk | 58.02 | 74.7 | +| rock | 52.97 | 84.95 | +| wardrobe | 54.24 | 71.61 | +| lamp | 74.65 | 84.87 | +| bathtub | 84.53 | 86.87 | +| railing | 40.19 | 57.57 | +| cushion | 70.35 | 83.88 | +| base | 42.79 | 60.61 | +| box | 38.95 | 53.07 | +| column | 54.4 | 65.16 | +| signboard | 41.34 | 55.74 | +| chest of drawers | 45.42 | 67.03 | +| counter | 44.23 | 57.74 | +| sand | 53.21 | 79.9 | +| sink | 78.54 | 83.82 | +| skyscraper | 49.06 | 59.99 | +| fireplace | 77.76 | 92.86 | +| refrigerator | 79.34 | 91.66 | +| grandstand | 47.66 | 77.98 | +| path | 26.11 | 35.04 | +| stairs | 29.38 | 40.32 | +| runway | 74.07 | 97.59 | +| case | 60.01 | 83.62 | +| pool table | 94.99 | 97.92 | +| pillow | 70.57 | 80.69 | +| screen door | 81.65 | 84.64 | +| stairway | 47.02 | 56.67 | +| river | 14.19 | 22.96 | +| bridge | 77.06 | 87.53 | +| bookcase | 43.33 | 54.12 | +| blind | 48.32 | 57.65 | +| coffee table | 65.42 | 87.25 | +| toilet | 90.19 | 94.02 | +| flower | 47.5 | 64.04 | +| book | 57.14 | 79.19 | +| hill | 7.52 | 11.36 | +| bench | 54.21 | 64.68 | +| countertop | 63.42 | 85.6 | +| stove | 87.07 | 93.92 | +| palm | 56.66 | 83.06 | +| kitchen island | 45.88 | 78.94 | +| computer | 79.28 | 92.45 | +| swivel chair | 51.3 | 78.25 | +| boat | 71.04 | 89.5 | +| bar | 55.36 | 74.37 | +| arcade machine | 78.76 | 82.67 | +| hovel | 42.11 | 49.58 | +| bus | 93.09 | 96.16 | +| towel | 77.54 | 86.17 | +| light | 61.94 | 73.41 | +| truck | 46.34 | 62.96 | +| tower | 11.87 | 16.84 | +| chandelier | 71.19 | 86.83 | +| awning | 45.39 | 57.81 | +| streetlight | 33.69 | 44.79 | +| booth | 52.09 | 65.31 | +| television receiver | 82.04 | 86.14 | +| airplane | 81.79 | 89.98 | +| dirt track | 7.65 | 36.97 | +| apparel | 49.85 | 69.18 | +| pole | 27.89 | 38.35 | +| land | 3.91 | 6.5 | +| bannister | 18.34 | 28.52 | +| escalator | 55.17 | 81.0 | +| ottoman | 48.09 | 64.51 | +| bottle | 42.98 | 60.29 | +| buffet | 49.75 | 63.58 | +| poster | 37.43 | 56.79 | +| stage | 26.37 | 45.2 | +| van | 44.6 | 60.47 | +| ship | 54.74 | 56.4 | +| fountain | 28.42 | 28.89 | +| conveyer belt | 78.19 | 92.44 | +| canopy | 54.48 | 80.44 | +| washer | 83.65 | 89.22 | +| plaything | 39.88 | 53.53 | +| swimming pool | 64.71 | 84.31 | +| stool | 49.35 | 70.7 | +| barrel | 56.04 | 64.73 | +| basket | 44.87 | 58.32 | +| waterfall | 57.92 | 74.93 | +| tent | 90.11 | 98.86 | +| bag | 21.21 | 24.61 | +| minibike | 74.9 | 89.79 | +| cradle | 86.08 | 97.23 | +| oven | 60.37 | 75.52 | +| ball | 53.33 | 57.56 | +| food | 54.6 | 63.52 | +| step | 12.37 | 15.18 | +| tank | 58.47 | 62.28 | +| trade name | 25.4 | 27.57 | +| microwave | 90.37 | 95.82 | +| pot | 59.66 | 70.82 | +| animal | 64.54 | 65.84 | +| bicycle | 58.89 | 77.13 | +| lake | 55.99 | 62.74 | +| dishwasher | 71.64 | 82.93 | +| screen | 56.54 | 91.38 | +| blanket | 33.53 | 38.1 | +| sculpture | 73.71 | 88.83 | +| hood | 61.49 | 74.37 | +| sconce | 54.83 | 61.51 | +| vase | 47.75 | 62.16 | +| traffic light | 40.23 | 61.44 | +| tray | 12.42 | 15.82 | +| ashcan | 49.48 | 66.29 | +| fan | 67.56 | 83.44 | +| pier | 36.88 | 44.31 | +| crt screen | 17.31 | 21.78 | +| plate | 59.47 | 72.81 | +| monitor | 66.02 | 78.52 | +| bulletin board | 51.21 | 53.33 | +| shower | 6.48 | 6.57 | +| radiator | 65.84 | 75.27 | +| glass | 17.8 | 18.73 | +| clock | 45.51 | 51.26 | +| flag | 71.21 | 81.27 | ++---------------------+-------+-------+ +2024-06-19 01:21:02,828 - mmseg - INFO - Summary: +2024-06-19 01:21:02,828 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 86.1 | 57.18 | 69.74 | ++------+-------+-------+ +2024-06-19 01:21:02,829 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 01:21:02,829 - mmseg - INFO - Iter(val) [250] aAcc: 0.8610, mIoU: 0.5718, mAcc: 0.6974, IoU.wall: 0.8217, IoU.building: 0.8528, IoU.sky: 0.9496, IoU.floor: 0.8474, IoU.tree: 0.7770, IoU.ceiling: 0.8671, IoU.road: 0.8697, IoU.bed : 0.9233, IoU.windowpane: 0.6629, IoU.grass: 0.6697, IoU.cabinet: 0.6486, IoU.sidewalk: 0.7190, IoU.person: 0.8534, IoU.earth: 0.3873, IoU.door: 0.6151, IoU.table: 0.6979, IoU.mountain: 0.5979, IoU.plant: 0.5615, IoU.curtain: 0.7723, IoU.chair: 0.6778, IoU.car: 0.8702, IoU.water: 0.6168, IoU.painting: 0.7787, IoU.sofa: 0.8202, IoU.shelf: 0.5220, IoU.house: 0.5488, IoU.sea: 0.6817, IoU.mirror: 0.7755, IoU.rug: 0.6811, IoU.field: 0.3557, IoU.armchair: 0.6034, IoU.seat: 0.6356, IoU.fence: 0.4979, IoU.desk: 0.5802, IoU.rock: 0.5297, IoU.wardrobe: 0.5424, IoU.lamp: 0.7465, IoU.bathtub: 0.8453, IoU.railing: 0.4019, IoU.cushion: 0.7035, IoU.base: 0.4279, IoU.box: 0.3895, IoU.column: 0.5440, IoU.signboard: 0.4134, IoU.chest of drawers: 0.4542, IoU.counter: 0.4423, IoU.sand: 0.5321, IoU.sink: 0.7854, IoU.skyscraper: 0.4906, IoU.fireplace: 0.7776, IoU.refrigerator: 0.7934, IoU.grandstand: 0.4766, IoU.path: 0.2611, IoU.stairs: 0.2938, IoU.runway: 0.7407, IoU.case: 0.6001, IoU.pool table: 0.9499, IoU.pillow: 0.7057, IoU.screen door: 0.8165, IoU.stairway: 0.4702, IoU.river: 0.1419, IoU.bridge: 0.7706, IoU.bookcase: 0.4333, IoU.blind: 0.4832, IoU.coffee table: 0.6542, IoU.toilet: 0.9019, IoU.flower: 0.4750, IoU.book: 0.5714, IoU.hill: 0.0752, IoU.bench: 0.5421, IoU.countertop: 0.6342, IoU.stove: 0.8707, IoU.palm: 0.5666, IoU.kitchen island: 0.4588, IoU.computer: 0.7928, IoU.swivel chair: 0.5130, IoU.boat: 0.7104, IoU.bar: 0.5536, IoU.arcade machine: 0.7876, IoU.hovel: 0.4211, IoU.bus: 0.9309, IoU.towel: 0.7754, IoU.light: 0.6194, IoU.truck: 0.4634, IoU.tower: 0.1187, IoU.chandelier: 0.7119, IoU.awning: 0.4539, IoU.streetlight: 0.3369, IoU.booth: 0.5209, IoU.television receiver: 0.8204, IoU.airplane: 0.8179, IoU.dirt track: 0.0765, IoU.apparel: 0.4985, IoU.pole: 0.2789, IoU.land: 0.0391, IoU.bannister: 0.1834, IoU.escalator: 0.5517, IoU.ottoman: 0.4809, IoU.bottle: 0.4298, IoU.buffet: 0.4975, IoU.poster: 0.3743, IoU.stage: 0.2637, IoU.van: 0.4460, IoU.ship: 0.5474, IoU.fountain: 0.2842, IoU.conveyer belt: 0.7819, IoU.canopy: 0.5448, IoU.washer: 0.8365, IoU.plaything: 0.3988, IoU.swimming pool: 0.6471, IoU.stool: 0.4935, IoU.barrel: 0.5604, IoU.basket: 0.4487, IoU.waterfall: 0.5792, IoU.tent: 0.9011, IoU.bag: 0.2121, IoU.minibike: 0.7490, IoU.cradle: 0.8608, IoU.oven: 0.6037, IoU.ball: 0.5333, IoU.food: 0.5460, IoU.step: 0.1237, IoU.tank: 0.5847, IoU.trade name: 0.2540, IoU.microwave: 0.9037, IoU.pot: 0.5966, IoU.animal: 0.6454, IoU.bicycle: 0.5889, IoU.lake: 0.5599, IoU.dishwasher: 0.7164, IoU.screen: 0.5654, IoU.blanket: 0.3353, IoU.sculpture: 0.7371, IoU.hood: 0.6149, IoU.sconce: 0.5483, IoU.vase: 0.4775, IoU.traffic light: 0.4023, IoU.tray: 0.1242, IoU.ashcan: 0.4948, IoU.fan: 0.6756, IoU.pier: 0.3688, IoU.crt screen: 0.1731, IoU.plate: 0.5947, IoU.monitor: 0.6602, IoU.bulletin board: 0.5121, IoU.shower: 0.0648, IoU.radiator: 0.6584, IoU.glass: 0.1780, IoU.clock: 0.4551, IoU.flag: 0.7121, Acc.wall: 0.8962, Acc.building: 0.9366, Acc.sky: 0.9747, Acc.floor: 0.9160, Acc.tree: 0.8967, Acc.ceiling: 0.9308, Acc.road: 0.9108, Acc.bed : 0.9679, Acc.windowpane: 0.8045, Acc.grass: 0.8062, Acc.cabinet: 0.7638, Acc.sidewalk: 0.8741, Acc.person: 0.9507, Acc.earth: 0.5131, Acc.door: 0.8051, Acc.table: 0.8163, Acc.mountain: 0.7115, Acc.plant: 0.6642, Acc.curtain: 0.8704, Acc.chair: 0.7828, Acc.car: 0.9412, Acc.water: 0.7643, Acc.painting: 0.9058, Acc.sofa: 0.9160, Acc.shelf: 0.6803, Acc.house: 0.7009, Acc.sea: 0.8728, Acc.mirror: 0.8323, Acc.rug: 0.8105, Acc.field: 0.6673, Acc.armchair: 0.7800, Acc.seat: 0.8839, Acc.fence: 0.6184, Acc.desk: 0.7470, Acc.rock: 0.8495, Acc.wardrobe: 0.7161, Acc.lamp: 0.8487, Acc.bathtub: 0.8687, Acc.railing: 0.5757, Acc.cushion: 0.8388, Acc.base: 0.6061, Acc.box: 0.5307, Acc.column: 0.6516, Acc.signboard: 0.5574, Acc.chest of drawers: 0.6703, Acc.counter: 0.5774, Acc.sand: 0.7990, Acc.sink: 0.8382, Acc.skyscraper: 0.5999, Acc.fireplace: 0.9286, Acc.refrigerator: 0.9166, Acc.grandstand: 0.7798, Acc.path: 0.3504, Acc.stairs: 0.4032, Acc.runway: 0.9759, Acc.case: 0.8362, Acc.pool table: 0.9792, Acc.pillow: 0.8069, Acc.screen door: 0.8464, Acc.stairway: 0.5667, Acc.river: 0.2296, Acc.bridge: 0.8753, Acc.bookcase: 0.5412, Acc.blind: 0.5765, Acc.coffee table: 0.8725, Acc.toilet: 0.9402, Acc.flower: 0.6404, Acc.book: 0.7919, Acc.hill: 0.1136, Acc.bench: 0.6468, Acc.countertop: 0.8560, Acc.stove: 0.9392, Acc.palm: 0.8306, Acc.kitchen island: 0.7894, Acc.computer: 0.9245, Acc.swivel chair: 0.7825, Acc.boat: 0.8950, Acc.bar: 0.7437, Acc.arcade machine: 0.8267, Acc.hovel: 0.4958, Acc.bus: 0.9616, Acc.towel: 0.8617, Acc.light: 0.7341, Acc.truck: 0.6296, Acc.tower: 0.1684, Acc.chandelier: 0.8683, Acc.awning: 0.5781, Acc.streetlight: 0.4479, Acc.booth: 0.6531, Acc.television receiver: 0.8614, Acc.airplane: 0.8998, Acc.dirt track: 0.3697, Acc.apparel: 0.6918, Acc.pole: 0.3835, Acc.land: 0.0650, Acc.bannister: 0.2852, Acc.escalator: 0.8100, Acc.ottoman: 0.6451, Acc.bottle: 0.6029, Acc.buffet: 0.6358, Acc.poster: 0.5679, Acc.stage: 0.4520, Acc.van: 0.6047, Acc.ship: 0.5640, Acc.fountain: 0.2889, Acc.conveyer belt: 0.9244, Acc.canopy: 0.8044, Acc.washer: 0.8922, Acc.plaything: 0.5353, Acc.swimming pool: 0.8431, Acc.stool: 0.7070, Acc.barrel: 0.6473, Acc.basket: 0.5832, Acc.waterfall: 0.7493, Acc.tent: 0.9886, Acc.bag: 0.2461, Acc.minibike: 0.8979, Acc.cradle: 0.9723, Acc.oven: 0.7552, Acc.ball: 0.5756, Acc.food: 0.6352, Acc.step: 0.1518, Acc.tank: 0.6228, Acc.trade name: 0.2757, Acc.microwave: 0.9582, Acc.pot: 0.7082, Acc.animal: 0.6584, Acc.bicycle: 0.7713, Acc.lake: 0.6274, Acc.dishwasher: 0.8293, Acc.screen: 0.9138, Acc.blanket: 0.3810, Acc.sculpture: 0.8883, Acc.hood: 0.7437, Acc.sconce: 0.6151, Acc.vase: 0.6216, Acc.traffic light: 0.6144, Acc.tray: 0.1582, Acc.ashcan: 0.6629, Acc.fan: 0.8344, Acc.pier: 0.4431, Acc.crt screen: 0.2178, Acc.plate: 0.7281, Acc.monitor: 0.7852, Acc.bulletin board: 0.5333, Acc.shower: 0.0657, Acc.radiator: 0.7527, Acc.glass: 0.1873, Acc.clock: 0.5126, Acc.flag: 0.8127 +2024-06-19 01:22:09,468 - mmseg - INFO - Iter [63050/80000] lr: 8.476e-06, eta: 6:58:07, time: 3.299, data_time: 1.982, memory: 70498, decode.loss_ce: 0.1603, decode.acc_seg: 92.9525, aux.loss_ce: 0.0682, aux.acc_seg: 92.5218, loss: 0.2285 +2024-06-19 01:23:15,965 - mmseg - INFO - Iter [63100/80000] lr: 8.451e-06, eta: 6:56:51, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1684, decode.acc_seg: 92.5804, aux.loss_ce: 0.0722, aux.acc_seg: 92.0758, loss: 0.2405 +2024-06-19 01:24:22,303 - mmseg - INFO - Iter [63150/80000] lr: 8.426e-06, eta: 6:55:35, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1479, decode.acc_seg: 93.4516, aux.loss_ce: 0.0631, aux.acc_seg: 93.0258, loss: 0.2109 +2024-06-19 01:25:30,972 - mmseg - INFO - Iter [63200/80000] lr: 8.401e-06, eta: 6:54:20, time: 1.373, data_time: 0.057, memory: 70498, decode.loss_ce: 0.1539, decode.acc_seg: 93.2376, aux.loss_ce: 0.0656, aux.acc_seg: 92.8080, loss: 0.2195 +2024-06-19 01:26:37,283 - mmseg - INFO - Iter [63250/80000] lr: 8.375e-06, eta: 6:53:04, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1597, decode.acc_seg: 92.9172, aux.loss_ce: 0.0681, aux.acc_seg: 92.4866, loss: 0.2278 +2024-06-19 01:27:44,003 - mmseg - INFO - Iter [63300/80000] lr: 8.350e-06, eta: 6:51:48, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1610, decode.acc_seg: 92.9597, aux.loss_ce: 0.0687, aux.acc_seg: 92.5187, loss: 0.2296 +2024-06-19 01:28:50,331 - mmseg - INFO - Iter [63350/80000] lr: 8.326e-06, eta: 6:50:32, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1643, decode.acc_seg: 93.0276, aux.loss_ce: 0.0700, aux.acc_seg: 92.6109, loss: 0.2343 +2024-06-19 01:29:56,735 - mmseg - INFO - Iter [63400/80000] lr: 8.300e-06, eta: 6:49:16, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1682, decode.acc_seg: 92.8027, aux.loss_ce: 0.0725, aux.acc_seg: 92.2609, loss: 0.2406 +2024-06-19 01:31:03,389 - mmseg - INFO - Iter [63450/80000] lr: 8.276e-06, eta: 6:48:00, time: 1.333, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1570, decode.acc_seg: 93.2372, aux.loss_ce: 0.0672, aux.acc_seg: 92.7674, loss: 0.2242 +2024-06-19 01:32:09,828 - mmseg - INFO - Iter [63500/80000] lr: 8.251e-06, eta: 6:46:44, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1555, decode.acc_seg: 93.3505, aux.loss_ce: 0.0664, aux.acc_seg: 92.9684, loss: 0.2219 +2024-06-19 01:33:16,304 - mmseg - INFO - Iter [63550/80000] lr: 8.226e-06, eta: 6:45:28, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1631, decode.acc_seg: 92.8782, aux.loss_ce: 0.0696, aux.acc_seg: 92.3993, loss: 0.2327 +2024-06-19 01:34:22,757 - mmseg - INFO - Iter [63600/80000] lr: 8.201e-06, eta: 6:44:12, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1662, decode.acc_seg: 92.9669, aux.loss_ce: 0.0715, aux.acc_seg: 92.5123, loss: 0.2377 +2024-06-19 01:35:29,564 - mmseg - INFO - Iter [63650/80000] lr: 8.176e-06, eta: 6:42:57, time: 1.336, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1667, decode.acc_seg: 92.7464, aux.loss_ce: 0.0717, aux.acc_seg: 92.2189, loss: 0.2383 +2024-06-19 01:36:35,914 - mmseg - INFO - Iter [63700/80000] lr: 8.150e-06, eta: 6:41:41, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1612, decode.acc_seg: 93.1482, aux.loss_ce: 0.0687, aux.acc_seg: 92.6743, loss: 0.2299 +2024-06-19 01:37:42,632 - mmseg - INFO - Iter [63750/80000] lr: 8.125e-06, eta: 6:40:25, time: 1.334, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1549, decode.acc_seg: 93.0906, aux.loss_ce: 0.0661, aux.acc_seg: 92.6484, loss: 0.2211 +2024-06-19 01:38:48,997 - mmseg - INFO - Iter [63800/80000] lr: 8.100e-06, eta: 6:39:09, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1625, decode.acc_seg: 93.0299, aux.loss_ce: 0.0692, aux.acc_seg: 92.6126, loss: 0.2317 +2024-06-19 01:39:55,768 - mmseg - INFO - Iter [63850/80000] lr: 8.076e-06, eta: 6:37:53, time: 1.335, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1575, decode.acc_seg: 93.1863, aux.loss_ce: 0.0679, aux.acc_seg: 92.7502, loss: 0.2254 +2024-06-19 01:41:02,114 - mmseg - INFO - Iter [63900/80000] lr: 8.051e-06, eta: 6:36:37, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1578, decode.acc_seg: 93.0766, aux.loss_ce: 0.0677, aux.acc_seg: 92.6541, loss: 0.2255 +2024-06-19 01:42:08,716 - mmseg - INFO - Iter [63950/80000] lr: 8.026e-06, eta: 6:35:22, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1615, decode.acc_seg: 92.7627, aux.loss_ce: 0.0685, aux.acc_seg: 92.3665, loss: 0.2300 +2024-06-19 01:43:15,146 - mmseg - INFO - Saving checkpoint at 64000 iterations +2024-06-19 01:44:59,432 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 01:44:59,433 - mmseg - INFO - Iter [64000/80000] lr: 8.001e-06, eta: 6:34:32, time: 3.414, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1578, decode.acc_seg: 93.1152, aux.loss_ce: 0.0675, aux.acc_seg: 92.6393, loss: 0.2253 +2024-06-19 01:46:56,358 - mmseg - INFO - per class results: +2024-06-19 01:46:56,364 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.27 | 90.54 | +| building | 85.42 | 93.85 | +| sky | 95.04 | 97.94 | +| floor | 85.32 | 91.28 | +| tree | 78.03 | 88.83 | +| ceiling | 87.13 | 94.21 | +| road | 87.13 | 92.07 | +| bed | 92.32 | 96.24 | +| windowpane | 66.39 | 81.22 | +| grass | 66.47 | 79.89 | +| cabinet | 64.96 | 75.3 | +| sidewalk | 72.22 | 84.46 | +| person | 85.63 | 93.45 | +| earth | 39.5 | 52.58 | +| door | 61.36 | 77.96 | +| table | 69.95 | 82.63 | +| mountain | 60.74 | 72.91 | +| plant | 57.12 | 68.16 | +| curtain | 77.13 | 85.68 | +| chair | 67.54 | 77.33 | +| car | 87.52 | 93.79 | +| water | 62.56 | 75.35 | +| painting | 78.13 | 91.04 | +| sofa | 82.19 | 90.37 | +| shelf | 53.17 | 74.14 | +| house | 50.22 | 60.14 | +| sea | 67.54 | 85.52 | +| mirror | 77.05 | 82.35 | +| rug | 68.91 | 83.58 | +| field | 33.79 | 64.85 | +| armchair | 61.01 | 78.95 | +| seat | 65.83 | 88.36 | +| fence | 49.48 | 66.14 | +| desk | 58.16 | 77.38 | +| rock | 54.54 | 79.05 | +| wardrobe | 52.29 | 68.41 | +| lamp | 74.48 | 86.3 | +| bathtub | 84.39 | 86.84 | +| railing | 40.44 | 58.19 | +| cushion | 70.09 | 79.04 | +| base | 42.62 | 62.08 | +| box | 37.17 | 49.25 | +| column | 55.13 | 66.45 | +| signboard | 42.15 | 58.97 | +| chest of drawers | 43.64 | 71.35 | +| counter | 42.21 | 51.03 | +| sand | 52.65 | 74.93 | +| sink | 77.39 | 83.13 | +| skyscraper | 49.19 | 60.37 | +| fireplace | 74.98 | 94.66 | +| refrigerator | 79.88 | 89.95 | +| grandstand | 49.15 | 84.82 | +| path | 28.7 | 46.88 | +| stairs | 22.96 | 30.16 | +| runway | 74.25 | 96.17 | +| case | 60.55 | 81.16 | +| pool table | 94.78 | 97.5 | +| pillow | 70.61 | 85.08 | +| screen door | 77.03 | 79.24 | +| stairway | 38.65 | 53.78 | +| river | 20.99 | 39.33 | +| bridge | 74.71 | 89.02 | +| bookcase | 47.61 | 55.0 | +| blind | 45.45 | 49.33 | +| coffee table | 67.36 | 87.57 | +| toilet | 89.78 | 93.19 | +| flower | 46.62 | 60.69 | +| book | 57.54 | 78.56 | +| hill | 11.66 | 21.76 | +| bench | 56.02 | 65.3 | +| countertop | 62.3 | 85.22 | +| stove | 86.93 | 92.66 | +| palm | 55.87 | 85.83 | +| kitchen island | 39.78 | 65.39 | +| computer | 80.24 | 92.68 | +| swivel chair | 52.25 | 75.79 | +| boat | 69.39 | 86.03 | +| bar | 54.09 | 74.41 | +| arcade machine | 78.46 | 83.82 | +| hovel | 44.04 | 48.69 | +| bus | 93.3 | 95.54 | +| towel | 74.3 | 82.5 | +| light | 60.8 | 68.61 | +| truck | 46.1 | 59.16 | +| tower | 12.55 | 17.41 | +| chandelier | 70.4 | 82.59 | +| awning | 48.4 | 64.61 | +| streetlight | 35.62 | 52.67 | +| booth | 54.8 | 61.01 | +| television receiver | 82.22 | 85.85 | +| airplane | 82.04 | 88.14 | +| dirt track | 8.66 | 33.4 | +| apparel | 44.73 | 62.07 | +| pole | 25.58 | 34.76 | +| land | 4.01 | 6.39 | +| bannister | 16.45 | 24.06 | +| escalator | 58.29 | 79.52 | +| ottoman | 49.37 | 68.63 | +| bottle | 44.03 | 57.02 | +| buffet | 51.04 | 66.24 | +| poster | 38.19 | 51.98 | +| stage | 25.05 | 41.26 | +| van | 44.22 | 60.25 | +| ship | 80.56 | 83.29 | +| fountain | 27.64 | 28.14 | +| conveyer belt | 80.03 | 92.93 | +| canopy | 56.43 | 74.44 | +| washer | 81.64 | 84.35 | +| plaything | 37.26 | 51.79 | +| swimming pool | 63.33 | 89.1 | +| stool | 49.96 | 68.39 | +| barrel | 55.81 | 64.72 | +| basket | 43.77 | 57.23 | +| waterfall | 62.95 | 84.12 | +| tent | 90.7 | 98.7 | +| bag | 19.45 | 23.01 | +| minibike | 75.96 | 86.54 | +| cradle | 85.95 | 96.86 | +| oven | 49.02 | 61.01 | +| ball | 36.67 | 38.07 | +| food | 56.62 | 68.94 | +| step | 12.01 | 14.55 | +| tank | 58.96 | 64.19 | +| trade name | 27.82 | 31.42 | +| microwave | 87.13 | 96.29 | +| pot | 58.88 | 70.26 | +| animal | 59.22 | 60.5 | +| bicycle | 60.23 | 77.37 | +| lake | 40.83 | 48.97 | +| dishwasher | 72.58 | 84.45 | +| screen | 56.97 | 83.02 | +| blanket | 34.45 | 40.43 | +| sculpture | 72.59 | 89.13 | +| hood | 61.83 | 72.94 | +| sconce | 53.84 | 59.54 | +| vase | 49.07 | 64.46 | +| traffic light | 43.16 | 59.03 | +| tray | 15.92 | 20.1 | +| ashcan | 49.51 | 63.34 | +| fan | 65.8 | 77.14 | +| pier | 33.13 | 47.57 | +| crt screen | 17.01 | 24.92 | +| plate | 59.66 | 77.85 | +| monitor | 67.7 | 84.58 | +| bulletin board | 47.19 | 54.87 | +| shower | 2.13 | 2.29 | +| radiator | 64.15 | 74.85 | +| glass | 19.28 | 20.76 | +| clock | 41.46 | 46.62 | +| flag | 71.81 | 77.22 | ++---------------------+-------+-------+ +2024-06-19 01:46:56,364 - mmseg - INFO - Summary: +2024-06-19 01:46:56,364 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.16 | 56.91 | 69.18 | ++-------+-------+-------+ +2024-06-19 01:46:56,365 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 01:46:56,365 - mmseg - INFO - Iter(val) [250] aAcc: 0.8616, mIoU: 0.5691, mAcc: 0.6918, IoU.wall: 0.8227, IoU.building: 0.8542, IoU.sky: 0.9504, IoU.floor: 0.8532, IoU.tree: 0.7803, IoU.ceiling: 0.8713, IoU.road: 0.8713, IoU.bed : 0.9232, IoU.windowpane: 0.6639, IoU.grass: 0.6647, IoU.cabinet: 0.6496, IoU.sidewalk: 0.7222, IoU.person: 0.8563, IoU.earth: 0.3950, IoU.door: 0.6136, IoU.table: 0.6995, IoU.mountain: 0.6074, IoU.plant: 0.5712, IoU.curtain: 0.7713, IoU.chair: 0.6754, IoU.car: 0.8752, IoU.water: 0.6256, IoU.painting: 0.7813, IoU.sofa: 0.8219, IoU.shelf: 0.5317, IoU.house: 0.5022, IoU.sea: 0.6754, IoU.mirror: 0.7705, IoU.rug: 0.6891, IoU.field: 0.3379, IoU.armchair: 0.6101, IoU.seat: 0.6583, IoU.fence: 0.4948, IoU.desk: 0.5816, IoU.rock: 0.5454, IoU.wardrobe: 0.5229, IoU.lamp: 0.7448, IoU.bathtub: 0.8439, IoU.railing: 0.4044, IoU.cushion: 0.7009, IoU.base: 0.4262, IoU.box: 0.3717, IoU.column: 0.5513, IoU.signboard: 0.4215, IoU.chest of drawers: 0.4364, IoU.counter: 0.4221, IoU.sand: 0.5265, IoU.sink: 0.7739, IoU.skyscraper: 0.4919, IoU.fireplace: 0.7498, IoU.refrigerator: 0.7988, IoU.grandstand: 0.4915, IoU.path: 0.2870, IoU.stairs: 0.2296, IoU.runway: 0.7425, IoU.case: 0.6055, IoU.pool table: 0.9478, IoU.pillow: 0.7061, IoU.screen door: 0.7703, IoU.stairway: 0.3865, IoU.river: 0.2099, IoU.bridge: 0.7471, IoU.bookcase: 0.4761, IoU.blind: 0.4545, IoU.coffee table: 0.6736, IoU.toilet: 0.8978, IoU.flower: 0.4662, IoU.book: 0.5754, IoU.hill: 0.1166, IoU.bench: 0.5602, IoU.countertop: 0.6230, IoU.stove: 0.8693, IoU.palm: 0.5587, IoU.kitchen island: 0.3978, IoU.computer: 0.8024, IoU.swivel chair: 0.5225, IoU.boat: 0.6939, IoU.bar: 0.5409, IoU.arcade machine: 0.7846, IoU.hovel: 0.4404, IoU.bus: 0.9330, IoU.towel: 0.7430, IoU.light: 0.6080, IoU.truck: 0.4610, IoU.tower: 0.1255, IoU.chandelier: 0.7040, IoU.awning: 0.4840, IoU.streetlight: 0.3562, IoU.booth: 0.5480, IoU.television receiver: 0.8222, IoU.airplane: 0.8204, IoU.dirt track: 0.0866, IoU.apparel: 0.4473, IoU.pole: 0.2558, IoU.land: 0.0401, IoU.bannister: 0.1645, IoU.escalator: 0.5829, IoU.ottoman: 0.4937, IoU.bottle: 0.4403, IoU.buffet: 0.5104, IoU.poster: 0.3819, IoU.stage: 0.2505, IoU.van: 0.4422, IoU.ship: 0.8056, IoU.fountain: 0.2764, IoU.conveyer belt: 0.8003, IoU.canopy: 0.5643, IoU.washer: 0.8164, IoU.plaything: 0.3726, IoU.swimming pool: 0.6333, IoU.stool: 0.4996, IoU.barrel: 0.5581, IoU.basket: 0.4377, IoU.waterfall: 0.6295, IoU.tent: 0.9070, IoU.bag: 0.1945, IoU.minibike: 0.7596, IoU.cradle: 0.8595, IoU.oven: 0.4902, IoU.ball: 0.3667, IoU.food: 0.5662, IoU.step: 0.1201, IoU.tank: 0.5896, IoU.trade name: 0.2782, IoU.microwave: 0.8713, IoU.pot: 0.5888, IoU.animal: 0.5922, IoU.bicycle: 0.6023, IoU.lake: 0.4083, IoU.dishwasher: 0.7258, IoU.screen: 0.5697, IoU.blanket: 0.3445, IoU.sculpture: 0.7259, IoU.hood: 0.6183, IoU.sconce: 0.5384, IoU.vase: 0.4907, IoU.traffic light: 0.4316, IoU.tray: 0.1592, IoU.ashcan: 0.4951, IoU.fan: 0.6580, IoU.pier: 0.3313, IoU.crt screen: 0.1701, IoU.plate: 0.5966, IoU.monitor: 0.6770, IoU.bulletin board: 0.4719, IoU.shower: 0.0213, IoU.radiator: 0.6415, IoU.glass: 0.1928, IoU.clock: 0.4146, IoU.flag: 0.7181, Acc.wall: 0.9054, Acc.building: 0.9385, Acc.sky: 0.9794, Acc.floor: 0.9128, Acc.tree: 0.8883, Acc.ceiling: 0.9421, Acc.road: 0.9207, Acc.bed : 0.9624, Acc.windowpane: 0.8122, Acc.grass: 0.7989, Acc.cabinet: 0.7530, Acc.sidewalk: 0.8446, Acc.person: 0.9345, Acc.earth: 0.5258, Acc.door: 0.7796, Acc.table: 0.8263, Acc.mountain: 0.7291, Acc.plant: 0.6816, Acc.curtain: 0.8568, Acc.chair: 0.7733, Acc.car: 0.9379, Acc.water: 0.7535, Acc.painting: 0.9104, Acc.sofa: 0.9037, Acc.shelf: 0.7414, Acc.house: 0.6014, Acc.sea: 0.8552, Acc.mirror: 0.8235, Acc.rug: 0.8358, Acc.field: 0.6485, Acc.armchair: 0.7895, Acc.seat: 0.8836, Acc.fence: 0.6614, Acc.desk: 0.7738, Acc.rock: 0.7905, Acc.wardrobe: 0.6841, Acc.lamp: 0.8630, Acc.bathtub: 0.8684, Acc.railing: 0.5819, Acc.cushion: 0.7904, Acc.base: 0.6208, Acc.box: 0.4925, Acc.column: 0.6645, Acc.signboard: 0.5897, Acc.chest of drawers: 0.7135, Acc.counter: 0.5103, Acc.sand: 0.7493, Acc.sink: 0.8313, Acc.skyscraper: 0.6037, Acc.fireplace: 0.9466, Acc.refrigerator: 0.8995, Acc.grandstand: 0.8482, Acc.path: 0.4688, Acc.stairs: 0.3016, Acc.runway: 0.9617, Acc.case: 0.8116, Acc.pool table: 0.9750, Acc.pillow: 0.8508, Acc.screen door: 0.7924, Acc.stairway: 0.5378, Acc.river: 0.3933, Acc.bridge: 0.8902, Acc.bookcase: 0.5500, Acc.blind: 0.4933, Acc.coffee table: 0.8757, Acc.toilet: 0.9319, Acc.flower: 0.6069, Acc.book: 0.7856, Acc.hill: 0.2176, Acc.bench: 0.6530, Acc.countertop: 0.8522, Acc.stove: 0.9266, Acc.palm: 0.8583, Acc.kitchen island: 0.6539, Acc.computer: 0.9268, Acc.swivel chair: 0.7579, Acc.boat: 0.8603, Acc.bar: 0.7441, Acc.arcade machine: 0.8382, Acc.hovel: 0.4869, Acc.bus: 0.9554, Acc.towel: 0.8250, Acc.light: 0.6861, Acc.truck: 0.5916, Acc.tower: 0.1741, Acc.chandelier: 0.8259, Acc.awning: 0.6461, Acc.streetlight: 0.5267, Acc.booth: 0.6101, Acc.television receiver: 0.8585, Acc.airplane: 0.8814, Acc.dirt track: 0.3340, Acc.apparel: 0.6207, Acc.pole: 0.3476, Acc.land: 0.0639, Acc.bannister: 0.2406, Acc.escalator: 0.7952, Acc.ottoman: 0.6863, Acc.bottle: 0.5702, Acc.buffet: 0.6624, Acc.poster: 0.5198, Acc.stage: 0.4126, Acc.van: 0.6025, Acc.ship: 0.8329, Acc.fountain: 0.2814, Acc.conveyer belt: 0.9293, Acc.canopy: 0.7444, Acc.washer: 0.8435, Acc.plaything: 0.5179, Acc.swimming pool: 0.8910, Acc.stool: 0.6839, Acc.barrel: 0.6472, Acc.basket: 0.5723, Acc.waterfall: 0.8412, Acc.tent: 0.9870, Acc.bag: 0.2301, Acc.minibike: 0.8654, Acc.cradle: 0.9686, Acc.oven: 0.6101, Acc.ball: 0.3807, Acc.food: 0.6894, Acc.step: 0.1455, Acc.tank: 0.6419, Acc.trade name: 0.3142, Acc.microwave: 0.9629, Acc.pot: 0.7026, Acc.animal: 0.6050, Acc.bicycle: 0.7737, Acc.lake: 0.4897, Acc.dishwasher: 0.8445, Acc.screen: 0.8302, Acc.blanket: 0.4043, Acc.sculpture: 0.8913, Acc.hood: 0.7294, Acc.sconce: 0.5954, Acc.vase: 0.6446, Acc.traffic light: 0.5903, Acc.tray: 0.2010, Acc.ashcan: 0.6334, Acc.fan: 0.7714, Acc.pier: 0.4757, Acc.crt screen: 0.2492, Acc.plate: 0.7785, Acc.monitor: 0.8458, Acc.bulletin board: 0.5487, Acc.shower: 0.0229, Acc.radiator: 0.7485, Acc.glass: 0.2076, Acc.clock: 0.4662, Acc.flag: 0.7722 +2024-06-19 01:48:03,420 - mmseg - INFO - Iter [64050/80000] lr: 7.976e-06, eta: 6:33:45, time: 3.680, data_time: 2.356, memory: 70498, decode.loss_ce: 0.1627, decode.acc_seg: 92.9796, aux.loss_ce: 0.0705, aux.acc_seg: 92.4352, loss: 0.2332 +2024-06-19 01:49:09,756 - mmseg - INFO - Iter [64100/80000] lr: 7.950e-06, eta: 6:32:29, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1715, decode.acc_seg: 92.5501, aux.loss_ce: 0.0727, aux.acc_seg: 92.0856, loss: 0.2442 +2024-06-19 01:50:16,336 - mmseg - INFO - Iter [64150/80000] lr: 7.925e-06, eta: 6:31:14, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1629, decode.acc_seg: 92.9572, aux.loss_ce: 0.0697, aux.acc_seg: 92.4702, loss: 0.2326 +2024-06-19 01:51:22,735 - mmseg - INFO - Iter [64200/80000] lr: 7.900e-06, eta: 6:29:58, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1548, decode.acc_seg: 93.2245, aux.loss_ce: 0.0662, aux.acc_seg: 92.8210, loss: 0.2210 +2024-06-19 01:52:29,115 - mmseg - INFO - Iter [64250/80000] lr: 7.876e-06, eta: 6:28:42, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1594, decode.acc_seg: 93.0374, aux.loss_ce: 0.0680, aux.acc_seg: 92.6459, loss: 0.2274 +2024-06-19 01:53:35,637 - mmseg - INFO - Iter [64300/80000] lr: 7.851e-06, eta: 6:27:26, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1581, decode.acc_seg: 93.1444, aux.loss_ce: 0.0673, aux.acc_seg: 92.7013, loss: 0.2254 +2024-06-19 01:54:42,038 - mmseg - INFO - Iter [64350/80000] lr: 7.826e-06, eta: 6:26:10, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1644, decode.acc_seg: 92.8378, aux.loss_ce: 0.0707, aux.acc_seg: 92.3306, loss: 0.2351 +2024-06-19 01:55:48,548 - mmseg - INFO - Iter [64400/80000] lr: 7.801e-06, eta: 6:24:54, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1558, decode.acc_seg: 93.1999, aux.loss_ce: 0.0667, aux.acc_seg: 92.8283, loss: 0.2225 +2024-06-19 01:56:57,430 - mmseg - INFO - Iter [64450/80000] lr: 7.776e-06, eta: 6:23:39, time: 1.378, data_time: 0.062, memory: 70498, decode.loss_ce: 0.1560, decode.acc_seg: 93.2498, aux.loss_ce: 0.0665, aux.acc_seg: 92.8369, loss: 0.2225 +2024-06-19 01:58:03,771 - mmseg - INFO - Iter [64500/80000] lr: 7.750e-06, eta: 6:22:23, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1541, decode.acc_seg: 93.2995, aux.loss_ce: 0.0656, aux.acc_seg: 92.9115, loss: 0.2196 +2024-06-19 01:59:10,219 - mmseg - INFO - Iter [64550/80000] lr: 7.725e-06, eta: 6:21:07, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1600, decode.acc_seg: 93.1579, aux.loss_ce: 0.0686, aux.acc_seg: 92.7465, loss: 0.2286 +2024-06-19 02:00:16,636 - mmseg - INFO - Iter [64600/80000] lr: 7.701e-06, eta: 6:19:51, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1467, decode.acc_seg: 93.6852, aux.loss_ce: 0.0632, aux.acc_seg: 93.1780, loss: 0.2099 +2024-06-19 02:01:22,682 - mmseg - INFO - Iter [64650/80000] lr: 7.675e-06, eta: 6:18:35, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1637, decode.acc_seg: 92.9344, aux.loss_ce: 0.0697, aux.acc_seg: 92.5582, loss: 0.2334 +2024-06-19 02:02:28,698 - mmseg - INFO - Iter [64700/80000] lr: 7.651e-06, eta: 6:17:20, time: 1.320, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1588, decode.acc_seg: 93.2068, aux.loss_ce: 0.0678, aux.acc_seg: 92.7964, loss: 0.2266 +2024-06-19 02:03:34,766 - mmseg - INFO - Iter [64750/80000] lr: 7.626e-06, eta: 6:16:04, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1589, decode.acc_seg: 93.0937, aux.loss_ce: 0.0680, aux.acc_seg: 92.6805, loss: 0.2269 +2024-06-19 02:04:41,035 - mmseg - INFO - Iter [64800/80000] lr: 7.601e-06, eta: 6:14:48, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1554, decode.acc_seg: 93.2710, aux.loss_ce: 0.0667, aux.acc_seg: 92.8122, loss: 0.2221 +2024-06-19 02:05:47,421 - mmseg - INFO - Iter [64850/80000] lr: 7.576e-06, eta: 6:13:32, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1623, decode.acc_seg: 93.0957, aux.loss_ce: 0.0691, aux.acc_seg: 92.6095, loss: 0.2314 +2024-06-19 02:06:53,964 - mmseg - INFO - Iter [64900/80000] lr: 7.551e-06, eta: 6:12:17, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1547, decode.acc_seg: 93.4238, aux.loss_ce: 0.0668, aux.acc_seg: 92.9137, loss: 0.2215 +2024-06-19 02:08:00,657 - mmseg - INFO - Iter [64950/80000] lr: 7.525e-06, eta: 6:11:01, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1575, decode.acc_seg: 93.2124, aux.loss_ce: 0.0669, aux.acc_seg: 92.8165, loss: 0.2244 +2024-06-19 02:09:06,877 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 02:09:06,877 - mmseg - INFO - Iter [65000/80000] lr: 7.500e-06, eta: 6:09:45, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1542, decode.acc_seg: 93.3100, aux.loss_ce: 0.0666, aux.acc_seg: 92.8581, loss: 0.2208 +2024-06-19 02:10:44,942 - mmseg - INFO - per class results: +2024-06-19 02:10:44,948 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.19 | 90.04 | +| building | 85.71 | 93.46 | +| sky | 95.03 | 97.4 | +| floor | 85.34 | 91.75 | +| tree | 77.94 | 91.33 | +| ceiling | 87.27 | 93.39 | +| road | 86.14 | 88.98 | +| bed | 92.44 | 96.85 | +| windowpane | 66.2 | 81.73 | +| grass | 66.8 | 80.89 | +| cabinet | 65.09 | 74.51 | +| sidewalk | 71.38 | 90.81 | +| person | 85.62 | 94.34 | +| earth | 39.66 | 52.95 | +| door | 59.78 | 75.0 | +| table | 69.76 | 81.52 | +| mountain | 61.03 | 73.35 | +| plant | 56.03 | 65.93 | +| curtain | 78.58 | 86.8 | +| chair | 67.48 | 79.9 | +| car | 87.4 | 94.48 | +| water | 64.68 | 77.67 | +| painting | 76.14 | 92.05 | +| sofa | 82.98 | 91.2 | +| shelf | 51.52 | 68.39 | +| house | 57.72 | 71.72 | +| sea | 68.36 | 82.41 | +| mirror | 77.39 | 82.73 | +| rug | 69.14 | 82.26 | +| field | 34.75 | 65.82 | +| armchair | 60.91 | 76.08 | +| seat | 65.5 | 88.03 | +| fence | 50.95 | 63.06 | +| desk | 57.01 | 75.95 | +| rock | 56.51 | 82.94 | +| wardrobe | 53.89 | 75.89 | +| lamp | 74.24 | 84.62 | +| bathtub | 84.85 | 86.97 | +| railing | 40.85 | 57.87 | +| cushion | 70.66 | 84.68 | +| base | 42.8 | 59.31 | +| box | 38.69 | 51.44 | +| column | 56.35 | 70.9 | +| signboard | 42.01 | 56.67 | +| chest of drawers | 47.05 | 69.68 | +| counter | 46.18 | 55.24 | +| sand | 49.96 | 76.56 | +| sink | 75.98 | 83.15 | +| skyscraper | 48.99 | 61.38 | +| fireplace | 74.5 | 94.75 | +| refrigerator | 78.18 | 91.07 | +| grandstand | 48.36 | 82.59 | +| path | 28.11 | 38.38 | +| stairs | 22.3 | 29.18 | +| runway | 74.36 | 97.96 | +| case | 61.95 | 79.74 | +| pool table | 95.2 | 98.11 | +| pillow | 70.09 | 82.32 | +| screen door | 76.61 | 78.96 | +| stairway | 38.42 | 55.41 | +| river | 20.87 | 40.33 | +| bridge | 74.72 | 85.91 | +| bookcase | 46.5 | 62.99 | +| blind | 45.42 | 50.07 | +| coffee table | 66.47 | 87.66 | +| toilet | 89.63 | 93.45 | +| flower | 43.0 | 53.29 | +| book | 57.4 | 79.35 | +| hill | 6.02 | 7.87 | +| bench | 55.26 | 63.75 | +| countertop | 62.25 | 81.53 | +| stove | 88.48 | 94.28 | +| palm | 58.11 | 81.23 | +| kitchen island | 45.71 | 83.65 | +| computer | 79.02 | 92.56 | +| swivel chair | 52.32 | 72.79 | +| boat | 72.28 | 88.07 | +| bar | 54.88 | 74.16 | +| arcade machine | 78.56 | 82.56 | +| hovel | 43.17 | 49.46 | +| bus | 93.04 | 96.26 | +| towel | 75.07 | 83.89 | +| light | 60.62 | 69.5 | +| truck | 45.0 | 60.85 | +| tower | 8.49 | 12.02 | +| chandelier | 70.82 | 84.04 | +| awning | 41.63 | 52.43 | +| streetlight | 34.19 | 45.16 | +| booth | 44.34 | 71.82 | +| television receiver | 81.68 | 89.16 | +| airplane | 82.35 | 89.66 | +| dirt track | 6.28 | 23.86 | +| apparel | 47.37 | 68.88 | +| pole | 28.62 | 39.08 | +| land | 4.13 | 6.4 | +| bannister | 18.37 | 23.65 | +| escalator | 58.76 | 79.37 | +| ottoman | 47.55 | 65.48 | +| bottle | 44.84 | 61.17 | +| buffet | 50.36 | 66.84 | +| poster | 34.98 | 47.5 | +| stage | 24.38 | 41.82 | +| van | 45.76 | 58.74 | +| ship | 76.24 | 78.79 | +| fountain | 29.3 | 29.97 | +| conveyer belt | 77.83 | 93.43 | +| canopy | 51.03 | 78.54 | +| washer | 81.91 | 84.91 | +| plaything | 32.06 | 43.95 | +| swimming pool | 64.06 | 91.49 | +| stool | 49.36 | 69.66 | +| barrel | 57.6 | 66.46 | +| basket | 41.66 | 59.78 | +| waterfall | 63.13 | 86.36 | +| tent | 89.96 | 98.7 | +| bag | 21.51 | 26.21 | +| minibike | 76.09 | 88.54 | +| cradle | 86.2 | 97.23 | +| oven | 52.45 | 60.75 | +| ball | 51.25 | 55.91 | +| food | 64.64 | 76.67 | +| step | 12.61 | 15.27 | +| tank | 59.29 | 66.37 | +| trade name | 31.76 | 37.2 | +| microwave | 86.6 | 95.94 | +| pot | 58.48 | 68.84 | +| animal | 60.57 | 61.91 | +| bicycle | 59.71 | 79.73 | +| lake | 47.34 | 58.45 | +| dishwasher | 73.31 | 84.78 | +| screen | 58.16 | 87.26 | +| blanket | 31.04 | 34.96 | +| sculpture | 73.17 | 89.08 | +| hood | 62.46 | 75.7 | +| sconce | 57.9 | 68.02 | +| vase | 46.93 | 62.56 | +| traffic light | 42.37 | 63.34 | +| tray | 15.56 | 20.74 | +| ashcan | 47.44 | 67.6 | +| fan | 66.69 | 79.62 | +| pier | 35.04 | 43.67 | +| crt screen | 19.79 | 27.53 | +| plate | 58.36 | 74.35 | +| monitor | 67.74 | 83.94 | +| bulletin board | 