diff --git "a/segmentation/upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.log" "b/segmentation/upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.log" new file mode 100644--- /dev/null +++ "b/segmentation/upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.log" @@ -0,0 +1,24475 @@ +2024-06-15 21:22:02,184 - mmseg - INFO - Multi-processing start method is `None` +2024-06-15 21:22:02,189 - mmseg - INFO - OpenCV num_threads is `128 +2024-06-15 21:22:02,474 - 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 +------------------------------------------------------------ + +2024-06-15 21:22:02,475 - mmseg - INFO - Distributed training: True +2024-06-15 21:22:03,556 - 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=256, + img_size=256, + 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_256_512_80k_ade20k_bs16_lr4e-5' +gpu_ids = range(0, 8) +auto_resume = False + +2024-06-15 21:22:08,724 - mmseg - INFO - Set random seed to 942084185, deterministic: False +2024-06-15 21:23:12,010 - 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']) +2024-06-15 21:23:23,091 - 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']) +2024-06-15 21:24:29,193 - mmseg - INFO - initialize UPerHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} +2024-06-15 21:24:30,958 - 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, 257, 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 +2024-06-15 21:24:30,996 - 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'}} +) +2024-06-15 21:24:31,515 - mmseg - INFO - Loaded 20210 images +2024-06-15 21:24:32,667 - mmseg - INFO - {'num_layers': 48, 'layer_decay_rate': 0.95, 'skip_stride': 1.5} +2024-06-15 21:24:32,667 - mmseg - INFO - Build LayerDecayOptimizerConstructor 0.950000 - 50 +2024-06-15 21:24:32,677 - 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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21:25:13,053 - 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 + -------------------- +2024-06-15 21:25:13,053 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters +2024-06-15 21:25:13,077 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/PIIP/mmsegmentation/work_dirs/upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5 by HardDiskBackend. +2024-06-15 21:27:42,873 - mmseg - INFO - Iter [50/80000] lr: 1.306e-06, eta: 1 day, 12:28:51, time: 1.643, data_time: 0.013, memory: 70722, decode.loss_ce: 4.1137, decode.acc_seg: 0.8824, aux.loss_ce: 1.6309, aux.acc_seg: 0.8035, loss: 5.7446 +2024-06-15 21:28:51,091 - mmseg - INFO - Iter [100/80000] lr: 2.637e-06, eta: 1 day, 9:22:10, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 4.0144, decode.acc_seg: 3.6680, aux.loss_ce: 1.6065, aux.acc_seg: 2.4156, loss: 5.6209 +2024-06-15 21:29:59,238 - mmseg - INFO - Iter [150/80000] lr: 3.966e-06, eta: 1 day, 8:18:33, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 3.5706, decode.acc_seg: 21.2312, aux.loss_ce: 1.4824, aux.acc_seg: 15.3443, loss: 5.0530 +2024-06-15 21:31:07,735 - mmseg - INFO - Iter [200/80000] lr: 5.294e-06, eta: 1 day, 7:48:30, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 3.0457, decode.acc_seg: 34.6415, aux.loss_ce: 1.3739, aux.acc_seg: 28.7409, loss: 4.4196 +2024-06-15 21:32:15,859 - mmseg - INFO - Iter [250/80000] lr: 6.619e-06, eta: 1 day, 7:28:02, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 2.5205, decode.acc_seg: 43.0047, aux.loss_ce: 1.1495, aux.acc_seg: 38.7751, loss: 3.6700 +2024-06-15 21:33:24,339 - mmseg - INFO - Iter [300/80000] lr: 7.944e-06, eta: 1 day, 7:15:35, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 2.1158, decode.acc_seg: 48.9499, aux.loss_ce: 0.9507, aux.acc_seg: 45.5844, loss: 3.0665 +2024-06-15 21:34:32,550 - mmseg - INFO - Iter [350/80000] lr: 9.266e-06, eta: 1 day, 7:05:21, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 1.7927, decode.acc_seg: 55.3103, aux.loss_ce: 0.8110, aux.acc_seg: 51.7406, loss: 2.6037 +2024-06-15 21:35:40,872 - mmseg - INFO - Iter [400/80000] lr: 1.059e-05, eta: 1 day, 6:57:45, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 1.5994, decode.acc_seg: 58.9507, aux.loss_ce: 0.7149, aux.acc_seg: 56.1274, loss: 2.3143 +2024-06-15 21:36:49,240 - mmseg - INFO - Iter [450/80000] lr: 1.191e-05, eta: 1 day, 6:51:44, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 1.4880, decode.acc_seg: 60.2060, aux.loss_ce: 0.6546, aux.acc_seg: 58.8152, loss: 2.1426 +2024-06-15 21:37:57,489 - mmseg - INFO - Iter [500/80000] lr: 1.322e-05, eta: 1 day, 6:46:22, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 1.3907, decode.acc_seg: 63.1752, aux.loss_ce: 0.6149, aux.acc_seg: 61.5207, loss: 2.0056 +2024-06-15 21:39:06,075 - mmseg - INFO - Iter [550/80000] lr: 1.454e-05, eta: 1 day, 6:42:35, time: 1.372, data_time: 0.009, memory: 70722, decode.loss_ce: 1.3166, decode.acc_seg: 63.8473, aux.loss_ce: 0.5753, aux.acc_seg: 62.6402, loss: 1.8919 +2024-06-15 21:40:14,366 - mmseg - INFO - Iter [600/80000] lr: 1.585e-05, eta: 1 day, 6:38:35, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 1.1623, decode.acc_seg: 66.8428, aux.loss_ce: 0.5067, aux.acc_seg: 65.8987, loss: 1.6690 +2024-06-15 21:41:22,828 - mmseg - INFO - Iter [650/80000] lr: 1.717e-05, eta: 1 day, 6:35:23, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 1.1564, decode.acc_seg: 67.1462, aux.loss_ce: 0.4992, aux.acc_seg: 66.4112, loss: 1.6556 +2024-06-15 21:42:31,208 - mmseg - INFO - Iter [700/80000] lr: 1.848e-05, eta: 1 day, 6:32:19, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 1.1062, decode.acc_seg: 67.5835, aux.loss_ce: 0.4693, aux.acc_seg: 67.6871, loss: 1.5755 +2024-06-15 21:43:39,609 - mmseg - INFO - Iter [750/80000] lr: 1.979e-05, eta: 1 day, 6:29:33, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 1.0197, decode.acc_seg: 69.5374, aux.loss_ce: 0.4332, aux.acc_seg: 69.4308, loss: 1.4529 +2024-06-15 21:44:47,855 - mmseg - INFO - Iter [800/80000] lr: 2.109e-05, eta: 1 day, 6:26:43, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.9992, decode.acc_seg: 69.3310, aux.loss_ce: 0.4223, aux.acc_seg: 69.2448, loss: 1.4215 +2024-06-15 21:45:56,130 - mmseg - INFO - Iter [850/80000] lr: 2.240e-05, eta: 1 day, 6:24:08, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.9725, decode.acc_seg: 70.1679, aux.loss_ce: 0.4051, aux.acc_seg: 70.4334, loss: 1.3775 +2024-06-15 21:47:04,503 - mmseg - INFO - Iter [900/80000] lr: 2.370e-05, eta: 1 day, 6:21:52, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.9320, decode.acc_seg: 70.7106, aux.loss_ce: 0.3907, aux.acc_seg: 70.6634, loss: 1.3227 +2024-06-15 21:48:12,772 - mmseg - INFO - Iter [950/80000] lr: 2.501e-05, eta: 1 day, 6:19:34, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.9096, decode.acc_seg: 70.9004, aux.loss_ce: 0.3780, aux.acc_seg: 70.8808, loss: 1.2875 +2024-06-15 21:49:21,473 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 21:49:21,474 - mmseg - INFO - Iter [1000/80000] lr: 2.631e-05, eta: 1 day, 6:17:57, time: 1.374, data_time: 0.010, memory: 70722, decode.loss_ce: 0.8740, decode.acc_seg: 70.7776, aux.loss_ce: 0.3616, aux.acc_seg: 70.6430, loss: 1.2356 +2024-06-15 21:51:56,038 - mmseg - INFO - per class results: +2024-06-15 21:51:56,103 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 68.07 | 84.73 | +| building | 77.86 | 90.25 | +| sky | 88.9 | 92.08 | +| floor | 70.61 | 83.91 | +| tree | 67.62 | 82.95 | +| ceiling | 76.21 | 85.52 | +| road | 75.9 | 90.04 | +| bed | 80.21 | 94.55 | +| windowpane | 45.0 | 66.57 | +| grass | 60.07 | 72.38 | +| cabinet | 53.4 | 74.77 | +| sidewalk | 53.77 | 75.55 | +| person | 65.38 | 83.97 | +| earth | 23.67 | 28.51 | +| door | 41.67 | 48.34 | +| table | 42.97 | 52.65 | +| mountain | 52.84 | 67.92 | +| plant | 42.43 | 77.41 | +| curtain | 45.31 | 48.67 | +| chair | 44.6 | 68.31 | +| car | 70.72 | 83.59 | +| water | 48.27 | 85.21 | +| painting | 56.03 | 82.02 | +| sofa | 57.54 | 91.53 | +| shelf | 35.13 | 59.63 | +| house | 45.1 | 69.32 | +| sea | 24.72 | 27.22 | +| mirror | 55.99 | 81.24 | +| rug | 49.92 | 70.98 | +| field | 23.68 | 42.37 | +| armchair | 32.23 | 42.02 | +| seat | 54.46 | 81.78 | +| fence | 15.84 | 20.09 | +| desk | 27.32 | 71.04 | +| rock | 45.32 | 71.47 | +| wardrobe | 54.5 | 75.87 | +| lamp | 35.69 | 50.29 | +| bathtub | 66.71 | 76.94 | +| railing | 23.34 | 29.52 | +| cushion | 35.41 | 53.97 | +| base | 18.88 | 27.78 | +| box | 8.61 | 11.09 | +| column | 0.2 | 0.2 | +| signboard | 7.57 | 7.78 | +| chest of drawers | 27.53 | 38.33 | +| counter | 41.81 | 51.8 | +| sand | 47.2 | 52.85 | +| sink | 49.08 | 71.54 | +| skyscraper | 42.9 | 68.74 | +| fireplace | 63.92 | 83.88 | +| refrigerator | 61.87 | 80.03 | +| grandstand | 37.92 | 69.55 | +| path | 5.59 | 5.78 | +| stairs | 11.13 | 11.95 | +| runway | 61.8 | 91.28 | +| case | 47.7 | 84.13 | +| pool table | 66.57 | 98.18 | +| pillow | 0.95 | 0.96 | +| screen door | 14.09 | 14.39 | +| stairway | 27.66 | 63.56 | +| river | 0.0 | 0.0 | +| bridge | 43.42 | 81.24 | +| bookcase | 12.69 | 16.46 | +| blind | 0.0 | 0.0 | +| coffee table | 41.38 | 62.42 | +| toilet | 61.79 | 94.86 | +| flower | 18.1 | 23.37 | +| book | 34.22 | 69.43 | +| hill | 0.25 | 0.25 | +| bench | 32.88 | 37.75 | +| countertop | 33.66 | 43.41 | +| stove | 50.83 | 83.66 | +| palm | 43.21 | 60.59 | +| kitchen island | 28.0 | 61.56 | +| computer | 53.61 | 84.63 | +| swivel chair | 32.18 | 52.02 | +| boat | 36.07 | 60.0 | +| bar | 46.29 | 61.35 | +| arcade machine | 81.14 | 91.63 | +| hovel | 0.0 | 0.0 | +| bus | 79.05 | 87.71 | +| towel | 30.04 | 31.52 | +| light | 0.0 | 0.0 | +| truck | 27.46 | 29.25 | +| tower | 0.0 | 0.0 | +| chandelier | 4.16 | 4.22 | +| awning | 0.0 | 0.0 | +| streetlight | 0.0 | 0.0 | +| booth | 0.0 | 0.0 | +| television receiver | 11.91 | 11.91 | +| airplane | 28.76 | 29.94 | +| dirt track | 0.0 | 0.0 | +| apparel | 1.06 | 1.11 | +| pole | 0.0 | 0.0 | +| land | 0.0 | 0.0 | +| bannister | 0.0 | 0.0 | +| escalator | 0.0 | 0.0 | +| ottoman | 0.15 | 0.15 | +| bottle | 0.0 | 0.0 | +| buffet | 0.0 | 0.0 | +| poster | 0.0 | 0.0 | +| stage | 0.03 | 0.03 | +| van | 0.0 | 0.0 | +| ship | 0.0 | 0.0 | +| fountain | 0.0 | 0.0 | +| conveyer belt | 0.08 | 0.08 | +| canopy | 0.0 | 0.0 | +| washer | 78.92 | 85.24 | +| plaything | 0.0 | 0.0 | +| swimming pool | 28.43 | 51.91 | +| stool | 0.0 | 0.0 | +| barrel | 10.66 | 10.66 | +| basket | 0.0 | 0.0 | +| waterfall | 41.1 | 46.12 | +| tent | 68.58 | 99.79 | +| bag | 0.0 | 0.0 | +| minibike | 0.0 | 0.0 | +| cradle | 66.6 | 94.51 | +| oven | 0.0 | 0.0 | +| ball | 33.82 | 49.77 | +| food | 0.0 | 0.0 | +| step | 0.0 | 0.0 | +| tank | 0.0 | 0.0 | +| trade name | 0.0 | 0.0 | +| microwave | 0.18 | 0.18 | +| 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 | 39.33 | 41.0 | +| 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 | ++---------------------+-------+-------+ +2024-06-15 21:51:56,103 - mmseg - INFO - Summary: +2024-06-15 21:51:56,103 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 75.73 | 25.85 | 35.52 | ++-------+-------+-------+ +2024-06-15 21:51:56,104 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 21:51:56,104 - mmseg - INFO - Iter(val) [250] aAcc: 0.7573, mIoU: 0.2585, mAcc: 0.3552, IoU.wall: 0.6807, IoU.building: 0.7786, IoU.sky: 0.8890, IoU.floor: 0.7061, IoU.tree: 0.6762, IoU.ceiling: 0.7621, IoU.road: 0.7590, IoU.bed : 0.8021, IoU.windowpane: 0.4500, IoU.grass: 0.6007, IoU.cabinet: 0.5340, IoU.sidewalk: 0.5377, IoU.person: 0.6538, IoU.earth: 0.2367, IoU.door: 0.4167, IoU.table: 0.4297, IoU.mountain: 0.5284, IoU.plant: 0.4243, IoU.curtain: 0.4531, IoU.chair: 0.4460, IoU.car: 0.7072, IoU.water: 0.4827, IoU.painting: 0.5603, IoU.sofa: 0.5754, IoU.shelf: 0.3513, IoU.house: 0.4510, IoU.sea: 0.2472, IoU.mirror: 0.5599, IoU.rug: 0.4992, IoU.field: 0.2368, IoU.armchair: 0.3223, IoU.seat: 0.5446, IoU.fence: 0.1584, IoU.desk: 0.2732, IoU.rock: 0.4532, IoU.wardrobe: 0.5450, IoU.lamp: 0.3569, IoU.bathtub: 0.6671, IoU.railing: 0.2334, IoU.cushion: 0.3541, IoU.base: 0.1888, IoU.box: 0.0861, IoU.column: 0.0020, IoU.signboard: 0.0757, IoU.chest of drawers: 0.2753, IoU.counter: 0.4181, IoU.sand: 0.4720, IoU.sink: 0.4908, IoU.skyscraper: 0.4290, IoU.fireplace: 0.6392, IoU.refrigerator: 0.6187, IoU.grandstand: 0.3792, IoU.path: 0.0559, IoU.stairs: 0.1113, IoU.runway: 0.6180, IoU.case: 0.4770, IoU.pool table: 0.6657, IoU.pillow: 0.0095, IoU.screen door: 0.1409, IoU.stairway: 0.2766, IoU.river: 0.0000, IoU.bridge: 0.4342, IoU.bookcase: 0.1269, IoU.blind: 0.0000, IoU.coffee table: 0.4138, IoU.toilet: 0.6179, IoU.flower: 0.1810, IoU.book: 0.3422, IoU.hill: 0.0025, IoU.bench: 0.3288, IoU.countertop: 0.3366, IoU.stove: 0.5083, IoU.palm: 0.4321, IoU.kitchen island: 0.2800, IoU.computer: 0.5361, IoU.swivel chair: 0.3218, IoU.boat: 0.3607, IoU.bar: 0.4629, IoU.arcade machine: 0.8114, IoU.hovel: 0.0000, IoU.bus: 0.7905, IoU.towel: 0.3004, IoU.light: 0.0000, IoU.truck: 0.2746, IoU.tower: 0.0000, IoU.chandelier: 0.0416, IoU.awning: 0.0000, IoU.streetlight: 0.0000, IoU.booth: 0.0000, IoU.television receiver: 0.1191, IoU.airplane: 0.2876, IoU.dirt track: 0.0000, IoU.apparel: 0.0106, IoU.pole: 0.0000, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.0000, IoU.ottoman: 0.0015, IoU.bottle: 0.0000, IoU.buffet: 0.0000, IoU.poster: 0.0000, IoU.stage: 0.0003, IoU.van: 0.0000, IoU.ship: 0.0000, IoU.fountain: 0.0000, IoU.conveyer belt: 0.0008, IoU.canopy: 0.0000, IoU.washer: 0.7892, IoU.plaything: 0.0000, IoU.swimming pool: 0.2843, IoU.stool: 0.0000, IoU.barrel: 0.1066, IoU.basket: 0.0000, IoU.waterfall: 0.4110, IoU.tent: 0.6858, IoU.bag: 0.0000, IoU.minibike: 0.0000, IoU.cradle: 0.6660, IoU.oven: 0.0000, IoU.ball: 0.3382, IoU.food: 0.0000, IoU.step: 0.0000, IoU.tank: 0.0000, IoU.trade name: 0.0000, IoU.microwave: 0.0018, IoU.pot: 0.0000, IoU.animal: 0.0000, IoU.bicycle: 0.0000, IoU.lake: 0.0000, IoU.dishwasher: 0.0000, IoU.screen: 0.3933, 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: 0.8473, Acc.building: 0.9025, Acc.sky: 0.9208, Acc.floor: 0.8391, Acc.tree: 0.8295, Acc.ceiling: 0.8552, Acc.road: 0.9004, Acc.bed : 0.9455, Acc.windowpane: 0.6657, Acc.grass: 0.7238, Acc.cabinet: 0.7477, Acc.sidewalk: 0.7555, Acc.person: 0.8397, Acc.earth: 0.2851, Acc.door: 0.4834, Acc.table: 0.5265, Acc.mountain: 0.6792, Acc.plant: 0.7741, Acc.curtain: 0.4867, Acc.chair: 0.6831, Acc.car: 0.8359, Acc.water: 0.8521, Acc.painting: 0.8202, Acc.sofa: 0.9153, Acc.shelf: 0.5963, Acc.house: 0.6932, Acc.sea: 0.2722, Acc.mirror: 0.8124, Acc.rug: 0.7098, Acc.field: 0.4237, Acc.armchair: 0.4202, Acc.seat: 0.8178, Acc.fence: 0.2009, Acc.desk: 0.7104, Acc.rock: 0.7147, Acc.wardrobe: 0.7587, Acc.lamp: 0.5029, Acc.bathtub: 0.7694, Acc.railing: 0.2952, Acc.cushion: 0.5397, Acc.base: 0.2778, Acc.box: 0.1109, Acc.column: 0.0020, Acc.signboard: 0.0778, Acc.chest of drawers: 0.3833, Acc.counter: 0.5180, Acc.sand: 0.5285, Acc.sink: 0.7154, Acc.skyscraper: 0.6874, Acc.fireplace: 0.8388, Acc.refrigerator: 0.8003, Acc.grandstand: 0.6955, Acc.path: 0.0578, Acc.stairs: 0.1195, Acc.runway: 0.9128, Acc.case: 0.8413, Acc.pool table: 0.9818, Acc.pillow: 0.0096, Acc.screen door: 0.1439, Acc.stairway: 0.6356, Acc.river: 0.0000, Acc.bridge: 0.8124, Acc.bookcase: 0.1646, Acc.blind: 0.0000, Acc.coffee table: 0.6242, Acc.toilet: 0.9486, Acc.flower: 0.2337, Acc.book: 0.6943, Acc.hill: 0.0025, Acc.bench: 0.3775, Acc.countertop: 0.4341, Acc.stove: 0.8366, Acc.palm: 0.6059, Acc.kitchen island: 0.6156, Acc.computer: 0.8463, Acc.swivel chair: 0.5202, Acc.boat: 0.6000, Acc.bar: 0.6135, Acc.arcade machine: 0.9163, Acc.hovel: 0.0000, Acc.bus: 0.8771, Acc.towel: 0.3152, Acc.light: 0.0000, Acc.truck: 0.2925, Acc.tower: 0.0000, Acc.chandelier: 0.0422, Acc.awning: 0.0000, Acc.streetlight: 0.0000, Acc.booth: 0.0000, Acc.television receiver: 0.1191, Acc.airplane: 0.2994, Acc.dirt track: 0.0000, Acc.apparel: 0.0111, Acc.pole: 0.0000, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.0000, Acc.ottoman: 0.0015, Acc.bottle: 0.0000, Acc.buffet: 0.0000, Acc.poster: 0.0000, Acc.stage: 0.0003, Acc.van: 0.0000, Acc.ship: 0.0000, Acc.fountain: 0.0000, Acc.conveyer belt: 0.0008, Acc.canopy: 0.0000, Acc.washer: 0.8524, Acc.plaything: 0.0000, Acc.swimming pool: 0.5191, Acc.stool: 0.0000, Acc.barrel: 0.1066, Acc.basket: 0.0000, Acc.waterfall: 0.4612, Acc.tent: 0.9979, Acc.bag: 0.0000, Acc.minibike: 0.0000, Acc.cradle: 0.9451, Acc.oven: 0.0000, Acc.ball: 0.4977, Acc.food: 0.0000, Acc.step: 0.0000, Acc.tank: 0.0000, Acc.trade name: 0.0000, Acc.microwave: 0.0018, Acc.pot: 0.0000, Acc.animal: 0.0000, Acc.bicycle: 0.0000, Acc.lake: 0.0000, Acc.dishwasher: 0.0000, Acc.screen: 0.4100, 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 +2024-06-15 21:53:04,833 - mmseg - INFO - Iter [1050/80000] lr: 2.761e-05, eta: 1 day, 9:30:11, time: 4.467, data_time: 3.108, memory: 70722, decode.loss_ce: 0.8822, decode.acc_seg: 72.1549, aux.loss_ce: 0.3612, aux.acc_seg: 72.6719, loss: 1.2434 +2024-06-15 21:54:13,383 - mmseg - INFO - Iter [1100/80000] lr: 2.890e-05, eta: 1 day, 9:19:33, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.8519, decode.acc_seg: 71.7337, aux.loss_ce: 0.3510, aux.acc_seg: 72.3338, loss: 1.2030 +2024-06-15 21:55:21,831 - mmseg - INFO - Iter [1150/80000] lr: 3.020e-05, eta: 1 day, 9:09:37, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.8281, decode.acc_seg: 72.8440, aux.loss_ce: 0.3396, aux.acc_seg: 73.2852, loss: 1.1677 +2024-06-15 21:56:30,257 - mmseg - INFO - Iter [1200/80000] lr: 3.149e-05, eta: 1 day, 9:00:24, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.8505, decode.acc_seg: 72.8093, aux.loss_ce: 0.3459, aux.acc_seg: 72.9978, loss: 1.1964 +2024-06-15 21:57:38,641 - mmseg - INFO - Iter [1250/80000] lr: 3.279e-05, eta: 1 day, 8:51:46, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.8110, decode.acc_seg: 72.7887, aux.loss_ce: 0.3282, aux.acc_seg: 73.4748, loss: 1.1393 +2024-06-15 21:58:49,428 - mmseg - INFO - Iter [1300/80000] lr: 3.408e-05, eta: 1 day, 8:46:09, time: 1.416, data_time: 0.055, memory: 70722, decode.loss_ce: 0.8067, decode.acc_seg: 73.1344, aux.loss_ce: 0.3249, aux.acc_seg: 73.7035, loss: 1.1316 +2024-06-15 21:59:57,805 - mmseg - INFO - Iter [1350/80000] lr: 3.537e-05, eta: 1 day, 8:38:31, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7898, decode.acc_seg: 73.6358, aux.loss_ce: 0.3175, aux.acc_seg: 74.5122, loss: 1.1072 +2024-06-15 22:01:06,108 - mmseg - INFO - Iter [1400/80000] lr: 3.665e-05, eta: 1 day, 8:31:17, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7499, decode.acc_seg: 73.4808, aux.loss_ce: 0.3040, aux.acc_seg: 74.1579, loss: 1.0539 +2024-06-15 22:02:14,565 - mmseg - INFO - Iter [1450/80000] lr: 3.794e-05, eta: 1 day, 8:24:36, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7692, decode.acc_seg: 73.9200, aux.loss_ce: 0.3081, aux.acc_seg: 74.3642, loss: 1.0773 +2024-06-15 22:03:22,935 - mmseg - INFO - Iter [1500/80000] lr: 3.922e-05, eta: 1 day, 8:18:13, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7680, decode.acc_seg: 73.0758, aux.loss_ce: 0.3043, aux.acc_seg: 74.0299, loss: 1.0723 +2024-06-15 22:04:31,393 - mmseg - INFO - Iter [1550/80000] lr: 3.923e-05, eta: 1 day, 8:12:15, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7573, decode.acc_seg: 73.9120, aux.loss_ce: 0.3021, aux.acc_seg: 74.4305, loss: 1.0594 +2024-06-15 22:05:39,634 - mmseg - INFO - Iter [1600/80000] lr: 3.920e-05, eta: 1 day, 8:06:24, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.8122, decode.acc_seg: 72.2771, aux.loss_ce: 0.3205, aux.acc_seg: 73.1310, loss: 1.1326 +2024-06-15 22:06:47,929 - mmseg - INFO - Iter [1650/80000] lr: 3.918e-05, eta: 1 day, 8:00:53, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7309, decode.acc_seg: 74.5442, aux.loss_ce: 0.2917, aux.acc_seg: 75.0009, loss: 1.0226 +2024-06-15 22:07:56,470 - mmseg - INFO - Iter [1700/80000] lr: 3.915e-05, eta: 1 day, 7:55:49, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6949, decode.acc_seg: 75.5269, aux.loss_ce: 0.2767, aux.acc_seg: 76.0945, loss: 0.9716 +2024-06-15 22:09:04,677 - mmseg - INFO - Iter [1750/80000] lr: 3.913e-05, eta: 1 day, 7:50:43, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7332, decode.acc_seg: 74.4573, aux.loss_ce: 0.2910, aux.acc_seg: 74.9160, loss: 1.0242 +2024-06-15 22:10:13,301 - mmseg - INFO - Iter [1800/80000] lr: 3.910e-05, eta: 1 day, 7:46:09, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7474, decode.acc_seg: 74.5293, aux.loss_ce: 0.2956, aux.acc_seg: 75.3313, loss: 1.0430 +2024-06-15 22:11:21,621 - mmseg - INFO - Iter [1850/80000] lr: 3.908e-05, eta: 1 day, 7:41:33, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7249, decode.acc_seg: 74.6095, aux.loss_ce: 0.2884, aux.acc_seg: 75.1847, loss: 1.0133 +2024-06-15 22:12:30,014 - mmseg - INFO - Iter [1900/80000] lr: 3.905e-05, eta: 1 day, 7:37:10, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7071, decode.acc_seg: 74.6296, aux.loss_ce: 0.2843, aux.acc_seg: 74.8659, loss: 0.9914 +2024-06-15 22:13:38,428 - mmseg - INFO - Iter [1950/80000] lr: 3.903e-05, eta: 1 day, 7:32:59, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7185, decode.acc_seg: 74.6621, aux.loss_ce: 0.2859, aux.acc_seg: 75.1322, loss: 1.0045 +2024-06-15 22:14:46,923 - mmseg - INFO - Saving checkpoint at 2000 iterations +2024-06-15 22:16:05,163 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 22:16:05,163 - mmseg - INFO - Iter [2000/80000] lr: 3.900e-05, eta: 1 day, 8:19:51, time: 2.935, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6875, decode.acc_seg: 75.5710, aux.loss_ce: 0.2707, aux.acc_seg: 76.2529, loss: 0.9582 +2024-06-15 22:17:41,396 - mmseg - INFO - per class results: +2024-06-15 22:17:41,402 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 71.22 | 82.93 | +| building | 80.57 | 90.73 | +| sky | 91.96 | 96.41 | +| floor | 75.46 | 84.31 | +| tree | 69.86 | 83.11 | +| ceiling | 78.73 | 90.35 | +| road | 79.62 | 89.89 | +| bed | 83.1 | 90.56 | +| windowpane | 56.47 | 73.53 | +| grass | 55.95 | 95.62 | +| cabinet | 54.12 | 63.76 | +| sidewalk | 55.63 | 69.3 | +| person | 69.41 | 80.35 | +| earth | 25.41 | 32.34 | +| door | 47.81 | 75.58 | +| table | 50.21 | 64.97 | +| mountain | 47.29 | 63.11 | +| plant | 40.96 | 50.16 | +| curtain | 66.05 | 83.96 | +| chair | 46.14 | 57.36 | +| car | 72.22 | 85.1 | +| water | 33.07 | 42.36 | +| painting | 62.65 | 78.18 | +| sofa | 58.63 | 66.38 | +| shelf | 39.62 | 68.99 | +| house | 38.0 | 40.61 | +| sea | 49.43 | 77.09 | +| mirror | 60.77 | 85.4 | +| rug | 57.51 | 66.09 | +| field | 16.57 | 20.53 | +| armchair | 37.2 | 81.5 | +| seat | 60.37 | 85.12 | +| fence | 35.98 | 66.38 | +| desk | 35.54 | 43.11 | +| rock | 49.5 | 66.46 | +| wardrobe | 40.76 | 59.97 | +| lamp | 45.24 | 59.78 | +| bathtub | 70.28 | 75.86 | +| railing | 28.63 | 41.27 | +| cushion | 39.96 | 47.39 | +| base | 29.62 | 47.34 | +| box | 10.88 | 11.45 | +| column | 40.46 | 57.45 | +| signboard | 25.78 | 31.24 | +| chest of drawers | 46.3 | 68.18 | +| counter | 49.01 | 62.39 | +| sand | 41.09 | 60.86 | +| sink | 47.22 | 80.51 | +| skyscraper | 50.51 | 84.48 | +| fireplace | 54.79 | 95.91 | +| refrigerator | 67.84 | 82.0 | +| grandstand | 48.69 | 85.13 | +| path | 24.65 | 43.58 | +| stairs | 23.73 | 28.44 | +| runway | 66.32 | 85.72 | +| case | 58.65 | 87.95 | +| pool table | 75.72 | 98.05 | +| pillow | 47.37 | 85.39 | +| screen door | 2.55 | 2.55 | +| stairway | 45.54 | 62.81 | +| river | 14.15 | 72.72 | +| bridge | 71.82 | 81.98 | +| bookcase | 30.77 | 65.16 | +| blind | 37.99 | 47.4 | +| coffee table | 36.79 | 86.03 | +| toilet | 73.01 | 94.05 | +| flower | 28.6 | 34.79 | +| book | 36.06 | 45.54 | +| hill | 1.42 | 2.01 | +| bench | 52.44 | 66.14 | +| countertop | 14.27 | 15.12 | +| stove | 71.61 | 80.94 | +| palm | 45.77 | 71.63 | +| kitchen island | 33.66 | 72.91 | +| computer | 65.61 | 89.08 | +| swivel chair | 38.77 | 55.42 | +| boat | 54.54 | 71.76 | +| bar | 58.24 | 71.69 | +| arcade machine | 61.58 | 99.75 | +| hovel | 57.43 | 75.22 | +| bus | 82.2 | 92.22 | +| towel | 53.06 | 62.74 | +| light | 4.7 | 4.83 | +| truck | 27.91 | 56.41 | +| tower | 0.0 | 0.0 | +| chandelier | 52.99 | 67.09 | +| awning | 21.3 | 26.61 | +| streetlight | 2.53 | 2.67 | +| booth | 7.27 | 7.56 | +| television receiver | 54.57 | 85.2 | +| airplane | 46.46 | 64.3 | +| dirt track | 0.0 | 0.0 | +| apparel | 26.23 | 62.51 | +| pole | 0.0 | 0.0 | +| land | 0.0 | 0.0 | +| bannister | 0.0 | 0.0 | +| escalator | 49.15 | 91.9 | +| ottoman | 37.78 | 53.12 | +| bottle | 24.0 | 29.87 | +| buffet | 1.3 | 1.31 | +| poster | 0.66 | 0.66 | +| stage | 12.83 | 81.73 | +| van | 0.0 | 0.0 | +| ship | 42.79 | 46.52 | +| fountain | 16.59 | 17.23 | +| conveyer belt | 54.93 | 98.73 | +| canopy | 30.59 | 84.56 | +| washer | 85.12 | 94.69 | +| plaything | 25.04 | 35.6 | +| swimming pool | 57.01 | 97.06 | +| stool | 18.79 | 30.53 | +| barrel | 3.83 | 64.89 | +| basket | 11.09 | 12.28 | +| waterfall | 36.17 | 62.59 | +| tent | 86.78 | 98.94 | +| bag | 0.0 | 0.0 | +| minibike | 59.95 | 78.78 | +| cradle | 63.54 | 97.83 | +| oven | 1.02 | 1.02 | +| ball | 25.44 | 64.24 | +| food | 36.46 | 47.14 | +| step | 0.0 | 0.0 | +| tank | 22.38 | 22.54 | +| trade name | 7.34 | 7.66 | +| microwave | 74.68 | 91.69 | +| pot | 7.63 | 7.84 | +| animal | 41.31 | 42.68 | +| bicycle | 29.35 | 33.28 | +| lake | 0.0 | 0.0 | +| dishwasher | 35.1 | 80.07 | +| screen | 53.33 | 82.69 | +| blanket | 0.0 | 0.0 | +| sculpture | 41.23 | 48.85 | +| hood | 52.23 | 59.11 | +| sconce | 0.0 | 0.0 | +| vase | 12.09 | 13.31 | +| traffic light | 0.0 | 0.0 | +| tray | 0.0 | 0.0 | +| ashcan | 0.21 | 0.21 | +| fan | 6.16 | 6.23 | +| pier | 33.01 | 39.88 | +| crt screen | 0.0 | 0.0 | +| plate | 27.54 | 29.47 | +| monitor | 38.15 | 43.24 | +| bulletin board | 5.75 | 5.9 | +| shower | 0.0 | 0.0 | +| radiator | 0.0 | 0.0 | +| glass | 0.0 | 0.0 | +| clock | 15.88 | 15.94 | +| flag | 0.0 | 0.0 | ++---------------------+-------+-------+ +2024-06-15 22:17:41,402 - mmseg - INFO - Summary: +2024-06-15 22:17:41,402 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 78.38 | 37.22 | 51.94 | ++-------+-------+-------+ +2024-06-15 22:17:41,403 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 22:17:41,403 - mmseg - INFO - Iter(val) [250] aAcc: 0.7838, mIoU: 0.3722, mAcc: 0.5194, IoU.wall: 0.7122, IoU.building: 0.8057, IoU.sky: 0.9196, IoU.floor: 0.7546, IoU.tree: 0.6986, IoU.ceiling: 0.7873, IoU.road: 0.7962, IoU.bed : 0.8310, IoU.windowpane: 0.5647, IoU.grass: 0.5595, IoU.cabinet: 0.5412, IoU.sidewalk: 0.5563, IoU.person: 0.6941, IoU.earth: 0.2541, IoU.door: 0.4781, IoU.table: 0.5021, IoU.mountain: 0.4729, IoU.plant: 0.4096, IoU.curtain: 0.6605, IoU.chair: 0.4614, IoU.car: 0.7222, IoU.water: 0.3307, IoU.painting: 0.6265, IoU.sofa: 0.5863, IoU.shelf: 0.3962, IoU.house: 0.3800, IoU.sea: 0.4943, IoU.mirror: 0.6077, IoU.rug: 0.5751, IoU.field: 0.1657, IoU.armchair: 0.3720, IoU.seat: 0.6037, IoU.fence: 0.3598, IoU.desk: 0.3554, IoU.rock: 0.4950, IoU.wardrobe: 0.4076, IoU.lamp: 0.4524, IoU.bathtub: 0.7028, IoU.railing: 0.2863, IoU.cushion: 0.3996, IoU.base: 0.2962, IoU.box: 0.1088, IoU.column: 0.4046, IoU.signboard: 0.2578, IoU.chest of drawers: 0.4630, IoU.counter: 0.4901, IoU.sand: 0.4109, IoU.sink: 0.4722, IoU.skyscraper: 0.5051, IoU.fireplace: 0.5479, IoU.refrigerator: 0.6784, IoU.grandstand: 0.4869, IoU.path: 0.2465, IoU.stairs: 0.2373, IoU.runway: 0.6632, IoU.case: 0.5865, IoU.pool table: 0.7572, IoU.pillow: 0.4737, IoU.screen door: 0.0255, IoU.stairway: 0.4554, IoU.river: 0.1415, IoU.bridge: 0.7182, IoU.bookcase: 0.3077, IoU.blind: 0.3799, IoU.coffee table: 0.3679, IoU.toilet: 0.7301, IoU.flower: 0.2860, IoU.book: 0.3606, IoU.hill: 0.0142, IoU.bench: 0.5244, IoU.countertop: 0.1427, IoU.stove: 0.7161, IoU.palm: 0.4577, IoU.kitchen island: 0.3366, IoU.computer: 0.6561, IoU.swivel chair: 0.3877, IoU.boat: 0.5454, IoU.bar: 0.5824, IoU.arcade machine: 0.6158, IoU.hovel: 0.5743, IoU.bus: 0.8220, IoU.towel: 0.5306, IoU.light: 0.0470, IoU.truck: 0.2791, IoU.tower: 0.0000, IoU.chandelier: 0.5299, IoU.awning: 0.2130, IoU.streetlight: 0.0253, IoU.booth: 0.0727, IoU.television receiver: 0.5457, IoU.airplane: 0.4646, IoU.dirt track: 0.0000, IoU.apparel: 0.2623, IoU.pole: 0.0000, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.4915, IoU.ottoman: 0.3778, IoU.bottle: 0.2400, IoU.buffet: 0.0130, IoU.poster: 0.0066, IoU.stage: 0.1283, IoU.van: 0.0000, IoU.ship: 0.4279, IoU.fountain: 0.1659, IoU.conveyer belt: 0.5493, IoU.canopy: 0.3059, IoU.washer: 0.8512, IoU.plaything: 0.2504, IoU.swimming pool: 0.5701, IoU.stool: 0.1879, IoU.barrel: 0.0383, IoU.basket: 0.1109, IoU.waterfall: 0.3617, IoU.tent: 0.8678, IoU.bag: 0.0000, IoU.minibike: 0.5995, IoU.cradle: 0.6354, IoU.oven: 0.0102, IoU.ball: 0.2544, IoU.food: 0.3646, IoU.step: 0.0000, IoU.tank: 0.2238, IoU.trade name: 0.0734, IoU.microwave: 0.7468, IoU.pot: 0.0763, IoU.animal: 0.4131, IoU.bicycle: 0.2935, IoU.lake: 0.0000, IoU.dishwasher: 0.3510, IoU.screen: 0.5333, IoU.blanket: 0.0000, IoU.sculpture: 0.4123, IoU.hood: 0.5223, IoU.sconce: 0.0000, IoU.vase: 0.1209, IoU.traffic light: 0.0000, IoU.tray: 0.0000, IoU.ashcan: 0.0021, IoU.fan: 0.0616, IoU.pier: 0.3301, IoU.crt screen: 0.0000, IoU.plate: 0.2754, IoU.monitor: 0.3815, IoU.bulletin board: 0.0575, IoU.shower: 0.0000, IoU.radiator: 0.0000, IoU.glass: 0.0000, IoU.clock: 0.1588, IoU.flag: 0.0000, Acc.wall: 0.8293, Acc.building: 0.9073, Acc.sky: 0.9641, Acc.floor: 0.8431, Acc.tree: 0.8311, Acc.ceiling: 0.9035, Acc.road: 0.8989, Acc.bed : 0.9056, Acc.windowpane: 0.7353, Acc.grass: 0.9562, Acc.cabinet: 0.6376, Acc.sidewalk: 0.6930, Acc.person: 0.8035, Acc.earth: 0.3234, Acc.door: 0.7558, Acc.table: 0.6497, Acc.mountain: 0.6311, Acc.plant: 0.5016, Acc.curtain: 0.8396, Acc.chair: 0.5736, Acc.car: 0.8510, Acc.water: 0.4236, Acc.painting: 0.7818, Acc.sofa: 0.6638, Acc.shelf: 0.6899, Acc.house: 0.4061, Acc.sea: 0.7709, Acc.mirror: 0.8540, Acc.rug: 0.6609, Acc.field: 0.2053, Acc.armchair: 0.8150, Acc.seat: 0.8512, Acc.fence: 0.6638, Acc.desk: 0.4311, Acc.rock: 0.6646, Acc.wardrobe: 0.5997, Acc.lamp: 0.5978, Acc.bathtub: 0.7586, Acc.railing: 0.4127, Acc.cushion: 0.4739, Acc.base: 0.4734, Acc.box: 0.1145, Acc.column: 0.5745, Acc.signboard: 0.3124, Acc.chest of drawers: 0.6818, Acc.counter: 0.6239, Acc.sand: 0.6086, Acc.sink: 0.8051, Acc.skyscraper: 0.8448, Acc.fireplace: 0.9591, Acc.refrigerator: 0.8200, Acc.grandstand: 0.8513, Acc.path: 0.4358, Acc.stairs: 0.2844, Acc.runway: 0.8572, Acc.case: 0.8795, Acc.pool table: 0.9805, Acc.pillow: 0.8539, Acc.screen door: 0.0255, Acc.stairway: 0.6281, Acc.river: 0.7272, Acc.bridge: 0.8198, Acc.bookcase: 0.6516, Acc.blind: 0.4740, Acc.coffee table: 0.8603, Acc.toilet: 0.9405, Acc.flower: 0.3479, Acc.book: 0.4554, Acc.hill: 0.0201, Acc.bench: 0.6614, Acc.countertop: 0.1512, Acc.stove: 0.8094, Acc.palm: 0.7163, Acc.kitchen island: 0.7291, Acc.computer: 0.8908, Acc.swivel chair: 0.5542, Acc.boat: 0.7176, Acc.bar: 0.7169, Acc.arcade machine: 0.9975, Acc.hovel: 0.7522, Acc.bus: 0.9222, Acc.towel: 0.6274, Acc.light: 0.0483, Acc.truck: 0.5641, Acc.tower: 0.0000, Acc.chandelier: 0.6709, Acc.awning: 0.2661, Acc.streetlight: 0.0267, Acc.booth: 0.0756, Acc.television receiver: 0.8520, Acc.airplane: 0.6430, Acc.dirt track: 0.0000, Acc.apparel: 0.6251, Acc.pole: 0.0000, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.9190, Acc.ottoman: 0.5312, Acc.bottle: 0.2987, Acc.buffet: 0.0131, Acc.poster: 0.0066, Acc.stage: 0.8173, Acc.van: 0.0000, Acc.ship: 0.4652, Acc.fountain: 0.1723, Acc.conveyer belt: 0.9873, Acc.canopy: 0.8456, Acc.washer: 0.9469, Acc.plaything: 0.3560, Acc.swimming pool: 0.9706, Acc.stool: 0.3053, Acc.barrel: 0.6489, Acc.basket: 0.1228, Acc.waterfall: 0.6259, Acc.tent: 0.9894, Acc.bag: 0.0000, Acc.minibike: 0.7878, Acc.cradle: 0.9783, Acc.oven: 0.0102, Acc.ball: 0.6424, Acc.food: 0.4714, Acc.step: 0.0000, Acc.tank: 0.2254, Acc.trade name: 0.0766, Acc.microwave: 0.9169, Acc.pot: 0.0784, Acc.animal: 0.4268, Acc.bicycle: 0.3328, Acc.lake: 0.0000, Acc.dishwasher: 0.8007, Acc.screen: 0.8269, Acc.blanket: 0.0000, Acc.sculpture: 0.4885, Acc.hood: 0.5911, Acc.sconce: 0.0000, Acc.vase: 0.1331, Acc.traffic light: 0.0000, Acc.tray: 0.0000, Acc.ashcan: 0.0021, Acc.fan: 0.0623, Acc.pier: 0.3988, Acc.crt screen: 0.0000, Acc.plate: 0.2947, Acc.monitor: 0.4324, Acc.bulletin board: 0.0590, Acc.shower: 0.0000, Acc.radiator: 0.0000, Acc.glass: 0.0000, Acc.clock: 0.1594, Acc.flag: 0.0000 +2024-06-15 22:18:50,314 - mmseg - INFO - Iter [2050/80000] lr: 3.898e-05, eta: 1 day, 9:15:59, time: 3.303, data_time: 1.942, memory: 70722, decode.loss_ce: 0.7330, decode.acc_seg: 73.9554, aux.loss_ce: 0.2911, aux.acc_seg: 74.7504, loss: 1.0241 +2024-06-15 22:19:58,953 - mmseg - INFO - Iter [2100/80000] lr: 3.895e-05, eta: 1 day, 9:09:39, time: 1.373, data_time: 0.010, memory: 70722, decode.loss_ce: 0.7148, decode.acc_seg: 74.8376, aux.loss_ce: 0.2809, aux.acc_seg: 75.3832, loss: 0.9957 +2024-06-15 22:21:07,392 - mmseg - INFO - Iter [2150/80000] lr: 3.893e-05, eta: 1 day, 9:03:26, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6726, decode.acc_seg: 74.9401, aux.loss_ce: 0.2691, aux.acc_seg: 75.2166, loss: 0.9417 +2024-06-15 22:22:15,609 - mmseg - INFO - Iter [2200/80000] lr: 3.890e-05, eta: 1 day, 8:57:19, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6559, decode.acc_seg: 75.9641, aux.loss_ce: 0.2610, aux.acc_seg: 76.4361, loss: 0.9169 +2024-06-15 22:23:24,288 - mmseg - INFO - Iter [2250/80000] lr: 3.888e-05, eta: 1 day, 8:51:41, time: 1.374, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6689, decode.acc_seg: 75.7744, aux.loss_ce: 0.2655, aux.acc_seg: 76.1789, loss: 0.9344 +2024-06-15 22:24:32,486 - mmseg - INFO - Iter [2300/80000] lr: 3.885e-05, eta: 1 day, 8:45:59, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6383, decode.acc_seg: 76.6484, aux.loss_ce: 0.2539, aux.acc_seg: 76.8998, loss: 0.8922 +2024-06-15 22:25:40,865 - mmseg - INFO - Iter [2350/80000] lr: 3.883e-05, eta: 1 day, 8:40:34, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6913, decode.acc_seg: 75.0066, aux.loss_ce: 0.2723, aux.acc_seg: 75.6398, loss: 0.9636 +2024-06-15 22:26:49,293 - mmseg - INFO - Iter [2400/80000] lr: 3.880e-05, eta: 1 day, 8:35:22, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6593, decode.acc_seg: 76.7593, aux.loss_ce: 0.2609, aux.acc_seg: 77.0341, loss: 0.9202 +2024-06-15 22:27:57,622 - mmseg - INFO - Iter [2450/80000] lr: 3.878e-05, eta: 1 day, 8:30:16, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6573, decode.acc_seg: 76.2930, aux.loss_ce: 0.2584, aux.acc_seg: 76.8048, loss: 0.9157 +2024-06-15 22:29:05,905 - mmseg - INFO - Iter [2500/80000] lr: 3.875e-05, eta: 1 day, 8:25:19, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6443, decode.acc_seg: 76.6293, aux.loss_ce: 0.2534, aux.acc_seg: 77.1986, loss: 0.8977 +2024-06-15 22:30:16,533 - mmseg - INFO - Iter [2550/80000] lr: 3.873e-05, eta: 1 day, 8:21:41, time: 1.413, data_time: 0.052, memory: 70722, decode.loss_ce: 0.6410, decode.acc_seg: 76.7828, aux.loss_ce: 0.2521, aux.acc_seg: 77.4058, loss: 0.8931 +2024-06-15 22:31:25,433 - mmseg - INFO - Iter [2600/80000] lr: 3.870e-05, eta: 1 day, 8:17:18, time: 1.378, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6308, decode.acc_seg: 76.6621, aux.loss_ce: 0.2513, aux.acc_seg: 76.9561, loss: 0.8821 +2024-06-15 22:32:33,763 - mmseg - INFO - Iter [2650/80000] lr: 3.868e-05, eta: 1 day, 8:12:46, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5989, decode.acc_seg: 77.7554, aux.loss_ce: 0.2362, aux.acc_seg: 78.4694, loss: 0.8351 +2024-06-15 22:33:42,075 - mmseg - INFO - Iter [2700/80000] lr: 3.865e-05, eta: 1 day, 8:08:21, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6320, decode.acc_seg: 76.9419, aux.loss_ce: 0.2522, aux.acc_seg: 77.0908, loss: 0.8842 +2024-06-15 22:34:50,602 - mmseg - INFO - Iter [2750/80000] lr: 3.863e-05, eta: 1 day, 8:04:08, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6446, decode.acc_seg: 76.9573, aux.loss_ce: 0.2549, aux.acc_seg: 77.4952, loss: 0.8995 +2024-06-15 22:35:59,022 - mmseg - INFO - Iter [2800/80000] lr: 3.860e-05, eta: 1 day, 8:00:00, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6020, decode.acc_seg: 77.5503, aux.loss_ce: 0.2363, aux.acc_seg: 78.1149, loss: 0.8383 +2024-06-15 22:37:07,593 - mmseg - INFO - Iter [2850/80000] lr: 3.858e-05, eta: 1 day, 7:56:02, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5912, decode.acc_seg: 78.5698, aux.loss_ce: 0.2348, aux.acc_seg: 78.7305, loss: 0.8259 +2024-06-15 22:38:16,054 - mmseg - INFO - Iter [2900/80000] lr: 3.855e-05, eta: 1 day, 7:52:07, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5706, decode.acc_seg: 78.2938, aux.loss_ce: 0.2272, aux.acc_seg: 78.4981, loss: 0.7978 +2024-06-15 22:39:24,305 - mmseg - INFO - Iter [2950/80000] lr: 3.853e-05, eta: 1 day, 7:48:11, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6325, decode.acc_seg: 76.5684, aux.loss_ce: 0.2499, aux.acc_seg: 76.8943, loss: 0.8824 +2024-06-15 22:40:32,781 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 22:40:32,781 - mmseg - INFO - Iter [3000/80000] lr: 3.850e-05, eta: 1 day, 7:44:28, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6187, decode.acc_seg: 77.5606, aux.loss_ce: 0.2448, aux.acc_seg: 78.0591, loss: 0.8635 +2024-06-15 22:42:08,574 - mmseg - INFO - per class results: +2024-06-15 22:42:08,581 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 73.03 | 81.28 | +| building | 80.98 | 90.36 | +| sky | 92.89 | 96.77 | +| floor | 77.16 | 83.8 | +| tree | 72.56 | 87.87 | +| ceiling | 80.08 | 92.5 | +| road | 81.16 | 91.1 | +| bed | 85.83 | 94.06 | +| windowpane | 56.64 | 75.94 | +| grass | 64.02 | 79.39 | +| cabinet | 57.21 | 77.16 | +| sidewalk | 61.62 | 75.47 | +| person | 76.35 | 88.9 | +| earth | 34.25 | 45.79 | +| door | 49.76 | 70.88 | +| table | 54.83 | 72.38 | +| mountain | 49.64 | 63.15 | +| plant | 50.71 | 57.69 | +| curtain | 70.44 | 84.13 | +| chair | 52.08 | 66.52 | +| car | 79.97 | 89.83 | +| water | 56.06 | 82.66 | +| painting | 65.03 | 86.95 | +| sofa | 70.66 | 81.22 | +| shelf | 35.81 | 49.34 | +| house | 52.54 | 82.75 | +| sea | 60.66 | 72.87 | +| mirror | 67.02 | 81.06 | +| rug | 61.7 | 78.88 | +| field | 31.41 | 78.63 | +| armchair | 48.31 | 64.85 | +| seat | 54.47 | 91.13 | +| fence | 44.6 | 56.42 | +| desk | 37.71 | 46.71 | +| rock | 51.35 | 58.37 | +| wardrobe | 51.8 | 79.96 | +| lamp | 52.93 | 61.33 | +| bathtub | 70.85 | 83.79 | +| railing | 35.75 | 54.78 | +| cushion | 55.39 | 75.55 | +| base | 30.08 | 52.63 | +| box | 22.84 | 29.11 | +| column | 46.61 | 62.88 | +| signboard | 32.61 | 44.79 | +| chest of drawers | 39.5 | 63.16 | +| counter | 50.3 | 63.09 | +| sand | 45.76 | 74.14 | +| sink | 61.1 | 82.7 | +| skyscraper | 42.93 | 77.85 | +| fireplace | 60.8 | 94.38 | +| refrigerator | 66.81 | 85.13 | +| grandstand | 41.6 | 88.79 | +| path | 19.7 | 30.37 | +| stairs | 30.04 | 50.93 | +| runway | 68.09 | 91.83 | +| case | 52.04 | 68.74 | +| pool table | 73.24 | 99.12 | +| pillow | 56.78 | 69.24 | +| screen door | 55.58 | 94.61 | +| stairway | 12.37 | 12.88 | +| river | 17.4 | 21.69 | +| bridge | 44.92 | 51.54 | +| bookcase | 17.3 | 22.36 | +| blind | 0.0 | 0.0 | +| coffee table | 50.96 | 83.54 | +| toilet | 79.83 | 95.19 | +| flower | 36.26 | 50.51 | +| book | 40.9 | 71.22 | +| hill | 2.21 | 3.07 | +| bench | 45.62 | 60.34 | +| countertop | 45.75 | 59.88 | +| stove | 73.23 | 89.23 | +| palm | 50.11 | 65.63 | +| kitchen island | 26.72 | 54.37 | +| computer | 64.82 | 90.96 | +| swivel chair | 35.42 | 44.23 | +| boat | 34.99 | 52.73 | +| bar | 63.54 | 70.57 | +| arcade machine | 83.45 | 97.36 | +| hovel | 11.7 | 12.65 | +| bus | 82.82 | 95.91 | +| towel | 52.04 | 84.93 | +| light | 30.29 | 35.09 | +| truck | 31.21 | 53.53 | +| tower | 2.89 | 3.37 | +| chandelier | 54.21 | 87.75 | +| awning | 27.74 | 52.0 | +| streetlight | 8.07 | 8.58 | +| booth | 12.12 | 20.57 | +| television receiver | 62.62 | 78.13 | +| airplane | 63.44 | 79.78 | +| dirt track | 1.84 | 4.56 | +| apparel | 40.39 | 54.55 | +| pole | 18.03 | 22.71 | +| land | 0.08 | 0.08 | +| bannister | 0.31 | 0.35 | +| escalator | 49.95 | 65.02 | +| ottoman | 46.22 | 57.53 | +| bottle | 28.36 | 31.01 | +| buffet | 0.0 | 0.0 | +| poster | 20.69 | 26.14 | +| stage | 10.09 | 12.88 | +| van | 37.9 | 56.73 | +| ship | 35.3 | 37.02 | +| fountain | 17.13 | 17.78 | +| conveyer belt | 54.06 | 98.3 | +| canopy | 34.68 | 42.54 | +| washer | 82.69 | 89.98 | +| plaything | 26.38 | 32.68 | +| swimming pool | 41.32 | 98.12 | +| stool | 17.85 | 22.88 | +| barrel | 35.93 | 64.94 | +| basket | 28.93 | 38.19 | +| waterfall | 48.0 | 78.26 | +| tent | 52.82 | 99.69 | +| bag | 17.33 | 18.69 | +| minibike | 65.29 | 77.27 | +| cradle | 64.35 | 98.71 | +| oven | 15.95 | 16.8 | +| ball | 26.62 | 64.24 | +| food | 46.19 | 87.57 | +| step | 0.61 | 0.61 | +| tank | 49.43 | 69.24 | +| trade name | 2.59 | 2.6 | +| microwave | 75.39 | 90.97 | +| pot | 48.47 | 57.7 | +| animal | 61.11 | 66.96 | +| bicycle | 46.13 | 57.2 | +| lake | 0.0 | 0.0 | +| dishwasher | 51.15 | 57.58 | +| screen | 35.78 | 97.08 | +| blanket | 1.34 | 1.39 | +| sculpture | 47.52 | 61.68 | +| hood | 56.52 | 65.56 | +| sconce | 26.88 | 30.77 | +| vase | 21.62 | 28.18 | +| traffic light | 13.11 | 16.5 | +| tray | 4.69 | 7.1 | +| ashcan | 36.4 | 43.21 | +| fan | 27.62 | 29.24 | +| pier | 0.0 | 0.0 | +| crt screen | 0.18 | 0.32 | +| plate | 36.38 | 49.43 | +| monitor | 0.83 | 0.84 | +| bulletin board | 45.25 | 60.62 | +| shower | 0.0 | 0.0 | +| radiator | 39.81 | 42.64 | +| glass | 0.0 | 0.0 | +| clock | 27.17 | 27.78 | +| flag | 60.88 | 71.07 | ++---------------------+-------+-------+ +2024-06-15 22:42:08,581 - mmseg - INFO - Summary: +2024-06-15 22:42:08,581 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 80.63 | 42.32 | 56.78 | ++-------+-------+-------+ +2024-06-15 22:42:08,582 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 22:42:08,582 - mmseg - INFO - Iter(val) [250] aAcc: 0.8063, mIoU: 0.4232, mAcc: 0.5678, IoU.wall: 0.7303, IoU.building: 0.8098, IoU.sky: 0.9289, IoU.floor: 0.7716, IoU.tree: 0.7256, IoU.ceiling: 0.8008, IoU.road: 0.8116, IoU.bed : 0.8583, IoU.windowpane: 0.5664, IoU.grass: 0.6402, IoU.cabinet: 0.5721, IoU.sidewalk: 0.6162, IoU.person: 0.7635, IoU.earth: 0.3425, IoU.door: 0.4976, IoU.table: 0.5483, IoU.mountain: 0.4964, IoU.plant: 0.5071, IoU.curtain: 0.7044, IoU.chair: 0.5208, IoU.car: 0.7997, IoU.water: 0.5606, IoU.painting: 0.6503, IoU.sofa: 0.7066, IoU.shelf: 0.3581, IoU.house: 0.5254, IoU.sea: 0.6066, IoU.mirror: 0.6702, IoU.rug: 0.6170, IoU.field: 0.3141, IoU.armchair: 0.4831, IoU.seat: 0.5447, IoU.fence: 0.4460, IoU.desk: 0.3771, IoU.rock: 0.5135, IoU.wardrobe: 0.5180, IoU.lamp: 0.5293, IoU.bathtub: 0.7085, IoU.railing: 0.3575, IoU.cushion: 0.5539, IoU.base: 0.3008, IoU.box: 0.2284, IoU.column: 0.4661, IoU.signboard: 0.3261, IoU.chest of drawers: 0.3950, IoU.counter: 0.5030, IoU.sand: 0.4576, IoU.sink: 0.6110, IoU.skyscraper: 0.4293, IoU.fireplace: 0.6080, IoU.refrigerator: 0.6681, IoU.grandstand: 0.4160, IoU.path: 0.1970, IoU.stairs: 0.3004, IoU.runway: 0.6809, IoU.case: 0.5204, IoU.pool table: 0.7324, IoU.pillow: 0.5678, IoU.screen door: 0.5558, IoU.stairway: 0.1237, IoU.river: 0.1740, IoU.bridge: 0.4492, IoU.bookcase: 0.1730, IoU.blind: 0.0000, IoU.coffee table: 0.5096, IoU.toilet: 0.7983, IoU.flower: 0.3626, IoU.book: 0.4090, IoU.hill: 0.0221, IoU.bench: 0.4562, IoU.countertop: 0.4575, IoU.stove: 0.7323, IoU.palm: 0.5011, IoU.kitchen island: 0.2672, IoU.computer: 0.6482, IoU.swivel chair: 0.3542, IoU.boat: 0.3499, IoU.bar: 0.6354, IoU.arcade machine: 0.8345, IoU.hovel: 0.1170, IoU.bus: 0.8282, IoU.towel: 0.5204, IoU.light: 0.3029, IoU.truck: 0.3121, IoU.tower: 0.0289, IoU.chandelier: 0.5421, IoU.awning: 0.2774, IoU.streetlight: 0.0807, IoU.booth: 0.1212, IoU.television receiver: 0.6262, IoU.airplane: 0.6344, IoU.dirt track: 0.0184, IoU.apparel: 0.4039, IoU.pole: 0.1803, IoU.land: 0.0008, IoU.bannister: 0.0031, IoU.escalator: 0.4995, IoU.ottoman: 0.4622, IoU.bottle: 0.2836, IoU.buffet: 0.0000, IoU.poster: 0.2069, IoU.stage: 0.1009, IoU.van: 0.3790, IoU.ship: 0.3530, IoU.fountain: 0.1713, IoU.conveyer belt: 0.5406, IoU.canopy: 0.3468, IoU.washer: 0.8269, IoU.plaything: 0.2638, IoU.swimming pool: 0.4132, IoU.stool: 0.1785, IoU.barrel: 0.3593, IoU.basket: 0.2893, IoU.waterfall: 0.4800, IoU.tent: 0.5282, IoU.bag: 0.1733, IoU.minibike: 0.6529, IoU.cradle: 0.6435, IoU.oven: 0.1595, IoU.ball: 0.2662, IoU.food: 0.4619, IoU.step: 0.0061, IoU.tank: 0.4943, IoU.trade name: 0.0259, IoU.microwave: 0.7539, IoU.pot: 0.4847, IoU.animal: 0.6111, IoU.bicycle: 0.4613, IoU.lake: 0.0000, IoU.dishwasher: 0.5115, IoU.screen: 0.3578, IoU.blanket: 0.0134, IoU.sculpture: 0.4752, IoU.hood: 0.5652, IoU.sconce: 0.2688, IoU.vase: 0.2162, IoU.traffic light: 0.1311, IoU.tray: 0.0469, IoU.ashcan: 0.3640, IoU.fan: 0.2762, IoU.pier: 0.0000, IoU.crt screen: 0.0018, IoU.plate: 0.3638, IoU.monitor: 0.0083, IoU.bulletin board: 0.4525, IoU.shower: 0.0000, IoU.radiator: 0.3981, IoU.glass: 0.0000, IoU.clock: 0.2717, IoU.flag: 0.6088, Acc.wall: 0.8128, Acc.building: 0.9036, Acc.sky: 0.9677, Acc.floor: 0.8380, Acc.tree: 0.8787, Acc.ceiling: 0.9250, Acc.road: 0.9110, Acc.bed : 0.9406, Acc.windowpane: 0.7594, Acc.grass: 0.7939, Acc.cabinet: 0.7716, Acc.sidewalk: 0.7547, Acc.person: 0.8890, Acc.earth: 0.4579, Acc.door: 0.7088, Acc.table: 0.7238, Acc.mountain: 0.6315, Acc.plant: 0.5769, Acc.curtain: 0.8413, Acc.chair: 0.6652, Acc.car: 0.8983, Acc.water: 0.8266, Acc.painting: 0.8695, Acc.sofa: 0.8122, Acc.shelf: 0.4934, Acc.house: 0.8275, Acc.sea: 0.7287, Acc.mirror: 0.8106, Acc.rug: 0.7888, Acc.field: 0.7863, Acc.armchair: 0.6485, Acc.seat: 0.9113, Acc.fence: 0.5642, Acc.desk: 0.4671, Acc.rock: 0.5837, Acc.wardrobe: 0.7996, Acc.lamp: 0.6133, Acc.bathtub: 0.8379, Acc.railing: 0.5478, Acc.cushion: 0.7555, Acc.base: 0.5263, Acc.box: 0.2911, Acc.column: 0.6288, Acc.signboard: 0.4479, Acc.chest of drawers: 0.6316, Acc.counter: 0.6309, Acc.sand: 0.7414, Acc.sink: 0.8270, Acc.skyscraper: 0.7785, Acc.fireplace: 0.9438, Acc.refrigerator: 0.8513, Acc.grandstand: 0.8879, Acc.path: 0.3037, Acc.stairs: 0.5093, Acc.runway: 0.9183, Acc.case: 0.6874, Acc.pool table: 0.9912, Acc.pillow: 0.6924, Acc.screen door: 0.9461, Acc.stairway: 0.1288, Acc.river: 0.2169, Acc.bridge: 0.5154, Acc.bookcase: 0.2236, Acc.blind: 0.0000, Acc.coffee table: 0.8354, Acc.toilet: 0.9519, Acc.flower: 0.5051, Acc.book: 0.7122, Acc.hill: 0.0307, Acc.bench: 0.6034, Acc.countertop: 0.5988, Acc.stove: 0.8923, Acc.palm: 0.6563, Acc.kitchen island: 0.5437, Acc.computer: 0.9096, Acc.swivel chair: 0.4423, Acc.boat: 0.5273, Acc.bar: 0.7057, Acc.arcade machine: 0.9736, Acc.hovel: 0.1265, Acc.bus: 0.9591, Acc.towel: 0.8493, Acc.light: 0.3509, Acc.truck: 0.5353, Acc.tower: 0.0337, Acc.chandelier: 0.8775, Acc.awning: 0.5200, Acc.streetlight: 0.0858, Acc.booth: 0.2057, Acc.television receiver: 0.7813, Acc.airplane: 0.7978, Acc.dirt track: 0.0456, Acc.apparel: 0.5455, Acc.pole: 0.2271, Acc.land: 0.0008, Acc.bannister: 0.0035, Acc.escalator: 0.6502, Acc.ottoman: 0.5753, Acc.bottle: 0.3101, Acc.buffet: 0.0000, Acc.poster: 0.2614, Acc.stage: 0.1288, Acc.van: 0.5673, Acc.ship: 0.3702, Acc.fountain: 0.1778, Acc.conveyer belt: 0.9830, Acc.canopy: 0.4254, Acc.washer: 0.8998, Acc.plaything: 0.3268, Acc.swimming pool: 0.9812, Acc.stool: 0.2288, Acc.barrel: 0.6494, Acc.basket: 0.3819, Acc.waterfall: 0.7826, Acc.tent: 0.9969, Acc.bag: 0.1869, Acc.minibike: 0.7727, Acc.cradle: 0.9871, Acc.oven: 0.1680, Acc.ball: 0.6424, Acc.food: 0.8757, Acc.step: 0.0061, Acc.tank: 0.6924, Acc.trade name: 0.0260, Acc.microwave: 0.9097, Acc.pot: 0.5770, Acc.animal: 0.6696, Acc.bicycle: 0.5720, Acc.lake: 0.0000, Acc.dishwasher: 0.5758, Acc.screen: 0.9708, Acc.blanket: 0.0139, Acc.sculpture: 0.6168, Acc.hood: 0.6556, Acc.sconce: 0.3077, Acc.vase: 0.2818, Acc.traffic light: 0.1650, Acc.tray: 0.0710, Acc.ashcan: 0.4321, Acc.fan: 0.2924, Acc.pier: 0.0000, Acc.crt screen: 0.0032, Acc.plate: 0.4943, Acc.monitor: 0.0084, Acc.bulletin board: 0.6062, Acc.shower: 0.0000, Acc.radiator: 0.4264, Acc.glass: 0.0000, Acc.clock: 0.2778, Acc.flag: 0.7107 +2024-06-15 22:43:17,492 - mmseg - INFO - Iter [3050/80000] lr: 3.848e-05, eta: 1 day, 8:21:17, time: 3.294, data_time: 1.932, memory: 70722, decode.loss_ce: 0.6353, decode.acc_seg: 76.9605, aux.loss_ce: 0.2499, aux.acc_seg: 77.3210, loss: 0.8852 +2024-06-15 22:44:25,797 - mmseg - INFO - Iter [3100/80000] lr: 3.845e-05, eta: 1 day, 8:16:58, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6336, decode.acc_seg: 76.5725, aux.loss_ce: 0.2491, aux.acc_seg: 77.2802, loss: 0.8827 +2024-06-15 22:45:34,119 - mmseg - INFO - Iter [3150/80000] lr: 3.843e-05, eta: 1 day, 8:12:46, time: 1.366, data_time: 0.009, memory: 70722, decode.loss_ce: 0.6079, decode.acc_seg: 77.9475, aux.loss_ce: 0.2416, aux.acc_seg: 78.3450, loss: 0.8495 +2024-06-15 22:46:42,511 - mmseg - INFO - Iter [3200/80000] lr: 3.840e-05, eta: 1 day, 8:08:41, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5796, decode.acc_seg: 78.5636, aux.loss_ce: 0.2276, aux.acc_seg: 79.1356, loss: 0.8072 +2024-06-15 22:47:50,846 - mmseg - INFO - Iter [3250/80000] lr: 3.838e-05, eta: 1 day, 8:04:40, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5930, decode.acc_seg: 77.7691, aux.loss_ce: 0.2353, aux.acc_seg: 78.3580, loss: 0.8284 +2024-06-15 22:48:59,442 - mmseg - INFO - Iter [3300/80000] lr: 3.835e-05, eta: 1 day, 8:00:51, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6062, decode.acc_seg: 78.2521, aux.loss_ce: 0.2387, aux.acc_seg: 78.4946, loss: 0.8449 +2024-06-15 22:50:07,985 - mmseg - INFO - Iter [3350/80000] lr: 3.833e-05, eta: 1 day, 7:57:05, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6072, decode.acc_seg: 77.9040, aux.loss_ce: 0.2397, aux.acc_seg: 78.4559, loss: 0.8469 +2024-06-15 22:51:16,563 - mmseg - INFO - Iter [3400/80000] lr: 3.830e-05, eta: 1 day, 7:53:25, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5982, decode.acc_seg: 78.3884, aux.loss_ce: 0.2369, aux.acc_seg: 78.6842, loss: 0.8351 +2024-06-15 22:52:24,922 - mmseg - INFO - Iter [3450/80000] lr: 3.828e-05, eta: 1 day, 7:49:44, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6353, decode.acc_seg: 76.5678, aux.loss_ce: 0.2536, aux.acc_seg: 76.8669, loss: 0.8890 +2024-06-15 22:53:33,550 - mmseg - INFO - Iter [3500/80000] lr: 3.825e-05, eta: 1 day, 7:46:13, time: 1.373, data_time: 0.010, memory: 70722, decode.loss_ce: 0.6118, decode.acc_seg: 77.3660, aux.loss_ce: 0.2427, aux.acc_seg: 77.9531, loss: 0.8545 +2024-06-15 22:54:42,037 - mmseg - INFO - Iter [3550/80000] lr: 3.823e-05, eta: 1 day, 7:42:43, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5887, decode.acc_seg: 77.9126, aux.loss_ce: 0.2325, aux.acc_seg: 78.4823, loss: 0.8212 +2024-06-15 22:55:50,349 - mmseg - INFO - Iter [3600/80000] lr: 3.820e-05, eta: 1 day, 7:39:14, time: 1.366, data_time: 0.009, memory: 70722, decode.loss_ce: 0.6186, decode.acc_seg: 78.0612, aux.loss_ce: 0.2443, aux.acc_seg: 78.4478, loss: 0.8630 +2024-06-15 22:56:58,762 - mmseg - INFO - Iter [3650/80000] lr: 3.818e-05, eta: 1 day, 7:35:50, time: 1.368, data_time: 0.009, memory: 70722, decode.loss_ce: 0.5883, decode.acc_seg: 77.9052, aux.loss_ce: 0.2324, aux.acc_seg: 78.1938, loss: 0.8207 +2024-06-15 22:58:07,344 - mmseg - INFO - Iter [3700/80000] lr: 3.815e-05, eta: 1 day, 7:32:34, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5981, decode.acc_seg: 78.5011, aux.loss_ce: 0.2344, aux.acc_seg: 78.8073, loss: 0.8325 +2024-06-15 22:59:15,591 - mmseg - INFO - Iter [3750/80000] lr: 3.813e-05, eta: 1 day, 7:29:14, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5394, decode.acc_seg: 79.8396, aux.loss_ce: 0.2146, aux.acc_seg: 80.1836, loss: 0.7540 +2024-06-15 23:00:26,472 - mmseg - INFO - Iter [3800/80000] lr: 3.810e-05, eta: 1 day, 7:26:50, time: 1.418, data_time: 0.055, memory: 70722, decode.loss_ce: 0.5775, decode.acc_seg: 78.2288, aux.loss_ce: 0.2299, aux.acc_seg: 78.5833, loss: 0.8074 +2024-06-15 23:01:34,840 - mmseg - INFO - Iter [3850/80000] lr: 3.808e-05, eta: 1 day, 7:23:39, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5289, decode.acc_seg: 80.0565, aux.loss_ce: 0.2093, aux.acc_seg: 80.2899, loss: 0.7382 +2024-06-15 23:02:43,240 - mmseg - INFO - Iter [3900/80000] lr: 3.805e-05, eta: 1 day, 7:20:32, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5548, decode.acc_seg: 79.1243, aux.loss_ce: 0.2184, aux.acc_seg: 79.5975, loss: 0.7732 +2024-06-15 23:03:51,654 - mmseg - INFO - Iter [3950/80000] lr: 3.803e-05, eta: 1 day, 7:17:27, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5620, decode.acc_seg: 79.1524, aux.loss_ce: 0.2234, aux.acc_seg: 79.4214, loss: 0.7855 +2024-06-15 23:05:00,402 - mmseg - INFO - Saving checkpoint at 4000 iterations +2024-06-15 23:06:25,467 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 23:06:25,467 - mmseg - INFO - Iter [4000/80000] lr: 3.800e-05, eta: 1 day, 7:41:29, time: 3.076, data_time: 0.009, memory: 70722, decode.loss_ce: 0.5668, decode.acc_seg: 78.7292, aux.loss_ce: 0.2236, aux.acc_seg: 79.1561, loss: 0.7905 +2024-06-15 23:07:57,569 - mmseg - INFO - per class results: +2024-06-15 23:07:57,575 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 75.68 | 85.49 | +| building | 80.67 | 92.13 | +| sky | 93.02 | 96.63 | +| floor | 80.28 | 84.73 | +| tree | 72.6 | 84.96 | +| ceiling | 79.5 | 87.81 | +| road | 75.79 | 93.98 | +| bed | 86.96 | 95.31 | +| windowpane | 59.14 | 77.47 | +| grass | 64.39 | 74.69 | +| cabinet | 55.24 | 72.72 | +| sidewalk | 48.1 | 53.34 | +| person | 77.65 | 90.27 | +| earth | 34.32 | 48.97 | +| door | 49.19 | 64.79 | +| table | 51.75 | 76.3 | +| mountain | 52.22 | 73.3 | +| plant | 49.16 | 55.31 | +| curtain | 68.52 | 89.34 | +| chair | 54.16 | 70.78 | +| car | 80.93 | 91.75 | +| water | 53.16 | 79.58 | +| painting | 61.5 | 88.88 | +| sofa | 68.54 | 75.12 | +| shelf | 39.42 | 55.67 | +| house | 46.3 | 55.16 | +| sea | 34.47 | 37.02 | +| mirror | 66.1 | 75.98 | +| rug | 67.77 | 80.34 | +| field | 31.04 | 68.96 | +| armchair | 50.27 | 73.64 | +| seat | 61.84 | 88.64 | +| fence | 41.39 | 51.9 | +| desk | 47.0 | 67.1 | +| rock | 58.52 | 73.11 | +| wardrobe | 54.94 | 78.25 | +| lamp | 56.5 | 70.28 | +| bathtub | 75.67 | 83.88 | +| railing | 32.23 | 41.56 | +| cushion | 51.79 | 83.31 | +| base | 25.46 | 39.86 | +| box | 20.16 | 23.89 | +| column | 44.7 | 67.23 | +| signboard | 32.36 | 53.84 | +| chest of drawers | 29.62 | 77.05 | +| counter | 16.18 | 16.2 | +| sand | 43.33 | 66.94 | +| sink | 66.46 | 79.83 | +| skyscraper | 45.3 | 64.37 | +| fireplace | 69.94 | 87.4 | +| refrigerator | 71.0 | 82.64 | +| grandstand | 49.52 | 69.82 | +| path | 16.65 | 21.37 | +| stairs | 37.92 | 47.24 | +| runway | 69.7 | 93.38 | +| case | 61.95 | 86.45 | +| pool table | 84.49 | 98.01 | +| pillow | 53.97 | 64.09 | +| screen door | 61.17 | 69.26 | +| stairway | 36.31 | 42.9 | +| river | 24.42 | 66.46 | +| bridge | 51.99 | 64.82 | +| bookcase | 26.26 | 42.73 | +| blind | 28.95 | 29.99 | +| coffee table | 54.63 | 65.58 | +| toilet | 82.26 | 93.26 | +| flower | 35.57 | 44.59 | +| book | 39.78 | 53.89 | +| hill | 0.1 | 0.1 | +| bench | 45.97 | 61.89 | +| countertop | 40.92 | 46.16 | +| stove | 66.41 | 68.69 | +| palm | 49.84 | 73.45 | +| kitchen island | 31.37 | 80.06 | +| computer | 71.09 | 87.07 | +| swivel chair | 45.27 | 72.16 | +| boat | 47.41 | 83.33 | +| bar | 52.09 | 73.14 | +| arcade machine | 0.0 | 0.0 | +| hovel | 56.41 | 68.21 | +| bus | 83.81 | 88.72 | +| towel | 56.38 | 75.67 | +| light | 22.63 | 23.96 | +| truck | 31.69 | 45.88 | +| tower | 40.89 | 58.88 | +| chandelier | 59.03 | 82.43 | +| awning | 35.89 | 56.3 | +| streetlight | 14.1 | 19.4 | +| booth | 40.95 | 62.01 | +| television receiver | 58.13 | 61.46 | +| airplane | 47.37 | 63.3 | +| dirt track | 4.05 | 21.81 | +| apparel | 40.62 | 68.14 | +| pole | 9.37 | 10.37 | +| land | 0.0 | 0.0 | +| bannister | 4.75 | 6.07 | +| escalator | 55.43 | 77.9 | +| ottoman | 47.81 | 73.33 | +| bottle | 29.31 | 33.8 | +| buffet | 5.15 | 5.39 | +| poster | 4.9 | 5.78 | +| stage | 9.99 | 21.39 | +| van | 45.29 | 57.92 | +| ship | 79.46 | 91.78 | +| fountain | 32.03 | 34.97 | +| conveyer belt | 54.95 | 98.01 | +| canopy | 50.03 | 57.83 | +| washer | 80.78 | 91.02 | +| plaything | 26.71 | 31.94 | +| swimming pool | 71.27 | 96.42 | +| stool | 35.98 | 46.02 | +| barrel | 50.85 | 64.67 | +| basket | 27.6 | 34.23 | +| waterfall | 52.92 | 93.58 | +| tent | 66.04 | 99.83 | +| bag | 17.62 | 18.65 | +| minibike | 61.68 | 81.7 | +| cradle | 74.33 | 96.88 | +| oven | 34.22 | 54.58 | +| ball | 30.97 | 64.86 | +| food | 44.87 | 62.69 | +| step | 1.4 | 1.67 | +| tank | 46.29 | 66.4 | +| trade name | 2.35 | 2.38 | +| microwave | 80.29 | 89.87 | +| pot | 50.73 | 61.09 | +| animal | 70.53 | 78.1 | +| bicycle | 47.43 | 61.9 | +| lake | 0.0 | 0.0 | +| dishwasher | 56.88 | 67.65 | +| screen | 54.48 | 92.02 | +| blanket | 18.59 | 23.67 | +| sculpture | 46.89 | 56.88 | +| hood | 52.71 | 58.61 | +| sconce | 36.01 | 43.09 | +| vase | 29.03 | 41.34 | +| traffic light | 15.8 | 16.87 | +| tray | 2.19 | 2.42 | +| ashcan | 42.07 | 61.22 | +| fan | 53.59 | 66.8 | +| pier | 49.81 | 54.42 | +| crt screen | 0.0 | 0.01 | +| plate | 41.81 | 63.8 | +| monitor | 0.0 | 0.0 | +| bulletin board | 15.61 | 15.64 | +| shower | 0.04 | 0.05 | +| radiator | 51.96 | 61.13 | +| glass | 0.2 | 0.2 | +| clock | 32.06 | 34.15 | +| flag | 59.91 | 74.02 | ++---------------------+-------+-------+ +2024-06-15 23:07:57,575 - mmseg - INFO - Summary: +2024-06-15 23:07:57,575 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 81.0 | 45.15 | 59.01 | ++------+-------+-------+ +2024-06-15 23:07:57,576 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 23:07:57,577 - mmseg - INFO - Iter(val) [250] aAcc: 0.8100, mIoU: 0.4515, mAcc: 0.5901, IoU.wall: 0.7568, IoU.building: 0.8067, IoU.sky: 0.9302, IoU.floor: 0.8028, IoU.tree: 0.7260, IoU.ceiling: 0.7950, IoU.road: 0.7579, IoU.bed : 0.8696, IoU.windowpane: 0.5914, IoU.grass: 0.6439, IoU.cabinet: 0.5524, IoU.sidewalk: 0.4810, IoU.person: 0.7765, IoU.earth: 0.3432, IoU.door: 0.4919, IoU.table: 0.5175, IoU.mountain: 0.5222, IoU.plant: 0.4916, IoU.curtain: 0.6852, IoU.chair: 0.5416, IoU.car: 0.8093, IoU.water: 0.5316, IoU.painting: 0.6150, IoU.sofa: 0.6854, IoU.shelf: 0.3942, IoU.house: 0.4630, IoU.sea: 0.3447, IoU.mirror: 0.6610, IoU.rug: 0.6777, IoU.field: 0.3104, IoU.armchair: 0.5027, IoU.seat: 0.6184, IoU.fence: 0.4139, IoU.desk: 0.4700, IoU.rock: 0.5852, IoU.wardrobe: 0.5494, IoU.lamp: 0.5650, IoU.bathtub: 0.7567, IoU.railing: 0.3223, IoU.cushion: 0.5179, IoU.base: 0.2546, IoU.box: 0.2016, IoU.column: 0.4470, IoU.signboard: 0.3236, IoU.chest of drawers: 0.2962, IoU.counter: 0.1618, IoU.sand: 0.4333, IoU.sink: 0.6646, IoU.skyscraper: 0.4530, IoU.fireplace: 0.6994, IoU.refrigerator: 0.7100, IoU.grandstand: 0.4952, IoU.path: 0.1665, IoU.stairs: 0.3792, IoU.runway: 0.6970, IoU.case: 0.6195, IoU.pool table: 0.8449, IoU.pillow: 0.5397, IoU.screen door: 0.6117, IoU.stairway: 0.3631, IoU.river: 0.2442, IoU.bridge: 0.5199, IoU.bookcase: 0.2626, IoU.blind: 0.2895, IoU.coffee table: 0.5463, IoU.toilet: 0.8226, IoU.flower: 0.3557, IoU.book: 0.3978, IoU.hill: 0.0010, IoU.bench: 0.4597, IoU.countertop: 0.4092, IoU.stove: 0.6641, IoU.palm: 0.4984, IoU.kitchen island: 0.3137, IoU.computer: 0.7109, IoU.swivel chair: 0.4527, IoU.boat: 0.4741, IoU.bar: 0.5209, IoU.arcade machine: 0.0000, IoU.hovel: 0.5641, IoU.bus: 0.8381, IoU.towel: 0.5638, IoU.light: 0.2263, IoU.truck: 0.3169, IoU.tower: 0.4089, IoU.chandelier: 0.5903, IoU.awning: 0.3589, IoU.streetlight: 0.1410, IoU.booth: 0.4095, IoU.television receiver: 0.5813, IoU.airplane: 0.4737, IoU.dirt track: 0.0405, IoU.apparel: 0.4062, IoU.pole: 0.0937, IoU.land: 0.0000, IoU.bannister: 0.0475, IoU.escalator: 0.5543, IoU.ottoman: 0.4781, IoU.bottle: 0.2931, IoU.buffet: 0.0515, IoU.poster: 0.0490, IoU.stage: 0.0999, IoU.van: 0.4529, IoU.ship: 0.7946, IoU.fountain: 0.3203, IoU.conveyer belt: 0.5495, IoU.canopy: 0.5003, IoU.washer: 0.8078, IoU.plaything: 0.2671, IoU.swimming pool: 0.7127, IoU.stool: 0.3598, IoU.barrel: 0.5085, IoU.basket: 0.2760, IoU.waterfall: 0.5292, IoU.tent: 0.6604, IoU.bag: 0.1762, IoU.minibike: 0.6168, IoU.cradle: 0.7433, IoU.oven: 0.3422, IoU.ball: 0.3097, IoU.food: 0.4487, IoU.step: 0.0140, IoU.tank: 0.4629, IoU.trade name: 0.0235, IoU.microwave: 0.8029, IoU.pot: 0.5073, IoU.animal: 0.7053, IoU.bicycle: 0.4743, IoU.lake: 0.0000, IoU.dishwasher: 0.5688, IoU.screen: 0.5448, IoU.blanket: 0.1859, IoU.sculpture: 0.4689, IoU.hood: 0.5271, IoU.sconce: 0.3601, IoU.vase: 0.2903, IoU.traffic light: 0.1580, IoU.tray: 0.0219, IoU.ashcan: 0.4207, IoU.fan: 0.5359, IoU.pier: 0.4981, IoU.crt screen: 0.0000, IoU.plate: 0.4181, IoU.monitor: 0.0000, IoU.bulletin board: 0.1561, IoU.shower: 0.0004, IoU.radiator: 0.5196, IoU.glass: 0.0020, IoU.clock: 0.3206, IoU.flag: 0.5991, Acc.wall: 0.8549, Acc.building: 0.9213, Acc.sky: 0.9663, Acc.floor: 0.8473, Acc.tree: 0.8496, Acc.ceiling: 0.8781, Acc.road: 0.9398, Acc.bed : 0.9531, Acc.windowpane: 0.7747, Acc.grass: 0.7469, Acc.cabinet: 0.7272, Acc.sidewalk: 0.5334, Acc.person: 0.9027, Acc.earth: 0.4897, Acc.door: 0.6479, Acc.table: 0.7630, Acc.mountain: 0.7330, Acc.plant: 0.5531, Acc.curtain: 0.8934, Acc.chair: 0.7078, Acc.car: 0.9175, Acc.water: 0.7958, Acc.painting: 0.8888, Acc.sofa: 0.7512, Acc.shelf: 0.5567, Acc.house: 0.5516, Acc.sea: 0.3702, Acc.mirror: 0.7598, Acc.rug: 0.8034, Acc.field: 0.6896, Acc.armchair: 0.7364, Acc.seat: 0.8864, Acc.fence: 0.5190, Acc.desk: 0.6710, Acc.rock: 0.7311, Acc.wardrobe: 0.7825, Acc.lamp: 0.7028, Acc.bathtub: 0.8388, Acc.railing: 0.4156, Acc.cushion: 0.8331, Acc.base: 0.3986, Acc.box: 0.2389, Acc.column: 0.6723, Acc.signboard: 0.5384, Acc.chest of drawers: 0.7705, Acc.counter: 0.1620, Acc.sand: 0.6694, Acc.sink: 0.7983, Acc.skyscraper: 0.6437, Acc.fireplace: 0.8740, Acc.refrigerator: 0.8264, Acc.grandstand: 0.6982, Acc.path: 0.2137, Acc.stairs: 0.4724, Acc.runway: 0.9338, Acc.case: 0.8645, Acc.pool table: 0.9801, Acc.pillow: 0.6409, Acc.screen door: 0.6926, Acc.stairway: 0.4290, Acc.river: 0.6646, Acc.bridge: 0.6482, Acc.bookcase: 0.4273, Acc.blind: 0.2999, Acc.coffee table: 0.6558, Acc.toilet: 0.9326, Acc.flower: 0.4459, Acc.book: 0.5389, Acc.hill: 0.0010, Acc.bench: 0.6189, Acc.countertop: 0.4616, Acc.stove: 0.6869, Acc.palm: 0.7345, Acc.kitchen island: 0.8006, Acc.computer: 0.8707, Acc.swivel chair: 0.7216, Acc.boat: 0.8333, Acc.bar: 0.7314, Acc.arcade machine: 0.0000, Acc.hovel: 0.6821, Acc.bus: 0.8872, Acc.towel: 0.7567, Acc.light: 0.2396, Acc.truck: 0.4588, Acc.tower: 0.5888, Acc.chandelier: 0.8243, Acc.awning: 0.5630, Acc.streetlight: 0.1940, Acc.booth: 0.6201, Acc.television receiver: 0.6146, Acc.airplane: 0.6330, Acc.dirt track: 0.2181, Acc.apparel: 0.6814, Acc.pole: 0.1037, Acc.land: 0.0000, Acc.bannister: 0.0607, Acc.escalator: 0.7790, Acc.ottoman: 0.7333, Acc.bottle: 0.3380, Acc.buffet: 0.0539, Acc.poster: 0.0578, Acc.stage: 0.2139, Acc.van: 0.5792, Acc.ship: 0.9178, Acc.fountain: 0.3497, Acc.conveyer belt: 0.9801, Acc.canopy: 0.5783, Acc.washer: 0.9102, Acc.plaything: 0.3194, Acc.swimming pool: 0.9642, Acc.stool: 0.4602, Acc.barrel: 0.6467, Acc.basket: 0.3423, Acc.waterfall: 0.9358, Acc.tent: 0.9983, Acc.bag: 0.1865, Acc.minibike: 0.8170, Acc.cradle: 0.9688, Acc.oven: 0.5458, Acc.ball: 0.6486, Acc.food: 0.6269, Acc.step: 0.0167, Acc.tank: 0.6640, Acc.trade name: 0.0238, Acc.microwave: 0.8987, Acc.pot: 0.6109, Acc.animal: 0.7810, Acc.bicycle: 0.6190, Acc.lake: 0.0000, Acc.dishwasher: 0.6765, Acc.screen: 0.9202, Acc.blanket: 0.2367, Acc.sculpture: 0.5688, Acc.hood: 0.5861, Acc.sconce: 0.4309, Acc.vase: 0.4134, Acc.traffic light: 0.1687, Acc.tray: 0.0242, Acc.ashcan: 0.6122, Acc.fan: 0.6680, Acc.pier: 0.5442, Acc.crt screen: 0.0001, Acc.plate: 0.6380, Acc.monitor: 0.0000, Acc.bulletin board: 0.1564, Acc.shower: 0.0005, Acc.radiator: 0.6113, Acc.glass: 0.0020, Acc.clock: 0.3415, Acc.flag: 0.7402 +2024-06-15 23:09:06,434 - mmseg - INFO - Iter [4050/80000] lr: 3.798e-05, eta: 1 day, 8:07:05, time: 3.219, data_time: 1.858, memory: 70722, decode.loss_ce: 0.5632, decode.acc_seg: 79.4943, aux.loss_ce: 0.2241, aux.acc_seg: 79.8217, loss: 0.7873 +2024-06-15 23:10:14,837 - mmseg - INFO - Iter [4100/80000] lr: 3.795e-05, eta: 1 day, 8:03:26, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5296, decode.acc_seg: 79.9864, aux.loss_ce: 0.2092, aux.acc_seg: 80.5139, loss: 0.7388 +2024-06-15 23:11:23,152 - mmseg - INFO - Iter [4150/80000] lr: 3.793e-05, eta: 1 day, 7:59:49, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5514, decode.acc_seg: 79.1422, aux.loss_ce: 0.2185, aux.acc_seg: 79.4218, loss: 0.7699 +2024-06-15 23:12:31,555 - mmseg - INFO - Iter [4200/80000] lr: 3.790e-05, eta: 1 day, 7:56:17, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5412, decode.acc_seg: 78.9401, aux.loss_ce: 0.2138, aux.acc_seg: 79.4244, loss: 0.7550 +2024-06-15 23:13:39,970 - mmseg - INFO - Iter [4250/80000] lr: 3.788e-05, eta: 1 day, 7:52:49, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5607, decode.acc_seg: 78.1874, aux.loss_ce: 0.2214, aux.acc_seg: 78.4233, loss: 0.7821 +2024-06-15 23:14:48,468 - mmseg - INFO - Iter [4300/80000] lr: 3.785e-05, eta: 1 day, 7:49:25, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5395, decode.acc_seg: 79.3139, aux.loss_ce: 0.2142, aux.acc_seg: 79.5125, loss: 0.7537 +2024-06-15 23:15:56,791 - mmseg - INFO - Iter [4350/80000] lr: 3.783e-05, eta: 1 day, 7:46:02, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5325, decode.acc_seg: 79.9921, aux.loss_ce: 0.2108, aux.acc_seg: 80.3505, loss: 0.7433 +2024-06-15 23:17:05,330 - mmseg - INFO - Iter [4400/80000] lr: 3.780e-05, eta: 1 day, 7:42:45, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5468, decode.acc_seg: 79.4300, aux.loss_ce: 0.2163, aux.acc_seg: 79.6236, loss: 0.7630 +2024-06-15 23:18:13,942 - mmseg - INFO - Iter [4450/80000] lr: 3.778e-05, eta: 1 day, 7:39:32, time: 1.372, data_time: 0.009, memory: 70722, decode.loss_ce: 0.5100, decode.acc_seg: 81.2100, aux.loss_ce: 0.2011, aux.acc_seg: 81.3248, loss: 0.7112 +2024-06-15 23:19:22,212 - mmseg - INFO - Iter [4500/80000] lr: 3.775e-05, eta: 1 day, 7:36:17, time: 1.365, data_time: 0.009, memory: 70722, decode.loss_ce: 0.5586, decode.acc_seg: 79.0238, aux.loss_ce: 0.2221, aux.acc_seg: 79.2923, loss: 0.7807 +2024-06-15 23:20:30,712 - mmseg - INFO - Iter [4550/80000] lr: 3.773e-05, eta: 1 day, 7:33:08, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5463, decode.acc_seg: 79.2220, aux.loss_ce: 0.2156, aux.acc_seg: 79.6223, loss: 0.7620 +2024-06-15 23:21:39,014 - mmseg - INFO - Iter [4600/80000] lr: 3.770e-05, eta: 1 day, 7:29:58, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5380, decode.acc_seg: 79.6344, aux.loss_ce: 0.2137, aux.acc_seg: 79.9340, loss: 0.7517 +2024-06-15 23:22:47,508 - mmseg - INFO - Iter [4650/80000] lr: 3.768e-05, eta: 1 day, 7:26:54, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5280, decode.acc_seg: 79.9425, aux.loss_ce: 0.2101, aux.acc_seg: 80.0786, loss: 0.7380 +2024-06-15 23:23:55,819 - mmseg - INFO - Iter [4700/80000] lr: 3.765e-05, eta: 1 day, 7:23:50, time: 1.366, data_time: 0.009, memory: 70722, decode.loss_ce: 0.5422, decode.acc_seg: 79.5963, aux.loss_ce: 0.2149, aux.acc_seg: 79.9366, loss: 0.7572 +2024-06-15 23:25:04,166 - mmseg - INFO - Iter [4750/80000] lr: 3.763e-05, eta: 1 day, 7:20:49, time: 1.367, data_time: 0.009, memory: 70722, decode.loss_ce: 0.5741, decode.acc_seg: 78.8583, aux.loss_ce: 0.2284, aux.acc_seg: 78.6958, loss: 0.8025 +2024-06-15 23:26:12,393 - mmseg - INFO - Iter [4800/80000] lr: 3.760e-05, eta: 1 day, 7:17:48, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5702, decode.acc_seg: 78.8116, aux.loss_ce: 0.2240, aux.acc_seg: 79.0434, loss: 0.7942 +2024-06-15 23:27:21,246 - mmseg - INFO - Iter [4850/80000] lr: 3.758e-05, eta: 1 day, 7:14:59, time: 1.377, data_time: 0.020, memory: 70722, decode.loss_ce: 0.5119, decode.acc_seg: 80.5528, aux.loss_ce: 0.2024, aux.acc_seg: 81.1267, loss: 0.7144 +2024-06-15 23:28:29,611 - mmseg - INFO - Iter [4900/80000] lr: 3.755e-05, eta: 1 day, 7:12:05, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5314, decode.acc_seg: 79.6322, aux.loss_ce: 0.2095, aux.acc_seg: 79.9749, loss: 0.7409 +2024-06-15 23:29:38,062 - mmseg - INFO - Iter [4950/80000] lr: 3.753e-05, eta: 1 day, 7:09:14, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5736, decode.acc_seg: 78.7867, aux.loss_ce: 0.2267, aux.acc_seg: 78.9520, loss: 0.8003 +2024-06-15 23:30:46,436 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 23:30:46,436 - mmseg - INFO - Iter [5000/80000] lr: 3.750e-05, eta: 1 day, 7:06:24, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5653, decode.acc_seg: 78.6511, aux.loss_ce: 0.2225, aux.acc_seg: 79.1854, loss: 0.7878 +2024-06-15 23:32:19,514 - mmseg - INFO - per class results: +2024-06-15 23:32:19,520 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 76.69 | 85.56 | +| building | 82.93 | 93.74 | +| sky | 93.4 | 95.44 | +| floor | 81.1 | 89.08 | +| tree | 73.78 | 89.4 | +| ceiling | 83.63 | 93.28 | +| road | 82.39 | 88.85 | +| bed | 88.54 | 94.68 | +| windowpane | 59.95 | 82.34 | +| grass | 64.74 | 90.27 | +| cabinet | 59.47 | 70.95 | +| sidewalk | 60.33 | 78.88 | +| person | 79.2 | 89.07 | +| earth | 33.61 | 43.9 | +| door | 52.3 | 77.59 | +| table | 55.81 | 66.52 | +| mountain | 59.04 | 72.04 | +| plant | 51.09 | 56.95 | +| curtain | 70.6 | 83.32 | +| chair | 54.23 | 64.76 | +| car | 82.14 | 92.67 | +| water | 62.05 | 84.38 | +| painting | 73.18 | 81.95 | +| sofa | 72.79 | 84.16 | +| shelf | 30.41 | 46.89 | +| house | 38.66 | 40.74 | +| sea | 65.09 | 74.49 | +| mirror | 71.15 | 79.3 | +| rug | 64.78 | 85.98 | +| field | 36.27 | 54.48 | +| armchair | 50.04 | 72.13 | +| seat | 62.71 | 76.96 | +| fence | 44.45 | 67.68 | +| desk | 38.21 | 78.24 | +| rock | 57.83 | 84.47 | +| wardrobe | 39.0 | 46.17 | +| lamp | 54.77 | 61.4 | +| bathtub | 77.03 | 86.48 | +| railing | 36.11 | 50.97 | +| cushion | 57.99 | 65.73 | +| base | 31.07 | 39.78 | +| box | 22.86 | 26.2 | +| column | 49.36 | 63.55 | +| signboard | 33.23 | 43.69 | +| chest of drawers | 41.36 | 67.4 | +| counter | 45.4 | 71.63 | +| sand | 37.23 | 47.71 | +| sink | 64.04 | 81.03 | +| skyscraper | 49.19 | 67.04 | +| fireplace | 61.19 | 95.77 | +| refrigerator | 71.36 | 83.09 | +| grandstand | 46.44 | 89.46 | +| path | 21.74 | 31.01 | +| stairs | 6.86 | 7.22 | +| runway | 69.56 | 95.32 | +| case | 37.33 | 96.93 | +| pool table | 81.8 | 98.89 | +| pillow | 63.33 | 81.5 | +| screen door | 65.94 | 79.77 | +| stairway | 32.65 | 60.51 | +| river | 26.24 | 29.94 | +| bridge | 70.89 | 87.96 | +| bookcase | 30.49 | 52.81 | +| blind | 20.39 | 20.6 | +| coffee table | 50.47 | 81.97 | +| toilet | 85.42 | 91.21 | +| flower | 34.09 | 38.53 | +| book | 46.16 | 72.12 | +| hill | 5.14 | 8.8 | +| bench | 54.66 | 67.06 | +| countertop | 57.15 | 72.36 | +| stove | 74.86 | 90.18 | +| palm | 48.46 | 77.83 | +| kitchen island | 35.33 | 69.3 | +| computer | 69.03 | 92.55 | +| swivel chair | 26.67 | 30.32 | +| boat | 68.02 | 85.03 | +| bar | 45.79 | 50.1 | +| arcade machine | 87.93 | 96.76 | +| hovel | 37.66 | 41.07 | +| bus | 87.31 | 94.73 | +| towel | 61.97 | 71.32 | +| light | 37.32 | 42.37 | +| truck | 25.43 | 27.43 | +| tower | 19.61 | 29.0 | +| chandelier | 61.21 | 78.73 | +| awning | 37.65 | 52.34 | +| streetlight | 17.91 | 21.47 | +| booth | 20.14 | 41.16 | +| television receiver | 65.29 | 78.33 | +| airplane | 65.46 | 75.83 | +| dirt track | 17.84 | 20.3 | +| apparel | 33.45 | 46.13 | +| pole | 4.72 | 4.97 | +| land | 0.0 | 0.0 | +| bannister | 7.17 | 7.89 | +| escalator | 57.43 | 76.66 | +| ottoman | 42.31 | 78.44 | +| bottle | 32.2 | 40.75 | +| buffet | 42.8 | 56.04 | +| poster | 20.39 | 22.21 | +| stage | 16.69 | 35.76 | +| van | 41.53 | 58.81 | +| ship | 24.92 | 26.54 | +| fountain | 17.67 | 18.01 | +| conveyer belt | 63.33 | 94.06 | +| canopy | 43.7 | 55.58 | +| washer | 84.78 | 91.78 | +| plaything | 31.92 | 56.52 | +| swimming pool | 62.56 | 93.91 | +| stool | 39.26 | 58.34 | +| barrel | 49.78 | 70.08 | +| basket | 29.54 | 34.14 | +| waterfall | 60.04 | 97.13 | +| tent | 75.95 | 98.01 | +| bag | 3.46 | 3.49 | +| minibike | 67.89 | 80.74 | +| cradle | 76.26 | 97.62 | +| oven | 40.68 | 42.94 | +| ball | 36.12 | 61.16 | +| food | 36.12 | 38.75 | +| step | 1.79 | 1.89 | +| tank | 49.07 | 70.29 | +| trade name | 15.75 | 17.39 | +| microwave | 77.84 | 94.34 | +| pot | 46.64 | 52.12 | +| animal | 69.3 | 72.27 | +| bicycle | 44.24 | 53.44 | +| lake | 0.0 | 0.0 | +| dishwasher | 56.38 | 71.12 | +| screen | 54.32 | 88.62 | +| blanket | 19.39 | 22.85 | +| sculpture | 56.58 | 59.05 | +| hood | 61.02 | 71.04 | +| sconce | 44.61 | 56.19 | +| vase | 28.84 | 37.79 | +| traffic light | 16.29 | 58.22 | +| tray | 7.95 | 10.91 | +| ashcan | 41.16 | 60.66 | +| fan | 51.67 | 62.3 | +| pier | 27.48 | 29.05 | +| crt screen | 0.14 | 0.45 | +| plate | 43.15 | 70.38 | +| monitor | 1.26 | 1.36 | +| bulletin board | 46.85 | 62.23 | +| shower | 0.0 | 0.0 | +| radiator | 58.62 | 71.75 | +| glass | 0.73 | 0.73 | +| clock | 28.14 | 28.77 | +| flag | 54.81 | 56.21 | ++---------------------+-------+-------+ +2024-06-15 23:32:19,521 - mmseg - INFO - Summary: +2024-06-15 23:32:19,521 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 82.42 | 46.99 | 60.44 | ++-------+-------+-------+ +2024-06-15 23:32:19,522 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 23:32:19,522 - mmseg - INFO - Iter(val) [250] aAcc: 0.8242, mIoU: 0.4699, mAcc: 0.6044, IoU.wall: 0.7669, IoU.building: 0.8293, IoU.sky: 0.9340, IoU.floor: 0.8110, IoU.tree: 0.7378, IoU.ceiling: 0.8363, IoU.road: 0.8239, IoU.bed : 0.8854, IoU.windowpane: 0.5995, IoU.grass: 0.6474, IoU.cabinet: 0.5947, IoU.sidewalk: 0.6033, IoU.person: 0.7920, IoU.earth: 0.3361, IoU.door: 0.5230, IoU.table: 0.5581, IoU.mountain: 0.5904, IoU.plant: 0.5109, IoU.curtain: 0.7060, IoU.chair: 0.5423, IoU.car: 0.8214, IoU.water: 0.6205, IoU.painting: 0.7318, IoU.sofa: 0.7279, IoU.shelf: 0.3041, IoU.house: 0.3866, IoU.sea: 0.6509, IoU.mirror: 0.7115, IoU.rug: 0.6478, IoU.field: 0.3627, IoU.armchair: 0.5004, IoU.seat: 0.6271, IoU.fence: 0.4445, IoU.desk: 0.3821, IoU.rock: 0.5783, IoU.wardrobe: 0.3900, IoU.lamp: 0.5477, IoU.bathtub: 0.7703, IoU.railing: 0.3611, IoU.cushion: 0.5799, IoU.base: 0.3107, IoU.box: 0.2286, IoU.column: 0.4936, IoU.signboard: 0.3323, IoU.chest of drawers: 0.4136, IoU.counter: 0.4540, IoU.sand: 0.3723, IoU.sink: 0.6404, IoU.skyscraper: 0.4919, IoU.fireplace: 0.6119, IoU.refrigerator: 0.7136, IoU.grandstand: 0.4644, IoU.path: 0.2174, IoU.stairs: 0.0686, IoU.runway: 0.6956, IoU.case: 0.3733, IoU.pool table: 0.8180, IoU.pillow: 0.6333, IoU.screen door: 0.6594, IoU.stairway: 0.3265, IoU.river: 0.2624, IoU.bridge: 0.7089, IoU.bookcase: 0.3049, IoU.blind: 0.2039, IoU.coffee table: 0.5047, IoU.toilet: 0.8542, IoU.flower: 0.3409, IoU.book: 0.4616, IoU.hill: 0.0514, IoU.bench: 0.5466, IoU.countertop: 0.5715, IoU.stove: 0.7486, IoU.palm: 0.4846, IoU.kitchen island: 0.3533, IoU.computer: 0.6903, IoU.swivel chair: 0.2667, IoU.boat: 0.6802, IoU.bar: 0.4579, IoU.arcade machine: 0.8793, IoU.hovel: 0.3766, IoU.bus: 0.8731, IoU.towel: 0.6197, IoU.light: 0.3732, IoU.truck: 0.2543, IoU.tower: 0.1961, IoU.chandelier: 0.6121, IoU.awning: 0.3765, IoU.streetlight: 0.1791, IoU.booth: 0.2014, IoU.television receiver: 0.6529, IoU.airplane: 0.6546, IoU.dirt track: 0.1784, IoU.apparel: 0.3345, IoU.pole: 0.0472, IoU.land: 0.0000, IoU.bannister: 0.0717, IoU.escalator: 0.5743, IoU.ottoman: 0.4231, IoU.bottle: 0.3220, IoU.buffet: 0.4280, IoU.poster: 0.2039, IoU.stage: 0.1669, IoU.van: 0.4153, IoU.ship: 0.2492, IoU.fountain: 0.1767, IoU.conveyer belt: 0.6333, IoU.canopy: 0.4370, IoU.washer: 0.8478, IoU.plaything: 0.3192, IoU.swimming pool: 0.6256, IoU.stool: 0.3926, IoU.barrel: 0.4978, IoU.basket: 0.2954, IoU.waterfall: 0.6004, IoU.tent: 0.7595, IoU.bag: 0.0346, IoU.minibike: 0.6789, IoU.cradle: 0.7626, IoU.oven: 0.4068, IoU.ball: 0.3612, IoU.food: 0.3612, IoU.step: 0.0179, IoU.tank: 0.4907, IoU.trade name: 0.1575, IoU.microwave: 0.7784, IoU.pot: 0.4664, IoU.animal: 0.6930, IoU.bicycle: 0.4424, IoU.lake: 0.0000, IoU.dishwasher: 0.5638, IoU.screen: 0.5432, IoU.blanket: 0.1939, IoU.sculpture: 0.5658, IoU.hood: 0.6102, IoU.sconce: 0.4461, IoU.vase: 0.2884, IoU.traffic light: 0.1629, IoU.tray: 0.0795, IoU.ashcan: 0.4116, IoU.fan: 0.5167, IoU.pier: 0.2748, IoU.crt screen: 0.0014, IoU.plate: 0.4315, IoU.monitor: 0.0126, IoU.bulletin board: 0.4685, IoU.shower: 0.0000, IoU.radiator: 0.5862, IoU.glass: 0.0073, IoU.clock: 0.2814, IoU.flag: 0.5481, Acc.wall: 0.8556, Acc.building: 0.9374, Acc.sky: 0.9544, Acc.floor: 0.8908, Acc.tree: 0.8940, Acc.ceiling: 0.9328, Acc.road: 0.8885, Acc.bed : 0.9468, Acc.windowpane: 0.8234, Acc.grass: 0.9027, Acc.cabinet: 0.7095, Acc.sidewalk: 0.7888, Acc.person: 0.8907, Acc.earth: 0.4390, Acc.door: 0.7759, Acc.table: 0.6652, Acc.mountain: 0.7204, Acc.plant: 0.5695, Acc.curtain: 0.8332, Acc.chair: 0.6476, Acc.car: 0.9267, Acc.water: 0.8438, Acc.painting: 0.8195, Acc.sofa: 0.8416, Acc.shelf: 0.4689, Acc.house: 0.4074, Acc.sea: 0.7449, Acc.mirror: 0.7930, Acc.rug: 0.8598, Acc.field: 0.5448, Acc.armchair: 0.7213, Acc.seat: 0.7696, Acc.fence: 0.6768, Acc.desk: 0.7824, Acc.rock: 0.8447, Acc.wardrobe: 0.4617, Acc.lamp: 0.6140, Acc.bathtub: 0.8648, Acc.railing: 0.5097, Acc.cushion: 0.6573, Acc.base: 0.3978, Acc.box: 0.2620, Acc.column: 0.6355, Acc.signboard: 0.4369, Acc.chest of drawers: 0.6740, Acc.counter: 0.7163, Acc.sand: 0.4771, Acc.sink: 0.8103, Acc.skyscraper: 0.6704, Acc.fireplace: 0.9577, Acc.refrigerator: 0.8309, Acc.grandstand: 0.8946, Acc.path: 0.3101, Acc.stairs: 0.0722, Acc.runway: 0.9532, Acc.case: 0.9693, Acc.pool table: 0.9889, Acc.pillow: 0.8150, Acc.screen door: 0.7977, Acc.stairway: 0.6051, Acc.river: 0.2994, Acc.bridge: 0.8796, Acc.bookcase: 0.5281, Acc.blind: 0.2060, Acc.coffee table: 0.8197, Acc.toilet: 0.9121, Acc.flower: 0.3853, Acc.book: 0.7212, Acc.hill: 0.0880, Acc.bench: 0.6706, Acc.countertop: 0.7236, Acc.stove: 0.9018, Acc.palm: 0.7783, Acc.kitchen island: 0.6930, Acc.computer: 0.9255, Acc.swivel chair: 0.3032, Acc.boat: 0.8503, Acc.bar: 0.5010, Acc.arcade machine: 0.9676, Acc.hovel: 0.4107, Acc.bus: 0.9473, Acc.towel: 0.7132, Acc.light: 0.4237, Acc.truck: 0.2743, Acc.tower: 0.2900, Acc.chandelier: 0.7873, Acc.awning: 0.5234, Acc.streetlight: 0.2147, Acc.booth: 0.4116, Acc.television receiver: 0.7833, Acc.airplane: 0.7583, Acc.dirt track: 0.2030, Acc.apparel: 0.4613, Acc.pole: 0.0497, Acc.land: 0.0000, Acc.bannister: 0.0789, Acc.escalator: 0.7666, Acc.ottoman: 0.7844, Acc.bottle: 0.4075, Acc.buffet: 0.5604, Acc.poster: 0.2221, Acc.stage: 0.3576, Acc.van: 0.5881, Acc.ship: 0.2654, Acc.fountain: 0.1801, Acc.conveyer belt: 0.9406, Acc.canopy: 0.5558, Acc.washer: 0.9178, Acc.plaything: 0.5652, Acc.swimming pool: 0.9391, Acc.stool: 0.5834, Acc.barrel: 0.7008, Acc.basket: 0.3414, Acc.waterfall: 0.9713, Acc.tent: 0.9801, Acc.bag: 0.0349, Acc.minibike: 0.8074, Acc.cradle: 0.9762, Acc.oven: 0.4294, Acc.ball: 0.6116, Acc.food: 0.3875, Acc.step: 0.0189, Acc.tank: 0.7029, Acc.trade name: 0.1739, Acc.microwave: 0.9434, Acc.pot: 0.5212, Acc.animal: 0.7227, Acc.bicycle: 0.5344, Acc.lake: 0.0000, Acc.dishwasher: 0.7112, Acc.screen: 0.8862, Acc.blanket: 0.2285, Acc.sculpture: 0.5905, Acc.hood: 0.7104, Acc.sconce: 0.5619, Acc.vase: 0.3779, Acc.traffic light: 0.5822, Acc.tray: 0.1091, Acc.ashcan: 0.6066, Acc.fan: 0.6230, Acc.pier: 0.2905, Acc.crt screen: 0.0045, Acc.plate: 0.7038, Acc.monitor: 0.0136, Acc.bulletin board: 0.6223, Acc.shower: 0.0000, Acc.radiator: 0.7175, Acc.glass: 0.0073, Acc.clock: 0.2877, Acc.flag: 0.5621 +2024-06-15 23:33:28,295 - mmseg - INFO - Iter [5050/80000] lr: 3.748e-05, eta: 1 day, 7:26:44, time: 3.237, data_time: 1.878, memory: 70722, decode.loss_ce: 0.5455, decode.acc_seg: 79.9331, aux.loss_ce: 0.2167, aux.acc_seg: 80.0353, loss: 0.7622 +2024-06-15 23:34:38,913 - mmseg - INFO - Iter [5100/80000] lr: 3.745e-05, eta: 1 day, 7:24:16, time: 1.412, data_time: 0.052, memory: 70722, decode.loss_ce: 0.5116, decode.acc_seg: 80.4173, aux.loss_ce: 0.2025, aux.acc_seg: 80.5944, loss: 0.7141 +2024-06-15 23:35:47,190 - mmseg - INFO - Iter [5150/80000] lr: 3.743e-05, eta: 1 day, 7:21:16, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4973, decode.acc_seg: 81.0370, aux.loss_ce: 0.1972, aux.acc_seg: 81.1830, loss: 0.6945 +2024-06-15 23:36:55,793 - mmseg - INFO - Iter [5200/80000] lr: 3.740e-05, eta: 1 day, 7:18:23, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4871, decode.acc_seg: 81.4396, aux.loss_ce: 0.1916, aux.acc_seg: 81.8729, loss: 0.6786 +2024-06-15 23:38:04,066 - mmseg - INFO - Iter [5250/80000] lr: 3.738e-05, eta: 1 day, 7:15:27, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4928, decode.acc_seg: 80.6880, aux.loss_ce: 0.1943, aux.acc_seg: 81.2208, loss: 0.6872 +2024-06-15 23:39:12,528 - mmseg - INFO - Iter [5300/80000] lr: 3.735e-05, eta: 1 day, 7:12:36, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5044, decode.acc_seg: 80.7141, aux.loss_ce: 0.2005, aux.acc_seg: 81.0454, loss: 0.7049 +2024-06-15 23:40:20,989 - mmseg - INFO - Iter [5350/80000] lr: 3.733e-05, eta: 1 day, 7:09:47, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5098, decode.acc_seg: 80.5676, aux.loss_ce: 0.2013, aux.acc_seg: 80.8662, loss: 0.7111 +2024-06-15 23:41:29,466 - mmseg - INFO - Iter [5400/80000] lr: 3.730e-05, eta: 1 day, 7:06:59, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5146, decode.acc_seg: 80.8295, aux.loss_ce: 0.2034, aux.acc_seg: 81.0816, loss: 0.7179 +2024-06-15 23:42:38,008 - mmseg - INFO - Iter [5450/80000] lr: 3.728e-05, eta: 1 day, 7:04:15, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5379, decode.acc_seg: 79.5969, aux.loss_ce: 0.2138, aux.acc_seg: 79.8358, loss: 0.7517 +2024-06-15 23:43:46,263 - mmseg - INFO - Iter [5500/80000] lr: 3.725e-05, eta: 1 day, 7:01:28, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5233, decode.acc_seg: 80.3169, aux.loss_ce: 0.2072, aux.acc_seg: 80.7330, loss: 0.7304 +2024-06-15 23:44:54,758 - mmseg - INFO - Iter [5550/80000] lr: 3.723e-05, eta: 1 day, 6:58:46, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5374, decode.acc_seg: 79.7546, aux.loss_ce: 0.2106, aux.acc_seg: 80.0498, loss: 0.7480 +2024-06-15 23:46:03,110 - mmseg - INFO - Iter [5600/80000] lr: 3.720e-05, eta: 1 day, 6:56:05, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5063, decode.acc_seg: 80.6822, aux.loss_ce: 0.2000, aux.acc_seg: 80.9229, loss: 0.7063 +2024-06-15 23:47:11,651 - mmseg - INFO - Iter [5650/80000] lr: 3.718e-05, eta: 1 day, 6:53:27, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5194, decode.acc_seg: 80.6363, aux.loss_ce: 0.2049, aux.acc_seg: 80.7854, loss: 0.7242 +2024-06-15 23:48:20,109 - mmseg - INFO - Iter [5700/80000] lr: 3.715e-05, eta: 1 day, 6:50:49, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5297, decode.acc_seg: 80.2834, aux.loss_ce: 0.2114, aux.acc_seg: 80.5014, loss: 0.7411 +2024-06-15 23:49:28,488 - mmseg - INFO - Iter [5750/80000] lr: 3.713e-05, eta: 1 day, 6:48:13, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4835, decode.acc_seg: 81.2784, aux.loss_ce: 0.1921, aux.acc_seg: 81.2779, loss: 0.6755 +2024-06-15 23:50:36,894 - mmseg - INFO - Iter [5800/80000] lr: 3.710e-05, eta: 1 day, 6:45:38, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5056, decode.acc_seg: 80.7700, aux.loss_ce: 0.2008, aux.acc_seg: 80.8775, loss: 0.7065 +2024-06-15 23:51:45,180 - mmseg - INFO - Iter [5850/80000] lr: 3.708e-05, eta: 1 day, 6:43:03, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4941, decode.acc_seg: 80.7895, aux.loss_ce: 0.1973, aux.acc_seg: 80.8071, loss: 0.6914 +2024-06-15 23:52:53,733 - mmseg - INFO - Iter [5900/80000] lr: 3.705e-05, eta: 1 day, 6:40:33, time: 1.371, data_time: 0.011, memory: 70722, decode.loss_ce: 0.4973, decode.acc_seg: 80.9347, aux.loss_ce: 0.1990, aux.acc_seg: 80.8574, loss: 0.6963 +2024-06-15 23:54:02,072 - mmseg - INFO - Iter [5950/80000] lr: 3.703e-05, eta: 1 day, 6:38:01, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5016, decode.acc_seg: 81.2013, aux.loss_ce: 0.2008, aux.acc_seg: 81.2545, loss: 0.7025 +2024-06-15 23:55:10,332 - mmseg - INFO - Saving checkpoint at 6000 iterations +2024-06-15 23:56:36,254 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 23:56:36,254 - mmseg - INFO - Iter [6000/80000] lr: 3.700e-05, eta: 1 day, 6:53:10, time: 3.084, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5114, decode.acc_seg: 80.7034, aux.loss_ce: 0.2031, aux.acc_seg: 80.9679, loss: 0.7145 +2024-06-15 23:58:11,207 - mmseg - INFO - per class results: +2024-06-15 23:58:11,213 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 77.21 | 83.68 | +| building | 81.9 | 90.98 | +| sky | 93.59 | 95.12 | +| floor | 81.51 | 87.12 | +| tree | 74.25 | 87.32 | +| ceiling | 83.6 | 94.89 | +| road | 80.96 | 90.99 | +| bed | 89.5 | 95.89 | +| windowpane | 59.22 | 85.02 | +| grass | 66.2 | 94.28 | +| cabinet | 58.6 | 64.9 | +| sidewalk | 65.28 | 78.85 | +| person | 80.64 | 91.08 | +| earth | 34.07 | 45.02 | +| door | 52.98 | 74.88 | +| table | 57.99 | 71.71 | +| mountain | 51.47 | 68.28 | +| plant | 51.26 | 59.59 | +| curtain | 75.37 | 86.16 | +| chair | 56.39 | 67.08 | +| car | 81.55 | 92.79 | +| water | 54.28 | 79.69 | +| painting | 67.75 | 89.36 | +| sofa | 72.25 | 87.43 | +| shelf | 44.7 | 69.09 | +| house | 47.28 | 86.45 | +| sea | 60.78 | 76.5 | +| mirror | 61.46 | 91.93 | +| rug | 64.16 | 85.52 | +| field | 22.35 | 29.25 | +| armchair | 49.17 | 66.55 | +| seat | 65.4 | 87.56 | +| fence | 45.0 | 65.99 | +| desk | 48.28 | 75.33 | +| rock | 52.3 | 70.99 | +| wardrobe | 50.36 | 69.56 | +| lamp | 59.02 | 75.17 | +| bathtub | 77.78 | 83.96 | +| railing | 39.13 | 51.66 | +| cushion | 55.43 | 66.52 | +| base | 34.0 | 50.82 | +| box | 30.75 | 38.84 | +| column | 52.15 | 61.36 | +| signboard | 32.75 | 43.06 | +| chest of drawers | 43.98 | 72.9 | +| counter | 51.4 | 65.66 | +| sand | 38.04 | 54.2 | +| sink | 69.21 | 73.59 | +| skyscraper | 53.94 | 74.73 | +| fireplace | 69.22 | 90.48 | +| refrigerator | 71.75 | 86.7 | +| grandstand | 40.94 | 85.93 | +| path | 12.68 | 15.26 | +| stairs | 13.11 | 13.68 | +| runway | 71.04 | 94.57 | +| case | 47.46 | 50.14 | +| pool table | 88.99 | 98.53 | +| pillow | 62.03 | 83.98 | +| screen door | 70.79 | 88.54 | +| stairway | 36.67 | 70.17 | +| river | 24.04 | 29.73 | +| bridge | 71.59 | 82.75 | +| bookcase | 32.52 | 42.41 | +| blind | 25.87 | 26.56 | +| coffee table | 48.94 | 83.61 | +| toilet | 85.63 | 90.46 | +| flower | 33.65 | 48.93 | +| book | 46.67 | 75.47 | +| hill | 4.56 | 7.16 | +| bench | 55.58 | 67.83 | +| countertop | 54.51 | 80.07 | +| stove | 74.47 | 91.38 | +| palm | 53.81 | 74.95 | +| kitchen island | 30.44 | 94.08 | +| computer | 70.62 | 90.61 | +| swivel chair | 44.16 | 70.73 | +| boat | 65.06 | 86.58 | +| bar | 55.34 | 67.48 | +| arcade machine | 80.26 | 99.75 | +| hovel | 7.84 | 8.21 | +| bus | 83.68 | 96.7 | +| towel | 60.91 | 87.05 | +| light | 38.0 | 43.57 | +| truck | 37.31 | 47.89 | +| tower | 28.46 | 52.09 | +| chandelier | 63.09 | 82.59 | +| awning | 13.22 | 14.2 | +| streetlight | 18.58 | 22.58 | +| booth | 31.28 | 54.3 | +| television receiver | 64.36 | 82.48 | +| airplane | 60.69 | 68.47 | +| dirt track | 6.95 | 40.68 | +| apparel | 46.46 | 69.75 | +| pole | 23.37 | 33.54 | +| land | 3.6 | 4.1 | +| bannister | 7.39 | 8.88 | +| escalator | 55.25 | 85.56 | +| ottoman | 44.06 | 71.08 | +| bottle | 39.14 | 63.18 | +| buffet | 53.07 | 64.09 | +| poster | 30.33 | 56.38 | +| stage | 16.61 | 23.26 | +| van | 18.21 | 19.57 | +| ship | 0.0 | 0.0 | +| fountain | 40.1 | 42.61 | +| conveyer belt | 70.44 | 96.33 | +| canopy | 45.22 | 59.34 | +| washer | 80.12 | 87.25 | +| plaything | 28.14 | 65.99 | +| swimming pool | 49.08 | 95.28 | +| stool | 39.6 | 49.86 | +| barrel | 55.82 | 66.11 | +| basket | 29.06 | 49.29 | +| waterfall | 47.22 | 50.86 | +| tent | 87.08 | 99.32 | +| bag | 25.49 | 32.64 | +| minibike | 66.98 | 76.44 | +| cradle | 81.74 | 98.23 | +| oven | 56.09 | 68.42 | +| ball | 35.51 | 66.55 | +| food | 62.75 | 72.21 | +| step | 6.66 | 7.31 | +| tank | 54.94 | 69.69 | +| trade name | 30.67 | 56.33 | +| microwave | 81.86 | 93.29 | +| pot | 42.16 | 47.03 | +| animal | 63.83 | 65.41 | +| bicycle | 48.32 | 61.68 | +| lake | 0.0 | 0.0 | +| dishwasher | 54.01 | 57.51 | +| screen | 59.2 | 93.69 | +| blanket | 15.46 | 17.32 | +| sculpture | 48.0 | 80.16 | +| hood | 61.18 | 77.74 | +| sconce | 35.46 | 43.28 | +| vase | 30.59 | 57.56 | +| traffic light | 24.68 | 35.77 | +| tray | 10.11 | 12.59 | +| ashcan | 46.89 | 54.77 | +| fan | 55.84 | 65.01 | +| pier | 30.26 | 31.04 | +| crt screen | 1.41 | 4.0 | +| plate | 51.93 | 69.58 | +| monitor | 7.96 | 9.29 | +| bulletin board | 55.76 | 64.0 | +| shower | 0.0 | 0.0 | +| radiator | 56.24 | 71.81 | +| glass | 8.1 | 8.47 | +| clock | 35.77 | 41.23 | +| flag | 66.95 | 71.54 | ++---------------------+-------+-------+ +2024-06-15 23:58:11,214 - mmseg - INFO - Summary: +2024-06-15 23:58:11,214 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 82.57 | 48.5 | 63.21 | ++-------+------+-------+ +2024-06-15 23:58:11,214 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 23:58:11,215 - mmseg - INFO - Iter(val) [250] aAcc: 0.8257, mIoU: 0.4850, mAcc: 0.6321, IoU.wall: 0.7721, IoU.building: 0.8190, IoU.sky: 0.9359, IoU.floor: 0.8151, IoU.tree: 0.7425, IoU.ceiling: 0.8360, IoU.road: 0.8096, IoU.bed : 0.8950, IoU.windowpane: 0.5922, IoU.grass: 0.6620, IoU.cabinet: 0.5860, IoU.sidewalk: 0.6528, IoU.person: 0.8064, IoU.earth: 0.3407, IoU.door: 0.5298, IoU.table: 0.5799, IoU.mountain: 0.5147, IoU.plant: 0.5126, IoU.curtain: 0.7537, IoU.chair: 0.5639, IoU.car: 0.8155, IoU.water: 0.5428, IoU.painting: 0.6775, IoU.sofa: 0.7225, IoU.shelf: 0.4470, IoU.house: 0.4728, IoU.sea: 0.6078, IoU.mirror: 0.6146, IoU.rug: 0.6416, IoU.field: 0.2235, IoU.armchair: 0.4917, IoU.seat: 0.6540, IoU.fence: 0.4500, IoU.desk: 0.4828, IoU.rock: 0.5230, IoU.wardrobe: 0.5036, IoU.lamp: 0.5902, IoU.bathtub: 0.7778, IoU.railing: 0.3913, IoU.cushion: 0.5543, IoU.base: 0.3400, IoU.box: 0.3075, IoU.column: 0.5215, IoU.signboard: 0.3275, IoU.chest of drawers: 0.4398, IoU.counter: 0.5140, IoU.sand: 0.3804, IoU.sink: 0.6921, IoU.skyscraper: 0.5394, IoU.fireplace: 0.6922, IoU.refrigerator: 0.7175, IoU.grandstand: 0.4094, IoU.path: 0.1268, IoU.stairs: 0.1311, IoU.runway: 0.7104, IoU.case: 0.4746, IoU.pool table: 0.8899, IoU.pillow: 0.6203, IoU.screen door: 0.7079, IoU.stairway: 0.3667, IoU.river: 0.2404, IoU.bridge: 0.7159, IoU.bookcase: 0.3252, IoU.blind: 0.2587, IoU.coffee table: 0.4894, IoU.toilet: 0.8563, IoU.flower: 0.3365, IoU.book: 0.4667, IoU.hill: 0.0456, IoU.bench: 0.5558, IoU.countertop: 0.5451, IoU.stove: 0.7447, IoU.palm: 0.5381, IoU.kitchen island: 0.3044, IoU.computer: 0.7062, IoU.swivel chair: 0.4416, IoU.boat: 0.6506, IoU.bar: 0.5534, IoU.arcade machine: 0.8026, IoU.hovel: 0.0784, IoU.bus: 0.8368, IoU.towel: 0.6091, IoU.light: 0.3800, IoU.truck: 0.3731, IoU.tower: 0.2846, IoU.chandelier: 0.6309, IoU.awning: 0.1322, IoU.streetlight: 0.1858, IoU.booth: 0.3128, IoU.television receiver: 0.6436, IoU.airplane: 0.6069, IoU.dirt track: 0.0695, IoU.apparel: 0.4646, IoU.pole: 0.2337, IoU.land: 0.0360, IoU.bannister: 0.0739, IoU.escalator: 0.5525, IoU.ottoman: 0.4406, IoU.bottle: 0.3914, IoU.buffet: 0.5307, IoU.poster: 0.3033, IoU.stage: 0.1661, IoU.van: 0.1821, IoU.ship: 0.0000, IoU.fountain: 0.4010, IoU.conveyer belt: 0.7044, IoU.canopy: 0.4522, IoU.washer: 0.8012, IoU.plaything: 0.2814, IoU.swimming pool: 0.4908, IoU.stool: 0.3960, IoU.barrel: 0.5582, IoU.basket: 0.2906, IoU.waterfall: 0.4722, IoU.tent: 0.8708, IoU.bag: 0.2549, IoU.minibike: 0.6698, IoU.cradle: 0.8174, IoU.oven: 0.5609, IoU.ball: 0.3551, IoU.food: 0.6275, IoU.step: 0.0666, IoU.tank: 0.5494, IoU.trade name: 0.3067, IoU.microwave: 0.8186, IoU.pot: 0.4216, IoU.animal: 0.6383, IoU.bicycle: 0.4832, IoU.lake: 0.0000, IoU.dishwasher: 0.5401, IoU.screen: 0.5920, IoU.blanket: 0.1546, IoU.sculpture: 0.4800, IoU.hood: 0.6118, IoU.sconce: 0.3546, IoU.vase: 0.3059, IoU.traffic light: 0.2468, IoU.tray: 0.1011, IoU.ashcan: 0.4689, IoU.fan: 0.5584, IoU.pier: 0.3026, IoU.crt screen: 0.0141, IoU.plate: 0.5193, IoU.monitor: 0.0796, IoU.bulletin board: 0.5576, IoU.shower: 0.0000, IoU.radiator: 0.5624, IoU.glass: 0.0810, IoU.clock: 0.3577, IoU.flag: 0.6695, Acc.wall: 0.8368, Acc.building: 0.9098, Acc.sky: 0.9512, Acc.floor: 0.8712, Acc.tree: 0.8732, Acc.ceiling: 0.9489, Acc.road: 0.9099, Acc.bed : 0.9589, Acc.windowpane: 0.8502, Acc.grass: 0.9428, Acc.cabinet: 0.6490, Acc.sidewalk: 0.7885, Acc.person: 0.9108, Acc.earth: 0.4502, Acc.door: 0.7488, Acc.table: 0.7171, Acc.mountain: 0.6828, Acc.plant: 0.5959, Acc.curtain: 0.8616, Acc.chair: 0.6708, Acc.car: 0.9279, Acc.water: 0.7969, Acc.painting: 0.8936, Acc.sofa: 0.8743, Acc.shelf: 0.6909, Acc.house: 0.8645, Acc.sea: 0.7650, Acc.mirror: 0.9193, Acc.rug: 0.8552, Acc.field: 0.2925, Acc.armchair: 0.6655, Acc.seat: 0.8756, Acc.fence: 0.6599, Acc.desk: 0.7533, Acc.rock: 0.7099, Acc.wardrobe: 0.6956, Acc.lamp: 0.7517, Acc.bathtub: 0.8396, Acc.railing: 0.5166, Acc.cushion: 0.6652, Acc.base: 0.5082, Acc.box: 0.3884, Acc.column: 0.6136, Acc.signboard: 0.4306, Acc.chest of drawers: 0.7290, Acc.counter: 0.6566, Acc.sand: 0.5420, Acc.sink: 0.7359, Acc.skyscraper: 0.7473, Acc.fireplace: 0.9048, Acc.refrigerator: 0.8670, Acc.grandstand: 0.8593, Acc.path: 0.1526, Acc.stairs: 0.1368, Acc.runway: 0.9457, Acc.case: 0.5014, Acc.pool table: 0.9853, Acc.pillow: 0.8398, Acc.screen door: 0.8854, Acc.stairway: 0.7017, Acc.river: 0.2973, Acc.bridge: 0.8275, Acc.bookcase: 0.4241, Acc.blind: 0.2656, Acc.coffee table: 0.8361, Acc.toilet: 0.9046, Acc.flower: 0.4893, Acc.book: 0.7547, Acc.hill: 0.0716, Acc.bench: 0.6783, Acc.countertop: 0.8007, Acc.stove: 0.9138, Acc.palm: 0.7495, Acc.kitchen island: 0.9408, Acc.computer: 0.9061, Acc.swivel chair: 0.7073, Acc.boat: 0.8658, Acc.bar: 0.6748, Acc.arcade machine: 0.9975, Acc.hovel: 0.0821, Acc.bus: 0.9670, Acc.towel: 0.8705, Acc.light: 0.4357, Acc.truck: 0.4789, Acc.tower: 0.5209, Acc.chandelier: 0.8259, Acc.awning: 0.1420, Acc.streetlight: 0.2258, Acc.booth: 0.5430, Acc.television receiver: 0.8248, Acc.airplane: 0.6847, Acc.dirt track: 0.4068, Acc.apparel: 0.6975, Acc.pole: 0.3354, Acc.land: 0.0410, Acc.bannister: 0.0888, Acc.escalator: 0.8556, Acc.ottoman: 0.7108, Acc.bottle: 0.6318, Acc.buffet: 0.6409, Acc.poster: 0.5638, Acc.stage: 0.2326, Acc.van: 0.1957, Acc.ship: 0.0000, Acc.fountain: 0.4261, Acc.conveyer belt: 0.9633, Acc.canopy: 0.5934, Acc.washer: 0.8725, Acc.plaything: 0.6599, Acc.swimming pool: 0.9528, Acc.stool: 0.4986, Acc.barrel: 0.6611, Acc.basket: 0.4929, Acc.waterfall: 0.5086, Acc.tent: 0.9932, Acc.bag: 0.3264, Acc.minibike: 0.7644, Acc.cradle: 0.9823, Acc.oven: 0.6842, Acc.ball: 0.6655, Acc.food: 0.7221, Acc.step: 0.0731, Acc.tank: 0.6969, Acc.trade name: 0.5633, Acc.microwave: 0.9329, Acc.pot: 0.4703, Acc.animal: 0.6541, Acc.bicycle: 0.6168, Acc.lake: 0.0000, Acc.dishwasher: 0.5751, Acc.screen: 0.9369, Acc.blanket: 0.1732, Acc.sculpture: 0.8016, Acc.hood: 0.7774, Acc.sconce: 0.4328, Acc.vase: 0.5756, Acc.traffic light: 0.3577, Acc.tray: 0.1259, Acc.ashcan: 0.5477, Acc.fan: 0.6501, Acc.pier: 0.3104, Acc.crt screen: 0.0400, Acc.plate: 0.6958, Acc.monitor: 0.0929, Acc.bulletin board: 0.6400, Acc.shower: 0.0000, Acc.radiator: 0.7181, Acc.glass: 0.0847, Acc.clock: 0.4123, Acc.flag: 0.7154 +2024-06-15 23:59:19,990 - mmseg - INFO - Iter [6050/80000] lr: 3.698e-05, eta: 1 day, 7:09:58, time: 3.275, data_time: 1.915, memory: 70722, decode.loss_ce: 0.5162, decode.acc_seg: 80.3086, aux.loss_ce: 0.2054, aux.acc_seg: 80.4632, loss: 0.7216 +2024-06-16 00:00:28,447 - mmseg - INFO - Iter [6100/80000] lr: 3.695e-05, eta: 1 day, 7:07:12, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5140, decode.acc_seg: 80.3508, aux.loss_ce: 0.2027, aux.acc_seg: 80.8200, loss: 0.7167 +2024-06-16 00:01:36,693 - mmseg - INFO - Iter [6150/80000] lr: 3.693e-05, eta: 1 day, 7:04:26, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5138, decode.acc_seg: 80.8583, aux.loss_ce: 0.2040, aux.acc_seg: 80.6937, loss: 0.7178 +2024-06-16 00:02:45,171 - mmseg - INFO - Iter [6200/80000] lr: 3.690e-05, eta: 1 day, 7:01:44, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5195, decode.acc_seg: 80.1912, aux.loss_ce: 0.2047, aux.acc_seg: 80.4706, loss: 0.7243 +2024-06-16 00:03:53,426 - mmseg - INFO - Iter [6250/80000] lr: 3.688e-05, eta: 1 day, 6:59:00, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5344, decode.acc_seg: 80.2758, aux.loss_ce: 0.2126, aux.acc_seg: 80.3708, loss: 0.7470 +2024-06-16 00:05:01,733 - mmseg - INFO - Iter [6300/80000] lr: 3.685e-05, eta: 1 day, 6:56:19, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5466, decode.acc_seg: 80.4435, aux.loss_ce: 0.2142, aux.acc_seg: 80.7806, loss: 0.7608 +2024-06-16 00:06:12,373 - mmseg - INFO - Iter [6350/80000] lr: 3.683e-05, eta: 1 day, 6:54:07, time: 1.413, data_time: 0.051, memory: 70722, decode.loss_ce: 0.4819, decode.acc_seg: 81.6722, aux.loss_ce: 0.1912, aux.acc_seg: 81.8852, loss: 0.6731 +2024-06-16 00:07:20,792 - mmseg - INFO - Iter [6400/80000] lr: 3.680e-05, eta: 1 day, 6:51:29, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4730, decode.acc_seg: 81.6505, aux.loss_ce: 0.1899, aux.acc_seg: 81.5864, loss: 0.6629 +2024-06-16 00:08:29,387 - mmseg - INFO - Iter [6450/80000] lr: 3.678e-05, eta: 1 day, 6:48:56, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4918, decode.acc_seg: 82.0871, aux.loss_ce: 0.1956, aux.acc_seg: 82.3193, loss: 0.6874 +2024-06-16 00:09:37,735 - mmseg - INFO - Iter [6500/80000] lr: 3.675e-05, eta: 1 day, 6:46:20, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4708, decode.acc_seg: 82.1349, aux.loss_ce: 0.1885, aux.acc_seg: 82.2616, loss: 0.6593 +2024-06-16 00:10:45,975 - mmseg - INFO - Iter [6550/80000] lr: 3.673e-05, eta: 1 day, 6:43:45, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4627, decode.acc_seg: 82.1025, aux.loss_ce: 0.1857, aux.acc_seg: 82.0652, loss: 0.6484 +2024-06-16 00:11:54,379 - mmseg - INFO - Iter [6600/80000] lr: 3.670e-05, eta: 1 day, 6:41:13, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4814, decode.acc_seg: 81.3803, aux.loss_ce: 0.1923, aux.acc_seg: 81.5111, loss: 0.6736 +2024-06-16 00:13:02,654 - mmseg - INFO - Iter [6650/80000] lr: 3.668e-05, eta: 1 day, 6:38:41, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4699, decode.acc_seg: 81.6173, aux.loss_ce: 0.1871, aux.acc_seg: 81.8045, loss: 0.6570 +2024-06-16 00:14:11,222 - mmseg - INFO - Iter [6700/80000] lr: 3.665e-05, eta: 1 day, 6:36:13, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4673, decode.acc_seg: 82.7730, aux.loss_ce: 0.1868, aux.acc_seg: 82.5664, loss: 0.6541 +2024-06-16 00:15:19,544 - mmseg - INFO - Iter [6750/80000] lr: 3.663e-05, eta: 1 day, 6:33:44, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4732, decode.acc_seg: 81.6246, aux.loss_ce: 0.1874, aux.acc_seg: 81.9307, loss: 0.6606 +2024-06-16 00:16:27,863 - mmseg - INFO - Iter [6800/80000] lr: 3.660e-05, eta: 1 day, 6:31:15, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4644, decode.acc_seg: 82.0097, aux.loss_ce: 0.1847, aux.acc_seg: 82.0700, loss: 0.6491 +2024-06-16 00:17:36,129 - mmseg - INFO - Iter [6850/80000] lr: 3.658e-05, eta: 1 day, 6:28:48, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4965, decode.acc_seg: 81.2610, aux.loss_ce: 0.1989, aux.acc_seg: 81.3460, loss: 0.6955 +2024-06-16 00:18:44,428 - mmseg - INFO - Iter [6900/80000] lr: 3.655e-05, eta: 1 day, 6:26:22, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4708, decode.acc_seg: 81.6605, aux.loss_ce: 0.1856, aux.acc_seg: 81.9489, loss: 0.6564 +2024-06-16 00:19:52,911 - mmseg - INFO - Iter [6950/80000] lr: 3.653e-05, eta: 1 day, 6:23:59, time: 1.370, data_time: 0.009, memory: 70722, decode.loss_ce: 0.4716, decode.acc_seg: 82.2803, aux.loss_ce: 0.1889, aux.acc_seg: 82.3308, loss: 0.6605 +2024-06-16 00:21:01,459 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 00:21:01,459 - mmseg - INFO - Iter [7000/80000] lr: 3.650e-05, eta: 1 day, 6:21:38, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4552, decode.acc_seg: 82.4198, aux.loss_ce: 0.1817, aux.acc_seg: 82.5740, loss: 0.6369 +2024-06-16 00:22:36,078 - mmseg - INFO - per class results: +2024-06-16 00:22:36,084 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 78.43 | 85.86 | +| building | 83.81 | 91.25 | +| sky | 94.2 | 96.82 | +| floor | 81.92 | 89.46 | +| tree | 74.93 | 90.59 | +| ceiling | 83.98 | 94.36 | +| road | 83.82 | 90.6 | +| bed | 89.45 | 95.98 | +| windowpane | 63.26 | 80.77 | +| grass | 69.66 | 82.73 | +| cabinet | 59.72 | 69.51 | +| sidewalk | 64.37 | 81.32 | +| person | 81.75 | 90.17 | +| earth | 33.06 | 41.76 | +| door | 55.08 | 71.3 | +| table | 60.41 | 74.55 | +| mountain | 54.97 | 63.16 | +| plant | 56.57 | 70.4 | +| curtain | 76.4 | 90.55 | +| chair | 59.64 | 72.92 | +| car | 84.3 | 92.97 | +| water | 55.56 | 76.07 | +| painting | 75.83 | 84.82 | +| sofa | 74.2 | 89.99 | +| shelf | 37.53 | 49.59 | +| house | 54.88 | 78.44 | +| sea | 66.91 | 94.93 | +| mirror | 71.32 | 80.98 | +| rug | 65.68 | 85.62 | +| field | 35.24 | 64.31 | +| armchair | 48.78 | 62.44 | +| seat | 66.24 | 89.1 | +| fence | 46.32 | 66.63 | +| desk | 48.26 | 76.08 | +| rock | 55.57 | 79.36 | +| wardrobe | 49.39 | 86.75 | +| lamp | 62.58 | 79.05 | +| bathtub | 79.81 | 83.53 | +| railing | 37.06 | 51.17 | +| cushion | 61.59 | 80.84 | +| base | 29.71 | 38.69 | +| box | 30.97 | 44.86 | +| column | 52.49 | 61.7 | +| signboard | 35.64 | 43.02 | +| chest of drawers | 47.64 | 61.7 | +| counter | 42.07 | 44.53 | +| sand | 51.66 | 85.83 | +| sink | 49.39 | 52.87 | +| skyscraper | 48.71 | 70.14 | +| fireplace | 62.31 | 97.08 | +| refrigerator | 72.11 | 88.9 | +| grandstand | 47.32 | 88.23 | +| path | 18.47 | 32.43 | +| stairs | 14.43 | 17.58 | +| runway | 66.59 | 92.82 | +| case | 58.82 | 85.33 | +| pool table | 84.59 | 98.8 | +| pillow | 44.8 | 46.9 | +| screen door | 68.51 | 88.72 | +| stairway | 36.01 | 75.91 | +| river | 23.31 | 27.68 | +| bridge | 50.56 | 92.0 | +| bookcase | 27.54 | 69.95 | +| blind | 38.35 | 39.73 | +| coffee table | 54.51 | 85.16 | +| toilet | 67.24 | 95.29 | +| flower | 39.87 | 52.09 | +| book | 44.59 | 66.3 | +| hill | 5.6 | 5.92 | +| bench | 57.07 | 73.7 | +| countertop | 59.4 | 64.18 | +| stove | 76.74 | 86.67 | +| palm | 54.05 | 78.82 | +| kitchen island | 42.54 | 83.12 | +| computer | 68.24 | 91.84 | +| swivel chair | 46.1 | 62.26 | +| boat | 53.33 | 91.76 | +| bar | 57.26 | 75.04 | +| arcade machine | 77.08 | 79.86 | +| hovel | 19.59 | 22.43 | +| bus | 87.29 | 95.79 | +| towel | 64.74 | 70.73 | +| light | 42.58 | 46.6 | +| truck | 35.82 | 52.7 | +| tower | 7.16 | 9.05 | +| chandelier | 63.9 | 76.11 | +| awning | 34.58 | 40.64 | +| streetlight | 26.6 | 43.03 | +| booth | 38.1 | 59.26 | +| television receiver | 64.28 | 80.91 | +| airplane | 66.45 | 80.43 | +| dirt track | 7.69 | 29.26 | +| apparel | 34.05 | 39.67 | +| pole | 21.64 | 31.44 | +| land | 5.97 | 8.59 | +| bannister | 14.06 | 23.31 | +| escalator | 52.17 | 81.25 | +| ottoman | 51.0 | 71.38 | +| bottle | 38.35 | 64.55 | +| buffet | 45.07 | 57.94 | +| poster | 29.27 | 48.37 | +| stage | 16.06 | 34.75 | +| van | 31.05 | 35.12 | +| ship | 32.43 | 32.46 | +| fountain | 25.38 | 28.7 | +| conveyer belt | 44.95 | 98.89 | +| canopy | 41.62 | 74.37 | +| washer | 76.89 | 87.03 | +| plaything | 37.04 | 57.31 | +| swimming pool | 65.96 | 83.3 | +| stool | 42.61 | 55.24 | +| barrel | 53.28 | 65.02 | +| basket | 30.73 | 46.67 | +| waterfall | 16.29 | 16.44 | +| tent | 92.2 | 96.5 | +| bag | 17.79 | 20.25 | +| minibike | 68.5 | 85.92 | +| cradle | 75.34 | 99.05 | +| oven | 41.35 | 46.23 | +| ball | 45.7 | 69.36 | +| food | 50.18 | 63.46 | +| step | 9.11 | 9.89 | +| tank | 44.72 | 73.16 | +| trade name | 29.3 | 35.29 | +| microwave | 81.85 | 94.19 | +| pot | 49.22 | 58.96 | +| animal | 69.55 | 73.04 | +| bicycle | 44.26 | 82.85 | +| lake | 0.86 | 0.93 | +| dishwasher | 63.6 | 68.84 | +| screen | 60.21 | 91.13 | +| blanket | 21.23 | 25.16 | +| sculpture | 52.61 | 67.22 | +| hood | 64.09 | 68.72 | +| sconce | 44.84 | 55.34 | +| vase | 32.85 | 44.38 | +| traffic light | 24.45 | 35.55 | +| tray | 5.87 | 6.86 | +| ashcan | 44.56 | 66.24 | +| fan | 60.33 | 72.54 | +| pier | 31.55 | 36.45 | +| crt screen | 0.12 | 0.12 | +| plate | 50.35 | 58.58 | +| monitor | 61.73 | 82.8 | +| bulletin board | 49.05 | 81.31 | +| shower | 0.0 | 0.0 | +| radiator | 56.16 | 74.01 | +| glass | 4.88 | 4.96 | +| clock | 31.5 | 35.5 | +| flag | 65.58 | 75.3 | ++---------------------+-------+-------+ +2024-06-16 00:22:36,084 - mmseg - INFO - Summary: +2024-06-16 00:22:36,084 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 83.53 | 49.32 | 64.15 | ++-------+-------+-------+ +2024-06-16 00:22:36,085 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 00:22:36,085 - mmseg - INFO - Iter(val) [250] aAcc: 0.8353, mIoU: 0.4932, mAcc: 0.6415, IoU.wall: 0.7843, IoU.building: 0.8381, IoU.sky: 0.9420, IoU.floor: 0.8192, IoU.tree: 0.7493, IoU.ceiling: 0.8398, IoU.road: 0.8382, IoU.bed : 0.8945, IoU.windowpane: 0.6326, IoU.grass: 0.6966, IoU.cabinet: 0.5972, IoU.sidewalk: 0.6437, IoU.person: 0.8175, IoU.earth: 0.3306, IoU.door: 0.5508, IoU.table: 0.6041, IoU.mountain: 0.5497, IoU.plant: 0.5657, IoU.curtain: 0.7640, IoU.chair: 0.5964, IoU.car: 0.8430, IoU.water: 0.5556, IoU.painting: 0.7583, IoU.sofa: 0.7420, IoU.shelf: 0.3753, IoU.house: 0.5488, IoU.sea: 0.6691, IoU.mirror: 0.7132, IoU.rug: 0.6568, IoU.field: 0.3524, IoU.armchair: 0.4878, IoU.seat: 0.6624, IoU.fence: 0.4632, IoU.desk: 0.4826, IoU.rock: 0.5557, IoU.wardrobe: 0.4939, IoU.lamp: 0.6258, IoU.bathtub: 0.7981, IoU.railing: 0.3706, IoU.cushion: 0.6159, IoU.base: 0.2971, IoU.box: 0.3097, IoU.column: 0.5249, IoU.signboard: 0.3564, IoU.chest of drawers: 0.4764, IoU.counter: 0.4207, IoU.sand: 0.5166, IoU.sink: 0.4939, IoU.skyscraper: 0.4871, IoU.fireplace: 0.6231, IoU.refrigerator: 0.7211, IoU.grandstand: 0.4732, IoU.path: 0.1847, IoU.stairs: 0.1443, IoU.runway: 0.6659, IoU.case: 0.5882, IoU.pool table: 0.8459, IoU.pillow: 0.4480, IoU.screen door: 0.6851, IoU.stairway: 0.3601, IoU.river: 0.2331, IoU.bridge: 0.5056, IoU.bookcase: 0.2754, IoU.blind: 0.3835, IoU.coffee table: 0.5451, IoU.toilet: 0.6724, IoU.flower: 0.3987, IoU.book: 0.4459, IoU.hill: 0.0560, IoU.bench: 0.5707, IoU.countertop: 0.5940, IoU.stove: 0.7674, IoU.palm: 0.5405, IoU.kitchen island: 0.4254, IoU.computer: 0.6824, IoU.swivel chair: 0.4610, IoU.boat: 0.5333, IoU.bar: 0.5726, IoU.arcade machine: 0.7708, IoU.hovel: 0.1959, IoU.bus: 0.8729, IoU.towel: 0.6474, IoU.light: 0.4258, IoU.truck: 0.3582, IoU.tower: 0.0716, IoU.chandelier: 0.6390, IoU.awning: 0.3458, IoU.streetlight: 0.2660, IoU.booth: 0.3810, IoU.television receiver: 0.6428, IoU.airplane: 0.6645, IoU.dirt track: 0.0769, IoU.apparel: 0.3405, IoU.pole: 0.2164, IoU.land: 0.0597, IoU.bannister: 0.1406, IoU.escalator: 0.5217, IoU.ottoman: 0.5100, IoU.bottle: 0.3835, IoU.buffet: 0.4507, IoU.poster: 0.2927, IoU.stage: 0.1606, IoU.van: 0.3105, IoU.ship: 0.3243, IoU.fountain: 0.2538, IoU.conveyer belt: 0.4495, IoU.canopy: 0.4162, IoU.washer: 0.7689, IoU.plaything: 0.3704, IoU.swimming pool: 0.6596, IoU.stool: 0.4261, IoU.barrel: 0.5328, IoU.basket: 0.3073, IoU.waterfall: 0.1629, IoU.tent: 0.9220, IoU.bag: 0.1779, IoU.minibike: 0.6850, IoU.cradle: 0.7534, IoU.oven: 0.4135, IoU.ball: 0.4570, IoU.food: 0.5018, IoU.step: 0.0911, IoU.tank: 0.4472, IoU.trade name: 0.2930, IoU.microwave: 0.8185, IoU.pot: 0.4922, IoU.animal: 0.6955, IoU.bicycle: 0.4426, IoU.lake: 0.0086, IoU.dishwasher: 0.6360, IoU.screen: 0.6021, IoU.blanket: 0.2123, IoU.sculpture: 0.5261, IoU.hood: 0.6409, IoU.sconce: 0.4484, IoU.vase: 0.3285, IoU.traffic light: 0.2445, IoU.tray: 0.0587, IoU.ashcan: 0.4456, IoU.fan: 0.6033, IoU.pier: 0.3155, IoU.crt screen: 0.0012, IoU.plate: 0.5035, IoU.monitor: 0.6173, IoU.bulletin board: 0.4905, IoU.shower: 0.0000, IoU.radiator: 0.5616, IoU.glass: 0.0488, IoU.clock: 0.3150, IoU.flag: 0.6558, Acc.wall: 0.8586, Acc.building: 0.9125, Acc.sky: 0.9682, Acc.floor: 0.8946, Acc.tree: 0.9059, Acc.ceiling: 0.9436, Acc.road: 0.9060, Acc.bed : 0.9598, Acc.windowpane: 0.8077, Acc.grass: 0.8273, Acc.cabinet: 0.6951, Acc.sidewalk: 0.8132, Acc.person: 0.9017, Acc.earth: 0.4176, Acc.door: 0.7130, Acc.table: 0.7455, Acc.mountain: 0.6316, Acc.plant: 0.7040, Acc.curtain: 0.9055, Acc.chair: 0.7292, Acc.car: 0.9297, Acc.water: 0.7607, Acc.painting: 0.8482, Acc.sofa: 0.8999, Acc.shelf: 0.4959, Acc.house: 0.7844, Acc.sea: 0.9493, Acc.mirror: 0.8098, Acc.rug: 0.8562, Acc.field: 0.6431, Acc.armchair: 0.6244, Acc.seat: 0.8910, Acc.fence: 0.6663, Acc.desk: 0.7608, Acc.rock: 0.7936, Acc.wardrobe: 0.8675, Acc.lamp: 0.7905, Acc.bathtub: 0.8353, Acc.railing: 0.5117, Acc.cushion: 0.8084, Acc.base: 0.3869, Acc.box: 0.4486, Acc.column: 0.6170, Acc.signboard: 0.4302, Acc.chest of drawers: 0.6170, Acc.counter: 0.4453, Acc.sand: 0.8583, Acc.sink: 0.5287, Acc.skyscraper: 0.7014, Acc.fireplace: 0.9708, Acc.refrigerator: 0.8890, Acc.grandstand: 0.8823, Acc.path: 0.3243, Acc.stairs: 0.1758, Acc.runway: 0.9282, Acc.case: 0.8533, Acc.pool table: 0.9880, Acc.pillow: 0.4690, Acc.screen door: 0.8872, Acc.stairway: 0.7591, Acc.river: 0.2768, Acc.bridge: 0.9200, Acc.bookcase: 0.6995, Acc.blind: 0.3973, Acc.coffee table: 0.8516, Acc.toilet: 0.9529, Acc.flower: 0.5209, Acc.book: 0.6630, Acc.hill: 0.0592, Acc.bench: 0.7370, Acc.countertop: 0.6418, Acc.stove: 0.8667, Acc.palm: 0.7882, Acc.kitchen island: 0.8312, Acc.computer: 0.9184, Acc.swivel chair: 0.6226, Acc.boat: 0.9176, Acc.bar: 0.7504, Acc.arcade machine: 0.7986, Acc.hovel: 0.2243, Acc.bus: 0.9579, Acc.towel: 0.7073, Acc.light: 0.4660, Acc.truck: 0.5270, Acc.tower: 0.0905, Acc.chandelier: 0.7611, Acc.awning: 0.4064, Acc.streetlight: 0.4303, Acc.booth: 0.5926, Acc.television receiver: 0.8091, Acc.airplane: 0.8043, Acc.dirt track: 0.2926, Acc.apparel: 0.3967, Acc.pole: 0.3144, Acc.land: 0.0859, Acc.bannister: 0.2331, Acc.escalator: 0.8125, Acc.ottoman: 0.7138, Acc.bottle: 0.6455, Acc.buffet: 0.5794, Acc.poster: 0.4837, Acc.stage: 0.3475, Acc.van: 0.3512, Acc.ship: 0.3246, Acc.fountain: 0.2870, Acc.conveyer belt: 0.9889, Acc.canopy: 0.7437, Acc.washer: 0.8703, Acc.plaything: 0.5731, Acc.swimming pool: 0.8330, Acc.stool: 0.5524, Acc.barrel: 0.6502, Acc.basket: 0.4667, Acc.waterfall: 0.1644, Acc.tent: 0.9650, Acc.bag: 0.2025, Acc.minibike: 0.8592, Acc.cradle: 0.9905, Acc.oven: 0.4623, Acc.ball: 0.6936, Acc.food: 0.6346, Acc.step: 0.0989, Acc.tank: 0.7316, Acc.trade name: 0.3529, Acc.microwave: 0.9419, Acc.pot: 0.5896, Acc.animal: 0.7304, Acc.bicycle: 0.8285, Acc.lake: 0.0093, Acc.dishwasher: 0.6884, Acc.screen: 0.9113, Acc.blanket: 0.2516, Acc.sculpture: 0.6722, Acc.hood: 0.6872, Acc.sconce: 0.5534, Acc.vase: 0.4438, Acc.traffic light: 0.3555, Acc.tray: 0.0686, Acc.ashcan: 0.6624, Acc.fan: 0.7254, Acc.pier: 0.3645, Acc.crt screen: 0.0012, Acc.plate: 0.5858, Acc.monitor: 0.8280, Acc.bulletin board: 0.8131, Acc.shower: 0.0000, Acc.radiator: 0.7401, Acc.glass: 0.0496, Acc.clock: 0.3550, Acc.flag: 0.7530 +2024-06-16 00:23:45,013 - mmseg - INFO - Iter [7050/80000] lr: 3.648e-05, eta: 1 day, 6:35:41, time: 3.271, data_time: 1.908, memory: 70722, decode.loss_ce: 0.4536, decode.acc_seg: 83.1639, aux.loss_ce: 0.1818, aux.acc_seg: 83.1746, loss: 0.6355 +2024-06-16 00:24:53,610 - mmseg - INFO - Iter [7100/80000] lr: 3.645e-05, eta: 1 day, 6:33:14, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4803, decode.acc_seg: 81.6822, aux.loss_ce: 0.1918, aux.acc_seg: 81.7450, loss: 0.6721 +2024-06-16 00:26:02,049 - mmseg - INFO - Iter [7150/80000] lr: 3.643e-05, eta: 1 day, 6:30:48, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4874, decode.acc_seg: 80.9591, aux.loss_ce: 0.1946, aux.acc_seg: 80.9824, loss: 0.6820 +2024-06-16 00:27:10,358 - mmseg - INFO - Iter [7200/80000] lr: 3.640e-05, eta: 1 day, 6:28:20, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4752, decode.acc_seg: 82.0420, aux.loss_ce: 0.1886, aux.acc_seg: 82.0124, loss: 0.6638 +2024-06-16 00:28:18,924 - mmseg - INFO - Iter [7250/80000] lr: 3.638e-05, eta: 1 day, 6:25:57, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4561, decode.acc_seg: 82.4521, aux.loss_ce: 0.1815, aux.acc_seg: 82.4584, loss: 0.6376 +2024-06-16 00:29:27,121 - mmseg - INFO - Iter [7300/80000] lr: 3.635e-05, eta: 1 day, 6:23:31, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5008, decode.acc_seg: 81.3104, aux.loss_ce: 0.1971, aux.acc_seg: 81.4970, loss: 0.6978 +2024-06-16 00:30:35,522 - mmseg - INFO - Iter [7350/80000] lr: 3.633e-05, eta: 1 day, 6:21:08, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4909, decode.acc_seg: 81.9788, aux.loss_ce: 0.1956, aux.acc_seg: 82.1807, loss: 0.6865 +2024-06-16 00:31:43,951 - mmseg - INFO - Iter [7400/80000] lr: 3.630e-05, eta: 1 day, 6:18:47, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.5064, decode.acc_seg: 80.4417, aux.loss_ce: 0.2010, aux.acc_seg: 80.4654, loss: 0.7073 +2024-06-16 00:32:52,444 - mmseg - INFO - Iter [7450/80000] lr: 3.628e-05, eta: 1 day, 6:16:26, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4779, decode.acc_seg: 81.8033, aux.loss_ce: 0.1886, aux.acc_seg: 81.9726, loss: 0.6665 +2024-06-16 00:34:00,684 - mmseg - INFO - Iter [7500/80000] lr: 3.625e-05, eta: 1 day, 6:14:05, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4778, decode.acc_seg: 81.2918, aux.loss_ce: 0.1888, aux.acc_seg: 81.5343, loss: 0.6666 +2024-06-16 00:35:09,158 - mmseg - INFO - Iter [7550/80000] lr: 3.623e-05, eta: 1 day, 6:11:47, time: 1.369, data_time: 0.011, memory: 70722, decode.loss_ce: 0.4580, decode.acc_seg: 82.0478, aux.loss_ce: 0.1819, aux.acc_seg: 82.1002, loss: 0.6399 +2024-06-16 00:36:19,608 - mmseg - INFO - Iter [7600/80000] lr: 3.620e-05, eta: 1 day, 6:09:48, time: 1.409, data_time: 0.052, memory: 70722, decode.loss_ce: 0.4711, decode.acc_seg: 82.1840, aux.loss_ce: 0.1870, aux.acc_seg: 82.2331, loss: 0.6581 +2024-06-16 00:37:28,007 - mmseg - INFO - Iter [7650/80000] lr: 3.618e-05, eta: 1 day, 6:07:31, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4525, decode.acc_seg: 82.1180, aux.loss_ce: 0.1822, aux.acc_seg: 82.0937, loss: 0.6347 +2024-06-16 00:38:36,532 - mmseg - INFO - Iter [7700/80000] lr: 3.615e-05, eta: 1 day, 6:05:15, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4326, decode.acc_seg: 82.6977, aux.loss_ce: 0.1732, aux.acc_seg: 82.8138, loss: 0.6058 +2024-06-16 00:39:44,944 - mmseg - INFO - Iter [7750/80000] lr: 3.613e-05, eta: 1 day, 6:03:00, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4252, decode.acc_seg: 83.7087, aux.loss_ce: 0.1705, aux.acc_seg: 83.5401, loss: 0.5957 +2024-06-16 00:40:53,389 - mmseg - INFO - Iter [7800/80000] lr: 3.610e-05, eta: 1 day, 6:00:46, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4318, decode.acc_seg: 83.1098, aux.loss_ce: 0.1717, aux.acc_seg: 83.1312, loss: 0.6034 +2024-06-16 00:42:01,486 - mmseg - INFO - Iter [7850/80000] lr: 3.608e-05, eta: 1 day, 5:58:29, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4461, decode.acc_seg: 82.8939, aux.loss_ce: 0.1762, aux.acc_seg: 82.9449, loss: 0.6222 +2024-06-16 00:43:09,961 - mmseg - INFO - Iter [7900/80000] lr: 3.605e-05, eta: 1 day, 5:56:17, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4907, decode.acc_seg: 81.7635, aux.loss_ce: 0.1943, aux.acc_seg: 81.8272, loss: 0.6851 +2024-06-16 00:44:18,119 - mmseg - INFO - Iter [7950/80000] lr: 3.603e-05, eta: 1 day, 5:54:02, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4433, decode.acc_seg: 82.8061, aux.loss_ce: 0.1765, aux.acc_seg: 82.8257, loss: 0.6198 +2024-06-16 00:45:26,647 - mmseg - INFO - Saving checkpoint at 8000 iterations +2024-06-16 00:46:53,797 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 00:46:53,797 - mmseg - INFO - Iter [8000/80000] lr: 3.600e-05, eta: 1 day, 6:04:56, time: 3.114, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4694, decode.acc_seg: 81.6538, aux.loss_ce: 0.1862, aux.acc_seg: 81.8423, loss: 0.6555 +2024-06-16 00:48:28,287 - mmseg - INFO - per class results: +2024-06-16 00:48:28,293 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 78.52 | 88.29 | +| building | 83.82 | 92.95 | +| sky | 94.35 | 97.29 | +| floor | 82.22 | 89.96 | +| tree | 76.08 | 90.35 | +| ceiling | 85.02 | 90.54 | +| road | 80.84 | 83.94 | +| bed | 89.08 | 96.58 | +| windowpane | 62.11 | 77.66 | +| grass | 69.93 | 81.81 | +| cabinet | 62.24 | 74.84 | +| sidewalk | 61.17 | 87.69 | +| person | 80.94 | 93.29 | +| earth | 32.94 | 43.76 | +| door | 55.46 | 76.46 | +| table | 60.52 | 74.64 | +| mountain | 56.17 | 69.22 | +| plant | 54.91 | 67.81 | +| curtain | 75.62 | 91.37 | +| chair | 59.85 | 70.58 | +| car | 84.51 | 94.45 | +| water | 50.61 | 62.65 | +| painting | 64.48 | 92.29 | +| sofa | 78.79 | 88.12 | +| shelf | 31.32 | 36.41 | +| house | 42.07 | 45.42 | +| sea | 64.92 | 84.01 | +| mirror | 71.75 | 78.05 | +| rug | 65.48 | 81.54 | +| field | 31.11 | 57.25 | +| armchair | 55.13 | 70.89 | +| seat | 62.7 | 83.21 | +| fence | 39.58 | 47.44 | +| desk | 49.06 | 69.34 | +| rock | 51.83 | 68.07 | +| wardrobe | 53.55 | 81.32 | +| lamp | 63.52 | 72.0 | +| bathtub | 79.72 | 82.1 | +| railing | 34.29 | 61.46 | +| cushion | 61.45 | 75.21 | +| base | 31.48 | 51.33 | +| box | 26.15 | 31.51 | +| column | 51.46 | 72.88 | +| signboard | 36.13 | 44.35 | +| chest of drawers | 45.6 | 49.62 | +| counter | 34.44 | 37.82 | +| sand | 35.9 | 48.5 | +| sink | 70.6 | 79.52 | +| skyscraper | 51.92 | 61.82 | +| fireplace | 67.28 | 88.09 | +| refrigerator | 73.71 | 90.8 | +| grandstand | 39.49 | 89.74 | +| path | 27.57 | 41.99 | +| stairs | 36.99 | 60.43 | +| runway | 71.41 | 97.62 | +| case | 48.69 | 85.14 | +| pool table | 86.22 | 97.78 | +| pillow | 57.84 | 66.18 | +| screen door | 53.4 | 54.62 | +| stairway | 47.02 | 55.08 | +| river | 20.95 | 50.86 | +| bridge | 71.64 | 81.36 | +| bookcase | 21.83 | 45.85 | +| blind | 21.18 | 21.44 | +| coffee table | 56.51 | 87.68 | +| toilet | 85.05 | 86.95 | +| flower | 41.91 | 47.66 | +| book | 49.13 | 75.06 | +| hill | 7.77 | 19.05 | +| bench | 54.38 | 70.89 | +| countertop | 47.46 | 49.64 | +| stove | 76.19 | 90.09 | +| palm | 50.53 | 78.21 | +| kitchen island | 46.91 | 75.86 | +| computer | 74.68 | 90.38 | +| swivel chair | 48.92 | 71.07 | +| boat | 76.41 | 86.18 | +| bar | 51.83 | 64.0 | +| arcade machine | 86.96 | 98.86 | +| hovel | 53.19 | 58.26 | +| bus | 91.29 | 93.68 | +| towel | 66.54 | 72.96 | +| light | 49.82 | 61.04 | +| truck | 42.16 | 49.35 | +| tower | 19.8 | 34.57 | +| chandelier | 64.0 | 77.18 | +| awning | 43.7 | 53.04 | +| streetlight | 20.44 | 26.25 | +| booth | 31.43 | 32.19 | +| television receiver | 67.09 | 83.81 | +| airplane | 83.77 | 89.62 | +| dirt track | 16.22 | 34.67 | +| apparel | 45.32 | 78.94 | +| pole | 17.85 | 22.95 | +| land | 1.79 | 1.98 | +| bannister | 11.81 | 16.1 | +| escalator | 47.76 | 64.41 | +| ottoman | 45.11 | 60.6 | +| bottle | 35.56 | 57.43 | +| buffet | 58.0 | 84.35 | +| poster | 30.68 | 36.15 | +| stage | 14.6 | 54.51 | +| van | 30.14 | 34.59 | +| ship | 51.84 | 62.3 | +| fountain | 40.62 | 41.67 | +| conveyer belt | 59.44 | 97.64 | +| canopy | 42.5 | 65.42 | +| washer | 74.83 | 80.27 | +| plaything | 19.4 | 24.07 | +| swimming pool | 49.05 | 96.86 | +| stool | 45.21 | 61.61 | +| barrel | 61.53 | 71.81 | +| basket | 30.46 | 41.89 | +| waterfall | 48.76 | 98.84 | +| tent | 92.69 | 98.71 | +| bag | 18.49 | 20.14 | +| minibike | 67.78 | 81.45 | +| cradle | 78.72 | 96.54 | +| oven | 40.82 | 42.48 | +| ball | 39.0 | 67.51 | +| food | 54.36 | 57.72 | +| step | 13.73 | 17.01 | +| tank | 60.16 | 76.49 | +| trade name | 24.57 | 27.69 | +| microwave | 85.38 | 90.15 | +| pot | 46.72 | 52.06 | +| animal | 46.94 | 47.2 | +| bicycle | 52.83 | 69.75 | +| lake | 3.19 | 6.02 | +| dishwasher | 64.07 | 67.46 | +| screen | 59.63 | 90.76 | +| blanket | 12.04 | 12.68 | +| sculpture | 53.6 | 60.92 | +| hood | 60.98 | 69.71 | +| sconce | 40.85 | 46.32 | +| vase | 36.36 | 51.12 | +| traffic light | 25.75 | 49.51 | +| tray | 7.05 | 9.65 | +| ashcan | 42.69 | 52.45 | +| fan | 57.11 | 64.35 | +| pier | 34.64 | 48.14 | +| crt screen | 0.47 | 0.62 | +| plate | 48.13 | 53.13 | +| monitor | 54.96 | 62.74 | +| bulletin board | 50.37 | 70.43 | +| shower | 0.03 | 0.1 | +| radiator | 60.71 | 73.01 | +| glass | 10.45 | 11.05 | +| clock | 35.85 | 41.72 | +| flag | 65.8 | 75.35 | ++---------------------+-------+-------+ +2024-06-16 00:48:28,293 - mmseg - INFO - Summary: +2024-06-16 00:48:28,294 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 83.44 | 50.53 | 63.66 | ++-------+-------+-------+ +2024-06-16 00:48:28,294 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 00:48:28,295 - mmseg - INFO - Iter(val) [250] aAcc: 0.8344, mIoU: 0.5053, mAcc: 0.6366, IoU.wall: 0.7852, IoU.building: 0.8382, IoU.sky: 0.9435, IoU.floor: 0.8222, IoU.tree: 0.7608, IoU.ceiling: 0.8502, IoU.road: 0.8084, IoU.bed : 0.8908, IoU.windowpane: 0.6211, IoU.grass: 0.6993, IoU.cabinet: 0.6224, IoU.sidewalk: 0.6117, IoU.person: 0.8094, IoU.earth: 0.3294, IoU.door: 0.5546, IoU.table: 0.6052, IoU.mountain: 0.5617, IoU.plant: 0.5491, IoU.curtain: 0.7562, IoU.chair: 0.5985, IoU.car: 0.8451, IoU.water: 0.5061, IoU.painting: 0.6448, IoU.sofa: 0.7879, IoU.shelf: 0.3132, IoU.house: 0.4207, IoU.sea: 0.6492, IoU.mirror: 0.7175, IoU.rug: 0.6548, IoU.field: 0.3111, IoU.armchair: 0.5513, IoU.seat: 0.6270, IoU.fence: 0.3958, IoU.desk: 0.4906, IoU.rock: 0.5183, IoU.wardrobe: 0.5355, IoU.lamp: 0.6352, IoU.bathtub: 0.7972, IoU.railing: 0.3429, IoU.cushion: 0.6145, IoU.base: 0.3148, IoU.box: 0.2615, IoU.column: 0.5146, IoU.signboard: 0.3613, IoU.chest of drawers: 0.4560, IoU.counter: 0.3444, IoU.sand: 0.3590, IoU.sink: 0.7060, IoU.skyscraper: 0.5192, IoU.fireplace: 0.6728, IoU.refrigerator: 0.7371, IoU.grandstand: 0.3949, IoU.path: 0.2757, IoU.stairs: 0.3699, IoU.runway: 0.7141, IoU.case: 0.4869, IoU.pool table: 0.8622, IoU.pillow: 0.5784, IoU.screen door: 0.5340, IoU.stairway: 0.4702, IoU.river: 0.2095, IoU.bridge: 0.7164, IoU.bookcase: 0.2183, IoU.blind: 0.2118, IoU.coffee table: 0.5651, IoU.toilet: 0.8505, IoU.flower: 0.4191, IoU.book: 0.4913, IoU.hill: 0.0777, IoU.bench: 0.5438, IoU.countertop: 0.4746, IoU.stove: 0.7619, IoU.palm: 0.5053, IoU.kitchen island: 0.4691, IoU.computer: 0.7468, IoU.swivel chair: 0.4892, IoU.boat: 0.7641, IoU.bar: 0.5183, IoU.arcade machine: 0.8696, IoU.hovel: 0.5319, IoU.bus: 0.9129, IoU.towel: 0.6654, IoU.light: 0.4982, IoU.truck: 0.4216, IoU.tower: 0.1980, IoU.chandelier: 0.6400, IoU.awning: 0.4370, IoU.streetlight: 0.2044, IoU.booth: 0.3143, IoU.television receiver: 0.6709, IoU.airplane: 0.8377, IoU.dirt track: 0.1622, IoU.apparel: 0.4532, IoU.pole: 0.1785, IoU.land: 0.0179, IoU.bannister: 0.1181, IoU.escalator: 0.4776, IoU.ottoman: 0.4511, IoU.bottle: 0.3556, IoU.buffet: 0.5800, IoU.poster: 0.3068, IoU.stage: 0.1460, IoU.van: 0.3014, IoU.ship: 0.5184, IoU.fountain: 0.4062, IoU.conveyer belt: 0.5944, IoU.canopy: 0.4250, IoU.washer: 0.7483, IoU.plaything: 0.1940, IoU.swimming pool: 0.4905, IoU.stool: 0.4521, IoU.barrel: 0.6153, IoU.basket: 0.3046, IoU.waterfall: 0.4876, IoU.tent: 0.9269, IoU.bag: 0.1849, IoU.minibike: 0.6778, IoU.cradle: 0.7872, IoU.oven: 0.4082, IoU.ball: 0.3900, IoU.food: 0.5436, IoU.step: 0.1373, IoU.tank: 0.6016, IoU.trade name: 0.2457, IoU.microwave: 0.8538, IoU.pot: 0.4672, IoU.animal: 0.4694, IoU.bicycle: 0.5283, IoU.lake: 0.0319, IoU.dishwasher: 0.6407, IoU.screen: 0.5963, IoU.blanket: 0.1204, IoU.sculpture: 0.5360, IoU.hood: 0.6098, IoU.sconce: 0.4085, IoU.vase: 0.3636, IoU.traffic light: 0.2575, IoU.tray: 0.0705, IoU.ashcan: 0.4269, IoU.fan: 0.5711, IoU.pier: 0.3464, IoU.crt screen: 0.0047, IoU.plate: 0.4813, IoU.monitor: 0.5496, IoU.bulletin board: 0.5037, IoU.shower: 0.0003, IoU.radiator: 0.6071, IoU.glass: 0.1045, IoU.clock: 0.3585, IoU.flag: 0.6580, Acc.wall: 0.8829, Acc.building: 0.9295, Acc.sky: 0.9729, Acc.floor: 0.8996, Acc.tree: 0.9035, Acc.ceiling: 0.9054, Acc.road: 0.8394, Acc.bed : 0.9658, Acc.windowpane: 0.7766, Acc.grass: 0.8181, Acc.cabinet: 0.7484, Acc.sidewalk: 0.8769, Acc.person: 0.9329, Acc.earth: 0.4376, Acc.door: 0.7646, Acc.table: 0.7464, Acc.mountain: 0.6922, Acc.plant: 0.6781, Acc.curtain: 0.9137, Acc.chair: 0.7058, Acc.car: 0.9445, Acc.water: 0.6265, Acc.painting: 0.9229, Acc.sofa: 0.8812, Acc.shelf: 0.3641, Acc.house: 0.4542, Acc.sea: 0.8401, Acc.mirror: 0.7805, Acc.rug: 0.8154, Acc.field: 0.5725, Acc.armchair: 0.7089, Acc.seat: 0.8321, Acc.fence: 0.4744, Acc.desk: 0.6934, Acc.rock: 0.6807, Acc.wardrobe: 0.8132, Acc.lamp: 0.7200, Acc.bathtub: 0.8210, Acc.railing: 0.6146, Acc.cushion: 0.7521, Acc.base: 0.5133, Acc.box: 0.3151, Acc.column: 0.7288, Acc.signboard: 0.4435, Acc.chest of drawers: 0.4962, Acc.counter: 0.3782, Acc.sand: 0.4850, Acc.sink: 0.7952, Acc.skyscraper: 0.6182, Acc.fireplace: 0.8809, Acc.refrigerator: 0.9080, Acc.grandstand: 0.8974, Acc.path: 0.4199, Acc.stairs: 0.6043, Acc.runway: 0.9762, Acc.case: 0.8514, Acc.pool table: 0.9778, Acc.pillow: 0.6618, Acc.screen door: 0.5462, Acc.stairway: 0.5508, Acc.river: 0.5086, Acc.bridge: 0.8136, Acc.bookcase: 0.4585, Acc.blind: 0.2144, Acc.coffee table: 0.8768, Acc.toilet: 0.8695, Acc.flower: 0.4766, Acc.book: 0.7506, Acc.hill: 0.1905, Acc.bench: 0.7089, Acc.countertop: 0.4964, Acc.stove: 0.9009, Acc.palm: 0.7821, Acc.kitchen island: 0.7586, Acc.computer: 0.9038, Acc.swivel chair: 0.7107, Acc.boat: 0.8618, Acc.bar: 0.6400, Acc.arcade machine: 0.9886, Acc.hovel: 0.5826, Acc.bus: 0.9368, Acc.towel: 0.7296, Acc.light: 0.6104, Acc.truck: 0.4935, Acc.tower: 0.3457, Acc.chandelier: 0.7718, Acc.awning: 0.5304, Acc.streetlight: 0.2625, Acc.booth: 0.3219, Acc.television receiver: 0.8381, Acc.airplane: 0.8962, Acc.dirt track: 0.3467, Acc.apparel: 0.7894, Acc.pole: 0.2295, Acc.land: 0.0198, Acc.bannister: 0.1610, Acc.escalator: 0.6441, Acc.ottoman: 0.6060, Acc.bottle: 0.5743, Acc.buffet: 0.8435, Acc.poster: 0.3615, Acc.stage: 0.5451, Acc.van: 0.3459, Acc.ship: 0.6230, Acc.fountain: 0.4167, Acc.conveyer belt: 0.9764, Acc.canopy: 0.6542, Acc.washer: 0.8027, Acc.plaything: 0.2407, Acc.swimming pool: 0.9686, Acc.stool: 0.6161, Acc.barrel: 0.7181, Acc.basket: 0.4189, Acc.waterfall: 0.9884, Acc.tent: 0.9871, Acc.bag: 0.2014, Acc.minibike: 0.8145, Acc.cradle: 0.9654, Acc.oven: 0.4248, Acc.ball: 0.6751, Acc.food: 0.5772, Acc.step: 0.1701, Acc.tank: 0.7649, Acc.trade name: 0.2769, Acc.microwave: 0.9015, Acc.pot: 0.5206, Acc.animal: 0.4720, Acc.bicycle: 0.6975, Acc.lake: 0.0602, Acc.dishwasher: 0.6746, Acc.screen: 0.9076, Acc.blanket: 0.1268, Acc.sculpture: 0.6092, Acc.hood: 0.6971, Acc.sconce: 0.4632, Acc.vase: 0.5112, Acc.traffic light: 0.4951, Acc.tray: 0.0965, Acc.ashcan: 0.5245, Acc.fan: 0.6435, Acc.pier: 0.4814, Acc.crt screen: 0.0062, Acc.plate: 0.5313, Acc.monitor: 0.6274, Acc.bulletin board: 0.7043, Acc.shower: 0.0010, Acc.radiator: 0.7301, Acc.glass: 0.1105, Acc.clock: 0.4172, Acc.flag: 0.7535 +2024-06-16 00:49:36,961 - mmseg - INFO - Iter [8050/80000] lr: 3.598e-05, eta: 1 day, 6:16:47, time: 3.263, data_time: 1.905, memory: 70722, decode.loss_ce: 0.4348, decode.acc_seg: 83.2486, aux.loss_ce: 0.1750, aux.acc_seg: 83.1653, loss: 0.6098 +2024-06-16 00:50:45,314 - mmseg - INFO - Iter [8100/80000] lr: 3.595e-05, eta: 1 day, 6:14:26, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4509, decode.acc_seg: 82.1940, aux.loss_ce: 0.1784, aux.acc_seg: 82.4605, loss: 0.6294 +2024-06-16 00:51:53,414 - mmseg - INFO - Iter [8150/80000] lr: 3.593e-05, eta: 1 day, 6:12:03, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4489, decode.acc_seg: 81.9649, aux.loss_ce: 0.1809, aux.acc_seg: 81.7735, loss: 0.6297 +2024-06-16 00:53:01,821 - mmseg - INFO - Iter [8200/80000] lr: 3.590e-05, eta: 1 day, 6:09:44, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4479, decode.acc_seg: 82.7100, aux.loss_ce: 0.1783, aux.acc_seg: 82.8148, loss: 0.6263 +2024-06-16 00:54:10,182 - mmseg - INFO - Iter [8250/80000] lr: 3.588e-05, eta: 1 day, 6:07:25, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4374, decode.acc_seg: 83.2304, aux.loss_ce: 0.1738, aux.acc_seg: 83.1876, loss: 0.6112 +2024-06-16 00:55:18,371 - mmseg - INFO - Iter [8300/80000] lr: 3.585e-05, eta: 1 day, 6:05:06, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4346, decode.acc_seg: 83.4974, aux.loss_ce: 0.1738, aux.acc_seg: 83.5243, loss: 0.6084 +2024-06-16 00:56:26,617 - mmseg - INFO - Iter [8350/80000] lr: 3.583e-05, eta: 1 day, 6:02:48, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4256, decode.acc_seg: 82.9731, aux.loss_ce: 0.1693, aux.acc_seg: 83.0483, loss: 0.5949 +2024-06-16 00:57:35,058 - mmseg - INFO - Iter [8400/80000] lr: 3.580e-05, eta: 1 day, 6:00:32, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4325, decode.acc_seg: 83.2412, aux.loss_ce: 0.1722, aux.acc_seg: 83.3663, loss: 0.6048 +2024-06-16 00:58:43,330 - mmseg - INFO - Iter [8450/80000] lr: 3.578e-05, eta: 1 day, 5:58:16, time: 1.365, data_time: 0.009, memory: 70722, decode.loss_ce: 0.4593, decode.acc_seg: 82.7365, aux.loss_ce: 0.1835, aux.acc_seg: 82.7109, loss: 0.6428 +2024-06-16 00:59:51,437 - mmseg - INFO - Iter [8500/80000] lr: 3.575e-05, eta: 1 day, 5:55:59, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4548, decode.acc_seg: 82.4054, aux.loss_ce: 0.1804, aux.acc_seg: 82.5488, loss: 0.6352 +2024-06-16 01:01:00,055 - mmseg - INFO - Iter [8550/80000] lr: 3.573e-05, eta: 1 day, 5:53:48, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4639, decode.acc_seg: 81.9582, aux.loss_ce: 0.1847, aux.acc_seg: 82.1863, loss: 0.6486 +2024-06-16 01:02:08,207 - mmseg - INFO - Iter [8600/80000] lr: 3.570e-05, eta: 1 day, 5:51:33, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4355, decode.acc_seg: 83.1708, aux.loss_ce: 0.1737, aux.acc_seg: 83.4023, loss: 0.6092 +2024-06-16 01:03:16,335 - mmseg - INFO - Iter [8650/80000] lr: 3.568e-05, eta: 1 day, 5:49:19, time: 1.363, data_time: 0.009, memory: 70722, decode.loss_ce: 0.4318, decode.acc_seg: 82.8425, aux.loss_ce: 0.1727, aux.acc_seg: 82.6875, loss: 0.6045 +2024-06-16 01:04:24,631 - mmseg - INFO - Iter [8700/80000] lr: 3.565e-05, eta: 1 day, 5:47:07, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4539, decode.acc_seg: 82.1494, aux.loss_ce: 0.1814, aux.acc_seg: 82.1836, loss: 0.6353 +2024-06-16 01:05:32,925 - mmseg - INFO - Iter [8750/80000] lr: 3.563e-05, eta: 1 day, 5:44:55, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4537, decode.acc_seg: 82.1795, aux.loss_ce: 0.1818, aux.acc_seg: 81.9675, loss: 0.6355 +2024-06-16 01:06:41,283 - mmseg - INFO - Iter [8800/80000] lr: 3.560e-05, eta: 1 day, 5:42:45, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4663, decode.acc_seg: 82.5968, aux.loss_ce: 0.1842, aux.acc_seg: 82.8744, loss: 0.6506 +2024-06-16 01:07:52,505 - mmseg - INFO - Iter [8850/80000] lr: 3.558e-05, eta: 1 day, 5:40:59, time: 1.424, data_time: 0.068, memory: 70722, decode.loss_ce: 0.4519, decode.acc_seg: 82.7212, aux.loss_ce: 0.1806, aux.acc_seg: 82.6660, loss: 0.6324 +2024-06-16 01:09:00,747 - mmseg - INFO - Iter [8900/80000] lr: 3.555e-05, eta: 1 day, 5:38:49, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4348, decode.acc_seg: 83.3074, aux.loss_ce: 0.1739, aux.acc_seg: 83.3215, loss: 0.6086 +2024-06-16 01:10:09,207 - mmseg - INFO - Iter [8950/80000] lr: 3.553e-05, eta: 1 day, 5:36:41, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4333, decode.acc_seg: 83.0896, aux.loss_ce: 0.1734, aux.acc_seg: 83.1023, loss: 0.6067 +2024-06-16 01:11:17,787 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 01:11:17,787 - mmseg - INFO - Iter [9000/80000] lr: 3.550e-05, eta: 1 day, 5:34:36, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4092, decode.acc_seg: 84.4431, aux.loss_ce: 0.1639, aux.acc_seg: 84.3623, loss: 0.5731 +2024-06-16 01:12:54,244 - mmseg - INFO - per class results: +2024-06-16 01:12:54,250 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 78.32 | 88.43 | +| building | 83.45 | 93.47 | +| sky | 94.2 | 97.97 | +| floor | 83.58 | 91.31 | +| tree | 75.59 | 86.97 | +| ceiling | 79.52 | 82.59 | +| road | 81.91 | 90.22 | +| bed | 90.28 | 95.48 | +| windowpane | 63.75 | 80.29 | +| grass | 64.79 | 80.32 | +| cabinet | 58.77 | 77.51 | +| sidewalk | 65.81 | 85.75 | +| person | 82.4 | 88.98 | +| earth | 26.92 | 30.72 | +| door | 56.64 | 74.26 | +| table | 59.92 | 75.29 | +| mountain | 59.26 | 71.89 | +| plant | 55.71 | 67.54 | +| curtain | 77.24 | 89.39 | +| chair | 57.22 | 63.96 | +| car | 85.41 | 93.55 | +| water | 60.04 | 78.45 | +| painting | 74.99 | 89.57 | +| sofa | 75.22 | 92.43 | +| shelf | 37.88 | 49.11 | +| house | 59.22 | 74.03 | +| sea | 68.95 | 86.08 | +| mirror | 71.74 | 79.37 | +| rug | 70.69 | 78.39 | +| field | 35.79 | 76.41 | +| armchair | 48.8 | 73.04 | +| seat | 66.44 | 86.6 | +| fence | 47.9 | 61.21 | +| desk | 49.62 | 74.3 | +| rock | 54.1 | 82.06 | +| wardrobe | 52.23 | 88.27 | +| lamp | 64.72 | 72.96 | +| bathtub | 79.94 | 85.52 | +| railing | 38.69 | 56.53 | +| cushion | 64.57 | 80.46 | +| base | 21.65 | 56.88 | +| box | 30.2 | 40.11 | +| column | 50.01 | 58.87 | +| signboard | 37.06 | 46.91 | +| chest of drawers | 44.43 | 73.69 | +| counter | 39.54 | 43.17 | +| sand | 50.75 | 75.16 | +| sink | 73.24 | 79.72 | +| skyscraper | 55.26 | 69.38 | +| fireplace | 67.13 | 92.66 | +| refrigerator | 65.24 | 68.34 | +| grandstand | 52.83 | 71.81 | +| path | 17.01 | 22.31 | +| stairs | 18.37 | 20.07 | +| runway | 74.51 | 95.7 | +| case | 65.22 | 90.83 | +| pool table | 89.74 | 97.44 | +| pillow | 60.91 | 68.0 | +| screen door | 76.64 | 78.87 | +| stairway | 28.82 | 39.84 | +| river | 10.62 | 11.34 | +| bridge | 39.56 | 52.48 | +| bookcase | 30.61 | 46.67 | +| blind | 42.03 | 45.13 | +| coffee table | 55.82 | 89.29 | +| toilet | 87.49 | 92.79 | +| flower | 41.19 | 53.74 | +| book | 48.06 | 64.3 | +| hill | 4.7 | 6.57 | +| bench | 58.83 | 76.82 | +| countertop | 56.37 | 61.92 | +| stove | 79.75 | 84.7 | +| palm | 49.52 | 78.94 | +| kitchen island | 43.33 | 78.58 | +| computer | 72.83 | 92.29 | +| swivel chair | 45.91 | 78.06 | +| boat | 73.18 | 81.08 | +| bar | 55.89 | 56.79 | +| arcade machine | 91.39 | 97.97 | +| hovel | 10.22 | 10.37 | +| bus | 91.47 | 94.85 | +| towel | 64.97 | 79.81 | +| light | 44.16 | 46.86 | +| truck | 39.15 | 55.98 | +| tower | 25.64 | 41.41 | +| chandelier | 62.69 | 76.27 | +| awning | 42.55 | 69.17 | +| streetlight | 21.02 | 27.25 | +| booth | 41.64 | 44.39 | +| television receiver | 64.98 | 71.74 | +| airplane | 81.73 | 95.4 | +| dirt track | 1.16 | 2.73 | +| apparel | 34.55 | 43.43 | +| pole | 9.6 | 10.35 | +| land | 7.47 | 27.8 | +| bannister | 11.65 | 19.0 | +| escalator | 52.1 | 80.67 | +| ottoman | 46.15 | 65.45 | +| bottle | 37.7 | 58.52 | +| buffet | 51.25 | 88.2 | +| poster | 26.32 | 27.4 | +| stage | 15.54 | 37.57 | +| van | 38.23 | 45.34 | +| ship | 72.29 | 73.73 | +| fountain | 39.21 | 44.53 | +| conveyer belt | 60.39 | 96.82 | +| canopy | 43.91 | 59.9 | +| washer | 78.94 | 83.72 | +| plaything | 11.61 | 15.66 | +| swimming pool | 56.5 | 87.32 | +| stool | 46.73 | 55.4 | +| barrel | 53.69 | 65.06 | +| basket | 34.15 | 40.95 | +| waterfall | 57.67 | 97.65 | +| tent | 82.92 | 98.76 | +| bag | 11.27 | 12.22 | +| minibike | 65.9 | 81.32 | +| cradle | 78.86 | 96.76 | +| oven | 40.82 | 44.52 | +| ball | 42.94 | 60.41 | +| food | 58.26 | 72.3 | +| step | 6.17 | 6.44 | +| tank | 63.68 | 69.55 | +| trade name | 38.17 | 56.92 | +| microwave | 81.46 | 87.39 | +| pot | 49.88 | 58.59 | +| animal | 65.21 | 67.42 | +| bicycle | 52.88 | 66.38 | +| lake | 3.07 | 5.73 | +| dishwasher | 61.09 | 77.19 | +| screen | 59.06 | 93.46 | +| blanket | 20.11 | 22.21 | +| sculpture | 50.59 | 63.0 | +| hood | 64.57 | 67.48 | +| sconce | 48.25 | 55.82 | +| vase | 36.98 | 49.53 | +| traffic light | 27.19 | 53.16 | +| tray | 7.78 | 9.92 | +| ashcan | 42.38 | 62.83 | +| fan | 61.01 | 79.07 | +| pier | 33.11 | 35.07 | +| crt screen | 3.18 | 8.35 | +| plate | 54.62 | 62.65 | +| monitor | 10.28 | 10.92 | +| bulletin board | 60.84 | 66.36 | +| shower | 0.0 | 0.0 | +| radiator | 57.33 | 75.51 | +| glass | 5.7 | 5.84 | +| clock | 36.24 | 39.91 | +| flag | 66.34 | 69.63 | ++---------------------+-------+-------+ +2024-06-16 01:12:54,250 - mmseg - INFO - Summary: +2024-06-16 01:12:54,250 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 83.64 | 50.89 | 63.62 | ++-------+-------+-------+ +2024-06-16 01:12:54,251 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 01:12:54,251 - mmseg - INFO - Iter(val) [250] aAcc: 0.8364, mIoU: 0.5089, mAcc: 0.6362, IoU.wall: 0.7832, IoU.building: 0.8345, IoU.sky: 0.9420, IoU.floor: 0.8358, IoU.tree: 0.7559, IoU.ceiling: 0.7952, IoU.road: 0.8191, IoU.bed : 0.9028, IoU.windowpane: 0.6375, IoU.grass: 0.6479, IoU.cabinet: 0.5877, IoU.sidewalk: 0.6581, IoU.person: 0.8240, IoU.earth: 0.2692, IoU.door: 0.5664, IoU.table: 0.5992, IoU.mountain: 0.5926, IoU.plant: 0.5571, IoU.curtain: 0.7724, IoU.chair: 0.5722, IoU.car: 0.8541, IoU.water: 0.6004, IoU.painting: 0.7499, IoU.sofa: 0.7522, IoU.shelf: 0.3788, IoU.house: 0.5922, IoU.sea: 0.6895, IoU.mirror: 0.7174, IoU.rug: 0.7069, IoU.field: 0.3579, IoU.armchair: 0.4880, IoU.seat: 0.6644, IoU.fence: 0.4790, IoU.desk: 0.4962, IoU.rock: 0.5410, IoU.wardrobe: 0.5223, IoU.lamp: 0.6472, IoU.bathtub: 0.7994, IoU.railing: 0.3869, IoU.cushion: 0.6457, IoU.base: 0.2165, IoU.box: 0.3020, IoU.column: 0.5001, IoU.signboard: 0.3706, IoU.chest of drawers: 0.4443, IoU.counter: 0.3954, IoU.sand: 0.5075, IoU.sink: 0.7324, IoU.skyscraper: 0.5526, IoU.fireplace: 0.6713, IoU.refrigerator: 0.6524, IoU.grandstand: 0.5283, IoU.path: 0.1701, IoU.stairs: 0.1837, IoU.runway: 0.7451, IoU.case: 0.6522, IoU.pool table: 0.8974, IoU.pillow: 0.6091, IoU.screen door: 0.7664, IoU.stairway: 0.2882, IoU.river: 0.1062, IoU.bridge: 0.3956, IoU.bookcase: 0.3061, IoU.blind: 0.4203, IoU.coffee table: 0.5582, IoU.toilet: 0.8749, IoU.flower: 0.4119, IoU.book: 0.4806, IoU.hill: 0.0470, IoU.bench: 0.5883, IoU.countertop: 0.5637, IoU.stove: 0.7975, IoU.palm: 0.4952, IoU.kitchen island: 0.4333, IoU.computer: 0.7283, IoU.swivel chair: 0.4591, IoU.boat: 0.7318, IoU.bar: 0.5589, IoU.arcade machine: 0.9139, IoU.hovel: 0.1022, IoU.bus: 0.9147, IoU.towel: 0.6497, IoU.light: 0.4416, IoU.truck: 0.3915, IoU.tower: 0.2564, IoU.chandelier: 0.6269, IoU.awning: 0.4255, IoU.streetlight: 0.2102, IoU.booth: 0.4164, IoU.television receiver: 0.6498, IoU.airplane: 0.8173, IoU.dirt track: 0.0116, IoU.apparel: 0.3455, IoU.pole: 0.0960, IoU.land: 0.0747, IoU.bannister: 0.1165, IoU.escalator: 0.5210, IoU.ottoman: 0.4615, IoU.bottle: 0.3770, IoU.buffet: 0.5125, IoU.poster: 0.2632, IoU.stage: 0.1554, IoU.van: 0.3823, IoU.ship: 0.7229, IoU.fountain: 0.3921, IoU.conveyer belt: 0.6039, IoU.canopy: 0.4391, IoU.washer: 0.7894, IoU.plaything: 0.1161, IoU.swimming pool: 0.5650, IoU.stool: 0.4673, IoU.barrel: 0.5369, IoU.basket: 0.3415, IoU.waterfall: 0.5767, IoU.tent: 0.8292, IoU.bag: 0.1127, IoU.minibike: 0.6590, IoU.cradle: 0.7886, IoU.oven: 0.4082, IoU.ball: 0.4294, IoU.food: 0.5826, IoU.step: 0.0617, IoU.tank: 0.6368, IoU.trade name: 0.3817, IoU.microwave: 0.8146, IoU.pot: 0.4988, IoU.animal: 0.6521, IoU.bicycle: 0.5288, IoU.lake: 0.0307, IoU.dishwasher: 0.6109, IoU.screen: 0.5906, IoU.blanket: 0.2011, IoU.sculpture: 0.5059, IoU.hood: 0.6457, IoU.sconce: 0.4825, IoU.vase: 0.3698, IoU.traffic light: 0.2719, IoU.tray: 0.0778, IoU.ashcan: 0.4238, IoU.fan: 0.6101, IoU.pier: 0.3311, IoU.crt screen: 0.0318, IoU.plate: 0.5462, IoU.monitor: 0.1028, IoU.bulletin board: 0.6084, IoU.shower: 0.0000, IoU.radiator: 0.5733, IoU.glass: 0.0570, IoU.clock: 0.3624, IoU.flag: 0.6634, Acc.wall: 0.8843, Acc.building: 0.9347, Acc.sky: 0.9797, Acc.floor: 0.9131, Acc.tree: 0.8697, Acc.ceiling: 0.8259, Acc.road: 0.9022, Acc.bed : 0.9548, Acc.windowpane: 0.8029, Acc.grass: 0.8032, Acc.cabinet: 0.7751, Acc.sidewalk: 0.8575, Acc.person: 0.8898, Acc.earth: 0.3072, Acc.door: 0.7426, Acc.table: 0.7529, Acc.mountain: 0.7189, Acc.plant: 0.6754, Acc.curtain: 0.8939, Acc.chair: 0.6396, Acc.car: 0.9355, Acc.water: 0.7845, Acc.painting: 0.8957, Acc.sofa: 0.9243, Acc.shelf: 0.4911, Acc.house: 0.7403, Acc.sea: 0.8608, Acc.mirror: 0.7937, Acc.rug: 0.7839, Acc.field: 0.7641, Acc.armchair: 0.7304, Acc.seat: 0.8660, Acc.fence: 0.6121, Acc.desk: 0.7430, Acc.rock: 0.8206, Acc.wardrobe: 0.8827, Acc.lamp: 0.7296, Acc.bathtub: 0.8552, Acc.railing: 0.5653, Acc.cushion: 0.8046, Acc.base: 0.5688, Acc.box: 0.4011, Acc.column: 0.5887, Acc.signboard: 0.4691, Acc.chest of drawers: 0.7369, Acc.counter: 0.4317, Acc.sand: 0.7516, Acc.sink: 0.7972, Acc.skyscraper: 0.6938, Acc.fireplace: 0.9266, Acc.refrigerator: 0.6834, Acc.grandstand: 0.7181, Acc.path: 0.2231, Acc.stairs: 0.2007, Acc.runway: 0.9570, Acc.case: 0.9083, Acc.pool table: 0.9744, Acc.pillow: 0.6800, Acc.screen door: 0.7887, Acc.stairway: 0.3984, Acc.river: 0.1134, Acc.bridge: 0.5248, Acc.bookcase: 0.4667, Acc.blind: 0.4513, Acc.coffee table: 0.8929, Acc.toilet: 0.9279, Acc.flower: 0.5374, Acc.book: 0.6430, Acc.hill: 0.0657, Acc.bench: 0.7682, Acc.countertop: 0.6192, Acc.stove: 0.8470, Acc.palm: 0.7894, Acc.kitchen island: 0.7858, Acc.computer: 0.9229, Acc.swivel chair: 0.7806, Acc.boat: 0.8108, Acc.bar: 0.5679, Acc.arcade machine: 0.9797, Acc.hovel: 0.1037, Acc.bus: 0.9485, Acc.towel: 0.7981, Acc.light: 0.4686, Acc.truck: 0.5598, Acc.tower: 0.4141, Acc.chandelier: 0.7627, Acc.awning: 0.6917, Acc.streetlight: 0.2725, Acc.booth: 0.4439, Acc.television receiver: 0.7174, Acc.airplane: 0.9540, Acc.dirt track: 0.0273, Acc.apparel: 0.4343, Acc.pole: 0.1035, Acc.land: 0.2780, Acc.bannister: 0.1900, Acc.escalator: 0.8067, Acc.ottoman: 0.6545, Acc.bottle: 0.5852, Acc.buffet: 0.8820, Acc.poster: 0.2740, Acc.stage: 0.3757, Acc.van: 0.4534, Acc.ship: 0.7373, Acc.fountain: 0.4453, Acc.conveyer belt: 0.9682, Acc.canopy: 0.5990, Acc.washer: 0.8372, Acc.plaything: 0.1566, Acc.swimming pool: 0.8732, Acc.stool: 0.5540, Acc.barrel: 0.6506, Acc.basket: 0.4095, Acc.waterfall: 0.9765, Acc.tent: 0.9876, Acc.bag: 0.1222, Acc.minibike: 0.8132, Acc.cradle: 0.9676, Acc.oven: 0.4452, Acc.ball: 0.6041, Acc.food: 0.7230, Acc.step: 0.0644, Acc.tank: 0.6955, Acc.trade name: 0.5692, Acc.microwave: 0.8739, Acc.pot: 0.5859, Acc.animal: 0.6742, Acc.bicycle: 0.6638, Acc.lake: 0.0573, Acc.dishwasher: 0.7719, Acc.screen: 0.9346, Acc.blanket: 0.2221, Acc.sculpture: 0.6300, Acc.hood: 0.6748, Acc.sconce: 0.5582, Acc.vase: 0.4953, Acc.traffic light: 0.5316, Acc.tray: 0.0992, Acc.ashcan: 0.6283, Acc.fan: 0.7907, Acc.pier: 0.3507, Acc.crt screen: 0.0835, Acc.plate: 0.6265, Acc.monitor: 0.1092, Acc.bulletin board: 0.6636, Acc.shower: 0.0000, Acc.radiator: 0.7551, Acc.glass: 0.0584, Acc.clock: 0.3991, Acc.flag: 0.6963 +2024-06-16 01:14:03,038 - mmseg - INFO - Iter [9050/80000] lr: 3.548e-05, eta: 1 day, 5:45:08, time: 3.305, data_time: 1.945, memory: 70722, decode.loss_ce: 0.4278, decode.acc_seg: 82.6796, aux.loss_ce: 0.1705, aux.acc_seg: 82.7665, loss: 0.5983 +2024-06-16 01:15:11,410 - mmseg - INFO - Iter [9100/80000] lr: 3.545e-05, eta: 1 day, 5:42:57, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4136, decode.acc_seg: 83.8036, aux.loss_ce: 0.1646, aux.acc_seg: 83.7082, loss: 0.5782 +2024-06-16 01:16:19,769 - mmseg - INFO - Iter [9150/80000] lr: 3.543e-05, eta: 1 day, 5:40:47, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3994, decode.acc_seg: 84.5099, aux.loss_ce: 0.1613, aux.acc_seg: 84.3252, loss: 0.5607 +2024-06-16 01:17:28,041 - mmseg - INFO - Iter [9200/80000] lr: 3.540e-05, eta: 1 day, 5:38:37, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4314, decode.acc_seg: 83.2448, aux.loss_ce: 0.1713, aux.acc_seg: 83.3296, loss: 0.6027 +2024-06-16 01:18:36,484 - mmseg - INFO - Iter [9250/80000] lr: 3.538e-05, eta: 1 day, 5:36:28, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3960, decode.acc_seg: 84.3218, aux.loss_ce: 0.1589, aux.acc_seg: 84.2454, loss: 0.5549 +2024-06-16 01:19:44,828 - mmseg - INFO - Iter [9300/80000] lr: 3.535e-05, eta: 1 day, 5:34:20, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4258, decode.acc_seg: 83.6763, aux.loss_ce: 0.1704, aux.acc_seg: 83.7051, loss: 0.5962 +2024-06-16 01:20:53,068 - mmseg - INFO - Iter [9350/80000] lr: 3.533e-05, eta: 1 day, 5:32:11, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4231, decode.acc_seg: 83.8802, aux.loss_ce: 0.1682, aux.acc_seg: 83.9027, loss: 0.5912 +2024-06-16 01:22:01,748 - mmseg - INFO - Iter [9400/80000] lr: 3.530e-05, eta: 1 day, 5:30:07, time: 1.374, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4197, decode.acc_seg: 83.0594, aux.loss_ce: 0.1659, aux.acc_seg: 83.3658, loss: 0.5856 +2024-06-16 01:23:09,833 - mmseg - INFO - Iter [9450/80000] lr: 3.528e-05, eta: 1 day, 5:27:58, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4237, decode.acc_seg: 82.8254, aux.loss_ce: 0.1686, aux.acc_seg: 83.1108, loss: 0.5923 +2024-06-16 01:24:18,219 - mmseg - INFO - Iter [9500/80000] lr: 3.525e-05, eta: 1 day, 5:25:53, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4468, decode.acc_seg: 82.9416, aux.loss_ce: 0.1781, aux.acc_seg: 82.8928, loss: 0.6249 +2024-06-16 01:25:26,505 - mmseg - INFO - Iter [9550/80000] lr: 3.523e-05, eta: 1 day, 5:23:47, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4618, decode.acc_seg: 82.3134, aux.loss_ce: 0.1842, aux.acc_seg: 82.4641, loss: 0.6460 +2024-06-16 01:26:34,963 - mmseg - INFO - Iter [9600/80000] lr: 3.520e-05, eta: 1 day, 5:21:43, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4288, decode.acc_seg: 83.0867, aux.loss_ce: 0.1706, aux.acc_seg: 83.2335, loss: 0.5994 +2024-06-16 01:27:43,231 - mmseg - INFO - Iter [9650/80000] lr: 3.518e-05, eta: 1 day, 5:19:38, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4178, decode.acc_seg: 83.5299, aux.loss_ce: 0.1652, aux.acc_seg: 83.5124, loss: 0.5830 +2024-06-16 01:28:51,241 - mmseg - INFO - Iter [9700/80000] lr: 3.515e-05, eta: 1 day, 5:17:32, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4229, decode.acc_seg: 83.3076, aux.loss_ce: 0.1699, aux.acc_seg: 83.2208, loss: 0.5927 +2024-06-16 01:29:59,550 - mmseg - INFO - Iter [9750/80000] lr: 3.513e-05, eta: 1 day, 5:15:29, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4556, decode.acc_seg: 82.8013, aux.loss_ce: 0.1799, aux.acc_seg: 82.8163, loss: 0.6356 +2024-06-16 01:31:07,852 - mmseg - INFO - Iter [9800/80000] lr: 3.510e-05, eta: 1 day, 5:13:27, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4206, decode.acc_seg: 82.9913, aux.loss_ce: 0.1701, aux.acc_seg: 82.6558, loss: 0.5907 +2024-06-16 01:32:16,401 - mmseg - INFO - Iter [9850/80000] lr: 3.508e-05, eta: 1 day, 5:11:26, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4454, decode.acc_seg: 83.0635, aux.loss_ce: 0.1775, aux.acc_seg: 82.8788, loss: 0.6229 +2024-06-16 01:33:24,616 - mmseg - INFO - Iter [9900/80000] lr: 3.505e-05, eta: 1 day, 5:09:24, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4224, decode.acc_seg: 83.7271, aux.loss_ce: 0.1687, aux.acc_seg: 83.7289, loss: 0.5912 +2024-06-16 01:34:33,229 - mmseg - INFO - Iter [9950/80000] lr: 3.503e-05, eta: 1 day, 5:07:25, time: 1.372, data_time: 0.009, memory: 70722, decode.loss_ce: 0.4191, decode.acc_seg: 83.1668, aux.loss_ce: 0.1672, aux.acc_seg: 83.0710, loss: 0.5863 +2024-06-16 01:35:41,430 - mmseg - INFO - Saving checkpoint at 10000 iterations +2024-06-16 01:37:07,407 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 01:37:07,407 - mmseg - INFO - Iter [10000/80000] lr: 3.500e-05, eta: 1 day, 5:15:26, time: 3.084, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4164, decode.acc_seg: 84.0897, aux.loss_ce: 0.1661, aux.acc_seg: 84.0204, loss: 0.5825 +2024-06-16 01:38:43,719 - mmseg - INFO - per class results: +2024-06-16 01:38:43,725 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.5 | 87.66 | +| building | 83.57 | 93.66 | +| sky | 94.47 | 96.67 | +| floor | 83.6 | 91.33 | +| tree | 75.69 | 89.12 | +| ceiling | 85.11 | 93.79 | +| road | 85.61 | 90.94 | +| bed | 91.31 | 96.01 | +| windowpane | 61.18 | 78.65 | +| grass | 74.62 | 86.07 | +| cabinet | 58.77 | 64.44 | +| sidewalk | 70.24 | 81.65 | +| person | 80.48 | 94.12 | +| earth | 40.08 | 57.77 | +| door | 57.06 | 73.56 | +| table | 63.27 | 74.1 | +| mountain | 58.18 | 69.02 | +| plant | 51.93 | 64.86 | +| curtain | 78.61 | 89.15 | +| chair | 63.04 | 74.83 | +| car | 85.05 | 92.38 | +| water | 48.53 | 56.64 | +| painting | 77.4 | 85.67 | +| sofa | 75.0 | 90.55 | +| shelf | 46.16 | 66.03 | +| house | 54.65 | 71.79 | +| sea | 64.83 | 81.68 | +| mirror | 73.43 | 80.97 | +| rug | 65.82 | 72.95 | +| field | 39.71 | 60.58 | +| armchair | 53.21 | 61.25 | +| seat | 60.11 | 89.62 | +| fence | 47.11 | 62.53 | +| desk | 53.46 | 73.97 | +| rock | 51.9 | 74.1 | +| wardrobe | 49.46 | 78.79 | +| lamp | 65.81 | 80.45 | +| bathtub | 79.72 | 84.31 | +| railing | 40.21 | 60.73 | +| cushion | 62.87 | 72.48 | +| base | 33.83 | 58.18 | +| box | 28.83 | 36.17 | +| column | 52.31 | 67.44 | +| signboard | 36.63 | 53.34 | +| chest of drawers | 50.34 | 72.64 | +| counter | 53.75 | 67.1 | +| sand | 42.08 | 60.54 | +| sink | 71.56 | 79.58 | +| skyscraper | 50.24 | 66.76 | +| fireplace | 60.31 | 67.88 | +| refrigerator | 78.03 | 88.0 | +| grandstand | 46.39 | 76.97 | +| path | 18.46 | 22.27 | +| stairs | 36.33 | 41.27 | +| runway | 70.85 | 96.67 | +| case | 61.5 | 89.55 | +| pool table | 92.36 | 98.2 | +| pillow | 62.15 | 70.51 | +| screen door | 63.02 | 97.08 | +| stairway | 37.86 | 39.44 | +| river | 13.14 | 53.96 | +| bridge | 66.96 | 88.15 | +| bookcase | 24.68 | 27.49 | +| blind | 49.95 | 69.77 | +| coffee table | 55.79 | 83.8 | +| toilet | 85.9 | 89.89 | +| flower | 37.08 | 48.34 | +| book | 49.01 | 75.64 | +| hill | 10.32 | 19.56 | +| bench | 45.7 | 51.0 | +| countertop | 58.25 | 62.4 | +| stove | 78.22 | 91.59 | +| palm | 53.62 | 76.69 | +| kitchen island | 44.28 | 86.84 | +| computer | 74.92 | 91.69 | +| swivel chair | 46.38 | 68.59 | +| boat | 66.0 | 72.15 | +| bar | 60.31 | 67.88 | +| arcade machine | 83.99 | 99.38 | +| hovel | 50.93 | 59.02 | +| bus | 88.97 | 96.38 | +| towel | 65.14 | 70.2 | +| light | 52.09 | 60.14 | +| truck | 41.99 | 57.65 | +| tower | 25.81 | 43.1 | +| chandelier | 65.81 | 85.61 | +| awning | 42.9 | 52.53 | +| streetlight | 26.82 | 39.17 | +| booth | 24.94 | 83.89 | +| television receiver | 71.41 | 83.91 | +| airplane | 72.69 | 88.13 | +| dirt track | 6.92 | 35.8 | +| apparel | 30.29 | 36.0 | +| pole | 18.36 | 22.08 | +| land | 7.73 | 12.43 | +| bannister | 7.04 | 7.16 | +| escalator | 56.81 | 68.16 | +| ottoman | 44.56 | 63.39 | +| bottle | 36.32 | 53.05 | +| buffet | 41.29 | 90.97 | +| poster | 28.39 | 32.8 | +| stage | 10.75 | 13.49 | +| van | 46.55 | 63.15 | +| ship | 1.49 | 1.49 | +| fountain | 38.91 | 39.67 | +| conveyer belt | 66.54 | 96.01 | +| canopy | 36.91 | 56.24 | +| washer | 86.66 | 96.76 | +| plaything | 40.05 | 48.32 | +| swimming pool | 73.78 | 83.8 | +| stool | 38.92 | 41.69 | +| barrel | 53.73 | 68.34 | +| basket | 33.67 | 49.59 | +| waterfall | 78.67 | 94.23 | +| tent | 91.33 | 97.93 | +| bag | 16.94 | 18.61 | +| minibike | 68.18 | 85.66 | +| cradle | 77.27 | 98.0 | +| oven | 61.58 | 75.41 | +| ball | 46.52 | 69.44 | +| food | 48.03 | 50.4 | +| step | 16.38 | 19.31 | +| tank | 67.39 | 96.47 | +| trade name | 1.04 | 1.05 | +| microwave | 86.18 | 94.97 | +| pot | 49.65 | 56.82 | +| animal | 70.77 | 75.6 | +| bicycle | 52.85 | 69.31 | +| lake | 0.0 | 0.0 | +| dishwasher | 59.68 | 72.24 | +| screen | 59.64 | 94.73 | +| blanket | 23.65 | 28.07 | +| sculpture | 58.6 | 61.88 | +| hood | 67.2 | 73.62 | +| sconce | 51.35 | 70.38 | +| vase | 39.55 | 49.76 | +| traffic light | 29.68 | 49.63 | +| tray | 5.31 | 5.93 | +| ashcan | 44.28 | 59.96 | +| fan | 62.48 | 76.77 | +| pier | 31.75 | 42.62 | +| crt screen | 0.0 | 0.0 | +| plate | 50.91 | 79.42 | +| monitor | 50.0 | 84.46 | +| bulletin board | 54.21 | 62.11 | +| shower | 0.0 | 0.0 | +| radiator | 62.35 | 70.97 | +| glass | 8.27 | 8.6 | +| clock | 32.8 | 36.18 | +| flag | 67.03 | 69.44 | ++---------------------+-------+-------+ +2024-06-16 01:38:43,725 - mmseg - INFO - Summary: +2024-06-16 01:38:43,726 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 84.4 | 52.06 | 65.46 | ++------+-------+-------+ +2024-06-16 01:38:43,726 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 01:38:43,727 - mmseg - INFO - Iter(val) [250] aAcc: 0.8440, mIoU: 0.5206, mAcc: 0.6546, IoU.wall: 0.7950, IoU.building: 0.8357, IoU.sky: 0.9447, IoU.floor: 0.8360, IoU.tree: 0.7569, IoU.ceiling: 0.8511, IoU.road: 0.8561, IoU.bed : 0.9131, IoU.windowpane: 0.6118, IoU.grass: 0.7462, IoU.cabinet: 0.5877, IoU.sidewalk: 0.7024, IoU.person: 0.8048, IoU.earth: 0.4008, IoU.door: 0.5706, IoU.table: 0.6327, IoU.mountain: 0.5818, IoU.plant: 0.5193, IoU.curtain: 0.7861, IoU.chair: 0.6304, IoU.car: 0.8505, IoU.water: 0.4853, IoU.painting: 0.7740, IoU.sofa: 0.7500, IoU.shelf: 0.4616, IoU.house: 0.5465, IoU.sea: 0.6483, IoU.mirror: 0.7343, IoU.rug: 0.6582, IoU.field: 0.3971, IoU.armchair: 0.5321, IoU.seat: 0.6011, IoU.fence: 0.4711, IoU.desk: 0.5346, IoU.rock: 0.5190, IoU.wardrobe: 0.4946, IoU.lamp: 0.6581, IoU.bathtub: 0.7972, IoU.railing: 0.4021, IoU.cushion: 0.6287, IoU.base: 0.3383, IoU.box: 0.2883, IoU.column: 0.5231, IoU.signboard: 0.3663, IoU.chest of drawers: 0.5034, IoU.counter: 0.5375, IoU.sand: 0.4208, IoU.sink: 0.7156, IoU.skyscraper: 0.5024, IoU.fireplace: 0.6031, IoU.refrigerator: 0.7803, IoU.grandstand: 0.4639, IoU.path: 0.1846, IoU.stairs: 0.3633, IoU.runway: 0.7085, IoU.case: 0.6150, IoU.pool table: 0.9236, IoU.pillow: 0.6215, IoU.screen door: 0.6302, IoU.stairway: 0.3786, IoU.river: 0.1314, IoU.bridge: 0.6696, IoU.bookcase: 0.2468, IoU.blind: 0.4995, IoU.coffee table: 0.5579, IoU.toilet: 0.8590, IoU.flower: 0.3708, IoU.book: 0.4901, IoU.hill: 0.1032, IoU.bench: 0.4570, IoU.countertop: 0.5825, IoU.stove: 0.7822, IoU.palm: 0.5362, IoU.kitchen island: 0.4428, IoU.computer: 0.7492, IoU.swivel chair: 0.4638, IoU.boat: 0.6600, IoU.bar: 0.6031, IoU.arcade machine: 0.8399, IoU.hovel: 0.5093, IoU.bus: 0.8897, IoU.towel: 0.6514, IoU.light: 0.5209, IoU.truck: 0.4199, IoU.tower: 0.2581, IoU.chandelier: 0.6581, IoU.awning: 0.4290, IoU.streetlight: 0.2682, IoU.booth: 0.2494, IoU.television receiver: 0.7141, IoU.airplane: 0.7269, IoU.dirt track: 0.0692, IoU.apparel: 0.3029, IoU.pole: 0.1836, IoU.land: 0.0773, IoU.bannister: 0.0704, IoU.escalator: 0.5681, IoU.ottoman: 0.4456, IoU.bottle: 0.3632, IoU.buffet: 0.4129, IoU.poster: 0.2839, IoU.stage: 0.1075, IoU.van: 0.4655, IoU.ship: 0.0149, IoU.fountain: 0.3891, IoU.conveyer belt: 0.6654, IoU.canopy: 0.3691, IoU.washer: 0.8666, IoU.plaything: 0.4005, IoU.swimming pool: 0.7378, IoU.stool: 0.3892, IoU.barrel: 0.5373, IoU.basket: 0.3367, IoU.waterfall: 0.7867, IoU.tent: 0.9133, IoU.bag: 0.1694, IoU.minibike: 0.6818, IoU.cradle: 0.7727, IoU.oven: 0.6158, IoU.ball: 0.4652, IoU.food: 0.4803, IoU.step: 0.1638, IoU.tank: 0.6739, IoU.trade name: 0.0104, IoU.microwave: 0.8618, IoU.pot: 0.4965, IoU.animal: 0.7077, IoU.bicycle: 0.5285, IoU.lake: 0.0000, IoU.dishwasher: 0.5968, IoU.screen: 0.5964, IoU.blanket: 0.2365, IoU.sculpture: 0.5860, IoU.hood: 0.6720, IoU.sconce: 0.5135, IoU.vase: 0.3955, IoU.traffic light: 0.2968, IoU.tray: 0.0531, IoU.ashcan: 0.4428, IoU.fan: 0.6248, IoU.pier: 0.3175, IoU.crt screen: 0.0000, IoU.plate: 0.5091, IoU.monitor: 0.5000, IoU.bulletin board: 0.5421, IoU.shower: 0.0000, IoU.radiator: 0.6235, IoU.glass: 0.0827, IoU.clock: 0.3280, IoU.flag: 0.6703, Acc.wall: 0.8766, Acc.building: 0.9366, Acc.sky: 0.9667, Acc.floor: 0.9133, Acc.tree: 0.8912, Acc.ceiling: 0.9379, Acc.road: 0.9094, Acc.bed : 0.9601, Acc.windowpane: 0.7865, Acc.grass: 0.8607, Acc.cabinet: 0.6444, Acc.sidewalk: 0.8165, Acc.person: 0.9412, Acc.earth: 0.5777, Acc.door: 0.7356, Acc.table: 0.7410, Acc.mountain: 0.6902, Acc.plant: 0.6486, Acc.curtain: 0.8915, Acc.chair: 0.7483, Acc.car: 0.9238, Acc.water: 0.5664, Acc.painting: 0.8567, Acc.sofa: 0.9055, Acc.shelf: 0.6603, Acc.house: 0.7179, Acc.sea: 0.8168, Acc.mirror: 0.8097, Acc.rug: 0.7295, Acc.field: 0.6058, Acc.armchair: 0.6125, Acc.seat: 0.8962, Acc.fence: 0.6253, Acc.desk: 0.7397, Acc.rock: 0.7410, Acc.wardrobe: 0.7879, Acc.lamp: 0.8045, Acc.bathtub: 0.8431, Acc.railing: 0.6073, Acc.cushion: 0.7248, Acc.base: 0.5818, Acc.box: 0.3617, Acc.column: 0.6744, Acc.signboard: 0.5334, Acc.chest of drawers: 0.7264, Acc.counter: 0.6710, Acc.sand: 0.6054, Acc.sink: 0.7958, Acc.skyscraper: 0.6676, Acc.fireplace: 0.6788, Acc.refrigerator: 0.8800, Acc.grandstand: 0.7697, Acc.path: 0.2227, Acc.stairs: 0.4127, Acc.runway: 0.9667, Acc.case: 0.8955, Acc.pool table: 0.9820, Acc.pillow: 0.7051, Acc.screen door: 0.9708, Acc.stairway: 0.3944, Acc.river: 0.5396, Acc.bridge: 0.8815, Acc.bookcase: 0.2749, Acc.blind: 0.6977, Acc.coffee table: 0.8380, Acc.toilet: 0.8989, Acc.flower: 0.4834, Acc.book: 0.7564, Acc.hill: 0.1956, Acc.bench: 0.5100, Acc.countertop: 0.6240, Acc.stove: 0.9159, Acc.palm: 0.7669, Acc.kitchen island: 0.8684, Acc.computer: 0.9169, Acc.swivel chair: 0.6859, Acc.boat: 0.7215, Acc.bar: 0.6788, Acc.arcade machine: 