diff --git "a/segmentation/upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.log" "b/segmentation/upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.log" new file mode 100644--- /dev/null +++ "b/segmentation/upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.log" @@ -0,0 +1,24475 @@ +2024-06-15 21:21:30,455 - mmseg - INFO - Multi-processing start method is `None` +2024-06-15 21:21:30,462 - mmseg - INFO - OpenCV num_threads is `128 +2024-06-15 21:21:30,699 - 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 21:21:30,699 - mmseg - INFO - Distributed training: True +2024-06-15 21:21:31,780 - 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=384, + img_size=384, + 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_384_512_80k_ade20k_bs16_lr4e-5' +gpu_ids = range(0, 8) +auto_resume = False + +2024-06-15 21:21:36,857 - mmseg - INFO - Set random seed to 897239737, deterministic: False +2024-06-15 21:22:41,093 - 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 21:22:50,554 - 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 21:23:50,162 - mmseg - INFO - initialize UPerHead with init_cfg {'type': 'Normal', 'std': 0.01, 'override': {'name': 'conv_seg'}} +2024-06-15 21:23:51,562 - 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, 577, 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 21:23:51,642 - 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 21:23:52,105 - mmseg - INFO - Loaded 20210 images +2024-06-15 21:23:53,173 - mmseg - INFO - {'num_layers': 48, 'layer_decay_rate': 0.95, 'skip_stride': 1.5} +2024-06-15 21:23:53,174 - mmseg - INFO - Build LayerDecayOptimizerConstructor 0.950000 - 50 +2024-06-15 21:23:53,183 - 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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21:24:34,977 - 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 21:24:34,977 - mmseg - INFO - workflow: [('train', 1)], max: 80000 iters +2024-06-15 21:24:34,999 - mmseg - INFO - Checkpoints will be saved to /mnt/petrelfs/PIIP/mmsegmentation/work_dirs/upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5 by HardDiskBackend. +2024-06-15 21:27:22,344 - mmseg - INFO - Iter [50/80000] lr: 1.306e-06, eta: 1 day, 18:22:53, time: 1.908, data_time: 0.016, memory: 71384, decode.loss_ce: 4.0621, decode.acc_seg: 0.5748, aux.loss_ce: 1.6459, aux.acc_seg: 0.3996, loss: 5.7080 +2024-06-15 21:28:43,636 - mmseg - INFO - Iter [100/80000] lr: 2.637e-06, eta: 1 day, 15:13:10, time: 1.626, data_time: 0.009, memory: 71384, decode.loss_ce: 3.9918, decode.acc_seg: 7.2690, aux.loss_ce: 1.6407, aux.acc_seg: 2.7940, loss: 5.6325 +2024-06-15 21:30:04,673 - mmseg - INFO - Iter [150/80000] lr: 3.966e-06, eta: 1 day, 14:06:46, time: 1.621, data_time: 0.009, memory: 71384, decode.loss_ce: 3.4956, decode.acc_seg: 27.8132, aux.loss_ce: 1.5363, aux.acc_seg: 14.4094, loss: 5.0319 +2024-06-15 21:31:25,786 - mmseg - INFO - Iter [200/80000] lr: 5.294e-06, eta: 1 day, 13:33:24, time: 1.622, data_time: 0.009, memory: 71384, decode.loss_ce: 2.8357, decode.acc_seg: 37.5009, aux.loss_ce: 1.3577, aux.acc_seg: 29.7083, loss: 4.1934 +2024-06-15 21:32:46,877 - mmseg - INFO - Iter [250/80000] lr: 6.619e-06, eta: 1 day, 13:12:43, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 2.3061, decode.acc_seg: 45.4462, aux.loss_ce: 1.0868, aux.acc_seg: 39.6263, loss: 3.3929 +2024-06-15 21:34:08,158 - mmseg - INFO - Iter [300/80000] lr: 7.944e-06, eta: 1 day, 12:59:20, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 1.9577, decode.acc_seg: 52.7190, aux.loss_ce: 0.9066, aux.acc_seg: 48.5278, loss: 2.8643 +2024-06-15 21:35:29,224 - mmseg - INFO - Iter [350/80000] lr: 9.266e-06, eta: 1 day, 12:48:33, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 1.7188, decode.acc_seg: 57.7787, aux.loss_ce: 0.7959, aux.acc_seg: 54.1534, loss: 2.5147 +2024-06-15 21:36:50,577 - mmseg - INFO - Iter [400/80000] lr: 1.059e-05, eta: 1 day, 12:41:05, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 1.5225, decode.acc_seg: 59.5972, aux.loss_ce: 0.6857, aux.acc_seg: 57.4853, loss: 2.2081 +2024-06-15 21:38:11,778 - mmseg - INFO - Iter [450/80000] lr: 1.191e-05, eta: 1 day, 12:34:32, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 1.3807, decode.acc_seg: 62.4739, aux.loss_ce: 0.6154, aux.acc_seg: 60.8825, loss: 1.9961 +2024-06-15 21:39:33,100 - mmseg - INFO - Iter [500/80000] lr: 1.322e-05, eta: 1 day, 12:29:21, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 1.2769, decode.acc_seg: 65.8932, aux.loss_ce: 0.5649, aux.acc_seg: 64.5396, loss: 1.8418 +2024-06-15 21:40:54,412 - mmseg - INFO - Iter [550/80000] lr: 1.454e-05, eta: 1 day, 12:24:49, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 1.1943, decode.acc_seg: 66.3630, aux.loss_ce: 0.5271, aux.acc_seg: 65.2680, loss: 1.7214 +2024-06-15 21:42:15,589 - mmseg - INFO - Iter [600/80000] lr: 1.585e-05, eta: 1 day, 12:20:32, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 1.1634, decode.acc_seg: 67.7123, aux.loss_ce: 0.5059, aux.acc_seg: 66.9539, loss: 1.6693 +2024-06-15 21:43:36,836 - mmseg - INFO - Iter [650/80000] lr: 1.717e-05, eta: 1 day, 12:16:50, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 1.0535, decode.acc_seg: 69.1944, aux.loss_ce: 0.4548, aux.acc_seg: 68.8608, loss: 1.5083 +2024-06-15 21:44:57,963 - mmseg - INFO - Iter [700/80000] lr: 1.848e-05, eta: 1 day, 12:13:15, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 1.0562, decode.acc_seg: 68.8778, aux.loss_ce: 0.4531, aux.acc_seg: 68.1657, loss: 1.5093 +2024-06-15 21:46:19,315 - mmseg - INFO - Iter [750/80000] lr: 1.979e-05, eta: 1 day, 12:10:21, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.9644, decode.acc_seg: 70.5886, aux.loss_ce: 0.4096, aux.acc_seg: 70.5209, loss: 1.3740 +2024-06-15 21:47:40,485 - mmseg - INFO - Iter [800/80000] lr: 2.109e-05, eta: 1 day, 12:07:21, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.9473, decode.acc_seg: 71.6398, aux.loss_ce: 0.4013, aux.acc_seg: 71.1388, loss: 1.3487 +2024-06-15 21:49:01,920 - mmseg - INFO - Iter [850/80000] lr: 2.240e-05, eta: 1 day, 12:04:57, time: 1.629, data_time: 0.010, memory: 71384, decode.loss_ce: 0.9299, decode.acc_seg: 71.7652, aux.loss_ce: 0.3870, aux.acc_seg: 71.5272, loss: 1.3169 +2024-06-15 21:50:23,202 - mmseg - INFO - Iter [900/80000] lr: 2.370e-05, eta: 1 day, 12:02:27, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.8642, decode.acc_seg: 72.9502, aux.loss_ce: 0.3637, aux.acc_seg: 72.7366, loss: 1.2279 +2024-06-15 21:51:44,387 - mmseg - INFO - Iter [950/80000] lr: 2.501e-05, eta: 1 day, 11:59:56, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.8612, decode.acc_seg: 73.7327, aux.loss_ce: 0.3561, aux.acc_seg: 73.9866, loss: 1.2172 +2024-06-15 21:53:05,649 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 21:53:05,649 - mmseg - INFO - Iter [1000/80000] lr: 2.631e-05, eta: 1 day, 11:57:38, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.8517, decode.acc_seg: 73.0102, aux.loss_ce: 0.3521, aux.acc_seg: 73.0728, loss: 1.2038 +2024-06-15 21:55:35,135 - mmseg - INFO - per class results: +2024-06-15 21:55:35,157 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 71.78 | 84.11 | +| building | 76.81 | 95.3 | +| sky | 89.72 | 92.82 | +| floor | 73.72 | 86.59 | +| tree | 68.74 | 85.86 | +| ceiling | 79.2 | 89.77 | +| road | 78.37 | 84.95 | +| bed | 79.01 | 97.68 | +| windowpane | 53.18 | 82.05 | +| grass | 57.38 | 69.88 | +| cabinet | 52.48 | 59.79 | +| sidewalk | 54.97 | 85.27 | +| person | 68.88 | 88.23 | +| earth | 27.01 | 33.56 | +| door | 40.92 | 47.83 | +| table | 47.3 | 60.49 | +| mountain | 54.38 | 83.12 | +| plant | 48.26 | 59.51 | +| curtain | 62.99 | 70.69 | +| chair | 46.2 | 70.59 | +| car | 74.2 | 86.92 | +| water | 42.72 | 50.46 | +| painting | 61.85 | 80.27 | +| sofa | 65.75 | 81.41 | +| shelf | 30.98 | 42.99 | +| house | 7.87 | 8.7 | +| sea | 54.03 | 85.0 | +| mirror | 60.57 | 70.14 | +| rug | 53.04 | 58.43 | +| field | 22.37 | 67.3 | +| armchair | 40.86 | 64.6 | +| seat | 53.97 | 90.29 | +| fence | 35.27 | 45.49 | +| desk | 33.66 | 64.23 | +| rock | 49.73 | 76.73 | +| wardrobe | 47.36 | 80.67 | +| lamp | 46.38 | 61.53 | +| bathtub | 63.85 | 86.11 | +| railing | 28.87 | 43.01 | +| cushion | 42.68 | 49.75 | +| base | 35.09 | 57.21 | +| box | 17.61 | 21.1 | +| column | 23.23 | 24.08 | +| signboard | 10.39 | 11.45 | +| chest of drawers | 38.98 | 69.22 | +| counter | 35.82 | 65.24 | +| sand | 50.03 | 61.33 | +| sink | 57.89 | 71.96 | +| skyscraper | 23.09 | 24.09 | +| fireplace | 62.56 | 85.69 | +| refrigerator | 62.61 | 86.61 | +| grandstand | 36.68 | 82.27 | +| path | 2.92 | 2.97 | +| stairs | 28.99 | 46.66 | +| runway | 62.75 | 81.55 | +| case | 28.93 | 90.44 | +| pool table | 77.29 | 97.02 | +| pillow | 18.45 | 20.48 | +| screen door | 41.13 | 46.4 | +| stairway | 0.3 | 0.3 | +| river | 2.22 | 2.24 | +| bridge | 50.7 | 64.47 | +| bookcase | 2.22 | 2.28 | +| blind | 0.0 | 0.0 | +| coffee table | 47.68 | 78.09 | +| toilet | 75.79 | 87.02 | +| flower | 18.05 | 20.12 | +| book | 36.21 | 71.96 | +| hill | 0.0 | 0.0 | +| bench | 26.06 | 26.41 | +| countertop | 36.46 | 46.43 | +| stove | 59.86 | 88.55 | +| palm | 42.28 | 56.5 | +| kitchen island | 27.12 | 87.82 | +| computer | 58.26 | 87.25 | +| swivel chair | 16.26 | 19.41 | +| boat | 57.98 | 75.84 | +| bar | 41.55 | 46.34 | +| arcade machine | 78.65 | 86.81 | +| hovel | 0.0 | 0.0 | +| bus | 81.78 | 91.53 | +| towel | 43.42 | 51.65 | +| light | 0.0 | 0.0 | +| truck | 20.88 | 23.54 | +| tower | 0.0 | 0.0 | +| chandelier | 48.49 | 55.2 | +| awning | 0.0 | 0.0 | +| streetlight | 0.0 | 0.0 | +| booth | 0.0 | 0.0 | +| television receiver | 57.16 | 69.22 | +| airplane | 44.05 | 54.6 | +| dirt track | 0.0 | 0.0 | +| apparel | 24.48 | 31.52 | +| pole | 0.0 | 0.0 | +| land | 0.0 | 0.0 | +| bannister | 0.0 | 0.0 | +| escalator | 13.27 | 13.8 | +| ottoman | 12.86 | 13.04 | +| bottle | 0.0 | 0.0 | +| buffet | 0.0 | 0.0 | +| poster | 0.0 | 0.0 | +| stage | 9.16 | 14.09 | +| van | 6.95 | 7.44 | +| ship | 0.0 | 0.0 | +| fountain | 7.33 | 7.37 | +| conveyer belt | 75.06 | 88.26 | +| canopy | 0.0 | 0.0 | +| washer | 83.55 | 94.42 | +| plaything | 0.0 | 0.0 | +| swimming pool | 32.08 | 77.59 | +| stool | 0.0 | 0.0 | +| barrel | 19.75 | 19.75 | +| basket | 0.0 | 0.0 | +| waterfall | 48.63 | 95.42 | +| tent | 91.63 | 95.24 | +| bag | 0.0 | 0.0 | +| minibike | 0.0 | 0.0 | +| cradle | 67.69 | 95.06 | +| oven | 0.0 | 0.0 | +| ball | 0.0 | 0.0 | +| food | 0.0 | 0.0 | +| step | 0.0 | 0.0 | +| tank | 0.0 | 0.0 | +| trade name | 0.0 | 0.0 | +| microwave | 63.64 | 70.36 | +| pot | 0.0 | 0.0 | +| animal | 0.0 | 0.0 | +| bicycle | 0.0 | 0.0 | +| lake | 0.0 | 0.0 | +| dishwasher | 9.57 | 9.65 | +| screen | 0.0 | 0.0 | +| blanket | 0.0 | 0.0 | +| sculpture | 0.0 | 0.0 | +| hood | 0.0 | 0.0 | +| sconce | 0.0 | 0.0 | +| vase | 0.0 | 0.0 | +| traffic light | 0.0 | 0.0 | +| tray | 0.0 | 0.0 | +| ashcan | 0.0 | 0.0 | +| fan | 0.0 | 0.0 | +| pier | 0.0 | 0.0 | +| crt screen | 0.0 | 0.0 | +| plate | 0.0 | 0.0 | +| monitor | 0.0 | 0.0 | +| bulletin board | 0.0 | 0.0 | +| shower | 0.0 | 0.0 | +| radiator | 0.0 | 0.0 | +| glass | 0.0 | 0.0 | +| clock | 0.0 | 0.0 | +| flag | 0.0 | 0.0 | ++---------------------+-------+-------+ +2024-06-15 21:55:35,157 - mmseg - INFO - Summary: +2024-06-15 21:55:35,157 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 77.43 | 29.34 | 39.3 | ++-------+-------+------+ +2024-06-15 21:55:35,158 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 21:55:35,158 - mmseg - INFO - Iter(val) [250] aAcc: 0.7743, mIoU: 0.2934, mAcc: 0.3930, IoU.wall: 0.7178, IoU.building: 0.7681, IoU.sky: 0.8972, IoU.floor: 0.7372, IoU.tree: 0.6874, IoU.ceiling: 0.7920, IoU.road: 0.7837, IoU.bed : 0.7901, IoU.windowpane: 0.5318, IoU.grass: 0.5738, IoU.cabinet: 0.5248, IoU.sidewalk: 0.5497, IoU.person: 0.6888, IoU.earth: 0.2701, IoU.door: 0.4092, IoU.table: 0.4730, IoU.mountain: 0.5438, IoU.plant: 0.4826, IoU.curtain: 0.6299, IoU.chair: 0.4620, IoU.car: 0.7420, IoU.water: 0.4272, IoU.painting: 0.6185, IoU.sofa: 0.6575, IoU.shelf: 0.3098, IoU.house: 0.0787, IoU.sea: 0.5403, IoU.mirror: 0.6057, IoU.rug: 0.5304, IoU.field: 0.2237, IoU.armchair: 0.4086, IoU.seat: 0.5397, IoU.fence: 0.3527, IoU.desk: 0.3366, IoU.rock: 0.4973, IoU.wardrobe: 0.4736, IoU.lamp: 0.4638, IoU.bathtub: 0.6385, IoU.railing: 0.2887, IoU.cushion: 0.4268, IoU.base: 0.3509, IoU.box: 0.1761, IoU.column: 0.2323, IoU.signboard: 0.1039, IoU.chest of drawers: 0.3898, IoU.counter: 0.3582, IoU.sand: 0.5003, IoU.sink: 0.5789, IoU.skyscraper: 0.2309, IoU.fireplace: 0.6256, IoU.refrigerator: 0.6261, IoU.grandstand: 0.3668, IoU.path: 0.0292, IoU.stairs: 0.2899, IoU.runway: 0.6275, IoU.case: 0.2893, IoU.pool table: 0.7729, IoU.pillow: 0.1845, IoU.screen door: 0.4113, IoU.stairway: 0.0030, IoU.river: 0.0222, IoU.bridge: 0.5070, IoU.bookcase: 0.0222, IoU.blind: 0.0000, IoU.coffee table: 0.4768, IoU.toilet: 0.7579, IoU.flower: 0.1805, IoU.book: 0.3621, IoU.hill: 0.0000, IoU.bench: 0.2606, IoU.countertop: 0.3646, IoU.stove: 0.5986, IoU.palm: 0.4228, IoU.kitchen island: 0.2712, IoU.computer: 0.5826, IoU.swivel chair: 0.1626, IoU.boat: 0.5798, IoU.bar: 0.4155, IoU.arcade machine: 0.7865, IoU.hovel: 0.0000, IoU.bus: 0.8178, IoU.towel: 0.4342, IoU.light: 0.0000, IoU.truck: 0.2088, IoU.tower: 0.0000, IoU.chandelier: 0.4849, IoU.awning: 0.0000, IoU.streetlight: 0.0000, IoU.booth: 0.0000, IoU.television receiver: 0.5716, IoU.airplane: 0.4405, IoU.dirt track: 0.0000, IoU.apparel: 0.2448, IoU.pole: 0.0000, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.1327, IoU.ottoman: 0.1286, IoU.bottle: 0.0000, IoU.buffet: 0.0000, IoU.poster: 0.0000, IoU.stage: 0.0916, IoU.van: 0.0695, IoU.ship: 0.0000, IoU.fountain: 0.0733, IoU.conveyer belt: 0.7506, IoU.canopy: 0.0000, IoU.washer: 0.8355, IoU.plaything: 0.0000, IoU.swimming pool: 0.3208, IoU.stool: 0.0000, IoU.barrel: 0.1975, IoU.basket: 0.0000, IoU.waterfall: 0.4863, IoU.tent: 0.9163, IoU.bag: 0.0000, IoU.minibike: 0.0000, IoU.cradle: 0.6769, IoU.oven: 0.0000, IoU.ball: 0.0000, IoU.food: 0.0000, IoU.step: 0.0000, IoU.tank: 0.0000, IoU.trade name: 0.0000, IoU.microwave: 0.6364, IoU.pot: 0.0000, IoU.animal: 0.0000, IoU.bicycle: 0.0000, IoU.lake: 0.0000, IoU.dishwasher: 0.0957, IoU.screen: 0.0000, IoU.blanket: 0.0000, IoU.sculpture: 0.0000, IoU.hood: 0.0000, IoU.sconce: 0.0000, IoU.vase: 0.0000, IoU.traffic light: 0.0000, IoU.tray: 0.0000, IoU.ashcan: 0.0000, IoU.fan: 0.0000, IoU.pier: 0.0000, IoU.crt screen: 0.0000, IoU.plate: 0.0000, IoU.monitor: 0.0000, IoU.bulletin board: 0.0000, IoU.shower: 0.0000, IoU.radiator: 0.0000, IoU.glass: 0.0000, IoU.clock: 0.0000, IoU.flag: 0.0000, Acc.wall: 0.8411, Acc.building: 0.9530, Acc.sky: 0.9282, Acc.floor: 0.8659, Acc.tree: 0.8586, Acc.ceiling: 0.8977, Acc.road: 0.8495, Acc.bed : 0.9768, Acc.windowpane: 0.8205, Acc.grass: 0.6988, Acc.cabinet: 0.5979, Acc.sidewalk: 0.8527, Acc.person: 0.8823, Acc.earth: 0.3356, Acc.door: 0.4783, Acc.table: 0.6049, Acc.mountain: 0.8312, Acc.plant: 0.5951, Acc.curtain: 0.7069, Acc.chair: 0.7059, Acc.car: 0.8692, Acc.water: 0.5046, Acc.painting: 0.8027, Acc.sofa: 0.8141, Acc.shelf: 0.4299, Acc.house: 0.0870, Acc.sea: 0.8500, Acc.mirror: 0.7014, Acc.rug: 0.5843, Acc.field: 0.6730, Acc.armchair: 0.6460, Acc.seat: 0.9029, Acc.fence: 0.4549, Acc.desk: 0.6423, Acc.rock: 0.7673, Acc.wardrobe: 0.8067, Acc.lamp: 0.6153, Acc.bathtub: 0.8611, Acc.railing: 0.4301, Acc.cushion: 0.4975, Acc.base: 0.5721, Acc.box: 0.2110, Acc.column: 0.2408, Acc.signboard: 0.1145, Acc.chest of drawers: 0.6922, Acc.counter: 0.6524, Acc.sand: 0.6133, Acc.sink: 0.7196, Acc.skyscraper: 0.2409, Acc.fireplace: 0.8569, Acc.refrigerator: 0.8661, Acc.grandstand: 0.8227, Acc.path: 0.0297, Acc.stairs: 0.4666, Acc.runway: 0.8155, Acc.case: 0.9044, Acc.pool table: 0.9702, Acc.pillow: 0.2048, Acc.screen door: 0.4640, Acc.stairway: 0.0030, Acc.river: 0.0224, Acc.bridge: 0.6447, Acc.bookcase: 0.0228, Acc.blind: 0.0000, Acc.coffee table: 0.7809, Acc.toilet: 0.8702, Acc.flower: 0.2012, Acc.book: 0.7196, Acc.hill: 0.0000, Acc.bench: 0.2641, Acc.countertop: 0.4643, Acc.stove: 0.8855, Acc.palm: 0.5650, Acc.kitchen island: 0.8782, Acc.computer: 0.8725, Acc.swivel chair: 0.1941, Acc.boat: 0.7584, Acc.bar: 0.4634, Acc.arcade machine: 0.8681, Acc.hovel: 0.0000, Acc.bus: 0.9153, Acc.towel: 0.5165, Acc.light: 0.0000, Acc.truck: 0.2354, Acc.tower: 0.0000, Acc.chandelier: 0.5520, Acc.awning: 0.0000, Acc.streetlight: 0.0000, Acc.booth: 0.0000, Acc.television receiver: 0.6922, Acc.airplane: 0.5460, Acc.dirt track: 0.0000, Acc.apparel: 0.3152, Acc.pole: 0.0000, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.1380, Acc.ottoman: 0.1304, Acc.bottle: 0.0000, Acc.buffet: 0.0000, Acc.poster: 0.0000, Acc.stage: 0.1409, Acc.van: 0.0744, Acc.ship: 0.0000, Acc.fountain: 0.0737, Acc.conveyer belt: 0.8826, Acc.canopy: 0.0000, Acc.washer: 0.9442, Acc.plaything: 0.0000, Acc.swimming pool: 0.7759, Acc.stool: 0.0000, Acc.barrel: 0.1975, Acc.basket: 0.0000, Acc.waterfall: 0.9542, Acc.tent: 0.9524, Acc.bag: 0.0000, Acc.minibike: 0.0000, Acc.cradle: 0.9506, Acc.oven: 0.0000, Acc.ball: 0.0000, Acc.food: 0.0000, Acc.step: 0.0000, Acc.tank: 0.0000, Acc.trade name: 0.0000, Acc.microwave: 0.7036, Acc.pot: 0.0000, Acc.animal: 0.0000, Acc.bicycle: 0.0000, Acc.lake: 0.0000, Acc.dishwasher: 0.0965, Acc.screen: 0.0000, Acc.blanket: 0.0000, Acc.sculpture: 0.0000, Acc.hood: 0.0000, Acc.sconce: 0.0000, Acc.vase: 0.0000, Acc.traffic light: 0.0000, Acc.tray: 0.0000, Acc.ashcan: 0.0000, Acc.fan: 0.0000, Acc.pier: 0.0000, Acc.crt screen: 0.0000, Acc.plate: 0.0000, Acc.monitor: 0.0000, Acc.bulletin board: 0.0000, Acc.shower: 0.0000, Acc.radiator: 0.0000, Acc.glass: 0.0000, Acc.clock: 0.0000, Acc.flag: 0.0000 +2024-06-15 21:56:56,788 - mmseg - INFO - Iter [1050/80000] lr: 2.761e-05, eta: 1 day, 15:03:15, time: 4.623, data_time: 3.007, memory: 71384, decode.loss_ce: 0.7791, decode.acc_seg: 74.2509, aux.loss_ce: 0.3199, aux.acc_seg: 74.4842, loss: 1.0990 +2024-06-15 21:58:18,207 - mmseg - INFO - Iter [1100/80000] lr: 2.890e-05, eta: 1 day, 14:52:39, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.7889, decode.acc_seg: 73.9263, aux.loss_ce: 0.3232, aux.acc_seg: 74.5136, loss: 1.1121 +2024-06-15 21:59:39,353 - mmseg - INFO - Iter [1150/80000] lr: 3.020e-05, eta: 1 day, 14:42:33, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.8076, decode.acc_seg: 73.3690, aux.loss_ce: 0.3275, aux.acc_seg: 73.9402, loss: 1.1351 +2024-06-15 22:01:00,748 - mmseg - INFO - Iter [1200/80000] lr: 3.149e-05, eta: 1 day, 14:33:27, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.7693, decode.acc_seg: 75.5559, aux.loss_ce: 0.3152, aux.acc_seg: 75.5395, loss: 1.0846 +2024-06-15 22:02:22,044 - mmseg - INFO - Iter [1250/80000] lr: 3.279e-05, eta: 1 day, 14:24:51, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.7958, decode.acc_seg: 72.8123, aux.loss_ce: 0.3167, aux.acc_seg: 73.5950, loss: 1.1125 +2024-06-15 22:03:45,540 - mmseg - INFO - Iter [1300/80000] lr: 3.408e-05, eta: 1 day, 14:19:03, time: 1.670, data_time: 0.052, memory: 71384, decode.loss_ce: 0.7618, decode.acc_seg: 74.8471, aux.loss_ce: 0.3075, aux.acc_seg: 75.4197, loss: 1.0693 +2024-06-15 22:05:06,711 - mmseg - INFO - Iter [1350/80000] lr: 3.537e-05, eta: 1 day, 14:11:18, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.7435, decode.acc_seg: 74.1814, aux.loss_ce: 0.2974, aux.acc_seg: 75.3249, loss: 1.0409 +2024-06-15 22:06:27,873 - mmseg - INFO - Iter [1400/80000] lr: 3.665e-05, eta: 1 day, 14:04:00, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.7149, decode.acc_seg: 75.3467, aux.loss_ce: 0.2876, aux.acc_seg: 75.8803, loss: 1.0025 +2024-06-15 22:07:49,254 - mmseg - INFO - Iter [1450/80000] lr: 3.794e-05, eta: 1 day, 13:57:19, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.7395, decode.acc_seg: 74.4248, aux.loss_ce: 0.2944, aux.acc_seg: 75.3876, loss: 1.0339 +2024-06-15 22:09:10,481 - mmseg - INFO - Iter [1500/80000] lr: 3.922e-05, eta: 1 day, 13:50:52, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.7115, decode.acc_seg: 75.1392, aux.loss_ce: 0.2865, aux.acc_seg: 75.4919, loss: 0.9981 +2024-06-15 22:10:31,979 - mmseg - INFO - Iter [1550/80000] lr: 3.923e-05, eta: 1 day, 13:44:57, time: 1.630, data_time: 0.010, memory: 71384, decode.loss_ce: 0.7381, decode.acc_seg: 75.5735, aux.loss_ce: 0.2954, aux.acc_seg: 76.1930, loss: 1.0335 +2024-06-15 22:11:53,261 - mmseg - INFO - Iter [1600/80000] lr: 3.920e-05, eta: 1 day, 13:39:09, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6908, decode.acc_seg: 76.0208, aux.loss_ce: 0.2761, aux.acc_seg: 76.2954, loss: 0.9669 +2024-06-15 22:13:14,455 - mmseg - INFO - Iter [1650/80000] lr: 3.918e-05, eta: 1 day, 13:33:33, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6624, decode.acc_seg: 76.5811, aux.loss_ce: 0.2622, aux.acc_seg: 77.0481, loss: 0.9246 +2024-06-15 22:14:35,661 - mmseg - INFO - Iter [1700/80000] lr: 3.915e-05, eta: 1 day, 13:28:13, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6685, decode.acc_seg: 76.2903, aux.loss_ce: 0.2652, aux.acc_seg: 77.0780, loss: 0.9338 +2024-06-15 22:15:56,984 - mmseg - INFO - Iter [1750/80000] lr: 3.913e-05, eta: 1 day, 13:23:11, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6871, decode.acc_seg: 75.6400, aux.loss_ce: 0.2724, aux.acc_seg: 76.3581, loss: 0.9594 +2024-06-15 22:17:18,252 - mmseg - INFO - Iter [1800/80000] lr: 3.910e-05, eta: 1 day, 13:18:20, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.7111, decode.acc_seg: 75.3484, aux.loss_ce: 0.2816, aux.acc_seg: 76.0682, loss: 0.9927 +2024-06-15 22:18:39,597 - mmseg - INFO - Iter [1850/80000] lr: 3.908e-05, eta: 1 day, 13:13:43, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6920, decode.acc_seg: 75.5579, aux.loss_ce: 0.2717, aux.acc_seg: 76.3042, loss: 0.9637 +2024-06-15 22:20:00,917 - mmseg - INFO - Iter [1900/80000] lr: 3.905e-05, eta: 1 day, 13:09:15, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6694, decode.acc_seg: 76.2619, aux.loss_ce: 0.2636, aux.acc_seg: 77.0355, loss: 0.9330 +2024-06-15 22:21:22,036 - mmseg - INFO - Iter [1950/80000] lr: 3.903e-05, eta: 1 day, 13:04:49, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6587, decode.acc_seg: 76.3053, aux.loss_ce: 0.2624, aux.acc_seg: 77.0983, loss: 0.9212 +2024-06-15 22:22:43,323 - mmseg - INFO - Saving checkpoint at 2000 iterations +2024-06-15 22:23:59,185 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 22:23:59,185 - mmseg - INFO - Iter [2000/80000] lr: 3.900e-05, eta: 1 day, 13:49:57, time: 3.143, data_time: 0.009, memory: 71384, decode.loss_ce: 0.6365, decode.acc_seg: 77.2344, aux.loss_ce: 0.2554, aux.acc_seg: 77.5193, loss: 0.8919 +2024-06-15 22:25:33,910 - mmseg - INFO - per class results: +2024-06-15 22:25:33,916 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 74.18 | 85.33 | +| building | 79.65 | 92.56 | +| sky | 90.94 | 93.31 | +| floor | 77.16 | 87.13 | +| tree | 70.99 | 87.19 | +| ceiling | 80.88 | 90.37 | +| road | 78.85 | 84.84 | +| bed | 85.92 | 96.14 | +| windowpane | 55.92 | 83.04 | +| grass | 65.46 | 82.44 | +| cabinet | 57.46 | 74.8 | +| sidewalk | 58.77 | 83.53 | +| person | 73.89 | 87.28 | +| earth | 36.88 | 49.64 | +| door | 48.71 | 56.39 | +| table | 53.3 | 65.89 | +| mountain | 58.45 | 74.72 | +| plant | 48.97 | 57.45 | +| curtain | 68.43 | 80.53 | +| chair | 50.74 | 67.82 | +| car | 72.81 | 93.15 | +| water | 48.67 | 56.53 | +| painting | 64.26 | 72.3 | +| sofa | 66.66 | 91.42 | +| shelf | 37.77 | 51.01 | +| house | 51.71 | 75.52 | +| sea | 58.61 | 87.15 | +| mirror | 67.11 | 86.92 | +| rug | 51.66 | 54.86 | +| field | 42.01 | 61.98 | +| armchair | 41.35 | 60.58 | +| seat | 63.55 | 86.57 | +| fence | 46.56 | 59.32 | +| desk | 41.94 | 59.94 | +| rock | 56.31 | 77.48 | +| wardrobe | 56.72 | 78.78 | +| lamp | 50.08 | 61.87 | +| bathtub | 74.79 | 82.51 | +| railing | 34.39 | 46.77 | +| cushion | 55.13 | 68.93 | +| base | 27.92 | 38.75 | +| box | 25.54 | 32.29 | +| column | 41.79 | 69.54 | +| signboard | 25.96 | 33.89 | +| chest of drawers | 42.27 | 68.33 | +| counter | 47.95 | 59.44 | +| sand | 37.92 | 51.78 | +| sink | 66.39 | 76.04 | +| skyscraper | 47.35 | 69.71 | +| fireplace | 67.51 | 90.05 | +| refrigerator | 68.0 | 83.8 | +| grandstand | 50.88 | 82.0 | +| path | 19.03 | 28.42 | +| stairs | 26.51 | 29.97 | +| runway | 51.17 | 97.63 | +| case | 57.56 | 72.92 | +| pool table | 86.4 | 91.74 | +| pillow | 49.13 | 54.45 | +| screen door | 67.04 | 85.53 | +| stairway | 34.49 | 50.01 | +| river | 19.79 | 50.28 | +| bridge | 39.07 | 49.78 | +| bookcase | 27.98 | 54.7 | +| blind | 0.41 | 0.41 | +| coffee table | 46.88 | 86.65 | +| toilet | 81.89 | 92.2 | +| flower | 28.61 | 45.67 | +| book | 42.8 | 57.0 | +| hill | 3.69 | 3.93 | +| bench | 61.32 | 65.53 | +| countertop | 54.89 | 66.93 | +| stove | 73.56 | 90.23 | +| palm | 37.69 | 42.56 | +| kitchen island | 29.53 | 91.09 | +| computer | 65.78 | 92.12 | +| swivel chair | 36.66 | 49.38 | +| boat | 49.89 | 79.67 | +| bar | 57.42 | 67.64 | +| arcade machine | 29.48 | 29.84 | +| hovel | 19.94 | 21.02 | +| bus | 54.41 | 54.98 | +| towel | 56.09 | 64.55 | +| light | 4.64 | 4.68 | +| truck | 19.46 | 50.16 | +| tower | 2.43 | 2.72 | +| chandelier | 55.31 | 77.34 | +| awning | 29.03 | 35.64 | +| streetlight | 6.44 | 7.05 | +| booth | 44.19 | 55.26 | +| television receiver | 68.9 | 77.3 | +| airplane | 53.07 | 67.06 | +| dirt track | 0.0 | 0.0 | +| apparel | 33.4 | 47.08 | +| pole | 0.07 | 0.07 | +| land | 0.0 | 0.0 | +| bannister | 0.0 | 0.0 | +| escalator | 54.85 | 74.74 | +| ottoman | 40.99 | 69.94 | +| bottle | 21.66 | 22.23 | +| buffet | 11.62 | 11.66 | +| poster | 2.85 | 2.95 | +| stage | 14.23 | 19.3 | +| van | 0.0 | 0.0 | +| ship | 0.0 | 0.0 | +| fountain | 70.64 | 96.28 | +| conveyer belt | 61.07 | 96.49 | +| canopy | 25.02 | 25.47 | +| washer | 82.78 | 96.82 | +| plaything | 28.7 | 39.5 | +| swimming pool | 23.61 | 24.21 | +| stool | 8.93 | 9.55 | +| barrel | 16.34 | 65.12 | +| basket | 26.19 | 31.06 | +| waterfall | 59.73 | 94.72 | +| tent | 71.74 | 99.74 | +| bag | 1.04 | 1.04 | +| minibike | 57.48 | 64.25 | +| cradle | 70.83 | 96.64 | +| oven | 20.48 | 21.2 | +| ball | 46.83 | 60.27 | +| food | 43.44 | 62.88 | +| step | 0.0 | 0.0 | +| tank | 54.29 | 58.79 | +| trade name | 0.0 | 0.0 | +| microwave | 76.61 | 91.37 | +| pot | 44.91 | 54.51 | +| animal | 51.07 | 53.84 | +| bicycle | 40.4 | 46.9 | +| lake | 0.0 | 0.0 | +| dishwasher | 50.81 | 69.29 | +| screen | 45.99 | 88.71 | +| blanket | 0.0 | 0.0 | +| sculpture | 35.4 | 37.11 | +| hood | 52.96 | 65.75 | +| sconce | 2.04 | 2.04 | +| vase | 19.28 | 23.34 | +| traffic light | 0.0 | 0.0 | +| tray | 0.0 | 0.0 | +| ashcan | 0.0 | 0.0 | +| fan | 42.16 | 56.88 | +| pier | 37.05 | 42.91 | +| crt screen | 0.0 | 0.0 | +| plate | 14.78 | 16.31 | +| monitor | 0.0 | 0.0 | +| bulletin board | 13.99 | 14.1 | +| shower | 0.0 | 0.0 | +| radiator | 8.17 | 8.17 | +| glass | 0.0 | 0.0 | +| clock | 0.0 | 0.0 | +| flag | 0.0 | 0.0 | ++---------------------+-------+-------+ +2024-06-15 22:25:33,916 - mmseg - INFO - Summary: +2024-06-15 22:25:33,916 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 80.58 | 40.16 | 52.38 | ++-------+-------+-------+ +2024-06-15 22:25:33,917 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 22:25:33,917 - mmseg - INFO - Iter(val) [250] aAcc: 0.8058, mIoU: 0.4016, mAcc: 0.5238, IoU.wall: 0.7418, IoU.building: 0.7965, IoU.sky: 0.9094, IoU.floor: 0.7716, IoU.tree: 0.7099, IoU.ceiling: 0.8088, IoU.road: 0.7885, IoU.bed : 0.8592, IoU.windowpane: 0.5592, IoU.grass: 0.6546, IoU.cabinet: 0.5746, IoU.sidewalk: 0.5877, IoU.person: 0.7389, IoU.earth: 0.3688, IoU.door: 0.4871, IoU.table: 0.5330, IoU.mountain: 0.5845, IoU.plant: 0.4897, IoU.curtain: 0.6843, IoU.chair: 0.5074, IoU.car: 0.7281, IoU.water: 0.4867, IoU.painting: 0.6426, IoU.sofa: 0.6666, IoU.shelf: 0.3777, IoU.house: 0.5171, IoU.sea: 0.5861, IoU.mirror: 0.6711, IoU.rug: 0.5166, IoU.field: 0.4201, IoU.armchair: 0.4135, IoU.seat: 0.6355, IoU.fence: 0.4656, IoU.desk: 0.4194, IoU.rock: 0.5631, IoU.wardrobe: 0.5672, IoU.lamp: 0.5008, IoU.bathtub: 0.7479, IoU.railing: 0.3439, IoU.cushion: 0.5513, IoU.base: 0.2792, IoU.box: 0.2554, IoU.column: 0.4179, IoU.signboard: 0.2596, IoU.chest of drawers: 0.4227, IoU.counter: 0.4795, IoU.sand: 0.3792, IoU.sink: 0.6639, IoU.skyscraper: 0.4735, IoU.fireplace: 0.6751, IoU.refrigerator: 0.6800, IoU.grandstand: 0.5088, IoU.path: 0.1903, IoU.stairs: 0.2651, IoU.runway: 0.5117, IoU.case: 0.5756, IoU.pool table: 0.8640, IoU.pillow: 0.4913, IoU.screen door: 0.6704, IoU.stairway: 0.3449, IoU.river: 0.1979, IoU.bridge: 0.3907, IoU.bookcase: 0.2798, IoU.blind: 0.0041, IoU.coffee table: 0.4688, IoU.toilet: 0.8189, IoU.flower: 0.2861, IoU.book: 0.4280, IoU.hill: 0.0369, IoU.bench: 0.6132, IoU.countertop: 0.5489, IoU.stove: 0.7356, IoU.palm: 0.3769, IoU.kitchen island: 0.2953, IoU.computer: 0.6578, IoU.swivel chair: 0.3666, IoU.boat: 0.4989, IoU.bar: 0.5742, IoU.arcade machine: 0.2948, IoU.hovel: 0.1994, IoU.bus: 0.5441, IoU.towel: 0.5609, IoU.light: 0.0464, IoU.truck: 0.1946, IoU.tower: 0.0243, IoU.chandelier: 0.5531, IoU.awning: 0.2903, IoU.streetlight: 0.0644, IoU.booth: 0.4419, IoU.television receiver: 0.6890, IoU.airplane: 0.5307, IoU.dirt track: 0.0000, IoU.apparel: 0.3340, IoU.pole: 0.0007, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.5485, IoU.ottoman: 0.4099, IoU.bottle: 0.2166, IoU.buffet: 0.1162, IoU.poster: 0.0285, IoU.stage: 0.1423, IoU.van: 0.0000, IoU.ship: 0.0000, IoU.fountain: 0.7064, IoU.conveyer belt: 0.6107, IoU.canopy: 0.2502, IoU.washer: 0.8278, IoU.plaything: 0.2870, IoU.swimming pool: 0.2361, IoU.stool: 0.0893, IoU.barrel: 0.1634, IoU.basket: 0.2619, IoU.waterfall: 0.5973, IoU.tent: 0.7174, IoU.bag: 0.0104, IoU.minibike: 0.5748, IoU.cradle: 0.7083, IoU.oven: 0.2048, IoU.ball: 0.4683, IoU.food: 0.4344, IoU.step: 0.0000, IoU.tank: 0.5429, IoU.trade name: 0.0000, IoU.microwave: 0.7661, IoU.pot: 0.4491, IoU.animal: 0.5107, IoU.bicycle: 0.4040, IoU.lake: 0.0000, IoU.dishwasher: 0.5081, IoU.screen: 0.4599, IoU.blanket: 0.0000, IoU.sculpture: 0.3540, IoU.hood: 0.5296, IoU.sconce: 0.0204, IoU.vase: 0.1928, IoU.traffic light: 0.0000, IoU.tray: 0.0000, IoU.ashcan: 0.0000, IoU.fan: 0.4216, IoU.pier: 0.3705, IoU.crt screen: 0.0000, IoU.plate: 0.1478, IoU.monitor: 0.0000, IoU.bulletin board: 0.1399, IoU.shower: 0.0000, IoU.radiator: 0.0817, IoU.glass: 0.0000, IoU.clock: 0.0000, IoU.flag: 0.0000, Acc.wall: 0.8533, Acc.building: 0.9256, Acc.sky: 0.9331, Acc.floor: 0.8713, Acc.tree: 0.8719, Acc.ceiling: 0.9037, Acc.road: 0.8484, Acc.bed : 0.9614, Acc.windowpane: 0.8304, Acc.grass: 0.8244, Acc.cabinet: 0.7480, Acc.sidewalk: 0.8353, Acc.person: 0.8728, Acc.earth: 0.4964, Acc.door: 0.5639, Acc.table: 0.6589, Acc.mountain: 0.7472, Acc.plant: 0.5745, Acc.curtain: 0.8053, Acc.chair: 0.6782, Acc.car: 0.9315, Acc.water: 0.5653, Acc.painting: 0.7230, Acc.sofa: 0.9142, Acc.shelf: 0.5101, Acc.house: 0.7552, Acc.sea: 0.8715, Acc.mirror: 0.8692, Acc.rug: 0.5486, Acc.field: 0.6198, Acc.armchair: 0.6058, Acc.seat: 0.8657, Acc.fence: 0.5932, Acc.desk: 0.5994, Acc.rock: 0.7748, Acc.wardrobe: 0.7878, Acc.lamp: 0.6187, Acc.bathtub: 0.8251, Acc.railing: 0.4677, Acc.cushion: 0.6893, Acc.base: 0.3875, Acc.box: 0.3229, Acc.column: 0.6954, Acc.signboard: 0.3389, Acc.chest of drawers: 0.6833, Acc.counter: 0.5944, Acc.sand: 0.5178, Acc.sink: 0.7604, Acc.skyscraper: 0.6971, Acc.fireplace: 0.9005, Acc.refrigerator: 0.8380, Acc.grandstand: 0.8200, Acc.path: 0.2842, Acc.stairs: 0.2997, Acc.runway: 0.9763, Acc.case: 0.7292, Acc.pool table: 0.9174, Acc.pillow: 0.5445, Acc.screen door: 0.8553, Acc.stairway: 0.5001, Acc.river: 0.5028, Acc.bridge: 0.4978, Acc.bookcase: 0.5470, Acc.blind: 0.0041, Acc.coffee table: 0.8665, Acc.toilet: 0.9220, Acc.flower: 0.4567, Acc.book: 0.5700, Acc.hill: 0.0393, Acc.bench: 0.6553, Acc.countertop: 0.6693, Acc.stove: 0.9023, Acc.palm: 0.4256, Acc.kitchen island: 0.9109, Acc.computer: 0.9212, Acc.swivel chair: 0.4938, Acc.boat: 0.7967, Acc.bar: 0.6764, Acc.arcade machine: 0.2984, Acc.hovel: 0.2102, Acc.bus: 0.5498, Acc.towel: 0.6455, Acc.light: 0.0468, Acc.truck: 0.5016, Acc.tower: 0.0272, Acc.chandelier: 0.7734, Acc.awning: 0.3564, Acc.streetlight: 0.0705, Acc.booth: 0.5526, Acc.television receiver: 0.7730, Acc.airplane: 0.6706, Acc.dirt track: 0.0000, Acc.apparel: 0.4708, Acc.pole: 0.0007, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.7474, Acc.ottoman: 0.6994, Acc.bottle: 0.2223, Acc.buffet: 0.1166, Acc.poster: 0.0295, Acc.stage: 0.1930, Acc.van: 0.0000, Acc.ship: 0.0000, Acc.fountain: 0.9628, Acc.conveyer belt: 0.9649, Acc.canopy: 0.2547, Acc.washer: 0.9682, Acc.plaything: 0.3950, Acc.swimming pool: 0.2421, Acc.stool: 0.0955, Acc.barrel: 0.6512, Acc.basket: 0.3106, Acc.waterfall: 0.9472, Acc.tent: 0.9974, Acc.bag: 0.0104, Acc.minibike: 0.6425, Acc.cradle: 0.9664, Acc.oven: 0.2120, Acc.ball: 0.6027, Acc.food: 0.6288, Acc.step: 0.0000, Acc.tank: 0.5879, Acc.trade name: 0.0000, Acc.microwave: 0.9137, Acc.pot: 0.5451, Acc.animal: 0.5384, Acc.bicycle: 0.4690, Acc.lake: 0.0000, Acc.dishwasher: 0.6929, Acc.screen: 0.8871, Acc.blanket: 0.0000, Acc.sculpture: 0.3711, Acc.hood: 0.6575, Acc.sconce: 0.0204, Acc.vase: 0.2334, Acc.traffic light: 0.0000, Acc.tray: 0.0000, Acc.ashcan: 0.0000, Acc.fan: 0.5688, Acc.pier: 0.4291, Acc.crt screen: 0.0000, Acc.plate: 0.1631, Acc.monitor: 0.0000, Acc.bulletin board: 0.1410, Acc.shower: 0.0000, Acc.radiator: 0.0817, Acc.glass: 0.0000, Acc.clock: 0.0000, Acc.flag: 0.0000 +2024-06-15 22:26:55,718 - mmseg - INFO - Iter [2050/80000] lr: 3.898e-05, eta: 1 day, 14:45:02, time: 3.531, data_time: 1.912, memory: 71384, decode.loss_ce: 0.6281, decode.acc_seg: 77.9978, aux.loss_ce: 0.2489, aux.acc_seg: 78.2605, loss: 0.8770 +2024-06-15 22:28:17,083 - mmseg - INFO - Iter [2100/80000] lr: 3.895e-05, eta: 1 day, 14:38:32, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6631, decode.acc_seg: 75.5676, aux.loss_ce: 0.2617, aux.acc_seg: 76.2653, loss: 0.9249 +2024-06-15 22:29:38,526 - mmseg - INFO - Iter [2150/80000] lr: 3.893e-05, eta: 1 day, 14:32:18, time: 1.629, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6401, decode.acc_seg: 76.6898, aux.loss_ce: 0.2541, aux.acc_seg: 77.0188, loss: 0.8942 +2024-06-15 22:30:59,922 - mmseg - INFO - Iter [2200/80000] lr: 3.890e-05, eta: 1 day, 14:26:17, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6917, decode.acc_seg: 75.5601, aux.loss_ce: 0.2749, aux.acc_seg: 76.0570, loss: 0.9666 +2024-06-15 22:32:21,147 - mmseg - INFO - Iter [2250/80000] lr: 3.888e-05, eta: 1 day, 14:20:21, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6528, decode.acc_seg: 77.1344, aux.loss_ce: 0.2567, aux.acc_seg: 77.6735, loss: 0.9094 +2024-06-15 22:33:42,380 - mmseg - INFO - Iter [2300/80000] lr: 3.885e-05, eta: 1 day, 14:14:38, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6623, decode.acc_seg: 76.4475, aux.loss_ce: 0.2598, aux.acc_seg: 76.9296, loss: 0.9221 +2024-06-15 22:35:03,625 - mmseg - INFO - Iter [2350/80000] lr: 3.883e-05, eta: 1 day, 14:09:07, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6427, decode.acc_seg: 76.8129, aux.loss_ce: 0.2556, aux.acc_seg: 77.2861, loss: 0.8983 +2024-06-15 22:36:24,846 - mmseg - INFO - Iter [2400/80000] lr: 3.880e-05, eta: 1 day, 14:03:45, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6061, decode.acc_seg: 77.0895, aux.loss_ce: 0.2428, aux.acc_seg: 77.4534, loss: 0.8490 +2024-06-15 22:37:46,154 - mmseg - INFO - Iter [2450/80000] lr: 3.878e-05, eta: 1 day, 13:58:36, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6214, decode.acc_seg: 77.2666, aux.loss_ce: 0.2459, aux.acc_seg: 77.7511, loss: 0.8673 +2024-06-15 22:39:07,506 - mmseg - INFO - Iter [2500/80000] lr: 3.875e-05, eta: 1 day, 13:53:37, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6469, decode.acc_seg: 76.4927, aux.loss_ce: 0.2567, aux.acc_seg: 76.8941, loss: 0.9035 +2024-06-15 22:40:31,583 - mmseg - INFO - Iter [2550/80000] lr: 3.873e-05, eta: 1 day, 13:50:09, time: 1.682, data_time: 0.066, memory: 71384, decode.loss_ce: 0.6190, decode.acc_seg: 77.3551, aux.loss_ce: 0.2450, aux.acc_seg: 77.8476, loss: 0.8640 +2024-06-15 22:41:52,800 - mmseg - INFO - Iter [2600/80000] lr: 3.870e-05, eta: 1 day, 13:45:21, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5966, decode.acc_seg: 78.1826, aux.loss_ce: 0.2363, aux.acc_seg: 78.6193, loss: 0.8329 +2024-06-15 22:43:14,017 - mmseg - INFO - Iter [2650/80000] lr: 3.868e-05, eta: 1 day, 13:40:41, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5944, decode.acc_seg: 77.8098, aux.loss_ce: 0.2357, aux.acc_seg: 78.4496, loss: 0.8301 +2024-06-15 22:44:35,559 - mmseg - INFO - Iter [2700/80000] lr: 3.865e-05, eta: 1 day, 13:36:18, time: 1.631, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6132, decode.acc_seg: 77.5697, aux.loss_ce: 0.2438, aux.acc_seg: 77.8792, loss: 0.8570 +2024-06-15 22:45:56,838 - mmseg - INFO - Iter [2750/80000] lr: 3.863e-05, eta: 1 day, 13:31:54, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5886, decode.acc_seg: 77.6663, aux.loss_ce: 0.2311, aux.acc_seg: 78.3083, loss: 0.8197 +2024-06-15 22:47:18,148 - mmseg - INFO - Iter [2800/80000] lr: 3.860e-05, eta: 1 day, 13:27:37, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6011, decode.acc_seg: 77.7638, aux.loss_ce: 0.2368, aux.acc_seg: 78.3379, loss: 0.8379 +2024-06-15 22:48:39,411 - mmseg - INFO - Iter [2850/80000] lr: 3.858e-05, eta: 1 day, 13:23:25, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5894, decode.acc_seg: 78.2744, aux.loss_ce: 0.2343, aux.acc_seg: 78.7059, loss: 0.8237 +2024-06-15 22:50:00,751 - mmseg - INFO - Iter [2900/80000] lr: 3.855e-05, eta: 1 day, 13:19:21, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5565, decode.acc_seg: 79.3160, aux.loss_ce: 0.2200, aux.acc_seg: 79.7820, loss: 0.7765 +2024-06-15 22:51:21,983 - mmseg - INFO - Iter [2950/80000] lr: 3.853e-05, eta: 1 day, 13:15:20, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5607, decode.acc_seg: 78.9729, aux.loss_ce: 0.2223, aux.acc_seg: 79.4642, loss: 0.7830 +2024-06-15 22:52:43,232 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 22:52:43,232 - mmseg - INFO - Iter [3000/80000] lr: 3.850e-05, eta: 1 day, 13:11:24, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6129, decode.acc_seg: 77.0680, aux.loss_ce: 0.2413, aux.acc_seg: 77.6700, loss: 0.8542 +2024-06-15 22:54:18,416 - mmseg - INFO - per class results: +2024-06-15 22:54:18,423 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 74.73 | 86.14 | +| building | 79.95 | 90.33 | +| sky | 92.83 | 95.81 | +| floor | 78.2 | 90.13 | +| tree | 73.32 | 88.23 | +| ceiling | 81.28 | 89.92 | +| road | 82.66 | 86.74 | +| bed | 86.97 | 95.16 | +| windowpane | 54.36 | 61.89 | +| grass | 59.62 | 74.78 | +| cabinet | 57.14 | 64.64 | +| sidewalk | 62.36 | 75.91 | +| person | 75.31 | 90.1 | +| earth | 32.57 | 39.39 | +| door | 46.83 | 78.26 | +| table | 52.88 | 61.74 | +| mountain | 48.23 | 53.17 | +| plant | 49.62 | 55.36 | +| curtain | 71.91 | 85.61 | +| chair | 52.23 | 62.1 | +| car | 76.98 | 93.53 | +| water | 53.96 | 64.56 | +| painting | 62.26 | 82.84 | +| sofa | 63.72 | 84.6 | +| shelf | 36.15 | 56.07 | +| house | 46.59 | 81.29 | +| sea | 60.26 | 79.89 | +| mirror | 70.83 | 80.55 | +| rug | 42.23 | 43.87 | +| field | 29.05 | 75.28 | +| armchair | 39.08 | 51.74 | +| seat | 67.63 | 85.85 | +| fence | 44.83 | 65.97 | +| desk | 41.89 | 65.18 | +| rock | 56.81 | 81.51 | +| wardrobe | 41.05 | 90.18 | +| lamp | 55.34 | 68.51 | +| bathtub | 77.09 | 85.78 | +| railing | 35.92 | 67.38 | +| cushion | 52.61 | 67.85 | +| base | 31.16 | 48.7 | +| box | 25.21 | 30.55 | +| column | 41.93 | 52.26 | +| signboard | 31.53 | 36.98 | +| chest of drawers | 43.69 | 60.05 | +| counter | 39.63 | 46.93 | +| sand | 52.52 | 78.51 | +| sink | 65.74 | 73.81 | +| skyscraper | 26.59 | 26.65 | +| fireplace | 66.07 | 89.57 | +| refrigerator | 58.8 | 89.46 | +| grandstand | 41.89 | 89.9 | +| path | 20.95 | 31.24 | +| stairs | 27.1 | 33.11 | +| runway | 68.88 | 93.21 | +| case | 31.76 | 31.9 | +| pool table | 83.72 | 97.83 | +| pillow | 57.85 | 75.13 | +| screen door | 42.9 | 44.3 | +| stairway | 46.72 | 63.23 | +| river | 23.89 | 58.94 | +| bridge | 68.15 | 81.76 | +| bookcase | 36.26 | 66.69 | +| blind | 46.49 | 62.12 | +| coffee table | 43.63 | 90.02 | +| toilet | 83.67 | 89.68 | +| flower | 34.36 | 57.08 | +| book | 36.55 | 44.63 | +| hill | 6.47 | 20.6 | +| bench | 60.75 | 73.38 | +| countertop | 55.1 | 75.07 | +| stove | 77.03 | 88.04 | +| palm | 54.37 | 73.25 | +| kitchen island | 33.86 | 75.14 | +| computer | 65.9 | 94.15 | +| swivel chair | 43.47 | 72.53 | +| boat | 63.53 | 87.03 | +| bar | 55.41 | 67.75 | +| arcade machine | 82.2 | 97.99 | +| hovel | 29.5 | 33.24 | +| bus | 61.32 | 96.56 | +| towel | 56.2 | 67.9 | +| light | 21.08 | 22.26 | +| truck | 37.71 | 45.49 | +| tower | 7.92 | 9.32 | +| chandelier | 58.57 | 82.3 | +| awning | 23.47 | 27.27 | +| streetlight | 14.05 | 17.54 | +| booth | 37.59 | 50.01 | +| television receiver | 66.99 | 73.96 | +| airplane | 62.84 | 94.64 | +| dirt track | 0.0 | 0.0 | +| apparel | 24.29 | 35.39 | +| pole | 17.88 | 23.29 | +| land | 0.01 | 0.01 | +| bannister | 3.91 | 4.7 | +| escalator | 51.25 | 90.56 | +| ottoman | 44.93 | 73.84 | +| bottle | 34.93 | 65.77 | +| buffet | 50.27 | 69.87 | +| poster | 10.17 | 13.26 | +| stage | 12.26 | 26.16 | +| van | 28.06 | 34.61 | +| ship | 29.34 | 29.58 | +| fountain | 52.91 | 64.95 | +| conveyer belt | 69.92 | 97.02 | +| canopy | 46.75 | 87.72 | +| washer | 84.02 | 93.27 | +| plaything | 26.51 | 48.81 | +| swimming pool | 48.11 | 80.21 | +| stool | 32.27 | 45.1 | +| barrel | 46.57 | 64.56 | +| basket | 32.75 | 42.56 | +| waterfall | 47.76 | 62.54 | +| tent | 80.83 | 99.7 | +| bag | 15.55 | 16.05 | +| minibike | 67.65 | 84.23 | +| cradle | 65.86 | 98.47 | +| oven | 43.36 | 47.67 | +| ball | 43.95 | 64.09 | +| food | 49.61 | 75.6 | +| step | 0.0 | 0.0 | +| tank | 53.61 | 69.96 | +| trade name | 0.3 | 0.3 | +| microwave | 76.98 | 94.42 | +| pot | 42.27 | 50.53 | +| animal | 52.55 | 54.11 | +| bicycle | 48.07 | 63.41 | +| lake | 0.0 | 0.0 | +| dishwasher | 45.71 | 81.09 | +| screen | 45.06 | 93.33 | +| blanket | 2.49 | 2.73 | +| sculpture | 51.64 | 60.15 | +| hood | 56.31 | 68.24 | +| sconce | 22.72 | 25.25 | +| vase | 25.75 | 31.93 | +| traffic light | 11.24 | 12.44 | +| tray | 0.33 | 0.33 | +| ashcan | 39.2 | 45.12 | +| fan | 47.32 | 68.48 | +| pier | 34.05 | 38.01 | +| crt screen | 1.09 | 3.54 | +| plate | 43.96 | 62.73 | +| monitor | 0.0 | 0.0 | +| bulletin board | 49.88 | 51.84 | +| shower | 0.0 | 0.0 | +| radiator | 46.05 | 47.63 | +| glass | 0.22 | 0.22 | +| clock | 29.2 | 31.38 | +| flag | 60.47 | 66.55 | ++---------------------+-------+-------+ +2024-06-15 22:54:18,423 - mmseg - INFO - Summary: +2024-06-15 22:54:18,423 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 80.79 | 45.12 | 59.67 | ++-------+-------+-------+ +2024-06-15 22:54:18,424 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 22:54:18,424 - mmseg - INFO - Iter(val) [250] aAcc: 0.8079, mIoU: 0.4512, mAcc: 0.5967, IoU.wall: 0.7473, IoU.building: 0.7995, IoU.sky: 0.9283, IoU.floor: 0.7820, IoU.tree: 0.7332, IoU.ceiling: 0.8128, IoU.road: 0.8266, IoU.bed : 0.8697, IoU.windowpane: 0.5436, IoU.grass: 0.5962, IoU.cabinet: 0.5714, IoU.sidewalk: 0.6236, IoU.person: 0.7531, IoU.earth: 0.3257, IoU.door: 0.4683, IoU.table: 0.5288, IoU.mountain: 0.4823, IoU.plant: 0.4962, IoU.curtain: 0.7191, IoU.chair: 0.5223, IoU.car: 0.7698, IoU.water: 0.5396, IoU.painting: 0.6226, IoU.sofa: 0.6372, IoU.shelf: 0.3615, IoU.house: 0.4659, IoU.sea: 0.6026, IoU.mirror: 0.7083, IoU.rug: 0.4223, IoU.field: 0.2905, IoU.armchair: 0.3908, IoU.seat: 0.6763, IoU.fence: 0.4483, IoU.desk: 0.4189, IoU.rock: 0.5681, IoU.wardrobe: 0.4105, IoU.lamp: 0.5534, IoU.bathtub: 0.7709, IoU.railing: 0.3592, IoU.cushion: 0.5261, IoU.base: 0.3116, IoU.box: 0.2521, IoU.column: 0.4193, IoU.signboard: 0.3153, IoU.chest of drawers: 0.4369, IoU.counter: 0.3963, IoU.sand: 0.5252, IoU.sink: 0.6574, IoU.skyscraper: 0.2659, IoU.fireplace: 0.6607, IoU.refrigerator: 0.5880, IoU.grandstand: 0.4189, IoU.path: 0.2095, IoU.stairs: 0.2710, IoU.runway: 0.6888, IoU.case: 0.3176, IoU.pool table: 0.8372, IoU.pillow: 0.5785, IoU.screen door: 0.4290, IoU.stairway: 0.4672, IoU.river: 0.2389, IoU.bridge: 0.6815, IoU.bookcase: 0.3626, IoU.blind: 0.4649, IoU.coffee table: 0.4363, IoU.toilet: 0.8367, IoU.flower: 0.3436, IoU.book: 0.3655, IoU.hill: 0.0647, IoU.bench: 0.6075, IoU.countertop: 0.5510, IoU.stove: 0.7703, IoU.palm: 0.5437, IoU.kitchen island: 0.3386, IoU.computer: 0.6590, IoU.swivel chair: 0.4347, IoU.boat: 0.6353, IoU.bar: 0.5541, IoU.arcade machine: 0.8220, IoU.hovel: 0.2950, IoU.bus: 0.6132, IoU.towel: 0.5620, IoU.light: 0.2108, IoU.truck: 0.3771, IoU.tower: 0.0792, IoU.chandelier: 0.5857, IoU.awning: 0.2347, IoU.streetlight: 0.1405, IoU.booth: 0.3759, IoU.television receiver: 0.6699, IoU.airplane: 0.6284, IoU.dirt track: 0.0000, IoU.apparel: 0.2429, IoU.pole: 0.1788, IoU.land: 0.0001, IoU.bannister: 0.0391, IoU.escalator: 0.5125, IoU.ottoman: 0.4493, IoU.bottle: 0.3493, IoU.buffet: 0.5027, IoU.poster: 0.1017, IoU.stage: 0.1226, IoU.van: 0.2806, IoU.ship: 0.2934, IoU.fountain: 0.5291, IoU.conveyer belt: 0.6992, IoU.canopy: 0.4675, IoU.washer: 0.8402, IoU.plaything: 0.2651, IoU.swimming pool: 0.4811, IoU.stool: 0.3227, IoU.barrel: 0.4657, IoU.basket: 0.3275, IoU.waterfall: 0.4776, IoU.tent: 0.8083, IoU.bag: 0.1555, IoU.minibike: 0.6765, IoU.cradle: 0.6586, IoU.oven: 0.4336, IoU.ball: 0.4395, IoU.food: 0.4961, IoU.step: 0.0000, IoU.tank: 0.5361, IoU.trade name: 0.0030, IoU.microwave: 0.7698, IoU.pot: 0.4227, IoU.animal: 0.5255, IoU.bicycle: 0.4807, IoU.lake: 0.0000, IoU.dishwasher: 0.4571, IoU.screen: 0.4506, IoU.blanket: 0.0249, IoU.sculpture: 0.5164, IoU.hood: 0.5631, IoU.sconce: 0.2272, IoU.vase: 0.2575, IoU.traffic light: 0.1124, IoU.tray: 0.0033, IoU.ashcan: 0.3920, IoU.fan: 0.4732, IoU.pier: 0.3405, IoU.crt screen: 0.0109, IoU.plate: 0.4396, IoU.monitor: 0.0000, IoU.bulletin board: 0.4988, IoU.shower: 0.0000, IoU.radiator: 0.4605, IoU.glass: 0.0022, IoU.clock: 0.2920, IoU.flag: 0.6047, Acc.wall: 0.8614, Acc.building: 0.9033, Acc.sky: 0.9581, Acc.floor: 0.9013, Acc.tree: 0.8823, Acc.ceiling: 0.8992, Acc.road: 0.8674, Acc.bed : 0.9516, Acc.windowpane: 0.6189, Acc.grass: 0.7478, Acc.cabinet: 0.6464, Acc.sidewalk: 0.7591, Acc.person: 0.9010, Acc.earth: 0.3939, Acc.door: 0.7826, Acc.table: 0.6174, Acc.mountain: 0.5317, Acc.plant: 0.5536, Acc.curtain: 0.8561, Acc.chair: 0.6210, Acc.car: 0.9353, Acc.water: 0.6456, Acc.painting: 0.8284, Acc.sofa: 0.8460, Acc.shelf: 0.5607, Acc.house: 0.8129, Acc.sea: 0.7989, Acc.mirror: 0.8055, Acc.rug: 0.4387, Acc.field: 0.7528, Acc.armchair: 0.5174, Acc.seat: 0.8585, Acc.fence: 0.6597, Acc.desk: 0.6518, Acc.rock: 0.8151, Acc.wardrobe: 0.9018, Acc.lamp: 0.6851, Acc.bathtub: 0.8578, Acc.railing: 0.6738, Acc.cushion: 0.6785, Acc.base: 0.4870, Acc.box: 0.3055, Acc.column: 0.5226, Acc.signboard: 0.3698, Acc.chest of drawers: 0.6005, Acc.counter: 0.4693, Acc.sand: 0.7851, Acc.sink: 0.7381, Acc.skyscraper: 0.2665, Acc.fireplace: 0.8957, Acc.refrigerator: 0.8946, Acc.grandstand: 0.8990, Acc.path: 0.3124, Acc.stairs: 0.3311, Acc.runway: 0.9321, Acc.case: 0.3190, Acc.pool table: 0.9783, Acc.pillow: 0.7513, Acc.screen door: 0.4430, Acc.stairway: 0.6323, Acc.river: 0.5894, Acc.bridge: 0.8176, Acc.bookcase: 0.6669, Acc.blind: 0.6212, Acc.coffee table: 0.9002, Acc.toilet: 0.8968, Acc.flower: 0.5708, Acc.book: 0.4463, Acc.hill: 0.2060, Acc.bench: 0.7338, Acc.countertop: 0.7507, Acc.stove: 0.8804, Acc.palm: 0.7325, Acc.kitchen island: 0.7514, Acc.computer: 0.9415, Acc.swivel chair: 0.7253, Acc.boat: 0.8703, Acc.bar: 0.6775, Acc.arcade machine: 0.9799, Acc.hovel: 0.3324, Acc.bus: 0.9656, Acc.towel: 0.6790, Acc.light: 0.2226, Acc.truck: 0.4549, Acc.tower: 0.0932, Acc.chandelier: 0.8230, Acc.awning: 0.2727, Acc.streetlight: 0.1754, Acc.booth: 0.5001, Acc.television receiver: 0.7396, Acc.airplane: 0.9464, Acc.dirt track: 0.0000, Acc.apparel: 0.3539, Acc.pole: 0.2329, Acc.land: 0.0001, Acc.bannister: 0.0470, Acc.escalator: 0.9056, Acc.ottoman: 0.7384, Acc.bottle: 0.6577, Acc.buffet: 0.6987, Acc.poster: 0.1326, Acc.stage: 0.2616, Acc.van: 0.3461, Acc.ship: 0.2958, Acc.fountain: 0.6495, Acc.conveyer belt: 0.9702, Acc.canopy: 0.8772, Acc.washer: 0.9327, Acc.plaything: 0.4881, Acc.swimming pool: 0.8021, Acc.stool: 0.4510, Acc.barrel: 0.6456, Acc.basket: 0.4256, Acc.waterfall: 0.6254, Acc.tent: 0.9970, Acc.bag: 0.1605, Acc.minibike: 0.8423, Acc.cradle: 0.9847, Acc.oven: 0.4767, Acc.ball: 0.6409, Acc.food: 0.7560, Acc.step: 0.0000, Acc.tank: 0.6996, Acc.trade name: 0.0030, Acc.microwave: 0.9442, Acc.pot: 0.5053, Acc.animal: 0.5411, Acc.bicycle: 0.6341, Acc.lake: 0.0000, Acc.dishwasher: 0.8109, Acc.screen: 0.9333, Acc.blanket: 0.0273, Acc.sculpture: 0.6015, Acc.hood: 0.6824, Acc.sconce: 0.2525, Acc.vase: 0.3193, Acc.traffic light: 0.1244, Acc.tray: 0.0033, Acc.ashcan: 0.4512, Acc.fan: 0.6848, Acc.pier: 0.3801, Acc.crt screen: 0.0354, Acc.plate: 0.6273, Acc.monitor: 0.0000, Acc.bulletin board: 0.5184, Acc.shower: 0.0000, Acc.radiator: 0.4763, Acc.glass: 0.0022, Acc.clock: 0.3138, Acc.flag: 0.6655 +2024-06-15 22:55:40,003 - mmseg - INFO - Iter [3050/80000] lr: 3.848e-05, eta: 1 day, 13:47:43, time: 3.535, data_time: 1.920, memory: 71384, decode.loss_ce: 0.5932, decode.acc_seg: 78.5549, aux.loss_ce: 0.2331, aux.acc_seg: 79.0133, loss: 0.8263 +2024-06-15 22:57:01,317 - mmseg - INFO - Iter [3100/80000] lr: 3.845e-05, eta: 1 day, 13:43:19, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5948, decode.acc_seg: 78.3851, aux.loss_ce: 0.2360, aux.acc_seg: 78.5589, loss: 0.8308 +2024-06-15 22:58:22,562 - mmseg - INFO - Iter [3150/80000] lr: 3.843e-05, eta: 1 day, 13:38:59, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5560, decode.acc_seg: 79.1913, aux.loss_ce: 0.2187, aux.acc_seg: 79.8895, loss: 0.7747 +2024-06-15 22:59:43,869 - mmseg - INFO - Iter [3200/80000] lr: 3.840e-05, eta: 1 day, 13:34:45, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5715, decode.acc_seg: 79.1129, aux.loss_ce: 0.2246, aux.acc_seg: 79.6206, loss: 0.7961 +2024-06-15 23:01:05,284 - mmseg - INFO - Iter [3250/80000] lr: 3.838e-05, eta: 1 day, 13:30:40, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5687, decode.acc_seg: 78.6530, aux.loss_ce: 0.2258, aux.acc_seg: 79.0991, loss: 0.7945 +2024-06-15 23:02:26,552 - mmseg - INFO - Iter [3300/80000] lr: 3.835e-05, eta: 1 day, 13:26:36, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6114, decode.acc_seg: 77.5903, aux.loss_ce: 0.2415, aux.acc_seg: 77.8733, loss: 0.8528 +2024-06-15 23:03:47,782 - mmseg - INFO - Iter [3350/80000] lr: 3.833e-05, eta: 1 day, 13:22:36, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5884, decode.acc_seg: 78.9067, aux.loss_ce: 0.2320, aux.acc_seg: 79.3703, loss: 0.8204 +2024-06-15 23:05:08,947 - mmseg - INFO - Iter [3400/80000] lr: 3.830e-05, eta: 1 day, 13:18:40, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.6080, decode.acc_seg: 77.3251, aux.loss_ce: 0.2410, aux.acc_seg: 77.7460, loss: 0.8489 +2024-06-15 23:06:30,201 - mmseg - INFO - Iter [3450/80000] lr: 3.828e-05, eta: 1 day, 13:14:49, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5589, decode.acc_seg: 79.0810, aux.loss_ce: 0.2203, aux.acc_seg: 79.2863, loss: 0.7793 +2024-06-15 23:07:51,483 - mmseg - INFO - Iter [3500/80000] lr: 3.825e-05, eta: 1 day, 13:11:04, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5455, decode.acc_seg: 79.4941, aux.loss_ce: 0.2155, aux.acc_seg: 80.0020, loss: 0.7610 +2024-06-15 23:09:12,912 - mmseg - INFO - Iter [3550/80000] lr: 3.823e-05, eta: 1 day, 13:07:26, time: 1.629, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5509, decode.acc_seg: 79.9287, aux.loss_ce: 0.2178, aux.acc_seg: 80.1383, loss: 0.7687 +2024-06-15 23:10:34,209 - mmseg - INFO - Iter [3600/80000] lr: 3.820e-05, eta: 1 day, 13:03:49, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5378, decode.acc_seg: 79.6491, aux.loss_ce: 0.2131, aux.acc_seg: 79.9359, loss: 0.7509 +2024-06-15 23:11:55,580 - mmseg - INFO - Iter [3650/80000] lr: 3.818e-05, eta: 1 day, 13:00:17, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5850, decode.acc_seg: 78.5018, aux.loss_ce: 0.2307, aux.acc_seg: 78.6871, loss: 0.8157 +2024-06-15 23:13:16,843 - mmseg - INFO - Iter [3700/80000] lr: 3.815e-05, eta: 1 day, 12:56:47, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5608, decode.acc_seg: 78.8048, aux.loss_ce: 0.2216, aux.acc_seg: 79.4290, loss: 0.7824 +2024-06-15 23:14:38,173 - mmseg - INFO - Iter [3750/80000] lr: 3.813e-05, eta: 1 day, 12:53:21, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5529, decode.acc_seg: 79.2326, aux.loss_ce: 0.2172, aux.acc_seg: 79.5279, loss: 0.7701 +2024-06-15 23:16:01,501 - mmseg - INFO - Iter [3800/80000] lr: 3.810e-05, eta: 1 day, 12:50:38, time: 1.667, data_time: 0.052, memory: 71384, decode.loss_ce: 0.5410, decode.acc_seg: 79.3342, aux.loss_ce: 0.2135, aux.acc_seg: 79.7993, loss: 0.7545 +2024-06-15 23:17:22,778 - mmseg - INFO - Iter [3850/80000] lr: 3.808e-05, eta: 1 day, 12:47:18, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5267, decode.acc_seg: 79.9580, aux.loss_ce: 0.2077, aux.acc_seg: 80.2786, loss: 0.7345 +2024-06-15 23:18:44,156 - mmseg - INFO - Iter [3900/80000] lr: 3.805e-05, eta: 1 day, 12:44:02, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4895, decode.acc_seg: 80.9927, aux.loss_ce: 0.1926, aux.acc_seg: 81.4022, loss: 0.6822 +2024-06-15 23:20:05,397 - mmseg - INFO - Iter [3950/80000] lr: 3.803e-05, eta: 1 day, 12:40:46, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5302, decode.acc_seg: 80.1766, aux.loss_ce: 0.2108, aux.acc_seg: 80.4861, loss: 0.7410 +2024-06-15 23:21:26,751 - mmseg - INFO - Saving checkpoint at 4000 iterations +2024-06-15 23:22:50,652 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 23:22:50,652 - mmseg - INFO - Iter [4000/80000] lr: 3.800e-05, eta: 1 day, 13:04:10, time: 3.305, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5169, decode.acc_seg: 80.0072, aux.loss_ce: 0.2037, aux.acc_seg: 80.3645, loss: 0.7205 +2024-06-15 23:24:26,741 - mmseg - INFO - per class results: +2024-06-15 23:24:26,747 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 75.34 | 83.6 | +| building | 81.31 | 90.48 | +| sky | 93.43 | 95.62 | +| floor | 80.59 | 86.58 | +| tree | 74.38 | 85.29 | +| ceiling | 83.05 | 91.51 | +| road | 83.57 | 90.36 | +| bed | 85.29 | 97.7 | +| windowpane | 57.05 | 87.91 | +| grass | 63.32 | 82.64 | +| cabinet | 59.49 | 76.33 | +| sidewalk | 66.25 | 81.47 | +| person | 78.2 | 89.36 | +| earth | 30.97 | 37.57 | +| door | 53.64 | 71.86 | +| table | 56.82 | 68.94 | +| mountain | 47.86 | 76.08 | +| plant | 54.73 | 66.54 | +| curtain | 66.12 | 72.94 | +| chair | 57.83 | 73.65 | +| car | 82.65 | 92.43 | +| water | 55.97 | 71.99 | +| painting | 70.77 | 85.59 | +| sofa | 69.99 | 88.28 | +| shelf | 35.77 | 48.38 | +| house | 48.61 | 84.08 | +| sea | 68.52 | 92.35 | +| mirror | 71.46 | 81.37 | +| rug | 67.6 | 84.5 | +| field | 32.02 | 62.82 | +| armchair | 45.12 | 51.37 | +| seat | 68.61 | 83.09 | +| fence | 42.05 | 51.5 | +| desk | 43.89 | 61.7 | +| rock | 25.82 | 31.55 | +| wardrobe | 47.07 | 51.2 | +| lamp | 56.15 | 64.95 | +| bathtub | 78.5 | 83.33 | +| railing | 39.19 | 51.16 | +| cushion | 58.87 | 78.28 | +| base | 35.85 | 72.84 | +| box | 30.35 | 40.76 | +| column | 46.78 | 69.34 | +| signboard | 31.85 | 45.27 | +| chest of drawers | 44.16 | 69.76 | +| counter | 43.39 | 59.71 | +| sand | 48.5 | 73.45 | +| sink | 69.5 | 82.11 | +| skyscraper | 48.9 | 67.38 | +| fireplace | 66.5 | 91.91 | +| refrigerator | 71.8 | 80.09 | +| grandstand | 45.2 | 81.79 | +| path | 22.82 | 30.33 | +| stairs | 9.49 | 10.2 | +| runway | 68.9 | 94.71 | +| case | 51.37 | 89.59 | +| pool table | 83.47 | 98.31 | +| pillow | 26.46 | 27.01 | +| screen door | 60.41 | 96.45 | +| stairway | 33.5 | 77.67 | +| river | 23.33 | 38.54 | +| bridge | 42.79 | 50.51 | +| bookcase | 30.17 | 47.2 | +| blind | 27.51 | 28.33 | +| coffee table | 49.55 | 87.79 | +| toilet | 83.74 | 92.76 | +| flower | 33.58 | 55.12 | +| book | 44.01 | 62.44 | +| hill | 2.12 | 3.43 | +| bench | 48.48 | 79.33 | +| countertop | 58.61 | 74.0 | +| stove | 77.12 | 92.79 | +| palm | 52.11 | 80.36 | +| kitchen island | 29.81 | 96.37 | +| computer | 69.74 | 93.16 | +| swivel chair | 46.05 | 68.85 | +| boat | 45.56 | 47.56 | +| bar | 49.92 | 55.5 | +| arcade machine | 85.35 | 98.77 | +| hovel | 39.6 | 50.23 | +| bus | 88.25 | 94.15 | +| towel | 60.26 | 73.57 | +| light | 34.32 | 40.51 | +| truck | 40.55 | 56.87 | +| tower | 24.93 | 39.28 | +| chandelier | 61.08 | 83.47 | +| awning | 19.14 | 20.81 | +| streetlight | 18.63 | 25.09 | +| booth | 40.15 | 59.47 | +| television receiver | 71.39 | 88.4 | +| airplane | 73.87 | 95.47 | +| dirt track | 2.72 | 3.27 | +| apparel | 36.72 | 43.87 | +| pole | 17.62 | 22.08 | +| land | 0.0 | 0.0 | +| bannister | 0.0 | 0.0 | +| escalator | 58.92 | 80.94 | +| ottoman | 27.88 | 37.69 | +| bottle | 33.96 | 42.9 | +| buffet | 48.09 | 84.96 | +| poster | 20.37 | 25.25 | +| stage | 20.29 | 48.24 | +| van | 43.75 | 60.34 | +| ship | 54.67 | 94.99 | +| fountain | 26.4 | 29.33 | +| conveyer belt | 71.35 | 93.72 | +| canopy | 43.73 | 74.78 | +| washer | 76.43 | 85.03 | +| plaything | 22.05 | 58.44 | +| swimming pool | 58.08 | 85.83 | +| stool | 24.4 | 46.42 | +| barrel | 41.58 | 78.19 | +| basket | 36.3 | 55.98 | +| waterfall | 55.25 | 98.51 | +| tent | 76.8 | 95.66 | +| bag | 17.98 | 19.21 | +| minibike | 64.71 | 90.33 | +| cradle | 68.98 | 98.99 | +| oven | 31.85 | 36.76 | +| ball | 50.01 | 62.71 | +| food | 46.15 | 49.34 | +| step | 7.86 | 7.99 | +| tank | 58.2 | 90.5 | +| trade name | 17.7 | 19.92 | +| microwave | 69.23 | 97.17 | +| pot | 50.02 | 59.81 | +| animal | 56.63 | 58.2 | +| bicycle | 51.88 | 72.8 | +| lake | 1.87 | 3.05 | +| dishwasher | 55.87 | 69.82 | +| screen | 54.6 | 92.96 | +| blanket | 2.61 | 2.71 | +| sculpture | 60.75 | 72.95 | +| hood | 59.35 | 70.31 | +| sconce | 45.05 | 64.25 | +| vase | 31.16 | 46.68 | +| traffic light | 22.53 | 39.28 | +| tray | 5.8 | 6.1 | +| ashcan | 40.34 | 47.98 | +| fan | 54.42 | 77.93 | +| pier | 44.25 | 49.09 | +| crt screen | 3.24 | 11.66 | +| plate | 48.58 | 65.2 | +| monitor | 2.02 | 2.1 | +| bulletin board | 52.1 | 61.88 | +| shower | 0.0 | 0.0 | +| radiator | 61.15 | 78.03 | +| glass | 6.93 | 7.12 | +| clock | 36.71 | 38.87 | +| flag | 42.0 | 43.1 | ++---------------------+-------+-------+ +2024-06-15 23:24:26,747 - mmseg - INFO - Summary: +2024-06-15 23:24:26,747 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 81.84 | 47.26 | 62.71 | ++-------+-------+-------+ +2024-06-15 23:24:26,748 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 23:24:26,748 - mmseg - INFO - Iter(val) [250] aAcc: 0.8184, mIoU: 0.4726, mAcc: 0.6271, IoU.wall: 0.7534, IoU.building: 0.8131, IoU.sky: 0.9343, IoU.floor: 0.8059, IoU.tree: 0.7438, IoU.ceiling: 0.8305, IoU.road: 0.8357, IoU.bed : 0.8529, IoU.windowpane: 0.5705, IoU.grass: 0.6332, IoU.cabinet: 0.5949, IoU.sidewalk: 0.6625, IoU.person: 0.7820, IoU.earth: 0.3097, IoU.door: 0.5364, IoU.table: 0.5682, IoU.mountain: 0.4786, IoU.plant: 0.5473, IoU.curtain: 0.6612, IoU.chair: 0.5783, IoU.car: 0.8265, IoU.water: 0.5597, IoU.painting: 0.7077, IoU.sofa: 0.6999, IoU.shelf: 0.3577, IoU.house: 0.4861, IoU.sea: 0.6852, IoU.mirror: 0.7146, IoU.rug: 0.6760, IoU.field: 0.3202, IoU.armchair: 0.4512, IoU.seat: 0.6861, IoU.fence: 0.4205, IoU.desk: 0.4389, IoU.rock: 0.2582, IoU.wardrobe: 0.4707, IoU.lamp: 0.5615, IoU.bathtub: 0.7850, IoU.railing: 0.3919, IoU.cushion: 0.5887, IoU.base: 0.3585, IoU.box: 0.3035, IoU.column: 0.4678, IoU.signboard: 0.3185, IoU.chest of drawers: 0.4416, IoU.counter: 0.4339, IoU.sand: 0.4850, IoU.sink: 0.6950, IoU.skyscraper: 0.4890, IoU.fireplace: 0.6650, IoU.refrigerator: 0.7180, IoU.grandstand: 0.4520, IoU.path: 0.2282, IoU.stairs: 0.0949, IoU.runway: 0.6890, IoU.case: 0.5137, IoU.pool table: 0.8347, IoU.pillow: 0.2646, IoU.screen door: 0.6041, IoU.stairway: 0.3350, IoU.river: 0.2333, IoU.bridge: 0.4279, IoU.bookcase: 0.3017, IoU.blind: 0.2751, IoU.coffee table: 0.4955, IoU.toilet: 0.8374, IoU.flower: 0.3358, IoU.book: 0.4401, IoU.hill: 0.0212, IoU.bench: 0.4848, IoU.countertop: 0.5861, IoU.stove: 0.7712, IoU.palm: 0.5211, IoU.kitchen island: 0.2981, IoU.computer: 0.6974, IoU.swivel chair: 0.4605, IoU.boat: 0.4556, IoU.bar: 0.4992, IoU.arcade machine: 0.8535, IoU.hovel: 0.3960, IoU.bus: 0.8825, IoU.towel: 0.6026, IoU.light: 0.3432, IoU.truck: 0.4055, IoU.tower: 0.2493, IoU.chandelier: 0.6108, IoU.awning: 0.1914, IoU.streetlight: 0.1863, IoU.booth: 0.4015, IoU.television receiver: 0.7139, IoU.airplane: 0.7387, IoU.dirt track: 0.0272, IoU.apparel: 0.3672, IoU.pole: 0.1762, IoU.land: 0.0000, IoU.bannister: 0.0000, IoU.escalator: 0.5892, IoU.ottoman: 0.2788, IoU.bottle: 0.3396, IoU.buffet: 0.4809, IoU.poster: 0.2037, IoU.stage: 0.2029, IoU.van: 0.4375, IoU.ship: 0.5467, IoU.fountain: 0.2640, IoU.conveyer belt: 0.7135, IoU.canopy: 0.4373, IoU.washer: 0.7643, IoU.plaything: 0.2205, IoU.swimming pool: 0.5808, IoU.stool: 0.2440, IoU.barrel: 0.4158, IoU.basket: 0.3630, IoU.waterfall: 0.5525, IoU.tent: 0.7680, IoU.bag: 0.1798, IoU.minibike: 0.6471, IoU.cradle: 0.6898, IoU.oven: 0.3185, IoU.ball: 0.5001, IoU.food: 0.4615, IoU.step: 0.0786, IoU.tank: 0.5820, IoU.trade name: 0.1770, IoU.microwave: 0.6923, IoU.pot: 0.5002, IoU.animal: 0.5663, IoU.bicycle: 0.5188, IoU.lake: 0.0187, IoU.dishwasher: 0.5587, IoU.screen: 0.5460, IoU.blanket: 0.0261, IoU.sculpture: 0.6075, IoU.hood: 0.5935, IoU.sconce: 0.4505, IoU.vase: 0.3116, IoU.traffic light: 0.2253, IoU.tray: 0.0580, IoU.ashcan: 0.4034, IoU.fan: 0.5442, IoU.pier: 0.4425, IoU.crt screen: 0.0324, IoU.plate: 0.4858, IoU.monitor: 0.0202, IoU.bulletin board: 0.5210, IoU.shower: 0.0000, IoU.radiator: 0.6115, IoU.glass: 0.0693, IoU.clock: 0.3671, IoU.flag: 0.4200, Acc.wall: 0.8360, Acc.building: 0.9048, Acc.sky: 0.9562, Acc.floor: 0.8658, Acc.tree: 0.8529, Acc.ceiling: 0.9151, Acc.road: 0.9036, Acc.bed : 0.9770, Acc.windowpane: 0.8791, Acc.grass: 0.8264, Acc.cabinet: 0.7633, Acc.sidewalk: 0.8147, Acc.person: 0.8936, Acc.earth: 0.3757, Acc.door: 0.7186, Acc.table: 0.6894, Acc.mountain: 0.7608, Acc.plant: 0.6654, Acc.curtain: 0.7294, Acc.chair: 0.7365, Acc.car: 0.9243, Acc.water: 0.7199, Acc.painting: 0.8559, Acc.sofa: 0.8828, Acc.shelf: 0.4838, Acc.house: 0.8408, Acc.sea: 0.9235, Acc.mirror: 0.8137, Acc.rug: 0.8450, Acc.field: 0.6282, Acc.armchair: 0.5137, Acc.seat: 0.8309, Acc.fence: 0.5150, Acc.desk: 0.6170, Acc.rock: 0.3155, Acc.wardrobe: 0.5120, Acc.lamp: 0.6495, Acc.bathtub: 0.8333, Acc.railing: 0.5116, Acc.cushion: 0.7828, Acc.base: 0.7284, Acc.box: 0.4076, Acc.column: 0.6934, Acc.signboard: 0.4527, Acc.chest of drawers: 0.6976, Acc.counter: 0.5971, Acc.sand: 0.7345, Acc.sink: 0.8211, Acc.skyscraper: 0.6738, Acc.fireplace: 0.9191, Acc.refrigerator: 0.8009, Acc.grandstand: 0.8179, Acc.path: 0.3033, Acc.stairs: 0.1020, Acc.runway: 0.9471, Acc.case: 0.8959, Acc.pool table: 0.9831, Acc.pillow: 0.2701, Acc.screen door: 0.9645, Acc.stairway: 0.7767, Acc.river: 0.3854, Acc.bridge: 0.5051, Acc.bookcase: 0.4720, Acc.blind: 0.2833, Acc.coffee table: 0.8779, Acc.toilet: 0.9276, Acc.flower: 0.5512, Acc.book: 0.6244, Acc.hill: 0.0343, Acc.bench: 0.7933, Acc.countertop: 0.7400, Acc.stove: 0.9279, Acc.palm: 0.8036, Acc.kitchen island: 0.9637, Acc.computer: 0.9316, Acc.swivel chair: 0.6885, Acc.boat: 0.4756, Acc.bar: 0.5550, Acc.arcade machine: 0.9877, Acc.hovel: 0.5023, Acc.bus: 0.9415, Acc.towel: 0.7357, Acc.light: 0.4051, Acc.truck: 0.5687, Acc.tower: 0.3928, Acc.chandelier: 0.8347, Acc.awning: 0.2081, Acc.streetlight: 0.2509, Acc.booth: 0.5947, Acc.television receiver: 0.8840, Acc.airplane: 0.9547, Acc.dirt track: 0.0327, Acc.apparel: 0.4387, Acc.pole: 0.2208, Acc.land: 0.0000, Acc.bannister: 0.0000, Acc.escalator: 0.8094, Acc.ottoman: 0.3769, Acc.bottle: 0.4290, Acc.buffet: 0.8496, Acc.poster: 0.2525, Acc.stage: 0.4824, Acc.van: 0.6034, Acc.ship: 0.9499, Acc.fountain: 0.2933, Acc.conveyer belt: 0.9372, Acc.canopy: 0.7478, Acc.washer: 0.8503, Acc.plaything: 0.5844, Acc.swimming pool: 0.8583, Acc.stool: 0.4642, Acc.barrel: 0.7819, Acc.basket: 0.5598, Acc.waterfall: 0.9851, Acc.tent: 0.9566, Acc.bag: 0.1921, Acc.minibike: 0.9033, Acc.cradle: 0.9899, Acc.oven: 0.3676, Acc.ball: 0.6271, Acc.food: 0.4934, Acc.step: 0.0799, Acc.tank: 0.9050, Acc.trade name: 0.1992, Acc.microwave: 0.9717, Acc.pot: 0.5981, Acc.animal: 0.5820, Acc.bicycle: 0.7280, Acc.lake: 0.0305, Acc.dishwasher: 0.6982, Acc.screen: 0.9296, Acc.blanket: 0.0271, Acc.sculpture: 0.7295, Acc.hood: 0.7031, Acc.sconce: 0.6425, Acc.vase: 0.4668, Acc.traffic light: 0.3928, Acc.tray: 0.0610, Acc.ashcan: 0.4798, Acc.fan: 0.7793, Acc.pier: 0.4909, Acc.crt screen: 0.1166, Acc.plate: 0.6520, Acc.monitor: 0.0210, Acc.bulletin board: 0.6188, Acc.shower: 0.0000, Acc.radiator: 0.7803, Acc.glass: 0.0712, Acc.clock: 0.3887, Acc.flag: 0.4310 +2024-06-15 23:25:48,402 - mmseg - INFO - Iter [4050/80000] lr: 3.798e-05, eta: 1 day, 13:30:49, time: 3.555, data_time: 1.938, memory: 71384, decode.loss_ce: 0.5099, decode.acc_seg: 80.2002, aux.loss_ce: 0.2004, aux.acc_seg: 80.7014, loss: 0.7103 +2024-06-15 23:27:09,725 - mmseg - INFO - Iter [4100/80000] lr: 3.795e-05, eta: 1 day, 13:26:59, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5509, decode.acc_seg: 79.4410, aux.loss_ce: 0.2189, aux.acc_seg: 79.8834, loss: 0.7698 +2024-06-15 23:28:30,924 - mmseg - INFO - Iter [4150/80000] lr: 3.793e-05, eta: 1 day, 13:23:11, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5460, decode.acc_seg: 79.5658, aux.loss_ce: 0.2155, aux.acc_seg: 79.7266, loss: 0.7615 +2024-06-15 23:29:52,325 - mmseg - INFO - Iter [4200/80000] lr: 3.790e-05, eta: 1 day, 13:19:31, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5426, decode.acc_seg: 80.0456, aux.loss_ce: 0.2157, aux.acc_seg: 80.4172, loss: 0.7583 +2024-06-15 23:31:13,627 - mmseg - INFO - Iter [4250/80000] lr: 3.788e-05, eta: 1 day, 13:15:51, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5111, decode.acc_seg: 80.5100, aux.loss_ce: 0.2026, aux.acc_seg: 80.8950, loss: 0.7136 +2024-06-15 23:32:34,906 - mmseg - INFO - Iter [4300/80000] lr: 3.785e-05, eta: 1 day, 13:12:15, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5264, decode.acc_seg: 79.9948, aux.loss_ce: 0.2090, aux.acc_seg: 80.3275, loss: 0.7354 +2024-06-15 23:33:56,110 - mmseg - INFO - Iter [4350/80000] lr: 3.783e-05, eta: 1 day, 13:08:40, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5327, decode.acc_seg: 79.9867, aux.loss_ce: 0.2116, aux.acc_seg: 80.3633, loss: 0.7443 +2024-06-15 23:35:17,407 - mmseg - INFO - Iter [4400/80000] lr: 3.780e-05, eta: 1 day, 13:05:10, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5102, decode.acc_seg: 80.9716, aux.loss_ce: 0.2003, aux.acc_seg: 81.5008, loss: 0.7105 +2024-06-15 23:36:38,774 - mmseg - INFO - Iter [4450/80000] lr: 3.778e-05, eta: 1 day, 13:01:44, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5115, decode.acc_seg: 80.6393, aux.loss_ce: 0.2030, aux.acc_seg: 81.2799, loss: 0.7145 +2024-06-15 23:38:00,096 - mmseg - INFO - Iter [4500/80000] lr: 3.775e-05, eta: 1 day, 12:58:20, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5766, decode.acc_seg: 78.1647, aux.loss_ce: 0.2253, aux.acc_seg: 78.4692, loss: 0.8019 +2024-06-15 23:39:21,397 - mmseg - INFO - Iter [4550/80000] lr: 3.773e-05, eta: 1 day, 12:54:58, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5710, decode.acc_seg: 79.3725, aux.loss_ce: 0.2246, aux.acc_seg: 79.5353, loss: 0.7955 +2024-06-15 23:40:42,571 - mmseg - INFO - Iter [4600/80000] lr: 3.770e-05, eta: 1 day, 12:51:37, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5256, decode.acc_seg: 79.4450, aux.loss_ce: 0.2062, aux.acc_seg: 79.7983, loss: 0.7317 +2024-06-15 23:42:03,877 - mmseg - INFO - Iter [4650/80000] lr: 3.768e-05, eta: 1 day, 12:48:21, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5192, decode.acc_seg: 79.7073, aux.loss_ce: 0.2046, aux.acc_seg: 80.1296, loss: 0.7239 +2024-06-15 23:43:25,013 - mmseg - INFO - Iter [4700/80000] lr: 3.765e-05, eta: 1 day, 12:45:04, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5251, decode.acc_seg: 80.0303, aux.loss_ce: 0.2068, aux.acc_seg: 80.5749, loss: 0.7320 +2024-06-15 23:44:46,186 - mmseg - INFO - Iter [4750/80000] lr: 3.763e-05, eta: 1 day, 12:41:50, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4938, decode.acc_seg: 81.1196, aux.loss_ce: 0.1974, aux.acc_seg: 81.3930, loss: 0.6912 +2024-06-15 23:46:07,524 - mmseg - INFO - Iter [4800/80000] lr: 3.760e-05, eta: 1 day, 12:38:41, time: 1.627, data_time: 0.011, memory: 71384, decode.loss_ce: 0.5402, decode.acc_seg: 79.9400, aux.loss_ce: 0.2138, aux.acc_seg: 80.1659, loss: 0.7540 +2024-06-15 23:47:28,919 - mmseg - INFO - Iter [4850/80000] lr: 3.758e-05, eta: 1 day, 12:35:36, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5452, decode.acc_seg: 78.8710, aux.loss_ce: 0.2142, aux.acc_seg: 79.4052, loss: 0.7594 +2024-06-15 23:48:50,139 - mmseg - INFO - Iter [4900/80000] lr: 3.755e-05, eta: 1 day, 12:32:30, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5817, decode.acc_seg: 77.9280, aux.loss_ce: 0.2289, aux.acc_seg: 78.5585, loss: 0.8106 +2024-06-15 23:50:11,382 - mmseg - INFO - Iter [4950/80000] lr: 3.753e-05, eta: 1 day, 12:29:26, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5346, decode.acc_seg: 79.9960, aux.loss_ce: 0.2107, aux.acc_seg: 80.3462, loss: 0.7453 +2024-06-15 23:51:32,879 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 23:51:32,879 - mmseg - INFO - Iter [5000/80000] lr: 3.750e-05, eta: 1 day, 12:26:28, time: 1.630, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5288, decode.acc_seg: 80.1991, aux.loss_ce: 0.2080, aux.acc_seg: 80.8862, loss: 0.7368 +2024-06-15 23:53:07,952 - mmseg - INFO - per class results: +2024-06-15 23:53:07,958 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 77.12 | 85.65 | +| building | 81.87 | 92.75 | +| sky | 93.77 | 96.95 | +| floor | 79.91 | 85.1 | +| tree | 73.82 | 89.46 | +| ceiling | 82.39 | 92.77 | +| road | 83.85 | 88.87 | +| bed | 89.48 | 94.23 | +| windowpane | 61.17 | 79.52 | +| grass | 71.46 | 86.29 | +| cabinet | 61.89 | 70.41 | +| sidewalk | 64.59 | 85.51 | +| person | 79.08 | 90.17 | +| earth | 40.18 | 52.89 | +| door | 57.64 | 74.55 | +| table | 60.02 | 75.22 | +| mountain | 58.2 | 75.37 | +| plant | 53.34 | 70.53 | +| curtain | 71.91 | 87.95 | +| chair | 59.6 | 75.25 | +| car | 81.09 | 93.65 | +| water | 55.37 | 72.72 | +| painting | 73.28 | 86.16 | +| sofa | 73.0 | 92.15 | +| shelf | 45.8 | 68.11 | +| house | 50.17 | 57.24 | +| sea | 62.04 | 88.6 | +| mirror | 73.86 | 84.12 | +| rug | 61.89 | 88.28 | +| field | 42.1 | 60.6 | +| armchair | 55.68 | 68.43 | +| seat | 63.66 | 87.05 | +| fence | 45.15 | 59.09 | +| desk | 45.99 | 73.41 | +| rock | 45.38 | 54.12 | +| wardrobe | 53.81 | 75.76 | +| lamp | 60.43 | 70.57 | +| bathtub | 80.57 | 84.38 | +| railing | 36.09 | 45.8 | +| cushion | 57.27 | 63.98 | +| base | 22.73 | 26.25 | +| box | 32.98 | 49.68 | +| column | 50.52 | 65.95 | +| signboard | 33.73 | 56.03 | +| chest of drawers | 45.6 | 67.73 | +| counter | 51.67 | 69.63 | +| sand | 41.1 | 56.62 | +| sink | 69.57 | 75.96 | +| skyscraper | 49.42 | 71.62 | +| fireplace | 65.23 | 92.91 | +| refrigerator | 74.05 | 82.25 | +| grandstand | 50.11 | 81.25 | +| path | 22.68 | 31.78 | +| stairs | 36.02 | 48.73 | +| runway | 69.59 | 94.6 | +| case | 57.61 | 75.36 | +| pool table | 90.71 | 96.88 | +| pillow | 63.84 | 76.19 | +| screen door | 65.82 | 73.97 | +| stairway | 55.59 | 62.84 | +| river | 8.62 | 9.23 | +| bridge | 39.31 | 43.4 | +| bookcase | 30.21 | 36.15 | +| blind | 39.32 | 44.73 | +| coffee table | 54.42 | 85.88 | +| toilet | 85.76 | 89.94 | +| flower | 33.64 | 53.85 | +| book | 44.14 | 76.86 | +| hill | 0.82 | 0.82 | +| bench | 59.81 | 71.64 | +| countertop | 59.16 | 67.93 | +| stove | 78.47 | 85.33 | +| palm | 54.53 | 68.79 | +| kitchen island | 29.84 | 41.88 | +| computer | 68.46 | 92.39 | +| swivel chair | 48.52 | 72.26 | +| boat | 71.93 | 84.52 | +| bar | 65.69 | 78.96 | +| arcade machine | 88.92 | 96.14 | +| hovel | 23.76 | 26.52 | +| bus | 89.52 | 94.69 | +| towel | 60.96 | 84.99 | +| light | 32.91 | 34.8 | +| truck | 42.08 | 53.01 | +| tower | 24.98 | 57.21 | +| chandelier | 60.86 | 86.15 | +| awning | 28.33 | 35.97 | +| streetlight | 19.8 | 29.81 | +| booth | 37.58 | 71.17 | +| television receiver | 70.51 | 74.41 | +| airplane | 61.02 | 77.89 | +| dirt track | 5.51 | 23.52 | +| apparel | 25.89 | 29.56 | +| pole | 12.02 | 13.55 | +| land | 2.32 | 3.12 | +| bannister | 8.89 | 12.33 | +| escalator | 51.48 | 90.81 | +| ottoman | 51.16 | 67.05 | +| bottle | 18.42 | 21.95 | +| buffet | 56.88 | 78.48 | +| poster | 31.0 | 39.57 | +| stage | 15.87 | 53.38 | +| van | 1.68 | 1.74 | +| ship | 7.3 | 7.3 | +| fountain | 34.44 | 36.34 | +| conveyer belt | 78.72 | 89.88 | +| canopy | 24.69 | 48.95 | +| washer | 74.45 | 80.64 | +| plaything | 21.03 | 53.34 | +| swimming pool | 51.23 | 79.26 | +| stool | 33.17 | 36.79 | +| barrel | 55.11 | 57.82 | +| basket | 30.0 | 44.26 | +| waterfall | 61.82 | 72.01 | +| tent | 70.23 | 99.37 | +| bag | 22.55 | 27.59 | +| minibike | 68.29 | 85.04 | +| cradle | 50.45 | 99.09 | +| oven | 57.47 | 63.0 | +| ball | 43.25 | 57.06 | +| food | 53.32 | 56.99 | +| step | 0.03 | 0.03 | +| tank | 60.02 | 67.38 | +| trade name | 25.59 | 31.25 | +| microwave | 84.9 | 89.89 | +| pot | 42.3 | 45.68 | +| animal | 67.92 | 70.79 | +| bicycle | 55.54 | 75.4 | +| lake | 0.0 | 0.0 | +| dishwasher | 57.64 | 68.31 | +| screen | 56.43 | 90.3 | +| blanket | 11.64 | 14.13 | +| sculpture | 64.97 | 71.75 | +| hood | 57.09 | 67.86 | +| sconce | 49.26 | 67.1 | +| vase | 31.89 | 49.92 | +| traffic light | 18.22 | 19.25 | +| tray | 2.54 | 2.59 | +| ashcan | 44.05 | 56.52 | +| fan | 51.05 | 57.4 | +| pier | 36.11 | 37.51 | +| crt screen | 0.12 | 0.34 | +| plate | 49.39 | 61.2 | +| monitor | 2.97 | 2.97 | +| bulletin board | 50.72 | 73.35 | +| shower | 0.0 | 0.0 | +| radiator | 61.87 | 69.62 | +| glass | 9.87 | 10.37 | +| clock | 35.68 | 37.71 | +| flag | 63.17 | 67.54 | ++---------------------+-------+-------+ +2024-06-15 23:53:07,959 - mmseg - INFO - Summary: +2024-06-15 23:53:07,959 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 83.25 | 48.91 | 61.62 | ++-------+-------+-------+ +2024-06-15 23:53:07,959 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-15 23:53:07,960 - mmseg - INFO - Iter(val) [250] aAcc: 0.8325, mIoU: 0.4891, mAcc: 0.6162, IoU.wall: 0.7712, IoU.building: 0.8187, IoU.sky: 0.9377, IoU.floor: 0.7991, IoU.tree: 0.7382, IoU.ceiling: 0.8239, IoU.road: 0.8385, IoU.bed : 0.8948, IoU.windowpane: 0.6117, IoU.grass: 0.7146, IoU.cabinet: 0.6189, IoU.sidewalk: 0.6459, IoU.person: 0.7908, IoU.earth: 0.4018, IoU.door: 0.5764, IoU.table: 0.6002, IoU.mountain: 0.5820, IoU.plant: 0.5334, IoU.curtain: 0.7191, IoU.chair: 0.5960, IoU.car: 0.8109, IoU.water: 0.5537, IoU.painting: 0.7328, IoU.sofa: 0.7300, IoU.shelf: 0.4580, IoU.house: 0.5017, IoU.sea: 0.6204, IoU.mirror: 0.7386, IoU.rug: 0.6189, IoU.field: 0.4210, IoU.armchair: 0.5568, IoU.seat: 0.6366, IoU.fence: 0.4515, IoU.desk: 0.4599, IoU.rock: 0.4538, IoU.wardrobe: 0.5381, IoU.lamp: 0.6043, IoU.bathtub: 0.8057, IoU.railing: 0.3609, IoU.cushion: 0.5727, IoU.base: 0.2273, IoU.box: 0.3298, IoU.column: 0.5052, IoU.signboard: 0.3373, IoU.chest of drawers: 0.4560, IoU.counter: 0.5167, IoU.sand: 0.4110, IoU.sink: 0.6957, IoU.skyscraper: 0.4942, IoU.fireplace: 0.6523, IoU.refrigerator: 0.7405, IoU.grandstand: 0.5011, IoU.path: 0.2268, IoU.stairs: 0.3602, IoU.runway: 0.6959, IoU.case: 0.5761, IoU.pool table: 0.9071, IoU.pillow: 0.6384, IoU.screen door: 0.6582, IoU.stairway: 0.5559, IoU.river: 0.0862, IoU.bridge: 0.3931, IoU.bookcase: 0.3021, IoU.blind: 0.3932, IoU.coffee table: 0.5442, IoU.toilet: 0.8576, IoU.flower: 0.3364, IoU.book: 0.4414, IoU.hill: 0.0082, IoU.bench: 0.5981, IoU.countertop: 0.5916, IoU.stove: 0.7847, IoU.palm: 0.5453, IoU.kitchen island: 0.2984, IoU.computer: 0.6846, IoU.swivel chair: 0.4852, IoU.boat: 0.7193, IoU.bar: 0.6569, IoU.arcade machine: 0.8892, IoU.hovel: 0.2376, IoU.bus: 0.8952, IoU.towel: 0.6096, IoU.light: 0.3291, IoU.truck: 0.4208, IoU.tower: 0.2498, IoU.chandelier: 0.6086, IoU.awning: 0.2833, IoU.streetlight: 0.1980, IoU.booth: 0.3758, IoU.television receiver: 0.7051, IoU.airplane: 0.6102, IoU.dirt track: 0.0551, IoU.apparel: 0.2589, IoU.pole: 0.1202, IoU.land: 0.0232, IoU.bannister: 0.0889, IoU.escalator: 0.5148, IoU.ottoman: 0.5116, IoU.bottle: 0.1842, IoU.buffet: 0.5688, IoU.poster: 0.3100, IoU.stage: 0.1587, IoU.van: 0.0168, IoU.ship: 0.0730, IoU.fountain: 0.3444, IoU.conveyer belt: 0.7872, IoU.canopy: 0.2469, IoU.washer: 0.7445, IoU.plaything: 0.2103, IoU.swimming pool: 0.5123, IoU.stool: 0.3317, IoU.barrel: 0.5511, IoU.basket: 0.3000, IoU.waterfall: 0.6182, IoU.tent: 0.7023, IoU.bag: 0.2255, IoU.minibike: 0.6829, IoU.cradle: 0.5045, IoU.oven: 0.5747, IoU.ball: 0.4325, IoU.food: 0.5332, IoU.step: 0.0003, IoU.tank: 0.6002, IoU.trade name: 0.2559, IoU.microwave: 0.8490, IoU.pot: 0.4230, IoU.animal: 0.6792, IoU.bicycle: 0.5554, IoU.lake: 0.0000, IoU.dishwasher: 0.5764, IoU.screen: 0.5643, IoU.blanket: 0.1164, IoU.sculpture: 0.6497, IoU.hood: 0.5709, IoU.sconce: 0.4926, IoU.vase: 0.3189, IoU.traffic light: 0.1822, IoU.tray: 0.0254, IoU.ashcan: 0.4405, IoU.fan: 0.5105, IoU.pier: 0.3611, IoU.crt screen: 0.0012, IoU.plate: 0.4939, IoU.monitor: 0.0297, IoU.bulletin board: 0.5072, IoU.shower: 0.0000, IoU.radiator: 0.6187, IoU.glass: 0.0987, IoU.clock: 0.3568, IoU.flag: 0.6317, Acc.wall: 0.8565, Acc.building: 0.9275, Acc.sky: 0.9695, Acc.floor: 0.8510, Acc.tree: 0.8946, Acc.ceiling: 0.9277, Acc.road: 0.8887, Acc.bed : 0.9423, Acc.windowpane: 0.7952, Acc.grass: 0.8629, Acc.cabinet: 0.7041, Acc.sidewalk: 0.8551, Acc.person: 0.9017, Acc.earth: 0.5289, Acc.door: 0.7455, Acc.table: 0.7522, Acc.mountain: 0.7537, Acc.plant: 0.7053, Acc.curtain: 0.8795, Acc.chair: 0.7525, Acc.car: 0.9365, Acc.water: 0.7272, Acc.painting: 0.8616, Acc.sofa: 0.9215, Acc.shelf: 0.6811, Acc.house: 0.5724, Acc.sea: 0.8860, Acc.mirror: 0.8412, Acc.rug: 0.8828, Acc.field: 0.6060, Acc.armchair: 0.6843, Acc.seat: 0.8705, Acc.fence: 0.5909, Acc.desk: 0.7341, Acc.rock: 0.5412, Acc.wardrobe: 0.7576, Acc.lamp: 0.7057, Acc.bathtub: 0.8438, Acc.railing: 0.4580, Acc.cushion: 0.6398, Acc.base: 0.2625, Acc.box: 0.4968, Acc.column: 0.6595, Acc.signboard: 0.5603, Acc.chest of drawers: 0.6773, Acc.counter: 0.6963, Acc.sand: 0.5662, Acc.sink: 0.7596, Acc.skyscraper: 0.7162, Acc.fireplace: 0.9291, Acc.refrigerator: 0.8225, Acc.grandstand: 0.8125, Acc.path: 0.3178, Acc.stairs: 0.4873, Acc.runway: 0.9460, Acc.case: 0.7536, Acc.pool table: 0.9688, Acc.pillow: 0.7619, Acc.screen door: 0.7397, Acc.stairway: 0.6284, Acc.river: 0.0923, Acc.bridge: 0.4340, Acc.bookcase: 0.3615, Acc.blind: 0.4473, Acc.coffee table: 0.8588, Acc.toilet: 0.8994, Acc.flower: 0.5385, Acc.book: 0.7686, Acc.hill: 0.0082, Acc.bench: 0.7164, Acc.countertop: 0.6793, Acc.stove: 0.8533, Acc.palm: 0.6879, Acc.kitchen island: 0.4188, Acc.computer: 0.9239, Acc.swivel chair: 0.7226, Acc.boat: 0.8452, Acc.bar: 0.7896, Acc.arcade machine: 0.9614, Acc.hovel: 0.2652, Acc.bus: 0.9469, Acc.towel: 0.8499, Acc.light: 0.3480, Acc.truck: 0.5301, Acc.tower: 0.5721, Acc.chandelier: 0.8615, Acc.awning: 0.3597, Acc.streetlight: 0.2981, Acc.booth: 0.7117, Acc.television receiver: 0.7441, Acc.airplane: 0.7789, Acc.dirt track: 0.2352, Acc.apparel: 0.2956, Acc.pole: 0.1355, Acc.land: 0.0312, Acc.bannister: 0.1233, Acc.escalator: 0.9081, Acc.ottoman: 0.6705, Acc.bottle: 0.2195, Acc.buffet: 0.7848, Acc.poster: 0.3957, Acc.stage: 0.5338, Acc.van: 0.0174, Acc.ship: 0.0730, Acc.fountain: 0.3634, Acc.conveyer belt: 0.8988, Acc.canopy: 0.4895, Acc.washer: 0.8064, Acc.plaything: 0.5334, Acc.swimming pool: 0.7926, Acc.stool: 0.3679, Acc.barrel: 0.5782, Acc.basket: 0.4426, Acc.waterfall: 0.7201, Acc.tent: 0.9937, Acc.bag: 0.2759, Acc.minibike: 0.8504, Acc.cradle: 0.9909, Acc.oven: 0.6300, Acc.ball: 0.5706, Acc.food: 0.5699, Acc.step: 0.0003, Acc.tank: 0.6738, Acc.trade name: 0.3125, Acc.microwave: 0.8989, Acc.pot: 0.4568, Acc.animal: 0.7079, Acc.bicycle: 0.7540, Acc.lake: 0.0000, Acc.dishwasher: 0.6831, Acc.screen: 0.9030, Acc.blanket: 0.1413, Acc.sculpture: 0.7175, Acc.hood: 0.6786, Acc.sconce: 0.6710, Acc.vase: 0.4992, Acc.traffic light: 0.1925, Acc.tray: 0.0259, Acc.ashcan: 0.5652, Acc.fan: 0.5740, Acc.pier: 0.3751, Acc.crt screen: 0.0034, Acc.plate: 0.6120, Acc.monitor: 0.0297, Acc.bulletin board: 0.7335, Acc.shower: 0.0000, Acc.radiator: 0.6962, Acc.glass: 0.1037, Acc.clock: 0.3771, Acc.flag: 0.6754 +2024-06-15 23:54:29,576 - mmseg - INFO - Iter [5050/80000] lr: 3.748e-05, eta: 1 day, 12:47:05, time: 3.534, data_time: 1.918, memory: 71384, decode.loss_ce: 0.5231, decode.acc_seg: 80.7087, aux.loss_ce: 0.2065, aux.acc_seg: 81.0963, loss: 0.7296 +2024-06-15 23:55:53,341 - mmseg - INFO - Iter [5100/80000] lr: 3.745e-05, eta: 1 day, 12:44:29, time: 1.675, data_time: 0.054, memory: 71384, decode.loss_ce: 0.5033, decode.acc_seg: 80.8059, aux.loss_ce: 0.1962, aux.acc_seg: 81.0133, loss: 0.6994 +2024-06-15 23:57:14,537 - mmseg - INFO - Iter [5150/80000] lr: 3.743e-05, eta: 1 day, 12:41:18, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5282, decode.acc_seg: 79.7263, aux.loss_ce: 0.2096, aux.acc_seg: 79.8882, loss: 0.7379 +2024-06-15 23:58:35,670 - mmseg - INFO - Iter [5200/80000] lr: 3.740e-05, eta: 1 day, 12:38:08, time: 1.623, data_time: 0.009, memory: 71384, decode.loss_ce: 0.4590, decode.acc_seg: 82.1122, aux.loss_ce: 0.1818, aux.acc_seg: 82.3149, loss: 0.6408 +2024-06-15 23:59:56,795 - mmseg - INFO - Iter [5250/80000] lr: 3.738e-05, eta: 1 day, 12:34:59, time: 1.622, data_time: 0.009, memory: 71384, decode.loss_ce: 0.5084, decode.acc_seg: 80.3945, aux.loss_ce: 0.2004, aux.acc_seg: 80.6456, loss: 0.7088 +2024-06-16 00:01:18,083 - mmseg - INFO - Iter [5300/80000] lr: 3.735e-05, eta: 1 day, 12:31:55, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4956, decode.acc_seg: 81.6189, aux.loss_ce: 0.1975, aux.acc_seg: 81.7127, loss: 0.6931 +2024-06-16 00:02:39,361 - mmseg - INFO - Iter [5350/80000] lr: 3.733e-05, eta: 1 day, 12:28:53, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4999, decode.acc_seg: 81.1844, aux.loss_ce: 0.1968, aux.acc_seg: 81.4745, loss: 0.6967 +2024-06-16 00:04:00,627 - mmseg - INFO - Iter [5400/80000] lr: 3.730e-05, eta: 1 day, 12:25:53, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5124, decode.acc_seg: 80.6457, aux.loss_ce: 0.2020, aux.acc_seg: 81.1551, loss: 0.7143 +2024-06-16 00:05:21,985 - mmseg - INFO - Iter [5450/80000] lr: 3.728e-05, eta: 1 day, 12:22:55, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5065, decode.acc_seg: 80.9182, aux.loss_ce: 0.2016, aux.acc_seg: 81.3430, loss: 0.7082 +2024-06-16 00:06:43,209 - mmseg - INFO - Iter [5500/80000] lr: 3.725e-05, eta: 1 day, 12:19:58, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4427, decode.acc_seg: 82.8163, aux.loss_ce: 0.1768, aux.acc_seg: 83.0332, loss: 0.6195 +2024-06-16 00:08:04,396 - mmseg - INFO - Iter [5550/80000] lr: 3.723e-05, eta: 1 day, 12:17:01, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4798, decode.acc_seg: 81.3229, aux.loss_ce: 0.1908, aux.acc_seg: 81.5411, loss: 0.6705 +2024-06-16 00:09:25,537 - mmseg - INFO - Iter [5600/80000] lr: 3.720e-05, eta: 1 day, 12:14:06, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4747, decode.acc_seg: 82.4864, aux.loss_ce: 0.1877, aux.acc_seg: 82.7484, loss: 0.6624 +2024-06-16 00:10:46,766 - mmseg - INFO - Iter [5650/80000] lr: 3.718e-05, eta: 1 day, 12:11:14, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4905, decode.acc_seg: 81.9721, aux.loss_ce: 0.1961, aux.acc_seg: 82.0017, loss: 0.6865 +2024-06-16 00:12:07,911 - mmseg - INFO - Iter [5700/80000] lr: 3.715e-05, eta: 1 day, 12:08:22, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4859, decode.acc_seg: 81.3596, aux.loss_ce: 0.1926, aux.acc_seg: 81.6687, loss: 0.6785 +2024-06-16 00:13:29,205 - mmseg - INFO - Iter [5750/80000] lr: 3.713e-05, eta: 1 day, 12:05:33, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5122, decode.acc_seg: 80.7073, aux.loss_ce: 0.2025, aux.acc_seg: 80.9989, loss: 0.7147 +2024-06-16 00:14:50,408 - mmseg - INFO - Iter [5800/80000] lr: 3.710e-05, eta: 1 day, 12:02:45, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4710, decode.acc_seg: 81.2077, aux.loss_ce: 0.1851, aux.acc_seg: 81.5802, loss: 0.6561 +2024-06-16 00:16:11,631 - mmseg - INFO - Iter [5850/80000] lr: 3.708e-05, eta: 1 day, 11:59:59, time: 1.624, data_time: 0.009, memory: 71384, decode.loss_ce: 0.4620, decode.acc_seg: 82.0556, aux.loss_ce: 0.1822, aux.acc_seg: 82.3050, loss: 0.6442 +2024-06-16 00:17:32,776 - mmseg - INFO - Iter [5900/80000] lr: 3.705e-05, eta: 1 day, 11:57:13, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4910, decode.acc_seg: 81.4864, aux.loss_ce: 0.1946, aux.acc_seg: 81.6795, loss: 0.6856 +2024-06-16 00:18:54,056 - mmseg - INFO - Iter [5950/80000] lr: 3.703e-05, eta: 1 day, 11:54:31, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4712, decode.acc_seg: 81.6932, aux.loss_ce: 0.1844, aux.acc_seg: 81.9511, loss: 0.6556 +2024-06-16 00:20:15,320 - mmseg - INFO - Saving checkpoint at 6000 iterations +2024-06-16 00:21:41,688 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 00:21:41,688 - mmseg - INFO - Iter [6000/80000] lr: 3.700e-05, eta: 1 day, 12:09:34, time: 3.353, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4814, decode.acc_seg: 82.6366, aux.loss_ce: 0.1915, aux.acc_seg: 82.7086, loss: 0.6729 +2024-06-16 00:23:17,574 - mmseg - INFO - per class results: +2024-06-16 00:23:17,580 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 77.24 | 84.0 | +| building | 83.0 | 93.72 | +| sky | 93.63 | 96.62 | +| floor | 81.34 | 88.15 | +| tree | 75.43 | 88.24 | +| ceiling | 83.74 | 92.38 | +| road | 80.98 | 93.16 | +| bed | 88.78 | 97.0 | +| windowpane | 61.79 | 78.73 | +| grass | 59.94 | 74.8 | +| cabinet | 57.05 | 65.91 | +| sidewalk | 59.86 | 75.26 | +| person | 81.22 | 92.4 | +| earth | 34.03 | 45.94 | +| door | 53.72 | 67.26 | +| table | 61.69 | 81.74 | +| mountain | 57.88 | 72.63 | +| plant | 57.18 | 68.18 | +| curtain | 70.94 | 91.43 | +| chair | 55.5 | 63.31 | +| car | 82.47 | 92.86 | +| water | 63.3 | 80.72 | +| painting | 72.73 | 87.73 | +| sofa | 76.14 | 84.71 | +| shelf | 25.45 | 28.6 | +| house | 46.73 | 53.92 | +| sea | 69.7 | 81.1 | +| mirror | 73.1 | 79.04 | +| rug | 66.19 | 83.96 | +| field | 28.44 | 75.84 | +| armchair | 56.84 | 79.76 | +| seat | 64.54 | 87.98 | +| fence | 49.07 | 69.82 | +| desk | 47.62 | 73.09 | +| rock | 47.42 | 76.52 | +| wardrobe | 47.69 | 86.65 | +| lamp | 63.86 | 78.78 | +| bathtub | 80.25 | 83.85 | +| railing | 30.71 | 38.72 | +| cushion | 63.08 | 74.02 | +| base | 34.57 | 49.75 | +| box | 22.95 | 26.44 | +| column | 45.57 | 79.25 | +| signboard | 35.05 | 57.86 | +| chest of drawers | 50.66 | 71.35 | +| counter | 39.28 | 50.26 | +| sand | 36.16 | 53.85 | +| sink | 71.62 | 80.32 | +| skyscraper | 41.49 | 44.49 | +| fireplace | 71.1 | 85.67 | +| refrigerator | 65.18 | 88.71 | +| grandstand | 49.88 | 81.8 | +| path | 17.32 | 22.39 | +| stairs | 30.69 | 51.58 | +| runway | 67.63 | 86.88 | +| case | 58.18 | 84.04 | +| pool table | 91.24 | 97.96 | +| pillow | 57.57 | 63.08 | +| screen door | 67.8 | 94.86 | +| stairway | 40.15 | 46.1 | +| river | 20.28 | 25.36 | +| bridge | 59.1 | 66.52 | +| bookcase | 27.25 | 43.26 | +| blind | 33.67 | 36.69 | +| coffee table | 62.44 | 73.73 | +| toilet | 83.56 | 94.23 | +| flower | 36.26 | 46.17 | +| book | 45.91 | 69.03 | +| hill | 1.42 | 1.68 | +| bench | 58.86 | 69.57 | +| countertop | 58.2 | 81.09 | +| stove | 75.32 | 94.33 | +| palm | 55.32 | 72.15 | +| kitchen island | 40.72 | 88.03 | +| computer | 69.67 | 92.64 | +| swivel chair | 46.73 | 76.84 | +| boat | 48.33 | 52.12 | +| bar | 59.25 | 90.16 | +| arcade machine | 81.07 | 86.52 | +| hovel | 24.49 | 37.46 | +| bus | 90.99 | 95.46 | +| towel | 69.18 | 80.01 | +| light | 46.42 | 55.21 | +| truck | 46.3 | 60.6 | +| tower | 19.75 | 30.35 | +| chandelier | 64.34 | 88.42 | +| awning | 46.98 | 55.17 | +| streetlight | 22.04 | 26.81 | +| booth | 37.43 | 58.95 | +| television receiver | 73.25 | 85.25 | +| airplane | 81.53 | 94.11 | +| dirt track | 2.77 | 4.54 | +| apparel | 48.16 | 69.3 | +| pole | 23.63 | 36.41 | +| land | 0.11 | 0.15 | +| bannister | 9.67 | 13.69 | +| escalator | 55.12 | 90.5 | +| ottoman | 42.61 | 72.85 | +| bottle | 34.03 | 65.4 | +| buffet | 42.29 | 91.71 | +| poster | 24.75 | 29.29 | +| stage | 23.64 | 32.73 | +| van | 41.69 | 51.69 | +| ship | 24.03 | 35.32 | +| fountain | 37.53 | 39.11 | +| conveyer belt | 68.17 | 96.23 | +| canopy | 56.97 | 66.1 | +| washer | 82.0 | 90.63 | +| plaything | 23.98 | 71.02 | +| swimming pool | 55.93 | 85.89 | +| stool | 36.25 | 53.29 | +| barrel | 50.61 | 66.89 | +| basket | 33.0 | 45.01 | +| waterfall | 74.77 | 88.1 | +| tent | 87.51 | 99.38 | +| bag | 24.38 | 29.65 | +| minibike | 71.97 | 81.14 | +| cradle | 56.98 | 99.57 | +| oven | 48.02 | 52.77 | +| ball | 36.61 | 74.44 | +| food | 59.93 | 74.38 | +| step | 5.6 | 6.32 | +| tank | 42.42 | 55.27 | +| trade name | 28.2 | 36.07 | +| microwave | 83.86 | 94.49 | +| pot | 49.66 | 62.19 | +| animal | 70.38 | 73.88 | +| bicycle | 56.36 | 76.11 | +| lake | 0.0 | 0.0 | +| dishwasher | 54.78 | 79.9 | +| screen | 57.8 | 88.23 | +| blanket | 19.43 | 22.55 | +| sculpture | 60.38 | 76.34 | +| hood | 62.23 | 75.13 | +| sconce | 50.48 | 61.35 | +| vase | 37.39 | 49.81 | +| traffic light | 20.25 | 22.31 | +| tray | 8.91 | 12.51 | +| ashcan | 40.81 | 67.95 | +| fan | 58.53 | 80.25 | +| pier | 38.52 | 41.96 | +| crt screen | 7.7 | 33.11 | +| plate | 51.42 | 66.86 | +| monitor | 0.03 | 0.03 | +| bulletin board | 57.6 | 72.23 | +| shower | 1.33 | 1.52 | +| radiator | 66.1 | 76.42 | +| glass | 9.61 | 9.99 | +| clock | 38.9 | 51.32 | +| flag | 65.44 | 81.36 | ++---------------------+-------+-------+ +2024-06-16 00:23:17,580 - mmseg - INFO - Summary: +2024-06-16 00:23:17,580 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 82.89 | 50.21 | 64.9 | ++-------+-------+------+ +2024-06-16 00:23:17,581 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 00:23:17,582 - mmseg - INFO - Iter(val) [250] aAcc: 0.8289, mIoU: 0.5021, mAcc: 0.6490, IoU.wall: 0.7724, IoU.building: 0.8300, IoU.sky: 0.9363, IoU.floor: 0.8134, IoU.tree: 0.7543, IoU.ceiling: 0.8374, IoU.road: 0.8098, IoU.bed : 0.8878, IoU.windowpane: 0.6179, IoU.grass: 0.5994, IoU.cabinet: 0.5705, IoU.sidewalk: 0.5986, IoU.person: 0.8122, IoU.earth: 0.3403, IoU.door: 0.5372, IoU.table: 0.6169, IoU.mountain: 0.5788, IoU.plant: 0.5718, IoU.curtain: 0.7094, IoU.chair: 0.5550, IoU.car: 0.8247, IoU.water: 0.6330, IoU.painting: 0.7273, IoU.sofa: 0.7614, IoU.shelf: 0.2545, IoU.house: 0.4673, IoU.sea: 0.6970, IoU.mirror: 0.7310, IoU.rug: 0.6619, IoU.field: 0.2844, IoU.armchair: 0.5684, IoU.seat: 0.6454, IoU.fence: 0.4907, IoU.desk: 0.4762, IoU.rock: 0.4742, IoU.wardrobe: 0.4769, IoU.lamp: 0.6386, IoU.bathtub: 0.8025, IoU.railing: 0.3071, IoU.cushion: 0.6308, IoU.base: 0.3457, IoU.box: 0.2295, IoU.column: 0.4557, IoU.signboard: 0.3505, IoU.chest of drawers: 0.5066, IoU.counter: 0.3928, IoU.sand: 0.3616, IoU.sink: 0.7162, IoU.skyscraper: 0.4149, IoU.fireplace: 0.7110, IoU.refrigerator: 0.6518, IoU.grandstand: 0.4988, IoU.path: 0.1732, IoU.stairs: 0.3069, IoU.runway: 0.6763, IoU.case: 0.5818, IoU.pool table: 0.9124, IoU.pillow: 0.5757, IoU.screen door: 0.6780, IoU.stairway: 0.4015, IoU.river: 0.2028, IoU.bridge: 0.5910, IoU.bookcase: 0.2725, IoU.blind: 0.3367, IoU.coffee table: 0.6244, IoU.toilet: 0.8356, IoU.flower: 0.3626, IoU.book: 0.4591, IoU.hill: 0.0142, IoU.bench: 0.5886, IoU.countertop: 0.5820, IoU.stove: 0.7532, IoU.palm: 0.5532, IoU.kitchen island: 0.4072, IoU.computer: 0.6967, IoU.swivel chair: 0.4673, IoU.boat: 0.4833, IoU.bar: 0.5925, IoU.arcade machine: 0.8107, IoU.hovel: 0.2449, IoU.bus: 0.9099, IoU.towel: 0.6918, IoU.light: 0.4642, IoU.truck: 0.4630, IoU.tower: 0.1975, IoU.chandelier: 0.6434, IoU.awning: 0.4698, IoU.streetlight: 0.2204, IoU.booth: 0.3743, IoU.television receiver: 0.7325, IoU.airplane: 0.8153, IoU.dirt track: 0.0277, IoU.apparel: 0.4816, IoU.pole: 0.2363, IoU.land: 0.0011, IoU.bannister: 0.0967, IoU.escalator: 0.5512, IoU.ottoman: 0.4261, IoU.bottle: 0.3403, IoU.buffet: 0.4229, IoU.poster: 0.2475, IoU.stage: 0.2364, IoU.van: 0.4169, IoU.ship: 0.2403, IoU.fountain: 0.3753, IoU.conveyer belt: 0.6817, IoU.canopy: 0.5697, IoU.washer: 0.8200, IoU.plaything: 0.2398, IoU.swimming pool: 0.5593, IoU.stool: 0.3625, IoU.barrel: 0.5061, IoU.basket: 0.3300, IoU.waterfall: 0.7477, IoU.tent: 0.8751, IoU.bag: 0.2438, IoU.minibike: 0.7197, IoU.cradle: 0.5698, IoU.oven: 0.4802, IoU.ball: 0.3661, IoU.food: 0.5993, IoU.step: 0.0560, IoU.tank: 0.4242, IoU.trade name: 0.2820, IoU.microwave: 0.8386, IoU.pot: 0.4966, IoU.animal: 0.7038, IoU.bicycle: 0.5636, IoU.lake: 0.0000, IoU.dishwasher: 0.5478, IoU.screen: 0.5780, IoU.blanket: 0.1943, IoU.sculpture: 0.6038, IoU.hood: 0.6223, IoU.sconce: 0.5048, IoU.vase: 0.3739, IoU.traffic light: 0.2025, IoU.tray: 0.0891, IoU.ashcan: 0.4081, IoU.fan: 0.5853, IoU.pier: 0.3852, IoU.crt screen: 0.0770, IoU.plate: 0.5142, IoU.monitor: 0.0003, IoU.bulletin board: 0.5760, IoU.shower: 0.0133, IoU.radiator: 0.6610, IoU.glass: 0.0961, IoU.clock: 0.3890, IoU.flag: 0.6544, Acc.wall: 0.8400, Acc.building: 0.9372, Acc.sky: 0.9662, Acc.floor: 0.8815, Acc.tree: 0.8824, Acc.ceiling: 0.9238, Acc.road: 0.9316, Acc.bed : 0.9700, Acc.windowpane: 0.7873, Acc.grass: 0.7480, Acc.cabinet: 0.6591, Acc.sidewalk: 0.7526, Acc.person: 0.9240, Acc.earth: 0.4594, Acc.door: 0.6726, Acc.table: 0.8174, Acc.mountain: 0.7263, Acc.plant: 0.6818, Acc.curtain: 0.9143, Acc.chair: 0.6331, Acc.car: 0.9286, Acc.water: 0.8072, Acc.painting: 0.8773, Acc.sofa: 0.8471, Acc.shelf: 0.2860, Acc.house: 0.5392, Acc.sea: 0.8110, Acc.mirror: 0.7904, Acc.rug: 0.8396, Acc.field: 0.7584, Acc.armchair: 0.7976, Acc.seat: 0.8798, Acc.fence: 0.6982, Acc.desk: 0.7309, Acc.rock: 0.7652, Acc.wardrobe: 0.8665, Acc.lamp: 0.7878, Acc.bathtub: 0.8385, Acc.railing: 0.3872, Acc.cushion: 0.7402, Acc.base: 0.4975, Acc.box: 0.2644, Acc.column: 0.7925, Acc.signboard: 0.5786, Acc.chest of drawers: 0.7135, Acc.counter: 0.5026, Acc.sand: 0.5385, Acc.sink: 0.8032, Acc.skyscraper: 0.4449, Acc.fireplace: 0.8567, Acc.refrigerator: 0.8871, Acc.grandstand: 0.8180, Acc.path: 0.2239, Acc.stairs: 0.5158, Acc.runway: 0.8688, Acc.case: 0.8404, Acc.pool table: 0.9796, Acc.pillow: 0.6308, Acc.screen door: 0.9486, Acc.stairway: 0.4610, Acc.river: 0.2536, Acc.bridge: 0.6652, Acc.bookcase: 0.4326, Acc.blind: 0.3669, Acc.coffee table: 0.7373, Acc.toilet: 0.9423, Acc.flower: 0.4617, Acc.book: 0.6903, Acc.hill: 0.0168, Acc.bench: 0.6957, Acc.countertop: 0.8109, Acc.stove: 0.9433, Acc.palm: 0.7215, Acc.kitchen island: 0.8803, Acc.computer: 0.9264, Acc.swivel chair: 0.7684, Acc.boat: 0.5212, Acc.bar: 0.9016, Acc.arcade machine: 0.8652, Acc.hovel: 0.3746, Acc.bus: 0.9546, Acc.towel: 0.8001, Acc.light: 0.5521, Acc.truck: 0.6060, Acc.tower: 0.3035, Acc.chandelier: 0.8842, Acc.awning: 0.5517, Acc.streetlight: 0.2681, Acc.booth: 0.5895, Acc.television receiver: 0.8525, Acc.airplane: 0.9411, Acc.dirt track: 0.0454, Acc.apparel: 0.6930, Acc.pole: 0.3641, Acc.land: 0.0015, Acc.bannister: 0.1369, Acc.escalator: 0.9050, Acc.ottoman: 0.7285, Acc.bottle: 0.6540, Acc.buffet: 0.9171, Acc.poster: 0.2929, Acc.stage: 0.3273, Acc.van: 0.5169, Acc.ship: 0.3532, Acc.fountain: 0.3911, Acc.conveyer belt: 0.9623, Acc.canopy: 0.6610, Acc.washer: 0.9063, Acc.plaything: 0.7102, Acc.swimming pool: 0.8589, Acc.stool: 0.5329, Acc.barrel: 0.6689, Acc.basket: 0.4501, Acc.waterfall: 0.8810, Acc.tent: 0.9938, Acc.bag: 0.2965, Acc.minibike: 0.8114, Acc.cradle: 0.9957, Acc.oven: 0.5277, Acc.ball: 0.7444, Acc.food: 0.7438, Acc.step: 0.0632, Acc.tank: 0.5527, Acc.trade name: 0.3607, Acc.microwave: 0.9449, Acc.pot: 0.6219, Acc.animal: 0.7388, Acc.bicycle: 0.7611, Acc.lake: 0.0000, Acc.dishwasher: 0.7990, Acc.screen: 0.8823, Acc.blanket: 0.2255, Acc.sculpture: 0.7634, Acc.hood: 0.7513, Acc.sconce: 0.6135, Acc.vase: 0.4981, Acc.traffic light: 0.2231, Acc.tray: 0.1251, Acc.ashcan: 0.6795, Acc.fan: 0.8025, Acc.pier: 0.4196, Acc.crt screen: 0.3311, Acc.plate: 0.6686, Acc.monitor: 0.0003, Acc.bulletin board: 0.7223, Acc.shower: 0.0152, Acc.radiator: 0.7642, Acc.glass: 0.0999, Acc.clock: 0.5132, Acc.flag: 0.8136 +2024-06-16 00:24:39,208 - mmseg - INFO - Iter [6050/80000] lr: 3.698e-05, eta: 1 day, 12:26:21, time: 3.550, data_time: 1.935, memory: 71384, decode.loss_ce: 0.5143, decode.acc_seg: 80.9033, aux.loss_ce: 0.2023, aux.acc_seg: 81.0711, loss: 0.7166 +2024-06-16 00:26:00,337 - mmseg - INFO - Iter [6100/80000] lr: 3.695e-05, eta: 1 day, 12:23:21, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4876, decode.acc_seg: 81.0558, aux.loss_ce: 0.1929, aux.acc_seg: 81.3917, loss: 0.6805 +2024-06-16 00:27:21,409 - mmseg - INFO - Iter [6150/80000] lr: 3.693e-05, eta: 1 day, 12:20:21, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5177, decode.acc_seg: 80.4780, aux.loss_ce: 0.2042, aux.acc_seg: 80.6228, loss: 0.7218 +2024-06-16 00:28:42,722 - mmseg - INFO - Iter [6200/80000] lr: 3.690e-05, eta: 1 day, 12:17:26, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5171, decode.acc_seg: 80.0713, aux.loss_ce: 0.2060, aux.acc_seg: 80.1371, loss: 0.7231 +2024-06-16 00:30:03,782 - mmseg - INFO - Iter [6250/80000] lr: 3.688e-05, eta: 1 day, 12:14:30, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.5059, decode.acc_seg: 80.1783, aux.loss_ce: 0.2010, aux.acc_seg: 80.5733, loss: 0.7069 +2024-06-16 00:31:25,135 - mmseg - INFO - Iter [6300/80000] lr: 3.685e-05, eta: 1 day, 12:11:38, time: 1.627, data_time: 0.009, memory: 71384, decode.loss_ce: 0.4660, decode.acc_seg: 82.4295, aux.loss_ce: 0.1849, aux.acc_seg: 82.5546, loss: 0.6509 +2024-06-16 00:32:48,465 - mmseg - INFO - Iter [6350/80000] lr: 3.683e-05, eta: 1 day, 12:09:11, time: 1.667, data_time: 0.053, memory: 71384, decode.loss_ce: 0.4571, decode.acc_seg: 82.2675, aux.loss_ce: 0.1819, aux.acc_seg: 82.4345, loss: 0.6390 +2024-06-16 00:34:09,831 - mmseg - INFO - Iter [6400/80000] lr: 3.680e-05, eta: 1 day, 12:06:22, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4466, decode.acc_seg: 82.4824, aux.loss_ce: 0.1781, aux.acc_seg: 82.6156, loss: 0.6247 +2024-06-16 00:35:30,929 - mmseg - INFO - Iter [6450/80000] lr: 3.678e-05, eta: 1 day, 12:03:32, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4435, decode.acc_seg: 83.3874, aux.loss_ce: 0.1765, aux.acc_seg: 83.3055, loss: 0.6200 +2024-06-16 00:36:52,135 - mmseg - INFO - Iter [6500/80000] lr: 3.675e-05, eta: 1 day, 12:00:44, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4569, decode.acc_seg: 82.0290, aux.loss_ce: 0.1817, aux.acc_seg: 82.2305, loss: 0.6386 +2024-06-16 00:38:13,456 - mmseg - INFO - Iter [6550/80000] lr: 3.673e-05, eta: 1 day, 11:57:59, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4507, decode.acc_seg: 82.1680, aux.loss_ce: 0.1795, aux.acc_seg: 82.5382, loss: 0.6302 +2024-06-16 00:39:34,612 - mmseg - INFO - Iter [6600/80000] lr: 3.670e-05, eta: 1 day, 11:55:13, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4361, decode.acc_seg: 82.8685, aux.loss_ce: 0.1762, aux.acc_seg: 82.6465, loss: 0.6123 +2024-06-16 00:40:56,057 - mmseg - INFO - Iter [6650/80000] lr: 3.668e-05, eta: 1 day, 11:52:31, time: 1.629, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4671, decode.acc_seg: 81.6942, aux.loss_ce: 0.1851, aux.acc_seg: 81.9768, loss: 0.6522 +2024-06-16 00:42:17,170 - mmseg - INFO - Iter [6700/80000] lr: 3.665e-05, eta: 1 day, 11:49:48, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4721, decode.acc_seg: 81.9340, aux.loss_ce: 0.1860, aux.acc_seg: 82.0935, loss: 0.6580 +2024-06-16 00:43:38,309 - mmseg - INFO - Iter [6750/80000] lr: 3.663e-05, eta: 1 day, 11:47:05, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4825, decode.acc_seg: 81.4436, aux.loss_ce: 0.1912, aux.acc_seg: 81.5752, loss: 0.6737 +2024-06-16 00:44:59,473 - mmseg - INFO - Iter [6800/80000] lr: 3.660e-05, eta: 1 day, 11:44:24, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4370, decode.acc_seg: 82.3833, aux.loss_ce: 0.1734, aux.acc_seg: 82.5552, loss: 0.6103 +2024-06-16 00:46:20,866 - mmseg - INFO - Iter [6850/80000] lr: 3.658e-05, eta: 1 day, 11:41:47, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4555, decode.acc_seg: 82.4573, aux.loss_ce: 0.1809, aux.acc_seg: 82.4561, loss: 0.6364 +2024-06-16 00:47:41,976 - mmseg - INFO - Iter [6900/80000] lr: 3.655e-05, eta: 1 day, 11:39:08, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4665, decode.acc_seg: 82.2523, aux.loss_ce: 0.1853, aux.acc_seg: 82.2201, loss: 0.6518 +2024-06-16 00:49:03,097 - mmseg - INFO - Iter [6950/80000] lr: 3.653e-05, eta: 1 day, 11:36:30, time: 1.622, data_time: 0.009, memory: 71384, decode.loss_ce: 0.4772, decode.acc_seg: 81.6496, aux.loss_ce: 0.1883, aux.acc_seg: 81.9302, loss: 0.6655 +2024-06-16 00:50:24,316 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 00:50:24,316 - mmseg - INFO - Iter [7000/80000] lr: 3.650e-05, eta: 1 day, 11:33:54, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4576, decode.acc_seg: 82.1130, aux.loss_ce: 0.1829, aux.acc_seg: 82.1487, loss: 0.6404 +2024-06-16 00:52:05,498 - mmseg - INFO - per class results: +2024-06-16 00:52:05,504 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.09 | 87.7 | +| building | 84.23 | 92.41 | +| sky | 94.11 | 96.39 | +| floor | 82.52 | 87.3 | +| tree | 75.07 | 87.49 | +| ceiling | 83.26 | 95.01 | +| road | 84.68 | 90.49 | +| bed | 90.9 | 94.79 | +| windowpane | 61.83 | 76.02 | +| grass | 66.01 | 79.91 | +| cabinet | 59.91 | 66.84 | +| sidewalk | 66.2 | 84.28 | +| person | 81.74 | 93.17 | +| earth | 32.42 | 44.56 | +| door | 58.76 | 69.92 | +| table | 60.5 | 74.64 | +| mountain | 52.67 | 58.42 | +| plant | 56.36 | 72.25 | +| curtain | 73.93 | 81.5 | +| chair | 61.57 | 79.93 | +| car | 80.01 | 93.94 | +| water | 42.93 | 49.91 | +| painting | 75.79 | 87.24 | +| sofa | 72.38 | 84.21 | +| shelf | 40.89 | 50.73 | +| house | 54.32 | 75.43 | +| sea | 61.58 | 93.58 | +| mirror | 75.03 | 83.95 | +| rug | 66.74 | 85.03 | +| field | 27.75 | 73.78 | +| armchair | 52.05 | 63.04 | +| seat | 64.85 | 83.17 | +| fence | 36.89 | 41.89 | +| desk | 48.18 | 77.38 | +| rock | 59.34 | 77.85 | +| wardrobe | 54.04 | 79.93 | +| lamp | 62.98 | 73.57 | +| bathtub | 80.5 | 84.2 | +| railing | 40.04 | 65.31 | +| cushion | 60.27 | 85.14 | +| base | 48.46 | 61.52 | +| box | 26.13 | 37.05 | +| column | 50.92 | 67.7 | +| signboard | 36.45 | 50.28 | +| chest of drawers | 41.84 | 75.34 | +| counter | 42.11 | 51.07 | +| sand | 40.64 | 55.96 | +| sink | 70.96 | 76.63 | +| skyscraper | 32.52 | 32.88 | +| fireplace | 69.43 | 77.53 | +| refrigerator | 71.23 | 86.89 | +| grandstand | 50.54 | 86.77 | +| path | 15.56 | 17.79 | +| stairs | 38.06 | 45.34 | +| runway | 71.04 | 96.18 | +| case | 44.64 | 91.41 | +| pool table | 88.57 | 90.55 | +| pillow | 61.99 | 71.09 | +| screen door | 72.22 | 90.06 | +| stairway | 53.1 | 72.47 | +| river | 19.04 | 21.39 | +| bridge | 53.81 | 66.49 | +| bookcase | 37.08 | 48.51 | +| blind | 44.87 | 59.82 | +| coffee table | 59.01 | 84.69 | +| toilet | 86.28 | 95.74 | +| flower | 40.64 | 53.55 | +| book | 48.18 | 72.56 | +| hill | 4.75 | 16.06 | +| bench | 57.04 | 67.31 | +| countertop | 48.49 | 85.74 | +| stove | 77.35 | 90.85 | +| palm | 52.28 | 63.48 | +| kitchen island | 42.44 | 81.19 | +| computer | 74.72 | 90.27 | +| swivel chair | 48.01 | 75.05 | +| boat | 48.09 | 66.47 | +| bar | 57.56 | 80.06 | +| arcade machine | 78.27 | 96.1 | +| hovel | 23.67 | 26.99 | +| bus | 90.3 | 96.14 | +| towel | 60.95 | 87.93 | +| light | 46.77 | 52.02 | +| truck | 43.04 | 62.52 | +| tower | 22.73 | 36.64 | +| chandelier | 66.41 | 83.73 | +| awning | 45.47 | 58.06 | +| streetlight | 26.47 | 34.84 | +| booth | 27.05 | 56.58 | +| television receiver | 74.66 | 87.77 | +| airplane | 67.12 | 95.5 | +| dirt track | 0.0 | 0.0 | +| apparel | 38.63 | 52.3 | +| pole | 23.07 | 31.1 | +| land | 0.92 | 1.49 | +| bannister | 10.37 | 16.74 | +| escalator | 58.41 | 88.96 | +| ottoman | 45.09 | 76.19 | +| bottle | 36.01 | 63.76 | +| buffet | 43.35 | 58.89 | +| poster | 29.03 | 33.6 | +| stage | 24.62 | 36.14 | +| van | 25.98 | 28.77 | +| ship | 68.4 | 96.22 | +| fountain | 32.73 | 32.89 | +| conveyer belt | 82.16 | 89.91 | +| canopy | 39.22 | 73.17 | +| washer | 79.63 | 86.97 | +| plaything | 26.24 | 72.69 | +| swimming pool | 58.85 | 94.02 | +| stool | 44.0 | 53.74 | +| barrel | 53.68 | 65.09 | +| basket | 36.61 | 58.99 | +| waterfall | 52.42 | 55.46 | +| tent | 80.74 | 98.83 | +| bag | 21.71 | 26.87 | +| minibike | 68.12 | 82.59 | +| cradle | 80.55 | 97.87 | +| oven | 61.72 | 77.62 | +| ball | 34.14 | 74.8 | +| food | 50.75 | 81.27 | +| step | 22.07 | 30.39 | +| tank | 53.31 | 74.15 | +| trade name | 3.99 | 4.07 | +| microwave | 85.55 | 95.01 | +| pot | 47.97 | 53.45 | +| animal | 60.94 | 62.44 | +| bicycle | 11.5 | 11.69 | +| lake | 21.12 | 96.25 | +| dishwasher | 60.25 | 75.59 | +| screen | 57.8 | 94.47 | +| blanket | 34.89 | 40.22 | +| sculpture | 72.32 | 86.1 | +| hood | 65.59 | 87.57 | +| sconce | 50.43 | 58.32 | +| vase | 37.23 | 57.04 | +| traffic light | 28.07 | 42.63 | +| tray | 14.78 | 17.64 | +| ashcan | 44.47 | 66.22 | +| fan | 60.32 | 73.55 | +| pier | 41.34 | 42.61 | +| crt screen | 0.0 | 0.0 | +| plate | 54.81 | 69.25 | +| monitor | 49.66 | 81.52 | +| bulletin board | 41.57 | 83.95 | +| shower | 0.0 | 0.0 | +| radiator | 65.27 | 76.3 | +| glass | 14.83 | 16.14 | +| clock | 37.75 | 42.19 | +| flag | 67.07 | 71.72 | ++---------------------+-------+-------+ +2024-06-16 00:52:05,504 - mmseg - INFO - Summary: +2024-06-16 00:52:05,505 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 83.53 | 51.16 | 66.46 | ++-------+-------+-------+ +2024-06-16 00:52:05,505 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 00:52:05,506 - mmseg - INFO - Iter(val) [250] aAcc: 0.8353, mIoU: 0.5116, mAcc: 0.6646, IoU.wall: 0.7909, IoU.building: 0.8423, IoU.sky: 0.9411, IoU.floor: 0.8252, IoU.tree: 0.7507, IoU.ceiling: 0.8326, IoU.road: 0.8468, IoU.bed : 0.9090, IoU.windowpane: 0.6183, IoU.grass: 0.6601, IoU.cabinet: 0.5991, IoU.sidewalk: 0.6620, IoU.person: 0.8174, IoU.earth: 0.3242, IoU.door: 0.5876, IoU.table: 0.6050, IoU.mountain: 0.5267, IoU.plant: 0.5636, IoU.curtain: 0.7393, IoU.chair: 0.6157, IoU.car: 0.8001, IoU.water: 0.4293, IoU.painting: 0.7579, IoU.sofa: 0.7238, IoU.shelf: 0.4089, IoU.house: 0.5432, IoU.sea: 0.6158, IoU.mirror: 0.7503, IoU.rug: 0.6674, IoU.field: 0.2775, IoU.armchair: 0.5205, IoU.seat: 0.6485, IoU.fence: 0.3689, IoU.desk: 0.4818, IoU.rock: 0.5934, IoU.wardrobe: 0.5404, IoU.lamp: 0.6298, IoU.bathtub: 0.8050, IoU.railing: 0.4004, IoU.cushion: 0.6027, IoU.base: 0.4846, IoU.box: 0.2613, IoU.column: 0.5092, IoU.signboard: 0.3645, IoU.chest of drawers: 0.4184, IoU.counter: 0.4211, IoU.sand: 0.4064, IoU.sink: 0.7096, IoU.skyscraper: 0.3252, IoU.fireplace: 0.6943, IoU.refrigerator: 0.7123, IoU.grandstand: 0.5054, IoU.path: 0.1556, IoU.stairs: 0.3806, IoU.runway: 0.7104, IoU.case: 0.4464, IoU.pool table: 0.8857, IoU.pillow: 0.6199, IoU.screen door: 0.7222, IoU.stairway: 0.5310, IoU.river: 0.1904, IoU.bridge: 0.5381, IoU.bookcase: 0.3708, IoU.blind: 0.4487, IoU.coffee table: 0.5901, IoU.toilet: 0.8628, IoU.flower: 0.4064, IoU.book: 0.4818, IoU.hill: 0.0475, IoU.bench: 0.5704, IoU.countertop: 0.4849, IoU.stove: 0.7735, IoU.palm: 0.5228, IoU.kitchen island: 0.4244, IoU.computer: 0.7472, IoU.swivel chair: 0.4801, IoU.boat: 0.4809, IoU.bar: 0.5756, IoU.arcade machine: 0.7827, IoU.hovel: 0.2367, IoU.bus: 0.9030, IoU.towel: 0.6095, IoU.light: 0.4677, IoU.truck: 0.4304, IoU.tower: 0.2273, IoU.chandelier: 0.6641, IoU.awning: 0.4547, IoU.streetlight: 0.2647, IoU.booth: 0.2705, IoU.television receiver: 0.7466, IoU.airplane: 0.6712, IoU.dirt track: 0.0000, IoU.apparel: 0.3863, IoU.pole: 0.2307, IoU.land: 0.0092, IoU.bannister: 0.1037, IoU.escalator: 0.5841, IoU.ottoman: 0.4509, IoU.bottle: 0.3601, IoU.buffet: 0.4335, IoU.poster: 0.2903, IoU.stage: 0.2462, IoU.van: 0.2598, IoU.ship: 0.6840, IoU.fountain: 0.3273, IoU.conveyer belt: 0.8216, IoU.canopy: 0.3922, IoU.washer: 0.7963, IoU.plaything: 0.2624, IoU.swimming pool: 0.5885, IoU.stool: 0.4400, IoU.barrel: 0.5368, IoU.basket: 0.3661, IoU.waterfall: 0.5242, IoU.tent: 0.8074, IoU.bag: 0.2171, IoU.minibike: 0.6812, IoU.cradle: 0.8055, IoU.oven: 0.6172, IoU.ball: 0.3414, IoU.food: 0.5075, IoU.step: 0.2207, IoU.tank: 0.5331, IoU.trade name: 0.0399, IoU.microwave: 0.8555, IoU.pot: 0.4797, IoU.animal: 0.6094, IoU.bicycle: 0.1150, IoU.lake: 0.2112, IoU.dishwasher: 0.6025, IoU.screen: 0.5780, IoU.blanket: 0.3489, IoU.sculpture: 0.7232, IoU.hood: 0.6559, IoU.sconce: 0.5043, IoU.vase: 0.3723, IoU.traffic light: 0.2807, IoU.tray: 0.1478, IoU.ashcan: 0.4447, IoU.fan: 0.6032, IoU.pier: 0.4134, IoU.crt screen: 0.0000, IoU.plate: 0.5481, IoU.monitor: 0.4966, IoU.bulletin board: 0.4157, IoU.shower: 0.0000, IoU.radiator: 0.6527, IoU.glass: 0.1483, IoU.clock: 0.3775, IoU.flag: 0.6707, Acc.wall: 0.8770, Acc.building: 0.9241, Acc.sky: 0.9639, Acc.floor: 0.8730, Acc.tree: 0.8749, Acc.ceiling: 0.9501, Acc.road: 0.9049, Acc.bed : 0.9479, Acc.windowpane: 0.7602, Acc.grass: 0.7991, Acc.cabinet: 0.6684, Acc.sidewalk: 0.8428, Acc.person: 0.9317, Acc.earth: 0.4456, Acc.door: 0.6992, Acc.table: 0.7464, Acc.mountain: 0.5842, Acc.plant: 0.7225, Acc.curtain: 0.8150, Acc.chair: 0.7993, Acc.car: 0.9394, Acc.water: 0.4991, Acc.painting: 0.8724, Acc.sofa: 0.8421, Acc.shelf: 0.5073, Acc.house: 0.7543, Acc.sea: 0.9358, Acc.mirror: 0.8395, Acc.rug: 0.8503, Acc.field: 0.7378, Acc.armchair: 0.6304, Acc.seat: 0.8317, Acc.fence: 0.4189, Acc.desk: 0.7738, Acc.rock: 0.7785, Acc.wardrobe: 0.7993, Acc.lamp: 0.7357, Acc.bathtub: 0.8420, Acc.railing: 0.6531, Acc.cushion: 0.8514, Acc.base: 0.6152, Acc.box: 0.3705, Acc.column: 0.6770, Acc.signboard: 0.5028, Acc.chest of drawers: 0.7534, Acc.counter: 0.5107, Acc.sand: 0.5596, Acc.sink: 0.7663, Acc.skyscraper: 0.3288, Acc.fireplace: 0.7753, Acc.refrigerator: 0.8689, Acc.grandstand: 0.8677, Acc.path: 0.1779, Acc.stairs: 0.4534, Acc.runway: 0.9618, Acc.case: 0.9141, Acc.pool table: 0.9055, Acc.pillow: 0.7109, Acc.screen door: 0.9006, Acc.stairway: 0.7247, Acc.river: 0.2139, Acc.bridge: 0.6649, Acc.bookcase: 0.4851, Acc.blind: 0.5982, Acc.coffee table: 0.8469, Acc.toilet: 0.9574, Acc.flower: 0.5355, Acc.book: 0.7256, Acc.hill: 0.1606, Acc.bench: 0.6731, Acc.countertop: 0.8574, Acc.stove: 0.9085, Acc.palm: 0.6348, Acc.kitchen island: 0.8119, Acc.computer: 0.9027, Acc.swivel chair: 0.7505, Acc.boat: 0.6647, Acc.bar: 0.8006, Acc.arcade machine: 0.9610, Acc.hovel: 0.2699, Acc.bus: 0.9614, Acc.towel: 0.8793, Acc.light: 0.5202, Acc.truck: 0.6252, Acc.tower: 0.3664, Acc.chandelier: 0.8373, Acc.awning: 0.5806, Acc.streetlight: 0.3484, Acc.booth: 0.5658, Acc.television receiver: 0.8777, Acc.airplane: 0.9550, Acc.dirt track: 0.0000, Acc.apparel: 0.5230, Acc.pole: 0.3110, Acc.land: 0.0149, Acc.bannister: 0.1674, Acc.escalator: 0.8896, Acc.ottoman: 0.7619, Acc.bottle: 0.6376, Acc.buffet: 0.5889, Acc.poster: 0.3360, Acc.stage: 0.3614, Acc.van: 0.2877, Acc.ship: 0.9622, Acc.fountain: 0.3289, Acc.conveyer belt: 0.8991, Acc.canopy: 0.7317, Acc.washer: 0.8697, Acc.plaything: 0.7269, Acc.swimming pool: 0.9402, Acc.stool: 0.5374, Acc.barrel: 0.6509, Acc.basket: 0.5899, Acc.waterfall: 0.5546, Acc.tent: 0.9883, Acc.bag: 0.2687, Acc.minibike: 0.8259, Acc.cradle: 0.9787, Acc.oven: 0.7762, Acc.ball: 0.7480, Acc.food: 0.8127, Acc.step: 0.3039, Acc.tank: 0.7415, Acc.trade name: 0.0407, Acc.microwave: 0.9501, Acc.pot: 0.5345, Acc.animal: 0.6244, Acc.bicycle: 0.1169, Acc.lake: 0.9625, Acc.dishwasher: 0.7559, Acc.screen: 0.9447, Acc.blanket: 0.4022, Acc.sculpture: 0.8610, Acc.hood: 0.8757, Acc.sconce: 0.5832, Acc.vase: 0.5704, Acc.traffic light: 0.4263, Acc.tray: 0.1764, Acc.ashcan: 0.6622, Acc.fan: 0.7355, Acc.pier: 0.4261, Acc.crt screen: 0.0000, Acc.plate: 0.6925, Acc.monitor: 0.8152, Acc.bulletin board: 0.8395, Acc.shower: 0.0000, Acc.radiator: 0.7630, Acc.glass: 0.1614, Acc.clock: 0.4219, Acc.flag: 0.7172 +2024-06-16 00:53:27,313 - mmseg - INFO - Iter [7050/80000] lr: 3.648e-05, eta: 1 day, 11:48:53, time: 3.660, data_time: 2.040, memory: 71384, decode.loss_ce: 0.4707, decode.acc_seg: 82.1850, aux.loss_ce: 0.1876, aux.acc_seg: 82.1982, loss: 0.6584 +2024-06-16 00:54:48,522 - mmseg - INFO - Iter [7100/80000] lr: 3.645e-05, eta: 1 day, 11:46:11, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4495, decode.acc_seg: 82.9759, aux.loss_ce: 0.1794, aux.acc_seg: 83.2237, loss: 0.6289 +2024-06-16 00:56:09,682 - mmseg - INFO - Iter [7150/80000] lr: 3.643e-05, eta: 1 day, 11:43:30, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4539, decode.acc_seg: 82.4919, aux.loss_ce: 0.1801, aux.acc_seg: 82.6661, loss: 0.6340 +2024-06-16 00:57:30,850 - mmseg - INFO - Iter [7200/80000] lr: 3.640e-05, eta: 1 day, 11:40:50, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4843, decode.acc_seg: 81.3626, aux.loss_ce: 0.1918, aux.acc_seg: 81.7044, loss: 0.6761 +2024-06-16 00:58:52,148 - mmseg - INFO - Iter [7250/80000] lr: 3.638e-05, eta: 1 day, 11:38:12, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4688, decode.acc_seg: 82.3069, aux.loss_ce: 0.1853, aux.acc_seg: 82.4862, loss: 0.6541 +2024-06-16 01:00:13,543 - mmseg - INFO - Iter [7300/80000] lr: 3.635e-05, eta: 1 day, 11:35:36, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4395, decode.acc_seg: 83.1218, aux.loss_ce: 0.1746, aux.acc_seg: 83.2620, loss: 0.6141 +2024-06-16 01:01:34,725 - mmseg - INFO - Iter [7350/80000] lr: 3.633e-05, eta: 1 day, 11:32:59, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4334, decode.acc_seg: 83.1890, aux.loss_ce: 0.1699, aux.acc_seg: 83.7186, loss: 0.6033 +2024-06-16 01:02:55,947 - mmseg - INFO - Iter [7400/80000] lr: 3.630e-05, eta: 1 day, 11:30:24, time: 1.624, data_time: 0.009, memory: 71384, decode.loss_ce: 0.4819, decode.acc_seg: 81.8276, aux.loss_ce: 0.1917, aux.acc_seg: 81.9364, loss: 0.6736 +2024-06-16 01:04:17,072 - mmseg - INFO - Iter [7450/80000] lr: 3.628e-05, eta: 1 day, 11:27:49, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4424, decode.acc_seg: 82.6942, aux.loss_ce: 0.1776, aux.acc_seg: 82.7716, loss: 0.6200 +2024-06-16 01:05:38,366 - mmseg - INFO - Iter [7500/80000] lr: 3.625e-05, eta: 1 day, 11:25:16, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4634, decode.acc_seg: 81.7500, aux.loss_ce: 0.1848, aux.acc_seg: 81.8316, loss: 0.6482 +2024-06-16 01:06:59,450 - mmseg - INFO - Iter [7550/80000] lr: 3.623e-05, eta: 1 day, 11:22:42, time: 1.622, data_time: 0.009, memory: 71384, decode.loss_ce: 0.4721, decode.acc_seg: 81.8731, aux.loss_ce: 0.1874, aux.acc_seg: 81.9121, loss: 0.6595 +2024-06-16 01:08:23,529 - mmseg - INFO - Iter [7600/80000] lr: 3.620e-05, eta: 1 day, 11:20:38, time: 1.682, data_time: 0.061, memory: 71384, decode.loss_ce: 0.4488, decode.acc_seg: 82.6800, aux.loss_ce: 0.1786, aux.acc_seg: 82.7022, loss: 0.6274 +2024-06-16 01:09:44,707 - mmseg - INFO - Iter [7650/80000] lr: 3.618e-05, eta: 1 day, 11:18:07, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4177, decode.acc_seg: 83.3401, aux.loss_ce: 0.1659, aux.acc_seg: 83.3974, loss: 0.5836 +2024-06-16 01:11:05,806 - mmseg - INFO - Iter [7700/80000] lr: 3.615e-05, eta: 1 day, 11:15:36, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4506, decode.acc_seg: 82.1080, aux.loss_ce: 0.1802, aux.acc_seg: 82.2968, loss: 0.6308 +2024-06-16 01:12:27,151 - mmseg - INFO - Iter [7750/80000] lr: 3.613e-05, eta: 1 day, 11:13:08, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4235, decode.acc_seg: 83.5996, aux.loss_ce: 0.1685, aux.acc_seg: 83.5647, loss: 0.5920 +2024-06-16 01:13:48,252 - mmseg - INFO - Iter [7800/80000] lr: 3.610e-05, eta: 1 day, 11:10:39, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4335, decode.acc_seg: 83.4916, aux.loss_ce: 0.1727, aux.acc_seg: 83.5814, loss: 0.6062 +2024-06-16 01:15:09,644 - mmseg - INFO - Iter [7850/80000] lr: 3.608e-05, eta: 1 day, 11:08:13, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4357, decode.acc_seg: 83.3124, aux.loss_ce: 0.1745, aux.acc_seg: 83.1533, loss: 0.6101 +2024-06-16 01:16:30,826 - mmseg - INFO - Iter [7900/80000] lr: 3.605e-05, eta: 1 day, 11:05:46, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4511, decode.acc_seg: 82.6994, aux.loss_ce: 0.1787, aux.acc_seg: 82.8405, loss: 0.6297 +2024-06-16 01:17:52,008 - mmseg - INFO - Iter [7950/80000] lr: 3.603e-05, eta: 1 day, 11:03:20, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4295, decode.acc_seg: 83.6058, aux.loss_ce: 0.1717, aux.acc_seg: 83.6506, loss: 0.6012 +2024-06-16 01:19:13,302 - mmseg - INFO - Saving checkpoint at 8000 iterations +2024-06-16 01:20:40,028 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 01:20:40,028 - mmseg - INFO - Iter [8000/80000] lr: 3.600e-05, eta: 1 day, 11:13:57, time: 3.360, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4344, decode.acc_seg: 83.0607, aux.loss_ce: 0.1728, aux.acc_seg: 83.0960, loss: 0.6071 +2024-06-16 01:22:15,549 - mmseg - INFO - per class results: +2024-06-16 01:22:15,555 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 78.62 | 89.74 | +| building | 82.84 | 90.07 | +| sky | 94.1 | 97.21 | +| floor | 83.0 | 89.63 | +| tree | 76.13 | 86.8 | +| ceiling | 84.34 | 90.42 | +| road | 81.98 | 85.27 | +| bed | 90.62 | 95.67 | +| windowpane | 63.12 | 77.47 | +| grass | 63.24 | 79.49 | +| cabinet | 63.74 | 76.92 | +| sidewalk | 64.01 | 87.25 | +| person | 82.66 | 91.73 | +| earth | 35.83 | 46.41 | +| door | 54.82 | 67.61 | +| table | 60.27 | 72.19 | +| mountain | 58.21 | 77.32 | +| plant | 55.64 | 71.38 | +| curtain | 72.18 | 90.12 | +| chair | 60.23 | 72.56 | +| car | 83.76 | 91.55 | +| water | 59.99 | 75.73 | +| painting | 69.98 | 91.97 | +| sofa | 76.39 | 91.17 | +| shelf | 44.05 | 56.01 | +| house | 45.3 | 66.89 | +| sea | 69.03 | 83.27 | +| mirror | 72.09 | 77.76 | +| rug | 67.77 | 84.96 | +| field | 31.41 | 70.81 | +| armchair | 56.95 | 71.27 | +| seat | 63.55 | 87.38 | +| fence | 45.65 | 58.42 | +| desk | 50.84 | 75.86 | +| rock | 25.08 | 26.15 | +| wardrobe | 56.08 | 79.95 | +| lamp | 68.31 | 81.95 | +| bathtub | 87.52 | 93.15 | +| railing | 40.85 | 51.31 | +| cushion | 57.93 | 62.08 | +| base | 34.29 | 70.13 | +| box | 28.29 | 39.85 | +| column | 46.36 | 49.71 | +| signboard | 35.95 | 62.43 | +| chest of drawers | 51.59 | 69.2 | +| counter | 48.71 | 64.36 | +| sand | 47.96 | 57.61 | +| sink | 74.13 | 80.9 | +| skyscraper | 53.72 | 67.32 | +| fireplace | 65.9 | 75.32 | +| refrigerator | 76.07 | 83.52 | +| grandstand | 46.93 | 88.9 | +| path | 24.56 | 29.38 | +| stairs | 21.3 | 26.06 | +| runway | 68.07 | 90.2 | +| case | 56.6 | 68.31 | +| pool table | 92.49 | 98.1 | +| pillow | 63.46 | 84.5 | +| screen door | 71.23 | 93.97 | +| stairway | 49.07 | 77.73 | +| river | 19.04 | 44.61 | +| bridge | 57.62 | 91.09 | +| bookcase | 34.76 | 62.31 | +| blind | 16.78 | 17.25 | +| coffee table | 53.85 | 87.44 | +| toilet | 87.69 | 92.19 | +| flower | 39.54 | 53.14 | +| book | 45.13 | 69.38 | +| hill | 10.21 | 16.3 | +| bench | 47.41 | 55.01 | +| countertop | 59.94 | 69.37 | +| stove | 62.6 | 68.39 | +| palm | 54.41 | 68.86 | +| kitchen island | 31.15 | 95.94 | +| computer | 76.18 | 89.73 | +| swivel chair | 49.81 | 77.09 | +| boat | 47.28 | 59.0 | +| bar | 61.56 | 64.87 | +| arcade machine | 89.89 | 98.1 | +| hovel | 18.31 | 19.63 | +| bus | 92.12 | 95.59 | +| towel | 69.19 | 85.83 | +| light | 40.55 | 42.63 | +| truck | 37.21 | 47.42 | +| tower | 4.05 | 5.03 | +| chandelier | 69.59 | 83.39 | +| awning | 31.49 | 34.58 | +| streetlight | 24.51 | 33.73 | +| booth | 35.37 | 37.3 | +| television receiver | 74.04 | 82.34 | +| airplane | 80.9 | 92.68 | +| dirt track | 4.48 | 22.57 | +| apparel | 44.33 | 84.24 | +| pole | 12.95 | 14.77 | +| land | 0.05 | 0.06 | +| bannister | 10.91 | 12.94 | +| escalator | 59.69 | 84.64 | +| ottoman | 48.97 | 70.76 | +| bottle | 31.61 | 37.6 | +| buffet | 43.22 | 54.54 | +| poster | 31.41 | 46.41 | +| stage | 15.21 | 58.29 | +| van | 29.6 | 37.24 | +| ship | 59.79 | 99.07 | +| fountain | 27.3 | 27.96 | +| conveyer belt | 84.06 | 90.69 | +| canopy | 45.81 | 62.91 | +| washer | 82.72 | 89.37 | +| plaything | 24.88 | 43.85 | +| swimming pool | 53.56 | 94.25 | +| stool | 38.28 | 47.69 | +| barrel | 49.97 | 65.1 | +| basket | 37.28 | 48.67 | +| waterfall | 62.45 | 97.06 | +| tent | 83.22 | 99.22 | +| bag | 22.12 | 25.88 | +| minibike | 69.54 | 87.18 | +| cradle | 70.66 | 96.77 | +| oven | 33.92 | 61.62 | +| ball | 39.17 | 72.79 | +| food | 60.0 | 70.16 | +| step | 9.24 | 9.92 | +| tank | 53.52 | 71.68 | +| trade name | 8.5 | 9.17 | +| microwave | 85.43 | 94.24 | +| pot | 53.64 | 65.3 | +| animal | 70.12 | 76.61 | +| bicycle | 54.94 | 69.42 | +| lake | 0.0 | 0.0 | +| dishwasher | 63.39 | 70.29 | +| screen | 61.32 | 92.62 | +| blanket | 27.71 | 33.34 | +| sculpture | 62.89 | 88.2 | +| hood | 66.24 | 70.24 | +| sconce | 51.04 | 58.73 | +| vase | 40.07 | 55.63 | +| traffic light | 27.21 | 40.91 | +| tray | 17.31 | 40.13 | +| ashcan | 47.05 | 61.76 | +| fan | 61.23 | 70.19 | +| pier | 52.17 | 64.65 | +| crt screen | 5.86 | 15.97 | +| plate | 55.8 | 78.51 | +| monitor | 4.41 | 4.56 | +| bulletin board | 64.43 | 69.86 | +| shower | 0.0 | 0.0 | +| radiator | 34.99 | 35.11 | +| glass | 11.72 | 12.22 | +| clock | 29.68 | 32.48 | +| flag | 67.3 | 73.58 | ++---------------------+-------+-------+ +2024-06-16 01:22:15,555 - mmseg - INFO - Summary: +2024-06-16 01:22:15,555 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 83.62 | 50.81 | 64.67 | ++-------+-------+-------+ +2024-06-16 01:22:15,556 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 01:22:15,556 - mmseg - INFO - Iter(val) [250] aAcc: 0.8362, mIoU: 0.5081, mAcc: 0.6467, IoU.wall: 0.7862, IoU.building: 0.8284, IoU.sky: 0.9410, IoU.floor: 0.8300, IoU.tree: 0.7613, IoU.ceiling: 0.8434, IoU.road: 0.8198, IoU.bed : 0.9062, IoU.windowpane: 0.6312, IoU.grass: 0.6324, IoU.cabinet: 0.6374, IoU.sidewalk: 0.6401, IoU.person: 0.8266, IoU.earth: 0.3583, IoU.door: 0.5482, IoU.table: 0.6027, IoU.mountain: 0.5821, IoU.plant: 0.5564, IoU.curtain: 0.7218, IoU.chair: 0.6023, IoU.car: 0.8376, IoU.water: 0.5999, IoU.painting: 0.6998, IoU.sofa: 0.7639, IoU.shelf: 0.4405, IoU.house: 0.4530, IoU.sea: 0.6903, IoU.mirror: 0.7209, IoU.rug: 0.6777, IoU.field: 0.3141, IoU.armchair: 0.5695, IoU.seat: 0.6355, IoU.fence: 0.4565, IoU.desk: 0.5084, IoU.rock: 0.2508, IoU.wardrobe: 0.5608, IoU.lamp: 0.6831, IoU.bathtub: 0.8752, IoU.railing: 0.4085, IoU.cushion: 0.5793, IoU.base: 0.3429, IoU.box: 0.2829, IoU.column: 0.4636, IoU.signboard: 0.3595, IoU.chest of drawers: 0.5159, IoU.counter: 0.4871, IoU.sand: 0.4796, IoU.sink: 0.7413, IoU.skyscraper: 0.5372, IoU.fireplace: 0.6590, IoU.refrigerator: 0.7607, IoU.grandstand: 0.4693, IoU.path: 0.2456, IoU.stairs: 0.2130, IoU.runway: 0.6807, IoU.case: 0.5660, IoU.pool table: 0.9249, IoU.pillow: 0.6346, IoU.screen door: 0.7123, IoU.stairway: 0.4907, IoU.river: 0.1904, IoU.bridge: 0.5762, IoU.bookcase: 0.3476, IoU.blind: 0.1678, IoU.coffee table: 0.5385, IoU.toilet: 0.8769, IoU.flower: 0.3954, IoU.book: 0.4513, IoU.hill: 0.1021, IoU.bench: 0.4741, IoU.countertop: 0.5994, IoU.stove: 0.6260, IoU.palm: 0.5441, IoU.kitchen island: 0.3115, IoU.computer: 0.7618, IoU.swivel chair: 0.4981, IoU.boat: 0.4728, IoU.bar: 0.6156, IoU.arcade machine: 0.8989, IoU.hovel: 0.1831, IoU.bus: 0.9212, IoU.towel: 0.6919, IoU.light: 0.4055, IoU.truck: 0.3721, IoU.tower: 0.0405, IoU.chandelier: 0.6959, IoU.awning: 0.3149, IoU.streetlight: 0.2451, IoU.booth: 0.3537, IoU.television receiver: 0.7404, IoU.airplane: 0.8090, IoU.dirt track: 0.0448, IoU.apparel: 0.4433, IoU.pole: 0.1295, IoU.land: 0.0005, IoU.bannister: 0.1091, IoU.escalator: 0.5969, IoU.ottoman: 0.4897, IoU.bottle: 0.3161, IoU.buffet: 0.4322, IoU.poster: 0.3141, IoU.stage: 0.1521, IoU.van: 0.2960, IoU.ship: 0.5979, IoU.fountain: 0.2730, IoU.conveyer belt: 0.8406, IoU.canopy: 0.4581, IoU.washer: 0.8272, IoU.plaything: 0.2488, IoU.swimming pool: 0.5356, IoU.stool: 0.3828, IoU.barrel: 0.4997, IoU.basket: 0.3728, IoU.waterfall: 0.6245, IoU.tent: 0.8322, IoU.bag: 0.2212, IoU.minibike: 0.6954, IoU.cradle: 0.7066, IoU.oven: 0.3392, IoU.ball: 0.3917, IoU.food: 0.6000, IoU.step: 0.0924, IoU.tank: 0.5352, IoU.trade name: 0.0850, IoU.microwave: 0.8543, IoU.pot: 0.5364, IoU.animal: 0.7012, IoU.bicycle: 0.5494, IoU.lake: 0.0000, IoU.dishwasher: 0.6339, IoU.screen: 0.6132, IoU.blanket: 0.2771, IoU.sculpture: 0.6289, IoU.hood: 0.6624, IoU.sconce: 0.5104, IoU.vase: 0.4007, IoU.traffic light: 0.2721, IoU.tray: 0.1731, IoU.ashcan: 0.4705, IoU.fan: 0.6123, IoU.pier: 0.5217, IoU.crt screen: 0.0586, IoU.plate: 0.5580, IoU.monitor: 0.0441, IoU.bulletin board: 0.6443, IoU.shower: 0.0000, IoU.radiator: 0.3499, IoU.glass: 0.1172, IoU.clock: 0.2968, IoU.flag: 0.6730, Acc.wall: 0.8974, Acc.building: 0.9007, Acc.sky: 0.9721, Acc.floor: 0.8963, Acc.tree: 0.8680, Acc.ceiling: 0.9042, Acc.road: 0.8527, Acc.bed : 0.9567, Acc.windowpane: 0.7747, Acc.grass: 0.7949, Acc.cabinet: 0.7692, Acc.sidewalk: 0.8725, Acc.person: 0.9173, Acc.earth: 0.4641, Acc.door: 0.6761, Acc.table: 0.7219, Acc.mountain: 0.7732, Acc.plant: 0.7138, Acc.curtain: 0.9012, Acc.chair: 0.7256, Acc.car: 0.9155, Acc.water: 0.7573, Acc.painting: 0.9197, Acc.sofa: 0.9117, Acc.shelf: 0.5601, Acc.house: 0.6689, Acc.sea: 0.8327, Acc.mirror: 0.7776, Acc.rug: 0.8496, Acc.field: 0.7081, Acc.armchair: 0.7127, Acc.seat: 0.8738, Acc.fence: 0.5842, Acc.desk: 0.7586, Acc.rock: 0.2615, Acc.wardrobe: 0.7995, Acc.lamp: 0.8195, Acc.bathtub: 0.9315, Acc.railing: 0.5131, Acc.cushion: 0.6208, Acc.base: 0.7013, Acc.box: 0.3985, Acc.column: 0.4971, Acc.signboard: 0.6243, Acc.chest of drawers: 0.6920, Acc.counter: 0.6436, Acc.sand: 0.5761, Acc.sink: 0.8090, Acc.skyscraper: 0.6732, Acc.fireplace: 0.7532, Acc.refrigerator: 0.8352, Acc.grandstand: 0.8890, Acc.path: 0.2938, Acc.stairs: 0.2606, Acc.runway: 0.9020, Acc.case: 0.6831, Acc.pool table: 0.9810, Acc.pillow: 0.8450, Acc.screen door: 0.9397, Acc.stairway: 0.7773, Acc.river: 0.4461, Acc.bridge: 0.9109, Acc.bookcase: 0.6231, Acc.blind: 0.1725, Acc.coffee table: 0.8744, Acc.toilet: 0.9219, Acc.flower: 0.5314, Acc.book: 0.6938, Acc.hill: 0.1630, Acc.bench: 0.5501, Acc.countertop: 0.6937, Acc.stove: 0.6839, Acc.palm: 0.6886, Acc.kitchen island: 0.9594, Acc.computer: 0.8973, Acc.swivel chair: 0.7709, Acc.boat: 0.5900, Acc.bar: 0.6487, Acc.arcade machine: 0.9810, Acc.hovel: 0.1963, Acc.bus: 0.9559, Acc.towel: 0.8583, Acc.light: 0.4263, Acc.truck: 0.4742, Acc.tower: 0.0503, Acc.chandelier: 0.8339, Acc.awning: 0.3458, Acc.streetlight: 0.3373, Acc.booth: 0.3730, Acc.television receiver: 0.8234, Acc.airplane: 0.9268, Acc.dirt track: 0.2257, Acc.apparel: 0.8424, Acc.pole: 0.1477, Acc.land: 0.0006, Acc.bannister: 0.1294, Acc.escalator: 0.8464, Acc.ottoman: 0.7076, Acc.bottle: 0.3760, Acc.buffet: 0.5454, Acc.poster: 0.4641, Acc.stage: 0.5829, Acc.van: 0.3724, Acc.ship: 0.9907, Acc.fountain: 0.2796, Acc.conveyer belt: 0.9069, Acc.canopy: 0.6291, Acc.washer: 0.8937, Acc.plaything: 0.4385, Acc.swimming pool: 0.9425, Acc.stool: 0.4769, Acc.barrel: 0.6510, Acc.basket: 0.4867, Acc.waterfall: 0.9706, Acc.tent: 0.9922, Acc.bag: 0.2588, Acc.minibike: 0.8718, Acc.cradle: 0.9677, Acc.oven: 0.6162, Acc.ball: 0.7279, Acc.food: 0.7016, Acc.step: 0.0992, Acc.tank: 0.7168, Acc.trade name: 0.0917, Acc.microwave: 0.9424, Acc.pot: 0.6530, Acc.animal: 0.7661, Acc.bicycle: 0.6942, Acc.lake: 0.0000, Acc.dishwasher: 0.7029, Acc.screen: 0.9262, Acc.blanket: 0.3334, Acc.sculpture: 0.8820, Acc.hood: 0.7024, Acc.sconce: 0.5873, Acc.vase: 0.5563, Acc.traffic light: 0.4091, Acc.tray: 0.4013, Acc.ashcan: 0.6176, Acc.fan: 0.7019, Acc.pier: 0.6465, Acc.crt screen: 0.1597, Acc.plate: 0.7851, Acc.monitor: 0.0456, Acc.bulletin board: 0.6986, Acc.shower: 0.0000, Acc.radiator: 0.3511, Acc.glass: 0.1222, Acc.clock: 0.3248, Acc.flag: 0.7358 +2024-06-16 01:23:37,182 - mmseg - INFO - Iter [8050/80000] lr: 3.598e-05, eta: 1 day, 11:25:45, time: 3.543, data_time: 1.927, memory: 71384, decode.loss_ce: 0.4501, decode.acc_seg: 82.6927, aux.loss_ce: 0.1800, aux.acc_seg: 82.6586, loss: 0.6300 +2024-06-16 01:24:58,286 - mmseg - INFO - Iter [8100/80000] lr: 3.595e-05, eta: 1 day, 11:23:09, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4112, decode.acc_seg: 83.9776, aux.loss_ce: 0.1646, aux.acc_seg: 83.7572, loss: 0.5757 +2024-06-16 01:26:19,419 - mmseg - INFO - Iter [8150/80000] lr: 3.593e-05, eta: 1 day, 11:20:35, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4155, decode.acc_seg: 84.6634, aux.loss_ce: 0.1656, aux.acc_seg: 84.6077, loss: 0.5811 +2024-06-16 01:27:40,556 - mmseg - INFO - Iter [8200/80000] lr: 3.590e-05, eta: 1 day, 11:18:02, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4196, decode.acc_seg: 83.4066, aux.loss_ce: 0.1671, aux.acc_seg: 83.5132, loss: 0.5867 +2024-06-16 01:29:01,904 - mmseg - INFO - Iter [8250/80000] lr: 3.588e-05, eta: 1 day, 11:15:31, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4382, decode.acc_seg: 82.6527, aux.loss_ce: 0.1746, aux.acc_seg: 82.8083, loss: 0.6128 +2024-06-16 01:30:23,109 - mmseg - INFO - Iter [8300/80000] lr: 3.585e-05, eta: 1 day, 11:13:00, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4077, decode.acc_seg: 83.6563, aux.loss_ce: 0.1630, aux.acc_seg: 83.6287, loss: 0.5707 +2024-06-16 01:31:44,364 - mmseg - INFO - Iter [8350/80000] lr: 3.583e-05, eta: 1 day, 11:10:30, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4350, decode.acc_seg: 82.9283, aux.loss_ce: 0.1747, aux.acc_seg: 82.9840, loss: 0.6097 +2024-06-16 01:33:05,745 - mmseg - INFO - Iter [8400/80000] lr: 3.580e-05, eta: 1 day, 11:08:02, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4474, decode.acc_seg: 82.4776, aux.loss_ce: 0.1785, aux.acc_seg: 82.4536, loss: 0.6260 +2024-06-16 01:34:26,862 - mmseg - INFO - Iter [8450/80000] lr: 3.578e-05, eta: 1 day, 11:05:33, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4283, decode.acc_seg: 83.3985, aux.loss_ce: 0.1714, aux.acc_seg: 83.5164, loss: 0.5997 +2024-06-16 01:35:48,134 - mmseg - INFO - Iter [8500/80000] lr: 3.575e-05, eta: 1 day, 11:03:06, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4058, decode.acc_seg: 83.4782, aux.loss_ce: 0.1628, aux.acc_seg: 83.6789, loss: 0.5687 +2024-06-16 01:37:09,218 - mmseg - INFO - Iter [8550/80000] lr: 3.573e-05, eta: 1 day, 11:00:37, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4113, decode.acc_seg: 83.5939, aux.loss_ce: 0.1633, aux.acc_seg: 83.7318, loss: 0.5746 +2024-06-16 01:38:30,372 - mmseg - INFO - Iter [8600/80000] lr: 3.570e-05, eta: 1 day, 10:58:11, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4531, decode.acc_seg: 83.0979, aux.loss_ce: 0.1808, aux.acc_seg: 83.0990, loss: 0.6339 +2024-06-16 01:39:51,464 - mmseg - INFO - Iter [8650/80000] lr: 3.568e-05, eta: 1 day, 10:55:44, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4671, decode.acc_seg: 81.9857, aux.loss_ce: 0.1833, aux.acc_seg: 82.0634, loss: 0.6504 +2024-06-16 01:41:12,758 - mmseg - INFO - Iter [8700/80000] lr: 3.565e-05, eta: 1 day, 10:53:20, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4713, decode.acc_seg: 82.1131, aux.loss_ce: 0.1840, aux.acc_seg: 82.3945, loss: 0.6552 +2024-06-16 01:42:34,113 - mmseg - INFO - Iter [8750/80000] lr: 3.563e-05, eta: 1 day, 10:50:57, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4405, decode.acc_seg: 83.3107, aux.loss_ce: 0.1747, aux.acc_seg: 83.3236, loss: 0.6151 +2024-06-16 01:43:55,351 - mmseg - INFO - Iter [8800/80000] lr: 3.560e-05, eta: 1 day, 10:48:34, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4216, decode.acc_seg: 83.4153, aux.loss_ce: 0.1675, aux.acc_seg: 83.4401, loss: 0.5891 +2024-06-16 01:45:19,037 - mmseg - INFO - Iter [8850/80000] lr: 3.558e-05, eta: 1 day, 10:46:32, time: 1.674, data_time: 0.055, memory: 71384, decode.loss_ce: 0.4387, decode.acc_seg: 83.2659, aux.loss_ce: 0.1736, aux.acc_seg: 83.4145, loss: 0.6123 +2024-06-16 01:46:40,210 - mmseg - INFO - Iter [8900/80000] lr: 3.555e-05, eta: 1 day, 10:44:09, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4037, decode.acc_seg: 83.9864, aux.loss_ce: 0.1611, aux.acc_seg: 84.0707, loss: 0.5648 +2024-06-16 01:48:01,465 - mmseg - INFO - Iter [8950/80000] lr: 3.553e-05, eta: 1 day, 10:41:48, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4056, decode.acc_seg: 83.9268, aux.loss_ce: 0.1624, aux.acc_seg: 83.9421, loss: 0.5680 +2024-06-16 01:49:22,558 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 01:49:22,558 - mmseg - INFO - Iter [9000/80000] lr: 3.550e-05, eta: 1 day, 10:39:27, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4174, decode.acc_seg: 83.9747, aux.loss_ce: 0.1665, aux.acc_seg: 84.0026, loss: 0.5839 +2024-06-16 01:50:59,687 - mmseg - INFO - per class results: +2024-06-16 01:50:59,693 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.81 | 88.76 | +| building | 84.01 | 93.46 | +| sky | 94.36 | 97.59 | +| floor | 82.83 | 88.42 | +| tree | 75.98 | 85.24 | +| ceiling | 85.33 | 91.85 | +| road | 84.64 | 90.37 | +| bed | 90.1 | 96.55 | +| windowpane | 64.7 | 77.67 | +| grass | 69.89 | 82.6 | +| cabinet | 63.29 | 72.67 | +| sidewalk | 67.81 | 82.83 | +| person | 81.85 | 91.28 | +| earth | 40.15 | 55.62 | +| door | 59.2 | 77.97 | +| table | 62.98 | 74.48 | +| mountain | 60.54 | 78.22 | +| plant | 56.21 | 67.13 | +| curtain | 77.8 | 86.99 | +| chair | 59.98 | 69.38 | +| car | 83.54 | 94.56 | +| water | 59.23 | 73.12 | +| painting | 75.96 | 88.32 | +| sofa | 79.24 | 90.4 | +| shelf | 46.66 | 61.93 | +| house | 54.77 | 66.2 | +| sea | 68.95 | 79.0 | +| mirror | 77.63 | 87.46 | +| rug | 66.78 | 81.99 | +| field | 26.17 | 40.24 | +| armchair | 60.47 | 80.3 | +| seat | 65.81 | 89.69 | +| fence | 43.55 | 56.86 | +| desk | 43.45 | 56.35 | +| rock | 55.45 | 71.74 | +| wardrobe | 54.51 | 82.18 | +| lamp | 66.49 | 81.69 | +| bathtub | 79.82 | 83.8 | +| railing | 36.96 | 51.6 | +| cushion | 62.47 | 70.35 | +| base | 42.59 | 65.27 | +| box | 33.87 | 46.53 | +| column | 51.33 | 68.04 | +| signboard | 37.39 | 45.26 | +| chest of drawers | 39.78 | 79.57 | +| counter | 41.62 | 46.59 | +| sand | 43.13 | 58.42 | +| sink | 46.09 | 82.38 | +| skyscraper | 50.57 | 63.29 | +| fireplace | 69.56 | 90.97 | +| refrigerator | 82.74 | 89.07 | +| grandstand | 36.69 | 93.13 | +| path | 27.8 | 41.69 | +| stairs | 31.45 | 38.2 | +| runway | 67.35 | 88.15 | +| case | 52.58 | 75.19 | +| pool table | 90.69 | 98.85 | +| pillow | 65.32 | 76.7 | +| screen door | 75.25 | 89.68 | +| stairway | 43.64 | 73.94 | +| river | 18.34 | 56.11 | +| bridge | 50.06 | 61.33 | +| bookcase | 35.08 | 63.25 | +| blind | 40.76 | 47.31 | +| coffee table | 61.07 | 88.32 | +| toilet | 23.1 | 23.15 | +| flower | 36.6 | 44.87 | +| book | 48.26 | 73.84 | +| hill | 4.98 | 10.32 | +| bench | 58.56 | 77.77 | +| countertop | 61.42 | 80.26 | +| stove | 78.06 | 90.37 | +| palm | 55.68 | 81.6 | +| kitchen island | 46.25 | 81.72 | +| computer | 70.74 | 93.61 | +| swivel chair | 49.03 | 90.23 | +| boat | 63.5 | 77.32 | +| bar | 61.36 | 83.0 | +| arcade machine | 81.46 | 99.51 | +| hovel | 52.64 | 64.72 | +| bus | 85.68 | 97.39 | +| towel | 69.1 | 76.98 | +| light | 44.23 | 46.55 | +| truck | 37.4 | 46.99 | +| tower | 40.86 | 78.11 | +| chandelier | 69.12 | 82.52 | +| awning | 44.21 | 66.95 | +| streetlight | 26.81 | 34.3 | +| booth | 26.9 | 50.22 | +| television receiver | 73.16 | 81.4 | +| airplane | 85.66 | 92.1 | +| dirt track | 0.01 | 0.01 | +| apparel | 30.62 | 41.16 | +| pole | 24.69 | 33.46 | +| land | 1.88 | 3.49 | +| bannister | 13.02 | 23.39 | +| escalator | 57.86 | 90.5 | +| ottoman | 51.27 | 73.93 | +| bottle | 39.95 | 57.65 | +| buffet | 50.28 | 57.69 | +| poster | 29.86 | 34.64 | +| stage | 17.73 | 57.63 | +| van | 15.83 | 17.92 | +| ship | 88.61 | 96.74 | +| fountain | 45.15 | 55.44 | +| conveyer belt | 62.24 | 98.77 | +| canopy | 49.14 | 60.91 | +| washer | 82.36 | 89.98 | +| plaything | 18.79 | 25.73 | +| swimming pool | 59.34 | 93.05 | +| stool | 37.76 | 60.11 | +| barrel | 51.21 | 65.08 | +| basket | 38.11 | 50.84 | +| waterfall | 59.9 | 67.55 | +| tent | 94.53 | 98.62 | +| bag | 25.94 | 29.47 | +| minibike | 70.66 | 87.01 | +| cradle | 62.12 | 98.9 | +| oven | 65.78 | 77.44 | +| ball | 42.56 | 76.22 | +| food | 55.76 | 61.96 | +| step | 19.74 | 27.6 | +| tank | 63.91 | 95.53 | +| trade name | 23.74 | 27.2 | +| microwave | 87.72 | 94.39 | +| pot | 45.61 | 72.23 | +| animal | 45.2 | 45.98 | +| bicycle | 55.92 | 76.07 | +| lake | 0.0 | 0.0 | +| dishwasher | 61.66 | 66.96 | +| screen | 52.81 | 93.59 | +| blanket | 13.01 | 14.05 | +| sculpture | 70.61 | 85.25 | +| hood | 62.86 | 78.68 | +| sconce | 53.45 | 63.12 | +| vase | 41.98 | 56.36 | +| traffic light | 29.53 | 54.21 | +| tray | 17.0 | 23.48 | +| ashcan | 45.85 | 63.48 | +| fan | 65.36 | 80.69 | +| pier | 28.35 | 74.54 | +| crt screen | 0.0 | 0.0 | +| plate | 57.57 | 73.44 | +| monitor | 62.18 | 69.02 | +| bulletin board | 54.68 | 63.62 | +| shower | 0.09 | 0.12 | +| radiator | 65.31 | 75.85 | +| glass | 15.15 | 16.48 | +| clock | 39.37 | 47.82 | +| flag | 67.41 | 71.32 | ++---------------------+-------+-------+ +2024-06-16 01:50:59,693 - mmseg - INFO - Summary: +2024-06-16 01:50:59,693 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 84.5 | 52.45 | 67.24 | ++------+-------+-------+ +2024-06-16 01:50:59,694 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 01:50:59,694 - mmseg - INFO - Iter(val) [250] aAcc: 0.8450, mIoU: 0.5245, mAcc: 0.6724, IoU.wall: 0.7981, IoU.building: 0.8401, IoU.sky: 0.9436, IoU.floor: 0.8283, IoU.tree: 0.7598, IoU.ceiling: 0.8533, IoU.road: 0.8464, IoU.bed : 0.9010, IoU.windowpane: 0.6470, IoU.grass: 0.6989, IoU.cabinet: 0.6329, IoU.sidewalk: 0.6781, IoU.person: 0.8185, IoU.earth: 0.4015, IoU.door: 0.5920, IoU.table: 0.6298, IoU.mountain: 0.6054, IoU.plant: 0.5621, IoU.curtain: 0.7780, IoU.chair: 0.5998, IoU.car: 0.8354, IoU.water: 0.5923, IoU.painting: 0.7596, IoU.sofa: 0.7924, IoU.shelf: 0.4666, IoU.house: 0.5477, IoU.sea: 0.6895, IoU.mirror: 0.7763, IoU.rug: 0.6678, IoU.field: 0.2617, IoU.armchair: 0.6047, IoU.seat: 0.6581, IoU.fence: 0.4355, IoU.desk: 0.4345, IoU.rock: 0.5545, IoU.wardrobe: 0.5451, IoU.lamp: 0.6649, IoU.bathtub: 0.7982, IoU.railing: 0.3696, IoU.cushion: 0.6247, IoU.base: 0.4259, IoU.box: 0.3387, IoU.column: 0.5133, IoU.signboard: 0.3739, IoU.chest of drawers: 0.3978, IoU.counter: 0.4162, IoU.sand: 0.4313, IoU.sink: 0.4609, IoU.skyscraper: 0.5057, IoU.fireplace: 0.6956, IoU.refrigerator: 0.8274, IoU.grandstand: 0.3669, IoU.path: 0.2780, IoU.stairs: 0.3145, IoU.runway: 0.6735, IoU.case: 0.5258, IoU.pool table: 0.9069, IoU.pillow: 0.6532, IoU.screen door: 0.7525, IoU.stairway: 0.4364, IoU.river: 0.1834, IoU.bridge: 0.5006, IoU.bookcase: 0.3508, IoU.blind: 0.4076, IoU.coffee table: 0.6107, IoU.toilet: 0.2310, IoU.flower: 0.3660, IoU.book: 0.4826, IoU.hill: 0.0498, IoU.bench: 0.5856, IoU.countertop: 0.6142, IoU.stove: 0.7806, IoU.palm: 0.5568, IoU.kitchen island: 0.4625, IoU.computer: 0.7074, IoU.swivel chair: 0.4903, IoU.boat: 0.6350, IoU.bar: 0.6136, IoU.arcade machine: 0.8146, IoU.hovel: 0.5264, IoU.bus: 0.8568, IoU.towel: 0.6910, IoU.light: 0.4423, IoU.truck: 0.3740, IoU.tower: 0.4086, IoU.chandelier: 0.6912, IoU.awning: 0.4421, IoU.streetlight: 0.2681, IoU.booth: 0.2690, IoU.television receiver: 0.7316, IoU.airplane: 0.8566, IoU.dirt track: 0.0001, IoU.apparel: 0.3062, IoU.pole: 0.2469, IoU.land: 0.0188, IoU.bannister: 0.1302, IoU.escalator: 0.5786, IoU.ottoman: 0.5127, IoU.bottle: 0.3995, IoU.buffet: 0.5028, IoU.poster: 0.2986, IoU.stage: 0.1773, IoU.van: 0.1583, IoU.ship: 0.8861, IoU.fountain: 0.4515, IoU.conveyer belt: 0.6224, IoU.canopy: 0.4914, IoU.washer: 0.8236, IoU.plaything: 0.1879, IoU.swimming pool: 0.5934, IoU.stool: 0.3776, IoU.barrel: 0.5121, IoU.basket: 0.3811, IoU.waterfall: 0.5990, IoU.tent: 0.9453, IoU.bag: 0.2594, IoU.minibike: 0.7066, IoU.cradle: 0.6212, IoU.oven: 0.6578, IoU.ball: 0.4256, IoU.food: 0.5576, IoU.step: 0.1974, IoU.tank: 0.6391, IoU.trade name: 0.2374, IoU.microwave: 0.8772, IoU.pot: 0.4561, IoU.animal: 0.4520, IoU.bicycle: 0.5592, IoU.lake: 0.0000, IoU.dishwasher: 0.6166, IoU.screen: 0.5281, IoU.blanket: 0.1301, IoU.sculpture: 0.7061, IoU.hood: 0.6286, IoU.sconce: 0.5345, IoU.vase: 0.4198, IoU.traffic light: 0.2953, IoU.tray: 0.1700, IoU.ashcan: 0.4585, IoU.fan: 0.6536, IoU.pier: 0.2835, IoU.crt screen: 0.0000, IoU.plate: 0.5757, IoU.monitor: 0.6218, IoU.bulletin board: 0.5468, IoU.shower: 0.0009, IoU.radiator: 0.6531, IoU.glass: 0.1515, IoU.clock: 0.3937, IoU.flag: 0.6741, Acc.wall: 0.8876, Acc.building: 0.9346, Acc.sky: 0.9759, Acc.floor: 0.8842, Acc.tree: 0.8524, Acc.ceiling: 0.9185, Acc.road: 0.9037, Acc.bed : 0.9655, Acc.windowpane: 0.7767, Acc.grass: 0.8260, Acc.cabinet: 0.7267, Acc.sidewalk: 0.8283, Acc.person: 0.9128, Acc.earth: 0.5562, Acc.door: 0.7797, Acc.table: 0.7448, Acc.mountain: 0.7822, Acc.plant: 0.6713, Acc.curtain: 0.8699, Acc.chair: 0.6938, Acc.car: 0.9456, Acc.water: 0.7312, Acc.painting: 0.8832, Acc.sofa: 0.9040, Acc.shelf: 0.6193, Acc.house: 0.6620, Acc.sea: 0.7900, Acc.mirror: 0.8746, Acc.rug: 0.8199, Acc.field: 0.4024, Acc.armchair: 0.8030, Acc.seat: 0.8969, Acc.fence: 0.5686, Acc.desk: 0.5635, Acc.rock: 0.7174, Acc.wardrobe: 0.8218, Acc.lamp: 0.8169, Acc.bathtub: 0.8380, Acc.railing: 0.5160, Acc.cushion: 0.7035, Acc.base: 0.6527, Acc.box: 0.4653, Acc.column: 0.6804, Acc.signboard: 0.4526, Acc.chest of drawers: 0.7957, Acc.counter: 0.4659, Acc.sand: 0.5842, Acc.sink: 0.8238, Acc.skyscraper: 0.6329, Acc.fireplace: 0.9097, Acc.refrigerator: 0.8907, Acc.grandstand: 0.9313, Acc.path: 0.4169, Acc.stairs: 0.3820, Acc.runway: 0.8815, Acc.case: 0.7519, Acc.pool table: 0.9885, Acc.pillow: 0.7670, Acc.screen door: 0.8968, Acc.stairway: 0.7394, Acc.river: 0.5611, Acc.bridge: 0.6133, Acc.bookcase: 0.6325, Acc.blind: 0.4731, Acc.coffee table: 0.8832, Acc.toilet: 0.2315, Acc.flower: 0.4487, Acc.book: 0.7384, Acc.hill: 0.1032, Acc.bench: 0.7777, Acc.countertop: 0.8026, Acc.stove: 0.9037, Acc.palm: 0.8160, Acc.kitchen island: 0.8172, Acc.computer: 0.9361, Acc.swivel chair: 0.9023, Acc.boat: 0.7732, Acc.bar: 0.8300, Acc.arcade machine: 0.9951, Acc.hovel: 0.6472, Acc.bus: 0.9739, Acc.towel: 0.7698, Acc.light: 0.4655, Acc.truck: 0.4699, Acc.tower: 0.7811, Acc.chandelier: 0.8252, Acc.awning: 0.6695, Acc.streetlight: 0.3430, Acc.booth: 0.5022, Acc.television receiver: 0.8140, Acc.airplane: 0.9210, Acc.dirt track: 0.0001, Acc.apparel: 0.4116, Acc.pole: 0.3346, Acc.land: 0.0349, Acc.bannister: 0.2339, Acc.escalator: 0.9050, Acc.ottoman: 0.7393, Acc.bottle: 0.5765, Acc.buffet: 0.5769, Acc.poster: 0.3464, Acc.stage: 0.5763, Acc.van: 0.1792, Acc.ship: 0.9674, Acc.fountain: 0.5544, Acc.conveyer belt: 0.9877, Acc.canopy: 0.6091, Acc.washer: 0.8998, Acc.plaything: 0.2573, Acc.swimming pool: 0.9305, Acc.stool: 0.6011, Acc.barrel: 0.6508, Acc.basket: 0.5084, Acc.waterfall: 0.6755, Acc.tent: 0.9862, Acc.bag: 0.2947, Acc.minibike: 0.8701, Acc.cradle: 0.9890, Acc.oven: 0.7744, Acc.ball: 0.7622, Acc.food: 0.6196, Acc.step: 0.2760, Acc.tank: 0.9553, Acc.trade name: 0.2720, Acc.microwave: 0.9439, Acc.pot: 0.7223, Acc.animal: 0.4598, Acc.bicycle: 0.7607, Acc.lake: 0.0000, Acc.dishwasher: 0.6696, Acc.screen: 0.9359, Acc.blanket: 0.1405, Acc.sculpture: 0.8525, Acc.hood: 0.7868, Acc.sconce: 0.6312, Acc.vase: 0.5636, Acc.traffic light: 0.5421, Acc.tray: 0.2348, Acc.ashcan: 0.6348, Acc.fan: 0.8069, Acc.pier: 0.7454, Acc.crt screen: 0.0000, Acc.plate: 0.7344, Acc.monitor: 0.6902, Acc.bulletin board: 0.6362, Acc.shower: 0.0012, Acc.radiator: 0.7585, Acc.glass: 0.1648, Acc.clock: 0.4782, Acc.flag: 0.7132 +2024-06-16 01:52:21,346 - mmseg - INFO - Iter [9050/80000] lr: 3.548e-05, eta: 1 day, 10:49:52, time: 3.576, data_time: 1.959, memory: 71384, decode.loss_ce: 0.4049, decode.acc_seg: 84.1072, aux.loss_ce: 0.1623, aux.acc_seg: 84.1934, loss: 0.5672 +2024-06-16 01:53:42,722 - mmseg - INFO - Iter [9100/80000] lr: 3.545e-05, eta: 1 day, 10:47:29, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4279, decode.acc_seg: 83.8290, aux.loss_ce: 0.1702, aux.acc_seg: 83.7647, loss: 0.5981 +2024-06-16 01:55:03,864 - mmseg - INFO - Iter [9150/80000] lr: 3.543e-05, eta: 1 day, 10:45:05, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3910, decode.acc_seg: 84.4465, aux.loss_ce: 0.1573, aux.acc_seg: 84.4508, loss: 0.5482 +2024-06-16 01:56:25,218 - mmseg - INFO - Iter [9200/80000] lr: 3.540e-05, eta: 1 day, 10:42:43, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4177, decode.acc_seg: 83.9025, aux.loss_ce: 0.1685, aux.acc_seg: 83.5650, loss: 0.5862 +2024-06-16 01:57:46,387 - mmseg - INFO - Iter [9250/80000] lr: 3.538e-05, eta: 1 day, 10:40:21, time: 1.623, data_time: 0.009, memory: 71384, decode.loss_ce: 0.3842, decode.acc_seg: 84.9126, aux.loss_ce: 0.1551, aux.acc_seg: 84.7024, loss: 0.5393 +2024-06-16 01:59:07,572 - mmseg - INFO - Iter [9300/80000] lr: 3.535e-05, eta: 1 day, 10:37:59, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3924, decode.acc_seg: 85.0323, aux.loss_ce: 0.1571, aux.acc_seg: 85.1215, loss: 0.5495 +2024-06-16 02:00:28,921 - mmseg - INFO - Iter [9350/80000] lr: 3.533e-05, eta: 1 day, 10:35:39, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4017, decode.acc_seg: 84.1892, aux.loss_ce: 0.1615, aux.acc_seg: 84.1685, loss: 0.5633 +2024-06-16 02:01:50,170 - mmseg - INFO - Iter [9400/80000] lr: 3.530e-05, eta: 1 day, 10:33:19, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4386, decode.acc_seg: 82.6902, aux.loss_ce: 0.1765, aux.acc_seg: 82.6829, loss: 0.6151 +2024-06-16 02:03:11,586 - mmseg - INFO - Iter [9450/80000] lr: 3.528e-05, eta: 1 day, 10:31:01, time: 1.628, data_time: 0.009, memory: 71384, decode.loss_ce: 0.4170, decode.acc_seg: 83.7295, aux.loss_ce: 0.1659, aux.acc_seg: 83.9408, loss: 0.5829 +2024-06-16 02:04:32,676 - mmseg - INFO - Iter [9500/80000] lr: 3.525e-05, eta: 1 day, 10:28:42, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3953, decode.acc_seg: 84.2380, aux.loss_ce: 0.1587, aux.acc_seg: 84.2660, loss: 0.5540 +2024-06-16 02:05:54,014 - mmseg - INFO - Iter [9550/80000] lr: 3.523e-05, eta: 1 day, 10:26:24, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4105, decode.acc_seg: 84.1131, aux.loss_ce: 0.1646, aux.acc_seg: 84.0915, loss: 0.5752 +2024-06-16 02:07:15,225 - mmseg - INFO - Iter [9600/80000] lr: 3.520e-05, eta: 1 day, 10:24:06, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3773, decode.acc_seg: 85.1090, aux.loss_ce: 0.1516, aux.acc_seg: 85.0160, loss: 0.5290 +2024-06-16 02:08:36,289 - mmseg - INFO - Iter [9650/80000] lr: 3.518e-05, eta: 1 day, 10:21:48, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4269, decode.acc_seg: 83.5139, aux.loss_ce: 0.1700, aux.acc_seg: 83.5965, loss: 0.5969 +2024-06-16 02:09:57,427 - mmseg - INFO - Iter [9700/80000] lr: 3.515e-05, eta: 1 day, 10:19:31, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3943, decode.acc_seg: 84.8822, aux.loss_ce: 0.1586, aux.acc_seg: 84.8444, loss: 0.5530 +2024-06-16 02:11:18,777 - mmseg - INFO - Iter [9750/80000] lr: 3.513e-05, eta: 1 day, 10:17:16, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4052, decode.acc_seg: 84.1454, aux.loss_ce: 0.1621, aux.acc_seg: 84.1277, loss: 0.5673 +2024-06-16 02:12:39,927 - mmseg - INFO - Iter [9800/80000] lr: 3.510e-05, eta: 1 day, 10:15:00, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4300, decode.acc_seg: 82.9470, aux.loss_ce: 0.1721, aux.acc_seg: 83.0066, loss: 0.6020 +2024-06-16 02:14:01,104 - mmseg - INFO - Iter [9850/80000] lr: 3.508e-05, eta: 1 day, 10:12:45, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4065, decode.acc_seg: 83.7989, aux.loss_ce: 0.1606, aux.acc_seg: 83.6425, loss: 0.5670 +2024-06-16 02:15:22,308 - mmseg - INFO - Iter [9900/80000] lr: 3.505e-05, eta: 1 day, 10:10:31, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4030, decode.acc_seg: 84.3822, aux.loss_ce: 0.1611, aux.acc_seg: 84.4500, loss: 0.5641 +2024-06-16 02:16:43,487 - mmseg - INFO - Iter [9950/80000] lr: 3.503e-05, eta: 1 day, 10:08:17, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3972, decode.acc_seg: 83.7758, aux.loss_ce: 0.1575, aux.acc_seg: 83.8611, loss: 0.5547 +2024-06-16 02:18:04,608 - mmseg - INFO - Saving checkpoint at 10000 iterations +2024-06-16 02:19:26,780 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 02:19:26,780 - mmseg - INFO - Iter [10000/80000] lr: 3.500e-05, eta: 1 day, 10:15:38, time: 3.266, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4213, decode.acc_seg: 83.7971, aux.loss_ce: 0.1675, aux.acc_seg: 83.7390, loss: 0.5888 +2024-06-16 02:21:02,714 - mmseg - INFO - per class results: +2024-06-16 02:21:02,720 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 78.58 | 86.38 | +| building | 83.89 | 93.73 | +| sky | 94.4 | 97.4 | +| floor | 83.53 | 89.03 | +| tree | 76.1 | 88.33 | +| ceiling | 84.9 | 92.41 | +| road | 82.46 | 89.93 | +| bed | 90.79 | 95.31 | +| windowpane | 63.96 | 82.14 | +| grass | 68.53 | 82.2 | +| cabinet | 63.46 | 70.66 | +| sidewalk | 65.42 | 83.25 | +| person | 83.69 | 93.77 | +| earth | 40.44 | 55.9 | +| door | 56.07 | 72.72 | +| table | 64.37 | 76.75 | +| mountain | 53.71 | 66.52 | +| plant | 54.68 | 65.22 | +| curtain | 78.79 | 85.92 | +| chair | 60.13 | 69.36 | +| car | 85.04 | 94.72 | +| water | 54.87 | 64.59 | +| painting | 72.71 | 90.6 | +| sofa | 74.98 | 80.36 | +| shelf | 43.83 | 58.57 | +| house | 51.38 | 60.99 | +| sea | 68.65 | 79.57 | +| mirror | 76.22 | 82.08 | +| rug | 68.95 | 83.99 | +| field | 35.05 | 50.15 | +| armchair | 54.96 | 87.32 | +| seat | 64.3 | 86.18 | +| fence | 44.99 | 72.76 | +| desk | 51.58 | 78.31 | +| rock | 55.29 | 73.66 | +| wardrobe | 54.76 | 80.87 | +| lamp | 68.27 | 79.57 | +| bathtub | 80.42 | 82.78 | +| railing | 31.58 | 45.68 | +| cushion | 63.93 | 79.92 | +| base | 38.32 | 50.76 | +| box | 30.87 | 38.64 | +| column | 52.7 | 76.8 | +| signboard | 39.55 | 49.71 | +| chest of drawers | 50.65 | 71.53 | +| counter | 37.31 | 46.24 | +| sand | 38.25 | 64.11 | +| sink | 73.3 | 85.56 | +| skyscraper | 50.55 | 56.84 | +| fireplace | 67.13 | 94.85 | +| refrigerator | 79.02 | 88.85 | +| grandstand | 49.47 | 80.03 | +| path | 25.9 | 39.7 | +| stairs | 34.63 | 46.65 | +| runway | 69.77 | 90.6 | +| case | 59.72 | 80.55 | +| pool table | 92.86 | 98.5 | +| pillow | 67.91 | 84.37 | +| screen door | 77.58 | 93.87 | +| stairway | 51.88 | 68.35 | +| river | 16.11 | 61.44 | +| bridge | 70.64 | 90.46 | +| bookcase | 33.72 | 57.22 | +| blind | 33.27 | 35.22 | +| coffee table | 57.81 | 85.9 | +| toilet | 85.63 | 94.19 | +| flower | 38.18 | 49.69 | +| book | 50.79 | 70.04 | +| hill | 6.28 | 13.99 | +| bench | 51.78 | 73.31 | +| countertop | 55.63 | 67.08 | +| stove | 80.67 | 88.67 | +| palm | 54.88 | 83.84 | +| kitchen island | 43.38 | 88.83 | +| computer | 77.26 | 90.46 | +| swivel chair | 51.69 | 81.23 | +| boat | 60.49 | 91.01 | +| bar | 54.41 | 90.71 | +| arcade machine | 90.82 | 97.32 | +| hovel | 69.56 | 87.06 | +| bus | 92.29 | 96.08 | +| towel | 70.62 | 82.68 | +| light | 38.24 | 39.85 | +| truck | 39.91 | 49.37 | +| tower | 24.64 | 41.34 | +| chandelier | 66.22 | 87.6 | +| awning | 38.32 | 43.43 | +| streetlight | 27.78 | 38.3 | +| booth | 37.39 | 56.18 | +| television receiver | 72.08 | 87.45 | +| airplane | 85.34 | 95.25 | +| dirt track | 17.16 | 19.28 | +| apparel | 49.86 | 69.38 | +| pole | 21.39 | 28.65 | +| land | 0.01 | 0.01 | +| bannister | 12.24 | 42.42 | +| escalator | 57.19 | 82.81 | +| ottoman | 48.06 | 75.13 | +| bottle | 41.69 | 62.18 | +| buffet | 59.12 | 84.61 | +| poster | 31.46 | 37.54 | +| stage | 27.66 | 50.99 | +| van | 44.02 | 57.18 | +| ship | 51.38 | 51.38 | +| fountain | 26.68 | 29.62 | +| conveyer belt | 80.84 | 92.92 | +| canopy | 37.44 | 59.5 | +| washer | 79.22 | 84.23 | +| plaything | 26.27 | 45.86 | +| swimming pool | 58.58 | 84.67 | +| stool | 37.16 | 71.65 | +| barrel | 58.41 | 70.43 | +| basket | 41.77 | 63.32 | +| waterfall | 72.3 | 93.52 | +| tent | 93.55 | 98.96 | +| bag | 28.34 | 35.76 | +| minibike | 69.76 | 88.43 | +| cradle | 82.9 | 98.24 | +| oven | 61.39 | 71.61 | +| ball | 39.63 | 73.01 | +| food | 50.73 | 52.77 | +| step | 15.38 | 28.01 | +| tank | 71.77 | 91.19 | +| trade name | 19.79 | 21.33 | +| microwave | 85.88 | 95.16 | +| pot | 52.16 | 61.06 | +| animal | 68.32 | 72.11 | +| bicycle | 58.62 | 73.4 | +| lake | 9.69 | 10.54 | +| dishwasher | 63.62 | 83.56 | +| screen | 53.22 | 94.8 | +| blanket | 34.88 | 43.66 | +| sculpture | 67.24 | 82.3 | +| hood | 61.22 | 71.12 | +| sconce | 56.08 | 71.12 | +| vase | 37.42 | 60.82 | +| traffic light | 29.11 | 56.04 | +| tray | 13.09 | 13.97 | +| ashcan | 44.09 | 62.77 | +| fan | 63.3 | 72.6 | +| pier | 35.75 | 37.25 | +| crt screen | 0.02 | 0.04 | +| plate | 52.44 | 80.87 | +| monitor | 25.06 | 28.65 | +| bulletin board | 61.37 | 76.14 | +| shower | 0.0 | 0.0 | +| radiator | 65.89 | 82.17 | +| glass | 14.3 | 15.29 | +| clock | 35.45 | 45.99 | +| flag | 69.08 | 73.29 | ++---------------------+-------+-------+ +2024-06-16 02:21:02,720 - mmseg - INFO - Summary: +2024-06-16 02:21:02,720 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.29 | 53.76 | 68.35 | ++-------+-------+-------+ +2024-06-16 02:21:02,721 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 02:21:02,721 - mmseg - INFO - Iter(val) [250] aAcc: 0.8429, mIoU: 0.5376, mAcc: 0.6835, IoU.wall: 0.7858, IoU.building: 0.8389, IoU.sky: 0.9440, IoU.floor: 0.8353, IoU.tree: 0.7610, IoU.ceiling: 0.8490, IoU.road: 0.8246, IoU.bed : 0.9079, IoU.windowpane: 0.6396, IoU.grass: 0.6853, IoU.cabinet: 0.6346, IoU.sidewalk: 0.6542, IoU.person: 0.8369, IoU.earth: 0.4044, IoU.door: 0.5607, IoU.table: 0.6437, IoU.mountain: 0.5371, IoU.plant: 0.5468, IoU.curtain: 0.7879, IoU.chair: 0.6013, IoU.car: 0.8504, IoU.water: 0.5487, IoU.painting: 0.7271, IoU.sofa: 0.7498, IoU.shelf: 0.4383, IoU.house: 0.5138, IoU.sea: 0.6865, IoU.mirror: 0.7622, IoU.rug: 0.6895, IoU.field: 0.3505, IoU.armchair: 0.5496, IoU.seat: 0.6430, IoU.fence: 0.4499, IoU.desk: 0.5158, IoU.rock: 0.5529, IoU.wardrobe: 0.5476, IoU.lamp: 0.6827, IoU.bathtub: 0.8042, IoU.railing: 0.3158, IoU.cushion: 0.6393, IoU.base: 0.3832, IoU.box: 0.3087, IoU.column: 0.5270, IoU.signboard: 0.3955, IoU.chest of drawers: 0.5065, IoU.counter: 0.3731, IoU.sand: 0.3825, IoU.sink: 0.7330, IoU.skyscraper: 0.5055, IoU.fireplace: 0.6713, IoU.refrigerator: 0.7902, IoU.grandstand: 0.4947, IoU.path: 0.2590, IoU.stairs: 0.3463, IoU.runway: 0.6977, IoU.case: 0.5972, IoU.pool table: 0.9286, IoU.pillow: 0.6791, IoU.screen door: 0.7758, IoU.stairway: 0.5188, IoU.river: 0.1611, IoU.bridge: 0.7064, IoU.bookcase: 0.3372, IoU.blind: 0.3327, IoU.coffee table: 0.5781, IoU.toilet: 0.8563, IoU.flower: 0.3818, IoU.book: 0.5079, IoU.hill: 0.0628, IoU.bench: 0.5178, IoU.countertop: 0.5563, IoU.stove: 0.8067, IoU.palm: 0.5488, IoU.kitchen island: 0.4338, IoU.computer: 0.7726, IoU.swivel chair: 0.5169, IoU.boat: 0.6049, IoU.bar: 0.5441, IoU.arcade machine: 0.9082, IoU.hovel: 0.6956, IoU.bus: 0.9229, IoU.towel: 0.7062, IoU.light: 0.3824, IoU.truck: 0.3991, IoU.tower: 0.2464, IoU.chandelier: 0.6622, IoU.awning: 0.3832, IoU.streetlight: 0.2778, IoU.booth: 0.3739, IoU.television receiver: 0.7208, IoU.airplane: 0.8534, IoU.dirt track: 0.1716, IoU.apparel: 0.4986, IoU.pole: 0.2139, IoU.land: 0.0001, IoU.bannister: 0.1224, IoU.escalator: 0.5719, IoU.ottoman: 0.4806, IoU.bottle: 0.4169, IoU.buffet: 0.5912, IoU.poster: 0.3146, IoU.stage: 0.2766, IoU.van: 0.4402, IoU.ship: 0.5138, IoU.fountain: 0.2668, IoU.conveyer belt: 0.8084, IoU.canopy: 0.3744, IoU.washer: 0.7922, IoU.plaything: 0.2627, IoU.swimming pool: 0.5858, IoU.stool: 0.3716, IoU.barrel: 0.5841, IoU.basket: 0.4177, IoU.waterfall: 0.7230, IoU.tent: 0.9355, IoU.bag: 0.2834, IoU.minibike: 0.6976, IoU.cradle: 0.8290, IoU.oven: 0.6139, IoU.ball: 0.3963, IoU.food: 0.5073, IoU.step: 0.1538, IoU.tank: 0.7177, IoU.trade name: 0.1979, IoU.microwave: 0.8588, IoU.pot: 0.5216, IoU.animal: 0.6832, IoU.bicycle: 0.5862, IoU.lake: 0.0969, IoU.dishwasher: 0.6362, IoU.screen: 0.5322, IoU.blanket: 0.3488, IoU.sculpture: 0.6724, IoU.hood: 0.6122, IoU.sconce: 0.5608, IoU.vase: 0.3742, IoU.traffic light: 0.2911, IoU.tray: 0.1309, IoU.ashcan: 0.4409, IoU.fan: 0.6330, IoU.pier: 0.3575, IoU.crt screen: 0.0002, IoU.plate: 0.5244, IoU.monitor: 0.2506, IoU.bulletin board: 0.6137, IoU.shower: 0.0000, IoU.radiator: 0.6589, IoU.glass: 0.1430, IoU.clock: 0.3545, IoU.flag: 0.6908, Acc.wall: 0.8638, Acc.building: 0.9373, Acc.sky: 0.9740, Acc.floor: 0.8903, Acc.tree: 0.8833, Acc.ceiling: 0.9241, Acc.road: 0.8993, Acc.bed : 0.9531, Acc.windowpane: 0.8214, Acc.grass: 0.8220, Acc.cabinet: 0.7066, Acc.sidewalk: 0.8325, Acc.person: 0.9377, Acc.earth: 0.5590, Acc.door: 0.7272, Acc.table: 0.7675, Acc.mountain: 0.6652, Acc.plant: 0.6522, Acc.curtain: 0.8592, Acc.chair: 0.6936, Acc.car: 0.9472, Acc.water: 0.6459, Acc.painting: 0.9060, Acc.sofa: 0.8036, Acc.shelf: 0.5857, Acc.house: 0.6099, Acc.sea: 0.7957, Acc.mirror: 0.8208, Acc.rug: 0.8399, Acc.field: 0.5015, Acc.armchair: 0.8732, Acc.seat: 0.8618, Acc.fence: 0.7276, Acc.desk: 0.7831, Acc.rock: 0.7366, Acc.wardrobe: 0.8087, Acc.lamp: 0.7957, Acc.bathtub: 0.8278, Acc.railing: 0.4568, Acc.cushion: 0.7992, Acc.base: 0.5076, Acc.box: 0.3864, Acc.column: 0.7680, Acc.signboard: 0.4971, Acc.chest of drawers: 0.7153, Acc.counter: 0.4624, Acc.sand: 0.6411, Acc.sink: 0.8556, Acc.skyscraper: 0.5684, Acc.fireplace: 0.9485, Acc.refrigerator: 0.8885, Acc.grandstand: 0.8003, Acc.path: 0.3970, Acc.stairs: 0.4665, Acc.runway: 0.9060, Acc.case: 0.8055, Acc.pool table: 0.9850, Acc.pillow: 0.8437, Acc.screen door: 0.9387, Acc.stairway: 0.6835, Acc.river: 0.6144, Acc.bridge: 0.9046, Acc.bookcase: 0.5722, Acc.blind: 0.3522, Acc.coffee table: 0.8590, Acc.toilet: 0.9419, Acc.flower: 0.4969, Acc.book: 0.7004, Acc.hill: 0.1399, Acc.bench: 0.7331, Acc.countertop: 0.6708, Acc.stove: 0.8867, Acc.palm: 0.8384, Acc.kitchen island: 0.8883, Acc.computer: 0.9046, Acc.swivel chair: 0.8123, Acc.boat: 0.9101, Acc.bar: 0.9071, Acc.arcade machine: 0.9732, Acc.hovel: 0.8706, Acc.bus: 0.9608, Acc.towel: 0.8268, Acc.light: 0.3985, Acc.truck: 0.4937, Acc.tower: 0.4134, Acc.chandelier: 0.8760, Acc.awning: 0.4343, Acc.streetlight: 0.3830, Acc.booth: 0.5618, Acc.television receiver: 0.8745, Acc.airplane: 0.9525, Acc.dirt track: 0.1928, Acc.apparel: 0.6938, Acc.pole: 0.2865, Acc.land: 0.0001, Acc.bannister: 0.4242, Acc.escalator: 0.8281, Acc.ottoman: 0.7513, Acc.bottle: 0.6218, Acc.buffet: 0.8461, Acc.poster: 0.3754, Acc.stage: 0.5099, Acc.van: 0.5718, Acc.ship: 0.5138, Acc.fountain: 0.2962, Acc.conveyer belt: 0.9292, Acc.canopy: 0.5950, Acc.washer: 0.8423, Acc.plaything: 0.4586, Acc.swimming pool: 0.8467, Acc.stool: 0.7165, Acc.barrel: 0.7043, Acc.basket: 0.6332, Acc.waterfall: 0.9352, Acc.tent: 0.9896, Acc.bag: 0.3576, Acc.minibike: 0.8843, Acc.cradle: 0.9824, Acc.oven: 0.7161, Acc.ball: 0.7301, Acc.food: 0.5277, Acc.step: 0.2801, Acc.tank: 0.9119, Acc.trade name: 0.2133, Acc.microwave: 0.9516, Acc.pot: 0.6106, Acc.animal: 0.7211, Acc.bicycle: 0.7340, Acc.lake: 0.1054, Acc.dishwasher: 0.8356, Acc.screen: 0.9480, Acc.blanket: 0.4366, Acc.sculpture: 0.8230, Acc.hood: 0.7112, Acc.sconce: 0.7112, Acc.vase: 0.6082, Acc.traffic light: 0.5604, Acc.tray: 0.1397, Acc.ashcan: 0.6277, Acc.fan: 0.7260, Acc.pier: 0.3725, Acc.crt screen: 0.0004, Acc.plate: 0.8087, Acc.monitor: 0.2865, Acc.bulletin board: 0.7614, Acc.shower: 0.0000, Acc.radiator: 0.8217, Acc.glass: 0.1529, Acc.clock: 0.4599, Acc.flag: 0.7329 +2024-06-16 02:22:24,341 - mmseg - INFO - Iter [10050/80000] lr: 3.498e-05, eta: 1 day, 10:24:32, time: 3.551, data_time: 1.936, memory: 71384, decode.loss_ce: 0.4043, decode.acc_seg: 84.4572, aux.loss_ce: 0.1616, aux.acc_seg: 84.2740, loss: 0.5659 +2024-06-16 02:23:45,623 - mmseg - INFO - Iter [10100/80000] lr: 3.495e-05, eta: 1 day, 10:22:14, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3936, decode.acc_seg: 84.5699, aux.loss_ce: 0.1595, aux.acc_seg: 84.2840, loss: 0.5531 +2024-06-16 02:25:09,971 - mmseg - INFO - Iter [10150/80000] lr: 3.493e-05, eta: 1 day, 10:20:17, time: 1.687, data_time: 0.063, memory: 71384, decode.loss_ce: 0.3658, decode.acc_seg: 85.8895, aux.loss_ce: 0.1467, aux.acc_seg: 86.0140, loss: 0.5125 +2024-06-16 02:26:31,209 - mmseg - INFO - Iter [10200/80000] lr: 3.490e-05, eta: 1 day, 10:17:58, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3747, decode.acc_seg: 84.9472, aux.loss_ce: 0.1497, aux.acc_seg: 84.9858, loss: 0.5244 +2024-06-16 02:27:52,442 - mmseg - INFO - Iter [10250/80000] lr: 3.488e-05, eta: 1 day, 10:15:41, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3561, decode.acc_seg: 86.1353, aux.loss_ce: 0.1442, aux.acc_seg: 86.0593, loss: 0.5003 +2024-06-16 02:29:13,582 - mmseg - INFO - Iter [10300/80000] lr: 3.485e-05, eta: 1 day, 10:13:23, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3631, decode.acc_seg: 85.7417, aux.loss_ce: 0.1457, aux.acc_seg: 85.7618, loss: 0.5088 +2024-06-16 02:30:34,897 - mmseg - INFO - Iter [10350/80000] lr: 3.483e-05, eta: 1 day, 10:11:07, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4090, decode.acc_seg: 84.0563, aux.loss_ce: 0.1652, aux.acc_seg: 83.7900, loss: 0.5741 +2024-06-16 02:31:56,225 - mmseg - INFO - Iter [10400/80000] lr: 3.480e-05, eta: 1 day, 10:08:52, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3578, decode.acc_seg: 85.1002, aux.loss_ce: 0.1436, aux.acc_seg: 85.0114, loss: 0.5015 +2024-06-16 02:33:17,418 - mmseg - INFO - Iter [10450/80000] lr: 3.478e-05, eta: 1 day, 10:06:36, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3914, decode.acc_seg: 84.6455, aux.loss_ce: 0.1569, aux.acc_seg: 84.5568, loss: 0.5483 +2024-06-16 02:34:38,682 - mmseg - INFO - Iter [10500/80000] lr: 3.475e-05, eta: 1 day, 10:04:21, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3719, decode.acc_seg: 85.1765, aux.loss_ce: 0.1490, aux.acc_seg: 85.1192, loss: 0.5210 +2024-06-16 02:35:59,920 - mmseg - INFO - Iter [10550/80000] lr: 3.473e-05, eta: 1 day, 10:02:07, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3904, decode.acc_seg: 84.4477, aux.loss_ce: 0.1565, aux.acc_seg: 84.3784, loss: 0.5469 +2024-06-16 02:37:21,070 - mmseg - INFO - Iter [10600/80000] lr: 3.470e-05, eta: 1 day, 9:59:53, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4112, decode.acc_seg: 84.2584, aux.loss_ce: 0.1632, aux.acc_seg: 84.3499, loss: 0.5744 +2024-06-16 02:38:42,339 - mmseg - INFO - Iter [10650/80000] lr: 3.468e-05, eta: 1 day, 9:57:39, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3877, decode.acc_seg: 84.7237, aux.loss_ce: 0.1544, aux.acc_seg: 84.6535, loss: 0.5421 +2024-06-16 02:40:03,564 - mmseg - INFO - Iter [10700/80000] lr: 3.465e-05, eta: 1 day, 9:55:26, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3694, decode.acc_seg: 85.1331, aux.loss_ce: 0.1486, aux.acc_seg: 84.7675, loss: 0.5180 +2024-06-16 02:41:24,946 - mmseg - INFO - Iter [10750/80000] lr: 3.463e-05, eta: 1 day, 9:53:15, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3998, decode.acc_seg: 84.1464, aux.loss_ce: 0.1600, aux.acc_seg: 84.2561, loss: 0.5599 +2024-06-16 02:42:46,144 - mmseg - INFO - Iter [10800/80000] lr: 3.460e-05, eta: 1 day, 9:51:03, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3903, decode.acc_seg: 84.2222, aux.loss_ce: 0.1566, aux.acc_seg: 84.0469, loss: 0.5468 +2024-06-16 02:44:07,339 - mmseg - INFO - Iter [10850/80000] lr: 3.458e-05, eta: 1 day, 9:48:51, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4055, decode.acc_seg: 84.3561, aux.loss_ce: 0.1624, aux.acc_seg: 84.2385, loss: 0.5680 +2024-06-16 02:45:28,494 - mmseg - INFO - Iter [10900/80000] lr: 3.455e-05, eta: 1 day, 9:46:39, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4050, decode.acc_seg: 83.9432, aux.loss_ce: 0.1620, aux.acc_seg: 84.0466, loss: 0.5669 +2024-06-16 02:46:49,810 - mmseg - INFO - Iter [10950/80000] lr: 3.453e-05, eta: 1 day, 9:44:29, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3868, decode.acc_seg: 84.5413, aux.loss_ce: 0.1561, aux.acc_seg: 84.4304, loss: 0.5429 +2024-06-16 02:48:11,117 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 02:48:11,117 - mmseg - INFO - Iter [11000/80000] lr: 3.450e-05, eta: 1 day, 9:42:20, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3973, decode.acc_seg: 84.4006, aux.loss_ce: 0.1588, aux.acc_seg: 84.3739, loss: 0.5561 +2024-06-16 02:49:48,864 - mmseg - INFO - per class results: +2024-06-16 02:49:48,871 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.75 | 87.47 | +| building | 83.74 | 91.06 | +| sky | 94.54 | 97.76 | +| floor | 84.72 | 90.07 | +| tree | 75.74 | 89.9 | +| ceiling | 85.55 | 93.24 | +| road | 85.92 | 90.67 | +| bed | 91.27 | 95.8 | +| windowpane | 65.24 | 81.4 | +| grass | 70.24 | 80.1 | +| cabinet | 64.42 | 73.99 | +| sidewalk | 68.78 | 83.97 | +| person | 83.6 | 94.11 | +| earth | 38.33 | 52.56 | +| door | 59.53 | 77.8 | +| table | 62.35 | 72.97 | +| mountain | 50.78 | 69.03 | +| plant | 55.67 | 65.4 | +| curtain | 76.63 | 87.72 | +| chair | 64.14 | 77.46 | +| car | 85.79 | 92.79 | +| water | 54.88 | 64.05 | +| painting | 73.93 | 89.35 | +| sofa | 79.22 | 88.07 | +| shelf | 46.22 | 58.07 | +| house | 47.06 | 88.82 | +| sea | 67.16 | 95.63 | +| mirror | 76.25 | 90.76 | +| rug | 71.24 | 87.33 | +| field | 35.93 | 58.92 | +| armchair | 60.85 | 76.78 | +| seat | 69.43 | 82.05 | +| fence | 47.53 | 62.57 | +| desk | 51.16 | 74.25 | +| rock | 47.32 | 66.95 | +| wardrobe | 56.01 | 76.56 | +| lamp | 68.9 | 79.71 | +| bathtub | 86.54 | 91.85 | +| railing | 42.07 | 58.64 | +| cushion | 65.98 | 78.16 | +| base | 43.1 | 65.88 | +| box | 31.66 | 44.44 | +| column | 53.92 | 73.94 | +| signboard | 41.69 | 52.19 | +| chest of drawers | 52.94 | 74.05 | +| counter | 47.79 | 66.13 | +| sand | 57.53 | 86.76 | +| sink | 72.84 | 84.2 | +| skyscraper | 47.13 | 49.22 | +| fireplace | 68.65 | 93.5 | +| refrigerator | 79.58 | 93.26 | +| grandstand | 49.25 | 84.14 | +| path | 26.06 | 37.19 | +| stairs | 24.76 | 28.51 | +| runway | 73.73 | 94.95 | +| case | 63.06 | 68.36 | +| pool table | 93.84 | 97.42 | +| pillow | 66.81 | 79.73 | +| screen door | 78.36 | 94.01 | +| stairway | 42.08 | 68.34 | +| river | 19.71 | 52.2 | +| bridge | 71.25 | 87.13 | +| bookcase | 32.34 | 50.4 | +| blind | 42.12 | 49.17 | +| coffee table | 58.57 | 86.9 | +| toilet | 89.52 | 93.79 | +| flower | 40.1 | 49.76 | +| book | 48.71 | 77.4 | +| hill | 3.13 | 6.17 | +| bench | 61.85 | 76.88 | +| countertop | 61.7 | 81.55 | +| stove | 78.97 | 92.43 | +| palm | 53.01 | 57.78 | +| kitchen island | 36.15 | 67.12 | +| computer | 58.17 | 93.03 | +| swivel chair | 47.07 | 92.57 | +| boat | 67.66 | 88.04 | +| bar | 59.86 | 84.51 | +| arcade machine | 91.17 | 97.15 | +| hovel | 29.4 | 33.19 | +| bus | 92.9 | 94.71 | +| towel | 68.69 | 87.96 | +| light | 44.35 | 46.32 | +| truck | 45.53 | 55.9 | +| tower | 22.72 | 38.25 | +| chandelier | 68.83 | 82.77 | +| awning | 44.33 | 55.47 | +| streetlight | 25.13 | 30.32 | +| booth | 59.83 | 66.68 | +| television receiver | 74.51 | 92.49 | +| airplane | 80.58 | 88.36 | +| dirt track | 16.0 | 65.2 | +| apparel | 37.68 | 48.15 | +| pole | 15.55 | 17.64 | +| land | 3.48 | 10.6 | +| bannister | 16.34 | 23.54 | +| escalator | 64.34 | 86.61 | +| ottoman | 53.74 | 72.66 | +| bottle | 40.73 | 60.81 | +| buffet | 58.67 | 79.93 | +| poster | 33.27 | 60.02 | +| stage | 27.77 | 38.51 | +| van | 45.42 | 72.9 | +| ship | 72.28 | 82.65 | +| fountain | 33.54 | 35.86 | +| conveyer belt | 75.19 | 97.06 | +| canopy | 51.69 | 84.43 | +| washer | 84.22 | 89.09 | +| plaything | 27.17 | 31.92 | +| swimming pool | 57.94 | 84.25 | +| stool | 43.2 | 49.97 | +| barrel | 53.84 | 65.41 | +| basket | 33.32 | 42.95 | +| waterfall | 53.65 | 60.34 | +| tent | 94.83 | 98.21 | +| bag | 28.27 | 36.22 | +| minibike | 72.24 | 88.66 | +| cradle | 79.14 | 96.39 | +| oven | 53.86 | 64.44 | +| ball | 43.05 | 72.26 | +| food | 63.1 | 70.57 | +| step | 14.99 | 18.05 | +| tank | 55.28 | 75.97 | +| trade name | 30.62 | 38.34 | +| microwave | 84.21 | 95.69 | +| pot | 54.18 | 61.59 | +| animal | 58.09 | 58.71 | +| bicycle | 54.36 | 67.82 | +| lake | 0.0 | 0.0 | +| dishwasher | 64.04 | 68.37 | +| screen | 54.12 | 79.81 | +| blanket | 23.46 | 28.89 | +| sculpture | 65.4 | 69.52 | +| hood | 65.65 | 78.22 | +| sconce | 50.26 | 61.29 | +| vase | 41.5 | 53.1 | +| traffic light | 28.01 | 49.24 | +| tray | 17.56 | 21.24 | +| ashcan | 46.77 | 61.56 | +| fan | 63.99 | 73.45 | +| pier | 39.33 | 42.73 | +| crt screen | 16.05 | 21.13 | +| plate | 50.42 | 80.93 | +| monitor | 3.84 | 4.52 | +| bulletin board | 64.26 | 72.95 | +| shower | 4.61 | 5.1 | +| radiator | 65.17 | 74.0 | +| glass | 13.5 | 14.16 | +| clock | 39.22 | 45.94 | +| flag | 70.54 | 79.5 | ++---------------------+-------+-------+ +2024-06-16 02:49:48,871 - mmseg - INFO - Summary: +2024-06-16 02:49:48,871 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.81 | 54.27 | 67.97 | ++-------+-------+-------+ +2024-06-16 02:49:48,872 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 02:49:48,872 - mmseg - INFO - Iter(val) [250] aAcc: 0.8481, mIoU: 0.5427, mAcc: 0.6797, IoU.wall: 0.7975, IoU.building: 0.8374, IoU.sky: 0.9454, IoU.floor: 0.8472, IoU.tree: 0.7574, IoU.ceiling: 0.8555, IoU.road: 0.8592, IoU.bed : 0.9127, IoU.windowpane: 0.6524, IoU.grass: 0.7024, IoU.cabinet: 0.6442, IoU.sidewalk: 0.6878, IoU.person: 0.8360, IoU.earth: 0.3833, IoU.door: 0.5953, IoU.table: 0.6235, IoU.mountain: 0.5078, IoU.plant: 0.5567, IoU.curtain: 0.7663, IoU.chair: 0.6414, IoU.car: 0.8579, IoU.water: 0.5488, IoU.painting: 0.7393, IoU.sofa: 0.7922, IoU.shelf: 0.4622, IoU.house: 0.4706, IoU.sea: 0.6716, IoU.mirror: 0.7625, IoU.rug: 0.7124, IoU.field: 0.3593, IoU.armchair: 0.6085, IoU.seat: 0.6943, IoU.fence: 0.4753, IoU.desk: 0.5116, IoU.rock: 0.4732, IoU.wardrobe: 0.5601, IoU.lamp: 0.6890, IoU.bathtub: 0.8654, IoU.railing: 0.4207, IoU.cushion: 0.6598, IoU.base: 0.4310, IoU.box: 0.3166, IoU.column: 0.5392, IoU.signboard: 0.4169, IoU.chest of drawers: 0.5294, IoU.counter: 0.4779, IoU.sand: 0.5753, IoU.sink: 0.7284, IoU.skyscraper: 0.4713, IoU.fireplace: 0.6865, IoU.refrigerator: 0.7958, IoU.grandstand: 0.4925, IoU.path: 0.2606, IoU.stairs: 0.2476, IoU.runway: 0.7373, IoU.case: 0.6306, IoU.pool table: 0.9384, IoU.pillow: 0.6681, IoU.screen door: 0.7836, IoU.stairway: 0.4208, IoU.river: 0.1971, IoU.bridge: 0.7125, IoU.bookcase: 0.3234, IoU.blind: 0.4212, IoU.coffee table: 0.5857, IoU.toilet: 0.8952, IoU.flower: 0.4010, IoU.book: 0.4871, IoU.hill: 0.0313, IoU.bench: 0.6185, IoU.countertop: 0.6170, IoU.stove: 0.7897, IoU.palm: 0.5301, IoU.kitchen island: 0.3615, IoU.computer: 0.5817, IoU.swivel chair: 0.4707, IoU.boat: 0.6766, IoU.bar: 0.5986, IoU.arcade machine: 0.9117, IoU.hovel: 0.2940, IoU.bus: 0.9290, IoU.towel: 0.6869, IoU.light: 0.4435, IoU.truck: 0.4553, IoU.tower: 0.2272, IoU.chandelier: 0.6883, IoU.awning: 0.4433, IoU.streetlight: 0.2513, IoU.booth: 0.5983, IoU.television receiver: 0.7451, IoU.airplane: 0.8058, IoU.dirt track: 0.1600, IoU.apparel: 0.3768, IoU.pole: 0.1555, IoU.land: 0.0348, IoU.bannister: 0.1634, IoU.escalator: 0.6434, IoU.ottoman: 0.5374, IoU.bottle: 0.4073, IoU.buffet: 0.5867, IoU.poster: 0.3327, IoU.stage: 0.2777, IoU.van: 0.4542, IoU.ship: 0.7228, IoU.fountain: 0.3354, IoU.conveyer belt: 0.7519, IoU.canopy: 0.5169, IoU.washer: 0.8422, IoU.plaything: 0.2717, IoU.swimming pool: 0.5794, IoU.stool: 0.4320, IoU.barrel: 0.5384, IoU.basket: 0.3332, IoU.waterfall: 0.5365, IoU.tent: 0.9483, IoU.bag: 0.2827, IoU.minibike: 0.7224, IoU.cradle: 0.7914, IoU.oven: 0.5386, IoU.ball: 0.4305, IoU.food: 0.6310, IoU.step: 0.1499, IoU.tank: 0.5528, IoU.trade name: 0.3062, IoU.microwave: 0.8421, IoU.pot: 0.5418, IoU.animal: 0.5809, IoU.bicycle: 0.5436, IoU.lake: 0.0000, IoU.dishwasher: 0.6404, IoU.screen: 0.5412, IoU.blanket: 0.2346, IoU.sculpture: 0.6540, IoU.hood: 0.6565, IoU.sconce: 0.5026, IoU.vase: 0.4150, IoU.traffic light: 0.2801, IoU.tray: 0.1756, IoU.ashcan: 0.4677, IoU.fan: 0.6399, IoU.pier: 0.3933, IoU.crt screen: 0.1605, IoU.plate: 0.5042, IoU.monitor: 0.0384, IoU.bulletin board: 0.6426, IoU.shower: 0.0461, IoU.radiator: 0.6517, IoU.glass: 0.1350, IoU.clock: 0.3922, IoU.flag: 0.7054, Acc.wall: 0.8747, Acc.building: 0.9106, Acc.sky: 0.9776, Acc.floor: 0.9007, Acc.tree: 0.8990, Acc.ceiling: 0.9324, Acc.road: 0.9067, Acc.bed : 0.9580, Acc.windowpane: 0.8140, Acc.grass: 0.8010, Acc.cabinet: 0.7399, Acc.sidewalk: 0.8397, Acc.person: 0.9411, Acc.earth: 0.5256, Acc.door: 0.7780, Acc.table: 0.7297, Acc.mountain: 0.6903, Acc.plant: 0.6540, Acc.curtain: 0.8772, Acc.chair: 0.7746, Acc.car: 0.9279, Acc.water: 0.6405, Acc.painting: 0.8935, Acc.sofa: 0.8807, Acc.shelf: 0.5807, Acc.house: 0.8882, Acc.sea: 0.9563, Acc.mirror: 0.9076, Acc.rug: 0.8733, Acc.field: 0.5892, Acc.armchair: 0.7678, Acc.seat: 0.8205, Acc.fence: 0.6257, Acc.desk: 0.7425, Acc.rock: 0.6695, Acc.wardrobe: 0.7656, Acc.lamp: 0.7971, Acc.bathtub: 0.9185, Acc.railing: 0.5864, Acc.cushion: 0.7816, Acc.base: 0.6588, Acc.box: 0.4444, Acc.column: 0.7394, Acc.signboard: 0.5219, Acc.chest of drawers: 0.7405, Acc.counter: 0.6613, Acc.sand: 0.8676, Acc.sink: 0.8420, Acc.skyscraper: 0.4922, Acc.fireplace: 0.9350, Acc.refrigerator: 0.9326, Acc.grandstand: 0.8414, Acc.path: 0.3719, Acc.stairs: 0.2851, Acc.runway: 0.9495, Acc.case: 0.6836, Acc.pool table: 0.9742, Acc.pillow: 0.7973, Acc.screen door: 0.9401, Acc.stairway: 0.6834, Acc.river: 0.5220, Acc.bridge: 0.8713, Acc.bookcase: 0.5040, Acc.blind: 0.4917, Acc.coffee table: 0.8690, Acc.toilet: 0.9379, Acc.flower: 0.4976, Acc.book: 0.7740, Acc.hill: 0.0617, Acc.bench: 0.7688, Acc.countertop: 0.8155, Acc.stove: 0.9243, Acc.palm: 0.5778, Acc.kitchen island: 0.6712, Acc.computer: 0.9303, Acc.swivel chair: 0.9257, Acc.boat: 0.8804, Acc.bar: 0.8451, Acc.arcade machine: 0.9715, Acc.hovel: 0.3319, Acc.bus: 0.9471, Acc.towel: 0.8796, Acc.light: 0.4632, Acc.truck: 0.5590, Acc.tower: 0.3825, Acc.chandelier: 0.8277, Acc.awning: 0.5547, Acc.streetlight: 0.3032, Acc.booth: 0.6668, Acc.television receiver: 0.9249, Acc.airplane: 0.8836, Acc.dirt track: 0.6520, Acc.apparel: 0.4815, Acc.pole: 0.1764, Acc.land: 0.1060, Acc.bannister: 0.2354, Acc.escalator: 0.8661, Acc.ottoman: 0.7266, Acc.bottle: 0.6081, Acc.buffet: 0.7993, Acc.poster: 0.6002, Acc.stage: 0.3851, Acc.van: 0.7290, Acc.ship: 0.8265, Acc.fountain: 0.3586, Acc.conveyer belt: 0.9706, Acc.canopy: 0.8443, Acc.washer: 0.8909, Acc.plaything: 0.3192, Acc.swimming pool: 0.8425, Acc.stool: 0.4997, Acc.barrel: 0.6541, Acc.basket: 0.4295, Acc.waterfall: 0.6034, Acc.tent: 0.9821, Acc.bag: 0.3622, Acc.minibike: 0.8866, Acc.cradle: 0.9639, Acc.oven: 0.6444, Acc.ball: 0.7226, Acc.food: 0.7057, Acc.step: 0.1805, Acc.tank: 0.7597, Acc.trade name: 0.3834, Acc.microwave: 0.9569, Acc.pot: 0.6159, Acc.animal: 0.5871, Acc.bicycle: 0.6782, Acc.lake: 0.0000, Acc.dishwasher: 0.6837, Acc.screen: 0.7981, Acc.blanket: 0.2889, Acc.sculpture: 0.6952, Acc.hood: 0.7822, Acc.sconce: 0.6129, Acc.vase: 0.5310, Acc.traffic light: 0.4924, Acc.tray: 0.2124, Acc.ashcan: 0.6156, Acc.fan: 0.7345, Acc.pier: 0.4273, Acc.crt screen: 0.2113, Acc.plate: 0.8093, Acc.monitor: 0.0452, Acc.bulletin board: 0.7295, Acc.shower: 0.0510, Acc.radiator: 0.7400, Acc.glass: 0.1416, Acc.clock: 0.4594, Acc.flag: 0.7950 +2024-06-16 02:51:10,509 - mmseg - INFO - Iter [11050/80000] lr: 3.448e-05, eta: 1 day, 9:50:23, time: 3.588, data_time: 1.970, memory: 71384, decode.loss_ce: 0.4277, decode.acc_seg: 83.5410, aux.loss_ce: 0.1693, aux.acc_seg: 83.4564, loss: 0.5970 +2024-06-16 02:52:31,603 - mmseg - INFO - Iter [11100/80000] lr: 3.445e-05, eta: 1 day, 9:48:09, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4003, decode.acc_seg: 84.6134, aux.loss_ce: 0.1603, aux.acc_seg: 84.5764, loss: 0.5605 +2024-06-16 02:53:52,773 - mmseg - INFO - Iter [11150/80000] lr: 3.443e-05, eta: 1 day, 9:45:57, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3797, decode.acc_seg: 84.6042, aux.loss_ce: 0.1531, aux.acc_seg: 84.5099, loss: 0.5328 +2024-06-16 02:55:14,053 - mmseg - INFO - Iter [11200/80000] lr: 3.440e-05, eta: 1 day, 9:43:46, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3885, decode.acc_seg: 85.0969, aux.loss_ce: 0.1549, aux.acc_seg: 85.2194, loss: 0.5434 +2024-06-16 02:56:35,233 - mmseg - INFO - Iter [11250/80000] lr: 3.438e-05, eta: 1 day, 9:41:34, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3903, decode.acc_seg: 84.7347, aux.loss_ce: 0.1562, aux.acc_seg: 84.8339, loss: 0.5465 +2024-06-16 02:57:56,457 - mmseg - INFO - Iter [11300/80000] lr: 3.435e-05, eta: 1 day, 9:39:23, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3958, decode.acc_seg: 84.5253, aux.loss_ce: 0.1582, aux.acc_seg: 84.4360, loss: 0.5539 +2024-06-16 02:59:17,625 - mmseg - INFO - Iter [11350/80000] lr: 3.433e-05, eta: 1 day, 9:37:13, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3915, decode.acc_seg: 84.6131, aux.loss_ce: 0.1571, aux.acc_seg: 84.6884, loss: 0.5486 +2024-06-16 03:00:41,142 - mmseg - INFO - Iter [11400/80000] lr: 3.430e-05, eta: 1 day, 9:35:17, time: 1.670, data_time: 0.052, memory: 71384, decode.loss_ce: 0.3498, decode.acc_seg: 85.8095, aux.loss_ce: 0.1402, aux.acc_seg: 85.6488, loss: 0.4899 +2024-06-16 03:02:02,369 - mmseg - INFO - Iter [11450/80000] lr: 3.428e-05, eta: 1 day, 9:33:07, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3663, decode.acc_seg: 85.1985, aux.loss_ce: 0.1482, aux.acc_seg: 85.0365, loss: 0.5145 +2024-06-16 03:03:23,534 - mmseg - INFO - Iter [11500/80000] lr: 3.425e-05, eta: 1 day, 9:30:58, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3600, decode.acc_seg: 86.2013, aux.loss_ce: 0.1442, aux.acc_seg: 86.0518, loss: 0.5043 +2024-06-16 03:04:44,842 - mmseg - INFO - Iter [11550/80000] lr: 3.423e-05, eta: 1 day, 9:28:50, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3429, decode.acc_seg: 86.4056, aux.loss_ce: 0.1389, aux.acc_seg: 86.3277, loss: 0.4818 +2024-06-16 03:06:06,070 - mmseg - INFO - Iter [11600/80000] lr: 3.420e-05, eta: 1 day, 9:26:41, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3851, decode.acc_seg: 85.1660, aux.loss_ce: 0.1548, aux.acc_seg: 85.0670, loss: 0.5399 +2024-06-16 03:07:27,247 - mmseg - INFO - Iter [11650/80000] lr: 3.418e-05, eta: 1 day, 9:24:33, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3656, decode.acc_seg: 85.5143, aux.loss_ce: 0.1467, aux.acc_seg: 85.5201, loss: 0.5123 +2024-06-16 03:08:48,325 - mmseg - INFO - Iter [11700/80000] lr: 3.415e-05, eta: 1 day, 9:22:25, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3784, decode.acc_seg: 85.2146, aux.loss_ce: 0.1517, aux.acc_seg: 85.1385, loss: 0.5301 +2024-06-16 03:10:09,725 - mmseg - INFO - Iter [11750/80000] lr: 3.413e-05, eta: 1 day, 9:20:19, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3929, decode.acc_seg: 84.5802, aux.loss_ce: 0.1577, aux.acc_seg: 84.3624, loss: 0.5506 +2024-06-16 03:11:30,843 - mmseg - INFO - Iter [11800/80000] lr: 3.410e-05, eta: 1 day, 9:18:12, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3742, decode.acc_seg: 85.4406, aux.loss_ce: 0.1513, aux.acc_seg: 85.2762, loss: 0.5255 +2024-06-16 03:12:52,099 - mmseg - INFO - Iter [11850/80000] lr: 3.408e-05, eta: 1 day, 9:16:06, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3802, decode.acc_seg: 85.5064, aux.loss_ce: 0.1526, aux.acc_seg: 85.3631, loss: 0.5327 +2024-06-16 03:14:13,146 - mmseg - INFO - Iter [11900/80000] lr: 3.405e-05, eta: 1 day, 9:13:59, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3504, decode.acc_seg: 85.9020, aux.loss_ce: 0.1412, aux.acc_seg: 85.7929, loss: 0.4916 +2024-06-16 03:15:34,591 - mmseg - INFO - Iter [11950/80000] lr: 3.403e-05, eta: 1 day, 9:11:54, time: 1.629, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3739, decode.acc_seg: 85.1538, aux.loss_ce: 0.1512, aux.acc_seg: 85.0812, loss: 0.5250 +2024-06-16 03:16:55,713 - mmseg - INFO - Saving checkpoint at 12000 iterations +2024-06-16 03:18:19,137 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 03:18:19,137 - mmseg - INFO - Iter [12000/80000] lr: 3.400e-05, eta: 1 day, 9:17:41, time: 3.291, data_time: 0.010, memory: 71384, decode.loss_ce: 0.4022, decode.acc_seg: 84.3516, aux.loss_ce: 0.1608, aux.acc_seg: 84.4992, loss: 0.5630 +2024-06-16 03:19:54,350 - mmseg - INFO - per class results: +2024-06-16 03:19:54,356 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 79.51 | 90.36 | +| building | 84.33 | 90.99 | +| sky | 94.54 | 97.73 | +| floor | 84.5 | 90.45 | +| tree | 75.46 | 90.76 | +| ceiling | 84.24 | 88.64 | +| road | 84.2 | 88.65 | +| bed | 91.12 | 97.03 | +| windowpane | 63.94 | 76.7 | +| grass | 67.74 | 76.55 | +| cabinet | 61.83 | 72.32 | +| sidewalk | 68.45 | 85.41 | +| person | 83.55 | 91.79 | +| earth | 35.47 | 45.82 | +| door | 56.04 | 71.06 | +| table | 63.84 | 77.41 | +| mountain | 58.33 | 74.58 | +| plant | 54.1 | 61.78 | +| curtain | 77.7 | 84.46 | +| chair | 62.36 | 73.29 | +| car | 86.52 | 94.22 | +| water | 60.85 | 80.5 | +| painting | 76.48 | 85.34 | +| sofa | 80.14 | 89.42 | +| shelf | 43.19 | 53.22 | +| house | 54.67 | 87.42 | +| sea | 60.58 | 67.71 | +| mirror | 78.6 | 85.73 | +| rug | 72.95 | 83.66 | +| field | 38.09 | 67.92 | +| armchair | 55.04 | 63.27 | +| seat | 68.93 | 90.33 | +| fence | 47.38 | 66.92 | +| desk | 41.88 | 77.47 | +| rock | 51.63 | 79.31 | +| wardrobe | 55.16 | 90.57 | +| lamp | 68.45 | 76.5 | +| bathtub | 75.63 | 77.4 | +| railing | 41.78 | 58.12 | +| cushion | 67.73 | 82.75 | +| base | 37.72 | 67.13 | +| box | 33.73 | 48.87 | +| column | 45.68 | 51.8 | +| signboard | 39.59 | 54.02 | +| chest of drawers | 49.89 | 74.16 | +| counter | 3.96 | 4.07 | +| sand | 49.81 | 88.49 | +| sink | 74.86 | 85.01 | +| skyscraper | 44.67 | 51.27 | +| fireplace | 71.36 | 93.93 | +| refrigerator | 81.26 | 92.63 | +| grandstand | 40.22 | 91.45 | +| path | 30.82 | 41.07 | +| stairs | 12.74 | 13.83 | +| runway | 72.51 | 97.78 | +| case | 57.84 | 92.53 | +| pool table | 92.8 | 98.49 | +| pillow | 65.58 | 75.83 | +| screen door | 59.46 | 94.83 | +| stairway | 32.27 | 87.75 | +| river | 19.94 | 51.14 | +| bridge | 75.58 | 84.31 | +| bookcase | 25.98 | 29.13 | +| blind | 37.11 | 38.14 | +| coffee table | 52.46 | 91.69 | +| toilet | 88.27 | 92.54 | +| flower | 42.96 | 55.74 | +| book | 51.8 | 74.92 | +| hill | 4.93 | 9.85 | +| bench | 58.19 | 77.91 | +| countertop | 53.39 | 57.31 | +| stove | 74.79 | 93.86 | +| palm | 53.7 | 72.85 | +| kitchen island | 42.43 | 85.98 | +| computer | 72.44 | 93.99 | +| swivel chair | 50.31 | 77.09 | +| boat | 50.61 | 67.11 | +| bar | 59.66 | 87.0 | +| arcade machine | 91.51 | 97.11 | +| hovel | 34.17 | 43.81 | +| bus | 89.61 | 97.15 | +| towel | 66.69 | 75.54 | +| light | 55.42 | 63.1 | +| truck | 32.07 | 39.71 | +| tower | 28.24 | 48.38 | +| chandelier | 67.48 | 83.16 | +| awning | 39.1 | 54.95 | +| streetlight | 25.68 | 32.55 | +| booth | 35.15 | 40.1 | +| television receiver | 72.76 | 79.25 | +| airplane | 85.57 | 95.45 | +| dirt track | 5.25 | 18.83 | +| apparel | 18.58 | 22.97 | +| pole | 18.37 | 22.04 | +| land | 0.22 | 0.32 | +| bannister | 11.75 | 14.95 | +| escalator | 45.36 | 56.63 | +| ottoman | 40.24 | 46.63 | +| bottle | 22.99 | 24.54 | +| buffet | 57.4 | 80.0 | +| poster | 21.61 | 23.45 | +| stage | 26.9 | 46.9 | +| van | 43.35 | 57.4 | +| ship | 57.95 | 92.12 | +| fountain | 35.05 | 38.73 | +| conveyer belt | 56.29 | 97.49 | +| canopy | 46.96 | 72.63 | +| washer | 80.95 | 88.0 | +| plaything | 20.23 | 33.33 | +| swimming pool | 60.78 | 89.91 | +| stool | 40.32 | 60.13 | +| barrel | 56.43 | 64.97 | +| basket | 37.85 | 54.97 | +| waterfall | 59.62 | 65.12 | +| tent | 87.76 | 99.14 | +| bag | 23.16 | 27.05 | +| minibike | 71.56 | 87.53 | +| cradle | 83.35 | 98.77 | +| oven | 66.79 | 78.54 | +| ball | 32.73 | 35.57 | +| food | 55.62 | 64.04 | +| step | 17.11 | 17.93 | +| tank | 55.97 | 76.59 | +| trade name | 8.76 | 9.29 | +| microwave | 86.65 | 95.81 | +| pot | 54.42 | 64.82 | +| animal | 63.58 | 65.04 | +| bicycle | 57.69 | 78.01 | +| lake | 0.0 | 0.0 | +| dishwasher | 57.57 | 78.37 | +| screen | 56.92 | 86.93 | +| blanket | 24.8 | 31.02 | +| sculpture | 74.39 | 82.79 | +| hood | 66.93 | 75.21 | +| sconce | 53.91 | 61.2 | +| vase | 40.37 | 64.56 | +| traffic light | 30.29 | 51.17 | +| tray | 18.06 | 24.58 | +| ashcan | 45.81 | 58.82 | +| fan | 63.85 | 75.26 | +| pier | 36.56 | 44.23 | +| crt screen | 0.42 | 1.21 | +| plate | 58.15 | 77.53 | +| monitor | 4.02 | 4.31 | +| bulletin board | 57.61 | 73.95 | +| shower | 0.0 | 0.0 | +| radiator | 65.53 | 80.25 | +| glass | 12.2 | 12.58 | +| clock | 39.93 | 42.95 | +| flag | 68.67 | 71.24 | ++---------------------+-------+-------+ +2024-06-16 03:19:54,356 - mmseg - INFO - Summary: +2024-06-16 03:19:54,356 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.45 | 51.98 | 65.65 | ++-------+-------+-------+ +2024-06-16 03:19:54,357 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 03:19:54,357 - mmseg - INFO - Iter(val) [250] aAcc: 0.8445, mIoU: 0.5198, mAcc: 0.6565, IoU.wall: 0.7951, IoU.building: 0.8433, IoU.sky: 0.9454, IoU.floor: 0.8450, IoU.tree: 0.7546, IoU.ceiling: 0.8424, IoU.road: 0.8420, IoU.bed : 0.9112, IoU.windowpane: 0.6394, IoU.grass: 0.6774, IoU.cabinet: 0.6183, IoU.sidewalk: 0.6845, IoU.person: 0.8355, IoU.earth: 0.3547, IoU.door: 0.5604, IoU.table: 0.6384, IoU.mountain: 0.5833, IoU.plant: 0.5410, IoU.curtain: 0.7770, IoU.chair: 0.6236, IoU.car: 0.8652, IoU.water: 0.6085, IoU.painting: 0.7648, IoU.sofa: 0.8014, IoU.shelf: 0.4319, IoU.house: 0.5467, IoU.sea: 0.6058, IoU.mirror: 0.7860, IoU.rug: 0.7295, IoU.field: 0.3809, IoU.armchair: 0.5504, IoU.seat: 0.6893, IoU.fence: 0.4738, IoU.desk: 0.4188, IoU.rock: 0.5163, IoU.wardrobe: 0.5516, IoU.lamp: 0.6845, IoU.bathtub: 0.7563, IoU.railing: 0.4178, IoU.cushion: 0.6773, IoU.base: 0.3772, IoU.box: 0.3373, IoU.column: 0.4568, IoU.signboard: 0.3959, IoU.chest of drawers: 0.4989, IoU.counter: 0.0396, IoU.sand: 0.4981, IoU.sink: 0.7486, IoU.skyscraper: 0.4467, IoU.fireplace: 0.7136, IoU.refrigerator: 0.8126, IoU.grandstand: 0.4022, IoU.path: 0.3082, IoU.stairs: 0.1274, IoU.runway: 0.7251, IoU.case: 0.5784, IoU.pool table: 0.9280, IoU.pillow: 0.6558, IoU.screen door: 0.5946, IoU.stairway: 0.3227, IoU.river: 0.1994, IoU.bridge: 0.7558, IoU.bookcase: 0.2598, IoU.blind: 0.3711, IoU.coffee table: 0.5246, IoU.toilet: 0.8827, IoU.flower: 0.4296, IoU.book: 0.5180, IoU.hill: 0.0493, IoU.bench: 0.5819, IoU.countertop: 0.5339, IoU.stove: 0.7479, IoU.palm: 0.5370, IoU.kitchen island: 0.4243, IoU.computer: 0.7244, IoU.swivel chair: 0.5031, IoU.boat: 0.5061, IoU.bar: 0.5966, IoU.arcade machine: 0.9151, IoU.hovel: 0.3417, IoU.bus: 0.8961, IoU.towel: 0.6669, IoU.light: 0.5542, IoU.truck: 0.3207, IoU.tower: 0.2824, IoU.chandelier: 0.6748, IoU.awning: 0.3910, IoU.streetlight: 0.2568, IoU.booth: 0.3515, IoU.television receiver: 0.7276, IoU.airplane: 0.8557, IoU.dirt track: 0.0525, IoU.apparel: 0.1858, IoU.pole: 0.1837, IoU.land: 0.0022, IoU.bannister: 0.1175, IoU.escalator: 0.4536, IoU.ottoman: 0.4024, IoU.bottle: 0.2299, IoU.buffet: 0.5740, IoU.poster: 0.2161, IoU.stage: 0.2690, IoU.van: 0.4335, IoU.ship: 0.5795, IoU.fountain: 0.3505, IoU.conveyer belt: 0.5629, IoU.canopy: 0.4696, IoU.washer: 0.8095, IoU.plaything: 0.2023, IoU.swimming pool: 0.6078, IoU.stool: 0.4032, IoU.barrel: 0.5643, IoU.basket: 0.3785, IoU.waterfall: 0.5962, IoU.tent: 0.8776, IoU.bag: 0.2316, IoU.minibike: 0.7156, IoU.cradle: 0.8335, IoU.oven: 0.6679, IoU.ball: 0.3273, IoU.food: 0.5562, IoU.step: 0.1711, IoU.tank: 0.5597, IoU.trade name: 0.0876, IoU.microwave: 0.8665, IoU.pot: 0.5442, IoU.animal: 0.6358, IoU.bicycle: 0.5769, IoU.lake: 0.0000, IoU.dishwasher: 0.5757, IoU.screen: 0.5692, IoU.blanket: 0.2480, IoU.sculpture: 0.7439, IoU.hood: 0.6693, IoU.sconce: 0.5391, IoU.vase: 0.4037, IoU.traffic light: 0.3029, IoU.tray: 0.1806, IoU.ashcan: 0.4581, IoU.fan: 0.6385, IoU.pier: 0.3656, IoU.crt screen: 0.0042, IoU.plate: 0.5815, IoU.monitor: 0.0402, IoU.bulletin board: 0.5761, IoU.shower: 0.0000, IoU.radiator: 0.6553, IoU.glass: 0.1220, IoU.clock: 0.3993, IoU.flag: 0.6867, Acc.wall: 0.9036, Acc.building: 0.9099, Acc.sky: 0.9773, Acc.floor: 0.9045, Acc.tree: 0.9076, Acc.ceiling: 0.8864, Acc.road: 0.8865, Acc.bed : 0.9703, Acc.windowpane: 0.7670, Acc.grass: 0.7655, Acc.cabinet: 0.7232, Acc.sidewalk: 0.8541, Acc.person: 0.9179, Acc.earth: 0.4582, Acc.door: 0.7106, Acc.table: 0.7741, Acc.mountain: 0.7458, Acc.plant: 0.6178, Acc.curtain: 0.8446, Acc.chair: 0.7329, Acc.car: 0.9422, Acc.water: 0.8050, Acc.painting: 0.8534, Acc.sofa: 0.8942, Acc.shelf: 0.5322, Acc.house: 0.8742, Acc.sea: 0.6771, Acc.mirror: 0.8573, Acc.rug: 0.8366, Acc.field: 0.6792, Acc.armchair: 0.6327, Acc.seat: 0.9033, Acc.fence: 0.6692, Acc.desk: 0.7747, Acc.rock: 0.7931, Acc.wardrobe: 0.9057, Acc.lamp: 0.7650, Acc.bathtub: 0.7740, Acc.railing: 0.5812, Acc.cushion: 0.8275, Acc.base: 0.6713, Acc.box: 0.4887, Acc.column: 0.5180, Acc.signboard: 0.5402, Acc.chest of drawers: 0.7416, Acc.counter: 0.0407, Acc.sand: 0.8849, Acc.sink: 0.8501, Acc.skyscraper: 0.5127, Acc.fireplace: 0.9393, Acc.refrigerator: 0.9263, Acc.grandstand: 0.9145, Acc.path: 0.4107, Acc.stairs: 0.1383, Acc.runway: 0.9778, Acc.case: 0.9253, Acc.pool table: 0.9849, Acc.pillow: 0.7583, Acc.screen door: 0.9483, Acc.stairway: 0.8775, Acc.river: 0.5114, Acc.bridge: 0.8431, Acc.bookcase: 0.2913, Acc.blind: 0.3814, Acc.coffee table: 0.9169, Acc.toilet: 0.9254, Acc.flower: 0.5574, Acc.book: 0.7492, Acc.hill: 0.0985, Acc.bench: 0.7791, Acc.countertop: 0.5731, Acc.stove: 0.9386, Acc.palm: 0.7285, Acc.kitchen island: 0.8598, Acc.computer: 0.9399, Acc.swivel chair: 0.7709, Acc.boat: 0.6711, Acc.bar: 0.8700, Acc.arcade machine: 0.9711, Acc.hovel: 0.4381, Acc.bus: 0.9715, Acc.towel: 0.7554, Acc.light: 0.6310, Acc.truck: 0.3971, Acc.tower: 0.4838, Acc.chandelier: 0.8316, Acc.awning: 0.5495, Acc.streetlight: 0.3255, Acc.booth: 0.4010, Acc.television receiver: 0.7925, Acc.airplane: 0.9545, Acc.dirt track: 0.1883, Acc.apparel: 0.2297, Acc.pole: 0.2204, Acc.land: 0.0032, Acc.bannister: 0.1495, Acc.escalator: 0.5663, Acc.ottoman: 0.4663, Acc.bottle: 0.2454, Acc.buffet: 0.8000, Acc.poster: 0.2345, Acc.stage: 0.4690, Acc.van: 0.5740, Acc.ship: 0.9212, Acc.fountain: 0.3873, Acc.conveyer belt: 0.9749, Acc.canopy: 0.7263, Acc.washer: 0.8800, Acc.plaything: 0.3333, Acc.swimming pool: 0.8991, Acc.stool: 0.6013, Acc.barrel: 0.6497, Acc.basket: 0.5497, Acc.waterfall: 0.6512, Acc.tent: 0.9914, Acc.bag: 0.2705, Acc.minibike: 0.8753, Acc.cradle: 0.9877, Acc.oven: 0.7854, Acc.ball: 0.3557, Acc.food: 0.6404, Acc.step: 0.1793, Acc.tank: 0.7659, Acc.trade name: 0.0929, Acc.microwave: 0.9581, Acc.pot: 0.6482, Acc.animal: 0.6504, Acc.bicycle: 0.7801, Acc.lake: 0.0000, Acc.dishwasher: 0.7837, Acc.screen: 0.8693, Acc.blanket: 0.3102, Acc.sculpture: 0.8279, Acc.hood: 0.7521, Acc.sconce: 0.6120, Acc.vase: 0.6456, Acc.traffic light: 0.5117, Acc.tray: 0.2458, Acc.ashcan: 0.5882, Acc.fan: 0.7526, Acc.pier: 0.4423, Acc.crt screen: 0.0121, Acc.plate: 0.7753, Acc.monitor: 0.0431, Acc.bulletin board: 0.7395, Acc.shower: 0.0000, Acc.radiator: 0.8025, Acc.glass: 0.1258, Acc.clock: 0.4295, Acc.flag: 0.7124 +2024-06-16 03:21:15,847 - mmseg - INFO - Iter [12050/80000] lr: 3.398e-05, eta: 1 day, 9:24:33, time: 3.534, data_time: 1.921, memory: 71384, decode.loss_ce: 0.3812, decode.acc_seg: 84.8725, aux.loss_ce: 0.1530, aux.acc_seg: 84.8650, loss: 0.5341 +2024-06-16 03:22:36,909 - mmseg - INFO - Iter [12100/80000] lr: 3.395e-05, eta: 1 day, 9:22:23, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3788, decode.acc_seg: 85.4496, aux.loss_ce: 0.1533, aux.acc_seg: 85.4076, loss: 0.5320 +2024-06-16 03:23:58,271 - mmseg - INFO - Iter [12150/80000] lr: 3.393e-05, eta: 1 day, 9:20:14, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3464, decode.acc_seg: 86.2607, aux.loss_ce: 0.1396, aux.acc_seg: 86.1470, loss: 0.4860 +2024-06-16 03:25:19,303 - mmseg - INFO - Iter [12200/80000] lr: 3.390e-05, eta: 1 day, 9:18:05, time: 1.621, data_time: 0.009, memory: 71384, decode.loss_ce: 0.3405, decode.acc_seg: 86.1207, aux.loss_ce: 0.1365, aux.acc_seg: 86.1083, loss: 0.4770 +2024-06-16 03:26:40,379 - mmseg - INFO - Iter [12250/80000] lr: 3.388e-05, eta: 1 day, 9:15:56, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3679, decode.acc_seg: 85.9509, aux.loss_ce: 0.1478, aux.acc_seg: 85.8179, loss: 0.5157 +2024-06-16 03:28:01,605 - mmseg - INFO - Iter [12300/80000] lr: 3.385e-05, eta: 1 day, 9:13:48, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3703, decode.acc_seg: 85.4121, aux.loss_ce: 0.1488, aux.acc_seg: 85.2543, loss: 0.5191 +2024-06-16 03:29:22,747 - mmseg - INFO - Iter [12350/80000] lr: 3.383e-05, eta: 1 day, 9:11:40, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3611, decode.acc_seg: 86.0465, aux.loss_ce: 0.1450, aux.acc_seg: 86.0413, loss: 0.5061 +2024-06-16 03:30:43,822 - mmseg - INFO - Iter [12400/80000] lr: 3.380e-05, eta: 1 day, 9:09:32, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3833, decode.acc_seg: 84.7503, aux.loss_ce: 0.1539, aux.acc_seg: 84.8121, loss: 0.5372 +2024-06-16 03:32:04,903 - mmseg - INFO - Iter [12450/80000] lr: 3.378e-05, eta: 1 day, 9:07:25, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3665, decode.acc_seg: 85.4447, aux.loss_ce: 0.1475, aux.acc_seg: 85.4094, loss: 0.5140 +2024-06-16 03:33:26,131 - mmseg - INFO - Iter [12500/80000] lr: 3.375e-05, eta: 1 day, 9:05:19, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3576, decode.acc_seg: 85.9228, aux.loss_ce: 0.1440, aux.acc_seg: 85.8425, loss: 0.5016 +2024-06-16 03:34:47,130 - mmseg - INFO - Iter [12550/80000] lr: 3.373e-05, eta: 1 day, 9:03:12, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3765, decode.acc_seg: 85.1499, aux.loss_ce: 0.1506, aux.acc_seg: 85.0217, loss: 0.5271 +2024-06-16 03:36:08,537 - mmseg - INFO - Iter [12600/80000] lr: 3.370e-05, eta: 1 day, 9:01:07, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3716, decode.acc_seg: 85.1547, aux.loss_ce: 0.1496, aux.acc_seg: 85.0111, loss: 0.5212 +2024-06-16 03:37:32,085 - mmseg - INFO - Iter [12650/80000] lr: 3.368e-05, eta: 1 day, 8:59:14, time: 1.671, data_time: 0.058, memory: 71384, decode.loss_ce: 0.3754, decode.acc_seg: 85.4479, aux.loss_ce: 0.1505, aux.acc_seg: 85.4017, loss: 0.5259 +2024-06-16 03:38:53,144 - mmseg - INFO - Iter [12700/80000] lr: 3.365e-05, eta: 1 day, 8:57:08, time: 1.621, data_time: 0.009, memory: 71384, decode.loss_ce: 0.3409, decode.acc_seg: 86.5026, aux.loss_ce: 0.1376, aux.acc_seg: 86.2389, loss: 0.4784 +2024-06-16 03:40:14,384 - mmseg - INFO - Iter [12750/80000] lr: 3.363e-05, eta: 1 day, 8:55:04, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3830, decode.acc_seg: 85.1723, aux.loss_ce: 0.1535, aux.acc_seg: 84.9519, loss: 0.5365 +2024-06-16 03:41:35,604 - mmseg - INFO - Iter [12800/80000] lr: 3.360e-05, eta: 1 day, 8:52:59, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3423, decode.acc_seg: 86.7495, aux.loss_ce: 0.1373, aux.acc_seg: 86.6323, loss: 0.4796 +2024-06-16 03:42:56,700 - mmseg - INFO - Iter [12850/80000] lr: 3.358e-05, eta: 1 day, 8:50:55, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3360, decode.acc_seg: 86.4860, aux.loss_ce: 0.1356, aux.acc_seg: 86.2868, loss: 0.4716 +2024-06-16 03:44:17,757 - mmseg - INFO - Iter [12900/80000] lr: 3.355e-05, eta: 1 day, 8:48:50, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3613, decode.acc_seg: 85.8551, aux.loss_ce: 0.1452, aux.acc_seg: 85.6593, loss: 0.5065 +2024-06-16 03:45:38,951 - mmseg - INFO - Iter [12950/80000] lr: 3.353e-05, eta: 1 day, 8:46:47, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3631, decode.acc_seg: 85.0682, aux.loss_ce: 0.1451, aux.acc_seg: 85.1819, loss: 0.5082 +2024-06-16 03:47:00,325 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 03:47:00,325 - mmseg - INFO - Iter [13000/80000] lr: 3.350e-05, eta: 1 day, 8:44:45, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3436, decode.acc_seg: 86.2670, aux.loss_ce: 0.1396, aux.acc_seg: 85.9635, loss: 0.4833 +2024-06-16 03:48:39,886 - mmseg - INFO - per class results: +2024-06-16 03:48:39,892 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.01 | 88.72 | +| building | 84.57 | 93.23 | +| sky | 94.61 | 97.66 | +| floor | 83.84 | 88.65 | +| tree | 77.37 | 88.23 | +| ceiling | 86.48 | 92.64 | +| road | 83.79 | 89.93 | +| bed | 91.82 | 96.36 | +| windowpane | 65.67 | 82.34 | +| grass | 65.06 | 81.56 | +| cabinet | 65.2 | 76.44 | +| sidewalk | 67.95 | 83.57 | +| person | 83.39 | 93.87 | +| earth | 36.59 | 50.15 | +| door | 56.93 | 72.22 | +| table | 63.89 | 71.28 | +| mountain | 58.19 | 65.28 | +| plant | 56.84 | 65.73 | +| curtain | 78.28 | 89.34 | +| chair | 60.79 | 68.51 | +| car | 83.74 | 94.85 | +| water | 61.85 | 82.1 | +| painting | 76.51 | 90.57 | +| sofa | 77.71 | 88.94 | +| shelf | 45.91 | 58.25 | +| house | 48.16 | 54.59 | +| sea | 67.18 | 72.83 | +| mirror | 76.23 | 83.57 | +| rug | 69.6 | 84.36 | +| field | 35.21 | 71.15 | +| armchair | 55.68 | 86.27 | +| seat | 62.13 | 91.31 | +| fence | 39.37 | 80.69 | +| desk | 51.24 | 76.58 | +| rock | 57.19 | 76.94 | +| wardrobe | 57.37 | 86.37 | +| lamp | 67.76 | 74.22 | +| bathtub | 85.16 | 88.34 | +| railing | 39.95 | 58.45 | +| cushion | 67.05 | 76.82 | +| base | 46.23 | 67.75 | +| box | 33.68 | 49.43 | +| column | 50.14 | 58.85 | +| signboard | 41.8 | 58.7 | +| chest of drawers | 42.52 | 59.2 | +| counter | 48.47 | 74.96 | +| sand | 40.56 | 57.55 | +| sink | 76.34 | 84.8 | +| skyscraper | 48.41 | 58.65 | +| fireplace | 68.34 | 95.8 | +| refrigerator | 78.62 | 89.34 | +| grandstand | 52.36 | 84.35 | +| path | 26.62 | 29.87 | +| stairs | 38.72 | 46.31 | +| runway | 73.22 | 95.21 | +| case | 58.41 | 76.69 | +| pool table | 93.85 | 98.09 | +| pillow | 68.24 | 82.88 | +| screen door | 70.21 | 94.55 | +| stairway | 60.51 | 67.72 | +| river | 6.12 | 9.76 | +| bridge | 74.27 | 88.22 | +| bookcase | 30.96 | 43.89 | +| blind | 39.47 | 42.94 | +| coffee table | 57.91 | 88.54 | +| toilet | 89.4 | 93.07 | +| flower | 44.03 | 59.34 | +| book | 50.06 | 79.5 | +| hill | 3.77 | 7.23 | +| bench | 61.83 | 75.29 | +| countertop | 62.26 | 74.15 | +| stove | 80.47 | 93.31 | +| palm | 52.04 | 82.84 | +| kitchen island | 49.29 | 86.51 | +| computer | 77.51 | 93.39 | +| swivel chair | 48.69 | 79.66 | +| boat | 58.87 | 89.2 | +| bar | 70.3 | 79.04 | +| arcade machine | 86.63 | 99.33 | +| hovel | 31.37 | 33.4 | +| bus | 93.36 | 95.94 | +| towel | 70.94 | 87.42 | +| light | 56.32 | 66.13 | +| truck | 33.81 | 41.0 | +| tower | 22.29 | 39.22 | +| chandelier | 64.34 | 90.29 | +| awning | 49.19 | 61.6 | +| streetlight | 29.51 | 37.03 | +| booth | 34.15 | 59.39 | +| television receiver | 76.12 | 89.88 | +| airplane | 79.56 | 97.54 | +| dirt track | 4.91 | 19.96 | +| apparel | 44.06 | 65.64 | +| pole | 25.36 | 36.23 | +| land | 3.37 | 4.54 | +| bannister | 18.34 | 21.7 | +| escalator | 62.78 | 87.97 | +| ottoman | 48.9 | 67.6 | +| bottle | 41.45 | 60.58 | +| buffet | 65.16 | 79.6 | +| poster | 22.61 | 23.85 | +| stage | 15.96 | 45.77 | +| van | 0.0 | 0.0 | +| ship | 53.9 | 57.86 | +| fountain | 70.46 | 76.94 | +| conveyer belt | 67.31 | 98.79 | +| canopy | 48.85 | 65.59 | +| washer | 85.62 | 95.49 | +| plaything | 26.87 | 33.74 | +| swimming pool | 58.54 | 87.26 | +| stool | 47.62 | 68.59 | +| barrel | 54.64 | 64.84 | +| basket | 37.73 | 50.15 | +| waterfall | 59.09 | 73.36 | +| tent | 86.07 | 98.69 | +| bag | 20.83 | 24.38 | +| minibike | 71.52 | 86.43 | +| cradle | 86.95 | 92.86 | +| oven | 50.8 | 57.28 | +| ball | 6.72 | 6.73 | +| food | 56.86 | 60.32 | +| step | 17.6 | 28.07 | +| tank | 59.38 | 72.95 | +| trade name | 31.0 | 38.36 | +| microwave | 86.41 | 93.62 | +| pot | 54.92 | 63.46 | +| animal | 67.53 | 70.22 | +| bicycle | 54.55 | 67.47 | +| lake | 0.0 | 0.0 | +| dishwasher | 67.17 | 72.27 | +| screen | 61.63 | 87.88 | +| blanket | 22.38 | 27.77 | +| sculpture | 74.57 | 86.02 | +| hood | 65.21 | 77.01 | +| sconce | 58.87 | 66.35 | +| vase | 44.35 | 55.14 | +| traffic light | 30.17 | 63.58 | +| tray | 11.06 | 13.24 | +| ashcan | 49.29 | 65.25 | +| fan | 65.96 | 76.19 | +| pier | 33.65 | 43.13 | +| crt screen | 0.5 | 0.53 | +| plate | 58.3 | 76.68 | +| monitor | 67.97 | 78.65 | +| bulletin board | 59.03 | 77.02 | +| shower | 2.25 | 2.74 | +| radiator | 61.99 | 77.74 | +| glass | 16.78 | 17.9 | +| clock | 39.22 | 42.27 | +| flag | 70.38 | 74.25 | ++---------------------+-------+-------+ +2024-06-16 03:48:39,892 - mmseg - INFO - Summary: +2024-06-16 03:48:39,892 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.97 | 54.29 | 67.57 | ++-------+-------+-------+ +2024-06-16 03:48:39,893 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 03:48:39,893 - mmseg - INFO - Iter(val) [250] aAcc: 0.8497, mIoU: 0.5429, mAcc: 0.6757, IoU.wall: 0.8001, IoU.building: 0.8457, IoU.sky: 0.9461, IoU.floor: 0.8384, IoU.tree: 0.7737, IoU.ceiling: 0.8648, IoU.road: 0.8379, IoU.bed : 0.9182, IoU.windowpane: 0.6567, IoU.grass: 0.6506, IoU.cabinet: 0.6520, IoU.sidewalk: 0.6795, IoU.person: 0.8339, IoU.earth: 0.3659, IoU.door: 0.5693, IoU.table: 0.6389, IoU.mountain: 0.5819, IoU.plant: 0.5684, IoU.curtain: 0.7828, IoU.chair: 0.6079, IoU.car: 0.8374, IoU.water: 0.6185, IoU.painting: 0.7651, IoU.sofa: 0.7771, IoU.shelf: 0.4591, IoU.house: 0.4816, IoU.sea: 0.6718, IoU.mirror: 0.7623, IoU.rug: 0.6960, IoU.field: 0.3521, IoU.armchair: 0.5568, IoU.seat: 0.6213, IoU.fence: 0.3937, IoU.desk: 0.5124, IoU.rock: 0.5719, IoU.wardrobe: 0.5737, IoU.lamp: 0.6776, IoU.bathtub: 0.8516, IoU.railing: 0.3995, IoU.cushion: 0.6705, IoU.base: 0.4623, IoU.box: 0.3368, IoU.column: 0.5014, IoU.signboard: 0.4180, IoU.chest of drawers: 0.4252, IoU.counter: 0.4847, IoU.sand: 0.4056, IoU.sink: 0.7634, IoU.skyscraper: 0.4841, IoU.fireplace: 0.6834, IoU.refrigerator: 0.7862, IoU.grandstand: 0.5236, IoU.path: 0.2662, IoU.stairs: 0.3872, IoU.runway: 0.7322, IoU.case: 0.5841, IoU.pool table: 0.9385, IoU.pillow: 0.6824, IoU.screen door: 0.7021, IoU.stairway: 0.6051, IoU.river: 0.0612, IoU.bridge: 0.7427, IoU.bookcase: 0.3096, IoU.blind: 0.3947, IoU.coffee table: 0.5791, IoU.toilet: 0.8940, IoU.flower: 0.4403, IoU.book: 0.5006, IoU.hill: 0.0377, IoU.bench: 0.6183, IoU.countertop: 0.6226, IoU.stove: 0.8047, IoU.palm: 0.5204, IoU.kitchen island: 0.4929, IoU.computer: 0.7751, IoU.swivel chair: 0.4869, IoU.boat: 0.5887, IoU.bar: 0.7030, IoU.arcade machine: 0.8663, IoU.hovel: 0.3137, IoU.bus: 0.9336, IoU.towel: 0.7094, IoU.light: 0.5632, IoU.truck: 0.3381, IoU.tower: 0.2229, IoU.chandelier: 0.6434, IoU.awning: 0.4919, IoU.streetlight: 0.2951, IoU.booth: 0.3415, IoU.television receiver: 0.7612, IoU.airplane: 0.7956, IoU.dirt track: 0.0491, IoU.apparel: 0.4406, IoU.pole: 0.2536, IoU.land: 0.0337, IoU.bannister: 0.1834, IoU.escalator: 0.6278, IoU.ottoman: 0.4890, IoU.bottle: 0.4145, IoU.buffet: 0.6516, IoU.poster: 0.2261, IoU.stage: 0.1596, IoU.van: 0.0000, IoU.ship: 0.5390, IoU.fountain: 0.7046, IoU.conveyer belt: 0.6731, IoU.canopy: 0.4885, IoU.washer: 0.8562, IoU.plaything: 0.2687, IoU.swimming pool: 0.5854, IoU.stool: 0.4762, IoU.barrel: 0.5464, IoU.basket: 0.3773, IoU.waterfall: 0.5909, IoU.tent: 0.8607, IoU.bag: 0.2083, IoU.minibike: 0.7152, IoU.cradle: 0.8695, IoU.oven: 0.5080, IoU.ball: 0.0672, IoU.food: 0.5686, IoU.step: 0.1760, IoU.tank: 0.5938, IoU.trade name: 0.3100, IoU.microwave: 0.8641, IoU.pot: 0.5492, IoU.animal: 0.6753, IoU.bicycle: 0.5455, IoU.lake: 0.0000, IoU.dishwasher: 0.6717, IoU.screen: 0.6163, IoU.blanket: 0.2238, IoU.sculpture: 0.7457, IoU.hood: 0.6521, IoU.sconce: 0.5887, IoU.vase: 0.4435, IoU.traffic light: 0.3017, IoU.tray: 0.1106, IoU.ashcan: 0.4929, IoU.fan: 0.6596, IoU.pier: 0.3365, IoU.crt screen: 0.0050, IoU.plate: 0.5830, IoU.monitor: 0.6797, IoU.bulletin board: 0.5903, IoU.shower: 0.0225, IoU.radiator: 0.6199, IoU.glass: 0.1678, IoU.clock: 0.3922, IoU.flag: 0.7038, Acc.wall: 0.8872, Acc.building: 0.9323, Acc.sky: 0.9766, Acc.floor: 0.8865, Acc.tree: 0.8823, Acc.ceiling: 0.9264, Acc.road: 0.8993, Acc.bed : 0.9636, Acc.windowpane: 0.8234, Acc.grass: 0.8156, Acc.cabinet: 0.7644, Acc.sidewalk: 0.8357, Acc.person: 0.9387, Acc.earth: 0.5015, Acc.door: 0.7222, Acc.table: 0.7128, Acc.mountain: 0.6528, Acc.plant: 0.6573, Acc.curtain: 0.8934, Acc.chair: 0.6851, Acc.car: 0.9485, Acc.water: 0.8210, Acc.painting: 0.9057, Acc.sofa: 0.8894, Acc.shelf: 0.5825, Acc.house: 0.5459, Acc.sea: 0.7283, Acc.mirror: 0.8357, Acc.rug: 0.8436, Acc.field: 0.7115, Acc.armchair: 0.8627, Acc.seat: 0.9131, Acc.fence: 0.8069, Acc.desk: 0.7658, Acc.rock: 0.7694, Acc.wardrobe: 0.8637, Acc.lamp: 0.7422, Acc.bathtub: 0.8834, Acc.railing: 0.5845, Acc.cushion: 0.7682, Acc.base: 0.6775, Acc.box: 0.4943, Acc.column: 0.5885, Acc.signboard: 0.5870, Acc.chest of drawers: 0.5920, Acc.counter: 0.7496, Acc.sand: 0.5755, Acc.sink: 0.8480, Acc.skyscraper: 0.5865, Acc.fireplace: 0.9580, Acc.refrigerator: 0.8934, Acc.grandstand: 0.8435, Acc.path: 0.2987, Acc.stairs: 0.4631, Acc.runway: 0.9521, Acc.case: 0.7669, Acc.pool table: 0.9809, Acc.pillow: 0.8288, Acc.screen door: 0.9455, Acc.stairway: 0.6772, Acc.river: 0.0976, Acc.bridge: 0.8822, Acc.bookcase: 0.4389, Acc.blind: 0.4294, Acc.coffee table: 0.8854, Acc.toilet: 0.9307, Acc.flower: 0.5934, Acc.book: 0.7950, Acc.hill: 0.0723, Acc.bench: 0.7529, Acc.countertop: 0.7415, Acc.stove: 0.9331, Acc.palm: 0.8284, Acc.kitchen island: 0.8651, Acc.computer: 0.9339, Acc.swivel chair: 0.7966, Acc.boat: 0.8920, Acc.bar: 0.7904, Acc.arcade machine: 0.9933, Acc.hovel: 0.3340, Acc.bus: 0.9594, Acc.towel: 0.8742, Acc.light: 0.6613, Acc.truck: 0.4100, Acc.tower: 0.3922, Acc.chandelier: 0.9029, Acc.awning: 0.6160, Acc.streetlight: 0.3703, Acc.booth: 0.5939, Acc.television receiver: 0.8988, Acc.airplane: 0.9754, Acc.dirt track: 0.1996, Acc.apparel: 0.6564, Acc.pole: 0.3623, Acc.land: 0.0454, Acc.bannister: 0.2170, Acc.escalator: 0.8797, Acc.ottoman: 0.6760, Acc.bottle: 0.6058, Acc.buffet: 0.7960, Acc.poster: 0.2385, Acc.stage: 0.4577, Acc.van: 0.0000, Acc.ship: 0.5786, Acc.fountain: 0.7694, Acc.conveyer belt: 0.9879, Acc.canopy: 0.6559, Acc.washer: 0.9549, Acc.plaything: 0.3374, Acc.swimming pool: 0.8726, Acc.stool: 0.6859, Acc.barrel: 0.6484, Acc.basket: 0.5015, Acc.waterfall: 0.7336, Acc.tent: 0.9869, Acc.bag: 0.2438, Acc.minibike: 0.8643, Acc.cradle: 0.9286, Acc.oven: 0.5728, Acc.ball: 0.0673, Acc.food: 0.6032, Acc.step: 0.2807, Acc.tank: 0.7295, Acc.trade name: 0.3836, Acc.microwave: 0.9362, Acc.pot: 0.6346, Acc.animal: 0.7022, Acc.bicycle: 0.6747, Acc.lake: 0.0000, Acc.dishwasher: 0.7227, Acc.screen: 0.8788, Acc.blanket: 0.2777, Acc.sculpture: 0.8602, Acc.hood: 0.7701, Acc.sconce: 0.6635, Acc.vase: 0.5514, Acc.traffic light: 0.6358, Acc.tray: 0.1324, Acc.ashcan: 0.6525, Acc.fan: 0.7619, Acc.pier: 0.4313, Acc.crt screen: 0.0053, Acc.plate: 0.7668, Acc.monitor: 0.7865, Acc.bulletin board: 0.7702, Acc.shower: 0.0274, Acc.radiator: 0.7774, Acc.glass: 0.1790, Acc.clock: 0.4227, Acc.flag: 0.7425 +2024-06-16 03:50:01,468 - mmseg - INFO - Iter [13050/80000] lr: 3.348e-05, eta: 1 day, 8:51:15, time: 3.623, data_time: 2.007, memory: 71384, decode.loss_ce: 0.3586, decode.acc_seg: 85.8672, aux.loss_ce: 0.1442, aux.acc_seg: 85.9041, loss: 0.5028 +2024-06-16 03:51:22,602 - mmseg - INFO - Iter [13100/80000] lr: 3.345e-05, eta: 1 day, 8:49:10, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3466, decode.acc_seg: 86.3054, aux.loss_ce: 0.1393, aux.acc_seg: 86.4506, loss: 0.4859 +2024-06-16 03:52:43,780 - mmseg - INFO - Iter [13150/80000] lr: 3.343e-05, eta: 1 day, 8:47:05, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3395, decode.acc_seg: 86.5059, aux.loss_ce: 0.1360, aux.acc_seg: 86.4997, loss: 0.4755 +2024-06-16 03:54:04,942 - mmseg - INFO - Iter [13200/80000] lr: 3.340e-05, eta: 1 day, 8:45:01, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3446, decode.acc_seg: 86.4396, aux.loss_ce: 0.1380, aux.acc_seg: 86.3547, loss: 0.4826 +2024-06-16 03:55:26,212 - mmseg - INFO - Iter [13250/80000] lr: 3.338e-05, eta: 1 day, 8:42:58, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3565, decode.acc_seg: 85.6048, aux.loss_ce: 0.1441, aux.acc_seg: 85.4843, loss: 0.5006 +2024-06-16 03:56:47,376 - mmseg - INFO - Iter [13300/80000] lr: 3.335e-05, eta: 1 day, 8:40:54, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3585, decode.acc_seg: 85.7851, aux.loss_ce: 0.1440, aux.acc_seg: 85.7829, loss: 0.5025 +2024-06-16 03:58:08,471 - mmseg - INFO - Iter [13350/80000] lr: 3.333e-05, eta: 1 day, 8:38:50, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3456, decode.acc_seg: 86.4244, aux.loss_ce: 0.1390, aux.acc_seg: 86.2498, loss: 0.4846 +2024-06-16 03:59:29,672 - mmseg - INFO - Iter [13400/80000] lr: 3.330e-05, eta: 1 day, 8:36:48, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3490, decode.acc_seg: 86.1211, aux.loss_ce: 0.1398, aux.acc_seg: 86.2082, loss: 0.4889 +2024-06-16 04:00:51,031 - mmseg - INFO - Iter [13450/80000] lr: 3.328e-05, eta: 1 day, 8:34:46, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3747, decode.acc_seg: 85.6602, aux.loss_ce: 0.1529, aux.acc_seg: 85.3104, loss: 0.5276 +2024-06-16 04:02:12,255 - mmseg - INFO - Iter [13500/80000] lr: 3.325e-05, eta: 1 day, 8:32:44, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3471, decode.acc_seg: 86.2366, aux.loss_ce: 0.1411, aux.acc_seg: 85.9886, loss: 0.4882 +2024-06-16 04:03:33,301 - mmseg - INFO - Iter [13550/80000] lr: 3.323e-05, eta: 1 day, 8:30:41, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3584, decode.acc_seg: 85.4880, aux.loss_ce: 0.1446, aux.acc_seg: 85.4942, loss: 0.5029 +2024-06-16 04:04:54,629 - mmseg - INFO - Iter [13600/80000] lr: 3.320e-05, eta: 1 day, 8:28:40, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3523, decode.acc_seg: 85.8787, aux.loss_ce: 0.1420, aux.acc_seg: 85.6921, loss: 0.4943 +2024-06-16 04:06:15,854 - mmseg - INFO - Iter [13650/80000] lr: 3.318e-05, eta: 1 day, 8:26:39, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3440, decode.acc_seg: 86.4275, aux.loss_ce: 0.1391, aux.acc_seg: 86.3278, loss: 0.4831 +2024-06-16 04:07:37,067 - mmseg - INFO - Iter [13700/80000] lr: 3.315e-05, eta: 1 day, 8:24:38, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3591, decode.acc_seg: 85.9683, aux.loss_ce: 0.1459, aux.acc_seg: 85.6959, loss: 0.5049 +2024-06-16 04:08:58,262 - mmseg - INFO - Iter [13750/80000] lr: 3.313e-05, eta: 1 day, 8:22:37, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3777, decode.acc_seg: 85.6187, aux.loss_ce: 0.1528, aux.acc_seg: 85.4067, loss: 0.5305 +2024-06-16 04:10:19,549 - mmseg - INFO - Iter [13800/80000] lr: 3.310e-05, eta: 1 day, 8:20:37, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3412, decode.acc_seg: 86.8087, aux.loss_ce: 0.1377, aux.acc_seg: 86.7337, loss: 0.4790 +2024-06-16 04:11:40,696 - mmseg - INFO - Iter [13850/80000] lr: 3.308e-05, eta: 1 day, 8:18:37, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3745, decode.acc_seg: 85.2088, aux.loss_ce: 0.1503, aux.acc_seg: 85.1331, loss: 0.5248 +2024-06-16 04:13:04,642 - mmseg - INFO - Iter [13900/80000] lr: 3.305e-05, eta: 1 day, 8:16:50, time: 1.679, data_time: 0.063, memory: 71384, decode.loss_ce: 0.3561, decode.acc_seg: 86.0411, aux.loss_ce: 0.1440, aux.acc_seg: 86.0661, loss: 0.5002 +2024-06-16 04:14:25,961 - mmseg - INFO - Iter [13950/80000] lr: 3.303e-05, eta: 1 day, 8:14:51, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3265, decode.acc_seg: 86.8511, aux.loss_ce: 0.1324, aux.acc_seg: 86.8128, loss: 0.4590 +2024-06-16 04:15:47,120 - mmseg - INFO - Saving checkpoint at 14000 iterations +2024-06-16 04:17:14,859 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 04:17:14,859 - mmseg - INFO - Iter [14000/80000] lr: 3.300e-05, eta: 1 day, 8:19:45, time: 3.378, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3245, decode.acc_seg: 86.4321, aux.loss_ce: 0.1298, aux.acc_seg: 86.4235, loss: 0.4543 +2024-06-16 04:18:51,183 - mmseg - INFO - per class results: +2024-06-16 04:18:51,190 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.83 | 88.17 | +| building | 84.49 | 91.71 | +| sky | 94.74 | 97.37 | +| floor | 84.38 | 89.68 | +| tree | 76.66 | 91.03 | +| ceiling | 85.89 | 91.33 | +| road | 84.05 | 92.25 | +| bed | 90.99 | 96.35 | +| windowpane | 64.61 | 84.01 | +| grass | 67.6 | 85.36 | +| cabinet | 64.83 | 75.3 | +| sidewalk | 67.1 | 80.08 | +| person | 84.27 | 92.36 | +| earth | 35.65 | 44.32 | +| door | 54.99 | 68.38 | +| table | 64.2 | 77.1 | +| mountain | 61.69 | 76.64 | +| plant | 56.81 | 67.16 | +| curtain | 78.37 | 89.39 | +| chair | 64.72 | 75.19 | +| car | 85.87 | 94.66 | +| water | 52.84 | 60.27 | +| painting | 76.16 | 91.9 | +| sofa | 78.29 | 92.53 | +| shelf | 46.59 | 76.1 | +| house | 58.01 | 82.97 | +| sea | 66.64 | 92.42 | +| mirror | 78.98 | 90.93 | +| rug | 71.76 | 85.16 | +| field | 34.03 | 58.96 | +| armchair | 61.47 | 72.49 | +| seat | 67.01 | 88.89 | +| fence | 46.65 | 72.83 | +| desk | 49.97 | 78.28 | +| rock | 52.22 | 74.83 | +| wardrobe | 55.82 | 78.83 | +| lamp | 70.63 | 86.16 | +| bathtub | 87.32 | 90.76 | +| railing | 40.83 | 58.78 | +| cushion | 67.67 | 77.82 | +| base | 41.5 | 53.48 | +| box | 31.93 | 41.97 | +| column | 53.37 | 64.5 | +| signboard | 41.02 | 56.77 | +| chest of drawers | 52.31 | 78.01 | +| counter | 48.05 | 61.65 | +| sand | 56.61 | 87.77 | +| sink | 74.75 | 86.2 | +| skyscraper | 48.31 | 57.1 | +| fireplace | 69.29 | 96.27 | +| refrigerator | 76.55 | 88.67 | +| grandstand | 45.89 | 87.72 | +| path | 29.85 | 40.13 | +| stairs | 33.31 | 40.24 | +| runway | 69.07 | 87.83 | +| case | 52.93 | 59.91 | +| pool table | 93.55 | 98.01 | +| pillow | 68.01 | 80.64 | +| screen door | 75.81 | 89.12 | +| stairway | 46.75 | 61.15 | +| river | 17.93 | 26.15 | +| bridge | 77.48 | 86.03 | +| bookcase | 38.66 | 55.61 | +| blind | 41.71 | 45.5 | +| coffee table | 59.8 | 85.83 | +| toilet | 89.64 | 94.17 | +| flower | 43.4 | 53.02 | +| book | 49.13 | 79.91 | +| hill | 4.82 | 7.06 | +| bench | 60.46 | 79.92 | +| countertop | 61.7 | 69.59 | +| stove | 82.13 | 92.95 | +| palm | 56.56 | 79.56 | +| kitchen island | 49.14 | 90.0 | +| computer | 74.02 | 93.36 | +| swivel chair | 52.15 | 77.71 | +| boat | 65.32 | 91.45 | +| bar | 63.94 | 87.26 | +| arcade machine | 87.88 | 98.82 | +| hovel | 66.16 | 76.85 | +| bus | 91.18 | 96.91 | +| towel | 68.77 | 75.93 | +| light | 52.76 | 56.75 | +| truck | 38.01 | 47.02 | +| tower | 24.36 | 40.4 | +| chandelier | 71.34 | 84.09 | +| awning | 39.63 | 71.3 | +| streetlight | 25.96 | 31.92 | +| booth | 51.47 | 83.69 | +| television receiver | 74.62 | 87.93 | +| airplane | 78.55 | 96.51 | +| dirt track | 0.0 | 0.0 | +| apparel | 45.47 | 62.41 | +| pole | 26.1 | 36.81 | +| land | 0.0 | 0.0 | +| bannister | 14.18 | 21.99 | +| escalator | 58.35 | 85.98 | +| ottoman | 50.35 | 62.58 | +| bottle | 24.67 | 32.18 | +| buffet | 44.87 | 48.91 | +| poster | 34.19 | 41.24 | +| stage | 10.68 | 49.16 | +| van | 38.39 | 51.13 | +| ship | 68.57 | 83.96 | +| fountain | 74.16 | 85.28 | +| conveyer belt | 77.58 | 95.62 | +| canopy | 33.02 | 44.98 | +| washer | 86.63 | 96.88 | +| plaything | 21.37 | 34.11 | +| swimming pool | 40.99 | 96.15 | +| stool | 48.57 | 55.92 | +| barrel | 48.6 | 67.68 | +| basket | 40.14 | 53.43 | +| waterfall | 57.07 | 77.69 | +| tent | 93.31 | 99.0 | +| bag | 25.47 | 28.67 | +| minibike | 73.28 | 86.11 | +| cradle | 89.47 | 97.48 | +| oven | 43.41 | 46.38 | +| ball | 33.91 | 74.06 | +| food | 60.85 | 78.61 | +| step | 14.04 | 16.63 | +| tank | 62.09 | 75.09 | +| trade name | 9.8 | 10.39 | +| microwave | 83.24 | 96.33 | +| pot | 56.29 | 68.11 | +| animal | 66.46 | 69.37 | +| bicycle | 58.78 | 74.13 | +| lake | 35.95 | 38.62 | +| dishwasher | 73.03 | 79.75 | +| screen | 57.26 | 92.05 | +| blanket | 21.26 | 24.72 | +| sculpture | 57.28 | 87.87 | +| hood | 62.22 | 71.47 | +| sconce | 60.77 | 74.04 | +| vase | 30.71 | 34.99 | +| traffic light | 32.99 | 58.44 | +| tray | 13.34 | 16.49 | +| ashcan | 45.8 | 65.84 | +| fan | 67.12 | 80.73 | +| pier | 39.41 | 44.24 | +| crt screen | 0.84 | 1.61 | +| plate | 58.17 | 77.94 | +| monitor | 33.73 | 41.07 | +| bulletin board | 63.06 | 75.91 | +| shower | 0.15 | 0.15 | +| radiator | 67.71 | 78.49 | +| glass | 16.23 | 17.31 | +| clock | 41.97 | 47.05 | +| flag | 70.95 | 78.67 | ++---------------------+-------+-------+ +2024-06-16 04:18:51,190 - mmseg - INFO - Summary: +2024-06-16 04:18:51,190 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.22 | 54.85 | 68.74 | ++-------+-------+-------+ +2024-06-16 04:18:51,191 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 04:18:51,191 - mmseg - INFO - Iter(val) [250] aAcc: 0.8522, mIoU: 0.5485, mAcc: 0.6874, IoU.wall: 0.8083, IoU.building: 0.8449, IoU.sky: 0.9474, IoU.floor: 0.8438, IoU.tree: 0.7666, IoU.ceiling: 0.8589, IoU.road: 0.8405, IoU.bed : 0.9099, IoU.windowpane: 0.6461, IoU.grass: 0.6760, IoU.cabinet: 0.6483, IoU.sidewalk: 0.6710, IoU.person: 0.8427, IoU.earth: 0.3565, IoU.door: 0.5499, IoU.table: 0.6420, IoU.mountain: 0.6169, IoU.plant: 0.5681, IoU.curtain: 0.7837, IoU.chair: 0.6472, IoU.car: 0.8587, IoU.water: 0.5284, IoU.painting: 0.7616, IoU.sofa: 0.7829, IoU.shelf: 0.4659, IoU.house: 0.5801, IoU.sea: 0.6664, IoU.mirror: 0.7898, IoU.rug: 0.7176, IoU.field: 0.3403, IoU.armchair: 0.6147, IoU.seat: 0.6701, IoU.fence: 0.4665, IoU.desk: 0.4997, IoU.rock: 0.5222, IoU.wardrobe: 0.5582, IoU.lamp: 0.7063, IoU.bathtub: 0.8732, IoU.railing: 0.4083, IoU.cushion: 0.6767, IoU.base: 0.4150, IoU.box: 0.3193, IoU.column: 0.5337, IoU.signboard: 0.4102, IoU.chest of drawers: 0.5231, IoU.counter: 0.4805, IoU.sand: 0.5661, IoU.sink: 0.7475, IoU.skyscraper: 0.4831, IoU.fireplace: 0.6929, IoU.refrigerator: 0.7655, IoU.grandstand: 0.4589, IoU.path: 0.2985, IoU.stairs: 0.3331, IoU.runway: 0.6907, IoU.case: 0.5293, IoU.pool table: 0.9355, IoU.pillow: 0.6801, IoU.screen door: 0.7581, IoU.stairway: 0.4675, IoU.river: 0.1793, IoU.bridge: 0.7748, IoU.bookcase: 0.3866, IoU.blind: 0.4171, IoU.coffee table: 0.5980, IoU.toilet: 0.8964, IoU.flower: 0.4340, IoU.book: 0.4913, IoU.hill: 0.0482, IoU.bench: 0.6046, IoU.countertop: 0.6170, IoU.stove: 0.8213, IoU.palm: 0.5656, IoU.kitchen island: 0.4914, IoU.computer: 0.7402, IoU.swivel chair: 0.5215, IoU.boat: 0.6532, IoU.bar: 0.6394, IoU.arcade machine: 0.8788, IoU.hovel: 0.6616, IoU.bus: 0.9118, IoU.towel: 0.6877, IoU.light: 0.5276, IoU.truck: 0.3801, IoU.tower: 0.2436, IoU.chandelier: 0.7134, IoU.awning: 0.3963, IoU.streetlight: 0.2596, IoU.booth: 0.5147, IoU.television receiver: 0.7462, IoU.airplane: 0.7855, IoU.dirt track: 0.0000, IoU.apparel: 0.4547, IoU.pole: 0.2610, IoU.land: 0.0000, IoU.bannister: 0.1418, IoU.escalator: 0.5835, IoU.ottoman: 0.5035, IoU.bottle: 0.2467, IoU.buffet: 0.4487, IoU.poster: 0.3419, IoU.stage: 0.1068, IoU.van: 0.3839, IoU.ship: 0.6857, IoU.fountain: 0.7416, IoU.conveyer belt: 0.7758, IoU.canopy: 0.3302, IoU.washer: 0.8663, IoU.plaything: 0.2137, IoU.swimming pool: 0.4099, IoU.stool: 0.4857, IoU.barrel: 0.4860, IoU.basket: 0.4014, IoU.waterfall: 0.5707, IoU.tent: 0.9331, IoU.bag: 0.2547, IoU.minibike: 0.7328, IoU.cradle: 0.8947, IoU.oven: 0.4341, IoU.ball: 0.3391, IoU.food: 0.6085, IoU.step: 0.1404, IoU.tank: 0.6209, IoU.trade name: 0.0980, IoU.microwave: 0.8324, IoU.pot: 0.5629, IoU.animal: 0.6646, IoU.bicycle: 0.5878, IoU.lake: 0.3595, IoU.dishwasher: 0.7303, IoU.screen: 0.5726, IoU.blanket: 0.2126, IoU.sculpture: 0.5728, IoU.hood: 0.6222, IoU.sconce: 0.6077, IoU.vase: 0.3071, IoU.traffic light: 0.3299, IoU.tray: 0.1334, IoU.ashcan: 0.4580, IoU.fan: 0.6712, IoU.pier: 0.3941, IoU.crt screen: 0.0084, IoU.plate: 0.5817, IoU.monitor: 0.3373, IoU.bulletin board: 0.6306, IoU.shower: 0.0015, IoU.radiator: 0.6771, IoU.glass: 0.1623, IoU.clock: 0.4197, IoU.flag: 0.7095, Acc.wall: 0.8817, Acc.building: 0.9171, Acc.sky: 0.9737, Acc.floor: 0.8968, Acc.tree: 0.9103, Acc.ceiling: 0.9133, Acc.road: 0.9225, Acc.bed : 0.9635, Acc.windowpane: 0.8401, Acc.grass: 0.8536, Acc.cabinet: 0.7530, Acc.sidewalk: 0.8008, Acc.person: 0.9236, Acc.earth: 0.4432, Acc.door: 0.6838, Acc.table: 0.7710, Acc.mountain: 0.7664, Acc.plant: 0.6716, Acc.curtain: 0.8939, Acc.chair: 0.7519, Acc.car: 0.9466, Acc.water: 0.6027, Acc.painting: 0.9190, Acc.sofa: 0.9253, Acc.shelf: 0.7610, Acc.house: 0.8297, Acc.sea: 0.9242, Acc.mirror: 0.9093, Acc.rug: 0.8516, Acc.field: 0.5896, Acc.armchair: 0.7249, Acc.seat: 0.8889, Acc.fence: 0.7283, Acc.desk: 0.7828, Acc.rock: 0.7483, Acc.wardrobe: 0.7883, Acc.lamp: 0.8616, Acc.bathtub: 0.9076, Acc.railing: 0.5878, Acc.cushion: 0.7782, Acc.base: 0.5348, Acc.box: 0.4197, Acc.column: 0.6450, Acc.signboard: 0.5677, Acc.chest of drawers: 0.7801, Acc.counter: 0.6165, Acc.sand: 0.8777, Acc.sink: 0.8620, Acc.skyscraper: 0.5710, Acc.fireplace: 0.9627, Acc.refrigerator: 0.8867, Acc.grandstand: 0.8772, Acc.path: 0.4013, Acc.stairs: 0.4024, Acc.runway: 0.8783, Acc.case: 0.5991, Acc.pool table: 0.9801, Acc.pillow: 0.8064, Acc.screen door: 0.8912, Acc.stairway: 0.6115, Acc.river: 0.2615, Acc.bridge: 0.8603, Acc.bookcase: 0.5561, Acc.blind: 0.4550, Acc.coffee table: 0.8583, Acc.toilet: 0.9417, Acc.flower: 0.5302, Acc.book: 0.7991, Acc.hill: 0.0706, Acc.bench: 0.7992, Acc.countertop: 0.6959, Acc.stove: 0.9295, Acc.palm: 0.7956, Acc.kitchen island: 0.9000, Acc.computer: 0.9336, Acc.swivel chair: 0.7771, Acc.boat: 0.9145, Acc.bar: 0.8726, Acc.arcade machine: 0.9882, Acc.hovel: 0.7685, Acc.bus: 0.9691, Acc.towel: 0.7593, Acc.light: 0.5675, Acc.truck: 0.4702, Acc.tower: 0.4040, Acc.chandelier: 0.8409, Acc.awning: 0.7130, Acc.streetlight: 0.3192, Acc.booth: 0.8369, Acc.television receiver: 0.8793, Acc.airplane: 0.9651, Acc.dirt track: 0.0000, Acc.apparel: 0.6241, Acc.pole: 0.3681, Acc.land: 0.0000, Acc.bannister: 0.2199, Acc.escalator: 0.8598, Acc.ottoman: 0.6258, Acc.bottle: 0.3218, Acc.buffet: 0.4891, Acc.poster: 0.4124, Acc.stage: 0.4916, Acc.van: 0.5113, Acc.ship: 0.8396, Acc.fountain: 0.8528, Acc.conveyer belt: 0.9562, Acc.canopy: 0.4498, Acc.washer: 0.9688, Acc.plaything: 0.3411, Acc.swimming pool: 0.9615, Acc.stool: 0.5592, Acc.barrel: 0.6768, Acc.basket: 0.5343, Acc.waterfall: 0.7769, Acc.tent: 0.9900, Acc.bag: 0.2867, Acc.minibike: 0.8611, Acc.cradle: 0.9748, Acc.oven: 0.4638, Acc.ball: 0.7406, Acc.food: 0.7861, Acc.step: 0.1663, Acc.tank: 0.7509, Acc.trade name: 0.1039, Acc.microwave: 0.9633, Acc.pot: 0.6811, Acc.animal: 0.6937, Acc.bicycle: 0.7413, Acc.lake: 0.3862, Acc.dishwasher: 0.7975, Acc.screen: 0.9205, Acc.blanket: 0.2472, Acc.sculpture: 0.8787, Acc.hood: 0.7147, Acc.sconce: 0.7404, Acc.vase: 0.3499, Acc.traffic light: 0.5844, Acc.tray: 0.1649, Acc.ashcan: 0.6584, Acc.fan: 0.8073, Acc.pier: 0.4424, Acc.crt screen: 0.0161, Acc.plate: 0.7794, Acc.monitor: 0.4107, Acc.bulletin board: 0.7591, Acc.shower: 0.0015, Acc.radiator: 0.7849, Acc.glass: 0.1731, Acc.clock: 0.4705, Acc.flag: 0.7867 +2024-06-16 04:20:12,773 - mmseg - INFO - Iter [14050/80000] lr: 3.298e-05, eta: 1 day, 8:25:18, time: 3.558, data_time: 1.945, memory: 71384, decode.loss_ce: 0.3203, decode.acc_seg: 87.2722, aux.loss_ce: 0.1299, aux.acc_seg: 87.0603, loss: 0.4502 +2024-06-16 04:21:33,927 - mmseg - INFO - Iter [14100/80000] lr: 3.295e-05, eta: 1 day, 8:23:15, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3242, decode.acc_seg: 87.5761, aux.loss_ce: 0.1314, aux.acc_seg: 87.3472, loss: 0.4555 +2024-06-16 04:22:55,277 - mmseg - INFO - Iter [14150/80000] lr: 3.293e-05, eta: 1 day, 8:21:14, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3470, decode.acc_seg: 86.4457, aux.loss_ce: 0.1403, aux.acc_seg: 86.2544, loss: 0.4873 +2024-06-16 04:24:16,488 - mmseg - INFO - Iter [14200/80000] lr: 3.290e-05, eta: 1 day, 8:19:12, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3200, decode.acc_seg: 87.0035, aux.loss_ce: 0.1292, aux.acc_seg: 86.8779, loss: 0.4492 +2024-06-16 04:25:37,505 - mmseg - INFO - Iter [14250/80000] lr: 3.288e-05, eta: 1 day, 8:17:09, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3385, decode.acc_seg: 86.5393, aux.loss_ce: 0.1357, aux.acc_seg: 86.4805, loss: 0.4742 +2024-06-16 04:26:58,606 - mmseg - INFO - Iter [14300/80000] lr: 3.285e-05, eta: 1 day, 8:15:07, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3427, decode.acc_seg: 86.0719, aux.loss_ce: 0.1379, aux.acc_seg: 86.0844, loss: 0.4806 +2024-06-16 04:28:20,045 - mmseg - INFO - Iter [14350/80000] lr: 3.283e-05, eta: 1 day, 8:13:07, time: 1.629, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3344, decode.acc_seg: 86.6241, aux.loss_ce: 0.1343, aux.acc_seg: 86.5454, loss: 0.4686 +2024-06-16 04:29:41,391 - mmseg - INFO - Iter [14400/80000] lr: 3.280e-05, eta: 1 day, 8:11:07, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3430, decode.acc_seg: 86.0383, aux.loss_ce: 0.1384, aux.acc_seg: 85.8780, loss: 0.4814 +2024-06-16 04:31:02,532 - mmseg - INFO - Iter [14450/80000] lr: 3.278e-05, eta: 1 day, 8:09:06, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3471, decode.acc_seg: 86.4195, aux.loss_ce: 0.1406, aux.acc_seg: 86.2007, loss: 0.4877 +2024-06-16 04:32:23,899 - mmseg - INFO - Iter [14500/80000] lr: 3.275e-05, eta: 1 day, 8:07:07, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3457, decode.acc_seg: 86.0032, aux.loss_ce: 0.1385, aux.acc_seg: 86.0243, loss: 0.4842 +2024-06-16 04:33:45,115 - mmseg - INFO - Iter [14550/80000] lr: 3.273e-05, eta: 1 day, 8:05:07, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3378, decode.acc_seg: 86.1752, aux.loss_ce: 0.1361, aux.acc_seg: 85.9626, loss: 0.4739 +2024-06-16 04:35:06,328 - mmseg - INFO - Iter [14600/80000] lr: 3.270e-05, eta: 1 day, 8:03:07, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3742, decode.acc_seg: 85.5395, aux.loss_ce: 0.1509, aux.acc_seg: 85.3340, loss: 0.5251 +2024-06-16 04:36:27,525 - mmseg - INFO - Iter [14650/80000] lr: 3.268e-05, eta: 1 day, 8:01:07, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3214, decode.acc_seg: 87.1047, aux.loss_ce: 0.1288, aux.acc_seg: 87.0904, loss: 0.4502 +2024-06-16 04:37:48,778 - mmseg - INFO - Iter [14700/80000] lr: 3.265e-05, eta: 1 day, 7:59:08, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3255, decode.acc_seg: 87.0445, aux.loss_ce: 0.1311, aux.acc_seg: 86.8745, loss: 0.4566 +2024-06-16 04:39:10,108 - mmseg - INFO - Iter [14750/80000] lr: 3.263e-05, eta: 1 day, 7:57:10, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3462, decode.acc_seg: 86.7826, aux.loss_ce: 0.1392, aux.acc_seg: 86.6416, loss: 0.4854 +2024-06-16 04:40:31,314 - mmseg - INFO - Iter [14800/80000] lr: 3.260e-05, eta: 1 day, 7:55:11, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3111, decode.acc_seg: 87.1343, aux.loss_ce: 0.1263, aux.acc_seg: 86.8795, loss: 0.4375 +2024-06-16 04:41:52,393 - mmseg - INFO - Iter [14850/80000] lr: 3.258e-05, eta: 1 day, 7:53:12, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3348, decode.acc_seg: 86.6504, aux.loss_ce: 0.1351, aux.acc_seg: 86.5668, loss: 0.4699 +2024-06-16 04:43:13,667 - mmseg - INFO - Iter [14900/80000] lr: 3.255e-05, eta: 1 day, 7:51:14, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3480, decode.acc_seg: 85.6240, aux.loss_ce: 0.1395, aux.acc_seg: 85.6234, loss: 0.4875 +2024-06-16 04:44:35,024 - mmseg - INFO - Iter [14950/80000] lr: 3.253e-05, eta: 1 day, 7:49:17, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3520, decode.acc_seg: 85.8273, aux.loss_ce: 0.1416, aux.acc_seg: 85.7661, loss: 0.4936 +2024-06-16 04:45:56,152 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 04:45:56,152 - mmseg - INFO - Iter [15000/80000] lr: 3.250e-05, eta: 1 day, 7:47:19, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3393, decode.acc_seg: 86.5721, aux.loss_ce: 0.1365, aux.acc_seg: 86.5629, loss: 0.4758 +2024-06-16 04:47:34,129 - mmseg - INFO - per class results: +2024-06-16 04:47:34,135 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.97 | 88.88 | +| building | 83.01 | 89.61 | +| sky | 94.51 | 97.1 | +| floor | 84.6 | 89.91 | +| tree | 76.8 | 87.93 | +| ceiling | 85.72 | 91.68 | +| road | 85.35 | 90.69 | +| bed | 91.39 | 95.16 | +| windowpane | 65.5 | 77.28 | +| grass | 67.75 | 80.38 | +| cabinet | 62.98 | 71.92 | +| sidewalk | 70.52 | 82.16 | +| person | 83.94 | 94.12 | +| earth | 33.89 | 42.25 | +| door | 59.34 | 74.21 | +| table | 65.29 | 74.38 | +| mountain | 58.69 | 75.69 | +| plant | 58.07 | 70.79 | +| curtain | 77.77 | 90.54 | +| chair | 65.85 | 77.42 | +| car | 86.23 | 94.53 | +| water | 64.06 | 81.14 | +| painting | 77.4 | 88.89 | +| sofa | 81.1 | 90.96 | +| shelf | 43.44 | 56.44 | +| house | 46.34 | 92.96 | +| sea | 69.44 | 79.56 | +| mirror | 79.63 | 88.52 | +| rug | 72.38 | 88.74 | +| field | 34.75 | 75.48 | +| armchair | 64.65 | 77.28 | +| seat | 62.13 | 88.98 | +| fence | 49.04 | 63.92 | +| desk | 50.93 | 79.94 | +| rock | 56.61 | 84.01 | +| wardrobe | 50.36 | 93.61 | +| lamp | 71.52 | 83.2 | +| bathtub | 83.45 | 86.75 | +| railing | 38.74 | 49.82 | +| cushion | 66.1 | 73.61 | +| base | 39.43 | 75.79 | +| box | 32.14 | 44.5 | +| column | 54.46 | 67.83 | +| signboard | 40.71 | 53.25 | +| chest of drawers | 46.49 | 77.1 | +| counter | 49.21 | 58.98 | +| sand | 56.82 | 87.3 | +| sink | 76.14 | 81.71 | +| skyscraper | 47.52 | 66.23 | +| fireplace | 69.88 | 94.62 | +| refrigerator | 83.54 | 93.33 | +| grandstand | 53.11 | 83.51 | +| path | 25.47 | 38.22 | +| stairs | 35.63 | 44.44 | +| runway | 67.9 | 88.05 | +| case | 57.77 | 87.57 | +| pool table | 90.32 | 98.46 | +| pillow | 67.53 | 84.76 | +| screen door | 81.66 | 95.61 | +| stairway | 52.53 | 62.73 | +| river | 10.86 | 17.1 | +| bridge | 58.47 | 86.67 | +| bookcase | 35.49 | 55.14 | +| blind | 40.98 | 43.58 | +| coffee table | 61.78 | 90.56 | +| toilet | 88.18 | 95.42 | +| flower | 42.49 | 49.82 | +| book | 52.93 | 74.68 | +| hill | 4.19 | 17.6 | +| bench | 51.08 | 58.89 | +| countertop | 64.17 | 80.68 | +| stove | 81.92 | 91.64 | +| palm | 57.07 | 73.65 | +| kitchen island | 45.79 | 86.92 | +| computer | 72.67 | 94.43 | +| swivel chair | 53.01 | 85.67 | +| boat | 47.37 | 57.34 | +| bar | 63.15 | 72.65 | +| arcade machine | 87.14 | 93.26 | +| hovel | 29.23 | 39.42 | +| bus | 93.16 | 96.24 | +| towel | 70.83 | 85.13 | +| light | 55.48 | 61.24 | +| truck | 40.2 | 57.81 | +| tower | 23.55 | 41.23 | +| chandelier | 70.01 | 81.41 | +| awning | 44.9 | 58.7 | +| streetlight | 31.21 | 42.94 | +| booth | 41.64 | 50.37 | +| television receiver | 74.98 | 87.26 | +| airplane | 80.32 | 96.68 | +| dirt track | 3.27 | 17.63 | +| apparel | 43.08 | 58.38 | +| pole | 22.1 | 29.51 | +| land | 0.28 | 0.54 | +| bannister | 16.75 | 23.96 | +| escalator | 53.7 | 89.32 | +| ottoman | 50.54 | 68.85 | +| bottle | 25.95 | 33.88 | +| buffet | 57.61 | 87.03 | +| poster | 35.93 | 49.41 | +| stage | 29.02 | 42.27 | +| van | 48.1 | 66.73 | +| ship | 56.03 | 82.0 | +| fountain | 53.73 | 57.81 | +| conveyer belt | 65.63 | 97.62 | +| canopy | 28.44 | 42.06 | +| washer | 78.29 | 84.51 | +| plaything | 33.52 | 51.69 | +| swimming pool | 54.92 | 90.38 | +| stool | 50.23 | 63.49 | +| barrel | 58.87 | 64.33 | +| basket | 34.41 | 44.51 | +| waterfall | 44.24 | 46.67 | +| tent | 93.17 | 98.34 | +| bag | 18.08 | 21.97 | +| minibike | 73.11 | 86.33 | +| cradle | 70.49 | 97.95 | +| oven | 64.0 | 80.88 | +| ball | 30.47 | 33.62 | +| food | 52.48 | 56.39 | +| step | 13.13 | 16.09 | +| tank | 64.99 | 96.78 | +| trade name | 16.33 | 17.54 | +| microwave | 88.58 | 95.85 | +| pot | 55.89 | 63.3 | +| animal | 60.15 | 61.28 | +| bicycle | 57.84 | 79.21 | +| lake | 52.67 | 75.2 | +| dishwasher | 71.41 | 78.44 | +| screen | 58.61 | 94.05 | +| blanket | 27.08 | 31.45 | +| sculpture | 68.11 | 87.33 | +| hood | 64.77 | 82.0 | +| sconce | 57.61 | 64.49 | +| vase | 41.57 | 57.32 | +| traffic light | 28.81 | 68.67 | +| tray | 14.8 | 18.69 | +| ashcan | 47.95 | 62.26 | +| fan | 66.45 | 78.37 | +| pier | 42.07 | 48.31 | +| crt screen | 0.0 | 0.0 | +| plate | 59.91 | 75.61 | +| monitor | 55.56 | 82.66 | +| bulletin board | 46.54 | 75.9 | +| shower | 0.0 | 0.0 | +| radiator | 64.09 | 74.52 | +| glass | 17.51 | 19.17 | +| clock | 39.84 | 47.15 | +| flag | 69.07 | 72.1 | ++---------------------+-------+-------+ +2024-06-16 04:47:34,135 - mmseg - INFO - Summary: +2024-06-16 04:47:34,135 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.94 | 54.67 | 69.07 | ++-------+-------+-------+ +2024-06-16 04:47:34,136 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 04:47:34,136 - mmseg - INFO - Iter(val) [250] aAcc: 0.8494, mIoU: 0.5467, mAcc: 0.6907, IoU.wall: 0.8097, IoU.building: 0.8301, IoU.sky: 0.9451, IoU.floor: 0.8460, IoU.tree: 0.7680, IoU.ceiling: 0.8572, IoU.road: 0.8535, IoU.bed : 0.9139, IoU.windowpane: 0.6550, IoU.grass: 0.6775, IoU.cabinet: 0.6298, IoU.sidewalk: 0.7052, IoU.person: 0.8394, IoU.earth: 0.3389, IoU.door: 0.5934, IoU.table: 0.6529, IoU.mountain: 0.5869, IoU.plant: 0.5807, IoU.curtain: 0.7777, IoU.chair: 0.6585, IoU.car: 0.8623, IoU.water: 0.6406, IoU.painting: 0.7740, IoU.sofa: 0.8110, IoU.shelf: 0.4344, IoU.house: 0.4634, IoU.sea: 0.6944, IoU.mirror: 0.7963, IoU.rug: 0.7238, IoU.field: 0.3475, IoU.armchair: 0.6465, IoU.seat: 0.6213, IoU.fence: 0.4904, IoU.desk: 0.5093, IoU.rock: 0.5661, IoU.wardrobe: 0.5036, IoU.lamp: 0.7152, IoU.bathtub: 0.8345, IoU.railing: 0.3874, IoU.cushion: 0.6610, IoU.base: 0.3943, IoU.box: 0.3214, IoU.column: 0.5446, IoU.signboard: 0.4071, IoU.chest of drawers: 0.4649, IoU.counter: 0.4921, IoU.sand: 0.5682, IoU.sink: 0.7614, IoU.skyscraper: 0.4752, IoU.fireplace: 0.6988, IoU.refrigerator: 0.8354, IoU.grandstand: 0.5311, IoU.path: 0.2547, IoU.stairs: 0.3563, IoU.runway: 0.6790, IoU.case: 0.5777, IoU.pool table: 0.9032, IoU.pillow: 0.6753, IoU.screen door: 0.8166, IoU.stairway: 0.5253, IoU.river: 0.1086, IoU.bridge: 0.5847, IoU.bookcase: 0.3549, IoU.blind: 0.4098, IoU.coffee table: 0.6178, IoU.toilet: 0.8818, IoU.flower: 0.4249, IoU.book: 0.5293, IoU.hill: 0.0419, IoU.bench: 0.5108, IoU.countertop: 0.6417, IoU.stove: 0.8192, IoU.palm: 0.5707, IoU.kitchen island: 0.4579, IoU.computer: 0.7267, IoU.swivel chair: 0.5301, IoU.boat: 0.4737, IoU.bar: 0.6315, IoU.arcade machine: 0.8714, IoU.hovel: 0.2923, IoU.bus: 0.9316, IoU.towel: 0.7083, IoU.light: 0.5548, IoU.truck: 0.4020, IoU.tower: 0.2355, IoU.chandelier: 0.7001, IoU.awning: 0.4490, IoU.streetlight: 0.3121, IoU.booth: 0.4164, IoU.television receiver: 0.7498, IoU.airplane: 0.8032, IoU.dirt track: 0.0327, IoU.apparel: 0.4308, IoU.pole: 0.2210, IoU.land: 0.0028, IoU.bannister: 0.1675, IoU.escalator: 0.5370, IoU.ottoman: 0.5054, IoU.bottle: 0.2595, IoU.buffet: 0.5761, IoU.poster: 0.3593, IoU.stage: 0.2902, IoU.van: 0.4810, IoU.ship: 0.5603, IoU.fountain: 0.5373, IoU.conveyer belt: 0.6563, IoU.canopy: 0.2844, IoU.washer: 0.7829, IoU.plaything: 0.3352, IoU.swimming pool: 0.5492, IoU.stool: 0.5023, IoU.barrel: 0.5887, IoU.basket: 0.3441, IoU.waterfall: 0.4424, IoU.tent: 0.9317, IoU.bag: 0.1808, IoU.minibike: 0.7311, IoU.cradle: 0.7049, IoU.oven: 0.6400, IoU.ball: 0.3047, IoU.food: 0.5248, IoU.step: 0.1313, IoU.tank: 0.6499, IoU.trade name: 0.1633, IoU.microwave: 0.8858, IoU.pot: 0.5589, IoU.animal: 0.6015, IoU.bicycle: 0.5784, IoU.lake: 0.5267, IoU.dishwasher: 0.7141, IoU.screen: 0.5861, IoU.blanket: 0.2708, IoU.sculpture: 0.6811, IoU.hood: 0.6477, IoU.sconce: 0.5761, IoU.vase: 0.4157, IoU.traffic light: 0.2881, IoU.tray: 0.1480, IoU.ashcan: 0.4795, IoU.fan: 0.6645, IoU.pier: 0.4207, IoU.crt screen: 0.0000, IoU.plate: 0.5991, IoU.monitor: 0.5556, IoU.bulletin board: 0.4654, IoU.shower: 0.0000, IoU.radiator: 0.6409, IoU.glass: 0.1751, IoU.clock: 0.3984, IoU.flag: 0.6907, Acc.wall: 0.8888, Acc.building: 0.8961, Acc.sky: 0.9710, Acc.floor: 0.8991, Acc.tree: 0.8793, Acc.ceiling: 0.9168, Acc.road: 0.9069, Acc.bed : 0.9516, Acc.windowpane: 0.7728, Acc.grass: 0.8038, Acc.cabinet: 0.7192, Acc.sidewalk: 0.8216, Acc.person: 0.9412, Acc.earth: 0.4225, Acc.door: 0.7421, Acc.table: 0.7438, Acc.mountain: 0.7569, Acc.plant: 0.7079, Acc.curtain: 0.9054, Acc.chair: 0.7742, Acc.car: 0.9453, Acc.water: 0.8114, Acc.painting: 0.8889, Acc.sofa: 0.9096, Acc.shelf: 0.5644, Acc.house: 0.9296, Acc.sea: 0.7956, Acc.mirror: 0.8852, Acc.rug: 0.8874, Acc.field: 0.7548, Acc.armchair: 0.7728, Acc.seat: 0.8898, Acc.fence: 0.6392, Acc.desk: 0.7994, Acc.rock: 0.8401, Acc.wardrobe: 0.9361, Acc.lamp: 0.8320, Acc.bathtub: 0.8675, Acc.railing: 0.4982, Acc.cushion: 0.7361, Acc.base: 0.7579, Acc.box: 0.4450, Acc.column: 0.6783, Acc.signboard: 0.5325, Acc.chest of drawers: 0.7710, Acc.counter: 0.5898, Acc.sand: 0.8730, Acc.sink: 0.8171, Acc.skyscraper: 0.6623, Acc.fireplace: 0.9462, Acc.refrigerator: 0.9333, Acc.grandstand: 0.8351, Acc.path: 0.3822, Acc.stairs: 0.4444, Acc.runway: 0.8805, Acc.case: 0.8757, Acc.pool table: 0.9846, Acc.pillow: 0.8476, Acc.screen door: 0.9561, Acc.stairway: 0.6273, Acc.river: 0.1710, Acc.bridge: 0.8667, Acc.bookcase: 0.5514, Acc.blind: 0.4358, Acc.coffee table: 0.9056, Acc.toilet: 0.9542, Acc.flower: 0.4982, Acc.book: 0.7468, Acc.hill: 0.1760, Acc.bench: 0.5889, Acc.countertop: 0.8068, Acc.stove: 0.9164, Acc.palm: 0.7365, Acc.kitchen island: 0.8692, Acc.computer: 0.9443, Acc.swivel chair: 0.8567, Acc.boat: 0.5734, Acc.bar: 0.7265, Acc.arcade machine: 0.9326, Acc.hovel: 0.3942, Acc.bus: 0.9624, Acc.towel: 0.8513, Acc.light: 0.6124, Acc.truck: 0.5781, Acc.tower: 0.4123, Acc.chandelier: 0.8141, Acc.awning: 0.5870, Acc.streetlight: 0.4294, Acc.booth: 0.5037, Acc.television receiver: 0.8726, Acc.airplane: 0.9668, Acc.dirt track: 0.1763, Acc.apparel: 0.5838, Acc.pole: 0.2951, Acc.land: 0.0054, Acc.bannister: 0.2396, Acc.escalator: 0.8932, Acc.ottoman: 0.6885, Acc.bottle: 0.3388, Acc.buffet: 0.8703, Acc.poster: 0.4941, Acc.stage: 0.4227, Acc.van: 0.6673, Acc.ship: 0.8200, Acc.fountain: 0.5781, Acc.conveyer belt: 0.9762, Acc.canopy: 0.4206, Acc.washer: 0.8451, Acc.plaything: 0.5169, Acc.swimming pool: 0.9038, Acc.stool: 0.6349, Acc.barrel: 0.6433, Acc.basket: 0.4451, Acc.waterfall: 0.4667, Acc.tent: 0.9834, Acc.bag: 0.2197, Acc.minibike: 0.8633, Acc.cradle: 0.9795, Acc.oven: 0.8088, Acc.ball: 0.3362, Acc.food: 0.5639, Acc.step: 0.1609, Acc.tank: 0.9678, Acc.trade name: 0.1754, Acc.microwave: 0.9585, Acc.pot: 0.6330, Acc.animal: 0.6128, Acc.bicycle: 0.7921, Acc.lake: 0.7520, Acc.dishwasher: 0.7844, Acc.screen: 0.9405, Acc.blanket: 0.3145, Acc.sculpture: 0.8733, Acc.hood: 0.8200, Acc.sconce: 0.6449, Acc.vase: 0.5732, Acc.traffic light: 0.6867, Acc.tray: 0.1869, Acc.ashcan: 0.6226, Acc.fan: 0.7837, Acc.pier: 0.4831, Acc.crt screen: 0.0000, Acc.plate: 0.7561, Acc.monitor: 0.8266, Acc.bulletin board: 0.7590, Acc.shower: 0.0000, Acc.radiator: 0.7452, Acc.glass: 0.1917, Acc.clock: 0.4715, Acc.flag: 0.7210 +2024-06-16 04:48:55,673 - mmseg - INFO - Iter [15050/80000] lr: 3.248e-05, eta: 1 day, 7:52:26, time: 3.590, data_time: 1.977, memory: 71384, decode.loss_ce: 0.3240, decode.acc_seg: 86.9208, aux.loss_ce: 0.1320, aux.acc_seg: 86.6927, loss: 0.4560 +2024-06-16 04:50:16,939 - mmseg - INFO - Iter [15100/80000] lr: 3.245e-05, eta: 1 day, 7:50:27, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3481, decode.acc_seg: 86.1726, aux.loss_ce: 0.1396, aux.acc_seg: 86.0802, loss: 0.4877 +2024-06-16 04:51:38,115 - mmseg - INFO - Iter [15150/80000] lr: 3.243e-05, eta: 1 day, 7:48:28, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3605, decode.acc_seg: 86.1149, aux.loss_ce: 0.1449, aux.acc_seg: 85.8262, loss: 0.5054 +2024-06-16 04:53:01,337 - mmseg - INFO - Iter [15200/80000] lr: 3.240e-05, eta: 1 day, 7:46:38, time: 1.664, data_time: 0.052, memory: 71384, decode.loss_ce: 0.3150, decode.acc_seg: 87.4552, aux.loss_ce: 0.1266, aux.acc_seg: 87.3956, loss: 0.4416 +2024-06-16 04:54:22,639 - mmseg - INFO - Iter [15250/80000] lr: 3.238e-05, eta: 1 day, 7:44:40, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3230, decode.acc_seg: 87.2982, aux.loss_ce: 0.1309, aux.acc_seg: 86.9358, loss: 0.4539 +2024-06-16 04:55:43,873 - mmseg - INFO - Iter [15300/80000] lr: 3.235e-05, eta: 1 day, 7:42:42, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3250, decode.acc_seg: 87.1503, aux.loss_ce: 0.1308, aux.acc_seg: 87.0388, loss: 0.4558 +2024-06-16 04:57:05,024 - mmseg - INFO - Iter [15350/80000] lr: 3.233e-05, eta: 1 day, 7:40:44, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3165, decode.acc_seg: 86.7960, aux.loss_ce: 0.1280, aux.acc_seg: 86.5940, loss: 0.4445 +2024-06-16 04:58:26,262 - mmseg - INFO - Iter [15400/80000] lr: 3.230e-05, eta: 1 day, 7:38:47, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3258, decode.acc_seg: 87.0885, aux.loss_ce: 0.1311, aux.acc_seg: 86.9892, loss: 0.4570 +2024-06-16 04:59:47,500 - mmseg - INFO - Iter [15450/80000] lr: 3.228e-05, eta: 1 day, 7:36:50, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3349, decode.acc_seg: 87.2079, aux.loss_ce: 0.1350, aux.acc_seg: 86.9252, loss: 0.4699 +2024-06-16 05:01:08,702 - mmseg - INFO - Iter [15500/80000] lr: 3.225e-05, eta: 1 day, 7:34:53, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3003, decode.acc_seg: 87.7261, aux.loss_ce: 0.1219, aux.acc_seg: 87.4784, loss: 0.4222 +2024-06-16 05:02:29,828 - mmseg - INFO - Iter [15550/80000] lr: 3.223e-05, eta: 1 day, 7:32:56, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3101, decode.acc_seg: 87.3017, aux.loss_ce: 0.1259, aux.acc_seg: 87.0608, loss: 0.4360 +2024-06-16 05:03:51,043 - mmseg - INFO - Iter [15600/80000] lr: 3.220e-05, eta: 1 day, 7:30:59, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3099, decode.acc_seg: 87.3912, aux.loss_ce: 0.1254, aux.acc_seg: 87.1711, loss: 0.4354 +2024-06-16 05:05:12,405 - mmseg - INFO - Iter [15650/80000] lr: 3.218e-05, eta: 1 day, 7:29:03, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3172, decode.acc_seg: 87.2027, aux.loss_ce: 0.1282, aux.acc_seg: 87.0114, loss: 0.4454 +2024-06-16 05:06:33,556 - mmseg - INFO - Iter [15700/80000] lr: 3.215e-05, eta: 1 day, 7:27:07, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3282, decode.acc_seg: 86.7610, aux.loss_ce: 0.1325, aux.acc_seg: 86.6407, loss: 0.4607 +2024-06-16 05:07:54,641 - mmseg - INFO - Iter [15750/80000] lr: 3.213e-05, eta: 1 day, 7:25:10, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3230, decode.acc_seg: 87.2360, aux.loss_ce: 0.1301, aux.acc_seg: 87.1384, loss: 0.4530 +2024-06-16 05:09:15,820 - mmseg - INFO - Iter [15800/80000] lr: 3.210e-05, eta: 1 day, 7:23:15, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2936, decode.acc_seg: 87.6882, aux.loss_ce: 0.1187, aux.acc_seg: 87.5464, loss: 0.4123 +2024-06-16 05:10:37,055 - mmseg - INFO - Iter [15850/80000] lr: 3.208e-05, eta: 1 day, 7:21:19, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3121, decode.acc_seg: 87.9406, aux.loss_ce: 0.1258, aux.acc_seg: 87.7127, loss: 0.4379 +2024-06-16 05:11:58,199 - mmseg - INFO - Iter [15900/80000] lr: 3.205e-05, eta: 1 day, 7:19:24, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3482, decode.acc_seg: 87.1046, aux.loss_ce: 0.1398, aux.acc_seg: 86.8429, loss: 0.4880 +2024-06-16 05:13:19,505 - mmseg - INFO - Iter [15950/80000] lr: 3.203e-05, eta: 1 day, 7:17:29, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3322, decode.acc_seg: 86.6807, aux.loss_ce: 0.1341, aux.acc_seg: 86.5229, loss: 0.4663 +2024-06-16 05:14:40,637 - mmseg - INFO - Saving checkpoint at 16000 iterations +2024-06-16 05:16:06,706 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 05:16:06,706 - mmseg - INFO - Iter [16000/80000] lr: 3.200e-05, eta: 1 day, 7:21:18, time: 3.344, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3217, decode.acc_seg: 86.9826, aux.loss_ce: 0.1299, aux.acc_seg: 86.8065, loss: 0.4515 +2024-06-16 05:17:42,038 - mmseg - INFO - per class results: +2024-06-16 05:17:42,044 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.65 | 88.7 | +| building | 84.79 | 92.47 | +| sky | 94.09 | 95.92 | +| floor | 83.76 | 91.88 | +| tree | 76.67 | 92.01 | +| ceiling | 85.31 | 93.69 | +| road | 85.25 | 90.8 | +| bed | 91.99 | 96.34 | +| windowpane | 65.32 | 79.43 | +| grass | 69.66 | 90.92 | +| cabinet | 62.4 | 72.38 | +| sidewalk | 69.6 | 86.61 | +| person | 84.54 | 91.49 | +| earth | 35.48 | 44.13 | +| door | 58.34 | 74.27 | +| table | 62.44 | 70.57 | +| mountain | 52.9 | 59.6 | +| plant | 58.29 | 73.7 | +| curtain | 77.64 | 90.32 | +| chair | 66.16 | 77.07 | +| car | 87.26 | 93.76 | +| water | 67.17 | 84.49 | +| painting | 78.04 | 88.4 | +| sofa | 80.24 | 91.15 | +| shelf | 47.16 | 62.56 | +| house | 53.93 | 63.16 | +| sea | 78.03 | 87.45 | +| mirror | 79.81 | 87.91 | +| rug | 69.94 | 79.84 | +| field | 32.48 | 50.55 | +| armchair | 59.89 | 71.41 | +| seat | 61.47 | 91.38 | +| fence | 45.96 | 60.11 | +| desk | 48.63 | 79.74 | +| rock | 53.54 | 78.82 | +| wardrobe | 53.84 | 80.7 | +| lamp | 72.55 | 80.76 | +| bathtub | 88.58 | 92.58 | +| railing | 36.03 | 45.02 | +| cushion | 68.26 | 83.84 | +| base | 42.88 | 57.83 | +| box | 34.9 | 47.8 | +| column | 54.36 | 67.3 | +| signboard | 37.07 | 59.17 | +| chest of drawers | 44.15 | 62.91 | +| counter | 50.59 | 62.74 | +| sand | 49.67 | 72.38 | +| sink | 75.54 | 85.71 | +| skyscraper | 49.6 | 58.71 | +| fireplace | 70.99 | 95.21 | +| refrigerator | 77.25 | 92.16 | +| grandstand | 42.8 | 85.27 | +| path | 28.39 | 39.24 | +| stairs | 37.12 | 44.76 | +| runway | 69.33 | 89.35 | +| case | 46.48 | 95.96 | +| pool table | 94.03 | 97.7 | +| pillow | 67.43 | 75.92 | +| screen door | 68.04 | 96.57 | +| stairway | 54.04 | 69.69 | +| river | 20.14 | 28.33 | +| bridge | 27.68 | 28.43 | +| bookcase | 34.09 | 52.02 | +| blind | 41.81 | 45.92 | +| coffee table | 58.11 | 83.54 | +| toilet | 78.35 | 96.8 | +| flower | 43.15 | 50.15 | +| book | 51.94 | 81.72 | +| hill | 7.66 | 21.07 | +| bench | 44.8 | 51.47 | +| countertop | 64.52 | 77.64 | +| stove | 79.4 | 87.67 | +| palm | 56.23 | 80.74 | +| kitchen island | 39.18 | 64.34 | +| computer | 78.48 | 92.91 | +| swivel chair | 53.74 | 86.3 | +| boat | 70.16 | 88.29 | +| bar | 60.47 | 63.22 | +| arcade machine | 79.14 | 86.94 | +| hovel | 18.63 | 19.79 | +| bus | 90.57 | 95.96 | +| towel | 72.02 | 90.45 | +| light | 56.14 | 63.15 | +| truck | 40.3 | 50.1 | +| tower | 26.54 | 38.77 | +| chandelier | 71.63 | 83.91 | +| awning | 43.84 | 69.12 | +| streetlight | 30.0 | 38.31 | +| booth | 34.16 | 53.22 | +| television receiver | 77.39 | 88.71 | +| airplane | 62.78 | 73.22 | +| dirt track | 0.77 | 3.64 | +| apparel | 51.97 | 63.09 | +| pole | 17.74 | 23.8 | +| land | 0.0 | 0.0 | +| bannister | 14.33 | 18.42 | +| escalator | 56.76 | 80.03 | +| ottoman | 43.26 | 59.24 | +| bottle | 25.15 | 32.66 | +| buffet | 55.62 | 90.18 | +| poster | 31.41 | 42.04 | +| stage | 17.23 | 33.55 | +| van | 47.87 | 67.31 | +| ship | 8.93 | 8.96 | +| fountain | 58.52 | 67.96 | +| conveyer belt | 65.15 | 96.18 | +| canopy | 55.85 | 68.27 | +| washer | 84.83 | 96.41 | +| plaything | 35.34 | 66.38 | +| swimming pool | 58.57 | 91.12 | +| stool | 48.81 | 58.91 | +| barrel | 51.82 | 64.89 | +| basket | 34.02 | 48.2 | +| waterfall | 49.64 | 55.56 | +| tent | 92.22 | 98.77 | +| bag | 24.89 | 29.6 | +| minibike | 72.0 | 88.74 | +| cradle | 72.23 | 98.73 | +| oven | 54.7 | 64.46 | +| ball | 10.47 | 11.08 | +| food | 53.41 | 57.65 | +| step | 16.63 | 19.74 | +| tank | 70.52 | 98.17 | +| trade name | 15.21 | 17.74 | +| microwave | 83.73 | 96.48 | +| pot | 55.46 | 62.92 | +| animal | 60.18 | 62.12 | +| bicycle | 57.68 | 80.63 | +| lake | 56.06 | 62.02 | +| dishwasher | 62.56 | 69.87 | +| screen | 58.39 | 96.59 | +| blanket | 28.77 | 32.67 | +| sculpture | 65.08 | 82.96 | +| hood | 65.65 | 76.14 | +| sconce | 58.28 | 69.19 | +| vase | 45.43 | 62.43 | +| traffic light | 27.18 | 63.21 | +| tray | 13.36 | 18.07 | +| ashcan | 43.68 | 68.41 | +| fan | 69.21 | 82.5 | +| pier | 39.69 | 59.87 | +| crt screen | 0.62 | 0.66 | +| plate | 54.06 | 85.64 | +| monitor | 55.52 | 80.19 | +| bulletin board | 50.81 | 59.04 | +| shower | 0.0 | 0.0 | +| radiator | 66.03 | 75.18 | +| glass | 17.89 | 19.87 | +| clock | 46.58 | 54.43 | +| flag | 69.74 | 81.09 | ++---------------------+-------+-------+ +2024-06-16 05:17:42,044 - mmseg - INFO - Summary: +2024-06-16 05:17:42,045 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.19 | 53.76 | 67.45 | ++-------+-------+-------+ +2024-06-16 05:17:42,045 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 05:17:42,046 - mmseg - INFO - Iter(val) [250] aAcc: 0.8519, mIoU: 0.5376, mAcc: 0.6745, IoU.wall: 0.8065, IoU.building: 0.8479, IoU.sky: 0.9409, IoU.floor: 0.8376, IoU.tree: 0.7667, IoU.ceiling: 0.8531, IoU.road: 0.8525, IoU.bed : 0.9199, IoU.windowpane: 0.6532, IoU.grass: 0.6966, IoU.cabinet: 0.6240, IoU.sidewalk: 0.6960, IoU.person: 0.8454, IoU.earth: 0.3548, IoU.door: 0.5834, IoU.table: 0.6244, IoU.mountain: 0.5290, IoU.plant: 0.5829, IoU.curtain: 0.7764, IoU.chair: 0.6616, IoU.car: 0.8726, IoU.water: 0.6717, IoU.painting: 0.7804, IoU.sofa: 0.8024, IoU.shelf: 0.4716, IoU.house: 0.5393, IoU.sea: 0.7803, IoU.mirror: 0.7981, IoU.rug: 0.6994, IoU.field: 0.3248, IoU.armchair: 0.5989, IoU.seat: 0.6147, IoU.fence: 0.4596, IoU.desk: 0.4863, IoU.rock: 0.5354, IoU.wardrobe: 0.5384, IoU.lamp: 0.7255, IoU.bathtub: 0.8858, IoU.railing: 0.3603, IoU.cushion: 0.6826, IoU.base: 0.4288, IoU.box: 0.3490, IoU.column: 0.5436, IoU.signboard: 0.3707, IoU.chest of drawers: 0.4415, IoU.counter: 0.5059, IoU.sand: 0.4967, IoU.sink: 0.7554, IoU.skyscraper: 0.4960, IoU.fireplace: 0.7099, IoU.refrigerator: 0.7725, IoU.grandstand: 0.4280, IoU.path: 0.2839, IoU.stairs: 0.3712, IoU.runway: 0.6933, IoU.case: 0.4648, IoU.pool table: 0.9403, IoU.pillow: 0.6743, IoU.screen door: 0.6804, IoU.stairway: 0.5404, IoU.river: 0.2014, IoU.bridge: 0.2768, IoU.bookcase: 0.3409, IoU.blind: 0.4181, IoU.coffee table: 0.5811, IoU.toilet: 0.7835, IoU.flower: 0.4315, IoU.book: 0.5194, IoU.hill: 0.0766, IoU.bench: 0.4480, IoU.countertop: 0.6452, IoU.stove: 0.7940, IoU.palm: 0.5623, IoU.kitchen island: 0.3918, IoU.computer: 0.7848, IoU.swivel chair: 0.5374, IoU.boat: 0.7016, IoU.bar: 0.6047, IoU.arcade machine: 0.7914, IoU.hovel: 0.1863, IoU.bus: 0.9057, IoU.towel: 0.7202, IoU.light: 0.5614, IoU.truck: 0.4030, IoU.tower: 0.2654, IoU.chandelier: 0.7163, IoU.awning: 0.4384, IoU.streetlight: 0.3000, IoU.booth: 0.3416, IoU.television receiver: 0.7739, IoU.airplane: 0.6278, IoU.dirt track: 0.0077, IoU.apparel: 0.5197, IoU.pole: 0.1774, IoU.land: 0.0000, IoU.bannister: 0.1433, IoU.escalator: 0.5676, IoU.ottoman: 0.4326, IoU.bottle: 0.2515, IoU.buffet: 0.5562, IoU.poster: 0.3141, IoU.stage: 0.1723, IoU.van: 0.4787, IoU.ship: 0.0893, IoU.fountain: 0.5852, IoU.conveyer belt: 0.6515, IoU.canopy: 0.5585, IoU.washer: 0.8483, IoU.plaything: 0.3534, IoU.swimming pool: 0.5857, IoU.stool: 0.4881, IoU.barrel: 0.5182, IoU.basket: 0.3402, IoU.waterfall: 0.4964, IoU.tent: 0.9222, IoU.bag: 0.2489, IoU.minibike: 0.7200, IoU.cradle: 0.7223, IoU.oven: 0.5470, IoU.ball: 0.1047, IoU.food: 0.5341, IoU.step: 0.1663, IoU.tank: 0.7052, IoU.trade name: 0.1521, IoU.microwave: 0.8373, IoU.pot: 0.5546, IoU.animal: 0.6018, IoU.bicycle: 0.5768, IoU.lake: 0.5606, IoU.dishwasher: 0.6256, IoU.screen: 0.5839, IoU.blanket: 0.2877, IoU.sculpture: 0.6508, IoU.hood: 0.6565, IoU.sconce: 0.5828, IoU.vase: 0.4543, IoU.traffic light: 0.2718, IoU.tray: 0.1336, IoU.ashcan: 0.4368, IoU.fan: 0.6921, IoU.pier: 0.3969, IoU.crt screen: 0.0062, IoU.plate: 0.5406, IoU.monitor: 0.5552, IoU.bulletin board: 0.5081, IoU.shower: 0.0000, IoU.radiator: 0.6603, IoU.glass: 0.1789, IoU.clock: 0.4658, IoU.flag: 0.6974, Acc.wall: 0.8870, Acc.building: 0.9247, Acc.sky: 0.9592, Acc.floor: 0.9188, Acc.tree: 0.9201, Acc.ceiling: 0.9369, Acc.road: 0.9080, Acc.bed : 0.9634, Acc.windowpane: 0.7943, Acc.grass: 0.9092, Acc.cabinet: 0.7238, Acc.sidewalk: 0.8661, Acc.person: 0.9149, Acc.earth: 0.4413, Acc.door: 0.7427, Acc.table: 0.7057, Acc.mountain: 0.5960, Acc.plant: 0.7370, Acc.curtain: 0.9032, Acc.chair: 0.7707, Acc.car: 0.9376, Acc.water: 0.8449, Acc.painting: 0.8840, Acc.sofa: 0.9115, Acc.shelf: 0.6256, Acc.house: 0.6316, Acc.sea: 0.8745, Acc.mirror: 0.8791, Acc.rug: 0.7984, Acc.field: 0.5055, Acc.armchair: 0.7141, Acc.seat: 0.9138, Acc.fence: 0.6011, Acc.desk: 0.7974, Acc.rock: 0.7882, Acc.wardrobe: 0.8070, Acc.lamp: 0.8076, Acc.bathtub: 0.9258, Acc.railing: 0.4502, Acc.cushion: 0.8384, Acc.base: 0.5783, Acc.box: 0.4780, Acc.column: 0.6730, Acc.signboard: 0.5917, Acc.chest of drawers: 0.6291, Acc.counter: 0.6274, Acc.sand: 0.7238, Acc.sink: 0.8571, Acc.skyscraper: 0.5871, Acc.fireplace: 0.9521, Acc.refrigerator: 0.9216, Acc.grandstand: 0.8527, Acc.path: 0.3924, Acc.stairs: 0.4476, Acc.runway: 0.8935, Acc.case: 0.9596, Acc.pool table: 0.9770, Acc.pillow: 0.7592, Acc.screen door: 0.9657, Acc.stairway: 0.6969, Acc.river: 0.2833, Acc.bridge: 0.2843, Acc.bookcase: 0.5202, Acc.blind: 0.4592, Acc.coffee table: 0.8354, Acc.toilet: 0.9680, Acc.flower: 0.5015, Acc.book: 0.8172, Acc.hill: 0.2107, Acc.bench: 0.5147, Acc.countertop: 0.7764, Acc.stove: 0.8767, Acc.palm: 0.8074, Acc.kitchen island: 0.6434, Acc.computer: 0.9291, Acc.swivel chair: 0.8630, Acc.boat: 0.8829, Acc.bar: 0.6322, Acc.arcade machine: 0.8694, Acc.hovel: 0.1979, Acc.bus: 0.9596, Acc.towel: 0.9045, Acc.light: 0.6315, Acc.truck: 0.5010, Acc.tower: 0.3877, Acc.chandelier: 0.8391, Acc.awning: 0.6912, Acc.streetlight: 0.3831, Acc.booth: 0.5322, Acc.television receiver: 0.8871, Acc.airplane: 0.7322, Acc.dirt track: 0.0364, Acc.apparel: 0.6309, Acc.pole: 0.2380, Acc.land: 0.0000, Acc.bannister: 0.1842, Acc.escalator: 0.8003, Acc.ottoman: 0.5924, Acc.bottle: 0.3266, Acc.buffet: 0.9018, Acc.poster: 0.4204, Acc.stage: 0.3355, Acc.van: 0.6731, Acc.ship: 0.0896, Acc.fountain: 0.6796, Acc.conveyer belt: 0.9618, Acc.canopy: 0.6827, Acc.washer: 0.9641, Acc.plaything: 0.6638, Acc.swimming pool: 0.9112, Acc.stool: 0.5891, Acc.barrel: 0.6489, Acc.basket: 0.4820, Acc.waterfall: 0.5556, Acc.tent: 0.9877, Acc.bag: 0.2960, Acc.minibike: 0.8874, Acc.cradle: 0.9873, Acc.oven: 0.6446, Acc.ball: 0.1108, Acc.food: 0.5765, Acc.step: 0.1974, Acc.tank: 0.9817, Acc.trade name: 0.1774, Acc.microwave: 0.9648, Acc.pot: 0.6292, Acc.animal: 0.6212, Acc.bicycle: 0.8063, Acc.lake: 0.6202, Acc.dishwasher: 0.6987, Acc.screen: 0.9659, Acc.blanket: 0.3267, Acc.sculpture: 0.8296, Acc.hood: 0.7614, Acc.sconce: 0.6919, Acc.vase: 0.6243, Acc.traffic light: 0.6321, Acc.tray: 0.1807, Acc.ashcan: 0.6841, Acc.fan: 0.8250, Acc.pier: 0.5987, Acc.crt screen: 0.0066, Acc.plate: 0.8564, Acc.monitor: 0.8019, Acc.bulletin board: 0.5904, Acc.shower: 0.0000, Acc.radiator: 0.7518, Acc.glass: 0.1987, Acc.clock: 0.5443, Acc.flag: 0.8109 +2024-06-16 05:19:03,560 - mmseg - INFO - Iter [16050/80000] lr: 3.198e-05, eta: 1 day, 7:25:43, time: 3.537, data_time: 1.922, memory: 71384, decode.loss_ce: 0.3339, decode.acc_seg: 86.5595, aux.loss_ce: 0.1339, aux.acc_seg: 86.4322, loss: 0.4677 +2024-06-16 05:20:24,930 - mmseg - INFO - Iter [16100/80000] lr: 3.195e-05, eta: 1 day, 7:23:47, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3070, decode.acc_seg: 88.1253, aux.loss_ce: 0.1238, aux.acc_seg: 87.8471, loss: 0.4307 +2024-06-16 05:21:46,116 - mmseg - INFO - Iter [16150/80000] lr: 3.193e-05, eta: 1 day, 7:21:49, time: 1.624, data_time: 0.009, memory: 71384, decode.loss_ce: 0.3303, decode.acc_seg: 86.4808, aux.loss_ce: 0.1337, aux.acc_seg: 86.2383, loss: 0.4639 +2024-06-16 05:23:07,319 - mmseg - INFO - Iter [16200/80000] lr: 3.190e-05, eta: 1 day, 7:19:53, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3318, decode.acc_seg: 86.7544, aux.loss_ce: 0.1342, aux.acc_seg: 86.6099, loss: 0.4660 +2024-06-16 05:24:28,410 - mmseg - INFO - Iter [16250/80000] lr: 3.188e-05, eta: 1 day, 7:17:56, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3242, decode.acc_seg: 86.9338, aux.loss_ce: 0.1311, aux.acc_seg: 86.6854, loss: 0.4553 +2024-06-16 05:25:49,645 - mmseg - INFO - Iter [16300/80000] lr: 3.185e-05, eta: 1 day, 7:15:59, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3353, decode.acc_seg: 86.5116, aux.loss_ce: 0.1363, aux.acc_seg: 86.3783, loss: 0.4716 +2024-06-16 05:27:10,785 - mmseg - INFO - Iter [16350/80000] lr: 3.183e-05, eta: 1 day, 7:14:03, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3540, decode.acc_seg: 85.7347, aux.loss_ce: 0.1432, aux.acc_seg: 85.5452, loss: 0.4971 +2024-06-16 05:28:31,913 - mmseg - INFO - Iter [16400/80000] lr: 3.180e-05, eta: 1 day, 7:12:07, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3436, decode.acc_seg: 86.0329, aux.loss_ce: 0.1372, aux.acc_seg: 86.0084, loss: 0.4808 +2024-06-16 05:29:55,482 - mmseg - INFO - Iter [16450/80000] lr: 3.178e-05, eta: 1 day, 7:10:20, time: 1.671, data_time: 0.058, memory: 71384, decode.loss_ce: 0.3348, decode.acc_seg: 86.8805, aux.loss_ce: 0.1363, aux.acc_seg: 86.6573, loss: 0.4710 +2024-06-16 05:31:16,770 - mmseg - INFO - Iter [16500/80000] lr: 3.175e-05, eta: 1 day, 7:08:25, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3202, decode.acc_seg: 87.4080, aux.loss_ce: 0.1295, aux.acc_seg: 87.2719, loss: 0.4497 +2024-06-16 05:32:37,940 - mmseg - INFO - Iter [16550/80000] lr: 3.173e-05, eta: 1 day, 7:06:29, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3075, decode.acc_seg: 87.7452, aux.loss_ce: 0.1248, aux.acc_seg: 87.5963, loss: 0.4323 +2024-06-16 05:34:00,516 - mmseg - INFO - Iter [16600/80000] lr: 3.170e-05, eta: 1 day, 7:04:39, time: 1.652, data_time: 0.037, memory: 71384, decode.loss_ce: 0.2946, decode.acc_seg: 88.3699, aux.loss_ce: 0.1193, aux.acc_seg: 88.2145, loss: 0.4139 +2024-06-16 05:35:21,773 - mmseg - INFO - Iter [16650/80000] lr: 3.168e-05, eta: 1 day, 7:02:45, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2964, decode.acc_seg: 87.9244, aux.loss_ce: 0.1202, aux.acc_seg: 87.7673, loss: 0.4166 +2024-06-16 05:36:42,913 - mmseg - INFO - Iter [16700/80000] lr: 3.165e-05, eta: 1 day, 7:00:50, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3068, decode.acc_seg: 87.3932, aux.loss_ce: 0.1233, aux.acc_seg: 87.3667, loss: 0.4301 +2024-06-16 05:38:03,906 - mmseg - INFO - Iter [16750/80000] lr: 3.163e-05, eta: 1 day, 6:58:54, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3052, decode.acc_seg: 87.8955, aux.loss_ce: 0.1238, aux.acc_seg: 87.7781, loss: 0.4290 +2024-06-16 05:39:25,095 - mmseg - INFO - Iter [16800/80000] lr: 3.160e-05, eta: 1 day, 6:57:00, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3092, decode.acc_seg: 87.6554, aux.loss_ce: 0.1247, aux.acc_seg: 87.5100, loss: 0.4339 +2024-06-16 05:40:46,408 - mmseg - INFO - Iter [16850/80000] lr: 3.158e-05, eta: 1 day, 6:55:06, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3198, decode.acc_seg: 87.5218, aux.loss_ce: 0.1294, aux.acc_seg: 87.4550, loss: 0.4492 +2024-06-16 05:42:07,720 - mmseg - INFO - Iter [16900/80000] lr: 3.155e-05, eta: 1 day, 6:53:12, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3257, decode.acc_seg: 87.2025, aux.loss_ce: 0.1311, aux.acc_seg: 86.9488, loss: 0.4569 +2024-06-16 05:43:28,890 - mmseg - INFO - Iter [16950/80000] lr: 3.153e-05, eta: 1 day, 6:51:18, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3123, decode.acc_seg: 87.8477, aux.loss_ce: 0.1270, aux.acc_seg: 87.5387, loss: 0.4393 +2024-06-16 05:44:50,001 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 05:44:50,001 - mmseg - INFO - Iter [17000/80000] lr: 3.150e-05, eta: 1 day, 6:49:24, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3051, decode.acc_seg: 87.8535, aux.loss_ce: 0.1234, aux.acc_seg: 87.5918, loss: 0.4285 +2024-06-16 05:46:28,068 - mmseg - INFO - per class results: +2024-06-16 05:46:28,074 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.22 | 86.81 | +| building | 84.92 | 92.87 | +| sky | 94.6 | 97.11 | +| floor | 84.74 | 90.43 | +| tree | 77.16 | 91.17 | +| ceiling | 86.03 | 94.93 | +| road | 85.67 | 91.02 | +| bed | 92.01 | 96.89 | +| windowpane | 66.83 | 81.26 | +| grass | 64.11 | 77.77 | +| cabinet | 64.79 | 73.15 | +| sidewalk | 69.13 | 81.48 | +| person | 85.08 | 92.96 | +| earth | 41.68 | 55.28 | +| door | 62.77 | 82.45 | +| table | 63.88 | 72.76 | +| mountain | 60.36 | 73.81 | +| plant | 57.84 | 71.44 | +| curtain | 76.48 | 90.41 | +| chair | 62.05 | 70.06 | +| car | 86.76 | 93.02 | +| water | 57.06 | 66.49 | +| painting | 75.78 | 89.67 | +| sofa | 81.38 | 92.5 | +| shelf | 51.1 | 67.59 | +| house | 63.42 | 78.35 | +| sea | 74.56 | 87.32 | +| mirror | 79.16 | 89.62 | +| rug | 69.37 | 84.66 | +| field | 29.12 | 53.14 | +| armchair | 55.74 | 78.79 | +| seat | 64.02 | 90.82 | +| fence | 48.58 | 71.17 | +| desk | 51.61 | 76.75 | +| rock | 51.33 | 72.13 | +| wardrobe | 54.36 | 84.95 | +| lamp | 72.65 | 82.13 | +| bathtub | 84.39 | 87.63 | +| railing | 42.2 | 56.35 | +| cushion | 69.43 | 86.08 | +| base | 41.99 | 55.58 | +| box | 32.88 | 41.29 | +| column | 51.74 | 63.0 | +| signboard | 40.72 | 59.4 | +| chest of drawers | 53.14 | 79.16 | +| counter | 50.44 | 72.7 | +| sand | 47.75 | 67.7 | +| sink | 76.41 | 83.78 | +| skyscraper | 46.84 | 55.58 | +| fireplace | 72.03 | 95.66 | +| refrigerator | 78.12 | 95.14 | +| grandstand | 63.5 | 86.13 | +| path | 27.93 | 44.41 | +| stairs | 26.44 | 33.94 | +| runway | 74.62 | 98.17 | +| case | 51.62 | 66.44 | +| pool table | 93.59 | 98.29 | +| pillow | 64.82 | 71.0 | +| screen door | 58.08 | 94.22 | +| stairway | 43.86 | 68.77 | +| river | 17.55 | 48.55 | +| bridge | 62.82 | 91.58 | +| bookcase | 39.87 | 47.82 | +| blind | 35.43 | 39.07 | +| coffee table | 59.62 | 86.44 | +| toilet | 88.43 | 92.22 | +| flower | 48.19 | 63.25 | +| book | 53.28 | 78.55 | +| hill | 5.57 | 9.72 | +| bench | 47.26 | 55.95 | +| countertop | 60.25 | 84.76 | +| stove | 77.1 | 95.11 | +| palm | 52.42 | 62.82 | +| kitchen island | 48.04 | 88.92 | +| computer | 79.42 | 91.07 | +| swivel chair | 52.6 | 86.17 | +| boat | 63.57 | 76.25 | +| bar | 65.91 | 86.35 | +| arcade machine | 80.55 | 84.86 | +| hovel | 39.65 | 43.2 | +| bus | 93.19 | 96.39 | +| towel | 73.48 | 80.83 | +| light | 56.07 | 60.78 | +| truck | 47.85 | 65.13 | +| tower | 25.0 | 43.05 | +| chandelier | 72.0 | 83.39 | +| awning | 50.13 | 63.87 | +| streetlight | 31.25 | 41.96 | +| booth | 26.16 | 39.5 | +| television receiver | 77.63 | 91.56 | +| airplane | 85.67 | 95.99 | +| dirt track | 2.31 | 9.8 | +| apparel | 45.05 | 52.28 | +| pole | 20.57 | 24.8 | +| land | 0.49 | 0.74 | +| bannister | 12.33 | 20.63 | +| escalator | 58.67 | 87.58 | +| ottoman | 45.41 | 56.22 | +| bottle | 42.0 | 59.72 | +| buffet | 55.19 | 85.03 | +| poster | 35.4 | 49.03 | +| stage | 20.51 | 50.11 | +| van | 49.4 | 70.13 | +| ship | 67.03 | 93.6 | +| fountain | 59.61 | 63.86 | +| conveyer belt | 72.65 | 97.76 | +| canopy | 49.53 | 68.97 | +| washer | 83.27 | 89.18 | +| plaything | 31.41 | 69.74 | +| swimming pool | 55.02 | 80.77 | +| stool | 46.4 | 65.43 | +| barrel | 60.7 | 66.33 | +| basket | 34.53 | 46.35 | +| waterfall | 32.12 | 32.64 | +| tent | 96.45 | 98.13 | +| bag | 21.79 | 25.57 | +| minibike | 71.35 | 88.7 | +| cradle | 78.97 | 95.06 | +| oven | 63.01 | 71.35 | +| ball | 21.43 | 22.49 | +| food | 59.97 | 87.57 | +| step | 21.61 | 37.81 | +| tank | 66.78 | 78.35 | +| trade name | 32.78 | 49.48 | +| microwave | 89.09 | 95.7 | +| pot | 57.58 | 70.13 | +| animal | 65.99 | 67.32 | +| bicycle | 51.85 | 58.62 | +| lake | 48.05 | 63.06 | +| dishwasher | 67.35 | 75.07 | +| screen | 53.67 | 95.45 | +| blanket | 26.04 | 31.87 | +| sculpture | 64.76 | 89.3 | +| hood | 69.56 | 85.43 | +| sconce | 57.71 | 74.02 | +| vase | 43.45 | 64.99 | +| traffic light | 29.79 | 65.34 | +| tray | 20.02 | 26.61 | +| ashcan | 47.94 | 63.24 | +| fan | 67.85 | 76.75 | +| pier | 42.63 | 48.84 | +| crt screen | 4.14 | 4.72 | +| plate | 59.77 | 74.9 | +| monitor | 63.45 | 83.74 | +| bulletin board | 48.82 | 55.08 | +| shower | 0.79 | 0.86 | +| radiator | 63.79 | 73.68 | +| glass | 17.18 | 18.14 | +| clock | 42.45 | 51.61 | +| flag | 70.91 | 78.23 | ++---------------------+-------+-------+ +2024-06-16 05:46:28,075 - mmseg - INFO - Summary: +2024-06-16 05:46:28,075 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.36 | 55.49 | 69.64 | ++-------+-------+-------+ +2024-06-16 05:46:28,075 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 05:46:28,076 - mmseg - INFO - Iter(val) [250] aAcc: 0.8536, mIoU: 0.5549, mAcc: 0.6964, IoU.wall: 0.8022, IoU.building: 0.8492, IoU.sky: 0.9460, IoU.floor: 0.8474, IoU.tree: 0.7716, IoU.ceiling: 0.8603, IoU.road: 0.8567, IoU.bed : 0.9201, IoU.windowpane: 0.6683, IoU.grass: 0.6411, IoU.cabinet: 0.6479, IoU.sidewalk: 0.6913, IoU.person: 0.8508, IoU.earth: 0.4168, IoU.door: 0.6277, IoU.table: 0.6388, IoU.mountain: 0.6036, IoU.plant: 0.5784, IoU.curtain: 0.7648, IoU.chair: 0.6205, IoU.car: 0.8676, IoU.water: 0.5706, IoU.painting: 0.7578, IoU.sofa: 0.8138, IoU.shelf: 0.5110, IoU.house: 0.6342, IoU.sea: 0.7456, IoU.mirror: 0.7916, IoU.rug: 0.6937, IoU.field: 0.2912, IoU.armchair: 0.5574, IoU.seat: 0.6402, IoU.fence: 0.4858, IoU.desk: 0.5161, IoU.rock: 0.5133, IoU.wardrobe: 0.5436, IoU.lamp: 0.7265, IoU.bathtub: 0.8439, IoU.railing: 0.4220, IoU.cushion: 0.6943, IoU.base: 0.4199, IoU.box: 0.3288, IoU.column: 0.5174, IoU.signboard: 0.4072, IoU.chest of drawers: 0.5314, IoU.counter: 0.5044, IoU.sand: 0.4775, IoU.sink: 0.7641, IoU.skyscraper: 0.4684, IoU.fireplace: 0.7203, IoU.refrigerator: 0.7812, IoU.grandstand: 0.6350, IoU.path: 0.2793, IoU.stairs: 0.2644, IoU.runway: 0.7462, IoU.case: 0.5162, IoU.pool table: 0.9359, IoU.pillow: 0.6482, IoU.screen door: 0.5808, IoU.stairway: 0.4386, IoU.river: 0.1755, IoU.bridge: 0.6282, IoU.bookcase: 0.3987, IoU.blind: 0.3543, IoU.coffee table: 0.5962, IoU.toilet: 0.8843, IoU.flower: 0.4819, IoU.book: 0.5328, IoU.hill: 0.0557, IoU.bench: 0.4726, IoU.countertop: 0.6025, IoU.stove: 0.7710, IoU.palm: 0.5242, IoU.kitchen island: 0.4804, IoU.computer: 0.7942, IoU.swivel chair: 0.5260, IoU.boat: 0.6357, IoU.bar: 0.6591, IoU.arcade machine: 0.8055, IoU.hovel: 0.3965, IoU.bus: 0.9319, IoU.towel: 0.7348, IoU.light: 0.5607, IoU.truck: 0.4785, IoU.tower: 0.2500, IoU.chandelier: 0.7200, IoU.awning: 0.5013, IoU.streetlight: 0.3125, IoU.booth: 0.2616, IoU.television receiver: 0.7763, IoU.airplane: 0.8567, IoU.dirt track: 0.0231, IoU.apparel: 0.4505, IoU.pole: 0.2057, IoU.land: 0.0049, IoU.bannister: 0.1233, IoU.escalator: 0.5867, IoU.ottoman: 0.4541, IoU.bottle: 0.4200, IoU.buffet: 0.5519, IoU.poster: 0.3540, IoU.stage: 0.2051, IoU.van: 0.4940, IoU.ship: 0.6703, IoU.fountain: 0.5961, IoU.conveyer belt: 0.7265, IoU.canopy: 0.4953, IoU.washer: 0.8327, IoU.plaything: 0.3141, IoU.swimming pool: 0.5502, IoU.stool: 0.4640, IoU.barrel: 0.6070, IoU.basket: 0.3453, IoU.waterfall: 0.3212, IoU.tent: 0.9645, IoU.bag: 0.2179, IoU.minibike: 0.7135, IoU.cradle: 0.7897, IoU.oven: 0.6301, IoU.ball: 0.2143, IoU.food: 0.5997, IoU.step: 0.2161, IoU.tank: 0.6678, IoU.trade name: 0.3278, IoU.microwave: 0.8909, IoU.pot: 0.5758, IoU.animal: 0.6599, IoU.bicycle: 0.5185, IoU.lake: 0.4805, IoU.dishwasher: 0.6735, IoU.screen: 0.5367, IoU.blanket: 0.2604, IoU.sculpture: 0.6476, IoU.hood: 0.6956, IoU.sconce: 0.5771, IoU.vase: 0.4345, IoU.traffic light: 0.2979, IoU.tray: 0.2002, IoU.ashcan: 0.4794, IoU.fan: 0.6785, IoU.pier: 0.4263, IoU.crt screen: 0.0414, IoU.plate: 0.5977, IoU.monitor: 0.6345, IoU.bulletin board: 0.4882, IoU.shower: 0.0079, IoU.radiator: 0.6379, IoU.glass: 0.1718, IoU.clock: 0.4245, IoU.flag: 0.7091, Acc.wall: 0.8681, Acc.building: 0.9287, Acc.sky: 0.9711, Acc.floor: 0.9043, Acc.tree: 0.9117, Acc.ceiling: 0.9493, Acc.road: 0.9102, Acc.bed : 0.9689, Acc.windowpane: 0.8126, Acc.grass: 0.7777, Acc.cabinet: 0.7315, Acc.sidewalk: 0.8148, Acc.person: 0.9296, Acc.earth: 0.5528, Acc.door: 0.8245, Acc.table: 0.7276, Acc.mountain: 0.7381, Acc.plant: 0.7144, Acc.curtain: 0.9041, Acc.chair: 0.7006, Acc.car: 0.9302, Acc.water: 0.6649, Acc.painting: 0.8967, Acc.sofa: 0.9250, Acc.shelf: 0.6759, Acc.house: 0.7835, Acc.sea: 0.8732, Acc.mirror: 0.8962, Acc.rug: 0.8466, Acc.field: 0.5314, Acc.armchair: 0.7879, Acc.seat: 0.9082, Acc.fence: 0.7117, Acc.desk: 0.7675, Acc.rock: 0.7213, Acc.wardrobe: 0.8495, Acc.lamp: 0.8213, Acc.bathtub: 0.8763, Acc.railing: 0.5635, Acc.cushion: 0.8608, Acc.base: 0.5558, Acc.box: 0.4129, Acc.column: 0.6300, Acc.signboard: 0.5940, Acc.chest of drawers: 0.7916, Acc.counter: 0.7270, Acc.sand: 0.6770, Acc.sink: 0.8378, Acc.skyscraper: 0.5558, Acc.fireplace: 0.9566, Acc.refrigerator: 0.9514, Acc.grandstand: 0.8613, Acc.path: 0.4441, Acc.stairs: 0.3394, Acc.runway: 0.9817, Acc.case: 0.6644, Acc.pool table: 0.9829, Acc.pillow: 0.7100, Acc.screen door: 0.9422, Acc.stairway: 0.6877, Acc.river: 0.4855, Acc.bridge: 0.9158, Acc.bookcase: 0.4782, Acc.blind: 0.3907, Acc.coffee table: 0.8644, Acc.toilet: 0.9222, Acc.flower: 0.6325, Acc.book: 0.7855, Acc.hill: 0.0972, Acc.bench: 0.5595, Acc.countertop: 0.8476, Acc.stove: 0.9511, Acc.palm: 0.6282, Acc.kitchen island: 0.8892, Acc.computer: 0.9107, Acc.swivel chair: 0.8617, Acc.boat: 0.7625, Acc.bar: 0.8635, Acc.arcade machine: 0.8486, Acc.hovel: 0.4320, Acc.bus: 0.9639, Acc.towel: 0.8083, Acc.light: 0.6078, Acc.truck: 0.6513, Acc.tower: 0.4305, Acc.chandelier: 0.8339, Acc.awning: 0.6387, Acc.streetlight: 0.4196, Acc.booth: 0.3950, Acc.television receiver: 0.9156, Acc.airplane: 0.9599, Acc.dirt track: 0.0980, Acc.apparel: 0.5228, Acc.pole: 0.2480, Acc.land: 0.0074, Acc.bannister: 0.2063, Acc.escalator: 0.8758, Acc.ottoman: 0.5622, Acc.bottle: 0.5972, Acc.buffet: 0.8503, Acc.poster: 0.4903, Acc.stage: 0.5011, Acc.van: 0.7013, Acc.ship: 0.9360, Acc.fountain: 0.6386, Acc.conveyer belt: 0.9776, Acc.canopy: 0.6897, Acc.washer: 0.8918, Acc.plaything: 0.6974, Acc.swimming pool: 0.8077, Acc.stool: 0.6543, Acc.barrel: 0.6633, Acc.basket: 0.4635, Acc.waterfall: 0.3264, Acc.tent: 0.9813, Acc.bag: 0.2557, Acc.minibike: 0.8870, Acc.cradle: 0.9506, Acc.oven: 0.7135, Acc.ball: 0.2249, Acc.food: 0.8757, Acc.step: 0.3781, Acc.tank: 0.7835, Acc.trade name: 0.4948, Acc.microwave: 0.9570, Acc.pot: 0.7013, Acc.animal: 0.6732, Acc.bicycle: 0.5862, Acc.lake: 0.6306, Acc.dishwasher: 0.7507, Acc.screen: 0.9545, Acc.blanket: 0.3187, Acc.sculpture: 0.8930, Acc.hood: 0.8543, Acc.sconce: 0.7402, Acc.vase: 0.6499, Acc.traffic light: 0.6534, Acc.tray: 0.2661, Acc.ashcan: 0.6324, Acc.fan: 0.7675, Acc.pier: 0.4884, Acc.crt screen: 0.0472, Acc.plate: 0.7490, Acc.monitor: 0.8374, Acc.bulletin board: 0.5508, Acc.shower: 0.0086, Acc.radiator: 0.7368, Acc.glass: 0.1814, Acc.clock: 0.5161, Acc.flag: 0.7823 +2024-06-16 05:47:49,541 - mmseg - INFO - Iter [17050/80000] lr: 3.148e-05, eta: 1 day, 6:53:34, time: 3.591, data_time: 1.978, memory: 71384, decode.loss_ce: 0.3256, decode.acc_seg: 86.7375, aux.loss_ce: 0.1314, aux.acc_seg: 86.5312, loss: 0.4570 +2024-06-16 05:49:10,721 - mmseg - INFO - Iter [17100/80000] lr: 3.145e-05, eta: 1 day, 6:51:39, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3075, decode.acc_seg: 87.5682, aux.loss_ce: 0.1242, aux.acc_seg: 87.4112, loss: 0.4317 +2024-06-16 05:50:32,379 - mmseg - INFO - Iter [17150/80000] lr: 3.143e-05, eta: 1 day, 6:49:47, time: 1.633, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3136, decode.acc_seg: 87.3954, aux.loss_ce: 0.1269, aux.acc_seg: 87.2632, loss: 0.4405 +2024-06-16 05:51:53,407 - mmseg - INFO - Iter [17200/80000] lr: 3.140e-05, eta: 1 day, 6:47:52, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3398, decode.acc_seg: 86.8660, aux.loss_ce: 0.1371, aux.acc_seg: 86.7265, loss: 0.4769 +2024-06-16 05:53:14,743 - mmseg - INFO - Iter [17250/80000] lr: 3.138e-05, eta: 1 day, 6:45:58, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3389, decode.acc_seg: 86.2863, aux.loss_ce: 0.1359, aux.acc_seg: 86.3334, loss: 0.4748 +2024-06-16 05:54:35,940 - mmseg - INFO - Iter [17300/80000] lr: 3.135e-05, eta: 1 day, 6:44:05, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3206, decode.acc_seg: 87.1436, aux.loss_ce: 0.1281, aux.acc_seg: 87.1982, loss: 0.4487 +2024-06-16 05:55:56,978 - mmseg - INFO - Iter [17350/80000] lr: 3.133e-05, eta: 1 day, 6:42:10, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3152, decode.acc_seg: 87.5898, aux.loss_ce: 0.1266, aux.acc_seg: 87.3536, loss: 0.4418 +2024-06-16 05:57:18,112 - mmseg - INFO - Iter [17400/80000] lr: 3.130e-05, eta: 1 day, 6:40:17, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3282, decode.acc_seg: 87.3904, aux.loss_ce: 0.1333, aux.acc_seg: 87.1815, loss: 0.4615 +2024-06-16 05:58:39,405 - mmseg - INFO - Iter [17450/80000] lr: 3.128e-05, eta: 1 day, 6:38:24, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3047, decode.acc_seg: 87.8219, aux.loss_ce: 0.1239, aux.acc_seg: 87.6234, loss: 0.4286 +2024-06-16 06:00:00,680 - mmseg - INFO - Iter [17500/80000] lr: 3.125e-05, eta: 1 day, 6:36:31, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3344, decode.acc_seg: 86.7451, aux.loss_ce: 0.1345, aux.acc_seg: 86.7241, loss: 0.4689 +2024-06-16 06:01:21,870 - mmseg - INFO - Iter [17550/80000] lr: 3.123e-05, eta: 1 day, 6:34:38, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3190, decode.acc_seg: 87.3091, aux.loss_ce: 0.1293, aux.acc_seg: 87.0745, loss: 0.4483 +2024-06-16 06:02:43,291 - mmseg - INFO - Iter [17600/80000] lr: 3.120e-05, eta: 1 day, 6:32:46, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3260, decode.acc_seg: 86.7385, aux.loss_ce: 0.1306, aux.acc_seg: 86.5403, loss: 0.4566 +2024-06-16 06:04:04,504 - mmseg - INFO - Iter [17650/80000] lr: 3.118e-05, eta: 1 day, 6:30:54, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3210, decode.acc_seg: 86.8233, aux.loss_ce: 0.1297, aux.acc_seg: 86.6650, loss: 0.4507 +2024-06-16 06:05:28,711 - mmseg - INFO - Iter [17700/80000] lr: 3.115e-05, eta: 1 day, 6:29:12, time: 1.684, data_time: 0.070, memory: 71384, decode.loss_ce: 0.3534, decode.acc_seg: 85.5002, aux.loss_ce: 0.1422, aux.acc_seg: 85.4236, loss: 0.4956 +2024-06-16 06:06:49,926 - mmseg - INFO - Iter [17750/80000] lr: 3.113e-05, eta: 1 day, 6:27:20, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3123, decode.acc_seg: 87.7951, aux.loss_ce: 0.1261, aux.acc_seg: 87.6806, loss: 0.4384 +2024-06-16 06:08:11,187 - mmseg - INFO - Iter [17800/80000] lr: 3.110e-05, eta: 1 day, 6:25:28, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2898, decode.acc_seg: 87.9592, aux.loss_ce: 0.1176, aux.acc_seg: 87.8843, loss: 0.4074 +2024-06-16 06:09:32,355 - mmseg - INFO - Iter [17850/80000] lr: 3.108e-05, eta: 1 day, 6:23:36, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3022, decode.acc_seg: 88.0799, aux.loss_ce: 0.1216, aux.acc_seg: 87.8176, loss: 0.4238 +2024-06-16 06:10:53,419 - mmseg - INFO - Iter [17900/80000] lr: 3.105e-05, eta: 1 day, 6:21:44, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3162, decode.acc_seg: 87.3970, aux.loss_ce: 0.1286, aux.acc_seg: 87.1436, loss: 0.4447 +2024-06-16 06:12:14,613 - mmseg - INFO - Iter [17950/80000] lr: 3.103e-05, eta: 1 day, 6:19:52, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3106, decode.acc_seg: 87.3528, aux.loss_ce: 0.1267, aux.acc_seg: 87.1346, loss: 0.4373 +2024-06-16 06:13:35,867 - mmseg - INFO - Saving checkpoint at 18000 iterations +2024-06-16 06:14:59,662 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 06:14:59,663 - mmseg - INFO - Iter [18000/80000] lr: 3.100e-05, eta: 1 day, 6:22:50, time: 3.301, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3013, decode.acc_seg: 87.7334, aux.loss_ce: 0.1221, aux.acc_seg: 87.6974, loss: 0.4234 +2024-06-16 06:16:35,785 - mmseg - INFO - per class results: +2024-06-16 06:16:35,792 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.7 | 89.14 | +| building | 84.29 | 92.66 | +| sky | 94.49 | 97.25 | +| floor | 84.46 | 90.55 | +| tree | 76.54 | 87.71 | +| ceiling | 86.65 | 93.03 | +| road | 84.75 | 90.49 | +| bed | 91.91 | 96.64 | +| windowpane | 65.8 | 83.92 | +| grass | 66.26 | 75.3 | +| cabinet | 63.07 | 76.17 | +| sidewalk | 68.91 | 85.7 | +| person | 84.26 | 90.18 | +| earth | 36.48 | 51.4 | +| door | 55.69 | 63.29 | +| table | 65.29 | 78.3 | +| mountain | 62.61 | 77.47 | +| plant | 57.28 | 68.84 | +| curtain | 79.91 | 89.21 | +| chair | 65.02 | 75.68 | +| car | 86.55 | 92.34 | +| water | 35.09 | 40.12 | +| painting | 76.78 | 89.2 | +| sofa | 80.08 | 89.49 | +| shelf | 48.53 | 68.0 | +| house | 54.07 | 81.79 | +| sea | 62.57 | 73.96 | +| mirror | 73.31 | 76.17 | +| rug | 72.31 | 81.5 | +| field | 36.58 | 73.74 | +| armchair | 57.89 | 77.52 | +| seat | 66.96 | 86.19 | +| fence | 45.28 | 56.09 | +| desk | 54.44 | 80.87 | +| rock | 53.14 | 77.13 | +| wardrobe | 52.5 | 90.09 | +| lamp | 73.53 | 80.4 | +| bathtub | 83.87 | 86.11 | +| railing | 37.18 | 56.8 | +| cushion | 68.0 | 76.55 | +| base | 43.2 | 51.9 | +| box | 33.51 | 43.89 | +| column | 53.9 | 69.72 | +| signboard | 41.27 | 50.79 | +| chest of drawers | 52.29 | 78.05 | +| counter | 39.3 | 44.44 | +| sand | 52.4 | 87.07 | +| sink | 73.78 | 78.72 | +| skyscraper | 48.65 | 59.85 | +| fireplace | 70.18 | 94.93 | +| refrigerator | 78.46 | 85.39 | +| grandstand | 41.9 | 86.68 | +| path | 27.79 | 36.38 | +| stairs | 20.42 | 21.99 | +| runway | 71.41 | 99.19 | +| case | 58.61 | 86.59 | +| pool table | 93.27 | 98.09 | +| pillow | 67.67 | 78.75 | +| screen door | 82.09 | 84.27 | +| stairway | 42.39 | 73.56 | +| river | 13.15 | 83.62 | +| bridge | 62.93 | 91.14 | +| bookcase | 41.27 | 52.35 | +| blind | 46.12 | 52.21 | +| coffee table | 60.98 | 84.83 | +| toilet | 88.86 | 92.45 | +| flower | 40.18 | 44.02 | +| book | 50.3 | 71.96 | +| hill | 3.98 | 8.04 | +| bench | 57.08 | 63.94 | +| countertop | 63.01 | 86.19 | +| stove | 79.35 | 91.52 | +| palm | 54.6 | 78.8 | +| kitchen island | 41.67 | 73.52 | +| computer | 76.59 | 92.31 | +| swivel chair | 50.81 | 75.86 | +| boat | 72.69 | 87.41 | +| bar | 62.97 | 85.66 | +| arcade machine | 87.04 | 97.37 | +| hovel | 41.43 | 48.39 | +| bus | 91.75 | 96.8 | +| towel | 73.66 | 86.73 | +| light | 57.92 | 67.07 | +| truck | 44.14 | 62.99 | +| tower | 29.38 | 41.81 | +| chandelier | 72.51 | 86.33 | +| awning | 37.45 | 43.07 | +| streetlight | 27.33 | 35.74 | +| booth | 36.59 | 66.8 | +| television receiver | 78.07 | 88.1 | +| airplane | 85.2 | 93.3 | +| dirt track | 4.52 | 18.76 | +| apparel | 42.89 | 56.01 | +| pole | 19.91 | 23.66 | +| land | 1.04 | 2.24 | +| bannister | 16.2 | 22.33 | +| escalator | 57.66 | 78.72 | +| ottoman | 50.68 | 68.59 | +| bottle | 42.16 | 58.89 | +| buffet | 41.79 | 46.38 | +| poster | 28.7 | 34.02 | +| stage | 22.64 | 39.82 | +| van | 43.55 | 68.23 | +| ship | 60.17 | 61.81 | +| fountain | 29.72 | 32.66 | +| conveyer belt | 49.08 | 99.07 | +| canopy | 36.46 | 51.76 | +| washer | 85.46 | 91.58 | +| plaything | 26.73 | 38.79 | +| swimming pool | 57.39 | 90.51 | +| stool | 52.7 | 66.12 | +| barrel | 51.67 | 68.68 | +| basket | 38.28 | 54.17 | +| waterfall | 64.78 | 72.56 | +| tent | 95.88 | 98.65 | +| bag | 21.73 | 24.39 | +| minibike | 72.65 | 88.02 | +| cradle | 71.49 | 99.01 | +| oven | 59.81 | 81.16 | +| ball | 51.1 | 60.73 | +| food | 56.54 | 66.3 | +| step | 14.19 | 16.81 | +| tank | 62.87 | 66.23 | +| trade name | 22.34 | 25.57 | +| microwave | 88.97 | 93.5 | +| pot | 56.4 | 68.86 | +| animal | 68.04 | 70.47 | +| bicycle | 56.29 | 70.91 | +| lake | 8.37 | 10.07 | +| dishwasher | 67.27 | 77.7 | +| screen | 55.98 | 94.53 | +| blanket | 21.76 | 24.28 | +| sculpture | 75.83 | 82.4 | +| hood | 60.65 | 72.96 | +| sconce | 57.67 | 65.54 | +| vase | 45.07 | 60.72 | +| traffic light | 32.66 | 54.7 | +| tray | 15.97 | 20.58 | +| ashcan | 48.46 | 65.05 | +| fan | 69.76 | 83.13 | +| pier | 37.53 | 38.45 | +| crt screen | 0.65 | 0.65 | +| plate | 58.09 | 67.33 | +| monitor | 65.52 | 80.82 | +| bulletin board | 55.32 | 82.28 | +| shower | 0.12 | 0.15 | +| radiator | 67.83 | 75.64 | +| glass | 13.04 | 13.37 | +| clock | 37.47 | 42.94 | +| flag | 69.96 | 75.58 | ++---------------------+-------+-------+ +2024-06-16 06:16:35,792 - mmseg - INFO - Summary: +2024-06-16 06:16:35,793 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 84.87 | 54.55 | 67.95 | ++-------+-------+-------+ +2024-06-16 06:16:35,794 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 06:16:35,794 - mmseg - INFO - Iter(val) [250] aAcc: 0.8487, mIoU: 0.5455, mAcc: 0.6795, IoU.wall: 0.8070, IoU.building: 0.8429, IoU.sky: 0.9449, IoU.floor: 0.8446, IoU.tree: 0.7654, IoU.ceiling: 0.8665, IoU.road: 0.8475, IoU.bed : 0.9191, IoU.windowpane: 0.6580, IoU.grass: 0.6626, IoU.cabinet: 0.6307, IoU.sidewalk: 0.6891, IoU.person: 0.8426, IoU.earth: 0.3648, IoU.door: 0.5569, IoU.table: 0.6529, IoU.mountain: 0.6261, IoU.plant: 0.5728, IoU.curtain: 0.7991, IoU.chair: 0.6502, IoU.car: 0.8655, IoU.water: 0.3509, IoU.painting: 0.7678, IoU.sofa: 0.8008, IoU.shelf: 0.4853, IoU.house: 0.5407, IoU.sea: 0.6257, IoU.mirror: 0.7331, IoU.rug: 0.7231, IoU.field: 0.3658, IoU.armchair: 0.5789, IoU.seat: 0.6696, IoU.fence: 0.4528, IoU.desk: 0.5444, IoU.rock: 0.5314, IoU.wardrobe: 0.5250, IoU.lamp: 0.7353, IoU.bathtub: 0.8387, IoU.railing: 0.3718, IoU.cushion: 0.6800, IoU.base: 0.4320, IoU.box: 0.3351, IoU.column: 0.5390, IoU.signboard: 0.4127, IoU.chest of drawers: 0.5229, IoU.counter: 0.3930, IoU.sand: 0.5240, IoU.sink: 0.7378, IoU.skyscraper: 0.4865, IoU.fireplace: 0.7018, IoU.refrigerator: 0.7846, IoU.grandstand: 0.4190, IoU.path: 0.2779, IoU.stairs: 0.2042, IoU.runway: 0.7141, IoU.case: 0.5861, IoU.pool table: 0.9327, IoU.pillow: 0.6767, IoU.screen door: 0.8209, IoU.stairway: 0.4239, IoU.river: 0.1315, IoU.bridge: 0.6293, IoU.bookcase: 0.4127, IoU.blind: 0.4612, IoU.coffee table: 0.6098, IoU.toilet: 0.8886, IoU.flower: 0.4018, IoU.book: 0.5030, IoU.hill: 0.0398, IoU.bench: 0.5708, IoU.countertop: 0.6301, IoU.stove: 0.7935, IoU.palm: 0.5460, IoU.kitchen island: 0.4167, IoU.computer: 0.7659, IoU.swivel chair: 0.5081, IoU.boat: 0.7269, IoU.bar: 0.6297, IoU.arcade machine: 0.8704, IoU.hovel: 0.4143, IoU.bus: 0.9175, IoU.towel: 0.7366, IoU.light: 0.5792, IoU.truck: 0.4414, IoU.tower: 0.2938, IoU.chandelier: 0.7251, IoU.awning: 0.3745, IoU.streetlight: 0.2733, IoU.booth: 0.3659, IoU.television receiver: 0.7807, IoU.airplane: 0.8520, IoU.dirt track: 0.0452, IoU.apparel: 0.4289, IoU.pole: 0.1991, IoU.land: 0.0104, IoU.bannister: 0.1620, IoU.escalator: 0.5766, IoU.ottoman: 0.5068, IoU.bottle: 0.4216, IoU.buffet: 0.4179, IoU.poster: 0.2870, IoU.stage: 0.2264, IoU.van: 0.4355, IoU.ship: 0.6017, IoU.fountain: 0.2972, IoU.conveyer belt: 0.4908, IoU.canopy: 0.3646, IoU.washer: 0.8546, IoU.plaything: 0.2673, IoU.swimming pool: 0.5739, IoU.stool: 0.5270, IoU.barrel: 0.5167, IoU.basket: 0.3828, IoU.waterfall: 0.6478, IoU.tent: 0.9588, IoU.bag: 0.2173, IoU.minibike: 0.7265, IoU.cradle: 0.7149, IoU.oven: 0.5981, IoU.ball: 0.5110, IoU.food: 0.5654, IoU.step: 0.1419, IoU.tank: 0.6287, IoU.trade name: 0.2234, IoU.microwave: 0.8897, IoU.pot: 0.5640, IoU.animal: 0.6804, IoU.bicycle: 0.5629, IoU.lake: 0.0837, IoU.dishwasher: 0.6727, IoU.screen: 0.5598, IoU.blanket: 0.2176, IoU.sculpture: 0.7583, IoU.hood: 0.6065, IoU.sconce: 0.5767, IoU.vase: 0.4507, IoU.traffic light: 0.3266, IoU.tray: 0.1597, IoU.ashcan: 0.4846, IoU.fan: 0.6976, IoU.pier: 0.3753, IoU.crt screen: 0.0065, IoU.plate: 0.5809, IoU.monitor: 0.6552, IoU.bulletin board: 0.5532, IoU.shower: 0.0012, IoU.radiator: 0.6783, IoU.glass: 0.1304, IoU.clock: 0.3747, IoU.flag: 0.6996, Acc.wall: 0.8914, Acc.building: 0.9266, Acc.sky: 0.9725, Acc.floor: 0.9055, Acc.tree: 0.8771, Acc.ceiling: 0.9303, Acc.road: 0.9049, Acc.bed : 0.9664, Acc.windowpane: 0.8392, Acc.grass: 0.7530, Acc.cabinet: 0.7617, Acc.sidewalk: 0.8570, Acc.person: 0.9018, Acc.earth: 0.5140, Acc.door: 0.6329, Acc.table: 0.7830, Acc.mountain: 0.7747, Acc.plant: 0.6884, Acc.curtain: 0.8921, Acc.chair: 0.7568, Acc.car: 0.9234, Acc.water: 0.4012, Acc.painting: 0.8920, Acc.sofa: 0.8949, Acc.shelf: 0.6800, Acc.house: 0.8179, Acc.sea: 0.7396, Acc.mirror: 0.7617, Acc.rug: 0.8150, Acc.field: 0.7374, Acc.armchair: 0.7752, Acc.seat: 0.8619, Acc.fence: 0.5609, Acc.desk: 0.8087, Acc.rock: 0.7713, Acc.wardrobe: 0.9009, Acc.lamp: 0.8040, Acc.bathtub: 0.8611, Acc.railing: 0.5680, Acc.cushion: 0.7655, Acc.base: 0.5190, Acc.box: 0.4389, Acc.column: 0.6972, Acc.signboard: 0.5079, Acc.chest of drawers: 0.7805, Acc.counter: 0.4444, Acc.sand: 0.8707, Acc.sink: 0.7872, Acc.skyscraper: 0.5985, Acc.fireplace: 0.9493, Acc.refrigerator: 0.8539, Acc.grandstand: 0.8668, Acc.path: 0.3638, Acc.stairs: 0.2199, Acc.runway: 0.9919, Acc.case: 0.8659, Acc.pool table: 0.9809, Acc.pillow: 0.7875, Acc.screen door: 0.8427, Acc.stairway: 0.7356, Acc.river: 0.8362, Acc.bridge: 0.9114, Acc.bookcase: 0.5235, Acc.blind: 0.5221, Acc.coffee table: 0.8483, Acc.toilet: 0.9245, Acc.flower: 0.4402, Acc.book: 0.7196, Acc.hill: 0.0804, Acc.bench: 0.6394, Acc.countertop: 0.8619, Acc.stove: 0.9152, Acc.palm: 0.7880, Acc.kitchen island: 0.7352, Acc.computer: 0.9231, Acc.swivel chair: 0.7586, Acc.boat: 0.8741, Acc.bar: 0.8566, Acc.arcade machine: 0.9737, Acc.hovel: 0.4839, Acc.bus: 0.9680, Acc.towel: 0.8673, Acc.light: 0.6707, Acc.truck: 0.6299, Acc.tower: 0.4181, Acc.chandelier: 0.8633, Acc.awning: 0.4307, Acc.streetlight: 0.3574, Acc.booth: 0.6680, Acc.television receiver: 0.8810, Acc.airplane: 0.9330, Acc.dirt track: 0.1876, Acc.apparel: 0.5601, Acc.pole: 0.2366, Acc.land: 0.0224, Acc.bannister: 0.2233, Acc.escalator: 0.7872, Acc.ottoman: 0.6859, Acc.bottle: 0.5889, Acc.buffet: 0.4638, Acc.poster: 0.3402, Acc.stage: 0.3982, Acc.van: 0.6823, Acc.ship: 0.6181, Acc.fountain: 0.3266, Acc.conveyer belt: 0.9907, Acc.canopy: 0.5176, Acc.washer: 0.9158, Acc.plaything: 0.3879, Acc.swimming pool: 0.9051, Acc.stool: 0.6612, Acc.barrel: 0.6868, Acc.basket: 0.5417, Acc.waterfall: 0.7256, Acc.tent: 0.9865, Acc.bag: 0.2439, Acc.minibike: 0.8802, Acc.cradle: 0.9901, Acc.oven: 0.8116, Acc.ball: 0.6073, Acc.food: 0.6630, Acc.step: 0.1681, Acc.tank: 0.6623, Acc.trade name: 0.2557, Acc.microwave: 0.9350, Acc.pot: 0.6886, Acc.animal: 0.7047, Acc.bicycle: 0.7091, Acc.lake: 0.1007, Acc.dishwasher: 0.7770, Acc.screen: 0.9453, Acc.blanket: 0.2428, Acc.sculpture: 0.8240, Acc.hood: 0.7296, Acc.sconce: 0.6554, Acc.vase: 0.6072, Acc.traffic light: 0.5470, Acc.tray: 0.2058, Acc.ashcan: 0.6505, Acc.fan: 0.8313, Acc.pier: 0.3845, Acc.crt screen: 0.0065, Acc.plate: 0.6733, Acc.monitor: 0.8082, Acc.bulletin board: 0.8228, Acc.shower: 0.0015, Acc.radiator: 0.7564, Acc.glass: 0.1337, Acc.clock: 0.4294, Acc.flag: 0.7558 +2024-06-16 06:17:57,530 - mmseg - INFO - Iter [18050/80000] lr: 3.098e-05, eta: 1 day, 6:26:29, time: 3.557, data_time: 1.941, memory: 71384, decode.loss_ce: 0.3041, decode.acc_seg: 87.8872, aux.loss_ce: 0.1237, aux.acc_seg: 87.8031, loss: 0.4278 +2024-06-16 06:19:18,674 - mmseg - INFO - Iter [18100/80000] lr: 3.095e-05, eta: 1 day, 6:24:36, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3245, decode.acc_seg: 87.3272, aux.loss_ce: 0.1304, aux.acc_seg: 87.1844, loss: 0.4549 +2024-06-16 06:20:40,000 - mmseg - INFO - Iter [18150/80000] lr: 3.093e-05, eta: 1 day, 6:22:43, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3029, decode.acc_seg: 88.2895, aux.loss_ce: 0.1218, aux.acc_seg: 88.0620, loss: 0.4247 +2024-06-16 06:22:01,213 - mmseg - INFO - Iter [18200/80000] lr: 3.090e-05, eta: 1 day, 6:20:50, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3034, decode.acc_seg: 87.9555, aux.loss_ce: 0.1227, aux.acc_seg: 87.8292, loss: 0.4261 +2024-06-16 06:23:22,285 - mmseg - INFO - Iter [18250/80000] lr: 3.088e-05, eta: 1 day, 6:18:57, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2800, decode.acc_seg: 88.5025, aux.loss_ce: 0.1136, aux.acc_seg: 88.3379, loss: 0.3936 +2024-06-16 06:24:43,452 - mmseg - INFO - Iter [18300/80000] lr: 3.085e-05, eta: 1 day, 6:17:04, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3052, decode.acc_seg: 87.7871, aux.loss_ce: 0.1242, aux.acc_seg: 87.5631, loss: 0.4295 +2024-06-16 06:26:04,613 - mmseg - INFO - Iter [18350/80000] lr: 3.083e-05, eta: 1 day, 6:15:12, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3170, decode.acc_seg: 87.4173, aux.loss_ce: 0.1272, aux.acc_seg: 87.2856, loss: 0.4442 +2024-06-16 06:27:25,818 - mmseg - INFO - Iter [18400/80000] lr: 3.080e-05, eta: 1 day, 6:13:20, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3133, decode.acc_seg: 87.2023, aux.loss_ce: 0.1275, aux.acc_seg: 86.9592, loss: 0.4408 +2024-06-16 06:28:46,865 - mmseg - INFO - Iter [18450/80000] lr: 3.078e-05, eta: 1 day, 6:11:27, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3048, decode.acc_seg: 87.5064, aux.loss_ce: 0.1234, aux.acc_seg: 87.3489, loss: 0.4282 +2024-06-16 06:30:08,049 - mmseg - INFO - Iter [18500/80000] lr: 3.075e-05, eta: 1 day, 6:09:35, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3094, decode.acc_seg: 87.1942, aux.loss_ce: 0.1257, aux.acc_seg: 87.0064, loss: 0.4351 +2024-06-16 06:31:29,439 - mmseg - INFO - Iter [18550/80000] lr: 3.073e-05, eta: 1 day, 6:07:44, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3008, decode.acc_seg: 87.6839, aux.loss_ce: 0.1218, aux.acc_seg: 87.6853, loss: 0.4226 +2024-06-16 06:32:50,610 - mmseg - INFO - Iter [18600/80000] lr: 3.070e-05, eta: 1 day, 6:05:52, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2806, decode.acc_seg: 88.1370, aux.loss_ce: 0.1144, aux.acc_seg: 88.0169, loss: 0.3950 +2024-06-16 06:34:11,762 - mmseg - INFO - Iter [18650/80000] lr: 3.068e-05, eta: 1 day, 6:04:01, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3152, decode.acc_seg: 87.3455, aux.loss_ce: 0.1270, aux.acc_seg: 87.2191, loss: 0.4421 +2024-06-16 06:35:32,860 - mmseg - INFO - Iter [18700/80000] lr: 3.065e-05, eta: 1 day, 6:02:09, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3051, decode.acc_seg: 87.8530, aux.loss_ce: 0.1231, aux.acc_seg: 87.6925, loss: 0.4282 +2024-06-16 06:36:54,200 - mmseg - INFO - Iter [18750/80000] lr: 3.063e-05, eta: 1 day, 6:00:19, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3011, decode.acc_seg: 87.9799, aux.loss_ce: 0.1206, aux.acc_seg: 87.8441, loss: 0.4216 +2024-06-16 06:38:15,435 - mmseg - INFO - Iter [18800/80000] lr: 3.060e-05, eta: 1 day, 5:58:28, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3012, decode.acc_seg: 88.2902, aux.loss_ce: 0.1224, aux.acc_seg: 87.9853, loss: 0.4236 +2024-06-16 06:39:36,674 - mmseg - INFO - Iter [18850/80000] lr: 3.058e-05, eta: 1 day, 5:56:37, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3160, decode.acc_seg: 87.4505, aux.loss_ce: 0.1284, aux.acc_seg: 87.3185, loss: 0.4444 +2024-06-16 06:40:57,737 - mmseg - INFO - Iter [18900/80000] lr: 3.055e-05, eta: 1 day, 5:54:46, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2946, decode.acc_seg: 88.3819, aux.loss_ce: 0.1192, aux.acc_seg: 88.1450, loss: 0.4138 +2024-06-16 06:42:21,568 - mmseg - INFO - Iter [18950/80000] lr: 3.053e-05, eta: 1 day, 5:53:04, time: 1.677, data_time: 0.060, memory: 71384, decode.loss_ce: 0.3319, decode.acc_seg: 86.3648, aux.loss_ce: 0.1343, aux.acc_seg: 86.1728, loss: 0.4663 +2024-06-16 06:43:42,799 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 06:43:42,799 - mmseg - INFO - Iter [19000/80000] lr: 3.050e-05, eta: 1 day, 5:51:14, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3069, decode.acc_seg: 87.2161, aux.loss_ce: 0.1247, aux.acc_seg: 86.8759, loss: 0.4316 +2024-06-16 06:45:20,743 - mmseg - INFO - per class results: +2024-06-16 06:45:20,749 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.63 | 90.83 | +| building | 83.66 | 90.65 | +| sky | 94.7 | 96.95 | +| floor | 85.31 | 91.25 | +| tree | 77.4 | 91.47 | +| ceiling | 86.4 | 95.15 | +| road | 86.31 | 91.85 | +| bed | 92.21 | 96.55 | +| windowpane | 65.1 | 74.48 | +| grass | 68.22 | 81.54 | +| cabinet | 65.03 | 73.1 | +| sidewalk | 69.78 | 79.75 | +| person | 84.76 | 94.22 | +| earth | 36.49 | 51.11 | +| door | 58.92 | 72.11 | +| table | 65.87 | 76.14 | +| mountain | 62.53 | 74.34 | +| plant | 56.2 | 73.29 | +| curtain | 78.0 | 90.54 | +| chair | 60.38 | 66.94 | +| car | 87.18 | 92.98 | +| water | 55.92 | 67.66 | +| painting | 77.7 | 89.12 | +| sofa | 81.89 | 91.56 | +| shelf | 50.65 | 68.04 | +| house | 51.24 | 76.25 | +| sea | 64.73 | 84.5 | +| mirror | 79.58 | 86.56 | +| rug | 71.85 | 85.11 | +| field | 36.85 | 60.75 | +| armchair | 57.08 | 84.02 | +| seat | 66.45 | 88.24 | +| fence | 42.26 | 48.89 | +| desk | 51.87 | 80.57 | +| rock | 61.73 | 74.01 | +| wardrobe | 50.93 | 67.76 | +| lamp | 73.37 | 82.18 | +| bathtub | 88.89 | 92.66 | +| railing | 40.36 | 53.19 | +| cushion | 59.65 | 64.46 | +| base | 34.52 | 54.62 | +| box | 33.66 | 43.59 | +| column | 52.62 | 61.02 | +| signboard | 40.91 | 50.48 | +| chest of drawers | 46.65 | 71.65 | +| counter | 45.85 | 61.73 | +| sand | 45.29 | 67.03 | +| sink | 76.22 | 82.66 | +| skyscraper | 46.98 | 59.19 | +| fireplace | 67.08 | 89.08 | +| refrigerator | 84.94 | 91.33 | +| grandstand | 57.31 | 84.12 | +| path | 28.38 | 44.61 | +| stairs | 34.98 | 43.09 | +| runway | 70.36 | 92.45 | +| case | 54.45 | 71.69 | +| pool table | 92.06 | 98.85 | +| pillow | 64.08 | 85.47 | +| screen door | 85.09 | 89.52 | +| stairway | 50.33 | 73.42 | +| river | 13.53 | 33.34 | +| bridge | 50.41 | 76.76 | +| bookcase | 41.06 | 48.16 | +| blind | 41.25 | 44.21 | +| coffee table | 60.86 | 84.86 | +| toilet | 89.63 | 92.76 | +| flower | 42.37 | 50.25 | +| book | 54.14 | 73.79 | +| hill | 5.3 | 10.34 | +| bench | 56.82 | 65.34 | +| countertop | 62.56 | 78.95 | +| stove | 81.8 | 90.49 | +| palm | 52.62 | 87.16 | +| kitchen island | 44.49 | 90.85 | +| computer | 76.77 | 90.02 | +| swivel chair | 49.17 | 82.63 | +| boat | 53.06 | 71.3 | +| bar | 71.88 | 81.75 | +| arcade machine | 85.48 | 98.08 | +| hovel | 43.55 | 51.43 | +| bus | 92.8 | 95.8 | +| towel | 74.29 | 82.01 | +| light | 54.71 | 58.97 | +| truck | 44.57 | 54.9 | +| tower | 23.59 | 38.77 | +| chandelier | 72.74 | 83.8 | +| awning | 38.94 | 46.73 | +| streetlight | 30.42 | 42.6 | +| booth | 45.36 | 50.35 | +| television receiver | 76.58 | 91.82 | +| airplane | 82.19 | 91.86 | +| dirt track | 5.37 | 26.12 | +| apparel | 52.9 | 79.44 | +| pole | 17.4 | 20.25 | +| land | 3.42 | 4.39 | +| bannister | 16.61 | 23.49 | +| escalator | 61.47 | 87.8 | +| ottoman | 48.86 | 66.06 | +| bottle | 39.61 | 68.8 | +| buffet | 58.69 | 73.45 | +| poster | 28.17 | 40.76 | +| stage | 23.41 | 34.25 | +| van | 43.5 | 67.08 | +| ship | 65.81 | 99.25 | +| fountain | 38.0 | 40.83 | +| conveyer belt | 79.9 | 93.76 | +| canopy | 54.76 | 62.13 | +| washer | 85.62 | 92.32 | +| plaything | 26.09 | 47.19 | +| swimming pool | 72.92 | 82.57 | +| stool | 47.28 | 73.87 | +| barrel | 61.89 | 88.46 | +| basket | 44.34 | 60.68 | +| waterfall | 54.04 | 60.46 | +| tent | 95.48 | 98.76 | +| bag | 24.55 | 30.49 | +| minibike | 74.02 | 86.41 | +| cradle | 78.65 | 98.83 | +| oven | 61.77 | 68.6 | +| ball | 41.55 | 53.21 | +| food | 56.55 | 69.06 | +| step | 14.88 | 18.14 | +| tank | 63.76 | 68.48 | +| trade name | 36.59 | 57.59 | +| microwave | 87.47 | 95.37 | +| pot | 59.15 | 71.5 | +| animal | 67.21 | 70.27 | +| bicycle | 56.11 | 69.19 | +| lake | 58.65 | 62.0 | +| dishwasher | 68.07 | 83.2 | +| screen | 57.85 | 95.27 | +| blanket | 27.02 | 32.34 | +| sculpture | 76.3 | 86.2 | +| hood | 62.16 | 73.0 | +| sconce | 54.76 | 62.01 | +| vase | 45.41 | 52.95 | +| traffic light | 32.73 | 64.74 | +| tray | 20.88 | 25.07 | +| ashcan | 49.73 | 67.01 | +| fan | 68.54 | 82.46 | +| pier | 54.1 | 61.83 | +| crt screen | 3.42 | 9.13 | +| plate | 58.4 | 78.85 | +| monitor | 8.29 | 9.22 | +| bulletin board | 61.87 | 81.36 | +| shower | 0.27 | 0.28 | +| radiator | 67.96 | 78.66 | +| glass | 14.49 | 15.05 | +| clock | 40.66 | 53.05 | +| flag | 68.55 | 77.97 | ++---------------------+-------+-------+ +2024-06-16 06:45:20,749 - mmseg - INFO - Summary: +2024-06-16 06:45:20,749 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 85.4 | 55.87 | 68.98 | ++------+-------+-------+ +2024-06-16 06:45:20,750 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 06:45:20,750 - mmseg - INFO - Iter(val) [250] aAcc: 0.8540, mIoU: 0.5587, mAcc: 0.6898, IoU.wall: 0.8063, IoU.building: 0.8366, IoU.sky: 0.9470, IoU.floor: 0.8531, IoU.tree: 0.7740, IoU.ceiling: 0.8640, IoU.road: 0.8631, IoU.bed : 0.9221, IoU.windowpane: 0.6510, IoU.grass: 0.6822, IoU.cabinet: 0.6503, IoU.sidewalk: 0.6978, IoU.person: 0.8476, IoU.earth: 0.3649, IoU.door: 0.5892, IoU.table: 0.6587, IoU.mountain: 0.6253, IoU.plant: 0.5620, IoU.curtain: 0.7800, IoU.chair: 0.6038, IoU.car: 0.8718, IoU.water: 0.5592, IoU.painting: 0.7770, IoU.sofa: 0.8189, IoU.shelf: 0.5065, IoU.house: 0.5124, IoU.sea: 0.6473, IoU.mirror: 0.7958, IoU.rug: 0.7185, IoU.field: 0.3685, IoU.armchair: 0.5708, IoU.seat: 0.6645, IoU.fence: 0.4226, IoU.desk: 0.5187, IoU.rock: 0.6173, IoU.wardrobe: 0.5093, IoU.lamp: 0.7337, IoU.bathtub: 0.8889, IoU.railing: 0.4036, IoU.cushion: 0.5965, IoU.base: 0.3452, IoU.box: 0.3366, IoU.column: 0.5262, IoU.signboard: 0.4091, IoU.chest of drawers: 0.4665, IoU.counter: 0.4585, IoU.sand: 0.4529, IoU.sink: 0.7622, IoU.skyscraper: 0.4698, IoU.fireplace: 0.6708, IoU.refrigerator: 0.8494, IoU.grandstand: 0.5731, IoU.path: 0.2838, IoU.stairs: 0.3498, IoU.runway: 0.7036, IoU.case: 0.5445, IoU.pool table: 0.9206, IoU.pillow: 0.6408, IoU.screen door: 0.8509, IoU.stairway: 0.5033, IoU.river: 0.1353, IoU.bridge: 0.5041, IoU.bookcase: 0.4106, IoU.blind: 0.4125, IoU.coffee table: 0.6086, IoU.toilet: 0.8963, IoU.flower: 0.4237, IoU.book: 0.5414, IoU.hill: 0.0530, IoU.bench: 0.5682, IoU.countertop: 0.6256, IoU.stove: 0.8180, IoU.palm: 0.5262, IoU.kitchen island: 0.4449, IoU.computer: 0.7677, IoU.swivel chair: 0.4917, IoU.boat: 0.5306, IoU.bar: 0.7188, IoU.arcade machine: 0.8548, IoU.hovel: 0.4355, IoU.bus: 0.9280, IoU.towel: 0.7429, IoU.light: 0.5471, IoU.truck: 0.4457, IoU.tower: 0.2359, IoU.chandelier: 0.7274, IoU.awning: 0.3894, IoU.streetlight: 0.3042, IoU.booth: 0.4536, IoU.television receiver: 0.7658, IoU.airplane: 0.8219, IoU.dirt track: 0.0537, IoU.apparel: 0.5290, IoU.pole: 0.1740, IoU.land: 0.0342, IoU.bannister: 0.1661, IoU.escalator: 0.6147, IoU.ottoman: 0.4886, IoU.bottle: 0.3961, IoU.buffet: 0.5869, IoU.poster: 0.2817, IoU.stage: 0.2341, IoU.van: 0.4350, IoU.ship: 0.6581, IoU.fountain: 0.3800, IoU.conveyer belt: 0.7990, IoU.canopy: 0.5476, IoU.washer: 0.8562, IoU.plaything: 0.2609, IoU.swimming pool: 0.7292, IoU.stool: 0.4728, IoU.barrel: 0.6189, IoU.basket: 0.4434, IoU.waterfall: 0.5404, IoU.tent: 0.9548, IoU.bag: 0.2455, IoU.minibike: 0.7402, IoU.cradle: 0.7865, IoU.oven: 0.6177, IoU.ball: 0.4155, IoU.food: 0.5655, IoU.step: 0.1488, IoU.tank: 0.6376, IoU.trade name: 0.3659, IoU.microwave: 0.8747, IoU.pot: 0.5915, IoU.animal: 0.6721, IoU.bicycle: 0.5611, IoU.lake: 0.5865, IoU.dishwasher: 0.6807, IoU.screen: 0.5785, IoU.blanket: 0.2702, IoU.sculpture: 0.7630, IoU.hood: 0.6216, IoU.sconce: 0.5476, IoU.vase: 0.4541, IoU.traffic light: 0.3273, IoU.tray: 0.2088, IoU.ashcan: 0.4973, IoU.fan: 0.6854, IoU.pier: 0.5410, IoU.crt screen: 0.0342, IoU.plate: 0.5840, IoU.monitor: 0.0829, IoU.bulletin board: 0.6187, IoU.shower: 0.0027, IoU.radiator: 0.6796, IoU.glass: 0.1449, IoU.clock: 0.4066, IoU.flag: 0.6855, Acc.wall: 0.9083, Acc.building: 0.9065, Acc.sky: 0.9695, Acc.floor: 0.9125, Acc.tree: 0.9147, Acc.ceiling: 0.9515, Acc.road: 0.9185, Acc.bed : 0.9655, Acc.windowpane: 0.7448, Acc.grass: 0.8154, Acc.cabinet: 0.7310, Acc.sidewalk: 0.7975, Acc.person: 0.9422, Acc.earth: 0.5111, Acc.door: 0.7211, Acc.table: 0.7614, Acc.mountain: 0.7434, Acc.plant: 0.7329, Acc.curtain: 0.9054, Acc.chair: 0.6694, Acc.car: 0.9298, Acc.water: 0.6766, Acc.painting: 0.8912, Acc.sofa: 0.9156, Acc.shelf: 0.6804, Acc.house: 0.7625, Acc.sea: 0.8450, Acc.mirror: 0.8656, Acc.rug: 0.8511, Acc.field: 0.6075, Acc.armchair: 0.8402, Acc.seat: 0.8824, Acc.fence: 0.4889, Acc.desk: 0.8057, Acc.rock: 0.7401, Acc.wardrobe: 0.6776, Acc.lamp: 0.8218, Acc.bathtub: 0.9266, Acc.railing: 0.5319, Acc.cushion: 0.6446, Acc.base: 0.5462, Acc.box: 0.4359, Acc.column: 0.6102, Acc.signboard: 0.5048, Acc.chest of drawers: 0.7165, Acc.counter: 0.6173, Acc.sand: 0.6703, Acc.sink: 0.8266, Acc.skyscraper: 0.5919, Acc.fireplace: 0.8908, Acc.refrigerator: 0.9133, Acc.grandstand: 0.8412, Acc.path: 0.4461, Acc.stairs: 0.4309, Acc.runway: 0.9245, Acc.case: 0.7169, Acc.pool table: 0.9885, Acc.pillow: 0.8547, Acc.screen door: 0.8952, Acc.stairway: 0.7342, Acc.river: 0.3334, Acc.bridge: 0.7676, Acc.bookcase: 0.4816, Acc.blind: 0.4421, Acc.coffee table: 0.8486, Acc.toilet: 0.9276, Acc.flower: 0.5025, Acc.book: 0.7379, Acc.hill: 0.1034, Acc.bench: 0.6534, Acc.countertop: 0.7895, Acc.stove: 0.9049, Acc.palm: 0.8716, Acc.kitchen island: 0.9085, Acc.computer: 0.9002, Acc.swivel chair: 0.8263, Acc.boat: 0.7130, Acc.bar: 0.8175, Acc.arcade machine: 0.9808, Acc.hovel: 0.5143, Acc.bus: 0.9580, Acc.towel: 0.8201, Acc.light: 0.5897, Acc.truck: 0.5490, Acc.tower: 0.3877, Acc.chandelier: 0.8380, Acc.awning: 0.4673, Acc.streetlight: 0.4260, Acc.booth: 0.5035, Acc.television receiver: 0.9182, Acc.airplane: 0.9186, Acc.dirt track: 0.2612, Acc.apparel: 0.7944, Acc.pole: 0.2025, Acc.land: 0.0439, Acc.bannister: 0.2349, Acc.escalator: 0.8780, Acc.ottoman: 0.6606, Acc.bottle: 0.6880, Acc.buffet: 0.7345, Acc.poster: 0.4076, Acc.stage: 0.3425, Acc.van: 0.6708, Acc.ship: 0.9925, Acc.fountain: 0.4083, Acc.conveyer belt: 0.9376, Acc.canopy: 0.6213, Acc.washer: 0.9232, Acc.plaything: 0.4719, Acc.swimming pool: 0.8257, Acc.stool: 0.7387, Acc.barrel: 0.8846, Acc.basket: 0.6068, Acc.waterfall: 0.6046, Acc.tent: 0.9876, Acc.bag: 0.3049, Acc.minibike: 0.8641, Acc.cradle: 0.9883, Acc.oven: 0.6860, Acc.ball: 0.5321, Acc.food: 0.6906, Acc.step: 0.1814, Acc.tank: 0.6848, Acc.trade name: 0.5759, Acc.microwave: 0.9537, Acc.pot: 0.7150, Acc.animal: 0.7027, Acc.bicycle: 0.6919, Acc.lake: 0.6200, Acc.dishwasher: 0.8320, Acc.screen: 0.9527, Acc.blanket: 0.3234, Acc.sculpture: 0.8620, Acc.hood: 0.7300, Acc.sconce: 0.6201, Acc.vase: 0.5295, Acc.traffic light: 0.6474, Acc.tray: 0.2507, Acc.ashcan: 0.6701, Acc.fan: 0.8246, Acc.pier: 0.6183, Acc.crt screen: 0.0913, Acc.plate: 0.7885, Acc.monitor: 0.0922, Acc.bulletin board: 0.8136, Acc.shower: 0.0028, Acc.radiator: 0.7866, Acc.glass: 0.1505, Acc.clock: 0.5305, Acc.flag: 0.7797 +2024-06-16 06:46:42,236 - mmseg - INFO - Iter [19050/80000] lr: 3.048e-05, eta: 1 day, 5:54:38, time: 3.589, data_time: 1.976, memory: 71384, decode.loss_ce: 0.2797, decode.acc_seg: 88.6174, aux.loss_ce: 0.1132, aux.acc_seg: 88.5104, loss: 0.3929 +2024-06-16 06:48:03,380 - mmseg - INFO - Iter [19100/80000] lr: 3.045e-05, eta: 1 day, 5:52:47, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2774, decode.acc_seg: 88.3156, aux.loss_ce: 0.1130, aux.acc_seg: 88.0432, loss: 0.3904 +2024-06-16 06:49:24,517 - mmseg - INFO - Iter [19150/80000] lr: 3.043e-05, eta: 1 day, 5:50:56, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2902, decode.acc_seg: 87.9181, aux.loss_ce: 0.1184, aux.acc_seg: 87.7883, loss: 0.4086 +2024-06-16 06:50:45,803 - mmseg - INFO - Iter [19200/80000] lr: 3.040e-05, eta: 1 day, 5:49:05, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2870, decode.acc_seg: 88.4632, aux.loss_ce: 0.1170, aux.acc_seg: 88.1721, loss: 0.4040 +2024-06-16 06:52:06,980 - mmseg - INFO - Iter [19250/80000] lr: 3.038e-05, eta: 1 day, 5:47:15, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2850, decode.acc_seg: 88.5349, aux.loss_ce: 0.1155, aux.acc_seg: 88.3403, loss: 0.4005 +2024-06-16 06:53:28,208 - mmseg - INFO - Iter [19300/80000] lr: 3.035e-05, eta: 1 day, 5:45:24, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3129, decode.acc_seg: 88.0574, aux.loss_ce: 0.1263, aux.acc_seg: 87.7992, loss: 0.4392 +2024-06-16 06:54:49,438 - mmseg - INFO - Iter [19350/80000] lr: 3.033e-05, eta: 1 day, 5:43:34, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2846, decode.acc_seg: 88.5949, aux.loss_ce: 0.1156, aux.acc_seg: 88.4946, loss: 0.4002 +2024-06-16 06:56:10,645 - mmseg - INFO - Iter [19400/80000] lr: 3.030e-05, eta: 1 day, 5:41:44, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2846, decode.acc_seg: 88.4656, aux.loss_ce: 0.1173, aux.acc_seg: 88.1792, loss: 0.4019 +2024-06-16 06:57:31,658 - mmseg - INFO - Iter [19450/80000] lr: 3.028e-05, eta: 1 day, 5:39:53, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2809, decode.acc_seg: 88.5053, aux.loss_ce: 0.1142, aux.acc_seg: 88.3133, loss: 0.3950 +2024-06-16 06:58:52,984 - mmseg - INFO - Iter [19500/80000] lr: 3.025e-05, eta: 1 day, 5:38:04, time: 1.627, data_time: 0.011, memory: 71384, decode.loss_ce: 0.2887, decode.acc_seg: 88.0748, aux.loss_ce: 0.1167, aux.acc_seg: 87.8843, loss: 0.4055 +2024-06-16 07:00:14,198 - mmseg - INFO - Iter [19550/80000] lr: 3.023e-05, eta: 1 day, 5:36:14, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2685, decode.acc_seg: 88.8689, aux.loss_ce: 0.1102, aux.acc_seg: 88.7027, loss: 0.3787 +2024-06-16 07:01:35,432 - mmseg - INFO - Iter [19600/80000] lr: 3.020e-05, eta: 1 day, 5:34:25, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2773, decode.acc_seg: 88.6010, aux.loss_ce: 0.1125, aux.acc_seg: 88.5198, loss: 0.3897 +2024-06-16 07:02:56,688 - mmseg - INFO - Iter [19650/80000] lr: 3.018e-05, eta: 1 day, 5:32:36, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3001, decode.acc_seg: 88.1961, aux.loss_ce: 0.1222, aux.acc_seg: 87.9433, loss: 0.4224 +2024-06-16 07:04:17,753 - mmseg - INFO - Iter [19700/80000] lr: 3.015e-05, eta: 1 day, 5:30:46, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2826, decode.acc_seg: 88.5360, aux.loss_ce: 0.1146, aux.acc_seg: 88.4477, loss: 0.3972 +2024-06-16 07:05:38,991 - mmseg - INFO - Iter [19750/80000] lr: 3.013e-05, eta: 1 day, 5:28:57, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2898, decode.acc_seg: 87.8958, aux.loss_ce: 0.1189, aux.acc_seg: 87.6255, loss: 0.4087 +2024-06-16 07:07:00,219 - mmseg - INFO - Iter [19800/80000] lr: 3.010e-05, eta: 1 day, 5:27:08, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3044, decode.acc_seg: 87.4894, aux.loss_ce: 0.1221, aux.acc_seg: 87.4315, loss: 0.4265 +2024-06-16 07:08:21,503 - mmseg - INFO - Iter [19850/80000] lr: 3.008e-05, eta: 1 day, 5:25:19, time: 1.626, data_time: 0.011, memory: 71384, decode.loss_ce: 0.2856, decode.acc_seg: 88.4156, aux.loss_ce: 0.1150, aux.acc_seg: 88.1593, loss: 0.4007 +2024-06-16 07:09:42,751 - mmseg - INFO - Iter [19900/80000] lr: 3.005e-05, eta: 1 day, 5:23:31, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2923, decode.acc_seg: 88.2980, aux.loss_ce: 0.1194, aux.acc_seg: 88.0933, loss: 0.4117 +2024-06-16 07:11:03,850 - mmseg - INFO - Iter [19950/80000] lr: 3.003e-05, eta: 1 day, 5:21:42, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2792, decode.acc_seg: 88.7035, aux.loss_ce: 0.1138, aux.acc_seg: 88.4666, loss: 0.3930 +2024-06-16 07:12:24,855 - mmseg - INFO - Saving checkpoint at 20000 iterations +2024-06-16 07:13:49,973 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 07:13:49,973 - mmseg - INFO - Iter [20000/80000] lr: 3.000e-05, eta: 1 day, 5:24:08, time: 3.322, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2866, decode.acc_seg: 88.2764, aux.loss_ce: 0.1162, aux.acc_seg: 88.1057, loss: 0.4029 +2024-06-16 07:15:27,589 - mmseg - INFO - per class results: +2024-06-16 07:15:27,595 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.52 | 88.18 | +| building | 85.43 | 92.57 | +| sky | 94.9 | 97.16 | +| floor | 84.62 | 91.5 | +| tree | 77.94 | 90.77 | +| ceiling | 86.19 | 95.14 | +| road | 86.22 | 90.81 | +| bed | 91.65 | 96.76 | +| windowpane | 65.22 | 80.58 | +| grass | 71.4 | 88.67 | +| cabinet | 63.81 | 72.08 | +| sidewalk | 69.57 | 83.93 | +| person | 84.62 | 94.53 | +| earth | 35.56 | 48.41 | +| door | 59.22 | 73.58 | +| table | 64.06 | 80.87 | +| mountain | 60.76 | 65.18 | +| plant | 55.22 | 70.97 | +| curtain | 79.92 | 90.71 | +| chair | 65.77 | 83.31 | +| car | 86.7 | 93.74 | +| water | 57.77 | 74.57 | +| painting | 79.55 | 88.98 | +| sofa | 80.1 | 89.7 | +| shelf | 45.45 | 67.76 | +| house | 59.08 | 74.71 | +| sea | 58.57 | 80.93 | +| mirror | 79.41 | 87.04 | +| rug | 67.64 | 76.13 | +| field | 30.6 | 43.56 | +| armchair | 57.43 | 72.19 | +| seat | 63.43 | 89.52 | +| fence | 42.18 | 55.66 | +| desk | 53.45 | 71.84 | +| rock | 54.41 | 86.47 | +| wardrobe | 55.13 | 78.87 | +| lamp | 72.55 | 80.8 | +| bathtub | 90.08 | 93.41 | +| railing | 40.43 | 52.79 | +| cushion | 68.58 | 79.37 | +| base | 44.55 | 66.83 | +| box | 33.21 | 44.36 | +| column | 55.31 | 67.82 | +| signboard | 41.28 | 59.4 | +| chest of drawers | 46.69 | 59.92 | +| counter | 46.3 | 64.37 | +| sand | 51.72 | 83.56 | +| sink | 80.67 | 87.72 | +| skyscraper | 50.51 | 61.58 | +| fireplace | 71.46 | 91.47 | +| refrigerator | 79.08 | 84.99 | +| grandstand | 56.21 | 87.37 | +| path | 28.8 | 43.93 | +| stairs | 36.29 | 44.1 | +| runway | 70.27 | 98.97 | +| case | 61.26 | 75.93 | +| pool table | 94.29 | 97.49 | +| pillow | 66.42 | 74.11 | +| screen door | 83.45 | 89.96 | +| stairway | 53.65 | 70.35 | +| river | 24.53 | 30.22 | +| bridge | 67.57 | 85.13 | +| bookcase | 34.34 | 77.59 | +| blind | 42.79 | 47.15 | +| coffee table | 60.08 | 88.9 | +| toilet | 90.37 | 93.71 | +| flower | 47.37 | 57.6 | +| book | 34.76 | 41.0 | +| hill | 7.34 | 19.78 | +| bench | 54.07 | 58.95 | +| countertop | 56.42 | 67.52 | +| stove | 82.27 | 94.58 | +| palm | 57.05 | 80.35 | +| kitchen island | 44.23 | 90.87 | +| computer | 76.06 | 93.01 | +| swivel chair | 47.49 | 72.5 | +| boat | 80.95 | 89.13 | +| bar | 66.76 | 81.46 | +| arcade machine | 87.81 | 97.87 | +| hovel | 51.63 | 70.15 | +| bus | 92.1 | 96.5 | +| towel | 76.52 | 84.64 | +| light | 58.98 | 66.38 | +| truck | 46.48 | 57.28 | +| tower | 31.5 | 57.65 | +| chandelier | 68.66 | 86.15 | +| awning | 43.44 | 55.87 | +| streetlight | 32.56 | 44.33 | +| booth | 45.86 | 66.79 | +| television receiver | 76.06 | 86.73 | +| airplane | 87.74 | 95.24 | +| dirt track | 6.47 | 44.4 | +| apparel | 53.74 | 71.62 | +| pole | 20.86 | 27.47 | +| land | 0.85 | 1.44 | +| bannister | 15.05 | 21.79 | +| escalator | 64.69 | 82.82 | +| ottoman | 53.32 | 74.89 | +| bottle | 21.53 | 24.99 | +| buffet | 56.56 | 90.49 | +| poster | 33.55 | 43.27 | +| stage | 19.54 | 54.17 | +| van | 46.2 | 71.51 | +| ship | 13.62 | 13.78 | +| fountain | 45.47 | 46.89 | +| conveyer belt | 80.16 | 92.68 | +| canopy | 49.52 | 79.28 | +| washer | 79.53 | 84.67 | +| plaything | 29.42 | 41.44 | +| swimming pool | 57.16 | 89.11 | +| stool | 53.44 | 63.32 | +| barrel | 46.16 | 65.09 | +| basket | 41.31 | 64.09 | +| waterfall | 58.6 | 66.53 | +| tent | 96.09 | 98.85 | +| bag | 20.18 | 23.71 | +| minibike | 74.01 | 90.58 | +| cradle | 83.98 | 97.48 | +| oven | 67.33 | 76.21 | +| ball | 11.29 | 11.61 | +| food | 38.19 | 41.1 | +| step | 19.83 | 23.82 | +| tank | 78.51 | 97.22 | +| trade name | 17.21 | 19.09 | +| microwave | 89.31 | 95.96 | +| pot | 56.43 | 69.7 | +| animal | 61.36 | 62.25 | +| bicycle | 59.18 | 80.82 | +| lake | 57.81 | 63.71 | +| dishwasher | 72.76 | 83.37 | +| screen | 57.21 | 93.3 | +| blanket | 32.99 | 38.28 | +| sculpture | 76.3 | 80.72 | +| hood | 65.08 | 81.72 | +| sconce | 60.05 | 69.48 | +| vase | 43.31 | 67.09 | +| traffic light | 26.68 | 73.98 | +| tray | 12.37 | 13.44 | +| ashcan | 47.61 | 65.51 | +| fan | 68.59 | 82.84 | +| pier | 44.49 | 51.85 | +| crt screen | 2.88 | 3.21 | +| plate | 61.67 | 72.8 | +| monitor | 62.69 | 71.82 | +| bulletin board | 55.16 | 77.98 | +| shower | 0.15 | 0.16 | +| radiator | 68.01 | 77.54 | +| glass | 17.19 | 18.09 | +| clock | 45.34 | 53.68 | +| flag | 69.57 | 78.54 | ++---------------------+-------+-------+ +2024-06-16 07:15:27,595 - mmseg - INFO - Summary: +2024-06-16 07:15:27,596 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.61 | 55.99 | 69.51 | ++-------+-------+-------+ +2024-06-16 07:15:27,597 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 07:15:27,597 - mmseg - INFO - Iter(val) [250] aAcc: 0.8561, mIoU: 0.5599, mAcc: 0.6951, IoU.wall: 0.8152, IoU.building: 0.8543, IoU.sky: 0.9490, IoU.floor: 0.8462, IoU.tree: 0.7794, IoU.ceiling: 0.8619, IoU.road: 0.8622, IoU.bed : 0.9165, IoU.windowpane: 0.6522, IoU.grass: 0.7140, IoU.cabinet: 0.6381, IoU.sidewalk: 0.6957, IoU.person: 