53.24 | 59.19 | +| shower | 4.72 | 4.79 | +| radiator | 64.4 | 76.62 | +| glass | 18.95 | 20.2 | +| clock | 41.85 | 47.73 | +| flag | 71.25 | 80.43 | ++---------------------+-------+-------+ +2024-06-19 02:10:44,948 - mmseg - INFO - Summary: +2024-06-19 02:10:44,948 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 86.2 | 57.08 | 69.76 | ++------+-------+-------+ +2024-06-19 02:10:44,949 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 02:10:44,950 - mmseg - INFO - Iter(val) [250] aAcc: 0.8620, mIoU: 0.5708, mAcc: 0.6976, IoU.wall: 0.8219, IoU.building: 0.8571, IoU.sky: 0.9503, IoU.floor: 0.8534, IoU.tree: 0.7794, IoU.ceiling: 0.8727, IoU.road: 0.8614, IoU.bed : 0.9244, IoU.windowpane: 0.6620, IoU.grass: 0.6680, IoU.cabinet: 0.6509, IoU.sidewalk: 0.7138, IoU.person: 0.8562, IoU.earth: 0.3966, IoU.door: 0.5978, IoU.table: 0.6976, IoU.mountain: 0.6103, IoU.plant: 0.5603, IoU.curtain: 0.7858, IoU.chair: 0.6748, IoU.car: 0.8740, IoU.water: 0.6468, IoU.painting: 0.7614, IoU.sofa: 0.8298, IoU.shelf: 0.5152, IoU.house: 0.5772, IoU.sea: 0.6836, IoU.mirror: 0.7739, IoU.rug: 0.6914, IoU.field: 0.3475, IoU.armchair: 0.6091, IoU.seat: 0.6550, IoU.fence: 0.5095, IoU.desk: 0.5701, IoU.rock: 0.5651, IoU.wardrobe: 0.5389, IoU.lamp: 0.7424, IoU.bathtub: 0.8485, IoU.railing: 0.4085, IoU.cushion: 0.7066, IoU.base: 0.4280, IoU.box: 0.3869, IoU.column: 0.5635, IoU.signboard: 0.4201, IoU.chest of drawers: 0.4705, IoU.counter: 0.4618, IoU.sand: 0.4996, IoU.sink: 0.7598, IoU.skyscraper: 0.4899, IoU.fireplace: 0.7450, IoU.refrigerator: 0.7818, IoU.grandstand: 0.4836, IoU.path: 0.2811, IoU.stairs: 0.2230, IoU.runway: 0.7436, IoU.case: 0.6195, IoU.pool table: 0.9520, IoU.pillow: 0.7009, IoU.screen door: 0.7661, IoU.stairway: 0.3842, IoU.river: 0.2087, IoU.bridge: 0.7472, IoU.bookcase: 0.4650, IoU.blind: 0.4542, IoU.coffee table: 0.6647, IoU.toilet: 0.8963, IoU.flower: 0.4300, IoU.book: 0.5740, IoU.hill: 0.0602, IoU.bench: 0.5526, IoU.countertop: 0.6225, IoU.stove: 0.8848, IoU.palm: 0.5811, IoU.kitchen island: 0.4571, IoU.computer: 0.7902, IoU.swivel chair: 0.5232, IoU.boat: 0.7228, IoU.bar: 0.5488, IoU.arcade machine: 0.7856, IoU.hovel: 0.4317, IoU.bus: 0.9304, IoU.towel: 0.7507, IoU.light: 0.6062, IoU.truck: 0.4500, IoU.tower: 0.0849, IoU.chandelier: 0.7082, IoU.awning: 0.4163, IoU.streetlight: 0.3419, IoU.booth: 0.4434, IoU.television receiver: 0.8168, IoU.airplane: 0.8235, IoU.dirt track: 0.0628, IoU.apparel: 0.4737, IoU.pole: 0.2862, IoU.land: 0.0413, IoU.bannister: 0.1837, IoU.escalator: 0.5876, IoU.ottoman: 0.4755, IoU.bottle: 0.4484, IoU.buffet: 0.5036, IoU.poster: 0.3498, IoU.stage: 0.2438, IoU.van: 0.4576, IoU.ship: 0.7624, IoU.fountain: 0.2930, IoU.conveyer belt: 0.7783, IoU.canopy: 0.5103, IoU.washer: 0.8191, IoU.plaything: 0.3206, IoU.swimming pool: 0.6406, IoU.stool: 0.4936, IoU.barrel: 0.5760, IoU.basket: 0.4166, IoU.waterfall: 0.6313, IoU.tent: 0.8996, IoU.bag: 0.2151, IoU.minibike: 0.7609, IoU.cradle: 0.8620, IoU.oven: 0.5245, IoU.ball: 0.5125, IoU.food: 0.6464, IoU.step: 0.1261, IoU.tank: 0.5929, IoU.trade name: 0.3176, IoU.microwave: 0.8660, IoU.pot: 0.5848, IoU.animal: 0.6057, IoU.bicycle: 0.5971, IoU.lake: 0.4734, IoU.dishwasher: 0.7331, IoU.screen: 0.5816, IoU.blanket: 0.3104, IoU.sculpture: 0.7317, IoU.hood: 0.6246, IoU.sconce: 0.5790, IoU.vase: 0.4693, IoU.traffic light: 0.4237, IoU.tray: 0.1556, IoU.ashcan: 0.4744, IoU.fan: 0.6669, IoU.pier: 0.3504, IoU.crt screen: 0.1979, IoU.plate: 0.5836, IoU.monitor: 0.6774, IoU.bulletin board: 0.5324, IoU.shower: 0.0472, IoU.radiator: 0.6440, IoU.glass: 0.1895, IoU.clock: 0.4185, IoU.flag: 0.7125, Acc.wall: 0.9004, Acc.building: 0.9346, Acc.sky: 0.9740, Acc.floor: 0.9175, Acc.tree: 0.9133, Acc.ceiling: 0.9339, Acc.road: 0.8898, Acc.bed : 0.9685, Acc.windowpane: 0.8173, Acc.grass: 0.8089, Acc.cabinet: 0.7451, Acc.sidewalk: 0.9081, Acc.person: 0.9434, Acc.earth: 0.5295, Acc.door: 0.7500, Acc.table: 0.8152, Acc.mountain: 0.7335, Acc.plant: 0.6593, Acc.curtain: 0.8680, Acc.chair: 0.7990, Acc.car: 0.9448, Acc.water: 0.7767, Acc.painting: 0.9205, Acc.sofa: 0.9120, Acc.shelf: 0.6839, Acc.house: 0.7172, Acc.sea: 0.8241, Acc.mirror: 0.8273, Acc.rug: 0.8226, Acc.field: 0.6582, Acc.armchair: 0.7608, Acc.seat: 0.8803, Acc.fence: 0.6306, Acc.desk: 0.7595, Acc.rock: 0.8294, Acc.wardrobe: 0.7589, Acc.lamp: 0.8462, Acc.bathtub: 0.8697, Acc.railing: 0.5787, Acc.cushion: 0.8468, Acc.base: 0.5931, Acc.box: 0.5144, Acc.column: 0.7090, Acc.signboard: 0.5667, Acc.chest of drawers: 0.6968, Acc.counter: 0.5524, Acc.sand: 0.7656, Acc.sink: 0.8315, Acc.skyscraper: 0.6138, Acc.fireplace: 0.9475, Acc.refrigerator: 0.9107, Acc.grandstand: 0.8259, Acc.path: 0.3838, Acc.stairs: 0.2918, Acc.runway: 0.9796, Acc.case: 0.7974, Acc.pool table: 0.9811, Acc.pillow: 0.8232, Acc.screen door: 0.7896, Acc.stairway: 0.5541, Acc.river: 0.4033, Acc.bridge: 0.8591, Acc.bookcase: 0.6299, Acc.blind: 0.5007, Acc.coffee table: 0.8766, Acc.toilet: 0.9345, Acc.flower: 0.5329, Acc.book: 0.7935, Acc.hill: 0.0787, Acc.bench: 0.6375, Acc.countertop: 0.8153, Acc.stove: 0.9428, Acc.palm: 0.8123, Acc.kitchen island: 0.8365, Acc.computer: 0.9256, Acc.swivel chair: 0.7279, Acc.boat: 0.8807, Acc.bar: 0.7416, Acc.arcade machine: 0.8256, Acc.hovel: 0.4946, Acc.bus: 0.9626, Acc.towel: 0.8389, Acc.light: 0.6950, Acc.truck: 0.6085, Acc.tower: 0.1202, Acc.chandelier: 0.8404, Acc.awning: 0.5243, Acc.streetlight: 0.4516, Acc.booth: 0.7182, Acc.television receiver: 0.8916, Acc.airplane: 0.8966, Acc.dirt track: 0.2386, Acc.apparel: 0.6888, Acc.pole: 0.3908, Acc.land: 0.0640, Acc.bannister: 0.2365, Acc.escalator: 0.7937, Acc.ottoman: 0.6548, Acc.bottle: 0.6117, Acc.buffet: 0.6684, Acc.poster: 0.4750, Acc.stage: 0.4182, Acc.van: 0.5874, Acc.ship: 0.7879, Acc.fountain: 0.2997, Acc.conveyer belt: 0.9343, Acc.canopy: 0.7854, Acc.washer: 0.8491, Acc.plaything: 0.4395, Acc.swimming pool: 0.9149, Acc.stool: 0.6966, Acc.barrel: 0.6646, Acc.basket: 0.5978, Acc.waterfall: 0.8636, Acc.tent: 0.9870, Acc.bag: 0.2621, Acc.minibike: 0.8854, Acc.cradle: 0.9723, Acc.oven: 0.6075, Acc.ball: 0.5591, Acc.food: 0.7667, Acc.step: 0.1527, Acc.tank: 0.6637, Acc.trade name: 0.3720, Acc.microwave: 0.9594, Acc.pot: 0.6884, Acc.animal: 0.6191, Acc.bicycle: 0.7973, Acc.lake: 0.5845, Acc.dishwasher: 0.8478, Acc.screen: 0.8726, Acc.blanket: 0.3496, Acc.sculpture: 0.8908, Acc.hood: 0.7570, Acc.sconce: 0.6802, Acc.vase: 0.6256, Acc.traffic light: 0.6334, Acc.tray: 0.2074, Acc.ashcan: 0.6760, Acc.fan: 0.7962, Acc.pier: 0.4367, Acc.crt screen: 0.2753, Acc.plate: 0.7435, Acc.monitor: 0.8394, Acc.bulletin board: 0.5919, Acc.shower: 0.0479, Acc.radiator: 0.7662, Acc.glass: 0.2020, Acc.clock: 0.4773, Acc.flag: 0.8043 +2024-06-19 02:11:52,004 - mmseg - INFO - Iter [65050/80000] lr: 7.475e-06, eta: 6:08:52, time: 3.303, data_time: 1.978, memory: 70498, decode.loss_ce: 0.1586, decode.acc_seg: 92.8747, aux.loss_ce: 0.0680, aux.acc_seg: 92.4541, loss: 0.2266 +2024-06-19 02:12:58,583 - mmseg - INFO - Iter [65100/80000] lr: 7.451e-06, eta: 6:07:36, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1610, decode.acc_seg: 92.9709, aux.loss_ce: 0.0685, aux.acc_seg: 92.5580, loss: 0.2295 +2024-06-19 02:14:04,831 - mmseg - INFO - Iter [65150/80000] lr: 7.426e-06, eta: 6:06:21, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1519, decode.acc_seg: 93.3213, aux.loss_ce: 0.0653, aux.acc_seg: 92.8488, loss: 0.2172 +2024-06-19 02:15:11,227 - mmseg - INFO - Iter [65200/80000] lr: 7.401e-06, eta: 6:05:05, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1567, decode.acc_seg: 93.0611, aux.loss_ce: 0.0672, aux.acc_seg: 92.5714, loss: 0.2239 +2024-06-19 02:16:17,915 - mmseg - INFO - Iter [65250/80000] lr: 7.376e-06, eta: 6:03:49, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1583, decode.acc_seg: 93.0443, aux.loss_ce: 0.0678, aux.acc_seg: 92.6361, loss: 0.2261 +2024-06-19 02:17:24,336 - mmseg - INFO - Iter [65300/80000] lr: 7.351e-06, eta: 6:02:33, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1574, decode.acc_seg: 93.0620, aux.loss_ce: 0.0672, aux.acc_seg: 92.5798, loss: 0.2246 +2024-06-19 02:18:30,557 - mmseg - INFO - Iter [65350/80000] lr: 7.325e-06, eta: 6:01:18, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1594, decode.acc_seg: 92.9800, aux.loss_ce: 0.0689, aux.acc_seg: 92.4421, loss: 0.2283 +2024-06-19 02:19:37,024 - mmseg - INFO - Iter [65400/80000] lr: 7.300e-06, eta: 6:00:02, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1553, decode.acc_seg: 93.2865, aux.loss_ce: 0.0663, aux.acc_seg: 92.8764, loss: 0.2217 +2024-06-19 02:20:43,422 - mmseg - INFO - Iter [65450/80000] lr: 7.276e-06, eta: 5:58:46, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1555, decode.acc_seg: 93.2714, aux.loss_ce: 0.0665, aux.acc_seg: 92.8998, loss: 0.2220 +2024-06-19 02:21:49,743 - mmseg - INFO - Iter [65500/80000] lr: 7.251e-06, eta: 5:57:31, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1579, decode.acc_seg: 93.1302, aux.loss_ce: 0.0677, aux.acc_seg: 92.6763, loss: 0.2256 +2024-06-19 02:22:56,023 - mmseg - INFO - Iter [65550/80000] lr: 7.226e-06, eta: 5:56:15, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1533, decode.acc_seg: 93.2398, aux.loss_ce: 0.0659, aux.acc_seg: 92.8500, loss: 0.2192 +2024-06-19 02:24:02,663 - mmseg - INFO - Iter [65600/80000] lr: 7.201e-06, eta: 5:55:00, time: 1.333, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1633, decode.acc_seg: 92.9905, aux.loss_ce: 0.0695, aux.acc_seg: 92.5187, loss: 0.2327 +2024-06-19 02:25:09,040 - mmseg - INFO - Iter [65650/80000] lr: 7.176e-06, eta: 5:53:44, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1638, decode.acc_seg: 93.0181, aux.loss_ce: 0.0700, aux.acc_seg: 92.5594, loss: 0.2338 +2024-06-19 02:26:18,652 - mmseg - INFO - Iter [65700/80000] lr: 7.151e-06, eta: 5:52:29, time: 1.392, data_time: 0.075, memory: 70498, decode.loss_ce: 0.1609, decode.acc_seg: 92.9833, aux.loss_ce: 0.0691, aux.acc_seg: 92.5448, loss: 0.2300 +2024-06-19 02:27:24,875 - mmseg - INFO - Iter [65750/80000] lr: 7.125e-06, eta: 5:51:13, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1466, decode.acc_seg: 93.4279, aux.loss_ce: 0.0634, aux.acc_seg: 92.9405, loss: 0.2099 +2024-06-19 02:28:31,543 - mmseg - INFO - Iter [65800/80000] lr: 7.100e-06, eta: 5:49:58, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1547, decode.acc_seg: 93.2710, aux.loss_ce: 0.0665, aux.acc_seg: 92.7759, loss: 0.2213 +2024-06-19 02:29:37,844 - mmseg - INFO - Iter [65850/80000] lr: 7.075e-06, eta: 5:48:42, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1523, decode.acc_seg: 93.2949, aux.loss_ce: 0.0656, aux.acc_seg: 92.8598, loss: 0.2178 +2024-06-19 02:30:44,286 - mmseg - INFO - Iter [65900/80000] lr: 7.051e-06, eta: 5:47:27, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1517, decode.acc_seg: 93.3428, aux.loss_ce: 0.0648, aux.acc_seg: 92.8689, loss: 0.2165 +2024-06-19 02:31:50,520 - mmseg - INFO - Iter [65950/80000] lr: 7.026e-06, eta: 5:46:11, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1519, decode.acc_seg: 93.2848, aux.loss_ce: 0.0654, aux.acc_seg: 92.8178, loss: 0.2173 +2024-06-19 02:32:56,882 - mmseg - INFO - Saving checkpoint at 66000 iterations +2024-06-19 02:34:45,315 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 02:34:45,316 - mmseg - INFO - Iter [66000/80000] lr: 7.001e-06, eta: 5:45:19, time: 3.496, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1592, decode.acc_seg: 93.1266, aux.loss_ce: 0.0678, aux.acc_seg: 92.7226, loss: 0.2271 +2024-06-19 02:36:23,255 - mmseg - INFO - per class results: +2024-06-19 02:36:23,261 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.11 | 89.34 | +| building | 85.77 | 93.83 | +| sky | 94.94 | 97.52 | +| floor | 84.78 | 91.72 | +| tree | 77.63 | 89.96 | +| ceiling | 86.88 | 94.18 | +| road | 86.69 | 92.01 | +| bed | 92.43 | 97.02 | +| windowpane | 65.8 | 81.84 | +| grass | 67.22 | 78.45 | +| cabinet | 64.92 | 76.74 | +| sidewalk | 72.5 | 85.39 | +| person | 85.53 | 94.75 | +| earth | 40.11 | 53.28 | +| door | 59.42 | 74.23 | +| table | 69.63 | 83.31 | +| mountain | 60.85 | 74.08 | +| plant | 56.15 | 65.1 | +| curtain | 77.43 | 87.63 | +| chair | 67.4 | 78.76 | +| car | 87.31 | 94.64 | +| water | 64.95 | 78.64 | +| painting | 76.85 | 91.63 | +| sofa | 81.44 | 92.76 | +| shelf | 54.46 | 74.08 | +| house | 56.03 | 70.27 | +| sea | 69.82 | 83.6 | +| mirror | 77.28 | 83.72 | +| rug | 67.23 | 79.57 | +| field | 33.08 | 66.32 | +| armchair | 58.92 | 72.63 | +| seat | 65.31 | 88.64 | +| fence | 50.14 | 62.73 | +| desk | 57.27 | 76.11 | +| rock | 55.47 | 80.07 | +| wardrobe | 54.57 | 76.65 | +| lamp | 74.37 | 85.86 | +| bathtub | 84.06 | 86.68 | +| railing | 39.13 | 58.12 | +| cushion | 69.2 | 79.15 | +| base | 41.62 | 57.29 | +| box | 38.62 | 51.02 | +| column | 56.65 | 68.04 | +| signboard | 41.81 | 56.88 | +| chest of drawers | 45.58 | 66.49 | +| counter | 45.37 | 59.83 | +| sand | 51.05 | 75.93 | +| sink | 76.28 | 83.95 | +| skyscraper | 50.16 | 61.49 | +| fireplace | 77.78 | 93.18 | +| refrigerator | 79.58 | 87.17 | +| grandstand | 48.03 | 82.2 | +| path | 25.83 | 39.87 | +| stairs | 24.74 | 32.46 | +| runway | 74.5 | 97.67 | +| case | 59.74 | 82.25 | +| pool table | 95.0 | 98.02 | +| pillow | 69.98 | 82.77 | +| screen door | 73.74 | 76.21 | +| stairway | 40.22 | 54.6 | +| river | 21.0 | 42.78 | +| bridge | 77.01 | 88.94 | +| bookcase | 48.14 | 56.11 | +| blind | 43.05 | 45.1 | +| coffee table | 65.87 | 88.2 | +| toilet | 90.08 | 94.15 | +| flower | 41.09 | 52.33 | +| book | 57.89 | 77.46 | +| hill | 8.08 | 12.31 | +| bench | 54.85 | 64.4 | +| countertop | 63.24 | 83.59 | +| stove | 88.72 | 94.99 | +| palm | 57.8 | 79.13 | +| kitchen island | 43.85 | 69.42 | +| computer | 80.13 | 92.49 | +| swivel chair | 51.44 | 77.09 | +| boat | 69.85 | 89.98 | +| bar | 52.52 | 71.22 | +| arcade machine | 76.82 | 83.6 | +| hovel | 43.69 | 49.31 | +| bus | 93.14 | 96.77 | +| towel | 73.97 | 80.47 | +| light | 61.1 | 70.75 | +| truck | 44.11 | 56.3 | +| tower | 10.71 | 14.45 | +| chandelier | 70.95 | 85.01 | +| awning | 36.46 | 45.47 | +| streetlight | 33.77 | 43.92 | +| booth | 46.91 | 66.17 | +| television receiver | 81.11 | 90.26 | +| airplane | 83.56 | 90.4 | +| dirt track | 5.67 | 30.72 | +| apparel | 45.52 | 60.58 | +| pole | 28.7 | 39.81 | +| land | 4.45 | 7.88 | +| bannister | 17.98 | 24.87 | +| escalator | 57.21 | 79.77 | +| ottoman | 46.28 | 63.59 | +| bottle | 44.21 | 58.56 | +| buffet | 47.96 | 59.18 | +| poster | 34.82 | 53.23 | +| stage | 26.07 | 48.04 | +| van | 45.05 | 57.3 | +| ship | 71.88 | 74.31 | +| fountain | 25.53 | 26.15 | +| conveyer belt | 76.75 | 93.42 | +| canopy | 56.27 | 79.06 | +| washer | 82.97 | 87.05 | +| plaything | 37.73 | 58.12 | +| swimming pool | 67.68 | 92.28 | +| stool | 52.69 | 66.14 | +| barrel | 55.28 | 64.76 | +| basket | 41.44 | 57.71 | +| waterfall | 61.14 | 86.7 | +| tent | 88.93 | 98.95 | +| bag | 19.11 | 23.05 | +| minibike | 75.27 | 89.1 | +| cradle | 83.44 | 97.85 | +| oven | 54.46 | 62.52 | +| ball | 52.51 | 60.38 | +| food | 57.55 | 69.64 | +| step | 12.1 | 15.12 | +| tank | 66.75 | 71.94 | +| trade name | 26.96 | 30.18 | +| microwave | 86.53 | 95.96 | +| pot | 58.18 | 66.38 | +| animal | 63.5 | 65.33 | +| bicycle | 60.25 | 77.74 | +| lake | 50.0 | 60.4 | +| dishwasher | 72.37 | 84.86 | +| screen | 59.47 | 91.52 | +| blanket | 29.25 | 32.44 | +| sculpture | 77.45 | 87.62 | +| hood | 62.67 | 74.56 | +| sconce | 57.62 | 67.2 | +| vase | 48.66 | 60.65 | +| traffic light | 41.04 | 62.97 | +| tray | 14.87 | 19.16 | +| ashcan | 49.06 | 61.89 | +| fan | 66.7 | 81.66 | +| pier | 34.03 | 47.27 | +| crt screen | 18.94 | 24.83 | +| plate | 58.57 | 74.78 | +| monitor | 67.2 | 82.22 | +| bulletin board | 48.71 | 50.69 | +| shower | 1.77 | 8.21 | +| radiator | 64.19 | 74.55 | +| glass | 19.05 | 20.65 | +| clock | 44.73 | 52.6 | +| flag | 71.42 | 78.42 | ++---------------------+-------+-------+ +2024-06-19 02:36:23,261 - mmseg - INFO - Summary: +2024-06-19 02:36:23,262 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.11 | 56.93 | 69.42 | ++-------+-------+-------+ +2024-06-19 02:36:23,262 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 02:36:23,263 - mmseg - INFO - Iter(val) [250] aAcc: 0.8611, mIoU: 0.5693, mAcc: 0.6942, IoU.wall: 0.8211, IoU.building: 0.8577, IoU.sky: 0.9494, IoU.floor: 0.8478, IoU.tree: 0.7763, IoU.ceiling: 0.8688, IoU.road: 0.8669, IoU.bed : 0.9243, IoU.windowpane: 0.6580, IoU.grass: 0.6722, IoU.cabinet: 0.6492, IoU.sidewalk: 0.7250, IoU.person: 0.8553, IoU.earth: 0.4011, IoU.door: 0.5942, IoU.table: 0.6963, IoU.mountain: 0.6085, IoU.plant: 0.5615, IoU.curtain: 0.7743, IoU.chair: 0.6740, IoU.car: 0.8731, IoU.water: 0.6495, IoU.painting: 0.7685, IoU.sofa: 0.8144, IoU.shelf: 0.5446, IoU.house: 0.5603, IoU.sea: 0.6982, IoU.mirror: 0.7728, IoU.rug: 0.6723, IoU.field: 0.3308, IoU.armchair: 0.5892, IoU.seat: 0.6531, IoU.fence: 0.5014, IoU.desk: 0.5727, IoU.rock: 0.5547, IoU.wardrobe: 0.5457, IoU.lamp: 0.7437, IoU.bathtub: 0.8406, IoU.railing: 0.3913, IoU.cushion: 0.6920, IoU.base: 0.4162, IoU.box: 0.3862, IoU.column: 0.5665, IoU.signboard: 0.4181, IoU.chest of drawers: 0.4558, IoU.counter: 0.4537, IoU.sand: 0.5105, IoU.sink: 0.7628, IoU.skyscraper: 0.5016, IoU.fireplace: 0.7778, IoU.refrigerator: 0.7958, IoU.grandstand: 0.4803, IoU.path: 0.2583, IoU.stairs: 0.2474, IoU.runway: 0.7450, IoU.case: 0.5974, IoU.pool table: 0.9500, IoU.pillow: 0.6998, IoU.screen door: 0.7374, IoU.stairway: 0.4022, IoU.river: 0.2100, IoU.bridge: 0.7701, IoU.bookcase: 0.4814, IoU.blind: 0.4305, IoU.coffee table: 0.6587, IoU.toilet: 0.9008, IoU.flower: 0.4109, IoU.book: 0.5789, IoU.hill: 0.0808, IoU.bench: 0.5485, IoU.countertop: 0.6324, IoU.stove: 0.8872, IoU.palm: 0.5780, IoU.kitchen island: 0.4385, IoU.computer: 0.8013, IoU.swivel chair: 0.5144, IoU.boat: 0.6985, IoU.bar: 0.5252, IoU.arcade machine: 0.7682, IoU.hovel: 0.4369, IoU.bus: 0.9314, IoU.towel: 0.7397, IoU.light: 0.6110, IoU.truck: 0.4411, IoU.tower: 0.1071, IoU.chandelier: 0.7095, IoU.awning: 0.3646, IoU.streetlight: 0.3377, IoU.booth: 0.4691, IoU.television receiver: 0.8111, IoU.airplane: 0.8356, IoU.dirt track: 0.0567, IoU.apparel: 0.4552, IoU.pole: 0.2870, IoU.land: 0.0445, IoU.bannister: 0.1798, IoU.escalator: 0.5721, IoU.ottoman: 0.4628, IoU.bottle: 0.4421, IoU.buffet: 0.4796, IoU.poster: 0.3482, IoU.stage: 0.2607, IoU.van: 0.4505, IoU.ship: 0.7188, IoU.fountain: 0.2553, IoU.conveyer belt: 0.7675, IoU.canopy: 0.5627, IoU.washer: 0.8297, IoU.plaything: 0.3773, IoU.swimming pool: 0.6768, IoU.stool: 0.5269, IoU.barrel: 0.5528, IoU.basket: 0.4144, IoU.waterfall: 0.6114, IoU.tent: 0.8893, IoU.bag: 0.1911, IoU.minibike: 0.7527, IoU.cradle: 0.8344, IoU.oven: 0.5446, IoU.ball: 0.5251, IoU.food: 0.5755, IoU.step: 0.1210, IoU.tank: 0.6675, IoU.trade name: 0.2696, IoU.microwave: 0.8653, IoU.pot: 0.5818, IoU.animal: 0.6350, IoU.bicycle: 0.6025, IoU.lake: 0.5000, IoU.dishwasher: 0.7237, IoU.screen: 0.5947, IoU.blanket: 0.2925, IoU.sculpture: 0.7745, IoU.hood: 0.6267, IoU.sconce: 0.5762, IoU.vase: 0.4866, IoU.traffic light: 0.4104, IoU.tray: 0.1487, IoU.ashcan: 0.4906, IoU.fan: 0.6670, IoU.pier: 0.3403, IoU.crt screen: 0.1894, IoU.plate: 0.5857, IoU.monitor: 0.6720, IoU.bulletin board: 0.4871, IoU.shower: 0.0177, IoU.radiator: 0.6419, IoU.glass: 0.1905, IoU.clock: 0.4473, IoU.flag: 0.7142, Acc.wall: 0.8934, Acc.building: 0.9383, Acc.sky: 0.9752, Acc.floor: 0.9172, Acc.tree: 0.8996, Acc.ceiling: 0.9418, Acc.road: 0.9201, Acc.bed : 0.9702, Acc.windowpane: 0.8184, Acc.grass: 0.7845, Acc.cabinet: 0.7674, Acc.sidewalk: 0.8539, Acc.person: 0.9475, Acc.earth: 0.5328, Acc.door: 0.7423, Acc.table: 0.8331, Acc.mountain: 0.7408, Acc.plant: 0.6510, Acc.curtain: 0.8763, Acc.chair: 0.7876, Acc.car: 0.9464, Acc.water: 0.7864, Acc.painting: 0.9163, Acc.sofa: 0.9276, Acc.shelf: 0.7408, Acc.house: 0.7027, Acc.sea: 0.8360, Acc.mirror: 0.8372, Acc.rug: 0.7957, Acc.field: 0.6632, Acc.armchair: 0.7263, Acc.seat: 0.8864, Acc.fence: 0.6273, Acc.desk: 0.7611, Acc.rock: 0.8007, Acc.wardrobe: 0.7665, Acc.lamp: 0.8586, Acc.bathtub: 0.8668, Acc.railing: 0.5812, Acc.cushion: 0.7915, Acc.base: 0.5729, Acc.box: 0.5102, Acc.column: 0.6804, Acc.signboard: 0.5688, Acc.chest of drawers: 0.6649, Acc.counter: 0.5983, Acc.sand: 0.7593, Acc.sink: 0.8395, Acc.skyscraper: 0.6149, Acc.fireplace: 0.9318, Acc.refrigerator: 0.8717, Acc.grandstand: 0.8220, Acc.path: 0.3987, Acc.stairs: 0.3246, Acc.runway: 0.9767, Acc.case: 0.8225, Acc.pool table: 0.9802, Acc.pillow: 0.8277, Acc.screen door: 0.7621, Acc.stairway: 0.5460, Acc.river: 0.4278, Acc.bridge: 0.8894, Acc.bookcase: 0.5611, Acc.blind: 0.4510, Acc.coffee table: 0.8820, Acc.toilet: 0.9415, Acc.flower: 0.5233, Acc.book: 0.7746, Acc.hill: 0.1231, Acc.bench: 0.6440, Acc.countertop: 0.8359, Acc.stove: 0.9499, Acc.palm: 0.7913, Acc.kitchen island: 0.6942, Acc.computer: 0.9249, Acc.swivel chair: 0.7709, Acc.boat: 0.8998, Acc.bar: 0.7122, Acc.arcade machine: 0.8360, Acc.hovel: 0.4931, Acc.bus: 0.9677, Acc.towel: 0.8047, Acc.light: 0.7075, Acc.truck: 0.5630, Acc.tower: 0.1445, Acc.chandelier: 0.8501, Acc.awning: 0.4547, Acc.streetlight: 0.4392, Acc.booth: 0.6617, Acc.television receiver: 0.9026, Acc.airplane: 0.9040, Acc.dirt track: 0.3072, Acc.apparel: 0.6058, Acc.pole: 0.3981, Acc.land: 0.0788, Acc.bannister: 0.2487, Acc.escalator: 0.7977, Acc.ottoman: 0.6359, Acc.bottle: 0.5856, Acc.buffet: 0.5918, Acc.poster: 0.5323, Acc.stage: 0.4804, Acc.van: 0.5730, Acc.ship: 0.7431, Acc.fountain: 0.2615, Acc.conveyer belt: 0.9342, Acc.canopy: 0.7906, Acc.washer: 0.8705, Acc.plaything: 0.5812, Acc.swimming pool: 0.9228, Acc.stool: 0.6614, Acc.barrel: 0.6476, Acc.basket: 0.5771, Acc.waterfall: 0.8670, Acc.tent: 0.9895, Acc.bag: 0.2305, Acc.minibike: 0.8910, Acc.cradle: 0.9785, Acc.oven: 0.6252, Acc.ball: 0.6038, Acc.food: 0.6964, Acc.step: 0.1512, Acc.tank: 0.7194, Acc.trade name: 0.3018, Acc.microwave: 0.9596, Acc.pot: 0.6638, Acc.animal: 0.6533, Acc.bicycle: 0.7774, Acc.lake: 0.6040, Acc.dishwasher: 0.8486, Acc.screen: 0.9152, Acc.blanket: 0.3244, Acc.sculpture: 0.8762, Acc.hood: 0.7456, Acc.sconce: 0.6720, Acc.vase: 0.6065, Acc.traffic light: 0.6297, Acc.tray: 0.1916, Acc.ashcan: 0.6189, Acc.fan: 0.8166, Acc.pier: 0.4727, Acc.crt screen: 0.2483, Acc.plate: 0.7478, Acc.monitor: 0.8222, Acc.bulletin board: 0.5069, Acc.shower: 0.0821, Acc.radiator: 0.7455, Acc.glass: 0.2065, Acc.clock: 0.5260, Acc.flag: 0.7842 +2024-06-19 02:37:30,334 - mmseg - INFO - Iter [66050/80000] lr: 6.976e-06, eta: 5:44:24, time: 3.300, data_time: 1.976, memory: 70498, decode.loss_ce: 0.1582, decode.acc_seg: 93.2834, aux.loss_ce: 0.0674, aux.acc_seg: 92.7964, loss: 0.2256 +2024-06-19 02:38:36,789 - mmseg - INFO - Iter [66100/80000] lr: 6.951e-06, eta: 5:43:08, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1646, decode.acc_seg: 92.9614, aux.loss_ce: 0.0703, aux.acc_seg: 92.4062, loss: 0.2349 +2024-06-19 02:39:42,980 - mmseg - INFO - Iter [66150/80000] lr: 6.926e-06, eta: 5:41:53, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1579, decode.acc_seg: 93.2003, aux.loss_ce: 0.0674, aux.acc_seg: 92.7276, loss: 0.2253 +2024-06-19 02:40:49,711 - mmseg - INFO - Iter [66200/80000] lr: 6.900e-06, eta: 5:40:37, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1597, decode.acc_seg: 93.0576, aux.loss_ce: 0.0683, aux.acc_seg: 92.6177, loss: 0.2280 +2024-06-19 02:41:56,185 - mmseg - INFO - Iter [66250/80000] lr: 6.875e-06, eta: 5:39:21, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1529, decode.acc_seg: 93.3971, aux.loss_ce: 0.0657, aux.acc_seg: 92.8805, loss: 0.2186 +2024-06-19 02:43:02,422 - mmseg - INFO - Iter [66300/80000] lr: 6.850e-06, eta: 5:38:06, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1577, decode.acc_seg: 92.9918, aux.loss_ce: 0.0683, aux.acc_seg: 92.4588, loss: 0.2260 +2024-06-19 02:44:09,141 - mmseg - INFO - Iter [66350/80000] lr: 6.826e-06, eta: 5:36:50, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1603, decode.acc_seg: 93.0756, aux.loss_ce: 0.0690, aux.acc_seg: 92.5511, loss: 0.2293 +2024-06-19 02:45:15,545 - mmseg - INFO - Iter [66400/80000] lr: 6.801e-06, eta: 5:35:35, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1536, decode.acc_seg: 93.2135, aux.loss_ce: 0.0661, aux.acc_seg: 92.7484, loss: 0.2197 +2024-06-19 02:46:22,017 - mmseg - INFO - Iter [66450/80000] lr: 6.776e-06, eta: 5:34:19, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1497, decode.acc_seg: 93.3752, aux.loss_ce: 0.0645, aux.acc_seg: 92.9039, loss: 0.2143 +2024-06-19 02:47:28,848 - mmseg - INFO - Iter [66500/80000] lr: 6.751e-06, eta: 5:33:03, time: 1.337, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1625, decode.acc_seg: 92.9715, aux.loss_ce: 0.0693, aux.acc_seg: 92.5324, loss: 0.2318 +2024-06-19 02:48:35,417 - mmseg - INFO - Iter [66550/80000] lr: 6.726e-06, eta: 5:31:48, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1518, decode.acc_seg: 93.3841, aux.loss_ce: 0.0650, aux.acc_seg: 92.9712, loss: 0.2168 +2024-06-19 02:49:41,703 - mmseg - INFO - Iter [66600/80000] lr: 6.700e-06, eta: 5:30:32, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1455, decode.acc_seg: 93.5425, aux.loss_ce: 0.0628, aux.acc_seg: 93.0772, loss: 0.2083 +2024-06-19 02:50:48,014 - mmseg - INFO - Iter [66650/80000] lr: 6.675e-06, eta: 5:29:17, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1604, decode.acc_seg: 92.8934, aux.loss_ce: 0.0686, aux.acc_seg: 92.5078, loss: 0.2290 +2024-06-19 02:51:54,669 - mmseg - INFO - Iter [66700/80000] lr: 6.651e-06, eta: 5:28:01, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1466, decode.acc_seg: 93.3528, aux.loss_ce: 0.0631, aux.acc_seg: 92.8968, loss: 0.2097 +2024-06-19 02:53:01,007 - mmseg - INFO - Iter [66750/80000] lr: 6.626e-06, eta: 5:26:46, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1537, decode.acc_seg: 93.1955, aux.loss_ce: 0.0662, aux.acc_seg: 92.7053, loss: 0.2199 +2024-06-19 02:54:07,486 - mmseg - INFO - Iter [66800/80000] lr: 6.601e-06, eta: 5:25:30, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1508, decode.acc_seg: 93.3876, aux.loss_ce: 0.0645, aux.acc_seg: 92.9646, loss: 0.2153 +2024-06-19 02:55:13,848 - mmseg - INFO - Iter [66850/80000] lr: 6.576e-06, eta: 5:24:15, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1588, decode.acc_seg: 93.0330, aux.loss_ce: 0.0684, aux.acc_seg: 92.5801, loss: 0.2272 +2024-06-19 02:56:20,428 - mmseg - INFO - Iter [66900/80000] lr: 6.551e-06, eta: 5:23:00, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1569, decode.acc_seg: 93.0173, aux.loss_ce: 0.0676, aux.acc_seg: 92.5330, loss: 0.2245 +2024-06-19 02:57:29,400 - mmseg - INFO - Iter [66950/80000] lr: 6.526e-06, eta: 5:21:45, time: 1.379, data_time: 0.062, memory: 70498, decode.loss_ce: 0.1507, decode.acc_seg: 93.3525, aux.loss_ce: 0.0651, aux.acc_seg: 92.7687, loss: 0.2157 +2024-06-19 02:58:35,772 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 02:58:35,772 - mmseg - INFO - Iter [67000/80000] lr: 6.500e-06, eta: 5:20:29, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1500, decode.acc_seg: 93.3342, aux.loss_ce: 0.0643, aux.acc_seg: 92.8725, loss: 0.2143 +2024-06-19 03:00:13,436 - mmseg - INFO - per class results: +2024-06-19 03:00:13,442 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.29 | 89.78 | +| building | 85.55 | 93.48 | +| sky | 95.01 | 97.85 | +| floor | 85.23 | 91.05 | +| tree | 78.11 | 89.26 | +| ceiling | 87.44 | 94.17 | +| road | 86.93 | 92.05 | +| bed | 92.25 | 96.93 | +| windowpane | 66.54 | 81.6 | +| grass | 67.02 | 81.59 | +| cabinet | 64.34 | 73.8 | +| sidewalk | 73.03 | 86.36 | +| person | 86.05 | 93.73 | +| earth | 38.25 | 50.1 | +| door | 60.77 | 77.15 | +| table | 69.46 | 82.82 | +| mountain | 61.71 | 76.64 | +| plant | 57.59 | 70.58 | +| curtain | 77.67 | 86.48 | +| chair | 68.15 | 78.15 | +| car | 87.05 | 94.31 | +| water | 64.64 | 77.56 | +| painting | 77.68 | 90.68 | +| sofa | 82.74 | 91.76 | +| shelf | 53.19 | 70.28 | +| house | 49.82 | 60.78 | +| sea | 69.56 | 83.81 | +| mirror | 77.74 | 84.19 | +| rug | 68.79 | 82.4 | +| field | 35.19 | 65.38 | +| armchair | 62.91 | 80.34 | +| seat | 64.58 | 89.39 | +| fence | 50.2 | 65.14 | +| desk | 58.31 | 75.71 | +| rock | 58.1 | 79.76 | +| wardrobe | 53.2 | 76.93 | +| lamp | 74.2 | 85.96 | +| bathtub | 84.42 | 87.17 | +| railing | 40.87 | 61.25 | +| cushion | 69.33 | 80.46 | +| base | 42.18 | 58.84 | +| box | 39.21 | 53.32 | +| column | 57.07 | 71.11 | +| signboard | 41.52 | 59.33 | +| chest of drawers | 47.07 | 68.86 | +| counter | 39.03 | 47.46 | +| sand | 49.9 | 74.98 | +| sink | 74.37 | 86.25 | +| skyscraper | 48.13 | 60.33 | +| fireplace | 74.52 | 94.12 | +| refrigerator | 80.43 | 91.93 | +| grandstand | 48.28 | 80.91 | +| path | 26.86 | 37.52 | +| stairs | 22.61 | 29.0 | +| runway | 73.68 | 96.38 | +| case | 59.9 | 81.1 | +| pool table | 95.02 | 97.83 | +| pillow | 69.6 | 80.6 | +| screen door | 77.91 | 80.21 | +| stairway | 37.75 | 56.83 | +| river | 22.32 | 44.18 | +| bridge | 77.97 | 88.62 | +| bookcase | 52.85 | 62.66 | +| blind | 46.58 | 55.47 | +| coffee table | 66.66 | 87.66 | +| toilet | 90.15 | 93.85 | +| flower | 43.82 | 54.09 | +| book | 60.26 | 77.18 | +| hill | 7.73 | 12.82 | +| bench | 54.51 | 62.8 | +| countertop | 63.11 | 80.58 | +| stove | 88.71 | 95.62 | +| palm | 57.57 | 78.7 | +| kitchen island | 47.92 | 86.78 | +| computer | 80.12 | 92.84 | +| swivel chair | 53.13 | 71.14 | +| boat | 60.77 | 91.09 | +| bar | 55.1 | 75.1 | +| arcade machine | 76.35 | 79.79 | +| hovel | 43.87 | 49.19 | +| bus | 92.88 | 95.93 | +| towel | 73.65 | 82.96 | +| light | 61.75 | 73.49 | +| truck | 44.78 | 59.93 | +| tower | 17.52 | 27.84 | +| chandelier | 72.3 | 86.54 | +| awning | 40.53 | 52.99 | +| streetlight | 35.96 | 52.25 | +| booth | 44.54 | 65.12 | +| television receiver | 80.11 | 86.42 | +| airplane | 81.9 | 87.79 | +| dirt track | 6.41 | 36.13 | +| apparel | 45.35 | 59.57 | +| pole | 25.35 | 34.69 | +| land | 3.8 | 6.44 | +| bannister | 18.68 | 25.59 | +| escalator | 60.23 | 78.99 | +| ottoman | 47.05 | 63.14 | +| bottle | 42.74 | 63.93 | +| buffet | 50.35 | 63.18 | +| poster | 37.94 | 53.22 | +| stage | 24.88 | 46.13 | +| van | 43.64 | 60.48 | +| ship | 89.78 | 94.03 | +| fountain | 21.53 | 21.92 | +| conveyer belt | 78.86 | 93.09 | +| canopy | 57.16 | 80.36 | +| washer | 82.92 | 85.75 | +| plaything | 37.38 | 55.69 | +| swimming pool | 67.4 | 88.69 | +| stool | 50.63 | 67.51 | +| barrel | 57.08 | 64.57 | +| basket | 40.63 | 57.68 | +| waterfall | 61.33 | 86.21 | +| tent | 90.75 | 98.51 | +| bag | 20.55 | 24.64 | +| minibike | 76.0 | 88.99 | +| cradle | 85.78 | 97.4 | +| oven | 67.36 | 78.41 | +| ball | 42.43 | 44.78 | +| food | 58.48 | 67.94 | +| step | 11.9 | 13.83 | +| tank | 64.45 | 71.12 | +| trade name | 27.95 | 31.02 | +| microwave | 90.7 | 95.9 | +| pot | 58.52 | 69.35 | +| animal | 63.79 | 65.21 | +| bicycle | 60.95 | 81.75 | +| lake | 52.98 | 63.72 | +| dishwasher | 73.32 | 83.99 | +| screen | 54.07 | 86.54 | +| blanket | 34.06 | 38.91 | +| sculpture | 73.55 | 88.53 | +| hood | 63.41 | 75.72 | +| sconce | 57.51 | 67.67 | +| vase | 49.05 | 59.9 | +| traffic light | 43.22 | 63.12 | +| tray | 16.99 | 21.91 | +| ashcan | 47.73 | 65.42 | +| fan | 66.63 | 79.6 | +| pier | 38.63 | 53.27 | +| crt screen | 18.84 | 27.31 | +| plate | 57.94 | 74.89 | +| monitor | 68.19 | 82.28 | +| bulletin board | 51.99 | 57.9 | +| shower | 6.66 | 7.37 | +| radiator | 65.02 | 76.04 | +| glass | 19.12 | 20.7 | +| clock | 45.83 | 53.12 | +| flag | 71.44 | 82.11 | ++---------------------+-------+-------+ +2024-06-19 03:00:13,442 - mmseg - INFO - Summary: +2024-06-19 03:00:13,442 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.22 | 57.42 | 70.21 | ++-------+-------+-------+ +2024-06-19 03:00:13,443 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 03:00:13,443 - mmseg - INFO - Iter(val) [250] aAcc: 0.8622, mIoU: 0.5742, mAcc: 0.7021, IoU.wall: 0.8229, IoU.building: 0.8555, IoU.sky: 0.9501, IoU.floor: 0.8523, IoU.tree: 0.7811, IoU.ceiling: 0.8744, IoU.road: 0.8693, IoU.bed : 0.9225, IoU.windowpane: 0.6654, IoU.grass: 0.6702, IoU.cabinet: 0.6434, IoU.sidewalk: 0.7303, IoU.person: 0.8605, IoU.earth: 0.3825, IoU.door: 0.6077, IoU.table: 0.6946, IoU.mountain: 0.6171, IoU.plant: 0.5759, IoU.curtain: 0.7767, IoU.chair: 0.6815, IoU.car: 0.8705, IoU.water: 0.6464, IoU.painting: 0.7768, IoU.sofa: 0.8274, IoU.shelf: 0.5319, IoU.house: 0.4982, IoU.sea: 0.6956, IoU.mirror: 0.7774, IoU.rug: 0.6879, IoU.field: 0.3519, IoU.armchair: 0.6291, IoU.seat: 0.6458, IoU.fence: 0.5020, IoU.desk: 0.5831, IoU.rock: 0.5810, IoU.wardrobe: 0.5320, IoU.lamp: 0.7420, IoU.bathtub: 0.8442, IoU.railing: 0.4087, IoU.cushion: 0.6933, IoU.base: 0.4218, IoU.box: 0.3921, IoU.column: 0.5707, IoU.signboard: 0.4152, IoU.chest of drawers: 0.4707, IoU.counter: 0.3903, IoU.sand: 0.4990, IoU.sink: 0.7437, IoU.skyscraper: 0.4813, IoU.fireplace: 0.7452, IoU.refrigerator: 0.8043, IoU.grandstand: 0.4828, IoU.path: 0.2686, IoU.stairs: 0.2261, IoU.runway: 0.7368, IoU.case: 0.5990, IoU.pool table: 0.9502, IoU.pillow: 0.6960, IoU.screen door: 0.7791, IoU.stairway: 0.3775, IoU.river: 0.2232, IoU.bridge: 0.7797, IoU.bookcase: 0.5285, IoU.blind: 0.4658, IoU.coffee table: 0.6666, IoU.toilet: 0.9015, IoU.flower: 0.4382, IoU.book: 0.6026, IoU.hill: 0.0773, IoU.bench: 0.5451, IoU.countertop: 0.6311, IoU.stove: 0.8871, IoU.palm: 0.5757, IoU.kitchen island: 0.4792, IoU.computer: 0.8012, IoU.swivel chair: 0.5313, IoU.boat: 0.6077, IoU.bar: 0.5510, IoU.arcade machine: 0.7635, IoU.hovel: 0.4387, IoU.bus: 0.9288, IoU.towel: 0.7365, IoU.light: 0.6175, IoU.truck: 0.4478, IoU.tower: 0.1752, IoU.chandelier: 0.7230, IoU.awning: 0.4053, IoU.streetlight: 0.3596, IoU.booth: 0.4454, IoU.television receiver: 0.8011, IoU.airplane: 0.8190, IoU.dirt track: 0.0641, IoU.apparel: 0.4535, IoU.pole: 0.2535, IoU.land: 0.0380, IoU.bannister: 0.1868, IoU.escalator: 0.6023, IoU.ottoman: 0.4705, IoU.bottle: 0.4274, IoU.buffet: 0.5035, IoU.poster: 0.3794, IoU.stage: 0.2488, IoU.van: 0.4364, IoU.ship: 0.8978, IoU.fountain: 0.2153, IoU.conveyer belt: 0.7886, IoU.canopy: 0.5716, IoU.washer: 0.8292, IoU.plaything: 0.3738, IoU.swimming pool: 0.6740, IoU.stool: 0.5063, IoU.barrel: 0.5708, IoU.basket: 0.4063, IoU.waterfall: 0.6133, IoU.tent: 0.9075, IoU.bag: 0.2055, IoU.minibike: 0.7600, IoU.cradle: 0.8578, IoU.oven: 0.6736, IoU.ball: 0.4243, IoU.food: 0.5848, IoU.step: 0.1190, IoU.tank: 0.6445, IoU.trade name: 0.2795, IoU.microwave: 0.9070, IoU.pot: 0.5852, IoU.animal: 0.6379, IoU.bicycle: 0.6095, IoU.lake: 0.5298, IoU.dishwasher: 0.7332, IoU.screen: 0.5407, IoU.blanket: 