0.9938, Acc.hovel: 0.5902, Acc.bus: 0.9638, Acc.towel: 0.7020, Acc.light: 0.6014, Acc.truck: 0.5765, Acc.tower: 0.4310, Acc.chandelier: 0.8561, Acc.awning: 0.5253, Acc.streetlight: 0.3917, Acc.booth: 0.8389, Acc.television receiver: 0.8391, Acc.airplane: 0.8813, Acc.dirt track: 0.3580, Acc.apparel: 0.3600, Acc.pole: 0.2208, Acc.land: 0.1243, Acc.bannister: 0.0716, Acc.escalator: 0.6816, Acc.ottoman: 0.6339, Acc.bottle: 0.5305, Acc.buffet: 0.9097, Acc.poster: 0.3280, Acc.stage: 0.1349, Acc.van: 0.6315, Acc.ship: 0.0149, Acc.fountain: 0.3967, Acc.conveyer belt: 0.9601, Acc.canopy: 0.5624, Acc.washer: 0.9676, Acc.plaything: 0.4832, Acc.swimming pool: 0.8380, Acc.stool: 0.4169, Acc.barrel: 0.6834, Acc.basket: 0.4959, Acc.waterfall: 0.9423, Acc.tent: 0.9793, Acc.bag: 0.1861, Acc.minibike: 0.8566, Acc.cradle: 0.9800, Acc.oven: 0.7541, Acc.ball: 0.6944, Acc.food: 0.5040, Acc.step: 0.1931, Acc.tank: 0.9647, Acc.trade name: 0.0105, Acc.microwave: 0.9497, Acc.pot: 0.5682, Acc.animal: 0.7560, Acc.bicycle: 0.6931, Acc.lake: 0.0000, Acc.dishwasher: 0.7224, Acc.screen: 0.9473, Acc.blanket: 0.2807, Acc.sculpture: 0.6188, Acc.hood: 0.7362, Acc.sconce: 0.7038, Acc.vase: 0.4976, Acc.traffic light: 0.4963, Acc.tray: 0.0593, Acc.ashcan: 0.5996, Acc.fan: 0.7677, Acc.pier: 0.4262, Acc.crt screen: 0.0000, Acc.plate: 0.7942, Acc.monitor: 0.8446, Acc.bulletin board: 0.6211, Acc.shower: 0.0000, Acc.radiator: 0.7097, Acc.glass: 0.0860, Acc.clock: 0.3618, Acc.flag: 0.6944 +2024-06-16 01:39:52,883 - mmseg - INFO - Iter [10050/80000] lr: 3.498e-05, eta: 1 day, 5:24:38, time: 3.310, data_time: 1.944, memory: 70722, decode.loss_ce: 0.4545, decode.acc_seg: 83.5358, aux.loss_ce: 0.1820, aux.acc_seg: 83.3673, loss: 0.6365 +2024-06-16 01:41:01,072 - mmseg - INFO - Iter [10100/80000] lr: 3.495e-05, eta: 1 day, 5:22:31, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4537, decode.acc_seg: 82.4625, aux.loss_ce: 0.1803, aux.acc_seg: 82.6974, loss: 0.6340 +2024-06-16 01:42:11,547 - mmseg - INFO - Iter [10150/80000] lr: 3.493e-05, eta: 1 day, 5:20:40, time: 1.410, data_time: 0.052, memory: 70722, decode.loss_ce: 0.4016, decode.acc_seg: 84.0006, aux.loss_ce: 0.1609, aux.acc_seg: 84.0164, loss: 0.5625 +2024-06-16 01:43:19,897 - mmseg - INFO - Iter [10200/80000] lr: 3.490e-05, eta: 1 day, 5:18:34, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3810, decode.acc_seg: 84.9637, aux.loss_ce: 0.1549, aux.acc_seg: 84.6057, loss: 0.5359 +2024-06-16 01:44:28,121 - mmseg - INFO - Iter [10250/80000] lr: 3.488e-05, eta: 1 day, 5:16:29, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3763, decode.acc_seg: 85.1319, aux.loss_ce: 0.1520, aux.acc_seg: 85.0183, loss: 0.5283 +2024-06-16 01:45:36,457 - mmseg - INFO - Iter [10300/80000] lr: 3.485e-05, eta: 1 day, 5:14:24, time: 1.367, data_time: 0.009, memory: 70722, decode.loss_ce: 0.3689, decode.acc_seg: 85.1999, aux.loss_ce: 0.1471, aux.acc_seg: 85.3591, loss: 0.5160 +2024-06-16 01:46:44,679 - mmseg - INFO - Iter [10350/80000] lr: 3.483e-05, eta: 1 day, 5:12:20, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4016, decode.acc_seg: 83.7364, aux.loss_ce: 0.1615, aux.acc_seg: 83.7182, loss: 0.5632 +2024-06-16 01:47:53,167 - mmseg - INFO - Iter [10400/80000] lr: 3.480e-05, eta: 1 day, 5:10:17, time: 1.370, data_time: 0.009, memory: 70722, decode.loss_ce: 0.4097, decode.acc_seg: 83.8797, aux.loss_ce: 0.1643, aux.acc_seg: 83.9038, loss: 0.5740 +2024-06-16 01:49:01,281 - mmseg - INFO - Iter [10450/80000] lr: 3.478e-05, eta: 1 day, 5:08:13, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3877, decode.acc_seg: 84.3519, aux.loss_ce: 0.1553, aux.acc_seg: 84.2401, loss: 0.5430 +2024-06-16 01:50:09,551 - mmseg - INFO - Iter [10500/80000] lr: 3.475e-05, eta: 1 day, 5:06:10, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3904, decode.acc_seg: 84.6851, aux.loss_ce: 0.1568, aux.acc_seg: 84.6676, loss: 0.5472 +2024-06-16 01:51:17,924 - mmseg - INFO - Iter [10550/80000] lr: 3.473e-05, eta: 1 day, 5:04:09, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4144, decode.acc_seg: 84.4857, aux.loss_ce: 0.1670, aux.acc_seg: 84.2368, loss: 0.5815 +2024-06-16 01:52:26,322 - mmseg - INFO - Iter [10600/80000] lr: 3.470e-05, eta: 1 day, 5:02:08, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3829, decode.acc_seg: 85.0840, aux.loss_ce: 0.1541, aux.acc_seg: 85.1673, loss: 0.5369 +2024-06-16 01:53:34,693 - mmseg - INFO - Iter [10650/80000] lr: 3.468e-05, eta: 1 day, 5:00:08, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4103, decode.acc_seg: 83.9374, aux.loss_ce: 0.1646, aux.acc_seg: 83.9736, loss: 0.5749 +2024-06-16 01:54:43,085 - mmseg - INFO - Iter [10700/80000] lr: 3.465e-05, eta: 1 day, 4:58:08, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3815, decode.acc_seg: 84.6269, aux.loss_ce: 0.1538, aux.acc_seg: 84.4466, loss: 0.5353 +2024-06-16 01:55:51,521 - mmseg - INFO - Iter [10750/80000] lr: 3.463e-05, eta: 1 day, 4:56:09, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3970, decode.acc_seg: 84.5319, aux.loss_ce: 0.1585, aux.acc_seg: 84.7448, loss: 0.5555 +2024-06-16 01:56:59,640 - mmseg - INFO - Iter [10800/80000] lr: 3.460e-05, eta: 1 day, 4:54:08, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3996, decode.acc_seg: 84.3470, aux.loss_ce: 0.1601, aux.acc_seg: 84.2516, loss: 0.5596 +2024-06-16 01:58:07,925 - mmseg - INFO - Iter [10850/80000] lr: 3.458e-05, eta: 1 day, 4:52:09, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4001, decode.acc_seg: 84.3861, aux.loss_ce: 0.1614, aux.acc_seg: 84.3365, loss: 0.5616 +2024-06-16 01:59:16,274 - mmseg - INFO - Iter [10900/80000] lr: 3.455e-05, eta: 1 day, 4:50:11, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4062, decode.acc_seg: 84.0828, aux.loss_ce: 0.1613, aux.acc_seg: 84.0687, loss: 0.5674 +2024-06-16 02:00:24,733 - mmseg - INFO - Iter [10950/80000] lr: 3.453e-05, eta: 1 day, 4:48:14, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4236, decode.acc_seg: 83.2922, aux.loss_ce: 0.1701, aux.acc_seg: 83.2425, loss: 0.5937 +2024-06-16 02:01:33,014 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 02:01:33,014 - mmseg - INFO - Iter [11000/80000] lr: 3.450e-05, eta: 1 day, 4:46:16, time: 1.366, data_time: 0.009, memory: 70722, decode.loss_ce: 0.4230, decode.acc_seg: 83.4625, aux.loss_ce: 0.1694, aux.acc_seg: 83.5418, loss: 0.5924 +2024-06-16 02:03:08,796 - mmseg - INFO - per class results: +2024-06-16 02:03:08,802 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.02 | 86.89 | +| building | 83.42 | 93.95 | +| sky | 94.12 | 97.27 | +| floor | 81.31 | 87.01 | +| tree | 75.41 | 91.49 | +| ceiling | 85.6 | 92.82 | +| road | 83.29 | 87.86 | +| bed | 89.62 | 96.96 | +| windowpane | 62.86 | 84.04 | +| grass | 63.06 | 77.91 | +| cabinet | 60.31 | 67.91 | +| sidewalk | 68.76 | 84.49 | +| person | 81.1 | 88.56 | +| earth | 29.52 | 38.75 | +| door | 50.13 | 59.81 | +| table | 64.55 | 77.39 | +| mountain | 55.4 | 71.55 | +| plant | 50.52 | 56.63 | +| curtain | 78.03 | 88.4 | +| chair | 61.92 | 75.6 | +| car | 84.76 | 91.48 | +| water | 63.33 | 78.76 | +| painting | 74.27 | 90.97 | +| sofa | 78.13 | 93.31 | +| shelf | 49.38 | 71.84 | +| house | 50.72 | 57.4 | +| sea | 71.14 | 82.74 | +| mirror | 72.23 | 86.32 | +| rug | 69.79 | 83.3 | +| field | 30.17 | 71.38 | +| armchair | 58.18 | 71.6 | +| seat | 65.46 | 81.39 | +| fence | 47.02 | 68.86 | +| desk | 52.19 | 67.2 | +| rock | 52.79 | 82.35 | +| wardrobe | 54.96 | 74.92 | +| lamp | 68.84 | 80.1 | +| bathtub | 81.03 | 84.18 | +| railing | 29.31 | 34.59 | +| cushion | 63.59 | 70.96 | +| base | 30.59 | 75.89 | +| box | 27.98 | 39.33 | +| column | 47.07 | 58.32 | +| signboard | 38.3 | 54.84 | +| chest of drawers | 42.7 | 76.36 | +| counter | 40.55 | 51.16 | +| sand | 45.87 | 80.15 | +| sink | 70.9 | 79.22 | +| skyscraper | 49.26 | 66.03 | +| fireplace | 67.93 | 95.73 | +| refrigerator | 67.98 | 94.03 | +| grandstand | 46.46 | 88.19 | +| path | 25.6 | 32.62 | +| stairs | 29.5 | 33.93 | +| runway | 63.96 | 96.36 | +| case | 61.01 | 77.15 | +| pool table | 92.29 | 98.98 | +| pillow | 56.61 | 63.67 | +| screen door | 73.45 | 93.58 | +| stairway | 50.1 | 77.33 | +| river | 16.36 | 31.08 | +| bridge | 66.06 | 86.72 | +| bookcase | 28.96 | 30.9 | +| blind | 40.46 | 43.97 | +| coffee table | 63.51 | 86.84 | +| toilet | 86.31 | 92.61 | +| flower | 38.77 | 46.84 | +| book | 44.53 | 60.46 | +| hill | 3.53 | 4.14 | +| bench | 52.88 | 65.34 | +| countertop | 54.26 | 82.48 | +| stove | 79.92 | 86.97 | +| palm | 47.06 | 64.86 | +| kitchen island | 38.23 | 91.97 | +| computer | 69.84 | 91.07 | +| swivel chair | 45.16 | 73.43 | +| boat | 42.67 | 89.09 | +| bar | 64.66 | 82.14 | +| arcade machine | 86.96 | 98.17 | +| hovel | 42.0 | 44.52 | +| bus | 91.33 | 95.32 | +| towel | 67.36 | 76.71 | +| light | 46.88 | 49.85 | +| truck | 40.56 | 55.32 | +| tower | 36.98 | 49.89 | +| chandelier | 69.22 | 86.49 | +| awning | 29.03 | 34.51 | +| streetlight | 23.75 | 29.48 | +| booth | 51.66 | 77.76 | +| television receiver | 68.97 | 75.26 | +| airplane | 66.5 | 87.91 | +| dirt track | 0.04 | 0.05 | +| apparel | 47.79 | 63.77 | +| pole | 21.08 | 24.85 | +| land | 0.0 | 0.0 | +| bannister | 20.82 | 35.46 | +| escalator | 45.33 | 82.07 | +| ottoman | 46.09 | 62.23 | +| bottle | 27.01 | 34.47 | +| buffet | 50.46 | 76.03 | +| poster | 35.24 | 41.2 | +| stage | 20.01 | 34.75 | +| van | 46.28 | 68.27 | +| ship | 68.51 | 97.17 | +| fountain | 38.53 | 40.98 | +| conveyer belt | 20.11 | 100.0 | +| canopy | 27.35 | 44.04 | +| washer | 85.41 | 93.11 | +| plaything | 17.4 | 24.38 | +| swimming pool | 59.6 | 95.95 | +| stool | 54.0 | 67.35 | +| barrel | 46.03 | 65.12 | +| basket | 33.58 | 46.59 | +| waterfall | 54.25 | 61.53 | +| tent | 71.73 | 98.97 | +| bag | 14.24 | 15.3 | +| minibike | 71.39 | 84.32 | +| cradle | 79.9 | 97.54 | +| oven | 53.32 | 75.46 | +| ball | 48.58 | 59.2 | +| food | 61.51 | 66.37 | +| step | 17.48 | 22.44 | +| tank | 51.27 | 72.62 | +| trade name | 14.04 | 14.92 | +| microwave | 84.27 | 95.36 | +| pot | 49.11 | 57.79 | +| animal | 46.42 | 47.02 | +| bicycle | 54.94 | 65.23 | +| lake | 45.55 | 71.21 | +| dishwasher | 61.09 | 83.37 | +| screen | 60.44 | 93.96 | +| blanket | 18.95 | 21.21 | +| sculpture | 40.35 | 64.71 | +| hood | 62.04 | 72.56 | +| sconce | 41.01 | 44.92 | +| vase | 37.51 | 48.39 | +| traffic light | 28.88 | 50.79 | +| tray | 15.49 | 19.84 | +| ashcan | 44.94 | 57.36 | +| fan | 58.77 | 67.55 | +| pier | 49.95 | 77.34 | +| crt screen | 0.0 | 0.0 | +| plate | 54.97 | 68.04 | +| monitor | 62.06 | 84.32 | +| bulletin board | 59.99 | 72.78 | +| shower | 0.0 | 0.0 | +| radiator | 59.55 | 66.27 | +| glass | 10.76 | 11.18 | +| clock | 34.07 | 41.36 | +| flag | 67.04 | 69.28 | ++---------------------+-------+-------+ +2024-06-16 02:03:08,802 - mmseg - INFO - Summary: +2024-06-16 02:03:08,802 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 83.79 | 51.63 | 66.35 | ++-------+-------+-------+ +2024-06-16 02:03:08,802 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 02:03:08,803 - mmseg - INFO - Iter(val) [250] aAcc: 0.8379, mIoU: 0.5163, mAcc: 0.6635, IoU.wall: 0.7902, IoU.building: 0.8342, IoU.sky: 0.9412, IoU.floor: 0.8131, IoU.tree: 0.7541, IoU.ceiling: 0.8560, IoU.road: 0.8329, IoU.bed : 0.8962, IoU.windowpane: 0.6286, IoU.grass: 0.6306, IoU.cabinet: 0.6031, IoU.sidewalk: 0.6876, IoU.person: 0.8110, IoU.earth: 0.2952, IoU.door: 0.5013, IoU.table: 0.6455, IoU.mountain: 0.5540, IoU.plant: 0.5052, IoU.curtain: 0.7803, IoU.chair: 0.6192, IoU.car: 0.8476, IoU.water: 0.6333, IoU.painting: 0.7427, IoU.sofa: 0.7813, IoU.shelf: 0.4938, IoU.house: 0.5072, IoU.sea: 0.7114, IoU.mirror: 0.7223, IoU.rug: 0.6979, IoU.field: 0.3017, IoU.armchair: 0.5818, IoU.seat: 0.6546, IoU.fence: 0.4702, IoU.desk: 0.5219, IoU.rock: 0.5279, IoU.wardrobe: 0.5496, IoU.lamp: 0.6884, IoU.bathtub: 0.8103, IoU.railing: 0.2931, IoU.cushion: 0.6359, IoU.base: 0.3059, IoU.box: 0.2798, IoU.column: 0.4707, IoU.signboard: 0.3830, IoU.chest of drawers: 0.4270, IoU.counter: 0.4055, IoU.sand: 0.4587, IoU.sink: 0.7090, IoU.skyscraper: 0.4926, IoU.fireplace: 0.6793, IoU.refrigerator: 0.6798, IoU.grandstand: 0.4646, IoU.path: 0.2560, IoU.stairs: 0.2950, IoU.runway: 0.6396, IoU.case: 0.6101, IoU.pool table: 0.9229, IoU.pillow: 0.5661, IoU.screen door: 0.7345, IoU.stairway: 0.5010, IoU.river: 0.1636, IoU.bridge: 0.6606, IoU.bookcase: 0.2896, IoU.blind: 0.4046, IoU.coffee table: 0.6351, IoU.toilet: 0.8631, IoU.flower: 0.3877, IoU.book: 0.4453, IoU.hill: 0.0353, IoU.bench: 0.5288, IoU.countertop: 0.5426, IoU.stove: 0.7992, IoU.palm: 0.4706, IoU.kitchen island: 0.3823, IoU.computer: 0.6984, IoU.swivel chair: 0.4516, IoU.boat: 0.4267, IoU.bar: 0.6466, IoU.arcade machine: 0.8696, IoU.hovel: 0.4200, IoU.bus: 0.9133, IoU.towel: 0.6736, IoU.light: 0.4688, IoU.truck: 0.4056, IoU.tower: 0.3698, IoU.chandelier: 0.6922, IoU.awning: 0.2903, IoU.streetlight: 0.2375, IoU.booth: 0.5166, IoU.television receiver: 0.6897, IoU.airplane: 0.6650, IoU.dirt track: 0.0004, IoU.apparel: 0.4779, IoU.pole: 0.2108, IoU.land: 0.0000, IoU.bannister: 0.2082, IoU.escalator: 0.4533, IoU.ottoman: 0.4609, IoU.bottle: 0.2701, IoU.buffet: 0.5046, IoU.poster: 0.3524, IoU.stage: 0.2001, IoU.van: 0.4628, IoU.ship: 0.6851, IoU.fountain: 0.3853, IoU.conveyer belt: 0.2011, IoU.canopy: 0.2735, IoU.washer: 0.8541, IoU.plaything: 0.1740, IoU.swimming pool: 0.5960, IoU.stool: 0.5400, IoU.barrel: 0.4603, IoU.basket: 0.3358, IoU.waterfall: 0.5425, IoU.tent: 0.7173, IoU.bag: 0.1424, IoU.minibike: 0.7139, IoU.cradle: 0.7990, IoU.oven: 0.5332, IoU.ball: 0.4858, IoU.food: 0.6151, IoU.step: 0.1748, IoU.tank: 0.5127, IoU.trade name: 0.1404, IoU.microwave: 0.8427, IoU.pot: 0.4911, IoU.animal: 0.4642, IoU.bicycle: 0.5494, IoU.lake: 0.4555, IoU.dishwasher: 0.6109, IoU.screen: 0.6044, IoU.blanket: 0.1895, IoU.sculpture: 0.4035, IoU.hood: 0.6204, IoU.sconce: 0.4101, IoU.vase: 0.3751, IoU.traffic light: 0.2888, IoU.tray: 0.1549, IoU.ashcan: 0.4494, IoU.fan: 0.5877, IoU.pier: 0.4995, IoU.crt screen: 0.0000, IoU.plate: 0.5497, IoU.monitor: 0.6206, IoU.bulletin board: 0.5999, IoU.shower: 0.0000, IoU.radiator: 0.5955, IoU.glass: 0.1076, IoU.clock: 0.3407, IoU.flag: 0.6704, Acc.wall: 0.8689, Acc.building: 0.9395, Acc.sky: 0.9727, Acc.floor: 0.8701, Acc.tree: 0.9149, Acc.ceiling: 0.9282, Acc.road: 0.8786, Acc.bed : 0.9696, Acc.windowpane: 0.8404, Acc.grass: 0.7791, Acc.cabinet: 0.6791, Acc.sidewalk: 0.8449, Acc.person: 0.8856, Acc.earth: 0.3875, Acc.door: 0.5981, Acc.table: 0.7739, Acc.mountain: 0.7155, Acc.plant: 0.5663, Acc.curtain: 0.8840, Acc.chair: 0.7560, Acc.car: 0.9148, Acc.water: 0.7876, Acc.painting: 0.9097, Acc.sofa: 0.9331, Acc.shelf: 0.7184, Acc.house: 0.5740, Acc.sea: 0.8274, Acc.mirror: 0.8632, Acc.rug: 0.8330, Acc.field: 0.7138, Acc.armchair: 0.7160, Acc.seat: 0.8139, Acc.fence: 0.6886, Acc.desk: 0.6720, Acc.rock: 0.8235, Acc.wardrobe: 0.7492, Acc.lamp: 0.8010, Acc.bathtub: 0.8418, Acc.railing: 0.3459, Acc.cushion: 0.7096, Acc.base: 0.7589, Acc.box: 0.3933, Acc.column: 0.5832, Acc.signboard: 0.5484, Acc.chest of drawers: 0.7636, Acc.counter: 0.5116, Acc.sand: 0.8015, Acc.sink: 0.7922, Acc.skyscraper: 0.6603, Acc.fireplace: 0.9573, Acc.refrigerator: 0.9403, Acc.grandstand: 0.8819, Acc.path: 0.3262, Acc.stairs: 0.3393, Acc.runway: 0.9636, Acc.case: 0.7715, Acc.pool table: 0.9898, Acc.pillow: 0.6367, Acc.screen door: 0.9358, Acc.stairway: 0.7733, Acc.river: 0.3108, Acc.bridge: 0.8672, Acc.bookcase: 0.3090, Acc.blind: 0.4397, Acc.coffee table: 0.8684, Acc.toilet: 0.9261, Acc.flower: 0.4684, Acc.book: 0.6046, Acc.hill: 0.0414, Acc.bench: 0.6534, Acc.countertop: 0.8248, Acc.stove: 0.8697, Acc.palm: 0.6486, Acc.kitchen island: 0.9197, Acc.computer: 0.9107, Acc.swivel chair: 0.7343, Acc.boat: 0.8909, Acc.bar: 0.8214, Acc.arcade machine: 0.9817, Acc.hovel: 0.4452, Acc.bus: 0.9532, Acc.towel: 0.7671, Acc.light: 0.4985, Acc.truck: 0.5532, Acc.tower: 0.4989, Acc.chandelier: 0.8649, Acc.awning: 0.3451, Acc.streetlight: 0.2948, Acc.booth: 0.7776, Acc.television receiver: 0.7526, Acc.airplane: 0.8791, Acc.dirt track: 0.0005, Acc.apparel: 0.6377, Acc.pole: 0.2485, Acc.land: 0.0000, Acc.bannister: 0.3546, Acc.escalator: 0.8207, Acc.ottoman: 0.6223, Acc.bottle: 0.3447, Acc.buffet: 0.7603, Acc.poster: 0.4120, Acc.stage: 0.3475, Acc.van: 0.6827, Acc.ship: 0.9717, Acc.fountain: 0.4098, Acc.conveyer belt: 1.0000, Acc.canopy: 0.4404, Acc.washer: 0.9311, Acc.plaything: 0.2438, Acc.swimming pool: 0.9595, Acc.stool: 0.6735, Acc.barrel: 0.6512, Acc.basket: 0.4659, Acc.waterfall: 0.6153, Acc.tent: 0.9897, Acc.bag: 0.1530, Acc.minibike: 0.8432, Acc.cradle: 0.9754, Acc.oven: 0.7546, Acc.ball: 0.5920, Acc.food: 0.6637, Acc.step: 0.2244, Acc.tank: 0.7262, Acc.trade name: 0.1492, Acc.microwave: 0.9536, Acc.pot: 0.5779, Acc.animal: 0.4702, Acc.bicycle: 0.6523, Acc.lake: 0.7121, Acc.dishwasher: 0.8337, Acc.screen: 0.9396, Acc.blanket: 0.2121, Acc.sculpture: 0.6471, Acc.hood: 0.7256, Acc.sconce: 0.4492, Acc.vase: 0.4839, Acc.traffic light: 0.5079, Acc.tray: 0.1984, Acc.ashcan: 0.5736, Acc.fan: 0.6755, Acc.pier: 0.7734, Acc.crt screen: 0.0000, Acc.plate: 0.6804, Acc.monitor: 0.8432, Acc.bulletin board: 0.7278, Acc.shower: 0.0000, Acc.radiator: 0.6627, Acc.glass: 0.1118, Acc.clock: 0.4136, Acc.flag: 0.6928 +2024-06-16 02:04:17,773 - mmseg - INFO - Iter [11050/80000] lr: 3.448e-05, eta: 1 day, 4:54:21, time: 3.295, data_time: 1.933, memory: 70722, decode.loss_ce: 0.4320, decode.acc_seg: 82.9018, aux.loss_ce: 0.1729, aux.acc_seg: 82.8955, loss: 0.6049 +2024-06-16 02:05:26,032 - mmseg - INFO - Iter [11100/80000] lr: 3.445e-05, eta: 1 day, 4:52:20, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4119, decode.acc_seg: 83.6862, aux.loss_ce: 0.1647, aux.acc_seg: 83.6899, loss: 0.5766 +2024-06-16 02:06:34,269 - mmseg - INFO - Iter [11150/80000] lr: 3.443e-05, eta: 1 day, 4:50:21, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4126, decode.acc_seg: 84.1966, aux.loss_ce: 0.1652, aux.acc_seg: 84.1855, loss: 0.5778 +2024-06-16 02:07:42,438 - mmseg - INFO - Iter [11200/80000] lr: 3.440e-05, eta: 1 day, 4:48:21, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4145, decode.acc_seg: 83.5477, aux.loss_ce: 0.1665, aux.acc_seg: 83.4955, loss: 0.5810 +2024-06-16 02:08:50,919 - mmseg - INFO - Iter [11250/80000] lr: 3.438e-05, eta: 1 day, 4:46:23, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4133, decode.acc_seg: 84.2021, aux.loss_ce: 0.1646, aux.acc_seg: 84.3500, loss: 0.5779 +2024-06-16 02:09:59,092 - mmseg - INFO - Iter [11300/80000] lr: 3.435e-05, eta: 1 day, 4:44:24, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4205, decode.acc_seg: 84.4217, aux.loss_ce: 0.1665, aux.acc_seg: 84.3208, loss: 0.5870 +2024-06-16 02:11:07,432 - mmseg - INFO - Iter [11350/80000] lr: 3.433e-05, eta: 1 day, 4:42:27, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4227, decode.acc_seg: 83.3248, aux.loss_ce: 0.1688, aux.acc_seg: 83.3068, loss: 0.5914 +2024-06-16 02:12:18,415 - mmseg - INFO - Iter [11400/80000] lr: 3.430e-05, eta: 1 day, 4:40:46, time: 1.420, data_time: 0.058, memory: 70722, decode.loss_ce: 0.3854, decode.acc_seg: 84.7699, aux.loss_ce: 0.1535, aux.acc_seg: 84.7492, loss: 0.5389 +2024-06-16 02:13:26,847 - mmseg - INFO - Iter [11450/80000] lr: 3.428e-05, eta: 1 day, 4:38:50, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3991, decode.acc_seg: 84.4239, aux.loss_ce: 0.1602, aux.acc_seg: 84.5255, loss: 0.5593 +2024-06-16 02:14:34,939 - mmseg - INFO - Iter [11500/80000] lr: 3.425e-05, eta: 1 day, 4:36:52, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3754, decode.acc_seg: 85.5433, aux.loss_ce: 0.1506, aux.acc_seg: 85.4751, loss: 0.5260 +2024-06-16 02:15:43,259 - mmseg - INFO - Iter [11550/80000] lr: 3.423e-05, eta: 1 day, 4:34:56, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3904, decode.acc_seg: 84.7504, aux.loss_ce: 0.1562, aux.acc_seg: 84.6501, loss: 0.5466 +2024-06-16 02:16:51,546 - mmseg - INFO - Iter [11600/80000] lr: 3.420e-05, eta: 1 day, 4:33:00, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3836, decode.acc_seg: 85.1492, aux.loss_ce: 0.1557, aux.acc_seg: 85.0341, loss: 0.5393 +2024-06-16 02:18:00,119 - mmseg - INFO - Iter [11650/80000] lr: 3.418e-05, eta: 1 day, 4:31:07, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4159, decode.acc_seg: 83.9031, aux.loss_ce: 0.1676, aux.acc_seg: 83.7816, loss: 0.5836 +2024-06-16 02:19:08,290 - mmseg - INFO - Iter [11700/80000] lr: 3.415e-05, eta: 1 day, 4:29:11, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3660, decode.acc_seg: 85.5486, aux.loss_ce: 0.1472, aux.acc_seg: 85.4829, loss: 0.5131 +2024-06-16 02:20:16,831 - mmseg - INFO - Iter [11750/80000] lr: 3.413e-05, eta: 1 day, 4:27:18, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4060, decode.acc_seg: 84.4314, aux.loss_ce: 0.1622, aux.acc_seg: 84.2535, loss: 0.5682 +2024-06-16 02:21:25,137 - mmseg - INFO - Iter [11800/80000] lr: 3.410e-05, eta: 1 day, 4:25:24, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3698, decode.acc_seg: 84.9385, aux.loss_ce: 0.1493, aux.acc_seg: 84.7479, loss: 0.5191 +2024-06-16 02:22:33,397 - mmseg - INFO - Iter [11850/80000] lr: 3.408e-05, eta: 1 day, 4:23:30, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3646, decode.acc_seg: 85.6953, aux.loss_ce: 0.1470, aux.acc_seg: 85.7023, loss: 0.5116 +2024-06-16 02:23:41,600 - mmseg - INFO - Iter [11900/80000] lr: 3.405e-05, eta: 1 day, 4:21:37, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3880, decode.acc_seg: 84.7730, aux.loss_ce: 0.1554, aux.acc_seg: 84.7216, loss: 0.5433 +2024-06-16 02:24:49,713 - mmseg - INFO - Iter [11950/80000] lr: 3.403e-05, eta: 1 day, 4:19:43, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3812, decode.acc_seg: 84.8139, aux.loss_ce: 0.1526, aux.acc_seg: 84.6682, loss: 0.5338 +2024-06-16 02:25:58,121 - mmseg - INFO - Saving checkpoint at 12000 iterations +2024-06-16 02:27:23,814 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 02:27:23,815 - mmseg - INFO - Iter [12000/80000] lr: 3.400e-05, eta: 1 day, 4:25:56, time: 3.082, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3867, decode.acc_seg: 84.4459, aux.loss_ce: 0.1544, aux.acc_seg: 84.4078, loss: 0.5411 +2024-06-16 02:28:59,977 - mmseg - INFO - per class results: +2024-06-16 02:28:59,983 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.55 | 88.26 | +| building | 83.88 | 93.77 | +| sky | 94.28 | 96.72 | +| floor | 83.86 | 90.89 | +| tree | 75.92 | 88.66 | +| ceiling | 84.94 | 93.21 | +| road | 85.62 | 90.75 | +| bed | 91.32 | 96.37 | +| windowpane | 64.12 | 82.23 | +| grass | 64.98 | 75.1 | +| cabinet | 61.06 | 71.58 | +| sidewalk | 69.49 | 85.08 | +| person | 84.13 | 91.98 | +| earth | 36.45 | 48.81 | +| door | 53.65 | 69.74 | +| table | 59.79 | 70.02 | +| mountain | 56.03 | 62.76 | +| plant | 56.26 | 63.74 | +| curtain | 78.13 | 87.23 | +| chair | 62.27 | 73.95 | +| car | 85.2 | 93.97 | +| water | 56.97 | 71.91 | +| painting | 74.57 | 84.85 | +| sofa | 66.8 | 71.17 | +| shelf | 33.41 | 42.73 | +| house | 54.35 | 65.34 | +| sea | 65.47 | 85.22 | +| mirror | 74.2 | 85.08 | +| rug | 70.53 | 77.97 | +| field | 34.26 | 77.68 | +| armchair | 45.88 | 85.36 | +| seat | 66.42 | 88.69 | +| fence | 46.63 | 69.49 | +| desk | 49.53 | 80.55 | +| rock | 49.16 | 86.48 | +| wardrobe | 48.9 | 77.07 | +| lamp | 67.92 | 77.87 | +| bathtub | 79.17 | 82.97 | +| railing | 39.45 | 57.85 | +| cushion | 62.09 | 71.04 | +| base | 33.78 | 56.65 | +| box | 29.53 | 42.23 | +| column | 52.49 | 69.97 | +| signboard | 34.4 | 42.91 | +| chest of drawers | 50.95 | 66.25 | +| counter | 43.32 | 54.51 | +| sand | 40.5 | 56.03 | +| sink | 68.95 | 84.97 | +| skyscraper | 48.06 | 66.25 | +| fireplace | 69.89 | 91.03 | +| refrigerator | 78.82 | 85.78 | +| grandstand | 50.22 | 77.58 | +| path | 33.51 | 41.4 | +| stairs | 14.29 | 15.5 | +| runway | 71.76 | 98.26 | +| case | 53.5 | 71.43 | +| pool table | 92.32 | 98.11 | +| pillow | 65.52 | 76.76 | +| screen door | 64.73 | 92.32 | +| stairway | 43.37 | 71.85 | +| river | 20.71 | 40.33 | +| bridge | 74.5 | 88.08 | +| bookcase | 33.23 | 54.69 | +| blind | 43.55 | 49.4 | +| coffee table | 48.27 | 90.8 | +| toilet | 85.92 | 92.72 | +| flower | 34.87 | 47.04 | +| book | 42.7 | 65.2 | +| hill | 6.96 | 9.7 | +| bench | 61.2 | 75.55 | +| countertop | 59.86 | 80.33 | +| stove | 81.21 | 85.33 | +| palm | 54.65 | 80.48 | +| kitchen island | 42.53 | 60.23 | +| computer | 73.56 | 88.14 | +| swivel chair | 49.83 | 71.3 | +| boat | 74.48 | 88.97 | +| bar | 68.75 | 76.4 | +| arcade machine | 75.6 | 79.99 | +| hovel | 52.53 | 60.95 | +| bus | 90.71 | 95.96 | +| towel | 68.12 | 79.58 | +| light | 50.17 | 54.35 | +| truck | 38.52 | 46.54 | +| tower | 27.55 | 57.57 | +| chandelier | 69.02 | 84.71 | +| awning | 42.21 | 50.18 | +| streetlight | 27.6 | 37.82 | +| booth | 21.43 | 84.16 | +| television receiver | 67.27 | 78.99 | +| airplane | 50.18 | 72.89 | +| dirt track | 0.43 | 0.56 | +| apparel | 47.2 | 75.24 | +| pole | 21.78 | 25.68 | +| land | 0.48 | 0.94 | +| bannister | 13.62 | 19.67 | +| escalator | 57.28 | 73.39 | +| ottoman | 47.9 | 66.2 | +| bottle | 39.01 | 65.67 | +| buffet | 44.51 | 57.18 | +| poster | 32.91 | 49.21 | +| stage | 15.18 | 23.56 | +| van | 43.95 | 56.49 | +| ship | 67.76 | 99.65 | +| fountain | 49.29 | 52.87 | +| conveyer belt | 66.38 | 95.41 | +| canopy | 32.2 | 35.75 | +| washer | 78.17 | 82.52 | +| plaything | 23.58 | 31.24 | +| swimming pool | 58.47 | 88.02 | +| stool | 50.74 | 59.78 | +| barrel | 61.24 | 68.75 | +| basket | 27.77 | 39.71 | +| waterfall | 63.24 | 72.18 | +| tent | 91.04 | 98.76 | +| bag | 16.58 | 17.92 | +| minibike | 69.88 | 81.8 | +| cradle | 75.54 | 98.47 | +| oven | 37.06 | 39.45 | +| ball | 43.63 | 68.07 | +| food | 65.21 | 76.34 | +| step | 9.03 | 10.35 | +| tank | 54.96 | 58.65 | +| trade name | 12.33 | 12.87 | +| microwave | 86.0 | 92.19 | +| pot | 50.85 | 60.77 | +| animal | 69.55 | 76.78 | +| bicycle | 50.34 | 65.04 | +| lake | 43.94 | 47.85 | +| dishwasher | 47.17 | 84.37 | +| screen | 56.98 | 94.28 | +| blanket | 24.46 | 26.96 | +| sculpture | 51.17 | 64.86 | +| hood | 66.16 | 71.01 | +| sconce | 42.05 | 47.22 | +| vase | 37.44 | 50.25 | +| traffic light | 28.38 | 47.53 | +| tray | 18.61 | 30.15 | +| ashcan | 43.73 | 52.37 | +| fan | 62.55 | 74.93 | +| pier | 37.37 | 45.0 | +| crt screen | 0.49 | 1.38 | +| plate | 55.46 | 71.31 | +| monitor | 10.38 | 11.24 | +| bulletin board | 54.49 | 67.07 | +| shower | 0.0 | 0.0 | +| radiator | 57.94 | 69.98 | +| glass | 13.01 | 13.76 | +| clock | 29.21 | 30.84 | +| flag | 67.69 | 70.19 | ++---------------------+-------+-------+ +2024-06-16 02:28:59,983 - mmseg - INFO - Summary: +2024-06-16 02:28:59,983 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.21 | 51.99 | 65.41 | ++-------+-------+-------+ +2024-06-16 02:28:59,984 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 02:28:59,984 - mmseg - INFO - Iter(val) [250] aAcc: 0.8421, mIoU: 0.5199, mAcc: 0.6541, IoU.wall: 0.7955, IoU.building: 0.8388, IoU.sky: 0.9428, IoU.floor: 0.8386, IoU.tree: 0.7592, IoU.ceiling: 0.8494, IoU.road: 0.8562, IoU.bed : 0.9132, IoU.windowpane: 0.6412, IoU.grass: 0.6498, IoU.cabinet: 0.6106, IoU.sidewalk: 0.6949, IoU.person: 0.8413, IoU.earth: 0.3645, IoU.door: 0.5365, IoU.table: 0.5979, IoU.mountain: 0.5603, IoU.plant: 0.5626, IoU.curtain: 0.7813, IoU.chair: 0.6227, IoU.car: 0.8520, IoU.water: 0.5697, IoU.painting: 0.7457, IoU.sofa: 0.6680, IoU.shelf: 0.3341, IoU.house: 0.5435, IoU.sea: 0.6547, IoU.mirror: 0.7420, IoU.rug: 0.7053, IoU.field: 0.3426, IoU.armchair: 0.4588, IoU.seat: 0.6642, IoU.fence: 0.4663, IoU.desk: 0.4953, IoU.rock: 0.4916, IoU.wardrobe: 0.4890, IoU.lamp: 0.6792, IoU.bathtub: 0.7917, IoU.railing: 0.3945, IoU.cushion: 0.6209, IoU.base: 0.3378, IoU.box: 0.2953, IoU.column: 0.5249, IoU.signboard: 0.3440, IoU.chest of drawers: 0.5095, IoU.counter: 0.4332, IoU.sand: 0.4050, IoU.sink: 0.6895, IoU.skyscraper: 0.4806, IoU.fireplace: 0.6989, IoU.refrigerator: 0.7882, IoU.grandstand: 0.5022, IoU.path: 0.3351, IoU.stairs: 0.1429, IoU.runway: 0.7176, IoU.case: 0.5350, IoU.pool table: 0.9232, IoU.pillow: 0.6552, IoU.screen door: 0.6473, IoU.stairway: 0.4337, IoU.river: 0.2071, IoU.bridge: 0.7450, IoU.bookcase: 0.3323, IoU.blind: 0.4355, IoU.coffee table: 0.4827, IoU.toilet: 0.8592, IoU.flower: 0.3487, IoU.book: 0.4270, IoU.hill: 0.0696, IoU.bench: 0.6120, IoU.countertop: 0.5986, IoU.stove: 0.8121, IoU.palm: 0.5465, IoU.kitchen island: 0.4253, IoU.computer: 0.7356, IoU.swivel chair: 0.4983, IoU.boat: 0.7448, IoU.bar: 0.6875, IoU.arcade machine: 0.7560, IoU.hovel: 0.5253, IoU.bus: 0.9071, IoU.towel: 0.6812, IoU.light: 0.5017, IoU.truck: 0.3852, IoU.tower: 0.2755, IoU.chandelier: 0.6902, IoU.awning: 0.4221, IoU.streetlight: 0.2760, IoU.booth: 0.2143, IoU.television receiver: 0.6727, IoU.airplane: 0.5018, IoU.dirt track: 0.0043, IoU.apparel: 0.4720, IoU.pole: 0.2178, IoU.land: 0.0048, IoU.bannister: 0.1362, IoU.escalator: 0.5728, IoU.ottoman: 0.4790, IoU.bottle: 0.3901, IoU.buffet: 0.4451, IoU.poster: 0.3291, IoU.stage: 0.1518, IoU.van: 0.4395, IoU.ship: 0.6776, IoU.fountain: 0.4929, IoU.conveyer belt: 0.6638, IoU.canopy: 0.3220, IoU.washer: 0.7817, IoU.plaything: 0.2358, IoU.swimming pool: 0.5847, IoU.stool: 0.5074, IoU.barrel: 0.6124, IoU.basket: 0.2777, IoU.waterfall: 0.6324, IoU.tent: 0.9104, IoU.bag: 0.1658, IoU.minibike: 0.6988, IoU.cradle: 0.7554, IoU.oven: 0.3706, IoU.ball: 0.4363, IoU.food: 0.6521, IoU.step: 0.0903, IoU.tank: 0.5496, IoU.trade name: 0.1233, IoU.microwave: 0.8600, IoU.pot: 0.5085, IoU.animal: 0.6955, IoU.bicycle: 0.5034, IoU.lake: 0.4394, IoU.dishwasher: 0.4717, IoU.screen: 0.5698, IoU.blanket: 0.2446, IoU.sculpture: 0.5117, IoU.hood: 0.6616, IoU.sconce: 0.4205, IoU.vase: 0.3744, IoU.traffic light: 0.2838, IoU.tray: 0.1861, IoU.ashcan: 0.4373, IoU.fan: 0.6255, IoU.pier: 0.3737, IoU.crt screen: 0.0049, IoU.plate: 0.5546, IoU.monitor: 0.1038, IoU.bulletin board: 0.5449, IoU.shower: 0.0000, IoU.radiator: 0.5794, IoU.glass: 0.1301, IoU.clock: 0.2921, IoU.flag: 0.6769, Acc.wall: 0.8826, Acc.building: 0.9377, Acc.sky: 0.9672, Acc.floor: 0.9089, Acc.tree: 0.8866, Acc.ceiling: 0.9321, Acc.road: 0.9075, Acc.bed : 0.9637, Acc.windowpane: 0.8223, Acc.grass: 0.7510, Acc.cabinet: 0.7158, Acc.sidewalk: 0.8508, Acc.person: 0.9198, Acc.earth: 0.4881, Acc.door: 0.6974, Acc.table: 0.7002, Acc.mountain: 0.6276, Acc.plant: 0.6374, Acc.curtain: 0.8723, Acc.chair: 0.7395, Acc.car: 0.9397, Acc.water: 0.7191, Acc.painting: 0.8485, Acc.sofa: 0.7117, Acc.shelf: 0.4273, Acc.house: 0.6534, Acc.sea: 0.8522, Acc.mirror: 0.8508, Acc.rug: 0.7797, Acc.field: 0.7768, Acc.armchair: 0.8536, Acc.seat: 0.8869, Acc.fence: 0.6949, Acc.desk: 0.8055, Acc.rock: 0.8648, Acc.wardrobe: 0.7707, Acc.lamp: 0.7787, Acc.bathtub: 0.8297, Acc.railing: 0.5785, Acc.cushion: 0.7104, Acc.base: 0.5665, Acc.box: 0.4223, Acc.column: 0.6997, Acc.signboard: 0.4291, Acc.chest of drawers: 0.6625, Acc.counter: 0.5451, Acc.sand: 0.5603, Acc.sink: 0.8497, Acc.skyscraper: 0.6625, Acc.fireplace: 0.9103, Acc.refrigerator: 0.8578, Acc.grandstand: 0.7758, Acc.path: 0.4140, Acc.stairs: 0.1550, Acc.runway: 0.9826, Acc.case: 0.7143, Acc.pool table: 0.9811, Acc.pillow: 0.7676, Acc.screen door: 0.9232, Acc.stairway: 0.7185, Acc.river: 0.4033, Acc.bridge: 0.8808, Acc.bookcase: 0.5469, Acc.blind: 0.4940, Acc.coffee table: 0.9080, Acc.toilet: 0.9272, Acc.flower: 0.4704, Acc.book: 0.6520, Acc.hill: 0.0970, Acc.bench: 0.7555, Acc.countertop: 0.8033, Acc.stove: 0.8533, Acc.palm: 0.8048, Acc.kitchen island: 0.6023, Acc.computer: 0.8814, Acc.swivel chair: 0.7130, Acc.boat: 0.8897, Acc.bar: 0.7640, Acc.arcade machine: 0.7999, Acc.hovel: 0.6095, Acc.bus: 0.9596, Acc.towel: 0.7958, Acc.light: 0.5435, Acc.truck: 0.4654, Acc.tower: 0.5757, Acc.chandelier: 0.8471, Acc.awning: 0.5018, Acc.streetlight: 0.3782, Acc.booth: 0.8416, Acc.television receiver: 0.7899, Acc.airplane: 0.7289, Acc.dirt track: 0.0056, Acc.apparel: 0.7524, Acc.pole: 0.2568, Acc.land: 0.0094, Acc.bannister: 0.1967, Acc.escalator: 0.7339, Acc.ottoman: 0.6620, Acc.bottle: 0.6567, Acc.buffet: 0.5718, Acc.poster: 0.4921, Acc.stage: 0.2356, Acc.van: 0.5649, Acc.ship: 0.9965, Acc.fountain: 0.5287, Acc.conveyer belt: 0.9541, Acc.canopy: 0.3575, Acc.washer: 0.8252, Acc.plaything: 0.3124, Acc.swimming pool: 0.8802, Acc.stool: 0.5978, Acc.barrel: 0.6875, Acc.basket: 0.3971, Acc.waterfall: 0.7218, Acc.tent: 0.9876, Acc.bag: 0.1792, Acc.minibike: 0.8180, Acc.cradle: 0.9847, Acc.oven: 0.3945, Acc.ball: 0.6807, Acc.food: 0.7634, Acc.step: 0.1035, Acc.tank: 0.5865, Acc.trade name: 0.1287, Acc.microwave: 0.9219, Acc.pot: 0.6077, Acc.animal: 0.7678, Acc.bicycle: 0.6504, Acc.lake: 0.4785, Acc.dishwasher: 0.8437, Acc.screen: 0.9428, Acc.blanket: 0.2696, Acc.sculpture: 0.6486, Acc.hood: 0.7101, Acc.sconce: 0.4722, Acc.vase: 0.5025, Acc.traffic light: 0.4753, Acc.tray: 0.3015, Acc.ashcan: 0.5237, Acc.fan: 0.7493, Acc.pier: 0.4500, Acc.crt screen: 0.0138, Acc.plate: 0.7131, Acc.monitor: 0.1124, Acc.bulletin board: 0.6707, Acc.shower: 0.0000, Acc.radiator: 0.6998, Acc.glass: 0.1376, Acc.clock: 0.3084, Acc.flag: 0.7019 +2024-06-16 02:30:08,741 - mmseg - INFO - Iter [12050/80000] lr: 3.398e-05, eta: 1 day, 4:33:07, time: 3.299, data_time: 1.940, memory: 70722, decode.loss_ce: 0.3861, decode.acc_seg: 85.3614, aux.loss_ce: 0.1549, aux.acc_seg: 85.1621, loss: 0.5410 +2024-06-16 02:31:16,984 - mmseg - INFO - Iter [12100/80000] lr: 3.395e-05, eta: 1 day, 4:31:10, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3762, decode.acc_seg: 85.1362, aux.loss_ce: 0.1501, aux.acc_seg: 85.0603, loss: 0.5263 +2024-06-16 02:32:25,331 - mmseg - INFO - Iter [12150/80000] lr: 3.393e-05, eta: 1 day, 4:29:13, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4161, decode.acc_seg: 83.5359, aux.loss_ce: 0.1668, aux.acc_seg: 83.4440, loss: 0.5829 +2024-06-16 02:33:33,800 - mmseg - INFO - Iter [12200/80000] lr: 3.390e-05, eta: 1 day, 4:27:18, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3678, decode.acc_seg: 85.3645, aux.loss_ce: 0.1484, aux.acc_seg: 85.2191, loss: 0.5163 +2024-06-16 02:34:41,844 - mmseg - INFO - Iter [12250/80000] lr: 3.388e-05, eta: 1 day, 4:25:21, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3943, decode.acc_seg: 84.3982, aux.loss_ce: 0.1571, aux.acc_seg: 84.4636, loss: 0.5514 +2024-06-16 02:35:50,103 - mmseg - INFO - Iter [12300/80000] lr: 3.385e-05, eta: 1 day, 4:23:26, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3844, decode.acc_seg: 84.4372, aux.loss_ce: 0.1538, aux.acc_seg: 84.4211, loss: 0.5382 +2024-06-16 02:36:58,368 - mmseg - INFO - Iter [12350/80000] lr: 3.383e-05, eta: 1 day, 4:21:31, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4038, decode.acc_seg: 84.5103, aux.loss_ce: 0.1607, aux.acc_seg: 84.3694, loss: 0.5645 +2024-06-16 02:38:06,769 - mmseg - INFO - Iter [12400/80000] lr: 3.380e-05, eta: 1 day, 4:19:37, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3795, decode.acc_seg: 85.0203, aux.loss_ce: 0.1524, aux.acc_seg: 84.8807, loss: 0.5319 +2024-06-16 02:39:15,031 - mmseg - INFO - Iter [12450/80000] lr: 3.378e-05, eta: 1 day, 4:17:43, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3843, decode.acc_seg: 85.2159, aux.loss_ce: 0.1560, aux.acc_seg: 85.0092, loss: 0.5403 +2024-06-16 02:40:23,458 - mmseg - INFO - Iter [12500/80000] lr: 3.375e-05, eta: 1 day, 4:15:50, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4010, decode.acc_seg: 84.6306, aux.loss_ce: 0.1610, aux.acc_seg: 84.4654, loss: 0.5621 +2024-06-16 02:41:32,071 - mmseg - INFO - Iter [12550/80000] lr: 3.373e-05, eta: 1 day, 4:13:58, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4041, decode.acc_seg: 84.1244, aux.loss_ce: 0.1608, aux.acc_seg: 84.0483, loss: 0.5649 +2024-06-16 02:42:40,243 - mmseg - INFO - Iter [12600/80000] lr: 3.370e-05, eta: 1 day, 4:12:04, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3805, decode.acc_seg: 84.6900, aux.loss_ce: 0.1532, aux.acc_seg: 84.6926, loss: 0.5337 +2024-06-16 02:43:50,711 - mmseg - INFO - Iter [12650/80000] lr: 3.368e-05, eta: 1 day, 4:10:23, time: 1.409, data_time: 0.051, memory: 70722, decode.loss_ce: 0.3791, decode.acc_seg: 84.9347, aux.loss_ce: 0.1527, aux.acc_seg: 84.8718, loss: 0.5319 +2024-06-16 02:44:58,932 - mmseg - INFO - Iter [12700/80000] lr: 3.365e-05, eta: 1 day, 4:08:30, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3411, decode.acc_seg: 86.3868, aux.loss_ce: 0.1368, aux.acc_seg: 86.3993, loss: 0.4779 +2024-06-16 02:46:07,179 - mmseg - INFO - Iter [12750/80000] lr: 3.363e-05, eta: 1 day, 4:06:38, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3626, decode.acc_seg: 85.5682, aux.loss_ce: 0.1468, aux.acc_seg: 85.2614, loss: 0.5094 +2024-06-16 02:47:15,549 - mmseg - INFO - Iter [12800/80000] lr: 3.360e-05, eta: 1 day, 4:04:47, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3777, decode.acc_seg: 84.9202, aux.loss_ce: 0.1515, aux.acc_seg: 84.8064, loss: 0.5292 +2024-06-16 02:48:23,793 - mmseg - INFO - Iter [12850/80000] lr: 3.358e-05, eta: 1 day, 4:02:55, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3858, decode.acc_seg: 84.8445, aux.loss_ce: 0.1537, aux.acc_seg: 84.8020, loss: 0.5395 +2024-06-16 02:49:31,993 - mmseg - INFO - Iter [12900/80000] lr: 3.355e-05, eta: 1 day, 4:01:03, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3672, decode.acc_seg: 85.2831, aux.loss_ce: 0.1475, aux.acc_seg: 85.2782, loss: 0.5147 +2024-06-16 02:50:40,242 - mmseg - INFO - Iter [12950/80000] lr: 3.353e-05, eta: 1 day, 3:59:13, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3704, decode.acc_seg: 85.8134, aux.loss_ce: 0.1480, aux.acc_seg: 85.8395, loss: 0.5185 +2024-06-16 02:51:48,319 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 02:51:48,319 - mmseg - INFO - Iter [13000/80000] lr: 3.350e-05, eta: 1 day, 3:57:21, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3718, decode.acc_seg: 84.7591, aux.loss_ce: 0.1482, aux.acc_seg: 84.8964, loss: 0.5200 +2024-06-16 02:53:24,765 - mmseg - INFO - per class results: +2024-06-16 02:53:24,772 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.68 | 85.55 | +| building | 83.36 | 92.0 | +| sky | 93.44 | 94.89 | +| floor | 82.39 | 87.8 | +| tree | 73.11 | 94.76 | +| ceiling | 85.18 | 94.74 | +| road | 84.46 | 93.75 | +| bed | 90.87 | 97.11 | +| windowpane | 62.12 | 82.18 | +| grass | 64.92 | 75.99 | +| cabinet | 61.59 | 69.57 | +| sidewalk | 69.91 | 81.8 | +| person | 83.35 | 93.66 | +| earth | 35.62 | 46.37 | +| door | 54.34 | 77.2 | +| table | 63.34 | 78.9 | +| mountain | 56.4 | 69.89 | +| plant | 47.14 | 54.89 | +| curtain | 78.95 | 88.52 | +| chair | 62.29 | 75.54 | +| car | 85.49 | 93.09 | +| water | 58.59 | 64.45 | +| painting | 74.11 | 88.98 | +| sofa | 79.48 | 88.26 | +| shelf | 42.39 | 57.17 | +| house | 28.74 | 30.07 | +| sea | 75.44 | 88.24 | +| mirror | 75.09 | 84.46 | +| rug | 66.78 | 87.34 | +| field | 33.05 | 65.79 | +| armchair | 57.5 | 81.31 | +| seat | 64.83 | 87.79 | +| fence | 48.6 | 67.2 | +| desk | 50.29 | 81.52 | +| rock | 44.03 | 67.35 | +| wardrobe | 51.05 | 80.96 | +| lamp | 63.85 | 80.24 | +| bathtub | 80.8 | 85.2 | +| railing | 37.39 | 48.9 | +| cushion | 62.25 | 87.08 | +| base | 42.09 | 69.86 | +| box | 34.66 | 46.31 | +| column | 55.87 | 67.25 | +| signboard | 36.74 | 43.98 | +| chest of drawers | 48.21 | 75.67 | +| counter | 42.43 | 46.73 | +| sand | 44.51 | 48.24 | +| sink | 71.94 | 81.62 | +| skyscraper | 46.75 | 66.71 | +| fireplace | 70.08 | 94.02 | +| refrigerator | 82.25 | 92.63 | +| grandstand | 53.41 | 87.03 | +| path | 27.02 | 33.89 | +| stairs | 36.68 | 46.57 | +| runway | 56.28 | 92.78 | +| case | 61.1 | 75.6 | +| pool table | 92.94 | 97.51 | +| pillow | 61.04 | 68.15 | +| screen door | 74.38 | 88.67 | +| stairway | 53.06 | 68.29 | +| river | 14.7 | 52.36 | +| bridge | 73.98 | 85.8 | +| bookcase | 43.25 | 62.52 | +| blind | 35.35 | 40.27 | +| coffee table | 59.22 | 86.89 | +| toilet | 87.45 | 90.6 | +| flower | 40.59 | 53.25 | +| book | 45.91 | 83.36 | +| hill | 9.85 | 21.09 | +| bench | 51.24 | 59.26 | +| countertop | 64.93 | 80.31 | +| stove | 82.39 | 93.89 | +| palm | 50.54 | 74.05 | +| kitchen island | 42.56 | 92.52 | +| computer | 71.31 | 94.46 | +| swivel chair | 47.68 | 68.38 | +| boat | 68.98 | 90.68 | +| bar | 52.28 | 76.94 | +| arcade machine | 82.36 | 92.17 | +| hovel | 57.14 | 83.43 | +| bus | 73.28 | 97.55 | +| towel | 70.05 | 77.1 | +| light | 55.81 | 72.46 | +| truck | 16.68 | 17.15 | +| tower | 26.11 | 38.81 | +| chandelier | 66.84 | 88.98 | +| awning | 36.06 | 46.36 | +| streetlight | 31.03 | 49.69 | +| booth | 45.9 | 80.63 | +| television receiver | 67.21 | 86.67 | +| airplane | 53.45 | 70.94 | +| dirt track | 1.85 | 2.1 | +| apparel | 44.95 | 70.46 | +| pole | 14.01 | 16.39 | +| land | 0.15 | 0.29 | +| bannister | 11.34 | 15.58 | +| escalator | 58.72 | 86.75 | +| ottoman | 45.65 | 72.24 | +| bottle | 38.7 | 68.13 | +| buffet | 57.99 | 82.38 | +| poster | 29.9 | 38.01 | +| stage | 13.34 | 33.96 | +| van | 39.28 | 51.4 | +| ship | 82.82 | 90.23 | +| fountain | 59.36 | 95.59 | +| conveyer belt | 69.31 | 95.72 | +| canopy | 44.97 | 71.81 | +| washer | 81.68 | 89.15 | +| plaything | 32.06 | 53.73 | +| swimming pool | 53.34 | 78.91 | +| stool | 45.94 | 62.08 | +| barrel | 50.99 | 65.1 | +| basket | 33.71 | 44.16 | +| waterfall | 63.27 | 97.18 | +| tent | 94.97 | 97.91 | +| bag | 20.84 | 28.35 | +| minibike | 63.67 | 89.83 | +| cradle | 90.03 | 97.1 | +| oven | 55.18 | 63.32 | +| ball | 49.89 | 62.56 | +| food | 64.27 | 85.9 | +| step | 11.57 | 14.5 | +| tank | 63.77 | 85.27 | +| trade name | 28.81 | 32.37 | +| microwave | 84.27 | 95.06 | +| pot | 52.82 | 61.36 | +| animal | 57.75 | 59.65 | +| bicycle | 54.48 | 78.04 | +| lake | 0.0 | 0.0 | +| dishwasher | 61.87 | 74.82 | +| screen | 59.98 | 94.52 | +| blanket | 27.33 | 30.78 | +| sculpture | 59.94 | 66.39 | +| hood | 61.41 | 76.57 | +| sconce | 52.86 | 62.44 | +| vase | 39.21 | 55.78 | +| traffic light | 26.88 | 65.66 | +| tray | 17.26 | 21.54 | +| ashcan | 46.83 | 64.52 | +| fan | 63.56 | 77.53 | +| pier | 40.2 | 43.38 | +| crt screen | 1.07 | 3.13 | +| plate | 57.56 | 65.59 | +| monitor | 3.66 | 4.85 | +| bulletin board | 48.85 | 71.59 | +| shower | 0.0 | 0.0 | +| radiator | 63.14 | 72.5 | +| glass | 14.83 | 16.01 | +| clock | 39.31 | 53.78 | +| flag | 67.73 | 77.45 | ++---------------------+-------+-------+ +2024-06-16 02:53:24,772 - mmseg - INFO - Summary: +2024-06-16 02:53:24,772 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 83.96 | 52.85 | 67.88 | ++-------+-------+-------+ +2024-06-16 02:53:24,773 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 02:53:24,773 - mmseg - INFO - Iter(val) [250] aAcc: 0.8396, mIoU: 0.5285, mAcc: 0.6788, IoU.wall: 0.7968, IoU.building: 0.8336, IoU.sky: 0.9344, IoU.floor: 0.8239, IoU.tree: 0.7311, IoU.ceiling: 0.8518, IoU.road: 0.8446, IoU.bed : 0.9087, IoU.windowpane: 0.6212, IoU.grass: 0.6492, IoU.cabinet: 0.6159, IoU.sidewalk: 0.6991, IoU.person: 0.8335, IoU.earth: 0.3562, IoU.door: 0.5434, IoU.table: 0.6334, IoU.mountain: 0.5640, IoU.plant: 0.4714, IoU.curtain: 0.7895, IoU.chair: 0.6229, IoU.car: 0.8549, IoU.water: 0.5859, IoU.painting: 0.7411, IoU.sofa: 0.7948, IoU.shelf: 0.4239, IoU.house: 0.2874, IoU.sea: 0.7544, IoU.mirror: 0.7509, IoU.rug: 0.6678, IoU.field: 0.3305, IoU.armchair: 0.5750, IoU.seat: 0.6483, IoU.fence: 0.4860, IoU.desk: 0.5029, IoU.rock: 0.4403, IoU.wardrobe: 0.5105, IoU.lamp: 0.6385, IoU.bathtub: 0.8080, IoU.railing: 0.3739, IoU.cushion: 0.6225, IoU.base: 0.4209, IoU.box: 0.3466, IoU.column: 0.5587, IoU.signboard: 0.3674, IoU.chest of drawers: 0.4821, IoU.counter: 0.4243, IoU.sand: 0.4451, IoU.sink: 0.7194, IoU.skyscraper: 0.4675, IoU.fireplace: 0.7008, IoU.refrigerator: 0.8225, IoU.grandstand: 0.5341, IoU.path: 0.2702, IoU.stairs: 0.3668, IoU.runway: 0.5628, IoU.case: 0.6110, IoU.pool table: 0.9294, IoU.pillow: 0.6104, IoU.screen door: 0.7438, IoU.stairway: 0.5306, IoU.river: 0.1470, IoU.bridge: 0.7398, IoU.bookcase: 0.4325, IoU.blind: 0.3535, IoU.coffee table: 0.5922, IoU.toilet: 0.8745, IoU.flower: 0.4059, IoU.book: 0.4591, IoU.hill: 0.0985, IoU.bench: 0.5124, IoU.countertop: 0.6493, IoU.stove: 0.8239, IoU.palm: 0.5054, IoU.kitchen island: 0.4256, IoU.computer: 0.7131, IoU.swivel chair: 0.4768, IoU.boat: 0.6898, IoU.bar: 0.5228, IoU.arcade machine: 0.8236, IoU.hovel: 0.5714, IoU.bus: 0.7328, IoU.towel: 0.7005, IoU.light: 0.5581, IoU.truck: 0.1668, IoU.tower: 0.2611, IoU.chandelier: 0.6684, IoU.awning: 0.3606, IoU.streetlight: 0.3103, IoU.booth: 0.4590, IoU.television receiver: 0.6721, IoU.airplane: 0.5345, IoU.dirt track: 0.0185, IoU.apparel: 0.4495, IoU.pole: 0.1401, IoU.land: 0.0015, IoU.bannister: 0.1134, IoU.escalator: 0.5872, IoU.ottoman: 0.4565, IoU.bottle: 0.3870, IoU.buffet: 0.5799, IoU.poster: 0.2990, IoU.stage: 0.1334, IoU.van: 0.3928, IoU.ship: 0.8282, IoU.fountain: 0.5936, IoU.conveyer belt: 0.6931, IoU.canopy: 0.4497, IoU.washer: 0.8168, IoU.plaything: 0.3206, IoU.swimming pool: 0.5334, IoU.stool: 0.4594, IoU.barrel: 0.5099, IoU.basket: 0.3371, IoU.waterfall: 0.6327, IoU.tent: 0.9497, IoU.bag: 0.2084, IoU.minibike: 0.6367, IoU.cradle: 0.9003, IoU.oven: 0.5518, IoU.ball: 0.4989, IoU.food: 0.6427, IoU.step: 0.1157, IoU.tank: 0.6377, IoU.trade name: 0.2881, IoU.microwave: 0.8427, IoU.pot: 0.5282, IoU.animal: 0.5775, IoU.bicycle: 0.5448, IoU.lake: 0.0000, IoU.dishwasher: 0.6187, IoU.screen: 0.5998, IoU.blanket: 0.2733, IoU.sculpture: 0.5994, IoU.hood: 0.6141, IoU.sconce: 0.5286, IoU.vase: 0.3921, IoU.traffic light: 0.2688, IoU.tray: 0.1726, IoU.ashcan: 0.4683, IoU.fan: 0.6356, IoU.pier: 0.4020, IoU.crt screen: 0.0107, IoU.plate: 0.5756, IoU.monitor: 0.0366, IoU.bulletin board: 0.4885, IoU.shower: 0.0000, IoU.radiator: 0.6314, IoU.glass: 0.1483, IoU.clock: 0.3931, IoU.flag: 0.6773, Acc.wall: 0.8555, Acc.building: 0.9200, Acc.sky: 0.9489, Acc.floor: 0.8780, Acc.tree: 0.9476, Acc.ceiling: 0.9474, Acc.road: 0.9375, Acc.bed : 0.9711, Acc.windowpane: 0.8218, Acc.grass: 0.7599, Acc.cabinet: 0.6957, Acc.sidewalk: 0.8180, Acc.person: 0.9366, Acc.earth: 0.4637, Acc.door: 0.7720, Acc.table: 0.7890, Acc.mountain: 0.6989, Acc.plant: 0.5489, Acc.curtain: 0.8852, Acc.chair: 0.7554, Acc.car: 0.9309, Acc.water: 0.6445, Acc.painting: 0.8898, Acc.sofa: 0.8826, Acc.shelf: 0.5717, Acc.house: 0.3007, Acc.sea: 0.8824, Acc.mirror: 0.8446, Acc.rug: 0.8734, Acc.field: 0.6579, Acc.armchair: 0.8131, Acc.seat: 0.8779, Acc.fence: 0.6720, Acc.desk: 0.8152, Acc.rock: 0.6735, Acc.wardrobe: 0.8096, Acc.lamp: 0.8024, Acc.bathtub: 0.8520, Acc.railing: 0.4890, Acc.cushion: 0.8708, Acc.base: 0.6986, Acc.box: 0.4631, Acc.column: 0.6725, Acc.signboard: 0.4398, Acc.chest of drawers: 0.7567, Acc.counter: 0.4673, Acc.sand: 0.4824, Acc.sink: 0.8162, Acc.skyscraper: 0.6671, Acc.fireplace: 0.9402, Acc.refrigerator: 0.9263, Acc.grandstand: 0.8703, Acc.path: 0.3389, Acc.stairs: 0.4657, Acc.runway: 0.9278, Acc.case: 0.7560, Acc.pool table: 0.9751, Acc.pillow: 0.6815, Acc.screen door: 0.8867, Acc.stairway: 0.6829, Acc.river: 0.5236, Acc.bridge: 0.8580, Acc.bookcase: 0.6252, Acc.blind: 0.4027, Acc.coffee table: 0.8689, Acc.toilet: 0.9060, Acc.flower: 0.5325, Acc.book: 0.8336, Acc.hill: 0.2109, Acc.bench: 0.5926, Acc.countertop: 0.8031, Acc.stove: 0.9389, Acc.palm: 0.7405, Acc.kitchen island: 0.9252, Acc.computer: 0.9446, Acc.swivel chair: 0.6838, Acc.boat: 0.9068, Acc.bar: 0.7694, Acc.arcade machine: 0.9217, Acc.hovel: 0.8343, Acc.bus: 0.9755, Acc.towel: 0.7710, Acc.light: 0.7246, Acc.truck: 0.1715, Acc.tower: 0.3881, Acc.chandelier: 0.8898, Acc.awning: 0.4636, Acc.streetlight: 0.4969, Acc.booth: 0.8063, Acc.television receiver: 0.8667, Acc.airplane: 0.7094, Acc.dirt track: 0.0210, Acc.apparel: 0.7046, Acc.pole: 0.1639, Acc.land: 0.0029, Acc.bannister: 0.1558, Acc.escalator: 0.8675, Acc.ottoman: 0.7224, Acc.bottle: 0.6813, Acc.buffet: 0.8238, Acc.poster: 0.3801, Acc.stage: 0.3396, Acc.van: 0.5140, Acc.ship: 0.9023, Acc.fountain: 0.9559, Acc.conveyer belt: 0.9572, Acc.canopy: 0.7181, Acc.washer: 0.8915, Acc.plaything: 0.5373, Acc.swimming pool: 0.7891, Acc.stool: 0.6208, Acc.barrel: 0.6510, Acc.basket: 0.4416, Acc.waterfall: 0.9718, Acc.tent: 0.9791, Acc.bag: 0.2835, Acc.minibike: 0.8983, Acc.cradle: 0.9710, Acc.oven: 0.6332, Acc.ball: 0.6256, Acc.food: 0.8590, Acc.step: 0.1450, Acc.tank: 0.8527, Acc.trade name: 0.3237, Acc.microwave: 0.9506, Acc.pot: 0.6136, Acc.animal: 0.5965, Acc.bicycle: 0.7804, Acc.lake: 0.0000, Acc.dishwasher: 0.7482, Acc.screen: 0.9452, Acc.blanket: 0.3078, Acc.sculpture: 0.6639, Acc.hood: 0.7657, Acc.sconce: 0.6244, Acc.vase: 0.5578, Acc.traffic light: 0.6566, Acc.tray: 0.2154, Acc.ashcan: 0.6452, Acc.fan: 0.7753, Acc.pier: 0.4338, Acc.crt screen: 0.0313, Acc.plate: 0.6559, Acc.monitor: 0.0485, Acc.bulletin board: 0.7159, Acc.shower: 0.0000, Acc.radiator: 0.7250, Acc.glass: 0.1601, Acc.clock: 0.5378, Acc.flag: 0.7745 +2024-06-16 02:54:33,595 - mmseg - INFO - Iter [13050/80000] lr: 3.348e-05, eta: 1 day, 4:03:49, time: 3.306, data_time: 1.945, memory: 70722, decode.loss_ce: 0.3874, decode.acc_seg: 84.8680, aux.loss_ce: 0.1550, aux.acc_seg: 84.8657, loss: 0.5425 +2024-06-16 02:55:42,047 - mmseg - INFO - Iter [13100/80000] lr: 3.345e-05, eta: 1 day, 4:01:57, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3566, decode.acc_seg: 85.7271, aux.loss_ce: 0.1451, aux.acc_seg: 85.5412, loss: 0.5018 +2024-06-16 02:56:50,153 - mmseg - INFO - Iter [13150/80000] lr: 3.343e-05, eta: 1 day, 4:00:05, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3616, decode.acc_seg: 86.0228, aux.loss_ce: 0.1441, aux.acc_seg: 86.0584, loss: 0.5057 +2024-06-16 02:57:58,465 - mmseg - INFO - Iter [13200/80000] lr: 3.340e-05, eta: 1 day, 3:58:13, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3885, decode.acc_seg: 84.6926, aux.loss_ce: 0.1562, aux.acc_seg: 84.4534, loss: 0.5448 +2024-06-16 02:59:06,699 - mmseg - INFO - Iter [13250/80000] lr: 3.338e-05, eta: 1 day, 3:56:22, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3516, decode.acc_seg: 85.8214, aux.loss_ce: 0.1412, aux.acc_seg: 85.7375, loss: 0.4928 +2024-06-16 03:00:15,100 - mmseg - INFO - Iter [13300/80000] lr: 3.335e-05, eta: 1 day, 3:54:32, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3662, decode.acc_seg: 85.6181, aux.loss_ce: 0.1474, aux.acc_seg: 85.5788, loss: 0.5136 +2024-06-16 03:01:23,368 - mmseg - INFO - Iter [13350/80000] lr: 3.333e-05, eta: 1 day, 3:52:41, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3318, decode.acc_seg: 87.1174, aux.loss_ce: 0.1341, aux.acc_seg: 86.9100, loss: 0.4659 +2024-06-16 03:02:31,576 - mmseg - INFO - Iter [13400/80000] lr: 3.330e-05, eta: 1 day, 3:50:51, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3470, decode.acc_seg: 86.1848, aux.loss_ce: 0.1391, aux.acc_seg: 86.1734, loss: 0.4862 +2024-06-16 03:03:40,090 - mmseg - INFO - Iter [13450/80000] lr: 3.328e-05, eta: 1 day, 3:49:02, time: 1.370, data_time: 0.009, memory: 70722, decode.loss_ce: 0.3641, decode.acc_seg: 85.3854, aux.loss_ce: 0.1475, aux.acc_seg: 85.2816, loss: 0.5116 +2024-06-16 03:04:48,427 - mmseg - INFO - Iter [13500/80000] lr: 3.325e-05, eta: 1 day, 3:47:13, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3620, decode.acc_seg: 85.5192, aux.loss_ce: 0.1461, aux.acc_seg: 85.3518, loss: 0.5081 +2024-06-16 03:05:56,778 - mmseg - INFO - Iter [13550/80000] lr: 3.323e-05, eta: 1 day, 3:45:24, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3881, decode.acc_seg: 84.6472, aux.loss_ce: 0.1550, aux.acc_seg: 84.7273, loss: 0.5432 +2024-06-16 03:07:04,993 - mmseg - INFO - Iter [13600/80000] lr: 3.320e-05, eta: 1 day, 3:43:35, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3733, decode.acc_seg: 84.7395, aux.loss_ce: 0.1498, aux.acc_seg: 84.7944, loss: 0.5231 +2024-06-16 03:08:13,386 - mmseg - INFO - Iter [13650/80000] lr: 3.318e-05, eta: 1 day, 3:41:47, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3731, decode.acc_seg: 84.9842, aux.loss_ce: 0.1495, aux.acc_seg: 84.9701, loss: 0.5226 +2024-06-16 03:09:21,923 - mmseg - INFO - Iter [13700/80000] lr: 3.315e-05, eta: 1 day, 3:40:00, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3834, decode.acc_seg: 84.3675, aux.loss_ce: 0.1541, aux.acc_seg: 84.3354, loss: 0.5375 +2024-06-16 03:10:30,204 - mmseg - INFO - Iter [13750/80000] lr: 3.313e-05, eta: 1 day, 3:38:12, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3781, decode.acc_seg: 85.4930, aux.loss_ce: 0.1518, aux.acc_seg: 85.2858, loss: 0.5299 +2024-06-16 03:11:38,499 - mmseg - INFO - Iter [13800/80000] lr: 3.310e-05, eta: 1 day, 3:36:24, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3918, decode.acc_seg: 84.8366, aux.loss_ce: 0.1554, aux.acc_seg: 84.9644, loss: 0.5472 +2024-06-16 03:12:46,792 - mmseg - INFO - Iter [13850/80000] lr: 3.308e-05, eta: 1 day, 3:34:37, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.4020, decode.acc_seg: 84.2424, aux.loss_ce: 0.1618, aux.acc_seg: 84.1050, loss: 0.5638 +2024-06-16 03:13:57,814 - mmseg - INFO - Iter [13900/80000] lr: 3.305e-05, eta: 1 day, 3:33:03, time: 1.420, data_time: 0.061, memory: 70722, decode.loss_ce: 0.3695, decode.acc_seg: 85.2598, aux.loss_ce: 0.1483, aux.acc_seg: 85.2612, loss: 0.5177 +2024-06-16 03:15:06,118 - mmseg - INFO - Iter [13950/80000] lr: 3.303e-05, eta: 1 day, 3:31:16, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3594, decode.acc_seg: 85.5513, aux.loss_ce: 0.1457, aux.acc_seg: 85.5013, loss: 0.5051 +2024-06-16 03:16:14,452 - mmseg - INFO - Saving checkpoint at 14000 iterations +2024-06-16 03:17:39,804 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 03:17:39,805 - mmseg - INFO - Iter [14000/80000] lr: 3.300e-05, eta: 1 day, 3:36:12, time: 3.074, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3530, decode.acc_seg: 85.9997, aux.loss_ce: 0.1419, aux.acc_seg: 85.8719, loss: 0.4950 +2024-06-16 03:19:14,612 - mmseg - INFO - per class results: +2024-06-16 03:19:14,618 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.85 | 87.7 | +| building | 83.29 | 92.99 | +| sky | 94.1 | 95.98 | +| floor | 81.89 | 88.43 | +| tree | 75.96 | 91.89 | +| ceiling | 85.41 | 94.21 | +| road | 83.34 | 91.93 | +| bed | 91.44 | 96.48 | +| windowpane | 64.03 | 82.48 | +| grass | 65.05 | 76.62 | +| cabinet | 66.52 | 76.15 | +| sidewalk | 65.91 | 87.22 | +| person | 83.39 | 91.07 | +| earth | 34.46 | 41.84 | +| door | 53.93 | 67.28 | +| table | 64.67 | 80.68 | +| mountain | 55.99 | 66.24 | +| plant | 54.73 | 70.46 | +| curtain | 78.75 | 87.97 | +| chair | 60.35 | 67.59 | +| car | 86.26 | 92.68 | +| water | 49.77 | 56.32 | +| painting | 75.59 | 89.91 | +| sofa | 75.06 | 81.18 | +| shelf | 41.66 | 71.78 | +| house | 57.49 | 79.9 | +| sea | 70.81 | 95.71 | +| mirror | 72.83 | 80.01 | +| rug | 70.68 | 84.79 | +| field | 30.19 | 65.09 | +| armchair | 54.97 | 81.34 | +| seat | 66.69 | 88.86 | +| fence | 49.79 | 67.43 | +| desk | 53.85 | 78.77 | +| rock | 52.09 | 69.22 | +| wardrobe | 47.34 | 55.77 | +| lamp | 66.97 | 76.97 | +| bathtub | 79.92 | 85.77 | +| railing | 38.77 | 58.24 | +| cushion | 65.35 | 81.47 | +| base | 39.46 | 46.72 | +| box | 28.67 | 34.11 | +| column | 51.83 | 60.81 | +| signboard | 40.23 | 53.13 | +| chest of drawers | 47.46 | 70.4 | +| counter | 38.61 | 43.02 | +| sand | 55.62 | 82.25 | +| sink | 72.95 | 82.58 | +| skyscraper | 49.96 | 59.31 | +| fireplace | 67.31 | 93.96 | +| refrigerator | 76.14 | 84.91 | +| grandstand | 50.94 | 85.79 | +| path | 22.28 | 28.5 | +| stairs | 36.29 | 50.15 | +| runway | 72.91 | 97.2 | +| case | 44.84 | 58.18 | +| pool table | 94.09 | 97.89 | +| pillow | 58.75 | 63.94 | +| screen door | 71.41 | 90.65 | +| stairway | 54.39 | 64.41 | +| river | 19.22 | 62.5 | +| bridge | 63.31 | 75.89 | +| bookcase | 35.78 | 60.97 | +| blind | 45.67 | 49.54 | +| coffee table | 58.39 | 88.63 | +| toilet | 88.03 | 93.72 | +| flower | 40.31 | 57.52 | +| book | 51.37 | 72.97 | +| hill | 8.24 | 23.14 | +| bench | 57.92 | 72.79 | +| countertop | 63.44 | 79.68 | +| stove | 82.2 | 91.13 | +| palm | 55.44 | 73.39 | +| kitchen island | 43.76 | 66.31 | +| computer | 73.56 | 94.86 | +| swivel chair | 45.23 | 81.35 | +| boat | 79.95 | 88.06 | +| bar | 59.43 | 82.84 | +| arcade machine | 48.95 | 49.43 | +| hovel | 22.06 | 23.27 | +| bus | 92.1 | 95.14 | +| towel | 62.38 | 82.78 | +| light | 54.8 | 63.54 | +| truck | 42.27 | 58.47 | +| tower | 24.98 | 36.16 | +| chandelier | 65.97 | 86.71 | +| awning | 32.48 | 38.63 | +| streetlight | 29.91 | 40.71 | +| booth | 36.14 | 45.82 | +| television receiver | 67.57 | 79.23 | +| airplane | 59.91 | 80.88 | +| dirt track | 12.36 | 30.2 | +| apparel | 40.66 | 68.08 | +| pole | 22.45 | 28.21 | +| land | 0.0 | 0.0 | +| bannister | 12.12 | 14.65 | +| escalator | 56.15 | 78.45 | +| ottoman | 46.75 | 63.36 | +| bottle | 30.02 | 35.02 | +| buffet | 58.4 | 80.74 | +| poster | 31.03 | 32.7 | +| stage | 19.81 | 29.52 | +| van | 48.22 | 66.23 | +| ship | 8.28 | 8.29 | +| fountain | 41.25 | 45.49 | +| conveyer belt | 75.3 | 94.94 | +| canopy | 37.15 | 47.67 | +| washer | 80.73 | 84.59 | +| plaything | 30.5 | 65.56 | +| swimming pool | 54.19 | 78.12 | +| stool | 46.3 | 62.43 | +| barrel | 54.08 | 64.61 | +| basket | 30.7 | 57.25 | +| waterfall | 74.17 | 93.16 | +| tent | 92.4 | 98.0 | +| bag | 19.81 | 22.35 | +| minibike | 65.85 | 75.19 | +| cradle | 80.22 | 97.9 | +| oven | 60.26 | 75.92 | +| ball | 48.96 | 65.42 | +| food | 53.42 | 61.87 | +| step | 5.34 | 5.8 | +| tank | 69.3 | 92.32 | +| trade name | 25.11 | 27.77 | +| microwave | 85.67 | 95.81 | +| pot | 47.15 | 54.67 | +| animal | 61.1 | 62.04 | +| bicycle | 49.31 | 64.07 | +| lake | 37.08 | 45.41 | +| dishwasher | 62.15 | 83.07 | +| screen | 66.59 | 90.68 | +| blanket | 34.45 | 40.98 | +| sculpture | 70.27 | 81.51 | +| hood | 68.26 | 73.02 | +| sconce | 53.96 | 69.14 | +| vase | 41.32 | 54.72 | +| traffic light | 31.27 | 48.89 | +| tray | 16.49 | 18.89 | +| ashcan | 44.37 | 60.62 | +| fan | 58.51 | 65.97 | +| pier | 38.39 | 44.34 | +| crt screen | 0.69 | 0.85 | +| plate | 59.1 | 74.03 | +| monitor | 58.98 | 83.68 | +| bulletin board | 51.84 | 79.61 | +| shower | 0.0 | 0.0 | +| radiator | 63.09 | 72.82 | +| glass | 16.98 | 18.56 | +| clock | 38.27 | 44.22 | +| flag | 68.87 | 71.92 | ++---------------------+-------+-------+ +2024-06-16 03:19:14,618 - mmseg - INFO - Summary: +2024-06-16 03:19:14,618 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.34 | 53.13 | 66.48 | ++-------+-------+-------+ +2024-06-16 03:19:14,619 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 03:19:14,619 - mmseg - INFO - Iter(val) [250] aAcc: 0.8434, mIoU: 0.5313, mAcc: 0.6648, IoU.wall: 0.7985, IoU.building: 0.8329, IoU.sky: 0.9410, IoU.floor: 0.8189, IoU.tree: 0.7596, IoU.ceiling: 0.8541, IoU.road: 0.8334, IoU.bed : 0.9144, IoU.windowpane: 0.6403, IoU.grass: 0.6505, IoU.cabinet: 0.6652, IoU.sidewalk: 0.6591, IoU.person: 0.8339, IoU.earth: 0.3446, IoU.door: 0.5393, IoU.table: 0.6467, IoU.mountain: 0.5599, IoU.plant: 0.5473, IoU.curtain: 0.7875, IoU.chair: 0.6035, IoU.car: 0.8626, IoU.water: 0.4977, IoU.painting: 0.7559, IoU.sofa: 0.7506, IoU.shelf: 0.4166, IoU.house: 0.5749, IoU.sea: 0.7081, IoU.mirror: 0.7283, IoU.rug: 0.7068, IoU.field: 0.3019, IoU.armchair: 0.5497, IoU.seat: 0.6669, IoU.fence: 0.4979, IoU.desk: 0.5385, IoU.rock: 0.5209, IoU.wardrobe: 0.4734, IoU.lamp: 0.6697, IoU.bathtub: 0.7992, IoU.railing: 0.3877, IoU.cushion: 0.6535, IoU.base: 0.3946, IoU.box: 0.2867, IoU.column: 0.5183, IoU.signboard: 0.4023, IoU.chest of drawers: 0.4746, IoU.counter: 0.3861, IoU.sand: 0.5562, IoU.sink: 0.7295, IoU.skyscraper: 0.4996, IoU.fireplace: 0.6731, IoU.refrigerator: 0.7614, IoU.grandstand: 0.5094, IoU.path: 0.2228, IoU.stairs: 0.3629, IoU.runway: 0.7291, IoU.case: 0.4484, IoU.pool table: 0.9409, IoU.pillow: 0.5875, IoU.screen door: 0.7141, IoU.stairway: 0.5439, IoU.river: 0.1922, IoU.bridge: 0.6331, IoU.bookcase: 0.3578, IoU.blind: 0.4567, IoU.coffee table: 0.5839, IoU.toilet: 0.8803, IoU.flower: 0.4031, IoU.book: 0.5137, IoU.hill: 0.0824, IoU.bench: 0.5792, IoU.countertop: 0.6344, IoU.stove: 0.8220, IoU.palm: 0.5544, IoU.kitchen island: 0.4376, IoU.computer: 0.7356, IoU.swivel chair: 0.4523, IoU.boat: 0.7995, IoU.bar: 0.5943, IoU.arcade machine: 0.4895, IoU.hovel: 0.2206, IoU.bus: 0.9210, IoU.towel: 0.6238, IoU.light: 0.5480, IoU.truck: 0.4227, IoU.tower: 0.2498, IoU.chandelier: 0.6597, IoU.awning: 0.3248, IoU.streetlight: 0.2991, IoU.booth: 0.3614, IoU.television receiver: 0.6757, IoU.airplane: 0.5991, IoU.dirt track: 0.1236, IoU.apparel: 0.4066, IoU.pole: 0.2245, IoU.land: 0.0000, IoU.bannister: 0.1212, IoU.escalator: 0.5615, IoU.ottoman: 0.4675, IoU.bottle: 0.3002, IoU.buffet: 0.5840, IoU.poster: 0.3103, IoU.stage: 0.1981, IoU.van: 0.4822, IoU.ship: 0.0828, IoU.fountain: 0.4125, IoU.conveyer belt: 0.7530, IoU.canopy: 0.3715, IoU.washer: 0.8073, IoU.plaything: 0.3050, IoU.swimming pool: 0.5419, IoU.stool: 0.4630, IoU.barrel: 0.5408, IoU.basket: 0.3070, IoU.waterfall: 0.7417, IoU.tent: 0.9240, IoU.bag: 0.1981, IoU.minibike: 0.6585, IoU.cradle: 0.8022, IoU.oven: 0.6026, IoU.ball: 0.4896, IoU.food: 0.5342, IoU.step: 0.0534, IoU.tank: 0.6930, IoU.trade name: 0.2511, IoU.microwave: 0.8567, IoU.pot: 0.4715, IoU.animal: 0.6110, IoU.bicycle: 0.4931, IoU.lake: 0.3708, IoU.dishwasher: 0.6215, IoU.screen: 0.6659, IoU.blanket: 0.3445, IoU.sculpture: 0.7027, IoU.hood: 0.6826, IoU.sconce: 0.5396, IoU.vase: 0.4132, IoU.traffic light: 0.3127, IoU.tray: 0.1649, IoU.ashcan: 0.4437, IoU.fan: 0.5851, IoU.pier: 0.3839, IoU.crt screen: 0.0069, IoU.plate: 0.5910, IoU.monitor: 0.5898, IoU.bulletin board: 0.5184, IoU.shower: 0.0000, IoU.radiator: 0.6309, IoU.glass: 0.1698, IoU.clock: 0.3827, IoU.flag: 0.6887, Acc.wall: 0.8770, Acc.building: 0.9299, Acc.sky: 0.9598, Acc.floor: 0.8843, Acc.tree: 0.9189, Acc.ceiling: 0.9421, Acc.road: 0.9193, Acc.bed : 0.9648, Acc.windowpane: 0.8248, Acc.grass: 0.7662, Acc.cabinet: 0.7615, Acc.sidewalk: 0.8722, Acc.person: 0.9107, Acc.earth: 0.4184, Acc.door: 0.6728, Acc.table: 0.8068, Acc.mountain: 0.6624, Acc.plant: 0.7046, Acc.curtain: 0.8797, Acc.chair: 0.6759, Acc.car: 0.9268, Acc.water: 0.5632, Acc.painting: 0.8991, Acc.sofa: 0.8118, Acc.shelf: 0.7178, Acc.house: 0.7990, Acc.sea: 0.9571, Acc.mirror: 0.8001, Acc.rug: 0.8479, Acc.field: 0.6509, Acc.armchair: 0.8134, Acc.seat: 0.8886, Acc.fence: 0.6743, Acc.desk: 0.7877, Acc.rock: 0.6922, Acc.wardrobe: 0.5577, Acc.lamp: 0.7697, Acc.bathtub: 0.8577, Acc.railing: 0.5824, Acc.cushion: 0.8147, Acc.base: 0.4672, Acc.box: 0.3411, Acc.column: 0.6081, Acc.signboard: 0.5313, Acc.chest of drawers: 0.7040, Acc.counter: 0.4302, Acc.sand: 0.8225, Acc.sink: 0.8258, Acc.skyscraper: 0.5931, Acc.fireplace: 0.9396, Acc.refrigerator: 0.8491, Acc.grandstand: 0.8579, Acc.path: 0.2850, Acc.stairs: 0.5015, Acc.runway: 0.9720, Acc.case: 0.5818, Acc.pool table: 0.9789, Acc.pillow: 0.6394, Acc.screen door: 0.9065, Acc.stairway: 0.6441, Acc.river: 0.6250, Acc.bridge: 0.7589, Acc.bookcase: 0.6097, Acc.blind: 0.4954, Acc.coffee table: 0.8863, Acc.toilet: 0.9372, Acc.flower: 0.5752, Acc.book: 0.7297, Acc.hill: 0.2314, Acc.bench: 0.7279, Acc.countertop: 0.7968, Acc.stove: 0.9113, Acc.palm: 0.7339, Acc.kitchen island: 0.6631, Acc.computer: 0.9486, Acc.swivel chair: 0.8135, Acc.boat: 0.8806, Acc.bar: 0.8284, Acc.arcade machine: 0.4943, Acc.hovel: 0.2327, Acc.bus: 0.9514, Acc.towel: 0.8278, Acc.light: 0.6354, Acc.truck: 0.5847, Acc.tower: 0.3616, Acc.chandelier: 0.8671, Acc.awning: 0.3863, Acc.streetlight: 0.4071, Acc.booth: 0.4582, Acc.television receiver: 0.7923, Acc.airplane: 0.8088, Acc.dirt track: 0.3020, Acc.apparel: 0.6808, Acc.pole: 0.2821, Acc.land: 0.0000, Acc.bannister: 0.1465, Acc.escalator: 0.7845, Acc.ottoman: 0.6336, Acc.bottle: 0.3502, Acc.buffet: 0.8074, Acc.poster: 0.3270, Acc.stage: 0.2952, Acc.van: 0.6623, Acc.ship: 0.0829, Acc.fountain: 0.4549, Acc.conveyer belt: 0.9494, Acc.canopy: 0.4767, Acc.washer: 0.8459, Acc.plaything: 0.6556, Acc.swimming pool: 0.7812, Acc.stool: 0.6243, Acc.barrel: 0.6461, Acc.basket: 0.5725, Acc.waterfall: 0.9316, Acc.tent: 0.9800, Acc.bag: 0.2235, Acc.minibike: 0.7519, Acc.cradle: 0.9790, Acc.oven: 0.7592, Acc.ball: 0.6542, Acc.food: 0.6187, Acc.step: 0.0580, Acc.tank: 0.9232, Acc.trade name: 0.2777, Acc.microwave: 0.9581, Acc.pot: 0.5467, Acc.animal: 0.6204, Acc.bicycle: 0.6407, Acc.lake: 0.4541, Acc.dishwasher: 0.8307, Acc.screen: 0.9068, Acc.blanket: 0.4098, Acc.sculpture: 0.8151, Acc.hood: 0.7302, Acc.sconce: 0.6914, Acc.vase: 0.5472, Acc.traffic light: 0.4889, Acc.tray: 0.1889, Acc.ashcan: 0.6062, Acc.fan: 0.6597, Acc.pier: 0.4434, Acc.crt screen: 0.0085, Acc.plate: 0.7403, Acc.monitor: 0.8368, Acc.bulletin board: 0.7961, Acc.shower: 0.0000, Acc.radiator: 0.7282, Acc.glass: 0.1856, Acc.clock: 0.4422, Acc.flag: 0.7192 +2024-06-16 03:20:23,547 - mmseg - INFO - Iter [14050/80000] lr: 3.298e-05, eta: 1 day, 3:41:52, time: 3.275, data_time: 1.913, memory: 70722, decode.loss_ce: 0.3580, decode.acc_seg: 85.3082, aux.loss_ce: 0.1440, aux.acc_seg: 85.1989, loss: 0.5020 +2024-06-16 03:21:31,815 - mmseg - INFO - Iter [14100/80000] lr: 3.295e-05, eta: 1 day, 3:40:02, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3500, decode.acc_seg: 85.6819, aux.loss_ce: 0.1410, aux.acc_seg: 85.7231, loss: 0.4910 +2024-06-16 03:22:40,273 - mmseg - INFO - Iter [14150/80000] lr: 3.293e-05, eta: 1 day, 3:38:13, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3738, decode.acc_seg: 85.4696, aux.loss_ce: 0.1500, aux.acc_seg: 85.3752, loss: 0.5239 +2024-06-16 03:23:48,524 - mmseg - INFO - Iter [14200/80000] lr: 3.290e-05, eta: 1 day, 3:36:24, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3687, decode.acc_seg: 85.1345, aux.loss_ce: 0.1483, aux.acc_seg: 84.9819, loss: 0.5170 +2024-06-16 03:24:56,893 - mmseg - INFO - Iter [14250/80000] lr: 3.288e-05, eta: 1 day, 3:34:35, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3622, decode.acc_seg: 85.5224, aux.loss_ce: 0.1464, aux.acc_seg: 85.5255, loss: 0.5087 +2024-06-16 03:26:05,279 - mmseg - INFO - Iter [14300/80000] lr: 3.285e-05, eta: 1 day, 3:32:47, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3646, decode.acc_seg: 85.9104, aux.loss_ce: 0.1456, aux.acc_seg: 85.8439, loss: 0.5102 +2024-06-16 03:27:13,779 - mmseg - INFO - Iter [14350/80000] lr: 3.283e-05, eta: 1 day, 3:31:00, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3481, decode.acc_seg: 86.0382, aux.loss_ce: 0.1397, aux.acc_seg: 86.0356, loss: 0.4878 +2024-06-16 03:28:22,146 - mmseg - INFO - Iter [14400/80000] lr: 3.280e-05, eta: 1 day, 3:29:12, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3422, decode.acc_seg: 86.6746, aux.loss_ce: 0.1385, aux.acc_seg: 86.5628, loss: 0.4807 +2024-06-16 03:29:30,584 - mmseg - INFO - Iter [14450/80000] lr: 3.278e-05, eta: 1 day, 3:27:25, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3494, decode.acc_seg: 86.0164, aux.loss_ce: 0.1412, aux.acc_seg: 85.8902, loss: 0.4905 +2024-06-16 03:30:38,712 - mmseg - INFO - Iter [14500/80000] lr: 3.275e-05, eta: 1 day, 3:25:37, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3610, decode.acc_seg: 86.2546, aux.loss_ce: 0.1441, aux.acc_seg: 86.3811, loss: 0.5050 +2024-06-16 03:31:47,037 - mmseg - INFO - Iter [14550/80000] lr: 3.273e-05, eta: 1 day, 3:23:50, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3586, decode.acc_seg: 85.6210, aux.loss_ce: 0.1429, aux.acc_seg: 85.7262, loss: 0.5016 +2024-06-16 03:32:55,365 - mmseg - INFO - Iter [14600/80000] lr: 3.270e-05, eta: 1 day, 3:22:03, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3755, decode.acc_seg: 84.7593, aux.loss_ce: 0.1515, aux.acc_seg: 84.6238, loss: 0.5271 +2024-06-16 03:34:03,588 - mmseg - INFO - Iter [14650/80000] lr: 3.268e-05, eta: 1 day, 3:20:16, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3721, decode.acc_seg: 85.0665, aux.loss_ce: 0.1498, aux.acc_seg: 84.9922, loss: 0.5218 +2024-06-16 03:35:12,248 - mmseg - INFO - Iter [14700/80000] lr: 3.265e-05, eta: 1 day, 3:18:31, time: 1.373, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3623, decode.acc_seg: 85.6257, aux.loss_ce: 0.1466, aux.acc_seg: 85.4956, loss: 0.5089 +2024-06-16 03:36:20,732 - mmseg - INFO - Iter [14750/80000] lr: 3.263e-05, eta: 1 day, 3:16:46, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3562, decode.acc_seg: 85.6223, aux.loss_ce: 0.1432, aux.acc_seg: 85.5342, loss: 0.4994 +2024-06-16 03:37:28,938 - mmseg - INFO - Iter [14800/80000] lr: 3.260e-05, eta: 1 day, 3:14:59, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3827, decode.acc_seg: 85.0224, aux.loss_ce: 0.1538, aux.acc_seg: 84.9130, loss: 0.5364 +2024-06-16 03:38:37,213 - mmseg - INFO - Iter [14850/80000] lr: 3.258e-05, eta: 1 day, 3:13:14, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3557, decode.acc_seg: 85.4466, aux.loss_ce: 0.1433, aux.acc_seg: 85.3598, loss: 0.4990 +2024-06-16 03:39:45,625 - mmseg - INFO - Iter [14900/80000] lr: 3.255e-05, eta: 1 day, 3:11:29, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3550, decode.acc_seg: 86.0327, aux.loss_ce: 0.1426, aux.acc_seg: 85.9184, loss: 0.4976 +2024-06-16 03:40:54,152 - mmseg - INFO - Iter [14950/80000] lr: 3.253e-05, eta: 1 day, 3:09:45, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3593, decode.acc_seg: 85.7729, aux.loss_ce: 0.1446, aux.acc_seg: 85.6668, loss: 0.5040 +2024-06-16 03:42:02,531 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 03:42:02,531 - mmseg - INFO - Iter [15000/80000] lr: 3.250e-05, eta: 1 day, 3:08:00, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3459, decode.acc_seg: 86.6338, aux.loss_ce: 0.1413, aux.acc_seg: 86.3818, loss: 0.4873 +2024-06-16 03:43:39,363 - mmseg - INFO - per class results: +2024-06-16 03:43:39,369 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.27 | 89.36 | +| building | 83.78 | 93.34 | +| sky | 94.5 | 96.32 | +| floor | 83.83 | 90.93 | +| tree | 76.13 | 89.57 | +| ceiling | 85.82 | 90.73 | +| road | 84.58 | 88.15 | +| bed | 91.13 | 96.64 | +| windowpane | 65.23 | 79.77 | +| grass | 64.95 | 87.3 | +| cabinet | 62.73 | 74.92 | +| sidewalk | 69.89 | 86.55 | +| person | 84.19 | 91.76 | +| earth | 38.53 | 55.64 | +| door | 57.19 | 76.01 | +| table | 65.66 | 76.37 | +| mountain | 57.99 | 73.74 | +| plant | 52.13 | 58.97 | +| curtain | 76.95 | 89.83 | +| chair | 62.02 | 71.72 | +| car | 86.6 | 94.39 | +| water | 55.2 | 64.89 | +| painting | 77.0 | 89.8 | +| sofa | 77.5 | 82.97 | +| shelf | 35.27 | 40.84 | +| house | 53.78 | 72.01 | +| sea | 66.45 | 84.85 | +| mirror | 72.74 | 79.82 | +| rug | 70.87 | 83.95 | +| field | 18.93 | 20.57 | +| armchair | 55.29 | 80.27 | +| seat | 66.13 | 87.74 | +| fence | 52.65 | 69.29 | +| desk | 54.69 | 82.76 | +| rock | 47.39 | 77.05 | +| wardrobe | 53.07 | 79.77 | +| lamp | 66.48 | 75.35 | +| bathtub | 76.83 | 77.84 | +| railing | 38.59 | 53.08 | +| cushion | 61.46 | 71.37 | +| base | 38.2 | 68.51 | +| box | 33.62 | 44.08 | +| column | 49.73 | 59.06 | +| signboard | 38.92 | 60.39 | +| chest of drawers | 51.3 | 64.48 | +| counter | 47.16 | 53.22 | +| sand | 48.44 | 67.62 | +| sink | 74.85 | 82.43 | +| skyscraper | 52.97 | 66.86 | +| fireplace | 73.46 | 94.68 | +| refrigerator | 77.47 | 85.99 | +| grandstand | 55.29 | 82.37 | +| path | 31.39 | 40.83 | +| stairs | 40.39 | 52.16 | +| runway | 74.91 | 94.76 | +| case | 59.65 | 76.13 | +| pool table | 93.97 | 98.11 | +| pillow | 64.06 | 89.35 | +| screen door | 75.44 | 84.71 | +| stairway | 52.67 | 56.12 | +| river | 11.79 | 25.74 | +| bridge | 68.95 | 82.9 | +| bookcase | 32.56 | 50.82 | +| blind | 46.68 | 54.53 | +| coffee table | 54.31 | 90.02 | +| toilet | 88.76 | 91.57 | +| flower | 39.11 | 54.5 | +| book | 46.72 | 82.13 | +| hill | 5.96 | 10.8 | +| bench | 47.75 | 57.76 | +| countertop | 61.06 | 67.02 | +| stove | 80.22 | 90.94 | +| palm | 50.68 | 86.3 | +| kitchen island | 45.43 | 58.1 | +| computer | 77.52 | 90.94 | +| swivel chair | 44.39 | 76.8 | +| boat | 41.83 | 93.88 | +| bar | 70.11 | 79.23 | +| arcade machine | 82.14 | 88.39 | +| hovel | 20.45 | 22.52 | +| bus | 91.91 | 95.98 | +| towel | 65.38 | 70.64 | +| light | 50.67 | 54.97 | +| truck | 43.44 | 61.26 | +| tower | 25.47 | 39.11 | +| chandelier | 66.72 | 86.87 | +| awning | 35.65 | 50.45 | +| streetlight | 30.19 | 52.15 | +| booth | 38.15 | 61.96 | +| television receiver | 75.77 | 84.51 | +| airplane | 60.64 | 75.21 | +| dirt track | 6.13 | 25.68 | +| apparel | 46.71 | 58.29 | +| pole | 17.14 | 20.67 | +| land | 0.0 | 0.0 | +| bannister | 12.65 | 16.28 | +| escalator | 54.7 | 85.57 | +| ottoman | 52.04 | 71.67 | +| bottle | 40.57 | 62.92 | +| buffet | 54.34 | 91.87 | +| poster | 30.09 | 33.31 | +| stage | 14.5 | 23.57 | +| van | 48.23 | 56.79 | +| ship | 81.04 | 92.56 | +| fountain | 45.09 | 49.03 | +| conveyer belt | 84.34 | 92.25 | +| canopy | 29.74 | 39.84 | +| washer | 83.98 | 88.14 | +| plaything | 15.42 | 18.71 | +| swimming pool | 56.63 | 87.0 | +| stool | 48.07 | 68.69 | +| barrel | 57.6 | 64.47 | +| basket | 34.31 | 45.88 | +| waterfall | 74.41 | 94.23 | +| tent | 94.55 | 99.14 | +| bag | 9.09 | 9.61 | +| minibike | 68.41 | 88.8 | +| cradle | 88.26 | 95.29 | +| oven | 51.14 | 60.75 | +| ball | 7.87 | 8.24 | +| food | 57.8 | 84.64 | +| step | 6.72 | 7.22 | +| tank | 64.16 | 66.53 | +| trade name | 24.26 | 29.42 | +| microwave | 84.88 | 94.61 | +| pot | 49.92 | 55.68 | +| animal | 63.23 | 64.81 | +| bicycle | 53.63 | 59.09 | +| lake | 44.98 | 91.43 | +| dishwasher | 54.9 | 58.55 | +| screen | 62.45 | 91.68 | +| blanket | 21.76 | 23.62 | +| sculpture | 61.33 | 67.96 | +| hood | 60.36 | 69.99 | +| sconce | 54.04 | 68.57 | +| vase | 39.12 | 54.17 | +| traffic light | 28.76 | 42.97 | +| tray | 8.46 | 9.05 | +| ashcan | 46.7 | 58.14 | +| fan | 65.91 | 76.16 | +| pier | 48.87 | 54.23 | +| crt screen | 0.17 | 0.18 | +| plate | 53.67 | 78.04 | +| monitor | 67.25 | 82.24 | +| bulletin board | 39.6 | 76.8 | +| shower | 0.33 | 0.42 | +| radiator | 59.93 | 65.57 | +| glass | 14.13 | 14.88 | +| clock | 28.71 | 32.77 | +| flag | 67.93 | 72.83 | ++---------------------+-------+-------+ +2024-06-16 03:43:39,369 - mmseg - INFO - Summary: +2024-06-16 03:43:39,369 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.76 | 53.33 | 66.25 | ++-------+-------+-------+ +2024-06-16 03:43:39,370 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 03:43:39,370 - mmseg - INFO - Iter(val) [250] aAcc: 0.8476, mIoU: 0.5333, mAcc: 0.6625, IoU.wall: 0.8027, IoU.building: 0.8378, IoU.sky: 0.9450, IoU.floor: 0.8383, IoU.tree: 0.7613, IoU.ceiling: 0.8582, IoU.road: 0.8458, IoU.bed : 0.9113, IoU.windowpane: 0.6523, IoU.grass: 0.6495, IoU.cabinet: 0.6273, IoU.sidewalk: 0.6989, IoU.person: 0.8419, IoU.earth: 0.3853, IoU.door: 0.5719, IoU.table: 0.6566, IoU.mountain: 0.5799, IoU.plant: 0.5213, IoU.curtain: 0.7695, IoU.chair: 0.6202, IoU.car: 0.8660, IoU.water: 0.5520, IoU.painting: 0.7700, IoU.sofa: 0.7750, IoU.shelf: 0.3527, IoU.house: 0.5378, IoU.sea: 0.6645, IoU.mirror: 0.7274, IoU.rug: 0.7087, IoU.field: 0.1893, IoU.armchair: 0.5529, IoU.seat: 0.6613, IoU.fence: 0.5265, IoU.desk: 0.5469, IoU.rock: 0.4739, IoU.wardrobe: 0.5307, IoU.lamp: 0.6648, IoU.bathtub: 0.7683, IoU.railing: 0.3859, IoU.cushion: 0.6146, IoU.base: 0.3820, IoU.box: 0.3362, IoU.column: 0.4973, IoU.signboard: 0.3892, IoU.chest of drawers: 0.5130, IoU.counter: 0.4716, IoU.sand: 0.4844, IoU.sink: 0.7485, IoU.skyscraper: 0.5297, IoU.fireplace: 0.7346, IoU.refrigerator: 0.7747, IoU.grandstand: 0.5529, IoU.path: 0.3139, IoU.stairs: 0.4039, IoU.runway: 0.7491, IoU.case: 0.5965, IoU.pool table: 0.9397, IoU.pillow: 0.6406, IoU.screen door: 0.7544, IoU.stairway: 0.5267, IoU.river: 0.1179, IoU.bridge: 0.6895, IoU.bookcase: 0.3256, IoU.blind: 0.4668, IoU.coffee table: 0.5431, IoU.toilet: 0.8876, IoU.flower: 0.3911, IoU.book: 0.4672, IoU.hill: 0.0596, IoU.bench: 0.4775, IoU.countertop: 0.6106, IoU.stove: 0.8022, IoU.palm: 0.5068, IoU.kitchen island: 0.4543, IoU.computer: 0.7752, IoU.swivel chair: 0.4439, IoU.boat: 0.4183, IoU.bar: 0.7011, IoU.arcade machine: 0.8214, IoU.hovel: 0.2045, IoU.bus: 0.9191, IoU.towel: 0.6538, IoU.light: 0.5067, IoU.truck: 0.4344, IoU.tower: 0.2547, IoU.chandelier: 0.6672, IoU.awning: 0.3565, IoU.streetlight: 0.3019, IoU.booth: 0.3815, IoU.television receiver: 0.7577, IoU.airplane: 0.6064, IoU.dirt track: 0.0613, IoU.apparel: 0.4671, IoU.pole: 0.1714, IoU.land: 0.0000, IoU.bannister: 0.1265, IoU.escalator: 0.5470, IoU.ottoman: 0.5204, IoU.bottle: 0.4057, IoU.buffet: 0.5434, IoU.poster: 0.3009, IoU.stage: 0.1450, IoU.van: 0.4823, IoU.ship: 0.8104, IoU.fountain: 0.4509, IoU.conveyer belt: 0.8434, IoU.canopy: 0.2974, IoU.washer: 0.8398, IoU.plaything: 0.1542, IoU.swimming pool: 0.5663, IoU.stool: 0.4807, IoU.barrel: 0.5760, IoU.basket: 0.3431, IoU.waterfall: 0.7441, IoU.tent: 0.9455, IoU.bag: 0.0909, IoU.minibike: 0.6841, IoU.cradle: 0.8826, IoU.oven: 0.5114, IoU.ball: 0.0787, IoU.food: 0.5780, IoU.step: 0.0672, IoU.tank: 0.6416, IoU.trade name: 0.2426, IoU.microwave: 0.8488, IoU.pot: 0.4992, IoU.animal: 0.6323, IoU.bicycle: 0.5363, IoU.lake: 0.4498, IoU.dishwasher: 0.5490, IoU.screen: 0.6245, IoU.blanket: 0.2176, IoU.sculpture: 0.6133, IoU.hood: 0.6036, IoU.sconce: 0.5404, IoU.vase: 0.3912, IoU.traffic light: 0.2876, IoU.tray: 0.0846, IoU.ashcan: 0.4670, IoU.fan: 0.6591, IoU.pier: 0.4887, IoU.crt screen: 0.0017, IoU.plate: 0.5367, IoU.monitor: 0.6725, IoU.bulletin board: 0.3960, IoU.shower: 0.0033, IoU.radiator: 0.5993, IoU.glass: 0.1413, IoU.clock: 0.2871, IoU.flag: 0.6793, Acc.wall: 0.8936, Acc.building: 0.9334, Acc.sky: 0.9632, Acc.floor: 0.9093, Acc.tree: 0.8957, Acc.ceiling: 0.9073, Acc.road: 0.8815, Acc.bed : 0.9664, Acc.windowpane: 0.7977, Acc.grass: 0.8730, Acc.cabinet: 0.7492, Acc.sidewalk: 0.8655, Acc.person: 0.9176, Acc.earth: 0.5564, Acc.door: 0.7601, Acc.table: 0.7637, Acc.mountain: 0.7374, Acc.plant: 0.5897, Acc.curtain: 0.8983, Acc.chair: 0.7172, Acc.car: 0.9439, Acc.water: 0.6489, Acc.painting: 0.8980, Acc.sofa: 0.8297, Acc.shelf: 0.4084, Acc.house: 0.7201, Acc.sea: 0.8485, Acc.mirror: 0.7982, Acc.rug: 0.8395, Acc.field: 0.2057, Acc.armchair: 0.8027, Acc.seat: 0.8774, Acc.fence: 0.6929, Acc.desk: 0.8276, Acc.rock: 0.7705, Acc.wardrobe: 0.7977, Acc.lamp: 0.7535, Acc.bathtub: 0.7784, Acc.railing: 0.5308, Acc.cushion: 0.7137, Acc.base: 0.6851, Acc.box: 0.4408, Acc.column: 0.5906, Acc.signboard: 0.6039, Acc.chest of drawers: 0.6448, Acc.counter: 0.5322, Acc.sand: 0.6762, Acc.sink: 0.8243, Acc.skyscraper: 0.6686, Acc.fireplace: 0.9468, Acc.refrigerator: 0.8599, Acc.grandstand: 0.8237, Acc.path: 0.4083, Acc.stairs: 0.5216, Acc.runway: 0.9476, Acc.case: 0.7613, Acc.pool table: 0.9811, Acc.pillow: 0.8935, Acc.screen door: 0.8471, Acc.stairway: 0.5612, Acc.river: 0.2574, Acc.bridge: 0.8290, Acc.bookcase: 0.5082, Acc.blind: 0.5453, Acc.coffee table: 0.9002, Acc.toilet: 0.9157, Acc.flower: 0.5450, Acc.book: 0.8213, Acc.hill: 0.1080, Acc.bench: 0.5776, Acc.countertop: 0.6702, Acc.stove: 0.9094, Acc.palm: 0.8630, Acc.kitchen island: 0.5810, Acc.computer: 0.9094, Acc.swivel chair: 0.7680, Acc.boat: 0.9388, Acc.bar: 0.7923, Acc.arcade machine: 0.8839, Acc.hovel: 0.2252, Acc.bus: 0.9598, Acc.towel: 0.7064, Acc.light: 0.5497, Acc.truck: 0.6126, Acc.tower: 0.3911, Acc.chandelier: 0.8687, Acc.awning: 0.5045, Acc.streetlight: 0.5215, Acc.booth: 0.6196, Acc.television receiver: 0.8451, Acc.airplane: 0.7521, Acc.dirt track: 0.2568, Acc.apparel: 0.5829, Acc.pole: 0.2067, Acc.land: 0.0000, Acc.bannister: 0.1628, Acc.escalator: 0.8557, Acc.ottoman: 0.7167, Acc.bottle: 0.6292, Acc.buffet: 0.9187, Acc.poster: 0.3331, Acc.stage: 0.2357, Acc.van: 0.5679, Acc.ship: 0.9256, Acc.fountain: 0.4903, Acc.conveyer belt: 0.9225, Acc.canopy: 0.3984, Acc.washer: 0.8814, Acc.plaything: 0.1871, Acc.swimming pool: 0.8700, Acc.stool: 0.6869, Acc.barrel: 0.6447, Acc.basket: 0.4588, Acc.waterfall: 0.9423, Acc.tent: 0.9914, Acc.bag: 0.0961, Acc.minibike: 0.8880, Acc.cradle: 0.9529, Acc.oven: 0.6075, Acc.ball: 0.0824, Acc.food: 0.8464, Acc.step: 0.0722, Acc.tank: 0.6653, Acc.trade name: 0.2942, Acc.microwave: 0.9461, Acc.pot: 0.5568, Acc.animal: 0.6481, Acc.bicycle: 0.5909, Acc.lake: 0.9143, Acc.dishwasher: 0.5855, Acc.screen: 0.9168, Acc.blanket: 0.2362, Acc.sculpture: 0.6796, Acc.hood: 0.6999, Acc.sconce: 0.6857, Acc.vase: 0.5417, Acc.traffic light: 0.4297, Acc.tray: 0.0905, Acc.ashcan: 0.5814, Acc.fan: 0.7616, Acc.pier: 0.5423, Acc.crt screen: 0.0018, Acc.plate: 0.7804, Acc.monitor: 0.8224, Acc.bulletin board: 0.7680, Acc.shower: 0.0042, Acc.radiator: 0.6557, Acc.glass: 0.1488, Acc.clock: 0.3277, Acc.flag: 0.7283 +2024-06-16 03:44:48,453 - mmseg - INFO - Iter [15050/80000] lr: 3.248e-05, eta: 1 day, 3:13:17, time: 3.318, data_time: 1.953, memory: 70722, decode.loss_ce: 0.3791, decode.acc_seg: 85.1952, aux.loss_ce: 0.1511, aux.acc_seg: 85.2858, loss: 0.5302 +2024-06-16 03:45:56,666 - mmseg - INFO - Iter [15100/80000] lr: 3.245e-05, eta: 1 day, 3:11:30, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3889, decode.acc_seg: 85.1339, aux.loss_ce: 0.1556, aux.acc_seg: 85.1726, loss: 0.5445 +2024-06-16 03:47:04,879 - mmseg - INFO - Iter [15150/80000] lr: 3.243e-05, eta: 1 day, 3:09:44, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3818, decode.acc_seg: 85.0041, aux.loss_ce: 0.1519, aux.acc_seg: 84.9826, loss: 0.5337 +2024-06-16 03:48:15,598 - mmseg - INFO - Iter [15200/80000] lr: 3.240e-05, eta: 1 day, 3:08:09, time: 1.414, data_time: 0.061, memory: 70722, decode.loss_ce: 0.3395, decode.acc_seg: 86.2480, aux.loss_ce: 0.1370, aux.acc_seg: 86.0086, loss: 0.4765 +2024-06-16 03:49:24,093 - mmseg - INFO - Iter [15250/80000] lr: 3.238e-05, eta: 1 day, 3:06:24, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3378, decode.acc_seg: 86.4270, aux.loss_ce: 0.1365, aux.acc_seg: 86.2755, loss: 0.4743 +2024-06-16 03:50:32,466 - mmseg - INFO - Iter [15300/80000] lr: 3.235e-05, eta: 1 day, 3:04:39, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3230, decode.acc_seg: 86.6311, aux.loss_ce: 0.1314, aux.acc_seg: 86.6452, loss: 0.4544 +2024-06-16 03:51:40,503 - mmseg - INFO - Iter [15350/80000] lr: 3.233e-05, eta: 1 day, 3:02:53, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3484, decode.acc_seg: 86.3780, aux.loss_ce: 0.1392, aux.acc_seg: 86.3083, loss: 0.4876 +2024-06-16 03:52:48,797 - mmseg - INFO - Iter [15400/80000] lr: 3.230e-05, eta: 1 day, 3:01:08, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3314, decode.acc_seg: 86.7767, aux.loss_ce: 0.1333, aux.acc_seg: 86.6546, loss: 0.4647 +2024-06-16 03:53:56,977 - mmseg - INFO - Iter [15450/80000] lr: 3.228e-05, eta: 1 day, 2:59:23, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3693, decode.acc_seg: 85.2166, aux.loss_ce: 0.1486, aux.acc_seg: 85.0880, loss: 0.5179 +2024-06-16 03:55:05,290 - mmseg - INFO - Iter [15500/80000] lr: 3.225e-05, eta: 1 day, 2:57:39, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3540, decode.acc_seg: 85.9046, aux.loss_ce: 0.1425, aux.acc_seg: 85.9727, loss: 0.4965 +2024-06-16 03:56:13,446 - mmseg - INFO - Iter [15550/80000] lr: 3.223e-05, eta: 1 day, 2:55:55, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3505, decode.acc_seg: 85.8997, aux.loss_ce: 0.1410, aux.acc_seg: 85.6846, loss: 0.4914 +2024-06-16 03:57:21,838 - mmseg - INFO - Iter [15600/80000] lr: 3.220e-05, eta: 1 day, 2:54:11, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3517, decode.acc_seg: 86.0954, aux.loss_ce: 0.1417, aux.acc_seg: 85.9341, loss: 0.4934 +2024-06-16 03:58:29,977 - mmseg - INFO - Iter [15650/80000] lr: 3.218e-05, eta: 1 day, 2:52:27, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3440, decode.acc_seg: 86.6114, aux.loss_ce: 0.1389, aux.acc_seg: 86.4460, loss: 0.4829 +2024-06-16 03:59:38,430 - mmseg - INFO - Iter [15700/80000] lr: 3.215e-05, eta: 1 day, 2:50:44, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3485, decode.acc_seg: 85.9648, aux.loss_ce: 0.1411, aux.acc_seg: 85.6650, loss: 0.4897 +2024-06-16 04:00:46,522 - mmseg - INFO - Iter [15750/80000] lr: 3.213e-05, eta: 1 day, 2:49:00, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3283, decode.acc_seg: 86.7773, aux.loss_ce: 0.1326, aux.acc_seg: 86.7407, loss: 0.4610 +2024-06-16 04:01:55,061 - mmseg - INFO - Iter [15800/80000] lr: 3.210e-05, eta: 1 day, 2:47:18, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3297, decode.acc_seg: 86.9032, aux.loss_ce: 0.1340, aux.acc_seg: 86.7683, loss: 0.4636 +2024-06-16 04:03:03,499 - mmseg - INFO - Iter [15850/80000] lr: 3.208e-05, eta: 1 day, 2:45:36, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3326, decode.acc_seg: 86.7331, aux.loss_ce: 0.1337, aux.acc_seg: 86.5952, loss: 0.4663 +2024-06-16 04:04:11,731 - mmseg - INFO - Iter [15900/80000] lr: 3.205e-05, eta: 1 day, 2:43:54, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3407, decode.acc_seg: 86.2612, aux.loss_ce: 0.1381, aux.acc_seg: 86.0404, loss: 0.4788 +2024-06-16 04:05:20,081 - mmseg - INFO - Iter [15950/80000] lr: 3.203e-05, eta: 1 day, 2:42:11, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3576, decode.acc_seg: 85.7639, aux.loss_ce: 0.1441, aux.acc_seg: 85.5918, loss: 0.5017 +2024-06-16 04:06:28,113 - mmseg - INFO - Saving checkpoint at 16000 iterations +2024-06-16 04:07:54,136 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 04:07:54,136 - mmseg - INFO - Iter [16000/80000] lr: 3.200e-05, eta: 1 day, 2:46:12, time: 3.081, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3425, decode.acc_seg: 86.3791, aux.loss_ce: 0.1382, aux.acc_seg: 86.1635, loss: 0.4807 +2024-06-16 04:09:29,888 - mmseg - INFO - per class results: +2024-06-16 04:09:29,895 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.86 | 87.02 | +| building | 84.19 | 92.69 | +| sky | 94.66 | 97.08 | +| floor | 83.33 | 89.13 | +| tree | 75.04 | 93.39 | +| ceiling | 85.54 | 92.32 | +| road | 83.08 | 90.63 | +| bed | 90.86 | 97.09 | +| windowpane | 65.8 | 79.16 | +| grass | 66.4 | 81.88 | +| cabinet | 63.68 | 75.03 | +| sidewalk | 67.19 | 86.21 | +| person | 84.32 | 94.62 | +| earth | 37.61 | 50.44 | +| door | 56.49 | 82.0 | +| table | 64.01 | 80.84 | +| mountain | 62.97 | 77.75 | +| plant | 48.64 | 55.62 | +| curtain | 79.55 | 87.08 | +| chair | 63.64 | 73.58 | +| car | 85.8 | 94.55 | +| water | 57.37 | 70.28 | +| painting | 75.5 | 89.32 | +| sofa | 79.56 | 90.55 | +| shelf | 42.51 | 57.24 | +| house | 57.78 | 68.56 | +| sea | 60.45 | 83.92 | +| mirror | 71.46 | 75.68 | +| rug | 72.88 | 81.71 | +| field | 30.53 | 46.2 | +| armchair | 57.35 | 76.5 | +| seat | 67.9 | 88.59 | +| fence | 44.87 | 59.24 | +| desk | 53.06 | 66.0 | +| rock | 49.25 | 66.65 | +| wardrobe | 52.25 | 69.54 | +| lamp | 68.18 | 83.08 | +| bathtub | 79.52 | 84.87 | +| railing | 38.99 | 54.29 | +| cushion | 66.12 | 79.63 | +| base | 41.32 | 66.19 | +| box | 37.63 | 50.95 | +| column | 54.91 | 65.11 | +| signboard | 39.6 | 55.41 | +| chest of drawers | 46.91 | 58.75 | +| counter | 48.28 | 58.8 | +| sand | 46.15 | 77.53 | +| sink | 73.45 | 84.86 | +| skyscraper | 48.1 | 61.6 | +| fireplace | 73.07 | 93.16 | +| refrigerator | 80.36 | 91.33 | +| grandstand | 47.6 | 81.33 | +| path | 24.63 | 37.44 | +| stairs | 37.08 | 46.24 | +| runway | 72.49 | 96.26 | +| case | 63.71 | 86.95 | +| pool table | 93.11 | 98.47 | +| pillow | 63.64 | 72.16 | +| screen door | 71.67 | 91.25 | +| stairway | 52.32 | 75.33 | +| river | 10.64 | 19.74 | +| bridge | 76.54 | 84.76 | +| bookcase | 38.19 | 59.9 | +| blind | 44.7 | 49.22 | +| coffee table | 55.82 | 89.84 | +| toilet | 87.32 | 92.42 | +| flower | 42.71 | 60.2 | +| book | 48.45 | 79.33 | +| hill | 5.9 | 8.54 | +| bench | 55.02 | 77.77 | +| countertop | 59.71 | 86.22 | +| stove | 81.55 | 86.21 | +| palm | 56.89 | 76.56 | +| kitchen island | 44.66 | 90.47 | +| computer | 73.1 | 92.19 | +| swivel chair | 48.03 | 74.33 | +| boat | 48.86 | 88.33 | +| bar | 56.14 | 69.94 | +| arcade machine | 74.25 | 79.61 | +| hovel | 62.4 | 78.14 | +| bus | 90.05 | 93.01 | +| towel | 69.07 | 78.79 | +| light | 52.64 | 59.29 | +| truck | 44.36 | 61.9 | +| tower | 11.7 | 14.98 | +| chandelier | 68.77 | 84.92 | +| awning | 33.41 | 42.81 | +| streetlight | 32.85 | 45.01 | +| booth | 51.47 | 58.98 | +| television receiver | 69.11 | 84.01 | +| airplane | 66.06 | 74.25 | +| dirt track | 14.36 | 48.91 | +| apparel | 41.08 | 50.16 | +| pole | 22.36 | 27.61 | +| land | 1.66 | 2.53 | +| bannister | 14.63 | 30.26 | +| escalator | 58.92 | 83.55 | +| ottoman | 49.87 | 63.07 | +| bottle | 28.8 | 45.01 | +| buffet | 57.64 | 84.58 | +| poster | 33.52 | 42.5 | +| stage | 16.7 | 28.86 | +| van | 50.55 | 69.75 | +| ship | 73.75 | 73.9 | +| fountain | 52.07 | 62.01 | +| conveyer belt | 74.53 | 97.03 | +| canopy | 50.37 | 72.01 | +| washer | 74.67 | 77.09 | +| plaything | 15.35 | 21.19 | +| swimming pool | 52.61 | 79.42 | +| stool | 54.22 | 69.3 | +| barrel | 4.45 | 72.49 | +| basket | 33.93 | 50.05 | +| waterfall | 65.7 | 97.79 | +| tent | 96.05 | 98.18 | +| bag | 21.5 | 28.75 | +| minibike | 69.16 | 85.86 | +| cradle | 86.17 | 97.2 | +| oven | 57.13 | 66.89 | +| ball | 50.06 | 62.99 | +| food | 64.32 | 71.63 | +| step | 15.23 | 18.58 | +| tank | 55.85 | 58.03 | +| trade name | 25.91 | 30.77 | +| microwave | 88.98 | 93.0 | +| pot | 51.25 | 65.57 | +| animal | 60.17 | 61.69 | +| bicycle | 50.06 | 75.38 | +| lake | 34.01 | 38.3 | +| dishwasher | 65.35 | 72.8 | +| screen | 72.34 | 86.71 | +| blanket | 32.05 | 36.86 | +| sculpture | 69.47 | 80.38 | +| hood | 57.58 | 64.5 | +| sconce | 49.2 | 55.45 | +| vase | 41.08 | 60.89 | +| traffic light | 27.66 | 60.3 | +| tray | 5.39 | 6.08 | +| ashcan | 45.4 | 55.8 | +| fan | 61.58 | 70.26 | +| pier | 33.28 | 44.91 | +| crt screen | 9.87 | 28.92 | +| plate | 58.69 | 72.23 | +| monitor | 10.58 | 12.55 | +| bulletin board | 49.1 | 73.85 | +| shower | 0.42 | 0.48 | +| radiator | 61.02 | 70.81 | +| glass | 13.03 | 13.57 | +| clock | 33.94 | 38.32 | +| flag | 70.09 | 77.65 | ++---------------------+-------+-------+ +2024-06-16 04:09:29,895 - mmseg - INFO - Summary: +2024-06-16 04:09:29,895 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.75 | 53.73 | 67.51 | ++-------+-------+-------+ +2024-06-16 04:09:29,896 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 04:09:29,896 - mmseg - INFO - Iter(val) [250] aAcc: 0.8475, mIoU: 0.5373, mAcc: 0.6751, IoU.wall: 0.7986, IoU.building: 0.8419, IoU.sky: 0.9466, IoU.floor: 0.8333, IoU.tree: 0.7504, IoU.ceiling: 0.8554, IoU.road: 0.8308, IoU.bed : 0.9086, IoU.windowpane: 0.6580, IoU.grass: 0.6640, IoU.cabinet: 0.6368, IoU.sidewalk: 0.6719, IoU.person: 0.8432, IoU.earth: 0.3761, IoU.door: 0.5649, IoU.table: 0.6401, IoU.mountain: 0.6297, IoU.plant: 0.4864, IoU.curtain: 0.7955, IoU.chair: 0.6364, IoU.car: 0.8580, IoU.water: 0.5737, IoU.painting: 0.7550, IoU.sofa: 0.7956, IoU.shelf: 0.4251, IoU.house: 0.5778, IoU.sea: 0.6045, IoU.mirror: 0.7146, IoU.rug: 0.7288, IoU.field: 0.3053, IoU.armchair: 0.5735, IoU.seat: 0.6790, IoU.fence: 0.4487, IoU.desk: 0.5306, IoU.rock: 0.4925, IoU.wardrobe: 0.5225, IoU.lamp: 0.6818, IoU.bathtub: 0.7952, IoU.railing: 0.3899, IoU.cushion: 0.6612, IoU.base: 0.4132, IoU.box: 0.3763, IoU.column: 0.5491, IoU.signboard: 0.3960, IoU.chest of drawers: 0.4691, IoU.counter: 0.4828, IoU.sand: 0.4615, IoU.sink: 0.7345, IoU.skyscraper: 0.4810, IoU.fireplace: 0.7307, IoU.refrigerator: 0.8036, IoU.grandstand: 0.4760, IoU.path: 0.2463, IoU.stairs: 0.3708, IoU.runway: 0.7249, IoU.case: 0.6371, IoU.pool table: 0.9311, IoU.pillow: 0.6364, IoU.screen door: 0.7167, IoU.stairway: 0.5232, IoU.river: 0.1064, IoU.bridge: 0.7654, IoU.bookcase: 0.3819, IoU.blind: 0.4470, IoU.coffee table: 0.5582, IoU.toilet: 0.8732, IoU.flower: 0.4271, IoU.book: 0.4845, IoU.hill: 0.0590, IoU.bench: 0.5502, IoU.countertop: 0.5971, IoU.stove: 0.8155, IoU.palm: 0.5689, IoU.kitchen island: 0.4466, IoU.computer: 0.7310, IoU.swivel chair: 0.4803, IoU.boat: 0.4886, IoU.bar: 0.5614, IoU.arcade machine: 0.7425, IoU.hovel: 0.6240, IoU.bus: 0.9005, IoU.towel: 0.6907, IoU.light: 0.5264, IoU.truck: 0.4436, IoU.tower: 0.1170, IoU.chandelier: 0.6877, IoU.awning: 0.3341, IoU.streetlight: 0.3285, IoU.booth: 0.5147, IoU.television receiver: 0.6911, IoU.airplane: 0.6606, IoU.dirt track: 0.1436, IoU.apparel: 0.4108, IoU.pole: 0.2236, IoU.land: 0.0166, IoU.bannister: 0.1463, IoU.escalator: 0.5892, IoU.ottoman: 0.4987, IoU.bottle: 0.2880, IoU.buffet: 0.5764, IoU.poster: 0.3352, IoU.stage: 0.1670, IoU.van: 0.5055, IoU.ship: 0.7375, IoU.fountain: 0.5207, IoU.conveyer belt: 0.7453, IoU.canopy: 0.5037, IoU.washer: 0.7467, IoU.plaything: 0.1535, IoU.swimming pool: 0.5261, IoU.stool: 0.5422, IoU.barrel: 0.0445, IoU.basket: 0.3393, IoU.waterfall: 0.6570, IoU.tent: 0.9605, IoU.bag: 0.2150, IoU.minibike: 0.6916, IoU.cradle: 0.8617, IoU.oven: 0.5713, IoU.ball: 0.5006, IoU.food: 0.6432, IoU.step: 0.1523, IoU.tank: 0.5585, IoU.trade name: 0.2591, IoU.microwave: 0.8898, IoU.pot: 0.5125, IoU.animal: 0.6017, IoU.bicycle: 0.5006, IoU.lake: 0.3401, IoU.dishwasher: 0.6535, IoU.screen: 0.7234, IoU.blanket: 0.3205, IoU.sculpture: 0.6947, IoU.hood: 0.5758, IoU.sconce: 0.4920, IoU.vase: 0.4108, IoU.traffic light: 0.2766, IoU.tray: 0.0539, IoU.ashcan: 0.4540, IoU.fan: 0.6158, IoU.pier: 0.3328, IoU.crt screen: 0.0987, IoU.plate: 0.5869, IoU.monitor: 0.1058, IoU.bulletin board: 0.4910, IoU.shower: 0.0042, IoU.radiator: 0.6102, IoU.glass: 0.1303, IoU.clock: 0.3394, IoU.flag: 0.7009, Acc.wall: 0.8702, Acc.building: 0.9269, Acc.sky: 0.9708, Acc.floor: 0.8913, Acc.tree: 0.9339, Acc.ceiling: 0.9232, Acc.road: 0.9063, Acc.bed : 0.9709, Acc.windowpane: 0.7916, Acc.grass: 0.8188, Acc.cabinet: 0.7503, Acc.sidewalk: 0.8621, Acc.person: 0.9462, Acc.earth: 0.5044, Acc.door: 0.8200, Acc.table: 0.8084, Acc.mountain: 0.7775, Acc.plant: 0.5562, Acc.curtain: 0.8708, Acc.chair: 0.7358, Acc.car: 0.9455, Acc.water: 0.7028, Acc.painting: 0.8932, Acc.sofa: 0.9055, Acc.shelf: 0.5724, Acc.house: 0.6856, Acc.sea: 0.8392, Acc.mirror: 0.7568, Acc.rug: 0.8171, Acc.field: 0.4620, Acc.armchair: 0.7650, Acc.seat: 0.8859, Acc.fence: 0.5924, Acc.desk: 0.6600, Acc.rock: 0.6665, Acc.wardrobe: 0.6954, Acc.lamp: 0.8308, Acc.bathtub: 0.8487, Acc.railing: 0.5429, Acc.cushion: 0.7963, Acc.base: 0.6619, Acc.box: 0.5095, Acc.column: 0.6511, Acc.signboard: 0.5541, Acc.chest of drawers: 0.5875, Acc.counter: 0.5880, Acc.sand: 0.7753, Acc.sink: 0.8486, Acc.skyscraper: 0.6160, Acc.fireplace: 0.9316, Acc.refrigerator: 0.9133, Acc.grandstand: 0.8133, Acc.path: 0.3744, Acc.stairs: 0.4624, Acc.runway: 0.9626, Acc.case: 0.8695, Acc.pool table: 0.9847, Acc.pillow: 0.7216, Acc.screen door: 0.9125, Acc.stairway: 0.7533, Acc.river: 0.1974, Acc.bridge: 0.8476, Acc.bookcase: 0.5990, Acc.blind: 0.4922, Acc.coffee table: 0.8984, Acc.toilet: 0.9242, Acc.flower: 0.6020, Acc.book: 0.7933, Acc.hill: 0.0854, Acc.bench: 0.7777, Acc.countertop: 0.8622, Acc.stove: 0.8621, Acc.palm: 0.7656, Acc.kitchen island: 0.9047, Acc.computer: 0.9219, Acc.swivel chair: 0.7433, Acc.boat: 0.8833, Acc.bar: 0.6994, Acc.arcade machine: 0.7961, Acc.hovel: 0.7814, Acc.bus: 0.9301, Acc.towel: 0.7879, Acc.light: 0.5929, Acc.truck: 0.6190, Acc.tower: 0.1498, Acc.chandelier: 0.8492, Acc.awning: 0.4281, Acc.streetlight: 0.4501, Acc.booth: 0.5898, Acc.television receiver: 0.8401, Acc.airplane: 0.7425, Acc.dirt track: 0.4891, Acc.apparel: 0.5016, Acc.pole: 0.2761, Acc.land: 0.0253, Acc.bannister: 0.3026, Acc.escalator: 0.8355, Acc.ottoman: 0.6307, Acc.bottle: 0.4501, Acc.buffet: 0.8458, Acc.poster: 0.4250, Acc.stage: 0.2886, Acc.van: 0.6975, Acc.ship: 0.7390, Acc.fountain: 0.6201, Acc.conveyer belt: 0.9703, Acc.canopy: 0.7201, Acc.washer: 0.7709, Acc.plaything: 0.2119, Acc.swimming pool: 0.7942, Acc.stool: 0.6930, Acc.barrel: 0.7249, Acc.basket: 0.5005, Acc.waterfall: 0.9779, Acc.tent: 0.9818, Acc.bag: 0.2875, Acc.minibike: 0.8586, Acc.cradle: 0.9720, Acc.oven: 0.6689, Acc.ball: 0.6299, Acc.food: 0.7163, Acc.step: 0.1858, Acc.tank: 0.5803, Acc.trade name: 0.3077, Acc.microwave: 0.9300, Acc.pot: 0.6557, Acc.animal: 0.6169, Acc.bicycle: 0.7538, Acc.lake: 0.3830, Acc.dishwasher: 0.7280, Acc.screen: 0.8671, Acc.blanket: 0.3686, Acc.sculpture: 0.8038, Acc.hood: 0.6450, Acc.sconce: 0.5545, Acc.vase: 0.6089, Acc.traffic light: 0.6030, Acc.tray: 0.0608, Acc.ashcan: 0.5580, Acc.fan: 0.7026, Acc.pier: 0.4491, Acc.crt screen: 0.2892, Acc.plate: 0.7223, Acc.monitor: 0.1255, Acc.bulletin board: 0.7385, Acc.shower: 0.0048, Acc.radiator: 0.7081, Acc.glass: 0.1357, Acc.clock: 0.3832, Acc.flag: 0.7765 +2024-06-16 04:10:38,754 - mmseg - INFO - Iter [16050/80000] lr: 3.198e-05, eta: 1 day, 2:50:53, time: 3.292, data_time: 1.934, memory: 70722, decode.loss_ce: 0.3347, decode.acc_seg: 86.9043, aux.loss_ce: 0.1346, aux.acc_seg: 86.7739, loss: 0.4693 +2024-06-16 04:11:47,018 - mmseg - INFO - Iter [16100/80000] lr: 3.195e-05, eta: 1 day, 2:49:09, time: 1.365, data_time: 0.011, memory: 70722, decode.loss_ce: 0.3381, decode.acc_seg: 86.4718, aux.loss_ce: 0.1374, aux.acc_seg: 86.2476, loss: 0.4756 +2024-06-16 04:12:55,631 - mmseg - INFO - Iter [16150/80000] lr: 3.193e-05, eta: 1 day, 2:47:26, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3550, decode.acc_seg: 85.7768, aux.loss_ce: 0.1416, aux.acc_seg: 85.7504, loss: 0.4966 +2024-06-16 04:14:03,751 - mmseg - INFO - Iter [16200/80000] lr: 3.190e-05, eta: 1 day, 2:45:41, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3536, decode.acc_seg: 86.3376, aux.loss_ce: 0.1415, aux.acc_seg: 86.2952, loss: 0.4951 +2024-06-16 04:15:12,019 - mmseg - INFO - Iter [16250/80000] lr: 3.188e-05, eta: 1 day, 2:43:57, time: 1.365, data_time: 0.009, memory: 70722, decode.loss_ce: 0.3593, decode.acc_seg: 85.9039, aux.loss_ce: 0.1445, aux.acc_seg: 85.7359, loss: 0.5038 +2024-06-16 04:16:20,170 - mmseg - INFO - Iter [16300/80000] lr: 3.185e-05, eta: 1 day, 2:42:13, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3307, decode.acc_seg: 86.9833, aux.loss_ce: 0.1340, aux.acc_seg: 86.8913, loss: 0.4647 +2024-06-16 04:17:28,672 - mmseg - INFO - Iter [16350/80000] lr: 3.183e-05, eta: 1 day, 2:40:30, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3704, decode.acc_seg: 84.8172, aux.loss_ce: 0.1492, aux.acc_seg: 84.7165, loss: 0.5196 +2024-06-16 04:18:36,938 - mmseg - INFO - Iter [16400/80000] lr: 3.180e-05, eta: 1 day, 2:38:47, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3691, decode.acc_seg: 85.1378, aux.loss_ce: 0.1480, aux.acc_seg: 84.9289, loss: 0.5172 +2024-06-16 04:19:48,040 - mmseg - INFO - Iter [16450/80000] lr: 3.178e-05, eta: 1 day, 2:37:15, time: 1.422, data_time: 0.063, memory: 70722, decode.loss_ce: 0.3635, decode.acc_seg: 85.6622, aux.loss_ce: 0.1460, aux.acc_seg: 85.6636, loss: 0.5096 +2024-06-16 04:20:56,464 - mmseg - INFO - Iter [16500/80000] lr: 3.175e-05, eta: 1 day, 2:35:33, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3171, decode.acc_seg: 86.9238, aux.loss_ce: 0.1286, aux.acc_seg: 86.7112, loss: 0.4457 +2024-06-16 04:22:04,554 - mmseg - INFO - Iter [16550/80000] lr: 3.173e-05, eta: 1 day, 2:33:49, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3160, decode.acc_seg: 87.3472, aux.loss_ce: 0.1280, aux.acc_seg: 87.0857, loss: 0.4440 +2024-06-16 04:23:12,829 - mmseg - INFO - Iter [16600/80000] lr: 3.170e-05, eta: 1 day, 2:32:07, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3252, decode.acc_seg: 86.6762, aux.loss_ce: 0.1304, aux.acc_seg: 86.5799, loss: 0.4557 +2024-06-16 04:24:20,959 - mmseg - INFO - Iter [16650/80000] lr: 3.168e-05, eta: 1 day, 2:30:24, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3166, decode.acc_seg: 87.4225, aux.loss_ce: 0.1274, aux.acc_seg: 87.3315, loss: 0.4440 +2024-06-16 04:25:29,216 - mmseg - INFO - Iter [16700/80000] lr: 3.165e-05, eta: 1 day, 2:28:42, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3484, decode.acc_seg: 86.5976, aux.loss_ce: 0.1411, aux.acc_seg: 86.3974, loss: 0.4895 +2024-06-16 04:26:37,369 - mmseg - INFO - Iter [16750/80000] lr: 3.163e-05, eta: 1 day, 2:27:00, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3204, decode.acc_seg: 87.0358, aux.loss_ce: 0.1284, aux.acc_seg: 86.9739, loss: 0.4488 +2024-06-16 04:27:45,717 - mmseg - INFO - Iter [16800/80000] lr: 3.160e-05, eta: 1 day, 2:25:19, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3295, decode.acc_seg: 87.0380, aux.loss_ce: 0.1331, aux.acc_seg: 86.9335, loss: 0.4626 +2024-06-16 04:28:54,012 - mmseg - INFO - Iter [16850/80000] lr: 3.158e-05, eta: 1 day, 2:23:37, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3371, decode.acc_seg: 86.7345, aux.loss_ce: 0.1354, aux.acc_seg: 86.6249, loss: 0.4725 +2024-06-16 04:30:02,079 - mmseg - INFO - Iter [16900/80000] lr: 3.155e-05, eta: 1 day, 2:21:55, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3299, decode.acc_seg: 86.7215, aux.loss_ce: 0.1330, aux.acc_seg: 86.5866, loss: 0.4629 +2024-06-16 04:31:10,469 - mmseg - INFO - Iter [16950/80000] lr: 3.153e-05, eta: 1 day, 2:20:15, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3388, decode.acc_seg: 86.8729, aux.loss_ce: 0.1364, aux.acc_seg: 86.7671, loss: 0.4753 +2024-06-16 04:32:18,676 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 04:32:18,677 - mmseg - INFO - Iter [17000/80000] lr: 3.150e-05, eta: 1 day, 2:18:34, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3253, decode.acc_seg: 87.3004, aux.loss_ce: 0.1313, aux.acc_seg: 87.0365, loss: 0.4566 +2024-06-16 04:33:56,884 - mmseg - INFO - per class results: +2024-06-16 04:33:56,891 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.97 | 86.13 | +| building | 84.07 | 93.33 | +| sky | 94.46 | 97.08 | +| floor | 83.2 | 88.1 | +| tree | 76.13 | 92.46 | +| ceiling | 86.5 | 92.65 | +| road | 85.22 | 90.56 | +| bed | 91.51 | 96.42 | +| windowpane | 62.43 | 84.06 | +| grass | 70.55 | 87.66 | +| cabinet | 63.43 | 75.33 | +| sidewalk | 68.51 | 88.59 | +| person | 83.71 | 90.25 | +| earth | 35.04 | 45.27 | +| door | 56.83 | 75.78 | +| table | 65.93 | 81.35 | +| mountain | 59.37 | 71.38 | +| plant | 51.83 | 60.52 | +| curtain | 77.39 | 89.55 | +| chair | 63.15 | 72.76 | +| car | 84.93 | 92.37 | +| water | 61.25 | 77.17 | +| painting | 71.84 | 90.76 | +| sofa | 79.84 | 86.42 | +| shelf | 46.06 | 64.63 | +| house | 54.48 | 69.25 | +| sea | 67.5 | 92.73 | +| mirror | 76.44 | 84.76 | +| rug | 65.22 | 89.32 | +| field | 40.95 | 57.7 | +| armchair | 57.44 | 83.21 | +| seat | 71.26 | 87.89 | +| fence | 47.48 | 66.88 | +| desk | 60.1 | 80.47 | +| rock | 51.42 | 65.79 | +| wardrobe | 51.48 | 78.24 | +| lamp | 67.51 | 83.43 | +| bathtub | 81.76 | 83.88 | +| railing | 38.66 | 60.38 | +| cushion | 67.37 | 76.77 | +| base | 36.16 | 57.74 | +| box | 29.23 | 34.6 | +| column | 52.95 | 71.98 | +| signboard | 39.62 | 55.59 | +| chest of drawers | 47.38 | 74.45 | +| counter | 35.49 | 38.48 | +| sand | 50.85 | 87.0 | +| sink | 75.26 | 84.16 | +| skyscraper | 49.6 | 63.18 | +| fireplace | 67.48 | 96.57 | +| refrigerator | 83.59 | 91.63 | +| grandstand | 60.46 | 81.4 | +| path | 25.2 | 29.56 | +| stairs | 45.22 | 55.69 | +| runway | 71.38 | 94.55 | +| case | 62.89 | 85.2 | +| pool table | 93.94 | 98.23 | +| pillow | 67.54 | 78.09 | +| screen door | 57.48 | 60.33 | +| stairway | 56.99 | 68.16 | +| river | 22.32 | 25.63 | +| bridge | 42.65 | 48.28 | +| bookcase | 39.14 | 61.12 | +| blind | 44.49 | 48.24 | +| coffee table | 63.25 | 87.5 | +| toilet | 89.22 | 92.42 | +| flower | 48.48 | 61.79 | +| book | 52.97 | 73.56 | +| hill | 4.92 | 11.2 | +| bench | 61.41 | 77.45 | +| countertop | 64.63 | 74.58 | +| stove | 77.15 | 93.43 | +| palm | 51.21 | 82.91 | +| kitchen island | 47.69 | 82.61 | +| computer | 67.29 | 76.16 | +| swivel chair | 44.95 | 79.89 | +| boat | 60.02 | 87.64 | +| bar | 52.48 | 75.26 | +| arcade machine | 79.82 | 89.56 | +| hovel | 50.1 | 55.19 | +| bus | 90.58 | 93.63 | +| towel | 72.81 | 82.9 | +| light | 57.83 | 68.38 | +| truck | 39.17 | 48.55 | +| tower | 42.1 | 57.6 | +| chandelier | 66.45 | 91.53 | +| awning | 33.95 | 39.86 | +| streetlight | 31.33 | 44.19 | +| booth | 41.45 | 70.26 | +| television receiver | 75.9 | 86.1 | +| airplane | 60.98 | 79.44 | +| dirt track | 26.15 | 28.04 | +| apparel | 41.53 | 56.8 | +| pole | 21.98 | 27.62 | +| land | 0.84 | 1.56 | +| bannister | 16.93 | 27.83 | +| escalator | 56.57 | 79.45 | +| ottoman | 47.44 | 69.21 | +| bottle | 38.69 | 58.72 | +| buffet | 48.99 | 66.59 | +| poster | 33.72 | 41.55 | +| stage | 17.58 | 53.33 | +| van | 42.58 | 76.13 | +| ship | 74.31 | 75.05 | +| fountain | 61.27 | 66.41 | +| conveyer belt | 81.12 | 96.18 | +| canopy | 52.08 | 70.57 | +| washer | 79.36 | 83.52 | +| plaything | 21.16 | 30.55 | +| swimming pool | 54.72 | 85.39 | +| stool | 49.6 | 73.1 | +| barrel | 55.58 | 70.99 | +| basket | 32.86 | 47.16 | +| waterfall | 75.48 | 95.85 | +| tent | 91.83 | 98.54 | +| bag | 11.56 | 12.36 | +| minibike | 71.1 | 87.26 | +| cradle | 79.63 | 98.02 | +| oven | 50.96 | 62.16 | +| ball | 44.09 | 76.13 | +| food | 58.23 | 62.75 | +| step | 18.75 | 30.71 | +| tank | 59.97 | 70.28 | +| trade name | 28.69 | 32.02 | +| microwave | 86.18 | 95.89 | +| pot | 50.67 | 57.27 | +| animal | 65.08 | 67.76 | +| bicycle | 49.98 | 79.08 | +| lake | 0.0 | 0.0 | +| dishwasher | 61.17 | 71.52 | +| screen | 48.84 | 62.83 | +| blanket | 23.76 | 26.03 | +| sculpture | 71.19 | 81.88 | +| hood | 58.11 | 71.45 | +| sconce | 51.73 | 62.27 | +| vase | 36.21 | 66.04 | +| traffic light | 30.35 | 51.11 | +| tray | 10.7 | 11.85 | +| ashcan | 46.22 | 60.98 | +| fan | 64.86 | 81.25 | +| pier | 33.03 | 45.07 | +| crt screen | 8.84 | 11.1 | +| plate | 58.76 | 77.97 | +| monitor | 30.0 | 90.3 | +| bulletin board | 55.09 | 71.34 | +| shower | 0.45 | 5.32 | +| radiator | 65.18 | 77.98 | +| glass | 16.62 | 18.5 | +| clock | 37.09 | 48.72 | +| flag | 69.73 | 72.71 | ++---------------------+-------+-------+ +2024-06-16 04:33:56,891 - mmseg - INFO - Summary: +2024-06-16 04:33:56,891 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.89 | 54.29 | 68.42 | ++-------+-------+-------+ +2024-06-16 04:33:56,892 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 04:33:56,892 - mmseg - INFO - Iter(val) [250] aAcc: 0.8489, mIoU: 0.5429, mAcc: 0.6842, IoU.wall: 0.7997, IoU.building: 0.8407, IoU.sky: 0.9446, IoU.floor: 0.8320, IoU.tree: 0.7613, IoU.ceiling: 0.8650, IoU.road: 0.8522, IoU.bed : 0.9151, IoU.windowpane: 0.6243, IoU.grass: 0.7055, IoU.cabinet: 0.6343, IoU.sidewalk: 0.6851, IoU.person: 0.8371, IoU.earth: 0.3504, IoU.door: 0.5683, IoU.table: 0.6593, IoU.mountain: 0.5937, IoU.plant: 0.5183, IoU.curtain: 0.7739, IoU.chair: 0.6315, IoU.car: 0.8493, IoU.water: 0.6125, IoU.painting: 0.7184, IoU.sofa: 0.7984, IoU.shelf: 0.4606, IoU.house: 0.5448, IoU.sea: 0.6750, IoU.mirror: 0.7644, IoU.rug: 0.6522, IoU.field: 0.4095, IoU.armchair: 0.5744, IoU.seat: 0.7126, IoU.fence: 0.4748, IoU.desk: 0.6010, IoU.rock: 0.5142, IoU.wardrobe: 0.5148, IoU.lamp: 0.6751, IoU.bathtub: 0.8176, IoU.railing: 0.3866, IoU.cushion: 0.6737, IoU.base: 0.3616, IoU.box: 0.2923, IoU.column: 0.5295, IoU.signboard: 0.3962, IoU.chest of drawers: 0.4738, IoU.counter: 0.3549, IoU.sand: 0.5085, IoU.sink: 0.7526, IoU.skyscraper: 0.4960, IoU.fireplace: 0.6748, IoU.refrigerator: 0.8359, IoU.grandstand: 0.6046, IoU.path: 0.2520, IoU.stairs: 0.4522, IoU.runway: 0.7138, IoU.case: 0.6289, IoU.pool table: 0.9394, IoU.pillow: 0.6754, IoU.screen door: 0.5748, IoU.stairway: 0.5699, IoU.river: 0.2232, IoU.bridge: 0.4265, IoU.bookcase: 0.3914, IoU.blind: 0.4449, IoU.coffee table: 0.6325, IoU.toilet: 0.8922, IoU.flower: 0.4848, IoU.book: 0.5297, IoU.hill: 0.0492, IoU.bench: 0.6141, IoU.countertop: 0.6463, IoU.stove: 0.7715, IoU.palm: 0.5121, IoU.kitchen island: 0.4769, IoU.computer: 0.6729, IoU.swivel chair: 0.4495, IoU.boat: 0.6002, IoU.bar: 0.5248, IoU.arcade machine: 0.7982, IoU.hovel: 0.5010, IoU.bus: 0.9058, IoU.towel: 0.7281, IoU.light: 0.5783, IoU.truck: 0.3917, IoU.tower: 0.4210, IoU.chandelier: 0.6645, IoU.awning: 0.3395, IoU.streetlight: 0.3133, IoU.booth: 0.4145, IoU.television receiver: 0.7590, IoU.airplane: 0.6098, IoU.dirt track: 0.2615, IoU.apparel: 0.4153, IoU.pole: 0.2198, IoU.land: 0.0084, IoU.bannister: 0.1693, IoU.escalator: 0.5657, IoU.ottoman: 0.4744, IoU.bottle: 0.3869, IoU.buffet: 0.4899, IoU.poster: 0.3372, IoU.stage: 0.1758, IoU.van: 0.4258, IoU.ship: 0.7431, IoU.fountain: 0.6127, IoU.conveyer belt: 0.8112, IoU.canopy: 0.5208, IoU.washer: 0.7936, IoU.plaything: 0.2116, IoU.swimming pool: 0.5472, IoU.stool: 0.4960, IoU.barrel: 0.5558, IoU.basket: 0.3286, IoU.waterfall: 0.7548, IoU.tent: 0.9183, IoU.bag: 0.1156, IoU.minibike: 0.7110, IoU.cradle: 0.7963, IoU.oven: 0.5096, IoU.ball: 0.4409, IoU.food: 0.5823, IoU.step: 0.1875, IoU.tank: 0.5997, IoU.trade name: 0.2869, IoU.microwave: 0.8618, IoU.pot: 0.5067, IoU.animal: 0.6508, IoU.bicycle: 0.4998, IoU.lake: 0.0000, IoU.dishwasher: 0.6117, IoU.screen: 0.4884, IoU.blanket: 0.2376, IoU.sculpture: 0.7119, IoU.hood: 0.5811, IoU.sconce: 0.5173, IoU.vase: 0.3621, IoU.traffic light: 0.3035, IoU.tray: 0.1070, IoU.ashcan: 0.4622, IoU.fan: 0.6486, IoU.pier: 0.3303, IoU.crt screen: 0.0884, IoU.plate: 0.5876, IoU.monitor: 0.3000, IoU.bulletin board: 0.5509, IoU.shower: 0.0045, IoU.radiator: 0.6518, IoU.glass: 0.1662, IoU.clock: 0.3709, IoU.flag: 0.6973, Acc.wall: 0.8613, Acc.building: 0.9333, Acc.sky: 0.9708, Acc.floor: 0.8810, Acc.tree: 0.9246, Acc.ceiling: 0.9265, Acc.road: 0.9056, Acc.bed : 0.9642, Acc.windowpane: 0.8406, Acc.grass: 0.8766, Acc.cabinet: 0.7533, Acc.sidewalk: 0.8859, Acc.person: 0.9025, Acc.earth: 0.4527, Acc.door: 0.7578, Acc.table: 0.8135, Acc.mountain: 0.7138, Acc.plant: 0.6052, Acc.curtain: 0.8955, Acc.chair: 0.7276, Acc.car: 0.9237, Acc.water: 0.7717, Acc.painting: 0.9076, Acc.sofa: 0.8642, Acc.shelf: 0.6463, Acc.house: 0.6925, Acc.sea: 0.9273, Acc.mirror: 0.8476, Acc.rug: 0.8932, Acc.field: 0.5770, Acc.armchair: 0.8321, Acc.seat: 0.8789, Acc.fence: 0.6688, Acc.desk: 0.8047, Acc.rock: 0.6579, Acc.wardrobe: 0.7824, Acc.lamp: 0.8343, Acc.bathtub: 0.8388, Acc.railing: 0.6038, Acc.cushion: 0.7677, Acc.base: 0.5774, Acc.box: 0.3460, Acc.column: 0.7198, Acc.signboard: 0.5559, Acc.chest of drawers: 0.7445, Acc.counter: 0.3848, Acc.sand: 0.8700, Acc.sink: 0.8416, Acc.skyscraper: 0.6318, Acc.fireplace: 0.9657, Acc.refrigerator: 0.9163, Acc.grandstand: 0.8140, Acc.path: 0.2956, Acc.stairs: 0.5569, Acc.runway: 0.9455, Acc.case: 0.8520, Acc.pool table: 0.9823, Acc.pillow: 0.7809, Acc.screen door: 0.6033, Acc.stairway: 0.6816, Acc.river: 0.2563, Acc.bridge: 0.4828, Acc.bookcase: 0.6112, Acc.blind: 0.4824, Acc.coffee table: 0.8750, Acc.toilet: 0.9242, Acc.flower: 0.6179, Acc.book: 0.7356, Acc.hill: 0.1120, Acc.bench: 0.7745, Acc.countertop: 0.7458, Acc.stove: 0.9343, Acc.palm: 0.8291, Acc.kitchen island: 0.8261, Acc.computer: 0.7616, Acc.swivel chair: 0.7989, Acc.boat: 0.8764, Acc.bar: 0.7526, Acc.arcade machine: 0.8956, Acc.hovel: 0.5519, Acc.bus: 0.9363, Acc.towel: 0.8290, Acc.light: 0.6838, Acc.truck: 0.4855, Acc.tower: 0.5760, Acc.chandelier: 0.9153, Acc.awning: 0.3986, Acc.streetlight: 0.4419, Acc.booth: 0.7026, Acc.television receiver: 0.8610, Acc.airplane: 0.7944, Acc.dirt track: 0.2804, Acc.apparel: 0.5680, Acc.pole: 0.2762, Acc.land: 0.0156, Acc.bannister: 0.2783, Acc.escalator: 0.7945, Acc.ottoman: 0.6921, Acc.bottle: 0.5872, Acc.buffet: 0.6659, Acc.poster: 0.4155, Acc.stage: 0.5333, Acc.van: 0.7613, Acc.ship: 0.7505, Acc.fountain: 0.6641, Acc.conveyer belt: 0.9618, Acc.canopy: 0.7057, Acc.washer: 0.8352, Acc.plaything: 0.3055, Acc.swimming pool: 0.8539, Acc.stool: 0.7310, Acc.barrel: 0.7099, Acc.basket: 0.4716, Acc.waterfall: 0.9585, Acc.tent: 0.9854, Acc.bag: 0.1236, Acc.minibike: 0.8726, Acc.cradle: 0.9802, Acc.oven: 0.6216, Acc.ball: 0.7613, Acc.food: 0.6275, Acc.step: 0.3071, Acc.tank: 0.7028, Acc.trade name: 0.3202, Acc.microwave: 0.9589, Acc.pot: 0.5727, Acc.animal: 0.6776, Acc.bicycle: 0.7908, Acc.lake: 0.0000, Acc.dishwasher: 0.7152, Acc.screen: 0.6283, Acc.blanket: 0.2603, Acc.sculpture: 0.8188, Acc.hood: 0.7145, Acc.sconce: 0.6227, Acc.vase: 0.6604, Acc.traffic light: 0.5111, Acc.tray: 0.1185, Acc.ashcan: 0.6098, Acc.fan: 0.8125, Acc.pier: 0.4507, Acc.crt screen: 0.1110, Acc.plate: 0.7797, Acc.monitor: 0.9030, Acc.bulletin board: 0.7134, Acc.shower: 0.0532, Acc.radiator: 0.7798, Acc.glass: 0.1850, Acc.clock: 0.4872, Acc.flag: 0.7271 +2024-06-16 04:35:06,093 - mmseg - INFO - Iter [17050/80000] lr: 3.148e-05, eta: 1 day, 2:22:59, time: 3.348, data_time: 1.981, memory: 70722, decode.loss_ce: 0.3176, decode.acc_seg: 86.9725, aux.loss_ce: 0.1291, aux.acc_seg: 86.7190, loss: 0.4467 +2024-06-16 04:36:14,079 - mmseg - INFO - Iter [17100/80000] lr: 3.145e-05, eta: 1 day, 2:21:16, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3350, decode.acc_seg: 86.6222, aux.loss_ce: 0.1359, aux.acc_seg: 86.4439, loss: 0.4709 +2024-06-16 04:37:22,154 - mmseg - INFO - Iter [17150/80000] lr: 3.143e-05, eta: 1 day, 2:19:34, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3080, decode.acc_seg: 87.5629, aux.loss_ce: 0.1239, aux.acc_seg: 87.4355, loss: 0.4320 +2024-06-16 04:38:30,545 - mmseg - INFO - Iter [17200/80000] lr: 3.140e-05, eta: 1 day, 2:17:53, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3322, decode.acc_seg: 86.6384, aux.loss_ce: 0.1345, aux.acc_seg: 86.5307, loss: 0.4667 +2024-06-16 04:39:38,908 - mmseg - INFO - Iter [17250/80000] lr: 3.138e-05, eta: 1 day, 2:16:12, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3354, decode.acc_seg: 86.0970, aux.loss_ce: 0.1354, aux.acc_seg: 86.0161, loss: 0.4708 +2024-06-16 04:40:47,071 - mmseg - INFO - Iter [17300/80000] lr: 3.135e-05, eta: 1 day, 2:14:31, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3343, decode.acc_seg: 86.6473, aux.loss_ce: 0.1351, aux.acc_seg: 86.5808, loss: 0.4694 +2024-06-16 04:41:55,152 - mmseg - INFO - Iter [17350/80000] lr: 3.133e-05, eta: 1 day, 2:12:49, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3369, decode.acc_seg: 86.5332, aux.loss_ce: 0.1357, aux.acc_seg: 86.3876, loss: 0.4726 +2024-06-16 04:43:03,739 - mmseg - INFO - Iter [17400/80000] lr: 3.130e-05, eta: 1 day, 2:11:09, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3576, decode.acc_seg: 85.0517, aux.loss_ce: 0.1422, aux.acc_seg: 85.2303, loss: 0.4998 +2024-06-16 04:44:11,894 - mmseg - INFO - Iter [17450/80000] lr: 3.128e-05, eta: 1 day, 2:09:29, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3370, decode.acc_seg: 86.8149, aux.loss_ce: 0.1374, aux.acc_seg: 86.6238, loss: 0.4744 +2024-06-16 04:45:19,971 - mmseg - INFO - Iter [17500/80000] lr: 3.125e-05, eta: 1 day, 2:07:48, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3262, decode.acc_seg: 87.0758, aux.loss_ce: 0.1314, aux.acc_seg: 86.8803, loss: 0.4575 +2024-06-16 04:46:28,220 - mmseg - INFO - Iter [17550/80000] lr: 3.123e-05, eta: 1 day, 2:06:07, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3198, decode.acc_seg: 87.2673, aux.loss_ce: 0.1296, aux.acc_seg: 87.0702, loss: 0.4494 +2024-06-16 04:47:36,657 - mmseg - INFO - Iter [17600/80000] lr: 3.120e-05, eta: 1 day, 2:04:28, time: 1.369, data_time: 0.009, memory: 70722, decode.loss_ce: 0.3635, decode.acc_seg: 85.5475, aux.loss_ce: 0.1464, aux.acc_seg: 85.4776, loss: 0.5099 +2024-06-16 04:48:44,707 - mmseg - INFO - Iter [17650/80000] lr: 3.118e-05, eta: 1 day, 2:02:48, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3327, decode.acc_seg: 86.7133, aux.loss_ce: 0.1345, aux.acc_seg: 86.6888, loss: 0.4671 +2024-06-16 04:49:54,971 - mmseg - INFO - Iter [17700/80000] lr: 3.115e-05, eta: 1 day, 2:01:15, time: 1.405, data_time: 0.051, memory: 70722, decode.loss_ce: 0.3313, decode.acc_seg: 86.7671, aux.loss_ce: 0.1333, aux.acc_seg: 86.7071, loss: 0.4646 +2024-06-16 04:51:03,277 - mmseg - INFO - Iter [17750/80000] lr: 3.113e-05, eta: 1 day, 1:59:36, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3124, decode.acc_seg: 87.3823, aux.loss_ce: 0.1263, aux.acc_seg: 87.2579, loss: 0.4387 +2024-06-16 04:52:11,766 - mmseg - INFO - Iter [17800/80000] lr: 3.110e-05, eta: 1 day, 1:57:57, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3335, decode.acc_seg: 86.6961, aux.loss_ce: 0.1359, aux.acc_seg: 86.4655, loss: 0.4695 +2024-06-16 04:53:20,114 - mmseg - INFO - Iter [17850/80000] lr: 3.108e-05, eta: 1 day, 1:56:18, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3297, decode.acc_seg: 86.6426, aux.loss_ce: 0.1323, aux.acc_seg: 86.6151, loss: 0.4619 +2024-06-16 04:54:28,338 - mmseg - INFO - Iter [17900/80000] lr: 3.105e-05, eta: 1 day, 1:54:39, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3133, decode.acc_seg: 87.6241, aux.loss_ce: 0.1262, aux.acc_seg: 87.4758, loss: 0.4395 +2024-06-16 04:55:36,654 - mmseg - INFO - Iter [17950/80000] lr: 3.103e-05, eta: 1 day, 1:53:01, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3225, decode.acc_seg: 87.4396, aux.loss_ce: 0.1320, aux.acc_seg: 87.1221, loss: 0.4545 +2024-06-16 04:56:44,763 - mmseg - INFO - Saving checkpoint at 18000 iterations +2024-06-16 04:58:16,264 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 04:58:16,264 - mmseg - INFO - Iter [18000/80000] lr: 3.100e-05, eta: 1 day, 1:56:37, time: 3.192, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3031, decode.acc_seg: 87.9892, aux.loss_ce: 0.1232, aux.acc_seg: 87.6719, loss: 0.4263 +2024-06-16 04:59:52,635 - mmseg - INFO - per class results: +2024-06-16 04:59:52,642 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.62 | 88.58 | +| building | 84.33 | 94.06 | +| sky | 94.62 | 96.97 | +| floor | 84.01 | 89.09 | +| tree | 77.38 | 89.21 | +| ceiling | 86.31 | 94.07 | +| road | 85.37 | 91.78 | +| bed | 91.7 | 97.06 | +| windowpane | 65.97 | 78.09 | +| grass | 70.4 | 81.79 | +| cabinet | 62.43 | 72.0 | +| sidewalk | 68.67 | 82.39 | +| person | 84.5 | 93.18 | +| earth | 35.97 | 54.66 | +| door | 56.29 | 72.09 | +| table | 66.89 | 79.11 | +| mountain | 65.08 | 76.86 | +| plant | 52.85 | 66.7 | +| curtain | 78.84 | 89.18 | +| chair | 59.22 | 68.07 | +| car | 86.74 | 94.2 | +| water | 49.55 | 59.47 | +| painting | 75.78 | 91.41 | +| sofa | 76.29 | 86.9 | +| shelf | 43.59 | 56.74 | +| house | 54.11 | 63.23 | +| sea | 69.88 | 86.37 | +| mirror | 73.45 | 80.77 | +| rug | 71.93 | 80.62 | +| field | 31.01 | 37.72 | +| armchair | 49.02 | 84.28 | +| seat | 69.75 | 86.29 | +| fence | 49.37 | 64.14 | +| desk | 52.52 | 82.2 | +| rock | 55.64 | 79.97 | +| wardrobe | 50.69 | 74.99 | +| lamp | 68.95 | 80.66 | +| bathtub | 81.09 | 84.64 | +| railing | 39.66 | 52.61 | +| cushion | 64.12 | 79.34 | +| base | 40.41 | 51.95 | +| box | 34.74 | 49.97 | +| column | 53.94 | 77.18 | +| signboard | 39.74 | 56.71 | +| chest of drawers | 47.43 | 79.16 | +| counter | 33.81 | 38.67 | +| sand | 48.1 | 72.8 | +| sink | 76.04 | 83.08 | +| skyscraper | 47.94 | 58.05 | +| fireplace | 70.39 | 93.27 | +| refrigerator | 82.83 | 91.51 | +| grandstand | 55.42 | 84.27 | +| path | 28.83 | 40.51 | +| stairs | 46.21 | 59.17 | +| runway | 72.97 | 95.3 | +| case | 56.75 | 82.81 | +| pool table | 93.66 | 98.29 | +| pillow | 61.76 | 69.42 | +| screen door | 70.41 | 74.3 | +| stairway | 52.17 | 71.31 | +| river | 15.25 | 55.77 | +| bridge | 76.51 | 90.69 | +| bookcase | 36.22 | 64.48 | +| blind | 47.86 | 57.72 | +| coffee table | 64.29 | 89.24 | +| toilet | 87.91 | 93.94 | +| flower | 48.08 | 61.48 | +| book | 52.43 | 72.24 | +| hill | 10.41 | 20.2 | +| bench | 52.79 | 73.9 | +| countertop | 60.34 | 83.93 | +| stove | 77.95 | 91.14 | +| palm | 55.39 | 77.75 | +| kitchen island | 48.08 | 72.93 | +| computer | 79.26 | 88.77 | +| swivel chair | 45.83 | 72.28 | +| boat | 80.94 | 85.69 | +| bar | 58.13 | 81.68 | +| arcade machine | 80.46 | 93.64 | +| hovel | 53.91 | 60.06 | +| bus | 92.01 | 95.89 | +| towel | 72.77 | 81.55 | +| light | 57.82 | 69.01 | +| truck | 44.61 | 55.27 | +| tower | 38.72 | 54.78 | +| chandelier | 66.23 | 80.23 | +| awning | 40.35 | 48.05 | +| streetlight | 29.69 | 37.86 | +| booth | 32.91 | 39.31 | +| television receiver | 74.99 | 87.43 | +| airplane | 62.01 | 79.24 | +| dirt track | 1.64 | 1.64 | +| apparel | 45.73 | 63.46 | +| pole | 30.63 | 42.31 | +| land | 4.04 | 14.3 | +| bannister | 10.09 | 12.21 | +| escalator | 59.59 | 78.82 | +| ottoman | 46.92 | 65.25 | +| bottle | 40.59 | 67.47 | +| buffet | 52.79 | 59.19 | +| poster | 34.7 | 43.75 | +| stage | 30.61 | 40.4 | +| van | 51.81 | 66.81 | +| ship | 56.14 | 57.61 | +| fountain | 40.95 | 42.02 | +| conveyer belt | 64.76 | 97.59 | +| canopy | 28.02 | 47.15 | +| washer | 80.07 | 86.43 | +| plaything | 25.12 | 77.5 | +| swimming pool | 58.23 | 77.5 | +| stool | 39.21 | 75.66 | +| barrel | 59.15 | 74.45 | +| basket | 36.18 | 53.93 | +| waterfall | 50.25 | 55.56 | +| tent | 91.09 | 99.01 | +| bag | 21.58 | 24.68 | +| minibike | 70.82 | 87.44 | +| cradle | 85.78 | 97.49 | +| oven | 51.49 | 71.12 | +| ball | 52.08 | 69.81 | +| food | 59.05 | 68.57 | +| step | 12.53 | 17.12 | +| tank | 60.03 | 69.82 | +| trade name | 27.98 | 31.55 | +| microwave | 86.83 | 95.92 | +| pot | 51.83 | 63.02 | +| animal | 57.21 | 58.62 | +| bicycle | 52.77 | 75.63 | +| lake | 47.63 | 55.29 | +| dishwasher | 60.46 | 65.49 | +| screen | 63.57 | 92.79 | +| blanket | 34.98 | 38.59 | +| sculpture | 52.09 | 85.17 | +| hood | 52.06 | 64.93 | +| sconce | 53.72 | 68.98 | +| vase | 40.27 | 66.52 | +| traffic light | 35.09 | 53.94 | +| tray | 6.96 | 7.33 | +| ashcan | 40.98 | 63.65 | +| fan | 65.63 | 78.05 | +| pier | 38.18 | 42.69 | +| crt screen | 10.04 | 29.88 | +| plate | 56.39 | 76.07 | +| monitor | 13.34 | 15.58 | +| bulletin board | 62.09 | 72.92 | +| shower | 0.17 | 1.39 | +| radiator | 64.42 | 78.39 | +| glass | 13.99 | 14.78 | +| clock | 30.39 | 40.03 | +| flag | 70.59 | 74.88 | ++---------------------+-------+-------+ +2024-06-16 04:59:52,642 - mmseg - INFO - Summary: +2024-06-16 04:59:52,643 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.02 | 54.42 | 68.16 | ++-------+-------+-------+ +2024-06-16 04:59:52,643 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 04:59:52,644 - mmseg - INFO - Iter(val) [250] aAcc: 0.8502, mIoU: 0.5442, mAcc: 0.6816, IoU.wall: 0.8062, IoU.building: 0.8433, IoU.sky: 0.9462, IoU.floor: 0.8401, IoU.tree: 0.7738, IoU.ceiling: 0.8631, IoU.road: 0.8537, IoU.bed : 0.9170, IoU.windowpane: 0.6597, IoU.grass: 0.7040, IoU.cabinet: 0.6243, IoU.sidewalk: 0.6867, IoU.person: 0.8450, IoU.earth: 0.3597, IoU.door: 0.5629, IoU.table: 0.6689, IoU.mountain: 0.6508, IoU.plant: 0.5285, IoU.curtain: 0.7884, IoU.chair: 0.5922, IoU.car: 0.8674, IoU.water: 0.4955, IoU.painting: 0.7578, IoU.sofa: 0.7629, IoU.shelf: 0.4359, IoU.house: 0.5411, IoU.sea: 0.6988, IoU.mirror: 0.7345, IoU.rug: 0.7193, IoU.field: 0.3101, IoU.armchair: 0.4902, IoU.seat: 0.6975, IoU.fence: 0.4937, IoU.desk: 0.5252, IoU.rock: 0.5564, IoU.wardrobe: 0.5069, IoU.lamp: 0.6895, IoU.bathtub: 0.8109, IoU.railing: 0.3966, IoU.cushion: 0.6412, IoU.base: 0.4041, IoU.box: 0.3474, IoU.column: 0.5394, IoU.signboard: 0.3974, IoU.chest of drawers: 0.4743, IoU.counter: 0.3381, IoU.sand: 0.4810, IoU.sink: 0.7604, IoU.skyscraper: 0.4794, IoU.fireplace: 0.7039, IoU.refrigerator: 0.8283, IoU.grandstand: 0.5542, IoU.path: 0.2883, IoU.stairs: 0.4621, IoU.runway: 0.7297, IoU.case: 0.5675, IoU.pool table: 0.9366, IoU.pillow: 0.6176, IoU.screen door: 0.7041, IoU.stairway: 0.5217, IoU.river: 0.1525, IoU.bridge: 0.7651, IoU.bookcase: 0.3622, IoU.blind: 0.4786, IoU.coffee table: 0.6429, IoU.toilet: 0.8791, IoU.flower: 0.4808, IoU.book: 0.5243, IoU.hill: 0.1041, IoU.bench: 0.5279, IoU.countertop: 0.6034, IoU.stove: 0.7795, IoU.palm: 0.5539, IoU.kitchen island: 0.4808, IoU.computer: 0.7926, IoU.swivel chair: 0.4583, IoU.boat: 0.8094, IoU.bar: 0.5813, IoU.arcade machine: 0.8046, IoU.hovel: 0.5391, IoU.bus: 0.9201, IoU.towel: 0.7277, IoU.light: 0.5782, IoU.truck: 0.4461, IoU.tower: 0.3872, IoU.chandelier: 0.6623, IoU.awning: 0.4035, IoU.streetlight: 0.2969, IoU.booth: 0.3291, IoU.television receiver: 0.7499, IoU.airplane: 0.6201, IoU.dirt track: 0.0164, IoU.apparel: 0.4573, IoU.pole: 0.3063, IoU.land: 0.0404, IoU.bannister: 0.1009, IoU.escalator: 0.5959, IoU.ottoman: 0.4692, IoU.bottle: 0.4059, IoU.buffet: 0.5279, IoU.poster: 0.3470, IoU.stage: 0.3061, IoU.van: 0.5181, IoU.ship: 0.5614, IoU.fountain: 0.4095, IoU.conveyer belt: 0.6476, IoU.canopy: 0.2802, IoU.washer: 0.8007, IoU.plaything: 0.2512, IoU.swimming pool: 0.5823, IoU.stool: 0.3921, IoU.barrel: 0.5915, IoU.basket: 0.3618, IoU.waterfall: 0.5025, IoU.tent: 0.9109, IoU.bag: 0.2158, IoU.minibike: 0.7082, IoU.cradle: 0.8578, IoU.oven: 0.5149, IoU.ball: 0.5208, IoU.food: 0.5905, IoU.step: 0.1253, IoU.tank: 0.6003, IoU.trade name: 0.2798, IoU.microwave: 0.8683, IoU.pot: 0.5183, IoU.animal: 0.5721, IoU.bicycle: 0.5277, IoU.lake: 0.4763, IoU.dishwasher: 0.6046, IoU.screen: 0.6357, IoU.blanket: 0.3498, IoU.sculpture: 0.5209, IoU.hood: 0.5206, IoU.sconce: 0.5372, IoU.vase: 0.4027, IoU.traffic light: 0.3509, IoU.tray: 0.0696, IoU.ashcan: 0.4098, IoU.fan: 0.6563, IoU.pier: 0.3818, IoU.crt screen: 0.1004, IoU.plate: 0.5639, IoU.monitor: 0.1334, IoU.bulletin board: 0.6209, IoU.shower: 0.0017, IoU.radiator: 0.6442, IoU.glass: 0.1399, IoU.clock: 0.3039, IoU.flag: 0.7059, Acc.wall: 0.8858, Acc.building: 0.9406, Acc.sky: 0.9697, Acc.floor: 0.8909, Acc.tree: 0.8921, Acc.ceiling: 0.9407, Acc.road: 0.9178, Acc.bed : 0.9706, Acc.windowpane: 0.7809, Acc.grass: 0.8179, Acc.cabinet: 0.7200, Acc.sidewalk: 0.8239, Acc.person: 0.9318, Acc.earth: 0.5466, Acc.door: 0.7209, Acc.table: 0.7911, Acc.mountain: 0.7686, Acc.plant: 0.6670, Acc.curtain: 0.8918, Acc.chair: 0.6807, Acc.car: 0.9420, Acc.water: 0.5947, Acc.painting: 0.9141, Acc.sofa: 0.8690, Acc.shelf: 0.5674, Acc.house: 0.6323, Acc.sea: 0.8637, Acc.mirror: 0.8077, Acc.rug: 0.8062, Acc.field: 0.3772, Acc.armchair: 0.8428, Acc.seat: 0.8629, Acc.fence: 0.6414, Acc.desk: 0.8220, Acc.rock: 0.7997, Acc.wardrobe: 0.7499, Acc.lamp: 0.8066, Acc.bathtub: 0.8464, Acc.railing: 0.5261, Acc.cushion: 0.7934, Acc.base: 0.5195, Acc.box: 0.4997, Acc.column: 0.7718, Acc.signboard: 0.5671, Acc.chest of drawers: 0.7916, Acc.counter: 0.3867, Acc.sand: 0.7280, Acc.sink: 0.8308, Acc.skyscraper: 0.5805, Acc.fireplace: 0.9327, Acc.refrigerator: 0.9151, Acc.grandstand: 0.8427, Acc.path: 0.4051, Acc.stairs: 0.5917, Acc.runway: 0.9530, Acc.case: 0.8281, Acc.pool table: 0.9829, Acc.pillow: 0.6942, Acc.screen door: 0.7430, Acc.stairway: 0.7131, Acc.river: 0.5577, Acc.bridge: 0.9069, Acc.bookcase: 0.6448, Acc.blind: 0.5772, Acc.coffee table: 0.8924, Acc.toilet: 0.9394, Acc.flower: 0.6148, Acc.book: 0.7224, Acc.hill: 0.2020, Acc.bench: 0.7390, Acc.countertop: 0.8393, Acc.stove: 0.9114, Acc.palm: 0.7775, Acc.kitchen island: 0.7293, Acc.computer: 0.8877, Acc.swivel chair: 0.7228, Acc.boat: 0.8569, Acc.bar: 0.8168, Acc.arcade machine: 0.9364, Acc.hovel: 0.6006, Acc.bus: 0.9589, Acc.towel: 0.8155, Acc.light: 0.6901, Acc.truck: 0.5527, Acc.tower: 0.5478, Acc.chandelier: 0.8023, Acc.awning: 0.4805, Acc.streetlight: 0.3786, Acc.booth: 0.3931, Acc.television receiver: 0.8743, Acc.airplane: 0.7924, Acc.dirt track: 0.0164, Acc.apparel: 0.6346, Acc.pole: 0.4231, Acc.land: 0.1430, Acc.bannister: 0.1221, Acc.escalator: 0.7882, Acc.ottoman: 0.6525, Acc.bottle: 0.6747, Acc.buffet: 0.5919, Acc.poster: 0.4375, Acc.stage: 0.4040, Acc.van: 0.6681, Acc.ship: 0.5761, Acc.fountain: 0.4202, Acc.conveyer belt: 0.9759, Acc.canopy: 0.4715, Acc.washer: 0.8643, Acc.plaything: 0.7750, Acc.swimming pool: 0.7750, Acc.stool: 0.7566, Acc.barrel: 0.7445, Acc.basket: 0.5393, Acc.waterfall: 0.5556, Acc.tent: 0.9901, Acc.bag: 0.2468, Acc.minibike: 0.8744, Acc.cradle: 0.9749, Acc.oven: 0.7112, Acc.ball: 0.6981, Acc.food: 0.6857, Acc.step: 0.1712, Acc.tank: 0.6982, Acc.trade name: 0.3155, Acc.microwave: 0.9592, Acc.pot: 0.6302, Acc.animal: 0.5862, Acc.bicycle: 0.7563, Acc.lake: 0.5529, Acc.dishwasher: 0.6549, Acc.screen: 0.9279, Acc.blanket: 0.3859, Acc.sculpture: 0.8517, Acc.hood: 0.6493, Acc.sconce: 0.6898, Acc.vase: 0.6652, Acc.traffic light: 0.5394, Acc.tray: 0.0733, Acc.ashcan: 0.6365, Acc.fan: 0.7805, Acc.pier: 0.4269, Acc.crt screen: 0.2988, Acc.plate: 0.7607, Acc.monitor: 0.1558, Acc.bulletin board: 0.7292, Acc.shower: 0.0139, Acc.radiator: 0.7839, Acc.glass: 0.1478, Acc.clock: 0.4003, Acc.flag: 0.7488 +2024-06-16 05:01:02,124 - mmseg - INFO - Iter [18050/80000] lr: 3.098e-05, eta: 1 day, 2:00:32, time: 3.317, data_time: 1.943, memory: 70722, decode.loss_ce: 0.3112, decode.acc_seg: 87.2543, aux.loss_ce: 0.1251, aux.acc_seg: 87.1668, loss: 0.4362 +2024-06-16 05:02:10,325 - mmseg - INFO - Iter [18100/80000] lr: 3.095e-05, eta: 1 day, 1:58:51, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3074, decode.acc_seg: 87.7553, aux.loss_ce: 0.1245, aux.acc_seg: 87.5335, loss: 0.4319 +2024-06-16 05:03:18,510 - mmseg - INFO - Iter [18150/80000] lr: 3.093e-05, eta: 1 day, 1:57:11, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3136, decode.acc_seg: 87.5000, aux.loss_ce: 0.1269, aux.acc_seg: 87.3286, loss: 0.4406 +2024-06-16 05:04:26,681 - mmseg - INFO - Iter [18200/80000] lr: 3.090e-05, eta: 1 day, 1:55:30, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3043, decode.acc_seg: 87.6640, aux.loss_ce: 0.1238, aux.acc_seg: 87.4501, loss: 0.4280 +2024-06-16 05:05:35,022 - mmseg - INFO - Iter [18250/80000] lr: 3.088e-05, eta: 1 day, 1:53:51, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3371, decode.acc_seg: 86.6144, aux.loss_ce: 0.1354, aux.acc_seg: 86.5437, loss: 0.4725 +2024-06-16 05:06:43,267 - mmseg - INFO - Iter [18300/80000] lr: 3.085e-05, eta: 1 day, 1:52:11, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3076, decode.acc_seg: 87.2884, aux.loss_ce: 0.1239, aux.acc_seg: 87.2646, loss: 0.4315 +2024-06-16 05:07:51,500 - mmseg - INFO - Iter [18350/80000] lr: 3.083e-05, eta: 1 day, 1:50:31, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3352, decode.acc_seg: 86.9757, aux.loss_ce: 0.1349, aux.acc_seg: 86.7766, loss: 0.4701 +2024-06-16 05:08:59,791 - mmseg - INFO - Iter [18400/80000] lr: 3.080e-05, eta: 1 day, 1:48:51, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3393, decode.acc_seg: 86.6590, aux.loss_ce: 0.1373, aux.acc_seg: 86.4551, loss: 0.4766 +2024-06-16 05:10:08,048 - mmseg - INFO - Iter [18450/80000] lr: 3.078e-05, eta: 1 day, 1:47:12, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3094, decode.acc_seg: 87.3788, aux.loss_ce: 0.1252, aux.acc_seg: 87.1935, loss: 0.4346 +2024-06-16 05:11:16,215 - mmseg - INFO - Iter [18500/80000] lr: 3.075e-05, eta: 1 day, 1:45:33, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3310, decode.acc_seg: 86.3570, aux.loss_ce: 0.1329, aux.acc_seg: 86.2207, loss: 0.4639 +2024-06-16 05:12:24,665 - mmseg - INFO - Iter [18550/80000] lr: 3.073e-05, eta: 1 day, 1:43:54, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3194, decode.acc_seg: 86.9268, aux.loss_ce: 0.1300, aux.acc_seg: 86.7969, loss: 0.4494 +2024-06-16 05:13:33,044 - mmseg - INFO - Iter [18600/80000] lr: 3.070e-05, eta: 1 day, 1:42:16, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3139, decode.acc_seg: 87.2744, aux.loss_ce: 0.1267, aux.acc_seg: 87.1200, loss: 0.4407 +2024-06-16 05:14:41,136 - mmseg - INFO - Iter [18650/80000] lr: 3.068e-05, eta: 1 day, 1:40:36, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3231, decode.acc_seg: 86.8154, aux.loss_ce: 0.1311, aux.acc_seg: 86.6252, loss: 0.4541 +2024-06-16 05:15:49,549 - mmseg - INFO - Iter [18700/80000] lr: 3.065e-05, eta: 1 day, 1:38:58, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3044, decode.acc_seg: 87.3885, aux.loss_ce: 0.1230, aux.acc_seg: 87.1760, loss: 0.4274 +2024-06-16 05:16:57,738 - mmseg - INFO - Iter [18750/80000] lr: 3.063e-05, eta: 1 day, 1:37:20, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3231, decode.acc_seg: 86.6570, aux.loss_ce: 0.1311, aux.acc_seg: 86.4485, loss: 0.4542 +2024-06-16 05:18:05,932 - mmseg - INFO - Iter [18800/80000] lr: 3.060e-05, eta: 1 day, 1:35:41, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3193, decode.acc_seg: 87.0654, aux.loss_ce: 0.1290, aux.acc_seg: 86.9788, loss: 0.4483 +2024-06-16 05:19:14,318 - mmseg - INFO - Iter [18850/80000] lr: 3.058e-05, eta: 1 day, 1:34:04, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3401, decode.acc_seg: 86.1995, aux.loss_ce: 0.1372, aux.acc_seg: 85.9751, loss: 0.4772 +2024-06-16 05:20:22,728 - mmseg - INFO - Iter [18900/80000] lr: 3.055e-05, eta: 1 day, 1:32:26, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3352, decode.acc_seg: 86.2248, aux.loss_ce: 0.1336, aux.acc_seg: 86.2046, loss: 0.4688 +2024-06-16 05:21:34,096 - mmseg - INFO - Iter [18950/80000] lr: 3.053e-05, eta: 1 day, 1:30:59, time: 1.427, data_time: 0.073, memory: 70722, decode.loss_ce: 0.3198, decode.acc_seg: 87.3419, aux.loss_ce: 0.1290, aux.acc_seg: 87.2289, loss: 0.4488 +2024-06-16 05:22:42,286 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 05:22:42,286 - mmseg - INFO - Iter [19000/80000] lr: 3.050e-05, eta: 1 day, 1:29:21, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2877, decode.acc_seg: 88.1177, aux.loss_ce: 0.1169, aux.acc_seg: 87.9763, loss: 0.4046 +2024-06-16 05:24:18,420 - mmseg - INFO - per class results: +2024-06-16 05:24:18,427 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.35 | 87.66 | +| building | 84.37 | 93.98 | +| sky | 94.88 | 97.13 | +| floor | 84.79 | 91.58 | +| tree | 77.27 | 88.25 | +| ceiling | 86.91 | 92.96 | +| road | 84.86 | 90.76 | +| bed | 91.88 | 96.72 | +| windowpane | 64.41 | 82.06 | +| grass | 69.67 | 77.12 | +| cabinet | 63.02 | 74.13 | +| sidewalk | 69.51 | 86.13 | +| person | 85.06 | 94.29 | +| earth | 40.58 | 61.01 | +| door | 55.22 | 73.72 | +| table | 66.77 | 78.6 | +| mountain | 58.28 | 68.07 | +| plant | 55.03 | 69.22 | +| curtain | 79.43 | 86.38 | +| chair | 65.3 | 80.92 | +| car | 86.95 | 93.75 | +| water | 65.87 | 88.18 | +| painting | 73.88 | 89.99 | +| sofa | 78.62 | 89.29 | +| shelf | 39.82 | 52.04 | +| house | 60.88 | 73.8 | +| sea | 76.46 | 85.4 | +| mirror | 72.8 | 78.51 | +| rug | 71.35 | 79.78 | +| field | 26.79 | 45.94 | +| armchair | 56.99 | 68.17 | +| seat | 61.95 | 82.77 | +| fence | 48.9 | 65.15 | +| desk | 57.49 | 81.24 | +| rock | 54.61 | 83.61 | +| wardrobe | 54.51 | 77.69 | +| lamp | 68.76 | 79.17 | +| bathtub | 83.66 | 87.98 | +| railing | 41.0 | 54.45 | +| cushion | 66.05 | 81.96 | +| base | 36.51 | 55.37 | +| box | 36.4 | 46.33 | +| column | 51.19 | 62.29 | +| signboard | 37.85 | 48.34 | +| chest of drawers | 41.54 | 79.53 | +| counter | 35.54 | 38.72 | +| sand | 36.68 | 54.47 | +| sink | 75.49 | 81.02 | +| skyscraper | 48.53 | 63.25 | +| fireplace | 72.05 | 95.08 | +| refrigerator | 84.75 | 87.37 | +| grandstand | 51.06 | 79.22 | +| path | 16.62 | 19.72 | +| stairs | 18.53 | 23.06 | +| runway | 71.21 | 98.09 | +| case | 63.56 | 87.08 | +| pool table | 93.3 | 98.5 | +| pillow | 66.21 | 75.31 | +| screen door | 68.4 | 72.86 | +| stairway | 43.2 | 79.1 | +| river | 15.6 | 17.23 | +| bridge | 75.16 | 89.02 | +| bookcase | 35.26 | 61.83 | +| blind | 47.73 | 56.95 | +| coffee table | 65.84 | 86.93 | +| toilet | 88.67 | 94.74 | +| flower | 41.45 | 48.92 | +| book | 51.0 | 68.81 | +| hill | 3.41 | 7.03 | +| bench | 55.55 | 64.22 | +| countertop | 59.97 | 87.23 | +| stove | 82.66 | 91.95 | +| palm | 50.94 | 84.16 | +| kitchen island | 47.03 | 85.56 | +| computer | 75.94 | 91.54 | +| swivel chair | 47.72 | 68.39 | +| boat | 67.64 | 87.35 | +| bar | 58.42 | 84.09 | +| arcade machine | 78.9 | 84.52 | +| hovel | 47.85 | 53.13 | +| bus | 92.6 | 94.63 | +| towel | 72.18 | 86.7 | +| light | 57.75 | 69.38 | +| truck | 44.62 | 63.69 | +| tower | 26.65 | 49.04 | +| chandelier | 66.72 | 87.59 | +| awning | 41.18 | 50.08 | +| streetlight | 29.0 | 35.76 | +| booth | 38.16 | 38.91 | +| television receiver | 71.53 | 88.82 | +| airplane | 80.75 | 88.92 | +| dirt track | 20.51 | 44.76 | +| apparel | 53.09 | 75.74 | +| pole | 23.01 | 28.13 | +| land | 0.21 | 0.34 | +| bannister | 13.41 | 17.34 | +| escalator | 60.74 | 78.28 | +| ottoman | 51.72 | 75.0 | +| bottle | 39.39 | 65.51 | +| buffet | 61.24 | 87.9 | +| poster | 29.66 | 40.36 | +| stage | 20.4 | 51.8 | +| van | 47.94 | 57.49 | +| ship | 86.0 | 95.48 | +| fountain | 45.27 | 48.47 | +| conveyer belt | 67.67 | 97.38 | +| canopy | 40.34 | 60.82 | +| washer | 79.44 | 86.08 | +| plaything | 35.38 | 65.65 | +| swimming pool | 52.17 | 74.77 | +| stool | 43.65 | 71.93 | +| barrel | 52.96 | 71.81 | +| basket | 37.64 | 55.23 | +| waterfall | 53.14 | 59.6 | +| tent | 95.95 | 98.88 | +| bag | 16.06 | 18.08 | +| minibike | 71.71 | 78.59 | +| cradle | 81.48 | 98.67 | +| oven | 59.48 | 68.03 | +| ball | 48.81 | 55.69 | +| food | 64.9 | 77.9 | +| step | 16.63 | 22.78 | +| tank | 58.51 | 69.13 | +| trade name | 16.65 | 17.64 | +| microwave | 87.98 | 95.5 | +| pot | 52.67 | 63.05 | +| animal | 65.63 | 67.56 | +| bicycle | 55.04 | 68.05 | +| lake | 0.0 | 0.0 | +| dishwasher | 72.3 | 77.59 | +| screen | 63.66 | 88.89 | +| blanket | 31.1 | 35.31 | +| sculpture | 54.57 | 66.28 | +| hood | 62.15 | 71.32 | +| sconce | 54.43 | 68.68 | +| vase | 43.79 | 55.06 | +| traffic light | 27.24 | 57.56 | +| tray | 9.74 | 11.2 | +| ashcan | 42.93 | 63.01 | +| fan | 65.92 | 83.71 | +| pier | 37.82 | 42.63 | +| crt screen | 13.33 | 30.33 | +| plate | 58.41 | 79.36 | +| monitor | 34.28 | 48.86 | +| bulletin board | 54.94 | 71.72 | +| shower | 0.02 | 0.05 | +| radiator | 60.74 | 81.79 | +| glass | 17.3 | 19.35 | +| clock | 38.4 | 48.23 | +| flag | 68.14 | 78.63 | ++---------------------+-------+-------+ +2024-06-16 05:24:18,427 - mmseg - INFO - Summary: +2024-06-16 05:24:18,427 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.22 | 54.59 | 68.07 | ++-------+-------+-------+ +2024-06-16 05:24:18,428 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 05:24:18,428 - mmseg - INFO - Iter(val) [250] aAcc: 0.8522, mIoU: 0.5459, mAcc: 0.6807, IoU.wall: 0.8035, IoU.building: 0.8437, IoU.sky: 0.9488, IoU.floor: 0.8479, IoU.tree: 0.7727, IoU.ceiling: 0.8691, IoU.road: 0.8486, IoU.bed : 0.9188, IoU.windowpane: 0.6441, IoU.grass: 0.6967, IoU.cabinet: 0.6302, IoU.sidewalk: 0.6951, IoU.person: 0.8506, IoU.earth: 0.4058, IoU.door: 0.5522, IoU.table: 0.6677, IoU.mountain: 0.5828, IoU.plant: 0.5503, IoU.curtain: 0.7943, IoU.chair: 0.6530, IoU.car: 0.8695, IoU.water: 0.6587, IoU.painting: 0.7388, IoU.sofa: 0.7862, IoU.shelf: 0.3982, IoU.house: 0.6088, IoU.sea: 0.7646, IoU.mirror: 0.7280, IoU.rug: 0.7135, IoU.field: 0.2679, IoU.armchair: 0.5699, IoU.seat: 0.6195, IoU.fence: 0.4890, IoU.desk: 0.5749, IoU.rock: 0.5461, IoU.wardrobe: 0.5451, IoU.lamp: 0.6876, IoU.bathtub: 0.8366, IoU.railing: 0.4100, IoU.cushion: 0.6605, IoU.base: 0.3651, IoU.box: 0.3640, IoU.column: 0.5119, IoU.signboard: 0.3785, IoU.chest of drawers: 0.4154, IoU.counter: 0.3554, IoU.sand: 0.3668, IoU.sink: 0.7549, IoU.skyscraper: 0.4853, IoU.fireplace: 0.7205, IoU.refrigerator: 0.8475, IoU.grandstand: 0.5106, IoU.path: 0.1662, IoU.stairs: 0.1853, IoU.runway: 0.7121, IoU.case: 0.6356, IoU.pool table: 0.9330, IoU.pillow: 0.6621, IoU.screen door: 0.6840, IoU.stairway: 0.4320, IoU.river: 0.1560, IoU.bridge: 0.7516, IoU.bookcase: 0.3526, IoU.blind: 0.4773, IoU.coffee table: 0.6584, IoU.toilet: 0.8867, IoU.flower: 0.4145, IoU.book: 0.5100, IoU.hill: 0.0341, IoU.bench: 0.5555, IoU.countertop: 0.5997, IoU.stove: 0.8266, IoU.palm: 0.5094, IoU.kitchen island: 0.4703, IoU.computer: 0.7594, IoU.swivel chair: 0.4772, IoU.boat: 0.6764, IoU.bar: 0.5842, IoU.arcade machine: 0.7890, IoU.hovel: 0.4785, IoU.bus: 0.9260, IoU.towel: 0.7218, IoU.light: 0.5775, IoU.truck: 0.4462, IoU.tower: 0.2665, IoU.chandelier: 0.6672, IoU.awning: 0.4118, IoU.streetlight: 0.2900, IoU.booth: 0.3816, IoU.television receiver: 0.7153, IoU.airplane: 0.8075, IoU.dirt track: 0.2051, IoU.apparel: 0.5309, IoU.pole: 0.2301, IoU.land: 0.0021, IoU.bannister: 0.1341, IoU.escalator: 0.6074, IoU.ottoman: 0.5172, IoU.bottle: 0.3939, IoU.buffet: 0.6124, IoU.poster: 0.2966, IoU.stage: 0.2040, IoU.van: 0.4794, IoU.ship: 0.8600, IoU.fountain: 0.4527, IoU.conveyer belt: 0.6767, IoU.canopy: 0.4034, IoU.washer: 0.7944, IoU.plaything: 0.3538, IoU.swimming pool: 0.5217, IoU.stool: 0.4365, IoU.barrel: 0.5296, IoU.basket: 0.3764, IoU.waterfall: 0.5314, IoU.tent: 0.9595, IoU.bag: 0.1606, IoU.minibike: 0.7171, IoU.cradle: 0.8148, IoU.oven: 0.5948, IoU.ball: 0.4881, IoU.food: 0.6490, IoU.step: 0.1663, IoU.tank: 0.5851, IoU.trade name: 0.1665, IoU.microwave: 0.8798, IoU.pot: 0.5267, IoU.animal: 0.6563, IoU.bicycle: 0.5504, IoU.lake: 0.0000, IoU.dishwasher: 0.7230, IoU.screen: 0.6366, IoU.blanket: 0.3110, IoU.sculpture: 0.5457, IoU.hood: 0.6215, IoU.sconce: 0.5443, IoU.vase: 0.4379, IoU.traffic light: 0.2724, IoU.tray: 0.0974, IoU.ashcan: 0.4293, IoU.fan: 0.6592, IoU.pier: 0.3782, IoU.crt screen: 0.1333, IoU.plate: 0.5841, IoU.monitor: 0.3428, IoU.bulletin board: 0.5494, IoU.shower: 0.0002, IoU.radiator: 0.6074, IoU.glass: 0.1730, IoU.clock: 0.3840, IoU.flag: 0.6814, Acc.wall: 0.8766, Acc.building: 0.9398, Acc.sky: 0.9713, Acc.floor: 0.9158, Acc.tree: 0.8825, Acc.ceiling: 0.9296, Acc.road: 0.9076, Acc.bed : 0.9672, Acc.windowpane: 0.8206, Acc.grass: 0.7712, Acc.cabinet: 0.7413, Acc.sidewalk: 0.8613, Acc.person: 0.9429, Acc.earth: 0.6101, Acc.door: 0.7372, Acc.table: 0.7860, Acc.mountain: 0.6807, Acc.plant: 0.6922, Acc.curtain: 0.8638, Acc.chair: 0.8092, Acc.car: 0.9375, Acc.water: 0.8818, Acc.painting: 0.8999, Acc.sofa: 0.8929, Acc.shelf: 0.5204, Acc.house: 0.7380, Acc.sea: 0.8540, Acc.mirror: 0.7851, Acc.rug: 0.7978, Acc.field: 0.4594, Acc.armchair: 0.6817, Acc.seat: 0.8277, Acc.fence: 0.6515, Acc.desk: 0.8124, Acc.rock: 0.8361, Acc.wardrobe: 0.7769, Acc.lamp: 0.7917, Acc.bathtub: 0.8798, Acc.railing: 0.5445, Acc.cushion: 0.8196, Acc.base: 0.5537, Acc.box: 0.4633, Acc.column: 0.6229, Acc.signboard: 0.4834, Acc.chest of drawers: 0.7953, Acc.counter: 0.3872, Acc.sand: 0.5447, Acc.sink: 0.8102, Acc.skyscraper: 0.6325, Acc.fireplace: 0.9508, Acc.refrigerator: 0.8737, Acc.grandstand: 0.7922, Acc.path: 0.1972, Acc.stairs: 0.2306, Acc.runway: 0.9809, Acc.case: 0.8708, Acc.pool table: 0.9850, Acc.pillow: 0.7531, Acc.screen door: 0.7286, Acc.stairway: 0.7910, Acc.river: 0.1723, Acc.bridge: 0.8902, Acc.bookcase: 0.6183, Acc.blind: 0.5695, Acc.coffee table: 0.8693, Acc.toilet: 0.9474, Acc.flower: 0.4892, Acc.book: 0.6881, Acc.hill: 0.0703, Acc.bench: 0.6422, Acc.countertop: 0.8723, Acc.stove: 0.9195, Acc.palm: 0.8416, Acc.kitchen island: 0.8556, Acc.computer: 0.9154, Acc.swivel chair: 0.6839, Acc.boat: 0.8735, Acc.bar: 0.8409, Acc.arcade machine: 0.8452, Acc.hovel: 0.5313, Acc.bus: 0.9463, Acc.towel: 0.8670, Acc.light: 0.6938, Acc.truck: 0.6369, Acc.tower: 0.4904, Acc.chandelier: 0.8759, Acc.awning: 0.5008, Acc.streetlight: 0.3576, Acc.booth: 0.3891, Acc.television receiver: 0.8882, Acc.airplane: 0.8892, Acc.dirt track: 0.4476, Acc.apparel: 0.7574, Acc.pole: 0.2813, Acc.land: 0.0034, Acc.bannister: 0.1734, Acc.escalator: 0.7828, Acc.ottoman: 0.7500, Acc.bottle: 0.6551, Acc.buffet: 0.8790, Acc.poster: 0.4036, Acc.stage: 0.5180, Acc.van: 0.5749, Acc.ship: 0.9548, Acc.fountain: 0.4847, Acc.conveyer belt: 0.9738, Acc.canopy: 0.6082, Acc.washer: 0.8608, Acc.plaything: 0.6565, Acc.swimming pool: 0.7477, Acc.stool: 0.7193, Acc.barrel: 0.7181, Acc.basket: 0.5523, Acc.waterfall: 0.5960, Acc.tent: 0.9888, Acc.bag: 0.1808, Acc.minibike: 0.7859, Acc.cradle: 0.9867, Acc.oven: 0.6803, Acc.ball: 0.5569, Acc.food: 0.7790, Acc.step: 0.2278, Acc.tank: 0.6913, Acc.trade name: 0.1764, Acc.microwave: 0.9550, Acc.pot: 0.6305, Acc.animal: 0.6756, Acc.bicycle: 0.6805, Acc.lake: 0.0000, Acc.dishwasher: 0.7759, Acc.screen: 0.8889, Acc.blanket: 0.3531, Acc.sculpture: 0.6628, Acc.hood: 0.7132, Acc.sconce: 0.6868, Acc.vase: 0.5506, Acc.traffic light: 0.5756, Acc.tray: 0.1120, Acc.ashcan: 0.6301, Acc.fan: 0.8371, Acc.pier: 0.4263, Acc.crt screen: 0.3033, Acc.plate: 0.7936, Acc.monitor: 0.4886, Acc.bulletin board: 0.7172, Acc.shower: 0.0005, Acc.radiator: 0.8179, Acc.glass: 0.1935, Acc.clock: 0.4823, Acc.flag: 0.7863 +2024-06-16 05:25:27,359 - mmseg - INFO - Iter [19050/80000] lr: 3.048e-05, eta: 1 day, 1:32:53, time: 3.301, data_time: 1.938, memory: 70722, decode.loss_ce: 0.3034, decode.acc_seg: 87.7648, aux.loss_ce: 0.1239, aux.acc_seg: 87.5625, loss: 0.4273 +2024-06-16 05:26:35,580 - mmseg - INFO - Iter [19100/80000] lr: 3.045e-05, eta: 1 day, 1:31:15, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2964, decode.acc_seg: 88.2499, aux.loss_ce: 0.1211, aux.acc_seg: 87.9910, loss: 0.4174 +2024-06-16 05:27:43,695 - mmseg - INFO - Iter [19150/80000] lr: 3.043e-05, eta: 1 day, 1:29:36, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2911, decode.acc_seg: 88.0976, aux.loss_ce: 0.1186, aux.acc_seg: 87.9755, loss: 0.4096 +2024-06-16 05:28:51,863 - mmseg - INFO - Iter [19200/80000] lr: 3.040e-05, eta: 1 day, 1:27:57, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3054, decode.acc_seg: 88.0227, aux.loss_ce: 0.1241, aux.acc_seg: 87.8534, loss: 0.4295 +2024-06-16 05:30:00,281 - mmseg - INFO - Iter [19250/80000] lr: 3.038e-05, eta: 1 day, 1:26:20, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3072, decode.acc_seg: 87.3044, aux.loss_ce: 0.1253, aux.acc_seg: 87.0498, loss: 0.4324 +2024-06-16 05:31:08,369 - mmseg - INFO - Iter [19300/80000] lr: 3.035e-05, eta: 1 day, 1:24:42, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2995, decode.acc_seg: 88.0677, aux.loss_ce: 0.1224, aux.acc_seg: 87.9557, loss: 0.4219 +2024-06-16 05:32:16,666 - mmseg - INFO - Iter [19350/80000] lr: 3.033e-05, eta: 1 day, 1:23:04, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3298, decode.acc_seg: 86.7349, aux.loss_ce: 0.1328, aux.acc_seg: 86.5766, loss: 0.4625 +2024-06-16 05:33:27,777 - mmseg - INFO - Iter [19400/80000] lr: 3.030e-05, eta: 1 day, 1:21:36, time: 1.422, data_time: 0.064, memory: 70722, decode.loss_ce: 0.3183, decode.acc_seg: 87.0647, aux.loss_ce: 0.1285, aux.acc_seg: 86.8414, loss: 0.4468 +2024-06-16 05:34:35,924 - mmseg - INFO - Iter [19450/80000] lr: 3.028e-05, eta: 1 day, 1:19:58, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3119, decode.acc_seg: 87.2071, aux.loss_ce: 0.1265, aux.acc_seg: 87.0035, loss: 0.4384 +2024-06-16 05:35:44,207 - mmseg - INFO - Iter [19500/80000] lr: 3.025e-05, eta: 1 day, 1:18:21, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3145, decode.acc_seg: 87.2952, aux.loss_ce: 0.1281, aux.acc_seg: 87.0238, loss: 0.4426 +2024-06-16 05:36:52,465 - mmseg - INFO - Iter [19550/80000] lr: 3.023e-05, eta: 1 day, 1:16:44, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3191, decode.acc_seg: 87.3879, aux.loss_ce: 0.1302, aux.acc_seg: 87.1432, loss: 0.4493 +2024-06-16 05:38:00,839 - mmseg - INFO - Iter [19600/80000] lr: 3.020e-05, eta: 1 day, 1:15:07, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2933, decode.acc_seg: 88.1645, aux.loss_ce: 0.1190, aux.acc_seg: 88.0301, loss: 0.4123 +2024-06-16 05:39:08,993 - mmseg - INFO - Iter [19650/80000] lr: 3.018e-05, eta: 1 day, 1:13:30, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3269, decode.acc_seg: 86.9670, aux.loss_ce: 0.1309, aux.acc_seg: 86.7207, loss: 0.4578 +2024-06-16 05:40:17,219 - mmseg - INFO - Iter [19700/80000] lr: 3.015e-05, eta: 1 day, 1:11:54, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3103, decode.acc_seg: 87.5835, aux.loss_ce: 0.1249, aux.acc_seg: 87.3743, loss: 0.4352 +2024-06-16 05:41:25,234 - mmseg - INFO - Iter [19750/80000] lr: 3.013e-05, eta: 1 day, 1:10:16, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3199, decode.acc_seg: 87.4204, aux.loss_ce: 0.1286, aux.acc_seg: 87.3866, loss: 0.4485 +2024-06-16 05:42:33,454 - mmseg - INFO - Iter [19800/80000] lr: 3.010e-05, eta: 1 day, 1:08:40, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3145, decode.acc_seg: 87.4456, aux.loss_ce: 0.1269, aux.acc_seg: 87.3330, loss: 0.4413 +2024-06-16 05:43:41,727 - mmseg - INFO - Iter [19850/80000] lr: 3.008e-05, eta: 1 day, 1:07:04, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3047, decode.acc_seg: 87.7192, aux.loss_ce: 0.1234, aux.acc_seg: 87.5818, loss: 0.4281 +2024-06-16 05:44:49,836 - mmseg - INFO - Iter [19900/80000] lr: 3.005e-05, eta: 1 day, 1:05:27, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3177, decode.acc_seg: 87.1689, aux.loss_ce: 0.1279, aux.acc_seg: 87.0948, loss: 0.4456 +2024-06-16 05:45:58,165 - mmseg - INFO - Iter [19950/80000] lr: 3.003e-05, eta: 1 day, 1:03:52, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3062, decode.acc_seg: 87.6648, aux.loss_ce: 0.1239, aux.acc_seg: 87.4541, loss: 0.4301 +2024-06-16 05:47:06,491 - mmseg - INFO - Saving checkpoint at 20000 iterations +2024-06-16 05:48:35,752 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 05:48:35,753 - mmseg - INFO - Iter [20000/80000] lr: 3.000e-05, eta: 1 day, 1:06:44, time: 3.152, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3273, decode.acc_seg: 86.9099, aux.loss_ce: 0.1327, aux.acc_seg: 86.8509, loss: 0.4600 +2024-06-16 05:50:13,017 - mmseg - INFO - per class results: +2024-06-16 05:50:13,023 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.11 | 89.32 | +| building | 83.06 | 92.68 | +| sky | 94.59 | 96.76 | +| floor | 85.11 | 91.97 | +| tree | 77.42 | 90.87 | +| ceiling | 86.67 | 93.25 | +| road | 85.75 | 89.38 | +| bed | 91.88 | 96.44 | +| windowpane | 64.5 | 77.25 | +| grass | 67.61 | 83.22 | +| cabinet | 64.19 | 73.63 | +| sidewalk | 70.77 | 87.14 | +| person | 84.76 | 94.07 | +| earth | 39.23 | 51.38 | +| door | 58.6 | 74.2 | +| table | 64.8 | 72.81 | +| mountain | 64.37 | 76.83 | +| plant | 54.37 | 64.91 | +| curtain | 78.34 | 91.16 | +| chair | 64.64 | 77.07 | +| car | 86.65 | 93.23 | +| water | 63.63 | 76.33 | +| painting | 77.34 | 86.97 | +| sofa | 78.67 | 90.47 | +| shelf | 45.1 | 58.03 | +| house | 56.63 | 86.85 | +| sea | 68.57 | 83.93 | +| mirror | 75.84 | 82.23 | +| rug | 72.66 | 81.02 | +| field | 30.63 | 54.45 | +| armchair | 56.37 | 72.64 | +| seat | 71.35 | 87.74 | +| fence | 53.05 | 65.07 | +| desk | 53.07 | 83.62 | +| rock | 60.16 | 77.15 | +| wardrobe | 53.33 | 81.61 | +| lamp | 68.31 | 80.6 | +| bathtub | 82.96 | 86.19 | +| railing | 35.73 | 48.04 | +| cushion | 66.31 | 79.35 | +| base | 41.35 | 51.61 | +| box | 32.97 | 42.26 | +| column | 52.22 | 62.55 | +| signboard | 39.19 | 51.46 | +| chest of drawers | 46.88 | 77.15 | +| counter | 49.44 | 64.03 | +| sand | 38.54 | 57.54 | +| sink | 72.75 | 84.21 | +| skyscraper | 48.81 | 65.6 | +| fireplace | 72.27 | 91.47 | +| refrigerator | 84.16 | 93.5 | +| grandstand | 51.77 | 87.02 | +| path | 28.85 | 40.78 | +| stairs | 21.96 | 25.47 | +| runway | 70.58 | 91.53 | +| case | 54.46 | 92.31 | +| pool table | 94.89 | 97.78 | +| pillow | 65.19 | 75.8 | +| screen door | 80.89 | 84.09 | +| stairway | 38.9 | 74.41 | +| river | 16.37 | 28.64 | +| bridge | 69.45 | 83.52 | +| bookcase | 42.76 | 57.7 | +| blind | 43.85 | 59.29 | +| coffee table | 66.74 | 87.79 | +| toilet | 89.36 | 92.34 | +| flower | 39.63 | 44.84 | +| book | 51.7 | 63.76 | +| hill | 4.77 | 12.38 | +| bench | 55.8 | 62.39 | +| countertop | 59.98 | 68.77 | +| stove | 80.03 | 91.15 | +| palm | 52.3 | 64.25 | +| kitchen island | 46.47 | 91.44 | +| computer | 78.33 | 89.58 | +| swivel chair | 37.21 | 44.83 | +| boat | 53.14 | 89.53 | +| bar | 61.18 | 79.06 | +| arcade machine | 79.17 | 84.58 | +| hovel | 30.67 | 32.98 | +| bus | 91.97 | 92.92 | +| towel | 72.87 | 77.99 | +| light | 57.89 | 71.34 | +| truck | 42.54 | 62.27 | +| tower | 33.44 | 56.36 | +| chandelier | 64.23 | 73.56 | +| awning | 37.42 | 52.33 | +| streetlight | 29.87 | 37.83 | +| booth | 43.97 | 47.25 | +| television receiver | 76.87 | 88.05 | +| airplane | 73.55 | 91.78 | +| dirt track | 9.94 | 46.27 | +| apparel | 59.21 | 78.65 | +| pole | 23.36 | 29.37 | +| land | 0.99 | 2.46 | +| bannister | 13.49 | 17.32 | +| escalator | 55.84 | 83.17 | +| ottoman | 47.58 | 68.24 | +| bottle | 38.96 | 62.05 | +| buffet | 63.57 | 74.27 | +| poster | 36.97 | 46.54 | +| stage | 28.57 | 40.0 | +| van | 49.5 | 65.32 | +| ship | 79.25 | 93.62 | +| fountain | 46.78 | 51.84 | +| conveyer belt | 77.49 | 97.26 | +| canopy | 40.86 | 56.29 | +| washer | 75.05 | 77.41 | +| plaything | 17.78 | 30.42 | +| swimming pool | 55.76 | 81.26 | +| stool | 44.75 | 70.58 | +| barrel | 57.72 | 70.85 | +| basket | 35.28 | 48.25 | +| waterfall | 70.17 | 91.9 | +| tent | 91.43 | 98.55 | +| bag | 23.58 | 29.05 | +| minibike | 70.57 | 78.46 | +| cradle | 81.85 | 97.46 | +| oven | 48.65 | 58.6 | +| ball | 32.48 | 34.03 | +| food | 56.92 | 65.33 | +| step | 9.98 | 12.41 | +| tank | 54.5 | 67.93 | +| trade name | 26.26 | 30.23 | +| microwave | 84.9 | 96.03 | +| pot | 56.24 | 67.59 | +| animal | 56.93 | 58.15 | +| bicycle | 55.17 | 72.95 | +| lake | 44.41 | 47.67 | +| dishwasher | 62.25 | 68.18 | +| screen | 48.34 | 93.84 | +| blanket | 22.25 | 23.81 | +| sculpture | 54.46 | 64.88 | +| hood | 65.54 | 85.79 | +| sconce | 54.58 | 65.24 | +| vase | 42.72 | 56.55 | +| traffic light | 30.2 | 57.9 | +| tray | 14.75 | 18.83 | +| ashcan | 46.76 | 58.96 | +| fan | 64.84 | 72.34 | +| pier | 61.14 | 73.95 | +| crt screen | 1.46 | 2.15 | +| plate | 58.52 | 69.66 | +| monitor | 48.88 | 60.49 | +| bulletin board | 56.04 | 71.08 | +| shower | 0.83 | 2.8 | +| radiator | 67.2 | 74.94 | +| glass | 14.21 | 14.92 | +| clock | 37.42 | 48.54 | +| flag | 67.6 | 74.27 | ++---------------------+-------+-------+ +2024-06-16 05:50:13,023 - mmseg - INFO - Summary: +2024-06-16 05:50:13,023 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.37 | 54.87 | 67.79 | ++-------+-------+-------+ +2024-06-16 05:50:13,024 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 05:50:13,024 - mmseg - INFO - Iter(val) [250] aAcc: 0.8537, mIoU: 0.5487, mAcc: 0.6779, IoU.wall: 0.8111, IoU.building: 0.8306, IoU.sky: 0.9459, IoU.floor: 0.8511, IoU.tree: 0.7742, IoU.ceiling: 0.8667, IoU.road: 0.8575, IoU.bed : 0.9188, IoU.windowpane: 0.6450, IoU.grass: 0.6761, IoU.cabinet: 0.6419, IoU.sidewalk: 0.7077, IoU.person: 0.8476, IoU.earth: 0.3923, IoU.door: 0.5860, IoU.table: 0.6480, IoU.mountain: 0.6437, IoU.plant: 0.5437, IoU.curtain: 0.7834, IoU.chair: 0.6464, IoU.car: 0.8665, IoU.water: 0.6363, IoU.painting: 0.7734, IoU.sofa: 0.7867, IoU.shelf: 0.4510, IoU.house: 0.5663, IoU.sea: 0.6857, IoU.mirror: 0.7584, IoU.rug: 0.7266, IoU.field: 0.3063, IoU.armchair: 0.5637, IoU.seat: 0.7135, IoU.fence: 0.5305, IoU.desk: 0.5307, IoU.rock: 0.6016, IoU.wardrobe: 0.5333, IoU.lamp: 0.6831, IoU.bathtub: 0.8296, IoU.railing: 0.3573, IoU.cushion: 0.6631, IoU.base: 0.4135, IoU.box: 0.3297, IoU.column: 0.5222, IoU.signboard: 0.3919, IoU.chest of drawers: 0.4688, IoU.counter: 0.4944, IoU.sand: 0.3854, IoU.sink: 0.7275, IoU.skyscraper: 0.4881, IoU.fireplace: 0.7227, IoU.refrigerator: 0.8416, IoU.grandstand: 0.5177, IoU.path: 0.2885, IoU.stairs: 0.2196, IoU.runway: 0.7058, IoU.case: 0.5446, IoU.pool table: 0.9489, IoU.pillow: 0.6519, IoU.screen door: 0.8089, IoU.stairway: 0.3890, IoU.river: 0.1637, IoU.bridge: 0.6945, IoU.bookcase: 0.4276, IoU.blind: 0.4385, IoU.coffee table: 0.6674, IoU.toilet: 0.8936, IoU.flower: 0.3963, IoU.book: 0.5170, IoU.hill: 0.0477, IoU.bench: 0.5580, IoU.countertop: 0.5998, IoU.stove: 0.8003, IoU.palm: 0.5230, IoU.kitchen island: 0.4647, IoU.computer: 0.7833, IoU.swivel chair: 0.3721, IoU.boat: 0.5314, IoU.bar: 0.6118, IoU.arcade machine: 0.7917, IoU.hovel: 0.3067, IoU.bus: 0.9197, IoU.towel: 0.7287, IoU.light: 0.5789, IoU.truck: 0.4254, IoU.tower: 0.3344, IoU.chandelier: 0.6423, IoU.awning: 0.3742, IoU.streetlight: 0.2987, IoU.booth: 0.4397, IoU.television receiver: 0.7687, IoU.airplane: 0.7355, IoU.dirt track: 0.0994, IoU.apparel: 0.5921, IoU.pole: 0.2336, IoU.land: 0.0099, IoU.bannister: 0.1349, IoU.escalator: 0.5584, IoU.ottoman: 0.4758, IoU.bottle: 0.3896, IoU.buffet: 0.6357, IoU.poster: 0.3697, IoU.stage: 0.2857, IoU.van: 0.4950, IoU.ship: 0.7925, IoU.fountain: 0.4678, IoU.conveyer belt: 0.7749, IoU.canopy: 0.4086, IoU.washer: 0.7505, IoU.plaything: 0.1778, IoU.swimming pool: 0.5576, IoU.stool: 0.4475, IoU.barrel: 0.5772, IoU.basket: 0.3528, IoU.waterfall: 0.7017, IoU.tent: 0.9143, IoU.bag: 0.2358, IoU.minibike: 0.7057, IoU.cradle: 0.8185, IoU.oven: 0.4865, IoU.ball: 0.3248, IoU.food: 0.5692, IoU.step: 0.0998, IoU.tank: 0.5450, IoU.trade name: 0.2626, IoU.microwave: 0.8490, IoU.pot: 0.5624, IoU.animal: 0.5693, IoU.bicycle: 0.5517, IoU.lake: 0.4441, IoU.dishwasher: 0.6225, IoU.screen: 0.4834, IoU.blanket: 0.2225, IoU.sculpture: 0.5446, IoU.hood: 0.6554, IoU.sconce: 0.5458, IoU.vase: 0.4272, IoU.traffic light: 0.3020, IoU.tray: 0.1475, IoU.ashcan: 0.4676, IoU.fan: 0.6484, IoU.pier: 0.6114, IoU.crt screen: 0.0146, IoU.plate: 0.5852, IoU.monitor: 0.4888, IoU.bulletin board: 0.5604, IoU.shower: 0.0083, IoU.radiator: 0.6720, IoU.glass: 0.1421, IoU.clock: 0.3742, IoU.flag: 0.6760, Acc.wall: 0.8932, Acc.building: 0.9268, Acc.sky: 0.9676, Acc.floor: 0.9197, Acc.tree: 0.9087, Acc.ceiling: 0.9325, Acc.road: 0.8938, Acc.bed : 0.9644, Acc.windowpane: 0.7725, Acc.grass: 0.8322, Acc.cabinet: 0.7363, Acc.sidewalk: 0.8714, Acc.person: 0.9407, Acc.earth: 0.5138, Acc.door: 0.7420, Acc.table: 0.7281, Acc.mountain: 0.7683, Acc.plant: 0.6491, Acc.curtain: 0.9116, Acc.chair: 0.7707, Acc.car: 0.9323, Acc.water: 0.7633, Acc.painting: 0.8697, Acc.sofa: 0.9047, Acc.shelf: 0.5803, Acc.house: 0.8685, Acc.sea: 0.8393, Acc.mirror: 0.8223, Acc.rug: 0.8102, Acc.field: 0.5445, Acc.armchair: 0.7264, Acc.seat: 0.8774, Acc.fence: 0.6507, Acc.desk: 0.8362, Acc.rock: 0.7715, Acc.wardrobe: 0.8161, Acc.lamp: 0.8060, Acc.bathtub: 0.8619, Acc.railing: 0.4804, Acc.cushion: 0.7935, Acc.base: 