0.8462, IoU.earth: 0.3556, IoU.door: 0.5922, IoU.table: 0.6406, IoU.mountain: 0.6076, IoU.plant: 0.5522, IoU.curtain: 0.7992, IoU.chair: 0.6577, IoU.car: 0.8670, IoU.water: 0.5777, IoU.painting: 0.7955, IoU.sofa: 0.8010, IoU.shelf: 0.4545, IoU.house: 0.5908, IoU.sea: 0.5857, IoU.mirror: 0.7941, IoU.rug: 0.6764, IoU.field: 0.3060, IoU.armchair: 0.5743, IoU.seat: 0.6343, IoU.fence: 0.4218, IoU.desk: 0.5345, IoU.rock: 0.5441, IoU.wardrobe: 0.5513, IoU.lamp: 0.7255, IoU.bathtub: 0.9008, IoU.railing: 0.4043, IoU.cushion: 0.6858, IoU.base: 0.4455, IoU.box: 0.3321, IoU.column: 0.5531, IoU.signboard: 0.4128, IoU.chest of drawers: 0.4669, IoU.counter: 0.4630, IoU.sand: 0.5172, IoU.sink: 0.8067, IoU.skyscraper: 0.5051, IoU.fireplace: 0.7146, IoU.refrigerator: 0.7908, IoU.grandstand: 0.5621, IoU.path: 0.2880, IoU.stairs: 0.3629, IoU.runway: 0.7027, IoU.case: 0.6126, IoU.pool table: 0.9429, IoU.pillow: 0.6642, IoU.screen door: 0.8345, IoU.stairway: 0.5365, IoU.river: 0.2453, IoU.bridge: 0.6757, IoU.bookcase: 0.3434, IoU.blind: 0.4279, IoU.coffee table: 0.6008, IoU.toilet: 0.9037, IoU.flower: 0.4737, IoU.book: 0.3476, IoU.hill: 0.0734, IoU.bench: 0.5407, IoU.countertop: 0.5642, IoU.stove: 0.8227, IoU.palm: 0.5705, IoU.kitchen island: 0.4423, IoU.computer: 0.7606, IoU.swivel chair: 0.4749, IoU.boat: 0.8095, IoU.bar: 0.6676, IoU.arcade machine: 0.8781, IoU.hovel: 0.5163, IoU.bus: 0.9210, IoU.towel: 0.7652, IoU.light: 0.5898, IoU.truck: 0.4648, IoU.tower: 0.3150, IoU.chandelier: 0.6866, IoU.awning: 0.4344, IoU.streetlight: 0.3256, IoU.booth: 0.4586, IoU.television receiver: 0.7606, IoU.airplane: 0.8774, IoU.dirt track: 0.0647, IoU.apparel: 0.5374, IoU.pole: 0.2086, IoU.land: 0.0085, IoU.bannister: 0.1505, IoU.escalator: 0.6469, IoU.ottoman: 0.5332, IoU.bottle: 0.2153, IoU.buffet: 0.5656, IoU.poster: 0.3355, IoU.stage: 0.1954, IoU.van: 0.4620, IoU.ship: 0.1362, IoU.fountain: 0.4547, IoU.conveyer belt: 0.8016, IoU.canopy: 0.4952, IoU.washer: 0.7953, IoU.plaything: 0.2942, IoU.swimming pool: 0.5716, IoU.stool: 0.5344, IoU.barrel: 0.4616, IoU.basket: 0.4131, IoU.waterfall: 0.5860, IoU.tent: 0.9609, IoU.bag: 0.2018, IoU.minibike: 0.7401, IoU.cradle: 0.8398, IoU.oven: 0.6733, IoU.ball: 0.1129, IoU.food: 0.3819, IoU.step: 0.1983, IoU.tank: 0.7851, IoU.trade name: 0.1721, IoU.microwave: 0.8931, IoU.pot: 0.5643, IoU.animal: 0.6136, IoU.bicycle: 0.5918, IoU.lake: 0.5781, IoU.dishwasher: 0.7276, IoU.screen: 0.5721, IoU.blanket: 0.3299, IoU.sculpture: 0.7630, IoU.hood: 0.6508, IoU.sconce: 0.6005, IoU.vase: 0.4331, IoU.traffic light: 0.2668, IoU.tray: 0.1237, IoU.ashcan: 0.4761, IoU.fan: 0.6859, IoU.pier: 0.4449, IoU.crt screen: 0.0288, IoU.plate: 0.6167, IoU.monitor: 0.6269, IoU.bulletin board: 0.5516, IoU.shower: 0.0015, IoU.radiator: 0.6801, IoU.glass: 0.1719, IoU.clock: 0.4534, IoU.flag: 0.6957, Acc.wall: 0.8818, Acc.building: 0.9257, Acc.sky: 0.9716, Acc.floor: 0.9150, Acc.tree: 0.9077, Acc.ceiling: 0.9514, Acc.road: 0.9081, Acc.bed : 0.9676, Acc.windowpane: 0.8058, Acc.grass: 0.8867, Acc.cabinet: 0.7208, Acc.sidewalk: 0.8393, Acc.person: 0.9453, Acc.earth: 0.4841, Acc.door: 0.7358, Acc.table: 0.8087, Acc.mountain: 0.6518, Acc.plant: 0.7097, Acc.curtain: 0.9071, Acc.chair: 0.8331, Acc.car: 0.9374, Acc.water: 0.7457, Acc.painting: 0.8898, Acc.sofa: 0.8970, Acc.shelf: 0.6776, Acc.house: 0.7471, Acc.sea: 0.8093, Acc.mirror: 0.8704, Acc.rug: 0.7613, Acc.field: 0.4356, Acc.armchair: 0.7219, Acc.seat: 0.8952, Acc.fence: 0.5566, Acc.desk: 0.7184, Acc.rock: 0.8647, Acc.wardrobe: 0.7887, Acc.lamp: 0.8080, Acc.bathtub: 0.9341, Acc.railing: 0.5279, Acc.cushion: 0.7937, Acc.base: 0.6683, Acc.box: 0.4436, Acc.column: 0.6782, Acc.signboard: 0.5940, Acc.chest of drawers: 0.5992, Acc.counter: 0.6437, Acc.sand: 0.8356, Acc.sink: 0.8772, Acc.skyscraper: 0.6158, Acc.fireplace: 0.9147, Acc.refrigerator: 0.8499, Acc.grandstand: 0.8737, Acc.path: 0.4393, Acc.stairs: 0.4410, Acc.runway: 0.9897, Acc.case: 0.7593, Acc.pool table: 0.9749, Acc.pillow: 0.7411, Acc.screen door: 0.8996, Acc.stairway: 0.7035, Acc.river: 0.3022, Acc.bridge: 0.8513, Acc.bookcase: 0.7759, Acc.blind: 0.4715, Acc.coffee table: 0.8890, Acc.toilet: 0.9371, Acc.flower: 0.5760, Acc.book: 0.4100, Acc.hill: 0.1978, Acc.bench: 0.5895, Acc.countertop: 0.6752, Acc.stove: 0.9458, Acc.palm: 0.8035, Acc.kitchen island: 0.9087, Acc.computer: 0.9301, Acc.swivel chair: 0.7250, Acc.boat: 0.8913, Acc.bar: 0.8146, Acc.arcade machine: 0.9787, Acc.hovel: 0.7015, Acc.bus: 0.9650, Acc.towel: 0.8464, Acc.light: 0.6638, Acc.truck: 0.5728, Acc.tower: 0.5765, Acc.chandelier: 0.8615, Acc.awning: 0.5587, Acc.streetlight: 0.4433, Acc.booth: 0.6679, Acc.television receiver: 0.8673, Acc.airplane: 0.9524, Acc.dirt track: 0.4440, Acc.apparel: 0.7162, Acc.pole: 0.2747, Acc.land: 0.0144, Acc.bannister: 0.2179, Acc.escalator: 0.8282, Acc.ottoman: 0.7489, Acc.bottle: 0.2499, Acc.buffet: 0.9049, Acc.poster: 0.4327, Acc.stage: 0.5417, Acc.van: 0.7151, Acc.ship: 0.1378, Acc.fountain: 0.4689, Acc.conveyer belt: 0.9268, Acc.canopy: 0.7928, Acc.washer: 0.8467, Acc.plaything: 0.4144, Acc.swimming pool: 0.8911, Acc.stool: 0.6332, Acc.barrel: 0.6509, Acc.basket: 0.6409, Acc.waterfall: 0.6653, Acc.tent: 0.9885, Acc.bag: 0.2371, Acc.minibike: 0.9058, Acc.cradle: 0.9748, Acc.oven: 0.7621, Acc.ball: 0.1161, Acc.food: 0.4110, Acc.step: 0.2382, Acc.tank: 0.9722, Acc.trade name: 0.1909, Acc.microwave: 0.9596, Acc.pot: 0.6970, Acc.animal: 0.6225, Acc.bicycle: 0.8082, Acc.lake: 0.6371, Acc.dishwasher: 0.8337, Acc.screen: 0.9330, Acc.blanket: 0.3828, Acc.sculpture: 0.8072, Acc.hood: 0.8172, Acc.sconce: 0.6948, Acc.vase: 0.6709, Acc.traffic light: 0.7398, Acc.tray: 0.1344, Acc.ashcan: 0.6551, Acc.fan: 0.8284, Acc.pier: 0.5185, Acc.crt screen: 0.0321, Acc.plate: 0.7280, Acc.monitor: 0.7182, Acc.bulletin board: 0.7798, Acc.shower: 0.0016, Acc.radiator: 0.7754, Acc.glass: 0.1809, Acc.clock: 0.5368, Acc.flag: 0.7854 +2024-06-16 07:16:49,335 - mmseg - INFO - Iter [20050/80000] lr: 2.998e-05, eta: 1 day, 5:27:12, time: 3.587, data_time: 1.969, memory: 71384, decode.loss_ce: 0.2958, decode.acc_seg: 88.0485, aux.loss_ce: 0.1198, aux.acc_seg: 87.8328, loss: 0.4156 +2024-06-16 07:18:10,328 - mmseg - INFO - Iter [20100/80000] lr: 2.995e-05, eta: 1 day, 5:25:22, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3005, decode.acc_seg: 87.9078, aux.loss_ce: 0.1235, aux.acc_seg: 87.5387, loss: 0.4240 +2024-06-16 07:19:31,427 - mmseg - INFO - Iter [20150/80000] lr: 2.993e-05, eta: 1 day, 5:23:32, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2770, decode.acc_seg: 87.9855, aux.loss_ce: 0.1134, aux.acc_seg: 87.7979, loss: 0.3904 +2024-06-16 07:20:52,626 - mmseg - INFO - Iter [20200/80000] lr: 2.990e-05, eta: 1 day, 5:21:42, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2934, decode.acc_seg: 88.0637, aux.loss_ce: 0.1200, aux.acc_seg: 87.7693, loss: 0.4135 +2024-06-16 07:22:16,389 - mmseg - INFO - Iter [20250/80000] lr: 2.988e-05, eta: 1 day, 5:20:00, time: 1.675, data_time: 0.062, memory: 71384, decode.loss_ce: 0.2718, decode.acc_seg: 89.2235, aux.loss_ce: 0.1121, aux.acc_seg: 88.8760, loss: 0.3840 +2024-06-16 07:23:37,519 - mmseg - INFO - Iter [20300/80000] lr: 2.985e-05, eta: 1 day, 5:18:10, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2816, decode.acc_seg: 88.2506, aux.loss_ce: 0.1151, aux.acc_seg: 87.9969, loss: 0.3967 +2024-06-16 07:24:58,831 - mmseg - INFO - Iter [20350/80000] lr: 2.983e-05, eta: 1 day, 5:16:21, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2917, decode.acc_seg: 88.1528, aux.loss_ce: 0.1180, aux.acc_seg: 87.9297, loss: 0.4097 +2024-06-16 07:26:20,107 - mmseg - INFO - Iter [20400/80000] lr: 2.980e-05, eta: 1 day, 5:14:32, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2698, decode.acc_seg: 89.0520, aux.loss_ce: 0.1097, aux.acc_seg: 88.7314, loss: 0.3795 +2024-06-16 07:27:41,294 - mmseg - INFO - Iter [20450/80000] lr: 2.978e-05, eta: 1 day, 5:12:43, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2904, decode.acc_seg: 88.3928, aux.loss_ce: 0.1179, aux.acc_seg: 88.1944, loss: 0.4083 +2024-06-16 07:29:02,354 - mmseg - INFO - Iter [20500/80000] lr: 2.975e-05, eta: 1 day, 5:10:54, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2797, decode.acc_seg: 88.4552, aux.loss_ce: 0.1134, aux.acc_seg: 88.3748, loss: 0.3931 +2024-06-16 07:30:23,667 - mmseg - INFO - Iter [20550/80000] lr: 2.973e-05, eta: 1 day, 5:09:06, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2793, decode.acc_seg: 88.4029, aux.loss_ce: 0.1137, aux.acc_seg: 88.2069, loss: 0.3930 +2024-06-16 07:31:44,877 - mmseg - INFO - Iter [20600/80000] lr: 2.970e-05, eta: 1 day, 5:07:17, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2818, decode.acc_seg: 88.4698, aux.loss_ce: 0.1147, aux.acc_seg: 88.2324, loss: 0.3964 +2024-06-16 07:33:06,076 - mmseg - INFO - Iter [20650/80000] lr: 2.968e-05, eta: 1 day, 5:05:28, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2813, decode.acc_seg: 88.6346, aux.loss_ce: 0.1142, aux.acc_seg: 88.4350, loss: 0.3954 +2024-06-16 07:34:27,131 - mmseg - INFO - Iter [20700/80000] lr: 2.965e-05, eta: 1 day, 5:03:40, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2800, decode.acc_seg: 88.5454, aux.loss_ce: 0.1133, aux.acc_seg: 88.4594, loss: 0.3933 +2024-06-16 07:35:59,457 - mmseg - INFO - Iter [20750/80000] lr: 2.963e-05, eta: 1 day, 5:02:23, time: 1.847, data_time: 0.227, memory: 71384, decode.loss_ce: 0.2783, decode.acc_seg: 88.8511, aux.loss_ce: 0.1145, aux.acc_seg: 88.4673, loss: 0.3929 +2024-06-16 07:37:20,661 - mmseg - INFO - Iter [20800/80000] lr: 2.960e-05, eta: 1 day, 5:00:35, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2762, decode.acc_seg: 88.3475, aux.loss_ce: 0.1131, aux.acc_seg: 88.1395, loss: 0.3893 +2024-06-16 07:38:41,790 - mmseg - INFO - Iter [20850/80000] lr: 2.958e-05, eta: 1 day, 4:58:47, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2790, decode.acc_seg: 88.9023, aux.loss_ce: 0.1135, aux.acc_seg: 88.7253, loss: 0.3925 +2024-06-16 07:40:03,078 - mmseg - INFO - Iter [20900/80000] lr: 2.955e-05, eta: 1 day, 4:56:59, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2817, decode.acc_seg: 89.1076, aux.loss_ce: 0.1137, aux.acc_seg: 88.9394, loss: 0.3954 +2024-06-16 07:41:24,305 - mmseg - INFO - Iter [20950/80000] lr: 2.953e-05, eta: 1 day, 4:55:11, time: 1.625, data_time: 0.011, memory: 71384, decode.loss_ce: 0.2897, decode.acc_seg: 88.0936, aux.loss_ce: 0.1178, aux.acc_seg: 87.9379, loss: 0.4075 +2024-06-16 07:42:45,618 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 07:42:45,618 - mmseg - INFO - Iter [21000/80000] lr: 2.950e-05, eta: 1 day, 4:53:24, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2773, decode.acc_seg: 88.4833, aux.loss_ce: 0.1123, aux.acc_seg: 88.3258, loss: 0.3896 +2024-06-16 07:44:24,402 - mmseg - INFO - per class results: +2024-06-16 07:44:24,408 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.17 | 89.16 | +| building | 84.66 | 93.04 | +| sky | 94.78 | 97.8 | +| floor | 84.25 | 90.3 | +| tree | 76.81 | 90.13 | +| ceiling | 86.92 | 93.01 | +| road | 85.87 | 91.68 | +| bed | 92.04 | 95.93 | +| windowpane | 66.42 | 84.11 | +| grass | 67.94 | 83.92 | +| cabinet | 64.86 | 76.93 | +| sidewalk | 70.4 | 84.5 | +| person | 84.87 | 93.99 | +| earth | 38.45 | 50.77 | +| door | 59.39 | 78.84 | +| table | 64.19 | 75.6 | +| mountain | 60.78 | 72.87 | +| plant | 57.9 | 70.49 | +| curtain | 78.2 | 91.16 | +| chair | 63.35 | 70.93 | +| car | 86.61 | 92.44 | +| water | 65.04 | 79.55 | +| painting | 78.36 | 89.12 | +| sofa | 76.4 | 83.73 | +| shelf | 46.08 | 60.91 | +| house | 45.48 | 53.69 | +| sea | 74.33 | 91.25 | +| mirror | 77.06 | 85.28 | +| rug | 70.67 | 82.99 | +| field | 37.07 | 57.8 | +| armchair | 57.3 | 84.21 | +| seat | 66.03 | 90.55 | +| fence | 44.83 | 68.83 | +| desk | 54.89 | 77.18 | +| rock | 49.48 | 63.61 | +| wardrobe | 52.12 | 66.57 | +| lamp | 74.08 | 84.39 | +| bathtub | 90.17 | 94.04 | +| railing | 41.87 | 53.75 | +| cushion | 69.14 | 79.9 | +| base | 37.79 | 70.85 | +| box | 31.57 | 40.05 | +| column | 53.36 | 63.74 | +| signboard | 38.11 | 50.55 | +| chest of drawers | 34.5 | 44.31 | +| counter | 43.6 | 55.66 | +| sand | 57.14 | 79.42 | +| sink | 77.9 | 85.19 | +| skyscraper | 50.68 | 65.29 | +| fireplace | 68.22 | 94.92 | +| refrigerator | 81.34 | 93.09 | +| grandstand | 50.5 | 85.86 | +| path | 32.37 | 44.05 | +| stairs | 31.77 | 39.44 | +| runway | 71.64 | 94.56 | +| case | 56.83 | 69.15 | +| pool table | 92.22 | 98.49 | +| pillow | 68.34 | 87.04 | +| screen door | 80.67 | 85.3 | +| stairway | 48.37 | 59.89 | +| river | 19.2 | 29.58 | +| bridge | 70.8 | 82.85 | +| bookcase | 38.22 | 69.9 | +| blind | 38.81 | 41.28 | +| coffee table | 62.98 | 89.44 | +| toilet | 89.23 | 92.54 | +| flower | 47.29 | 59.89 | +| book | 49.99 | 70.29 | +| hill | 4.47 | 8.52 | +| bench | 62.82 | 73.09 | +| countertop | 57.93 | 74.56 | +| stove | 82.59 | 92.2 | +| palm | 53.62 | 77.23 | +| kitchen island | 45.14 | 92.91 | +| computer | 79.4 | 91.17 | +| swivel chair | 54.7 | 88.71 | +| boat | 80.97 | 86.64 | +| bar | 64.49 | 75.68 | +| arcade machine | 73.95 | 79.15 | +| hovel | 35.18 | 38.58 | +| bus | 90.36 | 93.34 | +| towel | 78.22 | 87.76 | +| light | 58.15 | 64.56 | +| truck | 45.77 | 61.51 | +| tower | 24.25 | 44.2 | +| chandelier | 70.6 | 78.08 | +| awning | 33.24 | 38.1 | +| streetlight | 29.84 | 36.72 | +| booth | 60.0 | 67.61 | +| television receiver | 74.64 | 93.28 | +| airplane | 87.96 | 94.57 | +| dirt track | 7.12 | 30.95 | +| apparel | 53.48 | 74.48 | +| pole | 22.83 | 30.07 | +| land | 0.0 | 0.0 | +| bannister | 17.49 | 24.0 | +| escalator | 54.99 | 87.91 | +| ottoman | 48.46 | 66.9 | +| bottle | 26.04 | 29.7 | +| buffet | 61.32 | 89.81 | +| poster | 29.23 | 39.23 | +| stage | 23.85 | 43.63 | +| van | 44.41 | 72.13 | +| ship | 8.47 | 8.48 | +| fountain | 38.62 | 39.17 | +| conveyer belt | 68.33 | 97.46 | +| canopy | 52.8 | 74.82 | +| washer | 83.2 | 87.17 | +| plaything | 30.66 | 56.29 | +| swimming pool | 61.72 | 88.8 | +| stool | 35.38 | 80.64 | +| barrel | 50.21 | 65.07 | +| basket | 42.21 | 70.37 | +| waterfall | 50.59 | 55.67 | +| tent | 96.68 | 97.88 | +| bag | 28.41 | 45.99 | +| minibike | 73.23 | 82.63 | +| cradle | 83.27 | 98.15 | +| oven | 66.51 | 78.63 | +| ball | 28.0 | 29.33 | +| food | 62.54 | 66.06 | +| step | 10.62 | 12.77 | +| tank | 61.94 | 70.45 | +| trade name | 3.53 | 3.72 | +| microwave | 88.9 | 95.96 | +| pot | 54.74 | 65.14 | +| animal | 66.24 | 68.98 | +| bicycle | 56.98 | 74.03 | +| lake | 51.19 | 68.52 | +| dishwasher | 67.26 | 84.28 | +| screen | 51.76 | 74.92 | +| blanket | 32.36 | 38.2 | +| sculpture | 73.14 | 87.59 | +| hood | 66.54 | 79.16 | +| sconce | 58.89 | 65.51 | +| vase | 45.27 | 63.28 | +| traffic light | 32.58 | 59.42 | +| tray | 14.05 | 15.26 | +| ashcan | 45.72 | 69.53 | +| fan | 68.76 | 79.9 | +| pier | 37.26 | 42.19 | +| crt screen | 10.05 | 17.79 | +| plate | 59.84 | 82.4 | +| monitor | 56.97 | 67.59 | +| bulletin board | 59.13 | 71.57 | +| shower | 0.0 | 0.0 | +| radiator | 64.31 | 82.22 | +| glass | 18.86 | 20.02 | +| clock | 45.71 | 49.39 | +| flag | 69.47 | 77.11 | ++---------------------+-------+-------+ +2024-06-16 07:44:24,408 - mmseg - INFO - Summary: +2024-06-16 07:44:24,408 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.59 | 55.43 | 68.63 | ++-------+-------+-------+ +2024-06-16 07:44:24,409 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 07:44:24,409 - mmseg - INFO - Iter(val) [250] aAcc: 0.8559, mIoU: 0.5543, mAcc: 0.6863, IoU.wall: 0.8117, IoU.building: 0.8466, IoU.sky: 0.9478, IoU.floor: 0.8425, IoU.tree: 0.7681, IoU.ceiling: 0.8692, IoU.road: 0.8587, IoU.bed : 0.9204, IoU.windowpane: 0.6642, IoU.grass: 0.6794, IoU.cabinet: 0.6486, IoU.sidewalk: 0.7040, IoU.person: 0.8487, IoU.earth: 0.3845, IoU.door: 0.5939, IoU.table: 0.6419, IoU.mountain: 0.6078, IoU.plant: 0.5790, IoU.curtain: 0.7820, IoU.chair: 0.6335, IoU.car: 0.8661, IoU.water: 0.6504, IoU.painting: 0.7836, IoU.sofa: 0.7640, IoU.shelf: 0.4608, IoU.house: 0.4548, IoU.sea: 0.7433, IoU.mirror: 0.7706, IoU.rug: 0.7067, IoU.field: 0.3707, IoU.armchair: 0.5730, IoU.seat: 0.6603, IoU.fence: 0.4483, IoU.desk: 0.5489, IoU.rock: 0.4948, IoU.wardrobe: 0.5212, IoU.lamp: 0.7408, IoU.bathtub: 0.9017, IoU.railing: 0.4187, IoU.cushion: 0.6914, IoU.base: 0.3779, IoU.box: 0.3157, IoU.column: 0.5336, IoU.signboard: 0.3811, IoU.chest of drawers: 0.3450, IoU.counter: 0.4360, IoU.sand: 0.5714, IoU.sink: 0.7790, IoU.skyscraper: 0.5068, IoU.fireplace: 0.6822, IoU.refrigerator: 0.8134, IoU.grandstand: 0.5050, IoU.path: 0.3237, IoU.stairs: 0.3177, IoU.runway: 0.7164, IoU.case: 0.5683, IoU.pool table: 0.9222, IoU.pillow: 0.6834, IoU.screen door: 0.8067, IoU.stairway: 0.4837, IoU.river: 0.1920, IoU.bridge: 0.7080, IoU.bookcase: 0.3822, IoU.blind: 0.3881, IoU.coffee table: 0.6298, IoU.toilet: 0.8923, IoU.flower: 0.4729, IoU.book: 0.4999, IoU.hill: 0.0447, IoU.bench: 0.6282, IoU.countertop: 0.5793, IoU.stove: 0.8259, IoU.palm: 0.5362, IoU.kitchen island: 0.4514, IoU.computer: 0.7940, IoU.swivel chair: 0.5470, IoU.boat: 0.8097, IoU.bar: 0.6449, IoU.arcade machine: 0.7395, IoU.hovel: 0.3518, IoU.bus: 0.9036, IoU.towel: 0.7822, IoU.light: 0.5815, IoU.truck: 0.4577, IoU.tower: 0.2425, IoU.chandelier: 0.7060, IoU.awning: 0.3324, IoU.streetlight: 0.2984, IoU.booth: 0.6000, IoU.television receiver: 0.7464, IoU.airplane: 0.8796, IoU.dirt track: 0.0712, IoU.apparel: 0.5348, IoU.pole: 0.2283, IoU.land: 0.0000, IoU.bannister: 0.1749, IoU.escalator: 0.5499, IoU.ottoman: 0.4846, IoU.bottle: 0.2604, IoU.buffet: 0.6132, IoU.poster: 0.2923, IoU.stage: 0.2385, IoU.van: 0.4441, IoU.ship: 0.0847, IoU.fountain: 0.3862, IoU.conveyer belt: 0.6833, IoU.canopy: 0.5280, IoU.washer: 0.8320, IoU.plaything: 0.3066, IoU.swimming pool: 0.6172, IoU.stool: 0.3538, IoU.barrel: 0.5021, IoU.basket: 0.4221, IoU.waterfall: 0.5059, IoU.tent: 0.9668, IoU.bag: 0.2841, IoU.minibike: 0.7323, IoU.cradle: 0.8327, IoU.oven: 0.6651, IoU.ball: 0.2800, IoU.food: 0.6254, IoU.step: 0.1062, IoU.tank: 0.6194, IoU.trade name: 0.0353, IoU.microwave: 0.8890, IoU.pot: 0.5474, IoU.animal: 0.6624, IoU.bicycle: 0.5698, IoU.lake: 0.5119, IoU.dishwasher: 0.6726, IoU.screen: 0.5176, IoU.blanket: 0.3236, IoU.sculpture: 0.7314, IoU.hood: 0.6654, IoU.sconce: 0.5889, IoU.vase: 0.4527, IoU.traffic light: 0.3258, IoU.tray: 0.1405, IoU.ashcan: 0.4572, IoU.fan: 0.6876, IoU.pier: 0.3726, IoU.crt screen: 0.1005, IoU.plate: 0.5984, IoU.monitor: 0.5697, IoU.bulletin board: 0.5913, IoU.shower: 0.0000, IoU.radiator: 0.6431, IoU.glass: 0.1886, IoU.clock: 0.4571, IoU.flag: 0.6947, Acc.wall: 0.8916, Acc.building: 0.9304, Acc.sky: 0.9780, Acc.floor: 0.9030, Acc.tree: 0.9013, Acc.ceiling: 0.9301, Acc.road: 0.9168, Acc.bed : 0.9593, Acc.windowpane: 0.8411, Acc.grass: 0.8392, Acc.cabinet: 0.7693, Acc.sidewalk: 0.8450, Acc.person: 0.9399, Acc.earth: 0.5077, Acc.door: 0.7884, Acc.table: 0.7560, Acc.mountain: 0.7287, Acc.plant: 0.7049, Acc.curtain: 0.9116, Acc.chair: 0.7093, Acc.car: 0.9244, Acc.water: 0.7955, Acc.painting: 0.8912, Acc.sofa: 0.8373, Acc.shelf: 0.6091, Acc.house: 0.5369, Acc.sea: 0.9125, Acc.mirror: 0.8528, Acc.rug: 0.8299, Acc.field: 0.5780, Acc.armchair: 0.8421, Acc.seat: 0.9055, Acc.fence: 0.6883, Acc.desk: 0.7718, Acc.rock: 0.6361, Acc.wardrobe: 0.6657, Acc.lamp: 0.8439, Acc.bathtub: 0.9404, Acc.railing: 0.5375, Acc.cushion: 0.7990, Acc.base: 0.7085, Acc.box: 0.4005, Acc.column: 0.6374, Acc.signboard: 0.5055, Acc.chest of drawers: 0.4431, Acc.counter: 0.5566, Acc.sand: 0.7942, Acc.sink: 0.8519, Acc.skyscraper: 0.6529, Acc.fireplace: 0.9492, Acc.refrigerator: 0.9309, Acc.grandstand: 0.8586, Acc.path: 0.4405, Acc.stairs: 0.3944, Acc.runway: 0.9456, Acc.case: 0.6915, Acc.pool table: 0.9849, Acc.pillow: 0.8704, Acc.screen door: 0.8530, Acc.stairway: 0.5989, Acc.river: 0.2958, Acc.bridge: 0.8285, Acc.bookcase: 0.6990, Acc.blind: 0.4128, Acc.coffee table: 0.8944, Acc.toilet: 0.9254, Acc.flower: 0.5989, Acc.book: 0.7029, Acc.hill: 0.0852, Acc.bench: 0.7309, Acc.countertop: 0.7456, Acc.stove: 0.9220, Acc.palm: 0.7723, Acc.kitchen island: 0.9291, Acc.computer: 0.9117, Acc.swivel chair: 0.8871, Acc.boat: 0.8664, Acc.bar: 0.7568, Acc.arcade machine: 0.7915, Acc.hovel: 0.3858, Acc.bus: 0.9334, Acc.towel: 0.8776, Acc.light: 0.6456, Acc.truck: 0.6151, Acc.tower: 0.4420, Acc.chandelier: 0.7808, Acc.awning: 0.3810, Acc.streetlight: 0.3672, Acc.booth: 0.6761, Acc.television receiver: 0.9328, Acc.airplane: 0.9457, Acc.dirt track: 0.3095, Acc.apparel: 0.7448, Acc.pole: 0.3007, Acc.land: 0.0000, Acc.bannister: 0.2400, Acc.escalator: 0.8791, Acc.ottoman: 0.6690, Acc.bottle: 0.2970, Acc.buffet: 0.8981, Acc.poster: 0.3923, Acc.stage: 0.4363, Acc.van: 0.7213, Acc.ship: 0.0848, Acc.fountain: 0.3917, Acc.conveyer belt: 0.9746, Acc.canopy: 0.7482, Acc.washer: 0.8717, Acc.plaything: 0.5629, Acc.swimming pool: 0.8880, Acc.stool: 0.8064, Acc.barrel: 0.6507, Acc.basket: 0.7037, Acc.waterfall: 0.5567, Acc.tent: 0.9788, Acc.bag: 0.4599, Acc.minibike: 0.8263, Acc.cradle: 0.9815, Acc.oven: 0.7863, Acc.ball: 0.2933, Acc.food: 0.6606, Acc.step: 0.1277, Acc.tank: 0.7045, Acc.trade name: 0.0372, Acc.microwave: 0.9596, Acc.pot: 0.6514, Acc.animal: 0.6898, Acc.bicycle: 0.7403, Acc.lake: 0.6852, Acc.dishwasher: 0.8428, Acc.screen: 0.7492, Acc.blanket: 0.3820, Acc.sculpture: 0.8759, Acc.hood: 0.7916, Acc.sconce: 0.6551, Acc.vase: 0.6328, Acc.traffic light: 0.5942, Acc.tray: 0.1526, Acc.ashcan: 0.6953, Acc.fan: 0.7990, Acc.pier: 0.4219, Acc.crt screen: 0.1779, Acc.plate: 0.8240, Acc.monitor: 0.6759, Acc.bulletin board: 0.7157, Acc.shower: 0.0000, Acc.radiator: 0.8222, Acc.glass: 0.2002, Acc.clock: 0.4939, Acc.flag: 0.7711 +2024-06-16 07:45:46,010 - mmseg - INFO - Iter [21050/80000] lr: 2.948e-05, eta: 1 day, 4:56:14, time: 3.608, data_time: 1.994, memory: 71384, decode.loss_ce: 0.2713, decode.acc_seg: 89.0137, aux.loss_ce: 0.1102, aux.acc_seg: 88.8170, loss: 0.3815 +2024-06-16 07:47:07,370 - mmseg - INFO - Iter [21100/80000] lr: 2.945e-05, eta: 1 day, 4:54:26, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2857, decode.acc_seg: 88.3168, aux.loss_ce: 0.1158, aux.acc_seg: 88.1820, loss: 0.4015 +2024-06-16 07:48:28,363 - mmseg - INFO - Iter [21150/80000] lr: 2.943e-05, eta: 1 day, 4:52:37, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2732, decode.acc_seg: 88.2655, aux.loss_ce: 0.1112, aux.acc_seg: 88.1309, loss: 0.3844 +2024-06-16 07:49:49,557 - mmseg - INFO - Iter [21200/80000] lr: 2.940e-05, eta: 1 day, 4:50:49, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2868, decode.acc_seg: 88.0178, aux.loss_ce: 0.1159, aux.acc_seg: 87.8465, loss: 0.4027 +2024-06-16 07:51:10,717 - mmseg - INFO - Iter [21250/80000] lr: 2.938e-05, eta: 1 day, 4:49:01, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2638, decode.acc_seg: 89.0982, aux.loss_ce: 0.1072, aux.acc_seg: 88.8605, loss: 0.3709 +2024-06-16 07:52:31,964 - mmseg - INFO - Iter [21300/80000] lr: 2.935e-05, eta: 1 day, 4:47:14, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3081, decode.acc_seg: 87.5067, aux.loss_ce: 0.1249, aux.acc_seg: 87.3884, loss: 0.4329 +2024-06-16 07:53:53,178 - mmseg - INFO - Iter [21350/80000] lr: 2.933e-05, eta: 1 day, 4:45:26, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2808, decode.acc_seg: 88.9631, aux.loss_ce: 0.1140, aux.acc_seg: 88.6910, loss: 0.3948 +2024-06-16 07:55:14,172 - mmseg - INFO - Iter [21400/80000] lr: 2.930e-05, eta: 1 day, 4:43:38, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2762, decode.acc_seg: 88.5899, aux.loss_ce: 0.1125, aux.acc_seg: 88.3495, loss: 0.3887 +2024-06-16 07:56:35,479 - mmseg - INFO - Iter [21450/80000] lr: 2.928e-05, eta: 1 day, 4:41:51, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2972, decode.acc_seg: 87.9065, aux.loss_ce: 0.1206, aux.acc_seg: 87.6889, loss: 0.4178 +2024-06-16 07:57:58,836 - mmseg - INFO - Iter [21500/80000] lr: 2.925e-05, eta: 1 day, 4:40:09, time: 1.667, data_time: 0.055, memory: 71384, decode.loss_ce: 0.2690, decode.acc_seg: 89.1182, aux.loss_ce: 0.1092, aux.acc_seg: 88.8444, loss: 0.3782 +2024-06-16 07:59:20,031 - mmseg - INFO - Iter [21550/80000] lr: 2.923e-05, eta: 1 day, 4:38:22, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2702, decode.acc_seg: 88.5651, aux.loss_ce: 0.1104, aux.acc_seg: 88.3292, loss: 0.3807 +2024-06-16 08:00:41,118 - mmseg - INFO - Iter [21600/80000] lr: 2.920e-05, eta: 1 day, 4:36:34, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2607, decode.acc_seg: 89.2253, aux.loss_ce: 0.1069, aux.acc_seg: 88.9699, loss: 0.3676 +2024-06-16 08:02:02,326 - mmseg - INFO - Iter [21650/80000] lr: 2.918e-05, eta: 1 day, 4:34:47, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2675, decode.acc_seg: 89.2217, aux.loss_ce: 0.1087, aux.acc_seg: 89.1176, loss: 0.3762 +2024-06-16 08:03:23,508 - mmseg - INFO - Iter [21700/80000] lr: 2.915e-05, eta: 1 day, 4:33:01, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2390, decode.acc_seg: 89.7011, aux.loss_ce: 0.0984, aux.acc_seg: 89.4770, loss: 0.3374 +2024-06-16 08:04:44,591 - mmseg - INFO - Iter [21750/80000] lr: 2.913e-05, eta: 1 day, 4:31:13, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2790, decode.acc_seg: 88.0834, aux.loss_ce: 0.1146, aux.acc_seg: 87.7862, loss: 0.3936 +2024-06-16 08:06:05,820 - mmseg - INFO - Iter [21800/80000] lr: 2.910e-05, eta: 1 day, 4:29:27, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2713, decode.acc_seg: 88.5567, aux.loss_ce: 0.1087, aux.acc_seg: 88.4803, loss: 0.3801 +2024-06-16 08:07:26,986 - mmseg - INFO - Iter [21850/80000] lr: 2.908e-05, eta: 1 day, 4:27:40, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2726, decode.acc_seg: 88.9869, aux.loss_ce: 0.1110, aux.acc_seg: 88.8101, loss: 0.3836 +2024-06-16 08:08:48,130 - mmseg - INFO - Iter [21900/80000] lr: 2.905e-05, eta: 1 day, 4:25:54, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2669, decode.acc_seg: 88.9362, aux.loss_ce: 0.1091, aux.acc_seg: 88.6944, loss: 0.3760 +2024-06-16 08:10:09,373 - mmseg - INFO - Iter [21950/80000] lr: 2.903e-05, eta: 1 day, 4:24:08, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2655, decode.acc_seg: 89.2267, aux.loss_ce: 0.1092, aux.acc_seg: 88.8611, loss: 0.3747 +2024-06-16 08:11:30,592 - mmseg - INFO - Saving checkpoint at 22000 iterations +2024-06-16 08:12:58,635 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 08:12:58,635 - mmseg - INFO - Iter [22000/80000] lr: 2.900e-05, eta: 1 day, 4:26:13, time: 3.385, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2606, decode.acc_seg: 89.3679, aux.loss_ce: 0.1060, aux.acc_seg: 89.2452, loss: 0.3666 +2024-06-16 08:14:35,178 - mmseg - INFO - per class results: +2024-06-16 08:14:35,184 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.88 | 89.29 | +| building | 85.2 | 93.15 | +| sky | 94.88 | 97.72 | +| floor | 84.37 | 90.65 | +| tree | 77.31 | 88.0 | +| ceiling | 86.13 | 91.66 | +| road | 86.96 | 92.3 | +| bed | 92.05 | 96.88 | +| windowpane | 65.69 | 78.82 | +| grass | 67.14 | 81.35 | +| cabinet | 62.17 | 70.68 | +| sidewalk | 71.64 | 85.64 | +| person | 85.08 | 93.32 | +| earth | 31.72 | 39.45 | +| door | 58.22 | 74.62 | +| table | 64.76 | 74.18 | +| mountain | 59.96 | 76.95 | +| plant | 55.11 | 61.21 | +| curtain | 79.58 | 90.75 | +| chair | 63.34 | 70.85 | +| car | 86.71 | 91.95 | +| water | 60.29 | 77.85 | +| painting | 75.06 | 93.52 | +| sofa | 78.51 | 87.95 | +| shelf | 47.12 | 65.32 | +| house | 57.75 | 72.28 | +| sea | 75.07 | 90.83 | +| mirror | 79.79 | 89.49 | +| rug | 67.65 | 87.45 | +| field | 33.57 | 73.88 | +| armchair | 57.57 | 85.55 | +| seat | 66.75 | 90.06 | +| fence | 53.05 | 67.52 | +| desk | 51.15 | 81.78 | +| rock | 52.88 | 67.32 | +| wardrobe | 55.01 | 82.43 | +| lamp | 71.3 | 89.68 | +| bathtub | 84.09 | 88.7 | +| railing | 45.89 | 62.21 | +| cushion | 68.43 | 79.0 | +| base | 39.38 | 67.3 | +| box | 32.45 | 40.17 | +| column | 57.33 | 79.13 | +| signboard | 42.77 | 59.1 | +| chest of drawers | 41.24 | 87.29 | +| counter | 45.86 | 72.01 | +| sand | 57.14 | 87.53 | +| sink | 78.89 | 84.02 | +| skyscraper | 49.39 | 63.15 | +| fireplace | 70.41 | 96.98 | +| refrigerator | 81.64 | 92.14 | +| grandstand | 50.8 | 83.93 | +| path | 30.94 | 43.55 | +| stairs | 32.55 | 40.05 | +| runway | 74.15 | 97.92 | +| case | 54.87 | 67.07 | +| pool table | 93.9 | 98.32 | +| pillow | 68.83 | 81.12 | +| screen door | 85.8 | 88.96 | +| stairway | 48.53 | 63.69 | +| river | 16.27 | 33.46 | +| bridge | 72.08 | 89.5 | +| bookcase | 36.86 | 65.13 | +| blind | 40.38 | 42.19 | +| coffee table | 59.73 | 87.47 | +| toilet | 90.3 | 95.58 | +| flower | 42.68 | 51.16 | +| book | 51.88 | 72.24 | +| hill | 5.49 | 12.01 | +| bench | 54.91 | 61.54 | +| countertop | 63.51 | 84.38 | +| stove | 84.32 | 90.27 | +| palm | 56.67 | 78.92 | +| kitchen island | 42.35 | 77.6 | +| computer | 79.62 | 91.08 | +| swivel chair | 55.45 | 83.95 | +| boat | 58.78 | 66.89 | +| bar | 65.73 | 70.09 | +| arcade machine | 83.72 | 90.0 | +| hovel | 16.82 | 18.18 | +| bus | 89.92 | 97.18 | +| towel | 71.94 | 78.72 | +| light | 55.85 | 60.74 | +| truck | 48.56 | 58.7 | +| tower | 7.58 | 9.78 | +| chandelier | 71.89 | 89.3 | +| awning | 38.75 | 47.99 | +| streetlight | 35.86 | 51.99 | +| booth | 35.43 | 54.89 | +| television receiver | 75.73 | 85.61 | +| airplane | 87.66 | 96.29 | +| dirt track | 8.37 | 21.89 | +| apparel | 55.96 | 84.87 | +| pole | 26.37 | 40.75 | +| land | 3.72 | 15.36 | +| bannister | 15.45 | 23.9 | +| escalator | 65.16 | 84.6 | +| ottoman | 50.05 | 71.17 | +| bottle | 28.88 | 33.21 | +| buffet | 53.81 | 60.21 | +| poster | 35.43 | 42.07 | +| stage | 21.71 | 38.9 | +| van | 46.74 | 72.4 | +| ship | 8.94 | 8.95 | +| fountain | 35.71 | 36.7 | +| conveyer belt | 82.42 | 92.83 | +| canopy | 39.63 | 68.77 | +| washer | 82.1 | 88.65 | +| plaything | 27.44 | 45.74 | +| swimming pool | 61.24 | 88.0 | +| stool | 42.71 | 73.58 | +| barrel | 51.54 | 65.07 | +| basket | 41.55 | 55.76 | +| waterfall | 51.86 | 58.29 | +| tent | 93.38 | 98.93 | +| bag | 12.02 | 12.43 | +| minibike | 72.09 | 87.74 | +| cradle | 79.87 | 98.9 | +| oven | 63.6 | 74.77 | +| ball | 45.53 | 79.82 | +| food | 63.34 | 79.45 | +| step | 12.66 | 13.53 | +| tank | 61.46 | 66.84 | +| trade name | 23.79 | 28.92 | +| microwave | 90.01 | 95.44 | +| pot | 57.91 | 69.75 | +| animal | 62.78 | 64.85 | +| bicycle | 59.51 | 75.27 | +| lake | 45.67 | 63.66 | +| dishwasher | 73.26 | 78.32 | +| screen | 58.54 | 96.88 | +| blanket | 21.34 | 24.9 | +| sculpture | 53.89 | 91.32 | +| hood | 49.17 | 50.17 | +| sconce | 59.57 | 74.7 | +| vase | 45.28 | 63.72 | +| traffic light | 34.38 | 58.71 | +| tray | 15.3 | 16.63 | +| ashcan | 52.21 | 67.88 | +| fan | 67.82 | 81.77 | +| pier | 39.54 | 44.97 | +| crt screen | 8.63 | 9.84 | +| plate | 59.82 | 82.81 | +| monitor | 63.57 | 80.08 | +| bulletin board | 60.09 | 67.16 | +| shower | 0.0 | 0.0 | +| radiator | 69.89 | 82.12 | +| glass | 17.56 | 18.48 | +| clock | 46.51 | 54.63 | +| flag | 69.63 | 80.88 | ++---------------------+-------+-------+ +2024-06-16 08:14:35,185 - mmseg - INFO - Summary: +2024-06-16 08:14:35,185 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.39 | 55.33 | 69.04 | ++-------+-------+-------+ +2024-06-16 08:14:35,185 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 08:14:35,186 - mmseg - INFO - Iter(val) [250] aAcc: 0.8539, mIoU: 0.5533, mAcc: 0.6904, IoU.wall: 0.8088, IoU.building: 0.8520, IoU.sky: 0.9488, IoU.floor: 0.8437, IoU.tree: 0.7731, IoU.ceiling: 0.8613, IoU.road: 0.8696, IoU.bed : 0.9205, IoU.windowpane: 0.6569, IoU.grass: 0.6714, IoU.cabinet: 0.6217, IoU.sidewalk: 0.7164, IoU.person: 0.8508, IoU.earth: 0.3172, IoU.door: 0.5822, IoU.table: 0.6476, IoU.mountain: 0.5996, IoU.plant: 0.5511, IoU.curtain: 0.7958, IoU.chair: 0.6334, IoU.car: 0.8671, IoU.water: 0.6029, IoU.painting: 0.7506, IoU.sofa: 0.7851, IoU.shelf: 0.4712, IoU.house: 0.5775, IoU.sea: 0.7507, IoU.mirror: 0.7979, IoU.rug: 0.6765, IoU.field: 0.3357, IoU.armchair: 0.5757, IoU.seat: 0.6675, IoU.fence: 0.5305, IoU.desk: 0.5115, IoU.rock: 0.5288, IoU.wardrobe: 0.5501, IoU.lamp: 0.7130, IoU.bathtub: 0.8409, IoU.railing: 0.4589, IoU.cushion: 0.6843, IoU.base: 0.3938, IoU.box: 0.3245, IoU.column: 0.5733, IoU.signboard: 0.4277, IoU.chest of drawers: 0.4124, IoU.counter: 0.4586, IoU.sand: 0.5714, IoU.sink: 0.7889, IoU.skyscraper: 0.4939, IoU.fireplace: 0.7041, IoU.refrigerator: 0.8164, IoU.grandstand: 0.5080, IoU.path: 0.3094, IoU.stairs: 0.3255, IoU.runway: 0.7415, IoU.case: 0.5487, IoU.pool table: 0.9390, IoU.pillow: 0.6883, IoU.screen door: 0.8580, IoU.stairway: 0.4853, IoU.river: 0.1627, IoU.bridge: 0.7208, IoU.bookcase: 0.3686, IoU.blind: 0.4038, IoU.coffee table: 0.5973, IoU.toilet: 0.9030, IoU.flower: 0.4268, IoU.book: 0.5188, IoU.hill: 0.0549, IoU.bench: 0.5491, IoU.countertop: 0.6351, IoU.stove: 0.8432, IoU.palm: 0.5667, IoU.kitchen island: 0.4235, IoU.computer: 0.7962, IoU.swivel chair: 0.5545, IoU.boat: 0.5878, IoU.bar: 0.6573, IoU.arcade machine: 0.8372, IoU.hovel: 0.1682, IoU.bus: 0.8992, IoU.towel: 0.7194, IoU.light: 0.5585, IoU.truck: 0.4856, IoU.tower: 0.0758, IoU.chandelier: 0.7189, IoU.awning: 0.3875, IoU.streetlight: 0.3586, IoU.booth: 0.3543, IoU.television receiver: 0.7573, IoU.airplane: 0.8766, IoU.dirt track: 0.0837, IoU.apparel: 0.5596, IoU.pole: 0.2637, IoU.land: 0.0372, IoU.bannister: 0.1545, IoU.escalator: 0.6516, IoU.ottoman: 0.5005, IoU.bottle: 0.2888, IoU.buffet: 0.5381, IoU.poster: 0.3543, IoU.stage: 0.2171, IoU.van: 0.4674, IoU.ship: 0.0894, IoU.fountain: 0.3571, IoU.conveyer belt: 0.8242, IoU.canopy: 0.3963, IoU.washer: 0.8210, IoU.plaything: 0.2744, IoU.swimming pool: 0.6124, IoU.stool: 0.4271, IoU.barrel: 0.5154, IoU.basket: 0.4155, IoU.waterfall: 0.5186, IoU.tent: 0.9338, IoU.bag: 0.1202, IoU.minibike: 0.7209, IoU.cradle: 0.7987, IoU.oven: 0.6360, IoU.ball: 0.4553, IoU.food: 0.6334, IoU.step: 0.1266, IoU.tank: 0.6146, IoU.trade name: 0.2379, IoU.microwave: 0.9001, IoU.pot: 0.5791, IoU.animal: 0.6278, IoU.bicycle: 0.5951, IoU.lake: 0.4567, IoU.dishwasher: 0.7326, IoU.screen: 0.5854, IoU.blanket: 0.2134, IoU.sculpture: 0.5389, IoU.hood: 0.4917, IoU.sconce: 0.5957, IoU.vase: 0.4528, IoU.traffic light: 0.3438, IoU.tray: 0.1530, IoU.ashcan: 0.5221, IoU.fan: 0.6782, IoU.pier: 0.3954, IoU.crt screen: 0.0863, IoU.plate: 0.5982, IoU.monitor: 0.6357, IoU.bulletin board: 0.6009, IoU.shower: 0.0000, IoU.radiator: 0.6989, IoU.glass: 0.1756, IoU.clock: 0.4651, IoU.flag: 0.6963, Acc.wall: 0.8929, Acc.building: 0.9315, Acc.sky: 0.9772, Acc.floor: 0.9065, Acc.tree: 0.8800, Acc.ceiling: 0.9166, Acc.road: 0.9230, Acc.bed : 0.9688, Acc.windowpane: 0.7882, Acc.grass: 0.8135, Acc.cabinet: 0.7068, Acc.sidewalk: 0.8564, Acc.person: 0.9332, Acc.earth: 0.3945, Acc.door: 0.7462, Acc.table: 0.7418, Acc.mountain: 0.7695, Acc.plant: 0.6121, Acc.curtain: 0.9075, Acc.chair: 0.7085, Acc.car: 0.9195, Acc.water: 0.7785, Acc.painting: 0.9352, Acc.sofa: 0.8795, Acc.shelf: 0.6532, Acc.house: 0.7228, Acc.sea: 0.9083, Acc.mirror: 0.8949, Acc.rug: 0.8745, Acc.field: 0.7388, Acc.armchair: 0.8555, Acc.seat: 0.9006, Acc.fence: 0.6752, Acc.desk: 0.8178, Acc.rock: 0.6732, Acc.wardrobe: 0.8243, Acc.lamp: 0.8968, Acc.bathtub: 0.8870, Acc.railing: 0.6221, Acc.cushion: 0.7900, Acc.base: 0.6730, Acc.box: 0.4017, Acc.column: 0.7913, Acc.signboard: 0.5910, Acc.chest of drawers: 0.8729, Acc.counter: 0.7201, Acc.sand: 0.8753, Acc.sink: 0.8402, Acc.skyscraper: 0.6315, Acc.fireplace: 0.9698, Acc.refrigerator: 0.9214, Acc.grandstand: 0.8393, Acc.path: 0.4355, Acc.stairs: 0.4005, Acc.runway: 0.9792, Acc.case: 0.6707, Acc.pool table: 0.9832, Acc.pillow: 0.8112, Acc.screen door: 0.8896, Acc.stairway: 0.6369, Acc.river: 0.3346, Acc.bridge: 0.8950, Acc.bookcase: 0.6513, Acc.blind: 0.4219, Acc.coffee table: 0.8747, Acc.toilet: 0.9558, Acc.flower: 0.5116, Acc.book: 0.7224, Acc.hill: 0.1201, Acc.bench: 0.6154, Acc.countertop: 0.8438, Acc.stove: 0.9027, Acc.palm: 0.7892, Acc.kitchen island: 0.7760, Acc.computer: 0.9108, Acc.swivel chair: 0.8395, Acc.boat: 0.6689, Acc.bar: 0.7009, Acc.arcade machine: 0.9000, Acc.hovel: 0.1818, Acc.bus: 0.9718, Acc.towel: 0.7872, Acc.light: 0.6074, Acc.truck: 0.5870, Acc.tower: 0.0978, Acc.chandelier: 0.8930, Acc.awning: 0.4799, Acc.streetlight: 0.5199, Acc.booth: 0.5489, Acc.television receiver: 0.8561, Acc.airplane: 0.9629, Acc.dirt track: 0.2189, Acc.apparel: 0.8487, Acc.pole: 0.4075, Acc.land: 0.1536, Acc.bannister: 0.2390, Acc.escalator: 0.8460, Acc.ottoman: 0.7117, Acc.bottle: 0.3321, Acc.buffet: 0.6021, Acc.poster: 0.4207, Acc.stage: 0.3890, Acc.van: 0.7240, Acc.ship: 0.0895, Acc.fountain: 0.3670, Acc.conveyer belt: 0.9283, Acc.canopy: 0.6877, Acc.washer: 0.8865, Acc.plaything: 0.4574, Acc.swimming pool: 0.8800, Acc.stool: 0.7358, Acc.barrel: 0.6507, Acc.basket: 0.5576, Acc.waterfall: 0.5829, Acc.tent: 0.9893, Acc.bag: 0.1243, Acc.minibike: 0.8774, Acc.cradle: 0.9890, Acc.oven: 0.7477, Acc.ball: 0.7982, Acc.food: 0.7945, Acc.step: 0.1353, Acc.tank: 0.6684, Acc.trade name: 0.2892, Acc.microwave: 0.9544, Acc.pot: 0.6975, Acc.animal: 0.6485, Acc.bicycle: 0.7527, Acc.lake: 0.6366, Acc.dishwasher: 0.7832, Acc.screen: 0.9688, Acc.blanket: 0.2490, Acc.sculpture: 0.9132, Acc.hood: 0.5017, Acc.sconce: 0.7470, Acc.vase: 0.6372, Acc.traffic light: 0.5871, Acc.tray: 0.1663, Acc.ashcan: 0.6788, Acc.fan: 0.8177, Acc.pier: 0.4497, Acc.crt screen: 0.0984, Acc.plate: 0.8281, Acc.monitor: 0.8008, Acc.bulletin board: 0.6716, Acc.shower: 0.0000, Acc.radiator: 0.8212, Acc.glass: 0.1848, Acc.clock: 0.5463, Acc.flag: 0.8088 +2024-06-16 08:15:56,837 - mmseg - INFO - Iter [22050/80000] lr: 2.898e-05, eta: 1 day, 4:28:42, time: 3.564, data_time: 1.948, memory: 71384, decode.loss_ce: 0.2661, decode.acc_seg: 89.0269, aux.loss_ce: 0.1089, aux.acc_seg: 88.7109, loss: 0.3750 +2024-06-16 08:17:18,017 - mmseg - INFO - Iter [22100/80000] lr: 2.895e-05, eta: 1 day, 4:26:54, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2877, decode.acc_seg: 88.6198, aux.loss_ce: 0.1160, aux.acc_seg: 88.4840, loss: 0.4037 +2024-06-16 08:18:39,288 - mmseg - INFO - Iter [22150/80000] lr: 2.893e-05, eta: 1 day, 4:25:07, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2659, decode.acc_seg: 89.1015, aux.loss_ce: 0.1086, aux.acc_seg: 88.8406, loss: 0.3745 +2024-06-16 08:20:00,446 - mmseg - INFO - Iter [22200/80000] lr: 2.890e-05, eta: 1 day, 4:23:20, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.3169, decode.acc_seg: 87.4669, aux.loss_ce: 0.1275, aux.acc_seg: 87.3224, loss: 0.4444 +2024-06-16 08:21:21,625 - mmseg - INFO - Iter [22250/80000] lr: 2.888e-05, eta: 1 day, 4:21:32, time: 1.624, data_time: 0.011, memory: 71384, decode.loss_ce: 0.2788, decode.acc_seg: 88.9155, aux.loss_ce: 0.1131, aux.acc_seg: 88.6324, loss: 0.3920 +2024-06-16 08:22:42,665 - mmseg - INFO - Iter [22300/80000] lr: 2.885e-05, eta: 1 day, 4:19:45, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2808, decode.acc_seg: 88.5105, aux.loss_ce: 0.1154, aux.acc_seg: 88.2192, loss: 0.3962 +2024-06-16 08:24:03,924 - mmseg - INFO - Iter [22350/80000] lr: 2.883e-05, eta: 1 day, 4:17:58, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2740, decode.acc_seg: 88.9105, aux.loss_ce: 0.1122, aux.acc_seg: 88.6425, loss: 0.3862 +2024-06-16 08:25:25,140 - mmseg - INFO - Iter [22400/80000] lr: 2.880e-05, eta: 1 day, 4:16:12, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2705, decode.acc_seg: 88.7667, aux.loss_ce: 0.1105, aux.acc_seg: 88.5090, loss: 0.3810 +2024-06-16 08:26:46,333 - mmseg - INFO - Iter [22450/80000] lr: 2.878e-05, eta: 1 day, 4:14:25, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2994, decode.acc_seg: 88.1867, aux.loss_ce: 0.1214, aux.acc_seg: 87.9566, loss: 0.4207 +2024-06-16 08:28:07,489 - mmseg - INFO - Iter [22500/80000] lr: 2.875e-05, eta: 1 day, 4:12:38, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2807, decode.acc_seg: 88.7157, aux.loss_ce: 0.1130, aux.acc_seg: 88.7203, loss: 0.3937 +2024-06-16 08:29:28,558 - mmseg - INFO - Iter [22550/80000] lr: 2.873e-05, eta: 1 day, 4:10:51, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2697, decode.acc_seg: 89.0113, aux.loss_ce: 0.1098, aux.acc_seg: 88.7772, loss: 0.3795 +2024-06-16 08:30:49,888 - mmseg - INFO - Iter [22600/80000] lr: 2.870e-05, eta: 1 day, 4:09:05, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2667, decode.acc_seg: 89.0467, aux.loss_ce: 0.1096, aux.acc_seg: 88.6550, loss: 0.3763 +2024-06-16 08:32:11,023 - mmseg - INFO - Iter [22650/80000] lr: 2.868e-05, eta: 1 day, 4:07:19, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2778, decode.acc_seg: 88.8367, aux.loss_ce: 0.1143, aux.acc_seg: 88.4763, loss: 0.3922 +2024-06-16 08:33:32,316 - mmseg - INFO - Iter [22700/80000] lr: 2.865e-05, eta: 1 day, 4:05:33, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2703, decode.acc_seg: 89.0686, aux.loss_ce: 0.1106, aux.acc_seg: 88.8681, loss: 0.3808 +2024-06-16 08:34:55,557 - mmseg - INFO - Iter [22750/80000] lr: 2.863e-05, eta: 1 day, 4:03:52, time: 1.665, data_time: 0.051, memory: 71384, decode.loss_ce: 0.2740, decode.acc_seg: 88.5849, aux.loss_ce: 0.1113, aux.acc_seg: 88.4307, loss: 0.3854 +2024-06-16 08:36:16,821 - mmseg - INFO - Iter [22800/80000] lr: 2.860e-05, eta: 1 day, 4:02:07, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2830, decode.acc_seg: 88.5448, aux.loss_ce: 0.1152, aux.acc_seg: 88.4903, loss: 0.3982 +2024-06-16 08:37:37,968 - mmseg - INFO - Iter [22850/80000] lr: 2.858e-05, eta: 1 day, 4:00:21, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2892, decode.acc_seg: 88.5249, aux.loss_ce: 0.1172, aux.acc_seg: 88.4231, loss: 0.4064 +2024-06-16 08:38:59,152 - mmseg - INFO - Iter [22900/80000] lr: 2.855e-05, eta: 1 day, 3:58:35, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2532, decode.acc_seg: 89.3805, aux.loss_ce: 0.1036, aux.acc_seg: 89.1343, loss: 0.3568 +2024-06-16 08:40:20,226 - mmseg - INFO - Iter [22950/80000] lr: 2.853e-05, eta: 1 day, 3:56:49, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2616, decode.acc_seg: 89.4444, aux.loss_ce: 0.1074, aux.acc_seg: 89.1931, loss: 0.3690 +2024-06-16 08:41:41,297 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 08:41:41,298 - mmseg - INFO - Iter [23000/80000] lr: 2.850e-05, eta: 1 day, 3:55:03, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2756, decode.acc_seg: 88.6188, aux.loss_ce: 0.1120, aux.acc_seg: 88.3444, loss: 0.3875 +2024-06-16 08:43:18,444 - mmseg - INFO - per class results: +2024-06-16 08:43:18,450 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 80.97 | 87.45 | +| building | 84.7 | 93.36 | +| sky | 94.87 | 97.1 | +| floor | 84.66 | 90.76 | +| tree | 77.59 | 90.18 | +| ceiling | 86.3 | 94.47 | +| road | 85.21 | 90.37 | +| bed | 92.15 | 96.4 | +| windowpane | 65.54 | 80.18 | +| grass | 69.21 | 83.65 | +| cabinet | 66.79 | 75.94 | +| sidewalk | 69.21 | 86.62 | +| person | 85.07 | 92.96 | +| earth | 34.85 | 46.17 | +| door | 61.6 | 80.15 | +| table | 66.76 | 79.68 | +| mountain | 56.57 | 65.98 | +| plant | 58.07 | 71.2 | +| curtain | 78.72 | 90.56 | +| chair | 66.12 | 76.17 | +| car | 86.91 | 94.16 | +| water | 62.92 | 80.24 | +| painting | 75.76 | 90.47 | +| sofa | 81.37 | 92.33 | +| shelf | 47.29 | 63.51 | +| house | 43.67 | 52.82 | +| sea | 67.1 | 72.67 | +| mirror | 79.76 | 89.21 | +| rug | 69.77 | 82.85 | +| field | 36.22 | 52.01 | +| armchair | 59.79 | 81.31 | +| seat | 67.42 | 89.05 | +| fence | 50.99 | 69.01 | +| desk | 51.89 | 79.03 | +| rock | 53.24 | 80.91 | +| wardrobe | 51.88 | 66.58 | +| lamp | 73.83 | 85.75 | +| bathtub | 90.12 | 93.98 | +| railing | 45.82 | 62.49 | +| cushion | 68.5 | 79.69 | +| base | 46.16 | 64.6 | +| box | 39.35 | 54.81 | +| column | 55.06 | 69.79 | +| signboard | 42.18 | 61.43 | +| chest of drawers | 49.33 | 69.54 | +| counter | 47.27 | 65.27 | +| sand | 53.97 | 90.2 | +| sink | 76.46 | 87.71 | +| skyscraper | 49.3 | 62.19 | +| fireplace | 71.3 | 85.72 | +| refrigerator | 81.59 | 93.4 | +| grandstand | 53.74 | 84.46 | +| path | 28.83 | 44.4 | +| stairs | 35.42 | 40.78 | +| runway | 68.62 | 91.44 | +| case | 56.53 | 79.22 | +| pool table | 94.71 | 97.81 | +| pillow | 70.07 | 84.66 | +| screen door | 81.57 | 88.92 | +| stairway | 51.25 | 66.36 | +| river | 12.26 | 27.43 | +| bridge | 73.96 | 82.52 | +| bookcase | 40.48 | 64.73 | +| blind | 45.78 | 57.42 | +| coffee table | 60.59 | 88.0 | +| toilet | 89.98 | 94.92 | +| flower | 42.4 | 53.92 | +| book | 50.59 | 78.93 | +| hill | 7.41 | 24.72 | +| bench | 55.94 | 63.1 | +| countertop | 62.55 | 80.39 | +| stove | 82.09 | 92.73 | +| palm | 58.03 | 77.66 | +| kitchen island | 54.51 | 83.7 | +| computer | 74.97 | 92.83 | +| swivel chair | 52.21 | 75.84 | +| boat | 65.72 | 83.74 | +| bar | 67.07 | 83.97 | +| arcade machine | 87.63 | 93.3 | +| hovel | 24.47 | 26.12 | +| bus | 92.19 | 96.44 | +| towel | 74.14 | 83.57 | +| light | 57.29 | 64.11 | +| truck | 47.53 | 62.25 | +| tower | 26.4 | 40.13 | +| chandelier | 72.24 | 89.89 | +| awning | 38.84 | 52.29 | +| streetlight | 35.67 | 48.43 | +| booth | 33.18 | 58.74 | +| television receiver | 77.82 | 91.47 | +| airplane | 88.36 | 95.6 | +| dirt track | 7.03 | 38.7 | +| apparel | 51.5 | 80.7 | +| pole | 24.26 | 32.6 | +| land | 2.18 | 4.62 | +| bannister | 17.86 | 27.53 | +| escalator | 62.42 | 88.91 | +| ottoman | 50.87 | 73.23 | +| bottle | 40.75 | 53.98 | +| buffet | 58.89 | 71.97 | +| poster | 31.0 | 42.6 | +| stage | 27.63 | 48.04 | +| van | 50.92 | 65.07 | +| ship | 10.52 | 10.8 | +| fountain | 43.17 | 45.49 | +| conveyer belt | 73.84 | 94.31 | +| canopy | 45.65 | 52.67 | +| washer | 82.31 | 91.5 | +| plaything | 29.6 | 58.18 | +| swimming pool | 55.46 | 80.12 | +| stool | 57.03 | 67.56 | +| barrel | 52.58 | 84.54 | +| basket | 43.6 | 58.3 | +| waterfall | 57.33 | 68.84 | +| tent | 87.66 | 97.53 | +| bag | 28.42 | 36.09 | +| minibike | 74.36 | 87.55 | +| cradle | 79.36 | 98.54 | +| oven | 58.3 | 70.93 | +| ball | 52.29 | 66.96 | +| food | 53.96 | 59.94 | +| step | 18.71 | 22.28 | +| tank | 59.33 | 73.96 | +| trade name | 37.33 | 53.57 | +| microwave | 85.84 | 97.26 | +| pot | 59.04 | 69.44 | +| animal | 58.33 | 59.08 | +| bicycle | 59.98 | 75.7 | +| lake | 52.69 | 77.88 | +| dishwasher | 70.12 | 84.81 | +| screen | 56.06 | 95.39 | +| blanket | 34.01 | 58.38 | +| sculpture | 75.65 | 87.26 | +| hood | 65.69 | 78.45 | +| sconce | 58.64 | 69.91 | +| vase | 46.91 | 64.21 | +| traffic light | 33.59 | 64.21 | +| tray | 15.59 | 21.29 | +| ashcan | 47.15 | 64.24 | +| fan | 66.94 | 84.67 | +| pier | 53.1 | 64.72 | +| crt screen | 0.59 | 0.64 | +| plate | 56.05 | 84.18 | +| monitor | 53.15 | 80.07 | +| bulletin board | 59.13 | 67.07 | +| shower | 0.0 | 0.0 | +| radiator | 68.83 | 78.15 | +| glass | 20.9 | 23.2 | +| clock | 44.71 | 68.26 | +| flag | 69.83 | 79.55 | ++---------------------+-------+-------+ +2024-06-16 08:43:18,450 - mmseg - INFO - Summary: +2024-06-16 08:43:18,450 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.57 | 56.49 | 70.83 | ++-------+-------+-------+ +2024-06-16 08:43:18,451 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 08:43:18,451 - mmseg - INFO - Iter(val) [250] aAcc: 0.8557, mIoU: 0.5649, mAcc: 0.7083, IoU.wall: 0.8097, IoU.building: 0.8470, IoU.sky: 0.9487, IoU.floor: 0.8466, IoU.tree: 0.7759, IoU.ceiling: 0.8630, IoU.road: 0.8521, IoU.bed : 0.9215, IoU.windowpane: 0.6554, IoU.grass: 0.6921, IoU.cabinet: 0.6679, IoU.sidewalk: 0.6921, IoU.person: 0.8507, IoU.earth: 0.3485, IoU.door: 0.6160, IoU.table: 0.6676, IoU.mountain: 0.5657, IoU.plant: 0.5807, IoU.curtain: 0.7872, IoU.chair: 0.6612, IoU.car: 0.8691, IoU.water: 0.6292, IoU.painting: 0.7576, IoU.sofa: 0.8137, IoU.shelf: 0.4729, IoU.house: 0.4367, IoU.sea: 0.6710, IoU.mirror: 0.7976, IoU.rug: 0.6977, IoU.field: 0.3622, IoU.armchair: 0.5979, IoU.seat: 0.6742, IoU.fence: 0.5099, IoU.desk: 0.5189, IoU.rock: 0.5324, IoU.wardrobe: 0.5188, IoU.lamp: 0.7383, IoU.bathtub: 0.9012, IoU.railing: 0.4582, IoU.cushion: 0.6850, IoU.base: 0.4616, IoU.box: 0.3935, IoU.column: 0.5506, IoU.signboard: 0.4218, IoU.chest of drawers: 0.4933, IoU.counter: 0.4727, IoU.sand: 0.5397, IoU.sink: 0.7646, IoU.skyscraper: 0.4930, IoU.fireplace: 0.7130, IoU.refrigerator: 0.8159, IoU.grandstand: 0.5374, IoU.path: 0.2883, IoU.stairs: 0.3542, IoU.runway: 0.6862, IoU.case: 0.5653, IoU.pool table: 0.9471, IoU.pillow: 0.7007, IoU.screen door: 0.8157, IoU.stairway: 0.5125, IoU.river: 0.1226, IoU.bridge: 0.7396, IoU.bookcase: 0.4048, IoU.blind: 0.4578, IoU.coffee table: 0.6059, IoU.toilet: 0.8998, IoU.flower: 0.4240, IoU.book: 0.5059, IoU.hill: 0.0741, IoU.bench: 0.5594, IoU.countertop: 0.6255, IoU.stove: 0.8209, IoU.palm: 0.5803, IoU.kitchen island: 0.5451, IoU.computer: 0.7497, IoU.swivel chair: 0.5221, IoU.boat: 0.6572, IoU.bar: 0.6707, IoU.arcade machine: 0.8763, IoU.hovel: 0.2447, IoU.bus: 0.9219, IoU.towel: 0.7414, IoU.light: 0.5729, IoU.truck: 0.4753, IoU.tower: 0.2640, IoU.chandelier: 0.7224, IoU.awning: 0.3884, IoU.streetlight: 0.3567, IoU.booth: 0.3318, IoU.television receiver: 0.7782, IoU.airplane: 0.8836, IoU.dirt track: 0.0703, IoU.apparel: 0.5150, IoU.pole: 0.2426, IoU.land: 0.0218, IoU.bannister: 0.1786, IoU.escalator: 0.6242, IoU.ottoman: 0.5087, IoU.bottle: 0.4075, IoU.buffet: 0.5889, IoU.poster: 0.3100, IoU.stage: 0.2763, IoU.van: 0.5092, IoU.ship: 0.1052, IoU.fountain: 0.4317, IoU.conveyer belt: 0.7384, IoU.canopy: 0.4565, IoU.washer: 0.8231, IoU.plaything: 0.2960, IoU.swimming pool: 0.5546, IoU.stool: 0.5703, IoU.barrel: 0.5258, IoU.basket: 0.4360, IoU.waterfall: 0.5733, IoU.tent: 0.8766, IoU.bag: 0.2842, IoU.minibike: 0.7436, IoU.cradle: 0.7936, IoU.oven: 0.5830, IoU.ball: 0.5229, IoU.food: 0.5396, IoU.step: 0.1871, IoU.tank: 0.5933, IoU.trade name: 0.3733, IoU.microwave: 0.8584, IoU.pot: 0.5904, IoU.animal: 0.5833, IoU.bicycle: 0.5998, IoU.lake: 0.5269, IoU.dishwasher: 0.7012, IoU.screen: 0.5606, IoU.blanket: 0.3401, IoU.sculpture: 0.7565, IoU.hood: 0.6569, IoU.sconce: 0.5864, IoU.vase: 0.4691, IoU.traffic light: 0.3359, IoU.tray: 0.1559, IoU.ashcan: 0.4715, IoU.fan: 0.6694, IoU.pier: 0.5310, IoU.crt screen: 0.0059, IoU.plate: 0.5605, IoU.monitor: 0.5315, IoU.bulletin board: 0.5913, IoU.shower: 0.0000, IoU.radiator: 0.6883, IoU.glass: 0.2090, IoU.clock: 0.4471, IoU.flag: 0.6983, Acc.wall: 0.8745, Acc.building: 0.9336, Acc.sky: 0.9710, Acc.floor: 0.9076, Acc.tree: 0.9018, Acc.ceiling: 0.9447, Acc.road: 0.9037, Acc.bed : 0.9640, Acc.windowpane: 0.8018, Acc.grass: 0.8365, Acc.cabinet: 0.7594, Acc.sidewalk: 0.8662, Acc.person: 0.9296, Acc.earth: 0.4617, Acc.door: 0.8015, Acc.table: 0.7968, Acc.mountain: 0.6598, Acc.plant: 0.7120, Acc.curtain: 0.9056, Acc.chair: 0.7617, Acc.car: 0.9416, Acc.water: 0.8024, Acc.painting: 0.9047, Acc.sofa: 0.9233, Acc.shelf: 0.6351, Acc.house: 0.5282, Acc.sea: 0.7267, Acc.mirror: 0.8921, Acc.rug: 0.8285, Acc.field: 0.5201, Acc.armchair: 0.8131, Acc.seat: 0.8905, Acc.fence: 0.6901, Acc.desk: 0.7903, Acc.rock: 0.8091, Acc.wardrobe: 0.6658, Acc.lamp: 0.8575, Acc.bathtub: 0.9398, Acc.railing: 0.6249, Acc.cushion: 0.7969, Acc.base: 0.6460, Acc.box: 0.5481, Acc.column: 0.6979, Acc.signboard: 0.6143, Acc.chest of drawers: 0.6954, Acc.counter: 0.6527, Acc.sand: 0.9020, Acc.sink: 0.8771, Acc.skyscraper: 0.6219, Acc.fireplace: 0.8572, Acc.refrigerator: 0.9340, Acc.grandstand: 0.8446, Acc.path: 0.4440, Acc.stairs: 0.4078, Acc.runway: 0.9144, Acc.case: 0.7922, Acc.pool table: 0.9781, Acc.pillow: 0.8466, Acc.screen door: 0.8892, Acc.stairway: 0.6636, Acc.river: 0.2743, Acc.bridge: 0.8252, Acc.bookcase: 0.6473, Acc.blind: 0.5742, Acc.coffee table: 0.8800, Acc.toilet: 0.9492, Acc.flower: 0.5392, Acc.book: 0.7893, Acc.hill: 0.2472, Acc.bench: 0.6310, Acc.countertop: 0.8039, Acc.stove: 0.9273, Acc.palm: 0.7766, Acc.kitchen island: 0.8370, Acc.computer: 0.9283, Acc.swivel chair: 0.7584, Acc.boat: 0.8374, Acc.bar: 0.8397, Acc.arcade machine: 0.9330, Acc.hovel: 0.2612, Acc.bus: 0.9644, Acc.towel: 0.8357, Acc.light: 0.6411, Acc.truck: 0.6225, Acc.tower: 0.4013, Acc.chandelier: 0.8989, Acc.awning: 0.5229, Acc.streetlight: 0.4843, Acc.booth: 0.5874, Acc.television receiver: 0.9147, Acc.airplane: 0.9560, Acc.dirt track: 0.3870, Acc.apparel: 0.8070, Acc.pole: 0.3260, Acc.land: 0.0462, Acc.bannister: 0.2753, Acc.escalator: 0.8891, Acc.ottoman: 0.7323, Acc.bottle: 0.5398, Acc.buffet: 0.7197, Acc.poster: 0.4260, Acc.stage: 0.4804, Acc.van: 0.6507, Acc.ship: 0.1080, Acc.fountain: 0.4549, Acc.conveyer belt: 0.9431, Acc.canopy: 0.5267, Acc.washer: 0.9150, Acc.plaything: 0.5818, Acc.swimming pool: 0.8012, Acc.stool: 0.6756, Acc.barrel: 0.8454, Acc.basket: 0.5830, Acc.waterfall: 0.6884, Acc.tent: 0.9753, Acc.bag: 0.3609, Acc.minibike: 0.8755, Acc.cradle: 0.9854, Acc.oven: 0.7093, Acc.ball: 0.6696, Acc.food: 0.5994, Acc.step: 0.2228, Acc.tank: 0.7396, Acc.trade name: 0.5357, Acc.microwave: 0.9726, Acc.pot: 0.6944, Acc.animal: 0.5908, Acc.bicycle: 0.7570, Acc.lake: 0.7788, Acc.dishwasher: 0.8481, Acc.screen: 0.9539, Acc.blanket: 0.5838, Acc.sculpture: 0.8726, Acc.hood: 0.7845, Acc.sconce: 0.6991, Acc.vase: 0.6421, Acc.traffic light: 0.6421, Acc.tray: 0.2129, Acc.ashcan: 0.6424, Acc.fan: 0.8467, Acc.pier: 0.6472, Acc.crt screen: 0.0064, Acc.plate: 0.8418, Acc.monitor: 0.8007, Acc.bulletin board: 0.6707, Acc.shower: 0.0000, Acc.radiator: 0.7815, Acc.glass: 0.2320, Acc.clock: 0.6826, Acc.flag: 0.7955 +2024-06-16 08:44:39,886 - mmseg - INFO - Iter [23050/80000] lr: 2.848e-05, eta: 1 day, 3:57:19, time: 3.572, data_time: 1.959, memory: 71384, decode.loss_ce: 0.2463, decode.acc_seg: 89.5173, aux.loss_ce: 0.1014, aux.acc_seg: 89.1441, loss: 0.3476 +2024-06-16 08:46:01,161 - mmseg - INFO - Iter [23100/80000] lr: 2.845e-05, eta: 1 day, 3:55:33, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2728, decode.acc_seg: 89.1344, aux.loss_ce: 0.1116, aux.acc_seg: 88.8516, loss: 0.3845 +2024-06-16 08:47:22,315 - mmseg - INFO - Iter [23150/80000] lr: 2.843e-05, eta: 1 day, 3:53:47, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2807, decode.acc_seg: 88.7796, aux.loss_ce: 0.1136, aux.acc_seg: 88.5356, loss: 0.3943 +2024-06-16 08:48:43,549 - mmseg - INFO - Iter [23200/80000] lr: 2.840e-05, eta: 1 day, 3:52:01, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2594, decode.acc_seg: 89.4795, aux.loss_ce: 0.1066, aux.acc_seg: 89.2439, loss: 0.3659 +2024-06-16 08:50:04,593 - mmseg - INFO - Iter [23250/80000] lr: 2.838e-05, eta: 1 day, 3:50:15, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2623, decode.acc_seg: 89.5436, aux.loss_ce: 0.1073, aux.acc_seg: 89.2729, loss: 0.3696 +2024-06-16 08:51:25,651 - mmseg - INFO - Iter [23300/80000] lr: 2.835e-05, eta: 1 day, 3:48:29, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2656, decode.acc_seg: 88.7910, aux.loss_ce: 0.1081, aux.acc_seg: 88.5673, loss: 0.3736 +2024-06-16 08:52:46,917 - mmseg - INFO - Iter [23350/80000] lr: 2.833e-05, eta: 1 day, 3:46:44, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2754, decode.acc_seg: 88.9601, aux.loss_ce: 0.1129, aux.acc_seg: 88.6892, loss: 0.3883 +2024-06-16 08:54:07,923 - mmseg - INFO - Iter [23400/80000] lr: 2.830e-05, eta: 1 day, 3:44:58, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2664, decode.acc_seg: 88.9886, aux.loss_ce: 0.1089, aux.acc_seg: 88.6463, loss: 0.3753 +2024-06-16 08:55:29,055 - mmseg - INFO - Iter [23450/80000] lr: 2.828e-05, eta: 1 day, 3:43:13, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2646, decode.acc_seg: 89.1899, aux.loss_ce: 0.1085, aux.acc_seg: 88.9641, loss: 0.3732 +2024-06-16 08:56:50,026 - mmseg - INFO - Iter [23500/80000] lr: 2.825e-05, eta: 1 day, 3:41:27, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2646, decode.acc_seg: 89.5151, aux.loss_ce: 0.1079, aux.acc_seg: 89.2946, loss: 0.3725 +2024-06-16 08:58:11,074 - mmseg - INFO - Iter [23550/80000] lr: 2.823e-05, eta: 1 day, 3:39:41, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2522, decode.acc_seg: 89.6940, aux.loss_ce: 0.1035, aux.acc_seg: 89.3594, loss: 0.3557 +2024-06-16 08:59:32,109 - mmseg - INFO - Iter [23600/80000] lr: 2.820e-05, eta: 1 day, 3:37:56, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2693, decode.acc_seg: 89.0180, aux.loss_ce: 0.1103, aux.acc_seg: 88.7433, loss: 0.3795 +2024-06-16 09:00:53,187 - mmseg - INFO - Iter [23650/80000] lr: 2.818e-05, eta: 1 day, 3:36:11, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2658, decode.acc_seg: 88.9148, aux.loss_ce: 0.1078, aux.acc_seg: 88.7194, loss: 0.3736 +2024-06-16 09:02:14,399 - mmseg - INFO - Iter [23700/80000] lr: 2.815e-05, eta: 1 day, 3:34:26, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2614, decode.acc_seg: 89.2817, aux.loss_ce: 0.1072, aux.acc_seg: 89.0743, loss: 0.3686 +2024-06-16 09:03:35,597 - mmseg - INFO - Iter [23750/80000] lr: 2.813e-05, eta: 1 day, 3:32:42, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2341, decode.acc_seg: 90.1506, aux.loss_ce: 0.0955, aux.acc_seg: 89.9347, loss: 0.3297 +2024-06-16 09:04:56,730 - mmseg - INFO - Iter [23800/80000] lr: 2.810e-05, eta: 1 day, 3:30:57, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2657, decode.acc_seg: 89.0398, aux.loss_ce: 0.1093, aux.acc_seg: 88.7202, loss: 0.3749 +2024-06-16 09:06:17,870 - mmseg - INFO - Iter [23850/80000] lr: 2.808e-05, eta: 1 day, 3:29:12, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2540, decode.acc_seg: 89.7031, aux.loss_ce: 0.1040, aux.acc_seg: 89.5034, loss: 0.3580 +2024-06-16 09:07:38,906 - mmseg - INFO - Iter [23900/80000] lr: 2.805e-05, eta: 1 day, 3:27:28, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2469, decode.acc_seg: 89.4293, aux.loss_ce: 0.1009, aux.acc_seg: 89.2582, loss: 0.3477 +2024-06-16 09:09:00,081 - mmseg - INFO - Iter [23950/80000] lr: 2.803e-05, eta: 1 day, 3:25:43, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2776, decode.acc_seg: 88.8931, aux.loss_ce: 0.1128, aux.acc_seg: 88.6583, loss: 0.3904 +2024-06-16 09:10:24,250 - mmseg - INFO - Saving checkpoint at 24000 iterations +2024-06-16 09:11:49,634 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 09:11:49,635 - mmseg - INFO - Iter [24000/80000] lr: 2.800e-05, eta: 1 day, 3:27:25, time: 3.391, data_time: 0.069, memory: 71384, decode.loss_ce: 0.2589, decode.acc_seg: 89.3733, aux.loss_ce: 0.1056, aux.acc_seg: 89.1857, loss: 0.3645 +2024-06-16 09:13:26,515 - mmseg - INFO - per class results: +2024-06-16 09:13:26,522 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.83 | 89.86 | +| building | 84.34 | 93.29 | +| sky | 94.88 | 97.58 | +| floor | 85.3 | 91.83 | +| tree | 78.02 | 88.4 | +| ceiling | 86.81 | 92.11 | +| road | 86.64 | 90.5 | +| bed | 91.84 | 96.93 | +| windowpane | 65.65 | 79.09 | +| grass | 68.35 | 76.48 | +| cabinet | 65.79 | 77.36 | +| sidewalk | 71.04 | 84.71 | +| person | 85.58 | 92.97 | +| earth | 39.2 | 64.06 | +| door | 60.71 | 73.91 | +| table | 65.56 | 73.58 | +| mountain | 57.95 | 64.88 | +| plant | 55.67 | 72.84 | +| curtain | 78.18 | 89.07 | +| chair | 67.16 | 76.06 | +| car | 87.3 | 92.85 | +| water | 55.6 | 62.18 | +| painting | 77.52 | 90.63 | +| sofa | 79.23 | 89.37 | +| shelf | 47.56 | 65.5 | +| house | 49.39 | 64.41 | +| sea | 68.13 | 92.0 | +| mirror | 77.85 | 85.27 | +| rug | 70.23 | 83.59 | +| field | 31.16 | 44.61 | +| armchair | 58.1 | 70.83 | +| seat | 66.3 | 88.8 | +| fence | 53.8 | 71.83 | +| desk | 52.03 | 79.38 | +| rock | 54.92 | 71.86 | +| wardrobe | 54.98 | 82.22 | +| lamp | 74.09 | 87.37 | +| bathtub | 87.94 | 89.78 | +| railing | 44.02 | 61.68 | +| cushion | 65.21 | 87.6 | +| base | 39.93 | 49.58 | +| box | 37.13 | 49.61 | +| column | 53.26 | 63.65 | +| signboard | 39.8 | 47.6 | +| chest of drawers | 53.13 | 73.32 | +| counter | 44.21 | 53.93 | +| sand | 58.55 | 85.49 | +| sink | 77.35 | 81.89 | +| skyscraper | 44.7 | 52.39 | +| fireplace | 75.16 | 89.35 | +| refrigerator | 82.42 | 91.97 | +| grandstand | 50.89 | 85.18 | +| path | 32.65 | 40.97 | +| stairs | 36.0 | 41.57 | +| runway | 65.39 | 87.98 | +| case | 62.81 | 85.87 | +| pool table | 94.41 | 98.49 | +| pillow | 59.95 | 64.24 | +| screen door | 85.14 | 88.08 | +| stairway | 44.24 | 55.73 | +| river | 25.09 | 68.88 | +| bridge | 68.94 | 89.7 | +| bookcase | 36.3 | 62.0 | +| blind | 49.76 | 67.39 | +| coffee table | 60.92 | 85.96 | +| toilet | 89.97 | 93.33 | +| flower | 40.71 | 56.27 | +| book | 52.1 | 78.55 | +| hill | 7.29 | 18.98 | +| bench | 53.28 | 68.43 | +| countertop | 64.46 | 82.77 | +| stove | 80.79 | 91.43 | +| palm | 56.39 | 70.88 | +| kitchen island | 46.68 | 64.1 | +| computer | 77.63 | 93.47 | +| swivel chair | 52.32 | 74.55 | +| boat | 73.0 | 91.63 | +| bar | 65.94 | 87.97 | +| arcade machine | 77.15 | 83.85 | +| hovel | 29.07 | 36.47 | +| bus | 89.36 | 97.66 | +| towel | 74.95 | 88.48 | +| light | 59.75 | 79.82 | +| truck | 47.94 | 61.89 | +| tower | 26.56 | 57.6 | +| chandelier | 72.19 | 88.35 | +| awning | 41.29 | 58.62 | +| streetlight | 33.04 | 45.72 | +| booth | 50.73 | 71.47 | +| television receiver | 75.24 | 88.47 | +| airplane | 86.99 | 94.7 | +| dirt track | 7.65 | 41.42 | +| apparel | 48.97 | 61.11 | +| pole | 22.75 | 28.72 | +| land | 1.37 | 3.59 | +| bannister | 18.22 | 26.38 | +| escalator | 62.25 | 84.29 | +| ottoman | 50.38 | 72.38 | +| bottle | 34.28 | 45.02 | +| buffet | 58.46 | 69.94 | +| poster | 24.72 | 25.03 | +| stage | 21.15 | 31.52 | +| van | 49.71 | 72.9 | +| ship | 67.36 | 69.15 | +| fountain | 38.89 | 41.06 | +| conveyer belt | 83.43 | 92.3 | +| canopy | 53.08 | 79.92 | +| washer | 79.75 | 85.19 | +| plaything | 25.38 | 40.83 | +| swimming pool | 53.85 | 78.46 | +| stool | 52.91 | 65.73 | +| barrel | 53.0 | 67.79 | +| basket | 40.24 | 57.78 | +| waterfall | 64.16 | 76.14 | +| tent | 89.46 | 98.91 | +| bag | 25.06 | 31.57 | +| minibike | 73.5 | 86.67 | +| cradle | 79.37 | 98.2 | +| oven | 70.23 | 79.28 | +| ball | 24.52 | 26.97 | +| food | 57.17 | 67.81 | +| step | 20.17 | 22.39 | +| tank | 73.95 | 87.45 | +| trade name | 35.76 | 48.84 | +| microwave | 90.09 | 96.47 | +| pot | 57.85 | 69.17 | +| animal | 67.79 | 70.28 | +| bicycle | 58.04 | 79.41 | +| lake | 57.06 | 64.27 | +| dishwasher | 73.52 | 83.8 | +| screen | 60.03 | 95.18 | +| blanket | 26.94 | 30.69 | +| sculpture | 67.96 | 88.82 | +| hood | 67.4 | 79.79 | +| sconce | 60.86 | 71.51 | +| vase | 45.31 | 64.38 | +| traffic light | 34.84 | 58.34 | +| tray | 15.51 | 18.28 | +| ashcan | 47.39 | 59.22 | +| fan | 68.51 | 85.05 | +| pier | 30.09 | 31.48 | +| crt screen | 1.51 | 4.0 | +| plate | 60.69 | 82.06 | +| monitor | 6.72 | 7.16 | +| bulletin board | 64.66 | 72.81 | +| shower | 0.0 | 0.0 | +| radiator | 65.12 | 80.09 | +| glass | 19.53 | 20.8 | +| clock | 44.65 | 49.82 | +| flag | 69.38 | 76.19 | ++---------------------+-------+-------+ +2024-06-16 09:13:26,522 - mmseg - INFO - Summary: +2024-06-16 09:13:26,522 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 85.7 | 56.34 | 69.45 | ++------+-------+-------+ +2024-06-16 09:13:26,522 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 09:13:26,523 - mmseg - INFO - Iter(val) [250] aAcc: 0.8570, mIoU: 0.5634, mAcc: 0.6945, IoU.wall: 0.8183, IoU.building: 0.8434, IoU.sky: 0.9488, IoU.floor: 0.8530, IoU.tree: 0.7802, IoU.ceiling: 0.8681, IoU.road: 0.8664, IoU.bed : 0.9184, IoU.windowpane: 0.6565, IoU.grass: 0.6835, IoU.cabinet: 0.6579, IoU.sidewalk: 0.7104, IoU.person: 0.8558, IoU.earth: 0.3920, IoU.door: 0.6071, IoU.table: 0.6556, IoU.mountain: 0.5795, IoU.plant: 0.5567, IoU.curtain: 0.7818, IoU.chair: 0.6716, IoU.car: 0.8730, IoU.water: 0.5560, IoU.painting: 0.7752, IoU.sofa: 0.7923, IoU.shelf: 0.4756, IoU.house: 0.4939, IoU.sea: 0.6813, IoU.mirror: 0.7785, IoU.rug: 0.7023, IoU.field: 0.3116, IoU.armchair: 0.5810, IoU.seat: 0.6630, IoU.fence: 0.5380, IoU.desk: 0.5203, IoU.rock: 0.5492, IoU.wardrobe: 0.5498, IoU.lamp: 0.7409, IoU.bathtub: 0.8794, IoU.railing: 0.4402, IoU.cushion: 0.6521, IoU.base: 0.3993, IoU.box: 0.3713, IoU.column: 0.5326, IoU.signboard: 0.3980, IoU.chest of drawers: 0.5313, IoU.counter: 0.4421, IoU.sand: 0.5855, IoU.sink: 0.7735, IoU.skyscraper: 0.4470, IoU.fireplace: 0.7516, IoU.refrigerator: 0.8242, IoU.grandstand: 0.5089, IoU.path: 0.3265, IoU.stairs: 0.3600, IoU.runway: 0.6539, IoU.case: 0.6281, IoU.pool table: 0.9441, IoU.pillow: 0.5995, IoU.screen door: 0.8514, IoU.stairway: 0.4424, IoU.river: 0.2509, IoU.bridge: 0.6894, IoU.bookcase: 0.3630, IoU.blind: 0.4976, IoU.coffee table: 0.6092, IoU.toilet: 0.8997, IoU.flower: 0.4071, IoU.book: 0.5210, IoU.hill: 0.0729, IoU.bench: 0.5328, IoU.countertop: 0.6446, IoU.stove: 0.8079, IoU.palm: 0.5639, IoU.kitchen island: 0.4668, IoU.computer: 0.7763, IoU.swivel chair: 0.5232, IoU.boat: 0.7300, IoU.bar: 0.6594, IoU.arcade machine: 0.7715, IoU.hovel: 0.2907, IoU.bus: 0.8936, IoU.towel: 0.7495, IoU.light: 0.5975, IoU.truck: 0.4794, IoU.tower: 0.2656, IoU.chandelier: 0.7219, IoU.awning: 0.4129, IoU.streetlight: 0.3304, IoU.booth: 0.5073, IoU.television receiver: 0.7524, IoU.airplane: 0.8699, IoU.dirt track: 0.0765, IoU.apparel: 0.4897, IoU.pole: 0.2275, IoU.land: 0.0137, IoU.bannister: 0.1822, IoU.escalator: 0.6225, IoU.ottoman: 0.5038, IoU.bottle: 0.3428, IoU.buffet: 0.5846, IoU.poster: 0.2472, IoU.stage: 0.2115, IoU.van: 0.4971, IoU.ship: 0.6736, IoU.fountain: 0.3889, IoU.conveyer belt: 0.8343, IoU.canopy: 0.5308, IoU.washer: 0.7975, IoU.plaything: 0.2538, IoU.swimming pool: 0.5385, IoU.stool: 0.5291, IoU.barrel: 0.5300, IoU.basket: 0.4024, IoU.waterfall: 0.6416, IoU.tent: 0.8946, IoU.bag: 0.2506, IoU.minibike: 0.7350, IoU.cradle: 0.7937, IoU.oven: 0.7023, IoU.ball: 0.2452, IoU.food: 0.5717, IoU.step: 0.2017, IoU.tank: 0.7395, IoU.trade name: 0.3576, IoU.microwave: 0.9009, IoU.pot: 0.5785, IoU.animal: 0.6779, IoU.bicycle: 0.5804, IoU.lake: 0.5706, IoU.dishwasher: 0.7352, IoU.screen: 0.6003, IoU.blanket: 0.2694, IoU.sculpture: 0.6796, IoU.hood: 0.6740, IoU.sconce: 0.6086, IoU.vase: 0.4531, IoU.traffic light: 0.3484, IoU.tray: 0.1551, IoU.ashcan: 0.4739, IoU.fan: 0.6851, IoU.pier: 0.3009, IoU.crt screen: 0.0151, IoU.plate: 0.6069, IoU.monitor: 0.0672, IoU.bulletin board: 0.6466, IoU.shower: 0.0000, IoU.radiator: 0.6512, IoU.glass: 0.1953, IoU.clock: 0.4465, IoU.flag: 0.6938, Acc.wall: 0.8986, Acc.building: 0.9329, Acc.sky: 0.9758, Acc.floor: 0.9183, Acc.tree: 0.8840, Acc.ceiling: 0.9211, Acc.road: 0.9050, Acc.bed : 0.9693, Acc.windowpane: 0.7909, Acc.grass: 0.7648, Acc.cabinet: 0.7736, Acc.sidewalk: 0.8471, Acc.person: 0.9297, Acc.earth: 0.6406, Acc.door: 0.7391, Acc.table: 0.7358, Acc.mountain: 0.6488, Acc.plant: 0.7284, Acc.curtain: 0.8907, Acc.chair: 0.7606, Acc.car: 0.9285, Acc.water: 0.6218, Acc.painting: 0.9063, Acc.sofa: 0.8937, Acc.shelf: 0.6550, Acc.house: 0.6441, Acc.sea: 0.9200, Acc.mirror: 0.8527, Acc.rug: 0.8359, Acc.field: 0.4461, Acc.armchair: 0.7083, Acc.seat: 0.8880, Acc.fence: 0.7183, Acc.desk: 0.7938, Acc.rock: 0.7186, Acc.wardrobe: 0.8222, Acc.lamp: 0.8737, Acc.bathtub: 0.8978, Acc.railing: 0.6168, Acc.cushion: 0.8760, Acc.base: 0.4958, Acc.box: 0.4961, Acc.column: 0.6365, Acc.signboard: 0.4760, Acc.chest of drawers: 0.7332, Acc.counter: 0.5393, Acc.sand: 0.8549, Acc.sink: 0.8189, Acc.skyscraper: 0.5239, Acc.fireplace: 0.8935, Acc.refrigerator: 0.9197, Acc.grandstand: 0.8518, Acc.path: 0.4097, Acc.stairs: 0.4157, Acc.runway: 0.8798, Acc.case: 0.8587, Acc.pool table: 0.9849, Acc.pillow: 0.6424, Acc.screen door: 0.8808, Acc.stairway: 0.5573, Acc.river: 0.6888, Acc.bridge: 0.8970, Acc.bookcase: 0.6200, Acc.blind: 0.6739, Acc.coffee table: 0.8596, Acc.toilet: 0.9333, Acc.flower: 0.5627, Acc.book: 0.7855, Acc.hill: 0.1898, Acc.bench: 0.6843, Acc.countertop: 0.8277, Acc.stove: 0.9143, Acc.palm: 0.7088, Acc.kitchen island: 0.6410, Acc.computer: 0.9347, Acc.swivel chair: 0.7455, Acc.boat: 0.9163, Acc.bar: 0.8797, Acc.arcade machine: 0.8385, Acc.hovel: 0.3647, Acc.bus: 0.9766, Acc.towel: 0.8848, Acc.light: 0.7982, Acc.truck: 0.6189, Acc.tower: 0.5760, Acc.chandelier: 0.8835, Acc.awning: 0.5862, Acc.streetlight: 0.4572, Acc.booth: 0.7147, Acc.television receiver: 0.8847, Acc.airplane: 0.9470, Acc.dirt track: 0.4142, Acc.apparel: 0.6111, Acc.pole: 0.2872, Acc.land: 0.0359, Acc.bannister: 0.2638, Acc.escalator: 0.8429, Acc.ottoman: 0.7238, Acc.bottle: 0.4502, Acc.buffet: 0.6994, Acc.poster: 0.2503, Acc.stage: 0.3152, Acc.van: 0.7290, Acc.ship: 0.6915, Acc.fountain: 0.4106, Acc.conveyer belt: 0.9230, Acc.canopy: 0.7992, Acc.washer: 0.8519, Acc.plaything: 0.4083, Acc.swimming pool: 0.7846, Acc.stool: 0.6573, Acc.barrel: 0.6779, Acc.basket: 0.5778, Acc.waterfall: 0.7614, Acc.tent: 0.9891, Acc.bag: 0.3157, Acc.minibike: 0.8667, Acc.cradle: 0.9820, Acc.oven: 0.7928, Acc.ball: 0.2697, Acc.food: 0.6781, Acc.step: 0.2239, Acc.tank: 0.8745, Acc.trade name: 0.4884, Acc.microwave: 0.9647, Acc.pot: 0.6917, Acc.animal: 0.7028, Acc.bicycle: 0.7941, Acc.lake: 0.6427, Acc.dishwasher: 0.8380, Acc.screen: 0.9518, Acc.blanket: 0.3069, Acc.sculpture: 0.8882, Acc.hood: 0.7979, Acc.sconce: 0.7151, Acc.vase: 0.6438, Acc.traffic light: 0.5834, Acc.tray: 0.1828, Acc.ashcan: 0.5922, Acc.fan: 0.8505, Acc.pier: 0.3148, Acc.crt screen: 0.0400, Acc.plate: 0.8206, Acc.monitor: 0.0716, Acc.bulletin board: 0.7281, Acc.shower: 0.0000, Acc.radiator: 0.8009, Acc.glass: 0.2080, Acc.clock: 0.4982, Acc.flag: 0.7619 +2024-06-16 09:14:48,015 - mmseg - INFO - Iter [24050/80000] lr: 2.798e-05, eta: 1 day, 3:29:27, time: 3.568, data_time: 1.954, memory: 71384, decode.loss_ce: 0.2560, decode.acc_seg: 89.4802, aux.loss_ce: 0.1042, aux.acc_seg: 89.2629, loss: 0.3602 +2024-06-16 09:16:09,063 - mmseg - INFO - Iter [24100/80000] lr: 2.795e-05, eta: 1 day, 3:27:41, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2586, decode.acc_seg: 89.3631, aux.loss_ce: 0.1056, aux.acc_seg: 89.2250, loss: 0.3642 +2024-06-16 09:17:30,211 - mmseg - INFO - Iter [24150/80000] lr: 2.793e-05, eta: 1 day, 3:25:56, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2587, decode.acc_seg: 89.5406, aux.loss_ce: 0.1054, aux.acc_seg: 89.2729, loss: 0.3641 +2024-06-16 09:18:51,449 - mmseg - INFO - Iter [24200/80000] lr: 2.790e-05, eta: 1 day, 3:24:11, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2494, decode.acc_seg: 89.5523, aux.loss_ce: 0.1025, aux.acc_seg: 89.3228, loss: 0.3519 +2024-06-16 09:20:12,523 - mmseg - INFO - Iter [24250/80000] lr: 2.788e-05, eta: 1 day, 3:22:26, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2505, decode.acc_seg: 89.7782, aux.loss_ce: 0.1025, aux.acc_seg: 89.4501, loss: 0.3530 +2024-06-16 09:21:33,837 - mmseg - INFO - Iter [24300/80000] lr: 2.785e-05, eta: 1 day, 3:20:41, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2435, decode.acc_seg: 90.0771, aux.loss_ce: 0.1001, aux.acc_seg: 89.8604, loss: 0.3436 +2024-06-16 09:22:54,853 - mmseg - INFO - Iter [24350/80000] lr: 2.783e-05, eta: 1 day, 3:18:56, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2595, decode.acc_seg: 89.4179, aux.loss_ce: 0.1064, aux.acc_seg: 89.1034, loss: 0.3660 +2024-06-16 09:24:16,238 - mmseg - INFO - Iter [24400/80000] lr: 2.780e-05, eta: 1 day, 3:17:12, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2485, decode.acc_seg: 89.7492, aux.loss_ce: 0.1014, aux.acc_seg: 89.4935, loss: 0.3499 +2024-06-16 09:25:37,360 - mmseg - INFO - Iter [24450/80000] lr: 2.778e-05, eta: 1 day, 3:15:27, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2522, decode.acc_seg: 89.5196, aux.loss_ce: 0.1039, aux.acc_seg: 89.3214, loss: 0.3561 +2024-06-16 09:26:58,348 - mmseg - INFO - Iter [24500/80000] lr: 2.775e-05, eta: 1 day, 3:13:42, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2515, decode.acc_seg: 89.6612, aux.loss_ce: 0.1034, aux.acc_seg: 89.3649, loss: 0.3549 +2024-06-16 09:28:19,365 - mmseg - INFO - Iter [24550/80000] lr: 2.773e-05, eta: 1 day, 3:11:57, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2441, decode.acc_seg: 89.7135, aux.loss_ce: 0.0997, aux.acc_seg: 89.5386, loss: 0.3438 +2024-06-16 09:29:40,467 - mmseg - INFO - Iter [24600/80000] lr: 2.770e-05, eta: 1 day, 3:10:13, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2657, decode.acc_seg: 88.8914, aux.loss_ce: 0.1094, aux.acc_seg: 88.4968, loss: 0.3751 +2024-06-16 09:31:01,875 - mmseg - INFO - Iter [24650/80000] lr: 2.768e-05, eta: 1 day, 3:08:29, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2601, decode.acc_seg: 89.1676, aux.loss_ce: 0.1067, aux.acc_seg: 88.9915, loss: 0.3668 +2024-06-16 09:32:22,987 - mmseg - INFO - Iter [24700/80000] lr: 2.765e-05, eta: 1 day, 3:06:45, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2635, decode.acc_seg: 89.3763, aux.loss_ce: 0.1070, aux.acc_seg: 89.1847, loss: 0.3706 +2024-06-16 09:33:44,198 - mmseg - INFO - Iter [24750/80000] lr: 2.763e-05, eta: 1 day, 3:05:01, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2580, decode.acc_seg: 89.5596, aux.loss_ce: 0.1046, aux.acc_seg: 89.3767, loss: 0.3625 +2024-06-16 09:35:05,421 - mmseg - INFO - Iter [24800/80000] lr: 2.760e-05, eta: 1 day, 3:03:17, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2582, decode.acc_seg: 89.4420, aux.loss_ce: 0.1056, aux.acc_seg: 89.2002, loss: 0.3638 +2024-06-16 09:36:26,623 - mmseg - INFO - Iter [24850/80000] lr: 2.758e-05, eta: 1 day, 3:01:33, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2603, decode.acc_seg: 89.1369, aux.loss_ce: 0.1060, aux.acc_seg: 89.0647, loss: 0.3663 +2024-06-16 09:38:03,489 - mmseg - INFO - Iter [24900/80000] lr: 2.755e-05, eta: 1 day, 3:00:24, time: 1.937, data_time: 0.325, memory: 71384, decode.loss_ce: 0.2617, decode.acc_seg: 89.1211, aux.loss_ce: 0.1067, aux.acc_seg: 88.8485, loss: 0.3684 +2024-06-16 09:39:24,605 - mmseg - INFO - Iter [24950/80000] lr: 2.753e-05, eta: 1 day, 2:58:40, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2825, decode.acc_seg: 88.4409, aux.loss_ce: 0.1150, aux.acc_seg: 88.3062, loss: 0.3974 +2024-06-16 09:40:45,740 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 09:40:45,740 - mmseg - INFO - Iter [25000/80000] lr: 2.750e-05, eta: 1 day, 2:56:56, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2653, decode.acc_seg: 89.4009, aux.loss_ce: 0.1078, aux.acc_seg: 89.1862, loss: 0.3731 +2024-06-16 09:42:23,969 - mmseg - INFO - per class results: +2024-06-16 09:42:23,975 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.61 | 88.65 | +| building | 84.67 | 93.03 | +| sky | 94.78 | 97.91 | +| floor | 85.1 | 92.13 | +| tree | 76.93 | 87.66 | +| ceiling | 86.41 | 94.75 | +| road | 85.13 | 88.11 | +| bed | 92.51 | 97.33 | +| windowpane | 64.82 | 82.61 | +| grass | 69.74 | 82.76 | +| cabinet | 65.58 | 78.84 | +| sidewalk | 69.28 | 85.89 | +| person | 84.87 | 93.17 | +| earth | 40.24 | 56.78 | +| door | 57.84 | 70.46 | +| table | 67.17 | 79.59 | +| mountain | 56.57 | 64.06 | +| plant | 55.96 | 70.84 | +| curtain | 79.32 | 90.54 | +| chair | 64.91 | 73.14 | +| car | 87.18 | 93.84 | +| water | 62.51 | 87.11 | +| painting | 78.11 | 89.03 | +| sofa | 79.89 | 87.59 | +| shelf | 52.46 | 74.16 | +| house | 57.4 | 77.8 | +| sea | 56.66 | 64.3 | +| mirror | 78.95 | 87.41 | +| rug | 69.88 | 82.39 | +| field | 34.27 | 53.08 | +| armchair | 59.04 | 79.3 | +| seat | 68.8 | 87.08 | +| fence | 46.43 | 56.98 | +| desk | 59.12 | 77.17 | +| rock | 52.39 | 69.36 | +| wardrobe | 54.4 | 74.01 | +| lamp | 74.81 | 84.27 | +| bathtub | 84.31 | 86.64 | +| railing | 43.44 | 58.6 | +| cushion | 67.06 | 76.14 | +| base | 43.76 | 68.28 | +| box | 35.29 | 43.59 | +| column | 55.48 | 75.15 | +| signboard | 40.21 | 51.02 | +| chest of drawers | 43.33 | 62.65 | +| counter | 43.37 | 47.21 | +| sand | 43.95 | 79.0 | +| sink | 79.47 | 87.85 | +| skyscraper | 47.6 | 63.27 | +| fireplace | 75.06 | 96.12 | +| refrigerator | 83.8 | 92.66 | +| grandstand | 47.83 | 77.12 | +| path | 28.05 | 41.84 | +| stairs | 31.83 | 36.36 | +| runway | 68.81 | 88.98 | +| case | 61.46 | 89.33 | +| pool table | 94.52 | 97.78 | +| pillow | 69.34 | 79.57 | +| screen door | 83.24 | 92.14 | +| stairway | 45.48 | 62.31 | +| river | 15.24 | 24.92 | +| bridge | 63.77 | 82.09 | +| bookcase | 46.48 | 57.31 | +| blind | 39.67 | 42.03 | +| coffee table | 59.88 | 90.1 | +| toilet | 87.3 | 91.01 | +| flower | 40.04 | 48.69 | +| book | 53.92 | 75.84 | +| hill | 4.22 | 8.36 | +| bench | 59.2 | 76.8 | +| countertop | 63.45 | 77.92 | +| stove | 83.1 | 91.72 | +| palm | 53.11 | 86.51 | +| kitchen island | 52.9 | 76.73 | +| computer | 79.29 | 92.5 | +| swivel chair | 46.27 | 75.19 | +| boat | 71.91 | 89.22 | +| bar | 64.6 | 82.91 | +| arcade machine | 77.33 | 84.02 | +| hovel | 3.44 | 3.73 | +| bus | 92.25 | 96.91 | +| towel | 74.24 | 85.91 | +| light | 60.56 | 71.89 | +| truck | 48.26 | 61.56 | +| tower | 25.56 | 61.03 | +| chandelier | 73.72 | 86.28 | +| awning | 39.29 | 50.34 | +| streetlight | 35.03 | 50.34 | +| booth | 59.65 | 70.12 | +| television receiver | 80.96 | 87.71 | +| airplane | 87.53 | 93.93 | +| dirt track | 7.58 | 32.09 | +| apparel | 55.43 | 81.61 | +| pole | 25.35 | 35.87 | +| land | 1.84 | 2.76 | +| bannister | 16.82 | 25.5 | +| escalator | 56.45 | 87.12 | +| ottoman | 48.12 | 70.66 | +| bottle | 37.37 | 46.88 | +| buffet | 57.12 | 87.37 | +| poster | 33.85 | 45.42 | +| stage | 20.56 | 37.61 | +| van | 49.33 | 66.81 | +| ship | 33.26 | 35.18 | +| fountain | 40.28 | 40.57 | +| conveyer belt | 75.34 | 93.41 | +| canopy | 50.06 | 82.28 | +| washer | 83.19 | 88.67 | +| plaything | 27.45 | 47.87 | +| swimming pool | 51.54 | 79.05 | +| stool | 49.94 | 69.15 | +| barrel | 56.3 | 64.46 | +| basket | 39.06 | 66.64 | +| waterfall | 54.56 | 63.24 | +| tent | 95.32 | 97.78 | +| bag | 26.58 | 31.18 | +| minibike | 75.42 | 86.14 | +| cradle | 88.28 | 97.91 | +| oven | 58.31 | 69.04 | +| ball | 49.69 | 55.13 | +| food | 57.35 | 70.24 | +| step | 24.63 | 27.19 | +| tank | 63.54 | 70.52 | +| trade name | 9.07 | 9.64 | +| microwave | 87.35 | 96.42 | +| pot | 57.99 | 72.8 | +| animal | 62.28 | 64.69 | +| bicycle | 57.5 | 76.3 | +| lake | 62.2 | 63.61 | +| dishwasher | 74.77 | 82.63 | +| screen | 56.43 | 97.85 | +| blanket | 22.97 | 26.82 | +| sculpture | 69.64 | 84.52 | +| hood | 62.94 | 78.97 | +| sconce | 61.6 | 74.02 | +| vase | 44.96 | 60.83 | +| traffic light | 35.18 | 64.73 | +| tray | 17.42 | 22.03 | +| ashcan | 48.04 | 61.9 | +| fan | 68.78 | 82.21 | +| pier | 45.37 | 59.75 | +| crt screen | 5.87 | 7.09 | +| plate | 61.25 | 81.72 | +| monitor | 61.65 | 73.18 | +| bulletin board | 57.38 | 77.76 | +| shower | 0.0 | 0.0 | +| radiator | 65.42 | 77.37 | +| glass | 16.85 | 17.56 | +| clock | 47.37 | 59.49 | +| flag | 69.56 | 79.52 | ++---------------------+-------+-------+ +2024-06-16 09:42:23,975 - mmseg - INFO - Summary: +2024-06-16 09:42:23,975 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 85.7 | 56.23 | 69.53 | ++------+-------+-------+ +2024-06-16 09:42:23,976 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 09:42:23,976 - mmseg - INFO - Iter(val) [250] aAcc: 0.8570, mIoU: 0.5623, mAcc: 0.6953, IoU.wall: 0.8161, IoU.building: 0.8467, IoU.sky: 0.9478, IoU.floor: 0.8510, IoU.tree: 0.7693, IoU.ceiling: 0.8641, IoU.road: 0.8513, IoU.bed : 0.9251, IoU.windowpane: 0.6482, IoU.grass: 0.6974, IoU.cabinet: 0.6558, IoU.sidewalk: 0.6928, IoU.person: 0.8487, IoU.earth: 0.4024, IoU.door: 0.5784, IoU.table: 0.6717, IoU.mountain: 0.5657, IoU.plant: 0.5596, IoU.curtain: 0.7932, IoU.chair: 0.6491, IoU.car: 0.8718, IoU.water: 0.6251, IoU.painting: 0.7811, IoU.sofa: 0.7989, IoU.shelf: 0.5246, IoU.house: 0.5740, IoU.sea: 0.5666, IoU.mirror: 0.7895, IoU.rug: 0.6988, IoU.field: 0.3427, IoU.armchair: 0.5904, IoU.seat: 0.6880, IoU.fence: 0.4643, IoU.desk: 0.5912, IoU.rock: 0.5239, IoU.wardrobe: 0.5440, IoU.lamp: 0.7481, IoU.bathtub: 0.8431, IoU.railing: 0.4344, IoU.cushion: 0.6706, IoU.base: 0.4376, IoU.box: 0.3529, IoU.column: 0.5548, IoU.signboard: 0.4021, IoU.chest of drawers: 0.4333, IoU.counter: 0.4337, IoU.sand: 0.4395, IoU.sink: 0.7947, IoU.skyscraper: 0.4760, IoU.fireplace: 0.7506, IoU.refrigerator: 0.8380, IoU.grandstand: 0.4783, IoU.path: 0.2805, IoU.stairs: 0.3183, IoU.runway: 0.6881, IoU.case: 0.6146, IoU.pool table: 0.9452, IoU.pillow: 0.6934, IoU.screen door: 0.8324, IoU.stairway: 0.4548, IoU.river: 0.1524, IoU.bridge: 0.6377, IoU.bookcase: 0.4648, IoU.blind: 0.3967, IoU.coffee table: 0.5988, IoU.toilet: 0.8730, IoU.flower: 0.4004, IoU.book: 0.5392, IoU.hill: 0.0422, IoU.bench: 0.5920, IoU.countertop: 0.6345, IoU.stove: 0.8310, IoU.palm: 0.5311, IoU.kitchen island: 0.5290, IoU.computer: 0.7929, IoU.swivel chair: 0.4627, IoU.boat: 0.7191, IoU.bar: 0.6460, IoU.arcade machine: 0.7733, IoU.hovel: 0.0344, IoU.bus: 0.9225, IoU.towel: 0.7424, IoU.light: 0.6056, IoU.truck: 0.4826, IoU.tower: 0.2556, IoU.chandelier: 0.7372, IoU.awning: 0.3929, IoU.streetlight: 0.3503, IoU.booth: 0.5965, IoU.television receiver: 0.8096, IoU.airplane: 0.8753, IoU.dirt track: 0.0758, IoU.apparel: 0.5543, IoU.pole: 0.2535, IoU.land: 0.0184, IoU.bannister: 0.1682, IoU.escalator: 0.5645, IoU.ottoman: 0.4812, IoU.bottle: 0.3737, IoU.buffet: 0.5712, IoU.poster: 0.3385, IoU.stage: 0.2056, IoU.van: 0.4933, IoU.ship: 0.3326, IoU.fountain: 0.4028, IoU.conveyer belt: 0.7534, IoU.canopy: 0.5006, IoU.washer: 0.8319, IoU.plaything: 0.2745, IoU.swimming pool: 0.5154, IoU.stool: 0.4994, IoU.barrel: 0.5630, IoU.basket: 0.3906, IoU.waterfall: 0.5456, IoU.tent: 0.9532, IoU.bag: 0.2658, IoU.minibike: 0.7542, IoU.cradle: 0.8828, IoU.oven: 0.5831, IoU.ball: 0.4969, IoU.food: 0.5735, IoU.step: 0.2463, IoU.tank: 0.6354, IoU.trade name: 0.0907, IoU.microwave: 0.8735, IoU.pot: 0.5799, IoU.animal: 0.6228, IoU.bicycle: 0.5750, IoU.lake: 0.6220, IoU.dishwasher: 0.7477, IoU.screen: 0.5643, IoU.blanket: 0.2297, IoU.sculpture: 0.6964, IoU.hood: 0.6294, IoU.sconce: 0.6160, IoU.vase: 0.4496, IoU.traffic light: 0.3518, IoU.tray: 0.1742, IoU.ashcan: 0.4804, IoU.fan: 0.6878, IoU.pier: 0.4537, IoU.crt screen: 0.0587, IoU.plate: 0.6125, IoU.monitor: 0.6165, IoU.bulletin board: 0.5738, IoU.shower: 0.0000, IoU.radiator: 0.6542, IoU.glass: 0.1685, IoU.clock: 0.4737, IoU.flag: 0.6956, Acc.wall: 0.8865, Acc.building: 0.9303, Acc.sky: 0.9791, Acc.floor: 0.9213, Acc.tree: 0.8766, Acc.ceiling: 0.9475, Acc.road: 0.8811, Acc.bed : 0.9733, Acc.windowpane: 0.8261, Acc.grass: 0.8276, Acc.cabinet: 0.7884, Acc.sidewalk: 0.8589, Acc.person: 0.9317, Acc.earth: 0.5678, Acc.door: 0.7046, Acc.table: 0.7959, Acc.mountain: 0.6406, Acc.plant: 0.7084, Acc.curtain: 0.9054, Acc.chair: 0.7314, Acc.car: 0.9384, Acc.water: 0.8711, Acc.painting: 0.8903, Acc.sofa: 0.8759, Acc.shelf: 0.7416, Acc.house: 0.7780, Acc.sea: 0.6430, Acc.mirror: 0.8741, Acc.rug: 0.8239, Acc.field: 0.5308, Acc.armchair: 0.7930, Acc.seat: 0.8708, Acc.fence: 0.5698, Acc.desk: 0.7717, Acc.rock: 0.6936, Acc.wardrobe: 0.7401, Acc.lamp: 0.8427, Acc.bathtub: 0.8664, Acc.railing: 0.5860, Acc.cushion: 0.7614, Acc.base: 0.6828, Acc.box: 0.4359, Acc.column: 0.7515, Acc.signboard: 0.5102, Acc.chest of drawers: 0.6265, Acc.counter: 0.4721, Acc.sand: 0.7900, Acc.sink: 0.8785, Acc.skyscraper: 0.6327, Acc.fireplace: 0.9612, Acc.refrigerator: 0.9266, Acc.grandstand: 0.7712, Acc.path: 0.4184, Acc.stairs: 0.3636, Acc.runway: 0.8898, Acc.case: 0.8933, Acc.pool table: 0.9778, Acc.pillow: 0.7957, Acc.screen door: 0.9214, Acc.stairway: 0.6231, Acc.river: 0.2492, Acc.bridge: 0.8209, Acc.bookcase: 0.5731, Acc.blind: 0.4203, Acc.coffee table: 0.9010, Acc.toilet: 0.9101, Acc.flower: 0.4869, Acc.book: 0.7584, Acc.hill: 0.0836, Acc.bench: 0.7680, Acc.countertop: 0.7792, Acc.stove: 0.9172, Acc.palm: 0.8651, Acc.kitchen island: 0.7673, Acc.computer: 0.9250, Acc.swivel chair: 0.7519, Acc.boat: 0.8922, Acc.bar: 0.8291, Acc.arcade machine: 0.8402, Acc.hovel: 0.0373, Acc.bus: 0.9691, Acc.towel: 0.8591, Acc.light: 0.7189, Acc.truck: 0.6156, Acc.tower: 0.6103, Acc.chandelier: 0.8628, Acc.awning: 0.5034, Acc.streetlight: 0.5034, Acc.booth: 0.7012, Acc.television receiver: 0.8771, Acc.airplane: 0.9393, Acc.dirt track: 0.3209, Acc.apparel: 0.8161, Acc.pole: 0.3587, Acc.land: 0.0276, Acc.bannister: 0.2550, Acc.escalator: 0.8712, Acc.ottoman: 0.7066, Acc.bottle: 0.4688, Acc.buffet: 0.8737, Acc.poster: 0.4542, Acc.stage: 0.3761, Acc.van: 0.6681, Acc.ship: 0.3518, Acc.fountain: 0.4057, Acc.conveyer belt: 0.9341, Acc.canopy: 0.8228, Acc.washer: 0.8867, Acc.plaything: 0.4787, Acc.swimming pool: 0.7905, Acc.stool: 0.6915, Acc.barrel: 0.6446, Acc.basket: 0.6664, Acc.waterfall: 0.6324, Acc.tent: 0.9778, Acc.bag: 0.3118, Acc.minibike: 0.8614, Acc.cradle: 0.9791, Acc.oven: 0.6904, Acc.ball: 0.5513, Acc.food: 0.7024, Acc.step: 0.2719, Acc.tank: 0.7052, Acc.trade name: 0.0964, Acc.microwave: 0.9642, Acc.pot: 0.7280, Acc.animal: 0.6469, Acc.bicycle: 0.7630, Acc.lake: 0.6361, Acc.dishwasher: 0.8263, Acc.screen: 0.9785, Acc.blanket: 0.2682, Acc.sculpture: 0.8452, Acc.hood: 0.7897, Acc.sconce: 0.7402, Acc.vase: 0.6083, Acc.traffic light: 0.6473, Acc.tray: 0.2203, Acc.ashcan: 0.6190, Acc.fan: 0.8221, Acc.pier: 0.5975, Acc.crt screen: 0.0709, Acc.plate: 0.8172, Acc.monitor: 0.7318, Acc.bulletin board: 0.7776, Acc.shower: 0.0000, Acc.radiator: 0.7737, Acc.glass: 0.1756, Acc.clock: 0.5949, Acc.flag: 0.7952 +2024-06-16 09:43:45,529 - mmseg - INFO - Iter [25050/80000] lr: 2.748e-05, eta: 1 day, 2:58:49, time: 3.596, data_time: 1.981, memory: 71384, decode.loss_ce: 0.2567, decode.acc_seg: 89.2602, aux.loss_ce: 0.1050, aux.acc_seg: 89.0688, loss: 0.3617 +2024-06-16 09:45:07,362 - mmseg - INFO - Iter [25100/80000] lr: 2.745e-05, eta: 1 day, 2:57:06, time: 1.637, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2584, decode.acc_seg: 89.8903, aux.loss_ce: 0.1054, aux.acc_seg: 89.6314, loss: 0.3639 +2024-06-16 09:46:28,495 - mmseg - INFO - Iter [25150/80000] lr: 2.743e-05, eta: 1 day, 2:55:22, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2668, decode.acc_seg: 89.4299, aux.loss_ce: 0.1089, aux.acc_seg: 89.1761, loss: 0.3758 +2024-06-16 09:47:49,559 - mmseg - INFO - Iter [25200/80000] lr: 2.740e-05, eta: 1 day, 2:53:38, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2608, decode.acc_seg: 89.3471, aux.loss_ce: 0.1067, aux.acc_seg: 89.0462, loss: 0.3675 +2024-06-16 09:49:10,728 - mmseg - INFO - Iter [25250/80000] lr: 2.738e-05, eta: 1 day, 2:51:54, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2380, decode.acc_seg: 90.2432, aux.loss_ce: 0.0975, aux.acc_seg: 89.8561, loss: 0.3355 +2024-06-16 09:50:34,031 - mmseg - INFO - Iter [25300/80000] lr: 2.735e-05, eta: 1 day, 2:50:15, time: 1.666, data_time: 0.052, memory: 71384, decode.loss_ce: 0.2408, decode.acc_seg: 90.0548, aux.loss_ce: 0.0999, aux.acc_seg: 89.6901, loss: 0.3407 +2024-06-16 09:51:55,277 - mmseg - INFO - Iter [25350/80000] lr: 2.733e-05, eta: 1 day, 2:48:31, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2690, decode.acc_seg: 88.9762, aux.loss_ce: 0.1102, aux.acc_seg: 88.7582, loss: 0.3793 +2024-06-16 09:53:16,337 - mmseg - INFO - Iter [25400/80000] lr: 2.730e-05, eta: 1 day, 2:46:48, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2691, decode.acc_seg: 88.7395, aux.loss_ce: 0.1106, aux.acc_seg: 88.4074, loss: 0.3798 +2024-06-16 09:54:37,549 - mmseg - INFO - Iter [25450/80000] lr: 2.728e-05, eta: 1 day, 2:45:04, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2437, decode.acc_seg: 89.9240, aux.loss_ce: 0.1009, aux.acc_seg: 89.6171, loss: 0.3446 +2024-06-16 09:55:58,670 - mmseg - INFO - Iter [25500/80000] lr: 2.725e-05, eta: 1 day, 2:43:21, time: 1.622, data_time: 0.009, memory: 71384, decode.loss_ce: 0.2515, decode.acc_seg: 89.9568, aux.loss_ce: 0.1020, aux.acc_seg: 89.7404, loss: 0.3535 +2024-06-16 09:57:19,783 - mmseg - INFO - Iter [25550/80000] lr: 2.723e-05, eta: 1 day, 2:41:37, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2811, decode.acc_seg: 89.1131, aux.loss_ce: 0.1146, aux.acc_seg: 88.8840, loss: 0.3957 +2024-06-16 09:58:40,964 - mmseg - INFO - Iter [25600/80000] lr: 2.720e-05, eta: 1 day, 2:39:54, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2683, decode.acc_seg: 88.9373, aux.loss_ce: 0.1093, aux.acc_seg: 88.7827, loss: 0.3776 +2024-06-16 10:00:02,123 - mmseg - INFO - Iter [25650/80000] lr: 2.718e-05, eta: 1 day, 2:38:11, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2579, decode.acc_seg: 89.7460, aux.loss_ce: 0.1048, aux.acc_seg: 89.5320, loss: 0.3627 +2024-06-16 10:01:23,348 - mmseg - INFO - Iter [25700/80000] lr: 2.715e-05, eta: 1 day, 2:36:28, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2486, decode.acc_seg: 89.6493, aux.loss_ce: 0.1016, aux.acc_seg: 89.5028, loss: 0.3502 +2024-06-16 10:02:44,451 - mmseg - INFO - Iter [25750/80000] lr: 2.713e-05, eta: 1 day, 2:34:44, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2467, decode.acc_seg: 89.9991, aux.loss_ce: 0.1013, aux.acc_seg: 89.7594, loss: 0.3480 +2024-06-16 10:04:05,689 - mmseg - INFO - Iter [25800/80000] lr: 2.710e-05, eta: 1 day, 2:33:02, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2495, decode.acc_seg: 89.5383, aux.loss_ce: 0.1021, aux.acc_seg: 89.3471, loss: 0.3516 +2024-06-16 10:05:26,864 - mmseg - INFO - Iter [25850/80000] lr: 2.708e-05, eta: 1 day, 2:31:19, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2458, decode.acc_seg: 89.9094, aux.loss_ce: 0.1004, aux.acc_seg: 89.6857, loss: 0.3462 +2024-06-16 10:06:48,043 - mmseg - INFO - Iter [25900/80000] lr: 2.705e-05, eta: 1 day, 2:29:36, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2536, decode.acc_seg: 89.6684, aux.loss_ce: 0.1041, aux.acc_seg: 89.3956, loss: 0.3577 +2024-06-16 10:08:09,148 - mmseg - INFO - Iter [25950/80000] lr: 2.703e-05, eta: 1 day, 2:27:53, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2477, decode.acc_seg: 89.5563, aux.loss_ce: 0.1022, aux.acc_seg: 89.2761, loss: 0.3499 +2024-06-16 10:09:30,324 - mmseg - INFO - Saving checkpoint at 26000 iterations +2024-06-16 10:10:53,153 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 10:10:53,153 - mmseg - INFO - Iter [26000/80000] lr: 2.700e-05, eta: 1 day, 2:29:03, time: 3.280, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2511, decode.acc_seg: 89.6676, aux.loss_ce: 0.1023, aux.acc_seg: 89.4313, loss: 0.3534 +2024-06-16 10:12:30,879 - mmseg - INFO - per class results: +2024-06-16 10:12:30,885 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.58 | 89.28 | +| building | 85.37 | 92.93 | +| sky | 94.95 | 97.54 | +| floor | 85.52 | 91.18 | +| tree | 77.81 | 90.8 | +| ceiling | 87.27 | 94.07 | +| road | 85.31 | 90.59 | +| bed | 91.75 | 97.77 | +| windowpane | 66.1 | 79.21 | +| grass | 70.88 | 85.56 | +| cabinet | 65.78 | 76.49 | +| sidewalk | 69.2 | 85.57 | +| person | 85.46 | 93.51 | +| earth | 40.09 | 54.53 | +| door | 59.85 | 75.7 | +| table | 68.66 | 82.3 | +| mountain | 60.49 | 71.26 | +| plant | 57.38 | 71.99 | +| curtain | 79.02 | 90.49 | +| chair | 67.7 | 77.06 | +| car | 87.53 | 92.6 | +| water | 59.52 | 76.17 | +| painting | 78.04 | 90.13 | +| sofa | 80.64 | 90.09 | +| shelf | 48.05 | 71.1 | +| house | 58.11 | 73.2 | +| sea | 61.06 | 69.56 | +| mirror | 77.72 | 90.35 | +| rug | 71.86 | 80.85 | +| field | 34.27 | 48.11 | +| armchair | 57.44 | 78.63 | +| seat | 72.59 | 88.31 | +| fence | 45.76 | 53.38 | +| desk | 58.66 | 79.6 | +| rock | 54.9 | 69.53 | +| wardrobe | 56.42 | 75.21 | +| lamp | 73.73 | 86.24 | +| bathtub | 89.4 | 91.63 | +| railing | 42.98 | 56.64 | +| cushion | 68.63 | 80.68 | +| base | 36.79 | 64.71 | +| box | 32.45 | 43.81 | +| column | 51.14 | 57.68 | +| signboard | 43.25 | 57.69 | +| chest of drawers | 48.78 | 69.3 | +| counter | 39.0 | 43.32 | +| sand | 56.54 | 88.79 | +| sink | 79.71 | 85.42 | +| skyscraper | 46.33 | 57.88 | +| fireplace | 71.5 | 94.44 | +| refrigerator | 85.47 | 91.38 | +| grandstand | 50.04 | 82.94 | +| path | 29.34 | 38.96 | +| stairs | 27.1 | 32.06 | +| runway | 72.74 | 98.55 | +| case | 63.71 | 79.33 | +| pool table | 94.37 | 97.83 | +| pillow | 63.53 | 69.61 | +| screen door | 46.03 | 47.45 | +| stairway | 42.96 | 66.95 | +| river | 16.19 | 39.36 | +| bridge | 70.92 | 80.14 | +| bookcase | 37.79 | 49.51 | +| blind | 50.16 | 62.99 | +| coffee table | 66.43 | 84.26 | +| toilet | 89.33 | 94.39 | +| flower | 43.55 | 54.59 | +| book | 49.73 | 83.65 | +| hill | 7.84 | 23.05 | +| bench | 57.64 | 74.76 | +| countertop | 66.4 | 82.75 | +| stove | 78.31 | 81.14 | +| palm | 57.63 | 78.49 | +| kitchen island | 51.11 | 83.3 | +| computer | 78.31 | 91.58 | +| swivel chair | 53.65 | 77.61 | +| boat | 67.89 | 91.38 | +| bar | 65.97 | 82.16 | +| arcade machine | 75.21 | 81.57 | +| hovel | 19.74 | 21.19 | +| bus | 92.11 | 96.64 | +| towel | 74.9 | 88.75 | +| light | 57.38 | 62.04 | +| truck | 42.63 | 48.23 | +| tower | 14.23 | 20.14 | +| chandelier | 72.19 | 85.99 | +| awning | 40.32 | 55.34 | +| streetlight | 35.79 | 53.29 | +| booth | 43.31 | 58.51 | +| television receiver | 72.57 | 89.63 | +| airplane | 87.09 | 95.94 | +| dirt track | 10.49 | 56.34 | +| apparel | 47.36 | 66.46 | +| pole | 18.37 | 21.96 | +| land | 0.95 | 1.52 | +| bannister | 18.1 | 23.22 | +| escalator | 63.27 | 81.73 | +| ottoman | 47.08 | 75.22 | +| bottle | 24.69 | 27.94 | +| buffet | 58.28 | 89.59 | +| poster | 34.15 | 45.69 | +| stage | 17.73 | 50.63 | +| van | 44.69 | 76.12 | +| ship | 82.43 | 89.97 | +| fountain | 35.19 | 37.43 | +| conveyer belt | 73.94 | 94.28 | +| canopy | 51.0 | 73.53 | +| washer | 80.69 | 86.44 | +| plaything | 29.91 | 40.02 | +| swimming pool | 53.73 | 77.6 | +| stool | 54.42 | 62.92 | +| barrel | 53.33 | 64.92 | +| basket | 42.51 | 59.76 | +| waterfall | 67.45 | 80.03 | +| tent | 92.59 | 97.6 | +| bag | 24.18 | 25.8 | +| minibike | 74.63 | 90.81 | +| cradle | 88.92 | 97.7 | +| oven | 51.35 | 81.34 | +| ball | 24.42 | 25.37 | +| food | 64.86 | 74.59 | +| step | 19.39 | 21.37 | +| tank | 63.22 | 66.77 | +| trade name | 33.99 | 43.86 | +| microwave | 90.08 | 95.36 | +| pot | 60.04 | 71.73 | +| animal | 62.76 | 65.26 | +| bicycle | 59.52 | 78.72 | +| lake | 57.79 | 63.66 | +| dishwasher | 72.05 | 81.51 | +| screen | 64.08 | 94.04 | +| blanket | 30.02 | 37.43 | +| sculpture | 74.17 | 87.18 | +| hood | 63.44 | 75.46 | +| sconce | 60.46 | 73.04 | +| vase | 47.77 | 61.56 | +| traffic light | 37.63 | 62.74 | +| tray | 14.91 | 16.81 | +| ashcan | 47.74 | 63.62 | +| fan | 68.36 | 79.91 | +| pier | 69.03 | 86.98 | +| crt screen | 4.66 | 5.34 | +| plate | 59.64 | 82.4 | +| monitor | 61.23 | 72.37 | +| bulletin board | 46.65 | 54.33 | +| shower | 0.72 | 9.65 | +| radiator | 63.86 | 81.43 | +| glass | 19.88 | 21.23 | +| clock | 51.52 | 61.89 | +| flag | 71.71 | 80.48 | ++---------------------+-------+-------+ +2024-06-16 10:12:30,885 - mmseg - INFO - Summary: +2024-06-16 10:12:30,885 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.94 | 56.54 | 69.62 | ++-------+-------+-------+ +2024-06-16 10:12:30,886 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 10:12:30,886 - mmseg - INFO - Iter(val) [250] aAcc: 0.8594, mIoU: 0.5654, mAcc: 0.6962, IoU.wall: 0.8158, IoU.building: 0.8537, IoU.sky: 0.9495, IoU.floor: 0.8552, IoU.tree: 0.7781, IoU.ceiling: 0.8727, IoU.road: 0.8531, IoU.bed : 0.9175, IoU.windowpane: 0.6610, IoU.grass: 0.7088, IoU.cabinet: 0.6578, IoU.sidewalk: 0.6920, IoU.person: 0.8546, IoU.earth: 0.4009, IoU.door: 0.5985, IoU.table: 0.6866, IoU.mountain: 0.6049, IoU.plant: 0.5738, IoU.curtain: 0.7902, IoU.chair: 0.6770, IoU.car: 0.8753, IoU.water: 0.5952, IoU.painting: 0.7804, IoU.sofa: 0.8064, IoU.shelf: 0.4805, IoU.house: 0.5811, IoU.sea: 0.6106, IoU.mirror: 0.7772, IoU.rug: 0.7186, IoU.field: 0.3427, IoU.armchair: 0.5744, IoU.seat: 0.7259, IoU.fence: 0.4576, IoU.desk: 0.5866, IoU.rock: 0.5490, IoU.wardrobe: 0.5642, IoU.lamp: 0.7373, IoU.bathtub: 0.8940, IoU.railing: 0.4298, IoU.cushion: 0.6863, IoU.base: 0.3679, IoU.box: 0.3245, IoU.column: 0.5114, IoU.signboard: 0.4325, IoU.chest of drawers: 0.4878, IoU.counter: 0.3900, IoU.sand: 0.5654, IoU.sink: 0.7971, IoU.skyscraper: 0.4633, IoU.fireplace: 0.7150, IoU.refrigerator: 0.8547, IoU.grandstand: 0.5004, IoU.path: 0.2934, IoU.stairs: 0.2710, IoU.runway: 0.7274, IoU.case: 0.6371, IoU.pool table: 0.9437, IoU.pillow: 0.6353, IoU.screen door: 0.4603, IoU.stairway: 0.4296, IoU.river: 0.1619, IoU.bridge: 0.7092, IoU.bookcase: 0.3779, IoU.blind: 0.5016, IoU.coffee table: 0.6643, IoU.toilet: 0.8933, IoU.flower: 0.4355, IoU.book: 0.4973, IoU.hill: 0.0784, IoU.bench: 0.5764, IoU.countertop: 0.6640, IoU.stove: 0.7831, IoU.palm: 0.5763, IoU.kitchen island: 0.5111, IoU.computer: 0.7831, IoU.swivel chair: 0.5365, IoU.boat: 0.6789, IoU.bar: 0.6597, IoU.arcade machine: 0.7521, IoU.hovel: 0.1974, IoU.bus: 0.9211, IoU.towel: 0.7490, IoU.light: 0.5738, IoU.truck: 0.4263, IoU.tower: 0.1423, IoU.chandelier: 0.7219, IoU.awning: 0.4032, IoU.streetlight: 0.3579, IoU.booth: 0.4331, IoU.television receiver: 0.7257, IoU.airplane: 0.8709, IoU.dirt track: 0.1049, IoU.apparel: 0.4736, IoU.pole: 0.1837, IoU.land: 0.0095, IoU.bannister: 0.1810, IoU.escalator: 0.6327, IoU.ottoman: 0.4708, IoU.bottle: 0.2469, IoU.buffet: 0.5828, IoU.poster: 0.3415, IoU.stage: 0.1773, IoU.van: 0.4469, IoU.ship: 0.8243, IoU.fountain: 0.3519, IoU.conveyer belt: 0.7394, IoU.canopy: 0.5100, IoU.washer: 0.8069, IoU.plaything: 0.2991, IoU.swimming pool: 0.5373, IoU.stool: 0.5442, IoU.barrel: 0.5333, IoU.basket: 0.4251, IoU.waterfall: 0.6745, IoU.tent: 0.9259, IoU.bag: 0.2418, IoU.minibike: 0.7463, IoU.cradle: 0.8892, IoU.oven: 0.5135, IoU.ball: 0.2442, IoU.food: 0.6486, IoU.step: 0.1939, IoU.tank: 0.6322, IoU.trade name: 0.3399, IoU.microwave: 0.9008, IoU.pot: 0.6004, IoU.animal: 0.6276, IoU.bicycle: 0.5952, IoU.lake: 0.5779, IoU.dishwasher: 0.7205, IoU.screen: 0.6408, IoU.blanket: 0.3002, IoU.sculpture: 0.7417, IoU.hood: 0.6344, IoU.sconce: 0.6046, IoU.vase: 0.4777, IoU.traffic light: 0.3763, IoU.tray: 0.1491, IoU.ashcan: 0.4774, IoU.fan: 0.6836, IoU.pier: 0.6903, IoU.crt screen: 0.0466, IoU.plate: 0.5964, IoU.monitor: 0.6123, IoU.bulletin board: 0.4665, IoU.shower: 0.0072, IoU.radiator: 0.6386, IoU.glass: 0.1988, IoU.clock: 0.5152, IoU.flag: 0.7171, Acc.wall: 0.8928, Acc.building: 0.9293, Acc.sky: 0.9754, Acc.floor: 0.9118, Acc.tree: 0.9080, Acc.ceiling: 0.9407, Acc.road: 0.9059, Acc.bed : 0.9777, Acc.windowpane: 0.7921, Acc.grass: 0.8556, Acc.cabinet: 0.7649, Acc.sidewalk: 0.8557, Acc.person: 0.9351, Acc.earth: 0.5453, Acc.door: 0.7570, Acc.table: 0.8230, Acc.mountain: 0.7126, Acc.plant: 0.7199, Acc.curtain: 0.9049, Acc.chair: 0.7706, Acc.car: 0.9260, Acc.water: 0.7617, Acc.painting: 0.9013, Acc.sofa: 0.9009, Acc.shelf: 0.7110, Acc.house: 0.7320, Acc.sea: 0.6956, Acc.mirror: 0.9035, Acc.rug: 0.8085, Acc.field: 0.4811, Acc.armchair: 0.7863, Acc.seat: 0.8831, Acc.fence: 0.5338, Acc.desk: 0.7960, Acc.rock: 0.6953, Acc.wardrobe: 0.7521, Acc.lamp: 0.8624, Acc.bathtub: 0.9163, Acc.railing: 0.5664, Acc.cushion: 0.8068, Acc.base: 0.6471, Acc.box: 0.4381, Acc.column: 0.5768, Acc.signboard: 0.5769, Acc.chest of drawers: 0.6930, Acc.counter: 0.4332, Acc.sand: 0.8879, Acc.sink: 0.8542, Acc.skyscraper: 0.5788, Acc.fireplace: 0.9444, Acc.refrigerator: 0.9138, Acc.grandstand: 0.8294, Acc.path: 0.3896, Acc.stairs: 0.3206, Acc.runway: 0.9855, Acc.case: 0.7933, Acc.pool table: 0.9783, Acc.pillow: 0.6961, Acc.screen door: 0.4745, Acc.stairway: 0.6695, Acc.river: 0.3936, Acc.bridge: 0.8014, Acc.bookcase: 0.4951, Acc.blind: 0.6299, Acc.coffee table: 0.8426, Acc.toilet: 0.9439, Acc.flower: 0.5459, Acc.book: 0.8365, Acc.hill: 0.2305, Acc.bench: 0.7476, Acc.countertop: 0.8275, Acc.stove: 0.8114, Acc.palm: 0.7849, Acc.kitchen island: 0.8330, Acc.computer: 0.9158, Acc.swivel chair: 0.7761, Acc.boat: 0.9138, Acc.bar: 0.8216, Acc.arcade machine: 0.8157, Acc.hovel: 0.2119, Acc.bus: 0.9664, Acc.towel: 0.8875, Acc.light: 0.6204, Acc.truck: 0.4823, Acc.tower: 0.2014, Acc.chandelier: 0.8599, Acc.awning: 0.5534, Acc.streetlight: 0.5329, Acc.booth: 0.5851, Acc.television receiver: 0.8963, Acc.airplane: 0.9594, Acc.dirt track: 0.5634, Acc.apparel: 0.6646, Acc.pole: 0.2196, Acc.land: 0.0152, Acc.bannister: 0.2322, Acc.escalator: 0.8173, Acc.ottoman: 0.7522, Acc.bottle: 0.2794, Acc.buffet: 0.8959, Acc.poster: 0.4569, Acc.stage: 0.5063, Acc.van: 0.7612, Acc.ship: 0.8997, Acc.fountain: 0.3743, Acc.conveyer belt: 0.9428, Acc.canopy: 0.7353, Acc.washer: 0.8644, Acc.plaything: 0.4002, Acc.swimming pool: 0.7760, Acc.stool: 0.6292, Acc.barrel: 0.6492, Acc.basket: 0.5976, Acc.waterfall: 0.8003, Acc.tent: 0.9760, Acc.bag: 0.2580, Acc.minibike: 0.9081, Acc.cradle: 0.9770, Acc.oven: 0.8134, Acc.ball: 0.2537, Acc.food: 0.7459, Acc.step: 0.2137, Acc.tank: 0.6677, Acc.trade name: 0.4386, Acc.microwave: 0.9536, Acc.pot: 0.7173, Acc.animal: 0.6526, Acc.bicycle: 0.7872, Acc.lake: 0.6366, Acc.dishwasher: 0.8151, Acc.screen: 0.9404, Acc.blanket: 0.3743, Acc.sculpture: 0.8718, Acc.hood: 0.7546, Acc.sconce: 0.7304, Acc.vase: 0.6156, Acc.traffic light: 0.6274, Acc.tray: 0.1681, Acc.ashcan: 0.6362, Acc.fan: 0.7991, Acc.pier: 0.8698, Acc.crt screen: 0.0534, Acc.plate: 0.8240, Acc.monitor: 0.7237, Acc.bulletin board: 0.5433, Acc.shower: 0.0965, Acc.radiator: 0.8143, Acc.glass: 0.2123, Acc.clock: 0.6189, Acc.flag: 0.8048 +2024-06-16 10:13:52,452 - mmseg - INFO - Iter [26050/80000] lr: 2.698e-05, eta: 1 day, 2:30:43, time: 3.586, data_time: 1.971, memory: 71384, decode.loss_ce: 0.2399, decode.acc_seg: 90.0470, aux.loss_ce: 0.0989, aux.acc_seg: 89.7069, loss: 0.3388 +2024-06-16 10:15:13,615 - mmseg - INFO - Iter [26100/80000] lr: 2.695e-05, eta: 1 day, 2:28:59, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2596, decode.acc_seg: 89.3520, aux.loss_ce: 0.1052, aux.acc_seg: 89.2084, loss: 0.3649 +2024-06-16 10:16:34,847 - mmseg - INFO - Iter [26150/80000] lr: 2.693e-05, eta: 1 day, 2:27:16, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2511, decode.acc_seg: 89.6584, aux.loss_ce: 0.1027, aux.acc_seg: 89.3648, loss: 0.3538 +2024-06-16 10:17:56,014 - mmseg - INFO - Iter [26200/80000] lr: 2.690e-05, eta: 1 day, 2:25:33, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2598, decode.acc_seg: 89.1024, aux.loss_ce: 0.1066, aux.acc_seg: 88.7993, loss: 0.3664 +2024-06-16 10:19:17,114 - mmseg - INFO - Iter [26250/80000] lr: 2.688e-05, eta: 1 day, 2:23:49, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2405, decode.acc_seg: 90.2987, aux.loss_ce: 0.0990, aux.acc_seg: 90.0145, loss: 0.3394 +2024-06-16 10:20:38,171 - mmseg - INFO - Iter [26300/80000] lr: 2.685e-05, eta: 1 day, 2:22:06, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2507, decode.acc_seg: 89.3766, aux.loss_ce: 0.1017, aux.acc_seg: 89.1682, loss: 0.3524 +2024-06-16 10:21:59,323 - mmseg - INFO - Iter [26350/80000] lr: 2.683e-05, eta: 1 day, 2:20:23, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2544, decode.acc_seg: 89.5950, aux.loss_ce: 0.1023, aux.acc_seg: 89.3641, loss: 0.3567 +2024-06-16 10:23:20,410 - mmseg - INFO - Iter [26400/80000] lr: 2.680e-05, eta: 1 day, 2:18:40, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2351, decode.acc_seg: 90.6019, aux.loss_ce: 0.0968, aux.acc_seg: 90.2204, loss: 0.3318 +2024-06-16 10:24:41,533 - mmseg - INFO - Iter [26450/80000] lr: 2.678e-05, eta: 1 day, 2:16:57, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2482, decode.acc_seg: 89.7905, aux.loss_ce: 0.1011, aux.acc_seg: 89.6358, loss: 0.3493 +2024-06-16 10:26:02,637 - mmseg - INFO - Iter [26500/80000] lr: 2.675e-05, eta: 1 day, 2:15:14, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2417, decode.acc_seg: 90.1988, aux.loss_ce: 0.0989, aux.acc_seg: 89.8975, loss: 0.3406 +2024-06-16 10:27:26,844 - mmseg - INFO - Iter [26550/80000] lr: 2.673e-05, eta: 1 day, 2:13:37, time: 1.684, data_time: 0.072, memory: 71384, decode.loss_ce: 0.2460, decode.acc_seg: 89.9288, aux.loss_ce: 0.1007, aux.acc_seg: 89.5916, loss: 0.3466 +2024-06-16 10:28:47,939 - mmseg - INFO - Iter [26600/80000] lr: 2.670e-05, eta: 1 day, 2:11:54, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2366, decode.acc_seg: 90.4276, aux.loss_ce: 0.0971, aux.acc_seg: 90.2413, loss: 0.3337 +2024-06-16 10:30:09,009 - mmseg - INFO - Iter [26650/80000] lr: 2.668e-05, eta: 1 day, 2:10:12, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2302, decode.acc_seg: 90.5777, aux.loss_ce: 0.0943, aux.acc_seg: 90.2903, loss: 0.3244 +2024-06-16 10:31:30,137 - mmseg - INFO - Iter [26700/80000] lr: 2.665e-05, eta: 1 day, 2:08:29, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2406, decode.acc_seg: 90.1085, aux.loss_ce: 0.0989, aux.acc_seg: 