0.3406, IoU.sculpture: 0.7355, IoU.hood: 0.6341, IoU.sconce: 0.5751, IoU.vase: 0.4905, IoU.traffic light: 0.4322, IoU.tray: 0.1699, IoU.ashcan: 0.4773, IoU.fan: 0.6663, IoU.pier: 0.3863, IoU.crt screen: 0.1884, IoU.plate: 0.5794, IoU.monitor: 0.6819, IoU.bulletin board: 0.5199, IoU.shower: 0.0666, IoU.radiator: 0.6502, IoU.glass: 0.1912, IoU.clock: 0.4583, IoU.flag: 0.7144, Acc.wall: 0.8978, Acc.building: 0.9348, Acc.sky: 0.9785, Acc.floor: 0.9105, Acc.tree: 0.8926, Acc.ceiling: 0.9417, Acc.road: 0.9205, Acc.bed : 0.9693, Acc.windowpane: 0.8160, Acc.grass: 0.8159, Acc.cabinet: 0.7380, Acc.sidewalk: 0.8636, Acc.person: 0.9373, Acc.earth: 0.5010, Acc.door: 0.7715, Acc.table: 0.8282, Acc.mountain: 0.7664, Acc.plant: 0.7058, Acc.curtain: 0.8648, Acc.chair: 0.7815, Acc.car: 0.9431, Acc.water: 0.7756, Acc.painting: 0.9068, Acc.sofa: 0.9176, Acc.shelf: 0.7028, Acc.house: 0.6078, Acc.sea: 0.8381, Acc.mirror: 0.8419, Acc.rug: 0.8240, Acc.field: 0.6538, Acc.armchair: 0.8034, Acc.seat: 0.8939, Acc.fence: 0.6514, Acc.desk: 0.7571, Acc.rock: 0.7976, Acc.wardrobe: 0.7693, Acc.lamp: 0.8596, Acc.bathtub: 0.8717, Acc.railing: 0.6125, Acc.cushion: 0.8046, Acc.base: 0.5884, Acc.box: 0.5332, Acc.column: 0.7111, Acc.signboard: 0.5933, Acc.chest of drawers: 0.6886, Acc.counter: 0.4746, Acc.sand: 0.7498, Acc.sink: 0.8625, Acc.skyscraper: 0.6033, Acc.fireplace: 0.9412, Acc.refrigerator: 0.9193, Acc.grandstand: 0.8091, Acc.path: 0.3752, Acc.stairs: 0.2900, Acc.runway: 0.9638, Acc.case: 0.8110, Acc.pool table: 0.9783, Acc.pillow: 0.8060, Acc.screen door: 0.8021, Acc.stairway: 0.5683, Acc.river: 0.4418, Acc.bridge: 0.8862, Acc.bookcase: 0.6266, Acc.blind: 0.5547, Acc.coffee table: 0.8766, Acc.toilet: 0.9385, Acc.flower: 0.5409, Acc.book: 0.7718, Acc.hill: 0.1282, Acc.bench: 0.6280, Acc.countertop: 0.8058, Acc.stove: 0.9562, Acc.palm: 0.7870, Acc.kitchen island: 0.8678, Acc.computer: 0.9284, Acc.swivel chair: 0.7114, Acc.boat: 0.9109, Acc.bar: 0.7510, Acc.arcade machine: 0.7979, Acc.hovel: 0.4919, Acc.bus: 0.9593, Acc.towel: 0.8296, Acc.light: 0.7349, Acc.truck: 0.5993, Acc.tower: 0.2784, Acc.chandelier: 0.8654, Acc.awning: 0.5299, Acc.streetlight: 0.5225, Acc.booth: 0.6512, Acc.television receiver: 0.8642, Acc.airplane: 0.8779, Acc.dirt track: 0.3613, Acc.apparel: 0.5957, Acc.pole: 0.3469, Acc.land: 0.0644, Acc.bannister: 0.2559, Acc.escalator: 0.7899, Acc.ottoman: 0.6314, Acc.bottle: 0.6393, Acc.buffet: 0.6318, Acc.poster: 0.5322, Acc.stage: 0.4613, Acc.van: 0.6048, Acc.ship: 0.9403, Acc.fountain: 0.2192, Acc.conveyer belt: 0.9309, Acc.canopy: 0.8036, Acc.washer: 0.8575, Acc.plaything: 0.5569, Acc.swimming pool: 0.8869, Acc.stool: 0.6751, Acc.barrel: 0.6457, Acc.basket: 0.5768, Acc.waterfall: 0.8621, Acc.tent: 0.9851, Acc.bag: 0.2464, Acc.minibike: 0.8899, Acc.cradle: 0.9740, Acc.oven: 0.7841, Acc.ball: 0.4478, Acc.food: 0.6794, Acc.step: 0.1383, Acc.tank: 0.7112, Acc.trade name: 0.3102, Acc.microwave: 0.9590, Acc.pot: 0.6935, Acc.animal: 0.6521, Acc.bicycle: 0.8175, Acc.lake: 0.6372, Acc.dishwasher: 0.8399, Acc.screen: 0.8654, Acc.blanket: 0.3891, Acc.sculpture: 0.8853, Acc.hood: 0.7572, Acc.sconce: 0.6767, Acc.vase: 0.5990, Acc.traffic light: 0.6312, Acc.tray: 0.2191, Acc.ashcan: 0.6542, Acc.fan: 0.7960, Acc.pier: 0.5327, Acc.crt screen: 0.2731, Acc.plate: 0.7489, Acc.monitor: 0.8228, Acc.bulletin board: 0.5790, Acc.shower: 0.0737, Acc.radiator: 0.7604, Acc.glass: 0.2070, Acc.clock: 0.5312, Acc.flag: 0.8211 +2024-06-19 03:01:20,467 - mmseg - INFO - Iter [67050/80000] lr: 6.475e-06, eta: 5:19:33, time: 3.294, data_time: 1.971, memory: 70498, decode.loss_ce: 0.1612, decode.acc_seg: 92.9812, aux.loss_ce: 0.0693, aux.acc_seg: 92.4874, loss: 0.2306 +2024-06-19 03:02:26,816 - mmseg - INFO - Iter [67100/80000] lr: 6.450e-06, eta: 5:18:17, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1606, decode.acc_seg: 93.0937, aux.loss_ce: 0.0686, aux.acc_seg: 92.6606, loss: 0.2291 +2024-06-19 03:03:33,333 - mmseg - INFO - Iter [67150/80000] lr: 6.425e-06, eta: 5:17:02, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1606, decode.acc_seg: 92.9687, aux.loss_ce: 0.0686, aux.acc_seg: 92.4628, loss: 0.2292 +2024-06-19 03:04:39,735 - mmseg - INFO - Iter [67200/80000] lr: 6.401e-06, eta: 5:15:46, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1588, decode.acc_seg: 93.0201, aux.loss_ce: 0.0683, aux.acc_seg: 92.5397, loss: 0.2271 +2024-06-19 03:05:46,026 - mmseg - INFO - Iter [67250/80000] lr: 6.376e-06, eta: 5:14:31, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1514, decode.acc_seg: 93.4317, aux.loss_ce: 0.0650, aux.acc_seg: 92.9934, loss: 0.2165 +2024-06-19 03:06:52,526 - mmseg - INFO - Iter [67300/80000] lr: 6.351e-06, eta: 5:13:15, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1478, decode.acc_seg: 93.4572, aux.loss_ce: 0.0643, aux.acc_seg: 92.9029, loss: 0.2122 +2024-06-19 03:07:58,942 - mmseg - INFO - Iter [67350/80000] lr: 6.326e-06, eta: 5:12:00, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1644, decode.acc_seg: 92.8958, aux.loss_ce: 0.0702, aux.acc_seg: 92.4708, loss: 0.2347 +2024-06-19 03:09:05,265 - mmseg - INFO - Iter [67400/80000] lr: 6.301e-06, eta: 5:10:45, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1570, decode.acc_seg: 93.0129, aux.loss_ce: 0.0672, aux.acc_seg: 92.5911, loss: 0.2242 +2024-06-19 03:10:11,811 - mmseg - INFO - Iter [67450/80000] lr: 6.275e-06, eta: 5:09:29, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1479, decode.acc_seg: 93.4818, aux.loss_ce: 0.0639, aux.acc_seg: 92.9802, loss: 0.2118 +2024-06-19 03:11:18,451 - mmseg - INFO - Iter [67500/80000] lr: 6.250e-06, eta: 5:08:14, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1599, decode.acc_seg: 93.0002, aux.loss_ce: 0.0685, aux.acc_seg: 92.5450, loss: 0.2284 +2024-06-19 03:12:24,606 - mmseg - INFO - Iter [67550/80000] lr: 6.225e-06, eta: 5:06:58, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1588, decode.acc_seg: 92.9768, aux.loss_ce: 0.0676, aux.acc_seg: 92.5692, loss: 0.2264 +2024-06-19 03:13:31,055 - mmseg - INFO - Iter [67600/80000] lr: 6.201e-06, eta: 5:05:43, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1632, decode.acc_seg: 93.0995, aux.loss_ce: 0.0700, aux.acc_seg: 92.6033, loss: 0.2332 +2024-06-19 03:14:37,305 - mmseg - INFO - Iter [67650/80000] lr: 6.176e-06, eta: 5:04:28, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1573, decode.acc_seg: 93.2823, aux.loss_ce: 0.0674, aux.acc_seg: 92.7966, loss: 0.2247 +2024-06-19 03:15:43,663 - mmseg - INFO - Iter [67700/80000] lr: 6.151e-06, eta: 5:03:12, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1497, decode.acc_seg: 93.4583, aux.loss_ce: 0.0639, aux.acc_seg: 93.0722, loss: 0.2136 +2024-06-19 03:16:49,974 - mmseg - INFO - Iter [67750/80000] lr: 6.126e-06, eta: 5:01:57, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1592, decode.acc_seg: 93.0734, aux.loss_ce: 0.0680, aux.acc_seg: 92.6632, loss: 0.2272 +2024-06-19 03:17:56,501 - mmseg - INFO - Iter [67800/80000] lr: 6.101e-06, eta: 5:00:42, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1469, decode.acc_seg: 93.5416, aux.loss_ce: 0.0633, aux.acc_seg: 93.1260, loss: 0.2103 +2024-06-19 03:19:02,802 - mmseg - INFO - Iter [67850/80000] lr: 6.075e-06, eta: 4:59:26, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1527, decode.acc_seg: 93.2380, aux.loss_ce: 0.0654, aux.acc_seg: 92.7516, loss: 0.2181 +2024-06-19 03:20:09,014 - mmseg - INFO - Iter [67900/80000] lr: 6.050e-06, eta: 4:58:11, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1522, decode.acc_seg: 93.4584, aux.loss_ce: 0.0656, aux.acc_seg: 92.9599, loss: 0.2178 +2024-06-19 03:21:15,413 - mmseg - INFO - Iter [67950/80000] lr: 6.025e-06, eta: 4:56:56, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1544, decode.acc_seg: 93.2252, aux.loss_ce: 0.0667, aux.acc_seg: 92.7066, loss: 0.2211 +2024-06-19 03:22:21,695 - mmseg - INFO - Saving checkpoint at 68000 iterations +2024-06-19 03:24:02,674 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 03:24:02,674 - mmseg - INFO - Iter [68000/80000] lr: 6.001e-06, eta: 4:55:58, time: 3.345, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1581, decode.acc_seg: 93.0987, aux.loss_ce: 0.0679, aux.acc_seg: 92.6403, loss: 0.2260 +2024-06-19 03:25:38,661 - mmseg - INFO - per class results: +2024-06-19 03:25:38,668 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.16 | 90.32 | +| building | 85.81 | 93.77 | +| sky | 95.03 | 97.46 | +| floor | 84.71 | 92.15 | +| tree | 77.85 | 90.06 | +| ceiling | 87.55 | 94.02 | +| road | 86.83 | 92.27 | +| bed | 92.3 | 97.17 | +| windowpane | 65.91 | 82.08 | +| grass | 67.34 | 80.41 | +| cabinet | 64.31 | 73.66 | +| sidewalk | 72.18 | 86.23 | +| person | 85.94 | 93.81 | +| earth | 38.2 | 49.79 | +| door | 58.5 | 71.47 | +| table | 70.54 | 82.06 | +| mountain | 60.67 | 74.43 | +| plant | 57.22 | 68.38 | +| curtain | 78.74 | 87.47 | +| chair | 67.89 | 78.31 | +| car | 87.54 | 94.05 | +| water | 65.82 | 79.2 | +| painting | 76.92 | 89.99 | +| sofa | 81.92 | 91.93 | +| shelf | 52.3 | 71.62 | +| house | 55.48 | 72.89 | +| sea | 69.7 | 84.92 | +| mirror | 77.73 | 84.36 | +| rug | 66.16 | 77.47 | +| field | 34.81 | 68.23 | +| armchair | 59.87 | 76.54 | +| seat | 65.98 | 87.67 | +| fence | 50.73 | 63.44 | +| desk | 59.11 | 78.38 | +| rock | 57.3 | 80.36 | +| wardrobe | 52.77 | 75.65 | +| lamp | 74.75 | 83.62 | +| bathtub | 84.78 | 86.71 | +| railing | 40.4 | 56.04 | +| cushion | 70.86 | 80.19 | +| base | 44.01 | 58.29 | +| box | 37.5 | 46.83 | +| column | 55.1 | 65.24 | +| signboard | 41.23 | 54.54 | +| chest of drawers | 47.14 | 69.05 | +| counter | 39.12 | 48.52 | +| sand | 47.91 | 76.48 | +| sink | 75.8 | 84.32 | +| skyscraper | 48.97 | 59.7 | +| fireplace | 75.27 | 93.39 | +| refrigerator | 80.48 | 91.38 | +| grandstand | 48.35 | 83.32 | +| path | 25.47 | 34.92 | +| stairs | 24.13 | 30.6 | +| runway | 73.81 | 96.37 | +| case | 58.4 | 83.39 | +| pool table | 95.19 | 97.8 | +| pillow | 68.99 | 80.28 | +| screen door | 80.08 | 83.1 | +| stairway | 42.3 | 56.74 | +| river | 22.22 | 43.4 | +| bridge | 72.82 | 82.23 | +| bookcase | 48.69 | 61.85 | +| blind | 44.93 | 49.66 | +| coffee table | 67.03 | 87.0 | +| toilet | 90.02 | 93.21 | +| flower | 45.14 | 56.14 | +| book | 58.89 | 79.26 | +| hill | 9.35 | 15.03 | +| bench | 55.81 | 63.31 | +| countertop | 62.39 | 84.05 | +| stove | 88.73 | 94.36 | +| palm | 57.71 | 81.54 | +| kitchen island | 49.99 | 84.2 | +| computer | 80.92 | 92.43 | +| swivel chair | 51.48 | 74.25 | +| boat | 69.93 | 89.22 | +| bar | 54.86 | 74.76 | +| arcade machine | 77.91 | 82.89 | +| hovel | 43.53 | 49.05 | +| bus | 93.71 | 96.09 | +| towel | 73.56 | 83.64 | +| light | 60.39 | 69.83 | +| truck | 43.85 | 54.05 | +| tower | 11.91 | 16.2 | +| chandelier | 71.54 | 84.36 | +| awning | 39.58 | 48.33 | +| streetlight | 36.1 | 47.24 | +| booth | 44.28 | 67.42 | +| television receiver | 79.65 | 85.78 | +| airplane | 81.98 | 87.73 | +| dirt track | 7.32 | 38.54 | +| apparel | 44.28 | 61.49 | +| pole | 28.48 | 39.32 | +| land | 3.79 | 6.11 | +| bannister | 20.09 | 25.99 | +| escalator | 58.58 | 81.44 | +| ottoman | 47.84 | 64.17 | +| bottle | 43.57 | 65.82 | +| buffet | 43.12 | 51.26 | +| poster | 37.27 | 50.95 | +| stage | 24.8 | 43.73 | +| van | 42.53 | 59.98 | +| ship | 93.3 | 96.77 | +| fountain | 23.83 | 24.31 | +| conveyer belt | 78.85 | 93.2 | +| canopy | 58.57 | 77.74 | +| washer | 81.89 | 84.78 | +| plaything | 42.24 | 64.06 | +| swimming pool | 70.28 | 86.59 | +| stool | 49.48 | 65.2 | +| barrel | 54.36 | 64.79 | +| basket | 40.07 | 58.04 | +| waterfall | 57.13 | 80.22 | +| tent | 89.1 | 98.83 | +| bag | 20.1 | 23.86 | +| minibike | 76.85 | 87.37 | +| cradle | 85.12 | 97.62 | +| oven | 59.2 | 68.41 | +| ball | 48.85 | 55.42 | +| food | 56.86 | 67.65 | +| step | 10.52 | 12.22 | +| tank | 62.18 | 66.76 | +| trade name | 25.59 | 27.81 | +| microwave | 88.09 | 94.85 | +| pot | 58.54 | 68.69 | +| animal | 65.66 | 67.51 | +| bicycle | 60.03 | 74.9 | +| lake | 59.03 | 63.67 | +| dishwasher | 74.26 | 83.45 | +| screen | 52.27 | 77.1 | +| blanket | 32.57 | 36.8 | +| sculpture | 72.44 | 88.44 | +| hood | 62.4 | 74.78 | +| sconce | 56.25 | 64.83 | +| vase | 48.44 | 61.57 | +| traffic light | 42.28 | 58.21 | +| tray | 15.14 | 19.71 | +| ashcan | 48.46 | 63.39 | +| fan | 66.59 | 79.93 | +| pier | 37.74 | 47.38 | +| crt screen | 18.22 | 31.78 | +| plate | 58.24 | 76.4 | +| monitor | 71.03 | 81.93 | +| bulletin board | 61.23 | 71.12 | +| shower | 6.28 | 6.93 | +| radiator | 64.74 | 75.33 | +| glass | 18.85 | 20.16 | +| clock | 45.0 | 53.12 | +| flag | 71.65 | 77.8 | ++---------------------+-------+-------+ +2024-06-19 03:25:38,668 - mmseg - INFO - Summary: +2024-06-19 03:25:38,668 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.22 | 57.36 | 69.61 | ++-------+-------+-------+ +2024-06-19 03:25:38,669 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 03:25:38,669 - mmseg - INFO - Iter(val) [250] aAcc: 0.8622, mIoU: 0.5736, mAcc: 0.6961, IoU.wall: 0.8216, IoU.building: 0.8581, IoU.sky: 0.9503, IoU.floor: 0.8471, IoU.tree: 0.7785, IoU.ceiling: 0.8755, IoU.road: 0.8683, IoU.bed : 0.9230, IoU.windowpane: 0.6591, IoU.grass: 0.6734, IoU.cabinet: 0.6431, IoU.sidewalk: 0.7218, IoU.person: 0.8594, IoU.earth: 0.3820, IoU.door: 0.5850, IoU.table: 0.7054, IoU.mountain: 0.6067, IoU.plant: 0.5722, IoU.curtain: 0.7874, IoU.chair: 0.6789, IoU.car: 0.8754, IoU.water: 0.6582, IoU.painting: 0.7692, IoU.sofa: 0.8192, IoU.shelf: 0.5230, IoU.house: 0.5548, IoU.sea: 0.6970, IoU.mirror: 0.7773, IoU.rug: 0.6616, IoU.field: 0.3481, IoU.armchair: 0.5987, IoU.seat: 0.6598, IoU.fence: 0.5073, IoU.desk: 0.5911, IoU.rock: 0.5730, IoU.wardrobe: 0.5277, IoU.lamp: 0.7475, IoU.bathtub: 0.8478, IoU.railing: 0.4040, IoU.cushion: 0.7086, IoU.base: 0.4401, IoU.box: 0.3750, IoU.column: 0.5510, IoU.signboard: 0.4123, IoU.chest of drawers: 0.4714, IoU.counter: 0.3912, IoU.sand: 0.4791, IoU.sink: 0.7580, IoU.skyscraper: 0.4897, IoU.fireplace: 0.7527, IoU.refrigerator: 0.8048, IoU.grandstand: 0.4835, IoU.path: 0.2547, IoU.stairs: 0.2413, IoU.runway: 0.7381, IoU.case: 0.5840, IoU.pool table: 0.9519, IoU.pillow: 0.6899, IoU.screen door: 0.8008, IoU.stairway: 0.4230, IoU.river: 0.2222, IoU.bridge: 0.7282, IoU.bookcase: 0.4869, IoU.blind: 0.4493, IoU.coffee table: 0.6703, IoU.toilet: 0.9002, IoU.flower: 0.4514, IoU.book: 0.5889, IoU.hill: 0.0935, IoU.bench: 0.5581, IoU.countertop: 0.6239, IoU.stove: 0.8873, IoU.palm: 0.5771, IoU.kitchen island: 0.4999, IoU.computer: 0.8092, IoU.swivel chair: 0.5148, IoU.boat: 0.6993, IoU.bar: 0.5486, IoU.arcade machine: 0.7791, IoU.hovel: 0.4353, IoU.bus: 0.9371, IoU.towel: 0.7356, IoU.light: 0.6039, IoU.truck: 0.4385, IoU.tower: 0.1191, IoU.chandelier: 0.7154, IoU.awning: 0.3958, IoU.streetlight: 0.3610, IoU.booth: 0.4428, IoU.television receiver: 0.7965, IoU.airplane: 0.8198, IoU.dirt track: 0.0732, IoU.apparel: 0.4428, IoU.pole: 0.2848, IoU.land: 0.0379, IoU.bannister: 0.2009, IoU.escalator: 0.5858, IoU.ottoman: 0.4784, IoU.bottle: 0.4357, IoU.buffet: 0.4312, IoU.poster: 0.3727, IoU.stage: 0.2480, IoU.van: 0.4253, IoU.ship: 0.9330, IoU.fountain: 0.2383, IoU.conveyer belt: 0.7885, IoU.canopy: 0.5857, IoU.washer: 0.8189, IoU.plaything: 0.4224, IoU.swimming pool: 0.7028, IoU.stool: 0.4948, IoU.barrel: 0.5436, IoU.basket: 0.4007, IoU.waterfall: 0.5713, IoU.tent: 0.8910, IoU.bag: 0.2010, IoU.minibike: 0.7685, IoU.cradle: 0.8512, IoU.oven: 0.5920, IoU.ball: 0.4885, IoU.food: 0.5686, IoU.step: 0.1052, IoU.tank: 0.6218, IoU.trade name: 0.2559, IoU.microwave: 0.8809, IoU.pot: 0.5854, IoU.animal: 0.6566, IoU.bicycle: 0.6003, IoU.lake: 0.5903, IoU.dishwasher: 0.7426, IoU.screen: 0.5227, IoU.blanket: 0.3257, IoU.sculpture: 0.7244, IoU.hood: 0.6240, IoU.sconce: 0.5625, IoU.vase: 0.4844, IoU.traffic light: 0.4228, IoU.tray: 0.1514, IoU.ashcan: 0.4846, IoU.fan: 0.6659, IoU.pier: 0.3774, IoU.crt screen: 0.1822, IoU.plate: 0.5824, IoU.monitor: 0.7103, IoU.bulletin board: 0.6123, IoU.shower: 0.0628, IoU.radiator: 0.6474, IoU.glass: 0.1885, IoU.clock: 0.4500, IoU.flag: 0.7165, Acc.wall: 0.9032, Acc.building: 0.9377, Acc.sky: 0.9746, Acc.floor: 0.9215, Acc.tree: 0.9006, Acc.ceiling: 0.9402, Acc.road: 0.9227, Acc.bed : 0.9717, Acc.windowpane: 0.8208, Acc.grass: 0.8041, Acc.cabinet: 0.7366, Acc.sidewalk: 0.8623, Acc.person: 0.9381, Acc.earth: 0.4979, Acc.door: 0.7147, Acc.table: 0.8206, Acc.mountain: 0.7443, Acc.plant: 0.6838, Acc.curtain: 0.8747, Acc.chair: 0.7831, Acc.car: 0.9405, Acc.water: 0.7920, Acc.painting: 0.8999, Acc.sofa: 0.9193, Acc.shelf: 0.7162, Acc.house: 0.7289, Acc.sea: 0.8492, Acc.mirror: 0.8436, Acc.rug: 0.7747, Acc.field: 0.6823, Acc.armchair: 0.7654, Acc.seat: 0.8767, Acc.fence: 0.6344, Acc.desk: 0.7838, Acc.rock: 0.8036, Acc.wardrobe: 0.7565, Acc.lamp: 0.8362, Acc.bathtub: 0.8671, Acc.railing: 0.5604, Acc.cushion: 0.8019, Acc.base: 0.5829, Acc.box: 0.4683, Acc.column: 0.6524, Acc.signboard: 0.5454, Acc.chest of drawers: 0.6905, Acc.counter: 0.4852, Acc.sand: 0.7648, Acc.sink: 0.8432, Acc.skyscraper: 0.5970, Acc.fireplace: 0.9339, Acc.refrigerator: 0.9138, Acc.grandstand: 0.8332, Acc.path: 0.3492, Acc.stairs: 0.3060, Acc.runway: 0.9637, Acc.case: 0.8339, Acc.pool table: 0.9780, Acc.pillow: 0.8028, Acc.screen door: 0.8310, Acc.stairway: 0.5674, Acc.river: 0.4340, Acc.bridge: 0.8223, Acc.bookcase: 0.6185, Acc.blind: 0.4966, Acc.coffee table: 0.8700, Acc.toilet: 0.9321, Acc.flower: 0.5614, Acc.book: 0.7926, Acc.hill: 0.1503, Acc.bench: 0.6331, Acc.countertop: 0.8405, Acc.stove: 0.9436, Acc.palm: 0.8154, Acc.kitchen island: 0.8420, Acc.computer: 0.9243, Acc.swivel chair: 0.7425, Acc.boat: 0.8922, Acc.bar: 0.7476, Acc.arcade machine: 0.8289, Acc.hovel: 0.4905, Acc.bus: 0.9609, Acc.towel: 0.8364, Acc.light: 0.6983, Acc.truck: 0.5405, Acc.tower: 0.1620, Acc.chandelier: 0.8436, Acc.awning: 0.4833, Acc.streetlight: 0.4724, Acc.booth: 0.6742, Acc.television receiver: 0.8578, Acc.airplane: 0.8773, Acc.dirt track: 0.3854, Acc.apparel: 0.6149, Acc.pole: 0.3932, Acc.land: 0.0611, Acc.bannister: 0.2599, Acc.escalator: 0.8144, Acc.ottoman: 0.6417, Acc.bottle: 0.6582, Acc.buffet: 0.5126, Acc.poster: 0.5095, Acc.stage: 0.4373, Acc.van: 0.5998, Acc.ship: 0.9677, Acc.fountain: 0.2431, Acc.conveyer belt: 0.9320, Acc.canopy: 0.7774, Acc.washer: 0.8478, Acc.plaything: 0.6406, Acc.swimming pool: 0.8659, Acc.stool: 0.6520, Acc.barrel: 0.6479, Acc.basket: 0.5804, Acc.waterfall: 0.8022, Acc.tent: 0.9883, Acc.bag: 0.2386, Acc.minibike: 0.8737, Acc.cradle: 0.9762, Acc.oven: 0.6841, Acc.ball: 0.5542, Acc.food: 0.6765, Acc.step: 0.1222, Acc.tank: 0.6676, Acc.trade name: 0.2781, Acc.microwave: 0.9485, Acc.pot: 0.6869, Acc.animal: 0.6751, Acc.bicycle: 0.7490, Acc.lake: 0.6367, Acc.dishwasher: 0.8345, Acc.screen: 0.7710, Acc.blanket: 0.3680, Acc.sculpture: 0.8844, Acc.hood: 0.7478, Acc.sconce: 0.6483, Acc.vase: 0.6157, Acc.traffic light: 0.5821, Acc.tray: 0.1971, Acc.ashcan: 0.6339, Acc.fan: 0.7993, Acc.pier: 0.4738, Acc.crt screen: 0.3178, Acc.plate: 0.7640, Acc.monitor: 0.8193, Acc.bulletin board: 0.7112, Acc.shower: 0.0693, Acc.radiator: 0.7533, Acc.glass: 0.2016, Acc.clock: 0.5312, Acc.flag: 0.7780 +2024-06-19 03:26:45,773 - mmseg - INFO - Iter [68050/80000] lr: 5.976e-06, eta: 4:55:00, time: 3.262, data_time: 1.937, memory: 70498, decode.loss_ce: 0.1572, decode.acc_seg: 93.2657, aux.loss_ce: 0.0677, aux.acc_seg: 92.7160, loss: 0.2249 +2024-06-19 03:27:52,195 - mmseg - INFO - Iter [68100/80000] lr: 5.951e-06, eta: 4:53:45, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1597, decode.acc_seg: 93.0149, aux.loss_ce: 0.0680, aux.acc_seg: 92.5712, loss: 0.2277 +2024-06-19 03:28:59,697 - mmseg - INFO - Iter [68150/80000] lr: 5.926e-06, eta: 4:52:29, time: 1.350, data_time: 0.022, memory: 70498, decode.loss_ce: 0.1483, decode.acc_seg: 93.4028, aux.loss_ce: 0.0636, aux.acc_seg: 92.9396, loss: 0.2119 +2024-06-19 03:30:05,903 - mmseg - INFO - Iter [68200/80000] lr: 5.901e-06, eta: 4:51:14, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1598, decode.acc_seg: 93.1095, aux.loss_ce: 0.0682, aux.acc_seg: 92.6512, loss: 0.2280 +2024-06-19 03:31:14,472 - mmseg - INFO - Iter [68250/80000] lr: 5.875e-06, eta: 4:49:59, time: 1.371, data_time: 0.052, memory: 70498, decode.loss_ce: 0.1488, decode.acc_seg: 93.4856, aux.loss_ce: 0.0644, aux.acc_seg: 92.9912, loss: 0.2132 +2024-06-19 03:32:20,892 - mmseg - INFO - Iter [68300/80000] lr: 5.850e-06, eta: 4:48:44, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1435, decode.acc_seg: 93.5952, aux.loss_ce: 0.0622, aux.acc_seg: 93.0727, loss: 0.2058 +2024-06-19 03:33:27,420 - mmseg - INFO - Iter [68350/80000] lr: 5.825e-06, eta: 4:47:28, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1607, decode.acc_seg: 92.9753, aux.loss_ce: 0.0690, aux.acc_seg: 92.5121, loss: 0.2296 +2024-06-19 03:34:33,777 - mmseg - INFO - Iter [68400/80000] lr: 5.800e-06, eta: 4:46:13, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1546, decode.acc_seg: 93.2094, aux.loss_ce: 0.0664, aux.acc_seg: 92.7388, loss: 0.2210 +2024-06-19 03:35:40,251 - mmseg - INFO - Iter [68450/80000] lr: 5.776e-06, eta: 4:44:58, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1511, decode.acc_seg: 93.5379, aux.loss_ce: 0.0646, aux.acc_seg: 93.0534, loss: 0.2157 +2024-06-19 03:36:46,826 - mmseg - INFO - Iter [68500/80000] lr: 5.751e-06, eta: 4:43:42, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1554, decode.acc_seg: 93.2217, aux.loss_ce: 0.0669, aux.acc_seg: 92.7775, loss: 0.2224 +2024-06-19 03:37:53,156 - mmseg - INFO - Iter [68550/80000] lr: 5.726e-06, eta: 4:42:27, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1540, decode.acc_seg: 93.2119, aux.loss_ce: 0.0659, aux.acc_seg: 92.7915, loss: 0.2198 +2024-06-19 03:38:59,965 - mmseg - INFO - Iter [68600/80000] lr: 5.701e-06, eta: 4:41:12, time: 1.336, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1501, decode.acc_seg: 93.4582, aux.loss_ce: 0.0648, aux.acc_seg: 92.9870, loss: 0.2149 +2024-06-19 03:40:06,240 - mmseg - INFO - Iter [68650/80000] lr: 5.676e-06, eta: 4:39:57, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1527, decode.acc_seg: 93.4658, aux.loss_ce: 0.0654, aux.acc_seg: 92.9723, loss: 0.2180 +2024-06-19 03:41:12,619 - mmseg - INFO - Iter [68700/80000] lr: 5.650e-06, eta: 4:38:41, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1535, decode.acc_seg: 93.1854, aux.loss_ce: 0.0659, aux.acc_seg: 92.7203, loss: 0.2194 +2024-06-19 03:42:19,082 - mmseg - INFO - Iter [68750/80000] lr: 5.625e-06, eta: 4:37:26, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1454, decode.acc_seg: 93.4370, aux.loss_ce: 0.0626, aux.acc_seg: 92.9730, loss: 0.2079 +2024-06-19 03:43:25,549 - mmseg - INFO - Iter [68800/80000] lr: 5.600e-06, eta: 4:36:11, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1564, decode.acc_seg: 93.2588, aux.loss_ce: 0.0667, aux.acc_seg: 92.8016, loss: 0.2230 +2024-06-19 03:44:32,082 - mmseg - INFO - Iter [68850/80000] lr: 5.576e-06, eta: 4:34:56, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1542, decode.acc_seg: 93.1773, aux.loss_ce: 0.0663, aux.acc_seg: 92.6990, loss: 0.2206 +2024-06-19 03:45:38,450 - mmseg - INFO - Iter [68900/80000] lr: 5.551e-06, eta: 4:33:41, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1473, decode.acc_seg: 93.5705, aux.loss_ce: 0.0633, aux.acc_seg: 93.0768, loss: 0.2106 +2024-06-19 03:46:45,239 - mmseg - INFO - Iter [68950/80000] lr: 5.526e-06, eta: 4:32:25, time: 1.336, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1533, decode.acc_seg: 93.2184, aux.loss_ce: 0.0661, aux.acc_seg: 92.7161, loss: 0.2194 +2024-06-19 03:47:51,481 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 03:47:51,481 - mmseg - INFO - Iter [69000/80000] lr: 5.501e-06, eta: 4:31:10, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1531, decode.acc_seg: 93.3666, aux.loss_ce: 0.0665, aux.acc_seg: 92.8631, loss: 0.2196 +2024-06-19 03:49:28,130 - mmseg - INFO - per class results: +2024-06-19 03:49:28,137 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.27 | 90.21 | +| building | 85.65 | 93.47 | +| sky | 95.03 | 97.63 | +| floor | 84.62 | 91.94 | +| tree | 77.73 | 90.84 | +| ceiling | 87.27 | 92.29 | +| road | 86.83 | 91.38 | +| bed | 92.52 | 96.93 | +| windowpane | 66.72 | 81.59 | +| grass | 66.28 | 78.29 | +| cabinet | 65.04 | 75.68 | +| sidewalk | 72.28 | 87.43 | +| person | 85.88 | 94.25 | +| earth | 38.57 | 50.57 | +| door | 58.75 | 75.14 | +| table | 70.42 | 82.37 | +| mountain | 61.1 | 74.3 | +| plant | 57.46 | 68.59 | +| curtain | 77.97 | 87.02 | +| chair | 67.91 | 79.57 | +| car | 87.3 | 94.32 | +| water | 66.68 | 81.06 | +| painting | 78.02 | 90.21 | +| sofa | 82.78 | 90.38 | +| shelf | 52.47 | 70.61 | +| house | 57.59 | 73.79 | +| sea | 70.04 | 83.88 | +| mirror | 78.2 | 84.63 | +| rug | 65.55 | 75.3 | +| field | 34.96 | 69.01 | +| armchair | 61.29 | 78.96 | +| seat | 64.74 | 88.88 | +| fence | 50.53 | 66.19 | +| desk | 58.87 | 77.14 | +| rock | 56.18 | 79.28 | +| wardrobe | 55.78 | 72.43 | +| lamp | 74.67 | 85.88 | +| bathtub | 84.84 | 86.93 | +| railing | 39.43 | 54.89 | +| cushion | 70.64 | 83.61 | +| base | 40.79 | 59.36 | +| box | 38.09 | 48.4 | +| column | 56.85 | 68.57 | +| signboard | 41.66 | 58.86 | +| chest of drawers | 48.09 | 70.39 | +| counter | 45.7 | 59.83 | +| sand | 50.62 | 76.81 | +| sink | 77.3 | 84.62 | +| skyscraper | 49.57 | 59.96 | +| fireplace | 77.39 | 92.69 | +| refrigerator | 79.14 | 87.62 | +| grandstand | 47.77 | 80.78 | +| path | 26.04 | 35.48 | +| stairs | 24.44 | 31.44 | +| runway | 73.66 | 97.75 | +| case | 59.41 | 82.17 | +| pool table | 95.06 | 97.85 | +| pillow | 69.42 | 79.9 | +| screen door | 74.18 | 76.5 | +| stairway | 41.64 | 54.89 | +| river | 22.13 | 42.75 | +| bridge | 75.8 | 87.28 | +| bookcase | 49.55 | 64.69 | +| blind | 45.15 | 48.54 | +| coffee table | 66.65 | 86.86 | +| toilet | 90.12 | 93.7 | +| flower | 47.18 | 63.43 | +| book | 58.86 | 78.48 | +| hill | 8.97 | 13.45 | +| bench | 55.82 | 63.18 | +| countertop | 62.19 | 85.24 | +| stove | 87.37 | 93.9 | +| palm | 58.36 | 82.23 | +| kitchen island | 47.56 | 79.92 | +| computer | 80.06 | 93.65 | +| swivel chair | 52.4 | 73.69 | +| boat | 65.34 | 88.55 | +| bar | 52.82 | 69.37 | +| arcade machine | 76.54 | 80.05 | +| hovel | 44.28 | 48.62 | +| bus | 92.99 | 96.3 | +| towel | 72.89 | 82.84 | +| light | 61.01 | 71.46 | +| truck | 45.44 | 58.08 | +| tower | 12.12 | 16.5 | +| chandelier | 71.3 | 86.96 | +| awning | 40.74 | 51.3 | +| streetlight | 36.58 | 49.23 | +| booth | 51.99 | 66.86 | +| television receiver | 79.2 | 87.06 | +| airplane | 80.97 | 89.67 | +| dirt track | 7.85 | 36.8 | +| apparel | 47.44 | 67.12 | +| pole | 27.65 | 38.24 | +| land | 4.06 | 6.14 | +| bannister | 19.82 | 29.19 | +| escalator | 57.58 | 80.4 | +| ottoman | 47.79 | 64.82 | +| bottle | 43.93 | 64.69 | +| buffet | 50.89 | 69.35 | +| poster | 36.32 | 48.71 | +| stage | 25.06 | 45.17 | +| van | 42.74 | 61.51 | +| ship | 91.7 | 94.8 | +| fountain | 25.34 | 25.85 | +| conveyer belt | 79.75 | 93.31 | +| canopy | 54.06 | 76.25 | +| washer | 82.49 | 85.45 | +| plaything | 42.16 | 61.1 | +| swimming pool | 74.47 | 89.57 | +| stool | 49.0 | 66.72 | +| barrel | 44.06 | 67.97 | +| basket | 41.46 | 57.57 | +| waterfall | 57.16 | 75.69 | +| tent | 90.95 | 98.85 | +| bag | 22.05 | 25.67 | +| minibike | 75.38 | 89.59 | +| cradle | 84.77 | 97.71 | +| oven | 58.23 | 68.76 | +| ball | 46.08 | 52.5 | +| food | 58.87 | 67.54 | +| step | 11.38 | 13.62 | +| tank | 61.97 | 68.0 | +| trade name | 30.25 | 34.98 | +| microwave | 87.21 | 95.41 | +| pot | 59.4 | 70.44 | +| animal | 66.52 | 68.43 | +| bicycle | 61.04 | 76.84 | +| lake | 59.26 | 63.58 | +| dishwasher | 73.63 | 82.82 | +| screen | 55.38 | 81.81 | +| blanket | 35.15 | 40.08 | +| sculpture | 64.51 | 90.19 | +| hood | 62.26 | 74.2 | +| sconce | 54.57 | 62.55 | +| vase | 48.82 | 60.57 | +| traffic light | 43.99 | 59.98 | +| tray | 16.56 | 21.98 | +| ashcan | 48.52 | 64.11 | +| fan | 66.9 | 80.56 | +| pier | 37.44 | 49.44 | +| crt screen | 20.52 | 31.24 | +| plate | 59.11 | 78.12 | +| monitor | 69.92 | 84.5 | +| bulletin board | 51.33 | 61.68 | +| shower | 4.22 | 4.55 | +| radiator | 64.39 | 74.73 | +| glass | 20.37 | 22.31 | +| clock | 43.38 | 53.21 | +| flag | 71.57 | 80.33 | ++---------------------+-------+-------+ +2024-06-19 03:49:28,137 - mmseg - INFO - Summary: +2024-06-19 03:49:28,137 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.25 | 57.46 | 70.15 | ++-------+-------+-------+ +2024-06-19 03:49:28,138 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 03:49:28,138 - mmseg - INFO - Iter(val) [250] aAcc: 0.8625, mIoU: 0.5746, mAcc: 0.7015, IoU.wall: 0.8227, IoU.building: 0.8565, IoU.sky: 0.9503, IoU.floor: 0.8462, IoU.tree: 0.7773, IoU.ceiling: 0.8727, IoU.road: 0.8683, IoU.bed : 0.9252, IoU.windowpane: 0.6672, IoU.grass: 0.6628, IoU.cabinet: 0.6504, IoU.sidewalk: 0.7228, IoU.person: 0.8588, IoU.earth: 0.3857, IoU.door: 0.5875, IoU.table: 0.7042, IoU.mountain: 0.6110, IoU.plant: 0.5746, IoU.curtain: 0.7797, IoU.chair: 0.6791, IoU.car: 0.8730, IoU.water: 0.6668, IoU.painting: 0.7802, IoU.sofa: 0.8278, IoU.shelf: 0.5247, IoU.house: 0.5759, IoU.sea: 0.7004, IoU.mirror: 0.7820, IoU.rug: 0.6555, IoU.field: 0.3496, IoU.armchair: 0.6129, IoU.seat: 0.6474, IoU.fence: 0.5053, IoU.desk: 0.5887, IoU.rock: 0.5618, IoU.wardrobe: 0.5578, IoU.lamp: 0.7467, IoU.bathtub: 0.8484, IoU.railing: 0.3943, IoU.cushion: 0.7064, IoU.base: 0.4079, IoU.box: 0.3809, IoU.column: 0.5685, IoU.signboard: 0.4166, IoU.chest of drawers: 0.4809, IoU.counter: 0.4570, IoU.sand: 0.5062, IoU.sink: 0.7730, IoU.skyscraper: 0.4957, IoU.fireplace: 0.7739, IoU.refrigerator: 0.7914, IoU.grandstand: 0.4777, IoU.path: 0.2604, IoU.stairs: 0.2444, IoU.runway: 0.7366, IoU.case: 0.5941, IoU.pool table: 0.9506, IoU.pillow: 0.6942, IoU.screen door: 0.7418, IoU.stairway: 0.4164, IoU.river: 0.2213, IoU.bridge: 0.7580, IoU.bookcase: 0.4955, IoU.blind: 0.4515, IoU.coffee table: 0.6665, IoU.toilet: 0.9012, IoU.flower: 0.4718, IoU.book: 0.5886, IoU.hill: 0.0897, IoU.bench: 0.5582, IoU.countertop: 0.6219, IoU.stove: 0.8737, IoU.palm: 0.5836, IoU.kitchen island: 0.4756, IoU.computer: 0.8006, IoU.swivel chair: 0.5240, IoU.boat: 0.6534, IoU.bar: 0.5282, IoU.arcade machine: 0.7654, IoU.hovel: 0.4428, IoU.bus: 0.9299, IoU.towel: 0.7289, IoU.light: 0.6101, IoU.truck: 0.4544, IoU.tower: 0.1212, IoU.chandelier: 0.7130, IoU.awning: 0.4074, IoU.streetlight: 0.3658, IoU.booth: 0.5199, IoU.television receiver: 0.7920, IoU.airplane: 0.8097, IoU.dirt track: 0.0785, IoU.apparel: 0.4744, IoU.pole: 0.2765, IoU.land: 0.0406, IoU.bannister: 0.1982, IoU.escalator: 0.5758, IoU.ottoman: 0.4779, IoU.bottle: 0.4393, IoU.buffet: 0.5089, IoU.poster: 0.3632, IoU.stage: 0.2506, IoU.van: 0.4274, IoU.ship: 0.9170, IoU.fountain: 0.2534, IoU.conveyer belt: 0.7975, IoU.canopy: 0.5406, IoU.washer: 0.8249, IoU.plaything: 0.4216, IoU.swimming pool: 0.7447, IoU.stool: 0.4900, IoU.barrel: 0.4406, IoU.basket: 0.4146, IoU.waterfall: 0.5716, IoU.tent: 0.9095, IoU.bag: 0.2205, IoU.minibike: 0.7538, IoU.cradle: 0.8477, IoU.oven: 0.5823, IoU.ball: 0.4608, IoU.food: 0.5887, IoU.step: 0.1138, IoU.tank: 0.6197, IoU.trade name: 0.3025, IoU.microwave: 0.8721, IoU.pot: 0.5940, IoU.animal: 0.6652, IoU.bicycle: 0.6104, IoU.lake: 0.5926, IoU.dishwasher: 0.7363, IoU.screen: 0.5538, IoU.blanket: 0.3515, IoU.sculpture: 0.6451, IoU.hood: 0.6226, IoU.sconce: 0.5457, IoU.vase: 0.4882, IoU.traffic light: 0.4399, IoU.tray: 0.1656, IoU.ashcan: 0.4852, IoU.fan: 0.6690, IoU.pier: 0.3744, IoU.crt screen: 0.2052, IoU.plate: 0.5911, IoU.monitor: 0.6992, IoU.bulletin board: 0.5133, IoU.shower: 0.0422, IoU.radiator: 0.6439, IoU.glass: 0.2037, IoU.clock: 0.4338, IoU.flag: 0.7157, Acc.wall: 0.9021, Acc.building: 0.9347, Acc.sky: 0.9763, Acc.floor: 0.9194, Acc.tree: 0.9084, Acc.ceiling: 0.9229, Acc.road: 0.9138, Acc.bed : 0.9693, Acc.windowpane: 0.8159, Acc.grass: 0.7829, Acc.cabinet: 0.7568, Acc.sidewalk: 0.8743, Acc.person: 0.9425, Acc.earth: 0.5057, Acc.door: 0.7514, Acc.table: 0.8237, Acc.mountain: 0.7430, Acc.plant: 0.6859, Acc.curtain: 0.8702, Acc.chair: 0.7957, Acc.car: 0.9432, Acc.water: 0.8106, Acc.painting: 0.9021, Acc.sofa: 0.9038, Acc.shelf: 0.7061, Acc.house: 0.7379, Acc.sea: 0.8388, Acc.mirror: 0.8463, Acc.rug: 0.7530, Acc.field: 0.6901, Acc.armchair: 0.7896, Acc.seat: 0.8888, Acc.fence: 0.6619, Acc.desk: 0.7714, Acc.rock: 0.7928, Acc.wardrobe: 0.7243, Acc.lamp: 0.8588, Acc.bathtub: 0.8693, Acc.railing: 0.5489, Acc.cushion: 0.8361, Acc.base: 0.5936, Acc.box: 0.4840, Acc.column: 0.6857, Acc.signboard: 0.5886, Acc.chest of drawers: 0.7039, Acc.counter: 0.5983, Acc.sand: 0.7681, Acc.sink: 0.8462, Acc.skyscraper: 0.5996, Acc.fireplace: 0.9269, Acc.refrigerator: 0.8762, Acc.grandstand: 0.8078, Acc.path: 0.3548, Acc.stairs: 0.3144, Acc.runway: 0.9775, Acc.case: 0.8217, Acc.pool table: 0.9785, Acc.pillow: 0.7990, Acc.screen door: 0.7650, Acc.stairway: 0.5489, Acc.river: 0.4275, Acc.bridge: 0.8728, Acc.bookcase: 0.6469, Acc.blind: 0.4854, Acc.coffee table: 0.8686, Acc.toilet: 0.9370, Acc.flower: 0.6343, Acc.book: 0.7848, Acc.hill: 0.1345, Acc.bench: 0.6318, Acc.countertop: 0.8524, Acc.stove: 0.9390, Acc.palm: 0.8223, Acc.kitchen island: 0.7992, Acc.computer: 0.9365, Acc.swivel chair: 0.7369, Acc.boat: 0.8855, Acc.bar: 0.6937, Acc.arcade machine: 0.8005, Acc.hovel: 0.4862, Acc.bus: 0.9630, Acc.towel: 0.8284, Acc.light: 0.7146, Acc.truck: 0.5808, Acc.tower: 0.1650, Acc.chandelier: 0.8696, Acc.awning: 0.5130, Acc.streetlight: 0.4923, Acc.booth: 0.6686, Acc.television receiver: 0.8706, Acc.airplane: 0.8967, Acc.dirt track: 0.3680, Acc.apparel: 0.6712, Acc.pole: 0.3824, Acc.land: 0.0614, Acc.bannister: 0.2919, Acc.escalator: 0.8040, Acc.ottoman: 0.6482, Acc.bottle: 0.6469, Acc.buffet: 0.6935, Acc.poster: 0.4871, Acc.stage: 0.4517, Acc.van: 0.6151, Acc.ship: 0.9480, Acc.fountain: 0.2585, Acc.conveyer belt: 0.9331, Acc.canopy: 0.7625, Acc.washer: 0.8545, Acc.plaything: 0.6110, Acc.swimming pool: 0.8957, Acc.stool: 0.6672, Acc.barrel: 0.6797, Acc.basket: 0.5757, Acc.waterfall: 0.7569, Acc.tent: 0.9885, Acc.bag: 0.2567, Acc.minibike: 0.8959, Acc.cradle: 0.9771, Acc.oven: 0.6876, Acc.ball: 0.5250, Acc.food: 0.6754, Acc.step: 0.1362, Acc.tank: 0.6800, Acc.trade name: 0.3498, Acc.microwave: 0.9541, Acc.pot: 0.7044, Acc.animal: 0.6843, Acc.bicycle: 0.7684, Acc.lake: 0.6358, Acc.dishwasher: 0.8282, Acc.screen: 0.8181, Acc.blanket: 0.4008, Acc.sculpture: 0.9019, Acc.hood: 0.7420, Acc.sconce: 0.6255, Acc.vase: 0.6057, Acc.traffic light: 0.5998, Acc.tray: 0.2198, Acc.ashcan: 0.6411, Acc.fan: 