0.5161, Acc.box: 0.4226, Acc.column: 0.6255, Acc.signboard: 0.5146, Acc.chest of drawers: 0.7715, Acc.counter: 0.6403, Acc.sand: 0.5754, Acc.sink: 0.8421, Acc.skyscraper: 0.6560, Acc.fireplace: 0.9147, Acc.refrigerator: 0.9350, Acc.grandstand: 0.8702, Acc.path: 0.4078, Acc.stairs: 0.2547, Acc.runway: 0.9153, Acc.case: 0.9231, Acc.pool table: 0.9778, Acc.pillow: 0.7580, Acc.screen door: 0.8409, Acc.stairway: 0.7441, Acc.river: 0.2864, Acc.bridge: 0.8352, Acc.bookcase: 0.5770, Acc.blind: 0.5929, Acc.coffee table: 0.8779, Acc.toilet: 0.9234, Acc.flower: 0.4484, Acc.book: 0.6376, Acc.hill: 0.1238, Acc.bench: 0.6239, Acc.countertop: 0.6877, Acc.stove: 0.9115, Acc.palm: 0.6425, Acc.kitchen island: 0.9144, Acc.computer: 0.8958, Acc.swivel chair: 0.4483, Acc.boat: 0.8953, Acc.bar: 0.7906, Acc.arcade machine: 0.8458, Acc.hovel: 0.3298, Acc.bus: 0.9292, Acc.towel: 0.7799, Acc.light: 0.7134, Acc.truck: 0.6227, Acc.tower: 0.5636, Acc.chandelier: 0.7356, Acc.awning: 0.5233, Acc.streetlight: 0.3783, Acc.booth: 0.4725, Acc.television receiver: 0.8805, Acc.airplane: 0.9178, Acc.dirt track: 0.4627, Acc.apparel: 0.7865, Acc.pole: 0.2937, Acc.land: 0.0246, Acc.bannister: 0.1732, Acc.escalator: 0.8317, Acc.ottoman: 0.6824, Acc.bottle: 0.6205, Acc.buffet: 0.7427, Acc.poster: 0.4654, Acc.stage: 0.4000, Acc.van: 0.6532, Acc.ship: 0.9362, Acc.fountain: 0.5184, Acc.conveyer belt: 0.9726, Acc.canopy: 0.5629, Acc.washer: 0.7741, Acc.plaything: 0.3042, Acc.swimming pool: 0.8126, Acc.stool: 0.7058, Acc.barrel: 0.7085, Acc.basket: 0.4825, Acc.waterfall: 0.9190, Acc.tent: 0.9855, Acc.bag: 0.2905, Acc.minibike: 0.7846, Acc.cradle: 0.9746, Acc.oven: 0.5860, Acc.ball: 0.3403, Acc.food: 0.6533, Acc.step: 0.1241, Acc.tank: 0.6793, Acc.trade name: 0.3023, Acc.microwave: 0.9603, Acc.pot: 0.6759, Acc.animal: 0.5815, Acc.bicycle: 0.7295, Acc.lake: 0.4767, Acc.dishwasher: 0.6818, Acc.screen: 0.9384, Acc.blanket: 0.2381, Acc.sculpture: 0.6488, Acc.hood: 0.8579, Acc.sconce: 0.6524, Acc.vase: 0.5655, Acc.traffic light: 0.5790, Acc.tray: 0.1883, Acc.ashcan: 0.5896, Acc.fan: 0.7234, Acc.pier: 0.7395, Acc.crt screen: 0.0215, Acc.plate: 0.6966, Acc.monitor: 0.6049, Acc.bulletin board: 0.7108, Acc.shower: 0.0280, Acc.radiator: 0.7494, Acc.glass: 0.1492, Acc.clock: 0.4854, Acc.flag: 0.7427 +2024-06-16 05:51:21,640 - mmseg - INFO - Iter [20050/80000] lr: 2.998e-05, eta: 1 day, 1:09:59, time: 3.318, data_time: 1.961, memory: 70722, decode.loss_ce: 0.3064, decode.acc_seg: 87.4895, aux.loss_ce: 0.1252, aux.acc_seg: 87.2354, loss: 0.4315 +2024-06-16 05:52:29,976 - mmseg - INFO - Iter [20100/80000] lr: 2.995e-05, eta: 1 day, 1:08:22, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3107, decode.acc_seg: 87.5998, aux.loss_ce: 0.1258, aux.acc_seg: 87.2927, loss: 0.4365 +2024-06-16 05:53:38,072 - mmseg - INFO - Iter [20150/80000] lr: 2.993e-05, eta: 1 day, 1:06:45, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3185, decode.acc_seg: 87.1637, aux.loss_ce: 0.1300, aux.acc_seg: 86.9957, loss: 0.4485 +2024-06-16 05:54:46,082 - mmseg - INFO - Iter [20200/80000] lr: 2.990e-05, eta: 1 day, 1:05:07, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3323, decode.acc_seg: 86.8256, aux.loss_ce: 0.1339, aux.acc_seg: 86.7289, loss: 0.4662 +2024-06-16 05:55:57,899 - mmseg - INFO - Iter [20250/80000] lr: 2.988e-05, eta: 1 day, 1:03:40, time: 1.436, data_time: 0.074, memory: 70722, decode.loss_ce: 0.3123, decode.acc_seg: 87.3759, aux.loss_ce: 0.1260, aux.acc_seg: 87.3243, loss: 0.4382 +2024-06-16 05:57:06,091 - mmseg - INFO - Iter [20300/80000] lr: 2.985e-05, eta: 1 day, 1:02:03, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2937, decode.acc_seg: 88.0432, aux.loss_ce: 0.1194, aux.acc_seg: 87.8843, loss: 0.4131 +2024-06-16 05:58:14,404 - mmseg - INFO - Iter [20350/80000] lr: 2.983e-05, eta: 1 day, 1:00:27, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2857, decode.acc_seg: 88.4009, aux.loss_ce: 0.1156, aux.acc_seg: 88.2818, loss: 0.4013 +2024-06-16 05:59:22,769 - mmseg - INFO - Iter [20400/80000] lr: 2.980e-05, eta: 1 day, 0:58:51, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2850, decode.acc_seg: 88.3155, aux.loss_ce: 0.1155, aux.acc_seg: 88.1252, loss: 0.4004 +2024-06-16 06:00:31,077 - mmseg - INFO - Iter [20450/80000] lr: 2.978e-05, eta: 1 day, 0:57:15, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2898, decode.acc_seg: 88.3181, aux.loss_ce: 0.1174, aux.acc_seg: 88.0897, loss: 0.4071 +2024-06-16 06:01:39,260 - mmseg - INFO - Iter [20500/80000] lr: 2.975e-05, eta: 1 day, 0:55:38, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3283, decode.acc_seg: 86.6882, aux.loss_ce: 0.1331, aux.acc_seg: 86.4464, loss: 0.4613 +2024-06-16 06:02:47,360 - mmseg - INFO - Iter [20550/80000] lr: 2.973e-05, eta: 1 day, 0:54:02, time: 1.362, data_time: 0.009, memory: 70722, decode.loss_ce: 0.3089, decode.acc_seg: 87.7500, aux.loss_ce: 0.1251, aux.acc_seg: 87.6820, loss: 0.4340 +2024-06-16 06:03:55,633 - mmseg - INFO - Iter [20600/80000] lr: 2.970e-05, eta: 1 day, 0:52:26, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2948, decode.acc_seg: 87.6961, aux.loss_ce: 0.1195, aux.acc_seg: 87.5916, loss: 0.4143 +2024-06-16 06:05:03,827 - mmseg - INFO - Iter [20650/80000] lr: 2.968e-05, eta: 1 day, 0:50:50, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2955, decode.acc_seg: 88.1739, aux.loss_ce: 0.1213, aux.acc_seg: 87.8715, loss: 0.4168 +2024-06-16 06:06:12,012 - mmseg - INFO - Iter [20700/80000] lr: 2.965e-05, eta: 1 day, 0:49:14, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3180, decode.acc_seg: 87.5215, aux.loss_ce: 0.1294, aux.acc_seg: 87.3436, loss: 0.4474 +2024-06-16 06:07:20,118 - mmseg - INFO - Iter [20750/80000] lr: 2.963e-05, eta: 1 day, 0:47:38, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3080, decode.acc_seg: 87.4717, aux.loss_ce: 0.1238, aux.acc_seg: 87.4472, loss: 0.4318 +2024-06-16 06:08:28,615 - mmseg - INFO - Iter [20800/80000] lr: 2.960e-05, eta: 1 day, 0:46:03, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2997, decode.acc_seg: 87.9194, aux.loss_ce: 0.1220, aux.acc_seg: 87.5446, loss: 0.4217 +2024-06-16 06:09:36,752 - mmseg - INFO - Iter [20850/80000] lr: 2.958e-05, eta: 1 day, 0:44:27, time: 1.363, data_time: 0.009, memory: 70722, decode.loss_ce: 0.3018, decode.acc_seg: 87.5212, aux.loss_ce: 0.1230, aux.acc_seg: 87.3097, loss: 0.4248 +2024-06-16 06:10:44,989 - mmseg - INFO - Iter [20900/80000] lr: 2.955e-05, eta: 1 day, 0:42:52, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3018, decode.acc_seg: 88.0568, aux.loss_ce: 0.1225, aux.acc_seg: 87.8014, loss: 0.4243 +2024-06-16 06:11:53,206 - mmseg - INFO - Iter [20950/80000] lr: 2.953e-05, eta: 1 day, 0:41:17, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3052, decode.acc_seg: 87.6122, aux.loss_ce: 0.1232, aux.acc_seg: 87.5017, loss: 0.4284 +2024-06-16 06:13:01,234 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 06:13:01,234 - mmseg - INFO - Iter [21000/80000] lr: 2.950e-05, eta: 1 day, 0:39:41, time: 1.361, data_time: 0.009, memory: 70722, decode.loss_ce: 0.3102, decode.acc_seg: 87.7940, aux.loss_ce: 0.1262, aux.acc_seg: 87.5975, loss: 0.4364 +2024-06-16 06:14:38,970 - mmseg - INFO - per class results: +2024-06-16 06:14:38,976 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.02 | 87.63 | +| building | 84.43 | 94.15 | +| sky | 94.72 | 97.64 | +| floor | 83.5 | 90.34 | +| tree | 77.1 | 88.23 | +| ceiling | 83.18 | 89.08 | +| road | 84.55 | 92.81 | +| bed | 92.18 | 97.28 | +| windowpane | 66.1 | 80.24 | +| grass | 66.8 | 80.9 | +| cabinet | 63.86 | 70.76 | +| sidewalk | 68.18 | 77.0 | +| person | 84.63 | 92.6 | +| earth | 37.19 | 51.26 | +| door | 58.88 | 79.01 | +| table | 66.49 | 79.41 | +| mountain | 63.99 | 76.37 | +| plant | 54.61 | 70.35 | +| curtain | 78.86 | 92.19 | +| chair | 64.44 | 76.21 | +| car | 85.47 | 94.31 | +| water | 62.98 | 78.03 | +| painting | 77.71 | 90.24 | +| sofa | 75.85 | 83.22 | +| shelf | 47.8 | 61.83 | +| house | 54.8 | 68.36 | +| sea | 66.16 | 79.92 | +| mirror | 72.57 | 82.12 | +| rug | 69.83 | 88.25 | +| field | 26.9 | 50.76 | +| armchair | 55.99 | 79.49 | +| seat | 65.03 | 87.91 | +| fence | 48.1 | 67.28 | +| desk | 54.56 | 85.25 | +| rock | 58.97 | 85.05 | +| wardrobe | 54.2 | 72.62 | +| lamp | 69.4 | 82.16 | +| bathtub | 77.76 | 86.93 | +| railing | 39.77 | 57.98 | +| cushion | 67.82 | 80.44 | +| base | 43.78 | 67.18 | +| box | 27.97 | 32.1 | +| column | 54.5 | 64.67 | +| signboard | 38.03 | 46.88 | +| chest of drawers | 49.81 | 75.54 | +| counter | 48.05 | 64.28 | +| sand | 42.43 | 61.94 | +| sink | 71.31 | 86.49 | +| skyscraper | 47.46 | 59.7 | +| fireplace | 70.98 | 85.83 | +| refrigerator | 75.77 | 92.43 | +| grandstand | 52.37 | 83.16 | +| path | 28.43 | 38.18 | +| stairs | 38.24 | 54.99 | +| runway | 60.88 | 77.72 | +| case | 49.61 | 65.48 | +| pool table | 93.74 | 98.84 | +| pillow | 66.84 | 79.26 | +| screen door | 63.59 | 92.7 | +| stairway | 43.19 | 59.88 | +| river | 10.09 | 17.38 | +| bridge | 61.6 | 78.84 | +| bookcase | 39.05 | 52.11 | +| blind | 46.9 | 56.85 | +| coffee table | 66.46 | 88.45 | +| toilet | 87.24 | 95.7 | +| flower | 41.74 | 62.87 | +| book | 52.06 | 77.47 | +| hill | 2.41 | 2.84 | +| bench | 50.34 | 67.6 | +| countertop | 60.55 | 80.56 | +| stove | 81.23 | 92.0 | +| palm | 55.18 | 77.08 | +| kitchen island | 52.97 | 88.25 | +| computer | 76.93 | 88.81 | +| swivel chair | 47.69 | 82.1 | +| boat | 69.98 | 88.95 | +| bar | 62.77 | 72.93 | +| arcade machine | 80.74 | 87.17 | +| hovel | 41.38 | 46.97 | +| bus | 92.18 | 96.74 | +| towel | 67.31 | 90.05 | +| light | 51.13 | 56.15 | +| truck | 43.44 | 60.07 | +| tower | 7.36 | 9.46 | +| chandelier | 67.66 | 84.86 | +| awning | 34.16 | 40.73 | +| streetlight | 26.2 | 32.46 | +| booth | 57.19 | 72.87 | +| television receiver | 74.27 | 90.87 | +| airplane | 60.9 | 72.11 | +| dirt track | 19.03 | 42.32 | +| apparel | 40.41 | 60.82 | +| pole | 20.78 | 24.94 | +| land | 0.0 | 0.0 | +| bannister | 14.43 | 20.91 | +| escalator | 53.66 | 77.74 | +| ottoman | 48.02 | 68.04 | +| bottle | 40.03 | 65.62 | +| buffet | 66.44 | 85.65 | +| poster | 31.01 | 36.09 | +| stage | 18.41 | 61.36 | +| van | 48.78 | 58.09 | +| ship | 76.49 | 82.69 | +| fountain | 36.0 | 36.58 | +| conveyer belt | 67.48 | 97.1 | +| canopy | 44.21 | 67.28 | +| washer | 81.58 | 88.42 | +| plaything | 29.84 | 46.58 | +| swimming pool | 54.36 | 82.29 | +| stool | 47.75 | 72.95 | +| barrel | 59.16 | 73.38 | +| basket | 34.72 | 53.97 | +| waterfall | 69.13 | 95.61 | +| tent | 94.41 | 98.52 | +| bag | 18.82 | 20.97 | +| minibike | 70.22 | 84.94 | +| cradle | 85.82 | 98.57 | +| oven | 56.65 | 61.16 | +| ball | 47.35 | 74.52 | +| food | 63.32 | 78.42 | +| step | 20.42 | 29.06 | +| tank | 60.74 | 72.65 | +| trade name | 4.46 | 4.52 | +| microwave | 89.75 | 95.52 | +| pot | 52.99 | 60.63 | +| animal | 55.73 | 56.89 | +| bicycle | 56.04 | 76.66 | +| lake | 42.65 | 50.06 | +| dishwasher | 62.49 | 79.62 | +| screen | 58.47 | 94.25 | +| blanket | 27.77 | 32.49 | +| sculpture | 67.91 | 72.7 | +| hood | 66.66 | 70.9 | +| sconce | 53.59 | 62.37 | +| vase | 42.91 | 59.45 | +| traffic light | 29.97 | 53.29 | +| tray | 11.21 | 12.76 | +| ashcan | 42.97 | 61.23 | +| fan | 63.56 | 74.38 | +| pier | 40.95 | 51.63 | +| crt screen | 1.23 | 1.43 | +| plate | 57.03 | 80.6 | +| monitor | 59.11 | 85.7 | +| bulletin board | 50.93 | 65.75 | +| shower | 0.0 | 0.0 | +| radiator | 60.57 | 76.58 | +| glass | 13.97 | 14.82 | +| clock | 32.76 | 40.34 | +| flag | 69.19 | 74.4 | ++---------------------+-------+-------+ +2024-06-16 06:14:38,976 - mmseg - INFO - Summary: +2024-06-16 06:14:38,977 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.03 | 54.37 | 68.34 | ++-------+-------+-------+ +2024-06-16 06:14:38,977 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 06:14:38,978 - mmseg - INFO - Iter(val) [250] aAcc: 0.8503, mIoU: 0.5437, mAcc: 0.6834, IoU.wall: 0.8002, IoU.building: 0.8443, IoU.sky: 0.9472, IoU.floor: 0.8350, IoU.tree: 0.7710, IoU.ceiling: 0.8318, IoU.road: 0.8455, IoU.bed : 0.9218, IoU.windowpane: 0.6610, IoU.grass: 0.6680, IoU.cabinet: 0.6386, IoU.sidewalk: 0.6818, IoU.person: 0.8463, IoU.earth: 0.3719, IoU.door: 0.5888, IoU.table: 0.6649, IoU.mountain: 0.6399, IoU.plant: 0.5461, IoU.curtain: 0.7886, IoU.chair: 0.6444, IoU.car: 0.8547, IoU.water: 0.6298, IoU.painting: 0.7771, IoU.sofa: 0.7585, IoU.shelf: 0.4780, IoU.house: 0.5480, IoU.sea: 0.6616, IoU.mirror: 0.7257, IoU.rug: 0.6983, IoU.field: 0.2690, IoU.armchair: 0.5599, IoU.seat: 0.6503, IoU.fence: 0.4810, IoU.desk: 0.5456, IoU.rock: 0.5897, IoU.wardrobe: 0.5420, IoU.lamp: 0.6940, IoU.bathtub: 0.7776, IoU.railing: 0.3977, IoU.cushion: 0.6782, IoU.base: 0.4378, IoU.box: 0.2797, IoU.column: 0.5450, IoU.signboard: 0.3803, IoU.chest of drawers: 0.4981, IoU.counter: 0.4805, IoU.sand: 0.4243, IoU.sink: 0.7131, IoU.skyscraper: 0.4746, IoU.fireplace: 0.7098, IoU.refrigerator: 0.7577, IoU.grandstand: 0.5237, IoU.path: 0.2843, IoU.stairs: 0.3824, IoU.runway: 0.6088, IoU.case: 0.4961, IoU.pool table: 0.9374, IoU.pillow: 0.6684, IoU.screen door: 0.6359, IoU.stairway: 0.4319, IoU.river: 0.1009, IoU.bridge: 0.6160, IoU.bookcase: 0.3905, IoU.blind: 0.4690, IoU.coffee table: 0.6646, IoU.toilet: 0.8724, IoU.flower: 0.4174, IoU.book: 0.5206, IoU.hill: 0.0241, IoU.bench: 0.5034, IoU.countertop: 0.6055, IoU.stove: 0.8123, IoU.palm: 0.5518, IoU.kitchen island: 0.5297, IoU.computer: 0.7693, IoU.swivel chair: 0.4769, IoU.boat: 0.6998, IoU.bar: 0.6277, IoU.arcade machine: 0.8074, IoU.hovel: 0.4138, IoU.bus: 0.9218, IoU.towel: 0.6731, IoU.light: 0.5113, IoU.truck: 0.4344, IoU.tower: 0.0736, IoU.chandelier: 0.6766, IoU.awning: 0.3416, IoU.streetlight: 0.2620, IoU.booth: 0.5719, IoU.television receiver: 0.7427, IoU.airplane: 0.6090, IoU.dirt track: 0.1903, IoU.apparel: 0.4041, IoU.pole: 0.2078, IoU.land: 0.0000, IoU.bannister: 0.1443, IoU.escalator: 0.5366, IoU.ottoman: 0.4802, IoU.bottle: 0.4003, IoU.buffet: 0.6644, IoU.poster: 0.3101, IoU.stage: 0.1841, IoU.van: 0.4878, IoU.ship: 0.7649, IoU.fountain: 0.3600, IoU.conveyer belt: 0.6748, IoU.canopy: 0.4421, IoU.washer: 0.8158, IoU.plaything: 0.2984, IoU.swimming pool: 0.5436, IoU.stool: 0.4775, IoU.barrel: 0.5916, IoU.basket: 0.3472, IoU.waterfall: 0.6913, IoU.tent: 0.9441, IoU.bag: 0.1882, IoU.minibike: 0.7022, IoU.cradle: 0.8582, IoU.oven: 0.5665, IoU.ball: 0.4735, IoU.food: 0.6332, IoU.step: 0.2042, IoU.tank: 0.6074, IoU.trade name: 0.0446, IoU.microwave: 0.8975, IoU.pot: 0.5299, IoU.animal: 0.5573, IoU.bicycle: 0.5604, IoU.lake: 0.4265, IoU.dishwasher: 0.6249, IoU.screen: 0.5847, IoU.blanket: 0.2777, IoU.sculpture: 0.6791, IoU.hood: 0.6666, IoU.sconce: 0.5359, IoU.vase: 0.4291, IoU.traffic light: 0.2997, IoU.tray: 0.1121, IoU.ashcan: 0.4297, IoU.fan: 0.6356, IoU.pier: 0.4095, IoU.crt screen: 0.0123, IoU.plate: 0.5703, IoU.monitor: 0.5911, IoU.bulletin board: 0.5093, IoU.shower: 0.0000, IoU.radiator: 0.6057, IoU.glass: 0.1397, IoU.clock: 0.3276, IoU.flag: 0.6919, Acc.wall: 0.8763, Acc.building: 0.9415, Acc.sky: 0.9764, Acc.floor: 0.9034, Acc.tree: 0.8823, Acc.ceiling: 0.8908, Acc.road: 0.9281, Acc.bed : 0.9728, Acc.windowpane: 0.8024, Acc.grass: 0.8090, Acc.cabinet: 0.7076, Acc.sidewalk: 0.7700, Acc.person: 0.9260, Acc.earth: 0.5126, Acc.door: 0.7901, Acc.table: 0.7941, Acc.mountain: 0.7637, Acc.plant: 0.7035, Acc.curtain: 0.9219, Acc.chair: 0.7621, Acc.car: 0.9431, Acc.water: 0.7803, Acc.painting: 0.9024, Acc.sofa: 0.8322, Acc.shelf: 0.6183, Acc.house: 0.6836, Acc.sea: 0.7992, Acc.mirror: 0.8212, Acc.rug: 0.8825, Acc.field: 0.5076, Acc.armchair: 0.7949, Acc.seat: 0.8791, Acc.fence: 0.6728, Acc.desk: 0.8525, Acc.rock: 0.8505, Acc.wardrobe: 0.7262, Acc.lamp: 0.8216, Acc.bathtub: 0.8693, Acc.railing: 0.5798, Acc.cushion: 0.8044, Acc.base: 0.6718, Acc.box: 0.3210, Acc.column: 0.6467, Acc.signboard: 0.4688, Acc.chest of drawers: 0.7554, Acc.counter: 0.6428, Acc.sand: 0.6194, Acc.sink: 0.8649, Acc.skyscraper: 0.5970, Acc.fireplace: 0.8583, Acc.refrigerator: 0.9243, Acc.grandstand: 0.8316, Acc.path: 0.3818, Acc.stairs: 0.5499, Acc.runway: 0.7772, Acc.case: 0.6548, Acc.pool table: 0.9884, Acc.pillow: 0.7926, Acc.screen door: 0.9270, Acc.stairway: 0.5988, Acc.river: 0.1738, Acc.bridge: 0.7884, Acc.bookcase: 0.5211, Acc.blind: 0.5685, Acc.coffee table: 0.8845, Acc.toilet: 0.9570, Acc.flower: 0.6287, Acc.book: 0.7747, Acc.hill: 0.0284, Acc.bench: 0.6760, Acc.countertop: 0.8056, Acc.stove: 0.9200, Acc.palm: 0.7708, Acc.kitchen island: 0.8825, Acc.computer: 0.8881, Acc.swivel chair: 0.8210, Acc.boat: 0.8895, Acc.bar: 0.7293, Acc.arcade machine: 0.8717, Acc.hovel: 0.4697, Acc.bus: 0.9674, Acc.towel: 0.9005, Acc.light: 0.5615, Acc.truck: 0.6007, Acc.tower: 0.0946, Acc.chandelier: 0.8486, Acc.awning: 0.4073, Acc.streetlight: 0.3246, Acc.booth: 0.7287, Acc.television receiver: 0.9087, Acc.airplane: 0.7211, Acc.dirt track: 0.4232, Acc.apparel: 0.6082, Acc.pole: 0.2494, Acc.land: 0.0000, Acc.bannister: 0.2091, Acc.escalator: 0.7774, Acc.ottoman: 0.6804, Acc.bottle: 0.6562, Acc.buffet: 0.8565, Acc.poster: 0.3609, Acc.stage: 0.6136, Acc.van: 0.5809, Acc.ship: 0.8269, Acc.fountain: 0.3658, Acc.conveyer belt: 0.9710, Acc.canopy: 0.6728, Acc.washer: 0.8842, Acc.plaything: 0.4658, Acc.swimming pool: 0.8229, Acc.stool: 0.7295, Acc.barrel: 0.7338, Acc.basket: 0.5397, Acc.waterfall: 0.9561, Acc.tent: 0.9852, Acc.bag: 0.2097, Acc.minibike: 0.8494, Acc.cradle: 0.9857, Acc.oven: 0.6116, Acc.ball: 0.7452, Acc.food: 0.7842, Acc.step: 0.2906, Acc.tank: 0.7265, Acc.trade name: 0.0452, Acc.microwave: 0.9552, Acc.pot: 0.6063, Acc.animal: 0.5689, Acc.bicycle: 0.7666, Acc.lake: 0.5006, Acc.dishwasher: 0.7962, Acc.screen: 0.9425, Acc.blanket: 0.3249, Acc.sculpture: 0.7270, Acc.hood: 0.7090, Acc.sconce: 0.6237, Acc.vase: 0.5945, Acc.traffic light: 0.5329, Acc.tray: 0.1276, Acc.ashcan: 0.6123, Acc.fan: 0.7438, Acc.pier: 0.5163, Acc.crt screen: 0.0143, Acc.plate: 0.8060, Acc.monitor: 0.8570, Acc.bulletin board: 0.6575, Acc.shower: 0.0000, Acc.radiator: 0.7658, Acc.glass: 0.1482, Acc.clock: 0.4034, Acc.flag: 0.7440 +2024-06-16 06:15:47,663 - mmseg - INFO - Iter [21050/80000] lr: 2.948e-05, eta: 1 day, 0:42:41, time: 3.329, data_time: 1.971, memory: 70722, decode.loss_ce: 0.3020, decode.acc_seg: 87.9976, aux.loss_ce: 0.1226, aux.acc_seg: 87.8717, loss: 0.4246 +2024-06-16 06:16:55,858 - mmseg - INFO - Iter [21100/80000] lr: 2.945e-05, eta: 1 day, 0:41:06, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2798, decode.acc_seg: 88.8002, aux.loss_ce: 0.1146, aux.acc_seg: 88.5215, loss: 0.3944 +2024-06-16 06:18:04,209 - mmseg - INFO - Iter [21150/80000] lr: 2.943e-05, eta: 1 day, 0:39:31, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3018, decode.acc_seg: 87.8726, aux.loss_ce: 0.1223, aux.acc_seg: 87.7451, loss: 0.4241 +2024-06-16 06:19:12,338 - mmseg - INFO - Iter [21200/80000] lr: 2.940e-05, eta: 1 day, 0:37:55, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3160, decode.acc_seg: 87.7091, aux.loss_ce: 0.1295, aux.acc_seg: 87.3628, loss: 0.4455 +2024-06-16 06:20:20,571 - mmseg - INFO - Iter [21250/80000] lr: 2.938e-05, eta: 1 day, 0:36:20, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2987, decode.acc_seg: 87.8372, aux.loss_ce: 0.1206, aux.acc_seg: 87.6951, loss: 0.4192 +2024-06-16 06:21:28,789 - mmseg - INFO - Iter [21300/80000] lr: 2.935e-05, eta: 1 day, 0:34:45, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2987, decode.acc_seg: 87.9658, aux.loss_ce: 0.1209, aux.acc_seg: 87.8031, loss: 0.4196 +2024-06-16 06:22:36,956 - mmseg - INFO - Iter [21350/80000] lr: 2.933e-05, eta: 1 day, 0:33:09, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3132, decode.acc_seg: 87.0210, aux.loss_ce: 0.1269, aux.acc_seg: 86.8268, loss: 0.4401 +2024-06-16 06:23:45,125 - mmseg - INFO - Iter [21400/80000] lr: 2.930e-05, eta: 1 day, 0:31:34, time: 1.363, data_time: 0.009, memory: 70722, decode.loss_ce: 0.3073, decode.acc_seg: 87.5600, aux.loss_ce: 0.1248, aux.acc_seg: 87.3487, loss: 0.4321 +2024-06-16 06:24:53,302 - mmseg - INFO - Iter [21450/80000] lr: 2.928e-05, eta: 1 day, 0:29:59, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3057, decode.acc_seg: 87.5761, aux.loss_ce: 0.1239, aux.acc_seg: 87.3875, loss: 0.4296 +2024-06-16 06:26:04,484 - mmseg - INFO - Iter [21500/80000] lr: 2.925e-05, eta: 1 day, 0:28:33, time: 1.424, data_time: 0.060, memory: 70722, decode.loss_ce: 0.3067, decode.acc_seg: 87.9745, aux.loss_ce: 0.1248, aux.acc_seg: 87.8344, loss: 0.4315 +2024-06-16 06:27:12,572 - mmseg - INFO - Iter [21550/80000] lr: 2.923e-05, eta: 1 day, 0:26:58, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2750, decode.acc_seg: 88.6478, aux.loss_ce: 0.1118, aux.acc_seg: 88.4632, loss: 0.3868 +2024-06-16 06:28:21,023 - mmseg - INFO - Iter [21600/80000] lr: 2.920e-05, eta: 1 day, 0:25:24, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3016, decode.acc_seg: 87.9203, aux.loss_ce: 0.1212, aux.acc_seg: 87.6990, loss: 0.4228 +2024-06-16 06:29:29,036 - mmseg - INFO - Iter [21650/80000] lr: 2.918e-05, eta: 1 day, 0:23:49, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2764, decode.acc_seg: 88.5896, aux.loss_ce: 0.1143, aux.acc_seg: 88.2803, loss: 0.3907 +2024-06-16 06:30:37,334 - mmseg - INFO - Iter [21700/80000] lr: 2.915e-05, eta: 1 day, 0:22:15, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2626, decode.acc_seg: 88.9713, aux.loss_ce: 0.1072, aux.acc_seg: 88.7217, loss: 0.3698 +2024-06-16 06:31:45,454 - mmseg - INFO - Iter [21750/80000] lr: 2.913e-05, eta: 1 day, 0:20:41, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2586, decode.acc_seg: 89.3239, aux.loss_ce: 0.1063, aux.acc_seg: 89.1456, loss: 0.3649 +2024-06-16 06:32:53,536 - mmseg - INFO - Iter [21800/80000] lr: 2.910e-05, eta: 1 day, 0:19:07, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2785, decode.acc_seg: 88.3665, aux.loss_ce: 0.1129, aux.acc_seg: 88.2446, loss: 0.3914 +2024-06-16 06:34:01,775 - mmseg - INFO - Iter [21850/80000] lr: 2.908e-05, eta: 1 day, 0:17:33, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3095, decode.acc_seg: 87.3381, aux.loss_ce: 0.1260, aux.acc_seg: 87.0835, loss: 0.4355 +2024-06-16 06:35:09,965 - mmseg - INFO - Iter [21900/80000] lr: 2.905e-05, eta: 1 day, 0:15:59, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2934, decode.acc_seg: 87.9893, aux.loss_ce: 0.1195, aux.acc_seg: 87.7756, loss: 0.4130 +2024-06-16 06:36:18,295 - mmseg - INFO - Iter [21950/80000] lr: 2.903e-05, eta: 1 day, 0:14:26, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2833, decode.acc_seg: 87.9754, aux.loss_ce: 0.1144, aux.acc_seg: 87.8976, loss: 0.3977 +2024-06-16 06:37:26,394 - mmseg - INFO - Saving checkpoint at 22000 iterations +2024-06-16 06:38:51,442 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 06:38:51,442 - mmseg - INFO - Iter [22000/80000] lr: 2.900e-05, eta: 1 day, 0:16:36, time: 3.063, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2781, decode.acc_seg: 88.4140, aux.loss_ce: 0.1121, aux.acc_seg: 88.3609, loss: 0.3902 +2024-06-16 06:40:27,840 - mmseg - INFO - per class results: +2024-06-16 06:40:27,846 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.27 | 89.47 | +| building | 85.05 | 92.28 | +| sky | 94.88 | 97.6 | +| floor | 84.07 | 90.89 | +| tree | 77.36 | 89.06 | +| ceiling | 85.97 | 91.8 | +| road | 86.4 | 90.57 | +| bed | 91.94 | 97.09 | +| windowpane | 67.04 | 79.86 | +| grass | 63.94 | 73.04 | +| cabinet | 63.35 | 72.09 | +| sidewalk | 72.23 | 86.99 | +| person | 84.49 | 94.53 | +| earth | 35.45 | 48.15 | +| door | 59.05 | 75.51 | +| table | 66.96 | 81.18 | +| mountain | 60.28 | 70.79 | +| plant | 54.49 | 66.73 | +| curtain | 79.81 | 89.42 | +| chair | 60.0 | 67.25 | +| car | 86.35 | 92.02 | +| water | 58.39 | 72.34 | +| painting | 77.82 | 89.91 | +| sofa | 79.14 | 87.12 | +| shelf | 41.73 | 55.83 | +| house | 56.73 | 86.72 | +| sea | 64.72 | 84.03 | +| mirror | 77.15 | 84.73 | +| rug | 68.1 | 85.38 | +| field | 24.93 | 52.44 | +| armchair | 53.42 | 82.06 | +| seat | 63.44 | 88.17 | +| fence | 51.97 | 74.75 | +| desk | 51.9 | 83.76 | +| rock | 58.1 | 85.68 | +| wardrobe | 52.31 | 67.89 | +| lamp | 67.86 | 83.32 | +| bathtub | 81.81 | 87.39 | +| railing | 42.63 | 64.2 | +| cushion | 70.65 | 81.1 | +| base | 41.95 | 56.59 | +| box | 34.68 | 44.91 | +| column | 49.22 | 57.61 | +| signboard | 39.17 | 47.53 | +| chest of drawers | 43.86 | 71.09 | +| counter | 37.05 | 41.73 | +| sand | 50.6 | 78.37 | +| sink | 70.91 | 81.36 | +| skyscraper | 47.75 | 61.22 | +| fireplace | 72.64 | 89.58 | +| refrigerator | 83.96 | 91.38 | +| grandstand | 50.15 | 87.55 | +| path | 30.07 | 43.91 | +| stairs | 36.32 | 43.07 | +| runway | 67.36 | 98.65 | +| case | 58.16 | 84.87 | +| pool table | 91.28 | 98.57 | +| pillow | 67.55 | 81.29 | +| screen door | 83.11 | 89.28 | +| stairway | 45.7 | 50.59 | +| river | 7.93 | 15.79 | +| bridge | 72.48 | 82.13 | +| bookcase | 33.32 | 63.18 | +| blind | 52.33 | 66.99 | +| coffee table | 66.7 | 86.96 | +| toilet | 88.15 | 92.35 | +| flower | 37.38 | 59.79 | +| book | 48.33 | 73.2 | +| hill | 7.66 | 18.73 | +| bench | 41.31 | 44.95 | +| countertop | 60.05 | 86.91 | +| stove | 81.19 | 90.85 | +| palm | 53.66 | 81.49 | +| kitchen island | 53.11 | 86.38 | +| computer | 77.65 | 89.45 | +| swivel chair | 48.4 | 74.75 | +| boat | 63.87 | 91.15 | +| bar | 55.56 | 78.82 | +| arcade machine | 77.67 | 80.29 | +| hovel | 28.02 | 30.99 | +| bus | 91.15 | 96.32 | +| towel | 72.0 | 81.96 | +| light | 55.65 | 62.31 | +| truck | 41.03 | 57.27 | +| tower | 33.24 | 57.63 | +| chandelier | 67.88 | 86.91 | +| awning | 42.34 | 58.63 | +| streetlight | 30.3 | 40.9 | +| booth | 34.53 | 58.05 | +| television receiver | 77.76 | 86.02 | +| airplane | 67.78 | 79.74 | +| dirt track | 14.67 | 67.96 | +| apparel | 54.55 | 72.93 | +| pole | 25.0 | 33.0 | +| land | 0.91 | 1.31 | +| bannister | 15.8 | 23.09 | +| escalator | 45.99 | 57.22 | +| ottoman | 53.95 | 69.09 | +| bottle | 39.13 | 72.35 | +| buffet | 62.13 | 85.01 | +| poster | 30.56 | 38.52 | +| stage | 25.98 | 71.76 | +| van | 44.56 | 68.68 | +| ship | 86.91 | 92.04 | +| fountain | 49.52 | 54.02 | +| conveyer belt | 82.83 | 92.55 | +| canopy | 43.86 | 69.81 | +| washer | 77.64 | 80.38 | +| plaything | 32.83 | 51.78 | +| swimming pool | 59.21 | 90.48 | +| stool | 55.4 | 68.32 | +| barrel | 57.33 | 72.67 | +| basket | 39.56 | 50.58 | +| waterfall | 74.8 | 90.81 | +| tent | 95.54 | 98.41 | +| bag | 15.28 | 17.03 | +| minibike | 73.37 | 89.41 | +| cradle | 62.62 | 98.94 | +| oven | 64.05 | 81.26 | +| ball | 12.37 | 12.9 | +| food | 57.7 | 69.97 | +| step | 11.63 | 12.32 | +| tank | 83.91 | 96.65 | +| trade name | 29.76 | 35.54 | +| microwave | 90.19 | 95.62 | +| pot | 53.91 | 62.93 | +| animal | 63.07 | 66.0 | +| bicycle | 55.96 | 78.52 | +| lake | 49.56 | 66.65 | +| dishwasher | 64.96 | 73.65 | +| screen | 61.71 | 94.79 | +| blanket | 18.92 | 20.92 | +| sculpture | 68.57 | 74.2 | +| hood | 66.13 | 72.41 | +| sconce | 52.97 | 59.26 | +| vase | 42.27 | 64.23 | +| traffic light | 30.61 | 62.65 | +| tray | 10.56 | 13.29 | +| ashcan | 40.78 | 63.06 | +| fan | 67.1 | 75.87 | +| pier | 40.55 | 44.01 | +| crt screen | 3.23 | 9.01 | +| plate | 59.02 | 75.37 | +| monitor | 15.26 | 16.71 | +| bulletin board | 57.04 | 66.44 | +| shower | 1.09 | 4.53 | +| radiator | 64.39 | 76.46 | +| glass | 18.24 | 20.77 | +| clock | 37.99 | 48.16 | +| flag | 66.18 | 72.55 | ++---------------------+-------+-------+ +2024-06-16 06:40:27,846 - mmseg - INFO - Summary: +2024-06-16 06:40:27,846 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.19 | 54.88 | 69.05 | ++-------+-------+-------+ +2024-06-16 06:40:27,847 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 06:40:27,847 - mmseg - INFO - Iter(val) [250] aAcc: 0.8519, mIoU: 0.5488, mAcc: 0.6905, IoU.wall: 0.8127, IoU.building: 0.8505, IoU.sky: 0.9488, IoU.floor: 0.8407, IoU.tree: 0.7736, IoU.ceiling: 0.8597, IoU.road: 0.8640, IoU.bed : 0.9194, IoU.windowpane: 0.6704, IoU.grass: 0.6394, IoU.cabinet: 0.6335, IoU.sidewalk: 0.7223, IoU.person: 0.8449, IoU.earth: 0.3545, IoU.door: 0.5905, IoU.table: 0.6696, IoU.mountain: 0.6028, IoU.plant: 0.5449, IoU.curtain: 0.7981, IoU.chair: 0.6000, IoU.car: 0.8635, IoU.water: 0.5839, IoU.painting: 0.7782, IoU.sofa: 0.7914, IoU.shelf: 0.4173, IoU.house: 0.5673, IoU.sea: 0.6472, IoU.mirror: 0.7715, IoU.rug: 0.6810, IoU.field: 0.2493, IoU.armchair: 0.5342, IoU.seat: 0.6344, IoU.fence: 0.5197, IoU.desk: 0.5190, IoU.rock: 0.5810, IoU.wardrobe: 0.5231, IoU.lamp: 0.6786, IoU.bathtub: 0.8181, IoU.railing: 0.4263, IoU.cushion: 0.7065, IoU.base: 0.4195, IoU.box: 0.3468, IoU.column: 0.4922, IoU.signboard: 0.3917, IoU.chest of drawers: 0.4386, IoU.counter: 0.3705, IoU.sand: 0.5060, IoU.sink: 0.7091, IoU.skyscraper: 0.4775, IoU.fireplace: 0.7264, IoU.refrigerator: 0.8396, IoU.grandstand: 0.5015, IoU.path: 0.3007, IoU.stairs: 0.3632, IoU.runway: 0.6736, IoU.case: 0.5816, IoU.pool table: 0.9128, IoU.pillow: 0.6755, IoU.screen door: 0.8311, IoU.stairway: 0.4570, IoU.river: 0.0793, IoU.bridge: 0.7248, IoU.bookcase: 0.3332, IoU.blind: 0.5233, IoU.coffee table: 0.6670, IoU.toilet: 0.8815, IoU.flower: 0.3738, IoU.book: 0.4833, IoU.hill: 0.0766, IoU.bench: 0.4131, IoU.countertop: 0.6005, IoU.stove: 0.8119, IoU.palm: 0.5366, IoU.kitchen island: 0.5311, IoU.computer: 0.7765, IoU.swivel chair: 0.4840, IoU.boat: 0.6387, IoU.bar: 0.5556, IoU.arcade machine: 0.7767, IoU.hovel: 0.2802, IoU.bus: 0.9115, IoU.towel: 0.7200, IoU.light: 0.5565, IoU.truck: 0.4103, IoU.tower: 0.3324, IoU.chandelier: 0.6788, IoU.awning: 0.4234, IoU.streetlight: 0.3030, IoU.booth: 0.3453, IoU.television receiver: 0.7776, IoU.airplane: 0.6778, IoU.dirt track: 0.1467, IoU.apparel: 0.5455, IoU.pole: 0.2500, IoU.land: 0.0091, IoU.bannister: 0.1580, IoU.escalator: 0.4599, IoU.ottoman: 0.5395, IoU.bottle: 0.3913, IoU.buffet: 0.6213, IoU.poster: 0.3056, IoU.stage: 0.2598, IoU.van: 0.4456, IoU.ship: 0.8691, IoU.fountain: 0.4952, IoU.conveyer belt: 0.8283, IoU.canopy: 0.4386, IoU.washer: 0.7764, IoU.plaything: 0.3283, IoU.swimming pool: 0.5921, IoU.stool: 0.5540, IoU.barrel: 0.5733, IoU.basket: 0.3956, IoU.waterfall: 0.7480, IoU.tent: 0.9554, IoU.bag: 0.1528, IoU.minibike: 0.7337, IoU.cradle: 0.6262, IoU.oven: 0.6405, IoU.ball: 0.1237, IoU.food: 0.5770, IoU.step: 0.1163, IoU.tank: 0.8391, IoU.trade name: 0.2976, IoU.microwave: 0.9019, IoU.pot: 0.5391, IoU.animal: 0.6307, IoU.bicycle: 0.5596, IoU.lake: 0.4956, IoU.dishwasher: 0.6496, IoU.screen: 0.6171, IoU.blanket: 0.1892, IoU.sculpture: 0.6857, IoU.hood: 0.6613, IoU.sconce: 0.5297, IoU.vase: 0.4227, IoU.traffic light: 0.3061, IoU.tray: 0.1056, IoU.ashcan: 0.4078, IoU.fan: 0.6710, IoU.pier: 0.4055, IoU.crt screen: 0.0323, IoU.plate: 0.5902, IoU.monitor: 0.1526, IoU.bulletin board: 0.5704, IoU.shower: 0.0109, IoU.radiator: 0.6439, IoU.glass: 0.1824, IoU.clock: 0.3799, IoU.flag: 0.6618, Acc.wall: 0.8947, Acc.building: 0.9228, Acc.sky: 0.9760, Acc.floor: 0.9089, Acc.tree: 0.8906, Acc.ceiling: 0.9180, Acc.road: 0.9057, Acc.bed : 0.9709, Acc.windowpane: 0.7986, Acc.grass: 0.7304, Acc.cabinet: 0.7209, Acc.sidewalk: 0.8699, Acc.person: 0.9453, Acc.earth: 0.4815, Acc.door: 0.7551, Acc.table: 0.8118, Acc.mountain: 0.7079, Acc.plant: 0.6673, Acc.curtain: 0.8942, Acc.chair: 0.6725, Acc.car: 0.9202, Acc.water: 0.7234, Acc.painting: 0.8991, Acc.sofa: 0.8712, Acc.shelf: 0.5583, Acc.house: 0.8672, Acc.sea: 0.8403, Acc.mirror: 0.8473, Acc.rug: 0.8538, Acc.field: 0.5244, Acc.armchair: 0.8206, Acc.seat: 0.8817, Acc.fence: 0.7475, Acc.desk: 0.8376, Acc.rock: 0.8568, Acc.wardrobe: 0.6789, Acc.lamp: 0.8332, Acc.bathtub: 0.8739, Acc.railing: 0.6420, Acc.cushion: 0.8110, Acc.base: 0.5659, Acc.box: 0.4491, Acc.column: 0.5761, Acc.signboard: 0.4753, Acc.chest of drawers: 0.7109, Acc.counter: 0.4173, Acc.sand: 0.7837, Acc.sink: 0.8136, Acc.skyscraper: 0.6122, Acc.fireplace: 0.8958, Acc.refrigerator: 0.9138, Acc.grandstand: 0.8755, Acc.path: 0.4391, Acc.stairs: 0.4307, Acc.runway: 0.9865, Acc.case: 0.8487, Acc.pool table: 0.9857, Acc.pillow: 0.8129, Acc.screen door: 0.8928, Acc.stairway: 0.5059, Acc.river: 0.1579, Acc.bridge: 0.8213, Acc.bookcase: 0.6318, Acc.blind: 0.6699, Acc.coffee table: 0.8696, Acc.toilet: 0.9235, Acc.flower: 0.5979, Acc.book: 0.7320, Acc.hill: 0.1873, Acc.bench: 0.4495, Acc.countertop: 0.8691, Acc.stove: 0.9085, Acc.palm: 0.8149, Acc.kitchen island: 0.8638, Acc.computer: 0.8945, Acc.swivel chair: 0.7475, Acc.boat: 0.9115, Acc.bar: 0.7882, Acc.arcade machine: 0.8029, Acc.hovel: 0.3099, Acc.bus: 0.9632, Acc.towel: 0.8196, Acc.light: 0.6231, Acc.truck: 0.5727, Acc.tower: 0.5763, Acc.chandelier: 0.8691, Acc.awning: 0.5863, Acc.streetlight: 0.4090, Acc.booth: 0.5805, Acc.television receiver: 0.8602, Acc.airplane: 0.7974, Acc.dirt track: 0.6796, Acc.apparel: 0.7293, Acc.pole: 0.3300, Acc.land: 0.0131, Acc.bannister: 0.2309, Acc.escalator: 0.5722, Acc.ottoman: 0.6909, Acc.bottle: 0.7235, Acc.buffet: 0.8501, Acc.poster: 0.3852, Acc.stage: 0.7176, Acc.van: 0.6868, Acc.ship: 0.9204, Acc.fountain: 0.5402, Acc.conveyer belt: 0.9255, Acc.canopy: 0.6981, Acc.washer: 0.8038, Acc.plaything: 0.5178, Acc.swimming pool: 0.9048, Acc.stool: 0.6832, Acc.barrel: 0.7267, Acc.basket: 0.5058, Acc.waterfall: 0.9081, Acc.tent: 0.9841, Acc.bag: 0.1703, Acc.minibike: 0.8941, Acc.cradle: 0.9894, Acc.oven: 0.8126, Acc.ball: 0.1290, Acc.food: 0.6997, Acc.step: 0.1232, Acc.tank: 0.9665, Acc.trade name: 0.3554, Acc.microwave: 0.9562, Acc.pot: 0.6293, Acc.animal: 0.6600, Acc.bicycle: 0.7852, Acc.lake: 0.6665, Acc.dishwasher: 0.7365, Acc.screen: 0.9479, Acc.blanket: 0.2092, Acc.sculpture: 0.7420, Acc.hood: 0.7241, Acc.sconce: 0.5926, Acc.vase: 0.6423, Acc.traffic light: 0.6265, Acc.tray: 0.1329, Acc.ashcan: 0.6306, Acc.fan: 0.7587, Acc.pier: 0.4401, Acc.crt screen: 0.0901, Acc.plate: 0.7537, Acc.monitor: 0.1671, Acc.bulletin board: 0.6644, Acc.shower: 0.0453, Acc.radiator: 0.7646, Acc.glass: 0.2077, Acc.clock: 0.4816, Acc.flag: 0.7255 +2024-06-16 06:41:36,652 - mmseg - INFO - Iter [22050/80000] lr: 2.898e-05, eta: 1 day, 0:19:17, time: 3.304, data_time: 1.945, memory: 70722, decode.loss_ce: 0.2940, decode.acc_seg: 88.3313, aux.loss_ce: 0.1207, aux.acc_seg: 88.0858, loss: 0.4147 +2024-06-16 06:42:44,649 - mmseg - INFO - Iter [22100/80000] lr: 2.895e-05, eta: 1 day, 0:17:42, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3071, decode.acc_seg: 87.7932, aux.loss_ce: 0.1245, aux.acc_seg: 87.6126, loss: 0.4316 +2024-06-16 06:43:52,888 - mmseg - INFO - Iter [22150/80000] lr: 2.893e-05, eta: 1 day, 0:16:07, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2799, decode.acc_seg: 88.5831, aux.loss_ce: 0.1132, aux.acc_seg: 88.4575, loss: 0.3931 +2024-06-16 06:45:01,279 - mmseg - INFO - Iter [22200/80000] lr: 2.890e-05, eta: 1 day, 0:14:33, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2924, decode.acc_seg: 88.3585, aux.loss_ce: 0.1184, aux.acc_seg: 88.1591, loss: 0.4108 +2024-06-16 06:46:09,619 - mmseg - INFO - Iter [22250/80000] lr: 2.888e-05, eta: 1 day, 0:12:59, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3106, decode.acc_seg: 87.1446, aux.loss_ce: 0.1248, aux.acc_seg: 87.0917, loss: 0.4355 +2024-06-16 06:47:17,748 - mmseg - INFO - Iter [22300/80000] lr: 2.885e-05, eta: 1 day, 0:11:25, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2985, decode.acc_seg: 87.8830, aux.loss_ce: 0.1218, aux.acc_seg: 87.6087, loss: 0.4203 +2024-06-16 06:48:26,016 - mmseg - INFO - Iter [22350/80000] lr: 2.883e-05, eta: 1 day, 0:09:51, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2881, decode.acc_seg: 88.3245, aux.loss_ce: 0.1172, aux.acc_seg: 88.1016, loss: 0.4053 +2024-06-16 06:49:34,346 - mmseg - INFO - Iter [22400/80000] lr: 2.880e-05, eta: 1 day, 0:08:17, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3060, decode.acc_seg: 87.3506, aux.loss_ce: 0.1232, aux.acc_seg: 87.2031, loss: 0.4292 +2024-06-16 06:50:42,496 - mmseg - INFO - Iter [22450/80000] lr: 2.878e-05, eta: 1 day, 0:06:43, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2977, decode.acc_seg: 87.6758, aux.loss_ce: 0.1213, aux.acc_seg: 87.4831, loss: 0.4190 +2024-06-16 06:51:50,786 - mmseg - INFO - Iter [22500/80000] lr: 2.875e-05, eta: 1 day, 0:05:09, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3154, decode.acc_seg: 87.0694, aux.loss_ce: 0.1283, aux.acc_seg: 86.8289, loss: 0.4437 +2024-06-16 06:52:58,904 - mmseg - INFO - Iter [22550/80000] lr: 2.873e-05, eta: 1 day, 0:03:35, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2795, decode.acc_seg: 88.7535, aux.loss_ce: 0.1145, aux.acc_seg: 88.5467, loss: 0.3940 +2024-06-16 06:54:07,247 - mmseg - INFO - Iter [22600/80000] lr: 2.870e-05, eta: 1 day, 0:02:02, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3130, decode.acc_seg: 87.2364, aux.loss_ce: 0.1275, aux.acc_seg: 86.8793, loss: 0.4405 +2024-06-16 06:55:15,396 - mmseg - INFO - Iter [22650/80000] lr: 2.868e-05, eta: 1 day, 0:00:28, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2972, decode.acc_seg: 88.2038, aux.loss_ce: 0.1207, aux.acc_seg: 87.9391, loss: 0.4179 +2024-06-16 06:56:23,695 - mmseg - INFO - Iter [22700/80000] lr: 2.865e-05, eta: 23:58:55, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3039, decode.acc_seg: 87.4064, aux.loss_ce: 0.1248, aux.acc_seg: 87.2304, loss: 0.4287 +2024-06-16 06:57:34,298 - mmseg - INFO - Iter [22750/80000] lr: 2.863e-05, eta: 23:57:28, time: 1.412, data_time: 0.052, memory: 70722, decode.loss_ce: 0.2945, decode.acc_seg: 88.0393, aux.loss_ce: 0.1194, aux.acc_seg: 87.8576, loss: 0.4139 +2024-06-16 06:58:42,571 - mmseg - INFO - Iter [22800/80000] lr: 2.860e-05, eta: 23:55:55, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2701, decode.acc_seg: 88.9677, aux.loss_ce: 0.1100, aux.acc_seg: 88.8571, loss: 0.3801 +2024-06-16 06:59:51,130 - mmseg - INFO - Iter [22850/80000] lr: 2.858e-05, eta: 23:54:23, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2617, decode.acc_seg: 89.1937, aux.loss_ce: 0.1079, aux.acc_seg: 88.8614, loss: 0.3696 +2024-06-16 07:00:59,255 - mmseg - INFO - Iter [22900/80000] lr: 2.855e-05, eta: 23:52:49, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2682, decode.acc_seg: 89.0722, aux.loss_ce: 0.1100, aux.acc_seg: 88.7166, loss: 0.3782 +2024-06-16 07:02:07,541 - mmseg - INFO - Iter [22950/80000] lr: 2.853e-05, eta: 23:51:17, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2878, decode.acc_seg: 88.3227, aux.loss_ce: 0.1178, aux.acc_seg: 88.0282, loss: 0.4056 +2024-06-16 07:03:15,787 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 07:03:15,787 - mmseg - INFO - Iter [23000/80000] lr: 2.850e-05, eta: 23:49:44, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2684, decode.acc_seg: 88.7070, aux.loss_ce: 0.1093, aux.acc_seg: 88.6376, loss: 0.3777 +2024-06-16 07:04:51,663 - mmseg - INFO - per class results: +2024-06-16 07:04:51,669 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.37 | 87.95 | +| building | 83.96 | 92.73 | +| sky | 94.59 | 98.12 | +| floor | 84.32 | 90.66 | +| tree | 76.35 | 86.9 | +| ceiling | 85.92 | 94.97 | +| road | 83.96 | 87.81 | +| bed | 92.07 | 96.71 | +| windowpane | 65.3 | 80.36 | +| grass | 67.43 | 79.13 | +| cabinet | 65.5 | 74.35 | +| sidewalk | 68.94 | 89.44 | +| person | 84.7 | 94.14 | +| earth | 36.96 | 51.41 | +| door | 56.57 | 70.44 | +| table | 66.2 | 81.16 | +| mountain | 57.23 | 73.33 | +| plant | 57.65 | 70.62 | +| curtain | 78.58 | 90.31 | +| chair | 65.65 | 77.94 | +| car | 86.68 | 93.66 | +| water | 54.68 | 61.98 | +| painting | 76.99 | 92.99 | +| sofa | 78.13 | 88.8 | +| shelf | 46.7 | 62.86 | +| house | 49.75 | 71.8 | +| sea | 66.9 | 89.65 | +| mirror | 77.28 | 86.22 | +| rug | 72.01 | 88.14 | +| field | 30.19 | 58.34 | +| armchair | 55.48 | 73.76 | +| seat | 63.58 | 86.56 | +| fence | 50.4 | 61.95 | +| desk | 55.62 | 77.41 | +| rock | 45.83 | 62.54 | +| wardrobe | 55.51 | 72.9 | +| lamp | 70.21 | 82.34 | +| bathtub | 84.79 | 87.8 | +| railing | 36.06 | 45.64 | +| cushion | 67.73 | 81.68 | +| base | 37.77 | 55.42 | +| box | 36.27 | 47.26 | +| column | 55.82 | 71.78 | +| signboard | 41.03 | 57.85 | +| chest of drawers | 41.43 | 58.43 | +| counter | 34.89 | 40.64 | +| sand | 52.02 | 79.91 | +| sink | 72.63 | 82.23 | +| skyscraper | 53.33 | 66.18 | +| fireplace | 69.18 | 95.66 | +| refrigerator | 82.98 | 93.08 | +| grandstand | 50.6 | 86.68 | +| path | 34.05 | 47.69 | +| stairs | 38.61 | 48.09 | +| runway | 70.82 | 96.16 | +| case | 50.79 | 60.66 | +| pool table | 93.33 | 98.81 | +| pillow | 64.61 | 74.65 | +| screen door | 88.5 | 95.02 | +| stairway | 53.2 | 63.85 | +| river | 18.23 | 26.02 | +| bridge | 77.04 | 85.14 | +| bookcase | 35.96 | 54.35 | +| blind | 44.96 | 51.05 | +| coffee table | 57.58 | 89.04 | +| toilet | 88.69 | 94.39 | +| flower | 44.41 | 60.86 | +| book | 49.7 | 79.46 | +| hill | 3.73 | 6.02 | +| bench | 46.41 | 62.54 | +| countertop | 63.82 | 87.08 | +| stove | 83.14 | 94.33 | +| palm | 51.34 | 85.48 | +| kitchen island | 53.9 | 87.8 | +| computer | 79.46 | 88.19 | +| swivel chair | 48.27 | 67.21 | +| boat | 82.04 | 89.15 | +| bar | 60.12 | 84.18 | +| arcade machine | 81.7 | 89.25 | +| hovel | 29.92 | 33.75 | +| bus | 90.48 | 96.32 | +| towel | 74.91 | 82.41 | +| light | 58.28 | 66.46 | +| truck | 44.75 | 58.92 | +| tower | 26.67 | 40.1 | +| chandelier | 66.73 | 89.09 | +| awning | 40.12 | 51.39 | +| streetlight | 28.27 | 35.71 | +| booth | 51.14 | 74.71 | +| television receiver | 77.98 | 90.63 | +| airplane | 60.13 | 70.4 | +| dirt track | 9.87 | 39.37 | +| apparel | 54.24 | 70.78 | +| pole | 29.9 | 41.16 | +| land | 5.23 | 16.25 | +| bannister | 18.11 | 27.52 | +| escalator | 52.06 | 68.04 | +| ottoman | 49.58 | 68.18 | +| bottle | 38.47 | 60.52 | +| buffet | 68.28 | 85.91 | +| poster | 35.06 | 44.42 | +| stage | 19.58 | 51.31 | +| van | 48.07 | 62.73 | +| ship | 89.4 | 94.82 | +| fountain | 56.52 | 58.92 | +| conveyer belt | 75.21 | 97.0 | +| canopy | 33.13 | 49.94 | +| washer | 77.1 | 81.3 | +| plaything | 42.75 | 67.39 | +| swimming pool | 77.77 | 82.73 | +| stool | 49.1 | 74.13 | +| barrel | 57.57 | 64.5 | +| basket | 40.03 | 55.11 | +| waterfall | 74.82 | 95.71 | +| tent | 95.25 | 98.78 | +| bag | 17.68 | 20.19 | +| minibike | 72.12 | 82.26 | +| cradle | 73.51 | 98.24 | +| oven | 47.41 | 50.71 | +| ball | 49.17 | 71.0 | +| food | 65.43 | 83.75 | +| step | 21.89 | 31.65 | +| tank | 84.47 | 95.4 | +| trade name | 15.47 | 17.08 | +| microwave | 86.49 | 93.77 | +| pot | 55.49 | 66.23 | +| animal | 60.88 | 63.25 | +| bicycle | 56.88 | 69.12 | +| lake | 32.63 | 95.41 | +| dishwasher | 66.15 | 77.1 | +| screen | 65.42 | 92.57 | +| blanket | 20.72 | 22.57 | +| sculpture | 63.93 | 85.21 | +| hood | 67.69 | 82.36 | +| sconce | 57.88 | 70.35 | +| vase | 44.36 | 60.08 | +| traffic light | 35.56 | 54.69 | +| tray | 19.07 | 31.44 | +| ashcan | 43.06 | 59.36 | +| fan | 65.03 | 76.52 | +| pier | 44.44 | 56.78 | +| crt screen | 8.78 | 23.93 | +| plate | 61.3 | 74.17 | +| monitor | 11.86 | 12.39 | +| bulletin board | 61.18 | 66.82 | +| shower | 0.77 | 2.57 | +| radiator | 62.12 | 78.12 | +| glass | 15.66 | 16.38 | +| clock | 38.55 | 48.53 | +| flag | 68.88 | 81.2 | ++---------------------+-------+-------+ +2024-06-16 07:04:51,669 - mmseg - INFO - Summary: +2024-06-16 07:04:51,669 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 85.21 | 55.86 | 69.7 | ++-------+-------+------+ +2024-06-16 07:04:51,670 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 07:04:51,670 - mmseg - INFO - Iter(val) [250] aAcc: 0.8521, mIoU: 0.5586, mAcc: 0.6970, IoU.wall: 0.8137, IoU.building: 0.8396, IoU.sky: 0.9459, IoU.floor: 0.8432, IoU.tree: 0.7635, IoU.ceiling: 0.8592, IoU.road: 0.8396, IoU.bed : 0.9207, IoU.windowpane: 0.6530, IoU.grass: 0.6743, IoU.cabinet: 0.6550, IoU.sidewalk: 0.6894, IoU.person: 0.8470, IoU.earth: 0.3696, IoU.door: 0.5657, IoU.table: 0.6620, IoU.mountain: 0.5723, IoU.plant: 0.5765, IoU.curtain: 0.7858, IoU.chair: 0.6565, IoU.car: 0.8668, IoU.water: 0.5468, IoU.painting: 0.7699, IoU.sofa: 0.7813, IoU.shelf: 0.4670, IoU.house: 0.4975, IoU.sea: 0.6690, IoU.mirror: 0.7728, IoU.rug: 0.7201, IoU.field: 0.3019, IoU.armchair: 0.5548, IoU.seat: 0.6358, IoU.fence: 0.5040, IoU.desk: 0.5562, IoU.rock: 0.4583, IoU.wardrobe: 0.5551, IoU.lamp: 0.7021, IoU.bathtub: 0.8479, IoU.railing: 0.3606, IoU.cushion: 0.6773, IoU.base: 0.3777, IoU.box: 0.3627, IoU.column: 0.5582, IoU.signboard: 0.4103, IoU.chest of drawers: 0.4143, IoU.counter: 0.3489, IoU.sand: 0.5202, IoU.sink: 0.7263, IoU.skyscraper: 0.5333, IoU.fireplace: 0.6918, IoU.refrigerator: 0.8298, IoU.grandstand: 0.5060, IoU.path: 0.3405, IoU.stairs: 0.3861, IoU.runway: 0.7082, IoU.case: 0.5079, IoU.pool table: 0.9333, IoU.pillow: 0.6461, IoU.screen door: 0.8850, IoU.stairway: 0.5320, IoU.river: 0.1823, IoU.bridge: 0.7704, IoU.bookcase: 0.3596, IoU.blind: 0.4496, IoU.coffee table: 0.5758, IoU.toilet: 0.8869, IoU.flower: 0.4441, IoU.book: 0.4970, IoU.hill: 0.0373, IoU.bench: 0.4641, IoU.countertop: 0.6382, IoU.stove: 0.8314, IoU.palm: 0.5134, IoU.kitchen island: 0.5390, IoU.computer: 0.7946, IoU.swivel chair: 0.4827, IoU.boat: 0.8204, IoU.bar: 0.6012, IoU.arcade machine: 0.8170, IoU.hovel: 0.2992, IoU.bus: 0.9048, IoU.towel: 0.7491, IoU.light: 0.5828, IoU.truck: 0.4475, IoU.tower: 0.2667, IoU.chandelier: 0.6673, IoU.awning: 0.4012, IoU.streetlight: 0.2827, IoU.booth: 0.5114, IoU.television receiver: 0.7798, IoU.airplane: 0.6013, IoU.dirt track: 0.0987, IoU.apparel: 0.5424, IoU.pole: 0.2990, IoU.land: 0.0523, IoU.bannister: 0.1811, IoU.escalator: 0.5206, IoU.ottoman: 0.4958, IoU.bottle: 0.3847, IoU.buffet: 0.6828, IoU.poster: 0.3506, IoU.stage: 0.1958, IoU.van: 0.4807, IoU.ship: 0.8940, IoU.fountain: 0.5652, IoU.conveyer belt: 0.7521, IoU.canopy: 0.3313, IoU.washer: 0.7710, IoU.plaything: 0.4275, IoU.swimming pool: 0.7777, IoU.stool: 0.4910, IoU.barrel: 0.5757, IoU.basket: 0.4003, IoU.waterfall: 0.7482, IoU.tent: 0.9525, IoU.bag: 0.1768, IoU.minibike: 0.7212, IoU.cradle: 0.7351, IoU.oven: 0.4741, IoU.ball: 0.4917, IoU.food: 0.6543, IoU.step: 0.2189, IoU.tank: 0.8447, IoU.trade name: 0.1547, IoU.microwave: 0.8649, IoU.pot: 0.5549, IoU.animal: 0.6088, IoU.bicycle: 0.5688, IoU.lake: 0.3263, IoU.dishwasher: 0.6615, IoU.screen: 0.6542, IoU.blanket: 0.2072, IoU.sculpture: 0.6393, IoU.hood: 0.6769, IoU.sconce: 0.5788, IoU.vase: 0.4436, IoU.traffic light: 0.3556, IoU.tray: 0.1907, IoU.ashcan: 0.4306, IoU.fan: 0.6503, IoU.pier: 0.4444, IoU.crt screen: 0.0878, IoU.plate: 0.6130, IoU.monitor: 0.1186, IoU.bulletin board: 0.6118, IoU.shower: 0.0077, IoU.radiator: 0.6212, IoU.glass: 0.1566, IoU.clock: 0.3855, IoU.flag: 0.6888, Acc.wall: 0.8795, Acc.building: 0.9273, Acc.sky: 0.9812, Acc.floor: 0.9066, Acc.tree: 0.8690, Acc.ceiling: 0.9497, Acc.road: 0.8781, Acc.bed : 0.9671, Acc.windowpane: 0.8036, Acc.grass: 0.7913, Acc.cabinet: 0.7435, Acc.sidewalk: 0.8944, Acc.person: 0.9414, Acc.earth: 0.5141, Acc.door: 0.7044, Acc.table: 0.8116, Acc.mountain: 0.7333, Acc.plant: 0.7062, Acc.curtain: 0.9031, Acc.chair: 0.7794, Acc.car: 0.9366, Acc.water: 0.6198, Acc.painting: 0.9299, Acc.sofa: 0.8880, Acc.shelf: 0.6286, Acc.house: 0.7180, Acc.sea: 0.8965, Acc.mirror: 0.8622, Acc.rug: 0.8814, Acc.field: 0.5834, Acc.armchair: 0.7376, Acc.seat: 0.8656, Acc.fence: 0.6195, Acc.desk: 0.7741, Acc.rock: 0.6254, Acc.wardrobe: 0.7290, Acc.lamp: 0.8234, Acc.bathtub: 0.8780, Acc.railing: 0.4564, Acc.cushion: 0.8168, Acc.base: 0.5542, Acc.box: 0.4726, Acc.column: 0.7178, Acc.signboard: 0.5785, Acc.chest of drawers: 0.5843, Acc.counter: 0.4064, Acc.sand: 0.7991, Acc.sink: 0.8223, Acc.skyscraper: 0.6618, Acc.fireplace: 0.9566, Acc.refrigerator: 0.9308, Acc.grandstand: 0.8668, Acc.path: 0.4769, Acc.stairs: 0.4809, Acc.runway: 0.9616, Acc.case: 0.6066, Acc.pool table: 0.9881, Acc.pillow: 0.7465, Acc.screen door: 0.9502, Acc.stairway: 0.6385, Acc.river: 0.2602, Acc.bridge: 0.8514, Acc.bookcase: 0.5435, Acc.blind: 0.5105, Acc.coffee table: 0.8904, Acc.toilet: 0.9439, Acc.flower: 0.6086, Acc.book: 0.7946, Acc.hill: 0.0602, Acc.bench: 0.6254, Acc.countertop: 0.8708, Acc.stove: 0.9433, Acc.palm: 0.8548, Acc.kitchen island: 0.8780, Acc.computer: 0.8819, Acc.swivel chair: 0.6721, Acc.boat: 0.8915, Acc.bar: 0.8418, Acc.arcade machine: 0.8925, Acc.hovel: 0.3375, Acc.bus: 0.9632, Acc.towel: 0.8241, Acc.light: 0.6646, Acc.truck: 0.5892, Acc.tower: 0.4010, Acc.chandelier: 0.8909, Acc.awning: 0.5139, Acc.streetlight: 0.3571, Acc.booth: 0.7471, Acc.television receiver: 0.9063, Acc.airplane: 0.7040, Acc.dirt track: 0.3937, Acc.apparel: 0.7078, Acc.pole: 0.4116, Acc.land: 0.1625, Acc.bannister: 0.2752, Acc.escalator: 0.6804, Acc.ottoman: 0.6818, Acc.bottle: 0.6052, Acc.buffet: 0.8591, Acc.poster: 0.4442, Acc.stage: 0.5131, Acc.van: 0.6273, Acc.ship: 0.9482, Acc.fountain: 0.5892, Acc.conveyer belt: 0.9700, Acc.canopy: 0.4994, Acc.washer: 0.8130, Acc.plaything: 0.6739, Acc.swimming pool: 0.8273, Acc.stool: 0.7413, Acc.barrel: 0.6450, Acc.basket: 0.5511, Acc.waterfall: 0.9571, Acc.tent: 0.9878, Acc.bag: 0.2019, Acc.minibike: 0.8226, Acc.cradle: 0.9824, Acc.oven: 0.5071, Acc.ball: 0.7100, Acc.food: 0.8375, Acc.step: 0.3165, Acc.tank: 0.9540, Acc.trade name: 0.1708, Acc.microwave: 0.9377, Acc.pot: 0.6623, Acc.animal: 0.6325, Acc.bicycle: 0.6912, Acc.lake: 0.9541, Acc.dishwasher: 0.7710, Acc.screen: 0.9257, Acc.blanket: 0.2257, Acc.sculpture: 0.8521, Acc.hood: 0.8236, Acc.sconce: 0.7035, Acc.vase: 0.6008, Acc.traffic light: 0.5469, Acc.tray: 0.3144, Acc.ashcan: 0.5936, Acc.fan: 0.7652, Acc.pier: 0.5678, Acc.crt screen: 0.2393, Acc.plate: 0.7417, Acc.monitor: 0.1239, Acc.bulletin board: 0.6682, Acc.shower: 0.0257, Acc.radiator: 0.7812, Acc.glass: 0.1638, Acc.clock: 0.4853, Acc.flag: 0.8120 +2024-06-16 07:06:00,485 - mmseg - INFO - Iter [23050/80000] lr: 2.848e-05, eta: 23:52:10, time: 3.294, data_time: 1.934, memory: 70722, decode.loss_ce: 0.2669, decode.acc_seg: 89.0061, aux.loss_ce: 0.1083, aux.acc_seg: 88.8578, loss: 0.3752 +2024-06-16 07:07:08,578 - mmseg - INFO - Iter [23100/80000] lr: 2.845e-05, eta: 23:50:36, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2831, decode.acc_seg: 88.8910, aux.loss_ce: 0.1157, aux.acc_seg: 88.6375, loss: 0.3988 +2024-06-16 07:08:17,082 - mmseg - INFO - Iter [23150/80000] lr: 2.843e-05, eta: 23:49:04, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2693, decode.acc_seg: 89.0313, aux.loss_ce: 0.1095, aux.acc_seg: 88.7420, loss: 0.3788 +2024-06-16 07:09:25,319 - mmseg - INFO - Iter [23200/80000] lr: 2.840e-05, eta: 23:47:31, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2821, decode.acc_seg: 88.6473, aux.loss_ce: 0.1150, aux.acc_seg: 88.4361, loss: 0.3971 +2024-06-16 07:10:33,637 - mmseg - INFO - Iter [23250/80000] lr: 2.838e-05, eta: 23:45:58, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2603, decode.acc_seg: 89.3818, aux.loss_ce: 0.1056, aux.acc_seg: 89.1719, loss: 0.3659 +2024-06-16 07:11:41,781 - mmseg - INFO - Iter [23300/80000] lr: 2.835e-05, eta: 23:44:25, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2958, decode.acc_seg: 87.9873, aux.loss_ce: 0.1197, aux.acc_seg: 87.8002, loss: 0.4154 +2024-06-16 07:12:49,924 - mmseg - INFO - Iter [23350/80000] lr: 2.833e-05, eta: 23:42:52, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2688, decode.acc_seg: 89.2094, aux.loss_ce: 0.1097, aux.acc_seg: 89.0314, loss: 0.3785 +2024-06-16 07:13:58,576 - mmseg - INFO - Iter [23400/80000] lr: 2.830e-05, eta: 23:41:21, time: 1.373, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2787, decode.acc_seg: 88.8517, aux.loss_ce: 0.1138, aux.acc_seg: 88.6168, loss: 0.3925 +2024-06-16 07:15:06,746 - mmseg - INFO - Iter [23450/80000] lr: 2.828e-05, eta: 23:39:48, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2710, decode.acc_seg: 88.6352, aux.loss_ce: 0.1110, aux.acc_seg: 88.4590, loss: 0.3820 +2024-06-16 07:16:14,989 - mmseg - INFO - Iter [23500/80000] lr: 2.825e-05, eta: 23:38:16, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2671, decode.acc_seg: 89.1468, aux.loss_ce: 0.1091, aux.acc_seg: 88.9094, loss: 0.3761 +2024-06-16 07:17:23,649 - mmseg - INFO - Iter [23550/80000] lr: 2.823e-05, eta: 23:36:45, time: 1.373, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2793, decode.acc_seg: 88.4016, aux.loss_ce: 0.1142, aux.acc_seg: 88.3632, loss: 0.3934 +2024-06-16 07:18:31,772 - mmseg - INFO - Iter [23600/80000] lr: 2.820e-05, eta: 23:35:12, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2842, decode.acc_seg: 88.4545, aux.loss_ce: 0.1158, aux.acc_seg: 88.3537, loss: 0.4001 +2024-06-16 07:19:39,873 - mmseg - INFO - Iter [23650/80000] lr: 2.818e-05, eta: 23:33:40, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2882, decode.acc_seg: 88.2353, aux.loss_ce: 0.1175, aux.acc_seg: 88.0385, loss: 0.4057 +2024-06-16 07:20:47,915 - mmseg - INFO - Iter [23700/80000] lr: 2.815e-05, eta: 23:32:07, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2960, decode.acc_seg: 87.9109, aux.loss_ce: 0.1206, aux.acc_seg: 87.6465, loss: 0.4166 +2024-06-16 07:21:56,578 - mmseg - INFO - Iter [23750/80000] lr: 2.813e-05, eta: 23:30:37, time: 1.373, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2811, decode.acc_seg: 88.7859, aux.loss_ce: 0.1143, aux.acc_seg: 88.5401, loss: 0.3954 +2024-06-16 07:23:04,715 - mmseg - INFO - Iter [23800/80000] lr: 2.810e-05, eta: 23:29:05, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2823, decode.acc_seg: 88.5896, aux.loss_ce: 0.1152, aux.acc_seg: 88.5418, loss: 0.3975 +2024-06-16 07:24:13,096 - mmseg - INFO - Iter [23850/80000] lr: 2.808e-05, eta: 23:27:33, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.3045, decode.acc_seg: 88.0050, aux.loss_ce: 0.1235, aux.acc_seg: 87.7244, loss: 0.4280 +2024-06-16 07:25:21,273 - mmseg - INFO - Iter [23900/80000] lr: 2.805e-05, eta: 23:26:01, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2797, decode.acc_seg: 88.9659, aux.loss_ce: 0.1152, aux.acc_seg: 88.6834, loss: 0.3949 +2024-06-16 07:26:29,845 - mmseg - INFO - Iter [23950/80000] lr: 2.803e-05, eta: 23:24:31, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2974, decode.acc_seg: 88.2278, aux.loss_ce: 0.1203, aux.acc_seg: 87.9367, loss: 0.4176 +2024-06-16 07:27:40,196 - mmseg - INFO - Saving checkpoint at 24000 iterations +2024-06-16 07:29:08,270 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 07:29:08,270 - mmseg - INFO - Iter [24000/80000] lr: 2.800e-05, eta: 23:26:30, time: 3.168, data_time: 0.053, memory: 70722, decode.loss_ce: 0.2821, decode.acc_seg: 88.9236, aux.loss_ce: 0.1148, aux.acc_seg: 88.6391, loss: 0.3969 +2024-06-16 07:30:43,754 - mmseg - INFO - per class results: +2024-06-16 07:30:43,760 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.02 | 87.02 | +| building | 84.36 | 93.27 | +| sky | 94.77 | 97.41 | +| floor | 83.57 | 89.92 | +| tree | 76.92 | 88.98 | +| ceiling | 86.06 | 93.54 | +| road | 85.61 | 91.5 | +| bed | 91.87 | 97.0 | +| windowpane | 62.35 | 86.49 | +| grass | 66.99 | 76.27 | +| cabinet | 64.3 | 70.96 | +| sidewalk | 71.43 | 82.89 | +| person | 84.83 | 92.72 | +| earth | 39.04 | 53.76 | +| door | 57.55 | 73.98 | +| table | 67.4 | 84.03 | +| mountain | 60.18 | 78.3 | +| plant | 54.81 | 63.52 | +| curtain | 79.64 | 89.75 | +| chair | 64.52 | 79.31 | +| car | 85.56 | 94.67 | +| water | 65.18 | 79.72 | +| painting | 77.28 | 92.15 | +| sofa | 77.94 | 89.01 | +| shelf | 45.81 | 69.15 | +| house | 56.03 | 72.66 | +| sea | 70.97 | 83.58 | +| mirror | 77.3 | 85.36 | +| rug | 72.99 | 83.07 | +| field | 33.92 | 74.28 | +| armchair | 57.5 | 71.95 | +| seat | 61.5 | 89.28 | +| fence | 53.26 | 72.33 | +| desk | 53.91 | 80.95 | +| rock | 54.09 | 63.27 | +| wardrobe | 53.68 | 71.73 | +| lamp | 71.6 | 84.84 | +| bathtub | 85.09 | 87.37 | +| railing | 34.22 | 50.8 | +| cushion | 65.81 | 72.71 | +| base | 36.74 | 58.35 | +| box | 35.32 | 45.69 | +| column | 52.57 | 69.57 | +| signboard | 41.08 | 51.51 | +| chest of drawers | 52.24 | 75.38 | +| counter | 39.58 | 47.05 | +| sand | 50.15 | 79.39 | +| sink | 71.63 | 83.91 | +| skyscraper | 44.81 | 57.55 | +| fireplace | 71.05 | 94.79 | +| refrigerator | 83.14 | 91.24 | +| grandstand | 56.72 | 86.82 | +| path | 32.87 | 43.44 | +| stairs | 44.25 | 59.28 | +| runway | 73.09 | 95.63 | +| case | 49.62 | 63.1 | +| pool table | 94.07 | 98.25 | +| pillow | 66.56 | 78.88 | +| screen door | 76.3 | 77.92 | +| stairway | 42.61 | 47.66 | +| river | 13.46 | 23.38 | +| bridge | 71.94 | 90.79 | +| bookcase | 39.98 | 57.14 | +| blind | 37.72 | 41.12 | +| coffee table | 62.25 | 89.61 | +| toilet | 87.11 | 95.1 | +| flower | 39.91 | 44.7 | +| book | 51.26 | 74.13 | +| hill | 4.7 | 10.59 | +| bench | 44.27 | 59.55 | +| countertop | 60.64 | 79.61 | +| stove | 81.66 | 93.59 | +| palm | 56.54 | 80.24 | +| kitchen island | 50.71 | 86.23 | +| computer | 75.65 | 93.19 | +| swivel chair | 44.28 | 83.39 | +| boat | 73.72 | 90.83 | +| bar | 64.95 | 76.08 | +| arcade machine | 82.16 | 89.81 | +| hovel | 34.68 | 38.1 | +| bus | 90.04 | 96.7 | +| towel | 71.12 | 89.6 | +| light | 53.05 | 56.97 | +| truck | 44.34 | 57.62 | +| tower | 29.31 | 51.73 | +| chandelier | 70.18 | 81.81 | +| awning | 37.62 | 53.84 | +| streetlight | 29.73 | 37.44 | +| booth | 34.7 | 38.93 | +| television receiver | 75.55 | 93.59 | +| airplane | 70.34 | 80.37 | +| dirt track | 4.97 | 23.08 | +| apparel | 52.91 | 65.77 | +| pole | 29.52 | 42.1 | +| land | 0.23 | 0.35 | +| bannister | 15.58 | 22.26 | +| escalator | 51.64 | 84.15 | +| ottoman | 48.52 | 72.58 | +| bottle | 41.32 | 69.17 | +| buffet | 55.71 | 91.55 | +| poster | 33.91 | 45.9 | +| stage | 14.93 | 32.15 | +| van | 44.54 | 62.36 | +| ship | 10.21 | 10.22 | +| fountain | 38.02 | 39.38 | +| conveyer belt | 78.7 | 90.53 | +| canopy | 36.73 | 61.1 | +| washer | 86.73 | 93.18 | +| plaything | 40.67 | 71.01 | +| swimming pool | 79.76 | 84.7 | +| stool | 52.3 | 62.53 | +| barrel | 58.32 | 74.02 | +| basket | 38.22 | 54.19 | +| waterfall | 73.98 | 93.83 | +| tent | 92.06 | 99.05 | +| bag | 21.62 | 24.94 | +| minibike | 71.41 | 88.41 | +| cradle | 83.83 | 98.92 | +| oven | 64.53 | 76.92 | +| ball | 9.46 | 9.54 | +| food | 61.01 | 85.89 | +| step | 8.22 | 8.63 | +| tank | 78.86 | 96.59 | +| trade name | 34.82 | 43.93 | +| microwave | 87.65 | 96.21 | +| pot | 58.28 | 71.4 | +| animal | 60.52 | 62.78 | +| bicycle | 50.74 | 82.19 | +| lake | 54.67 | 66.82 | +| dishwasher | 68.99 | 76.51 | +| screen | 61.99 | 93.18 | +| blanket | 28.63 | 35.36 | +| sculpture | 69.68 | 75.68 | +| hood | 60.14 | 71.56 | +| sconce | 54.93 | 65.29 | +| vase | 41.73 | 62.17 | +| traffic light | 35.24 | 55.19 | +| tray | 15.52 | 20.34 | +| ashcan | 39.0 | 62.9 | +| fan | 64.82 | 80.93 | +| pier | 34.3 | 37.37 | +| crt screen | 1.24 | 1.26 | +| plate | 58.24 | 77.41 | +| monitor | 59.87 | 79.14 | +| bulletin board | 48.05 | 70.85 | +| shower | 0.0 | 0.0 | +| radiator | 62.36 | 76.58 | +| glass | 14.74 | 15.38 | +| clock | 31.55 | 45.36 | +| flag | 69.18 | 75.86 | ++---------------------+-------+-------+ +2024-06-16 07:30:43,760 - mmseg - INFO - Summary: +2024-06-16 07:30:43,760 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.29 | 55.03 | 68.78 | ++-------+-------+-------+ +2024-06-16 07:30:43,761 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 07:30:43,761 - mmseg - INFO - Iter(val) [250] aAcc: 0.8529, mIoU: 0.5503, mAcc: 0.6878, IoU.wall: 0.8102, IoU.building: 0.8436, IoU.sky: 0.9477, IoU.floor: 0.8357, IoU.tree: 0.7692, IoU.ceiling: 0.8606, IoU.road: 0.8561, IoU.bed : 0.9187, IoU.windowpane: 0.6235, IoU.grass: 0.6699, IoU.cabinet: 0.6430, IoU.sidewalk: 0.7143, IoU.person: 0.8483, IoU.earth: 0.3904, IoU.door: 0.5755, IoU.table: 0.6740, IoU.mountain: 0.6018, IoU.plant: 0.5481, IoU.curtain: 0.7964, IoU.chair: 0.6452, IoU.car: 0.8556, IoU.water: 0.6518, IoU.painting: 0.7728, IoU.sofa: 0.7794, IoU.shelf: 0.4581, IoU.house: 0.5603, IoU.sea: 0.7097, IoU.mirror: 0.7730, IoU.rug: 0.7299, IoU.field: 0.3392, IoU.armchair: 0.5750, IoU.seat: 0.6150, IoU.fence: 0.5326, IoU.desk: 0.5391, IoU.rock: 0.5409, IoU.wardrobe: 0.5368, IoU.lamp: 0.7160, IoU.bathtub: 0.8509, IoU.railing: 0.3422, IoU.cushion: 0.6581, IoU.base: 0.3674, IoU.box: 0.3532, IoU.column: 0.5257, IoU.signboard: 0.4108, IoU.chest of drawers: 0.5224, IoU.counter: 0.3958, IoU.sand: 0.5015, IoU.sink: 0.7163, IoU.skyscraper: 0.4481, IoU.fireplace: 0.7105, IoU.refrigerator: 0.8314, IoU.grandstand: 0.5672, IoU.path: 0.3287, IoU.stairs: 0.4425, IoU.runway: 0.7309, IoU.case: 0.4962, IoU.pool table: 0.9407, IoU.pillow: 0.6656, IoU.screen door: 0.7630, IoU.stairway: 0.4261, IoU.river: 0.1346, IoU.bridge: 0.7194, IoU.bookcase: 0.3998, IoU.blind: 0.3772, IoU.coffee table: 0.6225, IoU.toilet: 0.8711, IoU.flower: 0.3991, IoU.book: 0.5126, IoU.hill: 0.0470, IoU.bench: 0.4427, IoU.countertop: 0.6064, IoU.stove: 0.8166, IoU.palm: 0.5654, IoU.kitchen island: 0.5071, IoU.computer: 0.7565, IoU.swivel chair: 0.4428, IoU.boat: 0.7372, IoU.bar: 0.6495, IoU.arcade machine: 0.8216, IoU.hovel: 0.3468, IoU.bus: 0.9004, IoU.towel: 0.7112, IoU.light: 0.5305, IoU.truck: 0.4434, IoU.tower: 0.2931, IoU.chandelier: 0.7018, IoU.awning: 0.3762, IoU.streetlight: 0.2973, IoU.booth: 0.3470, IoU.television receiver: 0.7555, IoU.airplane: 0.7034, IoU.dirt track: 0.0497, IoU.apparel: 0.5291, IoU.pole: 0.2952, IoU.land: 0.0023, IoU.bannister: 0.1558, IoU.escalator: 0.5164, IoU.ottoman: 0.4852, IoU.bottle: 0.4132, IoU.buffet: 0.5571, IoU.poster: 0.3391, IoU.stage: 0.1493, IoU.van: 0.4454, IoU.ship: 0.1021, IoU.fountain: 0.3802, IoU.conveyer belt: 0.7870, IoU.canopy: 0.3673, IoU.washer: 0.8673, IoU.plaything: 0.4067, IoU.swimming pool: 0.7976, IoU.stool: 0.5230, IoU.barrel: 0.5832, IoU.basket: 0.3822, IoU.waterfall: 0.7398, IoU.tent: 0.9206, IoU.bag: 0.2162, IoU.minibike: 0.7141, IoU.cradle: 0.8383, IoU.oven: 0.6453, IoU.ball: 0.0946, IoU.food: 0.6101, IoU.step: 0.0822, IoU.tank: 0.7886, IoU.trade name: 0.3482, IoU.microwave: 0.8765, IoU.pot: 0.5828, IoU.animal: 0.6052, IoU.bicycle: 0.5074, IoU.lake: 0.5467, IoU.dishwasher: 0.6899, IoU.screen: 0.6199, IoU.blanket: 0.2863, IoU.sculpture: 0.6968, IoU.hood: 0.6014, IoU.sconce: 0.5493, IoU.vase: 0.4173, IoU.traffic light: 0.3524, IoU.tray: 0.1552, IoU.ashcan: 0.3900, IoU.fan: 0.6482, IoU.pier: 0.3430, IoU.crt screen: 0.0124, IoU.plate: 0.5824, IoU.monitor: 0.5987, IoU.bulletin board: 0.4805, IoU.shower: 0.0000, IoU.radiator: 0.6236, IoU.glass: 0.1474, IoU.clock: 0.3155, IoU.flag: 0.6918, Acc.wall: 0.8702, Acc.building: 0.9327, Acc.sky: 0.9741, Acc.floor: 0.8992, Acc.tree: 0.8898, Acc.ceiling: 0.9354, Acc.road: 0.9150, Acc.bed : 0.9700, Acc.windowpane: 0.8649, Acc.grass: 0.7627, Acc.cabinet: 0.7096, Acc.sidewalk: 0.8289, Acc.person: 0.9272, Acc.earth: 0.5376, Acc.door: 0.7398, Acc.table: 0.8403, Acc.mountain: 0.7830, Acc.plant: 0.6352, Acc.curtain: 0.8975, Acc.chair: 0.7931, Acc.car: 0.9467, Acc.water: 0.7972, Acc.painting: 0.9215, Acc.sofa: 0.8901, Acc.shelf: 0.6915, Acc.house: 0.7266, Acc.sea: 0.8358, Acc.mirror: 0.8536, Acc.rug: 0.8307, Acc.field: 0.7428, Acc.armchair: 0.7195, Acc.seat: 0.8928, Acc.fence: 0.7233, Acc.desk: 0.8095, Acc.rock: 0.6327, Acc.wardrobe: 0.7173, Acc.lamp: 0.8484, Acc.bathtub: 0.8737, Acc.railing: 0.5080, Acc.cushion: 0.7271, Acc.base: 0.5835, Acc.box: 0.4569, Acc.column: 0.6957, Acc.signboard: 0.5151, Acc.chest of drawers: 0.7538, Acc.counter: 0.4705, Acc.sand: 0.7939, Acc.sink: 0.8391, Acc.skyscraper: 0.5755, Acc.fireplace: 0.9479, Acc.refrigerator: 0.9124, Acc.grandstand: 0.8682, Acc.path: 0.4344, Acc.stairs: 0.5928, Acc.runway: 0.9563, Acc.case: 0.6310, Acc.pool table: 0.9825, Acc.pillow: 0.7888, Acc.screen door: 0.7792, Acc.stairway: 0.4766, Acc.river: 0.2338, Acc.bridge: 0.9079, Acc.bookcase: 0.5714, Acc.blind: 0.4112, Acc.coffee table: 0.8961, Acc.toilet: 0.9510, Acc.flower: 0.4470, Acc.book: 0.7413, Acc.hill: 0.1059, Acc.bench: 0.5955, Acc.countertop: 0.7961, Acc.stove: 0.9359, Acc.palm: 0.8024, Acc.kitchen island: 0.8623, Acc.computer: 0.9319, Acc.swivel chair: 0.8339, Acc.boat: 0.9083, Acc.bar: 0.7608, Acc.arcade machine: 0.8981, Acc.hovel: 0.3810, Acc.bus: 0.9670, Acc.towel: 0.8960, Acc.light: 0.5697, Acc.truck: 0.5762, Acc.tower: 0.5173, Acc.chandelier: 0.8181, Acc.awning: 0.5384, Acc.streetlight: 0.3744, Acc.booth: 0.3893, Acc.television receiver: 0.9359, Acc.airplane: 0.8037, Acc.dirt track: 0.2308, Acc.apparel: 0.6577, Acc.pole: 0.4210, Acc.land: 0.0035, Acc.bannister: 0.2226, Acc.escalator: 0.8415, Acc.ottoman: 0.7258, Acc.bottle: 0.6917, Acc.buffet: 0.9155, Acc.poster: 0.4590, Acc.stage: 0.3215, Acc.van: 0.6236, Acc.ship: 0.1022, Acc.fountain: 0.3938, Acc.conveyer belt: 0.9053, Acc.canopy: 0.6110, Acc.washer: 0.9318, Acc.plaything: 0.7101, Acc.swimming pool: 0.8470, Acc.stool: 0.6253, Acc.barrel: 0.7402, Acc.basket: 0.5419, Acc.waterfall: 0.9383, Acc.tent: 0.9905, Acc.bag: 0.2494, Acc.minibike: 0.8841, Acc.cradle: 0.9892, Acc.oven: 0.7692, Acc.ball: 0.0954, Acc.food: 0.8589, Acc.step: 0.0863, Acc.tank: 0.9659, Acc.trade name: 0.4393, Acc.microwave: 0.9621, Acc.pot: 0.7140, Acc.animal: 0.6278, Acc.bicycle: 0.8219, Acc.lake: 0.6682, Acc.dishwasher: 0.7651, Acc.screen: 0.9318, Acc.blanket: 0.3536, Acc.sculpture: 0.7568, Acc.hood: 0.7156, Acc.sconce: 0.6529, Acc.vase: 0.6217, Acc.traffic light: 0.5519, Acc.tray: 0.2034, Acc.ashcan: 0.6290, Acc.fan: 0.8093, Acc.pier: 0.3737, Acc.crt screen: 0.0126, Acc.plate: 0.7741, Acc.monitor: 0.7914, Acc.bulletin board: 0.7085, Acc.shower: 0.0000, Acc.radiator: 0.7658, Acc.glass: 0.1538, Acc.clock: 0.4536, Acc.flag: 0.7586 +2024-06-16 07:31:52,489 - mmseg - INFO - Iter [24050/80000] lr: 2.798e-05, eta: 23:28:41, time: 3.284, data_time: 1.927, memory: 70722, decode.loss_ce: 0.2745, decode.acc_seg: 88.6027, aux.loss_ce: 0.1114, aux.acc_seg: 88.3763, loss: 0.3859 +2024-06-16 07:33:00,639 - mmseg - INFO - Iter [24100/80000] lr: 2.795e-05, eta: 23:27:09, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2876, decode.acc_seg: 88.0926, aux.loss_ce: 0.1175, aux.acc_seg: 87.9445, loss: 0.4051 +2024-06-16 07:34:09,188 - mmseg - INFO - Iter [24150/80000] lr: 2.793e-05, eta: 23:25:37, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2692, decode.acc_seg: 89.1294, aux.loss_ce: 0.1093, aux.acc_seg: 88.9719, loss: 0.3785 +2024-06-16 07:35:30,284 - mmseg - INFO - Iter [24200/80000] lr: 2.790e-05, eta: 23:24:34, time: 1.622, data_time: 0.270, memory: 70722, decode.loss_ce: 0.2725, decode.acc_seg: 88.3330, aux.loss_ce: 0.1121, aux.acc_seg: 88.0610, loss: 0.3846 +2024-06-16 07:36:38,452 - mmseg - INFO - Iter [24250/80000] lr: 2.788e-05, eta: 23:23:02, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2664, decode.acc_seg: 88.9752, aux.loss_ce: 0.1083, aux.acc_seg: 88.8554, loss: 0.3747 +2024-06-16 07:37:46,781 - mmseg - INFO - Iter [24300/80000] lr: 2.785e-05, eta: 23:21:30, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2637, decode.acc_seg: 89.2613, aux.loss_ce: 0.1068, aux.acc_seg: 89.0141, loss: 0.3705 +2024-06-16 07:38:54,809 - mmseg - INFO - Iter [24350/80000] lr: 2.783e-05, eta: 23:19:58, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2714, decode.acc_seg: 88.9218, aux.loss_ce: 0.1111, aux.acc_seg: 88.6396, loss: 0.3825 +2024-06-16 07:40:03,358 - mmseg - INFO - Iter [24400/80000] lr: 2.780e-05, eta: 23:18:26, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2710, decode.acc_seg: 88.9644, aux.loss_ce: 0.1103, aux.acc_seg: 88.7045, loss: 0.3813 +2024-06-16 07:41:11,544 - mmseg - INFO - Iter [24450/80000] lr: 2.778e-05, eta: 23:16:54, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2579, decode.acc_seg: 89.6152, aux.loss_ce: 0.1052, aux.acc_seg: 89.3435, loss: 0.3631 +2024-06-16 07:42:19,905 - mmseg - INFO - Iter [24500/80000] lr: 2.775e-05, eta: 23:15:23, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2549, decode.acc_seg: 89.7671, aux.loss_ce: 0.1043, aux.acc_seg: 89.6042, loss: 0.3592 +2024-06-16 07:43:28,033 - mmseg - INFO - Iter [24550/80000] lr: 2.773e-05, eta: 23:13:51, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2701, decode.acc_seg: 88.8870, aux.loss_ce: 0.1122, aux.acc_seg: 88.4594, loss: 0.3823 +2024-06-16 07:44:36,300 - mmseg - INFO - Iter [24600/80000] lr: 2.770e-05, eta: 23:12:19, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2606, decode.acc_seg: 89.2789, aux.loss_ce: 0.1070, aux.acc_seg: 89.0781, loss: 0.3675 +2024-06-16 07:45:44,599 - mmseg - INFO - Iter [24650/80000] lr: 2.768e-05, eta: 23:10:48, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2937, decode.acc_seg: 87.9705, aux.loss_ce: 0.1189, aux.acc_seg: 87.8909, loss: 0.4126 +2024-06-16 07:46:52,818 - mmseg - INFO - Iter [24700/80000] lr: 2.765e-05, eta: 23:09:17, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2634, decode.acc_seg: 89.3347, aux.loss_ce: 0.1077, aux.acc_seg: 89.0655, loss: 0.3711 +2024-06-16 07:48:01,181 - mmseg - INFO - Iter [24750/80000] lr: 2.763e-05, eta: 23:07:46, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2740, decode.acc_seg: 88.5920, aux.loss_ce: 0.1124, aux.acc_seg: 88.3677, loss: 0.3864 +2024-06-16 07:49:09,440 - mmseg - INFO - Iter [24800/80000] lr: 2.760e-05, eta: 23:06:14, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2784, decode.acc_seg: 88.4197, aux.loss_ce: 0.1135, aux.acc_seg: 88.2038, loss: 0.3919 +2024-06-16 07:50:17,578 - mmseg - INFO - Iter [24850/80000] lr: 2.758e-05, eta: 23:04:43, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2640, decode.acc_seg: 89.0131, aux.loss_ce: 0.1074, aux.acc_seg: 88.8260, loss: 0.3714 +2024-06-16 07:51:25,828 - mmseg - INFO - Iter [24900/80000] lr: 2.755e-05, eta: 23:03:12, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2770, decode.acc_seg: 88.7458, aux.loss_ce: 0.1127, aux.acc_seg: 88.5649, loss: 0.3897 +2024-06-16 07:52:34,343 - mmseg - INFO - Iter [24950/80000] lr: 2.753e-05, eta: 23:01:42, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2740, decode.acc_seg: 89.0327, aux.loss_ce: 0.1117, aux.acc_seg: 88.8652, loss: 0.3857 +2024-06-16 07:53:42,572 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 07:53:42,572 - mmseg - INFO - Iter [25000/80000] lr: 2.750e-05, eta: 23:00:11, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2520, decode.acc_seg: 89.4953, aux.loss_ce: 0.1036, aux.acc_seg: 89.2000, loss: 0.3556 +2024-06-16 07:55:20,441 - mmseg - INFO - per class results: +2024-06-16 07:55:20,447 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.38 | 89.15 | +| building | 83.82 | 91.93 | +| sky | 94.59 | 96.91 | +| floor | 84.55 | 91.2 | +| tree | 77.21 | 90.89 | +| ceiling | 86.34 | 93.61 | +| road | 85.88 | 91.79 | +| bed | 92.09 | 97.33 | +| windowpane | 64.54 | 80.92 | +| grass | 67.72 | 83.88 | +| cabinet | 64.8 | 74.42 | +| sidewalk | 70.9 | 84.33 | +| person | 85.33 | 94.29 | +| earth | 34.99 | 43.66 | +| door | 57.25 | 70.86 | +| table | 70.33 | 81.76 | +| mountain | 56.5 | 74.35 | +| plant | 56.97 | 66.73 | +| curtain | 78.46 | 90.21 | +| chair | 63.61 | 73.34 | +| car | 86.24 | 93.96 | +| water | 63.43 | 78.73 | +| painting | 77.96 | 87.43 | +| sofa | 76.95 | 84.11 | +| shelf | 49.28 | 66.58 | +| house | 50.52 | 68.79 | +| sea | 70.07 | 82.06 | +| mirror | 77.16 | 84.7 | +| rug | 74.52 | 81.89 | +| field | 40.22 | 69.89 | +| armchair | 52.76 | 83.2 | +| seat | 67.14 | 86.38 | +| fence | 50.96 | 66.27 | +| desk | 58.42 | 82.62 | +| rock | 44.64 | 74.64 | +| wardrobe | 54.41 | 75.54 | +| lamp | 71.7 | 82.45 | +| bathtub | 82.67 | 86.32 | +| railing | 37.66 | 55.09 | +| cushion | 68.48 | 78.57 | +| base | 39.35 | 60.11 | +| box | 34.8 | 44.34 | +| column | 56.43 | 66.19 | +| signboard | 42.49 | 56.8 | +| chest of drawers | 49.78 | 73.72 | +| counter | 41.41 | 54.69 | +| sand | 49.64 | 74.29 | +| sink | 72.06 | 82.95 | +| skyscraper | 46.61 | 64.4 | +| fireplace | 72.6 | 94.74 | +| refrigerator | 83.85 | 88.22 | +| grandstand | 61.31 | 82.52 | +| path | 32.67 | 43.05 | +| stairs | 40.61 | 50.97 | +| runway | 71.56 | 97.89 | +| case | 53.25 | 73.05 | +| pool table | 94.52 | 98.21 | +| pillow | 64.0 | 72.3 | +| screen door | 80.52 | 86.33 | +| stairway | 52.44 | 62.6 | +| river | 9.55 | 24.27 | +| bridge | 77.44 | 88.38 | +| bookcase | 43.08 | 47.66 | +| blind | 43.09 | 48.21 | +| coffee table | 68.23 | 85.05 | +| toilet | 88.56 | 94.12 | +| flower | 42.71 | 51.74 | +| book | 52.3 | 72.3 | +| hill | 7.5 | 12.47 | +| bench | 58.79 | 68.42 | +| countertop | 63.73 | 92.5 | +| stove | 81.53 | 90.15 | +| palm | 53.71 | 83.57 | +| kitchen island | 52.35 | 82.13 | +| computer | 78.08 | 91.3 | +| swivel chair | 47.85 | 77.42 | +| boat | 72.07 | 90.89 | +| bar | 58.79 | 81.62 | +| arcade machine | 89.74 | 97.21 | +| hovel | 58.31 | 63.89 | +| bus | 91.7 | 96.07 | +| towel | 73.08 | 87.31 | +| light | 58.17 | 65.9 | +| truck | 42.45 | 60.91 | +| tower | 20.81 | 28.16 | +| chandelier | 69.7 | 86.65 | +| awning | 37.39 | 51.48 | +| streetlight | 32.65 | 42.62 | +| booth | 43.54 | 51.49 | +| television receiver | 77.25 | 87.28 | +| airplane | 70.44 | 92.96 | +| dirt track | 9.2 | 67.01 | +| apparel | 58.5 | 78.8 | +| pole | 30.3 | 46.27 | +| land | 9.48 | 18.86 | +| bannister | 16.35 | 24.32 | +| escalator | 54.89 | 84.82 | +| ottoman | 49.57 | 64.77 | +| bottle | 38.19 | 68.54 | +| buffet | 45.86 | 50.88 | +| poster | 40.88 | 59.2 | +| stage | 18.47 | 30.95 | +| van | 48.34 | 71.3 | +| ship | 17.12 | 17.41 | +| fountain | 31.18 | 32.08 | +| conveyer belt | 75.19 | 92.24 | +| canopy | 29.26 | 55.83 | +| washer | 79.14 | 83.94 | +| plaything | 42.33 | 65.08 | +| swimming pool | 59.98 | 92.49 | +| stool | 54.86 | 69.0 | +| barrel | 57.43 | 69.64 | +| basket | 37.69 | 50.49 | +| waterfall | 73.07 | 88.46 | +| tent | 85.21 | 98.79 | +| bag | 24.93 | 28.72 | +| minibike | 72.95 | 87.98 | +| cradle | 78.13 | 98.63 | +| oven | 61.47 | 68.53 | +| ball | 53.59 | 61.94 | +| food | 54.96 | 60.23 | +| step | 10.73 | 14.64 | +| tank | 62.04 | 66.53 | +| trade name | 30.76 | 37.14 | +| microwave | 87.75 | 95.17 | +| pot | 57.25 | 67.96 | +| animal | 59.94 | 61.91 | +| bicycle | 57.64 | 78.02 | +| lake | 43.8 | 59.75 | +| dishwasher | 60.3 | 75.45 | +| screen | 58.02 | 74.06 | +| blanket | 22.76 | 24.8 | +| sculpture | 67.28 | 82.99 | +| hood | 67.85 | 73.8 | +| sconce | 56.49 | 65.89 | +| vase | 41.21 | 62.73 | +| traffic light | 34.68 | 57.27 | +| tray | 11.84 | 13.09 | +| ashcan | 38.22 | 68.95 | +| fan | 67.01 | 80.97 | +| pier | 37.02 | 44.73 | +| crt screen | 24.92 | 33.85 | +| plate | 60.85 | 76.54 | +| monitor | 58.26 | 87.32 | +| bulletin board | 55.54 | 64.88 | +| shower | 2.16 | 2.3 | +| radiator | 63.57 | 79.43 | +| glass | 17.63 | 18.92 | +| clock | 36.88 | 47.11 | +| flag | 70.31 | 74.95 | ++---------------------+-------+-------+ +2024-06-16 07:55:20,448 - mmseg - INFO - Summary: +2024-06-16 07:55:20,448 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.49 | 55.92 | 69.54 | ++-------+-------+-------+ +2024-06-16 07:55:20,449 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 07:55:20,449 - mmseg - INFO - Iter(val) [250] aAcc: 0.8549, mIoU: 0.5592, mAcc: 0.6954, IoU.wall: 0.8138, IoU.building: 0.8382, IoU.sky: 0.9459, IoU.floor: 0.8455, IoU.tree: 0.7721, IoU.ceiling: 0.8634, IoU.road: 0.8588, IoU.bed : 0.9209, IoU.windowpane: 0.6454, IoU.grass: 0.6772, IoU.cabinet: 0.6480, IoU.sidewalk: 0.7090, IoU.person: 0.8533, IoU.earth: 0.3499, IoU.door: 0.5725, IoU.table: 0.7033, IoU.mountain: 0.5650, IoU.plant: 0.5697, IoU.curtain: 0.7846, IoU.chair: 0.6361, IoU.car: 0.8624, IoU.water: 0.6343, IoU.painting: 0.7796, IoU.sofa: 0.7695, IoU.shelf: 0.4928, IoU.house: 0.5052, IoU.sea: 0.7007, IoU.mirror: 0.7716, IoU.rug: 0.7452, IoU.field: 0.4022, IoU.armchair: 0.5276, IoU.seat: 0.6714, IoU.fence: 0.5096, IoU.desk: 0.5842, IoU.rock: 0.4464, IoU.wardrobe: 0.5441, IoU.lamp: 0.7170, IoU.bathtub: 0.8267, IoU.railing: 0.3766, IoU.cushion: 0.6848, IoU.base: 0.3935, IoU.box: 0.3480, IoU.column: 0.5643, IoU.signboard: 0.4249, IoU.chest of drawers: 0.4978, IoU.counter: 0.4141, IoU.sand: 0.4964, IoU.sink: 0.7206, IoU.skyscraper: 0.4661, IoU.fireplace: 0.7260, IoU.refrigerator: 0.8385, IoU.grandstand: 0.6131, IoU.path: 0.3267, IoU.stairs: 0.4061, IoU.runway: 0.7156, IoU.case: 0.5325, IoU.pool table: 0.9452, IoU.pillow: 0.6400, IoU.screen door: 0.8052, IoU.stairway: 0.5244, IoU.river: 0.0955, IoU.bridge: 0.7744, IoU.bookcase: 0.4308, IoU.blind: 0.4309, IoU.coffee table: 0.6823, IoU.toilet: 0.8856, IoU.flower: 0.4271, IoU.book: 0.5230, IoU.hill: 0.0750, IoU.bench: 0.5879, IoU.countertop: 0.6373, IoU.stove: 0.8153, IoU.palm: 0.5371, IoU.kitchen island: 0.5235, IoU.computer: 0.7808, IoU.swivel chair: 0.4785, IoU.boat: 0.7207, IoU.bar: 0.5879, IoU.arcade machine: 0.8974, IoU.hovel: 0.5831, IoU.bus: 0.9170, IoU.towel: 0.7308, IoU.light: 0.5817, IoU.truck: 0.4245, IoU.tower: 0.2081, IoU.chandelier: 0.6970, IoU.awning: 0.3739, IoU.streetlight: 0.3265, IoU.booth: 0.4354, IoU.television receiver: 0.7725, IoU.airplane: 0.7044, IoU.dirt track: 0.0920, IoU.apparel: 0.5850, IoU.pole: 0.3030, IoU.land: 0.0948, IoU.bannister: 0.1635, IoU.escalator: 0.5489, IoU.ottoman: 0.4957, IoU.bottle: 0.3819, IoU.buffet: 0.4586, IoU.poster: 0.4088, IoU.stage: 0.1847, IoU.van: 0.4834, IoU.ship: 0.1712, IoU.fountain: 0.3118, IoU.conveyer belt: 0.7519, IoU.canopy: 0.2926, IoU.washer: 0.7914, IoU.plaything: 0.4233, IoU.swimming pool: 0.5998, IoU.stool: 0.5486, IoU.barrel: 0.5743, IoU.basket: 0.3769, IoU.waterfall: 0.7307, IoU.tent: 0.8521, IoU.bag: 0.2493, IoU.minibike: 0.7295, IoU.cradle: 0.7813, IoU.oven: 0.6147, IoU.ball: 0.5359, IoU.food: 0.5496, IoU.step: 0.1073, IoU.tank: 0.6204, IoU.trade name: 0.3076, IoU.microwave: 0.8775, IoU.pot: 0.5725, IoU.animal: 0.5994, IoU.bicycle: 0.5764, IoU.lake: 0.4380, IoU.dishwasher: 0.6030, IoU.screen: 0.5802, IoU.blanket: 0.2276, IoU.sculpture: 0.6728, IoU.hood: 0.6785, IoU.sconce: 0.5649, IoU.vase: 0.4121, IoU.traffic light: 0.3468, IoU.tray: 0.1184, IoU.ashcan: 0.3822, IoU.fan: 0.6701, IoU.pier: 0.3702, IoU.crt screen: 0.2492, IoU.plate: 0.6085, IoU.monitor: 0.5826, IoU.bulletin board: 0.5554, IoU.shower: 0.0216, IoU.radiator: 0.6357, IoU.glass: 0.1763, IoU.clock: 0.3688, IoU.flag: 0.7031, Acc.wall: 0.8915, Acc.building: 0.9193, Acc.sky: 0.9691, Acc.floor: 0.9120, Acc.tree: 0.9089, Acc.ceiling: 0.9361, Acc.road: 0.9179, Acc.bed : 0.9733, Acc.windowpane: 0.8092, Acc.grass: 0.8388, Acc.cabinet: 0.7442, Acc.sidewalk: 0.8433, Acc.person: 0.9429, Acc.earth: 0.4366, Acc.door: 0.7086, Acc.table: 0.8176, Acc.mountain: 0.7435, Acc.plant: 0.6673, Acc.curtain: 0.9021, Acc.chair: 0.7334, Acc.car: 0.9396, Acc.water: 0.7873, Acc.painting: 0.8743, Acc.sofa: 0.8411, Acc.shelf: 0.6658, Acc.house: 0.6879, Acc.sea: 0.8206, Acc.mirror: 0.8470, Acc.rug: 0.8189, Acc.field: 0.6989, Acc.armchair: 0.8320, Acc.seat: 0.8638, Acc.fence: 0.6627, Acc.desk: 0.8262, Acc.rock: 0.7464, Acc.wardrobe: 0.7554, Acc.lamp: 0.8245, Acc.bathtub: 0.8632, Acc.railing: 0.5509, Acc.cushion: 0.7857, Acc.base: 0.6011, Acc.box: 0.4434, Acc.column: 0.6619, Acc.signboard: 0.5680, Acc.chest of drawers: 0.7372, Acc.counter: 0.5469, Acc.sand: 0.7429, Acc.sink: 0.8295, Acc.skyscraper: 0.6440, Acc.fireplace: 0.9474, Acc.refrigerator: 0.8822, Acc.grandstand: 0.8252, Acc.path: 0.4305, Acc.stairs: 0.5097, Acc.runway: 0.9789, Acc.case: 0.7305, Acc.pool table: 0.9821, Acc.pillow: 0.7230, Acc.screen door: 0.8633, Acc.stairway: 0.6260, Acc.river: 0.2427, Acc.bridge: 0.8838, Acc.bookcase: 0.4766, Acc.blind: 0.4821, Acc.coffee table: 0.8505, Acc.toilet: 0.9412, Acc.flower: 0.5174, Acc.book: 0.7230, Acc.hill: 0.1247, Acc.bench: 0.6842, Acc.countertop: 0.9250, Acc.stove: 0.9015, Acc.palm: 0.8357, Acc.kitchen island: 0.8213, Acc.computer: 0.9130, Acc.swivel chair: 0.7742, Acc.boat: 0.9089, Acc.bar: 0.8162, Acc.arcade machine: 0.9721, Acc.hovel: 0.6389, Acc.bus: 0.9607, Acc.towel: 0.8731, Acc.light: 0.6590, Acc.truck: 0.6091, Acc.tower: 0.2816, Acc.chandelier: 0.8665, Acc.awning: 0.5148, Acc.streetlight: 0.4262, Acc.booth: 0.5149, Acc.television receiver: 0.8728, Acc.airplane: 0.9296, Acc.dirt track: 0.6701, Acc.apparel: 0.7880, Acc.pole: 0.4627, Acc.land: 0.1886, Acc.bannister: 0.2432, Acc.escalator: 0.8482, Acc.ottoman: 0.6477, Acc.bottle: 0.6854, Acc.buffet: 0.5088, Acc.poster: 0.5920, Acc.stage: 0.3095, Acc.van: 0.7130, Acc.ship: 0.1741, Acc.fountain: 0.3208, Acc.conveyer belt: 0.9224, Acc.canopy: 0.5583, Acc.washer: 0.8394, Acc.plaything: 0.6508, Acc.swimming pool: 0.9249, Acc.stool: 0.6900, Acc.barrel: 0.6964, Acc.basket: 0.5049, Acc.waterfall: 0.8846, Acc.tent: 0.9879, Acc.bag: 0.2872, Acc.minibike: 0.8798, Acc.cradle: 0.9863, Acc.oven: 0.6853, Acc.ball: 0.6194, Acc.food: 0.6023, Acc.step: 0.1464, Acc.tank: 0.6653, Acc.trade name: 0.3714, Acc.microwave: 0.9517, Acc.pot: 0.6796, Acc.animal: 0.6191, Acc.bicycle: 0.7802, Acc.lake: 0.5975, Acc.dishwasher: 0.7545, Acc.screen: 0.7406, Acc.blanket: 0.2480, Acc.sculpture: 0.8299, Acc.hood: 0.7380, Acc.sconce: 0.6589, Acc.vase: 0.6273, Acc.traffic light: 0.5727, Acc.tray: 0.1309, Acc.ashcan: 0.6895, Acc.fan: 0.8097, Acc.pier: 0.4473, Acc.crt screen: 0.3385, Acc.plate: 0.7654, Acc.monitor: 0.8732, Acc.bulletin board: 0.6488, Acc.shower: 0.0230, Acc.radiator: 0.7943, Acc.glass: 0.1892, Acc.clock: 0.4711, Acc.flag: 0.7495 +2024-06-16 07:56:29,131 - mmseg - INFO - Iter [25050/80000] lr: 2.748e-05, eta: 23:02:16, time: 3.331, data_time: 1.973, memory: 70722, decode.loss_ce: 0.2722, decode.acc_seg: 89.1377, aux.loss_ce: 0.1112, aux.acc_seg: 88.8310, loss: 0.3834 +2024-06-16 07:57:37,523 - mmseg - INFO - Iter [25100/80000] lr: 2.745e-05, eta: 23:00:45, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2641, decode.acc_seg: 88.9692, aux.loss_ce: 0.1083, aux.acc_seg: 88.7194, loss: 0.3723 +2024-06-16 07:58:46,028 - mmseg - INFO - Iter [25150/80000] lr: 2.743e-05, eta: 22:59:14, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2782, decode.acc_seg: 88.5644, aux.loss_ce: 0.1126, aux.acc_seg: 88.4418, loss: 0.3907 +2024-06-16 07:59:54,029 - mmseg - INFO - Iter [25200/80000] lr: 2.740e-05, eta: 22:57:43, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2766, decode.acc_seg: 88.7125, aux.loss_ce: 0.1123, aux.acc_seg: 88.5352, loss: 0.3889 +2024-06-16 08:01:02,238 - mmseg - INFO - Iter [25250/80000] lr: 2.738e-05, eta: 22:56:12, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2759, decode.acc_seg: 88.3477, aux.loss_ce: 0.1119, aux.acc_seg: 88.1420, loss: 0.3878 +2024-06-16 08:02:13,242 - mmseg - INFO - Iter [25300/80000] lr: 2.735e-05, eta: 22:54:47, time: 1.420, data_time: 0.060, memory: 70722, decode.loss_ce: 0.2555, decode.acc_seg: 89.5360, aux.loss_ce: 0.1044, aux.acc_seg: 89.2418, loss: 0.3600 +2024-06-16 08:03:21,600 - mmseg - INFO - Iter [25350/80000] lr: 2.733e-05, eta: 22:53:16, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2637, decode.acc_seg: 89.1105, aux.loss_ce: 0.1092, aux.acc_seg: 88.7144, loss: 0.3729 +2024-06-16 08:04:29,763 - mmseg - INFO - Iter [25400/80000] lr: 2.730e-05, eta: 22:51:45, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2616, decode.acc_seg: 89.2064, aux.loss_ce: 0.1071, aux.acc_seg: 88.9285, loss: 0.3688 +2024-06-16 08:05:37,914 - mmseg - INFO - Iter [25450/80000] lr: 2.728e-05, eta: 22:50:14, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2744, decode.acc_seg: 89.1858, aux.loss_ce: 0.1116, aux.acc_seg: 88.9281, loss: 0.3860 +2024-06-16 08:06:46,319 - mmseg - INFO - Iter [25500/80000] lr: 2.725e-05, eta: 22:48:44, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2759, decode.acc_seg: 88.5825, aux.loss_ce: 0.1120, aux.acc_seg: 88.3722, loss: 0.3879 +2024-06-16 08:07:54,481 - mmseg - INFO - Iter [25550/80000] lr: 2.723e-05, eta: 22:47:14, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2597, decode.acc_seg: 89.1875, aux.loss_ce: 0.1065, aux.acc_seg: 88.9434, loss: 0.3662 +2024-06-16 08:09:02,809 - mmseg - INFO - Iter [25600/80000] lr: 2.720e-05, eta: 22:45:43, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2610, decode.acc_seg: 89.5724, aux.loss_ce: 0.1076, aux.acc_seg: 89.3502, loss: 0.3686 +2024-06-16 08:10:11,130 - mmseg - INFO - Iter [25650/80000] lr: 2.718e-05, eta: 22:44:13, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2718, decode.acc_seg: 88.9166, aux.loss_ce: 0.1115, aux.acc_seg: 88.6018, loss: 0.3833 +2024-06-16 08:11:19,364 - mmseg - INFO - Iter [25700/80000] lr: 2.715e-05, eta: 22:42:43, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2656, decode.acc_seg: 89.4770, aux.loss_ce: 0.1094, aux.acc_seg: 89.1250, loss: 0.3749 +2024-06-16 08:12:27,658 - mmseg - INFO - Iter [25750/80000] lr: 2.713e-05, eta: 22:41:13, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2683, decode.acc_seg: 89.1281, aux.loss_ce: 0.1097, aux.acc_seg: 88.9408, loss: 0.3779 +2024-06-16 08:13:35,945 - mmseg - INFO - Iter [25800/80000] lr: 2.710e-05, eta: 22:39:43, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2826, decode.acc_seg: 88.6530, aux.loss_ce: 0.1154, aux.acc_seg: 88.3919, loss: 0.3979 +2024-06-16 08:14:44,525 - mmseg - INFO - Iter [25850/80000] lr: 2.708e-05, eta: 22:38:14, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2459, decode.acc_seg: 89.6324, aux.loss_ce: 0.1002, aux.acc_seg: 89.4837, loss: 0.3461 +2024-06-16 08:15:52,582 - mmseg - INFO - Iter [25900/80000] lr: 2.705e-05, eta: 22:36:44, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2614, decode.acc_seg: 89.0625, aux.loss_ce: 0.1064, aux.acc_seg: 88.8965, loss: 0.3678 +2024-06-16 08:17:00,860 - mmseg - INFO - Iter [25950/80000] lr: 2.703e-05, eta: 22:35:14, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2821, decode.acc_seg: 88.5308, aux.loss_ce: 0.1147, aux.acc_seg: 88.3462, loss: 0.3969 +2024-06-16 08:18:09,045 - mmseg - INFO - Saving checkpoint at 26000 iterations +2024-06-16 08:19:36,197 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 08:19:36,198 - mmseg - INFO - Iter [26000/80000] lr: 2.700e-05, eta: 22:36:45, time: 3.107, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2634, decode.acc_seg: 89.3853, aux.loss_ce: 0.1083, aux.acc_seg: 89.1167, loss: 0.3717 +2024-06-16 08:21:11,190 - mmseg - INFO - per class results: +2024-06-16 08:21:11,201 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.27 | 90.06 | +| building | 84.43 | 92.33 | +| sky | 94.76 | 97.61 | +| floor | 83.9 | 90.7 | +| tree | 77.79 | 88.77 | +| ceiling | 87.0 | 92.41 | +| road | 86.9 | 91.74 | +| bed | 91.2 | 98.16 | +| windowpane | 66.29 | 79.21 | +| grass | 66.84 | 77.1 | +| cabinet | 65.18 | 73.4 | +| sidewalk | 70.97 | 84.27 | +| person | 85.21 | 91.94 | +| earth | 39.76 | 57.16 | +| door | 58.27 | 76.33 | +| table | 67.97 | 79.61 | +| mountain | 56.97 | 68.4 | +| plant | 58.64 | 70.74 | +| curtain | 79.26 | 89.25 | +| chair | 61.56 | 70.42 | +| car | 86.8 | 93.62 | +| water | 62.04 | 73.39 | +| painting | 75.52 | 91.76 | +| sofa | 79.65 | 88.78 | +| shelf | 46.79 | 65.93 | +| house | 50.95 | 70.15 | +| sea | 64.44 | 82.8 | +| mirror | 78.55 | 85.4 | +| rug | 68.66 | 84.3 | +| field | 32.04 | 57.68 | +| armchair | 54.05 | 80.38 | +| seat | 63.68 | 87.57 | +| fence | 49.8 | 65.78 | +| desk | 61.32 | 80.19 | +| rock | 49.06 | 65.17 | +| wardrobe | 54.25 | 72.63 | +| lamp | 70.43 | 87.66 | +| bathtub | 82.86 | 86.33 | +| railing | 34.56 | 48.34 | +| cushion | 68.28 | 80.46 | +| base | 38.88 | 56.67 | +| box | 36.2 | 47.45 | +| column | 55.03 | 66.22 | +| signboard | 42.32 | 54.07 | +| chest of drawers | 50.53 | 75.48 | +| counter | 43.29 | 56.36 | +| sand | 44.51 | 72.7 | +| sink | 73.84 | 85.07 | +| skyscraper | 45.53 | 58.65 | +| fireplace | 72.84 | 91.54 | +| refrigerator | 79.53 | 84.4 | +| grandstand | 53.35 | 83.03 | +| path | 34.47 | 48.05 | +| stairs | 34.51 | 42.4 | +| runway | 71.92 | 96.21 | +| case | 58.25 | 76.67 | +| pool table | 92.92 | 98.47 | +| pillow | 64.94 | 73.12 | +| screen door | 84.44 | 89.15 | +| stairway | 53.53 | 71.03 | +| river | 11.39 | 24.57 | +| bridge | 68.05 | 73.8 | +| bookcase | 41.78 | 62.44 | +| blind | 54.01 | 69.9 | +| coffee table | 63.11 | 87.59 | +| toilet | 89.26 | 94.61 | +| flower | 44.18 | 54.34 | +| book | 50.7 | 72.28 | +| hill | 6.62 | 15.12 | +| bench | 48.46 | 55.51 | +| countertop | 64.45 | 82.63 | +| stove | 81.79 | 90.79 | +| palm | 52.45 | 80.2 | +| kitchen island | 52.73 | 88.71 | +| computer | 80.62 | 91.39 | +| swivel chair | 47.26 | 79.52 | +| boat | 67.3 | 90.03 | +| bar | 59.86 | 76.64 | +| arcade machine | 83.5 | 89.33 | +| hovel | 27.73 | 30.02 | +| bus | 92.71 | 95.15 | +| towel | 71.1 | 81.66 | +| light | 58.34 | 68.39 | +| truck | 41.86 | 55.97 | +| tower | 27.67 | 40.88 | +| chandelier | 69.96 | 86.37 | +| awning | 36.38 | 46.48 | +| streetlight | 32.56 | 50.42 | +| booth | 40.56 | 47.7 | +| television receiver | 78.3 | 91.25 | +| airplane | 80.85 | 95.23 | +| dirt track | 9.73 | 51.9 | +| apparel | 45.68 | 53.18 | +| pole | 28.11 | 39.83 | +| land | 0.43 | 0.67 | +| bannister | 15.74 | 23.0 | +| escalator | 53.42 | 87.3 | +| ottoman | 53.15 | 69.13 | +| bottle | 38.4 | 51.69 | +| buffet | 60.57 | 72.83 | +| poster | 32.19 | 45.2 | +| stage | 16.63 | 27.08 | +| van | 47.3 | 68.19 | +| ship | 60.77 | 61.4 | +| fountain | 42.15 | 42.98 | +| conveyer belt | 80.1 | 89.95 | +| canopy | 32.17 | 46.11 | +| washer | 81.83 | 87.1 | +| plaything | 46.47 | 60.16 | +| swimming pool | 66.39 | 90.81 | +| stool | 56.9 | 68.05 | +| barrel | 56.3 | 64.81 | +| basket | 42.08 | 50.72 | +| waterfall | 72.6 | 87.55 | +| tent | 96.14 | 98.2 | +| bag | 20.53 | 23.17 | +| minibike | 73.92 | 84.85 | +| cradle | 87.99 | 97.77 | +| oven | 54.68 | 78.5 | +| ball | 49.25 | 56.89 | +| food | 64.58 | 77.88 | +| step | 26.05 | 35.65 | +| tank | 78.52 | 96.51 | +| trade name | 31.14 | 36.47 | +| microwave | 89.02 | 95.23 | +| pot | 57.5 | 68.89 | +| animal | 60.02 | 61.81 | +| bicycle | 55.51 | 77.85 | +| lake | 38.7 | 60.59 | +| dishwasher | 67.63 | 72.89 | +| screen | 53.74 | 69.85 | +| blanket | 28.31 | 31.61 | +| sculpture | 77.36 | 83.91 | +| hood | 62.81 | 79.08 | +| sconce | 51.42 | 58.73 | +| vase | 45.35 | 63.72 | +| traffic light | 35.43 | 55.02 | +| tray | 9.39 | 10.79 | +| ashcan | 43.66 | 63.54 | +| fan | 66.54 | 80.56 | +| pier | 38.03 | 41.41 | +| crt screen | 20.49 | 55.16 | +| plate | 58.71 | 74.33 | +| monitor | 39.63 | 49.46 | +| bulletin board | 50.16 | 64.88 | +| shower | 4.13 | 4.27 | +| radiator | 64.26 | 79.5 | +| glass | 18.86 | 21.08 | +| clock | 37.66 | 44.27 | +| flag | 70.49 | 75.67 | ++---------------------+-------+-------+ +2024-06-16 08:21:11,201 - mmseg - INFO - Summary: +2024-06-16 08:21:11,202 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.52 | 56.14 | 69.15 | ++-------+-------+-------+ +2024-06-16 08:21:11,203 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 08:21:11,203 - mmseg - INFO - Iter(val) [250] aAcc: 0.8552, mIoU: 0.5614, mAcc: 0.6915, IoU.wall: 0.8127, IoU.building: 0.8443, IoU.sky: 0.9476, IoU.floor: 0.8390, IoU.tree: 0.7779, IoU.ceiling: 0.8700, IoU.road: 0.8690, IoU.bed : 0.9120, IoU.windowpane: 0.6629, IoU.grass: 0.6684, IoU.cabinet: 0.6518, IoU.sidewalk: 0.7097, IoU.person: 0.8521, IoU.earth: 0.3976, IoU.door: 0.5827, IoU.table: 0.6797, IoU.mountain: 0.5697, IoU.plant: 0.5864, IoU.curtain: 0.7926, IoU.chair: 0.6156, IoU.car: 0.8680, IoU.water: 0.6204, IoU.painting: 0.7552, IoU.sofa: 0.7965, IoU.shelf: 0.4679, IoU.house: 0.5095, IoU.sea: 0.6444, IoU.mirror: 0.7855, IoU.rug: 0.6866, IoU.field: 0.3204, IoU.armchair: 0.5405, IoU.seat: 0.6368, IoU.fence: 0.4980, IoU.desk: 0.6132, IoU.rock: 0.4906, IoU.wardrobe: 0.5425, IoU.lamp: 0.7043, IoU.bathtub: 0.8286, IoU.railing: 0.3456, IoU.cushion: 0.6828, IoU.base: 0.3888, IoU.box: 0.3620, IoU.column: 0.5503, IoU.signboard: 0.4232, IoU.chest of drawers: 0.5053, IoU.counter: 0.4329, IoU.sand: 0.4451, IoU.sink: 0.7384, IoU.skyscraper: 0.4553, IoU.fireplace: 0.7284, IoU.refrigerator: 0.7953, IoU.grandstand: 0.5335, IoU.path: 0.3447, IoU.stairs: 0.3451, IoU.runway: 0.7192, IoU.case: 0.5825, IoU.pool table: 0.9292, IoU.pillow: 0.6494, IoU.screen door: 0.8444, IoU.stairway: 0.5353, IoU.river: 0.1139, IoU.bridge: 0.6805, IoU.bookcase: 0.4178, IoU.blind: 0.5401, IoU.coffee table: 0.6311, IoU.toilet: 0.8926, IoU.flower: 0.4418, IoU.book: 0.5070, IoU.hill: 0.0662, IoU.bench: 0.4846, IoU.countertop: 0.6445, IoU.stove: 0.8179, IoU.palm: 0.5245, IoU.kitchen island: 0.5273, IoU.computer: 0.8062, IoU.swivel chair: 0.4726, IoU.boat: 0.6730, IoU.bar: 0.5986, IoU.arcade machine: 0.8350, IoU.hovel: 0.2773, IoU.bus: 0.9271, IoU.towel: 0.7110, IoU.light: 0.5834, IoU.truck: 0.4186, IoU.tower: 0.2767, IoU.chandelier: 0.6996, IoU.awning: 0.3638, IoU.streetlight: 0.3256, IoU.booth: 0.4056, IoU.television receiver: 0.7830, IoU.airplane: 0.8085, IoU.dirt track: 0.0973, IoU.apparel: 0.4568, IoU.pole: 0.2811, IoU.land: 0.0043, IoU.bannister: 0.1574, IoU.escalator: 0.5342, IoU.ottoman: 0.5315, IoU.bottle: 0.3840, IoU.buffet: 0.6057, IoU.poster: 0.3219, IoU.stage: 0.1663, IoU.van: 0.4730, IoU.ship: 0.6077, IoU.fountain: 0.4215, IoU.conveyer belt: 0.8010, IoU.canopy: 0.3217, IoU.washer: 0.8183, IoU.plaything: 0.4647, IoU.swimming pool: 0.6639, IoU.stool: 0.5690, IoU.barrel: 0.5630, IoU.basket: 0.4208, IoU.waterfall: 0.7260, IoU.tent: 0.9614, IoU.bag: 0.2053, IoU.minibike: 0.7392, IoU.cradle: 0.8799, IoU.oven: 0.5468, IoU.ball: 0.4925, IoU.food: 0.6458, IoU.step: 0.2605, IoU.tank: 0.7852, IoU.trade name: 0.3114, IoU.microwave: 0.8902, IoU.pot: 0.5750, IoU.animal: 0.6002, IoU.bicycle: 0.5551, IoU.lake: 0.3870, IoU.dishwasher: 0.6763, IoU.screen: 0.5374, IoU.blanket: 0.2831, IoU.sculpture: 0.7736, IoU.hood: 0.6281, IoU.sconce: 0.5142, IoU.vase: 0.4535, IoU.traffic light: 0.3543, IoU.tray: 0.0939, IoU.ashcan: 0.4366, IoU.fan: 0.6654, IoU.pier: 0.3803, IoU.crt screen: 0.2049, IoU.plate: 0.5871, IoU.monitor: 0.3963, IoU.bulletin board: 0.5016, IoU.shower: 0.0413, IoU.radiator: 0.6426, IoU.glass: 0.1886, IoU.clock: 0.3766, IoU.flag: 0.7049, Acc.wall: 0.9006, Acc.building: 0.9233, Acc.sky: 0.9761, Acc.floor: 0.9070, Acc.tree: 0.8877, Acc.ceiling: 0.9241, Acc.road: 0.9174, Acc.bed : 0.9816, Acc.windowpane: 0.7921, Acc.grass: 0.7710, Acc.cabinet: 0.7340, Acc.sidewalk: 0.8427, Acc.person: 0.9194, Acc.earth: 0.5716, Acc.door: 0.7633, Acc.table: 0.7961, Acc.mountain: 0.6840, Acc.plant: 0.7074, Acc.curtain: 0.8925, Acc.chair: 0.7042, Acc.car: 0.9362, Acc.water: 0.7339, Acc.painting: 0.9176, Acc.sofa: 0.8878, Acc.shelf: 0.6593, Acc.house: 0.7015, Acc.sea: 0.8280, Acc.mirror: 0.8540, Acc.rug: 0.8430, Acc.field: 0.5768, Acc.armchair: 0.8038, Acc.seat: 0.8757, Acc.fence: 0.6578, Acc.desk: 0.8019, Acc.rock: 0.6517, Acc.wardrobe: 0.7263, Acc.lamp: 0.8766, Acc.bathtub: 0.8633, Acc.railing: 0.4834, Acc.cushion: 0.8046, Acc.base: 0.5667, Acc.box: 0.4745, Acc.column: 0.6622, Acc.signboard: 0.5407, Acc.chest of drawers: 0.7548, Acc.counter: 0.5636, Acc.sand: 0.7270, Acc.sink: 0.8507, Acc.skyscraper: 0.5865, Acc.fireplace: 0.9154, Acc.refrigerator: 0.8440, Acc.grandstand: 0.8303, Acc.path: 0.4805, Acc.stairs: 0.4240, Acc.runway: 0.9621, Acc.case: 0.7667, Acc.pool table: 0.9847, Acc.pillow: 0.7312, Acc.screen door: 0.8915, Acc.stairway: 0.7103, Acc.river: 0.2457, Acc.bridge: 0.7380, Acc.bookcase: 0.6244, Acc.blind: 0.6990, Acc.coffee table: 0.8759, Acc.toilet: 0.9461, Acc.flower: 0.5434, Acc.book: 0.7228, Acc.hill: 0.1512, Acc.bench: 0.5551, Acc.countertop: 0.8263, Acc.stove: 0.9079, Acc.palm: 0.8020, Acc.kitchen island: 0.8871, Acc.computer: 0.9139, Acc.swivel chair: 0.7952, Acc.boat: 0.9003, Acc.bar: 0.7664, Acc.arcade machine: 0.8933, Acc.hovel: 0.3002, Acc.bus: 0.9515, Acc.towel: 0.8166, Acc.light: 0.6839, Acc.truck: 0.5597, Acc.tower: 0.4088, Acc.chandelier: 0.8637, Acc.awning: 0.4648, Acc.streetlight: 0.5042, Acc.booth: 0.4770, Acc.television receiver: 0.9125, Acc.airplane: 0.9523, Acc.dirt track: 0.5190, Acc.apparel: 0.5318, Acc.pole: 0.3983, Acc.land: 0.0067, Acc.bannister: 0.2300, Acc.escalator: 0.8730, Acc.ottoman: 0.6913, Acc.bottle: 0.5169, Acc.buffet: 0.7283, Acc.poster: 0.4520, Acc.stage: 0.2708, Acc.van: 0.6819, Acc.ship: 0.6140, Acc.fountain: 0.4298, Acc.conveyer belt: 0.8995, Acc.canopy: 0.4611, Acc.washer: 0.8710, Acc.plaything: 0.6016, Acc.swimming pool: 0.9081, Acc.stool: 0.6805, Acc.barrel: 0.6481, Acc.basket: 0.5072, Acc.waterfall: 0.8755, Acc.tent: 0.9820, Acc.bag: 0.2317, Acc.minibike: 0.8485, Acc.cradle: 0.9777, Acc.oven: 0.7850, Acc.ball: 0.5689, Acc.food: 0.7788, Acc.step: 0.3565, Acc.tank: 0.9651, Acc.trade name: 0.3647, Acc.microwave: 0.9523, Acc.pot: 0.6889, Acc.animal: 0.6181, Acc.bicycle: 0.7785, Acc.lake: 0.6059, Acc.dishwasher: 0.7289, Acc.screen: 0.6985, Acc.blanket: 0.3161, Acc.sculpture: 0.8391, Acc.hood: 0.7908, Acc.sconce: 0.5873, Acc.vase: 0.6372, Acc.traffic light: 0.5502, Acc.tray: 0.1079, Acc.ashcan: 0.6354, Acc.fan: 0.8056, Acc.pier: 0.4141, Acc.crt screen: 0.5516, Acc.plate: 0.7433, Acc.monitor: 0.4946, Acc.bulletin board: 0.6488, Acc.shower: 0.0427, Acc.radiator: 0.7950, Acc.glass: 0.2108, Acc.clock: 0.4427, Acc.flag: 0.7567 +2024-06-16 08:22:20,165 - mmseg - INFO - Iter [26050/80000] lr: 2.698e-05, eta: 22:38:33, time: 3.279, data_time: 1.917, memory: 70722, decode.loss_ce: 0.2735, decode.acc_seg: 88.8184, aux.loss_ce: 0.1113, aux.acc_seg: 88.5546, loss: 0.3848 +2024-06-16 08:23:28,294 - mmseg - INFO - Iter [26100/80000] lr: 2.695e-05, eta: 22:37:02, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2591, decode.acc_seg: 89.2062, aux.loss_ce: 0.1054, aux.acc_seg: 89.0982, loss: 0.3645 +2024-06-16 08:24:36,531 - mmseg - INFO - Iter [26150/80000] lr: 2.693e-05, eta: 22:35:32, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2665, decode.acc_seg: 88.7786, aux.loss_ce: 0.1090, aux.acc_seg: 88.5104, loss: 0.3755 +2024-06-16 08:25:44,747 - mmseg - INFO - Iter [26200/80000] lr: 2.690e-05, eta: 22:34:01, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2591, decode.acc_seg: 89.3733, aux.loss_ce: 0.1062, aux.acc_seg: 89.0331, loss: 0.3652 +2024-06-16 08:26:52,841 - mmseg - INFO - Iter [26250/80000] lr: 2.688e-05, eta: 22:32:30, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2666, decode.acc_seg: 89.1426, aux.loss_ce: 0.1087, aux.acc_seg: 89.0086, loss: 0.3753 +2024-06-16 08:28:01,109 - mmseg - INFO - Iter [26300/80000] lr: 2.685e-05, eta: 22:31:00, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2579, decode.acc_seg: 89.4091, aux.loss_ce: 0.1064, aux.acc_seg: 89.0842, loss: 0.3643 +2024-06-16 08:29:09,268 - mmseg - INFO - Iter [26350/80000] lr: 2.683e-05, eta: 22:29:30, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2753, decode.acc_seg: 88.7693, aux.loss_ce: 0.1126, aux.acc_seg: 88.5185, loss: 0.3879 +2024-06-16 08:30:17,934 - mmseg - INFO - Iter [26400/80000] lr: 2.680e-05, eta: 22:28:01, time: 1.373, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2800, decode.acc_seg: 88.6964, aux.loss_ce: 0.1147, aux.acc_seg: 88.4603, loss: 0.3948 +2024-06-16 08:31:26,196 - mmseg - INFO - Iter [26450/80000] lr: 2.678e-05, eta: 22:26:31, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2606, decode.acc_seg: 89.3297, aux.loss_ce: 0.1070, aux.acc_seg: 89.1259, loss: 0.3676 +2024-06-16 08:32:34,577 - mmseg - INFO - Iter [26500/80000] lr: 2.675e-05, eta: 22:25:01, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2687, decode.acc_seg: 89.2705, aux.loss_ce: 0.1094, aux.acc_seg: 88.9488, loss: 0.3781 +2024-06-16 08:33:46,175 - mmseg - INFO - Iter [26550/80000] lr: 2.673e-05, eta: 22:23:38, time: 1.432, data_time: 0.074, memory: 70722, decode.loss_ce: 0.2624, decode.acc_seg: 89.3203, aux.loss_ce: 0.1070, aux.acc_seg: 89.1393, loss: 0.3693 +2024-06-16 08:34:54,431 - mmseg - INFO - Iter [26600/80000] lr: 2.670e-05, eta: 22:22:08, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2472, decode.acc_seg: 89.6261, aux.loss_ce: 0.1012, aux.acc_seg: 89.3631, loss: 0.3483 +2024-06-16 08:36:02,898 - mmseg - INFO - Iter [26650/80000] lr: 2.668e-05, eta: 22:20:39, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2555, decode.acc_seg: 89.4973, aux.loss_ce: 0.1035, aux.acc_seg: 89.4095, loss: 0.3591 +2024-06-16 08:37:10,977 - mmseg - INFO - Iter [26700/80000] lr: 2.665e-05, eta: 22:19:09, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2602, decode.acc_seg: 89.1295, aux.loss_ce: 0.1067, aux.acc_seg: 88.9372, loss: 0.3669 +2024-06-16 08:38:19,422 - mmseg - INFO - Iter [26750/80000] lr: 2.663e-05, eta: 22:17:40, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2444, decode.acc_seg: 89.7685, aux.loss_ce: 0.1007, aux.acc_seg: 89.5845, loss: 0.3452 +2024-06-16 08:39:27,722 - mmseg - INFO - Iter [26800/80000] lr: 2.660e-05, eta: 22:16:10, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2606, decode.acc_seg: 89.1960, aux.loss_ce: 0.1071, aux.acc_seg: 88.9260, loss: 0.3677 +2024-06-16 08:40:35,793 - mmseg - INFO - Iter [26850/80000] lr: 2.658e-05, eta: 22:14:40, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2521, decode.acc_seg: 89.5852, aux.loss_ce: 0.1037, aux.acc_seg: 89.3119, loss: 0.3558 +2024-06-16 08:41:44,165 - mmseg - INFO - Iter [26900/80000] lr: 2.655e-05, eta: 22:13:11, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2427, decode.acc_seg: 90.2542, aux.loss_ce: 0.1004, aux.acc_seg: 89.8966, loss: 0.3431 +2024-06-16 08:42:52,421 - mmseg - INFO - Iter [26950/80000] lr: 2.653e-05, eta: 22:11:42, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2442, decode.acc_seg: 89.8027, aux.loss_ce: 0.0999, aux.acc_seg: 89.6369, loss: 0.3441 +2024-06-16 08:44:00,714 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 08:44:00,714 - mmseg - INFO - Iter [27000/80000] lr: 2.650e-05, eta: 22:10:13, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2662, decode.acc_seg: 89.1777, aux.loss_ce: 0.1096, aux.acc_seg: 88.8212, loss: 0.3758 +2024-06-16 08:45:36,271 - mmseg - INFO - per class results: +2024-06-16 08:45:36,278 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.98 | 88.28 | +| building | 84.02 | 94.27 | +| sky | 94.88 | 97.73 | +| floor | 84.83 | 91.51 | +| tree | 76.83 | 85.89 | +| ceiling | 86.41 | 94.1 | +| road | 85.67 | 92.35 | +| bed | 92.26 | 97.23 | +| windowpane | 65.72 | 81.57 | +| grass | 67.9 | 79.11 | +| cabinet | 64.39 | 72.07 | +| sidewalk | 69.68 | 79.86 | +| person | 85.57 | 92.75 | +| earth | 40.35 | 56.91 | +| door | 55.54 | 71.54 | +| table | 68.24 | 82.08 | +| mountain | 60.03 | 72.53 | +| plant | 55.65 | 68.05 | +| curtain | 79.34 | 85.49 | +| chair | 65.95 | 77.47 | +| car | 87.04 | 93.67 | +| water | 65.24 | 80.0 | +| painting | 75.83 | 92.38 | +| sofa | 79.23 | 85.1 | +| shelf | 43.85 | 62.29 | +| house | 44.13 | 59.23 | +| sea | 67.43 | 84.0 | +| mirror | 74.7 | 79.38 | +| rug | 71.65 | 83.22 | +| field | 33.37 | 56.33 | +| armchair | 56.82 | 81.28 | +| seat | 67.86 | 88.48 | +| fence | 52.3 | 66.9 | +| desk | 50.11 | 83.78 | +| rock | 58.17 | 80.74 | +| wardrobe | 51.39 | 73.4 | +| lamp | 72.12 | 85.73 | +| bathtub | 83.21 | 87.52 | +| railing | 39.41 | 55.14 | +| cushion | 67.15 | 77.26 | +| base | 38.94 | 53.88 | +| box | 36.31 | 46.92 | +| column | 56.06 | 69.1 | +| signboard | 41.66 | 56.71 | +| chest of drawers | 49.69 | 77.77 | +| counter | 28.29 | 34.32 | +| sand | 53.5 | 81.68 | +| sink | 71.87 | 86.39 | +| skyscraper | 48.27 | 62.96 | +| fireplace | 70.8 | 88.54 | +| refrigerator | 80.41 | 87.81 | +| grandstand | 52.67 | 85.94 | +| path | 29.86 | 40.62 | +| stairs | 27.04 | 29.97 | +| runway | 73.4 | 95.56 | +| case | 57.97 | 78.34 | +| pool table | 94.76 | 97.96 | +| pillow | 68.17 | 80.81 | +| screen door | 57.56 | 59.85 | +| stairway | 43.2 | 64.53 | +| river | 12.15 | 16.93 | +| bridge | 79.3 | 87.36 | +| bookcase | 30.31 | 63.27 | +| blind | 48.03 | 53.6 | +| coffee table | 66.62 | 88.36 | +| toilet | 89.48 | 92.97 | +| flower | 47.54 | 55.47 | +| book | 47.72 | 77.97 | +| hill | 5.87 | 14.65 | +| bench | 47.63 | 58.36 | +| countertop | 62.85 | 78.8 | +| stove | 81.39 | 93.45 | +| palm | 50.46 | 85.96 | +| kitchen island | 50.44 | 92.72 | +| computer | 77.57 | 92.5 | +| swivel chair | 50.91 | 75.91 | +| boat | 71.78 | 91.7 | +| bar | 59.95 | 78.26 | +| arcade machine | 86.09 | 91.6 | +| hovel | 12.59 | 13.23 | +| bus | 90.67 | 96.62 | +| towel | 75.49 | 81.26 | +| light | 58.47 | 66.05 | +| truck | 44.55 | 60.75 | +| tower | 33.85 | 50.61 | +| chandelier | 70.38 | 86.2 | +| awning | 30.8 | 38.54 | +| streetlight | 31.88 | 53.34 | +| booth | 37.57 | 60.76 | +| television receiver | 73.84 | 84.9 | +| airplane | 75.84 | 93.21 | +| dirt track | 8.62 | 38.14 | +| apparel | 55.32 | 74.78 | +| pole | 25.11 | 32.7 | +| land | 1.15 | 1.94 | +| bannister | 15.8 | 21.74 | +| escalator | 56.19 | 77.16 | +| ottoman | 50.41 | 76.98 | +| bottle | 36.14 | 48.6 | +| buffet | 58.62 | 64.17 | +| poster | 35.36 | 43.41 | +| stage | 18.82 | 43.25 | +| van | 48.68 | 65.0 | +| ship | 76.7 | 96.82 | +| fountain | 45.52 | 46.62 | +| conveyer belt | 81.72 | 91.85 | +| canopy | 52.18 | 69.77 | +| washer | 80.75 | 84.58 | +| plaything | 39.31 | 72.1 | +| swimming pool | 68.83 | 92.59 | +| stool | 57.37 | 69.55 | +| barrel | 41.65 | 65.09 | +| basket | 38.46 | 49.25 | +| waterfall | 73.15 | 87.5 | +| tent | 96.35 | 98.51 | +| bag | 12.32 | 12.64 | +| minibike | 68.33 | 90.32 | +| cradle | 86.08 | 97.98 | +| oven | 60.84 | 76.97 | +| ball | 51.83 | 61.2 | +| food | 54.61 | 64.0 | +| step | 21.27 | 28.58 | +| tank | 83.23 | 94.74 | +| trade name | 29.66 | 35.96 | +| microwave | 88.17 | 96.14 | +| pot | 54.86 | 71.27 | +| animal | 65.88 | 68.74 | +| bicycle | 56.89 | 80.25 | +| lake | 41.37 | 63.64 | +| dishwasher | 60.69 | 62.98 | +| screen | 44.82 | 60.97 | +| blanket | 29.81 | 37.04 | +| sculpture | 73.2 | 86.67 | +| hood | 63.97 | 80.58 | +| sconce | 57.52 | 67.5 | +| vase | 46.66 | 63.28 | +| traffic light | 34.28 | 62.49 | +| tray | 19.39 | 27.25 | +| ashcan | 41.68 | 60.95 | +| fan | 64.24 | 78.43 | +| pier | 38.11 | 45.77 | +| crt screen | 22.3 | 50.98 | +| plate | 59.19 | 70.58 | +| monitor | 62.95 | 78.47 | +| bulletin board | 58.83 | 71.61 | +| shower | 0.5 | 5.41 | +| radiator | 68.35 | 77.33 | +| glass | 14.09 | 14.46 | +| clock | 38.43 | 49.05 | +| flag | 71.72 | 75.7 | ++---------------------+-------+-------+ +2024-06-16 08:45:36,278 - mmseg - INFO - Summary: +2024-06-16 08:45:36,278 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.41 | 55.97 | 69.69 | ++-------+-------+-------+ +2024-06-16 08:45:36,279 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 08:45:36,279 - mmseg - INFO - Iter(val) [250] aAcc: 0.8541, mIoU: 0.5597, mAcc: 0.6969, IoU.wall: 0.8098, IoU.building: 0.8402, IoU.sky: 0.9488, IoU.floor: 0.8483, IoU.tree: 0.7683, IoU.ceiling: 0.8641, IoU.road: 0.8567, IoU.bed : 0.9226, IoU.windowpane: 0.6572, IoU.grass: 0.6790, IoU.cabinet: 0.6439, IoU.sidewalk: 0.6968, IoU.person: 0.8557, IoU.earth: 0.4035, IoU.door: 0.5554, IoU.table: 0.6824, IoU.mountain: 0.6003, IoU.plant: 0.5565, IoU.curtain: 0.7934, IoU.chair: 0.6595, IoU.car: 0.8704, IoU.water: 0.6524, IoU.painting: 0.7583, IoU.sofa: 0.7923, IoU.shelf: 0.4385, IoU.house: 0.4413, IoU.sea: 0.6743, IoU.mirror: 0.7470, IoU.rug: 0.7165, IoU.field: 0.3337, IoU.armchair: 0.5682, IoU.seat: 0.6786, IoU.fence: 0.5230, IoU.desk: 0.5011, IoU.rock: 0.5817, IoU.wardrobe: 0.5139, IoU.lamp: 0.7212, IoU.bathtub: 0.8321, IoU.railing: 0.3941, IoU.cushion: 0.6715, IoU.base: 0.3894, IoU.box: 0.3631, IoU.column: 0.5606, IoU.signboard: 0.4166, IoU.chest of drawers: 0.4969, IoU.counter: 0.2829, IoU.sand: 0.5350, IoU.sink: 0.7187, IoU.skyscraper: 0.4827, IoU.fireplace: 0.7080, IoU.refrigerator: 0.8041, IoU.grandstand: 0.5267, IoU.path: 0.2986, IoU.stairs: 0.2704, IoU.runway: 0.7340, IoU.case: 0.5797, IoU.pool table: 0.9476, IoU.pillow: 0.6817, IoU.screen door: 0.5756, IoU.stairway: 0.4320, IoU.river: 0.1215, IoU.bridge: 0.7930, IoU.bookcase: 0.3031, IoU.blind: 0.4803, IoU.coffee table: 0.6662, IoU.toilet: 0.8948, IoU.flower: 0.4754, IoU.book: 0.4772, IoU.hill: 0.0587, IoU.bench: 0.4763, IoU.countertop: 0.6285, IoU.stove: 0.8139, IoU.palm: 0.5046, IoU.kitchen island: 0.5044, IoU.computer: 0.7757, IoU.swivel chair: 0.5091, IoU.boat: 0.7178, IoU.bar: 0.5995, IoU.arcade machine: 0.8609, IoU.hovel: 0.1259, IoU.bus: 0.9067, IoU.towel: 0.7549, IoU.light: 0.5847, IoU.truck: 0.4455, IoU.tower: 0.3385, IoU.chandelier: 0.7038, IoU.awning: 0.3080, IoU.streetlight: 0.3188, IoU.booth: 0.3757, IoU.television receiver: 0.7384, IoU.airplane: 0.7584, IoU.dirt track: 0.0862, IoU.apparel: 0.5532, IoU.pole: 0.2511, IoU.land: 0.0115, IoU.bannister: 0.1580, IoU.escalator: 0.5619, IoU.ottoman: 0.5041, IoU.bottle: 0.3614, IoU.buffet: 0.5862, IoU.poster: 0.3536, IoU.stage: 0.1882, IoU.van: 0.4868, IoU.ship: 0.7670, IoU.fountain: 0.4552, IoU.conveyer belt: 0.8172, IoU.canopy: 0.5218, IoU.washer: 0.8075, IoU.plaything: 0.3931, IoU.swimming pool: 0.6883, IoU.stool: 0.5737, IoU.barrel: 0.4165, IoU.basket: 0.3846, IoU.waterfall: 0.7315, IoU.tent: 0.9635, IoU.bag: 0.1232, IoU.minibike: 0.6833, IoU.cradle: 0.8608, IoU.oven: 0.6084, IoU.ball: 0.5183, IoU.food: 0.5461, IoU.step: 0.2127, IoU.tank: 0.8323, IoU.trade name: 0.2966, IoU.microwave: 0.8817, IoU.pot: 0.5486, IoU.animal: 0.6588, IoU.bicycle: 0.5689, IoU.lake: 0.4137, IoU.dishwasher: 0.6069, IoU.screen: 0.4482, IoU.blanket: 0.2981, IoU.sculpture: 0.7320, IoU.hood: 0.6397, IoU.sconce: 0.5752, IoU.vase: 0.4666, IoU.traffic light: 0.3428, IoU.tray: 0.1939, IoU.ashcan: 0.4168, IoU.fan: 0.6424, IoU.pier: 0.3811, IoU.crt screen: 0.2230, IoU.plate: 0.5919, IoU.monitor: 0.6295, IoU.bulletin board: 0.5883, IoU.shower: 0.0050, IoU.radiator: 0.6835, IoU.glass: 0.1409, IoU.clock: 0.3843, IoU.flag: 0.7172, Acc.wall: 0.8828, Acc.building: 0.9427, Acc.sky: 0.9773, Acc.floor: 0.9151, Acc.tree: 0.8589, Acc.ceiling: 0.9410, Acc.road: 0.9235, Acc.bed : 0.9723, Acc.windowpane: 0.8157, Acc.grass: 0.7911, Acc.cabinet: 0.7207, Acc.sidewalk: 0.7986, Acc.person: 0.9275, Acc.earth: 0.5691, Acc.door: 0.7154, Acc.table: 0.8208, Acc.mountain: 0.7253, Acc.plant: 0.6805, Acc.curtain: 0.8549, Acc.chair: 0.7747, Acc.car: 0.9367, Acc.water: 0.8000, Acc.painting: 0.9238, Acc.sofa: 0.8510, Acc.shelf: 0.6229, Acc.house: 0.5923, Acc.sea: 0.8400, Acc.mirror: 0.7938, Acc.rug: 0.8322, Acc.field: 0.5633, Acc.armchair: 0.8128, Acc.seat: 0.8848, Acc.fence: 0.6690, Acc.desk: 0.8378, Acc.rock: 0.8074, Acc.wardrobe: 0.7340, Acc.lamp: 0.8573, Acc.bathtub: 0.8752, Acc.railing: 0.5514, Acc.cushion: 0.7726, Acc.base: 0.5388, Acc.box: 0.4692, Acc.column: 0.6910, Acc.signboard: 0.5671, Acc.chest of drawers: 0.7777, Acc.counter: 0.3432, Acc.sand: 0.8168, Acc.sink: 0.8639, Acc.skyscraper: 0.6296, Acc.fireplace: 0.8854, Acc.refrigerator: 0.8781, Acc.grandstand: 0.8594, Acc.path: 0.4062, Acc.stairs: 0.2997, Acc.runway: 0.9556, Acc.case: 0.7834, Acc.pool table: 0.9796, Acc.pillow: 0.8081, Acc.screen door: 0.5985, Acc.stairway: 0.6453, Acc.river: 0.1693, Acc.bridge: 0.8736, Acc.bookcase: 0.6327, Acc.blind: 0.5360, Acc.coffee table: 0.8836, Acc.toilet: 0.9297, Acc.flower: 0.5547, Acc.book: 0.7797, Acc.hill: 0.1465, Acc.bench: 0.5836, Acc.countertop: 0.7880, Acc.stove: 0.9345, Acc.palm: 0.8596, Acc.kitchen island: 0.9272, Acc.computer: 0.9250, Acc.swivel chair: 0.7591, Acc.boat: 0.9170, Acc.bar: 0.7826, Acc.arcade machine: 0.9160, Acc.hovel: 0.1323, Acc.bus: 0.9662, Acc.towel: 0.8126, Acc.light: 0.6605, Acc.truck: 0.6075, Acc.tower: 0.5061, Acc.chandelier: 0.8620, Acc.awning: 0.3854, Acc.streetlight: 0.5334, Acc.booth: 0.6076, Acc.television receiver: 0.8490, Acc.airplane: 0.9321, Acc.dirt track: 0.3814, Acc.apparel: 0.7478, Acc.pole: 0.3270, Acc.land: 0.0194, Acc.bannister: 0.2174, Acc.escalator: 0.7716, Acc.ottoman: 0.7698, Acc.bottle: 0.4860, Acc.buffet: 0.6417, Acc.poster: 0.4341, Acc.stage: 0.4325, Acc.van: 0.6500, Acc.ship: 0.9682, Acc.fountain: 0.4662, Acc.conveyer belt: 0.9185, Acc.canopy: 0.6977, Acc.washer: 0.8458, Acc.plaything: 0.7210, Acc.swimming pool: 0.9259, Acc.stool: 0.6955, Acc.barrel: 0.6509, Acc.basket: 0.4925, Acc.waterfall: 0.8750, Acc.tent: 0.9851, Acc.bag: 0.1264, Acc.minibike: 0.9032, Acc.cradle: 0.9798, Acc.oven: 0.7697, Acc.ball: 0.6120, Acc.food: 0.6400, Acc.step: 0.2858, Acc.tank: 0.9474, Acc.trade name: 0.3596, Acc.microwave: 0.9614, Acc.pot: 0.7127, Acc.animal: 0.6874, Acc.bicycle: 0.8025, Acc.lake: 0.6364, Acc.dishwasher: 0.6298, Acc.screen: 0.6097, Acc.blanket: 0.3704, Acc.sculpture: 0.8667, Acc.hood: 0.8058, Acc.sconce: 0.6750, Acc.vase: 0.6328, Acc.traffic light: 0.6249, Acc.tray: 0.2725, Acc.ashcan: 0.6095, Acc.fan: 0.7843, Acc.pier: 0.4577, Acc.crt screen: 0.5098, Acc.plate: 0.7058, Acc.monitor: 0.7847, Acc.bulletin board: 0.7161, Acc.shower: 0.0541, Acc.radiator: 0.7733, Acc.glass: 0.1446, Acc.clock: 0.4905, Acc.flag: 0.7570 +2024-06-16 08:46:44,980 - mmseg - INFO - Iter [27050/80000] lr: 2.648e-05, eta: 22:11:52, time: 3.285, data_time: 1.928, memory: 70722, decode.loss_ce: 0.2439, decode.acc_seg: 89.8719, aux.loss_ce: 0.0998, aux.acc_seg: 89.6357, loss: 0.3437 +2024-06-16 08:47:53,210 - mmseg - INFO - Iter [27100/80000] lr: 2.645e-05, eta: 22:10:22, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2576, decode.acc_seg: 89.4472, aux.loss_ce: 0.1059, aux.acc_seg: 89.1808, loss: 0.3634 +2024-06-16 08:49:01,439 - mmseg - INFO - Iter [27150/80000] lr: 2.643e-05, eta: 22:08:53, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2760, decode.acc_seg: 88.7104, aux.loss_ce: 0.1127, aux.acc_seg: 88.5155, loss: 0.3887 +2024-06-16 08:50:09,708 - mmseg - INFO - Iter [27200/80000] lr: 2.640e-05, eta: 22:07:23, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2585, decode.acc_seg: 89.3784, aux.loss_ce: 0.1051, aux.acc_seg: 89.1392, loss: 0.3636 +2024-06-16 08:51:18,021 - mmseg - INFO - Iter [27250/80000] lr: 2.638e-05, eta: 22:05:54, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2676, decode.acc_seg: 89.4069, aux.loss_ce: 0.1088, aux.acc_seg: 89.2694, loss: 0.3764 +2024-06-16 08:52:26,111 - mmseg - INFO - Iter [27300/80000] lr: 2.635e-05, eta: 22:04:25, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2716, decode.acc_seg: 88.6692, aux.loss_ce: 0.1105, aux.acc_seg: 88.4892, loss: 0.3820 +2024-06-16 08:53:34,329 - mmseg - INFO - Iter [27350/80000] lr: 2.633e-05, eta: 22:02:56, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2585, decode.acc_seg: 89.6764, aux.loss_ce: 0.1063, aux.acc_seg: 89.3948, loss: 0.3647 +2024-06-16 08:54:42,589 - mmseg - INFO - Iter [27400/80000] lr: 2.630e-05, eta: 22:01:27, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2722, decode.acc_seg: 88.5498, aux.loss_ce: 0.1112, aux.acc_seg: 88.2803, loss: 0.3835 +2024-06-16 08:55:50,817 - mmseg - INFO - Iter [27450/80000] lr: 2.628e-05, eta: 21:59:57, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2596, decode.acc_seg: 89.5073, aux.loss_ce: 0.1061, aux.acc_seg: 89.3387, loss: 0.3658 +2024-06-16 08:56:59,100 - mmseg - INFO - Iter [27500/80000] lr: 2.625e-05, eta: 21:58:29, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2486, decode.acc_seg: 89.3837, aux.loss_ce: 0.1016, aux.acc_seg: 89.2472, loss: 0.3502 +2024-06-16 08:58:07,402 - mmseg - INFO - Iter [27550/80000] lr: 2.623e-05, eta: 21:57:00, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2709, decode.acc_seg: 88.9440, aux.loss_ce: 0.1097, aux.acc_seg: 88.7882, loss: 0.3805 +2024-06-16 08:59:15,656 - mmseg - INFO - Iter [27600/80000] lr: 2.620e-05, eta: 21:55:31, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2750, decode.acc_seg: 89.2713, aux.loss_ce: 0.1124, aux.acc_seg: 89.1004, loss: 0.3875 +2024-06-16 09:00:23,860 - mmseg - INFO - Iter [27650/80000] lr: 2.618e-05, eta: 21:54:02, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2640, decode.acc_seg: 89.4389, aux.loss_ce: 0.1081, aux.acc_seg: 89.2021, loss: 0.3721 +2024-06-16 09:01:32,200 - mmseg - INFO - Iter [27700/80000] lr: 2.615e-05, eta: 21:52:34, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2725, decode.acc_seg: 88.5608, aux.loss_ce: 0.1118, aux.acc_seg: 88.2740, loss: 0.3843 +2024-06-16 09:02:40,645 - mmseg - INFO - Iter [27750/80000] lr: 2.613e-05, eta: 21:51:06, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2687, decode.acc_seg: 89.2935, aux.loss_ce: 0.1102, aux.acc_seg: 88.9999, loss: 0.3788 +2024-06-16 09:03:51,701 - mmseg - INFO - Iter [27800/80000] lr: 2.610e-05, eta: 21:49:42, time: 1.421, data_time: 0.060, memory: 70722, decode.loss_ce: 0.2493, decode.acc_seg: 90.1628, aux.loss_ce: 0.1020, aux.acc_seg: 89.8598, loss: 0.3513 +2024-06-16 09:04:59,766 - mmseg - INFO - Iter [27850/80000] lr: 2.608e-05, eta: 21:48:14, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2470, decode.acc_seg: 89.8252, aux.loss_ce: 0.1014, aux.acc_seg: 89.5188, loss: 0.3484 +2024-06-16 09:06:08,069 - mmseg - INFO - Iter [27900/80000] lr: 2.605e-05, eta: 21:46:45, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2604, decode.acc_seg: 89.7749, aux.loss_ce: 0.1066, aux.acc_seg: 89.5204, loss: 0.3670 +2024-06-16 09:07:16,413 - mmseg - INFO - Iter [27950/80000] lr: 2.603e-05, eta: 21:45:17, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2585, decode.acc_seg: 89.4629, aux.loss_ce: 0.1056, aux.acc_seg: 89.2180, loss: 0.3642 +2024-06-16 09:08:24,799 - mmseg - INFO - Saving checkpoint at 28000 iterations +2024-06-16 09:09:51,911 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 09:09:51,911 - mmseg - INFO - Iter [28000/80000] lr: 2.600e-05, eta: 21:46:31, time: 3.110, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2398, decode.acc_seg: 90.1584, aux.loss_ce: 0.0989, aux.acc_seg: 89.8537, loss: 0.3387 +2024-06-16 09:11:27,244 - mmseg - INFO - per class results: +2024-06-16 09:11:27,250 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.32 | 87.5 | +| building | 84.1 | 92.46 | +| sky | 94.8 | 97.62 | +| floor | 84.16 | 91.42 | +| tree | 77.96 | 89.98 | +| ceiling | 86.21 | 92.71 | +| road | 85.59 | 91.77 | +| bed | 91.94 | 96.12 | +| windowpane | 64.41 | 84.08 | +| grass | 70.62 | 82.98 | +| cabinet | 65.53 | 75.61 | +| sidewalk | 70.74 | 83.8 | +| person | 84.6 | 94.17 | +| earth | 35.26 | 47.39 | +| door | 59.01 | 79.64 | +| table | 68.54 | 83.68 | +| mountain | 60.21 | 67.13 | +| plant | 57.17 | 67.86 | +| curtain | 78.32 | 89.98 | +| chair | 65.31 | 76.94 | +| car | 86.54 | 93.73 | +| water | 59.96 | 72.08 | +| painting | 74.87 | 92.12 | +| sofa | 75.48 | 80.85 | +| shelf | 47.47 | 65.86 | +| house | 52.88 | 68.26 | +| sea | 64.79 | 82.99 | +| mirror | 75.53 | 82.89 | +| rug | 70.86 | 82.23 | +| field | 41.96 | 71.66 | +| armchair | 55.85 | 80.83 | +| seat | 69.46 | 87.6 | +| fence | 50.88 | 69.06 | +| desk | 58.1 | 74.56 | +| rock | 53.27 | 90.41 | +| wardrobe | 52.29 | 76.41 | +| lamp | 72.37 | 84.02 | +| bathtub | 83.72 | 85.62 | +| railing | 41.29 | 54.43 | +| cushion | 69.06 | 84.65 | +| base | 39.34 | 49.17 | +| box | 33.44 | 40.27 | +| column | 58.65 | 78.26 | +| signboard | 41.55 | 56.69 | +| chest of drawers | 48.09 | 59.97 | +| counter | 40.38 | 42.99 | +| sand | 52.68 | 84.37 | +| sink | 71.04 | 85.48 | +| skyscraper | 43.61 | 68.42 | +| fireplace | 76.19 | 90.45 | +| refrigerator | 81.91 | 93.74 | +| grandstand | 51.97 | 79.4 | +| path | 29.4 | 48.94 | +| stairs | 34.14 | 46.08 | +| runway | 70.88 | 93.78 | +| case | 63.63 | 88.02 | +| pool table | 94.69 | 97.97 | +| pillow | 66.8 | 78.12 | +| screen door | 83.62 | 86.64 | +| stairway | 52.59 | 60.88 | +| river | 10.67 | 22.25 | +| bridge | 73.29 | 92.23 | +| bookcase | 36.49 | 55.72 | +| blind | 44.54 | 48.84 | +| coffee table | 67.7 | 87.38 | +| toilet | 89.64 | 94.93 | +| flower | 44.56 | 56.6 | +| book | 47.75 | 81.13 | +| hill | 5.65 | 13.42 | +| bench | 55.52 | 65.38 | +| countertop | 66.9 | 84.07 | +| stove | 81.25 | 91.79 | +| palm | 54.39 | 76.2 | +| kitchen island | 46.59 | 81.77 | +| computer | 77.64 | 91.57 | +| swivel chair | 48.58 | 76.82 | +| boat | 63.74 | 88.49 | +| bar | 56.79 | 76.73 | +| arcade machine | 81.5 | 86.41 | +| hovel | 13.44 | 14.03 | +| bus | 92.84 | 95.04 | +| towel | 71.73 | 84.36 | +| light | 59.88 | 73.01 | +| truck | 40.44 | 65.95 | +| tower | 26.68 | 49.99 | +| chandelier | 69.97 | 84.77 | +| awning | 49.17 | 64.48 | +| streetlight | 33.27 | 43.15 | +| booth | 43.89 | 49.22 | +| television receiver | 77.91 | 88.99 | +| airplane | 67.73 | 70.71 | +| dirt track | 7.64 | 26.01 | +| apparel | 41.09 | 51.42 | +| pole | 31.14 | 45.65 | +| land | 0.99 | 1.49 | +| bannister | 14.29 | 23.32 | +| escalator | 56.99 | 80.16 | +| ottoman | 47.94 | 59.35 | +| bottle | 40.61 | 58.53 | +| buffet | 61.81 | 72.6 | +| poster | 28.47 | 46.04 | +| stage | 18.24 | 47.67 | +| van | 48.11 | 61.51 | +| ship | 86.53 | 92.05 | +| fountain | 53.33 | 54.53 | +| conveyer belt | 71.98 | 93.38 | +| canopy | 42.93 | 75.44 | +| washer | 81.82 | 86.0 | +| plaything | 27.98 | 79.9 | +| swimming pool | 59.61 | 92.24 | +| stool | 51.93 | 65.58 | +| barrel | 34.51 | 65.88 | +| basket | 40.08 | 47.6 | +| waterfall | 64.19 | 78.03 | +| tent | 77.47 | 98.28 | +| bag | 17.85 | 21.91 | +| minibike | 76.11 | 86.79 | +| cradle | 86.05 | 97.75 | +| oven | 57.16 | 67.88 | +| ball | 54.69 | 68.65 | +| food | 61.1 | 72.65 | +| step | 16.29 | 22.07 | +| tank | 86.58 | 93.19 | +| trade name | 29.23 | 33.78 | +| microwave | 86.69 | 96.12 | +| pot | 55.78 | 66.1 | +| animal | 61.67 | 63.81 | +| bicycle | 53.76 | 73.66 | +| lake | 35.76 | 54.7 | +| dishwasher | 63.23 | 73.12 | +| screen | 66.52 | 91.69 | +| blanket | 27.84 | 35.49 | +| sculpture | 71.75 | 84.65 | +| hood | 65.57 | 79.24 | +| sconce | 54.82 | 69.83 | +| vase | 44.8 | 62.14 | +| traffic light | 37.38 | 56.73 | +| tray | 11.26 | 14.4 | +| ashcan | 47.76 | 59.37 | +| fan | 66.22 | 75.1 | +| pier | 34.88 | 47.27 | +| crt screen | 25.8 | 27.5 | +| plate | 59.85 | 75.05 | +| monitor | 68.69 | 86.69 | +| bulletin board | 59.07 | 63.67 | +| shower | 2.09 | 2.21 | +| radiator | 64.67 | 77.69 | +| glass | 17.13 | 18.33 | +| clock | 34.39 | 41.84 | +| flag | 70.51 | 76.51 | ++---------------------+-------+-------+ +2024-06-16 09:11:27,250 - mmseg - INFO - Summary: +2024-06-16 09:11:27,250 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.54 | 56.13 | 69.86 | ++-------+-------+-------+ +2024-06-16 09:11:27,251 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 09:11:27,251 - mmseg - INFO - Iter(val) [250] aAcc: 0.8554, mIoU: 0.5613, mAcc: 0.6986, IoU.wall: 0.8132, IoU.building: 0.8410, IoU.sky: 0.9480, IoU.floor: 0.8416, IoU.tree: 0.7796, IoU.ceiling: 0.8621, IoU.road: 0.8559, IoU.bed : 0.9194, IoU.windowpane: 0.6441, IoU.grass: 0.7062, IoU.cabinet: 0.6553, IoU.sidewalk: 0.7074, IoU.person: 0.8460, IoU.earth: 0.3526, IoU.door: 0.5901, IoU.table: 0.6854, IoU.mountain: 0.6021, IoU.plant: 0.5717, IoU.curtain: 0.7832, IoU.chair: 0.6531, IoU.car: 0.8654, IoU.water: 0.5996, IoU.painting: 0.7487, IoU.sofa: 0.7548, IoU.shelf: 0.4747, IoU.house: 0.5288, IoU.sea: 0.6479, IoU.mirror: 0.7553, IoU.rug: 0.7086, IoU.field: 0.4196, IoU.armchair: 0.5585, IoU.seat: 0.6946, IoU.fence: 0.5088, IoU.desk: 0.5810, IoU.rock: 0.5327, IoU.wardrobe: 0.5229, IoU.lamp: 0.7237, IoU.bathtub: 0.8372, IoU.railing: 0.4129, IoU.cushion: 0.6906, IoU.base: 0.3934, IoU.box: 0.3344, IoU.column: 0.5865, IoU.signboard: 0.4155, IoU.chest of drawers: 0.4809, IoU.counter: 0.4038, IoU.sand: 0.5268, IoU.sink: 0.7104, IoU.skyscraper: 0.4361, IoU.fireplace: 0.7619, IoU.refrigerator: 0.8191, IoU.grandstand: 0.5197, IoU.path: 0.2940, IoU.stairs: 0.3414, IoU.runway: 0.7088, IoU.case: 0.6363, IoU.pool table: 0.9469, IoU.pillow: 0.6680, IoU.screen door: 0.8362, IoU.stairway: 0.5259, IoU.river: 0.1067, IoU.bridge: 0.7329, IoU.bookcase: 0.3649, IoU.blind: 0.4454, IoU.coffee table: 0.6770, IoU.toilet: 0.8964, IoU.flower: 0.4456, IoU.book: 0.4775, IoU.hill: 0.0565, IoU.bench: 0.5552, IoU.countertop: 0.6690, IoU.stove: 0.8125, IoU.palm: 0.5439, IoU.kitchen island: 0.4659, IoU.computer: 0.7764, IoU.swivel chair: 0.4858, IoU.boat: 0.6374, IoU.bar: 0.5679, IoU.arcade machine: 0.8150, IoU.hovel: 0.1344, IoU.bus: 0.9284, IoU.towel: 0.7173, IoU.light: 0.5988, IoU.truck: 0.4044, IoU.tower: 0.2668, IoU.chandelier: 0.6997, IoU.awning: 0.4917, IoU.streetlight: 0.3327, IoU.booth: 0.4389, IoU.television receiver: 0.7791, IoU.airplane: 0.6773, IoU.dirt track: 0.0764, IoU.apparel: 0.4109, IoU.pole: 0.3114, IoU.land: 0.0099, IoU.bannister: 0.1429, IoU.escalator: 0.5699, IoU.ottoman: 0.4794, IoU.bottle: 0.4061, IoU.buffet: 0.6181, IoU.poster: 0.2847, IoU.stage: 0.1824, IoU.van: 0.4811, IoU.ship: 0.8653, IoU.fountain: 0.5333, IoU.conveyer belt: 0.7198, IoU.canopy: 0.4293, IoU.washer: 0.8182, IoU.plaything: 0.2798, IoU.swimming pool: 0.5961, IoU.stool: 0.5193, IoU.barrel: 0.3451, IoU.basket: 0.4008, IoU.waterfall: 0.6419, IoU.tent: 0.7747, IoU.bag: 0.1785, IoU.minibike: 0.7611, IoU.cradle: 0.8605, IoU.oven: 0.5716, IoU.ball: 0.5469, IoU.food: 0.6110, IoU.step: 0.1629, IoU.tank: 0.8658, IoU.trade name: 0.2923, IoU.microwave: 0.8669, IoU.pot: 0.5578, IoU.animal: 0.6167, IoU.bicycle: 0.5376, IoU.lake: 0.3576, IoU.dishwasher: 0.6323, IoU.screen: 0.6652, IoU.blanket: 0.2784, IoU.sculpture: 0.7175, IoU.hood: 0.6557, IoU.sconce: 0.5482, IoU.vase: 0.4480, IoU.traffic light: 0.3738, IoU.tray: 0.1126, IoU.ashcan: 0.4776, IoU.fan: 0.6622, IoU.pier: 0.3488, IoU.crt screen: 0.2580, IoU.plate: 0.5985, IoU.monitor: 0.6869, IoU.bulletin board: 0.5907, IoU.shower: 0.0209, IoU.radiator: 0.6467, IoU.glass: 0.1713, IoU.clock: 0.3439, IoU.flag: 0.7051, Acc.wall: 0.8750, Acc.building: 0.9246, Acc.sky: 0.9762, Acc.floor: 0.9142, Acc.tree: 0.8998, Acc.ceiling: 0.9271, Acc.road: 0.9177, Acc.bed : 0.9612, Acc.windowpane: 0.8408, Acc.grass: 0.8298, Acc.cabinet: 0.7561, Acc.sidewalk: 0.8380, Acc.person: 0.9417, Acc.earth: 0.4739, Acc.door: 0.7964, Acc.table: 0.8368, Acc.mountain: 0.6713, Acc.plant: 0.6786, Acc.curtain: 0.8998, Acc.chair: 0.7694, Acc.car: 0.9373, Acc.water: 0.7208, Acc.painting: 0.9212, Acc.sofa: 0.8085, Acc.shelf: 0.6586, Acc.house: 0.6826, Acc.sea: 0.8299, Acc.mirror: 0.8289, Acc.rug: 0.8223, Acc.field: 0.7166, Acc.armchair: 0.8083, Acc.seat: 0.8760, Acc.fence: 0.6906, Acc.desk: 0.7456, Acc.rock: 0.9041, Acc.wardrobe: 0.7641, Acc.lamp: 0.8402, Acc.bathtub: 0.8562, Acc.railing: 0.5443, Acc.cushion: 0.8465, Acc.base: 0.4917, Acc.box: 0.4027, Acc.column: 0.7826, Acc.signboard: 0.5669, Acc.chest of drawers: 0.5997, Acc.counter: 0.4299, Acc.sand: 0.8437, Acc.sink: 0.8548, Acc.skyscraper: 0.6842, Acc.fireplace: 0.9045, Acc.refrigerator: 0.9374, Acc.grandstand: 0.7940, Acc.path: 0.4894, Acc.stairs: 0.4608, Acc.runway: 0.9378, Acc.case: 0.8802, Acc.pool table: 0.9797, Acc.pillow: 0.7812, Acc.screen door: 0.8664, Acc.stairway: 0.6088, Acc.river: 0.2225, Acc.bridge: 0.9223, Acc.bookcase: 0.5572, Acc.blind: 0.4884, Acc.coffee table: 0.8738, Acc.toilet: 0.9493, Acc.flower: 0.5660, Acc.book: 0.8113, Acc.hill: 0.1342, Acc.bench: 0.6538, Acc.countertop: 0.8407, Acc.stove: 0.9179, Acc.palm: 0.7620, Acc.kitchen island: 0.8177, Acc.computer: 0.9157, Acc.swivel chair: 0.7682, Acc.boat: 0.8849, Acc.bar: 0.7673, Acc.arcade machine: 0.8641, Acc.hovel: 0.1403, Acc.bus: 0.9504, Acc.towel: 0.8436, Acc.light: 0.7301, Acc.truck: 0.6595, Acc.tower: 0.4999, Acc.chandelier: 0.8477, Acc.awning: 0.6448, Acc.streetlight: 0.4315, Acc.booth: 0.4922, Acc.television receiver: 0.8899, Acc.airplane: 0.7071, Acc.dirt track: 0.2601, Acc.apparel: 0.5142, Acc.pole: 0.4565, Acc.land: 0.0149, Acc.bannister: 0.2332, Acc.escalator: 0.8016, Acc.ottoman: 0.5935, Acc.bottle: 0.5853, Acc.buffet: 0.7260, Acc.poster: 0.4604, Acc.stage: 0.4767, Acc.van: 0.6151, Acc.ship: 0.9205, Acc.fountain: 0.5453, Acc.conveyer belt: 0.9338, Acc.canopy: 0.7544, Acc.washer: 0.8600, Acc.plaything: 0.7990, Acc.swimming pool: 0.9224, Acc.stool: 0.6558, Acc.barrel: 0.6588, Acc.basket: 0.4760, Acc.waterfall: 0.7803, Acc.tent: 0.9828, Acc.bag: 0.2191, Acc.minibike: 0.8679, Acc.cradle: 0.9775, Acc.oven: 0.6788, Acc.ball: 0.6865, Acc.food: 0.7265, Acc.step: 0.2207, Acc.tank: 0.9319, Acc.trade name: 0.3378, Acc.microwave: 0.9612, Acc.pot: 0.6610, Acc.animal: 0.6381, Acc.bicycle: 0.7366, Acc.lake: 0.5470, Acc.dishwasher: 0.7312, Acc.screen: 0.9169, Acc.blanket: 0.3549, Acc.sculpture: 0.8465, Acc.hood: 0.7924, Acc.sconce: 0.6983, Acc.vase: 0.6214, Acc.traffic light: 0.5673, Acc.tray: 0.1440, Acc.ashcan: 0.5937, Acc.fan: 0.7510, Acc.pier: 0.4727, Acc.crt screen: 0.2750, Acc.plate: 0.7505, Acc.monitor: 0.8669, Acc.bulletin board: 0.6367, Acc.shower: 0.0221, Acc.radiator: 0.7769, Acc.glass: 0.1833, Acc.clock: 0.4184, Acc.flag: 0.7651 +2024-06-16 09:12:35,939 - mmseg - INFO - Iter [28050/80000] lr: 2.598e-05, eta: 21:48:00, time: 3.281, data_time: 1.923, memory: 70722, decode.loss_ce: 0.2520, decode.acc_seg: 89.5489, aux.loss_ce: 0.1025, aux.acc_seg: 89.1988, loss: 0.3545 +2024-06-16 09:13:44,496 - mmseg - INFO - Iter [28100/80000] lr: 2.595e-05, eta: 21:46:32, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2456, decode.acc_seg: 89.7992, aux.loss_ce: 0.1002, aux.acc_seg: 89.5680, loss: 0.3459 +2024-06-16 09:14:52,665 - mmseg - INFO - Iter [28150/80000] lr: 2.593e-05, eta: 21:45:03, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2563, decode.acc_seg: 89.4309, aux.loss_ce: 0.1045, aux.acc_seg: 89.2237, loss: 0.3608 +2024-06-16 09:16:01,065 - mmseg - INFO - Iter [28200/80000] lr: 2.590e-05, eta: 21:43:34, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2467, decode.acc_seg: 89.8461, aux.loss_ce: 0.1014, aux.acc_seg: 89.5653, loss: 0.3481 +2024-06-16 09:17:09,156 - mmseg - INFO - Iter [28250/80000] lr: 2.588e-05, eta: 21:42:05, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2679, decode.acc_seg: 88.7392, aux.loss_ce: 0.1088, aux.acc_seg: 88.5045, loss: 0.3767 +2024-06-16 09:18:17,656 - mmseg - INFO - Iter [28300/80000] lr: 2.585e-05, eta: 21:40:37, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2408, decode.acc_seg: 90.0758, aux.loss_ce: 0.0994, aux.acc_seg: 89.7168, loss: 0.3401 +2024-06-16 09:19:25,881 - mmseg - INFO - Iter [28350/80000] lr: 2.583e-05, eta: 21:39:08, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2353, decode.acc_seg: 89.9124, aux.loss_ce: 0.0974, aux.acc_seg: 89.5371, loss: 0.3327 +2024-06-16 09:20:34,103 - mmseg - INFO - Iter [28400/80000] lr: 2.580e-05, eta: 21:37:39, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2356, decode.acc_seg: 90.1089, aux.loss_ce: 0.0970, aux.acc_seg: 89.8764, loss: 0.3326 +2024-06-16 09:21:42,741 - mmseg - INFO - Iter [28450/80000] lr: 2.578e-05, eta: 21:36:12, time: 1.373, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2314, decode.acc_seg: 90.3219, aux.loss_ce: 0.0952, aux.acc_seg: 89.9786, loss: 0.3266 +2024-06-16 09:22:50,921 - mmseg - INFO - Iter [28500/80000] lr: 2.575e-05, eta: 21:34:43, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2465, decode.acc_seg: 90.0176, aux.loss_ce: 0.1012, aux.acc_seg: 89.7471, loss: 0.3477 +2024-06-16 09:23:59,234 - mmseg - INFO - Iter [28550/80000] lr: 2.573e-05, eta: 21:33:15, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2504, decode.acc_seg: 89.6866, aux.loss_ce: 0.1019, aux.acc_seg: 89.5595, loss: 0.3523 +2024-06-16 09:25:07,404 - mmseg - INFO - Iter [28600/80000] lr: 2.570e-05, eta: 21:31:46, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2399, decode.acc_seg: 90.0227, aux.loss_ce: 0.0996, aux.acc_seg: 89.5686, loss: 0.3395 +2024-06-16 09:26:15,647 - mmseg - INFO - Iter [28650/80000] lr: 2.568e-05, eta: 21:30:18, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2495, decode.acc_seg: 89.5218, aux.loss_ce: 0.1031, aux.acc_seg: 89.2525, loss: 0.3526 +2024-06-16 09:27:23,616 - mmseg - INFO - Iter [28700/80000] lr: 2.565e-05, eta: 21:28:50, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2603, decode.acc_seg: 89.4300, aux.loss_ce: 0.1069, aux.acc_seg: 89.1101, loss: 0.3672 +2024-06-16 09:28:31,821 - mmseg - INFO - Iter [28750/80000] lr: 2.563e-05, eta: 21:27:21, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2532, decode.acc_seg: 89.8772, aux.loss_ce: 0.1036, aux.acc_seg: 89.5713, loss: 0.3568 +2024-06-16 09:29:40,202 - mmseg - INFO - Iter [28800/80000] lr: 2.560e-05, eta: 21:25:54, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2674, decode.acc_seg: 88.7856, aux.loss_ce: 0.1096, aux.acc_seg: 88.6213, loss: 0.3770 +2024-06-16 09:30:48,557 - mmseg - INFO - Iter [28850/80000] lr: 2.558e-05, eta: 21:24:26, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2496, decode.acc_seg: 89.6519, aux.loss_ce: 0.1020, aux.acc_seg: 89.3630, loss: 0.3516 +2024-06-16 09:31:56,650 - mmseg - INFO - Iter [28900/80000] lr: 2.555e-05, eta: 21:22:58, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2586, decode.acc_seg: 89.3268, aux.loss_ce: 0.1064, aux.acc_seg: 89.0252, loss: 0.3649 +2024-06-16 09:33:04,682 - mmseg - INFO - Iter [28950/80000] lr: 2.553e-05, eta: 21:21:30, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2595, decode.acc_seg: 89.3734, aux.loss_ce: 0.1055, aux.acc_seg: 89.0910, loss: 0.3650 +2024-06-16 09:34:13,170 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 09:34:13,170 - mmseg - INFO - Iter [29000/80000] lr: 2.550e-05, eta: 21:20:02, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2516, decode.acc_seg: 89.5202, aux.loss_ce: 0.1034, aux.acc_seg: 89.3217, loss: 0.3550 +2024-06-16 09:35:50,434 - mmseg - INFO - per class results: +2024-06-16 09:35:50,440 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.86 | 89.97 | +| building | 85.15 | 92.25 | +| sky | 94.88 | 97.47 | +| floor | 83.61 | 89.6 | +| tree | 77.01 | 89.99 | +| ceiling | 86.38 | 92.75 | +| road | 85.58 | 90.46 | +| bed | 92.38 | 96.15 | +| windowpane | 64.75 | 81.81 | +| grass | 66.65 | 82.36 | +| cabinet | 65.2 | 73.95 | +| sidewalk | 71.17 | 84.42 | +| person | 85.22 | 93.84 | +| earth | 38.97 | 54.27 | +| door | 58.93 | 77.81 | +| table | 69.09 | 81.72 | +| mountain | 62.77 | 72.62 | +| plant | 54.83 | 64.81 | +| curtain | 78.5 | 88.73 | +| chair | 64.59 | 72.99 | +| car | 85.77 | 94.16 | +| water | 63.87 | 78.18 | +| painting | 79.12 | 89.08 | +| sofa | 81.28 | 91.12 | +| shelf | 45.7 | 62.42 | +| house | 53.84 | 71.3 | +| sea | 69.98 | 82.81 | +| mirror | 75.36 | 81.97 | +| rug | 68.79 | 85.39 | +| field | 32.2 | 51.76 | +| armchair | 56.98 | 74.29 | +| seat | 65.59 | 89.08 | +| fence | 48.16 | 72.27 | +| desk | 57.72 | 81.48 | +| rock | 54.45 | 80.66 | +| wardrobe | 56.3 | 76.72 | +| lamp | 71.77 | 84.34 | +| bathtub | 84.18 | 86.53 | +| railing | 42.65 | 56.61 | +| cushion | 68.68 | 82.06 | +| base | 35.84 | 63.86 | +| box | 32.4 | 37.76 | +| column | 49.56 | 62.77 | +| signboard | 41.96 | 55.12 | +| chest of drawers | 43.19 | 61.43 | +| counter | 39.48 | 43.72 | +| sand | 45.09 | 67.24 | +| sink | 74.11 | 83.65 | +| skyscraper | 52.18 | 56.86 | +| fireplace | 68.41 | 95.19 | +| refrigerator | 81.91 | 90.91 | +| grandstand | 47.74 | 87.29 | +| path | 29.24 | 39.68 | +| stairs | 35.41 | 50.89 | +| runway | 72.79 | 95.42 | +| case | 62.27 | 81.26 | +| pool table | 94.68 | 97.97 | +| pillow | 66.63 | 77.01 | +| screen door | 84.19 | 86.56 | +| stairway | 53.73 | 61.71 | +| river | 8.5 | 15.52 | +| bridge | 76.47 | 89.12 | +| bookcase | 37.73 | 63.54 | +| blind | 50.21 | 59.19 | +| coffee table | 67.81 | 90.09 | +| toilet | 89.63 | 92.59 | +| flower | 42.54 | 68.64 | +| book | 51.71 | 74.72 | +| hill | 8.17 | 14.16 | +| bench | 52.15 | 64.81 | +| countertop | 64.82 | 82.82 | +| stove | 81.85 | 94.17 | +| palm | 52.11 | 82.95 | +| kitchen island | 48.19 | 85.44 | +| computer | 77.5 | 91.08 | +| swivel chair | 48.86 | 78.66 | +| boat | 72.32 | 90.42 | +| bar | 53.76 | 75.42 | +| arcade machine | 70.15 | 74.73 | +| hovel | 43.38 | 48.98 | +| bus | 87.32 | 96.86 | +| towel | 73.37 | 89.06 | +| light | 56.3 | 61.96 | +| truck | 44.83 | 56.79 | +| tower | 28.99 | 53.72 | +| chandelier | 67.34 | 78.19 | +| awning | 40.25 | 50.41 | +| streetlight | 31.05 | 44.36 | +| booth | 54.72 | 56.41 | +| television receiver | 80.29 | 89.95 | +| airplane | 83.61 | 92.89 | +| dirt track | 10.91 | 44.64 | +| apparel | 51.75 | 64.78 | +| pole | 23.82 | 31.63 | +| land | 2.12 | 4.59 | +| bannister | 16.47 | 26.44 | +| escalator | 55.29 | 80.24 | +| ottoman | 56.13 | 72.35 | +| bottle | 39.45 | 54.07 | +| buffet | 52.55 | 61.33 | +| poster | 33.73 | 47.92 | +| stage | 16.95 | 49.55 | +| van | 42.24 | 58.61 | +| ship | 67.59 | 68.2 | +| fountain | 43.47 | 46.72 | +| conveyer belt | 74.83 | 92.92 | +| canopy | 43.26 | 63.72 | +| washer | 89.03 | 94.08 | +| plaything | 24.49 | 42.2 | +| swimming pool | 63.94 | 77.7 | +| stool | 49.35 | 61.97 | +| barrel | 53.6 | 64.67 | +| basket | 42.98 | 58.54 | +| waterfall | 69.07 | 84.46 | +| tent | 89.0 | 98.78 | +| bag | 13.25 | 14.14 | +| minibike | 74.0 | 88.89 | +| cradle | 75.8 | 98.25 | +| oven | 58.24 | 70.5 | +| ball | 32.87 | 33.25 | +| food | 63.24 | 80.48 | +| step | 8.44 | 9.74 | +| tank | 86.36 | 94.74 | +| trade name | 21.6 | 22.99 | +| microwave | 87.19 | 96.29 | +| pot | 54.74 | 61.7 | +| animal | 64.05 | 68.54 | +| bicycle | 57.69 | 79.93 | +| lake | 40.22 | 63.71 | +| dishwasher | 68.3 | 75.67 | +| screen | 57.2 | 92.38 | +| blanket | 28.97 | 37.87 | +| sculpture | 68.28 | 85.47 | +| hood | 62.57 | 74.15 | +| sconce | 55.68 | 62.4 | +| vase | 45.78 | 59.55 | +| traffic light | 33.84 | 60.19 | +| tray | 17.45 | 21.82 | +| ashcan | 46.05 | 65.28 | +| fan | 66.78 | 82.09 | +| pier | 27.78 | 49.81 | +| crt screen | 2.62 | 3.3 | +| plate | 59.42 | 80.27 | +| monitor | 62.02 | 79.88 | +| bulletin board | 49.99 | 71.69 | +| shower | 1.51 | 1.55 | +| radiator | 66.54 | 79.61 | +| glass | 17.79 | 18.79 | +| clock | 32.42 | 41.41 | +| flag | 71.53 | 80.89 | ++---------------------+-------+-------+ +2024-06-16 09:35:50,440 - mmseg - INFO - Summary: +2024-06-16 09:35:50,441 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.66 | 55.86 | 69.34 | ++-------+-------+-------+ +2024-06-16 09:35:50,441 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 09:35:50,442 - mmseg - INFO - Iter(val) [250] aAcc: 0.8566, mIoU: 0.5586, mAcc: 0.6934, IoU.wall: 0.8186, IoU.building: 0.8515, IoU.sky: 0.9488, IoU.floor: 0.8361, IoU.tree: 0.7701, IoU.ceiling: 0.8638, IoU.road: 0.8558, IoU.bed : 0.9238, IoU.windowpane: 0.6475, IoU.grass: 0.6665, IoU.cabinet: 0.6520, IoU.sidewalk: 0.7117, IoU.person: 0.8522, IoU.earth: 0.3897, IoU.door: 0.5893, IoU.table: 0.6909, IoU.mountain: 0.6277, IoU.plant: 0.5483, IoU.curtain: 0.7850, IoU.chair: 0.6459, IoU.car: 0.8577, IoU.water: 0.6387, IoU.painting: 0.7912, IoU.sofa: 0.8128, IoU.shelf: 0.4570, IoU.house: 0.5384, IoU.sea: 0.6998, IoU.mirror: 0.7536, IoU.rug: 0.6879, IoU.field: 0.3220, IoU.armchair: 0.5698, IoU.seat: 0.6559, IoU.fence: 0.4816, IoU.desk: 0.5772, IoU.rock: 0.5445, IoU.wardrobe: 0.5630, IoU.lamp: 0.7177, IoU.bathtub: 0.8418, IoU.railing: 0.4265, IoU.cushion: 0.6868, IoU.base: 0.3584, IoU.box: 0.3240, IoU.column: 0.4956, IoU.signboard: 0.4196, IoU.chest of drawers: 0.4319, IoU.counter: 0.3948, IoU.sand: 0.4509, IoU.sink: 0.7411, IoU.skyscraper: 0.5218, IoU.fireplace: 0.6841, IoU.refrigerator: 0.8191, IoU.grandstand: 0.4774, IoU.path: 0.2924, IoU.stairs: 0.3541, IoU.runway: 0.7279, IoU.case: 0.6227, IoU.pool table: 0.9468, IoU.pillow: 0.6663, IoU.screen door: 0.8419, IoU.stairway: 0.5373, IoU.river: 0.0850, IoU.bridge: 0.7647, IoU.bookcase: 0.3773, IoU.blind: 0.5021, IoU.coffee table: 0.6781, IoU.toilet: 0.8963, IoU.flower: 0.4254, IoU.book: 0.5171, IoU.hill: 0.0817, IoU.bench: 0.5215, IoU.countertop: 0.6482, IoU.stove: 0.8185, IoU.palm: 0.5211, IoU.kitchen island: 0.4819, IoU.computer: 0.7750, IoU.swivel chair: 0.4886, IoU.boat: 0.7232, IoU.bar: 0.5376, IoU.arcade machine: 0.7015, IoU.hovel: 0.4338, IoU.bus: 0.8732, IoU.towel: 0.7337, IoU.light: 0.5630, IoU.truck: 0.4483, IoU.tower: 0.2899, IoU.chandelier: 0.6734, IoU.awning: 0.4025, IoU.streetlight: 0.3105, IoU.booth: 0.5472, IoU.television receiver: 0.8029, IoU.airplane: 0.8361, IoU.dirt track: 0.1091, IoU.apparel: 0.5175, IoU.pole: 0.2382, IoU.land: 0.0212, IoU.bannister: 0.1647, IoU.escalator: 0.5529, IoU.ottoman: 0.5613, IoU.bottle: 0.3945, IoU.buffet: 0.5255, IoU.poster: 0.3373, IoU.stage: 0.1695, IoU.van: 0.4224, IoU.ship: 0.6759, IoU.fountain: 0.4347, IoU.conveyer belt: 0.7483, IoU.canopy: 0.4326, IoU.washer: 0.8903, IoU.plaything: 0.2449, IoU.swimming pool: 0.6394, IoU.stool: 0.4935, IoU.barrel: 0.5360, IoU.basket: 0.4298, IoU.waterfall: 0.6907, IoU.tent: 0.8900, IoU.bag: 0.1325, IoU.minibike: 0.7400, IoU.cradle: 0.7580, IoU.oven: 0.5824, IoU.ball: 0.3287, IoU.food: 0.6324, IoU.step: 0.0844, IoU.tank: 0.8636, IoU.trade name: 0.2160, IoU.microwave: 0.8719, IoU.pot: 0.5474, IoU.animal: 0.6405, IoU.bicycle: 0.5769, IoU.lake: 0.4022, IoU.dishwasher: 0.6830, IoU.screen: 0.5720, IoU.blanket: 0.2897, IoU.sculpture: 0.6828, IoU.hood: 0.6257, IoU.sconce: 0.5568, IoU.vase: 0.4578, IoU.traffic light: 0.3384, IoU.tray: 0.1745, IoU.ashcan: 0.4605, IoU.fan: 0.6678, IoU.pier: 0.2778, IoU.crt screen: 0.0262, IoU.plate: 0.5942, IoU.monitor: 0.6202, IoU.bulletin board: 0.4999, IoU.shower: 0.0151, IoU.radiator: 0.6654, IoU.glass: 0.1779, IoU.clock: 0.3242, IoU.flag: 0.7153, Acc.wall: 0.8997, Acc.building: 0.9225, Acc.sky: 0.9747, Acc.floor: 0.8960, Acc.tree: 0.8999, Acc.ceiling: 0.9275, Acc.road: 0.9046, Acc.bed : 0.9615, Acc.windowpane: 0.8181, Acc.grass: 0.8236, Acc.cabinet: 0.7395, Acc.sidewalk: 0.8442, Acc.person: 0.9384, Acc.earth: 0.5427, Acc.door: 0.7781, Acc.table: 0.8172, Acc.mountain: 0.7262, Acc.plant: 0.6481, Acc.curtain: 0.8873, Acc.chair: 0.7299, Acc.car: 0.9416, Acc.water: 0.7818, Acc.painting: 0.8908, Acc.sofa: 0.9112, Acc.shelf: 0.6242, Acc.house: 0.7130, Acc.sea: 0.8281, Acc.mirror: 0.8197, Acc.rug: 0.8539, Acc.field: 0.5176, Acc.armchair: 0.7429, Acc.seat: 0.8908, Acc.fence: 0.7227, Acc.desk: 0.8148, Acc.rock: 0.8066, Acc.wardrobe: 0.7672, Acc.lamp: 0.8434, Acc.bathtub: 0.8653, Acc.railing: 0.5661, Acc.cushion: 0.8206, Acc.base: 0.6386, Acc.box: 0.3776, Acc.column: 0.6277, Acc.signboard: 0.5512, Acc.chest of drawers: 0.6143, Acc.counter: 0.4372, Acc.sand: 0.6724, Acc.sink: 0.8365, Acc.skyscraper: 0.5686, Acc.fireplace: 0.9519, Acc.refrigerator: 0.9091, Acc.grandstand: 0.8729, Acc.path: 0.3968, Acc.stairs: 0.5089, Acc.runway: 0.9542, Acc.case: 0.8126, Acc.pool table: 0.9797, Acc.pillow: 0.7701, Acc.screen door: 0.8656, Acc.stairway: 0.6171, Acc.river: 0.1552, Acc.bridge: 0.8912, Acc.bookcase: 0.6354, Acc.blind: 0.5919, Acc.coffee table: 0.9009, Acc.toilet: 0.9259, Acc.flower: 0.6864, Acc.book: 0.7472, Acc.hill: 0.1416, Acc.bench: 0.6481, Acc.countertop: 0.8282, Acc.stove: 0.9417, Acc.palm: 0.8295, Acc.kitchen island: 0.8544, Acc.computer: 0.9108, Acc.swivel chair: 0.7866, Acc.boat: 0.9042, Acc.bar: 0.7542, Acc.arcade machine: 0.7473, Acc.hovel: 0.4898, Acc.bus: 0.9686, Acc.towel: 0.8906, Acc.light: 0.6196, Acc.truck: 0.5679, Acc.tower: 0.5372, Acc.chandelier: 0.7819, Acc.awning: 0.5041, Acc.streetlight: 0.4436, Acc.booth: 0.5641, Acc.television receiver: 0.8995, Acc.airplane: 0.9289, Acc.dirt track: 0.4464, Acc.apparel: 0.6478, Acc.pole: 0.3163, Acc.land: 0.0459, Acc.bannister: 0.2644, Acc.escalator: 0.8024, Acc.ottoman: 0.7235, Acc.bottle: 0.5407, Acc.buffet: 0.6133, Acc.poster: 0.4792, Acc.stage: 0.4955, Acc.van: 0.5861, Acc.ship: 0.6820, Acc.fountain: 0.4672, Acc.conveyer belt: 0.9292, Acc.canopy: 0.6372, Acc.washer: 0.9408, Acc.plaything: 0.4220, Acc.swimming pool: 0.7770, Acc.stool: 0.6197, Acc.barrel: 0.6467, Acc.basket: 0.5854, Acc.waterfall: 0.8446, Acc.tent: 0.9878, Acc.bag: 0.1414, Acc.minibike: 0.8889, Acc.cradle: 0.9825, Acc.oven: 0.7050, Acc.ball: 0.3325, Acc.food: 0.8048, Acc.step: 0.0974, Acc.tank: 0.9474, Acc.trade name: 0.2299, Acc.microwave: 0.9629, Acc.pot: 0.6170, Acc.animal: 0.6854, Acc.bicycle: 0.7993, Acc.lake: 0.6371, Acc.dishwasher: 0.7567, Acc.screen: 0.9238, Acc.blanket: 0.3787, Acc.sculpture: 0.8547, Acc.hood: 0.7415, Acc.sconce: 0.6240, Acc.vase: 0.5955, Acc.traffic light: 0.6019, Acc.tray: 0.2182, Acc.ashcan: 0.6528, Acc.fan: 0.8209, Acc.pier: 0.4981, Acc.crt screen: 0.0330, Acc.plate: 0.8027, Acc.monitor: 0.7988, Acc.bulletin board: 0.7169, Acc.shower: 0.0155, Acc.radiator: 0.7961, Acc.glass: 0.1879, Acc.clock: 0.4141, Acc.flag: 0.8089 +2024-06-16 09:37:17,424 - mmseg - INFO - Iter [29050/80000] lr: 2.548e-05, eta: 21:21:58, time: 3.685, data_time: 2.311, memory: 70722, decode.loss_ce: 0.2532, decode.acc_seg: 89.2437, aux.loss_ce: 0.1039, aux.acc_seg: 89.0231, loss: 0.3571 +2024-06-16 09:38:25,895 - mmseg - INFO - Iter [29100/80000] lr: 2.545e-05, eta: 21:20:30, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2493, decode.acc_seg: 89.8084, aux.loss_ce: 0.1018, aux.acc_seg: 89.5675, loss: 0.3511 +2024-06-16 09:39:34,244 - mmseg - INFO - Iter [29150/80000] lr: 2.543e-05, eta: 21:19:02, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2263, decode.acc_seg: 90.5244, aux.loss_ce: 0.0937, aux.acc_seg: 90.3517, loss: 0.3200 +2024-06-16 09:40:42,555 - mmseg - INFO - Iter [29200/80000] lr: 2.540e-05, eta: 21:17:35, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2355, decode.acc_seg: 90.3198, aux.loss_ce: 0.0975, aux.acc_seg: 90.0190, loss: 0.3330 +2024-06-16 09:41:51,039 - mmseg - INFO - Iter [29250/80000] lr: 2.538e-05, eta: 21:16:07, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2346, decode.acc_seg: 89.9543, aux.loss_ce: 0.0961, aux.acc_seg: 89.8161, loss: 0.3307 +2024-06-16 09:42:59,173 - mmseg - INFO - Iter [29300/80000] lr: 2.535e-05, eta: 21:14:39, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2554, decode.acc_seg: 89.9761, aux.loss_ce: 0.1037, aux.acc_seg: 89.7790, loss: 0.3592 +2024-06-16 09:44:07,373 - mmseg - INFO - Iter [29350/80000] lr: 2.533e-05, eta: 21:13:11, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2357, decode.acc_seg: 90.2162, aux.loss_ce: 0.0969, aux.acc_seg: 89.8962, loss: 0.3326 +2024-06-16 09:45:15,846 - mmseg - INFO - Iter [29400/80000] lr: 2.530e-05, eta: 21:11:44, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2444, decode.acc_seg: 89.6709, aux.loss_ce: 0.0998, aux.acc_seg: 89.4880, loss: 0.3443 +2024-06-16 09:46:24,154 - mmseg - INFO - Iter [29450/80000] lr: 2.528e-05, eta: 21:10:16, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2360, decode.acc_seg: 90.0444, aux.loss_ce: 0.0972, aux.acc_seg: 89.7746, loss: 0.3332 +2024-06-16 09:47:32,435 - mmseg - INFO - Iter [29500/80000] lr: 2.525e-05, eta: 21:08:49, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2349, decode.acc_seg: 90.2326, aux.loss_ce: 0.0970, aux.acc_seg: 89.8941, loss: 0.3319 +2024-06-16 09:48:40,721 - mmseg - INFO - Iter [29550/80000] lr: 2.523e-05, eta: 21:07:21, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2397, decode.acc_seg: 90.1509, aux.loss_ce: 0.0991, aux.acc_seg: 89.8230, loss: 0.3388 +2024-06-16 09:49:48,999 - mmseg - INFO - Iter [29600/80000] lr: 2.520e-05, eta: 21:05:54, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2484, decode.acc_seg: 89.6089, aux.loss_ce: 0.1017, aux.acc_seg: 89.4005, loss: 0.3501 +2024-06-16 09:50:57,247 - mmseg - INFO - Iter [29650/80000] lr: 2.518e-05, eta: 21:04:26, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2578, decode.acc_seg: 89.2593, aux.loss_ce: 0.1052, aux.acc_seg: 89.0639, loss: 0.3630 +2024-06-16 09:52:05,762 - mmseg - INFO - Iter [29700/80000] lr: 2.515e-05, eta: 21:02:59, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2313, decode.acc_seg: 90.1359, aux.loss_ce: 0.0951, aux.acc_seg: 89.9191, loss: 0.3264 +2024-06-16 09:53:13,930 - mmseg - INFO - Iter [29750/80000] lr: 2.513e-05, eta: 21:01:32, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2385, decode.acc_seg: 89.9919, aux.loss_ce: 0.0976, aux.acc_seg: 89.7879, loss: 0.3361 +2024-06-16 09:54:22,383 - mmseg - INFO - Iter [29800/80000] lr: 2.510e-05, eta: 21:00:05, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2432, decode.acc_seg: 90.1691, aux.loss_ce: 0.0994, aux.acc_seg: 89.8630, loss: 0.3426 +2024-06-16 09:55:30,770 - mmseg - INFO - Iter [29850/80000] lr: 2.508e-05, eta: 20:58:38, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2467, decode.acc_seg: 89.7989, aux.loss_ce: 0.1014, aux.acc_seg: 89.4387, loss: 0.3481 +2024-06-16 09:56:38,914 - mmseg - INFO - Iter [29900/80000] lr: 2.505e-05, eta: 20:57:11, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2332, decode.acc_seg: 90.4781, aux.loss_ce: 0.0960, aux.acc_seg: 90.1710, loss: 0.3291 +2024-06-16 09:57:47,007 - mmseg - INFO - Iter [29950/80000] lr: 2.503e-05, eta: 20:55:44, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2277, decode.acc_seg: 90.5094, aux.loss_ce: 0.0943, aux.acc_seg: 90.2078, loss: 0.3220 +2024-06-16 09:58:55,412 - mmseg - INFO - Saving checkpoint at 30000 iterations +2024-06-16 10:00:20,525 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 10:00:20,525 - mmseg - INFO - Iter [30000/80000] lr: 2.500e-05, eta: 20:56:39, time: 3.070, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2470, decode.acc_seg: 89.8772, aux.loss_ce: 0.1013, aux.acc_seg: 89.6487, loss: 0.3483 +2024-06-16 10:01:56,108 - mmseg - INFO - per class results: +2024-06-16 10:01:56,114 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.01 | 87.5 | +| building | 80.72 | 86.91 | +| sky | 94.66 | 97.4 | +| floor | 84.47 | 92.11 | +| tree | 77.22 | 89.55 | +| ceiling | 86.03 | 94.33 | +| road | 86.34 | 90.67 | +| bed | 92.34 | 97.34 | +| windowpane | 64.61 | 83.83 | +| grass | 68.3 | 83.08 | +| cabinet | 64.25 | 72.11 | +| sidewalk | 70.81 | 82.0 | +| person | 84.25 | 93.97 | +| earth | 39.06 | 58.02 | +| door | 58.31 | 73.95 | +| table | 67.85 | 81.61 | +| mountain | 60.42 | 69.59 | +| plant | 56.11 | 66.92 | +| curtain | 76.71 | 90.82 | +| chair | 66.28 | 76.71 | +| car | 86.12 | 94.4 | +| water | 59.92 | 71.95 | +| painting | 77.3 | 90.67 | +| sofa | 77.2 | 93.21 | +| shelf | 45.38 | 64.32 | +| house | 38.7 | 91.28 | +| sea | 67.53 | 89.17 | +| mirror | 72.02 | 77.37 | +| rug | 71.57 | 84.72 | +| field | 38.04 | 61.33 | +| armchair | 57.44 | 70.01 | +| seat | 65.87 | 87.5 | +| fence | 52.0 | 65.56 | +| desk | 58.2 | 80.02 | +| rock | 57.07 | 82.82 | +| wardrobe | 55.53 | 74.84 | +| lamp | 71.54 | 84.6 | +| bathtub | 80.68 | 84.58 | +| railing | 42.05 | 65.06 | +| cushion | 68.3 | 78.36 | +| base | 39.72 | 54.19 | +| box | 37.74 | 48.07 | +| column | 52.56 | 64.29 | +| signboard | 40.38 | 61.04 | +| chest of drawers | 48.55 | 72.5 | +| counter | 37.13 | 46.52 | +| sand | 52.78 | 70.89 | +| sink | 73.66 | 82.96 | +| skyscraper | 50.12 | 60.39 | +| fireplace | 73.63 | 88.13 | +| refrigerator | 81.72 | 93.67 | +| grandstand | 44.68 | 89.62 | +| path | 29.4 | 39.5 | +| stairs | 28.88 | 37.42 | +| runway | 73.52 | 95.03 | +| case | 58.89 | 83.55 | +| pool table | 92.61 | 98.53 | +| pillow | 68.27 | 81.42 | +| screen door | 80.8 | 85.55 | +| stairway | 52.19 | 72.11 | +| river | 11.6 | 18.52 | +| bridge | 43.25 | 51.12 | +| bookcase | 41.59 | 62.73 | +| blind | 46.19 | 50.31 | +| coffee table | 60.74 | 87.81 | +| toilet | 88.61 | 93.58 | +| flower | 44.58 | 59.04 | +| book | 52.5 | 77.72 | +| hill | 5.8 | 9.47 | +| bench | 49.77 | 60.09 | +| countertop | 62.28 | 78.59 | +| stove | 83.44 | 90.14 | +| palm | 48.57 | 86.98 | +| kitchen island | 48.9 | 88.04 | +| computer | 77.63 | 91.47 | +| swivel chair | 49.34 | 77.12 | +| boat | 63.49 | 88.35 | +| bar | 53.55 | 70.5 | +| arcade machine | 71.95 | 76.83 | +| hovel | 37.62 | 42.07 | +| bus | 92.69 | 95.46 | +| towel | 73.84 | 84.61 | +| light | 58.43 | 67.69 | +| truck | 44.55 | 61.6 | +| tower | 37.55 | 57.49 | +| chandelier | 69.58 | 87.73 | +| awning | 42.6 | 60.66 | +| streetlight | 33.92 | 49.25 | +| booth | 54.09 | 58.61 | +| television receiver | 76.63 | 88.89 | +| airplane | 85.71 | 94.41 | +| dirt track | 6.72 | 39.02 | +| apparel | 48.87 | 66.06 | +| pole | 27.73 | 36.79 | +| land | 4.76 | 10.87 | +| bannister | 14.67 | 21.86 | +| escalator | 55.54 | 80.58 | +| ottoman | 54.62 | 72.42 | +| bottle | 38.12 | 63.53 | +| buffet | 62.06 | 88.65 | +| poster | 34.13 | 48.88 | +| stage | 18.91 | 48.86 | +| van | 46.38 | 59.67 | +| ship | 61.72 | 62.1 | +| fountain | 46.07 | 47.26 | +| conveyer belt | 74.63 | 94.11 | +| canopy | 48.09 | 76.57 | +| washer | 84.39 | 89.54 | +| plaything | 36.56 | 47.47 | +| swimming pool | 61.93 | 87.68 | +| stool | 54.33 | 64.14 | +| barrel | 52.97 | 64.79 | +| basket | 41.28 | 58.21 | +| waterfall | 63.65 | 90.19 | +| tent | 92.04 | 98.61 | +| bag | 19.4 | 24.42 | +| minibike | 75.42 | 84.36 | +| cradle | 85.49 | 97.98 | +| oven | 59.88 | 74.34 | +| ball | 41.48 | 42.83 | +| food | 63.77 | 77.65 | +| step | 14.57 | 18.15 | +| tank | 64.97 | 72.34 | +| trade name | 26.73 | 33.86 | +| microwave | 86.22 | 96.99 | +| pot | 55.8 | 66.53 | +| animal | 62.53 | 65.78 | +| bicycle | 55.6 | 75.3 | +| lake | 50.96 | 63.25 | +| dishwasher | 61.74 | 79.69 | +| screen | 58.22 | 94.38 | +| blanket | 28.02 | 31.58 | +| sculpture | 71.79 | 81.64 | +| hood | 64.65 | 72.88 | +| sconce | 54.95 | 71.84 | +| vase | 46.57 | 63.58 | +| traffic light | 35.54 | 60.09 | +| tray | 18.76 | 25.61 | +| ashcan | 45.54 | 60.23 | +| fan | 64.46 | 81.21 | +| pier | 37.74 | 49.89 | +| crt screen | 2.51 | 2.69 | +| plate | 59.03 | 76.55 | +| monitor | 65.79 | 77.95 | +| bulletin board | 45.22 | 62.98 | +| shower | 1.44 | 1.48 | +| radiator | 65.53 | 79.58 | +| glass | 14.01 | 14.38 | +| clock | 36.29 | 46.23 | +| flag | 70.95 | 78.54 | ++---------------------+-------+-------+ +2024-06-16 10:01:56,114 - mmseg - INFO - Summary: +2024-06-16 10:01:56,114 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.03 | 55.81 | 69.85 | ++-------+-------+-------+ +2024-06-16 10:01:56,115 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 10:01:56,115 - mmseg - INFO - Iter(val) [250] aAcc: 0.8503, mIoU: 0.5581, mAcc: 0.6985, IoU.wall: 0.8101, IoU.building: 0.8072, IoU.sky: 0.9466, IoU.floor: 0.8447, IoU.tree: 0.7722, IoU.ceiling: 0.8603, IoU.road: 0.8634, IoU.bed : 0.9234, IoU.windowpane: 0.6461, IoU.grass: 0.6830, IoU.cabinet: 0.6425, IoU.sidewalk: 0.7081, IoU.person: 0.8425, IoU.earth: 0.3906, IoU.door: 0.5831, IoU.table: 0.6785, IoU.mountain: 0.6042, IoU.plant: 0.5611, IoU.curtain: 0.7671, IoU.chair: 0.6628, IoU.car: 0.8612, IoU.water: 0.5992, IoU.painting: 0.7730, IoU.sofa: 0.7720, IoU.shelf: 0.4538, IoU.house: 0.3870, IoU.sea: 0.6753, IoU.mirror: 0.7202, IoU.rug: 0.7157, IoU.field: 0.3804, IoU.armchair: 0.5744, IoU.seat: 0.6587, IoU.fence: 0.5200, IoU.desk: 0.5820, IoU.rock: 0.5707, IoU.wardrobe: 0.5553, IoU.lamp: 0.7154, IoU.bathtub: 0.8068, IoU.railing: 0.4205, IoU.cushion: 0.6830, IoU.base: 0.3972, IoU.box: 0.3774, IoU.column: 0.5256, IoU.signboard: 0.4038, IoU.chest of drawers: 0.4855, IoU.counter: 0.3713, IoU.sand: 0.5278, IoU.sink: 0.7366, IoU.skyscraper: 0.5012, IoU.fireplace: 0.7363, IoU.refrigerator: 0.8172, IoU.grandstand: 0.4468, IoU.path: 0.2940, IoU.stairs: 0.2888, IoU.runway: 0.7352, IoU.case: 0.5889, IoU.pool table: 0.9261, IoU.pillow: 0.6827, IoU.screen door: 0.8080, IoU.stairway: 0.5219, IoU.river: 0.1160, IoU.bridge: 0.4325, IoU.bookcase: 0.4159, IoU.blind: 0.4619, IoU.coffee table: 0.6074, IoU.toilet: 0.8861, IoU.flower: 0.4458, IoU.book: 0.5250, IoU.hill: 0.0580, IoU.bench: 0.4977, IoU.countertop: 0.6228, IoU.stove: 0.8344, IoU.palm: 0.4857, IoU.kitchen island: 0.4890, IoU.computer: 0.7763, IoU.swivel chair: 0.4934, IoU.boat: 0.6349, IoU.bar: 0.5355, IoU.arcade machine: 0.7195, IoU.hovel: 0.3762, IoU.bus: 0.9269, IoU.towel: 0.7384, IoU.light: 0.5843, IoU.truck: 0.4455, IoU.tower: 0.3755, IoU.chandelier: 0.6958, IoU.awning: 0.4260, IoU.streetlight: 0.3392, IoU.booth: 0.5409, IoU.television receiver: 0.7663, IoU.airplane: 0.8571, IoU.dirt track: 0.0672, IoU.apparel: 0.4887, IoU.pole: 0.2773, IoU.land: 0.0476, IoU.bannister: 0.1467, IoU.escalator: 0.5554, IoU.ottoman: 0.5462, IoU.bottle: 0.3812, IoU.buffet: 0.6206, IoU.poster: 0.3413, IoU.stage: 0.1891, IoU.van: 0.4638, IoU.ship: 0.6172, IoU.fountain: 0.4607, IoU.conveyer belt: 0.7463, IoU.canopy: 0.4809, IoU.washer: 0.8439, IoU.plaything: 0.3656, IoU.swimming pool: 0.6193, IoU.stool: 0.5433, IoU.barrel: 0.5297, IoU.basket: 0.4128, IoU.waterfall: 0.6365, IoU.tent: 0.9204, IoU.bag: 0.1940, IoU.minibike: 0.7542, IoU.cradle: 0.8549, IoU.oven: 0.5988, IoU.ball: 0.4148, IoU.food: 0.6377, IoU.step: 0.1457, IoU.tank: 0.6497, IoU.trade name: 0.2673, IoU.microwave: 0.8622, IoU.pot: 0.5580, IoU.animal: 0.6253, IoU.bicycle: 0.5560, IoU.lake: 0.5096, IoU.dishwasher: 0.6174, IoU.screen: 0.5822, IoU.blanket: 0.2802, IoU.sculpture: 0.7179, IoU.hood: 0.6465, IoU.sconce: 0.5495, IoU.vase: 0.4657, IoU.traffic light: 0.3554, IoU.tray: 0.1876, IoU.ashcan: 0.4554, IoU.fan: 0.6446, IoU.pier: 0.3774, IoU.crt screen: 0.0251, IoU.plate: 0.5903, IoU.monitor: 0.6579, IoU.bulletin board: 0.4522, IoU.shower: 0.0144, IoU.radiator: 0.6553, IoU.glass: 0.1401, IoU.clock: 0.3629, IoU.flag: 0.7095, Acc.wall: 0.8750, Acc.building: 0.8691, Acc.sky: 0.9740, Acc.floor: 0.9211, Acc.tree: 0.8955, Acc.ceiling: 0.9433, Acc.road: 0.9067, Acc.bed : 0.9734, Acc.windowpane: 0.8383, Acc.grass: 0.8308, Acc.cabinet: 0.7211, Acc.sidewalk: 0.8200, Acc.person: 0.9397, Acc.earth: 0.5802, Acc.door: 0.7395, Acc.table: 0.8161, Acc.mountain: 0.6959, Acc.plant: 0.6692, Acc.curtain: 0.9082, Acc.chair: 0.7671, Acc.car: 0.9440, Acc.water: 0.7195, Acc.painting: 0.9067, Acc.sofa: 0.9321, Acc.shelf: 0.6432, Acc.house: 0.9128, Acc.sea: 0.8917, Acc.mirror: 0.7737, Acc.rug: 0.8472, Acc.field: 0.6133, Acc.armchair: 0.7001, Acc.seat: 0.8750, Acc.fence: 0.6556, Acc.desk: 0.8002, Acc.rock: 0.8282, Acc.wardrobe: 0.7484, Acc.lamp: 0.8460, Acc.bathtub: 0.8458, Acc.railing: 0.6506, Acc.cushion: 0.7836, Acc.base: 0.5419, Acc.box: 0.4807, Acc.column: 0.6429, Acc.signboard: 