89.8409, loss: 0.3396 +2024-06-16 10:32:51,234 - mmseg - INFO - Iter [26750/80000] lr: 2.663e-05, eta: 1 day, 2:06:46, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2591, decode.acc_seg: 89.6447, aux.loss_ce: 0.1057, aux.acc_seg: 89.3440, loss: 0.3648 +2024-06-16 10:34:12,285 - mmseg - INFO - Iter [26800/80000] lr: 2.660e-05, eta: 1 day, 2:05:04, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2419, decode.acc_seg: 90.1650, aux.loss_ce: 0.0995, aux.acc_seg: 89.8696, loss: 0.3414 +2024-06-16 10:35:33,477 - mmseg - INFO - Iter [26850/80000] lr: 2.658e-05, eta: 1 day, 2:03:21, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2374, decode.acc_seg: 90.1904, aux.loss_ce: 0.0977, aux.acc_seg: 89.8668, loss: 0.3351 +2024-06-16 10:36:54,408 - mmseg - INFO - Iter [26900/80000] lr: 2.655e-05, eta: 1 day, 2:01:39, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2412, decode.acc_seg: 90.0099, aux.loss_ce: 0.0980, aux.acc_seg: 89.7615, loss: 0.3392 +2024-06-16 10:38:15,489 - mmseg - INFO - Iter [26950/80000] lr: 2.653e-05, eta: 1 day, 1:59:56, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2430, decode.acc_seg: 89.9879, aux.loss_ce: 0.0990, aux.acc_seg: 89.7687, loss: 0.3420 +2024-06-16 10:39:36,505 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 10:39:36,505 - mmseg - INFO - Iter [27000/80000] lr: 2.650e-05, eta: 1 day, 1:58:14, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2382, decode.acc_seg: 89.7641, aux.loss_ce: 0.0980, aux.acc_seg: 89.5683, loss: 0.3363 +2024-06-16 10:41:15,463 - mmseg - INFO - per class results: +2024-06-16 10:41:15,469 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.5 | 90.13 | +| building | 84.32 | 94.11 | +| sky | 95.03 | 97.3 | +| floor | 85.27 | 91.59 | +| tree | 77.44 | 88.58 | +| ceiling | 86.29 | 91.45 | +| road | 86.2 | 91.18 | +| bed | 91.99 | 96.91 | +| windowpane | 66.59 | 79.24 | +| grass | 66.45 | 81.14 | +| cabinet | 65.14 | 75.85 | +| sidewalk | 69.66 | 84.42 | +| person | 85.19 | 93.75 | +| earth | 37.28 | 51.72 | +| door | 57.33 | 75.99 | +| table | 68.39 | 81.07 | +| mountain | 60.2 | 79.94 | +| plant | 57.47 | 67.27 | +| curtain | 81.0 | 90.76 | +| chair | 66.15 | 76.84 | +| car | 86.38 | 93.96 | +| water | 50.82 | 58.77 | +| painting | 74.37 | 93.07 | +| sofa | 78.14 | 84.78 | +| shelf | 47.47 | 57.69 | +| house | 43.18 | 47.79 | +| sea | 62.27 | 84.26 | +| mirror | 72.9 | 77.74 | +| rug | 73.13 | 81.36 | +| field | 32.98 | 50.63 | +| armchair | 57.03 | 82.41 | +| seat | 67.59 | 89.76 | +| fence | 47.49 | 58.74 | +| desk | 56.85 | 80.64 | +| rock | 46.56 | 52.42 | +| wardrobe | 51.18 | 65.3 | +| lamp | 74.23 | 88.76 | +| bathtub | 87.45 | 89.89 | +| railing | 44.93 | 61.6 | +| cushion | 72.06 | 82.88 | +| base | 42.2 | 60.21 | +| box | 35.55 | 48.0 | +| column | 57.71 | 72.66 | +| signboard | 41.26 | 54.95 | +| chest of drawers | 45.37 | 74.53 | +| counter | 49.71 | 57.95 | +| sand | 57.06 | 86.42 | +| sink | 77.63 | 83.6 | +| skyscraper | 47.88 | 60.46 | +| fireplace | 73.83 | 96.09 | +| refrigerator | 85.27 | 94.02 | +| grandstand | 50.51 | 81.49 | +| path | 30.8 | 47.6 | +| stairs | 30.97 | 41.49 | +| runway | 72.82 | 94.81 | +| case | 62.25 | 77.79 | +| pool table | 94.14 | 98.24 | +| pillow | 70.94 | 81.17 | +| screen door | 65.94 | 68.8 | +| stairway | 34.61 | 44.76 | +| river | 14.35 | 46.54 | +| bridge | 66.07 | 75.26 | +| bookcase | 39.37 | 61.05 | +| blind | 48.08 | 62.08 | +| coffee table | 62.71 | 83.27 | +| toilet | 90.56 | 94.23 | +| flower | 39.96 | 52.52 | +| book | 53.51 | 76.67 | +| hill | 4.71 | 9.39 | +| bench | 53.46 | 64.35 | +| countertop | 64.59 | 83.94 | +| stove | 84.43 | 92.48 | +| palm | 57.06 | 76.45 | +| kitchen island | 46.29 | 87.0 | +| computer | 78.33 | 91.32 | +| swivel chair | 48.59 | 81.4 | +| boat | 72.38 | 89.31 | +| bar | 64.96 | 89.66 | +| arcade machine | 79.06 | 84.57 | +| hovel | 19.4 | 20.94 | +| bus | 91.49 | 97.07 | +| towel | 74.21 | 89.33 | +| light | 59.74 | 80.7 | +| truck | 46.5 | 64.53 | +| tower | 29.79 | 54.16 | +| chandelier | 71.19 | 84.18 | +| awning | 41.22 | 52.27 | +| streetlight | 35.59 | 47.6 | +| booth | 44.41 | 57.43 | +| television receiver | 78.54 | 87.35 | +| airplane | 88.94 | 96.06 | +| dirt track | 7.66 | 22.97 | +| apparel | 52.94 | 76.87 | +| pole | 24.43 | 31.32 | +| land | 3.19 | 6.41 | +| bannister | 16.46 | 21.97 | +| escalator | 63.25 | 80.46 | +| ottoman | 50.77 | 65.84 | +| bottle | 42.15 | 59.55 | +| buffet | 55.94 | 64.92 | +| poster | 35.19 | 42.65 | +| stage | 20.96 | 47.37 | +| van | 44.48 | 68.27 | +| ship | 85.74 | 92.53 | +| fountain | 34.18 | 38.52 | +| conveyer belt | 71.65 | 97.35 | +| canopy | 49.6 | 69.45 | +| washer | 77.59 | 82.83 | +| plaything | 25.38 | 30.37 | +| swimming pool | 59.35 | 77.95 | +| stool | 52.46 | 67.62 | +| barrel | 53.39 | 97.13 | +| basket | 35.95 | 43.69 | +| waterfall | 65.94 | 72.43 | +| tent | 95.46 | 97.8 | +| bag | 24.92 | 28.37 | +| minibike | 74.72 | 89.21 | +| cradle | 82.66 | 97.81 | +| oven | 65.58 | 80.73 | +| ball | 29.44 | 31.35 | +| food | 65.13 | 81.34 | +| step | 25.58 | 32.17 | +| tank | 62.74 | 67.28 | +| trade name | 31.67 | 43.37 | +| microwave | 90.17 | 96.09 | +| pot | 58.08 | 70.44 | +| animal | 64.8 | 67.36 | +| bicycle | 59.5 | 73.44 | +| lake | 60.74 | 63.46 | +| dishwasher | 74.42 | 84.42 | +| screen | 55.8 | 80.19 | +| blanket | 30.21 | 34.78 | +| sculpture | 75.1 | 86.87 | +| hood | 62.02 | 73.68 | +| sconce | 59.81 | 70.05 | +| vase | 45.51 | 65.54 | +| traffic light | 32.74 | 64.17 | +| tray | 19.6 | 23.98 | +| ashcan | 45.34 | 62.38 | +| fan | 70.39 | 84.39 | +| pier | 37.34 | 47.42 | +| crt screen | 13.57 | 17.08 | +| plate | 59.49 | 83.88 | +| monitor | 64.85 | 80.78 | +| bulletin board | 60.19 | 70.98 | +| shower | 0.44 | 0.45 | +| radiator | 67.13 | 75.34 | +| glass | 20.08 | 21.54 | +| clock | 42.04 | 50.97 | +| flag | 70.46 | 76.33 | ++---------------------+-------+-------+ +2024-06-16 10:41:15,469 - mmseg - INFO - Summary: +2024-06-16 10:41:15,469 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 85.6 | 56.65 | 69.69 | ++------+-------+-------+ +2024-06-16 10:41:15,470 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 10:41:15,470 - mmseg - INFO - Iter(val) [250] aAcc: 0.8560, mIoU: 0.5665, mAcc: 0.6969, IoU.wall: 0.8150, IoU.building: 0.8432, IoU.sky: 0.9503, IoU.floor: 0.8527, IoU.tree: 0.7744, IoU.ceiling: 0.8629, IoU.road: 0.8620, IoU.bed : 0.9199, IoU.windowpane: 0.6659, IoU.grass: 0.6645, IoU.cabinet: 0.6514, IoU.sidewalk: 0.6966, IoU.person: 0.8519, IoU.earth: 0.3728, IoU.door: 0.5733, IoU.table: 0.6839, IoU.mountain: 0.6020, IoU.plant: 0.5747, IoU.curtain: 0.8100, IoU.chair: 0.6615, IoU.car: 0.8638, IoU.water: 0.5082, IoU.painting: 0.7437, IoU.sofa: 0.7814, IoU.shelf: 0.4747, IoU.house: 0.4318, IoU.sea: 0.6227, IoU.mirror: 0.7290, IoU.rug: 0.7313, IoU.field: 0.3298, IoU.armchair: 0.5703, IoU.seat: 0.6759, IoU.fence: 0.4749, IoU.desk: 0.5685, IoU.rock: 0.4656, IoU.wardrobe: 0.5118, IoU.lamp: 0.7423, IoU.bathtub: 0.8745, IoU.railing: 0.4493, IoU.cushion: 0.7206, IoU.base: 0.4220, IoU.box: 0.3555, IoU.column: 0.5771, IoU.signboard: 0.4126, IoU.chest of drawers: 0.4537, IoU.counter: 0.4971, IoU.sand: 0.5706, IoU.sink: 0.7763, IoU.skyscraper: 0.4788, IoU.fireplace: 0.7383, IoU.refrigerator: 0.8527, IoU.grandstand: 0.5051, IoU.path: 0.3080, IoU.stairs: 0.3097, IoU.runway: 0.7282, IoU.case: 0.6225, IoU.pool table: 0.9414, IoU.pillow: 0.7094, IoU.screen door: 0.6594, IoU.stairway: 0.3461, IoU.river: 0.1435, IoU.bridge: 0.6607, IoU.bookcase: 0.3937, IoU.blind: 0.4808, IoU.coffee table: 0.6271, IoU.toilet: 0.9056, IoU.flower: 0.3996, IoU.book: 0.5351, IoU.hill: 0.0471, IoU.bench: 0.5346, IoU.countertop: 0.6459, IoU.stove: 0.8443, IoU.palm: 0.5706, IoU.kitchen island: 0.4629, IoU.computer: 0.7833, IoU.swivel chair: 0.4859, IoU.boat: 0.7238, IoU.bar: 0.6496, IoU.arcade machine: 0.7906, IoU.hovel: 0.1940, IoU.bus: 0.9149, IoU.towel: 0.7421, IoU.light: 0.5974, IoU.truck: 0.4650, IoU.tower: 0.2979, IoU.chandelier: 0.7119, IoU.awning: 0.4122, IoU.streetlight: 0.3559, IoU.booth: 0.4441, IoU.television receiver: 0.7854, IoU.airplane: 0.8894, IoU.dirt track: 0.0766, IoU.apparel: 0.5294, IoU.pole: 0.2443, IoU.land: 0.0319, IoU.bannister: 0.1646, IoU.escalator: 0.6325, IoU.ottoman: 0.5077, IoU.bottle: 0.4215, IoU.buffet: 0.5594, IoU.poster: 0.3519, IoU.stage: 0.2096, IoU.van: 0.4448, IoU.ship: 0.8574, IoU.fountain: 0.3418, IoU.conveyer belt: 0.7165, IoU.canopy: 0.4960, IoU.washer: 0.7759, IoU.plaything: 0.2538, IoU.swimming pool: 0.5935, IoU.stool: 0.5246, IoU.barrel: 0.5339, IoU.basket: 0.3595, IoU.waterfall: 0.6594, IoU.tent: 0.9546, IoU.bag: 0.2492, IoU.minibike: 0.7472, IoU.cradle: 0.8266, IoU.oven: 0.6558, IoU.ball: 0.2944, IoU.food: 0.6513, IoU.step: 0.2558, IoU.tank: 0.6274, IoU.trade name: 0.3167, IoU.microwave: 0.9017, IoU.pot: 0.5808, IoU.animal: 0.6480, IoU.bicycle: 0.5950, IoU.lake: 0.6074, IoU.dishwasher: 0.7442, IoU.screen: 0.5580, IoU.blanket: 0.3021, IoU.sculpture: 0.7510, IoU.hood: 0.6202, IoU.sconce: 0.5981, IoU.vase: 0.4551, IoU.traffic light: 0.3274, IoU.tray: 0.1960, IoU.ashcan: 0.4534, IoU.fan: 0.7039, IoU.pier: 0.3734, IoU.crt screen: 0.1357, IoU.plate: 0.5949, IoU.monitor: 0.6485, IoU.bulletin board: 0.6019, IoU.shower: 0.0044, IoU.radiator: 0.6713, IoU.glass: 0.2008, IoU.clock: 0.4204, IoU.flag: 0.7046, Acc.wall: 0.9013, Acc.building: 0.9411, Acc.sky: 0.9730, Acc.floor: 0.9159, Acc.tree: 0.8858, Acc.ceiling: 0.9145, Acc.road: 0.9118, Acc.bed : 0.9691, Acc.windowpane: 0.7924, Acc.grass: 0.8114, Acc.cabinet: 0.7585, Acc.sidewalk: 0.8442, Acc.person: 0.9375, Acc.earth: 0.5172, Acc.door: 0.7599, Acc.table: 0.8107, Acc.mountain: 0.7994, Acc.plant: 0.6727, Acc.curtain: 0.9076, Acc.chair: 0.7684, Acc.car: 0.9396, Acc.water: 0.5877, Acc.painting: 0.9307, Acc.sofa: 0.8478, Acc.shelf: 0.5769, Acc.house: 0.4779, Acc.sea: 0.8426, Acc.mirror: 0.7774, Acc.rug: 0.8136, Acc.field: 0.5063, Acc.armchair: 0.8241, Acc.seat: 0.8976, Acc.fence: 0.5874, Acc.desk: 0.8064, Acc.rock: 0.5242, Acc.wardrobe: 0.6530, Acc.lamp: 0.8876, Acc.bathtub: 0.8989, Acc.railing: 0.6160, Acc.cushion: 0.8288, Acc.base: 0.6021, Acc.box: 0.4800, Acc.column: 0.7266, Acc.signboard: 0.5495, Acc.chest of drawers: 0.7453, Acc.counter: 0.5795, Acc.sand: 0.8642, Acc.sink: 0.8360, Acc.skyscraper: 0.6046, Acc.fireplace: 0.9609, Acc.refrigerator: 0.9402, Acc.grandstand: 0.8149, Acc.path: 0.4760, Acc.stairs: 0.4149, Acc.runway: 0.9481, Acc.case: 0.7779, Acc.pool table: 0.9824, Acc.pillow: 0.8117, Acc.screen door: 0.6880, Acc.stairway: 0.4476, Acc.river: 0.4654, Acc.bridge: 0.7526, Acc.bookcase: 0.6105, Acc.blind: 0.6208, Acc.coffee table: 0.8327, Acc.toilet: 0.9423, Acc.flower: 0.5252, Acc.book: 0.7667, Acc.hill: 0.0939, Acc.bench: 0.6435, Acc.countertop: 0.8394, Acc.stove: 0.9248, Acc.palm: 0.7645, Acc.kitchen island: 0.8700, Acc.computer: 0.9132, Acc.swivel chair: 0.8140, Acc.boat: 0.8931, Acc.bar: 0.8966, Acc.arcade machine: 0.8457, Acc.hovel: 0.2094, Acc.bus: 0.9707, Acc.towel: 0.8933, Acc.light: 0.8070, Acc.truck: 0.6453, Acc.tower: 0.5416, Acc.chandelier: 0.8418, Acc.awning: 0.5227, Acc.streetlight: 0.4760, Acc.booth: 0.5743, Acc.television receiver: 0.8735, Acc.airplane: 0.9606, Acc.dirt track: 0.2297, Acc.apparel: 0.7687, Acc.pole: 0.3132, Acc.land: 0.0641, Acc.bannister: 0.2197, Acc.escalator: 0.8046, Acc.ottoman: 0.6584, Acc.bottle: 0.5955, Acc.buffet: 0.6492, Acc.poster: 0.4265, Acc.stage: 0.4737, Acc.van: 0.6827, Acc.ship: 0.9253, Acc.fountain: 0.3852, Acc.conveyer belt: 0.9735, Acc.canopy: 0.6945, Acc.washer: 0.8283, Acc.plaything: 0.3037, Acc.swimming pool: 0.7795, Acc.stool: 0.6762, Acc.barrel: 0.9713, Acc.basket: 0.4369, Acc.waterfall: 0.7243, Acc.tent: 0.9780, Acc.bag: 0.2837, Acc.minibike: 0.8921, Acc.cradle: 0.9781, Acc.oven: 0.8073, Acc.ball: 0.3135, Acc.food: 0.8134, Acc.step: 0.3217, Acc.tank: 0.6728, Acc.trade name: 0.4337, Acc.microwave: 0.9609, Acc.pot: 0.7044, Acc.animal: 0.6736, Acc.bicycle: 0.7344, Acc.lake: 0.6346, Acc.dishwasher: 0.8442, Acc.screen: 0.8019, Acc.blanket: 0.3478, Acc.sculpture: 0.8687, Acc.hood: 0.7368, Acc.sconce: 0.7005, Acc.vase: 0.6554, Acc.traffic light: 0.6417, Acc.tray: 0.2398, Acc.ashcan: 0.6238, Acc.fan: 0.8439, Acc.pier: 0.4742, Acc.crt screen: 0.1708, Acc.plate: 0.8388, Acc.monitor: 0.8078, Acc.bulletin board: 0.7098, Acc.shower: 0.0045, Acc.radiator: 0.7534, Acc.glass: 0.2154, Acc.clock: 0.5097, Acc.flag: 0.7633 +2024-06-16 10:42:37,141 - mmseg - INFO - Iter [27050/80000] lr: 2.648e-05, eta: 1 day, 1:59:47, time: 3.613, data_time: 1.995, memory: 71384, decode.loss_ce: 0.2629, decode.acc_seg: 89.0989, aux.loss_ce: 0.1083, aux.acc_seg: 88.8382, loss: 0.3712 +2024-06-16 10:43:58,278 - mmseg - INFO - Iter [27100/80000] lr: 2.645e-05, eta: 1 day, 1:58:04, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2664, decode.acc_seg: 88.9621, aux.loss_ce: 0.1088, aux.acc_seg: 88.6908, loss: 0.3752 +2024-06-16 10:45:19,433 - mmseg - INFO - Iter [27150/80000] lr: 2.643e-05, eta: 1 day, 1:56:22, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2521, decode.acc_seg: 89.5277, aux.loss_ce: 0.1036, aux.acc_seg: 89.2437, loss: 0.3556 +2024-06-16 10:46:40,561 - mmseg - INFO - Iter [27200/80000] lr: 2.640e-05, eta: 1 day, 1:54:40, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2455, decode.acc_seg: 90.4930, aux.loss_ce: 0.1009, aux.acc_seg: 90.1390, loss: 0.3464 +2024-06-16 10:48:01,702 - mmseg - INFO - Iter [27250/80000] lr: 2.638e-05, eta: 1 day, 1:52:57, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2470, decode.acc_seg: 90.0095, aux.loss_ce: 0.1018, aux.acc_seg: 89.7329, loss: 0.3488 +2024-06-16 10:49:22,744 - mmseg - INFO - Iter [27300/80000] lr: 2.635e-05, eta: 1 day, 1:51:15, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2328, decode.acc_seg: 90.1550, aux.loss_ce: 0.0957, aux.acc_seg: 89.8394, loss: 0.3285 +2024-06-16 10:50:43,958 - mmseg - INFO - Iter [27350/80000] lr: 2.633e-05, eta: 1 day, 1:49:33, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2539, decode.acc_seg: 89.4601, aux.loss_ce: 0.1037, aux.acc_seg: 89.2913, loss: 0.3575 +2024-06-16 10:52:05,356 - mmseg - INFO - Iter [27400/80000] lr: 2.630e-05, eta: 1 day, 1:47:52, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2509, decode.acc_seg: 89.6660, aux.loss_ce: 0.1025, aux.acc_seg: 89.4702, loss: 0.3534 +2024-06-16 10:53:26,437 - mmseg - INFO - Iter [27450/80000] lr: 2.628e-05, eta: 1 day, 1:46:09, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2450, decode.acc_seg: 89.9036, aux.loss_ce: 0.0999, aux.acc_seg: 89.7205, loss: 0.3449 +2024-06-16 10:54:47,595 - mmseg - INFO - Iter [27500/80000] lr: 2.625e-05, eta: 1 day, 1:44:28, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2435, decode.acc_seg: 90.1098, aux.loss_ce: 0.1003, aux.acc_seg: 89.8329, loss: 0.3438 +2024-06-16 10:56:08,737 - mmseg - INFO - Iter [27550/80000] lr: 2.623e-05, eta: 1 day, 1:42:46, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2481, decode.acc_seg: 89.6659, aux.loss_ce: 0.1022, aux.acc_seg: 89.4304, loss: 0.3502 +2024-06-16 10:57:29,798 - mmseg - INFO - Iter [27600/80000] lr: 2.620e-05, eta: 1 day, 1:41:04, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2417, decode.acc_seg: 89.8972, aux.loss_ce: 0.0992, aux.acc_seg: 89.6477, loss: 0.3409 +2024-06-16 10:58:50,779 - mmseg - INFO - Iter [27650/80000] lr: 2.618e-05, eta: 1 day, 1:39:22, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2640, decode.acc_seg: 89.2548, aux.loss_ce: 0.1083, aux.acc_seg: 88.9162, loss: 0.3723 +2024-06-16 11:00:11,970 - mmseg - INFO - Iter [27700/80000] lr: 2.615e-05, eta: 1 day, 1:37:40, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2367, decode.acc_seg: 89.9536, aux.loss_ce: 0.0968, aux.acc_seg: 89.7469, loss: 0.3335 +2024-06-16 11:01:33,073 - mmseg - INFO - Iter [27750/80000] lr: 2.613e-05, eta: 1 day, 1:35:59, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2496, decode.acc_seg: 89.7857, aux.loss_ce: 0.1017, aux.acc_seg: 89.6330, loss: 0.3513 +2024-06-16 11:02:56,247 - mmseg - INFO - Iter [27800/80000] lr: 2.610e-05, eta: 1 day, 1:34:21, time: 1.663, data_time: 0.051, memory: 71384, decode.loss_ce: 0.2360, decode.acc_seg: 90.5065, aux.loss_ce: 0.0971, aux.acc_seg: 90.2707, loss: 0.3331 +2024-06-16 11:04:17,317 - mmseg - INFO - Iter [27850/80000] lr: 2.608e-05, eta: 1 day, 1:32:40, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2294, decode.acc_seg: 90.6651, aux.loss_ce: 0.0940, aux.acc_seg: 90.3668, loss: 0.3233 +2024-06-16 11:05:38,360 - mmseg - INFO - Iter [27900/80000] lr: 2.605e-05, eta: 1 day, 1:30:58, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2455, decode.acc_seg: 90.1068, aux.loss_ce: 0.1007, aux.acc_seg: 89.8353, loss: 0.3462 +2024-06-16 11:06:59,522 - mmseg - INFO - Iter [27950/80000] lr: 2.603e-05, eta: 1 day, 1:29:17, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2398, decode.acc_seg: 90.1056, aux.loss_ce: 0.0985, aux.acc_seg: 89.8838, loss: 0.3383 +2024-06-16 11:08:20,654 - mmseg - INFO - Saving checkpoint at 28000 iterations +2024-06-16 11:09:45,856 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 11:09:45,856 - mmseg - INFO - Iter [28000/80000] lr: 2.600e-05, eta: 1 day, 1:30:14, time: 3.327, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2189, decode.acc_seg: 90.6469, aux.loss_ce: 0.0903, aux.acc_seg: 90.4146, loss: 0.3092 +2024-06-16 11:11:22,727 - mmseg - INFO - per class results: +2024-06-16 11:11:22,733 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.47 | 89.05 | +| building | 84.86 | 92.03 | +| sky | 94.98 | 97.58 | +| floor | 85.34 | 90.71 | +| tree | 77.84 | 91.04 | +| ceiling | 86.21 | 91.85 | +| road | 86.1 | 92.49 | +| bed | 92.33 | 96.58 | +| windowpane | 67.52 | 80.84 | +| grass | 66.78 | 80.06 | +| cabinet | 65.49 | 76.94 | +| sidewalk | 70.66 | 82.16 | +| person | 85.38 | 94.33 | +| earth | 36.28 | 46.92 | +| door | 59.78 | 77.33 | +| table | 68.7 | 81.51 | +| mountain | 58.57 | 72.5 | +| plant | 56.83 | 68.79 | +| curtain | 79.33 | 89.62 | +| chair | 66.98 | 80.49 | +| car | 86.88 | 93.72 | +| water | 61.61 | 72.59 | +| painting | 77.27 | 89.93 | +| sofa | 80.99 | 92.66 | +| shelf | 51.86 | 73.59 | +| house | 55.69 | 80.04 | +| sea | 63.68 | 82.37 | +| mirror | 78.24 | 87.95 | +| rug | 72.63 | 83.15 | +| field | 33.31 | 66.75 | +| armchair | 58.94 | 69.07 | +| seat | 63.7 | 88.81 | +| fence | 47.22 | 60.16 | +| desk | 58.12 | 76.45 | +| rock | 56.29 | 81.64 | +| wardrobe | 50.71 | 73.33 | +| lamp | 73.69 | 81.64 | +| bathtub | 87.58 | 90.86 | +| railing | 43.16 | 66.9 | +| cushion | 68.52 | 83.87 | +| base | 38.58 | 61.14 | +| box | 37.76 | 50.91 | +| column | 58.78 | 74.49 | +| signboard | 41.89 | 55.49 | +| chest of drawers | 48.34 | 81.79 | +| counter | 46.92 | 54.75 | +| sand | 51.62 | 78.21 | +| sink | 79.61 | 87.37 | +| skyscraper | 48.43 | 67.34 | +| fireplace | 74.36 | 93.82 | +| refrigerator | 86.37 | 94.26 | +| grandstand | 53.02 | 80.23 | +| path | 29.41 | 36.84 | +| stairs | 25.18 | 30.76 | +| runway | 69.6 | 90.51 | +| case | 62.54 | 80.42 | +| pool table | 94.25 | 98.19 | +| pillow | 65.01 | 71.76 | +| screen door | 84.63 | 89.96 | +| stairway | 31.25 | 44.3 | +| river | 10.76 | 19.58 | +| bridge | 77.68 | 90.12 | +| bookcase | 43.0 | 47.35 | +| blind | 46.38 | 55.5 | +| coffee table | 61.88 | 88.75 | +| toilet | 88.07 | 92.67 | +| flower | 41.19 | 59.64 | +| book | 53.42 | 76.5 | +| hill | 5.86 | 11.51 | +| bench | 54.73 | 65.6 | +| countertop | 62.28 | 80.81 | +| stove | 83.47 | 90.54 | +| palm | 57.31 | 78.15 | +| kitchen island | 49.95 | 69.2 | +| computer | 81.06 | 89.87 | +| swivel chair | 51.27 | 81.16 | +| boat | 64.95 | 90.81 | +| bar | 67.75 | 86.61 | +| arcade machine | 86.07 | 92.41 | +| hovel | 13.53 | 14.41 | +| bus | 93.3 | 96.36 | +| towel | 75.56 | 83.08 | +| light | 59.43 | 71.13 | +| truck | 44.85 | 61.84 | +| tower | 30.36 | 59.61 | +| chandelier | 73.11 | 86.8 | +| awning | 45.41 | 58.69 | +| streetlight | 33.88 | 45.71 | +| booth | 51.0 | 67.57 | +| television receiver | 79.49 | 91.68 | +| airplane | 89.15 | 95.88 | +| dirt track | 7.0 | 35.11 | +| apparel | 52.46 | 68.16 | +| pole | 23.54 | 31.42 | +| land | 0.05 | 0.09 | +| bannister | 19.98 | 29.05 | +| escalator | 64.25 | 86.07 | +| ottoman | 50.61 | 71.18 | +| bottle | 35.06 | 43.42 | +| buffet | 43.59 | 48.06 | +| poster | 37.57 | 47.39 | +| stage | 23.55 | 47.3 | +| van | 49.12 | 68.32 | +| ship | 75.37 | 79.23 | +| fountain | 40.77 | 41.62 | +| conveyer belt | 77.35 | 96.91 | +| canopy | 52.33 | 75.11 | +| washer | 82.2 | 88.01 | +| plaything | 44.49 | 63.76 | +| swimming pool | 50.35 | 71.67 | +| stool | 53.08 | 63.66 | +| barrel | 68.39 | 93.39 | +| basket | 38.77 | 52.51 | +| waterfall | 76.73 | 93.45 | +| tent | 94.82 | 98.21 | +| bag | 29.8 | 34.94 | +| minibike | 76.56 | 89.94 | +| cradle | 85.54 | 97.5 | +| oven | 68.92 | 80.18 | +| ball | 32.81 | 35.86 | +| food | 61.01 | 74.18 | +| step | 26.02 | 31.83 | +| tank | 49.99 | 73.97 | +| trade name | 24.54 | 28.65 | +| microwave | 90.45 | 95.71 | +| pot | 57.34 | 68.21 | +| animal | 63.48 | 65.51 | +| bicycle | 57.7 | 74.95 | +| lake | 41.32 | 63.7 | +| dishwasher | 71.29 | 84.31 | +| screen | 51.72 | 79.86 | +| blanket | 38.45 | 46.67 | +| sculpture | 71.26 | 87.0 | +| hood | 63.49 | 77.19 | +| sconce | 61.98 | 75.13 | +| vase | 44.48 | 62.57 | +| traffic light | 33.81 | 61.29 | +| tray | 15.73 | 17.17 | +| ashcan | 48.41 | 63.93 | +| fan | 67.73 | 84.85 | +| pier | 54.16 | 61.54 | +| crt screen | 9.06 | 16.44 | +| plate | 61.81 | 80.38 | +| monitor | 47.32 | 63.75 | +| bulletin board | 56.97 | 76.3 | +| shower | 0.92 | 0.97 | +| radiator | 67.36 | 77.12 | +| glass | 21.44 | 23.52 | +| clock | 44.14 | 56.88 | +| flag | 67.58 | 78.26 | ++---------------------+-------+-------+ +2024-06-16 11:11:22,733 - mmseg - INFO - Summary: +2024-06-16 11:11:22,734 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.81 | 57.14 | 70.49 | ++-------+-------+-------+ +2024-06-16 11:11:22,734 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 11:11:22,735 - mmseg - INFO - Iter(val) [250] aAcc: 0.8581, mIoU: 0.5714, mAcc: 0.7049, IoU.wall: 0.8147, IoU.building: 0.8486, IoU.sky: 0.9498, IoU.floor: 0.8534, IoU.tree: 0.7784, IoU.ceiling: 0.8621, IoU.road: 0.8610, IoU.bed : 0.9233, IoU.windowpane: 0.6752, IoU.grass: 0.6678, IoU.cabinet: 0.6549, IoU.sidewalk: 0.7066, IoU.person: 0.8538, IoU.earth: 0.3628, IoU.door: 0.5978, IoU.table: 0.6870, IoU.mountain: 0.5857, IoU.plant: 0.5683, IoU.curtain: 0.7933, IoU.chair: 0.6698, IoU.car: 0.8688, IoU.water: 0.6161, IoU.painting: 0.7727, IoU.sofa: 0.8099, IoU.shelf: 0.5186, IoU.house: 0.5569, IoU.sea: 0.6368, IoU.mirror: 0.7824, IoU.rug: 0.7263, IoU.field: 0.3331, IoU.armchair: 0.5894, IoU.seat: 0.6370, IoU.fence: 0.4722, IoU.desk: 0.5812, IoU.rock: 0.5629, IoU.wardrobe: 0.5071, IoU.lamp: 0.7369, IoU.bathtub: 0.8758, IoU.railing: 0.4316, IoU.cushion: 0.6852, IoU.base: 0.3858, IoU.box: 0.3776, IoU.column: 0.5878, IoU.signboard: 0.4189, IoU.chest of drawers: 0.4834, IoU.counter: 0.4692, IoU.sand: 0.5162, IoU.sink: 0.7961, IoU.skyscraper: 0.4843, IoU.fireplace: 0.7436, IoU.refrigerator: 0.8637, IoU.grandstand: 0.5302, IoU.path: 0.2941, IoU.stairs: 0.2518, IoU.runway: 0.6960, IoU.case: 0.6254, IoU.pool table: 0.9425, IoU.pillow: 0.6501, IoU.screen door: 0.8463, IoU.stairway: 0.3125, IoU.river: 0.1076, IoU.bridge: 0.7768, IoU.bookcase: 0.4300, IoU.blind: 0.4638, IoU.coffee table: 0.6188, IoU.toilet: 0.8807, IoU.flower: 0.4119, IoU.book: 0.5342, IoU.hill: 0.0586, IoU.bench: 0.5473, IoU.countertop: 0.6228, IoU.stove: 0.8347, IoU.palm: 0.5731, IoU.kitchen island: 0.4995, IoU.computer: 0.8106, IoU.swivel chair: 0.5127, IoU.boat: 0.6495, IoU.bar: 0.6775, IoU.arcade machine: 0.8607, IoU.hovel: 0.1353, IoU.bus: 0.9330, IoU.towel: 0.7556, IoU.light: 0.5943, IoU.truck: 0.4485, IoU.tower: 0.3036, IoU.chandelier: 0.7311, IoU.awning: 0.4541, IoU.streetlight: 0.3388, IoU.booth: 0.5100, IoU.television receiver: 0.7949, IoU.airplane: 0.8915, IoU.dirt track: 0.0700, IoU.apparel: 0.5246, IoU.pole: 0.2354, IoU.land: 0.0005, IoU.bannister: 0.1998, IoU.escalator: 0.6425, IoU.ottoman: 0.5061, IoU.bottle: 0.3506, IoU.buffet: 0.4359, IoU.poster: 0.3757, IoU.stage: 0.2355, IoU.van: 0.4912, IoU.ship: 0.7537, IoU.fountain: 0.4077, IoU.conveyer belt: 0.7735, IoU.canopy: 0.5233, IoU.washer: 0.8220, IoU.plaything: 0.4449, IoU.swimming pool: 0.5035, IoU.stool: 0.5308, IoU.barrel: 0.6839, IoU.basket: 0.3877, IoU.waterfall: 0.7673, IoU.tent: 0.9482, IoU.bag: 0.2980, IoU.minibike: 0.7656, IoU.cradle: 0.8554, IoU.oven: 0.6892, IoU.ball: 0.3281, IoU.food: 0.6101, IoU.step: 0.2602, IoU.tank: 0.4999, IoU.trade name: 0.2454, IoU.microwave: 0.9045, IoU.pot: 0.5734, IoU.animal: 0.6348, IoU.bicycle: 0.5770, IoU.lake: 0.4132, IoU.dishwasher: 0.7129, IoU.screen: 0.5172, IoU.blanket: 0.3845, IoU.sculpture: 0.7126, IoU.hood: 0.6349, IoU.sconce: 0.6198, IoU.vase: 0.4448, IoU.traffic light: 0.3381, IoU.tray: 0.1573, IoU.ashcan: 0.4841, IoU.fan: 0.6773, IoU.pier: 0.5416, IoU.crt screen: 0.0906, IoU.plate: 0.6181, IoU.monitor: 0.4732, IoU.bulletin board: 0.5697, IoU.shower: 0.0092, IoU.radiator: 0.6736, IoU.glass: 0.2144, IoU.clock: 0.4414, IoU.flag: 0.6758, Acc.wall: 0.8905, Acc.building: 0.9203, Acc.sky: 0.9758, Acc.floor: 0.9071, Acc.tree: 0.9104, Acc.ceiling: 0.9185, Acc.road: 0.9249, Acc.bed : 0.9658, Acc.windowpane: 0.8084, Acc.grass: 0.8006, Acc.cabinet: 0.7694, Acc.sidewalk: 0.8216, Acc.person: 0.9433, Acc.earth: 0.4692, Acc.door: 0.7733, Acc.table: 0.8151, Acc.mountain: 0.7250, Acc.plant: 0.6879, Acc.curtain: 0.8962, Acc.chair: 0.8049, Acc.car: 0.9372, Acc.water: 0.7259, Acc.painting: 0.8993, Acc.sofa: 0.9266, Acc.shelf: 0.7359, Acc.house: 0.8004, Acc.sea: 0.8237, Acc.mirror: 0.8795, Acc.rug: 0.8315, Acc.field: 0.6675, Acc.armchair: 0.6907, Acc.seat: 0.8881, Acc.fence: 0.6016, Acc.desk: 0.7645, Acc.rock: 0.8164, Acc.wardrobe: 0.7333, Acc.lamp: 0.8164, Acc.bathtub: 0.9086, Acc.railing: 0.6690, Acc.cushion: 0.8387, Acc.base: 0.6114, Acc.box: 0.5091, Acc.column: 0.7449, Acc.signboard: 0.5549, Acc.chest of drawers: 0.8179, Acc.counter: 0.5475, Acc.sand: 0.7821, Acc.sink: 0.8737, Acc.skyscraper: 0.6734, Acc.fireplace: 0.9382, Acc.refrigerator: 0.9426, Acc.grandstand: 0.8023, Acc.path: 0.3684, Acc.stairs: 0.3076, Acc.runway: 0.9051, Acc.case: 0.8042, Acc.pool table: 0.9819, Acc.pillow: 0.7176, Acc.screen door: 0.8996, Acc.stairway: 0.4430, Acc.river: 0.1958, Acc.bridge: 0.9012, Acc.bookcase: 0.4735, Acc.blind: 0.5550, Acc.coffee table: 0.8875, Acc.toilet: 0.9267, Acc.flower: 0.5964, Acc.book: 0.7650, Acc.hill: 0.1151, Acc.bench: 0.6560, Acc.countertop: 0.8081, Acc.stove: 0.9054, Acc.palm: 0.7815, Acc.kitchen island: 0.6920, Acc.computer: 0.8987, Acc.swivel chair: 0.8116, Acc.boat: 0.9081, Acc.bar: 0.8661, Acc.arcade machine: 0.9241, Acc.hovel: 0.1441, Acc.bus: 0.9636, Acc.towel: 0.8308, Acc.light: 0.7113, Acc.truck: 0.6184, Acc.tower: 0.5961, Acc.chandelier: 0.8680, Acc.awning: 0.5869, Acc.streetlight: 0.4571, Acc.booth: 0.6757, Acc.television receiver: 0.9168, Acc.airplane: 0.9588, Acc.dirt track: 0.3511, Acc.apparel: 0.6816, Acc.pole: 0.3142, Acc.land: 0.0009, Acc.bannister: 0.2905, Acc.escalator: 0.8607, Acc.ottoman: 0.7118, Acc.bottle: 0.4342, Acc.buffet: 0.4806, Acc.poster: 0.4739, Acc.stage: 0.4730, Acc.van: 0.6832, Acc.ship: 0.7923, Acc.fountain: 0.4162, Acc.conveyer belt: 0.9691, Acc.canopy: 0.7511, Acc.washer: 0.8801, Acc.plaything: 0.6376, Acc.swimming pool: 0.7167, Acc.stool: 0.6366, Acc.barrel: 0.9339, Acc.basket: 0.5251, Acc.waterfall: 0.9345, Acc.tent: 0.9821, Acc.bag: 0.3494, Acc.minibike: 0.8994, Acc.cradle: 0.9750, Acc.oven: 0.8018, Acc.ball: 0.3586, Acc.food: 0.7418, Acc.step: 0.3183, Acc.tank: 0.7397, Acc.trade name: 0.2865, Acc.microwave: 0.9571, Acc.pot: 0.6821, Acc.animal: 0.6551, Acc.bicycle: 0.7495, Acc.lake: 0.6370, Acc.dishwasher: 0.8431, Acc.screen: 0.7986, Acc.blanket: 0.4667, Acc.sculpture: 0.8700, Acc.hood: 0.7719, Acc.sconce: 0.7513, Acc.vase: 0.6257, Acc.traffic light: 0.6129, Acc.tray: 0.1717, Acc.ashcan: 0.6393, Acc.fan: 0.8485, Acc.pier: 0.6154, Acc.crt screen: 0.1644, Acc.plate: 0.8038, Acc.monitor: 0.6375, Acc.bulletin board: 0.7630, Acc.shower: 0.0097, Acc.radiator: 0.7712, Acc.glass: 0.2352, Acc.clock: 0.5688, Acc.flag: 0.7826 +2024-06-16 11:12:44,362 - mmseg - INFO - Iter [28050/80000] lr: 2.598e-05, eta: 1 day, 1:31:33, time: 3.570, data_time: 1.955, memory: 71384, decode.loss_ce: 0.2416, decode.acc_seg: 90.2636, aux.loss_ce: 0.1003, aux.acc_seg: 89.8446, loss: 0.3418 +2024-06-16 11:14:05,363 - mmseg - INFO - Iter [28100/80000] lr: 2.595e-05, eta: 1 day, 1:29:51, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2615, decode.acc_seg: 89.7318, aux.loss_ce: 0.1067, aux.acc_seg: 89.4722, loss: 0.3682 +2024-06-16 11:15:26,461 - mmseg - INFO - Iter [28150/80000] lr: 2.593e-05, eta: 1 day, 1:28:09, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2516, decode.acc_seg: 89.7966, aux.loss_ce: 0.1023, aux.acc_seg: 89.6295, loss: 0.3538 +2024-06-16 11:16:47,645 - mmseg - INFO - Iter [28200/80000] lr: 2.590e-05, eta: 1 day, 1:26:27, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2404, decode.acc_seg: 90.2362, aux.loss_ce: 0.0978, aux.acc_seg: 89.9576, loss: 0.3383 +2024-06-16 11:18:08,850 - mmseg - INFO - Iter [28250/80000] lr: 2.588e-05, eta: 1 day, 1:24:46, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2448, decode.acc_seg: 89.8321, aux.loss_ce: 0.1007, aux.acc_seg: 89.5425, loss: 0.3454 +2024-06-16 11:19:29,939 - mmseg - INFO - Iter [28300/80000] lr: 2.585e-05, eta: 1 day, 1:23:04, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2265, decode.acc_seg: 90.4518, aux.loss_ce: 0.0935, aux.acc_seg: 90.1794, loss: 0.3200 +2024-06-16 11:20:51,191 - mmseg - INFO - Iter [28350/80000] lr: 2.583e-05, eta: 1 day, 1:21:22, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2347, decode.acc_seg: 90.3070, aux.loss_ce: 0.0961, aux.acc_seg: 90.0387, loss: 0.3308 +2024-06-16 11:22:12,230 - mmseg - INFO - Iter [28400/80000] lr: 2.580e-05, eta: 1 day, 1:19:41, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2412, decode.acc_seg: 90.0376, aux.loss_ce: 0.0985, aux.acc_seg: 89.8288, loss: 0.3397 +2024-06-16 11:23:33,355 - mmseg - INFO - Iter [28450/80000] lr: 2.578e-05, eta: 1 day, 1:17:59, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2306, decode.acc_seg: 90.5281, aux.loss_ce: 0.0950, aux.acc_seg: 90.2651, loss: 0.3256 +2024-06-16 11:24:54,407 - mmseg - INFO - Iter [28500/80000] lr: 2.575e-05, eta: 1 day, 1:16:18, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2249, decode.acc_seg: 90.5597, aux.loss_ce: 0.0934, aux.acc_seg: 90.1309, loss: 0.3183 +2024-06-16 11:26:15,558 - mmseg - INFO - Iter [28550/80000] lr: 2.573e-05, eta: 1 day, 1:14:37, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2385, decode.acc_seg: 89.8093, aux.loss_ce: 0.0980, aux.acc_seg: 89.6721, loss: 0.3364 +2024-06-16 11:27:36,632 - mmseg - INFO - Iter [28600/80000] lr: 2.570e-05, eta: 1 day, 1:12:55, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2434, decode.acc_seg: 89.8798, aux.loss_ce: 0.0998, aux.acc_seg: 89.5991, loss: 0.3432 +2024-06-16 11:28:57,956 - mmseg - INFO - Iter [28650/80000] lr: 2.568e-05, eta: 1 day, 1:11:14, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2523, decode.acc_seg: 89.7316, aux.loss_ce: 0.1029, aux.acc_seg: 89.4438, loss: 0.3552 +2024-06-16 11:30:19,211 - mmseg - INFO - Iter [28700/80000] lr: 2.565e-05, eta: 1 day, 1:09:34, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2542, decode.acc_seg: 89.4380, aux.loss_ce: 0.1042, aux.acc_seg: 89.1845, loss: 0.3584 +2024-06-16 11:31:40,156 - mmseg - INFO - Iter [28750/80000] lr: 2.563e-05, eta: 1 day, 1:07:52, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2620, decode.acc_seg: 89.2244, aux.loss_ce: 0.1085, aux.acc_seg: 88.8392, loss: 0.3705 +2024-06-16 11:33:01,511 - mmseg - INFO - Iter [28800/80000] lr: 2.560e-05, eta: 1 day, 1:06:12, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2319, decode.acc_seg: 90.1553, aux.loss_ce: 0.0958, aux.acc_seg: 89.9444, loss: 0.3277 +2024-06-16 11:34:22,548 - mmseg - INFO - Iter [28850/80000] lr: 2.558e-05, eta: 1 day, 1:04:31, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2259, decode.acc_seg: 90.7086, aux.loss_ce: 0.0920, aux.acc_seg: 90.5406, loss: 0.3179 +2024-06-16 11:35:43,661 - mmseg - INFO - Iter [28900/80000] lr: 2.555e-05, eta: 1 day, 1:02:50, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2321, decode.acc_seg: 90.2417, aux.loss_ce: 0.0946, aux.acc_seg: 90.1251, loss: 0.3267 +2024-06-16 11:37:04,631 - mmseg - INFO - Iter [28950/80000] lr: 2.553e-05, eta: 1 day, 1:01:09, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2387, decode.acc_seg: 90.2361, aux.loss_ce: 0.0984, aux.acc_seg: 89.9747, loss: 0.3371 +2024-06-16 11:38:25,975 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 11:38:25,976 - mmseg - INFO - Iter [29000/80000] lr: 2.550e-05, eta: 1 day, 0:59:28, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2365, decode.acc_seg: 90.2438, aux.loss_ce: 0.0966, aux.acc_seg: 90.0087, loss: 0.3330 +2024-06-16 11:40:25,030 - mmseg - INFO - per class results: +2024-06-16 11:40:25,036 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.9 | 90.35 | +| building | 85.69 | 92.83 | +| sky | 94.74 | 97.85 | +| floor | 85.27 | 91.99 | +| tree | 77.87 | 88.24 | +| ceiling | 86.76 | 93.08 | +| road | 87.33 | 92.18 | +| bed | 92.56 | 96.98 | +| windowpane | 66.35 | 79.35 | +| grass | 67.39 | 82.9 | +| cabinet | 66.19 | 74.93 | +| sidewalk | 71.48 | 83.52 | +| person | 85.92 | 93.22 | +| earth | 41.01 | 51.49 | +| door | 60.97 | 76.37 | +| table | 67.61 | 80.47 | +| mountain | 62.56 | 72.82 | +| plant | 57.22 | 67.23 | +| curtain | 79.73 | 90.68 | +| chair | 65.25 | 75.76 | +| car | 86.4 | 94.6 | +| water | 65.62 | 84.87 | +| painting | 74.77 | 88.67 | +| sofa | 78.21 | 85.58 | +| shelf | 49.8 | 66.7 | +| house | 59.74 | 83.59 | +| sea | 66.47 | 75.3 | +| mirror | 76.83 | 84.01 | +| rug | 70.14 | 75.5 | +| field | 32.58 | 60.81 | +| armchair | 57.68 | 85.99 | +| seat | 65.93 | 89.54 | +| fence | 51.46 | 72.07 | +| desk | 54.21 | 77.98 | +| rock | 62.66 | 82.39 | +| wardrobe | 54.12 | 71.43 | +| lamp | 74.69 | 87.94 | +| bathtub | 87.32 | 89.77 | +| railing | 45.61 | 64.66 | +| cushion | 63.67 | 68.44 | +| base | 42.56 | 58.45 | +| box | 36.27 | 49.95 | +| column | 54.63 | 65.49 | +| signboard | 40.14 | 52.22 | +| chest of drawers | 51.54 | 75.72 | +| counter | 46.4 | 54.98 | +| sand | 57.82 | 80.78 | +| sink | 81.56 | 86.44 | +| skyscraper | 47.28 | 60.66 | +| fireplace | 75.45 | 90.3 | +| refrigerator | 86.14 | 94.74 | +| grandstand | 51.96 | 84.71 | +| path | 31.39 | 43.1 | +| stairs | 37.54 | 49.58 | +| runway | 71.46 | 92.45 | +| case | 64.12 | 89.17 | +| pool table | 94.64 | 98.13 | +| pillow | 67.89 | 86.2 | +| screen door | 80.22 | 84.34 | +| stairway | 37.32 | 45.04 | +| river | 9.84 | 17.98 | +| bridge | 69.0 | 78.0 | +| bookcase | 44.09 | 58.69 | +| blind | 46.33 | 54.27 | +| coffee table | 61.8 | 87.26 | +| toilet | 90.16 | 95.45 | +| flower | 40.91 | 53.7 | +| book | 54.2 | 76.7 | +| hill | 9.39 | 27.42 | +| bench | 55.7 | 65.04 | +| countertop | 64.32 | 81.9 | +| stove | 84.17 | 92.6 | +| palm | 54.52 | 77.49 | +| kitchen island | 51.01 | 71.72 | +| computer | 78.52 | 92.41 | +| swivel chair | 52.5 | 86.83 | +| boat | 73.93 | 88.68 | +| bar | 66.14 | 88.59 | +| arcade machine | 79.86 | 87.16 | +| hovel | 14.56 | 15.96 | +| bus | 93.59 | 96.38 | +| towel | 75.39 | 88.76 | +| light | 60.59 | 69.18 | +| truck | 45.09 | 61.5 | +| tower | 27.07 | 49.31 | +| chandelier | 73.5 | 86.59 | +| awning | 36.55 | 42.89 | +| streetlight | 34.65 | 49.52 | +| booth | 55.46 | 74.69 | +| television receiver | 77.35 | 88.85 | +| airplane | 85.36 | 92.3 | +| dirt track | 8.85 | 39.99 | +| apparel | 55.22 | 76.8 | +| pole | 25.2 | 35.13 | +| land | 1.38 | 2.91 | +| bannister | 16.84 | 23.35 | +| escalator | 59.95 | 88.14 | +| ottoman | 51.07 | 67.58 | +| bottle | 21.53 | 24.35 | +| buffet | 59.15 | 90.37 | +| poster | 33.59 | 39.51 | +| stage | 17.69 | 41.29 | +| van | 27.97 | 32.36 | +| ship | 48.19 | 49.2 | +| fountain | 36.09 | 36.43 | +| conveyer belt | 83.16 | 94.99 | +| canopy | 50.1 | 68.6 | +| washer | 81.37 | 87.0 | +| plaything | 29.5 | 57.92 | +| swimming pool | 61.95 | 80.44 | +| stool | 49.81 | 72.08 | +| barrel | 58.1 | 67.78 | +| basket | 39.96 | 52.15 | +| waterfall | 68.44 | 91.75 | +| tent | 95.94 | 98.11 | +| bag | 30.96 | 36.82 | +| minibike | 73.72 | 90.23 | +| cradle | 82.79 | 97.98 | +| oven | 71.33 | 80.38 | +| ball | 48.66 | 78.03 | +| food | 59.94 | 72.16 | +| step | 26.63 | 40.43 | +| tank | 61.81 | 67.17 | +| trade name | 25.31 | 28.95 | +| microwave | 90.41 | 95.92 | +| pot | 58.9 | 69.92 | +| animal | 59.71 | 61.02 | +| bicycle | 59.42 | 78.99 | +| lake | 53.95 | 63.81 | +| dishwasher | 75.32 | 85.89 | +| screen | 58.84 | 91.88 | +| blanket | 28.21 | 32.63 | +| sculpture | 74.53 | 87.34 | +| hood | 62.17 | 72.2 | +| sconce | 61.48 | 72.58 | +| vase | 47.04 | 64.72 | +| traffic light | 35.76 | 64.9 | +| tray | 16.7 | 19.54 | +| ashcan | 48.62 | 64.58 | +| fan | 69.15 | 81.76 | +| pier | 50.39 | 57.94 | +| crt screen | 1.44 | 1.49 | +| plate | 63.48 | 75.9 | +| monitor | 57.56 | 82.59 | +| bulletin board | 45.34 | 50.47 | +| shower | 1.29 | 1.32 | +| radiator | 67.24 | 77.61 | +| glass | 21.62 | 24.77 | +| clock | 46.19 | 57.85 | +| flag | 68.64 | 75.28 | ++---------------------+-------+-------+ +2024-06-16 11:40:25,036 - mmseg - INFO - Summary: +2024-06-16 11:40:25,036 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.17 | 57.13 | 70.17 | ++-------+-------+-------+ +2024-06-16 11:40:25,037 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 11:40:25,037 - mmseg - INFO - Iter(val) [250] aAcc: 0.8617, mIoU: 0.5713, mAcc: 0.7017, IoU.wall: 0.8190, IoU.building: 0.8569, IoU.sky: 0.9474, IoU.floor: 0.8527, IoU.tree: 0.7787, IoU.ceiling: 0.8676, IoU.road: 0.8733, IoU.bed : 0.9256, IoU.windowpane: 0.6635, IoU.grass: 0.6739, IoU.cabinet: 0.6619, IoU.sidewalk: 0.7148, IoU.person: 0.8592, IoU.earth: 0.4101, IoU.door: 0.6097, IoU.table: 0.6761, IoU.mountain: 0.6256, IoU.plant: 0.5722, IoU.curtain: 0.7973, IoU.chair: 0.6525, IoU.car: 0.8640, IoU.water: 0.6562, IoU.painting: 0.7477, IoU.sofa: 0.7821, IoU.shelf: 0.4980, IoU.house: 0.5974, IoU.sea: 0.6647, IoU.mirror: 0.7683, IoU.rug: 0.7014, IoU.field: 0.3258, IoU.armchair: 0.5768, IoU.seat: 0.6593, IoU.fence: 0.5146, IoU.desk: 0.5421, IoU.rock: 0.6266, IoU.wardrobe: 0.5412, IoU.lamp: 0.7469, IoU.bathtub: 0.8732, IoU.railing: 0.4561, IoU.cushion: 0.6367, IoU.base: 0.4256, IoU.box: 0.3627, IoU.column: 0.5463, IoU.signboard: 0.4014, IoU.chest of drawers: 0.5154, IoU.counter: 0.4640, IoU.sand: 0.5782, IoU.sink: 0.8156, IoU.skyscraper: 0.4728, IoU.fireplace: 0.7545, IoU.refrigerator: 0.8614, IoU.grandstand: 0.5196, IoU.path: 0.3139, IoU.stairs: 0.3754, IoU.runway: 0.7146, IoU.case: 0.6412, IoU.pool table: 0.9464, IoU.pillow: 0.6789, IoU.screen door: 0.8022, IoU.stairway: 0.3732, IoU.river: 0.0984, IoU.bridge: 0.6900, IoU.bookcase: 0.4409, IoU.blind: 0.4633, IoU.coffee table: 0.6180, IoU.toilet: 0.9016, IoU.flower: 0.4091, IoU.book: 0.5420, IoU.hill: 0.0939, IoU.bench: 0.5570, IoU.countertop: 0.6432, IoU.stove: 0.8417, IoU.palm: 0.5452, IoU.kitchen island: 0.5101, IoU.computer: 0.7852, IoU.swivel chair: 0.5250, IoU.boat: 0.7393, IoU.bar: 0.6614, IoU.arcade machine: 0.7986, IoU.hovel: 0.1456, IoU.bus: 0.9359, IoU.towel: 0.7539, IoU.light: 0.6059, IoU.truck: 0.4509, IoU.tower: 0.2707, IoU.chandelier: 0.7350, IoU.awning: 0.3655, IoU.streetlight: 0.3465, IoU.booth: 0.5546, IoU.television receiver: 0.7735, IoU.airplane: 0.8536, IoU.dirt track: 0.0885, IoU.apparel: 0.5522, IoU.pole: 0.2520, IoU.land: 0.0138, IoU.bannister: 0.1684, IoU.escalator: 0.5995, IoU.ottoman: 0.5107, IoU.bottle: 0.2153, IoU.buffet: 0.5915, IoU.poster: 0.3359, IoU.stage: 0.1769, IoU.van: 0.2797, IoU.ship: 0.4819, IoU.fountain: 0.3609, IoU.conveyer belt: 0.8316, IoU.canopy: 0.5010, IoU.washer: 0.8137, IoU.plaything: 0.2950, IoU.swimming pool: 0.6195, IoU.stool: 0.4981, IoU.barrel: 0.5810, IoU.basket: 0.3996, IoU.waterfall: 0.6844, IoU.tent: 0.9594, IoU.bag: 0.3096, IoU.minibike: 0.7372, IoU.cradle: 0.8279, IoU.oven: 0.7133, IoU.ball: 0.4866, IoU.food: 0.5994, IoU.step: 0.2663, IoU.tank: 0.6181, IoU.trade name: 0.2531, IoU.microwave: 0.9041, IoU.pot: 0.5890, IoU.animal: 0.5971, IoU.bicycle: 0.5942, IoU.lake: 0.5395, IoU.dishwasher: 0.7532, IoU.screen: 0.5884, IoU.blanket: 0.2821, IoU.sculpture: 0.7453, IoU.hood: 0.6217, IoU.sconce: 0.6148, IoU.vase: 0.4704, IoU.traffic light: 0.3576, IoU.tray: 0.1670, IoU.ashcan: 0.4862, IoU.fan: 0.6915, IoU.pier: 0.5039, IoU.crt screen: 0.0144, IoU.plate: 0.6348, IoU.monitor: 0.5756, IoU.bulletin board: 0.4534, IoU.shower: 0.0129, IoU.radiator: 0.6724, IoU.glass: 0.2162, IoU.clock: 0.4619, IoU.flag: 0.6864, Acc.wall: 0.9035, Acc.building: 0.9283, Acc.sky: 0.9785, Acc.floor: 0.9199, Acc.tree: 0.8824, Acc.ceiling: 0.9308, Acc.road: 0.9218, Acc.bed : 0.9698, Acc.windowpane: 0.7935, Acc.grass: 0.8290, Acc.cabinet: 0.7493, Acc.sidewalk: 0.8352, Acc.person: 0.9322, Acc.earth: 0.5149, Acc.door: 0.7637, Acc.table: 0.8047, Acc.mountain: 0.7282, Acc.plant: 0.6723, Acc.curtain: 0.9068, Acc.chair: 0.7576, Acc.car: 0.9460, Acc.water: 0.8487, Acc.painting: 0.8867, Acc.sofa: 0.8558, Acc.shelf: 0.6670, Acc.house: 0.8359, Acc.sea: 0.7530, Acc.mirror: 0.8401, Acc.rug: 0.7550, Acc.field: 0.6081, Acc.armchair: 0.8599, Acc.seat: 0.8954, Acc.fence: 0.7207, Acc.desk: 0.7798, Acc.rock: 0.8239, Acc.wardrobe: 0.7143, Acc.lamp: 0.8794, Acc.bathtub: 0.8977, Acc.railing: 0.6466, Acc.cushion: 0.6844, Acc.base: 0.5845, Acc.box: 0.4995, Acc.column: 0.6549, Acc.signboard: 0.5222, Acc.chest of drawers: 0.7572, Acc.counter: 0.5498, Acc.sand: 0.8078, Acc.sink: 0.8644, Acc.skyscraper: 0.6066, Acc.fireplace: 0.9030, Acc.refrigerator: 0.9474, Acc.grandstand: 0.8471, Acc.path: 0.4310, Acc.stairs: 0.4958, Acc.runway: 0.9245, Acc.case: 0.8917, Acc.pool table: 0.9813, Acc.pillow: 0.8620, Acc.screen door: 0.8434, Acc.stairway: 0.4504, Acc.river: 0.1798, Acc.bridge: 0.7800, Acc.bookcase: 0.5869, Acc.blind: 0.5427, Acc.coffee table: 0.8726, Acc.toilet: 0.9545, Acc.flower: 0.5370, Acc.book: 0.7670, Acc.hill: 0.2742, Acc.bench: 0.6504, Acc.countertop: 0.8190, Acc.stove: 0.9260, Acc.palm: 0.7749, Acc.kitchen island: 0.7172, Acc.computer: 0.9241, Acc.swivel chair: 0.8683, Acc.boat: 0.8868, Acc.bar: 0.8859, Acc.arcade machine: 0.8716, Acc.hovel: 0.1596, Acc.bus: 0.9638, Acc.towel: 0.8876, Acc.light: 0.6918, Acc.truck: 0.6150, Acc.tower: 0.4931, Acc.chandelier: 0.8659, Acc.awning: 0.4289, Acc.streetlight: 0.4952, Acc.booth: 0.7469, Acc.television receiver: 0.8885, Acc.airplane: 0.9230, Acc.dirt track: 0.3999, Acc.apparel: 0.7680, Acc.pole: 0.3513, Acc.land: 0.0291, Acc.bannister: 0.2335, Acc.escalator: 0.8814, Acc.ottoman: 0.6758, Acc.bottle: 0.2435, Acc.buffet: 0.9037, Acc.poster: 0.3951, Acc.stage: 0.4129, Acc.van: 0.3236, Acc.ship: 0.4920, Acc.fountain: 0.3643, Acc.conveyer belt: 0.9499, Acc.canopy: 0.6860, Acc.washer: 0.8700, Acc.plaything: 0.5792, Acc.swimming pool: 0.8044, Acc.stool: 0.7208, Acc.barrel: 0.6778, Acc.basket: 0.5215, Acc.waterfall: 0.9175, Acc.tent: 0.9811, Acc.bag: 0.3682, Acc.minibike: 0.9023, Acc.cradle: 0.9798, Acc.oven: 0.8038, Acc.ball: 0.7803, Acc.food: 0.7216, Acc.step: 0.4043, Acc.tank: 0.6717, Acc.trade name: 0.2895, Acc.microwave: 0.9592, Acc.pot: 0.6992, Acc.animal: 0.6102, Acc.bicycle: 0.7899, Acc.lake: 0.6381, Acc.dishwasher: 0.8589, Acc.screen: 0.9188, Acc.blanket: 0.3263, Acc.sculpture: 0.8734, Acc.hood: 0.7220, Acc.sconce: 0.7258, Acc.vase: 0.6472, Acc.traffic light: 0.6490, Acc.tray: 0.1954, Acc.ashcan: 0.6458, Acc.fan: 0.8176, Acc.pier: 0.5794, Acc.crt screen: 0.0149, Acc.plate: 0.7590, Acc.monitor: 0.8259, Acc.bulletin board: 0.5047, Acc.shower: 0.0132, Acc.radiator: 0.7761, Acc.glass: 0.2477, Acc.clock: 0.5785, Acc.flag: 0.7528 +2024-06-16 11:41:49,313 - mmseg - INFO - Iter [29050/80000] lr: 2.548e-05, eta: 1 day, 1:01:22, time: 4.067, data_time: 2.453, memory: 71384, decode.loss_ce: 0.2354, decode.acc_seg: 89.8613, aux.loss_ce: 0.0968, aux.acc_seg: 89.5821, loss: 0.3322 +2024-06-16 11:43:10,509 - mmseg - INFO - Iter [29100/80000] lr: 2.545e-05, eta: 1 day, 0:59:41, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2178, decode.acc_seg: 90.8593, aux.loss_ce: 0.0897, aux.acc_seg: 90.6821, loss: 0.3075 +2024-06-16 11:44:31,615 - mmseg - INFO - Iter [29150/80000] lr: 2.543e-05, eta: 1 day, 0:58:00, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2380, decode.acc_seg: 90.2593, aux.loss_ce: 0.0972, aux.acc_seg: 89.8672, loss: 0.3353 +2024-06-16 11:45:52,625 - mmseg - INFO - Iter [29200/80000] lr: 2.540e-05, eta: 1 day, 0:56:19, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2383, decode.acc_seg: 90.2376, aux.loss_ce: 0.0985, aux.acc_seg: 89.9039, loss: 0.3368 +2024-06-16 11:47:13,732 - mmseg - INFO - Iter [29250/80000] lr: 2.538e-05, eta: 1 day, 0:54:38, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2412, decode.acc_seg: 90.0866, aux.loss_ce: 0.0988, aux.acc_seg: 89.8036, loss: 0.3401 +2024-06-16 11:48:34,999 - mmseg - INFO - Iter [29300/80000] lr: 2.535e-05, eta: 1 day, 0:52:57, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2295, decode.acc_seg: 90.1466, aux.loss_ce: 0.0943, aux.acc_seg: 89.9321, loss: 0.3238 +2024-06-16 11:49:56,153 - mmseg - INFO - Iter [29350/80000] lr: 2.533e-05, eta: 1 day, 0:51:17, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2094, decode.acc_seg: 91.4126, aux.loss_ce: 0.0868, aux.acc_seg: 91.0624, loss: 0.2962 +2024-06-16 11:51:17,247 - mmseg - INFO - Iter [29400/80000] lr: 2.530e-05, eta: 1 day, 0:49:36, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2217, decode.acc_seg: 91.0340, aux.loss_ce: 0.0917, aux.acc_seg: 90.7793, loss: 0.3134 +2024-06-16 11:52:38,246 - mmseg - INFO - Iter [29450/80000] lr: 2.528e-05, eta: 1 day, 0:47:55, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2256, decode.acc_seg: 90.7772, aux.loss_ce: 0.0921, aux.acc_seg: 90.5373, loss: 0.3178 +2024-06-16 11:53:59,449 - mmseg - INFO - Iter [29500/80000] lr: 2.525e-05, eta: 1 day, 0:46:14, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2219, decode.acc_seg: 90.7415, aux.loss_ce: 0.0911, aux.acc_seg: 90.4773, loss: 0.3130 +2024-06-16 11:55:20,484 - mmseg - INFO - Iter [29550/80000] lr: 2.523e-05, eta: 1 day, 0:44:34, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2155, decode.acc_seg: 90.8541, aux.loss_ce: 0.0893, aux.acc_seg: 90.5128, loss: 0.3048 +2024-06-16 11:56:41,490 - mmseg - INFO - Iter [29600/80000] lr: 2.520e-05, eta: 1 day, 0:42:53, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2342, decode.acc_seg: 89.9834, aux.loss_ce: 0.0957, aux.acc_seg: 89.7458, loss: 0.3300 +2024-06-16 11:58:02,686 - mmseg - INFO - Iter [29650/80000] lr: 2.518e-05, eta: 1 day, 0:41:13, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2275, decode.acc_seg: 90.2580, aux.loss_ce: 0.0934, aux.acc_seg: 90.0460, loss: 0.3209 +2024-06-16 11:59:24,099 - mmseg - INFO - Iter [29700/80000] lr: 2.515e-05, eta: 1 day, 0:39:33, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2200, decode.acc_seg: 90.9556, aux.loss_ce: 0.0908, aux.acc_seg: 90.5679, loss: 0.3108 +2024-06-16 12:00:45,167 - mmseg - INFO - Iter [29750/80000] lr: 2.513e-05, eta: 1 day, 0:37:53, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2289, decode.acc_seg: 90.4928, aux.loss_ce: 0.0942, aux.acc_seg: 90.2418, loss: 0.3231 +2024-06-16 12:02:06,208 - mmseg - INFO - Iter [29800/80000] lr: 2.510e-05, eta: 1 day, 0:36:12, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2322, decode.acc_seg: 90.6487, aux.loss_ce: 0.0958, aux.acc_seg: 90.3382, loss: 0.3280 +2024-06-16 12:03:27,487 - mmseg - INFO - Iter [29850/80000] lr: 2.508e-05, eta: 1 day, 0:34:32, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2435, decode.acc_seg: 90.1884, aux.loss_ce: 0.1004, aux.acc_seg: 89.8794, loss: 0.3439 +2024-06-16 12:04:48,485 - mmseg - INFO - Iter [29900/80000] lr: 2.505e-05, eta: 1 day, 0:32:52, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2417, decode.acc_seg: 90.0682, aux.loss_ce: 0.0985, aux.acc_seg: 89.8847, loss: 0.3402 +2024-06-16 12:06:09,596 - mmseg - INFO - Iter [29950/80000] lr: 2.503e-05, eta: 1 day, 0:31:12, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2308, decode.acc_seg: 90.4609, aux.loss_ce: 0.0952, aux.acc_seg: 90.0590, loss: 0.3260 +2024-06-16 12:07:30,627 - mmseg - INFO - Saving checkpoint at 30000 iterations +2024-06-16 12:08:53,759 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 12:08:53,759 - mmseg - INFO - Iter [30000/80000] lr: 2.500e-05, eta: 1 day, 0:31:50, time: 3.283, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2251, decode.acc_seg: 90.4587, aux.loss_ce: 0.0921, aux.acc_seg: 90.2223, loss: 0.3173 +2024-06-16 12:10:30,053 - mmseg - INFO - per class results: +2024-06-16 12:10:30,059 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.4 | 90.4 | +| building | 85.69 | 92.54 | +| sky | 94.9 | 97.07 | +| floor | 85.32 | 92.8 | +| tree | 77.88 | 89.9 | +| ceiling | 86.42 | 93.44 | +| road | 87.47 | 92.29 | +| bed | 92.13 | 97.58 | +| windowpane | 66.39 | 82.05 | +| grass | 67.52 | 82.34 | +| cabinet | 66.93 | 77.62 | +| sidewalk | 70.09 | 83.68 | +| person | 85.71 | 92.6 | +| earth | 38.69 | 49.48 | +| door | 59.83 | 73.77 | +| table | 68.37 | 80.92 | +| mountain | 63.58 | 76.58 | +| plant | 59.03 | 72.46 | +| curtain | 80.07 | 89.52 | +| chair | 68.76 | 79.22 | +| car | 86.86 | 92.44 | +| water | 65.25 | 78.57 | +| painting | 79.67 | 89.91 | +| sofa | 82.04 | 88.76 | +| shelf | 51.75 | 67.78 | +| house | 58.95 | 79.39 | +| sea | 70.81 | 83.69 | +| mirror | 75.14 | 80.64 | +| rug | 71.81 | 81.6 | +| field | 33.69 | 62.6 | +| armchair | 62.39 | 78.87 | +| seat | 70.38 | 87.73 | +| fence | 50.84 | 71.65 | +| desk | 55.87 | 77.9 | +| rock | 59.23 | 79.87 | +| wardrobe | 56.49 | 75.03 | +| lamp | 74.91 | 83.35 | +| bathtub | 83.67 | 85.17 | +| railing | 46.34 | 62.62 | +| cushion | 71.27 | 78.88 | +| base | 41.83 | 55.69 | +| box | 39.6 | 54.04 | +| column | 59.35 | 69.89 | +| signboard | 39.33 | 48.15 | +| chest of drawers | 48.14 | 67.12 | +| counter | 44.33 | 54.74 | +| sand | 50.96 | 89.1 | +| sink | 79.16 | 83.2 | +| skyscraper | 50.76 | 66.33 | +| fireplace | 72.31 | 91.02 | +| refrigerator | 81.81 | 85.69 | +| grandstand | 51.99 | 82.52 | +| path | 23.95 | 38.79 | +| stairs | 35.79 | 43.65 | +| runway | 73.74 | 98.16 | +| case | 64.89 | 80.91 | +| pool table | 94.89 | 97.94 | +| pillow | 66.44 | 75.31 | +| screen door | 80.25 | 84.96 | +| stairway | 44.32 | 61.85 | +| river | 10.65 | 20.35 | +| bridge | 64.79 | 74.2 | +| bookcase | 48.86 | 62.9 | +| blind | 48.67 | 57.1 | +| coffee table | 60.66 | 87.88 | +| toilet | 89.58 | 92.04 | +| flower | 44.79 | 53.16 | +| book | 53.6 | 75.12 | +| hill | 9.81 | 19.25 | +| bench | 62.45 | 70.26 | +| countertop | 63.97 | 85.56 | +| stove | 84.01 | 93.41 | +| palm | 53.02 | 87.31 | +| kitchen island | 57.32 | 85.39 | +| computer | 80.11 | 90.45 | +| swivel chair | 50.95 | 77.2 | +| boat | 75.76 | 89.41 | +| bar | 65.06 | 89.63 | +| arcade machine | 80.21 | 83.29 | +| hovel | 24.22 | 26.14 | +| bus | 91.76 | 97.11 | +| towel | 75.94 | 86.47 | +| light | 62.58 | 72.27 | +| truck | 49.29 | 59.46 | +| tower | 22.86 | 41.22 | +| chandelier | 74.66 | 83.78 | +| awning | 40.89 | 49.36 | +| streetlight | 33.62 | 49.84 | +| booth | 47.72 | 62.86 | +| television receiver | 77.45 | 91.85 | +| airplane | 86.73 | 91.73 | +| dirt track | 6.57 | 28.93 | +| apparel | 52.87 | 65.3 | +| pole | 18.65 | 23.29 | +| land | 2.2 | 3.27 | +| bannister | 13.88 | 15.87 | +| escalator | 62.36 | 86.67 | +| ottoman | 50.83 | 68.87 | +| bottle | 38.69 | 51.75 | +| buffet | 54.96 | 65.13 | +| poster | 38.15 | 43.51 | +| stage | 20.17 | 44.94 | +| van | 44.55 | 70.45 | +| ship | 89.02 | 93.52 | +| fountain | 52.15 | 55.85 | +| conveyer belt | 76.56 | 95.4 | +| canopy | 48.63 | 69.44 | +| washer | 80.24 | 85.06 | +| plaything | 37.89 | 62.67 | +| swimming pool | 56.46 | 83.57 | +| stool | 51.69 | 65.14 | +| barrel | 54.02 | 66.91 | +| basket | 40.98 | 59.11 | +| waterfall | 62.08 | 83.93 | +| tent | 93.93 | 98.32 | +| bag | 21.73 | 24.53 | +| minibike | 76.23 | 88.28 | +| cradle | 83.96 | 97.21 | +| oven | 65.31 | 80.41 | +| ball | 54.53 | 69.51 | +| food | 65.18 | 77.95 | +| step | 18.94 | 21.31 | +| tank | 57.58 | 67.35 | +| trade name | 29.23 | 33.77 | +| microwave | 90.31 | 95.08 | +| pot | 58.76 | 68.8 | +| animal | 60.9 | 61.55 | +| bicycle | 57.1 | 72.81 | +| lake | 46.26 | 63.76 | +| dishwasher | 70.47 | 76.88 | +| screen | 62.48 | 90.91 | +| blanket | 31.33 | 39.75 | +| sculpture | 78.1 | 85.82 | +| hood | 63.02 | 75.69 | +| sconce | 59.62 | 68.24 | +| vase | 46.71 | 62.0 | +| traffic light | 35.18 | 63.35 | +| tray | 23.48 | 29.07 | +| ashcan | 49.5 | 60.27 | +| fan | 66.18 | 81.97 | +| pier | 39.61 | 49.38 | +| crt screen | 1.17 | 1.42 | +| plate | 63.11 | 80.63 | +| monitor | 56.22 | 71.17 | +| bulletin board | 47.98 | 57.29 | +| shower | 0.89 | 0.9 | +| radiator | 69.41 | 75.94 | +| glass | 18.55 | 19.55 | +| clock | 48.06 | 54.63 | +| flag | 64.67 | 72.39 | ++---------------------+-------+-------+ +2024-06-16 12:10:30,059 - mmseg - INFO - Summary: +2024-06-16 12:10:30,059 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.39 | 57.73 | 70.23 | ++-------+-------+-------+ +2024-06-16 12:10:30,060 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 12:10:30,060 - mmseg - INFO - Iter(val) [250] aAcc: 0.8639, mIoU: 0.5773, mAcc: 0.7023, IoU.wall: 0.8240, IoU.building: 0.8569, IoU.sky: 0.9490, IoU.floor: 0.8532, IoU.tree: 0.7788, IoU.ceiling: 0.8642, IoU.road: 0.8747, IoU.bed : 0.9213, IoU.windowpane: 0.6639, IoU.grass: 0.6752, IoU.cabinet: 0.6693, IoU.sidewalk: 0.7009, IoU.person: 0.8571, IoU.earth: 0.3869, IoU.door: 0.5983, IoU.table: 0.6837, IoU.mountain: 0.6358, IoU.plant: 0.5903, IoU.curtain: 0.8007, IoU.chair: 0.6876, IoU.car: 0.8686, IoU.water: 0.6525, IoU.painting: 0.7967, IoU.sofa: 0.8204, IoU.shelf: 0.5175, IoU.house: 0.5895, IoU.sea: 0.7081, IoU.mirror: 0.7514, IoU.rug: 0.7181, IoU.field: 0.3369, IoU.armchair: 0.6239, IoU.seat: 0.7038, IoU.fence: 0.5084, IoU.desk: 0.5587, IoU.rock: 0.5923, IoU.wardrobe: 0.5649, IoU.lamp: 0.7491, IoU.bathtub: 0.8367, IoU.railing: 0.4634, IoU.cushion: 0.7127, IoU.base: 0.4183, IoU.box: 0.3960, IoU.column: 0.5935, IoU.signboard: 0.3933, IoU.chest of drawers: 0.4814, IoU.counter: 0.4433, IoU.sand: 0.5096, IoU.sink: 0.7916, IoU.skyscraper: 0.5076, IoU.fireplace: 0.7231, IoU.refrigerator: 0.8181, IoU.grandstand: 0.5199, IoU.path: 0.2395, IoU.stairs: 0.3579, IoU.runway: 0.7374, IoU.case: 0.6489, IoU.pool table: 0.9489, IoU.pillow: 0.6644, IoU.screen door: 0.8025, IoU.stairway: 0.4432, IoU.river: 0.1065, IoU.bridge: 0.6479, IoU.bookcase: 0.4886, IoU.blind: 0.4867, IoU.coffee table: 0.6066, IoU.toilet: 0.8958, IoU.flower: 0.4479, IoU.book: 0.5360, IoU.hill: 0.0981, IoU.bench: 0.6245, IoU.countertop: 0.6397, IoU.stove: 0.8401, IoU.palm: 0.5302, IoU.kitchen island: 0.5732, IoU.computer: 0.8011, IoU.swivel chair: 0.5095, IoU.boat: 0.7576, IoU.bar: 0.6506, IoU.arcade machine: 0.8021, IoU.hovel: 0.2422, IoU.bus: 0.9176, IoU.towel: 0.7594, IoU.light: 0.6258, IoU.truck: 0.4929, IoU.tower: 0.2286, IoU.chandelier: 0.7466, IoU.awning: 0.4089, IoU.streetlight: 0.3362, IoU.booth: 0.4772, IoU.television receiver: 0.7745, IoU.airplane: 0.8673, IoU.dirt track: 0.0657, IoU.apparel: 0.5287, IoU.pole: 0.1865, IoU.land: 0.0220, IoU.bannister: 0.1388, IoU.escalator: 0.6236, IoU.ottoman: 0.5083, IoU.bottle: 0.3869, IoU.buffet: 0.5496, IoU.poster: 0.3815, IoU.stage: 0.2017, IoU.van: 0.4455, IoU.ship: 0.8902, IoU.fountain: 0.5215, IoU.conveyer belt: 0.7656, IoU.canopy: 0.4863, IoU.washer: 0.8024, IoU.plaything: 0.3789, IoU.swimming pool: 0.5646, IoU.stool: 0.5169, IoU.barrel: 0.5402, IoU.basket: 0.4098, IoU.waterfall: 0.6208, IoU.tent: 0.9393, IoU.bag: 0.2173, IoU.minibike: 0.7623, IoU.cradle: 0.8396, IoU.oven: 0.6531, IoU.ball: 0.5453, IoU.food: 0.6518, IoU.step: 0.1894, IoU.tank: 0.5758, IoU.trade name: 0.2923, IoU.microwave: 0.9031, IoU.pot: 0.5876, IoU.animal: 0.6090, IoU.bicycle: 0.5710, IoU.lake: 0.4626, IoU.dishwasher: 0.7047, IoU.screen: 0.6248, IoU.blanket: 0.3133, IoU.sculpture: 0.7810, IoU.hood: 0.6302, IoU.sconce: 0.5962, IoU.vase: 0.4671, IoU.traffic light: 0.3518, IoU.tray: 0.2348, IoU.ashcan: 0.4950, IoU.fan: 0.6618, IoU.pier: 0.3961, IoU.crt screen: 0.0117, IoU.plate: 0.6311, IoU.monitor: 0.5622, IoU.bulletin board: 0.4798, IoU.shower: 0.0089, IoU.radiator: 0.6941, IoU.glass: 0.1855, IoU.clock: 0.4806, IoU.flag: 0.6467, Acc.wall: 0.9040, Acc.building: 0.9254, Acc.sky: 0.9707, Acc.floor: 0.9280, Acc.tree: 0.8990, Acc.ceiling: 0.9344, Acc.road: 0.9229, Acc.bed : 0.9758, Acc.windowpane: 0.8205, Acc.grass: 0.8234, Acc.cabinet: 0.7762, Acc.sidewalk: 0.8368, Acc.person: 0.9260, Acc.earth: 0.4948, Acc.door: 0.7377, Acc.table: 0.8092, Acc.mountain: 0.7658, Acc.plant: 0.7246, Acc.curtain: 0.8952, Acc.chair: 0.7922, Acc.car: 0.9244, Acc.water: 0.7857, Acc.painting: 0.8991, Acc.sofa: 0.8876, Acc.shelf: 0.6778, Acc.house: 0.7939, Acc.sea: 0.8369, Acc.mirror: 0.8064, Acc.rug: 0.8160, Acc.field: 0.6260, Acc.armchair: 0.7887, Acc.seat: 0.8773, Acc.fence: 0.7165, Acc.desk: 0.7790, Acc.rock: 0.7987, Acc.wardrobe: 0.7503, Acc.lamp: 0.8335, Acc.bathtub: 0.8517, Acc.railing: 0.6262, Acc.cushion: 0.7888, Acc.base: 0.5569, Acc.box: 0.5404, Acc.column: 0.6989, Acc.signboard: 0.4815, Acc.chest of drawers: 0.6712, Acc.counter: 0.5474, Acc.sand: 0.8910, Acc.sink: 0.8320, Acc.skyscraper: 0.6633, Acc.fireplace: 0.9102, Acc.refrigerator: 0.8569, Acc.grandstand: 0.8252, Acc.path: 0.3879, Acc.stairs: 0.4365, Acc.runway: 0.9816, Acc.case: 0.8091, Acc.pool table: 0.9794, Acc.pillow: 0.7531, Acc.screen door: 0.8496, Acc.stairway: 0.6185, Acc.river: 0.2035, Acc.bridge: 0.7420, Acc.bookcase: 0.6290, Acc.blind: 0.5710, Acc.coffee table: 0.8788, Acc.toilet: 0.9204, Acc.flower: 0.5316, Acc.book: 0.7512, Acc.hill: 0.1925, Acc.bench: 0.7026, Acc.countertop: 0.8556, Acc.stove: 0.9341, Acc.palm: 0.8731, Acc.kitchen island: 0.8539, Acc.computer: 0.9045, Acc.swivel chair: 0.7720, Acc.boat: 0.8941, Acc.bar: 0.8963, Acc.arcade machine: 0.8329, Acc.hovel: 0.2614, Acc.bus: 0.9711, Acc.towel: 0.8647, Acc.light: 0.7227, Acc.truck: 0.5946, Acc.tower: 0.4122, Acc.chandelier: 0.8378, Acc.awning: 0.4936, Acc.streetlight: 0.4984, Acc.booth: 0.6286, Acc.television receiver: 0.9185, Acc.airplane: 0.9173, Acc.dirt track: 0.2893, Acc.apparel: 0.6530, Acc.pole: 0.2329, Acc.land: 0.0327, Acc.bannister: 0.1587, Acc.escalator: 0.8667, Acc.ottoman: 0.6887, Acc.bottle: 0.5175, Acc.buffet: 0.6513, Acc.poster: 0.4351, Acc.stage: 0.4494, Acc.van: 0.7045, Acc.ship: 0.9352, Acc.fountain: 0.5585, Acc.conveyer belt: 0.9540, Acc.canopy: 0.6944, Acc.washer: 0.8506, Acc.plaything: 0.6267, Acc.swimming pool: 0.8357, Acc.stool: 0.6514, Acc.barrel: 0.6691, Acc.basket: 0.5911, Acc.waterfall: 0.8393, Acc.tent: 0.9832, Acc.bag: 0.2453, Acc.minibike: 0.8828, Acc.cradle: 0.9721, Acc.oven: 0.8041, Acc.ball: 0.6951, Acc.food: 0.7795, Acc.step: 0.2131, Acc.tank: 0.6735, Acc.trade name: 0.3377, Acc.microwave: 0.9508, Acc.pot: 0.6880, Acc.animal: 0.6155, Acc.bicycle: 0.7281, Acc.lake: 0.6376, Acc.dishwasher: 0.7688, Acc.screen: 0.9091, Acc.blanket: 0.3975, Acc.sculpture: 0.8582, Acc.hood: 0.7569, Acc.sconce: 0.6824, Acc.vase: 0.6200, Acc.traffic light: 0.6335, Acc.tray: 0.2907, Acc.ashcan: 0.6027, Acc.fan: 0.8197, Acc.pier: 0.4938, Acc.crt screen: 0.0142, Acc.plate: 0.8063, Acc.monitor: 0.7117, Acc.bulletin board: 0.5729, Acc.shower: 0.0090, Acc.radiator: 0.7594, Acc.glass: 0.1955, Acc.clock: 0.5463, Acc.flag: 0.7239 +2024-06-16 12:11:51,740 - mmseg - INFO - Iter [30050/80000] lr: 2.498e-05, eta: 1 day, 0:32:51, time: 3.560, data_time: 1.943, memory: 71384, decode.loss_ce: 0.2324, decode.acc_seg: 90.4749, aux.loss_ce: 0.0958, aux.acc_seg: 90.1382, loss: 0.3282 +2024-06-16 12:13:12,792 - mmseg - INFO - Iter [30100/80000] lr: 2.495e-05, eta: 1 day, 0:31:10, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2258, decode.acc_seg: 90.5382, aux.loss_ce: 0.0932, aux.acc_seg: 90.1735, loss: 0.3190 +2024-06-16 12:14:33,912 - mmseg - INFO - Iter [30150/80000] lr: 2.493e-05, eta: 1 day, 0:29:30, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2108, decode.acc_seg: 90.8626, aux.loss_ce: 0.0874, aux.acc_seg: 90.5770, loss: 0.2982 +2024-06-16 12:15:55,230 - mmseg - INFO - Iter [30200/80000] lr: 2.490e-05, eta: 1 day, 0:27:50, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2298, decode.acc_seg: 90.1476, aux.loss_ce: 0.0947, aux.acc_seg: 89.9757, loss: 0.3245 +2024-06-16 12:17:16,365 - mmseg - INFO - Iter [30250/80000] lr: 2.488e-05, eta: 1 day, 0:26:09, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2421, decode.acc_seg: 89.9380, aux.loss_ce: 0.0991, aux.acc_seg: 89.7297, loss: 0.3412 +2024-06-16 12:18:37,606 - mmseg - INFO - Iter [30300/80000] lr: 2.485e-05, eta: 1 day, 0:24:29, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2214, decode.acc_seg: 91.0476, aux.loss_ce: 0.0910, aux.acc_seg: 90.7965, loss: 0.3124 +2024-06-16 12:20:00,916 - mmseg - INFO - Iter [30350/80000] lr: 2.483e-05, eta: 1 day, 0:22:52, time: 1.666, data_time: 0.052, memory: 71384, decode.loss_ce: 0.2246, decode.acc_seg: 90.3225, aux.loss_ce: 0.0924, aux.acc_seg: 90.0852, loss: 0.3170 +2024-06-16 12:21:22,066 - mmseg - INFO - Iter [30400/80000] lr: 2.480e-05, eta: 1 day, 0:21:12, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2160, decode.acc_seg: 91.0523, aux.loss_ce: 0.0901, aux.acc_seg: 90.6463, loss: 0.3061 +2024-06-16 12:22:42,999 - mmseg - INFO - Iter [30450/80000] lr: 2.478e-05, eta: 1 day, 0:19:32, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2152, decode.acc_seg: 90.8718, aux.loss_ce: 0.0890, aux.acc_seg: 90.5603, loss: 0.3042 +2024-06-16 12:24:04,375 - mmseg - INFO - Iter [30500/80000] lr: 2.475e-05, eta: 1 day, 0:17:52, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2153, decode.acc_seg: 90.7174, aux.loss_ce: 0.0888, aux.acc_seg: 90.4530, loss: 0.3041 +2024-06-16 12:25:25,434 - mmseg - INFO - Iter [30550/80000] lr: 2.473e-05, eta: 1 day, 0:16:12, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2126, decode.acc_seg: 91.0204, aux.loss_ce: 0.0884, aux.acc_seg: 90.6887, loss: 0.3010 +2024-06-16 12:26:46,554 - mmseg - INFO - Iter [30600/80000] lr: 2.470e-05, eta: 1 day, 0:14:32, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2260, decode.acc_seg: 90.4374, aux.loss_ce: 0.0928, aux.acc_seg: 90.1656, loss: 0.3188 +2024-06-16 12:28:07,643 - mmseg - INFO - Iter [30650/80000] lr: 2.468e-05, eta: 1 day, 0:12:52, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2065, decode.acc_seg: 91.2695, aux.loss_ce: 0.0857, aux.acc_seg: 90.9885, loss: 0.2922 +2024-06-16 12:29:28,806 - mmseg - INFO - Iter [30700/80000] lr: 2.465e-05, eta: 1 day, 0:11:12, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2143, decode.acc_seg: 90.9300, aux.loss_ce: 0.0886, aux.acc_seg: 90.5435, loss: 0.3028 +2024-06-16 12:30:49,870 - mmseg - INFO - Iter [30750/80000] lr: 2.463e-05, eta: 1 day, 0:09:32, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2162, decode.acc_seg: 91.1778, aux.loss_ce: 0.0891, aux.acc_seg: 90.9296, loss: 0.3053 +2024-06-16 12:32:10,977 - mmseg - INFO - Iter [30800/80000] lr: 2.460e-05, eta: 1 day, 0:07:52, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2099, decode.acc_seg: 91.1810, aux.loss_ce: 0.0865, aux.acc_seg: 90.8324, loss: 0.2964 +2024-06-16 12:33:32,244 - mmseg - INFO - Iter [30850/80000] lr: 2.458e-05, eta: 1 day, 0:06:13, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2365, decode.acc_seg: 90.5675, aux.loss_ce: 0.0972, aux.acc_seg: 90.3149, loss: 0.3336 +2024-06-16 12:34:53,287 - mmseg - INFO - Iter [30900/80000] lr: 2.455e-05, eta: 1 day, 0:04:33, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2304, decode.acc_seg: 90.2024, aux.loss_ce: 0.0951, aux.acc_seg: 90.0090, loss: 0.3255 +2024-06-16 12:36:14,596 - mmseg - INFO - Iter [30950/80000] lr: 2.453e-05, eta: 1 day, 0:02:54, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2253, decode.acc_seg: 90.7667, aux.loss_ce: 0.0928, aux.acc_seg: 90.3581, loss: 0.3181 +2024-06-16 12:37:35,707 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 12:37:35,707 - mmseg - INFO - Iter [31000/80000] lr: 2.450e-05, eta: 1 day, 0:01:14, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2281, decode.acc_seg: 90.5302, aux.loss_ce: 0.0934, aux.acc_seg: 90.2989, loss: 0.3215 +2024-06-16 12:39:13,990 - mmseg - INFO - per class results: +2024-06-16 12:39:13,996 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.99 | 89.03 | +| building | 85.22 | 92.95 | +| sky | 94.94 | 97.19 | +| floor | 85.12 | 92.48 | +| tree | 78.35 | 90.37 | +| ceiling | 86.95 | 93.8 | +| road | 85.97 | 91.44 | +| bed | 92.6 | 97.59 | +| windowpane | 65.94 | 79.93 | +| grass | 67.31 | 80.91 | +| cabinet | 63.01 | 73.79 | +| sidewalk | 68.53 | 78.9 | +| person | 85.54 | 93.31 | +| earth | 40.4 | 55.14 | +| door | 59.5 | 76.84 | +| table | 61.92 | 71.15 | +| mountain | 64.93 | 74.67 | +| plant | 58.85 | 71.39 | +| curtain | 77.39 | 91.83 | +| chair | 68.99 | 82.2 | +| car | 87.65 | 92.92 | +| water | 62.31 | 81.47 | +| painting | 77.29 | 91.57 | +| sofa | 81.83 | 89.02 | +| shelf | 50.44 | 70.71 | +| house | 59.17 | 86.41 | +| sea | 61.12 | 70.32 | +| mirror | 78.22 | 88.05 | +| rug | 67.72 | 77.59 | +| field | 35.66 | 64.08 | +| armchair | 61.42 | 81.43 | +| seat | 69.8 | 88.48 | +| fence | 53.94 | 67.47 | +| desk | 59.2 | 77.32 | +| rock | 67.3 | 79.75 | +| wardrobe | 49.73 | 77.2 | +| lamp | 76.06 | 84.23 | +| bathtub | 83.09 | 84.98 | +| railing | 45.3 | 66.99 | +| cushion | 70.65 | 82.5 | +| base | 40.28 | 59.27 | +| box | 34.94 | 45.92 | +| column | 59.46 | 70.44 | +| signboard | 41.03 | 52.12 | +| chest of drawers | 45.07 | 68.66 | +| counter | 43.01 | 56.6 | +| sand | 55.26 | 88.39 | +| sink | 77.98 | 86.41 | +| skyscraper | 49.69 | 62.33 | +| fireplace | 73.84 | 93.54 | +| refrigerator | 79.27 | 82.93 | +| grandstand | 56.52 | 86.84 | +| path | 26.54 | 41.57 | +| stairs | 35.05 | 51.85 | +| runway | 74.5 | 98.53 | +| case | 65.71 | 84.31 | +| pool table | 94.05 | 97.88 | +| pillow | 70.05 | 80.78 | +| screen door | 61.05 | 63.67 | +| stairway | 29.89 | 39.03 | +| river | 12.35 | 26.69 | +| bridge | 63.04 | 75.63 | +| bookcase | 44.1 | 64.16 | +| blind | 48.14 | 54.29 | +| coffee table | 62.87 | 90.6 | +| toilet | 89.74 | 93.71 | +| flower | 44.18 | 53.88 | +| book | 54.76 | 77.87 | +| hill | 9.76 | 20.55 | +| bench | 55.36 | 66.92 | +| countertop | 63.79 | 79.85 | +| stove | 84.04 | 91.51 | +| palm | 49.62 | 59.78 | +| kitchen island | 48.67 | 76.19 | +| computer | 78.02 | 93.01 | +| swivel chair | 50.3 | 75.36 | +| boat | 71.13 | 90.54 | +| bar | 53.15 | 61.22 | +| arcade machine | 69.58 | 71.4 | +| hovel | 13.67 | 14.47 | +| bus | 91.93 | 97.66 | +| towel | 75.56 | 86.64 | +| light | 59.76 | 66.08 | +| truck | 45.35 | 63.23 | +| tower | 23.43 | 41.04 | +| chandelier | 74.59 | 88.59 | +| awning | 37.11 | 44.2 | +| streetlight | 33.09 | 41.75 | +| booth | 52.14 | 71.86 | +| television receiver | 78.47 | 86.0 | +| airplane | 88.79 | 94.89 | +| dirt track | 4.11 | 20.7 | +| apparel | 54.8 | 83.18 | +| pole | 25.71 | 36.39 | +| land | 0.23 | 0.31 | +| bannister | 19.59 | 28.89 | +| escalator | 36.87 | 44.93 | +| ottoman | 50.83 | 65.58 | +| bottle | 38.34 | 50.48 | +| buffet | 56.92 | 86.11 | +| poster | 35.39 | 39.78 | +| stage | 24.47 | 43.69 | +| van | 47.4 | 64.98 | +| ship | 82.97 | 86.0 | +| fountain | 34.33 | 36.57 | +| conveyer belt | 80.01 | 92.09 | +| canopy | 51.98 | 68.03 | +| washer | 78.69 | 84.3 | +| plaything | 40.77 | 53.98 | +| swimming pool | 74.88 | 91.71 | +| stool | 58.28 | 68.82 | +| barrel | 56.32 | 70.09 | +| basket | 37.7 | 58.06 | +| waterfall | 56.7 | 69.7 | +| tent | 95.98 | 97.76 | +| bag | 28.26 | 32.49 | +| minibike | 76.08 | 86.15 | +| cradle | 81.48 | 98.51 | +| oven | 66.11 | 75.1 | +| ball | 53.47 | 77.77 | +| food | 61.64 | 69.24 | +| step | 16.86 | 18.31 | +| tank | 80.18 | 88.33 | +| trade name | 18.34 | 20.06 | +| microwave | 89.66 | 96.08 | +| pot | 58.62 | 73.61 | +| animal | 65.7 | 67.25 | +| bicycle | 57.41 | 75.26 | +| lake | 49.25 | 63.78 | +| dishwasher | 72.72 | 81.23 | +| screen | 54.89 | 74.48 | +| blanket | 23.3 | 28.73 | +| sculpture | 74.84 | 88.94 | +| hood | 64.21 | 77.55 | +| sconce | 58.6 | 66.49 | +| vase | 42.43 | 73.26 | +| traffic light | 34.3 | 67.16 | +| tray | 20.07 | 25.77 | +| ashcan | 50.88 | 62.35 | +| fan | 66.72 | 81.25 | +| pier | 50.65 | 63.06 | +| crt screen | 6.88 | 18.47 | +| plate | 60.97 | 77.5 | +| monitor | 33.67 | 41.91 | +| bulletin board | 61.05 | 77.85 | +| shower | 0.99 | 4.48 | +| radiator | 68.13 | 79.63 | +| glass | 20.76 | 22.23 | +| clock | 43.1 | 48.15 | +| flag | 69.04 | 77.76 | ++---------------------+-------+-------+ +2024-06-16 12:39:13,996 - mmseg - INFO - Summary: +2024-06-16 12:39:13,996 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 85.98 | 56.9 | 69.54 | ++-------+------+-------+ +2024-06-16 12:39:13,997 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 12:39:13,997 - mmseg - INFO - Iter(val) [250] aAcc: 0.8598, mIoU: 0.5690, mAcc: 0.6954, IoU.wall: 0.8199, IoU.building: 0.8522, IoU.sky: 0.9494, IoU.floor: 0.8512, IoU.tree: 0.7835, IoU.ceiling: 0.8695, IoU.road: 0.8597, IoU.bed : 0.9260, IoU.windowpane: 0.6594, IoU.grass: 0.6731, IoU.cabinet: 0.6301, IoU.sidewalk: 0.6853, IoU.person: 0.8554, IoU.earth: 0.4040, IoU.door: 0.5950, IoU.table: 0.6192, IoU.mountain: 0.6493, IoU.plant: 0.5885, IoU.curtain: 0.7739, IoU.chair: 0.6899, IoU.car: 0.8765, IoU.water: 0.6231, IoU.painting: 0.7729, IoU.sofa: 0.8183, IoU.shelf: 0.5044, IoU.house: 0.5917, IoU.sea: 0.6112, IoU.mirror: 0.7822, IoU.rug: 0.6772, IoU.field: 0.3566, IoU.armchair: 0.6142, IoU.seat: 0.6980, IoU.fence: 0.5394, IoU.desk: 0.5920, IoU.rock: 0.6730, IoU.wardrobe: 0.4973, IoU.lamp: 0.7606, IoU.bathtub: 0.8309, IoU.railing: 0.4530, IoU.cushion: 0.7065, IoU.base: 0.4028, IoU.box: 0.3494, IoU.column: 0.5946, IoU.signboard: 0.4103, IoU.chest of drawers: 0.4507, IoU.counter: 0.4301, IoU.sand: 0.5526, IoU.sink: 0.7798, IoU.skyscraper: 0.4969, IoU.fireplace: 0.7384, IoU.refrigerator: 0.7927, IoU.grandstand: 0.5652, IoU.path: 0.2654, IoU.stairs: 0.3505, IoU.runway: 0.7450, IoU.case: 0.6571, IoU.pool table: 0.9405, IoU.pillow: 0.7005, IoU.screen door: 0.6105, IoU.stairway: 0.2989, IoU.river: 0.1235, IoU.bridge: 0.6304, IoU.bookcase: 0.4410, IoU.blind: 0.4814, IoU.coffee table: 0.6287, IoU.toilet: 0.8974, IoU.flower: 0.4418, IoU.book: 0.5476, IoU.hill: 0.0976, IoU.bench: 0.5536, IoU.countertop: 0.6379, IoU.stove: 0.8404, IoU.palm: 0.4962, IoU.kitchen island: 0.4867, IoU.computer: 0.7802, IoU.swivel chair: 0.5030, IoU.boat: 0.7113, IoU.bar: 0.5315, IoU.arcade machine: 0.6958, IoU.hovel: 0.1367, IoU.bus: 0.9193, IoU.towel: 0.7556, IoU.light: 0.5976, IoU.truck: 0.4535, IoU.tower: 0.2343, IoU.chandelier: 0.7459, IoU.awning: 0.3711, IoU.streetlight: 0.3309, IoU.booth: 0.5214, IoU.television receiver: 0.7847, IoU.airplane: 0.8879, IoU.dirt track: 0.0411, IoU.apparel: 0.5480, IoU.pole: 0.2571, IoU.land: 0.0023, IoU.bannister: 0.1959, IoU.escalator: 0.3687, IoU.ottoman: 0.5083, IoU.bottle: 0.3834, IoU.buffet: 0.5692, IoU.poster: 0.3539, IoU.stage: 0.2447, IoU.van: 0.4740, IoU.ship: 0.8297, IoU.fountain: 0.3433, IoU.conveyer belt: 0.8001, IoU.canopy: 0.5198, IoU.washer: 0.7869, IoU.plaything: 0.4077, IoU.swimming pool: 0.7488, IoU.stool: 0.5828, IoU.barrel: 0.5632, IoU.basket: 0.3770, IoU.waterfall: 0.5670, IoU.tent: 0.9598, IoU.bag: 0.2826, IoU.minibike: 0.7608, IoU.cradle: 0.8148, IoU.oven: 0.6611, IoU.ball: 0.5347, IoU.food: 0.6164, IoU.step: 0.1686, IoU.tank: 0.8018, IoU.trade name: 0.1834, IoU.microwave: 0.8966, IoU.pot: 0.5862, IoU.animal: 0.6570, IoU.bicycle: 0.5741, IoU.lake: 0.4925, IoU.dishwasher: 0.7272, IoU.screen: 0.5489, IoU.blanket: 0.2330, IoU.sculpture: 0.7484, IoU.hood: 0.6421, IoU.sconce: 0.5860, IoU.vase: 0.4243, IoU.traffic light: 0.3430, IoU.tray: 0.2007, IoU.ashcan: 0.5088, IoU.fan: 0.6672, IoU.pier: 0.5065, IoU.crt screen: 0.0688, IoU.plate: 0.6097, IoU.monitor: 0.3367, IoU.bulletin board: 0.6105, IoU.shower: 0.0099, IoU.radiator: 0.6813, IoU.glass: 0.2076, IoU.clock: 0.4310, IoU.flag: 0.6904, Acc.wall: 0.8903, Acc.building: 0.9295, Acc.sky: 0.9719, Acc.floor: 0.9248, Acc.tree: 0.9037, Acc.ceiling: 0.9380, Acc.road: 0.9144, Acc.bed : 0.9759, Acc.windowpane: 0.7993, Acc.grass: 0.8091, Acc.cabinet: 0.7379, Acc.sidewalk: 0.7890, Acc.person: 0.9331, Acc.earth: 0.5514, Acc.door: 0.7684, Acc.table: 0.7115, Acc.mountain: 0.7467, Acc.plant: 0.7139, Acc.curtain: 0.9183, Acc.chair: 0.8220, Acc.car: 0.9292, Acc.water: 0.8147, Acc.painting: 0.9157, Acc.sofa: 0.8902, Acc.shelf: 0.7071, Acc.house: 0.8641, Acc.sea: 0.7032, Acc.mirror: 0.8805, Acc.rug: 0.7759, Acc.field: 0.6408, Acc.armchair: 0.8143, Acc.seat: 0.8848, Acc.fence: 0.6747, Acc.desk: 0.7732, Acc.rock: 0.7975, Acc.wardrobe: 0.7720, Acc.lamp: 0.8423, Acc.bathtub: 0.8498, Acc.railing: 0.6699, Acc.cushion: 0.8250, Acc.base: 0.5927, Acc.box: 0.4592, Acc.column: 0.7044, Acc.signboard: 0.5212, Acc.chest of drawers: 0.6866, Acc.counter: 0.5660, Acc.sand: 0.8839, Acc.sink: 0.8641, Acc.skyscraper: 0.6233, Acc.fireplace: 0.9354, Acc.refrigerator: 0.8293, Acc.grandstand: 0.8684, Acc.path: 0.4157, Acc.stairs: 0.5185, Acc.runway: 0.9853, Acc.case: 0.8431, Acc.pool table: 0.9788, Acc.pillow: 0.8078, Acc.screen door: 0.6367, Acc.stairway: 0.3903, Acc.river: 0.2669, Acc.bridge: 0.7563, Acc.bookcase: 0.6416, Acc.blind: 0.5429, Acc.coffee table: 0.9060, Acc.toilet: 0.9371, Acc.flower: 0.5388, Acc.book: 0.7787, Acc.hill: 0.2055, Acc.bench: 0.6692, Acc.countertop: 0.7985, Acc.stove: 0.9151, Acc.palm: 0.5978, Acc.kitchen island: 0.7619, Acc.computer: 0.9301, Acc.swivel chair: 0.7536, Acc.boat: 0.9054, Acc.bar: 0.6122, Acc.arcade machine: 0.7140, Acc.hovel: 0.1447, Acc.bus: 0.9766, Acc.towel: 0.8664, Acc.light: 0.6608, Acc.truck: 0.6323, Acc.tower: 0.4104, Acc.chandelier: 0.8859, Acc.awning: 0.4420, Acc.streetlight: 0.4175, Acc.booth: 0.7186, Acc.television receiver: 0.8600, Acc.airplane: 0.9489, Acc.dirt track: 0.2070, Acc.apparel: 0.8318, Acc.pole: 0.3639, Acc.land: 0.0031, Acc.bannister: 0.2889, Acc.escalator: 0.4493, Acc.ottoman: 0.6558, Acc.bottle: 0.5048, Acc.buffet: 0.8611, Acc.poster: 0.3978, Acc.stage: 0.4369, Acc.van: 0.6498, Acc.ship: 0.8600, Acc.fountain: 0.3657, Acc.conveyer belt: 0.9209, Acc.canopy: 0.6803, Acc.washer: 0.8430, Acc.plaything: 0.5398, Acc.swimming pool: 0.9171, Acc.stool: 0.6882, Acc.barrel: 0.7009, Acc.basket: 0.5806, Acc.waterfall: 0.6970, Acc.tent: 0.9776, Acc.bag: 0.3249, Acc.minibike: 0.8615, Acc.cradle: 0.9851, Acc.oven: 0.7510, Acc.ball: 0.7777, Acc.food: 0.6924, Acc.step: 0.1831, Acc.tank: 0.8833, Acc.trade name: 0.2006, Acc.microwave: 0.9608, Acc.pot: 0.7361, Acc.animal: 0.6725, Acc.bicycle: 0.7526, Acc.lake: 0.6378, Acc.dishwasher: 0.8123, Acc.screen: 0.7448, Acc.blanket: 0.2873, Acc.sculpture: 0.8894, Acc.hood: 0.7755, Acc.sconce: 0.6649, Acc.vase: 0.7326, Acc.traffic light: 0.6716, Acc.tray: 0.2577, Acc.ashcan: 0.6235, Acc.fan: 0.8125, Acc.pier: 0.6306, Acc.crt screen: 0.1847, Acc.plate: 0.7750, Acc.monitor: 0.4191, Acc.bulletin board: 0.7785, Acc.shower: 0.0448, Acc.radiator: 0.7963, Acc.glass: 0.2223, Acc.clock: 0.4815, Acc.flag: 0.7776 +2024-06-16 12:40:35,530 - mmseg - INFO - Iter [31050/80000] lr: 2.448e-05, eta: 1 day, 0:02:10, time: 3.596, data_time: 1.983, memory: 71384, decode.loss_ce: 0.2225, decode.acc_seg: 90.6293, aux.loss_ce: 0.0923, aux.acc_seg: 90.3129, loss: 0.3148 +2024-06-16 12:41:56,669 - mmseg - INFO - Iter [31100/80000] lr: 2.445e-05, eta: 1 day, 0:00:31, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2258, decode.acc_seg: 90.4643, aux.loss_ce: 0.0936, aux.acc_seg: 90.1613, loss: 0.3194 +2024-06-16 12:43:17,760 - mmseg - INFO - Iter [31150/80000] lr: 2.443e-05, eta: 23:58:51, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2353, decode.acc_seg: 89.9458, aux.loss_ce: 0.0970, aux.acc_seg: 89.6496, loss: 0.3323 +2024-06-16 12:44:39,061 - mmseg - INFO - Iter [31200/80000] lr: 2.440e-05, eta: 23:57:11, time: 1.626, data_time: 0.011, memory: 71384, decode.loss_ce: 0.2337, decode.acc_seg: 90.6889, aux.loss_ce: 0.0954, aux.acc_seg: 90.4854, loss: 0.3291 +2024-06-16 12:46:00,088 - mmseg - INFO - Iter [31250/80000] lr: 2.438e-05, eta: 23:55:32, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2268, decode.acc_seg: 90.8951, aux.loss_ce: 0.0936, aux.acc_seg: 90.5823, loss: 0.3205 +2024-06-16 12:47:21,226 - mmseg - INFO - Iter [31300/80000] lr: 2.435e-05, eta: 23:53:52, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2450, decode.acc_seg: 89.8928, aux.loss_ce: 0.1002, aux.acc_seg: 89.6827, loss: 0.3452 +2024-06-16 12:48:42,283 - mmseg - INFO - Iter [31350/80000] lr: 2.433e-05, eta: 23:52:12, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2240, decode.acc_seg: 90.4635, aux.loss_ce: 0.0913, aux.acc_seg: 90.2506, loss: 0.3153 +2024-06-16 12:50:03,522 - mmseg - INFO - Iter [31400/80000] lr: 2.430e-05, eta: 23:50:33, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2389, decode.acc_seg: 90.1440, aux.loss_ce: 0.0977, aux.acc_seg: 89.9643, loss: 0.3367 +2024-06-16 12:51:24,679 - mmseg - INFO - Iter [31450/80000] lr: 2.428e-05, eta: 23:48:54, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2398, decode.acc_seg: 89.8317, aux.loss_ce: 0.0989, aux.acc_seg: 89.5341, loss: 0.3386 +2024-06-16 12:52:45,945 - mmseg - INFO - Iter [31500/80000] lr: 2.425e-05, eta: 23:47:15, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2308, decode.acc_seg: 90.4577, aux.loss_ce: 0.0947, aux.acc_seg: 90.0977, loss: 0.3256 +2024-06-16 12:54:07,218 - mmseg - INFO - Iter [31550/80000] lr: 2.423e-05, eta: 23:45:36, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2229, decode.acc_seg: 90.9210, aux.loss_ce: 0.0922, aux.acc_seg: 90.6082, loss: 0.3150 +2024-06-16 12:55:30,535 - mmseg - INFO - Iter [31600/80000] lr: 2.420e-05, eta: 23:44:00, time: 1.666, data_time: 0.051, memory: 71384, decode.loss_ce: 0.2158, decode.acc_seg: 91.1608, aux.loss_ce: 0.0891, aux.acc_seg: 90.8854, loss: 0.3049 +2024-06-16 12:56:51,690 - mmseg - INFO - Iter [31650/80000] lr: 2.418e-05, eta: 23:42:21, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2257, decode.acc_seg: 90.7730, aux.loss_ce: 0.0932, aux.acc_seg: 90.4891, loss: 0.3189 +2024-06-16 12:58:12,749 - mmseg - INFO - Iter [31700/80000] lr: 2.415e-05, eta: 23:40:41, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2289, decode.acc_seg: 90.8347, aux.loss_ce: 0.0946, aux.acc_seg: 90.5947, loss: 0.3235 +2024-06-16 12:59:33,796 - mmseg - INFO - Iter [31750/80000] lr: 2.413e-05, eta: 23:39:02, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2168, decode.acc_seg: 91.0035, aux.loss_ce: 0.0898, aux.acc_seg: 90.6542, loss: 0.3065 +2024-06-16 13:00:54,916 - mmseg - INFO - Iter [31800/80000] lr: 2.410e-05, eta: 23:37:23, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2293, decode.acc_seg: 90.1616, aux.loss_ce: 0.0949, aux.acc_seg: 89.8688, loss: 0.3242 +2024-06-16 13:02:16,046 - mmseg - INFO - Iter [31850/80000] lr: 2.408e-05, eta: 23:35:44, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2240, decode.acc_seg: 90.5204, aux.loss_ce: 0.0928, aux.acc_seg: 90.1954, loss: 0.3167 +2024-06-16 13:03:37,172 - mmseg - INFO - Iter [31900/80000] lr: 2.405e-05, eta: 23:34:05, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2236, decode.acc_seg: 90.7810, aux.loss_ce: 0.0921, aux.acc_seg: 90.5060, loss: 0.3157 +2024-06-16 13:04:58,384 - mmseg - INFO - Iter [31950/80000] lr: 2.403e-05, eta: 23:32:27, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2291, decode.acc_seg: 90.8535, aux.loss_ce: 0.0941, aux.acc_seg: 90.5251, loss: 0.3232 +2024-06-16 13:06:19,608 - mmseg - INFO - Saving checkpoint at 32000 iterations +2024-06-16 13:07:48,089 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 13:07:48,089 - mmseg - INFO - Iter [32000/80000] lr: 2.400e-05, eta: 23:33:01, time: 3.394, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1999, decode.acc_seg: 91.4077, aux.loss_ce: 0.0826, aux.acc_seg: 91.1568, loss: 0.2825 +2024-06-16 13:09:24,806 - mmseg - INFO - per class results: +2024-06-16 13:09:24,812 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.89 | 89.47 | +| building | 84.47 | 93.58 | +| sky | 95.03 | 97.38 | +| floor | 85.1 | 91.39 | +| tree | 77.82 | 91.22 | +| ceiling | 86.95 | 92.77 | +| road | 85.82 | 91.49 | +| bed | 92.68 | 97.12 | +| windowpane | 66.48 | 83.47 | +| grass | 70.24 | 83.44 | +| cabinet | 65.73 | 76.97 | +| sidewalk | 69.26 | 81.37 | +| person | 86.01 | 93.57 | +| earth | 39.41 | 55.2 | +| door | 61.8 | 77.98 | +| table | 69.15 | 85.71 | +| mountain | 63.59 | 77.06 | +| plant | 54.74 | 65.31 | +| curtain | 79.47 | 87.34 | +| chair | 68.38 | 80.35 | +| car | 87.02 | 93.56 | +| water | 59.87 | 72.99 | +| painting | 74.34 | 91.1 | +| sofa | 80.87 | 88.69 | +| shelf | 50.84 | 72.12 | +| house | 48.96 | 54.82 | +| sea | 71.13 | 83.61 | +| mirror | 80.65 | 89.21 | +| rug | 71.87 | 83.58 | +| field | 40.06 | 54.84 | +| armchair | 59.77 | 80.91 | +| seat | 72.22 | 87.5 | +| fence | 46.5 | 60.8 | +| desk | 57.58 | 73.56 | +| rock | 57.15 | 73.34 | +| wardrobe | 52.5 | 76.28 | +| lamp | 74.54 | 85.08 | +| bathtub | 87.25 | 89.94 | +| railing | 38.2 | 64.13 | +| cushion | 71.84 | 80.32 | +| base | 44.24 | 60.46 | +| box | 35.78 | 47.82 | +| column | 56.55 | 69.09 | +| signboard | 39.35 | 46.74 | +| chest of drawers | 43.33 | 67.08 | +| counter | 39.59 | 50.01 | +| sand | 56.57 | 87.6 | +| sink | 78.75 | 84.95 | +| skyscraper | 46.03 | 57.13 | +| fireplace | 71.01 | 92.17 | +| refrigerator | 80.43 | 86.59 | +| grandstand | 51.98 | 81.1 | +| path | 27.3 | 36.4 | +| stairs | 37.19 | 45.7 | +| runway | 75.51 | 98.57 | +| case | 58.53 | 88.89 | +| pool table | 93.81 | 97.7 | +| pillow | 69.58 | 82.7 | +| screen door | 70.14 | 72.33 | +| stairway | 43.18 | 55.0 | +| river | 10.0 | 18.51 | +| bridge | 58.79 | 70.85 | +| bookcase | 45.35 | 64.04 | +| blind | 39.77 | 46.03 | +| coffee table | 68.13 | 88.74 | +| toilet | 89.21 | 96.11 | +| flower | 43.07 | 55.96 | +| book | 52.5 | 69.75 | +| hill | 12.62 | 26.58 | +| bench | 65.18 | 74.51 | +| countertop | 63.15 | 86.14 | +| stove | 84.85 | 90.14 | +| palm | 55.74 | 82.04 | +| kitchen island | 41.88 | 52.7 | +| computer | 79.6 | 91.35 | +| swivel chair | 50.25 | 75.27 | +| boat | 60.97 | 91.68 | +| bar | 70.22 | 89.83 | +| arcade machine | 78.13 | 83.38 | +| hovel | 15.42 | 16.35 | +| bus | 92.64 | 96.86 | +| towel | 76.02 | 83.9 | +| light | 61.16 | 71.32 | +| truck | 44.8 | 62.31 | +| tower | 14.0 | 23.62 | +| chandelier | 72.75 | 89.27 | +| awning | 34.04 | 37.96 | +| streetlight | 33.52 | 44.86 | +| booth | 49.32 | 61.35 | +| television receiver | 81.29 | 92.05 | +| airplane | 87.58 | 95.55 | +| dirt track | 4.25 | 18.91 | +| apparel | 63.58 | 84.95 | +| pole | 23.08 | 29.4 | +| land | 0.37 | 0.68 | +| bannister | 19.09 | 25.91 | +| escalator | 63.88 | 82.85 | +| ottoman | 50.81 | 72.44 | +| bottle | 39.94 | 51.49 | +| buffet | 61.39 | 71.84 | +| poster | 27.19 | 31.74 | +| stage | 18.28 | 50.02 | +| van | 44.44 | 66.78 | +| ship | 86.34 | 89.6 | +| fountain | 30.71 | 31.82 | +| conveyer belt | 85.55 | 91.8 | +| canopy | 31.53 | 44.75 | +| washer | 76.31 | 81.41 | +| plaything | 38.63 | 65.67 | +| swimming pool | 49.86 | 72.5 | +| stool | 53.99 | 71.74 | +| barrel | 54.03 | 64.9 | +| basket | 42.68 | 61.35 | +| waterfall | 58.12 | 72.41 | +| tent | 95.81 | 98.07 | +| bag | 25.5 | 28.81 | +| minibike | 76.44 | 89.96 | +| cradle | 84.21 | 97.24 | +| oven | 50.46 | 56.3 | +| ball | 55.04 | 74.87 | +| food | 57.82 | 63.48 | +| step | 19.99 | 23.86 | +| tank | 61.83 | 66.94 | +| trade name | 18.61 | 20.35 | +| microwave | 87.38 | 95.83 | +| pot | 56.25 | 64.72 | +| animal | 60.89 | 62.57 | +| bicycle | 57.22 | 78.44 | +| lake | 52.67 | 63.73 | +| dishwasher | 72.93 | 82.92 | +| screen | 60.65 | 97.14 | +| blanket | 22.76 | 26.72 | +| sculpture | 74.56 | 87.27 | +| hood | 63.08 | 72.26 | +| sconce | 59.81 | 74.69 | +| vase | 45.37 | 61.27 | +| traffic light | 35.42 | 65.45 | +| tray | 19.25 | 22.64 | +| ashcan | 46.95 | 65.57 | +| fan | 69.29 | 83.23 | +| pier | 40.0 | 42.07 | +| crt screen | 1.23 | 2.64 | +| plate | 61.64 | 79.02 | +| monitor | 30.29 | 34.4 | +| bulletin board | 59.52 | 75.51 | +| shower | 0.0 | 0.0 | +| radiator | 66.78 | 80.65 | +| glass | 20.29 | 21.88 | +| clock | 49.15 | 60.4 | +| flag | 67.07 | 75.02 | ++---------------------+-------+-------+ +2024-06-16 13:09:24,812 - mmseg - INFO - Summary: +2024-06-16 13:09:24,813 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.01 | 56.45 | 68.78 | ++-------+-------+-------+ +2024-06-16 13:09:24,814 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 13:09:24,814 - mmseg - INFO - Iter(val) [250] aAcc: 0.8601, mIoU: 0.5645, mAcc: 0.6878, IoU.wall: 0.8189, IoU.building: 0.8447, IoU.sky: 0.9503, IoU.floor: 0.8510, IoU.tree: 0.7782, IoU.ceiling: 0.8695, IoU.road: 0.8582, IoU.bed : 0.9268, IoU.windowpane: 0.6648, IoU.grass: 0.7024, IoU.cabinet: 0.6573, IoU.sidewalk: 0.6926, IoU.person: 0.8601, IoU.earth: 0.3941, IoU.door: 0.6180, IoU.table: 0.6915, IoU.mountain: 0.6359, IoU.plant: 0.5474, IoU.curtain: 0.7947, IoU.chair: 0.6838, IoU.car: 0.8702, IoU.water: 0.5987, IoU.painting: 0.7434, IoU.sofa: 0.8087, IoU.shelf: 0.5084, IoU.house: 0.4896, IoU.sea: 0.7113, IoU.mirror: 0.8065, IoU.rug: 0.7187, IoU.field: 0.4006, IoU.armchair: 0.5977, IoU.seat: 0.7222, IoU.fence: 0.4650, IoU.desk: 0.5758, IoU.rock: 0.5715, IoU.wardrobe: 0.5250, IoU.lamp: 0.7454, IoU.bathtub: 0.8725, IoU.railing: 0.3820, IoU.cushion: 0.7184, IoU.base: 0.4424, IoU.box: 0.3578, IoU.column: 0.5655, IoU.signboard: 0.3935, IoU.chest of drawers: 0.4333, IoU.counter: 0.3959, IoU.sand: 0.5657, IoU.sink: 0.7875, IoU.skyscraper: 0.4603, IoU.fireplace: 0.7101, IoU.refrigerator: 0.8043, IoU.grandstand: 0.5198, IoU.path: 0.2730, IoU.stairs: 0.3719, IoU.runway: 0.7551, IoU.case: 0.5853, IoU.pool table: 0.9381, IoU.pillow: 0.6958, IoU.screen door: 0.7014, IoU.stairway: 0.4318, IoU.river: 0.1000, IoU.bridge: 0.5879, IoU.bookcase: 0.4535, IoU.blind: 0.3977, IoU.coffee table: 0.6813, IoU.toilet: 0.8921, IoU.flower: 0.4307, IoU.book: 0.5250, IoU.hill: 0.1262, IoU.bench: 0.6518, IoU.countertop: 0.6315, IoU.stove: 0.8485, IoU.palm: 0.5574, IoU.kitchen island: 0.4188, IoU.computer: 0.7960, IoU.swivel chair: 0.5025, IoU.boat: 0.6097, IoU.bar: 0.7022, IoU.arcade machine: 0.7813, IoU.hovel: 0.1542, IoU.bus: 0.9264, IoU.towel: 0.7602, IoU.light: 0.6116, IoU.truck: 0.4480, IoU.tower: 0.1400, IoU.chandelier: 0.7275, IoU.awning: 0.3404, IoU.streetlight: 0.3352, IoU.booth: 0.4932, IoU.television receiver: 0.8129, IoU.airplane: 0.8758, IoU.dirt track: 0.0425, IoU.apparel: 0.6358, IoU.pole: 0.2308, IoU.land: 0.0037, IoU.bannister: 0.1909, IoU.escalator: 0.6388, IoU.ottoman: 0.5081, IoU.bottle: 0.3994, IoU.buffet: 0.6139, IoU.poster: 0.2719, IoU.stage: 0.1828, IoU.van: 0.4444, IoU.ship: 0.8634, IoU.fountain: 0.3071, IoU.conveyer belt: 0.8555, IoU.canopy: 0.3153, IoU.washer: 0.7631, IoU.plaything: 0.3863, IoU.swimming pool: 0.4986, IoU.stool: 0.5399, IoU.barrel: 0.5403, IoU.basket: 0.4268, IoU.waterfall: 0.5812, IoU.tent: 0.9581, IoU.bag: 0.2550, IoU.minibike: 0.7644, IoU.cradle: 0.8421, IoU.oven: 0.5046, IoU.ball: 0.5504, IoU.food: 0.5782, IoU.step: 0.1999, IoU.tank: 0.6183, IoU.trade name: 0.1861, IoU.microwave: 0.8738, IoU.pot: 0.5625, IoU.animal: 0.6089, IoU.bicycle: 0.5722, IoU.lake: 0.5267, IoU.dishwasher: 0.7293, IoU.screen: 0.6065, IoU.blanket: 0.2276, IoU.sculpture: 0.7456, IoU.hood: 0.6308, IoU.sconce: 0.5981, IoU.vase: 0.4537, IoU.traffic light: 0.3542, IoU.tray: 0.1925, IoU.ashcan: 0.4695, IoU.fan: 0.6929, IoU.pier: 0.4000, IoU.crt screen: 0.0123, IoU.plate: 0.6164, IoU.monitor: 0.3029, IoU.bulletin board: 0.5952, IoU.shower: 0.0000, IoU.radiator: 0.6678, IoU.glass: 0.2029, IoU.clock: 0.4915, IoU.flag: 0.6707, Acc.wall: 0.8947, Acc.building: 0.9358, Acc.sky: 0.9738, Acc.floor: 0.9139, Acc.tree: 0.9122, Acc.ceiling: 0.9277, Acc.road: 0.9149, Acc.bed : 0.9712, Acc.windowpane: 0.8347, Acc.grass: 0.8344, Acc.cabinet: 0.7697, Acc.sidewalk: 0.8137, Acc.person: 0.9357, Acc.earth: 0.5520, Acc.door: 0.7798, Acc.table: 0.8571, Acc.mountain: 0.7706, Acc.plant: 0.6531, Acc.curtain: 0.8734, Acc.chair: 0.8035, Acc.car: 0.9356, Acc.water: 0.7299, Acc.painting: 0.9110, Acc.sofa: 0.8869, Acc.shelf: 0.7212, Acc.house: 0.5482, Acc.sea: 0.8361, Acc.mirror: 0.8921, Acc.rug: 0.8358, Acc.field: 0.5484, Acc.armchair: 0.8091, Acc.seat: 0.8750, Acc.fence: 0.6080, Acc.desk: 0.7356, Acc.rock: 0.7334, Acc.wardrobe: 0.7628, Acc.lamp: 0.8508, Acc.bathtub: 0.8994, Acc.railing: 0.6413, Acc.cushion: 0.8032, Acc.base: 0.6046, Acc.box: 0.4782, Acc.column: 0.6909, Acc.signboard: 0.4674, Acc.chest of drawers: 0.6708, Acc.counter: 0.5001, Acc.sand: 0.8760, Acc.sink: 0.8495, Acc.skyscraper: 0.5713, Acc.fireplace: 0.9217, Acc.refrigerator: 0.8659, Acc.grandstand: 0.8110, Acc.path: 0.3640, Acc.stairs: 0.4570, Acc.runway: 0.9857, Acc.case: 0.8889, Acc.pool table: 0.9770, Acc.pillow: 0.8270, Acc.screen door: 0.7233, Acc.stairway: 0.5500, Acc.river: 0.1851, Acc.bridge: 0.7085, Acc.bookcase: 0.6404, Acc.blind: 0.4603, Acc.coffee table: 0.8874, Acc.toilet: 0.9611, Acc.flower: 0.5596, Acc.book: 0.6975, Acc.hill: 0.2658, Acc.bench: 0.7451, Acc.countertop: 0.8614, Acc.stove: 0.9014, Acc.palm: 0.8204, Acc.kitchen island: 0.5270, Acc.computer: 0.9135, Acc.swivel chair: 0.7527, Acc.boat: 0.9168, Acc.bar: 0.8983, Acc.arcade machine: 0.8338, Acc.hovel: 0.1635, Acc.bus: 0.9686, Acc.towel: 0.8390, Acc.light: 0.7132, Acc.truck: 0.6231, Acc.tower: 0.2362, Acc.chandelier: 0.8927, Acc.awning: 0.3796, Acc.streetlight: 0.4486, Acc.booth: 0.6135, Acc.television receiver: 0.9205, Acc.airplane: 0.9555, Acc.dirt track: 0.1891, Acc.apparel: 0.8495, Acc.pole: 0.2940, Acc.land: 0.0068, Acc.bannister: 0.2591, Acc.escalator: 0.8285, Acc.ottoman: 0.7244, Acc.bottle: 0.5149, Acc.buffet: 0.7184, Acc.poster: 0.3174, Acc.stage: 0.5002, Acc.van: 0.6678, Acc.ship: 0.8960, Acc.fountain: 0.3182, Acc.conveyer belt: 0.9180, Acc.canopy: 0.4475, Acc.washer: 0.8141, Acc.plaything: 0.6567, Acc.swimming pool: 0.7250, Acc.stool: 0.7174, Acc.barrel: 0.6490, Acc.basket: 0.6135, Acc.waterfall: 0.7241, Acc.tent: 0.9807, Acc.bag: 0.2881, Acc.minibike: 0.8996, Acc.cradle: 0.9724, Acc.oven: 0.5630, Acc.ball: 0.7487, Acc.food: 0.6348, Acc.step: 0.2386, Acc.tank: 0.6694, Acc.trade name: 0.2035, Acc.microwave: 0.9583, Acc.pot: 0.6472, Acc.animal: 0.6257, Acc.bicycle: 0.7844, Acc.lake: 0.6373, Acc.dishwasher: 0.8292, Acc.screen: 0.9714, Acc.blanket: 0.2672, Acc.sculpture: 0.8727, Acc.hood: 0.7226, Acc.sconce: 0.7469, Acc.vase: 0.6127, Acc.traffic light: 0.6545, Acc.tray: 0.2264, Acc.ashcan: 0.6557, Acc.fan: 0.8323, Acc.pier: 0.4207, Acc.crt screen: 0.0264, Acc.plate: 0.7902, Acc.monitor: 0.3440, Acc.bulletin board: 0.7551, Acc.shower: 0.0000, Acc.radiator: 0.8065, Acc.glass: 0.2188, Acc.clock: 0.6040, Acc.flag: 0.7502 +2024-06-16 13:10:46,446 - mmseg - INFO - Iter [32050/80000] lr: 2.398e-05, eta: 23:33:47, time: 3.567, data_time: 1.953, memory: 71384, decode.loss_ce: 0.2205, decode.acc_seg: 90.9734, aux.loss_ce: 0.0903, aux.acc_seg: 90.7048, loss: 0.3108 +2024-06-16 13:12:07,534 - mmseg - INFO - Iter [32100/80000] lr: 2.395e-05, eta: 23:32:08, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2237, decode.acc_seg: 90.5597, aux.loss_ce: 0.0927, aux.acc_seg: 90.2838, loss: 0.3164 +2024-06-16 13:13:28,618 - mmseg - INFO - Iter [32150/80000] lr: 2.393e-05, eta: 23:30:28, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2256, decode.acc_seg: 90.9025, aux.loss_ce: 0.0935, aux.acc_seg: 90.5303, loss: 0.3191 +2024-06-16 13:14:49,819 - mmseg - INFO - Iter [32200/80000] lr: 2.390e-05, eta: 23:28:49, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2149, decode.acc_seg: 90.9629, aux.loss_ce: 0.0885, aux.acc_seg: 90.6330, loss: 0.3034 +2024-06-16 13:16:11,139 - mmseg - INFO - Iter [32250/80000] lr: 2.388e-05, eta: 23:27:10, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2178, decode.acc_seg: 90.7438, aux.loss_ce: 0.0897, aux.acc_seg: 90.6374, loss: 0.3075 +2024-06-16 13:17:32,220 - mmseg - INFO - Iter [32300/80000] lr: 2.385e-05, eta: 23:25:31, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2192, decode.acc_seg: 91.1123, aux.loss_ce: 0.0898, aux.acc_seg: 90.8501, loss: 0.3089 +2024-06-16 13:18:53,212 - mmseg - INFO - Iter [32350/80000] lr: 2.383e-05, eta: 23:23:52, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2201, decode.acc_seg: 90.9781, aux.loss_ce: 0.0908, aux.acc_seg: 90.7117, loss: 0.3109 +2024-06-16 13:20:14,432 - mmseg - INFO - Iter [32400/80000] lr: 2.380e-05, eta: 23:22:13, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2464, decode.acc_seg: 89.8698, aux.loss_ce: 0.1016, aux.acc_seg: 89.5788, loss: 0.3480 +2024-06-16 13:21:35,556 - mmseg - INFO - Iter [32450/80000] lr: 2.378e-05, eta: 23:20:34, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2387, decode.acc_seg: 90.2157, aux.loss_ce: 0.0986, aux.acc_seg: 89.8380, loss: 0.3373 +2024-06-16 13:22:56,794 - mmseg - INFO - Iter [32500/80000] lr: 2.375e-05, eta: 23:18:55, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2277, decode.acc_seg: 90.3731, aux.loss_ce: 0.0934, aux.acc_seg: 90.0809, loss: 0.3212 +2024-06-16 13:24:17,894 - mmseg - INFO - Iter [32550/80000] lr: 2.373e-05, eta: 23:17:16, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2103, decode.acc_seg: 91.2526, aux.loss_ce: 0.0868, aux.acc_seg: 90.9104, loss: 0.2970 +2024-06-16 13:25:39,084 - mmseg - INFO - Iter [32600/80000] lr: 2.370e-05, eta: 23:15:37, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2130, decode.acc_seg: 91.2047, aux.loss_ce: 0.0882, aux.acc_seg: 90.9040, loss: 0.3012 +2024-06-16 13:27:00,304 - mmseg - INFO - Iter [32650/80000] lr: 2.368e-05, eta: 23:13:59, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2095, decode.acc_seg: 91.2129, aux.loss_ce: 0.0867, aux.acc_seg: 90.9072, loss: 0.2962 +2024-06-16 13:28:21,504 - mmseg - INFO - Iter [32700/80000] lr: 2.365e-05, eta: 23:12:20, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2453, decode.acc_seg: 90.0076, aux.loss_ce: 0.1013, aux.acc_seg: 89.7200, loss: 0.3466 +2024-06-16 13:29:42,834 - mmseg - INFO - Iter [32750/80000] lr: 2.363e-05, eta: 23:10:42, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2238, decode.acc_seg: 90.6699, aux.loss_ce: 0.0918, aux.acc_seg: 90.3361, loss: 0.3156 +2024-06-16 13:31:03,993 - mmseg - INFO - Iter [32800/80000] lr: 2.360e-05, eta: 23:09:03, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2249, decode.acc_seg: 90.6336, aux.loss_ce: 0.0929, aux.acc_seg: 90.3996, loss: 0.3178 +2024-06-16 13:32:27,882 - mmseg - INFO - Iter [32850/80000] lr: 2.358e-05, eta: 23:07:29, time: 1.678, data_time: 0.064, memory: 71384, decode.loss_ce: 0.2104, decode.acc_seg: 90.8353, aux.loss_ce: 0.0872, aux.acc_seg: 90.5780, loss: 0.2976 +2024-06-16 13:33:48,921 - mmseg - INFO - Iter [32900/80000] lr: 2.355e-05, eta: 23:05:50, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2113, decode.acc_seg: 91.0350, aux.loss_ce: 0.0875, aux.acc_seg: 90.7221, loss: 0.2988 +2024-06-16 13:35:10,204 - mmseg - INFO - Iter [32950/80000] lr: 2.353e-05, eta: 23:04:12, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2101, decode.acc_seg: 90.9374, aux.loss_ce: 0.0863, aux.acc_seg: 90.7328, loss: 0.2964 +2024-06-16 13:36:31,410 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 13:36:31,410 - mmseg - INFO - Iter [33000/80000] lr: 2.350e-05, eta: 23:02:33, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2112, decode.acc_seg: 91.1187, aux.loss_ce: 0.0879, aux.acc_seg: 90.8101, loss: 0.2991 +2024-06-16 13:38:09,360 - mmseg - INFO - per class results: +2024-06-16 13:38:09,366 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.29 | 89.62 | +| building | 85.35 | 93.2 | +| sky | 95.06 | 97.33 | +| floor | 85.31 | 90.67 | +| tree | 77.51 | 90.63 | +| ceiling | 86.93 | 92.29 | +| road | 86.35 | 91.37 | +| bed | 92.37 | 97.05 | +| windowpane | 68.11 | 81.38 | +| grass | 67.52 | 84.83 | +| cabinet | 65.42 | 75.67 | +| sidewalk | 71.9 | 84.57 | +| person | 85.84 | 93.94 | +| earth | 39.98 | 55.08 | +| door | 61.26 | 79.52 | +| table | 68.9 | 80.37 | +| mountain | 59.44 | 74.08 | +| plant | 55.3 | 66.87 | +| curtain | 79.16 | 90.48 | +| chair | 67.93 | 78.44 | +| car | 87.2 | 94.18 | +| water | 61.86 | 74.75 | +| painting | 76.48 | 90.37 | +| sofa | 81.82 | 91.16 | +| shelf | 50.35 | 67.11 | +| house | 53.44 | 73.12 | +| sea | 71.39 | 84.18 | +| mirror | 78.21 | 85.42 | +| rug | 69.85 | 84.9 | +| field | 32.7 | 44.84 | +| armchair | 61.15 | 81.39 | +| seat | 67.58 | 89.53 | +| fence | 48.05 | 60.84 | +| desk | 54.7 | 77.05 | +| rock | 54.81 | 79.92 | +| wardrobe | 54.64 | 76.98 | +| lamp | 73.6 | 85.09 | +| bathtub | 89.91 | 92.42 | +| railing | 45.54 | 64.73 | +| cushion | 70.25 | 78.48 | +| base | 46.2 | 67.87 | +| box | 37.21 | 49.01 | +| column | 60.34 | 75.32 | +| signboard | 39.48 | 51.6 | +| chest of drawers | 42.81 | 62.02 | +| counter | 40.4 | 51.59 | +| sand | 53.36 | 79.08 | +| sink | 78.73 | 84.83 | +| skyscraper | 46.85 | 53.02 | +| fireplace | 70.2 | 93.01 | +| refrigerator | 85.74 | 95.53 | +| grandstand | 56.47 | 78.94 | +| path | 26.37 | 35.45 | +| stairs | 37.66 | 56.73 | +| runway | 74.73 | 96.03 | +| case | 63.85 | 84.91 | +| pool table | 94.27 | 98.37 | +| pillow | 69.69 | 84.7 | +| screen door | 78.29 | 81.55 | +| stairway | 32.53 | 34.77 | +| river | 15.6 | 35.02 | +| bridge | 49.91 | 57.66 | +| bookcase | 42.56 | 65.58 | +| blind | 51.19 | 62.48 | +| coffee table | 60.18 | 90.78 | +| toilet | 91.2 | 94.94 | +| flower | 40.18 | 48.16 | +| book | 53.49 | 77.73 | +| hill | 5.45 | 9.66 | +| bench | 56.61 | 63.56 | +| countertop | 62.72 | 85.54 | +| stove | 84.81 | 93.45 | +| palm | 56.42 | 83.64 | +| kitchen island | 49.83 | 81.31 | +| computer | 78.92 | 92.5 | +| swivel chair | 51.26 | 75.29 | +| boat | 77.33 | 90.07 | +| bar | 69.23 | 83.82 | +| arcade machine | 86.22 | 91.89 | +| hovel | 14.07 | 15.86 | +| bus | 92.84 | 96.51 | +| towel | 76.33 | 85.05 | +| light | 61.99 | 75.57 | +| truck | 39.7 | 49.94 | +| tower | 16.47 | 24.7 | +| chandelier | 70.76 | 88.12 | +| awning | 41.43 | 53.98 | +| streetlight | 37.03 | 51.35 | +| booth | 42.58 | 63.64 | +| television receiver | 77.94 | 89.81 | +| airplane | 89.82 | 94.21 | +| dirt track | 5.07 | 28.28 | +| apparel | 56.87 | 78.32 | +| pole | 27.2 | 40.55 | +| land | 0.0 | 0.0 | +| bannister | 19.62 | 23.63 | +| escalator | 64.25 | 83.03 | +| ottoman | 50.81 | 71.3 | +| bottle | 37.57 | 47.69 | +| buffet | 51.74 | 57.0 | +| poster | 30.16 | 34.0 | +| stage | 27.76 | 46.28 | +| van | 45.54 | 66.31 | +| ship | 80.43 | 90.46 | +| fountain | 37.6 | 40.63 | +| conveyer belt | 76.93 | 96.11 | +| canopy | 51.43 | 79.13 | +| washer | 83.53 | 89.03 | +| plaything | 44.95 | 64.65 | +| swimming pool | 57.71 | 87.38 | +| stool | 50.25 | 69.12 | +| barrel | 52.23 | 67.28 | +| basket | 42.64 | 69.13 | +| waterfall | 53.69 | 60.18 | +| tent | 95.88 | 98.35 | +| bag | 25.54 | 29.56 | +| minibike | 76.04 | 88.77 | +| cradle | 82.53 | 98.56 | +| oven | 59.9 | 69.38 | +| ball | 54.64 | 75.77 | +| food | 65.12 | 75.65 | +| step | 23.76 | 34.86 | +| tank | 59.07 | 69.55 | +| trade name | 20.06 | 23.1 | +| microwave | 87.43 | 96.23 | +| pot | 58.74 | 69.67 | +| animal | 61.29 | 62.4 | +| bicycle | 59.06 | 74.35 | +| lake | 54.69 | 63.65 | +| dishwasher | 73.76 | 79.82 | +| screen | 53.86 | 77.84 | +| blanket | 27.39 | 31.56 | +| sculpture | 74.21 | 89.08 | +| hood | 63.11 | 82.22 | +| sconce | 63.26 | 77.67 | +| vase | 45.17 | 62.43 | +| traffic light | 34.59 | 64.99 | +| tray | 26.76 | 46.06 | +| ashcan | 51.2 | 65.64 | +| fan | 68.08 | 87.3 | +| pier | 39.56 | 53.14 | +| crt screen | 1.72 | 2.95 | +| plate | 64.33 | 80.31 | +| monitor | 62.19 | 70.2 | +| bulletin board | 60.42 | 72.58 | +| shower | 0.92 | 0.94 | +| radiator | 67.66 | 80.71 | +| glass | 19.56 | 20.66 | +| clock | 48.12 | 58.95 | +| flag | 68.07 | 74.51 | ++---------------------+-------+-------+ +2024-06-16 13:38:09,366 - mmseg - INFO - Summary: +2024-06-16 13:38:09,366 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.16 | 57.27 | 70.33 | ++-------+-------+-------+ +2024-06-16 13:38:09,367 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 13:38:09,367 - mmseg - INFO - Iter(val) [250] aAcc: 0.8616, mIoU: 0.5727, mAcc: 0.7033, IoU.wall: 0.8229, IoU.building: 0.8535, IoU.sky: 0.9506, IoU.floor: 0.8531, IoU.tree: 0.7751, IoU.ceiling: 0.8693, IoU.road: 0.8635, IoU.bed : 0.9237, IoU.windowpane: 0.6811, IoU.grass: 0.6752, IoU.cabinet: 0.6542, IoU.sidewalk: 0.7190, IoU.person: 0.8584, IoU.earth: 0.3998, IoU.door: 0.6126, IoU.table: 0.6890, IoU.mountain: 0.5944, IoU.plant: 0.5530, IoU.curtain: 0.7916, IoU.chair: 0.6793, IoU.car: 0.8720, IoU.water: 0.6186, IoU.painting: 0.7648, IoU.sofa: 0.8182, IoU.shelf: 0.5035, IoU.house: 0.5344, IoU.sea: 0.7139, IoU.mirror: 0.7821, IoU.rug: 0.6985, IoU.field: 0.3270, IoU.armchair: 0.6115, IoU.seat: 0.6758, IoU.fence: 0.4805, IoU.desk: 0.5470, IoU.rock: 0.5481, IoU.wardrobe: 0.5464, IoU.lamp: 0.7360, IoU.bathtub: 0.8991, IoU.railing: 0.4554, IoU.cushion: 0.7025, IoU.base: 0.4620, IoU.box: 0.3721, IoU.column: 0.6034, IoU.signboard: 0.3948, IoU.chest of drawers: 0.4281, IoU.counter: 0.4040, IoU.sand: 0.5336, IoU.sink: 0.7873, IoU.skyscraper: 0.4685, IoU.fireplace: 0.7020, IoU.refrigerator: 0.8574, IoU.grandstand: 0.5647, IoU.path: 0.2637, IoU.stairs: 0.3766, IoU.runway: 0.7473, IoU.case: 0.6385, IoU.pool table: 0.9427, IoU.pillow: 0.6969, IoU.screen door: 0.7829, IoU.stairway: 0.3253, IoU.river: 0.1560, IoU.bridge: 0.4991, IoU.bookcase: 0.4256, IoU.blind: 0.5119, IoU.coffee table: 0.6018, IoU.toilet: 0.9120, IoU.flower: 0.4018, IoU.book: 0.5349, IoU.hill: 0.0545, IoU.bench: 0.5661, IoU.countertop: 0.6272, IoU.stove: 0.8481, IoU.palm: 0.5642, IoU.kitchen island: 0.4983, IoU.computer: 0.7892, IoU.swivel chair: 0.5126, IoU.boat: 0.7733, IoU.bar: 0.6923, IoU.arcade machine: 0.8622, IoU.hovel: 0.1407, IoU.bus: 0.9284, IoU.towel: 0.7633, IoU.light: 0.6199, IoU.truck: 0.3970, IoU.tower: 0.1647, IoU.chandelier: 0.7076, IoU.awning: 0.4143, IoU.streetlight: 0.3703, IoU.booth: 0.4258, IoU.television receiver: 0.7794, IoU.airplane: 0.8982, IoU.dirt track: 0.0507, IoU.apparel: 0.5687, IoU.pole: 0.2720, IoU.land: 0.0000, IoU.bannister: 0.1962, IoU.escalator: 0.6425, IoU.ottoman: 0.5081, IoU.bottle: 0.3757, IoU.buffet: 0.5174, IoU.poster: 0.3016, IoU.stage: 0.2776, IoU.van: 0.4554, IoU.ship: 0.8043, IoU.fountain: 0.3760, IoU.conveyer belt: 0.7693, IoU.canopy: 0.5143, IoU.washer: 0.8353, IoU.plaything: 0.4495, IoU.swimming pool: 0.5771, IoU.stool: 0.5025, IoU.barrel: 0.5223, IoU.basket: 0.4264, IoU.waterfall: 0.5369, IoU.tent: 0.9588, IoU.bag: 0.2554, IoU.minibike: 0.7604, IoU.cradle: 0.8253, IoU.oven: 0.5990, IoU.ball: 0.5464, IoU.food: 0.6512, IoU.step: 0.2376, IoU.tank: 0.5907, IoU.trade name: 0.2006, IoU.microwave: 0.8743, IoU.pot: 0.5874, IoU.animal: 0.6129, IoU.bicycle: 0.5906, IoU.lake: 0.5469, IoU.dishwasher: 0.7376, IoU.screen: 0.5386, IoU.blanket: 0.2739, IoU.sculpture: 0.7421, IoU.hood: 0.6311, IoU.sconce: 0.6326, IoU.vase: 0.4517, IoU.traffic light: 0.3459, IoU.tray: 0.2676, IoU.ashcan: 0.5120, IoU.fan: 0.6808, IoU.pier: 0.3956, IoU.crt screen: 0.0172, IoU.plate: 0.6433, IoU.monitor: 0.6219, IoU.bulletin board: 0.6042, IoU.shower: 0.0092, IoU.radiator: 0.6766, IoU.glass: 0.1956, IoU.clock: 0.4812, IoU.flag: 0.6807, Acc.wall: 0.8962, Acc.building: 0.9320, Acc.sky: 0.9733, Acc.floor: 0.9067, Acc.tree: 0.9063, Acc.ceiling: 0.9229, Acc.road: 0.9137, Acc.bed : 0.9705, Acc.windowpane: 0.8138, Acc.grass: 0.8483, Acc.cabinet: 0.7567, Acc.sidewalk: 0.8457, Acc.person: 0.9394, Acc.earth: 0.5508, Acc.door: 0.7952, Acc.table: 0.8037, Acc.mountain: 0.7408, Acc.plant: 0.6687, Acc.curtain: 0.9048, Acc.chair: 0.7844, Acc.car: 0.9418, Acc.water: 0.7475, Acc.painting: 0.9037, Acc.sofa: 0.9116, Acc.shelf: 0.6711, Acc.house: 0.7312, Acc.sea: 0.8418, Acc.mirror: 0.8542, Acc.rug: 0.8490, Acc.field: 0.4484, Acc.armchair: 0.8139, Acc.seat: 0.8953, Acc.fence: 0.6084, Acc.desk: 0.7705, Acc.rock: 0.7992, Acc.wardrobe: 0.7698, Acc.lamp: 0.8509, Acc.bathtub: 0.9242, Acc.railing: 0.6473, Acc.cushion: 0.7848, Acc.base: 0.6787, Acc.box: 0.4901, Acc.column: 0.7532, Acc.signboard: 0.5160, Acc.chest of drawers: 0.6202, Acc.counter: 0.5159, Acc.sand: 0.7908, Acc.sink: 0.8483, Acc.skyscraper: 0.5302, Acc.fireplace: 0.9301, Acc.refrigerator: 0.9553, Acc.grandstand: 0.7894, Acc.path: 0.3545, Acc.stairs: 0.5673, Acc.runway: 0.9603, Acc.case: 0.8491, Acc.pool table: 0.9837, Acc.pillow: 0.8470, Acc.screen door: 0.8155, Acc.stairway: 0.3477, Acc.river: 0.3502, Acc.bridge: 0.5766, Acc.bookcase: 0.6558, Acc.blind: 0.6248, Acc.coffee table: 0.9078, Acc.toilet: 0.9494, Acc.flower: 0.4816, Acc.book: 0.7773, Acc.hill: 0.0966, Acc.bench: 0.6356, Acc.countertop: 0.8554, Acc.stove: 0.9345, Acc.palm: 0.8364, Acc.kitchen island: 0.8131, Acc.computer: 0.9250, Acc.swivel chair: 0.7529, Acc.boat: 0.9007, Acc.bar: 0.8382, Acc.arcade machine: 0.9189, Acc.hovel: 0.1586, Acc.bus: 0.9651, Acc.towel: 0.8505, Acc.light: 0.7557, Acc.truck: 0.4994, Acc.tower: 0.2470, Acc.chandelier: 0.8812, Acc.awning: 0.5398, Acc.streetlight: 0.5135, Acc.booth: 0.6364, Acc.television receiver: 0.8981, Acc.airplane: 0.9421, Acc.dirt track: 0.2828, Acc.apparel: 0.7832, Acc.pole: 0.4055, Acc.land: 0.0000, Acc.bannister: 0.2363, Acc.escalator: 0.8303, Acc.ottoman: 0.7130, Acc.bottle: 0.4769, Acc.buffet: 0.5700, Acc.poster: 0.3400, Acc.stage: 0.4628, Acc.van: 0.6631, Acc.ship: 0.9046, Acc.fountain: 0.4063, Acc.conveyer belt: 0.9611, Acc.canopy: 0.7913, Acc.washer: 0.8903, Acc.plaything: 0.6465, Acc.swimming pool: 0.8738, Acc.stool: 0.6912, Acc.barrel: 0.6728, Acc.basket: 0.6913, Acc.waterfall: 0.6018, Acc.tent: 0.9835, Acc.bag: 0.2956, Acc.minibike: 0.8877, Acc.cradle: 0.9856, Acc.oven: 0.6938, Acc.ball: 0.7577, Acc.food: 0.7565, Acc.step: 0.3486, Acc.tank: 0.6955, Acc.trade name: 0.2310, Acc.microwave: 0.9623, Acc.pot: 0.6967, Acc.animal: 0.6240, Acc.bicycle: 0.7435, Acc.lake: 0.6365, Acc.dishwasher: 0.7982, Acc.screen: 0.7784, Acc.blanket: 0.3156, Acc.sculpture: 0.8908, Acc.hood: 0.8222, Acc.sconce: 0.7767, Acc.vase: 0.6243, Acc.traffic light: 0.6499, Acc.tray: 0.4606, Acc.ashcan: 0.6564, Acc.fan: 0.8730, Acc.pier: 0.5314, Acc.crt screen: 0.0295, Acc.plate: 0.8031, Acc.monitor: 0.7020, Acc.bulletin board: 0.7258, Acc.shower: 0.0094, Acc.radiator: 0.8071, Acc.glass: 0.2066, Acc.clock: 0.5895, Acc.flag: 0.7451 +2024-06-16 13:39:31,077 - mmseg - INFO - Iter [33050/80000] lr: 2.348e-05, eta: 23:03:15, time: 3.593, data_time: 1.977, memory: 71384, decode.loss_ce: 0.2121, decode.acc_seg: 91.1954, aux.loss_ce: 0.0875, aux.acc_seg: 90.9297, loss: 0.2995 +2024-06-16 13:40:52,253 - mmseg - INFO - Iter [33100/80000] lr: 2.345e-05, eta: 23:01:36, time: 1.624, data_time: 0.011, memory: 71384, decode.loss_ce: 0.2176, decode.acc_seg: 90.7287, aux.loss_ce: 0.0901, aux.acc_seg: 90.4560, loss: 0.3077 +2024-06-16 13:42:13,288 - mmseg - INFO - Iter [33150/80000] lr: 2.343e-05, eta: 22:59:58, time: 1.621, data_time: 0.011, memory: 71384, decode.loss_ce: 0.2173, decode.acc_seg: 90.3698, aux.loss_ce: 0.0897, aux.acc_seg: 90.0819, loss: 0.3070 +2024-06-16 13:43:34,763 - mmseg - INFO - Iter [33200/80000] lr: 2.340e-05, eta: 22:58:20, time: 1.629, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2184, decode.acc_seg: 90.7714, aux.loss_ce: 0.0894, aux.acc_seg: 90.5835, loss: 0.3078 +2024-06-16 13:44:55,801 - mmseg - INFO - Iter [33250/80000] lr: 2.338e-05, eta: 22:56:41, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2121, decode.acc_seg: 91.1306, aux.loss_ce: 0.0886, aux.acc_seg: 90.7744, loss: 0.3007 +2024-06-16 13:46:16,846 - mmseg - INFO - Iter [33300/80000] lr: 2.335e-05, eta: 22:55:02, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2007, decode.acc_seg: 91.4006, aux.loss_ce: 0.0836, aux.acc_seg: 91.0515, loss: 0.2843 +2024-06-16 13:47:37,920 - mmseg - INFO - Iter [33350/80000] lr: 2.333e-05, eta: 22:53:24, time: 1.621, data_time: 0.011, memory: 71384, decode.loss_ce: 0.2167, decode.acc_seg: 91.2512, aux.loss_ce: 0.0902, aux.acc_seg: 90.8520, loss: 0.3069 +2024-06-16 13:48:59,078 - mmseg - INFO - Iter [33400/80000] lr: 2.330e-05, eta: 22:51:46, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2094, decode.acc_seg: 91.0312, aux.loss_ce: 0.0861, aux.acc_seg: 90.8427, loss: 0.2955 +2024-06-16 13:50:20,223 - mmseg - INFO - Iter [33450/80000] lr: 2.328e-05, eta: 22:50:07, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2166, decode.acc_seg: 91.1155, aux.loss_ce: 0.0895, aux.acc_seg: 90.7860, loss: 0.3060 +2024-06-16 13:51:41,292 - mmseg - INFO - Iter [33500/80000] lr: 2.325e-05, eta: 22:48:29, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2178, decode.acc_seg: 91.0294, aux.loss_ce: 0.0897, aux.acc_seg: 90.7846, loss: 0.3075 +2024-06-16 13:53:02,593 - mmseg - INFO - Iter [33550/80000] lr: 2.323e-05, eta: 22:46:51, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2245, decode.acc_seg: 90.9146, aux.loss_ce: 0.0928, aux.acc_seg: 90.5682, loss: 0.3174 +2024-06-16 13:54:23,661 - mmseg - INFO - Iter [33600/80000] lr: 2.320e-05, eta: 22:45:13, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2119, decode.acc_seg: 90.9802, aux.loss_ce: 0.0874, aux.acc_seg: 90.6989, loss: 0.2993 +2024-06-16 13:55:44,819 - mmseg - INFO - Iter [33650/80000] lr: 2.318e-05, eta: 22:43:35, time: 1.623, data_time: 0.011, memory: 71384, decode.loss_ce: 0.2083, decode.acc_seg: 91.4526, aux.loss_ce: 0.0861, aux.acc_seg: 91.1426, loss: 0.2944 +2024-06-16 13:57:06,016 - mmseg - INFO - Iter [33700/80000] lr: 2.315e-05, eta: 22:41:57, time: 1.624, data_time: 0.011, memory: 71384, decode.loss_ce: 0.2046, decode.acc_seg: 91.3736, aux.loss_ce: 0.0852, aux.acc_seg: 90.9969, loss: 0.2898 +2024-06-16 13:58:27,237 - mmseg - INFO - Iter [33750/80000] lr: 2.313e-05, eta: 22:40:19, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2259, decode.acc_seg: 90.5612, aux.loss_ce: 0.0933, aux.acc_seg: 90.2581, loss: 0.3192 +2024-06-16 13:59:48,243 - mmseg - INFO - Iter [33800/80000] lr: 2.310e-05, eta: 22:38:41, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2164, decode.acc_seg: 91.0541, aux.loss_ce: 0.0891, aux.acc_seg: 90.7391, loss: 0.3055 +2024-06-16 14:01:09,479 - mmseg - INFO - Iter [33850/80000] lr: 2.308e-05, eta: 22:37:03, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2144, decode.acc_seg: 91.2307, aux.loss_ce: 0.0888, aux.acc_seg: 90.8859, loss: 0.3032 +2024-06-16 14:02:30,554 - mmseg - INFO - Iter [33900/80000] lr: 2.305e-05, eta: 22:35:25, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2110, decode.acc_seg: 91.6838, aux.loss_ce: 0.0871, aux.acc_seg: 91.3913, loss: 0.2981 +2024-06-16 14:03:51,655 - mmseg - INFO - Iter [33950/80000] lr: 2.303e-05, eta: 22:33:47, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2412, decode.acc_seg: 90.2207, aux.loss_ce: 0.1001, aux.acc_seg: 89.8849, loss: 0.3413 +2024-06-16 14:05:12,867 - mmseg - INFO - Saving checkpoint at 34000 iterations +2024-06-16 14:06:35,698 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 14:06:35,698 - mmseg - INFO - Iter [34000/80000] lr: 2.300e-05, eta: 22:34:02, time: 3.281, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2213, decode.acc_seg: 90.6208, aux.loss_ce: 0.0894, aux.acc_seg: 90.4571, loss: 0.3107 +2024-06-16 14:08:12,360 - mmseg - INFO - per class results: +2024-06-16 14:08:12,366 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.85 | 90.45 | +| building | 84.75 | 92.08 | +| sky | 94.8 | 96.58 | +| floor | 85.29 | 90.96 | +| tree | 76.86 | 93.11 | +| ceiling | 87.09 | 92.43 | +| road | 85.9 | 91.25 | +| bed | 92.55 | 97.45 | +| windowpane | 66.55 | 84.27 | +| grass | 67.63 | 79.66 | +| cabinet | 65.06 | 75.68 | +| sidewalk | 70.22 | 85.74 | +| person | 85.61 | 94.2 | +| earth | 38.14 | 53.39 | +| door | 60.08 | 73.94 | +| table | 68.98 | 81.83 | +| mountain | 60.42 | 71.95 | +| plant | 56.02 | 67.88 | +| curtain | 78.94 | 86.04 | +| chair | 67.41 | 77.36 | +| car | 85.74 | 95.18 | +| water | 63.74 | 76.38 | +| painting | 75.56 | 90.34 | +| sofa | 81.44 | 91.5 | +| shelf | 49.89 | 64.78 | +| house | 53.61 | 76.22 | +| sea | 73.65 | 85.09 | +| mirror | 77.78 | 85.92 | +| rug | 69.86 | 82.57 | +| field | 35.4 | 48.92 | +| armchair | 62.29 | 77.65 | +| seat | 66.05 | 88.92 | +| fence | 48.19 | 62.07 | +| desk | 54.74 | 79.4 | +| rock | 55.35 | 79.62 | +| wardrobe | 52.97 | 68.45 | +| lamp | 75.11 | 86.33 | +| bathtub | 90.1 | 93.45 | +| railing | 46.06 | 62.22 | +| cushion | 72.52 | 84.54 | +| base | 40.06 | 61.61 | +| box | 38.96 | 50.94 | +| column | 53.79 | 60.15 | +| signboard | 42.59 | 52.81 | +| chest of drawers | 47.76 | 73.23 | +| counter | 38.93 | 50.17 | +| sand | 55.86 | 86.45 | +| sink | 78.48 | 84.13 | +| skyscraper | 47.58 | 57.56 | +| fireplace | 68.79 | 92.88 | +| refrigerator | 83.61 | 91.23 | +| grandstand | 49.48 | 78.29 | +| path | 26.3 | 34.69 | +| stairs | 28.58 | 31.57 | +| runway | 71.54 | 92.83 | +| case | 66.78 | 86.56 | +| pool table | 94.59 | 97.95 | +| pillow | 68.49 | 76.69 | +| screen door | 85.69 | 90.78 | +| stairway | 41.68 | 57.32 | +| river | 10.12 | 23.98 | +| bridge | 43.13 | 50.02 | +| bookcase | 47.26 | 66.72 | +| blind | 43.04 | 46.59 | +| coffee table | 62.13 | 87.21 | +| toilet | 90.95 | 94.75 | +| flower | 45.05 | 55.76 | +| book | 55.1 | 72.25 | +| hill | 9.95 | 19.46 | +| bench | 51.67 | 59.48 | +| countertop | 63.66 | 80.4 | +| stove | 84.67 | 93.02 | +| palm | 57.57 | 81.64 | +| kitchen island | 53.24 | 80.74 | +| computer | 79.92 | 90.9 | +| swivel chair | 47.84 | 84.95 | +| boat | 70.44 | 92.04 | +| bar | 66.35 | 81.26 | +| arcade machine | 85.08 | 91.88 | +| hovel | 6.07 | 6.17 | +| bus | 92.16 | 96.81 | +| towel | 80.15 | 85.53 | +| light | 61.37 | 78.46 | +| truck | 40.27 | 50.65 | +| tower | 24.9 | 43.73 | +| chandelier | 73.24 | 90.28 | +| awning | 37.96 | 47.71 | +| streetlight | 35.19 | 51.42 | +| booth | 47.04 | 71.67 | +| television receiver | 78.23 | 85.63 | +| airplane | 86.73 | 97.4 | +| dirt track | 5.08 | 27.39 | +| apparel | 60.14 | 83.25 | +| pole | 27.71 | 35.76 | +| land | 0.71 | 1.28 | +| bannister | 16.61 | 17.75 | +| escalator | 64.52 | 83.06 | +| ottoman | 47.63 | 54.54 | +| bottle | 29.59 | 34.37 | +| buffet | 48.54 | 55.2 | +| poster | 36.35 | 50.92 | +| stage | 23.7 | 49.48 | +| van | 7.63 | 8.04 | +| ship | 87.62 | 90.72 | +| fountain | 34.78 | 38.48 | +| conveyer belt | 73.71 | 96.92 | +| canopy | 49.59 | 69.35 | +| washer | 82.69 | 88.25 | +| plaything | 42.7 | 73.62 | +| swimming pool | 53.48 | 79.93 | +| stool | 46.54 | 78.4 | +| barrel | 57.36 | 71.46 | +| basket | 43.75 | 63.47 | +| waterfall | 68.79 | 83.23 | +| tent | 79.96 | 98.24 | +| bag | 16.84 | 17.84 | +| minibike | 77.12 | 89.61 | +| cradle | 85.71 | 97.7 | +| oven | 48.98 | 54.79 | +| ball | 57.57 | 71.35 | +| food | 64.17 | 80.37 | +| step | 21.16 | 24.94 | +| tank | 54.96 | 68.7 | +| trade name | 29.37 | 36.04 | +| microwave | 86.91 | 95.98 | +| pot | 59.1 | 68.89 | +| animal | 60.76 | 61.43 | +| bicycle | 56.47 | 70.6 | +| lake | 43.45 | 66.95 | +| dishwasher | 75.64 | 82.65 | +| screen | 53.66 | 82.26 | +| blanket | 34.22 | 39.38 | +| sculpture | 73.29 | 88.0 | +| hood | 63.83 | 75.7 | +| sconce | 63.19 | 71.89 | +| vase | 47.32 | 66.31 | +| traffic light | 38.83 | 58.08 | +| tray | 19.27 | 23.55 | +| ashcan | 45.65 | 66.23 | +| fan | 70.26 | 87.26 | +| pier | 42.15 | 47.04 | +| crt screen | 0.99 | 1.62 | +| plate | 63.57 | 78.48 | +| monitor | 57.13 | 77.5 | +| bulletin board | 63.85 | 72.69 | +| shower | 0.87 | 0.88 | +| radiator | 67.29 | 85.21 | +| glass | 22.01 | 25.17 | +| clock | 44.02 | 52.99 | +| flag | 68.21 | 77.26 | ++---------------------+-------+-------+ +2024-06-16 14:08:12,366 - mmseg - INFO - Summary: +2024-06-16 14:08:12,366 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.93 | 56.61 | 69.47 | ++-------+-------+-------+ +2024-06-16 14:08:12,367 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 14:08:12,368 - mmseg - INFO - Iter(val) [250] aAcc: 0.8593, mIoU: 0.5661, mAcc: 0.6947, IoU.wall: 0.8185, IoU.building: 0.8475, IoU.sky: 0.9480, IoU.floor: 0.8529, IoU.tree: 0.7686, IoU.ceiling: 0.8709, IoU.road: 0.8590, IoU.bed : 0.9255, IoU.windowpane: 0.6655, IoU.grass: 0.6763, IoU.cabinet: 0.6506, IoU.sidewalk: 0.7022, IoU.person: 0.8561, IoU.earth: 0.3814, IoU.door: 0.6008, IoU.table: 0.6898, IoU.mountain: 0.6042, IoU.plant: 0.5602, IoU.curtain: 0.7894, IoU.chair: 0.6741, IoU.car: 0.8574, IoU.water: 0.6374, IoU.painting: 0.7556, IoU.sofa: 0.8144, IoU.shelf: 0.4989, IoU.house: 0.5361, IoU.sea: 0.7365, IoU.mirror: 0.7778, IoU.rug: 0.6986, IoU.field: 0.3540, IoU.armchair: 0.6229, IoU.seat: 0.6605, IoU.fence: 0.4819, IoU.desk: 0.5474, IoU.rock: 0.5535, IoU.wardrobe: 0.5297, IoU.lamp: 0.7511, IoU.bathtub: 0.9010, IoU.railing: 0.4606, IoU.cushion: 0.7252, IoU.base: 0.4006, IoU.box: 0.3896, IoU.column: 0.5379, IoU.signboard: 0.4259, IoU.chest of drawers: 0.4776, IoU.counter: 0.3893, IoU.sand: 0.5586, IoU.sink: 0.7848, IoU.skyscraper: 0.4758, IoU.fireplace: 0.6879, IoU.refrigerator: 0.8361, IoU.grandstand: 0.4948, IoU.path: 0.2630, IoU.stairs: 0.2858, IoU.runway: 0.7154, IoU.case: 0.6678, IoU.pool table: 0.9459, IoU.pillow: 0.6849, IoU.screen door: 0.8569, IoU.stairway: 0.4168, IoU.river: 0.1012, IoU.bridge: 0.4313, IoU.bookcase: 0.4726, IoU.blind: 0.4304, IoU.coffee table: 0.6213, IoU.toilet: 0.9095, IoU.flower: 0.4505, IoU.book: 0.5510, IoU.hill: 0.0995, IoU.bench: 0.5167, IoU.countertop: 0.6366, IoU.stove: 0.8467, IoU.palm: 0.5757, IoU.kitchen island: 0.5324, IoU.computer: 0.7992, IoU.swivel chair: 0.4784, IoU.boat: 0.7044, IoU.bar: 0.6635, IoU.arcade machine: 0.8508, IoU.hovel: 0.0607, IoU.bus: 0.9216, IoU.towel: 0.8015, IoU.light: 0.6137, IoU.truck: 0.4027, IoU.tower: 0.2490, IoU.chandelier: 0.7324, IoU.awning: 0.3796, IoU.streetlight: 0.3519, IoU.booth: 0.4704, IoU.television receiver: 0.7823, IoU.airplane: 0.8673, IoU.dirt track: 0.0508, IoU.apparel: 0.6014, IoU.pole: 0.2771, IoU.land: 0.0071, IoU.bannister: 0.1661, IoU.escalator: 0.6452, IoU.ottoman: 0.4763, IoU.bottle: 0.2959, IoU.buffet: 0.4854, IoU.poster: 0.3635, IoU.stage: 0.2370, IoU.van: 0.0763, IoU.ship: 0.8762, IoU.fountain: 0.3478, IoU.conveyer belt: 0.7371, IoU.canopy: 0.4959, IoU.washer: 0.8269, IoU.plaything: 0.4270, IoU.swimming pool: 0.5348, IoU.stool: 0.4654, IoU.barrel: 0.5736, IoU.basket: 0.4375, IoU.waterfall: 0.6879, IoU.tent: 0.7996, IoU.bag: 0.1684, IoU.minibike: 0.7712, IoU.cradle: 0.8571, IoU.oven: 0.4898, IoU.ball: 0.5757, IoU.food: 0.6417, IoU.step: 0.2116, IoU.tank: 0.5496, IoU.trade name: 0.2937, IoU.microwave: 0.8691, IoU.pot: 0.5910, IoU.animal: 0.6076, IoU.bicycle: 0.5647, IoU.lake: 0.4345, IoU.dishwasher: 0.7564, IoU.screen: 0.5366, IoU.blanket: 0.3422, IoU.sculpture: 0.7329, IoU.hood: 0.6383, IoU.sconce: 0.6319, IoU.vase: 0.4732, IoU.traffic light: 0.3883, IoU.tray: 0.1927, IoU.ashcan: 0.4565, IoU.fan: 0.7026, IoU.pier: 0.4215, IoU.crt screen: 0.0099, IoU.plate: 0.6357, IoU.monitor: 0.5713, IoU.bulletin board: 0.6385, IoU.shower: 0.0087, IoU.radiator: 0.6729, IoU.glass: 0.2201, IoU.clock: 0.4402, IoU.flag: 0.6821, Acc.wall: 0.9045, Acc.building: 0.9208, Acc.sky: 0.9658, Acc.floor: 0.9096, Acc.tree: 0.9311, Acc.ceiling: 0.9243, Acc.road: 0.9125, Acc.bed : 0.9745, Acc.windowpane: 0.8427, Acc.grass: 0.7966, Acc.cabinet: 0.7568, Acc.sidewalk: 0.8574, Acc.person: 0.9420, Acc.earth: 0.5339, Acc.door: 0.7394, Acc.table: 0.8183, Acc.mountain: 0.7195, Acc.plant: 0.6788, Acc.curtain: 0.8604, Acc.chair: 0.7736, Acc.car: 0.9518, Acc.water: 0.7638, Acc.painting: 0.9034, Acc.sofa: 0.9150, Acc.shelf: 0.6478, Acc.house: 0.7622, Acc.sea: 0.8509, Acc.mirror: 0.8592, Acc.rug: 0.8257, Acc.field: 0.4892, Acc.armchair: 0.7765, Acc.seat: 0.8892, Acc.fence: 0.6207, Acc.desk: 0.7940, Acc.rock: 0.7962, Acc.wardrobe: 0.6845, Acc.lamp: 0.8633, Acc.bathtub: 0.9345, Acc.railing: 0.6222, Acc.cushion: 0.8454, Acc.base: 0.6161, Acc.box: 0.5094, Acc.column: 0.6015, Acc.signboard: 0.5281, Acc.chest of drawers: 0.7323, Acc.counter: 0.5017, Acc.sand: 0.8645, Acc.sink: 0.8413, Acc.skyscraper: 0.5756, Acc.fireplace: 0.9288, Acc.refrigerator: 0.9123, Acc.grandstand: 0.7829, Acc.path: 0.3469, Acc.stairs: 0.3157, Acc.runway: 0.9283, Acc.case: 0.8656, Acc.pool table: 0.9795, Acc.pillow: 0.7669, Acc.screen door: 0.9078, Acc.stairway: 0.5732, Acc.river: 0.2398, Acc.bridge: 0.5002, Acc.bookcase: 0.6672, Acc.blind: 0.4659, Acc.coffee table: 0.8721, Acc.toilet: 0.9475, Acc.flower: 0.5576, Acc.book: 0.7225, Acc.hill: 0.1946, Acc.bench: 0.5948, Acc.countertop: 0.8040, Acc.stove: 0.9302, Acc.palm: 0.8164, Acc.kitchen island: 0.8074, Acc.computer: 0.9090, Acc.swivel chair: 0.8495, Acc.boat: 0.9204, Acc.bar: 0.8126, Acc.arcade machine: 0.9188, Acc.hovel: 0.0617, Acc.bus: 0.9681, Acc.towel: 0.8553, Acc.light: 0.7846, Acc.truck: 0.5065, Acc.tower: 0.4373, Acc.chandelier: 0.9028, Acc.awning: 0.4771, Acc.streetlight: 0.5142, Acc.booth: 0.7167, Acc.television receiver: 0.8563, Acc.airplane: 0.9740, Acc.dirt track: 0.2739, Acc.apparel: 0.8325, Acc.pole: 0.3576, Acc.land: 0.0128, Acc.bannister: 0.1775, Acc.escalator: 0.8306, Acc.ottoman: 0.5454, Acc.bottle: 0.3437, Acc.buffet: 0.5520, Acc.poster: 0.5092, Acc.stage: 0.4948, Acc.van: 0.0804, Acc.ship: 0.9072, Acc.fountain: 0.3848, Acc.conveyer belt: 0.9692, Acc.canopy: 0.6935, Acc.washer: 0.8825, Acc.plaything: 0.7362, Acc.swimming pool: 0.7993, Acc.stool: 0.7840, Acc.barrel: 0.7146, Acc.basket: 0.6347, Acc.waterfall: 0.8323, Acc.tent: 0.9824, Acc.bag: 0.1784, Acc.minibike: 0.8961, Acc.cradle: 0.9770, Acc.oven: 0.5479, Acc.ball: 0.7135, Acc.food: 0.8037, Acc.step: 0.2494, Acc.tank: 0.6870, Acc.trade name: 0.3604, Acc.microwave: 0.9598, Acc.pot: 0.6889, Acc.animal: 0.6143, Acc.bicycle: 0.7060, Acc.lake: 0.6695, Acc.dishwasher: 0.8265, Acc.screen: 0.8226, Acc.blanket: 0.3938, Acc.sculpture: 0.8800, Acc.hood: 0.7570, Acc.sconce: 0.7189, Acc.vase: 0.6631, Acc.traffic light: 0.5808, Acc.tray: 0.2355, Acc.ashcan: 0.6623, Acc.fan: 0.8726, Acc.pier: 0.4704, Acc.crt screen: 0.0162, Acc.plate: 0.7848, Acc.monitor: 0.7750, Acc.bulletin board: 0.7269, Acc.shower: 0.0088, Acc.radiator: 0.8521, Acc.glass: 0.2517, Acc.clock: 0.5299, Acc.flag: 0.7726 +2024-06-16 14:09:34,015 - mmseg - INFO - Iter [34050/80000] lr: 2.298e-05, eta: 22:34:35, time: 3.566, data_time: 1.950, memory: 71384, decode.loss_ce: 0.2058, decode.acc_seg: 91.3715, aux.loss_ce: 0.0843, aux.acc_seg: 91.1648, loss: 0.2901 +2024-06-16 14:10:55,192 - mmseg - INFO - Iter [34100/80000] lr: 2.295e-05, eta: 22:32:57, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2294, decode.acc_seg: 90.7998, aux.loss_ce: 0.0945, aux.acc_seg: 90.4497, loss: 0.3238 +2024-06-16 14:12:18,752 - mmseg - INFO - Iter [34150/80000] lr: 2.293e-05, eta: 22:31:22, time: 1.671, data_time: 0.058, memory: 71384, decode.loss_ce: 0.2087, decode.acc_seg: 91.1937, aux.loss_ce: 0.0862, aux.acc_seg: 90.8548, loss: 0.2949 +2024-06-16 14:13:40,042 - mmseg - INFO - Iter [34200/80000] lr: 2.290e-05, eta: 22:29:44, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2060, decode.acc_seg: 91.3130, aux.loss_ce: 0.0854, aux.acc_seg: 90.9592, loss: 0.2914 +2024-06-16 14:15:01,051 - mmseg - INFO - Iter [34250/80000] lr: 2.288e-05, eta: 22:28:05, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2182, decode.acc_seg: 91.1257, aux.loss_ce: 0.0897, aux.acc_seg: 90.9317, loss: 0.3079 +2024-06-16 14:16:22,295 - mmseg - INFO - Iter [34300/80000] lr: 2.285e-05, eta: 22:26:27, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2079, decode.acc_seg: 91.1715, aux.loss_ce: 0.0867, aux.acc_seg: 90.9040, loss: 0.2945 +2024-06-16 14:17:43,292 - mmseg - INFO - Iter [34350/80000] lr: 2.283e-05, eta: 22:24:49, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2076, decode.acc_seg: 91.4075, aux.loss_ce: 0.0859, aux.acc_seg: 91.0178, loss: 0.2935 +2024-06-16 14:19:04,442 - mmseg - INFO - Iter [34400/80000] lr: 2.280e-05, eta: 22:23:11, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2088, decode.acc_seg: 90.9515, aux.loss_ce: 0.0874, aux.acc_seg: 90.5556, loss: 0.2961 +2024-06-16 14:20:25,533 - mmseg - INFO - Iter [34450/80000] lr: 2.278e-05, eta: 22:21:33, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1982, decode.acc_seg: 91.6586, aux.loss_ce: 0.0825, aux.acc_seg: 91.3298, loss: 0.2807 +2024-06-16 14:21:46,548 - mmseg - INFO - Iter [34500/80000] lr: 2.275e-05, eta: 22:19:55, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2134, decode.acc_seg: 91.2335, aux.loss_ce: 0.0878, aux.acc_seg: 90.9551, loss: 0.3012 +2024-06-16 14:23:07,876 - mmseg - INFO - Iter [34550/80000] lr: 2.273e-05, eta: 22:18:18, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2189, decode.acc_seg: 90.9154, aux.loss_ce: 0.0895, aux.acc_seg: 90.7230, loss: 0.3085 +2024-06-16 14:24:29,017 - mmseg - INFO - Iter [34600/80000] lr: 2.270e-05, eta: 22:16:40, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2274, decode.acc_seg: 90.6714, aux.loss_ce: 0.0933, aux.acc_seg: 90.4053, loss: 0.3208 +2024-06-16 14:25:50,145 - mmseg - INFO - Iter [34650/80000] lr: 2.268e-05, eta: 22:15:02, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2095, decode.acc_seg: 91.1675, aux.loss_ce: 0.0875, aux.acc_seg: 90.7832, loss: 0.2970 +2024-06-16 14:27:11,195 - mmseg - INFO - Iter [34700/80000] lr: 2.265e-05, eta: 22:13:24, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2154, decode.acc_seg: 91.0753, aux.loss_ce: 0.0886, aux.acc_seg: 90.8635, loss: 0.3040 +2024-06-16 14:28:32,370 - mmseg - INFO - Iter [34750/80000] lr: 2.263e-05, eta: 22:11:47, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2078, decode.acc_seg: 91.1221, aux.loss_ce: 0.0856, aux.acc_seg: 90.8011, loss: 0.2933 +2024-06-16 14:29:53,543 - mmseg - INFO - Iter [34800/80000] lr: 2.260e-05, eta: 22:10:09, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2139, decode.acc_seg: 91.0286, aux.loss_ce: 0.0888, aux.acc_seg: 90.7489, loss: 0.3026 +2024-06-16 14:31:14,757 - mmseg - INFO - Iter [34850/80000] lr: 2.258e-05, eta: 22:08:32, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2099, decode.acc_seg: 91.5435, aux.loss_ce: 0.0868, aux.acc_seg: 91.2494, loss: 0.2966 +2024-06-16 14:32:35,957 - mmseg - INFO - Iter [34900/80000] lr: 2.255e-05, eta: 22:06:54, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2198, decode.acc_seg: 90.7566, aux.loss_ce: 0.0912, aux.acc_seg: 90.3738, loss: 0.3110 +2024-06-16 14:33:56,966 - mmseg - INFO - Iter [34950/80000] lr: 2.253e-05, eta: 22:05:17, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2125, decode.acc_seg: 91.1314, aux.loss_ce: 0.0870, aux.acc_seg: 90.9601, loss: 0.2995 +2024-06-16 14:35:18,124 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 14:35:18,125 - mmseg - INFO - Iter [35000/80000] lr: 2.250e-05, eta: 22:03:39, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2261, decode.acc_seg: 90.7314, aux.loss_ce: 0.0931, aux.acc_seg: 90.5343, loss: 0.3192 +2024-06-16 14:36:55,702 - mmseg - INFO - per class results: +2024-06-16 14:36:55,708 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.3 | 89.94 | +| building | 85.48 | 94.31 | +| sky | 95.05 | 97.65 | +| floor | 84.87 | 91.2 | +| tree | 78.09 | 88.72 | +| ceiling | 87.12 | 93.51 | +| road | 87.0 | 91.66 | +| bed | 92.62 | 97.2 | +| windowpane | 64.35 | 77.22 | +| grass | 68.58 | 81.88 | +| cabinet | 64.8 | 71.07 | +| sidewalk | 71.24 | 84.6 | +| person | 86.07 | 94.03 | +| earth | 40.38 | 57.24 | +| door | 60.14 | 75.15 | +| table | 70.01 | 82.4 | +| mountain | 61.03 | 70.71 | +| plant | 55.38 | 63.76 | +| curtain | 71.97 | 88.48 | +| chair | 68.15 | 81.05 | +| car | 87.56 | 93.91 | +| water | 61.96 | 74.62 | +| painting | 78.32 | 87.68 | +| sofa | 82.09 | 89.88 | +| shelf | 52.46 | 75.51 | +| house | 54.42 | 64.96 | +| sea | 72.08 | 82.46 | +| mirror | 74.2 | 91.6 | +| rug | 69.19 | 81.12 | +| field | 36.93 | 61.83 | +| armchair | 62.78 | 78.7 | +| seat | 64.92 | 89.46 | +| fence | 52.45 | 64.5 | +| desk | 56.49 | 80.89 | +| rock | 53.38 | 76.61 | +| wardrobe | 52.52 | 73.98 | +| lamp | 74.87 | 84.33 | +| bathtub | 88.12 | 89.81 | +| railing | 46.15 | 70.99 | +| cushion | 69.75 | 86.15 | +| base | 39.18 | 52.58 | +| box | 38.39 | 50.2 | +| column | 53.28 | 59.35 | +| signboard | 41.41 | 55.65 | +| chest of drawers | 43.92 | 82.14 | +| counter | 39.69 | 49.17 | +| sand | 54.93 | 89.1 | +| sink | 79.78 | 84.87 | +| skyscraper | 47.59 | 60.3 | +| fireplace | 74.43 | 95.82 | +| refrigerator | 82.67 | 90.08 | +| grandstand | 51.68 | 84.56 | +| path | 23.39 | 30.71 | +| stairs | 38.84 | 48.01 | +| runway | 73.9 | 98.37 | +| case | 67.35 | 88.99 | +| pool table | 94.37 | 98.11 | +| pillow | 70.8 | 80.92 | +| screen door | 68.72 | 72.39 | +| stairway | 54.39 | 63.46 | +| river | 11.42 | 26.82 | +| bridge | 41.79 | 49.86 | +| bookcase | 51.43 | 69.26 | +| blind | 46.21 | 55.93 | +| coffee table | 68.07 | 86.49 | +| toilet | 89.6 | 92.59 | +| flower | 41.22 | 47.36 | +| book | 53.36 | 72.68 | +| hill | 5.78 | 12.37 | +| bench | 53.76 | 62.46 | +| countertop | 63.04 | 80.67 | +| stove | 83.85 | 94.54 | +| palm | 54.37 | 84.96 | +| kitchen island | 51.74 | 87.5 | +| computer | 77.26 | 90.82 | +| swivel chair | 52.49 | 77.09 | +| boat | 59.67 | 93.06 | +| bar | 66.14 | 90.21 | +| arcade machine | 89.38 | 96.78 | +| hovel | 15.71 | 16.7 | +| bus | 93.01 | 96.1 | +| towel | 79.98 | 87.31 | +| light | 61.97 | 71.86 | +| truck | 45.61 | 59.53 | +| tower | 15.79 | 21.61 | +| chandelier | 73.64 | 89.03 | +| awning | 42.99 | 55.8 | +| streetlight | 35.97 | 50.39 | +| booth | 35.12 | 52.17 | +| television receiver | 81.05 | 88.21 | +| airplane | 88.22 | 95.85 | +| dirt track | 1.27 | 4.54 | +| apparel | 59.03 | 77.05 | +| pole | 23.34 | 29.13 | +| land | 0.82 | 1.11 | +| bannister | 19.02 | 26.14 | +| escalator | 65.35 | 83.2 | +| ottoman | 48.93 | 72.47 | +| bottle | 34.25 | 41.33 | +| buffet | 62.63 | 75.19 | +| poster | 39.89 | 50.57 | +| stage | 27.29 | 48.77 | +| van | 44.83 | 65.12 | +| ship | 84.17 | 89.0 | +| fountain | 39.69 | 40.24 | +| conveyer belt | 74.97 | 96.34 | +| canopy | 52.13 | 78.51 | +| washer | 82.23 | 87.22 | +| plaything | 43.63 | 63.98 | +| swimming pool | 57.2 | 87.43 | +| stool | 50.44 | 65.0 | +| barrel | 52.87 | 65.58 | +| basket | 40.03 | 66.13 | +| waterfall | 54.82 | 64.15 | +| tent | 88.86 | 98.2 | +| bag | 26.09 | 31.8 | +| minibike | 76.23 | 89.69 | +| cradle | 80.9 | 98.01 | +| oven | 61.34 | 72.02 | +| ball | 21.67 | 23.91 | +| food | 62.3 | 70.58 | +| step | 21.27 | 24.12 | +| tank | 68.24 | 84.54 | +| trade name | 28.64 | 37.42 | +| microwave | 87.26 | 96.55 | +| pot | 58.18 | 69.08 | +| animal | 65.19 | 66.88 | +| bicycle | 60.51 | 82.83 | +| lake | 51.79 | 63.68 | +| dishwasher | 76.08 | 83.45 | +| screen | 60.1 | 97.56 | +| blanket | 30.99 | 35.08 | +| sculpture | 80.48 | 87.22 | +| hood | 72.38 | 87.33 | +| sconce | 58.31 | 65.47 | +| vase | 47.88 | 66.4 | +| traffic light | 35.29 | 63.01 | +| tray | 22.48 | 30.99 | +| ashcan | 44.83 | 63.29 | +| fan | 69.53 | 83.1 | +| pier | 58.27 | 77.86 | +| crt screen | 2.02 | 2.63 | +| plate | 64.46 | 77.85 | +| monitor | 62.28 | 75.42 | +| bulletin board | 62.68 | 75.35 | +| shower | 2.13 | 14.01 | +| radiator | 67.36 | 80.02 | +| glass | 20.5 | 22.27 | +| clock | 52.18 | 71.94 | +| flag | 69.57 | 77.25 | ++---------------------+-------+-------+ +2024-06-16 14:36:55,708 - mmseg - INFO - Summary: +2024-06-16 14:36:55,708 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.14 | 57.41 | 70.58 | ++-------+-------+-------+ +2024-06-16 14:36:55,709 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 14:36:55,709 - mmseg - INFO - Iter(val) [250] aAcc: 0.8614, mIoU: 0.5741, mAcc: 0.7058, IoU.wall: 0.8230, IoU.building: 0.8548, IoU.sky: 0.9505, IoU.floor: 0.8487, IoU.tree: 0.7809, IoU.ceiling: 0.8712, IoU.road: 0.8700, IoU.bed : 0.9262, IoU.windowpane: 0.6435, IoU.grass: 0.6858, IoU.cabinet: 0.6480, IoU.sidewalk: 0.7124, IoU.person: 0.8607, IoU.earth: 0.4038, IoU.door: 0.6014, IoU.table: 0.7001, IoU.mountain: 0.6103, IoU.plant: 0.5538, IoU.curtain: 0.7197, IoU.chair: 0.6815, IoU.car: 0.8756, IoU.water: 0.6196, IoU.painting: 0.7832, IoU.sofa: 0.8209, IoU.shelf: 0.5246, IoU.house: 0.5442, IoU.sea: 0.7208, IoU.mirror: 0.7420, IoU.rug: 0.6919, IoU.field: 0.3693, IoU.armchair: 0.6278, IoU.seat: 0.6492, IoU.fence: 0.5245, IoU.desk: 0.5649, IoU.rock: 0.5338, IoU.wardrobe: 0.5252, IoU.lamp: 0.7487, IoU.bathtub: 0.8812, IoU.railing: 0.4615, IoU.cushion: 0.6975, IoU.base: 0.3918, IoU.box: 0.3839, IoU.column: 0.5328, IoU.signboard: 0.4141, IoU.chest of drawers: 0.4392, IoU.counter: 0.3969, IoU.sand: 0.5493, IoU.sink: 0.7978, IoU.skyscraper: 0.4759, IoU.fireplace: 0.7443, IoU.refrigerator: 0.8267, IoU.grandstand: 0.5168, IoU.path: 0.2339, IoU.stairs: 0.3884, IoU.runway: 0.7390, IoU.case: 0.6735, IoU.pool table: 0.9437, IoU.pillow: 0.7080, IoU.screen door: 0.6872, IoU.stairway: 0.5439, IoU.river: 0.1142, IoU.bridge: 0.4179, IoU.bookcase: 0.5143, IoU.blind: 0.4621, IoU.coffee table: 0.6807, IoU.toilet: 0.8960, IoU.flower: 0.4122, IoU.book: 0.5336, IoU.hill: 0.0578, IoU.bench: 0.5376, IoU.countertop: 0.6304, IoU.stove: 0.8385, IoU.palm: 0.5437, IoU.kitchen island: 0.5174, IoU.computer: 0.7726, IoU.swivel chair: 0.5249, IoU.boat: 0.5967, IoU.bar: 0.6614, IoU.arcade machine: 0.8938, IoU.hovel: 0.1571, IoU.bus: 0.9301, IoU.towel: 0.7998, IoU.light: 0.6197, IoU.truck: 0.4561, IoU.tower: 0.1579, IoU.chandelier: 0.7364, IoU.awning: 0.4299, IoU.streetlight: 0.3597, IoU.booth: 0.3512, IoU.television receiver: 0.8105, IoU.airplane: 0.8822, IoU.dirt track: 0.0127, IoU.apparel: 0.5903, IoU.pole: 0.2334, IoU.land: 0.0082, IoU.bannister: 0.1902, IoU.escalator: 0.6535, IoU.ottoman: 0.4893, IoU.bottle: 0.3425, IoU.buffet: 0.6263, IoU.poster: 0.3989, IoU.stage: 0.2729, IoU.van: 0.4483, IoU.ship: 0.8417, IoU.fountain: 0.3969, IoU.conveyer belt: 0.7497, IoU.canopy: 0.5213, IoU.washer: 0.8223, IoU.plaything: 0.4363, IoU.swimming pool: 0.5720, IoU.stool: 0.5044, IoU.barrel: 0.5287, IoU.basket: 0.4003, IoU.waterfall: 0.5482, IoU.tent: 0.8886, IoU.bag: 0.2609, IoU.minibike: 0.7623, IoU.cradle: 0.8090, IoU.oven: 0.6134, IoU.ball: 0.2167, IoU.food: 0.6230, IoU.step: 0.2127, IoU.tank: 0.6824, IoU.trade name: 0.2864, IoU.microwave: 0.8726, IoU.pot: 0.5818, IoU.animal: 0.6519, IoU.bicycle: 0.6051, IoU.lake: 0.5179, IoU.dishwasher: 0.7608, IoU.screen: 0.6010, IoU.blanket: 0.3099, IoU.sculpture: 0.8048, IoU.hood: 0.7238, IoU.sconce: 0.5831, IoU.vase: 0.4788, IoU.traffic light: 0.3529, IoU.tray: 0.2248, IoU.ashcan: 0.4483, IoU.fan: 0.6953, IoU.pier: 0.5827, IoU.crt screen: 0.0202, IoU.plate: 0.6446, IoU.monitor: 0.6228, IoU.bulletin board: 0.6268, IoU.shower: 0.0213, IoU.radiator: 0.6736, IoU.glass: 0.2050, IoU.clock: 0.5218, IoU.flag: 0.6957, Acc.wall: 0.8994, Acc.building: 0.9431, Acc.sky: 0.9765, Acc.floor: 0.9120, Acc.tree: 0.8872, Acc.ceiling: 0.9351, Acc.road: 0.9166, Acc.bed : 0.9720, Acc.windowpane: 0.7722, Acc.grass: 0.8188, Acc.cabinet: 0.7107, Acc.sidewalk: 0.8460, Acc.person: 0.9403, Acc.earth: 0.5724, Acc.door: 0.7515, Acc.table: 0.8240, Acc.mountain: 0.7071, Acc.plant: 0.6376, Acc.curtain: 0.8848, Acc.chair: 0.8105, Acc.car: 0.9391, Acc.water: 0.7462, Acc.painting: 0.8768, Acc.sofa: 0.8988, Acc.shelf: 0.7551, Acc.house: 0.6496, Acc.sea: 0.8246, Acc.mirror: 0.9160, Acc.rug: 0.8112, Acc.field: 0.6183, Acc.armchair: 0.7870, Acc.seat: 0.8946, Acc.fence: 0.6450, Acc.desk: 0.8089, Acc.rock: 0.7661, Acc.wardrobe: 0.7398, Acc.lamp: 0.8433, Acc.bathtub: 0.8981, Acc.railing: 0.7099, Acc.cushion: 0.8615, Acc.base: 0.5258, Acc.box: 0.5020, Acc.column: 0.5935, Acc.signboard: 0.5565, Acc.chest of drawers: 0.8214, Acc.counter: 0.4917, Acc.sand: 0.8910, Acc.sink: 0.8487, Acc.skyscraper: 0.6030, Acc.fireplace: 0.9582, Acc.refrigerator: 0.9008, Acc.grandstand: 0.8456, Acc.path: 0.3071, Acc.stairs: 0.4801, Acc.runway: 0.9837, Acc.case: 0.8899, Acc.pool table: 0.9811, Acc.pillow: 0.8092, Acc.screen door: 0.7239, Acc.stairway: 0.6346, Acc.river: 0.2682, Acc.bridge: 0.4986, Acc.bookcase: 0.6926, Acc.blind: 0.5593, Acc.coffee table: 0.8649, Acc.toilet: 0.9259, Acc.flower: 0.4736, Acc.book: 0.7268, Acc.hill: 0.1237, Acc.bench: 0.6246, Acc.countertop: 0.8067, Acc.stove: 0.9454, Acc.palm: 0.8496, Acc.kitchen island: 0.8750, Acc.computer: 0.9082, Acc.swivel chair: 0.7709, Acc.boat: 0.9306, Acc.bar: 0.9021, Acc.arcade machine: 0.9678, Acc.hovel: 0.1670, Acc.bus: 0.9610, Acc.towel: 0.8731, Acc.light: 0.7186, Acc.truck: 0.5953, Acc.tower: 0.2161, Acc.chandelier: 0.8903, Acc.awning: 0.5580, Acc.streetlight: 0.5039, Acc.booth: 0.5217, Acc.television receiver: 0.8821, Acc.airplane: 0.9585, Acc.dirt track: 0.0454, Acc.apparel: 0.7705, Acc.pole: 0.2913, Acc.land: 0.0111, Acc.bannister: 0.2614, Acc.escalator: 0.8320, Acc.ottoman: 0.7247, Acc.bottle: 0.4133, Acc.buffet: 0.7519, Acc.poster: 0.5057, Acc.stage: 0.4877, Acc.van: 0.6512, Acc.ship: 0.8900, Acc.fountain: 0.4024, Acc.conveyer belt: 0.9634, Acc.canopy: 0.7851, Acc.washer: 0.8722, Acc.plaything: 0.6398, Acc.swimming pool: 0.8743, Acc.stool: 0.6500, Acc.barrel: 0.6558, Acc.basket: 0.6613, Acc.waterfall: 0.6415, Acc.tent: 0.9820, Acc.bag: 0.3180, Acc.minibike: 0.8969, Acc.cradle: 0.9801, Acc.oven: 0.7202, Acc.ball: 0.2391, Acc.food: 0.7058, Acc.step: 0.2412, Acc.tank: 0.8454, Acc.trade name: 0.3742, Acc.microwave: 0.9655, Acc.pot: 0.6908, Acc.animal: 0.6688, Acc.bicycle: 0.8283, Acc.lake: 0.6368, Acc.dishwasher: 0.8345, Acc.screen: 0.9756, Acc.blanket: 0.3508, Acc.sculpture: 0.8722, Acc.hood: 0.8733, Acc.sconce: 0.6547, Acc.vase: 0.6640, Acc.traffic light: 0.6301, Acc.tray: 0.3099, Acc.ashcan: 0.6329, Acc.fan: 0.8310, Acc.pier: 0.7786, Acc.crt screen: 0.0263, Acc.plate: 0.7785, Acc.monitor: 0.7542, Acc.bulletin board: 0.7535, Acc.shower: 0.1401, Acc.radiator: 0.8002, Acc.glass: 0.2227, Acc.clock: 0.7194, Acc.flag: 0.7725 +2024-06-16 14:38:17,263 - mmseg - INFO - Iter [35050/80000] lr: 2.248e-05, eta: 22:04:08, time: 3.583, data_time: 1.968, memory: 71384, decode.loss_ce: 0.2091, decode.acc_seg: 91.3695, aux.loss_ce: 0.0867, aux.acc_seg: 90.9863, loss: 0.2958 +2024-06-16 14:39:38,405 - mmseg - INFO - Iter [35100/80000] lr: 2.245e-05, eta: 22:02:30, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2316, decode.acc_seg: 90.5706, aux.loss_ce: 0.0938, aux.acc_seg: 90.2592, loss: 0.3255 +2024-06-16 14:40:59,396 - mmseg - INFO - Iter [35150/80000] lr: 2.243e-05, eta: 22:00:52, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2243, decode.acc_seg: 90.6901, aux.loss_ce: 0.0921, aux.acc_seg: 90.4718, loss: 0.3163 +2024-06-16 14:42:20,518 - mmseg - INFO - Iter [35200/80000] lr: 2.240e-05, eta: 21:59:15, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2067, decode.acc_seg: 91.4304, aux.loss_ce: 0.0856, aux.acc_seg: 91.1251, loss: 0.2922 +2024-06-16 14:43:41,919 - mmseg - INFO - Iter [35250/80000] lr: 2.238e-05, eta: 21:57:37, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2116, decode.acc_seg: 91.0116, aux.loss_ce: 0.0876, aux.acc_seg: 90.5695, loss: 0.2992 +2024-06-16 14:45:02,965 - mmseg - INFO - Iter [35300/80000] lr: 2.235e-05, eta: 21:56:00, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2156, decode.acc_seg: 90.9864, aux.loss_ce: 0.0883, aux.acc_seg: 90.6468, loss: 0.3040 +2024-06-16 14:46:24,098 - mmseg - INFO - Iter [35350/80000] lr: 2.233e-05, eta: 21:54:22, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2357, decode.acc_seg: 90.3759, aux.loss_ce: 0.0969, aux.acc_seg: 90.0618, loss: 0.3326 +2024-06-16 14:47:48,191 - mmseg - INFO - Iter [35400/80000] lr: 2.230e-05, eta: 21:52:49, time: 1.682, data_time: 0.066, memory: 71384, decode.loss_ce: 0.2103, decode.acc_seg: 91.1991, aux.loss_ce: 0.0872, aux.acc_seg: 90.8742, loss: 0.2975 +2024-06-16 14:49:09,189 - mmseg - INFO - Iter [35450/80000] lr: 2.228e-05, eta: 21:51:11, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2017, decode.acc_seg: 91.5436, aux.loss_ce: 0.0839, aux.acc_seg: 91.2133, loss: 0.2856 +2024-06-16 14:50:30,339 - mmseg - INFO - Iter [35500/80000] lr: 2.225e-05, eta: 21:49:34, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2087, decode.acc_seg: 91.2486, aux.loss_ce: 0.0862, aux.acc_seg: 91.0299, loss: 0.2950 +2024-06-16 14:51:51,241 - mmseg - INFO - Iter [35550/80000] lr: 2.223e-05, eta: 21:47:57, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2017, decode.acc_seg: 91.7722, aux.loss_ce: 0.0830, aux.acc_seg: 91.4650, loss: 0.2847 +2024-06-16 14:53:12,470 - mmseg - INFO - Iter [35600/80000] lr: 2.220e-05, eta: 21:46:20, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1894, decode.acc_seg: 92.0041, aux.loss_ce: 0.0791, aux.acc_seg: 91.6568, loss: 0.2684 +2024-06-16 14:54:33,441 - mmseg - INFO - Iter [35650/80000] lr: 2.218e-05, eta: 21:44:42, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2088, decode.acc_seg: 90.9997, aux.loss_ce: 0.0865, aux.acc_seg: 90.6853, loss: 0.2953 +2024-06-16 14:55:54,641 - mmseg - INFO - Iter [35700/80000] lr: 2.215e-05, eta: 21:43:05, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2110, decode.acc_seg: 91.0329, aux.loss_ce: 0.0875, aux.acc_seg: 90.7732, loss: 0.2985 +2024-06-16 14:57:15,814 - mmseg - INFO - Iter [35750/80000] lr: 2.213e-05, eta: 21:41:28, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1960, decode.acc_seg: 91.8084, aux.loss_ce: 0.0814, aux.acc_seg: 91.4511, loss: 0.2774 +2024-06-16 14:58:37,082 - mmseg - INFO - Iter [35800/80000] lr: 2.210e-05, eta: 21:39:51, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2114, decode.acc_seg: 91.0904, aux.loss_ce: 0.0875, aux.acc_seg: 90.8054, loss: 0.2989 +2024-06-16 14:59:58,112 - mmseg - INFO - Iter [35850/80000] lr: 2.208e-05, eta: 21:38:14, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2138, decode.acc_seg: 91.2654, aux.loss_ce: 0.0884, aux.acc_seg: 90.9237, loss: 0.3022 +2024-06-16 15:01:19,160 - mmseg - INFO - Iter [35900/80000] lr: 2.205e-05, eta: 21:36:37, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2036, decode.acc_seg: 91.3602, aux.loss_ce: 0.0847, aux.acc_seg: 91.0294, loss: 0.2883 +2024-06-16 15:02:40,320 - mmseg - INFO - Iter [35950/80000] lr: 2.203e-05, eta: 21:35:00, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1905, decode.acc_seg: 91.9267, aux.loss_ce: 0.0792, aux.acc_seg: 91.6304, loss: 0.2697 +2024-06-16 15:04:01,512 - mmseg - INFO - Saving checkpoint at 36000 iterations +2024-06-16 15:05:26,117 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 15:05:26,117 - mmseg - INFO - Iter [36000/80000] lr: 2.200e-05, eta: 21:35:07, time: 3.316, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2104, decode.acc_seg: 90.9946, aux.loss_ce: 0.0871, aux.acc_seg: 90.6816, loss: 0.2975 +2024-06-16 15:07:02,277 - mmseg - INFO - per class results: +2024-06-16 15:07:02,283 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.33 | 89.33 | +| building | 85.4 | 94.58 | +| sky | 94.45 | 96.58 | +| floor | 85.31 | 91.73 | +| tree | 78.08 | 89.31 | +| ceiling | 87.42 | 95.06 | +| road | 86.12 | 93.19 | +| bed | 92.58 | 96.61 | +| windowpane | 67.27 | 81.98 | +| grass | 68.93 | 82.91 | +| cabinet | 66.51 | 76.89 | +| sidewalk | 68.82 | 82.12 | +| person | 85.24 | 94.16 | +| earth | 39.68 | 53.09 | +| door | 59.49 | 73.0 | +| table | 69.39 | 81.78 | +| mountain | 61.67 | 73.1 | +| plant | 55.7 | 64.48 | +| curtain | 78.93 | 90.33 | +| chair | 64.83 | 72.45 | +| car | 88.11 | 94.33 | +| water | 57.6 | 69.29 | +| painting | 77.43 | 90.05 | +| sofa | 82.86 | 91.62 | +| shelf | 46.58 | 67.8 | +| house | 51.95 | 65.2 | +| sea | 68.85 | 83.65 | +| mirror | 77.05 | 85.73 | +| rug | 71.71 | 79.07 | +| field | 35.52 | 55.06 | +| armchair | 61.76 | 78.01 | +| seat | 63.05 | 89.73 | +| fence | 50.4 | 70.08 | +| desk | 56.65 | 81.94 | +| rock | 55.61 | 81.52 | +| wardrobe | 53.56 | 72.7 | +| lamp | 75.09 | 86.6 | +| bathtub | 86.85 | 88.87 | +| railing | 46.16 | 60.66 | +| cushion | 70.96 | 83.11 | +| base | 42.16 | 59.8 | +| box | 39.97 | 55.58 | +| column | 57.42 | 70.04 | +| signboard | 41.87 | 54.51 | +| chest of drawers | 42.97 | 66.25 | +| counter | 50.13 | 66.86 | +| sand | 52.33 | 87.39 | +| sink | 79.19 | 83.7 | +| skyscraper | 50.28 | 59.33 | +| fireplace | 72.42 | 96.28 | +| refrigerator | 81.11 | 86.3 | +| grandstand | 50.31 | 80.56 | +| path | 23.2 | 30.14 | +| stairs | 29.51 | 34.12 | +| runway | 71.64 | 94.38 | +| case | 66.67 | 81.25 | +| pool table | 94.83 | 97.73 | +| pillow | 69.97 | 84.36 | +| screen door | 66.26 | 67.9 | +| stairway | 41.43 | 65.73 | +| river | 11.76 | 35.14 | +| bridge | 70.51 | 82.42 | +| bookcase | 40.84 | 64.41 | +| blind | 40.15 | 44.95 | +| coffee table | 62.85 | 87.55 | +| toilet | 90.33 | 94.01 | +| flower | 41.92 | 56.98 | +| book | 52.78 | 78.95 | +| hill | 5.27 | 10.4 | +| bench | 54.57 | 64.38 | +| countertop | 61.92 | 89.05 | +| stove | 86.59 | 92.65 | +| palm | 52.01 | 79.47 | +| kitchen island | 57.6 | 73.63 | +| computer | 68.61 | 75.05 | +| swivel chair | 51.08 | 81.35 | +| boat | 68.46 | 93.68 | +| bar | 67.25 | 88.13 | +| arcade machine | 76.57 | 81.97 | +| hovel | 7.88 | 8.34 | +| bus | 92.46 | 96.64 | +| towel | 78.7 | 86.88 | +| light | 62.55 | 72.63 | +| truck | 47.44 | 62.61 | +| tower | 16.93 | 29.25 | +| chandelier | 73.77 | 88.12 | +| awning | 43.1 | 58.77 | +| streetlight | 38.04 | 54.46 | +| booth | 39.77 | 58.1 | +| television receiver | 81.46 | 90.93 | +| airplane | 88.32 | 95.88 | +| dirt track | 11.74 | 14.36 | +| apparel | 52.63 | 67.7 | +| pole | 23.64 | 32.47 | +| land | 1.84 | 2.72 | +| bannister | 20.64 | 27.18 | +| escalator | 65.41 | 86.28 | +| ottoman | 52.18 | 69.4 | +| bottle | 38.11 | 45.48 | +| buffet | 45.63 | 55.63 | +| poster | 35.8 | 42.52 | +| stage | 25.33 | 46.76 | +| van | 50.32 | 63.23 | +| ship | 68.15 | 89.23 | +| fountain | 40.69 | 43.79 | +| conveyer belt | 74.03 | 96.64 | +| canopy | 52.29 | 71.62 | +| washer | 85.12 | 90.85 | +| plaything | 40.78 | 76.13 | +| swimming pool | 52.01 | 74.21 | +| stool | 44.57 | 74.67 | +| barrel | 53.76 | 66.7 | +| basket | 36.16 | 48.44 | +| waterfall | 58.87 | 66.94 | +| tent | 93.33 | 98.11 | +| bag | 17.47 | 18.98 | +| minibike | 75.88 | 89.83 | +| cradle | 89.13 | 96.57 | +| oven | 65.24 | 77.79 | +| ball | 53.1 | 64.99 | +| food | 65.68 | 82.25 | +| step | 24.61 | 34.45 | +| tank | 67.69 | 73.59 | +| trade name | 27.93 | 33.47 | +| microwave | 88.73 | 96.24 | +| pot | 57.89 | 68.75 | +| animal | 64.57 | 66.47 | +| bicycle | 54.3 | 65.21 | +| lake | 48.09 | 63.78 | +| dishwasher | 72.12 | 86.84 | +| screen | 52.01 | 80.1 | +| blanket | 33.39 | 39.73 | +| sculpture | 77.68 | 86.6 | +| hood | 63.63 | 73.97 | +| sconce | 62.11 | 70.46 | +| vase | 48.22 | 63.36 | +| traffic light | 34.48 | 62.29 | +| tray | 20.42 | 23.67 | +| ashcan | 50.13 | 67.71 | +| fan | 69.32 | 81.6 | +| pier | 47.85 | 56.46 | +| crt screen | 7.66 | 28.94 | +| plate | 61.35 | 82.2 | +| monitor | 38.98 | 44.56 | +| bulletin board | 53.88 | 78.95 | +| shower | 0.16 | 1.09 | +| radiator | 67.65 | 77.72 | +| glass | 21.15 | 23.04 | +| clock | 48.66 | 57.38 | +| flag | 66.85 | 79.79 | ++---------------------+-------+-------+ +2024-06-16 15:07:02,284 - mmseg - INFO - Summary: +2024-06-16 15:07:02,284 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.03 | 56.88 | 69.98 | ++-------+-------+-------+ +2024-06-16 15:07:02,284 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 15:07:02,285 - mmseg - INFO - Iter(val) [250] aAcc: 0.8603, mIoU: 0.5688, mAcc: 0.6998, IoU.wall: 0.8233, IoU.building: 0.8540, IoU.sky: 0.9445, IoU.floor: 0.8531, IoU.tree: 0.7808, IoU.ceiling: 0.8742, IoU.road: 0.8612, IoU.bed : 0.9258, IoU.windowpane: 0.6727, IoU.grass: 0.6893, IoU.cabinet: 0.6651, IoU.sidewalk: 0.6882, IoU.person: 0.8524, IoU.earth: 0.3968, IoU.door: 0.5949, IoU.table: 0.6939, IoU.mountain: 0.6167, IoU.plant: 0.5570, IoU.curtain: 0.7893, IoU.chair: 0.6483, IoU.car: 0.8811, IoU.water: 0.5760, IoU.painting: 0.7743, IoU.sofa: 0.8286, IoU.shelf: 0.4658, IoU.house: 0.5195, IoU.sea: 0.6885, IoU.mirror: 0.7705, IoU.rug: 0.7171, IoU.field: 0.3552, IoU.armchair: 0.6176, IoU.seat: 0.6305, IoU.fence: 0.5040, IoU.desk: 0.5665, IoU.rock: 0.5561, IoU.wardrobe: 0.5356, IoU.lamp: 0.7509, IoU.bathtub: 0.8685, IoU.railing: 0.4616, IoU.cushion: 0.7096, IoU.base: 0.4216, IoU.box: 0.3997, IoU.column: 0.5742, IoU.signboard: 0.4187, IoU.chest of drawers: 0.4297, IoU.counter: 0.5013, IoU.sand: 0.5233, IoU.sink: 0.7919, IoU.skyscraper: 0.5028, IoU.fireplace: 0.7242, IoU.refrigerator: 0.8111, IoU.grandstand: 0.5031, IoU.path: 0.2320, IoU.stairs: 0.2951, IoU.runway: 0.7164, IoU.case: 0.6667, IoU.pool table: 0.9483, IoU.pillow: 0.6997, IoU.screen door: 0.6626, IoU.stairway: 0.4143, IoU.river: 0.1176, IoU.bridge: 0.7051, IoU.bookcase: 0.4084, IoU.blind: 0.4015, IoU.coffee table: 0.6285, IoU.toilet: 0.9033, IoU.flower: 0.4192, IoU.book: 0.5278, IoU.hill: 0.0527, IoU.bench: 0.5457, IoU.countertop: 0.6192, IoU.stove: 0.8659, IoU.palm: 0.5201, IoU.kitchen island: 0.5760, IoU.computer: 0.6861, IoU.swivel chair: 0.5108, IoU.boat: 0.6846, IoU.bar: 0.6725, IoU.arcade machine: 0.7657, IoU.hovel: 0.0788, IoU.bus: 0.9246, IoU.towel: 0.7870, IoU.light: 0.6255, IoU.truck: 0.4744, IoU.tower: 0.1693, IoU.chandelier: 0.7377, IoU.awning: 0.4310, IoU.streetlight: 0.3804, IoU.booth: 0.3977, IoU.television receiver: 0.8146, IoU.airplane: 0.8832, IoU.dirt track: 0.1174, IoU.apparel: 0.5263, IoU.pole: 0.2364, IoU.land: 0.0184, IoU.bannister: 0.2064, IoU.escalator: 0.6541, IoU.ottoman: 0.5218, IoU.bottle: 0.3811, IoU.buffet: 0.4563, IoU.poster: 0.3580, IoU.stage: 0.2533, IoU.van: 0.5032, IoU.ship: 0.6815, IoU.fountain: 0.4069, IoU.conveyer belt: 0.7403, IoU.canopy: 0.5229, IoU.washer: 0.8512, IoU.plaything: 0.4078, IoU.swimming pool: 0.5201, IoU.stool: 0.4457, IoU.barrel: 0.5376, IoU.basket: 0.3616, IoU.waterfall: 0.5887, IoU.tent: 0.9333, IoU.bag: 0.1747, IoU.minibike: 0.7588, IoU.cradle: 0.8913, IoU.oven: 0.6524, IoU.ball: 0.5310, IoU.food: 0.6568, IoU.step: 0.2461, IoU.tank: 0.6769, IoU.trade name: 0.2793, IoU.microwave: 0.8873, IoU.pot: 0.5789, IoU.animal: 0.6457, IoU.bicycle: 0.5430, IoU.lake: 0.4809, IoU.dishwasher: 0.7212, IoU.screen: 0.5201, IoU.blanket: 0.3339, IoU.sculpture: 0.7768, IoU.hood: 0.6363, IoU.sconce: 0.6211, IoU.vase: 0.4822, IoU.traffic light: 0.3448, IoU.tray: 0.2042, IoU.ashcan: 0.5013, IoU.fan: 0.6932, IoU.pier: 0.4785, IoU.crt screen: 0.0766, IoU.plate: 0.6135, IoU.monitor: 0.3898, IoU.bulletin board: 0.5388, IoU.shower: 0.0016, IoU.radiator: 0.6765, IoU.glass: 0.2115, IoU.clock: 0.4866, IoU.flag: 0.6685, Acc.wall: 0.8933, Acc.building: 0.9458, Acc.sky: 0.9658, Acc.floor: 0.9173, Acc.tree: 0.8931, Acc.ceiling: 0.9506, Acc.road: 0.9319, Acc.bed : 0.9661, Acc.windowpane: 0.8198, Acc.grass: 0.8291, Acc.cabinet: 0.7689, Acc.sidewalk: 0.8212, Acc.person: 0.9416, Acc.earth: 0.5309, Acc.door: 0.7300, Acc.table: 0.8178, Acc.mountain: 0.7310, Acc.plant: 0.6448, Acc.curtain: 0.9033, Acc.chair: 0.7245, Acc.car: 0.9433, Acc.water: 0.6929, Acc.painting: 0.9005, Acc.sofa: 0.9162, Acc.shelf: 0.6780, Acc.house: 0.6520, Acc.sea: 0.8365, Acc.mirror: 0.8573, Acc.rug: 0.7907, Acc.field: 0.5506, Acc.armchair: 0.7801, Acc.seat: 0.8973, Acc.fence: 0.7008, Acc.desk: 0.8194, Acc.rock: 0.8152, Acc.wardrobe: 0.7270, Acc.lamp: 0.8660, Acc.bathtub: 0.8887, Acc.railing: 0.6066, Acc.cushion: 0.8311, Acc.base: 0.5980, Acc.box: 0.5558, Acc.column: 0.7004, Acc.signboard: 0.5451, Acc.chest of drawers: 0.6625, Acc.counter: 0.6686, Acc.sand: 0.8739, Acc.sink: 0.8370, Acc.skyscraper: 0.5933, Acc.fireplace: 0.9628, Acc.refrigerator: 0.8630, Acc.grandstand: 0.8056, Acc.path: 0.3014, Acc.stairs: 0.3412, Acc.runway: 0.9438, Acc.case: 0.8125, Acc.pool table: 0.9773, Acc.pillow: 0.8436, Acc.screen door: 0.6790, Acc.stairway: 0.6573, Acc.river: 0.3514, Acc.bridge: 0.8242, Acc.bookcase: 0.6441, Acc.blind: 0.4495, Acc.coffee table: 0.8755, Acc.toilet: 0.9401, Acc.flower: 0.5698, Acc.book: 0.7895, Acc.hill: 0.1040, Acc.bench: 0.6438, Acc.countertop: 0.8905, Acc.stove: 0.9265, Acc.palm: 0.7947, Acc.kitchen island: 0.7363, Acc.computer: 0.7505, Acc.swivel chair: 0.8135, Acc.boat: 0.9368, Acc.bar: 0.8813, Acc.arcade machine: 0.8197, Acc.hovel: 0.0834, Acc.bus: 0.9664, Acc.towel: 0.8688, Acc.light: 0.7263, Acc.truck: 0.6261, Acc.tower: 0.2925, Acc.chandelier: 0.8812, Acc.awning: 0.5877, Acc.streetlight: 0.5446, Acc.booth: 0.5810, Acc.television receiver: 0.9093, Acc.airplane: 0.9588, Acc.dirt track: 0.1436, Acc.apparel: 0.6770, Acc.pole: 0.3247, Acc.land: 0.0272, Acc.bannister: 0.2718, Acc.escalator: 0.8628, Acc.ottoman: 0.6940, Acc.bottle: 0.4548, Acc.buffet: 0.5563, Acc.poster: 0.4252, Acc.stage: 0.4676, Acc.van: 0.6323, Acc.ship: 0.8923, Acc.fountain: 0.4379, Acc.conveyer belt: 0.9664, Acc.canopy: 0.7162, Acc.washer: 0.9085, Acc.plaything: 0.7613, Acc.swimming pool: 0.7421, Acc.stool: 0.7467, Acc.barrel: 0.6670, Acc.basket: 0.4844, Acc.waterfall: 0.6694, Acc.tent: 0.9811, Acc.bag: 0.1898, Acc.minibike: 0.8983, Acc.cradle: 0.9657, Acc.oven: 0.7779, Acc.ball: 0.6499, Acc.food: 0.8225, Acc.step: 0.3445, Acc.tank: 0.7359, Acc.trade name: 0.3347, Acc.microwave: 0.9624, Acc.pot: 0.6875, Acc.animal: 0.6647, Acc.bicycle: 0.6521, Acc.lake: 0.6378, Acc.dishwasher: 0.8684, Acc.screen: 0.8010, Acc.blanket: 0.3973, Acc.sculpture: 0.8660, Acc.hood: 0.7397, Acc.sconce: 0.7046, Acc.vase: 0.6336, Acc.traffic light: 0.6229, Acc.tray: 0.2367, Acc.ashcan: 0.6771, Acc.fan: 0.8160, Acc.pier: 0.5646, Acc.crt screen: 0.2894, Acc.plate: 0.8220, Acc.monitor: 0.4456, Acc.bulletin board: 0.7895, Acc.shower: 0.0109, Acc.radiator: 0.7772, Acc.glass: 0.2304, Acc.clock: 0.5738, Acc.flag: 0.7979 +2024-06-16 15:08:23,782 - mmseg - INFO - Iter [36050/80000] lr: 2.198e-05, eta: 21:35:28, time: 3.553, data_time: 1.940, memory: 71384, decode.loss_ce: 0.2118, decode.acc_seg: 91.0665, aux.loss_ce: 0.0874, aux.acc_seg: 90.7769, loss: 0.2992 +2024-06-16 15:09:45,061 - mmseg - INFO - Iter [36100/80000] lr: 2.195e-05, eta: 21:33:51, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2040, decode.acc_seg: 91.4657, aux.loss_ce: 0.0849, aux.acc_seg: 91.0761, loss: 0.2889 +2024-06-16 15:11:06,157 - mmseg - INFO - Iter [36150/80000] lr: 2.193e-05, eta: 21:32:13, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1999, decode.acc_seg: 91.4723, aux.loss_ce: 0.0833, aux.acc_seg: 91.0352, loss: 0.2832 +2024-06-16 15:12:27,193 - mmseg - INFO - Iter [36200/80000] lr: 2.190e-05, eta: 21:30:36, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2053, decode.acc_seg: 91.1764, aux.loss_ce: 0.0856, aux.acc_seg: 90.7414, loss: 0.2909 +2024-06-16 15:13:48,338 - mmseg - INFO - Iter [36250/80000] lr: 2.188e-05, eta: 21:28:59, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1882, decode.acc_seg: 92.0655, aux.loss_ce: 0.0785, aux.acc_seg: 91.7132, loss: 0.2667 +2024-06-16 15:15:09,493 - mmseg - INFO - Iter [36300/80000] lr: 2.185e-05, eta: 21:27:22, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2148, decode.acc_seg: 91.1924, aux.loss_ce: 0.0891, aux.acc_seg: 90.8910, loss: 0.3039 +2024-06-16 15:16:30,555 - mmseg - INFO - Iter [36350/80000] lr: 2.183e-05, eta: 21:25:45, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2031, decode.acc_seg: 91.1769, aux.loss_ce: 0.0833, aux.acc_seg: 90.9276, loss: 0.2864 +2024-06-16 15:17:51,535 - mmseg - INFO - Iter [36400/80000] lr: 2.180e-05, eta: 21:24:07, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2051, decode.acc_seg: 91.3701, aux.loss_ce: 0.0846, aux.acc_seg: 91.0435, loss: 0.2896 +2024-06-16 15:19:12,702 - mmseg - INFO - Iter [36450/80000] lr: 2.178e-05, eta: 21:22:30, time: 1.623, data_time: 0.009, memory: 71384, decode.loss_ce: 0.2093, decode.acc_seg: 91.3147, aux.loss_ce: 0.0871, aux.acc_seg: 90.9451, loss: 0.2964 +2024-06-16 15:20:33,673 - mmseg - INFO - Iter [36500/80000] lr: 2.175e-05, eta: 21:20:53, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2071, decode.acc_seg: 91.0714, aux.loss_ce: 0.0848, aux.acc_seg: 90.8019, loss: 0.2918 +2024-06-16 15:21:54,672 - mmseg - INFO - Iter [36550/80000] lr: 2.173e-05, eta: 21:19:16, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2184, decode.acc_seg: 91.0688, aux.loss_ce: 0.0901, aux.acc_seg: 90.7787, loss: 0.3085 +2024-06-16 15:23:15,775 - mmseg - INFO - Iter [36600/80000] lr: 2.170e-05, eta: 21:17:39, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2080, decode.acc_seg: 91.2198, aux.loss_ce: 0.0863, aux.acc_seg: 90.8986, loss: 0.2943 +2024-06-16 15:24:39,363 - mmseg - INFO - Iter [36650/80000] lr: 2.168e-05, eta: 21:16:05, time: 1.672, data_time: 0.055, memory: 71384, decode.loss_ce: 0.2099, decode.acc_seg: 91.3526, aux.loss_ce: 0.0866, aux.acc_seg: 91.0591, loss: 0.2965 +2024-06-16 15:26:00,458 - mmseg - INFO - Iter [36700/80000] lr: 2.165e-05, eta: 21:14:29, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2184, decode.acc_seg: 91.2122, aux.loss_ce: 0.0899, aux.acc_seg: 90.9777, loss: 0.3083 +2024-06-16 15:27:21,570 - mmseg - INFO - Iter [36750/80000] lr: 2.163e-05, eta: 21:12:52, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2178, decode.acc_seg: 90.9689, aux.loss_ce: 0.0892, aux.acc_seg: 90.7667, loss: 0.3070 +2024-06-16 15:28:42,727 - mmseg - INFO - Iter [36800/80000] lr: 2.160e-05, eta: 21:11:15, time: 1.623, data_time: 0.009, memory: 71384, decode.loss_ce: 0.1905, decode.acc_seg: 91.8016, aux.loss_ce: 0.0784, aux.acc_seg: 91.5167, loss: 0.2688 +2024-06-16 15:30:03,918 - mmseg - INFO - Iter [36850/80000] lr: 2.158e-05, eta: 21:09:38, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2176, decode.acc_seg: 90.9859, aux.loss_ce: 0.0903, aux.acc_seg: 90.6710, loss: 0.3079 +2024-06-16 15:31:24,883 - mmseg - INFO - Iter [36900/80000] lr: 2.155e-05, eta: 21:08:02, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2073, decode.acc_seg: 91.1598, aux.loss_ce: 0.0856, aux.acc_seg: 90.8471, loss: 0.2930 +2024-06-16 15:32:45,978 - mmseg - INFO - Iter [36950/80000] lr: 2.153e-05, eta: 21:06:25, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2082, decode.acc_seg: 91.0779, aux.loss_ce: 0.0861, aux.acc_seg: 90.7941, loss: 0.2943 +2024-06-16 15:34:07,132 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 15:34:07,132 - mmseg - INFO - Iter [37000/80000] lr: 2.150e-05, eta: 21:04:49, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2028, decode.acc_seg: 91.3753, aux.loss_ce: 0.0850, aux.acc_seg: 90.9553, loss: 0.2877 +2024-06-16 15:35:45,773 - mmseg - INFO - per class results: +2024-06-16 15:35:45,780 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.15 | 89.87 | +| building | 85.29 | 91.69 | +| sky | 94.77 | 97.59 | +| floor | 84.76 | 90.59 | +| tree | 77.96 | 89.43 | +| ceiling | 87.64 | 94.65 | +| road | 85.65 | 90.66 | +| bed | 92.75 | 97.13 | +| windowpane | 66.44 | 80.19 | +| grass | 68.35 | 79.87 | +| cabinet | 65.27 | 74.64 | +| sidewalk | 71.0 | 86.89 | +| person | 85.94 | 93.17 | +| earth | 37.5 | 50.86 | +| door | 60.13 | 77.57 | +| table | 69.13 | 80.68 | +| mountain | 58.98 | 74.84 | +| plant | 55.6 | 65.65 | +| curtain | 78.84 | 90.24 | +| chair | 67.26 | 77.7 | +| car | 87.94 | 94.09 | +| water | 60.52 | 73.59 | +| painting | 77.45 | 90.85 | +| sofa | 81.77 | 89.14 | +| shelf | 47.31 | 65.1 | +| house | 55.72 | 88.28 | +| sea | 70.33 | 84.08 | +| mirror | 78.08 | 84.55 | +| rug | 69.91 | 85.15 | +| field | 28.47 | 51.07 | +| armchair | 61.78 | 81.98 | +| seat | 63.74 | 89.62 | +| fence | 46.27 | 65.49 | +| desk | 58.69 | 76.67 | +| rock | 55.33 | 83.15 | +| wardrobe | 55.39 | 83.08 | +| lamp | 75.32 | 85.61 | +| bathtub | 87.66 | 89.4 | +| railing | 42.17 | 63.95 | +| cushion | 70.25 | 83.27 | +| base | 38.32 | 57.84 | +| box | 40.51 | 53.13 | +| column | 55.2 | 71.15 | +| signboard | 40.58 | 55.1 | +| chest of drawers | 44.7 | 63.32 | +| counter | 27.33 | 33.3 | +| sand | 58.08 | 86.06 | +| sink | 80.35 | 86.23 | +| skyscraper | 47.99 | 64.39 | +| fireplace | 73.53 | 96.71 | +| refrigerator | 84.07 | 90.31 | +| grandstand | 49.33 | 83.97 | +| path | 25.92 | 35.06 | +| stairs | 23.9 | 29.63 | +| runway | 66.29 | 86.3 | +| case | 63.5 | 84.16 | +| pool table | 94.72 | 97.6 | +| pillow | 67.22 | 74.8 | +| screen door | 77.96 | 80.01 | +| stairway | 32.85 | 43.09 | +| river | 11.14 | 28.1 | +| bridge | 68.3 | 78.41 | +| bookcase | 36.66 | 67.59 | +| blind | 45.14 | 49.89 | +| coffee table | 61.56 | 86.35 | +| toilet | 89.13 | 93.8 | +| flower | 41.87 | 51.85 | +| book | 52.69 | 75.77 | +| hill | 6.84 | 17.75 | +| bench | 50.2 | 58.09 | +| countertop | 64.39 | 81.64 | +| stove | 86.08 | 92.08 | +| palm | 50.91 | 82.34 | +| kitchen island | 55.49 | 89.5 | +| computer | 80.08 | 90.38 | +| swivel chair | 51.6 | 76.25 | +| boat | 75.59 | 89.71 | +| bar | 62.59 | 83.12 | +| arcade machine | 77.18 | 83.51 | +| hovel | 14.4 | 15.55 | +| bus | 92.1 | 96.29 | +| towel | 79.07 | 86.76 | +| light | 62.87 | 73.04 | +| truck | 49.46 | 59.85 | +| tower | 32.65 | 60.59 | +| chandelier | 71.56 | 83.72 | +| awning | 49.93 | 68.7 | +| streetlight | 35.9 | 48.85 | +| booth | 39.57 | 58.77 | +| television receiver | 82.54 | 87.93 | +| airplane | 80.67 | 85.59 | +| dirt track | 5.02 | 28.97 | +| apparel | 56.46 | 76.08 | +| pole | 26.57 | 35.99 | +| land | 3.49 | 4.92 | +| bannister | 22.92 | 31.13 | +| escalator | 63.57 | 85.04 | +| ottoman | 54.31 | 78.19 | +| bottle | 31.79 | 37.78 | +| buffet | 57.38 | 72.04 | +| poster | 35.93 | 52.01 | +| stage | 25.52 | 47.13 | +| van | 47.01 | 62.64 | +| ship | 50.44 | 50.73 | +| fountain | 37.49 | 39.33 | +| conveyer belt | 82.12 | 92.78 | +| canopy | 52.44 | 80.28 | +| washer | 79.52 | 84.83 | +| plaything | 41.54 | 74.05 | +| swimming pool | 55.99 | 77.47 | +| stool | 49.99 | 74.91 | +| barrel | 49.8 | 65.01 | +| basket | 41.53 | 59.28 | +| waterfall | 62.61 | 75.12 | +| tent | 89.71 | 99.18 | +| bag | 20.88 | 23.48 | +| minibike | 76.98 | 90.16 | +| cradle | 85.56 | 97.36 | +| oven | 61.5 | 72.87 | +| ball | 51.67 | 64.81 | +| food | 56.67 | 62.09 | +| step | 21.97 | 27.87 | +| tank | 64.23 | 68.35 | +| trade name | 18.02 | 20.92 | +| microwave | 88.95 | 95.4 | +| pot | 59.19 | 68.94 | +| animal | 59.64 | 60.38 | +| bicycle | 59.69 | 75.4 | +| lake | 53.47 | 63.84 | +| dishwasher | 73.57 | 84.19 | +| screen | 62.93 | 95.42 | +| blanket | 38.05 | 46.94 | +| sculpture | 77.02 | 87.41 | +| hood | 64.1 | 77.65 | +| sconce | 61.63 | 69.55 | +| vase | 47.21 | 63.19 | +| traffic light | 36.63 | 60.9 | +| tray | 23.12 | 29.3 | +| ashcan | 47.68 | 58.28 | +| fan | 70.73 | 82.53 | +| pier | 40.04 | 43.34 | +| crt screen | 3.13 | 5.29 | +| plate | 64.16 | 78.46 | +| monitor | 59.21 | 67.25 | +| bulletin board | 54.76 | 59.46 | +| shower | 0.83 | 1.26 | +| radiator | 68.29 | 79.4 | +| glass | 20.04 | 21.03 | +| clock | 40.49 | 43.3 | +| flag | 71.37 | 77.05 | ++---------------------+-------+-------+ +2024-06-16 15:35:45,780 - mmseg - INFO - Summary: +2024-06-16 15:35:45,780 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 85.82 | 56.84 | 69.76 | ++-------+-------+-------+ +2024-06-16 15:35:45,781 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 15:35:45,781 - mmseg - INFO - Iter(val) [250] aAcc: 0.8582, mIoU: 0.5684, mAcc: 0.6976, IoU.wall: 0.8215, IoU.building: 0.8529, IoU.sky: 0.9477, IoU.floor: 0.8476, IoU.tree: 0.7796, IoU.ceiling: 0.8764, IoU.road: 0.8565, IoU.bed : 0.9275, IoU.windowpane: 0.6644, IoU.grass: 0.6835, IoU.cabinet: 0.6527, IoU.sidewalk: 0.7100, IoU.person: 0.8594, IoU.earth: 0.3750, IoU.door: 0.6013, IoU.table: 0.6913, IoU.mountain: 0.5898, IoU.plant: 0.5560, IoU.curtain: 0.7884, IoU.chair: 0.6726, IoU.car: 0.8794, IoU.water: 0.6052, IoU.painting: 0.7745, IoU.sofa: 0.8177, IoU.shelf: 0.4731, IoU.house: 0.5572, IoU.sea: 0.7033, IoU.mirror: 0.7808, IoU.rug: 0.6991, IoU.field: 0.2847, IoU.armchair: 0.6178, IoU.seat: 0.6374, IoU.fence: 0.4627, IoU.desk: 0.5869, IoU.rock: 0.5533, IoU.wardrobe: 0.5539, IoU.lamp: 0.7532, IoU.bathtub: 0.8766, IoU.railing: 0.4217, IoU.cushion: 0.7025, IoU.base: 0.3832, IoU.box: 0.4051, IoU.column: 0.5520, IoU.signboard: 0.4058, IoU.chest of drawers: 0.4470, IoU.counter: 0.2733, IoU.sand: 0.5808, IoU.sink: 0.8035, IoU.skyscraper: 0.4799, IoU.fireplace: 0.7353, IoU.refrigerator: 0.8407, IoU.grandstand: 0.4933, IoU.path: 0.2592, IoU.stairs: 0.2390, IoU.runway: 0.6629, IoU.case: 0.6350, IoU.pool table: 0.9472, IoU.pillow: 0.6722, IoU.screen door: 0.7796, IoU.stairway: 0.3285, IoU.river: 0.1114, IoU.bridge: 0.6830, IoU.bookcase: 0.3666, IoU.blind: 0.4514, IoU.coffee table: 0.6156, IoU.toilet: 0.8913, IoU.flower: 0.4187, IoU.book: 0.5269, IoU.hill: 0.0684, IoU.bench: 0.5020, IoU.countertop: 0.6439, IoU.stove: 0.8608, IoU.palm: 0.5091, IoU.kitchen island: 0.5549, IoU.computer: 0.8008, IoU.swivel chair: 0.5160, IoU.boat: 0.7559, IoU.bar: 0.6259, IoU.arcade machine: 0.7718, IoU.hovel: 0.1440, IoU.bus: 0.9210, IoU.towel: 0.7907, IoU.light: 0.6287, IoU.truck: 0.4946, IoU.tower: 0.3265, IoU.chandelier: 0.7156, IoU.awning: 0.4993, IoU.streetlight: 0.3590, IoU.booth: 0.3957, IoU.television receiver: 0.8254, IoU.airplane: 0.8067, IoU.dirt track: 0.0502, IoU.apparel: 0.5646, IoU.pole: 0.2657, IoU.land: 0.0349, IoU.bannister: 0.2292, IoU.escalator: 0.6357, IoU.ottoman: 0.5431, IoU.bottle: 0.3179, IoU.buffet: 0.5738, IoU.poster: 0.3593, IoU.stage: 0.2552, IoU.van: 0.4701, IoU.ship: 0.5044, IoU.fountain: 0.3749, IoU.conveyer belt: 0.8212, IoU.canopy: 0.5244, IoU.washer: 0.7952, IoU.plaything: 0.4154, IoU.swimming pool: 0.5599, IoU.stool: 0.4999, IoU.barrel: 0.4980, IoU.basket: 0.4153, IoU.waterfall: 0.6261, IoU.tent: 0.8971, IoU.bag: 0.2088, IoU.minibike: 0.7698, IoU.cradle: 0.8556, IoU.oven: 0.6150, IoU.ball: 0.5167, IoU.food: 0.5667, IoU.step: 0.2197, IoU.tank: 0.6423, IoU.trade name: 0.1802, IoU.microwave: 0.8895, IoU.pot: 0.5919, IoU.animal: 0.5964, IoU.bicycle: 0.5969, IoU.lake: 0.5347, IoU.dishwasher: 0.7357, IoU.screen: 0.6293, IoU.blanket: 0.3805, IoU.sculpture: 0.7702, IoU.hood: 0.6410, IoU.sconce: 0.6163, IoU.vase: 0.4721, IoU.traffic light: 0.3663, IoU.tray: 0.2312, IoU.ashcan: 0.4768, IoU.fan: 0.7073, IoU.pier: 0.4004, IoU.crt screen: 0.0313, IoU.plate: 0.6416, IoU.monitor: 0.5921, IoU.bulletin board: 0.5476, IoU.shower: 0.0083, IoU.radiator: 0.6829, IoU.glass: 0.2004, IoU.clock: 0.4049, IoU.flag: 0.7137, Acc.wall: 0.8987, Acc.building: 0.9169, Acc.sky: 0.9759, Acc.floor: 0.9059, Acc.tree: 0.8943, Acc.ceiling: 0.9465, Acc.road: 0.9066, Acc.bed : 0.9713, Acc.windowpane: 0.8019, Acc.grass: 0.7987, Acc.cabinet: 0.7464, Acc.sidewalk: 0.8689, Acc.person: 0.9317, Acc.earth: 0.5086, Acc.door: 0.7757, Acc.table: 0.8068, Acc.mountain: 0.7484, Acc.plant: 0.6565, Acc.curtain: 0.9024, Acc.chair: 0.7770, Acc.car: 0.9409, Acc.water: 0.7359, Acc.painting: 0.9085, Acc.sofa: 0.8914, Acc.shelf: 0.6510, Acc.house: 0.8828, Acc.sea: 0.8408, Acc.mirror: 0.8455, Acc.rug: 0.8515, Acc.field: 0.5107, Acc.armchair: 0.8198, Acc.seat: 0.8962, Acc.fence: 0.6549, Acc.desk: 0.7667, Acc.rock: 0.8315, Acc.wardrobe: 0.8308, Acc.lamp: 0.8561, Acc.bathtub: 0.8940, Acc.railing: 0.6395, Acc.cushion: 0.8327, Acc.base: 0.5784, Acc.box: 0.5313, Acc.column: 0.7115, Acc.signboard: 0.5510, Acc.chest of drawers: 0.6332, Acc.counter: 0.3330, Acc.sand: 0.8606, Acc.sink: 0.8623, Acc.skyscraper: 0.6439, Acc.fireplace: 0.9671, Acc.refrigerator: 0.9031, Acc.grandstand: 0.8397, Acc.path: 0.3506, Acc.stairs: 0.2963, Acc.runway: 0.8630, Acc.case: 0.8416, Acc.pool table: 0.9760, Acc.pillow: 0.7480, Acc.screen door: 0.8001, Acc.stairway: 0.4309, Acc.river: 0.2810, Acc.bridge: 0.7841, Acc.bookcase: 0.6759, Acc.blind: 0.4989, Acc.coffee table: 0.8635, Acc.toilet: 0.9380, Acc.flower: 0.5185, Acc.book: 0.7577, Acc.hill: 0.1775, Acc.bench: 0.5809, Acc.countertop: 0.8164, Acc.stove: 0.9208, Acc.palm: 0.8234, Acc.kitchen island: 0.8950, Acc.computer: 0.9038, Acc.swivel chair: 0.7625, Acc.boat: 0.8971, Acc.bar: 0.8312, Acc.arcade machine: 0.8351, Acc.hovel: 0.1555, Acc.bus: 0.9629, Acc.towel: 0.8676, Acc.light: 0.7304, Acc.truck: 0.5985, Acc.tower: 0.6059, Acc.chandelier: 0.8372, Acc.awning: 0.6870, Acc.streetlight: 0.4885, Acc.booth: 0.5877, Acc.television receiver: 0.8793, Acc.airplane: 0.8559, Acc.dirt track: 0.2897, Acc.apparel: 0.7608, Acc.pole: 0.3599, Acc.land: 0.0492, Acc.bannister: 0.3113, Acc.escalator: 0.8504, Acc.ottoman: 0.7819, Acc.bottle: 0.3778, Acc.buffet: 0.7204, Acc.poster: 0.5201, Acc.stage: 0.4713, Acc.van: 0.6264, Acc.ship: 0.5073, Acc.fountain: 0.3933, Acc.conveyer belt: 0.9278, Acc.canopy: 0.8028, Acc.washer: 0.8483, Acc.plaything: 0.7405, Acc.swimming pool: 0.7747, Acc.stool: 0.7491, Acc.barrel: 0.6501, Acc.basket: 0.5928, Acc.waterfall: 0.7512, Acc.tent: 0.9918, Acc.bag: 0.2348, Acc.minibike: 0.9016, Acc.cradle: 0.9736, Acc.oven: 0.7287, Acc.ball: 0.6481, Acc.food: 0.6209, Acc.step: 0.2787, Acc.tank: 0.6835, Acc.trade name: 0.2092, Acc.microwave: 0.9540, Acc.pot: 0.6894, Acc.animal: 0.6038, Acc.bicycle: 0.7540, Acc.lake: 0.6384, Acc.dishwasher: 0.8419, Acc.screen: 0.9542, Acc.blanket: 0.4694, Acc.sculpture: 0.8741, Acc.hood: 0.7765, Acc.sconce: 0.6955, Acc.vase: 0.6319, Acc.traffic light: 0.6090, Acc.tray: 0.2930, Acc.ashcan: 0.5828, Acc.fan: 0.8253, Acc.pier: 0.4334, Acc.crt screen: 0.0529, Acc.plate: 0.7846, Acc.monitor: 0.6725, Acc.bulletin board: 0.5946, Acc.shower: 0.0126, Acc.radiator: 0.7940, Acc.glass: 0.2103, Acc.clock: 0.4330, Acc.flag: 0.7705 +2024-06-16 15:37:07,258 - mmseg - INFO - Iter [37050/80000] lr: 2.148e-05, eta: 21:05:07, time: 3.603, data_time: 1.990, memory: 71384, decode.loss_ce: 0.1986, decode.acc_seg: 91.4927, aux.loss_ce: 0.0825, aux.acc_seg: 91.1117, loss: 0.2811 +2024-06-16 15:38:28,406 - mmseg - INFO - Iter [37100/80000] lr: 2.145e-05, eta: 21:03:30, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1883, decode.acc_seg: 91.9759, aux.loss_ce: 0.0779, aux.acc_seg: 91.6732, loss: 0.2662 +2024-06-16 15:39:49,483 - mmseg - INFO - Iter [37150/80000] lr: 2.143e-05, eta: 21:01:53, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1966, decode.acc_seg: 91.6599, aux.loss_ce: 0.0814, aux.acc_seg: 91.3831, loss: 0.2780 +2024-06-16 15:41:10,576 - mmseg - INFO - Iter [37200/80000] lr: 2.140e-05, eta: 21:00:17, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2211, decode.acc_seg: 90.9311, aux.loss_ce: 0.0908, aux.acc_seg: 90.6431, loss: 0.3119 +2024-06-16 15:42:31,652 - mmseg - INFO - Iter [37250/80000] lr: 2.138e-05, eta: 20:58:40, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2013, decode.acc_seg: 91.3452, aux.loss_ce: 0.0834, aux.acc_seg: 90.9951, loss: 0.2846 +2024-06-16 15:43:52,799 - mmseg - INFO - Iter [37300/80000] lr: 2.135e-05, eta: 20:57:03, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2273, decode.acc_seg: 90.4516, aux.loss_ce: 0.0933, aux.acc_seg: 90.2664, loss: 0.3206 +2024-06-16 15:45:13,857 - mmseg - INFO - Iter [37350/80000] lr: 2.133e-05, eta: 20:55:27, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1925, decode.acc_seg: 91.6113, aux.loss_ce: 0.0798, aux.acc_seg: 91.3745, loss: 0.2723 +2024-06-16 15:46:34,887 - mmseg - INFO - Iter [37400/80000] lr: 2.130e-05, eta: 20:53:50, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2079, decode.acc_seg: 91.4190, aux.loss_ce: 0.0857, aux.acc_seg: 91.0975, loss: 0.2936 +2024-06-16 15:47:55,968 - mmseg - INFO - Iter [37450/80000] lr: 2.128e-05, eta: 20:52:14, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2157, decode.acc_seg: 90.9960, aux.loss_ce: 0.0888, aux.acc_seg: 90.7199, loss: 0.3044 +2024-06-16 15:49:17,169 - mmseg - INFO - Iter [37500/80000] lr: 2.125e-05, eta: 20:50:37, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2181, decode.acc_seg: 91.2035, aux.loss_ce: 0.0897, aux.acc_seg: 90.8975, loss: 0.3079 +2024-06-16 15:50:38,277 - mmseg - INFO - Iter [37550/80000] lr: 2.123e-05, eta: 20:49:01, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2030, decode.acc_seg: 91.4664, aux.loss_ce: 0.0838, aux.acc_seg: 91.1376, loss: 0.2868 +2024-06-16 15:51:59,220 - mmseg - INFO - Iter [37600/80000] lr: 2.120e-05, eta: 20:47:24, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2128, decode.acc_seg: 90.9898, aux.loss_ce: 0.0882, aux.acc_seg: 90.6511, loss: 0.3010 +2024-06-16 15:53:20,393 - mmseg - INFO - Iter [37650/80000] lr: 2.118e-05, eta: 20:45:48, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2007, decode.acc_seg: 91.5356, aux.loss_ce: 0.0833, aux.acc_seg: 91.1796, loss: 0.2840 +2024-06-16 15:54:41,407 - mmseg - INFO - Iter [37700/80000] lr: 2.115e-05, eta: 20:44:12, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2002, decode.acc_seg: 91.4682, aux.loss_ce: 0.0839, aux.acc_seg: 91.0416, loss: 0.2841 +2024-06-16 15:56:02,627 - mmseg - INFO - Iter [37750/80000] lr: 2.113e-05, eta: 20:42:36, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2063, decode.acc_seg: 91.2737, aux.loss_ce: 0.0858, aux.acc_seg: 91.0024, loss: 0.2921 +2024-06-16 15:57:23,708 - mmseg - INFO - Iter [37800/80000] lr: 2.110e-05, eta: 20:41:00, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2037, decode.acc_seg: 91.5945, aux.loss_ce: 0.0850, aux.acc_seg: 91.2346, loss: 0.2886 +2024-06-16 15:58:44,724 - mmseg - INFO - Iter [37850/80000] lr: 2.108e-05, eta: 20:39:23, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1985, decode.acc_seg: 91.2761, aux.loss_ce: 0.0828, aux.acc_seg: 90.9055, loss: 0.2813 +2024-06-16 16:00:08,544 - mmseg - INFO - Iter [37900/80000] lr: 2.105e-05, eta: 20:37:50, time: 1.676, data_time: 0.061, memory: 71384, decode.loss_ce: 0.1950, decode.acc_seg: 91.5489, aux.loss_ce: 0.0809, aux.acc_seg: 91.3068, loss: 0.2759 +2024-06-16 16:01:29,735 - mmseg - INFO - Iter [37950/80000] lr: 2.103e-05, eta: 20:36:14, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1955, decode.acc_seg: 91.5275, aux.loss_ce: 0.0816, aux.acc_seg: 91.1652, loss: 0.2771 +2024-06-16 16:02:50,744 - mmseg - INFO - Saving checkpoint at 38000 iterations +2024-06-16 16:04:15,458 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 16:04:15,458 - mmseg - INFO - Iter [38000/80000] lr: 2.100e-05, eta: 20:36:12, time: 3.314, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2036, decode.acc_seg: 91.3601, aux.loss_ce: 0.0842, aux.acc_seg: 91.1380, loss: 0.2878 +2024-06-16 16:05:51,649 - mmseg - INFO - per class results: +2024-06-16 16:05:51,655 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.51 | 89.66 | +| building | 85.35 | 93.64 | +| sky | 94.87 | 97.52 | +| floor | 85.31 | 93.05 | +| tree | 78.16 | 89.53 | +| ceiling | 87.73 | 93.36 | +| road | 85.73 | 90.06 | +| bed | 92.63 | 96.99 | +| windowpane | 67.11 | 81.26 | +| grass | 68.42 | 84.49 | +| cabinet | 66.3 | 75.47 | +| sidewalk | 71.73 | 87.39 | +| person | 85.85 | 93.6 | +| earth | 36.6 | 46.16 | +| door | 60.79 | 78.96 | +| table | 70.85 | 82.27 | +| mountain | 60.33 | 83.3 | +| plant | 56.48 | 63.92 | +| curtain | 80.95 | 89.0 | +| chair | 65.63 | 75.12 | +| car | 87.56 | 94.17 | +| water | 62.66 | 76.57 | +| painting | 77.65 | 90.89 | +| sofa | 80.73 | 93.53 | +| shelf | 53.79 | 70.03 | +| house | 53.48 | 66.72 | +| sea | 73.98 | 83.55 | +| mirror | 79.03 | 85.56 | +| rug | 68.47 | 79.37 | +| field | 41.77 | 70.08 | +| armchair | 58.39 | 70.3 | +| seat | 64.77 | 89.15 | +| fence | 52.13 | 70.68 | +| desk | 59.29 | 79.34 | +| rock | 56.6 | 75.65 | +| wardrobe | 53.97 | 75.08 | +| lamp | 74.02 | 85.15 | +| bathtub | 89.15 | 91.75 | +| railing | 45.51 | 66.66 | +| cushion | 71.84 | 85.12 | +| base | 44.17 | 58.54 | +| box | 39.07 | 50.26 | +| column | 61.67 | 77.45 | +| signboard | 41.77 | 55.72 | +| chest of drawers | 41.69 | 63.98 | +| counter | 43.16 | 62.0 | +| sand | 57.42 | 84.63 | +| sink | 77.99 | 87.01 | +| skyscraper | 47.0 | 60.02 | +| fireplace | 73.86 | 95.97 | +| refrigerator | 84.7 | 95.22 | +| grandstand | 48.29 | 82.98 | +| path | 31.24 | 42.06 | +| stairs | 30.83 | 37.64 | +| runway | 69.44 | 89.86 | +| case | 68.02 | 83.73 | +| pool table | 94.9 | 97.7 | +| pillow | 70.45 | 81.08 | +| screen door | 76.3 | 77.82 | +| stairway | 44.27 | 65.81 | +| river | 12.11 | 27.57 | +| bridge | 52.28 | 60.33 | +| bookcase | 42.51 | 46.11 | +| blind | 43.61 | 51.75 | +| coffee table | 67.26 | 84.87 | +| toilet | 90.26 | 93.39 | +| flower | 42.47 | 54.86 | +| book | 53.37 | 80.08 | +| hill | 4.68 | 9.41 | +| bench | 54.62 | 61.48 | +| countertop | 64.29 | 81.55 | +| stove | 85.36 | 91.46 | +| palm | 54.17 | 78.35 | +| kitchen island | 54.14 | 77.08 | +| computer | 78.48 | 92.28 | +| swivel chair | 50.76 | 79.37 | +| boat | 75.37 | 90.36 | +| bar | 65.13 | 88.96 | +| arcade machine | 79.08 | 84.2 | +| hovel | 7.87 | 8.48 | +| bus | 92.29 | 97.26 | +| towel | 79.78 | 85.41 | +| light | 61.32 | 68.93 | +| truck | 47.88 | 61.9 | +| tower | 16.19 | 23.83 | +| chandelier | 71.98 | 90.04 | +| awning | 39.94 | 49.99 | +| streetlight | 38.18 | 52.67 | +| booth | 43.32 | 60.52 | +| television receiver | 79.98 | 89.08 | +| airplane | 88.14 | 95.56 | +| dirt track | 7.81 | 29.93 | +| apparel | 61.66 | 81.29 | +| pole | 23.91 | 31.5 | +| land | 4.52 | 6.02 | +| bannister | 20.68 | 27.9 | +| escalator | 64.91 | 84.64 | +| ottoman | 51.64 | 68.76 | +| bottle | 45.5 | 67.44 | +| buffet | 47.69 | 57.64 | +| poster | 32.88 | 43.07 | +| stage | 20.89 | 46.28 | +| van | 45.07 | 65.75 | +| ship | 23.75 | 24.44 | +| fountain | 39.54 | 40.2 | +| conveyer belt | 82.58 | 92.76 | +| canopy | 52.31 | 83.88 | +| washer | 79.68 | 84.37 | +| plaything | 43.01 | 71.57 | +| swimming pool | 51.17 | 72.96 | +| stool | 41.3 | 76.55 | +| barrel | 58.63 | 71.51 | +| basket | 43.89 | 61.09 | +| waterfall | 51.02 | 56.55 | +| tent | 96.5 | 97.86 | +| bag | 28.41 | 35.45 | +| minibike | 76.82 | 86.95 | +| cradle | 85.68 | 97.7 | +| oven | 62.87 | 75.51 | +| ball | 39.23 | 42.97 | +| food | 66.43 | 84.14 | +| step | 24.42 | 31.63 | +| tank | 58.39 | 69.58 | +| trade name | 26.74 | 36.26 | +| microwave | 90.36 | 95.89 | +| pot | 57.34 | 71.34 | +| animal | 60.43 | 61.82 | +| bicycle | 58.61 | 72.83 | +| lake | 51.59 | 63.75 | +| dishwasher | 70.22 | 84.02 | +| screen | 58.76 | 93.74 | +| blanket | 36.14 | 41.73 | +| sculpture | 73.78 | 88.2 | +| hood | 63.77 | 77.04 | +| sconce | 63.07 | 72.69 | +| vase | 47.8 | 65.32 | +| traffic light | 34.8 | 61.7 | +| tray | 20.23 | 23.35 | +| ashcan | 46.02 | 69.24 | +| fan | 69.12 | 86.91 | +| pier | 62.51 | 71.9 | +| crt screen | 2.05 | 3.54 | +| plate | 61.87 | 84.14 | +| monitor | 48.89 | 61.0 | +| bulletin board | 68.38 | 78.18 | +| shower | 1.13 | 1.35 | +| radiator | 67.94 | 82.19 | +| glass | 21.84 | 23.58 | +| clock | 53.85 | 63.67 | +| flag | 70.71 | 79.44 | ++---------------------+-------+-------+ +2024-06-16 16:05:51,655 - mmseg - INFO - Summary: +2024-06-16 16:05:51,656 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.28 | 57.28 | 70.22 | ++-------+-------+-------+ +2024-06-16 16:05:51,656 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 16:05:51,657 - mmseg - INFO - Iter(val) [250] aAcc: 0.8628, mIoU: 0.5728, mAcc: 0.7022, IoU.wall: 0.8251, IoU.building: 0.8535, IoU.sky: 0.9487, IoU.floor: 0.8531, IoU.tree: 0.7816, IoU.ceiling: 0.8773, IoU.road: 0.8573, IoU.bed : 0.9263, IoU.windowpane: 0.6711, IoU.grass: 0.6842, IoU.cabinet: 0.6630, IoU.sidewalk: 0.7173, IoU.person: 0.8585, IoU.earth: 0.3660, IoU.door: 0.6079, IoU.table: 0.7085, IoU.mountain: 0.6033, IoU.plant: 0.5648, IoU.curtain: 0.8095, IoU.chair: 0.6563, IoU.car: 0.8756, IoU.water: 0.6266, IoU.painting: 0.7765, IoU.sofa: 0.8073, IoU.shelf: 0.5379, IoU.house: 0.5348, IoU.sea: 0.7398, IoU.mirror: 0.7903, IoU.rug: 0.6847, IoU.field: 0.4177, IoU.armchair: 0.5839, IoU.seat: 0.6477, IoU.fence: 0.5213, IoU.desk: 0.5929, IoU.rock: 0.5660, IoU.wardrobe: 0.5397, IoU.lamp: 0.7402, IoU.bathtub: 0.8915, IoU.railing: 0.4551, IoU.cushion: 0.7184, IoU.base: 0.4417, IoU.box: 0.3907, IoU.column: 0.6167, IoU.signboard: 0.4177, IoU.chest of drawers: 0.4169, IoU.counter: 0.4316, IoU.sand: 0.5742, IoU.sink: 0.7799, IoU.skyscraper: 0.4700, IoU.fireplace: 0.7386, IoU.refrigerator: 0.8470, IoU.grandstand: 0.4829, IoU.path: 0.3124, IoU.stairs: 0.3083, IoU.runway: 0.6944, IoU.case: 0.6802, IoU.pool table: 0.9490, IoU.pillow: 0.7045, IoU.screen door: 0.7630, IoU.stairway: 0.4427, IoU.river: 0.1211, IoU.bridge: 0.5228, IoU.bookcase: 0.4251, IoU.blind: 0.4361, IoU.coffee table: 0.6726, IoU.toilet: 0.9026, IoU.flower: 0.4247, IoU.book: 0.5337, IoU.hill: 0.0468, IoU.bench: 0.5462, IoU.countertop: 0.6429, IoU.stove: 0.8536, IoU.palm: 0.5417, IoU.kitchen island: 0.5414, IoU.computer: 0.7848, IoU.swivel chair: 0.5076, IoU.boat: 0.7537, IoU.bar: 0.6513, IoU.arcade machine: 0.7908, IoU.hovel: 0.0787, IoU.bus: 0.9229, IoU.towel: 0.7978, IoU.light: 0.6132, IoU.truck: 0.4788, IoU.tower: 0.1619, IoU.chandelier: 0.7198, IoU.awning: 0.3994, IoU.streetlight: 0.3818, IoU.booth: 0.4332, IoU.television receiver: 0.7998, IoU.airplane: 0.8814, IoU.dirt track: 0.0781, IoU.apparel: 0.6166, IoU.pole: 0.2391, IoU.land: 0.0452, IoU.bannister: 0.2068, IoU.escalator: 0.6491, IoU.ottoman: 0.5164, IoU.bottle: 0.4550, IoU.buffet: 0.4769, IoU.poster: 0.3288, IoU.stage: 0.2089, IoU.van: 0.4507, IoU.ship: 0.2375, IoU.fountain: 0.3954, IoU.conveyer belt: 0.8258, IoU.canopy: 0.5231, IoU.washer: 0.7968, IoU.plaything: 0.4301, IoU.swimming pool: 0.5117, IoU.stool: 0.4130, IoU.barrel: 0.5863, IoU.basket: 0.4389, IoU.waterfall: 0.5102, IoU.tent: 0.9650, IoU.bag: 0.2841, IoU.minibike: 0.7682, IoU.cradle: 0.8568, IoU.oven: 0.6287, IoU.ball: 0.3923, IoU.food: 0.6643, IoU.step: 0.2442, IoU.tank: 0.5839, IoU.trade name: 0.2674, IoU.microwave: 0.9036, IoU.pot: 0.5734, IoU.animal: 0.6043, IoU.bicycle: 0.5861, IoU.lake: 0.5159, IoU.dishwasher: 0.7022, IoU.screen: 0.5876, IoU.blanket: 0.3614, IoU.sculpture: 0.7378, IoU.hood: 0.6377, IoU.sconce: 0.6307, IoU.vase: 0.4780, IoU.traffic light: 0.3480, IoU.tray: 0.2023, IoU.ashcan: 0.4602, IoU.fan: 0.6912, IoU.pier: 0.6251, IoU.crt screen: 0.0205, IoU.plate: 0.6187, IoU.monitor: 0.4889, IoU.bulletin board: 0.6838, IoU.shower: 0.0113, IoU.radiator: 0.6794, IoU.glass: 0.2184, IoU.clock: 0.5385, IoU.flag: 0.7071, Acc.wall: 0.8966, Acc.building: 0.9364, Acc.sky: 0.9752, Acc.floor: 0.9305, Acc.tree: 0.8953, Acc.ceiling: 0.9336, Acc.road: 0.9006, Acc.bed : 0.9699, Acc.windowpane: 0.8126, Acc.grass: 0.8449, Acc.cabinet: 0.7547, Acc.sidewalk: 0.8739, Acc.person: 0.9360, Acc.earth: 0.4616, Acc.door: 0.7896, Acc.table: 0.8227, Acc.mountain: 0.8330, Acc.plant: 0.6392, Acc.curtain: 0.8900, Acc.chair: 0.7512, Acc.car: 0.9417, Acc.water: 0.7657, Acc.painting: 0.9089, Acc.sofa: 0.9353, Acc.shelf: 0.7003, Acc.house: 0.6672, Acc.sea: 0.8355, Acc.mirror: 0.8556, Acc.rug: 0.7937, Acc.field: 0.7008, Acc.armchair: 0.7030, Acc.seat: 0.8915, Acc.fence: 0.7068, Acc.desk: 0.7934, Acc.rock: 0.7565, Acc.wardrobe: 0.7508, Acc.lamp: 0.8515, Acc.bathtub: 0.9175, Acc.railing: 0.6666, Acc.cushion: 0.8512, Acc.base: 0.5854, Acc.box: 0.5026, Acc.column: 0.7745, Acc.signboard: 0.5572, Acc.chest of drawers: 0.6398, Acc.counter: 0.6200, Acc.sand: 0.8463, Acc.sink: 0.8701, Acc.skyscraper: 0.6002, Acc.fireplace: 0.9597, Acc.refrigerator: 0.9522, Acc.grandstand: 0.8298, Acc.path: 0.4206, Acc.stairs: 0.3764, Acc.runway: 0.8986, Acc.case: 0.8373, Acc.pool table: 0.9770, Acc.pillow: 0.8108, Acc.screen door: 0.7782, Acc.stairway: 0.6581, Acc.river: 0.2757, Acc.bridge: 0.6033, Acc.bookcase: 0.4611, Acc.blind: 0.5175, Acc.coffee table: 0.8487, Acc.toilet: 0.9339, Acc.flower: 0.5486, Acc.book: 0.8008, Acc.hill: 0.0941, Acc.bench: 0.6148, Acc.countertop: 0.8155, Acc.stove: 0.9146, Acc.palm: 0.7835, Acc.kitchen island: 0.7708, Acc.computer: 0.9228, Acc.swivel chair: 0.7937, Acc.boat: 0.9036, Acc.bar: 0.8896, Acc.arcade machine: 0.8420, Acc.hovel: 0.0848, Acc.bus: 0.9726, Acc.towel: 0.8541, Acc.light: 0.6893, Acc.truck: 0.6190, Acc.tower: 0.2383, Acc.chandelier: 0.9004, Acc.awning: 0.4999, Acc.streetlight: 0.5267, Acc.booth: 0.6052, Acc.television receiver: 0.8908, Acc.airplane: 0.9556, Acc.dirt track: 0.2993, Acc.apparel: 0.8129, Acc.pole: 0.3150, Acc.land: 0.0602, Acc.bannister: 0.2790, Acc.escalator: 0.8464, Acc.ottoman: 0.6876, Acc.bottle: 0.6744, Acc.buffet: 0.5764, Acc.poster: 0.4307, Acc.stage: 0.4628, Acc.van: 0.6575, Acc.ship: 0.2444, Acc.fountain: 0.4020, Acc.conveyer belt: 0.9276, Acc.canopy: 0.8388, Acc.washer: 0.8437, Acc.plaything: 0.7157, Acc.swimming pool: 0.7296, Acc.stool: 0.7655, Acc.barrel: 0.7151, Acc.basket: 0.6109, Acc.waterfall: 0.5655, Acc.tent: 0.9786, Acc.bag: 0.3545, Acc.minibike: 0.8695, Acc.cradle: 0.9770, Acc.oven: 0.7551, Acc.ball: 0.4297, Acc.food: 0.8414, Acc.step: 0.3163, Acc.tank: 0.6958, Acc.trade name: 0.3626, Acc.microwave: 0.9589, Acc.pot: 0.7134, Acc.animal: 0.6182, Acc.bicycle: 0.7283, Acc.lake: 0.6375, Acc.dishwasher: 0.8402, Acc.screen: 0.9374, Acc.blanket: 0.4173, Acc.sculpture: 0.8820, Acc.hood: 0.7704, Acc.sconce: 0.7269, Acc.vase: 0.6532, Acc.traffic light: 0.6170, Acc.tray: 0.2335, Acc.ashcan: 0.6924, Acc.fan: 0.8691, Acc.pier: 0.7190, Acc.crt screen: 0.0354, Acc.plate: 0.8414, Acc.monitor: 0.6100, Acc.bulletin board: 0.7818, Acc.shower: 0.0135, Acc.radiator: 0.8219, Acc.glass: 0.2358, Acc.clock: 0.6367, Acc.flag: 0.7944 +2024-06-16 16:07:13,245 - mmseg - INFO - Iter [38050/80000] lr: 2.098e-05, eta: 20:36:22, time: 3.556, data_time: 1.942, memory: 71384, decode.loss_ce: 0.2049, decode.acc_seg: 91.3654, aux.loss_ce: 0.0850, aux.acc_seg: 91.0243, loss: 0.2899 +2024-06-16 16:08:34,491 - mmseg - INFO - Iter [38100/80000] lr: 2.095e-05, eta: 20:34:46, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2026, decode.acc_seg: 91.5639, aux.loss_ce: 0.0839, aux.acc_seg: 91.2592, loss: 0.2864 +2024-06-16 16:09:55,492 - mmseg - INFO - Iter [38150/80000] lr: 2.093e-05, eta: 20:33:09, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2088, decode.acc_seg: 91.5657, aux.loss_ce: 0.0864, aux.acc_seg: 91.1757, loss: 0.2952 +2024-06-16 16:11:16,648 - mmseg - INFO - Iter [38200/80000] lr: 2.090e-05, eta: 20:31:33, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2427, decode.acc_seg: 90.3577, aux.loss_ce: 0.1000, aux.acc_seg: 90.0255, loss: 0.3427 +2024-06-16 16:12:37,584 - mmseg - INFO - Iter [38250/80000] lr: 2.088e-05, eta: 20:29:56, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2042, decode.acc_seg: 91.5680, aux.loss_ce: 0.0842, aux.acc_seg: 91.3042, loss: 0.2884 +2024-06-16 16:13:58,611 - mmseg - INFO - Iter [38300/80000] lr: 2.085e-05, eta: 20:28:20, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2035, decode.acc_seg: 91.4607, aux.loss_ce: 0.0847, aux.acc_seg: 91.1621, loss: 0.2882 +2024-06-16 16:15:19,653 - mmseg - INFO - Iter [38350/80000] lr: 2.083e-05, eta: 20:26:44, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1908, decode.acc_seg: 91.9367, aux.loss_ce: 0.0794, aux.acc_seg: 91.5701, loss: 0.2702 +2024-06-16 16:16:40,833 - mmseg - INFO - Iter [38400/80000] lr: 2.080e-05, eta: 20:25:08, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1931, decode.acc_seg: 91.9869, aux.loss_ce: 0.0798, aux.acc_seg: 91.7134, loss: 0.2729 +2024-06-16 16:18:01,891 - mmseg - INFO - Iter [38450/80000] lr: 2.078e-05, eta: 20:23:31, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1934, decode.acc_seg: 91.6861, aux.loss_ce: 0.0808, aux.acc_seg: 91.3341, loss: 0.2742 +2024-06-16 16:19:22,869 - mmseg - INFO - Iter [38500/80000] lr: 2.075e-05, eta: 20:21:55, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1926, decode.acc_seg: 91.7544, aux.loss_ce: 0.0801, aux.acc_seg: 91.4723, loss: 0.2727 +2024-06-16 16:20:44,050 - mmseg - INFO - Iter [38550/80000] lr: 2.073e-05, eta: 20:20:19, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1911, decode.acc_seg: 92.1395, aux.loss_ce: 0.0796, aux.acc_seg: 91.8312, loss: 0.2707 +2024-06-16 16:22:05,089 - mmseg - INFO - Iter [38600/80000] lr: 2.070e-05, eta: 20:18:43, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1898, decode.acc_seg: 92.0505, aux.loss_ce: 0.0791, aux.acc_seg: 91.7524, loss: 0.2689 +2024-06-16 16:23:26,283 - mmseg - INFO - Iter [38650/80000] lr: 2.068e-05, eta: 20:17:07, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1935, decode.acc_seg: 91.7072, aux.loss_ce: 0.0804, aux.acc_seg: 91.4402, loss: 0.2739 +2024-06-16 16:24:47,191 - mmseg - INFO - Iter [38700/80000] lr: 2.065e-05, eta: 20:15:31, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1964, decode.acc_seg: 91.8559, aux.loss_ce: 0.0815, aux.acc_seg: 91.4813, loss: 0.2779 +2024-06-16 16:26:08,280 - mmseg - INFO - Iter [38750/80000] lr: 2.063e-05, eta: 20:13:55, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1931, decode.acc_seg: 91.6469, aux.loss_ce: 0.0802, aux.acc_seg: 91.2592, loss: 0.2734 +2024-06-16 16:27:29,303 - mmseg - INFO - Iter [38800/80000] lr: 2.060e-05, eta: 20:12:19, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2068, decode.acc_seg: 91.2976, aux.loss_ce: 0.0857, aux.acc_seg: 91.0146, loss: 0.2925 +2024-06-16 16:28:50,313 - mmseg - INFO - Iter [38850/80000] lr: 2.058e-05, eta: 20:10:43, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1949, decode.acc_seg: 91.9069, aux.loss_ce: 0.0808, aux.acc_seg: 91.5314, loss: 0.2757 +2024-06-16 16:30:11,423 - mmseg - INFO - Iter [38900/80000] lr: 2.055e-05, eta: 20:09:07, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1949, decode.acc_seg: 91.8222, aux.loss_ce: 0.0807, aux.acc_seg: 91.4872, loss: 0.2756 +2024-06-16 16:31:32,520 - mmseg - INFO - Iter [38950/80000] lr: 2.053e-05, eta: 20:07:31, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1912, decode.acc_seg: 91.9229, aux.loss_ce: 0.0794, aux.acc_seg: 91.5870, loss: 0.2707 +2024-06-16 16:32:53,605 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 16:32:53,605 - mmseg - INFO - Iter [39000/80000] lr: 2.050e-05, eta: 20:05:55, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1994, decode.acc_seg: 91.6280, aux.loss_ce: 0.0831, aux.acc_seg: 91.3550, loss: 0.2824 +2024-06-16 16:34:32,340 - mmseg - INFO - per class results: +2024-06-16 16:34:32,346 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.21 | 90.57 | +| building | 85.02 | 93.02 | +| sky | 94.75 | 97.14 | +| floor | 85.2 | 91.21 | +| tree | 77.59 | 91.42 | +| ceiling | 86.97 | 94.16 | +| road | 85.58 | 92.27 | +| bed | 92.55 | 97.21 | +| windowpane | 67.39 | 79.0 | +| grass | 69.37 | 82.67 | +| cabinet | 65.47 | 72.04 | +| sidewalk | 70.4 | 84.24 | +| person | 85.96 | 94.55 | +| earth | 37.21 | 48.11 | +| door | 59.37 | 72.82 | +| table | 69.85 | 80.86 | +| mountain | 57.33 | 64.21 | +| plant | 56.96 | 70.21 | +| curtain | 79.91 | 90.64 | +| chair | 69.33 | 80.57 | +| car | 87.61 | 92.92 | +| water | 62.56 | 75.66 | +| painting | 77.53 | 89.59 | +| sofa | 83.96 | 93.05 | +| shelf | 49.95 | 67.93 | +| house | 50.19 | 62.66 | +| sea | 71.95 | 83.89 | +| mirror | 78.64 | 85.93 | +| rug | 69.58 | 82.18 | +| field | 36.42 | 57.31 | +| armchair | 63.08 | 78.2 | +| seat | 65.87 | 88.27 | +| fence | 45.29 | 59.62 | +| desk | 56.18 | 79.88 | +| rock | 57.08 | 83.32 | +| wardrobe | 53.97 | 75.12 | +| lamp | 75.09 | 83.48 | +| bathtub | 88.7 | 91.61 | +| railing | 43.8 | 63.08 | +| cushion | 69.52 | 84.29 | +| base | 35.51 | 50.04 | +| box | 38.13 | 45.64 | +| column | 54.77 | 67.27 | +| signboard | 39.36 | 53.22 | +| chest of drawers | 46.81 | 74.81 | +| counter | 40.85 | 53.71 | +| sand | 60.12 | 86.42 | +| sink | 81.25 | 87.43 | +| skyscraper | 49.41 | 62.43 | +| fireplace | 76.32 | 93.49 | +| refrigerator | 85.09 | 94.37 | +| grandstand | 51.79 | 82.88 | +| path | 26.26 | 32.12 | +| stairs | 33.8 | 40.04 | +| runway | 71.95 | 96.44 | +| case | 65.91 | 83.92 | +| pool table | 94.52 | 97.82 | +| pillow | 65.21 | 73.64 | +| screen door | 72.89 | 74.57 | +| stairway | 47.42 | 59.55 | +| river | 12.71 | 29.79 | +| bridge | 71.72 | 87.09 | +| bookcase | 43.77 | 60.76 | +| blind | 45.8 | 54.41 | +| coffee table | 64.5 | 86.52 | +| toilet | 90.96 | 94.23 | +| flower | 45.54 | 58.9 | +| book | 52.56 | 80.26 | +| hill | 7.3 | 29.17 | +| bench | 50.7 | 55.69 | +| countertop | 64.79 | 80.19 | +| stove | 85.52 | 93.75 | +| palm | 56.6 | 78.88 | +| kitchen island | 56.73 | 89.38 | +| computer | 79.97 | 90.37 | +| swivel chair | 52.44 | 75.52 | +| boat | 59.22 | 91.9 | +| bar | 64.49 | 83.63 | +| arcade machine | 79.28 | 85.2 | +| hovel | 16.68 | 17.92 | +| bus | 93.11 | 96.96 | +| towel | 76.69 | 88.4 | +| light | 60.99 | 69.41 | +| truck | 47.22 | 53.27 | +| tower | 29.7 | 51.58 | +| chandelier | 74.74 | 86.37 | +| awning | 43.47 | 58.11 | +| streetlight | 37.95 | 53.05 | +| booth | 46.17 | 78.8 | +| television receiver | 81.73 | 86.04 | +| airplane | 89.07 | 95.61 | +| dirt track | 14.2 | 26.43 | +| apparel | 60.98 | 81.55 | +| pole | 28.54 | 40.57 | +| land | 1.87 | 2.75 | +| bannister | 21.44 | 26.01 | +| escalator | 65.97 | 85.66 | +| ottoman | 51.63 | 70.67 | +| bottle | 44.09 | 61.03 | +| buffet | 46.95 | 55.25 | +| poster | 37.45 | 47.31 | +| stage | 28.99 | 46.9 | +| van | 45.34 | 69.45 | +| ship | 89.55 | 94.8 | +| fountain | 41.36 | 42.18 | +| conveyer belt | 78.26 | 95.26 | +| canopy | 55.57 | 80.07 | +| washer | 88.12 | 95.04 | +| plaything | 45.26 | 74.08 | +| swimming pool | 51.61 | 74.1 | +| stool | 55.8 | 69.5 | +| barrel | 57.92 | 74.22 | +| basket | 39.19 | 60.78 | +| waterfall | 49.38 | 63.04 | +| tent | 88.82 | 97.46 | +| bag | 27.7 | 31.59 | +| minibike | 77.15 | 88.62 | +| cradle | 85.25 | 96.76 | +| oven | 55.22 | 66.61 | +| ball | 55.25 | 77.59 | +| food | 66.22 | 81.29 | +| step | 22.38 | 34.15 | +| tank | 63.02 | 76.17 | +| trade name | 22.73 | 27.85 | +| microwave | 84.48 | 96.92 | +| pot | 57.94 | 69.45 | +| animal | 63.27 | 64.62 | +| bicycle | 59.4 | 76.73 | +| lake | 47.25 | 63.74 | +| dishwasher | 71.83 | 84.24 | +| screen | 61.44 | 92.66 | +| blanket | 39.04 | 48.41 | +| sculpture | 78.3 | 87.93 | +| hood | 61.82 | 74.33 | +| sconce | 61.7 | 71.75 | +| vase | 47.37 | 63.11 | +| traffic light | 34.75 | 62.72 | +| tray | 22.1 | 31.12 | +| ashcan | 49.8 | 65.0 | +| fan | 67.31 | 82.39 | +| pier | 61.55 | 70.74 | +| crt screen | 2.46 | 3.87 | +| plate | 62.48 | 75.14 | +| monitor | 62.12 | 75.24 | +| bulletin board | 56.02 | 62.98 | +| shower | 0.81 | 0.86 | +| radiator | 67.42 | 81.12 | +| glass | 21.37 | 22.83 | +| clock | 51.38 | 63.18 | +| flag | 69.51 | 77.87 | ++---------------------+-------+-------+ +2024-06-16 16:34:32,346 - mmseg - INFO - Summary: +2024-06-16 16:34:32,346 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 86.12 | 58.07 | 71.2 | ++-------+-------+------+ +2024-06-16 16:34:32,347 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 16:34:32,347 - mmseg - INFO - Iter(val) [250] aAcc: 0.8612, mIoU: 0.5807, mAcc: 0.7120, IoU.wall: 0.8221, IoU.building: 0.8502, IoU.sky: 0.9475, IoU.floor: 0.8520, IoU.tree: 0.7759, IoU.ceiling: 0.8697, IoU.road: 0.8558, IoU.bed : 0.9255, IoU.windowpane: 0.6739, IoU.grass: 0.6937, IoU.cabinet: 0.6547, IoU.sidewalk: 0.7040, IoU.person: 0.8596, IoU.earth: 0.3721, IoU.door: 0.5937, IoU.table: 0.6985, IoU.mountain: 0.5733, IoU.plant: 0.5696, IoU.curtain: 0.7991, IoU.chair: 0.6933, IoU.car: 0.8761, IoU.water: 0.6256, IoU.painting: 0.7753, IoU.sofa: 0.8396, IoU.shelf: 0.4995, IoU.house: 0.5019, IoU.sea: 0.7195, IoU.mirror: 0.7864, IoU.rug: 0.6958, IoU.field: 0.3642, IoU.armchair: 0.6308, IoU.seat: 0.6587, IoU.fence: 0.4529, IoU.desk: 0.5618, IoU.rock: 0.5708, IoU.wardrobe: 0.5397, IoU.lamp: 0.7509, IoU.bathtub: 0.8870, IoU.railing: 0.4380, IoU.cushion: 0.6952, IoU.base: 0.3551, IoU.box: 0.3813, IoU.column: 0.5477, IoU.signboard: 0.3936, IoU.chest of drawers: 0.4681, IoU.counter: 0.4085, IoU.sand: 0.6012, IoU.sink: 0.8125, IoU.skyscraper: 0.4941, IoU.fireplace: 0.7632, IoU.refrigerator: 0.8509, IoU.grandstand: 0.5179, IoU.path: 0.2626, IoU.stairs: 0.3380, IoU.runway: 0.7195, IoU.case: 0.6591, IoU.pool table: 0.9452, IoU.pillow: 0.6521, IoU.screen door: 0.7289, IoU.stairway: 0.4742, IoU.river: 0.1271, IoU.bridge: 0.7172, IoU.bookcase: 0.4377, IoU.blind: 0.4580, IoU.coffee table: 0.6450, IoU.toilet: 0.9096, IoU.flower: 0.4554, IoU.book: 0.5256, IoU.hill: 0.0730, IoU.bench: 0.5070, IoU.countertop: 0.6479, IoU.stove: 0.8552, IoU.palm: 0.5660, IoU.kitchen island: 0.5673, IoU.computer: 0.7997, IoU.swivel chair: 0.5244, IoU.boat: 0.5922, IoU.bar: 0.6449, IoU.arcade machine: 0.7928, IoU.hovel: 0.1668, IoU.bus: 0.9311, IoU.towel: 0.7669, IoU.light: 0.6099, IoU.truck: 0.4722, IoU.tower: 0.2970, IoU.chandelier: 0.7474, IoU.awning: 0.4347, IoU.streetlight: 0.3795, IoU.booth: 0.4617, IoU.television receiver: 0.8173, IoU.airplane: 0.8907, IoU.dirt track: 0.1420, IoU.apparel: 0.6098, IoU.pole: 0.2854, IoU.land: 0.0187, IoU.bannister: 0.2144, IoU.escalator: 0.6597, IoU.ottoman: 0.5163, IoU.bottle: 0.4409, IoU.buffet: 0.4695, IoU.poster: 0.3745, IoU.stage: 0.2899, IoU.van: 0.4534, IoU.ship: 0.8955, IoU.fountain: 0.4136, IoU.conveyer belt: 0.7826, IoU.canopy: 0.5557, IoU.washer: 0.8812, IoU.plaything: 0.4526, IoU.swimming pool: 0.5161, IoU.stool: 0.5580, IoU.barrel: 0.5792, IoU.basket: 0.3919, IoU.waterfall: 0.4938, IoU.tent: 0.8882, IoU.bag: 0.2770, IoU.minibike: 0.7715, IoU.cradle: 0.8525, IoU.oven: 0.5522, IoU.ball: 0.5525, IoU.food: 0.6622, IoU.step: 0.2238, IoU.tank: 0.6302, IoU.trade name: 0.2273, IoU.microwave: 0.8448, IoU.pot: 0.5794, IoU.animal: 0.6327, IoU.bicycle: 0.5940, IoU.lake: 0.4725, IoU.dishwasher: 0.7183, IoU.screen: 0.6144, IoU.blanket: 0.3904, IoU.sculpture: 0.7830, IoU.hood: 0.6182, IoU.sconce: 0.6170, IoU.vase: 0.4737, IoU.traffic light: 0.3475, IoU.tray: 0.2210, IoU.ashcan: 0.4980, IoU.fan: 0.6731, IoU.pier: 0.6155, IoU.crt screen: 0.0246, IoU.plate: 0.6248, IoU.monitor: 0.6212, IoU.bulletin board: 0.5602, IoU.shower: 0.0081, IoU.radiator: 0.6742, IoU.glass: 0.2137, IoU.clock: 0.5138, IoU.flag: 0.6951, Acc.wall: 0.9057, Acc.building: 0.9302, Acc.sky: 0.9714, Acc.floor: 0.9121, Acc.tree: 0.9142, Acc.ceiling: 0.9416, Acc.road: 0.9227, Acc.bed : 0.9721, Acc.windowpane: 0.7900, Acc.grass: 0.8267, Acc.cabinet: 0.7204, Acc.sidewalk: 0.8424, Acc.person: 0.9455, Acc.earth: 0.4811, Acc.door: 0.7282, Acc.table: 0.8086, Acc.mountain: 0.6421, Acc.plant: 0.7021, Acc.curtain: 0.9064, Acc.chair: 0.8057, Acc.car: 0.9292, Acc.water: 0.7566, Acc.painting: 0.8959, Acc.sofa: 0.9305, Acc.shelf: 0.6793, Acc.house: 0.6266, Acc.sea: 0.8389, Acc.mirror: 0.8593, Acc.rug: 0.8218, Acc.field: 0.5731, Acc.armchair: 0.7820, Acc.seat: 0.8827, Acc.fence: 0.5962, Acc.desk: 0.7988, Acc.rock: 0.8332, Acc.wardrobe: 0.7512, Acc.lamp: 0.8348, Acc.bathtub: 0.9161, Acc.railing: 0.6308, Acc.cushion: 0.8429, Acc.base: 0.5004, Acc.box: 0.4564, Acc.column: 0.6727, Acc.signboard: 0.5322, Acc.chest of drawers: 0.7481, Acc.counter: 0.5371, Acc.sand: 0.8642, Acc.sink: 0.8743, Acc.skyscraper: 0.6243, Acc.fireplace: 0.9349, Acc.refrigerator: 0.9437, Acc.grandstand: 0.8288, Acc.path: 0.3212, Acc.stairs: 0.4004, Acc.runway: 0.9644, Acc.case: 0.8392, Acc.pool table: 0.9782, Acc.pillow: 0.7364, Acc.screen door: 0.7457, Acc.stairway: 0.5955, Acc.river: 0.2979, Acc.bridge: 0.8709, Acc.bookcase: 0.6076, Acc.blind: 0.5441, Acc.coffee table: 0.8652, Acc.toilet: 0.9423, Acc.flower: 0.5890, Acc.book: 0.8026, Acc.hill: 0.2917, Acc.bench: 0.5569, Acc.countertop: 0.8019, Acc.stove: 0.9375, Acc.palm: 0.7888, Acc.kitchen island: 0.8938, Acc.computer: 0.9037, Acc.swivel chair: 0.7552, Acc.boat: 0.9190, Acc.bar: 0.8363, Acc.arcade machine: 0.8520, Acc.hovel: 0.1792, Acc.bus: 0.9696, Acc.towel: 0.8840, Acc.light: 0.6941, Acc.truck: 0.5327, Acc.tower: 0.5158, Acc.chandelier: 0.8637, Acc.awning: 0.5811, Acc.streetlight: 0.5305, Acc.booth: 0.7880, Acc.television receiver: 0.8604, Acc.airplane: 0.9561, Acc.dirt track: 0.2643, Acc.apparel: 0.8155, Acc.pole: 0.4057, Acc.land: 0.0275, Acc.bannister: 0.2601, Acc.escalator: 0.8566, Acc.ottoman: 0.7067, Acc.bottle: 0.6103, Acc.buffet: 0.5525, Acc.poster: 0.4731, Acc.stage: 0.4690, Acc.van: 0.6945, Acc.ship: 0.9480, Acc.fountain: 0.4218, Acc.conveyer belt: 0.9526, Acc.canopy: 0.8007, Acc.washer: 0.9504, Acc.plaything: 0.7408, Acc.swimming pool: 0.7410, Acc.stool: 0.6950, Acc.barrel: 0.7422, Acc.basket: 0.6078, Acc.waterfall: 0.6304, Acc.tent: 0.9746, Acc.bag: 0.3159, Acc.minibike: 0.8862, Acc.cradle: 0.9676, Acc.oven: 0.6661, Acc.ball: 0.7759, Acc.food: 0.8129, Acc.step: 0.3415, Acc.tank: 0.7617, Acc.trade name: 0.2785, Acc.microwave: 0.9692, Acc.pot: 0.6945, Acc.animal: 0.6462, Acc.bicycle: 0.7673, Acc.lake: 0.6374, Acc.dishwasher: 0.8424, Acc.screen: 0.9266, Acc.blanket: 0.4841, Acc.sculpture: 0.8793, Acc.hood: 0.7433, Acc.sconce: 0.7175, Acc.vase: 0.6311, Acc.traffic light: 0.6272, Acc.tray: 0.3112, Acc.ashcan: 0.6500, Acc.fan: 0.8239, Acc.pier: 0.7074, Acc.crt screen: 0.0387, Acc.plate: 0.7514, Acc.monitor: 0.7524, Acc.bulletin board: 0.6298, Acc.shower: 0.0086, Acc.radiator: 0.8112, Acc.glass: 0.2283, Acc.clock: 0.6318, Acc.flag: 0.7787 +2024-06-16 16:35:53,906 - mmseg - INFO - Iter [39050/80000] lr: 2.048e-05, eta: 20:06:04, time: 3.606, data_time: 1.992, memory: 71384, decode.loss_ce: 0.2130, decode.acc_seg: 91.1647, aux.loss_ce: 0.0886, aux.acc_seg: 90.8643, loss: 0.3017 +2024-06-16 16:37:14,956 - mmseg - INFO - Iter [39100/80000] lr: 2.045e-05, eta: 20:04:28, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2063, decode.acc_seg: 91.2877, aux.loss_ce: 0.0855, aux.acc_seg: 90.9887, loss: 0.2918 +2024-06-16 16:38:35,917 - mmseg - INFO - Iter [39150/80000] lr: 2.043e-05, eta: 20:02:52, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1849, decode.acc_seg: 92.1456, aux.loss_ce: 0.0773, aux.acc_seg: 91.8273, loss: 0.2622 +2024-06-16 16:39:59,606 - mmseg - INFO - Iter [39200/80000] lr: 2.040e-05, eta: 20:01:18, time: 1.674, data_time: 0.059, memory: 71384, decode.loss_ce: 0.1982, decode.acc_seg: 91.4042, aux.loss_ce: 0.0826, aux.acc_seg: 91.0030, loss: 0.2808 +2024-06-16 16:41:20,689 - mmseg - INFO - Iter [39250/80000] lr: 2.038e-05, eta: 19:59:43, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2045, decode.acc_seg: 91.1211, aux.loss_ce: 0.0849, aux.acc_seg: 90.8199, loss: 0.2894 +2024-06-16 16:42:41,645 - mmseg - INFO - Iter [39300/80000] lr: 2.035e-05, eta: 19:58:07, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1901, decode.acc_seg: 91.9335, aux.loss_ce: 0.0791, aux.acc_seg: 91.6077, loss: 0.2692 +2024-06-16 16:44:02,828 - mmseg - INFO - Iter [39350/80000] lr: 2.033e-05, eta: 19:56:31, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2026, decode.acc_seg: 91.4445, aux.loss_ce: 0.0841, aux.acc_seg: 91.1709, loss: 0.2867 +2024-06-16 16:45:23,996 - mmseg - INFO - Iter [39400/80000] lr: 2.030e-05, eta: 19:54:55, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1960, decode.acc_seg: 91.7784, aux.loss_ce: 0.0813, aux.acc_seg: 91.4078, loss: 0.2773 +2024-06-16 16:46:45,136 - mmseg - INFO - Iter [39450/80000] lr: 2.028e-05, eta: 19:53:20, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1804, decode.acc_seg: 92.3853, aux.loss_ce: 0.0750, aux.acc_seg: 92.1027, loss: 0.2554 +2024-06-16 16:48:06,147 - mmseg - INFO - Iter [39500/80000] lr: 2.025e-05, eta: 19:51:44, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1860, decode.acc_seg: 92.1644, aux.loss_ce: 0.0770, aux.acc_seg: 91.8791, loss: 0.2629 +2024-06-16 16:49:27,375 - mmseg - INFO - Iter [39550/80000] lr: 2.023e-05, eta: 19:50:08, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1952, decode.acc_seg: 91.9505, aux.loss_ce: 0.0810, aux.acc_seg: 91.6234, loss: 0.2762 +2024-06-16 16:50:48,434 - mmseg - INFO - Iter [39600/80000] lr: 2.020e-05, eta: 19:48:33, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2049, decode.acc_seg: 91.3337, aux.loss_ce: 0.0843, aux.acc_seg: 91.0382, loss: 0.2892 +2024-06-16 16:52:09,520 - mmseg - INFO - Iter [39650/80000] lr: 2.018e-05, eta: 19:46:57, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1909, decode.acc_seg: 92.0243, aux.loss_ce: 0.0799, aux.acc_seg: 91.5801, loss: 0.2709 +2024-06-16 16:53:30,721 - mmseg - INFO - Iter [39700/80000] lr: 2.015e-05, eta: 19:45:22, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1804, decode.acc_seg: 92.2494, aux.loss_ce: 0.0752, aux.acc_seg: 91.8786, loss: 0.2556 +2024-06-16 16:54:51,735 - mmseg - INFO - Iter [39750/80000] lr: 2.013e-05, eta: 19:43:46, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1884, decode.acc_seg: 91.8499, aux.loss_ce: 0.0781, aux.acc_seg: 91.5232, loss: 0.2664 +2024-06-16 16:56:12,887 - mmseg - INFO - Iter [39800/80000] lr: 2.010e-05, eta: 19:42:11, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1880, decode.acc_seg: 91.9998, aux.loss_ce: 0.0786, aux.acc_seg: 91.6674, loss: 0.2665 +2024-06-16 16:57:33,927 - mmseg - INFO - Iter [39850/80000] lr: 2.008e-05, eta: 19:40:35, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1839, decode.acc_seg: 92.0945, aux.loss_ce: 0.0772, aux.acc_seg: 91.6553, loss: 0.2611 +2024-06-16 16:58:55,004 - mmseg - INFO - Iter [39900/80000] lr: 2.005e-05, eta: 19:39:00, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1796, decode.acc_seg: 92.1355, aux.loss_ce: 0.0744, aux.acc_seg: 91.9589, loss: 0.2539 +2024-06-16 17:00:16,114 - mmseg - INFO - Iter [39950/80000] lr: 2.003e-05, eta: 19:37:25, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1933, decode.acc_seg: 91.8558, aux.loss_ce: 0.0809, aux.acc_seg: 91.5044, loss: 0.2742 +2024-06-16 17:01:37,144 - mmseg - INFO - Saving checkpoint at 40000 iterations +2024-06-16 17:03:01,538 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 17:03:01,538 - mmseg - INFO - Iter [40000/80000] lr: 2.000e-05, eta: 19:37:14, time: 3.308, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1937, decode.acc_seg: 92.0597, aux.loss_ce: 0.0803, aux.acc_seg: 91.7458, loss: 0.2740 +2024-06-16 17:04:37,460 - mmseg - INFO - per class results: +2024-06-16 17:04:37,466 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.33 | 89.27 | +| building | 85.41 | 93.5 | +| sky | 94.88 | 97.63 | +| floor | 84.8 | 90.17 | +| tree | 77.33 | 88.36 | +| ceiling | 87.31 | 95.06 | +| road | 85.95 | 92.35 | +| bed | 93.03 | 97.64 | +| windowpane | 67.32 | 79.87 | +| grass | 68.0 | 79.98 | +| cabinet | 65.74 | 76.29 | +| sidewalk | 70.32 | 82.29 | +| person | 86.09 | 94.47 | +| earth | 38.31 | 51.78 | +| door | 61.08 | 77.67 | +| table | 69.45 | 81.26 | +| mountain | 61.24 | 88.07 | +| plant | 58.05 | 66.94 | +| curtain | 79.34 | 89.93 | +| chair | 68.84 | 83.35 | +| car | 87.82 | 93.89 | +| water | 61.95 | 76.23 | +| painting | 76.9 | 91.25 | +| sofa | 83.47 | 89.75 | +| shelf | 51.91 | 67.92 | +| house | 51.38 | 62.77 | +| sea | 72.45 | 84.83 | +| mirror | 79.81 | 88.74 | +| rug | 70.29 | 84.73 | +| field | 37.18 | 57.95 | +| armchair | 59.25 | 79.31 | +| seat | 69.58 | 89.06 | +| fence | 53.61 | 68.53 | +| desk | 56.16 | 80.45 | +| rock | 60.93 | 84.74 | +| wardrobe | 54.87 | 74.23 | +| lamp | 75.44 | 84.92 | +| bathtub | 88.15 | 91.15 | +| railing | 46.21 | 63.75 | +| cushion | 71.47 | 82.86 | +| base | 38.58 | 61.29 | +| box | 39.98 | 52.01 | +| column | 53.47 | 67.24 | +| signboard | 41.92 | 56.51 | +| chest of drawers | 43.1 | 65.88 | +| counter | 38.9 | 50.18 | +| sand | 60.97 | 84.99 | +| sink | 82.56 | 89.34 | +| skyscraper | 47.75 | 62.54 | +| fireplace | 73.47 | 94.32 | +| refrigerator | 83.91 | 93.73 | +| grandstand | 50.5 | 81.73 | +| path | 30.65 | 43.68 | +| stairs | 33.33 | 37.43 | +| runway | 73.74 | 98.54 | +| case | 66.5 | 83.77 | +| pool table | 94.77 | 97.69 | +| pillow | 68.11 | 78.47 | +| screen door | 86.66 | 90.26 | +| stairway | 47.82 | 65.25 | +| river | 8.55 | 19.78 | +| bridge | 68.42 | 89.63 | +| bookcase | 45.46 | 66.15 | +| blind | 46.25 | 59.37 | +| coffee table | 62.23 | 85.92 | +| toilet | 91.73 | 95.12 | +| flower | 46.31 | 60.0 | +| book | 52.64 | 74.03 | +| hill | 5.67 | 6.47 | +| bench | 59.56 | 70.89 | +| countertop | 63.35 | 79.77 | +| stove | 87.04 | 92.41 | +| palm | 53.68 | 83.49 | +| kitchen island | 50.57 | 61.88 | +| computer | 77.85 | 92.77 | +| swivel chair | 52.3 | 78.39 | +| boat | 80.19 | 84.99 | +| bar | 60.78 | 87.08 | +| arcade machine | 76.0 | 80.24 | +| hovel | 13.64 | 14.6 | +| bus | 93.81 | 96.51 | +| towel | 81.17 | 88.05 | +| light | 60.02 | 67.94 | +| truck | 47.58 | 56.88 | +| tower | 24.26 | 41.63 | +| chandelier | 74.18 | 87.68 | +| awning | 39.38 | 48.12 | +| streetlight | 36.97 | 46.24 | +| booth | 33.84 | 35.97 | +| television receiver | 81.44 | 87.32 | +| airplane | 83.01 | 89.92 | +| dirt track | 11.99 | 40.16 | +| apparel | 61.12 | 88.49 | +| pole | 27.93 | 40.64 | +| land | 0.0 | 0.0 | +| bannister | 21.09 | 28.15 | +| escalator | 66.4 | 82.63 | +| ottoman | 46.86 | 59.1 | +| bottle | 45.64 | 65.93 | +| buffet | 47.27 | 57.25 | +| poster | 38.42 | 45.84 | +| stage | 27.84 | 44.47 | +| van | 45.09 | 71.55 | +| ship | 51.13 | 51.98 | +| fountain | 37.41 | 37.75 | +| conveyer belt | 81.22 | 95.38 | +| canopy | 49.87 | 74.77 | +| washer | 88.36 | 94.08 | +| plaything | 39.01 | 69.64 | +| swimming pool | 52.88 | 82.14 | +| stool | 50.88 | 71.01 | +| barrel | 73.55 | 90.44 | +| basket | 39.23 | 56.95 | +| waterfall | 48.8 | 56.97 | +| tent | 95.51 | 98.27 | +| bag | 26.05 | 30.56 | +| minibike | 74.42 | 91.8 | +| cradle | 90.11 | 97.36 | +| oven | 50.04 | 59.49 | +| ball | 54.67 | 71.71 | +| food | 69.94 | 82.93 | +| step | 23.03 | 27.58 | +| tank | 56.06 | 69.61 | +| trade name | 27.47 | 32.4 | +| microwave | 85.76 | 96.46 | +| pot | 58.1 | 68.51 | +| animal | 64.68 | 66.12 | +| bicycle | 58.64 | 74.4 | +| lake | 48.28 | 75.7 | +| dishwasher | 70.91 | 84.38 | +| screen | 59.08 | 95.97 | +| blanket | 37.55 | 44.29 | +| sculpture | 72.33 | 89.09 | +| hood | 63.96 | 77.58 | +| sconce | 62.99 | 72.29 | +| vase | 47.42 | 65.09 | +| traffic light | 35.85 | 66.41 | +| tray | 24.18 | 34.45 | +| ashcan | 49.11 | 66.86 | +| fan | 70.03 | 79.54 | +| pier | 48.19 | 57.08 | +| crt screen | 3.93 | 4.69 | +| plate | 63.95 | 75.14 | +| monitor | 67.85 | 76.96 | +| bulletin board | 60.34 | 63.75 | +| shower | 3.16 | 3.42 | +| radiator | 69.58 | 82.86 | +| glass | 20.87 | 22.18 | +| clock | 48.64 | 57.19 | +| flag | 71.24 | 78.93 | ++---------------------+-------+-------+ +2024-06-16 17:04:37,467 - mmseg - INFO - Summary: +2024-06-16 17:04:37,467 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.32 | 57.96 | 70.82 | ++-------+-------+-------+ +2024-06-16 17:04:37,468 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 17:04:37,468 - mmseg - INFO - Iter(val) [250] aAcc: 0.8632, mIoU: 0.5796, mAcc: 0.7082, IoU.wall: 0.8233, IoU.building: 0.8541, IoU.sky: 0.9488, IoU.floor: 0.8480, IoU.tree: 0.7733, IoU.ceiling: 0.8731, IoU.road: 0.8595, IoU.bed : 0.9303, IoU.windowpane: 0.6732, IoU.grass: 0.6800, IoU.cabinet: 0.6574, IoU.sidewalk: 0.7032, IoU.person: 0.8609, IoU.earth: 0.3831, IoU.door: 0.6108, IoU.table: 0.6945, IoU.mountain: 0.6124, IoU.plant: 0.5805, IoU.curtain: 0.7934, IoU.chair: 0.6884, IoU.car: 0.8782, IoU.water: 0.6195, IoU.painting: 0.7690, IoU.sofa: 0.8347, IoU.shelf: 0.5191, IoU.house: 0.5138, IoU.sea: 0.7245, IoU.mirror: 0.7981, IoU.rug: 0.7029, IoU.field: 0.3718, IoU.armchair: 0.5925, IoU.seat: 0.6958, IoU.fence: 0.5361, IoU.desk: 0.5616, IoU.rock: 0.6093, IoU.wardrobe: 0.5487, IoU.lamp: 0.7544, IoU.bathtub: 0.8815, IoU.railing: 0.4621, IoU.cushion: 0.7147, IoU.base: 0.3858, IoU.box: 0.3998, IoU.column: 0.5347, IoU.signboard: 0.4192, IoU.chest of drawers: 0.4310, IoU.counter: 0.3890, IoU.sand: 0.6097, IoU.sink: 0.8256, IoU.skyscraper: 0.4775, IoU.fireplace: 0.7347, IoU.refrigerator: 0.8391, IoU.grandstand: 0.5050, IoU.path: 0.3065, IoU.stairs: 0.3333, IoU.runway: 0.7374, IoU.case: 0.6650, IoU.pool table: 0.9477, IoU.pillow: 0.6811, IoU.screen door: 0.8666, IoU.stairway: 0.4782, IoU.river: 0.0855, IoU.bridge: 0.6842, IoU.bookcase: 0.4546, IoU.blind: 0.4625, IoU.coffee table: 0.6223, IoU.toilet: 0.9173, IoU.flower: 0.4631, IoU.book: 0.5264, IoU.hill: 0.0567, IoU.bench: 0.5956, IoU.countertop: 0.6335, IoU.stove: 0.8704, IoU.palm: 0.5368, IoU.kitchen island: 0.5057, IoU.computer: 0.7785, IoU.swivel chair: 0.5230, IoU.boat: 0.8019, IoU.bar: 0.6078, IoU.arcade machine: 0.7600, IoU.hovel: 0.1364, IoU.bus: 0.9381, IoU.towel: 0.8117, IoU.light: 0.6002, IoU.truck: 0.4758, IoU.tower: 0.2426, IoU.chandelier: 0.7418, IoU.awning: 0.3938, IoU.streetlight: 0.3697, IoU.booth: 0.3384, IoU.television receiver: 0.8144, IoU.airplane: 0.8301, IoU.dirt track: 0.1199, IoU.apparel: 0.6112, IoU.pole: 0.2793, IoU.land: 0.0000, IoU.bannister: 0.2109, IoU.escalator: 0.6640, IoU.ottoman: 0.4686, IoU.bottle: 0.4564, IoU.buffet: 0.4727, IoU.poster: 0.3842, IoU.stage: 0.2784, IoU.van: 0.4509, IoU.ship: 0.5113, IoU.fountain: 0.3741, IoU.conveyer belt: 0.8122, IoU.canopy: 0.4987, IoU.washer: 0.8836, IoU.plaything: 0.3901, IoU.swimming pool: 0.5288, IoU.stool: 0.5088, IoU.barrel: 0.7355, IoU.basket: 0.3923, IoU.waterfall: 0.4880, IoU.tent: 0.9551, IoU.bag: 0.2605, IoU.minibike: 0.7442, IoU.cradle: 0.9011, IoU.oven: 0.5004, IoU.ball: 0.5467, IoU.food: 0.6994, IoU.step: 0.2303, IoU.tank: 0.5606, IoU.trade name: 0.2747, IoU.microwave: 0.8576, IoU.pot: 0.5810, IoU.animal: 0.6468, IoU.bicycle: 0.5864, IoU.lake: 0.4828, IoU.dishwasher: 0.7091, IoU.screen: 0.5908, IoU.blanket: 0.3755, IoU.sculpture: 0.7233, IoU.hood: 0.6396, IoU.sconce: 0.6299, IoU.vase: 0.4742, IoU.traffic light: 0.3585, IoU.tray: 0.2418, IoU.ashcan: 0.4911, IoU.fan: 0.7003, IoU.pier: 0.4819, IoU.crt screen: 0.0393, IoU.plate: 0.6395, IoU.monitor: 0.6785, IoU.bulletin board: 0.6034, IoU.shower: 0.0316, IoU.radiator: 0.6958, IoU.glass: 0.2087, IoU.clock: 0.4864, IoU.flag: 0.7124, Acc.wall: 0.8927, Acc.building: 0.9350, Acc.sky: 0.9763, Acc.floor: 0.9017, Acc.tree: 0.8836, Acc.ceiling: 0.9506, Acc.road: 0.9235, Acc.bed : 0.9764, Acc.windowpane: 0.7987, Acc.grass: 0.7998, Acc.cabinet: 0.7629, Acc.sidewalk: 0.8229, Acc.person: 0.9447, Acc.earth: 0.5178, Acc.door: 0.7767, Acc.table: 0.8126, Acc.mountain: 0.8807, Acc.plant: 0.6694, Acc.curtain: 0.8993, Acc.chair: 0.8335, Acc.car: 0.9389, Acc.water: 0.7623, Acc.painting: 0.9125, Acc.sofa: 0.8975, Acc.shelf: 0.6792, Acc.house: 0.6277, Acc.sea: 0.8483, Acc.mirror: 0.8874, Acc.rug: 0.8473, Acc.field: 0.5795, Acc.armchair: 0.7931, Acc.seat: 0.8906, Acc.fence: 0.6853, Acc.desk: 0.8045, Acc.rock: 0.8474, Acc.wardrobe: 0.7423, Acc.lamp: 0.8492, Acc.bathtub: 0.9115, Acc.railing: 0.6375, Acc.cushion: 0.8286, Acc.base: 0.6129, Acc.box: 0.5201, Acc.column: 0.6724, Acc.signboard: 0.5651, Acc.chest of drawers: 0.6588, Acc.counter: 0.5018, Acc.sand: 0.8499, Acc.sink: 0.8934, Acc.skyscraper: 0.6254, Acc.fireplace: 0.9432, Acc.refrigerator: 0.9373, Acc.grandstand: 0.8173, Acc.path: 0.4368, Acc.stairs: 0.3743, Acc.runway: 0.9854, Acc.case: 0.8377, Acc.pool table: 0.9769, Acc.pillow: 0.7847, Acc.screen door: 0.9026, Acc.stairway: 0.6525, Acc.river: 0.1978, Acc.bridge: 0.8963, Acc.bookcase: 0.6615, Acc.blind: 0.5937, Acc.coffee table: 0.8592, Acc.toilet: 0.9512, Acc.flower: 0.6000, Acc.book: 0.7403, Acc.hill: 0.0647, Acc.bench: 0.7089, Acc.countertop: 0.7977, Acc.stove: 0.9241, Acc.palm: 0.8349, Acc.kitchen island: 0.6188, Acc.computer: 0.9277, Acc.swivel chair: 0.7839, Acc.boat: 0.8499, Acc.bar: 0.8708, Acc.arcade machine: 0.8024, Acc.hovel: 0.1460, Acc.bus: 0.9651, Acc.towel: 0.8805, Acc.light: 0.6794, Acc.truck: 0.5688, Acc.tower: 0.4163, Acc.chandelier: 0.8768, Acc.awning: 0.4812, Acc.streetlight: 0.4624, Acc.booth: 0.3597, Acc.television receiver: 0.8732, Acc.airplane: 0.8992, Acc.dirt track: 0.4016, Acc.apparel: 0.8849, Acc.pole: 0.4064, Acc.land: 0.0000, Acc.bannister: 0.2815, Acc.escalator: 0.8263, Acc.ottoman: 0.5910, Acc.bottle: 0.6593, Acc.buffet: 0.5725, Acc.poster: 0.4584, Acc.stage: 0.4447, Acc.van: 0.7155, Acc.ship: 0.5198, Acc.fountain: 0.3775, Acc.conveyer belt: 0.9538, Acc.canopy: 0.7477, Acc.washer: 0.9408, Acc.plaything: 0.6964, Acc.swimming pool: 0.8214, Acc.stool: 0.7101, Acc.barrel: 0.9044, Acc.basket: 0.5695, Acc.waterfall: 0.5697, Acc.tent: 0.9827, Acc.bag: 0.3056, Acc.minibike: 0.9180, Acc.cradle: 0.9736, Acc.oven: 0.5949, Acc.ball: 0.7171, Acc.food: 0.8293, Acc.step: 0.2758, Acc.tank: 0.6961, Acc.trade name: 0.3240, Acc.microwave: 0.9646, Acc.pot: 0.6851, Acc.animal: 0.6612, Acc.bicycle: 0.7440, Acc.lake: 0.7570, Acc.dishwasher: 0.8438, Acc.screen: 0.9597, Acc.blanket: 0.4429, Acc.sculpture: 0.8909, Acc.hood: 0.7758, Acc.sconce: 0.7229, Acc.vase: 0.6509, Acc.traffic light: 0.6641, Acc.tray: 0.3445, Acc.ashcan: 0.6686, Acc.fan: 0.7954, Acc.pier: 0.5708, Acc.crt screen: 0.0469, Acc.plate: 0.7514, Acc.monitor: 0.7696, Acc.bulletin board: 0.6375, Acc.shower: 0.0342, Acc.radiator: 0.8286, Acc.glass: 0.2218, Acc.clock: 0.5719, Acc.flag: 0.7893 +2024-06-16 17:05:58,865 - mmseg - INFO - Iter [40050/80000] lr: 1.998e-05, eta: 19:37:14, time: 3.547, data_time: 1.935, memory: 71384, decode.loss_ce: 0.1922, decode.acc_seg: 91.8422, aux.loss_ce: 0.0801, aux.acc_seg: 91.5019, loss: 0.2723 +2024-06-16 17:07:20,041 - mmseg - INFO - Iter [40100/80000] lr: 1.995e-05, eta: 19:35:39, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1874, decode.acc_seg: 91.7409, aux.loss_ce: 0.0779, aux.acc_seg: 91.4567, loss: 0.2653 +2024-06-16 17:08:41,124 - mmseg - INFO - Iter [40150/80000] lr: 1.993e-05, eta: 19:34:03, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1881, decode.acc_seg: 91.9197, aux.loss_ce: 0.0788, aux.acc_seg: 91.5883, loss: 0.2670 +2024-06-16 17:10:02,217 - mmseg - INFO - Iter [40200/80000] lr: 1.990e-05, eta: 19:32:27, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1775, decode.acc_seg: 92.5317, aux.loss_ce: 0.0742, aux.acc_seg: 92.2311, loss: 0.2517 +2024-06-16 17:11:23,384 - mmseg - INFO - Iter [40250/80000] lr: 1.988e-05, eta: 19:30:52, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2075, decode.acc_seg: 91.4670, aux.loss_ce: 0.0852, aux.acc_seg: 91.2186, loss: 0.2927 +2024-06-16 17:12:44,429 - mmseg - INFO - Iter [40300/80000] lr: 1.985e-05, eta: 19:29:16, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1977, decode.acc_seg: 91.7805, aux.loss_ce: 0.0827, aux.acc_seg: 91.4447, loss: 0.2804 +2024-06-16 17:14:05,507 - mmseg - INFO - Iter [40350/80000] lr: 1.983e-05, eta: 19:27:41, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2063, decode.acc_seg: 91.2845, aux.loss_ce: 0.0853, aux.acc_seg: 91.0937, loss: 0.2915 +2024-06-16 17:15:26,548 - mmseg - INFO - Iter [40400/80000] lr: 1.980e-05, eta: 19:26:05, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1981, decode.acc_seg: 91.7904, aux.loss_ce: 0.0817, aux.acc_seg: 91.4451, loss: 0.2798 +2024-06-16 17:16:50,405 - mmseg - INFO - Iter [40450/80000] lr: 1.978e-05, eta: 19:24:33, time: 1.677, data_time: 0.061, memory: 71384, decode.loss_ce: 0.1832, decode.acc_seg: 91.9495, aux.loss_ce: 0.0768, aux.acc_seg: 91.5790, loss: 0.2600 +2024-06-16 17:18:11,367 - mmseg - INFO - Iter [40500/80000] lr: 1.975e-05, eta: 19:22:57, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1902, decode.acc_seg: 91.8929, aux.loss_ce: 0.0796, aux.acc_seg: 91.5690, loss: 0.2698 +2024-06-16 17:19:32,431 - mmseg - INFO - Iter [40550/80000] lr: 1.973e-05, eta: 19:21:22, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1824, decode.acc_seg: 92.5174, aux.loss_ce: 0.0758, aux.acc_seg: 92.1733, loss: 0.2582 +2024-06-16 17:20:53,559 - mmseg - INFO - Iter [40600/80000] lr: 1.970e-05, eta: 19:19:46, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1836, decode.acc_seg: 92.3546, aux.loss_ce: 0.0771, aux.acc_seg: 91.9529, loss: 0.2607 +2024-06-16 17:22:14,538 - mmseg - INFO - Iter [40650/80000] lr: 1.968e-05, eta: 19:18:11, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1882, decode.acc_seg: 92.0412, aux.loss_ce: 0.0788, aux.acc_seg: 91.6279, loss: 0.2670 +2024-06-16 17:23:35,705 - mmseg - INFO - Iter [40700/80000] lr: 1.965e-05, eta: 19:16:36, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1872, decode.acc_seg: 92.2853, aux.loss_ce: 0.0779, aux.acc_seg: 92.0023, loss: 0.2651 +2024-06-16 17:24:56,863 - mmseg - INFO - Iter [40750/80000] lr: 1.963e-05, eta: 19:15:01, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1948, decode.acc_seg: 91.7677, aux.loss_ce: 0.0816, aux.acc_seg: 91.3908, loss: 0.2765 +2024-06-16 17:26:18,063 - mmseg - INFO - Iter [40800/80000] lr: 1.960e-05, eta: 19:13:26, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1809, decode.acc_seg: 92.2845, aux.loss_ce: 0.0753, aux.acc_seg: 91.9850, loss: 0.2561 +2024-06-16 17:27:39,052 - mmseg - INFO - Iter [40850/80000] lr: 1.958e-05, eta: 19:11:50, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1967, decode.acc_seg: 91.7144, aux.loss_ce: 0.0814, aux.acc_seg: 91.4183, loss: 0.2781 +2024-06-16 17:29:00,035 - mmseg - INFO - Iter [40900/80000] lr: 1.955e-05, eta: 19:10:15, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.2014, decode.acc_seg: 91.5692, aux.loss_ce: 0.0830, aux.acc_seg: 91.2785, loss: 0.2844 +2024-06-16 17:30:21,132 - mmseg - INFO - Iter [40950/80000] lr: 1.953e-05, eta: 19:08:40, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1901, decode.acc_seg: 91.8515, aux.loss_ce: 0.0793, aux.acc_seg: 91.5047, loss: 0.2694 +2024-06-16 17:31:42,336 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 17:31:42,337 - mmseg - INFO - Iter [41000/80000] lr: 1.950e-05, eta: 19:07:05, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1899, decode.acc_seg: 92.1512, aux.loss_ce: 0.0795, aux.acc_seg: 91.7298, loss: 0.2693 +2024-06-16 17:33:18,301 - mmseg - INFO - per class results: +2024-06-16 17:33:18,308 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.15 | 89.57 | +| building | 85.63 | 93.17 | +| sky | 94.77 | 97.46 | +| floor | 85.33 | 92.08 | +| tree | 77.35 | 89.71 | +| ceiling | 87.66 | 93.62 | +| road | 85.69 | 91.16 | +| bed | 92.49 | 96.34 | +| windowpane | 66.47 | 83.36 | +| grass | 67.23 | 81.16 | +| cabinet | 70.26 | 82.83 | +| sidewalk | 69.53 | 84.16 | +| person | 86.41 | 93.76 | +| earth | 37.79 | 49.82 | +| door | 58.53 | 73.11 | +| table | 68.97 | 79.84 | +| mountain | 63.39 | 74.23 | +| plant | 56.81 | 67.45 | +| curtain | 79.83 | 89.61 | +| chair | 66.67 | 78.3 | +| car | 87.66 | 93.14 | +| water | 60.03 | 69.56 | +| painting | 78.95 | 91.05 | +| sofa | 82.99 | 91.78 | +| shelf | 54.33 | 78.42 | +| house | 53.61 | 63.82 | +| sea | 68.54 | 84.09 | +| mirror | 76.66 | 84.39 | +| rug | 67.42 | 76.62 | +| field | 35.27 | 62.87 | +| armchair | 60.05 | 77.98 | +| seat | 63.29 | 88.26 | +| fence | 49.87 | 63.68 | +| desk | 57.13 | 79.68 | +| rock | 59.23 | 85.12 | +| wardrobe | 60.33 | 65.08 | +| lamp | 75.42 | 86.91 | +| bathtub | 84.87 | 87.99 | +| railing | 44.08 | 60.44 | +| cushion | 71.9 | 82.12 | +| base | 41.89 | 61.73 | +| box | 37.19 | 46.95 | +| column | 57.36 | 86.26 | +| signboard | 40.77 | 60.99 | +| chest of drawers | 41.41 | 48.6 | +| counter | 34.86 | 43.14 | +| sand | 51.83 | 88.12 | +| sink | 81.96 | 87.04 | +| skyscraper | 50.66 | 64.72 | +| fireplace | 73.08 | 94.94 | +| refrigerator | 83.95 | 93.99 | +| grandstand | 52.43 | 83.64 | +| path | 27.47 | 37.78 | +| stairs | 27.98 | 34.82 | +| runway | 72.01 | 95.47 | +| case | 64.8 | 78.03 | +| pool table | 94.5 | 97.89 | +| pillow | 69.33 | 82.82 | +| screen door | 76.19 | 78.86 | +| stairway | 42.2 | 60.51 | +| river | 11.69 | 29.84 | +| bridge | 69.74 | 90.05 | +| bookcase | 54.17 | 62.23 | +| blind | 42.03 | 49.13 | +| coffee table | 59.94 | 89.95 | +| toilet | 91.56 | 94.93 | +| flower | 49.81 | 59.89 | +| book | 56.22 | 75.9 | +| hill | 6.75 | 11.67 | +| bench | 57.69 | 62.87 | +| countertop | 66.11 | 82.22 | +| stove | 86.03 | 90.51 | +| palm | 53.98 | 86.74 | +| kitchen island | 51.12 | 90.99 | +| computer | 81.9 | 90.4 | +| swivel chair | 53.32 | 78.31 | +| boat | 73.34 | 91.17 | +| bar | 59.78 | 79.58 | +| arcade machine | 83.17 | 89.06 | +| hovel | 36.36 | 39.89 | +| bus | 93.54 | 97.05 | +| towel | 77.59 | 85.53 | +| light | 62.52 | 73.68 | +| truck | 43.34 | 56.28 | +| tower | 21.55 | 35.06 | +| chandelier | 73.45 | 90.66 | +| awning | 40.65 | 48.59 | +| streetlight | 38.43 | 50.97 | +| booth | 43.2 | 64.26 | +| television receiver | 74.63 | 88.33 | +| airplane | 88.86 | 95.54 | +| dirt track | 8.82 | 45.79 | +| apparel | 66.11 | 78.16 | +| pole | 25.92 | 37.67 | +| land | 4.68 | 7.72 | +| bannister | 21.96 | 27.35 | +| escalator | 65.37 | 82.67 | +| ottoman | 50.58 | 70.31 | +| bottle | 36.34 | 44.2 | +| buffet | 54.09 | 70.87 | +| poster | 37.91 | 48.54 | +| stage | 19.65 | 50.52 | +| van | 46.33 | 72.59 | +| ship | 59.1 | 62.01 | +| fountain | 39.18 | 40.71 | +| conveyer belt | 77.16 | 93.77 | +| canopy | 54.84 | 80.45 | +| washer | 83.21 | 88.21 | +| plaything | 34.22 | 51.46 | +| swimming pool | 53.32 | 77.34 | +| stool | 49.02 | 74.97 | +| barrel | 39.73 | 90.89 | +| basket | 38.0 | 56.43 | +| waterfall | 66.1 | 85.28 | +| tent | 95.56 | 98.3 | +| bag | 28.78 | 32.78 | +| minibike | 75.46 | 89.6 | +| cradle | 89.17 | 97.76 | +| oven | 62.15 | 82.07 | +| ball | 48.23 | 77.18 | +| food | 69.35 | 83.13 | +| step | 21.42 | 26.66 | +| tank | 67.5 | 76.03 | +| trade name | 10.8 | 11.84 | +| microwave | 89.6 | 96.21 | +| pot | 59.43 | 69.94 | +| animal | 62.78 | 64.06 | +| bicycle | 59.21 | 74.65 | +| lake | 42.67 | 74.64 | +| dishwasher | 71.03 | 78.57 | +| screen | 57.55 | 96.88 | +| blanket | 35.42 | 39.49 | +| sculpture | 75.33 | 88.7 | +| hood | 62.48 | 74.23 | +| sconce | 58.54 | 65.33 | +| vase | 45.64 | 67.18 | +| traffic light | 33.42 | 69.98 | +| tray | 25.32 | 33.92 | +| ashcan | 51.0 | 67.89 | +| fan | 68.9 | 86.47 | +| pier | 62.88 | 79.29 | +| crt screen | 11.14 | 15.67 | +| plate | 63.36 | 79.88 | +| monitor | 61.37 | 74.3 | +| bulletin board | 56.19 | 67.63 | +| shower | 2.17 | 2.61 | +| radiator | 69.83 | 79.71 | +| glass | 20.76 | 22.32 | +| clock | 49.69 | 56.52 | +| flag | 71.93 | 79.68 | ++---------------------+-------+-------+ +2024-06-16 17:33:18,308 - mmseg - INFO - Summary: +2024-06-16 17:33:18,308 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 86.2 | 57.78 | 71.54 | ++------+-------+-------+ +2024-06-16 17:33:18,309 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 17:33:18,309 - mmseg - INFO - Iter(val) [250] aAcc: 0.8620, mIoU: 0.5778, mAcc: 0.7154, IoU.wall: 0.8215, IoU.building: 0.8563, IoU.sky: 0.9477, IoU.floor: 0.8533, IoU.tree: 0.7735, IoU.ceiling: 0.8766, IoU.road: 0.8569, IoU.bed : 0.9249, IoU.windowpane: 0.6647, IoU.grass: 0.6723, IoU.cabinet: 0.7026, IoU.sidewalk: 0.6953, IoU.person: 0.8641, IoU.earth: 0.3779, IoU.door: 0.5853, IoU.table: 0.6897, IoU.mountain: 0.6339, IoU.plant: 0.5681, IoU.curtain: 0.7983, IoU.chair: 0.6667, IoU.car: 0.8766, IoU.water: 0.6003, IoU.painting: 0.7895, IoU.sofa: 0.8299, IoU.shelf: 0.5433, IoU.house: 0.5361, IoU.sea: 0.6854, IoU.mirror: 0.7666, IoU.rug: 0.6742, IoU.field: 0.3527, IoU.armchair: 0.6005, IoU.seat: 0.6329, IoU.fence: 0.4987, IoU.desk: 0.5713, IoU.rock: 0.5923, IoU.wardrobe: 0.6033, IoU.lamp: 0.7542, IoU.bathtub: 0.8487, IoU.railing: 0.4408, IoU.cushion: 0.7190, IoU.base: 0.4189, IoU.box: 0.3719, IoU.column: 0.5736, IoU.signboard: 0.4077, IoU.chest of drawers: 0.4141, IoU.counter: 0.3486, IoU.sand: 0.5183, IoU.sink: 0.8196, IoU.skyscraper: 0.5066, IoU.fireplace: 0.7308, IoU.refrigerator: 0.8395, IoU.grandstand: 0.5243, IoU.path: 0.2747, IoU.stairs: 0.2798, IoU.runway: 0.7201, IoU.case: 0.6480, IoU.pool table: 0.9450, IoU.pillow: 0.6933, IoU.screen door: 0.7619, IoU.stairway: 0.4220, IoU.river: 0.1169, IoU.bridge: 0.6974, IoU.bookcase: 0.5417, IoU.blind: 0.4203, IoU.coffee table: 0.5994, IoU.toilet: 0.9156, IoU.flower: 0.4981, IoU.book: 0.5622, IoU.hill: 0.0675, IoU.bench: 0.5769, IoU.countertop: 0.6611, IoU.stove: 0.8603, IoU.palm: 0.5398, IoU.kitchen island: 0.5112, IoU.computer: 0.8190, IoU.swivel chair: 0.5332, IoU.boat: 0.7334, IoU.bar: 0.5978, IoU.arcade machine: 0.8317, IoU.hovel: 0.3636, IoU.bus: 0.9354, IoU.towel: 0.7759, IoU.light: 0.6252, IoU.truck: 0.4334, IoU.tower: 0.2155, IoU.chandelier: 0.7345, IoU.awning: 0.4065, IoU.streetlight: 0.3843, IoU.booth: 0.4320, IoU.television receiver: 0.7463, IoU.airplane: 0.8886, IoU.dirt track: 0.0882, IoU.apparel: 0.6611, IoU.pole: 0.2592, IoU.land: 0.0468, IoU.bannister: 0.2196, IoU.escalator: 0.6537, IoU.ottoman: 0.5058, IoU.bottle: 0.3634, IoU.buffet: 0.5409, IoU.poster: 0.3791, IoU.stage: 0.1965, IoU.van: 0.4633, IoU.ship: 0.5910, IoU.fountain: 0.3918, IoU.conveyer belt: 0.7716, IoU.canopy: 0.5484, IoU.washer: 0.8321, IoU.plaything: 0.3422, IoU.swimming pool: 0.5332, IoU.stool: 0.4902, IoU.barrel: 0.3973, IoU.basket: 0.3800, IoU.waterfall: 0.6610, IoU.tent: 0.9556, IoU.bag: 0.2878, IoU.minibike: 0.7546, IoU.cradle: 0.8917, IoU.oven: 0.6215, IoU.ball: 0.4823, IoU.food: 0.6935, IoU.step: 0.2142, IoU.tank: 0.6750, IoU.trade name: 0.1080, IoU.microwave: 0.8960, IoU.pot: 0.5943, IoU.animal: 0.6278, IoU.bicycle: 0.5921, IoU.lake: 0.4267, IoU.dishwasher: 0.7103, IoU.screen: 0.5755, IoU.blanket: 0.3542, IoU.sculpture: 0.7533, IoU.hood: 0.6248, IoU.sconce: 0.5854, IoU.vase: 0.4564, IoU.traffic light: 0.3342, IoU.tray: 0.2532, IoU.ashcan: 0.5100, IoU.fan: 0.6890, IoU.pier: 0.6288, IoU.crt screen: 0.1114, IoU.plate: 0.6336, IoU.monitor: 0.6137, IoU.bulletin board: 0.5619, IoU.shower: 0.0217, IoU.radiator: 0.6983, IoU.glass: 0.2076, IoU.clock: 0.4969, IoU.flag: 0.7193, Acc.wall: 0.8957, Acc.building: 0.9317, Acc.sky: 0.9746, Acc.floor: 0.9208, Acc.tree: 0.8971, Acc.ceiling: 0.9362, Acc.road: 0.9116, Acc.bed : 0.9634, Acc.windowpane: 0.8336, Acc.grass: 0.8116, Acc.cabinet: 0.8283, Acc.sidewalk: 0.8416, Acc.person: 0.9376, Acc.earth: 0.4982, Acc.door: 0.7311, Acc.table: 0.7984, Acc.mountain: 0.7423, Acc.plant: 0.6745, Acc.curtain: 0.8961, Acc.chair: 0.7830, Acc.car: 0.9314, Acc.water: 0.6956, Acc.painting: 0.9105, Acc.sofa: 0.9178, Acc.shelf: 0.7842, Acc.house: 0.6382, Acc.sea: 0.8409, Acc.mirror: 0.8439, Acc.rug: 0.7662, Acc.field: 0.6287, Acc.armchair: 0.7798, Acc.seat: 0.8826, Acc.fence: 0.6368, Acc.desk: 0.7968, Acc.rock: 0.8512, Acc.wardrobe: 0.6508, Acc.lamp: 0.8691, Acc.bathtub: 0.8799, Acc.railing: 0.6044, Acc.cushion: 0.8212, Acc.base: 0.6173, Acc.box: 0.4695, Acc.column: 0.8626, Acc.signboard: 0.6099, Acc.chest of drawers: 0.4860, Acc.counter: 0.4314, Acc.sand: 0.8812, Acc.sink: 0.8704, Acc.skyscraper: 0.6472, Acc.fireplace: 0.9494, Acc.refrigerator: 0.9399, Acc.grandstand: 0.8364, Acc.path: 0.3778, Acc.stairs: 0.3482, Acc.runway: 0.9547, Acc.case: 0.7803, Acc.pool table: 0.9789, Acc.pillow: 0.8282, Acc.screen door: 0.7886, Acc.stairway: 0.6051, Acc.river: 0.2984, Acc.bridge: 0.9005, Acc.bookcase: 0.6223, Acc.blind: 0.4913, Acc.coffee table: 0.8995, Acc.toilet: 0.9493, Acc.flower: 0.5989, Acc.book: 0.7590, Acc.hill: 0.1167, Acc.bench: 0.6287, Acc.countertop: 0.8222, Acc.stove: 0.9051, Acc.palm: 0.8674, Acc.kitchen island: 0.9099, Acc.computer: 0.9040, Acc.swivel chair: 0.7831, Acc.boat: 0.9117, Acc.bar: 0.7958, Acc.arcade machine: 0.8906, Acc.hovel: 0.3989, Acc.bus: 0.9705, Acc.towel: 0.8553, Acc.light: 0.7368, Acc.truck: 0.5628, Acc.tower: 0.3506, Acc.chandelier: 0.9066, Acc.awning: 0.4859, Acc.streetlight: 0.5097, Acc.booth: 0.6426, Acc.television receiver: 0.8833, Acc.airplane: 0.9554, Acc.dirt track: 0.4579, Acc.apparel: 0.7816, Acc.pole: 0.3767, Acc.land: 0.0772, Acc.bannister: 0.2735, Acc.escalator: 0.8267, Acc.ottoman: 0.7031, Acc.bottle: 0.4420, Acc.buffet: 0.7087, Acc.poster: 0.4854, Acc.stage: 0.5052, Acc.van: 0.7259, Acc.ship: 0.6201, Acc.fountain: 0.4071, Acc.conveyer belt: 0.9377, Acc.canopy: 0.8045, Acc.washer: 0.8821, Acc.plaything: 0.5146, Acc.swimming pool: 0.7734, Acc.stool: 0.7497, Acc.barrel: 0.9089, Acc.basket: 0.5643, Acc.waterfall: 0.8528, Acc.tent: 0.9830, Acc.bag: 0.3278, Acc.minibike: 0.8960, Acc.cradle: 0.9776, Acc.oven: 0.8207, Acc.ball: 0.7718, Acc.food: 0.8313, Acc.step: 0.2666, Acc.tank: 0.7603, Acc.trade name: 0.1184, Acc.microwave: 0.9621, Acc.pot: 0.6994, Acc.animal: 0.6406, Acc.bicycle: 0.7465, Acc.lake: 0.7464, Acc.dishwasher: 0.7857, Acc.screen: 0.9688, Acc.blanket: 0.3949, Acc.sculpture: 0.8870, Acc.hood: 0.7423, Acc.sconce: 0.6533, Acc.vase: 0.6718, Acc.traffic light: 0.6998, Acc.tray: 0.3392, Acc.ashcan: 0.6789, Acc.fan: 0.8647, Acc.pier: 0.7929, Acc.crt screen: 0.1567, Acc.plate: 0.7988, Acc.monitor: 0.7430, Acc.bulletin board: 0.6763, Acc.shower: 0.0261, Acc.radiator: 0.7971, Acc.glass: 0.2232, Acc.clock: 0.5652, Acc.flag: 0.7968 +2024-06-16 17:34:39,772 - mmseg - INFO - Iter [41050/80000] lr: 1.948e-05, eta: 19:07:01, time: 3.549, data_time: 1.938, memory: 71384, decode.loss_ce: 0.1971, decode.acc_seg: 91.5834, aux.loss_ce: 0.0827, aux.acc_seg: 91.1579, loss: 0.2798 +2024-06-16 17:36:00,809 - mmseg - INFO - Iter [41100/80000] lr: 1.945e-05, eta: 19:05:26, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1909, decode.acc_seg: 92.0167, aux.loss_ce: 0.0796, aux.acc_seg: 91.6661, loss: 0.2705 +2024-06-16 17:37:22,001 - mmseg - INFO - Iter [41150/80000] lr: 1.943e-05, eta: 19:03:51, time: 1.624, data_time: 0.009, memory: 71384, decode.loss_ce: 0.1840, decode.acc_seg: 92.1764, aux.loss_ce: 0.0764, aux.acc_seg: 91.8708, loss: 0.2604 +2024-06-16 17:38:43,052 - mmseg - INFO - Iter [41200/80000] lr: 1.940e-05, eta: 19:02:16, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1799, decode.acc_seg: 92.1208, aux.loss_ce: 0.0748, aux.acc_seg: 91.8417, loss: 0.2546 +2024-06-16 17:40:04,157 - mmseg - INFO - Iter [41250/80000] lr: 1.938e-05, eta: 19:00:41, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1916, decode.acc_seg: 92.0838, aux.loss_ce: 0.0801, aux.acc_seg: 91.6672, loss: 0.2717 +2024-06-16 17:41:25,151 - mmseg - INFO - Iter [41300/80000] lr: 1.935e-05, eta: 18:59:06, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1784, decode.acc_seg: 92.2275, aux.loss_ce: 0.0744, aux.acc_seg: 91.8977, loss: 0.2528 +2024-06-16 17:42:46,387 - mmseg - INFO - Iter [41350/80000] lr: 1.933e-05, eta: 18:57:31, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1851, decode.acc_seg: 92.1400, aux.loss_ce: 0.0769, aux.acc_seg: 91.8178, loss: 0.2620 +2024-06-16 17:44:07,403 - mmseg - INFO - Iter [41400/80000] lr: 1.930e-05, eta: 18:55:56, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1897, decode.acc_seg: 91.9580, aux.loss_ce: 0.0793, aux.acc_seg: 91.6823, loss: 0.2690 +2024-06-16 17:45:28,437 - mmseg - INFO - Iter [41450/80000] lr: 1.928e-05, eta: 18:54:21, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1990, decode.acc_seg: 91.5952, aux.loss_ce: 0.0827, aux.acc_seg: 91.2135, loss: 0.2817 +2024-06-16 17:46:49,539 - mmseg - INFO - Iter [41500/80000] lr: 1.925e-05, eta: 18:52:46, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1822, decode.acc_seg: 92.4399, aux.loss_ce: 0.0765, aux.acc_seg: 92.0431, loss: 0.2587 +2024-06-16 17:48:10,685 - mmseg - INFO - Iter [41550/80000] lr: 1.923e-05, eta: 18:51:11, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1865, decode.acc_seg: 92.0457, aux.loss_ce: 0.0775, aux.acc_seg: 91.6976, loss: 0.2640 +2024-06-16 17:49:31,828 - mmseg - INFO - Iter [41600/80000] lr: 1.920e-05, eta: 18:49:36, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1843, decode.acc_seg: 92.0297, aux.loss_ce: 0.0772, aux.acc_seg: 91.6541, loss: 0.2615 +2024-06-16 17:51:09,929 - mmseg - INFO - Iter [41650/80000] lr: 1.918e-05, eta: 18:48:17, time: 1.962, data_time: 0.348, memory: 71384, decode.loss_ce: 0.1985, decode.acc_seg: 91.4684, aux.loss_ce: 0.0816, aux.acc_seg: 91.1633, loss: 0.2801 +2024-06-16 17:52:33,208 - mmseg - INFO - Iter [41700/80000] lr: 1.915e-05, eta: 18:46:44, time: 1.666, data_time: 0.051, memory: 71384, decode.loss_ce: 0.1913, decode.acc_seg: 92.0373, aux.loss_ce: 0.0798, aux.acc_seg: 91.6818, loss: 0.2711 +2024-06-16 17:53:54,194 - mmseg - INFO - Iter [41750/80000] lr: 1.913e-05, eta: 18:45:09, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1839, decode.acc_seg: 92.4178, aux.loss_ce: 0.0768, aux.acc_seg: 92.0266, loss: 0.2607 +2024-06-16 17:55:15,248 - mmseg - INFO - Iter [41800/80000] lr: 1.910e-05, eta: 18:43:34, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1863, decode.acc_seg: 92.1318, aux.loss_ce: 0.0769, aux.acc_seg: 91.8481, loss: 0.2632 +2024-06-16 17:56:36,347 - mmseg - INFO - Iter [41850/80000] lr: 1.908e-05, eta: 18:41:59, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1811, decode.acc_seg: 92.3216, aux.loss_ce: 0.0756, aux.acc_seg: 91.9119, loss: 0.2567 +2024-06-16 17:57:57,512 - mmseg - INFO - Iter [41900/80000] lr: 1.905e-05, eta: 18:40:25, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1766, decode.acc_seg: 92.4732, aux.loss_ce: 0.0738, aux.acc_seg: 92.1244, loss: 0.2504 +2024-06-16 17:59:18,486 - mmseg - INFO - Iter [41950/80000] lr: 1.903e-05, eta: 18:38:50, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1914, decode.acc_seg: 91.8476, aux.loss_ce: 0.0794, aux.acc_seg: 91.4961, loss: 0.2708 +2024-06-16 18:00:39,740 - mmseg - INFO - Saving checkpoint at 42000 iterations +2024-06-16 18:02:03,939 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 18:02:03,939 - mmseg - INFO - Iter [42000/80000] lr: 1.900e-05, eta: 18:38:32, time: 3.309, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1846, decode.acc_seg: 91.9361, aux.loss_ce: 0.0773, aux.acc_seg: 91.5631, loss: 0.2619 +2024-06-16 18:03:39,503 - mmseg - INFO - per class results: +2024-06-16 18:03:39,509 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.91 | 89.83 | +| building | 85.72 | 94.1 | +| sky | 94.92 | 97.89 | +| floor | 85.24 | 91.15 | +| tree | 77.94 | 88.63 | +| ceiling | 87.82 | 93.41 | +| road | 86.04 | 90.77 | +| bed | 92.8 | 96.98 | +| windowpane | 67.47 | 82.23 | +| grass | 68.61 | 83.44 | +| cabinet | 65.1 | 79.0 | +| sidewalk | 71.02 | 86.51 | +| person | 86.22 | 94.2 | +| earth | 39.95 | 54.33 | +| door | 59.11 | 74.1 | +| table | 71.08 | 83.29 | +| mountain | 61.1 | 71.76 | +| plant | 55.48 | 64.76 | +| curtain | 78.92 | 89.24 | +| chair | 67.77 | 77.69 | +| car | 88.02 | 93.04 | +| water | 62.31 | 74.2 | +| painting | 77.84 | 89.96 | +| sofa | 82.86 | 91.71 | +| shelf | 51.84 | 69.61 | +| house | 55.75 | 64.08 | +| sea | 74.19 | 84.14 | +| mirror | 76.51 | 83.11 | +| rug | 66.75 | 77.85 | +| field | 27.1 | 44.87 | +| armchair | 61.6 | 80.55 | +| seat | 65.49 | 90.18 | +| fence | 48.41 | 60.61 | +| desk | 56.79 | 77.2 | +| rock | 58.16 | 89.34 | +| wardrobe | 53.49 | 78.16 | +| lamp | 75.29 | 86.28 | +| bathtub | 84.18 | 87.78 | +| railing | 44.66 | 60.87 | +| cushion | 71.65 | 86.02 | +| base | 39.56 | 54.16 | +| box | 36.5 | 45.86 | +| column | 55.66 | 71.18 | +| signboard | 40.23 | 59.8 | +| chest of drawers | 47.86 | 64.34 | +| counter | 35.83 | 47.99 | +| sand | 57.84 | 86.7 | +| sink | 78.32 | 81.62 | +| skyscraper | 49.68 | 63.18 | +| fireplace | 78.75 | 90.33 | +| refrigerator | 83.15 | 94.11 | +| grandstand | 51.84 | 84.6 | +| path | 32.07 | 43.23 | +| stairs | 28.96 | 31.01 | +| runway | 72.8 | 95.36 | +| case | 64.04 | 81.21 | +| pool table | 94.86 | 97.68 | +| pillow | 67.66 | 76.27 | +| screen door | 83.89 | 88.45 | +| stairway | 47.96 | 71.02 | +| river | 11.16 | 25.64 | +| bridge | 74.72 | 86.49 | +| bookcase | 49.68 | 54.28 | +| blind | 44.15 | 51.5 | +| coffee table | 65.74 | 84.92 | +| toilet | 90.47 | 94.18 | +| flower | 44.16 | 53.11 | +| book | 53.92 | 73.46 | +| hill | 7.65 | 20.65 | +| bench | 56.99 | 62.02 | +| countertop | 63.65 | 82.5 | +| stove | 85.69 | 92.8 | +| palm | 54.23 | 76.33 | +| kitchen island | 41.58 | 54.15 | +| computer | 76.97 | 92.86 | +| swivel chair | 49.51 | 83.36 | +| boat | 64.58 | 91.38 | +| bar | 61.74 | 84.55 | +| arcade machine | 79.04 | 84.03 | +| hovel | 35.05 | 37.83 | +| bus | 93.46 | 96.93 | +| towel | 80.76 | 88.53 | +| light | 60.83 | 67.78 | +| truck | 45.88 | 55.03 | +| tower | 18.26 | 31.11 | +| chandelier | 73.97 | 86.71 | +| awning | 40.19 | 49.66 | +| streetlight | 37.87 | 47.71 | +| booth | 33.34 | 58.73 | +| television receiver | 78.89 | 87.38 | +| airplane | 89.3 | 95.41 | +| dirt track | 6.91 | 34.92 | +| apparel | 60.5 | 90.41 | +| pole | 28.09 | 38.77 | +| land | 1.94 | 3.14 | +| bannister | 22.51 | 28.46 | +| escalator | 65.21 | 83.04 | +| ottoman | 51.19 | 74.02 | +| bottle | 41.75 | 53.76 | +| buffet | 45.85 | 56.42 | +| poster | 38.93 | 47.92 | +| stage | 31.11 | 47.47 | +| van | 42.17 | 71.22 | +| ship | 83.44 | 92.91 | +| fountain | 36.3 | 36.8 | +| conveyer belt | 79.65 | 93.95 | +| canopy | 57.56 | 77.33 | +| washer | 87.73 | 93.34 | +| plaything | 35.97 | 74.21 | +| swimming pool | 54.61 | 78.85 | +| stool | 56.89 | 69.36 | +| barrel | 54.63 | 69.64 | +| basket | 39.22 | 60.57 | +| waterfall | 59.75 | 66.24 | +| tent | 94.28 | 98.88 | +| bag | 20.71 | 22.23 | +| minibike | 75.7 | 89.77 | +| cradle | 85.18 | 96.89 | +| oven | 56.88 | 64.42 | +| ball | 38.9 | 41.49 | +| food | 70.07 | 84.57 | +| step | 24.84 | 32.47 | +| tank | 61.34 | 67.26 | +| trade name | 16.3 | 18.73 | +| microwave | 88.07 | 96.31 | +| pot | 60.79 | 71.75 | +| animal | 63.0 | 64.35 | +| bicycle | 60.38 | 78.52 | +| lake | 44.57 | 77.49 | +| dishwasher | 73.39 | 85.56 | +| screen | 58.78 | 93.94 | +| blanket | 40.95 | 47.58 | +| sculpture | 74.75 | 88.49 | +| hood | 62.34 | 73.79 | +| sconce | 59.88 | 67.4 | +| vase | 47.0 | 64.23 | +| traffic light | 33.46 | 64.85 | +| tray | 25.06 | 36.78 | +| ashcan | 50.21 | 65.01 | +| fan | 69.13 | 82.62 | +| pier | 70.9 | 85.88 | +| crt screen | 2.0 | 3.39 | +| plate | 63.0 | 78.04 | +| monitor | 47.22 | 54.17 | +| bulletin board | 49.69 | 65.12 | +| shower | 5.28 | 13.32 | +| radiator | 67.19 | 77.81 | +| glass | 22.41 | 24.56 | +| clock | 51.89 | 60.51 | +| flag | 70.01 | 77.95 | ++---------------------+-------+-------+ +2024-06-16 18:03:39,509 - mmseg - INFO - Summary: +2024-06-16 18:03:39,509 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.19 | 57.77 | 70.61 | ++-------+-------+-------+ +2024-06-16 18:03:39,510 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 18:03:39,511 - mmseg - INFO - Iter(val) [250] aAcc: 0.8619, mIoU: 0.5777, mAcc: 0.7061, IoU.wall: 0.8191, IoU.building: 0.8572, IoU.sky: 0.9492, IoU.floor: 0.8524, IoU.tree: 0.7794, IoU.ceiling: 0.8782, IoU.road: 0.8604, IoU.bed : 0.9280, IoU.windowpane: 0.6747, IoU.grass: 0.6861, IoU.cabinet: 0.6510, IoU.sidewalk: 0.7102, IoU.person: 0.8622, IoU.earth: 0.3995, IoU.door: 0.5911, IoU.table: 0.7108, IoU.mountain: 0.6110, IoU.plant: 0.5548, IoU.curtain: 0.7892, IoU.chair: 0.6777, IoU.car: 0.8802, IoU.water: 0.6231, IoU.painting: 0.7784, IoU.sofa: 0.8286, IoU.shelf: 0.5184, IoU.house: 0.5575, IoU.sea: 0.7419, IoU.mirror: 0.7651, IoU.rug: 0.6675, IoU.field: 0.2710, IoU.armchair: 0.6160, IoU.seat: 0.6549, IoU.fence: 0.4841, IoU.desk: 0.5679, IoU.rock: 0.5816, IoU.wardrobe: 0.5349, IoU.lamp: 0.7529, IoU.bathtub: 0.8418, IoU.railing: 0.4466, IoU.cushion: 0.7165, IoU.base: 0.3956, IoU.box: 0.3650, IoU.column: 0.5566, IoU.signboard: 0.4023, IoU.chest of drawers: 0.4786, IoU.counter: 0.3583, IoU.sand: 0.5784, IoU.sink: 0.7832, IoU.skyscraper: 0.4968, IoU.fireplace: 0.7875, IoU.refrigerator: 0.8315, IoU.grandstand: 0.5184, IoU.path: 0.3207, IoU.stairs: 0.2896, IoU.runway: 0.7280, IoU.case: 0.6404, IoU.pool table: 0.9486, IoU.pillow: 0.6766, IoU.screen door: 0.8389, IoU.stairway: 0.4796, IoU.river: 0.1116, IoU.bridge: 0.7472, IoU.bookcase: 0.4968, IoU.blind: 0.4415, IoU.coffee table: 0.6574, IoU.toilet: 0.9047, IoU.flower: 0.4416, IoU.book: 0.5392, IoU.hill: 0.0765, IoU.bench: 0.5699, IoU.countertop: 0.6365, IoU.stove: 0.8569, IoU.palm: 0.5423, IoU.kitchen island: 0.4158, IoU.computer: 0.7697, IoU.swivel chair: 0.4951, IoU.boat: 0.6458, IoU.bar: 0.6174, IoU.arcade machine: 0.7904, IoU.hovel: 0.3505, IoU.bus: 0.9346, IoU.towel: 0.8076, IoU.light: 0.6083, IoU.truck: 0.4588, IoU.tower: 0.1826, IoU.chandelier: 0.7397, IoU.awning: 0.4019, IoU.streetlight: 0.3787, IoU.booth: 0.3334, IoU.television receiver: 0.7889, IoU.airplane: 0.8930, IoU.dirt track: 0.0691, IoU.apparel: 0.6050, IoU.pole: 0.2809, IoU.land: 0.0194, IoU.bannister: 0.2251, IoU.escalator: 0.6521, IoU.ottoman: 0.5119, IoU.bottle: 0.4175, IoU.buffet: 0.4585, IoU.poster: 0.3893, IoU.stage: 0.3111, IoU.van: 0.4217, IoU.ship: 0.8344, IoU.fountain: 0.3630, IoU.conveyer belt: 0.7965, IoU.canopy: 0.5756, IoU.washer: 0.8773, IoU.plaything: 0.3597, IoU.swimming pool: 0.5461, IoU.stool: 0.5689, IoU.barrel: 0.5463, IoU.basket: 0.3922, IoU.waterfall: 0.5975, IoU.tent: 0.9428, IoU.bag: 0.2071, IoU.minibike: 0.7570, IoU.cradle: 0.8518, IoU.oven: 0.5688, IoU.ball: 0.3890, IoU.food: 0.7007, IoU.step: 0.2484, IoU.tank: 0.6134, IoU.trade name: 0.1630, IoU.microwave: 0.8807, IoU.pot: 0.6079, IoU.animal: 0.6300, IoU.bicycle: 0.6038, IoU.lake: 0.4457, IoU.dishwasher: 0.7339, IoU.screen: 0.5878, IoU.blanket: 0.4095, IoU.sculpture: 0.7475, IoU.hood: 0.6234, IoU.sconce: 0.5988, IoU.vase: 0.4700, IoU.traffic light: 0.3346, IoU.tray: 0.2506, IoU.ashcan: 0.5021, IoU.fan: 0.6913, IoU.pier: 0.7090, IoU.crt screen: 0.0200, IoU.plate: 0.6300, IoU.monitor: 0.4722, IoU.bulletin board: 0.4969, IoU.shower: 0.0528, IoU.radiator: 0.6719, IoU.glass: 0.2241, IoU.clock: 0.5189, IoU.flag: 0.7001, Acc.wall: 0.8983, Acc.building: 0.9410, Acc.sky: 0.9789, Acc.floor: 0.9115, Acc.tree: 0.8863, Acc.ceiling: 0.9341, Acc.road: 0.9077, Acc.bed : 0.9698, Acc.windowpane: 0.8223, Acc.grass: 0.8344, Acc.cabinet: 0.7900, Acc.sidewalk: 0.8651, Acc.person: 0.9420, Acc.earth: 0.5433, Acc.door: 0.7410, Acc.table: 0.8329, Acc.mountain: 0.7176, Acc.plant: 0.6476, Acc.curtain: 0.8924, Acc.chair: 0.7769, Acc.car: 0.9304, Acc.water: 0.7420, Acc.painting: 0.8996, Acc.sofa: 0.9171, Acc.shelf: 0.6961, Acc.house: 0.6408, Acc.sea: 0.8414, Acc.mirror: 0.8311, Acc.rug: 0.7785, Acc.field: 0.4487, Acc.armchair: 0.8055, Acc.seat: 0.9018, Acc.fence: 0.6061, Acc.desk: 0.7720, Acc.rock: 0.8934, Acc.wardrobe: 0.7816, Acc.lamp: 0.8628, Acc.bathtub: 0.8778, Acc.railing: 0.6087, Acc.cushion: 0.8602, Acc.base: 0.5416, Acc.box: 0.4586, Acc.column: 0.7118, Acc.signboard: 0.5980, Acc.chest of drawers: 0.6434, Acc.counter: 0.4799, Acc.sand: 0.8670, Acc.sink: 0.8162, Acc.skyscraper: 0.6318, Acc.fireplace: 0.9033, Acc.refrigerator: 0.9411, Acc.grandstand: 0.8460, Acc.path: 0.4323, Acc.stairs: 0.3101, Acc.runway: 0.9536, Acc.case: 0.8121, Acc.pool table: 0.9768, Acc.pillow: 0.7627, Acc.screen door: 0.8845, Acc.stairway: 0.7102, Acc.river: 0.2564, Acc.bridge: 0.8649, Acc.bookcase: 0.5428, Acc.blind: 0.5150, Acc.coffee table: 0.8492, Acc.toilet: 0.9418, Acc.flower: 0.5311, Acc.book: 0.7346, Acc.hill: 0.2065, Acc.bench: 0.6202, Acc.countertop: 0.8250, Acc.stove: 0.9280, Acc.palm: 0.7633, Acc.kitchen island: 0.5415, Acc.computer: 0.9286, Acc.swivel chair: 0.8336, Acc.boat: 0.9138, Acc.bar: 0.8455, Acc.arcade machine: 0.8403, Acc.hovel: 0.3783, Acc.bus: 0.9693, Acc.towel: 0.8853, Acc.light: 0.6778, Acc.truck: 0.5503, Acc.tower: 0.3111, Acc.chandelier: 0.8671, Acc.awning: 0.4966, Acc.streetlight: 0.4771, Acc.booth: 0.5873, Acc.television receiver: 0.8738, Acc.airplane: 0.9541, Acc.dirt track: 0.3492, Acc.apparel: 0.9041, Acc.pole: 0.3877, Acc.land: 0.0314, Acc.bannister: 0.2846, Acc.escalator: 0.8304, Acc.ottoman: 0.7402, Acc.bottle: 0.5376, Acc.buffet: 0.5642, Acc.poster: 0.4792, Acc.stage: 0.4747, Acc.van: 0.7122, Acc.ship: 0.9291, Acc.fountain: 0.3680, Acc.conveyer belt: 0.9395, Acc.canopy: 0.7733, Acc.washer: 0.9334, Acc.plaything: 0.7421, Acc.swimming pool: 0.7885, Acc.stool: 0.6936, Acc.barrel: 0.6964, Acc.basket: 0.6057, Acc.waterfall: 0.6624, Acc.tent: 0.9888, Acc.bag: 0.2223, Acc.minibike: 0.8977, Acc.cradle: 0.9689, Acc.oven: 0.6442, Acc.ball: 0.4149, Acc.food: 0.8457, Acc.step: 0.3247, Acc.tank: 0.6726, Acc.trade name: 0.1873, Acc.microwave: 0.9631, Acc.pot: 0.7175, Acc.animal: 0.6435, Acc.bicycle: 0.7852, Acc.lake: 0.7749, Acc.dishwasher: 0.8556, Acc.screen: 0.9394, Acc.blanket: 0.4758, Acc.sculpture: 0.8849, Acc.hood: 0.7379, Acc.sconce: 0.6740, Acc.vase: 0.6423, Acc.traffic light: 0.6485, Acc.tray: 0.3678, Acc.ashcan: 0.6501, Acc.fan: 0.8262, Acc.pier: 0.8588, Acc.crt screen: 0.0339, Acc.plate: 0.7804, Acc.monitor: 0.5417, Acc.bulletin board: 0.6512, Acc.shower: 0.1332, Acc.radiator: 0.7781, Acc.glass: 0.2456, Acc.clock: 0.6051, Acc.flag: 0.7795 +2024-06-16 18:05:01,058 - mmseg - INFO - Iter [42050/80000] lr: 1.898e-05, eta: 18:38:24, time: 3.542, data_time: 1.929, memory: 71384, decode.loss_ce: 0.1807, decode.acc_seg: 92.3039, aux.loss_ce: 0.0756, aux.acc_seg: 91.8545, loss: 0.2563 +2024-06-16 18:06:22,119 - mmseg - INFO - Iter [42100/80000] lr: 1.895e-05, eta: 18:36:49, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1807, decode.acc_seg: 92.6025, aux.loss_ce: 0.0757, aux.acc_seg: 92.2422, loss: 0.2564 +2024-06-16 18:07:43,055 - mmseg - INFO - Iter [42150/80000] lr: 1.893e-05, eta: 18:35:13, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1830, decode.acc_seg: 92.0780, aux.loss_ce: 0.0765, aux.acc_seg: 91.7689, loss: 0.2594 +2024-06-16 18:09:04,059 - mmseg - INFO - Iter [42200/80000] lr: 1.890e-05, eta: 18:33:38, time: 1.620, data_time: 0.009, memory: 71384, decode.loss_ce: 0.1907, decode.acc_seg: 91.8366, aux.loss_ce: 0.0792, aux.acc_seg: 91.5477, loss: 0.2699 +2024-06-16 18:10:25,104 - mmseg - INFO - Iter [42250/80000] lr: 1.888e-05, eta: 18:32:03, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1903, decode.acc_seg: 92.3517, aux.loss_ce: 0.0793, aux.acc_seg: 91.9720, loss: 0.2696 +2024-06-16 18:11:46,107 - mmseg - INFO - Iter [42300/80000] lr: 1.885e-05, eta: 18:30:29, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1856, decode.acc_seg: 92.2596, aux.loss_ce: 0.0777, aux.acc_seg: 91.8752, loss: 0.2634 +2024-06-16 18:13:07,092 - mmseg - INFO - Iter [42350/80000] lr: 1.883e-05, eta: 18:28:54, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1809, decode.acc_seg: 92.1938, aux.loss_ce: 0.0757, aux.acc_seg: 91.8060, loss: 0.2566 +2024-06-16 18:14:28,269 - mmseg - INFO - Iter [42400/80000] lr: 1.880e-05, eta: 18:27:19, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1782, decode.acc_seg: 92.4909, aux.loss_ce: 0.0749, aux.acc_seg: 92.0727, loss: 0.2531 +2024-06-16 18:15:49,378 - mmseg - INFO - Iter [42450/80000] lr: 1.878e-05, eta: 18:25:44, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1918, decode.acc_seg: 91.8806, aux.loss_ce: 0.0801, aux.acc_seg: 91.4989, loss: 0.2719 +2024-06-16 18:17:10,417 - mmseg - INFO - Iter [42500/80000] lr: 1.875e-05, eta: 18:24:09, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1880, decode.acc_seg: 92.1456, aux.loss_ce: 0.0788, aux.acc_seg: 91.8261, loss: 0.2668 +2024-06-16 18:18:31,769 - mmseg - INFO - Iter [42550/80000] lr: 1.873e-05, eta: 18:22:35, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1852, decode.acc_seg: 92.0585, aux.loss_ce: 0.0770, aux.acc_seg: 91.7423, loss: 0.2622 +2024-06-16 18:19:52,852 - mmseg - INFO - Iter [42600/80000] lr: 1.870e-05, eta: 18:21:00, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1840, decode.acc_seg: 92.3056, aux.loss_ce: 0.0767, aux.acc_seg: 91.9157, loss: 0.2607 +2024-06-16 18:21:13,899 - mmseg - INFO - Iter [42650/80000] lr: 1.868e-05, eta: 18:19:25, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1975, decode.acc_seg: 91.7642, aux.loss_ce: 0.0814, aux.acc_seg: 91.4864, loss: 0.2789 +2024-06-16 18:22:34,909 - mmseg - INFO - Iter [42700/80000] lr: 1.865e-05, eta: 18:17:51, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1815, decode.acc_seg: 92.1650, aux.loss_ce: 0.0751, aux.acc_seg: 91.8950, loss: 0.2565 +2024-06-16 18:23:56,072 - mmseg - INFO - Iter [42750/80000] lr: 1.863e-05, eta: 18:16:16, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1996, decode.acc_seg: 91.6209, aux.loss_ce: 0.0835, aux.acc_seg: 91.2663, loss: 0.2830 +2024-06-16 18:25:17,190 - mmseg - INFO - Iter [42800/80000] lr: 1.860e-05, eta: 18:14:42, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1964, decode.acc_seg: 91.9236, aux.loss_ce: 0.0809, aux.acc_seg: 91.5968, loss: 0.2772 +2024-06-16 18:26:38,211 - mmseg - INFO - Iter [42850/80000] lr: 1.858e-05, eta: 18:13:07, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1920, decode.acc_seg: 92.0067, aux.loss_ce: 0.0804, aux.acc_seg: 91.6256, loss: 0.2724 +2024-06-16 18:27:59,298 - mmseg - INFO - Iter [42900/80000] lr: 1.855e-05, eta: 18:11:33, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1765, decode.acc_seg: 92.3530, aux.loss_ce: 0.0732, aux.acc_seg: 92.0927, loss: 0.2496 +2024-06-16 18:29:22,742 - mmseg - INFO - Iter [42950/80000] lr: 1.853e-05, eta: 18:10:00, time: 1.669, data_time: 0.054, memory: 71384, decode.loss_ce: 0.1875, decode.acc_seg: 91.7621, aux.loss_ce: 0.0778, aux.acc_seg: 91.4864, loss: 0.2653 +2024-06-16 18:30:43,900 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 18:30:43,900 - mmseg - INFO - Iter [43000/80000] lr: 1.850e-05, eta: 18:08:26, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1891, decode.acc_seg: 91.8894, aux.loss_ce: 0.0792, aux.acc_seg: 91.4775, loss: 0.2684 +2024-06-16 18:32:21,737 - mmseg - INFO - per class results: +2024-06-16 18:32:21,745 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.33 | 90.66 | +| building | 85.36 | 93.43 | +| sky | 94.88 | 97.55 | +| floor | 85.72 | 92.7 | +| tree | 77.46 | 89.86 | +| ceiling | 87.76 | 93.71 | +| road | 86.07 | 91.67 | +| bed | 91.2 | 95.55 | +| windowpane | 67.16 | 82.97 | +| grass | 71.87 | 92.42 | +| cabinet | 65.72 | 75.89 | +| sidewalk | 69.22 | 83.57 | +| person | 86.12 | 95.09 | +| earth | 38.81 | 49.47 | +| door | 59.19 | 73.37 | +| table | 68.1 | 81.48 | +| mountain | 61.64 | 71.63 | +| plant | 53.12 | 60.84 | +| curtain | 80.43 | 90.36 | +| chair | 68.93 | 80.68 | +| car | 87.9 | 94.43 | +| water | 62.69 | 76.15 | +| painting | 79.01 | 88.31 | +| sofa | 82.31 | 92.64 | +| shelf | 47.96 | 63.2 | +| house | 57.67 | 68.61 | +| sea | 71.8 | 83.48 | +| mirror | 78.46 | 86.52 | +| rug | 69.97 | 78.35 | +| field | 30.83 | 42.16 | +| armchair | 60.23 | 74.98 | +| seat | 68.49 | 87.87 | +| fence | 49.0 | 56.97 | +| desk | 59.84 | 79.01 | +| rock | 54.73 | 79.11 | +| wardrobe | 54.58 | 76.0 | +| lamp | 75.67 | 84.51 | +| bathtub | 86.35 | 88.94 | +| railing | 45.71 | 64.87 | +| cushion | 71.33 | 81.57 | +| base | 40.74 | 53.39 | +| box | 35.84 | 43.66 | +| column | 53.43 | 64.92 | +| signboard | 41.05 | 56.56 | +| chest of drawers | 42.73 | 62.65 | +| counter | 32.31 | 42.23 | +| sand | 55.42 | 87.93 | +| sink | 79.63 | 85.84 | +| skyscraper | 49.12 | 61.8 | +| fireplace | 75.77 | 93.2 | +| refrigerator | 84.74 | 95.09 | +| grandstand | 53.52 | 81.77 | +| path | 28.27 | 43.74 | +| stairs | 28.37 | 32.02 | +| runway | 69.53 | 98.04 | +| case | 61.57 | 81.18 | +| pool table | 94.83 | 98.34 | +| pillow | 68.49 | 78.58 | +| screen door | 85.21 | 88.68 | +| stairway | 40.34 | 65.95 | +| river | 13.7 | 33.58 | +| bridge | 72.62 | 85.94 | +| bookcase | 43.36 | 65.0 | +| blind | 42.24 | 51.48 | +| coffee table | 60.9 | 87.08 | +| toilet | 90.57 | 93.0 | +| flower | 44.07 | 57.57 | +| book | 54.35 | 77.71 | +| hill | 10.05 | 22.34 | +| bench | 56.1 | 63.21 | +| countertop | 62.31 | 82.92 | +| stove | 87.3 | 93.27 | +| palm | 53.24 | 76.5 | +| kitchen island | 51.13 | 87.94 | +| computer | 79.69 | 92.63 | +| swivel chair | 50.9 | 76.29 | +| boat | 71.27 | 90.82 | +| bar | 59.67 | 80.21 | +| arcade machine | 73.57 | 78.37 | +| hovel | 13.58 | 13.8 | +| bus | 93.81 | 96.65 | +| towel | 77.98 | 90.01 | +| light | 60.29 | 66.11 | +| truck | 40.15 | 46.94 | +| tower | 31.13 | 53.94 | +| chandelier | 73.93 | 83.8 | +| awning | 41.6 | 50.96 | +| streetlight | 34.59 | 44.45 | +| booth | 51.48 | 61.02 | +| television receiver | 77.93 | 89.91 | +| airplane | 87.41 | 93.24 | +| dirt track | 14.64 | 49.72 | +| apparel | 65.13 | 81.4 | +| pole | 29.29 | 40.59 | +| land | 5.62 | 9.92 | +| bannister | 23.04 | 28.24 | +| escalator | 65.63 | 85.76 | +| ottoman | 46.56 | 60.25 | +| bottle | 44.17 | 66.84 | +| buffet | 44.35 | 50.35 | +| poster | 34.54 | 44.72 | +| stage | 29.7 | 44.79 | +| van | 49.59 | 71.26 | +| ship | 85.66 | 89.02 | +| fountain | 35.67 | 37.79 | +| conveyer belt | 82.8 | 93.33 | +| canopy | 56.56 | 76.92 | +| washer | 87.38 | 93.6 | +| plaything | 27.12 | 39.5 | +| swimming pool | 52.17 | 75.57 | +| stool | 52.41 | 66.3 | +| barrel | 38.37 | 95.71 | +| basket | 42.86 | 59.64 | +| waterfall | 53.57 | 66.63 | +| tent | 96.19 | 98.27 | +| bag | 25.22 | 28.96 | +| minibike | 76.54 | 87.3 | +| cradle | 81.45 | 97.89 | +| oven | 63.68 | 73.06 | +| ball | 53.45 | 77.72 | +| food | 61.72 | 73.97 | +| step | 14.27 | 15.69 | +| tank | 58.12 | 65.88 | +| trade name | 23.14 | 27.89 | +| microwave | 90.33 | 95.77 | +| pot | 60.9 | 69.51 | +| animal | 66.94 | 68.44 | +| bicycle | 59.26 | 74.02 | +| lake | 57.77 | 66.46 | +| dishwasher | 71.39 | 84.57 | +| screen | 62.75 | 93.22 | +| blanket | 36.23 | 41.88 | +| sculpture | 77.22 | 85.74 | +| hood | 62.63 | 74.09 | +| sconce | 59.04 | 66.78 | +| vase | 47.53 | 62.07 | +| traffic light | 35.93 | 61.03 | +| tray | 21.28 | 27.85 | +| ashcan | 54.45 | 64.26 | +| fan | 69.28 | 83.78 | +| pier | 39.37 | 49.29 | +| crt screen | 2.1 | 3.36 | +| plate | 63.49 | 79.22 | +| monitor | 56.78 | 69.48 | +| bulletin board | 59.74 | 74.78 | +| shower | 0.49 | 0.49 | +| radiator | 68.26 | 81.03 | +| glass | 20.51 | 21.86 | +| clock | 50.74 | 57.48 | +| flag | 72.92 | 81.62 | ++---------------------+-------+-------+ +2024-06-16 18:32:21,745 - mmseg - INFO - Summary: +2024-06-16 18:32:21,745 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 86.31 | 57.64 | 70.3 | ++-------+-------+------+ +2024-06-16 18:32:21,746 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 18:32:21,746 - mmseg - INFO - Iter(val) [250] aAcc: 0.8631, mIoU: 0.5764, mAcc: 0.7030, IoU.wall: 0.8233, IoU.building: 0.8536, IoU.sky: 0.9488, IoU.floor: 0.8572, IoU.tree: 0.7746, IoU.ceiling: 0.8776, IoU.road: 0.8607, IoU.bed : 0.9120, IoU.windowpane: 0.6716, IoU.grass: 0.7187, IoU.cabinet: 0.6572, IoU.sidewalk: 0.6922, IoU.person: 0.8612, IoU.earth: 0.3881, IoU.door: 0.5919, IoU.table: 0.6810, IoU.mountain: 0.6164, IoU.plant: 0.5312, IoU.curtain: 0.8043, IoU.chair: 0.6893, IoU.car: 0.8790, IoU.water: 0.6269, IoU.painting: 0.7901, IoU.sofa: 0.8231, IoU.shelf: 0.4796, IoU.house: 0.5767, IoU.sea: 0.7180, IoU.mirror: 0.7846, IoU.rug: 0.6997, IoU.field: 0.3083, IoU.armchair: 0.6023, IoU.seat: 0.6849, IoU.fence: 0.4900, IoU.desk: 0.5984, IoU.rock: 0.5473, IoU.wardrobe: 0.5458, IoU.lamp: 0.7567, IoU.bathtub: 0.8635, IoU.railing: 0.4571, IoU.cushion: 0.7133, IoU.base: 0.4074, IoU.box: 0.3584, IoU.column: 0.5343, IoU.signboard: 0.4105, IoU.chest of drawers: 0.4273, IoU.counter: 0.3231, IoU.sand: 0.5542, IoU.sink: 0.7963, IoU.skyscraper: 0.4912, IoU.fireplace: 0.7577, IoU.refrigerator: 0.8474, IoU.grandstand: 0.5352, IoU.path: 0.2827, IoU.stairs: 0.2837, IoU.runway: 0.6953, IoU.case: 0.6157, IoU.pool table: 0.9483, IoU.pillow: 0.6849, IoU.screen door: 0.8521, IoU.stairway: 0.4034, IoU.river: 0.1370, IoU.bridge: 0.7262, IoU.bookcase: 0.4336, IoU.blind: 0.4224, IoU.coffee table: 0.6090, IoU.toilet: 0.9057, IoU.flower: 0.4407, IoU.book: 0.5435, IoU.hill: 0.1005, IoU.bench: 0.5610, IoU.countertop: 0.6231, IoU.stove: 0.8730, IoU.palm: 0.5324, IoU.kitchen island: 0.5113, IoU.computer: 0.7969, IoU.swivel chair: 0.5090, IoU.boat: 0.7127, IoU.bar: 0.5967, IoU.arcade machine: 0.7357, IoU.hovel: 0.1358, IoU.bus: 0.9381, IoU.towel: 0.7798, IoU.light: 0.6029, IoU.truck: 0.4015, IoU.tower: 0.3113, IoU.chandelier: 0.7393, IoU.awning: 0.4160, IoU.streetlight: 0.3459, IoU.booth: 0.5148, IoU.television receiver: 0.7793, IoU.airplane: 0.8741, IoU.dirt track: 0.1464, IoU.apparel: 0.6513, IoU.pole: 0.2929, IoU.land: 0.0562, IoU.bannister: 0.2304, IoU.escalator: 0.6563, IoU.ottoman: 0.4656, IoU.bottle: 0.4417, IoU.buffet: 0.4435, IoU.poster: 0.3454, IoU.stage: 0.2970, IoU.van: 0.4959, IoU.ship: 0.8566, IoU.fountain: 0.3567, IoU.conveyer belt: 0.8280, IoU.canopy: 0.5656, IoU.washer: 0.8738, IoU.plaything: 0.2712, IoU.swimming pool: 0.5217, IoU.stool: 0.5241, IoU.barrel: 0.3837, IoU.basket: 0.4286, IoU.waterfall: 0.5357, IoU.tent: 0.9619, IoU.bag: 0.2522, IoU.minibike: 0.7654, IoU.cradle: 0.8145, IoU.oven: 0.6368, IoU.ball: 0.5345, IoU.food: 0.6172, IoU.step: 0.1427, IoU.tank: 0.5812, IoU.trade name: 0.2314, IoU.microwave: 0.9033, IoU.pot: 0.6090, IoU.animal: 0.6694, IoU.bicycle: 0.5926, IoU.lake: 0.5777, IoU.dishwasher: 0.7139, IoU.screen: 0.6275, IoU.blanket: 0.3623, IoU.sculpture: 0.7722, IoU.hood: 0.6263, IoU.sconce: 0.5904, IoU.vase: 0.4753, IoU.traffic light: 0.3593, IoU.tray: 0.2128, IoU.ashcan: 0.5445, IoU.fan: 0.6928, IoU.pier: 0.3937, IoU.crt screen: 0.0210, IoU.plate: 0.6349, IoU.monitor: 0.5678, IoU.bulletin board: 0.5974, IoU.shower: 0.0049, IoU.radiator: 0.6826, IoU.glass: 0.2051, IoU.clock: 0.5074, IoU.flag: 0.7292, Acc.wall: 0.9066, Acc.building: 0.9343, Acc.sky: 0.9755, Acc.floor: 0.9270, Acc.tree: 0.8986, Acc.ceiling: 0.9371, Acc.road: 0.9167, Acc.bed : 0.9555, Acc.windowpane: 0.8297, Acc.grass: 0.9242, Acc.cabinet: 0.7589, Acc.sidewalk: 0.8357, Acc.person: 0.9509, Acc.earth: 0.4947, Acc.door: 0.7337, Acc.table: 0.8148, Acc.mountain: 0.7163, Acc.plant: 0.6084, Acc.curtain: 0.9036, Acc.chair: 0.8068, Acc.car: 0.9443, Acc.water: 0.7615, Acc.painting: 0.8831, Acc.sofa: 0.9264, Acc.shelf: 0.6320, Acc.house: 0.6861, Acc.sea: 0.8348, Acc.mirror: 0.8652, Acc.rug: 0.7835, Acc.field: 0.4216, Acc.armchair: 0.7498, Acc.seat: 0.8787, Acc.fence: 0.5697, Acc.desk: 0.7901, Acc.rock: 0.7911, Acc.wardrobe: 0.7600, Acc.lamp: 0.8451, Acc.bathtub: 0.8894, Acc.railing: 0.6487, Acc.cushion: 0.8157, Acc.base: 0.5339, Acc.box: 0.4366, Acc.column: 0.6492, Acc.signboard: 0.5656, Acc.chest of drawers: 0.6265, Acc.counter: 0.4223, Acc.sand: 0.8793, Acc.sink: 0.8584, Acc.skyscraper: 0.6180, Acc.fireplace: 0.9320, Acc.refrigerator: 0.9509, Acc.grandstand: 0.8177, Acc.path: 0.4374, Acc.stairs: 0.3202, Acc.runway: 0.9804, Acc.case: 0.8118, Acc.pool table: 0.9834, Acc.pillow: 0.7858, Acc.screen door: 0.8868, Acc.stairway: 0.6595, Acc.river: 0.3358, Acc.bridge: 0.8594, Acc.bookcase: 0.6500, Acc.blind: 0.5148, Acc.coffee table: 0.8708, Acc.toilet: 0.9300, Acc.flower: 0.5757, Acc.book: 0.7771, Acc.hill: 0.2234, Acc.bench: 0.6321, Acc.countertop: 0.8292, Acc.stove: 0.9327, Acc.palm: 0.7650, Acc.kitchen island: 0.8794, Acc.computer: 0.9263, Acc.swivel chair: 0.7629, Acc.boat: 0.9082, Acc.bar: 0.8021, Acc.arcade machine: 0.7837, Acc.hovel: 0.1380, Acc.bus: 0.9665, Acc.towel: 0.9001, Acc.light: 0.6611, Acc.truck: 0.4694, Acc.tower: 0.5394, Acc.chandelier: 0.8380, Acc.awning: 0.5096, Acc.streetlight: 0.4445, Acc.booth: 0.6102, Acc.television receiver: 0.8991, Acc.airplane: 0.9324, Acc.dirt track: 0.4972, Acc.apparel: 0.8140, Acc.pole: 0.4059, Acc.land: 0.0992, Acc.bannister: 0.2824, Acc.escalator: 0.8576, Acc.ottoman: 0.6025, Acc.bottle: 0.6684, Acc.buffet: 0.5035, Acc.poster: 0.4472, Acc.stage: 0.4479, Acc.van: 0.7126, Acc.ship: 0.8902, Acc.fountain: 0.3779, Acc.conveyer belt: 0.9333, Acc.canopy: 0.7692, Acc.washer: 0.9360, Acc.plaything: 0.3950, Acc.swimming pool: 0.7557, Acc.stool: 0.6630, Acc.barrel: 0.9571, Acc.basket: 0.5964, Acc.waterfall: 0.6663, Acc.tent: 0.9827, Acc.bag: 0.2896, Acc.minibike: 0.8730, Acc.cradle: 0.9789, Acc.oven: 0.7306, Acc.ball: 0.7772, Acc.food: 0.7397, Acc.step: 0.1569, Acc.tank: 0.6588, Acc.trade name: 0.2789, Acc.microwave: 0.9577, Acc.pot: 0.6951, Acc.animal: 0.6844, Acc.bicycle: 0.7402, Acc.lake: 0.6646, Acc.dishwasher: 0.8457, Acc.screen: 0.9322, Acc.blanket: 0.4188, Acc.sculpture: 0.8574, Acc.hood: 0.7409, Acc.sconce: 0.6678, Acc.vase: 0.6207, Acc.traffic light: 0.6103, Acc.tray: 0.2785, Acc.ashcan: 0.6426, Acc.fan: 0.8378, Acc.pier: 0.4929, Acc.crt screen: 0.0336, Acc.plate: 0.7922, Acc.monitor: 0.6948, Acc.bulletin board: 0.7478, Acc.shower: 0.0049, Acc.radiator: 0.8103, Acc.glass: 0.2186, Acc.clock: 0.5748, Acc.flag: 0.8162 +2024-06-16 18:33:43,269 - mmseg - INFO - Iter [43050/80000] lr: 1.848e-05, eta: 18:08:16, time: 3.587, data_time: 1.974, memory: 71384, decode.loss_ce: 0.1760, decode.acc_seg: 92.2881, aux.loss_ce: 0.0741, aux.acc_seg: 91.9379, loss: 0.2501 +2024-06-16 18:35:04,378 - mmseg - INFO - Iter [43100/80000] lr: 1.845e-05, eta: 18:06:41, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1706, decode.acc_seg: 92.7035, aux.loss_ce: 0.0710, aux.acc_seg: 92.3944, loss: 0.2416 +2024-06-16 18:36:25,459 - mmseg - INFO - Iter [43150/80000] lr: 1.843e-05, eta: 18:05:07, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1816, decode.acc_seg: 92.1949, aux.loss_ce: 0.0758, aux.acc_seg: 91.9163, loss: 0.2573 +2024-06-16 18:37:46,526 - mmseg - INFO - Iter [43200/80000] lr: 1.840e-05, eta: 18:03:32, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1744, decode.acc_seg: 92.5576, aux.loss_ce: 0.0730, aux.acc_seg: 92.1897, loss: 0.2474 +2024-06-16 18:39:07,480 - mmseg - INFO - Iter [43250/80000] lr: 1.838e-05, eta: 18:01:58, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1715, decode.acc_seg: 92.4414, aux.loss_ce: 0.0719, aux.acc_seg: 92.1661, loss: 0.2434 +2024-06-16 18:40:28,721 - mmseg - INFO - Iter [43300/80000] lr: 1.835e-05, eta: 18:00:23, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1817, decode.acc_seg: 92.1602, aux.loss_ce: 0.0764, aux.acc_seg: 91.7961, loss: 0.2581 +2024-06-16 18:41:49,801 - mmseg - INFO - Iter [43350/80000] lr: 1.833e-05, eta: 17:58:49, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1890, decode.acc_seg: 91.9631, aux.loss_ce: 0.0793, aux.acc_seg: 91.5642, loss: 0.2683 +2024-06-16 18:43:10,997 - mmseg - INFO - Iter [43400/80000] lr: 1.830e-05, eta: 17:57:15, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1862, decode.acc_seg: 92.2247, aux.loss_ce: 0.0775, aux.acc_seg: 91.9547, loss: 0.2637 +2024-06-16 18:44:32,060 - mmseg - INFO - Iter [43450/80000] lr: 1.828e-05, eta: 17:55:40, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1800, decode.acc_seg: 92.2579, aux.loss_ce: 0.0760, aux.acc_seg: 91.7991, loss: 0.2560 +2024-06-16 18:45:53,163 - mmseg - INFO - Iter [43500/80000] lr: 1.825e-05, eta: 17:54:06, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1748, decode.acc_seg: 92.4645, aux.loss_ce: 0.0734, aux.acc_seg: 92.0721, loss: 0.2483 +2024-06-16 18:47:14,151 - mmseg - INFO - Iter [43550/80000] lr: 1.823e-05, eta: 17:52:31, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1856, decode.acc_seg: 91.9698, aux.loss_ce: 0.0778, aux.acc_seg: 91.5948, loss: 0.2634 +2024-06-16 18:48:35,124 - mmseg - INFO - Iter [43600/80000] lr: 1.820e-05, eta: 17:50:57, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1862, decode.acc_seg: 92.2474, aux.loss_ce: 0.0775, aux.acc_seg: 91.8564, loss: 0.2637 +2024-06-16 18:49:56,245 - mmseg - INFO - Iter [43650/80000] lr: 1.818e-05, eta: 17:49:23, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1782, decode.acc_seg: 92.4262, aux.loss_ce: 0.0750, aux.acc_seg: 92.0432, loss: 0.2532 +2024-06-16 18:51:17,408 - mmseg - INFO - Iter [43700/80000] lr: 1.815e-05, eta: 17:47:49, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1794, decode.acc_seg: 92.2333, aux.loss_ce: 0.0750, aux.acc_seg: 91.8846, loss: 0.2544 +2024-06-16 18:52:38,580 - mmseg - INFO - Iter [43750/80000] lr: 1.813e-05, eta: 17:46:15, time: 1.623, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1807, decode.acc_seg: 92.2759, aux.loss_ce: 0.0760, aux.acc_seg: 91.8990, loss: 0.2566 +2024-06-16 18:53:59,648 - mmseg - INFO - Iter [43800/80000] lr: 1.810e-05, eta: 17:44:40, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1823, decode.acc_seg: 92.2826, aux.loss_ce: 0.0759, aux.acc_seg: 91.9440, loss: 0.2583 +2024-06-16 18:55:20,661 - mmseg - INFO - Iter [43850/80000] lr: 1.808e-05, eta: 17:43:06, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1830, decode.acc_seg: 91.8838, aux.loss_ce: 0.0759, aux.acc_seg: 91.5785, loss: 0.2588 +2024-06-16 18:56:41,830 - mmseg - INFO - Iter [43900/80000] lr: 1.805e-05, eta: 17:41:32, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1715, decode.acc_seg: 92.8003, aux.loss_ce: 0.0718, aux.acc_seg: 92.3969, loss: 0.2434 +2024-06-16 18:58:02,857 - mmseg - INFO - Iter [43950/80000] lr: 1.803e-05, eta: 17:39:58, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1763, decode.acc_seg: 92.4943, aux.loss_ce: 0.0743, aux.acc_seg: 92.1048, loss: 0.2506 +2024-06-16 18:59:23,960 - mmseg - INFO - Saving checkpoint at 44000 iterations +2024-06-16 19:00:47,901 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 19:00:47,902 - mmseg - INFO - Iter [44000/80000] lr: 1.800e-05, eta: 17:39:33, time: 3.301, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1843, decode.acc_seg: 92.0785, aux.loss_ce: 0.0769, aux.acc_seg: 91.7349, loss: 0.2612 +2024-06-16 19:02:24,091 - mmseg - INFO - per class results: +2024-06-16 19:02:24,097 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.17 | 89.76 | +| building | 85.41 | 93.31 | +| sky | 94.87 | 97.52 | +| floor | 85.14 | 92.95 | +| tree | 77.92 | 88.89 | +| ceiling | 87.77 | 93.63 | +| road | 85.98 | 90.23 | +| bed | 92.65 | 97.01 | +| windowpane | 67.19 | 82.37 | +| grass | 69.38 | 82.32 | +| cabinet | 64.61 | 75.05 | +| sidewalk | 71.53 | 87.24 | +| person | 86.47 | 94.43 | +| earth | 40.31 | 56.04 | +| door | 57.97 | 73.66 | +| table | 69.32 | 82.13 | +| mountain | 61.42 | 72.05 | +| plant | 56.26 | 66.83 | +| curtain | 80.32 | 92.01 | +| chair | 67.92 | 75.85 | +| car | 88.05 | 93.65 | +| water | 60.38 | 73.23 | +| painting | 79.07 | 89.9 | +| sofa | 79.8 | 94.31 | +| shelf | 51.19 | 66.56 | +| house | 58.07 | 74.09 | +| sea | 69.52 | 83.39 | +| mirror | 78.2 | 89.42 | +| rug | 68.63 | 80.5 | +| field | 32.68 | 48.16 | +| armchair | 57.3 | 71.25 | +| seat | 67.08 | 87.72 | +| fence | 48.93 | 60.4 | +| desk | 62.34 | 78.56 | +| rock | 53.02 | 81.73 | +| wardrobe | 54.9 | 76.69 | +| lamp | 75.83 | 86.34 | +| bathtub | 88.48 | 91.09 | +| railing | 44.16 | 62.23 | +| cushion | 70.34 | 79.14 | +| base | 43.41 | 59.8 | +| box | 35.64 | 45.85 | +| column | 53.1 | 61.68 | +| signboard | 40.65 | 52.46 | +| chest of drawers | 42.62 | 66.02 | +| counter | 35.8 | 50.9 | +| sand | 58.03 | 89.11 | +| sink | 79.92 | 86.29 | +| skyscraper | 49.24 | 62.92 | +| fireplace | 77.8 | 94.16 | +| refrigerator | 85.9 | 94.37 | +| grandstand | 51.82 | 85.05 | +| path | 30.87 | 41.99 | +| stairs | 27.17 | 33.25 | +| runway | 71.59 | 96.92 | +| case | 61.91 | 80.86 | +| pool table | 94.57 | 97.94 | +| pillow | 69.0 | 82.99 | +| screen door | 82.26 | 84.89 | +| stairway | 36.82 | 54.79 | +| river | 11.13 | 26.92 | +| bridge | 68.78 | 88.48 | +| bookcase | 46.6 | 70.25 | +| blind | 49.01 | 60.11 | +| coffee table | 62.01 | 86.12 | +| toilet | 90.31 | 92.94 | +| flower | 45.94 | 58.41 | +| book | 53.85 | 72.26 | +| hill | 6.01 | 10.17 | +| bench | 54.53 | 60.38 | +| countertop | 59.49 | 77.13 | +| stove | 86.87 | 93.36 | +| palm | 55.8 | 82.18 | +| kitchen island | 51.68 | 90.94 | +| computer | 80.77 | 90.81 | +| swivel chair | 49.77 | 75.28 | +| boat | 78.54 | 91.09 | +| bar | 60.29 | 84.32 | +| arcade machine | 84.16 | 90.94 | +| hovel | 33.42 | 38.79 | +| bus | 92.64 | 97.32 | +| towel | 80.53 | 87.35 | +| light | 62.25 | 70.87 | +| truck | 43.93 | 53.43 | +| tower | 23.71 | 41.01 | +| chandelier | 73.87 | 89.79 | +| awning | 42.21 | 52.75 | +| streetlight | 38.12 | 53.24 | +| booth | 47.06 | 71.07 | +| television receiver | 81.83 | 87.81 | +| airplane | 88.88 | 96.3 | +| dirt track | 10.1 | 56.61 | +| apparel | 65.1 | 81.55 | +| pole | 27.66 | 38.6 | +| land | 2.76 | 5.16 | +| bannister | 19.19 | 23.32 | +| escalator | 66.12 | 85.59 | +| ottoman | 49.37 | 65.56 | +| bottle | 45.15 | 68.56 | +| buffet | 44.07 | 48.23 | +| poster | 30.33 | 35.53 | +| stage | 30.35 | 47.86 | +| van | 46.53 | 71.57 | +| ship | 71.07 | 77.24 | +| fountain | 39.38 | 40.35 | +| conveyer belt | 77.89 | 95.22 | +| canopy | 55.68 | 79.75 | +| washer | 86.81 | 92.92 | +| plaything | 27.27 | 37.73 | +| swimming pool | 52.91 | 76.55 | +| stool | 53.23 | 66.06 | +| barrel | 57.82 | 71.08 | +| basket | 42.1 | 61.94 | +| waterfall | 52.36 | 66.62 | +| tent | 94.44 | 99.02 | +| bag | 24.34 | 26.97 | +| minibike | 71.82 | 93.89 | +| cradle | 89.64 | 97.12 | +| oven | 54.36 | 67.56 | +| ball | 41.9 | 45.0 | +| food | 62.11 | 74.5 | +| step | 24.39 | 30.29 | +| tank | 56.58 | 70.65 | +| trade name | 21.72 | 25.67 | +| microwave | 86.44 | 96.43 | +| pot | 58.47 | 67.98 | +| animal | 67.9 | 69.87 | +| bicycle | 59.99 | 78.63 | +| lake | 55.34 | 63.6 | +| dishwasher | 75.17 | 82.38 | +| screen | 62.41 | 95.53 | +| blanket | 39.22 | 46.84 | +| sculpture | 76.7 | 87.97 | +| hood | 63.14 | 72.85 | +| sconce | 63.43 | 74.95 | +| vase | 47.1 | 65.75 | +| traffic light | 36.58 | 65.9 | +| tray | 22.14 | 27.63 | +| ashcan | 49.74 | 68.95 | +| fan | 70.99 | 87.45 | +| pier | 41.43 | 48.66 | +| crt screen | 1.8 | 3.38 | +| plate | 63.44 | 79.31 | +| monitor | 46.58 | 55.18 | +| bulletin board | 59.51 | 77.55 | +| shower | 1.16 | 1.29 | +| radiator | 66.39 | 79.5 | +| glass | 21.58 | 23.13 | +| clock | 47.01 | 53.18 | +| flag | 70.51 | 82.93 | ++---------------------+-------+-------+ +2024-06-16 19:02:24,097 - mmseg - INFO - Summary: +2024-06-16 19:02:24,098 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.23 | 57.69 | 70.65 | ++-------+-------+-------+ +2024-06-16 19:02:24,098 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 19:02:24,099 - mmseg - INFO - Iter(val) [250] aAcc: 0.8623, mIoU: 0.5769, mAcc: 0.7065, IoU.wall: 0.8217, IoU.building: 0.8541, IoU.sky: 0.9487, IoU.floor: 0.8514, IoU.tree: 0.7792, IoU.ceiling: 0.8777, IoU.road: 0.8598, IoU.bed : 0.9265, IoU.windowpane: 0.6719, IoU.grass: 0.6938, IoU.cabinet: 0.6461, IoU.sidewalk: 0.7153, IoU.person: 0.8647, IoU.earth: 0.4031, IoU.door: 0.5797, IoU.table: 0.6932, IoU.mountain: 0.6142, IoU.plant: 0.5626, IoU.curtain: 0.8032, IoU.chair: 0.6792, IoU.car: 0.8805, IoU.water: 0.6038, IoU.painting: 0.7907, IoU.sofa: 0.7980, IoU.shelf: 0.5119, IoU.house: 0.5807, IoU.sea: 0.6952, IoU.mirror: 0.7820, IoU.rug: 0.6863, IoU.field: 0.3268, IoU.armchair: 0.5730, IoU.seat: 0.6708, IoU.fence: 0.4893, IoU.desk: 0.6234, IoU.rock: 0.5302, IoU.wardrobe: 0.5490, IoU.lamp: 0.7583, IoU.bathtub: 0.8848, IoU.railing: 0.4416, IoU.cushion: 0.7034, IoU.base: 0.4341, IoU.box: 0.3564, IoU.column: 0.5310, IoU.signboard: 0.4065, IoU.chest of drawers: 0.4262, IoU.counter: 0.3580, IoU.sand: 0.5803, IoU.sink: 0.7992, IoU.skyscraper: 0.4924, IoU.fireplace: 0.7780, IoU.refrigerator: 0.8590, IoU.grandstand: 0.5182, IoU.path: 0.3087, IoU.stairs: 0.2717, IoU.runway: 0.7159, IoU.case: 0.6191, IoU.pool table: 0.9457, IoU.pillow: 0.6900, IoU.screen door: 0.8226, IoU.stairway: 0.3682, IoU.river: 0.1113, IoU.bridge: 0.6878, IoU.bookcase: 0.4660, IoU.blind: 0.4901, IoU.coffee table: 0.6201, IoU.toilet: 0.9031, IoU.flower: 0.4594, IoU.book: 0.5385, IoU.hill: 0.0601, IoU.bench: 0.5453, IoU.countertop: 0.5949, IoU.stove: 0.8687, IoU.palm: 0.5580, IoU.kitchen island: 0.5168, IoU.computer: 0.8077, IoU.swivel chair: 0.4977, IoU.boat: 0.7854, IoU.bar: 0.6029, IoU.arcade machine: 0.8416, IoU.hovel: 0.3342, IoU.bus: 0.9264, IoU.towel: 0.8053, IoU.light: 0.6225, IoU.truck: 0.4393, IoU.tower: 0.2371, IoU.chandelier: 0.7387, IoU.awning: 0.4221, IoU.streetlight: 0.3812, IoU.booth: 0.4706, IoU.television receiver: 0.8183, IoU.airplane: 0.8888, IoU.dirt track: 0.1010, IoU.apparel: 0.6510, IoU.pole: 0.2766, IoU.land: 0.0276, IoU.bannister: 0.1919, IoU.escalator: 0.6612, IoU.ottoman: 0.4937, IoU.bottle: 0.4515, IoU.buffet: 0.4407, IoU.poster: 0.3033, IoU.stage: 0.3035, IoU.van: 0.4653, IoU.ship: 0.7107, IoU.fountain: 0.3938, IoU.conveyer belt: 0.7789, IoU.canopy: 0.5568, IoU.washer: 0.8681, IoU.plaything: 0.2727, IoU.swimming pool: 0.5291, IoU.stool: 0.5323, IoU.barrel: 0.5782, IoU.basket: 0.4210, IoU.waterfall: 0.5236, IoU.tent: 0.9444, IoU.bag: 0.2434, IoU.minibike: 0.7182, IoU.cradle: 0.8964, IoU.oven: 0.5436, IoU.ball: 0.4190, IoU.food: 0.6211, IoU.step: 0.2439, IoU.tank: 0.5658, IoU.trade name: 0.2172, IoU.microwave: 0.8644, IoU.pot: 0.5847, IoU.animal: 0.6790, IoU.bicycle: 0.5999, IoU.lake: 0.5534, IoU.dishwasher: 0.7517, IoU.screen: 0.6241, IoU.blanket: 0.3922, IoU.sculpture: 0.7670, IoU.hood: 0.6314, IoU.sconce: 0.6343, IoU.vase: 0.4710, IoU.traffic light: 0.3658, IoU.tray: 0.2214, IoU.ashcan: 0.4974, IoU.fan: 0.7099, IoU.pier: 0.4143, IoU.crt screen: 0.0180, IoU.plate: 0.6344, IoU.monitor: 0.4658, IoU.bulletin board: 0.5951, IoU.shower: 0.0116, IoU.radiator: 0.6639, IoU.glass: 0.2158, IoU.clock: 0.4701, IoU.flag: 0.7051, Acc.wall: 0.8976, Acc.building: 0.9331, Acc.sky: 0.9752, Acc.floor: 0.9295, Acc.tree: 0.8889, Acc.ceiling: 0.9363, Acc.road: 0.9023, Acc.bed : 0.9701, Acc.windowpane: 0.8237, Acc.grass: 0.8232, Acc.cabinet: 0.7505, Acc.sidewalk: 0.8724, Acc.person: 0.9443, Acc.earth: 0.5604, Acc.door: 0.7366, Acc.table: 0.8213, Acc.mountain: 0.7205, Acc.plant: 0.6683, Acc.curtain: 0.9201, Acc.chair: 0.7585, Acc.car: 0.9365, Acc.water: 0.7323, Acc.painting: 0.8990, Acc.sofa: 0.9431, Acc.shelf: 0.6656, Acc.house: 0.7409, Acc.sea: 0.8339, Acc.mirror: 0.8942, Acc.rug: 0.8050, Acc.field: 0.4816, Acc.armchair: 0.7125, Acc.seat: 0.8772, Acc.fence: 0.6040, Acc.desk: 0.7856, Acc.rock: 0.8173, Acc.wardrobe: 0.7669, Acc.lamp: 0.8634, Acc.bathtub: 0.9109, Acc.railing: 0.6223, Acc.cushion: 0.7914, Acc.base: 0.5980, Acc.box: 0.4585, Acc.column: 0.6168, Acc.signboard: 0.5246, Acc.chest of drawers: 0.6602, Acc.counter: 0.5090, Acc.sand: 0.8911, Acc.sink: 0.8629, Acc.skyscraper: 0.6292, Acc.fireplace: 0.9416, Acc.refrigerator: 0.9437, Acc.grandstand: 0.8505, Acc.path: 0.4199, Acc.stairs: 0.3325, Acc.runway: 0.9692, Acc.case: 0.8086, Acc.pool table: 0.9794, Acc.pillow: 0.8299, Acc.screen door: 0.8489, Acc.stairway: 0.5479, Acc.river: 0.2692, Acc.bridge: 0.8848, Acc.bookcase: 0.7025, Acc.blind: 0.6011, Acc.coffee table: 0.8612, Acc.toilet: 0.9294, Acc.flower: 0.5841, Acc.book: 0.7226, Acc.hill: 0.1017, Acc.bench: 0.6038, Acc.countertop: 0.7713, Acc.stove: 0.9336, Acc.palm: 0.8218, Acc.kitchen island: 0.9094, Acc.computer: 0.9081, Acc.swivel chair: 0.7528, Acc.boat: 0.9109, Acc.bar: 0.8432, Acc.arcade machine: 0.9094, Acc.hovel: 0.3879, Acc.bus: 0.9732, Acc.towel: 0.8735, Acc.light: 0.7087, Acc.truck: 0.5343, Acc.tower: 0.4101, Acc.chandelier: 0.8979, Acc.awning: 0.5275, Acc.streetlight: 0.5324, Acc.booth: 0.7107, Acc.television receiver: 0.8781, Acc.airplane: 0.9630, Acc.dirt track: 0.5661, Acc.apparel: 0.8155, Acc.pole: 0.3860, Acc.land: 0.0516, Acc.bannister: 0.2332, Acc.escalator: 0.8559, Acc.ottoman: 0.6556, Acc.bottle: 0.6856, Acc.buffet: 0.4823, Acc.poster: 0.3553, Acc.stage: 0.4786, Acc.van: 0.7157, Acc.ship: 0.7724, Acc.fountain: 0.4035, Acc.conveyer belt: 0.9522, Acc.canopy: 0.7975, Acc.washer: 0.9292, Acc.plaything: 0.3773, Acc.swimming pool: 0.7655, Acc.stool: 0.6606, Acc.barrel: 0.7108, Acc.basket: 0.6194, Acc.waterfall: 0.6662, Acc.tent: 0.9902, Acc.bag: 0.2697, Acc.minibike: 0.9389, Acc.cradle: 0.9712, Acc.oven: 0.6756, Acc.ball: 0.4500, Acc.food: 0.7450, Acc.step: 0.3029, Acc.tank: 0.7065, Acc.trade name: 0.2567, Acc.microwave: 0.9643, Acc.pot: 0.6798, Acc.animal: 0.6987, Acc.bicycle: 0.7863, Acc.lake: 0.6360, Acc.dishwasher: 0.8238, Acc.screen: 0.9553, Acc.blanket: 0.4684, Acc.sculpture: 0.8797, Acc.hood: 0.7285, Acc.sconce: 0.7495, Acc.vase: 0.6575, Acc.traffic light: 0.6590, Acc.tray: 0.2763, Acc.ashcan: 0.6895, Acc.fan: 0.8745, Acc.pier: 0.4866, Acc.crt screen: 0.0338, Acc.plate: 0.7931, Acc.monitor: 0.5518, Acc.bulletin board: 0.7755, Acc.shower: 0.0129, Acc.radiator: 0.7950, Acc.glass: 0.2313, Acc.clock: 0.5318, Acc.flag: 0.8293 +2024-06-16 19:03:45,528 - mmseg - INFO - Iter [44050/80000] lr: 1.798e-05, eta: 17:39:17, time: 3.553, data_time: 1.940, memory: 71384, decode.loss_ce: 0.1805, decode.acc_seg: 92.1512, aux.loss_ce: 0.0761, aux.acc_seg: 91.8096, loss: 0.2566 +2024-06-16 19:05:06,566 - mmseg - INFO - Iter [44100/80000] lr: 1.795e-05, eta: 17:37:43, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1908, decode.acc_seg: 92.0005, aux.loss_ce: 0.0793, aux.acc_seg: 91.6705, loss: 0.2701 +2024-06-16 19:06:27,603 - mmseg - INFO - Iter [44150/80000] lr: 1.793e-05, eta: 17:36:09, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1875, decode.acc_seg: 91.8690, aux.loss_ce: 0.0784, aux.acc_seg: 91.5077, loss: 0.2659 +2024-06-16 19:07:48,843 - mmseg - INFO - Iter [44200/80000] lr: 1.790e-05, eta: 17:34:34, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1823, decode.acc_seg: 92.2729, aux.loss_ce: 0.0756, aux.acc_seg: 91.9753, loss: 0.2579 +2024-06-16 19:09:12,458 - mmseg - INFO - Iter [44250/80000] lr: 1.788e-05, eta: 17:33:02, time: 1.672, data_time: 0.061, memory: 71384, decode.loss_ce: 0.1754, decode.acc_seg: 92.5236, aux.loss_ce: 0.0738, aux.acc_seg: 92.1731, loss: 0.2492 +2024-06-16 19:10:33,483 - mmseg - INFO - Iter [44300/80000] lr: 1.785e-05, eta: 17:31:28, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1707, decode.acc_seg: 92.6958, aux.loss_ce: 0.0718, aux.acc_seg: 92.3366, loss: 0.2425 +2024-06-16 19:11:54,787 - mmseg - INFO - Iter [44350/80000] lr: 1.783e-05, eta: 17:29:54, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1671, decode.acc_seg: 92.8306, aux.loss_ce: 0.0700, aux.acc_seg: 92.5249, loss: 0.2371 +2024-06-16 19:13:15,846 - mmseg - INFO - Iter [44400/80000] lr: 1.780e-05, eta: 17:28:20, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1779, decode.acc_seg: 92.2984, aux.loss_ce: 0.0746, aux.acc_seg: 91.8659, loss: 0.2525 +2024-06-16 19:14:36,953 - mmseg - INFO - Iter [44450/80000] lr: 1.778e-05, eta: 17:26:46, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1796, decode.acc_seg: 92.4424, aux.loss_ce: 0.0746, aux.acc_seg: 92.0532, loss: 0.2542 +2024-06-16 19:15:58,013 - mmseg - INFO - Iter [44500/80000] lr: 1.775e-05, eta: 17:25:11, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1710, decode.acc_seg: 92.5816, aux.loss_ce: 0.0722, aux.acc_seg: 92.1674, loss: 0.2432 +2024-06-16 19:17:19,144 - mmseg - INFO - Iter [44550/80000] lr: 1.773e-05, eta: 17:23:37, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1770, decode.acc_seg: 92.2419, aux.loss_ce: 0.0748, aux.acc_seg: 91.8136, loss: 0.2518 +2024-06-16 19:18:40,199 - mmseg - INFO - Iter [44600/80000] lr: 1.770e-05, eta: 17:22:03, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1758, decode.acc_seg: 92.4835, aux.loss_ce: 0.0737, aux.acc_seg: 92.1306, loss: 0.2495 +2024-06-16 19:20:01,172 - mmseg - INFO - Iter [44650/80000] lr: 1.768e-05, eta: 17:20:29, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1761, decode.acc_seg: 92.5401, aux.loss_ce: 0.0735, aux.acc_seg: 92.2230, loss: 0.2497 +2024-06-16 19:21:22,362 - mmseg - INFO - Iter [44700/80000] lr: 1.765e-05, eta: 17:18:55, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1808, decode.acc_seg: 92.2166, aux.loss_ce: 0.0762, aux.acc_seg: 91.8989, loss: 0.2570 +2024-06-16 19:22:43,384 - mmseg - INFO - Iter [44750/80000] lr: 1.763e-05, eta: 17:17:21, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1677, decode.acc_seg: 92.8809, aux.loss_ce: 0.0702, aux.acc_seg: 92.5831, loss: 0.2379 +2024-06-16 19:24:04,483 - mmseg - INFO - Iter [44800/80000] lr: 1.760e-05, eta: 17:15:47, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1745, decode.acc_seg: 92.5159, aux.loss_ce: 0.0734, aux.acc_seg: 92.1543, loss: 0.2479 +2024-06-16 19:25:25,548 - mmseg - INFO - Iter [44850/80000] lr: 1.758e-05, eta: 17:14:13, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1731, decode.acc_seg: 92.5208, aux.loss_ce: 0.0727, aux.acc_seg: 92.1828, loss: 0.2458 +2024-06-16 19:26:46,781 - mmseg - INFO - Iter [44900/80000] lr: 1.755e-05, eta: 17:12:40, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1874, decode.acc_seg: 92.3077, aux.loss_ce: 0.0781, aux.acc_seg: 91.9979, loss: 0.2655 +2024-06-16 19:28:07,744 - mmseg - INFO - Iter [44950/80000] lr: 1.753e-05, eta: 17:11:06, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1775, decode.acc_seg: 92.5167, aux.loss_ce: 0.0742, aux.acc_seg: 92.2109, loss: 0.2518 +2024-06-16 19:29:28,846 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 19:29:28,846 - mmseg - INFO - Iter [45000/80000] lr: 1.750e-05, eta: 17:09:32, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1828, decode.acc_seg: 92.3623, aux.loss_ce: 0.0764, aux.acc_seg: 92.0142, loss: 0.2592 +2024-06-16 19:31:08,143 - mmseg - INFO - per class results: +2024-06-16 19:31:08,149 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 81.78 | 89.6 | +| building | 85.16 | 93.62 | +| sky | 94.86 | 97.51 | +| floor | 85.47 | 91.31 | +| tree | 77.6 | 88.98 | +| ceiling | 87.89 | 93.84 | +| road | 85.89 | 91.27 | +| bed | 92.88 | 96.93 | +| windowpane | 66.45 | 85.24 | +| grass | 68.84 | 83.22 | +| cabinet | 66.35 | 74.9 | +| sidewalk | 69.85 | 84.19 | +| person | 85.9 | 94.16 | +| earth | 40.78 | 56.13 | +| door | 58.12 | 74.94 | +| table | 70.06 | 80.46 | +| mountain | 62.23 | 75.0 | +| plant | 54.63 | 63.92 | +| curtain | 80.08 | 89.78 | +| chair | 68.52 | 79.11 | +| car | 88.15 | 93.39 | +| water | 60.96 | 74.02 | +| painting | 75.87 | 90.89 | +| sofa | 82.33 | 88.4 | +| shelf | 49.4 | 66.46 | +| house | 56.86 | 67.83 | +| sea | 69.27 | 82.27 | +| mirror | 78.06 | 84.72 | +| rug | 69.43 | 84.14 | +| field | 31.18 | 48.48 | +| armchair | 61.06 | 82.15 | +| seat | 64.58 | 89.36 | +| fence | 46.59 | 56.02 | +| desk | 58.45 | 77.48 | +| rock | 55.61 | 81.7 | +| wardrobe | 57.19 | 74.87 | +| lamp | 76.16 | 86.37 | +| bathtub | 87.7 | 89.23 | +| railing | 44.7 | 57.53 | +| cushion | 73.36 | 85.12 | +| base | 42.14 | 54.89 | +| box | 38.92 | 52.24 | +| column | 57.25 | 70.23 | +| signboard | 41.45 | 56.91 | +| chest of drawers | 43.26 | 73.68 | +| counter | 34.04 | 44.1 | +| sand | 59.17 | 85.82 | +| sink | 80.22 | 87.15 | +| skyscraper | 46.79 | 58.23 | +| fireplace | 70.52 | 93.67 | +| refrigerator | 83.77 | 95.9 | +| grandstand | 53.65 | 84.42 | +| path | 31.19 | 42.02 | +| stairs | 25.58 | 27.94 | +| runway | 74.61 | 96.59 | +| case | 62.48 | 89.06 | +| pool table | 95.3 | 98.21 | +| pillow | 69.07 | 80.7 | +| screen door | 53.76 | 55.02 | +| stairway | 37.59 | 65.93 | +| river | 11.46 | 26.56 | +| bridge | 67.09 | 78.32 | +| bookcase | 50.59 | 76.3 | +| blind | 42.1 | 43.68 | +| coffee table | 62.48 | 86.18 | +| toilet | 91.45 | 94.16 | +| flower | 41.68 | 61.59 | +| book | 55.56 | 71.43 | +| hill | 7.88 | 13.25 | +| bench | 52.65 | 58.16 | +| countertop | 64.69 | 75.81 | +| stove | 85.96 | 93.48 | +| palm | 55.11 | 84.31 | +| kitchen island | 48.75 | 89.07 | +| computer | 79.21 | 93.19 | +| swivel chair | 50.16 | 77.39 | +| boat | 76.62 | 91.68 | +| bar | 65.77 | 91.49 | +| arcade machine | 86.16 | 91.0 | +| hovel | 20.27 | 21.53 | +| bus | 92.66 | 96.72 | +| towel | 81.61 | 89.47 | +| light | 62.8 | 75.09 | +| truck | 46.88 | 60.54 | +| tower | 31.16 | 56.82 | +| chandelier | 73.64 | 88.34 | +| awning | 40.72 | 46.49 | +| streetlight | 38.08 | 52.14 | +| booth | 41.73 | 61.78 | +| television receiver | 81.04 | 91.37 | +| airplane | 89.02 | 95.89 | +| dirt track | 6.89 | 42.08 | +| apparel | 56.65 | 87.26 | +| pole | 29.44 | 39.76 | +| land | 2.43 | 4.2 | +| bannister | 19.16 | 24.81 | +| escalator | 60.86 | 89.15 | +| ottoman | 48.54 | 64.81 | +| bottle | 45.0 | 59.07 | +| buffet | 65.49 | 82.88 | +| poster | 35.75 | 44.47 | +| stage | 25.51 | 41.35 | +| van | 46.74 | 77.33 | +| ship | 85.41 | 91.82 | +| fountain | 37.86 | 38.61 | +| conveyer belt | 78.36 | 94.81 | +| canopy | 45.76 | 62.58 | +| washer | 87.35 | 92.65 | +| plaything | 26.8 | 44.23 | +| swimming pool | 56.3 | 84.32 | +| stool | 52.41 | 68.24 | +| barrel | 56.49 | 72.31 | +| basket | 42.16 | 63.77 | +| waterfall | 56.57 | 71.18 | +| tent | 96.52 | 97.74 | +| bag | 31.14 | 39.12 | +| minibike | 75.44 | 91.13 | +| cradle | 87.68 | 97.98 | +| oven | 56.18 | 68.04 | +| ball | 53.83 | 61.06 | +| food | 57.79 | 66.31 | +| step | 13.73 | 15.33 | +| tank | 68.26 | 74.65 | +| trade name | 22.32 | 26.6 | +| microwave | 88.37 | 96.55 | +| pot | 58.16 | 71.71 | +| animal | 60.98 | 61.8 | +| bicycle | 59.93 | 76.94 | +| lake | 52.62 | 68.78 | +| dishwasher | 75.63 | 86.54 | +| screen | 40.35 | 73.4 | +| blanket | 38.8 | 44.82 | +| sculpture | 75.55 | 87.9 | +| hood | 67.31 | 81.33 | +| sconce | 62.48 | 71.28 | +| vase | 46.53 | 64.5 | +| traffic light | 36.76 | 64.36 | +| tray | 23.15 | 32.64 | +| ashcan | 46.04 | 66.03 | +| fan | 70.94 | 88.74 | +| pier | 42.55 | 47.31 | +| crt screen | 3.82 | 7.22 | +| plate | 61.26 | 78.23 | +| monitor | 62.42 | 75.05 | +| bulletin board | 50.13 | 58.46 | +| shower | 0.8 | 1.18 | +| radiator | 66.57 | 78.17 | +| glass | 20.15 | 20.99 | +| clock | 50.8 | 66.68 | +| flag | 73.01 | 80.13 | ++---------------------+-------+-------+ +2024-06-16 19:31:08,149 - mmseg - INFO - Summary: +2024-06-16 19:31:08,150 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.13 | 57.55 | 70.81 | ++-------+-------+-------+ +2024-06-16 19:31:08,150 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 19:31:08,151 - mmseg - INFO - Iter(val) [250] aAcc: 0.8613, mIoU: 0.5755, mAcc: 0.7081, IoU.wall: 0.8178, IoU.building: 0.8516, IoU.sky: 0.9486, IoU.floor: 0.8547, IoU.tree: 0.7760, IoU.ceiling: 0.8789, IoU.road: 0.8589, IoU.bed : 0.9288, IoU.windowpane: 0.6645, IoU.grass: 0.6884, IoU.cabinet: 0.6635, IoU.sidewalk: 0.6985, IoU.person: 0.8590, IoU.earth: 0.4078, IoU.door: 0.5812, IoU.table: 0.7006, IoU.mountain: 0.6223, IoU.plant: 0.5463, IoU.curtain: 0.8008, IoU.chair: 0.6852, IoU.car: 0.8815, IoU.water: 0.6096, IoU.painting: 0.7587, IoU.sofa: 0.8233, IoU.shelf: 0.4940, IoU.house: 0.5686, IoU.sea: 0.6927, IoU.mirror: 0.7806, IoU.rug: 0.6943, IoU.field: 0.3118, IoU.armchair: 0.6106, IoU.seat: 0.6458, IoU.fence: 0.4659, IoU.desk: 0.5845, IoU.rock: 0.5561, IoU.wardrobe: 0.5719, IoU.lamp: 0.7616, IoU.bathtub: 0.8770, IoU.railing: 0.4470, IoU.cushion: 0.7336, IoU.base: 0.4214, IoU.box: 0.3892, IoU.column: 0.5725, IoU.signboard: 0.4145, IoU.chest of drawers: 0.4326, IoU.counter: 0.3404, IoU.sand: 0.5917, IoU.sink: 0.8022, IoU.skyscraper: 0.4679, IoU.fireplace: 0.7052, IoU.refrigerator: 0.8377, IoU.grandstand: 0.5365, IoU.path: 0.3119, IoU.stairs: 0.2558, IoU.runway: 0.7461, IoU.case: 0.6248, IoU.pool table: 0.9530, IoU.pillow: 0.6907, IoU.screen door: 0.5376, IoU.stairway: 0.3759, IoU.river: 0.1146, IoU.bridge: 0.6709, IoU.bookcase: 0.5059, IoU.blind: 0.4210, IoU.coffee table: 0.6248, IoU.toilet: 0.9145, IoU.flower: 0.4168, IoU.book: 0.5556, IoU.hill: 0.0788, IoU.bench: 0.5265, IoU.countertop: 0.6469, IoU.stove: 0.8596, IoU.palm: 0.5511, IoU.kitchen island: 0.4875, IoU.computer: 0.7921, IoU.swivel chair: 0.5016, IoU.boat: 0.7662, IoU.bar: 0.6577, IoU.arcade machine: 0.8616, IoU.hovel: 0.2027, IoU.bus: 0.9266, IoU.towel: 0.8161, IoU.light: 0.6280, IoU.truck: 0.4688, IoU.tower: 0.3116, IoU.chandelier: 0.7364, IoU.awning: 0.4072, IoU.streetlight: 0.3808, IoU.booth: 0.4173, IoU.television receiver: 0.8104, IoU.airplane: 0.8902, IoU.dirt track: 0.0689, IoU.apparel: 0.5665, IoU.pole: 0.2944, IoU.land: 0.0243, IoU.bannister: 0.1916, IoU.escalator: 0.6086, IoU.ottoman: 0.4854, IoU.bottle: 0.4500, IoU.buffet: 0.6549, IoU.poster: 0.3575, IoU.stage: 0.2551, IoU.van: 0.4674, IoU.ship: 0.8541, IoU.fountain: 0.3786, IoU.conveyer belt: 0.7836, IoU.canopy: 0.4576, IoU.washer: 0.8735, IoU.plaything: 0.2680, IoU.swimming pool: 0.5630, IoU.stool: 0.5241, IoU.barrel: 0.5649, IoU.basket: 0.4216, IoU.waterfall: 0.5657, IoU.tent: 0.9652, IoU.bag: 0.3114, IoU.minibike: 0.7544, IoU.cradle: 0.8768, IoU.oven: 0.5618, IoU.ball: 0.5383, IoU.food: 0.5779, IoU.step: 0.1373, IoU.tank: 0.6826, IoU.trade name: 0.2232, IoU.microwave: 0.8837, IoU.pot: 0.5816, IoU.animal: 0.6098, IoU.bicycle: 0.5993, IoU.lake: 0.5262, IoU.dishwasher: 0.7563, IoU.screen: 0.4035, IoU.blanket: 0.3880, IoU.sculpture: 0.7555, IoU.hood: 0.6731, IoU.sconce: 0.6248, IoU.vase: 0.4653, IoU.traffic light: 0.3676, IoU.tray: 0.2315, IoU.ashcan: 0.4604, IoU.fan: 0.7094, IoU.pier: 0.4255, IoU.crt screen: 0.0382, IoU.plate: 0.6126, IoU.monitor: 0.6242, IoU.bulletin board: 0.5013, IoU.shower: 0.0080, IoU.radiator: 0.6657, IoU.glass: 0.2015, IoU.clock: 0.5080, IoU.flag: 0.7301, Acc.wall: 0.8960, Acc.building: 0.9362, Acc.sky: 0.9751, Acc.floor: 0.9131, Acc.tree: 0.8898, Acc.ceiling: 0.9384, Acc.road: 0.9127, Acc.bed : 0.9693, Acc.windowpane: 0.8524, Acc.grass: 0.8322, Acc.cabinet: 0.7490, Acc.sidewalk: 0.8419, Acc.person: 0.9416, Acc.earth: 0.5613, Acc.door: 0.7494, Acc.table: 0.8046, Acc.mountain: 0.7500, Acc.plant: 0.6392, Acc.curtain: 0.8978, Acc.chair: 0.7911, Acc.car: 0.9339, Acc.water: 0.7402, Acc.painting: 0.9089, Acc.sofa: 0.8840, Acc.shelf: 0.6646, Acc.house: 0.6783, Acc.sea: 0.8227, Acc.mirror: 0.8472, Acc.rug: 0.8414, Acc.field: 0.4848, Acc.armchair: 0.8215, Acc.seat: 0.8936, Acc.fence: 0.5602, Acc.desk: 0.7748, Acc.rock: 0.8170, Acc.wardrobe: 0.7487, Acc.lamp: 0.8637, Acc.bathtub: 0.8923, Acc.railing: 0.5753, Acc.cushion: 0.8512, Acc.base: 0.5489, Acc.box: 0.5224, Acc.column: 0.7023, Acc.signboard: 0.5691, Acc.chest of drawers: 0.7368, Acc.counter: 0.4410, Acc.sand: 0.8582, Acc.sink: 0.8715, Acc.skyscraper: 0.5823, Acc.fireplace: 0.9367, Acc.refrigerator: 0.9590, Acc.grandstand: 0.8442, Acc.path: 0.4202, Acc.stairs: 0.2794, Acc.runway: 0.9659, Acc.case: 0.8906, Acc.pool table: 0.9821, Acc.pillow: 0.8070, Acc.screen door: 0.5502, Acc.stairway: 0.6593, Acc.river: 0.2656, Acc.bridge: 0.7832, Acc.bookcase: 0.7630, Acc.blind: 0.4368, Acc.coffee table: 0.8618, Acc.toilet: 0.9416, Acc.flower: 0.6159, Acc.book: 0.7143, Acc.hill: 0.1325, Acc.bench: 0.5816, Acc.countertop: 0.7581, Acc.stove: 0.9348, Acc.palm: 0.8431, Acc.kitchen island: 0.8907, Acc.computer: 0.9319, Acc.swivel chair: 0.7739, Acc.boat: 0.9168, Acc.bar: 0.9149, Acc.arcade machine: 0.9100, Acc.hovel: 0.2153, Acc.bus: 0.9672, Acc.towel: 0.8947, Acc.light: 0.7509, Acc.truck: 0.6054, Acc.tower: 0.5682, Acc.chandelier: 0.8834, Acc.awning: 0.4649, Acc.streetlight: 0.5214, Acc.booth: 0.6178, Acc.television receiver: 0.9137, Acc.airplane: 0.9589, Acc.dirt track: 0.4208, Acc.apparel: 0.8726, Acc.pole: 0.3976, Acc.land: 0.0420, Acc.bannister: 0.2481, Acc.escalator: 0.8915, Acc.ottoman: 0.6481, Acc.bottle: 0.5907, Acc.buffet: 0.8288, Acc.poster: 0.4447, Acc.stage: 0.4135, Acc.van: 0.7733, Acc.ship: 0.9182, Acc.fountain: 0.3861, Acc.conveyer belt: 0.9481, Acc.canopy: 0.6258, Acc.washer: 0.9265, Acc.plaything: 0.4423, Acc.swimming pool: 0.8432, Acc.stool: 0.6824, Acc.barrel: 0.7231, Acc.basket: 0.6377, Acc.waterfall: 0.7118, Acc.tent: 0.9774, Acc.bag: 0.3912, Acc.minibike: 0.9113, Acc.cradle: 0.9798, Acc.oven: 0.6804, Acc.ball: 0.6106, Acc.food: 0.6631, Acc.step: 0.1533, Acc.tank: 0.7465, Acc.trade name: 0.2660, Acc.microwave: 0.9655, Acc.pot: 0.7171, Acc.animal: 0.6180, Acc.bicycle: 0.7694, Acc.lake: 0.6878, Acc.dishwasher: 0.8654, Acc.screen: 0.7340, Acc.blanket: 0.4482, Acc.sculpture: 0.8790, Acc.hood: 0.8133, Acc.sconce: 0.7128, Acc.vase: 0.6450, Acc.traffic light: 0.6436, Acc.tray: 0.3264, Acc.ashcan: 0.6603, Acc.fan: 0.8874, Acc.pier: 0.4731, Acc.crt screen: 0.0722, Acc.plate: 0.7823, Acc.monitor: 0.7505, Acc.bulletin board: 0.5846, Acc.shower: 0.0118, Acc.radiator: 0.7817, Acc.glass: 0.2099, Acc.clock: 0.6668, Acc.flag: 0.8013 +2024-06-16 19:32:29,795 - mmseg - INFO - Iter [45050/80000] lr: 1.748e-05, eta: 17:09:15, time: 3.619, data_time: 2.004, memory: 71384, decode.loss_ce: 0.1701, decode.acc_seg: 92.5137, aux.loss_ce: 0.0713, aux.acc_seg: 92.1013, loss: 0.2414 +2024-06-16 19:33:50,992 - mmseg - INFO - Iter [45100/80000] lr: 1.745e-05, eta: 17:07:42, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1804, decode.acc_seg: 92.3950, aux.loss_ce: 0.0758, aux.acc_seg: 91.9617, loss: 0.2561 +2024-06-16 19:35:12,059 - mmseg - INFO - Iter [45150/80000] lr: 1.743e-05, eta: 17:06:08, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1811, decode.acc_seg: 92.2860, aux.loss_ce: 0.0754, aux.acc_seg: 91.9603, loss: 0.2565 +2024-06-16 19:36:33,155 - mmseg - INFO - Iter [45200/80000] lr: 1.740e-05, eta: 17:04:34, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1901, decode.acc_seg: 91.8691, aux.loss_ce: 0.0784, aux.acc_seg: 91.5474, loss: 0.2685 +2024-06-16 19:37:54,279 - mmseg - INFO - Iter [45250/80000] lr: 1.738e-05, eta: 17:03:00, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1809, decode.acc_seg: 92.2346, aux.loss_ce: 0.0752, aux.acc_seg: 92.0036, loss: 0.2561 +2024-06-16 19:39:15,486 - mmseg - INFO - Iter [45300/80000] lr: 1.735e-05, eta: 17:01:26, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1838, decode.acc_seg: 92.2218, aux.loss_ce: 0.0774, aux.acc_seg: 91.8526, loss: 0.2612 +2024-06-16 19:40:36,600 - mmseg - INFO - Iter [45350/80000] lr: 1.733e-05, eta: 16:59:52, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1859, decode.acc_seg: 91.8694, aux.loss_ce: 0.0777, aux.acc_seg: 91.5359, loss: 0.2636 +2024-06-16 19:41:57,518 - mmseg - INFO - Iter [45400/80000] lr: 1.730e-05, eta: 16:58:18, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1849, decode.acc_seg: 92.4259, aux.loss_ce: 0.0775, aux.acc_seg: 92.0487, loss: 0.2624 +2024-06-16 19:43:18,580 - mmseg - INFO - Iter [45450/80000] lr: 1.728e-05, eta: 16:56:45, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1815, decode.acc_seg: 92.3681, aux.loss_ce: 0.0765, aux.acc_seg: 91.9355, loss: 0.2580 +2024-06-16 19:44:42,624 - mmseg - INFO - Iter [45500/80000] lr: 1.725e-05, eta: 16:55:13, time: 1.681, data_time: 0.060, memory: 71384, decode.loss_ce: 0.1645, decode.acc_seg: 92.7702, aux.loss_ce: 0.0695, aux.acc_seg: 92.4346, loss: 0.2340 +2024-06-16 19:46:03,741 - mmseg - INFO - Iter [45550/80000] lr: 1.723e-05, eta: 16:53:39, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1842, decode.acc_seg: 92.2095, aux.loss_ce: 0.0772, aux.acc_seg: 91.7873, loss: 0.2614 +2024-06-16 19:47:24,727 - mmseg - INFO - Iter [45600/80000] lr: 1.720e-05, eta: 16:52:06, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1691, decode.acc_seg: 92.7690, aux.loss_ce: 0.0703, aux.acc_seg: 92.4346, loss: 0.2394 +2024-06-16 19:48:45,777 - mmseg - INFO - Iter [45650/80000] lr: 1.718e-05, eta: 16:50:32, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1756, decode.acc_seg: 92.4648, aux.loss_ce: 0.0742, aux.acc_seg: 92.0388, loss: 0.2498 +2024-06-16 19:50:06,744 - mmseg - INFO - Iter [45700/80000] lr: 1.715e-05, eta: 16:48:58, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1639, decode.acc_seg: 92.9274, aux.loss_ce: 0.0687, aux.acc_seg: 92.6258, loss: 0.2326 +2024-06-16 19:51:27,856 - mmseg - INFO - Iter [45750/80000] lr: 1.713e-05, eta: 16:47:25, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1774, decode.acc_seg: 92.3802, aux.loss_ce: 0.0747, aux.acc_seg: 92.0136, loss: 0.2521 +2024-06-16 19:52:48,926 - mmseg - INFO - Iter [45800/80000] lr: 1.710e-05, eta: 16:45:51, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1687, decode.acc_seg: 92.7122, aux.loss_ce: 0.0713, aux.acc_seg: 92.2561, loss: 0.2399 +2024-06-16 19:54:09,949 - mmseg - INFO - Iter [45850/80000] lr: 1.708e-05, eta: 16:44:17, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1865, decode.acc_seg: 91.9539, aux.loss_ce: 0.0769, aux.acc_seg: 91.7325, loss: 0.2634 +2024-06-16 19:55:31,264 - mmseg - INFO - Iter [45900/80000] lr: 1.705e-05, eta: 16:42:44, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1763, decode.acc_seg: 92.4186, aux.loss_ce: 0.0745, aux.acc_seg: 92.0474, loss: 0.2508 +2024-06-16 19:56:52,305 - mmseg - INFO - Iter [45950/80000] lr: 1.703e-05, eta: 16:41:11, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1748, decode.acc_seg: 92.4718, aux.loss_ce: 0.0725, aux.acc_seg: 92.2223, loss: 0.2473 +2024-06-16 19:58:13,426 - mmseg - INFO - Saving checkpoint at 46000 iterations +2024-06-16 19:59:35,006 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 19:59:35,007 - mmseg - INFO - Iter [46000/80000] lr: 1.700e-05, eta: 16:40:37, time: 3.254, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1842, decode.acc_seg: 92.0373, aux.loss_ce: 0.0769, aux.acc_seg: 91.6471, loss: 0.2611 +2024-06-16 20:01:12,094 - mmseg - INFO - per class results: +2024-06-16 20:01:12,100 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.42 | 89.76 | +| building | 85.02 | 93.95 | +| sky | 94.89 | 97.17 | +| floor | 85.76 | 91.7 | +| tree | 77.88 | 89.73 | +| ceiling | 87.46 | 94.09 | +| road | 84.95 | 89.85 | +| bed | 92.83 | 97.18 | +| windowpane | 66.94 | 79.84 | +| grass | 67.94 | 82.4 | +| cabinet | 67.89 | 76.84 | +| sidewalk | 69.83 | 84.74 | +| person | 85.92 | 94.02 | +| earth | 38.71 | 54.66 | +| door | 58.79 | 73.95 | +| table | 69.17 | 81.44 | +| mountain | 60.53 | 73.4 | +| plant | 55.81 | 67.15 | +| curtain | 80.25 | 91.18 | +| chair | 69.29 | 82.08 | +| car | 87.99 | 94.06 | +| water | 60.55 | 72.88 | +| painting | 78.06 | 87.84 | +| sofa | 84.53 | 93.16 | +| shelf | 51.25 | 70.3 | +| house | 47.81 | 52.44 | +| sea | 70.43 | 82.2 | +| mirror | 77.53 | 85.31 | +| rug | 68.83 | 76.97 | +| field | 31.86 | 48.33 | +| armchair | 62.91 | 78.28 | +| seat | 67.69 | 89.48 | +| fence | 50.22 | 68.3 | +| desk | 56.83 | 81.92 | +| rock | 55.35 | 79.83 | +| wardrobe | 57.46 | 68.59 | +| lamp | 74.14 | 86.26 | +| bathtub | 86.91 | 89.82 | +| railing | 46.25 | 64.06 | +| cushion | 71.58 | 80.9 | +| base | 40.08 | 69.75 | +| box | 38.32 | 46.7 | +| column | 56.89 | 69.89 | +| signboard | 41.95 | 54.07 | +| chest of drawers | 45.91 | 80.73 | +| counter | 39.47 | 53.09 | +| sand | 54.26 | 77.14 | +| sink | 79.75 | 86.19 | +| skyscraper | 49.8 | 62.13 | +| fireplace | 74.19 | 95.15 | +| refrigerator | 87.78 | 94.83 | +| grandstand | 51.05 | 86.02 | +| path | 29.75 | 41.85 | +| stairs | 29.17 | 35.82 | +| runway | 72.92 | 95.26 | +| case | 64.17 | 87.84 | +| pool table | 94.58 | 96.18 | +| pillow | 67.53 | 76.84 | +| screen door | 84.29 | 88.62 | +| stairway | 44.87 | 62.11 | +| river | 11.03 | 29.05 | +| bridge | 70.35 | 79.89 | +| bookcase | 49.99 | 56.75 | +| blind | 47.99 | 57.8 | +| coffee table | 60.66 | 88.56 | +| toilet | 91.77 | 95.1 | +| flower | 43.92 | 61.34 | +| book | 56.4 | 78.84 | +| hill | 11.84 | 25.24 | +| bench | 55.04 | 60.76 | +| countertop | 63.37 | 82.39 | +| stove | 87.24 | 93.88 | +| palm | 53.07 | 86.2 | +| kitchen island | 50.96 | 92.67 | +| computer | 81.46 | 88.97 | +| swivel chair | 47.89 | 71.33 | +| boat | 81.3 | 90.43 | +| bar | 62.19 | 85.71 | +| arcade machine | 79.75 | 85.75 | +| hovel | 19.53 | 21.07 | +| bus | 92.62 | 95.97 | +| towel | 80.41 | 90.73 | +| light | 63.71 | 75.09 | +| truck | 51.48 | 60.62 | +| tower | 22.6 | 38.95 | +| chandelier | 71.44 | 83.76 | +| awning | 40.69 | 49.8 | +| streetlight | 37.63 | 50.11 | +| booth | 44.85 | 69.77 | +| television receiver | 81.87 | 86.4 | +| airplane | 87.52 | 91.96 | +| dirt track | 8.51 | 57.53 | +| apparel | 56.48 | 77.9 | +| pole | 28.01 | 40.64 | +| land | 1.37 | 2.36 | +| bannister | 21.91 | 26.55 | +| escalator | 65.37 | 87.21 | +| ottoman | 54.32 | 72.69 | +| bottle | 42.37 | 50.45 | +| buffet | 49.62 | 61.53 | +| poster | 33.06 | 42.23 | +| stage | 27.47 | 46.8 | +| van | 46.86 | 70.83 | +| ship | 81.41 | 84.36 | +| fountain | 49.29 | 56.93 | +| conveyer belt | 81.35 | 93.01 | +| canopy | 49.2 | 70.56 | +| washer | 84.65 | 89.42 | +| plaything | 39.79 | 66.63 | +| swimming pool | 54.39 | 80.6 | +| stool | 54.87 | 70.04 | +| barrel | 68.94 | 96.95 | +| basket | 41.95 | 63.4 | +| waterfall | 51.47 | 66.2 | +| tent | 95.57 | 98.71 | +| bag | 31.31 | 39.51 | +| minibike | 77.42 | 89.09 | +| cradle | 78.33 | 97.83 | +| oven | 56.51 | 68.92 | +| ball | 52.82 | 60.34 | +| food | 59.32 | 65.87 | +| step | 16.6 | 20.91 | +| tank | 67.54 | 98.12 | +| trade name | 18.93 | 21.15 | +| microwave | 85.93 | 95.86 | +| pot | 58.32 | 69.16 | +| animal | 63.05 | 64.95 | +| bicycle | 60.47 | 78.83 | +| lake | 45.67 | 66.42 | +| dishwasher | 72.63 | 84.32 | +| screen | 58.92 | 89.24 | +| blanket | 38.67 | 44.47 | +| sculpture | 78.18 | 87.24 | +| hood | 65.68 | 76.52 | +| sconce | 54.4 | 58.96 | +| vase | 47.53 | 65.67 | +| traffic light | 37.82 | 59.8 | +| tray | 24.27 | 31.8 | +| ashcan | 50.5 | 64.63 | +| fan | 72.96 | 84.04 | +| pier | 39.55 | 48.1 | +| crt screen | 9.75 | 14.78 | +| plate | 60.92 | 80.1 | +| monitor | 69.98 | 86.21 | +| bulletin board | 59.82 | 66.37 | +| shower | 7.32 | 7.6 | +| radiator | 67.22 | 82.62 | +| glass | 19.6 | 20.68 | +| clock | 48.87 | 59.26 | +| flag | 70.74 | 77.79 | ++---------------------+-------+-------+ +2024-06-16 20:01:12,100 - mmseg - INFO - Summary: +2024-06-16 20:01:12,100 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.19 | 58.22 | 71.48 | ++-------+-------+-------+ +2024-06-16 20:01:12,101 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:01:12,101 - mmseg - INFO - Iter(val) [250] aAcc: 0.8619, mIoU: 0.5822, mAcc: 0.7148, IoU.wall: 0.8242, IoU.building: 0.8502, IoU.sky: 0.9489, IoU.floor: 0.8576, IoU.tree: 0.7788, IoU.ceiling: 0.8746, IoU.road: 0.8495, IoU.bed : 0.9283, IoU.windowpane: 0.6694, IoU.grass: 0.6794, IoU.cabinet: 0.6789, IoU.sidewalk: 0.6983, IoU.person: 0.8592, IoU.earth: 0.3871, IoU.door: 0.5879, IoU.table: 0.6917, IoU.mountain: 0.6053, IoU.plant: 0.5581, IoU.curtain: 0.8025, IoU.chair: 0.6929, IoU.car: 0.8799, IoU.water: 0.6055, IoU.painting: 0.7806, IoU.sofa: 0.8453, IoU.shelf: 0.5125, IoU.house: 0.4781, IoU.sea: 0.7043, IoU.mirror: 0.7753, IoU.rug: 0.6883, IoU.field: 0.3186, IoU.armchair: 0.6291, IoU.seat: 0.6769, IoU.fence: 0.5022, IoU.desk: 0.5683, IoU.rock: 0.5535, IoU.wardrobe: 0.5746, IoU.lamp: 0.7414, IoU.bathtub: 0.8691, IoU.railing: 0.4625, IoU.cushion: 0.7158, IoU.base: 0.4008, IoU.box: 0.3832, IoU.column: 0.5689, IoU.signboard: 0.4195, IoU.chest of drawers: 0.4591, IoU.counter: 0.3947, IoU.sand: 0.5426, IoU.sink: 0.7975, IoU.skyscraper: 0.4980, IoU.fireplace: 0.7419, IoU.refrigerator: 0.8778, IoU.grandstand: 0.5105, IoU.path: 0.2975, IoU.stairs: 0.2917, IoU.runway: 0.7292, IoU.case: 0.6417, IoU.pool table: 0.9458, IoU.pillow: 0.6753, IoU.screen door: 0.8429, IoU.stairway: 0.4487, IoU.river: 0.1103, IoU.bridge: 0.7035, IoU.bookcase: 0.4999, IoU.blind: 0.4799, IoU.coffee table: 0.6066, IoU.toilet: 0.9177, IoU.flower: 0.4392, IoU.book: 0.5640, IoU.hill: 0.1184, IoU.bench: 0.5504, IoU.countertop: 0.6337, IoU.stove: 0.8724, IoU.palm: 0.5307, IoU.kitchen island: 0.5096, IoU.computer: 0.8146, IoU.swivel chair: 0.4789, IoU.boat: 0.8130, IoU.bar: 0.6219, IoU.arcade machine: 0.7975, IoU.hovel: 0.1953, IoU.bus: 0.9262, IoU.towel: 0.8041, IoU.light: 0.6371, IoU.truck: 0.5148, IoU.tower: 0.2260, IoU.chandelier: 0.7144, IoU.awning: 0.4069, IoU.streetlight: 0.3763, IoU.booth: 0.4485, IoU.television receiver: 0.8187, IoU.airplane: 0.8752, IoU.dirt track: 0.0851, IoU.apparel: 0.5648, IoU.pole: 0.2801, IoU.land: 0.0137, IoU.bannister: 0.2191, IoU.escalator: 0.6537, IoU.ottoman: 0.5432, IoU.bottle: 0.4237, IoU.buffet: 0.4962, IoU.poster: 0.3306, IoU.stage: 0.2747, IoU.van: 0.4686, IoU.ship: 0.8141, IoU.fountain: 0.4929, IoU.conveyer belt: 0.8135, IoU.canopy: 0.4920, IoU.washer: 0.8465, IoU.plaything: 0.3979, IoU.swimming pool: 0.5439, IoU.stool: 0.5487, IoU.barrel: 0.6894, IoU.basket: 0.4195, IoU.waterfall: 0.5147, IoU.tent: 0.9557, IoU.bag: 0.3131, IoU.minibike: 0.7742, IoU.cradle: 0.7833, IoU.oven: 0.5651, IoU.ball: 0.5282, IoU.food: 0.5932, IoU.step: 0.1660, IoU.tank: 0.6754, IoU.trade name: 0.1893, IoU.microwave: 0.8593, IoU.pot: 0.5832, IoU.animal: 0.6305, IoU.bicycle: 0.6047, IoU.lake: 0.4567, IoU.dishwasher: 0.7263, IoU.screen: 0.5892, IoU.blanket: 0.3867, IoU.sculpture: 0.7818, IoU.hood: 0.6568, IoU.sconce: 0.5440, IoU.vase: 0.4753, IoU.traffic light: 0.3782, IoU.tray: 0.2427, IoU.ashcan: 0.5050, IoU.fan: 0.7296, IoU.pier: 0.3955, IoU.crt screen: 0.0975, IoU.plate: 0.6092, IoU.monitor: 0.6998, IoU.bulletin board: 0.5982, IoU.shower: 0.0732, IoU.radiator: 0.6722, IoU.glass: 0.1960, IoU.clock: 0.4887, IoU.flag: 0.7074, Acc.wall: 0.8976, Acc.building: 0.9395, Acc.sky: 0.9717, Acc.floor: 0.9170, Acc.tree: 0.8973, Acc.ceiling: 0.9409, Acc.road: 0.8985, Acc.bed : 0.9718, Acc.windowpane: 0.7984, Acc.grass: 0.8240, Acc.cabinet: 0.7684, Acc.sidewalk: 0.8474, Acc.person: 0.9402, Acc.earth: 0.5466, Acc.door: 0.7395, Acc.table: 0.8144, Acc.mountain: 0.7340, Acc.plant: 0.6715, Acc.curtain: 0.9118, Acc.chair: 0.8208, Acc.car: 0.9406, Acc.water: 0.7288, Acc.painting: 0.8784, Acc.sofa: 0.9316, Acc.shelf: 0.7030, Acc.house: 0.5244, Acc.sea: 0.8220, Acc.mirror: 0.8531, Acc.rug: 0.7697, Acc.field: 0.4833, Acc.armchair: 0.7828, Acc.seat: 0.8948, Acc.fence: 0.6830, Acc.desk: 0.8192, Acc.rock: 0.7983, Acc.wardrobe: 0.6859, Acc.lamp: 0.8626, Acc.bathtub: 0.8982, Acc.railing: 0.6406, Acc.cushion: 0.8090, Acc.base: 0.6975, Acc.box: 0.4670, Acc.column: 0.6989, Acc.signboard: 0.5407, Acc.chest of drawers: 0.8073, Acc.counter: 0.5309, Acc.sand: 0.7714, Acc.sink: 0.8619, Acc.skyscraper: 0.6213, Acc.fireplace: 0.9515, Acc.refrigerator: 0.9483, Acc.grandstand: 0.8602, Acc.path: 0.4185, Acc.stairs: 0.3582, Acc.runway: 0.9526, Acc.case: 0.8784, Acc.pool table: 0.9618, Acc.pillow: 0.7684, Acc.screen door: 0.8862, Acc.stairway: 0.6211, Acc.river: 0.2905, Acc.bridge: 0.7989, Acc.bookcase: 0.5675, Acc.blind: 0.5780, Acc.coffee table: 0.8856, Acc.toilet: 0.9510, Acc.flower: 0.6134, Acc.book: 0.7884, Acc.hill: 0.2524, Acc.bench: 0.6076, Acc.countertop: 0.8239, Acc.stove: 0.9388, Acc.palm: 0.8620, Acc.kitchen island: 0.9267, Acc.computer: 0.8897, Acc.swivel chair: 0.7133, Acc.boat: 0.9043, Acc.bar: 0.8571, Acc.arcade machine: 0.8575, Acc.hovel: 0.2107, Acc.bus: 0.9597, Acc.towel: 0.9073, Acc.light: 0.7509, Acc.truck: 0.6062, Acc.tower: 0.3895, Acc.chandelier: 0.8376, Acc.awning: 0.4980, Acc.streetlight: 0.5011, Acc.booth: 0.6977, Acc.television receiver: 0.8640, Acc.airplane: 0.9196, Acc.dirt track: 0.5753, Acc.apparel: 0.7790, Acc.pole: 0.4064, Acc.land: 0.0236, Acc.bannister: 0.2655, Acc.escalator: 0.8721, Acc.ottoman: 0.7269, Acc.bottle: 0.5045, Acc.buffet: 0.6153, Acc.poster: 0.4223, Acc.stage: 0.4680, Acc.van: 0.7083, Acc.ship: 0.8436, Acc.fountain: 0.5693, Acc.conveyer belt: 0.9301, Acc.canopy: 0.7056, Acc.washer: 0.8942, Acc.plaything: 0.6663, Acc.swimming pool: 0.8060, Acc.stool: 0.7004, Acc.barrel: 0.9695, Acc.basket: 0.6340, Acc.waterfall: 0.6620, Acc.tent: 0.9871, Acc.bag: 0.3951, Acc.minibike: 0.8909, Acc.cradle: 0.9783, Acc.oven: 0.6892, Acc.ball: 0.6034, Acc.food: 0.6587, Acc.step: 0.2091, Acc.tank: 0.9812, Acc.trade name: 0.2115, Acc.microwave: 0.9586, Acc.pot: 0.6916, Acc.animal: 0.6495, Acc.bicycle: 0.7883, Acc.lake: 0.6642, Acc.dishwasher: 0.8432, Acc.screen: 0.8924, Acc.blanket: 0.4447, Acc.sculpture: 0.8724, Acc.hood: 0.7652, Acc.sconce: 0.5896, Acc.vase: 0.6567, Acc.traffic light: 0.5980, Acc.tray: 0.3180, Acc.ashcan: 0.6463, Acc.fan: 0.8404, Acc.pier: 0.4810, Acc.crt screen: 0.1478, Acc.plate: 0.8010, Acc.monitor: 0.8621, Acc.bulletin board: 0.6637, Acc.shower: 0.0760, Acc.radiator: 0.8262, Acc.glass: 0.2068, Acc.clock: 0.5926, Acc.flag: 0.7779 +2024-06-16 20:02:33,538 - mmseg - INFO - Iter [46050/80000] lr: 1.698e-05, eta: 16:40:16, time: 3.571, data_time: 1.958, memory: 71384, decode.loss_ce: 0.1669, decode.acc_seg: 92.7356, aux.loss_ce: 0.0703, aux.acc_seg: 92.3380, loss: 0.2372 +2024-06-16 20:03:54,524 - mmseg - INFO - Iter [46100/80000] lr: 1.695e-05, eta: 16:38:42, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1703, decode.acc_seg: 92.4802, aux.loss_ce: 0.0718, aux.acc_seg: 92.1137, loss: 0.2422 +2024-06-16 20:05:15,678 - mmseg - INFO - Iter [46150/80000] lr: 1.693e-05, eta: 16:37:08, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1676, decode.acc_seg: 92.7067, aux.loss_ce: 0.0706, aux.acc_seg: 92.3215, loss: 0.2382 +2024-06-16 20:06:36,786 - mmseg - INFO - Iter [46200/80000] lr: 1.690e-05, eta: 16:35:34, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1769, decode.acc_seg: 92.5014, aux.loss_ce: 0.0742, aux.acc_seg: 92.1222, loss: 0.2511 +2024-06-16 20:07:57,874 - mmseg - INFO - Iter [46250/80000] lr: 1.688e-05, eta: 16:34:01, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1795, decode.acc_seg: 92.2174, aux.loss_ce: 0.0758, aux.acc_seg: 