0.8056, Acc.pier: 0.4944, Acc.crt screen: 0.3124, Acc.plate: 0.7812, Acc.monitor: 0.8450, Acc.bulletin board: 0.6168, Acc.shower: 0.0455, Acc.radiator: 0.7473, Acc.glass: 0.2231, Acc.clock: 0.5321, Acc.flag: 0.8033 +2024-06-19 03:50:35,274 - mmseg - INFO - Iter [69050/80000] lr: 5.476e-06, eta: 4:30:11, time: 3.276, data_time: 1.950, memory: 70498, decode.loss_ce: 0.1599, decode.acc_seg: 93.1262, aux.loss_ce: 0.0686, aux.acc_seg: 92.6552, loss: 0.2285 +2024-06-19 03:51:41,475 - mmseg - INFO - Iter [69100/80000] lr: 5.450e-06, eta: 4:28:55, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1488, decode.acc_seg: 93.5821, aux.loss_ce: 0.0643, aux.acc_seg: 93.1200, loss: 0.2131 +2024-06-19 03:52:47,980 - mmseg - INFO - Iter [69150/80000] lr: 5.425e-06, eta: 4:27:40, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1495, decode.acc_seg: 93.4816, aux.loss_ce: 0.0641, aux.acc_seg: 93.0127, loss: 0.2135 +2024-06-19 03:53:54,296 - mmseg - INFO - Iter [69200/80000] lr: 5.400e-06, eta: 4:26:25, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1601, decode.acc_seg: 93.1610, aux.loss_ce: 0.0692, aux.acc_seg: 92.6628, loss: 0.2293 +2024-06-19 03:55:00,785 - mmseg - INFO - Iter [69250/80000] lr: 5.376e-06, eta: 4:25:10, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1622, decode.acc_seg: 92.8697, aux.loss_ce: 0.0695, aux.acc_seg: 92.3858, loss: 0.2317 +2024-06-19 03:56:07,053 - mmseg - INFO - Iter [69300/80000] lr: 5.351e-06, eta: 4:23:55, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1547, decode.acc_seg: 93.2550, aux.loss_ce: 0.0667, aux.acc_seg: 92.7355, loss: 0.2214 +2024-06-19 03:57:13,445 - mmseg - INFO - Iter [69350/80000] lr: 5.326e-06, eta: 4:22:39, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1525, decode.acc_seg: 93.2173, aux.loss_ce: 0.0658, aux.acc_seg: 92.7561, loss: 0.2183 +2024-06-19 03:58:20,098 - mmseg - INFO - Iter [69400/80000] lr: 5.301e-06, eta: 4:21:24, time: 1.333, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1515, decode.acc_seg: 93.3877, aux.loss_ce: 0.0648, aux.acc_seg: 92.9122, loss: 0.2163 +2024-06-19 03:59:26,373 - mmseg - INFO - Iter [69450/80000] lr: 5.276e-06, eta: 4:20:09, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1410, decode.acc_seg: 93.5870, aux.loss_ce: 0.0607, aux.acc_seg: 93.1699, loss: 0.2017 +2024-06-19 04:00:35,097 - mmseg - INFO - Iter [69500/80000] lr: 5.250e-06, eta: 4:18:54, time: 1.374, data_time: 0.058, memory: 70498, decode.loss_ce: 0.1429, decode.acc_seg: 93.7090, aux.loss_ce: 0.0615, aux.acc_seg: 93.2146, loss: 0.2044 +2024-06-19 04:01:41,504 - mmseg - INFO - Iter [69550/80000] lr: 5.225e-06, eta: 4:17:39, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1426, decode.acc_seg: 93.6497, aux.loss_ce: 0.0617, aux.acc_seg: 93.1774, loss: 0.2043 +2024-06-19 04:02:47,903 - mmseg - INFO - Iter [69600/80000] lr: 5.200e-06, eta: 4:16:24, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1569, decode.acc_seg: 93.2122, aux.loss_ce: 0.0674, aux.acc_seg: 92.7316, loss: 0.2243 +2024-06-19 04:03:54,472 - mmseg - INFO - Iter [69650/80000] lr: 5.175e-06, eta: 4:15:09, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1562, decode.acc_seg: 93.3075, aux.loss_ce: 0.0673, aux.acc_seg: 92.8055, loss: 0.2235 +2024-06-19 04:05:00,951 - mmseg - INFO - Iter [69700/80000] lr: 5.151e-06, eta: 4:13:54, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1513, decode.acc_seg: 93.3781, aux.loss_ce: 0.0657, aux.acc_seg: 92.8598, loss: 0.2170 +2024-06-19 04:06:07,322 - mmseg - INFO - Iter [69750/80000] lr: 5.126e-06, eta: 4:12:39, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1443, decode.acc_seg: 93.7338, aux.loss_ce: 0.0625, aux.acc_seg: 93.2083, loss: 0.2069 +2024-06-19 04:07:13,566 - mmseg - INFO - Iter [69800/80000] lr: 5.101e-06, eta: 4:11:24, time: 1.325, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1445, decode.acc_seg: 93.7573, aux.loss_ce: 0.0626, aux.acc_seg: 93.2859, loss: 0.2071 +2024-06-19 04:08:20,132 - mmseg - INFO - Iter [69850/80000] lr: 5.076e-06, eta: 4:10:09, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1457, decode.acc_seg: 93.6127, aux.loss_ce: 0.0629, aux.acc_seg: 93.0973, loss: 0.2086 +2024-06-19 04:09:26,545 - mmseg - INFO - Iter [69900/80000] lr: 5.051e-06, eta: 4:08:54, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1568, decode.acc_seg: 93.3030, aux.loss_ce: 0.0682, aux.acc_seg: 92.8226, loss: 0.2250 +2024-06-19 04:10:33,147 - mmseg - INFO - Iter [69950/80000] lr: 5.025e-06, eta: 4:07:39, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1542, decode.acc_seg: 93.3081, aux.loss_ce: 0.0665, aux.acc_seg: 92.8112, loss: 0.2207 +2024-06-19 04:11:39,466 - mmseg - INFO - Saving checkpoint at 70000 iterations +2024-06-19 04:13:24,315 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 04:13:24,315 - mmseg - INFO - Iter [70000/80000] lr: 5.000e-06, eta: 4:06:39, time: 3.423, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1441, decode.acc_seg: 93.6490, aux.loss_ce: 0.0617, aux.acc_seg: 93.2203, loss: 0.2058 +2024-06-19 04:15:00,295 - mmseg - INFO - per class results: +2024-06-19 04:15:00,301 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.39 | 90.21 | +| building | 86.0 | 93.7 | +| sky | 95.01 | 97.73 | +| floor | 84.71 | 92.37 | +| tree | 78.1 | 89.4 | +| ceiling | 87.46 | 94.48 | +| road | 86.79 | 92.53 | +| bed | 92.48 | 96.77 | +| windowpane | 66.52 | 81.3 | +| grass | 67.02 | 80.38 | +| cabinet | 65.43 | 75.45 | +| sidewalk | 72.37 | 85.12 | +| person | 86.05 | 93.55 | +| earth | 38.74 | 51.25 | +| door | 60.03 | 74.45 | +| table | 70.29 | 82.47 | +| mountain | 61.15 | 74.93 | +| plant | 57.39 | 68.71 | +| curtain | 77.8 | 87.76 | +| chair | 68.36 | 78.06 | +| car | 87.33 | 93.97 | +| water | 66.41 | 81.65 | +| painting | 76.59 | 91.79 | +| sofa | 82.24 | 92.07 | +| shelf | 51.05 | 68.94 | +| house | 57.12 | 71.73 | +| sea | 69.61 | 84.09 | +| mirror | 77.79 | 83.45 | +| rug | 67.35 | 78.41 | +| field | 34.84 | 66.59 | +| armchair | 60.74 | 77.56 | +| seat | 66.07 | 87.97 | +| fence | 52.93 | 66.27 | +| desk | 59.33 | 77.49 | +| rock | 56.62 | 78.28 | +| wardrobe | 55.51 | 73.36 | +| lamp | 74.38 | 85.2 | +| bathtub | 84.42 | 86.53 | +| railing | 40.37 | 56.75 | +| cushion | 71.19 | 83.01 | +| base | 43.86 | 60.05 | +| box | 37.19 | 46.31 | +| column | 55.15 | 67.83 | +| signboard | 41.78 | 56.23 | +| chest of drawers | 44.42 | 69.0 | +| counter | 44.28 | 53.15 | +| sand | 51.59 | 76.46 | +| sink | 77.87 | 83.23 | +| skyscraper | 49.06 | 60.03 | +| fireplace | 78.41 | 91.71 | +| refrigerator | 81.16 | 92.72 | +| grandstand | 48.92 | 84.54 | +| path | 26.33 | 36.32 | +| stairs | 24.19 | 31.43 | +| runway | 73.85 | 97.31 | +| case | 59.92 | 81.71 | +| pool table | 95.03 | 97.7 | +| pillow | 70.24 | 81.44 | +| screen door | 83.38 | 88.07 | +| stairway | 43.42 | 58.32 | +| river | 19.57 | 36.01 | +| bridge | 76.17 | 87.07 | +| bookcase | 47.99 | 65.31 | +| blind | 44.22 | 48.27 | +| coffee table | 67.28 | 86.44 | +| toilet | 89.69 | 93.31 | +| flower | 44.69 | 59.08 | +| book | 57.11 | 75.63 | +| hill | 7.77 | 10.76 | +| bench | 56.15 | 64.86 | +| countertop | 59.87 | 86.12 | +| stove | 87.94 | 94.24 | +| palm | 57.72 | 82.87 | +| kitchen island | 47.31 | 71.52 | +| computer | 80.81 | 91.86 | +| swivel chair | 52.17 | 74.11 | +| boat | 59.64 | 90.05 | +| bar | 55.83 | 74.9 | +| arcade machine | 79.28 | 83.5 | +| hovel | 43.82 | 49.38 | +| bus | 93.52 | 96.06 | +| towel | 73.43 | 82.16 | +| light | 60.19 | 68.66 | +| truck | 47.05 | 63.12 | +| tower | 12.66 | 17.26 | +| chandelier | 71.31 | 84.55 | +| awning | 46.91 | 63.25 | +| streetlight | 35.26 | 46.46 | +| booth | 40.9 | 65.7 | +| television receiver | 80.19 | 88.05 | +| airplane | 80.25 | 89.17 | +| dirt track | 6.54 | 27.84 | +| apparel | 47.18 | 74.95 | +| pole | 29.6 | 40.98 | +| land | 4.12 | 6.35 | +| bannister | 19.03 | 26.83 | +| escalator | 58.09 | 79.53 | +| ottoman | 48.89 | 65.53 | +| bottle | 44.26 | 66.16 | +| buffet | 48.78 | 65.98 | +| poster | 38.13 | 49.68 | +| stage | 25.38 | 46.33 | +| van | 43.7 | 59.84 | +| ship | 93.39 | 96.79 | +| fountain | 25.09 | 26.0 | +| conveyer belt | 75.89 | 93.21 | +| canopy | 59.81 | 77.82 | +| washer | 78.75 | 81.84 | +| plaything | 42.98 | 63.05 | +| swimming pool | 72.34 | 90.21 | +| stool | 54.94 | 64.99 | +| barrel | 47.17 | 64.72 | +| basket | 41.6 | 57.11 | +| waterfall | 51.3 | 71.82 | +| tent | 88.99 | 99.12 | +| bag | 20.75 | 24.6 | +| minibike | 76.81 | 88.87 | +| cradle | 84.81 | 97.79 | +| oven | 56.65 | 65.22 | +| ball | 51.83 | 58.15 | +| food | 56.82 | 69.91 | +| step | 12.19 | 14.49 | +| tank | 66.23 | 69.68 | +| trade name | 25.83 | 27.97 | +| microwave | 87.43 | 95.6 | +| pot | 58.86 | 67.57 | +| animal | 67.13 | 69.07 | +| bicycle | 60.8 | 77.01 | +| lake | 56.59 | 63.75 | +| dishwasher | 72.76 | 84.15 | +| screen | 48.85 | 72.64 | +| blanket | 33.98 | 38.66 | +| sculpture | 72.5 | 88.33 | +| hood | 62.09 | 74.26 | +| sconce | 57.2 | 66.04 | +| vase | 48.9 | 60.09 | +| traffic light | 42.26 | 62.83 | +| tray | 13.91 | 17.55 | +| ashcan | 48.97 | 60.53 | +| fan | 67.25 | 80.27 | +| pier | 39.62 | 50.98 | +| crt screen | 17.88 | 31.28 | +| plate | 60.29 | 75.4 | +| monitor | 68.99 | 82.4 | +| bulletin board | 55.8 | 61.07 | +| shower | 8.75 | 9.85 | +| radiator | 65.01 | 73.39 | +| glass | 19.57 | 21.16 | +| clock | 44.33 | 51.15 | +| flag | 72.15 | 80.21 | ++---------------------+-------+-------+ +2024-06-19 04:15:00,301 - mmseg - INFO - Summary: +2024-06-19 04:15:00,301 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.36 | 57.55 | 69.98 | ++-------+-------+-------+ +2024-06-19 04:15:00,302 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 04:15:00,302 - mmseg - INFO - Iter(val) [250] aAcc: 0.8636, mIoU: 0.5755, mAcc: 0.6998, IoU.wall: 0.8239, IoU.building: 0.8600, IoU.sky: 0.9501, IoU.floor: 0.8471, IoU.tree: 0.7810, IoU.ceiling: 0.8746, IoU.road: 0.8679, IoU.bed : 0.9248, IoU.windowpane: 0.6652, IoU.grass: 0.6702, IoU.cabinet: 0.6543, IoU.sidewalk: 0.7237, IoU.person: 0.8605, IoU.earth: 0.3874, IoU.door: 0.6003, IoU.table: 0.7029, IoU.mountain: 0.6115, IoU.plant: 0.5739, IoU.curtain: 0.7780, IoU.chair: 0.6836, IoU.car: 0.8733, IoU.water: 0.6641, IoU.painting: 0.7659, IoU.sofa: 0.8224, IoU.shelf: 0.5105, IoU.house: 0.5712, IoU.sea: 0.6961, IoU.mirror: 0.7779, IoU.rug: 0.6735, IoU.field: 0.3484, IoU.armchair: 0.6074, IoU.seat: 0.6607, IoU.fence: 0.5293, IoU.desk: 0.5933, IoU.rock: 0.5662, IoU.wardrobe: 0.5551, IoU.lamp: 0.7438, IoU.bathtub: 0.8442, IoU.railing: 0.4037, IoU.cushion: 0.7119, IoU.base: 0.4386, IoU.box: 0.3719, IoU.column: 0.5515, IoU.signboard: 0.4178, IoU.chest of drawers: 0.4442, IoU.counter: 0.4428, IoU.sand: 0.5159, IoU.sink: 0.7787, IoU.skyscraper: 0.4906, IoU.fireplace: 0.7841, IoU.refrigerator: 0.8116, IoU.grandstand: 0.4892, IoU.path: 0.2633, IoU.stairs: 0.2419, IoU.runway: 0.7385, IoU.case: 0.5992, IoU.pool table: 0.9503, IoU.pillow: 0.7024, IoU.screen door: 0.8338, IoU.stairway: 0.4342, IoU.river: 0.1957, IoU.bridge: 0.7617, IoU.bookcase: 0.4799, IoU.blind: 0.4422, IoU.coffee table: 0.6728, IoU.toilet: 0.8969, IoU.flower: 0.4469, IoU.book: 0.5711, IoU.hill: 0.0777, IoU.bench: 0.5615, IoU.countertop: 0.5987, IoU.stove: 0.8794, IoU.palm: 0.5772, IoU.kitchen island: 0.4731, IoU.computer: 0.8081, IoU.swivel chair: 0.5217, IoU.boat: 0.5964, IoU.bar: 0.5583, IoU.arcade machine: 0.7928, IoU.hovel: 0.4382, IoU.bus: 0.9352, IoU.towel: 0.7343, IoU.light: 0.6019, IoU.truck: 0.4705, IoU.tower: 0.1266, IoU.chandelier: 0.7131, IoU.awning: 0.4691, IoU.streetlight: 0.3526, IoU.booth: 0.4090, IoU.television receiver: 0.8019, IoU.airplane: 0.8025, IoU.dirt track: 0.0654, IoU.apparel: 0.4718, IoU.pole: 0.2960, IoU.land: 0.0412, IoU.bannister: 0.1903, IoU.escalator: 0.5809, IoU.ottoman: 0.4889, IoU.bottle: 0.4426, IoU.buffet: 0.4878, IoU.poster: 0.3813, IoU.stage: 0.2538, IoU.van: 0.4370, IoU.ship: 0.9339, IoU.fountain: 0.2509, IoU.conveyer belt: 0.7589, IoU.canopy: 0.5981, IoU.washer: 0.7875, IoU.plaything: 0.4298, IoU.swimming pool: 0.7234, IoU.stool: 0.5494, IoU.barrel: 0.4717, IoU.basket: 0.4160, IoU.waterfall: 0.5130, IoU.tent: 0.8899, IoU.bag: 0.2075, IoU.minibike: 0.7681, IoU.cradle: 0.8481, IoU.oven: 0.5665, IoU.ball: 0.5183, IoU.food: 0.5682, IoU.step: 0.1219, IoU.tank: 0.6623, IoU.trade name: 0.2583, IoU.microwave: 0.8743, IoU.pot: 0.5886, IoU.animal: 0.6713, IoU.bicycle: 0.6080, IoU.lake: 0.5659, IoU.dishwasher: 0.7276, IoU.screen: 0.4885, IoU.blanket: 0.3398, IoU.sculpture: 0.7250, IoU.hood: 0.6209, IoU.sconce: 0.5720, IoU.vase: 0.4890, IoU.traffic light: 0.4226, IoU.tray: 0.1391, IoU.ashcan: 0.4897, IoU.fan: 0.6725, IoU.pier: 0.3962, IoU.crt screen: 0.1788, IoU.plate: 0.6029, IoU.monitor: 0.6899, IoU.bulletin board: 0.5580, IoU.shower: 0.0875, IoU.radiator: 0.6501, IoU.glass: 0.1957, IoU.clock: 0.4433, IoU.flag: 0.7215, Acc.wall: 0.9021, Acc.building: 0.9370, Acc.sky: 0.9773, Acc.floor: 0.9237, Acc.tree: 0.8940, Acc.ceiling: 0.9448, Acc.road: 0.9253, Acc.bed : 0.9677, Acc.windowpane: 0.8130, Acc.grass: 0.8038, Acc.cabinet: 0.7545, Acc.sidewalk: 0.8512, Acc.person: 0.9355, Acc.earth: 0.5125, Acc.door: 0.7445, Acc.table: 0.8247, Acc.mountain: 0.7493, Acc.plant: 0.6871, Acc.curtain: 0.8776, Acc.chair: 0.7806, Acc.car: 0.9397, Acc.water: 0.8165, Acc.painting: 0.9179, Acc.sofa: 0.9207, Acc.shelf: 0.6894, Acc.house: 0.7173, Acc.sea: 0.8409, Acc.mirror: 0.8345, Acc.rug: 0.7841, Acc.field: 0.6659, Acc.armchair: 0.7756, Acc.seat: 0.8797, Acc.fence: 0.6627, Acc.desk: 0.7749, Acc.rock: 0.7828, Acc.wardrobe: 0.7336, Acc.lamp: 0.8520, Acc.bathtub: 0.8653, Acc.railing: 0.5675, Acc.cushion: 0.8301, Acc.base: 0.6005, Acc.box: 0.4631, Acc.column: 0.6783, Acc.signboard: 0.5623, Acc.chest of drawers: 0.6900, Acc.counter: 0.5315, Acc.sand: 0.7646, Acc.sink: 0.8323, Acc.skyscraper: 0.6003, Acc.fireplace: 0.9171, Acc.refrigerator: 0.9272, Acc.grandstand: 0.8454, Acc.path: 0.3632, Acc.stairs: 0.3143, Acc.runway: 0.9731, Acc.case: 0.8171, Acc.pool table: 0.9770, Acc.pillow: 0.8144, Acc.screen door: 0.8807, Acc.stairway: 0.5832, Acc.river: 0.3601, Acc.bridge: 0.8707, Acc.bookcase: 0.6531, Acc.blind: 0.4827, Acc.coffee table: 0.8644, Acc.toilet: 0.9331, Acc.flower: 0.5908, Acc.book: 0.7563, Acc.hill: 0.1076, Acc.bench: 0.6486, Acc.countertop: 0.8612, Acc.stove: 0.9424, Acc.palm: 0.8287, Acc.kitchen island: 0.7152, Acc.computer: 0.9186, Acc.swivel chair: 0.7411, Acc.boat: 0.9005, Acc.bar: 0.7490, Acc.arcade machine: 0.8350, Acc.hovel: 0.4938, Acc.bus: 0.9606, Acc.towel: 0.8216, Acc.light: 0.6866, Acc.truck: 0.6312, Acc.tower: 0.1726, Acc.chandelier: 0.8455, Acc.awning: 0.6325, Acc.streetlight: 0.4646, Acc.booth: 0.6570, Acc.television receiver: 0.8805, Acc.airplane: 0.8917, Acc.dirt track: 0.2784, Acc.apparel: 0.7495, Acc.pole: 0.4098, Acc.land: 0.0635, Acc.bannister: 0.2683, Acc.escalator: 0.7953, Acc.ottoman: 0.6553, Acc.bottle: 0.6616, Acc.buffet: 0.6598, Acc.poster: 0.4968, Acc.stage: 0.4633, Acc.van: 0.5984, Acc.ship: 0.9679, Acc.fountain: 0.2600, Acc.conveyer belt: 0.9321, Acc.canopy: 0.7782, Acc.washer: 0.8184, Acc.plaything: 0.6305, Acc.swimming pool: 0.9021, Acc.stool: 0.6499, Acc.barrel: 0.6472, Acc.basket: 0.5711, Acc.waterfall: 0.7182, Acc.tent: 0.9912, Acc.bag: 0.2460, Acc.minibike: 0.8887, Acc.cradle: 0.9779, Acc.oven: 0.6522, Acc.ball: 0.5815, Acc.food: 0.6991, Acc.step: 0.1449, Acc.tank: 0.6968, Acc.trade name: 0.2797, Acc.microwave: 0.9560, Acc.pot: 0.6757, Acc.animal: 0.6907, Acc.bicycle: 0.7701, Acc.lake: 0.6375, Acc.dishwasher: 0.8415, Acc.screen: 0.7264, Acc.blanket: 0.3866, Acc.sculpture: 0.8833, Acc.hood: 0.7426, Acc.sconce: 0.6604, Acc.vase: 0.6009, Acc.traffic light: 0.6283, Acc.tray: 0.1755, Acc.ashcan: 0.6053, Acc.fan: 0.8027, Acc.pier: 0.5098, Acc.crt screen: 0.3128, Acc.plate: 0.7540, Acc.monitor: 0.8240, Acc.bulletin board: 0.6107, Acc.shower: 0.0985, Acc.radiator: 0.7339, Acc.glass: 0.2116, Acc.clock: 0.5115, Acc.flag: 0.8021 +2024-06-19 04:16:07,116 - mmseg - INFO - Iter [70050/80000] lr: 4.976e-06, eta: 4:05:37, time: 3.256, data_time: 1.936, memory: 70498, decode.loss_ce: 0.1556, decode.acc_seg: 93.3359, aux.loss_ce: 0.0666, aux.acc_seg: 92.8758, loss: 0.2223 +2024-06-19 04:17:13,599 - mmseg - INFO - Iter [70100/80000] lr: 4.951e-06, eta: 4:04:22, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1566, decode.acc_seg: 93.2158, aux.loss_ce: 0.0675, aux.acc_seg: 92.7182, loss: 0.2241 +2024-06-19 04:18:20,111 - mmseg - INFO - Iter [70150/80000] lr: 4.926e-06, eta: 4:03:07, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1502, decode.acc_seg: 93.2638, aux.loss_ce: 0.0647, aux.acc_seg: 92.8600, loss: 0.2148 +2024-06-19 04:19:26,616 - mmseg - INFO - Iter [70200/80000] lr: 4.901e-06, eta: 4:01:52, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1563, decode.acc_seg: 93.1378, aux.loss_ce: 0.0674, aux.acc_seg: 92.6711, loss: 0.2236 +2024-06-19 04:20:32,914 - mmseg - INFO - Iter [70250/80000] lr: 4.876e-06, eta: 4:00:37, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1412, decode.acc_seg: 93.6258, aux.loss_ce: 0.0613, aux.acc_seg: 93.1189, loss: 0.2025 +2024-06-19 04:21:39,526 - mmseg - INFO - Iter [70300/80000] lr: 4.851e-06, eta: 3:59:22, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1468, decode.acc_seg: 93.4857, aux.loss_ce: 0.0631, aux.acc_seg: 93.0107, loss: 0.2099 +2024-06-19 04:22:46,026 - mmseg - INFO - Iter [70350/80000] lr: 4.825e-06, eta: 3:58:07, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1483, decode.acc_seg: 93.3459, aux.loss_ce: 0.0645, aux.acc_seg: 92.8260, loss: 0.2127 +2024-06-19 04:23:52,569 - mmseg - INFO - Iter [70400/80000] lr: 4.800e-06, eta: 3:56:52, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1477, decode.acc_seg: 93.4985, aux.loss_ce: 0.0640, aux.acc_seg: 92.9966, loss: 0.2117 +2024-06-19 04:24:58,973 - mmseg - INFO - Iter [70450/80000] lr: 4.775e-06, eta: 3:55:37, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1456, decode.acc_seg: 93.5355, aux.loss_ce: 0.0629, aux.acc_seg: 93.0274, loss: 0.2085 +2024-06-19 04:26:05,579 - mmseg - INFO - Iter [70500/80000] lr: 4.751e-06, eta: 3:54:22, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1471, decode.acc_seg: 93.7106, aux.loss_ce: 0.0640, aux.acc_seg: 93.1462, loss: 0.2111 +2024-06-19 04:27:12,033 - mmseg - INFO - Iter [70550/80000] lr: 4.726e-06, eta: 3:53:07, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1440, decode.acc_seg: 93.6725, aux.loss_ce: 0.0623, aux.acc_seg: 93.2244, loss: 0.2063 +2024-06-19 04:28:18,487 - mmseg - INFO - Iter [70600/80000] lr: 4.701e-06, eta: 3:51:52, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1496, decode.acc_seg: 93.4751, aux.loss_ce: 0.0639, aux.acc_seg: 93.0179, loss: 0.2135 +2024-06-19 04:29:24,789 - mmseg - INFO - Iter [70650/80000] lr: 4.676e-06, eta: 3:50:37, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1546, decode.acc_seg: 93.3089, aux.loss_ce: 0.0660, aux.acc_seg: 92.8890, loss: 0.2206 +2024-06-19 04:30:31,246 - mmseg - INFO - Iter [70700/80000] lr: 4.651e-06, eta: 3:49:22, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1591, decode.acc_seg: 93.1284, aux.loss_ce: 0.0686, aux.acc_seg: 92.6076, loss: 0.2277 +2024-06-19 04:31:40,368 - mmseg - INFO - Iter [70750/80000] lr: 4.625e-06, eta: 3:48:07, time: 1.382, data_time: 0.058, memory: 70498, decode.loss_ce: 0.1577, decode.acc_seg: 93.0386, aux.loss_ce: 0.0681, aux.acc_seg: 92.4215, loss: 0.2258 +2024-06-19 04:32:46,721 - mmseg - INFO - Iter [70800/80000] lr: 4.600e-06, eta: 3:46:52, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1406, decode.acc_seg: 93.7206, aux.loss_ce: 0.0608, aux.acc_seg: 93.2580, loss: 0.2014 +2024-06-19 04:33:53,156 - mmseg - INFO - Iter [70850/80000] lr: 4.575e-06, eta: 3:45:37, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1561, decode.acc_seg: 93.1379, aux.loss_ce: 0.0673, aux.acc_seg: 92.6232, loss: 0.2234 +2024-06-19 04:34:59,686 - mmseg - INFO - Iter [70900/80000] lr: 4.550e-06, eta: 3:44:22, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1473, decode.acc_seg: 93.5793, aux.loss_ce: 0.0632, aux.acc_seg: 93.1339, loss: 0.2104 +2024-06-19 04:36:05,904 - mmseg - INFO - Iter [70950/80000] lr: 4.526e-06, eta: 3:43:07, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1427, decode.acc_seg: 93.5955, aux.loss_ce: 0.0617, aux.acc_seg: 93.1881, loss: 0.2044 +2024-06-19 04:37:12,644 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 04:37:12,644 - mmseg - INFO - Iter [71000/80000] lr: 4.501e-06, eta: 3:41:52, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1405, decode.acc_seg: 93.6953, aux.loss_ce: 0.0611, aux.acc_seg: 93.1983, loss: 0.2017 +2024-06-19 04:38:50,696 - mmseg - INFO - per class results: +2024-06-19 04:38:50,702 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.35 | 90.3 | +| building | 85.57 | 94.05 | +| sky | 95.1 | 97.53 | +| floor | 84.85 | 91.56 | +| tree | 77.56 | 90.29 | +| ceiling | 87.6 | 94.16 | +| road | 86.83 | 91.94 | +| bed | 92.39 | 96.84 | +| windowpane | 65.82 | 80.93 | +| grass | 66.88 | 80.32 | +| cabinet | 65.2 | 75.46 | +| sidewalk | 72.32 | 86.25 | +| person | 85.86 | 93.56 | +| earth | 38.0 | 50.19 | +| door | 59.75 | 74.81 | +| table | 70.61 | 83.87 | +| mountain | 61.59 | 75.74 | +| plant | 55.21 | 65.56 | +| curtain | 78.02 | 86.04 | +| chair | 68.25 | 79.47 | +| car | 87.24 | 93.84 | +| water | 65.51 | 79.91 | +| painting | 76.83 | 90.73 | +| sofa | 82.77 | 90.49 | +| shelf | 51.7 | 68.58 | +| house | 50.48 | 61.15 | +| sea | 69.24 | 83.49 | +| mirror | 77.81 | 83.57 | +| rug | 68.39 | 79.87 | +| field | 33.71 | 63.02 | +| armchair | 61.88 | 80.72 | +| seat | 65.23 | 87.77 | +| fence | 52.19 | 64.05 | +| desk | 58.84 | 76.37 | +| rock | 55.94 | 79.94 | +| wardrobe | 54.64 | 76.42 | +| lamp | 74.75 | 84.91 | +| bathtub | 84.43 | 86.16 | +| railing | 40.32 | 56.08 | +| cushion | 70.47 | 84.67 | +| base | 43.2 | 57.09 | +| box | 37.85 | 47.57 | +| column | 55.65 | 68.18 | +| signboard | 41.34 | 57.56 | +| chest of drawers | 46.38 | 67.45 | +| counter | 44.97 | 53.54 | +| sand | 50.88 | 76.05 | +| sink | 77.8 | 83.31 | +| skyscraper | 48.91 | 62.45 | +| fireplace | 76.81 | 92.11 | +| refrigerator | 82.56 | 92.04 | +| grandstand | 49.29 | 84.59 | +| path | 26.84 | 37.95 | +| stairs | 23.53 | 28.82 | +| runway | 73.79 | 96.77 | +| case | 59.93 | 81.97 | +| pool table | 94.9 | 97.63 | +| pillow | 67.78 | 75.77 | +| screen door | 82.03 | 85.32 | +| stairway | 39.07 | 57.57 | +| river | 18.71 | 36.28 | +| bridge | 76.54 | 87.84 | +| bookcase | 48.25 | 63.59 | +| blind | 44.01 | 47.45 | +| coffee table | 66.53 | 87.7 | +| toilet | 90.06 | 93.69 | +| flower | 45.21 | 61.66 | +| book | 58.33 | 75.12 | +| hill | 6.67 | 9.05 | +| bench | 56.34 | 64.2 | +| countertop | 62.16 | 82.35 | +| stove | 88.11 | 94.56 | +| palm | 57.59 | 79.85 | +| kitchen island | 49.73 | 78.39 | +| computer | 80.72 | 91.95 | +| swivel chair | 51.63 | 76.43 | +| boat | 63.22 | 87.67 | +| bar | 55.24 | 74.38 | +| arcade machine | 79.9 | 83.62 | +| hovel | 43.15 | 49.6 | +| bus | 93.19 | 96.43 | +| towel | 74.29 | 82.63 | +| light | 61.02 | 69.51 | +| truck | 47.05 | 63.01 | +| tower | 18.1 | 24.56 | +| chandelier | 72.08 | 88.09 | +| awning | 44.64 | 58.0 | +| streetlight | 37.31 | 50.27 | +| booth | 41.1 | 68.52 | +| television receiver | 80.64 | 86.78 | +| airplane | 83.81 | 91.48 | +| dirt track | 6.96 | 32.46 | +| apparel | 44.16 | 59.53 | +| pole | 28.79 | 39.58 | +| land | 4.88 | 7.76 | +| bannister | 19.44 | 28.01 | +| escalator | 56.16 | 80.75 | +| ottoman | 48.57 | 64.23 | +| bottle | 43.27 | 61.65 | +| buffet | 50.58 | 66.37 | +| poster | 36.19 | 51.62 | +| stage | 24.03 | 43.93 | +| van | 44.21 | 58.08 | +| ship | 92.39 | 95.79 | +| fountain | 27.06 | 27.58 | +| conveyer belt | 77.48 | 93.33 | +| canopy | 55.2 | 80.13 | +| washer | 81.37 | 85.0 | +| plaything | 41.42 | 63.57 | +| swimming pool | 69.34 | 92.42 | +| stool | 52.69 | 66.22 | +| barrel | 40.31 | 67.12 | +| basket | 41.17 | 55.97 | +| waterfall | 61.77 | 86.16 | +| tent | 89.34 | 98.83 | +| bag | 20.93 | 24.68 | +| minibike | 75.93 | 89.6 | +| cradle | 84.8 | 97.52 | +| oven | 63.68 | 75.24 | +| ball | 47.85 | 53.28 | +| food | 57.73 | 71.0 | +| step | 10.29 | 12.24 | +| tank | 68.17 | 75.94 | +| trade name | 25.13 | 27.74 | +| microwave | 89.54 | 95.4 | +| pot | 59.04 | 68.57 | +| animal | 67.11 | 69.05 | +| bicycle | 60.42 | 77.19 | +| lake | 56.26 | 63.76 | +| dishwasher | 74.42 | 83.45 | +| screen | 53.24 | 78.95 | +| blanket | 36.08 | 40.54 | +| sculpture | 74.6 | 88.11 | +| hood | 62.09 | 73.53 | +| sconce | 55.15 | 62.35 | +| vase | 49.21 | 61.47 | +| traffic light | 41.19 | 63.14 | +| tray | 12.88 | 15.71 | +| ashcan | 48.01 | 60.48 | +| fan | 66.81 | 80.36 | +| pier | 37.41 | 49.13 | +| crt screen | 16.98 | 26.97 | +| plate | 60.43 | 77.2 | +| monitor | 67.84 | 84.11 | +| bulletin board | 56.77 | 63.37 | +| shower | 9.15 | 11.41 | +| radiator | 65.78 | 74.04 | +| glass | 19.19 | 20.62 | +| clock | 46.56 | 53.82 | +| flag | 72.4 | 79.84 | ++---------------------+-------+-------+ +2024-06-19 04:38:50,702 - mmseg - INFO - Summary: +2024-06-19 04:38:50,702 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 86.25 | 57.54 | 70.1 | ++-------+-------+------+ +2024-06-19 04:38:50,703 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 04:38:50,703 - mmseg - INFO - Iter(val) [250] aAcc: 0.8625, mIoU: 0.5754, mAcc: 0.7010, IoU.wall: 0.8235, IoU.building: 0.8557, IoU.sky: 0.9510, IoU.floor: 0.8485, IoU.tree: 0.7756, IoU.ceiling: 0.8760, IoU.road: 0.8683, IoU.bed : 0.9239, IoU.windowpane: 0.6582, IoU.grass: 0.6688, IoU.cabinet: 0.6520, IoU.sidewalk: 0.7232, IoU.person: 0.8586, IoU.earth: 0.3800, IoU.door: 0.5975, IoU.table: 0.7061, IoU.mountain: 0.6159, IoU.plant: 0.5521, IoU.curtain: 0.7802, IoU.chair: 0.6825, IoU.car: 0.8724, IoU.water: 0.6551, IoU.painting: 0.7683, IoU.sofa: 0.8277, IoU.shelf: 0.5170, IoU.house: 0.5048, IoU.sea: 0.6924, IoU.mirror: 0.7781, IoU.rug: 0.6839, IoU.field: 0.3371, IoU.armchair: 0.6188, IoU.seat: 0.6523, IoU.fence: 0.5219, IoU.desk: 0.5884, IoU.rock: 0.5594, IoU.wardrobe: 0.5464, IoU.lamp: 0.7475, IoU.bathtub: 0.8443, IoU.railing: 0.4032, IoU.cushion: 0.7047, IoU.base: 0.4320, IoU.box: 0.3785, IoU.column: 0.5565, IoU.signboard: 0.4134, IoU.chest of drawers: 0.4638, IoU.counter: 0.4497, IoU.sand: 0.5088, IoU.sink: 0.7780, IoU.skyscraper: 0.4891, IoU.fireplace: 0.7681, IoU.refrigerator: 0.8256, IoU.grandstand: 0.4929, IoU.path: 0.2684, IoU.stairs: 0.2353, IoU.runway: 0.7379, IoU.case: 0.5993, IoU.pool table: 0.9490, IoU.pillow: 0.6778, IoU.screen door: 0.8203, IoU.stairway: 0.3907, IoU.river: 0.1871, IoU.bridge: 0.7654, IoU.bookcase: 0.4825, IoU.blind: 0.4401, IoU.coffee table: 0.6653, IoU.toilet: 0.9006, IoU.flower: 0.4521, IoU.book: 0.5833, IoU.hill: 0.0667, IoU.bench: 0.5634, IoU.countertop: 0.6216, IoU.stove: 0.8811, IoU.palm: 0.5759, IoU.kitchen island: 0.4973, IoU.computer: 0.8072, IoU.swivel chair: 0.5163, IoU.boat: 0.6322, IoU.bar: 0.5524, IoU.arcade machine: 0.7990, IoU.hovel: 0.4315, IoU.bus: 0.9319, IoU.towel: 0.7429, IoU.light: 0.6102, IoU.truck: 0.4705, IoU.tower: 0.1810, IoU.chandelier: 0.7208, IoU.awning: 0.4464, IoU.streetlight: 0.3731, IoU.booth: 0.4110, IoU.television receiver: 0.8064, IoU.airplane: 0.8381, IoU.dirt track: 0.0696, IoU.apparel: 0.4416, IoU.pole: 0.2879, IoU.land: 0.0488, IoU.bannister: 0.1944, IoU.escalator: 0.5616, IoU.ottoman: 0.4857, IoU.bottle: 0.4327, IoU.buffet: 0.5058, IoU.poster: 0.3619, IoU.stage: 0.2403, IoU.van: 0.4421, IoU.ship: 0.9239, IoU.fountain: 0.2706, IoU.conveyer belt: 0.7748, IoU.canopy: 0.5520, IoU.washer: 0.8137, IoU.plaything: 0.4142, IoU.swimming pool: 0.6934, IoU.stool: 0.5269, IoU.barrel: 0.4031, IoU.basket: 0.4117, IoU.waterfall: 0.6177, IoU.tent: 0.8934, IoU.bag: 0.2093, IoU.minibike: 0.7593, IoU.cradle: 0.8480, IoU.oven: 0.6368, IoU.ball: 0.4785, IoU.food: 0.5773, IoU.step: 0.1029, IoU.tank: 0.6817, IoU.trade name: 0.2513, IoU.microwave: 0.8954, IoU.pot: 0.5904, IoU.animal: 0.6711, IoU.bicycle: 0.6042, IoU.lake: 0.5626, IoU.dishwasher: 0.7442, IoU.screen: 0.5324, IoU.blanket: 0.3608, IoU.sculpture: 0.7460, IoU.hood: 0.6209, IoU.sconce: 0.5515, IoU.vase: 0.4921, IoU.traffic light: 0.4119, IoU.tray: 0.1288, IoU.ashcan: 0.4801, IoU.fan: 0.6681, IoU.pier: 0.3741, IoU.crt screen: 0.1698, IoU.plate: 0.6043, IoU.monitor: 0.6784, IoU.bulletin board: 0.5677, IoU.shower: 0.0915, IoU.radiator: 0.6578, IoU.glass: 0.1919, IoU.clock: 0.4656, IoU.flag: 0.7240, Acc.wall: 0.9030, Acc.building: 0.9405, Acc.sky: 0.9753, Acc.floor: 0.9156, Acc.tree: 0.9029, Acc.ceiling: 0.9416, Acc.road: 0.9194, Acc.bed : 0.9684, Acc.windowpane: 0.8093, Acc.grass: 0.8032, Acc.cabinet: 0.7546, Acc.sidewalk: 0.8625, Acc.person: 0.9356, Acc.earth: 0.5019, Acc.door: 0.7481, Acc.table: 0.8387, Acc.mountain: 0.7574, Acc.plant: 0.6556, Acc.curtain: 0.8604, Acc.chair: 0.7947, Acc.car: 0.9384, Acc.water: 0.7991, Acc.painting: 0.9073, Acc.sofa: 0.9049, Acc.shelf: 0.6858, Acc.house: 0.6115, Acc.sea: 0.8349, Acc.mirror: 0.8357, Acc.rug: 0.7987, Acc.field: 0.6302, Acc.armchair: 0.8072, Acc.seat: 0.8777, Acc.fence: 0.6405, Acc.desk: 0.7637, Acc.rock: 0.7994, Acc.wardrobe: 0.7642, Acc.lamp: 0.8491, Acc.bathtub: 0.8616, Acc.railing: 0.5608, Acc.cushion: 0.8467, Acc.base: 0.5709, Acc.box: 0.4757, Acc.column: 0.6818, Acc.signboard: 0.5756, Acc.chest of drawers: 0.6745, Acc.counter: 0.5354, Acc.sand: 0.7605, Acc.sink: 0.8331, Acc.skyscraper: 0.6245, Acc.fireplace: 0.9211, Acc.refrigerator: 0.9204, Acc.grandstand: 0.8459, Acc.path: 0.3795, Acc.stairs: 0.2882, Acc.runway: 0.9677, Acc.case: 0.8197, Acc.pool table: 0.9763, Acc.pillow: 0.7577, Acc.screen door: 0.8532, Acc.stairway: 0.5757, Acc.river: 0.3628, Acc.bridge: 0.8784, Acc.bookcase: 0.6359, Acc.blind: 0.4745, Acc.coffee table: 0.8770, Acc.toilet: 0.9369, Acc.flower: 0.6166, Acc.book: 0.7512, Acc.hill: 0.0905, Acc.bench: 0.6420, Acc.countertop: 0.8235, Acc.stove: 0.9456, Acc.palm: 0.7985, Acc.kitchen island: 0.7839, Acc.computer: 0.9195, Acc.swivel chair: 0.7643, Acc.boat: 0.8767, Acc.bar: 0.7438, Acc.arcade machine: 0.8362, Acc.hovel: 0.4960, Acc.bus: 0.9643, Acc.towel: 0.8263, Acc.light: 0.6951, Acc.truck: 0.6301, Acc.tower: 0.2456, Acc.chandelier: 0.8809, Acc.awning: 0.5800, Acc.streetlight: 0.5027, Acc.booth: 0.6852, Acc.television receiver: 0.8678, Acc.airplane: 0.9148, Acc.dirt track: 0.3246, Acc.apparel: 0.5953, Acc.pole: 0.3958, Acc.land: 0.0776, Acc.bannister: 0.2801, Acc.escalator: 0.8075, Acc.ottoman: 0.6423, Acc.bottle: 0.6165, Acc.buffet: 0.6637, Acc.poster: 0.5162, Acc.stage: 0.4393, Acc.van: 0.5808, Acc.ship: 0.9579, Acc.fountain: 0.2758, Acc.conveyer belt: 0.9333, Acc.canopy: 0.8013, Acc.washer: 0.8500, Acc.plaything: 0.6357, Acc.swimming pool: 0.9242, Acc.stool: 0.6622, Acc.barrel: 0.6712, Acc.basket: 0.5597, Acc.waterfall: 0.8616, Acc.tent: 0.9883, Acc.bag: 0.2468, Acc.minibike: 0.8960, Acc.cradle: 0.9752, Acc.oven: 0.7524, Acc.ball: 0.5328, Acc.food: 0.7100, Acc.step: 0.1224, Acc.tank: 0.7594, Acc.trade name: 0.2774, Acc.microwave: 0.9540, Acc.pot: 0.6857, Acc.animal: 0.6905, Acc.bicycle: 0.7719, Acc.lake: 0.6376, Acc.dishwasher: 0.8345, Acc.screen: 0.7895, Acc.blanket: 0.4054, Acc.sculpture: 0.8811, Acc.hood: 0.7353, Acc.sconce: 0.6235, Acc.vase: 0.6147, Acc.traffic light: 0.6314, Acc.tray: 0.1571, Acc.ashcan: 0.6048, Acc.fan: 0.8036, Acc.pier: 0.4913, Acc.crt screen: 0.2697, Acc.plate: 0.7720, Acc.monitor: 0.8411, Acc.bulletin board: 0.6337, Acc.shower: 0.1141, Acc.radiator: 0.7404, Acc.glass: 0.2062, Acc.clock: 0.5382, Acc.flag: 0.7984 +2024-06-19 04:39:57,608 - mmseg - INFO - Iter [71050/80000] lr: 4.476e-06, eta: 3:40:50, time: 3.299, data_time: 1.977, memory: 70498, decode.loss_ce: 0.1533, decode.acc_seg: 93.3025, aux.loss_ce: 0.0661, aux.acc_seg: 92.8660, loss: 0.2193 +2024-06-19 04:41:04,298 - mmseg - INFO - Iter [71100/80000] lr: 4.451e-06, eta: 3:39:35, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1557, decode.acc_seg: 93.0633, aux.loss_ce: 0.0669, aux.acc_seg: 92.5729, loss: 0.2226 +2024-06-19 04:42:10,780 - mmseg - INFO - Iter [71150/80000] lr: 4.426e-06, eta: 3:38:20, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1534, decode.acc_seg: 93.3905, aux.loss_ce: 0.0664, aux.acc_seg: 92.8398, loss: 0.2197 +2024-06-19 04:43:17,271 - mmseg - INFO - Iter [71200/80000] lr: 4.400e-06, eta: 3:37:05, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1418, decode.acc_seg: 93.7421, aux.loss_ce: 0.0613, aux.acc_seg: 93.3069, loss: 0.2031 +2024-06-19 04:44:23,669 - mmseg - INFO - Iter [71250/80000] lr: 4.375e-06, eta: 3:35:50, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1485, decode.acc_seg: 93.5386, aux.loss_ce: 0.0640, aux.acc_seg: 93.0638, loss: 0.2125 +2024-06-19 04:45:30,298 - mmseg - INFO - Iter [71300/80000] lr: 4.351e-06, eta: 3:34:35, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1443, decode.acc_seg: 93.5517, aux.loss_ce: 0.0624, aux.acc_seg: 93.0768, loss: 0.2067 +2024-06-19 04:46:36,545 - mmseg - INFO - Iter [71350/80000] lr: 4.326e-06, eta: 3:33:20, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1474, decode.acc_seg: 93.4011, aux.loss_ce: 0.0637, aux.acc_seg: 92.8930, loss: 0.2111 +2024-06-19 04:47:43,062 - mmseg - INFO - Iter [71400/80000] lr: 4.301e-06, eta: 3:32:05, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1569, decode.acc_seg: 93.1737, aux.loss_ce: 0.0679, aux.acc_seg: 92.5750, loss: 0.2248 +2024-06-19 04:48:49,877 - mmseg - INFO - Iter [71450/80000] lr: 4.276e-06, eta: 3:30:50, time: 1.336, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1533, decode.acc_seg: 93.1997, aux.loss_ce: 0.0658, aux.acc_seg: 92.7833, loss: 0.2191 +2024-06-19 04:49:56,445 - mmseg - INFO - Iter [71500/80000] lr: 4.251e-06, eta: 3:29:36, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1448, decode.acc_seg: 93.3538, aux.loss_ce: 0.0620, aux.acc_seg: 92.9531, loss: 0.2068 +2024-06-19 04:51:03,152 - mmseg - INFO - Iter [71550/80000] lr: 4.226e-06, eta: 3:28:21, time: 1.334, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1494, decode.acc_seg: 93.5179, aux.loss_ce: 0.0649, aux.acc_seg: 93.0104, loss: 0.2143 +2024-06-19 04:52:09,528 - mmseg - INFO - Iter [71600/80000] lr: 4.200e-06, eta: 3:27:06, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1436, decode.acc_seg: 93.6380, aux.loss_ce: 0.0620, aux.acc_seg: 93.1781, loss: 0.2056 +2024-06-19 04:53:16,177 - mmseg - INFO - Iter [71650/80000] lr: 4.175e-06, eta: 3:25:51, time: 1.333, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1517, decode.acc_seg: 93.4030, aux.loss_ce: 0.0656, aux.acc_seg: 92.9132, loss: 