0.6104, Acc.chest of drawers: 0.7250, Acc.counter: 0.4652, Acc.sand: 0.7089, Acc.sink: 0.8296, Acc.skyscraper: 0.6039, Acc.fireplace: 0.8813, Acc.refrigerator: 0.9367, Acc.grandstand: 0.8962, Acc.path: 0.3950, Acc.stairs: 0.3742, Acc.runway: 0.9503, Acc.case: 0.8355, Acc.pool table: 0.9853, Acc.pillow: 0.8142, Acc.screen door: 0.8555, Acc.stairway: 0.7211, Acc.river: 0.1852, Acc.bridge: 0.5112, Acc.bookcase: 0.6273, Acc.blind: 0.5031, Acc.coffee table: 0.8781, Acc.toilet: 0.9358, Acc.flower: 0.5904, Acc.book: 0.7772, Acc.hill: 0.0947, Acc.bench: 0.6009, Acc.countertop: 0.7859, Acc.stove: 0.9014, Acc.palm: 0.8698, Acc.kitchen island: 0.8804, Acc.computer: 0.9147, Acc.swivel chair: 0.7712, Acc.boat: 0.8835, Acc.bar: 0.7050, Acc.arcade machine: 0.7683, Acc.hovel: 0.4207, Acc.bus: 0.9546, Acc.towel: 0.8461, Acc.light: 0.6769, Acc.truck: 0.6160, Acc.tower: 0.5749, Acc.chandelier: 0.8773, Acc.awning: 0.6066, Acc.streetlight: 0.4925, Acc.booth: 0.5861, Acc.television receiver: 0.8889, Acc.airplane: 0.9441, Acc.dirt track: 0.3902, Acc.apparel: 0.6606, Acc.pole: 0.3679, Acc.land: 0.1087, Acc.bannister: 0.2186, Acc.escalator: 0.8058, Acc.ottoman: 0.7242, Acc.bottle: 0.6353, Acc.buffet: 0.8865, Acc.poster: 0.4888, Acc.stage: 0.4886, Acc.van: 0.5967, Acc.ship: 0.6210, Acc.fountain: 0.4726, Acc.conveyer belt: 0.9411, Acc.canopy: 0.7657, Acc.washer: 0.8954, Acc.plaything: 0.4747, Acc.swimming pool: 0.8768, Acc.stool: 0.6414, Acc.barrel: 0.6479, Acc.basket: 0.5821, Acc.waterfall: 0.9019, Acc.tent: 0.9861, Acc.bag: 0.2442, Acc.minibike: 0.8436, Acc.cradle: 0.9798, Acc.oven: 0.7434, Acc.ball: 0.4283, Acc.food: 0.7765, Acc.step: 0.1815, Acc.tank: 0.7234, Acc.trade name: 0.3386, Acc.microwave: 0.9699, Acc.pot: 0.6653, Acc.animal: 0.6578, Acc.bicycle: 0.7530, Acc.lake: 0.6325, Acc.dishwasher: 0.7969, Acc.screen: 0.9438, Acc.blanket: 0.3158, Acc.sculpture: 0.8164, Acc.hood: 0.7288, Acc.sconce: 0.7184, Acc.vase: 0.6358, Acc.traffic light: 0.6009, Acc.tray: 0.2561, Acc.ashcan: 0.6023, Acc.fan: 0.8121, Acc.pier: 0.4989, Acc.crt screen: 0.0269, Acc.plate: 0.7655, Acc.monitor: 0.7795, Acc.bulletin board: 0.6298, Acc.shower: 0.0148, Acc.radiator: 0.7958, Acc.glass: 0.1438, Acc.clock: 0.4623, Acc.flag: 0.7854 +2024-06-16 10:03:05,219 - mmseg - INFO - Iter [30050/80000] lr: 2.498e-05, eta: 20:57:52, time: 3.294, data_time: 1.928, memory: 70722, decode.loss_ce: 0.2649, decode.acc_seg: 88.9659, aux.loss_ce: 0.1086, aux.acc_seg: 88.6281, loss: 0.3735 +2024-06-16 10:04:13,562 - mmseg - INFO - Iter [30100/80000] lr: 2.495e-05, eta: 20:56:24, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2548, decode.acc_seg: 89.6259, aux.loss_ce: 0.1040, aux.acc_seg: 89.3192, loss: 0.3589 +2024-06-16 10:05:21,604 - mmseg - INFO - Iter [30150/80000] lr: 2.493e-05, eta: 20:54:56, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2674, decode.acc_seg: 89.2301, aux.loss_ce: 0.1103, aux.acc_seg: 88.8986, loss: 0.3777 +2024-06-16 10:06:29,776 - mmseg - INFO - Iter [30200/80000] lr: 2.490e-05, eta: 20:53:29, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2563, decode.acc_seg: 89.2003, aux.loss_ce: 0.1049, aux.acc_seg: 88.8965, loss: 0.3613 +2024-06-16 10:07:37,953 - mmseg - INFO - Iter [30250/80000] lr: 2.488e-05, eta: 20:52:01, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2579, decode.acc_seg: 89.2333, aux.loss_ce: 0.1060, aux.acc_seg: 88.9551, loss: 0.3639 +2024-06-16 10:08:46,058 - mmseg - INFO - Iter [30300/80000] lr: 2.485e-05, eta: 20:50:33, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2499, decode.acc_seg: 89.7473, aux.loss_ce: 0.1018, aux.acc_seg: 89.4757, loss: 0.3517 +2024-06-16 10:09:56,521 - mmseg - INFO - Iter [30350/80000] lr: 2.483e-05, eta: 20:49:10, time: 1.409, data_time: 0.052, memory: 70722, decode.loss_ce: 0.2407, decode.acc_seg: 90.0315, aux.loss_ce: 0.0985, aux.acc_seg: 89.7503, loss: 0.3391 +2024-06-16 10:11:04,843 - mmseg - INFO - Iter [30400/80000] lr: 2.480e-05, eta: 20:47:43, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2457, decode.acc_seg: 89.7499, aux.loss_ce: 0.1001, aux.acc_seg: 89.5444, loss: 0.3458 +2024-06-16 10:12:13,131 - mmseg - INFO - Iter [30450/80000] lr: 2.478e-05, eta: 20:46:15, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2506, decode.acc_seg: 89.5931, aux.loss_ce: 0.1026, aux.acc_seg: 89.3843, loss: 0.3532 +2024-06-16 10:13:21,356 - mmseg - INFO - Iter [30500/80000] lr: 2.475e-05, eta: 20:44:48, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2512, decode.acc_seg: 89.7255, aux.loss_ce: 0.1030, aux.acc_seg: 89.5759, loss: 0.3542 +2024-06-16 10:14:29,453 - mmseg - INFO - Iter [30550/80000] lr: 2.473e-05, eta: 20:43:21, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2397, decode.acc_seg: 90.2825, aux.loss_ce: 0.0983, aux.acc_seg: 90.0202, loss: 0.3380 +2024-06-16 10:15:37,627 - mmseg - INFO - Iter [30600/80000] lr: 2.470e-05, eta: 20:41:54, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2323, decode.acc_seg: 90.2511, aux.loss_ce: 0.0959, aux.acc_seg: 89.8881, loss: 0.3283 +2024-06-16 10:16:45,814 - mmseg - INFO - Iter [30650/80000] lr: 2.468e-05, eta: 20:40:27, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2324, decode.acc_seg: 90.6513, aux.loss_ce: 0.0961, aux.acc_seg: 90.2732, loss: 0.3285 +2024-06-16 10:17:54,103 - mmseg - INFO - Iter [30700/80000] lr: 2.465e-05, eta: 20:39:00, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2490, decode.acc_seg: 89.8506, aux.loss_ce: 0.1024, aux.acc_seg: 89.5078, loss: 0.3514 +2024-06-16 10:19:02,294 - mmseg - INFO - Iter [30750/80000] lr: 2.463e-05, eta: 20:37:33, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2305, decode.acc_seg: 90.0903, aux.loss_ce: 0.0949, aux.acc_seg: 89.8444, loss: 0.3255 +2024-06-16 10:20:10,554 - mmseg - INFO - Iter [30800/80000] lr: 2.460e-05, eta: 20:36:06, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2432, decode.acc_seg: 90.1810, aux.loss_ce: 0.1002, aux.acc_seg: 89.8954, loss: 0.3435 +2024-06-16 10:21:18,650 - mmseg - INFO - Iter [30850/80000] lr: 2.458e-05, eta: 20:34:39, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2301, decode.acc_seg: 90.1704, aux.loss_ce: 0.0942, aux.acc_seg: 89.9737, loss: 0.3244 +2024-06-16 10:22:26,795 - mmseg - INFO - Iter [30900/80000] lr: 2.455e-05, eta: 20:33:12, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2285, decode.acc_seg: 90.6572, aux.loss_ce: 0.0947, aux.acc_seg: 90.3028, loss: 0.3232 +2024-06-16 10:23:34,945 - mmseg - INFO - Iter [30950/80000] lr: 2.453e-05, eta: 20:31:46, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2388, decode.acc_seg: 90.0570, aux.loss_ce: 0.0996, aux.acc_seg: 89.6183, loss: 0.3383 +2024-06-16 10:24:43,344 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 10:24:43,344 - mmseg - INFO - Iter [31000/80000] lr: 2.450e-05, eta: 20:30:19, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2339, decode.acc_seg: 90.2145, aux.loss_ce: 0.0970, aux.acc_seg: 89.9155, loss: 0.3309 +2024-06-16 10:26:20,007 - mmseg - INFO - per class results: +2024-06-16 10:26:20,013 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.58 | 88.83 | +| building | 85.44 | 92.94 | +| sky | 94.83 | 97.66 | +| floor | 84.1 | 90.43 | +| tree | 76.48 | 89.96 | +| ceiling | 87.13 | 94.18 | +| road | 85.68 | 89.3 | +| bed | 92.22 | 96.76 | +| windowpane | 65.49 | 82.36 | +| grass | 68.07 | 82.61 | +| cabinet | 63.86 | 72.74 | +| sidewalk | 70.14 | 88.05 | +| person | 84.34 | 93.96 | +| earth | 39.55 | 49.71 | +| door | 57.42 | 75.18 | +| table | 68.41 | 82.44 | +| mountain | 59.88 | 70.4 | +| plant | 54.78 | 63.06 | +| curtain | 78.56 | 89.34 | +| chair | 66.3 | 79.7 | +| car | 86.45 | 94.46 | +| water | 61.66 | 76.66 | +| painting | 76.17 | 88.44 | +| sofa | 79.07 | 90.78 | +| shelf | 43.35 | 59.47 | +| house | 58.57 | 77.18 | +| sea | 71.17 | 81.65 | +| mirror | 74.78 | 84.77 | +| rug | 68.22 | 84.69 | +| field | 36.48 | 65.33 | +| armchair | 57.24 | 75.91 | +| seat | 67.62 | 88.45 | +| fence | 50.92 | 59.93 | +| desk | 58.07 | 78.73 | +| rock | 53.82 | 87.64 | +| wardrobe | 50.69 | 80.18 | +| lamp | 73.45 | 83.84 | +| bathtub | 84.04 | 86.25 | +| railing | 39.53 | 51.78 | +| cushion | 68.07 | 79.05 | +| base | 38.92 | 56.25 | +| box | 37.99 | 50.87 | +| column | 57.76 | 72.13 | +| signboard | 41.43 | 53.05 | +| chest of drawers | 48.38 | 67.53 | +| counter | 32.25 | 34.95 | +| sand | 56.93 | 81.41 | +| sink | 73.5 | 85.75 | +| skyscraper | 49.17 | 63.55 | +| fireplace | 70.99 | 95.21 | +| refrigerator | 79.07 | 92.37 | +| grandstand | 48.16 | 86.8 | +| path | 29.15 | 39.84 | +| stairs | 28.55 | 38.31 | +| runway | 72.3 | 94.4 | +| case | 61.97 | 82.1 | +| pool table | 94.27 | 97.91 | +| pillow | 67.63 | 79.41 | +| screen door | 83.52 | 86.27 | +| stairway | 48.63 | 66.2 | +| river | 11.02 | 24.08 | +| bridge | 51.87 | 58.4 | +| bookcase | 37.26 | 59.04 | +| blind | 42.6 | 45.2 | +| coffee table | 64.42 | 88.48 | +| toilet | 88.81 | 92.98 | +| flower | 46.16 | 56.34 | +| book | 51.68 | 77.72 | +| hill | 7.59 | 14.11 | +| bench | 55.63 | 65.15 | +| countertop | 63.38 | 87.11 | +| stove | 82.86 | 94.33 | +| palm | 50.47 | 83.14 | +| kitchen island | 43.01 | 86.15 | +| computer | 77.64 | 92.54 | +| swivel chair | 51.67 | 80.13 | +| boat | 61.85 | 89.48 | +| bar | 53.65 | 73.83 | +| arcade machine | 79.3 | 84.13 | +| hovel | 26.02 | 30.03 | +| bus | 92.71 | 95.78 | +| towel | 76.49 | 85.82 | +| light | 59.47 | 67.98 | +| truck | 46.0 | 63.19 | +| tower | 34.97 | 48.28 | +| chandelier | 70.4 | 82.85 | +| awning | 52.32 | 65.82 | +| streetlight | 33.89 | 48.24 | +| booth | 38.93 | 64.53 | +| television receiver | 77.3 | 90.32 | +| airplane | 73.64 | 87.25 | +| dirt track | 9.49 | 55.83 | +| apparel | 47.62 | 53.04 | +| pole | 28.08 | 37.48 | +| land | 3.84 | 6.43 | +| bannister | 18.05 | 24.69 | +| escalator | 57.76 | 78.15 | +| ottoman | 49.6 | 63.32 | +| bottle | 39.67 | 65.69 | +| buffet | 57.93 | 65.95 | +| poster | 38.68 | 60.75 | +| stage | 21.58 | 50.64 | +| van | 46.8 | 66.99 | +| ship | 86.0 | 94.06 | +| fountain | 44.28 | 45.57 | +| conveyer belt | 80.82 | 95.58 | +| canopy | 51.03 | 73.93 | +| washer | 80.07 | 85.16 | +| plaything | 36.84 | 49.48 | +| swimming pool | 65.19 | 90.13 | +| stool | 51.25 | 67.99 | +| barrel | 58.49 | 70.55 | +| basket | 45.33 | 59.14 | +| waterfall | 49.28 | 59.35 | +| tent | 93.49 | 98.85 | +| bag | 21.08 | 23.35 | +| minibike | 73.66 | 88.89 | +| cradle | 78.53 | 98.64 | +| oven | 61.01 | 73.91 | +| ball | 45.72 | 50.67 | +| food | 60.52 | 70.97 | +| step | 13.23 | 17.38 | +| tank | 62.02 | 75.81 | +| trade name | 35.21 | 46.82 | +| microwave | 89.62 | 96.07 | +| pot | 57.04 | 69.56 | +| animal | 60.75 | 62.73 | +| bicycle | 58.13 | 73.56 | +| lake | 52.8 | 62.12 | +| dishwasher | 68.02 | 82.66 | +| screen | 60.48 | 94.46 | +| blanket | 22.67 | 24.2 | +| sculpture | 66.15 | 80.16 | +| hood | 62.63 | 71.58 | +| sconce | 59.32 | 71.95 | +| vase | 46.96 | 62.99 | +| traffic light | 32.61 | 58.05 | +| tray | 15.99 | 19.97 | +| ashcan | 44.55 | 66.42 | +| fan | 67.05 | 84.3 | +| pier | 36.09 | 47.09 | +| crt screen | 11.41 | 12.92 | +| plate | 58.76 | 76.31 | +| monitor | 66.86 | 85.51 | +| bulletin board | 55.45 | 72.24 | +| shower | 4.32 | 4.5 | +| radiator | 65.85 | 78.5 | +| glass | 16.33 | 16.93 | +| clock | 36.57 | 50.49 | +| flag | 70.94 | 77.9 | ++---------------------+-------+-------+ +2024-06-16 10:26:20,013 - mmseg - INFO - Summary: +2024-06-16 10:26:20,013 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.68 | 56.31 | 70.13 | ++-------+-------+-------+ +2024-06-16 10:26:20,014 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 10:26:20,015 - mmseg - INFO - Iter(val) [250] aAcc: 0.8568, mIoU: 0.5631, mAcc: 0.7013, IoU.wall: 0.8158, IoU.building: 0.8544, IoU.sky: 0.9483, IoU.floor: 0.8410, IoU.tree: 0.7648, IoU.ceiling: 0.8713, IoU.road: 0.8568, IoU.bed : 0.9222, IoU.windowpane: 0.6549, IoU.grass: 0.6807, IoU.cabinet: 0.6386, IoU.sidewalk: 0.7014, IoU.person: 0.8434, IoU.earth: 0.3955, IoU.door: 0.5742, IoU.table: 0.6841, IoU.mountain: 0.5988, IoU.plant: 0.5478, IoU.curtain: 0.7856, IoU.chair: 0.6630, IoU.car: 0.8645, IoU.water: 0.6166, IoU.painting: 0.7617, IoU.sofa: 0.7907, IoU.shelf: 0.4335, IoU.house: 0.5857, IoU.sea: 0.7117, IoU.mirror: 0.7478, IoU.rug: 0.6822, IoU.field: 0.3648, IoU.armchair: 0.5724, IoU.seat: 0.6762, IoU.fence: 0.5092, IoU.desk: 0.5807, IoU.rock: 0.5382, IoU.wardrobe: 0.5069, IoU.lamp: 0.7345, IoU.bathtub: 0.8404, IoU.railing: 0.3953, IoU.cushion: 0.6807, IoU.base: 0.3892, IoU.box: 0.3799, IoU.column: 0.5776, IoU.signboard: 0.4143, IoU.chest of drawers: 0.4838, IoU.counter: 0.3225, IoU.sand: 0.5693, IoU.sink: 0.7350, IoU.skyscraper: 0.4917, IoU.fireplace: 0.7099, IoU.refrigerator: 0.7907, IoU.grandstand: 0.4816, IoU.path: 0.2915, IoU.stairs: 0.2855, IoU.runway: 0.7230, IoU.case: 0.6197, IoU.pool table: 0.9427, IoU.pillow: 0.6763, IoU.screen door: 0.8352, IoU.stairway: 0.4863, IoU.river: 0.1102, IoU.bridge: 0.5187, IoU.bookcase: 0.3726, IoU.blind: 0.4260, IoU.coffee table: 0.6442, IoU.toilet: 0.8881, IoU.flower: 0.4616, IoU.book: 0.5168, IoU.hill: 0.0759, IoU.bench: 0.5563, IoU.countertop: 0.6338, IoU.stove: 0.8286, IoU.palm: 0.5047, IoU.kitchen island: 0.4301, IoU.computer: 0.7764, IoU.swivel chair: 0.5167, IoU.boat: 0.6185, IoU.bar: 0.5365, IoU.arcade machine: 0.7930, IoU.hovel: 0.2602, IoU.bus: 0.9271, IoU.towel: 0.7649, IoU.light: 0.5947, IoU.truck: 0.4600, IoU.tower: 0.3497, IoU.chandelier: 0.7040, IoU.awning: 0.5232, IoU.streetlight: 0.3389, IoU.booth: 0.3893, IoU.television receiver: 0.7730, IoU.airplane: 0.7364, IoU.dirt track: 0.0949, IoU.apparel: 0.4762, IoU.pole: 0.2808, IoU.land: 0.0384, IoU.bannister: 0.1805, IoU.escalator: 0.5776, IoU.ottoman: 0.4960, IoU.bottle: 0.3967, IoU.buffet: 0.5793, IoU.poster: 0.3868, IoU.stage: 0.2158, IoU.van: 0.4680, IoU.ship: 0.8600, IoU.fountain: 0.4428, IoU.conveyer belt: 0.8082, IoU.canopy: 0.5103, IoU.washer: 0.8007, IoU.plaything: 0.3684, IoU.swimming pool: 0.6519, IoU.stool: 0.5125, IoU.barrel: 0.5849, IoU.basket: 0.4533, IoU.waterfall: 0.4928, IoU.tent: 0.9349, IoU.bag: 0.2108, IoU.minibike: 0.7366, IoU.cradle: 0.7853, IoU.oven: 0.6101, IoU.ball: 0.4572, IoU.food: 0.6052, IoU.step: 0.1323, IoU.tank: 0.6202, IoU.trade name: 0.3521, IoU.microwave: 0.8962, IoU.pot: 0.5704, IoU.animal: 0.6075, IoU.bicycle: 0.5813, IoU.lake: 0.5280, IoU.dishwasher: 0.6802, IoU.screen: 0.6048, IoU.blanket: 0.2267, IoU.sculpture: 0.6615, IoU.hood: 0.6263, IoU.sconce: 0.5932, IoU.vase: 0.4696, IoU.traffic light: 0.3261, IoU.tray: 0.1599, IoU.ashcan: 0.4455, IoU.fan: 0.6705, IoU.pier: 0.3609, IoU.crt screen: 0.1141, IoU.plate: 0.5876, IoU.monitor: 0.6686, IoU.bulletin board: 0.5545, IoU.shower: 0.0432, IoU.radiator: 0.6585, IoU.glass: 0.1633, IoU.clock: 0.3657, IoU.flag: 0.7094, Acc.wall: 0.8883, Acc.building: 0.9294, Acc.sky: 0.9766, Acc.floor: 0.9043, Acc.tree: 0.8996, Acc.ceiling: 0.9418, Acc.road: 0.8930, Acc.bed : 0.9676, Acc.windowpane: 0.8236, Acc.grass: 0.8261, Acc.cabinet: 0.7274, Acc.sidewalk: 0.8805, Acc.person: 0.9396, Acc.earth: 0.4971, Acc.door: 0.7518, Acc.table: 0.8244, Acc.mountain: 0.7040, Acc.plant: 0.6306, Acc.curtain: 0.8934, Acc.chair: 0.7970, Acc.car: 0.9446, Acc.water: 0.7666, Acc.painting: 0.8844, Acc.sofa: 0.9078, Acc.shelf: 0.5947, Acc.house: 0.7718, Acc.sea: 0.8165, Acc.mirror: 0.8477, Acc.rug: 0.8469, Acc.field: 0.6533, Acc.armchair: 0.7591, Acc.seat: 0.8845, Acc.fence: 0.5993, Acc.desk: 0.7873, Acc.rock: 0.8764, Acc.wardrobe: 0.8018, Acc.lamp: 0.8384, Acc.bathtub: 0.8625, Acc.railing: 0.5178, Acc.cushion: 0.7905, Acc.base: 0.5625, Acc.box: 0.5087, Acc.column: 0.7213, Acc.signboard: 0.5305, Acc.chest of drawers: 0.6753, Acc.counter: 0.3495, Acc.sand: 0.8141, Acc.sink: 0.8575, Acc.skyscraper: 0.6355, Acc.fireplace: 0.9521, Acc.refrigerator: 0.9237, Acc.grandstand: 0.8680, Acc.path: 0.3984, Acc.stairs: 0.3831, Acc.runway: 0.9440, Acc.case: 0.8210, Acc.pool table: 0.9791, Acc.pillow: 0.7941, Acc.screen door: 0.8627, Acc.stairway: 0.6620, Acc.river: 0.2408, Acc.bridge: 0.5840, Acc.bookcase: 0.5904, Acc.blind: 0.4520, Acc.coffee table: 0.8848, Acc.toilet: 0.9298, Acc.flower: 0.5634, Acc.book: 0.7772, Acc.hill: 0.1411, Acc.bench: 0.6515, Acc.countertop: 0.8711, Acc.stove: 0.9433, Acc.palm: 0.8314, Acc.kitchen island: 0.8615, Acc.computer: 0.9254, Acc.swivel chair: 0.8013, Acc.boat: 0.8948, Acc.bar: 0.7383, Acc.arcade machine: 0.8413, Acc.hovel: 0.3003, Acc.bus: 0.9578, Acc.towel: 0.8582, Acc.light: 0.6798, Acc.truck: 0.6319, Acc.tower: 0.4828, Acc.chandelier: 0.8285, Acc.awning: 0.6582, Acc.streetlight: 0.4824, Acc.booth: 0.6453, Acc.television receiver: 0.9032, Acc.airplane: 0.8725, Acc.dirt track: 0.5583, Acc.apparel: 0.5304, Acc.pole: 0.3748, Acc.land: 0.0643, Acc.bannister: 0.2469, Acc.escalator: 0.7815, Acc.ottoman: 0.6332, Acc.bottle: 0.6569, Acc.buffet: 0.6595, Acc.poster: 0.6075, Acc.stage: 0.5064, Acc.van: 0.6699, Acc.ship: 0.9406, Acc.fountain: 0.4557, Acc.conveyer belt: 0.9558, Acc.canopy: 0.7393, Acc.washer: 0.8516, Acc.plaything: 0.4948, Acc.swimming pool: 0.9013, Acc.stool: 0.6799, Acc.barrel: 0.7055, Acc.basket: 0.5914, Acc.waterfall: 0.5935, Acc.tent: 0.9885, Acc.bag: 0.2335, Acc.minibike: 0.8889, Acc.cradle: 0.9864, Acc.oven: 0.7391, Acc.ball: 0.5067, Acc.food: 0.7097, Acc.step: 0.1738, Acc.tank: 0.7581, Acc.trade name: 0.4682, Acc.microwave: 0.9607, Acc.pot: 0.6956, Acc.animal: 0.6273, Acc.bicycle: 0.7356, Acc.lake: 0.6212, Acc.dishwasher: 0.8266, Acc.screen: 0.9446, Acc.blanket: 0.2420, Acc.sculpture: 0.8016, Acc.hood: 0.7158, Acc.sconce: 0.7195, Acc.vase: 0.6299, Acc.traffic light: 0.5805, Acc.tray: 0.1997, Acc.ashcan: 0.6642, Acc.fan: 0.8430, Acc.pier: 0.4709, Acc.crt screen: 0.1292, Acc.plate: 0.7631, Acc.monitor: 0.8551, Acc.bulletin board: 0.7224, Acc.shower: 0.0450, Acc.radiator: 0.7850, Acc.glass: 0.1693, Acc.clock: 0.5049, Acc.flag: 0.7790 +2024-06-16 10:27:28,847 - mmseg - INFO - Iter [31050/80000] lr: 2.448e-05, eta: 20:31:26, time: 3.310, data_time: 1.949, memory: 70722, decode.loss_ce: 0.2450, decode.acc_seg: 89.9456, aux.loss_ce: 0.1009, aux.acc_seg: 89.7065, loss: 0.3459 +2024-06-16 10:28:37,157 - mmseg - INFO - Iter [31100/80000] lr: 2.445e-05, eta: 20:29:59, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2513, decode.acc_seg: 89.7926, aux.loss_ce: 0.1030, aux.acc_seg: 89.5290, loss: 0.3544 +2024-06-16 10:29:45,160 - mmseg - INFO - Iter [31150/80000] lr: 2.443e-05, eta: 20:28:32, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2484, decode.acc_seg: 89.8424, aux.loss_ce: 0.1025, aux.acc_seg: 89.4818, loss: 0.3509 +2024-06-16 10:30:53,558 - mmseg - INFO - Iter [31200/80000] lr: 2.440e-05, eta: 20:27:06, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2458, decode.acc_seg: 90.1003, aux.loss_ce: 0.1008, aux.acc_seg: 89.7747, loss: 0.3465 +2024-06-16 10:32:01,851 - mmseg - INFO - Iter [31250/80000] lr: 2.438e-05, eta: 20:25:39, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2435, decode.acc_seg: 89.9771, aux.loss_ce: 0.0990, aux.acc_seg: 89.7603, loss: 0.3426 +2024-06-16 10:33:09,954 - mmseg - INFO - Iter [31300/80000] lr: 2.435e-05, eta: 20:24:12, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2336, decode.acc_seg: 90.0801, aux.loss_ce: 0.0966, aux.acc_seg: 89.8227, loss: 0.3302 +2024-06-16 10:34:18,143 - mmseg - INFO - Iter [31350/80000] lr: 2.433e-05, eta: 20:22:46, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2402, decode.acc_seg: 90.1872, aux.loss_ce: 0.0994, aux.acc_seg: 89.8458, loss: 0.3397 +2024-06-16 10:35:26,248 - mmseg - INFO - Iter [31400/80000] lr: 2.430e-05, eta: 20:21:19, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2501, decode.acc_seg: 89.7330, aux.loss_ce: 0.1027, aux.acc_seg: 89.4814, loss: 0.3528 +2024-06-16 10:36:34,520 - mmseg - INFO - Iter [31450/80000] lr: 2.428e-05, eta: 20:19:53, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2437, decode.acc_seg: 89.9610, aux.loss_ce: 0.0998, aux.acc_seg: 89.7914, loss: 0.3436 +2024-06-16 10:37:42,758 - mmseg - INFO - Iter [31500/80000] lr: 2.425e-05, eta: 20:18:26, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2415, decode.acc_seg: 90.2171, aux.loss_ce: 0.0987, aux.acc_seg: 89.9479, loss: 0.3402 +2024-06-16 10:38:50,948 - mmseg - INFO - Iter [31550/80000] lr: 2.423e-05, eta: 20:17:00, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2460, decode.acc_seg: 89.9067, aux.loss_ce: 0.1010, aux.acc_seg: 89.7003, loss: 0.3470 +2024-06-16 10:40:01,851 - mmseg - INFO - Iter [31600/80000] lr: 2.420e-05, eta: 20:15:38, time: 1.418, data_time: 0.062, memory: 70722, decode.loss_ce: 0.2319, decode.acc_seg: 90.0794, aux.loss_ce: 0.0949, aux.acc_seg: 89.8306, loss: 0.3268 +2024-06-16 10:41:10,063 - mmseg - INFO - Iter [31650/80000] lr: 2.418e-05, eta: 20:14:11, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2372, decode.acc_seg: 89.8241, aux.loss_ce: 0.0977, aux.acc_seg: 89.5609, loss: 0.3349 +2024-06-16 10:42:18,133 - mmseg - INFO - Iter [31700/80000] lr: 2.415e-05, eta: 20:12:45, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2311, decode.acc_seg: 90.2596, aux.loss_ce: 0.0954, aux.acc_seg: 89.9695, loss: 0.3265 +2024-06-16 10:43:26,434 - mmseg - INFO - Iter [31750/80000] lr: 2.413e-05, eta: 20:11:19, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2395, decode.acc_seg: 89.8341, aux.loss_ce: 0.0987, aux.acc_seg: 89.5737, loss: 0.3382 +2024-06-16 10:44:34,859 - mmseg - INFO - Iter [31800/80000] lr: 2.410e-05, eta: 20:09:53, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2409, decode.acc_seg: 90.0700, aux.loss_ce: 0.1002, aux.acc_seg: 89.6840, loss: 0.3411 +2024-06-16 10:45:42,986 - mmseg - INFO - Iter [31850/80000] lr: 2.408e-05, eta: 20:08:27, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2211, decode.acc_seg: 90.6046, aux.loss_ce: 0.0912, aux.acc_seg: 90.2614, loss: 0.3122 +2024-06-16 10:46:51,092 - mmseg - INFO - Iter [31900/80000] lr: 2.405e-05, eta: 20:07:01, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2292, decode.acc_seg: 90.5978, aux.loss_ce: 0.0944, aux.acc_seg: 90.3674, loss: 0.3236 +2024-06-16 10:47:59,386 - mmseg - INFO - Iter [31950/80000] lr: 2.403e-05, eta: 20:05:35, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2355, decode.acc_seg: 90.3513, aux.loss_ce: 0.0966, aux.acc_seg: 90.1025, loss: 0.3321 +2024-06-16 10:49:07,645 - mmseg - INFO - Saving checkpoint at 32000 iterations +2024-06-16 10:50:35,509 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 10:50:35,510 - mmseg - INFO - Iter [32000/80000] lr: 2.400e-05, eta: 20:06:21, time: 3.122, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2365, decode.acc_seg: 89.9751, aux.loss_ce: 0.0971, aux.acc_seg: 89.7664, loss: 0.3336 +2024-06-16 10:52:11,103 - mmseg - INFO - per class results: +2024-06-16 10:52:11,109 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.02 | 89.81 | +| building | 85.01 | 92.36 | +| sky | 94.8 | 97.97 | +| floor | 84.73 | 90.68 | +| tree | 77.38 | 89.16 | +| ceiling | 86.48 | 92.65 | +| road | 85.19 | 93.12 | +| bed | 92.45 | 96.74 | +| windowpane | 65.84 | 81.53 | +| grass | 66.15 | 76.65 | +| cabinet | 66.26 | 75.36 | +| sidewalk | 68.43 | 80.15 | +| person | 84.76 | 94.15 | +| earth | 37.62 | 54.08 | +| door | 58.22 | 74.72 | +| table | 68.62 | 81.77 | +| mountain | 59.83 | 67.03 | +| plant | 56.43 | 72.22 | +| curtain | 78.97 | 89.14 | +| chair | 64.84 | 76.61 | +| car | 86.55 | 93.51 | +| water | 61.33 | 80.14 | +| painting | 78.67 | 91.15 | +| sofa | 79.88 | 87.92 | +| shelf | 44.3 | 57.4 | +| house | 55.77 | 75.62 | +| sea | 61.06 | 69.8 | +| mirror | 78.16 | 85.25 | +| rug | 69.55 | 84.84 | +| field | 31.27 | 54.17 | +| armchair | 55.51 | 70.25 | +| seat | 63.81 | 89.42 | +| fence | 50.88 | 71.5 | +| desk | 51.83 | 84.0 | +| rock | 56.63 | 83.54 | +| wardrobe | 53.39 | 77.24 | +| lamp | 71.92 | 83.65 | +| bathtub | 84.75 | 86.96 | +| railing | 43.31 | 65.49 | +| cushion | 65.22 | 88.13 | +| base | 39.83 | 60.97 | +| box | 37.97 | 52.88 | +| column | 54.09 | 63.75 | +| signboard | 39.78 | 48.47 | +| chest of drawers | 46.28 | 61.43 | +| counter | 40.09 | 49.17 | +| sand | 52.37 | 78.38 | +| sink | 74.93 | 84.39 | +| skyscraper | 49.51 | 65.5 | +| fireplace | 74.94 | 88.84 | +| refrigerator | 83.64 | 93.25 | +| grandstand | 52.04 | 85.52 | +| path | 19.45 | 33.32 | +| stairs | 27.02 | 32.18 | +| runway | 68.48 | 88.47 | +| case | 61.58 | 81.56 | +| pool table | 94.43 | 97.89 | +| pillow | 60.2 | 69.15 | +| screen door | 79.89 | 84.51 | +| stairway | 44.36 | 65.31 | +| river | 10.02 | 17.46 | +| bridge | 55.92 | 62.51 | +| bookcase | 38.26 | 56.24 | +| blind | 43.23 | 48.36 | +| coffee table | 69.94 | 87.7 | +| toilet | 89.41 | 93.4 | +| flower | 43.86 | 53.32 | +| book | 50.7 | 78.14 | +| hill | 5.07 | 11.32 | +| bench | 52.61 | 60.3 | +| countertop | 64.49 | 85.75 | +| stove | 85.96 | 93.09 | +| palm | 51.2 | 83.82 | +| kitchen island | 49.37 | 76.37 | +| computer | 79.19 | 91.23 | +| swivel chair | 50.39 | 71.01 | +| boat | 72.27 | 88.34 | +| bar | 57.36 | 77.89 | +| arcade machine | 53.26 | 55.59 | +| hovel | 24.97 | 28.48 | +| bus | 89.65 | 96.96 | +| towel | 77.93 | 88.0 | +| light | 55.55 | 62.53 | +| truck | 41.22 | 56.67 | +| tower | 27.59 | 35.46 | +| chandelier | 69.61 | 88.44 | +| awning | 46.54 | 58.2 | +| streetlight | 32.69 | 46.06 | +| booth | 43.25 | 79.16 | +| television receiver | 77.75 | 87.87 | +| airplane | 68.19 | 80.48 | +| dirt track | 5.97 | 11.55 | +| apparel | 56.71 | 81.88 | +| pole | 24.56 | 30.36 | +| land | 1.52 | 2.4 | +| bannister | 17.33 | 25.83 | +| escalator | 56.95 | 79.56 | +| ottoman | 49.0 | 65.17 | +| bottle | 37.88 | 65.94 | +| buffet | 59.43 | 69.03 | +| poster | 40.18 | 47.68 | +| stage | 24.43 | 41.77 | +| van | 50.48 | 63.45 | +| ship | 60.66 | 62.89 | +| fountain | 40.32 | 41.93 | +| conveyer belt | 76.65 | 95.47 | +| canopy | 43.49 | 61.21 | +| washer | 80.55 | 85.52 | +| plaything | 27.71 | 30.49 | +| swimming pool | 61.78 | 83.22 | +| stool | 49.46 | 71.74 | +| barrel | 56.06 | 72.2 | +| basket | 38.85 | 51.32 | +| waterfall | 67.69 | 79.06 | +| tent | 96.29 | 97.8 | +| bag | 23.09 | 27.44 | +| minibike | 73.56 | 88.56 | +| cradle | 82.15 | 98.12 | +| oven | 67.58 | 79.28 | +| ball | 11.63 | 12.4 | +| food | 63.86 | 84.04 | +| step | 13.66 | 19.74 | +| tank | 80.14 | 95.19 | +| trade name | 23.97 | 26.39 | +| microwave | 89.48 | 95.8 | +| pot | 57.07 | 69.27 | +| animal | 59.17 | 62.16 | +| bicycle | 57.73 | 78.82 | +| lake | 55.01 | 63.58 | +| dishwasher | 73.1 | 81.28 | +| screen | 53.74 | 76.06 | +| blanket | 33.05 | 36.94 | +| sculpture | 70.61 | 84.71 | +| hood | 62.01 | 68.64 | +| sconce | 59.46 | 71.65 | +| vase | 49.35 | 61.19 | +| traffic light | 33.33 | 59.6 | +| tray | 16.39 | 20.28 | +| ashcan | 46.21 | 60.84 | +| fan | 68.58 | 78.67 | +| pier | 39.35 | 46.3 | +| crt screen | 17.99 | 27.54 | +| plate | 59.96 | 80.36 | +| monitor | 60.61 | 85.04 | +| bulletin board | 51.94 | 72.63 | +| shower | 1.15 | 2.98 | +| radiator | 66.72 | 77.82 | +| glass | 20.61 | 23.25 | +| clock | 39.82 | 46.31 | +| flag | 70.67 | 76.91 | ++---------------------+-------+-------+ +2024-06-16 10:52:11,109 - mmseg - INFO - Summary: +2024-06-16 10:52:11,109 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.59 | 55.75 | 68.59 | ++-------+-------+-------+ +2024-06-16 10:52:11,110 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 10:52:11,110 - mmseg - INFO - Iter(val) [250] aAcc: 0.8559, mIoU: 0.5575, mAcc: 0.6859, IoU.wall: 0.8202, IoU.building: 0.8501, IoU.sky: 0.9480, IoU.floor: 0.8473, IoU.tree: 0.7738, IoU.ceiling: 0.8648, IoU.road: 0.8519, IoU.bed : 0.9245, IoU.windowpane: 0.6584, IoU.grass: 0.6615, IoU.cabinet: 0.6626, IoU.sidewalk: 0.6843, IoU.person: 0.8476, IoU.earth: 0.3762, IoU.door: 0.5822, IoU.table: 0.6862, IoU.mountain: 0.5983, IoU.plant: 0.5643, IoU.curtain: 0.7897, IoU.chair: 0.6484, IoU.car: 0.8655, IoU.water: 0.6133, IoU.painting: 0.7867, IoU.sofa: 0.7988, IoU.shelf: 0.4430, IoU.house: 0.5577, IoU.sea: 0.6106, IoU.mirror: 0.7816, IoU.rug: 0.6955, IoU.field: 0.3127, IoU.armchair: 0.5551, IoU.seat: 0.6381, IoU.fence: 0.5088, IoU.desk: 0.5183, IoU.rock: 0.5663, IoU.wardrobe: 0.5339, IoU.lamp: 0.7192, IoU.bathtub: 0.8475, IoU.railing: 0.4331, IoU.cushion: 0.6522, IoU.base: 0.3983, IoU.box: 0.3797, IoU.column: 0.5409, IoU.signboard: 0.3978, IoU.chest of drawers: 0.4628, IoU.counter: 0.4009, IoU.sand: 0.5237, IoU.sink: 0.7493, IoU.skyscraper: 0.4951, IoU.fireplace: 0.7494, IoU.refrigerator: 0.8364, IoU.grandstand: 0.5204, IoU.path: 0.1945, IoU.stairs: 0.2702, IoU.runway: 0.6848, IoU.case: 0.6158, IoU.pool table: 0.9443, IoU.pillow: 0.6020, IoU.screen door: 0.7989, IoU.stairway: 0.4436, IoU.river: 0.1002, IoU.bridge: 0.5592, IoU.bookcase: 0.3826, IoU.blind: 0.4323, IoU.coffee table: 0.6994, IoU.toilet: 0.8941, IoU.flower: 0.4386, IoU.book: 0.5070, IoU.hill: 0.0507, IoU.bench: 0.5261, IoU.countertop: 0.6449, IoU.stove: 0.8596, IoU.palm: 0.5120, IoU.kitchen island: 0.4937, IoU.computer: 0.7919, IoU.swivel chair: 0.5039, IoU.boat: 0.7227, IoU.bar: 0.5736, IoU.arcade machine: 0.5326, IoU.hovel: 0.2497, IoU.bus: 0.8965, IoU.towel: 0.7793, IoU.light: 0.5555, IoU.truck: 0.4122, IoU.tower: 0.2759, IoU.chandelier: 0.6961, IoU.awning: 0.4654, IoU.streetlight: 0.3269, IoU.booth: 0.4325, IoU.television receiver: 0.7775, IoU.airplane: 0.6819, IoU.dirt track: 0.0597, IoU.apparel: 0.5671, IoU.pole: 0.2456, IoU.land: 0.0152, IoU.bannister: 0.1733, IoU.escalator: 0.5695, IoU.ottoman: 0.4900, IoU.bottle: 0.3788, IoU.buffet: 0.5943, IoU.poster: 0.4018, IoU.stage: 0.2443, IoU.van: 0.5048, IoU.ship: 0.6066, IoU.fountain: 0.4032, IoU.conveyer belt: 0.7665, IoU.canopy: 0.4349, IoU.washer: 0.8055, IoU.plaything: 0.2771, IoU.swimming pool: 0.6178, IoU.stool: 0.4946, IoU.barrel: 0.5606, IoU.basket: 0.3885, IoU.waterfall: 0.6769, IoU.tent: 0.9629, IoU.bag: 0.2309, IoU.minibike: 0.7356, IoU.cradle: 0.8215, IoU.oven: 0.6758, IoU.ball: 0.1163, IoU.food: 0.6386, IoU.step: 0.1366, IoU.tank: 0.8014, IoU.trade name: 0.2397, IoU.microwave: 0.8948, IoU.pot: 0.5707, IoU.animal: 0.5917, IoU.bicycle: 0.5773, IoU.lake: 0.5501, IoU.dishwasher: 0.7310, IoU.screen: 0.5374, IoU.blanket: 0.3305, IoU.sculpture: 0.7061, IoU.hood: 0.6201, IoU.sconce: 0.5946, IoU.vase: 0.4935, IoU.traffic light: 0.3333, IoU.tray: 0.1639, IoU.ashcan: 0.4621, IoU.fan: 0.6858, IoU.pier: 0.3935, IoU.crt screen: 0.1799, IoU.plate: 0.5996, IoU.monitor: 0.6061, IoU.bulletin board: 0.5194, IoU.shower: 0.0115, IoU.radiator: 0.6672, IoU.glass: 0.2061, IoU.clock: 0.3982, IoU.flag: 0.7067, Acc.wall: 0.8981, Acc.building: 0.9236, Acc.sky: 0.9797, Acc.floor: 0.9068, Acc.tree: 0.8916, Acc.ceiling: 0.9265, Acc.road: 0.9312, Acc.bed : 0.9674, Acc.windowpane: 0.8153, Acc.grass: 0.7665, Acc.cabinet: 0.7536, Acc.sidewalk: 0.8015, Acc.person: 0.9415, Acc.earth: 0.5408, Acc.door: 0.7472, Acc.table: 0.8177, Acc.mountain: 0.6703, Acc.plant: 0.7222, Acc.curtain: 0.8914, Acc.chair: 0.7661, Acc.car: 0.9351, Acc.water: 0.8014, Acc.painting: 0.9115, Acc.sofa: 0.8792, Acc.shelf: 0.5740, Acc.house: 0.7562, Acc.sea: 0.6980, Acc.mirror: 0.8525, Acc.rug: 0.8484, Acc.field: 0.5417, Acc.armchair: 0.7025, Acc.seat: 0.8942, Acc.fence: 0.7150, Acc.desk: 0.8400, Acc.rock: 0.8354, Acc.wardrobe: 0.7724, Acc.lamp: 0.8365, Acc.bathtub: 0.8696, Acc.railing: 0.6549, Acc.cushion: 0.8813, Acc.base: 0.6097, Acc.box: 0.5288, Acc.column: 0.6375, Acc.signboard: 0.4847, Acc.chest of drawers: 0.6143, Acc.counter: 0.4917, Acc.sand: 0.7838, Acc.sink: 0.8439, Acc.skyscraper: 0.6550, Acc.fireplace: 0.8884, Acc.refrigerator: 0.9325, Acc.grandstand: 0.8552, Acc.path: 0.3332, Acc.stairs: 0.3218, Acc.runway: 0.8847, Acc.case: 0.8156, Acc.pool table: 0.9789, Acc.pillow: 0.6915, Acc.screen door: 0.8451, Acc.stairway: 0.6531, Acc.river: 0.1746, Acc.bridge: 0.6251, Acc.bookcase: 0.5624, Acc.blind: 0.4836, Acc.coffee table: 0.8770, Acc.toilet: 0.9340, Acc.flower: 0.5332, Acc.book: 0.7814, Acc.hill: 0.1132, Acc.bench: 0.6030, Acc.countertop: 0.8575, Acc.stove: 0.9309, Acc.palm: 0.8382, Acc.kitchen island: 0.7637, Acc.computer: 0.9123, Acc.swivel chair: 0.7101, Acc.boat: 0.8834, Acc.bar: 0.7789, Acc.arcade machine: 0.5559, Acc.hovel: 0.2848, Acc.bus: 0.9696, Acc.towel: 0.8800, Acc.light: 0.6253, Acc.truck: 0.5667, Acc.tower: 0.3546, Acc.chandelier: 0.8844, Acc.awning: 0.5820, Acc.streetlight: 0.4606, Acc.booth: 0.7916, Acc.television receiver: 0.8787, Acc.airplane: 0.8048, Acc.dirt track: 0.1155, Acc.apparel: 0.8188, Acc.pole: 0.3036, Acc.land: 0.0240, Acc.bannister: 0.2583, Acc.escalator: 0.7956, Acc.ottoman: 0.6517, Acc.bottle: 0.6594, Acc.buffet: 0.6903, Acc.poster: 0.4768, Acc.stage: 0.4177, Acc.van: 0.6345, Acc.ship: 0.6289, Acc.fountain: 0.4193, Acc.conveyer belt: 0.9547, Acc.canopy: 0.6121, Acc.washer: 0.8552, Acc.plaything: 0.3049, Acc.swimming pool: 0.8322, Acc.stool: 0.7174, Acc.barrel: 0.7220, Acc.basket: 0.5132, Acc.waterfall: 0.7906, Acc.tent: 0.9780, Acc.bag: 0.2744, Acc.minibike: 0.8856, Acc.cradle: 0.9812, Acc.oven: 0.7928, Acc.ball: 0.1240, Acc.food: 0.8404, Acc.step: 0.1974, Acc.tank: 0.9519, Acc.trade name: 0.2639, Acc.microwave: 0.9580, Acc.pot: 0.6927, Acc.animal: 0.6216, Acc.bicycle: 0.7882, Acc.lake: 0.6358, Acc.dishwasher: 0.8128, Acc.screen: 0.7606, Acc.blanket: 0.3694, Acc.sculpture: 0.8471, Acc.hood: 0.6864, Acc.sconce: 0.7165, Acc.vase: 0.6119, Acc.traffic light: 0.5960, Acc.tray: 0.2028, Acc.ashcan: 0.6084, Acc.fan: 0.7867, Acc.pier: 0.4630, Acc.crt screen: 0.2754, Acc.plate: 0.8036, Acc.monitor: 0.8504, Acc.bulletin board: 0.7263, Acc.shower: 0.0298, Acc.radiator: 0.7782, Acc.glass: 0.2325, Acc.clock: 0.4631, Acc.flag: 0.7691 +2024-06-16 10:53:20,021 - mmseg - INFO - Iter [32050/80000] lr: 2.398e-05, eta: 20:07:19, time: 3.290, data_time: 1.928, memory: 70722, decode.loss_ce: 0.2349, decode.acc_seg: 89.9791, aux.loss_ce: 0.0960, aux.acc_seg: 89.7984, loss: 0.3309 +2024-06-16 10:54:28,115 - mmseg - INFO - Iter [32100/80000] lr: 2.395e-05, eta: 20:05:52, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2389, decode.acc_seg: 90.2934, aux.loss_ce: 0.0986, aux.acc_seg: 89.9018, loss: 0.3375 +2024-06-16 10:55:36,377 - mmseg - INFO - Iter [32150/80000] lr: 2.393e-05, eta: 20:04:26, time: 1.365, data_time: 0.009, memory: 70722, decode.loss_ce: 0.2305, decode.acc_seg: 90.3853, aux.loss_ce: 0.0951, aux.acc_seg: 90.1155, loss: 0.3256 +2024-06-16 10:56:44,621 - mmseg - INFO - Iter [32200/80000] lr: 2.390e-05, eta: 20:03:00, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2392, decode.acc_seg: 90.1670, aux.loss_ce: 0.0984, aux.acc_seg: 89.8423, loss: 0.3376 +2024-06-16 10:57:52,714 - mmseg - INFO - Iter [32250/80000] lr: 2.388e-05, eta: 20:01:33, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2305, decode.acc_seg: 90.5584, aux.loss_ce: 0.0947, aux.acc_seg: 90.3416, loss: 0.3252 +2024-06-16 10:59:00,995 - mmseg - INFO - Iter [32300/80000] lr: 2.385e-05, eta: 20:00:07, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2303, decode.acc_seg: 90.5901, aux.loss_ce: 0.0955, aux.acc_seg: 90.3545, loss: 0.3258 +2024-06-16 11:00:09,236 - mmseg - INFO - Iter [32350/80000] lr: 2.383e-05, eta: 19:58:41, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2280, decode.acc_seg: 90.2408, aux.loss_ce: 0.0933, aux.acc_seg: 90.0073, loss: 0.3213 +2024-06-16 11:01:17,489 - mmseg - INFO - Iter [32400/80000] lr: 2.380e-05, eta: 19:57:15, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2278, decode.acc_seg: 90.5624, aux.loss_ce: 0.0941, aux.acc_seg: 90.1940, loss: 0.3218 +2024-06-16 11:02:25,825 - mmseg - INFO - Iter [32450/80000] lr: 2.378e-05, eta: 19:55:49, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2259, decode.acc_seg: 90.5128, aux.loss_ce: 0.0931, aux.acc_seg: 90.2013, loss: 0.3190 +2024-06-16 11:03:33,822 - mmseg - INFO - Iter [32500/80000] lr: 2.375e-05, eta: 19:54:23, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2471, decode.acc_seg: 89.7774, aux.loss_ce: 0.1017, aux.acc_seg: 89.4984, loss: 0.3488 +2024-06-16 11:04:41,758 - mmseg - INFO - Iter [32550/80000] lr: 2.373e-05, eta: 19:52:56, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2387, decode.acc_seg: 90.2388, aux.loss_ce: 0.0978, aux.acc_seg: 89.9961, loss: 0.3365 +2024-06-16 11:05:50,124 - mmseg - INFO - Iter [32600/80000] lr: 2.370e-05, eta: 19:51:31, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2499, decode.acc_seg: 89.8196, aux.loss_ce: 0.1020, aux.acc_seg: 89.5683, loss: 0.3519 +2024-06-16 11:06:58,483 - mmseg - INFO - Iter [32650/80000] lr: 2.368e-05, eta: 19:50:05, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2429, decode.acc_seg: 90.0435, aux.loss_ce: 0.1007, aux.acc_seg: 89.7323, loss: 0.3436 +2024-06-16 11:08:06,633 - mmseg - INFO - Iter [32700/80000] lr: 2.365e-05, eta: 19:48:39, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2403, decode.acc_seg: 89.8939, aux.loss_ce: 0.0987, aux.acc_seg: 89.6314, loss: 0.3390 +2024-06-16 11:09:15,157 - mmseg - INFO - Iter [32750/80000] lr: 2.363e-05, eta: 19:47:14, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2258, decode.acc_seg: 90.6357, aux.loss_ce: 0.0931, aux.acc_seg: 90.2341, loss: 0.3190 +2024-06-16 11:10:23,388 - mmseg - INFO - Iter [32800/80000] lr: 2.360e-05, eta: 19:45:48, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2228, decode.acc_seg: 90.6925, aux.loss_ce: 0.0915, aux.acc_seg: 90.4348, loss: 0.3143 +2024-06-16 11:11:34,504 - mmseg - INFO - Iter [32850/80000] lr: 2.358e-05, eta: 19:44:27, time: 1.422, data_time: 0.065, memory: 70722, decode.loss_ce: 0.2366, decode.acc_seg: 89.9440, aux.loss_ce: 0.0977, aux.acc_seg: 89.6166, loss: 0.3343 +2024-06-16 11:12:42,572 - mmseg - INFO - Iter [32900/80000] lr: 2.355e-05, eta: 19:43:01, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2251, decode.acc_seg: 90.4296, aux.loss_ce: 0.0939, aux.acc_seg: 90.0221, loss: 0.3190 +2024-06-16 11:13:50,714 - mmseg - INFO - Iter [32950/80000] lr: 2.353e-05, eta: 19:41:35, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2277, decode.acc_seg: 90.4655, aux.loss_ce: 0.0938, aux.acc_seg: 90.1249, loss: 0.3215 +2024-06-16 11:14:59,098 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 11:14:59,098 - mmseg - INFO - Iter [33000/80000] lr: 2.350e-05, eta: 19:40:10, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2344, decode.acc_seg: 90.4959, aux.loss_ce: 0.0958, aux.acc_seg: 90.2997, loss: 0.3301 +2024-06-16 11:16:35,357 - mmseg - INFO - per class results: +2024-06-16 11:16:35,364 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.64 | 89.15 | +| building | 85.57 | 92.36 | +| sky | 94.86 | 97.87 | +| floor | 85.13 | 93.02 | +| tree | 77.73 | 87.9 | +| ceiling | 86.54 | 93.62 | +| road | 86.65 | 90.73 | +| bed | 92.31 | 96.67 | +| windowpane | 64.88 | 82.33 | +| grass | 66.85 | 78.41 | +| cabinet | 64.52 | 73.22 | +| sidewalk | 72.99 | 85.55 | +| person | 84.68 | 94.19 | +| earth | 39.9 | 56.62 | +| door | 56.54 | 71.3 | +| table | 67.89 | 79.53 | +| mountain | 58.96 | 75.38 | +| plant | 58.21 | 69.91 | +| curtain | 78.73 | 89.81 | +| chair | 66.13 | 78.71 | +| car | 87.2 | 93.94 | +| water | 59.06 | 73.71 | +| painting | 74.05 | 90.71 | +| sofa | 79.23 | 88.9 | +| shelf | 47.42 | 72.89 | +| house | 61.81 | 85.93 | +| sea | 56.27 | 70.46 | +| mirror | 77.38 | 86.39 | +| rug | 73.56 | 79.85 | +| field | 37.85 | 61.56 | +| armchair | 60.09 | 78.49 | +| seat | 66.01 | 87.59 | +| fence | 49.31 | 68.42 | +| desk | 53.74 | 85.21 | +| rock | 57.18 | 76.62 | +| wardrobe | 53.68 | 76.04 | +| lamp | 72.35 | 82.09 | +| bathtub | 84.7 | 86.25 | +| railing | 42.88 | 60.9 | +| cushion | 66.86 | 78.32 | +| base | 35.54 | 51.65 | +| box | 33.12 | 40.31 | +| column | 51.95 | 63.61 | +| signboard | 38.36 | 47.43 | +| chest of drawers | 43.19 | 64.84 | +| counter | 46.42 | 72.53 | +| sand | 57.24 | 82.28 | +| sink | 75.69 | 85.0 | +| skyscraper | 48.76 | 62.53 | +| fireplace | 73.18 | 92.88 | +| refrigerator | 82.22 | 92.05 | +| grandstand | 49.9 | 79.94 | +| path | 33.34 | 43.11 | +| stairs | 34.14 | 43.58 | +| runway | 74.07 | 94.66 | +| case | 52.8 | 63.13 | +| pool table | 94.38 | 98.38 | +| pillow | 66.45 | 76.97 | +| screen door | 66.05 | 67.61 | +| stairway | 49.25 | 61.91 | +| river | 11.78 | 24.66 | +| bridge | 53.28 | 58.92 | +| bookcase | 39.55 | 64.24 | +| blind | 44.95 | 50.15 | +| coffee table | 63.88 | 89.73 | +| toilet | 89.53 | 93.25 | +| flower | 41.29 | 49.95 | +| book | 53.27 | 71.87 | +| hill | 7.02 | 13.93 | +| bench | 51.55 | 63.06 | +| countertop | 62.83 | 84.38 | +| stove | 85.16 | 92.75 | +| palm | 52.37 | 85.32 | +| kitchen island | 45.8 | 72.96 | +| computer | 79.24 | 89.23 | +| swivel chair | 49.02 | 70.31 | +| boat | 69.17 | 91.95 | +| bar | 55.04 | 70.71 | +| arcade machine | 74.87 | 80.68 | +| hovel | 24.14 | 26.39 | +| bus | 93.07 | 97.09 | +| towel | 73.01 | 84.53 | +| light | 58.11 | 67.75 | +| truck | 47.63 | 66.54 | +| tower | 28.45 | 43.69 | +| chandelier | 68.55 | 90.07 | +| awning | 48.95 | 67.82 | +| streetlight | 32.77 | 42.11 | +| booth | 40.67 | 61.64 | +| television receiver | 72.13 | 88.62 | +| airplane | 65.78 | 78.57 | +| dirt track | 6.13 | 27.68 | +| apparel | 46.99 | 58.89 | +| pole | 31.65 | 45.18 | +| land | 3.02 | 5.25 | +| bannister | 15.59 | 21.39 | +| escalator | 56.18 | 73.53 | +| ottoman | 48.58 | 64.92 | +| bottle | 41.21 | 53.12 | +| buffet | 59.95 | 70.91 | +| poster | 33.81 | 39.0 | +| stage | 20.31 | 45.23 | +| van | 51.7 | 68.03 | +| ship | 45.68 | 46.77 | +| fountain | 56.11 | 57.34 | +| conveyer belt | 76.9 | 95.54 | +| canopy | 50.35 | 69.68 | +| washer | 78.6 | 82.93 | +| plaything | 23.68 | 27.39 | +| swimming pool | 59.98 | 89.15 | +| stool | 54.91 | 65.67 | +| barrel | 56.22 | 65.06 | +| basket | 38.5 | 53.71 | +| waterfall | 70.79 | 94.8 | +| tent | 95.19 | 98.66 | +| bag | 10.32 | 10.5 | +| minibike | 74.39 | 89.01 | +| cradle | 85.62 | 97.85 | +| oven | 61.55 | 68.54 | +| ball | 54.33 | 65.28 | +| food | 63.56 | 80.92 | +| step | 9.5 | 12.75 | +| tank | 65.33 | 68.82 | +| trade name | 35.38 | 53.58 | +| microwave | 89.82 | 94.22 | +| pot | 56.63 | 68.87 | +| animal | 63.02 | 65.04 | +| bicycle | 58.75 | 74.99 | +| lake | 50.8 | 67.21 | +| dishwasher | 69.86 | 79.88 | +| screen | 59.27 | 93.7 | +| blanket | 36.06 | 51.84 | +| sculpture | 73.89 | 80.17 | +| hood | 60.33 | 74.09 | +| sconce | 56.99 | 67.4 | +| vase | 46.02 | 60.3 | +| traffic light | 35.25 | 62.19 | +| tray | 17.26 | 21.94 | +| ashcan | 43.16 | 62.46 | +| fan | 68.82 | 78.45 | +| pier | 39.05 | 42.54 | +| crt screen | 3.04 | 3.53 | +| plate | 60.06 | 69.33 | +| monitor | 54.71 | 81.57 | +| bulletin board | 43.37 | 52.21 | +| shower | 0.23 | 1.01 | +| radiator | 63.11 | 78.49 | +| glass | 19.34 | 21.21 | +| clock | 38.38 | 46.77 | +| flag | 70.95 | 77.0 | ++---------------------+-------+-------+ +2024-06-16 11:16:35,364 - mmseg - INFO - Summary: +2024-06-16 11:16:35,364 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.76 | 55.87 | 68.81 | ++-------+-------+-------+ +2024-06-16 11:16:35,365 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 11:16:35,365 - mmseg - INFO - Iter(val) [250] aAcc: 0.8576, mIoU: 0.5587, mAcc: 0.6881, IoU.wall: 0.8164, IoU.building: 0.8557, IoU.sky: 0.9486, IoU.floor: 0.8513, IoU.tree: 0.7773, IoU.ceiling: 0.8654, IoU.road: 0.8665, IoU.bed : 0.9231, IoU.windowpane: 0.6488, IoU.grass: 0.6685, IoU.cabinet: 0.6452, IoU.sidewalk: 0.7299, IoU.person: 0.8468, IoU.earth: 0.3990, IoU.door: 0.5654, IoU.table: 0.6789, IoU.mountain: 0.5896, IoU.plant: 0.5821, IoU.curtain: 0.7873, IoU.chair: 0.6613, IoU.car: 0.8720, IoU.water: 0.5906, IoU.painting: 0.7405, IoU.sofa: 0.7923, IoU.shelf: 0.4742, IoU.house: 0.6181, IoU.sea: 0.5627, IoU.mirror: 0.7738, IoU.rug: 0.7356, IoU.field: 0.3785, IoU.armchair: 0.6009, IoU.seat: 0.6601, IoU.fence: 0.4931, IoU.desk: 0.5374, IoU.rock: 0.5718, IoU.wardrobe: 0.5368, IoU.lamp: 0.7235, IoU.bathtub: 0.8470, IoU.railing: 0.4288, IoU.cushion: 0.6686, IoU.base: 0.3554, IoU.box: 0.3312, IoU.column: 0.5195, IoU.signboard: 0.3836, IoU.chest of drawers: 0.4319, IoU.counter: 0.4642, IoU.sand: 0.5724, IoU.sink: 0.7569, IoU.skyscraper: 0.4876, IoU.fireplace: 0.7318, IoU.refrigerator: 0.8222, IoU.grandstand: 0.4990, IoU.path: 0.3334, IoU.stairs: 0.3414, IoU.runway: 0.7407, IoU.case: 0.5280, IoU.pool table: 0.9438, IoU.pillow: 0.6645, IoU.screen door: 0.6605, IoU.stairway: 0.4925, IoU.river: 0.1178, IoU.bridge: 0.5328, IoU.bookcase: 0.3955, IoU.blind: 0.4495, IoU.coffee table: 0.6388, IoU.toilet: 0.8953, IoU.flower: 0.4129, IoU.book: 0.5327, IoU.hill: 0.0702, IoU.bench: 0.5155, IoU.countertop: 0.6283, IoU.stove: 0.8516, IoU.palm: 0.5237, IoU.kitchen island: 0.4580, IoU.computer: 0.7924, IoU.swivel chair: 0.4902, IoU.boat: 0.6917, IoU.bar: 0.5504, IoU.arcade machine: 0.7487, IoU.hovel: 0.2414, IoU.bus: 0.9307, IoU.towel: 0.7301, IoU.light: 0.5811, IoU.truck: 0.4763, IoU.tower: 0.2845, IoU.chandelier: 0.6855, IoU.awning: 0.4895, IoU.streetlight: 0.3277, IoU.booth: 0.4067, IoU.television receiver: 0.7213, IoU.airplane: 0.6578, IoU.dirt track: 0.0613, IoU.apparel: 0.4699, IoU.pole: 0.3165, IoU.land: 0.0302, IoU.bannister: 0.1559, IoU.escalator: 0.5618, IoU.ottoman: 0.4858, IoU.bottle: 0.4121, IoU.buffet: 0.5995, IoU.poster: 0.3381, IoU.stage: 0.2031, IoU.van: 0.5170, IoU.ship: 0.4568, IoU.fountain: 0.5611, IoU.conveyer belt: 0.7690, IoU.canopy: 0.5035, IoU.washer: 0.7860, IoU.plaything: 0.2368, IoU.swimming pool: 0.5998, IoU.stool: 0.5491, IoU.barrel: 0.5622, IoU.basket: 0.3850, IoU.waterfall: 0.7079, IoU.tent: 0.9519, IoU.bag: 0.1032, IoU.minibike: 0.7439, IoU.cradle: 0.8562, IoU.oven: 0.6155, IoU.ball: 0.5433, IoU.food: 0.6356, IoU.step: 0.0950, IoU.tank: 0.6533, IoU.trade name: 0.3538, IoU.microwave: 0.8982, IoU.pot: 0.5663, IoU.animal: 0.6302, IoU.bicycle: 0.5875, IoU.lake: 0.5080, IoU.dishwasher: 0.6986, IoU.screen: 0.5927, IoU.blanket: 0.3606, IoU.sculpture: 0.7389, IoU.hood: 0.6033, IoU.sconce: 0.5699, IoU.vase: 0.4602, IoU.traffic light: 0.3525, IoU.tray: 0.1726, IoU.ashcan: 0.4316, IoU.fan: 0.6882, IoU.pier: 0.3905, IoU.crt screen: 0.0304, IoU.plate: 0.6006, IoU.monitor: 0.5471, IoU.bulletin board: 0.4337, IoU.shower: 0.0023, IoU.radiator: 0.6311, IoU.glass: 0.1934, IoU.clock: 0.3838, IoU.flag: 0.7095, Acc.wall: 0.8915, Acc.building: 0.9236, Acc.sky: 0.9787, Acc.floor: 0.9302, Acc.tree: 0.8790, Acc.ceiling: 0.9362, Acc.road: 0.9073, Acc.bed : 0.9667, Acc.windowpane: 0.8233, Acc.grass: 0.7841, Acc.cabinet: 0.7322, Acc.sidewalk: 0.8555, Acc.person: 0.9419, Acc.earth: 0.5662, Acc.door: 0.7130, Acc.table: 0.7953, Acc.mountain: 0.7538, Acc.plant: 0.6991, Acc.curtain: 0.8981, Acc.chair: 0.7871, Acc.car: 0.9394, Acc.water: 0.7371, Acc.painting: 0.9071, Acc.sofa: 0.8890, Acc.shelf: 0.7289, Acc.house: 0.8593, Acc.sea: 0.7046, Acc.mirror: 0.8639, Acc.rug: 0.7985, Acc.field: 0.6156, Acc.armchair: 0.7849, Acc.seat: 0.8759, Acc.fence: 0.6842, Acc.desk: 0.8521, Acc.rock: 0.7662, Acc.wardrobe: 0.7604, Acc.lamp: 0.8209, Acc.bathtub: 0.8625, Acc.railing: 0.6090, Acc.cushion: 0.7832, Acc.base: 0.5165, Acc.box: 0.4031, Acc.column: 0.6361, Acc.signboard: 0.4743, Acc.chest of drawers: 0.6484, Acc.counter: 0.7253, Acc.sand: 0.8228, Acc.sink: 0.8500, Acc.skyscraper: 0.6253, Acc.fireplace: 0.9288, Acc.refrigerator: 0.9205, Acc.grandstand: 0.7994, Acc.path: 0.4311, Acc.stairs: 0.4358, Acc.runway: 0.9466, Acc.case: 0.6313, Acc.pool table: 0.9838, Acc.pillow: 0.7697, Acc.screen door: 0.6761, Acc.stairway: 0.6191, Acc.river: 0.2466, Acc.bridge: 0.5892, Acc.bookcase: 0.6424, Acc.blind: 0.5015, Acc.coffee table: 0.8973, Acc.toilet: 0.9325, Acc.flower: 0.4995, Acc.book: 0.7187, Acc.hill: 0.1393, Acc.bench: 0.6306, Acc.countertop: 0.8438, Acc.stove: 0.9275, Acc.palm: 0.8532, Acc.kitchen island: 0.7296, Acc.computer: 0.8923, Acc.swivel chair: 0.7031, Acc.boat: 0.9195, Acc.bar: 0.7071, Acc.arcade machine: 0.8068, Acc.hovel: 0.2639, Acc.bus: 0.9709, Acc.towel: 0.8453, Acc.light: 0.6775, Acc.truck: 0.6654, Acc.tower: 0.4369, Acc.chandelier: 0.9007, Acc.awning: 0.6782, Acc.streetlight: 0.4211, Acc.booth: 0.6164, Acc.television receiver: 0.8862, Acc.airplane: 0.7857, Acc.dirt track: 0.2768, Acc.apparel: 0.5889, Acc.pole: 0.4518, Acc.land: 0.0525, Acc.bannister: 0.2139, Acc.escalator: 0.7353, Acc.ottoman: 0.6492, Acc.bottle: 0.5312, Acc.buffet: 0.7091, Acc.poster: 0.3900, Acc.stage: 0.4523, Acc.van: 0.6803, Acc.ship: 0.4677, Acc.fountain: 0.5734, Acc.conveyer belt: 0.9554, Acc.canopy: 0.6968, Acc.washer: 0.8293, Acc.plaything: 0.2739, Acc.swimming pool: 0.8915, Acc.stool: 0.6567, Acc.barrel: 0.6506, Acc.basket: 0.5371, Acc.waterfall: 0.9480, Acc.tent: 0.9866, Acc.bag: 0.1050, Acc.minibike: 0.8901, Acc.cradle: 0.9785, Acc.oven: 0.6854, Acc.ball: 0.6528, Acc.food: 0.8092, Acc.step: 0.1275, Acc.tank: 0.6882, Acc.trade name: 0.5358, Acc.microwave: 0.9422, Acc.pot: 0.6887, Acc.animal: 0.6504, Acc.bicycle: 0.7499, Acc.lake: 0.6721, Acc.dishwasher: 0.7988, Acc.screen: 0.9370, Acc.blanket: 0.5184, Acc.sculpture: 0.8017, Acc.hood: 0.7409, Acc.sconce: 0.6740, Acc.vase: 0.6030, Acc.traffic light: 0.6219, Acc.tray: 0.2194, Acc.ashcan: 0.6246, Acc.fan: 0.7845, Acc.pier: 0.4254, Acc.crt screen: 0.0353, Acc.plate: 0.6933, Acc.monitor: 0.8157, Acc.bulletin board: 0.5221, Acc.shower: 0.0101, Acc.radiator: 0.7849, Acc.glass: 0.2121, Acc.clock: 0.4677, Acc.flag: 0.7700 +2024-06-16 11:17:43,994 - mmseg - INFO - Iter [33050/80000] lr: 2.348e-05, eta: 19:41:02, time: 3.298, data_time: 1.941, memory: 70722, decode.loss_ce: 0.2193, decode.acc_seg: 90.8861, aux.loss_ce: 0.0909, aux.acc_seg: 90.5199, loss: 0.3102 +2024-06-16 11:18:52,317 - mmseg - INFO - Iter [33100/80000] lr: 2.345e-05, eta: 19:39:36, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2197, decode.acc_seg: 90.9932, aux.loss_ce: 0.0913, aux.acc_seg: 90.6987, loss: 0.3110 +2024-06-16 11:20:00,352 - mmseg - INFO - Iter [33150/80000] lr: 2.343e-05, eta: 19:38:10, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2345, decode.acc_seg: 90.2755, aux.loss_ce: 0.0961, aux.acc_seg: 90.0229, loss: 0.3306 +2024-06-16 11:21:08,659 - mmseg - INFO - Iter [33200/80000] lr: 2.340e-05, eta: 19:36:45, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2428, decode.acc_seg: 89.8325, aux.loss_ce: 0.1000, aux.acc_seg: 89.5253, loss: 0.3428 +2024-06-16 11:22:16,753 - mmseg - INFO - Iter [33250/80000] lr: 2.338e-05, eta: 19:35:19, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2180, decode.acc_seg: 90.8709, aux.loss_ce: 0.0909, aux.acc_seg: 90.5309, loss: 0.3090 +2024-06-16 11:23:24,969 - mmseg - INFO - Iter [33300/80000] lr: 2.335e-05, eta: 19:33:54, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2321, decode.acc_seg: 90.6489, aux.loss_ce: 0.0946, aux.acc_seg: 90.3872, loss: 0.3267 +2024-06-16 11:24:33,266 - mmseg - INFO - Iter [33350/80000] lr: 2.333e-05, eta: 19:32:28, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2264, decode.acc_seg: 90.1667, aux.loss_ce: 0.0937, aux.acc_seg: 89.7911, loss: 0.3201 +2024-06-16 11:25:41,513 - mmseg - INFO - Iter [33400/80000] lr: 2.330e-05, eta: 19:31:03, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2257, decode.acc_seg: 90.4057, aux.loss_ce: 0.0925, aux.acc_seg: 90.1490, loss: 0.3182 +2024-06-16 11:26:49,747 - mmseg - INFO - Iter [33450/80000] lr: 2.328e-05, eta: 19:29:38, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2199, decode.acc_seg: 90.8530, aux.loss_ce: 0.0903, aux.acc_seg: 90.5913, loss: 0.3102 +2024-06-16 11:27:57,892 - mmseg - INFO - Iter [33500/80000] lr: 2.325e-05, eta: 19:28:12, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2254, decode.acc_seg: 90.5647, aux.loss_ce: 0.0934, aux.acc_seg: 90.2280, loss: 0.3188 +2024-06-16 11:29:06,271 - mmseg - INFO - Iter [33550/80000] lr: 2.323e-05, eta: 19:26:47, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2452, decode.acc_seg: 89.8999, aux.loss_ce: 0.1020, aux.acc_seg: 89.5085, loss: 0.3472 +2024-06-16 11:30:14,579 - mmseg - INFO - Iter [33600/80000] lr: 2.320e-05, eta: 19:25:22, time: 1.366, data_time: 0.011, memory: 70722, decode.loss_ce: 0.2190, decode.acc_seg: 90.7142, aux.loss_ce: 0.0905, aux.acc_seg: 90.4148, loss: 0.3096 +2024-06-16 11:31:22,928 - mmseg - INFO - Iter [33650/80000] lr: 2.318e-05, eta: 19:23:57, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2307, decode.acc_seg: 90.4833, aux.loss_ce: 0.0958, aux.acc_seg: 90.1368, loss: 0.3265 +2024-06-16 11:32:31,235 - mmseg - INFO - Iter [33700/80000] lr: 2.315e-05, eta: 19:22:32, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2137, decode.acc_seg: 90.9208, aux.loss_ce: 0.0886, aux.acc_seg: 90.6606, loss: 0.3022 +2024-06-16 11:33:39,344 - mmseg - INFO - Iter [33750/80000] lr: 2.313e-05, eta: 19:21:07, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2175, decode.acc_seg: 90.7281, aux.loss_ce: 0.0900, aux.acc_seg: 90.5006, loss: 0.3075 +2024-06-16 11:34:47,836 - mmseg - INFO - Iter [33800/80000] lr: 2.310e-05, eta: 19:19:42, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2438, decode.acc_seg: 89.8503, aux.loss_ce: 0.1002, aux.acc_seg: 89.6032, loss: 0.3440 +2024-06-16 11:35:55,969 - mmseg - INFO - Iter [33850/80000] lr: 2.308e-05, eta: 19:18:17, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2320, decode.acc_seg: 90.5451, aux.loss_ce: 0.0948, aux.acc_seg: 90.3804, loss: 0.3268 +2024-06-16 11:37:04,189 - mmseg - INFO - Iter [33900/80000] lr: 2.305e-05, eta: 19:16:52, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2315, decode.acc_seg: 90.4784, aux.loss_ce: 0.0959, aux.acc_seg: 90.1491, loss: 0.3273 +2024-06-16 11:38:12,337 - mmseg - INFO - Iter [33950/80000] lr: 2.303e-05, eta: 19:15:27, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2365, decode.acc_seg: 90.4965, aux.loss_ce: 0.0987, aux.acc_seg: 90.1129, loss: 0.3352 +2024-06-16 11:39:20,548 - mmseg - INFO - Saving checkpoint at 34000 iterations +2024-06-16 11:40:48,581 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 11:40:48,581 - mmseg - INFO - Iter [34000/80000] lr: 2.300e-05, eta: 19:16:01, time: 3.125, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2318, decode.acc_seg: 90.2753, aux.loss_ce: 0.0961, aux.acc_seg: 90.0281, loss: 0.3279 +2024-06-16 11:42:24,739 - mmseg - INFO - per class results: +2024-06-16 11:42:24,745 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.52 | 89.02 | +| building | 84.43 | 93.93 | +| sky | 94.82 | 97.92 | +| floor | 84.63 | 92.37 | +| tree | 77.79 | 88.59 | +| ceiling | 86.37 | 92.75 | +| road | 86.59 | 91.93 | +| bed | 91.87 | 95.75 | +| windowpane | 65.68 | 82.41 | +| grass | 68.48 | 81.98 | +| cabinet | 65.53 | 75.41 | +| sidewalk | 72.75 | 87.32 | +| person | 85.06 | 93.45 | +| earth | 37.81 | 47.14 | +| door | 56.88 | 73.48 | +| table | 67.94 | 78.4 | +| mountain | 57.52 | 67.87 | +| plant | 54.89 | 61.53 | +| curtain | 78.99 | 90.32 | +| chair | 65.91 | 78.14 | +| car | 87.52 | 94.73 | +| water | 59.58 | 75.37 | +| painting | 75.99 | 91.95 | +| sofa | 79.89 | 91.53 | +| shelf | 48.33 | 68.34 | +| house | 55.87 | 68.23 | +| sea | 66.68 | 82.09 | +| mirror | 75.94 | 82.96 | +| rug | 70.03 | 74.39 | +| field | 36.63 | 71.54 | +| armchair | 56.4 | 76.57 | +| seat | 63.55 | 89.3 | +| fence | 52.01 | 65.72 | +| desk | 62.33 | 81.34 | +| rock | 55.41 | 81.34 | +| wardrobe | 51.33 | 67.7 | +| lamp | 72.93 | 82.95 | +| bathtub | 83.45 | 85.72 | +| railing | 42.68 | 58.92 | +| cushion | 68.61 | 80.93 | +| base | 39.55 | 48.01 | +| box | 36.13 | 49.72 | +| column | 49.64 | 60.27 | +| signboard | 39.43 | 51.63 | +| chest of drawers | 42.69 | 68.57 | +| counter | 45.66 | 50.86 | +| sand | 55.97 | 87.24 | +| sink | 77.29 | 83.07 | +| skyscraper | 48.78 | 61.62 | +| fireplace | 76.17 | 87.44 | +| refrigerator | 81.14 | 88.22 | +| grandstand | 51.73 | 82.0 | +| path | 33.89 | 46.52 | +| stairs | 26.44 | 34.82 | +| runway | 73.33 | 96.78 | +| case | 61.62 | 80.08 | +| pool table | 94.4 | 97.63 | +| pillow | 67.36 | 78.85 | +| screen door | 83.74 | 92.19 | +| stairway | 43.16 | 58.36 | +| river | 10.69 | 24.43 | +| bridge | 51.61 | 59.25 | +| bookcase | 47.17 | 64.64 | +| blind | 42.62 | 48.4 | +| coffee table | 61.3 | 88.31 | +| toilet | 89.84 | 93.0 | +| flower | 43.4 | 58.98 | +| book | 55.11 | 76.28 | +| hill | 5.69 | 17.74 | +| bench | 53.62 | 61.43 | +| countertop | 64.91 | 83.13 | +| stove | 83.12 | 89.34 | +| palm | 51.32 | 84.38 | +| kitchen island | 51.23 | 80.45 | +| computer | 78.32 | 90.81 | +| swivel chair | 51.15 | 73.65 | +| boat | 73.0 | 90.3 | +| bar | 58.13 | 88.42 | +| arcade machine | 72.18 | 76.85 | +| hovel | 39.33 | 43.12 | +| bus | 93.31 | 96.74 | +| towel | 74.94 | 85.99 | +| light | 61.29 | 72.54 | +| truck | 46.25 | 59.96 | +| tower | 29.86 | 56.53 | +| chandelier | 70.88 | 86.57 | +| awning | 53.07 | 74.56 | +| streetlight | 32.59 | 41.81 | +| booth | 49.53 | 72.79 | +| television receiver | 70.03 | 87.35 | +| airplane | 66.43 | 75.51 | +| dirt track | 8.48 | 31.83 | +| apparel | 47.24 | 62.59 | +| pole | 26.85 | 35.99 | +| land | 3.41 | 9.87 | +| bannister | 19.11 | 28.42 | +| escalator | 53.29 | 80.2 | +| ottoman | 47.51 | 63.84 | +| bottle | 38.77 | 65.64 | +| buffet | 46.27 | 53.37 | +| poster | 35.18 | 44.59 | +| stage | 29.54 | 47.81 | +| van | 55.56 | 68.06 | +| ship | 10.67 | 10.69 | +| fountain | 30.79 | 31.36 | +| conveyer belt | 78.68 | 95.39 | +| canopy | 50.99 | 78.13 | +| washer | 78.94 | 83.97 | +| plaything | 24.37 | 35.43 | +| swimming pool | 60.56 | 94.36 | +| stool | 55.8 | 72.65 | +| barrel | 53.17 | 67.79 | +| basket | 45.22 | 64.45 | +| waterfall | 64.93 | 86.28 | +| tent | 88.87 | 98.68 | +| bag | 21.38 | 25.65 | +| minibike | 73.96 | 87.05 | +| cradle | 80.98 | 97.92 | +| oven | 64.68 | 77.54 | +| ball | 50.87 | 57.1 | +| food | 65.39 | 81.13 | +| step | 12.47 | 17.31 | +| tank | 86.38 | 93.45 | +| trade name | 28.88 | 33.79 | +| microwave | 90.1 | 96.03 | +| pot | 57.79 | 66.85 | +| animal | 62.07 | 64.34 | +| bicycle | 46.12 | 49.62 | +| lake | 54.35 | 63.8 | +| dishwasher | 69.6 | 80.46 | +| screen | 62.31 | 91.61 | +| blanket | 27.32 | 34.78 | +| sculpture | 51.82 | 89.78 | +| hood | 63.93 | 74.19 | +| sconce | 59.11 | 72.11 | +| vase | 47.38 | 62.99 | +| traffic light | 34.69 | 59.17 | +| tray | 16.81 | 18.97 | +| ashcan | 46.09 | 62.51 | +| fan | 67.81 | 83.29 | +| pier | 36.04 | 48.16 | +| crt screen | 2.93 | 3.31 | +| plate | 59.77 | 81.94 | +| monitor | 63.75 | 77.61 | +| bulletin board | 50.99 | 56.26 | +| shower | 0.89 | 0.9 | +| radiator | 67.27 | 76.38 | +| glass | 17.9 | 19.01 | +| clock | 37.7 | 47.47 | +| flag | 71.23 | 77.28 | ++---------------------+-------+-------+ +2024-06-16 11:42:24,746 - mmseg - INFO - Summary: +2024-06-16 11:42:24,746 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 85.68 | 55.97 | 69.2 | ++-------+-------+------+ +2024-06-16 11:42:24,746 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 11:42:24,747 - mmseg - INFO - Iter(val) [250] aAcc: 0.8568, mIoU: 0.5597, mAcc: 0.6920, IoU.wall: 0.8152, IoU.building: 0.8443, IoU.sky: 0.9482, IoU.floor: 0.8463, IoU.tree: 0.7779, IoU.ceiling: 0.8637, IoU.road: 0.8659, IoU.bed : 0.9187, IoU.windowpane: 0.6568, IoU.grass: 0.6848, IoU.cabinet: 0.6553, IoU.sidewalk: 0.7275, IoU.person: 0.8506, IoU.earth: 0.3781, IoU.door: 0.5688, IoU.table: 0.6794, IoU.mountain: 0.5752, IoU.plant: 0.5489, IoU.curtain: 0.7899, IoU.chair: 0.6591, IoU.car: 0.8752, IoU.water: 0.5958, IoU.painting: 0.7599, IoU.sofa: 0.7989, IoU.shelf: 0.4833, IoU.house: 0.5587, IoU.sea: 0.6668, IoU.mirror: 0.7594, IoU.rug: 0.7003, IoU.field: 0.3663, IoU.armchair: 0.5640, IoU.seat: 0.6355, IoU.fence: 0.5201, IoU.desk: 0.6233, IoU.rock: 0.5541, IoU.wardrobe: 0.5133, IoU.lamp: 0.7293, IoU.bathtub: 0.8345, IoU.railing: 0.4268, IoU.cushion: 0.6861, IoU.base: 0.3955, IoU.box: 0.3613, IoU.column: 0.4964, IoU.signboard: 0.3943, IoU.chest of drawers: 0.4269, IoU.counter: 0.4566, IoU.sand: 0.5597, IoU.sink: 0.7729, IoU.skyscraper: 0.4878, IoU.fireplace: 0.7617, IoU.refrigerator: 0.8114, IoU.grandstand: 0.5173, IoU.path: 0.3389, IoU.stairs: 0.2644, IoU.runway: 0.7333, IoU.case: 0.6162, IoU.pool table: 0.9440, IoU.pillow: 0.6736, IoU.screen door: 0.8374, IoU.stairway: 0.4316, IoU.river: 0.1069, IoU.bridge: 0.5161, IoU.bookcase: 0.4717, IoU.blind: 0.4262, IoU.coffee table: 0.6130, IoU.toilet: 0.8984, IoU.flower: 0.4340, IoU.book: 0.5511, IoU.hill: 0.0569, IoU.bench: 0.5362, IoU.countertop: 0.6491, IoU.stove: 0.8312, IoU.palm: 0.5132, IoU.kitchen island: 0.5123, IoU.computer: 0.7832, IoU.swivel chair: 0.5115, IoU.boat: 0.7300, IoU.bar: 0.5813, IoU.arcade machine: 0.7218, IoU.hovel: 0.3933, IoU.bus: 0.9331, IoU.towel: 0.7494, IoU.light: 0.6129, IoU.truck: 0.4625, IoU.tower: 0.2986, IoU.chandelier: 0.7088, IoU.awning: 0.5307, IoU.streetlight: 0.3259, IoU.booth: 0.4953, IoU.television receiver: 0.7003, IoU.airplane: 0.6643, IoU.dirt track: 0.0848, IoU.apparel: 0.4724, IoU.pole: 0.2685, IoU.land: 0.0341, IoU.bannister: 0.1911, IoU.escalator: 0.5329, IoU.ottoman: 0.4751, IoU.bottle: 0.3877, IoU.buffet: 0.4627, IoU.poster: 0.3518, IoU.stage: 0.2954, IoU.van: 0.5556, IoU.ship: 0.1067, IoU.fountain: 0.3079, IoU.conveyer belt: 0.7868, IoU.canopy: 0.5099, IoU.washer: 0.7894, IoU.plaything: 0.2437, IoU.swimming pool: 0.6056, IoU.stool: 0.5580, IoU.barrel: 0.5317, IoU.basket: 0.4522, IoU.waterfall: 0.6493, IoU.tent: 0.8887, IoU.bag: 0.2138, IoU.minibike: 0.7396, IoU.cradle: 0.8098, IoU.oven: 0.6468, IoU.ball: 0.5087, IoU.food: 0.6539, IoU.step: 0.1247, IoU.tank: 0.8638, IoU.trade name: 0.2888, IoU.microwave: 0.9010, IoU.pot: 0.5779, IoU.animal: 0.6207, IoU.bicycle: 0.4612, IoU.lake: 0.5435, IoU.dishwasher: 0.6960, IoU.screen: 0.6231, IoU.blanket: 0.2732, IoU.sculpture: 0.5182, IoU.hood: 0.6393, IoU.sconce: 0.5911, IoU.vase: 0.4738, IoU.traffic light: 0.3469, IoU.tray: 0.1681, IoU.ashcan: 0.4609, IoU.fan: 0.6781, IoU.pier: 0.3604, IoU.crt screen: 0.0293, IoU.plate: 0.5977, IoU.monitor: 0.6375, IoU.bulletin board: 0.5099, IoU.shower: 0.0089, IoU.radiator: 0.6727, IoU.glass: 0.1790, IoU.clock: 0.3770, IoU.flag: 0.7123, Acc.wall: 0.8902, Acc.building: 0.9393, Acc.sky: 0.9792, Acc.floor: 0.9237, Acc.tree: 0.8859, Acc.ceiling: 0.9275, Acc.road: 0.9193, Acc.bed : 0.9575, Acc.windowpane: 0.8241, Acc.grass: 0.8198, Acc.cabinet: 0.7541, Acc.sidewalk: 0.8732, Acc.person: 0.9345, Acc.earth: 0.4714, Acc.door: 0.7348, Acc.table: 0.7840, Acc.mountain: 0.6787, Acc.plant: 0.6153, Acc.curtain: 0.9032, Acc.chair: 0.7814, Acc.car: 0.9473, Acc.water: 0.7537, Acc.painting: 0.9195, Acc.sofa: 0.9153, Acc.shelf: 0.6834, Acc.house: 0.6823, Acc.sea: 0.8209, Acc.mirror: 0.8296, Acc.rug: 0.7439, Acc.field: 0.7154, Acc.armchair: 0.7657, Acc.seat: 0.8930, Acc.fence: 0.6572, Acc.desk: 0.8134, Acc.rock: 0.8134, Acc.wardrobe: 0.6770, Acc.lamp: 0.8295, Acc.bathtub: 0.8572, Acc.railing: 0.5892, Acc.cushion: 0.8093, Acc.base: 0.4801, Acc.box: 0.4972, Acc.column: 0.6027, Acc.signboard: 0.5163, Acc.chest of drawers: 0.6857, Acc.counter: 0.5086, Acc.sand: 0.8724, Acc.sink: 0.8307, Acc.skyscraper: 0.6162, Acc.fireplace: 0.8744, Acc.refrigerator: 0.8822, Acc.grandstand: 0.8200, Acc.path: 0.4652, Acc.stairs: 0.3482, Acc.runway: 0.9678, Acc.case: 0.8008, Acc.pool table: 0.9763, Acc.pillow: 0.7885, Acc.screen door: 0.9219, Acc.stairway: 0.5836, Acc.river: 0.2443, Acc.bridge: 0.5925, Acc.bookcase: 0.6464, Acc.blind: 0.4840, Acc.coffee table: 0.8831, Acc.toilet: 0.9300, Acc.flower: 0.5898, Acc.book: 0.7628, Acc.hill: 0.1774, Acc.bench: 0.6143, Acc.countertop: 0.8313, Acc.stove: 0.8934, Acc.palm: 0.8438, Acc.kitchen island: 0.8045, Acc.computer: 0.9081, Acc.swivel chair: 0.7365, Acc.boat: 0.9030, Acc.bar: 0.8842, Acc.arcade machine: 0.7685, Acc.hovel: 0.4312, Acc.bus: 0.9674, Acc.towel: 0.8599, Acc.light: 0.7254, Acc.truck: 0.5996, Acc.tower: 0.5653, Acc.chandelier: 0.8657, Acc.awning: 0.7456, Acc.streetlight: 0.4181, Acc.booth: 0.7279, Acc.television receiver: 0.8735, Acc.airplane: 0.7551, Acc.dirt track: 0.3183, Acc.apparel: 0.6259, Acc.pole: 0.3599, Acc.land: 0.0987, Acc.bannister: 0.2842, Acc.escalator: 0.8020, Acc.ottoman: 0.6384, Acc.bottle: 0.6564, Acc.buffet: 0.5337, Acc.poster: 0.4459, Acc.stage: 0.4781, Acc.van: 0.6806, Acc.ship: 0.1069, Acc.fountain: 0.3136, Acc.conveyer belt: 0.9539, Acc.canopy: 0.7813, Acc.washer: 0.8397, Acc.plaything: 0.3543, Acc.swimming pool: 0.9436, Acc.stool: 0.7265, Acc.barrel: 0.6779, Acc.basket: 0.6445, Acc.waterfall: 0.8628, Acc.tent: 0.9868, Acc.bag: 0.2565, Acc.minibike: 0.8705, Acc.cradle: 0.9792, Acc.oven: 0.7754, Acc.ball: 0.5710, Acc.food: 0.8113, Acc.step: 0.1731, Acc.tank: 0.9345, Acc.trade name: 0.3379, Acc.microwave: 0.9603, Acc.pot: 0.6685, Acc.animal: 0.6434, Acc.bicycle: 0.4962, Acc.lake: 0.6380, Acc.dishwasher: 0.8046, Acc.screen: 0.9161, Acc.blanket: 0.3478, Acc.sculpture: 0.8978, Acc.hood: 0.7419, Acc.sconce: 0.7211, Acc.vase: 0.6299, Acc.traffic light: 0.5917, Acc.tray: 0.1897, Acc.ashcan: 0.6251, Acc.fan: 0.8329, Acc.pier: 0.4816, Acc.crt screen: 0.0331, Acc.plate: 0.8194, Acc.monitor: 0.7761, Acc.bulletin board: 0.5626, Acc.shower: 0.0090, Acc.radiator: 0.7638, Acc.glass: 0.1901, Acc.clock: 0.4747, Acc.flag: 0.7728 +2024-06-16 11:43:33,371 - mmseg - INFO - Iter [34050/80000] lr: 2.298e-05, eta: 19:16:47, time: 3.296, data_time: 1.939, memory: 70722, decode.loss_ce: 0.2261, decode.acc_seg: 90.6493, aux.loss_ce: 0.0934, aux.acc_seg: 90.3494, loss: 0.3195 +2024-06-16 11:44:41,722 - mmseg - INFO - Iter [34100/80000] lr: 2.295e-05, eta: 19:15:22, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2386, decode.acc_seg: 90.0483, aux.loss_ce: 0.0982, aux.acc_seg: 89.7234, loss: 0.3368 +2024-06-16 11:45:53,004 - mmseg - INFO - Iter [34150/80000] lr: 2.293e-05, eta: 19:14:00, time: 1.426, data_time: 0.066, memory: 70722, decode.loss_ce: 0.2270, decode.acc_seg: 90.5212, aux.loss_ce: 0.0935, aux.acc_seg: 90.2740, loss: 0.3205 +2024-06-16 11:47:01,025 - mmseg - INFO - Iter [34200/80000] lr: 2.290e-05, eta: 19:12:35, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2080, decode.acc_seg: 91.0091, aux.loss_ce: 0.0866, aux.acc_seg: 90.6150, loss: 0.2946 +2024-06-16 11:48:09,342 - mmseg - INFO - Iter [34250/80000] lr: 2.288e-05, eta: 19:11:10, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2376, decode.acc_seg: 90.0230, aux.loss_ce: 0.0980, aux.acc_seg: 89.7392, loss: 0.3355 +2024-06-16 11:49:17,571 - mmseg - INFO - Iter [34300/80000] lr: 2.285e-05, eta: 19:09:45, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2197, decode.acc_seg: 90.8083, aux.loss_ce: 0.0908, aux.acc_seg: 90.4585, loss: 0.3105 +2024-06-16 11:50:25,674 - mmseg - INFO - Iter [34350/80000] lr: 2.283e-05, eta: 19:08:19, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2202, decode.acc_seg: 90.7353, aux.loss_ce: 0.0914, aux.acc_seg: 90.4515, loss: 0.3116 +2024-06-16 11:51:33,840 - mmseg - INFO - Iter [34400/80000] lr: 2.280e-05, eta: 19:06:54, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2087, decode.acc_seg: 91.4205, aux.loss_ce: 0.0867, aux.acc_seg: 91.0923, loss: 0.2954 +2024-06-16 11:52:42,226 - mmseg - INFO - Iter [34450/80000] lr: 2.278e-05, eta: 19:05:29, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2200, decode.acc_seg: 90.8245, aux.loss_ce: 0.0913, aux.acc_seg: 90.4354, loss: 0.3113 +2024-06-16 11:53:50,587 - mmseg - INFO - Iter [34500/80000] lr: 2.275e-05, eta: 19:04:05, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2113, decode.acc_seg: 91.2322, aux.loss_ce: 0.0881, aux.acc_seg: 90.8630, loss: 0.2994 +2024-06-16 11:54:58,748 - mmseg - INFO - Iter [34550/80000] lr: 2.273e-05, eta: 19:02:40, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2313, decode.acc_seg: 90.5353, aux.loss_ce: 0.0955, aux.acc_seg: 90.2123, loss: 0.3268 +2024-06-16 11:56:06,916 - mmseg - INFO - Iter [34600/80000] lr: 2.270e-05, eta: 19:01:15, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2309, decode.acc_seg: 90.3161, aux.loss_ce: 0.0950, aux.acc_seg: 89.9763, loss: 0.3260 +2024-06-16 11:57:15,268 - mmseg - INFO - Iter [34650/80000] lr: 2.268e-05, eta: 18:59:50, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2196, decode.acc_seg: 90.8676, aux.loss_ce: 0.0909, aux.acc_seg: 90.5605, loss: 0.3104 +2024-06-16 11:58:23,468 - mmseg - INFO - Iter [34700/80000] lr: 2.265e-05, eta: 18:58:25, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2163, decode.acc_seg: 90.5961, aux.loss_ce: 0.0898, aux.acc_seg: 90.2664, loss: 0.3060 +2024-06-16 11:59:31,579 - mmseg - INFO - Iter [34750/80000] lr: 2.263e-05, eta: 18:57:00, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2286, decode.acc_seg: 90.2608, aux.loss_ce: 0.0939, aux.acc_seg: 90.0273, loss: 0.3224 +2024-06-16 12:00:39,773 - mmseg - INFO - Iter [34800/80000] lr: 2.260e-05, eta: 18:55:36, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2166, decode.acc_seg: 90.9098, aux.loss_ce: 0.0897, aux.acc_seg: 90.5893, loss: 0.3063 +2024-06-16 12:01:48,152 - mmseg - INFO - Iter [34850/80000] lr: 2.258e-05, eta: 18:54:11, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2406, decode.acc_seg: 90.1837, aux.loss_ce: 0.0989, aux.acc_seg: 89.8978, loss: 0.3394 +2024-06-16 12:02:56,414 - mmseg - INFO - Iter [34900/80000] lr: 2.255e-05, eta: 18:52:47, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2102, decode.acc_seg: 91.2204, aux.loss_ce: 0.0865, aux.acc_seg: 90.9350, loss: 0.2967 +2024-06-16 12:04:04,678 - mmseg - INFO - Iter [34950/80000] lr: 2.253e-05, eta: 18:51:22, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2161, decode.acc_seg: 91.0710, aux.loss_ce: 0.0893, aux.acc_seg: 90.6699, loss: 0.3055 +2024-06-16 12:05:12,925 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 12:05:12,925 - mmseg - INFO - Iter [35000/80000] lr: 2.250e-05, eta: 18:49:58, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2173, decode.acc_seg: 91.0174, aux.loss_ce: 0.0896, aux.acc_seg: 90.7145, loss: 0.3069 +2024-06-16 12:06:49,200 - mmseg - INFO - per class results: +2024-06-16 12:06:49,206 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.84 | 90.03 | +| building | 84.63 | 93.51 | +| sky | 94.92 | 97.75 | +| floor | 84.9 | 92.0 | +| tree | 77.7 | 89.49 | +| ceiling | 86.86 | 92.97 | +| road | 87.07 | 91.51 | +| bed | 92.41 | 97.09 | +| windowpane | 65.9 | 80.13 | +| grass | 69.91 | 83.69 | +| cabinet | 64.1 | 72.17 | +| sidewalk | 71.48 | 85.44 | +| person | 85.26 | 94.5 | +| earth | 40.44 | 54.81 | +| door | 55.53 | 72.82 | +| table | 69.08 | 83.12 | +| mountain | 62.16 | 71.12 | +| plant | 55.0 | 62.61 | +| curtain | 78.4 | 89.85 | +| chair | 66.4 | 80.11 | +| car | 87.21 | 92.99 | +| water | 66.41 | 82.76 | +| painting | 77.24 | 91.02 | +| sofa | 82.06 | 89.4 | +| shelf | 44.77 | 59.57 | +| house | 50.14 | 57.91 | +| sea | 72.76 | 82.55 | +| mirror | 75.56 | 82.64 | +| rug | 73.48 | 80.28 | +| field | 38.34 | 67.77 | +| armchair | 59.63 | 76.97 | +| seat | 66.07 | 87.58 | +| fence | 49.53 | 67.5 | +| desk | 59.77 | 81.86 | +| rock | 56.08 | 82.67 | +| wardrobe | 51.42 | 75.46 | +| lamp | 72.81 | 86.35 | +| bathtub | 84.42 | 86.59 | +| railing | 43.77 | 59.34 | +| cushion | 69.92 | 81.12 | +| base | 39.6 | 58.29 | +| box | 37.57 | 50.11 | +| column | 54.32 | 70.76 | +| signboard | 41.72 | 58.45 | +| chest of drawers | 41.52 | 68.53 | +| counter | 44.87 | 52.62 | +| sand | 52.78 | 78.55 | +| sink | 76.06 | 84.06 | +| skyscraper | 46.93 | 63.88 | +| fireplace | 75.67 | 90.92 | +| refrigerator | 83.48 | 93.49 | +| grandstand | 53.19 | 82.38 | +| path | 29.08 | 38.7 | +| stairs | 27.64 | 33.76 | +| runway | 72.03 | 96.44 | +| case | 60.83 | 81.18 | +| pool table | 93.41 | 98.52 | +| pillow | 69.1 | 82.0 | +| screen door | 61.53 | 62.34 | +| stairway | 47.14 | 65.15 | +| river | 12.82 | 24.81 | +| bridge | 45.65 | 49.58 | +| bookcase | 41.08 | 68.37 | +| blind | 46.14 | 52.43 | +| coffee table | 66.54 | 89.22 | +| toilet | 90.24 | 94.73 | +| flower | 42.2 | 53.23 | +| book | 51.3 | 69.14 | +| hill | 9.15 | 15.7 | +| bench | 52.31 | 60.69 | +| countertop | 63.86 | 82.5 | +| stove | 86.11 | 90.63 | +| palm | 57.38 | 74.44 | +| kitchen island | 50.46 | 83.63 | +| computer | 78.55 | 91.09 | +| swivel chair | 49.75 | 77.95 | +| boat | 69.57 | 84.64 | +| bar | 55.55 | 74.4 | +| arcade machine | 80.19 | 85.33 | +| hovel | 52.4 | 63.47 | +| bus | 93.47 | 97.13 | +| towel | 78.02 | 89.17 | +| light | 61.2 | 71.18 | +| truck | 42.0 | 54.53 | +| tower | 31.19 | 49.72 | +| chandelier | 71.18 | 86.79 | +| awning | 47.59 | 63.43 | +| streetlight | 33.54 | 45.27 | +| booth | 50.79 | 70.11 | +| television receiver | 75.34 | 89.44 | +| airplane | 70.61 | 75.31 | +| dirt track | 9.05 | 19.83 | +| apparel | 56.78 | 73.79 | +| pole | 26.35 | 35.99 | +| land | 2.67 | 4.15 | +| bannister | 17.31 | 22.43 | +| escalator | 56.66 | 79.7 | +| ottoman | 50.32 | 60.03 | +| bottle | 39.58 | 64.02 | +| buffet | 61.44 | 77.7 | +| poster | 36.31 | 41.97 | +| stage | 25.7 | 45.59 | +| van | 46.96 | 75.27 | +| ship | 12.26 | 12.28 | +| fountain | 41.14 | 42.16 | +| conveyer belt | 81.97 | 94.97 | +| canopy | 48.55 | 72.18 | +| washer | 79.62 | 84.05 | +| plaything | 25.87 | 40.33 | +| swimming pool | 61.21 | 94.36 | +| stool | 51.17 | 61.09 | +| barrel | 52.4 | 68.95 | +| basket | 43.28 | 56.85 | +| waterfall | 71.02 | 88.8 | +| tent | 77.29 | 98.99 | +| bag | 16.62 | 17.97 | +| minibike | 76.95 | 87.72 | +| cradle | 84.61 | 98.0 | +| oven | 56.79 | 65.4 | +| ball | 37.79 | 38.6 | +| food | 63.79 | 78.35 | +| step | 9.97 | 13.41 | +| tank | 61.73 | 67.89 | +| trade name | 30.62 | 38.21 | +| microwave | 88.53 | 95.31 | +| pot | 57.01 | 66.31 | +| animal | 63.79 | 66.6 | +| bicycle | 60.16 | 71.7 | +| lake | 49.64 | 63.73 | +| dishwasher | 66.19 | 77.08 | +| screen | 59.95 | 92.55 | +| blanket | 26.68 | 31.64 | +| sculpture | 70.14 | 85.73 | +| hood | 65.62 | 76.14 | +| sconce | 53.24 | 60.21 | +| vase | 46.56 | 64.28 | +| traffic light | 38.52 | 57.21 | +| tray | 11.5 | 12.99 | +| ashcan | 42.95 | 64.84 | +| fan | 67.2 | 78.67 | +| pier | 35.04 | 45.31 | +| crt screen | 2.14 | 2.32 | +| plate | 55.15 | 80.3 | +| monitor | 66.3 | 81.39 | +| bulletin board | 59.65 | 70.48 | +| shower | 0.01 | 0.07 | +| radiator | 67.82 | 74.76 | +| glass | 17.33 | 18.0 | +| clock | 40.96 | 46.94 | +| flag | 70.01 | 74.79 | ++---------------------+-------+-------+ +2024-06-16 12:06:49,206 - mmseg - INFO - Summary: +2024-06-16 12:06:49,207 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.97 | 56.11 | 68.77 | ++-------+-------+-------+ +2024-06-16 12:06:49,207 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 12:06:49,207 - mmseg - INFO - Iter(val) [250] aAcc: 0.8597, mIoU: 0.5611, mAcc: 0.6877, IoU.wall: 0.8184, IoU.building: 0.8463, IoU.sky: 0.9492, IoU.floor: 0.8490, IoU.tree: 0.7770, IoU.ceiling: 0.8686, IoU.road: 0.8707, IoU.bed : 0.9241, IoU.windowpane: 0.6590, IoU.grass: 0.6991, IoU.cabinet: 0.6410, IoU.sidewalk: 0.7148, IoU.person: 0.8526, IoU.earth: 0.4044, IoU.door: 0.5553, IoU.table: 0.6908, IoU.mountain: 0.6216, IoU.plant: 0.5500, IoU.curtain: 0.7840, IoU.chair: 0.6640, IoU.car: 0.8721, IoU.water: 0.6641, IoU.painting: 0.7724, IoU.sofa: 0.8206, IoU.shelf: 0.4477, IoU.house: 0.5014, IoU.sea: 0.7276, IoU.mirror: 0.7556, IoU.rug: 0.7348, IoU.field: 0.3834, IoU.armchair: 0.5963, IoU.seat: 0.6607, IoU.fence: 0.4953, IoU.desk: 0.5977, IoU.rock: 0.5608, IoU.wardrobe: 0.5142, IoU.lamp: 0.7281, IoU.bathtub: 0.8442, IoU.railing: 0.4377, IoU.cushion: 0.6992, IoU.base: 0.3960, IoU.box: 0.3757, IoU.column: 0.5432, IoU.signboard: 0.4172, IoU.chest of drawers: 0.4152, IoU.counter: 0.4487, IoU.sand: 0.5278, IoU.sink: 0.7606, IoU.skyscraper: 0.4693, IoU.fireplace: 0.7567, IoU.refrigerator: 0.8348, IoU.grandstand: 0.5319, IoU.path: 0.2908, IoU.stairs: 0.2764, IoU.runway: 0.7203, IoU.case: 0.6083, IoU.pool table: 0.9341, IoU.pillow: 0.6910, IoU.screen door: 0.6153, IoU.stairway: 0.4714, IoU.river: 0.1282, IoU.bridge: 0.4565, IoU.bookcase: 0.4108, IoU.blind: 0.4614, IoU.coffee table: 0.6654, IoU.toilet: 0.9024, IoU.flower: 0.4220, IoU.book: 0.5130, IoU.hill: 0.0915, IoU.bench: 0.5231, IoU.countertop: 0.6386, IoU.stove: 0.8611, IoU.palm: 0.5738, IoU.kitchen island: 0.5046, IoU.computer: 0.7855, IoU.swivel chair: 0.4975, IoU.boat: 0.6957, IoU.bar: 0.5555, IoU.arcade machine: 0.8019, IoU.hovel: 0.5240, IoU.bus: 0.9347, IoU.towel: 0.7802, IoU.light: 0.6120, IoU.truck: 0.4200, IoU.tower: 0.3119, IoU.chandelier: 0.7118, IoU.awning: 0.4759, IoU.streetlight: 0.3354, IoU.booth: 0.5079, IoU.television receiver: 0.7534, IoU.airplane: 0.7061, IoU.dirt track: 0.0905, IoU.apparel: 0.5678, IoU.pole: 0.2635, IoU.land: 0.0267, IoU.bannister: 0.1731, IoU.escalator: 0.5666, IoU.ottoman: 0.5032, IoU.bottle: 0.3958, IoU.buffet: 0.6144, IoU.poster: 0.3631, IoU.stage: 0.2570, IoU.van: 0.4696, IoU.ship: 0.1226, IoU.fountain: 0.4114, IoU.conveyer belt: 0.8197, IoU.canopy: 0.4855, IoU.washer: 0.7962, IoU.plaything: 0.2587, IoU.swimming pool: 0.6121, IoU.stool: 0.5117, IoU.barrel: 0.5240, IoU.basket: 0.4328, IoU.waterfall: 0.7102, IoU.tent: 0.7729, IoU.bag: 0.1662, IoU.minibike: 0.7695, IoU.cradle: 0.8461, IoU.oven: 0.5679, IoU.ball: 0.3779, IoU.food: 0.6379, IoU.step: 0.0997, IoU.tank: 0.6173, IoU.trade name: 0.3062, IoU.microwave: 0.8853, IoU.pot: 0.5701, IoU.animal: 0.6379, IoU.bicycle: 0.6016, IoU.lake: 0.4964, IoU.dishwasher: 0.6619, IoU.screen: 0.5995, IoU.blanket: 0.2668, IoU.sculpture: 0.7014, IoU.hood: 0.6562, IoU.sconce: 0.5324, IoU.vase: 0.4656, IoU.traffic light: 0.3852, IoU.tray: 0.1150, IoU.ashcan: 0.4295, IoU.fan: 0.6720, IoU.pier: 0.3504, IoU.crt screen: 0.0214, IoU.plate: 0.5515, IoU.monitor: 0.6630, IoU.bulletin board: 0.5965, IoU.shower: 0.0001, IoU.radiator: 0.6782, IoU.glass: 0.1733, IoU.clock: 0.4096, IoU.flag: 0.7001, Acc.wall: 0.9003, Acc.building: 0.9351, Acc.sky: 0.9775, Acc.floor: 0.9200, Acc.tree: 0.8949, Acc.ceiling: 0.9297, Acc.road: 0.9151, Acc.bed : 0.9709, Acc.windowpane: 0.8013, Acc.grass: 0.8369, Acc.cabinet: 0.7217, Acc.sidewalk: 0.8544, Acc.person: 0.9450, Acc.earth: 0.5481, Acc.door: 0.7282, Acc.table: 0.8312, Acc.mountain: 0.7112, Acc.plant: 0.6261, Acc.curtain: 0.8985, Acc.chair: 0.8011, Acc.car: 0.9299, Acc.water: 0.8276, Acc.painting: 0.9102, Acc.sofa: 0.8940, Acc.shelf: 0.5957, Acc.house: 0.5791, Acc.sea: 0.8255, Acc.mirror: 0.8264, Acc.rug: 0.8028, Acc.field: 0.6777, Acc.armchair: 0.7697, Acc.seat: 0.8758, Acc.fence: 0.6750, Acc.desk: 0.8186, Acc.rock: 0.8267, Acc.wardrobe: 0.7546, Acc.lamp: 0.8635, Acc.bathtub: 0.8659, Acc.railing: 0.5934, Acc.cushion: 0.8112, Acc.base: 0.5829, Acc.box: 0.5011, Acc.column: 0.7076, Acc.signboard: 0.5845, Acc.chest of drawers: 0.6853, Acc.counter: 0.5262, Acc.sand: 0.7855, Acc.sink: 0.8406, Acc.skyscraper: 0.6388, Acc.fireplace: 0.9092, Acc.refrigerator: 0.9349, Acc.grandstand: 0.8238, Acc.path: 0.3870, Acc.stairs: 0.3376, Acc.runway: 0.9644, Acc.case: 0.8118, Acc.pool table: 0.9852, Acc.pillow: 0.8200, Acc.screen door: 0.6234, Acc.stairway: 0.6515, Acc.river: 0.2481, Acc.bridge: 0.4958, Acc.bookcase: 0.6837, Acc.blind: 0.5243, Acc.coffee table: 0.8922, Acc.toilet: 0.9473, Acc.flower: 0.5323, Acc.book: 0.6914, Acc.hill: 0.1570, Acc.bench: 0.6069, Acc.countertop: 0.8250, Acc.stove: 0.9063, Acc.palm: 0.7444, Acc.kitchen island: 0.8363, Acc.computer: 0.9109, Acc.swivel chair: 0.7795, Acc.boat: 0.8464, Acc.bar: 0.7440, Acc.arcade machine: 0.8533, Acc.hovel: 0.6347, Acc.bus: 0.9713, Acc.towel: 0.8917, Acc.light: 0.7118, Acc.truck: 0.5453, Acc.tower: 0.4972, Acc.chandelier: 0.8679, Acc.awning: 0.6343, Acc.streetlight: 0.4527, Acc.booth: 0.7011, Acc.television receiver: 0.8944, Acc.airplane: 0.7531, Acc.dirt track: 0.1983, Acc.apparel: 0.7379, Acc.pole: 0.3599, Acc.land: 0.0415, Acc.bannister: 0.2243, Acc.escalator: 0.7970, Acc.ottoman: 0.6003, Acc.bottle: 0.6402, Acc.buffet: 0.7770, Acc.poster: 0.4197, Acc.stage: 0.4559, Acc.van: 0.7527, Acc.ship: 0.1228, Acc.fountain: 0.4216, Acc.conveyer belt: 0.9497, Acc.canopy: 0.7218, Acc.washer: 0.8405, Acc.plaything: 0.4033, Acc.swimming pool: 0.9436, Acc.stool: 0.6109, Acc.barrel: 0.6895, Acc.basket: 0.5685, Acc.waterfall: 0.8880, Acc.tent: 0.9899, Acc.bag: 0.1797, Acc.minibike: 0.8772, Acc.cradle: 0.9800, Acc.oven: 0.6540, Acc.ball: 0.3860, Acc.food: 0.7835, Acc.step: 0.1341, Acc.tank: 0.6789, Acc.trade name: 0.3821, Acc.microwave: 0.9531, Acc.pot: 0.6631, Acc.animal: 0.6660, Acc.bicycle: 0.7170, Acc.lake: 0.6373, Acc.dishwasher: 0.7708, Acc.screen: 0.9255, Acc.blanket: 0.3164, Acc.sculpture: 0.8573, Acc.hood: 0.7614, Acc.sconce: 0.6021, Acc.vase: 0.6428, Acc.traffic light: 0.5721, Acc.tray: 0.1299, Acc.ashcan: 0.6484, Acc.fan: 0.7867, Acc.pier: 0.4531, Acc.crt screen: 0.0232, Acc.plate: 0.8030, Acc.monitor: 0.8139, Acc.bulletin board: 0.7048, Acc.shower: 0.0007, Acc.radiator: 0.7476, Acc.glass: 0.1800, Acc.clock: 0.4694, Acc.flag: 0.7479 +2024-06-16 12:07:57,777 - mmseg - INFO - Iter [35050/80000] lr: 2.248e-05, eta: 18:50:37, time: 3.297, data_time: 1.942, memory: 70722, decode.loss_ce: 0.2152, decode.acc_seg: 90.9533, aux.loss_ce: 0.0895, aux.acc_seg: 90.5700, loss: 0.3047 +2024-06-16 12:09:06,024 - mmseg - INFO - Iter [35100/80000] lr: 2.245e-05, eta: 18:49:12, time: 1.365, data_time: 0.011, memory: 70722, decode.loss_ce: 0.2258, decode.acc_seg: 90.5398, aux.loss_ce: 0.0930, aux.acc_seg: 90.2673, loss: 0.3188 +2024-06-16 12:10:14,402 - mmseg - INFO - Iter [35150/80000] lr: 2.243e-05, eta: 18:47:48, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2286, decode.acc_seg: 90.2399, aux.loss_ce: 0.0936, aux.acc_seg: 90.0313, loss: 0.3222 +2024-06-16 12:11:22,524 - mmseg - INFO - Iter [35200/80000] lr: 2.240e-05, eta: 18:46:23, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2215, decode.acc_seg: 90.5973, aux.loss_ce: 0.0915, aux.acc_seg: 90.3941, loss: 0.3130 +2024-06-16 12:12:30,875 - mmseg - INFO - Iter [35250/80000] lr: 2.238e-05, eta: 18:44:59, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2371, decode.acc_seg: 90.0001, aux.loss_ce: 0.0987, aux.acc_seg: 89.6807, loss: 0.3358 +2024-06-16 12:13:39,158 - mmseg - INFO - Iter [35300/80000] lr: 2.235e-05, eta: 18:43:34, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2165, decode.acc_seg: 90.9950, aux.loss_ce: 0.0887, aux.acc_seg: 90.7529, loss: 0.3052 +2024-06-16 12:14:47,372 - mmseg - INFO - Iter [35350/80000] lr: 2.233e-05, eta: 18:42:10, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2169, decode.acc_seg: 90.9871, aux.loss_ce: 0.0900, aux.acc_seg: 90.5676, loss: 0.3068 +2024-06-16 12:15:58,510 - mmseg - INFO - Iter [35400/80000] lr: 2.230e-05, eta: 18:40:49, time: 1.423, data_time: 0.069, memory: 70722, decode.loss_ce: 0.2160, decode.acc_seg: 90.7571, aux.loss_ce: 0.0895, aux.acc_seg: 90.4845, loss: 0.3055 +2024-06-16 12:17:06,859 - mmseg - INFO - Iter [35450/80000] lr: 2.228e-05, eta: 18:39:25, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2161, decode.acc_seg: 90.9435, aux.loss_ce: 0.0900, aux.acc_seg: 90.6183, loss: 0.3061 +2024-06-16 12:18:15,188 - mmseg - INFO - Iter [35500/80000] lr: 2.225e-05, eta: 18:38:01, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2279, decode.acc_seg: 90.5122, aux.loss_ce: 0.0932, aux.acc_seg: 90.3176, loss: 0.3210 +2024-06-16 12:19:23,589 - mmseg - INFO - Iter [35550/80000] lr: 2.223e-05, eta: 18:36:36, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2118, decode.acc_seg: 91.1500, aux.loss_ce: 0.0878, aux.acc_seg: 90.7304, loss: 0.2996 +2024-06-16 12:20:31,847 - mmseg - INFO - Iter [35600/80000] lr: 2.220e-05, eta: 18:35:12, time: 1.365, data_time: 0.011, memory: 70722, decode.loss_ce: 0.2169, decode.acc_seg: 90.7713, aux.loss_ce: 0.0904, aux.acc_seg: 90.4004, loss: 0.3073 +2024-06-16 12:21:40,001 - mmseg - INFO - Iter [35650/80000] lr: 2.218e-05, eta: 18:33:48, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2127, decode.acc_seg: 90.9416, aux.loss_ce: 0.0878, aux.acc_seg: 90.6311, loss: 0.3006 +2024-06-16 12:22:47,983 - mmseg - INFO - Iter [35700/80000] lr: 2.215e-05, eta: 18:32:23, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2172, decode.acc_seg: 90.8990, aux.loss_ce: 0.0896, aux.acc_seg: 90.6187, loss: 0.3068 +2024-06-16 12:23:56,302 - mmseg - INFO - Iter [35750/80000] lr: 2.213e-05, eta: 18:30:59, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2286, decode.acc_seg: 90.4598, aux.loss_ce: 0.0945, aux.acc_seg: 90.0772, loss: 0.3231 +2024-06-16 12:25:04,547 - mmseg - INFO - Iter [35800/80000] lr: 2.210e-05, eta: 18:29:35, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2159, decode.acc_seg: 90.6538, aux.loss_ce: 0.0893, aux.acc_seg: 90.4151, loss: 0.3053 +2024-06-16 12:26:12,903 - mmseg - INFO - Iter [35850/80000] lr: 2.208e-05, eta: 18:28:12, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2186, decode.acc_seg: 90.6959, aux.loss_ce: 0.0905, aux.acc_seg: 90.3566, loss: 0.3091 +2024-06-16 12:27:21,017 - mmseg - INFO - Iter [35900/80000] lr: 2.205e-05, eta: 18:26:47, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2207, decode.acc_seg: 90.9773, aux.loss_ce: 0.0909, aux.acc_seg: 90.7388, loss: 0.3117 +2024-06-16 12:28:29,135 - mmseg - INFO - Iter [35950/80000] lr: 2.203e-05, eta: 18:25:23, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2164, decode.acc_seg: 90.7788, aux.loss_ce: 0.0898, aux.acc_seg: 90.5191, loss: 0.3062 +2024-06-16 12:29:37,555 - mmseg - INFO - Saving checkpoint at 36000 iterations +2024-06-16 12:31:03,644 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 12:31:03,644 - mmseg - INFO - Iter [36000/80000] lr: 2.200e-05, eta: 18:25:45, time: 3.090, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2184, decode.acc_seg: 90.8759, aux.loss_ce: 0.0899, aux.acc_seg: 90.4995, loss: 0.3082 +2024-06-16 12:32:39,050 - mmseg - INFO - per class results: +2024-06-16 12:32:39,056 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.3 | 89.08 | +| building | 84.86 | 93.7 | +| sky | 94.85 | 97.43 | +| floor | 84.97 | 91.89 | +| tree | 77.62 | 90.63 | +| ceiling | 86.9 | 93.48 | +| road | 85.33 | 92.16 | +| bed | 92.17 | 96.99 | +| windowpane | 65.06 | 79.58 | +| grass | 69.44 | 83.03 | +| cabinet | 63.67 | 73.63 | +| sidewalk | 71.24 | 85.04 | +| person | 85.45 | 93.66 | +| earth | 38.49 | 50.01 | +| door | 55.9 | 76.25 | +| table | 69.77 | 82.76 | +| mountain | 61.08 | 74.39 | +| plant | 55.2 | 64.61 | +| curtain | 77.27 | 86.11 | +| chair | 66.6 | 80.19 | +| car | 87.0 | 94.33 | +| water | 61.21 | 77.04 | +| painting | 77.04 | 89.58 | +| sofa | 80.08 | 89.19 | +| shelf | 47.83 | 67.47 | +| house | 53.48 | 61.64 | +| sea | 70.53 | 88.18 | +| mirror | 75.36 | 86.22 | +| rug | 70.72 | 75.86 | +| field | 38.62 | 69.06 | +| armchair | 57.64 | 74.56 | +| seat | 64.57 | 87.33 | +| fence | 50.02 | 62.18 | +| desk | 53.38 | 81.21 | +| rock | 54.14 | 77.86 | +| wardrobe | 56.66 | 72.83 | +| lamp | 70.99 | 88.05 | +| bathtub | 83.99 | 85.94 | +| railing | 43.7 | 59.52 | +| cushion | 68.09 | 79.53 | +| base | 35.9 | 50.2 | +| box | 36.18 | 44.92 | +| column | 56.21 | 76.56 | +| signboard | 41.04 | 55.83 | +| chest of drawers | 46.08 | 68.45 | +| counter | 43.5 | 51.79 | +| sand | 53.93 | 75.79 | +| sink | 75.53 | 83.24 | +| skyscraper | 48.8 | 65.9 | +| fireplace | 76.11 | 90.86 | +| refrigerator | 83.89 | 91.62 | +| grandstand | 55.03 | 81.76 | +| path | 24.34 | 31.5 | +| stairs | 26.66 | 31.94 | +| runway | 67.94 | 90.5 | +| case | 59.02 | 81.24 | +| pool table | 94.81 | 98.33 | +| pillow | 66.01 | 75.83 | +| screen door | 62.77 | 64.23 | +| stairway | 43.86 | 60.59 | +| river | 15.79 | 31.21 | +| bridge | 55.32 | 61.13 | +| bookcase | 45.83 | 60.13 | +| blind | 47.79 | 56.19 | +| coffee table | 67.21 | 89.3 | +| toilet | 89.45 | 93.44 | +| flower | 42.12 | 51.7 | +| book | 54.08 | 75.5 | +| hill | 5.55 | 9.83 | +| bench | 55.51 | 63.15 | +| countertop | 64.15 | 82.37 | +| stove | 82.85 | 89.19 | +| palm | 54.91 | 70.01 | +| kitchen island | 51.77 | 92.19 | +| computer | 80.25 | 89.34 | +| swivel chair | 43.81 | 61.8 | +| boat | 64.21 | 90.53 | +| bar | 54.6 | 66.42 | +| arcade machine | 70.31 | 74.02 | +| hovel | 43.14 | 47.06 | +| bus | 92.08 | 96.37 | +| towel | 77.31 | 89.18 | +| light | 60.85 | 72.78 | +| truck | 47.4 | 64.81 | +| tower | 27.11 | 38.96 | +| chandelier | 71.56 | 87.93 | +| awning | 40.27 | 52.83 | +| streetlight | 32.71 | 45.28 | +| booth | 41.09 | 81.29 | +| television receiver | 68.94 | 86.86 | +| airplane | 73.29 | 88.2 | +| dirt track | 6.55 | 23.0 | +| apparel | 49.89 | 83.61 | +| pole | 31.55 | 44.21 | +| land | 4.43 | 9.11 | +| bannister | 16.3 | 21.9 | +| escalator | 55.9 | 79.88 | +| ottoman | 47.91 | 62.65 | +| bottle | 41.53 | 66.64 | +| buffet | 44.45 | 49.08 | +| poster | 35.99 | 47.38 | +| stage | 15.98 | 28.22 | +| van | 49.41 | 63.75 | +| ship | 63.2 | 66.91 | +| fountain | 32.03 | 33.07 | +| conveyer belt | 77.47 | 95.88 | +| canopy | 49.84 | 75.25 | +| washer | 84.93 | 89.48 | +| plaything | 36.13 | 67.7 | +| swimming pool | 79.45 | 84.85 | +| stool | 54.84 | 72.51 | +| barrel | 39.78 | 66.28 | +| basket | 40.35 | 61.03 | +| waterfall | 70.97 | 87.21 | +| tent | 88.85 | 98.92 | +| bag | 19.78 | 22.26 | +| minibike | 75.92 | 87.96 | +| cradle | 84.38 | 97.7 | +| oven | 64.55 | 77.31 | +| ball | 50.46 | 53.36 | +| food | 57.17 | 67.28 | +| step | 9.89 | 12.09 | +| tank | 60.04 | 67.84 | +| trade name | 32.69 | 39.14 | +| microwave | 90.07 | 92.83 | +| pot | 54.29 | 65.28 | +| animal | 62.92 | 64.56 | +| bicycle | 56.82 | 74.14 | +| lake | 54.91 | 55.66 | +| dishwasher | 67.08 | 76.54 | +| screen | 54.65 | 90.42 | +| blanket | 25.68 | 27.92 | +| sculpture | 71.35 | 81.85 | +| hood | 63.47 | 74.32 | +| sconce | 54.0 | 63.33 | +| vase | 45.01 | 66.43 | +| traffic light | 37.41 | 53.58 | +| tray | 13.44 | 14.4 | +| ashcan | 42.9 | 65.79 | +| fan | 69.63 | 82.31 | +| pier | 38.25 | 40.61 | +| crt screen | 2.38 | 3.35 | +| plate | 58.29 | 75.74 | +| monitor | 61.01 | 76.26 | +| bulletin board | 53.55 | 69.2 | +| shower | 0.18 | 0.75 | +| radiator | 65.98 | 79.42 | +| glass | 18.55 | 19.43 | +| clock | 38.27 | 43.57 | +| flag | 70.77 | 76.73 | ++---------------------+-------+-------+ +2024-06-16 12:32:39,056 - mmseg - INFO - Summary: +2024-06-16 12:32:39,057 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.76 | 55.96 | 68.73 | ++-------+-------+-------+ +2024-06-16 12:32:39,057 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 12:32:39,057 - mmseg - INFO - Iter(val) [250] aAcc: 0.8576, mIoU: 0.5596, mAcc: 0.6873, IoU.wall: 0.8130, IoU.building: 0.8486, IoU.sky: 0.9485, IoU.floor: 0.8497, IoU.tree: 0.7762, IoU.ceiling: 0.8690, IoU.road: 0.8533, IoU.bed : 0.9217, IoU.windowpane: 0.6506, IoU.grass: 0.6944, IoU.cabinet: 0.6367, IoU.sidewalk: 0.7124, IoU.person: 0.8545, IoU.earth: 0.3849, IoU.door: 0.5590, IoU.table: 0.6977, IoU.mountain: 0.6108, IoU.plant: 0.5520, IoU.curtain: 0.7727, IoU.chair: 0.6660, IoU.car: 0.8700, IoU.water: 0.6121, IoU.painting: 0.7704, IoU.sofa: 0.8008, IoU.shelf: 0.4783, IoU.house: 0.5348, IoU.sea: 0.7053, IoU.mirror: 0.7536, IoU.rug: 0.7072, IoU.field: 0.3862, IoU.armchair: 0.5764, IoU.seat: 0.6457, IoU.fence: 0.5002, IoU.desk: 0.5338, IoU.rock: 0.5414, IoU.wardrobe: 0.5666, IoU.lamp: 0.7099, IoU.bathtub: 0.8399, IoU.railing: 0.4370, IoU.cushion: 0.6809, IoU.base: 0.3590, IoU.box: 0.3618, IoU.column: 0.5621, IoU.signboard: 0.4104, IoU.chest of drawers: 0.4608, IoU.counter: 0.4350, IoU.sand: 0.5393, IoU.sink: 0.7553, IoU.skyscraper: 0.4880, IoU.fireplace: 0.7611, IoU.refrigerator: 0.8389, IoU.grandstand: 0.5503, IoU.path: 0.2434, IoU.stairs: 0.2666, IoU.runway: 0.6794, IoU.case: 0.5902, IoU.pool table: 0.9481, IoU.pillow: 0.6601, IoU.screen door: 0.6277, IoU.stairway: 0.4386, IoU.river: 0.1579, IoU.bridge: 0.5532, IoU.bookcase: 0.4583, IoU.blind: 0.4779, IoU.coffee table: 0.6721, IoU.toilet: 0.8945, IoU.flower: 0.4212, IoU.book: 0.5408, IoU.hill: 0.0555, IoU.bench: 0.5551, IoU.countertop: 0.6415, IoU.stove: 0.8285, IoU.palm: 0.5491, IoU.kitchen island: 0.5177, IoU.computer: 0.8025, IoU.swivel chair: 0.4381, IoU.boat: 0.6421, IoU.bar: 0.5460, IoU.arcade machine: 0.7031, IoU.hovel: 0.4314, IoU.bus: 0.9208, IoU.towel: 0.7731, IoU.light: 0.6085, IoU.truck: 0.4740, IoU.tower: 0.2711, IoU.chandelier: 0.7156, IoU.awning: 0.4027, IoU.streetlight: 0.3271, IoU.booth: 0.4109, IoU.television receiver: 0.6894, IoU.airplane: 0.7329, IoU.dirt track: 0.0655, IoU.apparel: 0.4989, IoU.pole: 0.3155, IoU.land: 0.0443, IoU.bannister: 0.1630, IoU.escalator: 0.5590, IoU.ottoman: 0.4791, IoU.bottle: 0.4153, IoU.buffet: 0.4445, IoU.poster: 0.3599, IoU.stage: 0.1598, IoU.van: 0.4941, IoU.ship: 0.6320, IoU.fountain: 0.3203, IoU.conveyer belt: 0.7747, IoU.canopy: 0.4984, IoU.washer: 0.8493, IoU.plaything: 0.3613, IoU.swimming pool: 0.7945, IoU.stool: 0.5484, IoU.barrel: 0.3978, IoU.basket: 0.4035, IoU.waterfall: 0.7097, IoU.tent: 0.8885, IoU.bag: 0.1978, IoU.minibike: 0.7592, IoU.cradle: 0.8438, IoU.oven: 0.6455, IoU.ball: 0.5046, IoU.food: 0.5717, IoU.step: 0.0989, IoU.tank: 0.6004, IoU.trade name: 0.3269, IoU.microwave: 0.9007, IoU.pot: 0.5429, IoU.animal: 0.6292, IoU.bicycle: 0.5682, IoU.lake: 0.5491, IoU.dishwasher: 0.6708, IoU.screen: 0.5465, IoU.blanket: 0.2568, IoU.sculpture: 0.7135, IoU.hood: 0.6347, IoU.sconce: 0.5400, IoU.vase: 0.4501, IoU.traffic light: 0.3741, IoU.tray: 0.1344, IoU.ashcan: 0.4290, IoU.fan: 0.6963, IoU.pier: 0.3825, IoU.crt screen: 0.0238, IoU.plate: 0.5829, IoU.monitor: 0.6101, IoU.bulletin board: 0.5355, IoU.shower: 0.0018, IoU.radiator: 0.6598, IoU.glass: 0.1855, IoU.clock: 0.3827, IoU.flag: 0.7077, Acc.wall: 0.8908, Acc.building: 0.9370, Acc.sky: 0.9743, Acc.floor: 0.9189, Acc.tree: 0.9063, Acc.ceiling: 0.9348, Acc.road: 0.9216, Acc.bed : 0.9699, Acc.windowpane: 0.7958, Acc.grass: 0.8303, Acc.cabinet: 0.7363, Acc.sidewalk: 0.8504, Acc.person: 0.9366, Acc.earth: 0.5001, Acc.door: 0.7625, Acc.table: 0.8276, Acc.mountain: 0.7439, Acc.plant: 0.6461, Acc.curtain: 0.8611, Acc.chair: 0.8019, Acc.car: 0.9433, Acc.water: 0.7704, Acc.painting: 0.8958, Acc.sofa: 0.8919, Acc.shelf: 0.6747, Acc.house: 0.6164, Acc.sea: 0.8818, Acc.mirror: 0.8622, Acc.rug: 0.7586, Acc.field: 0.6906, Acc.armchair: 0.7456, Acc.seat: 0.8733, Acc.fence: 0.6218, Acc.desk: 0.8121, Acc.rock: 0.7786, Acc.wardrobe: 0.7283, Acc.lamp: 0.8805, Acc.bathtub: 0.8594, Acc.railing: 0.5952, Acc.cushion: 0.7953, Acc.base: 0.5020, Acc.box: 0.4492, Acc.column: 0.7656, Acc.signboard: 0.5583, Acc.chest of drawers: 0.6845, Acc.counter: 0.5179, Acc.sand: 0.7579, Acc.sink: 0.8324, Acc.skyscraper: 0.6590, Acc.fireplace: 0.9086, Acc.refrigerator: 0.9162, Acc.grandstand: 0.8176, Acc.path: 0.3150, Acc.stairs: 0.3194, Acc.runway: 0.9050, Acc.case: 0.8124, Acc.pool table: 0.9833, Acc.pillow: 0.7583, Acc.screen door: 0.6423, Acc.stairway: 0.6059, Acc.river: 0.3121, Acc.bridge: 0.6113, Acc.bookcase: 0.6013, Acc.blind: 0.5619, Acc.coffee table: 0.8930, Acc.toilet: 0.9344, Acc.flower: 0.5170, Acc.book: 0.7550, Acc.hill: 0.0983, Acc.bench: 0.6315, Acc.countertop: 0.8237, Acc.stove: 0.8919, Acc.palm: 0.7001, Acc.kitchen island: 0.9219, Acc.computer: 0.8934, Acc.swivel chair: 0.6180, Acc.boat: 0.9053, Acc.bar: 0.6642, Acc.arcade machine: 0.7402, Acc.hovel: 0.4706, Acc.bus: 0.9637, Acc.towel: 0.8918, Acc.light: 0.7278, Acc.truck: 0.6481, Acc.tower: 0.3896, Acc.chandelier: 0.8793, Acc.awning: 0.5283, Acc.streetlight: 0.4528, Acc.booth: 0.8129, Acc.television receiver: 0.8686, Acc.airplane: 0.8820, Acc.dirt track: 0.2300, Acc.apparel: 0.8361, Acc.pole: 0.4421, Acc.land: 0.0911, Acc.bannister: 0.2190, Acc.escalator: 0.7988, Acc.ottoman: 0.6265, Acc.bottle: 0.6664, Acc.buffet: 0.4908, Acc.poster: 0.4738, Acc.stage: 0.2822, Acc.van: 0.6375, Acc.ship: 0.6691, Acc.fountain: 0.3307, Acc.conveyer belt: 0.9588, Acc.canopy: 0.7525, Acc.washer: 0.8948, Acc.plaything: 0.6770, Acc.swimming pool: 0.8485, Acc.stool: 0.7251, Acc.barrel: 0.6628, Acc.basket: 0.6103, Acc.waterfall: 0.8721, Acc.tent: 0.9892, Acc.bag: 0.2226, Acc.minibike: 0.8796, Acc.cradle: 0.9770, Acc.oven: 0.7731, Acc.ball: 0.5336, Acc.food: 0.6728, Acc.step: 0.1209, Acc.tank: 0.6784, Acc.trade name: 0.3914, Acc.microwave: 0.9283, Acc.pot: 0.6528, Acc.animal: 0.6456, Acc.bicycle: 0.7414, Acc.lake: 0.5566, Acc.dishwasher: 0.7654, Acc.screen: 0.9042, Acc.blanket: 0.2792, Acc.sculpture: 0.8185, Acc.hood: 0.7432, Acc.sconce: 0.6333, Acc.vase: 0.6643, Acc.traffic light: 0.5358, Acc.tray: 0.1440, Acc.ashcan: 0.6579, Acc.fan: 0.8231, Acc.pier: 0.4061, Acc.crt screen: 0.0335, Acc.plate: 0.7574, Acc.monitor: 0.7626, Acc.bulletin board: 0.6920, Acc.shower: 0.0075, Acc.radiator: 0.7942, Acc.glass: 0.1943, Acc.clock: 0.4357, Acc.flag: 0.7673 +2024-06-16 12:33:47,742 - mmseg - INFO - Iter [36050/80000] lr: 2.198e-05, eta: 18:26:18, time: 3.282, data_time: 1.924, memory: 70722, decode.loss_ce: 0.2202, decode.acc_seg: 90.8421, aux.loss_ce: 0.0904, aux.acc_seg: 90.7052, loss: 0.3105 +2024-06-16 12:34:56,226 - mmseg - INFO - Iter [36100/80000] lr: 2.195e-05, eta: 18:24:54, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2387, decode.acc_seg: 89.7406, aux.loss_ce: 0.0977, aux.acc_seg: 89.5212, loss: 0.3365 +2024-06-16 12:36:04,253 - mmseg - INFO - Iter [36150/80000] lr: 2.193e-05, eta: 18:23:29, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2249, decode.acc_seg: 90.6204, aux.loss_ce: 0.0925, aux.acc_seg: 90.3071, loss: 0.3174 +2024-06-16 12:37:12,508 - mmseg - INFO - Iter [36200/80000] lr: 2.190e-05, eta: 18:22:05, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2250, decode.acc_seg: 90.7428, aux.loss_ce: 0.0932, aux.acc_seg: 90.4257, loss: 0.3182 +2024-06-16 12:38:20,806 - mmseg - INFO - Iter [36250/80000] lr: 2.188e-05, eta: 18:20:41, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2126, decode.acc_seg: 91.1006, aux.loss_ce: 0.0877, aux.acc_seg: 90.8132, loss: 0.3003 +2024-06-16 12:39:29,251 - mmseg - INFO - Iter [36300/80000] lr: 2.185e-05, eta: 18:19:17, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2398, decode.acc_seg: 90.1689, aux.loss_ce: 0.0986, aux.acc_seg: 90.0330, loss: 0.3383 +2024-06-16 12:40:37,529 - mmseg - INFO - Iter [36350/80000] lr: 2.183e-05, eta: 18:17:53, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2049, decode.acc_seg: 91.4727, aux.loss_ce: 0.0851, aux.acc_seg: 91.2164, loss: 0.2900 +2024-06-16 12:41:45,718 - mmseg - INFO - Iter [36400/80000] lr: 2.180e-05, eta: 18:16:28, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2155, decode.acc_seg: 90.9302, aux.loss_ce: 0.0896, aux.acc_seg: 90.5350, loss: 0.3051 +2024-06-16 12:42:53,890 - mmseg - INFO - Iter [36450/80000] lr: 2.178e-05, eta: 18:15:04, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2223, decode.acc_seg: 90.9133, aux.loss_ce: 0.0921, aux.acc_seg: 90.5891, loss: 0.3144 +2024-06-16 12:44:02,107 - mmseg - INFO - Iter [36500/80000] lr: 2.175e-05, eta: 18:13:40, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2198, decode.acc_seg: 90.7963, aux.loss_ce: 0.0912, aux.acc_seg: 90.5562, loss: 0.3110 +2024-06-16 12:45:10,241 - mmseg - INFO - Iter [36550/80000] lr: 2.173e-05, eta: 18:12:16, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2227, decode.acc_seg: 90.6234, aux.loss_ce: 0.0917, aux.acc_seg: 90.3337, loss: 0.3144 +2024-06-16 12:46:18,492 - mmseg - INFO - Iter [36600/80000] lr: 2.170e-05, eta: 18:10:52, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2283, decode.acc_seg: 90.6468, aux.loss_ce: 0.0937, aux.acc_seg: 90.3858, loss: 0.3220 +2024-06-16 12:47:29,073 - mmseg - INFO - Iter [36650/80000] lr: 2.168e-05, eta: 18:09:31, time: 1.412, data_time: 0.052, memory: 70722, decode.loss_ce: 0.2240, decode.acc_seg: 90.7329, aux.loss_ce: 0.0920, aux.acc_seg: 90.6016, loss: 0.3160 +2024-06-16 12:48:37,183 - mmseg - INFO - Iter [36700/80000] lr: 2.165e-05, eta: 18:08:07, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2209, decode.acc_seg: 90.8486, aux.loss_ce: 0.0916, aux.acc_seg: 90.4902, loss: 0.3126 +2024-06-16 12:49:45,229 - mmseg - INFO - Iter [36750/80000] lr: 2.163e-05, eta: 18:06:43, time: 1.361, data_time: 0.009, memory: 70722, decode.loss_ce: 0.2058, decode.acc_seg: 91.2108, aux.loss_ce: 0.0855, aux.acc_seg: 90.9199, loss: 0.2912 +2024-06-16 12:50:53,333 - mmseg - INFO - Iter [36800/80000] lr: 2.160e-05, eta: 18:05:19, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2013, decode.acc_seg: 91.4714, aux.loss_ce: 0.0832, aux.acc_seg: 91.1338, loss: 0.2845 +2024-06-16 12:52:01,513 - mmseg - INFO - Iter [36850/80000] lr: 2.158e-05, eta: 18:03:55, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2054, decode.acc_seg: 91.3865, aux.loss_ce: 0.0851, aux.acc_seg: 91.0852, loss: 0.2905 +2024-06-16 12:53:09,614 - mmseg - INFO - Iter [36900/80000] lr: 2.155e-05, eta: 18:02:31, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2182, decode.acc_seg: 90.6707, aux.loss_ce: 0.0899, aux.acc_seg: 90.4388, loss: 0.3081 +2024-06-16 12:54:18,019 - mmseg - INFO - Iter [36950/80000] lr: 2.153e-05, eta: 18:01:08, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2062, decode.acc_seg: 91.3121, aux.loss_ce: 0.0850, aux.acc_seg: 91.0833, loss: 0.2912 +2024-06-16 12:55:26,144 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 12:55:26,144 - mmseg - INFO - Iter [37000/80000] lr: 2.150e-05, eta: 17:59:44, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2191, decode.acc_seg: 90.8359, aux.loss_ce: 0.0908, aux.acc_seg: 90.5241, loss: 0.3099 +2024-06-16 12:57:02,476 - mmseg - INFO - per class results: +2024-06-16 12:57:02,482 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.26 | 88.28 | +| building | 83.98 | 92.31 | +| sky | 94.81 | 97.58 | +| floor | 84.66 | 92.42 | +| tree | 76.64 | 88.69 | +| ceiling | 86.68 | 93.36 | +| road | 86.78 | 91.19 | +| bed | 92.37 | 96.83 | +| windowpane | 66.35 | 79.28 | +| grass | 67.95 | 79.88 | +| cabinet | 64.54 | 76.38 | +| sidewalk | 72.55 | 87.14 | +| person | 85.6 | 94.31 | +| earth | 38.98 | 50.58 | +| door | 58.06 | 74.9 | +| table | 69.28 | 83.76 | +| mountain | 61.86 | 71.24 | +| plant | 54.96 | 65.18 | +| curtain | 78.41 | 88.65 | +| chair | 66.73 | 79.05 | +| car | 86.39 | 94.74 | +| water | 65.43 | 79.07 | +| painting | 76.74 | 91.38 | +| sofa | 80.44 | 89.58 | +| shelf | 47.8 | 63.37 | +| house | 56.82 | 88.86 | +| sea | 73.55 | 89.45 | +| mirror | 74.32 | 81.82 | +| rug | 71.44 | 76.61 | +| field | 34.36 | 75.54 | +| armchair | 57.95 | 74.29 | +| seat | 64.69 | 89.95 | +| fence | 54.06 | 68.98 | +| desk | 61.14 | 79.95 | +| rock | 60.13 | 86.49 | +| wardrobe | 53.34 | 71.47 | +| lamp | 72.48 | 85.95 | +| bathtub | 83.69 | 86.54 | +| railing | 42.69 | 60.21 | +| cushion | 70.07 | 79.87 | +| base | 36.29 | 77.59 | +| box | 30.7 | 36.29 | +| column | 56.54 | 76.24 | +| signboard | 41.12 | 53.77 | +| chest of drawers | 43.05 | 69.44 | +| counter | 41.22 | 47.62 | +| sand | 54.77 | 81.42 | +| sink | 74.53 | 83.51 | +| skyscraper | 46.51 | 59.36 | +| fireplace | 69.82 | 96.81 | +| refrigerator | 80.43 | 87.15 | +| grandstand | 52.92 | 76.92 | +| path | 33.1 | 47.19 | +| stairs | 30.05 | 37.79 | +| runway | 68.93 | 90.3 | +| case | 59.08 | 76.5 | +| pool table | 94.29 | 98.27 | +| pillow | 69.08 | 81.6 | +| screen door | 74.61 | 76.22 | +| stairway | 47.27 | 68.83 | +| river | 16.47 | 26.03 | +| bridge | 45.23 | 50.45 | +| bookcase | 39.58 | 44.6 | +| blind | 45.66 | 51.2 | +| coffee table | 65.79 | 87.06 | +| toilet | 89.78 | 93.02 | +| flower | 42.47 | 56.74 | +| book | 52.74 | 78.34 | +| hill | 7.33 | 12.8 | +| bench | 55.83 | 63.45 | +| countertop | 64.05 | 85.77 | +| stove | 83.97 | 91.36 | +| palm | 52.81 | 74.26 | +| kitchen island | 48.81 | 77.46 | +| computer | 78.54 | 91.55 | +| swivel chair | 49.69 | 73.87 | +| boat | 70.95 | 89.54 | +| bar | 59.19 | 78.96 | +| arcade machine | 78.08 | 83.08 | +| hovel | 36.02 | 40.52 | +| bus | 93.3 | 96.35 | +| towel | 76.38 | 89.38 | +| light | 59.86 | 70.79 | +| truck | 47.8 | 65.47 | +| tower | 31.12 | 54.24 | +| chandelier | 71.28 | 87.8 | +| awning | 43.59 | 59.57 | +| streetlight | 35.05 | 47.8 | +| booth | 54.32 | 61.79 | +| television receiver | 75.57 | 88.11 | +| airplane | 67.75 | 91.02 | +| dirt track | 8.58 | 33.96 | +| apparel | 50.47 | 85.48 | +| pole | 28.03 | 37.58 | +| land | 5.36 | 9.3 | +| bannister | 16.45 | 27.68 | +| escalator | 58.57 | 78.33 | +| ottoman | 45.96 | 60.47 | +| bottle | 40.99 | 70.6 | +| buffet | 38.85 | 42.88 | +| poster | 33.72 | 50.52 | +| stage | 22.05 | 45.17 | +| van | 44.16 | 53.44 | +| ship | 9.45 | 9.9 | +| fountain | 41.62 | 42.49 | +| conveyer belt | 82.68 | 93.26 | +| canopy | 50.92 | 76.22 | +| washer | 84.99 | 89.58 | +| plaything | 25.87 | 35.26 | +| swimming pool | 57.77 | 86.33 | +| stool | 49.64 | 71.9 | +| barrel | 58.58 | 68.57 | +| basket | 43.8 | 66.42 | +| waterfall | 71.7 | 81.39 | +| tent | 88.79 | 98.24 | +| bag | 23.42 | 27.24 | +| minibike | 76.49 | 89.19 | +| cradle | 81.43 | 98.02 | +| oven | 63.04 | 78.5 | +| ball | 54.6 | 69.37 | +| food | 65.07 | 75.74 | +| step | 12.93 | 18.23 | +| tank | 60.6 | 69.87 | +| trade name | 27.83 | 33.04 | +| microwave | 89.17 | 96.4 | +| pot | 56.28 | 67.11 | +| animal | 65.6 | 68.29 | +| bicycle | 58.17 | 67.87 | +| lake | 51.55 | 63.71 | +| dishwasher | 69.54 | 75.19 | +| screen | 57.41 | 88.04 | +| blanket | 28.56 | 32.76 | +| sculpture | 75.87 | 85.0 | +| hood | 62.41 | 71.22 | +| sconce | 59.03 | 68.99 | +| vase | 49.13 | 64.24 | +| traffic light | 33.63 | 61.96 | +| tray | 17.3 | 20.19 | +| ashcan | 45.42 | 65.89 | +| fan | 69.79 | 81.64 | +| pier | 37.05 | 40.48 | +| crt screen | 5.04 | 6.57 | +| plate | 61.44 | 71.22 | +| monitor | 64.16 | 78.38 | +| bulletin board | 55.22 | 69.84 | +| shower | 0.45 | 2.24 | +| radiator | 63.99 | 77.09 | +| glass | 19.45 | 20.6 | +| clock | 40.09 | 48.21 | +| flag | 71.49 | 77.48 | ++---------------------+-------+-------+ +2024-06-16 12:57:02,482 - mmseg - INFO - Summary: +2024-06-16 12:57:02,482 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.71 | 56.23 | 69.43 | ++-------+-------+-------+ +2024-06-16 12:57:02,483 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 12:57:02,483 - mmseg - INFO - Iter(val) [250] aAcc: 0.8571, mIoU: 0.5623, mAcc: 0.6943, IoU.wall: 0.8126, IoU.building: 0.8398, IoU.sky: 0.9481, IoU.floor: 0.8466, IoU.tree: 0.7664, IoU.ceiling: 0.8668, IoU.road: 0.8678, IoU.bed : 0.9237, IoU.windowpane: 0.6635, IoU.grass: 0.6795, IoU.cabinet: 0.6454, IoU.sidewalk: 0.7255, IoU.person: 0.8560, IoU.earth: 0.3898, IoU.door: 0.5806, IoU.table: 0.6928, IoU.mountain: 0.6186, IoU.plant: 0.5496, IoU.curtain: 0.7841, IoU.chair: 0.6673, IoU.car: 0.8639, IoU.water: 0.6543, IoU.painting: 0.7674, IoU.sofa: 0.8044, IoU.shelf: 0.4780, IoU.house: 0.5682, IoU.sea: 0.7355, IoU.mirror: 0.7432, IoU.rug: 0.7144, IoU.field: 0.3436, IoU.armchair: 0.5795, IoU.seat: 0.6469, IoU.fence: 0.5406, IoU.desk: 0.6114, IoU.rock: 0.6013, IoU.wardrobe: 0.5334, IoU.lamp: 0.7248, IoU.bathtub: 0.8369, IoU.railing: 0.4269, IoU.cushion: 0.7007, IoU.base: 0.3629, IoU.box: 0.3070, IoU.column: 0.5654, IoU.signboard: 0.4112, IoU.chest of drawers: 0.4305, IoU.counter: 0.4122, IoU.sand: 0.5477, IoU.sink: 0.7453, IoU.skyscraper: 0.4651, IoU.fireplace: 0.6982, IoU.refrigerator: 0.8043, IoU.grandstand: 0.5292, IoU.path: 0.3310, IoU.stairs: 0.3005, IoU.runway: 0.6893, IoU.case: 0.5908, IoU.pool table: 0.9429, IoU.pillow: 0.6908, IoU.screen door: 0.7461, IoU.stairway: 0.4727, IoU.river: 0.1647, IoU.bridge: 0.4523, IoU.bookcase: 0.3958, IoU.blind: 0.4566, IoU.coffee table: 0.6579, IoU.toilet: 0.8978, IoU.flower: 0.4247, IoU.book: 0.5274, IoU.hill: 0.0733, IoU.bench: 0.5583, IoU.countertop: 0.6405, IoU.stove: 0.8397, IoU.palm: 0.5281, IoU.kitchen island: 0.4881, IoU.computer: 0.7854, IoU.swivel chair: 0.4969, IoU.boat: 0.7095, IoU.bar: 0.5919, IoU.arcade machine: 0.7808, IoU.hovel: 0.3602, IoU.bus: 0.9330, IoU.towel: 0.7638, IoU.light: 0.5986, IoU.truck: 0.4780, IoU.tower: 0.3112, IoU.chandelier: 0.7128, IoU.awning: 0.4359, IoU.streetlight: 0.3505, IoU.booth: 0.5432, IoU.television receiver: 0.7557, IoU.airplane: 0.6775, IoU.dirt track: 0.0858, IoU.apparel: 0.5047, IoU.pole: 0.2803, IoU.land: 0.0536, IoU.bannister: 0.1645, IoU.escalator: 0.5857, IoU.ottoman: 0.4596, IoU.bottle: 0.4099, IoU.buffet: 0.3885, IoU.poster: 0.3372, IoU.stage: 0.2205, IoU.van: 0.4416, IoU.ship: 0.0945, IoU.fountain: 0.4162, IoU.conveyer belt: 0.8268, IoU.canopy: 0.5092, IoU.washer: 0.8499, IoU.plaything: 0.2587, IoU.swimming pool: 0.5777, IoU.stool: 0.4964, IoU.barrel: 0.5858, IoU.basket: 0.4380, IoU.waterfall: 0.7170, IoU.tent: 0.8879, IoU.bag: 0.2342, IoU.minibike: 0.7649, IoU.cradle: 0.8143, IoU.oven: 0.6304, IoU.ball: 0.5460, IoU.food: 0.6507, IoU.step: 0.1293, IoU.tank: 0.6060, IoU.trade name: 0.2783, IoU.microwave: 0.8917, IoU.pot: 0.5628, IoU.animal: 0.6560, IoU.bicycle: 0.5817, IoU.lake: 0.5155, IoU.dishwasher: 0.6954, IoU.screen: 0.5741, IoU.blanket: 0.2856, IoU.sculpture: 0.7587, IoU.hood: 0.6241, IoU.sconce: 0.5903, IoU.vase: 0.4913, IoU.traffic light: 0.3363, IoU.tray: 0.1730, IoU.ashcan: 0.4542, IoU.fan: 0.6979, IoU.pier: 0.3705, IoU.crt screen: 0.0504, IoU.plate: 0.6144, IoU.monitor: 0.6416, IoU.bulletin board: 0.5522, IoU.shower: 0.0045, IoU.radiator: 0.6399, IoU.glass: 0.1945, IoU.clock: 0.4009, IoU.flag: 0.7149, Acc.wall: 0.8828, Acc.building: 0.9231, Acc.sky: 0.9758, Acc.floor: 0.9242, Acc.tree: 0.8869, Acc.ceiling: 0.9336, Acc.road: 0.9119, Acc.bed : 0.9683, Acc.windowpane: 0.7928, Acc.grass: 0.7988, Acc.cabinet: 0.7638, Acc.sidewalk: 0.8714, Acc.person: 0.9431, Acc.earth: 0.5058, Acc.door: 0.7490, Acc.table: 0.8376, Acc.mountain: 0.7124, Acc.plant: 0.6518, Acc.curtain: 0.8865, Acc.chair: 0.7905, Acc.car: 0.9474, Acc.water: 0.7907, Acc.painting: 0.9138, Acc.sofa: 0.8958, Acc.shelf: 0.6337, Acc.house: 0.8886, Acc.sea: 0.8945, Acc.mirror: 0.8182, Acc.rug: 0.7661, Acc.field: 0.7554, Acc.armchair: 0.7429, Acc.seat: 0.8995, Acc.fence: 0.6898, Acc.desk: 0.7995, Acc.rock: 0.8649, Acc.wardrobe: 0.7147, Acc.lamp: 0.8595, Acc.bathtub: 0.8654, Acc.railing: 0.6021, Acc.cushion: 0.7987, Acc.base: 0.7759, Acc.box: 0.3629, Acc.column: 0.7624, Acc.signboard: 0.5377, Acc.chest of drawers: 0.6944, Acc.counter: 0.4762, Acc.sand: 0.8142, Acc.sink: 0.8351, Acc.skyscraper: 0.5936, Acc.fireplace: 0.9681, Acc.refrigerator: 0.8715, Acc.grandstand: 0.7692, Acc.path: 0.4719, Acc.stairs: 0.3779, Acc.runway: 0.9030, Acc.case: 0.7650, Acc.pool table: 0.9827, Acc.pillow: 0.8160, Acc.screen door: 0.7622, Acc.stairway: 0.6883, Acc.river: 0.2603, Acc.bridge: 0.5045, Acc.bookcase: 0.4460, Acc.blind: 0.5120, Acc.coffee table: 0.8706, Acc.toilet: 0.9302, Acc.flower: 0.5674, Acc.book: 0.7834, Acc.hill: 0.1280, Acc.bench: 0.6345, Acc.countertop: 0.8577, Acc.stove: 0.9136, Acc.palm: 0.7426, Acc.kitchen island: 0.7746, Acc.computer: 0.9155, Acc.swivel chair: 0.7387, Acc.boat: 0.8954, Acc.bar: 0.7896, Acc.arcade machine: 0.8308, Acc.hovel: 0.4052, Acc.bus: 0.9635, Acc.towel: 0.8938, Acc.light: 0.7079, Acc.truck: 0.6547, Acc.tower: 0.5424, Acc.chandelier: 0.8780, Acc.awning: 0.5957, Acc.streetlight: 0.4780, Acc.booth: 0.6179, Acc.television receiver: 0.8811, Acc.airplane: 0.9102, Acc.dirt track: 0.3396, Acc.apparel: 0.8548, Acc.pole: 0.3758, Acc.land: 0.0930, Acc.bannister: 0.2768, Acc.escalator: 0.7833, Acc.ottoman: 0.6047, Acc.bottle: 0.7060, Acc.buffet: 0.4288, Acc.poster: 0.5052, Acc.stage: 0.4517, Acc.van: 0.5344, Acc.ship: 0.0990, Acc.fountain: 0.4249, Acc.conveyer belt: 0.9326, Acc.canopy: 0.7622, Acc.washer: 0.8958, Acc.plaything: 0.3526, Acc.swimming pool: 0.8633, Acc.stool: 0.7190, Acc.barrel: 0.6857, Acc.basket: 0.6642, Acc.waterfall: 0.8139, Acc.tent: 0.9824, Acc.bag: 0.2724, Acc.minibike: 0.8919, Acc.cradle: 0.9802, Acc.oven: 0.7850, Acc.ball: 0.6937, Acc.food: 0.7574, Acc.step: 0.1823, Acc.tank: 0.6987, Acc.trade name: 0.3304, Acc.microwave: 0.9640, Acc.pot: 0.6711, Acc.animal: 0.6829, Acc.bicycle: 0.6787, Acc.lake: 0.6371, Acc.dishwasher: 0.7519, Acc.screen: 0.8804, Acc.blanket: 0.3276, Acc.sculpture: 0.8500, Acc.hood: 0.7122, Acc.sconce: 0.6899, Acc.vase: 0.6424, Acc.traffic light: 0.6196, Acc.tray: 0.2019, Acc.ashcan: 0.6589, Acc.fan: 0.8164, Acc.pier: 0.4048, Acc.crt screen: 0.0657, Acc.plate: 0.7122, Acc.monitor: 0.7838, Acc.bulletin board: 0.6984, Acc.shower: 0.0224, Acc.radiator: 0.7709, Acc.glass: 0.2060, Acc.clock: 0.4821, Acc.flag: 0.7748 +2024-06-16 12:58:11,162 - mmseg - INFO - Iter [37050/80000] lr: 2.148e-05, eta: 18:00:13, time: 3.300, data_time: 1.942, memory: 70722, decode.loss_ce: 0.2037, decode.acc_seg: 91.4962, aux.loss_ce: 0.0849, aux.acc_seg: 91.2103, loss: 0.2886 +2024-06-16 12:59:19,205 - mmseg - INFO - Iter [37100/80000] lr: 2.145e-05, eta: 17:58:49, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2124, decode.acc_seg: 91.1239, aux.loss_ce: 0.0883, aux.acc_seg: 90.7430, loss: 0.3007 +2024-06-16 13:00:27,442 - mmseg - INFO - Iter [37150/80000] lr: 2.143e-05, eta: 17:57:25, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2115, decode.acc_seg: 91.2505, aux.loss_ce: 0.0875, aux.acc_seg: 90.9560, loss: 0.2991 +2024-06-16 13:01:35,874 - mmseg - INFO - Iter [37200/80000] lr: 2.140e-05, eta: 17:56:02, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2253, decode.acc_seg: 90.7179, aux.loss_ce: 0.0929, aux.acc_seg: 90.3444, loss: 0.3182 +2024-06-16 13:02:44,056 - mmseg - INFO - Iter [37250/80000] lr: 2.138e-05, eta: 17:54:38, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2152, decode.acc_seg: 90.8946, aux.loss_ce: 0.0893, aux.acc_seg: 90.4787, loss: 0.3045 +2024-06-16 13:03:52,276 - mmseg - INFO - Iter [37300/80000] lr: 2.135e-05, eta: 17:53:14, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2000, decode.acc_seg: 91.5377, aux.loss_ce: 0.0832, aux.acc_seg: 91.1938, loss: 0.2832 +2024-06-16 13:05:00,592 - mmseg - INFO - Iter [37350/80000] lr: 2.133e-05, eta: 17:51:51, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2132, decode.acc_seg: 91.0322, aux.loss_ce: 0.0877, aux.acc_seg: 90.7103, loss: 0.3010 +2024-06-16 13:06:08,618 - mmseg - INFO - Iter [37400/80000] lr: 2.130e-05, eta: 17:50:27, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2253, decode.acc_seg: 90.4110, aux.loss_ce: 0.0934, aux.acc_seg: 90.0627, loss: 0.3187 +2024-06-16 13:07:16,983 - mmseg - INFO - Iter [37450/80000] lr: 2.128e-05, eta: 17:49:04, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2202, decode.acc_seg: 90.5762, aux.loss_ce: 0.0919, aux.acc_seg: 90.1777, loss: 0.3121 +2024-06-16 13:08:24,981 - mmseg - INFO - Iter [37500/80000] lr: 2.125e-05, eta: 17:47:40, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2246, decode.acc_seg: 90.6896, aux.loss_ce: 0.0926, aux.acc_seg: 90.4008, loss: 0.3172 +2024-06-16 13:09:33,147 - mmseg - INFO - Iter [37550/80000] lr: 2.123e-05, eta: 17:46:16, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2021, decode.acc_seg: 91.5628, aux.loss_ce: 0.0847, aux.acc_seg: 91.1927, loss: 0.2868 +2024-06-16 13:10:41,238 - mmseg - INFO - Iter [37600/80000] lr: 2.120e-05, eta: 17:44:53, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2307, decode.acc_seg: 90.5436, aux.loss_ce: 0.0945, aux.acc_seg: 90.2236, loss: 0.3253 +2024-06-16 13:11:49,259 - mmseg - INFO - Iter [37650/80000] lr: 2.118e-05, eta: 17:43:29, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2101, decode.acc_seg: 91.1649, aux.loss_ce: 0.0872, aux.acc_seg: 90.8448, loss: 0.2972 +2024-06-16 13:12:57,539 - mmseg - INFO - Iter [37700/80000] lr: 2.115e-05, eta: 17:42:06, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2224, decode.acc_seg: 90.6816, aux.loss_ce: 0.0925, aux.acc_seg: 90.2422, loss: 0.3149 +2024-06-16 13:14:05,682 - mmseg - INFO - Iter [37750/80000] lr: 2.113e-05, eta: 17:40:43, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2110, decode.acc_seg: 90.9190, aux.loss_ce: 0.0873, aux.acc_seg: 90.6045, loss: 0.2983 +2024-06-16 13:15:14,042 - mmseg - INFO - Iter [37800/80000] lr: 2.110e-05, eta: 17:39:20, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2066, decode.acc_seg: 91.2019, aux.loss_ce: 0.0856, aux.acc_seg: 90.7945, loss: 0.2922 +2024-06-16 13:16:22,133 - mmseg - INFO - Iter [37850/80000] lr: 2.108e-05, eta: 17:37:56, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2118, decode.acc_seg: 91.2868, aux.loss_ce: 0.0873, aux.acc_seg: 90.9983, loss: 0.2990 +2024-06-16 13:17:33,214 - mmseg - INFO - Iter [37900/80000] lr: 2.105e-05, eta: 17:36:36, time: 1.422, data_time: 0.062, memory: 70722, decode.loss_ce: 0.2138, decode.acc_seg: 91.5345, aux.loss_ce: 0.0885, aux.acc_seg: 91.1007, loss: 0.3023 +2024-06-16 13:18:41,538 - mmseg - INFO - Iter [37950/80000] lr: 2.103e-05, eta: 17:35:13, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2111, decode.acc_seg: 91.3072, aux.loss_ce: 0.0871, aux.acc_seg: 91.0534, loss: 0.2982 +2024-06-16 13:19:49,659 - mmseg - INFO - Saving checkpoint at 38000 iterations +2024-06-16 13:21:15,024 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 13:21:15,025 - mmseg - INFO - Iter [38000/80000] lr: 2.100e-05, eta: 17:35:24, time: 3.070, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1959, decode.acc_seg: 91.5505, aux.loss_ce: 0.0821, aux.acc_seg: 91.1537, loss: 0.2780 +2024-06-16 13:22:51,162 - mmseg - INFO - per class results: +2024-06-16 13:22:51,169 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.55 | 89.18 | +| building | 84.81 | 93.39 | +| sky | 94.85 | 97.38 | +| floor | 84.02 | 91.48 | +| tree | 77.47 | 89.56 | +| ceiling | 86.86 | 92.67 | +| road | 86.72 | 92.72 | +| bed | 92.06 | 97.28 | +| windowpane | 65.97 | 80.73 | +| grass | 66.93 | 81.03 | +| cabinet | 65.99 | 76.29 | +| sidewalk | 72.68 | 85.79 | +| person | 85.6 | 93.26 | +| earth | 35.4 | 44.57 | +| door | 56.75 | 72.51 | +| table | 68.21 | 81.31 | +| mountain | 62.43 | 72.06 | +| plant | 53.95 | 63.49 | +| curtain | 78.34 | 88.99 | +| chair | 65.02 | 73.83 | +| car | 87.04 | 94.04 | +| water | 67.26 | 85.0 | +| painting | 75.14 | 89.71 | +| sofa | 78.8 | 90.97 | +| shelf | 46.02 | 59.83 | +| house | 57.2 | 76.53 | +| sea | 76.83 | 86.05 | +| mirror | 74.29 | 84.18 | +| rug | 71.81 | 84.07 | +| field | 34.62 | 67.9 | +| armchair | 56.12 | 76.68 | +| seat | 66.83 | 90.18 | +| fence | 51.01 | 63.82 | +| desk | 58.54 | 82.23 | +| rock | 54.21 | 86.12 | +| wardrobe | 54.46 | 70.93 | +| lamp | 73.49 | 82.84 | +| bathtub | 84.45 | 86.57 | +| railing | 43.8 | 58.18 | +| cushion | 69.67 | 78.67 | +| base | 39.37 | 63.72 | +| box | 35.79 | 45.37 | +| column | 54.54 | 69.42 | +| signboard | 40.81 | 53.67 | +| chest of drawers | 45.6 | 70.12 | +| counter | 45.53 | 62.14 | +| sand | 41.22 | 80.85 | +| sink | 73.81 | 82.69 | +| skyscraper | 46.34 | 57.36 | +| fireplace | 72.32 | 95.89 | +| refrigerator | 80.53 | 87.87 | +| grandstand | 52.37 | 83.74 | +| path | 34.43 | 51.76 | +| stairs | 26.47 | 30.58 | +| runway | 72.8 | 95.88 | +| case | 59.19 | 81.26 | +| pool table | 91.82 | 98.34 | +| pillow | 66.93 | 76.75 | +| screen door | 84.0 | 87.38 | +| stairway | 43.84 | 61.28 | +| river | 11.08 | 16.56 | +| bridge | 58.35 | 64.47 | +| bookcase | 42.59 | 59.34 | +| blind | 45.27 | 52.95 | +| coffee table | 62.6 | 88.97 | +| toilet | 89.02 | 93.84 | +| flower | 40.2 | 57.95 | +| book | 53.29 | 81.51 | +| hill | 7.72 | 15.57 | +| bench | 50.16 | 64.16 | +| countertop | 64.03 | 85.64 | +| stove | 82.44 | 90.88 | +| palm | 57.07 | 80.45 | +| kitchen island | 46.04 | 90.57 | +| computer | 78.15 | 91.24 | +| swivel chair | 51.18 | 75.64 | +| boat | 75.71 | 88.46 | +| bar | 56.64 | 70.74 | +| arcade machine | 77.45 | 84.48 | +| hovel | 34.25 | 38.42 | +| bus | 92.82 | 96.64 | +| towel | 76.94 | 83.61 | +| light | 61.34 | 74.03 | +| truck | 45.44 | 60.35 | +| tower | 32.47 | 54.55 | +| chandelier | 70.09 | 89.25 | +| awning | 38.53 | 46.16 | +| streetlight | 35.78 | 48.94 | +| booth | 51.62 | 78.58 | +| television receiver | 72.95 | 86.81 | +| airplane | 81.3 | 86.24 | +| dirt track | 2.9 | 3.82 | +| apparel | 47.86 | 64.35 | +| pole | 28.41 | 40.3 | +| land | 0.75 | 1.21 | +| bannister | 15.8 | 23.99 | +| escalator | 59.1 | 77.41 | +| ottoman | 49.75 | 65.56 | +| bottle | 40.22 | 66.83 | +| buffet | 52.04 | 58.1 | +| poster | 35.89 | 46.21 | +| stage | 24.76 | 45.74 | +| van | 47.42 | 64.67 | +| ship | 28.54 | 31.59 | +| fountain | 36.25 | 37.08 | +| conveyer belt | 81.43 | 92.69 | +| canopy | 47.08 | 75.05 | +| washer | 82.95 | 87.99 | +| plaything | 26.18 | 41.49 | +| swimming pool | 59.65 | 88.78 | +| stool | 46.21 | 75.06 | +| barrel | 56.18 | 72.51 | +| basket | 42.98 | 58.72 | +| waterfall | 60.75 | 69.79 | +| tent | 94.8 | 98.87 | +| bag | 17.23 | 18.87 | +| minibike | 75.52 | 89.48 | +| cradle | 80.37 | 97.91 | +| oven | 63.6 | 76.85 | +| ball | 56.08 | 71.59 | +| food | 55.05 | 67.36 | +| step | 11.83 | 14.29 | +| tank | 62.9 | 69.27 | +| trade name | 33.33 | 40.96 | +| microwave | 89.19 | 96.6 | +| pot | 57.22 | 66.13 | +| animal | 61.11 | 62.39 | +| bicycle | 59.27 | 76.79 | +| lake | 59.66 | 63.42 | +| dishwasher | 63.71 | 71.46 | +| screen | 59.71 | 88.89 | +| blanket | 27.76 | 31.6 | +| sculpture | 72.17 | 85.03 | +| hood | 62.18 | 74.23 | +| sconce | 58.99 | 72.73 | +| vase | 47.21 | 64.06 | +| traffic light | 35.02 | 62.39 | +| tray | 15.53 | 16.2 | +| ashcan | 42.89 | 64.91 | +| fan | 68.26 | 82.16 | +| pier | 40.42 | 49.26 | +| crt screen | 4.22 | 5.62 | +| plate | 59.24 | 79.45 | +| monitor | 57.54 | 72.86 | +| bulletin board | 51.9 | 70.25 | +| shower | 1.7 | 1.91 | +| radiator | 63.18 | 78.38 | +| glass | 18.04 | 19.11 | +| clock | 38.75 | 45.46 | +| flag | 69.24 | 78.89 | ++---------------------+-------+-------+ +2024-06-16 13:22:51,169 - mmseg - INFO - Summary: +2024-06-16 13:22:51,169 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.76 | 56.04 | 69.33 | ++-------+-------+-------+ +2024-06-16 13:22:51,170 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 13:22:51,170 - mmseg - INFO - Iter(val) [250] aAcc: 0.8576, mIoU: 0.5604, mAcc: 0.6933, IoU.wall: 0.8155, IoU.building: 0.8481, IoU.sky: 0.9485, IoU.floor: 0.8402, IoU.tree: 0.7747, IoU.ceiling: 0.8686, IoU.road: 0.8672, IoU.bed : 0.9206, IoU.windowpane: 0.6597, IoU.grass: 0.6693, IoU.cabinet: 0.6599, IoU.sidewalk: 0.7268, IoU.person: 0.8560, IoU.earth: 0.3540, IoU.door: 0.5675, IoU.table: 0.6821, IoU.mountain: 0.6243, IoU.plant: 0.5395, IoU.curtain: 0.7834, IoU.chair: 0.6502, IoU.car: 0.8704, IoU.water: 0.6726, IoU.painting: 0.7514, IoU.sofa: 0.7880, IoU.shelf: 0.4602, IoU.house: 0.5720, IoU.sea: 0.7683, IoU.mirror: 0.7429, IoU.rug: 0.7181, IoU.field: 0.3462, IoU.armchair: 0.5612, IoU.seat: 0.6683, IoU.fence: 0.5101, IoU.desk: 0.5854, IoU.rock: 0.5421, IoU.wardrobe: 0.5446, IoU.lamp: 0.7349, IoU.bathtub: 0.8445, IoU.railing: 0.4380, IoU.cushion: 0.6967, IoU.base: 0.3937, IoU.box: 0.3579, IoU.column: 0.5454, IoU.signboard: 0.4081, IoU.chest of drawers: 0.4560, IoU.counter: 0.4553, IoU.sand: 0.4122, IoU.sink: 0.7381, IoU.skyscraper: 0.4634, IoU.fireplace: 0.7232, IoU.refrigerator: 0.8053, IoU.grandstand: 0.5237, IoU.path: 0.3443, IoU.stairs: 0.2647, IoU.runway: 0.7280, IoU.case: 0.5919, IoU.pool table: 0.9182, IoU.pillow: 0.6693, IoU.screen door: 0.8400, IoU.stairway: 0.4384, IoU.river: 0.1108, IoU.bridge: 0.5835, IoU.bookcase: 0.4259, IoU.blind: 0.4527, IoU.coffee table: 0.6260, IoU.toilet: 0.8902, IoU.flower: 0.4020, IoU.book: 0.5329, IoU.hill: 0.0772, IoU.bench: 0.5016, IoU.countertop: 0.6403, IoU.stove: 0.8244, IoU.palm: 0.5707, IoU.kitchen island: 0.4604, IoU.computer: 0.7815, IoU.swivel chair: 0.5118, IoU.boat: 0.7571, IoU.bar: 0.5664, IoU.arcade machine: 0.7745, IoU.hovel: 0.3425, IoU.bus: 0.9282, IoU.towel: 0.7694, IoU.light: 0.6134, IoU.truck: 0.4544, IoU.tower: 0.3247, IoU.chandelier: 0.7009, IoU.awning: 0.3853, IoU.streetlight: 0.3578, IoU.booth: 0.5162, IoU.television receiver: 0.7295, IoU.airplane: 0.8130, IoU.dirt track: 0.0290, IoU.apparel: 0.4786, IoU.pole: 0.2841, IoU.land: 0.0075, IoU.bannister: 0.1580, IoU.escalator: 0.5910, IoU.ottoman: 0.4975, IoU.bottle: 0.4022, IoU.buffet: 0.5204, IoU.poster: 0.3589, IoU.stage: 0.2476, IoU.van: 0.4742, IoU.ship: 0.2854, IoU.fountain: 0.3625, IoU.conveyer belt: 0.8143, IoU.canopy: 0.4708, IoU.washer: 0.8295, IoU.plaything: 0.2618, IoU.swimming pool: 0.5965, IoU.stool: 0.4621, IoU.barrel: 0.5618, IoU.basket: 0.4298, IoU.waterfall: 0.6075, IoU.tent: 0.9480, IoU.bag: 0.1723, IoU.minibike: 0.7552, IoU.cradle: 0.8037, IoU.oven: 0.6360, IoU.ball: 0.5608, IoU.food: 0.5505, IoU.step: 0.1183, IoU.tank: 0.6290, IoU.trade name: 0.3333, IoU.microwave: 0.8919, IoU.pot: 0.5722, IoU.animal: 0.6111, IoU.bicycle: 0.5927, IoU.lake: 0.5966, IoU.dishwasher: 0.6371, IoU.screen: 0.5971, IoU.blanket: 0.2776, IoU.sculpture: 0.7217, IoU.hood: 0.6218, IoU.sconce: 0.5899, IoU.vase: 0.4721, IoU.traffic light: 0.3502, IoU.tray: 0.1553, IoU.ashcan: 0.4289, IoU.fan: 0.6826, IoU.pier: 0.4042, IoU.crt screen: 0.0422, IoU.plate: 0.5924, IoU.monitor: 0.5754, IoU.bulletin board: 0.5190, IoU.shower: 0.0170, IoU.radiator: 0.6318, IoU.glass: 0.1804, IoU.clock: 0.3875, IoU.flag: 0.6924, Acc.wall: 0.8918, Acc.building: 0.9339, Acc.sky: 0.9738, Acc.floor: 0.9148, Acc.tree: 0.8956, Acc.ceiling: 0.9267, Acc.road: 0.9272, Acc.bed : 0.9728, Acc.windowpane: 0.8073, Acc.grass: 0.8103, Acc.cabinet: 0.7629, Acc.sidewalk: 0.8579, Acc.person: 0.9326, Acc.earth: 0.4457, Acc.door: 0.7251, Acc.table: 0.8131, Acc.mountain: 0.7206, Acc.plant: 0.6349, Acc.curtain: 0.8899, Acc.chair: 0.7383, Acc.car: 0.9404, Acc.water: 0.8500, Acc.painting: 0.8971, Acc.sofa: 0.9097, Acc.shelf: 0.5983, Acc.house: 0.7653, Acc.sea: 0.8605, Acc.mirror: 0.8418, Acc.rug: 0.8407, Acc.field: 0.6790, Acc.armchair: 0.7668, Acc.seat: 0.9018, Acc.fence: 0.6382, Acc.desk: 0.8223, Acc.rock: 0.8612, Acc.wardrobe: 0.7093, Acc.lamp: 0.8284, Acc.bathtub: 0.8657, Acc.railing: 0.5818, Acc.cushion: 0.7867, Acc.base: 0.6372, Acc.box: 0.4537, Acc.column: 0.6942, Acc.signboard: 0.5367, Acc.chest of drawers: 0.7012, Acc.counter: 0.6214, Acc.sand: 0.8085, Acc.sink: 0.8269, Acc.skyscraper: 0.5736, Acc.fireplace: 0.9589, Acc.refrigerator: 0.8787, Acc.grandstand: 0.8374, Acc.path: 0.5176, Acc.stairs: 0.3058, Acc.runway: 0.9588, Acc.case: 0.8126, Acc.pool table: 0.9834, Acc.pillow: 0.7675, Acc.screen door: 0.8738, Acc.stairway: 0.6128, Acc.river: 0.1656, Acc.bridge: 0.6447, Acc.bookcase: 0.5934, Acc.blind: 0.5295, Acc.coffee table: 0.8897, Acc.toilet: 0.9384, Acc.flower: 0.5795, Acc.book: 0.8151, Acc.hill: 0.1557, Acc.bench: 0.6416, Acc.countertop: 0.8564, Acc.stove: 0.9088, Acc.palm: 0.8045, Acc.kitchen island: 0.9057, Acc.computer: 0.9124, Acc.swivel chair: 0.7564, Acc.boat: 0.8846, Acc.bar: 0.7074, Acc.arcade machine: 0.8448, Acc.hovel: 0.3842, Acc.bus: 0.9664, Acc.towel: 0.8361, Acc.light: 0.7403, Acc.truck: 0.6035, Acc.tower: 0.5455, Acc.chandelier: 0.8925, Acc.awning: 0.4616, Acc.streetlight: 0.4894, Acc.booth: 0.7858, Acc.television receiver: 0.8681, Acc.airplane: 0.8624, Acc.dirt track: 0.0382, Acc.apparel: 0.6435, Acc.pole: 0.4030, Acc.land: 0.0121, Acc.bannister: 0.2399, Acc.escalator: 0.7741, Acc.ottoman: 0.6556, Acc.bottle: 0.6683, Acc.buffet: 0.5810, Acc.poster: 0.4621, Acc.stage: 0.4574, Acc.van: 0.6467, Acc.ship: 0.3159, Acc.fountain: 0.3708, Acc.conveyer belt: 0.9269, Acc.canopy: 0.7505, Acc.washer: 0.8799, Acc.plaything: 0.4149, Acc.swimming pool: 0.8878, Acc.stool: 0.7506, Acc.barrel: 0.7251, Acc.basket: 0.5872, Acc.waterfall: 0.6979, Acc.tent: 0.9887, Acc.bag: 0.1887, Acc.minibike: 0.8948, Acc.cradle: 0.9791, Acc.oven: 0.7685, Acc.ball: 0.7159, Acc.food: 0.6736, Acc.step: 0.1429, Acc.tank: 0.6927, Acc.trade name: 0.4096, Acc.microwave: 0.9660, Acc.pot: 0.6613, Acc.animal: 0.6239, Acc.bicycle: 0.7679, Acc.lake: 0.6342, Acc.dishwasher: 0.7146, Acc.screen: 0.8889, Acc.blanket: 0.3160, Acc.sculpture: 0.8503, Acc.hood: 0.7423, Acc.sconce: 0.7273, Acc.vase: 0.6406, Acc.traffic light: 0.6239, Acc.tray: 0.1620, Acc.ashcan: 0.6491, Acc.fan: 0.8216, Acc.pier: 0.4926, Acc.crt screen: 0.0562, Acc.plate: 0.7945, Acc.monitor: 0.7286, Acc.bulletin board: 0.7025, Acc.shower: 0.0191, Acc.radiator: 0.7838, Acc.glass: 0.1911, Acc.clock: 0.4546, Acc.flag: 0.7889 +2024-06-16 13:23:59,717 - mmseg - INFO - Iter [38050/80000] lr: 2.098e-05, eta: 17:35:47, time: 3.294, data_time: 1.939, memory: 70722, decode.loss_ce: 0.2140, decode.acc_seg: 91.1195, aux.loss_ce: 0.0888, aux.acc_seg: 90.7191, loss: 0.3028 +2024-06-16 13:25:07,833 - mmseg - INFO - Iter [38100/80000] lr: 2.095e-05, eta: 17:34:24, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2167, decode.acc_seg: 90.9575, aux.loss_ce: 0.0903, aux.acc_seg: 90.5469, loss: 0.3069 +2024-06-16 13:26:15,796 - mmseg - INFO - Iter [38150/80000] lr: 2.093e-05, eta: 17:33:00, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2057, decode.acc_seg: 91.0005, aux.loss_ce: 0.0847, aux.acc_seg: 90.7381, loss: 0.2904 +2024-06-16 13:27:23,976 - mmseg - INFO - Iter [38200/80000] lr: 2.090e-05, eta: 17:31:37, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2206, decode.acc_seg: 90.5576, aux.loss_ce: 0.0915, aux.acc_seg: 90.3252, loss: 0.3121 +2024-06-16 13:28:32,104 - mmseg - INFO - Iter [38250/80000] lr: 2.088e-05, eta: 17:30:13, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2263, decode.acc_seg: 90.3666, aux.loss_ce: 0.0936, aux.acc_seg: 90.0185, loss: 0.3199 +2024-06-16 13:29:40,391 - mmseg - INFO - Iter [38300/80000] lr: 2.085e-05, eta: 17:28:50, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2272, decode.acc_seg: 90.3002, aux.loss_ce: 0.0941, aux.acc_seg: 89.9496, loss: 0.3214 +2024-06-16 13:30:48,495 - mmseg - INFO - Iter [38350/80000] lr: 2.083e-05, eta: 17:27:26, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2183, decode.acc_seg: 90.9788, aux.loss_ce: 0.0902, aux.acc_seg: 90.7337, loss: 0.3085 +2024-06-16 13:31:56,946 - mmseg - INFO - Iter [38400/80000] lr: 2.080e-05, eta: 17:26:03, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2134, decode.acc_seg: 90.9474, aux.loss_ce: 0.0885, aux.acc_seg: 90.5768, loss: 0.3019 +2024-06-16 13:33:05,190 - mmseg - INFO - Iter [38450/80000] lr: 2.078e-05, eta: 17:24:40, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1983, decode.acc_seg: 91.4525, aux.loss_ce: 0.0828, aux.acc_seg: 91.0281, loss: 0.2811 +2024-06-16 13:34:13,352 - mmseg - INFO - Iter [38500/80000] lr: 2.075e-05, eta: 17:23:17, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2024, decode.acc_seg: 91.4618, aux.loss_ce: 0.0853, aux.acc_seg: 91.0577, loss: 0.2877 +2024-06-16 13:35:21,732 - mmseg - INFO - Iter [38550/80000] lr: 2.073e-05, eta: 17:21:54, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2012, decode.acc_seg: 91.7254, aux.loss_ce: 0.0838, aux.acc_seg: 91.4190, loss: 0.2850 +2024-06-16 13:36:29,871 - mmseg - INFO - Iter [38600/80000] lr: 2.070e-05, eta: 17:20:31, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2098, decode.acc_seg: 91.1826, aux.loss_ce: 0.0868, aux.acc_seg: 90.9265, loss: 0.2966 +2024-06-16 13:37:38,104 - mmseg - INFO - Iter [38650/80000] lr: 2.068e-05, eta: 17:19:08, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2075, decode.acc_seg: 91.1727, aux.loss_ce: 0.0859, aux.acc_seg: 90.9265, loss: 0.2934 +2024-06-16 13:38:46,348 - mmseg - INFO - Iter [38700/80000] lr: 2.065e-05, eta: 17:17:45, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1987, decode.acc_seg: 91.6369, aux.loss_ce: 0.0827, aux.acc_seg: 91.3483, loss: 0.2814 +2024-06-16 13:39:54,691 - mmseg - INFO - Iter [38750/80000] lr: 2.063e-05, eta: 17:16:22, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1906, decode.acc_seg: 91.8946, aux.loss_ce: 0.0793, aux.acc_seg: 91.5822, loss: 0.2699 +2024-06-16 13:41:02,807 - mmseg - INFO - Iter [38800/80000] lr: 2.060e-05, eta: 17:14:59, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2122, decode.acc_seg: 91.2764, aux.loss_ce: 0.0878, aux.acc_seg: 90.9550, loss: 0.2999 +2024-06-16 13:42:11,043 - mmseg - INFO - Iter [38850/80000] lr: 2.058e-05, eta: 17:13:36, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2110, decode.acc_seg: 90.9198, aux.loss_ce: 0.0875, aux.acc_seg: 90.6791, loss: 0.2985 +2024-06-16 13:43:19,059 - mmseg - INFO - Iter [38900/80000] lr: 2.055e-05, eta: 17:12:13, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2259, decode.acc_seg: 90.5209, aux.loss_ce: 0.0926, aux.acc_seg: 90.3180, loss: 0.3185 +2024-06-16 13:44:27,482 - mmseg - INFO - Iter [38950/80000] lr: 2.053e-05, eta: 17:10:50, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2081, decode.acc_seg: 90.9317, aux.loss_ce: 0.0858, aux.acc_seg: 90.5712, loss: 0.2940 +2024-06-16 13:45:35,705 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 13:45:35,705 - mmseg - INFO - Iter [39000/80000] lr: 2.050e-05, eta: 17:09:27, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2198, decode.acc_seg: 90.9834, aux.loss_ce: 0.0902, aux.acc_seg: 90.7836, loss: 0.3100 +2024-06-16 13:47:11,824 - mmseg - INFO - per class results: +2024-06-16 13:47:11,830 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.04 | 89.71 | +| building | 85.65 | 92.62 | +| sky | 94.94 | 97.68 | +| floor | 84.71 | 90.9 | +| tree | 76.9 | 90.66 | +| ceiling | 86.91 | 93.59 | +| road | 86.16 | 93.27 | +| bed | 92.1 | 97.53 | +| windowpane | 66.02 | 80.33 | +| grass | 68.45 | 81.16 | +| cabinet | 67.09 | 79.8 | +| sidewalk | 71.31 | 82.86 | +| person | 85.17 | 94.8 | +| earth | 37.21 | 50.12 | +| door | 59.27 | 77.49 | +| table | 69.51 | 81.17 | +| mountain | 65.21 | 72.03 | +| plant | 55.73 | 69.82 | +| curtain | 78.45 | 89.67 | +| chair | 67.76 | 82.26 | +| car | 86.41 | 94.83 | +| water | 65.5 | 79.11 | +| painting | 76.22 | 91.39 | +| sofa | 80.89 | 90.65 | +| shelf | 45.94 | 62.33 | +| house | 61.8 | 78.92 | +| sea | 77.81 | 89.57 | +| mirror | 75.04 | 80.88 | +| rug | 72.25 | 82.43 | +| field | 28.61 | 50.1 | +| armchair | 60.7 | 76.19 | +| seat | 68.28 | 87.01 | +| fence | 49.71 | 64.46 | +| desk | 62.12 | 81.2 | +| rock | 56.58 | 84.86 | +| wardrobe | 55.9 | 65.35 | +| lamp | 73.61 | 84.81 | +| bathtub | 84.63 | 86.74 | +| railing | 42.56 | 61.63 | +| cushion | 68.17 | 83.16 | +| base | 37.83 | 55.22 | +| box | 36.28 | 47.97 | +| column | 52.81 | 71.13 | +| signboard | 39.48 | 54.04 | +| chest of drawers | 44.74 | 75.11 | +| counter | 40.3 | 44.15 | +| sand | 50.28 | 81.76 | +| sink | 74.84 | 85.13 | +| skyscraper | 55.31 | 71.1 | +| fireplace | 75.33 | 90.51 | +| refrigerator | 78.1 | 92.02 | +| grandstand | 56.24 | 81.74 | +| path | 30.03 | 40.76 | +| stairs | 43.17 | 53.32 | +| runway | 68.03 | 88.01 | +| case | 59.61 | 79.78 | +| pool table | 94.36 | 98.5 | +| pillow | 60.4 | 66.44 | +| screen door | 81.19 | 83.83 | +| stairway | 56.07 | 65.45 | +| river | 13.72 | 27.38 | +| bridge | 63.6 | 71.26 | +| bookcase | 45.07 | 67.42 | +| blind | 47.43 | 54.02 | +| coffee table | 65.6 | 88.15 | +| toilet | 89.29 | 93.75 | +| flower | 44.99 | 53.76 | +| book | 52.05 | 69.59 | +| hill | 8.18 | 12.31 | +| bench | 55.44 | 64.14 | +| countertop | 63.92 | 79.19 | +| stove | 85.23 | 91.88 | +| palm | 53.08 | 82.72 | +| kitchen island | 51.32 | 87.0 | +| computer | 77.75 | 91.95 | +| swivel chair | 49.94 | 80.2 | +| boat | 75.0 | 92.51 | +| bar | 65.01 | 79.66 | +| arcade machine | 78.9 | 84.29 | +| hovel | 42.33 | 49.01 | +| bus | 93.01 | 95.47 | +| towel | 76.91 | 83.85 | +| light | 61.74 | 73.43 | +| truck | 45.3 | 67.12 | +| tower | 43.78 | 77.24 | +| chandelier | 70.13 | 89.7 | +| awning | 42.99 | 54.93 | +| streetlight | 31.66 | 40.66 | +| booth | 64.04 | 73.2 | +| television receiver | 73.69 | 84.84 | +| airplane | 73.55 | 86.13 | +| dirt track | 0.0 | 0.0 | +| apparel | 38.53 | 55.39 | +| pole | 28.26 | 38.71 | +| land | 1.52 | 2.65 | +| bannister | 14.98 | 20.01 | +| escalator | 55.33 | 80.17 | +| ottoman | 50.45 | 66.03 | +| bottle | 35.21 | 45.59 | +| buffet | 45.53 | 55.3 | +| poster | 40.84 | 49.24 | +| stage | 24.85 | 36.22 | +| van | 52.3 | 65.35 | +| ship | 14.62 | 14.95 | +| fountain | 33.45 | 34.38 | +| conveyer belt | 82.05 | 93.69 | +| canopy | 46.64 | 65.32 | +| washer | 80.51 | 84.55 | +| plaything | 28.84 | 38.27 | +| swimming pool | 60.73 | 92.89 | +| stool | 56.41 | 66.43 | +| barrel | 55.97 | 68.24 | +| basket | 41.05 | 55.24 | +| waterfall | 75.59 | 88.69 | +| tent | 88.15 | 98.7 | +| bag | 18.33 | 21.08 | +| minibike | 75.34 | 88.63 | +| cradle | 81.52 | 97.3 | +| oven | 56.17 | 66.1 | +| ball | 50.82 | 53.57 | +| food | 59.94 | 75.91 | +| step | 21.93 | 30.67 | +| tank | 61.45 | 66.58 | +| trade name | 23.39 | 25.67 | +| microwave | 87.29 | 96.6 | +| pot | 56.99 | 67.59 | +| animal | 59.91 | 61.46 | +| bicycle | 60.27 | 72.49 | +| lake | 49.44 | 63.69 | +| dishwasher | 64.74 | 77.96 | +| screen | 59.89 | 94.21 | +| blanket | 27.93 | 31.3 | +| sculpture | 60.21 | 88.98 | +| hood | 61.06 | 72.49 | +| sconce | 52.55 | 58.38 | +| vase | 47.83 | 62.95 | +| traffic light | 34.78 | 58.61 | +| tray | 22.78 | 30.84 | +| ashcan | 41.18 | 60.49 | +| fan | 67.0 | 80.09 | +| pier | 41.26 | 46.77 | +| crt screen | 2.17 | 2.33 | +| plate | 62.39 | 76.47 | +| monitor | 68.6 | 80.12 | +| bulletin board | 52.11 | 68.63 | +| shower | 2.79 | 3.63 | +| radiator | 62.28 | 79.04 | +| glass | 19.24 | 20.61 | +| clock | 41.38 | 49.83 | +| flag | 70.69 | 77.11 | ++---------------------+-------+-------+ +2024-06-16 13:47:11,830 - mmseg - INFO - Summary: +2024-06-16 13:47:11,830 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.15 | 56.6 | 69.07 | ++-------+------+-------+ +2024-06-16 13:47:11,831 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 13:47:11,831 - mmseg - INFO - Iter(val) [250] aAcc: 0.8615, mIoU: 0.5660, mAcc: 0.6907, IoU.wall: 0.8204, IoU.building: 0.8565, IoU.sky: 0.9494, IoU.floor: 0.8471, IoU.tree: 0.7690, IoU.ceiling: 0.8691, IoU.road: 0.8616, IoU.bed : 0.9210, IoU.windowpane: 0.6602, IoU.grass: 0.6845, IoU.cabinet: 0.6709, IoU.sidewalk: 0.7131, IoU.person: 0.8517, IoU.earth: 0.3721, IoU.door: 0.5927, IoU.table: 0.6951, IoU.mountain: 0.6521, IoU.plant: 0.5573, IoU.curtain: 0.7845, IoU.chair: 0.6776, IoU.car: 0.8641, IoU.water: 0.6550, IoU.painting: 0.7622, IoU.sofa: 0.8089, IoU.shelf: 0.4594, IoU.house: 0.6180, IoU.sea: 0.7781, IoU.mirror: 0.7504, IoU.rug: 0.7225, IoU.field: 0.2861, IoU.armchair: 0.6070, IoU.seat: 0.6828, IoU.fence: 0.4971, IoU.desk: 0.6212, IoU.rock: 0.5658, IoU.wardrobe: 0.5590, IoU.lamp: 0.7361, IoU.bathtub: 0.8463, IoU.railing: 0.4256, IoU.cushion: 0.6817, IoU.base: 0.3783, IoU.box: 0.3628, IoU.column: 0.5281, IoU.signboard: 0.3948, IoU.chest of drawers: 0.4474, IoU.counter: 0.4030, IoU.sand: 0.5028, IoU.sink: 0.7484, IoU.skyscraper: 0.5531, IoU.fireplace: 0.7533, IoU.refrigerator: 0.7810, IoU.grandstand: 0.5624, IoU.path: 0.3003, IoU.stairs: 0.4317, IoU.runway: 0.6803, IoU.case: 0.5961, IoU.pool table: 0.9436, IoU.pillow: 0.6040, IoU.screen door: 0.8119, IoU.stairway: 0.5607, IoU.river: 0.1372, IoU.bridge: 0.6360, IoU.bookcase: 0.4507, IoU.blind: 0.4743, IoU.coffee table: 0.6560, IoU.toilet: 0.8929, IoU.flower: 0.4499, IoU.book: 0.5205, IoU.hill: 0.0818, IoU.bench: 0.5544, IoU.countertop: 0.6392, IoU.stove: 0.8523, IoU.palm: 0.5308, IoU.kitchen island: 0.5132, IoU.computer: 0.7775, IoU.swivel chair: 0.4994, IoU.boat: 0.7500, IoU.bar: 0.6501, IoU.arcade machine: 0.7890, IoU.hovel: 0.4233, IoU.bus: 0.9301, IoU.towel: 0.7691, IoU.light: 0.6174, IoU.truck: 0.4530, IoU.tower: 0.4378, IoU.chandelier: 0.7013, IoU.awning: 0.4299, IoU.streetlight: 0.3166, IoU.booth: 0.6404, IoU.television receiver: 0.7369, IoU.airplane: 0.7355, IoU.dirt track: 0.0000, IoU.apparel: 0.3853, IoU.pole: 0.2826, IoU.land: 0.0152, IoU.bannister: 0.1498, IoU.escalator: 0.5533, IoU.ottoman: 0.5045, IoU.bottle: 0.3521, IoU.buffet: 0.4553, IoU.poster: 0.4084, IoU.stage: 0.2485, IoU.van: 0.5230, IoU.ship: 0.1462, IoU.fountain: 0.3345, IoU.conveyer belt: 0.8205, IoU.canopy: 0.4664, IoU.washer: 0.8051, IoU.plaything: 0.2884, IoU.swimming pool: 0.6073, IoU.stool: 0.5641, IoU.barrel: 0.5597, IoU.basket: 0.4105, IoU.waterfall: 0.7559, IoU.tent: 0.8815, IoU.bag: 0.1833, IoU.minibike: 0.7534, IoU.cradle: 0.8152, IoU.oven: 0.5617, IoU.ball: 0.5082, IoU.food: 0.5994, IoU.step: 0.2193, IoU.tank: 0.6145, IoU.trade name: 0.2339, IoU.microwave: 0.8729, IoU.pot: 0.5699, IoU.animal: 0.5991, IoU.bicycle: 0.6027, IoU.lake: 0.4944, IoU.dishwasher: 0.6474, IoU.screen: 0.5989, IoU.blanket: 0.2793, IoU.sculpture: 0.6021, IoU.hood: 0.6106, IoU.sconce: 0.5255, IoU.vase: 0.4783, IoU.traffic light: 0.3478, IoU.tray: 0.2278, IoU.ashcan: 0.4118, IoU.fan: 0.6700, IoU.pier: 0.4126, IoU.crt screen: 0.0217, IoU.plate: 0.6239, IoU.monitor: 0.6860, IoU.bulletin board: 0.5211, IoU.shower: 0.0279, IoU.radiator: 0.6228, IoU.glass: 0.1924, IoU.clock: 0.4138, IoU.flag: 0.7069, Acc.wall: 0.8971, Acc.building: 0.9262, Acc.sky: 0.9768, Acc.floor: 0.9090, Acc.tree: 0.9066, Acc.ceiling: 0.9359, Acc.road: 0.9327, Acc.bed : 0.9753, Acc.windowpane: 0.8033, Acc.grass: 0.8116, Acc.cabinet: 0.7980, Acc.sidewalk: 0.8286, Acc.person: 0.9480, Acc.earth: 0.5012, Acc.door: 0.7749, Acc.table: 0.8117, Acc.mountain: 0.7203, Acc.plant: 0.6982, Acc.curtain: 0.8967, Acc.chair: 0.8226, Acc.car: 0.9483, Acc.water: 0.7911, Acc.painting: 0.9139, Acc.sofa: 0.9065, Acc.shelf: 0.6233, Acc.house: 0.7892, Acc.sea: 0.8957, Acc.mirror: 0.8088, Acc.rug: 0.8243, Acc.field: 0.5010, Acc.armchair: 0.7619, Acc.seat: 0.8701, Acc.fence: 0.6446, Acc.desk: 0.8120, Acc.rock: 0.8486, Acc.wardrobe: 0.6535, Acc.lamp: 0.8481, Acc.bathtub: 0.8674, Acc.railing: 0.6163, Acc.cushion: 0.8316, Acc.base: 0.5522, Acc.box: 0.4797, Acc.column: 0.7113, Acc.signboard: 0.5404, Acc.chest of drawers: 0.7511, Acc.counter: 0.4415, Acc.sand: 0.8176, Acc.sink: 0.8513, Acc.skyscraper: 0.7110, Acc.fireplace: 0.9051, Acc.refrigerator: 0.9202, Acc.grandstand: 0.8174, Acc.path: 0.4076, Acc.stairs: 0.5332, Acc.runway: 0.8801, Acc.case: 0.7978, Acc.pool table: 0.9850, Acc.pillow: 0.6644, Acc.screen door: 0.8383, Acc.stairway: 0.6545, Acc.river: 0.2738, Acc.bridge: 0.7126, Acc.bookcase: 0.6742, Acc.blind: 0.5402, Acc.coffee table: 0.8815, Acc.toilet: 0.9375, Acc.flower: 0.5376, Acc.book: 0.6959, Acc.hill: 0.1231, Acc.bench: 0.6414, Acc.countertop: 0.7919, Acc.stove: 0.9188, Acc.palm: 0.8272, Acc.kitchen island: 0.8700, Acc.computer: 0.9195, Acc.swivel chair: 0.8020, Acc.boat: 0.9251, Acc.bar: 0.7966, Acc.arcade machine: 0.8429, Acc.hovel: 0.4901, Acc.bus: 0.9547, Acc.towel: 0.8385, Acc.light: 0.7343, Acc.truck: 0.6712, Acc.tower: 0.7724, Acc.chandelier: 0.8970, Acc.awning: 0.5493, Acc.streetlight: 0.4066, Acc.booth: 0.7320, Acc.television receiver: 0.8484, Acc.airplane: 0.8613, Acc.dirt track: 0.0000, Acc.apparel: 0.5539, Acc.pole: 0.3871, Acc.land: 0.0265, Acc.bannister: 0.2001, Acc.escalator: 0.8017, Acc.ottoman: 0.6603, Acc.bottle: 0.4559, Acc.buffet: 0.5530, Acc.poster: 0.4924, Acc.stage: 0.3622, Acc.van: 0.6535, Acc.ship: 0.1495, Acc.fountain: 0.3438, Acc.conveyer belt: 0.9369, Acc.canopy: 0.6532, Acc.washer: 0.8455, Acc.plaything: 0.3827, Acc.swimming pool: 0.9289, Acc.stool: 0.6643, Acc.barrel: 0.6824, Acc.basket: 0.5524, Acc.waterfall: 0.8869, Acc.tent: 0.9870, Acc.bag: 0.2108, Acc.minibike: 0.8863, Acc.cradle: 0.9730, Acc.oven: 0.6610, Acc.ball: 0.5357, Acc.food: 0.7591, Acc.step: 0.3067, Acc.tank: 0.6658, Acc.trade name: 0.2567, Acc.microwave: 0.9660, Acc.pot: 0.6759, Acc.animal: 0.6146, Acc.bicycle: 0.7249, Acc.lake: 0.6369, Acc.dishwasher: 0.7796, Acc.screen: 0.9421, Acc.blanket: 0.3130, Acc.sculpture: 0.8898, Acc.hood: 0.7249, Acc.sconce: 0.5838, Acc.vase: 0.6295, Acc.traffic light: 0.5861, Acc.tray: 0.3084, Acc.ashcan: 0.6049, Acc.fan: 0.8009, Acc.pier: 0.4677, Acc.crt screen: 0.0233, Acc.plate: 0.7647, Acc.monitor: 0.8012, Acc.bulletin board: 0.6863, Acc.shower: 0.0363, Acc.radiator: 0.7904, Acc.glass: 0.2061, Acc.clock: 0.4983, Acc.flag: 0.7711 +2024-06-16 13:48:20,516 - mmseg - INFO - Iter [39050/80000] lr: 2.048e-05, eta: 17:09:46, time: 3.296, data_time: 1.939, memory: 70722, decode.loss_ce: 0.2118, decode.acc_seg: 91.0484, aux.loss_ce: 0.0885, aux.acc_seg: 90.6261, loss: 0.3003 +2024-06-16 13:49:28,872 - mmseg - INFO - Iter [39100/80000] lr: 2.045e-05, eta: 17:08:23, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2273, decode.acc_seg: 90.4124, aux.loss_ce: 0.0928, aux.acc_seg: 90.1066, loss: 0.3201 +2024-06-16 13:50:36,900 - mmseg - INFO - Iter [39150/80000] lr: 2.043e-05, eta: 17:07:00, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2021, decode.acc_seg: 91.4314, aux.loss_ce: 0.0836, aux.acc_seg: 91.0910, loss: 0.2858 +2024-06-16 13:51:47,519 - mmseg - INFO - Iter [39200/80000] lr: 2.040e-05, eta: 17:05:39, time: 1.412, data_time: 0.055, memory: 70722, decode.loss_ce: 0.1971, decode.acc_seg: 91.9884, aux.loss_ce: 0.0822, aux.acc_seg: 91.7034, loss: 0.2793 +2024-06-16 13:52:55,760 - mmseg - INFO - Iter [39250/80000] lr: 2.038e-05, eta: 17:04:16, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1981, decode.acc_seg: 91.3114, aux.loss_ce: 0.0825, aux.acc_seg: 91.0131, loss: 0.2807 +2024-06-16 13:54:03,762 - mmseg - INFO - Iter [39300/80000] lr: 2.035e-05, eta: 17:02:53, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1996, decode.acc_seg: 91.4960, aux.loss_ce: 0.0827, aux.acc_seg: 91.2040, loss: 0.2823 +2024-06-16 13:55:11,789 - mmseg - INFO - Iter [39350/80000] lr: 2.033e-05, eta: 17:01:30, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1996, decode.acc_seg: 91.5932, aux.loss_ce: 0.0829, aux.acc_seg: 91.2499, loss: 0.2826 +2024-06-16 13:56:20,069 - mmseg - INFO - Iter [39400/80000] lr: 2.030e-05, eta: 17:00:08, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1967, decode.acc_seg: 91.4120, aux.loss_ce: 0.0813, aux.acc_seg: 91.1239, loss: 0.2780 +2024-06-16 13:57:28,174 - mmseg - INFO - Iter [39450/80000] lr: 2.028e-05, eta: 16:58:45, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2038, decode.acc_seg: 91.4219, aux.loss_ce: 0.0847, aux.acc_seg: 91.1196, loss: 0.2884 +2024-06-16 13:58:36,156 - mmseg - INFO - Iter [39500/80000] lr: 2.025e-05, eta: 16:57:22, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1893, decode.acc_seg: 92.0866, aux.loss_ce: 0.0790, aux.acc_seg: 91.7463, loss: 0.2683 +2024-06-16 13:59:44,257 - mmseg - INFO - Iter [39550/80000] lr: 2.023e-05, eta: 16:55:59, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2020, decode.acc_seg: 91.2879, aux.loss_ce: 0.0839, aux.acc_seg: 90.9405, loss: 0.2859 +2024-06-16 14:00:52,722 - mmseg - INFO - Iter [39600/80000] lr: 2.020e-05, eta: 16:54:37, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1957, decode.acc_seg: 91.6915, aux.loss_ce: 0.0814, aux.acc_seg: 91.4135, loss: 0.2772 +2024-06-16 14:02:00,830 - mmseg - INFO - Iter [39650/80000] lr: 2.018e-05, eta: 16:53:14, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2001, decode.acc_seg: 91.4367, aux.loss_ce: 0.0829, aux.acc_seg: 91.1097, loss: 0.2830 +2024-06-16 14:03:08,954 - mmseg - INFO - Iter [39700/80000] lr: 2.015e-05, eta: 16:51:51, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1900, decode.acc_seg: 92.0433, aux.loss_ce: 0.0792, aux.acc_seg: 91.7591, loss: 0.2692 +2024-06-16 14:04:17,055 - mmseg - INFO - Iter [39750/80000] lr: 2.013e-05, eta: 16:50:29, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2008, decode.acc_seg: 91.7059, aux.loss_ce: 0.0838, aux.acc_seg: 91.3670, loss: 0.2846 +2024-06-16 14:05:25,381 - mmseg - INFO - Iter [39800/80000] lr: 2.010e-05, eta: 16:49:06, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2028, decode.acc_seg: 91.2559, aux.loss_ce: 0.0850, aux.acc_seg: 90.8481, loss: 0.2878 +2024-06-16 14:06:33,628 - mmseg - INFO - Iter [39850/80000] lr: 2.008e-05, eta: 16:47:44, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2061, decode.acc_seg: 91.2329, aux.loss_ce: 0.0845, aux.acc_seg: 91.0506, loss: 0.2906 +2024-06-16 14:07:41,730 - mmseg - INFO - Iter [39900/80000] lr: 2.005e-05, eta: 16:46:21, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2150, decode.acc_seg: 90.7834, aux.loss_ce: 0.0892, aux.acc_seg: 90.5146, loss: 0.3042 +2024-06-16 14:08:49,987 - mmseg - INFO - Iter [39950/80000] lr: 2.003e-05, eta: 16:44:59, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2137, decode.acc_seg: 91.1560, aux.loss_ce: 0.0887, aux.acc_seg: 90.8271, loss: 0.3023 +2024-06-16 14:09:58,230 - mmseg - INFO - Saving checkpoint at 40000 iterations +2024-06-16 14:11:23,407 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 14:11:23,407 - mmseg - INFO - Iter [40000/80000] lr: 2.000e-05, eta: 16:45:02, time: 3.068, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1960, decode.acc_seg: 91.6755, aux.loss_ce: 0.0822, aux.acc_seg: 91.2673, loss: 0.2782 +2024-06-16 14:12:59,695 - mmseg - INFO - per class results: +2024-06-16 14:12:59,702 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.87 | 90.25 | +| building | 84.55 | 92.52 | +| sky | 94.84 | 97.47 | +| floor | 84.75 | 91.5 | +| tree | 77.89 | 89.96 | +| ceiling | 86.62 | 94.07 | +| road | 86.93 | 92.55 | +| bed | 92.57 | 96.73 | +| windowpane | 66.27 | 79.97 | +| grass | 68.27 | 80.55 | +| cabinet | 65.52 | 75.9 | +| sidewalk | 71.92 | 85.57 | +| person | 85.32 | 93.1 | +| earth | 35.65 | 47.11 | +| door | 58.72 | 71.88 | +| table | 69.99 | 82.26 | +| mountain | 62.28 | 74.85 | +| plant | 55.39 | 66.58 | +| curtain | 76.75 | 88.13 | +| chair | 68.35 | 80.67 | +| car | 86.86 | 94.58 | +| water | 65.34 | 79.56 | +| painting | 75.06 | 91.82 | +| sofa | 80.19 | 89.92 | +| shelf | 45.42 | 63.79 | +| house | 59.46 | 86.01 | +| sea | 76.76 | 88.36 | +| mirror | 75.53 | 81.94 | +| rug | 70.77 | 82.02 | +| field | 31.75 | 59.97 | +| armchair | 60.45 | 78.18 | +| seat | 66.91 | 86.55 | +| fence | 47.28 | 58.72 | +| desk | 62.26 | 78.26 | +| rock | 51.95 | 74.99 | +| wardrobe | 52.03 | 67.78 | +| lamp | 73.93 | 86.25 | +| bathtub | 84.42 | 86.46 | +| railing | 42.15 | 64.51 | +| cushion | 67.36 | 77.04 | +| base | 41.81 | 54.71 | +| box | 38.1 | 48.35 | +| column | 54.75 | 67.12 | +| signboard | 42.0 | 52.83 | +| chest of drawers | 42.19 | 70.65 | +| counter | 48.74 | 56.05 | +| sand | 55.58 | 82.0 | +| sink | 77.0 | 83.58 | +| skyscraper | 48.0 | 64.3 | +| fireplace | 73.6 | 94.87 | +| refrigerator | 81.31 | 88.12 | +| grandstand | 50.55 | 79.77 | +| path | 31.31 | 47.38 | +| stairs | 34.45 | 43.03 | +| runway | 72.65 | 95.48 | +| case | 56.13 | 75.58 | +| pool table | 94.36 | 98.6 | +| pillow | 69.07 | 84.14 | +| screen door | 76.07 | 77.55 | +| stairway | 42.23 | 50.67 | +| river | 16.69 | 35.37 | +| bridge | 64.47 | 72.98 | +| bookcase | 48.2 | 69.63 | +| blind | 42.7 | 46.7 | +| coffee table | 65.02 | 87.63 | +| toilet | 89.45 | 93.77 | +| flower | 41.7 | 62.72 | +| book | 50.8 | 71.49 | +| hill | 7.55 | 12.23 | +| bench | 50.79 | 55.82 | +| countertop | 63.9 | 81.27 | +| stove | 84.62 | 91.78 | +| palm | 55.73 | 80.18 | +| kitchen island | 51.36 | 88.54 | +| computer | 80.51 | 90.74 | +| swivel chair | 46.2 | 62.95 | +| boat | 71.72 | 87.54 | +| bar | 60.63 | 78.52 | +| arcade machine | 78.37 | 84.64 | +| hovel | 35.57 | 37.32 | +| bus | 93.36 | 96.14 | +| towel | 76.1 | 84.85 | +| light | 60.63 | 69.19 | +| truck | 43.88 | 57.33 | +| tower | 31.19 | 39.97 | +| chandelier | 71.82 | 87.55 | +| awning | 50.89 | 70.74 | +| streetlight | 35.26 | 47.77 | +| booth | 55.52 | 61.76 | +| television receiver | 77.31 | 88.68 | +| airplane | 70.6 | 77.39 | +| dirt track | 16.24 | 66.4 | +| apparel | 46.57 | 67.12 | +| pole | 30.45 | 44.22 | +| land | 3.66 | 4.58 | +| bannister | 14.67 | 20.66 | +| escalator | 56.14 | 80.05 | +| ottoman | 48.32 | 63.16 | +| bottle | 41.49 | 67.85 | +| buffet | 49.0 | 59.83 | +| poster | 35.87 | 45.5 | +| stage | 20.08 | 38.05 | +| van | 45.84 | 59.48 | +| ship | 56.39 | 59.85 | +| fountain | 37.33 | 37.97 | +| conveyer belt | 83.47 | 92.64 | +| canopy | 46.92 | 61.92 | +| washer | 85.96 | 91.34 | +| plaything | 28.12 | 44.36 | +| swimming pool | 60.43 | 92.38 | +| stool | 57.6 | 67.51 | +| barrel | 57.84 | 73.94 | +| basket | 39.75 | 54.12 | +| waterfall | 73.59 | 88.02 | +| tent | 92.9 | 98.62 | +| bag | 19.07 | 21.76 | +| minibike | 74.58 | 89.93 | +| cradle | 81.4 | 97.36 | +| oven | 52.94 | 68.08 | +| ball | 57.02 | 64.94 | +| food | 56.38 | 70.13 | +| step | 15.7 | 18.99 | +| tank | 59.54 | 62.5 | +| trade name | 33.06 | 41.21 | +| microwave | 87.5 | 96.26 | +| pot | 55.6 | 63.73 | +| animal | 58.15 | 60.05 | +| bicycle | 60.68 | 79.92 | +| lake | 54.93 | 63.63 | +| dishwasher | 68.64 | 73.85 | +| screen | 52.85 | 65.35 | +| blanket | 24.41 | 27.15 | +| sculpture | 62.18 | 88.51 | +| hood | 61.09 | 71.55 | +| sconce | 60.6 | 71.21 | +| vase | 45.63 | 64.51 | +| traffic light | 35.36 | 61.1 | +| tray | 18.82 | 24.09 | +| ashcan | 45.24 | 63.26 | +| fan | 67.34 | 77.99 | +| pier | 41.12 | 48.26 | +| crt screen | 15.05 | 30.37 | +| plate | 59.86 | 78.92 | +| monitor | 64.68 | 78.89 | +| bulletin board | 53.32 | 63.82 | +| shower | 0.01 | 0.04 | +| radiator | 63.65 | 77.55 | +| glass | 17.99 | 18.95 | +| clock | 41.16 | 51.2 | +| flag | 68.67 | 77.42 | ++---------------------+-------+-------+ +2024-06-16 14:12:59,702 - mmseg - INFO - Summary: +2024-06-16 14:12:59,702 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.96 | 56.77 | 69.52 | ++-------+-------+-------+ +2024-06-16 14:12:59,703 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 14:12:59,703 - mmseg - INFO - Iter(val) [250] aAcc: 0.8596, mIoU: 0.5677, mAcc: 0.6952, IoU.wall: 0.8187, IoU.building: 0.8455, IoU.sky: 0.9484, IoU.floor: 0.8475, IoU.tree: 0.7789, IoU.ceiling: 0.8662, IoU.road: 0.8693, IoU.bed : 0.9257, IoU.windowpane: 0.6627, IoU.grass: 0.6827, IoU.cabinet: 0.6552, IoU.sidewalk: 0.7192, IoU.person: 0.8532, IoU.earth: 0.3565, IoU.door: 0.5872, IoU.table: 0.6999, IoU.mountain: 0.6228, IoU.plant: 0.5539, IoU.curtain: 0.7675, IoU.chair: 0.6835, IoU.car: 0.8686, IoU.water: 0.6534, IoU.painting: 0.7506, IoU.sofa: 0.8019, IoU.shelf: 0.4542, IoU.house: 0.5946, IoU.sea: 0.7676, IoU.mirror: 0.7553, IoU.rug: 0.7077, IoU.field: 0.3175, IoU.armchair: 0.6045, IoU.seat: 0.6691, IoU.fence: 0.4728, IoU.desk: 0.6226, IoU.rock: 0.5195, IoU.wardrobe: 0.5203, IoU.lamp: 0.7393, IoU.bathtub: 0.8442, IoU.railing: 0.4215, IoU.cushion: 0.6736, IoU.base: 0.4181, IoU.box: 0.3810, IoU.column: 0.5475, IoU.signboard: 0.4200, IoU.chest of drawers: 0.4219, IoU.counter: 0.4874, IoU.sand: 0.5558, IoU.sink: 0.7700, IoU.skyscraper: 0.4800, IoU.fireplace: 0.7360, IoU.refrigerator: 0.8131, IoU.grandstand: 0.5055, IoU.path: 0.3131, IoU.stairs: 0.3445, IoU.runway: 0.7265, IoU.case: 0.5613, IoU.pool table: 0.9436, IoU.pillow: 0.6907, IoU.screen door: 0.7607, IoU.stairway: 0.4223, IoU.river: 0.1669, IoU.bridge: 0.6447, IoU.bookcase: 0.4820, IoU.blind: 0.4270, IoU.coffee table: 0.6502, IoU.toilet: 0.8945, IoU.flower: 0.4170, IoU.book: 0.5080, IoU.hill: 0.0755, IoU.bench: 0.5079, IoU.countertop: 0.6390, IoU.stove: 0.8462, IoU.palm: 0.5573, IoU.kitchen island: 0.5136, IoU.computer: 0.8051, IoU.swivel chair: 0.4620, IoU.boat: 0.7172, IoU.bar: 0.6063, IoU.arcade machine: 0.7837, IoU.hovel: 0.3557, IoU.bus: 0.9336, IoU.towel: 0.7610, IoU.light: 0.6063, IoU.truck: 0.4388, IoU.tower: 0.3119, IoU.chandelier: 0.7182, IoU.awning: 0.5089, IoU.streetlight: 0.3526, IoU.booth: 0.5552, IoU.television receiver: 0.7731, IoU.airplane: 0.7060, IoU.dirt track: 0.1624, IoU.apparel: 0.4657, IoU.pole: 0.3045, IoU.land: 0.0366, IoU.bannister: 0.1467, IoU.escalator: 0.5614, IoU.ottoman: 0.4832, IoU.bottle: 0.4149, IoU.buffet: 0.4900, IoU.poster: 0.3587, IoU.stage: 0.2008, IoU.van: 0.4584, IoU.ship: 0.5639, IoU.fountain: 0.3733, IoU.conveyer belt: 0.8347, IoU.canopy: 0.4692, IoU.washer: 0.8596, IoU.plaything: 0.2812, IoU.swimming pool: 0.6043, IoU.stool: 0.5760, IoU.barrel: 0.5784, IoU.basket: 0.3975, IoU.waterfall: 0.7359, IoU.tent: 0.9290, IoU.bag: 0.1907, IoU.minibike: 0.7458, IoU.cradle: 0.8140, IoU.oven: 0.5294, IoU.ball: 0.5702, IoU.food: 0.5638, IoU.step: 0.1570, IoU.tank: 0.5954, IoU.trade name: 0.3306, IoU.microwave: 0.8750, IoU.pot: 0.5560, IoU.animal: 0.5815, IoU.bicycle: 0.6068, IoU.lake: 0.5493, IoU.dishwasher: 0.6864, IoU.screen: 0.5285, IoU.blanket: 0.2441, IoU.sculpture: 0.6218, IoU.hood: 0.6109, IoU.sconce: 0.6060, IoU.vase: 0.4563, IoU.traffic light: 0.3536, IoU.tray: 0.1882, IoU.ashcan: 0.4524, IoU.fan: 0.6734, IoU.pier: 0.4112, IoU.crt screen: 0.1505, IoU.plate: 0.5986, IoU.monitor: 0.6468, IoU.bulletin board: 0.5332, IoU.shower: 0.0001, IoU.radiator: 0.6365, IoU.glass: 0.1799, IoU.clock: 0.4116, IoU.flag: 0.6867, Acc.wall: 0.9025, Acc.building: 0.9252, Acc.sky: 0.9747, Acc.floor: 0.9150, Acc.tree: 0.8996, Acc.ceiling: 0.9407, Acc.road: 0.9255, Acc.bed : 0.9673, Acc.windowpane: 0.7997, Acc.grass: 0.8055, Acc.cabinet: 0.7590, Acc.sidewalk: 0.8557, Acc.person: 0.9310, Acc.earth: 0.4711, Acc.door: 0.7188, Acc.table: 0.8226, Acc.mountain: 0.7485, Acc.plant: 0.6658, Acc.curtain: 0.8813, Acc.chair: 0.8067, Acc.car: 0.9458, Acc.water: 0.7956, Acc.painting: 0.9182, Acc.sofa: 0.8992, Acc.shelf: 0.6379, Acc.house: 0.8601, Acc.sea: 0.8836, Acc.mirror: 0.8194, Acc.rug: 0.8202, Acc.field: 0.5997, Acc.armchair: 0.7818, Acc.seat: 0.8655, Acc.fence: 0.5872, Acc.desk: 0.7826, Acc.rock: 0.7499, Acc.wardrobe: 0.6778, Acc.lamp: 0.8625, Acc.bathtub: 0.8646, Acc.railing: 0.6451, Acc.cushion: 0.7704, Acc.base: 0.5471, Acc.box: 0.4835, Acc.column: 0.6712, Acc.signboard: 0.5283, Acc.chest of drawers: 0.7065, Acc.counter: 0.5605, Acc.sand: 0.8200, Acc.sink: 0.8358, Acc.skyscraper: 0.6430, Acc.fireplace: 0.9487, Acc.refrigerator: 0.8812, Acc.grandstand: 0.7977, Acc.path: 0.4738, Acc.stairs: 0.4303, Acc.runway: 0.9548, Acc.case: 0.7558, Acc.pool table: 0.9860, Acc.pillow: 0.8414, Acc.screen door: 0.7755, Acc.stairway: 0.5067, Acc.river: 0.3537, Acc.bridge: 