0.2173 +2024-06-19 04:54:22,584 - mmseg - INFO - Iter [71700/80000] lr: 4.150e-06, eta: 3:24:36, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1480, decode.acc_seg: 93.6229, aux.loss_ce: 0.0644, aux.acc_seg: 93.0734, loss: 0.2124 +2024-06-19 04:55:28,987 - mmseg - INFO - Iter [71750/80000] lr: 4.125e-06, eta: 3:23:21, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1464, decode.acc_seg: 93.5744, aux.loss_ce: 0.0635, aux.acc_seg: 93.1033, loss: 0.2099 +2024-06-19 04:56:35,610 - mmseg - INFO - Iter [71800/80000] lr: 4.101e-06, eta: 3:22:07, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1565, decode.acc_seg: 93.1896, aux.loss_ce: 0.0674, aux.acc_seg: 92.7014, loss: 0.2239 +2024-06-19 04:57:42,198 - mmseg - INFO - Iter [71850/80000] lr: 4.076e-06, eta: 3:20:52, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1446, decode.acc_seg: 93.6835, aux.loss_ce: 0.0625, aux.acc_seg: 93.1881, loss: 0.2071 +2024-06-19 04:58:48,966 - mmseg - INFO - Iter [71900/80000] lr: 4.051e-06, eta: 3:19:37, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1500, decode.acc_seg: 93.3560, aux.loss_ce: 0.0649, aux.acc_seg: 92.9005, loss: 0.2149 +2024-06-19 04:59:55,224 - mmseg - INFO - Iter [71950/80000] lr: 4.026e-06, eta: 3:18:22, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1538, decode.acc_seg: 93.2548, aux.loss_ce: 0.0660, aux.acc_seg: 92.7765, loss: 0.2198 +2024-06-19 05:01:05,525 - mmseg - INFO - Saving checkpoint at 72000 iterations +2024-06-19 05:02:50,466 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 05:02:50,466 - mmseg - INFO - Iter [72000/80000] lr: 4.000e-06, eta: 3:17:20, time: 3.505, data_time: 0.086, memory: 70498, decode.loss_ce: 0.1518, decode.acc_seg: 93.1654, aux.loss_ce: 0.0652, aux.acc_seg: 92.6664, loss: 0.2171 +2024-06-19 05:04:28,355 - mmseg - INFO - per class results: +2024-06-19 05:04:28,361 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.45 | 90.54 | +| building | 85.85 | 93.68 | +| sky | 95.07 | 97.63 | +| floor | 84.8 | 92.02 | +| tree | 77.13 | 90.96 | +| ceiling | 87.87 | 93.88 | +| road | 86.82 | 91.91 | +| bed | 92.59 | 96.89 | +| windowpane | 66.36 | 82.58 | +| grass | 65.81 | 79.81 | +| cabinet | 66.67 | 77.96 | +| sidewalk | 72.41 | 86.46 | +| person | 85.62 | 94.63 | +| earth | 38.58 | 52.08 | +| door | 59.68 | 73.98 | +| table | 70.46 | 81.9 | +| mountain | 61.29 | 72.32 | +| plant | 54.81 | 63.66 | +| curtain | 77.73 | 86.79 | +| chair | 67.46 | 76.62 | +| car | 87.38 | 94.06 | +| water | 65.21 | 79.58 | +| painting | 77.91 | 89.95 | +| sofa | 82.09 | 92.53 | +| shelf | 50.59 | 67.14 | +| house | 53.72 | 62.05 | +| sea | 68.71 | 84.27 | +| mirror | 77.51 | 83.13 | +| rug | 68.08 | 79.42 | +| field | 31.53 | 61.48 | +| armchair | 61.03 | 76.98 | +| seat | 64.99 | 87.67 | +| fence | 51.33 | 62.84 | +| desk | 58.17 | 80.9 | +| rock | 58.31 | 86.12 | +| wardrobe | 56.21 | 74.62 | +| lamp | 74.14 | 84.95 | +| bathtub | 84.71 | 87.22 | +| railing | 38.47 | 52.41 | +| cushion | 70.53 | 80.78 | +| base | 41.08 | 56.33 | +| box | 38.57 | 49.7 | +| column | 55.8 | 69.24 | +| signboard | 41.16 | 56.87 | +| chest of drawers | 45.42 | 61.54 | +| counter | 44.85 | 54.7 | +| sand | 53.54 | 76.3 | +| sink | 77.93 | 82.88 | +| skyscraper | 49.56 | 62.12 | +| fireplace | 76.78 | 92.06 | +| refrigerator | 83.22 | 93.14 | +| grandstand | 50.0 | 82.26 | +| path | 29.46 | 40.64 | +| stairs | 23.78 | 30.5 | +| runway | 67.95 | 88.78 | +| case | 58.43 | 79.32 | +| pool table | 94.72 | 97.86 | +| pillow | 70.28 | 81.76 | +| screen door | 82.39 | 85.3 | +| stairway | 41.8 | 55.94 | +| river | 20.72 | 40.5 | +| bridge | 75.52 | 87.43 | +| bookcase | 43.54 | 57.01 | +| blind | 43.53 | 47.41 | +| coffee table | 65.74 | 88.92 | +| toilet | 90.01 | 93.94 | +| flower | 45.33 | 57.84 | +| book | 58.9 | 77.4 | +| hill | 11.82 | 17.38 | +| bench | 55.85 | 63.91 | +| countertop | 61.86 | 84.02 | +| stove | 87.97 | 94.15 | +| palm | 56.65 | 82.5 | +| kitchen island | 49.92 | 80.53 | +| computer | 79.64 | 92.53 | +| swivel chair | 51.51 | 75.97 | +| boat | 60.69 | 88.41 | +| bar | 55.23 | 73.1 | +| arcade machine | 79.46 | 83.79 | +| hovel | 43.87 | 49.23 | +| bus | 93.76 | 96.12 | +| towel | 73.88 | 83.59 | +| light | 60.53 | 70.05 | +| truck | 46.49 | 59.69 | +| tower | 16.27 | 22.86 | +| chandelier | 71.31 | 83.97 | +| awning | 44.84 | 58.7 | +| streetlight | 37.26 | 49.9 | +| booth | 48.54 | 64.86 | +| television receiver | 79.76 | 84.7 | +| airplane | 84.28 | 89.91 | +| dirt track | 7.41 | 28.95 | +| apparel | 46.5 | 60.95 | +| pole | 30.34 | 43.59 | +| land | 4.63 | 6.99 | +| bannister | 18.75 | 26.41 | +| escalator | 57.0 | 80.22 | +| ottoman | 48.8 | 64.61 | +| bottle | 43.37 | 64.66 | +| buffet | 48.36 | 66.04 | +| poster | 36.5 | 50.53 | +| stage | 25.06 | 44.95 | +| van | 44.15 | 59.31 | +| ship | 93.29 | 97.06 | +| fountain | 21.7 | 22.07 | +| conveyer belt | 77.16 | 93.32 | +| canopy | 59.61 | 76.24 | +| washer | 81.6 | 84.89 | +| plaything | 38.71 | 53.29 | +| swimming pool | 66.77 | 87.79 | +| stool | 50.86 | 69.55 | +| barrel | 42.41 | 69.59 | +| basket | 40.29 | 59.61 | +| waterfall | 61.83 | 82.4 | +| tent | 90.65 | 98.72 | +| bag | 21.0 | 24.0 | +| minibike | 76.87 | 88.63 | +| cradle | 84.08 | 97.42 | +| oven | 67.72 | 79.39 | +| ball | 45.91 | 50.2 | +| food | 58.63 | 68.46 | +| step | 13.2 | 16.1 | +| tank | 69.27 | 76.05 | +| trade name | 25.98 | 28.31 | +| microwave | 90.65 | 95.56 | +| pot | 58.61 | 68.18 | +| animal | 64.88 | 66.23 | +| bicycle | 59.86 | 77.14 | +| lake | 55.51 | 63.76 | +| dishwasher | 73.18 | 84.87 | +| screen | 51.68 | 77.44 | +| blanket | 30.44 | 33.67 | +| sculpture | 75.11 | 87.92 | +| hood | 62.64 | 75.52 | +| sconce | 55.19 | 63.41 | +| vase | 48.41 | 58.19 | +| traffic light | 41.29 | 62.05 | +| tray | 13.39 | 16.05 | +| ashcan | 48.87 | 64.03 | +| fan | 66.3 | 78.01 | +| pier | 38.33 | 49.25 | +| crt screen | 17.92 | 27.58 | +| plate | 58.93 | 72.33 | +| monitor | 69.74 | 83.83 | +| bulletin board | 58.3 | 64.12 | +| shower | 6.62 | 7.22 | +| radiator | 65.09 | 74.95 | +| glass | 19.61 | 21.29 | +| clock | 44.71 | 49.75 | +| flag | 72.63 | 80.04 | ++---------------------+-------+-------+ +2024-06-19 05:04:28,361 - mmseg - INFO - Summary: +2024-06-19 05:04:28,362 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.27 | 57.52 | 69.76 | ++-------+-------+-------+ +2024-06-19 05:04:28,362 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 05:04:28,363 - mmseg - INFO - Iter(val) [250] aAcc: 0.8627, mIoU: 0.5752, mAcc: 0.6976, IoU.wall: 0.8245, IoU.building: 0.8585, IoU.sky: 0.9507, IoU.floor: 0.8480, IoU.tree: 0.7713, IoU.ceiling: 0.8787, IoU.road: 0.8682, IoU.bed : 0.9259, IoU.windowpane: 0.6636, IoU.grass: 0.6581, IoU.cabinet: 0.6667, IoU.sidewalk: 0.7241, IoU.person: 0.8562, IoU.earth: 0.3858, IoU.door: 0.5968, IoU.table: 0.7046, IoU.mountain: 0.6129, IoU.plant: 0.5481, IoU.curtain: 0.7773, IoU.chair: 0.6746, IoU.car: 0.8738, IoU.water: 0.6521, IoU.painting: 0.7791, IoU.sofa: 0.8209, IoU.shelf: 0.5059, IoU.house: 0.5372, IoU.sea: 0.6871, IoU.mirror: 0.7751, IoU.rug: 0.6808, IoU.field: 0.3153, IoU.armchair: 0.6103, IoU.seat: 0.6499, IoU.fence: 0.5133, IoU.desk: 0.5817, IoU.rock: 0.5831, IoU.wardrobe: 0.5621, IoU.lamp: 0.7414, IoU.bathtub: 0.8471, IoU.railing: 0.3847, IoU.cushion: 0.7053, IoU.base: 0.4108, IoU.box: 0.3857, IoU.column: 0.5580, IoU.signboard: 0.4116, IoU.chest of drawers: 0.4542, IoU.counter: 0.4485, IoU.sand: 0.5354, IoU.sink: 0.7793, IoU.skyscraper: 0.4956, IoU.fireplace: 0.7678, IoU.refrigerator: 0.8322, IoU.grandstand: 0.5000, IoU.path: 0.2946, IoU.stairs: 0.2378, IoU.runway: 0.6795, IoU.case: 0.5843, IoU.pool table: 0.9472, IoU.pillow: 0.7028, IoU.screen door: 0.8239, IoU.stairway: 0.4180, IoU.river: 0.2072, IoU.bridge: 0.7552, IoU.bookcase: 0.4354, IoU.blind: 0.4353, IoU.coffee table: 0.6574, IoU.toilet: 0.9001, IoU.flower: 0.4533, IoU.book: 0.5890, IoU.hill: 0.1182, IoU.bench: 0.5585, IoU.countertop: 0.6186, IoU.stove: 0.8797, IoU.palm: 0.5665, IoU.kitchen island: 0.4992, IoU.computer: 0.7964, IoU.swivel chair: 0.5151, IoU.boat: 0.6069, IoU.bar: 0.5523, IoU.arcade machine: 0.7946, IoU.hovel: 0.4387, IoU.bus: 0.9376, IoU.towel: 0.7388, IoU.light: 0.6053, IoU.truck: 0.4649, IoU.tower: 0.1627, IoU.chandelier: 0.7131, IoU.awning: 0.4484, IoU.streetlight: 0.3726, IoU.booth: 0.4854, IoU.television receiver: 0.7976, IoU.airplane: 0.8428, IoU.dirt track: 0.0741, IoU.apparel: 0.4650, IoU.pole: 0.3034, IoU.land: 0.0463, IoU.bannister: 0.1875, IoU.escalator: 0.5700, IoU.ottoman: 0.4880, IoU.bottle: 0.4337, IoU.buffet: 0.4836, IoU.poster: 0.3650, IoU.stage: 0.2506, IoU.van: 0.4415, IoU.ship: 0.9329, IoU.fountain: 0.2170, IoU.conveyer belt: 0.7716, IoU.canopy: 0.5961, IoU.washer: 0.8160, IoU.plaything: 0.3871, IoU.swimming pool: 0.6677, IoU.stool: 0.5086, IoU.barrel: 0.4241, IoU.basket: 0.4029, IoU.waterfall: 0.6183, IoU.tent: 0.9065, IoU.bag: 0.2100, IoU.minibike: 0.7687, IoU.cradle: 0.8408, IoU.oven: 0.6772, IoU.ball: 0.4591, IoU.food: 0.5863, IoU.step: 0.1320, IoU.tank: 0.6927, IoU.trade name: 0.2598, IoU.microwave: 0.9065, IoU.pot: 0.5861, IoU.animal: 0.6488, IoU.bicycle: 0.5986, IoU.lake: 0.5551, IoU.dishwasher: 0.7318, IoU.screen: 0.5168, IoU.blanket: 0.3044, IoU.sculpture: 0.7511, IoU.hood: 0.6264, IoU.sconce: 0.5519, IoU.vase: 0.4841, IoU.traffic light: 0.4129, IoU.tray: 0.1339, IoU.ashcan: 0.4887, IoU.fan: 0.6630, IoU.pier: 0.3833, IoU.crt screen: 0.1792, IoU.plate: 0.5893, IoU.monitor: 0.6974, IoU.bulletin board: 0.5830, IoU.shower: 0.0662, IoU.radiator: 0.6509, IoU.glass: 0.1961, IoU.clock: 0.4471, IoU.flag: 0.7263, Acc.wall: 0.9054, Acc.building: 0.9368, Acc.sky: 0.9763, Acc.floor: 0.9202, Acc.tree: 0.9096, Acc.ceiling: 0.9388, Acc.road: 0.9191, Acc.bed : 0.9689, Acc.windowpane: 0.8258, Acc.grass: 0.7981, Acc.cabinet: 0.7796, Acc.sidewalk: 0.8646, Acc.person: 0.9463, Acc.earth: 0.5208, Acc.door: 0.7398, Acc.table: 0.8190, Acc.mountain: 0.7232, Acc.plant: 0.6366, Acc.curtain: 0.8679, Acc.chair: 0.7662, Acc.car: 0.9406, Acc.water: 0.7958, Acc.painting: 0.8995, Acc.sofa: 0.9253, Acc.shelf: 0.6714, Acc.house: 0.6205, Acc.sea: 0.8427, Acc.mirror: 0.8313, Acc.rug: 0.7942, Acc.field: 0.6148, Acc.armchair: 0.7698, Acc.seat: 0.8767, Acc.fence: 0.6284, Acc.desk: 0.8090, Acc.rock: 0.8612, Acc.wardrobe: 0.7462, Acc.lamp: 0.8495, Acc.bathtub: 0.8722, Acc.railing: 0.5241, Acc.cushion: 0.8078, Acc.base: 0.5633, Acc.box: 0.4970, Acc.column: 0.6924, Acc.signboard: 0.5687, Acc.chest of drawers: 0.6154, Acc.counter: 0.5470, Acc.sand: 0.7630, Acc.sink: 0.8288, Acc.skyscraper: 0.6212, Acc.fireplace: 0.9206, Acc.refrigerator: 0.9314, Acc.grandstand: 0.8226, Acc.path: 0.4064, Acc.stairs: 0.3050, Acc.runway: 0.8878, Acc.case: 0.7932, Acc.pool table: 0.9786, Acc.pillow: 0.8176, Acc.screen door: 0.8530, Acc.stairway: 0.5594, Acc.river: 0.4050, Acc.bridge: 0.8743, Acc.bookcase: 0.5701, Acc.blind: 0.4741, Acc.coffee table: 0.8892, Acc.toilet: 0.9394, Acc.flower: 0.5784, Acc.book: 0.7740, Acc.hill: 0.1738, Acc.bench: 0.6391, Acc.countertop: 0.8402, Acc.stove: 0.9415, Acc.palm: 0.8250, Acc.kitchen island: 0.8053, Acc.computer: 0.9253, Acc.swivel chair: 0.7597, Acc.boat: 0.8841, Acc.bar: 0.7310, Acc.arcade machine: 0.8379, Acc.hovel: 0.4923, Acc.bus: 0.9612, Acc.towel: 0.8359, Acc.light: 0.7005, Acc.truck: 0.5969, Acc.tower: 0.2286, Acc.chandelier: 0.8397, Acc.awning: 0.5870, Acc.streetlight: 0.4990, Acc.booth: 0.6486, Acc.television receiver: 0.8470, Acc.airplane: 0.8991, Acc.dirt track: 0.2895, Acc.apparel: 0.6095, Acc.pole: 0.4359, Acc.land: 0.0699, Acc.bannister: 0.2641, Acc.escalator: 0.8022, Acc.ottoman: 0.6461, Acc.bottle: 0.6466, Acc.buffet: 0.6604, Acc.poster: 0.5053, Acc.stage: 0.4495, Acc.van: 0.5931, Acc.ship: 0.9706, Acc.fountain: 0.2207, Acc.conveyer belt: 0.9332, Acc.canopy: 0.7624, Acc.washer: 0.8489, Acc.plaything: 0.5329, Acc.swimming pool: 0.8779, Acc.stool: 0.6955, Acc.barrel: 0.6959, Acc.basket: 0.5961, Acc.waterfall: 0.8240, Acc.tent: 0.9872, Acc.bag: 0.2400, Acc.minibike: 0.8863, Acc.cradle: 0.9742, Acc.oven: 0.7939, Acc.ball: 0.5020, Acc.food: 0.6846, Acc.step: 0.1610, Acc.tank: 0.7605, Acc.trade name: 0.2831, Acc.microwave: 0.9556, Acc.pot: 0.6818, Acc.animal: 0.6623, Acc.bicycle: 0.7714, Acc.lake: 0.6376, Acc.dishwasher: 0.8487, Acc.screen: 0.7744, Acc.blanket: 0.3367, Acc.sculpture: 0.8792, Acc.hood: 0.7552, Acc.sconce: 0.6341, Acc.vase: 0.5819, Acc.traffic light: 0.6205, Acc.tray: 0.1605, Acc.ashcan: 0.6403, Acc.fan: 0.7801, Acc.pier: 0.4925, Acc.crt screen: 0.2758, Acc.plate: 0.7233, Acc.monitor: 0.8383, Acc.bulletin board: 0.6412, Acc.shower: 0.0722, Acc.radiator: 0.7495, Acc.glass: 0.2129, Acc.clock: 0.4975, Acc.flag: 0.8004 +2024-06-19 05:05:35,303 - mmseg - INFO - Iter [72050/80000] lr: 3.975e-06, eta: 3:16:16, time: 3.297, data_time: 1.976, memory: 70498, decode.loss_ce: 0.1435, decode.acc_seg: 93.5775, aux.loss_ce: 0.0618, aux.acc_seg: 93.0974, loss: 0.2052 +2024-06-19 05:06:42,004 - mmseg - INFO - Iter [72100/80000] lr: 3.950e-06, eta: 3:15:01, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1522, decode.acc_seg: 93.3233, aux.loss_ce: 0.0662, aux.acc_seg: 92.7304, loss: 0.2184 +2024-06-19 05:07:48,401 - mmseg - INFO - Iter [72150/80000] lr: 3.925e-06, eta: 3:13:46, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1398, decode.acc_seg: 93.7194, aux.loss_ce: 0.0611, aux.acc_seg: 93.1531, loss: 0.2008 +2024-06-19 05:08:54,960 - mmseg - INFO - Iter [72200/80000] lr: 3.901e-06, eta: 3:12:31, time: 1.331, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1464, decode.acc_seg: 93.6667, aux.loss_ce: 0.0635, aux.acc_seg: 93.1604, loss: 0.2099 +2024-06-19 05:10:01,307 - mmseg - INFO - Iter [72250/80000] lr: 3.876e-06, eta: 3:11:16, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1410, decode.acc_seg: 93.8745, aux.loss_ce: 0.0607, aux.acc_seg: 93.4302, loss: 0.2016 +2024-06-19 05:11:07,889 - mmseg - INFO - Iter [72300/80000] lr: 3.851e-06, eta: 3:10:01, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1467, decode.acc_seg: 93.5462, aux.loss_ce: 0.0635, aux.acc_seg: 93.0347, loss: 0.2102 +2024-06-19 05:12:14,706 - mmseg - INFO - Iter [72350/80000] lr: 3.826e-06, eta: 3:08:47, time: 1.336, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1566, decode.acc_seg: 93.3605, aux.loss_ce: 0.0672, aux.acc_seg: 92.8803, loss: 0.2238 +2024-06-19 05:13:21,133 - mmseg - INFO - Iter [72400/80000] lr: 3.801e-06, eta: 3:07:32, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1490, decode.acc_seg: 93.5272, aux.loss_ce: 0.0644, aux.acc_seg: 93.0637, loss: 0.2134 +2024-06-19 05:14:27,666 - mmseg - INFO - Iter [72450/80000] lr: 3.775e-06, eta: 3:06:17, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1540, decode.acc_seg: 93.2276, aux.loss_ce: 0.0661, aux.acc_seg: 92.7104, loss: 0.2201 +2024-06-19 05:15:34,095 - mmseg - INFO - Iter [72500/80000] lr: 3.750e-06, eta: 3:05:02, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1463, decode.acc_seg: 93.6865, aux.loss_ce: 0.0624, aux.acc_seg: 93.2689, loss: 0.2087 +2024-06-19 05:16:40,419 - mmseg - INFO - Iter [72550/80000] lr: 3.725e-06, eta: 3:03:47, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1446, decode.acc_seg: 93.5529, aux.loss_ce: 0.0625, aux.acc_seg: 93.0818, loss: 0.2070 +2024-06-19 05:17:46,713 - mmseg - INFO - Iter [72600/80000] lr: 3.701e-06, eta: 3:02:32, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1499, decode.acc_seg: 93.4598, aux.loss_ce: 0.0651, aux.acc_seg: 92.9458, loss: 0.2150 +2024-06-19 05:18:53,343 - mmseg - INFO - Iter [72650/80000] lr: 3.676e-06, eta: 3:01:18, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1432, decode.acc_seg: 93.5492, aux.loss_ce: 0.0618, aux.acc_seg: 93.0890, loss: 0.2049 +2024-06-19 05:19:59,563 - mmseg - INFO - Iter [72700/80000] lr: 3.651e-06, eta: 3:00:03, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1431, decode.acc_seg: 93.7225, aux.loss_ce: 0.0622, aux.acc_seg: 93.2184, loss: 0.2054 +2024-06-19 05:21:06,007 - mmseg - INFO - Iter [72750/80000] lr: 3.626e-06, eta: 2:58:48, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1508, decode.acc_seg: 93.5339, aux.loss_ce: 0.0652, aux.acc_seg: 93.0381, loss: 0.2160 +2024-06-19 05:22:12,563 - mmseg - INFO - Iter [72800/80000] lr: 3.601e-06, eta: 2:57:33, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1457, decode.acc_seg: 93.6761, aux.loss_ce: 0.0634, aux.acc_seg: 93.1421, loss: 0.2091 +2024-06-19 05:23:18,913 - mmseg - INFO - Iter [72850/80000] lr: 3.575e-06, eta: 2:56:19, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1475, decode.acc_seg: 93.6174, aux.loss_ce: 0.0637, aux.acc_seg: 93.0992, loss: 0.2113 +2024-06-19 05:24:25,300 - mmseg - INFO - Iter [72900/80000] lr: 3.550e-06, eta: 2:55:04, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1435, decode.acc_seg: 93.6874, aux.loss_ce: 0.0620, aux.acc_seg: 93.1809, loss: 0.2055 +2024-06-19 05:25:31,644 - mmseg - INFO - Iter [72950/80000] lr: 3.525e-06, eta: 2:53:49, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1612, decode.acc_seg: 92.9895, aux.loss_ce: 0.0697, aux.acc_seg: 92.4211, loss: 0.2309 +2024-06-19 05:26:37,949 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 05:26:37,949 - mmseg - INFO - Iter [73000/80000] lr: 3.501e-06, eta: 2:52:35, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1467, decode.acc_seg: 93.4276, aux.loss_ce: 0.0639, aux.acc_seg: 92.8971, loss: 0.2105 +2024-06-19 05:28:15,651 - mmseg - INFO - per class results: +2024-06-19 05:28:15,657 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.57 | 90.14 | +| building | 85.66 | 94.07 | +| sky | 95.02 | 97.91 | +| floor | 84.6 | 91.9 | +| tree | 76.91 | 90.17 | +| ceiling | 87.83 | 93.83 | +| road | 86.47 | 91.91 | +| bed | 92.65 | 96.73 | +| windowpane | 66.44 | 83.51 | +| grass | 66.11 | 79.5 | +| cabinet | 65.68 | 75.7 | +| sidewalk | 72.11 | 86.85 | +| person | 85.91 | 94.17 | +| earth | 37.58 | 50.27 | +| door | 59.6 | 73.33 | +| table | 70.98 | 83.12 | +| mountain | 61.96 | 74.37 | +| plant | 54.99 | 64.89 | +| curtain | 77.85 | 87.41 | +| chair | 68.17 | 77.95 | +| car | 87.41 | 94.35 | +| water | 65.83 | 80.08 | +| painting | 77.2 | 90.16 | +| sofa | 82.35 | 91.82 | +| shelf | 51.44 | 69.14 | +| house | 51.8 | 61.17 | +| sea | 69.28 | 84.02 | +| mirror | 78.77 | 85.52 | +| rug | 66.74 | 76.24 | +| field | 32.87 | 60.76 | +| armchair | 61.76 | 79.24 | +| seat | 64.96 | 88.25 | +| fence | 51.18 | 65.96 | +| desk | 58.29 | 80.12 | +| rock | 57.31 | 80.07 | +| wardrobe | 55.3 | 75.63 | +| lamp | 75.11 | 85.27 | +| bathtub | 84.68 | 86.94 | +| railing | 38.82 | 54.39 | +| cushion | 71.32 | 83.73 | +| base | 42.09 | 57.28 | +| box | 37.91 | 47.35 | +| column | 56.15 | 67.79 | +| signboard | 41.0 | 56.56 | +| chest of drawers | 46.11 | 70.29 | +| counter | 43.71 | 53.79 | +| sand | 54.47 | 76.31 | +| sink | 76.85 | 83.54 | +| skyscraper | 49.85 | 61.11 | +| fireplace | 75.18 | 93.14 | +| refrigerator | 83.69 | 92.79 | +| grandstand | 50.25 | 84.5 | +| path | 26.95 | 37.19 | +| stairs | 25.43 | 33.1 | +| runway | 73.25 | 96.62 | +| case | 57.69 | 81.15 | +| pool table | 94.94 | 97.74 | +| pillow | 70.7 | 81.19 | +| screen door | 77.12 | 79.92 | +| stairway | 41.43 | 53.79 | +| river | 22.11 | 44.01 | +| bridge | 76.18 | 86.89 | +| bookcase | 46.5 | 61.45 | +| blind | 42.36 | 45.57 | +| coffee table | 67.57 | 86.9 | +| toilet | 90.01 | 93.8 | +| flower | 45.64 | 56.49 | +| book | 59.96 | 76.52 | +| hill | 7.33 | 10.37 | +| bench | 56.15 | 65.8 | +| countertop | 62.91 | 84.82 | +| stove | 88.6 | 95.35 | +| palm | 56.71 | 80.1 | +| kitchen island | 50.62 | 86.81 | +| computer | 79.89 | 92.24 | +| swivel chair | 51.33 | 75.47 | +| boat | 64.14 | 87.8 | +| bar | 56.34 | 74.77 | +| arcade machine | 79.18 | 84.18 | +| hovel | 43.78 | 49.32 | +| bus | 93.6 | 96.07 | +| towel | 73.81 | 82.53 | +| light | 61.33 | 70.97 | +| truck | 45.49 | 58.29 | +| tower | 12.06 | 16.64 | +| chandelier | 73.01 | 87.71 | +| awning | 45.05 | 58.69 | +| streetlight | 35.6 | 47.18 | +| booth | 50.7 | 68.75 | +| television receiver | 80.9 | 85.97 | +| airplane | 85.36 | 91.6 | +| dirt track | 8.62 | 36.57 | +| apparel | 49.56 | 70.89 | +| pole | 28.79 | 39.24 | +| land | 4.08 | 6.46 | +| bannister | 18.23 | 25.51 | +| escalator | 57.67 | 80.54 | +| ottoman | 47.92 | 65.52 | +| bottle | 43.41 | 65.64 | +| buffet | 53.2 | 64.86 | +| poster | 39.11 | 51.96 | +| stage | 25.67 | 45.74 | +| van | 43.66 | 59.53 | +| ship | 91.44 | 94.46 | +| fountain | 21.54 | 22.04 | +| conveyer belt | 76.59 | 93.26 | +| canopy | 58.12 | 78.89 | +| washer | 83.65 | 86.51 | +| plaything | 41.08 | 64.15 | +| swimming pool | 66.48 | 87.37 | +| stool | 54.82 | 69.34 | +| barrel | 47.88 | 69.02 | +| basket | 41.11 | 58.06 | +| waterfall | 61.42 | 86.31 | +| tent | 90.38 | 98.7 | +| bag | 21.28 | 25.1 | +| minibike | 76.09 | 89.33 | +| cradle | 84.59 | 97.33 | +| oven | 64.05 | 73.25 | +| ball | 52.88 | 63.43 | +| food | 57.21 | 69.33 | +| step | 13.43 | 16.22 | +| tank | 69.55 | 75.16 | +| trade name | 28.63 | 32.0 | +| microwave | 89.44 | 95.57 | +| pot | 59.08 | 68.43 | +| animal | 67.28 | 68.9 | +| bicycle | 59.63 | 75.84 | +| lake | 55.58 | 63.76 | +| dishwasher | 73.41 | 83.98 | +| screen | 57.63 | 87.08 | +| blanket | 32.67 | 36.23 | +| sculpture | 74.71 | 88.35 | +| hood | 61.43 | 73.18 | +| sconce | 55.69 | 63.25 | +| vase | 48.38 | 59.07 | +| traffic light | 41.03 | 62.95 | +| tray | 15.02 | 19.1 | +| ashcan | 48.71 | 63.93 | +| fan | 66.3 | 78.11 | +| pier | 37.37 | 48.77 | +| crt screen | 21.59 | 30.09 | +| plate | 59.25 | 76.76 | +| monitor | 68.27 | 84.35 | +| bulletin board | 52.67 | 57.61 | +| shower | 9.88 | 11.0 | +| radiator | 65.52 | 75.48 | +| glass | 19.56 | 21.31 | +| clock | 47.38 | 54.42 | +| flag | 71.78 | 78.09 | ++---------------------+-------+-------+ +2024-06-19 05:28:15,657 - mmseg - INFO - Summary: +2024-06-19 05:28:15,657 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.27 | 57.81 | 70.31 | ++-------+-------+-------+ +2024-06-19 05:28:15,658 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 05:28:15,658 - mmseg - INFO - Iter(val) [250] aAcc: 0.8627, mIoU: 0.5781, mAcc: 0.7031, IoU.wall: 0.8257, IoU.building: 0.8566, IoU.sky: 0.9502, IoU.floor: 0.8460, IoU.tree: 0.7691, IoU.ceiling: 0.8783, IoU.road: 0.8647, IoU.bed : 0.9265, IoU.windowpane: 0.6644, IoU.grass: 0.6611, IoU.cabinet: 0.6568, IoU.sidewalk: 0.7211, IoU.person: 0.8591, IoU.earth: 0.3758, IoU.door: 0.5960, IoU.table: 0.7098, IoU.mountain: 0.6196, IoU.plant: 0.5499, IoU.curtain: 0.7785, IoU.chair: 0.6817, IoU.car: 0.8741, IoU.water: 0.6583, IoU.painting: 0.7720, IoU.sofa: 0.8235, IoU.shelf: 0.5144, IoU.house: 0.5180, IoU.sea: 0.6928, IoU.mirror: 0.7877, IoU.rug: 0.6674, IoU.field: 0.3287, IoU.armchair: 0.6176, IoU.seat: 0.6496, IoU.fence: 0.5118, IoU.desk: 0.5829, IoU.rock: 0.5731, IoU.wardrobe: 0.5530, IoU.lamp: 0.7511, IoU.bathtub: 0.8468, IoU.railing: 0.3882, IoU.cushion: 0.7132, IoU.base: 0.4209, IoU.box: 0.3791, IoU.column: 0.5615, IoU.signboard: 0.4100, IoU.chest of drawers: 0.4611, IoU.counter: 0.4371, IoU.sand: 0.5447, IoU.sink: 0.7685, IoU.skyscraper: 0.4985, IoU.fireplace: 0.7518, IoU.refrigerator: 0.8369, IoU.grandstand: 0.5025, IoU.path: 0.2695, IoU.stairs: 0.2543, IoU.runway: 0.7325, IoU.case: 0.5769, IoU.pool table: 0.9494, IoU.pillow: 0.7070, IoU.screen door: 0.7712, IoU.stairway: 0.4143, IoU.river: 0.2211, IoU.bridge: 0.7618, IoU.bookcase: 0.4650, IoU.blind: 0.4236, IoU.coffee table: 0.6757, IoU.toilet: 0.9001, IoU.flower: 0.4564, IoU.book: 0.5996, IoU.hill: 0.0733, IoU.bench: 0.5615, IoU.countertop: 0.6291, IoU.stove: 0.8860, IoU.palm: 0.5671, IoU.kitchen island: 0.5062, IoU.computer: 0.7989, IoU.swivel chair: 0.5133, IoU.boat: 0.6414, IoU.bar: 0.5634, IoU.arcade machine: 0.7918, IoU.hovel: 0.4378, IoU.bus: 0.9360, IoU.towel: 0.7381, IoU.light: 0.6133, IoU.truck: 0.4549, IoU.tower: 0.1206, IoU.chandelier: 0.7301, IoU.awning: 0.4505, IoU.streetlight: 0.3560, IoU.booth: 0.5070, IoU.television receiver: 0.8090, IoU.airplane: 0.8536, IoU.dirt track: 0.0862, IoU.apparel: 0.4956, IoU.pole: 0.2879, IoU.land: 0.0408, IoU.bannister: 0.1823, IoU.escalator: 0.5767, IoU.ottoman: 0.4792, IoU.bottle: 0.4341, IoU.buffet: 0.5320, IoU.poster: 0.3911, IoU.stage: 0.2567, IoU.van: 0.4366, IoU.ship: 0.9144, IoU.fountain: 0.2154, IoU.conveyer belt: 0.7659, IoU.canopy: 0.5812, IoU.washer: 0.8365, IoU.plaything: 0.4108, IoU.swimming pool: 0.6648, IoU.stool: 0.5482, IoU.barrel: 0.4788, IoU.basket: 0.4111, IoU.waterfall: 0.6142, IoU.tent: 0.9038, IoU.bag: 0.2128, IoU.minibike: 0.7609, IoU.cradle: 0.8459, IoU.oven: 0.6405, IoU.ball: 0.5288, IoU.food: 0.5721, IoU.step: 0.1343, IoU.tank: 0.6955, IoU.trade name: 0.2863, IoU.microwave: 0.8944, IoU.pot: 0.5908, IoU.animal: 0.6728, IoU.bicycle: 0.5963, IoU.lake: 0.5558, IoU.dishwasher: 0.7341, IoU.screen: 0.5763, IoU.blanket: 0.3267, IoU.sculpture: 0.7471, IoU.hood: 0.6143, IoU.sconce: 0.5569, IoU.vase: 0.4838, IoU.traffic light: 0.4103, IoU.tray: 0.1502, IoU.ashcan: 0.4871, IoU.fan: 0.6630, IoU.pier: 0.3737, IoU.crt screen: 0.2159, IoU.plate: 0.5925, IoU.monitor: 0.6827, IoU.bulletin board: 0.5267, IoU.shower: 0.0988, IoU.radiator: 0.6552, IoU.glass: 0.1956, IoU.clock: 0.4738, IoU.flag: 0.7178, Acc.wall: 0.9014, Acc.building: 0.9407, Acc.sky: 0.9791, Acc.floor: 0.9190, Acc.tree: 0.9017, Acc.ceiling: 0.9383, Acc.road: 0.9191, Acc.bed : 0.9673, Acc.windowpane: 0.8351, Acc.grass: 0.7950, Acc.cabinet: 0.7570, Acc.sidewalk: 0.8685, Acc.person: 0.9417, Acc.earth: 0.5027, Acc.door: 0.7333, Acc.table: 0.8312, Acc.mountain: 0.7437, Acc.plant: 0.6489, Acc.curtain: 0.8741, Acc.chair: 0.7795, Acc.car: 0.9435, Acc.water: 0.8008, Acc.painting: 0.9016, Acc.sofa: 0.9182, Acc.shelf: 0.6914, Acc.house: 0.6117, Acc.sea: 0.8402, Acc.mirror: 0.8552, Acc.rug: 0.7624, Acc.field: 0.6076, Acc.armchair: 0.7924, Acc.seat: 0.8825, Acc.fence: 0.6596, Acc.desk: 0.8012, Acc.rock: 0.8007, Acc.wardrobe: 0.7563, Acc.lamp: 0.8527, Acc.bathtub: 0.8694, Acc.railing: 0.5439, Acc.cushion: 0.8373, Acc.base: 0.5728, Acc.box: 0.4735, Acc.column: 0.6779, Acc.signboard: 0.5656, Acc.chest of drawers: 0.7029, Acc.counter: 0.5379, Acc.sand: 0.7631, Acc.sink: 0.8354, Acc.skyscraper: 0.6111, Acc.fireplace: 0.9314, Acc.refrigerator: 0.9279, Acc.grandstand: 0.8450, Acc.path: 0.3719, Acc.stairs: 0.3310, Acc.runway: 0.9662, Acc.case: 0.8115, Acc.pool table: 0.9774, Acc.pillow: 0.8119, Acc.screen door: 0.7992, Acc.stairway: 0.5379, Acc.river: 0.4401, Acc.bridge: 0.8689, Acc.bookcase: 0.6145, Acc.blind: 0.4557, Acc.coffee table: 0.8690, Acc.toilet: 0.9380, Acc.flower: 0.5649, Acc.book: 0.7652, Acc.hill: 0.1037, Acc.bench: 0.6580, Acc.countertop: 0.8482, Acc.stove: 0.9535, Acc.palm: 0.8010, Acc.kitchen island: 0.8681, Acc.computer: 0.9224, Acc.swivel chair: 0.7547, Acc.boat: 0.8780, Acc.bar: 0.7477, Acc.arcade machine: 0.8418, Acc.hovel: 0.4932, Acc.bus: 0.9607, Acc.towel: 0.8253, Acc.light: 0.7097, Acc.truck: 0.5829, Acc.tower: 0.1664, Acc.chandelier: 0.8771, Acc.awning: 0.5869, Acc.streetlight: 0.4718, Acc.booth: 0.6875, Acc.television receiver: 0.8597, Acc.airplane: 0.9160, Acc.dirt track: 0.3657, Acc.apparel: 0.7089, Acc.pole: 0.3924, Acc.land: 0.0646, Acc.bannister: 0.2551, Acc.escalator: 0.8054, Acc.ottoman: 0.6552, Acc.bottle: 0.6564, Acc.buffet: 0.6486, Acc.poster: 0.5196, Acc.stage: 0.4574, Acc.van: 0.5953, Acc.ship: 0.9446, Acc.fountain: 0.2204, Acc.conveyer belt: 0.9326, Acc.canopy: 0.7889, Acc.washer: 0.8651, Acc.plaything: 0.6415, Acc.swimming pool: 0.8737, Acc.stool: 0.6934, Acc.barrel: 0.6902, Acc.basket: 0.5806, Acc.waterfall: 0.8631, Acc.tent: 0.9870, Acc.bag: 0.2510, Acc.minibike: 0.8933, Acc.cradle: 0.9733, Acc.oven: 0.7325, Acc.ball: 0.6343, Acc.food: 0.6933, Acc.step: 0.1622, Acc.tank: 0.7516, Acc.trade name: 0.3200, Acc.microwave: 0.9557, Acc.pot: 0.6843, Acc.animal: 0.6890, Acc.bicycle: 0.7584, Acc.lake: 0.6376, Acc.dishwasher: 0.8398, Acc.screen: 0.8708, Acc.blanket: 0.3623, Acc.sculpture: 0.8835, Acc.hood: 0.7318, Acc.sconce: 0.6325, Acc.vase: 0.5907, Acc.traffic light: 0.6295, Acc.tray: 0.1910, Acc.ashcan: 0.6393, Acc.fan: 0.7811, Acc.pier: 0.4877, Acc.crt screen: 0.3009, Acc.plate: 0.7676, Acc.monitor: 0.8435, Acc.bulletin board: 0.5761, Acc.shower: 0.1100, Acc.radiator: 0.7548, Acc.glass: 0.2131, Acc.clock: 0.5442, Acc.flag: 0.7809 +2024-06-19 05:29:22,386 - mmseg - INFO - Iter [73050/80000] lr: 3.476e-06, eta: 2:51:29, time: 3.289, data_time: 1.970, memory: 70498, decode.loss_ce: 0.1452, decode.acc_seg: 93.5632, aux.loss_ce: 0.0631, aux.acc_seg: 93.0746, loss: 0.2083 +2024-06-19 05:30:29,246 - mmseg - INFO - Iter [73100/80000] lr: 3.451e-06, eta: 2:50:15, time: 1.337, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1467, decode.acc_seg: 93.4440, aux.loss_ce: 0.0638, aux.acc_seg: 92.9330, loss: 0.2105 +2024-06-19 05:31:35,485 - mmseg - INFO - Iter [73150/80000] lr: 3.426e-06, eta: 2:49:00, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1522, decode.acc_seg: 93.2952, aux.loss_ce: 0.0656, aux.acc_seg: 92.7750, loss: 0.2178 +2024-06-19 05:32:41,952 - mmseg - INFO - Iter [73200/80000] lr: 3.401e-06, eta: 2:47:45, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1470, decode.acc_seg: 93.4940, aux.loss_ce: 0.0636, aux.acc_seg: 92.9644, loss: 0.2106 +2024-06-19 05:33:48,529 - mmseg - INFO - Iter [73250/80000] lr: 3.375e-06, eta: 2:46:30, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1481, decode.acc_seg: 93.4629, aux.loss_ce: 0.0636, aux.acc_seg: 93.0334, loss: 0.2117 +2024-06-19 05:34:57,754 - mmseg - INFO - Iter [73300/80000] lr: 3.350e-06, eta: 2:45:16, time: 1.384, data_time: 0.065, memory: 70498, decode.loss_ce: 0.1522, decode.acc_seg: 93.4350, aux.loss_ce: 0.0658, aux.acc_seg: 92.9399, loss: 0.2180 +2024-06-19 05:36:04,179 - mmseg - INFO - Iter [73350/80000] lr: 3.325e-06, eta: 2:44:01, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1368, decode.acc_seg: 94.0902, aux.loss_ce: 0.0592, aux.acc_seg: 93.6010, loss: 0.1961 +2024-06-19 05:37:10,556 - mmseg - INFO - Iter [73400/80000] lr: 3.300e-06, eta: 2:42:47, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1439, decode.acc_seg: 93.4996, aux.loss_ce: 0.0623, aux.acc_seg: 92.9761, loss: 0.2062 +2024-06-19 05:38:17,195 - mmseg - INFO - Iter [73450/80000] lr: 3.276e-06, eta: 2:41:32, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1517, decode.acc_seg: 93.2232, aux.loss_ce: 0.0656, aux.acc_seg: 92.6671, loss: 0.2173 +2024-06-19 05:39:23,800 - mmseg - INFO - Iter [73500/80000] lr: 3.251e-06, eta: 2:40:17, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1480, decode.acc_seg: 93.4506, aux.loss_ce: 0.0636, aux.acc_seg: 92.9697, loss: 0.2117 +2024-06-19 05:40:29,974 - mmseg - INFO - Iter [73550/80000] lr: 3.226e-06, eta: 2:39:03, time: 1.323, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1442, decode.acc_seg: 93.8059, aux.loss_ce: 0.0626, aux.acc_seg: 93.2983, loss: 0.2069 +2024-06-19 05:41:36,165 - mmseg - INFO - Iter [73600/80000] lr: 3.201e-06, eta: 2:37:48, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1488, decode.acc_seg: 93.3138, aux.loss_ce: 0.0643, aux.acc_seg: 92.7598, loss: 0.2132 +2024-06-19 05:42:42,757 - mmseg - INFO - Iter [73650/80000] lr: 3.176e-06, eta: 2:36:33, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1481, decode.acc_seg: 93.2049, aux.loss_ce: 0.0640, aux.acc_seg: 92.7204, loss: 0.2121 +2024-06-19 05:43:49,409 - mmseg - INFO - Iter [73700/80000] lr: 3.150e-06, eta: 2:35:19, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1457, decode.acc_seg: 93.6141, aux.loss_ce: 0.0632, aux.acc_seg: 93.1062, loss: 0.2089 +2024-06-19 05:44:55,659 - mmseg - INFO - Iter [73750/80000] lr: 3.125e-06, eta: 2:34:04, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1434, decode.acc_seg: 93.6879, aux.loss_ce: 0.0620, aux.acc_seg: 93.1747, loss: 0.2054 +2024-06-19 05:46:02,331 - mmseg - INFO - Iter [73800/80000] lr: 3.100e-06, eta: 2:32:50, time: 1.333, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1454, decode.acc_seg: 93.6376, aux.loss_ce: 0.0633, aux.acc_seg: 93.1359, loss: 0.2087 +2024-06-19 05:47:08,558 - mmseg - INFO - Iter [73850/80000] lr: 3.076e-06, eta: 2:31:35, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1523, decode.acc_seg: 93.4879, aux.loss_ce: 0.0663, aux.acc_seg: 92.9138, loss: 0.2186 +2024-06-19 05:48:14,934 - mmseg - INFO - Iter [73900/80000] lr: 3.051e-06, eta: 2:30:20, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1457, decode.acc_seg: 93.7285, aux.loss_ce: 0.0635, aux.acc_seg: 93.1931, loss: 0.2091 +2024-06-19 05:49:21,319 - mmseg - INFO - Iter [73950/80000] lr: 3.026e-06, eta: 2:29:06, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1433, decode.acc_seg: 93.5882, aux.loss_ce: 0.0621, aux.acc_seg: 93.0555, loss: 0.2054 +2024-06-19 05:50:27,941 - mmseg - INFO - Saving checkpoint at 74000 iterations +2024-06-19 05:52:09,527 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 05:52:09,527 - mmseg - INFO - Iter [74000/80000] lr: 3.001e-06, eta: 2:28:00, time: 3.364, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1449, decode.acc_seg: 93.5734, aux.loss_ce: 0.0632, aux.acc_seg: 93.0382, loss: 0.2081 +2024-06-19 05:53:59,702 - mmseg - INFO - per class results: +2024-06-19 05:53:59,708 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.62 | 90.09 | +| building | 85.64 | 93.63 | +| sky | 95.05 | 97.6 | +| floor | 84.94 | 91.92 | +| tree | 77.41 | 90.27 | +| ceiling | 87.86 | 94.5 | +| road | 86.62 | 92.14 | +| bed | 92.55 | 96.82 | +| windowpane | 66.46 | 82.33 | +| grass | 66.68 | 80.13 | +| cabinet | 65.35 | 75.64 | +| sidewalk | 71.97 | 85.46 | +| person | 85.81 | 94.2 | +| earth | 37.92 | 49.2 | +| door | 59.97 | 74.99 | +| table | 70.73 | 82.14 | +| mountain | 61.36 | 74.61 | +| plant | 56.12 | 66.5 | +| curtain | 77.81 | 87.68 | +| chair | 68.31 | 78.08 | +| car | 87.47 | 94.7 | +| water | 65.82 | 80.18 | +| painting | 76.99 | 91.06 | +| sofa | 82.53 | 91.24 | +| shelf | 51.18 | 69.33 | +| house | 50.49 | 61.02 | +| sea | 68.96 | 83.77 | +| mirror | 77.75 | 83.34 | +| rug | 67.63 | 78.93 | +| field | 33.99 | 65.94 | +| armchair | 62.2 | 80.24 | +| seat | 64.92 | 88.53 | +| fence | 51.6 | 64.35 | +| desk | 59.05 | 77.18 | +| rock | 56.26 | 82.13 | +| wardrobe | 54.12 | 76.12 | +| lamp | 74.58 | 86.08 | +| bathtub | 85.05 | 87.09 | +| railing | 40.57 | 58.52 | +| cushion | 71.34 | 84.62 | +| base | 42.25 | 56.57 | +| box | 38.73 | 50.41 | +| column | 56.11 | 69.82 | +| signboard | 41.68 | 58.34 | +| chest of drawers | 46.62 | 69.74 | +| counter | 45.68 | 56.05 | +| sand | 52.39 | 76.45 | +| sink | 