0.7298, Acc.bookcase: 0.6963, Acc.blind: 0.4670, Acc.coffee table: 0.8763, Acc.toilet: 0.9377, Acc.flower: 0.6272, Acc.book: 0.7149, Acc.hill: 0.1223, Acc.bench: 0.5582, Acc.countertop: 0.8127, Acc.stove: 0.9178, Acc.palm: 0.8018, Acc.kitchen island: 0.8854, Acc.computer: 0.9074, Acc.swivel chair: 0.6295, Acc.boat: 0.8754, Acc.bar: 0.7852, Acc.arcade machine: 0.8464, Acc.hovel: 0.3732, Acc.bus: 0.9614, Acc.towel: 0.8485, Acc.light: 0.6919, Acc.truck: 0.5733, Acc.tower: 0.3997, Acc.chandelier: 0.8755, Acc.awning: 0.7074, Acc.streetlight: 0.4777, Acc.booth: 0.6176, Acc.television receiver: 0.8868, Acc.airplane: 0.7739, Acc.dirt track: 0.6640, Acc.apparel: 0.6712, Acc.pole: 0.4422, Acc.land: 0.0458, Acc.bannister: 0.2066, Acc.escalator: 0.8005, Acc.ottoman: 0.6316, Acc.bottle: 0.6785, Acc.buffet: 0.5983, Acc.poster: 0.4550, Acc.stage: 0.3805, Acc.van: 0.5948, Acc.ship: 0.5985, Acc.fountain: 0.3797, Acc.conveyer belt: 0.9264, Acc.canopy: 0.6192, Acc.washer: 0.9134, Acc.plaything: 0.4436, Acc.swimming pool: 0.9238, Acc.stool: 0.6751, Acc.barrel: 0.7394, Acc.basket: 0.5412, Acc.waterfall: 0.8802, Acc.tent: 0.9862, Acc.bag: 0.2176, Acc.minibike: 0.8993, Acc.cradle: 0.9736, Acc.oven: 0.6808, Acc.ball: 0.6494, Acc.food: 0.7013, Acc.step: 0.1899, Acc.tank: 0.6250, Acc.trade name: 0.4121, Acc.microwave: 0.9626, Acc.pot: 0.6373, Acc.animal: 0.6005, Acc.bicycle: 0.7992, Acc.lake: 0.6363, Acc.dishwasher: 0.7385, Acc.screen: 0.6535, Acc.blanket: 0.2715, Acc.sculpture: 0.8851, Acc.hood: 0.7155, Acc.sconce: 0.7121, Acc.vase: 0.6451, Acc.traffic light: 0.6110, Acc.tray: 0.2409, Acc.ashcan: 0.6326, Acc.fan: 0.7799, Acc.pier: 0.4826, Acc.crt screen: 0.3037, Acc.plate: 0.7892, Acc.monitor: 0.7889, Acc.bulletin board: 0.6382, Acc.shower: 0.0004, Acc.radiator: 0.7755, Acc.glass: 0.1895, Acc.clock: 0.5120, Acc.flag: 0.7742 +2024-06-16 14:14:08,780 - mmseg - INFO - Iter [40050/80000] lr: 1.998e-05, eta: 16:45:16, time: 3.307, data_time: 1.943, memory: 70722, decode.loss_ce: 0.1916, decode.acc_seg: 91.6657, aux.loss_ce: 0.0803, aux.acc_seg: 91.3416, loss: 0.2718 +2024-06-16 14:15:16,934 - mmseg - INFO - Iter [40100/80000] lr: 1.995e-05, eta: 16:43:53, time: 1.363, data_time: 0.011, memory: 70722, decode.loss_ce: 0.1954, decode.acc_seg: 91.7079, aux.loss_ce: 0.0817, aux.acc_seg: 91.3056, loss: 0.2770 +2024-06-16 14:16:25,085 - mmseg - INFO - Iter [40150/80000] lr: 1.993e-05, eta: 16:42:31, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2054, decode.acc_seg: 91.0857, aux.loss_ce: 0.0857, aux.acc_seg: 90.7429, loss: 0.2911 +2024-06-16 14:17:33,523 - mmseg - INFO - Iter [40200/80000] lr: 1.990e-05, eta: 16:41:08, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2310, decode.acc_seg: 90.7432, aux.loss_ce: 0.0950, aux.acc_seg: 90.4685, loss: 0.3260 +2024-06-16 14:18:41,753 - mmseg - INFO - Iter [40250/80000] lr: 1.988e-05, eta: 16:39:46, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2106, decode.acc_seg: 91.2274, aux.loss_ce: 0.0876, aux.acc_seg: 90.9129, loss: 0.2983 +2024-06-16 14:19:50,030 - mmseg - INFO - Iter [40300/80000] lr: 1.985e-05, eta: 16:38:23, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2322, decode.acc_seg: 90.2507, aux.loss_ce: 0.0954, aux.acc_seg: 89.9438, loss: 0.3276 +2024-06-16 14:20:58,154 - mmseg - INFO - Iter [40350/80000] lr: 1.983e-05, eta: 16:37:00, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2150, decode.acc_seg: 90.8868, aux.loss_ce: 0.0891, aux.acc_seg: 90.5448, loss: 0.3042 +2024-06-16 14:22:06,190 - mmseg - INFO - Iter [40400/80000] lr: 1.980e-05, eta: 16:35:38, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2019, decode.acc_seg: 91.4680, aux.loss_ce: 0.0842, aux.acc_seg: 91.1171, loss: 0.2861 +2024-06-16 14:23:17,246 - mmseg - INFO - Iter [40450/80000] lr: 1.978e-05, eta: 16:34:18, time: 1.421, data_time: 0.060, memory: 70722, decode.loss_ce: 0.2013, decode.acc_seg: 91.7176, aux.loss_ce: 0.0840, aux.acc_seg: 91.3360, loss: 0.2854 +2024-06-16 14:24:25,312 - mmseg - INFO - Iter [40500/80000] lr: 1.975e-05, eta: 16:32:55, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1872, decode.acc_seg: 91.7032, aux.loss_ce: 0.0780, aux.acc_seg: 91.3324, loss: 0.2652 +2024-06-16 14:25:33,500 - mmseg - INFO - Iter [40550/80000] lr: 1.973e-05, eta: 16:31:33, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1942, decode.acc_seg: 91.8146, aux.loss_ce: 0.0811, aux.acc_seg: 91.5018, loss: 0.2753 +2024-06-16 14:26:41,858 - mmseg - INFO - Iter [40600/80000] lr: 1.970e-05, eta: 16:30:11, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1909, decode.acc_seg: 91.7727, aux.loss_ce: 0.0799, aux.acc_seg: 91.4452, loss: 0.2707 +2024-06-16 14:27:49,895 - mmseg - INFO - Iter [40650/80000] lr: 1.968e-05, eta: 16:28:48, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2027, decode.acc_seg: 91.4553, aux.loss_ce: 0.0846, aux.acc_seg: 91.1272, loss: 0.2873 +2024-06-16 14:28:58,103 - mmseg - INFO - Iter [40700/80000] lr: 1.965e-05, eta: 16:27:26, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2007, decode.acc_seg: 91.7536, aux.loss_ce: 0.0836, aux.acc_seg: 91.3245, loss: 0.2843 +2024-06-16 14:30:06,215 - mmseg - INFO - Iter [40750/80000] lr: 1.963e-05, eta: 16:26:04, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1895, decode.acc_seg: 92.1785, aux.loss_ce: 0.0790, aux.acc_seg: 91.8668, loss: 0.2685 +2024-06-16 14:31:14,422 - mmseg - INFO - Iter [40800/80000] lr: 1.960e-05, eta: 16:24:41, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2021, decode.acc_seg: 91.4463, aux.loss_ce: 0.0847, aux.acc_seg: 91.0318, loss: 0.2868 +2024-06-16 14:32:22,546 - mmseg - INFO - Iter [40850/80000] lr: 1.958e-05, eta: 16:23:19, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1996, decode.acc_seg: 91.7079, aux.loss_ce: 0.0827, aux.acc_seg: 91.3527, loss: 0.2823 +2024-06-16 14:33:30,954 - mmseg - INFO - Iter [40900/80000] lr: 1.955e-05, eta: 16:21:57, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2075, decode.acc_seg: 91.0870, aux.loss_ce: 0.0856, aux.acc_seg: 90.9296, loss: 0.2931 +2024-06-16 14:34:39,234 - mmseg - INFO - Iter [40950/80000] lr: 1.953e-05, eta: 16:20:35, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1979, decode.acc_seg: 91.6951, aux.loss_ce: 0.0830, aux.acc_seg: 91.3194, loss: 0.2808 +2024-06-16 14:35:47,369 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 14:35:47,369 - mmseg - INFO - Iter [41000/80000] lr: 1.950e-05, eta: 16:19:13, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2058, decode.acc_seg: 91.3471, aux.loss_ce: 0.0858, aux.acc_seg: 90.9648, loss: 0.2915 +2024-06-16 14:37:23,280 - mmseg - INFO - per class results: +2024-06-16 14:37:23,286 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.7 | 88.94 | +| building | 84.78 | 93.63 | +| sky | 95.0 | 97.51 | +| floor | 85.0 | 91.57 | +| tree | 77.99 | 90.73 | +| ceiling | 86.91 | 93.92 | +| road | 85.69 | 92.53 | +| bed | 92.32 | 97.23 | +| windowpane | 65.8 | 81.08 | +| grass | 69.37 | 81.98 | +| cabinet | 65.49 | 74.75 | +| sidewalk | 71.08 | 82.14 | +| person | 85.7 | 93.17 | +| earth | 35.12 | 44.51 | +| door | 59.23 | 76.21 | +| table | 68.76 | 80.7 | +| mountain | 60.84 | 71.09 | +| plant | 54.19 | 65.84 | +| curtain | 77.81 | 88.71 | +| chair | 67.43 | 78.5 | +| car | 87.09 | 92.79 | +| water | 61.87 | 75.34 | +| painting | 75.84 | 91.85 | +| sofa | 81.11 | 90.8 | +| shelf | 44.63 | 61.41 | +| house | 59.45 | 81.56 | +| sea | 71.39 | 86.76 | +| mirror | 78.88 | 87.11 | +| rug | 72.07 | 83.69 | +| field | 32.86 | 62.35 | +| armchair | 60.41 | 79.41 | +| seat | 66.8 | 90.23 | +| fence | 49.0 | 62.26 | +| desk | 59.08 | 82.7 | +| rock | 53.85 | 83.97 | +| wardrobe | 49.95 | 64.05 | +| lamp | 72.9 | 85.25 | +| bathtub | 84.44 | 86.54 | +| railing | 41.51 | 60.98 | +| cushion | 67.73 | 76.67 | +| base | 39.08 | 55.67 | +| box | 36.97 | 48.02 | +| column | 53.96 | 65.5 | +| signboard | 40.17 | 55.68 | +| chest of drawers | 44.6 | 66.75 | +| counter | 44.75 | 55.47 | +| sand | 58.96 | 85.05 | +| sink | 75.74 | 84.3 | +| skyscraper | 47.57 | 59.55 | +| fireplace | 65.75 | 97.68 | +| refrigerator | 83.49 | 88.73 | +| grandstand | 51.63 | 84.18 | +| path | 25.25 | 41.95 | +| stairs | 26.73 | 30.17 | +| runway | 69.66 | 88.96 | +| case | 60.39 | 74.57 | +| pool table | 91.5 | 98.03 | +| pillow | 66.93 | 76.22 | +| screen door | 72.86 | 75.29 | +| stairway | 43.34 | 61.61 | +| river | 14.41 | 33.68 | +| bridge | 63.87 | 71.39 | +| bookcase | 39.55 | 59.86 | +| blind | 48.2 | 57.05 | +| coffee table | 59.44 | 89.01 | +| toilet | 89.13 | 93.26 | +| flower | 48.57 | 65.43 | +| book | 51.32 | 82.61 | +| hill | 7.02 | 11.93 | +| bench | 55.41 | 62.28 | +| countertop | 65.63 | 79.3 | +| stove | 85.74 | 92.25 | +| palm | 55.66 | 72.68 | +| kitchen island | 56.77 | 84.5 | +| computer | 77.6 | 92.78 | +| swivel chair | 47.56 | 69.29 | +| boat | 67.92 | 92.42 | +| bar | 59.99 | 76.4 | +| arcade machine | 75.83 | 81.31 | +| hovel | 55.13 | 67.18 | +| bus | 93.85 | 96.07 | +| towel | 76.98 | 85.51 | +| light | 60.65 | 70.63 | +| truck | 44.07 | 59.13 | +| tower | 35.55 | 52.57 | +| chandelier | 69.8 | 88.2 | +| awning | 52.66 | 71.34 | +| streetlight | 31.51 | 40.31 | +| booth | 36.79 | 48.55 | +| television receiver | 72.59 | 88.24 | +| airplane | 80.81 | 88.44 | +| dirt track | 7.64 | 62.65 | +| apparel | 51.85 | 71.76 | +| pole | 26.39 | 34.63 | +| land | 0.01 | 0.02 | +| bannister | 15.66 | 20.87 | +| escalator | 56.6 | 79.0 | +| ottoman | 47.79 | 64.68 | +| bottle | 39.36 | 65.26 | +| buffet | 46.5 | 53.64 | +| poster | 38.4 | 50.8 | +| stage | 17.59 | 37.69 | +| van | 50.01 | 74.27 | +| ship | 46.16 | 49.02 | +| fountain | 36.06 | 36.79 | +| conveyer belt | 84.28 | 93.06 | +| canopy | 41.47 | 55.82 | +| washer | 81.61 | 86.61 | +| plaything | 27.94 | 46.17 | +| swimming pool | 61.95 | 82.28 | +| stool | 57.1 | 72.2 | +| barrel | 53.07 | 74.51 | +| basket | 41.05 | 54.48 | +| waterfall | 67.4 | 77.1 | +| tent | 92.53 | 98.36 | +| bag | 13.41 | 14.79 | +| minibike | 74.25 | 89.43 | +| cradle | 83.26 | 97.61 | +| oven | 62.97 | 82.32 | +| ball | 54.08 | 74.32 | +| food | 64.89 | 87.67 | +| step | 12.2 | 15.73 | +| tank | 57.47 | 60.71 | +| trade name | 21.95 | 24.31 | +| microwave | 89.42 | 96.41 | +| pot | 58.14 | 68.26 | +| animal | 60.65 | 62.44 | +| bicycle | 59.44 | 77.05 | +| lake | 47.8 | 63.66 | +| dishwasher | 71.38 | 80.05 | +| screen | 54.94 | 75.8 | +| blanket | 25.2 | 33.11 | +| sculpture | 70.36 | 87.35 | +| hood | 63.51 | 79.23 | +| sconce | 56.09 | 66.72 | +| vase | 47.2 | 59.11 | +| traffic light | 32.81 | 64.55 | +| tray | 21.09 | 26.35 | +| ashcan | 44.41 | 61.63 | +| fan | 68.13 | 83.74 | +| pier | 39.18 | 43.4 | +| crt screen | 22.26 | 33.83 | +| plate | 59.53 | 77.36 | +| monitor | 65.94 | 80.89 | +| bulletin board | 57.66 | 70.01 | +| shower | 0.19 | 0.62 | +| radiator | 64.21 | 78.55 | +| glass | 19.25 | 20.49 | +| clock | 39.09 | 47.43 | +| flag | 69.2 | 77.6 | ++---------------------+-------+-------+ +2024-06-16 14:37:23,287 - mmseg - INFO - Summary: +2024-06-16 14:37:23,287 - mmseg - INFO - ++------+------+-------+ +| aAcc | mIoU | mAcc | ++------+------+-------+ +| 85.8 | 56.4 | 69.81 | ++------+------+-------+ +2024-06-16 14:37:23,288 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 14:37:23,288 - mmseg - INFO - Iter(val) [250] aAcc: 0.8580, mIoU: 0.5640, mAcc: 0.6981, IoU.wall: 0.8170, IoU.building: 0.8478, IoU.sky: 0.9500, IoU.floor: 0.8500, IoU.tree: 0.7799, IoU.ceiling: 0.8691, IoU.road: 0.8569, IoU.bed : 0.9232, IoU.windowpane: 0.6580, IoU.grass: 0.6937, IoU.cabinet: 0.6549, IoU.sidewalk: 0.7108, IoU.person: 0.8570, IoU.earth: 0.3512, IoU.door: 0.5923, IoU.table: 0.6876, IoU.mountain: 0.6084, IoU.plant: 0.5419, IoU.curtain: 0.7781, IoU.chair: 0.6743, IoU.car: 0.8709, IoU.water: 0.6187, IoU.painting: 0.7584, IoU.sofa: 0.8111, IoU.shelf: 0.4463, IoU.house: 0.5945, IoU.sea: 0.7139, IoU.mirror: 0.7888, IoU.rug: 0.7207, IoU.field: 0.3286, IoU.armchair: 0.6041, IoU.seat: 0.6680, IoU.fence: 0.4900, IoU.desk: 0.5908, IoU.rock: 0.5385, IoU.wardrobe: 0.4995, IoU.lamp: 0.7290, IoU.bathtub: 0.8444, IoU.railing: 0.4151, IoU.cushion: 0.6773, IoU.base: 0.3908, IoU.box: 0.3697, IoU.column: 0.5396, IoU.signboard: 0.4017, IoU.chest of drawers: 0.4460, IoU.counter: 0.4475, IoU.sand: 0.5896, IoU.sink: 0.7574, IoU.skyscraper: 0.4757, IoU.fireplace: 0.6575, IoU.refrigerator: 0.8349, IoU.grandstand: 0.5163, IoU.path: 0.2525, IoU.stairs: 0.2673, IoU.runway: 0.6966, IoU.case: 0.6039, IoU.pool table: 0.9150, IoU.pillow: 0.6693, IoU.screen door: 0.7286, IoU.stairway: 0.4334, IoU.river: 0.1441, IoU.bridge: 0.6387, IoU.bookcase: 0.3955, IoU.blind: 0.4820, IoU.coffee table: 0.5944, IoU.toilet: 0.8913, IoU.flower: 0.4857, IoU.book: 0.5132, IoU.hill: 0.0702, IoU.bench: 0.5541, IoU.countertop: 0.6563, IoU.stove: 0.8574, IoU.palm: 0.5566, IoU.kitchen island: 0.5677, IoU.computer: 0.7760, IoU.swivel chair: 0.4756, IoU.boat: 0.6792, IoU.bar: 0.5999, IoU.arcade machine: 0.7583, IoU.hovel: 0.5513, IoU.bus: 0.9385, IoU.towel: 0.7698, IoU.light: 0.6065, IoU.truck: 0.4407, IoU.tower: 0.3555, IoU.chandelier: 0.6980, IoU.awning: 0.5266, IoU.streetlight: 0.3151, IoU.booth: 0.3679, IoU.television receiver: 0.7259, IoU.airplane: 0.8081, IoU.dirt track: 0.0764, IoU.apparel: 0.5185, IoU.pole: 0.2639, IoU.land: 0.0001, IoU.bannister: 0.1566, IoU.escalator: 0.5660, IoU.ottoman: 0.4779, IoU.bottle: 0.3936, IoU.buffet: 0.4650, IoU.poster: 0.3840, IoU.stage: 0.1759, IoU.van: 0.5001, IoU.ship: 0.4616, IoU.fountain: 0.3606, IoU.conveyer belt: 0.8428, IoU.canopy: 0.4147, IoU.washer: 0.8161, IoU.plaything: 0.2794, IoU.swimming pool: 0.6195, IoU.stool: 0.5710, IoU.barrel: 0.5307, IoU.basket: 0.4105, IoU.waterfall: 0.6740, IoU.tent: 0.9253, IoU.bag: 0.1341, IoU.minibike: 0.7425, IoU.cradle: 0.8326, IoU.oven: 0.6297, IoU.ball: 0.5408, IoU.food: 0.6489, IoU.step: 0.1220, IoU.tank: 0.5747, IoU.trade name: 0.2195, IoU.microwave: 0.8942, IoU.pot: 0.5814, IoU.animal: 0.6065, IoU.bicycle: 0.5944, IoU.lake: 0.4780, IoU.dishwasher: 0.7138, IoU.screen: 0.5494, IoU.blanket: 0.2520, IoU.sculpture: 0.7036, IoU.hood: 0.6351, IoU.sconce: 0.5609, IoU.vase: 0.4720, IoU.traffic light: 0.3281, IoU.tray: 0.2109, IoU.ashcan: 0.4441, IoU.fan: 0.6813, IoU.pier: 0.3918, IoU.crt screen: 0.2226, IoU.plate: 0.5953, IoU.monitor: 0.6594, IoU.bulletin board: 0.5766, IoU.shower: 0.0019, IoU.radiator: 0.6421, IoU.glass: 0.1925, IoU.clock: 0.3909, IoU.flag: 0.6920, Acc.wall: 0.8894, Acc.building: 0.9363, Acc.sky: 0.9751, Acc.floor: 0.9157, Acc.tree: 0.9073, Acc.ceiling: 0.9392, Acc.road: 0.9253, Acc.bed : 0.9723, Acc.windowpane: 0.8108, Acc.grass: 0.8198, Acc.cabinet: 0.7475, Acc.sidewalk: 0.8214, Acc.person: 0.9317, Acc.earth: 0.4451, Acc.door: 0.7621, Acc.table: 0.8070, Acc.mountain: 0.7109, Acc.plant: 0.6584, Acc.curtain: 0.8871, Acc.chair: 0.7850, Acc.car: 0.9279, Acc.water: 0.7534, Acc.painting: 0.9185, Acc.sofa: 0.9080, Acc.shelf: 0.6141, Acc.house: 0.8156, Acc.sea: 0.8676, Acc.mirror: 0.8711, Acc.rug: 0.8369, Acc.field: 0.6235, Acc.armchair: 0.7941, Acc.seat: 0.9023, Acc.fence: 0.6226, Acc.desk: 0.8270, Acc.rock: 0.8397, Acc.wardrobe: 0.6405, Acc.lamp: 0.8525, Acc.bathtub: 0.8654, Acc.railing: 0.6098, Acc.cushion: 0.7667, Acc.base: 0.5567, Acc.box: 0.4802, Acc.column: 0.6550, Acc.signboard: 0.5568, Acc.chest of drawers: 0.6675, Acc.counter: 0.5547, Acc.sand: 0.8505, Acc.sink: 0.8430, Acc.skyscraper: 0.5955, Acc.fireplace: 0.9768, Acc.refrigerator: 0.8873, Acc.grandstand: 0.8418, Acc.path: 0.4195, Acc.stairs: 0.3017, Acc.runway: 0.8896, Acc.case: 0.7457, Acc.pool table: 0.9803, Acc.pillow: 0.7622, Acc.screen door: 0.7529, Acc.stairway: 0.6161, Acc.river: 0.3368, Acc.bridge: 0.7139, Acc.bookcase: 0.5986, Acc.blind: 0.5705, Acc.coffee table: 0.8901, Acc.toilet: 0.9326, Acc.flower: 0.6543, Acc.book: 0.8261, Acc.hill: 0.1193, Acc.bench: 0.6228, Acc.countertop: 0.7930, Acc.stove: 0.9225, Acc.palm: 0.7268, Acc.kitchen island: 0.8450, Acc.computer: 0.9278, Acc.swivel chair: 0.6929, Acc.boat: 0.9242, Acc.bar: 0.7640, Acc.arcade machine: 0.8131, Acc.hovel: 0.6718, Acc.bus: 0.9607, Acc.towel: 0.8551, Acc.light: 0.7063, Acc.truck: 0.5913, Acc.tower: 0.5257, Acc.chandelier: 0.8820, Acc.awning: 0.7134, Acc.streetlight: 0.4031, Acc.booth: 0.4855, Acc.television receiver: 0.8824, Acc.airplane: 0.8844, Acc.dirt track: 0.6265, Acc.apparel: 0.7176, Acc.pole: 0.3463, Acc.land: 0.0002, Acc.bannister: 0.2087, Acc.escalator: 0.7900, Acc.ottoman: 0.6468, Acc.bottle: 0.6526, Acc.buffet: 0.5364, Acc.poster: 0.5080, Acc.stage: 0.3769, Acc.van: 0.7427, Acc.ship: 0.4902, Acc.fountain: 0.3679, Acc.conveyer belt: 0.9306, Acc.canopy: 0.5582, Acc.washer: 0.8661, Acc.plaything: 0.4617, Acc.swimming pool: 0.8228, Acc.stool: 0.7220, Acc.barrel: 0.7451, Acc.basket: 0.5448, Acc.waterfall: 0.7710, Acc.tent: 0.9836, Acc.bag: 0.1479, Acc.minibike: 0.8943, Acc.cradle: 0.9761, Acc.oven: 0.8232, Acc.ball: 0.7432, Acc.food: 0.8767, Acc.step: 0.1573, Acc.tank: 0.6071, Acc.trade name: 0.2431, Acc.microwave: 0.9641, Acc.pot: 0.6826, Acc.animal: 0.6244, Acc.bicycle: 0.7705, Acc.lake: 0.6366, Acc.dishwasher: 0.8005, Acc.screen: 0.7580, Acc.blanket: 0.3311, Acc.sculpture: 0.8735, Acc.hood: 0.7923, Acc.sconce: 0.6672, Acc.vase: 0.5911, Acc.traffic light: 0.6455, Acc.tray: 0.2635, Acc.ashcan: 0.6163, Acc.fan: 0.8374, Acc.pier: 0.4340, Acc.crt screen: 0.3383, Acc.plate: 0.7736, Acc.monitor: 0.8089, Acc.bulletin board: 0.7001, Acc.shower: 0.0062, Acc.radiator: 0.7855, Acc.glass: 0.2049, Acc.clock: 0.4743, Acc.flag: 0.7760 +2024-06-16 14:38:32,019 - mmseg - INFO - Iter [41050/80000] lr: 1.948e-05, eta: 16:19:22, time: 3.293, data_time: 1.935, memory: 70722, decode.loss_ce: 0.2049, decode.acc_seg: 91.6465, aux.loss_ce: 0.0851, aux.acc_seg: 91.2281, loss: 0.2900 +2024-06-16 14:39:40,124 - mmseg - INFO - Iter [41100/80000] lr: 1.945e-05, eta: 16:18:00, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1954, decode.acc_seg: 91.8268, aux.loss_ce: 0.0814, aux.acc_seg: 91.5271, loss: 0.2767 +2024-06-16 14:40:48,265 - mmseg - INFO - Iter [41150/80000] lr: 1.943e-05, eta: 16:16:38, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1974, decode.acc_seg: 91.6493, aux.loss_ce: 0.0822, aux.acc_seg: 91.2618, loss: 0.2797 +2024-06-16 14:41:56,569 - mmseg - INFO - Iter [41200/80000] lr: 1.940e-05, eta: 16:15:15, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2086, decode.acc_seg: 91.2816, aux.loss_ce: 0.0874, aux.acc_seg: 90.8558, loss: 0.2960 +2024-06-16 14:43:04,744 - mmseg - INFO - Iter [41250/80000] lr: 1.938e-05, eta: 16:13:53, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1929, decode.acc_seg: 91.8768, aux.loss_ce: 0.0803, aux.acc_seg: 91.6117, loss: 0.2731 +2024-06-16 14:44:12,805 - mmseg - INFO - Iter [41300/80000] lr: 1.935e-05, eta: 16:12:31, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2016, decode.acc_seg: 91.3283, aux.loss_ce: 0.0835, aux.acc_seg: 91.0174, loss: 0.2851 +2024-06-16 14:45:21,014 - mmseg - INFO - Iter [41350/80000] lr: 1.933e-05, eta: 16:11:09, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1882, decode.acc_seg: 92.3146, aux.loss_ce: 0.0786, aux.acc_seg: 91.9667, loss: 0.2668 +2024-06-16 14:46:29,314 - mmseg - INFO - Iter [41400/80000] lr: 1.930e-05, eta: 16:09:47, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2020, decode.acc_seg: 91.3050, aux.loss_ce: 0.0837, aux.acc_seg: 90.9538, loss: 0.2856 +2024-06-16 14:47:37,430 - mmseg - INFO - Iter [41450/80000] lr: 1.928e-05, eta: 16:08:25, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2060, decode.acc_seg: 91.1642, aux.loss_ce: 0.0864, aux.acc_seg: 90.7253, loss: 0.2924 +2024-06-16 14:48:45,641 - mmseg - INFO - Iter [41500/80000] lr: 1.925e-05, eta: 16:07:03, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2011, decode.acc_seg: 91.2703, aux.loss_ce: 0.0836, aux.acc_seg: 90.8947, loss: 0.2847 +2024-06-16 14:49:53,977 - mmseg - INFO - Iter [41550/80000] lr: 1.923e-05, eta: 16:05:41, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2086, decode.acc_seg: 91.2055, aux.loss_ce: 0.0860, aux.acc_seg: 90.8494, loss: 0.2947 +2024-06-16 14:51:02,227 - mmseg - INFO - Iter [41600/80000] lr: 1.920e-05, eta: 16:04:19, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1966, decode.acc_seg: 91.6850, aux.loss_ce: 0.0813, aux.acc_seg: 91.3996, loss: 0.2779 +2024-06-16 14:52:10,365 - mmseg - INFO - Iter [41650/80000] lr: 1.918e-05, eta: 16:02:57, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1958, decode.acc_seg: 91.5240, aux.loss_ce: 0.0818, aux.acc_seg: 91.1265, loss: 0.2776 +2024-06-16 14:53:20,640 - mmseg - INFO - Iter [41700/80000] lr: 1.915e-05, eta: 16:01:37, time: 1.405, data_time: 0.052, memory: 70722, decode.loss_ce: 0.1908, decode.acc_seg: 91.8346, aux.loss_ce: 0.0795, aux.acc_seg: 91.5051, loss: 0.2703 +2024-06-16 14:54:29,115 - mmseg - INFO - Iter [41750/80000] lr: 1.913e-05, eta: 16:00:15, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2084, decode.acc_seg: 91.2724, aux.loss_ce: 0.0864, aux.acc_seg: 91.0145, loss: 0.2948 +2024-06-16 14:55:37,227 - mmseg - INFO - Iter [41800/80000] lr: 1.910e-05, eta: 15:58:54, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1805, decode.acc_seg: 92.1041, aux.loss_ce: 0.0755, aux.acc_seg: 91.7165, loss: 0.2561 +2024-06-16 14:56:45,631 - mmseg - INFO - Iter [41850/80000] lr: 1.908e-05, eta: 15:57:32, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1947, decode.acc_seg: 91.5029, aux.loss_ce: 0.0813, aux.acc_seg: 91.1860, loss: 0.2761 +2024-06-16 14:57:53,876 - mmseg - INFO - Iter [41900/80000] lr: 1.905e-05, eta: 15:56:10, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2026, decode.acc_seg: 91.5604, aux.loss_ce: 0.0837, aux.acc_seg: 91.2927, loss: 0.2862 +2024-06-16 14:59:01,922 - mmseg - INFO - Iter [41950/80000] lr: 1.903e-05, eta: 15:54:48, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2106, decode.acc_seg: 91.2164, aux.loss_ce: 0.0877, aux.acc_seg: 90.8979, loss: 0.2983 +2024-06-16 15:00:10,107 - mmseg - INFO - Saving checkpoint at 42000 iterations +2024-06-16 15:01:39,592 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 15:01:39,592 - mmseg - INFO - Iter [42000/80000] lr: 1.900e-05, eta: 15:54:48, time: 3.153, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2013, decode.acc_seg: 91.5476, aux.loss_ce: 0.0831, aux.acc_seg: 91.2688, loss: 0.2844 +2024-06-16 15:03:16,319 - mmseg - INFO - per class results: +2024-06-16 15:03:16,325 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.51 | 89.06 | +| building | 84.91 | 93.68 | +| sky | 94.95 | 97.15 | +| floor | 83.74 | 89.4 | +| tree | 78.17 | 89.86 | +| ceiling | 87.18 | 94.78 | +| road | 86.31 | 92.75 | +| bed | 92.72 | 96.59 | +| windowpane | 64.76 | 80.47 | +| grass | 67.47 | 82.79 | +| cabinet | 66.98 | 77.17 | +| sidewalk | 67.98 | 86.0 | +| person | 85.31 | 95.33 | +| earth | 38.04 | 52.54 | +| door | 56.74 | 73.97 | +| table | 70.39 | 81.4 | +| mountain | 62.41 | 75.38 | +| plant | 54.24 | 64.61 | +| curtain | 77.55 | 88.59 | +| chair | 66.8 | 78.9 | +| car | 87.4 | 94.24 | +| water | 63.21 | 75.24 | +| painting | 76.45 | 91.47 | +| sofa | 82.31 | 91.69 | +| shelf | 48.94 | 69.03 | +| house | 57.83 | 77.94 | +| sea | 67.96 | 89.18 | +| mirror | 75.03 | 86.51 | +| rug | 72.44 | 80.93 | +| field | 27.33 | 42.27 | +| armchair | 60.04 | 79.28 | +| seat | 66.58 | 88.42 | +| fence | 49.17 | 69.09 | +| desk | 61.46 | 81.39 | +| rock | 54.84 | 85.33 | +| wardrobe | 51.34 | 64.18 | +| lamp | 73.89 | 84.63 | +| bathtub | 83.94 | 85.69 | +| railing | 41.74 | 57.17 | +| cushion | 70.41 | 79.94 | +| base | 39.77 | 54.7 | +| box | 36.35 | 47.5 | +| column | 54.98 | 69.79 | +| signboard | 39.88 | 54.88 | +| chest of drawers | 42.09 | 69.6 | +| counter | 48.67 | 53.96 | +| sand | 57.9 | 86.7 | +| sink | 75.01 | 84.97 | +| skyscraper | 45.79 | 56.89 | +| fireplace | 73.61 | 91.17 | +| refrigerator | 83.34 | 89.49 | +| grandstand | 51.87 | 81.04 | +| path | 20.61 | 25.14 | +| stairs | 32.18 | 39.41 | +| runway | 74.89 | 97.27 | +| case | 62.97 | 81.49 | +| pool table | 94.67 | 98.28 | +| pillow | 69.86 | 82.8 | +| screen door | 46.4 | 47.96 | +| stairway | 52.6 | 68.75 | +| river | 19.83 | 31.81 | +| bridge | 44.7 | 49.47 | +| bookcase | 46.9 | 64.52 | +| blind | 40.04 | 41.45 | +| coffee table | 62.78 | 90.37 | +| toilet | 88.47 | 91.93 | +| flower | 44.42 | 54.45 | +| book | 54.19 | 75.51 | +| hill | 5.09 | 8.27 | +| bench | 55.6 | 64.33 | +| countertop | 64.7 | 86.71 | +| stove | 82.33 | 89.0 | +| palm | 57.28 | 81.22 | +| kitchen island | 50.3 | 85.3 | +| computer | 78.83 | 91.45 | +| swivel chair | 45.96 | 71.22 | +| boat | 76.79 | 89.55 | +| bar | 60.32 | 77.76 | +| arcade machine | 76.69 | 82.1 | +| hovel | 32.39 | 34.34 | +| bus | 93.19 | 96.72 | +| towel | 74.0 | 84.19 | +| light | 61.52 | 71.2 | +| truck | 44.27 | 60.19 | +| tower | 35.94 | 53.9 | +| chandelier | 70.36 | 88.88 | +| awning | 54.4 | 72.11 | +| streetlight | 32.99 | 52.74 | +| booth | 54.89 | 71.32 | +| television receiver | 74.62 | 86.21 | +| airplane | 74.08 | 88.88 | +| dirt track | 12.94 | 31.02 | +| apparel | 51.94 | 66.72 | +| pole | 20.47 | 26.43 | +| land | 1.44 | 2.17 | +| bannister | 15.13 | 20.84 | +| escalator | 60.04 | 80.28 | +| ottoman | 49.12 | 63.96 | +| bottle | 41.31 | 70.24 | +| buffet | 59.5 | 71.86 | +| poster | 34.2 | 47.85 | +| stage | 18.36 | 34.07 | +| van | 51.45 | 68.45 | +| ship | 49.21 | 50.78 | +| fountain | 32.41 | 33.14 | +| conveyer belt | 76.36 | 95.5 | +| canopy | 34.21 | 44.93 | +| washer | 84.63 | 89.56 | +| plaything | 23.22 | 34.03 | +| swimming pool | 57.19 | 86.45 | +| stool | 56.22 | 72.92 | +| barrel | 61.59 | 73.93 | +| basket | 40.21 | 55.12 | +| waterfall | 72.17 | 86.24 | +| tent | 87.74 | 98.15 | +| bag | 20.9 | 24.43 | +| minibike | 75.5 | 88.4 | +| cradle | 74.32 | 98.59 | +| oven | 64.66 | 73.77 | +| ball | 36.48 | 38.42 | +| food | 63.35 | 77.77 | +| step | 15.14 | 18.9 | +| tank | 59.02 | 66.61 | +| trade name | 20.76 | 23.29 | +| microwave | 88.28 | 96.78 | +| pot | 58.78 | 68.45 | +| animal | 61.91 | 63.57 | +| bicycle | 60.11 | 81.35 | +| lake | 54.22 | 63.94 | +| dishwasher | 66.17 | 80.88 | +| screen | 59.11 | 84.77 | +| blanket | 22.24 | 28.36 | +| sculpture | 73.25 | 84.93 | +| hood | 65.59 | 74.37 | +| sconce | 57.62 | 71.03 | +| vase | 47.94 | 64.19 | +| traffic light | 32.02 | 58.91 | +| tray | 20.07 | 23.95 | +| ashcan | 41.96 | 64.33 | +| fan | 70.18 | 82.28 | +| pier | 40.03 | 45.93 | +| crt screen | 5.69 | 7.01 | +| plate | 59.37 | 79.6 | +| monitor | 70.2 | 80.04 | +| bulletin board | 56.12 | 69.05 | +| shower | 0.0 | 0.0 | +| radiator | 65.91 | 77.43 | +| glass | 19.09 | 20.39 | +| clock | 38.72 | 48.67 | +| flag | 67.9 | 79.87 | ++---------------------+-------+-------+ +2024-06-16 15:03:16,325 - mmseg - INFO - Summary: +2024-06-16 15:03:16,325 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.81 | 56.17 | 68.92 | ++-------+-------+-------+ +2024-06-16 15:03:16,326 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 15:03:16,326 - mmseg - INFO - Iter(val) [250] aAcc: 0.8581, mIoU: 0.5617, mAcc: 0.6892, IoU.wall: 0.8151, IoU.building: 0.8491, IoU.sky: 0.9495, IoU.floor: 0.8374, IoU.tree: 0.7817, IoU.ceiling: 0.8718, IoU.road: 0.8631, IoU.bed : 0.9272, IoU.windowpane: 0.6476, IoU.grass: 0.6747, IoU.cabinet: 0.6698, IoU.sidewalk: 0.6798, IoU.person: 0.8531, IoU.earth: 0.3804, IoU.door: 0.5674, IoU.table: 0.7039, IoU.mountain: 0.6241, IoU.plant: 0.5424, IoU.curtain: 0.7755, IoU.chair: 0.6680, IoU.car: 0.8740, IoU.water: 0.6321, IoU.painting: 0.7645, IoU.sofa: 0.8231, IoU.shelf: 0.4894, IoU.house: 0.5783, IoU.sea: 0.6796, IoU.mirror: 0.7503, IoU.rug: 0.7244, IoU.field: 0.2733, IoU.armchair: 0.6004, IoU.seat: 0.6658, IoU.fence: 0.4917, IoU.desk: 0.6146, IoU.rock: 0.5484, IoU.wardrobe: 0.5134, IoU.lamp: 0.7389, IoU.bathtub: 0.8394, IoU.railing: 0.4174, IoU.cushion: 0.7041, IoU.base: 0.3977, IoU.box: 0.3635, IoU.column: 0.5498, IoU.signboard: 0.3988, IoU.chest of drawers: 0.4209, IoU.counter: 0.4867, IoU.sand: 0.5790, IoU.sink: 0.7501, IoU.skyscraper: 0.4579, IoU.fireplace: 0.7361, IoU.refrigerator: 0.8334, IoU.grandstand: 0.5187, IoU.path: 0.2061, IoU.stairs: 0.3218, IoU.runway: 0.7489, IoU.case: 0.6297, IoU.pool table: 0.9467, IoU.pillow: 0.6986, IoU.screen door: 0.4640, IoU.stairway: 0.5260, IoU.river: 0.1983, IoU.bridge: 0.4470, IoU.bookcase: 0.4690, IoU.blind: 0.4004, IoU.coffee table: 0.6278, IoU.toilet: 0.8847, IoU.flower: 0.4442, IoU.book: 0.5419, IoU.hill: 0.0509, IoU.bench: 0.5560, IoU.countertop: 0.6470, IoU.stove: 0.8233, IoU.palm: 0.5728, IoU.kitchen island: 0.5030, IoU.computer: 0.7883, IoU.swivel chair: 0.4596, IoU.boat: 0.7679, IoU.bar: 0.6032, IoU.arcade machine: 0.7669, IoU.hovel: 0.3239, IoU.bus: 0.9319, IoU.towel: 0.7400, IoU.light: 0.6152, IoU.truck: 0.4427, IoU.tower: 0.3594, IoU.chandelier: 0.7036, IoU.awning: 0.5440, IoU.streetlight: 0.3299, IoU.booth: 0.5489, IoU.television receiver: 0.7462, IoU.airplane: 0.7408, IoU.dirt track: 0.1294, IoU.apparel: 0.5194, IoU.pole: 0.2047, IoU.land: 0.0144, IoU.bannister: 0.1513, IoU.escalator: 0.6004, IoU.ottoman: 0.4912, IoU.bottle: 0.4131, IoU.buffet: 0.5950, IoU.poster: 0.3420, IoU.stage: 0.1836, IoU.van: 0.5145, IoU.ship: 0.4921, IoU.fountain: 0.3241, IoU.conveyer belt: 0.7636, IoU.canopy: 0.3421, IoU.washer: 0.8463, IoU.plaything: 0.2322, IoU.swimming pool: 0.5719, IoU.stool: 0.5622, IoU.barrel: 0.6159, IoU.basket: 0.4021, IoU.waterfall: 0.7217, IoU.tent: 0.8774, IoU.bag: 0.2090, IoU.minibike: 0.7550, IoU.cradle: 0.7432, IoU.oven: 0.6466, IoU.ball: 0.3648, IoU.food: 0.6335, IoU.step: 0.1514, IoU.tank: 0.5902, IoU.trade name: 0.2076, IoU.microwave: 0.8828, IoU.pot: 0.5878, IoU.animal: 0.6191, IoU.bicycle: 0.6011, IoU.lake: 0.5422, IoU.dishwasher: 0.6617, IoU.screen: 0.5911, IoU.blanket: 0.2224, IoU.sculpture: 0.7325, IoU.hood: 0.6559, IoU.sconce: 0.5762, IoU.vase: 0.4794, IoU.traffic light: 0.3202, IoU.tray: 0.2007, IoU.ashcan: 0.4196, IoU.fan: 0.7018, IoU.pier: 0.4003, IoU.crt screen: 0.0569, IoU.plate: 0.5937, IoU.monitor: 0.7020, IoU.bulletin board: 0.5612, IoU.shower: 0.0000, IoU.radiator: 0.6591, IoU.glass: 0.1909, IoU.clock: 0.3872, IoU.flag: 0.6790, Acc.wall: 0.8906, Acc.building: 0.9368, Acc.sky: 0.9715, Acc.floor: 0.8940, Acc.tree: 0.8986, Acc.ceiling: 0.9478, Acc.road: 0.9275, Acc.bed : 0.9659, Acc.windowpane: 0.8047, Acc.grass: 0.8279, Acc.cabinet: 0.7717, Acc.sidewalk: 0.8600, Acc.person: 0.9533, Acc.earth: 0.5254, Acc.door: 0.7397, Acc.table: 0.8140, Acc.mountain: 0.7538, Acc.plant: 0.6461, Acc.curtain: 0.8859, Acc.chair: 0.7890, Acc.car: 0.9424, Acc.water: 0.7524, Acc.painting: 0.9147, Acc.sofa: 0.9169, Acc.shelf: 0.6903, Acc.house: 0.7794, Acc.sea: 0.8918, Acc.mirror: 0.8651, Acc.rug: 0.8093, Acc.field: 0.4227, Acc.armchair: 0.7928, Acc.seat: 0.8842, Acc.fence: 0.6909, Acc.desk: 0.8139, Acc.rock: 0.8533, Acc.wardrobe: 0.6418, Acc.lamp: 0.8463, Acc.bathtub: 0.8569, Acc.railing: 0.5717, Acc.cushion: 0.7994, Acc.base: 0.5470, Acc.box: 0.4750, Acc.column: 0.6979, Acc.signboard: 0.5488, Acc.chest of drawers: 0.6960, Acc.counter: 0.5396, Acc.sand: 0.8670, Acc.sink: 0.8497, Acc.skyscraper: 0.5689, Acc.fireplace: 0.9117, Acc.refrigerator: 0.8949, Acc.grandstand: 0.8104, Acc.path: 0.2514, Acc.stairs: 0.3941, Acc.runway: 0.9727, Acc.case: 0.8149, Acc.pool table: 0.9828, Acc.pillow: 0.8280, Acc.screen door: 0.4796, Acc.stairway: 0.6875, Acc.river: 0.3181, Acc.bridge: 0.4947, Acc.bookcase: 0.6452, Acc.blind: 0.4145, Acc.coffee table: 0.9037, Acc.toilet: 0.9193, Acc.flower: 0.5445, Acc.book: 0.7551, Acc.hill: 0.0827, Acc.bench: 0.6433, Acc.countertop: 0.8671, Acc.stove: 0.8900, Acc.palm: 0.8122, Acc.kitchen island: 0.8530, Acc.computer: 0.9145, Acc.swivel chair: 0.7122, Acc.boat: 0.8955, Acc.bar: 0.7776, Acc.arcade machine: 0.8210, Acc.hovel: 0.3434, Acc.bus: 0.9672, Acc.towel: 0.8419, Acc.light: 0.7120, Acc.truck: 0.6019, Acc.tower: 0.5390, Acc.chandelier: 0.8888, Acc.awning: 0.7211, Acc.streetlight: 0.5274, Acc.booth: 0.7132, Acc.television receiver: 0.8621, Acc.airplane: 0.8888, Acc.dirt track: 0.3102, Acc.apparel: 0.6672, Acc.pole: 0.2643, Acc.land: 0.0217, Acc.bannister: 0.2084, Acc.escalator: 0.8028, Acc.ottoman: 0.6396, Acc.bottle: 0.7024, Acc.buffet: 0.7186, Acc.poster: 0.4785, Acc.stage: 0.3407, Acc.van: 0.6845, Acc.ship: 0.5078, Acc.fountain: 0.3314, Acc.conveyer belt: 0.9550, Acc.canopy: 0.4493, Acc.washer: 0.8956, Acc.plaything: 0.3403, Acc.swimming pool: 0.8645, Acc.stool: 0.7292, Acc.barrel: 0.7393, Acc.basket: 0.5512, Acc.waterfall: 0.8624, Acc.tent: 0.9815, Acc.bag: 0.2443, Acc.minibike: 0.8840, Acc.cradle: 0.9859, Acc.oven: 0.7377, Acc.ball: 0.3842, Acc.food: 0.7777, Acc.step: 0.1890, Acc.tank: 0.6661, Acc.trade name: 0.2329, Acc.microwave: 0.9678, Acc.pot: 0.6845, Acc.animal: 0.6357, Acc.bicycle: 0.8135, Acc.lake: 0.6394, Acc.dishwasher: 0.8088, Acc.screen: 0.8477, Acc.blanket: 0.2836, Acc.sculpture: 0.8493, Acc.hood: 0.7437, Acc.sconce: 0.7103, Acc.vase: 0.6419, Acc.traffic light: 0.5891, Acc.tray: 0.2395, Acc.ashcan: 0.6433, Acc.fan: 0.8228, Acc.pier: 0.4593, Acc.crt screen: 0.0701, Acc.plate: 0.7960, Acc.monitor: 0.8004, Acc.bulletin board: 0.6905, Acc.shower: 0.0000, Acc.radiator: 0.7743, Acc.glass: 0.2039, Acc.clock: 0.4867, Acc.flag: 0.7987 +2024-06-16 15:04:25,179 - mmseg - INFO - Iter [42050/80000] lr: 1.898e-05, eta: 15:54:54, time: 3.312, data_time: 1.951, memory: 70722, decode.loss_ce: 0.2010, decode.acc_seg: 91.6287, aux.loss_ce: 0.0835, aux.acc_seg: 91.2385, loss: 0.2845 +2024-06-16 15:05:33,531 - mmseg - INFO - Iter [42100/80000] lr: 1.895e-05, eta: 15:53:32, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1909, decode.acc_seg: 91.7087, aux.loss_ce: 0.0789, aux.acc_seg: 91.3966, loss: 0.2698 +2024-06-16 15:06:41,777 - mmseg - INFO - Iter [42150/80000] lr: 1.893e-05, eta: 15:52:10, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1939, decode.acc_seg: 91.6132, aux.loss_ce: 0.0810, aux.acc_seg: 91.2900, loss: 0.2749 +2024-06-16 15:07:49,978 - mmseg - INFO - Iter [42200/80000] lr: 1.890e-05, eta: 15:50:48, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1983, decode.acc_seg: 91.5730, aux.loss_ce: 0.0831, aux.acc_seg: 91.2418, loss: 0.2814 +2024-06-16 15:08:58,200 - mmseg - INFO - Iter [42250/80000] lr: 1.888e-05, eta: 15:49:26, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1953, decode.acc_seg: 91.7475, aux.loss_ce: 0.0820, aux.acc_seg: 91.3921, loss: 0.2773 +2024-06-16 15:10:06,635 - mmseg - INFO - Iter [42300/80000] lr: 1.885e-05, eta: 15:48:04, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1994, decode.acc_seg: 91.7546, aux.loss_ce: 0.0831, aux.acc_seg: 91.3931, loss: 0.2826 +2024-06-16 15:11:14,787 - mmseg - INFO - Iter [42350/80000] lr: 1.883e-05, eta: 15:46:42, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1852, decode.acc_seg: 91.7853, aux.loss_ce: 0.0770, aux.acc_seg: 91.5352, loss: 0.2621 +2024-06-16 15:12:22,959 - mmseg - INFO - Iter [42400/80000] lr: 1.880e-05, eta: 15:45:20, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1933, decode.acc_seg: 91.8384, aux.loss_ce: 0.0810, aux.acc_seg: 91.4982, loss: 0.2743 +2024-06-16 15:13:31,262 - mmseg - INFO - Iter [42450/80000] lr: 1.878e-05, eta: 15:43:59, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1941, decode.acc_seg: 91.9656, aux.loss_ce: 0.0813, aux.acc_seg: 91.6059, loss: 0.2754 +2024-06-16 15:14:39,497 - mmseg - INFO - Iter [42500/80000] lr: 1.875e-05, eta: 15:42:37, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2016, decode.acc_seg: 91.6353, aux.loss_ce: 0.0840, aux.acc_seg: 91.2872, loss: 0.2857 +2024-06-16 15:15:47,542 - mmseg - INFO - Iter [42550/80000] lr: 1.873e-05, eta: 15:41:15, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2035, decode.acc_seg: 91.2798, aux.loss_ce: 0.0841, aux.acc_seg: 90.9946, loss: 0.2876 +2024-06-16 15:16:55,887 - mmseg - INFO - Iter [42600/80000] lr: 1.870e-05, eta: 15:39:53, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1892, decode.acc_seg: 91.7905, aux.loss_ce: 0.0790, aux.acc_seg: 91.4778, loss: 0.2683 +2024-06-16 15:18:04,126 - mmseg - INFO - Iter [42650/80000] lr: 1.868e-05, eta: 15:38:32, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1908, decode.acc_seg: 91.7881, aux.loss_ce: 0.0795, aux.acc_seg: 91.4920, loss: 0.2703 +2024-06-16 15:19:12,552 - mmseg - INFO - Iter [42700/80000] lr: 1.865e-05, eta: 15:37:10, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1916, decode.acc_seg: 91.7322, aux.loss_ce: 0.0799, aux.acc_seg: 91.4087, loss: 0.2714 +2024-06-16 15:20:20,615 - mmseg - INFO - Iter [42750/80000] lr: 1.863e-05, eta: 15:35:49, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1959, decode.acc_seg: 92.0120, aux.loss_ce: 0.0812, aux.acc_seg: 91.6786, loss: 0.2771 +2024-06-16 15:21:28,720 - mmseg - INFO - Iter [42800/80000] lr: 1.860e-05, eta: 15:34:27, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1968, decode.acc_seg: 91.8758, aux.loss_ce: 0.0823, aux.acc_seg: 91.4882, loss: 0.2790 +2024-06-16 15:22:36,977 - mmseg - INFO - Iter [42850/80000] lr: 1.858e-05, eta: 15:33:05, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2062, decode.acc_seg: 91.0126, aux.loss_ce: 0.0861, aux.acc_seg: 90.7677, loss: 0.2923 +2024-06-16 15:23:45,273 - mmseg - INFO - Iter [42900/80000] lr: 1.855e-05, eta: 15:31:44, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1999, decode.acc_seg: 91.5527, aux.loss_ce: 0.0829, aux.acc_seg: 91.2116, loss: 0.2828 +2024-06-16 15:24:56,393 - mmseg - INFO - Iter [42950/80000] lr: 1.853e-05, eta: 15:30:25, time: 1.422, data_time: 0.065, memory: 70722, decode.loss_ce: 0.1953, decode.acc_seg: 91.6128, aux.loss_ce: 0.0809, aux.acc_seg: 91.3088, loss: 0.2763 +2024-06-16 15:26:04,423 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 15:26:04,423 - mmseg - INFO - Iter [43000/80000] lr: 1.850e-05, eta: 15:29:03, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1856, decode.acc_seg: 92.2742, aux.loss_ce: 0.0778, aux.acc_seg: 91.9567, loss: 0.2633 +2024-06-16 15:27:41,068 - mmseg - INFO - per class results: +2024-06-16 15:27:41,075 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.96 | 89.96 | +| building | 84.91 | 94.56 | +| sky | 94.94 | 97.46 | +| floor | 84.31 | 90.53 | +| tree | 77.55 | 89.59 | +| ceiling | 86.85 | 92.93 | +| road | 87.38 | 92.97 | +| bed | 92.75 | 96.88 | +| windowpane | 66.05 | 80.34 | +| grass | 71.03 | 83.42 | +| cabinet | 64.59 | 73.14 | +| sidewalk | 72.57 | 84.07 | +| person | 85.33 | 93.79 | +| earth | 37.04 | 48.87 | +| door | 60.66 | 76.08 | +| table | 69.11 | 81.58 | +| mountain | 61.78 | 70.48 | +| plant | 54.12 | 67.15 | +| curtain | 77.04 | 89.07 | +| chair | 65.25 | 74.53 | +| car | 87.23 | 94.37 | +| water | 64.56 | 78.07 | +| painting | 76.97 | 90.07 | +| sofa | 80.5 | 92.33 | +| shelf | 46.39 | 63.53 | +| house | 52.39 | 59.35 | +| sea | 70.26 | 82.54 | +| mirror | 75.06 | 82.05 | +| rug | 68.62 | 88.38 | +| field | 30.93 | 56.7 | +| armchair | 58.12 | 77.68 | +| seat | 64.86 | 89.64 | +| fence | 50.97 | 65.76 | +| desk | 55.53 | 85.28 | +| rock | 55.19 | 86.97 | +| wardrobe | 51.96 | 71.97 | +| lamp | 72.69 | 86.17 | +| bathtub | 84.66 | 87.22 | +| railing | 41.9 | 56.01 | +| cushion | 67.63 | 83.6 | +| base | 37.32 | 58.13 | +| box | 36.08 | 45.56 | +| column | 54.99 | 66.84 | +| signboard | 40.26 | 49.71 | +| chest of drawers | 44.84 | 84.18 | +| counter | 46.4 | 56.04 | +| sand | 54.8 | 80.17 | +| sink | 74.87 | 84.44 | +| skyscraper | 48.66 | 62.0 | +| fireplace | 73.41 | 92.84 | +| refrigerator | 82.96 | 89.43 | +| grandstand | 50.06 | 85.99 | +| path | 28.68 | 39.93 | +| stairs | 25.84 | 33.16 | +| runway | 73.99 | 96.98 | +| case | 57.68 | 76.07 | +| pool table | 93.67 | 98.83 | +| pillow | 66.68 | 76.69 | +| screen door | 82.86 | 88.26 | +| stairway | 44.89 | 61.18 | +| river | 13.05 | 24.9 | +| bridge | 63.02 | 69.24 | +| bookcase | 41.36 | 67.1 | +| blind | 41.73 | 43.94 | +| coffee table | 66.45 | 88.56 | +| toilet | 89.43 | 95.01 | +| flower | 43.1 | 53.41 | +| book | 50.95 | 70.63 | +| hill | 7.9 | 14.28 | +| bench | 56.57 | 64.62 | +| countertop | 64.2 | 80.84 | +| stove | 86.34 | 93.32 | +| palm | 56.94 | 80.64 | +| kitchen island | 53.61 | 88.31 | +| computer | 79.19 | 91.2 | +| swivel chair | 49.59 | 76.11 | +| boat | 76.96 | 90.23 | +| bar | 61.68 | 81.05 | +| arcade machine | 78.57 | 85.54 | +| hovel | 47.3 | 51.14 | +| bus | 92.73 | 96.66 | +| towel | 74.52 | 85.97 | +| light | 61.45 | 73.05 | +| truck | 46.04 | 62.33 | +| tower | 18.6 | 24.49 | +| chandelier | 69.69 | 89.76 | +| awning | 54.95 | 71.23 | +| streetlight | 32.25 | 46.52 | +| booth | 49.78 | 56.98 | +| television receiver | 73.45 | 87.38 | +| airplane | 78.61 | 91.15 | +| dirt track | 14.36 | 29.86 | +| apparel | 43.96 | 58.73 | +| pole | 22.95 | 29.65 | +| land | 3.21 | 4.51 | +| bannister | 16.17 | 23.91 | +| escalator | 56.89 | 79.42 | +| ottoman | 48.68 | 60.22 | +| bottle | 37.39 | 53.46 | +| buffet | 52.6 | 61.53 | +| poster | 36.72 | 45.46 | +| stage | 22.97 | 47.93 | +| van | 48.28 | 60.84 | +| ship | 60.19 | 61.33 | +| fountain | 31.89 | 32.64 | +| conveyer belt | 75.14 | 94.05 | +| canopy | 48.8 | 73.77 | +| washer | 84.69 | 89.31 | +| plaything | 21.56 | 42.51 | +| swimming pool | 54.56 | 81.61 | +| stool | 55.01 | 76.43 | +| barrel | 61.15 | 74.17 | +| basket | 43.49 | 60.09 | +| waterfall | 77.56 | 93.03 | +| tent | 87.48 | 98.25 | +| bag | 19.52 | 21.17 | +| minibike | 76.0 | 88.57 | +| cradle | 84.38 | 98.01 | +| oven | 69.13 | 78.77 | +| ball | 55.21 | 71.08 | +| food | 59.69 | 71.38 | +| step | 16.4 | 25.46 | +| tank | 63.16 | 68.33 | +| trade name | 24.06 | 26.79 | +| microwave | 89.48 | 96.61 | +| pot | 58.3 | 69.57 | +| animal | 58.7 | 59.91 | +| bicycle | 59.14 | 77.98 | +| lake | 55.16 | 63.82 | +| dishwasher | 70.03 | 84.14 | +| screen | 60.72 | 92.96 | +| blanket | 26.17 | 29.32 | +| sculpture | 75.05 | 86.05 | +| hood | 61.95 | 74.76 | +| sconce | 56.56 | 65.22 | +| vase | 49.77 | 63.35 | +| traffic light | 34.95 | 64.0 | +| tray | 21.45 | 26.03 | +| ashcan | 42.44 | 63.47 | +| fan | 69.11 | 85.93 | +| pier | 40.53 | 44.88 | +| crt screen | 1.38 | 1.58 | +| plate | 60.42 | 76.82 | +| monitor | 62.5 | 76.61 | +| bulletin board | 57.5 | 69.89 | +| shower | 0.4 | 0.4 | +| radiator | 68.14 | 76.71 | +| glass | 18.1 | 19.03 | +| clock | 42.65 | 49.15 | +| flag | 69.91 | 77.57 | ++---------------------+-------+-------+ +2024-06-16 15:27:41,075 - mmseg - INFO - Summary: +2024-06-16 15:27:41,075 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.96 | 56.76 | 69.63 | ++-------+-------+-------+ +2024-06-16 15:27:41,075 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 15:27:41,076 - mmseg - INFO - Iter(val) [250] aAcc: 0.8596, mIoU: 0.5676, mAcc: 0.6963, IoU.wall: 0.8196, IoU.building: 0.8491, IoU.sky: 0.9494, IoU.floor: 0.8431, IoU.tree: 0.7755, IoU.ceiling: 0.8685, IoU.road: 0.8738, IoU.bed : 0.9275, IoU.windowpane: 0.6605, IoU.grass: 0.7103, IoU.cabinet: 0.6459, IoU.sidewalk: 0.7257, IoU.person: 0.8533, IoU.earth: 0.3704, IoU.door: 0.6066, IoU.table: 0.6911, IoU.mountain: 0.6178, IoU.plant: 0.5412, IoU.curtain: 0.7704, IoU.chair: 0.6525, IoU.car: 0.8723, IoU.water: 0.6456, IoU.painting: 0.7697, IoU.sofa: 0.8050, IoU.shelf: 0.4639, IoU.house: 0.5239, IoU.sea: 0.7026, IoU.mirror: 0.7506, IoU.rug: 0.6862, IoU.field: 0.3093, IoU.armchair: 0.5812, IoU.seat: 0.6486, IoU.fence: 0.5097, IoU.desk: 0.5553, IoU.rock: 0.5519, IoU.wardrobe: 0.5196, IoU.lamp: 0.7269, IoU.bathtub: 0.8466, IoU.railing: 0.4190, IoU.cushion: 0.6763, IoU.base: 0.3732, IoU.box: 0.3608, IoU.column: 0.5499, IoU.signboard: 0.4026, IoU.chest of drawers: 0.4484, IoU.counter: 0.4640, IoU.sand: 0.5480, IoU.sink: 0.7487, IoU.skyscraper: 0.4866, IoU.fireplace: 0.7341, IoU.refrigerator: 0.8296, IoU.grandstand: 0.5006, IoU.path: 0.2868, IoU.stairs: 0.2584, IoU.runway: 0.7399, IoU.case: 0.5768, IoU.pool table: 0.9367, IoU.pillow: 0.6668, IoU.screen door: 0.8286, IoU.stairway: 0.4489, IoU.river: 0.1305, IoU.bridge: 0.6302, IoU.bookcase: 0.4136, IoU.blind: 0.4173, IoU.coffee table: 0.6645, IoU.toilet: 0.8943, IoU.flower: 0.4310, IoU.book: 0.5095, IoU.hill: 0.0790, IoU.bench: 0.5657, IoU.countertop: 0.6420, IoU.stove: 0.8634, IoU.palm: 0.5694, IoU.kitchen island: 0.5361, IoU.computer: 0.7919, IoU.swivel chair: 0.4959, IoU.boat: 0.7696, IoU.bar: 0.6168, IoU.arcade machine: 0.7857, IoU.hovel: 0.4730, IoU.bus: 0.9273, IoU.towel: 0.7452, IoU.light: 0.6145, IoU.truck: 0.4604, IoU.tower: 0.1860, IoU.chandelier: 0.6969, IoU.awning: 0.5495, IoU.streetlight: 0.3225, IoU.booth: 0.4978, IoU.television receiver: 0.7345, IoU.airplane: 0.7861, IoU.dirt track: 0.1436, IoU.apparel: 0.4396, IoU.pole: 0.2295, IoU.land: 0.0321, IoU.bannister: 0.1617, IoU.escalator: 0.5689, IoU.ottoman: 0.4868, IoU.bottle: 0.3739, IoU.buffet: 0.5260, IoU.poster: 0.3672, IoU.stage: 0.2297, IoU.van: 0.4828, IoU.ship: 0.6019, IoU.fountain: 0.3189, IoU.conveyer belt: 0.7514, IoU.canopy: 0.4880, IoU.washer: 0.8469, IoU.plaything: 0.2156, IoU.swimming pool: 0.5456, IoU.stool: 0.5501, IoU.barrel: 0.6115, IoU.basket: 0.4349, IoU.waterfall: 0.7756, IoU.tent: 0.8748, IoU.bag: 0.1952, IoU.minibike: 0.7600, IoU.cradle: 0.8438, IoU.oven: 0.6913, IoU.ball: 0.5521, IoU.food: 0.5969, IoU.step: 0.1640, IoU.tank: 0.6316, IoU.trade name: 0.2406, IoU.microwave: 0.8948, IoU.pot: 0.5830, IoU.animal: 0.5870, IoU.bicycle: 0.5914, IoU.lake: 0.5516, IoU.dishwasher: 0.7003, IoU.screen: 0.6072, IoU.blanket: 0.2617, IoU.sculpture: 0.7505, IoU.hood: 0.6195, IoU.sconce: 0.5656, IoU.vase: 0.4977, IoU.traffic light: 0.3495, IoU.tray: 0.2145, IoU.ashcan: 0.4244, IoU.fan: 0.6911, IoU.pier: 0.4053, IoU.crt screen: 0.0138, IoU.plate: 0.6042, IoU.monitor: 0.6250, IoU.bulletin board: 0.5750, IoU.shower: 0.0040, IoU.radiator: 0.6814, IoU.glass: 0.1810, IoU.clock: 0.4265, IoU.flag: 0.6991, Acc.wall: 0.8996, Acc.building: 0.9456, Acc.sky: 0.9746, Acc.floor: 0.9053, Acc.tree: 0.8959, Acc.ceiling: 0.9293, Acc.road: 0.9297, Acc.bed : 0.9688, Acc.windowpane: 0.8034, Acc.grass: 0.8342, Acc.cabinet: 0.7314, Acc.sidewalk: 0.8407, Acc.person: 0.9379, Acc.earth: 0.4887, Acc.door: 0.7608, Acc.table: 0.8158, Acc.mountain: 0.7048, Acc.plant: 0.6715, Acc.curtain: 0.8907, Acc.chair: 0.7453, Acc.car: 0.9437, Acc.water: 0.7807, Acc.painting: 0.9007, Acc.sofa: 0.9233, Acc.shelf: 0.6353, Acc.house: 0.5935, Acc.sea: 0.8254, Acc.mirror: 0.8205, Acc.rug: 0.8838, Acc.field: 0.5670, Acc.armchair: 0.7768, Acc.seat: 0.8964, Acc.fence: 0.6576, Acc.desk: 0.8528, Acc.rock: 0.8697, Acc.wardrobe: 0.7197, Acc.lamp: 0.8617, Acc.bathtub: 0.8722, Acc.railing: 0.5601, Acc.cushion: 0.8360, Acc.base: 0.5813, Acc.box: 0.4556, Acc.column: 0.6684, Acc.signboard: 0.4971, Acc.chest of drawers: 0.8418, Acc.counter: 0.5604, Acc.sand: 0.8017, Acc.sink: 0.8444, Acc.skyscraper: 0.6200, Acc.fireplace: 0.9284, Acc.refrigerator: 0.8943, Acc.grandstand: 0.8599, Acc.path: 0.3993, Acc.stairs: 0.3316, Acc.runway: 0.9698, Acc.case: 0.7607, Acc.pool table: 0.9883, Acc.pillow: 0.7669, Acc.screen door: 0.8826, Acc.stairway: 0.6118, Acc.river: 0.2490, Acc.bridge: 0.6924, Acc.bookcase: 0.6710, Acc.blind: 0.4394, Acc.coffee table: 0.8856, Acc.toilet: 0.9501, Acc.flower: 0.5341, Acc.book: 0.7063, Acc.hill: 0.1428, Acc.bench: 0.6462, Acc.countertop: 0.8084, Acc.stove: 0.9332, Acc.palm: 0.8064, Acc.kitchen island: 0.8831, Acc.computer: 0.9120, Acc.swivel chair: 0.7611, Acc.boat: 0.9023, Acc.bar: 0.8105, Acc.arcade machine: 0.8554, Acc.hovel: 0.5114, Acc.bus: 0.9666, Acc.towel: 0.8597, Acc.light: 0.7305, Acc.truck: 0.6233, Acc.tower: 0.2449, Acc.chandelier: 0.8976, Acc.awning: 0.7123, Acc.streetlight: 0.4652, Acc.booth: 0.5698, Acc.television receiver: 0.8738, Acc.airplane: 0.9115, Acc.dirt track: 0.2986, Acc.apparel: 0.5873, Acc.pole: 0.2965, Acc.land: 0.0451, Acc.bannister: 0.2391, Acc.escalator: 0.7942, Acc.ottoman: 0.6022, Acc.bottle: 0.5346, Acc.buffet: 0.6153, Acc.poster: 0.4546, Acc.stage: 0.4793, Acc.van: 0.6084, Acc.ship: 0.6133, Acc.fountain: 0.3264, Acc.conveyer belt: 0.9405, Acc.canopy: 0.7377, Acc.washer: 0.8931, Acc.plaything: 0.4251, Acc.swimming pool: 0.8161, Acc.stool: 0.7643, Acc.barrel: 0.7417, Acc.basket: 0.6009, Acc.waterfall: 0.9303, Acc.tent: 0.9825, Acc.bag: 0.2117, Acc.minibike: 0.8857, Acc.cradle: 0.9801, Acc.oven: 0.7877, Acc.ball: 0.7108, Acc.food: 0.7138, Acc.step: 0.2546, Acc.tank: 0.6833, Acc.trade name: 0.2679, Acc.microwave: 0.9661, Acc.pot: 0.6957, Acc.animal: 0.5991, Acc.bicycle: 0.7798, Acc.lake: 0.6382, Acc.dishwasher: 0.8414, Acc.screen: 0.9296, Acc.blanket: 0.2932, Acc.sculpture: 0.8605, Acc.hood: 0.7476, Acc.sconce: 0.6522, Acc.vase: 0.6335, Acc.traffic light: 0.6400, Acc.tray: 0.2603, Acc.ashcan: 0.6347, Acc.fan: 0.8593, Acc.pier: 0.4488, Acc.crt screen: 0.0158, Acc.plate: 0.7682, Acc.monitor: 0.7661, Acc.bulletin board: 0.6989, Acc.shower: 0.0040, Acc.radiator: 0.7671, Acc.glass: 0.1903, Acc.clock: 0.4915, Acc.flag: 0.7757 +2024-06-16 15:28:49,842 - mmseg - INFO - Iter [43050/80000] lr: 1.848e-05, eta: 15:29:05, time: 3.308, data_time: 1.949, memory: 70722, decode.loss_ce: 0.1872, decode.acc_seg: 91.7822, aux.loss_ce: 0.0784, aux.acc_seg: 91.4033, loss: 0.2656 +2024-06-16 15:29:58,101 - mmseg - INFO - Iter [43100/80000] lr: 1.845e-05, eta: 15:27:44, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1813, decode.acc_seg: 92.1213, aux.loss_ce: 0.0754, aux.acc_seg: 91.8333, loss: 0.2567 +2024-06-16 15:31:06,181 - mmseg - INFO - Iter [43150/80000] lr: 1.843e-05, eta: 15:26:22, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1983, decode.acc_seg: 91.6843, aux.loss_ce: 0.0827, aux.acc_seg: 91.3370, loss: 0.2810 +2024-06-16 15:32:14,399 - mmseg - INFO - Iter [43200/80000] lr: 1.840e-05, eta: 15:25:00, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1918, decode.acc_seg: 91.8818, aux.loss_ce: 0.0802, aux.acc_seg: 91.5288, loss: 0.2720 +2024-06-16 15:33:22,501 - mmseg - INFO - Iter [43250/80000] lr: 1.838e-05, eta: 15:23:39, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1948, decode.acc_seg: 91.8730, aux.loss_ce: 0.0814, aux.acc_seg: 91.5032, loss: 0.2762 +2024-06-16 15:34:30,793 - mmseg - INFO - Iter [43300/80000] lr: 1.835e-05, eta: 15:22:17, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1981, decode.acc_seg: 91.7496, aux.loss_ce: 0.0824, aux.acc_seg: 91.3599, loss: 0.2805 +2024-06-16 15:35:39,086 - mmseg - INFO - Iter [43350/80000] lr: 1.833e-05, eta: 15:20:56, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1964, decode.acc_seg: 91.6338, aux.loss_ce: 0.0818, aux.acc_seg: 91.3279, loss: 0.2782 +2024-06-16 15:36:47,156 - mmseg - INFO - Iter [43400/80000] lr: 1.830e-05, eta: 15:19:34, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1983, decode.acc_seg: 91.4346, aux.loss_ce: 0.0824, aux.acc_seg: 91.0986, loss: 0.2807 +2024-06-16 15:37:55,254 - mmseg - INFO - Iter [43450/80000] lr: 1.828e-05, eta: 15:18:13, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1952, decode.acc_seg: 91.9883, aux.loss_ce: 0.0814, aux.acc_seg: 91.5560, loss: 0.2766 +2024-06-16 15:39:03,291 - mmseg - INFO - Iter [43500/80000] lr: 1.825e-05, eta: 15:16:51, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1893, decode.acc_seg: 91.6510, aux.loss_ce: 0.0783, aux.acc_seg: 91.3518, loss: 0.2676 +2024-06-16 15:40:11,442 - mmseg - INFO - Iter [43550/80000] lr: 1.823e-05, eta: 15:15:30, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1889, decode.acc_seg: 91.6896, aux.loss_ce: 0.0792, aux.acc_seg: 91.3332, loss: 0.2681 +2024-06-16 15:41:19,897 - mmseg - INFO - Iter [43600/80000] lr: 1.820e-05, eta: 15:14:09, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1924, decode.acc_seg: 91.7390, aux.loss_ce: 0.0799, aux.acc_seg: 91.4151, loss: 0.2723 +2024-06-16 15:42:28,265 - mmseg - INFO - Iter [43650/80000] lr: 1.818e-05, eta: 15:12:48, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1845, decode.acc_seg: 92.1434, aux.loss_ce: 0.0770, aux.acc_seg: 91.8466, loss: 0.2615 +2024-06-16 15:43:36,392 - mmseg - INFO - Iter [43700/80000] lr: 1.815e-05, eta: 15:11:26, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1966, decode.acc_seg: 91.4680, aux.loss_ce: 0.0825, aux.acc_seg: 91.0719, loss: 0.2791 +2024-06-16 15:44:44,482 - mmseg - INFO - Iter [43750/80000] lr: 1.813e-05, eta: 15:10:05, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1969, decode.acc_seg: 91.8052, aux.loss_ce: 0.0821, aux.acc_seg: 91.3976, loss: 0.2790 +2024-06-16 15:45:52,781 - mmseg - INFO - Iter [43800/80000] lr: 1.810e-05, eta: 15:08:44, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1901, decode.acc_seg: 92.1661, aux.loss_ce: 0.0795, aux.acc_seg: 91.8296, loss: 0.2696 +2024-06-16 15:47:01,222 - mmseg - INFO - Iter [43850/80000] lr: 1.808e-05, eta: 15:07:23, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1882, decode.acc_seg: 91.8468, aux.loss_ce: 0.0789, aux.acc_seg: 91.4552, loss: 0.2671 +2024-06-16 15:48:09,355 - mmseg - INFO - Iter [43900/80000] lr: 1.805e-05, eta: 15:06:02, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1951, decode.acc_seg: 91.6604, aux.loss_ce: 0.0818, aux.acc_seg: 91.3224, loss: 0.2769 +2024-06-16 15:49:17,493 - mmseg - INFO - Iter [43950/80000] lr: 1.803e-05, eta: 15:04:41, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1937, decode.acc_seg: 91.4996, aux.loss_ce: 0.0812, aux.acc_seg: 91.1057, loss: 0.2748 +2024-06-16 15:50:25,677 - mmseg - INFO - Saving checkpoint at 44000 iterations +2024-06-16 15:51:52,204 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 15:51:52,204 - mmseg - INFO - Iter [44000/80000] lr: 1.800e-05, eta: 15:04:30, time: 3.094, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1965, decode.acc_seg: 91.7080, aux.loss_ce: 0.0824, aux.acc_seg: 91.3701, loss: 0.2789 +2024-06-16 15:53:28,835 - mmseg - INFO - per class results: +2024-06-16 15:53:28,841 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.85 | 89.63 | +| building | 84.63 | 93.27 | +| sky | 95.04 | 97.72 | +| floor | 84.36 | 91.72 | +| tree | 77.6 | 90.61 | +| ceiling | 86.59 | 93.14 | +| road | 87.56 | 92.64 | +| bed | 92.72 | 97.5 | +| windowpane | 66.73 | 80.28 | +| grass | 68.96 | 84.21 | +| cabinet | 66.23 | 76.02 | +| sidewalk | 73.19 | 85.18 | +| person | 85.65 | 93.67 | +| earth | 37.73 | 49.89 | +| door | 59.2 | 76.5 | +| table | 69.37 | 82.93 | +| mountain | 63.2 | 73.03 | +| plant | 55.52 | 65.12 | +| curtain | 78.86 | 90.29 | +| chair | 68.01 | 79.26 | +| car | 87.4 | 94.78 | +| water | 61.22 | 74.34 | +| painting | 79.6 | 91.36 | +| sofa | 80.91 | 91.64 | +| shelf | 46.93 | 62.09 | +| house | 54.48 | 71.22 | +| sea | 67.82 | 83.68 | +| mirror | 73.59 | 78.45 | +| rug | 66.53 | 73.49 | +| field | 34.79 | 60.46 | +| armchair | 59.18 | 76.35 | +| seat | 67.41 | 88.24 | +| fence | 48.62 | 63.36 | +| desk | 58.68 | 79.59 | +| rock | 58.03 | 86.03 | +| wardrobe | 54.36 | 74.52 | +| lamp | 72.84 | 83.42 | +| bathtub | 84.58 | 87.14 | +| railing | 41.32 | 56.09 | +| cushion | 69.18 | 82.43 | +| base | 38.81 | 55.22 | +| box | 36.12 | 47.4 | +| column | 52.14 | 58.93 | +| signboard | 42.08 | 53.89 | +| chest of drawers | 47.92 | 67.64 | +| counter | 44.17 | 58.1 | +| sand | 55.16 | 84.71 | +| sink | 76.87 | 84.9 | +| skyscraper | 46.77 | 58.12 | +| fireplace | 72.46 | 92.33 | +| refrigerator | 79.46 | 83.26 | +| grandstand | 55.79 | 82.23 | +| path | 30.12 | 41.93 | +| stairs | 25.96 | 31.09 | +| runway | 70.66 | 92.38 | +| case | 57.36 | 73.46 | +| pool table | 94.75 | 98.37 | +| pillow | 68.61 | 79.69 | +| screen door | 82.87 | 86.19 | +| stairway | 47.15 | 66.91 | +| river | 11.59 | 23.09 | +| bridge | 64.32 | 73.22 | +| bookcase | 44.44 | 63.43 | +| blind | 46.81 | 51.83 | +| coffee table | 63.6 | 89.74 | +| toilet | 89.55 | 93.29 | +| flower | 45.81 | 56.33 | +| book | 53.21 | 73.8 | +| hill | 6.5 | 10.23 | +| bench | 56.5 | 64.84 | +| countertop | 63.51 | 85.56 | +| stove | 86.91 | 92.39 | +| palm | 54.11 | 79.7 | +| kitchen island | 56.58 | 86.09 | +| computer | 78.48 | 91.39 | +| swivel chair | 49.3 | 81.13 | +| boat | 77.78 | 90.54 | +| bar | 60.77 | 84.69 | +| arcade machine | 78.5 | 84.37 | +| hovel | 24.4 | 26.15 | +| bus | 92.25 | 97.09 | +| towel | 75.01 | 85.02 | +| light | 59.97 | 70.34 | +| truck | 46.03 | 61.46 | +| tower | 36.98 | 62.01 | +| chandelier | 70.8 | 87.41 | +| awning | 53.37 | 75.81 | +| streetlight | 33.41 | 44.26 | +| booth | 41.84 | 51.75 | +| television receiver | 73.94 | 87.61 | +| airplane | 76.77 | 91.15 | +| dirt track | 12.67 | 25.01 | +| apparel | 52.23 | 70.21 | +| pole | 28.88 | 38.44 | +| land | 6.54 | 9.73 | +| bannister | 16.22 | 27.55 | +| escalator | 60.08 | 77.02 | +| ottoman | 46.33 | 62.98 | +| bottle | 40.91 | 64.22 | +| buffet | 57.05 | 91.44 | +| poster | 40.45 | 44.94 | +| stage | 17.11 | 52.86 | +| van | 51.64 | 63.23 | +| ship | 67.89 | 69.81 | +| fountain | 33.83 | 35.78 | +| conveyer belt | 77.14 | 94.06 | +| canopy | 47.01 | 66.65 | +| washer | 81.99 | 87.52 | +| plaything | 31.8 | 49.51 | +| swimming pool | 56.28 | 72.38 | +| stool | 54.47 | 71.25 | +| barrel | 61.51 | 74.08 | +| basket | 39.73 | 55.67 | +| waterfall | 70.64 | 95.4 | +| tent | 90.12 | 98.24 | +| bag | 20.42 | 21.9 | +| minibike | 74.55 | 90.15 | +| cradle | 77.78 | 98.41 | +| oven | 59.15 | 67.95 | +| ball | 50.23 | 60.1 | +| food | 63.71 | 86.4 | +| step | 13.86 | 18.84 | +| tank | 61.33 | 70.63 | +| trade name | 33.58 | 42.92 | +| microwave | 86.42 | 96.47 | +| pot | 52.53 | 60.26 | +| animal | 60.21 | 61.73 | +| bicycle | 60.35 | 81.14 | +| lake | 53.34 | 63.72 | +| dishwasher | 68.9 | 82.5 | +| screen | 48.43 | 75.88 | +| blanket | 25.48 | 28.37 | +| sculpture | 76.37 | 88.5 | +| hood | 59.7 | 70.76 | +| sconce | 55.11 | 64.2 | +| vase | 48.59 | 61.61 | +| traffic light | 37.27 | 58.97 | +| tray | 22.5 | 33.67 | +| ashcan | 48.26 | 63.07 | +| fan | 64.96 | 78.76 | +| pier | 40.39 | 47.17 | +| crt screen | 3.3 | 4.85 | +| plate | 60.44 | 81.09 | +| monitor | 67.33 | 78.47 | +| bulletin board | 51.2 | 68.42 | +| shower | 6.55 | 7.04 | +| radiator | 68.11 | 77.22 | +| glass | 20.61 | 22.56 | +| clock | 40.0 | 49.05 | +| flag | 67.3 | 80.35 | ++---------------------+-------+-------+ +2024-06-16 15:53:28,841 - mmseg - INFO - Summary: +2024-06-16 15:53:28,841 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.05 | 56.93 | 69.97 | ++-------+-------+-------+ +2024-06-16 15:53:28,842 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 15:53:28,842 - mmseg - INFO - Iter(val) [250] aAcc: 0.8605, mIoU: 0.5693, mAcc: 0.6997, IoU.wall: 0.8185, IoU.building: 0.8463, IoU.sky: 0.9504, IoU.floor: 0.8436, IoU.tree: 0.7760, IoU.ceiling: 0.8659, IoU.road: 0.8756, IoU.bed : 0.9272, IoU.windowpane: 0.6673, IoU.grass: 0.6896, IoU.cabinet: 0.6623, IoU.sidewalk: 0.7319, IoU.person: 0.8565, IoU.earth: 0.3773, IoU.door: 0.5920, IoU.table: 0.6937, IoU.mountain: 0.6320, IoU.plant: 0.5552, IoU.curtain: 0.7886, IoU.chair: 0.6801, IoU.car: 0.8740, IoU.water: 0.6122, IoU.painting: 0.7960, IoU.sofa: 0.8091, IoU.shelf: 0.4693, IoU.house: 0.5448, IoU.sea: 0.6782, IoU.mirror: 0.7359, IoU.rug: 0.6653, IoU.field: 0.3479, IoU.armchair: 0.5918, IoU.seat: 0.6741, IoU.fence: 0.4862, IoU.desk: 0.5868, IoU.rock: 0.5803, IoU.wardrobe: 0.5436, IoU.lamp: 0.7284, IoU.bathtub: 0.8458, IoU.railing: 0.4132, IoU.cushion: 0.6918, IoU.base: 0.3881, IoU.box: 0.3612, IoU.column: 0.5214, IoU.signboard: 0.4208, IoU.chest of drawers: 0.4792, IoU.counter: 0.4417, IoU.sand: 0.5516, IoU.sink: 0.7687, IoU.skyscraper: 0.4677, IoU.fireplace: 0.7246, IoU.refrigerator: 0.7946, IoU.grandstand: 0.5579, IoU.path: 0.3012, IoU.stairs: 0.2596, IoU.runway: 0.7066, IoU.case: 0.5736, IoU.pool table: 0.9475, IoU.pillow: 0.6861, IoU.screen door: 0.8287, IoU.stairway: 0.4715, IoU.river: 0.1159, IoU.bridge: 0.6432, IoU.bookcase: 0.4444, IoU.blind: 0.4681, IoU.coffee table: 0.6360, IoU.toilet: 0.8955, IoU.flower: 0.4581, IoU.book: 0.5321, IoU.hill: 0.0650, IoU.bench: 0.5650, IoU.countertop: 0.6351, IoU.stove: 0.8691, IoU.palm: 0.5411, IoU.kitchen island: 0.5658, IoU.computer: 0.7848, IoU.swivel chair: 0.4930, IoU.boat: 0.7778, IoU.bar: 0.6077, IoU.arcade machine: 0.7850, IoU.hovel: 0.2440, IoU.bus: 0.9225, IoU.towel: 0.7501, IoU.light: 0.5997, IoU.truck: 0.4603, IoU.tower: 0.3698, IoU.chandelier: 0.7080, IoU.awning: 0.5337, IoU.streetlight: 0.3341, IoU.booth: 0.4184, IoU.television receiver: 0.7394, IoU.airplane: 0.7677, IoU.dirt track: 0.1267, IoU.apparel: 0.5223, IoU.pole: 0.2888, IoU.land: 0.0654, IoU.bannister: 0.1622, IoU.escalator: 0.6008, IoU.ottoman: 0.4633, IoU.bottle: 0.4091, IoU.buffet: 0.5705, IoU.poster: 0.4045, IoU.stage: 0.1711, IoU.van: 0.5164, IoU.ship: 0.6789, IoU.fountain: 0.3383, IoU.conveyer belt: 0.7714, IoU.canopy: 0.4701, IoU.washer: 0.8199, IoU.plaything: 0.3180, IoU.swimming pool: 0.5628, IoU.stool: 0.5447, IoU.barrel: 0.6151, IoU.basket: 0.3973, IoU.waterfall: 0.7064, IoU.tent: 0.9012, IoU.bag: 0.2042, IoU.minibike: 0.7455, IoU.cradle: 0.7778, IoU.oven: 0.5915, IoU.ball: 0.5023, IoU.food: 0.6371, IoU.step: 0.1386, IoU.tank: 0.6133, IoU.trade name: 0.3358, IoU.microwave: 0.8642, IoU.pot: 0.5253, IoU.animal: 0.6021, IoU.bicycle: 0.6035, IoU.lake: 0.5334, IoU.dishwasher: 0.6890, IoU.screen: 0.4843, IoU.blanket: 0.2548, IoU.sculpture: 0.7637, IoU.hood: 0.5970, IoU.sconce: 0.5511, IoU.vase: 0.4859, IoU.traffic light: 0.3727, IoU.tray: 0.2250, IoU.ashcan: 0.4826, IoU.fan: 0.6496, IoU.pier: 0.4039, IoU.crt screen: 0.0330, IoU.plate: 0.6044, IoU.monitor: 0.6733, IoU.bulletin board: 0.5120, IoU.shower: 0.0655, IoU.radiator: 0.6811, IoU.glass: 0.2061, IoU.clock: 0.4000, IoU.flag: 0.6730, Acc.wall: 0.8963, Acc.building: 0.9327, Acc.sky: 0.9772, Acc.floor: 0.9172, Acc.tree: 0.9061, Acc.ceiling: 0.9314, Acc.road: 0.9264, Acc.bed : 0.9750, Acc.windowpane: 0.8028, Acc.grass: 0.8421, Acc.cabinet: 0.7602, Acc.sidewalk: 0.8518, Acc.person: 0.9367, Acc.earth: 0.4989, Acc.door: 0.7650, Acc.table: 0.8293, Acc.mountain: 0.7303, Acc.plant: 0.6512, Acc.curtain: 0.9029, Acc.chair: 0.7926, Acc.car: 0.9478, Acc.water: 0.7434, Acc.painting: 0.9136, Acc.sofa: 0.9164, Acc.shelf: 0.6209, Acc.house: 0.7122, Acc.sea: 0.8368, Acc.mirror: 0.7845, Acc.rug: 0.7349, Acc.field: 0.6046, Acc.armchair: 0.7635, Acc.seat: 0.8824, Acc.fence: 0.6336, Acc.desk: 0.7959, Acc.rock: 0.8603, Acc.wardrobe: 0.7452, Acc.lamp: 0.8342, Acc.bathtub: 0.8714, Acc.railing: 0.5609, Acc.cushion: 0.8243, Acc.base: 0.5522, Acc.box: 0.4740, Acc.column: 0.5893, Acc.signboard: 0.5389, Acc.chest of drawers: 0.6764, Acc.counter: 0.5810, Acc.sand: 0.8471, Acc.sink: 0.8490, Acc.skyscraper: 0.5812, Acc.fireplace: 0.9233, Acc.refrigerator: 0.8326, Acc.grandstand: 0.8223, Acc.path: 0.4193, Acc.stairs: 0.3109, Acc.runway: 0.9238, Acc.case: 0.7346, Acc.pool table: 0.9837, Acc.pillow: 0.7969, Acc.screen door: 0.8619, Acc.stairway: 0.6691, Acc.river: 0.2309, Acc.bridge: 0.7322, Acc.bookcase: 0.6343, Acc.blind: 0.5183, Acc.coffee table: 0.8974, Acc.toilet: 0.9329, Acc.flower: 0.5633, Acc.book: 0.7380, Acc.hill: 0.1023, Acc.bench: 0.6484, Acc.countertop: 0.8556, Acc.stove: 0.9239, Acc.palm: 0.7970, Acc.kitchen island: 0.8609, Acc.computer: 0.9139, Acc.swivel chair: 0.8113, Acc.boat: 0.9054, Acc.bar: 0.8469, Acc.arcade machine: 0.8437, Acc.hovel: 0.2615, Acc.bus: 0.9709, Acc.towel: 0.8502, Acc.light: 0.7034, Acc.truck: 0.6146, Acc.tower: 0.6201, Acc.chandelier: 0.8741, Acc.awning: 0.7581, Acc.streetlight: 0.4426, Acc.booth: 0.5175, Acc.television receiver: 0.8761, Acc.airplane: 0.9115, Acc.dirt track: 0.2501, Acc.apparel: 0.7021, Acc.pole: 0.3844, Acc.land: 0.0973, Acc.bannister: 0.2755, Acc.escalator: 0.7702, Acc.ottoman: 0.6298, Acc.bottle: 0.6422, Acc.buffet: 0.9144, Acc.poster: 0.4494, Acc.stage: 0.5286, Acc.van: 0.6323, Acc.ship: 0.6981, Acc.fountain: 0.3578, Acc.conveyer belt: 0.9406, Acc.canopy: 0.6665, Acc.washer: 0.8752, Acc.plaything: 0.4951, Acc.swimming pool: 0.7238, Acc.stool: 0.7125, Acc.barrel: 0.7408, Acc.basket: 0.5567, Acc.waterfall: 0.9540, Acc.tent: 0.9824, Acc.bag: 0.2190, Acc.minibike: 0.9015, Acc.cradle: 0.9841, Acc.oven: 0.6795, Acc.ball: 0.6010, Acc.food: 0.8640, Acc.step: 0.1884, Acc.tank: 0.7063, Acc.trade name: 0.4292, Acc.microwave: 0.9647, Acc.pot: 0.6026, Acc.animal: 0.6173, Acc.bicycle: 0.8114, Acc.lake: 0.6372, Acc.dishwasher: 0.8250, Acc.screen: 0.7588, Acc.blanket: 0.2837, Acc.sculpture: 0.8850, Acc.hood: 0.7076, Acc.sconce: 0.6420, Acc.vase: 0.6161, Acc.traffic light: 0.5897, Acc.tray: 0.3367, Acc.ashcan: 0.6307, Acc.fan: 0.7876, Acc.pier: 0.4717, Acc.crt screen: 0.0485, Acc.plate: 0.8109, Acc.monitor: 0.7847, Acc.bulletin board: 0.6842, Acc.shower: 0.0704, Acc.radiator: 0.7722, Acc.glass: 0.2256, Acc.clock: 0.4905, Acc.flag: 0.8035 +2024-06-16 15:54:37,584 - mmseg - INFO - Iter [44050/80000] lr: 1.798e-05, eta: 15:04:28, time: 3.308, data_time: 1.949, memory: 70722, decode.loss_ce: 0.1857, decode.acc_seg: 92.1138, aux.loss_ce: 0.0767, aux.acc_seg: 91.8081, loss: 0.2625 +2024-06-16 15:55:45,861 - mmseg - INFO - Iter [44100/80000] lr: 1.795e-05, eta: 15:03:07, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1950, decode.acc_seg: 91.8629, aux.loss_ce: 0.0810, aux.acc_seg: 91.5170, loss: 0.2760 +2024-06-16 15:56:53,889 - mmseg - INFO - Iter [44150/80000] lr: 1.793e-05, eta: 15:01:46, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1907, decode.acc_seg: 91.9972, aux.loss_ce: 0.0782, aux.acc_seg: 91.6823, loss: 0.2688 +2024-06-16 15:58:02,051 - mmseg - INFO - Iter [44200/80000] lr: 1.790e-05, eta: 15:00:24, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2046, decode.acc_seg: 91.3737, aux.loss_ce: 0.0860, aux.acc_seg: 90.9749, loss: 0.2906 +2024-06-16 15:59:12,669 - mmseg - INFO - Iter [44250/80000] lr: 1.788e-05, eta: 14:59:05, time: 1.412, data_time: 0.052, memory: 70722, decode.loss_ce: 0.1846, decode.acc_seg: 92.3075, aux.loss_ce: 0.0772, aux.acc_seg: 91.9206, loss: 0.2618 +2024-06-16 16:00:20,700 - mmseg - INFO - Iter [44300/80000] lr: 1.785e-05, eta: 14:57:43, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1780, decode.acc_seg: 92.4647, aux.loss_ce: 0.0745, aux.acc_seg: 92.1013, loss: 0.2525 +2024-06-16 16:01:28,922 - mmseg - INFO - Iter [44350/80000] lr: 1.783e-05, eta: 14:56:22, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2041, decode.acc_seg: 91.7133, aux.loss_ce: 0.0849, aux.acc_seg: 91.3317, loss: 0.2890 +2024-06-16 16:02:36,978 - mmseg - INFO - Iter [44400/80000] lr: 1.780e-05, eta: 14:55:01, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1821, decode.acc_seg: 92.1282, aux.loss_ce: 0.0763, aux.acc_seg: 91.7678, loss: 0.2585 +2024-06-16 16:03:45,244 - mmseg - INFO - Iter [44450/80000] lr: 1.778e-05, eta: 14:53:40, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1864, decode.acc_seg: 91.9309, aux.loss_ce: 0.0782, aux.acc_seg: 91.5101, loss: 0.2646 +2024-06-16 16:04:53,565 - mmseg - INFO - Iter [44500/80000] lr: 1.775e-05, eta: 14:52:19, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1941, decode.acc_seg: 91.7805, aux.loss_ce: 0.0804, aux.acc_seg: 91.5890, loss: 0.2745 +2024-06-16 16:06:01,729 - mmseg - INFO - Iter [44550/80000] lr: 1.773e-05, eta: 14:50:57, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1893, decode.acc_seg: 91.8812, aux.loss_ce: 0.0787, aux.acc_seg: 91.6278, loss: 0.2680 +2024-06-16 16:07:09,766 - mmseg - INFO - Iter [44600/80000] lr: 1.770e-05, eta: 14:49:36, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2024, decode.acc_seg: 91.6567, aux.loss_ce: 0.0849, aux.acc_seg: 91.2512, loss: 0.2873 +2024-06-16 16:08:18,110 - mmseg - INFO - Iter [44650/80000] lr: 1.768e-05, eta: 14:48:15, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1916, decode.acc_seg: 91.8512, aux.loss_ce: 0.0796, aux.acc_seg: 91.5561, loss: 0.2712 +2024-06-16 16:09:26,397 - mmseg - INFO - Iter [44700/80000] lr: 1.765e-05, eta: 14:46:54, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1865, decode.acc_seg: 92.2093, aux.loss_ce: 0.0776, aux.acc_seg: 91.8521, loss: 0.2641 +2024-06-16 16:10:34,534 - mmseg - INFO - Iter [44750/80000] lr: 1.763e-05, eta: 14:45:33, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1902, decode.acc_seg: 91.9142, aux.loss_ce: 0.0791, aux.acc_seg: 91.6013, loss: 0.2693 +2024-06-16 16:11:42,711 - mmseg - INFO - Iter [44800/80000] lr: 1.760e-05, eta: 14:44:12, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2014, decode.acc_seg: 91.8913, aux.loss_ce: 0.0830, aux.acc_seg: 91.5884, loss: 0.2844 +2024-06-16 16:12:50,802 - mmseg - INFO - Iter [44850/80000] lr: 1.758e-05, eta: 14:42:51, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1893, decode.acc_seg: 91.8933, aux.loss_ce: 0.0788, aux.acc_seg: 91.5635, loss: 0.2681 +2024-06-16 16:13:59,137 - mmseg - INFO - Iter [44900/80000] lr: 1.755e-05, eta: 14:41:30, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1957, decode.acc_seg: 91.8110, aux.loss_ce: 0.0814, aux.acc_seg: 91.4644, loss: 0.2771 +2024-06-16 16:15:07,250 - mmseg - INFO - Iter [44950/80000] lr: 1.753e-05, eta: 14:40:09, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1883, decode.acc_seg: 91.8808, aux.loss_ce: 0.0777, aux.acc_seg: 91.5716, loss: 0.2660 +2024-06-16 16:16:15,644 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 16:16:15,644 - mmseg - INFO - Iter [45000/80000] lr: 1.750e-05, eta: 14:38:49, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1902, decode.acc_seg: 91.7848, aux.loss_ce: 0.0793, aux.acc_seg: 91.4689, loss: 0.2694 +2024-06-16 16:17:52,950 - mmseg - INFO - per class results: +2024-06-16 16:17:52,957 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.8 | 90.13 | +| building | 85.49 | 93.7 | +| sky | 94.89 | 97.99 | +| floor | 85.48 | 92.79 | +| tree | 77.6 | 89.44 | +| ceiling | 87.07 | 93.4 | +| road | 87.48 | 93.64 | +| bed | 92.76 | 97.01 | +| windowpane | 66.06 | 81.36 | +| grass | 69.05 | 82.42 | +| cabinet | 65.88 | 77.31 | +| sidewalk | 73.59 | 83.26 | +| person | 85.78 | 93.86 | +| earth | 35.62 | 46.88 | +| door | 58.61 | 77.5 | +| table | 70.35 | 82.02 | +| mountain | 60.78 | 70.14 | +| plant | 54.45 | 69.79 | +| curtain | 78.18 | 89.98 | +| chair | 68.46 | 79.32 | +| car | 87.27 | 93.8 | +| water | 61.65 | 74.49 | +| painting | 77.05 | 91.54 | +| sofa | 81.28 | 90.0 | +| shelf | 44.84 | 58.51 | +| house | 58.44 | 74.91 | +| sea | 67.45 | 82.83 | +| mirror | 74.37 | 79.95 | +| rug | 72.1 | 82.44 | +| field | 35.0 | 60.5 | +| armchair | 60.16 | 78.5 | +| seat | 68.2 | 86.68 | +| fence | 49.93 | 63.26 | +| desk | 60.59 | 78.08 | +| rock | 55.48 | 81.37 | +| wardrobe | 52.07 | 67.34 | +| lamp | 73.73 | 85.68 | +| bathtub | 84.57 | 87.02 | +| railing | 43.44 | 60.93 | +| cushion | 69.93 | 81.06 | +| base | 40.84 | 63.75 | +| box | 36.15 | 44.31 | +| column | 56.93 | 73.37 | +| signboard | 41.83 | 51.36 | +| chest of drawers | 42.74 | 67.71 | +| counter | 40.13 | 48.22 | +| sand | 52.2 | 79.39 | +| sink | 75.34 | 86.35 | +| skyscraper | 48.27 | 60.06 | +| fireplace | 72.68 | 88.13 | +| refrigerator | 83.13 | 89.99 | +| grandstand | 52.57 | 82.01 | +| path | 29.53 | 39.03 | +| stairs | 31.86 | 38.84 | +| runway | 67.52 | 88.43 | +| case | 58.74 | 71.59 | +| pool table | 94.16 | 97.54 | +| pillow | 67.77 | 77.72 | +| screen door | 77.04 | 79.13 | +| stairway | 41.64 | 48.86 | +| river | 9.04 | 18.56 | +| bridge | 59.03 | 66.68 | +| bookcase | 38.9 | 58.55 | +| blind | 42.14 | 45.56 | +| coffee table | 67.19 | 87.62 | +| toilet | 89.93 | 93.47 | +| flower | 43.41 | 55.66 | +| book | 51.61 | 79.31 | +| hill | 7.75 | 13.39 | +| bench | 50.92 | 56.19 | +| countertop | 65.6 | 80.79 | +| stove | 85.46 | 92.23 | +| palm | 56.26 | 77.45 | +| kitchen island | 55.65 | 87.4 | +| computer | 78.88 | 92.04 | +| swivel chair | 51.55 | 74.92 | +| boat | 73.24 | 91.22 | +| bar | 61.19 | 82.71 | +| arcade machine | 77.94 | 83.52 | +| hovel | 30.32 | 31.91 | +| bus | 92.1 | 96.61 | +| towel | 75.93 | 88.27 | +| light | 61.2 | 69.52 | +| truck | 45.17 | 56.06 | +| tower | 20.95 | 28.09 | +| chandelier | 69.91 | 88.28 | +| awning | 48.28 | 64.5 | +| streetlight | 33.7 | 44.75 | +| booth | 48.07 | 62.51 | +| television receiver | 72.56 | 88.0 | +| airplane | 74.77 | 87.96 | +| dirt track | 7.93 | 16.2 | +| apparel | 44.11 | 69.03 | +| pole | 32.78 | 47.41 | +| land | 2.08 | 3.01 | +| bannister | 15.83 | 22.82 | +| escalator | 61.05 | 76.83 | +| ottoman | 47.72 | 64.7 | +| bottle | 39.62 | 59.35 | +| buffet | 49.66 | 59.95 | +| poster | 33.48 | 44.39 | +| stage | 17.71 | 34.84 | +| van | 46.01 | 60.22 | +| ship | 89.06 | 94.33 | +| fountain | 31.83 | 33.79 | +| conveyer belt | 85.84 | 93.37 | +| canopy | 52.05 | 75.75 | +| washer | 81.63 | 87.36 | +| plaything | 34.91 | 60.79 | +| swimming pool | 52.95 | 81.37 | +| stool | 53.64 | 64.5 | +| barrel | 64.16 | 74.12 | +| basket | 37.62 | 52.29 | +| waterfall | 68.21 | 80.92 | +| tent | 92.19 | 98.65 | +| bag | 19.84 | 21.91 | +| minibike | 74.77 | 90.6 | +| cradle | 71.98 | 97.84 | +| oven | 60.27 | 66.94 | +| ball | 53.75 | 68.43 | +| food | 66.37 | 80.15 | +| step | 14.74 | 19.04 | +| tank | 61.95 | 67.76 | +| trade name | 27.53 | 31.69 | +| microwave | 85.86 | 96.58 | +| pot | 55.4 | 64.46 | +| animal | 58.11 | 59.22 | +| bicycle | 61.2 | 73.3 | +| lake | 53.69 | 63.81 | +| dishwasher | 67.74 | 78.51 | +| screen | 52.4 | 78.43 | +| blanket | 23.75 | 26.62 | +| sculpture | 76.05 | 85.94 | +| hood | 64.21 | 72.88 | +| sconce | 59.89 | 71.13 | +| vase | 49.35 | 65.94 | +| traffic light | 39.63 | 58.99 | +| tray | 25.42 | 35.01 | +| ashcan | 47.86 | 62.69 | +| fan | 68.46 | 82.32 | +| pier | 41.47 | 45.16 | +| crt screen | 12.5 | 18.0 | +| plate | 61.95 | 74.51 | +| monitor | 68.01 | 78.8 | +| bulletin board | 52.98 | 69.88 | +| shower | 1.57 | 1.6 | +| radiator | 68.27 | 77.46 | +| glass | 18.68 | 19.59 | +| clock | 45.52 | 52.5 | +| flag | 68.38 | 79.97 | ++---------------------+-------+-------+ +2024-06-16 16:17:52,957 - mmseg - INFO - Summary: +2024-06-16 16:17:52,957 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.12 | 56.86 | 69.03 | ++-------+-------+-------+ +2024-06-16 16:17:52,958 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 16:17:52,958 - mmseg - INFO - Iter(val) [250] aAcc: 0.8612, mIoU: 0.5686, mAcc: 0.6903, IoU.wall: 0.8180, IoU.building: 0.8549, IoU.sky: 0.9489, IoU.floor: 0.8548, IoU.tree: 0.7760, IoU.ceiling: 0.8707, IoU.road: 0.8748, IoU.bed : 0.9276, IoU.windowpane: 0.6606, IoU.grass: 0.6905, IoU.cabinet: 0.6588, IoU.sidewalk: 0.7359, IoU.person: 0.8578, IoU.earth: 0.3562, IoU.door: 0.5861, IoU.table: 0.7035, IoU.mountain: 0.6078, IoU.plant: 0.5445, IoU.curtain: 0.7818, IoU.chair: 0.6846, IoU.car: 0.8727, IoU.water: 0.6165, IoU.painting: 0.7705, IoU.sofa: 0.8128, IoU.shelf: 0.4484, IoU.house: 0.5844, IoU.sea: 0.6745, IoU.mirror: 0.7437, IoU.rug: 0.7210, IoU.field: 0.3500, IoU.armchair: 0.6016, IoU.seat: 0.6820, IoU.fence: 0.4993, IoU.desk: 0.6059, IoU.rock: 0.5548, IoU.wardrobe: 0.5207, IoU.lamp: 0.7373, IoU.bathtub: 0.8457, IoU.railing: 0.4344, IoU.cushion: 0.6993, IoU.base: 0.4084, IoU.box: 0.3615, IoU.column: 0.5693, IoU.signboard: 0.4183, IoU.chest of drawers: 0.4274, IoU.counter: 0.4013, IoU.sand: 0.5220, IoU.sink: 0.7534, IoU.skyscraper: 0.4827, IoU.fireplace: 0.7268, IoU.refrigerator: 0.8313, IoU.grandstand: 0.5257, IoU.path: 0.2953, IoU.stairs: 0.3186, IoU.runway: 0.6752, IoU.case: 0.5874, IoU.pool table: 0.9416, IoU.pillow: 0.6777, IoU.screen door: 0.7704, IoU.stairway: 0.4164, IoU.river: 0.0904, IoU.bridge: 0.5903, IoU.bookcase: 0.3890, IoU.blind: 0.4214, IoU.coffee table: 0.6719, IoU.toilet: 0.8993, IoU.flower: 0.4341, IoU.book: 0.5161, IoU.hill: 0.0775, IoU.bench: 0.5092, IoU.countertop: 0.6560, IoU.stove: 0.8546, IoU.palm: 0.5626, IoU.kitchen island: 0.5565, IoU.computer: 0.7888, IoU.swivel chair: 0.5155, IoU.boat: 0.7324, IoU.bar: 0.6119, IoU.arcade machine: 0.7794, IoU.hovel: 0.3032, IoU.bus: 0.9210, IoU.towel: 0.7593, IoU.light: 0.6120, IoU.truck: 0.4517, IoU.tower: 0.2095, IoU.chandelier: 0.6991, IoU.awning: 0.4828, IoU.streetlight: 0.3370, IoU.booth: 0.4807, IoU.television receiver: 0.7256, IoU.airplane: 0.7477, IoU.dirt track: 0.0793, IoU.apparel: 0.4411, IoU.pole: 0.3278, IoU.land: 0.0208, IoU.bannister: 0.1583, IoU.escalator: 0.6105, IoU.ottoman: 0.4772, IoU.bottle: 0.3962, IoU.buffet: 0.4966, IoU.poster: 0.3348, IoU.stage: 0.1771, IoU.van: 0.4601, IoU.ship: 0.8906, IoU.fountain: 0.3183, IoU.conveyer belt: 0.8584, IoU.canopy: 0.5205, IoU.washer: 0.8163, IoU.plaything: 0.3491, IoU.swimming pool: 0.5295, IoU.stool: 0.5364, IoU.barrel: 0.6416, IoU.basket: 0.3762, IoU.waterfall: 0.6821, IoU.tent: 0.9219, IoU.bag: 0.1984, IoU.minibike: 0.7477, IoU.cradle: 0.7198, IoU.oven: 0.6027, IoU.ball: 0.5375, IoU.food: 0.6637, IoU.step: 0.1474, IoU.tank: 0.6195, IoU.trade name: 0.2753, IoU.microwave: 0.8586, IoU.pot: 0.5540, IoU.animal: 0.5811, IoU.bicycle: 0.6120, IoU.lake: 0.5369, IoU.dishwasher: 0.6774, IoU.screen: 0.5240, IoU.blanket: 0.2375, IoU.sculpture: 0.7605, IoU.hood: 0.6421, IoU.sconce: 0.5989, IoU.vase: 0.4935, IoU.traffic light: 0.3963, IoU.tray: 0.2542, IoU.ashcan: 0.4786, IoU.fan: 0.6846, IoU.pier: 0.4147, IoU.crt screen: 0.1250, IoU.plate: 0.6195, IoU.monitor: 0.6801, IoU.bulletin board: 0.5298, IoU.shower: 0.0157, IoU.radiator: 0.6827, IoU.glass: 0.1868, IoU.clock: 0.4552, IoU.flag: 0.6838, Acc.wall: 0.9013, Acc.building: 0.9370, Acc.sky: 0.9799, Acc.floor: 0.9279, Acc.tree: 0.8944, Acc.ceiling: 0.9340, Acc.road: 0.9364, Acc.bed : 0.9701, Acc.windowpane: 0.8136, Acc.grass: 0.8242, Acc.cabinet: 0.7731, Acc.sidewalk: 0.8326, Acc.person: 0.9386, Acc.earth: 0.4688, Acc.door: 0.7750, Acc.table: 0.8202, Acc.mountain: 0.7014, Acc.plant: 0.6979, Acc.curtain: 0.8998, Acc.chair: 0.7932, Acc.car: 0.9380, Acc.water: 0.7449, Acc.painting: 0.9154, Acc.sofa: 0.9000, Acc.shelf: 0.5851, Acc.house: 0.7491, Acc.sea: 0.8283, Acc.mirror: 0.7995, Acc.rug: 0.8244, Acc.field: 0.6050, Acc.armchair: 0.7850, Acc.seat: 0.8668, Acc.fence: 0.6326, Acc.desk: 0.7808, Acc.rock: 0.8137, Acc.wardrobe: 0.6734, Acc.lamp: 0.8568, Acc.bathtub: 0.8702, Acc.railing: 0.6093, Acc.cushion: 0.8106, Acc.base: 0.6375, Acc.box: 0.4431, Acc.column: 0.7337, Acc.signboard: 0.5136, Acc.chest of drawers: 0.6771, Acc.counter: 0.4822, Acc.sand: 0.7939, Acc.sink: 0.8635, Acc.skyscraper: 0.6006, Acc.fireplace: 0.8813, Acc.refrigerator: 0.8999, Acc.grandstand: 0.8201, Acc.path: 0.3903, Acc.stairs: 0.3884, Acc.runway: 0.8843, Acc.case: 0.7159, Acc.pool table: 0.9754, Acc.pillow: 0.7772, Acc.screen door: 0.7913, Acc.stairway: 0.4886, Acc.river: 0.1856, Acc.bridge: 0.6668, Acc.bookcase: 0.5855, Acc.blind: 0.4556, Acc.coffee table: 0.8762, Acc.toilet: 0.9347, Acc.flower: 0.5566, Acc.book: 0.7931, Acc.hill: 0.1339, Acc.bench: 0.5619, Acc.countertop: 0.8079, Acc.stove: 0.9223, Acc.palm: 0.7745, Acc.kitchen island: 0.8740, Acc.computer: 0.9204, Acc.swivel chair: 0.7492, Acc.boat: 0.9122, Acc.bar: 0.8271, Acc.arcade machine: 0.8352, Acc.hovel: 0.3191, Acc.bus: 0.9661, Acc.towel: 0.8827, Acc.light: 0.6952, Acc.truck: 0.5606, Acc.tower: 0.2809, Acc.chandelier: 0.8828, Acc.awning: 0.6450, Acc.streetlight: 0.4475, Acc.booth: 0.6251, Acc.television receiver: 0.8800, Acc.airplane: 0.8796, Acc.dirt track: 0.1620, Acc.apparel: 0.6903, Acc.pole: 0.4741, Acc.land: 0.0301, Acc.bannister: 0.2282, Acc.escalator: 0.7683, Acc.ottoman: 0.6470, Acc.bottle: 0.5935, Acc.buffet: 0.5995, Acc.poster: 0.4439, Acc.stage: 0.3484, Acc.van: 0.6022, Acc.ship: 0.9433, Acc.fountain: 0.3379, Acc.conveyer belt: 0.9337, Acc.canopy: 0.7575, Acc.washer: 0.8736, Acc.plaything: 0.6079, Acc.swimming pool: 0.8137, Acc.stool: 0.6450, Acc.barrel: 0.7412, Acc.basket: 0.5229, Acc.waterfall: 0.8092, Acc.tent: 0.9865, Acc.bag: 0.2191, Acc.minibike: 0.9060, Acc.cradle: 0.9784, Acc.oven: 0.6694, Acc.ball: 0.6843, Acc.food: 0.8015, Acc.step: 0.1904, Acc.tank: 0.6776, Acc.trade name: 0.3169, Acc.microwave: 0.9658, Acc.pot: 0.6446, Acc.animal: 0.5922, Acc.bicycle: 0.7330, Acc.lake: 0.6381, Acc.dishwasher: 0.7851, Acc.screen: 0.7843, Acc.blanket: 0.2662, Acc.sculpture: 0.8594, Acc.hood: 0.7288, Acc.sconce: 0.7113, Acc.vase: 0.6594, Acc.traffic light: 0.5899, Acc.tray: 0.3501, Acc.ashcan: 0.6269, Acc.fan: 0.8232, Acc.pier: 0.4516, Acc.crt screen: 0.1800, Acc.plate: 0.7451, Acc.monitor: 0.7880, Acc.bulletin board: 0.6988, Acc.shower: 0.0160, Acc.radiator: 0.7746, Acc.glass: 0.1959, Acc.clock: 0.5250, Acc.flag: 0.7997 +2024-06-16 16:19:01,429 - mmseg - INFO - Iter [45050/80000] lr: 1.748e-05, eta: 14:38:43, time: 3.316, data_time: 1.963, memory: 70722, decode.loss_ce: 0.1895, decode.acc_seg: 91.9871, aux.loss_ce: 0.0793, aux.acc_seg: 91.6552, loss: 0.2687 +2024-06-16 16:20:09,692 - mmseg - INFO - Iter [45100/80000] lr: 1.745e-05, eta: 14:37:22, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1937, decode.acc_seg: 91.7796, aux.loss_ce: 0.0810, aux.acc_seg: 91.4493, loss: 0.2746 +2024-06-16 16:21:17,978 - mmseg - INFO - Iter [45150/80000] lr: 1.743e-05, eta: 14:36:01, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1944, decode.acc_seg: 91.7359, aux.loss_ce: 0.0817, aux.acc_seg: 91.2827, loss: 0.2761 +2024-06-16 16:22:26,123 - mmseg - INFO - Iter [45200/80000] lr: 1.740e-05, eta: 14:34:40, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1951, decode.acc_seg: 92.0731, aux.loss_ce: 0.0810, aux.acc_seg: 91.7409, loss: 0.2761 +2024-06-16 16:23:34,341 - mmseg - INFO - Iter [45250/80000] lr: 1.738e-05, eta: 14:33:20, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1850, decode.acc_seg: 92.1535, aux.loss_ce: 0.0774, aux.acc_seg: 91.8570, loss: 0.2624 +2024-06-16 16:24:42,455 - mmseg - INFO - Iter [45300/80000] lr: 1.735e-05, eta: 14:31:59, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1813, decode.acc_seg: 92.3826, aux.loss_ce: 0.0759, aux.acc_seg: 92.0186, loss: 0.2572 +2024-06-16 16:25:50,720 - mmseg - INFO - Iter [45350/80000] lr: 1.733e-05, eta: 14:30:38, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1957, decode.acc_seg: 91.6000, aux.loss_ce: 0.0820, aux.acc_seg: 91.2248, loss: 0.2777 +2024-06-16 16:26:59,059 - mmseg - INFO - Iter [45400/80000] lr: 1.730e-05, eta: 14:29:17, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1858, decode.acc_seg: 92.0608, aux.loss_ce: 0.0782, aux.acc_seg: 91.6271, loss: 0.2641 +2024-06-16 16:28:07,592 - mmseg - INFO - Iter [45450/80000] lr: 1.728e-05, eta: 14:27:56, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1968, decode.acc_seg: 91.7031, aux.loss_ce: 0.0821, aux.acc_seg: 91.3762, loss: 0.2789 +2024-06-16 16:29:17,970 - mmseg - INFO - Iter [45500/80000] lr: 1.725e-05, eta: 14:26:37, time: 1.408, data_time: 0.055, memory: 70722, decode.loss_ce: 0.1941, decode.acc_seg: 91.7392, aux.loss_ce: 0.0814, aux.acc_seg: 91.3991, loss: 0.2755 +2024-06-16 16:30:26,633 - mmseg - INFO - Iter [45550/80000] lr: 1.723e-05, eta: 14:25:17, time: 1.373, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1815, decode.acc_seg: 92.1897, aux.loss_ce: 0.0756, aux.acc_seg: 91.9074, loss: 0.2571 +2024-06-16 16:31:34,742 - mmseg - INFO - Iter [45600/80000] lr: 1.720e-05, eta: 14:23:56, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1907, decode.acc_seg: 91.8738, aux.loss_ce: 0.0792, aux.acc_seg: 91.5911, loss: 0.2699 +2024-06-16 16:32:42,797 - mmseg - INFO - Iter [45650/80000] lr: 1.718e-05, eta: 14:22:35, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1847, decode.acc_seg: 92.1213, aux.loss_ce: 0.0774, aux.acc_seg: 91.7428, loss: 0.2621 +2024-06-16 16:33:50,896 - mmseg - INFO - Iter [45700/80000] lr: 1.715e-05, eta: 14:21:14, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1874, decode.acc_seg: 92.2016, aux.loss_ce: 0.0798, aux.acc_seg: 91.7286, loss: 0.2672 +2024-06-16 16:34:59,010 - mmseg - INFO - Iter [45750/80000] lr: 1.713e-05, eta: 14:19:54, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1838, decode.acc_seg: 92.2681, aux.loss_ce: 0.0772, aux.acc_seg: 91.8665, loss: 0.2610 +2024-06-16 16:36:07,367 - mmseg - INFO - Iter [45800/80000] lr: 1.710e-05, eta: 14:18:33, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.2011, decode.acc_seg: 91.4329, aux.loss_ce: 0.0843, aux.acc_seg: 91.0619, loss: 0.2854 +2024-06-16 16:37:15,545 - mmseg - INFO - Iter [45850/80000] lr: 1.708e-05, eta: 14:17:13, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1958, decode.acc_seg: 91.8631, aux.loss_ce: 0.0819, aux.acc_seg: 91.4907, loss: 0.2776 +2024-06-16 16:38:24,041 - mmseg - INFO - Iter [45900/80000] lr: 1.705e-05, eta: 14:15:52, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1757, decode.acc_seg: 92.4899, aux.loss_ce: 0.0741, aux.acc_seg: 92.0228, loss: 0.2498 +2024-06-16 16:39:32,259 - mmseg - INFO - Iter [45950/80000] lr: 1.703e-05, eta: 14:14:32, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1874, decode.acc_seg: 92.0310, aux.loss_ce: 0.0786, aux.acc_seg: 91.6242, loss: 0.2660 +2024-06-16 16:40:40,460 - mmseg - INFO - Saving checkpoint at 46000 iterations +2024-06-16 16:42:05,869 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 16:42:05,869 - mmseg - INFO - Iter [46000/80000] lr: 1.700e-05, eta: 14:14:14, time: 3.072, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1790, decode.acc_seg: 92.2685, aux.loss_ce: 0.0759, aux.acc_seg: 91.9314, loss: 0.2549 +2024-06-16 16:43:40,869 - mmseg - INFO - per class results: +2024-06-16 16:43:40,876 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.11 | 90.06 | +| building | 85.56 | 93.68 | +| sky | 94.95 | 97.69 | +| floor | 84.97 | 91.54 | +| tree | 77.37 | 90.19 | +| ceiling | 86.66 | 93.74 | +| road | 86.8 | 93.44 | +| bed | 92.72 | 96.48 | +| windowpane | 65.87 | 81.5 | +| grass | 66.77 | 81.69 | +| cabinet | 65.35 | 75.61 | +| sidewalk | 71.03 | 81.87 | +| person | 85.53 | 94.42 | +| earth | 35.91 | 49.24 | +| door | 61.05 | 75.87 | +| table | 68.52 | 78.99 | +| mountain | 61.37 | 71.45 | +| plant | 54.08 | 66.64 | +| curtain | 77.1 | 89.98 | +| chair | 66.8 | 75.93 | +| car | 87.37 | 93.38 | +| water | 61.0 | 75.88 | +| painting | 77.31 | 90.65 | +| sofa | 81.12 | 89.75 | +| shelf | 44.15 | 56.72 | +| house | 59.4 | 74.8 | +| sea | 66.87 | 83.09 | +| mirror | 77.41 | 83.67 | +| rug | 72.1 | 85.28 | +| field | 29.5 | 50.37 | +| armchair | 60.97 | 77.65 | +| seat | 67.62 | 89.7 | +| fence | 42.54 | 50.61 | +| desk | 61.34 | 78.84 | +| rock | 58.45 | 86.23 | +| wardrobe | 52.35 | 72.91 | +| lamp | 73.98 | 86.9 | +| bathtub | 84.64 | 86.42 | +| railing | 41.31 | 53.31 | +| cushion | 68.43 | 84.78 | +| base | 42.19 | 56.99 | +| box | 38.71 | 51.39 | +| column | 55.2 | 67.27 | +| signboard | 42.47 | 53.99 | +| chest of drawers | 44.83 | 67.04 | +| counter | 40.92 | 46.98 | +| sand | 49.0 | 78.37 | +| sink | 72.82 | 85.12 | +| skyscraper | 46.42 | 58.11 | +| fireplace | 75.21 | 91.35 | +| refrigerator | 84.94 | 94.06 | +| grandstand | 50.4 | 83.3 | +| path | 29.88 | 40.35 | +| stairs | 24.35 | 29.73 | +| runway | 73.25 | 95.92 | +| case | 61.4 | 87.21 | +| pool table | 91.94 | 98.17 | +| pillow | 68.99 | 82.59 | +| screen door | 80.32 | 82.42 | +| stairway | 42.89 | 62.82 | +| river | 8.65 | 18.01 | +| bridge | 62.88 | 69.79 | +| bookcase | 43.4 | 69.2 | +| blind | 42.17 | 48.4 | +| coffee table | 61.75 | 88.62 | +| toilet | 90.49 | 94.03 | +| flower | 43.88 | 51.01 | +| book | 51.95 | 79.79 | +| hill | 6.01 | 9.4 | +| bench | 49.95 | 58.02 | +| countertop | 64.17 | 82.47 | +| stove | 83.05 | 88.3 | +| palm | 56.11 | 77.09 | +| kitchen island | 51.21 | 88.15 | +| computer | 77.83 | 92.17 | +| swivel chair | 43.65 | 83.13 | +| boat | 76.94 | 87.14 | +| bar | 58.67 | 82.54 | +| arcade machine | 77.74 | 81.41 | +| hovel | 24.78 | 27.62 | +| bus | 93.52 | 96.19 | +| towel | 73.96 | 87.14 | +| light | 60.52 | 69.12 | +| truck | 43.21 | 59.84 | +| tower | 30.98 | 45.6 | +| chandelier | 73.63 | 87.35 | +| awning | 41.44 | 56.88 | +| streetlight | 32.2 | 42.92 | +| booth | 55.88 | 67.59 | +| television receiver | 74.62 | 88.19 | +| airplane | 80.08 | 90.94 | +| dirt track | 17.89 | 35.6 | +| apparel | 40.82 | 57.43 | +| pole | 25.96 | 32.51 | +| land | 1.09 | 2.21 | +| bannister | 15.79 | 21.22 | +| escalator | 58.3 | 78.76 | +| ottoman | 49.22 | 66.74 | +| bottle | 42.0 | 69.84 | +| buffet | 47.6 | 55.16 | +| poster | 37.96 | 50.34 | +| stage | 18.63 | 34.52 | +| van | 49.88 | 71.94 | +| ship | 87.99 | 92.62 | +| fountain | 33.03 | 35.09 | +| conveyer belt | 82.98 | 93.71 | +| canopy | 54.48 | 79.81 | +| washer | 82.67 | 88.2 | +| plaything | 25.03 | 39.7 | +| swimming pool | 56.66 | 84.2 | +| stool | 56.03 | 69.02 | +| barrel | 59.78 | 74.34 | +| basket | 42.06 | 59.06 | +| waterfall | 58.07 | 67.27 | +| tent | 92.3 | 98.27 | +| bag | 24.63 | 28.07 | +| minibike | 76.93 | 88.71 | +| cradle | 84.06 | 97.18 | +| oven | 55.68 | 70.63 | +| ball | 51.41 | 56.98 | +| food | 57.7 | 67.71 | +| step | 24.57 | 34.91 | +| tank | 60.5 | 70.4 | +| trade name | 32.29 | 40.62 | +| microwave | 86.69 | 96.25 | +| pot | 60.08 | 70.43 | +| animal | 58.05 | 59.08 | +| bicycle | 60.01 | 73.25 | +| lake | 52.32 | 63.84 | +| dishwasher | 70.11 | 82.67 | +| screen | 56.43 | 89.31 | +| blanket | 22.42 | 24.56 | +| sculpture | 73.46 | 88.61 | +| hood | 61.75 | 75.45 | +| sconce | 51.21 | 58.3 | +| vase | 49.0 | 63.32 | +| traffic light | 36.11 | 59.17 | +| tray | 22.43 | 29.81 | +| ashcan | 47.02 | 62.79 | +| fan | 67.1 | 84.0 | +| pier | 38.49 | 47.56 | +| crt screen | 3.0 | 3.56 | +| plate | 60.42 | 80.68 | +| monitor | 66.77 | 78.14 | +| bulletin board | 56.33 | 63.9 | +| shower | 5.32 | 6.02 | +| radiator | 66.92 | 79.72 | +| glass | 19.88 | 21.46 | +| clock | 42.32 | 55.21 | +| flag | 70.56 | 75.78 | ++---------------------+-------+-------+ +2024-06-16 16:43:40,876 - mmseg - INFO - Summary: +2024-06-16 16:43:40,876 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.95 | 56.65 | 69.39 | ++-------+-------+-------+ +2024-06-16 16:43:40,877 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 16:43:40,877 - mmseg - INFO - Iter(val) [250] aAcc: 0.8595, mIoU: 0.5665, mAcc: 0.6939, IoU.wall: 0.8211, IoU.building: 0.8556, IoU.sky: 0.9495, IoU.floor: 0.8497, IoU.tree: 0.7737, IoU.ceiling: 0.8666, IoU.road: 0.8680, IoU.bed : 0.9272, IoU.windowpane: 0.6587, IoU.grass: 0.6677, IoU.cabinet: 0.6535, IoU.sidewalk: 0.7103, IoU.person: 0.8553, IoU.earth: 0.3591, IoU.door: 0.6105, IoU.table: 0.6852, IoU.mountain: 0.6137, IoU.plant: 0.5408, IoU.curtain: 0.7710, IoU.chair: 0.6680, IoU.car: 0.8737, IoU.water: 0.6100, IoU.painting: 0.7731, IoU.sofa: 0.8112, IoU.shelf: 0.4415, IoU.house: 0.5940, IoU.sea: 0.6687, IoU.mirror: 0.7741, IoU.rug: 0.7210, IoU.field: 0.2950, IoU.armchair: 0.6097, IoU.seat: 0.6762, IoU.fence: 0.4254, IoU.desk: 0.6134, IoU.rock: 0.5845, IoU.wardrobe: 0.5235, IoU.lamp: 0.7398, IoU.bathtub: 0.8464, IoU.railing: 0.4131, IoU.cushion: 0.6843, IoU.base: 0.4219, IoU.box: 0.3871, IoU.column: 0.5520, IoU.signboard: 0.4247, IoU.chest of drawers: 0.4483, IoU.counter: 0.4092, IoU.sand: 0.4900, IoU.sink: 0.7282, IoU.skyscraper: 0.4642, IoU.fireplace: 0.7521, IoU.refrigerator: 0.8494, IoU.grandstand: 0.5040, IoU.path: 0.2988, IoU.stairs: 0.2435, IoU.runway: 0.7325, IoU.case: 0.6140, IoU.pool table: 0.9194, IoU.pillow: 0.6899, IoU.screen door: 0.8032, IoU.stairway: 0.4289, IoU.river: 0.0865, IoU.bridge: 0.6288, IoU.bookcase: 0.4340, IoU.blind: 0.4217, IoU.coffee table: 0.6175, IoU.toilet: 0.9049, IoU.flower: 0.4388, IoU.book: 0.5195, IoU.hill: 0.0601, IoU.bench: 0.4995, IoU.countertop: 0.6417, IoU.stove: 0.8305, IoU.palm: 0.5611, IoU.kitchen island: 0.5121, IoU.computer: 0.7783, IoU.swivel chair: 0.4365, IoU.boat: 0.7694, IoU.bar: 0.5867, IoU.arcade machine: 0.7774, IoU.hovel: 0.2478, IoU.bus: 0.9352, IoU.towel: 0.7396, IoU.light: 0.6052, IoU.truck: 0.4321, IoU.tower: 0.3098, IoU.chandelier: 0.7363, IoU.awning: 0.4144, IoU.streetlight: 0.3220, IoU.booth: 0.5588, IoU.television receiver: 0.7462, IoU.airplane: 0.8008, IoU.dirt track: 0.1789, IoU.apparel: 0.4082, IoU.pole: 0.2596, IoU.land: 0.0109, IoU.bannister: 0.1579, IoU.escalator: 0.5830, IoU.ottoman: 0.4922, IoU.bottle: 0.4200, IoU.buffet: 0.4760, IoU.poster: 0.3796, IoU.stage: 0.1863, IoU.van: 0.4988, IoU.ship: 0.8799, IoU.fountain: 0.3303, IoU.conveyer belt: 0.8298, IoU.canopy: 0.5448, IoU.washer: 0.8267, IoU.plaything: 0.2503, IoU.swimming pool: 0.5666, IoU.stool: 0.5603, IoU.barrel: 0.5978, IoU.basket: 0.4206, IoU.waterfall: 0.5807, IoU.tent: 0.9230, IoU.bag: 0.2463, IoU.minibike: 0.7693, IoU.cradle: 0.8406, IoU.oven: 0.5568, IoU.ball: 0.5141, IoU.food: 0.5770, IoU.step: 0.2457, IoU.tank: 0.6050, IoU.trade name: 0.3229, IoU.microwave: 0.8669, IoU.pot: 0.6008, IoU.animal: 0.5805, IoU.bicycle: 0.6001, IoU.lake: 0.5232, IoU.dishwasher: 0.7011, IoU.screen: 0.5643, IoU.blanket: 0.2242, IoU.sculpture: 0.7346, IoU.hood: 0.6175, IoU.sconce: 0.5121, IoU.vase: 0.4900, IoU.traffic light: 0.3611, IoU.tray: 0.2243, IoU.ashcan: 0.4702, IoU.fan: 0.6710, IoU.pier: 0.3849, IoU.crt screen: 0.0300, IoU.plate: 0.6042, IoU.monitor: 0.6677, IoU.bulletin board: 0.5633, IoU.shower: 0.0532, IoU.radiator: 0.6692, IoU.glass: 0.1988, IoU.clock: 0.4232, IoU.flag: 0.7056, Acc.wall: 0.9006, Acc.building: 0.9368, Acc.sky: 0.9769, Acc.floor: 0.9154, Acc.tree: 0.9019, Acc.ceiling: 0.9374, Acc.road: 0.9344, Acc.bed : 0.9648, Acc.windowpane: 0.8150, Acc.grass: 0.8169, Acc.cabinet: 0.7561, Acc.sidewalk: 0.8187, Acc.person: 0.9442, Acc.earth: 0.4924, Acc.door: 0.7587, Acc.table: 0.7899, Acc.mountain: 0.7145, Acc.plant: 0.6664, Acc.curtain: 0.8998, Acc.chair: 0.7593, Acc.car: 0.9338, Acc.water: 0.7588, Acc.painting: 0.9065, Acc.sofa: 0.8975, Acc.shelf: 0.5672, Acc.house: 0.7480, Acc.sea: 0.8309, Acc.mirror: 0.8367, Acc.rug: 0.8528, Acc.field: 0.5037, Acc.armchair: 0.7765, Acc.seat: 0.8970, Acc.fence: 0.5061, Acc.desk: 0.7884, Acc.rock: 0.8623, Acc.wardrobe: 0.7291, Acc.lamp: 0.8690, Acc.bathtub: 0.8642, Acc.railing: 0.5331, Acc.cushion: 0.8478, Acc.base: 0.5699, Acc.box: 0.5139, Acc.column: 0.6727, Acc.signboard: 0.5399, Acc.chest of drawers: 0.6704, Acc.counter: 0.4698, Acc.sand: 0.7837, Acc.sink: 0.8512, Acc.skyscraper: 0.5811, Acc.fireplace: 0.9135, Acc.refrigerator: 0.9406, Acc.grandstand: 0.8330, Acc.path: 0.4035, Acc.stairs: 0.2973, Acc.runway: 0.9592, Acc.case: 0.8721, Acc.pool table: 0.9817, Acc.pillow: 0.8259, Acc.screen door: 0.8242, Acc.stairway: 0.6282, Acc.river: 0.1801, Acc.bridge: 0.6979, Acc.bookcase: 0.6920, Acc.blind: 0.4840, Acc.coffee table: 0.8862, Acc.toilet: 0.9403, Acc.flower: 0.5101, Acc.book: 0.7979, Acc.hill: 0.0940, Acc.bench: 0.5802, Acc.countertop: 0.8247, Acc.stove: 0.8830, Acc.palm: 0.7709, Acc.kitchen island: 0.8815, Acc.computer: 0.9217, Acc.swivel chair: 0.8313, Acc.boat: 0.8714, Acc.bar: 0.8254, Acc.arcade machine: 0.8141, Acc.hovel: 0.2762, Acc.bus: 0.9619, Acc.towel: 0.8714, Acc.light: 0.6912, Acc.truck: 0.5984, Acc.tower: 0.4560, Acc.chandelier: 0.8735, Acc.awning: 0.5688, Acc.streetlight: 0.4292, Acc.booth: 0.6759, Acc.television receiver: 0.8819, Acc.airplane: 0.9094, Acc.dirt track: 0.3560, Acc.apparel: 0.5743, Acc.pole: 0.3251, Acc.land: 0.0221, Acc.bannister: 0.2122, Acc.escalator: 0.7876, Acc.ottoman: 0.6674, Acc.bottle: 0.6984, Acc.buffet: 0.5516, Acc.poster: 0.5034, Acc.stage: 0.3452, Acc.van: 0.7194, Acc.ship: 0.9262, Acc.fountain: 0.3509, Acc.conveyer belt: 0.9371, Acc.canopy: 0.7981, Acc.washer: 0.8820, Acc.plaything: 0.3970, Acc.swimming pool: 0.8420, Acc.stool: 0.6902, Acc.barrel: 0.7434, Acc.basket: 0.5906, Acc.waterfall: 0.6727, Acc.tent: 0.9827, Acc.bag: 0.2807, Acc.minibike: 0.8871, Acc.cradle: 0.9718, Acc.oven: 0.7063, Acc.ball: 0.5698, Acc.food: 0.6771, Acc.step: 0.3491, Acc.tank: 0.7040, Acc.trade name: 0.4062, Acc.microwave: 0.9625, Acc.pot: 0.7043, Acc.animal: 0.5908, Acc.bicycle: 0.7325, Acc.lake: 0.6384, Acc.dishwasher: 0.8267, Acc.screen: 0.8931, Acc.blanket: 0.2456, Acc.sculpture: 0.8861, Acc.hood: 0.7545, Acc.sconce: 0.5830, Acc.vase: 0.6332, Acc.traffic light: 0.5917, Acc.tray: 0.2981, Acc.ashcan: 0.6279, Acc.fan: 0.8400, Acc.pier: 0.4756, Acc.crt screen: 0.0356, Acc.plate: 0.8068, Acc.monitor: 0.7814, Acc.bulletin board: 0.6390, Acc.shower: 0.0602, Acc.radiator: 0.7972, Acc.glass: 0.2146, Acc.clock: 0.5521, Acc.flag: 0.7578 +2024-06-16 16:44:49,632 - mmseg - INFO - Iter [46050/80000] lr: 1.698e-05, eta: 14:14:04, time: 3.275, data_time: 1.916, memory: 70722, decode.loss_ce: 0.1870, decode.acc_seg: 92.0313, aux.loss_ce: 0.0782, aux.acc_seg: 91.7011, loss: 0.2652 +2024-06-16 16:45:57,699 - mmseg - INFO - Iter [46100/80000] lr: 1.695e-05, eta: 14:12:43, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1795, decode.acc_seg: 92.2534, aux.loss_ce: 0.0744, aux.acc_seg: 91.9962, loss: 0.2539 +2024-06-16 16:47:06,056 - mmseg - INFO - Iter [46150/80000] lr: 1.693e-05, eta: 14:11:22, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1841, decode.acc_seg: 92.1984, aux.loss_ce: 0.0769, aux.acc_seg: 91.7793, loss: 0.2611 +2024-06-16 16:48:14,385 - mmseg - INFO - Iter [46200/80000] lr: 1.690e-05, eta: 14:10:02, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1877, decode.acc_seg: 92.1774, aux.loss_ce: 0.0782, aux.acc_seg: 91.8032, loss: 0.2660 +2024-06-16 16:49:22,563 - mmseg - INFO - Iter [46250/80000] lr: 1.688e-05, eta: 14:08:41, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1843, decode.acc_seg: 92.0674, aux.loss_ce: 0.0771, aux.acc_seg: 91.7046, loss: 0.2614 +2024-06-16 16:50:30,701 - mmseg - INFO - Iter [46300/80000] lr: 1.685e-05, eta: 14:07:20, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1816, decode.acc_seg: 92.2668, aux.loss_ce: 0.0765, aux.acc_seg: 91.8771, loss: 0.2581 +2024-06-16 16:51:38,994 - mmseg - INFO - Iter [46350/80000] lr: 1.683e-05, eta: 14:06:00, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1939, decode.acc_seg: 91.8538, aux.loss_ce: 0.0819, aux.acc_seg: 91.4699, loss: 0.2757 +2024-06-16 16:52:47,342 - mmseg - INFO - Iter [46400/80000] lr: 1.680e-05, eta: 14:04:39, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1851, decode.acc_seg: 92.0882, aux.loss_ce: 0.0770, aux.acc_seg: 91.8071, loss: 0.2621 +2024-06-16 16:53:55,489 - mmseg - INFO - Iter [46450/80000] lr: 1.678e-05, eta: 14:03:18, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1891, decode.acc_seg: 91.9611, aux.loss_ce: 0.0791, aux.acc_seg: 91.5841, loss: 0.2682 +2024-06-16 16:55:03,583 - mmseg - INFO - Iter [46500/80000] lr: 1.675e-05, eta: 14:01:58, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1861, decode.acc_seg: 91.9801, aux.loss_ce: 0.0779, aux.acc_seg: 91.6003, loss: 0.2639 +2024-06-16 16:56:11,720 - mmseg - INFO - Iter [46550/80000] lr: 1.673e-05, eta: 14:00:37, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1868, decode.acc_seg: 91.9660, aux.loss_ce: 0.0782, aux.acc_seg: 91.6490, loss: 0.2649 +2024-06-16 16:57:20,144 - mmseg - INFO - Iter [46600/80000] lr: 1.670e-05, eta: 13:59:17, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1844, decode.acc_seg: 92.0725, aux.loss_ce: 0.0772, aux.acc_seg: 91.6714, loss: 0.2616 +2024-06-16 16:58:28,330 - mmseg - INFO - Iter [46650/80000] lr: 1.668e-05, eta: 13:57:56, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1779, decode.acc_seg: 92.3910, aux.loss_ce: 0.0745, aux.acc_seg: 92.0824, loss: 0.2524 +2024-06-16 16:59:36,622 - mmseg - INFO - Iter [46700/80000] lr: 1.665e-05, eta: 13:56:36, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1854, decode.acc_seg: 91.7629, aux.loss_ce: 0.0773, aux.acc_seg: 91.4620, loss: 0.2627 +2024-06-16 17:00:47,627 - mmseg - INFO - Iter [46750/80000] lr: 1.663e-05, eta: 13:55:17, time: 1.420, data_time: 0.063, memory: 70722, decode.loss_ce: 0.1822, decode.acc_seg: 92.0501, aux.loss_ce: 0.0760, aux.acc_seg: 91.8000, loss: 0.2582 +2024-06-16 17:01:55,985 - mmseg - INFO - Iter [46800/80000] lr: 1.660e-05, eta: 13:53:57, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1744, decode.acc_seg: 92.3118, aux.loss_ce: 0.0727, aux.acc_seg: 92.0183, loss: 0.2471 +2024-06-16 17:03:04,162 - mmseg - INFO - Iter [46850/80000] lr: 1.658e-05, eta: 13:52:37, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1833, decode.acc_seg: 92.4962, aux.loss_ce: 0.0768, aux.acc_seg: 92.1404, loss: 0.2601 +2024-06-16 17:04:12,329 - mmseg - INFO - Iter [46900/80000] lr: 1.655e-05, eta: 13:51:16, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1871, decode.acc_seg: 92.1131, aux.loss_ce: 0.0785, aux.acc_seg: 91.7571, loss: 0.2656 +2024-06-16 17:05:20,489 - mmseg - INFO - Iter [46950/80000] lr: 1.653e-05, eta: 13:49:56, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1841, decode.acc_seg: 92.3047, aux.loss_ce: 0.0774, aux.acc_seg: 91.9494, loss: 0.2615 +2024-06-16 17:06:28,817 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 17:06:28,817 - mmseg - INFO - Iter [47000/80000] lr: 1.650e-05, eta: 13:48:36, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1765, decode.acc_seg: 92.6227, aux.loss_ce: 0.0743, aux.acc_seg: 92.2352, loss: 0.2507 +2024-06-16 17:08:04,454 - mmseg - INFO - per class results: +2024-06-16 17:08:04,460 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.8 | 88.95 | +| building | 85.2 | 93.25 | +| sky | 94.85 | 97.77 | +| floor | 85.29 | 91.94 | +| tree | 76.79 | 89.74 | +| ceiling | 86.35 | 93.84 | +| road | 86.12 | 93.41 | +| bed | 93.12 | 96.9 | +| windowpane | 66.11 | 83.94 | +| grass | 69.08 | 83.67 | +| cabinet | 66.47 | 77.94 | +| sidewalk | 72.17 | 83.09 | +| person | 85.53 | 95.0 | +| earth | 36.22 | 47.41 | +| door | 60.87 | 73.92 | +| table | 69.0 | 80.34 | +| mountain | 61.3 | 72.0 | +| plant | 50.82 | 61.58 | +| curtain | 77.31 | 87.86 | +| chair | 68.48 | 78.4 | +| car | 87.77 | 93.69 | +| water | 64.91 | 79.23 | +| painting | 74.0 | 92.83 | +| sofa | 81.05 | 89.66 | +| shelf | 47.02 | 62.68 | +| house | 56.74 | 78.3 | +| sea | 71.31 | 79.88 | +| mirror | 78.45 | 84.59 | +| rug | 72.91 | 82.19 | +| field | 32.34 | 55.1 | +| armchair | 60.68 | 77.46 | +| seat | 67.12 | 88.96 | +| fence | 48.25 | 60.7 | +| desk | 61.21 | 80.45 | +| rock | 59.68 | 82.22 | +| wardrobe | 51.91 | 71.35 | +| lamp | 73.86 | 83.46 | +| bathtub | 84.44 | 86.37 | +| railing | 42.74 | 62.29 | +| cushion | 67.54 | 85.86 | +| base | 40.69 | 58.56 | +| box | 36.13 | 48.07 | +| column | 52.85 | 71.15 | +| signboard | 38.96 | 57.46 | +| chest of drawers | 47.77 | 61.81 | +| counter | 45.09 | 55.68 | +| sand | 53.2 | 78.14 | +| sink | 76.97 | 84.34 | +| skyscraper | 47.15 | 60.24 | +| fireplace | 73.04 | 92.09 | +| refrigerator | 84.81 | 92.42 | +| grandstand | 51.42 | 83.52 | +| path | 29.46 | 40.85 | +| stairs | 24.05 | 29.51 | +| runway | 68.18 | 89.74 | +| case | 56.89 | 72.33 | +| pool table | 93.9 | 98.42 | +| pillow | 65.43 | 74.84 | +| screen door | 84.85 | 87.89 | +| stairway | 41.24 | 62.13 | +| river | 8.49 | 18.43 | +| bridge | 43.98 | 48.71 | +| bookcase | 45.18 | 59.94 | +| blind | 44.25 | 48.36 | +| coffee table | 61.6 | 89.46 | +| toilet | 90.7 | 94.52 | +| flower | 41.1 | 62.98 | +| book | 52.03 | 76.16 | +| hill | 7.52 | 17.04 | +| bench | 48.66 | 57.42 | +| countertop | 61.07 | 75.97 | +| stove | 84.38 | 91.56 | +| palm | 54.64 | 83.09 | +| kitchen island | 53.69 | 84.58 | +| computer | 77.07 | 91.7 | +| swivel chair | 50.63 | 78.36 | +| boat | 73.7 | 88.85 | +| bar | 66.46 | 87.66 | +| arcade machine | 77.3 | 81.28 | +| hovel | 19.24 | 20.14 | +| bus | 92.21 | 96.92 | +| towel | 75.54 | 84.23 | +| light | 61.76 | 72.72 | +| truck | 42.53 | 55.07 | +| tower | 25.45 | 35.48 | +| chandelier | 72.19 | 87.19 | +| awning | 50.06 | 65.77 | +| streetlight | 31.33 | 38.84 | +| booth | 42.93 | 81.78 | +| television receiver | 75.21 | 84.62 | +| airplane | 80.74 | 85.37 | +| dirt track | 11.01 | 21.75 | +| apparel | 47.63 | 62.23 | +| pole | 28.57 | 37.27 | +| land | 5.31 | 11.19 | +| bannister | 17.88 | 23.98 | +| escalator | 57.7 | 72.75 | +| ottoman | 52.47 | 71.96 | +| bottle | 40.23 | 66.72 | +| buffet | 46.18 | 56.24 | +| poster | 34.43 | 46.96 | +| stage | 17.12 | 35.16 | +| van | 51.5 | 71.37 | +| ship | 89.5 | 95.33 | +| fountain | 32.14 | 33.4 | +| conveyer belt | 76.19 | 95.01 | +| canopy | 53.6 | 78.12 | +| washer | 81.02 | 86.53 | +| plaything | 25.06 | 41.32 | +| swimming pool | 54.03 | 76.9 | +| stool | 53.08 | 69.03 | +| barrel | 62.72 | 74.29 | +| basket | 43.05 | 58.77 | +| waterfall | 69.55 | 95.89 | +| tent | 92.24 | 97.86 | +| bag | 22.16 | 26.13 | +| minibike | 75.91 | 89.71 | +| cradle | 84.7 | 96.87 | +| oven | 55.87 | 65.99 | +| ball | 49.96 | 69.15 | +| food | 63.68 | 83.89 | +| step | 17.51 | 22.05 | +| tank | 61.61 | 71.24 | +| trade name | 20.25 | 23.66 | +| microwave | 85.94 | 95.85 | +| pot | 56.21 | 69.38 | +| animal | 60.25 | 62.23 | +| bicycle | 60.27 | 76.85 | +| lake | 55.48 | 63.8 | +| dishwasher | 72.18 | 77.87 | +| screen | 49.13 | 78.74 | +| blanket | 28.71 | 32.54 | +| sculpture | 72.44 | 87.38 | +| hood | 61.23 | 71.91 | +| sconce | 55.96 | 66.41 | +| vase | 47.6 | 59.41 | +| traffic light | 34.29 | 63.85 | +| tray | 25.81 | 35.95 | +| ashcan | 46.35 | 62.93 | +| fan | 68.0 | 83.58 | +| pier | 39.71 | 49.05 | +| crt screen | 2.27 | 3.49 | +| plate | 59.81 | 77.7 | +| monitor | 65.82 | 77.47 | +| bulletin board | 47.68 | 68.88 | +| shower | 5.31 | 5.4 | +| radiator | 66.76 | 75.66 | +| glass | 18.7 | 19.79 | +| clock | 42.73 | 50.85 | +| flag | 69.94 | 78.59 | ++---------------------+-------+-------+ +2024-06-16 17:08:04,460 - mmseg - INFO - Summary: +2024-06-16 17:08:04,461 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.89 | 56.49 | 69.44 | ++-------+-------+-------+ +2024-06-16 17:08:04,461 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 17:08:04,461 - mmseg - INFO - Iter(val) [250] aAcc: 0.8589, mIoU: 0.5649, mAcc: 0.6944, IoU.wall: 0.8180, IoU.building: 0.8520, IoU.sky: 0.9485, IoU.floor: 0.8529, IoU.tree: 0.7679, IoU.ceiling: 0.8635, IoU.road: 0.8612, IoU.bed : 0.9312, IoU.windowpane: 0.6611, IoU.grass: 0.6908, IoU.cabinet: 0.6647, IoU.sidewalk: 0.7217, IoU.person: 0.8553, IoU.earth: 0.3622, IoU.door: 0.6087, IoU.table: 0.6900, IoU.mountain: 0.6130, IoU.plant: 0.5082, IoU.curtain: 0.7731, IoU.chair: 0.6848, IoU.car: 0.8777, IoU.water: 0.6491, IoU.painting: 0.7400, IoU.sofa: 0.8105, IoU.shelf: 0.4702, IoU.house: 0.5674, IoU.sea: 0.7131, IoU.mirror: 0.7845, IoU.rug: 0.7291, IoU.field: 0.3234, IoU.armchair: 0.6068, IoU.seat: 0.6712, IoU.fence: 0.4825, IoU.desk: 0.6121, IoU.rock: 0.5968, IoU.wardrobe: 0.5191, IoU.lamp: 0.7386, IoU.bathtub: 0.8444, IoU.railing: 0.4274, IoU.cushion: 0.6754, IoU.base: 0.4069, IoU.box: 0.3613, IoU.column: 0.5285, IoU.signboard: 0.3896, IoU.chest of drawers: 0.4777, IoU.counter: 0.4509, IoU.sand: 0.5320, IoU.sink: 0.7697, IoU.skyscraper: 0.4715, IoU.fireplace: 0.7304, IoU.refrigerator: 0.8481, IoU.grandstand: 0.5142, IoU.path: 0.2946, IoU.stairs: 0.2405, IoU.runway: 0.6818, IoU.case: 0.5689, IoU.pool table: 0.9390, IoU.pillow: 0.6543, IoU.screen door: 0.8485, IoU.stairway: 0.4124, IoU.river: 0.0849, IoU.bridge: 0.4398, IoU.bookcase: 0.4518, IoU.blind: 0.4425, IoU.coffee table: 0.6160, IoU.toilet: 0.9070, IoU.flower: 0.4110, IoU.book: 0.5203, IoU.hill: 0.0752, IoU.bench: 0.4866, IoU.countertop: 0.6107, IoU.stove: 0.8438, IoU.palm: 0.5464, IoU.kitchen island: 0.5369, IoU.computer: 0.7707, IoU.swivel chair: 0.5063, IoU.boat: 0.7370, IoU.bar: 0.6646, IoU.arcade machine: 0.7730, IoU.hovel: 0.1924, IoU.bus: 0.9221, IoU.towel: 0.7554, IoU.light: 0.6176, IoU.truck: 0.4253, IoU.tower: 0.2545, IoU.chandelier: 0.7219, IoU.awning: 0.5006, IoU.streetlight: 0.3133, IoU.booth: 0.4293, IoU.television receiver: 0.7521, IoU.airplane: 0.8074, IoU.dirt track: 0.1101, IoU.apparel: 0.4763, IoU.pole: 0.2857, IoU.land: 0.0531, IoU.bannister: 0.1788, IoU.escalator: 0.5770, IoU.ottoman: 0.5247, IoU.bottle: 0.4023, IoU.buffet: 0.4618, IoU.poster: 0.3443, IoU.stage: 0.1712, IoU.van: 0.5150, IoU.ship: 0.8950, IoU.fountain: 0.3214, IoU.conveyer belt: 0.7619, IoU.canopy: 0.5360, IoU.washer: 0.8102, IoU.plaything: 0.2506, IoU.swimming pool: 0.5403, IoU.stool: 0.5308, IoU.barrel: 0.6272, IoU.basket: 0.4305, IoU.waterfall: 0.6955, IoU.tent: 0.9224, IoU.bag: 0.2216, IoU.minibike: 0.7591, IoU.cradle: 0.8470, IoU.oven: 0.5587, IoU.ball: 0.4996, IoU.food: 0.6368, IoU.step: 0.1751, IoU.tank: 0.6161, IoU.trade name: 0.2025, IoU.microwave: 0.8594, IoU.pot: 0.5621, IoU.animal: 0.6025, IoU.bicycle: 0.6027, IoU.lake: 0.5548, IoU.dishwasher: 0.7218, IoU.screen: 0.4913, IoU.blanket: 0.2871, IoU.sculpture: 0.7244, IoU.hood: 0.6123, IoU.sconce: 0.5596, IoU.vase: 0.4760, IoU.traffic light: 0.3429, IoU.tray: 0.2581, IoU.ashcan: 0.4635, IoU.fan: 0.6800, IoU.pier: 0.3971, IoU.crt screen: 0.0227, IoU.plate: 0.5981, IoU.monitor: 0.6582, IoU.bulletin board: 0.4768, IoU.shower: 0.0531, IoU.radiator: 0.6676, IoU.glass: 0.1870, IoU.clock: 0.4273, IoU.flag: 0.6994, Acc.wall: 0.8895, Acc.building: 0.9325, Acc.sky: 0.9777, Acc.floor: 0.9194, Acc.tree: 0.8974, Acc.ceiling: 0.9384, Acc.road: 0.9341, Acc.bed : 0.9690, Acc.windowpane: 0.8394, Acc.grass: 0.8367, Acc.cabinet: 0.7794, Acc.sidewalk: 0.8309, Acc.person: 0.9500, Acc.earth: 0.4741, Acc.door: 0.7392, Acc.table: 0.8034, Acc.mountain: 0.7200, Acc.plant: 0.6158, Acc.curtain: 0.8786, Acc.chair: 0.7840, Acc.car: 0.9369, Acc.water: 0.7923, Acc.painting: 0.9283, Acc.sofa: 0.8966, Acc.shelf: 0.6268, Acc.house: 0.7830, Acc.sea: 0.7988, Acc.mirror: 0.8459, Acc.rug: 0.8219, Acc.field: 0.5510, Acc.armchair: 0.7746, Acc.seat: 0.8896, Acc.fence: 0.6070, Acc.desk: 0.8045, Acc.rock: 0.8222, Acc.wardrobe: 0.7135, Acc.lamp: 0.8346, Acc.bathtub: 0.8637, Acc.railing: 0.6229, Acc.cushion: 0.8586, Acc.base: 0.5856, Acc.box: 0.4807, Acc.column: 0.7115, Acc.signboard: 0.5746, Acc.chest of drawers: 0.6181, Acc.counter: 0.5568, Acc.sand: 0.7814, Acc.sink: 0.8434, Acc.skyscraper: 0.6024, Acc.fireplace: 0.9209, Acc.refrigerator: 0.9242, Acc.grandstand: 0.8352, Acc.path: 0.4085, Acc.stairs: 0.2951, Acc.runway: 0.8974, Acc.case: 0.7233, Acc.pool table: 0.9842, Acc.pillow: 0.7484, Acc.screen door: 0.8789, Acc.stairway: 0.6213, Acc.river: 0.1843, Acc.bridge: 0.4871, Acc.bookcase: 0.5994, Acc.blind: 0.4836, Acc.coffee table: 0.8946, Acc.toilet: 0.9452, Acc.flower: 0.6298, Acc.book: 0.7616, Acc.hill: 0.1704, Acc.bench: 0.5742, Acc.countertop: 0.7597, Acc.stove: 0.9156, Acc.palm: 0.8309, Acc.kitchen island: 0.8458, Acc.computer: 0.9170, Acc.swivel chair: 0.7836, Acc.boat: 0.8885, Acc.bar: 0.8766, Acc.arcade machine: 0.8128, Acc.hovel: 0.2014, Acc.bus: 0.9692, Acc.towel: 0.8423, Acc.light: 0.7272, Acc.truck: 0.5507, Acc.tower: 0.3548, Acc.chandelier: 0.8719, Acc.awning: 0.6577, Acc.streetlight: 0.3884, Acc.booth: 0.8178, Acc.television receiver: 0.8462, Acc.airplane: 0.8537, Acc.dirt track: 0.2175, Acc.apparel: 0.6223, Acc.pole: 0.3727, Acc.land: 0.1119, Acc.bannister: 0.2398, Acc.escalator: 0.7275, Acc.ottoman: 0.7196, Acc.bottle: 0.6672, Acc.buffet: 0.5624, Acc.poster: 0.4696, Acc.stage: 0.3516, Acc.van: 0.7137, Acc.ship: 0.9533, Acc.fountain: 0.3340, Acc.conveyer belt: 0.9501, Acc.canopy: 0.7812, Acc.washer: 0.8653, Acc.plaything: 0.4132, Acc.swimming pool: 0.7690, Acc.stool: 0.6903, Acc.barrel: 0.7429, Acc.basket: 0.5877, Acc.waterfall: 0.9589, Acc.tent: 0.9786, Acc.bag: 0.2613, Acc.minibike: 0.8971, Acc.cradle: 0.9687, Acc.oven: 0.6599, Acc.ball: 0.6915, Acc.food: 0.8389, Acc.step: 0.2205, Acc.tank: 0.7124, Acc.trade name: 0.2366, Acc.microwave: 0.9585, Acc.pot: 0.6938, Acc.animal: 0.6223, Acc.bicycle: 0.7685, Acc.lake: 0.6380, Acc.dishwasher: 0.7787, Acc.screen: 0.7874, Acc.blanket: 0.3254, Acc.sculpture: 0.8738, Acc.hood: 0.7191, Acc.sconce: 0.6641, Acc.vase: 0.5941, Acc.traffic light: 0.6385, Acc.tray: 0.3595, Acc.ashcan: 0.6293, Acc.fan: 0.8358, Acc.pier: 0.4905, Acc.crt screen: 0.0349, Acc.plate: 0.7770, Acc.monitor: 0.7747, Acc.bulletin board: 0.6888, Acc.shower: 0.0540, Acc.radiator: 0.7566, Acc.glass: 0.1979, Acc.clock: 0.5085, Acc.flag: 0.7859 +2024-06-16 17:09:13,137 - mmseg - INFO - Iter [47050/80000] lr: 1.648e-05, eta: 13:48:22, time: 3.286, data_time: 1.929, memory: 70722, decode.loss_ce: 0.1834, decode.acc_seg: 92.2122, aux.loss_ce: 0.0768, aux.acc_seg: 91.8547, loss: 0.2602 +2024-06-16 17:10:21,382 - mmseg - INFO - Iter [47100/80000] lr: 1.645e-05, eta: 13:47:02, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1758, decode.acc_seg: 92.5550, aux.loss_ce: 0.0737, aux.acc_seg: 92.2133, loss: 0.2495 +2024-06-16 17:11:29,607 - mmseg - INFO - Iter [47150/80000] lr: 1.643e-05, eta: 13:45:42, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1848, decode.acc_seg: 92.0038, aux.loss_ce: 0.0775, aux.acc_seg: 91.6396, loss: 0.2623 +2024-06-16 17:12:37,895 - mmseg - INFO - Iter [47200/80000] lr: 1.640e-05, eta: 13:44:21, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1875, decode.acc_seg: 92.0428, aux.loss_ce: 0.0786, aux.acc_seg: 91.6291, loss: 0.2661 +2024-06-16 17:13:46,112 - mmseg - INFO - Iter [47250/80000] lr: 1.638e-05, eta: 13:43:01, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1807, decode.acc_seg: 92.1633, aux.loss_ce: 0.0756, aux.acc_seg: 91.8395, loss: 0.2563 +2024-06-16 17:14:54,325 - mmseg - INFO - Iter [47300/80000] lr: 1.635e-05, eta: 13:41:41, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1896, decode.acc_seg: 92.0968, aux.loss_ce: 0.0791, aux.acc_seg: 91.7525, loss: 0.2688 +2024-06-16 17:16:02,621 - mmseg - INFO - Iter [47350/80000] lr: 1.633e-05, eta: 13:40:20, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1779, decode.acc_seg: 92.4605, aux.loss_ce: 0.0745, aux.acc_seg: 92.1342, loss: 0.2524 +2024-06-16 17:17:10,943 - mmseg - INFO - Iter [47400/80000] lr: 1.630e-05, eta: 13:39:00, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1732, decode.acc_seg: 92.5563, aux.loss_ce: 0.0730, aux.acc_seg: 92.2123, loss: 0.2462 +2024-06-16 17:18:19,072 - mmseg - INFO - Iter [47450/80000] lr: 1.628e-05, eta: 13:37:40, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1744, decode.acc_seg: 92.7115, aux.loss_ce: 0.0732, aux.acc_seg: 92.3802, loss: 0.2476 +2024-06-16 17:19:27,236 - mmseg - INFO - Iter [47500/80000] lr: 1.625e-05, eta: 13:36:19, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1845, decode.acc_seg: 91.9835, aux.loss_ce: 0.0772, aux.acc_seg: 91.6387, loss: 0.2616 +2024-06-16 17:20:35,501 - mmseg - INFO - Iter [47550/80000] lr: 1.623e-05, eta: 13:34:59, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1830, decode.acc_seg: 92.1099, aux.loss_ce: 0.0771, aux.acc_seg: 91.6952, loss: 0.2601 +2024-06-16 17:21:43,793 - mmseg - INFO - Iter [47600/80000] lr: 1.620e-05, eta: 13:33:39, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1837, decode.acc_seg: 92.1407, aux.loss_ce: 0.0777, aux.acc_seg: 91.7297, loss: 0.2613 +2024-06-16 17:22:52,392 - mmseg - INFO - Iter [47650/80000] lr: 1.618e-05, eta: 13:32:19, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1848, decode.acc_seg: 92.3584, aux.loss_ce: 0.0768, aux.acc_seg: 91.9899, loss: 0.2615 +2024-06-16 17:24:00,547 - mmseg - INFO - Iter [47700/80000] lr: 1.615e-05, eta: 13:30:59, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1889, decode.acc_seg: 91.9440, aux.loss_ce: 0.0782, aux.acc_seg: 91.6055, loss: 0.2671 +2024-06-16 17:25:08,863 - mmseg - INFO - Iter [47750/80000] lr: 1.613e-05, eta: 13:29:39, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1760, decode.acc_seg: 92.1506, aux.loss_ce: 0.0734, aux.acc_seg: 91.8707, loss: 0.2495 +2024-06-16 17:26:17,030 - mmseg - INFO - Iter [47800/80000] lr: 1.610e-05, eta: 13:28:19, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1912, decode.acc_seg: 92.0063, aux.loss_ce: 0.0794, aux.acc_seg: 91.7046, loss: 0.2706 +2024-06-16 17:27:25,311 - mmseg - INFO - Iter [47850/80000] lr: 1.608e-05, eta: 13:26:59, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1899, decode.acc_seg: 91.9219, aux.loss_ce: 0.0788, aux.acc_seg: 91.5898, loss: 0.2688 +2024-06-16 17:28:33,587 - mmseg - INFO - Iter [47900/80000] lr: 1.605e-05, eta: 13:25:39, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1822, decode.acc_seg: 92.1061, aux.loss_ce: 0.0761, aux.acc_seg: 91.7846, loss: 0.2584 +2024-06-16 17:29:42,026 - mmseg - INFO - Iter [47950/80000] lr: 1.603e-05, eta: 13:24:19, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1788, decode.acc_seg: 92.2038, aux.loss_ce: 0.0754, aux.acc_seg: 91.8471, loss: 0.2542 +2024-06-16 17:30:53,181 - mmseg - INFO - Saving checkpoint at 48000 iterations +2024-06-16 17:32:19,256 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 17:32:19,257 - mmseg - INFO - Iter [48000/80000] lr: 1.600e-05, eta: 13:23:58, time: 3.145, data_time: 0.067, memory: 70722, decode.loss_ce: 0.1855, decode.acc_seg: 92.0795, aux.loss_ce: 0.0783, aux.acc_seg: 91.6302, loss: 0.2638 +2024-06-16 17:33:54,466 - mmseg - INFO - per class results: +2024-06-16 17:33:54,472 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.82 | 89.45 | +| building | 84.67 | 92.78 | +| sky | 94.93 | 97.46 | +| floor | 85.71 | 91.97 | +| tree | 78.34 | 88.81 | +| ceiling | 86.57 | 93.77 | +| road | 86.24 | 92.83 | +| bed | 92.99 | 96.47 | +| windowpane | 66.28 | 78.88 | +| grass | 67.91 | 83.48 | +| cabinet | 68.82 | 80.62 | +| sidewalk | 70.3 | 81.62 | +| person | 85.34 | 93.87 | +| earth | 36.49 | 49.11 | +| door | 58.31 | 77.94 | +| table | 68.78 | 83.24 | +| mountain | 61.59 | 72.06 | +| plant | 55.29 | 69.43 | +| curtain | 78.09 | 90.24 | +| chair | 68.21 | 80.96 | +| car | 87.52 | 93.0 | +| water | 64.61 | 77.95 | +| painting | 76.58 | 89.64 | +| sofa | 82.17 | 90.14 | +| shelf | 45.09 | 58.39 | +| house | 56.3 | 82.02 | +| sea | 72.32 | 85.17 | +| mirror | 76.52 | 82.46 | +| rug | 74.98 | 83.67 | +| field | 29.77 | 51.76 | +| armchair | 60.46 | 76.06 | +| seat | 67.31 | 88.96 | +| fence | 51.79 | 65.48 | +| desk | 60.5 | 76.65 | +| rock | 59.78 | 83.37 | +| wardrobe | 55.79 | 66.86 | +| lamp | 74.54 | 85.75 | +| bathtub | 84.84 | 86.43 | +| railing | 42.79 | 61.1 | +| cushion | 70.33 | 82.19 | +| base | 40.44 | 60.02 | +| box | 36.24 | 47.52 | +| column | 52.74 | 69.27 | +| signboard | 40.84 | 54.4 | +| chest of drawers | 48.31 | 60.38 | +| counter | 44.32 | 53.49 | +| sand | 57.44 | 87.09 | +| sink | 76.18 | 84.88 | +| skyscraper | 48.38 | 63.51 | +| fireplace | 68.62 | 94.62 | +| refrigerator | 85.33 | 92.36 | +| grandstand | 55.39 | 84.34 | +| path | 31.02 | 44.69 | +| stairs | 26.13 | 35.4 | +| runway | 72.22 | 96.0 | +| case | 55.8 | 79.02 | +| pool table | 94.49 | 97.68 | +| pillow | 69.43 | 80.11 | +| screen door | 83.51 | 86.83 | +| stairway | 38.79 | 52.06 | +| river | 12.32 | 25.87 | +| bridge | 64.27 | 75.57 | +| bookcase | 43.64 | 62.61 | +| blind | 45.4 | 52.07 | +| coffee table | 62.4 | 91.79 | +| toilet | 89.75 | 93.28 | +| flower | 41.65 | 49.15 | +| book | 54.4 | 77.09 | +| hill | 8.22 | 13.86 | +| bench | 53.09 | 64.91 | +| countertop | 64.63 | 83.05 | +| stove | 85.33 | 91.49 | +| palm | 55.22 | 84.9 | +| kitchen island | 50.12 | 73.34 | +| computer | 79.17 | 91.32 | +| swivel chair | 51.65 | 76.88 | +| boat | 70.42 | 91.08 | +| bar | 66.74 | 84.61 | +| arcade machine | 80.34 | 88.02 | +| hovel | 42.1 | 46.22 | +| bus | 93.8 | 97.54 | +| towel | 77.03 | 85.13 | +| light | 62.94 | 72.7 | +| truck | 42.35 | 60.62 | +| tower | 23.11 | 32.07 | +| chandelier | 72.76 | 87.43 | +| awning | 41.66 | 53.46 | +| streetlight | 33.52 | 44.22 | +| booth | 47.31 | 71.23 | +| television receiver | 75.8 | 89.15 | +| airplane | 64.01 | 70.69 | +| dirt track | 15.05 | 38.89 | +| apparel | 39.02 | 56.85 | +| pole | 29.35 | 40.68 | +| land | 0.0 | 0.0 | +| bannister | 18.13 | 25.78 | +| escalator | 57.57 | 77.73 | +| ottoman | 49.43 | 64.34 | +| bottle | 39.94 | 68.1 | +| buffet | 51.14 | 62.94 | +| poster | 40.38 | 53.03 | +| stage | 16.85 | 33.98 | +| van | 50.25 | 67.6 | +| ship | 92.38 | 98.43 | +| fountain | 29.98 | 32.0 | +| conveyer belt | 76.14 | 95.41 | +| canopy | 52.98 | 71.38 | +| washer | 80.3 | 85.12 | +| plaything | 27.05 | 36.85 | +| swimming pool | 59.57 | 88.44 | +| stool | 57.49 | 72.03 | +| barrel | 60.66 | 74.47 | +| basket | 46.01 | 65.63 | +| waterfall | 74.38 | 94.52 | +| tent | 96.98 | 98.27 | +| bag | 21.81 | 26.23 | +| minibike | 77.21 | 87.66 | +| cradle | 82.75 | 97.89 | +| oven | 57.17 | 65.12 | +| ball | 51.11 | 56.08 | +| food | 58.06 | 69.25 | +| step | 9.4 | 10.44 | +| tank | 70.53 | 80.36 | +| trade name | 25.07 | 28.94 | +| microwave | 85.33 | 96.12 | +| pot | 54.25 | 65.99 | +| animal | 58.78 | 60.45 | +| bicycle | 59.14 | 79.0 | +| lake | 55.33 | 63.77 | +| dishwasher | 70.71 | 76.38 | +| screen | 51.78 | 72.39 | +| blanket | 29.5 | 33.41 | +| sculpture | 70.21 | 87.84 | +| hood | 64.17 | 74.49 | +| sconce | 57.91 | 67.04 | +| vase | 44.54 | 61.16 | +| traffic light | 34.64 | 63.09 | +| tray | 21.32 | 25.31 | +| ashcan | 48.21 | 64.68 | +| fan | 70.05 | 83.63 | +| pier | 41.16 | 48.5 | +| crt screen | 19.09 | 39.19 | +| plate | 61.07 | 80.12 | +| monitor | 57.1 | 66.74 | +| bulletin board | 54.86 | 66.14 | +| shower | 6.63 | 6.73 | +| radiator | 67.43 | 79.44 | +| glass | 19.76 | 21.21 | +| clock | 42.78 | 51.99 | +| flag | 69.75 | 74.89 | ++---------------------+-------+-------+ +2024-06-16 17:33:54,472 - mmseg - INFO - Summary: +2024-06-16 17:33:54,472 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.06 | 57.21 | 69.94 | ++-------+-------+-------+ +2024-06-16 17:33:54,473 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 17:33:54,473 - mmseg - INFO - Iter(val) [250] aAcc: 0.8606, mIoU: 0.5721, mAcc: 0.6994, IoU.wall: 0.8182, IoU.building: 0.8467, IoU.sky: 0.9493, IoU.floor: 0.8571, IoU.tree: 0.7834, IoU.ceiling: 0.8657, IoU.road: 0.8624, IoU.bed : 0.9299, IoU.windowpane: 0.6628, IoU.grass: 0.6791, IoU.cabinet: 0.6882, IoU.sidewalk: 0.7030, IoU.person: 0.8534, IoU.earth: 0.3649, IoU.door: 0.5831, IoU.table: 0.6878, IoU.mountain: 0.6159, IoU.plant: 0.5529, IoU.curtain: 0.7809, IoU.chair: 0.6821, IoU.car: 0.8752, IoU.water: 0.6461, IoU.painting: 0.7658, IoU.sofa: 0.8217, IoU.shelf: 0.4509, IoU.house: 0.5630, IoU.sea: 0.7232, IoU.mirror: 0.7652, IoU.rug: 0.7498, IoU.field: 0.2977, IoU.armchair: 0.6046, IoU.seat: 0.6731, IoU.fence: 0.5179, IoU.desk: 0.6050, IoU.rock: 0.5978, IoU.wardrobe: 0.5579, IoU.lamp: 0.7454, IoU.bathtub: 0.8484, IoU.railing: 0.4279, IoU.cushion: 0.7033, IoU.base: 0.4044, IoU.box: 0.3624, IoU.column: 0.5274, IoU.signboard: 0.4084, IoU.chest of drawers: 0.4831, IoU.counter: 0.4432, IoU.sand: 0.5744, IoU.sink: 0.7618, IoU.skyscraper: 0.4838, IoU.fireplace: 0.6862, IoU.refrigerator: 0.8533, IoU.grandstand: 0.5539, IoU.path: 0.3102, IoU.stairs: 0.2613, IoU.runway: 0.7222, IoU.case: 0.5580, IoU.pool table: 0.9449, IoU.pillow: 0.6943, IoU.screen door: 0.8351, IoU.stairway: 0.3879, IoU.river: 0.1232, IoU.bridge: 0.6427, IoU.bookcase: 0.4364, IoU.blind: 0.4540, IoU.coffee table: 0.6240, IoU.toilet: 0.8975, IoU.flower: 0.4165, IoU.book: 0.5440, IoU.hill: 0.0822, IoU.bench: 0.5309, IoU.countertop: 0.6463, IoU.stove: 0.8533, IoU.palm: 0.5522, IoU.kitchen island: 0.5012, IoU.computer: 0.7917, IoU.swivel chair: 0.5165, IoU.boat: 0.7042, IoU.bar: 0.6674, IoU.arcade machine: 0.8034, IoU.hovel: 0.4210, IoU.bus: 0.9380, IoU.towel: 0.7703, IoU.light: 0.6294, IoU.truck: 0.4235, IoU.tower: 0.2311, IoU.chandelier: 0.7276, IoU.awning: 0.4166, IoU.streetlight: 0.3352, IoU.booth: 0.4731, IoU.television receiver: 0.7580, IoU.airplane: 0.6401, IoU.dirt track: 0.1505, IoU.apparel: 0.3902, IoU.pole: 0.2935, IoU.land: 0.0000, IoU.bannister: 0.1813, IoU.escalator: 0.5757, IoU.ottoman: 0.4943, IoU.bottle: 0.3994, IoU.buffet: 0.5114, IoU.poster: 0.4038, IoU.stage: 0.1685, IoU.van: 0.5025, IoU.ship: 0.9238, IoU.fountain: 0.2998, IoU.conveyer belt: 0.7614, IoU.canopy: 0.5298, IoU.washer: 0.8030, IoU.plaything: 0.2705, IoU.swimming pool: 0.5957, IoU.stool: 0.5749, IoU.barrel: 0.6066, IoU.basket: 0.4601, IoU.waterfall: 0.7438, IoU.tent: 0.9698, IoU.bag: 0.2181, IoU.minibike: 0.7721, IoU.cradle: 0.8275, IoU.oven: 0.5717, IoU.ball: 0.5111, IoU.food: 0.5806, IoU.step: 0.0940, IoU.tank: 0.7053, IoU.trade name: 0.2507, IoU.microwave: 0.8533, IoU.pot: 0.5425, IoU.animal: 0.5878, IoU.bicycle: 0.5914, IoU.lake: 0.5533, IoU.dishwasher: 0.7071, IoU.screen: 0.5178, IoU.blanket: 0.2950, IoU.sculpture: 0.7021, IoU.hood: 0.6417, IoU.sconce: 0.5791, IoU.vase: 0.4454, IoU.traffic light: 0.3464, IoU.tray: 0.2132, IoU.ashcan: 0.4821, IoU.fan: 0.7005, IoU.pier: 0.4116, IoU.crt screen: 0.1909, IoU.plate: 0.6107, IoU.monitor: 0.5710, IoU.bulletin board: 0.5486, IoU.shower: 0.0663, IoU.radiator: 0.6743, IoU.glass: 0.1976, IoU.clock: 0.4278, IoU.flag: 0.6975, Acc.wall: 0.8945, Acc.building: 0.9278, Acc.sky: 0.9746, Acc.floor: 0.9197, Acc.tree: 0.8881, Acc.ceiling: 0.9377, Acc.road: 0.9283, Acc.bed : 0.9647, Acc.windowpane: 0.7888, Acc.grass: 0.8348, Acc.cabinet: 0.8062, Acc.sidewalk: 0.8162, Acc.person: 0.9387, Acc.earth: 0.4911, Acc.door: 0.7794, Acc.table: 0.8324, Acc.mountain: 0.7206, Acc.plant: 0.6943, Acc.curtain: 0.9024, Acc.chair: 0.8096, Acc.car: 0.9300, Acc.water: 0.7795, Acc.painting: 0.8964, Acc.sofa: 0.9014, Acc.shelf: 0.5839, Acc.house: 0.8202, Acc.sea: 0.8517, Acc.mirror: 0.8246, Acc.rug: 0.8367, Acc.field: 0.5176, Acc.armchair: 0.7606, Acc.seat: 0.8896, Acc.fence: 0.6548, Acc.desk: 0.7665, Acc.rock: 0.8337, Acc.wardrobe: 0.6686, Acc.lamp: 0.8575, Acc.bathtub: 0.8643, Acc.railing: 0.6110, Acc.cushion: 0.8219, Acc.base: 0.6002, Acc.box: 0.4752, Acc.column: 0.6927, Acc.signboard: 0.5440, Acc.chest of drawers: 0.6038, Acc.counter: 0.5349, Acc.sand: 0.8709, Acc.sink: 0.8488, Acc.skyscraper: 0.6351, Acc.fireplace: 0.9462, Acc.refrigerator: 0.9236, Acc.grandstand: 0.8434, Acc.path: 0.4469, Acc.stairs: 0.3540, Acc.runway: 0.9600, Acc.case: 0.7902, Acc.pool table: 0.9768, Acc.pillow: 0.8011, Acc.screen door: 0.8683, Acc.stairway: 0.5206, Acc.river: 0.2587, Acc.bridge: 0.7557, Acc.bookcase: 0.6261, Acc.blind: 0.5207, Acc.coffee table: 0.9179, Acc.toilet: 0.9328, Acc.flower: 0.4915, Acc.book: 0.7709, Acc.hill: 0.1386, Acc.bench: 0.6491, Acc.countertop: 0.8305, Acc.stove: 0.9149, Acc.palm: 0.8490, Acc.kitchen island: 0.7334, Acc.computer: 0.9132, Acc.swivel chair: 0.7688, Acc.boat: 0.9108, Acc.bar: 0.8461, Acc.arcade machine: 0.8802, Acc.hovel: 0.4622, Acc.bus: 0.9754, Acc.towel: 0.8513, Acc.light: 0.7270, Acc.truck: 0.6062, Acc.tower: 0.3207, Acc.chandelier: 0.8743, Acc.awning: 0.5346, Acc.streetlight: 0.4422, Acc.booth: 0.7123, Acc.television receiver: 0.8915, Acc.airplane: 0.7069, Acc.dirt track: 0.3889, Acc.apparel: 0.5685, Acc.pole: 0.4068, Acc.land: 0.0000, Acc.bannister: 0.2578, Acc.escalator: 0.7773, Acc.ottoman: 0.6434, Acc.bottle: 0.6810, Acc.buffet: 0.6294, Acc.poster: 0.5303, Acc.stage: 0.3398, Acc.van: 0.6760, Acc.ship: 0.9843, Acc.fountain: 0.3200, Acc.conveyer belt: 0.9541, Acc.canopy: 0.7138, Acc.washer: 0.8512, Acc.plaything: 0.3685, Acc.swimming pool: 0.8844, Acc.stool: 0.7203, Acc.barrel: 0.7447, Acc.basket: 0.6563, Acc.waterfall: 0.9452, Acc.tent: 0.9827, Acc.bag: 0.2623, Acc.minibike: 0.8766, Acc.cradle: 0.9789, Acc.oven: 0.6512, Acc.ball: 0.5608, Acc.food: 0.6925, Acc.step: 0.1044, Acc.tank: 0.8036, Acc.trade name: 0.2894, Acc.microwave: 0.9612, Acc.pot: 0.6599, Acc.animal: 0.6045, Acc.bicycle: 0.7900, Acc.lake: 0.6377, Acc.dishwasher: 0.7638, Acc.screen: 0.7239, Acc.blanket: 0.3341, Acc.sculpture: 0.8784, Acc.hood: 0.7449, Acc.sconce: 0.6704, Acc.vase: 0.6116, Acc.traffic light: 0.6309, Acc.tray: 0.2531, Acc.ashcan: 0.6468, Acc.fan: 0.8363, Acc.pier: 0.4850, Acc.crt screen: 0.3919, Acc.plate: 0.8012, Acc.monitor: 0.6674, Acc.bulletin board: 0.6614, Acc.shower: 0.0673, Acc.radiator: 0.7944, Acc.glass: 0.2121, Acc.clock: 0.5199, Acc.flag: 0.7489 +2024-06-16 17:35:03,470 - mmseg - INFO - Iter [48050/80000] lr: 1.598e-05, eta: 13:23:42, time: 3.284, data_time: 1.920, memory: 70722, decode.loss_ce: 0.1811, decode.acc_seg: 92.3263, aux.loss_ce: 0.0760, aux.acc_seg: 92.0217, loss: 0.2570 +2024-06-16 17:36:11,604 - mmseg - INFO - Iter [48100/80000] lr: 1.595e-05, eta: 13:22:22, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1777, decode.acc_seg: 92.5287, aux.loss_ce: 0.0746, aux.acc_seg: 92.1459, loss: 0.2523 +2024-06-16 17:37:19,870 - mmseg - INFO - Iter [48150/80000] lr: 1.593e-05, eta: 13:21:01, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1729, decode.acc_seg: 92.4191, aux.loss_ce: 0.0729, aux.acc_seg: 92.0623, loss: 0.2458 +2024-06-16 17:38:28,044 - mmseg - INFO - Iter [48200/80000] lr: 1.590e-05, eta: 13:19:41, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1722, decode.acc_seg: 92.4218, aux.loss_ce: 0.0726, aux.acc_seg: 92.0269, loss: 0.2448 +2024-06-16 17:39:36,230 - mmseg - INFO - Iter [48250/80000] lr: 1.588e-05, eta: 13:18:21, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1773, decode.acc_seg: 92.4142, aux.loss_ce: 0.0741, aux.acc_seg: 92.0589, loss: 0.2514 +2024-06-16 17:40:44,703 - mmseg - INFO - Iter [48300/80000] lr: 1.585e-05, eta: 13:17:01, time: 1.369, data_time: 0.011, memory: 70722, decode.loss_ce: 0.1776, decode.acc_seg: 92.3289, aux.loss_ce: 0.0748, aux.acc_seg: 91.9943, loss: 0.2524 +2024-06-16 17:41:52,776 - mmseg - INFO - Iter [48350/80000] lr: 1.583e-05, eta: 13:15:41, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1859, decode.acc_seg: 91.9332, aux.loss_ce: 0.0788, aux.acc_seg: 91.4638, loss: 0.2646 +2024-06-16 17:43:01,111 - mmseg - INFO - Iter [48400/80000] lr: 1.580e-05, eta: 13:14:21, time: 1.367, data_time: 0.011, memory: 70722, decode.loss_ce: 0.1778, decode.acc_seg: 92.5440, aux.loss_ce: 0.0749, aux.acc_seg: 92.1319, loss: 0.2527 +2024-06-16 17:44:09,323 - mmseg - INFO - Iter [48450/80000] lr: 1.578e-05, eta: 13:13:01, time: 1.364, data_time: 0.011, memory: 70722, decode.loss_ce: 0.1822, decode.acc_seg: 92.4969, aux.loss_ce: 0.0767, aux.acc_seg: 92.1438, loss: 0.2589 +2024-06-16 17:45:17,578 - mmseg - INFO - Iter [48500/80000] lr: 1.575e-05, eta: 13:11:40, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1819, decode.acc_seg: 92.3648, aux.loss_ce: 0.0760, aux.acc_seg: 92.0178, loss: 0.2579 +2024-06-16 17:46:25,842 - mmseg - INFO - Iter [48550/80000] lr: 1.573e-05, eta: 13:10:20, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1785, decode.acc_seg: 92.4432, aux.loss_ce: 0.0745, aux.acc_seg: 92.0630, loss: 0.2530 +2024-06-16 17:47:33,904 - mmseg - INFO - Iter [48600/80000] lr: 1.570e-05, eta: 13:09:00, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1910, decode.acc_seg: 91.6767, aux.loss_ce: 0.0798, aux.acc_seg: 91.3195, loss: 0.2707 +2024-06-16 17:48:42,152 - mmseg - INFO - Iter [48650/80000] lr: 1.568e-05, eta: 13:07:40, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1792, decode.acc_seg: 92.6512, aux.loss_ce: 0.0748, aux.acc_seg: 92.3052, loss: 0.2540 +2024-06-16 17:49:50,608 - mmseg - INFO - Iter [48700/80000] lr: 1.565e-05, eta: 13:06:20, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1814, decode.acc_seg: 92.2553, aux.loss_ce: 0.0765, aux.acc_seg: 91.8157, loss: 0.2580 +2024-06-16 17:51:18,313 - mmseg - INFO - Iter [48750/80000] lr: 1.563e-05, eta: 13:05:13, time: 1.754, data_time: 0.394, memory: 70722, decode.loss_ce: 0.1750, decode.acc_seg: 92.5138, aux.loss_ce: 0.0733, aux.acc_seg: 92.1733, loss: 0.2483 +2024-06-16 17:52:26,442 - mmseg - INFO - Iter [48800/80000] lr: 1.560e-05, eta: 13:03:53, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1757, decode.acc_seg: 92.4358, aux.loss_ce: 0.0738, aux.acc_seg: 92.0467, loss: 0.2495 +2024-06-16 17:53:34,698 - mmseg - INFO - Iter [48850/80000] lr: 1.558e-05, eta: 13:02:33, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1862, decode.acc_seg: 92.3509, aux.loss_ce: 0.0780, aux.acc_seg: 91.9504, loss: 0.2642 +2024-06-16 17:54:42,890 - mmseg - INFO - Iter [48900/80000] lr: 1.555e-05, eta: 13:01:13, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1665, decode.acc_seg: 92.6648, aux.loss_ce: 0.0696, aux.acc_seg: 92.3970, loss: 0.2361 +2024-06-16 17:55:51,190 - mmseg - INFO - Iter [48950/80000] lr: 1.553e-05, eta: 12:59:53, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1780, decode.acc_seg: 92.4031, aux.loss_ce: 0.0746, aux.acc_seg: 92.0783, loss: 0.2526 +2024-06-16 17:56:59,380 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 17:56:59,380 - mmseg - INFO - Iter [49000/80000] lr: 1.550e-05, eta: 12:58:33, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1843, decode.acc_seg: 91.8199, aux.loss_ce: 0.0771, aux.acc_seg: 91.4661, loss: 0.2614 +2024-06-16 17:58:38,182 - mmseg - INFO - per class results: +2024-06-16 17:58:38,188 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.51 | 88.94 | +| building | 84.69 | 92.97 | +| sky | 94.79 | 97.58 | +| floor | 85.33 | 92.33 | +| tree | 77.58 | 89.5 | +| ceiling | 86.21 | 93.16 | +| road | 87.58 | 91.92 | +| bed | 93.12 | 96.53 | +| windowpane | 66.68 | 82.27 | +| grass | 70.3 | 83.33 | +| cabinet | 65.71 | 75.08 | +| sidewalk | 73.54 | 85.87 | +| person | 85.77 | 94.32 | +| earth | 38.45 | 52.97 | +| door | 57.81 | 73.24 | +| table | 69.9 | 83.1 | +| mountain | 61.86 | 71.85 | +| plant | 56.54 | 67.25 | +| curtain | 77.48 | 90.0 | +| chair | 68.57 | 80.39 | +| car | 87.05 | 94.54 | +| water | 63.17 | 78.53 | +| painting | 76.4 | 90.67 | +| sofa | 81.25 | 92.16 | +| shelf | 46.39 | 60.7 | +| house | 60.43 | 80.25 | +| sea | 70.8 | 86.29 | +| mirror | 77.79 | 83.96 | +| rug | 72.56 | 84.19 | +| field | 32.78 | 62.6 | +| armchair | 60.1 | 77.2 | +| seat | 69.35 | 89.34 | +| fence | 52.06 | 66.94 | +| desk | 62.91 | 82.45 | +| rock | 59.15 | 85.31 | +| wardrobe | 50.96 | 74.74 | +| lamp | 74.79 | 84.3 | +| bathtub | 84.18 | 86.21 | +| railing | 42.85 | 61.22 | +| cushion | 63.95 | 69.77 | +| base | 40.48 | 60.47 | +| box | 38.86 | 54.1 | +| column | 53.31 | 67.81 | +| signboard | 40.5 | 59.0 | +| chest of drawers | 45.65 | 69.03 | +| counter | 44.58 | 54.99 | +| sand | 56.38 | 82.63 | +| sink | 75.55 | 84.32 | +| skyscraper | 49.65 | 63.13 | +| fireplace | 68.39 | 96.95 | +| refrigerator | 85.42 | 94.64 | +| grandstand | 56.19 | 84.53 | +| path | 34.3 | 47.2 | +| stairs | 24.43 | 33.29 | +| runway | 72.7 | 96.03 | +| case | 54.86 | 76.3 | +| pool table | 93.04 | 97.42 | +| pillow | 69.79 | 81.85 | +| screen door | 78.92 | 80.81 | +| stairway | 39.48 | 56.82 | +| river | 11.78 | 17.94 | +| bridge | 62.93 | 68.57 | +| bookcase | 46.38 | 62.8 | +| blind | 45.55 | 51.07 | +| coffee table | 68.68 | 85.77 | +| toilet | 89.86 | 93.16 | +| flower | 42.65 | 50.04 | +| book | 52.59 | 73.91 | +| hill | 6.89 | 10.89 | +| bench | 56.47 | 62.02 | +| countertop | 63.13 | 82.0 | +| stove | 86.27 | 92.73 | +| palm | 57.31 | 79.65 | +| kitchen island | 51.11 | 77.14 | +| computer | 79.61 | 89.72 | +| swivel chair | 52.32 | 76.03 | +| boat | 77.25 | 90.99 | +| bar | 65.0 | 79.25 | +| arcade machine | 80.77 | 84.41 | +| hovel | 39.16 | 42.03 | +| bus | 92.66 | 96.51 | +| towel | 72.7 | 90.25 | +| light | 59.47 | 66.42 | +| truck | 43.06 | 55.61 | +| tower | 35.64 | 53.96 | +| chandelier | 71.71 | 84.89 | +| awning | 40.59 | 51.62 | +| streetlight | 36.39 | 50.75 | +| booth | 57.2 | 65.24 | +| television receiver | 76.47 | 85.96 | +| airplane | 64.13 | 71.94 | +| dirt track | 14.77 | 37.01 | +| apparel | 43.98 | 64.02 | +| pole | 31.02 | 43.75 | +| land | 0.65 | 1.4 | +| bannister | 17.49 | 25.14 | +| escalator | 58.44 | 78.42 | +| ottoman | 49.05 | 66.54 | +| bottle | 40.94 | 71.06 | +| buffet | 47.7 | 59.49 | +| poster | 39.22 | 47.53 | +| stage | 19.9 | 36.55 | +| van | 42.49 | 55.25 | +| ship | 70.34 | 78.95 | +| fountain | 31.18 | 32.77 | +| conveyer belt | 79.79 | 93.4 | +| canopy | 53.46 | 76.08 | +| washer | 82.15 | 87.53 | +| plaything | 26.84 | 35.68 | +| swimming pool | 58.82 | 82.38 | +| stool | 53.35 | 73.71 | +| barrel | 61.84 | 74.17 | +| basket | 42.21 | 59.94 | +| waterfall | 74.45 | 91.22 | +| tent | 92.97 | 98.82 | +| bag | 17.89 | 19.59 | +| minibike | 75.51 | 89.16 | +| cradle | 81.0 | 96.45 | +| oven | 61.44 | 71.87 | +| ball | 19.14 | 19.62 | +| food | 66.2 | 78.27 | +| step | 11.18 | 14.7 | +| tank | 78.87 | 94.99 | +| trade name | 14.99 | 16.97 | +| microwave | 88.65 | 95.01 | +| pot | 57.17 | 67.31 | +| animal | 57.85 | 58.77 | +| bicycle | 60.04 | 75.49 | +| lake | 54.88 | 63.81 | +| dishwasher | 68.69 | 79.15 | +| screen | 50.04 | 72.78 | +| blanket | 31.24 | 35.28 | +| sculpture | 76.36 | 87.88 | +| hood | 70.47 | 84.62 | +| sconce | 57.83 | 65.82 | +| vase | 49.6 | 61.41 | +| traffic light | 37.35 | 63.2 | +| tray | 24.39 | 31.68 | +| ashcan | 48.34 | 67.14 | +| fan | 66.64 | 79.34 | +| pier | 41.74 | 46.51 | +| crt screen | 14.81 | 36.76 | +| plate | 60.03 | 68.66 | +| monitor | 50.97 | 60.17 | +| bulletin board | 55.8 | 64.31 | +| shower | 5.23 | 5.52 | +| radiator | 68.06 | 75.23 | +| glass | 18.72 | 19.57 | +| clock | 46.17 | 58.9 | +| flag | 70.43 | 77.02 | ++---------------------+-------+-------+ +2024-06-16 17:58:38,188 - mmseg - INFO - Summary: +2024-06-16 17:58:38,188 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 86.1 | 57.14 | 69.55 | ++------+-------+-------+ +2024-06-16 17:58:38,189 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 17:58:38,189 - mmseg - INFO - Iter(val) [250] aAcc: 0.8610, mIoU: 0.5714, mAcc: 0.6955, IoU.wall: 0.8151, IoU.building: 0.8469, IoU.sky: 0.9479, IoU.floor: 0.8533, IoU.tree: 0.7758, IoU.ceiling: 0.8621, IoU.road: 0.8758, IoU.bed : 0.9312, IoU.windowpane: 0.6668, IoU.grass: 0.7030, IoU.cabinet: 0.6571, IoU.sidewalk: 0.7354, IoU.person: 0.8577, IoU.earth: 0.3845, IoU.door: 0.5781, IoU.table: 0.6990, IoU.mountain: 0.6186, IoU.plant: 0.5654, IoU.curtain: 0.7748, IoU.chair: 0.6857, IoU.car: 0.8705, IoU.water: 0.6317, IoU.painting: 0.7640, IoU.sofa: 0.8125, IoU.shelf: 0.4639, IoU.house: 0.6043, IoU.sea: 0.7080, IoU.mirror: 0.7779, IoU.rug: 0.7256, IoU.field: 0.3278, IoU.armchair: 0.6010, IoU.seat: 0.6935, IoU.fence: 0.5206, IoU.desk: 0.6291, IoU.rock: 0.5915, IoU.wardrobe: 0.5096, IoU.lamp: 0.7479, IoU.bathtub: 0.8418, IoU.railing: 0.4285, IoU.cushion: 0.6395, IoU.base: 0.4048, IoU.box: 0.3886, IoU.column: 0.5331, IoU.signboard: 0.4050, IoU.chest of drawers: 0.4565, IoU.counter: 0.4458, IoU.sand: 0.5638, IoU.sink: 0.7555, IoU.skyscraper: 0.4965, IoU.fireplace: 0.6839, IoU.refrigerator: 0.8542, IoU.grandstand: 0.5619, IoU.path: 0.3430, IoU.stairs: 0.2443, IoU.runway: 0.7270, IoU.case: 0.5486, IoU.pool table: 0.9304, IoU.pillow: 0.6979, IoU.screen door: 0.7892, IoU.stairway: 0.3948, IoU.river: 0.1178, IoU.bridge: 0.6293, IoU.bookcase: 0.4638, IoU.blind: 0.4555, IoU.coffee table: 0.6868, IoU.toilet: 0.8986, IoU.flower: 0.4265, IoU.book: 0.5259, IoU.hill: 0.0689, IoU.bench: 0.5647, IoU.countertop: 0.6313, IoU.stove: 0.8627, IoU.palm: 0.5731, IoU.kitchen island: 0.5111, IoU.computer: 0.7961, IoU.swivel chair: 0.5232, IoU.boat: 0.7725, IoU.bar: 0.6500, IoU.arcade machine: 0.8077, IoU.hovel: 0.3916, IoU.bus: 0.9266, IoU.towel: 0.7270, IoU.light: 0.5947, IoU.truck: 0.4306, IoU.tower: 0.3564, IoU.chandelier: 0.7171, IoU.awning: 0.4059, IoU.streetlight: 0.3639, IoU.booth: 0.5720, IoU.television receiver: 0.7647, IoU.airplane: 0.6413, IoU.dirt track: 0.1477, IoU.apparel: 0.4398, IoU.pole: 0.3102, IoU.land: 0.0065, IoU.bannister: 0.1749, IoU.escalator: 0.5844, IoU.ottoman: 0.4905, IoU.bottle: 0.4094, IoU.buffet: 0.4770, IoU.poster: 0.3922, IoU.stage: 0.1990, IoU.van: 0.4249, IoU.ship: 0.7034, IoU.fountain: 0.3118, IoU.conveyer belt: 0.7979, IoU.canopy: 0.5346, IoU.washer: 0.8215, IoU.plaything: 0.2684, IoU.swimming pool: 0.5882, IoU.stool: 0.5335, IoU.barrel: 0.6184, IoU.basket: 0.4221, IoU.waterfall: 0.7445, IoU.tent: 0.9297, IoU.bag: 0.1789, IoU.minibike: 0.7551, IoU.cradle: 0.8100, IoU.oven: 0.6144, IoU.ball: 0.1914, IoU.food: 0.6620, IoU.step: 0.1118, IoU.tank: 0.7887, IoU.trade name: 0.1499, IoU.microwave: 0.8865, IoU.pot: 0.5717, IoU.animal: 0.5785, IoU.bicycle: 0.6004, IoU.lake: 0.5488, IoU.dishwasher: 0.6869, IoU.screen: 0.5004, IoU.blanket: 0.3124, IoU.sculpture: 0.7636, IoU.hood: 0.7047, IoU.sconce: 0.5783, IoU.vase: 0.4960, IoU.traffic light: 0.3735, IoU.tray: 0.2439, IoU.ashcan: 0.4834, IoU.fan: 0.6664, IoU.pier: 0.4174, IoU.crt screen: 0.1481, IoU.plate: 0.6003, IoU.monitor: 0.5097, IoU.bulletin board: 0.5580, IoU.shower: 0.0523, IoU.radiator: 0.6806, IoU.glass: 0.1872, IoU.clock: 0.4617, IoU.flag: 0.7043, Acc.wall: 0.8894, Acc.building: 0.9297, Acc.sky: 0.9758, Acc.floor: 0.9233, Acc.tree: 0.8950, Acc.ceiling: 0.9316, Acc.road: 0.9192, Acc.bed : 0.9653, Acc.windowpane: 0.8227, Acc.grass: 0.8333, Acc.cabinet: 0.7508, Acc.sidewalk: 0.8587, Acc.person: 0.9432, Acc.earth: 0.5297, Acc.door: 0.7324, Acc.table: 0.8310, Acc.mountain: 0.7185, Acc.plant: 0.6725, Acc.curtain: 0.9000, Acc.chair: 0.8039, Acc.car: 0.9454, Acc.water: 0.7853, Acc.painting: 0.9067, Acc.sofa: 0.9216, Acc.shelf: 0.6070, Acc.house: 0.8025, Acc.sea: 0.8629, Acc.mirror: 0.8396, Acc.rug: 0.8419, Acc.field: 0.6260, Acc.armchair: 0.7720, Acc.seat: 0.8934, Acc.fence: 0.6694, Acc.desk: 0.8245, Acc.rock: 0.8531, Acc.wardrobe: 0.7474, Acc.lamp: 0.8430, Acc.bathtub: 0.8621, Acc.railing: 0.6122, Acc.cushion: 0.6977, Acc.base: 0.6047, Acc.box: 0.5410, Acc.column: 0.6781, Acc.signboard: 0.5900, Acc.chest of drawers: 0.6903, Acc.counter: 0.5499, Acc.sand: 0.8263, Acc.sink: 0.8432, Acc.skyscraper: 0.6313, Acc.fireplace: 0.9695, Acc.refrigerator: 0.9464, Acc.grandstand: 0.8453, Acc.path: 0.4720, Acc.stairs: 0.3329, Acc.runway: 0.9603, Acc.case: 0.7630, Acc.pool table: 0.9742, Acc.pillow: 0.8185, Acc.screen door: 0.8081, Acc.stairway: 0.5682, Acc.river: 0.1794, Acc.bridge: 0.6857, Acc.bookcase: 0.6280, Acc.blind: 0.5107, Acc.coffee table: 0.8577, Acc.toilet: 0.9316, Acc.flower: 0.5004, Acc.book: 0.7391, Acc.hill: 0.1089, Acc.bench: 0.6202, Acc.countertop: 0.8200, Acc.stove: 0.9273, Acc.palm: 0.7965, Acc.kitchen island: 0.7714, Acc.computer: 0.8972, Acc.swivel chair: 0.7603, Acc.boat: 0.9099, Acc.bar: 0.7925, Acc.arcade machine: 0.8441, Acc.hovel: 0.4203, Acc.bus: 0.9651, Acc.towel: 0.9025, Acc.light: 0.6642, Acc.truck: 0.5561, Acc.tower: 0.5396, Acc.chandelier: 0.8489, Acc.awning: 0.5162, Acc.streetlight: 0.5075, Acc.booth: 0.6524, Acc.television receiver: 0.8596, Acc.airplane: 0.7194, Acc.dirt track: 0.3701, Acc.apparel: 0.6402, Acc.pole: 0.4375, Acc.land: 0.0140, Acc.bannister: 0.2514, Acc.escalator: 0.7842, Acc.ottoman: 0.6654, Acc.bottle: 0.7106, Acc.buffet: 0.5949, Acc.poster: 0.4753, Acc.stage: 0.3655, Acc.van: 0.5525, Acc.ship: 0.7895, Acc.fountain: 0.3277, Acc.conveyer belt: 0.9340, Acc.canopy: 0.7608, Acc.washer: 0.8753, Acc.plaything: 0.3568, Acc.swimming pool: 0.8238, Acc.stool: 0.7371, Acc.barrel: 0.7417, Acc.basket: 0.5994, Acc.waterfall: 0.9122, Acc.tent: 0.9882, Acc.bag: 0.1959, Acc.minibike: 0.8916, Acc.cradle: 0.9645, Acc.oven: 0.7187, Acc.ball: 0.1962, Acc.food: 0.7827, Acc.step: 0.1470, Acc.tank: 0.9499, Acc.trade name: 0.1697, Acc.microwave: 0.9501, Acc.pot: 0.6731, Acc.animal: 0.5877, Acc.bicycle: 0.7549, Acc.lake: 0.6381, Acc.dishwasher: 0.7915, Acc.screen: 0.7278, Acc.blanket: 0.3528, Acc.sculpture: 0.8788, Acc.hood: 0.8462, Acc.sconce: 0.6582, Acc.vase: 0.6141, Acc.traffic light: 0.6320, Acc.tray: 0.3168, Acc.ashcan: 0.6714, Acc.fan: 0.7934, Acc.pier: 0.4651, Acc.crt screen: 0.3676, Acc.plate: 0.6866, Acc.monitor: 0.6017, Acc.bulletin board: 0.6431, Acc.shower: 0.0552, Acc.radiator: 0.7523, Acc.glass: 0.1957, Acc.clock: 0.5890, Acc.flag: 0.7702 +2024-06-16 17:59:46,843 - mmseg - INFO - Iter [49050/80000] lr: 1.548e-05, eta: 12:58:16, time: 3.349, data_time: 1.992, memory: 70722, decode.loss_ce: 0.1784, decode.acc_seg: 92.5976, aux.loss_ce: 0.0745, aux.acc_seg: 92.1974, loss: 0.2530 +2024-06-16 18:00:54,925 - mmseg - INFO - Iter [49100/80000] lr: 1.545e-05, eta: 12:56:56, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1778, decode.acc_seg: 92.3247, aux.loss_ce: 0.0747, aux.acc_seg: 91.8930, loss: 0.2525 +2024-06-16 18:02:03,068 - mmseg - INFO - Iter [49150/80000] lr: 1.543e-05, eta: 12:55:36, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1768, decode.acc_seg: 92.4277, aux.loss_ce: 0.0742, aux.acc_seg: 92.1478, loss: 0.2511 +2024-06-16 18:03:11,271 - mmseg - INFO - Iter [49200/80000] lr: 1.540e-05, eta: 12:54:16, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1790, decode.acc_seg: 92.1970, aux.loss_ce: 0.0752, aux.acc_seg: 91.8550, loss: 0.2543 +2024-06-16 18:04:19,529 - mmseg - INFO - Iter [49250/80000] lr: 1.538e-05, eta: 12:52:56, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1817, decode.acc_seg: 92.3630, aux.loss_ce: 0.0766, aux.acc_seg: 91.9184, loss: 0.2584 +2024-06-16 18:05:30,224 - mmseg - INFO - Iter [49300/80000] lr: 1.535e-05, eta: 12:51:38, time: 1.414, data_time: 0.059, memory: 70722, decode.loss_ce: 0.1906, decode.acc_seg: 91.8489, aux.loss_ce: 0.0803, aux.acc_seg: 91.4978, loss: 0.2709 +2024-06-16 18:06:38,620 - mmseg - INFO - Iter [49350/80000] lr: 1.533e-05, eta: 12:50:18, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1779, decode.acc_seg: 92.5756, aux.loss_ce: 0.0744, aux.acc_seg: 92.1976, loss: 0.2523 +2024-06-16 18:07:46,705 - mmseg - INFO - Iter [49400/80000] lr: 1.530e-05, eta: 12:48:58, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1853, decode.acc_seg: 92.1075, aux.loss_ce: 0.0776, aux.acc_seg: 91.7326, loss: 0.2629 +2024-06-16 18:08:54,940 - mmseg - INFO - Iter [49450/80000] lr: 1.528e-05, eta: 12:47:38, time: 1.365, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1886, decode.acc_seg: 91.8347, aux.loss_ce: 0.0784, aux.acc_seg: 91.5305, loss: 0.2670 +2024-06-16 18:10:03,226 - mmseg - INFO - Iter [49500/80000] lr: 1.525e-05, eta: 12:46:19, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1737, decode.acc_seg: 92.5910, aux.loss_ce: 0.0730, aux.acc_seg: 92.2046, loss: 0.2467 +2024-06-16 18:11:11,265 - mmseg - INFO - Iter [49550/80000] lr: 1.523e-05, eta: 12:44:59, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1716, decode.acc_seg: 92.7315, aux.loss_ce: 0.0724, aux.acc_seg: 92.3538, loss: 0.2440 +2024-06-16 18:12:19,569 - mmseg - INFO - Iter [49600/80000] lr: 1.520e-05, eta: 12:43:39, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1762, decode.acc_seg: 92.5798, aux.loss_ce: 0.0737, aux.acc_seg: 92.2296, loss: 0.2498 +2024-06-16 18:13:27,544 - mmseg - INFO - Iter [49650/80000] lr: 1.518e-05, eta: 12:42:19, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1863, decode.acc_seg: 92.2429, aux.loss_ce: 0.0783, aux.acc_seg: 91.8651, loss: 0.2646 +2024-06-16 18:14:35,760 - mmseg - INFO - Iter [49700/80000] lr: 1.515e-05, eta: 12:41:00, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1768, decode.acc_seg: 92.3812, aux.loss_ce: 0.0740, aux.acc_seg: 92.0420, loss: 0.2508 +2024-06-16 18:15:43,977 - mmseg - INFO - Iter [49750/80000] lr: 1.513e-05, eta: 12:39:40, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1722, decode.acc_seg: 92.3620, aux.loss_ce: 0.0727, aux.acc_seg: 91.9838, loss: 0.2449 +2024-06-16 18:16:52,073 - mmseg - INFO - Iter [49800/80000] lr: 1.510e-05, eta: 12:38:20, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1766, decode.acc_seg: 92.5610, aux.loss_ce: 0.0743, aux.acc_seg: 92.2104, loss: 0.2510 +2024-06-16 18:18:00,358 - mmseg - INFO - Iter [49850/80000] lr: 1.508e-05, eta: 12:37:01, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1750, decode.acc_seg: 92.4577, aux.loss_ce: 0.0735, aux.acc_seg: 92.1246, loss: 0.2486 +2024-06-16 18:19:08,390 - mmseg - INFO - Iter [49900/80000] lr: 1.505e-05, eta: 12:35:41, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1754, decode.acc_seg: 92.3399, aux.loss_ce: 0.0733, aux.acc_seg: 92.0376, loss: 0.2486 +2024-06-16 18:20:16,884 - mmseg - INFO - Iter [49950/80000] lr: 1.503e-05, eta: 12:34:21, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1700, decode.acc_seg: 92.6609, aux.loss_ce: 0.0713, aux.acc_seg: 92.3087, loss: 0.2412 +2024-06-16 18:21:24,855 - mmseg - INFO - Saving checkpoint at 50000 iterations +2024-06-16 18:22:50,852 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 18:22:50,852 - mmseg - INFO - Iter [50000/80000] lr: 1.500e-05, eta: 12:33:53, time: 3.079, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1730, decode.acc_seg: 92.7308, aux.loss_ce: 0.0729, aux.acc_seg: 92.3389, loss: 0.2458 +2024-06-16 18:24:25,767 - mmseg - INFO - per class results: +2024-06-16 18:24:25,774 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.31 | 89.79 | +| building | 85.64 | 93.65 | +| sky | 94.85 | 97.6 | +| floor | 85.25 | 92.56 | +| tree | 76.95 | 90.21 | +| ceiling | 86.21 | 92.93 | +| road | 86.69 | 91.17 | +| bed | 93.06 | 96.89 | +| windowpane | 66.31 | 81.54 | +| grass | 67.4 | 79.43 | +| cabinet | 66.11 | 75.17 | +| sidewalk | 71.97 | 85.71 | +| person | 86.11 | 94.44 | +| earth | 36.52 | 47.36 | +| door | 59.9 | 75.28 | +| table | 69.65 | 80.29 | +| mountain | 60.73 | 72.8 | +| plant | 53.89 | 64.66 | +| curtain | 76.19 | 86.44 | +| chair | 68.1 | 78.49 | +| car | 86.61 | 94.85 | +| water | 60.19 | 71.9 | +| painting | 77.74 | 89.74 | +| sofa | 82.53 | 91.05 | +| shelf | 44.9 | 59.44 | +| house | 63.02 | 79.39 | +| sea | 67.12 | 91.32 | +| mirror | 78.05 | 85.47 | +| rug | 71.5 | 77.17 | +| field | 28.89 | 58.01 | +| armchair | 60.12 | 77.05 | +| seat | 67.97 | 88.88 | +| fence | 53.43 | 65.39 | +| desk | 63.27 | 78.3 | +| rock | 54.44 | 82.15 | +| wardrobe | 51.13 | 74.8 | +| lamp | 74.65 | 85.03 | +| bathtub | 84.51 | 86.78 | +| railing | 42.87 | 65.16 | +| cushion | 70.03 | 82.24 | +| base | 40.09 | 62.86 | +| box | 32.3 | 39.57 | +| column | 55.03 | 70.93 | +| signboard | 40.06 | 52.12 | +| chest of drawers | 42.4 | 69.91 | +| counter | 42.4 | 54.52 | +| sand | 53.36 | 87.7 | +| sink | 76.98 | 83.54 | +| skyscraper | 50.09 | 62.17 | +| fireplace | 71.31 | 94.27 | +| refrigerator | 84.94 | 93.37 | +| grandstand | 53.3 | 85.12 | +| path | 32.95 | 50.49 | +| stairs | 25.18 | 32.08 | +| runway | 73.26 | 96.28 | +| case | 52.22 | 71.37 | +| pool table | 94.86 | 97.93 | +| pillow | 69.98 | 81.1 | +| screen door | 82.77 | 85.05 | +| stairway | 44.33 | 62.76 | +| river | 16.2 | 28.2 | +| bridge | 67.96 | 75.35 | +| bookcase | 39.29 | 73.79 | +| blind | 43.59 | 48.29 | +| coffee table | 62.26 | 90.72 | +| toilet | 89.71 | 92.78 | +| flower | 43.08 | 52.69 | +| book | 54.26 | 70.06 | +| hill | 8.42 | 14.77 | +| bench | 51.54 | 61.6 | +| countertop | 64.65 | 87.07 | +| stove | 85.77 | 92.63 | +| palm | 55.43 | 79.74 | +| kitchen island | 55.31 | 87.36 | +| computer | 78.83 | 92.05 | +| swivel chair | 50.46 | 73.29 | +| boat | 70.45 | 92.18 | +| bar | 64.55 | 87.6 | +| arcade machine | 77.58 | 85.31 | +| hovel | 52.93 | 59.17 | +| bus | 92.86 | 96.41 | +| towel | 72.15 | 84.21 | +| light | 59.74 | 66.31 | +| truck | 42.96 | 54.95 | +| tower | 31.82 | 48.07 | +| chandelier | 71.77 | 83.42 | +| awning | 41.68 | 51.49 | +| streetlight | 34.23 | 48.59 | +| booth | 50.96 | 66.86 | +| television receiver | 77.45 | 93.13 | +| airplane | 62.99 | 73.49 | +| dirt track | 12.15 | 19.06 | +| apparel | 48.95 | 69.69 | +| pole | 28.43 | 39.59 | +| land | 0.93 | 2.01 | +| bannister | 18.57 | 30.63 | +| escalator | 57.63 | 78.89 | +| ottoman | 44.83 | 59.29 | +| bottle | 40.61 | 65.53 | +| buffet | 59.15 | 76.3 | +| poster | 36.11 | 44.23 | +| stage | 21.67 | 42.26 | +| van | 44.72 | 61.55 | +| ship | 74.11 | 90.62 | +| fountain | 30.78 | 32.76 | +| conveyer belt | 78.91 | 92.96 | +| canopy | 51.62 | 79.52 | +| washer | 82.37 | 87.01 | +| plaything | 23.11 | 41.36 | +| swimming pool | 53.94 | 77.55 | +| stool | 53.91 | 70.49 | +| barrel | 52.21 | 74.68 | +| basket | 42.33 | 63.99 | +| waterfall | 78.89 | 93.13 | +| tent | 91.29 | 98.64 | +| bag | 18.05 | 19.77 | +| minibike | 75.92 | 89.19 | +| cradle | 80.18 | 97.8 | +| oven | 65.56 | 76.21 | +| ball | 54.1 | 70.03 | +| food | 60.69 | 72.73 | +| step | 14.84 | 18.44 | +| tank | 84.13 | 91.72 | +| trade name | 17.98 | 19.74 | +| microwave | 89.22 | 96.5 | +| pot | 56.69 | 67.04 | +| animal | 60.13 | 61.89 | +| bicycle | 57.7 | 77.43 | +| lake | 56.48 | 63.79 | +| dishwasher | 67.05 | 76.85 | +| screen | 51.37 | 81.03 | +| blanket | 21.23 | 23.68 | +| sculpture | 77.75 | 85.48 | +| hood | 64.32 | 75.56 | +| sconce | 57.59 | 64.39 | +| vase | 48.81 | 63.48 | +| traffic light | 36.37 | 62.51 | +| tray | 23.54 | 26.98 | +| ashcan | 49.4 | 62.4 | +| fan | 70.23 | 81.07 | +| pier | 42.39 | 46.42 | +| crt screen | 3.4 | 4.79 | +| plate | 60.09 | 78.75 | +| monitor | 61.27 | 79.52 | +| bulletin board | 60.05 | 70.12 | +| shower | 4.89 | 7.64 | +| radiator | 68.12 | 79.07 | +| glass | 19.49 | 20.85 | +| clock | 49.24 | 56.03 | +| flag | 69.99 | 78.14 | ++---------------------+-------+-------+ +2024-06-16 18:24:25,774 - mmseg - INFO - Summary: +2024-06-16 18:24:25,774 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.03 | 57.14 | 70.23 | ++-------+-------+-------+ +2024-06-16 18:24:25,774 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 18:24:25,775 - mmseg - INFO - Iter(val) [250] aAcc: 0.8603, mIoU: 0.5714, mAcc: 0.7023, IoU.wall: 0.8231, IoU.building: 0.8564, IoU.sky: 0.9485, IoU.floor: 0.8525, IoU.tree: 0.7695, IoU.ceiling: 0.8621, IoU.road: 0.8669, IoU.bed : 0.9306, IoU.windowpane: 0.6631, IoU.grass: 0.6740, IoU.cabinet: 0.6611, IoU.sidewalk: 0.7197, IoU.person: 0.8611, IoU.earth: 0.3652, IoU.door: 0.5990, IoU.table: 0.6965, IoU.mountain: 0.6073, IoU.plant: 0.5389, IoU.curtain: 0.7619, IoU.chair: 0.6810, IoU.car: 0.8661, IoU.water: 0.6019, IoU.painting: 0.7774, IoU.sofa: 0.8253, IoU.shelf: 0.4490, IoU.house: 0.6302, IoU.sea: 0.6712, IoU.mirror: 0.7805, IoU.rug: 0.7150, IoU.field: 0.2889, IoU.armchair: 0.6012, IoU.seat: 0.6797, IoU.fence: 0.5343, IoU.desk: 0.6327, IoU.rock: 0.5444, IoU.wardrobe: 0.5113, IoU.lamp: 0.7465, IoU.bathtub: 0.8451, IoU.railing: 0.4287, IoU.cushion: 0.7003, IoU.base: 0.4009, IoU.box: 0.3230, IoU.column: 0.5503, IoU.signboard: 0.4006, IoU.chest of drawers: 0.4240, IoU.counter: 0.4240, IoU.sand: 0.5336, IoU.sink: 0.7698, IoU.skyscraper: 0.5009, IoU.fireplace: 0.7131, IoU.refrigerator: 0.8494, IoU.grandstand: 0.5330, IoU.path: 0.3295, IoU.stairs: 0.2518, IoU.runway: 0.7326, IoU.case: 0.5222, IoU.pool table: 0.9486, IoU.pillow: 0.6998, IoU.screen door: 0.8277, IoU.stairway: 0.4433, IoU.river: 0.1620, IoU.bridge: 0.6796, IoU.bookcase: 0.3929, IoU.blind: 0.4359, IoU.coffee table: 0.6226, IoU.toilet: 0.8971, IoU.flower: 0.4308, IoU.book: 0.5426, IoU.hill: 0.0842, IoU.bench: 0.5154, IoU.countertop: 0.6465, IoU.stove: 0.8577, IoU.palm: 0.5543, IoU.kitchen island: 0.5531, IoU.computer: 0.7883, IoU.swivel chair: 0.5046, IoU.boat: 0.7045, IoU.bar: 0.6455, IoU.arcade machine: 0.7758, IoU.hovel: 0.5293, IoU.bus: 0.9286, IoU.towel: 0.7215, IoU.light: 0.5974, IoU.truck: 0.4296, IoU.tower: 0.3182, IoU.chandelier: 0.7177, IoU.awning: 0.4168, IoU.streetlight: 0.3423, IoU.booth: 0.5096, IoU.television receiver: 0.7745, IoU.airplane: 0.6299, IoU.dirt track: 0.1215, IoU.apparel: 0.4895, IoU.pole: 0.2843, IoU.land: 0.0093, IoU.bannister: 0.1857, IoU.escalator: 0.5763, IoU.ottoman: 0.4483, IoU.bottle: 0.4061, IoU.buffet: 0.5915, IoU.poster: 0.3611, IoU.stage: 0.2167, IoU.van: 0.4472, IoU.ship: 0.7411, IoU.fountain: 0.3078, IoU.conveyer belt: 0.7891, IoU.canopy: 0.5162, IoU.washer: 0.8237, IoU.plaything: 0.2311, IoU.swimming pool: 0.5394, IoU.stool: 0.5391, IoU.barrel: 0.5221, IoU.basket: 0.4233, IoU.waterfall: 0.7889, IoU.tent: 0.9129, IoU.bag: 0.1805, IoU.minibike: 0.7592, IoU.cradle: 0.8018, IoU.oven: 0.6556, IoU.ball: 0.5410, IoU.food: 0.6069, IoU.step: 0.1484, IoU.tank: 0.8413, IoU.trade name: 0.1798, IoU.microwave: 0.8922, IoU.pot: 0.5669, IoU.animal: 0.6013, IoU.bicycle: 0.5770, IoU.lake: 0.5648, IoU.dishwasher: 0.6705, IoU.screen: 0.5137, IoU.blanket: 0.2123, IoU.sculpture: 0.7775, IoU.hood: 0.6432, IoU.sconce: 0.5759, IoU.vase: 0.4881, IoU.traffic light: 0.3637, IoU.tray: 0.2354, IoU.ashcan: 0.4940, IoU.fan: 0.7023, IoU.pier: 0.4239, IoU.crt screen: 0.0340, IoU.plate: 0.6009, IoU.monitor: 0.6127, IoU.bulletin board: 0.6005, IoU.shower: 0.0489, IoU.radiator: 0.6812, IoU.glass: 0.1949, IoU.clock: 0.4924, IoU.flag: 0.6999, Acc.wall: 0.8979, Acc.building: 0.9365, Acc.sky: 0.9760, Acc.floor: 0.9256, Acc.tree: 0.9021, Acc.ceiling: 0.9293, Acc.road: 0.9117, Acc.bed : 0.9689, Acc.windowpane: 0.8154, Acc.grass: 0.7943, Acc.cabinet: 0.7517, Acc.sidewalk: 0.8571, Acc.person: 0.9444, Acc.earth: 0.4736, Acc.door: 0.7528, Acc.table: 0.8029, Acc.mountain: 0.7280, Acc.plant: 0.6466, Acc.curtain: 0.8644, Acc.chair: 0.7849, Acc.car: 0.9485, Acc.water: 0.7190, Acc.painting: 0.8974, Acc.sofa: 0.9105, Acc.shelf: 0.5944, Acc.house: 0.7939, Acc.sea: 0.9132, Acc.mirror: 0.8547, Acc.rug: 0.7717, Acc.field: 0.5801, Acc.armchair: 0.7705, Acc.seat: 0.8888, Acc.fence: 0.6539, Acc.desk: 0.7830, Acc.rock: 0.8215, Acc.wardrobe: 0.7480, Acc.lamp: 0.8503, Acc.bathtub: 0.8678, Acc.railing: 0.6516, Acc.cushion: 0.8224, Acc.base: 0.6286, Acc.box: 0.3957, Acc.column: 0.7093, Acc.signboard: 0.5212, Acc.chest of drawers: 0.6991, Acc.counter: 0.5452, Acc.sand: 0.8770, Acc.sink: 0.8354, Acc.skyscraper: 0.6217, Acc.fireplace: 0.9427, Acc.refrigerator: 0.9337, Acc.grandstand: 0.8512, Acc.path: 0.5049, Acc.stairs: 0.3208, Acc.runway: 0.9628, Acc.case: 0.7137, Acc.pool table: 0.9793, Acc.pillow: 0.8110, Acc.screen door: 0.8505, Acc.stairway: 0.6276, Acc.river: 0.2820, Acc.bridge: 0.7535, Acc.bookcase: 0.7379, Acc.blind: 0.4829, Acc.coffee table: 0.9072, Acc.toilet: 0.9278, Acc.flower: 0.5269, Acc.book: 0.7006, Acc.hill: 0.1477, Acc.bench: 0.6160, Acc.countertop: 0.8707, Acc.stove: 0.9263, Acc.palm: 0.7974, Acc.kitchen island: 0.8736, Acc.computer: 0.9205, Acc.swivel chair: 0.7329, Acc.boat: 0.9218, Acc.bar: 0.8760, Acc.arcade machine: 0.8531, Acc.hovel: 0.5917, Acc.bus: 0.9641, Acc.towel: 0.8421, Acc.light: 0.6631, Acc.truck: 0.5495, Acc.tower: 0.4807, Acc.chandelier: 0.8342, Acc.awning: 0.5149, Acc.streetlight: 0.4859, Acc.booth: 0.6686, Acc.television receiver: 0.9313, Acc.airplane: 0.7349, Acc.dirt track: 0.1906, Acc.apparel: 0.6969, Acc.pole: 0.3959, Acc.land: 0.0201, Acc.bannister: 0.3063, Acc.escalator: 0.7889, Acc.ottoman: 0.5929, Acc.bottle: 0.6553, Acc.buffet: 0.7630, Acc.poster: 0.4423, Acc.stage: 0.4226, Acc.van: 0.6155, Acc.ship: 0.9062, Acc.fountain: 0.3276, Acc.conveyer belt: 0.9296, Acc.canopy: 0.7952, Acc.washer: 0.8701, Acc.plaything: 0.4136, Acc.swimming pool: 0.7755, Acc.stool: 0.7049, Acc.barrel: 0.7468, Acc.basket: 0.6399, Acc.waterfall: 0.9313, Acc.tent: 0.9864, Acc.bag: 0.1977, Acc.minibike: 0.8919, Acc.cradle: 0.9780, Acc.oven: 0.7621, Acc.ball: 0.7003, Acc.food: 0.7273, Acc.step: 0.1844, Acc.tank: 0.9172, Acc.trade name: 0.1974, Acc.microwave: 0.9650, Acc.pot: 0.6704, Acc.animal: 0.6189, Acc.bicycle: 0.7743, Acc.lake: 0.6379, Acc.dishwasher: 0.7685, Acc.screen: 0.8103, Acc.blanket: 0.2368, Acc.sculpture: 0.8548, Acc.hood: 0.7556, Acc.sconce: 0.6439, Acc.vase: 0.6348, Acc.traffic light: 0.6251, Acc.tray: 0.2698, Acc.ashcan: 0.6240, Acc.fan: 0.8107, Acc.pier: 0.4642, Acc.crt screen: 0.0479, Acc.plate: 0.7875, Acc.monitor: 0.7952, Acc.bulletin board: 0.7012, Acc.shower: 0.0764, Acc.radiator: 0.7907, Acc.glass: 0.2085, Acc.clock: 0.5603, Acc.flag: 0.7814 +2024-06-16 18:25:34,435 - mmseg - INFO - Iter [50050/80000] lr: 1.498e-05, eta: 12:33:31, time: 3.272, data_time: 1.915, memory: 70722, decode.loss_ce: 0.1784, decode.acc_seg: 92.4014, aux.loss_ce: 0.0749, aux.acc_seg: 92.0297, loss: 0.2533 +2024-06-16 18:26:42,597 - mmseg - INFO - Iter [50100/80000] lr: 1.495e-05, eta: 12:32:11, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1763, decode.acc_seg: 92.5247, aux.loss_ce: 0.0739, aux.acc_seg: 92.1044, loss: 0.2502 +2024-06-16 18:27:50,887 - mmseg - INFO - Iter [50150/80000] lr: 1.493e-05, eta: 12:30:51, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1716, decode.acc_seg: 92.4161, aux.loss_ce: 0.0718, aux.acc_seg: 92.1304, loss: 0.2434 +2024-06-16 18:28:59,155 - mmseg - INFO - Iter [50200/80000] lr: 1.490e-05, eta: 12:29:31, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1800, decode.acc_seg: 92.4646, aux.loss_ce: 0.0752, aux.acc_seg: 92.1637, loss: 0.2552 +2024-06-16 18:30:07,529 - mmseg - INFO - Iter [50250/80000] lr: 1.488e-05, eta: 12:28:12, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1718, decode.acc_seg: 92.6828, aux.loss_ce: 0.0724, aux.acc_seg: 92.3677, loss: 0.2441 +2024-06-16 18:31:15,651 - mmseg - INFO - Iter [50300/80000] lr: 1.485e-05, eta: 12:26:52, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1844, decode.acc_seg: 92.1373, aux.loss_ce: 0.0778, aux.acc_seg: 91.7111, loss: 0.2622 +2024-06-16 18:32:23,918 - mmseg - INFO - Iter [50350/80000] lr: 1.483e-05, eta: 12:25:32, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1743, decode.acc_seg: 92.5939, aux.loss_ce: 0.0731, aux.acc_seg: 92.2461, loss: 0.2474 +2024-06-16 18:33:32,163 - mmseg - INFO - Iter [50400/80000] lr: 1.480e-05, eta: 12:24:13, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1694, decode.acc_seg: 92.7417, aux.loss_ce: 0.0709, aux.acc_seg: 92.4216, loss: 0.2403 +2024-06-16 18:34:40,299 - mmseg - INFO - Iter [50450/80000] lr: 1.478e-05, eta: 12:22:53, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1803, decode.acc_seg: 92.3228, aux.loss_ce: 0.0755, aux.acc_seg: 91.9938, loss: 0.2558 +2024-06-16 18:35:48,480 - mmseg - INFO - Iter [50500/80000] lr: 1.475e-05, eta: 12:21:33, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1737, decode.acc_seg: 92.3625, aux.loss_ce: 0.0732, aux.acc_seg: 91.9753, loss: 0.2470 +2024-06-16 18:36:58,855 - mmseg - INFO - Iter [50550/80000] lr: 1.473e-05, eta: 12:20:15, time: 1.407, data_time: 0.052, memory: 70722, decode.loss_ce: 0.1702, decode.acc_seg: 92.4521, aux.loss_ce: 0.0721, aux.acc_seg: 92.0524, loss: 0.2423 +2024-06-16 18:38:06,880 - mmseg - INFO - Iter [50600/80000] lr: 1.470e-05, eta: 12:18:55, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1669, decode.acc_seg: 92.9059, aux.loss_ce: 0.0705, aux.acc_seg: 92.5335, loss: 0.2375 +2024-06-16 18:39:15,235 - mmseg - INFO - Iter [50650/80000] lr: 1.468e-05, eta: 12:17:36, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1796, decode.acc_seg: 92.2571, aux.loss_ce: 0.0751, aux.acc_seg: 91.8512, loss: 0.2547 +2024-06-16 18:40:23,199 - mmseg - INFO - Iter [50700/80000] lr: 1.465e-05, eta: 12:16:16, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1710, decode.acc_seg: 92.6630, aux.loss_ce: 0.0723, aux.acc_seg: 92.2583, loss: 0.2434 +2024-06-16 18:41:31,545 - mmseg - INFO - Iter [50750/80000] lr: 1.463e-05, eta: 12:14:57, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1734, decode.acc_seg: 92.7016, aux.loss_ce: 0.0732, aux.acc_seg: 92.3434, loss: 0.2466 +2024-06-16 18:42:39,615 - mmseg - INFO - Iter [50800/80000] lr: 1.460e-05, eta: 12:13:37, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1657, decode.acc_seg: 92.6999, aux.loss_ce: 0.0704, aux.acc_seg: 92.3615, loss: 0.2362 +2024-06-16 18:43:47,703 - mmseg - INFO - Iter [50850/80000] lr: 1.458e-05, eta: 12:12:17, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1695, decode.acc_seg: 92.4987, aux.loss_ce: 0.0716, aux.acc_seg: 92.1338, loss: 0.2411 +2024-06-16 18:44:55,829 - mmseg - INFO - Iter [50900/80000] lr: 1.455e-05, eta: 12:10:58, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1696, decode.acc_seg: 92.5634, aux.loss_ce: 0.0716, aux.acc_seg: 92.1880, loss: 0.2412 +2024-06-16 18:46:03,810 - mmseg - INFO - Iter [50950/80000] lr: 1.453e-05, eta: 12:09:38, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1722, decode.acc_seg: 92.5073, aux.loss_ce: 0.0724, aux.acc_seg: 92.1943, loss: 0.2446 +2024-06-16 18:47:12,083 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 18:47:12,083 - mmseg - INFO - Iter [51000/80000] lr: 1.450e-05, eta: 12:08:19, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1806, decode.acc_seg: 92.5772, aux.loss_ce: 0.0759, aux.acc_seg: 92.2655, loss: 0.2565 +2024-06-16 18:48:48,540 - mmseg - INFO - per class results: +2024-06-16 18:48:48,546 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.45 | 90.25 | +| building | 85.52 | 93.85 | +| sky | 94.89 | 97.54 | +| floor | 85.39 | 92.0 | +| tree | 77.31 | 89.52 | +| ceiling | 86.61 | 93.99 | +| road | 86.78 | 92.39 | +| bed | 92.69 | 96.75 | +| windowpane | 66.16 | 82.44 | +| grass | 69.06 | 83.93 | +| cabinet | 65.83 | 76.57 | +| sidewalk | 73.21 | 83.04 | +| person | 86.13 | 94.19 | +| earth | 37.23 | 50.64 | +| door | 57.81 | 69.64 | +| table | 70.65 | 81.39 | +| mountain | 60.91 | 70.57 | +| plant | 53.78 | 67.26 | +| curtain | 76.2 | 86.66 | +| chair | 67.45 | 78.53 | +| car | 87.2 | 94.23 | +| water | 67.72 | 84.26 | +| painting | 78.37 | 91.05 | +| sofa | 81.95 | 91.13 | +| shelf | 45.38 | 62.85 | +| house | 53.84 | 67.88 | +| sea | 77.7 | 87.17 | +| mirror | 78.16 | 84.52 | +| rug | 72.34 | 83.66 | +| field | 29.61 | 51.93 | +| armchair | 59.39 | 77.12 | +| seat | 66.0 | 89.53 | +| fence | 52.36 | 69.59 | +| desk | 61.38 | 80.32 | +| rock | 57.79 | 83.07 | +| wardrobe | 52.5 | 71.99 | +| lamp | 74.41 | 84.84 | +| bathtub | 84.39 | 86.72 | +| railing | 43.54 | 62.67 | +| cushion | 70.04 | 79.95 | +| base | 39.51 | 58.08 | +| box | 37.49 | 46.56 | +| column | 52.63 | 65.43 | +| signboard | 39.38 | 49.37 | +| chest of drawers | 42.21 | 69.66 | +| counter | 45.67 | 52.29 | +| sand | 53.32 | 79.85 | +| sink | 77.09 | 85.31 | +| skyscraper | 48.47 | 63.26 | +| fireplace | 74.54 | 93.48 | +| refrigerator | 84.2 | 93.35 | +| grandstand | 53.03 | 84.58 | +| path | 33.58 | 45.3 | +| stairs | 26.42 | 34.74 | +| runway | 66.3 | 85.1 | +| case | 61.62 | 82.99 | +| pool table | 94.18 | 98.79 | +| pillow | 70.78 | 83.31 | +| screen door | 76.11 | 78.18 | +| stairway | 43.21 | 57.62 | +| river | 16.74 | 25.72 | +| bridge | 76.39 | 85.93 | +| bookcase | 44.04 | 67.88 | +| blind | 42.42 | 46.87 | +| coffee table | 64.64 | 90.53 | +| toilet | 89.25 | 94.41 | +| flower | 42.18 | 51.16 | +| book | 54.22 | 78.38 | +| hill | 7.16 | 11.93 | +| bench | 53.85 | 63.6 | +| countertop | 64.41 | 85.17 | +| stove | 85.2 | 91.67 | +| palm | 55.29 | 79.34 | +| kitchen island | 54.7 | 86.38 | +| computer | 79.43 | 90.88 | +| swivel chair | 45.11 | 61.86 | +| boat | 73.94 | 92.38 | +| bar | 64.83 | 84.65 | +| arcade machine | 78.51 | 82.88 | +| hovel | 23.16 | 24.43 | +| bus | 93.02 | 96.63 | +| towel | 75.35 | 84.57 | +| light | 61.23 | 71.27 | +| truck | 45.96 | 66.18 | +| tower | 8.34 | 11.31 | +| chandelier | 72.35 | 85.02 | +| awning | 46.36 | 62.15 | +| streetlight | 34.45 | 44.88 | +| booth | 50.13 | 63.68 | +| television receiver | 77.92 | 87.59 | +| airplane | 64.8 | 70.03 | +| dirt track | 6.04 | 26.34 | +| apparel | 47.26 | 63.23 | +| pole | 31.17 | 41.64 | +| land | 7.2 | 12.42 | +| bannister | 17.76 | 25.29 | +| escalator | 60.24 | 79.04 | +| ottoman | 51.64 | 72.05 | +| bottle | 40.14 | 61.67 | +| buffet | 51.13 | 65.12 | +| poster | 35.71 | 47.12 | +| stage | 23.14 | 45.32 | +| van | 46.03 | 59.13 | +| ship | 62.35 | 66.28 | +| fountain | 29.61 | 30.22 | +| conveyer belt | 78.58 | 93.27 | +| canopy | 55.9 | 76.74 | +| washer | 84.21 | 89.55 | +| plaything | 29.42 | 40.83 | +| swimming pool | 55.69 | 80.62 | +| stool | 56.89 | 67.55 | +| barrel | 48.13 | 74.91 | +| basket | 40.65 | 58.91 | +| waterfall | 73.09 | 87.88 | +| tent | 94.88 | 98.53 | +| bag | 20.17 | 23.16 | +| minibike | 75.44 | 89.25 | +| cradle | 76.11 | 98.06 | +| oven | 55.83 | 66.32 | +| ball | 49.97 | 52.76 | +| food | 64.65 | 82.2 | +| step | 12.93 | 15.88 | +| tank | 74.88 | 85.29 | +| trade name | 26.33 | 30.16 | +| microwave | 87.12 | 96.21 | +| pot | 56.16 | 65.89 | +| animal | 60.96 | 62.77 | +| bicycle | 59.22 | 79.87 | +| lake | 57.76 | 63.72 | +| dishwasher | 68.4 | 74.34 | +| screen | 51.78 | 70.5 | +| blanket | 25.5 | 30.12 | +| sculpture | 72.03 | 88.64 | +| hood | 66.01 | 78.0 | +| sconce | 59.79 | 72.43 | +| vase | 48.49 | 59.66 | +| traffic light | 36.24 | 61.76 | +| tray | 24.69 | 34.25 | +| ashcan | 46.43 | 63.61 | +| fan | 69.61 | 81.54 | +| pier | 42.13 | 45.31 | +| crt screen | 16.9 | 37.51 | +| plate | 60.15 | 74.69 | +| monitor | 48.88 | 57.6 | +| bulletin board | 48.94 | 69.39 | +| shower | 3.11 | 11.93 | +| radiator | 66.59 | 74.84 | +| glass | 18.83 | 19.88 | +| clock | 46.52 | 54.3 | +| flag | 71.04 | 75.76 | ++---------------------+-------+-------+ +2024-06-16 18:48:48,546 - mmseg - INFO - Summary: +2024-06-16 18:48:48,546 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.17 | 56.88 | 69.42 | ++-------+-------+-------+ +2024-06-16 18:48:48,547 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 18:48:48,547 - mmseg - INFO - Iter(val) [250] aAcc: 0.8617, mIoU: 0.5688, mAcc: 0.6942, IoU.wall: 0.8245, IoU.building: 0.8552, IoU.sky: 0.9489, IoU.floor: 0.8539, IoU.tree: 0.7731, IoU.ceiling: 0.8661, IoU.road: 0.8678, IoU.bed : 0.9269, IoU.windowpane: 0.6616, IoU.grass: 0.6906, IoU.cabinet: 0.6583, IoU.sidewalk: 0.7321, IoU.person: 0.8613, IoU.earth: 0.3723, IoU.door: 0.5781, IoU.table: 0.7065, IoU.mountain: 0.6091, IoU.plant: 0.5378, IoU.curtain: 0.7620, IoU.chair: 0.6745, IoU.car: 0.8720, IoU.water: 0.6772, IoU.painting: 0.7837, IoU.sofa: 0.8195, IoU.shelf: 0.4538, IoU.house: 0.5384, IoU.sea: 0.7770, IoU.mirror: 0.7816, IoU.rug: 0.7234, IoU.field: 0.2961, IoU.armchair: 0.5939, IoU.seat: 0.6600, IoU.fence: 0.5236, IoU.desk: 0.6138, IoU.rock: 0.5779, IoU.wardrobe: 0.5250, IoU.lamp: 0.7441, IoU.bathtub: 0.8439, IoU.railing: 0.4354, IoU.cushion: 0.7004, IoU.base: 0.3951, IoU.box: 0.3749, IoU.column: 0.5263, IoU.signboard: 0.3938, IoU.chest of drawers: 0.4221, IoU.counter: 0.4567, IoU.sand: 0.5332, IoU.sink: 0.7709, IoU.skyscraper: 0.4847, IoU.fireplace: 0.7454, IoU.refrigerator: 0.8420, IoU.grandstand: 0.5303, IoU.path: 0.3358, IoU.stairs: 0.2642, IoU.runway: 0.6630, IoU.case: 0.6162, IoU.pool table: 0.9418, IoU.pillow: 0.7078, IoU.screen door: 0.7611, IoU.stairway: 0.4321, IoU.river: 0.1674, IoU.bridge: 0.7639, IoU.bookcase: 0.4404, IoU.blind: 0.4242, IoU.coffee table: 0.6464, IoU.toilet: 0.8925, IoU.flower: 0.4218, IoU.book: 0.5422, IoU.hill: 0.0716, IoU.bench: 0.5385, IoU.countertop: 0.6441, IoU.stove: 0.8520, IoU.palm: 0.5529, IoU.kitchen island: 0.5470, IoU.computer: 0.7943, IoU.swivel chair: 0.4511, IoU.boat: 0.7394, IoU.bar: 0.6483, IoU.arcade machine: 0.7851, IoU.hovel: 0.2316, IoU.bus: 0.9302, IoU.towel: 0.7535, IoU.light: 0.6123, IoU.truck: 0.4596, IoU.tower: 0.0834, IoU.chandelier: 0.7235, IoU.awning: 0.4636, IoU.streetlight: 0.3445, IoU.booth: 0.5013, IoU.television receiver: 0.7792, IoU.airplane: 0.6480, IoU.dirt track: 0.0604, IoU.apparel: 0.4726, IoU.pole: 0.3117, IoU.land: 0.0720, IoU.bannister: 0.1776, IoU.escalator: 0.6024, IoU.ottoman: 0.5164, IoU.bottle: 0.4014, IoU.buffet: 0.5113, IoU.poster: 0.3571, IoU.stage: 0.2314, IoU.van: 0.4603, IoU.ship: 0.6235, IoU.fountain: 0.2961, IoU.conveyer belt: 0.7858, IoU.canopy: 0.5590, IoU.washer: 0.8421, IoU.plaything: 0.2942, IoU.swimming pool: 0.5569, IoU.stool: 0.5689, IoU.barrel: 0.4813, IoU.basket: 0.4065, IoU.waterfall: 0.7309, IoU.tent: 0.9488, IoU.bag: 0.2017, IoU.minibike: 0.7544, IoU.cradle: 0.7611, IoU.oven: 0.5583, IoU.ball: 0.4997, IoU.food: 0.6465, IoU.step: 0.1293, IoU.tank: 0.7488, IoU.trade name: 0.2633, IoU.microwave: 0.8712, IoU.pot: 0.5616, IoU.animal: 0.6096, IoU.bicycle: 0.5922, IoU.lake: 0.5776, IoU.dishwasher: 0.6840, IoU.screen: 0.5178, IoU.blanket: 0.2550, IoU.sculpture: 0.7203, IoU.hood: 0.6601, IoU.sconce: 0.5979, IoU.vase: 0.4849, IoU.traffic light: 0.3624, IoU.tray: 0.2469, IoU.ashcan: 0.4643, IoU.fan: 0.6961, IoU.pier: 0.4213, IoU.crt screen: 0.1690, IoU.plate: 0.6015, IoU.monitor: 0.4888, IoU.bulletin board: 0.4894, IoU.shower: 0.0311, IoU.radiator: 0.6659, IoU.glass: 0.1883, IoU.clock: 0.4652, IoU.flag: 0.7104, Acc.wall: 0.9025, Acc.building: 0.9385, Acc.sky: 0.9754, Acc.floor: 0.9200, Acc.tree: 0.8952, Acc.ceiling: 0.9399, Acc.road: 0.9239, Acc.bed : 0.9675, Acc.windowpane: 0.8244, Acc.grass: 0.8393, Acc.cabinet: 0.7657, Acc.sidewalk: 0.8304, Acc.person: 0.9419, Acc.earth: 0.5064, Acc.door: 0.6964, Acc.table: 0.8139, Acc.mountain: 0.7057, Acc.plant: 0.6726, Acc.curtain: 0.8666, Acc.chair: 0.7853, Acc.car: 0.9423, Acc.water: 0.8426, Acc.painting: 0.9105, Acc.sofa: 0.9113, Acc.shelf: 0.6285, Acc.house: 0.6788, Acc.sea: 0.8717, Acc.mirror: 0.8452, Acc.rug: 0.8366, Acc.field: 0.5193, Acc.armchair: 0.7712, Acc.seat: 0.8953, Acc.fence: 0.6959, Acc.desk: 0.8032, Acc.rock: 0.8307, Acc.wardrobe: 0.7199, Acc.lamp: 0.8484, Acc.bathtub: 0.8672, Acc.railing: 0.6267, Acc.cushion: 0.7995, Acc.base: 0.5808, Acc.box: 0.4656, Acc.column: 0.6543, Acc.signboard: 0.4937, Acc.chest of drawers: 0.6966, Acc.counter: 0.5229, Acc.sand: 0.7985, Acc.sink: 0.8531, Acc.skyscraper: 0.6326, Acc.fireplace: 0.9348, Acc.refrigerator: 0.9335, Acc.grandstand: 0.8458, Acc.path: 0.4530, Acc.stairs: 0.3474, Acc.runway: 0.8510, Acc.case: 0.8299, Acc.pool table: 0.9879, Acc.pillow: 0.8331, Acc.screen door: 0.7818, Acc.stairway: 0.5762, Acc.river: 0.2572, Acc.bridge: 0.8593, Acc.bookcase: 0.6788, Acc.blind: 0.4687, Acc.coffee table: 0.9053, Acc.toilet: 0.9441, Acc.flower: 0.5116, Acc.book: 0.7838, Acc.hill: 0.1193, Acc.bench: 0.6360, Acc.countertop: 0.8517, Acc.stove: 0.9167, Acc.palm: 0.7934, Acc.kitchen island: 0.8638, Acc.computer: 0.9088, Acc.swivel chair: 0.6186, Acc.boat: 0.9238, Acc.bar: 0.8465, Acc.arcade machine: 0.8288, Acc.hovel: 0.2443, Acc.bus: 0.9663, Acc.towel: 0.8457, Acc.light: 0.7127, Acc.truck: 0.6618, Acc.tower: 0.1131, Acc.chandelier: 0.8502, Acc.awning: 0.6215, Acc.streetlight: 0.4488, Acc.booth: 0.6368, Acc.television receiver: 0.8759, Acc.airplane: 0.7003, Acc.dirt track: 0.2634, Acc.apparel: 0.6323, Acc.pole: 0.4164, Acc.land: 0.1242, Acc.bannister: 0.2529, Acc.escalator: 0.7904, Acc.ottoman: 0.7205, Acc.bottle: 0.6167, Acc.buffet: 0.6512, Acc.poster: 0.4712, Acc.stage: 0.4532, Acc.van: 0.5913, Acc.ship: 0.6628, Acc.fountain: 0.3022, Acc.conveyer belt: 0.9327, Acc.canopy: 0.7674, Acc.washer: 0.8955, Acc.plaything: 0.4083, Acc.swimming pool: 0.8062, Acc.stool: 0.6755, Acc.barrel: 0.7491, Acc.basket: 0.5891, Acc.waterfall: 0.8788, Acc.tent: 0.9853, Acc.bag: 0.2316, Acc.minibike: 0.8925, Acc.cradle: 0.9806, Acc.oven: 0.6632, Acc.ball: 0.5276, Acc.food: 0.8220, Acc.step: 0.1588, Acc.tank: 0.8529, Acc.trade name: 0.3016, Acc.microwave: 0.9621, Acc.pot: 0.6589, Acc.animal: 0.6277, Acc.bicycle: 0.7987, Acc.lake: 0.6372, Acc.dishwasher: 0.7434, Acc.screen: 0.7050, Acc.blanket: 0.3012, Acc.sculpture: 0.8864, Acc.hood: 0.7800, Acc.sconce: 0.7243, Acc.vase: 0.5966, Acc.traffic light: 0.6176, Acc.tray: 0.3425, Acc.ashcan: 0.6361, Acc.fan: 0.8154, Acc.pier: 0.4531, Acc.crt screen: 0.3751, Acc.plate: 0.7469, Acc.monitor: 0.5760, Acc.bulletin board: 0.6939, Acc.shower: 0.1193, Acc.radiator: 0.7484, Acc.glass: 0.1988, Acc.clock: 0.5430, Acc.flag: 0.7576 +2024-06-16 18:49:57,420 - mmseg - INFO - Iter [51050/80000] lr: 1.448e-05, eta: 12:07:55, time: 3.307, data_time: 1.945, memory: 70722, decode.loss_ce: 0.1745, decode.acc_seg: 92.6226, aux.loss_ce: 0.0736, aux.acc_seg: 92.2038, loss: 0.2481 +2024-06-16 18:51:05,613 - mmseg - INFO - Iter [51100/80000] lr: 1.445e-05, eta: 12:06:35, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1759, decode.acc_seg: 92.3484, aux.loss_ce: 0.0736, aux.acc_seg: 91.9954, loss: 0.2495 +2024-06-16 18:52:13,685 - mmseg - INFO - Iter [51150/80000] lr: 1.443e-05, eta: 12:05:16, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1728, decode.acc_seg: 92.3483, aux.loss_ce: 0.0734, aux.acc_seg: 91.8791, loss: 0.2462 +2024-06-16 18:53:21,953 - mmseg - INFO - Iter [51200/80000] lr: 1.440e-05, eta: 12:03:56, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1732, decode.acc_seg: 92.6135, aux.loss_ce: 0.0729, aux.acc_seg: 92.3214, loss: 0.2461 +2024-06-16 18:54:30,176 - mmseg - INFO - Iter [51250/80000] lr: 1.438e-05, eta: 12:02:37, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1781, decode.acc_seg: 92.3931, aux.loss_ce: 0.0752, aux.acc_seg: 92.0084, loss: 0.2533 +2024-06-16 18:55:38,394 - mmseg - INFO - Iter [51300/80000] lr: 1.435e-05, eta: 12:01:17, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1752, decode.acc_seg: 92.6420, aux.loss_ce: 0.0743, aux.acc_seg: 92.2062, loss: 0.2496 +2024-06-16 18:56:46,454 - mmseg - INFO - Iter [51350/80000] lr: 1.433e-05, eta: 11:59:58, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1713, decode.acc_seg: 92.5746, aux.loss_ce: 0.0717, aux.acc_seg: 92.2234, loss: 0.2430 +2024-06-16 18:57:54,936 - mmseg - INFO - Iter [51400/80000] lr: 1.430e-05, eta: 11:58:39, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1824, decode.acc_seg: 91.9631, aux.loss_ce: 0.0767, aux.acc_seg: 91.6247, loss: 0.2591 +2024-06-16 18:59:02,987 - mmseg - INFO - Iter [51450/80000] lr: 1.428e-05, eta: 11:57:19, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1858, decode.acc_seg: 92.1676, aux.loss_ce: 0.0780, aux.acc_seg: 91.8189, loss: 0.2637 +2024-06-16 19:00:11,144 - mmseg - INFO - Iter [51500/80000] lr: 1.425e-05, eta: 11:56:00, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1760, decode.acc_seg: 92.4092, aux.loss_ce: 0.0740, aux.acc_seg: 92.0322, loss: 0.2499 +2024-06-16 19:01:19,454 - mmseg - INFO - Iter [51550/80000] lr: 1.423e-05, eta: 11:54:40, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1691, decode.acc_seg: 92.6898, aux.loss_ce: 0.0711, aux.acc_seg: 92.3057, loss: 0.2401 +2024-06-16 19:02:27,595 - mmseg - INFO - Iter [51600/80000] lr: 1.420e-05, eta: 11:53:21, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1758, decode.acc_seg: 92.4883, aux.loss_ce: 0.0744, aux.acc_seg: 92.1056, loss: 0.2502 +2024-06-16 19:03:35,881 - mmseg - INFO - Iter [51650/80000] lr: 1.418e-05, eta: 11:52:02, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1719, decode.acc_seg: 92.6021, aux.loss_ce: 0.0725, aux.acc_seg: 92.2589, loss: 0.2444 +2024-06-16 19:04:44,257 - mmseg - INFO - Iter [51700/80000] lr: 1.415e-05, eta: 11:50:43, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1670, decode.acc_seg: 92.8323, aux.loss_ce: 0.0705, aux.acc_seg: 92.4503, loss: 0.2375 +2024-06-16 19:05:52,280 - mmseg - INFO - Iter [51750/80000] lr: 1.413e-05, eta: 11:49:23, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1699, decode.acc_seg: 92.5644, aux.loss_ce: 0.0717, aux.acc_seg: 92.1730, loss: 0.2416 +2024-06-16 19:07:02,947 - mmseg - INFO - Iter [51800/80000] lr: 1.410e-05, eta: 11:48:06, time: 1.413, data_time: 0.061, memory: 70722, decode.loss_ce: 0.1787, decode.acc_seg: 92.2204, aux.loss_ce: 0.0747, aux.acc_seg: 91.9760, loss: 0.2534 +2024-06-16 19:08:11,043 - mmseg - INFO - Iter [51850/80000] lr: 1.408e-05, eta: 11:46:46, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1781, decode.acc_seg: 92.2128, aux.loss_ce: 0.0749, aux.acc_seg: 91.8620, loss: 0.2530 +2024-06-16 19:09:19,059 - mmseg - INFO - Iter [51900/80000] lr: 1.405e-05, eta: 11:45:27, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1639, decode.acc_seg: 92.9891, aux.loss_ce: 0.0691, aux.acc_seg: 92.5766, loss: 0.2330 +2024-06-16 19:10:27,169 - mmseg - INFO - Iter [51950/80000] lr: 1.403e-05, eta: 11:44:08, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1830, decode.acc_seg: 92.3473, aux.loss_ce: 0.0762, aux.acc_seg: 91.9936, loss: 0.2593 +2024-06-16 19:11:35,707 - mmseg - INFO - Saving checkpoint at 52000 iterations +2024-06-16 19:13:03,709 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 19:13:03,709 - mmseg - INFO - Iter [52000/80000] lr: 1.400e-05, eta: 11:43:36, time: 3.131, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1807, decode.acc_seg: 92.4627, aux.loss_ce: 0.0761, aux.acc_seg: 92.0701, loss: 0.2569 +2024-06-16 19:14:38,533 - mmseg - INFO - per class results: +2024-06-16 19:14:38,539 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.02 | 89.5 | +| building | 85.53 | 93.74 | +| sky | 94.82 | 97.51 | +| floor | 85.44 | 91.89 | +| tree | 76.86 | 88.11 | +| ceiling | 86.6 | 94.93 | +| road | 86.5 | 91.14 | +| bed | 92.81 | 96.99 | +| windowpane | 66.38 | 80.26 | +| grass | 70.41 | 85.64 | +| cabinet | 66.3 | 78.36 | +| sidewalk | 71.75 | 85.36 | +| person | 85.64 | 94.85 | +| earth | 36.81 | 49.62 | +| door | 59.78 | 74.48 | +| table | 70.35 | 81.67 | +| mountain | 60.02 | 70.96 | +| plant | 52.82 | 66.77 | +| curtain | 77.96 | 88.53 | +| chair | 68.55 | 80.6 | +| car | 87.34 | 93.75 | +| water | 62.12 | 76.52 | +| painting | 77.23 | 92.21 | +| sofa | 80.91 | 94.95 | +| shelf | 43.01 | 56.1 | +| house | 57.05 | 71.12 | +| sea | 68.96 | 90.08 | +| mirror | 77.43 | 85.37 | +| rug | 74.1 | 82.58 | +| field | 33.56 | 52.82 | +| armchair | 60.16 | 74.41 | +| seat | 67.54 | 87.82 | +| fence | 50.08 | 67.9 | +| desk | 62.34 | 76.42 | +| rock | 57.2 | 87.17 | +| wardrobe | 54.0 | 72.06 | +| lamp | 74.6 | 84.48 | +| bathtub | 84.33 | 85.99 | +| railing | 42.17 | 64.57 | +| cushion | 69.92 | 76.31 | +| base | 37.47 | 43.39 | +| box | 34.37 | 41.05 | +| column | 53.36 | 70.28 | +| signboard | 38.93 | 52.3 | +| chest of drawers | 42.46 | 68.78 | +| counter | 41.38 | 49.68 | +| sand | 56.12 | 82.37 | +| sink | 78.38 | 84.31 | +| skyscraper | 48.31 | 61.8 | +| fireplace | 70.91 | 93.97 | +| refrigerator | 84.4 | 92.14 | +| grandstand | 52.12 | 80.48 | +| path | 25.95 | 33.79 | +| stairs | 29.47 | 37.31 | +| runway | 71.74 | 96.26 | +| case | 55.7 | 78.18 | +| pool table | 94.22 | 98.66 | +| pillow | 69.49 | 82.4 | +| screen door | 79.44 | 83.76 | +| stairway | 49.56 | 62.19 | +| river | 17.04 | 23.56 | +| bridge | 75.59 | 87.29 | +| bookcase | 38.15 | 67.59 | +| blind | 46.39 | 52.04 | +| coffee table | 66.06 | 89.78 | +| toilet | 89.64 | 94.67 | +| flower | 45.46 | 57.39 | +| book | 53.73 | 75.64 | +| hill | 7.31 | 15.27 | +| bench | 53.67 | 63.05 | +| countertop | 64.73 | 85.62 | +| stove | 85.64 | 92.41 | +| palm | 56.04 | 82.96 | +| kitchen island | 53.36 | 74.37 | +| computer | 78.34 | 92.12 | +| swivel chair | 50.46 | 72.79 | +| boat | 68.34 | 92.56 | +| bar | 59.32 | 81.34 | +| arcade machine | 79.98 | 87.26 | +| hovel | 39.17 | 41.42 | +| bus | 93.7 | 96.07 | +| towel | 75.81 | 88.2 | +| light | 60.74 | 69.56 | +| truck | 44.12 | 59.16 | +| tower | 24.05 | 33.63 | +| chandelier | 71.75 | 87.6 | +| awning | 47.84 | 64.53 | +| streetlight | 33.34 | 47.92 | +| booth | 35.96 | 45.74 | +| television receiver | 76.84 | 86.0 | +| airplane | 57.07 | 74.45 | +| dirt track | 11.22 | 22.8 | +| apparel | 45.51 | 68.27 | +| pole | 23.93 | 31.41 | +| land | 3.57 | 5.03 | +| bannister | 19.12 | 26.24 | +| escalator | 61.45 | 77.82 | +| ottoman | 52.92 | 69.94 | +| bottle | 39.58 | 62.03 | +| buffet | 44.35 | 54.05 | +| poster | 33.32 | 52.17 | +| stage | 19.88 | 47.51 | +| van | 46.73 | 64.73 | +| ship | 82.82 | 93.12 | +| fountain | 31.38 | 33.0 | +| conveyer belt | 74.38 | 93.5 | +| canopy | 51.87 | 70.17 | +| washer | 85.77 | 90.98 | +| plaything | 35.9 | 56.45 | +| swimming pool | 60.77 | 83.63 | +| stool | 55.4 | 70.32 | +| barrel | 45.53 | 74.7 | +| basket | 39.92 | 56.02 | +| waterfall | 71.82 | 84.62 | +| tent | 94.45 | 97.76 | +| bag | 22.83 | 29.66 | +| minibike | 76.73 | 88.41 | +| cradle | 73.89 | 98.4 | +| oven | 58.93 | 66.09 | +| ball | 49.14 | 55.0 | +| food | 63.2 | 80.68 | +| step | 15.82 | 20.98 | +| tank | 75.97 | 85.75 | +| trade name | 27.36 | 31.33 | +| microwave | 87.62 | 95.95 | +| pot | 55.97 | 63.9 | +| animal | 59.85 | 61.24 | +| bicycle | 57.93 | 80.87 | +| lake | 57.57 | 63.66 | +| dishwasher | 65.75 | 76.44 | +| screen | 49.28 | 75.12 | +| blanket | 26.25 | 30.86 | +| sculpture | 73.46 | 87.41 | +| hood | 62.76 | 75.88 | +| sconce | 58.3 | 66.45 | +| vase | 48.39 | 66.46 | +| traffic light | 34.6 | 61.12 | +| tray | 23.5 | 30.03 | +| ashcan | 50.16 | 65.79 | +| fan | 66.91 | 79.08 | +| pier | 42.58 | 48.21 | +| crt screen | 2.57 | 5.51 | +| plate | 55.5 | 83.51 | +| monitor | 41.67 | 48.25 | +| bulletin board | 55.1 | 64.26 | +| shower | 5.6 | 5.6 | +| radiator | 66.06 | 77.09 | +| glass | 20.02 | 21.41 | +| clock | 44.49 | 55.95 | +| flag | 68.0 | 80.71 | ++---------------------+-------+-------+ +2024-06-16 19:14:38,539 - mmseg - INFO - Summary: +2024-06-16 19:14:38,539 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.09 | 56.67 | 69.61 | ++-------+-------+-------+ +2024-06-16 19:14:38,540 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 19:14:38,540 - mmseg - INFO - Iter(val) [250] aAcc: 0.8609, mIoU: 0.5667, mAcc: 0.6961, IoU.wall: 0.8202, IoU.building: 0.8553, IoU.sky: 0.9482, IoU.floor: 0.8544, IoU.tree: 0.7686, IoU.ceiling: 0.8660, IoU.road: 0.8650, IoU.bed : 0.9281, IoU.windowpane: 0.6638, IoU.grass: 0.7041, IoU.cabinet: 0.6630, IoU.sidewalk: 0.7175, IoU.person: 0.8564, IoU.earth: 0.3681, IoU.door: 0.5978, IoU.table: 0.7035, IoU.mountain: 0.6002, IoU.plant: 0.5282, IoU.curtain: 0.7796, IoU.chair: 0.6855, IoU.car: 0.8734, IoU.water: 0.6212, IoU.painting: 0.7723, IoU.sofa: 0.8091, IoU.shelf: 0.4301, IoU.house: 0.5705, IoU.sea: 0.6896, IoU.mirror: 0.7743, IoU.rug: 0.7410, IoU.field: 0.3356, IoU.armchair: 0.6016, IoU.seat: 0.6754, IoU.fence: 0.5008, IoU.desk: 0.6234, IoU.rock: 0.5720, IoU.wardrobe: 0.5400, IoU.lamp: 0.7460, IoU.bathtub: 0.8433, IoU.railing: 0.4217, IoU.cushion: 0.6992, IoU.base: 0.3747, IoU.box: 0.3437, IoU.column: 0.5336, IoU.signboard: 0.3893, IoU.chest of drawers: 0.4246, IoU.counter: 0.4138, IoU.sand: 0.5612, IoU.sink: 0.7838, IoU.skyscraper: 0.4831, IoU.fireplace: 0.7091, IoU.refrigerator: 0.8440, IoU.grandstand: 0.5212, IoU.path: 0.2595, IoU.stairs: 0.2947, IoU.runway: 0.7174, IoU.case: 0.5570, IoU.pool table: 0.9422, IoU.pillow: 0.6949, IoU.screen door: 0.7944, IoU.stairway: 0.4956, IoU.river: 0.1704, IoU.bridge: 0.7559, IoU.bookcase: 0.3815, IoU.blind: 0.4639, IoU.coffee table: 0.6606, IoU.toilet: 0.8964, IoU.flower: 0.4546, IoU.book: 0.5373, IoU.hill: 0.0731, IoU.bench: 0.5367, IoU.countertop: 0.6473, IoU.stove: 0.8564, IoU.palm: 0.5604, IoU.kitchen island: 0.5336, IoU.computer: 0.7834, IoU.swivel chair: 0.5046, IoU.boat: 0.6834, IoU.bar: 0.5932, IoU.arcade machine: 0.7998, IoU.hovel: 0.3917, IoU.bus: 0.9370, IoU.towel: 0.7581, IoU.light: 0.6074, IoU.truck: 0.4412, IoU.tower: 0.2405, IoU.chandelier: 0.7175, IoU.awning: 0.4784, IoU.streetlight: 0.3334, IoU.booth: 0.3596, IoU.television receiver: 0.7684, IoU.airplane: 0.5707, IoU.dirt track: 0.1122, IoU.apparel: 0.4551, IoU.pole: 0.2393, IoU.land: 0.0357, IoU.bannister: 0.1912, IoU.escalator: 0.6145, IoU.ottoman: 0.5292, IoU.bottle: 0.3958, IoU.buffet: 0.4435, IoU.poster: 0.3332, IoU.stage: 0.1988, IoU.van: 0.4673, IoU.ship: 0.8282, IoU.fountain: 0.3138, IoU.conveyer belt: 0.7438, IoU.canopy: 0.5187, IoU.washer: 0.8577, IoU.plaything: 0.3590, IoU.swimming pool: 0.6077, IoU.stool: 0.5540, IoU.barrel: 0.4553, IoU.basket: 0.3992, IoU.waterfall: 0.7182, IoU.tent: 0.9445, IoU.bag: 0.2283, IoU.minibike: 0.7673, IoU.cradle: 0.7389, IoU.oven: 0.5893, IoU.ball: 0.4914, IoU.food: 0.6320, IoU.step: 0.1582, IoU.tank: 0.7597, IoU.trade name: 0.2736, IoU.microwave: 0.8762, IoU.pot: 0.5597, IoU.animal: 0.5985, IoU.bicycle: 0.5793, IoU.lake: 0.5757, IoU.dishwasher: 0.6575, IoU.screen: 0.4928, IoU.blanket: 0.2625, IoU.sculpture: 0.7346, IoU.hood: 0.6276, IoU.sconce: 0.5830, IoU.vase: 0.4839, IoU.traffic light: 0.3460, IoU.tray: 0.2350, IoU.ashcan: 0.5016, IoU.fan: 0.6691, IoU.pier: 0.4258, IoU.crt screen: 0.0257, IoU.plate: 0.5550, IoU.monitor: 0.4167, IoU.bulletin board: 0.5510, IoU.shower: 0.0560, IoU.radiator: 0.6606, IoU.glass: 0.2002, IoU.clock: 0.4449, IoU.flag: 0.6800, Acc.wall: 0.8950, Acc.building: 0.9374, Acc.sky: 0.9751, Acc.floor: 0.9189, Acc.tree: 0.8811, Acc.ceiling: 0.9493, Acc.road: 0.9114, Acc.bed : 0.9699, Acc.windowpane: 0.8026, Acc.grass: 0.8564, Acc.cabinet: 0.7836, Acc.sidewalk: 0.8536, Acc.person: 0.9485, Acc.earth: 0.4962, Acc.door: 0.7448, Acc.table: 0.8167, Acc.mountain: 0.7096, Acc.plant: 0.6677, Acc.curtain: 0.8853, Acc.chair: 0.8060, Acc.car: 0.9375, Acc.water: 0.7652, Acc.painting: 0.9221, Acc.sofa: 0.9495, Acc.shelf: 0.5610, Acc.house: 0.7112, Acc.sea: 0.9008, Acc.mirror: 0.8537, Acc.rug: 0.8258, Acc.field: 0.5282, Acc.armchair: 0.7441, Acc.seat: 0.8782, Acc.fence: 0.6790, Acc.desk: 0.7642, Acc.rock: 0.8717, Acc.wardrobe: 0.7206, Acc.lamp: 0.8448, Acc.bathtub: 0.8599, Acc.railing: 0.6457, Acc.cushion: 0.7631, Acc.base: 0.4339, Acc.box: 0.4105, Acc.column: 0.7028, Acc.signboard: 0.5230, Acc.chest of drawers: 0.6878, Acc.counter: 0.4968, Acc.sand: 0.8237, Acc.sink: 0.8431, Acc.skyscraper: 0.6180, Acc.fireplace: 0.9397, Acc.refrigerator: 0.9214, Acc.grandstand: 0.8048, Acc.path: 0.3379, Acc.stairs: 0.3731, Acc.runway: 0.9626, Acc.case: 0.7818, Acc.pool table: 0.9866, Acc.pillow: 0.8240, Acc.screen door: 0.8376, Acc.stairway: 0.6219, Acc.river: 0.2356, Acc.bridge: 0.8729, Acc.bookcase: 0.6759, Acc.blind: 0.5204, Acc.coffee table: 0.8978, Acc.toilet: 0.9467, Acc.flower: 0.5739, Acc.book: 0.7564, Acc.hill: 0.1527, Acc.bench: 0.6305, Acc.countertop: 0.8562, Acc.stove: 0.9241, Acc.palm: 0.8296, Acc.kitchen island: 0.7437, Acc.computer: 0.9212, Acc.swivel chair: 0.7279, Acc.boat: 0.9256, Acc.bar: 0.8134, Acc.arcade machine: 0.8726, Acc.hovel: 0.4142, Acc.bus: 0.9607, Acc.towel: 0.8820, Acc.light: 0.6956, Acc.truck: 0.5916, Acc.tower: 0.3363, Acc.chandelier: 0.8760, Acc.awning: 0.6453, Acc.streetlight: 0.4792, Acc.booth: 0.4574, Acc.television receiver: 0.8600, Acc.airplane: 0.7445, Acc.dirt track: 0.2280, Acc.apparel: 0.6827, Acc.pole: 0.3141, Acc.land: 0.0503, Acc.bannister: 0.2624, Acc.escalator: 0.7782, Acc.ottoman: 0.6994, Acc.bottle: 0.6203, Acc.buffet: 0.5405, Acc.poster: 0.5217, Acc.stage: 0.4751, Acc.van: 0.6473, Acc.ship: 0.9312, Acc.fountain: 0.3300, Acc.conveyer belt: 0.9350, Acc.canopy: 0.7017, Acc.washer: 0.9098, Acc.plaything: 0.5645, Acc.swimming pool: 0.8363, Acc.stool: 0.7032, Acc.barrel: 0.7470, Acc.basket: 0.5602, Acc.waterfall: 0.8462, Acc.tent: 0.9776, Acc.bag: 0.2966, Acc.minibike: 0.8841, Acc.cradle: 0.9840, Acc.oven: 0.6609, Acc.ball: 0.5500, Acc.food: 0.8068, Acc.step: 0.2098, Acc.tank: 0.8575, Acc.trade name: 0.3133, Acc.microwave: 0.9595, Acc.pot: 0.6390, Acc.animal: 0.6124, Acc.bicycle: 0.8087, Acc.lake: 0.6366, Acc.dishwasher: 0.7644, Acc.screen: 0.7512, Acc.blanket: 0.3086, Acc.sculpture: 0.8741, Acc.hood: 0.7588, Acc.sconce: 0.6645, Acc.vase: 0.6646, Acc.traffic light: 0.6112, Acc.tray: 0.3003, Acc.ashcan: 0.6579, Acc.fan: 0.7908, Acc.pier: 0.4821, Acc.crt screen: 0.0551, Acc.plate: 0.8351, Acc.monitor: 0.4825, Acc.bulletin board: 0.6426, Acc.shower: 0.0560, Acc.radiator: 0.7709, Acc.glass: 0.2141, Acc.clock: 0.5595, Acc.flag: 0.8071 +2024-06-16 19:15:47,211 - mmseg - INFO - Iter [52050/80000] lr: 1.398e-05, eta: 11:43:08, time: 3.270, data_time: 1.912, memory: 70722, decode.loss_ce: 0.1639, decode.acc_seg: 92.9491, aux.loss_ce: 0.0693, aux.acc_seg: 92.5520, loss: 0.2331 +2024-06-16 19:16:55,527 - mmseg - INFO - Iter [52100/80000] lr: 1.395e-05, eta: 11:41:49, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1699, decode.acc_seg: 92.5855, aux.loss_ce: 0.0712, aux.acc_seg: 92.2662, loss: 0.2410 +2024-06-16 19:18:03,814 - mmseg - INFO - Iter [52150/80000] lr: 1.393e-05, eta: 11:40:30, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1824, decode.acc_seg: 92.0892, aux.loss_ce: 0.0762, aux.acc_seg: 91.6873, loss: 0.2586 +2024-06-16 19:19:12,324 - mmseg - INFO - Iter [52200/80000] lr: 1.390e-05, eta: 11:39:10, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1692, decode.acc_seg: 92.8598, aux.loss_ce: 0.0713, aux.acc_seg: 92.4747, loss: 0.2405 +2024-06-16 19:20:20,447 - mmseg - INFO - Iter [52250/80000] lr: 1.388e-05, eta: 11:37:51, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1669, decode.acc_seg: 92.7061, aux.loss_ce: 0.0705, aux.acc_seg: 92.3286, loss: 0.2375 +2024-06-16 19:21:28,423 - mmseg - INFO - Iter [52300/80000] lr: 1.385e-05, eta: 11:36:32, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1653, decode.acc_seg: 92.7929, aux.loss_ce: 0.0699, aux.acc_seg: 92.4211, loss: 0.2351 +2024-06-16 19:22:36,513 - mmseg - INFO - Iter [52350/80000] lr: 1.383e-05, eta: 11:35:12, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1766, decode.acc_seg: 92.6564, aux.loss_ce: 0.0741, aux.acc_seg: 92.2881, loss: 0.2507 +2024-06-16 19:23:44,758 - mmseg - INFO - Iter [52400/80000] lr: 1.380e-05, eta: 11:33:53, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1616, decode.acc_seg: 92.8907, aux.loss_ce: 0.0681, aux.acc_seg: 92.5640, loss: 0.2296 +2024-06-16 19:24:52,822 - mmseg - INFO - Iter [52450/80000] lr: 1.378e-05, eta: 11:32:34, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1648, decode.acc_seg: 92.7341, aux.loss_ce: 0.0704, aux.acc_seg: 92.2488, loss: 0.2351 +2024-06-16 19:26:01,075 - mmseg - INFO - Iter [52500/80000] lr: 1.375e-05, eta: 11:31:15, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1718, decode.acc_seg: 92.7530, aux.loss_ce: 0.0724, aux.acc_seg: 92.3719, loss: 0.2442 +2024-06-16 19:27:09,321 - mmseg - INFO - Iter [52550/80000] lr: 1.373e-05, eta: 11:29:56, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1724, decode.acc_seg: 92.5328, aux.loss_ce: 0.0727, aux.acc_seg: 92.1923, loss: 0.2451 +2024-06-16 19:28:17,450 - mmseg - INFO - Iter [52600/80000] lr: 1.370e-05, eta: 11:28:36, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1689, decode.acc_seg: 92.5835, aux.loss_ce: 0.0708, aux.acc_seg: 92.2768, loss: 0.2397 +2024-06-16 19:29:25,747 - mmseg - INFO - Iter [52650/80000] lr: 1.368e-05, eta: 11:27:17, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1672, decode.acc_seg: 92.7388, aux.loss_ce: 0.0702, aux.acc_seg: 92.3526, loss: 0.2374 +2024-06-16 19:30:34,093 - mmseg - INFO - Iter [52700/80000] lr: 1.365e-05, eta: 11:25:58, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1651, decode.acc_seg: 92.9315, aux.loss_ce: 0.0695, aux.acc_seg: 92.5810, loss: 0.2346 +2024-06-16 19:31:42,340 - mmseg - INFO - Iter [52750/80000] lr: 1.363e-05, eta: 11:24:39, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1675, decode.acc_seg: 92.7518, aux.loss_ce: 0.0707, aux.acc_seg: 92.3839, loss: 0.2382 +2024-06-16 19:32:50,570 - mmseg - INFO - Iter [52800/80000] lr: 1.360e-05, eta: 11:23:20, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1704, decode.acc_seg: 92.7316, aux.loss_ce: 0.0719, aux.acc_seg: 92.3593, loss: 0.2422 +2024-06-16 19:33:58,597 - mmseg - INFO - Iter [52850/80000] lr: 1.358e-05, eta: 11:22:01, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1692, decode.acc_seg: 92.6158, aux.loss_ce: 0.0714, aux.acc_seg: 92.2576, loss: 0.2405 +2024-06-16 19:35:06,927 - mmseg - INFO - Iter [52900/80000] lr: 1.355e-05, eta: 11:20:42, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1775, decode.acc_seg: 92.2604, aux.loss_ce: 0.0747, aux.acc_seg: 91.8644, loss: 0.2522 +2024-06-16 19:36:14,985 - mmseg - INFO - Iter [52950/80000] lr: 1.353e-05, eta: 11:19:23, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1661, decode.acc_seg: 92.7897, aux.loss_ce: 0.0704, aux.acc_seg: 92.3716, loss: 0.2365 +2024-06-16 19:37:23,247 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 19:37:23,248 - mmseg - INFO - Iter [53000/80000] lr: 1.350e-05, eta: 11:18:04, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1710, decode.acc_seg: 92.5888, aux.loss_ce: 0.0714, aux.acc_seg: 92.3038, loss: 0.2424 +2024-06-16 19:38:59,314 - mmseg - INFO - per class results: +2024-06-16 19:38:59,320 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.42 | 88.9 | +| building | 85.92 | 93.6 | +| sky | 94.84 | 97.61 | +| floor | 85.72 | 91.96 | +| tree | 77.26 | 89.65 | +| ceiling | 86.61 | 94.91 | +| road | 86.74 | 92.56 | +| bed | 93.02 | 97.15 | +| windowpane | 64.99 | 80.43 | +| grass | 69.5 | 83.01 | +| cabinet | 65.11 | 76.72 | +| sidewalk | 72.62 | 84.3 | +| person | 85.78 | 95.09 | +| earth | 37.23 | 50.14 | +| door | 60.03 | 76.09 | +| table | 70.99 | 82.92 | +| mountain | 61.06 | 70.15 | +| plant | 54.75 | 64.33 | +| curtain | 77.66 | 88.6 | +| chair | 69.34 | 81.93 | +| car | 87.25 | 94.19 | +| water | 63.9 | 79.28 | +| painting | 76.0 | 91.39 | +| sofa | 83.13 | 92.4 | +| shelf | 41.01 | 54.11 | +| house | 56.06 | 68.15 | +| sea | 75.42 | 88.72 | +| mirror | 76.89 | 82.34 | +| rug | 74.37 | 83.84 | +| field | 32.09 | 59.36 | +| armchair | 60.98 | 75.83 | +| seat | 67.5 | 89.5 | +| fence | 51.0 | 71.18 | +| desk | 61.67 | 80.82 | +| rock | 59.12 | 84.91 | +| wardrobe | 55.06 | 76.56 | +| lamp | 74.21 | 84.99 | +| bathtub | 82.96 | 87.08 | +| railing | 42.35 | 61.93 | +| cushion | 71.94 | 86.34 | +| base | 41.69 | 60.1 | +| box | 34.36 | 43.55 | +| column | 54.46 | 69.67 | +| signboard | 38.0 | 57.09 | +| chest of drawers | 44.43 | 60.55 | +| counter | 46.53 | 58.73 | +| sand | 53.5 | 76.92 | +| sink | 75.17 | 84.62 | +| skyscraper | 48.91 | 60.94 | +| fireplace | 74.59 | 93.85 | +| refrigerator | 84.45 | 93.45 | +| grandstand | 52.77 | 83.05 | +| path | 32.37 | 48.61 | +| stairs | 25.25 | 31.87 | +| runway | 73.49 | 95.28 | +| case | 54.74 | 82.07 | +| pool table | 92.19 | 98.51 | +| pillow | 67.22 | 76.98 | +| screen door | 81.88 | 84.19 | +| stairway | 45.66 | 67.42 | +| river | 16.09 | 27.29 | +| bridge | 79.37 | 88.24 | +| bookcase | 39.35 | 69.26 | +| blind | 45.69 | 57.58 | +| coffee table | 68.6 | 87.73 | +| toilet | 89.9 | 92.66 | +| flower | 45.04 | 62.3 | +| book | 53.92 | 77.64 | +| hill | 7.47 | 11.31 | +| bench | 52.89 | 65.01 | +| countertop | 63.31 | 88.61 | +| stove | 86.16 | 92.72 | +| palm | 56.05 | 78.34 | +| kitchen island | 52.54 | 82.78 | +| computer | 78.95 | 91.63 | +| swivel chair | 46.9 | 67.8 | +| boat | 72.93 | 91.06 | +| bar | 62.81 | 77.27 | +| arcade machine | 76.01 | 84.71 | +| hovel | 43.58 | 47.55 | +| bus | 91.97 | 96.3 | +| towel | 74.51 | 90.15 | +| light | 60.11 | 68.91 | +| truck | 43.76 | 58.47 | +| tower | 30.52 | 43.44 | +| chandelier | 70.99 | 88.96 | +| awning | 49.88 | 68.34 | +| streetlight | 35.82 | 47.76 | +| booth | 39.55 | 59.03 | +| television receiver | 76.72 | 88.9 | +| airplane | 58.67 | 73.43 | +| dirt track | 4.69 | 11.75 | +| apparel | 44.29 | 57.29 | +| pole | 28.8 | 42.1 | +| land | 7.34 | 15.82 | +| bannister | 17.79 | 25.32 | +| escalator | 62.88 | 82.76 | +| ottoman | 53.59 | 67.92 | +| bottle | 41.08 | 65.35 | +| buffet | 43.59 | 53.36 | +| poster | 31.18 | 39.59 | +| stage | 19.29 | 42.34 | +| van | 43.64 | 62.24 | +| ship | 87.18 | 92.9 | +| fountain | 28.55 | 29.08 | +| conveyer belt | 79.57 | 93.25 | +| canopy | 56.34 | 81.46 | +| washer | 83.7 | 88.78 | +| plaything | 30.23 | 40.29 | +| swimming pool | 54.47 | 80.84 | +| stool | 54.71 | 67.05 | +| barrel | 52.34 | 74.66 | +| basket | 39.21 | 60.93 | +| waterfall | 66.11 | 85.27 | +| tent | 96.12 | 98.55 | +| bag | 19.64 | 22.42 | +| minibike | 77.24 | 87.24 | +| cradle | 75.28 | 98.27 | +| oven | 59.85 | 67.22 | +| ball | 56.6 | 65.99 | +| food | 64.69 | 82.23 | +| step | 9.96 | 12.55 | +| tank | 86.8 | 92.53 | +| trade name | 22.02 | 25.23 | +| microwave | 88.31 | 95.62 | +| pot | 54.49 | 62.04 | +| animal | 59.93 | 61.66 | +| bicycle | 58.37 | 79.59 | +| lake | 55.5 | 63.82 | +| dishwasher | 67.4 | 78.25 | +| screen | 53.83 | 82.66 | +| blanket | 27.05 | 31.1 | +| sculpture | 72.85 | 87.58 | +| hood | 62.51 | 74.87 | +| sconce | 59.12 | 71.51 | +| vase | 49.61 | 62.13 | +| traffic light | 35.8 | 62.82 | +| tray | 24.35 | 31.28 | +| ashcan | 48.23 | 64.62 | +| fan | 67.3 | 83.19 | +| pier | 41.31 | 50.99 | +| crt screen | 4.04 | 5.41 | +| plate | 59.98 | 78.91 | +| monitor | 64.68 | 81.26 | +| bulletin board | 53.4 | 65.42 | +| shower | 3.46 | 7.46 | +| radiator | 63.55 | 80.22 | +| glass | 20.49 | 22.29 | +| clock | 44.79 | 60.33 | +| flag | 71.3 | 78.17 | ++---------------------+-------+-------+ +2024-06-16 19:38:59,320 - mmseg - INFO - Summary: +2024-06-16 19:38:59,321 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.22 | 57.18 | 70.41 | ++-------+-------+-------+ +2024-06-16 19:38:59,321 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 19:38:59,322 - mmseg - INFO - Iter(val) [250] aAcc: 0.8622, mIoU: 0.5718, mAcc: 0.7041, IoU.wall: 0.8242, IoU.building: 0.8592, IoU.sky: 0.9484, IoU.floor: 0.8572, IoU.tree: 0.7726, IoU.ceiling: 0.8661, IoU.road: 0.8674, IoU.bed : 0.9302, IoU.windowpane: 0.6499, IoU.grass: 0.6950, IoU.cabinet: 0.6511, IoU.sidewalk: 0.7262, IoU.person: 0.8578, IoU.earth: 0.3723, IoU.door: 0.6003, IoU.table: 0.7099, IoU.mountain: 0.6106, IoU.plant: 0.5475, IoU.curtain: 0.7766, IoU.chair: 0.6934, IoU.car: 0.8725, IoU.water: 0.6390, IoU.painting: 0.7600, IoU.sofa: 0.8313, IoU.shelf: 0.4101, IoU.house: 0.5606, IoU.sea: 0.7542, IoU.mirror: 0.7689, IoU.rug: 0.7437, IoU.field: 0.3209, IoU.armchair: 0.6098, IoU.seat: 0.6750, IoU.fence: 0.5100, IoU.desk: 0.6167, IoU.rock: 0.5912, IoU.wardrobe: 0.5506, IoU.lamp: 0.7421, IoU.bathtub: 0.8296, IoU.railing: 0.4235, IoU.cushion: 0.7194, IoU.base: 0.4169, IoU.box: 0.3436, IoU.column: 0.5446, IoU.signboard: 0.3800, IoU.chest of drawers: 0.4443, IoU.counter: 0.4653, IoU.sand: 0.5350, IoU.sink: 0.7517, IoU.skyscraper: 0.4891, IoU.fireplace: 0.7459, IoU.refrigerator: 0.8445, IoU.grandstand: 0.5277, IoU.path: 0.3237, IoU.stairs: 0.2525, IoU.runway: 0.7349, IoU.case: 0.5474, IoU.pool table: 0.9219, IoU.pillow: 0.6722, IoU.screen door: 0.8188, IoU.stairway: 0.4566, IoU.river: 0.1609, IoU.bridge: 0.7937, IoU.bookcase: 0.3935, IoU.blind: 0.4569, IoU.coffee table: 0.6860, IoU.toilet: 0.8990, IoU.flower: 0.4504, IoU.book: 0.5392, IoU.hill: 0.0747, IoU.bench: 0.5289, IoU.countertop: 0.6331, IoU.stove: 0.8616, IoU.palm: 0.5605, IoU.kitchen island: 0.5254, IoU.computer: 0.7895, IoU.swivel chair: 0.4690, IoU.boat: 0.7293, IoU.bar: 0.6281, IoU.arcade machine: 0.7601, IoU.hovel: 0.4358, IoU.bus: 0.9197, IoU.towel: 0.7451, IoU.light: 0.6011, IoU.truck: 0.4376, IoU.tower: 0.3052, IoU.chandelier: 0.7099, IoU.awning: 0.4988, IoU.streetlight: 0.3582, IoU.booth: 0.3955, IoU.television receiver: 0.7672, IoU.airplane: 0.5867, IoU.dirt track: 0.0469, IoU.apparel: 0.4429, IoU.pole: 0.2880, IoU.land: 0.0734, IoU.bannister: 0.1779, IoU.escalator: 0.6288, IoU.ottoman: 0.5359, IoU.bottle: 0.4108, IoU.buffet: 0.4359, IoU.poster: 0.3118, IoU.stage: 0.1929, IoU.van: 0.4364, IoU.ship: 0.8718, IoU.fountain: 0.2855, IoU.conveyer belt: 0.7957, IoU.canopy: 0.5634, IoU.washer: 0.8370, IoU.plaything: 0.3023, IoU.swimming pool: 0.5447, IoU.stool: 0.5471, IoU.barrel: 0.5234, IoU.basket: 0.3921, IoU.waterfall: 0.6611, IoU.tent: 0.9612, IoU.bag: 0.1964, IoU.minibike: 0.7724, IoU.cradle: 0.7528, IoU.oven: 0.5985, IoU.ball: 0.5660, IoU.food: 0.6469, IoU.step: 0.0996, IoU.tank: 0.8680, IoU.trade name: 0.2202, IoU.microwave: 0.8831, IoU.pot: 0.5449, IoU.animal: 0.5993, IoU.bicycle: 0.5837, IoU.lake: 0.5550, IoU.dishwasher: 0.6740, IoU.screen: 0.5383, IoU.blanket: 0.2705, IoU.sculpture: 0.7285, IoU.hood: 0.6251, IoU.sconce: 0.5912, IoU.vase: 0.4961, IoU.traffic light: 0.3580, IoU.tray: 0.2435, IoU.ashcan: 0.4823, IoU.fan: 0.6730, IoU.pier: 0.4131, IoU.crt screen: 0.0404, IoU.plate: 0.5998, IoU.monitor: 0.6468, IoU.bulletin board: 0.5340, IoU.shower: 0.0346, IoU.radiator: 0.6355, IoU.glass: 0.2049, IoU.clock: 0.4479, IoU.flag: 0.7130, Acc.wall: 0.8890, Acc.building: 0.9360, Acc.sky: 0.9761, Acc.floor: 0.9196, Acc.tree: 0.8965, Acc.ceiling: 0.9491, Acc.road: 0.9256, Acc.bed : 0.9715, Acc.windowpane: 0.8043, Acc.grass: 0.8301, Acc.cabinet: 0.7672, Acc.sidewalk: 0.8430, Acc.person: 0.9509, Acc.earth: 0.5014, Acc.door: 0.7609, Acc.table: 0.8292, Acc.mountain: 0.7015, Acc.plant: 0.6433, Acc.curtain: 0.8860, Acc.chair: 0.8193, Acc.car: 0.9419, Acc.water: 0.7928, Acc.painting: 0.9139, Acc.sofa: 0.9240, Acc.shelf: 0.5411, Acc.house: 0.6815, Acc.sea: 0.8872, Acc.mirror: 0.8234, Acc.rug: 0.8384, Acc.field: 0.5936, Acc.armchair: 0.7583, Acc.seat: 0.8950, Acc.fence: 0.7118, Acc.desk: 0.8082, Acc.rock: 0.8491, Acc.wardrobe: 0.7656, Acc.lamp: 0.8499, Acc.bathtub: 0.8708, Acc.railing: 0.6193, Acc.cushion: 0.8634, Acc.base: 0.6010, Acc.box: 0.4355, Acc.column: 0.6967, Acc.signboard: 0.5709, Acc.chest of drawers: 0.6055, Acc.counter: 0.5873, Acc.sand: 0.7692, Acc.sink: 0.8462, Acc.skyscraper: 0.6094, Acc.fireplace: 0.9385, Acc.refrigerator: 0.9345, Acc.grandstand: 0.8305, Acc.path: 0.4861, Acc.stairs: 0.3187, Acc.runway: 0.9528, Acc.case: 0.8207, Acc.pool table: 0.9851, Acc.pillow: 0.7698, Acc.screen door: 0.8419, Acc.stairway: 0.6742, Acc.river: 0.2729, Acc.bridge: 0.8824, Acc.bookcase: 0.6926, Acc.blind: 0.5758, Acc.coffee table: 0.8773, Acc.toilet: 0.9266, Acc.flower: 0.6230, Acc.book: 0.7764, Acc.hill: 0.1131, Acc.bench: 0.6501, Acc.countertop: 0.8861, Acc.stove: 0.9272, Acc.palm: 0.7834, Acc.kitchen island: 0.8278, Acc.computer: 0.9163, Acc.swivel chair: 0.6780, Acc.boat: 0.9106, Acc.bar: 0.7727, Acc.arcade machine: 0.8471, Acc.hovel: 0.4755, Acc.bus: 0.9630, Acc.towel: 0.9015, Acc.light: 0.6891, Acc.truck: 0.5847, Acc.tower: 0.4344, Acc.chandelier: 0.8896, Acc.awning: 0.6834, Acc.streetlight: 0.4776, Acc.booth: 0.5903, Acc.television receiver: 0.8890, Acc.airplane: 0.7343, Acc.dirt track: 0.1175, Acc.apparel: 0.5729, Acc.pole: 0.4210, Acc.land: 0.1582, Acc.bannister: 0.2532, Acc.escalator: 0.8276, Acc.ottoman: 0.6792, Acc.bottle: 0.6535, Acc.buffet: 0.5336, Acc.poster: 0.3959, Acc.stage: 0.4234, Acc.van: 0.6224, Acc.ship: 0.9290, Acc.fountain: 0.2908, Acc.conveyer belt: 0.9325, Acc.canopy: 0.8146, Acc.washer: 0.8878, Acc.plaything: 0.4029, Acc.swimming pool: 0.8084, Acc.stool: 0.6705, Acc.barrel: 0.7466, Acc.basket: 0.6093, Acc.waterfall: 0.8527, Acc.tent: 0.9855, Acc.bag: 0.2242, Acc.minibike: 0.8724, Acc.cradle: 0.9827, Acc.oven: 0.6722, Acc.ball: 0.6599, Acc.food: 0.8223, Acc.step: 0.1255, Acc.tank: 0.9253, Acc.trade name: 0.2523, Acc.microwave: 0.9562, Acc.pot: 0.6204, Acc.animal: 0.6166, Acc.bicycle: 0.7959, Acc.lake: 0.6382, Acc.dishwasher: 0.7825, Acc.screen: 0.8266, Acc.blanket: 0.3110, Acc.sculpture: 0.8758, Acc.hood: 0.7487, Acc.sconce: 0.7151, Acc.vase: 0.6213, Acc.traffic light: 0.6282, Acc.tray: 0.3128, Acc.ashcan: 0.6462, Acc.fan: 0.8319, Acc.pier: 0.5099, Acc.crt screen: 0.0541, Acc.plate: 0.7891, Acc.monitor: 0.8126, Acc.bulletin board: 0.6542, Acc.shower: 0.0746, Acc.radiator: 0.8022, Acc.glass: 0.2229, Acc.clock: 0.6033, Acc.flag: 0.7817 +2024-06-16 19:40:10,826 - mmseg - INFO - Iter [53050/80000] lr: 1.348e-05, eta: 11:17:35, time: 3.352, data_time: 1.994, memory: 70722, decode.loss_ce: 0.1760, decode.acc_seg: 92.5110, aux.loss_ce: 0.0743, aux.acc_seg: 92.0770, loss: 0.2503 +2024-06-16 19:41:19,040 - mmseg - INFO - Iter [53100/80000] lr: 1.345e-05, eta: 11:16:16, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1726, decode.acc_seg: 92.5954, aux.loss_ce: 0.0722, aux.acc_seg: 92.2381, loss: 0.2448 +2024-06-16 19:42:27,326 - mmseg - INFO - Iter [53150/80000] lr: 1.343e-05, eta: 11:14:57, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1721, decode.acc_seg: 92.6851, aux.loss_ce: 0.0723, aux.acc_seg: 92.2887, loss: 0.2444 +2024-06-16 19:43:35,549 - mmseg - INFO - Iter [53200/80000] lr: 1.340e-05, eta: 11:13:38, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1611, decode.acc_seg: 92.7846, aux.loss_ce: 0.0685, aux.acc_seg: 92.3844, loss: 0.2296 +2024-06-16 19:44:43,701 - mmseg - INFO - Iter [53250/80000] lr: 1.338e-05, eta: 11:12:19, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1687, decode.acc_seg: 92.8104, aux.loss_ce: 0.0712, aux.acc_seg: 92.4643, loss: 0.2400 +2024-06-16 19:45:51,935 - mmseg - INFO - Iter [53300/80000] lr: 1.335e-05, eta: 11:11:00, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1647, decode.acc_seg: 92.8343, aux.loss_ce: 0.0690, aux.acc_seg: 92.4915, loss: 0.2337 +2024-06-16 19:47:00,333 - mmseg - INFO - Iter [53350/80000] lr: 1.333e-05, eta: 11:09:41, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1694, decode.acc_seg: 92.6642, aux.loss_ce: 0.0715, aux.acc_seg: 92.2651, loss: 0.2409 +2024-06-16 19:48:08,453 - mmseg - INFO - Iter [53400/80000] lr: 1.330e-05, eta: 11:08:22, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1723, decode.acc_seg: 92.5462, aux.loss_ce: 0.0733, aux.acc_seg: 92.1429, loss: 0.2456 +2024-06-16 19:49:16,601 - mmseg - INFO - Iter [53450/80000] lr: 1.328e-05, eta: 11:07:03, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1601, decode.acc_seg: 92.9403, aux.loss_ce: 0.0681, aux.acc_seg: 92.5856, loss: 0.2282 +2024-06-16 19:50:24,868 - mmseg - INFO - Iter [53500/80000] lr: 1.325e-05, eta: 11:05:44, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1665, decode.acc_seg: 92.8692, aux.loss_ce: 0.0694, aux.acc_seg: 92.6202, loss: 0.2360 +2024-06-16 19:51:33,000 - mmseg - INFO - Iter [53550/80000] lr: 1.323e-05, eta: 11:04:26, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1777, decode.acc_seg: 92.5757, aux.loss_ce: 0.0741, aux.acc_seg: 92.2033, loss: 0.2517 +2024-06-16 19:52:41,100 - mmseg - INFO - Iter [53600/80000] lr: 1.320e-05, eta: 11:03:07, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1772, decode.acc_seg: 92.2400, aux.loss_ce: 0.0750, aux.acc_seg: 91.8475, loss: 0.2522 +2024-06-16 19:53:49,372 - mmseg - INFO - Iter [53650/80000] lr: 1.318e-05, eta: 11:01:48, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1743, decode.acc_seg: 92.4750, aux.loss_ce: 0.0730, aux.acc_seg: 92.1529, loss: 0.2474 +2024-06-16 19:54:57,472 - mmseg - INFO - Iter [53700/80000] lr: 1.315e-05, eta: 11:00:29, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1754, decode.acc_seg: 92.5769, aux.loss_ce: 0.0741, aux.acc_seg: 92.2131, loss: 0.2496 +2024-06-16 19:56:05,735 - mmseg - INFO - Iter [53750/80000] lr: 1.313e-05, eta: 10:59:10, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1605, decode.acc_seg: 92.9985, aux.loss_ce: 0.0678, aux.acc_seg: 92.6346, loss: 0.2282 +2024-06-16 19:57:13,973 - mmseg - INFO - Iter [53800/80000] lr: 1.310e-05, eta: 10:57:51, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1714, decode.acc_seg: 92.6551, aux.loss_ce: 0.0725, aux.acc_seg: 92.2042, loss: 0.2439 +2024-06-16 19:58:22,114 - mmseg - INFO - Iter [53850/80000] lr: 1.308e-05, eta: 10:56:32, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1633, decode.acc_seg: 92.9771, aux.loss_ce: 0.0693, aux.acc_seg: 92.5796, loss: 0.2326 +2024-06-16 19:59:30,607 - mmseg - INFO - Iter [53900/80000] lr: 1.305e-05, eta: 10:55:14, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1690, decode.acc_seg: 92.8624, aux.loss_ce: 0.0716, aux.acc_seg: 92.5170, loss: 0.2405 +2024-06-16 20:00:38,767 - mmseg - INFO - Iter [53950/80000] lr: 1.303e-05, eta: 10:53:55, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1664, decode.acc_seg: 92.8799, aux.loss_ce: 0.0704, aux.acc_seg: 92.4914, loss: 0.2368 +2024-06-16 20:01:46,955 - mmseg - INFO - Saving checkpoint at 54000 iterations +2024-06-16 20:03:11,341 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:03:11,341 - mmseg - INFO - Iter [54000/80000] lr: 1.300e-05, eta: 10:53:17, time: 3.051, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1739, decode.acc_seg: 92.5412, aux.loss_ce: 0.0734, aux.acc_seg: 92.1772, loss: 0.2473 +2024-06-16 20:04:47,408 - mmseg - INFO - per class results: +2024-06-16 20:04:47,414 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.33 | 90.92 | +| building | 85.65 | 93.21 | +| sky | 94.98 | 97.75 | +| floor | 85.69 | 92.67 | +| tree | 77.35 | 90.12 | +| ceiling | 87.09 | 94.28 | +| road | 86.8 | 90.73 | +| bed | 93.2 | 96.64 | +| windowpane | 66.77 | 79.13 | +| grass | 71.3 | 85.44 | +| cabinet | 67.04 | 75.43 | +| sidewalk | 73.36 | 88.78 | +| person | 86.02 | 94.34 | +| earth | 39.46 | 54.12 | +| door | 60.11 | 72.8 | +| table | 70.3 | 84.74 | +| mountain | 61.84 | 73.0 | +| plant | 53.87 | 66.79 | +| curtain | 77.36 | 89.7 | +| chair | 68.01 | 77.48 | +| car | 86.85 | 94.36 | +| water | 62.72 | 78.33 | +| painting | 77.52 | 90.51 | +| sofa | 83.0 | 92.34 | +| shelf | 44.95 | 58.0 | +| house | 55.71 | 70.1 | +| sea | 70.39 | 86.62 | +| mirror | 76.63 | 81.62 | +| rug | 73.63 | 83.51 | +| field | 34.51 | 44.62 | +| armchair | 61.75 | 77.73 | +| seat | 69.54 | 89.86 | +| fence | 51.57 | 69.2 | +| desk | 63.82 | 77.47 | +| rock | 56.6 | 81.5 | +| wardrobe | 56.85 | 76.64 | +| lamp | 74.77 | 83.46 | +| bathtub | 84.44 | 87.45 | +| railing | 44.11 | 62.88 | +| cushion | 69.78 | 82.91 | +| base | 42.72 | 58.07 | +| box | 35.82 | 47.56 | +| column | 51.14 | 62.7 | +| signboard | 41.27 | 58.22 | +| chest of drawers | 47.18 | 66.04 | +| counter | 40.41 | 46.59 | +| sand | 54.69 | 86.98 | +| sink | 76.66 | 84.19 | +| skyscraper | 47.63 | 58.53 | +| fireplace | 73.54 | 94.08 | +| refrigerator | 83.78 | 93.48 | +| grandstand | 50.79 | 85.07 | +| path | 32.94 | 46.73 | +| stairs | 25.28 | 31.41 | +| runway | 73.4 | 95.21 | +| case | 59.43 | 77.17 | +| pool table | 94.55 | 98.18 | +| pillow | 67.51 | 77.73 | +| screen door | 72.98 | 74.6 | +| stairway | 45.77 | 60.7 | +| river | 10.23 | 17.75 | +| bridge | 69.49 | 77.36 | +| bookcase | 38.87 | 70.45 | +| blind | 50.27 | 57.24 | +| coffee table | 65.44 | 88.61 | +| toilet | 89.11 | 93.51 | +| flower | 44.03 | 52.28 | +| book | 53.34 | 71.03 | +| hill | 7.32 | 11.13 | +| bench | 52.03 | 62.83 | +| countertop | 65.37 | 83.27 | +| stove | 86.13 | 92.6 | +| palm | 55.5 | 81.57 | +| kitchen island | 46.5 | 71.95 | +| computer | 79.2 | 91.71 | +| swivel chair | 49.71 | 71.03 | +| boat | 73.54 | 90.54 | +| bar | 58.61 | 82.39 | +| arcade machine | 77.76 | 80.64 | +| hovel | 54.31 | 63.74 | +| bus | 93.85 | 95.97 | +| towel | 74.46 | 85.6 | +| light | 60.66 | 70.1 | +| truck | 44.1 | 59.21 | +| tower | 25.17 | 34.84 | +| chandelier | 72.75 | 85.2 | +| awning | 42.81 | 52.51 | +| streetlight | 32.41 | 43.96 | +| booth | 52.35 | 60.84 | +| television receiver | 76.49 | 88.08 | +| airplane | 64.24 | 71.94 | +| dirt track | 10.12 | 47.6 | +| apparel | 49.87 | 72.64 | +| pole | 22.19 | 27.84 | +| land | 4.2 | 6.78 | +| bannister | 16.46 | 24.75 | +| escalator | 58.37 | 79.34 | +| ottoman | 51.06 | 67.36 | +| bottle | 38.26 | 59.71 | +| buffet | 59.36 | 73.33 | +| poster | 38.2 | 46.19 | +| stage | 17.5 | 31.78 | +| van | 44.13 | 57.41 | +| ship | 89.0 | 95.12 | +| fountain | 29.08 | 30.47 | +| conveyer belt | 81.7 | 93.71 | +| canopy | 56.26 | 75.64 | +| washer | 80.87 | 85.57 | +| plaything | 27.44 | 39.16 | +| swimming pool | 61.84 | 95.26 | +| stool | 54.27 | 72.27 | +| barrel | 55.87 | 74.49 | +| basket | 42.29 | 60.96 | +| waterfall | 64.63 | 85.97 | +| tent | 94.93 | 98.61 | +| bag | 20.8 | 25.0 | +| minibike | 75.95 | 88.06 | +| cradle | 79.89 | 97.36 | +| oven | 61.43 | 70.41 | +| ball | 43.6 | 45.19 | +| food | 63.61 | 82.77 | +| step | 11.07 | 13.19 | +| tank | 64.1 | 69.67 | +| trade name | 28.55 | 34.41 | +| microwave | 89.79 | 96.09 | +| pot | 58.81 | 69.65 | +| animal | 58.37 | 59.69 | +| bicycle | 59.42 | 74.36 | +| lake | 56.39 | 63.83 | +| dishwasher | 67.21 | 78.1 | +| screen | 46.63 | 75.99 | +| blanket | 32.29 | 37.28 | +| sculpture | 72.25 | 88.06 | +| hood | 62.44 | 73.93 | +| sconce | 58.31 | 67.02 | +| vase | 50.71 | 62.65 | +| traffic light | 38.95 | 59.86 | +| tray | 23.38 | 27.4 | +| ashcan | 48.53 | 63.49 | +| fan | 66.75 | 80.06 | +| pier | 43.32 | 49.43 | +| crt screen | 2.13 | 3.51 | +| plate | 61.48 | 78.52 | +| monitor | 61.28 | 72.62 | +| bulletin board | 56.71 | 66.91 | +| shower | 2.16 | 10.99 | +| radiator | 67.73 | 77.84 | +| glass | 20.07 | 21.51 | +| clock | 41.59 | 53.15 | +| flag | 70.39 | 78.39 | ++---------------------+-------+-------+ +2024-06-16 20:04:47,414 - mmseg - INFO - Summary: +2024-06-16 20:04:47,414 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.42 | 57.23 | 69.59 | ++-------+-------+-------+ +2024-06-16 20:04:47,415 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:04:47,415 - mmseg - INFO - Iter(val) [250] aAcc: 0.8642, mIoU: 0.5723, mAcc: 0.6959, IoU.wall: 0.8233, IoU.building: 0.8565, IoU.sky: 0.9498, IoU.floor: 0.8569, IoU.tree: 0.7735, IoU.ceiling: 0.8709, IoU.road: 0.8680, IoU.bed : 0.9320, IoU.windowpane: 0.6677, IoU.grass: 0.7130, IoU.cabinet: 0.6704, IoU.sidewalk: 0.7336, IoU.person: 0.8602, IoU.earth: 0.3946, IoU.door: 0.6011, IoU.table: 0.7030, IoU.mountain: 0.6184, IoU.plant: 0.5387, IoU.curtain: 0.7736, IoU.chair: 0.6801, IoU.car: 0.8685, IoU.water: 0.6272, IoU.painting: 0.7752, IoU.sofa: 0.8300, IoU.shelf: 0.4495, IoU.house: 0.5571, IoU.sea: 0.7039, IoU.mirror: 0.7663, IoU.rug: 0.7363, IoU.field: 0.3451, IoU.armchair: 0.6175, IoU.seat: 0.6954, IoU.fence: 0.5157, IoU.desk: 0.6382, IoU.rock: 0.5660, IoU.wardrobe: 0.5685, IoU.lamp: 0.7477, IoU.bathtub: 0.8444, IoU.railing: 0.4411, IoU.cushion: 0.6978, IoU.base: 0.4272, IoU.box: 0.3582, IoU.column: 0.5114, IoU.signboard: 0.4127, IoU.chest of drawers: 0.4718, IoU.counter: 0.4041, IoU.sand: 0.5469, IoU.sink: 0.7666, IoU.skyscraper: 0.4763, IoU.fireplace: 0.7354, IoU.refrigerator: 0.8378, IoU.grandstand: 0.5079, IoU.path: 0.3294, IoU.stairs: 0.2528, IoU.runway: 0.7340, IoU.case: 0.5943, IoU.pool table: 0.9455, IoU.pillow: 0.6751, IoU.screen door: 0.7298, IoU.stairway: 0.4577, IoU.river: 0.1023, IoU.bridge: 0.6949, IoU.bookcase: 0.3887, IoU.blind: 0.5027, IoU.coffee table: 0.6544, IoU.toilet: 0.8911, IoU.flower: 0.4403, IoU.book: 0.5334, IoU.hill: 0.0732, IoU.bench: 0.5203, IoU.countertop: 0.6537, IoU.stove: 0.8613, IoU.palm: 0.5550, IoU.kitchen island: 0.4650, IoU.computer: 0.7920, IoU.swivel chair: 0.4971, IoU.boat: 0.7354, IoU.bar: 0.5861, IoU.arcade machine: 0.7776, IoU.hovel: 0.5431, IoU.bus: 0.9385, IoU.towel: 0.7446, IoU.light: 0.6066, IoU.truck: 0.4410, IoU.tower: 0.2517, IoU.chandelier: 0.7275, IoU.awning: 0.4281, IoU.streetlight: 0.3241, IoU.booth: 0.5235, IoU.television receiver: 0.7649, IoU.airplane: 0.6424, IoU.dirt track: 0.1012, IoU.apparel: 0.4987, IoU.pole: 0.2219, IoU.land: 0.0420, IoU.bannister: 0.1646, IoU.escalator: 0.5837, IoU.ottoman: 0.5106, IoU.bottle: 0.3826, IoU.buffet: 0.5936, IoU.poster: 0.3820, IoU.stage: 0.1750, IoU.van: 0.4413, IoU.ship: 0.8900, IoU.fountain: 0.2908, IoU.conveyer belt: 0.8170, IoU.canopy: 0.5626, IoU.washer: 0.8087, IoU.plaything: 0.2744, IoU.swimming pool: 0.6184, IoU.stool: 0.5427, IoU.barrel: 0.5587, IoU.basket: 0.4229, IoU.waterfall: 0.6463, IoU.tent: 0.9493, IoU.bag: 0.2080, IoU.minibike: 0.7595, IoU.cradle: 0.7989, IoU.oven: 0.6143, IoU.ball: 0.4360, IoU.food: 0.6361, IoU.step: 0.1107, IoU.tank: 0.6410, IoU.trade name: 0.2855, IoU.microwave: 0.8979, IoU.pot: 0.5881, IoU.animal: 0.5837, IoU.bicycle: 0.5942, IoU.lake: 0.5639, IoU.dishwasher: 0.6721, IoU.screen: 0.4663, IoU.blanket: 0.3229, IoU.sculpture: 0.7225, IoU.hood: 0.6244, IoU.sconce: 0.5831, IoU.vase: 0.5071, IoU.traffic light: 0.3895, IoU.tray: 0.2338, IoU.ashcan: 0.4853, IoU.fan: 0.6675, IoU.pier: 0.4332, IoU.crt screen: 0.0213, IoU.plate: 0.6148, IoU.monitor: 0.6128, IoU.bulletin board: 0.5671, IoU.shower: 0.0216, IoU.radiator: 0.6773, IoU.glass: 0.2007, IoU.clock: 0.4159, IoU.flag: 0.7039, Acc.wall: 0.9092, Acc.building: 0.9321, Acc.sky: 0.9775, Acc.floor: 0.9267, Acc.tree: 0.9012, Acc.ceiling: 0.9428, Acc.road: 0.9073, Acc.bed : 0.9664, Acc.windowpane: 0.7913, Acc.grass: 0.8544, Acc.cabinet: 0.7543, Acc.sidewalk: 0.8878, Acc.person: 0.9434, Acc.earth: 0.5412, Acc.door: 0.7280, Acc.table: 0.8474, Acc.mountain: 0.7300, Acc.plant: 0.6679, Acc.curtain: 0.8970, Acc.chair: 0.7748, Acc.car: 0.9436, Acc.water: 0.7833, Acc.painting: 0.9051, Acc.sofa: 0.9234, Acc.shelf: 0.5800, Acc.house: 0.7010, Acc.sea: 0.8662, Acc.mirror: 0.8162, Acc.rug: 0.8351, Acc.field: 0.4462, Acc.armchair: 0.7773, Acc.seat: 0.8986, Acc.fence: 0.6920, Acc.desk: 0.7747, Acc.rock: 0.8150, Acc.wardrobe: 0.7664, Acc.lamp: 0.8346, Acc.bathtub: 0.8745, Acc.railing: 0.6288, Acc.cushion: 0.8291, Acc.base: 0.5807, Acc.box: 0.4756, Acc.column: 0.6270, Acc.signboard: 0.5822, Acc.chest of drawers: 0.6604, Acc.counter: 0.4659, Acc.sand: 0.8698, Acc.sink: 0.8419, Acc.skyscraper: 0.5853, Acc.fireplace: 0.9408, Acc.refrigerator: 0.9348, Acc.grandstand: 0.8507, Acc.path: 0.4673, Acc.stairs: 0.3141, Acc.runway: 0.9521, Acc.case: 0.7717, Acc.pool table: 0.9818, Acc.pillow: 0.7773, Acc.screen door: 0.7460, Acc.stairway: 0.6070, Acc.river: 0.1775, Acc.bridge: 0.7736, Acc.bookcase: 0.7045, Acc.blind: 0.5724, Acc.coffee table: 0.8861, Acc.toilet: 0.9351, Acc.flower: 0.5228, Acc.book: 0.7103, Acc.hill: 0.1113, Acc.bench: 0.6283, Acc.countertop: 0.8327, Acc.stove: 0.9260, Acc.palm: 0.8157, Acc.kitchen island: 0.7195, Acc.computer: 0.9171, Acc.swivel chair: 0.7103, Acc.boat: 0.9054, Acc.bar: 0.8239, Acc.arcade machine: 0.8064, Acc.hovel: 0.6374, Acc.bus: 0.9597, Acc.towel: 0.8560, Acc.light: 0.7010, Acc.truck: 0.5921, Acc.tower: 0.3484, Acc.chandelier: 0.8520, Acc.awning: 0.5251, Acc.streetlight: 0.4396, Acc.booth: 0.6084, Acc.television receiver: 0.8808, Acc.airplane: 0.7194, Acc.dirt track: 0.4760, Acc.apparel: 0.7264, Acc.pole: 0.2784, Acc.land: 0.0678, Acc.bannister: 0.2475, Acc.escalator: 0.7934, Acc.ottoman: 0.6736, Acc.bottle: 0.5971, Acc.buffet: 0.7333, Acc.poster: 0.4619, Acc.stage: 0.3178, Acc.van: 0.5741, Acc.ship: 0.9512, Acc.fountain: 0.3047, Acc.conveyer belt: 0.9371, Acc.canopy: 0.7564, Acc.washer: 0.8557, Acc.plaything: 0.3916, Acc.swimming pool: 0.9526, Acc.stool: 0.7227, Acc.barrel: 0.7449, Acc.basket: 0.6096, Acc.waterfall: 0.8597, Acc.tent: 0.9861, Acc.bag: 0.2500, Acc.minibike: 0.8806, Acc.cradle: 0.9736, Acc.oven: 0.7041, Acc.ball: 0.4519, Acc.food: 0.8277, Acc.step: 0.1319, Acc.tank: 0.6967, Acc.trade name: 0.3441, Acc.microwave: 0.9609, Acc.pot: 0.6965, Acc.animal: 0.5969, Acc.bicycle: 0.7436, Acc.lake: 0.6383, Acc.dishwasher: 0.7810, Acc.screen: 0.7599, Acc.blanket: 0.3728, Acc.sculpture: 0.8806, Acc.hood: 0.7393, Acc.sconce: 0.6702, Acc.vase: 0.6265, Acc.traffic light: 0.5986, Acc.tray: 0.2740, Acc.ashcan: 0.6349, Acc.fan: 0.8006, Acc.pier: 0.4943, Acc.crt screen: 0.0351, Acc.plate: 0.7852, Acc.monitor: 0.7262, Acc.bulletin board: 0.6691, Acc.shower: 0.1099, Acc.radiator: 0.7784, Acc.glass: 0.2151, Acc.clock: 0.5315, Acc.flag: 0.7839 +2024-06-16 20:05:56,014 - mmseg - INFO - Iter [54050/80000] lr: 1.298e-05, eta: 10:52:44, time: 3.294, data_time: 1.937, memory: 70722, decode.loss_ce: 0.1673, decode.acc_seg: 92.9080, aux.loss_ce: 0.0705, aux.acc_seg: 92.5273, loss: 0.2378 +2024-06-16 20:07:04,498 - mmseg - INFO - Iter [54100/80000] lr: 1.295e-05, eta: 10:51:26, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1782, decode.acc_seg: 92.3539, aux.loss_ce: 0.0754, aux.acc_seg: 91.9301, loss: 0.2535 +2024-06-16 20:08:12,525 - mmseg - INFO - Iter [54150/80000] lr: 1.293e-05, eta: 10:50:07, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1678, decode.acc_seg: 92.5941, aux.loss_ce: 0.0704, aux.acc_seg: 92.2851, loss: 0.2382 +2024-06-16 20:09:20,525 - mmseg - INFO - Iter [54200/80000] lr: 1.290e-05, eta: 10:48:48, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1570, decode.acc_seg: 93.1083, aux.loss_ce: 0.0672, aux.acc_seg: 92.6906, loss: 0.2242 +2024-06-16 20:10:28,903 - mmseg - INFO - Iter [54250/80000] lr: 1.288e-05, eta: 10:47:29, time: 1.368, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1623, decode.acc_seg: 92.9621, aux.loss_ce: 0.0685, aux.acc_seg: 92.6234, loss: 0.2308 +2024-06-16 20:11:37,168 - mmseg - INFO - Iter [54300/80000] lr: 1.285e-05, eta: 10:46:10, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1739, decode.acc_seg: 92.4205, aux.loss_ce: 0.0740, aux.acc_seg: 91.9979, loss: 0.2479 +2024-06-16 20:12:48,041 - mmseg - INFO - Iter [54350/80000] lr: 1.283e-05, eta: 10:44:52, time: 1.417, data_time: 0.056, memory: 70722, decode.loss_ce: 0.1721, decode.acc_seg: 92.6743, aux.loss_ce: 0.0724, aux.acc_seg: 92.2249, loss: 0.2445 +2024-06-16 20:13:56,087 - mmseg - INFO - Iter [54400/80000] lr: 1.280e-05, eta: 10:43:34, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1530, decode.acc_seg: 93.4280, aux.loss_ce: 0.0649, aux.acc_seg: 93.0903, loss: 0.2179 +2024-06-16 20:15:04,294 - mmseg - INFO - Iter [54450/80000] lr: 1.278e-05, eta: 10:42:15, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1648, decode.acc_seg: 92.8319, aux.loss_ce: 0.0700, aux.acc_seg: 92.4080, loss: 0.2348 +2024-06-16 20:16:12,509 - mmseg - INFO - Iter [54500/80000] lr: 1.275e-05, eta: 10:40:56, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1587, decode.acc_seg: 93.1496, aux.loss_ce: 0.0674, aux.acc_seg: 92.7491, loss: 0.2261 +2024-06-16 20:17:21,072 - mmseg - INFO - Iter [54550/80000] lr: 1.273e-05, eta: 10:39:37, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1556, decode.acc_seg: 93.1788, aux.loss_ce: 0.0659, aux.acc_seg: 92.7701, loss: 0.2215 +2024-06-16 20:18:29,283 - mmseg - INFO - Iter [54600/80000] lr: 1.270e-05, eta: 10:38:19, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1599, decode.acc_seg: 92.9668, aux.loss_ce: 0.0678, aux.acc_seg: 92.5377, loss: 0.2276 +2024-06-16 20:19:37,622 - mmseg - INFO - Iter [54650/80000] lr: 1.268e-05, eta: 10:37:00, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1583, decode.acc_seg: 93.0995, aux.loss_ce: 0.0674, aux.acc_seg: 92.6754, loss: 0.2257 +2024-06-16 20:20:45,707 - mmseg - INFO - Iter [54700/80000] lr: 1.265e-05, eta: 10:35:41, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1546, decode.acc_seg: 93.1265, aux.loss_ce: 0.0653, aux.acc_seg: 92.7953, loss: 0.2199 +2024-06-16 20:21:53,965 - mmseg - INFO - Iter [54750/80000] lr: 1.263e-05, eta: 10:34:23, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1790, decode.acc_seg: 92.4702, aux.loss_ce: 0.0751, aux.acc_seg: 92.0761, loss: 0.2542 +2024-06-16 20:23:02,166 - mmseg - INFO - Iter [54800/80000] lr: 1.260e-05, eta: 10:33:04, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1673, decode.acc_seg: 92.8182, aux.loss_ce: 0.0712, aux.acc_seg: 92.4001, loss: 0.2385 +2024-06-16 20:24:10,183 - mmseg - INFO - Iter [54850/80000] lr: 1.258e-05, eta: 10:31:45, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1594, decode.acc_seg: 93.0219, aux.loss_ce: 0.0675, aux.acc_seg: 92.6136, loss: 0.2269 +2024-06-16 20:25:18,677 - mmseg - INFO - Iter [54900/80000] lr: 1.255e-05, eta: 10:30:27, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1771, decode.acc_seg: 92.5667, aux.loss_ce: 0.0743, aux.acc_seg: 92.1946, loss: 0.2515 +2024-06-16 20:26:26,764 - mmseg - INFO - Iter [54950/80000] lr: 1.253e-05, eta: 10:29:08, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1701, decode.acc_seg: 92.6748, aux.loss_ce: 0.0723, aux.acc_seg: 92.1878, loss: 0.2423 +2024-06-16 20:27:35,213 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:27:35,213 - mmseg - INFO - Iter [55000/80000] lr: 1.250e-05, eta: 10:27:49, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1614, decode.acc_seg: 93.0357, aux.loss_ce: 0.0686, aux.acc_seg: 92.6173, loss: 0.2300 +2024-06-16 20:29:12,158 - mmseg - INFO - per class results: +2024-06-16 20:29:12,164 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.24 | 89.62 | +| building | 85.76 | 93.78 | +| sky | 94.93 | 97.88 | +| floor | 85.93 | 92.02 | +| tree | 76.87 | 89.53 | +| ceiling | 87.08 | 94.18 | +| road | 87.16 | 92.0 | +| bed | 92.97 | 97.41 | +| windowpane | 66.02 | 82.74 | +| grass | 69.8 | 82.28 | +| cabinet | 66.72 | 78.34 | +| sidewalk | 73.09 | 84.96 | +| person | 85.91 | 94.75 | +| earth | 38.41 | 50.05 | +| door | 59.23 | 73.09 | +| table | 70.56 | 83.02 | +| mountain | 61.97 | 72.41 | +| plant | 54.02 | 62.75 | +| curtain | 77.21 | 88.74 | +| chair | 67.68 | 78.34 | +| car | 87.26 | 94.75 | +| water | 60.93 | 75.09 | +| painting | 77.44 | 91.69 | +| sofa | 82.69 | 91.41 | +| shelf | 43.94 | 57.56 | +| house | 53.3 | 65.89 | +| sea | 70.26 | 89.01 | +| mirror | 77.09 | 83.02 | +| rug | 73.51 | 81.23 | +| field | 37.97 | 70.79 | +| armchair | 60.99 | 79.19 | +| seat | 65.71 | 90.45 | +| fence | 52.26 | 67.67 | +| desk | 61.41 | 80.81 | +| rock | 56.4 | 87.46 | +| wardrobe | 52.93 | 69.31 | +| lamp | 75.51 | 86.43 | +| bathtub | 84.8 | 86.7 | +| railing | 43.28 | 60.22 | +| cushion | 70.39 | 82.6 | +| base | 43.51 | 59.45 | +| box | 39.48 | 55.77 | +| column | 53.49 | 68.87 | +| signboard | 40.19 | 58.28 | +| chest of drawers | 45.97 | 65.87 | +| counter | 41.64 | 50.19 | +| sand | 58.8 | 87.12 | +| sink | 76.74 | 85.44 | +| skyscraper | 49.79 | 61.22 | +| fireplace | 75.4 | 92.77 | +| refrigerator | 82.42 | 94.38 | +| grandstand | 49.69 | 83.51 | +| path | 32.09 | 45.29 | +| stairs | 25.13 | 31.56 | +| runway | 74.93 | 97.47 | +| case | 59.17 | 78.17 | +| pool table | 95.12 | 98.29 | +| pillow | 68.73 | 80.75 | +| screen door | 74.91 | 77.37 | +| stairway | 44.28 | 60.57 | +| river | 17.55 | 28.48 | +| bridge | 73.42 | 83.86 | +| bookcase | 39.31 | 66.28 | +| blind | 45.54 | 50.44 | +| coffee table | 66.21 | 88.73 | +| toilet | 89.51 | 93.75 | +| flower | 44.94 | 57.02 | +| book | 54.15 | 73.64 | +| hill | 7.99 | 13.66 | +| bench | 51.29 | 63.45 | +| countertop | 64.27 | 85.52 | +| stove | 83.26 | 90.25 | +| palm | 54.56 | 78.39 | +| kitchen island | 51.83 | 69.99 | +| computer | 78.22 | 92.93 | +| swivel chair | 50.42 | 78.95 | +| boat | 69.42 | 92.71 | +| bar | 61.06 | 84.09 | +| arcade machine | 78.38 | 83.86 | +| hovel | 40.66 | 43.34 | +| bus | 92.13 | 96.86 | +| towel | 74.5 | 84.96 | +| light | 61.97 | 72.68 | +| truck | 44.63 | 61.0 | +| tower | 34.12 | 56.9 | +| chandelier | 72.97 | 86.07 | +| awning | 51.15 | 63.74 | +| streetlight | 33.45 | 43.41 | +| booth | 45.16 | 62.73 | +| television receiver | 76.85 | 88.56 | +| airplane | 64.43 | 74.33 | +| dirt track | 9.39 | 40.43 | +| apparel | 47.99 | 62.8 | +| pole | 29.06 | 40.75 | +| land | 2.63 | 3.7 | +| bannister | 16.95 | 28.55 | +| escalator | 59.84 | 79.27 | +| ottoman | 49.86 | 62.28 | +| bottle | 37.62 | 62.09 | +| buffet | 45.96 | 56.22 | +| poster | 37.84 | 46.49 | +| stage | 18.82 | 38.3 | +| van | 49.82 | 68.29 | +| ship | 90.68 | 95.21 | +| fountain | 30.45 | 31.13 | +| conveyer belt | 82.06 | 93.43 | +| canopy | 53.23 | 72.59 | +| washer | 80.77 | 85.93 | +| plaything | 30.62 | 43.79 | +| swimming pool | 58.77 | 88.0 | +| stool | 55.65 | 69.94 | +| barrel | 53.39 | 74.96 | +| basket | 42.3 | 58.05 | +| waterfall | 55.67 | 65.76 | +| tent | 88.45 | 98.95 | +| bag | 20.78 | 27.26 | +| minibike | 76.74 | 90.55 | +| cradle | 77.22 | 97.85 | +| oven | 50.51 | 58.92 | +| ball | 32.89 | 33.5 | +| food | 65.01 | 80.87 | +| step | 16.24 | 19.64 | +| tank | 77.86 | 92.41 | +| trade name | 14.13 | 15.31 | +| microwave | 87.14 | 96.31 | +| pot | 58.56 | 68.35 | +| animal | 61.41 | 63.29 | +| bicycle | 60.61 | 78.05 | +| lake | 60.47 | 63.82 | +| dishwasher | 64.92 | 77.51 | +| screen | 47.24 | 69.21 | +| blanket | 30.75 | 34.56 | +| sculpture | 70.6 | 88.36 | +| hood | 63.1 | 75.62 | +| sconce | 57.95 | 68.62 | +| vase | 49.63 | 62.86 | +| traffic light | 37.84 | 58.76 | +| tray | 25.35 | 32.05 | +| ashcan | 49.84 | 63.79 | +| fan | 68.34 | 83.76 | +| pier | 43.99 | 49.52 | +| crt screen | 13.56 | 25.22 | +| plate | 60.45 | 78.12 | +| monitor | 66.49 | 78.2 | +| bulletin board | 50.47 | 66.93 | +| shower | 4.23 | 13.41 | +| radiator | 65.79 | 79.55 | +| glass | 21.25 | 23.3 | +| clock | 41.86 | 52.5 | +| flag | 70.33 | 77.04 | ++---------------------+-------+-------+ +2024-06-16 20:29:12,165 - mmseg - INFO - Summary: +2024-06-16 20:29:12,165 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.29 | 57.12 | 70.16 | ++-------+-------+-------+ +2024-06-16 20:29:12,166 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:29:12,166 - mmseg - INFO - Iter(val) [250] aAcc: 0.8629, mIoU: 0.5712, mAcc: 0.7016, IoU.wall: 0.8224, IoU.building: 0.8576, IoU.sky: 0.9493, IoU.floor: 0.8593, IoU.tree: 0.7687, IoU.ceiling: 0.8708, IoU.road: 0.8716, IoU.bed : 0.9297, IoU.windowpane: 0.6602, IoU.grass: 0.6980, IoU.cabinet: 0.6672, IoU.sidewalk: 0.7309, IoU.person: 0.8591, IoU.earth: 0.3841, IoU.door: 0.5923, IoU.table: 0.7056, IoU.mountain: 0.6197, IoU.plant: 0.5402, IoU.curtain: 0.7721, IoU.chair: 0.6768, IoU.car: 0.8726, IoU.water: 0.6093, IoU.painting: 0.7744, IoU.sofa: 0.8269, IoU.shelf: 0.4394, IoU.house: 0.5330, IoU.sea: 0.7026, IoU.mirror: 0.7709, IoU.rug: 0.7351, IoU.field: 0.3797, IoU.armchair: 0.6099, IoU.seat: 0.6571, IoU.fence: 0.5226, IoU.desk: 0.6141, IoU.rock: 0.5640, IoU.wardrobe: 0.5293, IoU.lamp: 0.7551, IoU.bathtub: 0.8480, IoU.railing: 0.4328, IoU.cushion: 0.7039, IoU.base: 0.4351, IoU.box: 0.3948, IoU.column: 0.5349, IoU.signboard: 0.4019, IoU.chest of drawers: 0.4597, IoU.counter: 0.4164, IoU.sand: 0.5880, IoU.sink: 0.7674, IoU.skyscraper: 0.4979, IoU.fireplace: 0.7540, IoU.refrigerator: 0.8242, IoU.grandstand: 0.4969, IoU.path: 0.3209, IoU.stairs: 0.2513, IoU.runway: 0.7493, IoU.case: 0.5917, IoU.pool table: 0.9512, IoU.pillow: 0.6873, IoU.screen door: 0.7491, IoU.stairway: 0.4428, IoU.river: 0.1755, IoU.bridge: 0.7342, IoU.bookcase: 0.3931, IoU.blind: 0.4554, IoU.coffee table: 0.6621, IoU.toilet: 0.8951, IoU.flower: 0.4494, IoU.book: 0.5415, IoU.hill: 0.0799, IoU.bench: 0.5129, IoU.countertop: 0.6427, IoU.stove: 0.8326, IoU.palm: 0.5456, IoU.kitchen island: 0.5183, IoU.computer: 0.7822, IoU.swivel chair: 0.5042, IoU.boat: 0.6942, IoU.bar: 0.6106, IoU.arcade machine: 0.7838, IoU.hovel: 0.4066, IoU.bus: 0.9213, IoU.towel: 0.7450, IoU.light: 0.6197, IoU.truck: 0.4463, IoU.tower: 0.3412, IoU.chandelier: 0.7297, IoU.awning: 0.5115, IoU.streetlight: 0.3345, IoU.booth: 0.4516, IoU.television receiver: 0.7685, IoU.airplane: 0.6443, IoU.dirt track: 0.0939, IoU.apparel: 0.4799, IoU.pole: 0.2906, IoU.land: 0.0263, IoU.bannister: 0.1695, IoU.escalator: 0.5984, IoU.ottoman: 0.4986, IoU.bottle: 0.3762, IoU.buffet: 0.4596, IoU.poster: 0.3784, IoU.stage: 0.1882, IoU.van: 0.4982, IoU.ship: 0.9068, IoU.fountain: 0.3045, IoU.conveyer belt: 0.8206, IoU.canopy: 0.5323, IoU.washer: 0.8077, IoU.plaything: 0.3062, IoU.swimming pool: 0.5877, IoU.stool: 0.5565, IoU.barrel: 0.5339, IoU.basket: 0.4230, IoU.waterfall: 0.5567, IoU.tent: 0.8845, IoU.bag: 0.2078, IoU.minibike: 0.7674, IoU.cradle: 0.7722, IoU.oven: 0.5051, IoU.ball: 0.3289, IoU.food: 0.6501, IoU.step: 0.1624, IoU.tank: 0.7786, IoU.trade name: 0.1413, IoU.microwave: 0.8714, IoU.pot: 0.5856, IoU.animal: 0.6141, IoU.bicycle: 0.6061, IoU.lake: 0.6047, IoU.dishwasher: 0.6492, IoU.screen: 0.4724, IoU.blanket: 0.3075, IoU.sculpture: 0.7060, IoU.hood: 0.6310, IoU.sconce: 0.5795, IoU.vase: 0.4963, IoU.traffic light: 0.3784, IoU.tray: 0.2535, IoU.ashcan: 0.4984, IoU.fan: 0.6834, IoU.pier: 0.4399, IoU.crt screen: 0.1356, IoU.plate: 0.6045, IoU.monitor: 0.6649, IoU.bulletin board: 0.5047, IoU.shower: 0.0423, IoU.radiator: 0.6579, IoU.glass: 0.2125, IoU.clock: 0.4186, IoU.flag: 0.7033, Acc.wall: 0.8962, Acc.building: 0.9378, Acc.sky: 0.9788, Acc.floor: 0.9202, Acc.tree: 0.8953, Acc.ceiling: 0.9418, Acc.road: 0.9200, Acc.bed : 0.9741, Acc.windowpane: 0.8274, Acc.grass: 0.8228, Acc.cabinet: 0.7834, Acc.sidewalk: 0.8496, Acc.person: 0.9475, Acc.earth: 0.5005, Acc.door: 0.7309, Acc.table: 0.8302, Acc.mountain: 0.7241, Acc.plant: 0.6275, Acc.curtain: 0.8874, Acc.chair: 0.7834, Acc.car: 0.9475, Acc.water: 0.7509, Acc.painting: 0.9169, Acc.sofa: 0.9141, Acc.shelf: 0.5756, Acc.house: 0.6589, Acc.sea: 0.8901, Acc.mirror: 0.8302, Acc.rug: 0.8123, Acc.field: 0.7079, Acc.armchair: 0.7919, Acc.seat: 0.9045, Acc.fence: 0.6767, Acc.desk: 0.8081, Acc.rock: 0.8746, Acc.wardrobe: 0.6931, Acc.lamp: 0.8643, Acc.bathtub: 0.8670, Acc.railing: 0.6022, Acc.cushion: 0.8260, Acc.base: 0.5945, Acc.box: 0.5577, Acc.column: 0.6887, Acc.signboard: 0.5828, Acc.chest of drawers: 0.6587, Acc.counter: 0.5019, Acc.sand: 0.8712, Acc.sink: 0.8544, Acc.skyscraper: 0.6122, Acc.fireplace: 0.9277, Acc.refrigerator: 0.9438, Acc.grandstand: 0.8351, Acc.path: 0.4529, Acc.stairs: 0.3156, Acc.runway: 0.9747, Acc.case: 0.7817, Acc.pool table: 0.9829, Acc.pillow: 0.8075, Acc.screen door: 0.7737, Acc.stairway: 0.6057, Acc.river: 0.2848, Acc.bridge: 0.8386, Acc.bookcase: 0.6628, Acc.blind: 0.5044, Acc.coffee table: 0.8873, Acc.toilet: 0.9375, Acc.flower: 0.5702, Acc.book: 0.7364, Acc.hill: 0.1366, Acc.bench: 0.6345, Acc.countertop: 0.8552, Acc.stove: 0.9025, Acc.palm: 0.7839, Acc.kitchen island: 0.6999, Acc.computer: 0.9293, Acc.swivel chair: 0.7895, Acc.boat: 0.9271, Acc.bar: 0.8409, Acc.arcade machine: 0.8386, Acc.hovel: 0.4334, Acc.bus: 0.9686, Acc.towel: 0.8496, Acc.light: 0.7268, Acc.truck: 0.6100, Acc.tower: 0.5690, Acc.chandelier: 0.8607, Acc.awning: 0.6374, Acc.streetlight: 0.4341, Acc.booth: 0.6273, Acc.television receiver: 0.8856, Acc.airplane: 0.7433, Acc.dirt track: 0.4043, Acc.apparel: 0.6280, Acc.pole: 0.4075, Acc.land: 0.0370, Acc.bannister: 0.2855, Acc.escalator: 0.7927, Acc.ottoman: 0.6228, Acc.bottle: 0.6209, Acc.buffet: 0.5622, Acc.poster: 0.4649, Acc.stage: 0.3830, Acc.van: 0.6829, Acc.ship: 0.9521, Acc.fountain: 0.3113, Acc.conveyer belt: 0.9343, Acc.canopy: 0.7259, Acc.washer: 0.8593, Acc.plaything: 0.4379, Acc.swimming pool: 0.8800, Acc.stool: 0.6994, Acc.barrel: 0.7496, Acc.basket: 0.5805, Acc.waterfall: 0.6576, Acc.tent: 0.9895, Acc.bag: 0.2726, Acc.minibike: 0.9055, Acc.cradle: 0.9785, Acc.oven: 0.5892, Acc.ball: 0.3350, Acc.food: 0.8087, Acc.step: 0.1964, Acc.tank: 0.9241, Acc.trade name: 0.1531, Acc.microwave: 0.9631, Acc.pot: 0.6835, Acc.animal: 0.6329, Acc.bicycle: 0.7805, Acc.lake: 0.6382, Acc.dishwasher: 0.7751, Acc.screen: 0.6921, Acc.blanket: 0.3456, Acc.sculpture: 0.8836, Acc.hood: 0.7562, Acc.sconce: 0.6862, Acc.vase: 0.6286, Acc.traffic light: 0.5876, Acc.tray: 0.3205, Acc.ashcan: 0.6379, Acc.fan: 0.8376, Acc.pier: 0.4952, Acc.crt screen: 0.2522, Acc.plate: 0.7812, Acc.monitor: 0.7820, Acc.bulletin board: 0.6693, Acc.shower: 0.1341, Acc.radiator: 0.7955, Acc.glass: 0.2330, Acc.clock: 0.5250, Acc.flag: 0.7704 +2024-06-16 20:30:20,878 - mmseg - INFO - Iter [55050/80000] lr: 1.248e-05, eta: 10:27:15, time: 3.313, data_time: 1.955, memory: 70722, decode.loss_ce: 0.1694, decode.acc_seg: 92.6392, aux.loss_ce: 0.0716, aux.acc_seg: 92.2447, loss: 0.2409 +2024-06-16 20:31:29,167 - mmseg - INFO - Iter [55100/80000] lr: 1.245e-05, eta: 10:25:56, time: 1.366, data_time: 0.011, memory: 70722, decode.loss_ce: 0.1599, decode.acc_seg: 92.8975, aux.loss_ce: 0.0677, aux.acc_seg: 92.4960, loss: 0.2275 +2024-06-16 20:32:37,256 - mmseg - INFO - Iter [55150/80000] lr: 1.243e-05, eta: 10:24:38, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1596, decode.acc_seg: 93.0433, aux.loss_ce: 0.0678, aux.acc_seg: 92.6479, loss: 0.2274 +2024-06-16 20:33:45,333 - mmseg - INFO - Iter [55200/80000] lr: 1.240e-05, eta: 10:23:19, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1680, decode.acc_seg: 92.7538, aux.loss_ce: 0.0713, aux.acc_seg: 92.2866, loss: 0.2393 +2024-06-16 20:34:53,472 - mmseg - INFO - Iter [55250/80000] lr: 1.238e-05, eta: 10:22:00, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1566, decode.acc_seg: 93.1543, aux.loss_ce: 0.0661, aux.acc_seg: 92.8614, loss: 0.2227 +2024-06-16 20:36:01,768 - mmseg - INFO - Iter [55300/80000] lr: 1.235e-05, eta: 10:20:42, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1584, decode.acc_seg: 93.0031, aux.loss_ce: 0.0670, aux.acc_seg: 92.5906, loss: 0.2254 +2024-06-16 20:37:09,942 - mmseg - INFO - Iter [55350/80000] lr: 1.233e-05, eta: 10:19:23, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1598, decode.acc_seg: 92.8644, aux.loss_ce: 0.0682, aux.acc_seg: 92.4796, loss: 0.2280 +2024-06-16 20:38:18,074 - mmseg - INFO - Iter [55400/80000] lr: 1.230e-05, eta: 10:18:05, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1563, decode.acc_seg: 93.0803, aux.loss_ce: 0.0663, aux.acc_seg: 92.6905, loss: 0.2226 +2024-06-16 20:39:26,384 - mmseg - INFO - Iter [55450/80000] lr: 1.228e-05, eta: 10:16:46, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1637, decode.acc_seg: 92.6979, aux.loss_ce: 0.0693, aux.acc_seg: 92.3215, loss: 0.2329 +2024-06-16 20:40:34,555 - mmseg - INFO - Iter [55500/80000] lr: 1.225e-05, eta: 10:15:28, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1619, decode.acc_seg: 93.1282, aux.loss_ce: 0.0680, aux.acc_seg: 92.7718, loss: 0.2299 +2024-06-16 20:41:42,912 - mmseg - INFO - Iter [55550/80000] lr: 1.223e-05, eta: 10:14:09, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1679, decode.acc_seg: 92.5273, aux.loss_ce: 0.0716, aux.acc_seg: 92.0853, loss: 0.2395 +2024-06-16 20:42:53,736 - mmseg - INFO - Iter [55600/80000] lr: 1.220e-05, eta: 10:12:52, time: 1.416, data_time: 0.061, memory: 70722, decode.loss_ce: 0.1576, decode.acc_seg: 93.0442, aux.loss_ce: 0.0672, aux.acc_seg: 92.5868, loss: 0.2248 +2024-06-16 20:44:01,902 - mmseg - INFO - Iter [55650/80000] lr: 1.218e-05, eta: 10:11:33, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1597, decode.acc_seg: 92.8946, aux.loss_ce: 0.0676, aux.acc_seg: 92.4813, loss: 0.2273 +2024-06-16 20:45:10,052 - mmseg - INFO - Iter [55700/80000] lr: 1.215e-05, eta: 10:10:15, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1570, decode.acc_seg: 93.0461, aux.loss_ce: 0.0666, aux.acc_seg: 92.6577, loss: 0.2236 +2024-06-16 20:46:18,189 - mmseg - INFO - Iter [55750/80000] lr: 1.213e-05, eta: 10:08:56, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1588, decode.acc_seg: 92.9910, aux.loss_ce: 0.0675, aux.acc_seg: 92.6085, loss: 0.2264 +2024-06-16 20:47:26,419 - mmseg - INFO - Iter [55800/80000] lr: 1.210e-05, eta: 10:07:38, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1638, decode.acc_seg: 92.7074, aux.loss_ce: 0.0696, aux.acc_seg: 92.2827, loss: 0.2334 +2024-06-16 20:48:34,607 - mmseg - INFO - Iter [55850/80000] lr: 1.208e-05, eta: 10:06:19, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1606, decode.acc_seg: 93.0154, aux.loss_ce: 0.0679, aux.acc_seg: 92.5893, loss: 0.2285 +2024-06-16 20:49:42,953 - mmseg - INFO - Iter [55900/80000] lr: 1.205e-05, eta: 10:05:01, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1642, decode.acc_seg: 92.8382, aux.loss_ce: 0.0695, aux.acc_seg: 92.4520, loss: 0.2337 +2024-06-16 20:50:50,942 - mmseg - INFO - Iter [55950/80000] lr: 1.203e-05, eta: 10:03:43, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1611, decode.acc_seg: 93.0593, aux.loss_ce: 0.0675, aux.acc_seg: 92.7252, loss: 0.2286 +2024-06-16 20:51:59,239 - mmseg - INFO - Saving checkpoint at 56000 iterations +2024-06-16 20:53:23,959 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:53:23,959 - mmseg - INFO - Iter [56000/80000] lr: 1.200e-05, eta: 10:03:01, time: 3.060, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1542, decode.acc_seg: 93.1625, aux.loss_ce: 0.0656, aux.acc_seg: 92.7680, loss: 0.2198 +2024-06-16 20:55:00,966 - mmseg - INFO - per class results: +2024-06-16 20:55:00,972 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.06 | 89.62 | +| building | 85.16 | 93.74 | +| sky | 94.93 | 97.67 | +| floor | 85.73 | 92.06 | +| tree | 76.86 | 89.89 | +| ceiling | 87.36 | 93.6 | +| road | 86.78 | 91.72 | +| bed | 93.08 | 97.16 | +| windowpane | 66.13 | 80.83 | +| grass | 70.29 | 83.18 | +| cabinet | 67.51 | 77.99 | +| sidewalk | 73.1 | 86.9 | +| person | 86.12 | 94.23 | +| earth | 36.66 | 49.79 | +| door | 58.01 | 72.67 | +| table | 70.42 | 82.39 | +| mountain | 63.71 | 75.09 | +| plant | 53.87 | 64.2 | +| curtain | 78.25 | 90.41 | +| chair | 68.45 | 80.52 | +| car | 87.76 | 94.43 | +| water | 61.28 | 75.77 | +| painting | 77.67 | 91.04 | +| sofa | 81.81 | 93.38 | +| shelf | 44.08 | 56.76 | +| house | 53.31 | 67.78 | +| sea | 72.0 | 89.59 | +| mirror | 78.43 | 84.27 | +| rug | 73.82 | 82.4 | +| field | 36.51 | 61.91 | +| armchair | 59.46 | 73.77 | +| seat | 68.23 | 90.31 | +| fence | 53.13 | 63.34 | +| desk | 63.21 | 78.03 | +| rock | 63.93 | 86.27 | +| wardrobe | 53.69 | 72.26 | +| lamp | 75.6 | 87.86 | +| bathtub | 83.89 | 86.49 | +| railing | 42.94 | 60.29 | +| cushion | 68.82 | 80.64 | +| base | 41.76 | 55.77 | +| box | 37.82 | 49.8 | +| column | 54.6 | 68.95 | +| signboard | 41.21 | 53.28 | +| chest of drawers | 46.74 | 66.94 | +| counter | 42.4 | 59.5 | +| sand | 56.75 | 87.9 | +| sink | 76.52 | 80.43 | +| skyscraper | 49.21 | 61.08 | +| fireplace | 73.78 | 94.87 | +| refrigerator | 84.44 | 95.17 | +| grandstand | 49.56 | 85.55 | +| path | 28.95 | 45.33 | +| stairs | 24.89 | 29.72 | +| runway | 72.97 | 94.89 | +| case | 57.74 | 78.4 | +| pool table | 94.67 | 98.39 | +| pillow | 68.98 | 79.78 | +| screen door | 78.1 | 79.84 | +| stairway | 45.55 | 67.76 | +| river | 15.16 | 26.3 | +| bridge | 75.32 | 83.45 | +| bookcase | 42.58 | 70.44 | +| blind | 45.18 | 49.93 | +| coffee table | 64.83 | 89.53 | +| toilet | 90.04 | 92.66 | +| flower | 43.14 | 53.52 | +| book | 55.99 | 75.32 | +| hill | 8.51 | 13.95 | +| bench | 52.71 | 65.73 | +| countertop | 62.41 | 86.17 | +| stove | 86.86 | 92.25 | +| palm | 54.32 | 80.42 | +| kitchen island | 48.27 | 72.65 | +| computer | 78.87 | 91.69 | +| swivel chair | 49.02 | 78.0 | +| boat | 71.52 | 91.97 | +| bar | 54.12 | 64.56 | +| arcade machine | 74.05 | 80.13 | +| hovel | 40.33 | 44.67 | +| bus | 93.52 | 97.07 | +| towel | 74.2 | 84.33 | +| light | 61.44 | 70.05 | +| truck | 45.75 | 59.58 | +| tower | 32.2 | 48.7 | +| chandelier | 74.07 | 85.77 | +| awning | 52.22 | 65.05 | +| streetlight | 34.91 | 45.8 | +| booth | 46.05 | 71.68 | +| television receiver | 72.34 | 86.4 | +| airplane | 67.51 | 72.82 | +| dirt track | 14.48 | 40.69 | +| apparel | 46.74 | 71.06 | +| pole | 27.51 | 38.4 | +| land | 10.04 | 19.33 | +| bannister | 17.71 | 27.03 | +| escalator | 60.49 | 80.13 | +| ottoman | 49.98 | 68.12 | +| bottle | 41.58 | 69.44 | +| buffet | 59.96 | 76.14 | +| poster | 40.18 | 46.09 | +| stage | 20.71 | 42.46 | +| van | 50.88 | 66.31 | +| ship | 90.08 | 93.5 | +| fountain | 29.77 | 30.34 | +| conveyer belt | 83.18 | 92.61 | +| canopy | 53.95 | 76.03 | +| washer | 80.37 | 85.57 | +| plaything | 29.6 | 43.44 | +| swimming pool | 56.74 | 83.19 | +| stool | 55.79 | 70.36 | +| barrel | 53.93 | 74.31 | +| basket | 42.05 | 61.06 | +| waterfall | 69.28 | 87.48 | +| tent | 86.44 | 98.55 | +| bag | 21.35 | 25.07 | +| minibike | 77.47 | 87.17 | +| cradle | 83.02 | 97.71 | +| oven | 62.77 | 72.55 | +| ball | 53.18 | 57.95 | +| food | 63.87 | 82.2 | +| step | 10.55 | 14.21 | +| tank | 80.72 | 89.41 | +| trade name | 28.45 | 33.84 | +| microwave | 88.46 | 96.19 | +| pot | 56.01 | 64.43 | +| animal | 61.25 | 63.24 | +| bicycle | 59.04 | 74.66 | +| lake | 50.91 | 63.82 | +| dishwasher | 66.58 | 78.31 | +| screen | 58.99 | 93.76 | +| blanket | 29.38 | 32.82 | +| sculpture | 73.73 | 87.68 | +| hood | 62.89 | 76.52 | +| sconce | 58.15 | 73.77 | +| vase | 49.07 | 63.9 | +| traffic light | 40.26 | 58.82 | +| tray | 24.24 | 29.39 | +| ashcan | 48.98 | 64.66 | +| fan | 69.89 | 82.32 | +| pier | 40.07 | 45.39 | +| crt screen | 3.23 | 3.42 | +| plate | 61.32 | 77.61 | +| monitor | 66.22 | 78.2 | +| bulletin board | 53.19 | 64.47 | +| shower | 4.65 | 5.08 | +| radiator | 67.87 | 77.83 | +| glass | 19.23 | 20.29 | +| clock | 44.66 | 54.6 | +| flag | 70.51 | 77.59 | ++---------------------+-------+-------+ +2024-06-16 20:55:00,972 - mmseg - INFO - Summary: +2024-06-16 20:55:00,972 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.29 | 57.73 | 70.63 | ++-------+-------+-------+ +2024-06-16 20:55:00,973 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:55:00,974 - mmseg - INFO - Iter(val) [250] aAcc: 0.8629, mIoU: 0.5773, mAcc: 0.7063, IoU.wall: 0.8206, IoU.building: 0.8516, IoU.sky: 0.9493, IoU.floor: 0.8573, IoU.tree: 0.7686, IoU.ceiling: 0.8736, IoU.road: 0.8678, IoU.bed : 0.9308, IoU.windowpane: 0.6613, IoU.grass: 0.7029, IoU.cabinet: 0.6751, IoU.sidewalk: 0.7310, IoU.person: 0.8612, IoU.earth: 0.3666, IoU.door: 0.5801, IoU.table: 0.7042, IoU.mountain: 0.6371, IoU.plant: 0.5387, IoU.curtain: 0.7825, IoU.chair: 0.6845, IoU.car: 0.8776, IoU.water: 0.6128, IoU.painting: 0.7767, IoU.sofa: 0.8181, IoU.shelf: 0.4408, IoU.house: 0.5331, IoU.sea: 0.7200, IoU.mirror: 0.7843, IoU.rug: 0.7382, IoU.field: 0.3651, IoU.armchair: 0.5946, IoU.seat: 0.6823, IoU.fence: 0.5313, IoU.desk: 0.6321, IoU.rock: 0.6393, IoU.wardrobe: 0.5369, IoU.lamp: 0.7560, IoU.bathtub: 0.8389, IoU.railing: 0.4294, IoU.cushion: 0.6882, IoU.base: 0.4176, IoU.box: 0.3782, IoU.column: 0.5460, IoU.signboard: 0.4121, IoU.chest of drawers: 0.4674, IoU.counter: 0.4240, IoU.sand: 0.5675, IoU.sink: 0.7652, IoU.skyscraper: 0.4921, IoU.fireplace: 0.7378, IoU.refrigerator: 0.8444, IoU.grandstand: 0.4956, IoU.path: 0.2895, IoU.stairs: 0.2489, IoU.runway: 0.7297, IoU.case: 0.5774, IoU.pool table: 0.9467, IoU.pillow: 0.6898, IoU.screen door: 0.7810, IoU.stairway: 0.4555, IoU.river: 0.1516, IoU.bridge: 0.7532, IoU.bookcase: 0.4258, IoU.blind: 0.4518, IoU.coffee table: 0.6483, IoU.toilet: 0.9004, IoU.flower: 0.4314, IoU.book: 0.5599, IoU.hill: 0.0851, IoU.bench: 0.5271, IoU.countertop: 0.6241, IoU.stove: 0.8686, IoU.palm: 0.5432, IoU.kitchen island: 0.4827, IoU.computer: 0.7887, IoU.swivel chair: 0.4902, IoU.boat: 0.7152, IoU.bar: 0.5412, IoU.arcade machine: 0.7405, IoU.hovel: 0.4033, IoU.bus: 0.9352, IoU.towel: 0.7420, IoU.light: 0.6144, IoU.truck: 0.4575, IoU.tower: 0.3220, IoU.chandelier: 0.7407, IoU.awning: 0.5222, IoU.streetlight: 0.3491, IoU.booth: 0.4605, IoU.television receiver: 0.7234, IoU.airplane: 0.6751, IoU.dirt track: 0.1448, IoU.apparel: 0.4674, IoU.pole: 0.2751, IoU.land: 0.1004, IoU.bannister: 0.1771, IoU.escalator: 0.6049, IoU.ottoman: 0.4998, IoU.bottle: 0.4158, IoU.buffet: 0.5996, IoU.poster: 0.4018, IoU.stage: 0.2071, IoU.van: 0.5088, IoU.ship: 0.9008, IoU.fountain: 0.2977, IoU.conveyer belt: 0.8318, IoU.canopy: 0.5395, IoU.washer: 0.8037, IoU.plaything: 0.2960, IoU.swimming pool: 0.5674, IoU.stool: 0.5579, IoU.barrel: 0.5393, IoU.basket: 0.4205, IoU.waterfall: 0.6928, IoU.tent: 0.8644, IoU.bag: 0.2135, IoU.minibike: 0.7747, IoU.cradle: 0.8302, IoU.oven: 0.6277, IoU.ball: 0.5318, IoU.food: 0.6387, IoU.step: 0.1055, IoU.tank: 0.8072, IoU.trade name: 0.2845, IoU.microwave: 0.8846, IoU.pot: 0.5601, IoU.animal: 0.6125, IoU.bicycle: 0.5904, IoU.lake: 0.5091, IoU.dishwasher: 0.6658, IoU.screen: 0.5899, IoU.blanket: 0.2938, IoU.sculpture: 0.7373, IoU.hood: 0.6289, IoU.sconce: 0.5815, IoU.vase: 0.4907, IoU.traffic light: 0.4026, IoU.tray: 0.2424, IoU.ashcan: 0.4898, IoU.fan: 0.6989, IoU.pier: 0.4007, IoU.crt screen: 0.0323, IoU.plate: 0.6132, IoU.monitor: 0.6622, IoU.bulletin board: 0.5319, IoU.shower: 0.0465, IoU.radiator: 0.6787, IoU.glass: 0.1923, IoU.clock: 0.4466, IoU.flag: 0.7051, Acc.wall: 0.8962, Acc.building: 0.9374, Acc.sky: 0.9767, Acc.floor: 0.9206, Acc.tree: 0.8989, Acc.ceiling: 0.9360, Acc.road: 0.9172, Acc.bed : 0.9716, Acc.windowpane: 0.8083, Acc.grass: 0.8318, Acc.cabinet: 0.7799, Acc.sidewalk: 0.8690, Acc.person: 0.9423, Acc.earth: 0.4979, Acc.door: 0.7267, Acc.table: 0.8239, Acc.mountain: 0.7509, Acc.plant: 0.6420, Acc.curtain: 0.9041, Acc.chair: 0.8052, Acc.car: 0.9443, Acc.water: 0.7577, Acc.painting: 0.9104, Acc.sofa: 0.9338, Acc.shelf: 0.5676, Acc.house: 0.6778, Acc.sea: 0.8959, Acc.mirror: 0.8427, Acc.rug: 0.8240, Acc.field: 0.6191, Acc.armchair: 0.7377, Acc.seat: 0.9031, Acc.fence: 0.6334, Acc.desk: 0.7803, Acc.rock: 0.8627, Acc.wardrobe: 0.7226, Acc.lamp: 0.8786, Acc.bathtub: 0.8649, Acc.railing: 0.6029, Acc.cushion: 0.8064, Acc.base: 0.5577, Acc.box: 0.4980, Acc.column: 0.6895, Acc.signboard: 0.5328, Acc.chest of drawers: 0.6694, Acc.counter: 0.5950, Acc.sand: 0.8790, Acc.sink: 0.8043, Acc.skyscraper: 0.6108, Acc.fireplace: 0.9487, Acc.refrigerator: 0.9517, Acc.grandstand: 0.8555, Acc.path: 0.4533, Acc.stairs: 0.2972, Acc.runway: 0.9489, Acc.case: 0.7840, Acc.pool table: 0.9839, Acc.pillow: 0.7978, Acc.screen door: 0.7984, Acc.stairway: 0.6776, Acc.river: 0.2630, Acc.bridge: 0.8345, Acc.bookcase: 0.7044, Acc.blind: 0.4993, Acc.coffee table: 0.8953, Acc.toilet: 0.9266, Acc.flower: 0.5352, Acc.book: 0.7532, Acc.hill: 0.1395, Acc.bench: 0.6573, Acc.countertop: 0.8617, Acc.stove: 0.9225, Acc.palm: 0.8042, Acc.kitchen island: 0.7265, Acc.computer: 0.9169, Acc.swivel chair: 0.7800, Acc.boat: 0.9197, Acc.bar: 0.6456, Acc.arcade machine: 0.8013, Acc.hovel: 0.4467, Acc.bus: 0.9707, Acc.towel: 0.8433, Acc.light: 0.7005, Acc.truck: 0.5958, Acc.tower: 0.4870, Acc.chandelier: 0.8577, Acc.awning: 0.6505, Acc.streetlight: 0.4580, Acc.booth: 0.7168, Acc.television receiver: 0.8640, Acc.airplane: 0.7282, Acc.dirt track: 0.4069, Acc.apparel: 0.7106, Acc.pole: 0.3840, Acc.land: 0.1933, Acc.bannister: 0.2703, Acc.escalator: 0.8013, Acc.ottoman: 0.6812, Acc.bottle: 0.6944, Acc.buffet: 0.7614, Acc.poster: 0.4609, Acc.stage: 0.4246, Acc.van: 0.6631, Acc.ship: 0.9350, Acc.fountain: 0.3034, Acc.conveyer belt: 0.9261, Acc.canopy: 0.7603, Acc.washer: 0.8557, Acc.plaything: 0.4344, Acc.swimming pool: 0.8319, Acc.stool: 0.7036, Acc.barrel: 0.7431, Acc.basket: 0.6106, Acc.waterfall: 0.8748, Acc.tent: 0.9855, Acc.bag: 0.2507, Acc.minibike: 0.8717, Acc.cradle: 0.9771, Acc.oven: 0.7255, Acc.ball: 0.5795, Acc.food: 0.8220, Acc.step: 0.1421, Acc.tank: 0.8941, Acc.trade name: 0.3384, Acc.microwave: 0.9619, Acc.pot: 0.6443, Acc.animal: 0.6324, Acc.bicycle: 0.7466, Acc.lake: 0.6382, Acc.dishwasher: 0.7831, Acc.screen: 0.9376, Acc.blanket: 0.3282, Acc.sculpture: 0.8768, Acc.hood: 0.7652, Acc.sconce: 0.7377, Acc.vase: 0.6390, Acc.traffic light: 0.5882, Acc.tray: 0.2939, Acc.ashcan: 0.6466, Acc.fan: 0.8232, Acc.pier: 0.4539, Acc.crt screen: 0.0342, Acc.plate: 0.7761, Acc.monitor: 0.7820, Acc.bulletin board: 0.6447, Acc.shower: 0.0508, Acc.radiator: 0.7783, Acc.glass: 0.2029, Acc.clock: 0.5460, Acc.flag: 0.7759 +2024-06-16 20:56:09,758 - mmseg - INFO - Iter [56050/80000] lr: 1.198e-05, eta: 10:02:24, time: 3.316, data_time: 1.957, memory: 70722, decode.loss_ce: 0.1649, decode.acc_seg: 92.7342, aux.loss_ce: 0.0698, aux.acc_seg: 92.3768, loss: 0.2346 +2024-06-16 20:57:18,081 - mmseg - INFO - Iter [56100/80000] lr: 1.195e-05, eta: 10:01:05, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1556, decode.acc_seg: 93.1838, aux.loss_ce: 0.0663, aux.acc_seg: 92.7646, loss: 0.2220 +2024-06-16 20:58:26,278 - mmseg - INFO - Iter [56150/80000] lr: 1.193e-05, eta: 9:59:47, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1723, decode.acc_seg: 92.6971, aux.loss_ce: 0.0728, aux.acc_seg: 92.2898, loss: 0.2452 +2024-06-16 20:59:34,354 - mmseg - INFO - Iter [56200/80000] lr: 1.190e-05, eta: 9:58:28, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1613, decode.acc_seg: 92.9390, aux.loss_ce: 0.0682, aux.acc_seg: 92.5763, loss: 0.2295 +2024-06-16 21:00:42,479 - mmseg - INFO - Iter [56250/80000] lr: 1.188e-05, eta: 9:57:10, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1659, decode.acc_seg: 92.6211, aux.loss_ce: 0.0695, aux.acc_seg: 92.4237, loss: 0.2354 +2024-06-16 21:01:50,756 - mmseg - INFO - Iter [56300/80000] lr: 1.185e-05, eta: 9:55:51, time: 1.366, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1703, decode.acc_seg: 92.6574, aux.loss_ce: 0.0719, aux.acc_seg: 92.2694, loss: 0.2422 +2024-06-16 21:02:58,796 - mmseg - INFO - Iter [56350/80000] lr: 1.183e-05, eta: 9:54:33, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1692, decode.acc_seg: 92.8955, aux.loss_ce: 0.0709, aux.acc_seg: 92.5607, loss: 0.2401 +2024-06-16 21:04:06,968 - mmseg - INFO - Iter [56400/80000] lr: 1.180e-05, eta: 9:53:14, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1611, decode.acc_seg: 92.8293, aux.loss_ce: 0.0684, aux.acc_seg: 92.4238, loss: 0.2295 +2024-06-16 21:05:15,139 - mmseg - INFO - Iter [56450/80000] lr: 1.178e-05, eta: 9:51:56, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1658, decode.acc_seg: 92.5637, aux.loss_ce: 0.0698, aux.acc_seg: 92.2080, loss: 0.2356 +2024-06-16 21:06:23,216 - mmseg - INFO - Iter [56500/80000] lr: 1.175e-05, eta: 9:50:37, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1649, decode.acc_seg: 92.8942, aux.loss_ce: 0.0700, aux.acc_seg: 92.4323, loss: 0.2349 +2024-06-16 21:07:31,500 - mmseg - INFO - Iter [56550/80000] lr: 1.173e-05, eta: 9:49:19, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1592, decode.acc_seg: 93.1012, aux.loss_ce: 0.0674, aux.acc_seg: 92.7638, loss: 0.2266 +2024-06-16 21:08:39,453 - mmseg - INFO - Iter [56600/80000] lr: 1.170e-05, eta: 9:48:01, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1702, decode.acc_seg: 92.6447, aux.loss_ce: 0.0716, aux.acc_seg: 92.3024, loss: 0.2418 +2024-06-16 21:09:47,900 - mmseg - INFO - Iter [56650/80000] lr: 1.168e-05, eta: 9:46:42, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1736, decode.acc_seg: 92.4116, aux.loss_ce: 0.0734, aux.acc_seg: 92.0193, loss: 0.2470 +2024-06-16 21:10:55,856 - mmseg - INFO - Iter [56700/80000] lr: 1.165e-05, eta: 9:45:24, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1598, decode.acc_seg: 93.2548, aux.loss_ce: 0.0679, aux.acc_seg: 92.8622, loss: 0.2276 +2024-06-16 21:12:04,079 - mmseg - INFO - Iter [56750/80000] lr: 1.163e-05, eta: 9:44:06, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1551, decode.acc_seg: 93.3168, aux.loss_ce: 0.0658, aux.acc_seg: 92.9445, loss: 0.2209 +2024-06-16 21:13:12,029 - mmseg - INFO - Iter [56800/80000] lr: 1.160e-05, eta: 9:42:47, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1690, decode.acc_seg: 92.6592, aux.loss_ce: 0.0719, aux.acc_seg: 92.2386, loss: 0.2409 +2024-06-16 21:14:22,510 - mmseg - INFO - Iter [56850/80000] lr: 1.158e-05, eta: 9:41:30, time: 1.410, data_time: 0.052, memory: 70722, decode.loss_ce: 0.1649, decode.acc_seg: 92.6796, aux.loss_ce: 0.0695, aux.acc_seg: 92.2614, loss: 0.2343 +2024-06-16 21:15:30,624 - mmseg - INFO - Iter [56900/80000] lr: 1.155e-05, eta: 9:40:12, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1621, decode.acc_seg: 93.1578, aux.loss_ce: 0.0684, aux.acc_seg: 92.7607, loss: 0.2306 +2024-06-16 21:16:38,683 - mmseg - INFO - Iter [56950/80000] lr: 1.153e-05, eta: 9:38:53, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1510, decode.acc_seg: 93.3211, aux.loss_ce: 0.0642, aux.acc_seg: 92.9296, loss: 0.2152 +2024-06-16 21:17:47,111 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 21:17:47,112 - mmseg - INFO - Iter [57000/80000] lr: 1.150e-05, eta: 9:37:35, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1535, decode.acc_seg: 93.2266, aux.loss_ce: 0.0649, aux.acc_seg: 92.8718, loss: 0.2184 +2024-06-16 21:19:22,783 - mmseg - INFO - per class results: +2024-06-16 21:19:22,789 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.94 | 90.25 | +| building | 85.16 | 93.49 | +| sky | 94.89 | 97.63 | +| floor | 85.8 | 92.37 | +| tree | 77.25 | 90.52 | +| ceiling | 87.59 | 94.23 | +| road | 86.37 | 91.88 | +| bed | 93.08 | 96.8 | +| windowpane | 66.45 | 79.53 | +| grass | 68.84 | 80.98 | +| cabinet | 67.62 | 77.16 | +| sidewalk | 72.17 | 85.84 | +| person | 85.87 | 94.47 | +| earth | 36.73 | 50.15 | +| door | 55.99 | 68.58 | +| table | 71.13 | 82.49 | +| mountain | 64.63 | 76.86 | +| plant | 53.76 | 66.29 | +| curtain | 77.92 | 88.8 | +| chair | 67.95 | 79.04 | +| car | 87.54 | 94.74 | +| water | 66.15 | 84.05 | +| painting | 76.99 | 91.53 | +| sofa | 83.57 | 93.05 | +| shelf | 48.55 | 67.46 | +| house | 57.56 | 70.62 | +| sea | 77.64 | 90.33 | +| mirror | 78.32 | 83.72 | +| rug | 71.95 | 78.56 | +| field | 27.79 | 48.75 | +| armchair | 62.19 | 77.25 | +| seat | 68.69 | 88.49 | +| fence | 51.05 | 63.59 | +| desk | 59.57 | 81.49 | +| rock | 61.74 | 85.83 | +| wardrobe | 53.83 | 74.33 | +| lamp | 76.01 | 86.25 | +| bathtub | 84.72 | 86.39 | +| railing | 42.96 | 59.92 | +| cushion | 70.19 | 78.94 | +| base | 40.44 | 54.86 | +| box | 36.76 | 48.0 | +| column | 55.39 | 64.98 | +| signboard | 41.93 | 57.9 | +| chest of drawers | 46.59 | 64.94 | +| counter | 44.11 | 58.53 | +| sand | 51.08 | 87.13 | +| sink | 76.3 | 82.44 | +| skyscraper | 49.86 | 62.02 | +| fireplace | 74.98 | 93.94 | +| refrigerator | 84.67 | 94.49 | +| grandstand | 51.97 | 84.53 | +| path | 29.79 | 41.91 | +| stairs | 25.66 | 31.44 | +| runway | 73.34 | 95.58 | +| case | 58.04 | 77.86 | +| pool table | 94.37 | 98.39 | +| pillow | 68.81 | 79.1 | +| screen door | 78.3 | 80.04 | +| stairway | 46.04 | 64.34 | +| river | 10.57 | 15.25 | +| bridge | 64.36 | 71.53 | +| bookcase | 43.7 | 61.78 | +| blind | 42.71 | 46.58 | +| coffee table | 67.69 | 85.63 | +| toilet | 88.61 | 93.27 | +| flower | 43.91 | 54.33 | +| book | 53.88 | 70.65 | +| hill | 9.31 | 16.88 | +| bench | 53.21 | 60.94 | +| countertop | 65.24 | 87.89 | +| stove | 83.14 | 88.66 | +| palm | 54.22 | 70.33 | +| kitchen island | 60.43 | 87.2 | +| computer | 79.34 | 91.57 | +| swivel chair | 46.29 | 65.86 | +| boat | 74.96 | 90.63 | +| bar | 62.19 | 80.61 | +| arcade machine | 76.58 | 82.13 | +| hovel | 39.59 | 43.13 | +| bus | 92.3 | 96.27 | +| towel | 75.62 | 89.76 | +| light | 60.79 | 68.39 | +| truck | 43.4 | 56.79 | +| tower | 44.28 | 65.69 | +| chandelier | 73.3 | 84.89 | +| awning | 41.06 | 49.11 | +| streetlight | 35.67 | 47.0 | +| booth | 47.13 | 68.18 | +| television receiver | 75.63 | 86.29 | +| airplane | 74.82 | 81.83 | +| dirt track | 10.85 | 56.69 | +| apparel | 45.17 | 64.53 | +| pole | 25.75 | 33.93 | +| land | 3.37 | 5.78 | +| bannister | 17.7 | 24.53 | +| escalator | 61.68 | 77.63 | +| ottoman | 51.36 | 67.79 | +| bottle | 39.53 | 62.1 | +| buffet | 48.57 | 57.28 | +| poster | 38.72 | 49.03 | +| stage | 20.92 | 48.72 | +| van | 49.38 | 61.79 | +| ship | 88.89 | 92.57 | +| fountain | 30.49 | 31.04 | +| conveyer belt | 79.41 | 93.5 | +| canopy | 58.82 | 74.86 | +| washer | 81.8 | 87.01 | +| plaything | 36.67 | 52.91 | +| swimming pool | 59.22 | 90.17 | +| stool | 42.97 | 75.79 | +| barrel | 43.74 | 70.52 | +| basket | 40.64 | 53.77 | +| waterfall | 65.74 | 81.35 | +| tent | 90.25 | 98.48 | +| bag | 21.3 | 23.93 | +| minibike | 78.57 | 88.0 | +| cradle | 75.6 | 97.87 | +| oven | 60.38 | 70.55 | +| ball | 33.14 | 33.72 | +| food | 58.04 | 72.25 | +| step | 10.91 | 13.74 | +| tank | 65.58 | 71.14 | +| trade name | 28.44 | 34.45 | +| microwave | 88.64 | 95.73 | +| pot | 57.58 | 65.43 | +| animal | 60.0 | 61.44 | +| bicycle | 58.6 | 73.38 | +| lake | 57.1 | 63.82 | +| dishwasher | 65.08 | 77.15 | +| screen | 50.15 | 76.08 | +| blanket | 29.86 | 34.37 | +| sculpture | 76.41 | 86.07 | +| hood | 63.02 | 73.27 | +| sconce | 56.66 | 65.41 | +| vase | 49.94 | 65.27 | +| traffic light | 38.81 | 60.21 | +| tray | 20.19 | 23.16 | +| ashcan | 46.99 | 63.91 | +| fan | 68.98 | 78.77 | +| pier | 38.25 | 43.56 | +| crt screen | 2.43 | 3.78 | +| plate | 60.76 | 76.4 | +| monitor | 65.64 | 77.83 | +| bulletin board | 51.2 | 61.52 | +| shower | 3.65 | 10.23 | +| radiator | 66.47 | 74.5 | +| glass | 19.78 | 20.94 | +| clock | 44.82 | 50.74 | +| flag | 71.05 | 79.29 | ++---------------------+-------+-------+ +2024-06-16 21:19:22,789 - mmseg - INFO - Summary: +2024-06-16 21:19:22,789 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.26 | 57.17 | 69.49 | ++-------+-------+-------+ +2024-06-16 21:19:22,790 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 21:19:22,790 - mmseg - INFO - Iter(val) [250] aAcc: 0.8626, mIoU: 0.5717, mAcc: 0.6949, IoU.wall: 0.8194, IoU.building: 0.8516, IoU.sky: 0.9489, IoU.floor: 0.8580, IoU.tree: 0.7725, IoU.ceiling: 0.8759, IoU.road: 0.8637, IoU.bed : 0.9308, IoU.windowpane: 0.6645, IoU.grass: 0.6884, IoU.cabinet: 0.6762, IoU.sidewalk: 0.7217, IoU.person: 0.8587, IoU.earth: 0.3673, IoU.door: 0.5599, IoU.table: 0.7113, IoU.mountain: 0.6463, IoU.plant: 0.5376, IoU.curtain: 0.7792, IoU.chair: 0.6795, IoU.car: 0.8754, IoU.water: 0.6615, IoU.painting: 0.7699, IoU.sofa: 0.8357, IoU.shelf: 0.4855, IoU.house: 0.5756, IoU.sea: 0.7764, IoU.mirror: 0.7832, IoU.rug: 0.7195, IoU.field: 0.2779, IoU.armchair: 0.6219, IoU.seat: 0.6869, IoU.fence: 0.5105, IoU.desk: 0.5957, IoU.rock: 0.6174, IoU.wardrobe: 0.5383, IoU.lamp: 0.7601, IoU.bathtub: 0.8472, IoU.railing: 0.4296, IoU.cushion: 0.7019, IoU.base: 0.4044, IoU.box: 0.3676, IoU.column: 0.5539, IoU.signboard: 0.4193, IoU.chest of drawers: 0.4659, IoU.counter: 0.4411, IoU.sand: 0.5108, IoU.sink: 0.7630, IoU.skyscraper: 0.4986, IoU.fireplace: 0.7498, IoU.refrigerator: 0.8467, IoU.grandstand: 0.5197, IoU.path: 0.2979, IoU.stairs: 0.2566, IoU.runway: 0.7334, IoU.case: 0.5804, IoU.pool table: 0.9437, IoU.pillow: 0.6881, IoU.screen door: 0.7830, IoU.stairway: 0.4604, IoU.river: 0.1057, IoU.bridge: 0.6436, IoU.bookcase: 0.4370, IoU.blind: 0.4271, IoU.coffee table: 0.6769, IoU.toilet: 0.8861, IoU.flower: 0.4391, IoU.book: 0.5388, IoU.hill: 0.0931, IoU.bench: 0.5321, IoU.countertop: 0.6524, IoU.stove: 0.8314, IoU.palm: 0.5422, IoU.kitchen island: 0.6043, IoU.computer: 0.7934, IoU.swivel chair: 0.4629, IoU.boat: 0.7496, IoU.bar: 0.6219, IoU.arcade machine: 0.7658, IoU.hovel: 0.3959, IoU.bus: 0.9230, IoU.towel: 0.7562, IoU.light: 0.6079, IoU.truck: 0.4340, IoU.tower: 0.4428, IoU.chandelier: 0.7330, IoU.awning: 0.4106, IoU.streetlight: 0.3567, IoU.booth: 0.4713, IoU.television receiver: 0.7563, IoU.airplane: 0.7482, IoU.dirt track: 0.1085, IoU.apparel: 0.4517, IoU.pole: 0.2575, IoU.land: 0.0337, IoU.bannister: 0.1770, IoU.escalator: 0.6168, IoU.ottoman: 0.5136, IoU.bottle: 0.3953, IoU.buffet: 0.4857, IoU.poster: 0.3872, IoU.stage: 0.2092, IoU.van: 0.4938, IoU.ship: 0.8889, IoU.fountain: 0.3049, IoU.conveyer belt: 0.7941, IoU.canopy: 0.5882, IoU.washer: 0.8180, IoU.plaything: 0.3667, IoU.swimming pool: 0.5922, IoU.stool: 0.4297, IoU.barrel: 0.4374, IoU.basket: 0.4064, IoU.waterfall: 0.6574, IoU.tent: 0.9025, IoU.bag: 0.2130, IoU.minibike: 0.7857, IoU.cradle: 0.7560, IoU.oven: 0.6038, IoU.ball: 0.3314, IoU.food: 0.5804, IoU.step: 0.1091, IoU.tank: 0.6558, IoU.trade name: 0.2844, IoU.microwave: 0.8864, IoU.pot: 0.5758, IoU.animal: 0.6000, IoU.bicycle: 0.5860, IoU.lake: 0.5710, IoU.dishwasher: 0.6508, IoU.screen: 0.5015, IoU.blanket: 0.2986, IoU.sculpture: 0.7641, IoU.hood: 0.6302, IoU.sconce: 0.5666, IoU.vase: 0.4994, IoU.traffic light: 0.3881, IoU.tray: 0.2019, IoU.ashcan: 0.4699, IoU.fan: 0.6898, IoU.pier: 0.3825, IoU.crt screen: 0.0243, IoU.plate: 0.6076, IoU.monitor: 0.6564, IoU.bulletin board: 0.5120, IoU.shower: 0.0365, IoU.radiator: 0.6647, IoU.glass: 0.1978, IoU.clock: 0.4482, IoU.flag: 0.7105, Acc.wall: 0.9025, Acc.building: 0.9349, Acc.sky: 0.9763, Acc.floor: 0.9237, Acc.tree: 0.9052, Acc.ceiling: 0.9423, Acc.road: 0.9188, Acc.bed : 0.9680, Acc.windowpane: 0.7953, Acc.grass: 0.8098, Acc.cabinet: 0.7716, Acc.sidewalk: 0.8584, Acc.person: 0.9447, Acc.earth: 0.5015, Acc.door: 0.6858, Acc.table: 0.8249, Acc.mountain: 0.7686, Acc.plant: 0.6629, Acc.curtain: 0.8880, Acc.chair: 0.7904, Acc.car: 0.9474, Acc.water: 0.8405, Acc.painting: 0.9153, Acc.sofa: 0.9305, Acc.shelf: 0.6746, Acc.house: 0.7062, Acc.sea: 0.9033, Acc.mirror: 0.8372, Acc.rug: 0.7856, Acc.field: 0.4875, Acc.armchair: 0.7725, Acc.seat: 0.8849, Acc.fence: 0.6359, Acc.desk: 0.8149, Acc.rock: 0.8583, Acc.wardrobe: 0.7433, Acc.lamp: 0.8625, Acc.bathtub: 0.8639, Acc.railing: 0.5992, Acc.cushion: 0.7894, Acc.base: 0.5486, Acc.box: 0.4800, Acc.column: 0.6498, Acc.signboard: 0.5790, Acc.chest of drawers: 0.6494, Acc.counter: 0.5853, Acc.sand: 0.8713, Acc.sink: 0.8244, Acc.skyscraper: 0.6202, Acc.fireplace: 0.9394, Acc.refrigerator: 0.9449, Acc.grandstand: 0.8453, Acc.path: 0.4191, Acc.stairs: 0.3144, Acc.runway: 0.9558, Acc.case: 0.7786, Acc.pool table: 0.9839, Acc.pillow: 0.7910, Acc.screen door: 0.8004, Acc.stairway: 0.6434, Acc.river: 0.1525, Acc.bridge: 0.7153, Acc.bookcase: 0.6178, Acc.blind: 0.4658, Acc.coffee table: 0.8563, Acc.toilet: 0.9327, Acc.flower: 0.5433, Acc.book: 0.7065, Acc.hill: 0.1688, Acc.bench: 0.6094, Acc.countertop: 0.8789, Acc.stove: 0.8866, Acc.palm: 0.7033, Acc.kitchen island: 0.8720, Acc.computer: 0.9157, Acc.swivel chair: 0.6586, Acc.boat: 0.9063, Acc.bar: 0.8061, Acc.arcade machine: 0.8213, Acc.hovel: 0.4313, Acc.bus: 0.9627, Acc.towel: 0.8976, Acc.light: 0.6839, Acc.truck: 0.5679, Acc.tower: 0.6569, Acc.chandelier: 0.8489, Acc.awning: 0.4911, Acc.streetlight: 0.4700, Acc.booth: 0.6818, Acc.television receiver: 0.8629, Acc.airplane: 0.8183, Acc.dirt track: 0.5669, Acc.apparel: 0.6453, Acc.pole: 0.3393, Acc.land: 0.0578, Acc.bannister: 0.2453, Acc.escalator: 0.7763, Acc.ottoman: 0.6779, Acc.bottle: 0.6210, Acc.buffet: 0.5728, Acc.poster: 0.4903, Acc.stage: 0.4872, Acc.van: 0.6179, Acc.ship: 0.9257, Acc.fountain: 0.3104, Acc.conveyer belt: 0.9350, Acc.canopy: 0.7486, Acc.washer: 0.8701, Acc.plaything: 0.5291, Acc.swimming pool: 0.9017, Acc.stool: 0.7579, Acc.barrel: 0.7052, Acc.basket: 0.5377, Acc.waterfall: 0.8135, Acc.tent: 0.9848, Acc.bag: 0.2393, Acc.minibike: 0.8800, Acc.cradle: 0.9787, Acc.oven: 0.7055, Acc.ball: 0.3372, Acc.food: 0.7225, Acc.step: 0.1374, Acc.tank: 0.7114, Acc.trade name: 0.3445, Acc.microwave: 0.9573, Acc.pot: 0.6543, Acc.animal: 0.6144, Acc.bicycle: 0.7338, Acc.lake: 0.6382, Acc.dishwasher: 0.7715, Acc.screen: 0.7608, Acc.blanket: 0.3437, Acc.sculpture: 0.8607, Acc.hood: 0.7327, Acc.sconce: 0.6541, Acc.vase: 0.6527, Acc.traffic light: 0.6021, Acc.tray: 0.2316, Acc.ashcan: 0.6391, Acc.fan: 0.7877, Acc.pier: 0.4356, Acc.crt screen: 0.0378, Acc.plate: 0.7640, Acc.monitor: 0.7783, Acc.bulletin board: 0.6152, Acc.shower: 0.1023, Acc.radiator: 0.7450, Acc.glass: 0.2094, Acc.clock: 0.5074, Acc.flag: 0.7929 +2024-06-16 21:20:31,341 - mmseg - INFO - Iter [57050/80000] lr: 1.148e-05, eta: 9:36:56, time: 3.285, data_time: 1.929, memory: 70722, decode.loss_ce: 0.1649, decode.acc_seg: 92.9384, aux.loss_ce: 0.0700, aux.acc_seg: 92.5250, loss: 0.2348 +2024-06-16 21:21:39,801 - mmseg - INFO - Iter [57100/80000] lr: 1.145e-05, eta: 9:35:37, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1604, decode.acc_seg: 93.2793, aux.loss_ce: 0.0678, aux.acc_seg: 92.8616, loss: 0.2283 +2024-06-16 21:22:47,767 - mmseg - INFO - Iter [57150/80000] lr: 1.143e-05, eta: 9:34:19, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1567, decode.acc_seg: 93.3146, aux.loss_ce: 0.0665, aux.acc_seg: 93.0072, loss: 0.2232 +2024-06-16 21:23:56,017 - mmseg - INFO - Iter [57200/80000] lr: 1.140e-05, eta: 9:33:01, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1599, decode.acc_seg: 92.9831, aux.loss_ce: 0.0676, aux.acc_seg: 92.6503, loss: 0.2274 +2024-06-16 21:25:04,045 - mmseg - INFO - Iter [57250/80000] lr: 1.138e-05, eta: 9:31:42, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1604, decode.acc_seg: 92.9097, aux.loss_ce: 0.0683, aux.acc_seg: 92.4568, loss: 0.2287 +2024-06-16 21:26:12,294 - mmseg - INFO - Iter [57300/80000] lr: 1.135e-05, eta: 9:30:24, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1618, decode.acc_seg: 93.0135, aux.loss_ce: 0.0686, aux.acc_seg: 92.5609, loss: 0.2304 +2024-06-16 21:27:20,487 - mmseg - INFO - Iter [57350/80000] lr: 1.133e-05, eta: 9:29:06, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1598, decode.acc_seg: 93.0169, aux.loss_ce: 0.0683, aux.acc_seg: 92.5972, loss: 0.2281 +2024-06-16 21:28:28,750 - mmseg - INFO - Iter [57400/80000] lr: 1.130e-05, eta: 9:27:48, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1688, decode.acc_seg: 92.9604, aux.loss_ce: 0.0712, aux.acc_seg: 92.5823, loss: 0.2400 +2024-06-16 21:29:37,084 - mmseg - INFO - Iter [57450/80000] lr: 1.128e-05, eta: 9:26:30, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1547, decode.acc_seg: 93.1178, aux.loss_ce: 0.0654, aux.acc_seg: 92.7601, loss: 0.2201 +2024-06-16 21:30:45,150 - mmseg - INFO - Iter [57500/80000] lr: 1.125e-05, eta: 9:25:11, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1679, decode.acc_seg: 92.7237, aux.loss_ce: 0.0706, aux.acc_seg: 92.4035, loss: 0.2385 +2024-06-16 21:31:53,633 - mmseg - INFO - Iter [57550/80000] lr: 1.123e-05, eta: 9:23:53, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1553, decode.acc_seg: 93.0737, aux.loss_ce: 0.0655, aux.acc_seg: 92.7038, loss: 0.2208 +2024-06-16 21:33:01,711 - mmseg - INFO - Iter [57600/80000] lr: 1.120e-05, eta: 9:22:35, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1652, decode.acc_seg: 92.8744, aux.loss_ce: 0.0702, aux.acc_seg: 92.4819, loss: 0.2354 +2024-06-16 21:34:09,775 - mmseg - INFO - Iter [57650/80000] lr: 1.118e-05, eta: 9:21:17, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1648, decode.acc_seg: 92.7285, aux.loss_ce: 0.0691, aux.acc_seg: 92.4217, loss: 0.2339 +2024-06-16 21:35:18,022 - mmseg - INFO - Iter [57700/80000] lr: 1.115e-05, eta: 9:19:59, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1624, decode.acc_seg: 93.0329, aux.loss_ce: 0.0689, aux.acc_seg: 92.6279, loss: 0.2313 +2024-06-16 21:36:26,143 - mmseg - INFO - Iter [57750/80000] lr: 1.113e-05, eta: 9:18:41, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1580, decode.acc_seg: 93.1030, aux.loss_ce: 0.0670, aux.acc_seg: 92.7082, loss: 0.2250 +2024-06-16 21:37:34,417 - mmseg - INFO - Iter [57800/80000] lr: 1.110e-05, eta: 9:17:23, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1567, decode.acc_seg: 93.0538, aux.loss_ce: 0.0664, aux.acc_seg: 92.6360, loss: 0.2231 +2024-06-16 21:38:42,372 - mmseg - INFO - Iter [57850/80000] lr: 1.108e-05, eta: 9:16:05, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1556, decode.acc_seg: 93.2088, aux.loss_ce: 0.0664, aux.acc_seg: 92.7958, loss: 0.2220 +2024-06-16 21:39:50,598 - mmseg - INFO - Iter [57900/80000] lr: 1.105e-05, eta: 9:14:47, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1650, decode.acc_seg: 92.9000, aux.loss_ce: 0.0703, aux.acc_seg: 92.4589, loss: 0.2353 +2024-06-16 21:40:58,785 - mmseg - INFO - Iter [57950/80000] lr: 1.103e-05, eta: 9:13:29, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1649, decode.acc_seg: 93.1522, aux.loss_ce: 0.0700, aux.acc_seg: 92.6962, loss: 0.2350 +2024-06-16 21:42:07,255 - mmseg - INFO - Saving checkpoint at 58000 iterations +2024-06-16 21:43:31,066 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 21:43:31,066 - mmseg - INFO - Iter [58000/80000] lr: 1.100e-05, eta: 9:12:43, time: 3.046, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1541, decode.acc_seg: 93.2880, aux.loss_ce: 0.0655, aux.acc_seg: 92.9034, loss: 0.2197 +2024-06-16 21:45:07,651 - mmseg - INFO - per class results: +2024-06-16 21:45:07,657 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.09 | 89.16 | +| building | 86.0 | 93.45 | +| sky | 94.93 | 97.18 | +| floor | 85.42 | 92.8 | +| tree | 76.87 | 89.64 | +| ceiling | 87.15 | 95.6 | +| road | 86.07 | 91.29 | +| bed | 93.25 | 96.99 | +| windowpane | 66.05 | 81.61 | +| grass | 66.8 | 79.3 | +| cabinet | 66.63 | 75.98 | +| sidewalk | 72.55 | 87.05 | +| person | 85.71 | 93.89 | +| earth | 37.12 | 48.15 | +| door | 58.07 | 76.16 | +| table | 70.09 | 82.92 | +| mountain | 61.27 | 75.52 | +| plant | 55.05 | 65.83 | +| curtain | 77.4 | 87.51 | +| chair | 68.97 | 81.2 | +| car | 87.85 | 93.62 | +| water | 64.33 | 80.31 | +| painting | 78.29 | 91.77 | +| sofa | 83.83 | 92.29 | +| shelf | 48.83 | 66.73 | +| house | 61.25 | 78.08 | +| sea | 71.19 | 82.62 | +| mirror | 79.45 | 85.92 | +| rug | 73.24 | 81.19 | +| field | 29.63 | 57.38 | +| armchair | 62.51 | 78.53 | +| seat | 69.41 | 89.4 | +| fence | 50.23 | 64.71 | +| desk | 63.19 | 76.71 | +| rock | 55.1 | 83.2 | +| wardrobe | 53.16 | 72.89 | +| lamp | 76.42 | 85.65 | +| bathtub | 84.5 | 86.92 | +| railing | 41.88 | 55.86 | +| cushion | 70.45 | 78.25 | +| base | 41.43 | 55.34 | +| box | 38.32 | 50.33 | +| column | 58.07 | 72.38 | +| signboard | 40.41 | 55.61 | +| chest of drawers | 46.66 | 66.84 | +| counter | 38.6 | 45.47 | +| sand | 56.34 | 86.88 | +| sink | 76.56 | 82.47 | +| skyscraper | 48.16 | 62.52 | +| fireplace | 73.76 | 94.8 | +| refrigerator | 81.23 | 95.9 | +| grandstand | 52.26 | 84.67 | +| path | 24.98 | 36.44 | +| stairs | 27.02 | 35.39 | +| runway | 71.69 | 94.69 | +| case | 60.69 | 86.29 | +| pool table | 94.91 | 98.44 | +| pillow | 70.49 | 83.99 | +| screen door | 69.78 | 71.43 | +| stairway | 46.45 | 61.84 | +| river | 9.5 | 17.58 | +| bridge | 71.4 | 78.94 | +| bookcase | 50.43 | 64.73 | +| blind | 46.16 | 51.0 | +| coffee table | 64.59 | 89.14 | +| toilet | 88.8 | 93.65 | +| flower | 46.08 | 55.76 | +| book | 53.56 | 75.93 | +| hill | 11.18 | 19.12 | +| bench | 51.46 | 62.04 | +| countertop | 64.87 | 88.53 | +| stove | 83.1 | 88.8 | +| palm | 55.62 | 80.99 | +| kitchen island | 49.57 | 87.87 | +| computer | 78.54 | 91.15 | +| swivel chair | 49.84 | 72.33 | +| boat | 74.34 | 90.61 | +| bar | 58.88 | 85.91 | +| arcade machine | 80.67 | 85.32 | +| hovel | 41.37 | 46.24 | +| bus | 92.25 | 96.8 | +| towel | 76.06 | 87.23 | +| light | 60.52 | 68.24 | +| truck | 43.31 | 56.65 | +| tower | 35.61 | 52.67 | +| chandelier | 73.38 | 84.75 | +| awning | 44.6 | 61.18 | +| streetlight | 35.2 | 45.98 | +| booth | 42.47 | 63.73 | +| television receiver | 73.6 | 84.02 | +| airplane | 67.9 | 77.69 | +| dirt track | 11.59 | 45.07 | +| apparel | 45.32 | 63.55 | +| pole | 30.29 | 43.88 | +| land | 3.75 | 5.76 | +| bannister | 17.24 | 25.72 | +| escalator | 61.0 | 79.19 | +| ottoman | 52.41 | 68.78 | +| bottle | 40.97 | 67.85 | +| buffet | 55.36 | 71.01 | +| poster | 39.29 | 52.23 | +| stage | 24.84 | 46.49 | +| van | 46.81 | 67.41 | +| ship | 76.27 | 81.46 | +| fountain | 30.53 | 31.44 | +| conveyer belt | 79.64 | 93.56 | +| canopy | 50.42 | 68.4 | +| washer | 80.34 | 85.27 | +| plaything | 33.66 | 48.98 | +| swimming pool | 51.49 | 73.9 | +| stool | 52.78 | 69.83 | +| barrel | 52.03 | 72.67 | +| basket | 41.54 | 57.01 | +| waterfall | 62.19 | 76.82 | +| tent | 96.25 | 98.63 | +| bag | 22.57 | 25.98 | +| minibike | 77.3 | 88.55 | +| cradle | 75.54 | 97.85 | +| oven | 63.75 | 76.19 | +| ball | 51.34 | 56.7 | +| food | 59.35 | 75.23 | +| step | 11.14 | 15.56 | +| tank | 70.16 | 77.88 | +| trade name | 26.73 | 31.9 | +| microwave | 88.86 | 95.61 | +| pot | 52.97 | 59.62 | +| animal | 60.6 | 62.39 | +| bicycle | 60.37 | 74.93 | +| lake | 58.39 | 63.81 | +| dishwasher | 63.91 | 78.4 | +| screen | 48.24 | 75.75 | +| blanket | 27.3 | 30.23 | +| sculpture | 75.02 | 86.96 | +| hood | 62.48 | 74.9 | +| sconce | 58.61 | 69.34 | +| vase | 49.48 | 61.1 | +| traffic light | 35.95 | 66.26 | +| tray | 21.39 | 24.78 | +| ashcan | 45.83 | 62.11 | +| fan | 68.42 | 80.47 | +| pier | 40.9 | 51.92 | +| crt screen | 2.46 | 3.99 | +| plate | 62.56 | 75.58 | +| monitor | 71.64 | 83.09 | +| bulletin board | 54.23 | 66.6 | +| shower | 6.69 | 7.5 | +| radiator | 68.58 | 76.48 | +| glass | 19.84 | 21.12 | +| clock | 47.32 | 53.99 | +| flag | 70.43 | 80.03 | ++---------------------+-------+-------+ +2024-06-16 21:45:07,657 - mmseg - INFO - Summary: +2024-06-16 21:45:07,657 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.19 | 57.28 | 70.12 | ++-------+-------+-------+ +2024-06-16 21:45:07,658 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 21:45:07,658 - mmseg - INFO - Iter(val) [250] aAcc: 0.8619, mIoU: 0.5728, mAcc: 0.7012, IoU.wall: 0.8209, IoU.building: 0.8600, IoU.sky: 0.9493, IoU.floor: 0.8542, IoU.tree: 0.7687, IoU.ceiling: 0.8715, IoU.road: 0.8607, IoU.bed : 0.9325, IoU.windowpane: 0.6605, IoU.grass: 0.6680, IoU.cabinet: 0.6663, IoU.sidewalk: 0.7255, IoU.person: 0.8571, IoU.earth: 0.3712, IoU.door: 0.5807, IoU.table: 0.7009, IoU.mountain: 0.6127, IoU.plant: 0.5505, IoU.curtain: 0.7740, IoU.chair: 0.6897, IoU.car: 0.8785, IoU.water: 0.6433, IoU.painting: 0.7829, IoU.sofa: 0.8383, IoU.shelf: 0.4883, IoU.house: 0.6125, IoU.sea: 0.7119, IoU.mirror: 0.7945, IoU.rug: 0.7324, IoU.field: 0.2963, IoU.armchair: 0.6251, IoU.seat: 0.6941, IoU.fence: 0.5023, IoU.desk: 0.6319, IoU.rock: 0.5510, IoU.wardrobe: 0.5316, IoU.lamp: 0.7642, IoU.bathtub: 0.8450, IoU.railing: 0.4188, IoU.cushion: 0.7045, IoU.base: 0.4143, IoU.box: 0.3832, IoU.column: 0.5807, IoU.signboard: 0.4041, IoU.chest of drawers: 0.4666, IoU.counter: 0.3860, IoU.sand: 0.5634, IoU.sink: 0.7656, IoU.skyscraper: 0.4816, IoU.fireplace: 0.7376, IoU.refrigerator: 0.8123, IoU.grandstand: 0.5226, IoU.path: 0.2498, IoU.stairs: 0.2702, IoU.runway: 0.7169, IoU.case: 0.6069, IoU.pool table: 0.9491, IoU.pillow: 0.7049, IoU.screen door: 0.6978, IoU.stairway: 0.4645, IoU.river: 0.0950, IoU.bridge: 0.7140, IoU.bookcase: 0.5043, IoU.blind: 0.4616, IoU.coffee table: 0.6459, IoU.toilet: 0.8880, IoU.flower: 0.4608, IoU.book: 0.5356, IoU.hill: 0.1118, IoU.bench: 0.5146, IoU.countertop: 0.6487, IoU.stove: 0.8310, IoU.palm: 0.5562, IoU.kitchen island: 0.4957, IoU.computer: 0.7854, IoU.swivel chair: 0.4984, IoU.boat: 0.7434, IoU.bar: 0.5888, IoU.arcade machine: 0.8067, IoU.hovel: 0.4137, IoU.bus: 0.9225, IoU.towel: 0.7606, IoU.light: 0.6052, IoU.truck: 0.4331, IoU.tower: 0.3561, IoU.chandelier: 0.7338, IoU.awning: 0.4460, IoU.streetlight: 0.3520, IoU.booth: 0.4247, IoU.television receiver: 0.7360, IoU.airplane: 0.6790, IoU.dirt track: 0.1159, IoU.apparel: 0.4532, IoU.pole: 0.3029, IoU.land: 0.0375, IoU.bannister: 0.1724, IoU.escalator: 0.6100, IoU.ottoman: 0.5241, IoU.bottle: 0.4097, IoU.buffet: 0.5536, IoU.poster: 0.3929, IoU.stage: 0.2484, IoU.van: 0.4681, IoU.ship: 0.7627, IoU.fountain: 0.3053, IoU.conveyer belt: 0.7964, IoU.canopy: 0.5042, IoU.washer: 0.8034, IoU.plaything: 0.3366, IoU.swimming pool: 0.5149, IoU.stool: 0.5278, IoU.barrel: 0.5203, IoU.basket: 0.4154, IoU.waterfall: 0.6219, IoU.tent: 0.9625, IoU.bag: 0.2257, IoU.minibike: 0.7730, IoU.cradle: 0.7554, IoU.oven: 0.6375, IoU.ball: 0.5134, IoU.food: 0.5935, IoU.step: 0.1114, IoU.tank: 0.7016, IoU.trade name: 0.2673, IoU.microwave: 0.8886, IoU.pot: 0.5297, IoU.animal: 0.6060, IoU.bicycle: 0.6037, IoU.lake: 0.5839, IoU.dishwasher: 0.6391, IoU.screen: 0.4824, IoU.blanket: 0.2730, IoU.sculpture: 0.7502, IoU.hood: 0.6248, IoU.sconce: 0.5861, IoU.vase: 0.4948, IoU.traffic light: 0.3595, IoU.tray: 0.2139, IoU.ashcan: 0.4583, IoU.fan: 0.6842, IoU.pier: 0.4090, IoU.crt screen: 0.0246, IoU.plate: 0.6256, IoU.monitor: 0.7164, IoU.bulletin board: 0.5423, IoU.shower: 0.0669, IoU.radiator: 0.6858, IoU.glass: 0.1984, IoU.clock: 0.4732, IoU.flag: 0.7043, Acc.wall: 0.8916, Acc.building: 0.9345, Acc.sky: 0.9718, Acc.floor: 0.9280, Acc.tree: 0.8964, Acc.ceiling: 0.9560, Acc.road: 0.9129, Acc.bed : 0.9699, Acc.windowpane: 0.8161, Acc.grass: 0.7930, Acc.cabinet: 0.7598, Acc.sidewalk: 0.8705, Acc.person: 0.9389, Acc.earth: 0.4815, Acc.door: 0.7616, Acc.table: 0.8292, Acc.mountain: 0.7552, Acc.plant: 0.6583, Acc.curtain: 0.8751, Acc.chair: 0.8120, Acc.car: 0.9362, Acc.water: 0.8031, Acc.painting: 0.9177, Acc.sofa: 0.9229, Acc.shelf: 0.6673, Acc.house: 0.7808, Acc.sea: 0.8262, Acc.mirror: 0.8592, Acc.rug: 0.8119, Acc.field: 0.5738, Acc.armchair: 0.7853, Acc.seat: 0.8940, Acc.fence: 0.6471, Acc.desk: 0.7671, Acc.rock: 0.8320, Acc.wardrobe: 0.7289, Acc.lamp: 0.8565, Acc.bathtub: 0.8692, Acc.railing: 0.5586, Acc.cushion: 0.7825, Acc.base: 0.5534, Acc.box: 0.5033, Acc.column: 0.7238, Acc.signboard: 0.5561, Acc.chest of drawers: 0.6684, Acc.counter: 0.4547, Acc.sand: 0.8688, Acc.sink: 0.8247, Acc.skyscraper: 0.6252, Acc.fireplace: 0.9480, Acc.refrigerator: 0.9590, Acc.grandstand: 0.8467, Acc.path: 0.3644, Acc.stairs: 0.3539, Acc.runway: 0.9469, Acc.case: 0.8629, Acc.pool table: 0.9844, Acc.pillow: 0.8399, Acc.screen door: 0.7143, Acc.stairway: 0.6184, Acc.river: 0.1758, Acc.bridge: 0.7894, Acc.bookcase: 0.6473, Acc.blind: 0.5100, Acc.coffee table: 0.8914, Acc.toilet: 0.9365, Acc.flower: 0.5576, Acc.book: 0.7593, Acc.hill: 0.1912, Acc.bench: 0.6204, Acc.countertop: 0.8853, Acc.stove: 0.8880, Acc.palm: 0.8099, Acc.kitchen island: 0.8787, Acc.computer: 0.9115, Acc.swivel chair: 0.7233, Acc.boat: 0.9061, Acc.bar: 0.8591, Acc.arcade machine: 0.8532, Acc.hovel: 0.4624, Acc.bus: 0.9680, Acc.towel: 0.8723, Acc.light: 0.6824, Acc.truck: 0.5665, Acc.tower: 0.5267, Acc.chandelier: 0.8475, Acc.awning: 0.6118, Acc.streetlight: 0.4598, Acc.booth: 0.6373, Acc.television receiver: 0.8402, Acc.airplane: 0.7769, Acc.dirt track: 0.4507, Acc.apparel: 0.6355, Acc.pole: 0.4388, Acc.land: 0.0576, Acc.bannister: 0.2572, Acc.escalator: 0.7919, Acc.ottoman: 0.6878, Acc.bottle: 0.6785, Acc.buffet: 0.7101, Acc.poster: 0.5223, Acc.stage: 0.4649, Acc.van: 0.6741, Acc.ship: 0.8146, Acc.fountain: 0.3144, Acc.conveyer belt: 0.9356, Acc.canopy: 0.6840, Acc.washer: 0.8527, Acc.plaything: 0.4898, Acc.swimming pool: 0.7390, Acc.stool: 0.6983, Acc.barrel: 0.7267, Acc.basket: 0.5701, Acc.waterfall: 0.7682, Acc.tent: 0.9863, Acc.bag: 0.2598, Acc.minibike: 0.8855, Acc.cradle: 0.9785, Acc.oven: 0.7619, Acc.ball: 0.5670, Acc.food: 0.7523, Acc.step: 0.1556, Acc.tank: 0.7788, Acc.trade name: 0.3190, Acc.microwave: 0.9561, Acc.pot: 0.5962, Acc.animal: 0.6239, Acc.bicycle: 0.7493, Acc.lake: 0.6381, Acc.dishwasher: 0.7840, Acc.screen: 0.7575, Acc.blanket: 0.3023, Acc.sculpture: 0.8696, Acc.hood: 0.7490, Acc.sconce: 0.6934, Acc.vase: 0.6110, Acc.traffic light: 0.6626, Acc.tray: 0.2478, Acc.ashcan: 0.6211, Acc.fan: 0.8047, Acc.pier: 0.5192, Acc.crt screen: 0.0399, Acc.plate: 0.7558, Acc.monitor: 0.8309, Acc.bulletin board: 0.6660, Acc.shower: 0.0750, Acc.radiator: 0.7648, Acc.glass: 0.2112, Acc.clock: 0.5399, Acc.flag: 0.8003 +2024-06-16 21:46:16,060 - mmseg - INFO - Iter [58050/80000] lr: 1.098e-05, eta: 9:12:01, time: 3.300, data_time: 1.948, memory: 70722, decode.loss_ce: 0.1565, decode.acc_seg: 93.2393, aux.loss_ce: 0.0670, aux.acc_seg: 92.7948, loss: 0.2235 +2024-06-16 21:47:27,009 - mmseg - INFO - Iter [58100/80000] lr: 1.095e-05, eta: 9:10:44, time: 1.419, data_time: 0.063, memory: 70722, decode.loss_ce: 0.1743, decode.acc_seg: 92.6750, aux.loss_ce: 0.0730, aux.acc_seg: 92.2877, loss: 0.2472 +2024-06-16 21:48:35,054 - mmseg - INFO - Iter [58150/80000] lr: 1.093e-05, eta: 9:09:26, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1560, decode.acc_seg: 93.1292, aux.loss_ce: 0.0662, aux.acc_seg: 92.7213, loss: 0.2222 +2024-06-16 21:49:43,425 - mmseg - INFO - Iter [58200/80000] lr: 1.090e-05, eta: 9:08:08, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1610, decode.acc_seg: 93.0842, aux.loss_ce: 0.0685, aux.acc_seg: 92.6122, loss: 0.2296 +2024-06-16 21:50:51,657 - mmseg - INFO - Iter [58250/80000] lr: 1.088e-05, eta: 9:06:49, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1700, decode.acc_seg: 92.7917, aux.loss_ce: 0.0720, aux.acc_seg: 92.3496, loss: 0.2420 +2024-06-16 21:51:59,675 - mmseg - INFO - Iter [58300/80000] lr: 1.085e-05, eta: 9:05:31, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1673, decode.acc_seg: 92.7565, aux.loss_ce: 0.0707, aux.acc_seg: 92.4371, loss: 0.2380 +2024-06-16 21:53:07,944 - mmseg - INFO - Iter [58350/80000] lr: 1.083e-05, eta: 9:04:13, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1522, decode.acc_seg: 93.3278, aux.loss_ce: 0.0648, aux.acc_seg: 92.8989, loss: 0.2170 +2024-06-16 21:54:15,891 - mmseg - INFO - Iter [58400/80000] lr: 1.080e-05, eta: 9:02:55, time: 1.359, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1532, decode.acc_seg: 93.2969, aux.loss_ce: 0.0651, aux.acc_seg: 92.9810, loss: 0.2183 +2024-06-16 21:55:23,922 - mmseg - INFO - Iter [58450/80000] lr: 1.078e-05, eta: 9:01:37, time: 1.361, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1560, decode.acc_seg: 93.1685, aux.loss_ce: 0.0665, aux.acc_seg: 92.7579, loss: 0.2225 +2024-06-16 21:56:31,934 - mmseg - INFO - Iter [58500/80000] lr: 1.075e-05, eta: 9:00:19, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1530, decode.acc_seg: 93.2123, aux.loss_ce: 0.0647, aux.acc_seg: 92.8479, loss: 0.2177 +2024-06-16 21:57:40,298 - mmseg - INFO - Iter [58550/80000] lr: 1.073e-05, eta: 8:59:01, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1627, decode.acc_seg: 93.0806, aux.loss_ce: 0.0693, aux.acc_seg: 92.6573, loss: 0.2320 +2024-06-16 21:58:59,666 - mmseg - INFO - Iter [58600/80000] lr: 1.070e-05, eta: 8:57:47, time: 1.587, data_time: 0.232, memory: 70722, decode.loss_ce: 0.1551, decode.acc_seg: 93.1311, aux.loss_ce: 0.0660, aux.acc_seg: 92.7443, loss: 0.2211 +2024-06-16 22:00:07,714 - mmseg - INFO - Iter [58650/80000] lr: 1.068e-05, eta: 8:56:29, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1637, decode.acc_seg: 92.8666, aux.loss_ce: 0.0696, aux.acc_seg: 92.4912, loss: 0.2333 +2024-06-16 22:01:16,327 - mmseg - INFO - Iter [58700/80000] lr: 1.065e-05, eta: 8:55:11, time: 1.372, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1544, decode.acc_seg: 93.2117, aux.loss_ce: 0.0657, aux.acc_seg: 92.8290, loss: 0.2201 +2024-06-16 22:02:24,380 - mmseg - INFO - Iter [58750/80000] lr: 1.063e-05, eta: 8:53:53, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1498, decode.acc_seg: 93.4041, aux.loss_ce: 0.0640, aux.acc_seg: 93.0109, loss: 0.2138 +2024-06-16 22:03:32,558 - mmseg - INFO - Iter [58800/80000] lr: 1.060e-05, eta: 8:52:35, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1599, decode.acc_seg: 93.0123, aux.loss_ce: 0.0681, aux.acc_seg: 92.5604, loss: 0.2281 +2024-06-16 22:04:40,728 - mmseg - INFO - Iter [58850/80000] lr: 1.058e-05, eta: 8:51:17, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1521, decode.acc_seg: 93.2457, aux.loss_ce: 0.0648, aux.acc_seg: 92.7786, loss: 0.2169 +2024-06-16 22:05:49,236 - mmseg - INFO - Iter [58900/80000] lr: 1.055e-05, eta: 8:49:59, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1541, decode.acc_seg: 93.3250, aux.loss_ce: 0.0654, aux.acc_seg: 92.8962, loss: 0.2196 +2024-06-16 22:06:57,252 - mmseg - INFO - Iter [58950/80000] lr: 1.053e-05, eta: 8:48:41, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1622, decode.acc_seg: 93.0480, aux.loss_ce: 0.0687, aux.acc_seg: 92.6998, loss: 0.2309 +2024-06-16 22:08:05,537 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 22:08:05,537 - mmseg - INFO - Iter [59000/80000] lr: 1.050e-05, eta: 8:47:23, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1530, decode.acc_seg: 93.1558, aux.loss_ce: 0.0647, aux.acc_seg: 92.7726, loss: 0.2177 +2024-06-16 22:09:42,147 - mmseg - INFO - per class results: +2024-06-16 22:09:42,153 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.3 | 90.17 | +| building | 85.91 | 93.7 | +| sky | 95.0 | 97.72 | +| floor | 86.06 | 92.01 | +| tree | 77.29 | 89.36 | +| ceiling | 87.27 | 94.38 | +| road | 86.45 | 91.53 | +| bed | 93.07 | 96.92 | +| windowpane | 65.8 | 80.94 | +| grass | 68.92 | 82.97 | +| cabinet | 67.02 | 77.68 | +| sidewalk | 71.63 | 83.89 | +| person | 86.06 | 94.23 | +| earth | 37.0 | 51.52 | +| door | 58.36 | 74.13 | +| table | 69.96 | 82.6 | +| mountain | 62.68 | 75.33 | +| plant | 54.33 | 64.39 | +| curtain | 77.06 | 88.39 | +| chair | 68.36 | 81.04 | +| car | 87.51 | 94.18 | +| water | 65.3 | 81.1 | +| painting | 77.11 | 91.47 | +| sofa | 83.76 | 92.93 | +| shelf | 46.11 | 61.2 | +| house | 56.3 | 69.13 | +| sea | 70.55 | 82.77 | +| mirror | 78.7 | 84.69 | +| rug | 72.65 | 83.81 | +| field | 30.99 | 55.23 | +| armchair | 61.09 | 74.31 | +| seat | 67.29 | 88.75 | +| fence | 51.59 | 68.8 | +| desk | 62.28 | 79.46 | +| rock | 58.01 | 80.72 | +| wardrobe | 53.44 | 70.83 | +| lamp | 76.42 | 87.34 | +| bathtub | 84.42 | 86.91 | +| railing | 42.58 | 59.0 | +| cushion | 69.5 | 82.82 | +| base | 41.74 | 63.59 | +| box | 38.26 | 50.78 | +| column | 55.31 | 70.24 | +| signboard | 40.41 | 52.36 | +| chest of drawers | 49.7 | 67.08 | +| counter | 39.77 | 46.51 | +| sand | 59.61 | 84.4 | +| sink | 76.85 | 84.55 | +| skyscraper | 48.54 | 65.92 | +| fireplace | 73.84 | 94.61 | +| refrigerator | 85.87 | 92.53 | +| grandstand | 53.23 | 85.93 | +| path | 28.83 | 46.07 | +| stairs | 29.37 | 35.98 | +| runway | 65.85 | 86.31 | +| case | 58.75 | 80.95 | +| pool table | 94.84 | 98.35 | +| pillow | 67.89 | 78.18 | +| screen door | 68.7 | 70.25 | +| stairway | 46.78 | 60.28 | +| river | 13.0 | 24.38 | +| bridge | 76.09 | 85.12 | +| bookcase | 45.6 | 70.59 | +| blind | 42.5 | 46.71 | +| coffee table | 64.45 | 88.53 | +| toilet | 90.13 | 92.47 | +| flower | 46.34 | 60.59 | +| book | 55.6 | 73.76 | +| hill | 9.84 | 15.38 | +| bench | 51.76 | 60.82 | +| countertop | 65.48 | 85.18 | +| stove | 83.17 | 89.22 | +| palm | 54.73 | 79.53 | +| kitchen island | 56.52 | 84.89 | +| computer | 79.43 | 91.12 | +| swivel chair | 50.54 | 75.37 | +| boat | 75.35 | 90.37 | +| bar | 56.27 | 77.86 | +| arcade machine | 75.14 | 78.9 | +| hovel | 52.52 | 57.76 | +| bus | 92.94 | 96.32 | +| towel | 77.61 | 86.93 | +| light | 61.43 | 69.47 | +| truck | 44.81 | 57.93 | +| tower | 37.04 | 58.37 | +| chandelier | 74.0 | 86.24 | +| awning | 50.71 | 70.59 | +| streetlight | 33.39 | 42.65 | +| booth | 50.47 | 62.06 | +| television receiver | 74.74 | 79.36 | +| airplane | 65.08 | 77.45 | +| dirt track | 12.22 | 18.56 | +| apparel | 42.85 | 68.86 | +| pole | 30.6 | 42.21 | +| land | 3.26 | 5.08 | +| bannister | 18.44 | 24.79 | +| escalator | 59.14 | 79.07 | +| ottoman | 50.06 | 66.79 | +| bottle | 41.49 | 70.03 | +| buffet | 46.14 | 55.6 | +| poster | 43.98 | 51.19 | +| stage | 20.1 | 43.55 | +| van | 49.66 | 64.63 | +| ship | 87.16 | 91.98 | +| fountain | 34.09 | 36.27 | +| conveyer belt | 82.75 | 93.0 | +| canopy | 51.95 | 71.69 | +| washer | 77.94 | 81.8 | +| plaything | 34.26 | 43.71 | +| swimming pool | 60.21 | 92.04 | +| stool | 55.05 | 70.46 | +| barrel | 57.87 | 71.44 | +| basket | 40.17 | 54.66 | +| waterfall | 70.12 | 87.82 | +| tent | 94.32 | 98.55 | +| bag | 20.89 | 23.37 | +| minibike | 77.58 | 87.69 | +| cradle | 79.1 | 97.72 | +| oven | 63.64 | 73.95 | +| ball | 54.74 | 63.19 | +| food | 54.94 | 65.93 | +| step | 12.98 | 16.35 | +| tank | 62.89 | 67.3 | +| trade name | 30.45 | 38.31 | +| microwave | 89.9 | 95.91 | +| pot | 57.94 | 66.54 | +| animal | 58.62 | 59.59 | +| bicycle | 56.18 | 63.71 | +| lake | 52.52 | 63.82 | +| dishwasher | 71.42 | 81.18 | +| screen | 46.18 | 76.24 | +| blanket | 26.54 | 29.51 | +| sculpture | 75.32 | 86.34 | +| hood | 63.08 | 76.56 | +| sconce | 59.3 | 68.0 | +| vase | 49.7 | 64.57 | +| traffic light | 37.49 | 58.03 | +| tray | 26.19 | 32.7 | +| ashcan | 49.04 | 63.15 | +| fan | 69.21 | 79.17 | +| pier | 40.71 | 45.04 | +| crt screen | 6.38 | 12.23 | +| plate | 62.38 | 77.45 | +| monitor | 55.08 | 62.37 | +| bulletin board | 56.2 | 66.91 | +| shower | 3.03 | 3.14 | +| radiator | 66.74 | 80.76 | +| glass | 20.56 | 21.88 | +| clock | 45.96 | 54.14 | +| flag | 70.45 | 80.16 | ++---------------------+-------+-------+ +2024-06-16 22:09:42,153 - mmseg - INFO - Summary: +2024-06-16 22:09:42,153 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.29 | 57.63 | 69.84 | ++-------+-------+-------+ +2024-06-16 22:09:42,154 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 22:09:42,154 - mmseg - INFO - Iter(val) [250] aAcc: 0.8629, mIoU: 0.5763, mAcc: 0.6984, IoU.wall: 0.8230, IoU.building: 0.8591, IoU.sky: 0.9500, IoU.floor: 0.8606, IoU.tree: 0.7729, IoU.ceiling: 0.8727, IoU.road: 0.8645, IoU.bed : 0.9307, IoU.windowpane: 0.6580, IoU.grass: 0.6892, IoU.cabinet: 0.6702, IoU.sidewalk: 0.7163, IoU.person: 0.8606, IoU.earth: 0.3700, IoU.door: 0.5836, IoU.table: 0.6996, IoU.mountain: 0.6268, IoU.plant: 0.5433, IoU.curtain: 0.7706, IoU.chair: 0.6836, IoU.car: 0.8751, IoU.water: 0.6530, IoU.painting: 0.7711, IoU.sofa: 0.8376, IoU.shelf: 0.4611, IoU.house: 0.5630, IoU.sea: 0.7055, IoU.mirror: 0.7870, IoU.rug: 0.7265, IoU.field: 0.3099, IoU.armchair: 0.6109, IoU.seat: 0.6729, IoU.fence: 0.5159, IoU.desk: 0.6228, IoU.rock: 0.5801, IoU.wardrobe: 0.5344, IoU.lamp: 0.7642, IoU.bathtub: 0.8442, IoU.railing: 0.4258, IoU.cushion: 0.6950, IoU.base: 0.4174, IoU.box: 0.3826, IoU.column: 0.5531, IoU.signboard: 0.4041, IoU.chest of drawers: 0.4970, IoU.counter: 0.3977, IoU.sand: 0.5961, IoU.sink: 0.7685, IoU.skyscraper: 0.4854, IoU.fireplace: 0.7384, IoU.refrigerator: 0.8587, IoU.grandstand: 0.5323, IoU.path: 0.2883, IoU.stairs: 0.2937, IoU.runway: 0.6585, IoU.case: 0.5875, IoU.pool table: 0.9484, IoU.pillow: 0.6789, IoU.screen door: 0.6870, IoU.stairway: 0.4678, IoU.river: 0.1300, IoU.bridge: 0.7609, IoU.bookcase: 0.4560, IoU.blind: 0.4250, IoU.coffee table: 0.6445, IoU.toilet: 0.9013, IoU.flower: 0.4634, IoU.book: 0.5560, IoU.hill: 0.0984, IoU.bench: 0.5176, IoU.countertop: 0.6548, IoU.stove: 0.8317, IoU.palm: 0.5473, IoU.kitchen island: 0.5652, IoU.computer: 0.7943, IoU.swivel chair: 0.5054, IoU.boat: 0.7535, IoU.bar: 0.5627, IoU.arcade machine: 0.7514, IoU.hovel: 0.5252, IoU.bus: 0.9294, IoU.towel: 0.7761, IoU.light: 0.6143, IoU.truck: 0.4481, IoU.tower: 0.3704, IoU.chandelier: 0.7400, IoU.awning: 0.5071, IoU.streetlight: 0.3339, IoU.booth: 0.5047, IoU.television receiver: 0.7474, IoU.airplane: 0.6508, IoU.dirt track: 0.1222, IoU.apparel: 0.4285, IoU.pole: 0.3060, IoU.land: 0.0326, IoU.bannister: 0.1844, IoU.escalator: 0.5914, IoU.ottoman: 0.5006, IoU.bottle: 0.4149, IoU.buffet: 0.4614, IoU.poster: 0.4398, IoU.stage: 0.2010, IoU.van: 0.4966, IoU.ship: 0.8716, IoU.fountain: 0.3409, IoU.conveyer belt: 0.8275, IoU.canopy: 0.5195, IoU.washer: 0.7794, IoU.plaything: 0.3426, IoU.swimming pool: 0.6021, IoU.stool: 0.5505, IoU.barrel: 0.5787, IoU.basket: 0.4017, IoU.waterfall: 0.7012, IoU.tent: 0.9432, IoU.bag: 0.2089, IoU.minibike: 0.7758, IoU.cradle: 0.7910, IoU.oven: 0.6364, IoU.ball: 0.5474, IoU.food: 0.5494, IoU.step: 0.1298, IoU.tank: 0.6289, IoU.trade name: 0.3045, IoU.microwave: 0.8990, IoU.pot: 0.5794, IoU.animal: 0.5862, IoU.bicycle: 0.5618, IoU.lake: 0.5252, IoU.dishwasher: 0.7142, IoU.screen: 0.4618, IoU.blanket: 0.2654, IoU.sculpture: 0.7532, IoU.hood: 0.6308, IoU.sconce: 0.5930, IoU.vase: 0.4970, IoU.traffic light: 0.3749, IoU.tray: 0.2619, IoU.ashcan: 0.4904, IoU.fan: 0.6921, IoU.pier: 0.4071, IoU.crt screen: 0.0638, IoU.plate: 0.6238, IoU.monitor: 0.5508, IoU.bulletin board: 0.5620, IoU.shower: 0.0303, IoU.radiator: 0.6674, IoU.glass: 0.2056, IoU.clock: 0.4596, IoU.flag: 0.7045, Acc.wall: 0.9017, Acc.building: 0.9370, Acc.sky: 0.9772, Acc.floor: 0.9201, Acc.tree: 0.8936, Acc.ceiling: 0.9438, Acc.road: 0.9153, Acc.bed : 0.9692, Acc.windowpane: 0.8094, Acc.grass: 0.8297, Acc.cabinet: 0.7768, Acc.sidewalk: 0.8389, Acc.person: 0.9423, Acc.earth: 0.5152, Acc.door: 0.7413, Acc.table: 0.8260, Acc.mountain: 0.7533, Acc.plant: 0.6439, Acc.curtain: 0.8839, Acc.chair: 0.8104, Acc.car: 0.9418, Acc.water: 0.8110, Acc.painting: 0.9147, Acc.sofa: 0.9293, Acc.shelf: 0.6120, Acc.house: 0.6913, Acc.sea: 0.8277, Acc.mirror: 0.8469, Acc.rug: 0.8381, Acc.field: 0.5523, Acc.armchair: 0.7431, Acc.seat: 0.8875, Acc.fence: 0.6880, Acc.desk: 0.7946, Acc.rock: 0.8072, Acc.wardrobe: 0.7083, Acc.lamp: 0.8734, Acc.bathtub: 0.8691, Acc.railing: 0.5900, Acc.cushion: 0.8282, Acc.base: 0.6359, Acc.box: 0.5078, Acc.column: 0.7024, Acc.signboard: 0.5236, Acc.chest of drawers: 0.6708, Acc.counter: 0.4651, Acc.sand: 0.8440, Acc.sink: 0.8455, Acc.skyscraper: 0.6592, Acc.fireplace: 0.9461, Acc.refrigerator: 0.9253, Acc.grandstand: 0.8593, Acc.path: 0.4607, Acc.stairs: 0.3598, Acc.runway: 0.8631, Acc.case: 0.8095, Acc.pool table: 0.9835, Acc.pillow: 0.7818, Acc.screen door: 0.7025, Acc.stairway: 0.6028, Acc.river: 0.2438, Acc.bridge: 0.8512, Acc.bookcase: 0.7059, Acc.blind: 0.4671, Acc.coffee table: 0.8853, Acc.toilet: 0.9247, Acc.flower: 0.6059, Acc.book: 0.7376, Acc.hill: 0.1538, Acc.bench: 0.6082, Acc.countertop: 0.8518, Acc.stove: 0.8922, Acc.palm: 0.7953, Acc.kitchen island: 0.8489, Acc.computer: 0.9112, Acc.swivel chair: 0.7537, Acc.boat: 0.9037, Acc.bar: 0.7786, Acc.arcade machine: 0.7890, Acc.hovel: 0.5776, Acc.bus: 0.9632, Acc.towel: 0.8693, Acc.light: 0.6947, Acc.truck: 0.5793, Acc.tower: 0.5837, Acc.chandelier: 0.8624, Acc.awning: 0.7059, Acc.streetlight: 0.4265, Acc.booth: 0.6206, Acc.television receiver: 0.7936, Acc.airplane: 0.7745, Acc.dirt track: 0.1856, Acc.apparel: 0.6886, Acc.pole: 0.4221, Acc.land: 0.0508, Acc.bannister: 0.2479, Acc.escalator: 0.7907, Acc.ottoman: 0.6679, Acc.bottle: 0.7003, Acc.buffet: 0.5560, Acc.poster: 0.5119, Acc.stage: 0.4355, Acc.van: 0.6463, Acc.ship: 0.9198, Acc.fountain: 0.3627, Acc.conveyer belt: 0.9300, Acc.canopy: 0.7169, Acc.washer: 0.8180, Acc.plaything: 0.4371, Acc.swimming pool: 0.9204, Acc.stool: 0.7046, Acc.barrel: 0.7144, Acc.basket: 0.5466, Acc.waterfall: 0.8782, Acc.tent: 0.9855, Acc.bag: 0.2337, Acc.minibike: 0.8769, Acc.cradle: 0.9772, Acc.oven: 0.7395, Acc.ball: 0.6319, Acc.food: 0.6593, Acc.step: 0.1635, Acc.tank: 0.6730, Acc.trade name: 0.3831, Acc.microwave: 0.9591, Acc.pot: 0.6654, Acc.animal: 0.5959, Acc.bicycle: 0.6371, Acc.lake: 0.6382, Acc.dishwasher: 0.8118, Acc.screen: 0.7624, Acc.blanket: 0.2951, Acc.sculpture: 0.8634, Acc.hood: 0.7656, Acc.sconce: 0.6800, Acc.vase: 0.6457, Acc.traffic light: 0.5803, Acc.tray: 0.3270, Acc.ashcan: 0.6315, Acc.fan: 0.7917, Acc.pier: 0.4504, Acc.crt screen: 0.1223, Acc.plate: 0.7745, Acc.monitor: 0.6237, Acc.bulletin board: 0.6691, Acc.shower: 0.0314, Acc.radiator: 0.8076, Acc.glass: 0.2188, Acc.clock: 0.5414, Acc.flag: 0.8016 +2024-06-16 22:10:51,082 - mmseg - INFO - Iter [59050/80000] lr: 1.048e-05, eta: 8:46:40, time: 3.311, data_time: 1.948, memory: 70722, decode.loss_ce: 0.1648, decode.acc_seg: 92.8354, aux.loss_ce: 0.0698, aux.acc_seg: 92.4300, loss: 0.2346 +2024-06-16 22:11:59,120 - mmseg - INFO - Iter [59100/80000] lr: 1.045e-05, eta: 8:45:22, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1580, decode.acc_seg: 93.0267, aux.loss_ce: 0.0671, aux.acc_seg: 92.6395, loss: 0.2252 +2024-06-16 22:13:07,353 - mmseg - INFO - Iter [59150/80000] lr: 1.043e-05, eta: 8:44:04, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1628, decode.acc_seg: 92.8601, aux.loss_ce: 0.0692, aux.acc_seg: 92.3980, loss: 0.2320 +2024-06-16 22:14:15,375 - mmseg - INFO - Iter [59200/80000] lr: 1.040e-05, eta: 8:42:46, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1709, decode.acc_seg: 92.7167, aux.loss_ce: 0.0718, aux.acc_seg: 92.3507, loss: 0.2427 +2024-06-16 22:15:23,689 - mmseg - INFO - Iter [59250/80000] lr: 1.038e-05, eta: 8:41:28, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1556, decode.acc_seg: 93.2345, aux.loss_ce: 0.0664, aux.acc_seg: 92.8023, loss: 0.2220 +2024-06-16 22:16:31,791 - mmseg - INFO - Iter [59300/80000] lr: 1.035e-05, eta: 8:40:10, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1589, decode.acc_seg: 93.1840, aux.loss_ce: 0.0670, aux.acc_seg: 92.8536, loss: 0.2259 +2024-06-16 22:17:39,976 - mmseg - INFO - Iter [59350/80000] lr: 1.033e-05, eta: 8:38:52, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1501, decode.acc_seg: 93.4282, aux.loss_ce: 0.0640, aux.acc_seg: 93.0253, loss: 0.2141 +2024-06-16 22:18:50,487 - mmseg - INFO - Iter [59400/80000] lr: 1.030e-05, eta: 8:37:35, time: 1.410, data_time: 0.052, memory: 70722, decode.loss_ce: 0.1504, decode.acc_seg: 93.4809, aux.loss_ce: 0.0643, aux.acc_seg: 93.0531, loss: 0.2147 +2024-06-16 22:19:58,546 - mmseg - INFO - Iter [59450/80000] lr: 1.028e-05, eta: 8:36:17, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1597, decode.acc_seg: 93.0225, aux.loss_ce: 0.0677, aux.acc_seg: 92.6296, loss: 0.2273 +2024-06-16 22:21:06,763 - mmseg - INFO - Iter [59500/80000] lr: 1.025e-05, eta: 8:35:00, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1583, decode.acc_seg: 92.9684, aux.loss_ce: 0.0674, aux.acc_seg: 92.5496, loss: 0.2257 +2024-06-16 22:22:14,993 - mmseg - INFO - Iter [59550/80000] lr: 1.023e-05, eta: 8:33:42, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1534, decode.acc_seg: 93.1604, aux.loss_ce: 0.0654, aux.acc_seg: 92.7399, loss: 0.2188 +2024-06-16 22:23:23,229 - mmseg - INFO - Iter [59600/80000] lr: 1.020e-05, eta: 8:32:24, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1539, decode.acc_seg: 93.4102, aux.loss_ce: 0.0650, aux.acc_seg: 93.0158, loss: 0.2189 +2024-06-16 22:24:31,341 - mmseg - INFO - Iter [59650/80000] lr: 1.018e-05, eta: 8:31:06, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1601, decode.acc_seg: 92.9649, aux.loss_ce: 0.0684, aux.acc_seg: 92.5564, loss: 0.2285 +2024-06-16 22:25:39,633 - mmseg - INFO - Iter [59700/80000] lr: 1.015e-05, eta: 8:29:48, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1623, decode.acc_seg: 93.0479, aux.loss_ce: 0.0689, aux.acc_seg: 92.6417, loss: 0.2312 +2024-06-16 22:26:47,725 - mmseg - INFO - Iter [59750/80000] lr: 1.013e-05, eta: 8:28:31, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1583, decode.acc_seg: 93.1675, aux.loss_ce: 0.0680, aux.acc_seg: 92.6891, loss: 0.2263 +2024-06-16 22:27:55,892 - mmseg - INFO - Iter [59800/80000] lr: 1.010e-05, eta: 8:27:13, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1499, decode.acc_seg: 93.3185, aux.loss_ce: 0.0635, aux.acc_seg: 92.9667, loss: 0.2135 +2024-06-16 22:29:03,981 - mmseg - INFO - Iter [59850/80000] lr: 1.008e-05, eta: 8:25:55, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1562, decode.acc_seg: 92.9974, aux.loss_ce: 0.0672, aux.acc_seg: 92.5543, loss: 0.2235 +2024-06-16 22:30:12,110 - mmseg - INFO - Iter [59900/80000] lr: 1.005e-05, eta: 8:24:37, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1549, decode.acc_seg: 93.0671, aux.loss_ce: 0.0666, aux.acc_seg: 92.6450, loss: 0.2214 +2024-06-16 22:31:20,544 - mmseg - INFO - Iter [59950/80000] lr: 1.003e-05, eta: 8:23:20, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1455, decode.acc_seg: 93.6544, aux.loss_ce: 0.0627, aux.acc_seg: 93.1932, loss: 0.2082 +2024-06-16 22:32:28,551 - mmseg - INFO - Saving checkpoint at 60000 iterations +2024-06-16 22:33:55,284 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 22:33:55,284 - mmseg - INFO - Iter [60000/80000] lr: 1.000e-05, eta: 8:22:31, time: 3.095, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1564, decode.acc_seg: 93.1492, aux.loss_ce: 0.0667, aux.acc_seg: 92.7426, loss: 0.2231 +2024-06-16 22:35:30,607 - mmseg - INFO - per class results: +2024-06-16 22:35:30,613 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.5 | 90.18 | +| building | 85.72 | 93.4 | +| sky | 95.06 | 97.57 | +| floor | 86.07 | 92.83 | +| tree | 77.42 | 89.17 | +| ceiling | 87.32 | 93.49 | +| road | 87.13 | 91.75 | +| bed | 93.19 | 97.27 | +| windowpane | 66.26 | 81.64 | +| grass | 68.32 | 82.59 | +| cabinet | 66.62 | 77.22 | +| sidewalk | 72.38 | 85.85 | +| person | 85.49 | 94.37 | +| earth | 37.9 | 51.4 | +| door | 59.53 | 75.29 | +| table | 69.33 | 80.78 | +| mountain | 62.57 | 74.03 | +| plant | 56.26 | 67.82 | +| curtain | 77.12 | 90.28 | +| chair | 69.01 | 79.97 | +| car | 87.85 | 93.96 | +| water | 65.31 | 80.48 | +| painting | 80.19 | 89.71 | +| sofa | 83.01 | 92.0 | +| shelf | 43.61 | 56.97 | +| house | 61.35 | 77.04 | +| sea | 72.99 | 83.15 | +| mirror | 78.77 | 84.38 | +| rug | 73.89 | 81.54 | +| field | 31.39 | 57.07 | +| armchair | 61.38 | 80.51 | +| seat | 68.59 | 88.92 | +| fence | 52.7 | 66.17 | +| desk | 61.69 | 79.79 | +| rock | 54.94 | 86.08 | +| wardrobe | 53.84 | 70.82 | +| lamp | 75.29 | 84.55 | +| bathtub | 84.3 | 86.75 | +| railing | 43.77 | 61.7 | +| cushion | 71.78 | 84.08 | +| base | 42.76 | 58.83 | +| box | 38.25 | 49.8 | +| column | 54.21 | 68.71 | +| signboard | 39.99 | 53.93 | +| chest of drawers | 46.1 | 67.37 | +| counter | 45.47 | 53.88 | +| sand | 60.08 | 85.45 | +| sink | 76.68 | 84.07 | +| skyscraper | 47.84 | 62.29 | +| fireplace | 73.24 | 93.8 | +| refrigerator | 85.13 | 93.48 | +| grandstand | 52.52 | 82.93 | +| path | 27.51 | 35.57 | +| stairs | 29.04 | 35.71 | +| runway | 69.57 | 90.75 | +| case | 57.14 | 81.97 | +| pool table | 94.23 | 98.36 | +| pillow | 68.8 | 79.6 | +| screen door | 77.31 | 80.15 | +| stairway | 45.91 | 61.25 | +| river | 13.0 | 26.63 | +| bridge | 66.14 | 72.25 | +| bookcase | 42.61 | 70.65 | +| blind | 46.19 | 50.9 | +| coffee table | 62.12 | 88.93 | +| toilet | 88.67 | 93.09 | +| flower | 45.64 | 54.66 | +| book | 54.18 | 74.15 | +| hill | 8.65 | 14.63 | +| bench | 54.12 | 61.38 | +| countertop | 65.37 | 86.28 | +| stove | 84.26 | 89.75 | +| palm | 55.99 | 79.45 | +| kitchen island | 53.86 | 76.89 | +| computer | 78.77 | 90.45 | +| swivel chair | 46.41 | 63.03 | +| boat | 77.87 | 89.16 | +| bar | 65.13 | 86.53 | +| arcade machine | 77.83 | 82.11 | +| hovel | 46.52 | 51.7 | +| bus | 92.56 | 96.4 | +| towel | 77.05 | 87.21 | +| light | 59.27 | 67.84 | +| truck | 42.78 | 58.01 | +| tower | 35.79 | 59.94 | +| chandelier | 72.59 | 89.3 | +| awning | 49.95 | 61.93 | +| streetlight | 36.11 | 50.77 | +| booth | 41.77 | 66.45 | +| television receiver | 73.99 | 84.68 | +| airplane | 64.67 | 77.71 | +| dirt track | 11.77 | 42.18 | +| apparel | 45.15 | 66.39 | +| pole | 29.36 | 38.86 | +| land | 3.93 | 5.99 | +| bannister | 17.76 | 23.8 | +| escalator | 60.39 | 78.16 | +| ottoman | 54.82 | 71.47 | +| bottle | 41.7 | 72.85 | +| buffet | 47.71 | 57.28 | +| poster | 44.09 | 51.39 | +| stage | 24.12 | 44.51 | +| van | 49.77 | 68.31 | +| ship | 89.51 | 93.89 | +| fountain | 38.31 | 40.26 | +| conveyer belt | 83.88 | 93.08 | +| canopy | 48.33 | 66.67 | +| washer | 79.96 | 84.52 | +| plaything | 35.76 | 55.32 | +| swimming pool | 59.13 | 90.01 | +| stool | 52.45 | 72.93 | +| barrel | 53.1 | 74.45 | +| basket | 42.83 | 59.84 | +| waterfall | 70.19 | 85.5 | +| tent | 95.18 | 98.92 | +| bag | 22.06 | 24.64 | +| minibike | 78.33 | 89.53 | +| cradle | 83.0 | 97.71 | +| oven | 64.59 | 75.76 | +| ball | 39.02 | 39.94 | +| food | 60.12 | 75.43 | +| step | 13.7 | 18.79 | +| tank | 73.9 | 80.88 | +| trade name | 28.76 | 35.43 | +| microwave | 90.25 | 95.74 | +| pot | 57.24 | 65.35 | +| animal | 58.66 | 59.72 | +| bicycle | 60.52 | 75.83 | +| lake | 50.82 | 63.85 | +| dishwasher | 70.53 | 81.75 | +| screen | 46.84 | 75.12 | +| blanket | 28.67 | 32.46 | +| sculpture | 74.26 | 86.71 | +| hood | 62.7 | 74.34 | +| sconce | 57.21 | 63.24 | +| vase | 49.61 | 65.53 | +| traffic light | 35.86 | 58.86 | +| tray | 23.91 | 29.27 | +| ashcan | 47.98 | 64.34 | +| fan | 69.1 | 81.74 | +| pier | 40.56 | 44.53 | +| crt screen | 2.73 | 4.74 | +| plate | 62.2 | 77.23 | +| monitor | 61.41 | 72.99 | +| bulletin board | 51.96 | 59.06 | +| shower | 6.83 | 7.01 | +| radiator | 67.46 | 78.87 | +| glass | 20.61 | 22.33 | +| clock | 44.95 | 54.86 | +| flag | 71.89 | 76.92 | ++---------------------+-------+-------+ +2024-06-16 22:35:30,613 - mmseg - INFO - Summary: +2024-06-16 22:35:30,613 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.41 | 57.74 | 70.27 | ++-------+-------+-------+ +2024-06-16 22:35:30,614 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 22:35:30,614 - mmseg - INFO - Iter(val) [250] aAcc: 0.8641, mIoU: 0.5774, mAcc: 0.7027, IoU.wall: 0.8250, IoU.building: 0.8572, IoU.sky: 0.9506, IoU.floor: 0.8607, IoU.tree: 0.7742, IoU.ceiling: 0.8732, IoU.road: 0.8713, IoU.bed : 0.9319, IoU.windowpane: 0.6626, IoU.grass: 0.6832, IoU.cabinet: 0.6662, IoU.sidewalk: 0.7238, IoU.person: 0.8549, IoU.earth: 0.3790, IoU.door: 0.5953, IoU.table: 0.6933, IoU.mountain: 0.6257, IoU.plant: 0.5626, IoU.curtain: 0.7712, IoU.chair: 0.6901, IoU.car: 0.8785, IoU.water: 0.6531, IoU.painting: 0.8019, IoU.sofa: 0.8301, IoU.shelf: 0.4361, IoU.house: 0.6135, IoU.sea: 0.7299, IoU.mirror: 0.7877, IoU.rug: 0.7389, IoU.field: 0.3139, IoU.armchair: 0.6138, IoU.seat: 0.6859, IoU.fence: 0.5270, IoU.desk: 0.6169, IoU.rock: 0.5494, IoU.wardrobe: 0.5384, IoU.lamp: 0.7529, IoU.bathtub: 0.8430, IoU.railing: 0.4377, IoU.cushion: 0.7178, IoU.base: 0.4276, IoU.box: 0.3825, IoU.column: 0.5421, IoU.signboard: 0.3999, IoU.chest of drawers: 0.4610, IoU.counter: 0.4547, IoU.sand: 0.6008, IoU.sink: 0.7668, IoU.skyscraper: 0.4784, IoU.fireplace: 0.7324, IoU.refrigerator: 0.8513, IoU.grandstand: 0.5252, IoU.path: 0.2751, IoU.stairs: 0.2904, IoU.runway: 0.6957, IoU.case: 0.5714, IoU.pool table: 0.9423, IoU.pillow: 0.6880, IoU.screen door: 0.7731, IoU.stairway: 0.4591, IoU.river: 0.1300, IoU.bridge: 0.6614, IoU.bookcase: 0.4261, IoU.blind: 0.4619, IoU.coffee table: 0.6212, IoU.toilet: 0.8867, IoU.flower: 0.4564, IoU.book: 0.5418, IoU.hill: 0.0865, IoU.bench: 0.5412, IoU.countertop: 0.6537, IoU.stove: 0.8426, IoU.palm: 0.5599, IoU.kitchen island: 0.5386, IoU.computer: 0.7877, IoU.swivel chair: 0.4641, IoU.boat: 0.7787, IoU.bar: 0.6513, IoU.arcade machine: 0.7783, IoU.hovel: 0.4652, IoU.bus: 0.9256, IoU.towel: 0.7705, IoU.light: 0.5927, IoU.truck: 0.4278, IoU.tower: 0.3579, IoU.chandelier: 0.7259, IoU.awning: 0.4995, IoU.streetlight: 0.3611, IoU.booth: 0.4177, IoU.television receiver: 0.7399, IoU.airplane: 0.6467, IoU.dirt track: 0.1177, IoU.apparel: 0.4515, IoU.pole: 0.2936, IoU.land: 0.0393, IoU.bannister: 0.1776, IoU.escalator: 0.6039, IoU.ottoman: 0.5482, IoU.bottle: 0.4170, IoU.buffet: 0.4771, IoU.poster: 0.4409, IoU.stage: 0.2412, IoU.van: 0.4977, IoU.ship: 0.8951, IoU.fountain: 0.3831, IoU.conveyer belt: 0.8388, IoU.canopy: 0.4833, IoU.washer: 0.7996, IoU.plaything: 0.3576, IoU.swimming pool: 0.5913, IoU.stool: 0.5245, IoU.barrel: 0.5310, IoU.basket: 0.4283, IoU.waterfall: 0.7019, IoU.tent: 0.9518, IoU.bag: 0.2206, IoU.minibike: 0.7833, IoU.cradle: 0.8300, IoU.oven: 0.6459, IoU.ball: 0.3902, IoU.food: 0.6012, IoU.step: 0.1370, IoU.tank: 0.7390, IoU.trade name: 0.2876, IoU.microwave: 0.9025, IoU.pot: 0.5724, IoU.animal: 0.5866, IoU.bicycle: 0.6052, IoU.lake: 0.5082, IoU.dishwasher: 0.7053, IoU.screen: 0.4684, IoU.blanket: 0.2867, IoU.sculpture: 0.7426, IoU.hood: 0.6270, IoU.sconce: 0.5721, IoU.vase: 0.4961, IoU.traffic light: 0.3586, IoU.tray: 0.2391, IoU.ashcan: 0.4798, IoU.fan: 0.6910, IoU.pier: 0.4056, IoU.crt screen: 0.0273, IoU.plate: 0.6220, IoU.monitor: 0.6141, IoU.bulletin board: 0.5196, IoU.shower: 0.0683, IoU.radiator: 0.6746, IoU.glass: 0.2061, IoU.clock: 0.4495, IoU.flag: 0.7189, Acc.wall: 0.9018, Acc.building: 0.9340, Acc.sky: 0.9757, Acc.floor: 0.9283, Acc.tree: 0.8917, Acc.ceiling: 0.9349, Acc.road: 0.9175, Acc.bed : 0.9727, Acc.windowpane: 0.8164, Acc.grass: 0.8259, Acc.cabinet: 0.7722, Acc.sidewalk: 0.8585, Acc.person: 0.9437, Acc.earth: 0.5140, Acc.door: 0.7529, Acc.table: 0.8078, Acc.mountain: 0.7403, Acc.plant: 0.6782, Acc.curtain: 0.9028, Acc.chair: 0.7997, Acc.car: 0.9396, Acc.water: 0.8048, Acc.painting: 0.8971, Acc.sofa: 0.9200, Acc.shelf: 0.5697, Acc.house: 0.7704, Acc.sea: 0.8315, Acc.mirror: 0.8438, Acc.rug: 0.8154, Acc.field: 0.5707, Acc.armchair: 0.8051, Acc.seat: 0.8892, Acc.fence: 0.6617, Acc.desk: 0.7979, Acc.rock: 0.8608, Acc.wardrobe: 0.7082, Acc.lamp: 0.8455, Acc.bathtub: 0.8675, Acc.railing: 0.6170, Acc.cushion: 0.8408, Acc.base: 0.5883, Acc.box: 0.4980, Acc.column: 0.6871, Acc.signboard: 0.5393, Acc.chest of drawers: 0.6737, Acc.counter: 0.5388, Acc.sand: 0.8545, Acc.sink: 0.8407, Acc.skyscraper: 0.6229, Acc.fireplace: 0.9380, Acc.refrigerator: 0.9348, Acc.grandstand: 0.8293, Acc.path: 0.3557, Acc.stairs: 0.3571, Acc.runway: 0.9075, Acc.case: 0.8197, Acc.pool table: 0.9836, Acc.pillow: 0.7960, Acc.screen door: 0.8015, Acc.stairway: 0.6125, Acc.river: 0.2663, Acc.bridge: 0.7225, Acc.bookcase: 0.7065, Acc.blind: 0.5090, Acc.coffee table: 0.8893, Acc.toilet: 0.9309, Acc.flower: 0.5466, Acc.book: 0.7415, Acc.hill: 0.1463, Acc.bench: 0.6138, Acc.countertop: 0.8628, Acc.stove: 0.8975, Acc.palm: 0.7945, Acc.kitchen island: 0.7689, Acc.computer: 0.9045, Acc.swivel chair: 0.6303, Acc.boat: 0.8916, Acc.bar: 0.8653, Acc.arcade machine: 0.8211, Acc.hovel: 0.5170, Acc.bus: 0.9640, Acc.towel: 0.8721, Acc.light: 0.6784, Acc.truck: 0.5801, Acc.tower: 0.5994, Acc.chandelier: 0.8930, Acc.awning: 0.6193, Acc.streetlight: 0.5077, Acc.booth: 0.6645, Acc.television receiver: 0.8468, Acc.airplane: 0.7771, Acc.dirt track: 0.4218, Acc.apparel: 0.6639, Acc.pole: 0.3886, Acc.land: 0.0599, Acc.bannister: 0.2380, Acc.escalator: 0.7816, Acc.ottoman: 0.7147, Acc.bottle: 0.7285, Acc.buffet: 0.5728, Acc.poster: 0.5139, Acc.stage: 0.4451, Acc.van: 