77.31 | 84.32 | +| skyscraper | 50.14 | 61.44 | +| fireplace | 74.8 | 93.15 | +| refrigerator | 81.75 | 92.88 | +| grandstand | 50.12 | 83.38 | +| path | 26.49 | 37.83 | +| stairs | 23.3 | 29.42 | +| runway | 72.34 | 95.3 | +| case | 58.21 | 80.37 | +| pool table | 94.92 | 97.75 | +| pillow | 69.66 | 78.86 | +| screen door | 77.51 | 79.91 | +| stairway | 40.33 | 58.5 | +| river | 21.15 | 42.35 | +| bridge | 75.74 | 86.25 | +| bookcase | 45.87 | 58.12 | +| blind | 43.6 | 48.33 | +| coffee table | 66.99 | 87.61 | +| toilet | 89.85 | 93.5 | +| flower | 45.15 | 59.94 | +| book | 57.63 | 78.12 | +| hill | 6.95 | 9.63 | +| bench | 56.36 | 64.5 | +| countertop | 64.0 | 82.61 | +| stove | 87.74 | 93.91 | +| palm | 56.72 | 81.88 | +| kitchen island | 51.07 | 84.85 | +| computer | 80.26 | 92.09 | +| swivel chair | 51.12 | 75.18 | +| boat | 66.47 | 88.82 | +| bar | 56.97 | 75.8 | +| arcade machine | 79.05 | 83.82 | +| hovel | 43.63 | 48.9 | +| bus | 93.59 | 96.1 | +| towel | 73.9 | 84.19 | +| light | 61.64 | 73.13 | +| truck | 47.29 | 61.07 | +| tower | 11.55 | 16.4 | +| chandelier | 71.86 | 86.57 | +| awning | 43.82 | 56.69 | +| streetlight | 34.54 | 47.18 | +| booth | 45.78 | 64.62 | +| television receiver | 81.54 | 87.52 | +| airplane | 84.63 | 91.56 | +| dirt track | 10.7 | 49.39 | +| apparel | 46.43 | 67.16 | +| pole | 28.55 | 39.76 | +| land | 4.13 | 6.43 | +| bannister | 18.79 | 27.4 | +| escalator | 58.91 | 79.02 | +| ottoman | 49.07 | 66.15 | +| bottle | 43.0 | 66.33 | +| buffet | 52.51 | 65.46 | +| poster | 38.18 | 47.23 | +| stage | 26.59 | 46.97 | +| van | 45.01 | 57.69 | +| ship | 92.65 | 96.52 | +| fountain | 21.23 | 21.98 | +| conveyer belt | 77.82 | 93.33 | +| canopy | 57.43 | 77.36 | +| washer | 82.15 | 85.4 | +| plaything | 41.04 | 56.42 | +| swimming pool | 70.39 | 90.86 | +| stool | 52.69 | 67.71 | +| barrel | 47.32 | 67.61 | +| basket | 40.66 | 58.65 | +| waterfall | 56.47 | 84.74 | +| tent | 90.19 | 98.95 | +| bag | 20.86 | 24.37 | +| minibike | 76.55 | 88.94 | +| cradle | 85.07 | 97.26 | +| oven | 59.24 | 70.36 | +| ball | 54.27 | 64.64 | +| food | 59.09 | 71.22 | +| step | 13.18 | 15.64 | +| tank | 67.37 | 72.02 | +| trade name | 31.75 | 37.7 | +| microwave | 89.16 | 95.8 | +| pot | 59.17 | 68.54 | +| animal | 65.76 | 67.42 | +| bicycle | 60.1 | 76.61 | +| lake | 57.4 | 63.72 | +| dishwasher | 74.11 | 83.89 | +| screen | 57.8 | 86.43 | +| blanket | 33.87 | 38.22 | +| sculpture | 73.34 | 88.74 | +| hood | 61.63 | 74.21 | +| sconce | 56.64 | 65.34 | +| vase | 48.03 | 60.03 | +| traffic light | 42.56 | 60.7 | +| tray | 15.98 | 20.86 | +| ashcan | 49.12 | 64.37 | +| fan | 66.86 | 80.79 | +| pier | 40.92 | 53.77 | +| crt screen | 20.91 | 28.97 | +| plate | 60.15 | 78.33 | +| monitor | 68.68 | 84.33 | +| bulletin board | 52.39 | 58.5 | +| shower | 5.27 | 5.37 | +| radiator | 65.42 | 75.15 | +| glass | 20.6 | 22.93 | +| clock | 46.39 | 54.59 | +| flag | 72.05 | 80.2 | ++---------------------+-------+-------+ +2024-06-19 05:53:59,708 - mmseg - INFO - Summary: +2024-06-19 05:53:59,709 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.29 | 57.76 | 70.48 | ++-------+-------+-------+ +2024-06-19 05:53:59,709 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 05:53:59,710 - mmseg - INFO - Iter(val) [250] aAcc: 0.8629, mIoU: 0.5776, mAcc: 0.7048, IoU.wall: 0.8262, IoU.building: 0.8564, IoU.sky: 0.9505, IoU.floor: 0.8494, IoU.tree: 0.7741, IoU.ceiling: 0.8786, IoU.road: 0.8662, IoU.bed : 0.9255, IoU.windowpane: 0.6646, IoU.grass: 0.6668, IoU.cabinet: 0.6535, IoU.sidewalk: 0.7197, IoU.person: 0.8581, IoU.earth: 0.3792, IoU.door: 0.5997, IoU.table: 0.7073, IoU.mountain: 0.6136, IoU.plant: 0.5612, IoU.curtain: 0.7781, IoU.chair: 0.6831, IoU.car: 0.8747, IoU.water: 0.6582, IoU.painting: 0.7699, IoU.sofa: 0.8253, IoU.shelf: 0.5118, IoU.house: 0.5049, IoU.sea: 0.6896, IoU.mirror: 0.7775, IoU.rug: 0.6763, IoU.field: 0.3399, IoU.armchair: 0.6220, IoU.seat: 0.6492, IoU.fence: 0.5160, IoU.desk: 0.5905, IoU.rock: 0.5626, IoU.wardrobe: 0.5412, IoU.lamp: 0.7458, IoU.bathtub: 0.8505, IoU.railing: 0.4057, IoU.cushion: 0.7134, IoU.base: 0.4225, IoU.box: 0.3873, IoU.column: 0.5611, IoU.signboard: 0.4168, IoU.chest of drawers: 0.4662, IoU.counter: 0.4568, IoU.sand: 0.5239, IoU.sink: 0.7731, IoU.skyscraper: 0.5014, IoU.fireplace: 0.7480, IoU.refrigerator: 0.8175, IoU.grandstand: 0.5012, IoU.path: 0.2649, IoU.stairs: 0.2330, IoU.runway: 0.7234, IoU.case: 0.5821, IoU.pool table: 0.9492, IoU.pillow: 0.6966, IoU.screen door: 0.7751, IoU.stairway: 0.4033, IoU.river: 0.2115, IoU.bridge: 0.7574, IoU.bookcase: 0.4587, IoU.blind: 0.4360, IoU.coffee table: 0.6699, IoU.toilet: 0.8985, IoU.flower: 0.4515, IoU.book: 0.5763, IoU.hill: 0.0695, IoU.bench: 0.5636, IoU.countertop: 0.6400, IoU.stove: 0.8774, IoU.palm: 0.5672, IoU.kitchen island: 0.5107, IoU.computer: 0.8026, IoU.swivel chair: 0.5112, IoU.boat: 0.6647, IoU.bar: 0.5697, IoU.arcade machine: 0.7905, IoU.hovel: 0.4363, IoU.bus: 0.9359, IoU.towel: 0.7390, IoU.light: 0.6164, IoU.truck: 0.4729, IoU.tower: 0.1155, IoU.chandelier: 0.7186, IoU.awning: 0.4382, IoU.streetlight: 0.3454, IoU.booth: 0.4578, IoU.television receiver: 0.8154, IoU.airplane: 0.8463, IoU.dirt track: 0.1070, IoU.apparel: 0.4643, IoU.pole: 0.2855, IoU.land: 0.0413, IoU.bannister: 0.1879, IoU.escalator: 0.5891, IoU.ottoman: 0.4907, IoU.bottle: 0.4300, IoU.buffet: 0.5251, IoU.poster: 0.3818, IoU.stage: 0.2659, IoU.van: 0.4501, IoU.ship: 0.9265, IoU.fountain: 0.2123, IoU.conveyer belt: 0.7782, IoU.canopy: 0.5743, IoU.washer: 0.8215, IoU.plaything: 0.4104, IoU.swimming pool: 0.7039, IoU.stool: 0.5269, IoU.barrel: 0.4732, IoU.basket: 0.4066, IoU.waterfall: 0.5647, IoU.tent: 0.9019, IoU.bag: 0.2086, IoU.minibike: 0.7655, IoU.cradle: 0.8507, IoU.oven: 0.5924, IoU.ball: 0.5427, IoU.food: 0.5909, IoU.step: 0.1318, IoU.tank: 0.6737, IoU.trade name: 0.3175, IoU.microwave: 0.8916, IoU.pot: 0.5917, IoU.animal: 0.6576, IoU.bicycle: 0.6010, IoU.lake: 0.5740, IoU.dishwasher: 0.7411, IoU.screen: 0.5780, IoU.blanket: 0.3387, IoU.sculpture: 0.7334, IoU.hood: 0.6163, IoU.sconce: 0.5664, IoU.vase: 0.4803, IoU.traffic light: 0.4256, IoU.tray: 0.1598, IoU.ashcan: 0.4912, IoU.fan: 0.6686, IoU.pier: 0.4092, IoU.crt screen: 0.2091, IoU.plate: 0.6015, IoU.monitor: 0.6868, IoU.bulletin board: 0.5239, IoU.shower: 0.0527, IoU.radiator: 0.6542, IoU.glass: 0.2060, IoU.clock: 0.4639, IoU.flag: 0.7205, Acc.wall: 0.9009, Acc.building: 0.9363, Acc.sky: 0.9760, Acc.floor: 0.9192, Acc.tree: 0.9027, Acc.ceiling: 0.9450, Acc.road: 0.9214, Acc.bed : 0.9682, Acc.windowpane: 0.8233, Acc.grass: 0.8013, Acc.cabinet: 0.7564, Acc.sidewalk: 0.8546, Acc.person: 0.9420, Acc.earth: 0.4920, Acc.door: 0.7499, Acc.table: 0.8214, Acc.mountain: 0.7461, Acc.plant: 0.6650, Acc.curtain: 0.8768, Acc.chair: 0.7808, Acc.car: 0.9470, Acc.water: 0.8018, Acc.painting: 0.9106, Acc.sofa: 0.9124, Acc.shelf: 0.6933, Acc.house: 0.6102, Acc.sea: 0.8377, Acc.mirror: 0.8334, Acc.rug: 0.7893, Acc.field: 0.6594, Acc.armchair: 0.8024, Acc.seat: 0.8853, Acc.fence: 0.6435, Acc.desk: 0.7718, Acc.rock: 0.8213, Acc.wardrobe: 0.7612, Acc.lamp: 0.8608, Acc.bathtub: 0.8709, Acc.railing: 0.5852, Acc.cushion: 0.8462, Acc.base: 0.5657, Acc.box: 0.5041, Acc.column: 0.6982, Acc.signboard: 0.5834, Acc.chest of drawers: 0.6974, Acc.counter: 0.5605, Acc.sand: 0.7645, Acc.sink: 0.8432, Acc.skyscraper: 0.6144, Acc.fireplace: 0.9315, Acc.refrigerator: 0.9288, Acc.grandstand: 0.8338, Acc.path: 0.3783, Acc.stairs: 0.2942, Acc.runway: 0.9530, Acc.case: 0.8037, Acc.pool table: 0.9775, Acc.pillow: 0.7886, Acc.screen door: 0.7991, Acc.stairway: 0.5850, Acc.river: 0.4235, Acc.bridge: 0.8625, Acc.bookcase: 0.5812, Acc.blind: 0.4833, Acc.coffee table: 0.8761, Acc.toilet: 0.9350, Acc.flower: 0.5994, Acc.book: 0.7812, Acc.hill: 0.0963, Acc.bench: 0.6450, Acc.countertop: 0.8261, Acc.stove: 0.9391, Acc.palm: 0.8188, Acc.kitchen island: 0.8485, Acc.computer: 0.9209, Acc.swivel chair: 0.7518, Acc.boat: 0.8882, Acc.bar: 0.7580, Acc.arcade machine: 0.8382, Acc.hovel: 0.4890, Acc.bus: 0.9610, Acc.towel: 0.8419, Acc.light: 0.7313, Acc.truck: 0.6107, Acc.tower: 0.1640, Acc.chandelier: 0.8657, Acc.awning: 0.5669, Acc.streetlight: 0.4718, Acc.booth: 0.6462, Acc.television receiver: 0.8752, Acc.airplane: 0.9156, Acc.dirt track: 0.4939, Acc.apparel: 0.6716, Acc.pole: 0.3976, Acc.land: 0.0643, Acc.bannister: 0.2740, Acc.escalator: 0.7902, Acc.ottoman: 0.6615, Acc.bottle: 0.6633, Acc.buffet: 0.6546, Acc.poster: 0.4723, Acc.stage: 0.4697, Acc.van: 0.5769, Acc.ship: 0.9652, Acc.fountain: 0.2198, Acc.conveyer belt: 0.9333, Acc.canopy: 0.7736, Acc.washer: 0.8540, Acc.plaything: 0.5642, Acc.swimming pool: 0.9086, Acc.stool: 0.6771, Acc.barrel: 0.6761, Acc.basket: 0.5865, Acc.waterfall: 0.8474, Acc.tent: 0.9895, Acc.bag: 0.2437, Acc.minibike: 0.8894, Acc.cradle: 0.9726, Acc.oven: 0.7036, Acc.ball: 0.6464, Acc.food: 0.7122, Acc.step: 0.1564, Acc.tank: 0.7202, Acc.trade name: 0.3770, Acc.microwave: 0.9580, Acc.pot: 0.6854, Acc.animal: 0.6742, Acc.bicycle: 0.7661, Acc.lake: 0.6372, Acc.dishwasher: 0.8389, Acc.screen: 0.8643, Acc.blanket: 0.3822, Acc.sculpture: 0.8874, Acc.hood: 0.7421, Acc.sconce: 0.6534, Acc.vase: 0.6003, Acc.traffic light: 0.6070, Acc.tray: 0.2086, Acc.ashcan: 0.6437, Acc.fan: 0.8079, Acc.pier: 0.5377, Acc.crt screen: 0.2897, Acc.plate: 0.7833, Acc.monitor: 0.8433, Acc.bulletin board: 0.5850, Acc.shower: 0.0537, Acc.radiator: 0.7515, Acc.glass: 0.2293, Acc.clock: 0.5459, Acc.flag: 0.8020 +2024-06-19 05:55:06,647 - mmseg - INFO - Iter [74050/80000] lr: 2.976e-06, eta: 2:26:54, time: 3.542, data_time: 2.220, memory: 70498, decode.loss_ce: 0.1518, decode.acc_seg: 93.2082, aux.loss_ce: 0.0655, aux.acc_seg: 92.7092, loss: 0.2173 +2024-06-19 05:56:13,233 - mmseg - INFO - Iter [74100/80000] lr: 2.950e-06, eta: 2:25:39, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1438, decode.acc_seg: 93.7372, aux.loss_ce: 0.0626, aux.acc_seg: 93.1890, loss: 0.2064 +2024-06-19 05:57:19,680 - mmseg - INFO - Iter [74150/80000] lr: 2.925e-06, eta: 2:24:24, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1458, decode.acc_seg: 93.5782, aux.loss_ce: 0.0630, aux.acc_seg: 93.1111, loss: 0.2088 +2024-06-19 05:58:26,188 - mmseg - INFO - Iter [74200/80000] lr: 2.900e-06, eta: 2:23:10, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1499, decode.acc_seg: 93.5624, aux.loss_ce: 0.0652, aux.acc_seg: 92.9921, loss: 0.2151 +2024-06-19 05:59:32,620 - mmseg - INFO - Iter [74250/80000] lr: 2.875e-06, eta: 2:21:55, time: 1.329, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1430, decode.acc_seg: 93.6555, aux.loss_ce: 0.0622, aux.acc_seg: 93.1472, loss: 0.2052 +2024-06-19 06:00:38,830 - mmseg - INFO - Iter [74300/80000] lr: 2.851e-06, eta: 2:20:41, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1440, decode.acc_seg: 93.6909, aux.loss_ce: 0.0621, aux.acc_seg: 93.2006, loss: 0.2062 +2024-06-19 06:01:45,245 - mmseg - INFO - Iter [74350/80000] lr: 2.826e-06, eta: 2:19:26, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1458, decode.acc_seg: 93.5449, aux.loss_ce: 0.0629, aux.acc_seg: 93.0836, loss: 0.2087 +2024-06-19 06:02:51,614 - mmseg - INFO - Iter [74400/80000] lr: 2.801e-06, eta: 2:18:11, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1452, decode.acc_seg: 93.6274, aux.loss_ce: 0.0627, aux.acc_seg: 93.0953, loss: 0.2079 +2024-06-19 06:03:58,178 - mmseg - INFO - Iter [74450/80000] lr: 2.776e-06, eta: 2:16:57, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1384, decode.acc_seg: 93.7331, aux.loss_ce: 0.0605, aux.acc_seg: 93.2180, loss: 0.1989 +2024-06-19 06:05:04,670 - mmseg - INFO - Iter [74500/80000] lr: 2.750e-06, eta: 2:15:42, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1427, decode.acc_seg: 93.7455, aux.loss_ce: 0.0615, aux.acc_seg: 93.2893, loss: 0.2041 +2024-06-19 06:06:13,108 - mmseg - INFO - Iter [74550/80000] lr: 2.725e-06, eta: 2:14:28, time: 1.369, data_time: 0.052, memory: 70498, decode.loss_ce: 0.1514, decode.acc_seg: 93.4563, aux.loss_ce: 0.0654, aux.acc_seg: 92.9868, loss: 0.2167 +2024-06-19 06:07:19,350 - mmseg - INFO - Iter [74600/80000] lr: 2.700e-06, eta: 2:13:13, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1447, decode.acc_seg: 93.6856, aux.loss_ce: 0.0627, aux.acc_seg: 93.1589, loss: 0.2074 +2024-06-19 06:08:25,572 - mmseg - INFO - Iter [74650/80000] lr: 2.675e-06, eta: 2:11:59, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1471, decode.acc_seg: 93.5836, aux.loss_ce: 0.0637, aux.acc_seg: 93.1060, loss: 0.2107 +2024-06-19 06:09:31,886 - mmseg - INFO - Iter [74700/80000] lr: 2.651e-06, eta: 2:10:44, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1478, decode.acc_seg: 93.4878, aux.loss_ce: 0.0644, aux.acc_seg: 92.9461, loss: 0.2122 +2024-06-19 06:10:38,151 - mmseg - INFO - Iter [74750/80000] lr: 2.626e-06, eta: 2:09:29, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1426, decode.acc_seg: 93.5852, aux.loss_ce: 0.0621, aux.acc_seg: 93.1043, loss: 0.2047 +2024-06-19 06:11:44,710 - mmseg - INFO - Iter [74800/80000] lr: 2.601e-06, eta: 2:08:15, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1522, decode.acc_seg: 93.4903, aux.loss_ce: 0.0656, aux.acc_seg: 92.9788, loss: 0.2178 +2024-06-19 06:12:51,132 - mmseg - INFO - Iter [74850/80000] lr: 2.576e-06, eta: 2:07:00, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1434, decode.acc_seg: 93.8030, aux.loss_ce: 0.0620, aux.acc_seg: 93.2672, loss: 0.2054 +2024-06-19 06:13:57,621 - mmseg - INFO - Iter [74900/80000] lr: 2.551e-06, eta: 2:05:46, time: 1.330, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1403, decode.acc_seg: 93.8054, aux.loss_ce: 0.0612, aux.acc_seg: 93.3139, loss: 0.2015 +2024-06-19 06:15:04,194 - mmseg - INFO - Iter [74950/80000] lr: 2.525e-06, eta: 2:04:31, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1450, decode.acc_seg: 93.4361, aux.loss_ce: 0.0629, aux.acc_seg: 92.9551, loss: 0.2079 +2024-06-19 06:16:10,429 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 06:16:10,429 - mmseg - INFO - Iter [75000/80000] lr: 2.500e-06, eta: 2:03:17, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1416, decode.acc_seg: 93.7767, aux.loss_ce: 0.0616, aux.acc_seg: 93.2833, loss: 0.2032 +2024-06-19 06:17:47,260 - mmseg - INFO - per class results: +2024-06-19 06:17:47,266 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.79 | 90.59 | +| building | 85.77 | 93.95 | +| sky | 95.02 | 97.82 | +| floor | 84.99 | 92.14 | +| tree | 77.23 | 89.62 | +| ceiling | 87.83 | 93.86 | +| road | 87.08 | 91.94 | +| bed | 92.43 | 96.89 | +| windowpane | 66.85 | 81.84 | +| grass | 66.55 | 80.27 | +| cabinet | 66.09 | 75.44 | +| sidewalk | 72.44 | 86.14 | +| person | 85.71 | 93.78 | +| earth | 38.88 | 51.37 | +| door | 60.42 | 75.45 | +| table | 70.73 | 82.96 | +| mountain | 61.08 | 73.18 | +| plant | 56.01 | 65.97 | +| curtain | 78.1 | 88.04 | +| chair | 68.05 | 78.94 | +| car | 87.5 | 94.07 | +| water | 65.44 | 78.81 | +| painting | 77.66 | 90.19 | +| sofa | 82.6 | 91.86 | +| shelf | 51.02 | 68.92 | +| house | 51.55 | 60.44 | +| sea | 69.33 | 83.85 | +| mirror | 79.12 | 85.5 | +| rug | 65.74 | 75.95 | +| field | 34.07 | 64.81 | +| armchair | 61.89 | 77.9 | +| seat | 65.71 | 88.41 | +| fence | 50.89 | 65.08 | +| desk | 58.34 | 79.26 | +| rock | 55.67 | 83.54 | +| wardrobe | 56.34 | 75.52 | +| lamp | 74.95 | 85.79 | +| bathtub | 84.87 | 86.93 | +| railing | 40.89 | 58.68 | +| cushion | 70.8 | 81.95 | +| base | 42.46 | 56.72 | +| box | 38.69 | 50.68 | +| column | 56.41 | 68.76 | +| signboard | 41.67 | 56.4 | +| chest of drawers | 47.04 | 70.27 | +| counter | 45.95 | 55.22 | +| sand | 53.66 | 75.36 | +| sink | 78.02 | 83.7 | +| skyscraper | 50.23 | 62.07 | +| fireplace | 76.57 | 92.61 | +| refrigerator | 81.72 | 93.18 | +| grandstand | 50.02 | 83.87 | +| path | 30.39 | 42.18 | +| stairs | 24.26 | 31.65 | +| runway | 73.04 | 96.09 | +| case | 58.78 | 81.47 | +| pool table | 94.83 | 97.77 | +| pillow | 69.31 | 78.39 | +| screen door | 76.35 | 78.8 | +| stairway | 43.1 | 57.85 | +| river | 21.01 | 43.74 | +| bridge | 75.86 | 86.7 | +| bookcase | 47.77 | 62.91 | +| blind | 44.29 | 50.73 | +| coffee table | 68.0 | 87.47 | +| toilet | 89.92 | 93.5 | +| flower | 45.28 | 57.92 | +| book | 59.04 | 77.37 | +| hill | 7.32 | 10.53 | +| bench | 56.04 | 65.42 | +| countertop | 62.96 | 84.14 | +| stove | 87.82 | 94.32 | +| palm | 57.38 | 83.04 | +| kitchen island | 47.02 | 75.32 | +| computer | 79.65 | 92.16 | +| swivel chair | 51.37 | 76.0 | +| boat | 69.44 | 89.04 | +| bar | 55.13 | 75.3 | +| arcade machine | 77.16 | 80.54 | +| hovel | 43.68 | 49.09 | +| bus | 93.22 | 96.32 | +| towel | 74.03 | 81.72 | +| light | 60.74 | 69.56 | +| truck | 46.1 | 59.65 | +| tower | 12.02 | 16.98 | +| chandelier | 71.91 | 86.05 | +| awning | 43.4 | 55.07 | +| streetlight | 36.01 | 47.94 | +| booth | 45.25 | 65.16 | +| television receiver | 81.75 | 87.11 | +| airplane | 84.22 | 90.84 | +| dirt track | 7.21 | 35.29 | +| apparel | 48.39 | 67.41 | +| pole | 29.12 | 41.11 | +| land | 4.06 | 6.06 | +| bannister | 18.01 | 27.5 | +| escalator | 57.51 | 79.47 | +| ottoman | 48.75 | 63.82 | +| bottle | 42.7 | 67.15 | +| buffet | 51.62 | 65.74 | +| poster | 36.78 | 51.7 | +| stage | 25.87 | 47.06 | +| van | 44.27 | 59.84 | +| ship | 92.89 | 96.74 | +| fountain | 21.61 | 22.01 | +| conveyer belt | 78.43 | 93.37 | +| canopy | 56.26 | 78.27 | +| washer | 80.52 | 82.76 | +| plaything | 40.23 | 52.89 | +| swimming pool | 65.22 | 91.83 | +| stool | 52.04 | 68.91 | +| barrel | 46.06 | 68.54 | +| basket | 39.55 | 58.1 | +| waterfall | 57.65 | 86.44 | +| tent | 90.2 | 98.83 | +| bag | 21.16 | 24.67 | +| minibike | 76.18 | 89.23 | +| cradle | 84.52 | 97.3 | +| oven | 65.08 | 75.78 | +| ball | 49.85 | 56.49 | +| food | 59.12 | 71.06 | +| step | 16.43 | 20.04 | +| tank | 66.38 | 71.51 | +| trade name | 30.87 | 35.72 | +| microwave | 90.32 | 95.28 | +| pot | 58.4 | 67.68 | +| animal | 65.3 | 66.73 | +| bicycle | 60.01 | 76.78 | +| lake | 58.53 | 63.4 | +| dishwasher | 74.23 | 83.56 | +| screen | 56.85 | 85.34 | +| blanket | 31.42 | 35.32 | +| sculpture | 75.2 | 88.36 | +| hood | 62.53 | 74.8 | +| sconce | 55.83 | 63.16 | +| vase | 48.87 | 62.02 | +| traffic light | 42.15 | 61.73 | +| tray | 15.41 | 19.39 | +| ashcan | 48.72 | 62.74 | +| fan | 66.6 | 79.17 | +| pier | 40.06 | 53.11 | +| crt screen | 20.32 | 29.8 | +| plate | 59.68 | 77.44 | +| monitor | 68.87 | 84.32 | +| bulletin board | 56.85 | 64.33 | +| shower | 5.95 | 6.49 | +| radiator | 64.98 | 75.96 | +| glass | 19.63 | 21.24 | +| clock | 45.66 | 52.7 | +| flag | 71.39 | 79.2 | ++---------------------+-------+-------+ +2024-06-19 06:17:47,266 - mmseg - INFO - Summary: +2024-06-19 06:17:47,266 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 86.37 | 57.76 | 70.3 | ++-------+-------+------+ +2024-06-19 06:17:47,267 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 06:17:47,267 - mmseg - INFO - Iter(val) [250] aAcc: 0.8637, mIoU: 0.5776, mAcc: 0.7030, IoU.wall: 0.8279, IoU.building: 0.8577, IoU.sky: 0.9502, IoU.floor: 0.8499, IoU.tree: 0.7723, IoU.ceiling: 0.8783, IoU.road: 0.8708, IoU.bed : 0.9243, IoU.windowpane: 0.6685, IoU.grass: 0.6655, IoU.cabinet: 0.6609, IoU.sidewalk: 0.7244, IoU.person: 0.8571, IoU.earth: 0.3888, IoU.door: 0.6042, IoU.table: 0.7073, IoU.mountain: 0.6108, IoU.plant: 0.5601, IoU.curtain: 0.7810, IoU.chair: 0.6805, IoU.car: 0.8750, IoU.water: 0.6544, IoU.painting: 0.7766, IoU.sofa: 0.8260, IoU.shelf: 0.5102, IoU.house: 0.5155, IoU.sea: 0.6933, IoU.mirror: 0.7912, IoU.rug: 0.6574, IoU.field: 0.3407, IoU.armchair: 0.6189, IoU.seat: 0.6571, IoU.fence: 0.5089, IoU.desk: 0.5834, IoU.rock: 0.5567, IoU.wardrobe: 0.5634, IoU.lamp: 0.7495, IoU.bathtub: 0.8487, IoU.railing: 0.4089, IoU.cushion: 0.7080, IoU.base: 0.4246, IoU.box: 0.3869, IoU.column: 0.5641, IoU.signboard: 0.4167, IoU.chest of drawers: 0.4704, IoU.counter: 0.4595, IoU.sand: 0.5366, IoU.sink: 0.7802, IoU.skyscraper: 0.5023, IoU.fireplace: 0.7657, IoU.refrigerator: 0.8172, IoU.grandstand: 0.5002, IoU.path: 0.3039, IoU.stairs: 0.2426, IoU.runway: 0.7304, IoU.case: 0.5878, IoU.pool table: 0.9483, IoU.pillow: 0.6931, IoU.screen door: 0.7635, IoU.stairway: 0.4310, IoU.river: 0.2101, IoU.bridge: 0.7586, IoU.bookcase: 0.4777, IoU.blind: 0.4429, IoU.coffee table: 0.6800, IoU.toilet: 0.8992, IoU.flower: 0.4528, IoU.book: 0.5904, IoU.hill: 0.0732, IoU.bench: 0.5604, IoU.countertop: 0.6296, IoU.stove: 0.8782, IoU.palm: 0.5738, IoU.kitchen island: 0.4702, IoU.computer: 0.7965, IoU.swivel chair: 0.5137, IoU.boat: 0.6944, IoU.bar: 0.5513, IoU.arcade machine: 0.7716, IoU.hovel: 0.4368, IoU.bus: 0.9322, IoU.towel: 0.7403, IoU.light: 0.6074, IoU.truck: 0.4610, IoU.tower: 0.1202, IoU.chandelier: 0.7191, IoU.awning: 0.4340, IoU.streetlight: 0.3601, IoU.booth: 0.4525, IoU.television receiver: 0.8175, IoU.airplane: 0.8422, IoU.dirt track: 0.0721, IoU.apparel: 0.4839, IoU.pole: 0.2912, IoU.land: 0.0406, IoU.bannister: 0.1801, IoU.escalator: 0.5751, IoU.ottoman: 0.4875, IoU.bottle: 0.4270, IoU.buffet: 0.5162, IoU.poster: 0.3678, IoU.stage: 0.2587, IoU.van: 0.4427, IoU.ship: 0.9289, IoU.fountain: 0.2161, IoU.conveyer belt: 0.7843, IoU.canopy: 0.5626, IoU.washer: 0.8052, IoU.plaything: 0.4023, IoU.swimming pool: 0.6522, IoU.stool: 0.5204, IoU.barrel: 0.4606, IoU.basket: 0.3955, IoU.waterfall: 0.5765, IoU.tent: 0.9020, IoU.bag: 0.2116, IoU.minibike: 0.7618, IoU.cradle: 0.8452, IoU.oven: 0.6508, IoU.ball: 0.4985, IoU.food: 0.5912, IoU.step: 0.1643, IoU.tank: 0.6638, IoU.trade name: 0.3087, IoU.microwave: 0.9032, IoU.pot: 0.5840, IoU.animal: 0.6530, IoU.bicycle: 0.6001, IoU.lake: 0.5853, IoU.dishwasher: 0.7423, IoU.screen: 0.5685, IoU.blanket: 0.3142, IoU.sculpture: 0.7520, IoU.hood: 0.6253, IoU.sconce: 0.5583, IoU.vase: 0.4887, IoU.traffic light: 0.4215, IoU.tray: 0.1541, IoU.ashcan: 0.4872, IoU.fan: 0.6660, IoU.pier: 0.4006, IoU.crt screen: 0.2032, IoU.plate: 0.5968, IoU.monitor: 0.6887, IoU.bulletin board: 0.5685, IoU.shower: 0.0595, IoU.radiator: 0.6498, IoU.glass: 0.1963, IoU.clock: 0.4566, IoU.flag: 0.7139, Acc.wall: 0.9059, Acc.building: 0.9395, Acc.sky: 0.9782, Acc.floor: 0.9214, Acc.tree: 0.8962, Acc.ceiling: 0.9386, Acc.road: 0.9194, Acc.bed : 0.9689, Acc.windowpane: 0.8184, Acc.grass: 0.8027, Acc.cabinet: 0.7544, Acc.sidewalk: 0.8614, Acc.person: 0.9378, Acc.earth: 0.5137, Acc.door: 0.7545, Acc.table: 0.8296, Acc.mountain: 0.7318, Acc.plant: 0.6597, Acc.curtain: 0.8804, Acc.chair: 0.7894, Acc.car: 0.9407, Acc.water: 0.7881, Acc.painting: 0.9019, Acc.sofa: 0.9186, Acc.shelf: 0.6892, Acc.house: 0.6044, Acc.sea: 0.8385, Acc.mirror: 0.8550, Acc.rug: 0.7595, Acc.field: 0.6481, Acc.armchair: 0.7790, Acc.seat: 0.8841, Acc.fence: 0.6508, Acc.desk: 0.7926, Acc.rock: 0.8354, Acc.wardrobe: 0.7552, Acc.lamp: 0.8579, Acc.bathtub: 0.8693, Acc.railing: 0.5868, Acc.cushion: 0.8195, Acc.base: 0.5672, Acc.box: 0.5068, Acc.column: 0.6876, Acc.signboard: 0.5640, Acc.chest of drawers: 0.7027, Acc.counter: 0.5522, Acc.sand: 0.7536, Acc.sink: 0.8370, Acc.skyscraper: 0.6207, Acc.fireplace: 0.9261, Acc.refrigerator: 0.9318, Acc.grandstand: 0.8387, Acc.path: 0.4218, Acc.stairs: 0.3165, Acc.runway: 0.9609, Acc.case: 0.8147, Acc.pool table: 0.9777, Acc.pillow: 0.7839, Acc.screen door: 0.7880, Acc.stairway: 0.5785, Acc.river: 0.4374, Acc.bridge: 0.8670, Acc.bookcase: 0.6291, Acc.blind: 0.5073, Acc.coffee table: 0.8747, Acc.toilet: 0.9350, Acc.flower: 0.5792, Acc.book: 0.7737, Acc.hill: 0.1053, Acc.bench: 0.6542, Acc.countertop: 0.8414, Acc.stove: 0.9432, Acc.palm: 0.8304, Acc.kitchen island: 0.7532, Acc.computer: 0.9216, Acc.swivel chair: 0.7600, Acc.boat: 0.8904, Acc.bar: 0.7530, Acc.arcade machine: 0.8054, Acc.hovel: 0.4909, Acc.bus: 0.9632, Acc.towel: 0.8172, Acc.light: 0.6956, Acc.truck: 0.5965, Acc.tower: 0.1698, Acc.chandelier: 0.8605, Acc.awning: 0.5507, Acc.streetlight: 0.4794, Acc.booth: 0.6516, Acc.television receiver: 0.8711, Acc.airplane: 0.9084, Acc.dirt track: 0.3529, Acc.apparel: 0.6741, Acc.pole: 0.4111, Acc.land: 0.0606, Acc.bannister: 0.2750, Acc.escalator: 0.7947, Acc.ottoman: 0.6382, Acc.bottle: 0.6715, Acc.buffet: 0.6574, Acc.poster: 0.5170, Acc.stage: 0.4706, Acc.van: 0.5984, Acc.ship: 0.9674, Acc.fountain: 0.2201, Acc.conveyer belt: 0.9337, Acc.canopy: 0.7827, Acc.washer: 0.8276, Acc.plaything: 0.5289, Acc.swimming pool: 0.9183, Acc.stool: 0.6891, Acc.barrel: 0.6854, Acc.basket: 0.5810, Acc.waterfall: 0.8644, Acc.tent: 0.9883, Acc.bag: 0.2467, Acc.minibike: 0.8923, Acc.cradle: 0.9730, Acc.oven: 0.7578, Acc.ball: 0.5649, Acc.food: 0.7106, Acc.step: 0.2004, Acc.tank: 0.7151, Acc.trade name: 0.3572, Acc.microwave: 0.9528, Acc.pot: 0.6768, Acc.animal: 0.6673, Acc.bicycle: 0.7678, Acc.lake: 0.6340, Acc.dishwasher: 0.8356, Acc.screen: 0.8534, Acc.blanket: 0.3532, Acc.sculpture: 0.8836, Acc.hood: 0.7480, Acc.sconce: 0.6316, Acc.vase: 0.6202, Acc.traffic light: 0.6173, Acc.tray: 0.1939, Acc.ashcan: 0.6274, Acc.fan: 0.7917, Acc.pier: 0.5311, Acc.crt screen: 0.2980, Acc.plate: 0.7744, Acc.monitor: 0.8432, Acc.bulletin board: 0.6433, Acc.shower: 0.0649, Acc.radiator: 0.7596, Acc.glass: 0.2124, Acc.clock: 0.5270, Acc.flag: 0.7920 +2024-06-19 06:18:54,121 - mmseg - INFO - Iter [75050/80000] lr: 2.475e-06, eta: 2:02:09, time: 3.274, data_time: 1.953, memory: 70498, decode.loss_ce: 0.1498, decode.acc_seg: 93.5126, aux.loss_ce: 0.0649, aux.acc_seg: 93.0401, loss: 0.2147 +2024-06-19 06:20:00,548 - mmseg - INFO - Iter [75100/80000] lr: 2.451e-06, eta: 2:00:54, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1473, decode.acc_seg: 93.3213, aux.loss_ce: 0.0636, aux.acc_seg: 92.8532, loss: 0.2108 +2024-06-19 06:21:07,089 - mmseg - INFO - Iter [75150/80000] lr: 2.426e-06, eta: 1:59:40, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1354, decode.acc_seg: 93.7998, aux.loss_ce: 0.0589, aux.acc_seg: 93.3308, loss: 0.1943 +2024-06-19 06:22:13,419 - mmseg - INFO - Iter [75200/80000] lr: 2.401e-06, eta: 1:58:25, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1483, decode.acc_seg: 93.7475, aux.loss_ce: 0.0642, aux.acc_seg: 93.2849, loss: 0.2125 +2024-06-19 06:23:19,638 - mmseg - INFO - Iter [75250/80000] lr: 2.376e-06, eta: 1:57:11, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1483, decode.acc_seg: 93.4465, aux.loss_ce: 0.0640, aux.acc_seg: 92.9797, loss: 0.2124 +2024-06-19 06:24:25,914 - mmseg - INFO - Iter [75300/80000] lr: 2.351e-06, eta: 1:55:56, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1394, decode.acc_seg: 93.9270, aux.loss_ce: 0.0603, aux.acc_seg: 93.4984, loss: 0.1997 +2024-06-19 06:25:32,131 - mmseg - INFO - Iter [75350/80000] lr: 2.325e-06, eta: 1:54:42, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1445, decode.acc_seg: 93.7409, aux.loss_ce: 0.0627, aux.acc_seg: 93.2234, loss: 0.2072 +2024-06-19 06:26:38,777 - mmseg - INFO - Iter [75400/80000] lr: 2.300e-06, eta: 1:53:27, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1414, decode.acc_seg: 93.7509, aux.loss_ce: 0.0611, aux.acc_seg: 93.2706, loss: 0.2025 +2024-06-19 06:27:45,187 - mmseg - INFO - Iter [75450/80000] lr: 2.275e-06, eta: 1:52:13, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1558, decode.acc_seg: 93.1605, aux.loss_ce: 0.0673, aux.acc_seg: 92.6614, loss: 0.2230 +2024-06-19 06:28:51,393 - mmseg - INFO - Iter [75500/80000] lr: 2.250e-06, eta: 1:50:58, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1491, decode.acc_seg: 93.3508, aux.loss_ce: 0.0643, aux.acc_seg: 92.9272, loss: 0.2134 +2024-06-19 06:29:57,693 - mmseg - INFO - Iter [75550/80000] lr: 2.226e-06, eta: 1:49:44, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1407, decode.acc_seg: 93.8171, aux.loss_ce: 0.0611, aux.acc_seg: 93.2743, loss: 0.2018 +2024-06-19 06:31:04,214 - mmseg - INFO - Iter [75600/80000] lr: 2.201e-06, eta: 1:48:30, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1425, decode.acc_seg: 93.6912, aux.loss_ce: 0.0621, aux.acc_seg: 93.1624, loss: 0.2046 +2024-06-19 06:32:10,642 - mmseg - INFO - Iter [75650/80000] lr: 2.176e-06, eta: 1:47:15, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1539, decode.acc_seg: 93.0897, aux.loss_ce: 0.0670, aux.acc_seg: 92.5415, loss: 0.2209 +2024-06-19 06:33:16,952 - mmseg - INFO - Iter [75700/80000] lr: 2.151e-06, eta: 1:46:01, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1517, decode.acc_seg: 93.3817, aux.loss_ce: 0.0653, aux.acc_seg: 92.9306, loss: 0.2170 +2024-06-19 06:34:23,131 - mmseg - INFO - Iter [75750/80000] lr: 2.125e-06, eta: 1:44:46, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1440, decode.acc_seg: 93.7722, aux.loss_ce: 0.0622, aux.acc_seg: 93.3131, loss: 0.2062 +2024-06-19 06:35:32,244 - mmseg - INFO - Iter [75800/80000] lr: 2.100e-06, eta: 1:43:32, time: 1.382, data_time: 0.063, memory: 70498, decode.loss_ce: 0.1467, decode.acc_seg: 93.7013, aux.loss_ce: 0.0633, aux.acc_seg: 93.2031, loss: 0.2100 +2024-06-19 06:36:38,684 - mmseg - INFO - Iter [75850/80000] lr: 2.075e-06, eta: 1:42:18, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1400, decode.acc_seg: 93.9425, aux.loss_ce: 0.0613, aux.acc_seg: 93.3579, loss: 0.2013 +2024-06-19 06:37:44,968 - mmseg - INFO - Iter [75900/80000] lr: 2.050e-06, eta: 1:41:03, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1465, decode.acc_seg: 93.6459, aux.loss_ce: 0.0631, aux.acc_seg: 93.1503, loss: 0.2095 +2024-06-19 06:38:51,427 - mmseg - INFO - Iter [75950/80000] lr: 2.026e-06, eta: 1:39:49, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1437, decode.acc_seg: 93.7387, aux.loss_ce: 0.0626, aux.acc_seg: 93.2560, loss: 0.2063 +2024-06-19 06:39:57,726 - mmseg - INFO - Saving checkpoint at 76000 iterations +2024-06-19 06:41:41,603 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 06:41:41,603 - mmseg - INFO - Iter [76000/80000] lr: 2.001e-06, eta: 1:38:40, time: 3.404, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1430, decode.acc_seg: 93.6987, aux.loss_ce: 0.0623, aux.acc_seg: 93.1395, loss: 0.2053 +2024-06-19 06:43:17,210 - mmseg - INFO - per class results: +2024-06-19 06:43:17,216 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.61 | 90.29 | +| building | 85.95 | 94.29 | +| sky | 94.98 | 97.93 | +| floor | 85.0 | 91.82 | +| tree | 77.12 | 89.37 | +| ceiling | 87.95 | 94.12 | +| road | 87.38 | 91.75 | +| bed | 92.56 | 96.65 | +| windowpane | 67.11 | 83.0 | +| grass | 66.25 | 80.53 | +| cabinet | 65.37 | 75.75 | +| sidewalk | 72.54 | 87.04 | +| person | 85.87 | 93.88 | +| earth | 38.24 | 50.74 | +| door | 60.22 | 73.88 | +| table | 70.4 | 82.97 | +| mountain | 61.53 | 73.55 | +| plant | 55.54 | 65.64 | +| curtain | 77.83 | 87.83 | +| chair | 68.19 | 79.62 | +| car | 87.43 | 94.31 | +| water | 64.88 | 78.97 | +| painting | 77.2 | 90.44 | +| sofa | 82.64 | 90.36 | +| shelf | 50.26 | 68.99 | +| house | 55.5 | 66.07 | +| sea | 69.06 | 83.85 | +| mirror | 78.82 | 84.54 | +| rug | 67.51 | 79.62 | +| field | 31.18 | 58.17 | +| armchair | 62.19 | 80.46 | +| seat | 65.28 | 88.68 | +| fence | 50.42 | 62.97 | +| desk | 58.69 | 77.11 | +| rock | 55.83 | 84.11 | +| wardrobe | 55.18 | 77.2 | +| lamp | 75.09 | 85.89 | +| bathtub | 84.96 | 87.06 | +| railing | 40.25 | 57.08 | +| cushion | 71.38 | 83.84 | +| base | 42.73 | 55.85 | +| box | 38.36 | 49.51 | +| column | 56.41 | 67.61 | +| signboard | 41.2 | 56.35 | +| chest of drawers | 46.64 | 67.79 | +| counter | 45.14 | 54.44 | +| sand | 52.32 | 76.79 | +| sink | 78.0 | 83.75 | +| skyscraper | 50.36 | 61.76 | +| fireplace | 77.52 | 92.53 | +| refrigerator | 82.58 | 92.78 | +| grandstand | 50.08 | 82.28 | +| path | 29.08 | 39.46 | +| stairs | 25.18 | 32.96 | +| runway | 73.12 | 96.08 | +| case | 58.24 | 81.06 | +| pool table | 95.0 | 97.55 | +| pillow | 70.87 | 81.9 | +| screen door | 81.54 | 84.18 | +| stairway | 43.63 | 56.67 | +| river | 21.05 | 42.32 | +| bridge | 75.86 | 86.84 | +| bookcase | 44.74 | 58.58 | +| blind | 43.26 | 47.2 | +| coffee table | 67.82 | 86.69 | +| toilet | 90.11 | 93.81 | +| flower | 46.63 | 61.27 | +| book | 57.38 | 80.29 | +| hill | 7.45 | 10.56 | +| bench | 55.93 | 64.5 | +| countertop | 62.5 | 84.41 | +| stove | 88.03 | 94.62 | +| palm | 58.04 | 82.1 | +| kitchen island | 45.77 | 74.58 | +| computer | 80.41 | 91.56 | +| swivel chair | 51.78 | 75.18 | +| boat | 69.37 | 88.85 | +| bar | 55.71 | 74.52 | +| arcade machine | 78.52 | 84.6 | +| hovel | 43.5 | 48.63 | +| bus | 93.76 | 96.0 | +| towel | 74.46 | 82.85 | +| light | 61.06 | 70.79 | +| truck | 46.84 | 59.43 | +| tower | 11.04 | 15.0 | +| chandelier | 72.39 | 86.91 | +| awning | 46.88 | 59.66 | +| streetlight | 35.94 | 48.98 | +| booth | 42.37 | 62.82 | +| television receiver | 81.83 | 86.66 | +| airplane | 83.31 | 90.41 | +| dirt track | 8.42 | 34.15 | +| apparel | 48.99 | 67.85 | +| pole | 29.0 | 39.51 | +| land | 4.15 | 6.58 | +| bannister | 17.27 | 24.12 | +| escalator | 57.54 | 79.71 | +| ottoman | 47.72 | 63.42 | +| bottle | 42.66 | 64.62 | +| buffet | 48.97 | 66.38 | +| poster | 37.98 | 51.01 | +| stage | 26.42 | 46.14 | +| van | 44.11 | 59.84 | +| ship | 93.68 | 96.54 | +| fountain | 21.83 | 22.46 | +| conveyer belt | 77.39 | 93.35 | +| canopy | 59.6 | 79.37 | +| washer | 82.84 | 85.21 | +| plaything | 42.38 | 59.05 | +| swimming pool | 64.37 | 89.18 | +| stool | 54.42 | 66.06 | +| barrel | 45.79 | 66.39 | +| basket | 41.38 | 58.18 | +| waterfall | 57.75 | 83.1 | +| tent | 91.2 | 98.76 | +| bag | 21.36 | 24.94 | +| minibike | 75.77 | 90.15 | +| cradle | 85.07 | 97.16 | +| oven | 61.29 | 70.62 | +| ball | 47.51 | 53.72 | +| food | 56.31 | 67.8 | +| step | 12.49 | 14.91 | +| tank | 65.21 | 69.85 | +| trade name | 29.19 | 33.1 | +| microwave | 89.03 | 95.71 | +| pot | 58.93 | 67.91 | +| animal | 65.45 | 66.94 | +| bicycle | 61.01 | 78.16 | +| lake | 56.84 | 63.73 | +| dishwasher | 74.23 | 83.41 | +| screen | 51.07 | 75.66 | +| blanket | 31.13 | 34.66 | +| sculpture | 75.44 | 87.74 | +| hood | 62.66 | 75.11 | +| sconce | 57.09 | 66.08 | +| vase | 48.86 | 63.44 | +| traffic light | 42.47 | 62.06 | +| tray | 16.11 | 21.47 | +| ashcan | 49.21 | 62.35 | +| fan | 66.87 | 79.58 | +| pier | 38.3 | 53.91 | +| crt screen | 18.59 | 30.39 | +| plate | 59.45 | 76.92 | +| monitor | 69.46 | 83.43 | +| bulletin board | 52.24 | 57.14 | +| shower | 5.9 | 6.51 | +| radiator | 64.73 | 73.61 | +| glass | 19.99 | 21.76 | +| clock | 44.59 | 50.63 | +| flag | 71.46 | 79.41 | ++---------------------+-------+-------+ +2024-06-19 06:43:17,216 - mmseg - INFO - Summary: +2024-06-19 06:43:17,216 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.35 | 57.66 | 70.02 | ++-------+-------+-------+ +2024-06-19 06:43:17,217 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 