0.6831, Acc.ship: 0.9389, Acc.fountain: 0.4026, Acc.conveyer belt: 0.9308, Acc.canopy: 0.6667, Acc.washer: 0.8452, Acc.plaything: 0.5532, Acc.swimming pool: 0.9001, Acc.stool: 0.7293, Acc.barrel: 0.7445, Acc.basket: 0.5984, Acc.waterfall: 0.8550, Acc.tent: 0.9892, Acc.bag: 0.2464, Acc.minibike: 0.8953, Acc.cradle: 0.9771, Acc.oven: 0.7576, Acc.ball: 0.3994, Acc.food: 0.7543, Acc.step: 0.1879, Acc.tank: 0.8088, Acc.trade name: 0.3543, Acc.microwave: 0.9574, Acc.pot: 0.6535, Acc.animal: 0.5972, Acc.bicycle: 0.7583, Acc.lake: 0.6385, Acc.dishwasher: 0.8175, Acc.screen: 0.7512, Acc.blanket: 0.3246, Acc.sculpture: 0.8671, Acc.hood: 0.7434, Acc.sconce: 0.6324, Acc.vase: 0.6553, Acc.traffic light: 0.5886, Acc.tray: 0.2927, Acc.ashcan: 0.6434, Acc.fan: 0.8174, Acc.pier: 0.4453, Acc.crt screen: 0.0474, Acc.plate: 0.7723, Acc.monitor: 0.7299, Acc.bulletin board: 0.5906, Acc.shower: 0.0701, Acc.radiator: 0.7887, Acc.glass: 0.2233, Acc.clock: 0.5486, Acc.flag: 0.7692 +2024-06-16 22:36:39,408 - mmseg - INFO - Iter [60050/80000] lr: 9.975e-06, eta: 8:21:45, time: 3.282, data_time: 1.923, memory: 70722, decode.loss_ce: 0.1492, decode.acc_seg: 93.3241, aux.loss_ce: 0.0634, aux.acc_seg: 93.0688, loss: 0.2126 +2024-06-16 22:37:47,513 - mmseg - INFO - Iter [60100/80000] lr: 9.951e-06, eta: 8:20:27, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1595, decode.acc_seg: 93.1030, aux.loss_ce: 0.0675, aux.acc_seg: 92.7098, loss: 0.2269 +2024-06-16 22:38:55,750 - mmseg - INFO - Iter [60150/80000] lr: 9.926e-06, eta: 8:19:09, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1655, decode.acc_seg: 92.8698, aux.loss_ce: 0.0702, aux.acc_seg: 92.4835, loss: 0.2358 +2024-06-16 22:40:03,792 - mmseg - INFO - Iter [60200/80000] lr: 9.901e-06, eta: 8:17:51, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1526, decode.acc_seg: 93.3324, aux.loss_ce: 0.0650, aux.acc_seg: 92.8946, loss: 0.2176 +2024-06-16 22:41:12,037 - mmseg - INFO - Iter [60250/80000] lr: 9.876e-06, eta: 8:16:34, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1525, decode.acc_seg: 93.1591, aux.loss_ce: 0.0652, aux.acc_seg: 92.7399, loss: 0.2177 +2024-06-16 22:42:20,361 - mmseg - INFO - Iter [60300/80000] lr: 9.851e-06, eta: 8:15:16, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1517, decode.acc_seg: 93.3645, aux.loss_ce: 0.0650, aux.acc_seg: 92.9281, loss: 0.2167 +2024-06-16 22:43:28,276 - mmseg - INFO - Iter [60350/80000] lr: 9.825e-06, eta: 8:13:58, time: 1.358, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1642, decode.acc_seg: 92.7941, aux.loss_ce: 0.0700, aux.acc_seg: 92.3497, loss: 0.2342 +2024-06-16 22:44:36,599 - mmseg - INFO - Iter [60400/80000] lr: 9.800e-06, eta: 8:12:40, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1533, decode.acc_seg: 93.2112, aux.loss_ce: 0.0652, aux.acc_seg: 92.8133, loss: 0.2185 +2024-06-16 22:45:44,570 - mmseg - INFO - Iter [60450/80000] lr: 9.775e-06, eta: 8:11:22, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1461, decode.acc_seg: 93.5374, aux.loss_ce: 0.0625, aux.acc_seg: 93.1366, loss: 0.2086 +2024-06-16 22:46:52,769 - mmseg - INFO - Iter [60500/80000] lr: 9.751e-06, eta: 8:10:05, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1471, decode.acc_seg: 93.6429, aux.loss_ce: 0.0630, aux.acc_seg: 93.1969, loss: 0.2101 +2024-06-16 22:48:01,073 - mmseg - INFO - Iter [60550/80000] lr: 9.726e-06, eta: 8:08:47, time: 1.366, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1569, decode.acc_seg: 93.3074, aux.loss_ce: 0.0669, aux.acc_seg: 92.9168, loss: 0.2238 +2024-06-16 22:49:09,306 - mmseg - INFO - Iter [60600/80000] lr: 9.701e-06, eta: 8:07:29, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1520, decode.acc_seg: 93.2811, aux.loss_ce: 0.0644, aux.acc_seg: 92.9368, loss: 0.2164 +2024-06-16 22:50:20,277 - mmseg - INFO - Iter [60650/80000] lr: 9.676e-06, eta: 8:06:13, time: 1.419, data_time: 0.062, memory: 70722, decode.loss_ce: 0.1559, decode.acc_seg: 93.1863, aux.loss_ce: 0.0671, aux.acc_seg: 92.6944, loss: 0.2231 +2024-06-16 22:51:28,543 - mmseg - INFO - Iter [60700/80000] lr: 9.651e-06, eta: 8:04:55, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1611, decode.acc_seg: 93.0111, aux.loss_ce: 0.0682, aux.acc_seg: 92.6255, loss: 0.2293 +2024-06-16 22:52:36,801 - mmseg - INFO - Iter [60750/80000] lr: 9.625e-06, eta: 8:03:37, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1571, decode.acc_seg: 93.3833, aux.loss_ce: 0.0668, aux.acc_seg: 92.9468, loss: 0.2239 +2024-06-16 22:53:45,036 - mmseg - INFO - Iter [60800/80000] lr: 9.600e-06, eta: 8:02:20, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1575, decode.acc_seg: 93.2181, aux.loss_ce: 0.0665, aux.acc_seg: 92.8727, loss: 0.2240 +2024-06-16 22:54:53,325 - mmseg - INFO - Iter [60850/80000] lr: 9.576e-06, eta: 8:01:02, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1545, decode.acc_seg: 93.4433, aux.loss_ce: 0.0657, aux.acc_seg: 92.9962, loss: 0.2202 +2024-06-16 22:56:01,382 - mmseg - INFO - Iter [60900/80000] lr: 9.551e-06, eta: 7:59:44, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1483, decode.acc_seg: 93.4387, aux.loss_ce: 0.0634, aux.acc_seg: 93.0418, loss: 0.2118 +2024-06-16 22:57:09,696 - mmseg - INFO - Iter [60950/80000] lr: 9.526e-06, eta: 7:58:27, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1558, decode.acc_seg: 93.0392, aux.loss_ce: 0.0664, aux.acc_seg: 92.5933, loss: 0.2223 +2024-06-16 22:58:17,997 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 22:58:17,998 - mmseg - INFO - Iter [61000/80000] lr: 9.501e-06, eta: 7:57:09, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1501, decode.acc_seg: 93.2438, aux.loss_ce: 0.0645, aux.acc_seg: 92.8150, loss: 0.2146 +2024-06-16 22:59:54,928 - mmseg - INFO - per class results: +2024-06-16 22:59:54,934 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.25 | 89.62 | +| building | 85.84 | 93.69 | +| sky | 95.01 | 97.58 | +| floor | 85.6 | 93.08 | +| tree | 76.72 | 89.6 | +| ceiling | 87.46 | 94.97 | +| road | 86.79 | 91.55 | +| bed | 93.26 | 96.87 | +| windowpane | 65.35 | 83.09 | +| grass | 68.04 | 84.36 | +| cabinet | 66.08 | 76.4 | +| sidewalk | 72.73 | 86.59 | +| person | 86.0 | 94.08 | +| earth | 38.5 | 49.22 | +| door | 58.45 | 70.55 | +| table | 70.74 | 81.16 | +| mountain | 61.24 | 71.61 | +| plant | 51.74 | 63.18 | +| curtain | 77.82 | 89.7 | +| chair | 69.6 | 80.67 | +| car | 87.82 | 93.88 | +| water | 63.67 | 77.89 | +| painting | 79.66 | 91.49 | +| sofa | 80.79 | 87.97 | +| shelf | 44.99 | 60.73 | +| house | 56.61 | 68.79 | +| sea | 69.32 | 81.59 | +| mirror | 78.14 | 83.67 | +| rug | 73.04 | 80.71 | +| field | 28.63 | 53.53 | +| armchair | 59.63 | 80.71 | +| seat | 69.2 | 88.3 | +| fence | 53.39 | 63.82 | +| desk | 62.75 | 80.16 | +| rock | 57.22 | 83.77 | +| wardrobe | 53.24 | 70.1 | +| lamp | 75.43 | 86.18 | +| bathtub | 84.56 | 86.29 | +| railing | 43.13 | 60.98 | +| cushion | 68.63 | 79.35 | +| base | 39.21 | 57.22 | +| box | 38.83 | 50.47 | +| column | 55.51 | 71.47 | +| signboard | 41.53 | 56.38 | +| chest of drawers | 43.71 | 69.5 | +| counter | 37.59 | 47.01 | +| sand | 58.91 | 87.7 | +| sink | 77.5 | 83.0 | +| skyscraper | 49.1 | 63.28 | +| fireplace | 72.53 | 95.17 | +| refrigerator | 86.15 | 94.62 | +| grandstand | 54.15 | 82.9 | +| path | 31.07 | 43.35 | +| stairs | 29.36 | 35.63 | +| runway | 71.55 | 93.6 | +| case | 57.79 | 72.3 | +| pool table | 94.69 | 97.86 | +| pillow | 68.06 | 79.06 | +| screen door | 80.51 | 83.07 | +| stairway | 49.55 | 67.1 | +| river | 13.07 | 28.32 | +| bridge | 66.48 | 73.96 | +| bookcase | 41.92 | 61.88 | +| blind | 42.74 | 46.28 | +| coffee table | 65.88 | 88.69 | +| toilet | 89.15 | 93.62 | +| flower | 44.75 | 57.83 | +| book | 55.84 | 80.42 | +| hill | 10.81 | 21.38 | +| bench | 54.58 | 63.63 | +| countertop | 64.18 | 84.7 | +| stove | 86.48 | 92.56 | +| palm | 55.69 | 77.37 | +| kitchen island | 54.61 | 75.96 | +| computer | 78.64 | 91.16 | +| swivel chair | 50.2 | 71.46 | +| boat | 72.25 | 90.77 | +| bar | 62.06 | 86.68 | +| arcade machine | 71.39 | 75.35 | +| hovel | 44.14 | 48.87 | +| bus | 93.24 | 95.33 | +| towel | 78.11 | 89.18 | +| light | 62.23 | 73.07 | +| truck | 45.36 | 59.33 | +| tower | 33.79 | 58.62 | +| chandelier | 72.89 | 87.31 | +| awning | 46.16 | 62.46 | +| streetlight | 34.21 | 43.11 | +| booth | 49.08 | 64.89 | +| television receiver | 72.78 | 87.84 | +| airplane | 76.52 | 91.55 | +| dirt track | 12.01 | 41.26 | +| apparel | 46.7 | 68.64 | +| pole | 33.9 | 49.61 | +| land | 3.93 | 6.16 | +| bannister | 19.82 | 29.1 | +| escalator | 59.08 | 78.89 | +| ottoman | 52.95 | 70.9 | +| bottle | 41.47 | 72.8 | +| buffet | 47.38 | 55.05 | +| poster | 41.2 | 54.66 | +| stage | 19.97 | 49.04 | +| van | 49.96 | 71.9 | +| ship | 91.09 | 95.22 | +| fountain | 37.77 | 38.55 | +| conveyer belt | 81.91 | 93.48 | +| canopy | 53.69 | 75.39 | +| washer | 78.86 | 82.7 | +| plaything | 33.91 | 44.43 | +| swimming pool | 59.47 | 89.48 | +| stool | 56.59 | 71.4 | +| barrel | 58.1 | 74.08 | +| basket | 42.29 | 63.01 | +| waterfall | 55.83 | 64.73 | +| tent | 96.24 | 98.39 | +| bag | 22.15 | 25.48 | +| minibike | 77.84 | 89.69 | +| cradle | 83.89 | 98.01 | +| oven | 68.92 | 76.66 | +| ball | 46.25 | 48.43 | +| food | 60.23 | 73.44 | +| step | 9.92 | 11.55 | +| tank | 63.61 | 69.33 | +| trade name | 27.27 | 32.02 | +| microwave | 89.99 | 96.48 | +| pot | 59.14 | 69.32 | +| animal | 58.71 | 60.1 | +| bicycle | 59.1 | 74.82 | +| lake | 51.68 | 63.8 | +| dishwasher | 69.4 | 81.59 | +| screen | 46.38 | 75.15 | +| blanket | 32.74 | 38.07 | +| sculpture | 75.51 | 87.09 | +| hood | 62.88 | 77.37 | +| sconce | 60.5 | 73.08 | +| vase | 49.18 | 64.8 | +| traffic light | 40.31 | 58.16 | +| tray | 25.04 | 34.91 | +| ashcan | 45.26 | 64.68 | +| fan | 69.93 | 80.51 | +| pier | 42.07 | 49.43 | +| crt screen | 2.83 | 5.03 | +| plate | 61.09 | 80.76 | +| monitor | 58.04 | 74.76 | +| bulletin board | 51.29 | 68.14 | +| shower | 8.05 | 8.3 | +| radiator | 67.77 | 78.34 | +| glass | 21.53 | 23.55 | +| clock | 45.66 | 58.96 | +| flag | 71.76 | 78.77 | ++---------------------+-------+-------+ +2024-06-16 22:59:54,934 - mmseg - INFO - Summary: +2024-06-16 22:59:54,934 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.23 | 57.7 | 70.54 | ++-------+------+-------+ +2024-06-16 22:59:54,935 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 22:59:54,935 - mmseg - INFO - Iter(val) [250] aAcc: 0.8623, mIoU: 0.5770, mAcc: 0.7054, IoU.wall: 0.8225, IoU.building: 0.8584, IoU.sky: 0.9501, IoU.floor: 0.8560, IoU.tree: 0.7672, IoU.ceiling: 0.8746, IoU.road: 0.8679, IoU.bed : 0.9326, IoU.windowpane: 0.6535, IoU.grass: 0.6804, IoU.cabinet: 0.6608, IoU.sidewalk: 0.7273, IoU.person: 0.8600, IoU.earth: 0.3850, IoU.door: 0.5845, IoU.table: 0.7074, IoU.mountain: 0.6124, IoU.plant: 0.5174, IoU.curtain: 0.7782, IoU.chair: 0.6960, IoU.car: 0.8782, IoU.water: 0.6367, IoU.painting: 0.7966, IoU.sofa: 0.8079, IoU.shelf: 0.4499, IoU.house: 0.5661, IoU.sea: 0.6932, IoU.mirror: 0.7814, IoU.rug: 0.7304, IoU.field: 0.2863, IoU.armchair: 0.5963, IoU.seat: 0.6920, IoU.fence: 0.5339, IoU.desk: 0.6275, IoU.rock: 0.5722, IoU.wardrobe: 0.5324, IoU.lamp: 0.7543, IoU.bathtub: 0.8456, IoU.railing: 0.4313, IoU.cushion: 0.6863, IoU.base: 0.3921, IoU.box: 0.3883, IoU.column: 0.5551, IoU.signboard: 0.4153, IoU.chest of drawers: 0.4371, IoU.counter: 0.3759, IoU.sand: 0.5891, IoU.sink: 0.7750, IoU.skyscraper: 0.4910, IoU.fireplace: 0.7253, IoU.refrigerator: 0.8615, IoU.grandstand: 0.5415, IoU.path: 0.3107, IoU.stairs: 0.2936, IoU.runway: 0.7155, IoU.case: 0.5779, IoU.pool table: 0.9469, IoU.pillow: 0.6806, IoU.screen door: 0.8051, IoU.stairway: 0.4955, IoU.river: 0.1307, IoU.bridge: 0.6648, IoU.bookcase: 0.4192, IoU.blind: 0.4274, IoU.coffee table: 0.6588, IoU.toilet: 0.8915, IoU.flower: 0.4475, IoU.book: 0.5584, IoU.hill: 0.1081, IoU.bench: 0.5458, IoU.countertop: 0.6418, IoU.stove: 0.8648, IoU.palm: 0.5569, IoU.kitchen island: 0.5461, IoU.computer: 0.7864, IoU.swivel chair: 0.5020, IoU.boat: 0.7225, IoU.bar: 0.6206, IoU.arcade machine: 0.7139, IoU.hovel: 0.4414, IoU.bus: 0.9324, IoU.towel: 0.7811, IoU.light: 0.6223, IoU.truck: 0.4536, IoU.tower: 0.3379, IoU.chandelier: 0.7289, IoU.awning: 0.4616, IoU.streetlight: 0.3421, IoU.booth: 0.4908, IoU.television receiver: 0.7278, IoU.airplane: 0.7652, IoU.dirt track: 0.1201, IoU.apparel: 0.4670, IoU.pole: 0.3390, IoU.land: 0.0393, IoU.bannister: 0.1982, IoU.escalator: 0.5908, IoU.ottoman: 0.5295, IoU.bottle: 0.4147, IoU.buffet: 0.4738, IoU.poster: 0.4120, IoU.stage: 0.1997, IoU.van: 0.4996, IoU.ship: 0.9109, IoU.fountain: 0.3777, IoU.conveyer belt: 0.8191, IoU.canopy: 0.5369, IoU.washer: 0.7886, IoU.plaything: 0.3391, IoU.swimming pool: 0.5947, IoU.stool: 0.5659, IoU.barrel: 0.5810, IoU.basket: 0.4229, IoU.waterfall: 0.5583, IoU.tent: 0.9624, IoU.bag: 0.2215, IoU.minibike: 0.7784, IoU.cradle: 0.8389, IoU.oven: 0.6892, IoU.ball: 0.4625, IoU.food: 0.6023, IoU.step: 0.0992, IoU.tank: 0.6361, IoU.trade name: 0.2727, IoU.microwave: 0.8999, IoU.pot: 0.5914, IoU.animal: 0.5871, IoU.bicycle: 0.5910, IoU.lake: 0.5168, IoU.dishwasher: 0.6940, IoU.screen: 0.4638, IoU.blanket: 0.3274, IoU.sculpture: 0.7551, IoU.hood: 0.6288, IoU.sconce: 0.6050, IoU.vase: 0.4918, IoU.traffic light: 0.4031, IoU.tray: 0.2504, IoU.ashcan: 0.4526, IoU.fan: 0.6993, IoU.pier: 0.4207, IoU.crt screen: 0.0283, IoU.plate: 0.6109, IoU.monitor: 0.5804, IoU.bulletin board: 0.5129, IoU.shower: 0.0805, IoU.radiator: 0.6777, IoU.glass: 0.2153, IoU.clock: 0.4566, IoU.flag: 0.7176, Acc.wall: 0.8962, Acc.building: 0.9369, Acc.sky: 0.9758, Acc.floor: 0.9308, Acc.tree: 0.8960, Acc.ceiling: 0.9497, Acc.road: 0.9155, Acc.bed : 0.9687, Acc.windowpane: 0.8309, Acc.grass: 0.8436, Acc.cabinet: 0.7640, Acc.sidewalk: 0.8659, Acc.person: 0.9408, Acc.earth: 0.4922, Acc.door: 0.7055, Acc.table: 0.8116, Acc.mountain: 0.7161, Acc.plant: 0.6318, Acc.curtain: 0.8970, Acc.chair: 0.8067, Acc.car: 0.9388, Acc.water: 0.7789, Acc.painting: 0.9149, Acc.sofa: 0.8797, Acc.shelf: 0.6073, Acc.house: 0.6879, Acc.sea: 0.8159, Acc.mirror: 0.8367, Acc.rug: 0.8071, Acc.field: 0.5353, Acc.armchair: 0.8071, Acc.seat: 0.8830, Acc.fence: 0.6382, Acc.desk: 0.8016, Acc.rock: 0.8377, Acc.wardrobe: 0.7010, Acc.lamp: 0.8618, Acc.bathtub: 0.8629, Acc.railing: 0.6098, Acc.cushion: 0.7935, Acc.base: 0.5722, Acc.box: 0.5047, Acc.column: 0.7147, Acc.signboard: 0.5638, Acc.chest of drawers: 0.6950, Acc.counter: 0.4701, Acc.sand: 0.8770, Acc.sink: 0.8300, Acc.skyscraper: 0.6328, Acc.fireplace: 0.9517, Acc.refrigerator: 0.9462, Acc.grandstand: 0.8290, Acc.path: 0.4335, Acc.stairs: 0.3563, Acc.runway: 0.9360, Acc.case: 0.7230, Acc.pool table: 0.9786, Acc.pillow: 0.7906, Acc.screen door: 0.8307, Acc.stairway: 0.6710, Acc.river: 0.2832, Acc.bridge: 0.7396, Acc.bookcase: 0.6188, Acc.blind: 0.4628, Acc.coffee table: 0.8869, Acc.toilet: 0.9362, Acc.flower: 0.5783, Acc.book: 0.8042, Acc.hill: 0.2138, Acc.bench: 0.6363, Acc.countertop: 0.8470, Acc.stove: 0.9256, Acc.palm: 0.7737, Acc.kitchen island: 0.7596, Acc.computer: 0.9116, Acc.swivel chair: 0.7146, Acc.boat: 0.9077, Acc.bar: 0.8668, Acc.arcade machine: 0.7535, Acc.hovel: 0.4887, Acc.bus: 0.9533, Acc.towel: 0.8918, Acc.light: 0.7307, Acc.truck: 0.5933, Acc.tower: 0.5862, Acc.chandelier: 0.8731, Acc.awning: 0.6246, Acc.streetlight: 0.4311, Acc.booth: 0.6489, Acc.television receiver: 0.8784, Acc.airplane: 0.9155, Acc.dirt track: 0.4126, Acc.apparel: 0.6864, Acc.pole: 0.4961, Acc.land: 0.0616, Acc.bannister: 0.2910, Acc.escalator: 0.7889, Acc.ottoman: 0.7090, Acc.bottle: 0.7280, Acc.buffet: 0.5505, Acc.poster: 0.5466, Acc.stage: 0.4904, Acc.van: 0.7190, Acc.ship: 0.9522, Acc.fountain: 0.3855, Acc.conveyer belt: 0.9348, Acc.canopy: 0.7539, Acc.washer: 0.8270, Acc.plaything: 0.4443, Acc.swimming pool: 0.8948, Acc.stool: 0.7140, Acc.barrel: 0.7408, Acc.basket: 0.6301, Acc.waterfall: 0.6473, Acc.tent: 0.9839, Acc.bag: 0.2548, Acc.minibike: 0.8969, Acc.cradle: 0.9801, Acc.oven: 0.7666, Acc.ball: 0.4843, Acc.food: 0.7344, Acc.step: 0.1155, Acc.tank: 0.6933, Acc.trade name: 0.3202, Acc.microwave: 0.9648, Acc.pot: 0.6932, Acc.animal: 0.6010, Acc.bicycle: 0.7482, Acc.lake: 0.6380, Acc.dishwasher: 0.8159, Acc.screen: 0.7515, Acc.blanket: 0.3807, Acc.sculpture: 0.8709, Acc.hood: 0.7737, Acc.sconce: 0.7308, Acc.vase: 0.6480, Acc.traffic light: 0.5816, Acc.tray: 0.3491, Acc.ashcan: 0.6468, Acc.fan: 0.8051, Acc.pier: 0.4943, Acc.crt screen: 0.0503, Acc.plate: 0.8076, Acc.monitor: 0.7476, Acc.bulletin board: 0.6814, Acc.shower: 0.0830, Acc.radiator: 0.7834, Acc.glass: 0.2355, Acc.clock: 0.5896, Acc.flag: 0.7877 +2024-06-16 23:01:04,157 - mmseg - INFO - Iter [61050/80000] lr: 9.476e-06, eta: 7:56:22, time: 3.323, data_time: 1.955, memory: 70722, decode.loss_ce: 0.1563, decode.acc_seg: 93.2175, aux.loss_ce: 0.0667, aux.acc_seg: 92.8036, loss: 0.2230 +2024-06-16 23:02:12,501 - mmseg - INFO - Iter [61100/80000] lr: 9.451e-06, eta: 7:55:05, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1459, decode.acc_seg: 93.6063, aux.loss_ce: 0.0621, aux.acc_seg: 93.2414, loss: 0.2081 +2024-06-16 23:03:20,582 - mmseg - INFO - Iter [61150/80000] lr: 9.426e-06, eta: 7:53:47, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1491, decode.acc_seg: 93.3876, aux.loss_ce: 0.0636, aux.acc_seg: 92.9815, loss: 0.2127 +2024-06-16 23:04:28,774 - mmseg - INFO - Iter [61200/80000] lr: 9.400e-06, eta: 7:52:29, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1542, decode.acc_seg: 93.1375, aux.loss_ce: 0.0657, aux.acc_seg: 92.7319, loss: 0.2199 +2024-06-16 23:05:36,926 - mmseg - INFO - Iter [61250/80000] lr: 9.376e-06, eta: 7:51:12, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1510, decode.acc_seg: 93.2882, aux.loss_ce: 0.0646, aux.acc_seg: 92.8923, loss: 0.2156 +2024-06-16 23:06:45,409 - mmseg - INFO - Iter [61300/80000] lr: 9.350e-06, eta: 7:49:54, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1589, decode.acc_seg: 93.1133, aux.loss_ce: 0.0676, aux.acc_seg: 92.6773, loss: 0.2264 +2024-06-16 23:07:53,510 - mmseg - INFO - Iter [61350/80000] lr: 9.326e-06, eta: 7:48:37, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1544, decode.acc_seg: 93.2583, aux.loss_ce: 0.0653, aux.acc_seg: 92.8722, loss: 0.2197 +2024-06-16 23:09:01,616 - mmseg - INFO - Iter [61400/80000] lr: 9.301e-06, eta: 7:47:19, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1442, decode.acc_seg: 93.4394, aux.loss_ce: 0.0622, aux.acc_seg: 92.9825, loss: 0.2064 +2024-06-16 23:10:09,687 - mmseg - INFO - Iter [61450/80000] lr: 9.276e-06, eta: 7:46:01, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1523, decode.acc_seg: 93.1722, aux.loss_ce: 0.0652, aux.acc_seg: 92.7300, loss: 0.2174 +2024-06-16 23:11:17,985 - mmseg - INFO - Iter [61500/80000] lr: 9.251e-06, eta: 7:44:44, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1494, decode.acc_seg: 93.5004, aux.loss_ce: 0.0640, aux.acc_seg: 93.0883, loss: 0.2134 +2024-06-16 23:12:26,070 - mmseg - INFO - Iter [61550/80000] lr: 9.226e-06, eta: 7:43:26, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1520, decode.acc_seg: 93.4386, aux.loss_ce: 0.0646, aux.acc_seg: 93.0856, loss: 0.2166 +2024-06-16 23:13:34,016 - mmseg - INFO - Iter [61600/80000] lr: 9.200e-06, eta: 7:42:09, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1606, decode.acc_seg: 93.1972, aux.loss_ce: 0.0680, aux.acc_seg: 92.8014, loss: 0.2285 +2024-06-16 23:14:42,261 - mmseg - INFO - Iter [61650/80000] lr: 9.175e-06, eta: 7:40:51, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1580, decode.acc_seg: 93.2097, aux.loss_ce: 0.0675, aux.acc_seg: 92.7653, loss: 0.2255 +2024-06-16 23:15:50,367 - mmseg - INFO - Iter [61700/80000] lr: 9.150e-06, eta: 7:39:34, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1619, decode.acc_seg: 92.9722, aux.loss_ce: 0.0688, aux.acc_seg: 92.6137, loss: 0.2308 +2024-06-16 23:16:58,583 - mmseg - INFO - Iter [61750/80000] lr: 9.126e-06, eta: 7:38:16, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1563, decode.acc_seg: 93.3642, aux.loss_ce: 0.0668, aux.acc_seg: 92.9500, loss: 0.2231 +2024-06-16 23:18:06,718 - mmseg - INFO - Iter [61800/80000] lr: 9.101e-06, eta: 7:36:59, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1608, decode.acc_seg: 93.0653, aux.loss_ce: 0.0690, aux.acc_seg: 92.6199, loss: 0.2298 +2024-06-16 23:19:15,087 - mmseg - INFO - Iter [61850/80000] lr: 9.076e-06, eta: 7:35:42, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1445, decode.acc_seg: 93.5893, aux.loss_ce: 0.0616, aux.acc_seg: 93.2407, loss: 0.2061 +2024-06-16 23:20:25,414 - mmseg - INFO - Iter [61900/80000] lr: 9.051e-06, eta: 7:34:25, time: 1.407, data_time: 0.052, memory: 70722, decode.loss_ce: 0.1462, decode.acc_seg: 93.5115, aux.loss_ce: 0.0626, aux.acc_seg: 93.0765, loss: 0.2088 +2024-06-16 23:21:33,586 - mmseg - INFO - Iter [61950/80000] lr: 9.026e-06, eta: 7:33:07, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1501, decode.acc_seg: 93.4074, aux.loss_ce: 0.0639, aux.acc_seg: 92.9145, loss: 0.2140 +2024-06-16 23:22:41,742 - mmseg - INFO - Saving checkpoint at 62000 iterations +2024-06-16 23:24:06,036 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 23:24:06,037 - mmseg - INFO - Iter [62000/80000] lr: 9.000e-06, eta: 7:32:14, time: 3.049, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1525, decode.acc_seg: 93.4675, aux.loss_ce: 0.0653, aux.acc_seg: 92.9501, loss: 0.2178 +2024-06-16 23:25:38,380 - mmseg - INFO - per class results: +2024-06-16 23:25:38,387 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.38 | 89.53 | +| building | 85.78 | 93.73 | +| sky | 95.0 | 97.77 | +| floor | 85.77 | 92.43 | +| tree | 77.23 | 88.69 | +| ceiling | 87.52 | 94.03 | +| road | 86.94 | 92.24 | +| bed | 92.97 | 97.18 | +| windowpane | 65.32 | 83.29 | +| grass | 68.36 | 83.07 | +| cabinet | 65.94 | 76.79 | +| sidewalk | 72.63 | 86.25 | +| person | 86.24 | 94.03 | +| earth | 39.27 | 54.31 | +| door | 59.0 | 73.04 | +| table | 71.03 | 83.56 | +| mountain | 63.36 | 75.34 | +| plant | 55.01 | 64.57 | +| curtain | 76.79 | 87.57 | +| chair | 69.28 | 81.14 | +| car | 87.87 | 93.85 | +| water | 62.79 | 77.03 | +| painting | 78.35 | 91.75 | +| sofa | 82.09 | 92.71 | +| shelf | 45.19 | 60.16 | +| house | 57.62 | 71.83 | +| sea | 69.01 | 82.9 | +| mirror | 79.61 | 86.04 | +| rug | 72.32 | 81.43 | +| field | 26.74 | 46.42 | +| armchair | 60.52 | 76.26 | +| seat | 68.12 | 88.6 | +| fence | 52.59 | 64.82 | +| desk | 62.39 | 81.97 | +| rock | 59.23 | 86.74 | +| wardrobe | 53.48 | 75.46 | +| lamp | 74.78 | 86.4 | +| bathtub | 84.36 | 87.13 | +| railing | 43.4 | 59.31 | +| cushion | 67.62 | 76.35 | +| base | 37.52 | 54.86 | +| box | 38.76 | 49.64 | +| column | 53.79 | 68.47 | +| signboard | 40.61 | 56.47 | +| chest of drawers | 47.75 | 68.45 | +| counter | 38.08 | 42.46 | +| sand | 60.05 | 85.85 | +| sink | 77.0 | 84.49 | +| skyscraper | 49.74 | 62.93 | +| fireplace | 72.24 | 93.39 | +| refrigerator | 86.2 | 93.26 | +| grandstand | 52.58 | 82.79 | +| path | 29.67 | 46.05 | +| stairs | 29.99 | 36.01 | +| runway | 73.14 | 94.99 | +| case | 63.73 | 85.2 | +| pool table | 94.49 | 98.4 | +| pillow | 69.26 | 81.08 | +| screen door | 79.54 | 83.75 | +| stairway | 48.9 | 63.66 | +| river | 12.89 | 26.12 | +| bridge | 60.18 | 66.0 | +| bookcase | 43.8 | 67.28 | +| blind | 41.8 | 45.14 | +| coffee table | 66.97 | 87.92 | +| toilet | 89.71 | 93.21 | +| flower | 43.17 | 48.91 | +| book | 55.05 | 77.41 | +| hill | 9.01 | 15.52 | +| bench | 53.63 | 63.7 | +| countertop | 63.84 | 83.14 | +| stove | 83.61 | 88.62 | +| palm | 55.38 | 83.85 | +| kitchen island | 53.35 | 86.46 | +| computer | 79.01 | 91.41 | +| swivel chair | 49.12 | 72.87 | +| boat | 78.18 | 91.58 | +| bar | 60.94 | 85.39 | +| arcade machine | 74.09 | 78.41 | +| hovel | 34.88 | 37.8 | +| bus | 92.55 | 96.65 | +| towel | 78.23 | 89.49 | +| light | 62.43 | 72.74 | +| truck | 44.79 | 59.5 | +| tower | 32.91 | 52.57 | +| chandelier | 71.54 | 85.77 | +| awning | 54.78 | 68.1 | +| streetlight | 35.36 | 47.57 | +| booth | 43.5 | 66.18 | +| television receiver | 73.45 | 88.64 | +| airplane | 80.91 | 88.91 | +| dirt track | 23.82 | 47.68 | +| apparel | 46.07 | 63.91 | +| pole | 27.24 | 37.24 | +| land | 3.16 | 4.78 | +| bannister | 17.38 | 25.32 | +| escalator | 60.06 | 78.25 | +| ottoman | 51.95 | 68.83 | +| bottle | 40.22 | 64.91 | +| buffet | 47.27 | 55.92 | +| poster | 40.96 | 53.31 | +| stage | 23.56 | 47.22 | +| van | 48.88 | 66.87 | +| ship | 85.25 | 92.43 | +| fountain | 37.1 | 38.14 | +| conveyer belt | 83.95 | 93.63 | +| canopy | 49.92 | 73.56 | +| washer | 81.36 | 86.0 | +| plaything | 38.15 | 58.62 | +| swimming pool | 60.95 | 92.68 | +| stool | 58.15 | 71.84 | +| barrel | 57.78 | 74.16 | +| basket | 41.6 | 57.11 | +| waterfall | 66.96 | 88.54 | +| tent | 94.58 | 98.27 | +| bag | 19.08 | 20.94 | +| minibike | 77.67 | 89.16 | +| cradle | 86.29 | 96.95 | +| oven | 63.54 | 75.24 | +| ball | 47.65 | 50.31 | +| food | 62.47 | 77.59 | +| step | 10.69 | 13.91 | +| tank | 66.5 | 74.42 | +| trade name | 29.18 | 35.39 | +| microwave | 89.89 | 95.89 | +| pot | 58.38 | 66.48 | +| animal | 58.8 | 60.23 | +| bicycle | 59.38 | 77.56 | +| lake | 51.95 | 63.83 | +| dishwasher | 71.57 | 79.38 | +| screen | 47.86 | 74.94 | +| blanket | 32.82 | 37.1 | +| sculpture | 72.27 | 88.13 | +| hood | 62.74 | 74.47 | +| sconce | 60.28 | 76.51 | +| vase | 50.2 | 62.77 | +| traffic light | 36.25 | 66.53 | +| tray | 26.63 | 32.59 | +| ashcan | 47.49 | 63.84 | +| fan | 69.45 | 83.09 | +| pier | 40.52 | 44.67 | +| crt screen | 3.42 | 5.45 | +| plate | 62.6 | 80.76 | +| monitor | 64.4 | 77.41 | +| bulletin board | 53.41 | 71.88 | +| shower | 4.63 | 4.69 | +| radiator | 67.57 | 77.52 | +| glass | 21.31 | 23.07 | +| clock | 48.79 | 57.58 | +| flag | 71.37 | 78.63 | ++---------------------+-------+-------+ +2024-06-16 23:25:38,387 - mmseg - INFO - Summary: +2024-06-16 23:25:38,387 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.32 | 57.86 | 70.62 | ++-------+-------+-------+ +2024-06-16 23:25:38,388 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 23:25:38,388 - mmseg - INFO - Iter(val) [250] aAcc: 0.8632, mIoU: 0.5786, mAcc: 0.7062, IoU.wall: 0.8238, IoU.building: 0.8578, IoU.sky: 0.9500, IoU.floor: 0.8577, IoU.tree: 0.7723, IoU.ceiling: 0.8752, IoU.road: 0.8694, IoU.bed : 0.9297, IoU.windowpane: 0.6532, IoU.grass: 0.6836, IoU.cabinet: 0.6594, IoU.sidewalk: 0.7263, IoU.person: 0.8624, IoU.earth: 0.3927, IoU.door: 0.5900, IoU.table: 0.7103, IoU.mountain: 0.6336, IoU.plant: 0.5501, IoU.curtain: 0.7679, IoU.chair: 0.6928, IoU.car: 0.8787, IoU.water: 0.6279, IoU.painting: 0.7835, IoU.sofa: 0.8209, IoU.shelf: 0.4519, IoU.house: 0.5762, IoU.sea: 0.6901, IoU.mirror: 0.7961, IoU.rug: 0.7232, IoU.field: 0.2674, IoU.armchair: 0.6052, IoU.seat: 0.6812, IoU.fence: 0.5259, IoU.desk: 0.6239, IoU.rock: 0.5923, IoU.wardrobe: 0.5348, IoU.lamp: 0.7478, IoU.bathtub: 0.8436, IoU.railing: 0.4340, IoU.cushion: 0.6762, IoU.base: 0.3752, IoU.box: 0.3876, IoU.column: 0.5379, IoU.signboard: 0.4061, IoU.chest of drawers: 0.4775, IoU.counter: 0.3808, IoU.sand: 0.6005, IoU.sink: 0.7700, IoU.skyscraper: 0.4974, IoU.fireplace: 0.7224, IoU.refrigerator: 0.8620, IoU.grandstand: 0.5258, IoU.path: 0.2967, IoU.stairs: 0.2999, IoU.runway: 0.7314, IoU.case: 0.6373, IoU.pool table: 0.9449, IoU.pillow: 0.6926, IoU.screen door: 0.7954, IoU.stairway: 0.4890, IoU.river: 0.1289, IoU.bridge: 0.6018, IoU.bookcase: 0.4380, IoU.blind: 0.4180, IoU.coffee table: 0.6697, IoU.toilet: 0.8971, IoU.flower: 0.4317, IoU.book: 0.5505, IoU.hill: 0.0901, IoU.bench: 0.5363, IoU.countertop: 0.6384, IoU.stove: 0.8361, IoU.palm: 0.5538, IoU.kitchen island: 0.5335, IoU.computer: 0.7901, IoU.swivel chair: 0.4912, IoU.boat: 0.7818, IoU.bar: 0.6094, IoU.arcade machine: 0.7409, IoU.hovel: 0.3488, IoU.bus: 0.9255, IoU.towel: 0.7823, IoU.light: 0.6243, IoU.truck: 0.4479, IoU.tower: 0.3291, IoU.chandelier: 0.7154, IoU.awning: 0.5478, IoU.streetlight: 0.3536, IoU.booth: 0.4350, IoU.television receiver: 0.7345, IoU.airplane: 0.8091, IoU.dirt track: 0.2382, IoU.apparel: 0.4607, IoU.pole: 0.2724, IoU.land: 0.0316, IoU.bannister: 0.1738, IoU.escalator: 0.6006, IoU.ottoman: 0.5195, IoU.bottle: 0.4022, IoU.buffet: 0.4727, IoU.poster: 0.4096, IoU.stage: 0.2356, IoU.van: 0.4888, IoU.ship: 0.8525, IoU.fountain: 0.3710, IoU.conveyer belt: 0.8395, IoU.canopy: 0.4992, IoU.washer: 0.8136, IoU.plaything: 0.3815, IoU.swimming pool: 0.6095, IoU.stool: 0.5815, IoU.barrel: 0.5778, IoU.basket: 0.4160, IoU.waterfall: 0.6696, IoU.tent: 0.9458, IoU.bag: 0.1908, IoU.minibike: 0.7767, IoU.cradle: 0.8629, IoU.oven: 0.6354, IoU.ball: 0.4765, IoU.food: 0.6247, IoU.step: 0.1069, IoU.tank: 0.6650, IoU.trade name: 0.2918, IoU.microwave: 0.8989, IoU.pot: 0.5838, IoU.animal: 0.5880, IoU.bicycle: 0.5938, IoU.lake: 0.5195, IoU.dishwasher: 0.7157, IoU.screen: 0.4786, IoU.blanket: 0.3282, IoU.sculpture: 0.7227, IoU.hood: 0.6274, IoU.sconce: 0.6028, IoU.vase: 0.5020, IoU.traffic light: 0.3625, IoU.tray: 0.2663, IoU.ashcan: 0.4749, IoU.fan: 0.6945, IoU.pier: 0.4052, IoU.crt screen: 0.0342, IoU.plate: 0.6260, IoU.monitor: 0.6440, IoU.bulletin board: 0.5341, IoU.shower: 0.0463, IoU.radiator: 0.6757, IoU.glass: 0.2131, IoU.clock: 0.4879, IoU.flag: 0.7137, Acc.wall: 0.8953, Acc.building: 0.9373, Acc.sky: 0.9777, Acc.floor: 0.9243, Acc.tree: 0.8869, Acc.ceiling: 0.9403, Acc.road: 0.9224, Acc.bed : 0.9718, Acc.windowpane: 0.8329, Acc.grass: 0.8307, Acc.cabinet: 0.7679, Acc.sidewalk: 0.8625, Acc.person: 0.9403, Acc.earth: 0.5431, Acc.door: 0.7304, Acc.table: 0.8356, Acc.mountain: 0.7534, Acc.plant: 0.6457, Acc.curtain: 0.8757, Acc.chair: 0.8114, Acc.car: 0.9385, Acc.water: 0.7703, Acc.painting: 0.9175, Acc.sofa: 0.9271, Acc.shelf: 0.6016, Acc.house: 0.7183, Acc.sea: 0.8290, Acc.mirror: 0.8604, Acc.rug: 0.8143, Acc.field: 0.4642, Acc.armchair: 0.7626, Acc.seat: 0.8860, Acc.fence: 0.6482, Acc.desk: 0.8197, Acc.rock: 0.8674, Acc.wardrobe: 0.7546, Acc.lamp: 0.8640, Acc.bathtub: 0.8713, Acc.railing: 0.5931, Acc.cushion: 0.7635, Acc.base: 0.5486, Acc.box: 0.4964, Acc.column: 0.6847, Acc.signboard: 0.5647, Acc.chest of drawers: 0.6845, Acc.counter: 0.4246, Acc.sand: 0.8585, Acc.sink: 0.8449, Acc.skyscraper: 0.6293, Acc.fireplace: 0.9339, Acc.refrigerator: 0.9326, Acc.grandstand: 0.8279, Acc.path: 0.4605, Acc.stairs: 0.3601, Acc.runway: 0.9499, Acc.case: 0.8520, Acc.pool table: 0.9840, Acc.pillow: 0.8108, Acc.screen door: 0.8375, Acc.stairway: 0.6366, Acc.river: 0.2612, Acc.bridge: 0.6600, Acc.bookcase: 0.6728, Acc.blind: 0.4514, Acc.coffee table: 0.8792, Acc.toilet: 0.9321, Acc.flower: 0.4891, Acc.book: 0.7741, Acc.hill: 0.1552, Acc.bench: 0.6370, Acc.countertop: 0.8314, Acc.stove: 0.8862, Acc.palm: 0.8385, Acc.kitchen island: 0.8646, Acc.computer: 0.9141, Acc.swivel chair: 0.7287, Acc.boat: 0.9158, Acc.bar: 0.8539, Acc.arcade machine: 0.7841, Acc.hovel: 0.3780, Acc.bus: 0.9665, Acc.towel: 0.8949, Acc.light: 0.7274, Acc.truck: 0.5950, Acc.tower: 0.5257, Acc.chandelier: 0.8577, Acc.awning: 0.6810, Acc.streetlight: 0.4757, Acc.booth: 0.6618, Acc.television receiver: 0.8864, Acc.airplane: 0.8891, Acc.dirt track: 0.4768, Acc.apparel: 0.6391, Acc.pole: 0.3724, Acc.land: 0.0478, Acc.bannister: 0.2532, Acc.escalator: 0.7825, Acc.ottoman: 0.6883, Acc.bottle: 0.6491, Acc.buffet: 0.5592, Acc.poster: 0.5331, Acc.stage: 0.4722, Acc.van: 0.6687, Acc.ship: 0.9243, Acc.fountain: 0.3814, Acc.conveyer belt: 0.9363, Acc.canopy: 0.7356, Acc.washer: 0.8600, Acc.plaything: 0.5862, Acc.swimming pool: 0.9268, Acc.stool: 0.7184, Acc.barrel: 0.7416, Acc.basket: 0.5711, Acc.waterfall: 0.8854, Acc.tent: 0.9827, Acc.bag: 0.2094, Acc.minibike: 0.8916, Acc.cradle: 0.9695, Acc.oven: 0.7524, Acc.ball: 0.5031, Acc.food: 0.7759, Acc.step: 0.1391, Acc.tank: 0.7442, Acc.trade name: 0.3539, Acc.microwave: 0.9589, Acc.pot: 0.6648, Acc.animal: 0.6023, Acc.bicycle: 0.7756, Acc.lake: 0.6383, Acc.dishwasher: 0.7938, Acc.screen: 0.7494, Acc.blanket: 0.3710, Acc.sculpture: 0.8813, Acc.hood: 0.7447, Acc.sconce: 0.7651, Acc.vase: 0.6277, Acc.traffic light: 0.6653, Acc.tray: 0.3259, Acc.ashcan: 0.6384, Acc.fan: 0.8309, Acc.pier: 0.4467, Acc.crt screen: 0.0545, Acc.plate: 0.8076, Acc.monitor: 0.7741, Acc.bulletin board: 0.7188, Acc.shower: 0.0469, Acc.radiator: 0.7752, Acc.glass: 0.2307, Acc.clock: 0.5758, Acc.flag: 0.7863 +2024-06-16 23:26:47,097 - mmseg - INFO - Iter [62050/80000] lr: 8.975e-06, eta: 7:31:24, time: 3.221, data_time: 1.863, memory: 70722, decode.loss_ce: 0.1498, decode.acc_seg: 93.4491, aux.loss_ce: 0.0642, aux.acc_seg: 93.0088, loss: 0.2141 +2024-06-16 23:27:55,126 - mmseg - INFO - Iter [62100/80000] lr: 8.951e-06, eta: 7:30:06, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1525, decode.acc_seg: 93.2891, aux.loss_ce: 0.0650, aux.acc_seg: 92.8603, loss: 0.2175 +2024-06-16 23:29:03,254 - mmseg - INFO - Iter [62150/80000] lr: 8.925e-06, eta: 7:28:49, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1522, decode.acc_seg: 93.3344, aux.loss_ce: 0.0653, aux.acc_seg: 92.8614, loss: 0.2174 +2024-06-16 23:30:11,481 - mmseg - INFO - Iter [62200/80000] lr: 8.901e-06, eta: 7:27:31, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1537, decode.acc_seg: 93.2951, aux.loss_ce: 0.0659, aux.acc_seg: 92.8251, loss: 0.2196 +2024-06-16 23:31:19,516 - mmseg - INFO - Iter [62250/80000] lr: 8.876e-06, eta: 7:26:14, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1592, decode.acc_seg: 93.1311, aux.loss_ce: 0.0675, aux.acc_seg: 92.7141, loss: 0.2266 +2024-06-16 23:32:27,691 - mmseg - INFO - Iter [62300/80000] lr: 8.851e-06, eta: 7:24:56, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1441, decode.acc_seg: 93.6264, aux.loss_ce: 0.0608, aux.acc_seg: 93.2747, loss: 0.2049 +2024-06-16 23:33:35,915 - mmseg - INFO - Iter [62350/80000] lr: 8.826e-06, eta: 7:23:39, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1506, decode.acc_seg: 93.3904, aux.loss_ce: 0.0641, aux.acc_seg: 92.9849, loss: 0.2146 +2024-06-16 23:34:43,987 - mmseg - INFO - Iter [62400/80000] lr: 8.801e-06, eta: 7:22:21, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1528, decode.acc_seg: 93.2753, aux.loss_ce: 0.0652, aux.acc_seg: 92.8613, loss: 0.2181 +2024-06-16 23:35:52,263 - mmseg - INFO - Iter [62450/80000] lr: 8.775e-06, eta: 7:21:04, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1586, decode.acc_seg: 93.0984, aux.loss_ce: 0.0677, aux.acc_seg: 92.7177, loss: 0.2263 +2024-06-16 23:37:00,459 - mmseg - INFO - Iter [62500/80000] lr: 8.751e-06, eta: 7:19:46, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1503, decode.acc_seg: 93.5056, aux.loss_ce: 0.0636, aux.acc_seg: 93.1636, loss: 0.2139 +2024-06-16 23:38:08,470 - mmseg - INFO - Iter [62550/80000] lr: 8.725e-06, eta: 7:18:29, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1497, decode.acc_seg: 93.5516, aux.loss_ce: 0.0640, aux.acc_seg: 93.1818, loss: 0.2137 +2024-06-16 23:39:16,920 - mmseg - INFO - Iter [62600/80000] lr: 8.701e-06, eta: 7:17:12, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1570, decode.acc_seg: 93.2443, aux.loss_ce: 0.0671, aux.acc_seg: 92.7600, loss: 0.2241 +2024-06-16 23:40:25,365 - mmseg - INFO - Iter [62650/80000] lr: 8.676e-06, eta: 7:15:54, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1498, decode.acc_seg: 93.4917, aux.loss_ce: 0.0640, aux.acc_seg: 93.0695, loss: 0.2139 +2024-06-16 23:41:33,539 - mmseg - INFO - Iter [62700/80000] lr: 8.651e-06, eta: 7:14:37, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1489, decode.acc_seg: 93.3881, aux.loss_ce: 0.0634, aux.acc_seg: 92.9877, loss: 0.2123 +2024-06-16 23:42:41,730 - mmseg - INFO - Iter [62750/80000] lr: 8.626e-06, eta: 7:13:20, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1623, decode.acc_seg: 93.1708, aux.loss_ce: 0.0691, aux.acc_seg: 92.7483, loss: 0.2314 +2024-06-16 23:43:49,899 - mmseg - INFO - Iter [62800/80000] lr: 8.601e-06, eta: 7:12:02, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1673, decode.acc_seg: 92.8529, aux.loss_ce: 0.0713, aux.acc_seg: 92.4225, loss: 0.2386 +2024-06-16 23:44:58,266 - mmseg - INFO - Iter [62850/80000] lr: 8.575e-06, eta: 7:10:45, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1506, decode.acc_seg: 93.4221, aux.loss_ce: 0.0639, aux.acc_seg: 93.0046, loss: 0.2145 +2024-06-16 23:46:06,434 - mmseg - INFO - Iter [62900/80000] lr: 8.550e-06, eta: 7:09:28, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1488, decode.acc_seg: 93.5321, aux.loss_ce: 0.0637, aux.acc_seg: 93.1489, loss: 0.2124 +2024-06-16 23:47:14,739 - mmseg - INFO - Iter [62950/80000] lr: 8.525e-06, eta: 7:08:11, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1605, decode.acc_seg: 93.2136, aux.loss_ce: 0.0688, aux.acc_seg: 92.7607, loss: 0.2293 +2024-06-16 23:48:23,072 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 23:48:23,072 - mmseg - INFO - Iter [63000/80000] lr: 8.501e-06, eta: 7:06:53, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1459, decode.acc_seg: 93.5999, aux.loss_ce: 0.0624, aux.acc_seg: 93.2181, loss: 0.2083 +2024-06-16 23:49:59,593 - mmseg - INFO - per class results: +2024-06-16 23:49:59,600 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.54 | 90.42 | +| building | 85.68 | 93.98 | +| sky | 95.07 | 97.76 | +| floor | 85.34 | 92.68 | +| tree | 77.11 | 89.33 | +| ceiling | 87.32 | 94.12 | +| road | 86.54 | 92.03 | +| bed | 93.15 | 97.27 | +| windowpane | 65.44 | 81.02 | +| grass | 69.18 | 82.98 | +| cabinet | 66.09 | 75.63 | +| sidewalk | 72.78 | 86.12 | +| person | 86.07 | 94.54 | +| earth | 36.66 | 48.06 | +| door | 58.91 | 72.73 | +| table | 70.01 | 80.67 | +| mountain | 62.51 | 79.35 | +| plant | 54.34 | 64.27 | +| curtain | 77.06 | 87.72 | +| chair | 69.1 | 81.43 | +| car | 87.83 | 93.8 | +| water | 63.0 | 77.88 | +| painting | 79.26 | 91.48 | +| sofa | 82.49 | 90.41 | +| shelf | 46.18 | 61.76 | +| house | 54.63 | 65.64 | +| sea | 69.58 | 83.61 | +| mirror | 77.97 | 83.04 | +| rug | 69.44 | 75.13 | +| field | 29.41 | 53.82 | +| armchair | 60.79 | 77.82 | +| seat | 67.46 | 88.82 | +| fence | 51.87 | 64.38 | +| desk | 61.77 | 80.25 | +| rock | 55.85 | 78.33 | +| wardrobe | 53.37 | 73.04 | +| lamp | 74.74 | 85.54 | +| bathtub | 84.76 | 86.74 | +| railing | 41.85 | 55.47 | +| cushion | 69.06 | 82.25 | +| base | 39.92 | 54.84 | +| box | 39.03 | 49.47 | +| column | 54.16 | 69.32 | +| signboard | 40.13 | 50.79 | +| chest of drawers | 44.97 | 70.23 | +| counter | 40.89 | 51.86 | +| sand | 57.74 | 84.25 | +| sink | 77.06 | 82.41 | +| skyscraper | 48.65 | 61.95 | +| fireplace | 74.73 | 92.04 | +| refrigerator | 85.79 | 92.16 | +| grandstand | 56.58 | 83.22 | +| path | 28.77 | 39.49 | +| stairs | 30.81 | 37.0 | +| runway | 72.11 | 94.26 | +| case | 60.87 | 80.8 | +| pool table | 94.47 | 97.49 | +| pillow | 65.59 | 75.03 | +| screen door | 77.68 | 79.56 | +| stairway | 44.23 | 60.5 | +| river | 12.75 | 26.3 | +| bridge | 66.28 | 73.37 | +| bookcase | 42.7 | 68.79 | +| blind | 45.01 | 50.76 | +| coffee table | 63.02 | 88.86 | +| toilet | 89.07 | 92.87 | +| flower | 44.99 | 55.11 | +| book | 54.39 | 75.59 | +| hill | 8.06 | 12.38 | +| bench | 52.35 | 61.95 | +| countertop | 65.32 | 85.57 | +| stove | 83.39 | 87.16 | +| palm | 56.37 | 80.5 | +| kitchen island | 51.66 | 87.5 | +| computer | 79.06 | 91.42 | +| swivel chair | 47.75 | 67.44 | +| boat | 71.81 | 92.4 | +| bar | 62.45 | 82.24 | +| arcade machine | 75.12 | 78.74 | +| hovel | 47.24 | 52.49 | +| bus | 92.9 | 96.22 | +| towel | 78.8 | 88.23 | +| light | 62.11 | 72.17 | +| truck | 43.42 | 57.17 | +| tower | 40.72 | 63.3 | +| chandelier | 70.61 | 84.47 | +| awning | 47.9 | 57.08 | +| streetlight | 34.1 | 46.45 | +| booth | 44.01 | 67.53 | +| television receiver | 74.38 | 89.18 | +| airplane | 81.44 | 88.03 | +| dirt track | 13.16 | 53.17 | +| apparel | 43.91 | 59.54 | +| pole | 25.6 | 35.09 | +| land | 3.55 | 5.76 | +| bannister | 17.84 | 26.3 | +| escalator | 62.43 | 77.93 | +| ottoman | 52.87 | 72.45 | +| bottle | 40.36 | 64.2 | +| buffet | 46.79 | 56.68 | +| poster | 42.09 | 51.43 | +| stage | 22.46 | 47.29 | +| van | 49.15 | 69.11 | +| ship | 86.39 | 96.19 | +| fountain | 33.62 | 34.26 | +| conveyer belt | 83.2 | 93.41 | +| canopy | 53.89 | 78.75 | +| washer | 80.56 | 85.1 | +| plaything | 28.54 | 42.91 | +| swimming pool | 61.39 | 90.5 | +| stool | 53.42 | 72.74 | +| barrel | 61.3 | 74.67 | +| basket | 43.3 | 64.88 | +| waterfall | 66.09 | 79.99 | +| tent | 96.79 | 98.46 | +| bag | 20.69 | 23.71 | +| minibike | 77.98 | 88.6 | +| cradle | 82.81 | 97.27 | +| oven | 62.78 | 74.13 | +| ball | 40.83 | 42.5 | +| food | 59.28 | 72.06 | +| step | 15.17 | 19.9 | +| tank | 72.97 | 81.0 | +| trade name | 30.58 | 36.21 | +| microwave | 88.77 | 96.22 | +| pot | 58.77 | 69.6 | +| animal | 59.78 | 61.66 | +| bicycle | 59.02 | 75.3 | +| lake | 53.12 | 63.82 | +| dishwasher | 71.93 | 82.16 | +| screen | 56.81 | 87.48 | +| blanket | 30.81 | 35.43 | +| sculpture | 73.98 | 86.19 | +| hood | 63.46 | 74.82 | +| sconce | 57.68 | 67.5 | +| vase | 49.78 | 66.7 | +| traffic light | 36.82 | 61.22 | +| tray | 26.35 | 36.78 | +| ashcan | 50.45 | 64.21 | +| fan | 70.78 | 80.6 | +| pier | 40.1 | 45.26 | +| crt screen | 2.49 | 3.44 | +| plate | 61.4 | 76.09 | +| monitor | 62.11 | 72.74 | +| bulletin board | 53.41 | 66.51 | +| shower | 2.44 | 2.47 | +| radiator | 67.34 | 78.6 | +| glass | 20.28 | 21.46 | +| clock | 46.55 | 58.4 | +| flag | 70.97 | 78.42 | ++---------------------+-------+-------+ +2024-06-16 23:49:59,600 - mmseg - INFO - Summary: +2024-06-16 23:49:59,600 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.28 | 57.69 | 70.26 | ++-------+-------+-------+ +2024-06-16 23:49:59,601 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 23:49:59,601 - mmseg - INFO - Iter(val) [250] aAcc: 0.8628, mIoU: 0.5769, mAcc: 0.7026, IoU.wall: 0.8254, IoU.building: 0.8568, IoU.sky: 0.9507, IoU.floor: 0.8534, IoU.tree: 0.7711, IoU.ceiling: 0.8732, IoU.road: 0.8654, IoU.bed : 0.9315, IoU.windowpane: 0.6544, IoU.grass: 0.6918, IoU.cabinet: 0.6609, IoU.sidewalk: 0.7278, IoU.person: 0.8607, IoU.earth: 0.3666, IoU.door: 0.5891, IoU.table: 0.7001, IoU.mountain: 0.6251, IoU.plant: 0.5434, IoU.curtain: 0.7706, IoU.chair: 0.6910, IoU.car: 0.8783, IoU.water: 0.6300, IoU.painting: 0.7926, IoU.sofa: 0.8249, IoU.shelf: 0.4618, IoU.house: 0.5463, IoU.sea: 0.6958, IoU.mirror: 0.7797, IoU.rug: 0.6944, IoU.field: 0.2941, IoU.armchair: 0.6079, IoU.seat: 0.6746, IoU.fence: 0.5187, IoU.desk: 0.6177, IoU.rock: 0.5585, IoU.wardrobe: 0.5337, IoU.lamp: 0.7474, IoU.bathtub: 0.8476, IoU.railing: 0.4185, IoU.cushion: 0.6906, IoU.base: 0.3992, IoU.box: 0.3903, IoU.column: 0.5416, IoU.signboard: 0.4013, IoU.chest of drawers: 0.4497, IoU.counter: 0.4089, IoU.sand: 0.5774, IoU.sink: 0.7706, IoU.skyscraper: 0.4865, IoU.fireplace: 0.7473, IoU.refrigerator: 0.8579, IoU.grandstand: 0.5658, IoU.path: 0.2877, IoU.stairs: 0.3081, IoU.runway: 0.7211, IoU.case: 0.6087, IoU.pool table: 0.9447, IoU.pillow: 0.6559, IoU.screen door: 0.7768, IoU.stairway: 0.4423, IoU.river: 0.1275, IoU.bridge: 0.6628, IoU.bookcase: 0.4270, IoU.blind: 0.4501, IoU.coffee table: 0.6302, IoU.toilet: 0.8907, IoU.flower: 0.4499, IoU.book: 0.5439, IoU.hill: 0.0806, IoU.bench: 0.5235, IoU.countertop: 0.6532, IoU.stove: 0.8339, IoU.palm: 0.5637, IoU.kitchen island: 0.5166, IoU.computer: 0.7906, IoU.swivel chair: 0.4775, IoU.boat: 0.7181, IoU.bar: 0.6245, IoU.arcade machine: 0.7512, IoU.hovel: 0.4724, IoU.bus: 0.9290, IoU.towel: 0.7880, IoU.light: 0.6211, IoU.truck: 0.4342, IoU.tower: 0.4072, IoU.chandelier: 0.7061, IoU.awning: 0.4790, IoU.streetlight: 0.3410, IoU.booth: 0.4401, IoU.television receiver: 0.7438, IoU.airplane: 0.8144, IoU.dirt track: 0.1316, IoU.apparel: 0.4391, IoU.pole: 0.2560, IoU.land: 0.0355, IoU.bannister: 0.1784, IoU.escalator: 0.6243, IoU.ottoman: 0.5287, IoU.bottle: 0.4036, IoU.buffet: 0.4679, IoU.poster: 0.4209, IoU.stage: 0.2246, IoU.van: 0.4915, IoU.ship: 0.8639, IoU.fountain: 0.3362, IoU.conveyer belt: 0.8320, IoU.canopy: 0.5389, IoU.washer: 0.8056, IoU.plaything: 0.2854, IoU.swimming pool: 0.6139, IoU.stool: 0.5342, IoU.barrel: 0.6130, IoU.basket: 0.4330, IoU.waterfall: 0.6609, IoU.tent: 0.9679, IoU.bag: 0.2069, IoU.minibike: 0.7798, IoU.cradle: 0.8281, IoU.oven: 0.6278, IoU.ball: 0.4083, IoU.food: 0.5928, IoU.step: 0.1517, IoU.tank: 0.7297, IoU.trade name: 0.3058, IoU.microwave: 0.8877, IoU.pot: 0.5877, IoU.animal: 0.5978, IoU.bicycle: 0.5902, IoU.lake: 0.5312, IoU.dishwasher: 0.7193, IoU.screen: 0.5681, IoU.blanket: 0.3081, IoU.sculpture: 0.7398, IoU.hood: 0.6346, IoU.sconce: 0.5768, IoU.vase: 0.4978, IoU.traffic light: 0.3682, IoU.tray: 0.2635, IoU.ashcan: 0.5045, IoU.fan: 0.7078, IoU.pier: 0.4010, IoU.crt screen: 0.0249, IoU.plate: 0.6140, IoU.monitor: 0.6211, IoU.bulletin board: 0.5341, IoU.shower: 0.0244, IoU.radiator: 0.6734, IoU.glass: 0.2028, IoU.clock: 0.4655, IoU.flag: 0.7097, Acc.wall: 0.9042, Acc.building: 0.9398, Acc.sky: 0.9776, Acc.floor: 0.9268, Acc.tree: 0.8933, Acc.ceiling: 0.9412, Acc.road: 0.9203, Acc.bed : 0.9727, Acc.windowpane: 0.8102, Acc.grass: 0.8298, Acc.cabinet: 0.7563, Acc.sidewalk: 0.8612, Acc.person: 0.9454, Acc.earth: 0.4806, Acc.door: 0.7273, Acc.table: 0.8067, Acc.mountain: 0.7935, Acc.plant: 0.6427, Acc.curtain: 0.8772, Acc.chair: 0.8143, Acc.car: 0.9380, Acc.water: 0.7788, Acc.painting: 0.9148, Acc.sofa: 0.9041, Acc.shelf: 0.6176, Acc.house: 0.6564, Acc.sea: 0.8361, Acc.mirror: 0.8304, Acc.rug: 0.7513, Acc.field: 0.5382, Acc.armchair: 0.7782, Acc.seat: 0.8882, Acc.fence: 0.6438, Acc.desk: 0.8025, Acc.rock: 0.7833, Acc.wardrobe: 0.7304, Acc.lamp: 0.8554, Acc.bathtub: 0.8674, Acc.railing: 0.5547, Acc.cushion: 0.8225, Acc.base: 0.5484, Acc.box: 0.4947, Acc.column: 0.6932, Acc.signboard: 0.5079, Acc.chest of drawers: 0.7023, Acc.counter: 0.5186, Acc.sand: 0.8425, Acc.sink: 0.8241, Acc.skyscraper: 0.6195, Acc.fireplace: 0.9204, Acc.refrigerator: 0.9216, Acc.grandstand: 0.8322, Acc.path: 0.3949, Acc.stairs: 0.3700, Acc.runway: 0.9426, Acc.case: 0.8080, Acc.pool table: 0.9749, Acc.pillow: 0.7503, Acc.screen door: 0.7956, Acc.stairway: 0.6050, Acc.river: 0.2630, Acc.bridge: 0.7337, Acc.bookcase: 0.6879, Acc.blind: 0.5076, Acc.coffee table: 0.8886, Acc.toilet: 0.9287, Acc.flower: 0.5511, Acc.book: 0.7559, Acc.hill: 0.1238, Acc.bench: 0.6195, Acc.countertop: 0.8557, Acc.stove: 0.8716, Acc.palm: 0.8050, Acc.kitchen island: 0.8750, Acc.computer: 0.9142, Acc.swivel chair: 0.6744, Acc.boat: 0.9240, Acc.bar: 0.8224, Acc.arcade machine: 0.7874, Acc.hovel: 0.5249, Acc.bus: 0.9622, Acc.towel: 0.8823, Acc.light: 0.7217, Acc.truck: 0.5717, Acc.tower: 0.6330, Acc.chandelier: 0.8447, Acc.awning: 0.5708, Acc.streetlight: 0.4645, Acc.booth: 0.6753, Acc.television receiver: 0.8918, Acc.airplane: 0.8803, Acc.dirt track: 0.5317, Acc.apparel: 0.5954, Acc.pole: 0.3509, Acc.land: 0.0576, Acc.bannister: 0.2630, Acc.escalator: 0.7793, Acc.ottoman: 0.7245, Acc.bottle: 0.6420, Acc.buffet: 0.5668, Acc.poster: 0.5143, Acc.stage: 0.4729, Acc.van: 0.6911, Acc.ship: 0.9619, Acc.fountain: 0.3426, Acc.conveyer belt: 0.9341, Acc.canopy: 0.7875, Acc.washer: 0.8510, Acc.plaything: 0.4291, Acc.swimming pool: 0.9050, Acc.stool: 0.7274, Acc.barrel: 0.7467, Acc.basket: 0.6488, Acc.waterfall: 0.7999, Acc.tent: 0.9846, Acc.bag: 0.2371, Acc.minibike: 0.8860, Acc.cradle: 0.9727, Acc.oven: 0.7413, Acc.ball: 0.4250, Acc.food: 0.7206, Acc.step: 0.1990, Acc.tank: 0.8100, Acc.trade name: 0.3621, Acc.microwave: 0.9622, Acc.pot: 0.6960, Acc.animal: 0.6166, Acc.bicycle: 0.7530, Acc.lake: 0.6382, Acc.dishwasher: 0.8216, Acc.screen: 0.8748, Acc.blanket: 0.3543, Acc.sculpture: 0.8619, Acc.hood: 0.7482, Acc.sconce: 0.6750, Acc.vase: 0.6670, Acc.traffic light: 0.6122, Acc.tray: 0.3678, Acc.ashcan: 0.6421, Acc.fan: 0.8060, Acc.pier: 0.4526, Acc.crt screen: 0.0344, Acc.plate: 0.7609, Acc.monitor: 0.7274, Acc.bulletin board: 0.6651, Acc.shower: 0.0247, Acc.radiator: 0.7860, Acc.glass: 0.2146, Acc.clock: 0.5840, Acc.flag: 0.7842 +2024-06-16 23:51:08,347 - mmseg - INFO - Iter [63050/80000] lr: 8.476e-06, eta: 7:06:02, time: 3.305, data_time: 1.947, memory: 70722, decode.loss_ce: 0.1494, decode.acc_seg: 93.4565, aux.loss_ce: 0.0635, aux.acc_seg: 93.0297, loss: 0.2129 +2024-06-16 23:52:16,641 - mmseg - INFO - Iter [63100/80000] lr: 8.451e-06, eta: 7:04:45, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1578, decode.acc_seg: 93.0181, aux.loss_ce: 0.0670, aux.acc_seg: 92.6323, loss: 0.2248 +2024-06-16 23:53:25,079 - mmseg - INFO - Iter [63150/80000] lr: 8.426e-06, eta: 7:03:28, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1480, decode.acc_seg: 93.4378, aux.loss_ce: 0.0636, aux.acc_seg: 92.9850, loss: 0.2116 +2024-06-16 23:54:35,546 - mmseg - INFO - Iter [63200/80000] lr: 8.401e-06, eta: 7:02:11, time: 1.409, data_time: 0.052, memory: 70722, decode.loss_ce: 0.1505, decode.acc_seg: 93.2897, aux.loss_ce: 0.0642, aux.acc_seg: 92.8909, loss: 0.2148 +2024-06-16 23:55:43,655 - mmseg - INFO - Iter [63250/80000] lr: 8.375e-06, eta: 7:00:54, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1516, decode.acc_seg: 93.5391, aux.loss_ce: 0.0645, aux.acc_seg: 93.1162, loss: 0.2161 +2024-06-16 23:56:51,840 - mmseg - INFO - Iter [63300/80000] lr: 8.350e-06, eta: 6:59:36, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1534, decode.acc_seg: 93.1329, aux.loss_ce: 0.0663, aux.acc_seg: 92.6705, loss: 0.2197 +2024-06-16 23:58:00,105 - mmseg - INFO - Iter [63350/80000] lr: 8.326e-06, eta: 6:58:19, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1474, decode.acc_seg: 93.5374, aux.loss_ce: 0.0631, aux.acc_seg: 93.1387, loss: 0.2105 +2024-06-16 23:59:08,179 - mmseg - INFO - Iter [63400/80000] lr: 8.300e-06, eta: 6:57:02, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1435, decode.acc_seg: 93.8541, aux.loss_ce: 0.0617, aux.acc_seg: 93.4011, loss: 0.2052 +2024-06-17 00:00:16,359 - mmseg - INFO - Iter [63450/80000] lr: 8.276e-06, eta: 6:55:44, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1514, decode.acc_seg: 93.5065, aux.loss_ce: 0.0646, aux.acc_seg: 93.1268, loss: 0.2160 +2024-06-17 00:01:24,507 - mmseg - INFO - Iter [63500/80000] lr: 8.251e-06, eta: 6:54:27, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1537, decode.acc_seg: 93.1682, aux.loss_ce: 0.0657, aux.acc_seg: 92.7585, loss: 0.2194 +2024-06-17 00:02:32,842 - mmseg - INFO - Iter [63550/80000] lr: 8.226e-06, eta: 6:53:10, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1448, decode.acc_seg: 93.5525, aux.loss_ce: 0.0624, aux.acc_seg: 93.0945, loss: 0.2072 +2024-06-17 00:03:41,047 - mmseg - INFO - Iter [63600/80000] lr: 8.201e-06, eta: 6:51:53, time: 1.364, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1426, decode.acc_seg: 93.6775, aux.loss_ce: 0.0610, aux.acc_seg: 93.2718, loss: 0.2036 +2024-06-17 00:04:49,375 - mmseg - INFO - Iter [63650/80000] lr: 8.176e-06, eta: 6:50:36, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1518, decode.acc_seg: 93.2976, aux.loss_ce: 0.0643, aux.acc_seg: 92.9122, loss: 0.2161 +2024-06-17 00:05:57,478 - mmseg - INFO - Iter [63700/80000] lr: 8.150e-06, eta: 6:49:19, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1539, decode.acc_seg: 93.3994, aux.loss_ce: 0.0655, aux.acc_seg: 93.0105, loss: 0.2195 +2024-06-17 00:07:05,660 - mmseg - INFO - Iter [63750/80000] lr: 8.125e-06, eta: 6:48:01, time: 1.364, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1550, decode.acc_seg: 93.1632, aux.loss_ce: 0.0665, aux.acc_seg: 92.7049, loss: 0.2215 +2024-06-17 00:08:14,021 - mmseg - INFO - Iter [63800/80000] lr: 8.100e-06, eta: 6:46:44, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1452, decode.acc_seg: 93.7967, aux.loss_ce: 0.0621, aux.acc_seg: 93.3666, loss: 0.2073 +2024-06-17 00:09:22,044 - mmseg - INFO - Iter [63850/80000] lr: 8.076e-06, eta: 6:45:27, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1572, decode.acc_seg: 93.0490, aux.loss_ce: 0.0674, aux.acc_seg: 92.5881, loss: 0.2246 +2024-06-17 00:10:30,185 - mmseg - INFO - Iter [63900/80000] lr: 8.051e-06, eta: 6:44:10, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1530, decode.acc_seg: 93.3488, aux.loss_ce: 0.0651, aux.acc_seg: 92.9701, loss: 0.2181 +2024-06-17 00:11:38,398 - mmseg - INFO - Iter [63950/80000] lr: 8.026e-06, eta: 6:42:53, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1478, decode.acc_seg: 93.4576, aux.loss_ce: 0.0631, aux.acc_seg: 93.0607, loss: 0.2109 +2024-06-17 00:12:46,606 - mmseg - INFO - Saving checkpoint at 64000 iterations +2024-06-17 00:14:14,970 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 00:14:14,971 - mmseg - INFO - Iter [64000/80000] lr: 8.001e-06, eta: 6:41:58, time: 3.131, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1523, decode.acc_seg: 93.3308, aux.loss_ce: 0.0646, aux.acc_seg: 92.9220, loss: 0.2169 +2024-06-17 00:15:50,476 - mmseg - INFO - per class results: +2024-06-17 00:15:50,482 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.64 | 89.86 | +| building | 85.93 | 93.77 | +| sky | 95.07 | 97.77 | +| floor | 85.71 | 92.08 | +| tree | 77.03 | 89.59 | +| ceiling | 87.65 | 93.93 | +| road | 86.49 | 91.48 | +| bed | 93.35 | 97.23 | +| windowpane | 64.87 | 81.24 | +| grass | 69.3 | 81.46 | +| cabinet | 66.22 | 76.36 | +| sidewalk | 72.76 | 86.44 | +| person | 86.27 | 94.36 | +| earth | 38.92 | 52.37 | +| door | 60.63 | 76.99 | +| table | 70.53 | 83.67 | +| mountain | 62.21 | 74.25 | +| plant | 54.86 | 64.49 | +| curtain | 76.29 | 88.02 | +| chair | 68.9 | 81.4 | +| car | 87.79 | 94.03 | +| water | 63.02 | 78.71 | +| painting | 76.91 | 93.05 | +| sofa | 80.39 | 89.83 | +| shelf | 45.27 | 59.85 | +| house | 58.89 | 70.85 | +| sea | 68.44 | 82.86 | +| mirror | 76.8 | 81.66 | +| rug | 73.2 | 81.52 | +| field | 30.5 | 57.38 | +| armchair | 59.1 | 79.44 | +| seat | 67.5 | 88.99 | +| fence | 51.71 | 64.18 | +| desk | 60.55 | 79.48 | +| rock | 56.83 | 85.37 | +| wardrobe | 54.61 | 75.13 | +| lamp | 75.29 | 87.03 | +| bathtub | 84.77 | 87.24 | +| railing | 42.41 | 57.1 | +| cushion | 66.02 | 75.02 | +| base | 44.16 | 63.0 | +| box | 38.19 | 49.18 | +| column | 56.51 | 75.51 | +| signboard | 41.77 | 53.3 | +| chest of drawers | 46.93 | 64.99 | +| counter | 39.78 | 50.06 | +| sand | 58.34 | 86.67 | +| sink | 76.6 | 84.27 | +| skyscraper | 49.31 | 61.44 | +| fireplace | 74.05 | 92.4 | +| refrigerator | 86.51 | 93.15 | +| grandstand | 53.92 | 85.36 | +| path | 30.93 | 47.66 | +| stairs | 32.3 | 38.93 | +| runway | 72.59 | 96.31 | +| case | 58.58 | 83.04 | +| pool table | 94.04 | 98.18 | +| pillow | 68.15 | 79.63 | +| screen door | 71.14 | 73.07 | +| stairway | 51.14 | 66.36 | +| river | 8.96 | 16.07 | +| bridge | 61.4 | 67.07 | +| bookcase | 43.85 | 62.89 | +| blind | 41.34 | 44.82 | +| coffee table | 66.62 | 89.11 | +| toilet | 89.61 | 93.67 | +| flower | 46.66 | 56.34 | +| book | 55.97 | 76.91 | +| hill | 8.93 | 17.44 | +| bench | 54.36 | 61.93 | +| countertop | 63.92 | 84.24 | +| stove | 83.35 | 88.85 | +| palm | 56.47 | 80.97 | +| kitchen island | 58.6 | 85.44 | +| computer | 79.27 | 90.2 | +| swivel chair | 50.2 | 75.36 | +| boat | 64.59 | 92.79 | +| bar | 60.56 | 84.32 | +| arcade machine | 79.3 | 84.04 | +| hovel | 45.67 | 51.6 | +| bus | 92.24 | 96.14 | +| towel | 77.15 | 87.1 | +| light | 62.61 | 76.62 | +| truck | 45.6 | 61.5 | +| tower | 37.53 | 60.01 | +| chandelier | 71.24 | 84.99 | +| awning | 53.33 | 71.88 | +| streetlight | 34.6 | 47.63 | +| booth | 47.71 | 66.06 | +| television receiver | 76.19 | 89.04 | +| airplane | 78.91 | 86.95 | +| dirt track | 11.74 | 55.99 | +| apparel | 45.9 | 57.38 | +| pole | 29.12 | 40.36 | +| land | 2.7 | 3.62 | +| bannister | 19.55 | 28.18 | +| escalator | 62.23 | 77.79 | +| ottoman | 49.66 | 66.68 | +| bottle | 41.61 | 69.21 | +| buffet | 47.98 | 56.72 | +| poster | 44.29 | 49.63 | +| stage | 23.53 | 42.69 | +| van | 49.78 | 69.47 | +| ship | 82.98 | 96.74 | +| fountain | 34.85 | 35.54 | +| conveyer belt | 82.29 | 94.26 | +| canopy | 53.39 | 76.35 | +| washer | 80.57 | 85.28 | +| plaything | 31.1 | 42.54 | +| swimming pool | 59.13 | 88.8 | +| stool | 56.11 | 70.26 | +| barrel | 58.92 | 74.71 | +| basket | 42.11 | 57.31 | +| waterfall | 55.97 | 68.64 | +| tent | 94.78 | 98.72 | +| bag | 20.82 | 22.22 | +| minibike | 77.41 | 89.09 | +| cradle | 83.03 | 97.84 | +| oven | 63.71 | 74.79 | +| ball | 53.68 | 66.19 | +| food | 57.87 | 71.09 | +| step | 14.63 | 18.53 | +| tank | 71.4 | 78.06 | +| trade name | 30.3 | 36.77 | +| microwave | 89.65 | 96.13 | +| pot | 58.66 | 67.65 | +| animal | 60.48 | 62.16 | +| bicycle | 60.13 | 78.87 | +| lake | 54.95 | 63.84 | +| dishwasher | 68.72 | 82.17 | +| screen | 57.41 | 88.33 | +| blanket | 30.45 | 34.63 | +| sculpture | 72.71 | 87.52 | +| hood | 63.52 | 76.14 | +| sconce | 59.54 | 71.23 | +| vase | 49.91 | 64.29 | +| traffic light | 39.36 | 61.39 | +| tray | 21.41 | 24.78 | +| ashcan | 49.77 | 63.01 | +| fan | 71.04 | 84.39 | +| pier | 41.32 | 47.31 | +| crt screen | 2.44 | 3.46 | +| plate | 61.86 | 78.43 | +| monitor | 59.13 | 71.88 | +| bulletin board | 52.66 | 70.9 | +| shower | 0.99 | 3.32 | +| radiator | 67.87 | 78.01 | +| glass | 20.92 | 22.66 | +| clock | 45.09 | 57.15 | +| flag | 71.58 | 77.7 | ++---------------------+-------+-------+ +2024-06-17 00:15:50,482 - mmseg - INFO - Summary: +2024-06-17 00:15:50,482 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.35 | 57.81 | 70.72 | ++-------+-------+-------+ +2024-06-17 00:15:50,483 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 00:15:50,483 - mmseg - INFO - Iter(val) [250] aAcc: 0.8635, mIoU: 0.5781, mAcc: 0.7072, IoU.wall: 0.8264, IoU.building: 0.8593, IoU.sky: 0.9507, IoU.floor: 0.8571, IoU.tree: 0.7703, IoU.ceiling: 0.8765, IoU.road: 0.8649, IoU.bed : 0.9335, IoU.windowpane: 0.6487, IoU.grass: 0.6930, IoU.cabinet: 0.6622, IoU.sidewalk: 0.7276, IoU.person: 0.8627, IoU.earth: 0.3892, IoU.door: 0.6063, IoU.table: 0.7053, IoU.mountain: 0.6221, IoU.plant: 0.5486, IoU.curtain: 0.7629, IoU.chair: 0.6890, IoU.car: 0.8779, IoU.water: 0.6302, IoU.painting: 0.7691, IoU.sofa: 0.8039, IoU.shelf: 0.4527, IoU.house: 0.5889, IoU.sea: 0.6844, IoU.mirror: 0.7680, IoU.rug: 0.7320, IoU.field: 0.3050, IoU.armchair: 0.5910, IoU.seat: 0.6750, IoU.fence: 0.5171, IoU.desk: 0.6055, IoU.rock: 0.5683, IoU.wardrobe: 0.5461, IoU.lamp: 0.7529, IoU.bathtub: 0.8477, IoU.railing: 0.4241, IoU.cushion: 0.6602, IoU.base: 0.4416, IoU.box: 0.3819, IoU.column: 0.5651, IoU.signboard: 0.4177, IoU.chest of drawers: 0.4693, IoU.counter: 0.3978, IoU.sand: 0.5834, IoU.sink: 0.7660, IoU.skyscraper: 0.4931, IoU.fireplace: 0.7405, IoU.refrigerator: 0.8651, IoU.grandstand: 0.5392, IoU.path: 0.3093, IoU.stairs: 0.3230, IoU.runway: 0.7259, IoU.case: 0.5858, IoU.pool table: 0.9404, IoU.pillow: 0.6815, IoU.screen door: 0.7114, IoU.stairway: 0.5114, IoU.river: 0.0896, IoU.bridge: 0.6140, IoU.bookcase: 0.4385, IoU.blind: 0.4134, IoU.coffee table: 0.6662, IoU.toilet: 0.8961, IoU.flower: 0.4666, IoU.book: 0.5597, IoU.hill: 0.0893, IoU.bench: 0.5436, IoU.countertop: 0.6392, IoU.stove: 0.8335, IoU.palm: 0.5647, IoU.kitchen island: 0.5860, IoU.computer: 0.7927, IoU.swivel chair: 0.5020, IoU.boat: 0.6459, IoU.bar: 0.6056, IoU.arcade machine: 0.7930, IoU.hovel: 0.4567, IoU.bus: 0.9224, IoU.towel: 0.7715, IoU.light: 0.6261, IoU.truck: 0.4560, IoU.tower: 0.3753, IoU.chandelier: 0.7124, IoU.awning: 0.5333, IoU.streetlight: 0.3460, IoU.booth: 0.4771, IoU.television receiver: 0.7619, IoU.airplane: 0.7891, IoU.dirt track: 0.1174, IoU.apparel: 0.4590, IoU.pole: 0.2912, IoU.land: 0.0270, IoU.bannister: 0.1955, IoU.escalator: 0.6223, IoU.ottoman: 0.4966, IoU.bottle: 0.4161, IoU.buffet: 0.4798, IoU.poster: 0.4429, IoU.stage: 0.2353, IoU.van: 0.4978, IoU.ship: 0.8298, IoU.fountain: 0.3485, IoU.conveyer belt: 0.8229, IoU.canopy: 0.5339, IoU.washer: 0.8057, IoU.plaything: 0.3110, IoU.swimming pool: 0.5913, IoU.stool: 0.5611, IoU.barrel: 0.5892, IoU.basket: 0.4211, IoU.waterfall: 0.5597, IoU.tent: 0.9478, IoU.bag: 0.2082, IoU.minibike: 0.7741, IoU.cradle: 0.8303, IoU.oven: 0.6371, IoU.ball: 0.5368, IoU.food: 0.5787, IoU.step: 0.1463, IoU.tank: 0.7140, IoU.trade name: 0.3030, IoU.microwave: 0.8965, IoU.pot: 0.5866, IoU.animal: 0.6048, IoU.bicycle: 0.6013, IoU.lake: 0.5495, IoU.dishwasher: 0.6872, IoU.screen: 0.5741, IoU.blanket: 0.3045, IoU.sculpture: 0.7271, IoU.hood: 0.6352, IoU.sconce: 0.5954, IoU.vase: 0.4991, IoU.traffic light: 0.3936, IoU.tray: 0.2141, IoU.ashcan: 0.4977, IoU.fan: 0.7104, IoU.pier: 0.4132, IoU.crt screen: 0.0244, IoU.plate: 0.6186, IoU.monitor: 0.5913, IoU.bulletin board: 0.5266, IoU.shower: 0.0099, IoU.radiator: 0.6787, IoU.glass: 0.2092, IoU.clock: 0.4509, IoU.flag: 0.7158, Acc.wall: 0.8986, Acc.building: 0.9377, Acc.sky: 0.9777, Acc.floor: 0.9208, Acc.tree: 0.8959, Acc.ceiling: 0.9393, Acc.road: 0.9148, Acc.bed : 0.9723, Acc.windowpane: 0.8124, Acc.grass: 0.8146, Acc.cabinet: 0.7636, Acc.sidewalk: 0.8644, Acc.person: 0.9436, Acc.earth: 0.5237, Acc.door: 0.7699, Acc.table: 0.8367, Acc.mountain: 0.7425, Acc.plant: 0.6449, Acc.curtain: 0.8802, Acc.chair: 0.8140, Acc.car: 0.9403, Acc.water: 0.7871, Acc.painting: 0.9305, Acc.sofa: 0.8983, Acc.shelf: 0.5985, Acc.house: 0.7085, Acc.sea: 0.8286, Acc.mirror: 0.8166, Acc.rug: 0.8152, Acc.field: 0.5738, Acc.armchair: 0.7944, Acc.seat: 0.8899, Acc.fence: 0.6418, Acc.desk: 0.7948, Acc.rock: 0.8537, Acc.wardrobe: 0.7513, Acc.lamp: 0.8703, Acc.bathtub: 0.8724, Acc.railing: 0.5710, Acc.cushion: 0.7502, Acc.base: 0.6300, Acc.box: 0.4918, Acc.column: 0.7551, Acc.signboard: 0.5330, Acc.chest of drawers: 0.6499, Acc.counter: 0.5006, Acc.sand: 0.8667, Acc.sink: 0.8427, Acc.skyscraper: 0.6144, Acc.fireplace: 0.9240, Acc.refrigerator: 0.9315, Acc.grandstand: 0.8536, Acc.path: 0.4766, Acc.stairs: 0.3893, Acc.runway: 0.9631, Acc.case: 0.8304, Acc.pool table: 0.9818, Acc.pillow: 0.7963, Acc.screen door: 0.7307, Acc.stairway: 0.6636, Acc.river: 0.1607, Acc.bridge: 0.6707, Acc.bookcase: 0.6289, Acc.blind: 0.4482, Acc.coffee table: 0.8911, Acc.toilet: 0.9367, Acc.flower: 0.5634, Acc.book: 0.7691, Acc.hill: 0.1744, Acc.bench: 0.6193, Acc.countertop: 0.8424, Acc.stove: 0.8885, Acc.palm: 0.8097, Acc.kitchen island: 0.8544, Acc.computer: 0.9020, Acc.swivel chair: 0.7536, Acc.boat: 0.9279, Acc.bar: 0.8432, Acc.arcade machine: 0.8404, Acc.hovel: 0.5160, Acc.bus: 0.9614, Acc.towel: 0.8710, Acc.light: 0.7662, Acc.truck: 0.6150, Acc.tower: 0.6001, Acc.chandelier: 0.8499, Acc.awning: 0.7188, Acc.streetlight: 0.4763, Acc.booth: 0.6606, Acc.television receiver: 0.8904, Acc.airplane: 0.8695, Acc.dirt track: 0.5599, Acc.apparel: 0.5738, Acc.pole: 0.4036, Acc.land: 0.0362, Acc.bannister: 0.2818, Acc.escalator: 0.7779, Acc.ottoman: 0.6668, Acc.bottle: 0.6921, Acc.buffet: 0.5672, Acc.poster: 0.4963, Acc.stage: 0.4269, Acc.van: 0.6947, Acc.ship: 0.9674, Acc.fountain: 0.3554, Acc.conveyer belt: 0.9426, Acc.canopy: 0.7635, Acc.washer: 0.8528, Acc.plaything: 0.4254, Acc.swimming pool: 0.8880, Acc.stool: 0.7026, Acc.barrel: 0.7471, Acc.basket: 0.5731, Acc.waterfall: 0.6864, Acc.tent: 0.9872, Acc.bag: 0.2222, Acc.minibike: 0.8909, Acc.cradle: 0.9784, Acc.oven: 0.7479, Acc.ball: 0.6619, Acc.food: 0.7109, Acc.step: 0.1853, Acc.tank: 0.7806, Acc.trade name: 0.3677, Acc.microwave: 0.9613, Acc.pot: 0.6765, Acc.animal: 0.6216, Acc.bicycle: 0.7887, Acc.lake: 0.6384, Acc.dishwasher: 0.8217, Acc.screen: 0.8833, Acc.blanket: 0.3463, Acc.sculpture: 0.8752, Acc.hood: 0.7614, Acc.sconce: 0.7123, Acc.vase: 0.6429, Acc.traffic light: 0.6139, Acc.tray: 0.2478, Acc.ashcan: 0.6301, Acc.fan: 0.8439, Acc.pier: 0.4731, Acc.crt screen: 0.0346, Acc.plate: 0.7843, Acc.monitor: 0.7188, Acc.bulletin board: 0.7090, Acc.shower: 0.0332, Acc.radiator: 0.7801, Acc.glass: 0.2266, Acc.clock: 0.5715, Acc.flag: 0.7770 +2024-06-17 00:16:59,808 - mmseg - INFO - Iter [64050/80000] lr: 7.976e-06, eta: 6:41:05, time: 3.297, data_time: 1.926, memory: 70722, decode.loss_ce: 0.1477, decode.acc_seg: 93.3153, aux.loss_ce: 0.0637, aux.acc_seg: 92.8100, loss: 0.2115 +2024-06-17 00:18:07,890 - mmseg - INFO - Iter [64100/80000] lr: 7.950e-06, eta: 6:39:48, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1483, decode.acc_seg: 93.4042, aux.loss_ce: 0.0636, aux.acc_seg: 92.9867, loss: 0.2119 +2024-06-17 00:19:16,119 - mmseg - INFO - Iter [64150/80000] lr: 7.925e-06, eta: 6:38:30, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1510, decode.acc_seg: 93.4336, aux.loss_ce: 0.0648, aux.acc_seg: 92.9504, loss: 0.2158 +2024-06-17 00:20:24,304 - mmseg - INFO - Iter [64200/80000] lr: 7.900e-06, eta: 6:37:13, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1480, decode.acc_seg: 93.5943, aux.loss_ce: 0.0637, aux.acc_seg: 93.1548, loss: 0.2117 +2024-06-17 00:21:32,237 - mmseg - INFO - Iter [64250/80000] lr: 7.876e-06, eta: 6:35:56, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1468, decode.acc_seg: 93.5627, aux.loss_ce: 0.0631, aux.acc_seg: 93.1784, loss: 0.2100 +2024-06-17 00:22:40,429 - mmseg - INFO - Iter [64300/80000] lr: 7.851e-06, eta: 6:34:39, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1438, decode.acc_seg: 93.5955, aux.loss_ce: 0.0617, aux.acc_seg: 93.1234, loss: 0.2055 +2024-06-17 00:23:48,519 - mmseg - INFO - Iter [64350/80000] lr: 7.826e-06, eta: 6:33:21, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1469, decode.acc_seg: 93.7069, aux.loss_ce: 0.0626, aux.acc_seg: 93.3364, loss: 0.2094 +2024-06-17 00:24:56,498 - mmseg - INFO - Iter [64400/80000] lr: 7.801e-06, eta: 6:32:04, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1532, decode.acc_seg: 93.3445, aux.loss_ce: 0.0654, aux.acc_seg: 92.9886, loss: 0.2186 +2024-06-17 00:26:07,607 - mmseg - INFO - Iter [64450/80000] lr: 7.776e-06, eta: 6:30:48, time: 1.422, data_time: 0.061, memory: 70722, decode.loss_ce: 0.1444, decode.acc_seg: 93.7891, aux.loss_ce: 0.0622, aux.acc_seg: 93.3482, loss: 0.2065 +2024-06-17 00:27:15,583 - mmseg - INFO - Iter [64500/80000] lr: 7.750e-06, eta: 6:29:31, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1407, decode.acc_seg: 93.8509, aux.loss_ce: 0.0599, aux.acc_seg: 93.4631, loss: 0.2005 +2024-06-17 00:28:23,672 - mmseg - INFO - Iter [64550/80000] lr: 7.725e-06, eta: 6:28:14, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1504, decode.acc_seg: 93.3909, aux.loss_ce: 0.0644, aux.acc_seg: 92.9441, loss: 0.2148 +2024-06-17 00:29:31,895 - mmseg - INFO - Iter [64600/80000] lr: 7.701e-06, eta: 6:26:56, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1447, decode.acc_seg: 93.8424, aux.loss_ce: 0.0623, aux.acc_seg: 93.4194, loss: 0.2070 +2024-06-17 00:30:40,046 - mmseg - INFO - Iter [64650/80000] lr: 7.675e-06, eta: 6:25:39, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1554, decode.acc_seg: 93.1947, aux.loss_ce: 0.0657, aux.acc_seg: 92.8355, loss: 0.2211 +2024-06-17 00:31:48,154 - mmseg - INFO - Iter [64700/80000] lr: 7.651e-06, eta: 6:24:22, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1509, decode.acc_seg: 93.3501, aux.loss_ce: 0.0650, aux.acc_seg: 92.8264, loss: 0.2160 +2024-06-17 00:32:56,188 - mmseg - INFO - Iter [64750/80000] lr: 7.626e-06, eta: 6:23:05, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1468, decode.acc_seg: 93.6309, aux.loss_ce: 0.0633, aux.acc_seg: 93.1775, loss: 0.2101 +2024-06-17 00:34:04,425 - mmseg - INFO - Iter [64800/80000] lr: 7.601e-06, eta: 6:21:48, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1365, decode.acc_seg: 93.9517, aux.loss_ce: 0.0583, aux.acc_seg: 93.5533, loss: 0.1948 +2024-06-17 00:35:12,570 - mmseg - INFO - Iter [64850/80000] lr: 7.576e-06, eta: 6:20:31, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1536, decode.acc_seg: 93.1652, aux.loss_ce: 0.0654, aux.acc_seg: 92.7862, loss: 0.2190 +2024-06-17 00:36:20,718 - mmseg - INFO - Iter [64900/80000] lr: 7.551e-06, eta: 6:19:14, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1455, decode.acc_seg: 93.6688, aux.loss_ce: 0.0620, aux.acc_seg: 93.2632, loss: 0.2075 +2024-06-17 00:37:28,927 - mmseg - INFO - Iter [64950/80000] lr: 7.525e-06, eta: 6:17:57, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1559, decode.acc_seg: 93.1083, aux.loss_ce: 0.0663, aux.acc_seg: 92.6821, loss: 0.2222 +2024-06-17 00:38:36,960 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 00:38:36,960 - mmseg - INFO - Iter [65000/80000] lr: 7.500e-06, eta: 6:16:40, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1540, decode.acc_seg: 93.1816, aux.loss_ce: 0.0659, aux.acc_seg: 92.7421, loss: 0.2199 +2024-06-17 00:40:25,326 - mmseg - INFO - per class results: +2024-06-17 00:40:25,332 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.53 | 89.93 | +| building | 86.45 | 93.87 | +| sky | 94.93 | 97.87 | +| floor | 85.74 | 93.12 | +| tree | 77.24 | 89.42 | +| ceiling | 87.28 | 94.59 | +| road | 86.92 | 91.8 | +| bed | 93.01 | 97.17 | +| windowpane | 65.44 | 79.94 | +| grass | 68.58 | 82.68 | +| cabinet | 66.5 | 76.58 | +| sidewalk | 73.38 | 87.08 | +| person | 86.25 | 93.6 | +| earth | 37.62 | 49.31 | +| door | 59.14 | 73.81 | +| table | 71.51 | 82.69 | +| mountain | 61.01 | 73.08 | +| plant | 55.37 | 64.34 | +| curtain | 76.16 | 89.97 | +| chair | 69.48 | 80.73 | +| car | 87.92 | 93.64 | +| water | 63.42 | 79.37 | +| painting | 77.98 | 92.36 | +| sofa | 83.38 | 91.85 | +| shelf | 45.22 | 58.76 | +| house | 61.05 | 72.98 | +| sea | 69.07 | 82.94 | +| mirror | 79.37 | 85.35 | +| rug | 72.04 | 80.09 | +| field | 34.2 | 66.45 | +| armchair | 61.42 | 77.18 | +| seat | 68.84 | 89.28 | +| fence | 52.49 | 63.83 | +| desk | 62.48 | 79.31 | +| rock | 53.44 | 79.68 | +| wardrobe | 53.86 | 71.73 | +| lamp | 74.85 | 85.16 | +| bathtub | 84.92 | 86.86 | +| railing | 43.47 | 62.66 | +| cushion | 68.36 | 78.05 | +| base | 40.05 | 63.89 | +| box | 37.59 | 47.79 | +| column | 57.05 | 73.79 | +| signboard | 41.32 | 57.26 | +| chest of drawers | 45.39 | 69.73 | +| counter | 37.56 | 46.79 | +| sand | 58.96 | 86.33 | +| sink | 75.84 | 85.42 | +| skyscraper | 49.18 | 60.72 | +| fireplace | 75.05 | 93.33 | +| refrigerator | 84.89 | 92.94 | +| grandstand | 53.69 | 86.49 | +| path | 34.71 | 46.36 | +| stairs | 29.48 | 37.21 | +| runway | 72.34 | 95.47 | +| case | 56.09 | 81.82 | +| pool table | 94.14 | 97.92 | +| pillow | 67.55 | 77.94 | +| screen door | 66.38 | 67.59 | +| stairway | 51.67 | 69.43 | +| river | 9.09 | 18.08 | +| bridge | 67.47 | 74.41 | +| bookcase | 45.22 | 64.56 | +| blind | 41.9 | 45.66 | +| coffee table | 67.96 | 87.79 | +| toilet | 89.94 | 93.16 | +| flower | 47.19 | 58.29 | +| book | 55.09 | 73.96 | +| hill | 7.91 | 13.69 | +| bench | 54.67 | 62.21 | +| countertop | 65.02 | 85.55 | +| stove | 83.02 | 88.29 | +| palm | 56.1 | 79.88 | +| kitchen island | 56.42 | 87.45 | +| computer | 79.75 | 91.13 | +| swivel chair | 51.07 | 74.64 | +| boat | 74.1 | 91.48 | +| bar | 60.48 | 81.64 | +| arcade machine | 76.78 | 81.29 | +| hovel | 42.24 | 46.01 | +| bus | 92.36 | 96.17 | +| towel | 75.07 | 84.94 | +| light | 61.89 | 73.43 | +| truck | 44.01 | 56.08 | +| tower | 47.3 | 65.72 | +| chandelier | 70.69 | 84.45 | +| awning | 52.8 | 71.72 | +| streetlight | 34.74 | 42.6 | +| booth | 51.81 | 68.14 | +| television receiver | 77.44 | 90.73 | +| airplane | 75.14 | 84.06 | +| dirt track | 5.82 | 24.38 | +| apparel | 48.48 | 67.91 | +| pole | 33.76 | 48.08 | +| land | 3.21 | 5.43 | +| bannister | 19.82 | 27.93 | +| escalator | 61.57 | 79.23 | +| ottoman | 51.44 | 68.75 | +| bottle | 42.59 | 72.65 | +| buffet | 48.6 | 60.84 | +| poster | 40.39 | 56.91 | +| stage | 23.88 | 45.72 | +| van | 49.01 | 67.46 | +| ship | 83.42 | 97.43 | +| fountain | 38.0 | 39.03 | +| conveyer belt | 82.54 | 93.65 | +| canopy | 53.08 | 73.58 | +| washer | 80.85 | 85.2 | +| plaything | 33.07 | 52.42 | +| swimming pool | 53.56 | 77.32 | +| stool | 57.12 | 70.41 | +| barrel | 60.44 | 74.39 | +| basket | 39.9 | 57.92 | +| waterfall | 67.91 | 86.06 | +| tent | 96.94 | 98.23 | +| bag | 20.92 | 23.99 | +| minibike | 77.38 | 89.69 | +| cradle | 84.35 | 97.52 | +| oven | 63.11 | 76.55 | +| ball | 53.27 | 65.7 | +| food | 60.3 | 76.64 | +| step | 13.57 | 17.26 | +| tank | 66.94 | 73.77 | +| trade name | 22.23 | 25.45 | +| microwave | 89.55 | 95.72 | +| pot | 58.58 | 69.41 | +| animal | 59.72 | 61.35 | +| bicycle | 60.83 | 79.12 | +| lake | 55.2 | 63.84 | +| dishwasher | 69.35 | 78.25 | +| screen | 55.58 | 85.15 | +| blanket | 30.84 | 35.57 | +| sculpture | 70.5 | 88.42 | +| hood | 63.72 | 75.08 | +| sconce | 58.79 | 68.25 | +| vase | 49.53 | 64.42 | +| traffic light | 39.71 | 63.33 | +| tray | 23.92 | 30.43 | +| ashcan | 47.12 | 64.17 | +| fan | 71.56 | 82.1 | +| pier | 39.01 | 42.97 | +| crt screen | 2.29 | 3.46 | +| plate | 62.01 | 77.83 | +| monitor | 62.54 | 73.42 | +| bulletin board | 53.53 | 67.69 | +| shower | 1.13 | 4.56 | +| radiator | 69.17 | 77.08 | +| glass | 20.2 | 21.52 | +| clock | 47.71 | 55.42 | +| flag | 73.28 | 79.66 | ++---------------------+-------+-------+ +2024-06-17 00:40:25,332 - mmseg - INFO - Summary: +2024-06-17 00:40:25,332 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.43 | 57.99 | 70.67 | ++-------+-------+-------+ +2024-06-17 00:40:25,333 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 00:40:25,333 - mmseg - INFO - Iter(val) [250] aAcc: 0.8643, mIoU: 0.5799, mAcc: 0.7067, IoU.wall: 0.8253, IoU.building: 0.8645, IoU.sky: 0.9493, IoU.floor: 0.8574, IoU.tree: 0.7724, IoU.ceiling: 0.8728, IoU.road: 0.8692, IoU.bed : 0.9301, IoU.windowpane: 0.6544, IoU.grass: 0.6858, IoU.cabinet: 0.6650, IoU.sidewalk: 0.7338, IoU.person: 0.8625, IoU.earth: 0.3762, IoU.door: 0.5914, IoU.table: 0.7151, IoU.mountain: 0.6101, IoU.plant: 0.5537, IoU.curtain: 0.7616, IoU.chair: 0.6948, IoU.car: 0.8792, IoU.water: 0.6342, IoU.painting: 0.7798, IoU.sofa: 0.8338, IoU.shelf: 0.4522, IoU.house: 0.6105, IoU.sea: 0.6907, IoU.mirror: 0.7937, IoU.rug: 0.7204, IoU.field: 0.3420, IoU.armchair: 0.6142, IoU.seat: 0.6884, IoU.fence: 0.5249, IoU.desk: 0.6248, IoU.rock: 0.5344, IoU.wardrobe: 0.5386, IoU.lamp: 0.7485, IoU.bathtub: 0.8492, IoU.railing: 0.4347, IoU.cushion: 0.6836, IoU.base: 0.4005, IoU.box: 0.3759, IoU.column: 0.5705, IoU.signboard: 0.4132, IoU.chest of drawers: 0.4539, IoU.counter: 0.3756, IoU.sand: 0.5896, IoU.sink: 0.7584, IoU.skyscraper: 0.4918, IoU.fireplace: 0.7505, IoU.refrigerator: 0.8489, IoU.grandstand: 0.5369, IoU.path: 0.3471, IoU.stairs: 0.2948, IoU.runway: 0.7234, IoU.case: 0.5609, IoU.pool table: 0.9414, IoU.pillow: 0.6755, IoU.screen door: 0.6638, IoU.stairway: 0.5167, IoU.river: 0.0909, IoU.bridge: 0.6747, IoU.bookcase: 0.4522, IoU.blind: 0.4190, IoU.coffee table: 0.6796, IoU.toilet: 0.8994, IoU.flower: 0.4719, IoU.book: 0.5509, IoU.hill: 0.0791, IoU.bench: 0.5467, IoU.countertop: 0.6502, IoU.stove: 0.8302, IoU.palm: 0.5610, IoU.kitchen island: 0.5642, IoU.computer: 0.7975, IoU.swivel chair: 0.5107, IoU.boat: 0.7410, IoU.bar: 0.6048, IoU.arcade machine: 0.7678, IoU.hovel: 0.4224, IoU.bus: 0.9236, IoU.towel: 0.7507, IoU.light: 0.6189, IoU.truck: 0.4401, IoU.tower: 0.4730, IoU.chandelier: 0.7069, IoU.awning: 0.5280, IoU.streetlight: 0.3474, IoU.booth: 0.5181, IoU.television receiver: 0.7744, IoU.airplane: 0.7514, IoU.dirt track: 0.0582, IoU.apparel: 0.4848, IoU.pole: 0.3376, IoU.land: 0.0321, IoU.bannister: 0.1982, IoU.escalator: 0.6157, IoU.ottoman: 0.5144, IoU.bottle: 0.4259, IoU.buffet: 0.4860, IoU.poster: 0.4039, IoU.stage: 0.2388, IoU.van: 0.4901, IoU.ship: 0.8342, IoU.fountain: 0.3800, IoU.conveyer belt: 0.8254, IoU.canopy: 0.5308, IoU.washer: 0.8085, IoU.plaything: 0.3307, IoU.swimming pool: 0.5356, IoU.stool: 0.5712, IoU.barrel: 0.6044, IoU.basket: 0.3990, IoU.waterfall: 0.6791, IoU.tent: 0.9694, IoU.bag: 0.2092, IoU.minibike: 0.7738, IoU.cradle: 0.8435, IoU.oven: 0.6311, IoU.ball: 0.5327, IoU.food: 0.6030, IoU.step: 0.1357, IoU.tank: 0.6694, IoU.trade name: 0.2223, IoU.microwave: 0.8955, IoU.pot: 0.5858, IoU.animal: 0.5972, IoU.bicycle: 0.6083, IoU.lake: 0.5520, IoU.dishwasher: 0.6935, IoU.screen: 0.5558, IoU.blanket: 0.3084, IoU.sculpture: 0.7050, IoU.hood: 0.6372, IoU.sconce: 0.5879, IoU.vase: 0.4953, IoU.traffic light: 0.3971, IoU.tray: 0.2392, IoU.ashcan: 0.4712, IoU.fan: 0.7156, IoU.pier: 0.3901, IoU.crt screen: 0.0229, IoU.plate: 0.6201, IoU.monitor: 0.6254, IoU.bulletin board: 0.5353, IoU.shower: 0.0113, IoU.radiator: 0.6917, IoU.glass: 0.2020, IoU.clock: 0.4771, IoU.flag: 0.7328, Acc.wall: 0.8993, Acc.building: 0.9387, Acc.sky: 0.9787, Acc.floor: 0.9312, Acc.tree: 0.8942, Acc.ceiling: 0.9459, Acc.road: 0.9180, Acc.bed : 0.9717, Acc.windowpane: 0.7994, Acc.grass: 0.8268, Acc.cabinet: 0.7658, Acc.sidewalk: 0.8708, Acc.person: 0.9360, Acc.earth: 0.4931, Acc.door: 0.7381, Acc.table: 0.8269, Acc.mountain: 0.7308, Acc.plant: 0.6434, Acc.curtain: 0.8997, Acc.chair: 0.8073, Acc.car: 0.9364, Acc.water: 0.7937, Acc.painting: 0.9236, Acc.sofa: 0.9185, Acc.shelf: 0.5876, Acc.house: 0.7298, Acc.sea: 0.8294, Acc.mirror: 0.8535, Acc.rug: 0.8009, Acc.field: 0.6645, Acc.armchair: 0.7718, Acc.seat: 0.8928, Acc.fence: 0.6383, Acc.desk: 0.7931, Acc.rock: 0.7968, Acc.wardrobe: 0.7173, Acc.lamp: 0.8516, Acc.bathtub: 0.8686, Acc.railing: 0.6266, Acc.cushion: 0.7805, Acc.base: 0.6389, Acc.box: 0.4779, Acc.column: 0.7379, Acc.signboard: 0.5726, Acc.chest of drawers: 0.6973, Acc.counter: 0.4679, Acc.sand: 0.8633, Acc.sink: 0.8542, Acc.skyscraper: 0.6072, Acc.fireplace: 0.9333, Acc.refrigerator: 0.9294, Acc.grandstand: 0.8649, Acc.path: 0.4636, Acc.stairs: 0.3721, Acc.runway: 0.9547, Acc.case: 0.8182, Acc.pool table: 0.9792, Acc.pillow: 0.7794, Acc.screen door: 0.6759, Acc.stairway: 0.6943, Acc.river: 0.1808, Acc.bridge: 0.7441, Acc.bookcase: 0.6456, Acc.blind: 0.4566, Acc.coffee table: 0.8779, Acc.toilet: 0.9316, Acc.flower: 0.5829, Acc.book: 0.7396, Acc.hill: 0.1369, Acc.bench: 0.6221, Acc.countertop: 0.8555, Acc.stove: 0.8829, Acc.palm: 0.7988, Acc.kitchen island: 0.8745, Acc.computer: 0.9113, Acc.swivel chair: 0.7464, Acc.boat: 0.9148, Acc.bar: 0.8164, Acc.arcade machine: 0.8129, Acc.hovel: 0.4601, Acc.bus: 0.9617, Acc.towel: 0.8494, Acc.light: 0.7343, Acc.truck: 0.5608, Acc.tower: 0.6572, Acc.chandelier: 0.8445, Acc.awning: 0.7172, Acc.streetlight: 0.4260, Acc.booth: 0.6814, Acc.television receiver: 0.9073, Acc.airplane: 0.8406, Acc.dirt track: 0.2438, Acc.apparel: 0.6791, Acc.pole: 0.4808, Acc.land: 0.0543, Acc.bannister: 0.2793, Acc.escalator: 0.7923, Acc.ottoman: 0.6875, Acc.bottle: 0.7265, Acc.buffet: 0.6084, Acc.poster: 0.5691, Acc.stage: 0.4572, Acc.van: 0.6746, Acc.ship: 0.9743, Acc.fountain: 0.3903, Acc.conveyer belt: 0.9365, Acc.canopy: 0.7358, Acc.washer: 0.8520, Acc.plaything: 0.5242, Acc.swimming pool: 0.7732, Acc.stool: 0.7041, Acc.barrel: 0.7439, Acc.basket: 0.5792, Acc.waterfall: 0.8606, Acc.tent: 0.9823, Acc.bag: 0.2399, Acc.minibike: 0.8969, Acc.cradle: 0.9752, Acc.oven: 0.7655, Acc.ball: 0.6570, Acc.food: 0.7664, Acc.step: 0.1726, Acc.tank: 0.7377, Acc.trade name: 0.2545, Acc.microwave: 0.9572, Acc.pot: 0.6941, Acc.animal: 0.6135, Acc.bicycle: 0.7912, Acc.lake: 0.6384, Acc.dishwasher: 0.7825, Acc.screen: 0.8515, Acc.blanket: 0.3557, Acc.sculpture: 0.8842, Acc.hood: 0.7508, Acc.sconce: 0.6825, Acc.vase: 0.6442, Acc.traffic light: 0.6333, Acc.tray: 0.3043, Acc.ashcan: 0.6417, Acc.fan: 0.8210, Acc.pier: 0.4297, Acc.crt screen: 0.0346, Acc.plate: 0.7783, Acc.monitor: 0.7342, Acc.bulletin board: 0.6769, Acc.shower: 0.0456, Acc.radiator: 0.7708, Acc.glass: 0.2152, Acc.clock: 0.5542, Acc.flag: 0.7966 +2024-06-17 00:41:33,801 - mmseg - INFO - Iter [65050/80000] lr: 7.475e-06, eta: 6:15:48, time: 3.537, data_time: 2.184, memory: 70722, decode.loss_ce: 0.1522, decode.acc_seg: 93.2061, aux.loss_ce: 0.0650, aux.acc_seg: 92.7778, loss: 0.2172 +2024-06-17 00:42:41,893 - mmseg - INFO - Iter [65100/80000] lr: 7.451e-06, eta: 6:14:31, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1451, decode.acc_seg: 93.4507, aux.loss_ce: 0.0622, aux.acc_seg: 93.0700, loss: 0.2073 +2024-06-17 00:43:50,083 - mmseg - INFO - Iter [65150/80000] lr: 7.426e-06, eta: 6:13:14, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1495, decode.acc_seg: 93.2045, aux.loss_ce: 0.0639, aux.acc_seg: 92.8016, loss: 0.2133 +2024-06-17 00:44:58,270 - mmseg - INFO - Iter [65200/80000] lr: 7.401e-06, eta: 6:11:57, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1490, decode.acc_seg: 93.4745, aux.loss_ce: 0.0635, aux.acc_seg: 93.0330, loss: 0.2125 +2024-06-17 00:46:06,298 - mmseg - INFO - Iter [65250/80000] lr: 7.376e-06, eta: 6:10:40, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1498, decode.acc_seg: 93.4555, aux.loss_ce: 0.0642, aux.acc_seg: 93.0121, loss: 0.2139 +2024-06-17 00:47:14,543 - mmseg - INFO - Iter [65300/80000] lr: 7.351e-06, eta: 6:09:23, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1477, decode.acc_seg: 93.6626, aux.loss_ce: 0.0630, aux.acc_seg: 93.1987, loss: 0.2107 +2024-06-17 00:48:22,706 - mmseg - INFO - Iter [65350/80000] lr: 7.325e-06, eta: 6:08:06, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1496, decode.acc_seg: 93.4214, aux.loss_ce: 0.0639, aux.acc_seg: 92.9778, loss: 0.2135 +2024-06-17 00:49:30,696 - mmseg - INFO - Iter [65400/80000] lr: 7.300e-06, eta: 6:06:49, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1549, decode.acc_seg: 93.2394, aux.loss_ce: 0.0666, aux.acc_seg: 92.7827, loss: 0.2215 +2024-06-17 00:50:39,007 - mmseg - INFO - Iter [65450/80000] lr: 7.276e-06, eta: 6:05:32, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1401, decode.acc_seg: 93.7307, aux.loss_ce: 0.0601, aux.acc_seg: 93.2895, loss: 0.2002 +2024-06-17 00:51:47,283 - mmseg - INFO - Iter [65500/80000] lr: 7.251e-06, eta: 6:04:15, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1492, decode.acc_seg: 93.2481, aux.loss_ce: 0.0638, aux.acc_seg: 92.7953, loss: 0.2129 +2024-06-17 00:52:55,371 - mmseg - INFO - Iter [65550/80000] lr: 7.226e-06, eta: 6:02:58, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1502, decode.acc_seg: 93.4595, aux.loss_ce: 0.0640, aux.acc_seg: 93.1142, loss: 0.2142 +2024-06-17 00:54:03,345 - mmseg - INFO - Iter [65600/80000] lr: 7.201e-06, eta: 6:01:41, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1410, decode.acc_seg: 93.7444, aux.loss_ce: 0.0604, aux.acc_seg: 93.3179, loss: 0.2014 +2024-06-17 00:55:11,551 - mmseg - INFO - Iter [65650/80000] lr: 7.176e-06, eta: 6:00:24, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1476, decode.acc_seg: 93.5312, aux.loss_ce: 0.0638, aux.acc_seg: 93.0152, loss: 0.2114 +2024-06-17 00:56:22,076 - mmseg - INFO - Iter [65700/80000] lr: 7.151e-06, eta: 5:59:08, time: 1.410, data_time: 0.051, memory: 70722, decode.loss_ce: 0.1529, decode.acc_seg: 93.3319, aux.loss_ce: 0.0651, aux.acc_seg: 92.8974, loss: 0.2180 +2024-06-17 00:57:30,170 - mmseg - INFO - Iter [65750/80000] lr: 7.125e-06, eta: 5:57:51, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1455, decode.acc_seg: 93.5642, aux.loss_ce: 0.0624, aux.acc_seg: 93.1282, loss: 0.2079 +2024-06-17 00:58:38,308 - mmseg - INFO - Iter [65800/80000] lr: 7.100e-06, eta: 5:56:34, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1436, decode.acc_seg: 93.7199, aux.loss_ce: 0.0613, aux.acc_seg: 93.3465, loss: 0.2049 +2024-06-17 00:59:46,411 - mmseg - INFO - Iter [65850/80000] lr: 7.075e-06, eta: 5:55:17, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1431, decode.acc_seg: 93.7271, aux.loss_ce: 0.0614, aux.acc_seg: 93.2750, loss: 0.2045 +2024-06-17 01:00:54,636 - mmseg - INFO - Iter [65900/80000] lr: 7.051e-06, eta: 5:54:00, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1486, decode.acc_seg: 93.4973, aux.loss_ce: 0.0637, aux.acc_seg: 93.0835, loss: 0.2123 +2024-06-17 01:02:02,735 - mmseg - INFO - Iter [65950/80000] lr: 7.026e-06, eta: 5:52:43, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1477, decode.acc_seg: 93.5637, aux.loss_ce: 0.0637, aux.acc_seg: 93.0723, loss: 0.2114 +2024-06-17 01:03:11,212 - mmseg - INFO - Saving checkpoint at 66000 iterations +2024-06-17 01:04:39,313 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 01:04:39,313 - mmseg - INFO - Iter [66000/80000] lr: 7.001e-06, eta: 5:51:45, time: 3.132, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1468, decode.acc_seg: 93.4322, aux.loss_ce: 0.0629, aux.acc_seg: 93.1026, loss: 0.2097 +2024-06-17 01:06:16,650 - mmseg - INFO - per class results: +2024-06-17 01:06:16,656 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.51 | 90.35 | +| building | 86.33 | 93.55 | +| sky | 95.01 | 97.59 | +| floor | 85.49 | 92.31 | +| tree | 77.45 | 89.71 | +| ceiling | 87.32 | 93.78 | +| road | 86.84 | 91.9 | +| bed | 93.18 | 96.68 | +| windowpane | 66.28 | 80.42 | +| grass | 67.97 | 82.85 | +| cabinet | 66.23 | 77.62 | +| sidewalk | 73.21 | 85.95 | +| person | 86.07 | 94.53 | +| earth | 37.0 | 49.33 | +| door | 58.75 | 75.38 | +| table | 71.03 | 84.15 | +| mountain | 60.39 | 70.7 | +| plant | 56.8 | 66.38 | +| curtain | 76.36 | 88.49 | +| chair | 69.31 | 81.25 | +| car | 87.83 | 93.77 | +| water | 62.02 | 76.81 | +| painting | 77.36 | 90.9 | +| sofa | 82.81 | 92.03 | +| shelf | 45.18 | 62.24 | +| house | 60.68 | 75.86 | +| sea | 68.58 | 82.46 | +| mirror | 79.68 | 84.86 | +| rug | 70.38 | 77.96 | +| field | 33.39 | 66.53 | +| armchair | 61.89 | 78.25 | +| seat | 68.74 | 89.32 | +| fence | 51.62 | 67.1 | +| desk | 61.71 | 76.89 | +| rock | 57.11 | 85.91 | +| wardrobe | 52.4 | 70.91 | +| lamp | 76.03 | 86.72 | +| bathtub | 84.82 | 87.54 | +| railing | 43.51 | 61.07 | +| cushion | 67.92 | 75.82 | +| base | 41.07 | 59.55 | +| box | 39.05 | 52.62 | +| column | 53.26 | 65.04 | +| signboard | 41.73 | 57.28 | +| chest of drawers | 46.01 | 67.97 | +| counter | 37.76 | 45.35 | +| sand | 58.37 | 84.33 | +| sink | 78.81 | 84.03 | +| skyscraper | 49.34 | 60.91 | +| fireplace | 75.81 | 91.1 | +| refrigerator | 81.68 | 86.66 | +| grandstand | 53.99 | 84.27 | +| path | 32.55 | 45.48 | +| stairs | 26.85 | 32.25 | +| runway | 71.03 | 94.84 | +| case | 57.14 | 81.71 | +| pool table | 94.35 | 98.35 | +| pillow | 68.93 | 81.29 | +| screen door | 63.82 | 64.94 | +| stairway | 46.54 | 65.13 | +| river | 8.48 | 18.76 | +| bridge | 64.72 | 71.64 | +| bookcase | 48.17 | 67.54 | +| blind | 47.02 | 52.88 | +| coffee table | 67.82 | 87.12 | +| toilet | 90.41 | 94.27 | +| flower | 45.72 | 54.65 | +| book | 55.26 | 77.78 | +| hill | 8.13 | 13.92 | +| bench | 53.92 | 61.25 | +| countertop | 63.76 | 84.04 | +| stove | 83.59 | 89.5 | +| palm | 55.62 | 78.85 | +| kitchen island | 60.07 | 83.26 | +| computer | 78.6 | 91.67 | +| swivel chair | 47.23 | 68.2 | +| boat | 79.11 | 91.41 | +| bar | 56.87 | 78.43 | +| arcade machine | 79.24 | 84.09 | +| hovel | 37.72 | 40.81 | +| bus | 91.84 | 96.48 | +| towel | 76.51 | 87.11 | +| light | 62.4 | 73.64 | +| truck | 44.73 | 61.73 | +| tower | 45.86 | 67.01 | +| chandelier | 73.08 | 85.0 | +| awning | 52.78 | 74.42 | +| streetlight | 35.19 | 46.45 | +| booth | 46.11 | 64.13 | +| television receiver | 77.74 | 88.85 | +| airplane | 79.43 | 88.92 | +| dirt track | 10.6 | 46.56 | +| apparel | 44.46 | 60.46 | +| pole | 28.7 | 38.67 | +| land | 2.35 | 3.57 | +| bannister | 20.49 | 28.27 | +| escalator | 60.41 | 79.36 | +| ottoman | 49.24 | 67.13 | +| bottle | 40.7 | 65.25 | +| buffet | 48.18 | 57.66 | +| poster | 38.85 | 56.05 | +| stage | 22.68 | 40.72 | +| van | 48.67 | 67.18 | +| ship | 82.24 | 89.42 | +| fountain | 33.63 | 34.16 | +| conveyer belt | 84.11 | 93.19 | +| canopy | 52.05 | 77.09 | +| washer | 80.55 | 85.35 | +| plaything | 30.17 | 44.68 | +| swimming pool | 61.51 | 93.37 | +| stool | 57.24 | 70.19 | +| barrel | 57.87 | 74.51 | +| basket | 39.33 | 57.22 | +| waterfall | 62.15 | 75.67 | +| tent | 95.26 | 98.61 | +| bag | 20.16 | 22.44 | +| minibike | 77.8 | 90.33 | +| cradle | 83.7 | 97.53 | +| oven | 67.42 | 76.74 | +| ball | 44.48 | 51.01 | +| food | 57.45 | 71.27 | +| step | 15.93 | 19.31 | +| tank | 62.74 | 66.8 | +| trade name | 28.09 | 34.08 | +| microwave | 90.69 | 95.83 | +| pot | 58.58 | 67.79 | +| animal | 59.05 | 60.65 | +| bicycle | 60.43 | 77.81 | +| lake | 53.75 | 63.79 | +| dishwasher | 71.39 | 79.45 | +| screen | 57.67 | 86.71 | +| blanket | 31.09 | 35.94 | +| sculpture | 72.15 | 87.97 | +| hood | 63.54 | 74.1 | +| sconce | 59.49 | 70.56 | +| vase | 49.57 | 63.1 | +| traffic light | 39.51 | 61.31 | +| tray | 25.32 | 31.49 | +| ashcan | 49.35 | 65.61 | +| fan | 70.88 | 82.6 | +| pier | 40.26 | 44.78 | +| crt screen | 2.47 | 3.42 | +| plate | 62.11 | 79.5 | +| monitor | 62.5 | 72.13 | +| bulletin board | 53.31 | 63.9 | +| shower | 6.79 | 7.62 | +| radiator | 68.3 | 77.29 | +| glass | 21.18 | 22.95 | +| clock | 48.14 | 57.53 | +| flag | 73.33 | 81.08 | ++---------------------+-------+-------+ +2024-06-17 01:06:16,656 - mmseg - INFO - Summary: +2024-06-17 01:06:16,656 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.34 | 57.82 | 70.28 | ++-------+-------+-------+ +2024-06-17 01:06:16,657 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 01:06:16,658 - mmseg - INFO - Iter(val) [250] aAcc: 0.8634, mIoU: 0.5782, mAcc: 0.7028, IoU.wall: 0.8251, IoU.building: 0.8633, IoU.sky: 0.9501, IoU.floor: 0.8549, IoU.tree: 0.7745, IoU.ceiling: 0.8732, IoU.road: 0.8684, IoU.bed : 0.9318, IoU.windowpane: 0.6628, IoU.grass: 0.6797, IoU.cabinet: 0.6623, IoU.sidewalk: 0.7321, IoU.person: 0.8607, IoU.earth: 0.3700, IoU.door: 0.5875, IoU.table: 0.7103, IoU.mountain: 0.6039, IoU.plant: 0.5680, IoU.curtain: 0.7636, IoU.chair: 0.6931, IoU.car: 0.8783, IoU.water: 0.6202, IoU.painting: 0.7736, IoU.sofa: 0.8281, IoU.shelf: 0.4518, IoU.house: 0.6068, IoU.sea: 0.6858, IoU.mirror: 0.7968, IoU.rug: 0.7038, IoU.field: 0.3339, IoU.armchair: 0.6189, IoU.seat: 0.6874, IoU.fence: 0.5162, IoU.desk: 0.6171, IoU.rock: 0.5711, IoU.wardrobe: 0.5240, IoU.lamp: 0.7603, IoU.bathtub: 0.8482, IoU.railing: 0.4351, IoU.cushion: 0.6792, IoU.base: 0.4107, IoU.box: 0.3905, IoU.column: 0.5326, IoU.signboard: 0.4173, IoU.chest of drawers: 0.4601, IoU.counter: 0.3776, IoU.sand: 0.5837, IoU.sink: 0.7881, IoU.skyscraper: 0.4934, IoU.fireplace: 0.7581, IoU.refrigerator: 0.8168, IoU.grandstand: 0.5399, IoU.path: 0.3255, IoU.stairs: 0.2685, IoU.runway: 0.7103, IoU.case: 0.5714, IoU.pool table: 0.9435, IoU.pillow: 0.6893, IoU.screen door: 0.6382, IoU.stairway: 0.4654, IoU.river: 0.0848, IoU.bridge: 0.6472, IoU.bookcase: 0.4817, IoU.blind: 0.4702, IoU.coffee table: 0.6782, IoU.toilet: 0.9041, IoU.flower: 0.4572, IoU.book: 0.5526, IoU.hill: 0.0813, IoU.bench: 0.5392, IoU.countertop: 0.6376, IoU.stove: 0.8359, IoU.palm: 0.5562, IoU.kitchen island: 0.6007, IoU.computer: 0.7860, IoU.swivel chair: 0.4723, IoU.boat: 0.7911, IoU.bar: 0.5687, IoU.arcade machine: 0.7924, IoU.hovel: 0.3772, IoU.bus: 0.9184, IoU.towel: 0.7651, IoU.light: 0.6240, IoU.truck: 0.4473, IoU.tower: 0.4586, IoU.chandelier: 0.7308, IoU.awning: 0.5278, IoU.streetlight: 0.3519, IoU.booth: 0.4611, IoU.television receiver: 0.7774, IoU.airplane: 0.7943, IoU.dirt track: 0.1060, IoU.apparel: 0.4446, IoU.pole: 0.2870, IoU.land: 0.0235, IoU.bannister: 0.2049, IoU.escalator: 0.6041, IoU.ottoman: 0.4924, IoU.bottle: 0.4070, IoU.buffet: 0.4818, IoU.poster: 0.3885, IoU.stage: 0.2268, IoU.van: 0.4867, IoU.ship: 0.8224, IoU.fountain: 0.3363, IoU.conveyer belt: 0.8411, IoU.canopy: 0.5205, IoU.washer: 0.8055, IoU.plaything: 0.3017, IoU.swimming pool: 0.6151, IoU.stool: 0.5724, IoU.barrel: 0.5787, IoU.basket: 0.3933, IoU.waterfall: 0.6215, IoU.tent: 0.9526, IoU.bag: 0.2016, IoU.minibike: 0.7780, IoU.cradle: 0.8370, IoU.oven: 0.6742, IoU.ball: 0.4448, IoU.food: 0.5745, IoU.step: 0.1593, IoU.tank: 0.6274, IoU.trade name: 0.2809, IoU.microwave: 0.9069, IoU.pot: 0.5858, IoU.animal: 0.5905, IoU.bicycle: 0.6043, IoU.lake: 0.5375, IoU.dishwasher: 0.7139, IoU.screen: 0.5767, IoU.blanket: 0.3109, IoU.sculpture: 0.7215, IoU.hood: 0.6354, IoU.sconce: 0.5949, IoU.vase: 0.4957, IoU.traffic light: 0.3951, IoU.tray: 0.2532, IoU.ashcan: 0.4935, IoU.fan: 0.7088, IoU.pier: 0.4026, IoU.crt screen: 0.0247, IoU.plate: 0.6211, IoU.monitor: 0.6250, IoU.bulletin board: 0.5331, IoU.shower: 0.0679, IoU.radiator: 0.6830, IoU.glass: 0.2118, IoU.clock: 0.4814, IoU.flag: 0.7333, Acc.wall: 0.9035, Acc.building: 0.9355, Acc.sky: 0.9759, Acc.floor: 0.9231, Acc.tree: 0.8971, Acc.ceiling: 0.9378, Acc.road: 0.9190, Acc.bed : 0.9668, Acc.windowpane: 0.8042, Acc.grass: 0.8285, Acc.cabinet: 0.7762, Acc.sidewalk: 0.8595, Acc.person: 0.9453, Acc.earth: 0.4933, Acc.door: 0.7538, Acc.table: 0.8415, Acc.mountain: 0.7070, Acc.plant: 0.6638, Acc.curtain: 0.8849, Acc.chair: 0.8125, Acc.car: 0.9377, Acc.water: 0.7681, Acc.painting: 0.9090, Acc.sofa: 0.9203, Acc.shelf: 0.6224, Acc.house: 0.7586, Acc.sea: 0.8246, Acc.mirror: 0.8486, Acc.rug: 0.7796, Acc.field: 0.6653, Acc.armchair: 0.7825, Acc.seat: 0.8932, Acc.fence: 0.6710, Acc.desk: 0.7689, Acc.rock: 0.8591, Acc.wardrobe: 0.7091, Acc.lamp: 0.8672, Acc.bathtub: 0.8754, Acc.railing: 0.6107, Acc.cushion: 0.7582, Acc.base: 0.5955, Acc.box: 0.5262, Acc.column: 0.6504, Acc.signboard: 0.5728, Acc.chest of drawers: 0.6797, Acc.counter: 0.4535, Acc.sand: 0.8433, Acc.sink: 0.8403, Acc.skyscraper: 0.6091, Acc.fireplace: 0.9110, Acc.refrigerator: 0.8666, Acc.grandstand: 0.8427, Acc.path: 0.4548, Acc.stairs: 0.3225, Acc.runway: 0.9484, Acc.case: 0.8171, Acc.pool table: 0.9835, Acc.pillow: 0.8129, Acc.screen door: 0.6494, Acc.stairway: 0.6513, Acc.river: 0.1876, Acc.bridge: 0.7164, Acc.bookcase: 0.6754, Acc.blind: 0.5288, Acc.coffee table: 0.8712, Acc.toilet: 0.9427, Acc.flower: 0.5465, Acc.book: 0.7778, Acc.hill: 0.1392, Acc.bench: 0.6125, Acc.countertop: 0.8404, Acc.stove: 0.8950, Acc.palm: 0.7885, Acc.kitchen island: 0.8326, Acc.computer: 0.9167, Acc.swivel chair: 0.6820, Acc.boat: 0.9141, Acc.bar: 0.7843, Acc.arcade machine: 0.8409, Acc.hovel: 0.4081, Acc.bus: 0.9648, Acc.towel: 0.8711, Acc.light: 0.7364, Acc.truck: 0.6173, Acc.tower: 0.6701, Acc.chandelier: 0.8500, Acc.awning: 0.7442, Acc.streetlight: 0.4645, Acc.booth: 0.6413, Acc.television receiver: 0.8885, Acc.airplane: 0.8892, Acc.dirt track: 0.4656, Acc.apparel: 0.6046, Acc.pole: 0.3867, Acc.land: 0.0357, Acc.bannister: 0.2827, Acc.escalator: 0.7936, Acc.ottoman: 0.6713, Acc.bottle: 0.6525, Acc.buffet: 0.5766, Acc.poster: 0.5605, Acc.stage: 0.4072, Acc.van: 0.6718, Acc.ship: 0.8942, Acc.fountain: 0.3416, Acc.conveyer belt: 0.9319, Acc.canopy: 0.7709, Acc.washer: 0.8535, Acc.plaything: 0.4468, Acc.swimming pool: 0.9337, Acc.stool: 0.7019, Acc.barrel: 0.7451, Acc.basket: 0.5722, Acc.waterfall: 0.7567, Acc.tent: 0.9861, Acc.bag: 0.2244, Acc.minibike: 0.9033, Acc.cradle: 0.9753, Acc.oven: 0.7674, Acc.ball: 0.5101, Acc.food: 0.7127, Acc.step: 0.1931, Acc.tank: 0.6680, Acc.trade name: 0.3408, Acc.microwave: 0.9583, Acc.pot: 0.6779, Acc.animal: 0.6065, Acc.bicycle: 0.7781, Acc.lake: 0.6379, Acc.dishwasher: 0.7945, Acc.screen: 0.8671, Acc.blanket: 0.3594, Acc.sculpture: 0.8797, Acc.hood: 0.7410, Acc.sconce: 0.7056, Acc.vase: 0.6310, Acc.traffic light: 0.6131, Acc.tray: 0.3149, Acc.ashcan: 0.6561, Acc.fan: 0.8260, Acc.pier: 0.4478, Acc.crt screen: 0.0342, Acc.plate: 0.7950, Acc.monitor: 0.7213, Acc.bulletin board: 0.6390, Acc.shower: 0.0762, Acc.radiator: 0.7729, Acc.glass: 0.2295, Acc.clock: 0.5753, Acc.flag: 0.8108 +2024-06-17 01:07:25,344 - mmseg - INFO - Iter [66050/80000] lr: 6.976e-06, eta: 5:50:49, time: 3.321, data_time: 1.964, memory: 70722, decode.loss_ce: 0.1451, decode.acc_seg: 93.7033, aux.loss_ce: 0.0623, aux.acc_seg: 93.2773, loss: 0.2075 +2024-06-17 01:08:33,394 - mmseg - INFO - Iter [66100/80000] lr: 6.951e-06, eta: 5:49:32, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1423, decode.acc_seg: 93.6338, aux.loss_ce: 0.0614, aux.acc_seg: 93.2251, loss: 0.2038 +2024-06-17 01:09:41,432 - mmseg - INFO - Iter [66150/80000] lr: 6.926e-06, eta: 5:48:15, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1541, decode.acc_seg: 93.3633, aux.loss_ce: 0.0651, aux.acc_seg: 92.9703, loss: 0.2191 +2024-06-17 01:10:49,495 - mmseg - INFO - Iter [66200/80000] lr: 6.900e-06, eta: 5:46:58, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1517, decode.acc_seg: 93.4424, aux.loss_ce: 0.0650, aux.acc_seg: 93.0066, loss: 0.2167 +2024-06-17 01:11:57,668 - mmseg - INFO - Iter [66250/80000] lr: 6.875e-06, eta: 5:45:41, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1412, decode.acc_seg: 93.9868, aux.loss_ce: 0.0604, aux.acc_seg: 93.5585, loss: 0.2016 +2024-06-17 01:13:05,949 - mmseg - INFO - Iter [66300/80000] lr: 6.850e-06, eta: 5:44:24, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1452, decode.acc_seg: 93.7090, aux.loss_ce: 0.0624, aux.acc_seg: 93.2706, loss: 0.2076 +2024-06-17 01:14:14,259 - mmseg - INFO - Iter [66350/80000] lr: 6.826e-06, eta: 5:43:07, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1449, decode.acc_seg: 93.5931, aux.loss_ce: 0.0623, aux.acc_seg: 93.1479, loss: 0.2072 +2024-06-17 01:15:22,527 - mmseg - INFO - Iter [66400/80000] lr: 6.801e-06, eta: 5:41:50, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1485, decode.acc_seg: 93.4689, aux.loss_ce: 0.0633, aux.acc_seg: 93.0504, loss: 0.2118 +2024-06-17 01:16:30,809 - mmseg - INFO - Iter [66450/80000] lr: 6.776e-06, eta: 5:40:33, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1420, decode.acc_seg: 93.8274, aux.loss_ce: 0.0615, aux.acc_seg: 93.3525, loss: 0.2035 +2024-06-17 01:17:38,898 - mmseg - INFO - Iter [66500/80000] lr: 6.751e-06, eta: 5:39:17, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1444, decode.acc_seg: 93.6275, aux.loss_ce: 0.0619, aux.acc_seg: 93.2239, loss: 0.2063 +2024-06-17 01:18:47,061 - mmseg - INFO - Iter [66550/80000] lr: 6.726e-06, eta: 5:38:00, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1455, decode.acc_seg: 93.7028, aux.loss_ce: 0.0627, aux.acc_seg: 93.2110, loss: 0.2082 +2024-06-17 01:19:55,125 - mmseg - INFO - Iter [66600/80000] lr: 6.700e-06, eta: 5:36:43, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1426, decode.acc_seg: 93.7094, aux.loss_ce: 0.0610, aux.acc_seg: 93.2873, loss: 0.2036 +2024-06-17 01:21:03,621 - mmseg - INFO - Iter [66650/80000] lr: 6.675e-06, eta: 5:35:26, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1543, decode.acc_seg: 93.2323, aux.loss_ce: 0.0660, aux.acc_seg: 92.8172, loss: 0.2202 +2024-06-17 01:22:11,808 - mmseg - INFO - Iter [66700/80000] lr: 6.651e-06, eta: 5:34:09, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1375, decode.acc_seg: 93.8867, aux.loss_ce: 0.0591, aux.acc_seg: 93.4735, loss: 0.1966 +2024-06-17 01:23:19,948 - mmseg - INFO - Iter [66750/80000] lr: 6.626e-06, eta: 5:32:52, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1538, decode.acc_seg: 93.2817, aux.loss_ce: 0.0660, aux.acc_seg: 92.7782, loss: 0.2198 +2024-06-17 01:24:28,067 - mmseg - INFO - Iter [66800/80000] lr: 6.601e-06, eta: 5:31:36, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1462, decode.acc_seg: 93.5574, aux.loss_ce: 0.0631, aux.acc_seg: 93.0991, loss: 0.2093 +2024-06-17 01:25:36,428 - mmseg - INFO - Iter [66850/80000] lr: 6.576e-06, eta: 5:30:19, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1450, decode.acc_seg: 93.4942, aux.loss_ce: 0.0622, aux.acc_seg: 93.0173, loss: 0.2072 +2024-06-17 01:26:44,784 - mmseg - INFO - Iter [66900/80000] lr: 6.551e-06, eta: 5:29:02, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1448, decode.acc_seg: 93.6439, aux.loss_ce: 0.0613, aux.acc_seg: 93.2548, loss: 0.2061 +2024-06-17 01:27:55,499 - mmseg - INFO - Iter [66950/80000] lr: 6.526e-06, eta: 5:27:46, time: 1.414, data_time: 0.061, memory: 70722, decode.loss_ce: 0.1469, decode.acc_seg: 93.5284, aux.loss_ce: 0.0635, aux.acc_seg: 93.1134, loss: 0.2103 +2024-06-17 01:29:03,685 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 01:29:03,685 - mmseg - INFO - Iter [67000/80000] lr: 6.500e-06, eta: 5:26:29, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1438, decode.acc_seg: 93.8050, aux.loss_ce: 0.0616, aux.acc_seg: 93.3421, loss: 0.2054 +2024-06-17 01:30:40,051 - mmseg - INFO - per class results: +2024-06-17 01:30:40,058 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.53 | 90.22 | +| building | 86.33 | 93.49 | +| sky | 94.95 | 97.8 | +| floor | 85.69 | 91.96 | +| tree | 77.34 | 89.31 | +| ceiling | 87.47 | 94.11 | +| road | 87.09 | 92.14 | +| bed | 93.03 | 97.4 | +| windowpane | 66.12 | 82.82 | +| grass | 69.2 | 81.98 | +| cabinet | 67.02 | 77.09 | +| sidewalk | 73.42 | 85.81 | +| person | 86.29 | 94.54 | +| earth | 37.18 | 50.18 | +| door | 60.4 | 74.75 | +| table | 71.07 | 84.2 | +| mountain | 60.93 | 73.84 | +| plant | 55.38 | 64.81 | +| curtain | 77.84 | 89.13 | +| chair | 69.66 | 82.98 | +| car | 87.67 | 93.97 | +| water | 64.79 | 80.56 | +| painting | 78.07 | 92.44 | +| sofa | 83.8 | 91.63 | +| shelf | 45.53 | 60.61 | +| house | 60.59 | 74.7 | +| sea | 77.07 | 88.86 | +| mirror | 78.56 | 83.93 | +| rug | 71.98 | 81.17 | +| field | 31.83 | 59.54 | +| armchair | 62.48 | 76.63 | +| seat | 67.92 | 89.95 | +| fence | 52.93 | 67.65 | +| desk | 60.03 | 78.76 | +| rock | 57.24 | 84.63 | +| wardrobe | 54.01 | 72.31 | +| lamp | 74.48 | 86.18 | +| bathtub | 85.0 | 87.3 | +| railing | 43.62 | 60.08 | +| cushion | 68.88 | 82.68 | +| base | 38.4 | 53.32 | +| box | 37.82 | 50.43 | +| column | 55.43 | 69.27 | +| signboard | 40.83 | 57.82 | +| chest of drawers | 45.85 | 68.28 | +| counter | 36.97 | 45.84 | +| sand | 57.06 | 86.11 | +| sink | 77.4 | 82.82 | +| skyscraper | 49.69 | 62.19 | +| fireplace | 75.17 | 91.56 | +| refrigerator | 84.23 | 93.09 | +| grandstand | 52.79 | 83.32 | +| path | 31.03 | 44.11 | +| stairs | 29.91 | 36.3 | +| runway | 71.48 | 94.84 | +| case | 55.14 | 78.01 | +| pool table | 94.6 | 98.29 | +| pillow | 64.28 | 73.14 | +| screen door | 82.77 | 86.28 | +| stairway | 46.0 | 59.92 | +| river | 10.5 | 17.89 | +| bridge | 65.19 | 72.21 | +| bookcase | 46.4 | 66.44 | +| blind | 40.31 | 43.43 | +| coffee table | 66.51 | 86.62 | +| toilet | 90.85 | 94.24 | +| flower | 43.24 | 62.69 | +| book | 56.35 | 76.56 | +| hill | 8.35 | 12.22 | +| bench | 53.75 | 62.27 | +| countertop | 63.5 | 88.31 | +| stove | 83.03 | 88.19 | +| palm | 56.64 | 82.63 | +| kitchen island | 59.91 | 86.53 | +| computer | 78.55 | 91.16 | +| swivel chair | 45.79 | 63.63 | +| boat | 74.43 | 93.07 | +| bar | 56.06 | 77.2 | +| arcade machine | 79.38 | 84.63 | +| hovel | 40.78 | 44.21 | +| bus | 91.67 | 96.77 | +| towel | 76.09 | 87.51 | +| light | 62.5 | 70.69 | +| truck | 43.51 | 57.27 | +| tower | 43.54 | 68.22 | +| chandelier | 68.83 | 85.35 | +| awning | 50.34 | 67.02 | +| streetlight | 34.98 | 46.37 | +| booth | 45.61 | 59.87 | +| television receiver | 77.86 | 87.32 | +| airplane | 70.14 | 76.6 | +| dirt track | 8.94 | 37.4 | +| apparel | 46.34 | 58.44 | +| pole | 30.97 | 44.86 | +| land | 2.98 | 5.56 | +| bannister | 19.94 | 26.94 | +| escalator | 62.2 | 78.37 | +| ottoman | 50.49 | 67.79 | +| bottle | 42.5 | 70.48 | +| buffet | 48.12 | 59.66 | +| poster | 39.98 | 51.61 | +| stage | 19.29 | 48.73 | +| van | 47.9 | 68.19 | +| ship | 85.31 | 90.45 | +| fountain | 33.06 | 33.64 | +| conveyer belt | 84.35 | 93.54 | +| canopy | 52.71 | 75.38 | +| washer | 80.62 | 85.64 | +| plaything | 29.07 | 40.88 | +| swimming pool | 59.12 | 88.34 | +| stool | 56.74 | 71.5 | +| barrel | 59.52 | 74.35 | +| basket | 41.03 | 58.14 | +| waterfall | 68.05 | 85.13 | +| tent | 96.38 | 98.52 | +| bag | 20.66 | 23.41 | +| minibike | 77.35 | 90.02 | +| cradle | 81.88 | 97.83 | +| oven | 66.02 | 78.83 | +| ball | 48.8 | 57.59 | +| food | 60.87 | 76.66 | +| step | 19.19 | 23.96 | +| tank | 61.98 | 65.8 | +| trade name | 31.53 | 40.62 | +| microwave | 90.13 | 96.04 | +| pot | 57.48 | 66.58 | +| animal | 58.82 | 60.37 | +| bicycle | 60.67 | 80.0 | +| lake | 52.67 | 63.85 | +| dishwasher | 68.63 | 75.91 | +| screen | 54.91 | 82.29 | +| blanket | 29.58 | 34.18 | +| sculpture | 70.02 | 87.53 | +| hood | 63.7 | 75.34 | +| sconce | 59.39 | 71.88 | +| vase | 49.6 | 65.01 | +| traffic light | 40.52 | 62.29 | +| tray | 27.35 | 34.68 | +| ashcan | 45.52 | 65.64 | +| fan | 68.61 | 82.4 | +| pier | 40.54 | 47.64 | +| crt screen | 2.1 | 3.24 | +| plate | 62.51 | 75.7 | +| monitor | 59.63 | 69.9 | +| bulletin board | 46.49 | 61.79 | +| shower | 7.69 | 7.81 | +| radiator | 68.48 | 77.74 | +| glass | 20.05 | 21.3 | +| clock | 47.3 | 55.44 | +| flag | 73.67 | 80.38 | ++---------------------+-------+-------+ +2024-06-17 01:30:40,058 - mmseg - INFO - Summary: +2024-06-17 01:30:40,058 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.46 | 57.78 | 70.45 | ++-------+-------+-------+ +2024-06-17 01:30:40,059 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 01:30:40,059 - mmseg - INFO - Iter(val) [250] aAcc: 0.8646, mIoU: 0.5778, mAcc: 0.7045, IoU.wall: 0.8253, IoU.building: 0.8633, IoU.sky: 0.9495, IoU.floor: 0.8569, IoU.tree: 0.7734, IoU.ceiling: 0.8747, IoU.road: 0.8709, IoU.bed : 0.9303, IoU.windowpane: 0.6612, IoU.grass: 0.6920, IoU.cabinet: 0.6702, IoU.sidewalk: 0.7342, IoU.person: 0.8629, IoU.earth: 0.3718, IoU.door: 0.6040, IoU.table: 0.7107, IoU.mountain: 0.6093, IoU.plant: 0.5538, IoU.curtain: 0.7784, IoU.chair: 0.6966, IoU.car: 0.8767, IoU.water: 0.6479, IoU.painting: 0.7807, IoU.sofa: 0.8380, IoU.shelf: 0.4553, IoU.house: 0.6059, IoU.sea: 0.7707, IoU.mirror: 0.7856, IoU.rug: 0.7198, IoU.field: 0.3183, IoU.armchair: 0.6248, IoU.seat: 0.6792, IoU.fence: 0.5293, IoU.desk: 0.6003, IoU.rock: 0.5724, IoU.wardrobe: 0.5401, IoU.lamp: 0.7448, IoU.bathtub: 0.8500, IoU.railing: 0.4362, IoU.cushion: 0.6888, IoU.base: 0.3840, IoU.box: 0.3782, IoU.column: 0.5543, IoU.signboard: 0.4083, IoU.chest of drawers: 0.4585, IoU.counter: 0.3697, IoU.sand: 0.5706, IoU.sink: 0.7740, IoU.skyscraper: 0.4969, IoU.fireplace: 0.7517, IoU.refrigerator: 0.8423, IoU.grandstand: 0.5279, IoU.path: 0.3103, IoU.stairs: 0.2991, IoU.runway: 0.7148, IoU.case: 0.5514, IoU.pool table: 0.9460, IoU.pillow: 0.6428, IoU.screen door: 0.8277, IoU.stairway: 0.4600, IoU.river: 0.1050, IoU.bridge: 0.6519, IoU.bookcase: 0.4640, IoU.blind: 0.4031, IoU.coffee table: 0.6651, IoU.toilet: 0.9085, IoU.flower: 0.4324, IoU.book: 0.5635, IoU.hill: 0.0835, IoU.bench: 0.5375, IoU.countertop: 0.6350, IoU.stove: 0.8303, IoU.palm: 0.5664, IoU.kitchen island: 0.5991, IoU.computer: 0.7855, IoU.swivel chair: 0.4579, IoU.boat: 0.7443, IoU.bar: 0.5606, IoU.arcade machine: 0.7938, IoU.hovel: 0.4078, IoU.bus: 0.9167, IoU.towel: 0.7609, IoU.light: 0.6250, IoU.truck: 0.4351, IoU.tower: 0.4354, IoU.chandelier: 0.6883, IoU.awning: 0.5034, IoU.streetlight: 0.3498, IoU.booth: 0.4561, IoU.television receiver: 0.7786, IoU.airplane: 0.7014, IoU.dirt track: 0.0894, IoU.apparel: 0.4634, IoU.pole: 0.3097, IoU.land: 0.0298, IoU.bannister: 0.1994, IoU.escalator: 0.6220, IoU.ottoman: 0.5049, IoU.bottle: 0.4250, IoU.buffet: 0.4812, IoU.poster: 0.3998, IoU.stage: 0.1929, IoU.van: 0.4790, IoU.ship: 0.8531, IoU.fountain: 0.3306, IoU.conveyer belt: 0.8435, IoU.canopy: 0.5271, IoU.washer: 0.8062, IoU.plaything: 0.2907, IoU.swimming pool: 0.5912, IoU.stool: 0.5674, IoU.barrel: 0.5952, IoU.basket: 0.4103, IoU.waterfall: 0.6805, IoU.tent: 0.9638, IoU.bag: 0.2066, IoU.minibike: 0.7735, IoU.cradle: 0.8188, IoU.oven: 0.6602, IoU.ball: 0.4880, IoU.food: 0.6087, IoU.step: 0.1919, IoU.tank: 0.6198, IoU.trade name: 0.3153, IoU.microwave: 0.9013, IoU.pot: 0.5748, IoU.animal: 0.5882, IoU.bicycle: 0.6067, IoU.lake: 0.5267, IoU.dishwasher: 0.6863, IoU.screen: 0.5491, IoU.blanket: 0.2958, IoU.sculpture: 0.7002, IoU.hood: 0.6370, IoU.sconce: 0.5939, IoU.vase: 0.4960, IoU.traffic light: 0.4052, IoU.tray: 0.2735, IoU.ashcan: 0.4552, IoU.fan: 0.6861, IoU.pier: 0.4054, IoU.crt screen: 0.0210, IoU.plate: 0.6251, IoU.monitor: 0.5963, IoU.bulletin board: 0.4649, IoU.shower: 0.0769, IoU.radiator: 0.6848, IoU.glass: 0.2005, IoU.clock: 0.4730, IoU.flag: 0.7367, Acc.wall: 0.9022, Acc.building: 0.9349, Acc.sky: 0.9780, Acc.floor: 0.9196, Acc.tree: 0.8931, Acc.ceiling: 0.9411, Acc.road: 0.9214, Acc.bed : 0.9740, Acc.windowpane: 0.8282, Acc.grass: 0.8198, Acc.cabinet: 0.7709, Acc.sidewalk: 0.8581, Acc.person: 0.9454, Acc.earth: 0.5018, Acc.door: 0.7475, Acc.table: 0.8420, Acc.mountain: 0.7384, Acc.plant: 0.6481, Acc.curtain: 0.8913, Acc.chair: 0.8298, Acc.car: 0.9397, Acc.water: 0.8056, Acc.painting: 0.9244, Acc.sofa: 0.9163, Acc.shelf: 0.6061, Acc.house: 0.7470, Acc.sea: 0.8886, Acc.mirror: 0.8393, Acc.rug: 0.8117, Acc.field: 0.5954, Acc.armchair: 0.7663, Acc.seat: 0.8995, Acc.fence: 0.6765, Acc.desk: 0.7876, Acc.rock: 0.8463, Acc.wardrobe: 0.7231, Acc.lamp: 0.8618, Acc.bathtub: 0.8730, Acc.railing: 0.6008, Acc.cushion: 0.8268, Acc.base: 0.5332, Acc.box: 0.5043, Acc.column: 0.6927, Acc.signboard: 0.5782, Acc.chest of drawers: 0.6828, Acc.counter: 0.4584, Acc.sand: 0.8611, Acc.sink: 0.8282, Acc.skyscraper: 0.6219, Acc.fireplace: 0.9156, Acc.refrigerator: 0.9309, Acc.grandstand: 0.8332, Acc.path: 0.4411, Acc.stairs: 0.3630, Acc.runway: 0.9484, Acc.case: 0.7801, Acc.pool table: 0.9829, Acc.pillow: 0.7314, Acc.screen door: 0.8628, Acc.stairway: 0.5992, Acc.river: 0.1789, Acc.bridge: 0.7221, Acc.bookcase: 0.6644, Acc.blind: 0.4343, Acc.coffee table: 0.8662, Acc.toilet: 0.9424, Acc.flower: 0.6269, Acc.book: 0.7656, Acc.hill: 0.1222, Acc.bench: 0.6227, Acc.countertop: 0.8831, Acc.stove: 0.8819, Acc.palm: 0.8263, Acc.kitchen island: 0.8653, Acc.computer: 0.9116, Acc.swivel chair: 0.6363, Acc.boat: 0.9307, Acc.bar: 0.7720, Acc.arcade machine: 0.8463, Acc.hovel: 0.4421, Acc.bus: 0.9677, Acc.towel: 0.8751, Acc.light: 0.7069, Acc.truck: 0.5727, Acc.tower: 0.6822, Acc.chandelier: 0.8535, Acc.awning: 0.6702, Acc.streetlight: 0.4637, Acc.booth: 0.5987, Acc.television receiver: 0.8732, Acc.airplane: 0.7660, Acc.dirt track: 0.3740, Acc.apparel: 0.5844, Acc.pole: 0.4486, Acc.land: 0.0556, Acc.bannister: 0.2694, Acc.escalator: 0.7837, Acc.ottoman: 0.6779, Acc.bottle: 0.7048, Acc.buffet: 0.5966, Acc.poster: 0.5161, Acc.stage: 0.4873, Acc.van: 0.6819, Acc.ship: 0.9045, Acc.fountain: 0.3364, Acc.conveyer belt: 0.9354, Acc.canopy: 0.7538, Acc.washer: 0.8564, Acc.plaything: 0.4088, Acc.swimming pool: 0.8834, Acc.stool: 0.7150, Acc.barrel: 0.7435, Acc.basket: 0.5814, Acc.waterfall: 0.8513, Acc.tent: 0.9852, Acc.bag: 0.2341, Acc.minibike: 0.9002, Acc.cradle: 0.9783, Acc.oven: 0.7883, Acc.ball: 0.5759, Acc.food: 0.7666, Acc.step: 0.2396, Acc.tank: 0.6580, Acc.trade name: 0.4062, Acc.microwave: 0.9604, Acc.pot: 0.6658, Acc.animal: 0.6037, Acc.bicycle: 0.8000, Acc.lake: 0.6385, Acc.dishwasher: 0.7591, Acc.screen: 0.8229, Acc.blanket: 0.3418, Acc.sculpture: 0.8753, Acc.hood: 0.7534, Acc.sconce: 0.7188, Acc.vase: 0.6501, Acc.traffic light: 0.6229, Acc.tray: 0.3468, Acc.ashcan: 0.6564, Acc.fan: 0.8240, Acc.pier: 0.4764, Acc.crt screen: 0.0324, Acc.plate: 0.7570, Acc.monitor: 0.6990, Acc.bulletin board: 0.6179, Acc.shower: 0.0781, Acc.radiator: 0.7774, Acc.glass: 0.2130, Acc.clock: 0.5544, Acc.flag: 0.8038 +2024-06-17 01:31:48,607 - mmseg - INFO - Iter [67050/80000] lr: 6.475e-06, eta: 5:25:31, time: 3.298, data_time: 1.943, memory: 70722, decode.loss_ce: 0.1496, decode.acc_seg: 93.5191, aux.loss_ce: 0.0645, aux.acc_seg: 92.9920, loss: 0.2140 +2024-06-17 01:32:56,772 - mmseg - INFO - Iter [67100/80000] lr: 6.450e-06, eta: 5:24:14, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1405, decode.acc_seg: 93.8319, aux.loss_ce: 0.0605, aux.acc_seg: 93.3502, loss: 0.2010 +2024-06-17 01:34:04,929 - mmseg - INFO - Iter [67150/80000] lr: 6.425e-06, eta: 5:22:58, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1405, decode.acc_seg: 93.7548, aux.loss_ce: 0.0608, aux.acc_seg: 93.2704, loss: 0.2013 +2024-06-17 01:35:13,387 - mmseg - INFO - Iter [67200/80000] lr: 6.401e-06, eta: 5:21:41, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1455, decode.acc_seg: 93.4924, aux.loss_ce: 0.0626, aux.acc_seg: 93.0557, loss: 0.2081 +2024-06-17 01:36:21,622 - mmseg - INFO - Iter [67250/80000] lr: 6.376e-06, eta: 5:20:24, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1540, decode.acc_seg: 93.5973, aux.loss_ce: 0.0655, aux.acc_seg: 93.1950, loss: 0.2195 +2024-06-17 01:37:29,844 - mmseg - INFO - Iter [67300/80000] lr: 6.351e-06, eta: 5:19:07, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1403, decode.acc_seg: 93.9922, aux.loss_ce: 0.0601, aux.acc_seg: 93.5647, loss: 0.2005 +2024-06-17 01:38:38,143 - mmseg - INFO - Iter [67350/80000] lr: 6.326e-06, eta: 5:17:51, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1406, decode.acc_seg: 93.8681, aux.loss_ce: 0.0607, aux.acc_seg: 93.3974, loss: 0.2013 +2024-06-17 01:39:46,263 - mmseg - INFO - Iter [67400/80000] lr: 6.301e-06, eta: 5:16:34, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1426, decode.acc_seg: 93.7463, aux.loss_ce: 0.0614, aux.acc_seg: 93.3217, loss: 0.2040 +2024-06-17 01:40:54,699 - mmseg - INFO - Iter [67450/80000] lr: 6.275e-06, eta: 5:15:17, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1433, decode.acc_seg: 93.7765, aux.loss_ce: 0.0614, aux.acc_seg: 93.3101, loss: 0.2047 +2024-06-17 01:42:03,172 - mmseg - INFO - Iter [67500/80000] lr: 6.250e-06, eta: 5:14:01, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1420, decode.acc_seg: 93.7335, aux.loss_ce: 0.0610, aux.acc_seg: 93.2969, loss: 0.2030 +2024-06-17 01:43:11,267 - mmseg - INFO - Iter [67550/80000] lr: 6.225e-06, eta: 5:12:44, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1447, decode.acc_seg: 93.5730, aux.loss_ce: 0.0625, aux.acc_seg: 93.0632, loss: 0.2072 +2024-06-17 01:44:19,515 - mmseg - INFO - Iter [67600/80000] lr: 6.201e-06, eta: 5:11:27, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1446, decode.acc_seg: 93.5073, aux.loss_ce: 0.0618, aux.acc_seg: 93.1830, loss: 0.2064 +2024-06-17 01:45:27,613 - mmseg - INFO - Iter [67650/80000] lr: 6.176e-06, eta: 5:10:11, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1450, decode.acc_seg: 93.7300, aux.loss_ce: 0.0624, aux.acc_seg: 93.1878, loss: 0.2075 +2024-06-17 01:46:36,105 - mmseg - INFO - Iter [67700/80000] lr: 6.151e-06, eta: 5:08:54, time: 1.370, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1433, decode.acc_seg: 93.6824, aux.loss_ce: 0.0614, aux.acc_seg: 93.2575, loss: 0.2047 +2024-06-17 01:47:44,237 - mmseg - INFO - Iter [67750/80000] lr: 6.126e-06, eta: 5:07:37, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1398, decode.acc_seg: 93.7656, aux.loss_ce: 0.0604, aux.acc_seg: 93.2596, loss: 0.2003 +2024-06-17 01:48:52,265 - mmseg - INFO - Iter [67800/80000] lr: 6.101e-06, eta: 5:06:21, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1428, decode.acc_seg: 93.6929, aux.loss_ce: 0.0616, aux.acc_seg: 93.2572, loss: 0.2044 +2024-06-17 01:50:00,455 - mmseg - INFO - Iter [67850/80000] lr: 6.075e-06, eta: 5:05:04, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1427, decode.acc_seg: 93.6660, aux.loss_ce: 0.0612, aux.acc_seg: 93.2392, loss: 0.2039 +2024-06-17 01:51:08,601 - mmseg - INFO - Iter [67900/80000] lr: 6.050e-06, eta: 5:03:47, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1462, decode.acc_seg: 93.4265, aux.loss_ce: 0.0627, aux.acc_seg: 93.0053, loss: 0.2090 +2024-06-17 01:52:16,885 - mmseg - INFO - Iter [67950/80000] lr: 6.025e-06, eta: 5:02:31, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1399, decode.acc_seg: 93.7274, aux.loss_ce: 0.0610, aux.acc_seg: 93.2235, loss: 0.2009 +2024-06-17 01:53:24,986 - mmseg - INFO - Saving checkpoint at 68000 iterations +2024-06-17 01:54:54,346 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 01:54:54,346 - mmseg - INFO - Iter [68000/80000] lr: 6.001e-06, eta: 5:01:30, time: 3.149, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1519, decode.acc_seg: 93.6003, aux.loss_ce: 0.0649, aux.acc_seg: 93.2021, loss: 0.2168 +2024-06-17 01:56:29,354 - mmseg - INFO - per class results: +2024-06-17 01:56:29,360 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.4 | 90.11 | +| building | 86.26 | 93.62 | +| sky | 95.07 | 97.58 | +| floor | 85.77 | 92.32 | +| tree | 77.43 | 90.51 | +| ceiling | 87.3 | 93.46 | +| road | 87.25 | 92.51 | +| bed | 93.1 | 97.15 | +| windowpane | 66.55 | 82.82 | +| grass | 69.61 | 85.18 | +| cabinet | 66.8 | 76.68 | +| sidewalk | 74.01 | 86.18 | +| person | 86.07 | 94.2 | +| earth | 36.9 | 51.3 | +| door | 59.99 | 74.74 | +| table | 71.08 | 83.92 | +| mountain | 62.58 | 74.8 | +| plant | 55.18 | 64.84 | +| curtain | 76.91 | 89.3 | +| chair | 69.55 | 80.66 | +| car | 87.45 | 94.32 | +| water | 66.81 | 82.47 | +| painting | 77.93 | 92.24 | +| sofa | 83.57 | 93.15 | +| shelf | 46.02 | 60.21 | +| house | 63.86 | 75.96 | +| sea | 80.04 | 89.39 | +| mirror | 78.68 | 84.36 | +| rug | 71.53 | 79.87 | +| field | 32.92 | 48.96 | +| armchair | 61.36 | 76.72 | +| seat | 67.98 | 89.72 | +| fence | 52.35 | 68.12 | +| desk | 61.6 | 79.43 | +| rock | 58.52 | 85.2 | +| wardrobe | 53.42 | 74.15 | +| lamp | 74.71 | 86.13 | +| bathtub | 84.8 | 86.99 | +| railing | 43.26 | 61.28 | +| cushion | 68.59 | 82.22 | +| base | 39.88 | 57.15 | +| box | 38.21 | 50.52 | +| column | 54.84 | 68.04 | +| signboard | 41.21 | 55.5 | +| chest of drawers | 45.24 | 62.81 | +| counter | 39.41 | 47.56 | +| sand | 58.88 | 86.57 | +| sink | 76.37 | 84.91 | +| skyscraper | 49.66 | 63.38 | +| fireplace | 72.01 | 92.48 | +| refrigerator | 84.09 | 92.28 | +| grandstand | 59.47 | 84.52 | +| path | 30.29 | 41.16 | +| stairs | 37.14 | 44.99 | +| runway | 72.01 | 94.82 | +| case | 57.4 | 82.55 | +| pool table | 93.95 | 98.45 | +| pillow | 63.48 | 72.15 | +| screen door | 81.89 | 85.13 | +| stairway | 45.95 | 61.12 | +| river | 10.6 | 18.18 | +| bridge | 61.0 | 68.2 | +| bookcase | 49.31 | 66.34 | +| blind | 44.09 | 48.71 | +| coffee table | 66.43 | 89.48 | +| toilet | 90.36 | 92.87 | +| flower | 46.71 | 56.56 | +| book | 56.55 | 76.05 | +| hill | 7.97 | 12.48 | +| bench | 53.81 | 62.05 | +| countertop | 66.79 | 84.41 | +| stove | 82.91 | 89.07 | +| palm | 55.11 | 80.41 | +| kitchen island | 60.83 | 86.77 | +| computer | 79.64 | 91.32 | +| swivel chair | 46.57 | 64.56 | +| boat | 76.94 | 90.44 | +| bar | 58.54 | 80.58 | +| arcade machine | 78.05 | 83.12 | +| hovel | 46.34 | 50.85 | +| bus | 90.81 | 97.53 | +| towel | 77.15 | 87.76 | +| light | 62.79 | 76.49 | +| truck | 46.0 | 61.59 | +| tower | 45.82 | 69.1 | +| chandelier | 70.46 | 86.03 | +| awning | 50.85 | 68.24 | +| streetlight | 36.81 | 49.88 | +| booth | 49.18 | 66.51 | +| television receiver | 79.0 | 90.36 | +| airplane | 81.06 | 87.17 | +| dirt track | 15.35 | 48.1 | +| apparel | 45.09 | 61.35 | +| pole | 30.85 | 44.2 | +| land | 2.25 | 3.83 | +| bannister | 18.95 | 26.34 | +| escalator | 60.68 | 78.8 | +| ottoman | 54.1 | 71.24 | +| bottle | 41.5 | 67.24 | +| buffet | 53.56 | 66.69 | +| poster | 39.88 | 50.6 | +| stage | 24.35 | 46.83 | +| van | 48.58 | 59.94 | +| ship | 84.08 | 88.93 | +| fountain | 36.47 | 37.16 | +| conveyer belt | 83.83 | 93.43 | +| canopy | 52.88 | 75.13 | +| washer | 80.99 | 85.77 | +| plaything | 29.12 | 44.21 | +| swimming pool | 60.03 | 90.44 | +| stool | 57.28 | 70.37 | +| barrel | 58.05 | 74.22 | +| basket | 40.83 | 56.46 | +| waterfall | 68.01 | 88.64 | +| tent | 93.29 | 98.48 | +| bag | 20.71 | 22.4 | +| minibike | 77.71 | 89.39 | +| cradle | 81.9 | 97.47 | +| oven | 62.9 | 74.17 | +| ball | 44.08 | 50.38 | +| food | 60.54 | 76.05 | +| step | 14.77 | 18.51 | +| tank | 63.49 | 69.72 | +| trade name | 28.62 | 34.16 | +| microwave | 89.39 | 96.02 | +| pot | 57.83 | 65.84 | +| animal | 59.35 | 60.89 | +| bicycle | 60.77 | 78.13 | +| lake | 52.43 | 63.85 | +| dishwasher | 69.94 | 76.87 | +| screen | 59.13 | 90.15 | +| blanket | 29.28 | 32.78 | +| sculpture | 72.51 | 87.28 | +| hood | 63.12 | 74.69 | +| sconce | 59.53 | 69.42 | +| vase | 49.89 | 62.97 | +| traffic light | 38.47 | 63.93 | +| tray | 27.92 | 35.49 | +| ashcan | 49.18 | 63.45 | +| fan | 68.69 | 83.07 | +| pier | 40.6 | 44.83 | +| crt screen | 2.51 | 3.44 | +| plate | 62.05 | 77.7 | +| monitor | 61.21 | 72.26 | +| bulletin board | 53.48 | 65.51 | +| shower | 6.66 | 6.89 | +| radiator | 68.37 | 78.73 | +| glass | 19.35 | 20.33 | +| clock | 48.09 | 56.54 | +| flag | 73.67 | 77.7 | ++---------------------+-------+-------+ +2024-06-17 01:56:29,360 - mmseg - INFO - Summary: +2024-06-17 01:56:29,360 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.61 | 58.35 | 70.74 | ++-------+-------+-------+ +2024-06-17 01:56:29,361 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 01:56:29,361 - mmseg - INFO - Iter(val) [250] aAcc: 0.8661, mIoU: 0.5835, mAcc: 0.7074, IoU.wall: 0.8240, IoU.building: 0.8626, IoU.sky: 0.9507, IoU.floor: 0.8577, IoU.tree: 0.7743, IoU.ceiling: 0.8730, IoU.road: 0.8725, IoU.bed : 0.9310, IoU.windowpane: 0.6655, IoU.grass: 0.6961, IoU.cabinet: 0.6680, IoU.sidewalk: 0.7401, IoU.person: 0.8607, IoU.earth: 0.3690, IoU.door: 0.5999, IoU.table: 0.7108, IoU.mountain: 0.6258, IoU.plant: 0.5518, IoU.curtain: 0.7691, IoU.chair: 0.6955, IoU.car: 0.8745, IoU.water: 0.6681, IoU.painting: 0.7793, IoU.sofa: 0.8357, IoU.shelf: 0.4602, IoU.house: 0.6386, IoU.sea: 0.8004, IoU.mirror: 0.7868, IoU.rug: 0.7153, IoU.field: 0.3292, IoU.armchair: 0.6136, IoU.seat: 0.6798, IoU.fence: 0.5235, IoU.desk: 0.6160, IoU.rock: 0.5852, IoU.wardrobe: 0.5342, IoU.lamp: 0.7471, IoU.bathtub: 0.8480, IoU.railing: 0.4326, IoU.cushion: 0.6859, IoU.base: 0.3988, IoU.box: 0.3821, IoU.column: 0.5484, IoU.signboard: 0.4121, IoU.chest of drawers: 0.4524, IoU.counter: 0.3941, IoU.sand: 0.5888, IoU.sink: 0.7637, IoU.skyscraper: 0.4966, IoU.fireplace: 0.7201, IoU.refrigerator: 0.8409, IoU.grandstand: 0.5947, IoU.path: 0.3029, IoU.stairs: 0.3714, IoU.runway: 0.7201, IoU.case: 0.5740, IoU.pool table: 0.9395, IoU.pillow: 0.6348, IoU.screen door: 0.8189, IoU.stairway: 0.4595, IoU.river: 0.1060, IoU.bridge: 0.6100, IoU.bookcase: 0.4931, IoU.blind: 0.4409, IoU.coffee table: 0.6643, IoU.toilet: 0.9036, IoU.flower: 0.4671, IoU.book: 0.5655, IoU.hill: 0.0797, IoU.bench: 0.5381, IoU.countertop: 0.6679, IoU.stove: 0.8291, IoU.palm: 0.5511, IoU.kitchen island: 0.6083, IoU.computer: 0.7964, IoU.swivel chair: 0.4657, IoU.boat: 0.7694, IoU.bar: 0.5854, IoU.arcade machine: 0.7805, IoU.hovel: 0.4634, IoU.bus: 0.9081, IoU.towel: 0.7715, IoU.light: 0.6279, IoU.truck: 0.4600, IoU.tower: 0.4582, IoU.chandelier: 0.7046, IoU.awning: 0.5085, IoU.streetlight: 0.3681, IoU.booth: 0.4918, IoU.television receiver: 0.7900, IoU.airplane: 0.8106, IoU.dirt track: 0.1535, IoU.apparel: 0.4509, IoU.pole: 0.3085, IoU.land: 0.0225, IoU.bannister: 0.1895, IoU.escalator: 0.6068, IoU.ottoman: 0.5410, IoU.bottle: 0.4150, IoU.buffet: 0.5356, IoU.poster: 0.3988, IoU.stage: 0.2435, IoU.van: 0.4858, IoU.ship: 0.8408, IoU.fountain: 0.3647, IoU.conveyer belt: 0.8383, IoU.canopy: 0.5288, IoU.washer: 0.8099, IoU.plaything: 0.2912, IoU.swimming pool: 0.6003, IoU.stool: 0.5728, IoU.barrel: 0.5805, IoU.basket: 0.4083, IoU.waterfall: 0.6801, IoU.tent: 0.9329, IoU.bag: 0.2071, IoU.minibike: 0.7771, IoU.cradle: 0.8190, IoU.oven: 0.6290, IoU.ball: 0.4408, IoU.food: 0.6054, IoU.step: 0.1477, IoU.tank: 0.6349, IoU.trade name: 0.2862, IoU.microwave: 0.8939, IoU.pot: 0.5783, IoU.animal: 0.5935, IoU.bicycle: 0.6077, IoU.lake: 0.5243, IoU.dishwasher: 0.6994, IoU.screen: 0.5913, IoU.blanket: 0.2928, IoU.sculpture: 0.7251, IoU.hood: 0.6312, IoU.sconce: 0.5953, IoU.vase: 0.4989, IoU.traffic light: 0.3847, IoU.tray: 0.2792, IoU.ashcan: 0.4918, IoU.fan: 0.6869, IoU.pier: 0.4060, IoU.crt screen: 0.0251, IoU.plate: 0.6205, IoU.monitor: 0.6121, IoU.bulletin board: 0.5348, IoU.shower: 0.0666, IoU.radiator: 0.6837, IoU.glass: 0.1935, IoU.clock: 0.4809, IoU.flag: 0.7367, Acc.wall: 0.9011, Acc.building: 0.9362, Acc.sky: 0.9758, Acc.floor: 0.9232, Acc.tree: 0.9051, Acc.ceiling: 0.9346, Acc.road: 0.9251, Acc.bed : 0.9715, Acc.windowpane: 0.8282, Acc.grass: 0.8518, Acc.cabinet: 0.7668, Acc.sidewalk: 0.8618, Acc.person: 0.9420, Acc.earth: 0.5130, Acc.door: 0.7474, Acc.table: 0.8392, Acc.mountain: 0.7480, Acc.plant: 0.6484, Acc.curtain: 0.8930, Acc.chair: 0.8066, Acc.car: 0.9432, Acc.water: 0.8247, Acc.painting: 0.9224, Acc.sofa: 0.9315, Acc.shelf: 0.6021, Acc.house: 0.7596, Acc.sea: 0.8939, Acc.mirror: 0.8436, Acc.rug: 0.7987, Acc.field: 0.4896, Acc.armchair: 0.7672, Acc.seat: 0.8972, Acc.fence: 0.6812, Acc.desk: 0.7943, Acc.rock: 0.8520, Acc.wardrobe: 0.7415, Acc.lamp: 0.8613, Acc.bathtub: 0.8699, Acc.railing: 0.6128, Acc.cushion: 0.8222, Acc.base: 0.5715, Acc.box: 0.5052, Acc.column: 0.6804, Acc.signboard: 0.5550, Acc.chest of drawers: 0.6281, Acc.counter: 0.4756, Acc.sand: 0.8657, Acc.sink: 0.8491, Acc.skyscraper: 0.6338, Acc.fireplace: 0.9248, Acc.refrigerator: 0.9228, Acc.grandstand: 0.8452, Acc.path: 0.4116, Acc.stairs: 0.4499, Acc.runway: 0.9482, Acc.case: 0.8255, Acc.pool table: 0.9845, Acc.pillow: 0.7215, Acc.screen door: 0.8513, Acc.stairway: 0.6112, Acc.river: 0.1818, Acc.bridge: 0.6820, Acc.bookcase: 0.6634, Acc.blind: 0.4871, Acc.coffee table: 0.8948, Acc.toilet: 0.9287, Acc.flower: 0.5656, Acc.book: 0.7605, Acc.hill: 0.1248, Acc.bench: 0.6205, Acc.countertop: 0.8441, Acc.stove: 0.8907, Acc.palm: 0.8041, Acc.kitchen island: 0.8677, Acc.computer: 0.9132, Acc.swivel chair: 0.6456, Acc.boat: 0.9044, Acc.bar: 0.8058, Acc.arcade machine: 0.8312, Acc.hovel: 0.5085, Acc.bus: 0.9753, Acc.towel: 0.8776, Acc.light: 0.7649, Acc.truck: 0.6159, Acc.tower: 0.6910, Acc.chandelier: 0.8603, Acc.awning: 0.6824, Acc.streetlight: 0.4988, Acc.booth: 0.6651, Acc.television receiver: 0.9036, Acc.airplane: 0.8717, Acc.dirt track: 0.4810, Acc.apparel: 0.6135, Acc.pole: 0.4420, Acc.land: 0.0383, Acc.bannister: 0.2634, Acc.escalator: 0.7880, Acc.ottoman: 0.7124, Acc.bottle: 0.6724, Acc.buffet: 0.6669, Acc.poster: 0.5060, Acc.stage: 0.4683, Acc.van: 0.5994, Acc.ship: 0.8893, Acc.fountain: 0.3716, Acc.conveyer belt: 0.9343, Acc.canopy: 0.7513, Acc.washer: 0.8577, Acc.plaything: 0.4421, Acc.swimming pool: 0.9044, Acc.stool: 0.7037, Acc.barrel: 0.7422, Acc.basket: 0.5646, Acc.waterfall: 0.8864, Acc.tent: 0.9848, Acc.bag: 0.2240, Acc.minibike: 0.8939, Acc.cradle: 0.9747, Acc.oven: 0.7417, Acc.ball: 0.5038, Acc.food: 0.7605, Acc.step: 0.1851, Acc.tank: 0.6972, Acc.trade name: 0.3416, Acc.microwave: 0.9602, Acc.pot: 0.6584, Acc.animal: 0.6089, Acc.bicycle: 0.7813, Acc.lake: 0.6385, Acc.dishwasher: 0.7687, Acc.screen: 0.9015, Acc.blanket: 0.3278, Acc.sculpture: 0.8728, Acc.hood: 0.7469, Acc.sconce: 0.6942, Acc.vase: 0.6297, Acc.traffic light: 0.6393, Acc.tray: 0.3549, Acc.ashcan: 0.6345, Acc.fan: 0.8307, Acc.pier: 0.4483, Acc.crt screen: 0.0344, Acc.plate: 0.7770, Acc.monitor: 0.7226, Acc.bulletin board: 0.6551, Acc.shower: 0.0689, Acc.radiator: 0.7873, Acc.glass: 0.2033, Acc.clock: 0.5654, Acc.flag: 0.7770 +2024-06-17 01:57:38,226 - mmseg - INFO - Iter [68050/80000] lr: 5.976e-06, eta: 5:00:30, time: 3.278, data_time: 1.917, memory: 70722, decode.loss_ce: 0.1423, decode.acc_seg: 93.7865, aux.loss_ce: 0.0616, aux.acc_seg: 93.2804, loss: 0.2039 +2024-06-17 01:58:46,257 - mmseg - INFO - Iter [68100/80000] lr: 5.951e-06, eta: 4:59:13, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1423, decode.acc_seg: 93.7096, aux.loss_ce: 0.0610, aux.acc_seg: 93.2987, loss: 0.2033 +2024-06-17 01:59:54,298 - mmseg - INFO - Iter [68150/80000] lr: 5.926e-06, eta: 4:57:57, time: 1.361, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1324, decode.acc_seg: 94.0835, aux.loss_ce: 0.0573, aux.acc_seg: 93.6330, loss: 0.1897 +2024-06-17 02:01:02,316 - mmseg - INFO - Iter [68200/80000] lr: 5.901e-06, eta: 4:56:40, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1408, decode.acc_seg: 93.7642, aux.loss_ce: 0.0605, aux.acc_seg: 93.3206, loss: 0.2013 +2024-06-17 02:02:12,718 - mmseg - INFO - Iter [68250/80000] lr: 5.875e-06, eta: 4:55:24, time: 1.408, data_time: 0.051, memory: 70722, decode.loss_ce: 0.1424, decode.acc_seg: 93.7171, aux.loss_ce: 0.0613, aux.acc_seg: 93.2892, loss: 0.2036 +2024-06-17 02:03:20,928 - mmseg - INFO - Iter [68300/80000] lr: 5.850e-06, eta: 4:54:07, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1469, decode.acc_seg: 93.4944, aux.loss_ce: 0.0638, aux.acc_seg: 92.9700, loss: 0.2107 +2024-06-17 02:04:28,993 - mmseg - INFO - Iter [68350/80000] lr: 5.825e-06, eta: 4:52:50, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1319, decode.acc_seg: 94.2305, aux.loss_ce: 0.0567, aux.acc_seg: 93.8346, loss: 0.1886 +2024-06-17 02:05:37,138 - mmseg - INFO - Iter [68400/80000] lr: 5.800e-06, eta: 4:51:34, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1374, decode.acc_seg: 94.0289, aux.loss_ce: 0.0598, aux.acc_seg: 93.5061, loss: 0.1972 +2024-06-17 02:06:45,180 - mmseg - INFO - Iter [68450/80000] lr: 5.776e-06, eta: 4:50:17, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1391, decode.acc_seg: 93.9355, aux.loss_ce: 0.0600, aux.acc_seg: 93.4732, loss: 0.1991 +2024-06-17 02:07:53,246 - mmseg - INFO - Iter [68500/80000] lr: 5.751e-06, eta: 4:49:00, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1453, decode.acc_seg: 93.6908, aux.loss_ce: 0.0623, aux.acc_seg: 93.3021, loss: 0.2077 +2024-06-17 02:09:01,504 - mmseg - INFO - Iter [68550/80000] lr: 5.726e-06, eta: 4:47:44, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1419, decode.acc_seg: 93.6585, aux.loss_ce: 0.0608, aux.acc_seg: 93.2828, loss: 0.2027 +2024-06-17 02:10:09,743 - mmseg - INFO - Iter [68600/80000] lr: 5.701e-06, eta: 4:46:27, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1389, decode.acc_seg: 93.9116, aux.loss_ce: 0.0600, aux.acc_seg: 93.4813, loss: 0.1990 +2024-06-17 02:11:17,967 - mmseg - INFO - Iter [68650/80000] lr: 5.676e-06, eta: 4:45:11, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1456, decode.acc_seg: 93.7414, aux.loss_ce: 0.0618, aux.acc_seg: 93.3513, loss: 0.2074 +2024-06-17 02:12:26,075 - mmseg - INFO - Iter [68700/80000] lr: 5.650e-06, eta: 4:43:54, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1432, decode.acc_seg: 93.6085, aux.loss_ce: 0.0619, aux.acc_seg: 93.0608, loss: 0.2051 +2024-06-17 02:13:34,186 - mmseg - INFO - Iter [68750/80000] lr: 5.625e-06, eta: 4:42:38, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1444, decode.acc_seg: 93.6097, aux.loss_ce: 0.0617, aux.acc_seg: 93.1993, loss: 0.2060 +2024-06-17 02:14:42,388 - mmseg - INFO - Iter [68800/80000] lr: 5.600e-06, eta: 4:41:21, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1460, decode.acc_seg: 93.6894, aux.loss_ce: 0.0627, aux.acc_seg: 93.2628, loss: 0.2087 +2024-06-17 02:15:50,462 - mmseg - INFO - Iter [68850/80000] lr: 5.576e-06, eta: 4:40:05, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1435, decode.acc_seg: 93.7261, aux.loss_ce: 0.0614, aux.acc_seg: 93.2686, loss: 0.2049 +2024-06-17 02:16:58,519 - mmseg - INFO - Iter [68900/80000] lr: 5.551e-06, eta: 4:38:48, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1453, decode.acc_seg: 93.6258, aux.loss_ce: 0.0625, aux.acc_seg: 93.2016, loss: 0.2078 +2024-06-17 02:18:06,640 - mmseg - INFO - Iter [68950/80000] lr: 5.526e-06, eta: 4:37:31, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1406, decode.acc_seg: 93.8231, aux.loss_ce: 0.0603, aux.acc_seg: 93.4424, loss: 0.2009 +2024-06-17 02:19:14,908 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 02:19:14,908 - mmseg - INFO - Iter [69000/80000] lr: 5.501e-06, eta: 4:36:15, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1398, decode.acc_seg: 93.9072, aux.loss_ce: 0.0599, aux.acc_seg: 93.4406, loss: 0.1997 +2024-06-17 02:20:51,788 - mmseg - INFO - per class results: +2024-06-17 02:20:51,794 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.6 | 90.5 | +| building | 86.25 | 93.51 | +| sky | 95.06 | 97.73 | +| floor | 85.79 | 92.38 | +| tree | 77.27 | 90.76 | +| ceiling | 87.52 | 93.4 | +| road | 86.71 | 91.77 | +| bed | 92.82 | 97.27 | +| windowpane | 66.36 | 82.41 | +| grass | 68.49 | 82.81 | +| cabinet | 66.22 | 75.91 | +| sidewalk | 73.73 | 86.61 | +| person | 86.11 | 94.42 | +| earth | 37.25 | 50.71 | +| door | 59.54 | 73.31 | +| table | 71.0 | 82.11 | +| mountain | 61.8 | 71.82 | +| plant | 54.17 | 62.83 | +| curtain | 76.85 | 88.57 | +| chair | 69.8 | 81.12 | +| car | 87.65 | 94.41 | +| water | 65.73 | 80.72 | +| painting | 77.75 | 92.12 | +| sofa | 83.49 | 92.56 | +| shelf | 46.27 | 63.14 | +| house | 64.8 | 77.73 | +| sea | 79.61 | 89.2 | +| mirror | 80.76 | 86.51 | +| rug | 70.09 | 77.22 | +| field | 31.1 | 56.02 | +| armchair | 61.55 | 76.67 | +| seat | 68.53 | 89.05 | +| fence | 53.13 | 67.14 | +| desk | 61.96 | 79.56 | +| rock | 56.15 | 88.24 | +| wardrobe | 53.84 | 72.37 | +| lamp | 75.76 | 86.49 | +| bathtub | 84.75 | 87.34 | +| railing | 44.4 | 63.41 | +| cushion | 69.85 | 80.92 | +| base | 39.95 | 53.75 | +| box | 37.82 | 49.55 | +| column | 55.08 | 68.03 | +| signboard | 40.98 | 54.33 | +| chest of drawers | 44.43 | 70.96 | +| counter | 41.49 | 52.56 | +| sand | 58.68 | 86.3 | +| sink | 77.95 | 84.66 | +| skyscraper | 49.01 | 60.78 | +| fireplace | 73.85 | 93.9 | +| refrigerator | 84.53 | 91.35 | +| grandstand | 55.34 | 85.04 | +| path | 31.21 | 39.94 | +| stairs | 31.15 | 38.51 | +| runway | 70.76 | 93.06 | +| case | 57.32 | 82.92 | +| pool table | 94.53 | 98.23 | +| pillow | 68.03 | 79.31 | +| screen door | 79.42 | 81.75 | +| stairway | 50.46 | 68.29 | +| river | 12.83 | 23.14 | +| bridge | 64.72 | 72.81 | +| bookcase | 46.4 | 69.95 | +| blind | 41.36 | 45.28 | +| coffee table | 65.1 | 88.2 | +| toilet | 90.32 | 94.54 | +| flower | 47.88 | 61.61 | +| book | 55.18 | 73.84 | +| hill | 8.67 | 15.67 | +| bench | 53.63 | 61.84 | +| countertop | 65.7 | 85.41 | +| stove | 83.85 | 88.69 | +| palm | 55.56 | 77.47 | +| kitchen island | 54.35 | 86.96 | +| computer | 78.73 | 91.96 | +| swivel chair | 50.77 | 71.88 | +| boat | 75.16 | 92.06 | +| bar | 55.02 | 71.46 | +| arcade machine | 79.44 | 84.07 | +| hovel | 46.32 | 51.29 | +| bus | 92.09 | 96.2 | +| towel | 75.36 | 84.03 | +| light | 63.29 | 73.69 | +| truck | 46.49 | 59.52 | +| tower | 41.78 | 74.69 | +| chandelier | 72.23 | 84.81 | +| awning | 49.23 | 62.61 | +| streetlight | 36.5 | 48.31 | +| booth | 46.55 | 68.34 | +| television receiver | 80.59 | 87.89 | +| airplane | 80.32 | 87.67 | +| dirt track | 7.18 | 35.7 | +| apparel | 46.13 | 61.17 | +| pole | 30.64 | 43.06 | +| land | 1.75 | 2.71 | +| bannister | 19.13 | 25.31 | +| escalator | 58.24 | 79.8 | +| ottoman | 54.26 | 71.28 | +| bottle | 41.21 | 66.74 | +| buffet | 48.11 | 59.45 | +| poster | 38.83 | 47.67 | +| stage | 24.27 | 43.98 | +| van | 47.57 | 63.84 | +| ship | 85.86 | 95.52 | +| fountain | 35.97 | 36.66 | +| conveyer belt | 84.75 | 93.36 | +| canopy | 54.45 | 75.41 | +| washer | 79.55 | 84.12 | +| plaything | 31.85 | 47.78 | +| swimming pool | 55.91 | 83.24 | +| stool | 57.22 | 71.97 | +| barrel | 57.55 | 74.43 | +| basket | 42.08 | 58.76 | +| waterfall | 68.28 | 86.38 | +| tent | 92.42 | 98.83 | +| bag | 21.73 | 25.03 | +| minibike | 77.15 | 90.69 | +| cradle | 81.83 | 97.68 | +| oven | 65.51 | 77.58 | +| ball | 46.68 | 52.78 | +| food | 58.56 | 73.0 | +| step | 11.38 | 13.79 | +| tank | 58.15 | 62.17 | +| trade name | 22.12 | 24.41 | +| microwave | 89.89 | 95.98 | +| pot | 58.06 | 66.86 | +| animal | 59.23 | 60.64 | +| bicycle | 61.13 | 78.26 | +| lake | 52.95 | 63.81 | +| dishwasher | 69.71 | 79.45 | +| screen | 50.97 | 75.44 | +| blanket | 28.16 | 31.47 | +| sculpture | 71.23 | 88.56 | +| hood | 63.7 | 75.19 | +| sconce | 58.73 | 68.9 | +| vase | 49.2 | 63.78 | +| traffic light | 38.67 | 63.3 | +| tray | 24.59 | 30.41 | +| ashcan | 46.55 | 63.65 | +| fan | 69.01 | 82.8 | +| pier | 41.88 | 46.37 | +| crt screen | 2.27 | 3.8 | +| plate | 62.41 | 77.38 | +| monitor | 66.0 | 78.04 | +| bulletin board | 52.89 | 62.71 | +| shower | 7.53 | 8.18 | +| radiator | 67.93 | 79.9 | +| glass | 18.91 | 19.74 | +| clock | 46.93 | 54.74 | +| flag | 72.6 | 78.67 | ++---------------------+-------+-------+ +2024-06-17 02:20:51,794 - mmseg - INFO - Summary: +2024-06-17 02:20:51,794 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.49 | 57.98 | 70.48 | ++-------+-------+-------+ +2024-06-17 02:20:51,795 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 02:20:51,795 - mmseg - INFO - Iter(val) [250] aAcc: 0.8649, mIoU: 0.5798, mAcc: 0.7048, IoU.wall: 0.8260, IoU.building: 0.8625, IoU.sky: 0.9506, IoU.floor: 0.8579, IoU.tree: 0.7727, IoU.ceiling: 0.8752, IoU.road: 0.8671, IoU.bed : 0.9282, IoU.windowpane: 0.6636, IoU.grass: 0.6849, IoU.cabinet: 0.6622, IoU.sidewalk: 0.7373, IoU.person: 0.8611, IoU.earth: 0.3725, IoU.door: 0.5954, IoU.table: 0.7100, IoU.mountain: 0.6180, IoU.plant: 0.5417, IoU.curtain: 0.7685, IoU.chair: 0.6980, IoU.car: 0.8765, IoU.water: 0.6573, IoU.painting: 0.7775, IoU.sofa: 0.8349, IoU.shelf: 0.4627, IoU.house: 0.6480, IoU.sea: 0.7961, IoU.mirror: 0.8076, IoU.rug: 0.7009, IoU.field: 0.3110, IoU.armchair: 0.6155, IoU.seat: 0.6853, IoU.fence: 0.5313, IoU.desk: 0.6196, IoU.rock: 0.5615, IoU.wardrobe: 0.5384, IoU.lamp: 0.7576, IoU.bathtub: 0.8475, IoU.railing: 0.4440, IoU.cushion: 0.6985, IoU.base: 0.3995, IoU.box: 0.3782, IoU.column: 0.5508, IoU.signboard: 0.4098, IoU.chest of drawers: 0.4443, IoU.counter: 0.4149, IoU.sand: 0.5868, IoU.sink: 0.7795, IoU.skyscraper: 0.4901, IoU.fireplace: 0.7385, IoU.refrigerator: 0.8453, IoU.grandstand: 0.5534, IoU.path: 0.3121, IoU.stairs: 0.3115, IoU.runway: 0.7076, IoU.case: 0.5732, IoU.pool table: 0.9453, IoU.pillow: 0.6803, IoU.screen door: 0.7942, IoU.stairway: 0.5046, IoU.river: 0.1283, IoU.bridge: 0.6472, IoU.bookcase: 0.4640, IoU.blind: 0.4136, IoU.coffee table: 0.6510, IoU.toilet: 0.9032, IoU.flower: 0.4788, IoU.book: 0.5518, IoU.hill: 0.0867, IoU.bench: 0.5363, IoU.countertop: 0.6570, IoU.stove: 0.8385, IoU.palm: 0.5556, IoU.kitchen island: 0.5435, IoU.computer: 0.7873, IoU.swivel chair: 0.5077, IoU.boat: 0.7516, IoU.bar: 0.5502, IoU.arcade machine: 0.7944, IoU.hovel: 0.4632, IoU.bus: 0.9209, IoU.towel: 0.7536, IoU.light: 0.6329, IoU.truck: 0.4649, IoU.tower: 0.4178, IoU.chandelier: 0.7223, IoU.awning: 0.4923, IoU.streetlight: 0.3650, IoU.booth: 0.4655, IoU.television receiver: 0.8059, IoU.airplane: 0.8032, IoU.dirt track: 0.0718, IoU.apparel: 0.4613, IoU.pole: 0.3064, IoU.land: 0.0175, IoU.bannister: 0.1913, IoU.escalator: 0.5824, IoU.ottoman: 0.5426, IoU.bottle: 0.4121, IoU.buffet: 0.4811, IoU.poster: 0.3883, IoU.stage: 0.2427, IoU.van: 0.4757, IoU.ship: 0.8586, IoU.fountain: 0.3597, IoU.conveyer belt: 0.8475, IoU.canopy: 0.5445, IoU.washer: 0.7955, IoU.plaything: 0.3185, IoU.swimming pool: 0.5591, IoU.stool: 0.5722, IoU.barrel: 0.5755, IoU.basket: 0.4208, IoU.waterfall: 0.6828, IoU.tent: 0.9242, IoU.bag: 0.2173, IoU.minibike: 0.7715, IoU.cradle: 0.8183, IoU.oven: 0.6551, IoU.ball: 0.4668, IoU.food: 0.5856, IoU.step: 0.1138, IoU.tank: 0.5815, IoU.trade name: 0.2212, IoU.microwave: 0.8989, IoU.pot: 0.5806, IoU.animal: 0.5923, IoU.bicycle: 0.6113, IoU.lake: 0.5295, IoU.dishwasher: 0.6971, IoU.screen: 0.5097, IoU.blanket: 0.2816, IoU.sculpture: 0.7123, IoU.hood: 0.6370, IoU.sconce: 0.5873, IoU.vase: 0.4920, IoU.traffic light: 0.3867, IoU.tray: 0.2459, IoU.ashcan: 0.4655, IoU.fan: 0.6901, IoU.pier: 0.4188, IoU.crt screen: 0.0227, IoU.plate: 0.6241, IoU.monitor: 0.6600, IoU.bulletin board: 0.5289, IoU.shower: 0.0753, IoU.radiator: 0.6793, IoU.glass: 0.1891, IoU.clock: 0.4693, IoU.flag: 0.7260, Acc.wall: 0.9050, Acc.building: 0.9351, Acc.sky: 0.9773, Acc.floor: 0.9238, Acc.tree: 0.9076, Acc.ceiling: 0.9340, Acc.road: 0.9177, Acc.bed : 0.9727, Acc.windowpane: 0.8241, Acc.grass: 0.8281, Acc.cabinet: 0.7591, Acc.sidewalk: 0.8661, Acc.person: 0.9442, Acc.earth: 0.5071, Acc.door: 0.7331, Acc.table: 0.8211, Acc.mountain: 0.7182, Acc.plant: 0.6283, Acc.curtain: 0.8857, Acc.chair: 0.8112, Acc.car: 0.9441, Acc.water: 0.8072, Acc.painting: 0.9212, Acc.sofa: 0.9256, Acc.shelf: 0.6314, Acc.house: 0.7773, Acc.sea: 0.8920, Acc.mirror: 0.8651, Acc.rug: 0.7722, Acc.field: 0.5602, Acc.armchair: 0.7667, Acc.seat: 0.8905, Acc.fence: 0.6714, Acc.desk: 0.7956, Acc.rock: 0.8824, Acc.wardrobe: 0.7237, Acc.lamp: 0.8649, Acc.bathtub: 0.8734, Acc.railing: 0.6341, Acc.cushion: 0.8092, Acc.base: 0.5375, Acc.box: 0.4955, Acc.column: 0.6803, Acc.signboard: 0.5433, Acc.chest of drawers: 0.7096, Acc.counter: 0.5256, Acc.sand: 0.8630, Acc.sink: 0.8466, Acc.skyscraper: 0.6078, Acc.fireplace: 0.9390, Acc.refrigerator: 0.9135, Acc.grandstand: 0.8504, Acc.path: 0.3994, Acc.stairs: 0.3851, Acc.runway: 0.9306, Acc.case: 0.8292, Acc.pool table: 0.9823, Acc.pillow: 0.7931, Acc.screen door: 0.8175, Acc.stairway: 0.6829, Acc.river: 0.2314, Acc.bridge: 0.7281, Acc.bookcase: 0.6995, Acc.blind: 0.4528, Acc.coffee table: 0.8820, Acc.toilet: 0.9454, Acc.flower: 0.6161, Acc.book: 0.7384, Acc.hill: 0.1567, Acc.bench: 0.6184, Acc.countertop: 0.8541, Acc.stove: 0.8869, Acc.palm: 0.7747, Acc.kitchen island: 0.8696, Acc.computer: 0.9196, Acc.swivel chair: 0.7188, Acc.boat: 0.9206, Acc.bar: 0.7146, Acc.arcade machine: 0.8407, Acc.hovel: 0.5129, Acc.bus: 0.9620, Acc.towel: 0.8403, Acc.light: 0.7369, Acc.truck: 0.5952, Acc.tower: 0.7469, Acc.chandelier: 0.8481, Acc.awning: 0.6261, Acc.streetlight: 0.4831, Acc.booth: 0.6834, Acc.television receiver: 0.8789, Acc.airplane: 0.8767, Acc.dirt track: 0.3570, Acc.apparel: 0.6117, Acc.pole: 0.4306, Acc.land: 0.0271, Acc.bannister: 0.2531, Acc.escalator: 0.7980, Acc.ottoman: 0.7128, Acc.bottle: 0.6674, Acc.buffet: 0.5945, Acc.poster: 0.4767, Acc.stage: 0.4398, Acc.van: 0.6384, Acc.ship: 0.9552, Acc.fountain: 0.3666, Acc.conveyer belt: 0.9336, Acc.canopy: 0.7541, Acc.washer: 0.8412, Acc.plaything: 0.4778, Acc.swimming pool: 0.8324, Acc.stool: 0.7197, Acc.barrel: 0.7443, Acc.basket: 0.5876, Acc.waterfall: 0.8638, Acc.tent: 0.9883, Acc.bag: 0.2503, Acc.minibike: 0.9069, Acc.cradle: 0.9768, Acc.oven: 0.7758, Acc.ball: 0.5278, Acc.food: 0.7300, Acc.step: 0.1379, Acc.tank: 0.6217, Acc.trade name: 0.2441, Acc.microwave: 0.9598, Acc.pot: 0.6686, Acc.animal: 0.6064, Acc.bicycle: 0.7826, Acc.lake: 0.6381, Acc.dishwasher: 0.7945, Acc.screen: 0.7544, Acc.blanket: 0.3147, Acc.sculpture: 0.8856, Acc.hood: 0.7519, Acc.sconce: 0.6890, Acc.vase: 0.6378, Acc.traffic light: 0.6330, Acc.tray: 0.3041, Acc.ashcan: 0.6365, Acc.fan: 0.8280, Acc.pier: 0.4637, Acc.crt screen: 0.0380, Acc.plate: 0.7738, Acc.monitor: 0.7804, Acc.bulletin board: 0.6271, Acc.shower: 0.0818, Acc.radiator: 0.7990, Acc.glass: 0.1974, Acc.clock: 0.5474, Acc.flag: 0.7867 +2024-06-17 02:22:00,481 - mmseg - INFO - Iter [69050/80000] lr: 5.476e-06, eta: 4:35:14, time: 3.311, data_time: 1.954, memory: 70722, decode.loss_ce: 0.1444, decode.acc_seg: 93.7190, aux.loss_ce: 0.0623, aux.acc_seg: 93.2312, loss: 0.2068 +2024-06-17 02:23:08,592 - mmseg - INFO - Iter [69100/80000] lr: 5.450e-06, eta: 4:33:57, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1391, decode.acc_seg: 93.9494, aux.loss_ce: 0.0598, aux.acc_seg: 93.5338, loss: 0.1988 +2024-06-17 02:24:16,828 - mmseg - INFO - Iter [69150/80000] lr: 5.425e-06, eta: 4:32:41, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1574, decode.acc_seg: 93.2620, aux.loss_ce: 0.0672, aux.acc_seg: 92.8922, loss: 0.2246 +2024-06-17 02:25:24,749 - mmseg - INFO - Iter [69200/80000] lr: 5.400e-06, eta: 4:31:24, time: 1.358, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1360, decode.acc_seg: 94.0973, aux.loss_ce: 0.0581, aux.acc_seg: 93.6992, loss: 0.1941 +2024-06-17 02:26:32,860 - mmseg - INFO - Iter [69250/80000] lr: 5.376e-06, eta: 4:30:08, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1407, decode.acc_seg: 93.7314, aux.loss_ce: 0.0609, aux.acc_seg: 93.2531, loss: 0.2016 +2024-06-17 02:27:41,169 - mmseg - INFO - Iter [69300/80000] lr: 5.351e-06, eta: 4:28:51, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1464, decode.acc_seg: 93.5474, aux.loss_ce: 0.0623, aux.acc_seg: 93.1401, loss: 0.2087 +2024-06-17 02:28:49,540 - mmseg - INFO - Iter [69350/80000] lr: 5.326e-06, eta: 4:27:35, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1454, decode.acc_seg: 93.5407, aux.loss_ce: 0.0627, aux.acc_seg: 93.0648, loss: 0.2081 +2024-06-17 02:29:57,536 - mmseg - INFO - Iter [69400/80000] lr: 5.301e-06, eta: 4:26:18, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1400, decode.acc_seg: 93.8803, aux.loss_ce: 0.0605, aux.acc_seg: 93.4392, loss: 0.2005 +2024-06-17 02:31:05,498 - mmseg - INFO - Iter [69450/80000] lr: 5.276e-06, eta: 4:25:02, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1380, decode.acc_seg: 93.9230, aux.loss_ce: 0.0594, aux.acc_seg: 93.4488, loss: 0.1974 +2024-06-17 02:32:16,176 - mmseg - INFO - Iter [69500/80000] lr: 5.250e-06, eta: 4:23:46, time: 1.414, data_time: 0.051, memory: 70722, decode.loss_ce: 0.1403, decode.acc_seg: 93.9113, aux.loss_ce: 0.0603, aux.acc_seg: 93.4240, loss: 0.2006 +2024-06-17 02:33:24,483 - mmseg - INFO - Iter [69550/80000] lr: 5.225e-06, eta: 4:22:29, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1374, decode.acc_seg: 93.9295, aux.loss_ce: 0.0592, aux.acc_seg: 93.5246, loss: 0.1966 +2024-06-17 02:34:32,497 - mmseg - INFO - Iter [69600/80000] lr: 5.200e-06, eta: 4:21:13, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1375, decode.acc_seg: 93.9333, aux.loss_ce: 0.0591, aux.acc_seg: 93.4650, loss: 0.1966 +2024-06-17 02:35:40,540 - mmseg - INFO - Iter [69650/80000] lr: 5.175e-06, eta: 4:19:56, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1517, decode.acc_seg: 93.4493, aux.loss_ce: 0.0656, aux.acc_seg: 92.9487, loss: 0.2173 +2024-06-17 02:36:48,758 - mmseg - INFO - Iter [69700/80000] lr: 5.151e-06, eta: 4:18:40, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1447, decode.acc_seg: 93.4406, aux.loss_ce: 0.0620, aux.acc_seg: 92.9843, loss: 0.2067 +2024-06-17 02:37:56,732 - mmseg - INFO - Iter [69750/80000] lr: 5.126e-06, eta: 4:17:24, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1437, decode.acc_seg: 93.7807, aux.loss_ce: 0.0618, aux.acc_seg: 93.3480, loss: 0.2055 +2024-06-17 02:39:04,774 - mmseg - INFO - Iter [69800/80000] lr: 5.101e-06, eta: 4:16:07, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1433, decode.acc_seg: 93.6520, aux.loss_ce: 0.0616, aux.acc_seg: 93.2159, loss: 0.2049 +2024-06-17 02:40:13,140 - mmseg - INFO - Iter [69850/80000] lr: 5.076e-06, eta: 4:14:51, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1400, decode.acc_seg: 93.8396, aux.loss_ce: 0.0602, aux.acc_seg: 93.3451, loss: 0.2002 +2024-06-17 02:41:21,462 - mmseg - INFO - Iter [69900/80000] lr: 5.051e-06, eta: 4:13:35, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1468, decode.acc_seg: 93.3846, aux.loss_ce: 0.0633, aux.acc_seg: 92.8926, loss: 0.2101 +2024-06-17 02:42:29,653 - mmseg - INFO - Iter [69950/80000] lr: 5.025e-06, eta: 4:12:18, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1460, decode.acc_seg: 93.4574, aux.loss_ce: 0.0625, aux.acc_seg: 93.0393, loss: 0.2086 +2024-06-17 02:43:37,690 - mmseg - INFO - Saving checkpoint at 70000 iterations +2024-06-17 02:45:06,577 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 02:45:06,577 - mmseg - INFO - Iter [70000/80000] lr: 5.000e-06, eta: 4:11:15, time: 3.138, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1404, decode.acc_seg: 93.7923, aux.loss_ce: 0.0607, aux.acc_seg: 93.3670, loss: 0.2012 +2024-06-17 02:46:41,352 - mmseg - INFO - per class results: +2024-06-17 02:46:41,359 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.62 | 90.01 | +| building | 86.16 | 94.15 | +| sky | 95.1 | 97.74 | +| floor | 85.61 | 92.17 | +| tree | 77.2 | 90.08 | +| ceiling | 87.73 | 94.26 | +| road | 86.75 | 91.71 | +| bed | 93.36 | 97.22 | +| windowpane | 65.5 | 81.35 | +| grass | 69.63 | 83.53 | +| cabinet | 66.41 | 76.12 | +| sidewalk | 73.04 | 86.45 | +| person | 86.24 | 94.27 | +| earth | 37.36 | 50.67 | +| door | 60.27 | 75.53 | +| table | 70.66 | 82.32 | +| mountain | 61.71 | 73.37 | +| plant | 53.47 | 62.11 | +| curtain | 77.02 | 88.89 | +| chair | 70.04 | 82.6 | +| car | 87.64 | 94.42 | +| water | 63.64 | 79.29 | +| painting | 77.98 | 92.33 | +| sofa | 82.99 | 90.58 | +| shelf | 46.36 | 61.6 | +| house | 65.68 | 77.29 | +| sea | 75.0 | 89.66 | +| mirror | 80.15 | 87.01 | +| rug | 70.56 | 80.26 | +| field | 30.34 | 52.72 | +| armchair | 61.46 | 76.1 | +| seat | 68.79 | 89.55 | +| fence | 53.06 | 63.91 | +| desk | 61.07 | 79.83 | +| rock | 57.4 | 88.79 | +| wardrobe | 53.74 | 72.62 | +| lamp | 75.87 | 88.12 | +| bathtub | 84.77 | 87.22 | +| railing | 43.31 | 58.95 | +| cushion | 69.22 | 83.52 | +| base | 40.5 | 64.06 | +| box | 38.09 | 50.08 | +| column | 54.19 | 66.19 | +| signboard | 41.17 | 56.13 | +| chest of drawers | 45.64 | 65.57 | +| counter | 38.82 | 48.16 | +| sand | 58.45 | 87.43 | +| sink | 78.71 | 84.96 | +| skyscraper | 48.98 | 60.66 | +| fireplace | 74.25 | 94.52 | +| refrigerator | 85.12 | 92.75 | +| grandstand | 56.08 | 81.91 | +| path | 28.53 | 39.0 | +| stairs | 35.44 | 43.0 | +| runway | 70.72 | 93.14 | +| case | 57.53 | 77.54 | +| pool table | 94.39 | 98.44 | +| pillow | 66.06 | 76.32 | +| screen door | 78.91 | 80.77 | +| stairway | 53.94 | 70.19 | +| river | 14.55 | 25.15 | +| bridge | 62.44 | 68.23 | +| bookcase | 46.63 | 69.8 | +| blind | 40.13 | 43.55 | +| coffee table | 65.06 | 87.38 | +| toilet | 90.82 | 94.29 | +| flower | 45.14 | 61.07 | +| book | 54.89 | 78.02 | +| hill | 8.21 | 13.0 | +| bench | 54.13 | 62.15 | +| countertop | 65.97 | 86.62 | +| stove | 83.63 | 89.01 | +| palm | 55.94 | 78.43 | +| kitchen island | 54.54 | 84.83 | +| computer | 78.63 | 91.55 | +| swivel chair | 45.66 | 63.99 | +| boat | 74.81 | 92.21 | +| bar | 55.39 | 75.63 | +| arcade machine | 77.81 | 82.25 | +| hovel | 51.8 | 57.9 | +| bus | 91.86 | 96.76 | +| towel | 76.77 | 88.19 | +| light | 63.36 | 72.85 | +| truck | 45.68 | 59.49 | +| tower | 16.25 | 21.86 | +| chandelier | 73.29 | 85.53 | +| awning | 54.01 | 66.52 | +| streetlight | 36.69 | 49.43 | +| booth | 42.04 | 60.12 | +| television receiver | 78.98 | 89.19 | +| airplane | 79.72 | 89.5 | +| dirt track | 6.66 | 32.62 | +| apparel | 48.01 | 63.82 | +| pole | 29.95 | 43.95 | +| land | 2.11 | 3.49 | +| bannister | 18.79 | 27.6 | +| escalator | 57.57 | 79.14 | +| ottoman | 54.56 | 73.9 | +| bottle | 42.22 | 68.67 | +| buffet | 47.66 | 58.2 | +| poster | 37.59 | 51.79 | +| stage | 23.75 | 46.07 | +| van | 48.24 | 62.66 | +| ship | 87.04 | 93.06 | +| fountain | 33.13 | 33.73 | +| conveyer belt | 83.44 | 93.56 | +| canopy | 55.92 | 77.58 | +| washer | 79.74 | 84.37 | +| plaything | 38.68 | 59.18 | +| swimming pool | 60.48 | 90.81 | +| stool | 56.77 | 70.23 | +| barrel | 55.02 | 74.59 | +| basket | 40.75 | 60.31 | +| waterfall | 65.46 | 83.14 | +| tent | 93.67 | 98.6 | +| bag | 21.29 | 24.1 | +| minibike | 76.93 | 91.17 | +| cradle | 84.47 | 97.64 | +| oven | 57.23 | 66.31 | +| ball | 52.3 | 61.82 | +| food | 58.84 | 72.59 | +| step | 14.39 | 17.77 | +| tank | 62.15 | 67.22 | +| trade name | 28.34 | 34.19 | +| microwave | 87.7 | 96.11 | +| pot | 58.72 | 68.57 | +| animal | 58.81 | 60.29 | +| bicycle | 60.52 | 77.3 | +| lake | 52.56 | 63.84 | +| dishwasher | 70.44 | 79.4 | +| screen | 50.52 | 74.8 | +| blanket | 30.91 | 35.63 | +| sculpture | 73.39 | 87.98 | +| hood | 64.04 | 77.15 | +| sconce | 58.85 | 68.69 | +| vase | 49.9 | 63.85 | +| traffic light | 39.43 | 62.48 | +| tray | 27.23 | 33.72 | +| ashcan | 44.6 | 64.53 | +| fan | 70.24 | 83.86 | +| pier | 42.19 | 47.34 | +| crt screen | 1.84 | 3.56 | +| plate | 62.11 | 79.93 | +| monitor | 55.14 | 64.37 | +| bulletin board | 51.81 | 65.09 | +| shower | 11.16 | 11.39 | +| radiator | 69.19 | 78.62 | +| glass | 20.44 | 21.83 | +| clock | 47.01 | 54.63 | +| flag | 73.68 | 80.2 | ++---------------------+-------+-------+ +2024-06-17 02:46:41,359 - mmseg - INFO - Summary: +2024-06-17 02:46:41,359 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.47 | 57.87 | 70.42 | ++-------+-------+-------+ +2024-06-17 02:46:41,360 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 02:46:41,360 - mmseg - INFO - Iter(val) [250] aAcc: 0.8647, mIoU: 0.5787, mAcc: 0.7042, IoU.wall: 0.8262, IoU.building: 0.8616, IoU.sky: 0.9510, IoU.floor: 0.8561, IoU.tree: 0.7720, IoU.ceiling: 0.8773, IoU.road: 0.8675, IoU.bed : 0.9336, IoU.windowpane: 0.6550, IoU.grass: 0.6963, IoU.cabinet: 0.6641, IoU.sidewalk: 0.7304, IoU.person: 0.8624, IoU.earth: 0.3736, IoU.door: 0.6027, IoU.table: 0.7066, IoU.mountain: 0.6171, IoU.plant: 0.5347, IoU.curtain: 0.7702, IoU.chair: 0.7004, IoU.car: 0.8764, IoU.water: 0.6364, IoU.painting: 0.7798, IoU.sofa: 0.8299, IoU.shelf: 0.4636, IoU.house: 0.6568, IoU.sea: 0.7500, IoU.mirror: 0.8015, IoU.rug: 0.7056, IoU.field: 0.3034, IoU.armchair: 0.6146, IoU.seat: 0.6879, IoU.fence: 0.5306, IoU.desk: 0.6107, IoU.rock: 0.5740, IoU.wardrobe: 0.5374, IoU.lamp: 0.7587, IoU.bathtub: 0.8477, IoU.railing: 0.4331, IoU.cushion: 0.6922, IoU.base: 0.4050, IoU.box: 0.3809, IoU.column: 0.5419, IoU.signboard: 0.4117, IoU.chest of drawers: 0.4564, IoU.counter: 0.3882, IoU.sand: 0.5845, IoU.sink: 0.7871, IoU.skyscraper: 0.4898, IoU.fireplace: 0.7425, IoU.refrigerator: 0.8512, IoU.grandstand: 0.5608, IoU.path: 0.2853, IoU.stairs: 0.3544, IoU.runway: 0.7072, IoU.case: 0.5753, IoU.pool table: 0.9439, IoU.pillow: 0.6606, IoU.screen door: 0.7891, IoU.stairway: 0.5394, IoU.river: 0.1455, IoU.bridge: 0.6244, IoU.bookcase: 0.4663, IoU.blind: 0.4013, IoU.coffee table: 0.6506, IoU.toilet: 0.9082, IoU.flower: 0.4514, IoU.book: 0.5489, IoU.hill: 0.0821, IoU.bench: 0.5413, IoU.countertop: 0.6597, IoU.stove: 0.8363, IoU.palm: 0.5594, IoU.kitchen island: 0.5454, IoU.computer: 0.7863, IoU.swivel chair: 0.4566, IoU.boat: 0.7481, IoU.bar: 0.5539, IoU.arcade machine: 0.7781, IoU.hovel: 0.5180, IoU.bus: 0.9186, IoU.towel: 0.7677, IoU.light: 0.6336, IoU.truck: 0.4568, IoU.tower: 0.1625, IoU.chandelier: 0.7329, IoU.awning: 0.5401, IoU.streetlight: 0.3669, IoU.booth: 0.4204, IoU.television receiver: 0.7898, IoU.airplane: 0.7972, IoU.dirt track: 0.0666, IoU.apparel: 0.4801, IoU.pole: 0.2995, IoU.land: 0.0211, IoU.bannister: 0.1879, IoU.escalator: 0.5757, IoU.ottoman: 0.5456, IoU.bottle: 0.4222, IoU.buffet: 0.4766, IoU.poster: 0.3759, IoU.stage: 0.2375, IoU.van: 0.4824, IoU.ship: 0.8704, IoU.fountain: 0.3313, IoU.conveyer belt: 0.8344, IoU.canopy: 0.5592, IoU.washer: 0.7974, IoU.plaything: 0.3868, IoU.swimming pool: 0.6048, IoU.stool: 0.5677, IoU.barrel: 0.5502, IoU.basket: 0.4075, IoU.waterfall: 0.6546, IoU.tent: 0.9367, IoU.bag: 0.2129, IoU.minibike: 0.7693, IoU.cradle: 0.8447, IoU.oven: 0.5723, IoU.ball: 0.5230, IoU.food: 0.5884, IoU.step: 0.1439, IoU.tank: 0.6215, IoU.trade name: 0.2834, IoU.microwave: 0.8770, IoU.pot: 0.5872, IoU.animal: 0.5881, IoU.bicycle: 0.6052, IoU.lake: 0.5256, IoU.dishwasher: 0.7044, IoU.screen: 0.5052, IoU.blanket: 0.3091, IoU.sculpture: 0.7339, IoU.hood: 0.6404, IoU.sconce: 0.5885, IoU.vase: 0.4990, IoU.traffic light: 0.3943, IoU.tray: 0.2723, IoU.ashcan: 0.4460, IoU.fan: 0.7024, IoU.pier: 0.4219, IoU.crt screen: 0.0184, IoU.plate: 0.6211, IoU.monitor: 0.5514, IoU.bulletin board: 0.5181, IoU.shower: 0.1116, IoU.radiator: 0.6919, IoU.glass: 0.2044, IoU.clock: 0.4701, IoU.flag: 0.7368, Acc.wall: 0.9001, Acc.building: 0.9415, Acc.sky: 0.9774, Acc.floor: 0.9217, Acc.tree: 0.9008, Acc.ceiling: 0.9426, Acc.road: 0.9171, Acc.bed : 0.9722, Acc.windowpane: 0.8135, Acc.grass: 0.8353, Acc.cabinet: 0.7612, Acc.sidewalk: 0.8645, Acc.person: 0.9427, Acc.earth: 0.5067, Acc.door: 0.7553, Acc.table: 0.8232, Acc.mountain: 0.7337, Acc.plant: 0.6211, Acc.curtain: 0.8889, Acc.chair: 0.8260, Acc.car: 0.9442, Acc.water: 0.7929, Acc.painting: 0.9233, Acc.sofa: 0.9058, Acc.shelf: 0.6160, Acc.house: 0.7729, Acc.sea: 0.8966, Acc.mirror: 0.8701, Acc.rug: 0.8026, Acc.field: 0.5272, Acc.armchair: 0.7610, Acc.seat: 0.8955, Acc.fence: 0.6391, Acc.desk: 0.7983, Acc.rock: 0.8879, Acc.wardrobe: 0.7262, Acc.lamp: 0.8812, Acc.bathtub: 0.8722, Acc.railing: 0.5895, Acc.cushion: 0.8352, Acc.base: 0.6406, Acc.box: 0.5008, Acc.column: 0.6619, Acc.signboard: 0.5613, Acc.chest of drawers: 0.6557, Acc.counter: 0.4816, Acc.sand: 0.8743, Acc.sink: 0.8496, Acc.skyscraper: 0.6066, Acc.fireplace: 0.9452, Acc.refrigerator: 0.9275, Acc.grandstand: 0.8191, Acc.path: 0.3900, Acc.stairs: 0.4300, Acc.runway: 0.9314, Acc.case: 0.7754, Acc.pool table: 0.9844, Acc.pillow: 0.7632, Acc.screen door: 0.8077, Acc.stairway: 0.7019, Acc.river: 0.2515, Acc.bridge: 0.6823, Acc.bookcase: 0.6980, Acc.blind: 0.4355, Acc.coffee table: 0.8738, Acc.toilet: 0.9429, Acc.flower: 0.6107, Acc.book: 0.7802, Acc.hill: 0.1300, Acc.bench: 0.6215, Acc.countertop: 0.8662, Acc.stove: 0.8901, Acc.palm: 0.7843, Acc.kitchen island: 0.8483, Acc.computer: 0.9155, Acc.swivel chair: 0.6399, Acc.boat: 0.9221, Acc.bar: 0.7563, Acc.arcade machine: 0.8225, Acc.hovel: 0.5790, Acc.bus: 0.9676, Acc.towel: 0.8819, Acc.light: 0.7285, Acc.truck: 0.5949, Acc.tower: 0.2186, Acc.chandelier: 0.8553, Acc.awning: 0.6652, Acc.streetlight: 0.4943, Acc.booth: 0.6012, Acc.television receiver: 0.8919, Acc.airplane: 0.8950, Acc.dirt track: 0.3262, Acc.apparel: 0.6382, Acc.pole: 0.4395, Acc.land: 0.0349, Acc.bannister: 0.2760, Acc.escalator: 0.7914, Acc.ottoman: 0.7390, Acc.bottle: 0.6867, Acc.buffet: 0.5820, Acc.poster: 0.5179, Acc.stage: 0.4607, Acc.van: 0.6266, Acc.ship: 0.9306, Acc.fountain: 0.3373, Acc.conveyer belt: 0.9356, Acc.canopy: 0.7758, Acc.washer: 0.8437, Acc.plaything: 0.5918, Acc.swimming pool: 0.9081, Acc.stool: 0.7023, Acc.barrel: 0.7459, Acc.basket: 0.6031, Acc.waterfall: 0.8314, Acc.tent: 0.9860, Acc.bag: 0.2410, Acc.minibike: 0.9117, Acc.cradle: 0.9764, Acc.oven: 0.6631, Acc.ball: 0.6182, Acc.food: 0.7259, Acc.step: 0.1777, Acc.tank: 0.6722, Acc.trade name: 0.3419, Acc.microwave: 0.9611, Acc.pot: 0.6857, Acc.animal: 0.6029, Acc.bicycle: 0.7730, Acc.lake: 0.6384, Acc.dishwasher: 0.7940, Acc.screen: 0.7480, Acc.blanket: 0.3563, Acc.sculpture: 0.8798, Acc.hood: 0.7715, Acc.sconce: 0.6869, Acc.vase: 0.6385, Acc.traffic light: 0.6248, Acc.tray: 0.3372, Acc.ashcan: 0.6453, Acc.fan: 0.8386, Acc.pier: 0.4734, Acc.crt screen: 0.0356, Acc.plate: 0.7993, Acc.monitor: 0.6437, Acc.bulletin board: 0.6509, Acc.shower: 0.1139, Acc.radiator: 0.7862, Acc.glass: 0.2183, Acc.clock: 0.5463, Acc.flag: 0.8020 +2024-06-17 02:47:50,291 - mmseg - INFO - Iter [70050/80000] lr: 4.976e-06, eta: 4:10:12, time: 3.274, data_time: 1.913, memory: 70722, decode.loss_ce: 0.1366, decode.acc_seg: 93.9141, aux.loss_ce: 0.0586, aux.acc_seg: 93.5205, loss: 0.1952 +2024-06-17 02:48:58,535 - mmseg - INFO - Iter [70100/80000] lr: 4.951e-06, eta: 4:08:55, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1358, decode.acc_seg: 93.8587, aux.loss_ce: 0.0586, aux.acc_seg: 93.4270, loss: 0.1944 +2024-06-17 02:50:06,667 - mmseg - INFO - Iter [70150/80000] lr: 4.926e-06, eta: 4:07:39, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1446, decode.acc_seg: 93.7626, aux.loss_ce: 0.0620, aux.acc_seg: 93.3311, loss: 0.2066 +2024-06-17 02:51:14,776 - mmseg - INFO - Iter [70200/80000] lr: 4.901e-06, eta: 4:06:22, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1376, decode.acc_seg: 93.7937, aux.loss_ce: 0.0591, aux.acc_seg: 93.3530, loss: 0.1967 +2024-06-17 02:52:22,830 - mmseg - INFO - Iter [70250/80000] lr: 4.876e-06, eta: 4:05:06, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1432, decode.acc_seg: 93.8146, aux.loss_ce: 0.0622, aux.acc_seg: 93.3118, loss: 0.2054 +2024-06-17 02:53:31,054 - mmseg - INFO - Iter [70300/80000] lr: 4.851e-06, eta: 4:03:49, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1387, decode.acc_seg: 93.9295, aux.loss_ce: 0.0600, aux.acc_seg: 93.4828, loss: 0.1986 +2024-06-17 02:54:39,220 - mmseg - INFO - Iter [70350/80000] lr: 4.825e-06, eta: 4:02:33, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1399, decode.acc_seg: 93.9131, aux.loss_ce: 0.0602, aux.acc_seg: 93.4930, loss: 0.2001 +2024-06-17 02:55:47,291 - mmseg - INFO - Iter [70400/80000] lr: 4.800e-06, eta: 4:01:17, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1409, decode.acc_seg: 93.6989, aux.loss_ce: 0.0610, aux.acc_seg: 93.2050, loss: 0.2019 +2024-06-17 02:56:55,636 - mmseg - INFO - Iter [70450/80000] lr: 4.775e-06, eta: 4:00:00, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1409, decode.acc_seg: 93.9658, aux.loss_ce: 0.0606, aux.acc_seg: 93.5429, loss: 0.2016 +2024-06-17 02:58:03,990 - mmseg - INFO - Iter [70500/80000] lr: 4.751e-06, eta: 3:58:44, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1398, decode.acc_seg: 93.7223, aux.loss_ce: 0.0604, aux.acc_seg: 93.2867, loss: 0.2002 +2024-06-17 02:59:11,919 - mmseg - INFO - Iter [70550/80000] lr: 4.726e-06, eta: 3:57:28, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1460, decode.acc_seg: 93.6448, aux.loss_ce: 0.0633, aux.acc_seg: 93.2097, loss: 0.2093 +2024-06-17 03:00:20,091 - mmseg - INFO - Iter [70600/80000] lr: 4.701e-06, eta: 3:56:11, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1455, decode.acc_seg: 93.7864, aux.loss_ce: 0.0624, aux.acc_seg: 93.3605, loss: 0.2078 +2024-06-17 03:01:28,345 - mmseg - INFO - Iter [70650/80000] lr: 4.676e-06, eta: 3:54:55, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1324, decode.acc_seg: 94.0917, aux.loss_ce: 0.0574, aux.acc_seg: 93.6120, loss: 0.1898 +2024-06-17 03:02:36,629 - mmseg - INFO - Iter [70700/80000] lr: 4.651e-06, eta: 3:53:39, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1395, decode.acc_seg: 93.5986, aux.loss_ce: 0.0601, aux.acc_seg: 93.1331, loss: 0.1996 +2024-06-17 03:03:47,426 - mmseg - INFO - Iter [70750/80000] lr: 4.625e-06, eta: 3:52:23, time: 1.416, data_time: 0.063, memory: 70722, decode.loss_ce: 0.1376, decode.acc_seg: 93.7779, aux.loss_ce: 0.0593, aux.acc_seg: 93.3461, loss: 0.1969 +2024-06-17 03:04:55,723 - mmseg - INFO - Iter [70800/80000] lr: 4.600e-06, eta: 3:51:06, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1363, decode.acc_seg: 94.0101, aux.loss_ce: 0.0592, aux.acc_seg: 93.5306, loss: 0.1955 +2024-06-17 03:06:03,928 - mmseg - INFO - Iter [70850/80000] lr: 4.575e-06, eta: 3:49:50, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1406, decode.acc_seg: 93.5874, aux.loss_ce: 0.0605, aux.acc_seg: 93.1605, loss: 0.2011 +2024-06-17 03:07:12,007 - mmseg - INFO - Iter [70900/80000] lr: 4.550e-06, eta: 3:48:34, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1406, decode.acc_seg: 93.6420, aux.loss_ce: 0.0610, aux.acc_seg: 93.1735, loss: 0.2016 +2024-06-17 03:08:20,107 - mmseg - INFO - Iter [70950/80000] lr: 4.526e-06, eta: 3:47:17, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1415, decode.acc_seg: 93.7659, aux.loss_ce: 0.0611, aux.acc_seg: 93.2930, loss: 0.2026 +2024-06-17 03:09:28,203 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 03:09:28,203 - mmseg - INFO - Iter [71000/80000] lr: 4.501e-06, eta: 3:46:01, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1360, decode.acc_seg: 93.9130, aux.loss_ce: 0.0586, aux.acc_seg: 93.5037, loss: 0.1947 +2024-06-17 03:11:04,833 - mmseg - INFO - per class results: +2024-06-17 03:11:04,839 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.67 | 90.23 | +| building | 86.22 | 93.89 | +| sky | 95.05 | 97.7 | +| floor | 85.73 | 92.32 | +| tree | 77.72 | 89.79 | +| ceiling | 87.53 | 94.1 | +| road | 87.24 | 91.93 | +| bed | 93.09 | 97.36 | +| windowpane | 66.22 | 80.84 | +| grass | 69.21 | 81.94 | +| cabinet | 66.34 | 76.41 | +| sidewalk | 73.1 | 86.4 | +| person | 86.17 | 94.05 | +| earth | 37.58 | 50.95 | +| door | 60.91 | 75.35 | +| table | 71.02 | 82.51 | +| mountain | 61.55 | 73.96 | +| plant | 54.7 | 64.71 | +| curtain | 77.32 | 89.18 | +| chair | 68.87 | 79.78 | +| car | 87.71 | 94.43 | +| water | 65.31 | 81.12 | +| painting | 78.1 | 91.73 | +| sofa | 83.16 | 92.62 | +| shelf | 45.59 | 60.51 | +| house | 65.96 | 77.52 | +| sea | 77.88 | 88.91 | +| mirror | 79.5 | 86.01 | +| rug | 70.95 | 80.78 | +| field | 32.36 | 59.57 | +| armchair | 61.78 | 77.54 | +| seat | 65.8 | 90.07 | +| fence | 53.07 | 65.39 | +| desk | 60.5 | 79.34 | +| rock | 56.27 | 86.75 | +| wardrobe | 54.24 | 74.1 | +| lamp | 75.55 | 86.85 | +| bathtub | 84.87 | 87.0 | +| railing | 43.74 | 61.84 | +| cushion | 67.88 | 83.97 | +| base | 41.2 | 57.4 | +| box | 37.34 | 47.34 | +| column | 54.11 | 67.34 | +| signboard | 41.33 | 57.47 | +| chest of drawers | 44.78 | 67.14 | +| counter | 39.33 | 46.7 | +| sand | 59.5 | 86.19 | +| sink | 78.3 | 85.52 | +| skyscraper | 49.12 | 61.03 | +| fireplace | 74.76 | 90.5 | +| refrigerator | 83.48 | 89.35 | +| grandstand | 54.19 | 83.82 | +| path | 29.6 | 43.74 | +| stairs | 31.87 | 37.45 | +| runway | 71.3 | 93.01 | +| case | 59.28 | 83.31 | +| pool table | 94.75 | 98.2 | +| pillow | 62.86 | 71.16 | +| screen door | 81.98 | 83.8 | +| stairway | 53.89 | 71.42 | +| river | 10.95 | 18.66 | +| bridge | 64.8 | 71.13 | +| bookcase | 47.34 | 67.99 | +| blind | 41.42 | 45.41 | +| coffee table | 64.77 | 87.28 | +| toilet | 91.27 | 94.58 | +| flower | 46.34 | 58.03 | +| book | 55.82 | 79.6 | +| hill | 9.47 | 15.2 | +| bench | 53.38 | 63.1 | +| countertop | 65.54 | 85.31 | +| stove | 83.71 | 88.4 | +| palm | 56.51 | 80.52 | +| kitchen island | 56.66 | 87.57 | +| computer | 78.42 | 91.4 | +| swivel chair | 50.31 | 74.08 | +| boat | 74.97 | 91.83 | +| bar | 57.39 | 81.25 | +| arcade machine | 77.42 | 83.21 | +| hovel | 51.43 | 57.92 | +| bus | 92.17 | 96.31 | +| towel | 76.17 | 87.52 | +| light | 63.08 | 75.08 | +| truck | 45.77 | 58.98 | +| tower | 17.55 | 24.0 | +| chandelier | 72.35 | 85.28 | +| awning | 50.34 | 65.93 | +| streetlight | 36.04 | 50.11 | +| booth | 45.86 | 62.87 | +| television receiver | 80.97 | 89.12 | +| airplane | 78.32 | 84.97 | +| dirt track | 5.93 | 24.7 | +| apparel | 47.4 | 64.66 | +| pole | 30.64 | 43.55 | +| land | 1.6 | 3.22 | +| bannister | 18.32 | 28.54 | +| escalator | 57.5 | 80.03 | +| ottoman | 53.6 | 70.99 | +| bottle | 41.82 | 69.23 | +| buffet | 50.61 | 64.47 | +| poster | 38.34 | 54.29 | +| stage | 22.59 | 45.69 | +| van | 48.04 | 64.76 | +| ship | 85.56 | 93.42 | +| fountain | 35.29 | 36.07 | +| conveyer belt | 83.0 | 94.14 | +| canopy | 56.72 | 78.53 | +| washer | 81.07 | 85.45 | +| plaything | 33.89 | 52.6 | +| swimming pool | 59.37 | 89.18 | +| stool | 55.81 | 69.97 | +| barrel | 55.13 | 74.4 | +| basket | 42.33 | 61.48 | +| waterfall | 66.46 | 81.21 | +| tent | 94.92 | 98.54 | +| bag | 21.25 | 24.24 | +| minibike | 77.21 | 91.42 | +| cradle | 82.85 | 97.57 | +| oven | 61.46 | 71.88 | +| ball | 50.46 | 59.82 | +| food | 59.34 | 74.98 | +| step | 15.81 | 19.64 | +| tank | 61.58 | 67.43 | +| trade name | 28.8 | 36.6 | +| microwave | 88.63 | 95.82 | +| pot | 56.62 | 65.6 | +| animal | 59.5 | 61.23 | +| bicycle | 60.59 | 80.26 | +| lake | 52.42 | 63.84 | +| dishwasher | 69.92 | 78.25 | +| screen | 51.11 | 73.11 | +| blanket | 28.44 | 31.97 | +| sculpture | 76.04 | 87.46 | +| hood | 63.52 | 75.36 | +| sconce | 59.41 | 71.76 | +| vase | 46.68 | 67.8 | +| traffic light | 42.29 | 58.76 | +| tray | 27.67 | 33.43 | +| ashcan | 47.07 | 64.23 | +| fan | 69.89 | 80.2 | +| pier | 39.48 | 42.7 | +| crt screen | 5.38 | 8.94 | +| plate | 61.5 | 80.93 | +| monitor | 68.88 | 81.87 | +| bulletin board | 51.3 | 60.69 | +| shower | 11.64 | 12.49 | +| radiator | 69.13 | 78.53 | +| glass | 19.15 | 20.11 | +| clock | 48.18 | 55.76 | +| flag | 73.15 | 78.79 | ++---------------------+-------+-------+ +2024-06-17 03:11:04,839 - mmseg - INFO - Summary: +2024-06-17 03:11:04,840 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.52 | 58.07 | 70.64 | ++-------+-------+-------+ +2024-06-17 03:11:04,840 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 03:11:04,840 - mmseg - INFO - Iter(val) [250] aAcc: 0.8652, mIoU: 0.5807, mAcc: 0.7064, IoU.wall: 0.8267, IoU.building: 0.8622, IoU.sky: 0.9505, IoU.floor: 0.8573, IoU.tree: 0.7772, IoU.ceiling: 0.8753, IoU.road: 0.8724, IoU.bed : 0.9309, IoU.windowpane: 0.6622, IoU.grass: 0.6921, IoU.cabinet: 0.6634, IoU.sidewalk: 0.7310, IoU.person: 0.8617, IoU.earth: 0.3758, IoU.door: 0.6091, IoU.table: 0.7102, IoU.mountain: 0.6155, IoU.plant: 0.5470, IoU.curtain: 0.7732, IoU.chair: 0.6887, IoU.car: 0.8771, IoU.water: 0.6531, IoU.painting: 0.7810, IoU.sofa: 0.8316, IoU.shelf: 0.4559, IoU.house: 0.6596, IoU.sea: 0.7788, IoU.mirror: 0.7950, IoU.rug: 0.7095, IoU.field: 0.3236, IoU.armchair: 0.6178, IoU.seat: 0.6580, IoU.fence: 0.5307, IoU.desk: 0.6050, IoU.rock: 0.5627, IoU.wardrobe: 0.5424, IoU.lamp: 0.7555, IoU.bathtub: 0.8487, IoU.railing: 0.4374, IoU.cushion: 0.6788, IoU.base: 0.4120, IoU.box: 0.3734, IoU.column: 0.5411, IoU.signboard: 0.4133, IoU.chest of drawers: 0.4478, IoU.counter: 0.3933, IoU.sand: 0.5950, IoU.sink: 0.7830, IoU.skyscraper: 0.4912, IoU.fireplace: 0.7476, IoU.refrigerator: 0.8348, IoU.grandstand: 0.5419, IoU.path: 0.2960, IoU.stairs: 0.3187, IoU.runway: 0.7130, IoU.case: 0.5928, IoU.pool table: 0.9475, IoU.pillow: 0.6286, IoU.screen door: 0.8198, IoU.stairway: 0.5389, IoU.river: 0.1095, IoU.bridge: 0.6480, IoU.bookcase: 0.4734, IoU.blind: 0.4142, IoU.coffee table: 0.6477, IoU.toilet: 0.9127, IoU.flower: 0.4634, IoU.book: 0.5582, IoU.hill: 0.0947, IoU.bench: 0.5338, IoU.countertop: 0.6554, IoU.stove: 0.8371, IoU.palm: 0.5651, IoU.kitchen island: 0.5666, IoU.computer: 0.7842, IoU.swivel chair: 0.5031, IoU.boat: 0.7497, IoU.bar: 0.5739, IoU.arcade machine: 0.7742, IoU.hovel: 0.5143, IoU.bus: 0.9217, IoU.towel: 0.7617, IoU.light: 0.6308, IoU.truck: 0.4577, IoU.tower: 0.1755, IoU.chandelier: 0.7235, IoU.awning: 0.5034, IoU.streetlight: 0.3604, IoU.booth: 0.4586, IoU.television receiver: 0.8097, IoU.airplane: 0.7832, IoU.dirt track: 0.0593, IoU.apparel: 0.4740, IoU.pole: 0.3064, IoU.land: 0.0160, IoU.bannister: 0.1832, IoU.escalator: 0.5750, IoU.ottoman: 0.5360, IoU.bottle: 0.4182, IoU.buffet: 0.5061, IoU.poster: 0.3834, IoU.stage: 0.2259, IoU.van: 0.4804, IoU.ship: 0.8556, IoU.fountain: 0.3529, IoU.conveyer belt: 0.8300, IoU.canopy: 0.5672, IoU.washer: 0.8107, IoU.plaything: 0.3389, IoU.swimming pool: 0.5937, IoU.stool: 0.5581, IoU.barrel: 0.5513, IoU.basket: 0.4233, IoU.waterfall: 0.6646, IoU.tent: 0.9492, IoU.bag: 0.2125, IoU.minibike: 0.7721, IoU.cradle: 0.8285, IoU.oven: 0.6146, IoU.ball: 0.5046, IoU.food: 0.5934, IoU.step: 0.1581, IoU.tank: 0.6158, IoU.trade name: 0.2880, IoU.microwave: 0.8863, IoU.pot: 0.5662, IoU.animal: 0.5950, IoU.bicycle: 0.6059, IoU.lake: 0.5242, IoU.dishwasher: 0.6992, IoU.screen: 0.5111, IoU.blanket: 0.2844, IoU.sculpture: 0.7604, IoU.hood: 0.6352, IoU.sconce: 0.5941, IoU.vase: 0.4668, IoU.traffic light: 0.4229, IoU.tray: 0.2767, IoU.ashcan: 0.4707, IoU.fan: 0.6989, IoU.pier: 0.3948, IoU.crt screen: 0.0538, IoU.plate: 0.6150, IoU.monitor: 0.6888, IoU.bulletin board: 0.5130, IoU.shower: 0.1164, IoU.radiator: 0.6913, IoU.glass: 0.1915, IoU.clock: 0.4818, IoU.flag: 0.7315, Acc.wall: 0.9023, Acc.building: 0.9389, Acc.sky: 0.9770, Acc.floor: 0.9232, Acc.tree: 0.8979, Acc.ceiling: 0.9410, Acc.road: 0.9193, Acc.bed : 0.9736, Acc.windowpane: 0.8084, Acc.grass: 0.8194, Acc.cabinet: 0.7641, Acc.sidewalk: 0.8640, Acc.person: 0.9405, Acc.earth: 0.5095, Acc.door: 0.7535, Acc.table: 0.8251, Acc.mountain: 0.7396, Acc.plant: 0.6471, Acc.curtain: 0.8918, Acc.chair: 0.7978, Acc.car: 0.9443, Acc.water: 0.8112, Acc.painting: 0.9173, Acc.sofa: 0.9262, Acc.shelf: 0.6051, Acc.house: 0.7752, Acc.sea: 0.8891, Acc.mirror: 0.8601, Acc.rug: 0.8078, Acc.field: 0.5957, Acc.armchair: 0.7754, Acc.seat: 0.9007, Acc.fence: 0.6539, Acc.desk: 0.7934, Acc.rock: 0.8675, Acc.wardrobe: 0.7410, Acc.lamp: 0.8685, Acc.bathtub: 0.8700, Acc.railing: 0.6184, Acc.cushion: 0.8397, Acc.base: 0.5740, Acc.box: 0.4734, Acc.column: 0.6734, Acc.signboard: 0.5747, Acc.chest of drawers: 0.6714, Acc.counter: 0.4670, Acc.sand: 0.8619, Acc.sink: 0.8552, Acc.skyscraper: 0.6103, Acc.fireplace: 0.9050, Acc.refrigerator: 0.8935, Acc.grandstand: 0.8382, Acc.path: 0.4374, Acc.stairs: 0.3745, Acc.runway: 0.9301, Acc.case: 0.8331, Acc.pool table: 0.9820, Acc.pillow: 0.7116, Acc.screen door: 0.8380, Acc.stairway: 0.7142, Acc.river: 0.1866, Acc.bridge: 0.7113, Acc.bookcase: 0.6799, Acc.blind: 0.4541, Acc.coffee table: 0.8728, Acc.toilet: 0.9458, Acc.flower: 0.5803, Acc.book: 0.7960, Acc.hill: 0.1520, Acc.bench: 0.6310, Acc.countertop: 0.8531, Acc.stove: 0.8840, Acc.palm: 0.8052, Acc.kitchen island: 0.8757, Acc.computer: 0.9140, Acc.swivel chair: 0.7408, Acc.boat: 0.9183, Acc.bar: 0.8125, Acc.arcade machine: 0.8321, Acc.hovel: 0.5792, Acc.bus: 0.9631, Acc.towel: 0.8752, Acc.light: 0.7508, Acc.truck: 0.5898, Acc.tower: 0.2400, Acc.chandelier: 0.8528, Acc.awning: 0.6593, Acc.streetlight: 0.5011, Acc.booth: 0.6287, Acc.television receiver: 0.8912, Acc.airplane: 0.8497, Acc.dirt track: 0.2470, Acc.apparel: 0.6466, Acc.pole: 0.4355, Acc.land: 0.0322, Acc.bannister: 0.2854, Acc.escalator: 0.8003, Acc.ottoman: 0.7099, Acc.bottle: 0.6923, Acc.buffet: 0.6447, Acc.poster: 0.5429, Acc.stage: 0.4569, Acc.van: 0.6476, Acc.ship: 0.9342, Acc.fountain: 0.3607, Acc.conveyer belt: 0.9414, Acc.canopy: 0.7853, Acc.washer: 0.8545, Acc.plaything: 0.5260, Acc.swimming pool: 0.8918, Acc.stool: 0.6997, Acc.barrel: 0.7440, Acc.basket: 0.6148, Acc.waterfall: 0.8121, Acc.tent: 0.9854, Acc.bag: 0.2424, Acc.minibike: 0.9142, Acc.cradle: 0.9757, Acc.oven: 0.7188, Acc.ball: 0.5982, Acc.food: 0.7498, Acc.step: 0.1964, Acc.tank: 0.6743, Acc.trade name: 0.3660, Acc.microwave: 0.9582, Acc.pot: 0.6560, Acc.animal: 0.6123, Acc.bicycle: 0.8026, Acc.lake: 0.6384, Acc.dishwasher: 0.7825, Acc.screen: 0.7311, Acc.blanket: 0.3197, Acc.sculpture: 0.8746, Acc.hood: 0.7536, Acc.sconce: 0.7176, Acc.vase: 0.6780, Acc.traffic light: 0.5876, Acc.tray: 0.3343, Acc.ashcan: 0.6423, Acc.fan: 0.8020, Acc.pier: 0.4270, Acc.crt screen: 0.0894, Acc.plate: 0.8093, Acc.monitor: 0.8187, Acc.bulletin board: 0.6069, Acc.shower: 0.1249, Acc.radiator: 0.7853, Acc.glass: 0.2011, Acc.clock: 0.5576, Acc.flag: 0.7879 +2024-06-17 03:12:13,479 - mmseg - INFO - Iter [71050/80000] lr: 4.476e-06, eta: 3:44:57, time: 3.306, data_time: 1.949, memory: 70722, decode.loss_ce: 0.1370, decode.acc_seg: 93.9542, aux.loss_ce: 0.0592, aux.acc_seg: 93.4717, loss: 0.1962 +2024-06-17 03:13:21,569 - mmseg - INFO - Iter [71100/80000] lr: 4.451e-06, eta: 3:43:41, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1460, decode.acc_seg: 93.8999, aux.loss_ce: 0.0626, aux.acc_seg: 93.5035, loss: 0.2086 +2024-06-17 03:14:29,800 - mmseg - INFO - Iter [71150/80000] lr: 4.426e-06, eta: 3:42:25, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1417, decode.acc_seg: 93.8340, aux.loss_ce: 0.0609, aux.acc_seg: 93.4170, loss: 0.2025 +2024-06-17 03:15:38,034 - mmseg - INFO - Iter [71200/80000] lr: 4.400e-06, eta: 3:41:08, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1524, decode.acc_seg: 93.2237, aux.loss_ce: 0.0652, aux.acc_seg: 92.7890, loss: 0.2176 +2024-06-17 03:16:46,186 - mmseg - INFO - Iter [71250/80000] lr: 4.375e-06, eta: 3:39:52, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1427, decode.acc_seg: 93.8428, aux.loss_ce: 0.0617, aux.acc_seg: 93.4187, loss: 0.2044 +2024-06-17 03:17:54,200 - mmseg - INFO - Iter [71300/80000] lr: 4.351e-06, eta: 3:38:36, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1422, decode.acc_seg: 93.7384, aux.loss_ce: 0.0616, aux.acc_seg: 93.2826, loss: 0.2038 +2024-06-17 03:19:02,264 - mmseg - INFO - Iter [71350/80000] lr: 4.326e-06, eta: 3:37:19, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1355, decode.acc_seg: 94.0187, aux.loss_ce: 0.0585, aux.acc_seg: 93.4949, loss: 0.1940 +2024-06-17 03:20:10,379 - mmseg - INFO - Iter [71400/80000] lr: 4.301e-06, eta: 3:36:03, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1335, decode.acc_seg: 94.0021, aux.loss_ce: 0.0581, aux.acc_seg: 93.5127, loss: 0.1916 +2024-06-17 03:21:18,522 - mmseg - INFO - Iter [71450/80000] lr: 4.276e-06, eta: 3:34:47, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1401, decode.acc_seg: 93.7874, aux.loss_ce: 0.0613, aux.acc_seg: 93.2708, loss: 0.2014 +2024-06-17 03:22:26,857 - mmseg - INFO - Iter [71500/80000] lr: 4.251e-06, eta: 3:33:31, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1317, decode.acc_seg: 94.0196, aux.loss_ce: 0.0572, aux.acc_seg: 93.5743, loss: 0.1889 +2024-06-17 03:23:35,029 - mmseg - INFO - Iter [71550/80000] lr: 4.226e-06, eta: 3:32:15, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1389, decode.acc_seg: 93.8627, aux.loss_ce: 0.0602, aux.acc_seg: 93.3295, loss: 0.1991 +2024-06-17 03:24:43,322 - mmseg - INFO - Iter [71600/80000] lr: 4.200e-06, eta: 3:30:58, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1473, decode.acc_seg: 93.4995, aux.loss_ce: 0.0636, aux.acc_seg: 93.0808, loss: 0.2109 +2024-06-17 03:25:51,247 - mmseg - INFO - Iter [71650/80000] lr: 4.175e-06, eta: 3:29:42, time: 1.358, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1449, decode.acc_seg: 93.7024, aux.loss_ce: 0.0622, aux.acc_seg: 93.2927, loss: 0.2071 +2024-06-17 03:26:59,573 - mmseg - INFO - Iter [71700/80000] lr: 4.150e-06, eta: 3:28:26, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1380, decode.acc_seg: 93.7926, aux.loss_ce: 0.0600, aux.acc_seg: 93.3258, loss: 0.1980 +2024-06-17 03:28:07,790 - mmseg - INFO - Iter [71750/80000] lr: 4.125e-06, eta: 3:27:10, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1425, decode.acc_seg: 93.7709, aux.loss_ce: 0.0611, aux.acc_seg: 93.2958, loss: 0.2036 +2024-06-17 03:29:15,856 - mmseg - INFO - Iter [71800/80000] lr: 4.101e-06, eta: 3:25:54, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1456, decode.acc_seg: 93.6595, aux.loss_ce: 0.0629, aux.acc_seg: 93.1643, loss: 0.2085 +2024-06-17 03:30:24,146 - mmseg - INFO - Iter [71850/80000] lr: 4.076e-06, eta: 3:24:38, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1439, decode.acc_seg: 93.5931, aux.loss_ce: 0.0619, aux.acc_seg: 93.1606, loss: 0.2058 +2024-06-17 03:31:32,154 - mmseg - INFO - Iter [71900/80000] lr: 4.051e-06, eta: 3:23:21, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1399, decode.acc_seg: 93.8684, aux.loss_ce: 0.0606, aux.acc_seg: 93.4027, loss: 0.2005 +2024-06-17 03:32:40,429 - mmseg - INFO - Iter [71950/80000] lr: 4.026e-06, eta: 3:22:05, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1286, decode.acc_seg: 94.1861, aux.loss_ce: 0.0555, aux.acc_seg: 93.7468, loss: 0.1841 +2024-06-17 03:33:50,965 - mmseg - INFO - Saving checkpoint at 72000 iterations +2024-06-17 03:35:18,457 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 03:35:18,457 - mmseg - INFO - Iter [72000/80000] lr: 4.000e-06, eta: 3:20:59, time: 3.161, data_time: 0.061, memory: 70722, decode.loss_ce: 0.1347, decode.acc_seg: 93.9498, aux.loss_ce: 0.0582, aux.acc_seg: 93.4610, loss: 0.1928 +2024-06-17 03:36:53,552 - mmseg - INFO - per class results: +2024-06-17 03:36:53,558 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.67 | 90.2 | +| building | 86.17 | 94.01 | +| sky | 95.05 | 97.78 | +| floor | 85.69 | 93.08 | +| tree | 77.45 | 89.16 | +| ceiling | 87.28 | 95.0 | +| road | 87.1 | 92.01 | +| bed | 93.28 | 97.29 | +| windowpane | 66.29 | 81.97 | +| grass | 69.6 | 84.36 | +| cabinet | 65.78 | 75.28 | +| sidewalk | 73.41 | 86.47 | +| person | 86.19 | 94.12 | +| earth | 36.87 | 47.34 | +| door | 60.13 | 73.93 | +| table | 70.95 | 82.21 | +| mountain | 62.83 | 75.77 | +| plant | 54.12 | 63.44 | +| curtain | 76.5 | 87.85 | +| chair | 68.34 | 79.1 | +| car | 87.77 | 93.94 | +| water | 66.25 | 82.07 | +| painting | 77.61 | 91.81 | +| sofa | 83.1 | 92.1 | +| shelf | 45.36 | 59.93 | +| house | 64.29 | 76.07 | +| sea | 79.04 | 89.36 | +| mirror | 80.09 | 86.04 | +| rug | 70.97 | 78.78 | +| field | 34.67 | 63.63 | +| armchair | 61.27 | 76.23 | +| seat | 65.89 | 89.83 | +| fence | 53.03 | 65.71 | +| desk | 61.07 | 78.6 | +| rock | 56.59 | 85.21 | +| wardrobe | 52.96 | 72.88 | +| lamp | 75.86 | 87.06 | +| bathtub | 85.02 | 87.06 | +| railing | 43.15 | 61.79 | +| cushion | 70.17 | 83.6 | +| base | 42.66 | 57.95 | +| box | 38.35 | 49.57 | +| column | 54.72 | 68.23 | +| signboard | 41.64 | 56.83 | +| chest of drawers | 45.71 | 68.7 | +| counter | 40.26 | 50.39 | +| sand | 61.03 | 85.4 | +| sink | 78.46 | 84.11 | +| skyscraper | 48.84 | 61.28 | +| fireplace | 73.01 | 92.04 | +| refrigerator | 84.64 | 92.54 | +| grandstand | 51.67 | 83.63 | +| path | 28.53 | 42.41 | +| stairs | 31.26 | 36.75 | +| runway | 70.36 | 92.67 | +| case | 56.11 | 81.93 | +| pool table | 94.64 | 98.17 | +| pillow | 67.44 | 78.51 | +| screen door | 78.45 | 80.88 | +| stairway | 51.48 | 67.2 | +| river | 11.82 | 21.15 | +| bridge | 66.32 | 72.36 | +| bookcase | 45.98 | 66.51 | +| blind | 40.85 | 44.59 | +| coffee table | 63.75 | 89.42 | +| toilet | 90.73 | 94.2 | +| flower | 45.4 | 59.07 | +| book | 54.27 | 77.65 | +| hill | 8.09 | 13.76 | +| bench | 53.5 | 61.55 | +| countertop | 65.89 | 87.99 | +| stove | 83.33 | 88.52 | +| palm | 54.95 | 81.15 | +| kitchen island | 53.84 | 87.64 | +| computer | 78.82 | 90.96 | +| swivel chair | 45.2 | 63.54 | +| boat | 75.85 | 91.57 | +| bar | 59.18 | 82.66 | +| arcade machine | 78.06 | 82.7 | +| hovel | 50.8 | 56.41 | +| bus | 92.32 | 96.15 | +| towel | 75.43 | 84.09 | +| light | 62.04 | 72.41 | +| truck | 44.73 | 58.47 | +| tower | 15.87 | 21.64 | +| chandelier | 73.16 | 84.54 | +| awning | 48.87 | 64.87 | +| streetlight | 36.58 | 50.02 | +| booth | 44.74 | 64.03 | +| television receiver | 79.25 | 89.14 | +| airplane | 73.93 | 82.23 | +| dirt track | 8.46 | 36.43 | +| apparel | 46.3 | 66.31 | +| pole | 30.63 | 42.73 | +| land | 2.61 | 4.47 | +| bannister | 18.5 | 25.51 | +| escalator | 59.13 | 78.72 | +| ottoman | 51.41 | 67.36 | +| bottle | 42.6 | 69.98 | +| buffet | 47.68 | 59.61 | +| poster | 37.97 | 50.4 | +| stage | 23.42 | 45.12 | +| van | 45.61 | 65.5 | +| ship | 86.62 | 91.95 | +| fountain | 32.6 | 33.13 | +| conveyer belt | 83.39 | 93.66 | +| canopy | 57.39 | 79.19 | +| washer | 81.01 | 85.7 | +| plaything | 36.33 | 49.61 | +| swimming pool | 58.55 | 87.67 | +| stool | 56.92 | 70.8 | +| barrel | 59.44 | 74.04 | +| basket | 42.96 | 61.76 | +| waterfall | 70.99 | 88.08 | +| tent | 96.49 | 98.38 | +| bag | 21.07 | 23.34 | +| minibike | 78.07 | 90.27 | +| cradle | 83.98 | 97.54 | +| oven | 60.03 | 69.91 | +| ball | 49.46 | 57.59 | +| food | 56.16 | 69.79 | +| step | 11.27 | 13.79 | +| tank | 61.27 | 65.08 | +| trade name | 25.53 | 30.01 | +| microwave | 88.47 | 96.03 | +| pot | 57.66 | 68.15 | +| animal | 58.83 | 60.33 | +| bicycle | 60.16 | 76.9 | +| lake | 52.48 | 63.82 | +| dishwasher | 69.13 | 78.07 | +| screen | 60.41 | 93.68 | +| blanket | 28.61 | 31.35 | +| sculpture | 73.74 | 87.91 | +| hood | 63.66 | 75.42 | +| sconce | 59.64 | 71.14 | +| vase | 48.67 | 65.95 | +| traffic light | 41.7 | 63.11 | +| tray | 24.57 | 28.64 | +| ashcan | 46.43 | 63.66 | +| fan | 71.37 | 82.43 | +| pier | 40.57 | 45.46 | +| crt screen | 2.94 | 3.45 | +| plate | 62.72 | 79.44 | +| monitor | 69.08 | 81.41 | +| bulletin board | 53.39 | 64.31 | +| shower | 7.82 | 7.89 | +| radiator | 68.6 | 77.85 | +| glass | 19.57 | 20.62 | +| clock | 47.72 | 56.4 | +| flag | 72.27 | 80.12 | ++---------------------+-------+-------+ +2024-06-17 03:36:53,558 - mmseg - INFO - Summary: +2024-06-17 03:36:53,558 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.53 | 57.91 | 70.39 | ++-------+-------+-------+ +2024-06-17 03:36:53,559 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 03:36:53,559 - mmseg - INFO - Iter(val) [250] aAcc: 0.8653, mIoU: 0.5791, mAcc: 0.7039, IoU.wall: 0.8267, IoU.building: 0.8617, IoU.sky: 0.9505, IoU.floor: 0.8569, IoU.tree: 0.7745, IoU.ceiling: 0.8728, IoU.road: 0.8710, IoU.bed : 0.9328, IoU.windowpane: 0.6629, IoU.grass: 0.6960, IoU.cabinet: 0.6578, IoU.sidewalk: 0.7341, IoU.person: 0.8619, IoU.earth: 0.3687, IoU.door: 0.6013, IoU.table: 0.7095, IoU.mountain: 0.6283, IoU.plant: 0.5412, IoU.curtain: 0.7650, IoU.chair: 0.6834, IoU.car: 0.8777, IoU.water: 0.6625, IoU.painting: 0.7761, IoU.sofa: 0.8310, IoU.shelf: 0.4536, IoU.house: 0.6429, IoU.sea: 0.7904, IoU.mirror: 0.8009, IoU.rug: 0.7097, IoU.field: 0.3467, IoU.armchair: 0.6127, IoU.seat: 0.6589, IoU.fence: 0.5303, IoU.desk: 0.6107, IoU.rock: 0.5659, IoU.wardrobe: 0.5296, IoU.lamp: 0.7586, IoU.bathtub: 0.8502, IoU.railing: 0.4315, IoU.cushion: 0.7017, IoU.base: 0.4266, IoU.box: 0.3835, IoU.column: 0.5472, IoU.signboard: 0.4164, IoU.chest of drawers: 0.4571, IoU.counter: 0.4026, IoU.sand: 0.6103, IoU.sink: 0.7846, IoU.skyscraper: 0.4884, IoU.fireplace: 0.7301, IoU.refrigerator: 0.8464, IoU.grandstand: 0.5167, IoU.path: 0.2853, IoU.stairs: 0.3126, IoU.runway: 0.7036, IoU.case: 0.5611, IoU.pool table: 0.9464, IoU.pillow: 0.6744, IoU.screen door: 0.7845, IoU.stairway: 0.5148, IoU.river: 0.1182, IoU.bridge: 0.6632, IoU.bookcase: 0.4598, IoU.blind: 0.4085, IoU.coffee table: 0.6375, IoU.toilet: 0.9073, IoU.flower: 0.4540, IoU.book: 0.5427, IoU.hill: 0.0809, IoU.bench: 0.5350, IoU.countertop: 0.6589, IoU.stove: 0.8333, IoU.palm: 0.5495, IoU.kitchen island: 0.5384, IoU.computer: 0.7882, IoU.swivel chair: 0.4520, IoU.boat: 0.7585, IoU.bar: 0.5918, IoU.arcade machine: 0.7806, IoU.hovel: 0.5080, IoU.bus: 0.9232, IoU.towel: 0.7543, IoU.light: 0.6204, IoU.truck: 0.4473, IoU.tower: 0.1587, IoU.chandelier: 0.7316, IoU.awning: 0.4887, IoU.streetlight: 0.3658, IoU.booth: 0.4474, IoU.television receiver: 0.7925, IoU.airplane: 0.7393, IoU.dirt track: 0.0846, IoU.apparel: 0.4630, IoU.pole: 0.3063, IoU.land: 0.0261, IoU.bannister: 0.1850, IoU.escalator: 0.5913, IoU.ottoman: 0.5141, IoU.bottle: 0.4260, IoU.buffet: 0.4768, IoU.poster: 0.3797, IoU.stage: 0.2342, IoU.van: 0.4561, IoU.ship: 0.8662, IoU.fountain: 0.3260, IoU.conveyer belt: 0.8339, IoU.canopy: 0.5739, IoU.washer: 0.8101, IoU.plaything: 0.3633, IoU.swimming pool: 0.5855, IoU.stool: 0.5692, IoU.barrel: 0.5944, IoU.basket: 0.4296, IoU.waterfall: 0.7099, IoU.tent: 0.9649, IoU.bag: 0.2107, IoU.minibike: 0.7807, IoU.cradle: 0.8398, IoU.oven: 0.6003, IoU.ball: 0.4946, IoU.food: 0.5616, IoU.step: 0.1127, IoU.tank: 0.6127, IoU.trade name: 0.2553, IoU.microwave: 0.8847, IoU.pot: 0.5766, IoU.animal: 0.5883, IoU.bicycle: 0.6016, IoU.lake: 0.5248, IoU.dishwasher: 0.6913, IoU.screen: 0.6041, IoU.blanket: 0.2861, IoU.sculpture: 0.7374, IoU.hood: 0.6366, IoU.sconce: 0.5964, IoU.vase: 0.4867, IoU.traffic light: 0.4170, IoU.tray: 0.2457, IoU.ashcan: 0.4643, IoU.fan: 0.7137, IoU.pier: 0.4057, IoU.crt screen: 0.0294, IoU.plate: 0.6272, IoU.monitor: 0.6908, IoU.bulletin board: 0.5339, IoU.shower: 0.0782, IoU.radiator: 0.6860, IoU.glass: 0.1957, IoU.clock: 0.4772, IoU.flag: 0.7227, Acc.wall: 0.9020, Acc.building: 0.9401, Acc.sky: 0.9778, Acc.floor: 0.9308, Acc.tree: 0.8916, Acc.ceiling: 0.9500, Acc.road: 0.9201, Acc.bed : 0.9729, Acc.windowpane: 0.8197, Acc.grass: 0.8436, Acc.cabinet: 0.7528, Acc.sidewalk: 0.8647, Acc.person: 0.9412, Acc.earth: 0.4734, Acc.door: 0.7393, Acc.table: 0.8221, Acc.mountain: 0.7577, Acc.plant: 0.6344, Acc.curtain: 0.8785, Acc.chair: 0.7910, Acc.car: 0.9394, Acc.water: 0.8207, Acc.painting: 0.9181, Acc.sofa: 0.9210, Acc.shelf: 0.5993, Acc.house: 0.7607, Acc.sea: 0.8936, Acc.mirror: 0.8604, Acc.rug: 0.7878, Acc.field: 0.6363, Acc.armchair: 0.7623, Acc.seat: 0.8983, Acc.fence: 0.6571, Acc.desk: 0.7860, Acc.rock: 0.8521, Acc.wardrobe: 0.7288, Acc.lamp: 0.8706, Acc.bathtub: 0.8706, Acc.railing: 0.6179, Acc.cushion: 0.8360, Acc.base: 0.5795, Acc.box: 0.4957, Acc.column: 0.6823, Acc.signboard: 0.5683, Acc.chest of drawers: 0.6870, Acc.counter: 0.5039, Acc.sand: 0.8540, Acc.sink: 0.8411, Acc.skyscraper: 0.6128, Acc.fireplace: 0.9204, Acc.refrigerator: 0.9254, Acc.grandstand: 0.8363, Acc.path: 0.4241, Acc.stairs: 0.3675, Acc.runway: 0.9267, Acc.case: 0.8193, Acc.pool table: 0.9817, Acc.pillow: 0.7851, Acc.screen door: 0.8088, Acc.stairway: 0.6720, Acc.river: 0.2115, Acc.bridge: 0.7236, Acc.bookcase: 0.6651, Acc.blind: 0.4459, Acc.coffee table: 0.8942, Acc.toilet: 0.9420, Acc.flower: 0.5907, Acc.book: 0.7765, Acc.hill: 0.1376, Acc.bench: 0.6155, Acc.countertop: 0.8799, Acc.stove: 0.8852, Acc.palm: 0.8115, Acc.kitchen island: 0.8764, Acc.computer: 0.9096, Acc.swivel chair: 0.6354, Acc.boat: 0.9157, Acc.bar: 0.8266, Acc.arcade machine: 0.8270, Acc.hovel: 0.5641, Acc.bus: 0.9615, Acc.towel: 0.8409, Acc.light: 0.7241, Acc.truck: 0.5847, Acc.tower: 0.2164, Acc.chandelier: 0.8454, Acc.awning: 0.6487, Acc.streetlight: 0.5002, Acc.booth: 0.6403, Acc.television receiver: 0.8914, Acc.airplane: 0.8223, Acc.dirt track: 0.3643, Acc.apparel: 0.6631, Acc.pole: 0.4273, Acc.land: 0.0447, Acc.bannister: 0.2551, Acc.escalator: 0.7872, Acc.ottoman: 0.6736, Acc.bottle: 0.6998, Acc.buffet: 0.5961, Acc.poster: 0.5040, Acc.stage: 0.4512, Acc.van: 0.6550, Acc.ship: 0.9195, Acc.fountain: 0.3313, Acc.conveyer belt: 0.9366, Acc.canopy: 0.7919, Acc.washer: 0.8570, Acc.plaything: 0.4961, Acc.swimming pool: 0.8767, Acc.stool: 0.7080, Acc.barrel: 0.7404, Acc.basket: 0.6176, Acc.waterfall: 0.8808, Acc.tent: 0.9838, Acc.bag: 0.2334, Acc.minibike: 0.9027, Acc.cradle: 0.9754, Acc.oven: 0.6991, Acc.ball: 0.5759, Acc.food: 0.6979, Acc.step: 0.1379, Acc.tank: 0.6508, Acc.trade name: 0.3001, Acc.microwave: 0.9603, Acc.pot: 0.6815, Acc.animal: 0.6033, Acc.bicycle: 0.7690, Acc.lake: 0.6382, Acc.dishwasher: 0.7807, Acc.screen: 0.9368, Acc.blanket: 0.3135, Acc.sculpture: 0.8791, Acc.hood: 0.7542, Acc.sconce: 0.7114, Acc.vase: 0.6595, Acc.traffic light: 0.6311, Acc.tray: 0.2864, Acc.ashcan: 0.6366, Acc.fan: 0.8243, Acc.pier: 0.4546, Acc.crt screen: 0.0345, Acc.plate: 0.7944, Acc.monitor: 0.8141, Acc.bulletin board: 0.6431, Acc.shower: 0.0789, Acc.radiator: 0.7785, Acc.glass: 0.2062, Acc.clock: 0.5640, Acc.flag: 0.8012 +2024-06-17 03:38:02,510 - mmseg - INFO - Iter [72050/80000] lr: 3.975e-06, eta: 3:19:54, time: 3.281, data_time: 1.918, memory: 70722, decode.loss_ce: 0.1408, decode.acc_seg: 93.6064, aux.loss_ce: 0.0607, aux.acc_seg: 93.0961, loss: 0.2015 +2024-06-17 03:39:10,616 - mmseg - INFO - Iter [72100/80000] lr: 3.950e-06, eta: 3:18:37, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1447, decode.acc_seg: 93.5494, aux.loss_ce: 0.0621, aux.acc_seg: 93.1378, loss: 0.2068 +2024-06-17 03:40:18,536 - mmseg - INFO - Iter [72150/80000] lr: 3.925e-06, eta: 3:17:21, time: 1.358, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1399, decode.acc_seg: 93.7473, aux.loss_ce: 0.0602, aux.acc_seg: 93.3132, loss: 0.2001 +2024-06-17 03:41:26,677 - mmseg - INFO - Iter [72200/80000] lr: 3.901e-06, eta: 3:16:05, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1400, decode.acc_seg: 93.8074, aux.loss_ce: 0.0606, aux.acc_seg: 93.3090, loss: 0.2006 +2024-06-17 03:42:34,873 - mmseg - INFO - Iter [72250/80000] lr: 3.876e-06, eta: 3:14:49, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1294, decode.acc_seg: 94.3004, aux.loss_ce: 0.0562, aux.acc_seg: 93.7658, loss: 0.1856 +2024-06-17 03:43:43,058 - mmseg - INFO - Iter [72300/80000] lr: 3.851e-06, eta: 3:13:33, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1409, decode.acc_seg: 93.9036, aux.loss_ce: 0.0610, aux.acc_seg: 93.4629, loss: 0.2019 +2024-06-17 03:44:51,022 - mmseg - INFO - Iter [72350/80000] lr: 3.826e-06, eta: 3:12:16, time: 1.359, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1324, decode.acc_seg: 94.1209, aux.loss_ce: 0.0573, aux.acc_seg: 93.6760, loss: 0.1897 +2024-06-17 03:45:59,060 - mmseg - INFO - Iter [72400/80000] lr: 3.801e-06, eta: 3:11:00, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1335, decode.acc_seg: 94.0795, aux.loss_ce: 0.0576, aux.acc_seg: 93.6691, loss: 0.1912 +2024-06-17 03:47:07,321 - mmseg - INFO - Iter [72450/80000] lr: 3.775e-06, eta: 3:09:44, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1339, decode.acc_seg: 94.0947, aux.loss_ce: 0.0579, aux.acc_seg: 93.6466, loss: 0.1918 +2024-06-17 03:48:15,453 - mmseg - INFO - Iter [72500/80000] lr: 3.750e-06, eta: 3:08:28, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1360, decode.acc_seg: 94.0181, aux.loss_ce: 0.0587, aux.acc_seg: 93.5986, loss: 0.1947 +2024-06-17 03:49:23,705 - mmseg - INFO - Iter [72550/80000] lr: 3.725e-06, eta: 3:07:12, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1331, decode.acc_seg: 94.0019, aux.loss_ce: 0.0575, aux.acc_seg: 93.5644, loss: 0.1906 +2024-06-17 03:50:31,973 - mmseg - INFO - Iter [72600/80000] lr: 3.701e-06, eta: 3:05:56, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1342, decode.acc_seg: 93.9803, aux.loss_ce: 0.0584, aux.acc_seg: 93.4687, loss: 0.1925 +2024-06-17 03:51:40,110 - mmseg - INFO - Iter [72650/80000] lr: 3.676e-06, eta: 3:04:40, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1365, decode.acc_seg: 93.9695, aux.loss_ce: 0.0591, aux.acc_seg: 93.4839, loss: 0.1956 +2024-06-17 03:52:48,302 - mmseg - INFO - Iter [72700/80000] lr: 3.651e-06, eta: 3:03:23, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1425, decode.acc_seg: 93.8328, aux.loss_ce: 0.0613, aux.acc_seg: 93.3943, loss: 0.2037 +2024-06-17 03:53:56,515 - mmseg - INFO - Iter [72750/80000] lr: 3.626e-06, eta: 3:02:07, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1368, decode.acc_seg: 93.8976, aux.loss_ce: 0.0588, aux.acc_seg: 93.4711, loss: 0.1956 +2024-06-17 03:55:04,730 - mmseg - INFO - Iter [72800/80000] lr: 3.601e-06, eta: 3:00:51, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1329, decode.acc_seg: 93.8637, aux.loss_ce: 0.0577, aux.acc_seg: 93.4422, loss: 0.1906 +2024-06-17 03:56:12,970 - mmseg - INFO - Iter [72850/80000] lr: 3.575e-06, eta: 2:59:35, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1356, decode.acc_seg: 93.9554, aux.loss_ce: 0.0585, aux.acc_seg: 93.5111, loss: 0.1941 +2024-06-17 03:57:21,189 - mmseg - INFO - Iter [72900/80000] lr: 3.550e-06, eta: 2:58:19, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1363, decode.acc_seg: 93.9132, aux.loss_ce: 0.0595, aux.acc_seg: 93.3949, loss: 0.1958 +2024-06-17 03:58:29,287 - mmseg - INFO - Iter [72950/80000] lr: 3.525e-06, eta: 2:57:03, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1341, decode.acc_seg: 93.9991, aux.loss_ce: 0.0581, aux.acc_seg: 93.5488, loss: 0.1922 +2024-06-17 03:59:37,385 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 03:59:37,385 - mmseg - INFO - Iter [73000/80000] lr: 3.501e-06, eta: 2:55:47, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1318, decode.acc_seg: 93.8534, aux.loss_ce: 0.0569, aux.acc_seg: 93.4717, loss: 0.1887 +2024-06-17 04:01:13,421 - mmseg - INFO - per class results: +2024-06-17 04:01:13,427 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.81 | 90.45 | +| building | 86.02 | 93.55 | +| sky | 95.05 | 97.59 | +| floor | 85.93 | 92.9 | +| tree | 77.37 | 90.6 | +| ceiling | 87.5 | 94.04 | +| road | 87.28 | 92.05 | +| bed | 92.97 | 97.21 | +| windowpane | 66.07 | 81.97 | +| grass | 69.39 | 83.54 | +| cabinet | 67.65 | 78.18 | +| sidewalk | 73.81 | 86.54 | +| person | 86.25 | 94.75 | +| earth | 37.78 | 50.37 | +| door | 60.14 | 75.25 | +| table | 71.65 | 82.21 | +| mountain | 62.87 | 73.89 | +| plant | 55.07 | 63.81 | +| curtain | 76.62 | 87.64 | +| chair | 69.35 | 81.35 | +| car | 87.77 | 94.09 | +| water | 66.25 | 82.11 | +| painting | 78.21 | 91.43 | +| sofa | 83.12 | 90.92 | +| shelf | 44.13 | 59.93 | +| house | 63.25 | 75.27 | +| sea | 78.44 | 88.32 | +| mirror | 79.71 | 85.2 | +| rug | 71.2 | 79.28 | +| field | 32.28 | 58.62 | +| armchair | 61.2 | 77.42 | +| seat | 68.3 | 89.48 | +| fence | 53.84 | 65.26 | +| desk | 61.3 | 79.68 | +| rock | 57.96 | 85.2 | +| wardrobe | 55.48 | 72.09 | +| lamp | 76.06 | 87.19 | +| bathtub | 85.07 | 87.47 | +| railing | 43.48 | 60.84 | +| cushion | 70.56 | 80.55 | +| base | 42.72 | 58.97 | +| box | 38.23 | 48.91 | +| column | 55.03 | 69.03 | +| signboard | 41.82 | 55.34 | +| chest of drawers | 45.68 | 65.89 | +| counter | 39.91 | 48.91 | +| sand | 61.16 | 86.36 | +| sink | 77.48 | 85.45 | +| skyscraper | 48.34 | 62.73 | +| fireplace | 74.61 | 93.13 | +| refrigerator | 83.83 | 91.8 | +| grandstand | 52.81 | 86.27 | +| path | 31.19 | 43.45 | +| stairs | 30.03 | 34.89 | +| runway | 71.46 | 94.32 | +| case | 56.8 | 82.23 | +| pool table | 94.63 | 98.25 | +| pillow | 68.07 | 78.8 | +| screen door | 77.36 | 79.54 | +| stairway | 49.3 | 65.61 | +| river | 11.62 | 21.51 | +| bridge | 65.87 | 72.78 | +| bookcase | 46.26 | 69.29 | +| blind | 41.09 | 45.08 | +| coffee table | 66.0 | 87.44 | +| toilet | 91.0 | 94.35 | +| flower | 46.02 | 55.62 | +| book | 55.67 | 77.67 | +| hill | 12.29 | 23.52 | +| bench | 54.84 | 62.48 | +| countertop | 66.99 | 83.51 | +| stove | 83.85 | 87.91 | +| palm | 55.86 | 78.68 | +| kitchen island | 61.09 | 84.61 | +| computer | 78.89 | 91.14 | +| swivel chair | 50.0 | 71.03 | +| boat | 74.89 | 90.64 | +| bar | 59.72 | 83.62 | +| arcade machine | 78.16 | 82.93 | +| hovel | 50.43 | 55.63 | +| bus | 92.67 | 96.24 | +| towel | 75.79 | 85.93 | +| light | 61.86 | 71.0 | +| truck | 44.6 | 58.07 | +| tower | 26.66 | 40.08 | +| chandelier | 73.27 | 84.35 | +| awning | 46.77 | 61.04 | +| streetlight | 35.98 | 48.79 | +| booth | 45.43 | 63.41 | +| television receiver | 80.76 | 88.81 | +| airplane | 79.05 | 87.04 | +| dirt track | 8.23 | 34.71 | +| apparel | 49.39 | 65.78 | +| pole | 30.59 | 43.5 | +| land | 1.73 | 2.95 | +| bannister | 18.43 | 27.06 | +| escalator | 58.24 | 79.38 | +| ottoman | 47.81 | 63.65 | +| bottle | 41.99 | 69.71 | +| buffet | 46.65 | 56.12 | +| poster | 37.68 | 47.98 | +| stage | 23.51 | 46.61 | +| van | 45.63 | 62.35 | +| ship | 87.1 | 91.81 | +| fountain | 35.41 | 36.05 | +| conveyer belt | 83.31 | 93.73 | +| canopy | 55.86 | 76.81 | +| washer | 81.44 | 86.21 | +| plaything | 34.64 | 46.96 | +| swimming pool | 58.53 | 87.05 | +| stool | 55.7 | 71.76 | +| barrel | 58.22 | 74.39 | +| basket | 41.99 | 61.71 | +| waterfall | 69.39 | 88.42 | +| tent | 96.41 | 98.46 | +| bag | 22.4 | 25.53 | +| minibike | 78.1 | 90.14 | +| cradle | 83.53 | 97.72 | +| oven | 62.47 | 72.95 | +| ball | 44.23 | 51.51 | +| food | 59.17 | 74.19 | +| step | 12.54 | 15.6 | +| tank | 61.68 | 65.6 | +| trade name | 29.17 | 35.84 | +| microwave | 88.55 | 96.13 | +| pot | 57.89 | 66.66 | +| animal | 59.0 | 60.47 | +| bicycle | 60.68 | 74.58 | +| lake | 51.96 | 63.84 | +| dishwasher | 68.56 | 77.5 | +| screen | 55.63 | 84.4 | +| blanket | 29.77 | 33.47 | +| sculpture | 75.82 | 87.81 | +| hood | 64.35 | 77.12 | +| sconce | 58.69 | 67.69 | +| vase | 49.88 | 63.19 | +| traffic light | 38.78 | 63.96 | +| tray | 25.19 | 29.76 | +| ashcan | 46.57 | 65.91 | +| fan | 71.16 | 82.44 | +| pier | 39.97 | 43.27 | +| crt screen | 2.24 | 3.51 | +| plate | 61.69 | 77.75 | +| monitor | 66.77 | 78.36 | +| bulletin board | 54.21 | 65.41 | +| shower | 7.81 | 7.98 | +| radiator | 68.66 | 77.61 | +| glass | 20.25 | 21.65 | +| clock | 47.1 | 56.36 | +| flag | 72.12 | 78.69 | ++---------------------+-------+-------+ +2024-06-17 04:01:13,427 - mmseg - INFO - Summary: +2024-06-17 04:01:13,427 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 86.61 | 58.18 | 70.4 | ++-------+-------+------+ +2024-06-17 04:01:13,428 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 04:01:13,428 - mmseg - INFO - Iter(val) [250] aAcc: 0.8661, mIoU: 0.5818, mAcc: 0.7040, IoU.wall: 0.8281, IoU.building: 0.8602, IoU.sky: 0.9505, IoU.floor: 0.8593, IoU.tree: 0.7737, IoU.ceiling: 0.8750, IoU.road: 0.8728, IoU.bed : 0.9297, IoU.windowpane: 0.6607, IoU.grass: 0.6939, IoU.cabinet: 0.6765, IoU.sidewalk: 0.7381, IoU.person: 0.8625, IoU.earth: 0.3778, IoU.door: 0.6014, IoU.table: 0.7165, IoU.mountain: 0.6287, IoU.plant: 0.5507, IoU.curtain: 0.7662, IoU.chair: 0.6935, IoU.car: 0.8777, IoU.water: 0.6625, IoU.painting: 0.7821, IoU.sofa: 0.8312, IoU.shelf: 0.4413, IoU.house: 0.6325, IoU.sea: 0.7844, IoU.mirror: 0.7971, IoU.rug: 0.7120, IoU.field: 0.3228, IoU.armchair: 0.6120, IoU.seat: 0.6830, IoU.fence: 0.5384, IoU.desk: 0.6130, IoU.rock: 0.5796, IoU.wardrobe: 0.5548, IoU.lamp: 0.7606, IoU.bathtub: 0.8507, IoU.railing: 0.4348, IoU.cushion: 0.7056, IoU.base: 0.4272, IoU.box: 0.3823, IoU.column: 0.5503, IoU.signboard: 0.4182, IoU.chest of drawers: 0.4568, IoU.counter: 0.3991, IoU.sand: 0.6116, IoU.sink: 0.7748, IoU.skyscraper: 0.4834, IoU.fireplace: 0.7461, IoU.refrigerator: 0.8383, IoU.grandstand: 0.5281, IoU.path: 0.3119, IoU.stairs: 0.3003, IoU.runway: 0.7146, IoU.case: 0.5680, IoU.pool table: 0.9463, IoU.pillow: 0.6807, IoU.screen door: 0.7736, IoU.stairway: 0.4930, IoU.river: 0.1162, IoU.bridge: 0.6587, IoU.bookcase: 0.4626, IoU.blind: 0.4109, IoU.coffee table: 0.6600, IoU.toilet: 0.9100, IoU.flower: 0.4602, IoU.book: 0.5567, IoU.hill: 0.1229, IoU.bench: 0.5484, IoU.countertop: 0.6699, IoU.stove: 0.8385, IoU.palm: 0.5586, IoU.kitchen island: 0.6109, IoU.computer: 0.7889, IoU.swivel chair: 0.5000, IoU.boat: 0.7489, IoU.bar: 0.5972, IoU.arcade machine: 0.7816, IoU.hovel: 0.5043, IoU.bus: 0.9267, IoU.towel: 0.7579, IoU.light: 0.6186, IoU.truck: 0.4460, IoU.tower: 0.2666, IoU.chandelier: 0.7327, IoU.awning: 0.4677, IoU.streetlight: 0.3598, IoU.booth: 0.4543, IoU.television receiver: 0.8076, IoU.airplane: 0.7905, IoU.dirt track: 0.0823, IoU.apparel: 0.4939, IoU.pole: 0.3059, IoU.land: 0.0173, IoU.bannister: 0.1843, IoU.escalator: 0.5824, IoU.ottoman: 0.4781, IoU.bottle: 0.4199, IoU.buffet: 0.4665, IoU.poster: 0.3768, IoU.stage: 0.2351, IoU.van: 0.4563, IoU.ship: 0.8710, IoU.fountain: 0.3541, IoU.conveyer belt: 0.8331, IoU.canopy: 0.5586, IoU.washer: 0.8144, IoU.plaything: 0.3464, IoU.swimming pool: 0.5853, IoU.stool: 0.5570, IoU.barrel: 0.5822, IoU.basket: 0.4199, IoU.waterfall: 0.6939, IoU.tent: 0.9641, IoU.bag: 0.2240, IoU.minibike: 0.7810, IoU.cradle: 0.8353, IoU.oven: 0.6247, IoU.ball: 0.4423, IoU.food: 0.5917, IoU.step: 0.1254, IoU.tank: 0.6168, IoU.trade name: 0.2917, IoU.microwave: 0.8855, IoU.pot: 0.5789, IoU.animal: 0.5900, IoU.bicycle: 0.6068, IoU.lake: 0.5196, IoU.dishwasher: 0.6856, IoU.screen: 0.5563, IoU.blanket: 0.2977, IoU.sculpture: 0.7582, IoU.hood: 0.6435, IoU.sconce: 0.5869, IoU.vase: 0.4988, IoU.traffic light: 0.3878, IoU.tray: 0.2519, IoU.ashcan: 0.4657, IoU.fan: 0.7116, IoU.pier: 0.3997, IoU.crt screen: 0.0224, IoU.plate: 0.6169, IoU.monitor: 0.6677, IoU.bulletin board: 0.5421, IoU.shower: 0.0781, IoU.radiator: 0.6866, IoU.glass: 0.2025, IoU.clock: 0.4710, IoU.flag: 0.7212, Acc.wall: 0.9045, Acc.building: 0.9355, Acc.sky: 0.9759, Acc.floor: 0.9290, Acc.tree: 0.9060, Acc.ceiling: 0.9404, Acc.road: 0.9205, Acc.bed : 0.9721, Acc.windowpane: 0.8197, Acc.grass: 0.8354, Acc.cabinet: 0.7818, Acc.sidewalk: 0.8654, Acc.person: 0.9475, Acc.earth: 0.5037, Acc.door: 0.7525, Acc.table: 0.8221, Acc.mountain: 0.7389, Acc.plant: 0.6381, Acc.curtain: 0.8764, Acc.chair: 0.8135, Acc.car: 0.9409, Acc.water: 0.8211, Acc.painting: 0.9143, Acc.sofa: 0.9092, Acc.shelf: 0.5993, Acc.house: 0.7527, Acc.sea: 0.8832, Acc.mirror: 0.8520, Acc.rug: 0.7928, Acc.field: 0.5862, Acc.armchair: 0.7742, Acc.seat: 0.8948, Acc.fence: 0.6526, Acc.desk: 0.7968, Acc.rock: 0.8520, Acc.wardrobe: 0.7209, Acc.lamp: 0.8719, Acc.bathtub: 0.8747, Acc.railing: 0.6084, Acc.cushion: 0.8055, Acc.base: 0.5897, Acc.box: 0.4891, Acc.column: 0.6903, Acc.signboard: 0.5534, Acc.chest of drawers: 0.6589, Acc.counter: 0.4891, Acc.sand: 0.8636, Acc.sink: 0.8545, Acc.skyscraper: 0.6273, Acc.fireplace: 0.9313, Acc.refrigerator: 0.9180, Acc.grandstand: 0.8627, Acc.path: 0.4345, Acc.stairs: 0.3489, Acc.runway: 0.9432, Acc.case: 0.8223, Acc.pool table: 0.9825, Acc.pillow: 0.7880, Acc.screen door: 0.7954, Acc.stairway: 0.6561, Acc.river: 0.2151, Acc.bridge: 0.7278, Acc.bookcase: 0.6929, Acc.blind: 0.4508, Acc.coffee table: 0.8744, Acc.toilet: 0.9435, Acc.flower: 0.5562, Acc.book: 0.7767, Acc.hill: 0.2352, Acc.bench: 0.6248, Acc.countertop: 0.8351, Acc.stove: 0.8791, Acc.palm: 0.7868, Acc.kitchen island: 0.8461, Acc.computer: 0.9114, Acc.swivel chair: 0.7103, Acc.boat: 0.9064, Acc.bar: 0.8362, Acc.arcade machine: 0.8293, Acc.hovel: 0.5563, Acc.bus: 0.9624, Acc.towel: 0.8593, Acc.light: 0.7100, Acc.truck: 0.5807, Acc.tower: 0.4008, Acc.chandelier: 0.8435, Acc.awning: 0.6104, Acc.streetlight: 0.4879, Acc.booth: 0.6341, Acc.television receiver: 0.8881, Acc.airplane: 0.8704, Acc.dirt track: 0.3471, Acc.apparel: 0.6578, Acc.pole: 0.4350, Acc.land: 0.0295, Acc.bannister: 0.2706, Acc.escalator: 0.7938, Acc.ottoman: 0.6365, Acc.bottle: 0.6971, Acc.buffet: 0.5612, Acc.poster: 0.4798, Acc.stage: 0.4661, Acc.van: 0.6235, Acc.ship: 0.9181, Acc.fountain: 0.3605, Acc.conveyer belt: 0.9373, Acc.canopy: 0.7681, Acc.washer: 0.8621, Acc.plaything: 0.4696, Acc.swimming pool: 0.8705, Acc.stool: 0.7176, Acc.barrel: 0.7439, Acc.basket: 0.6171, Acc.waterfall: 0.8842, Acc.tent: 0.9846, Acc.bag: 0.2553, Acc.minibike: 0.9014, Acc.cradle: 0.9772, Acc.oven: 0.7295, Acc.ball: 0.5151, Acc.food: 0.7419, Acc.step: 0.1560, Acc.tank: 0.6560, Acc.trade name: 0.3584, Acc.microwave: 0.9613, Acc.pot: 0.6666, Acc.animal: 0.6047, Acc.bicycle: 0.7458, Acc.lake: 0.6384, Acc.dishwasher: 0.7750, Acc.screen: 0.8440, Acc.blanket: 0.3347, Acc.sculpture: 0.8781, Acc.hood: 0.7712, Acc.sconce: 0.6769, Acc.vase: 0.6319, Acc.traffic light: 0.6396, Acc.tray: 0.2976, Acc.ashcan: 0.6591, Acc.fan: 0.8244, Acc.pier: 0.4327, Acc.crt screen: 0.0351, Acc.plate: 0.7775, Acc.monitor: 0.7836, Acc.bulletin board: 0.6541, Acc.shower: 0.0798, Acc.radiator: 0.7761, Acc.glass: 0.2165, Acc.clock: 0.5636, Acc.flag: 0.7869 +2024-06-17 04:02:22,004 - mmseg - INFO - Iter [73050/80000] lr: 3.476e-06, eta: 2:54:40, time: 3.292, data_time: 1.937, memory: 70722, decode.loss_ce: 0.1390, decode.acc_seg: 94.0608, aux.loss_ce: 0.0596, aux.acc_seg: 93.5993, loss: 0.1986 +2024-06-17 04:03:30,249 - mmseg - INFO - Iter [73100/80000] lr: 3.451e-06, eta: 2:53:24, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1372, decode.acc_seg: 93.9297, aux.loss_ce: 0.0591, aux.acc_seg: 93.5368, loss: 0.1963 +2024-06-17 04:04:38,570 - mmseg - INFO - Iter [73150/80000] lr: 3.426e-06, eta: 2:52:08, time: 1.366, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1394, decode.acc_seg: 93.9662, aux.loss_ce: 0.0604, aux.acc_seg: 93.4300, loss: 0.1999 +2024-06-17 04:05:46,638 - mmseg - INFO - Iter [73200/80000] lr: 3.401e-06, eta: 2:50:52, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1341, decode.acc_seg: 93.9643, aux.loss_ce: 0.0575, aux.acc_seg: 93.5465, loss: 0.1916 +2024-06-17 04:06:54,829 - mmseg - INFO - Iter [73250/80000] lr: 3.375e-06, eta: 2:49:36, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1348, decode.acc_seg: 94.0827, aux.loss_ce: 0.0581, aux.acc_seg: 93.6215, loss: 0.1929 +2024-06-17 04:08:05,321 - mmseg - INFO - Iter [73300/80000] lr: 3.350e-06, eta: 2:48:20, time: 1.410, data_time: 0.052, memory: 70722, decode.loss_ce: 0.1397, decode.acc_seg: 93.9909, aux.loss_ce: 0.0606, aux.acc_seg: 93.4723, loss: 0.2003 +2024-06-17 04:09:13,488 - mmseg - INFO - Iter [73350/80000] lr: 3.325e-06, eta: 2:47:04, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1381, decode.acc_seg: 93.9968, aux.loss_ce: 0.0595, aux.acc_seg: 93.5781, loss: 0.1975 +2024-06-17 04:10:21,618 - mmseg - INFO - Iter [73400/80000] lr: 3.300e-06, eta: 2:45:48, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1408, decode.acc_seg: 93.9751, aux.loss_ce: 0.0603, aux.acc_seg: 93.5334, loss: 0.2010 +2024-06-17 04:11:29,759 - mmseg - INFO - Iter [73450/80000] lr: 3.276e-06, eta: 2:44:32, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1330, decode.acc_seg: 94.0849, aux.loss_ce: 0.0575, aux.acc_seg: 93.6128, loss: 0.1904 +2024-06-17 04:12:38,120 - mmseg - INFO - Iter [73500/80000] lr: 3.251e-06, eta: 2:43:16, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1334, decode.acc_seg: 93.9746, aux.loss_ce: 0.0578, aux.acc_seg: 93.5362, loss: 0.1912 +2024-06-17 04:13:46,283 - mmseg - INFO - Iter [73550/80000] lr: 3.226e-06, eta: 2:42:00, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1419, decode.acc_seg: 93.6827, aux.loss_ce: 0.0613, aux.acc_seg: 93.2079, loss: 0.2032 +2024-06-17 04:14:54,461 - mmseg - INFO - Iter [73600/80000] lr: 3.201e-06, eta: 2:40:44, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1310, decode.acc_seg: 93.9813, aux.loss_ce: 0.0569, aux.acc_seg: 93.5147, loss: 0.1878 +2024-06-17 04:16:02,650 - mmseg - INFO - Iter [73650/80000] lr: 3.176e-06, eta: 2:39:28, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1408, decode.acc_seg: 93.7517, aux.loss_ce: 0.0609, aux.acc_seg: 93.2653, loss: 0.2017 +2024-06-17 04:17:10,838 - mmseg - INFO - Iter [73700/80000] lr: 3.150e-06, eta: 2:38:12, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1355, decode.acc_seg: 93.9427, aux.loss_ce: 0.0586, aux.acc_seg: 93.4311, loss: 0.1942 +2024-06-17 04:18:19,251 - mmseg - INFO - Iter [73750/80000] lr: 3.125e-06, eta: 2:36:56, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1370, decode.acc_seg: 94.0243, aux.loss_ce: 0.0592, aux.acc_seg: 93.5745, loss: 0.1962 +2024-06-17 04:19:27,521 - mmseg - INFO - Iter [73800/80000] lr: 3.100e-06, eta: 2:35:40, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1401, decode.acc_seg: 93.8231, aux.loss_ce: 0.0608, aux.acc_seg: 93.3419, loss: 0.2009 +2024-06-17 04:20:35,682 - mmseg - INFO - Iter [73850/80000] lr: 3.076e-06, eta: 2:34:24, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1419, decode.acc_seg: 93.7774, aux.loss_ce: 0.0609, aux.acc_seg: 93.3687, loss: 0.2028 +2024-06-17 04:21:43,795 - mmseg - INFO - Iter [73900/80000] lr: 3.051e-06, eta: 2:33:08, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1344, decode.acc_seg: 94.1185, aux.loss_ce: 0.0575, aux.acc_seg: 93.7433, loss: 0.1919 +2024-06-17 04:22:52,131 - mmseg - INFO - Iter [73950/80000] lr: 3.026e-06, eta: 2:31:53, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1315, decode.acc_seg: 93.9359, aux.loss_ce: 0.0570, aux.acc_seg: 93.4641, loss: 0.1885 +2024-06-17 04:24:00,294 - mmseg - INFO - Saving checkpoint at 74000 iterations +2024-06-17 04:25:30,437 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 04:25:30,437 - mmseg - INFO - Iter [74000/80000] lr: 3.001e-06, eta: 2:30:44, time: 3.166, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1389, decode.acc_seg: 93.8740, aux.loss_ce: 0.0600, aux.acc_seg: 93.4216, loss: 0.1989 +2024-06-17 04:27:05,191 - mmseg - INFO - per class results: +2024-06-17 04:27:05,197 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.74 | 90.62 | +| building | 86.01 | 93.93 | +| sky | 95.05 | 97.7 | +| floor | 85.88 | 92.76 | +| tree | 77.51 | 89.32 | +| ceiling | 87.33 | 94.45 | +| road | 87.21 | 91.99 | +| bed | 93.25 | 97.18 | +| windowpane | 65.88 | 80.97 | +| grass | 68.74 | 81.93 | +| cabinet | 67.09 | 77.69 | +| sidewalk | 73.23 | 85.96 | +| person | 86.33 | 94.27 | +| earth | 37.76 | 50.82 | +| door | 59.81 | 75.13 | +| table | 71.07 | 82.19 | +| mountain | 61.46 | 72.33 | +| plant | 56.06 | 65.66 | +| curtain | 77.07 | 88.25 | +| chair | 69.1 | 80.07 | +| car | 87.62 | 94.11 | +| water | 66.58 | 82.61 | +| painting | 78.55 | 91.3 | +| sofa | 81.54 | 89.89 | +| shelf | 44.41 | 59.56 | +| house | 60.69 | 69.47 | +| sea | 75.68 | 85.17 | +| mirror | 80.4 | 86.27 | +| rug | 70.84 | 78.88 | +| field | 31.1 | 58.58 | +| armchair | 60.77 | 79.75 | +| seat | 69.27 | 89.2 | +| fence | 52.84 | 66.09 | +| desk | 62.1 | 79.43 | +| rock | 56.12 | 87.94 | +| wardrobe | 55.39 | 73.65 | +| lamp | 75.9 | 87.01 | +| bathtub | 85.14 | 87.22 | +| railing | 43.44 | 59.84 | +| cushion | 67.93 | 79.66 | +| base | 42.0 | 54.99 | +| box | 37.77 | 47.54 | +| column | 56.18 | 69.58 | +| signboard | 41.24 | 56.99 | +| chest of drawers | 44.84 | 66.63 | +| counter | 39.46 | 47.68 | +| sand | 59.87 | 86.45 | +| sink | 78.27 | 83.52 | +| skyscraper | 47.96 | 61.16 | +| fireplace | 73.05 | 91.88 | +| refrigerator | 81.97 | 86.25 | +| grandstand | 53.79 | 85.44 | +| path | 30.02 | 46.08 | +| stairs | 30.61 | 36.29 | +| runway | 71.48 | 93.11 | +| case | 57.13 | 81.22 | +| pool table | 94.65 | 98.08 | +| pillow | 65.35 | 75.04 | +| screen door | 76.36 | 78.14 | +| stairway | 52.22 | 69.8 | +| river | 9.65 | 18.59 | +| bridge | 69.61 | 78.26 | +| bookcase | 44.61 | 66.05 | +| blind | 41.85 | 46.05 | +| coffee table | 65.16 | 88.05 | +| toilet | 90.6 | 94.0 | +| flower | 46.37 | 55.91 | +| book | 55.27 | 78.9 | +| hill | 9.09 | 16.25 | +| bench | 54.91 | 63.1 | +| countertop | 64.65 | 85.69 | +| stove | 84.09 | 89.39 | +| palm | 55.23 | 81.3 | +| kitchen island | 61.49 | 86.46 | +| computer | 79.27 | 90.29 | +| swivel chair | 47.48 | 67.13 | +| boat | 75.05 | 91.3 | +| bar | 59.9 | 84.84 | +| arcade machine | 77.86 | 82.97 | +| hovel | 49.83 | 54.79 | +| bus | 92.14 | 96.6 | +| towel | 76.74 | 87.27 | +| light | 62.1 | 71.68 | +| truck | 44.98 | 59.03 | +| tower | 23.49 | 37.42 | +| chandelier | 73.16 | 83.19 | +| awning | 47.08 | 61.68 | +| streetlight | 36.8 | 50.73 | +| booth | 42.87 | 62.8 | +| television receiver | 80.32 | 89.04 | +| airplane | 78.13 | 86.8 | +| dirt track | 5.59 | 22.66 | +| apparel | 48.95 | 65.06 | +| pole | 30.21 | 41.58 | +| land | 1.15 | 1.91 | +| bannister | 18.55 | 26.27 | +| escalator | 58.89 | 78.56 | +| ottoman | 47.35 | 62.72 | +| bottle | 41.75 | 68.99 | +| buffet | 46.66 | 57.27 | +| poster | 40.01 | 51.8 | +| stage | 23.86 | 45.49 | +| van | 47.31 | 61.64 | +| ship | 87.32 | 91.65 | +| fountain | 35.66 | 36.28 | +| conveyer belt | 84.04 | 93.75 | +| canopy | 54.45 | 74.57 | +| washer | 81.64 | 86.29 | +| plaything | 33.08 | 44.02 | +| swimming pool | 58.82 | 87.14 | +| stool | 55.51 | 70.19 | +| barrel | 54.47 | 74.83 | +| basket | 41.75 | 61.41 | +| waterfall | 69.33 | 86.37 | +| tent | 95.87 | 98.58 | +| bag | 21.53 | 24.0 | +| minibike | 77.78 | 90.29 | +| cradle | 83.72 | 97.46 | +| oven | 64.69 | 74.41 | +| ball | 43.43 | 49.27 | +| food | 58.95 | 74.15 | +| step | 12.79 | 15.84 | +| tank | 61.61 | 66.41 | +| trade name | 27.75 | 33.34 | +| microwave | 89.92 | 95.87 | +| pot | 58.33 | 68.49 | +| animal | 59.6 | 60.8 | +| bicycle | 60.51 | 77.24 | +| lake | 52.28 | 63.85 | +| dishwasher | 70.26 | 82.43 | +| screen | 59.41 | 89.55 | +| blanket | 29.41 | 32.9 | +| sculpture | 74.01 | 88.65 | +| hood | 63.65 | 75.97 | +| sconce | 58.98 | 68.06 | +| vase | 49.36 | 65.32 | +| traffic light | 40.46 | 59.8 | +| tray | 26.32 | 32.19 | +| ashcan | 47.25 | 65.32 | +| fan | 71.82 | 83.51 | +| pier | 40.41 | 44.6 | +| crt screen | 2.67 | 3.79 | +| plate | 62.09 | 80.19 | +| monitor | 64.05 | 76.72 | +| bulletin board | 51.39 | 61.32 | +| shower | 7.36 | 7.58 | +| radiator | 68.36 | 77.31 | +| glass | 20.13 | 21.4 | +| clock | 46.78 | 58.73 | +| flag | 72.24 | 79.1 | ++---------------------+-------+-------+ +2024-06-17 04:27:05,197 - mmseg - INFO - Summary: +2024-06-17 04:27:05,198 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 86.5 | 57.94 | 70.17 | ++------+-------+-------+ +2024-06-17 04:27:05,198 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 04:27:05,198 - mmseg - INFO - Iter(val) [250] aAcc: 0.8650, mIoU: 0.5794, mAcc: 0.7017, IoU.wall: 0.8274, IoU.building: 0.8601, IoU.sky: 0.9505, IoU.floor: 0.8588, IoU.tree: 0.7751, IoU.ceiling: 0.8733, IoU.road: 0.8721, IoU.bed : 0.9325, IoU.windowpane: 0.6588, IoU.grass: 0.6874, IoU.cabinet: 0.6709, IoU.sidewalk: 0.7323, IoU.person: 0.8633, IoU.earth: 0.3776, IoU.door: 0.5981, IoU.table: 0.7107, IoU.mountain: 0.6146, IoU.plant: 0.5606, IoU.curtain: 0.7707, IoU.chair: 0.6910, IoU.car: 0.8762, IoU.water: 0.6658, IoU.painting: 0.7855, IoU.sofa: 0.8154, IoU.shelf: 0.4441, IoU.house: 0.6069, IoU.sea: 0.7568, IoU.mirror: 0.8040, IoU.rug: 0.7084, IoU.field: 0.3110, IoU.armchair: 0.6077, IoU.seat: 0.6927, IoU.fence: 0.5284, IoU.desk: 0.6210, IoU.rock: 0.5612, IoU.wardrobe: 0.5539, IoU.lamp: 0.7590, IoU.bathtub: 0.8514, IoU.railing: 0.4344, IoU.cushion: 0.6793, IoU.base: 0.4200, IoU.box: 0.3777, IoU.column: 0.5618, IoU.signboard: 0.4124, IoU.chest of drawers: 0.4484, IoU.counter: 0.3946, IoU.sand: 0.5987, IoU.sink: 0.7827, IoU.skyscraper: 0.4796, IoU.fireplace: 0.7305, IoU.refrigerator: 0.8197, IoU.grandstand: 0.5379, IoU.path: 0.3002, IoU.stairs: 0.3061, IoU.runway: 0.7148, IoU.case: 0.5713, IoU.pool table: 0.9465, IoU.pillow: 0.6535, IoU.screen door: 0.7636, IoU.stairway: 0.5222, IoU.river: 0.0965, IoU.bridge: 0.6961, IoU.bookcase: 0.4461, IoU.blind: 0.4185, IoU.coffee table: 0.6516, IoU.toilet: 0.9060, IoU.flower: 0.4637, IoU.book: 0.5527, IoU.hill: 0.0909, IoU.bench: 0.5491, IoU.countertop: 0.6465, IoU.stove: 0.8409, IoU.palm: 0.5523, IoU.kitchen island: 0.6149, IoU.computer: 0.7927, IoU.swivel chair: 0.4748, IoU.boat: 0.7505, IoU.bar: 0.5990, IoU.arcade machine: 0.7786, IoU.hovel: 0.4983, IoU.bus: 0.9214, IoU.towel: 0.7674, IoU.light: 0.6210, IoU.truck: 0.4498, IoU.tower: 0.2349, IoU.chandelier: 0.7316, IoU.awning: 0.4708, IoU.streetlight: 0.3680, IoU.booth: 0.4287, IoU.television receiver: 0.8032, IoU.airplane: 0.7813, IoU.dirt track: 0.0559, IoU.apparel: 0.4895, IoU.pole: 0.3021, IoU.land: 0.0115, IoU.bannister: 0.1855, IoU.escalator: 0.5889, IoU.ottoman: 0.4735, IoU.bottle: 0.4175, IoU.buffet: 0.4666, IoU.poster: 0.4001, IoU.stage: 0.2386, IoU.van: 0.4731, IoU.ship: 0.8732, IoU.fountain: 0.3566, IoU.conveyer belt: 0.8404, IoU.canopy: 0.5445, IoU.washer: 0.8164, IoU.plaything: 0.3308, IoU.swimming pool: 0.5882, IoU.stool: 0.5551, IoU.barrel: 0.5447, IoU.basket: 0.4175, IoU.waterfall: 0.6933, IoU.tent: 0.9587, IoU.bag: 0.2153, IoU.minibike: 0.7778, IoU.cradle: 0.8372, IoU.oven: 0.6469, IoU.ball: 0.4343, IoU.food: 0.5895, IoU.step: 0.1279, IoU.tank: 0.6161, IoU.trade name: 0.2775, IoU.microwave: 0.8992, IoU.pot: 0.5833, IoU.animal: 0.5960, IoU.bicycle: 0.6051, IoU.lake: 0.5228, IoU.dishwasher: 0.7026, IoU.screen: 0.5941, IoU.blanket: 0.2941, IoU.sculpture: 0.7401, IoU.hood: 0.6365, IoU.sconce: 0.5898, IoU.vase: 0.4936, IoU.traffic light: 0.4046, IoU.tray: 0.2632, IoU.ashcan: 0.4725, IoU.fan: 0.7182, IoU.pier: 0.4041, IoU.crt screen: 0.0267, IoU.plate: 0.6209, IoU.monitor: 0.6405, IoU.bulletin board: 0.5139, IoU.shower: 0.0736, IoU.radiator: 0.6836, IoU.glass: 0.2013, IoU.clock: 0.4678, IoU.flag: 0.7224, Acc.wall: 0.9062, Acc.building: 0.9393, Acc.sky: 0.9770, Acc.floor: 0.9276, Acc.tree: 0.8932, Acc.ceiling: 0.9445, Acc.road: 0.9199, Acc.bed : 0.9718, Acc.windowpane: 0.8097, Acc.grass: 0.8193, Acc.cabinet: 0.7769, Acc.sidewalk: 0.8596, Acc.person: 0.9427, Acc.earth: 0.5082, Acc.door: 0.7513, Acc.table: 0.8219, Acc.mountain: 0.7233, Acc.plant: 0.6566, Acc.curtain: 0.8825, Acc.chair: 0.8007, Acc.car: 0.9411, Acc.water: 0.8261, Acc.painting: 0.9130, Acc.sofa: 0.8989, Acc.shelf: 0.5956, Acc.house: 0.6947, Acc.sea: 0.8517, Acc.mirror: 0.8627, Acc.rug: 0.7888, Acc.field: 0.5858, Acc.armchair: 0.7975, Acc.seat: 0.8920, Acc.fence: 0.6609, Acc.desk: 0.7943, Acc.rock: 0.8794, Acc.wardrobe: 0.7365, Acc.lamp: 0.8701, Acc.bathtub: 0.8722, Acc.railing: 0.5984, Acc.cushion: 0.7966, Acc.base: 0.5499, Acc.box: 0.4754, Acc.column: 0.6958, Acc.signboard: 0.5699, Acc.chest of drawers: 0.6663, Acc.counter: 0.4768, Acc.sand: 0.8645, Acc.sink: 0.8352, Acc.skyscraper: 0.6116, Acc.fireplace: 0.9188, Acc.refrigerator: 0.8625, Acc.grandstand: 0.8544, Acc.path: 0.4608, Acc.stairs: 0.3629, Acc.runway: 0.9311, Acc.case: 0.8122, Acc.pool table: 0.9808, Acc.pillow: 0.7504, Acc.screen door: 0.7814, Acc.stairway: 0.6980, Acc.river: 0.1859, Acc.bridge: 0.7826, Acc.bookcase: 0.6605, Acc.blind: 0.4605, Acc.coffee table: 0.8805, Acc.toilet: 0.9400, Acc.flower: 0.5591, Acc.book: 0.7890, Acc.hill: 0.1625, Acc.bench: 0.6310, Acc.countertop: 0.8569, Acc.stove: 0.8939, Acc.palm: 0.8130, Acc.kitchen island: 0.8646, Acc.computer: 0.9029, Acc.swivel chair: 0.6713, Acc.boat: 0.9130, Acc.bar: 0.8484, Acc.arcade machine: 0.8297, Acc.hovel: 0.5479, Acc.bus: 0.9660, Acc.towel: 0.8727, Acc.light: 0.7168, Acc.truck: 0.5903, Acc.tower: 0.3742, Acc.chandelier: 0.8319, Acc.awning: 0.6168, Acc.streetlight: 0.5073, Acc.booth: 0.6280, Acc.television receiver: 0.8904, Acc.airplane: 0.8680, Acc.dirt track: 0.2266, Acc.apparel: 0.6506, Acc.pole: 0.4158, Acc.land: 0.0191, Acc.bannister: 0.2627, Acc.escalator: 0.7856, Acc.ottoman: 0.6272, Acc.bottle: 0.6899, Acc.buffet: 0.5727, Acc.poster: 0.5180, Acc.stage: 0.4549, Acc.van: 0.6164, Acc.ship: 0.9165, Acc.fountain: 0.3628, Acc.conveyer belt: 0.9375, Acc.canopy: 0.7457, Acc.washer: 0.8629, Acc.plaything: 0.4402, Acc.swimming pool: 0.8714, Acc.stool: 0.7019, Acc.barrel: 0.7483, Acc.basket: 0.6141, Acc.waterfall: 0.8637, Acc.tent: 0.9858, Acc.bag: 0.2400, Acc.minibike: 0.9029, Acc.cradle: 0.9746, Acc.oven: 0.7441, Acc.ball: 0.4927, Acc.food: 0.7415, Acc.step: 0.1584, Acc.tank: 0.6641, Acc.trade name: 0.3334, Acc.microwave: 0.9587, Acc.pot: 0.6849, Acc.animal: 0.6080, Acc.bicycle: 0.7724, Acc.lake: 0.6385, Acc.dishwasher: 0.8243, Acc.screen: 0.8955, Acc.blanket: 0.3290, Acc.sculpture: 0.8865, Acc.hood: 0.7597, Acc.sconce: 0.6806, Acc.vase: 0.6532, Acc.traffic light: 0.5980, Acc.tray: 0.3219, Acc.ashcan: 0.6532, Acc.fan: 0.8351, Acc.pier: 0.4460, Acc.crt screen: 0.0379, Acc.plate: 0.8019, Acc.monitor: 0.7672, Acc.bulletin board: 0.6132, Acc.shower: 0.0758, Acc.radiator: 0.7731, Acc.glass: 0.2140, Acc.clock: 0.5873, Acc.flag: 0.7910 +2024-06-17 04:28:14,050 - mmseg - INFO - Iter [74050/80000] lr: 2.976e-06, eta: 2:29:36, time: 3.272, data_time: 1.912, memory: 70722, decode.loss_ce: 0.1341, decode.acc_seg: 93.9248, aux.loss_ce: 0.0579, aux.acc_seg: 93.4548, loss: 0.1920 +2024-06-17 04:29:22,212 - mmseg - INFO - Iter [74100/80000] lr: 2.950e-06, eta: 2:28:20, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1402, decode.acc_seg: 93.7579, aux.loss_ce: 0.0602, aux.acc_seg: 93.3254, loss: 0.2005 +2024-06-17 04:30:30,541 - mmseg - INFO - Iter [74150/80000] lr: 2.925e-06, eta: 2:27:04, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1284, decode.acc_seg: 94.2511, aux.loss_ce: 0.0558, aux.acc_seg: 93.7839, loss: 0.1842 +2024-06-17 04:31:38,638 - mmseg - INFO - Iter [74200/80000] lr: 2.900e-06, eta: 2:25:48, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1336, decode.acc_seg: 94.0483, aux.loss_ce: 0.0581, aux.acc_seg: 93.5156, loss: 0.1918 +2024-06-17 04:32:46,771 - mmseg - INFO - Iter [74250/80000] lr: 2.875e-06, eta: 2:24:32, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1377, decode.acc_seg: 94.0495, aux.loss_ce: 0.0595, aux.acc_seg: 93.6230, loss: 0.1973 +2024-06-17 04:33:54,894 - mmseg - INFO - Iter [74300/80000] lr: 2.851e-06, eta: 2:23:16, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1353, decode.acc_seg: 93.9829, aux.loss_ce: 0.0588, aux.acc_seg: 93.4654, loss: 0.1940 +2024-06-17 04:35:03,082 - mmseg - INFO - Iter [74350/80000] lr: 2.826e-06, eta: 2:22:00, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1474, decode.acc_seg: 93.4988, aux.loss_ce: 0.0638, aux.acc_seg: 92.9717, loss: 0.2112 +2024-06-17 04:36:11,311 - mmseg - INFO - Iter [74400/80000] lr: 2.801e-06, eta: 2:20:44, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1297, decode.acc_seg: 94.1608, aux.loss_ce: 0.0564, aux.acc_seg: 93.6925, loss: 0.1861 +2024-06-17 04:37:19,352 - mmseg - INFO - Iter [74450/80000] lr: 2.776e-06, eta: 2:19:28, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1396, decode.acc_seg: 93.7516, aux.loss_ce: 0.0608, aux.acc_seg: 93.2945, loss: 0.2004 +2024-06-17 04:38:27,598 - mmseg - INFO - Iter [74500/80000] lr: 2.750e-06, eta: 2:18:12, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1433, decode.acc_seg: 93.7060, aux.loss_ce: 0.0617, aux.acc_seg: 93.3168, loss: 0.2050 +2024-06-17 04:39:38,224 - mmseg - INFO - Iter [74550/80000] lr: 2.725e-06, eta: 2:16:56, time: 1.412, data_time: 0.052, memory: 70722, decode.loss_ce: 0.1368, decode.acc_seg: 93.9764, aux.loss_ce: 0.0592, aux.acc_seg: 93.5630, loss: 0.1960 +2024-06-17 04:40:46,645 - mmseg - INFO - Iter [74600/80000] lr: 2.700e-06, eta: 2:15:40, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1314, decode.acc_seg: 93.9802, aux.loss_ce: 0.0571, aux.acc_seg: 93.4976, loss: 0.1885 +2024-06-17 04:41:54,784 - mmseg - INFO - Iter [74650/80000] lr: 2.675e-06, eta: 2:14:25, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1401, decode.acc_seg: 93.8628, aux.loss_ce: 0.0603, aux.acc_seg: 93.4203, loss: 0.2004 +2024-06-17 04:43:02,802 - mmseg - INFO - Iter [74700/80000] lr: 2.651e-06, eta: 2:13:09, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1307, decode.acc_seg: 94.2145, aux.loss_ce: 0.0567, aux.acc_seg: 93.7331, loss: 0.1874 +2024-06-17 04:44:11,180 - mmseg - INFO - Iter [74750/80000] lr: 2.626e-06, eta: 2:11:53, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1316, decode.acc_seg: 94.1343, aux.loss_ce: 0.0568, aux.acc_seg: 93.6660, loss: 0.1884 +2024-06-17 04:45:19,400 - mmseg - INFO - Iter [74800/80000] lr: 2.601e-06, eta: 2:10:37, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1341, decode.acc_seg: 94.1492, aux.loss_ce: 0.0583, aux.acc_seg: 93.6861, loss: 0.1924 +2024-06-17 04:46:27,494 - mmseg - INFO - Iter [74850/80000] lr: 2.576e-06, eta: 2:09:21, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1370, decode.acc_seg: 93.9747, aux.loss_ce: 0.0595, aux.acc_seg: 93.4674, loss: 0.1965 +2024-06-17 04:47:35,652 - mmseg - INFO - Iter [74900/80000] lr: 2.551e-06, eta: 2:08:05, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1285, decode.acc_seg: 94.2068, aux.loss_ce: 0.0558, aux.acc_seg: 93.7560, loss: 0.1843 +2024-06-17 04:48:43,951 - mmseg - INFO - Iter [74950/80000] lr: 2.525e-06, eta: 2:06:49, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1414, decode.acc_seg: 93.8950, aux.loss_ce: 0.0613, aux.acc_seg: 93.4455, loss: 0.2027 +2024-06-17 04:49:52,142 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 04:49:52,142 - mmseg - INFO - Iter [75000/80000] lr: 2.500e-06, eta: 2:05:34, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1381, decode.acc_seg: 93.8260, aux.loss_ce: 0.0598, aux.acc_seg: 93.3546, loss: 0.1980 +2024-06-17 04:51:28,638 - mmseg - INFO - per class results: +2024-06-17 04:51:28,644 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.76 | 90.33 | +| building | 85.97 | 93.46 | +| sky | 95.05 | 97.8 | +| floor | 85.88 | 92.27 | +| tree | 77.6 | 89.71 | +| ceiling | 87.4 | 94.31 | +| road | 87.12 | 91.91 | +| bed | 93.16 | 97.38 | +| windowpane | 65.71 | 82.5 | +| grass | 68.61 | 82.32 | +| cabinet | 67.46 | 78.52 | +| sidewalk | 73.36 | 86.78 | +| person | 86.19 | 94.73 | +| earth | 38.15 | 51.79 | +| door | 59.91 | 73.44 | +| table | 70.82 | 82.11 | +| mountain | 62.53 | 74.24 | +| plant | 56.14 | 66.17 | +| curtain | 77.13 | 88.6 | +| chair | 69.58 | 81.67 | +| car | 87.5 | 94.51 | +| water | 66.02 | 81.52 | +| painting | 79.18 | 91.51 | +| sofa | 82.31 | 91.42 | +| shelf | 45.73 | 60.6 | +| house | 61.03 | 74.22 | +| sea | 78.28 | 88.43 | +| mirror | 79.81 | 85.28 | +| rug | 70.11 | 77.92 | +| field | 30.93 | 57.07 | +| armchair | 61.22 | 78.04 | +| seat | 67.87 | 89.61 | +| fence | 53.06 | 64.64 | +| desk | 61.85 | 79.93 | +| rock | 57.17 | 84.72 | +| wardrobe | 55.45 | 71.35 | +| lamp | 75.56 | 86.77 | +| bathtub | 85.19 | 87.29 | +| railing | 44.09 | 61.03 | +| cushion | 69.93 | 81.97 | +| base | 42.79 | 59.95 | +| box | 37.5 | 47.64 | +| column | 56.04 | 68.79 | +| signboard | 41.27 | 55.3 | +| chest of drawers | 44.76 | 68.04 | +| counter | 39.29 | 47.16 | +| sand | 60.72 | 86.59 | +| sink | 78.19 | 84.75 | +| skyscraper | 48.06 | 61.23 | +| fireplace | 72.69 | 91.13 | +| refrigerator | 84.39 | 91.14 | +| grandstand | 52.65 | 85.36 | +| path | 30.85 | 43.51 | +| stairs | 28.68 | 33.61 | +| runway | 70.7 | 92.38 | +| case | 57.5 | 82.05 | +| pool table | 94.54 | 98.3 | +| pillow | 68.14 | 79.37 | +| screen door | 77.55 | 79.4 | +| stairway | 51.21 | 72.24 | +| river | 10.49 | 18.21 | +| bridge | 66.99 | 75.05 | +| bookcase | 45.59 | 66.17 | +| blind | 39.71 | 43.0 | +| coffee table | 64.68 | 88.26 | +| toilet | 90.54 | 93.98 | +| flower | 46.42 | 57.34 | +| book | 55.86 | 79.01 | +| hill | 8.91 | 15.44 | +| bench | 54.27 | 62.99 | +| countertop | 66.62 | 84.07 | +| stove | 84.38 | 89.17 | +| palm | 54.96 | 79.69 | +| kitchen island | 59.94 | 83.72 | +| computer | 78.86 | 91.62 | +| swivel chair | 50.84 | 74.47 | +| boat | 75.5 | 91.83 | +| bar | 59.16 | 81.65 | +| arcade machine | 78.74 | 83.66 | +| hovel | 45.91 | 50.49 | +| bus | 91.59 | 96.42 | +| towel | 76.61 | 87.61 | +| light | 61.65 | 71.59 | +| truck | 45.04 | 58.86 | +| tower | 28.12 | 42.43 | +| chandelier | 72.86 | 85.12 | +| awning | 47.29 | 62.66 | +| streetlight | 36.33 | 50.35 | +| booth | 44.98 | 64.9 | +| television receiver | 79.19 | 88.71 | +| airplane | 80.36 | 89.18 | +| dirt track | 6.79 | 27.84 | +| apparel | 48.73 | 66.8 | +| pole | 28.61 | 39.32 | +| land | 0.92 | 1.6 | +| bannister | 18.94 | 27.02 | +| escalator | 59.59 | 78.5 | +| ottoman | 48.36 | 62.78 | +| bottle | 42.02 | 69.57 | +| buffet | 46.99 | 55.64 | +| poster | 43.54 | 53.65 | +| stage | 23.48 | 46.29 | +| van | 46.79 | 62.9 | +| ship | 86.53 | 90.42 | +| fountain | 32.5 | 33.04 | +| conveyer belt | 84.21 | 93.64 | +| canopy | 54.54 | 74.86 | +| washer | 81.25 | 85.83 | +| plaything | 33.62 | 48.32 | +| swimming pool | 58.13 | 85.81 | +| stool | 55.66 | 70.96 | +| barrel | 56.21 | 74.85 | +| basket | 42.15 | 62.38 | +| waterfall | 67.87 | 88.15 | +| tent | 95.69 | 98.61 | +| bag | 21.97 | 24.8 | +| minibike | 77.38 | 91.35 | +| cradle | 84.66 | 97.4 | +| oven | 64.91 | 77.07 | +| ball | 46.51 | 54.28 | +| food | 59.71 | 76.02 | +| step | 11.26 | 13.77 | +| tank | 62.52 | 67.2 | +| trade name | 29.87 | 36.84 | +| microwave | 89.37 | 96.23 | +| pot | 58.63 | 69.03 | +| animal | 59.95 | 61.27 | +| bicycle | 61.62 | 77.73 | +| lake | 52.72 | 63.84 | +| dishwasher | 67.75 | 79.31 | +| screen | 54.19 | 82.26 | +| blanket | 30.74 | 34.48 | +| sculpture | 74.61 | 88.37 | +| hood | 63.83 | 76.4 | +| sconce | 60.64 | 71.53 | +| vase | 50.18 | 65.34 | +| traffic light | 39.49 | 63.37 | +| tray | 26.62 | 34.91 | +| ashcan | 46.85 | 65.54 | +| fan | 71.33 | 82.41 | +| pier | 39.94 | 44.04 | +| crt screen | 2.27 | 3.45 | +| plate | 61.96 | 79.21 | +| monitor | 64.03 | 76.54 | +| bulletin board | 53.0 | 65.55 | +| shower | 9.28 | 9.35 | +| radiator | 68.67 | 77.48 | +| glass | 20.62 | 22.19 | +| clock | 47.16 | 57.2 | +| flag | 72.16 | 79.62 | ++---------------------+-------+-------+ +2024-06-17 04:51:28,644 - mmseg - INFO - Summary: +2024-06-17 04:51:28,644 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.54 | 58.08 | 70.52 | ++-------+-------+-------+ +2024-06-17 04:51:28,645 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 04:51:28,645 - mmseg - INFO - Iter(val) [250] aAcc: 0.8654, mIoU: 0.5808, mAcc: 0.7052, IoU.wall: 0.8276, IoU.building: 0.8597, IoU.sky: 0.9505, IoU.floor: 0.8588, IoU.tree: 0.7760, IoU.ceiling: 0.8740, IoU.road: 0.8712, IoU.bed : 0.9316, IoU.windowpane: 0.6571, IoU.grass: 0.6861, IoU.cabinet: 0.6746, IoU.sidewalk: 0.7336, IoU.person: 0.8619, IoU.earth: 0.3815, IoU.door: 0.5991, IoU.table: 0.7082, IoU.mountain: 0.6253, IoU.plant: 0.5614, IoU.curtain: 0.7713, IoU.chair: 0.6958, IoU.car: 0.8750, IoU.water: 0.6602, IoU.painting: 0.7918, IoU.sofa: 0.8231, IoU.shelf: 0.4573, IoU.house: 0.6103, IoU.sea: 0.7828, IoU.mirror: 0.7981, IoU.rug: 0.7011, IoU.field: 0.3093, IoU.armchair: 0.6122, IoU.seat: 0.6787, IoU.fence: 0.5306, IoU.desk: 0.6185, IoU.rock: 0.5717, IoU.wardrobe: 0.5545, IoU.lamp: 0.7556, IoU.bathtub: 0.8519, IoU.railing: 0.4409, IoU.cushion: 0.6993, IoU.base: 0.4279, IoU.box: 0.3750, IoU.column: 0.5604, IoU.signboard: 0.4127, IoU.chest of drawers: 0.4476, IoU.counter: 0.3929, IoU.sand: 0.6072, IoU.sink: 0.7819, IoU.skyscraper: 0.4806, IoU.fireplace: 0.7269, IoU.refrigerator: 0.8439, IoU.grandstand: 0.5265, IoU.path: 0.3085, IoU.stairs: 0.2868, IoU.runway: 0.7070, IoU.case: 0.5750, IoU.pool table: 0.9454, IoU.pillow: 0.6814, IoU.screen door: 0.7755, IoU.stairway: 0.5121, IoU.river: 0.1049, IoU.bridge: 0.6699, IoU.bookcase: 0.4559, IoU.blind: 0.3971, IoU.coffee table: 0.6468, IoU.toilet: 0.9054, IoU.flower: 0.4642, IoU.book: 0.5586, IoU.hill: 0.0891, IoU.bench: 0.5427, IoU.countertop: 0.6662, IoU.stove: 0.8438, IoU.palm: 0.5496, IoU.kitchen island: 0.5994, IoU.computer: 0.7886, IoU.swivel chair: 0.5084, IoU.boat: 0.7550, IoU.bar: 0.5916, IoU.arcade machine: 0.7874, IoU.hovel: 0.4591, IoU.bus: 0.9159, IoU.towel: 0.7661, IoU.light: 0.6165, IoU.truck: 0.4504, IoU.tower: 0.2812, IoU.chandelier: 0.7286, IoU.awning: 0.4729, IoU.streetlight: 0.3633, IoU.booth: 0.4498, IoU.television receiver: 0.7919, IoU.airplane: 0.8036, IoU.dirt track: 0.0679, IoU.apparel: 0.4873, IoU.pole: 0.2861, IoU.land: 0.0092, IoU.bannister: 0.1894, IoU.escalator: 0.5959, IoU.ottoman: 0.4836, IoU.bottle: 0.4202, IoU.buffet: 0.4699, IoU.poster: 0.4354, IoU.stage: 0.2348, IoU.van: 0.4679, IoU.ship: 0.8653, IoU.fountain: 0.3250, IoU.conveyer belt: 0.8421, IoU.canopy: 0.5454, IoU.washer: 0.8125, IoU.plaything: 0.3362, IoU.swimming pool: 0.5813, IoU.stool: 0.5566, IoU.barrel: 0.5621, IoU.basket: 0.4215, IoU.waterfall: 0.6787, IoU.tent: 0.9569, IoU.bag: 0.2197, IoU.minibike: 0.7738, IoU.cradle: 0.8466, IoU.oven: 0.6491, IoU.ball: 0.4651, IoU.food: 0.5971, IoU.step: 0.1126, IoU.tank: 0.6252, IoU.trade name: 0.2987, IoU.microwave: 0.8937, IoU.pot: 0.5863, IoU.animal: 0.5995, IoU.bicycle: 0.6162, IoU.lake: 0.5272, IoU.dishwasher: 0.6775, IoU.screen: 0.5419, IoU.blanket: 0.3074, IoU.sculpture: 0.7461, IoU.hood: 0.6383, IoU.sconce: 0.6064, IoU.vase: 0.5018, IoU.traffic light: 0.3949, IoU.tray: 0.2662, IoU.ashcan: 0.4685, IoU.fan: 0.7133, IoU.pier: 0.3994, IoU.crt screen: 0.0227, IoU.plate: 0.6196, IoU.monitor: 0.6403, IoU.bulletin board: 0.5300, IoU.shower: 0.0928, IoU.radiator: 0.6867, IoU.glass: 0.2062, IoU.clock: 0.4716, IoU.flag: 0.7216, Acc.wall: 0.9033, Acc.building: 0.9346, Acc.sky: 0.9780, Acc.floor: 0.9227, Acc.tree: 0.8971, Acc.ceiling: 0.9431, Acc.road: 0.9191, Acc.bed : 0.9738, Acc.windowpane: 0.8250, Acc.grass: 0.8232, Acc.cabinet: 0.7852, Acc.sidewalk: 0.8678, Acc.person: 0.9473, Acc.earth: 0.5179, Acc.door: 0.7344, Acc.table: 0.8211, Acc.mountain: 0.7424, Acc.plant: 0.6617, Acc.curtain: 0.8860, Acc.chair: 0.8167, Acc.car: 0.9451, Acc.water: 0.8152, Acc.painting: 0.9151, Acc.sofa: 0.9142, Acc.shelf: 0.6060, Acc.house: 0.7422, Acc.sea: 0.8843, Acc.mirror: 0.8528, Acc.rug: 0.7792, Acc.field: 0.5707, Acc.armchair: 0.7804, Acc.seat: 0.8961, Acc.fence: 0.6464, Acc.desk: 0.7993, Acc.rock: 0.8472, Acc.wardrobe: 0.7135, Acc.lamp: 0.8677, Acc.bathtub: 0.8729, Acc.railing: 0.6103, Acc.cushion: 0.8197, Acc.base: 0.5995, Acc.box: 0.4764, Acc.column: 0.6879, Acc.signboard: 0.5530, Acc.chest of drawers: 0.6804, Acc.counter: 0.4716, Acc.sand: 0.8659, Acc.sink: 0.8475, Acc.skyscraper: 0.6123, Acc.fireplace: 0.9113, Acc.refrigerator: 0.9114, Acc.grandstand: 0.8536, Acc.path: 0.4351, Acc.stairs: 0.3361, Acc.runway: 0.9238, Acc.case: 0.8205, Acc.pool table: 0.9830, Acc.pillow: 0.7937, Acc.screen door: 0.7940, Acc.stairway: 0.7224, Acc.river: 0.1821, Acc.bridge: 0.7505, Acc.bookcase: 0.6617, Acc.blind: 0.4300, Acc.coffee table: 0.8826, Acc.toilet: 0.9398, Acc.flower: 0.5734, Acc.book: 0.7901, Acc.hill: 0.1544, Acc.bench: 0.6299, Acc.countertop: 0.8407, Acc.stove: 0.8917, Acc.palm: 0.7969, Acc.kitchen island: 0.8372, Acc.computer: 0.9162, Acc.swivel chair: 0.7447, Acc.boat: 0.9183, Acc.bar: 0.8165, Acc.arcade machine: 0.8366, Acc.hovel: 0.5049, Acc.bus: 0.9642, Acc.towel: 0.8761, Acc.light: 0.7159, Acc.truck: 0.5886, Acc.tower: 0.4243, Acc.chandelier: 0.8512, Acc.awning: 0.6266, Acc.streetlight: 0.5035, Acc.booth: 0.6490, Acc.television receiver: 0.8871, Acc.airplane: 0.8918, Acc.dirt track: 0.2784, Acc.apparel: 0.6680, Acc.pole: 0.3932, Acc.land: 0.0160, Acc.bannister: 0.2702, Acc.escalator: 0.7850, Acc.ottoman: 0.6278, Acc.bottle: 0.6957, Acc.buffet: 0.5564, Acc.poster: 0.5365, Acc.stage: 0.4629, Acc.van: 0.6290, Acc.ship: 0.9042, Acc.fountain: 0.3304, Acc.conveyer belt: 0.9364, Acc.canopy: 0.7486, Acc.washer: 0.8583, Acc.plaything: 0.4832, Acc.swimming pool: 0.8581, Acc.stool: 0.7096, Acc.barrel: 0.7485, Acc.basket: 0.6238, Acc.waterfall: 0.8815, Acc.tent: 0.9861, Acc.bag: 0.2480, Acc.minibike: 0.9135, Acc.cradle: 0.9740, Acc.oven: 0.7707, Acc.ball: 0.5428, Acc.food: 0.7602, Acc.step: 0.1377, Acc.tank: 0.6720, Acc.trade name: 0.3684, Acc.microwave: 0.9623, Acc.pot: 0.6903, Acc.animal: 0.6127, Acc.bicycle: 0.7773, Acc.lake: 0.6384, Acc.dishwasher: 0.7931, Acc.screen: 0.8226, Acc.blanket: 0.3448, Acc.sculpture: 0.8837, Acc.hood: 0.7640, Acc.sconce: 0.7153, Acc.vase: 0.6534, Acc.traffic light: 0.6337, Acc.tray: 0.3491, Acc.ashcan: 0.6554, Acc.fan: 0.8241, Acc.pier: 0.4404, Acc.crt screen: 0.0345, Acc.plate: 0.7921, Acc.monitor: 0.7654, Acc.bulletin board: 0.6555, Acc.shower: 0.0935, Acc.radiator: 0.7748, Acc.glass: 0.2219, Acc.clock: 0.5720, Acc.flag: 0.7962 +2024-06-17 04:52:37,306 - mmseg - INFO - Iter [75050/80000] lr: 2.475e-06, eta: 2:04:24, time: 3.303, data_time: 1.946, memory: 70722, decode.loss_ce: 0.1325, decode.acc_seg: 94.0457, aux.loss_ce: 0.0576, aux.acc_seg: 93.5793, loss: 0.1901 +2024-06-17 04:53:45,569 - mmseg - INFO - Iter [75100/80000] lr: 2.451e-06, eta: 2:03:08, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1360, decode.acc_seg: 94.2647, aux.loss_ce: 0.0589, aux.acc_seg: 93.7614, loss: 0.1950 +2024-06-17 04:54:53,651 - mmseg - INFO - Iter [75150/80000] lr: 2.426e-06, eta: 2:01:52, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1368, decode.acc_seg: 93.8195, aux.loss_ce: 0.0595, aux.acc_seg: 93.2956, loss: 0.1963 +2024-06-17 04:56:01,807 - mmseg - INFO - Iter [75200/80000] lr: 2.401e-06, eta: 2:00:37, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1293, decode.acc_seg: 94.0553, aux.loss_ce: 0.0561, aux.acc_seg: 93.6121, loss: 0.1853 +2024-06-17 04:57:10,170 - mmseg - INFO - Iter [75250/80000] lr: 2.376e-06, eta: 1:59:21, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1378, decode.acc_seg: 94.0099, aux.loss_ce: 0.0599, aux.acc_seg: 93.5140, loss: 0.1977 +2024-06-17 04:58:18,535 - mmseg - INFO - Iter [75300/80000] lr: 2.351e-06, eta: 1:58:05, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1374, decode.acc_seg: 93.7953, aux.loss_ce: 0.0601, aux.acc_seg: 93.2430, loss: 0.1975 +2024-06-17 04:59:26,723 - mmseg - INFO - Iter [75350/80000] lr: 2.325e-06, eta: 1:56:49, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1308, decode.acc_seg: 94.1145, aux.loss_ce: 0.0569, aux.acc_seg: 93.6335, loss: 0.1877 +2024-06-17 05:00:35,035 - mmseg - INFO - Iter [75400/80000] lr: 2.300e-06, eta: 1:55:33, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1478, decode.acc_seg: 93.6286, aux.loss_ce: 0.0643, aux.acc_seg: 93.1189, loss: 0.2121 +2024-06-17 05:01:43,371 - mmseg - INFO - Iter [75450/80000] lr: 2.275e-06, eta: 1:54:17, time: 1.367, data_time: 0.011, memory: 70722, decode.loss_ce: 0.1372, decode.acc_seg: 93.8820, aux.loss_ce: 0.0593, aux.acc_seg: 93.3984, loss: 0.1965 +2024-06-17 05:02:51,439 - mmseg - INFO - Iter [75500/80000] lr: 2.250e-06, eta: 1:53:02, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1358, decode.acc_seg: 93.9190, aux.loss_ce: 0.0586, aux.acc_seg: 93.4713, loss: 0.1944 +2024-06-17 05:03:59,874 - mmseg - INFO - Iter [75550/80000] lr: 2.226e-06, eta: 1:51:46, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1359, decode.acc_seg: 93.9775, aux.loss_ce: 0.0591, aux.acc_seg: 93.4537, loss: 0.1950 +2024-06-17 05:05:08,256 - mmseg - INFO - Iter [75600/80000] lr: 2.201e-06, eta: 1:50:30, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1423, decode.acc_seg: 93.7100, aux.loss_ce: 0.0615, aux.acc_seg: 93.2129, loss: 0.2038 +2024-06-17 05:06:16,347 - mmseg - INFO - Iter [75650/80000] lr: 2.176e-06, eta: 1:49:14, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1351, decode.acc_seg: 93.9972, aux.loss_ce: 0.0589, aux.acc_seg: 93.5295, loss: 0.1940 +2024-06-17 05:07:24,560 - mmseg - INFO - Iter [75700/80000] lr: 2.151e-06, eta: 1:47:59, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1264, decode.acc_seg: 94.4905, aux.loss_ce: 0.0545, aux.acc_seg: 94.0636, loss: 0.1810 +2024-06-17 05:08:32,669 - mmseg - INFO - Iter [75750/80000] lr: 2.125e-06, eta: 1:46:43, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1408, decode.acc_seg: 93.6075, aux.loss_ce: 0.0614, aux.acc_seg: 93.0695, loss: 0.2022 +2024-06-17 05:09:44,054 - mmseg - INFO - Iter [75800/80000] lr: 2.100e-06, eta: 1:45:27, time: 1.428, data_time: 0.066, memory: 70722, decode.loss_ce: 0.1359, decode.acc_seg: 94.0815, aux.loss_ce: 0.0589, aux.acc_seg: 93.6413, loss: 0.1947 +2024-06-17 05:10:52,340 - mmseg - INFO - Iter [75850/80000] lr: 2.075e-06, eta: 1:44:12, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1385, decode.acc_seg: 93.8785, aux.loss_ce: 0.0604, aux.acc_seg: 93.3830, loss: 0.1988 +2024-06-17 05:12:00,681 - mmseg - INFO - Iter [75900/80000] lr: 2.050e-06, eta: 1:42:56, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1348, decode.acc_seg: 94.2368, aux.loss_ce: 0.0584, aux.acc_seg: 93.7664, loss: 0.1932 +2024-06-17 05:13:08,727 - mmseg - INFO - Iter [75950/80000] lr: 2.026e-06, eta: 1:41:40, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1392, decode.acc_seg: 93.8821, aux.loss_ce: 0.0601, aux.acc_seg: 93.4284, loss: 0.1993 +2024-06-17 05:14:16,824 - mmseg - INFO - Saving checkpoint at 76000 iterations +2024-06-17 05:15:44,061 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 05:15:44,061 - mmseg - INFO - Iter [76000/80000] lr: 2.001e-06, eta: 1:40:29, time: 3.107, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1317, decode.acc_seg: 94.1141, aux.loss_ce: 0.0569, aux.acc_seg: 93.6301, loss: 0.1887 +2024-06-17 05:17:17,500 - mmseg - INFO - per class results: +2024-06-17 05:17:17,507 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.8 | 90.25 | +| building | 85.88 | 93.64 | +| sky | 95.05 | 97.73 | +| floor | 85.84 | 92.62 | +| tree | 77.46 | 89.95 | +| ceiling | 87.37 | 94.2 | +| road | 87.13 | 92.23 | +| bed | 93.25 | 97.22 | +| windowpane | 66.04 | 81.15 | +| grass | 68.56 | 82.15 | +| cabinet | 67.48 | 78.21 | +| sidewalk | 73.72 | 86.64 | +| person | 86.07 | 94.73 | +| earth | 37.43 | 49.32 | +| door | 60.0 | 74.93 | +| table | 70.97 | 82.33 | +| mountain | 63.14 | 75.51 | +| plant | 55.82 | 65.66 | +| curtain | 76.69 | 88.92 | +| chair | 69.58 | 81.67 | +| car | 87.49 | 94.34 | +| water | 65.5 | 81.17 | +| painting | 78.77 | 92.18 | +| sofa | 83.16 | 91.47 | +| shelf | 45.92 | 60.87 | +| house | 56.55 | 65.73 | +| sea | 77.23 | 88.77 | +| mirror | 80.03 | 85.94 | +| rug | 70.52 | 79.62 | +| field | 32.45 | 61.16 | +| armchair | 61.82 | 77.99 | +| seat | 68.25 | 89.67 | +| fence | 53.05 | 65.86 | +| desk | 62.7 | 79.68 | +| rock | 57.39 | 85.96 | +| wardrobe | 55.41 | 71.97 | +| lamp | 75.71 | 87.4 | +| bathtub | 84.97 | 87.3 | +| railing | 43.67 | 60.82 | +| cushion | 70.03 | 82.05 | +| base | 41.2 | 56.22 | +| box | 37.53 | 48.44 | +| column | 54.77 | 65.98 | +| signboard | 40.81 | 56.11 | +| chest of drawers | 43.82 | 66.91 | +| counter | 39.84 | 48.41 | +| sand | 60.09 | 86.23 | +| sink | 78.29 | 84.93 | +| skyscraper | 47.84 | 61.71 | +| fireplace | 72.89 | 91.38 | +| refrigerator | 84.94 | 91.83 | +| grandstand | 54.07 | 84.25 | +| path | 29.98 | 41.05 | +| stairs | 32.0 | 38.65 | +| runway | 70.47 | 92.7 | +| case | 57.61 | 80.72 | +| pool table | 94.62 | 98.32 | +| pillow | 67.98 | 78.66 | +| screen door | 75.1 | 77.39 | +| stairway | 52.88 | 70.58 | +| river | 10.35 | 18.22 | +| bridge | 69.4 | 77.45 | +| bookcase | 47.05 | 66.71 | +| blind | 42.07 | 46.4 | +| coffee table | 64.48 | 88.17 | +| toilet | 90.59 | 93.84 | +| flower | 46.44 | 57.46 | +| book | 55.06 | 76.86 | +| hill | 7.65 | 12.97 | +| bench | 54.39 | 62.93 | +| countertop | 65.55 | 84.96 | +| stove | 84.64 | 90.89 | +| palm | 54.78 | 79.5 | +| kitchen island | 59.05 | 86.0 | +| computer | 79.11 | 91.46 | +| swivel chair | 49.69 | 72.39 | +| boat | 75.04 | 91.51 | +| bar | 58.42 | 80.82 | +| arcade machine | 79.17 | 84.27 | +| hovel | 49.07 | 54.43 | +| bus | 91.65 | 96.53 | +| towel | 76.39 | 86.57 | +| light | 62.08 | 72.32 | +| truck | 45.21 | 59.42 | +| tower | 28.02 | 43.5 | +| chandelier | 72.4 | 85.86 | +| awning | 47.16 | 62.71 | +| streetlight | 36.17 | 49.89 | +| booth | 44.28 | 64.15 | +| television receiver | 78.49 | 90.7 | +| airplane | 81.36 | 89.99 | +| dirt track | 8.2 | 30.54 | +| apparel | 48.09 | 68.01 | +| pole | 29.76 | 41.78 | +| land | 2.4 | 4.63 | +| bannister | 18.68 | 27.26 | +| escalator | 59.35 | 79.26 | +| ottoman | 47.09 | 61.26 | +| bottle | 42.0 | 69.9 | +| buffet | 49.01 | 60.93 | +| poster | 39.55 | 48.84 | +| stage | 23.41 | 46.06 | +| van | 47.02 | 62.22 | +| ship | 87.28 | 91.36 | +| fountain | 35.0 | 35.6 | +| conveyer belt | 84.81 | 93.49 | +| canopy | 53.24 | 73.27 | +| washer | 81.6 | 86.1 | +| plaything | 34.67 | 51.75 | +| swimming pool | 58.45 | 86.38 | +| stool | 55.09 | 69.7 | +| barrel | 57.44 | 74.52 | +| basket | 42.12 | 62.38 | +| waterfall | 69.28 | 87.63 | +| tent | 95.56 | 98.61 | +| bag | 22.38 | 25.78 | +| minibike | 77.79 | 90.05 | +| cradle | 84.29 | 97.51 | +| oven | 63.89 | 72.73 | +| ball | 45.75 | 52.34 | +| food | 60.49 | 76.08 | +| step | 14.19 | 17.56 | +| tank | 63.04 | 68.3 | +| trade name | 25.78 | 29.94 | +| microwave | 89.1 | 96.26 | +| pot | 58.48 | 69.62 | +| animal | 59.88 | 61.29 | +| bicycle | 61.09 | 78.71 | +| lake | 52.54 | 63.84 | +| dishwasher | 68.26 | 78.41 | +| screen | 54.25 | 81.18 | +| blanket | 31.16 | 35.18 | +| sculpture | 73.38 | 88.68 | +| hood | 63.4 | 75.39 | +| sconce | 60.53 | 70.34 | +| vase | 49.77 | 66.72 | +| traffic light | 39.7 | 62.31 | +| tray | 27.02 | 33.84 | +| ashcan | 47.2 | 65.48 | +| fan | 70.63 | 82.29 | +| pier | 41.81 | 46.52 | +| crt screen | 2.13 | 3.49 | +| plate | 61.67 | 78.96 | +| monitor | 59.84 | 71.53 | +| bulletin board | 54.08 | 64.73 | +| shower | 9.17 | 9.2 | +| radiator | 68.58 | 78.03 | +| glass | 20.58 | 22.1 | +| clock | 46.71 | 58.86 | +| flag | 72.71 | 79.75 | ++---------------------+-------+-------+ +2024-06-17 05:17:17,507 - mmseg - INFO - Summary: +2024-06-17 05:17:17,507 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.54 | 58.09 | 70.55 | ++-------+-------+-------+ +2024-06-17 05:17:17,508 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 05:17:17,508 - mmseg - INFO - Iter(val) [250] aAcc: 0.8654, mIoU: 0.5809, mAcc: 0.7055, IoU.wall: 0.8280, IoU.building: 0.8588, IoU.sky: 0.9505, IoU.floor: 0.8584, IoU.tree: 0.7746, IoU.ceiling: 0.8737, IoU.road: 0.8713, IoU.bed : 0.9325, IoU.windowpane: 0.6604, IoU.grass: 0.6856, IoU.cabinet: 0.6748, IoU.sidewalk: 0.7372, IoU.person: 0.8607, IoU.earth: 0.3743, IoU.door: 0.6000, IoU.table: 0.7097, IoU.mountain: 0.6314, IoU.plant: 0.5582, IoU.curtain: 0.7669, IoU.chair: 0.6958, IoU.car: 0.8749, IoU.water: 0.6550, IoU.painting: 0.7877, IoU.sofa: 0.8316, IoU.shelf: 0.4592, IoU.house: 0.5655, IoU.sea: 0.7723, IoU.mirror: 0.8003, IoU.rug: 0.7052, IoU.field: 0.3245, IoU.armchair: 0.6182, IoU.seat: 0.6825, IoU.fence: 0.5305, IoU.desk: 0.6270, IoU.rock: 0.5739, IoU.wardrobe: 0.5541, IoU.lamp: 0.7571, IoU.bathtub: 0.8497, IoU.railing: 0.4367, IoU.cushion: 0.7003, IoU.base: 0.4120, IoU.box: 0.3753, IoU.column: 0.5477, IoU.signboard: 0.4081, IoU.chest of drawers: 0.4382, IoU.counter: 0.3984, IoU.sand: 0.6009, IoU.sink: 0.7829, IoU.skyscraper: 0.4784, IoU.fireplace: 0.7289, IoU.refrigerator: 0.8494, IoU.grandstand: 0.5407, IoU.path: 0.2998, IoU.stairs: 0.3200, IoU.runway: 0.7047, IoU.case: 0.5761, IoU.pool table: 0.9462, IoU.pillow: 0.6798, IoU.screen door: 0.7510, IoU.stairway: 0.5288, IoU.river: 0.1035, IoU.bridge: 0.6940, IoU.bookcase: 0.4705, IoU.blind: 0.4207, IoU.coffee table: 0.6448, IoU.toilet: 0.9059, IoU.flower: 0.4644, IoU.book: 0.5506, IoU.hill: 0.0765, IoU.bench: 0.5439, IoU.countertop: 0.6555, IoU.stove: 0.8464, IoU.palm: 0.5478, IoU.kitchen island: 0.5905, IoU.computer: 0.7911, IoU.swivel chair: 0.4969, IoU.boat: 0.7504, IoU.bar: 0.5842, IoU.arcade machine: 0.7917, IoU.hovel: 0.4907, IoU.bus: 0.9165, IoU.towel: 0.7639, IoU.light: 0.6208, IoU.truck: 0.4521, IoU.tower: 0.2802, IoU.chandelier: 0.7240, IoU.awning: 0.4716, IoU.streetlight: 0.3617, IoU.booth: 0.4428, IoU.television receiver: 0.7849, IoU.airplane: 0.8136, IoU.dirt track: 0.0820, IoU.apparel: 0.4809, IoU.pole: 0.2976, IoU.land: 0.0240, IoU.bannister: 0.1868, IoU.escalator: 0.5935, IoU.ottoman: 0.4709, IoU.bottle: 0.4200, IoU.buffet: 0.4901, IoU.poster: 0.3955, IoU.stage: 0.2341, IoU.van: 0.4702, IoU.ship: 0.8728, IoU.fountain: 0.3500, IoU.conveyer belt: 0.8481, IoU.canopy: 0.5324, IoU.washer: 0.8160, IoU.plaything: 0.3467, IoU.swimming pool: 0.5845, IoU.stool: 0.5509, IoU.barrel: 0.5744, IoU.basket: 0.4212, IoU.waterfall: 0.6928, IoU.tent: 0.9556, IoU.bag: 0.2238, IoU.minibike: 0.7779, IoU.cradle: 0.8429, IoU.oven: 0.6389, IoU.ball: 0.4575, IoU.food: 0.6049, IoU.step: 0.1419, IoU.tank: 0.6304, IoU.trade name: 0.2578, IoU.microwave: 0.8910, IoU.pot: 0.5848, IoU.animal: 0.5988, IoU.bicycle: 0.6109, IoU.lake: 0.5254, IoU.dishwasher: 0.6826, IoU.screen: 0.5425, IoU.blanket: 0.3116, IoU.sculpture: 0.7338, IoU.hood: 0.6340, IoU.sconce: 0.6053, IoU.vase: 0.4977, IoU.traffic light: 0.3970, IoU.tray: 0.2702, IoU.ashcan: 0.4720, IoU.fan: 0.7063, IoU.pier: 0.4181, IoU.crt screen: 0.0213, IoU.plate: 0.6167, IoU.monitor: 0.5984, IoU.bulletin board: 0.5408, IoU.shower: 0.0917, IoU.radiator: 0.6858, IoU.glass: 0.2058, IoU.clock: 0.4671, IoU.flag: 0.7271, Acc.wall: 0.9025, Acc.building: 0.9364, Acc.sky: 0.9773, Acc.floor: 0.9262, Acc.tree: 0.8995, Acc.ceiling: 0.9420, Acc.road: 0.9223, Acc.bed : 0.9722, Acc.windowpane: 0.8115, Acc.grass: 0.8215, Acc.cabinet: 0.7821, Acc.sidewalk: 0.8664, Acc.person: 0.9473, Acc.earth: 0.4932, Acc.door: 0.7493, Acc.table: 0.8233, Acc.mountain: 0.7551, Acc.plant: 0.6566, Acc.curtain: 0.8892, Acc.chair: 0.8167, Acc.car: 0.9434, Acc.water: 0.8117, Acc.painting: 0.9218, Acc.sofa: 0.9147, Acc.shelf: 0.6087, Acc.house: 0.6573, Acc.sea: 0.8877, Acc.mirror: 0.8594, Acc.rug: 0.7962, Acc.field: 0.6116, Acc.armchair: 0.7799, Acc.seat: 0.8967, Acc.fence: 0.6586, Acc.desk: 0.7968, Acc.rock: 0.8596, Acc.wardrobe: 0.7197, Acc.lamp: 0.8740, Acc.bathtub: 0.8730, Acc.railing: 0.6082, Acc.cushion: 0.8205, Acc.base: 0.5622, Acc.box: 0.4844, Acc.column: 0.6598, Acc.signboard: 0.5611, Acc.chest of drawers: 0.6691, Acc.counter: 0.4841, Acc.sand: 0.8623, Acc.sink: 0.8493, Acc.skyscraper: 0.6171, Acc.fireplace: 0.9138, Acc.refrigerator: 0.9183, Acc.grandstand: 0.8425, Acc.path: 0.4105, Acc.stairs: 0.3865, Acc.runway: 0.9270, Acc.case: 0.8072, Acc.pool table: 0.9832, Acc.pillow: 0.7866, Acc.screen door: 0.7739, Acc.stairway: 0.7058, Acc.river: 0.1822, Acc.bridge: 0.7745, Acc.bookcase: 0.6671, Acc.blind: 0.4640, Acc.coffee table: 0.8817, Acc.toilet: 0.9384, Acc.flower: 0.5746, Acc.book: 0.7686, Acc.hill: 0.1297, Acc.bench: 0.6293, Acc.countertop: 0.8496, Acc.stove: 0.9089, Acc.palm: 0.7950, Acc.kitchen island: 0.8600, Acc.computer: 0.9146, Acc.swivel chair: 0.7239, Acc.boat: 0.9151, Acc.bar: 0.8082, Acc.arcade machine: 0.8427, Acc.hovel: 0.5443, Acc.bus: 0.9653, Acc.towel: 0.8657, Acc.light: 0.7232, Acc.truck: 0.5942, Acc.tower: 0.4350, Acc.chandelier: 0.8586, Acc.awning: 0.6271, Acc.streetlight: 0.4989, Acc.booth: 0.6415, Acc.television receiver: 0.9070, Acc.airplane: 0.8999, Acc.dirt track: 0.3054, Acc.apparel: 0.6801, Acc.pole: 0.4178, Acc.land: 0.0463, Acc.bannister: 0.2726, Acc.escalator: 0.7926, Acc.ottoman: 0.6126, Acc.bottle: 0.6990, Acc.buffet: 0.6093, Acc.poster: 0.4884, Acc.stage: 0.4606, Acc.van: 0.6222, Acc.ship: 0.9136, Acc.fountain: 0.3560, Acc.conveyer belt: 0.9349, Acc.canopy: 0.7327, Acc.washer: 0.8610, Acc.plaything: 0.5175, Acc.swimming pool: 0.8638, Acc.stool: 0.6970, Acc.barrel: 0.7452, Acc.basket: 0.6238, Acc.waterfall: 0.8763, Acc.tent: 0.9861, Acc.bag: 0.2578, Acc.minibike: 0.9005, Acc.cradle: 0.9751, Acc.oven: 0.7273, Acc.ball: 0.5234, Acc.food: 0.7608, Acc.step: 0.1756, Acc.tank: 0.6830, Acc.trade name: 0.2994, Acc.microwave: 0.9626, Acc.pot: 0.6962, Acc.animal: 0.6129, Acc.bicycle: 0.7871, Acc.lake: 0.6384, Acc.dishwasher: 0.7841, Acc.screen: 0.8118, Acc.blanket: 0.3518, Acc.sculpture: 0.8868, Acc.hood: 0.7539, Acc.sconce: 0.7034, Acc.vase: 0.6672, Acc.traffic light: 0.6231, Acc.tray: 0.3384, Acc.ashcan: 0.6548, Acc.fan: 0.8229, Acc.pier: 0.4652, Acc.crt screen: 0.0349, Acc.plate: 0.7896, Acc.monitor: 0.7153, Acc.bulletin board: 0.6473, Acc.shower: 0.0920, Acc.radiator: 0.7803, Acc.glass: 0.2210, Acc.clock: 0.5886, Acc.flag: 0.7975 +2024-06-17 05:18:26,473 - mmseg - INFO - Iter [76050/80000] lr: 1.976e-06, eta: 1:39:18, time: 3.248, data_time: 1.886, memory: 70722, decode.loss_ce: 0.1391, decode.acc_seg: 94.0353, aux.loss_ce: 0.0601, aux.acc_seg: 93.5803, loss: 0.1992 +2024-06-17 05:19:34,513 - mmseg - INFO - Iter [76100/80000] lr: 1.951e-06, eta: 1:38:02, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1352, decode.acc_seg: 93.7984, aux.loss_ce: 0.0586, aux.acc_seg: 93.3380, loss: 0.1938 +2024-06-17 05:20:42,788 - mmseg - INFO - Iter [76150/80000] lr: 1.926e-06, eta: 1:36:47, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1365, decode.acc_seg: 93.9381, aux.loss_ce: 0.0594, aux.acc_seg: 93.4053, loss: 0.1959 +2024-06-17 05:21:51,060 - mmseg - INFO - Iter [76200/80000] lr: 1.900e-06, eta: 1:35:31, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1280, decode.acc_seg: 94.0546, aux.loss_ce: 0.0558, aux.acc_seg: 93.5795, loss: 0.1838 +2024-06-17 05:22:59,290 - mmseg - INFO - Iter [76250/80000] lr: 1.875e-06, eta: 1:34:15, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1332, decode.acc_seg: 93.9410, aux.loss_ce: 0.0580, aux.acc_seg: 93.4309, loss: 0.1912 +2024-06-17 05:24:07,340 - mmseg - INFO - Iter [76300/80000] lr: 1.850e-06, eta: 1:32:59, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1278, decode.acc_seg: 94.1852, aux.loss_ce: 0.0553, aux.acc_seg: 93.7350, loss: 0.1831 +2024-06-17 05:25:15,388 - mmseg - INFO - Iter [76350/80000] lr: 1.826e-06, eta: 1:31:44, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1336, decode.acc_seg: 94.0994, aux.loss_ce: 0.0580, aux.acc_seg: 93.6180, loss: 0.1916 +2024-06-17 05:26:23,382 - mmseg - INFO - Iter [76400/80000] lr: 1.801e-06, eta: 1:30:28, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1399, decode.acc_seg: 93.8239, aux.loss_ce: 0.0606, aux.acc_seg: 93.3318, loss: 0.2005 +2024-06-17 05:27:31,550 - mmseg - INFO - Iter [76450/80000] lr: 1.776e-06, eta: 1:29:12, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1302, decode.acc_seg: 94.3098, aux.loss_ce: 0.0562, aux.acc_seg: 93.8567, loss: 0.1864 +2024-06-17 05:28:39,728 - mmseg - INFO - Iter [76500/80000] lr: 1.751e-06, eta: 1:27:56, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1397, decode.acc_seg: 93.7501, aux.loss_ce: 0.0604, aux.acc_seg: 93.2790, loss: 0.2000 +2024-06-17 05:29:47,728 - mmseg - INFO - Iter [76550/80000] lr: 1.726e-06, eta: 1:26:41, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1338, decode.acc_seg: 94.1384, aux.loss_ce: 0.0577, aux.acc_seg: 93.6771, loss: 0.1914 +2024-06-17 05:30:56,197 - mmseg - INFO - Iter [76600/80000] lr: 1.700e-06, eta: 1:25:25, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1365, decode.acc_seg: 94.1316, aux.loss_ce: 0.0592, aux.acc_seg: 93.6841, loss: 0.1957 +2024-06-17 05:32:04,386 - mmseg - INFO - Iter [76650/80000] lr: 1.675e-06, eta: 1:24:09, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1274, decode.acc_seg: 94.3242, aux.loss_ce: 0.0556, aux.acc_seg: 93.7753, loss: 0.1829 +2024-06-17 05:33:12,620 - mmseg - INFO - Iter [76700/80000] lr: 1.650e-06, eta: 1:22:54, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1364, decode.acc_seg: 93.6626, aux.loss_ce: 0.0593, aux.acc_seg: 93.1688, loss: 0.1957 +2024-06-17 05:34:20,877 - mmseg - INFO - Iter [76750/80000] lr: 1.625e-06, eta: 1:21:38, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1330, decode.acc_seg: 94.0381, aux.loss_ce: 0.0575, aux.acc_seg: 93.5920, loss: 0.1905 +2024-06-17 05:35:28,996 - mmseg - INFO - Iter [76800/80000] lr: 1.601e-06, eta: 1:20:22, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1334, decode.acc_seg: 94.0070, aux.loss_ce: 0.0579, aux.acc_seg: 93.5647, loss: 0.1913 +2024-06-17 05:36:37,087 - mmseg - INFO - Iter [76850/80000] lr: 1.576e-06, eta: 1:19:07, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1403, decode.acc_seg: 93.7910, aux.loss_ce: 0.0603, aux.acc_seg: 93.3584, loss: 0.2006 +2024-06-17 05:37:45,153 - mmseg - INFO - Iter [76900/80000] lr: 1.551e-06, eta: 1:17:51, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1338, decode.acc_seg: 94.0859, aux.loss_ce: 0.0578, aux.acc_seg: 93.6404, loss: 0.1915 +2024-06-17 05:38:53,588 - mmseg - INFO - Iter [76950/80000] lr: 1.526e-06, eta: 1:16:35, time: 1.369, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1320, decode.acc_seg: 94.0082, aux.loss_ce: 0.0576, aux.acc_seg: 93.5089, loss: 0.1896 +2024-06-17 05:40:01,668 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 05:40:01,668 - mmseg - INFO - Iter [77000/80000] lr: 1.500e-06, eta: 1:15:20, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1275, decode.acc_seg: 94.3593, aux.loss_ce: 0.0552, aux.acc_seg: 93.9374, loss: 0.1827 +2024-06-17 05:41:37,892 - mmseg - INFO - per class results: +2024-06-17 05:41:37,898 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.74 | 90.65 | +| building | 86.04 | 93.61 | +| sky | 95.07 | 97.75 | +| floor | 86.0 | 92.68 | +| tree | 77.59 | 89.83 | +| ceiling | 87.52 | 94.13 | +| road | 87.24 | 92.23 | +| bed | 93.35 | 97.08 | +| windowpane | 66.13 | 81.81 | +| grass | 68.73 | 82.37 | +| cabinet | 66.91 | 76.54 | +| sidewalk | 73.71 | 85.97 | +| person | 86.27 | 94.57 | +| earth | 37.7 | 49.39 | +| door | 60.17 | 74.87 | +| table | 70.85 | 81.75 | +| mountain | 62.64 | 73.87 | +| plant | 55.94 | 66.18 | +| curtain | 76.78 | 87.36 | +| chair | 69.22 | 81.13 | +| car | 87.61 | 94.32 | +| water | 65.44 | 80.13 | +| painting | 79.31 | 91.97 | +| sofa | 83.04 | 92.16 | +| shelf | 44.93 | 59.61 | +| house | 60.5 | 74.13 | +| sea | 76.16 | 86.46 | +| mirror | 79.6 | 84.91 | +| rug | 71.05 | 79.3 | +| field | 32.32 | 61.63 | +| armchair | 61.41 | 77.01 | +| seat | 68.25 | 89.76 | +| fence | 52.87 | 64.61 | +| desk | 61.33 | 79.92 | +| rock | 56.54 | 88.16 | +| wardrobe | 54.81 | 72.41 | +| lamp | 75.94 | 87.81 | +| bathtub | 84.85 | 87.33 | +| railing | 43.7 | 59.93 | +| cushion | 69.92 | 81.75 | +| base | 41.79 | 55.89 | +| box | 37.85 | 48.85 | +| column | 55.56 | 68.46 | +| signboard | 41.5 | 56.65 | +| chest of drawers | 44.93 | 68.09 | +| counter | 41.03 | 49.56 | +| sand | 60.65 | 86.29 | +| sink | 78.72 | 84.77 | +| skyscraper | 47.91 | 61.21 | +| fireplace | 72.75 | 91.87 | +| refrigerator | 84.88 | 92.66 | +| grandstand | 53.87 | 84.31 | +| path | 30.27 | 41.97 | +| stairs | 31.25 | 37.36 | +| runway | 71.53 | 94.47 | +| case | 57.05 | 83.11 | +| pool table | 94.47 | 98.42 | +| pillow | 68.04 | 79.36 | +| screen door | 78.71 | 81.03 | +| stairway | 51.26 | 67.4 | +| river | 12.81 | 25.05 | +| bridge | 68.93 | 76.1 | +| bookcase | 45.61 | 67.56 | +| blind | 40.87 | 44.94 | +| coffee table | 64.13 | 89.62 | +| toilet | 90.47 | 94.12 | +| flower | 46.45 | 56.06 | +| book | 55.77 | 79.57 | +| hill | 7.43 | 12.71 | +| bench | 54.49 | 62.38 | +| countertop | 65.53 | 83.4 | +| stove | 84.02 | 88.7 | +| palm | 54.97 | 78.75 | +| kitchen island | 57.7 | 86.17 | +| computer | 78.89 | 91.17 | +| swivel chair | 47.66 | 68.17 | +| boat | 76.67 | 91.51 | +| bar | 58.43 | 79.77 | +| arcade machine | 78.39 | 83.36 | +| hovel | 49.2 | 55.39 | +| bus | 92.47 | 96.1 | +| towel | 76.7 | 87.04 | +| light | 61.94 | 71.91 | +| truck | 45.81 | 59.92 | +| tower | 30.81 | 47.53 | +| chandelier | 73.14 | 83.87 | +| awning | 46.5 | 59.98 | +| streetlight | 36.33 | 50.07 | +| booth | 42.81 | 64.02 | +| television receiver | 78.8 | 87.85 | +| airplane | 80.84 | 89.35 | +| dirt track | 7.76 | 31.47 | +| apparel | 47.85 | 64.04 | +| pole | 30.43 | 42.9 | +| land | 2.43 | 4.29 | +| bannister | 18.64 | 26.4 | +| escalator | 59.19 | 79.01 | +| ottoman | 48.22 | 64.31 | +| bottle | 42.05 | 70.19 | +| buffet | 48.8 | 60.2 | +| poster | 38.8 | 51.12 | +| stage | 23.61 | 45.42 | +| van | 46.48 | 62.15 | +| ship | 88.24 | 92.97 | +| fountain | 33.51 | 34.06 | +| conveyer belt | 84.19 | 93.72 | +| canopy | 54.99 | 75.84 | +| washer | 81.11 | 85.51 | +| plaything | 32.75 | 46.21 | +| swimming pool | 58.31 | 86.19 | +| stool | 55.02 | 71.4 | +| barrel | 57.22 | 74.54 | +| basket | 42.02 | 61.52 | +| waterfall | 69.58 | 87.75 | +| tent | 95.54 | 98.48 | +| bag | 22.14 | 25.15 | +| minibike | 77.82 | 90.95 | +| cradle | 84.52 | 97.34 | +| oven | 62.64 | 73.48 | +| ball | 44.61 | 50.27 | +| food | 59.62 | 74.67 | +| step | 13.82 | 17.15 | +| tank | 62.38 | 67.52 | +| trade name | 28.82 | 34.02 | +| microwave | 88.71 | 96.12 | +| pot | 58.53 | 68.62 | +| animal | 59.81 | 61.27 | +| bicycle | 61.86 | 77.27 | +| lake | 52.95 | 63.83 | +| dishwasher | 68.17 | 77.6 | +| screen | 55.35 | 82.6 | +| blanket | 31.35 | 35.58 | +| sculpture | 72.32 | 88.85 | +| hood | 63.58 | 75.82 | +| sconce | 59.93 | 69.11 | +| vase | 50.17 | 64.73 | +| traffic light | 39.88 | 62.79 | +| tray | 27.29 | 35.24 | +| ashcan | 48.69 | 65.7 | +| fan | 71.12 | 82.17 | +| pier | 40.58 | 45.07 | +| crt screen | 2.26 | 3.46 | +| plate | 62.3 | 79.34 | +| monitor | 62.53 | 74.82 | +| bulletin board | 53.39 | 65.98 | +| shower | 9.66 | 9.83 | +| radiator | 68.19 | 77.48 | +| glass | 20.52 | 21.93 | +| clock | 46.6 | 57.48 | +| flag | 72.2 | 79.03 | ++---------------------+-------+-------+ +2024-06-17 05:41:37,899 - mmseg - INFO - Summary: +2024-06-17 05:41:37,899 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.57 | 58.13 | 70.55 | ++-------+-------+-------+ +2024-06-17 05:41:37,900 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 05:41:37,900 - mmseg - INFO - Iter(val) [250] aAcc: 0.8657, mIoU: 0.5813, mAcc: 0.7055, IoU.wall: 0.8274, IoU.building: 0.8604, IoU.sky: 0.9507, IoU.floor: 0.8600, IoU.tree: 0.7759, IoU.ceiling: 0.8752, IoU.road: 0.8724, IoU.bed : 0.9335, IoU.windowpane: 0.6613, IoU.grass: 0.6873, IoU.cabinet: 0.6691, IoU.sidewalk: 0.7371, IoU.person: 0.8627, IoU.earth: 0.3770, IoU.door: 0.6017, IoU.table: 0.7085, IoU.mountain: 0.6264, IoU.plant: 0.5594, IoU.curtain: 0.7678, IoU.chair: 0.6922, IoU.car: 0.8761, IoU.water: 0.6544, IoU.painting: 0.7931, IoU.sofa: 0.8304, IoU.shelf: 0.4493, IoU.house: 0.6050, IoU.sea: 0.7616, IoU.mirror: 0.7960, IoU.rug: 0.7105, IoU.field: 0.3232, IoU.armchair: 0.6141, IoU.seat: 0.6825, IoU.fence: 0.5287, IoU.desk: 0.6133, IoU.rock: 0.5654, IoU.wardrobe: 0.5481, IoU.lamp: 0.7594, IoU.bathtub: 0.8485, IoU.railing: 0.4370, IoU.cushion: 0.6992, IoU.base: 0.4179, IoU.box: 0.3785, IoU.column: 0.5556, IoU.signboard: 0.4150, IoU.chest of drawers: 0.4493, IoU.counter: 0.4103, IoU.sand: 0.6065, IoU.sink: 0.7872, IoU.skyscraper: 0.4791, IoU.fireplace: 0.7275, IoU.refrigerator: 0.8488, IoU.grandstand: 0.5387, IoU.path: 0.3027, IoU.stairs: 0.3125, IoU.runway: 0.7153, IoU.case: 0.5705, IoU.pool table: 0.9447, IoU.pillow: 0.6804, IoU.screen door: 0.7871, IoU.stairway: 0.5126, IoU.river: 0.1281, IoU.bridge: 0.6893, IoU.bookcase: 0.4561, IoU.blind: 0.4087, IoU.coffee table: 0.6413, IoU.toilet: 0.9047, IoU.flower: 0.4645, IoU.book: 0.5577, IoU.hill: 0.0743, IoU.bench: 0.5449, IoU.countertop: 0.6553, IoU.stove: 0.8402, IoU.palm: 0.5497, IoU.kitchen island: 0.5770, IoU.computer: 0.7889, IoU.swivel chair: 0.4766, IoU.boat: 0.7667, IoU.bar: 0.5843, IoU.arcade machine: 0.7839, IoU.hovel: 0.4920, IoU.bus: 0.9247, IoU.towel: 0.7670, IoU.light: 0.6194, IoU.truck: 0.4581, IoU.tower: 0.3081, IoU.chandelier: 0.7314, IoU.awning: 0.4650, IoU.streetlight: 0.3633, IoU.booth: 0.4281, IoU.television receiver: 0.7880, IoU.airplane: 0.8084, IoU.dirt track: 0.0776, IoU.apparel: 0.4785, IoU.pole: 0.3043, IoU.land: 0.0243, IoU.bannister: 0.1864, IoU.escalator: 0.5919, IoU.ottoman: 0.4822, IoU.bottle: 0.4205, IoU.buffet: 0.4880, IoU.poster: 0.3880, IoU.stage: 0.2361, IoU.van: 0.4648, IoU.ship: 0.8824, IoU.fountain: 0.3351, IoU.conveyer belt: 0.8419, IoU.canopy: 0.5499, IoU.washer: 0.8111, IoU.plaything: 0.3275, IoU.swimming pool: 0.5831, IoU.stool: 0.5502, IoU.barrel: 0.5722, IoU.basket: 0.4202, IoU.waterfall: 0.6958, IoU.tent: 0.9554, IoU.bag: 0.2214, IoU.minibike: 0.7782, IoU.cradle: 0.8452, IoU.oven: 0.6264, IoU.ball: 0.4461, IoU.food: 0.5962, IoU.step: 0.1382, IoU.tank: 0.6238, IoU.trade name: 0.2882, IoU.microwave: 0.8871, IoU.pot: 0.5853, IoU.animal: 0.5981, IoU.bicycle: 0.6186, IoU.lake: 0.5295, IoU.dishwasher: 0.6817, IoU.screen: 0.5535, IoU.blanket: 0.3135, IoU.sculpture: 0.7232, IoU.hood: 0.6358, IoU.sconce: 0.5993, IoU.vase: 0.5017, IoU.traffic light: 0.3988, IoU.tray: 0.2729, IoU.ashcan: 0.4869, IoU.fan: 0.7112, IoU.pier: 0.4058, IoU.crt screen: 0.0226, IoU.plate: 0.6230, IoU.monitor: 0.6253, IoU.bulletin board: 0.5339, IoU.shower: 0.0966, IoU.radiator: 0.6819, IoU.glass: 0.2052, IoU.clock: 0.4660, IoU.flag: 0.7220, Acc.wall: 0.9065, Acc.building: 0.9361, Acc.sky: 0.9775, Acc.floor: 0.9268, Acc.tree: 0.8983, Acc.ceiling: 0.9413, Acc.road: 0.9223, Acc.bed : 0.9708, Acc.windowpane: 0.8181, Acc.grass: 0.8237, Acc.cabinet: 0.7654, Acc.sidewalk: 0.8597, Acc.person: 0.9457, Acc.earth: 0.4939, Acc.door: 0.7487, Acc.table: 0.8175, Acc.mountain: 0.7387, Acc.plant: 0.6618, Acc.curtain: 0.8736, Acc.chair: 0.8113, Acc.car: 0.9432, Acc.water: 0.8013, Acc.painting: 0.9197, Acc.sofa: 0.9216, Acc.shelf: 0.5961, Acc.house: 0.7413, Acc.sea: 0.8646, Acc.mirror: 0.8491, Acc.rug: 0.7930, Acc.field: 0.6163, Acc.armchair: 0.7701, Acc.seat: 0.8976, Acc.fence: 0.6461, Acc.desk: 0.7992, Acc.rock: 0.8816, Acc.wardrobe: 0.7241, Acc.lamp: 0.8781, Acc.bathtub: 0.8733, Acc.railing: 0.5993, Acc.cushion: 0.8175, Acc.base: 0.5589, Acc.box: 0.4885, Acc.column: 0.6846, Acc.signboard: 0.5665, Acc.chest of drawers: 0.6809, Acc.counter: 0.4956, Acc.sand: 0.8629, Acc.sink: 0.8477, Acc.skyscraper: 0.6121, Acc.fireplace: 0.9187, Acc.refrigerator: 0.9266, Acc.grandstand: 0.8431, Acc.path: 0.4197, Acc.stairs: 0.3736, Acc.runway: 0.9447, Acc.case: 0.8311, Acc.pool table: 0.9842, Acc.pillow: 0.7936, Acc.screen door: 0.8103, Acc.stairway: 0.6740, Acc.river: 0.2505, Acc.bridge: 0.7610, Acc.bookcase: 0.6756, Acc.blind: 0.4494, Acc.coffee table: 0.8962, Acc.toilet: 0.9412, Acc.flower: 0.5606, Acc.book: 0.7957, Acc.hill: 0.1271, Acc.bench: 0.6238, Acc.countertop: 0.8340, Acc.stove: 0.8870, Acc.palm: 0.7875, Acc.kitchen island: 0.8617, Acc.computer: 0.9117, Acc.swivel chair: 0.6817, Acc.boat: 0.9151, Acc.bar: 0.7977, Acc.arcade machine: 0.8336, Acc.hovel: 0.5539, Acc.bus: 0.9610, Acc.towel: 0.8704, Acc.light: 0.7191, Acc.truck: 0.5992, Acc.tower: 0.4753, Acc.chandelier: 0.8387, Acc.awning: 0.5998, Acc.streetlight: 0.5007, Acc.booth: 0.6402, Acc.television receiver: 0.8785, Acc.airplane: 0.8935, Acc.dirt track: 0.3147, Acc.apparel: 0.6404, Acc.pole: 0.4290, Acc.land: 0.0429, Acc.bannister: 0.2640, Acc.escalator: 0.7901, Acc.ottoman: 0.6431, Acc.bottle: 0.7019, Acc.buffet: 0.6020, Acc.poster: 0.5112, Acc.stage: 0.4542, Acc.van: 0.6215, Acc.ship: 0.9297, Acc.fountain: 0.3406, Acc.conveyer belt: 0.9372, Acc.canopy: 0.7584, Acc.washer: 0.8551, Acc.plaything: 0.4621, Acc.swimming pool: 0.8619, Acc.stool: 0.7140, Acc.barrel: 0.7454, Acc.basket: 0.6152, Acc.waterfall: 0.8775, Acc.tent: 0.9848, Acc.bag: 0.2515, Acc.minibike: 0.9095, Acc.cradle: 0.9734, Acc.oven: 0.7348, Acc.ball: 0.5027, Acc.food: 0.7467, Acc.step: 0.1715, Acc.tank: 0.6752, Acc.trade name: 0.3402, Acc.microwave: 0.9612, Acc.pot: 0.6862, Acc.animal: 0.6127, Acc.bicycle: 0.7727, Acc.lake: 0.6383, Acc.dishwasher: 0.7760, Acc.screen: 0.8260, Acc.blanket: 0.3558, Acc.sculpture: 0.8885, Acc.hood: 0.7582, Acc.sconce: 0.6911, Acc.vase: 0.6473, Acc.traffic light: 0.6279, Acc.tray: 0.3524, Acc.ashcan: 0.6570, Acc.fan: 0.8217, Acc.pier: 0.4507, Acc.crt screen: 0.0346, Acc.plate: 0.7934, Acc.monitor: 0.7482, Acc.bulletin board: 0.6598, Acc.shower: 0.0983, Acc.radiator: 0.7748, Acc.glass: 0.2193, Acc.clock: 0.5748, Acc.flag: 0.7903 +2024-06-17 05:42:48,714 - mmseg - INFO - Iter [77050/80000] lr: 1.475e-06, eta: 1:14:08, time: 3.341, data_time: 1.982, memory: 70722, decode.loss_ce: 0.1347, decode.acc_seg: 94.0000, aux.loss_ce: 0.0587, aux.acc_seg: 93.4719, loss: 0.1934 +2024-06-17 05:43:56,939 - mmseg - INFO - Iter [77100/80000] lr: 1.450e-06, eta: 1:12:52, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1320, decode.acc_seg: 94.0918, aux.loss_ce: 0.0576, aux.acc_seg: 93.6251, loss: 0.1895 +2024-06-17 05:45:05,142 - mmseg - INFO - Iter [77150/80000] lr: 1.425e-06, eta: 1:11:37, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1376, decode.acc_seg: 93.8271, aux.loss_ce: 0.0598, aux.acc_seg: 93.3212, loss: 0.1974 +2024-06-17 05:46:13,558 - mmseg - INFO - Iter [77200/80000] lr: 1.401e-06, eta: 1:10:21, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1319, decode.acc_seg: 94.0353, aux.loss_ce: 0.0570, aux.acc_seg: 93.5877, loss: 0.1889 +2024-06-17 05:47:21,707 - mmseg - INFO - Iter [77250/80000] lr: 1.376e-06, eta: 1:09:05, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1301, decode.acc_seg: 94.1044, aux.loss_ce: 0.0569, aux.acc_seg: 93.5739, loss: 0.1871 +2024-06-17 05:48:29,695 - mmseg - INFO - Iter [77300/80000] lr: 1.351e-06, eta: 1:07:50, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1330, decode.acc_seg: 94.0875, aux.loss_ce: 0.0581, aux.acc_seg: 93.5436, loss: 0.1911 +2024-06-17 05:49:38,013 - mmseg - INFO - Iter [77350/80000] lr: 1.326e-06, eta: 1:06:34, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1338, decode.acc_seg: 94.0791, aux.loss_ce: 0.0578, aux.acc_seg: 93.6512, loss: 0.1916 +2024-06-17 05:50:45,994 - mmseg - INFO - Iter [77400/80000] lr: 1.301e-06, eta: 1:05:18, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1388, decode.acc_seg: 93.9346, aux.loss_ce: 0.0597, aux.acc_seg: 93.5286, loss: 0.1985 +2024-06-17 05:51:54,351 - mmseg - INFO - Iter [77450/80000] lr: 1.275e-06, eta: 1:04:03, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1298, decode.acc_seg: 94.3384, aux.loss_ce: 0.0560, aux.acc_seg: 93.9042, loss: 0.1858 +2024-06-17 05:53:02,501 - mmseg - INFO - Iter [77500/80000] lr: 1.250e-06, eta: 1:02:47, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1409, decode.acc_seg: 93.9203, aux.loss_ce: 0.0606, aux.acc_seg: 93.4983, loss: 0.2015 +2024-06-17 05:54:10,690 - mmseg - INFO - Iter [77550/80000] lr: 1.225e-06, eta: 1:01:32, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1247, decode.acc_seg: 94.5163, aux.loss_ce: 0.0544, aux.acc_seg: 94.0342, loss: 0.1791 +2024-06-17 05:55:19,004 - mmseg - INFO - Iter [77600/80000] lr: 1.200e-06, eta: 1:00:16, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1281, decode.acc_seg: 94.2296, aux.loss_ce: 0.0561, aux.acc_seg: 93.7273, loss: 0.1842 +2024-06-17 05:56:27,183 - mmseg - INFO - Iter [77650/80000] lr: 1.176e-06, eta: 0:59:01, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1362, decode.acc_seg: 94.2033, aux.loss_ce: 0.0581, aux.acc_seg: 93.6966, loss: 0.1943 +2024-06-17 05:57:35,479 - mmseg - INFO - Iter [77700/80000] lr: 1.151e-06, eta: 0:57:45, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1302, decode.acc_seg: 94.2648, aux.loss_ce: 0.0570, aux.acc_seg: 93.7724, loss: 0.1872 +2024-06-17 05:58:43,525 - mmseg - INFO - Iter [77750/80000] lr: 1.126e-06, eta: 0:56:29, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1337, decode.acc_seg: 94.0058, aux.loss_ce: 0.0586, aux.acc_seg: 93.4711, loss: 0.1923 +2024-06-17 05:59:51,938 - mmseg - INFO - Iter [77800/80000] lr: 1.101e-06, eta: 0:55:14, time: 1.368, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1418, decode.acc_seg: 93.7605, aux.loss_ce: 0.0616, aux.acc_seg: 93.2440, loss: 0.2034 +2024-06-17 06:00:59,951 - mmseg - INFO - Iter [77850/80000] lr: 1.075e-06, eta: 0:53:58, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1285, decode.acc_seg: 94.2850, aux.loss_ce: 0.0557, aux.acc_seg: 93.7835, loss: 0.1842 +2024-06-17 06:02:08,217 - mmseg - INFO - Iter [77900/80000] lr: 1.050e-06, eta: 0:52:43, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1305, decode.acc_seg: 94.0928, aux.loss_ce: 0.0569, aux.acc_seg: 93.5946, loss: 0.1874 +2024-06-17 06:03:16,437 - mmseg - INFO - Iter [77950/80000] lr: 1.025e-06, eta: 0:51:27, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1268, decode.acc_seg: 94.3330, aux.loss_ce: 0.0553, aux.acc_seg: 93.8334, loss: 0.1821 +2024-06-17 06:04:24,659 - mmseg - INFO - Saving checkpoint at 78000 iterations +2024-06-17 06:05:53,112 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 06:05:53,113 - mmseg - INFO - Iter [78000/80000] lr: 1.000e-06, eta: 0:50:14, time: 3.133, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1356, decode.acc_seg: 93.9828, aux.loss_ce: 0.0587, aux.acc_seg: 93.5398, loss: 0.1944 +2024-06-17 06:07:28,870 - mmseg - INFO - per class results: +2024-06-17 06:07:28,876 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.7 | 90.34 | +| building | 85.92 | 93.5 | +| sky | 95.06 | 97.79 | +| floor | 86.03 | 92.78 | +| tree | 77.66 | 89.94 | +| ceiling | 87.56 | 94.29 | +| road | 87.03 | 92.2 | +| bed | 93.39 | 97.12 | +| windowpane | 66.4 | 82.02 | +| grass | 68.86 | 81.94 | +| cabinet | 67.28 | 77.61 | +| sidewalk | 73.67 | 86.48 | +| person | 86.32 | 94.13 | +| earth | 37.06 | 49.1 | +| door | 60.14 | 75.23 | +| table | 71.46 | 82.22 | +| mountain | 62.29 | 72.89 | +| plant | 55.95 | 66.05 | +| curtain | 77.24 | 88.51 | +| chair | 69.28 | 81.74 | +| car | 87.72 | 94.21 | +| water | 65.72 | 80.61 | +| painting | 78.78 | 91.74 | +| sofa | 83.0 | 91.47 | +| shelf | 45.39 | 60.04 | +| house | 60.66 | 74.47 | +| sea | 77.35 | 87.72 | +| mirror | 80.11 | 85.74 | +| rug | 70.81 | 78.5 | +| field | 31.75 | 61.02 | +| armchair | 61.2 | 78.01 | +| seat | 68.32 | 89.78 | +| fence | 53.31 | 65.43 | +| desk | 61.93 | 79.5 | +| rock | 56.4 | 88.01 | +| wardrobe | 56.09 | 72.36 | +| lamp | 75.9 | 87.33 | +| bathtub | 84.96 | 87.4 | +| railing | 43.43 | 59.99 | +| cushion | 70.29 | 81.42 | +| base | 42.06 | 56.68 | +| box | 37.9 | 48.75 | +| column | 55.28 | 68.27 | +| signboard | 41.43 | 56.18 | +| chest of drawers | 44.63 | 68.61 | +| counter | 41.05 | 49.31 | +| sand | 59.72 | 86.77 | +| sink | 78.44 | 85.06 | +| skyscraper | 47.72 | 61.34 | +| fireplace | 72.95 | 92.16 | +| refrigerator | 84.85 | 92.28 | +| grandstand | 53.45 | 84.98 | +| path | 30.83 | 42.67 | +| stairs | 30.54 | 36.87 | +| runway | 71.46 | 94.42 | +| case | 57.85 | 81.79 | +| pool table | 94.52 | 98.39 | +| pillow | 68.91 | 80.76 | +| screen door | 78.48 | 80.84 | +| stairway | 53.36 | 71.37 | +| river | 13.34 | 24.63 | +| bridge | 67.99 | 74.76 | +| bookcase | 46.58 | 67.87 | +| blind | 41.44 | 45.6 | +| coffee table | 65.74 | 87.79 | +| toilet | 90.65 | 94.09 | +| flower | 46.38 | 55.38 | +| book | 55.53 | 78.29 | +| hill | 8.24 | 14.32 | +| bench | 54.27 | 62.75 | +| countertop | 65.26 | 84.23 | +| stove | 84.25 | 88.99 | +| palm | 55.24 | 78.13 | +| kitchen island | 56.39 | 86.82 | +| computer | 78.83 | 91.08 | +| swivel chair | 48.1 | 68.7 | +| boat | 76.04 | 91.86 | +| bar | 58.02 | 79.61 | +| arcade machine | 78.33 | 83.29 | +| hovel | 49.03 | 54.62 | +| bus | 92.33 | 96.27 | +| towel | 76.56 | 86.97 | +| light | 62.05 | 72.43 | +| truck | 45.53 | 59.89 | +| tower | 34.02 | 54.37 | +| chandelier | 73.21 | 85.24 | +| awning | 47.13 | 60.09 | +| streetlight | 35.19 | 46.76 | +| booth | 43.17 | 64.93 | +| television receiver | 78.29 | 87.65 | +| airplane | 80.37 | 88.83 | +| dirt track | 7.4 | 29.92 | +| apparel | 47.73 | 64.61 | +| pole | 30.8 | 43.54 | +| land | 2.34 | 4.35 | +| bannister | 18.48 | 26.39 | +| escalator | 58.8 | 79.25 | +| ottoman | 48.56 | 64.04 | +| bottle | 41.51 | 68.24 | +| buffet | 48.36 | 59.85 | +| poster | 39.73 | 50.5 | +| stage | 23.69 | 45.81 | +| van | 46.72 | 62.21 | +| ship | 86.87 | 91.15 | +| fountain | 34.56 | 35.19 | +| conveyer belt | 83.96 | 93.71 | +| canopy | 54.98 | 75.55 | +| washer | 81.55 | 86.1 | +| plaything | 33.17 | 47.13 | +| swimming pool | 58.3 | 86.19 | +| stool | 56.26 | 69.15 | +| barrel | 56.97 | 74.6 | +| basket | 42.19 | 61.49 | +| waterfall | 68.71 | 88.24 | +| tent | 95.68 | 98.46 | +| bag | 22.6 | 25.86 | +| minibike | 77.76 | 90.79 | +| cradle | 83.77 | 97.46 | +| oven | 61.98 | 72.21 | +| ball | 51.7 | 62.11 | +| food | 61.11 | 77.48 | +| step | 13.95 | 17.61 | +| tank | 62.44 | 68.22 | +| trade name | 28.53 | 33.25 | +| microwave | 88.51 | 96.1 | +| pot | 58.59 | 69.64 | +| animal | 59.83 | 61.24 | +| bicycle | 61.27 | 77.4 | +| lake | 52.64 | 63.84 | +| dishwasher | 68.18 | 78.45 | +| screen | 56.61 | 86.26 | +| blanket | 30.43 | 34.12 | +| sculpture | 72.36 | 88.62 | +| hood | 63.88 | 76.28 | +| sconce | 60.02 | 70.66 | +| vase | 49.78 | 65.28 | +| traffic light | 40.58 | 62.11 | +| tray | 26.3 | 32.6 | +| ashcan | 48.35 | 65.5 | +| fan | 70.9 | 82.61 | +| pier | 40.28 | 44.46 | +| crt screen | 2.44 | 3.46 | +| plate | 61.77 | 79.08 | +| monitor | 62.1 | 73.25 | +| bulletin board | 54.71 | 64.48 | +| shower | 10.19 | 10.41 | +| radiator | 68.37 | 78.44 | +| glass | 20.42 | 21.89 | +| clock | 46.58 | 57.62 | +| flag | 72.2 | 78.78 | ++---------------------+-------+-------+ +2024-06-17 06:07:28,876 - mmseg - INFO - Summary: +2024-06-17 06:07:28,876 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.57 | 58.24 | 70.72 | ++-------+-------+-------+ +2024-06-17 06:07:28,877 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 06:07:28,877 - mmseg - INFO - Iter(val) [250] aAcc: 0.8657, mIoU: 0.5824, mAcc: 0.7072, IoU.wall: 0.8270, IoU.building: 0.8592, IoU.sky: 0.9506, IoU.floor: 0.8603, IoU.tree: 0.7766, IoU.ceiling: 0.8756, IoU.road: 0.8703, IoU.bed : 0.9339, IoU.windowpane: 0.6640, IoU.grass: 0.6886, IoU.cabinet: 0.6728, IoU.sidewalk: 0.7367, IoU.person: 0.8632, IoU.earth: 0.3706, IoU.door: 0.6014, IoU.table: 0.7146, IoU.mountain: 0.6229, IoU.plant: 0.5595, IoU.curtain: 0.7724, IoU.chair: 0.6928, IoU.car: 0.8772, IoU.water: 0.6572, IoU.painting: 0.7878, IoU.sofa: 0.8300, IoU.shelf: 0.4539, IoU.house: 0.6066, IoU.sea: 0.7735, IoU.mirror: 0.8011, IoU.rug: 0.7081, IoU.field: 0.3175, IoU.armchair: 0.6120, IoU.seat: 0.6832, IoU.fence: 0.5331, IoU.desk: 0.6193, IoU.rock: 0.5640, IoU.wardrobe: 0.5609, IoU.lamp: 0.7590, IoU.bathtub: 0.8496, IoU.railing: 0.4343, IoU.cushion: 0.7029, IoU.base: 0.4206, IoU.box: 0.3790, IoU.column: 0.5528, IoU.signboard: 0.4143, IoU.chest of drawers: 0.4463, IoU.counter: 0.4105, IoU.sand: 0.5972, IoU.sink: 0.7844, IoU.skyscraper: 0.4772, IoU.fireplace: 0.7295, IoU.refrigerator: 0.8485, IoU.grandstand: 0.5345, IoU.path: 0.3083, IoU.stairs: 0.3054, IoU.runway: 0.7146, IoU.case: 0.5785, IoU.pool table: 0.9452, IoU.pillow: 0.6891, IoU.screen door: 0.7848, IoU.stairway: 0.5336, IoU.river: 0.1334, IoU.bridge: 0.6799, IoU.bookcase: 0.4658, IoU.blind: 0.4144, IoU.coffee table: 0.6574, IoU.toilet: 0.9065, IoU.flower: 0.4638, IoU.book: 0.5553, IoU.hill: 0.0824, IoU.bench: 0.5427, IoU.countertop: 0.6526, IoU.stove: 0.8425, IoU.palm: 0.5524, IoU.kitchen island: 0.5639, IoU.computer: 0.7883, IoU.swivel chair: 0.4810, IoU.boat: 0.7604, IoU.bar: 0.5802, IoU.arcade machine: 0.7833, IoU.hovel: 0.4903, IoU.bus: 0.9233, IoU.towel: 0.7656, IoU.light: 0.6205, IoU.truck: 0.4553, IoU.tower: 0.3402, IoU.chandelier: 0.7321, IoU.awning: 0.4713, IoU.streetlight: 0.3519, IoU.booth: 0.4317, IoU.television receiver: 0.7829, IoU.airplane: 0.8037, IoU.dirt track: 0.0740, IoU.apparel: 0.4773, IoU.pole: 0.3080, IoU.land: 0.0234, IoU.bannister: 0.1848, IoU.escalator: 0.5880, IoU.ottoman: 0.4856, IoU.bottle: 0.4151, IoU.buffet: 0.4836, IoU.poster: 0.3973, IoU.stage: 0.2369, IoU.van: 0.4672, IoU.ship: 0.8687, IoU.fountain: 0.3456, IoU.conveyer belt: 0.8396, IoU.canopy: 0.5498, IoU.washer: 0.8155, IoU.plaything: 0.3317, IoU.swimming pool: 0.5830, IoU.stool: 0.5626, IoU.barrel: 0.5697, IoU.basket: 0.4219, IoU.waterfall: 0.6871, IoU.tent: 0.9568, IoU.bag: 0.2260, IoU.minibike: 0.7776, IoU.cradle: 0.8377, IoU.oven: 0.6198, IoU.ball: 0.5170, IoU.food: 0.6111, IoU.step: 0.1395, IoU.tank: 0.6244, IoU.trade name: 0.2853, IoU.microwave: 0.8851, IoU.pot: 0.5859, IoU.animal: 0.5983, IoU.bicycle: 0.6127, IoU.lake: 0.5264, IoU.dishwasher: 0.6818, IoU.screen: 0.5661, IoU.blanket: 0.3043, IoU.sculpture: 0.7236, IoU.hood: 0.6388, IoU.sconce: 0.6002, IoU.vase: 0.4978, IoU.traffic light: 0.4058, IoU.tray: 0.2630, IoU.ashcan: 0.4835, IoU.fan: 0.7090, IoU.pier: 0.4028, IoU.crt screen: 0.0244, IoU.plate: 0.6177, IoU.monitor: 0.6210, IoU.bulletin board: 0.5471, IoU.shower: 0.1019, IoU.radiator: 0.6837, IoU.glass: 0.2042, IoU.clock: 0.4658, IoU.flag: 0.7220, Acc.wall: 0.9034, Acc.building: 0.9350, Acc.sky: 0.9779, Acc.floor: 0.9278, Acc.tree: 0.8994, Acc.ceiling: 0.9429, Acc.road: 0.9220, Acc.bed : 0.9712, Acc.windowpane: 0.8202, Acc.grass: 0.8194, Acc.cabinet: 0.7761, Acc.sidewalk: 0.8648, Acc.person: 0.9413, Acc.earth: 0.4910, Acc.door: 0.7523, Acc.table: 0.8222, Acc.mountain: 0.7289, Acc.plant: 0.6605, Acc.curtain: 0.8851, Acc.chair: 0.8174, Acc.car: 0.9421, Acc.water: 0.8061, Acc.painting: 0.9174, Acc.sofa: 0.9147, Acc.shelf: 0.6004, Acc.house: 0.7447, Acc.sea: 0.8772, Acc.mirror: 0.8574, Acc.rug: 0.7850, Acc.field: 0.6102, Acc.armchair: 0.7801, Acc.seat: 0.8978, Acc.fence: 0.6543, Acc.desk: 0.7950, Acc.rock: 0.8801, Acc.wardrobe: 0.7236, Acc.lamp: 0.8733, Acc.bathtub: 0.8740, Acc.railing: 0.5999, Acc.cushion: 0.8142, Acc.base: 0.5668, Acc.box: 0.4875, Acc.column: 0.6827, Acc.signboard: 0.5618, Acc.chest of drawers: 0.6861, Acc.counter: 0.4931, Acc.sand: 0.8677, Acc.sink: 0.8506, Acc.skyscraper: 0.6134, Acc.fireplace: 0.9216, Acc.refrigerator: 0.9228, Acc.grandstand: 0.8498, Acc.path: 0.4267, Acc.stairs: 0.3687, Acc.runway: 0.9442, Acc.case: 0.8179, Acc.pool table: 0.9839, Acc.pillow: 0.8076, Acc.screen door: 0.8084, Acc.stairway: 0.7137, Acc.river: 0.2463, Acc.bridge: 0.7476, Acc.bookcase: 0.6787, Acc.blind: 0.4560, Acc.coffee table: 0.8779, Acc.toilet: 0.9409, Acc.flower: 0.5538, Acc.book: 0.7829, Acc.hill: 0.1432, Acc.bench: 0.6275, Acc.countertop: 0.8423, Acc.stove: 0.8899, Acc.palm: 0.7813, Acc.kitchen island: 0.8682, Acc.computer: 0.9108, Acc.swivel chair: 0.6870, Acc.boat: 0.9186, Acc.bar: 0.7961, Acc.arcade machine: 0.8329, Acc.hovel: 0.5462, Acc.bus: 0.9627, Acc.towel: 0.8697, Acc.light: 0.7243, Acc.truck: 0.5989, Acc.tower: 0.5437, Acc.chandelier: 0.8524, Acc.awning: 0.6009, Acc.streetlight: 0.4676, Acc.booth: 0.6493, Acc.television receiver: 0.8765, Acc.airplane: 0.8883, Acc.dirt track: 0.2992, Acc.apparel: 0.6461, Acc.pole: 0.4354, Acc.land: 0.0435, Acc.bannister: 0.2639, Acc.escalator: 0.7925, Acc.ottoman: 0.6404, Acc.bottle: 0.6824, Acc.buffet: 0.5985, Acc.poster: 0.5050, Acc.stage: 0.4581, Acc.van: 0.6221, Acc.ship: 0.9115, Acc.fountain: 0.3519, Acc.conveyer belt: 0.9371, Acc.canopy: 0.7555, Acc.washer: 0.8610, Acc.plaything: 0.4713, Acc.swimming pool: 0.8619, Acc.stool: 0.6915, Acc.barrel: 0.7460, Acc.basket: 0.6149, Acc.waterfall: 0.8824, Acc.tent: 0.9846, Acc.bag: 0.2586, Acc.minibike: 0.9079, Acc.cradle: 0.9746, Acc.oven: 0.7221, Acc.ball: 0.6211, Acc.food: 0.7748, Acc.step: 0.1761, Acc.tank: 0.6822, Acc.trade name: 0.3325, Acc.microwave: 0.9610, Acc.pot: 0.6964, Acc.animal: 0.6124, Acc.bicycle: 0.7740, Acc.lake: 0.6384, Acc.dishwasher: 0.7845, Acc.screen: 0.8626, Acc.blanket: 0.3412, Acc.sculpture: 0.8862, Acc.hood: 0.7628, Acc.sconce: 0.7066, Acc.vase: 0.6528, Acc.traffic light: 0.6211, Acc.tray: 0.3260, Acc.ashcan: 0.6550, Acc.fan: 0.8261, Acc.pier: 0.4446, Acc.crt screen: 0.0346, Acc.plate: 0.7908, Acc.monitor: 0.7325, Acc.bulletin board: 0.6448, Acc.shower: 0.1041, Acc.radiator: 0.7844, Acc.glass: 0.2189, Acc.clock: 0.5762, Acc.flag: 0.7878 +2024-06-17 06:08:37,559 - mmseg - INFO - Iter [78050/80000] lr: 9.755e-07, eta: 0:49:01, time: 3.289, data_time: 1.931, memory: 70722, decode.loss_ce: 0.1386, decode.acc_seg: 93.8946, aux.loss_ce: 0.0604, aux.acc_seg: 93.3984, loss: 0.1990 +2024-06-17 06:09:45,640 - mmseg - INFO - Iter [78100/80000] lr: 9.505e-07, eta: 0:47:45, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1340, decode.acc_seg: 94.0501, aux.loss_ce: 0.0583, aux.acc_seg: 93.5468, loss: 0.1924 +2024-06-17 06:10:53,870 - mmseg - INFO - Iter [78150/80000] lr: 9.255e-07, eta: 0:46:30, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1314, decode.acc_seg: 94.1541, aux.loss_ce: 0.0569, aux.acc_seg: 93.6954, loss: 0.1883 +2024-06-17 06:12:01,881 - mmseg - INFO - Iter [78200/80000] lr: 9.005e-07, eta: 0:45:14, time: 1.360, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1353, decode.acc_seg: 93.9642, aux.loss_ce: 0.0584, aux.acc_seg: 93.4870, loss: 0.1937 +2024-06-17 06:13:10,147 - mmseg - INFO - Iter [78250/80000] lr: 8.755e-07, eta: 0:43:59, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1312, decode.acc_seg: 94.2292, aux.loss_ce: 0.0576, aux.acc_seg: 93.7448, loss: 0.1888 +2024-06-17 06:14:18,230 - mmseg - INFO - Iter [78300/80000] lr: 8.505e-07, eta: 0:42:43, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1376, decode.acc_seg: 93.8606, aux.loss_ce: 0.0594, aux.acc_seg: 93.4008, loss: 0.1970 +2024-06-17 06:15:30,779 - mmseg - INFO - Iter [78350/80000] lr: 8.255e-07, eta: 0:41:28, time: 1.451, data_time: 0.082, memory: 70722, decode.loss_ce: 0.1314, decode.acc_seg: 94.1745, aux.loss_ce: 0.0574, aux.acc_seg: 93.6640, loss: 0.1888 +2024-06-17 06:16:38,843 - mmseg - INFO - Iter [78400/80000] lr: 8.005e-07, eta: 0:40:12, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1348, decode.acc_seg: 94.0009, aux.loss_ce: 0.0584, aux.acc_seg: 93.5570, loss: 0.1932 +2024-06-17 06:17:47,248 - mmseg - INFO - Iter [78450/80000] lr: 7.755e-07, eta: 0:38:57, time: 1.368, data_time: 0.009, memory: 70722, decode.loss_ce: 0.1296, decode.acc_seg: 94.2595, aux.loss_ce: 0.0563, aux.acc_seg: 93.7817, loss: 0.1860 +2024-06-17 06:18:55,309 - mmseg - INFO - Iter [78500/80000] lr: 7.505e-07, eta: 0:37:41, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1447, decode.acc_seg: 93.6613, aux.loss_ce: 0.0620, aux.acc_seg: 93.2147, loss: 0.2067 +2024-06-17 06:20:03,366 - mmseg - INFO - Iter [78550/80000] lr: 7.255e-07, eta: 0:36:26, time: 1.361, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1362, decode.acc_seg: 94.1081, aux.loss_ce: 0.0595, aux.acc_seg: 93.5171, loss: 0.1957 +2024-06-17 06:21:11,535 - mmseg - INFO - Iter [78600/80000] lr: 7.005e-07, eta: 0:35:10, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1335, decode.acc_seg: 93.9042, aux.loss_ce: 0.0580, aux.acc_seg: 93.3964, loss: 0.1915 +2024-06-17 06:22:19,657 - mmseg - INFO - Iter [78650/80000] lr: 6.755e-07, eta: 0:33:55, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1308, decode.acc_seg: 94.2665, aux.loss_ce: 0.0573, aux.acc_seg: 93.7582, loss: 0.1880 +2024-06-17 06:23:28,013 - mmseg - INFO - Iter [78700/80000] lr: 6.505e-07, eta: 0:32:39, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1307, decode.acc_seg: 94.1371, aux.loss_ce: 0.0573, aux.acc_seg: 93.6261, loss: 0.1881 +2024-06-17 06:24:36,136 - mmseg - INFO - Iter [78750/80000] lr: 6.255e-07, eta: 0:31:24, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1310, decode.acc_seg: 94.1792, aux.loss_ce: 0.0567, aux.acc_seg: 93.7262, loss: 0.1877 +2024-06-17 06:25:44,335 - mmseg - INFO - Iter [78800/80000] lr: 6.005e-07, eta: 0:30:08, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1330, decode.acc_seg: 94.0270, aux.loss_ce: 0.0580, aux.acc_seg: 93.5045, loss: 0.1910 +2024-06-17 06:26:52,415 - mmseg - INFO - Iter [78850/80000] lr: 5.755e-07, eta: 0:28:53, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1350, decode.acc_seg: 94.0617, aux.loss_ce: 0.0589, aux.acc_seg: 93.5414, loss: 0.1939 +2024-06-17 06:28:00,537 - mmseg - INFO - Iter [78900/80000] lr: 5.505e-07, eta: 0:27:37, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1311, decode.acc_seg: 94.2028, aux.loss_ce: 0.0572, aux.acc_seg: 93.6913, loss: 0.1883 +2024-06-17 06:29:08,669 - mmseg - INFO - Iter [78950/80000] lr: 5.255e-07, eta: 0:26:22, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1349, decode.acc_seg: 93.9634, aux.loss_ce: 0.0587, aux.acc_seg: 93.4537, loss: 0.1936 +2024-06-17 06:30:16,822 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 06:30:16,822 - mmseg - INFO - Iter [79000/80000] lr: 5.005e-07, eta: 0:25:06, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1284, decode.acc_seg: 94.0473, aux.loss_ce: 0.0558, aux.acc_seg: 93.5567, loss: 0.1842 +2024-06-17 06:31:52,543 - mmseg - INFO - per class results: +2024-06-17 06:31:52,549 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.72 | 90.24 | +| building | 85.84 | 93.59 | +| sky | 95.05 | 97.74 | +| floor | 85.99 | 92.84 | +| tree | 77.6 | 90.05 | +| ceiling | 87.54 | 94.38 | +| road | 86.97 | 92.18 | +| bed | 93.34 | 97.2 | +| windowpane | 66.24 | 82.33 | +| grass | 68.71 | 81.97 | +| cabinet | 67.24 | 77.67 | +| sidewalk | 73.5 | 86.43 | +| person | 86.22 | 94.24 | +| earth | 37.25 | 49.42 | +| door | 60.05 | 75.06 | +| table | 71.25 | 82.02 | +| mountain | 62.38 | 73.53 | +| plant | 55.77 | 65.04 | +| curtain | 77.17 | 88.22 | +| chair | 69.22 | 81.04 | +| car | 87.61 | 94.22 | +| water | 66.16 | 81.54 | +| painting | 78.96 | 91.65 | +| sofa | 82.88 | 91.57 | +| shelf | 45.52 | 60.04 | +| house | 58.14 | 70.52 | +| sea | 78.11 | 88.92 | +| mirror | 80.11 | 85.95 | +| rug | 71.38 | 79.71 | +| field | 30.73 | 58.44 | +| armchair | 61.2 | 77.67 | +| seat | 68.36 | 89.59 | +| fence | 53.27 | 66.0 | +| desk | 62.51 | 80.03 | +| rock | 56.36 | 88.2 | +| wardrobe | 55.45 | 72.96 | +| lamp | 75.92 | 87.48 | +| bathtub | 84.94 | 87.29 | +| railing | 43.67 | 60.23 | +| cushion | 69.42 | 81.58 | +| base | 42.06 | 58.91 | +| box | 37.95 | 49.13 | +| column | 54.91 | 67.63 | +| signboard | 41.41 | 56.43 | +| chest of drawers | 44.18 | 67.1 | +| counter | 40.74 | 49.69 | +| sand | 60.09 | 86.59 | +| sink | 78.5 | 85.03 | +| skyscraper | 47.75 | 61.19 | +| fireplace | 73.52 | 92.29 | +| refrigerator | 85.17 | 93.12 | +| grandstand | 53.18 | 85.24 | +| path | 30.03 | 41.03 | +| stairs | 30.35 | 36.3 | +| runway | 71.27 | 94.16 | +| case | 57.35 | 80.74 | +| pool table | 94.64 | 98.24 | +| pillow | 67.35 | 78.25 | +| screen door | 78.2 | 80.28 | +| stairway | 52.74 | 69.91 | +| river | 13.55 | 23.88 | +| bridge | 67.95 | 74.52 | +| bookcase | 46.89 | 67.85 | +| blind | 40.27 | 44.11 | +| coffee table | 65.66 | 88.16 | +| toilet | 90.54 | 93.97 | +| flower | 46.77 | 56.25 | +| book | 55.34 | 77.23 | +| hill | 8.03 | 14.17 | +| bench | 54.34 | 62.19 | +| countertop | 65.24 | 85.58 | +| stove | 84.27 | 88.81 | +| palm | 55.19 | 78.54 | +| kitchen island | 57.22 | 86.76 | +| computer | 78.74 | 91.3 | +| swivel chair | 47.94 | 68.36 | +| boat | 76.52 | 91.18 | +| bar | 58.76 | 80.68 | +| arcade machine | 79.32 | 84.46 | +| hovel | 49.29 | 54.68 | +| bus | 92.44 | 96.07 | +| towel | 76.08 | 86.55 | +| light | 61.68 | 71.2 | +| truck | 45.51 | 60.54 | +| tower | 33.22 | 50.52 | +| chandelier | 73.13 | 83.78 | +| awning | 48.32 | 62.31 | +| streetlight | 36.09 | 48.67 | +| booth | 44.08 | 64.94 | +| television receiver | 78.2 | 87.8 | +| airplane | 80.21 | 88.41 | +| dirt track | 7.69 | 30.48 | +| apparel | 48.54 | 65.76 | +| pole | 30.29 | 42.95 | +| land | 2.35 | 4.33 | +| bannister | 18.58 | 26.68 | +| escalator | 58.97 | 79.05 | +| ottoman | 49.16 | 65.23 | +| bottle | 41.82 | 68.83 | +| buffet | 47.86 | 58.55 | +| poster | 40.93 | 51.84 | +| stage | 23.95 | 45.8 | +| van | 46.84 | 61.58 | +| ship | 87.56 | 91.71 | +| fountain | 32.88 | 33.42 | +| conveyer belt | 84.07 | 93.66 | +| canopy | 54.26 | 73.71 | +| washer | 81.72 | 86.31 | +| plaything | 33.81 | 49.89 | +| swimming pool | 58.62 | 86.67 | +| stool | 55.9 | 70.05 | +| barrel | 57.08 | 74.48 | +| basket | 42.36 | 61.28 | +| waterfall | 69.51 | 88.15 | +| tent | 95.7 | 98.4 | +| bag | 22.64 | 25.91 | +| minibike | 77.87 | 90.01 | +| cradle | 84.16 | 97.48 | +| oven | 63.67 | 74.5 | +| ball | 50.8 | 60.78 | +| food | 61.05 | 77.32 | +| step | 14.08 | 17.92 | +| tank | 62.78 | 67.8 | +| trade name | 28.55 | 33.53 | +| microwave | 89.04 | 96.03 | +| pot | 58.52 | 68.94 | +| animal | 59.9 | 61.38 | +| bicycle | 60.86 | 77.74 | +| lake | 52.61 | 63.85 | +| dishwasher | 68.14 | 77.61 | +| screen | 55.43 | 84.38 | +| blanket | 30.83 | 34.84 | +| sculpture | 73.95 | 88.01 | +| hood | 64.04 | 76.64 | +| sconce | 60.1 | 70.04 | +| vase | 50.07 | 64.95 | +| traffic light | 39.67 | 63.36 | +| tray | 26.01 | 31.87 | +| ashcan | 47.76 | 65.53 | +| fan | 71.14 | 83.54 | +| pier | 40.71 | 45.45 | +| crt screen | 2.32 | 3.47 | +| plate | 62.09 | 78.49 | +| monitor | 61.8 | 73.76 | +| bulletin board | 53.89 | 64.29 | +| shower | 9.45 | 9.62 | +| radiator | 68.57 | 78.0 | +| glass | 20.06 | 21.3 | +| clock | 46.48 | 56.74 | +| flag | 72.34 | 79.07 | ++---------------------+-------+-------+ +2024-06-17 06:31:52,550 - mmseg - INFO - Summary: +2024-06-17 06:31:52,550 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.54 | 58.23 | 70.64 | ++-------+-------+-------+ +2024-06-17 06:31:52,550 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 06:31:52,551 - mmseg - INFO - Iter(val) [250] aAcc: 0.8654, mIoU: 0.5823, mAcc: 0.7064, IoU.wall: 0.8272, IoU.building: 0.8584, IoU.sky: 0.9505, IoU.floor: 0.8599, IoU.tree: 0.7760, IoU.ceiling: 0.8754, IoU.road: 0.8697, IoU.bed : 0.9334, IoU.windowpane: 0.6624, IoU.grass: 0.6871, IoU.cabinet: 0.6724, IoU.sidewalk: 0.7350, IoU.person: 0.8622, IoU.earth: 0.3725, IoU.door: 0.6005, IoU.table: 0.7125, IoU.mountain: 0.6238, IoU.plant: 0.5577, IoU.curtain: 0.7717, IoU.chair: 0.6922, IoU.car: 0.8761, IoU.water: 0.6616, IoU.painting: 0.7896, IoU.sofa: 0.8288, IoU.shelf: 0.4552, IoU.house: 0.5814, IoU.sea: 0.7811, IoU.mirror: 0.8011, IoU.rug: 0.7138, IoU.field: 0.3073, IoU.armchair: 0.6120, IoU.seat: 0.6836, IoU.fence: 0.5327, IoU.desk: 0.6251, IoU.rock: 0.5636, IoU.wardrobe: 0.5545, IoU.lamp: 0.7592, IoU.bathtub: 0.8494, IoU.railing: 0.4367, IoU.cushion: 0.6942, IoU.base: 0.4206, IoU.box: 0.3795, IoU.column: 0.5491, IoU.signboard: 0.4141, IoU.chest of drawers: 0.4418, IoU.counter: 0.4074, IoU.sand: 0.6009, IoU.sink: 0.7850, IoU.skyscraper: 0.4775, IoU.fireplace: 0.7352, IoU.refrigerator: 0.8517, IoU.grandstand: 0.5318, IoU.path: 0.3003, IoU.stairs: 0.3035, IoU.runway: 0.7127, IoU.case: 0.5735, IoU.pool table: 0.9464, IoU.pillow: 0.6735, IoU.screen door: 0.7820, IoU.stairway: 0.5274, IoU.river: 0.1355, IoU.bridge: 0.6795, IoU.bookcase: 0.4689, IoU.blind: 0.4027, IoU.coffee table: 0.6566, IoU.toilet: 0.9054, IoU.flower: 0.4677, IoU.book: 0.5534, IoU.hill: 0.0803, IoU.bench: 0.5434, IoU.countertop: 0.6524, IoU.stove: 0.8427, IoU.palm: 0.5519, IoU.kitchen island: 0.5722, IoU.computer: 0.7874, IoU.swivel chair: 0.4794, IoU.boat: 0.7652, IoU.bar: 0.5876, IoU.arcade machine: 0.7932, IoU.hovel: 0.4929, IoU.bus: 0.9244, IoU.towel: 0.7608, IoU.light: 0.6168, IoU.truck: 0.4551, IoU.tower: 0.3322, IoU.chandelier: 0.7313, IoU.awning: 0.4832, IoU.streetlight: 0.3609, IoU.booth: 0.4408, IoU.television receiver: 0.7820, IoU.airplane: 0.8021, IoU.dirt track: 0.0769, IoU.apparel: 0.4854, IoU.pole: 0.3029, IoU.land: 0.0235, IoU.bannister: 0.1858, IoU.escalator: 0.5897, IoU.ottoman: 0.4916, IoU.bottle: 0.4182, IoU.buffet: 0.4786, IoU.poster: 0.4093, IoU.stage: 0.2395, IoU.van: 0.4684, IoU.ship: 0.8756, IoU.fountain: 0.3288, IoU.conveyer belt: 0.8407, IoU.canopy: 0.5426, IoU.washer: 0.8172, IoU.plaything: 0.3381, IoU.swimming pool: 0.5862, IoU.stool: 0.5590, IoU.barrel: 0.5708, IoU.basket: 0.4236, IoU.waterfall: 0.6951, IoU.tent: 0.9570, IoU.bag: 0.2264, IoU.minibike: 0.7787, IoU.cradle: 0.8416, IoU.oven: 0.6367, IoU.ball: 0.5080, IoU.food: 0.6105, IoU.step: 0.1408, IoU.tank: 0.6278, IoU.trade name: 0.2855, IoU.microwave: 0.8904, IoU.pot: 0.5852, IoU.animal: 0.5990, IoU.bicycle: 0.6086, IoU.lake: 0.5261, IoU.dishwasher: 0.6814, IoU.screen: 0.5543, IoU.blanket: 0.3083, IoU.sculpture: 0.7395, IoU.hood: 0.6404, IoU.sconce: 0.6010, IoU.vase: 0.5007, IoU.traffic light: 0.3967, IoU.tray: 0.2601, IoU.ashcan: 0.4776, IoU.fan: 0.7114, IoU.pier: 0.4071, IoU.crt screen: 0.0232, IoU.plate: 0.6209, IoU.monitor: 0.6180, IoU.bulletin board: 0.5389, IoU.shower: 0.0945, IoU.radiator: 0.6857, IoU.glass: 0.2006, IoU.clock: 0.4648, IoU.flag: 0.7234, Acc.wall: 0.9024, Acc.building: 0.9359, Acc.sky: 0.9774, Acc.floor: 0.9284, Acc.tree: 0.9005, Acc.ceiling: 0.9438, Acc.road: 0.9218, Acc.bed : 0.9720, Acc.windowpane: 0.8233, Acc.grass: 0.8197, Acc.cabinet: 0.7767, Acc.sidewalk: 0.8643, Acc.person: 0.9424, Acc.earth: 0.4942, Acc.door: 0.7506, Acc.table: 0.8202, Acc.mountain: 0.7353, Acc.plant: 0.6504, Acc.curtain: 0.8822, Acc.chair: 0.8104, Acc.car: 0.9422, Acc.water: 0.8154, Acc.painting: 0.9165, Acc.sofa: 0.9157, Acc.shelf: 0.6004, Acc.house: 0.7052, Acc.sea: 0.8892, Acc.mirror: 0.8595, Acc.rug: 0.7971, Acc.field: 0.5844, Acc.armchair: 0.7767, Acc.seat: 0.8959, Acc.fence: 0.6600, Acc.desk: 0.8003, Acc.rock: 0.8820, Acc.wardrobe: 0.7296, Acc.lamp: 0.8748, Acc.bathtub: 0.8729, Acc.railing: 0.6023, Acc.cushion: 0.8158, Acc.base: 0.5891, Acc.box: 0.4913, Acc.column: 0.6763, Acc.signboard: 0.5643, Acc.chest of drawers: 0.6710, Acc.counter: 0.4969, Acc.sand: 0.8659, Acc.sink: 0.8503, Acc.skyscraper: 0.6119, Acc.fireplace: 0.9229, Acc.refrigerator: 0.9312, Acc.grandstand: 0.8524, Acc.path: 0.4103, Acc.stairs: 0.3630, Acc.runway: 0.9416, Acc.case: 0.8074, Acc.pool table: 0.9824, Acc.pillow: 0.7825, Acc.screen door: 0.8028, Acc.stairway: 0.6991, Acc.river: 0.2388, Acc.bridge: 0.7452, Acc.bookcase: 0.6785, Acc.blind: 0.4411, Acc.coffee table: 0.8816, Acc.toilet: 0.9397, Acc.flower: 0.5625, Acc.book: 0.7723, Acc.hill: 0.1417, Acc.bench: 0.6219, Acc.countertop: 0.8558, Acc.stove: 0.8881, Acc.palm: 0.7854, Acc.kitchen island: 0.8676, Acc.computer: 0.9130, Acc.swivel chair: 0.6836, Acc.boat: 0.9118, Acc.bar: 0.8068, Acc.arcade machine: 0.8446, Acc.hovel: 0.5468, Acc.bus: 0.9607, Acc.towel: 0.8655, Acc.light: 0.7120, Acc.truck: 0.6054, Acc.tower: 0.5052, Acc.chandelier: 0.8378, Acc.awning: 0.6231, Acc.streetlight: 0.4867, Acc.booth: 0.6494, Acc.television receiver: 0.8780, Acc.airplane: 0.8841, Acc.dirt track: 0.3048, Acc.apparel: 0.6576, Acc.pole: 0.4295, Acc.land: 0.0433, Acc.bannister: 0.2668, Acc.escalator: 0.7905, Acc.ottoman: 0.6523, Acc.bottle: 0.6883, Acc.buffet: 0.5855, Acc.poster: 0.5184, Acc.stage: 0.4580, Acc.van: 0.6158, Acc.ship: 0.9171, Acc.fountain: 0.3342, Acc.conveyer belt: 0.9366, Acc.canopy: 0.7371, Acc.washer: 0.8631, Acc.plaything: 0.4989, Acc.swimming pool: 0.8667, Acc.stool: 0.7005, Acc.barrel: 0.7448, Acc.basket: 0.6128, Acc.waterfall: 0.8815, Acc.tent: 0.9840, Acc.bag: 0.2591, Acc.minibike: 0.9001, Acc.cradle: 0.9748, Acc.oven: 0.7450, Acc.ball: 0.6078, Acc.food: 0.7732, Acc.step: 0.1792, Acc.tank: 0.6780, Acc.trade name: 0.3353, Acc.microwave: 0.9603, Acc.pot: 0.6894, Acc.animal: 0.6138, Acc.bicycle: 0.7774, Acc.lake: 0.6385, Acc.dishwasher: 0.7761, Acc.screen: 0.8438, Acc.blanket: 0.3484, Acc.sculpture: 0.8801, Acc.hood: 0.7664, Acc.sconce: 0.7004, Acc.vase: 0.6495, Acc.traffic light: 0.6336, Acc.tray: 0.3187, Acc.ashcan: 0.6553, Acc.fan: 0.8354, Acc.pier: 0.4545, Acc.crt screen: 0.0347, Acc.plate: 0.7849, Acc.monitor: 0.7376, Acc.bulletin board: 0.6429, Acc.shower: 0.0962, Acc.radiator: 0.7800, Acc.glass: 0.2130, Acc.clock: 0.5674, Acc.flag: 0.7907 +2024-06-17 06:33:01,218 - mmseg - INFO - Iter [79050/80000] lr: 4.755e-07, eta: 0:23:52, time: 3.288, data_time: 1.930, memory: 70722, decode.loss_ce: 0.1350, decode.acc_seg: 94.0657, aux.loss_ce: 0.0585, aux.acc_seg: 93.6084, loss: 0.1935 +2024-06-17 06:34:09,116 - mmseg - INFO - Iter [79100/80000] lr: 4.505e-07, eta: 0:22:37, time: 1.358, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1294, decode.acc_seg: 94.1904, aux.loss_ce: 0.0564, aux.acc_seg: 93.7057, loss: 0.1858 +2024-06-17 06:35:17,237 - mmseg - INFO - Iter [79150/80000] lr: 4.255e-07, eta: 0:21:21, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1335, decode.acc_seg: 94.0026, aux.loss_ce: 0.0583, aux.acc_seg: 93.5025, loss: 0.1918 +2024-06-17 06:36:25,357 - mmseg - INFO - Iter [79200/80000] lr: 4.005e-07, eta: 0:20:06, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1355, decode.acc_seg: 94.0789, aux.loss_ce: 0.0584, aux.acc_seg: 93.6224, loss: 0.1939 +2024-06-17 06:37:33,465 - mmseg - INFO - Iter [79250/80000] lr: 3.755e-07, eta: 0:18:50, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1303, decode.acc_seg: 94.0772, aux.loss_ce: 0.0566, aux.acc_seg: 93.6166, loss: 0.1869 +2024-06-17 06:38:41,579 - mmseg - INFO - Iter [79300/80000] lr: 3.505e-07, eta: 0:17:35, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1271, decode.acc_seg: 94.2558, aux.loss_ce: 0.0555, aux.acc_seg: 93.8091, loss: 0.1827 +2024-06-17 06:39:49,804 - mmseg - INFO - Iter [79350/80000] lr: 3.255e-07, eta: 0:16:19, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1443, decode.acc_seg: 93.6877, aux.loss_ce: 0.0624, aux.acc_seg: 93.1500, loss: 0.2067 +2024-06-17 06:40:58,101 - mmseg - INFO - Iter [79400/80000] lr: 3.005e-07, eta: 0:15:04, time: 1.366, data_time: 0.011, memory: 70722, decode.loss_ce: 0.1300, decode.acc_seg: 94.2426, aux.loss_ce: 0.0567, aux.acc_seg: 93.7683, loss: 0.1867 +2024-06-17 06:42:06,203 - mmseg - INFO - Iter [79450/80000] lr: 2.755e-07, eta: 0:13:48, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1373, decode.acc_seg: 93.8622, aux.loss_ce: 0.0596, aux.acc_seg: 93.3495, loss: 0.1969 +2024-06-17 06:43:14,524 - mmseg - INFO - Iter [79500/80000] lr: 2.505e-07, eta: 0:12:33, time: 1.366, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1286, decode.acc_seg: 94.2850, aux.loss_ce: 0.0562, aux.acc_seg: 93.7417, loss: 0.1848 +2024-06-17 06:44:22,738 - mmseg - INFO - Iter [79550/80000] lr: 2.255e-07, eta: 0:11:18, time: 1.364, data_time: 0.011, memory: 70722, decode.loss_ce: 0.1277, decode.acc_seg: 94.2203, aux.loss_ce: 0.0554, aux.acc_seg: 93.7803, loss: 0.1831 +2024-06-17 06:45:33,369 - mmseg - INFO - Iter [79600/80000] lr: 2.005e-07, eta: 0:10:02, time: 1.413, data_time: 0.052, memory: 70722, decode.loss_ce: 0.1350, decode.acc_seg: 94.0977, aux.loss_ce: 0.0585, aux.acc_seg: 93.6094, loss: 0.1935 +2024-06-17 06:46:41,715 - mmseg - INFO - Iter [79650/80000] lr: 1.755e-07, eta: 0:08:47, time: 1.367, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1377, decode.acc_seg: 94.0300, aux.loss_ce: 0.0603, aux.acc_seg: 93.4997, loss: 0.1979 +2024-06-17 06:47:49,975 - mmseg - INFO - Iter [79700/80000] lr: 1.505e-07, eta: 0:07:32, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1321, decode.acc_seg: 94.2847, aux.loss_ce: 0.0573, aux.acc_seg: 93.7851, loss: 0.1894 +2024-06-17 06:48:58,092 - mmseg - INFO - Iter [79750/80000] lr: 1.255e-07, eta: 0:06:16, time: 1.362, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1286, decode.acc_seg: 94.2846, aux.loss_ce: 0.0559, aux.acc_seg: 93.8436, loss: 0.1845 +2024-06-17 06:50:06,627 - mmseg - INFO - Iter [79800/80000] lr: 1.005e-07, eta: 0:05:01, time: 1.371, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1263, decode.acc_seg: 94.2808, aux.loss_ce: 0.0552, aux.acc_seg: 93.7867, loss: 0.1815 +2024-06-17 06:51:14,771 - mmseg - INFO - Iter [79850/80000] lr: 7.550e-08, eta: 0:03:45, time: 1.363, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1345, decode.acc_seg: 93.9905, aux.loss_ce: 0.0589, aux.acc_seg: 93.5282, loss: 0.1934 +2024-06-17 06:52:22,972 - mmseg - INFO - Iter [79900/80000] lr: 5.050e-08, eta: 0:02:30, time: 1.364, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1403, decode.acc_seg: 93.7456, aux.loss_ce: 0.0606, aux.acc_seg: 93.2817, loss: 0.2009 +2024-06-17 06:53:31,236 - mmseg - INFO - Iter [79950/80000] lr: 2.550e-08, eta: 0:01:15, time: 1.365, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1353, decode.acc_seg: 94.1629, aux.loss_ce: 0.0595, aux.acc_seg: 93.6534, loss: 0.1949 +2024-06-17 06:54:39,429 - mmseg - INFO - Saving checkpoint at 80000 iterations +2024-06-17 06:56:07,837 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 06:56:07,837 - mmseg - INFO - Iter [80000/80000] lr: 5.000e-10, eta: 0:00:00, time: 3.132, data_time: 0.010, memory: 70722, decode.loss_ce: 0.1343, decode.acc_seg: 94.0710, aux.loss_ce: 0.0581, aux.acc_seg: 93.7097, loss: 0.1924 +2024-06-17 06:57:42,625 - mmseg - INFO - per class results: +2024-06-17 06:57:42,631 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.71 | 90.29 | +| building | 85.8 | 93.65 | +| sky | 95.07 | 97.74 | +| floor | 86.02 | 92.75 | +| tree | 77.62 | 89.98 | +| ceiling | 87.49 | 94.44 | +| road | 86.95 | 92.01 | +| bed | 93.38 | 97.18 | +| windowpane | 66.37 | 81.76 | +| grass | 68.75 | 81.99 | +| cabinet | 67.08 | 77.33 | +| sidewalk | 73.56 | 86.72 | +| person | 86.18 | 94.61 | +| earth | 37.13 | 49.02 | +| door | 60.22 | 74.89 | +| table | 71.49 | 82.85 | +| mountain | 62.59 | 73.89 | +| plant | 55.88 | 65.55 | +| curtain | 77.21 | 88.55 | +| chair | 69.36 | 81.12 | +| car | 87.71 | 94.2 | +| water | 66.07 | 81.11 | +| painting | 78.81 | 91.83 | +| sofa | 82.9 | 91.76 | +| shelf | 45.52 | 60.48 | +| house | 57.76 | 69.74 | +| sea | 78.24 | 89.31 | +| mirror | 80.01 | 85.89 | +| rug | 71.38 | 80.0 | +| field | 30.65 | 58.28 | +| armchair | 61.35 | 77.98 | +| seat | 68.75 | 89.38 | +| fence | 52.85 | 65.83 | +| desk | 62.53 | 79.84 | +| rock | 56.76 | 87.73 | +| wardrobe | 55.0 | 73.09 | +| lamp | 75.98 | 86.83 | +| bathtub | 84.98 | 87.32 | +| railing | 43.41 | 59.15 | +| cushion | 69.85 | 81.51 | +| base | 41.84 | 57.69 | +| box | 37.79 | 48.37 | +| column | 54.95 | 67.7 | +| signboard | 41.5 | 56.33 | +| chest of drawers | 44.45 | 67.99 | +| counter | 40.56 | 49.4 | +| sand | 59.63 | 86.51 | +| sink | 78.44 | 84.87 | +| skyscraper | 47.72 | 61.41 | +| fireplace | 73.59 | 92.46 | +| refrigerator | 85.34 | 93.55 | +| grandstand | 52.91 | 84.95 | +| path | 30.14 | 40.94 | +| stairs | 30.49 | 36.46 | +| runway | 71.15 | 93.97 | +| case | 57.29 | 80.75 | +| pool table | 94.67 | 98.26 | +| pillow | 67.8 | 78.76 | +| screen door | 78.8 | 80.97 | +| stairway | 52.85 | 69.9 | +| river | 13.62 | 23.66 | +| bridge | 67.82 | 74.49 | +| bookcase | 46.64 | 66.6 | +| blind | 41.42 | 45.6 | +| coffee table | 66.15 | 87.96 | +| toilet | 90.54 | 94.09 | +| flower | 46.78 | 56.54 | +| book | 55.23 | 77.91 | +| hill | 7.84 | 13.57 | +| bench | 54.53 | 62.82 | +| countertop | 65.24 | 85.41 | +| stove | 84.17 | 88.74 | +| palm | 55.11 | 78.84 | +| kitchen island | 58.09 | 86.29 | +| computer | 78.83 | 91.32 | +| swivel chair | 48.19 | 68.81 | +| boat | 76.05 | 91.66 | +| bar | 59.28 | 81.59 | +| arcade machine | 79.24 | 84.36 | +| hovel | 50.05 | 55.71 | +| bus | 92.59 | 96.04 | +| towel | 76.46 | 87.3 | +| light | 61.8 | 71.3 | +| truck | 45.11 | 59.84 | +| tower | 31.7 | 48.5 | +| chandelier | 73.28 | 84.84 | +| awning | 48.16 | 62.3 | +| streetlight | 36.08 | 48.24 | +| booth | 44.14 | 65.27 | +| television receiver | 78.38 | 87.66 | +| airplane | 80.72 | 88.26 | +| dirt track | 7.89 | 32.11 | +| apparel | 48.8 | 65.61 | +| pole | 31.52 | 45.33 | +| land | 2.36 | 4.35 | +| bannister | 18.49 | 27.0 | +| escalator | 59.0 | 79.18 | +| ottoman | 48.9 | 64.1 | +| bottle | 42.04 | 69.58 | +| buffet | 48.39 | 59.51 | +| poster | 40.54 | 51.02 | +| stage | 24.14 | 45.66 | +| van | 47.09 | 63.07 | +| ship | 88.14 | 93.25 | +| fountain | 34.22 | 34.82 | +| conveyer belt | 84.04 | 93.62 | +| canopy | 54.71 | 74.87 | +| washer | 81.78 | 86.4 | +| plaything | 33.3 | 49.52 | +| swimming pool | 58.61 | 86.93 | +| stool | 55.82 | 70.02 | +| barrel | 57.65 | 74.37 | +| basket | 42.16 | 61.41 | +| waterfall | 69.86 | 88.12 | +| tent | 95.75 | 98.46 | +| bag | 22.6 | 25.74 | +| minibike | 77.97 | 90.48 | +| cradle | 83.92 | 97.57 | +| oven | 63.27 | 74.37 | +| ball | 50.38 | 59.59 | +| food | 60.59 | 76.58 | +| step | 13.61 | 17.26 | +| tank | 62.94 | 68.39 | +| trade name | 28.45 | 33.32 | +| microwave | 88.97 | 96.09 | +| pot | 58.46 | 68.82 | +| animal | 59.87 | 61.27 | +| bicycle | 60.77 | 78.03 | +| lake | 52.62 | 63.85 | +| dishwasher | 67.76 | 78.06 | +| screen | 55.68 | 84.28 | +| blanket | 30.7 | 34.68 | +| sculpture | 73.5 | 88.37 | +| hood | 64.08 | 76.9 | +| sconce | 59.5 | 68.52 | +| vase | 50.1 | 64.64 | +| traffic light | 40.4 | 62.88 | +| tray | 26.71 | 33.77 | +| ashcan | 48.05 | 65.51 | +| fan | 70.9 | 83.99 | +| pier | 41.15 | 46.33 | +| crt screen | 2.41 | 3.47 | +| plate | 61.93 | 78.39 | +| monitor | 63.63 | 76.09 | +| bulletin board | 53.36 | 65.07 | +| shower | 10.68 | 10.96 | +| radiator | 68.58 | 78.18 | +| glass | 19.98 | 21.19 | +| clock | 46.58 | 57.24 | +| flag | 72.64 | 79.37 | ++---------------------+-------+-------+ +2024-06-17 06:57:42,631 - mmseg - INFO - Summary: +2024-06-17 06:57:42,631 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.56 | 58.3 | 70.75 | ++-------+------+-------+ +2024-06-17 06:57:42,632 - mmseg - INFO - Exp name: upernet_internvit_h6b_256_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 06:57:42,632 - mmseg - INFO - Iter(val) [250] aAcc: 0.8656, mIoU: 0.5830, mAcc: 0.7075, IoU.wall: 0.8271, IoU.building: 0.8580, IoU.sky: 0.9507, IoU.floor: 0.8602, IoU.tree: 0.7762, IoU.ceiling: 0.8749, IoU.road: 0.8695, IoU.bed : 0.9338, IoU.windowpane: 0.6637, IoU.grass: 0.6875, IoU.cabinet: 0.6708, IoU.sidewalk: 0.7356, IoU.person: 0.8618, IoU.earth: 0.3713, IoU.door: 0.6022, IoU.table: 0.7149, IoU.mountain: 0.6259, IoU.plant: 0.5588, IoU.curtain: 0.7721, IoU.chair: 0.6936, IoU.car: 0.8771, IoU.water: 0.6607, IoU.painting: 0.7881, IoU.sofa: 0.8290, IoU.shelf: 0.4552, IoU.house: 0.5776, IoU.sea: 0.7824, IoU.mirror: 0.8001, IoU.rug: 0.7138, IoU.field: 0.3065, IoU.armchair: 0.6135, IoU.seat: 0.6875, IoU.fence: 0.5285, IoU.desk: 0.6253, IoU.rock: 0.5676, IoU.wardrobe: 0.5500, IoU.lamp: 0.7598, IoU.bathtub: 0.8498, IoU.railing: 0.4341, IoU.cushion: 0.6985, IoU.base: 0.4184, IoU.box: 0.3779, IoU.column: 0.5495, IoU.signboard: 0.4150, IoU.chest of drawers: 0.4445, IoU.counter: 0.4056, IoU.sand: 0.5963, IoU.sink: 0.7844, IoU.skyscraper: 0.4772, IoU.fireplace: 0.7359, IoU.refrigerator: 0.8534, IoU.grandstand: 0.5291, IoU.path: 0.3014, IoU.stairs: 0.3049, IoU.runway: 0.7115, IoU.case: 0.5729, IoU.pool table: 0.9467, IoU.pillow: 0.6780, IoU.screen door: 0.7880, IoU.stairway: 0.5285, IoU.river: 0.1362, IoU.bridge: 0.6782, IoU.bookcase: 0.4664, IoU.blind: 0.4142, IoU.coffee table: 0.6615, IoU.toilet: 0.9054, IoU.flower: 0.4678, IoU.book: 0.5523, IoU.hill: 0.0784, IoU.bench: 0.5453, IoU.countertop: 0.6524, IoU.stove: 0.8417, IoU.palm: 0.5511, IoU.kitchen island: 0.5809, IoU.computer: 0.7883, IoU.swivel chair: 0.4819, IoU.boat: 0.7605, IoU.bar: 0.5928, IoU.arcade machine: 0.7924, IoU.hovel: 0.5005, IoU.bus: 0.9259, IoU.towel: 0.7646, IoU.light: 0.6180, IoU.truck: 0.4511, IoU.tower: 0.3170, IoU.chandelier: 0.7328, IoU.awning: 0.4816, IoU.streetlight: 0.3608, IoU.booth: 0.4414, IoU.television receiver: 0.7838, IoU.airplane: 0.8072, IoU.dirt track: 0.0789, IoU.apparel: 0.4880, IoU.pole: 0.3152, IoU.land: 0.0236, IoU.bannister: 0.1849, IoU.escalator: 0.5900, IoU.ottoman: 0.4890, IoU.bottle: 0.4204, IoU.buffet: 0.4839, IoU.poster: 0.4054, IoU.stage: 0.2414, IoU.van: 0.4709, IoU.ship: 0.8814, IoU.fountain: 0.3422, IoU.conveyer belt: 0.8404, IoU.canopy: 0.5471, IoU.washer: 0.8178, IoU.plaything: 0.3330, IoU.swimming pool: 0.5861, IoU.stool: 0.5582, IoU.barrel: 0.5765, IoU.basket: 0.4216, IoU.waterfall: 0.6986, IoU.tent: 0.9575, IoU.bag: 0.2260, IoU.minibike: 0.7797, IoU.cradle: 0.8392, IoU.oven: 0.6327, IoU.ball: 0.5038, IoU.food: 0.6059, IoU.step: 0.1361, IoU.tank: 0.6294, IoU.trade name: 0.2845, IoU.microwave: 0.8897, IoU.pot: 0.5846, IoU.animal: 0.5987, IoU.bicycle: 0.6077, IoU.lake: 0.5262, IoU.dishwasher: 0.6776, IoU.screen: 0.5568, IoU.blanket: 0.3070, IoU.sculpture: 0.7350, IoU.hood: 0.6408, IoU.sconce: 0.5950, IoU.vase: 0.5010, IoU.traffic light: 0.4040, IoU.tray: 0.2671, IoU.ashcan: 0.4805, IoU.fan: 0.7090, IoU.pier: 0.4115, IoU.crt screen: 0.0241, IoU.plate: 0.6193, IoU.monitor: 0.6363, IoU.bulletin board: 0.5336, IoU.shower: 0.1068, IoU.radiator: 0.6858, IoU.glass: 0.1998, IoU.clock: 0.4658, IoU.flag: 0.7264, Acc.wall: 0.9029, Acc.building: 0.9365, Acc.sky: 0.9774, Acc.floor: 0.9275, Acc.tree: 0.8998, Acc.ceiling: 0.9444, Acc.road: 0.9201, Acc.bed : 0.9718, Acc.windowpane: 0.8176, Acc.grass: 0.8199, Acc.cabinet: 0.7733, Acc.sidewalk: 0.8672, Acc.person: 0.9461, Acc.earth: 0.4902, Acc.door: 0.7489, Acc.table: 0.8285, Acc.mountain: 0.7389, Acc.plant: 0.6555, Acc.curtain: 0.8855, Acc.chair: 0.8112, Acc.car: 0.9420, Acc.water: 0.8111, Acc.painting: 0.9183, Acc.sofa: 0.9176, Acc.shelf: 0.6048, Acc.house: 0.6974, Acc.sea: 0.8931, Acc.mirror: 0.8589, Acc.rug: 0.8000, Acc.field: 0.5828, Acc.armchair: 0.7798, Acc.seat: 0.8938, Acc.fence: 0.6583, Acc.desk: 0.7984, Acc.rock: 0.8773, Acc.wardrobe: 0.7309, Acc.lamp: 0.8683, Acc.bathtub: 0.8732, Acc.railing: 0.5915, Acc.cushion: 0.8151, Acc.base: 0.5769, Acc.box: 0.4837, Acc.column: 0.6770, Acc.signboard: 0.5633, Acc.chest of drawers: 0.6799, Acc.counter: 0.4940, Acc.sand: 0.8651, Acc.sink: 0.8487, Acc.skyscraper: 0.6141, Acc.fireplace: 0.9246, Acc.refrigerator: 0.9355, Acc.grandstand: 0.8495, Acc.path: 0.4094, Acc.stairs: 0.3646, Acc.runway: 0.9397, Acc.case: 0.8075, Acc.pool table: 0.9826, Acc.pillow: 0.7876, Acc.screen door: 0.8097, Acc.stairway: 0.6990, Acc.river: 0.2366, Acc.bridge: 0.7449, Acc.bookcase: 0.6660, Acc.blind: 0.4560, Acc.coffee table: 0.8796, Acc.toilet: 0.9409, Acc.flower: 0.5654, Acc.book: 0.7791, Acc.hill: 0.1357, Acc.bench: 0.6282, Acc.countertop: 0.8541, Acc.stove: 0.8874, Acc.palm: 0.7884, Acc.kitchen island: 0.8629, Acc.computer: 0.9132, Acc.swivel chair: 0.6881, Acc.boat: 0.9166, Acc.bar: 0.8159, Acc.arcade machine: 0.8436, Acc.hovel: 0.5571, Acc.bus: 0.9604, Acc.towel: 0.8730, Acc.light: 0.7130, Acc.truck: 0.5984, Acc.tower: 0.4850, Acc.chandelier: 0.8484, Acc.awning: 0.6230, Acc.streetlight: 0.4824, Acc.booth: 0.6527, Acc.television receiver: 0.8766, Acc.airplane: 0.8826, Acc.dirt track: 0.3211, Acc.apparel: 0.6561, Acc.pole: 0.4533, Acc.land: 0.0435, Acc.bannister: 0.2700, Acc.escalator: 0.7918, Acc.ottoman: 0.6410, Acc.bottle: 0.6958, Acc.buffet: 0.5951, Acc.poster: 0.5102, Acc.stage: 0.4566, Acc.van: 0.6307, Acc.ship: 0.9325, Acc.fountain: 0.3482, Acc.conveyer belt: 0.9362, Acc.canopy: 0.7487, Acc.washer: 0.8640, Acc.plaything: 0.4952, Acc.swimming pool: 0.8693, Acc.stool: 0.7002, Acc.barrel: 0.7437, Acc.basket: 0.6141, Acc.waterfall: 0.8812, Acc.tent: 0.9846, Acc.bag: 0.2574, Acc.minibike: 0.9048, Acc.cradle: 0.9757, Acc.oven: 0.7437, Acc.ball: 0.5959, Acc.food: 0.7658, Acc.step: 0.1726, Acc.tank: 0.6839, Acc.trade name: 0.3332, Acc.microwave: 0.9609, Acc.pot: 0.6882, Acc.animal: 0.6127, Acc.bicycle: 0.7803, Acc.lake: 0.6385, Acc.dishwasher: 0.7806, Acc.screen: 0.8428, Acc.blanket: 0.3468, Acc.sculpture: 0.8837, Acc.hood: 0.7690, Acc.sconce: 0.6852, Acc.vase: 0.6464, Acc.traffic light: 0.6288, Acc.tray: 0.3377, Acc.ashcan: 0.6551, Acc.fan: 0.8399, Acc.pier: 0.4633, Acc.crt screen: 0.0347, Acc.plate: 0.7839, Acc.monitor: 0.7609, Acc.bulletin board: 0.6507, Acc.shower: 0.1096, Acc.radiator: 0.7818, Acc.glass: 0.2119, Acc.clock: 0.5724, Acc.flag: 0.7937