06:43:17,217 - mmseg - INFO - Iter(val) [250] aAcc: 0.8635, mIoU: 0.5766, mAcc: 0.7002, IoU.wall: 0.8261, IoU.building: 0.8595, IoU.sky: 0.9498, IoU.floor: 0.8500, IoU.tree: 0.7712, IoU.ceiling: 0.8795, IoU.road: 0.8738, IoU.bed : 0.9256, IoU.windowpane: 0.6711, IoU.grass: 0.6625, IoU.cabinet: 0.6537, IoU.sidewalk: 0.7254, IoU.person: 0.8587, IoU.earth: 0.3824, IoU.door: 0.6022, IoU.table: 0.7040, IoU.mountain: 0.6153, IoU.plant: 0.5554, IoU.curtain: 0.7783, IoU.chair: 0.6819, IoU.car: 0.8743, IoU.water: 0.6488, IoU.painting: 0.7720, IoU.sofa: 0.8264, IoU.shelf: 0.5026, IoU.house: 0.5550, IoU.sea: 0.6906, IoU.mirror: 0.7882, IoU.rug: 0.6751, IoU.field: 0.3118, IoU.armchair: 0.6219, IoU.seat: 0.6528, IoU.fence: 0.5042, IoU.desk: 0.5869, IoU.rock: 0.5583, IoU.wardrobe: 0.5518, IoU.lamp: 0.7509, IoU.bathtub: 0.8496, IoU.railing: 0.4025, IoU.cushion: 0.7138, IoU.base: 0.4273, IoU.box: 0.3836, IoU.column: 0.5641, IoU.signboard: 0.4120, IoU.chest of drawers: 0.4664, IoU.counter: 0.4514, IoU.sand: 0.5232, IoU.sink: 0.7800, IoU.skyscraper: 0.5036, IoU.fireplace: 0.7752, IoU.refrigerator: 0.8258, IoU.grandstand: 0.5008, IoU.path: 0.2908, IoU.stairs: 0.2518, IoU.runway: 0.7312, IoU.case: 0.5824, IoU.pool table: 0.9500, IoU.pillow: 0.7087, IoU.screen door: 0.8154, IoU.stairway: 0.4363, IoU.river: 0.2105, IoU.bridge: 0.7586, IoU.bookcase: 0.4474, IoU.blind: 0.4326, IoU.coffee table: 0.6782, IoU.toilet: 0.9011, IoU.flower: 0.4663, IoU.book: 0.5738, IoU.hill: 0.0745, IoU.bench: 0.5593, IoU.countertop: 0.6250, IoU.stove: 0.8803, IoU.palm: 0.5804, IoU.kitchen island: 0.4577, IoU.computer: 0.8041, IoU.swivel chair: 0.5178, IoU.boat: 0.6937, IoU.bar: 0.5571, IoU.arcade machine: 0.7852, IoU.hovel: 0.4350, IoU.bus: 0.9376, IoU.towel: 0.7446, IoU.light: 0.6106, IoU.truck: 0.4684, IoU.tower: 0.1104, IoU.chandelier: 0.7239, IoU.awning: 0.4688, IoU.streetlight: 0.3594, IoU.booth: 0.4237, IoU.television receiver: 0.8183, IoU.airplane: 0.8331, IoU.dirt track: 0.0842, IoU.apparel: 0.4899, IoU.pole: 0.2900, IoU.land: 0.0415, IoU.bannister: 0.1727, IoU.escalator: 0.5754, IoU.ottoman: 0.4772, IoU.bottle: 0.4266, IoU.buffet: 0.4897, IoU.poster: 0.3798, IoU.stage: 0.2642, IoU.van: 0.4411, IoU.ship: 0.9368, IoU.fountain: 0.2183, IoU.conveyer belt: 0.7739, IoU.canopy: 0.5960, IoU.washer: 0.8284, IoU.plaything: 0.4238, IoU.swimming pool: 0.6437, IoU.stool: 0.5442, IoU.barrel: 0.4579, IoU.basket: 0.4138, IoU.waterfall: 0.5775, IoU.tent: 0.9120, IoU.bag: 0.2136, IoU.minibike: 0.7577, IoU.cradle: 0.8507, IoU.oven: 0.6129, IoU.ball: 0.4751, IoU.food: 0.5631, IoU.step: 0.1249, IoU.tank: 0.6521, IoU.trade name: 0.2919, IoU.microwave: 0.8903, IoU.pot: 0.5893, IoU.animal: 0.6545, IoU.bicycle: 0.6101, IoU.lake: 0.5684, IoU.dishwasher: 0.7423, IoU.screen: 0.5107, IoU.blanket: 0.3113, IoU.sculpture: 0.7544, IoU.hood: 0.6266, IoU.sconce: 0.5709, IoU.vase: 0.4886, IoU.traffic light: 0.4247, IoU.tray: 0.1611, IoU.ashcan: 0.4921, IoU.fan: 0.6687, IoU.pier: 0.3830, IoU.crt screen: 0.1859, IoU.plate: 0.5945, IoU.monitor: 0.6946, IoU.bulletin board: 0.5224, IoU.shower: 0.0590, IoU.radiator: 0.6473, IoU.glass: 0.1999, IoU.clock: 0.4459, IoU.flag: 0.7146, Acc.wall: 0.9029, Acc.building: 0.9429, Acc.sky: 0.9793, Acc.floor: 0.9182, Acc.tree: 0.8937, Acc.ceiling: 0.9412, Acc.road: 0.9175, Acc.bed : 0.9665, Acc.windowpane: 0.8300, Acc.grass: 0.8053, Acc.cabinet: 0.7575, Acc.sidewalk: 0.8704, Acc.person: 0.9388, Acc.earth: 0.5074, Acc.door: 0.7388, Acc.table: 0.8297, Acc.mountain: 0.7355, Acc.plant: 0.6564, Acc.curtain: 0.8783, Acc.chair: 0.7962, Acc.car: 0.9431, Acc.water: 0.7897, Acc.painting: 0.9044, Acc.sofa: 0.9036, Acc.shelf: 0.6899, Acc.house: 0.6607, Acc.sea: 0.8385, Acc.mirror: 0.8454, Acc.rug: 0.7962, Acc.field: 0.5817, Acc.armchair: 0.8046, Acc.seat: 0.8868, Acc.fence: 0.6297, Acc.desk: 0.7711, Acc.rock: 0.8411, Acc.wardrobe: 0.7720, Acc.lamp: 0.8589, Acc.bathtub: 0.8706, Acc.railing: 0.5708, Acc.cushion: 0.8384, Acc.base: 0.5585, Acc.box: 0.4951, Acc.column: 0.6761, Acc.signboard: 0.5635, Acc.chest of drawers: 0.6779, Acc.counter: 0.5444, Acc.sand: 0.7679, Acc.sink: 0.8375, Acc.skyscraper: 0.6176, Acc.fireplace: 0.9253, Acc.refrigerator: 0.9278, Acc.grandstand: 0.8228, Acc.path: 0.3946, Acc.stairs: 0.3296, Acc.runway: 0.9608, Acc.case: 0.8106, Acc.pool table: 0.9755, Acc.pillow: 0.8190, Acc.screen door: 0.8418, Acc.stairway: 0.5667, Acc.river: 0.4232, Acc.bridge: 0.8684, Acc.bookcase: 0.5858, Acc.blind: 0.4720, Acc.coffee table: 0.8669, Acc.toilet: 0.9381, Acc.flower: 0.6127, Acc.book: 0.8029, Acc.hill: 0.1056, Acc.bench: 0.6450, Acc.countertop: 0.8441, Acc.stove: 0.9462, Acc.palm: 0.8210, Acc.kitchen island: 0.7458, Acc.computer: 0.9156, Acc.swivel chair: 0.7518, Acc.boat: 0.8885, Acc.bar: 0.7452, Acc.arcade machine: 0.8460, Acc.hovel: 0.4863, Acc.bus: 0.9600, Acc.towel: 0.8285, Acc.light: 0.7079, Acc.truck: 0.5943, Acc.tower: 0.1500, Acc.chandelier: 0.8691, Acc.awning: 0.5966, Acc.streetlight: 0.4898, Acc.booth: 0.6282, Acc.television receiver: 0.8666, Acc.airplane: 0.9041, Acc.dirt track: 0.3415, Acc.apparel: 0.6785, Acc.pole: 0.3951, Acc.land: 0.0658, Acc.bannister: 0.2412, Acc.escalator: 0.7971, Acc.ottoman: 0.6342, Acc.bottle: 0.6462, Acc.buffet: 0.6638, Acc.poster: 0.5101, Acc.stage: 0.4614, Acc.van: 0.5984, Acc.ship: 0.9654, Acc.fountain: 0.2246, Acc.conveyer belt: 0.9335, Acc.canopy: 0.7937, Acc.washer: 0.8521, Acc.plaything: 0.5905, Acc.swimming pool: 0.8918, Acc.stool: 0.6606, Acc.barrel: 0.6639, Acc.basket: 0.5818, Acc.waterfall: 0.8310, Acc.tent: 0.9876, Acc.bag: 0.2494, Acc.minibike: 0.9015, Acc.cradle: 0.9716, Acc.oven: 0.7062, Acc.ball: 0.5372, Acc.food: 0.6780, Acc.step: 0.1491, Acc.tank: 0.6985, Acc.trade name: 0.3310, Acc.microwave: 0.9571, Acc.pot: 0.6791, Acc.animal: 0.6694, Acc.bicycle: 0.7816, Acc.lake: 0.6373, Acc.dishwasher: 0.8341, Acc.screen: 0.7566, Acc.blanket: 0.3466, Acc.sculpture: 0.8774, Acc.hood: 0.7511, Acc.sconce: 0.6608, Acc.vase: 0.6344, Acc.traffic light: 0.6206, Acc.tray: 0.2147, Acc.ashcan: 0.6235, Acc.fan: 0.7958, Acc.pier: 0.5391, Acc.crt screen: 0.3039, Acc.plate: 0.7692, Acc.monitor: 0.8343, Acc.bulletin board: 0.5714, Acc.shower: 0.0651, Acc.radiator: 0.7361, Acc.glass: 0.2176, Acc.clock: 0.5063, Acc.flag: 0.7941 +2024-06-19 06:44:24,131 - mmseg - INFO - Iter [76050/80000] lr: 1.976e-06, eta: 1:37:31, time: 3.251, data_time: 1.928, memory: 70498, decode.loss_ce: 0.1465, decode.acc_seg: 93.5862, aux.loss_ce: 0.0632, aux.acc_seg: 93.1410, loss: 0.2098 +2024-06-19 06:45:30,795 - mmseg - INFO - Iter [76100/80000] lr: 1.951e-06, eta: 1:36:16, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1502, decode.acc_seg: 93.4603, aux.loss_ce: 0.0649, aux.acc_seg: 92.9717, loss: 0.2151 +2024-06-19 06:46:37,103 - mmseg - INFO - Iter [76150/80000] lr: 1.926e-06, eta: 1:35:02, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1403, decode.acc_seg: 93.8622, aux.loss_ce: 0.0609, aux.acc_seg: 93.3716, loss: 0.2012 +2024-06-19 06:47:43,621 - mmseg - INFO - Iter [76200/80000] lr: 1.900e-06, eta: 1:33:47, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1466, decode.acc_seg: 93.6069, aux.loss_ce: 0.0634, aux.acc_seg: 93.1289, loss: 0.2100 +2024-06-19 06:48:50,019 - mmseg - INFO - Iter [76250/80000] lr: 1.875e-06, eta: 1:32:33, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1462, decode.acc_seg: 93.6341, aux.loss_ce: 0.0633, aux.acc_seg: 93.1533, loss: 0.2095 +2024-06-19 06:49:56,481 - mmseg - INFO - Iter [76300/80000] lr: 1.850e-06, eta: 1:31:19, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1488, decode.acc_seg: 93.4208, aux.loss_ce: 0.0644, aux.acc_seg: 92.9359, loss: 0.2132 +2024-06-19 06:51:02,852 - mmseg - INFO - Iter [76350/80000] lr: 1.826e-06, eta: 1:30:04, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1395, decode.acc_seg: 93.8897, aux.loss_ce: 0.0608, aux.acc_seg: 93.3988, loss: 0.2003 +2024-06-19 06:52:09,493 - mmseg - INFO - Iter [76400/80000] lr: 1.801e-06, eta: 1:28:50, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1413, decode.acc_seg: 93.7148, aux.loss_ce: 0.0616, aux.acc_seg: 93.1570, loss: 0.2029 +2024-06-19 06:53:15,819 - mmseg - INFO - Iter [76450/80000] lr: 1.776e-06, eta: 1:27:35, time: 1.327, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1506, decode.acc_seg: 93.3347, aux.loss_ce: 0.0655, aux.acc_seg: 92.7795, loss: 0.2161 +2024-06-19 06:54:22,259 - mmseg - INFO - Iter [76500/80000] lr: 1.751e-06, eta: 1:26:21, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1445, decode.acc_seg: 93.7482, aux.loss_ce: 0.0626, aux.acc_seg: 93.2739, loss: 0.2071 +2024-06-19 06:55:28,533 - mmseg - INFO - Iter [76550/80000] lr: 1.726e-06, eta: 1:25:07, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1510, decode.acc_seg: 93.3985, aux.loss_ce: 0.0653, aux.acc_seg: 92.9215, loss: 0.2163 +2024-06-19 06:56:34,818 - mmseg - INFO - Iter [76600/80000] lr: 1.700e-06, eta: 1:23:52, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1492, decode.acc_seg: 93.3454, aux.loss_ce: 0.0646, aux.acc_seg: 92.9137, loss: 0.2137 +2024-06-19 06:57:41,273 - mmseg - INFO - Iter [76650/80000] lr: 1.675e-06, eta: 1:22:38, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1444, decode.acc_seg: 93.6463, aux.loss_ce: 0.0627, aux.acc_seg: 93.1839, loss: 0.2071 +2024-06-19 06:58:47,759 - mmseg - INFO - Iter [76700/80000] lr: 1.650e-06, eta: 1:21:24, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1448, decode.acc_seg: 93.7257, aux.loss_ce: 0.0624, aux.acc_seg: 93.2962, loss: 0.2072 +2024-06-19 06:59:54,318 - mmseg - INFO - Iter [76750/80000] lr: 1.625e-06, eta: 1:20:09, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1406, decode.acc_seg: 93.7759, aux.loss_ce: 0.0610, aux.acc_seg: 93.2807, loss: 0.2016 +2024-06-19 07:01:00,615 - mmseg - INFO - Iter [76800/80000] lr: 1.601e-06, eta: 1:18:55, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1476, decode.acc_seg: 93.4129, aux.loss_ce: 0.0640, aux.acc_seg: 92.9137, loss: 0.2115 +2024-06-19 07:02:07,352 - mmseg - INFO - Iter [76850/80000] lr: 1.576e-06, eta: 1:17:41, time: 1.335, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1452, decode.acc_seg: 93.7135, aux.loss_ce: 0.0634, aux.acc_seg: 93.1521, loss: 0.2086 +2024-06-19 07:03:14,274 - mmseg - INFO - Iter [76900/80000] lr: 1.551e-06, eta: 1:16:26, time: 1.338, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1445, decode.acc_seg: 93.6358, aux.loss_ce: 0.0629, aux.acc_seg: 93.1376, loss: 0.2074 +2024-06-19 07:04:20,779 - mmseg - INFO - Iter [76950/80000] lr: 1.526e-06, eta: 1:15:12, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1414, decode.acc_seg: 93.6000, aux.loss_ce: 0.0608, aux.acc_seg: 93.1673, loss: 0.2021 +2024-06-19 07:05:27,151 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 07:05:27,152 - mmseg - INFO - Iter [77000/80000] lr: 1.500e-06, eta: 1:13:58, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1523, decode.acc_seg: 93.0965, aux.loss_ce: 0.0660, aux.acc_seg: 92.6277, loss: 0.2184 +2024-06-19 07:07:04,547 - mmseg - INFO - per class results: +2024-06-19 07:07:04,553 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.55 | 90.13 | +| building | 86.0 | 93.92 | +| sky | 95.07 | 97.7 | +| floor | 84.92 | 91.89 | +| tree | 77.36 | 90.38 | +| ceiling | 87.73 | 94.23 | +| road | 87.47 | 91.96 | +| bed | 92.44 | 96.8 | +| windowpane | 66.98 | 82.44 | +| grass | 66.46 | 79.28 | +| cabinet | 66.09 | 76.03 | +| sidewalk | 72.44 | 86.72 | +| person | 85.78 | 93.9 | +| earth | 38.54 | 51.66 | +| door | 60.08 | 75.89 | +| table | 70.87 | 82.55 | +| mountain | 60.87 | 73.66 | +| plant | 56.25 | 66.83 | +| curtain | 78.45 | 88.2 | +| chair | 68.07 | 78.93 | +| car | 87.46 | 93.96 | +| water | 65.04 | 79.16 | +| painting | 77.11 | 90.52 | +| sofa | 82.69 | 91.59 | +| shelf | 49.73 | 66.38 | +| house | 55.65 | 67.66 | +| sea | 69.02 | 83.46 | +| mirror | 78.43 | 84.24 | +| rug | 67.65 | 79.38 | +| field | 32.38 | 61.22 | +| armchair | 61.8 | 78.42 | +| seat | 65.48 | 88.11 | +| fence | 50.58 | 63.49 | +| desk | 58.78 | 78.78 | +| rock | 54.45 | 78.49 | +| wardrobe | 56.79 | 75.37 | +| lamp | 74.86 | 85.67 | +| bathtub | 84.85 | 86.71 | +| railing | 39.94 | 56.99 | +| cushion | 71.32 | 81.93 | +| base | 43.54 | 58.26 | +| box | 38.12 | 50.47 | +| column | 56.04 | 66.75 | +| signboard | 41.3 | 55.52 | +| chest of drawers | 46.96 | 69.86 | +| counter | 44.14 | 53.81 | +| sand | 52.47 | 76.49 | +| sink | 78.17 | 83.88 | +| skyscraper | 50.41 | 62.1 | +| fireplace | 77.09 | 92.97 | +| refrigerator | 82.6 | 92.86 | +| grandstand | 50.15 | 82.35 | +| path | 28.58 | 37.72 | +| stairs | 25.03 | 32.61 | +| runway | 73.21 | 96.26 | +| case | 59.55 | 79.97 | +| pool table | 94.95 | 97.74 | +| pillow | 70.36 | 81.51 | +| screen door | 76.67 | 79.29 | +| stairway | 42.92 | 57.82 | +| river | 21.27 | 41.44 | +| bridge | 75.96 | 86.15 | +| bookcase | 46.06 | 65.71 | +| blind | 44.49 | 49.92 | +| coffee table | 67.4 | 87.21 | +| toilet | 90.0 | 94.08 | +| flower | 47.25 | 61.19 | +| book | 59.43 | 76.79 | +| hill | 7.78 | 11.92 | +| bench | 55.99 | 64.89 | +| countertop | 62.54 | 84.66 | +| stove | 87.85 | 94.53 | +| palm | 57.88 | 83.07 | +| kitchen island | 47.69 | 79.02 | +| computer | 80.29 | 92.24 | +| swivel chair | 51.66 | 75.4 | +| boat | 62.4 | 89.3 | +| bar | 55.28 | 74.16 | +| arcade machine | 79.79 | 84.22 | +| hovel | 43.54 | 48.8 | +| bus | 93.56 | 95.87 | +| towel | 74.56 | 83.08 | +| light | 61.31 | 71.24 | +| truck | 46.2 | 60.03 | +| tower | 11.69 | 15.85 | +| chandelier | 71.77 | 85.78 | +| awning | 45.66 | 60.09 | +| streetlight | 35.99 | 47.73 | +| booth | 40.14 | 65.98 | +| television receiver | 82.46 | 87.1 | +| airplane | 83.4 | 90.04 | +| dirt track | 7.93 | 34.01 | +| apparel | 49.34 | 69.3 | +| pole | 29.09 | 39.69 | +| land | 3.87 | 6.3 | +| bannister | 18.05 | 27.05 | +| escalator | 57.61 | 79.87 | +| ottoman | 48.45 | 65.24 | +| bottle | 42.71 | 67.91 | +| buffet | 50.58 | 66.09 | +| poster | 38.24 | 51.11 | +| stage | 25.87 | 45.65 | +| van | 43.92 | 59.87 | +| ship | 92.51 | 96.42 | +| fountain | 21.84 | 22.31 | +| conveyer belt | 80.03 | 93.13 | +| canopy | 57.81 | 80.46 | +| washer | 80.86 | 83.08 | +| plaything | 40.69 | 53.54 | +| swimming pool | 64.55 | 90.14 | +| stool | 52.61 | 67.26 | +| barrel | 43.3 | 67.71 | +| basket | 40.52 | 58.49 | +| waterfall | 59.94 | 86.24 | +| tent | 89.78 | 99.0 | +| bag | 21.46 | 25.34 | +| minibike | 76.42 | 89.36 | +| cradle | 85.66 | 97.17 | +| oven | 60.16 | 69.62 | +| ball | 45.38 | 50.68 | +| food | 58.38 | 71.56 | +| step | 13.08 | 15.83 | +| tank | 68.03 | 75.18 | +| trade name | 30.16 | 34.75 | +| microwave | 88.71 | 96.05 | +| pot | 58.98 | 69.47 | +| animal | 64.28 | 65.58 | +| bicycle | 60.36 | 80.2 | +| lake | 56.51 | 63.76 | +| dishwasher | 73.2 | 83.99 | +| screen | 50.08 | 74.39 | +| blanket | 32.41 | 36.85 | +| sculpture | 74.8 | 88.43 | +| hood | 62.32 | 75.55 | +| sconce | 56.41 | 63.99 | +| vase | 48.69 | 60.92 | +| traffic light | 42.02 | 61.15 | +| tray | 15.57 | 19.75 | +| ashcan | 48.66 | 63.21 | +| fan | 66.51 | 79.01 | +| pier | 37.6 | 51.84 | +| crt screen | 18.45 | 31.67 | +| plate | 60.01 | 77.17 | +| monitor | 69.42 | 83.4 | +| bulletin board | 51.64 | 57.6 | +| shower | 6.23 | 6.87 | +| radiator | 65.18 | 74.81 | +| glass | 19.93 | 21.67 | +| clock | 45.02 | 52.03 | +| flag | 71.33 | 79.09 | ++---------------------+-------+-------+ +2024-06-19 07:07:04,554 - mmseg - INFO - Summary: +2024-06-19 07:07:04,554 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.35 | 57.57 | 70.19 | ++-------+-------+-------+ +2024-06-19 07:07:04,554 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 07:07:04,555 - mmseg - INFO - Iter(val) [250] aAcc: 0.8635, mIoU: 0.5757, mAcc: 0.7019, IoU.wall: 0.8255, IoU.building: 0.8600, IoU.sky: 0.9507, IoU.floor: 0.8492, IoU.tree: 0.7736, IoU.ceiling: 0.8773, IoU.road: 0.8747, IoU.bed : 0.9244, IoU.windowpane: 0.6698, IoU.grass: 0.6646, IoU.cabinet: 0.6609, IoU.sidewalk: 0.7244, IoU.person: 0.8578, IoU.earth: 0.3854, IoU.door: 0.6008, IoU.table: 0.7087, IoU.mountain: 0.6087, IoU.plant: 0.5625, IoU.curtain: 0.7845, IoU.chair: 0.6807, IoU.car: 0.8746, IoU.water: 0.6504, IoU.painting: 0.7711, IoU.sofa: 0.8269, IoU.shelf: 0.4973, IoU.house: 0.5565, IoU.sea: 0.6902, IoU.mirror: 0.7843, IoU.rug: 0.6765, IoU.field: 0.3238, IoU.armchair: 0.6180, IoU.seat: 0.6548, IoU.fence: 0.5058, IoU.desk: 0.5878, IoU.rock: 0.5445, IoU.wardrobe: 0.5679, IoU.lamp: 0.7486, IoU.bathtub: 0.8485, IoU.railing: 0.3994, IoU.cushion: 0.7132, IoU.base: 0.4354, IoU.box: 0.3812, IoU.column: 0.5604, IoU.signboard: 0.4130, IoU.chest of drawers: 0.4696, IoU.counter: 0.4414, IoU.sand: 0.5247, IoU.sink: 0.7817, IoU.skyscraper: 0.5041, IoU.fireplace: 0.7709, IoU.refrigerator: 0.8260, IoU.grandstand: 0.5015, IoU.path: 0.2858, IoU.stairs: 0.2503, IoU.runway: 0.7321, IoU.case: 0.5955, IoU.pool table: 0.9495, IoU.pillow: 0.7036, IoU.screen door: 0.7667, IoU.stairway: 0.4292, IoU.river: 0.2127, IoU.bridge: 0.7596, IoU.bookcase: 0.4606, IoU.blind: 0.4449, IoU.coffee table: 0.6740, IoU.toilet: 0.9000, IoU.flower: 0.4725, IoU.book: 0.5943, IoU.hill: 0.0778, IoU.bench: 0.5599, IoU.countertop: 0.6254, IoU.stove: 0.8785, IoU.palm: 0.5788, IoU.kitchen island: 0.4769, IoU.computer: 0.8029, IoU.swivel chair: 0.5166, IoU.boat: 0.6240, IoU.bar: 0.5528, IoU.arcade machine: 0.7979, IoU.hovel: 0.4354, IoU.bus: 0.9356, IoU.towel: 0.7456, IoU.light: 0.6131, IoU.truck: 0.4620, IoU.tower: 0.1169, IoU.chandelier: 0.7177, IoU.awning: 0.4566, IoU.streetlight: 0.3599, IoU.booth: 0.4014, IoU.television receiver: 0.8246, IoU.airplane: 0.8340, IoU.dirt track: 0.0793, IoU.apparel: 0.4934, IoU.pole: 0.2909, IoU.land: 0.0387, IoU.bannister: 0.1805, IoU.escalator: 0.5761, IoU.ottoman: 0.4845, IoU.bottle: 0.4271, IoU.buffet: 0.5058, IoU.poster: 0.3824, IoU.stage: 0.2587, IoU.van: 0.4392, IoU.ship: 0.9251, IoU.fountain: 0.2184, IoU.conveyer belt: 0.8003, IoU.canopy: 0.5781, IoU.washer: 0.8086, IoU.plaything: 0.4069, IoU.swimming pool: 0.6455, IoU.stool: 0.5261, IoU.barrel: 0.4330, IoU.basket: 0.4052, IoU.waterfall: 0.5994, IoU.tent: 0.8978, IoU.bag: 0.2146, IoU.minibike: 0.7642, IoU.cradle: 0.8566, IoU.oven: 0.6016, IoU.ball: 0.4538, IoU.food: 0.5838, IoU.step: 0.1308, IoU.tank: 0.6803, IoU.trade name: 0.3016, IoU.microwave: 0.8871, IoU.pot: 0.5898, IoU.animal: 0.6428, IoU.bicycle: 0.6036, IoU.lake: 0.5651, IoU.dishwasher: 0.7320, IoU.screen: 0.5008, IoU.blanket: 0.3241, IoU.sculpture: 0.7480, IoU.hood: 0.6232, IoU.sconce: 0.5641, IoU.vase: 0.4869, IoU.traffic light: 0.4202, IoU.tray: 0.1557, IoU.ashcan: 0.4866, IoU.fan: 0.6651, IoU.pier: 0.3760, IoU.crt screen: 0.1845, IoU.plate: 0.6001, IoU.monitor: 0.6942, IoU.bulletin board: 0.5164, IoU.shower: 0.0623, IoU.radiator: 0.6518, IoU.glass: 0.1993, IoU.clock: 0.4502, IoU.flag: 0.7133, Acc.wall: 0.9013, Acc.building: 0.9392, Acc.sky: 0.9770, Acc.floor: 0.9189, Acc.tree: 0.9038, Acc.ceiling: 0.9423, Acc.road: 0.9196, Acc.bed : 0.9680, Acc.windowpane: 0.8244, Acc.grass: 0.7928, Acc.cabinet: 0.7603, Acc.sidewalk: 0.8672, Acc.person: 0.9390, Acc.earth: 0.5166, Acc.door: 0.7589, Acc.table: 0.8255, Acc.mountain: 0.7366, Acc.plant: 0.6683, Acc.curtain: 0.8820, Acc.chair: 0.7893, Acc.car: 0.9396, Acc.water: 0.7916, Acc.painting: 0.9052, Acc.sofa: 0.9159, Acc.shelf: 0.6638, Acc.house: 0.6766, Acc.sea: 0.8346, Acc.mirror: 0.8424, Acc.rug: 0.7938, Acc.field: 0.6122, Acc.armchair: 0.7842, Acc.seat: 0.8811, Acc.fence: 0.6349, Acc.desk: 0.7878, Acc.rock: 0.7849, Acc.wardrobe: 0.7537, Acc.lamp: 0.8567, Acc.bathtub: 0.8671, Acc.railing: 0.5699, Acc.cushion: 0.8193, Acc.base: 0.5826, Acc.box: 0.5047, Acc.column: 0.6675, Acc.signboard: 0.5552, Acc.chest of drawers: 0.6986, Acc.counter: 0.5381, Acc.sand: 0.7649, Acc.sink: 0.8388, Acc.skyscraper: 0.6210, Acc.fireplace: 0.9297, Acc.refrigerator: 0.9286, Acc.grandstand: 0.8235, Acc.path: 0.3772, Acc.stairs: 0.3261, Acc.runway: 0.9626, Acc.case: 0.7997, Acc.pool table: 0.9774, Acc.pillow: 0.8151, Acc.screen door: 0.7929, Acc.stairway: 0.5782, Acc.river: 0.4144, Acc.bridge: 0.8615, Acc.bookcase: 0.6571, Acc.blind: 0.4992, Acc.coffee table: 0.8721, Acc.toilet: 0.9408, Acc.flower: 0.6119, Acc.book: 0.7679, Acc.hill: 0.1192, Acc.bench: 0.6489, Acc.countertop: 0.8466, Acc.stove: 0.9453, Acc.palm: 0.8307, Acc.kitchen island: 0.7902, Acc.computer: 0.9224, Acc.swivel chair: 0.7540, Acc.boat: 0.8930, Acc.bar: 0.7416, Acc.arcade machine: 0.8422, Acc.hovel: 0.4880, Acc.bus: 0.9587, Acc.towel: 0.8308, Acc.light: 0.7124, Acc.truck: 0.6003, Acc.tower: 0.1585, Acc.chandelier: 0.8578, Acc.awning: 0.6009, Acc.streetlight: 0.4773, Acc.booth: 0.6598, Acc.television receiver: 0.8710, Acc.airplane: 0.9004, Acc.dirt track: 0.3401, Acc.apparel: 0.6930, Acc.pole: 0.3969, Acc.land: 0.0630, Acc.bannister: 0.2705, Acc.escalator: 0.7987, Acc.ottoman: 0.6524, Acc.bottle: 0.6791, Acc.buffet: 0.6609, Acc.poster: 0.5111, Acc.stage: 0.4565, Acc.van: 0.5987, Acc.ship: 0.9642, Acc.fountain: 0.2231, Acc.conveyer belt: 0.9313, Acc.canopy: 0.8046, Acc.washer: 0.8308, Acc.plaything: 0.5354, Acc.swimming pool: 0.9014, Acc.stool: 0.6726, Acc.barrel: 0.6771, Acc.basket: 0.5849, Acc.waterfall: 0.8624, Acc.tent: 0.9900, Acc.bag: 0.2534, Acc.minibike: 0.8936, Acc.cradle: 0.9717, Acc.oven: 0.6962, Acc.ball: 0.5068, Acc.food: 0.7156, Acc.step: 0.1583, Acc.tank: 0.7518, Acc.trade name: 0.3475, Acc.microwave: 0.9605, Acc.pot: 0.6947, Acc.animal: 0.6558, Acc.bicycle: 0.8020, Acc.lake: 0.6376, Acc.dishwasher: 0.8399, Acc.screen: 0.7439, Acc.blanket: 0.3685, Acc.sculpture: 0.8843, Acc.hood: 0.7555, Acc.sconce: 0.6399, Acc.vase: 0.6092, Acc.traffic light: 0.6115, Acc.tray: 0.1975, Acc.ashcan: 0.6321, Acc.fan: 0.7901, Acc.pier: 0.5184, Acc.crt screen: 0.3167, Acc.plate: 0.7717, Acc.monitor: 0.8340, Acc.bulletin board: 0.5760, Acc.shower: 0.0687, Acc.radiator: 0.7481, Acc.glass: 0.2167, Acc.clock: 0.5203, Acc.flag: 0.7909 +2024-06-19 07:08:13,600 - mmseg - INFO - Iter [77050/80000] lr: 1.475e-06, eta: 1:12:47, time: 3.329, data_time: 2.006, memory: 70498, decode.loss_ce: 0.1406, decode.acc_seg: 93.7333, aux.loss_ce: 0.0612, aux.acc_seg: 93.2441, loss: 0.2018 +2024-06-19 07:09:19,875 - mmseg - INFO - Iter [77100/80000] lr: 1.450e-06, eta: 1:11:33, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1432, decode.acc_seg: 93.6291, aux.loss_ce: 0.0625, aux.acc_seg: 93.0899, loss: 0.2056 +2024-06-19 07:10:26,113 - mmseg - INFO - Iter [77150/80000] lr: 1.425e-06, eta: 1:10:19, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1401, decode.acc_seg: 93.9054, aux.loss_ce: 0.0607, aux.acc_seg: 93.4146, loss: 0.2008 +2024-06-19 07:11:32,522 - mmseg - INFO - Iter [77200/80000] lr: 1.401e-06, eta: 1:09:04, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1417, decode.acc_seg: 93.8994, aux.loss_ce: 0.0612, aux.acc_seg: 93.4257, loss: 0.2029 +2024-06-19 07:12:39,252 - mmseg - INFO - Iter [77250/80000] lr: 1.376e-06, eta: 1:07:50, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1438, decode.acc_seg: 93.7590, aux.loss_ce: 0.0624, aux.acc_seg: 93.2435, loss: 0.2061 +2024-06-19 07:13:45,701 - mmseg - INFO - Iter [77300/80000] lr: 1.351e-06, eta: 1:06:36, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1476, decode.acc_seg: 93.2856, aux.loss_ce: 0.0640, aux.acc_seg: 92.7644, loss: 0.2116 +2024-06-19 07:14:52,412 - mmseg - INFO - Iter [77350/80000] lr: 1.326e-06, eta: 1:05:22, time: 1.334, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1425, decode.acc_seg: 93.6464, aux.loss_ce: 0.0624, aux.acc_seg: 93.1244, loss: 0.2049 +2024-06-19 07:15:58,789 - mmseg - INFO - Iter [77400/80000] lr: 1.301e-06, eta: 1:04:07, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1381, decode.acc_seg: 93.8859, aux.loss_ce: 0.0602, aux.acc_seg: 93.4141, loss: 0.1983 +2024-06-19 07:17:04,994 - mmseg - INFO - Iter [77450/80000] lr: 1.275e-06, eta: 1:02:53, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1330, decode.acc_seg: 93.9821, aux.loss_ce: 0.0582, aux.acc_seg: 93.4548, loss: 0.1912 +2024-06-19 07:18:11,448 - mmseg - INFO - Iter [77500/80000] lr: 1.250e-06, eta: 1:01:39, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1413, decode.acc_seg: 94.0507, aux.loss_ce: 0.0616, aux.acc_seg: 93.5185, loss: 0.2029 +2024-06-19 07:19:18,063 - mmseg - INFO - Iter [77550/80000] lr: 1.225e-06, eta: 1:00:25, time: 1.332, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1379, decode.acc_seg: 93.8716, aux.loss_ce: 0.0602, aux.acc_seg: 93.3058, loss: 0.1981 +2024-06-19 07:20:24,542 - mmseg - INFO - Iter [77600/80000] lr: 1.200e-06, eta: 0:59:10, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1618, decode.acc_seg: 93.1442, aux.loss_ce: 0.0697, aux.acc_seg: 92.6563, loss: 0.2316 +2024-06-19 07:21:31,194 - mmseg - INFO - Iter [77650/80000] lr: 1.176e-06, eta: 0:57:56, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1416, decode.acc_seg: 93.7661, aux.loss_ce: 0.0617, aux.acc_seg: 93.2903, loss: 0.2033 +2024-06-19 07:22:37,714 - mmseg - INFO - Iter [77700/80000] lr: 1.151e-06, eta: 0:56:42, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1495, decode.acc_seg: 93.6373, aux.loss_ce: 0.0648, aux.acc_seg: 93.1445, loss: 0.2143 +2024-06-19 07:23:43,917 - mmseg - INFO - Iter [77750/80000] lr: 1.126e-06, eta: 0:55:28, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1443, decode.acc_seg: 93.5697, aux.loss_ce: 0.0628, aux.acc_seg: 93.0695, loss: 0.2071 +2024-06-19 07:24:50,101 - mmseg - INFO - Iter [77800/80000] lr: 1.101e-06, eta: 0:54:14, time: 1.324, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1449, decode.acc_seg: 93.5934, aux.loss_ce: 0.0633, aux.acc_seg: 93.0588, loss: 0.2082 +2024-06-19 07:25:56,574 - mmseg - INFO - Iter [77850/80000] lr: 1.075e-06, eta: 0:53:00, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1451, decode.acc_seg: 93.7416, aux.loss_ce: 0.0628, aux.acc_seg: 93.2088, loss: 0.2079 +2024-06-19 07:27:02,848 - mmseg - INFO - Iter [77900/80000] lr: 1.050e-06, eta: 0:51:45, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1401, decode.acc_seg: 93.7481, aux.loss_ce: 0.0604, aux.acc_seg: 93.4335, loss: 0.2006 +2024-06-19 07:28:09,190 - mmseg - INFO - Iter [77950/80000] lr: 1.025e-06, eta: 0:50:31, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1546, decode.acc_seg: 93.2570, aux.loss_ce: 0.0672, aux.acc_seg: 92.7080, loss: 0.2217 +2024-06-19 07:29:15,710 - mmseg - INFO - Saving checkpoint at 78000 iterations +2024-06-19 07:31:00,088 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 07:31:00,088 - mmseg - INFO - Iter [78000/80000] lr: 1.000e-06, eta: 0:49:20, time: 3.418, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1446, decode.acc_seg: 93.7658, aux.loss_ce: 0.0626, aux.acc_seg: 93.2832, loss: 0.2072 +2024-06-19 07:32:36,621 - mmseg - INFO - per class results: +2024-06-19 07:32:36,627 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.65 | 90.4 | +| building | 85.96 | 93.73 | +| sky | 95.04 | 97.76 | +| floor | 84.86 | 91.83 | +| tree | 77.31 | 89.89 | +| ceiling | 87.71 | 94.02 | +| road | 87.61 | 92.01 | +| bed | 92.43 | 96.86 | +| windowpane | 67.07 | 82.11 | +| grass | 66.05 | 80.09 | +| cabinet | 65.97 | 75.77 | +| sidewalk | 72.53 | 86.69 | +| person | 85.71 | 94.36 | +| earth | 38.51 | 51.59 | +| door | 60.72 | 75.89 | +| table | 70.51 | 82.52 | +| mountain | 60.68 | 73.69 | +| plant | 56.08 | 66.32 | +| curtain | 78.34 | 87.98 | +| chair | 68.34 | 79.16 | +| car | 87.37 | 94.07 | +| water | 65.38 | 79.72 | +| painting | 77.43 | 90.15 | +| sofa | 82.9 | 91.22 | +| shelf | 50.42 | 68.49 | +| house | 56.64 | 70.86 | +| sea | 68.49 | 83.6 | +| mirror | 78.15 | 83.83 | +| rug | 67.19 | 78.47 | +| field | 32.06 | 60.27 | +| armchair | 62.34 | 79.57 | +| seat | 65.84 | 88.04 | +| fence | 50.93 | 63.42 | +| desk | 58.54 | 77.67 | +| rock | 54.2 | 78.38 | +| wardrobe | 55.55 | 75.56 | +| lamp | 74.96 | 85.22 | +| bathtub | 84.86 | 86.72 | +| railing | 39.78 | 55.68 | +| cushion | 71.46 | 82.74 | +| base | 42.94 | 56.14 | +| box | 37.82 | 48.88 | +| column | 56.01 | 68.31 | +| signboard | 41.14 | 57.56 | +| chest of drawers | 46.77 | 67.55 | +| counter | 43.76 | 53.22 | +| sand | 52.17 | 76.49 | +| sink | 78.22 | 83.65 | +| skyscraper | 50.22 | 62.05 | +| fireplace | 76.39 | 92.71 | +| refrigerator | 82.08 | 92.97 | +| grandstand | 50.1 | 81.61 | +| path | 29.74 | 39.8 | +| stairs | 23.95 | 30.87 | +| runway | 72.42 | 94.93 | +| case | 58.97 | 80.64 | +| pool table | 94.94 | 97.7 | +| pillow | 70.14 | 80.47 | +| screen door | 80.47 | 83.39 | +| stairway | 41.39 | 57.33 | +| river | 21.35 | 40.48 | +| bridge | 75.89 | 85.93 | +| bookcase | 46.62 | 62.77 | +| blind | 43.87 | 49.07 | +| coffee table | 66.91 | 87.62 | +| toilet | 90.05 | 93.89 | +| flower | 46.97 | 59.35 | +| book | 59.5 | 77.92 | +| hill | 7.66 | 11.42 | +| bench | 55.96 | 65.11 | +| countertop | 62.48 | 84.65 | +| stove | 87.86 | 94.45 | +| palm | 57.3 | 82.7 | +| kitchen island | 46.97 | 76.84 | +| computer | 80.11 | 92.2 | +| swivel chair | 51.81 | 74.39 | +| boat | 65.38 | 88.51 | +| bar | 55.31 | 74.62 | +| arcade machine | 79.56 | 84.38 | +| hovel | 43.57 | 48.98 | +| bus | 93.3 | 96.12 | +| towel | 74.35 | 83.06 | +| light | 61.07 | 71.48 | +| truck | 45.56 | 58.65 | +| tower | 11.46 | 15.59 | +| chandelier | 72.43 | 86.19 | +| awning | 43.3 | 55.3 | +| streetlight | 36.52 | 48.28 | +| booth | 40.54 | 66.05 | +| television receiver | 81.23 | 87.89 | +| airplane | 84.11 | 90.52 | +| dirt track | 7.44 | 30.05 | +| apparel | 48.86 | 68.41 | +| pole | 29.85 | 41.84 | +| land | 3.88 | 6.19 | +| bannister | 18.21 | 26.68 | +| escalator | 57.49 | 79.68 | +| ottoman | 47.72 | 63.77 | +| bottle | 42.89 | 67.58 | +| buffet | 51.79 | 65.94 | +| poster | 36.96 | 50.83 | +| stage | 26.04 | 45.57 | +| van | 44.08 | 59.2 | +| ship | 92.8 | 96.41 | +| fountain | 21.46 | 21.89 | +| conveyer belt | 78.51 | 93.12 | +| canopy | 56.56 | 80.1 | +| washer | 83.19 | 85.99 | +| plaything | 40.71 | 56.53 | +| swimming pool | 64.97 | 90.35 | +| stool | 53.87 | 67.1 | +| barrel | 47.91 | 67.44 | +| basket | 40.27 | 58.3 | +| waterfall | 59.64 | 85.45 | +| tent | 89.97 | 98.93 | +| bag | 21.91 | 26.13 | +| minibike | 76.59 | 89.04 | +| cradle | 85.45 | 97.19 | +| oven | 60.1 | 70.13 | +| ball | 45.45 | 50.97 | +| food | 57.76 | 70.63 | +| step | 12.58 | 14.98 | +| tank | 67.75 | 73.92 | +| trade name | 29.16 | 33.29 | +| microwave | 88.68 | 96.07 | +| pot | 58.8 | 68.56 | +| animal | 64.34 | 65.68 | +| bicycle | 60.26 | 78.43 | +| lake | 55.81 | 63.76 | +| dishwasher | 73.97 | 83.59 | +| screen | 50.9 | 75.51 | +| blanket | 31.86 | 36.34 | +| sculpture | 74.48 | 88.37 | +| hood | 62.25 | 75.23 | +| sconce | 56.72 | 64.82 | +| vase | 49.11 | 60.94 | +| traffic light | 41.98 | 62.55 | +| tray | 14.99 | 18.9 | +| ashcan | 49.07 | 62.76 | +| fan | 66.27 | 78.55 | +| pier | 37.31 | 50.75 | +| crt screen | 18.52 | 30.6 | +| plate | 59.65 | 77.49 | +| monitor | 70.03 | 83.81 | +| bulletin board | 49.87 | 54.4 | +| shower | 6.07 | 6.73 | +| radiator | 64.99 | 74.38 | +| glass | 19.69 | 21.34 | +| clock | 44.91 | 51.46 | +| flag | 71.6 | 79.49 | ++---------------------+-------+-------+ +2024-06-19 07:32:36,627 - mmseg - INFO - Summary: +2024-06-19 07:32:36,627 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.35 | 57.54 | 70.01 | ++-------+-------+-------+ +2024-06-19 07:32:36,628 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 07:32:36,628 - mmseg - INFO - Iter(val) [250] aAcc: 0.8635, mIoU: 0.5754, mAcc: 0.7001, IoU.wall: 0.8265, IoU.building: 0.8596, IoU.sky: 0.9504, IoU.floor: 0.8486, IoU.tree: 0.7731, IoU.ceiling: 0.8771, IoU.road: 0.8761, IoU.bed : 0.9243, IoU.windowpane: 0.6707, IoU.grass: 0.6605, IoU.cabinet: 0.6597, IoU.sidewalk: 0.7253, IoU.person: 0.8571, IoU.earth: 0.3851, IoU.door: 0.6072, IoU.table: 0.7051, IoU.mountain: 0.6068, IoU.plant: 0.5608, IoU.curtain: 0.7834, IoU.chair: 0.6834, IoU.car: 0.8737, IoU.water: 0.6538, IoU.painting: 0.7743, IoU.sofa: 0.8290, IoU.shelf: 0.5042, IoU.house: 0.5664, IoU.sea: 0.6849, IoU.mirror: 0.7815, IoU.rug: 0.6719, IoU.field: 0.3206, IoU.armchair: 0.6234, IoU.seat: 0.6584, IoU.fence: 0.5093, IoU.desk: 0.5854, IoU.rock: 0.5420, IoU.wardrobe: 0.5555, IoU.lamp: 0.7496, IoU.bathtub: 0.8486, IoU.railing: 0.3978, IoU.cushion: 0.7146, IoU.base: 0.4294, IoU.box: 0.3782, IoU.column: 0.5601, IoU.signboard: 0.4114, IoU.chest of drawers: 0.4677, IoU.counter: 0.4376, IoU.sand: 0.5217, IoU.sink: 0.7822, IoU.skyscraper: 0.5022, IoU.fireplace: 0.7639, IoU.refrigerator: 0.8208, IoU.grandstand: 0.5010, IoU.path: 0.2974, IoU.stairs: 0.2395, IoU.runway: 0.7242, IoU.case: 0.5897, IoU.pool table: 0.9494, IoU.pillow: 0.7014, IoU.screen door: 0.8047, IoU.stairway: 0.4139, IoU.river: 0.2135, IoU.bridge: 0.7589, IoU.bookcase: 0.4662, IoU.blind: 0.4387, IoU.coffee table: 0.6691, IoU.toilet: 0.9005, IoU.flower: 0.4697, IoU.book: 0.5950, IoU.hill: 0.0766, IoU.bench: 0.5596, IoU.countertop: 0.6248, IoU.stove: 0.8786, IoU.palm: 0.5730, IoU.kitchen island: 0.4697, IoU.computer: 0.8011, IoU.swivel chair: 0.5181, IoU.boat: 0.6538, IoU.bar: 0.5531, IoU.arcade machine: 0.7956, IoU.hovel: 0.4357, IoU.bus: 0.9330, IoU.towel: 0.7435, IoU.light: 0.6107, IoU.truck: 0.4556, IoU.tower: 0.1146, IoU.chandelier: 0.7243, IoU.awning: 0.4330, IoU.streetlight: 0.3652, IoU.booth: 0.4054, IoU.television receiver: 0.8123, IoU.airplane: 0.8411, IoU.dirt track: 0.0744, IoU.apparel: 0.4886, IoU.pole: 0.2985, IoU.land: 0.0388, IoU.bannister: 0.1821, IoU.escalator: 0.5749, IoU.ottoman: 0.4772, IoU.bottle: 0.4289, IoU.buffet: 0.5179, IoU.poster: 0.3696, IoU.stage: 0.2604, IoU.van: 0.4408, IoU.ship: 0.9280, IoU.fountain: 0.2146, IoU.conveyer belt: 0.7851, IoU.canopy: 0.5656, IoU.washer: 0.8319, IoU.plaything: 0.4071, IoU.swimming pool: 0.6497, IoU.stool: 0.5387, IoU.barrel: 0.4791, IoU.basket: 0.4027, IoU.waterfall: 0.5964, IoU.tent: 0.8997, IoU.bag: 0.2191, IoU.minibike: 0.7659, IoU.cradle: 0.8545, IoU.oven: 0.6010, IoU.ball: 0.4545, IoU.food: 0.5776, IoU.step: 0.1258, IoU.tank: 0.6775, IoU.trade name: 0.2916, IoU.microwave: 0.8868, IoU.pot: 0.5880, IoU.animal: 0.6434, IoU.bicycle: 0.6026, IoU.lake: 0.5581, IoU.dishwasher: 0.7397, IoU.screen: 0.5090, IoU.blanket: 0.3186, IoU.sculpture: 0.7448, IoU.hood: 0.6225, IoU.sconce: 0.5672, IoU.vase: 0.4911, IoU.traffic light: 0.4198, IoU.tray: 0.1499, IoU.ashcan: 0.4907, IoU.fan: 0.6627, IoU.pier: 0.3731, IoU.crt screen: 0.1852, IoU.plate: 0.5965, IoU.monitor: 0.7003, IoU.bulletin board: 0.4987, IoU.shower: 0.0607, IoU.radiator: 0.6499, IoU.glass: 0.1969, IoU.clock: 0.4491, IoU.flag: 0.7160, Acc.wall: 0.9040, Acc.building: 0.9373, Acc.sky: 0.9776, Acc.floor: 0.9183, Acc.tree: 0.8989, Acc.ceiling: 0.9402, Acc.road: 0.9201, Acc.bed : 0.9686, Acc.windowpane: 0.8211, Acc.grass: 0.8009, Acc.cabinet: 0.7577, Acc.sidewalk: 0.8669, Acc.person: 0.9436, Acc.earth: 0.5159, Acc.door: 0.7589, Acc.table: 0.8252, Acc.mountain: 0.7369, Acc.plant: 0.6632, Acc.curtain: 0.8798, Acc.chair: 0.7916, Acc.car: 0.9407, Acc.water: 0.7972, Acc.painting: 0.9015, Acc.sofa: 0.9122, Acc.shelf: 0.6849, Acc.house: 0.7086, Acc.sea: 0.8360, Acc.mirror: 0.8383, Acc.rug: 0.7847, Acc.field: 0.6027, Acc.armchair: 0.7957, Acc.seat: 0.8804, Acc.fence: 0.6342, Acc.desk: 0.7767, Acc.rock: 0.7838, Acc.wardrobe: 0.7556, Acc.lamp: 0.8522, Acc.bathtub: 0.8672, Acc.railing: 0.5568, Acc.cushion: 0.8274, Acc.base: 0.5614, Acc.box: 0.4888, Acc.column: 0.6831, Acc.signboard: 0.5756, Acc.chest of drawers: 0.6755, Acc.counter: 0.5322, Acc.sand: 0.7649, Acc.sink: 0.8365, Acc.skyscraper: 0.6205, Acc.fireplace: 0.9271, Acc.refrigerator: 0.9297, Acc.grandstand: 0.8161, Acc.path: 0.3980, Acc.stairs: 0.3087, Acc.runway: 0.9493, Acc.case: 0.8064, Acc.pool table: 0.9770, Acc.pillow: 0.8047, Acc.screen door: 0.8339, Acc.stairway: 0.5733, Acc.river: 0.4048, Acc.bridge: 0.8593, Acc.bookcase: 0.6277, Acc.blind: 0.4907, Acc.coffee table: 0.8762, Acc.toilet: 0.9389, Acc.flower: 0.5935, Acc.book: 0.7792, Acc.hill: 0.1142, Acc.bench: 0.6511, Acc.countertop: 0.8465, Acc.stove: 0.9445, Acc.palm: 0.8270, Acc.kitchen island: 0.7684, Acc.computer: 0.9220, Acc.swivel chair: 0.7439, Acc.boat: 0.8851, Acc.bar: 0.7462, Acc.arcade machine: 0.8438, Acc.hovel: 0.4898, Acc.bus: 0.9612, Acc.towel: 0.8306, Acc.light: 0.7148, Acc.truck: 0.5865, Acc.tower: 0.1559, Acc.chandelier: 0.8619, Acc.awning: 0.5530, Acc.streetlight: 0.4828, Acc.booth: 0.6605, Acc.television receiver: 0.8789, Acc.airplane: 0.9052, Acc.dirt track: 0.3005, Acc.apparel: 0.6841, Acc.pole: 0.4184, Acc.land: 0.0619, Acc.bannister: 0.2668, Acc.escalator: 0.7968, Acc.ottoman: 0.6377, Acc.bottle: 0.6758, Acc.buffet: 0.6594, Acc.poster: 0.5083, Acc.stage: 0.4557, Acc.van: 0.5920, Acc.ship: 0.9641, Acc.fountain: 0.2189, Acc.conveyer belt: 0.9312, Acc.canopy: 0.8010, Acc.washer: 0.8599, Acc.plaything: 0.5653, Acc.swimming pool: 0.9035, Acc.stool: 0.6710, Acc.barrel: 0.6744, Acc.basket: 0.5830, Acc.waterfall: 0.8545, Acc.tent: 0.9893, Acc.bag: 0.2613, Acc.minibike: 0.8904, Acc.cradle: 0.9719, Acc.oven: 0.7013, Acc.ball: 0.5097, Acc.food: 0.7063, Acc.step: 0.1498, Acc.tank: 0.7392, Acc.trade name: 0.3329, Acc.microwave: 0.9607, Acc.pot: 0.6856, Acc.animal: 0.6568, Acc.bicycle: 0.7843, Acc.lake: 0.6376, Acc.dishwasher: 0.8359, Acc.screen: 0.7551, Acc.blanket: 0.3634, Acc.sculpture: 0.8837, Acc.hood: 0.7523, Acc.sconce: 0.6482, Acc.vase: 0.6094, Acc.traffic light: 0.6255, Acc.tray: 0.1890, Acc.ashcan: 0.6276, Acc.fan: 0.7855, Acc.pier: 0.5075, Acc.crt screen: 0.3060, Acc.plate: 0.7749, Acc.monitor: 0.8381, Acc.bulletin board: 0.5440, Acc.shower: 0.0673, Acc.radiator: 0.7438, Acc.glass: 0.2134, Acc.clock: 0.5146, Acc.flag: 0.7949 +2024-06-19 07:33:43,267 - mmseg - INFO - Iter [78050/80000] lr: 9.755e-07, eta: 0:48:08, time: 3.264, data_time: 1.948, memory: 70498, decode.loss_ce: 0.1388, decode.acc_seg: 93.9731, aux.loss_ce: 0.0603, aux.acc_seg: 93.5192, loss: 0.1990 +2024-06-19 07:34:49,696 - mmseg - INFO - Iter [78100/80000] lr: 9.505e-07, eta: 0:46:54, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1411, decode.acc_seg: 93.9444, aux.loss_ce: 0.0612, aux.acc_seg: 93.4231, loss: 0.2023 +2024-06-19 07:35:56,297 - mmseg - INFO - Iter [78150/80000] lr: 9.255e-07, eta: 0:45:40, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1400, decode.acc_seg: 93.8549, aux.loss_ce: 0.0606, aux.acc_seg: 93.4237, loss: 0.2005 +2024-06-19 07:37:02,816 - mmseg - INFO - Iter [78200/80000] lr: 9.005e-07, eta: 0:44:25, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1468, decode.acc_seg: 93.5937, aux.loss_ce: 0.0638, aux.acc_seg: 93.0422, loss: 0.2106 +2024-06-19 07:38:09,361 - mmseg - INFO - Iter [78250/80000] lr: 8.755e-07, eta: 0:43:11, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1429, decode.acc_seg: 93.6049, aux.loss_ce: 0.0618, aux.acc_seg: 93.1128, loss: 0.2048 +2024-06-19 07:39:15,778 - mmseg - INFO - Iter [78300/80000] lr: 8.505e-07, eta: 0:41:57, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1441, decode.acc_seg: 93.7406, aux.loss_ce: 0.0625, aux.acc_seg: 93.2297, loss: 0.2066 +2024-06-19 07:40:24,938 - mmseg - INFO - Iter [78350/80000] lr: 8.255e-07, eta: 0:40:43, time: 1.383, data_time: 0.066, memory: 70498, decode.loss_ce: 0.1434, decode.acc_seg: 93.5878, aux.loss_ce: 0.0620, aux.acc_seg: 93.0737, loss: 0.2054 +2024-06-19 07:41:31,315 - mmseg - INFO - Iter [78400/80000] lr: 8.005e-07, eta: 0:39:29, time: 1.328, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1432, decode.acc_seg: 93.7588, aux.loss_ce: 0.0629, aux.acc_seg: 93.2279, loss: 0.2061 +2024-06-19 07:42:37,661 - mmseg - INFO - Iter [78450/80000] lr: 7.755e-07, eta: 0:38:14, time: 1.327, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1492, decode.acc_seg: 93.2834, aux.loss_ce: 0.0648, aux.acc_seg: 92.7907, loss: 0.2141 +2024-06-19 07:43:44,397 - mmseg - INFO - Iter [78500/80000] lr: 7.505e-07, eta: 0:37:00, time: 1.335, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1440, decode.acc_seg: 93.5686, aux.loss_ce: 0.0624, aux.acc_seg: 93.0553, loss: 0.2063 +2024-06-19 07:44:50,835 - mmseg - INFO - Iter [78550/80000] lr: 7.255e-07, eta: 0:35:46, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1477, decode.acc_seg: 93.4086, aux.loss_ce: 0.0638, aux.acc_seg: 92.9599, loss: 0.2115 +2024-06-19 07:45:57,347 - mmseg - INFO - Iter [78600/80000] lr: 7.005e-07, eta: 0:34:32, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1414, decode.acc_seg: 93.5851, aux.loss_ce: 0.0617, aux.acc_seg: 93.0503, loss: 0.2031 +2024-06-19 07:47:03,889 - mmseg - INFO - Iter [78650/80000] lr: 6.755e-07, eta: 0:33:18, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1408, decode.acc_seg: 93.8531, aux.loss_ce: 0.0610, aux.acc_seg: 93.3547, loss: 0.2019 +2024-06-19 07:48:10,180 - mmseg - INFO - Iter [78700/80000] lr: 6.505e-07, eta: 0:32:04, time: 1.326, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1460, decode.acc_seg: 93.5556, aux.loss_ce: 0.0633, aux.acc_seg: 93.0320, loss: 0.2094 +2024-06-19 07:49:16,439 - mmseg - INFO - Iter [78750/80000] lr: 6.255e-07, eta: 0:30:49, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1406, decode.acc_seg: 93.6444, aux.loss_ce: 0.0611, aux.acc_seg: 93.1463, loss: 0.2017 +2024-06-19 07:50:23,043 - mmseg - INFO - Iter [78800/80000] lr: 6.005e-07, eta: 0:29:35, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1378, decode.acc_seg: 93.9346, aux.loss_ce: 0.0599, aux.acc_seg: 93.4597, loss: 0.1976 +2024-06-19 07:51:29,552 - mmseg - INFO - Iter [78850/80000] lr: 5.755e-07, eta: 0:28:21, time: 1.330, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1374, decode.acc_seg: 93.8744, aux.loss_ce: 0.0598, aux.acc_seg: 93.3572, loss: 0.1973 +2024-06-19 07:52:36,118 - mmseg - INFO - Iter [78900/80000] lr: 5.505e-07, eta: 0:27:07, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1477, decode.acc_seg: 93.5271, aux.loss_ce: 0.0639, aux.acc_seg: 93.0334, loss: 0.2116 +2024-06-19 07:53:42,693 - mmseg - INFO - Iter [78950/80000] lr: 5.255e-07, eta: 0:25:53, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1423, decode.acc_seg: 93.7010, aux.loss_ce: 0.0618, aux.acc_seg: 93.1741, loss: 0.2041 +2024-06-19 07:54:49,241 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 07:54:49,241 - mmseg - INFO - Iter [79000/80000] lr: 5.005e-07, eta: 0:24:39, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1448, decode.acc_seg: 93.6361, aux.loss_ce: 0.0631, aux.acc_seg: 93.1060, loss: 0.2079 +2024-06-19 07:56:27,459 - mmseg - INFO - per class results: +2024-06-19 07:56:27,465 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.61 | 90.18 | +| building | 85.95 | 93.62 | +| sky | 95.05 | 97.72 | +| floor | 84.78 | 92.04 | +| tree | 77.43 | 90.0 | +| ceiling | 87.79 | 94.45 | +| road | 87.42 | 92.28 | +| bed | 92.45 | 96.89 | +| windowpane | 66.91 | 82.41 | +| grass | 66.34 | 79.56 | +| cabinet | 65.89 | 76.16 | +| sidewalk | 72.22 | 85.93 | +| person | 85.79 | 94.41 | +| earth | 38.64 | 51.91 | +| door | 60.47 | 75.49 | +| table | 70.73 | 82.36 | +| mountain | 60.69 | 73.37 | +| plant | 56.34 | 66.79 | +| curtain | 78.35 | 87.97 | +| chair | 68.3 | 78.86 | +| car | 87.45 | 94.24 | +| water | 65.3 | 79.47 | +| painting | 77.35 | 90.52 | +| sofa | 82.93 | 91.54 | +| shelf | 50.62 | 67.86 | +| house | 56.5 | 71.14 | +| sea | 69.0 | 83.78 | +| mirror | 78.0 | 83.68 | +| rug | 66.4 | 77.61 | +| field | 32.02 | 60.09 | +| armchair | 62.27 | 78.89 | +| seat | 65.63 | 87.83 | +| fence | 50.94 | 64.97 | +| desk | 58.59 | 78.42 | +| rock | 54.37 | 79.19 | +| wardrobe | 55.41 | 75.37 | +| lamp | 75.17 | 85.42 | +| bathtub | 84.85 | 86.64 | +| railing | 40.12 | 57.56 | +| cushion | 71.7 | 83.06 | +| base | 43.12 | 56.83 | +| box | 38.28 | 49.97 | +| column | 56.01 | 67.6 | +| signboard | 41.32 | 56.68 | +| chest of drawers | 46.75 | 68.71 | +| counter | 43.06 | 52.55 | +| sand | 52.39 | 76.08 | +| sink | 78.35 | 84.07 | +| skyscraper | 50.13 | 62.26 | +| fireplace | 77.11 | 92.49 | +| refrigerator | 82.87 | 92.6 | +| grandstand | 50.3 | 83.3 | +| path | 28.8 | 38.98 | +| stairs | 25.2 | 32.71 | +| runway | 72.94 | 96.11 | +| case | 58.87 | 81.2 | +| pool table | 94.88 | 97.67 | +| pillow | 70.15 | 80.3 | +| screen door | 80.85 | 83.76 | +| stairway | 42.62 | 58.12 | +| river | 21.34 | 40.68 | +| bridge | 76.75 | 87.69 | +| bookcase | 44.94 | 59.66 | +| blind | 45.07 | 50.93 | +| coffee table | 67.34 | 87.76 | +| toilet | 90.16 | 93.8 | +| flower | 46.3 | 58.97 | +| book | 59.45 | 79.36 | +| hill | 7.9 | 11.88 | +| bench | 56.23 | 65.59 | +| countertop | 63.32 | 84.42 | +| stove | 87.75 | 94.14 | +| palm | 57.44 | 83.15 | +| kitchen island | 45.57 | 73.0 | +| computer | 79.99 | 91.99 | +| swivel chair | 51.58 | 75.1 | +| boat | 66.02 | 88.53 | +| bar | 55.33 | 74.42 | +| arcade machine | 79.91 | 84.15 | +| hovel | 43.52 | 48.8 | +| bus | 93.52 | 95.82 | +| towel | 74.55 | 82.59 | +| light | 61.01 | 70.59 | +| truck | 45.82 | 59.22 | +| tower | 12.28 | 17.03 | +| chandelier | 72.67 | 87.25 | +| awning | 43.31 | 54.63 | +| streetlight | 36.37 | 48.23 | +| booth | 41.07 | 64.46 | +| television receiver | 81.29 | 86.93 | +| airplane | 84.0 | 90.12 | +| dirt track | 8.43 | 31.72 | +| apparel | 48.83 | 67.1 | +| pole | 30.15 | 42.1 | +| land | 3.87 | 6.24 | +| bannister | 18.65 | 26.97 | +| escalator | 57.24 | 79.7 | +| ottoman | 47.66 | 63.33 | +| bottle | 43.16 | 65.23 | +| buffet | 52.27 | 65.69 | +| poster | 36.67 | 51.28 | +| stage | 25.85 | 45.32 | +| van | 44.24 | 59.16 | +| ship | 92.4 | 95.74 | +| fountain | 21.76 | 22.25 | +| conveyer belt | 79.28 | 93.25 | +| canopy | 57.47 | 79.67 | +| washer | 82.45 | 85.28 | +| plaything | 40.31 | 53.26 | +| swimming pool | 64.48 | 90.83 | +| stool | 53.59 | 67.24 | +| barrel | 49.79 | 67.69 | +| basket | 40.03 | 57.87 | +| waterfall | 59.5 | 85.43 | +| tent | 90.46 | 98.91 | +| bag | 21.05 | 24.64 | +| minibike | 76.41 | 89.19 | +| cradle | 85.26 | 97.36 | +| oven | 61.31 | 71.51 | +| ball | 46.54 | 52.42 | +| food | 57.91 | 69.6 | +| step | 12.56 | 14.96 | +| tank | 67.48 | 73.35 | +| trade name | 29.56 | 34.09 | +| microwave | 89.01 | 95.85 | +| pot | 59.04 | 69.4 | +| animal | 63.56 | 64.88 | +| bicycle | 60.4 | 77.45 | +| lake | 56.12 | 63.75 | +| dishwasher | 73.36 | 83.91 | +| screen | 50.32 | 74.7 | +| blanket | 32.45 | 36.94 | +| sculpture | 74.73 | 88.2 | +| hood | 62.16 | 74.68 | +| sconce | 56.62 | 64.54 | +| vase | 49.15 | 60.21 | +| traffic light | 41.96 | 62.34 | +| tray | 15.12 | 19.33 | +| ashcan | 48.77 | 62.98 | +| fan | 65.98 | 77.69 | +| pier | 37.63 | 50.68 | +| crt screen | 18.2 | 31.24 | +| plate | 59.69 | 76.57 | +| monitor | 68.78 | 83.97 | +| bulletin board | 50.89 | 56.29 | +| shower | 6.5 | 7.29 | +| radiator | 65.59 | 75.46 | +| glass | 19.69 | 21.18 | +| clock | 44.67 | 51.14 | +| flag | 71.62 | 79.69 | ++---------------------+-------+-------+ +2024-06-19 07:56:27,465 - mmseg - INFO - Summary: +2024-06-19 07:56:27,465 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.35 | 57.62 | 70.01 | ++-------+-------+-------+ +2024-06-19 07:56:27,466 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 07:56:27,466 - mmseg - INFO - Iter(val) [250] aAcc: 0.8635, mIoU: 0.5762, mAcc: 0.7001, IoU.wall: 0.8261, IoU.building: 0.8595, IoU.sky: 0.9505, IoU.floor: 0.8478, IoU.tree: 0.7743, IoU.ceiling: 0.8779, IoU.road: 0.8742, IoU.bed : 0.9245, IoU.windowpane: 0.6691, IoU.grass: 0.6634, IoU.cabinet: 0.6589, IoU.sidewalk: 0.7222, IoU.person: 0.8579, IoU.earth: 0.3864, IoU.door: 0.6047, IoU.table: 0.7073, IoU.mountain: 0.6069, IoU.plant: 0.5634, IoU.curtain: 0.7835, IoU.chair: 0.6830, IoU.car: 0.8745, IoU.water: 0.6530, IoU.painting: 0.7735, IoU.sofa: 0.8293, IoU.shelf: 0.5062, IoU.house: 0.5650, IoU.sea: 0.6900, IoU.mirror: 0.7800, IoU.rug: 0.6640, IoU.field: 0.3202, IoU.armchair: 0.6227, IoU.seat: 0.6563, IoU.fence: 0.5094, IoU.desk: 0.5859, IoU.rock: 0.5437, IoU.wardrobe: 0.5541, IoU.lamp: 0.7517, IoU.bathtub: 0.8485, IoU.railing: 0.4012, IoU.cushion: 0.7170, IoU.base: 0.4312, IoU.box: 0.3828, IoU.column: 0.5601, IoU.signboard: 0.4132, IoU.chest of drawers: 0.4675, IoU.counter: 0.4306, IoU.sand: 0.5239, IoU.sink: 0.7835, IoU.skyscraper: 0.5013, IoU.fireplace: 0.7711, IoU.refrigerator: 0.8287, IoU.grandstand: 0.5030, IoU.path: 0.2880, IoU.stairs: 0.2520, IoU.runway: 0.7294, IoU.case: 0.5887, IoU.pool table: 0.9488, IoU.pillow: 0.7015, IoU.screen door: 0.8085, IoU.stairway: 0.4262, IoU.river: 0.2134, IoU.bridge: 0.7675, IoU.bookcase: 0.4494, IoU.blind: 0.4507, IoU.coffee table: 0.6734, IoU.toilet: 0.9016, IoU.flower: 0.4630, IoU.book: 0.5945, IoU.hill: 0.0790, IoU.bench: 0.5623, IoU.countertop: 0.6332, IoU.stove: 0.8775, IoU.palm: 0.5744, IoU.kitchen island: 0.4557, IoU.computer: 0.7999, IoU.swivel chair: 0.5158, IoU.boat: 0.6602, IoU.bar: 0.5533, IoU.arcade machine: 0.7991, IoU.hovel: 0.4352, IoU.bus: 0.9352, IoU.towel: 0.7455, IoU.light: 0.6101, IoU.truck: 0.4582, IoU.tower: 0.1228, IoU.chandelier: 0.7267, IoU.awning: 0.4331, IoU.streetlight: 0.3637, IoU.booth: 0.4107, IoU.television receiver: 0.8129, IoU.airplane: 0.8400, IoU.dirt track: 0.0843, IoU.apparel: 0.4883, IoU.pole: 0.3015, IoU.land: 0.0387, IoU.bannister: 0.1865, IoU.escalator: 0.5724, IoU.ottoman: 0.4766, IoU.bottle: 0.4316, IoU.buffet: 0.5227, IoU.poster: 0.3667, IoU.stage: 0.2585, IoU.van: 0.4424, IoU.ship: 0.9240, IoU.fountain: 0.2176, IoU.conveyer belt: 0.7928, IoU.canopy: 0.5747, IoU.washer: 0.8245, IoU.plaything: 0.4031, IoU.swimming pool: 0.6448, IoU.stool: 0.5359, IoU.barrel: 0.4979, IoU.basket: 0.4003, IoU.waterfall: 0.5950, IoU.tent: 0.9046, IoU.bag: 0.2105, IoU.minibike: 0.7641, IoU.cradle: 0.8526, IoU.oven: 0.6131, IoU.ball: 0.4654, IoU.food: 0.5791, IoU.step: 0.1256, IoU.tank: 0.6748, IoU.trade name: 0.2956, IoU.microwave: 0.8901, IoU.pot: 0.5904, IoU.animal: 0.6356, IoU.bicycle: 0.6040, IoU.lake: 0.5612, IoU.dishwasher: 0.7336, IoU.screen: 0.5032, IoU.blanket: 0.3245, IoU.sculpture: 0.7473, IoU.hood: 0.6216, IoU.sconce: 0.5662, IoU.vase: 0.4915, IoU.traffic light: 0.4196, IoU.tray: 0.1512, IoU.ashcan: 0.4877, IoU.fan: 0.6598, IoU.pier: 0.3763, IoU.crt screen: 0.1820, IoU.plate: 0.5969, IoU.monitor: 0.6878, IoU.bulletin board: 0.5089, IoU.shower: 0.0650, IoU.radiator: 0.6559, IoU.glass: 0.1969, IoU.clock: 0.4467, IoU.flag: 0.7162, Acc.wall: 0.9018, Acc.building: 0.9362, Acc.sky: 0.9772, Acc.floor: 0.9204, Acc.tree: 0.9000, Acc.ceiling: 0.9445, Acc.road: 0.9228, Acc.bed : 0.9689, Acc.windowpane: 0.8241, Acc.grass: 0.7956, Acc.cabinet: 0.7616, Acc.sidewalk: 0.8593, Acc.person: 0.9441, Acc.earth: 0.5191, Acc.door: 0.7549, Acc.table: 0.8236, Acc.mountain: 0.7337, Acc.plant: 0.6679, Acc.curtain: 0.8797, Acc.chair: 0.7886, Acc.car: 0.9424, Acc.water: 0.7947, Acc.painting: 0.9052, Acc.sofa: 0.9154, Acc.shelf: 0.6786, Acc.house: 0.7114, Acc.sea: 0.8378, Acc.mirror: 0.8368, Acc.rug: 0.7761, Acc.field: 0.6009, Acc.armchair: 0.7889, Acc.seat: 0.8783, Acc.fence: 0.6497, Acc.desk: 0.7842, Acc.rock: 0.7919, Acc.wardrobe: 0.7537, Acc.lamp: 0.8542, Acc.bathtub: 0.8664, Acc.railing: 0.5756, Acc.cushion: 0.8306, Acc.base: 0.5683, Acc.box: 0.4997, Acc.column: 0.6760, Acc.signboard: 0.5668, Acc.chest of drawers: 0.6871, Acc.counter: 0.5255, Acc.sand: 0.7608, Acc.sink: 0.8407, Acc.skyscraper: 0.6226, Acc.fireplace: 0.9249, Acc.refrigerator: 0.9260, Acc.grandstand: 0.8330, Acc.path: 0.3898, Acc.stairs: 0.3271, Acc.runway: 0.9611, Acc.case: 0.8120, Acc.pool table: 0.9767, Acc.pillow: 0.8030, Acc.screen door: 0.8376, Acc.stairway: 0.5812, Acc.river: 0.4068, Acc.bridge: 0.8769, Acc.bookcase: 0.5966, Acc.blind: 0.5093, Acc.coffee table: 0.8776, Acc.toilet: 0.9380, Acc.flower: 0.5897, Acc.book: 0.7936, Acc.hill: 0.1188, Acc.bench: 0.6559, Acc.countertop: 0.8442, Acc.stove: 0.9414, Acc.palm: 0.8315, Acc.kitchen island: 0.7300, Acc.computer: 0.9199, Acc.swivel chair: 0.7510, Acc.boat: 0.8853, Acc.bar: 0.7442, Acc.arcade machine: 0.8415, Acc.hovel: 0.4880, Acc.bus: 0.9582, Acc.towel: 0.8259, Acc.light: 0.7059, Acc.truck: 0.5922, Acc.tower: 0.1703, Acc.chandelier: 0.8725, Acc.awning: 0.5463, Acc.streetlight: 0.4823, Acc.booth: 0.6446, Acc.television receiver: 0.8693, Acc.airplane: 0.9012, Acc.dirt track: 0.3172, Acc.apparel: 0.6710, Acc.pole: 0.4210, Acc.land: 0.0624, Acc.bannister: 0.2697, Acc.escalator: 0.7970, Acc.ottoman: 0.6333, Acc.bottle: 0.6523, Acc.buffet: 0.6569, Acc.poster: 0.5128, Acc.stage: 0.4532, Acc.van: 0.5916, Acc.ship: 0.9574, Acc.fountain: 0.2225, Acc.conveyer belt: 0.9325, Acc.canopy: 0.7967, Acc.washer: 0.8528, Acc.plaything: 0.5326, Acc.swimming pool: 0.9083, Acc.stool: 0.6724, Acc.barrel: 0.6769, Acc.basket: 0.5787, Acc.waterfall: 0.8543, Acc.tent: 0.9891, Acc.bag: 0.2464, Acc.minibike: 0.8919, Acc.cradle: 0.9736, Acc.oven: 0.7151, Acc.ball: 0.5242, Acc.food: 0.6960, Acc.step: 0.1496, Acc.tank: 0.7335, Acc.trade name: 0.3409, Acc.microwave: 0.9585, Acc.pot: 0.6940, Acc.animal: 0.6488, Acc.bicycle: 0.7745, Acc.lake: 0.6375, Acc.dishwasher: 0.8391, Acc.screen: 0.7470, Acc.blanket: 0.3694, Acc.sculpture: 0.8820, Acc.hood: 0.7468, Acc.sconce: 0.6454, Acc.vase: 0.6021, Acc.traffic light: 0.6234, Acc.tray: 0.1933, Acc.ashcan: 0.6298, Acc.fan: 0.7769, Acc.pier: 0.5068, Acc.crt screen: 0.3124, Acc.plate: 0.7657, Acc.monitor: 0.8397, Acc.bulletin board: 0.5629, Acc.shower: 0.0729, Acc.radiator: 0.7546, Acc.glass: 0.2118, Acc.clock: 0.5114, Acc.flag: 0.7969 +2024-06-19 07:57:34,216 - mmseg - INFO - Iter [79050/80000] lr: 4.755e-07, eta: 0:23:26, time: 3.299, data_time: 1.982, memory: 70498, decode.loss_ce: 0.1395, decode.acc_seg: 93.8918, aux.loss_ce: 0.0611, aux.acc_seg: 93.3482, loss: 0.2007 +2024-06-19 07:58:40,881 - mmseg - INFO - Iter [79100/80000] lr: 4.505e-07, eta: 0:22:12, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1428, decode.acc_seg: 93.7421, aux.loss_ce: 0.0623, aux.acc_seg: 93.2164, loss: 0.2051 +2024-06-19 07:59:46,984 - mmseg - INFO - Iter [79150/80000] lr: 4.255e-07, eta: 0:20:58, time: 1.322, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1518, decode.acc_seg: 93.2766, aux.loss_ce: 0.0656, aux.acc_seg: 92.7757, loss: 0.2174 +2024-06-19 08:00:53,050 - mmseg - INFO - Iter [79200/80000] lr: 4.005e-07, eta: 0:19:44, time: 1.321, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1407, decode.acc_seg: 93.8175, aux.loss_ce: 0.0614, aux.acc_seg: 93.2554, loss: 0.2021 +2024-06-19 08:01:59,286 - mmseg - INFO - Iter [79250/80000] lr: 3.755e-07, eta: 0:18:30, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1421, decode.acc_seg: 93.8784, aux.loss_ce: 0.0618, aux.acc_seg: 93.3901, loss: 0.2039 +2024-06-19 08:03:05,697 - mmseg - INFO - Iter [79300/80000] lr: 3.505e-07, eta: 0:17:16, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1360, decode.acc_seg: 93.9426, aux.loss_ce: 0.0595, aux.acc_seg: 93.4348, loss: 0.1955 +2024-06-19 08:04:11,953 - mmseg - INFO - Iter [79350/80000] lr: 3.255e-07, eta: 0:16:02, time: 1.325, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1373, decode.acc_seg: 93.8817, aux.loss_ce: 0.0603, aux.acc_seg: 93.3265, loss: 0.1976 +2024-06-19 08:05:18,381 - mmseg - INFO - Iter [79400/80000] lr: 3.005e-07, eta: 0:14:47, time: 1.329, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1404, decode.acc_seg: 93.8318, aux.loss_ce: 0.0619, aux.acc_seg: 93.2284, loss: 0.2023 +2024-06-19 08:06:24,783 - mmseg - INFO - Iter [79450/80000] lr: 2.755e-07, eta: 0:13:33, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1389, decode.acc_seg: 93.8678, aux.loss_ce: 0.0606, aux.acc_seg: 93.3623, loss: 0.1995 +2024-06-19 08:07:31,186 - mmseg - INFO - Iter [79500/80000] lr: 2.505e-07, eta: 0:12:19, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1395, decode.acc_seg: 93.9114, aux.loss_ce: 0.0610, aux.acc_seg: 93.2782, loss: 0.2005 +2024-06-19 08:08:37,729 - mmseg - INFO - Iter [79550/80000] lr: 2.255e-07, eta: 0:11:05, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1400, decode.acc_seg: 93.8878, aux.loss_ce: 0.0612, aux.acc_seg: 93.3602, loss: 0.2012 +2024-06-19 08:09:46,649 - mmseg - INFO - Iter [79600/80000] lr: 2.005e-07, eta: 0:09:51, time: 1.378, data_time: 0.057, memory: 70498, decode.loss_ce: 0.1360, decode.acc_seg: 94.1716, aux.loss_ce: 0.0598, aux.acc_seg: 93.6341, loss: 0.1957 +2024-06-19 08:10:53,042 - mmseg - INFO - Iter [79650/80000] lr: 1.755e-07, eta: 0:08:37, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1390, decode.acc_seg: 93.8487, aux.loss_ce: 0.0604, aux.acc_seg: 93.3148, loss: 0.1994 +2024-06-19 08:11:59,439 - mmseg - INFO - Iter [79700/80000] lr: 1.505e-07, eta: 0:07:23, time: 1.328, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1382, decode.acc_seg: 93.9132, aux.loss_ce: 0.0604, aux.acc_seg: 93.3905, loss: 0.1986 +2024-06-19 08:13:06,026 - mmseg - INFO - Iter [79750/80000] lr: 1.255e-07, eta: 0:06:09, time: 1.332, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1410, decode.acc_seg: 93.7529, aux.loss_ce: 0.0611, aux.acc_seg: 93.2882, loss: 0.2021 +2024-06-19 08:14:12,225 - mmseg - INFO - Iter [79800/80000] lr: 1.005e-07, eta: 0:04:55, time: 1.324, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1548, decode.acc_seg: 93.2173, aux.loss_ce: 0.0668, aux.acc_seg: 92.6785, loss: 0.2216 +2024-06-19 08:15:18,530 - mmseg - INFO - Iter [79850/80000] lr: 7.550e-08, eta: 0:03:41, time: 1.326, data_time: 0.009, memory: 70498, decode.loss_ce: 0.1363, decode.acc_seg: 93.9819, aux.loss_ce: 0.0595, aux.acc_seg: 93.4471, loss: 0.1959 +2024-06-19 08:16:25,067 - mmseg - INFO - Iter [79900/80000] lr: 5.050e-08, eta: 0:02:27, time: 1.331, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1462, decode.acc_seg: 93.6416, aux.loss_ce: 0.0633, aux.acc_seg: 93.1876, loss: 0.2095 +2024-06-19 08:17:31,739 - mmseg - INFO - Iter [79950/80000] lr: 2.550e-08, eta: 0:01:13, time: 1.333, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1382, decode.acc_seg: 93.7992, aux.loss_ce: 0.0603, aux.acc_seg: 93.2836, loss: 0.1986 +2024-06-19 08:18:38,245 - mmseg - INFO - Saving checkpoint at 80000 iterations +2024-06-19 08:20:16,330 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 08:20:16,330 - mmseg - INFO - Iter [80000/80000] lr: 5.000e-10, eta: 0:00:00, time: 3.292, data_time: 0.010, memory: 70498, decode.loss_ce: 0.1408, decode.acc_seg: 93.8879, aux.loss_ce: 0.0610, aux.acc_seg: 93.4105, loss: 0.2019 +2024-06-19 08:21:52,982 - mmseg - INFO - per class results: +2024-06-19 08:21:52,988 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.67 | 90.49 | +| building | 86.03 | 93.57 | +| sky | 95.05 | 97.75 | +| floor | 84.87 | 91.87 | +| tree | 77.45 | 90.13 | +| ceiling | 87.77 | 94.52 | +| road | 87.52 | 92.1 | +| bed | 92.42 | 96.88 | +| windowpane | 66.92 | 81.84 | +| grass | 66.09 | 79.85 | +| cabinet | 65.92 | 76.03 | +| sidewalk | 72.25 | 86.38 | +| person | 85.8 | 94.33 | +| earth | 38.65 | 51.9 | +| door | 60.43 | 75.34 | +| table | 70.65 | 82.52 | +| mountain | 60.45 | 72.89 | +| plant | 56.5 | 67.0 | +| curtain | 78.47 | 87.74 | +| chair | 68.3 | 78.9 | +| car | 87.47 | 93.91 | +| water | 65.23 | 79.65 | +| painting | 77.39 | 90.33 | +| sofa | 82.8 | 91.68 | +| shelf | 50.63 | 68.28 | +| house | 57.07 | 71.56 | +| sea | 68.86 | 83.49 | +| mirror | 78.12 | 83.86 | +| rug | 66.81 | 78.28 | +| field | 31.86 | 60.19 | +| armchair | 62.21 | 78.73 | +| seat | 65.54 | 88.11 | +| fence | 50.86 | 63.84 | +| desk | 58.61 | 78.27 | +| rock | 53.88 | 79.46 | +| wardrobe | 55.18 | 75.48 | +| lamp | 75.03 | 85.51 | +| bathtub | 84.93 | 86.74 | +| railing | 40.02 | 56.76 | +| cushion | 71.49 | 82.92 | +| base | 42.97 | 56.31 | +| box | 38.09 | 49.37 | +| column | 56.0 | 67.49 | +| signboard | 41.16 | 56.81 | +| chest of drawers | 46.79 | 69.2 | +| counter | 43.05 | 52.13 | +| sand | 52.47 | 76.11 | +| sink | 78.4 | 83.98 | +| skyscraper | 50.17 | 62.03 | +| fireplace | 77.18 | 92.63 | +| refrigerator | 83.18 | 92.49 | +| grandstand | 50.41 | 83.46 | +| path | 28.85 | 38.88 | +| stairs | 24.65 | 31.77 | +| runway | 72.98 | 95.77 | +| case | 59.23 | 80.73 | +| pool table | 94.89 | 97.68 | +| pillow | 69.57 | 78.87 | +| screen door | 80.93 | 83.69 | +| stairway | 42.23 | 57.57 | +| river | 21.06 | 40.79 | +| bridge | 76.4 | 86.85 | +| bookcase | 45.64 | 61.45 | +| blind | 44.44 | 49.66 | +| coffee table | 67.17 | 87.79 | +| toilet | 90.16 | 93.67 | +| flower | 45.96 | 58.33 | +| book | 59.66 | 78.32 | +| hill | 7.82 | 11.62 | +| bench | 56.27 | 65.28 | +| countertop | 62.93 | 84.7 | +| stove | 87.71 | 93.87 | +| palm | 57.57 | 82.7 | +| kitchen island | 46.07 | 74.29 | +| computer | 80.33 | 91.81 | +| swivel chair | 51.55 | 75.15 | +| boat | 66.73 | 88.09 | +| bar | 55.43 | 74.47 | +| arcade machine | 79.99 | 84.15 | +| hovel | 43.58 | 48.84 | +| bus | 93.51 | 96.0 | +| towel | 74.49 | 82.8 | +| light | 60.96 | 70.19 | +| truck | 45.32 | 58.47 | +| tower | 11.62 | 15.75 | +| chandelier | 72.51 | 86.69 | +| awning | 43.22 | 54.87 | +| streetlight | 36.62 | 49.1 | +| booth | 41.22 | 64.19 | +| television receiver | 81.74 | 87.38 | +| airplane | 83.22 | 89.09 | +| dirt track | 7.98 | 30.85 | +| apparel | 48.61 | 66.54 | +| pole | 29.65 | 41.05 | +| land | 3.82 | 6.14 | +| bannister | 18.64 | 27.06 | +| escalator | 57.72 | 79.54 | +| ottoman | 47.96 | 64.0 | +| bottle | 43.2 | 64.49 | +| buffet | 52.54 | 65.47 | +| poster | 36.89 | 50.96 | +| stage | 26.01 | 45.03 | +| van | 44.01 | 59.73 | +| ship | 92.63 | 95.56 | +| fountain | 21.61 | 22.04 | +| conveyer belt | 79.81 | 93.16 | +| canopy | 57.5 | 78.81 | +| washer | 82.77 | 85.62 | +| plaything | 40.24 | 54.58 | +| swimming pool | 64.67 | 90.41 | +| stool | 53.46 | 67.72 | +| barrel | 49.95 | 67.07 | +| basket | 40.22 | 58.0 | +| waterfall | 59.46 | 85.52 | +| tent | 90.72 | 98.81 | +| bag | 20.77 | 24.14 | +| minibike | 76.5 | 89.12 | +| cradle | 85.21 | 97.35 | +| oven | 61.31 | 71.06 | +| ball | 46.27 | 52.25 | +| food | 57.62 | 69.86 | +| step | 12.31 | 14.62 | +| tank | 67.27 | 72.43 | +| trade name | 30.13 | 35.15 | +| microwave | 89.02 | 95.65 | +| pot | 59.03 | 68.99 | +| animal | 64.57 | 65.86 | +| bicycle | 60.31 | 77.4 | +| lake | 55.94 | 63.76 | +| dishwasher | 73.97 | 83.69 | +| screen | 51.7 | 76.5 | +| blanket | 32.29 | 36.71 | +| sculpture | 74.68 | 88.18 | +| hood | 62.14 | 74.59 | +| sconce | 56.64 | 64.45 | +| vase | 49.21 | 61.28 | +| traffic light | 42.07 | 62.4 | +| tray | 15.0 | 18.8 | +| ashcan | 48.87 | 62.57 | +| fan | 65.96 | 77.66 | +| pier | 37.98 | 50.06 | +| crt screen | 18.24 | 29.6 | +| plate | 59.62 | 77.21 | +| monitor | 68.88 | 83.67 | +| bulletin board | 49.84 | 54.16 | +| shower | 6.04 | 6.68 | +| radiator | 65.31 | 74.87 | +| glass | 19.86 | 21.48 | +| clock | 44.69 | 50.87 | +| flag | 71.66 | 79.48 | ++---------------------+-------+-------+ +2024-06-19 08:21:52,988 - mmseg - INFO - Summary: +2024-06-19 08:21:52,988 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.37 | 57.62 | 69.89 | ++-------+-------+-------+ +2024-06-19 08:21:52,989 - mmseg - INFO - Exp name: upernet_internvit_h6b_192_512_80k_ade20k_bs16_lr4e-5.py +2024-06-19 08:21:52,989 - mmseg - INFO - Iter(val) [250] aAcc: 0.8637, mIoU: 0.5762, mAcc: 0.6989, IoU.wall: 0.8267, IoU.building: 0.8603, IoU.sky: 0.9505, IoU.floor: 0.8487, IoU.tree: 0.7745, IoU.ceiling: 0.8777, IoU.road: 0.8752, IoU.bed : 0.9242, IoU.windowpane: 0.6692, IoU.grass: 0.6609, IoU.cabinet: 0.6592, IoU.sidewalk: 0.7225, IoU.person: 0.8580, IoU.earth: 0.3865, IoU.door: 0.6043, IoU.table: 0.7065, IoU.mountain: 0.6045, IoU.plant: 0.5650, IoU.curtain: 0.7847, IoU.chair: 0.6830, IoU.car: 0.8747, IoU.water: 0.6523, IoU.painting: 0.7739, IoU.sofa: 0.8280, IoU.shelf: 0.5063, IoU.house: 0.5707, IoU.sea: 0.6886, IoU.mirror: 0.7812, IoU.rug: 0.6681, IoU.field: 0.3186, IoU.armchair: 0.6221, IoU.seat: 0.6554, IoU.fence: 0.5086, IoU.desk: 0.5861, IoU.rock: 0.5388, IoU.wardrobe: 0.5518, IoU.lamp: 0.7503, IoU.bathtub: 0.8493, IoU.railing: 0.4002, IoU.cushion: 0.7149, IoU.base: 0.4297, IoU.box: 0.3809, IoU.column: 0.5600, IoU.signboard: 0.4116, IoU.chest of drawers: 0.4679, IoU.counter: 0.4305, IoU.sand: 0.5247, IoU.sink: 0.7840, IoU.skyscraper: 0.5017, IoU.fireplace: 0.7718, IoU.refrigerator: 0.8318, IoU.grandstand: 0.5041, IoU.path: 0.2885, IoU.stairs: 0.2465, IoU.runway: 0.7298, IoU.case: 0.5923, IoU.pool table: 0.9489, IoU.pillow: 0.6957, IoU.screen door: 0.8093, IoU.stairway: 0.4223, IoU.river: 0.2106, IoU.bridge: 0.7640, IoU.bookcase: 0.4564, IoU.blind: 0.4444, IoU.coffee table: 0.6717, IoU.toilet: 0.9016, IoU.flower: 0.4596, IoU.book: 0.5966, IoU.hill: 0.0782, IoU.bench: 0.5627, IoU.countertop: 0.6293, IoU.stove: 0.8771, IoU.palm: 0.5757, IoU.kitchen island: 0.4607, IoU.computer: 0.8033, IoU.swivel chair: 0.5155, IoU.boat: 0.6673, IoU.bar: 0.5543, IoU.arcade machine: 0.7999, IoU.hovel: 0.4358, IoU.bus: 0.9351, IoU.towel: 0.7449, IoU.light: 0.6096, IoU.truck: 0.4532, IoU.tower: 0.1162, IoU.chandelier: 0.7251, IoU.awning: 0.4322, IoU.streetlight: 0.3662, IoU.booth: 0.4122, IoU.television receiver: 0.8174, IoU.airplane: 0.8322, IoU.dirt track: 0.0798, IoU.apparel: 0.4861, IoU.pole: 0.2965, IoU.land: 0.0382, IoU.bannister: 0.1864, IoU.escalator: 0.5772, IoU.ottoman: 0.4796, IoU.bottle: 0.4320, IoU.buffet: 0.5254, IoU.poster: 0.3689, IoU.stage: 0.2601, IoU.van: 0.4401, IoU.ship: 0.9263, IoU.fountain: 0.2161, IoU.conveyer belt: 0.7981, IoU.canopy: 0.5750, IoU.washer: 0.8277, IoU.plaything: 0.4024, IoU.swimming pool: 0.6467, IoU.stool: 0.5346, IoU.barrel: 0.4995, IoU.basket: 0.4022, IoU.waterfall: 0.5946, IoU.tent: 0.9072, IoU.bag: 0.2077, IoU.minibike: 0.7650, IoU.cradle: 0.8521, IoU.oven: 0.6131, IoU.ball: 0.4627, IoU.food: 0.5762, IoU.step: 0.1231, IoU.tank: 0.6727, IoU.trade name: 0.3013, IoU.microwave: 0.8902, IoU.pot: 0.5903, IoU.animal: 0.6457, IoU.bicycle: 0.6031, IoU.lake: 0.5594, IoU.dishwasher: 0.7397, IoU.screen: 0.5170, IoU.blanket: 0.3229, IoU.sculpture: 0.7468, IoU.hood: 0.6214, IoU.sconce: 0.5664, IoU.vase: 0.4921, IoU.traffic light: 0.4207, IoU.tray: 0.1500, IoU.ashcan: 0.4887, IoU.fan: 0.6596, IoU.pier: 0.3798, IoU.crt screen: 0.1824, IoU.plate: 0.5962, IoU.monitor: 0.6888, IoU.bulletin board: 0.4984, IoU.shower: 0.0604, IoU.radiator: 0.6531, IoU.glass: 0.1986, IoU.clock: 0.4469, IoU.flag: 0.7166, Acc.wall: 0.9049, Acc.building: 0.9357, Acc.sky: 0.9775, Acc.floor: 0.9187, Acc.tree: 0.9013, Acc.ceiling: 0.9452, Acc.road: 0.9210, Acc.bed : 0.9688, Acc.windowpane: 0.8184, Acc.grass: 0.7985, Acc.cabinet: 0.7603, Acc.sidewalk: 0.8638, Acc.person: 0.9433, Acc.earth: 0.5190, Acc.door: 0.7534, Acc.table: 0.8252, Acc.mountain: 0.7289, Acc.plant: 0.6700, Acc.curtain: 0.8774, Acc.chair: 0.7890, Acc.car: 0.9391, Acc.water: 0.7965, Acc.painting: 0.9033, Acc.sofa: 0.9168, Acc.shelf: 0.6828, Acc.house: 0.7156, Acc.sea: 0.8349, Acc.mirror: 0.8386, Acc.rug: 0.7828, Acc.field: 0.6019, Acc.armchair: 0.7873, Acc.seat: 0.8811, Acc.fence: 0.6384, Acc.desk: 0.7827, Acc.rock: 0.7946, Acc.wardrobe: 0.7548, Acc.lamp: 0.8551, Acc.bathtub: 0.8674, Acc.railing: 0.5676, Acc.cushion: 0.8292, Acc.base: 0.5631, Acc.box: 0.4937, Acc.column: 0.6749, Acc.signboard: 0.5681, Acc.chest of drawers: 0.6920, Acc.counter: 0.5213, Acc.sand: 0.7611, Acc.sink: 0.8398, Acc.skyscraper: 0.6203, Acc.fireplace: 0.9263, Acc.refrigerator: 0.9249, Acc.grandstand: 0.8346, Acc.path: 0.3888, Acc.stairs: 0.3177, Acc.runway: 0.9577, Acc.case: 0.8073, Acc.pool table: 0.9768, Acc.pillow: 0.7887, Acc.screen door: 0.8369, Acc.stairway: 0.5757, Acc.river: 0.4079, Acc.bridge: 0.8685, Acc.bookcase: 0.6145, Acc.blind: 0.4966, Acc.coffee table: 0.8779, Acc.toilet: 0.9367, Acc.flower: 0.5833, Acc.book: 0.7832, Acc.hill: 0.1162, Acc.bench: 0.6528, Acc.countertop: 0.8470, Acc.stove: 0.9387, Acc.palm: 0.8270, Acc.kitchen island: 0.7429, Acc.computer: 0.9181, Acc.swivel chair: 0.7515, Acc.boat: 0.8809, Acc.bar: 0.7447, Acc.arcade machine: 0.8415, Acc.hovel: 0.4884, Acc.bus: 0.9600, Acc.towel: 0.8280, Acc.light: 0.7019, Acc.truck: 0.5847, Acc.tower: 0.1575, Acc.chandelier: 0.8669, Acc.awning: 0.5487, Acc.streetlight: 0.4910, Acc.booth: 0.6419, Acc.television receiver: 0.8738, Acc.airplane: 0.8909, Acc.dirt track: 0.3085, Acc.apparel: 0.6654, Acc.pole: 0.4105, Acc.land: 0.0614, Acc.bannister: 0.2706, Acc.escalator: 0.7954, Acc.ottoman: 0.6400, Acc.bottle: 0.6449, Acc.buffet: 0.6547, Acc.poster: 0.5096, Acc.stage: 0.4503, Acc.van: 0.5973, Acc.ship: 0.9556, Acc.fountain: 0.2204, Acc.conveyer belt: 0.9316, Acc.canopy: 0.7881, Acc.washer: 0.8562, Acc.plaything: 0.5458, Acc.swimming pool: 0.9041, Acc.stool: 0.6772, Acc.barrel: 0.6707, Acc.basket: 0.5800, Acc.waterfall: 0.8552, Acc.tent: 0.9881, Acc.bag: 0.2414, Acc.minibike: 0.8912, Acc.cradle: 0.9735, Acc.oven: 0.7106, Acc.ball: 0.5225, Acc.food: 0.6986, Acc.step: 0.1462, Acc.tank: 0.7243, Acc.trade name: 0.3515, Acc.microwave: 0.9565, Acc.pot: 0.6899, Acc.animal: 0.6586, Acc.bicycle: 0.7740, Acc.lake: 0.6376, Acc.dishwasher: 0.8369, Acc.screen: 0.7650, Acc.blanket: 0.3671, Acc.sculpture: 0.8818, Acc.hood: 0.7459, Acc.sconce: 0.6445, Acc.vase: 0.6128, Acc.traffic light: 0.6240, Acc.tray: 0.1880, Acc.ashcan: 0.6257, Acc.fan: 0.7766, Acc.pier: 0.5006, Acc.crt screen: 0.2960, Acc.plate: 0.7721, Acc.monitor: 0.8367, Acc.bulletin board: 0.5416, Acc.shower: 0.0668, Acc.radiator: 0.7487, Acc.glass: 0.2148, Acc.clock: 0.5087, Acc.flag: 0.7948