91.8176, loss: 0.2553 +2024-06-16 20:09:18,943 - mmseg - INFO - Iter [46300/80000] lr: 1.685e-05, eta: 16:32:27, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1702, decode.acc_seg: 92.6607, aux.loss_ce: 0.0723, aux.acc_seg: 92.2539, loss: 0.2425 +2024-06-16 20:10:40,040 - mmseg - INFO - Iter [46350/80000] lr: 1.683e-05, eta: 16:30:53, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1708, decode.acc_seg: 92.5972, aux.loss_ce: 0.0721, aux.acc_seg: 92.1705, loss: 0.2428 +2024-06-16 20:12:01,085 - mmseg - INFO - Iter [46400/80000] lr: 1.680e-05, eta: 16:29:20, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1720, decode.acc_seg: 92.5403, aux.loss_ce: 0.0720, aux.acc_seg: 92.1908, loss: 0.2441 +2024-06-16 20:13:22,194 - mmseg - INFO - Iter [46450/80000] lr: 1.678e-05, eta: 16:27:46, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1814, decode.acc_seg: 92.4365, aux.loss_ce: 0.0755, aux.acc_seg: 92.0964, loss: 0.2569 +2024-06-16 20:14:43,335 - mmseg - INFO - Iter [46500/80000] lr: 1.675e-05, eta: 16:26:13, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1779, decode.acc_seg: 92.3262, aux.loss_ce: 0.0745, aux.acc_seg: 91.9623, loss: 0.2524 +2024-06-16 20:16:04,417 - mmseg - INFO - Iter [46550/80000] lr: 1.673e-05, eta: 16:24:39, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1744, decode.acc_seg: 92.4670, aux.loss_ce: 0.0732, aux.acc_seg: 92.0747, loss: 0.2476 +2024-06-16 20:17:25,417 - mmseg - INFO - Iter [46600/80000] lr: 1.670e-05, eta: 16:23:06, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1708, decode.acc_seg: 92.5536, aux.loss_ce: 0.0716, aux.acc_seg: 92.2787, loss: 0.2424 +2024-06-16 20:18:46,463 - mmseg - INFO - Iter [46650/80000] lr: 1.668e-05, eta: 16:21:32, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1787, decode.acc_seg: 92.4349, aux.loss_ce: 0.0748, aux.acc_seg: 92.1076, loss: 0.2535 +2024-06-16 20:20:07,488 - mmseg - INFO - Iter [46700/80000] lr: 1.665e-05, eta: 16:19:59, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1813, decode.acc_seg: 92.3289, aux.loss_ce: 0.0748, aux.acc_seg: 92.0650, loss: 0.2561 +2024-06-16 20:21:31,407 - mmseg - INFO - Iter [46750/80000] lr: 1.663e-05, eta: 16:18:27, time: 1.678, data_time: 0.065, memory: 71384, decode.loss_ce: 0.1791, decode.acc_seg: 92.3825, aux.loss_ce: 0.0749, aux.acc_seg: 91.9819, loss: 0.2539 +2024-06-16 20:22:52,416 - mmseg - INFO - Iter [46800/80000] lr: 1.660e-05, eta: 16:16:54, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1712, decode.acc_seg: 92.5456, aux.loss_ce: 0.0720, aux.acc_seg: 92.2100, loss: 0.2431 +2024-06-16 20:24:13,430 - mmseg - INFO - Iter [46850/80000] lr: 1.658e-05, eta: 16:15:20, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1708, decode.acc_seg: 92.5901, aux.loss_ce: 0.0715, aux.acc_seg: 92.2542, loss: 0.2423 +2024-06-16 20:25:34,462 - mmseg - INFO - Iter [46900/80000] lr: 1.655e-05, eta: 16:13:47, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1747, decode.acc_seg: 92.5867, aux.loss_ce: 0.0732, aux.acc_seg: 92.2455, loss: 0.2479 +2024-06-16 20:26:55,438 - mmseg - INFO - Iter [46950/80000] lr: 1.653e-05, eta: 16:12:14, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1669, decode.acc_seg: 92.9008, aux.loss_ce: 0.0704, aux.acc_seg: 92.5179, loss: 0.2373 +2024-06-16 20:28:16,491 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:28:16,491 - mmseg - INFO - Iter [47000/80000] lr: 1.650e-05, eta: 16:10:40, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1677, decode.acc_seg: 92.9007, aux.loss_ce: 0.0701, aux.acc_seg: 92.5974, loss: 0.2378 +2024-06-16 20:29:54,019 - mmseg - INFO - per class results: +2024-06-16 20:29:54,025 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.62 | 90.0 | +| building | 85.42 | 93.62 | +| sky | 94.86 | 97.64 | +| floor | 85.45 | 91.92 | +| tree | 78.01 | 89.57 | +| ceiling | 87.18 | 94.27 | +| road | 85.87 | 91.25 | +| bed | 92.97 | 97.17 | +| windowpane | 66.73 | 83.08 | +| grass | 67.22 | 82.47 | +| cabinet | 68.52 | 79.01 | +| sidewalk | 69.16 | 82.89 | +| person | 85.69 | 94.51 | +| earth | 38.37 | 53.89 | +| door | 60.76 | 76.67 | +| table | 69.07 | 81.89 | +| mountain | 60.58 | 71.15 | +| plant | 56.05 | 67.05 | +| curtain | 79.42 | 87.49 | +| chair | 69.4 | 81.88 | +| car | 88.52 | 93.68 | +| water | 58.62 | 71.41 | +| painting | 76.5 | 90.21 | +| sofa | 83.4 | 89.45 | +| shelf | 50.45 | 65.03 | +| house | 47.91 | 58.4 | +| sea | 66.41 | 82.48 | +| mirror | 77.91 | 84.62 | +| rug | 70.28 | 79.32 | +| field | 27.82 | 44.05 | +| armchair | 63.4 | 82.36 | +| seat | 68.26 | 89.34 | +| fence | 49.28 | 62.13 | +| desk | 58.98 | 81.95 | +| rock | 55.62 | 87.02 | +| wardrobe | 56.07 | 73.27 | +| lamp | 74.21 | 83.21 | +| bathtub | 86.33 | 88.72 | +| railing | 45.69 | 63.41 | +| cushion | 72.07 | 86.6 | +| base | 35.35 | 49.29 | +| box | 36.37 | 45.16 | +| column | 56.61 | 70.78 | +| signboard | 41.39 | 55.93 | +| chest of drawers | 44.19 | 57.82 | +| counter | 26.81 | 32.23 | +| sand | 56.6 | 82.94 | +| sink | 79.94 | 85.69 | +| skyscraper | 49.13 | 63.8 | +| fireplace | 72.89 | 93.71 | +| refrigerator | 86.66 | 93.26 | +| grandstand | 52.23 | 76.85 | +| path | 27.45 | 37.64 | +| stairs | 32.64 | 39.51 | +| runway | 71.61 | 93.24 | +| case | 63.44 | 88.22 | +| pool table | 94.72 | 97.95 | +| pillow | 69.86 | 80.62 | +| screen door | 76.15 | 78.16 | +| stairway | 38.88 | 52.33 | +| river | 11.45 | 26.72 | +| bridge | 73.74 | 87.97 | +| bookcase | 47.86 | 66.84 | +| blind | 40.95 | 47.54 | +| coffee table | 59.56 | 88.96 | +| toilet | 91.37 | 93.97 | +| flower | 47.85 | 59.41 | +| book | 54.24 | 75.07 | +| hill | 6.78 | 12.8 | +| bench | 58.75 | 66.25 | +| countertop | 63.61 | 82.14 | +| stove | 86.24 | 92.81 | +| palm | 55.11 | 82.12 | +| kitchen island | 52.13 | 79.16 | +| computer | 79.45 | 87.62 | +| swivel chair | 49.24 | 73.5 | +| boat | 74.55 | 91.96 | +| bar | 62.36 | 87.28 | +| arcade machine | 79.19 | 83.9 | +| hovel | 15.97 | 18.05 | +| bus | 93.17 | 96.64 | +| towel | 80.51 | 87.08 | +| light | 59.84 | 65.62 | +| truck | 43.56 | 52.32 | +| tower | 36.18 | 63.4 | +| chandelier | 73.19 | 87.77 | +| awning | 41.57 | 55.85 | +| streetlight | 37.98 | 51.73 | +| booth | 39.39 | 55.21 | +| television receiver | 81.77 | 87.0 | +| airplane | 87.26 | 96.3 | +| dirt track | 7.2 | 34.67 | +| apparel | 54.09 | 76.9 | +| pole | 26.18 | 35.03 | +| land | 1.99 | 3.28 | +| bannister | 20.66 | 24.8 | +| escalator | 60.86 | 76.91 | +| ottoman | 52.69 | 73.4 | +| bottle | 44.86 | 64.44 | +| buffet | 48.71 | 57.61 | +| poster | 32.06 | 37.25 | +| stage | 23.84 | 45.58 | +| van | 49.88 | 75.5 | +| ship | 85.06 | 97.57 | +| fountain | 38.06 | 40.9 | +| conveyer belt | 80.29 | 92.61 | +| canopy | 49.64 | 76.51 | +| washer | 86.46 | 92.54 | +| plaything | 31.59 | 68.84 | +| swimming pool | 52.01 | 74.91 | +| stool | 57.47 | 67.17 | +| barrel | 76.81 | 95.32 | +| basket | 41.88 | 63.15 | +| waterfall | 54.88 | 64.68 | +| tent | 91.42 | 98.57 | +| bag | 26.1 | 31.19 | +| minibike | 76.67 | 90.46 | +| cradle | 88.52 | 97.71 | +| oven | 55.62 | 65.94 | +| ball | 53.84 | 73.5 | +| food | 59.83 | 68.55 | +| step | 15.69 | 18.98 | +| tank | 80.85 | 94.98 | +| trade name | 23.68 | 27.59 | +| microwave | 89.52 | 95.33 | +| pot | 61.17 | 72.22 | +| animal | 64.87 | 67.39 | +| bicycle | 59.66 | 77.32 | +| lake | 52.43 | 64.47 | +| dishwasher | 78.43 | 83.47 | +| screen | 49.35 | 76.49 | +| blanket | 39.58 | 49.02 | +| sculpture | 74.07 | 88.43 | +| hood | 63.28 | 75.11 | +| sconce | 63.03 | 72.13 | +| vase | 49.4 | 60.0 | +| traffic light | 36.56 | 64.01 | +| tray | 26.09 | 38.51 | +| ashcan | 49.5 | 64.59 | +| fan | 70.34 | 79.78 | +| pier | 43.1 | 54.8 | +| crt screen | 8.79 | 14.43 | +| plate | 62.12 | 78.31 | +| monitor | 64.99 | 90.15 | +| bulletin board | 55.87 | 60.97 | +| shower | 2.6 | 6.21 | +| radiator | 66.59 | 82.45 | +| glass | 21.99 | 24.08 | +| clock | 50.04 | 62.51 | +| flag | 73.19 | 80.38 | ++---------------------+-------+-------+ +2024-06-16 20:29:54,025 - mmseg - INFO - Summary: +2024-06-16 20:29:54,025 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.19 | 57.9 | 70.75 | ++-------+------+-------+ +2024-06-16 20:29:54,026 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:29:54,026 - mmseg - INFO - Iter(val) [250] aAcc: 0.8619, mIoU: 0.5790, mAcc: 0.7075, IoU.wall: 0.8262, IoU.building: 0.8542, IoU.sky: 0.9486, IoU.floor: 0.8545, IoU.tree: 0.7801, IoU.ceiling: 0.8718, IoU.road: 0.8587, IoU.bed : 0.9297, IoU.windowpane: 0.6673, IoU.grass: 0.6722, IoU.cabinet: 0.6852, IoU.sidewalk: 0.6916, IoU.person: 0.8569, IoU.earth: 0.3837, IoU.door: 0.6076, IoU.table: 0.6907, IoU.mountain: 0.6058, IoU.plant: 0.5605, IoU.curtain: 0.7942, IoU.chair: 0.6940, IoU.car: 0.8852, IoU.water: 0.5862, IoU.painting: 0.7650, IoU.sofa: 0.8340, IoU.shelf: 0.5045, IoU.house: 0.4791, IoU.sea: 0.6641, IoU.mirror: 0.7791, IoU.rug: 0.7028, IoU.field: 0.2782, IoU.armchair: 0.6340, IoU.seat: 0.6826, IoU.fence: 0.4928, IoU.desk: 0.5898, IoU.rock: 0.5562, IoU.wardrobe: 0.5607, IoU.lamp: 0.7421, IoU.bathtub: 0.8633, IoU.railing: 0.4569, IoU.cushion: 0.7207, IoU.base: 0.3535, IoU.box: 0.3637, IoU.column: 0.5661, IoU.signboard: 0.4139, IoU.chest of drawers: 0.4419, IoU.counter: 0.2681, IoU.sand: 0.5660, IoU.sink: 0.7994, IoU.skyscraper: 0.4913, IoU.fireplace: 0.7289, IoU.refrigerator: 0.8666, IoU.grandstand: 0.5223, IoU.path: 0.2745, IoU.stairs: 0.3264, IoU.runway: 0.7161, IoU.case: 0.6344, IoU.pool table: 0.9472, IoU.pillow: 0.6986, IoU.screen door: 0.7615, IoU.stairway: 0.3888, IoU.river: 0.1145, IoU.bridge: 0.7374, IoU.bookcase: 0.4786, IoU.blind: 0.4095, IoU.coffee table: 0.5956, IoU.toilet: 0.9137, IoU.flower: 0.4785, IoU.book: 0.5424, IoU.hill: 0.0678, IoU.bench: 0.5875, IoU.countertop: 0.6361, IoU.stove: 0.8624, IoU.palm: 0.5511, IoU.kitchen island: 0.5213, IoU.computer: 0.7945, IoU.swivel chair: 0.4924, IoU.boat: 0.7455, IoU.bar: 0.6236, IoU.arcade machine: 0.7919, IoU.hovel: 0.1597, IoU.bus: 0.9317, IoU.towel: 0.8051, IoU.light: 0.5984, IoU.truck: 0.4356, IoU.tower: 0.3618, IoU.chandelier: 0.7319, IoU.awning: 0.4157, IoU.streetlight: 0.3798, IoU.booth: 0.3939, IoU.television receiver: 0.8177, IoU.airplane: 0.8726, IoU.dirt track: 0.0720, IoU.apparel: 0.5409, IoU.pole: 0.2618, IoU.land: 0.0199, IoU.bannister: 0.2066, IoU.escalator: 0.6086, IoU.ottoman: 0.5269, IoU.bottle: 0.4486, IoU.buffet: 0.4871, IoU.poster: 0.3206, IoU.stage: 0.2384, IoU.van: 0.4988, IoU.ship: 0.8506, IoU.fountain: 0.3806, IoU.conveyer belt: 0.8029, IoU.canopy: 0.4964, IoU.washer: 0.8646, IoU.plaything: 0.3159, IoU.swimming pool: 0.5201, IoU.stool: 0.5747, IoU.barrel: 0.7681, IoU.basket: 0.4188, IoU.waterfall: 0.5488, IoU.tent: 0.9142, IoU.bag: 0.2610, IoU.minibike: 0.7667, IoU.cradle: 0.8852, IoU.oven: 0.5562, IoU.ball: 0.5384, IoU.food: 0.5983, IoU.step: 0.1569, IoU.tank: 0.8085, IoU.trade name: 0.2368, IoU.microwave: 0.8952, IoU.pot: 0.6117, IoU.animal: 0.6487, IoU.bicycle: 0.5966, IoU.lake: 0.5243, IoU.dishwasher: 0.7843, IoU.screen: 0.4935, IoU.blanket: 0.3958, IoU.sculpture: 0.7407, IoU.hood: 0.6328, IoU.sconce: 0.6303, IoU.vase: 0.4940, IoU.traffic light: 0.3656, IoU.tray: 0.2609, IoU.ashcan: 0.4950, IoU.fan: 0.7034, IoU.pier: 0.4310, IoU.crt screen: 0.0879, IoU.plate: 0.6212, IoU.monitor: 0.6499, IoU.bulletin board: 0.5587, IoU.shower: 0.0260, IoU.radiator: 0.6659, IoU.glass: 0.2199, IoU.clock: 0.5004, IoU.flag: 0.7319, Acc.wall: 0.9000, Acc.building: 0.9362, Acc.sky: 0.9764, Acc.floor: 0.9192, Acc.tree: 0.8957, Acc.ceiling: 0.9427, Acc.road: 0.9125, Acc.bed : 0.9717, Acc.windowpane: 0.8308, Acc.grass: 0.8247, Acc.cabinet: 0.7901, Acc.sidewalk: 0.8289, Acc.person: 0.9451, Acc.earth: 0.5389, Acc.door: 0.7667, Acc.table: 0.8189, Acc.mountain: 0.7115, Acc.plant: 0.6705, Acc.curtain: 0.8749, Acc.chair: 0.8188, Acc.car: 0.9368, Acc.water: 0.7141, Acc.painting: 0.9021, Acc.sofa: 0.8945, Acc.shelf: 0.6503, Acc.house: 0.5840, Acc.sea: 0.8248, Acc.mirror: 0.8462, Acc.rug: 0.7932, Acc.field: 0.4405, Acc.armchair: 0.8236, Acc.seat: 0.8934, Acc.fence: 0.6213, Acc.desk: 0.8195, Acc.rock: 0.8702, Acc.wardrobe: 0.7327, Acc.lamp: 0.8321, Acc.bathtub: 0.8872, Acc.railing: 0.6341, Acc.cushion: 0.8660, Acc.base: 0.4929, Acc.box: 0.4516, Acc.column: 0.7078, Acc.signboard: 0.5593, Acc.chest of drawers: 0.5782, Acc.counter: 0.3223, Acc.sand: 0.8294, Acc.sink: 0.8569, Acc.skyscraper: 0.6380, Acc.fireplace: 0.9371, Acc.refrigerator: 0.9326, Acc.grandstand: 0.7685, Acc.path: 0.3764, Acc.stairs: 0.3951, Acc.runway: 0.9324, Acc.case: 0.8822, Acc.pool table: 0.9795, Acc.pillow: 0.8062, Acc.screen door: 0.7816, Acc.stairway: 0.5233, Acc.river: 0.2672, Acc.bridge: 0.8797, Acc.bookcase: 0.6684, Acc.blind: 0.4754, Acc.coffee table: 0.8896, Acc.toilet: 0.9397, Acc.flower: 0.5941, Acc.book: 0.7507, Acc.hill: 0.1280, Acc.bench: 0.6625, Acc.countertop: 0.8214, Acc.stove: 0.9281, Acc.palm: 0.8212, Acc.kitchen island: 0.7916, Acc.computer: 0.8762, Acc.swivel chair: 0.7350, Acc.boat: 0.9196, Acc.bar: 0.8728, Acc.arcade machine: 0.8390, Acc.hovel: 0.1805, Acc.bus: 0.9664, Acc.towel: 0.8708, Acc.light: 0.6562, Acc.truck: 0.5232, Acc.tower: 0.6340, Acc.chandelier: 0.8777, Acc.awning: 0.5585, Acc.streetlight: 0.5173, Acc.booth: 0.5521, Acc.television receiver: 0.8700, Acc.airplane: 0.9630, Acc.dirt track: 0.3467, Acc.apparel: 0.7690, Acc.pole: 0.3503, Acc.land: 0.0328, Acc.bannister: 0.2480, Acc.escalator: 0.7691, Acc.ottoman: 0.7340, Acc.bottle: 0.6444, Acc.buffet: 0.5761, Acc.poster: 0.3725, Acc.stage: 0.4558, Acc.van: 0.7550, Acc.ship: 0.9757, Acc.fountain: 0.4090, Acc.conveyer belt: 0.9261, Acc.canopy: 0.7651, Acc.washer: 0.9254, Acc.plaything: 0.6884, Acc.swimming pool: 0.7491, Acc.stool: 0.6717, Acc.barrel: 0.9532, Acc.basket: 0.6315, Acc.waterfall: 0.6468, Acc.tent: 0.9857, Acc.bag: 0.3119, Acc.minibike: 0.9046, Acc.cradle: 0.9771, Acc.oven: 0.6594, Acc.ball: 0.7350, Acc.food: 0.6855, Acc.step: 0.1898, Acc.tank: 0.9498, Acc.trade name: 0.2759, Acc.microwave: 0.9533, Acc.pot: 0.7222, Acc.animal: 0.6739, Acc.bicycle: 0.7732, Acc.lake: 0.6447, Acc.dishwasher: 0.8347, Acc.screen: 0.7649, Acc.blanket: 0.4902, Acc.sculpture: 0.8843, Acc.hood: 0.7511, Acc.sconce: 0.7213, Acc.vase: 0.6000, Acc.traffic light: 0.6401, Acc.tray: 0.3851, Acc.ashcan: 0.6459, Acc.fan: 0.7978, Acc.pier: 0.5480, Acc.crt screen: 0.1443, Acc.plate: 0.7831, Acc.monitor: 0.9015, Acc.bulletin board: 0.6097, Acc.shower: 0.0621, Acc.radiator: 0.8245, Acc.glass: 0.2408, Acc.clock: 0.6251, Acc.flag: 0.8038 +2024-06-16 20:31:15,517 - mmseg - INFO - Iter [47050/80000] lr: 1.648e-05, eta: 16:10:16, time: 3.581, data_time: 1.966, memory: 71384, decode.loss_ce: 0.1731, decode.acc_seg: 92.7646, aux.loss_ce: 0.0725, aux.acc_seg: 92.3855, loss: 0.2455 +2024-06-16 20:32:36,575 - mmseg - INFO - Iter [47100/80000] lr: 1.645e-05, eta: 16:08:42, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1645, decode.acc_seg: 92.8548, aux.loss_ce: 0.0691, aux.acc_seg: 92.4920, loss: 0.2336 +2024-06-16 20:33:57,611 - mmseg - INFO - Iter [47150/80000] lr: 1.643e-05, eta: 16:07:09, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1616, decode.acc_seg: 92.9428, aux.loss_ce: 0.0679, aux.acc_seg: 92.5798, loss: 0.2295 +2024-06-16 20:35:18,658 - mmseg - INFO - Iter [47200/80000] lr: 1.640e-05, eta: 16:05:36, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1734, decode.acc_seg: 92.6699, aux.loss_ce: 0.0735, aux.acc_seg: 92.2368, loss: 0.2469 +2024-06-16 20:36:39,755 - mmseg - INFO - Iter [47250/80000] lr: 1.638e-05, eta: 16:04:02, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1745, decode.acc_seg: 92.0457, aux.loss_ce: 0.0733, aux.acc_seg: 91.7065, loss: 0.2478 +2024-06-16 20:38:00,702 - mmseg - INFO - Iter [47300/80000] lr: 1.635e-05, eta: 16:02:29, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1769, decode.acc_seg: 92.4640, aux.loss_ce: 0.0736, aux.acc_seg: 92.1811, loss: 0.2505 +2024-06-16 20:39:21,919 - mmseg - INFO - Iter [47350/80000] lr: 1.633e-05, eta: 16:00:56, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1700, decode.acc_seg: 92.8195, aux.loss_ce: 0.0712, aux.acc_seg: 92.4669, loss: 0.2412 +2024-06-16 20:40:42,942 - mmseg - INFO - Iter [47400/80000] lr: 1.630e-05, eta: 15:59:22, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1595, decode.acc_seg: 93.1621, aux.loss_ce: 0.0674, aux.acc_seg: 92.7674, loss: 0.2269 +2024-06-16 20:42:04,222 - mmseg - INFO - Iter [47450/80000] lr: 1.628e-05, eta: 15:57:49, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1696, decode.acc_seg: 92.8372, aux.loss_ce: 0.0712, aux.acc_seg: 92.4730, loss: 0.2408 +2024-06-16 20:43:25,219 - mmseg - INFO - Iter [47500/80000] lr: 1.625e-05, eta: 15:56:16, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1777, decode.acc_seg: 92.3575, aux.loss_ce: 0.0743, aux.acc_seg: 91.9479, loss: 0.2520 +2024-06-16 20:44:46,484 - mmseg - INFO - Iter [47550/80000] lr: 1.623e-05, eta: 15:54:43, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1811, decode.acc_seg: 92.4413, aux.loss_ce: 0.0758, aux.acc_seg: 92.1122, loss: 0.2569 +2024-06-16 20:46:07,525 - mmseg - INFO - Iter [47600/80000] lr: 1.620e-05, eta: 15:53:10, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1727, decode.acc_seg: 92.7587, aux.loss_ce: 0.0729, aux.acc_seg: 92.3277, loss: 0.2456 +2024-06-16 20:47:28,640 - mmseg - INFO - Iter [47650/80000] lr: 1.618e-05, eta: 15:51:37, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1732, decode.acc_seg: 92.7004, aux.loss_ce: 0.0732, aux.acc_seg: 92.2440, loss: 0.2463 +2024-06-16 20:48:49,676 - mmseg - INFO - Iter [47700/80000] lr: 1.615e-05, eta: 15:50:03, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1770, decode.acc_seg: 92.3987, aux.loss_ce: 0.0732, aux.acc_seg: 92.0939, loss: 0.2502 +2024-06-16 20:50:10,669 - mmseg - INFO - Iter [47750/80000] lr: 1.613e-05, eta: 15:48:30, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1726, decode.acc_seg: 92.5613, aux.loss_ce: 0.0716, aux.acc_seg: 92.4110, loss: 0.2442 +2024-06-16 20:51:31,798 - mmseg - INFO - Iter [47800/80000] lr: 1.610e-05, eta: 15:46:57, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1784, decode.acc_seg: 92.3554, aux.loss_ce: 0.0745, aux.acc_seg: 91.9940, loss: 0.2529 +2024-06-16 20:52:52,769 - mmseg - INFO - Iter [47850/80000] lr: 1.608e-05, eta: 15:45:24, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1792, decode.acc_seg: 92.1690, aux.loss_ce: 0.0752, aux.acc_seg: 91.8603, loss: 0.2545 +2024-06-16 20:54:13,923 - mmseg - INFO - Iter [47900/80000] lr: 1.605e-05, eta: 15:43:51, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1727, decode.acc_seg: 92.5413, aux.loss_ce: 0.0726, aux.acc_seg: 92.1594, loss: 0.2453 +2024-06-16 20:55:34,819 - mmseg - INFO - Iter [47950/80000] lr: 1.603e-05, eta: 15:42:18, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1762, decode.acc_seg: 92.3599, aux.loss_ce: 0.0739, aux.acc_seg: 91.9385, loss: 0.2501 +2024-06-16 20:56:58,172 - mmseg - INFO - Saving checkpoint at 48000 iterations +2024-06-16 20:58:20,130 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:58:20,130 - mmseg - INFO - Iter [48000/80000] lr: 1.600e-05, eta: 15:41:41, time: 3.306, data_time: 0.055, memory: 71384, decode.loss_ce: 0.1644, decode.acc_seg: 92.8006, aux.loss_ce: 0.0684, aux.acc_seg: 92.4830, loss: 0.2328 +2024-06-16 20:59:57,351 - mmseg - INFO - per class results: +2024-06-16 20:59:57,357 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.2 | 90.13 | +| building | 85.8 | 92.73 | +| sky | 94.85 | 97.11 | +| floor | 85.67 | 91.98 | +| tree | 78.2 | 90.66 | +| ceiling | 87.73 | 94.16 | +| road | 85.6 | 91.23 | +| bed | 92.79 | 96.77 | +| windowpane | 66.75 | 83.75 | +| grass | 67.41 | 81.13 | +| cabinet | 65.25 | 75.16 | +| sidewalk | 69.97 | 83.8 | +| person | 85.82 | 94.69 | +| earth | 39.21 | 53.43 | +| door | 60.0 | 71.44 | +| table | 69.79 | 82.94 | +| mountain | 62.24 | 71.14 | +| plant | 56.69 | 68.31 | +| curtain | 78.64 | 88.11 | +| chair | 68.73 | 77.59 | +| car | 88.51 | 93.79 | +| water | 60.8 | 74.57 | +| painting | 76.67 | 89.84 | +| sofa | 84.08 | 92.78 | +| shelf | 51.99 | 70.39 | +| house | 59.95 | 81.44 | +| sea | 69.7 | 81.72 | +| mirror | 77.05 | 85.02 | +| rug | 70.39 | 79.35 | +| field | 32.18 | 56.55 | +| armchair | 62.63 | 81.78 | +| seat | 67.91 | 86.49 | +| fence | 47.43 | 67.64 | +| desk | 59.47 | 76.75 | +| rock | 55.35 | 83.64 | +| wardrobe | 52.83 | 77.23 | +| lamp | 75.6 | 87.15 | +| bathtub | 87.11 | 89.37 | +| railing | 45.23 | 61.6 | +| cushion | 71.6 | 85.43 | +| base | 39.54 | 53.03 | +| box | 37.58 | 49.28 | +| column | 56.04 | 65.26 | +| signboard | 42.14 | 56.11 | +| chest of drawers | 41.25 | 66.49 | +| counter | 29.63 | 37.57 | +| sand | 53.55 | 85.8 | +| sink | 81.99 | 88.88 | +| skyscraper | 46.21 | 57.99 | +| fireplace | 74.69 | 95.62 | +| refrigerator | 86.56 | 92.61 | +| grandstand | 51.68 | 85.04 | +| path | 30.51 | 39.33 | +| stairs | 29.93 | 36.98 | +| runway | 70.82 | 91.35 | +| case | 64.95 | 83.84 | +| pool table | 94.73 | 98.35 | +| pillow | 68.21 | 78.11 | +| screen door | 78.71 | 81.25 | +| stairway | 40.18 | 57.53 | +| river | 9.13 | 21.28 | +| bridge | 63.4 | 71.96 | +| bookcase | 53.86 | 65.85 | +| blind | 46.27 | 54.99 | +| coffee table | 61.02 | 87.64 | +| toilet | 90.38 | 93.3 | +| flower | 43.32 | 53.82 | +| book | 55.24 | 77.27 | +| hill | 9.65 | 17.23 | +| bench | 61.57 | 69.64 | +| countertop | 56.65 | 69.29 | +| stove | 87.54 | 92.69 | +| palm | 54.01 | 85.86 | +| kitchen island | 54.55 | 89.79 | +| computer | 81.06 | 91.47 | +| swivel chair | 50.98 | 78.47 | +| boat | 75.55 | 91.16 | +| bar | 59.81 | 82.54 | +| arcade machine | 75.69 | 80.63 | +| hovel | 14.65 | 15.62 | +| bus | 92.55 | 97.06 | +| towel | 80.94 | 89.44 | +| light | 62.23 | 70.25 | +| truck | 38.6 | 48.07 | +| tower | 37.12 | 63.89 | +| chandelier | 74.81 | 87.93 | +| awning | 46.25 | 60.02 | +| streetlight | 38.06 | 51.2 | +| booth | 35.19 | 54.48 | +| television receiver | 75.6 | 92.25 | +| airplane | 88.04 | 96.22 | +| dirt track | 3.92 | 13.49 | +| apparel | 57.76 | 86.22 | +| pole | 24.52 | 30.69 | +| land | 3.89 | 5.89 | +| bannister | 22.56 | 28.6 | +| escalator | 56.47 | 73.12 | +| ottoman | 53.0 | 71.67 | +| bottle | 42.4 | 54.56 | +| buffet | 51.56 | 68.91 | +| poster | 33.4 | 39.9 | +| stage | 24.73 | 44.65 | +| van | 48.9 | 73.39 | +| ship | 88.33 | 97.49 | +| fountain | 33.78 | 34.43 | +| conveyer belt | 73.45 | 95.42 | +| canopy | 52.57 | 73.53 | +| washer | 84.98 | 90.81 | +| plaything | 24.56 | 28.75 | +| swimming pool | 50.88 | 73.56 | +| stool | 50.87 | 69.04 | +| barrel | 51.15 | 93.92 | +| basket | 41.67 | 64.98 | +| waterfall | 51.83 | 68.77 | +| tent | 92.38 | 98.55 | +| bag | 28.72 | 34.06 | +| minibike | 76.12 | 91.53 | +| cradle | 90.69 | 96.81 | +| oven | 56.98 | 69.38 | +| ball | 59.42 | 69.23 | +| food | 61.52 | 70.9 | +| step | 21.86 | 29.65 | +| tank | 80.92 | 94.6 | +| trade name | 25.61 | 30.59 | +| microwave | 88.12 | 96.68 | +| pot | 57.98 | 67.52 | +| animal | 64.12 | 66.13 | +| bicycle | 59.11 | 76.86 | +| lake | 49.14 | 63.67 | +| dishwasher | 78.02 | 84.11 | +| screen | 60.24 | 89.36 | +| blanket | 43.44 | 53.53 | +| sculpture | 72.39 | 89.02 | +| hood | 64.19 | 76.09 | +| sconce | 62.12 | 71.58 | +| vase | 47.41 | 67.74 | +| traffic light | 37.1 | 60.99 | +| tray | 25.9 | 38.54 | +| ashcan | 48.7 | 66.3 | +| fan | 70.6 | 87.44 | +| pier | 31.1 | 43.25 | +| crt screen | 3.22 | 5.12 | +| plate | 61.94 | 81.34 | +| monitor | 51.05 | 62.17 | +| bulletin board | 57.41 | 76.2 | +| shower | 0.99 | 2.44 | +| radiator | 67.56 | 80.98 | +| glass | 20.88 | 22.08 | +| clock | 48.99 | 55.25 | +| flag | 72.32 | 78.8 | ++---------------------+-------+-------+ +2024-06-16 20:59:57,357 - mmseg - INFO - Summary: +2024-06-16 20:59:57,357 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.24 | 57.59 | 70.74 | ++-------+-------+-------+ +2024-06-16 20:59:57,358 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 20:59:57,358 - mmseg - INFO - Iter(val) [250] aAcc: 0.8624, mIoU: 0.5759, mAcc: 0.7074, IoU.wall: 0.8220, IoU.building: 0.8580, IoU.sky: 0.9485, IoU.floor: 0.8567, IoU.tree: 0.7820, IoU.ceiling: 0.8773, IoU.road: 0.8560, IoU.bed : 0.9279, IoU.windowpane: 0.6675, IoU.grass: 0.6741, IoU.cabinet: 0.6525, IoU.sidewalk: 0.6997, IoU.person: 0.8582, IoU.earth: 0.3921, IoU.door: 0.6000, IoU.table: 0.6979, IoU.mountain: 0.6224, IoU.plant: 0.5669, IoU.curtain: 0.7864, IoU.chair: 0.6873, IoU.car: 0.8851, IoU.water: 0.6080, IoU.painting: 0.7667, IoU.sofa: 0.8408, IoU.shelf: 0.5199, IoU.house: 0.5995, IoU.sea: 0.6970, IoU.mirror: 0.7705, IoU.rug: 0.7039, IoU.field: 0.3218, IoU.armchair: 0.6263, IoU.seat: 0.6791, IoU.fence: 0.4743, IoU.desk: 0.5947, IoU.rock: 0.5535, IoU.wardrobe: 0.5283, IoU.lamp: 0.7560, IoU.bathtub: 0.8711, IoU.railing: 0.4523, IoU.cushion: 0.7160, IoU.base: 0.3954, IoU.box: 0.3758, IoU.column: 0.5604, IoU.signboard: 0.4214, IoU.chest of drawers: 0.4125, IoU.counter: 0.2963, IoU.sand: 0.5355, IoU.sink: 0.8199, IoU.skyscraper: 0.4621, IoU.fireplace: 0.7469, IoU.refrigerator: 0.8656, IoU.grandstand: 0.5168, IoU.path: 0.3051, IoU.stairs: 0.2993, IoU.runway: 0.7082, IoU.case: 0.6495, IoU.pool table: 0.9473, IoU.pillow: 0.6821, IoU.screen door: 0.7871, IoU.stairway: 0.4018, IoU.river: 0.0913, IoU.bridge: 0.6340, IoU.bookcase: 0.5386, IoU.blind: 0.4627, IoU.coffee table: 0.6102, IoU.toilet: 0.9038, IoU.flower: 0.4332, IoU.book: 0.5524, IoU.hill: 0.0965, IoU.bench: 0.6157, IoU.countertop: 0.5665, IoU.stove: 0.8754, IoU.palm: 0.5401, IoU.kitchen island: 0.5455, IoU.computer: 0.8106, IoU.swivel chair: 0.5098, IoU.boat: 0.7555, IoU.bar: 0.5981, IoU.arcade machine: 0.7569, IoU.hovel: 0.1465, IoU.bus: 0.9255, IoU.towel: 0.8094, IoU.light: 0.6223, IoU.truck: 0.3860, IoU.tower: 0.3712, IoU.chandelier: 0.7481, IoU.awning: 0.4625, IoU.streetlight: 0.3806, IoU.booth: 0.3519, IoU.television receiver: 0.7560, IoU.airplane: 0.8804, IoU.dirt track: 0.0392, IoU.apparel: 0.5776, IoU.pole: 0.2452, IoU.land: 0.0389, IoU.bannister: 0.2256, IoU.escalator: 0.5647, IoU.ottoman: 0.5300, IoU.bottle: 0.4240, IoU.buffet: 0.5156, IoU.poster: 0.3340, IoU.stage: 0.2473, IoU.van: 0.4890, IoU.ship: 0.8833, IoU.fountain: 0.3378, IoU.conveyer belt: 0.7345, IoU.canopy: 0.5257, IoU.washer: 0.8498, IoU.plaything: 0.2456, IoU.swimming pool: 0.5088, IoU.stool: 0.5087, IoU.barrel: 0.5115, IoU.basket: 0.4167, IoU.waterfall: 0.5183, IoU.tent: 0.9238, IoU.bag: 0.2872, IoU.minibike: 0.7612, IoU.cradle: 0.9069, IoU.oven: 0.5698, IoU.ball: 0.5942, IoU.food: 0.6152, IoU.step: 0.2186, IoU.tank: 0.8092, IoU.trade name: 0.2561, IoU.microwave: 0.8812, IoU.pot: 0.5798, IoU.animal: 0.6412, IoU.bicycle: 0.5911, IoU.lake: 0.4914, IoU.dishwasher: 0.7802, IoU.screen: 0.6024, IoU.blanket: 0.4344, IoU.sculpture: 0.7239, IoU.hood: 0.6419, IoU.sconce: 0.6212, IoU.vase: 0.4741, IoU.traffic light: 0.3710, IoU.tray: 0.2590, IoU.ashcan: 0.4870, IoU.fan: 0.7060, IoU.pier: 0.3110, IoU.crt screen: 0.0322, IoU.plate: 0.6194, IoU.monitor: 0.5105, IoU.bulletin board: 0.5741, IoU.shower: 0.0099, IoU.radiator: 0.6756, IoU.glass: 0.2088, IoU.clock: 0.4899, IoU.flag: 0.7232, Acc.wall: 0.9013, Acc.building: 0.9273, Acc.sky: 0.9711, Acc.floor: 0.9198, Acc.tree: 0.9066, Acc.ceiling: 0.9416, Acc.road: 0.9123, Acc.bed : 0.9677, Acc.windowpane: 0.8375, Acc.grass: 0.8113, Acc.cabinet: 0.7516, Acc.sidewalk: 0.8380, Acc.person: 0.9469, Acc.earth: 0.5343, Acc.door: 0.7144, Acc.table: 0.8294, Acc.mountain: 0.7114, Acc.plant: 0.6831, Acc.curtain: 0.8811, Acc.chair: 0.7759, Acc.car: 0.9379, Acc.water: 0.7457, Acc.painting: 0.8984, Acc.sofa: 0.9278, Acc.shelf: 0.7039, Acc.house: 0.8144, Acc.sea: 0.8172, Acc.mirror: 0.8502, Acc.rug: 0.7935, Acc.field: 0.5655, Acc.armchair: 0.8178, Acc.seat: 0.8649, Acc.fence: 0.6764, Acc.desk: 0.7675, Acc.rock: 0.8364, Acc.wardrobe: 0.7723, Acc.lamp: 0.8715, Acc.bathtub: 0.8937, Acc.railing: 0.6160, Acc.cushion: 0.8543, Acc.base: 0.5303, Acc.box: 0.4928, Acc.column: 0.6526, Acc.signboard: 0.5611, Acc.chest of drawers: 0.6649, Acc.counter: 0.3757, Acc.sand: 0.8580, Acc.sink: 0.8888, Acc.skyscraper: 0.5799, Acc.fireplace: 0.9562, Acc.refrigerator: 0.9261, Acc.grandstand: 0.8504, Acc.path: 0.3933, Acc.stairs: 0.3698, Acc.runway: 0.9135, Acc.case: 0.8384, Acc.pool table: 0.9835, Acc.pillow: 0.7811, Acc.screen door: 0.8125, Acc.stairway: 0.5753, Acc.river: 0.2128, Acc.bridge: 0.7196, Acc.bookcase: 0.6585, Acc.blind: 0.5499, Acc.coffee table: 0.8764, Acc.toilet: 0.9330, Acc.flower: 0.5382, Acc.book: 0.7727, Acc.hill: 0.1723, Acc.bench: 0.6964, Acc.countertop: 0.6929, Acc.stove: 0.9269, Acc.palm: 0.8586, Acc.kitchen island: 0.8979, Acc.computer: 0.9147, Acc.swivel chair: 0.7847, Acc.boat: 0.9116, Acc.bar: 0.8254, Acc.arcade machine: 0.8063, Acc.hovel: 0.1562, Acc.bus: 0.9706, Acc.towel: 0.8944, Acc.light: 0.7025, Acc.truck: 0.4807, Acc.tower: 0.6389, Acc.chandelier: 0.8793, Acc.awning: 0.6002, Acc.streetlight: 0.5120, Acc.booth: 0.5448, Acc.television receiver: 0.9225, Acc.airplane: 0.9622, Acc.dirt track: 0.1349, Acc.apparel: 0.8622, Acc.pole: 0.3069, Acc.land: 0.0589, Acc.bannister: 0.2860, Acc.escalator: 0.7312, Acc.ottoman: 0.7167, Acc.bottle: 0.5456, Acc.buffet: 0.6891, Acc.poster: 0.3990, Acc.stage: 0.4465, Acc.van: 0.7339, Acc.ship: 0.9749, Acc.fountain: 0.3443, Acc.conveyer belt: 0.9542, Acc.canopy: 0.7353, Acc.washer: 0.9081, Acc.plaything: 0.2875, Acc.swimming pool: 0.7356, Acc.stool: 0.6904, Acc.barrel: 0.9392, Acc.basket: 0.6498, Acc.waterfall: 0.6877, Acc.tent: 0.9855, Acc.bag: 0.3406, Acc.minibike: 0.9153, Acc.cradle: 0.9681, Acc.oven: 0.6938, Acc.ball: 0.6923, Acc.food: 0.7090, Acc.step: 0.2965, Acc.tank: 0.9460, Acc.trade name: 0.3059, Acc.microwave: 0.9668, Acc.pot: 0.6752, Acc.animal: 0.6613, Acc.bicycle: 0.7686, Acc.lake: 0.6367, Acc.dishwasher: 0.8411, Acc.screen: 0.8936, Acc.blanket: 0.5353, Acc.sculpture: 0.8902, Acc.hood: 0.7609, Acc.sconce: 0.7158, Acc.vase: 0.6774, Acc.traffic light: 0.6099, Acc.tray: 0.3854, Acc.ashcan: 0.6630, Acc.fan: 0.8744, Acc.pier: 0.4325, Acc.crt screen: 0.0512, Acc.plate: 0.8134, Acc.monitor: 0.6217, Acc.bulletin board: 0.7620, Acc.shower: 0.0244, Acc.radiator: 0.8098, Acc.glass: 0.2208, Acc.clock: 0.5525, Acc.flag: 0.7880 +2024-06-16 21:01:18,948 - mmseg - INFO - Iter [48050/80000] lr: 1.598e-05, eta: 15:41:13, time: 3.576, data_time: 1.962, memory: 71384, decode.loss_ce: 0.1720, decode.acc_seg: 92.7536, aux.loss_ce: 0.0724, aux.acc_seg: 92.4109, loss: 0.2444 +2024-06-16 21:02:40,124 - mmseg - INFO - Iter [48100/80000] lr: 1.595e-05, eta: 15:39:40, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1577, decode.acc_seg: 93.1621, aux.loss_ce: 0.0668, aux.acc_seg: 92.7754, loss: 0.2244 +2024-06-16 21:04:01,053 - mmseg - INFO - Iter [48150/80000] lr: 1.593e-05, eta: 15:38:07, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1773, decode.acc_seg: 92.6492, aux.loss_ce: 0.0749, aux.acc_seg: 92.3148, loss: 0.2522 +2024-06-16 21:05:22,081 - mmseg - INFO - Iter [48200/80000] lr: 1.590e-05, eta: 15:36:34, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1740, decode.acc_seg: 92.4302, aux.loss_ce: 0.0732, aux.acc_seg: 92.0583, loss: 0.2472 +2024-06-16 21:06:42,974 - mmseg - INFO - Iter [48250/80000] lr: 1.588e-05, eta: 15:35:00, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1714, decode.acc_seg: 92.5797, aux.loss_ce: 0.0722, aux.acc_seg: 92.1656, loss: 0.2436 +2024-06-16 21:08:04,017 - mmseg - INFO - Iter [48300/80000] lr: 1.585e-05, eta: 15:33:27, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1696, decode.acc_seg: 92.6328, aux.loss_ce: 0.0708, aux.acc_seg: 92.3393, loss: 0.2404 +2024-06-16 21:09:25,246 - mmseg - INFO - Iter [48350/80000] lr: 1.583e-05, eta: 15:31:54, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1677, decode.acc_seg: 92.9623, aux.loss_ce: 0.0705, aux.acc_seg: 92.5453, loss: 0.2382 +2024-06-16 21:10:46,244 - mmseg - INFO - Iter [48400/80000] lr: 1.580e-05, eta: 15:30:21, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1668, decode.acc_seg: 92.7250, aux.loss_ce: 0.0704, aux.acc_seg: 92.3506, loss: 0.2372 +2024-06-16 21:12:07,378 - mmseg - INFO - Iter [48450/80000] lr: 1.578e-05, eta: 15:28:48, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1636, decode.acc_seg: 93.0372, aux.loss_ce: 0.0690, aux.acc_seg: 92.6775, loss: 0.2326 +2024-06-16 21:13:28,451 - mmseg - INFO - Iter [48500/80000] lr: 1.575e-05, eta: 15:27:15, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1698, decode.acc_seg: 92.8022, aux.loss_ce: 0.0709, aux.acc_seg: 92.4711, loss: 0.2407 +2024-06-16 21:14:49,581 - mmseg - INFO - Iter [48550/80000] lr: 1.573e-05, eta: 15:25:42, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1629, decode.acc_seg: 92.9428, aux.loss_ce: 0.0690, aux.acc_seg: 92.4939, loss: 0.2319 +2024-06-16 21:16:10,523 - mmseg - INFO - Iter [48600/80000] lr: 1.570e-05, eta: 15:24:09, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1731, decode.acc_seg: 92.5963, aux.loss_ce: 0.0728, aux.acc_seg: 92.2493, loss: 0.2460 +2024-06-16 21:17:31,629 - mmseg - INFO - Iter [48650/80000] lr: 1.568e-05, eta: 15:22:36, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1789, decode.acc_seg: 92.0101, aux.loss_ce: 0.0748, aux.acc_seg: 91.7309, loss: 0.2537 +2024-06-16 21:18:52,736 - mmseg - INFO - Iter [48700/80000] lr: 1.565e-05, eta: 15:21:03, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1713, decode.acc_seg: 92.7137, aux.loss_ce: 0.0720, aux.acc_seg: 92.3510, loss: 0.2433 +2024-06-16 21:20:13,937 - mmseg - INFO - Iter [48750/80000] lr: 1.563e-05, eta: 15:19:30, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1641, decode.acc_seg: 92.8171, aux.loss_ce: 0.0690, aux.acc_seg: 92.4244, loss: 0.2330 +2024-06-16 21:21:34,982 - mmseg - INFO - Iter [48800/80000] lr: 1.560e-05, eta: 15:17:57, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1664, decode.acc_seg: 92.9370, aux.loss_ce: 0.0705, aux.acc_seg: 92.5274, loss: 0.2369 +2024-06-16 21:22:56,074 - mmseg - INFO - Iter [48850/80000] lr: 1.558e-05, eta: 15:16:25, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1667, decode.acc_seg: 92.8560, aux.loss_ce: 0.0700, aux.acc_seg: 92.5195, loss: 0.2367 +2024-06-16 21:24:17,219 - mmseg - INFO - Iter [48900/80000] lr: 1.555e-05, eta: 15:14:52, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1722, decode.acc_seg: 92.6393, aux.loss_ce: 0.0724, aux.acc_seg: 92.2423, loss: 0.2446 +2024-06-16 21:25:38,248 - mmseg - INFO - Iter [48950/80000] lr: 1.553e-05, eta: 15:13:19, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1637, decode.acc_seg: 93.0240, aux.loss_ce: 0.0689, aux.acc_seg: 92.6294, loss: 0.2326 +2024-06-16 21:26:59,358 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 21:26:59,358 - mmseg - INFO - Iter [49000/80000] lr: 1.550e-05, eta: 15:11:46, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1628, decode.acc_seg: 92.6853, aux.loss_ce: 0.0686, aux.acc_seg: 92.3371, loss: 0.2313 +2024-06-16 21:28:37,444 - mmseg - INFO - per class results: +2024-06-16 21:28:37,450 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.38 | 90.79 | +| building | 85.3 | 93.74 | +| sky | 95.02 | 97.7 | +| floor | 85.64 | 92.14 | +| tree | 77.78 | 89.37 | +| ceiling | 87.6 | 93.75 | +| road | 85.78 | 92.56 | +| bed | 91.83 | 95.82 | +| windowpane | 67.99 | 80.87 | +| grass | 69.2 | 81.55 | +| cabinet | 69.44 | 81.23 | +| sidewalk | 71.19 | 84.42 | +| person | 86.52 | 93.85 | +| earth | 39.04 | 54.11 | +| door | 60.75 | 77.82 | +| table | 69.53 | 83.51 | +| mountain | 60.31 | 69.27 | +| plant | 55.61 | 65.05 | +| curtain | 79.92 | 88.99 | +| chair | 69.91 | 80.85 | +| car | 88.52 | 93.45 | +| water | 64.94 | 81.95 | +| painting | 78.13 | 90.78 | +| sofa | 83.74 | 93.83 | +| shelf | 52.39 | 68.72 | +| house | 48.89 | 57.46 | +| sea | 72.79 | 81.21 | +| mirror | 76.87 | 82.67 | +| rug | 68.09 | 74.65 | +| field | 34.42 | 59.2 | +| armchair | 64.53 | 81.18 | +| seat | 66.01 | 89.18 | +| fence | 46.71 | 60.62 | +| desk | 61.27 | 79.03 | +| rock | 52.51 | 83.89 | +| wardrobe | 58.63 | 69.57 | +| lamp | 76.39 | 87.65 | +| bathtub | 85.83 | 87.89 | +| railing | 43.92 | 59.73 | +| cushion | 72.38 | 81.99 | +| base | 36.79 | 62.36 | +| box | 39.4 | 52.13 | +| column | 53.97 | 67.3 | +| signboard | 41.67 | 58.92 | +| chest of drawers | 45.11 | 65.95 | +| counter | 27.91 | 31.38 | +| sand | 52.04 | 79.06 | +| sink | 80.2 | 85.57 | +| skyscraper | 45.78 | 58.18 | +| fireplace | 75.66 | 95.36 | +| refrigerator | 86.18 | 93.38 | +| grandstand | 48.83 | 80.18 | +| path | 31.09 | 43.74 | +| stairs | 37.92 | 49.74 | +| runway | 62.7 | 68.56 | +| case | 65.93 | 82.71 | +| pool table | 94.54 | 98.05 | +| pillow | 69.03 | 79.96 | +| screen door | 74.65 | 76.98 | +| stairway | 56.06 | 66.51 | +| river | 11.56 | 23.21 | +| bridge | 75.74 | 85.69 | +| bookcase | 54.84 | 64.04 | +| blind | 42.48 | 47.96 | +| coffee table | 65.93 | 86.62 | +| toilet | 91.26 | 93.86 | +| flower | 42.17 | 57.73 | +| book | 54.75 | 79.56 | +| hill | 10.83 | 23.68 | +| bench | 56.02 | 61.65 | +| countertop | 64.11 | 85.01 | +| stove | 87.1 | 91.31 | +| palm | 54.7 | 80.95 | +| kitchen island | 61.97 | 86.21 | +| computer | 81.38 | 91.9 | +| swivel chair | 51.01 | 76.51 | +| boat | 80.02 | 91.4 | +| bar | 59.15 | 80.5 | +| arcade machine | 77.73 | 84.05 | +| hovel | 19.69 | 21.15 | +| bus | 92.44 | 97.4 | +| towel | 79.85 | 91.76 | +| light | 63.15 | 72.15 | +| truck | 38.95 | 50.89 | +| tower | 22.43 | 37.72 | +| chandelier | 73.61 | 82.41 | +| awning | 40.04 | 48.47 | +| streetlight | 38.75 | 51.52 | +| booth | 42.67 | 54.57 | +| television receiver | 80.36 | 86.77 | +| airplane | 89.46 | 96.07 | +| dirt track | 6.3 | 22.32 | +| apparel | 63.82 | 80.89 | +| pole | 28.46 | 39.42 | +| land | 4.2 | 5.86 | +| bannister | 18.91 | 24.59 | +| escalator | 63.65 | 83.41 | +| ottoman | 54.83 | 73.91 | +| bottle | 47.18 | 64.24 | +| buffet | 46.95 | 57.74 | +| poster | 28.67 | 31.04 | +| stage | 25.99 | 40.88 | +| van | 50.65 | 75.07 | +| ship | 90.93 | 94.53 | +| fountain | 39.85 | 40.93 | +| conveyer belt | 81.21 | 93.66 | +| canopy | 51.13 | 73.92 | +| washer | 82.23 | 87.47 | +| plaything | 31.1 | 41.4 | +| swimming pool | 55.72 | 81.59 | +| stool | 49.08 | 70.49 | +| barrel | 56.61 | 94.87 | +| basket | 41.68 | 60.9 | +| waterfall | 49.09 | 62.72 | +| tent | 95.03 | 98.21 | +| bag | 31.19 | 38.07 | +| minibike | 77.77 | 90.4 | +| cradle | 81.72 | 98.79 | +| oven | 56.24 | 67.86 | +| ball | 57.29 | 65.65 | +| food | 64.29 | 75.31 | +| step | 13.52 | 15.35 | +| tank | 81.95 | 88.72 | +| trade name | 18.64 | 21.13 | +| microwave | 88.63 | 95.15 | +| pot | 59.3 | 69.91 | +| animal | 62.86 | 64.03 | +| bicycle | 59.28 | 77.67 | +| lake | 52.17 | 63.75 | +| dishwasher | 76.74 | 83.47 | +| screen | 60.73 | 91.17 | +| blanket | 41.75 | 48.35 | +| sculpture | 75.72 | 84.93 | +| hood | 62.72 | 73.23 | +| sconce | 58.38 | 64.36 | +| vase | 49.95 | 66.44 | +| traffic light | 36.61 | 65.99 | +| tray | 22.03 | 27.3 | +| ashcan | 47.79 | 68.42 | +| fan | 72.76 | 83.82 | +| pier | 43.87 | 49.0 | +| crt screen | 8.89 | 12.68 | +| plate | 61.32 | 82.72 | +| monitor | 65.43 | 78.53 | +| bulletin board | 59.95 | 76.78 | +| shower | 0.95 | 2.39 | +| radiator | 67.73 | 79.92 | +| glass | 21.15 | 22.75 | +| clock | 47.68 | 56.26 | +| flag | 72.46 | 78.76 | ++---------------------+-------+-------+ +2024-06-16 21:28:37,450 - mmseg - INFO - Summary: +2024-06-16 21:28:37,451 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 86.42 | 58.27 | 70.4 | ++-------+-------+------+ +2024-06-16 21:28:37,451 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 21:28:37,452 - mmseg - INFO - Iter(val) [250] aAcc: 0.8642, mIoU: 0.5827, mAcc: 0.7040, IoU.wall: 0.8238, IoU.building: 0.8530, IoU.sky: 0.9502, IoU.floor: 0.8564, IoU.tree: 0.7778, IoU.ceiling: 0.8760, IoU.road: 0.8578, IoU.bed : 0.9183, IoU.windowpane: 0.6799, IoU.grass: 0.6920, IoU.cabinet: 0.6944, IoU.sidewalk: 0.7119, IoU.person: 0.8652, IoU.earth: 0.3904, IoU.door: 0.6075, IoU.table: 0.6953, IoU.mountain: 0.6031, IoU.plant: 0.5561, IoU.curtain: 0.7992, IoU.chair: 0.6991, IoU.car: 0.8852, IoU.water: 0.6494, IoU.painting: 0.7813, IoU.sofa: 0.8374, IoU.shelf: 0.5239, IoU.house: 0.4889, IoU.sea: 0.7279, IoU.mirror: 0.7687, IoU.rug: 0.6809, IoU.field: 0.3442, IoU.armchair: 0.6453, IoU.seat: 0.6601, IoU.fence: 0.4671, IoU.desk: 0.6127, IoU.rock: 0.5251, IoU.wardrobe: 0.5863, IoU.lamp: 0.7639, IoU.bathtub: 0.8583, IoU.railing: 0.4392, IoU.cushion: 0.7238, IoU.base: 0.3679, IoU.box: 0.3940, IoU.column: 0.5397, IoU.signboard: 0.4167, IoU.chest of drawers: 0.4511, IoU.counter: 0.2791, IoU.sand: 0.5204, IoU.sink: 0.8020, IoU.skyscraper: 0.4578, IoU.fireplace: 0.7566, IoU.refrigerator: 0.8618, IoU.grandstand: 0.4883, IoU.path: 0.3109, IoU.stairs: 0.3792, IoU.runway: 0.6270, IoU.case: 0.6593, IoU.pool table: 0.9454, IoU.pillow: 0.6903, IoU.screen door: 0.7465, IoU.stairway: 0.5606, IoU.river: 0.1156, IoU.bridge: 0.7574, IoU.bookcase: 0.5484, IoU.blind: 0.4248, IoU.coffee table: 0.6593, IoU.toilet: 0.9126, IoU.flower: 0.4217, IoU.book: 0.5475, IoU.hill: 0.1083, IoU.bench: 0.5602, IoU.countertop: 0.6411, IoU.stove: 0.8710, IoU.palm: 0.5470, IoU.kitchen island: 0.6197, IoU.computer: 0.8138, IoU.swivel chair: 0.5101, IoU.boat: 0.8002, IoU.bar: 0.5915, IoU.arcade machine: 0.7773, IoU.hovel: 0.1969, IoU.bus: 0.9244, IoU.towel: 0.7985, IoU.light: 0.6315, IoU.truck: 0.3895, IoU.tower: 0.2243, IoU.chandelier: 0.7361, IoU.awning: 0.4004, IoU.streetlight: 0.3875, IoU.booth: 0.4267, IoU.television receiver: 0.8036, IoU.airplane: 0.8946, IoU.dirt track: 0.0630, IoU.apparel: 0.6382, IoU.pole: 0.2846, IoU.land: 0.0420, IoU.bannister: 0.1891, IoU.escalator: 0.6365, IoU.ottoman: 0.5483, IoU.bottle: 0.4718, IoU.buffet: 0.4695, IoU.poster: 0.2867, IoU.stage: 0.2599, IoU.van: 0.5065, IoU.ship: 0.9093, IoU.fountain: 0.3985, IoU.conveyer belt: 0.8121, IoU.canopy: 0.5113, IoU.washer: 0.8223, IoU.plaything: 0.3110, IoU.swimming pool: 0.5572, IoU.stool: 0.4908, IoU.barrel: 0.5661, IoU.basket: 0.4168, IoU.waterfall: 0.4909, IoU.tent: 0.9503, IoU.bag: 0.3119, IoU.minibike: 0.7777, IoU.cradle: 0.8172, IoU.oven: 0.5624, IoU.ball: 0.5729, IoU.food: 0.6429, IoU.step: 0.1352, IoU.tank: 0.8195, IoU.trade name: 0.1864, IoU.microwave: 0.8863, IoU.pot: 0.5930, IoU.animal: 0.6286, IoU.bicycle: 0.5928, IoU.lake: 0.5217, IoU.dishwasher: 0.7674, IoU.screen: 0.6073, IoU.blanket: 0.4175, IoU.sculpture: 0.7572, IoU.hood: 0.6272, IoU.sconce: 0.5838, IoU.vase: 0.4995, IoU.traffic light: 0.3661, IoU.tray: 0.2203, IoU.ashcan: 0.4779, IoU.fan: 0.7276, IoU.pier: 0.4387, IoU.crt screen: 0.0889, IoU.plate: 0.6132, IoU.monitor: 0.6543, IoU.bulletin board: 0.5995, IoU.shower: 0.0095, IoU.radiator: 0.6773, IoU.glass: 0.2115, IoU.clock: 0.4768, IoU.flag: 0.7246, Acc.wall: 0.9079, Acc.building: 0.9374, Acc.sky: 0.9770, Acc.floor: 0.9214, Acc.tree: 0.8937, Acc.ceiling: 0.9375, Acc.road: 0.9256, Acc.bed : 0.9582, Acc.windowpane: 0.8087, Acc.grass: 0.8155, Acc.cabinet: 0.8123, Acc.sidewalk: 0.8442, Acc.person: 0.9385, Acc.earth: 0.5411, Acc.door: 0.7782, Acc.table: 0.8351, Acc.mountain: 0.6927, Acc.plant: 0.6505, Acc.curtain: 0.8899, Acc.chair: 0.8085, Acc.car: 0.9345, Acc.water: 0.8195, Acc.painting: 0.9078, Acc.sofa: 0.9383, Acc.shelf: 0.6872, Acc.house: 0.5746, Acc.sea: 0.8121, Acc.mirror: 0.8267, Acc.rug: 0.7465, Acc.field: 0.5920, Acc.armchair: 0.8118, Acc.seat: 0.8918, Acc.fence: 0.6062, Acc.desk: 0.7903, Acc.rock: 0.8389, Acc.wardrobe: 0.6957, Acc.lamp: 0.8765, Acc.bathtub: 0.8789, Acc.railing: 0.5973, Acc.cushion: 0.8199, Acc.base: 0.6236, Acc.box: 0.5213, Acc.column: 0.6730, Acc.signboard: 0.5892, Acc.chest of drawers: 0.6595, Acc.counter: 0.3138, Acc.sand: 0.7906, Acc.sink: 0.8557, Acc.skyscraper: 0.5818, Acc.fireplace: 0.9536, Acc.refrigerator: 0.9338, Acc.grandstand: 0.8018, Acc.path: 0.4374, Acc.stairs: 0.4974, Acc.runway: 0.6856, Acc.case: 0.8271, Acc.pool table: 0.9805, Acc.pillow: 0.7996, Acc.screen door: 0.7698, Acc.stairway: 0.6651, Acc.river: 0.2321, Acc.bridge: 0.8569, Acc.bookcase: 0.6404, Acc.blind: 0.4796, Acc.coffee table: 0.8662, Acc.toilet: 0.9386, Acc.flower: 0.5773, Acc.book: 0.7956, Acc.hill: 0.2368, Acc.bench: 0.6165, Acc.countertop: 0.8501, Acc.stove: 0.9131, Acc.palm: 0.8095, Acc.kitchen island: 0.8621, Acc.computer: 0.9190, Acc.swivel chair: 0.7651, Acc.boat: 0.9140, Acc.bar: 0.8050, Acc.arcade machine: 0.8405, Acc.hovel: 0.2115, Acc.bus: 0.9740, Acc.towel: 0.9176, Acc.light: 0.7215, Acc.truck: 0.5089, Acc.tower: 0.3772, Acc.chandelier: 0.8241, Acc.awning: 0.4847, Acc.streetlight: 0.5152, Acc.booth: 0.5457, Acc.television receiver: 0.8677, Acc.airplane: 0.9607, Acc.dirt track: 0.2232, Acc.apparel: 0.8089, Acc.pole: 0.3942, Acc.land: 0.0586, Acc.bannister: 0.2459, Acc.escalator: 0.8341, Acc.ottoman: 0.7391, Acc.bottle: 0.6424, Acc.buffet: 0.5774, Acc.poster: 0.3104, Acc.stage: 0.4088, Acc.van: 0.7507, Acc.ship: 0.9453, Acc.fountain: 0.4093, Acc.conveyer belt: 0.9366, Acc.canopy: 0.7392, Acc.washer: 0.8747, Acc.plaything: 0.4140, Acc.swimming pool: 0.8159, Acc.stool: 0.7049, Acc.barrel: 0.9487, Acc.basket: 0.6090, Acc.waterfall: 0.6272, Acc.tent: 0.9821, Acc.bag: 0.3807, Acc.minibike: 0.9040, Acc.cradle: 0.9879, Acc.oven: 0.6786, Acc.ball: 0.6565, Acc.food: 0.7531, Acc.step: 0.1535, Acc.tank: 0.8872, Acc.trade name: 0.2113, Acc.microwave: 0.9515, Acc.pot: 0.6991, Acc.animal: 0.6403, Acc.bicycle: 0.7767, Acc.lake: 0.6375, Acc.dishwasher: 0.8347, Acc.screen: 0.9117, Acc.blanket: 0.4835, Acc.sculpture: 0.8493, Acc.hood: 0.7323, Acc.sconce: 0.6436, Acc.vase: 0.6644, Acc.traffic light: 0.6599, Acc.tray: 0.2730, Acc.ashcan: 0.6842, Acc.fan: 0.8382, Acc.pier: 0.4900, Acc.crt screen: 0.1268, Acc.plate: 0.8272, Acc.monitor: 0.7853, Acc.bulletin board: 0.7678, Acc.shower: 0.0239, Acc.radiator: 0.7992, Acc.glass: 0.2275, Acc.clock: 0.5626, Acc.flag: 0.7876 +2024-06-16 21:29:58,886 - mmseg - INFO - Iter [49050/80000] lr: 1.548e-05, eta: 15:11:16, time: 3.591, data_time: 1.979, memory: 71384, decode.loss_ce: 0.1703, decode.acc_seg: 92.5860, aux.loss_ce: 0.0716, aux.acc_seg: 92.2084, loss: 0.2419 +2024-06-16 21:31:20,095 - mmseg - INFO - Iter [49100/80000] lr: 1.545e-05, eta: 15:09:43, time: 1.624, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1743, decode.acc_seg: 92.6378, aux.loss_ce: 0.0726, aux.acc_seg: 92.3161, loss: 0.2469 +2024-06-16 21:32:41,212 - mmseg - INFO - Iter [49150/80000] lr: 1.543e-05, eta: 15:08:10, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1696, decode.acc_seg: 92.6801, aux.loss_ce: 0.0709, aux.acc_seg: 92.3589, loss: 0.2405 +2024-06-16 21:34:02,173 - mmseg - INFO - Iter [49200/80000] lr: 1.540e-05, eta: 15:06:37, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1661, decode.acc_seg: 92.8628, aux.loss_ce: 0.0702, aux.acc_seg: 92.4379, loss: 0.2364 +2024-06-16 21:35:23,391 - mmseg - INFO - Iter [49250/80000] lr: 1.538e-05, eta: 15:05:04, time: 1.624, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1671, decode.acc_seg: 92.7145, aux.loss_ce: 0.0700, aux.acc_seg: 92.4188, loss: 0.2371 +2024-06-16 21:36:46,772 - mmseg - INFO - Iter [49300/80000] lr: 1.535e-05, eta: 15:03:33, time: 1.668, data_time: 0.052, memory: 71384, decode.loss_ce: 0.1624, decode.acc_seg: 93.1164, aux.loss_ce: 0.0683, aux.acc_seg: 92.7110, loss: 0.2306 +2024-06-16 21:38:07,839 - mmseg - INFO - Iter [49350/80000] lr: 1.533e-05, eta: 15:02:00, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1660, decode.acc_seg: 92.7813, aux.loss_ce: 0.0701, aux.acc_seg: 92.4221, loss: 0.2361 +2024-06-16 21:39:28,821 - mmseg - INFO - Iter [49400/80000] lr: 1.530e-05, eta: 15:00:27, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1603, decode.acc_seg: 92.8987, aux.loss_ce: 0.0675, aux.acc_seg: 92.5454, loss: 0.2278 +2024-06-16 21:40:49,879 - mmseg - INFO - Iter [49450/80000] lr: 1.528e-05, eta: 14:58:55, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1655, decode.acc_seg: 92.7784, aux.loss_ce: 0.0701, aux.acc_seg: 92.3851, loss: 0.2356 +2024-06-16 21:42:10,946 - mmseg - INFO - Iter [49500/80000] lr: 1.525e-05, eta: 14:57:22, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1639, decode.acc_seg: 92.8569, aux.loss_ce: 0.0688, aux.acc_seg: 92.4514, loss: 0.2327 +2024-06-16 21:43:31,977 - mmseg - INFO - Iter [49550/80000] lr: 1.523e-05, eta: 14:55:49, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1574, decode.acc_seg: 93.2769, aux.loss_ce: 0.0668, aux.acc_seg: 92.8898, loss: 0.2242 +2024-06-16 21:44:52,986 - mmseg - INFO - Iter [49600/80000] lr: 1.520e-05, eta: 14:54:17, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1688, decode.acc_seg: 92.7083, aux.loss_ce: 0.0706, aux.acc_seg: 92.3649, loss: 0.2393 +2024-06-16 21:46:14,149 - mmseg - INFO - Iter [49650/80000] lr: 1.518e-05, eta: 14:52:44, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1648, decode.acc_seg: 93.0179, aux.loss_ce: 0.0698, aux.acc_seg: 92.5718, loss: 0.2346 +2024-06-16 21:47:35,137 - mmseg - INFO - Iter [49700/80000] lr: 1.515e-05, eta: 14:51:11, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1687, decode.acc_seg: 92.5602, aux.loss_ce: 0.0716, aux.acc_seg: 92.1815, loss: 0.2402 +2024-06-16 21:48:56,155 - mmseg - INFO - Iter [49750/80000] lr: 1.513e-05, eta: 14:49:39, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1629, decode.acc_seg: 92.9907, aux.loss_ce: 0.0690, aux.acc_seg: 92.6035, loss: 0.2319 +2024-06-16 21:50:17,309 - mmseg - INFO - Iter [49800/80000] lr: 1.510e-05, eta: 14:48:06, time: 1.623, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1583, decode.acc_seg: 93.1596, aux.loss_ce: 0.0669, aux.acc_seg: 92.7675, loss: 0.2252 +2024-06-16 21:51:38,458 - mmseg - INFO - Iter [49850/80000] lr: 1.508e-05, eta: 14:46:34, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1610, decode.acc_seg: 92.9735, aux.loss_ce: 0.0683, aux.acc_seg: 92.5900, loss: 0.2293 +2024-06-16 21:52:59,622 - mmseg - INFO - Iter [49900/80000] lr: 1.505e-05, eta: 14:45:01, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1661, decode.acc_seg: 92.7178, aux.loss_ce: 0.0702, aux.acc_seg: 92.3892, loss: 0.2363 +2024-06-16 21:54:20,660 - mmseg - INFO - Iter [49950/80000] lr: 1.503e-05, eta: 14:43:29, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1704, decode.acc_seg: 92.6464, aux.loss_ce: 0.0721, aux.acc_seg: 92.2840, loss: 0.2425 +2024-06-16 21:55:41,948 - mmseg - INFO - Saving checkpoint at 50000 iterations +2024-06-16 21:57:04,271 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 21:57:04,271 - mmseg - INFO - Iter [50000/80000] lr: 1.500e-05, eta: 14:42:46, time: 3.272, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1637, decode.acc_seg: 93.0959, aux.loss_ce: 0.0693, aux.acc_seg: 92.7256, loss: 0.2329 +2024-06-16 21:59:10,353 - mmseg - INFO - per class results: +2024-06-16 21:59:10,359 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.63 | 90.88 | +| building | 85.53 | 94.45 | +| sky | 95.02 | 97.27 | +| floor | 85.51 | 92.69 | +| tree | 77.88 | 89.19 | +| ceiling | 87.38 | 93.68 | +| road | 87.35 | 91.83 | +| bed | 92.7 | 97.7 | +| windowpane | 67.89 | 81.88 | +| grass | 69.66 | 81.15 | +| cabinet | 66.48 | 76.05 | +| sidewalk | 71.27 | 84.81 | +| person | 85.94 | 94.7 | +| earth | 39.3 | 52.9 | +| door | 60.76 | 75.76 | +| table | 69.46 | 81.62 | +| mountain | 62.74 | 74.94 | +| plant | 56.65 | 65.9 | +| curtain | 79.46 | 89.57 | +| chair | 69.82 | 79.42 | +| car | 87.96 | 94.01 | +| water | 64.08 | 79.79 | +| painting | 79.44 | 89.78 | +| sofa | 84.42 | 93.8 | +| shelf | 51.43 | 70.31 | +| house | 47.92 | 55.38 | +| sea | 73.62 | 82.63 | +| mirror | 77.49 | 85.3 | +| rug | 69.09 | 77.34 | +| field | 35.07 | 61.1 | +| armchair | 64.79 | 78.99 | +| seat | 70.23 | 87.72 | +| fence | 44.66 | 53.97 | +| desk | 58.32 | 77.78 | +| rock | 55.11 | 80.33 | +| wardrobe | 54.59 | 74.46 | +| lamp | 76.68 | 86.35 | +| bathtub | 84.39 | 86.83 | +| railing | 46.45 | 65.85 | +| cushion | 73.05 | 82.47 | +| base | 39.86 | 61.26 | +| box | 36.86 | 47.74 | +| column | 57.48 | 66.16 | +| signboard | 41.14 | 55.29 | +| chest of drawers | 45.62 | 64.93 | +| counter | 38.09 | 48.93 | +| sand | 52.54 | 76.51 | +| sink | 79.99 | 84.38 | +| skyscraper | 46.3 | 56.02 | +| fireplace | 76.6 | 91.43 | +| refrigerator | 83.4 | 94.41 | +| grandstand | 53.08 | 79.73 | +| path | 31.66 | 43.26 | +| stairs | 38.06 | 46.2 | +| runway | 72.52 | 94.57 | +| case | 63.24 | 81.88 | +| pool table | 94.73 | 98.16 | +| pillow | 68.53 | 78.12 | +| screen door | 81.4 | 85.48 | +| stairway | 50.98 | 64.0 | +| river | 9.56 | 21.21 | +| bridge | 61.07 | 87.59 | +| bookcase | 53.25 | 75.91 | +| blind | 43.05 | 49.36 | +| coffee table | 61.74 | 87.7 | +| toilet | 90.73 | 94.64 | +| flower | 45.86 | 57.17 | +| book | 55.08 | 69.76 | +| hill | 6.92 | 12.51 | +| bench | 55.92 | 62.86 | +| countertop | 65.2 | 85.97 | +| stove | 87.11 | 92.57 | +| palm | 55.71 | 82.3 | +| kitchen island | 60.28 | 86.97 | +| computer | 79.34 | 91.58 | +| swivel chair | 51.02 | 78.99 | +| boat | 78.69 | 91.32 | +| bar | 61.73 | 81.36 | +| arcade machine | 78.64 | 84.36 | +| hovel | 19.93 | 21.52 | +| bus | 92.1 | 97.37 | +| towel | 78.62 | 83.52 | +| light | 60.42 | 66.14 | +| truck | 48.34 | 60.2 | +| tower | 31.7 | 55.78 | +| chandelier | 74.76 | 86.15 | +| awning | 42.7 | 54.18 | +| streetlight | 38.74 | 51.3 | +| booth | 43.64 | 66.46 | +| television receiver | 80.15 | 86.81 | +| airplane | 88.04 | 97.13 | +| dirt track | 9.66 | 19.63 | +| apparel | 66.06 | 86.79 | +| pole | 23.13 | 30.98 | +| land | 3.47 | 7.56 | +| bannister | 21.05 | 29.09 | +| escalator | 64.46 | 82.84 | +| ottoman | 54.93 | 77.95 | +| bottle | 41.96 | 50.63 | +| buffet | 50.12 | 63.13 | +| poster | 30.27 | 35.53 | +| stage | 22.57 | 51.07 | +| van | 47.64 | 75.51 | +| ship | 86.0 | 98.37 | +| fountain | 37.53 | 39.58 | +| conveyer belt | 77.33 | 95.62 | +| canopy | 38.74 | 53.54 | +| washer | 86.01 | 92.04 | +| plaything | 28.25 | 42.94 | +| swimming pool | 54.76 | 84.2 | +| stool | 56.06 | 69.19 | +| barrel | 73.43 | 94.48 | +| basket | 40.84 | 59.28 | +| waterfall | 47.64 | 67.13 | +| tent | 94.89 | 98.55 | +| bag | 29.75 | 35.66 | +| minibike | 77.05 | 89.5 | +| cradle | 86.42 | 98.08 | +| oven | 54.19 | 64.71 | +| ball | 11.65 | 11.94 | +| food | 57.86 | 68.23 | +| step | 16.76 | 22.13 | +| tank | 80.93 | 94.04 | +| trade name | 23.2 | 27.79 | +| microwave | 88.94 | 96.08 | +| pot | 58.86 | 65.58 | +| animal | 58.21 | 59.42 | +| bicycle | 58.85 | 79.56 | +| lake | 47.6 | 63.79 | +| dishwasher | 78.8 | 84.23 | +| screen | 61.06 | 92.16 | +| blanket | 35.61 | 43.63 | +| sculpture | 76.93 | 87.01 | +| hood | 62.53 | 73.74 | +| sconce | 60.39 | 67.88 | +| vase | 50.35 | 61.17 | +| traffic light | 34.63 | 64.72 | +| tray | 26.31 | 33.83 | +| ashcan | 50.95 | 64.08 | +| fan | 71.47 | 84.3 | +| pier | 71.18 | 87.86 | +| crt screen | 2.77 | 3.3 | +| plate | 64.21 | 75.0 | +| monitor | 68.75 | 82.05 | +| bulletin board | 61.18 | 70.66 | +| shower | 0.65 | 1.9 | +| radiator | 67.9 | 81.69 | +| glass | 18.39 | 19.04 | +| clock | 49.47 | 56.77 | +| flag | 72.27 | 78.79 | ++---------------------+-------+-------+ +2024-06-16 21:59:10,359 - mmseg - INFO - Summary: +2024-06-16 21:59:10,359 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.52 | 58.25 | 70.57 | ++-------+-------+-------+ +2024-06-16 21:59:10,360 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 21:59:10,360 - mmseg - INFO - Iter(val) [250] aAcc: 0.8652, mIoU: 0.5825, mAcc: 0.7057, IoU.wall: 0.8263, IoU.building: 0.8553, IoU.sky: 0.9502, IoU.floor: 0.8551, IoU.tree: 0.7788, IoU.ceiling: 0.8738, IoU.road: 0.8735, IoU.bed : 0.9270, IoU.windowpane: 0.6789, IoU.grass: 0.6966, IoU.cabinet: 0.6648, IoU.sidewalk: 0.7127, IoU.person: 0.8594, IoU.earth: 0.3930, IoU.door: 0.6076, IoU.table: 0.6946, IoU.mountain: 0.6274, IoU.plant: 0.5665, IoU.curtain: 0.7946, IoU.chair: 0.6982, IoU.car: 0.8796, IoU.water: 0.6408, IoU.painting: 0.7944, IoU.sofa: 0.8442, IoU.shelf: 0.5143, IoU.house: 0.4792, IoU.sea: 0.7362, IoU.mirror: 0.7749, IoU.rug: 0.6909, IoU.field: 0.3507, IoU.armchair: 0.6479, IoU.seat: 0.7023, IoU.fence: 0.4466, IoU.desk: 0.5832, IoU.rock: 0.5511, IoU.wardrobe: 0.5459, IoU.lamp: 0.7668, IoU.bathtub: 0.8439, IoU.railing: 0.4645, IoU.cushion: 0.7305, IoU.base: 0.3986, IoU.box: 0.3686, IoU.column: 0.5748, IoU.signboard: 0.4114, IoU.chest of drawers: 0.4562, IoU.counter: 0.3809, IoU.sand: 0.5254, IoU.sink: 0.7999, IoU.skyscraper: 0.4630, IoU.fireplace: 0.7660, IoU.refrigerator: 0.8340, IoU.grandstand: 0.5308, IoU.path: 0.3166, IoU.stairs: 0.3806, IoU.runway: 0.7252, IoU.case: 0.6324, IoU.pool table: 0.9473, IoU.pillow: 0.6853, IoU.screen door: 0.8140, IoU.stairway: 0.5098, IoU.river: 0.0956, IoU.bridge: 0.6107, IoU.bookcase: 0.5325, IoU.blind: 0.4305, IoU.coffee table: 0.6174, IoU.toilet: 0.9073, IoU.flower: 0.4586, IoU.book: 0.5508, IoU.hill: 0.0692, IoU.bench: 0.5592, IoU.countertop: 0.6520, IoU.stove: 0.8711, IoU.palm: 0.5571, IoU.kitchen island: 0.6028, IoU.computer: 0.7934, IoU.swivel chair: 0.5102, IoU.boat: 0.7869, IoU.bar: 0.6173, IoU.arcade machine: 0.7864, IoU.hovel: 0.1993, IoU.bus: 0.9210, IoU.towel: 0.7862, IoU.light: 0.6042, IoU.truck: 0.4834, IoU.tower: 0.3170, IoU.chandelier: 0.7476, IoU.awning: 0.4270, IoU.streetlight: 0.3874, IoU.booth: 0.4364, IoU.television receiver: 0.8015, IoU.airplane: 0.8804, IoU.dirt track: 0.0966, IoU.apparel: 0.6606, IoU.pole: 0.2313, IoU.land: 0.0347, IoU.bannister: 0.2105, IoU.escalator: 0.6446, IoU.ottoman: 0.5493, IoU.bottle: 0.4196, IoU.buffet: 0.5012, IoU.poster: 0.3027, IoU.stage: 0.2257, IoU.van: 0.4764, IoU.ship: 0.8600, IoU.fountain: 0.3753, IoU.conveyer belt: 0.7733, IoU.canopy: 0.3874, IoU.washer: 0.8601, IoU.plaything: 0.2825, IoU.swimming pool: 0.5476, IoU.stool: 0.5606, IoU.barrel: 0.7343, IoU.basket: 0.4084, IoU.waterfall: 0.4764, IoU.tent: 0.9489, IoU.bag: 0.2975, IoU.minibike: 0.7705, IoU.cradle: 0.8642, IoU.oven: 0.5419, IoU.ball: 0.1165, IoU.food: 0.5786, IoU.step: 0.1676, IoU.tank: 0.8093, IoU.trade name: 0.2320, IoU.microwave: 0.8894, IoU.pot: 0.5886, IoU.animal: 0.5821, IoU.bicycle: 0.5885, IoU.lake: 0.4760, IoU.dishwasher: 0.7880, IoU.screen: 0.6106, IoU.blanket: 0.3561, IoU.sculpture: 0.7693, IoU.hood: 0.6253, IoU.sconce: 0.6039, IoU.vase: 0.5035, IoU.traffic light: 0.3463, IoU.tray: 0.2631, IoU.ashcan: 0.5095, IoU.fan: 0.7147, IoU.pier: 0.7118, IoU.crt screen: 0.0277, IoU.plate: 0.6421, IoU.monitor: 0.6875, IoU.bulletin board: 0.6118, IoU.shower: 0.0065, IoU.radiator: 0.6790, IoU.glass: 0.1839, IoU.clock: 0.4947, IoU.flag: 0.7227, Acc.wall: 0.9088, Acc.building: 0.9445, Acc.sky: 0.9727, Acc.floor: 0.9269, Acc.tree: 0.8919, Acc.ceiling: 0.9368, Acc.road: 0.9183, Acc.bed : 0.9770, Acc.windowpane: 0.8188, Acc.grass: 0.8115, Acc.cabinet: 0.7605, Acc.sidewalk: 0.8481, Acc.person: 0.9470, Acc.earth: 0.5290, Acc.door: 0.7576, Acc.table: 0.8162, Acc.mountain: 0.7494, Acc.plant: 0.6590, Acc.curtain: 0.8957, Acc.chair: 0.7942, Acc.car: 0.9401, Acc.water: 0.7979, Acc.painting: 0.8978, Acc.sofa: 0.9380, Acc.shelf: 0.7031, Acc.house: 0.5538, Acc.sea: 0.8263, Acc.mirror: 0.8530, Acc.rug: 0.7734, Acc.field: 0.6110, Acc.armchair: 0.7899, Acc.seat: 0.8772, Acc.fence: 0.5397, Acc.desk: 0.7778, Acc.rock: 0.8033, Acc.wardrobe: 0.7446, Acc.lamp: 0.8635, Acc.bathtub: 0.8683, Acc.railing: 0.6585, Acc.cushion: 0.8247, Acc.base: 0.6126, Acc.box: 0.4774, Acc.column: 0.6616, Acc.signboard: 0.5529, Acc.chest of drawers: 0.6493, Acc.counter: 0.4893, Acc.sand: 0.7651, Acc.sink: 0.8438, Acc.skyscraper: 0.5602, Acc.fireplace: 0.9143, Acc.refrigerator: 0.9441, Acc.grandstand: 0.7973, Acc.path: 0.4326, Acc.stairs: 0.4620, Acc.runway: 0.9457, Acc.case: 0.8188, Acc.pool table: 0.9816, Acc.pillow: 0.7812, Acc.screen door: 0.8548, Acc.stairway: 0.6400, Acc.river: 0.2121, Acc.bridge: 0.8759, Acc.bookcase: 0.7591, Acc.blind: 0.4936, Acc.coffee table: 0.8770, Acc.toilet: 0.9464, Acc.flower: 0.5717, Acc.book: 0.6976, Acc.hill: 0.1251, Acc.bench: 0.6286, Acc.countertop: 0.8597, Acc.stove: 0.9257, Acc.palm: 0.8230, Acc.kitchen island: 0.8697, Acc.computer: 0.9158, Acc.swivel chair: 0.7899, Acc.boat: 0.9132, Acc.bar: 0.8136, Acc.arcade machine: 0.8436, Acc.hovel: 0.2152, Acc.bus: 0.9737, Acc.towel: 0.8352, Acc.light: 0.6614, Acc.truck: 0.6020, Acc.tower: 0.5578, Acc.chandelier: 0.8615, Acc.awning: 0.5418, Acc.streetlight: 0.5130, Acc.booth: 0.6646, Acc.television receiver: 0.8681, Acc.airplane: 0.9713, Acc.dirt track: 0.1963, Acc.apparel: 0.8679, Acc.pole: 0.3098, Acc.land: 0.0756, Acc.bannister: 0.2909, Acc.escalator: 0.8284, Acc.ottoman: 0.7795, Acc.bottle: 0.5063, Acc.buffet: 0.6313, Acc.poster: 0.3553, Acc.stage: 0.5107, Acc.van: 0.7551, Acc.ship: 0.9837, Acc.fountain: 0.3958, Acc.conveyer belt: 0.9562, Acc.canopy: 0.5354, Acc.washer: 0.9204, Acc.plaything: 0.4294, Acc.swimming pool: 0.8420, Acc.stool: 0.6919, Acc.barrel: 0.9448, Acc.basket: 0.5928, Acc.waterfall: 0.6713, Acc.tent: 0.9855, Acc.bag: 0.3566, Acc.minibike: 0.8950, Acc.cradle: 0.9808, Acc.oven: 0.6471, Acc.ball: 0.1194, Acc.food: 0.6823, Acc.step: 0.2213, Acc.tank: 0.9404, Acc.trade name: 0.2779, Acc.microwave: 0.9608, Acc.pot: 0.6558, Acc.animal: 0.5942, Acc.bicycle: 0.7956, Acc.lake: 0.6379, Acc.dishwasher: 0.8423, Acc.screen: 0.9216, Acc.blanket: 0.4363, Acc.sculpture: 0.8701, Acc.hood: 0.7374, Acc.sconce: 0.6788, Acc.vase: 0.6117, Acc.traffic light: 0.6472, Acc.tray: 0.3383, Acc.ashcan: 0.6408, Acc.fan: 0.8430, Acc.pier: 0.8786, Acc.crt screen: 0.0330, Acc.plate: 0.7500, Acc.monitor: 0.8205, Acc.bulletin board: 0.7066, Acc.shower: 0.0190, Acc.radiator: 0.8169, Acc.glass: 0.1904, Acc.clock: 0.5677, Acc.flag: 0.7879 +2024-06-16 22:00:32,406 - mmseg - INFO - Iter [50050/80000] lr: 1.498e-05, eta: 14:42:29, time: 4.163, data_time: 2.539, memory: 71384, decode.loss_ce: 0.1674, decode.acc_seg: 92.8833, aux.loss_ce: 0.0706, aux.acc_seg: 92.5327, loss: 0.2381 +2024-06-16 22:01:53,430 - mmseg - INFO - Iter [50100/80000] lr: 1.495e-05, eta: 14:40:56, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1672, decode.acc_seg: 92.6458, aux.loss_ce: 0.0708, aux.acc_seg: 92.2512, loss: 0.2380 +2024-06-16 22:03:14,564 - mmseg - INFO - Iter [50150/80000] lr: 1.493e-05, eta: 14:39:24, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1771, decode.acc_seg: 92.4816, aux.loss_ce: 0.0747, aux.acc_seg: 92.0775, loss: 0.2518 +2024-06-16 22:04:35,693 - mmseg - INFO - Iter [50200/80000] lr: 1.490e-05, eta: 14:37:51, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1508, decode.acc_seg: 93.3051, aux.loss_ce: 0.0638, aux.acc_seg: 92.9039, loss: 0.2146 +2024-06-16 22:05:56,770 - mmseg - INFO - Iter [50250/80000] lr: 1.488e-05, eta: 14:36:18, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1656, decode.acc_seg: 92.6547, aux.loss_ce: 0.0693, aux.acc_seg: 92.3108, loss: 0.2350 +2024-06-16 22:07:17,869 - mmseg - INFO - Iter [50300/80000] lr: 1.485e-05, eta: 14:34:46, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1602, decode.acc_seg: 93.2056, aux.loss_ce: 0.0675, aux.acc_seg: 92.8518, loss: 0.2277 +2024-06-16 22:08:38,902 - mmseg - INFO - Iter [50350/80000] lr: 1.483e-05, eta: 14:33:13, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1674, decode.acc_seg: 92.7896, aux.loss_ce: 0.0704, aux.acc_seg: 92.4196, loss: 0.2378 +2024-06-16 22:09:59,890 - mmseg - INFO - Iter [50400/80000] lr: 1.480e-05, eta: 14:31:40, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1662, decode.acc_seg: 92.9126, aux.loss_ce: 0.0701, aux.acc_seg: 92.5297, loss: 0.2362 +2024-06-16 22:11:21,052 - mmseg - INFO - Iter [50450/80000] lr: 1.478e-05, eta: 14:30:08, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1719, decode.acc_seg: 92.3940, aux.loss_ce: 0.0718, aux.acc_seg: 92.0415, loss: 0.2437 +2024-06-16 22:12:42,040 - mmseg - INFO - Iter [50500/80000] lr: 1.475e-05, eta: 14:28:35, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1644, decode.acc_seg: 92.8697, aux.loss_ce: 0.0694, aux.acc_seg: 92.5014, loss: 0.2338 +2024-06-16 22:14:05,893 - mmseg - INFO - Iter [50550/80000] lr: 1.473e-05, eta: 14:27:04, time: 1.677, data_time: 0.064, memory: 71384, decode.loss_ce: 0.1722, decode.acc_seg: 92.3875, aux.loss_ce: 0.0718, aux.acc_seg: 92.0810, loss: 0.2440 +2024-06-16 22:15:27,100 - mmseg - INFO - Iter [50600/80000] lr: 1.470e-05, eta: 14:25:32, time: 1.624, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1453, decode.acc_seg: 93.6125, aux.loss_ce: 0.0613, aux.acc_seg: 93.2839, loss: 0.2066 +2024-06-16 22:16:48,275 - mmseg - INFO - Iter [50650/80000] lr: 1.468e-05, eta: 14:23:59, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1672, decode.acc_seg: 92.8263, aux.loss_ce: 0.0701, aux.acc_seg: 92.4536, loss: 0.2373 +2024-06-16 22:18:09,277 - mmseg - INFO - Iter [50700/80000] lr: 1.465e-05, eta: 14:22:27, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1636, decode.acc_seg: 92.5591, aux.loss_ce: 0.0691, aux.acc_seg: 92.1672, loss: 0.2327 +2024-06-16 22:19:30,340 - mmseg - INFO - Iter [50750/80000] lr: 1.463e-05, eta: 14:20:54, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1582, decode.acc_seg: 93.3317, aux.loss_ce: 0.0665, aux.acc_seg: 92.9237, loss: 0.2247 +2024-06-16 22:20:51,404 - mmseg - INFO - Iter [50800/80000] lr: 1.460e-05, eta: 14:19:22, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1671, decode.acc_seg: 92.8750, aux.loss_ce: 0.0696, aux.acc_seg: 92.5523, loss: 0.2367 +2024-06-16 22:22:12,413 - mmseg - INFO - Iter [50850/80000] lr: 1.458e-05, eta: 14:17:49, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1594, decode.acc_seg: 93.1126, aux.loss_ce: 0.0679, aux.acc_seg: 92.6514, loss: 0.2272 +2024-06-16 22:23:33,477 - mmseg - INFO - Iter [50900/80000] lr: 1.455e-05, eta: 14:16:17, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1637, decode.acc_seg: 92.8385, aux.loss_ce: 0.0688, aux.acc_seg: 92.5060, loss: 0.2325 +2024-06-16 22:24:54,499 - mmseg - INFO - Iter [50950/80000] lr: 1.453e-05, eta: 14:14:44, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1675, decode.acc_seg: 92.9356, aux.loss_ce: 0.0704, aux.acc_seg: 92.5353, loss: 0.2379 +2024-06-16 22:26:15,624 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 22:26:15,624 - mmseg - INFO - Iter [51000/80000] lr: 1.450e-05, eta: 14:13:12, time: 1.622, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1599, decode.acc_seg: 93.1239, aux.loss_ce: 0.0673, aux.acc_seg: 92.7337, loss: 0.2271 +2024-06-16 22:27:54,890 - mmseg - INFO - per class results: +2024-06-16 22:27:54,896 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.73 | 90.39 | +| building | 85.25 | 93.33 | +| sky | 94.78 | 97.3 | +| floor | 85.88 | 92.11 | +| tree | 78.24 | 90.18 | +| ceiling | 87.63 | 94.03 | +| road | 87.26 | 92.1 | +| bed | 92.65 | 96.87 | +| windowpane | 67.81 | 81.37 | +| grass | 69.42 | 84.1 | +| cabinet | 67.41 | 77.21 | +| sidewalk | 71.24 | 85.17 | +| person | 86.3 | 94.45 | +| earth | 42.27 | 56.09 | +| door | 61.53 | 76.47 | +| table | 68.96 | 78.23 | +| mountain | 61.68 | 76.22 | +| plant | 56.71 | 64.61 | +| curtain | 80.21 | 89.88 | +| chair | 68.71 | 81.18 | +| car | 87.93 | 94.4 | +| water | 60.78 | 74.19 | +| painting | 77.91 | 91.24 | +| sofa | 83.5 | 93.48 | +| shelf | 51.52 | 69.6 | +| house | 50.94 | 65.4 | +| sea | 68.3 | 85.0 | +| mirror | 78.65 | 86.48 | +| rug | 70.05 | 77.94 | +| field | 30.98 | 50.56 | +| armchair | 62.98 | 80.64 | +| seat | 67.51 | 88.97 | +| fence | 47.34 | 59.12 | +| desk | 55.7 | 80.39 | +| rock | 55.84 | 82.81 | +| wardrobe | 54.59 | 72.77 | +| lamp | 75.35 | 85.63 | +| bathtub | 86.59 | 88.93 | +| railing | 47.42 | 66.97 | +| cushion | 73.07 | 81.16 | +| base | 38.16 | 55.36 | +| box | 35.27 | 45.43 | +| column | 57.54 | 71.23 | +| signboard | 41.45 | 55.57 | +| chest of drawers | 41.7 | 62.71 | +| counter | 42.33 | 60.65 | +| sand | 48.15 | 79.26 | +| sink | 80.25 | 85.82 | +| skyscraper | 44.77 | 58.8 | +| fireplace | 73.08 | 92.53 | +| refrigerator | 86.7 | 93.39 | +| grandstand | 50.37 | 86.29 | +| path | 31.62 | 43.24 | +| stairs | 33.29 | 40.97 | +| runway | 72.36 | 95.15 | +| case | 61.36 | 81.09 | +| pool table | 94.82 | 98.45 | +| pillow | 69.91 | 81.46 | +| screen door | 72.14 | 74.32 | +| stairway | 38.61 | 48.34 | +| river | 9.98 | 20.87 | +| bridge | 75.05 | 87.41 | +| bookcase | 49.0 | 64.65 | +| blind | 46.35 | 52.39 | +| coffee table | 59.86 | 88.58 | +| toilet | 90.47 | 94.05 | +| flower | 46.99 | 59.13 | +| book | 53.43 | 80.7 | +| hill | 6.42 | 10.23 | +| bench | 54.02 | 65.24 | +| countertop | 65.6 | 83.69 | +| stove | 87.32 | 91.9 | +| palm | 54.54 | 83.4 | +| kitchen island | 54.2 | 87.78 | +| computer | 80.35 | 91.32 | +| swivel chair | 49.58 | 77.23 | +| boat | 83.07 | 90.59 | +| bar | 59.99 | 81.36 | +| arcade machine | 76.36 | 80.45 | +| hovel | 14.38 | 15.44 | +| bus | 93.23 | 96.27 | +| towel | 81.93 | 90.72 | +| light | 62.66 | 70.85 | +| truck | 48.64 | 57.21 | +| tower | 31.12 | 54.19 | +| chandelier | 75.47 | 87.31 | +| awning | 38.08 | 45.18 | +| streetlight | 35.87 | 49.16 | +| booth | 43.47 | 56.47 | +| television receiver | 80.29 | 88.48 | +| airplane | 85.02 | 94.34 | +| dirt track | 15.45 | 46.52 | +| apparel | 63.58 | 87.64 | +| pole | 23.74 | 32.18 | +| land | 6.11 | 10.37 | +| bannister | 22.0 | 29.12 | +| escalator | 60.98 | 87.59 | +| ottoman | 50.75 | 64.95 | +| bottle | 41.92 | 53.89 | +| buffet | 49.91 | 59.34 | +| poster | 30.27 | 34.78 | +| stage | 28.73 | 45.92 | +| van | 44.83 | 62.99 | +| ship | 74.94 | 82.53 | +| fountain | 39.7 | 42.03 | +| conveyer belt | 85.06 | 94.75 | +| canopy | 54.81 | 78.58 | +| washer | 78.15 | 82.82 | +| plaything | 28.94 | 38.56 | +| swimming pool | 53.17 | 78.98 | +| stool | 52.08 | 65.13 | +| barrel | 69.38 | 94.72 | +| basket | 41.17 | 62.41 | +| waterfall | 55.44 | 74.52 | +| tent | 95.26 | 98.38 | +| bag | 25.25 | 28.47 | +| minibike | 74.58 | 91.43 | +| cradle | 90.11 | 97.77 | +| oven | 63.28 | 76.76 | +| ball | 56.2 | 71.59 | +| food | 55.79 | 64.6 | +| step | 15.64 | 17.95 | +| tank | 71.72 | 94.39 | +| trade name | 21.27 | 24.88 | +| microwave | 88.85 | 96.66 | +| pot | 60.72 | 70.67 | +| animal | 62.14 | 63.52 | +| bicycle | 59.81 | 76.62 | +| lake | 48.67 | 64.06 | +| dishwasher | 78.11 | 85.42 | +| screen | 61.26 | 91.62 | +| blanket | 39.56 | 47.21 | +| sculpture | 76.3 | 86.73 | +| hood | 64.65 | 75.04 | +| sconce | 64.45 | 73.95 | +| vase | 49.36 | 67.72 | +| traffic light | 39.01 | 60.32 | +| tray | 25.19 | 31.85 | +| ashcan | 49.38 | 65.8 | +| fan | 70.8 | 81.75 | +| pier | 47.28 | 54.46 | +| crt screen | 2.44 | 3.32 | +| plate | 58.8 | 82.68 | +| monitor | 59.61 | 74.07 | +| bulletin board | 64.4 | 82.28 | +| shower | 1.17 | 5.38 | +| radiator | 67.84 | 83.55 | +| glass | 21.16 | 22.62 | +| clock | 50.13 | 61.5 | +| flag | 69.89 | 77.85 | ++---------------------+-------+-------+ +2024-06-16 22:27:54,897 - mmseg - INFO - Summary: +2024-06-16 22:27:54,897 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.47 | 58.08 | 70.91 | ++-------+-------+-------+ +2024-06-16 22:27:54,898 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 22:27:54,898 - mmseg - INFO - Iter(val) [250] aAcc: 0.8647, mIoU: 0.5808, mAcc: 0.7091, IoU.wall: 0.8273, IoU.building: 0.8525, IoU.sky: 0.9478, IoU.floor: 0.8588, IoU.tree: 0.7824, IoU.ceiling: 0.8763, IoU.road: 0.8726, IoU.bed : 0.9265, IoU.windowpane: 0.6781, IoU.grass: 0.6942, IoU.cabinet: 0.6741, IoU.sidewalk: 0.7124, IoU.person: 0.8630, IoU.earth: 0.4227, IoU.door: 0.6153, IoU.table: 0.6896, IoU.mountain: 0.6168, IoU.plant: 0.5671, IoU.curtain: 0.8021, IoU.chair: 0.6871, IoU.car: 0.8793, IoU.water: 0.6078, IoU.painting: 0.7791, IoU.sofa: 0.8350, IoU.shelf: 0.5152, IoU.house: 0.5094, IoU.sea: 0.6830, IoU.mirror: 0.7865, IoU.rug: 0.7005, IoU.field: 0.3098, IoU.armchair: 0.6298, IoU.seat: 0.6751, IoU.fence: 0.4734, IoU.desk: 0.5570, IoU.rock: 0.5584, IoU.wardrobe: 0.5459, IoU.lamp: 0.7535, IoU.bathtub: 0.8659, IoU.railing: 0.4742, IoU.cushion: 0.7307, IoU.base: 0.3816, IoU.box: 0.3527, IoU.column: 0.5754, IoU.signboard: 0.4145, IoU.chest of drawers: 0.4170, IoU.counter: 0.4233, IoU.sand: 0.4815, IoU.sink: 0.8025, IoU.skyscraper: 0.4477, IoU.fireplace: 0.7308, IoU.refrigerator: 0.8670, IoU.grandstand: 0.5037, IoU.path: 0.3162, IoU.stairs: 0.3329, IoU.runway: 0.7236, IoU.case: 0.6136, IoU.pool table: 0.9482, IoU.pillow: 0.6991, IoU.screen door: 0.7214, IoU.stairway: 0.3861, IoU.river: 0.0998, IoU.bridge: 0.7505, IoU.bookcase: 0.4900, IoU.blind: 0.4635, IoU.coffee table: 0.5986, IoU.toilet: 0.9047, IoU.flower: 0.4699, IoU.book: 0.5343, IoU.hill: 0.0642, IoU.bench: 0.5402, IoU.countertop: 0.6560, IoU.stove: 0.8732, IoU.palm: 0.5454, IoU.kitchen island: 0.5420, IoU.computer: 0.8035, IoU.swivel chair: 0.4958, IoU.boat: 0.8307, IoU.bar: 0.5999, IoU.arcade machine: 0.7636, IoU.hovel: 0.1438, IoU.bus: 0.9323, IoU.towel: 0.8193, IoU.light: 0.6266, IoU.truck: 0.4864, IoU.tower: 0.3112, IoU.chandelier: 0.7547, IoU.awning: 0.3808, IoU.streetlight: 0.3587, IoU.booth: 0.4347, IoU.television receiver: 0.8029, IoU.airplane: 0.8502, IoU.dirt track: 0.1545, IoU.apparel: 0.6358, IoU.pole: 0.2374, IoU.land: 0.0611, IoU.bannister: 0.2200, IoU.escalator: 0.6098, IoU.ottoman: 0.5075, IoU.bottle: 0.4192, IoU.buffet: 0.4991, IoU.poster: 0.3027, IoU.stage: 0.2873, IoU.van: 0.4483, IoU.ship: 0.7494, IoU.fountain: 0.3970, IoU.conveyer belt: 0.8506, IoU.canopy: 0.5481, IoU.washer: 0.7815, IoU.plaything: 0.2894, IoU.swimming pool: 0.5317, IoU.stool: 0.5208, IoU.barrel: 0.6938, IoU.basket: 0.4117, IoU.waterfall: 0.5544, IoU.tent: 0.9526, IoU.bag: 0.2525, IoU.minibike: 0.7458, IoU.cradle: 0.9011, IoU.oven: 0.6328, IoU.ball: 0.5620, IoU.food: 0.5579, IoU.step: 0.1564, IoU.tank: 0.7172, IoU.trade name: 0.2127, IoU.microwave: 0.8885, IoU.pot: 0.6072, IoU.animal: 0.6214, IoU.bicycle: 0.5981, IoU.lake: 0.4867, IoU.dishwasher: 0.7811, IoU.screen: 0.6126, IoU.blanket: 0.3956, IoU.sculpture: 0.7630, IoU.hood: 0.6465, IoU.sconce: 0.6445, IoU.vase: 0.4936, IoU.traffic light: 0.3901, IoU.tray: 0.2519, IoU.ashcan: 0.4938, IoU.fan: 0.7080, IoU.pier: 0.4728, IoU.crt screen: 0.0244, IoU.plate: 0.5880, IoU.monitor: 0.5961, IoU.bulletin board: 0.6440, IoU.shower: 0.0117, IoU.radiator: 0.6784, IoU.glass: 0.2116, IoU.clock: 0.5013, IoU.flag: 0.6989, Acc.wall: 0.9039, Acc.building: 0.9333, Acc.sky: 0.9730, Acc.floor: 0.9211, Acc.tree: 0.9018, Acc.ceiling: 0.9403, Acc.road: 0.9210, Acc.bed : 0.9687, Acc.windowpane: 0.8137, Acc.grass: 0.8410, Acc.cabinet: 0.7721, Acc.sidewalk: 0.8517, Acc.person: 0.9445, Acc.earth: 0.5609, Acc.door: 0.7647, Acc.table: 0.7823, Acc.mountain: 0.7622, Acc.plant: 0.6461, Acc.curtain: 0.8988, Acc.chair: 0.8118, Acc.car: 0.9440, Acc.water: 0.7419, Acc.painting: 0.9124, Acc.sofa: 0.9348, Acc.shelf: 0.6960, Acc.house: 0.6540, Acc.sea: 0.8500, Acc.mirror: 0.8648, Acc.rug: 0.7794, Acc.field: 0.5056, Acc.armchair: 0.8064, Acc.seat: 0.8897, Acc.fence: 0.5912, Acc.desk: 0.8039, Acc.rock: 0.8281, Acc.wardrobe: 0.7277, Acc.lamp: 0.8563, Acc.bathtub: 0.8893, Acc.railing: 0.6697, Acc.cushion: 0.8116, Acc.base: 0.5536, Acc.box: 0.4543, Acc.column: 0.7123, Acc.signboard: 0.5557, Acc.chest of drawers: 0.6271, Acc.counter: 0.6065, Acc.sand: 0.7926, Acc.sink: 0.8582, Acc.skyscraper: 0.5880, Acc.fireplace: 0.9253, Acc.refrigerator: 0.9339, Acc.grandstand: 0.8629, Acc.path: 0.4324, Acc.stairs: 0.4097, Acc.runway: 0.9515, Acc.case: 0.8109, Acc.pool table: 0.9845, Acc.pillow: 0.8146, Acc.screen door: 0.7432, Acc.stairway: 0.4834, Acc.river: 0.2087, Acc.bridge: 0.8741, Acc.bookcase: 0.6465, Acc.blind: 0.5239, Acc.coffee table: 0.8858, Acc.toilet: 0.9405, Acc.flower: 0.5913, Acc.book: 0.8070, Acc.hill: 0.1023, Acc.bench: 0.6524, Acc.countertop: 0.8369, Acc.stove: 0.9190, Acc.palm: 0.8340, Acc.kitchen island: 0.8778, Acc.computer: 0.9132, Acc.swivel chair: 0.7723, Acc.boat: 0.9059, Acc.bar: 0.8136, Acc.arcade machine: 0.8045, Acc.hovel: 0.1544, Acc.bus: 0.9627, Acc.towel: 0.9072, Acc.light: 0.7085, Acc.truck: 0.5721, Acc.tower: 0.5419, Acc.chandelier: 0.8731, Acc.awning: 0.4518, Acc.streetlight: 0.4916, Acc.booth: 0.5647, Acc.television receiver: 0.8848, Acc.airplane: 0.9434, Acc.dirt track: 0.4652, Acc.apparel: 0.8764, Acc.pole: 0.3218, Acc.land: 0.1037, Acc.bannister: 0.2912, Acc.escalator: 0.8759, Acc.ottoman: 0.6495, Acc.bottle: 0.5389, Acc.buffet: 0.5934, Acc.poster: 0.3478, Acc.stage: 0.4592, Acc.van: 0.6299, Acc.ship: 0.8253, Acc.fountain: 0.4203, Acc.conveyer belt: 0.9475, Acc.canopy: 0.7858, Acc.washer: 0.8282, Acc.plaything: 0.3856, Acc.swimming pool: 0.7898, Acc.stool: 0.6513, Acc.barrel: 0.9472, Acc.basket: 0.6241, Acc.waterfall: 0.7452, Acc.tent: 0.9838, Acc.bag: 0.2847, Acc.minibike: 0.9143, Acc.cradle: 0.9777, Acc.oven: 0.7676, Acc.ball: 0.7159, Acc.food: 0.6460, Acc.step: 0.1795, Acc.tank: 0.9439, Acc.trade name: 0.2488, Acc.microwave: 0.9666, Acc.pot: 0.7067, Acc.animal: 0.6352, Acc.bicycle: 0.7662, Acc.lake: 0.6406, Acc.dishwasher: 0.8542, Acc.screen: 0.9162, Acc.blanket: 0.4721, Acc.sculpture: 0.8673, Acc.hood: 0.7504, Acc.sconce: 0.7395, Acc.vase: 0.6772, Acc.traffic light: 0.6032, Acc.tray: 0.3185, Acc.ashcan: 0.6580, Acc.fan: 0.8175, Acc.pier: 0.5446, Acc.crt screen: 0.0332, Acc.plate: 0.8268, Acc.monitor: 0.7407, Acc.bulletin board: 0.8228, Acc.shower: 0.0538, Acc.radiator: 0.8355, Acc.glass: 0.2262, Acc.clock: 0.6150, Acc.flag: 0.7785 +2024-06-16 22:29:16,462 - mmseg - INFO - Iter [51050/80000] lr: 1.448e-05, eta: 14:12:36, time: 3.617, data_time: 2.004, memory: 71384, decode.loss_ce: 0.1668, decode.acc_seg: 92.7128, aux.loss_ce: 0.0710, aux.acc_seg: 92.2016, loss: 0.2379 +2024-06-16 22:30:37,668 - mmseg - INFO - Iter [51100/80000] lr: 1.445e-05, eta: 14:11:04, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1755, decode.acc_seg: 92.3838, aux.loss_ce: 0.0734, aux.acc_seg: 92.0627, loss: 0.2489 +2024-06-16 22:31:58,516 - mmseg - INFO - Iter [51150/80000] lr: 1.443e-05, eta: 14:09:31, time: 1.617, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1567, decode.acc_seg: 93.3764, aux.loss_ce: 0.0656, aux.acc_seg: 93.0492, loss: 0.2223 +2024-06-16 22:33:19,479 - mmseg - INFO - Iter [51200/80000] lr: 1.440e-05, eta: 14:07:59, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1658, decode.acc_seg: 93.2762, aux.loss_ce: 0.0697, aux.acc_seg: 92.9229, loss: 0.2355 +2024-06-16 22:34:40,609 - mmseg - INFO - Iter [51250/80000] lr: 1.438e-05, eta: 14:06:26, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1731, decode.acc_seg: 92.4666, aux.loss_ce: 0.0733, aux.acc_seg: 92.0535, loss: 0.2464 +2024-06-16 22:36:01,692 - mmseg - INFO - Iter [51300/80000] lr: 1.435e-05, eta: 14:04:54, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1760, decode.acc_seg: 92.5762, aux.loss_ce: 0.0729, aux.acc_seg: 92.2955, loss: 0.2489 +2024-06-16 22:37:22,816 - mmseg - INFO - Iter [51350/80000] lr: 1.433e-05, eta: 14:03:22, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1702, decode.acc_seg: 92.4524, aux.loss_ce: 0.0717, aux.acc_seg: 92.0592, loss: 0.2419 +2024-06-16 22:38:43,973 - mmseg - INFO - Iter [51400/80000] lr: 1.430e-05, eta: 14:01:49, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1685, decode.acc_seg: 92.5132, aux.loss_ce: 0.0711, aux.acc_seg: 92.1783, loss: 0.2396 +2024-06-16 22:40:04,972 - mmseg - INFO - Iter [51450/80000] lr: 1.428e-05, eta: 14:00:17, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1633, decode.acc_seg: 92.7419, aux.loss_ce: 0.0689, aux.acc_seg: 92.3676, loss: 0.2323 +2024-06-16 22:41:26,058 - mmseg - INFO - Iter [51500/80000] lr: 1.425e-05, eta: 13:58:45, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1611, decode.acc_seg: 93.0338, aux.loss_ce: 0.0680, aux.acc_seg: 92.7381, loss: 0.2291 +2024-06-16 22:42:47,215 - mmseg - INFO - Iter [51550/80000] lr: 1.423e-05, eta: 13:57:13, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1553, decode.acc_seg: 93.2866, aux.loss_ce: 0.0662, aux.acc_seg: 92.8475, loss: 0.2215 +2024-06-16 22:44:08,268 - mmseg - INFO - Iter [51600/80000] lr: 1.420e-05, eta: 13:55:40, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1584, decode.acc_seg: 93.1658, aux.loss_ce: 0.0664, aux.acc_seg: 92.8045, loss: 0.2248 +2024-06-16 22:45:29,395 - mmseg - INFO - Iter [51650/80000] lr: 1.418e-05, eta: 13:54:08, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1598, decode.acc_seg: 92.8143, aux.loss_ce: 0.0674, aux.acc_seg: 92.3968, loss: 0.2272 +2024-06-16 22:46:50,493 - mmseg - INFO - Iter [51700/80000] lr: 1.415e-05, eta: 13:52:36, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1684, decode.acc_seg: 92.7599, aux.loss_ce: 0.0708, aux.acc_seg: 92.3715, loss: 0.2391 +2024-06-16 22:48:11,489 - mmseg - INFO - Iter [51750/80000] lr: 1.413e-05, eta: 13:51:04, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1649, decode.acc_seg: 92.8329, aux.loss_ce: 0.0687, aux.acc_seg: 92.5291, loss: 0.2336 +2024-06-16 22:49:35,434 - mmseg - INFO - Iter [51800/80000] lr: 1.410e-05, eta: 13:49:33, time: 1.679, data_time: 0.066, memory: 71384, decode.loss_ce: 0.1511, decode.acc_seg: 93.3727, aux.loss_ce: 0.0642, aux.acc_seg: 92.9428, loss: 0.2154 +2024-06-16 22:50:56,556 - mmseg - INFO - Iter [51850/80000] lr: 1.408e-05, eta: 13:48:01, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1597, decode.acc_seg: 92.8903, aux.loss_ce: 0.0680, aux.acc_seg: 92.4223, loss: 0.2278 +2024-06-16 22:52:17,659 - mmseg - INFO - Iter [51900/80000] lr: 1.405e-05, eta: 13:46:29, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1594, decode.acc_seg: 93.1300, aux.loss_ce: 0.0670, aux.acc_seg: 92.7490, loss: 0.2264 +2024-06-16 22:53:38,635 - mmseg - INFO - Iter [51950/80000] lr: 1.403e-05, eta: 13:44:57, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1603, decode.acc_seg: 93.0113, aux.loss_ce: 0.0676, aux.acc_seg: 92.6296, loss: 0.2279 +2024-06-16 22:54:59,632 - mmseg - INFO - Saving checkpoint at 52000 iterations +2024-06-16 22:56:23,347 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 22:56:23,347 - mmseg - INFO - Iter [52000/80000] lr: 1.400e-05, eta: 13:44:10, time: 3.294, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1664, decode.acc_seg: 92.9277, aux.loss_ce: 0.0703, aux.acc_seg: 92.5077, loss: 0.2367 +2024-06-16 22:58:00,068 - mmseg - INFO - per class results: +2024-06-16 22:58:00,074 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.26 | 90.51 | +| building | 85.16 | 92.44 | +| sky | 94.84 | 97.2 | +| floor | 85.5 | 92.1 | +| tree | 77.9 | 90.01 | +| ceiling | 87.39 | 93.32 | +| road | 87.44 | 92.62 | +| bed | 92.61 | 96.62 | +| windowpane | 68.05 | 82.42 | +| grass | 69.99 | 82.44 | +| cabinet | 66.88 | 75.57 | +| sidewalk | 71.43 | 82.8 | +| person | 86.18 | 95.18 | +| earth | 42.63 | 58.46 | +| door | 60.35 | 79.49 | +| table | 70.36 | 81.75 | +| mountain | 61.4 | 75.33 | +| plant | 57.65 | 68.48 | +| curtain | 80.21 | 90.19 | +| chair | 69.3 | 80.81 | +| car | 88.5 | 94.2 | +| water | 64.23 | 78.77 | +| painting | 77.04 | 89.89 | +| sofa | 84.53 | 92.78 | +| shelf | 51.08 | 70.33 | +| house | 57.45 | 75.51 | +| sea | 74.49 | 82.21 | +| mirror | 77.85 | 86.62 | +| rug | 69.42 | 78.92 | +| field | 34.51 | 59.54 | +| armchair | 64.11 | 78.66 | +| seat | 67.31 | 88.73 | +| fence | 47.29 | 60.77 | +| desk | 58.9 | 78.16 | +| rock | 53.53 | 83.71 | +| wardrobe | 53.06 | 70.08 | +| lamp | 75.48 | 83.37 | +| bathtub | 87.54 | 89.64 | +| railing | 44.84 | 60.96 | +| cushion | 72.81 | 87.29 | +| base | 37.4 | 57.7 | +| box | 39.67 | 53.84 | +| column | 56.74 | 64.91 | +| signboard | 41.28 | 56.62 | +| chest of drawers | 45.95 | 69.91 | +| counter | 36.75 | 43.07 | +| sand | 55.07 | 77.16 | +| sink | 80.58 | 86.04 | +| skyscraper | 45.36 | 59.45 | +| fireplace | 74.81 | 92.13 | +| refrigerator | 84.35 | 91.38 | +| grandstand | 54.19 | 80.1 | +| path | 30.55 | 40.97 | +| stairs | 26.22 | 29.38 | +| runway | 73.79 | 95.55 | +| case | 66.66 | 85.01 | +| pool table | 94.96 | 97.96 | +| pillow | 70.58 | 82.54 | +| screen door | 74.86 | 77.09 | +| stairway | 39.65 | 64.79 | +| river | 11.07 | 24.76 | +| bridge | 71.53 | 86.75 | +| bookcase | 49.87 | 63.62 | +| blind | 44.49 | 48.6 | +| coffee table | 61.38 | 88.48 | +| toilet | 90.38 | 94.44 | +| flower | 44.97 | 57.15 | +| book | 54.16 | 82.0 | +| hill | 10.46 | 17.45 | +| bench | 51.72 | 55.96 | +| countertop | 65.11 | 79.58 | +| stove | 87.12 | 92.72 | +| palm | 55.14 | 79.84 | +| kitchen island | 59.95 | 81.61 | +| computer | 79.17 | 92.19 | +| swivel chair | 50.3 | 76.32 | +| boat | 83.37 | 89.19 | +| bar | 63.09 | 80.42 | +| arcade machine | 79.42 | 84.94 | +| hovel | 16.37 | 18.99 | +| bus | 91.6 | 96.37 | +| towel | 81.11 | 89.9 | +| light | 61.01 | 67.14 | +| truck | 45.95 | 53.57 | +| tower | 9.32 | 14.88 | +| chandelier | 74.34 | 88.78 | +| awning | 40.31 | 44.84 | +| streetlight | 40.48 | 56.1 | +| booth | 44.59 | 60.83 | +| television receiver | 80.8 | 88.26 | +| airplane | 87.19 | 95.81 | +| dirt track | 17.11 | 47.94 | +| apparel | 62.73 | 85.26 | +| pole | 24.2 | 34.48 | +| land | 3.7 | 6.52 | +| bannister | 21.06 | 26.74 | +| escalator | 63.18 | 78.02 | +| ottoman | 48.05 | 58.5 | +| bottle | 42.94 | 57.1 | +| buffet | 46.08 | 54.94 | +| poster | 31.41 | 39.1 | +| stage | 26.24 | 47.54 | +| van | 49.69 | 72.07 | +| ship | 82.46 | 91.41 | +| fountain | 37.2 | 39.67 | +| conveyer belt | 82.92 | 93.07 | +| canopy | 49.03 | 72.55 | +| washer | 83.51 | 90.33 | +| plaything | 38.19 | 68.61 | +| swimming pool | 53.34 | 77.63 | +| stool | 53.32 | 63.16 | +| barrel | 64.97 | 94.15 | +| basket | 42.77 | 65.95 | +| waterfall | 50.64 | 61.76 | +| tent | 95.14 | 97.91 | +| bag | 28.53 | 33.25 | +| minibike | 75.92 | 88.95 | +| cradle | 86.66 | 97.34 | +| oven | 59.86 | 74.2 | +| ball | 46.39 | 54.34 | +| food | 67.27 | 76.34 | +| step | 14.64 | 17.03 | +| tank | 75.5 | 89.84 | +| trade name | 22.23 | 25.53 | +| microwave | 88.42 | 96.58 | +| pot | 58.89 | 68.89 | +| animal | 62.3 | 63.73 | +| bicycle | 60.74 | 78.63 | +| lake | 54.39 | 63.77 | +| dishwasher | 75.67 | 86.19 | +| screen | 62.12 | 91.96 | +| blanket | 34.2 | 39.86 | +| sculpture | 72.96 | 89.2 | +| hood | 62.21 | 71.79 | +| sconce | 63.52 | 74.19 | +| vase | 48.62 | 63.56 | +| traffic light | 35.19 | 67.82 | +| tray | 20.96 | 26.2 | +| ashcan | 50.46 | 64.76 | +| fan | 67.87 | 80.86 | +| pier | 48.62 | 55.38 | +| crt screen | 2.37 | 3.39 | +| plate | 63.42 | 78.06 | +| monitor | 60.45 | 74.06 | +| bulletin board | 47.8 | 56.4 | +| shower | 1.55 | 3.18 | +| radiator | 69.25 | 80.49 | +| glass | 20.83 | 22.14 | +| clock | 52.99 | 65.04 | +| flag | 72.31 | 81.82 | ++---------------------+-------+-------+ +2024-06-16 22:58:00,074 - mmseg - INFO - Summary: +2024-06-16 22:58:00,074 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.47 | 58.14 | 70.49 | ++-------+-------+-------+ +2024-06-16 22:58:00,075 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 22:58:00,076 - mmseg - INFO - Iter(val) [250] aAcc: 0.8647, mIoU: 0.5814, mAcc: 0.7049, IoU.wall: 0.8226, IoU.building: 0.8516, IoU.sky: 0.9484, IoU.floor: 0.8550, IoU.tree: 0.7790, IoU.ceiling: 0.8739, IoU.road: 0.8744, IoU.bed : 0.9261, IoU.windowpane: 0.6805, IoU.grass: 0.6999, IoU.cabinet: 0.6688, IoU.sidewalk: 0.7143, IoU.person: 0.8618, IoU.earth: 0.4263, IoU.door: 0.6035, IoU.table: 0.7036, IoU.mountain: 0.6140, IoU.plant: 0.5765, IoU.curtain: 0.8021, IoU.chair: 0.6930, IoU.car: 0.8850, IoU.water: 0.6423, IoU.painting: 0.7704, IoU.sofa: 0.8453, IoU.shelf: 0.5108, IoU.house: 0.5745, IoU.sea: 0.7449, IoU.mirror: 0.7785, IoU.rug: 0.6942, IoU.field: 0.3451, IoU.armchair: 0.6411, IoU.seat: 0.6731, IoU.fence: 0.4729, IoU.desk: 0.5890, IoU.rock: 0.5353, IoU.wardrobe: 0.5306, IoU.lamp: 0.7548, IoU.bathtub: 0.8754, IoU.railing: 0.4484, IoU.cushion: 0.7281, IoU.base: 0.3740, IoU.box: 0.3967, IoU.column: 0.5674, IoU.signboard: 0.4128, IoU.chest of drawers: 0.4595, IoU.counter: 0.3675, IoU.sand: 0.5507, IoU.sink: 0.8058, IoU.skyscraper: 0.4536, IoU.fireplace: 0.7481, IoU.refrigerator: 0.8435, IoU.grandstand: 0.5419, IoU.path: 0.3055, IoU.stairs: 0.2622, IoU.runway: 0.7379, IoU.case: 0.6666, IoU.pool table: 0.9496, IoU.pillow: 0.7058, IoU.screen door: 0.7486, IoU.stairway: 0.3965, IoU.river: 0.1107, IoU.bridge: 0.7153, IoU.bookcase: 0.4987, IoU.blind: 0.4449, IoU.coffee table: 0.6138, IoU.toilet: 0.9038, IoU.flower: 0.4497, IoU.book: 0.5416, IoU.hill: 0.1046, IoU.bench: 0.5172, IoU.countertop: 0.6511, IoU.stove: 0.8712, IoU.palm: 0.5514, IoU.kitchen island: 0.5995, IoU.computer: 0.7917, IoU.swivel chair: 0.5030, IoU.boat: 0.8337, IoU.bar: 0.6309, IoU.arcade machine: 0.7942, IoU.hovel: 0.1637, IoU.bus: 0.9160, IoU.towel: 0.8111, IoU.light: 0.6101, IoU.truck: 0.4595, IoU.tower: 0.0932, IoU.chandelier: 0.7434, IoU.awning: 0.4031, IoU.streetlight: 0.4048, IoU.booth: 0.4459, IoU.television receiver: 0.8080, IoU.airplane: 0.8719, IoU.dirt track: 0.1711, IoU.apparel: 0.6273, IoU.pole: 0.2420, IoU.land: 0.0370, IoU.bannister: 0.2106, IoU.escalator: 0.6318, IoU.ottoman: 0.4805, IoU.bottle: 0.4294, IoU.buffet: 0.4608, IoU.poster: 0.3141, IoU.stage: 0.2624, IoU.van: 0.4969, IoU.ship: 0.8246, IoU.fountain: 0.3720, IoU.conveyer belt: 0.8292, IoU.canopy: 0.4903, IoU.washer: 0.8351, IoU.plaything: 0.3819, IoU.swimming pool: 0.5334, IoU.stool: 0.5332, IoU.barrel: 0.6497, IoU.basket: 0.4277, IoU.waterfall: 0.5064, IoU.tent: 0.9514, IoU.bag: 0.2853, IoU.minibike: 0.7592, IoU.cradle: 0.8666, IoU.oven: 0.5986, IoU.ball: 0.4639, IoU.food: 0.6727, IoU.step: 0.1464, IoU.tank: 0.7550, IoU.trade name: 0.2223, IoU.microwave: 0.8842, IoU.pot: 0.5889, IoU.animal: 0.6230, IoU.bicycle: 0.6074, IoU.lake: 0.5439, IoU.dishwasher: 0.7567, IoU.screen: 0.6212, IoU.blanket: 0.3420, IoU.sculpture: 0.7296, IoU.hood: 0.6221, IoU.sconce: 0.6352, IoU.vase: 0.4862, IoU.traffic light: 0.3519, IoU.tray: 0.2096, IoU.ashcan: 0.5046, IoU.fan: 0.6787, IoU.pier: 0.4862, IoU.crt screen: 0.0237, IoU.plate: 0.6342, IoU.monitor: 0.6045, IoU.bulletin board: 0.4780, IoU.shower: 0.0155, IoU.radiator: 0.6925, IoU.glass: 0.2083, IoU.clock: 0.5299, IoU.flag: 0.7231, Acc.wall: 0.9051, Acc.building: 0.9244, Acc.sky: 0.9720, Acc.floor: 0.9210, Acc.tree: 0.9001, Acc.ceiling: 0.9332, Acc.road: 0.9262, Acc.bed : 0.9662, Acc.windowpane: 0.8242, Acc.grass: 0.8244, Acc.cabinet: 0.7557, Acc.sidewalk: 0.8280, Acc.person: 0.9518, Acc.earth: 0.5846, Acc.door: 0.7949, Acc.table: 0.8175, Acc.mountain: 0.7533, Acc.plant: 0.6848, Acc.curtain: 0.9019, Acc.chair: 0.8081, Acc.car: 0.9420, Acc.water: 0.7877, Acc.painting: 0.8989, Acc.sofa: 0.9278, Acc.shelf: 0.7033, Acc.house: 0.7551, Acc.sea: 0.8221, Acc.mirror: 0.8662, Acc.rug: 0.7892, Acc.field: 0.5954, Acc.armchair: 0.7866, Acc.seat: 0.8873, Acc.fence: 0.6077, Acc.desk: 0.7816, Acc.rock: 0.8371, Acc.wardrobe: 0.7008, Acc.lamp: 0.8337, Acc.bathtub: 0.8964, Acc.railing: 0.6096, Acc.cushion: 0.8729, Acc.base: 0.5770, Acc.box: 0.5384, Acc.column: 0.6491, Acc.signboard: 0.5662, Acc.chest of drawers: 0.6991, Acc.counter: 0.4307, Acc.sand: 0.7716, Acc.sink: 0.8604, Acc.skyscraper: 0.5945, Acc.fireplace: 0.9213, Acc.refrigerator: 0.9138, Acc.grandstand: 0.8010, Acc.path: 0.4097, Acc.stairs: 0.2938, Acc.runway: 0.9555, Acc.case: 0.8501, Acc.pool table: 0.9796, Acc.pillow: 0.8254, Acc.screen door: 0.7709, Acc.stairway: 0.6479, Acc.river: 0.2476, Acc.bridge: 0.8675, Acc.bookcase: 0.6362, Acc.blind: 0.4860, Acc.coffee table: 0.8848, Acc.toilet: 0.9444, Acc.flower: 0.5715, Acc.book: 0.8200, Acc.hill: 0.1745, Acc.bench: 0.5596, Acc.countertop: 0.7958, Acc.stove: 0.9272, Acc.palm: 0.7984, Acc.kitchen island: 0.8161, Acc.computer: 0.9219, Acc.swivel chair: 0.7632, Acc.boat: 0.8919, Acc.bar: 0.8042, Acc.arcade machine: 0.8494, Acc.hovel: 0.1899, Acc.bus: 0.9637, Acc.towel: 0.8990, Acc.light: 0.6714, Acc.truck: 0.5357, Acc.tower: 0.1488, Acc.chandelier: 0.8878, Acc.awning: 0.4484, Acc.streetlight: 0.5610, Acc.booth: 0.6083, Acc.television receiver: 0.8826, Acc.airplane: 0.9581, Acc.dirt track: 0.4794, Acc.apparel: 0.8526, Acc.pole: 0.3448, Acc.land: 0.0652, Acc.bannister: 0.2674, Acc.escalator: 0.7802, Acc.ottoman: 0.5850, Acc.bottle: 0.5710, Acc.buffet: 0.5494, Acc.poster: 0.3910, Acc.stage: 0.4754, Acc.van: 0.7207, Acc.ship: 0.9141, Acc.fountain: 0.3967, Acc.conveyer belt: 0.9307, Acc.canopy: 0.7255, Acc.washer: 0.9033, Acc.plaything: 0.6861, Acc.swimming pool: 0.7763, Acc.stool: 0.6316, Acc.barrel: 0.9415, Acc.basket: 0.6595, Acc.waterfall: 0.6176, Acc.tent: 0.9791, Acc.bag: 0.3325, Acc.minibike: 0.8895, Acc.cradle: 0.9734, Acc.oven: 0.7420, Acc.ball: 0.5434, Acc.food: 0.7634, Acc.step: 0.1703, Acc.tank: 0.8984, Acc.trade name: 0.2553, Acc.microwave: 0.9658, Acc.pot: 0.6889, Acc.animal: 0.6373, Acc.bicycle: 0.7863, Acc.lake: 0.6377, Acc.dishwasher: 0.8619, Acc.screen: 0.9196, Acc.blanket: 0.3986, Acc.sculpture: 0.8920, Acc.hood: 0.7179, Acc.sconce: 0.7419, Acc.vase: 0.6356, Acc.traffic light: 0.6782, Acc.tray: 0.2620, Acc.ashcan: 0.6476, Acc.fan: 0.8086, Acc.pier: 0.5538, Acc.crt screen: 0.0339, Acc.plate: 0.7806, Acc.monitor: 0.7406, Acc.bulletin board: 0.5640, Acc.shower: 0.0318, Acc.radiator: 0.8049, Acc.glass: 0.2214, Acc.clock: 0.6504, Acc.flag: 0.8182 +2024-06-16 22:59:21,694 - mmseg - INFO - Iter [52050/80000] lr: 1.398e-05, eta: 13:43:30, time: 3.567, data_time: 1.952, memory: 71384, decode.loss_ce: 0.1704, decode.acc_seg: 92.8560, aux.loss_ce: 0.0716, aux.acc_seg: 92.4812, loss: 0.2420 +2024-06-16 23:00:42,789 - mmseg - INFO - Iter [52100/80000] lr: 1.395e-05, eta: 13:41:57, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1756, decode.acc_seg: 92.5189, aux.loss_ce: 0.0731, aux.acc_seg: 92.2159, loss: 0.2487 +2024-06-16 23:02:03,716 - mmseg - INFO - Iter [52150/80000] lr: 1.393e-05, eta: 13:40:25, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1593, decode.acc_seg: 92.9827, aux.loss_ce: 0.0665, aux.acc_seg: 92.7034, loss: 0.2258 +2024-06-16 23:03:24,932 - mmseg - INFO - Iter [52200/80000] lr: 1.390e-05, eta: 13:38:53, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1594, decode.acc_seg: 93.1167, aux.loss_ce: 0.0675, aux.acc_seg: 92.7379, loss: 0.2269 +2024-06-16 23:04:45,792 - mmseg - INFO - Iter [52250/80000] lr: 1.388e-05, eta: 13:37:20, time: 1.617, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1655, decode.acc_seg: 92.8959, aux.loss_ce: 0.0693, aux.acc_seg: 92.5574, loss: 0.2348 +2024-06-16 23:06:06,840 - mmseg - INFO - Iter [52300/80000] lr: 1.385e-05, eta: 13:35:48, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1597, decode.acc_seg: 93.1405, aux.loss_ce: 0.0673, aux.acc_seg: 92.7410, loss: 0.2270 +2024-06-16 23:07:27,924 - mmseg - INFO - Iter [52350/80000] lr: 1.383e-05, eta: 13:34:16, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1612, decode.acc_seg: 92.9623, aux.loss_ce: 0.0677, aux.acc_seg: 92.6486, loss: 0.2289 +2024-06-16 23:08:49,389 - mmseg - INFO - Iter [52400/80000] lr: 1.380e-05, eta: 13:32:44, time: 1.629, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1609, decode.acc_seg: 92.9669, aux.loss_ce: 0.0681, aux.acc_seg: 92.5014, loss: 0.2291 +2024-06-16 23:10:10,543 - mmseg - INFO - Iter [52450/80000] lr: 1.378e-05, eta: 13:31:12, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1646, decode.acc_seg: 92.8469, aux.loss_ce: 0.0694, aux.acc_seg: 92.5139, loss: 0.2341 +2024-06-16 23:11:31,601 - mmseg - INFO - Iter [52500/80000] lr: 1.375e-05, eta: 13:29:40, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1630, decode.acc_seg: 92.9318, aux.loss_ce: 0.0690, aux.acc_seg: 92.5457, loss: 0.2320 +2024-06-16 23:12:52,659 - mmseg - INFO - Iter [52550/80000] lr: 1.373e-05, eta: 13:28:08, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1621, decode.acc_seg: 92.9860, aux.loss_ce: 0.0684, aux.acc_seg: 92.5748, loss: 0.2305 +2024-06-16 23:14:13,730 - mmseg - INFO - Iter [52600/80000] lr: 1.370e-05, eta: 13:26:36, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1644, decode.acc_seg: 92.9606, aux.loss_ce: 0.0696, aux.acc_seg: 92.5460, loss: 0.2340 +2024-06-16 23:15:34,883 - mmseg - INFO - Iter [52650/80000] lr: 1.368e-05, eta: 13:25:04, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1533, decode.acc_seg: 93.3125, aux.loss_ce: 0.0646, aux.acc_seg: 92.9594, loss: 0.2180 +2024-06-16 23:16:56,104 - mmseg - INFO - Iter [52700/80000] lr: 1.365e-05, eta: 13:23:32, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1602, decode.acc_seg: 93.2469, aux.loss_ce: 0.0678, aux.acc_seg: 92.8896, loss: 0.2280 +2024-06-16 23:18:17,306 - mmseg - INFO - Iter [52750/80000] lr: 1.363e-05, eta: 13:22:00, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1614, decode.acc_seg: 92.9754, aux.loss_ce: 0.0690, aux.acc_seg: 92.5581, loss: 0.2304 +2024-06-16 23:19:38,252 - mmseg - INFO - Iter [52800/80000] lr: 1.360e-05, eta: 13:20:28, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1596, decode.acc_seg: 92.9361, aux.loss_ce: 0.0670, aux.acc_seg: 92.5592, loss: 0.2267 +2024-06-16 23:20:59,464 - mmseg - INFO - Iter [52850/80000] lr: 1.358e-05, eta: 13:18:56, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1715, decode.acc_seg: 92.6299, aux.loss_ce: 0.0726, aux.acc_seg: 92.2018, loss: 0.2441 +2024-06-16 23:22:20,447 - mmseg - INFO - Iter [52900/80000] lr: 1.355e-05, eta: 13:17:24, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1650, decode.acc_seg: 92.8210, aux.loss_ce: 0.0699, aux.acc_seg: 92.4454, loss: 0.2349 +2024-06-16 23:23:41,469 - mmseg - INFO - Iter [52950/80000] lr: 1.353e-05, eta: 13:15:52, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1609, decode.acc_seg: 93.0630, aux.loss_ce: 0.0683, aux.acc_seg: 92.6342, loss: 0.2291 +2024-06-16 23:25:02,558 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 23:25:02,558 - mmseg - INFO - Iter [53000/80000] lr: 1.350e-05, eta: 13:14:20, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1529, decode.acc_seg: 93.1758, aux.loss_ce: 0.0645, aux.acc_seg: 92.8334, loss: 0.2174 +2024-06-16 23:26:40,400 - mmseg - INFO - per class results: +2024-06-16 23:26:40,406 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.64 | 89.91 | +| building | 85.51 | 93.68 | +| sky | 94.76 | 97.16 | +| floor | 85.82 | 91.91 | +| tree | 77.85 | 89.62 | +| ceiling | 87.72 | 94.58 | +| road | 86.93 | 92.73 | +| bed | 92.6 | 96.93 | +| windowpane | 67.01 | 83.02 | +| grass | 69.97 | 84.01 | +| cabinet | 66.26 | 77.18 | +| sidewalk | 70.29 | 83.76 | +| person | 86.18 | 94.1 | +| earth | 38.68 | 50.73 | +| door | 59.86 | 76.13 | +| table | 70.42 | 81.09 | +| mountain | 59.86 | 74.19 | +| plant | 58.67 | 68.8 | +| curtain | 79.95 | 90.3 | +| chair | 68.54 | 79.92 | +| car | 87.97 | 94.24 | +| water | 62.63 | 79.48 | +| painting | 78.31 | 90.59 | +| sofa | 83.99 | 92.56 | +| shelf | 48.39 | 59.66 | +| house | 49.93 | 60.07 | +| sea | 65.12 | 72.25 | +| mirror | 78.59 | 84.72 | +| rug | 69.87 | 79.36 | +| field | 40.25 | 69.65 | +| armchair | 62.6 | 77.03 | +| seat | 65.9 | 88.98 | +| fence | 49.36 | 63.16 | +| desk | 59.22 | 79.1 | +| rock | 46.89 | 73.26 | +| wardrobe | 54.74 | 77.29 | +| lamp | 76.19 | 86.98 | +| bathtub | 85.78 | 87.83 | +| railing | 44.52 | 64.67 | +| cushion | 72.78 | 84.41 | +| base | 39.66 | 58.56 | +| box | 38.86 | 49.35 | +| column | 53.74 | 63.48 | +| signboard | 40.77 | 58.6 | +| chest of drawers | 41.64 | 62.65 | +| counter | 42.55 | 54.32 | +| sand | 51.26 | 77.43 | +| sink | 80.92 | 86.4 | +| skyscraper | 47.27 | 58.57 | +| fireplace | 74.51 | 92.41 | +| refrigerator | 87.4 | 95.57 | +| grandstand | 52.37 | 82.61 | +| path | 31.6 | 43.53 | +| stairs | 31.74 | 37.49 | +| runway | 73.27 | 95.74 | +| case | 62.22 | 86.6 | +| pool table | 95.39 | 98.37 | +| pillow | 70.11 | 80.73 | +| screen door | 79.92 | 83.53 | +| stairway | 35.67 | 53.31 | +| river | 12.25 | 26.71 | +| bridge | 66.22 | 89.09 | +| bookcase | 45.21 | 68.41 | +| blind | 42.39 | 44.4 | +| coffee table | 61.25 | 88.67 | +| toilet | 90.51 | 93.32 | +| flower | 45.71 | 59.13 | +| book | 55.46 | 76.91 | +| hill | 8.6 | 18.03 | +| bench | 55.88 | 60.07 | +| countertop | 66.09 | 84.01 | +| stove | 86.8 | 93.19 | +| palm | 55.69 | 82.99 | +| kitchen island | 58.01 | 86.53 | +| computer | 79.01 | 89.02 | +| swivel chair | 50.98 | 80.75 | +| boat | 72.03 | 91.3 | +| bar | 66.19 | 80.38 | +| arcade machine | 78.05 | 83.1 | +| hovel | 14.78 | 15.73 | +| bus | 91.9 | 96.92 | +| towel | 81.68 | 89.02 | +| light | 63.08 | 72.25 | +| truck | 48.49 | 63.16 | +| tower | 36.95 | 67.35 | +| chandelier | 75.0 | 85.58 | +| awning | 40.27 | 47.74 | +| streetlight | 39.51 | 52.23 | +| booth | 40.68 | 50.66 | +| television receiver | 81.44 | 87.54 | +| airplane | 87.38 | 96.67 | +| dirt track | 9.02 | 10.8 | +| apparel | 61.18 | 84.99 | +| pole | 29.83 | 39.91 | +| land | 3.41 | 5.44 | +| bannister | 20.9 | 25.46 | +| escalator | 63.46 | 85.02 | +| ottoman | 48.75 | 64.06 | +| bottle | 44.22 | 60.15 | +| buffet | 47.17 | 54.89 | +| poster | 33.03 | 42.88 | +| stage | 23.39 | 47.7 | +| van | 49.99 | 70.06 | +| ship | 88.61 | 96.22 | +| fountain | 39.1 | 41.69 | +| conveyer belt | 80.09 | 95.22 | +| canopy | 48.11 | 67.63 | +| washer | 83.78 | 89.2 | +| plaything | 41.02 | 76.4 | +| swimming pool | 53.18 | 78.75 | +| stool | 53.22 | 67.9 | +| barrel | 58.34 | 73.76 | +| basket | 43.25 | 64.21 | +| waterfall | 50.68 | 66.77 | +| tent | 95.75 | 97.97 | +| bag | 28.33 | 32.03 | +| minibike | 76.21 | 91.71 | +| cradle | 81.67 | 97.41 | +| oven | 64.76 | 76.81 | +| ball | 47.11 | 53.07 | +| food | 69.21 | 85.82 | +| step | 14.22 | 16.6 | +| tank | 72.38 | 95.9 | +| trade name | 19.56 | 22.36 | +| microwave | 89.64 | 96.21 | +| pot | 58.75 | 67.94 | +| animal | 63.08 | 64.76 | +| bicycle | 60.05 | 78.13 | +| lake | 52.23 | 63.62 | +| dishwasher | 78.2 | 84.54 | +| screen | 49.08 | 74.61 | +| blanket | 34.07 | 40.13 | +| sculpture | 71.58 | 90.11 | +| hood | 63.49 | 74.29 | +| sconce | 64.39 | 75.16 | +| vase | 48.31 | 63.45 | +| traffic light | 37.7 | 65.09 | +| tray | 20.54 | 25.4 | +| ashcan | 50.15 | 64.95 | +| fan | 71.62 | 85.99 | +| pier | 50.61 | 66.9 | +| crt screen | 4.7 | 13.12 | +| plate | 63.78 | 77.0 | +| monitor | 30.46 | 39.2 | +| bulletin board | 45.35 | 51.27 | +| shower | 1.71 | 1.72 | +| radiator | 69.82 | 85.06 | +| glass | 21.63 | 23.38 | +| clock | 50.64 | 66.7 | +| flag | 72.34 | 82.24 | ++---------------------+-------+-------+ +2024-06-16 23:26:40,406 - mmseg - INFO - Summary: +2024-06-16 23:26:40,406 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.37 | 57.78 | 70.63 | ++-------+-------+-------+ +2024-06-16 23:26:40,407 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 23:26:40,407 - mmseg - INFO - Iter(val) [250] aAcc: 0.8637, mIoU: 0.5778, mAcc: 0.7063, IoU.wall: 0.8264, IoU.building: 0.8551, IoU.sky: 0.9476, IoU.floor: 0.8582, IoU.tree: 0.7785, IoU.ceiling: 0.8772, IoU.road: 0.8693, IoU.bed : 0.9260, IoU.windowpane: 0.6701, IoU.grass: 0.6997, IoU.cabinet: 0.6626, IoU.sidewalk: 0.7029, IoU.person: 0.8618, IoU.earth: 0.3868, IoU.door: 0.5986, IoU.table: 0.7042, IoU.mountain: 0.5986, IoU.plant: 0.5867, IoU.curtain: 0.7995, IoU.chair: 0.6854, IoU.car: 0.8797, IoU.water: 0.6263, IoU.painting: 0.7831, IoU.sofa: 0.8399, IoU.shelf: 0.4839, IoU.house: 0.4993, IoU.sea: 0.6512, IoU.mirror: 0.7859, IoU.rug: 0.6987, IoU.field: 0.4025, IoU.armchair: 0.6260, IoU.seat: 0.6590, IoU.fence: 0.4936, IoU.desk: 0.5922, IoU.rock: 0.4689, IoU.wardrobe: 0.5474, IoU.lamp: 0.7619, IoU.bathtub: 0.8578, IoU.railing: 0.4452, IoU.cushion: 0.7278, IoU.base: 0.3966, IoU.box: 0.3886, IoU.column: 0.5374, IoU.signboard: 0.4077, IoU.chest of drawers: 0.4164, IoU.counter: 0.4255, IoU.sand: 0.5126, IoU.sink: 0.8092, IoU.skyscraper: 0.4727, IoU.fireplace: 0.7451, IoU.refrigerator: 0.8740, IoU.grandstand: 0.5237, IoU.path: 0.3160, IoU.stairs: 0.3174, IoU.runway: 0.7327, IoU.case: 0.6222, IoU.pool table: 0.9539, IoU.pillow: 0.7011, IoU.screen door: 0.7992, IoU.stairway: 0.3567, IoU.river: 0.1225, IoU.bridge: 0.6622, IoU.bookcase: 0.4521, IoU.blind: 0.4239, IoU.coffee table: 0.6125, IoU.toilet: 0.9051, IoU.flower: 0.4571, IoU.book: 0.5546, IoU.hill: 0.0860, IoU.bench: 0.5588, IoU.countertop: 0.6609, IoU.stove: 0.8680, IoU.palm: 0.5569, IoU.kitchen island: 0.5801, IoU.computer: 0.7901, IoU.swivel chair: 0.5098, IoU.boat: 0.7203, IoU.bar: 0.6619, IoU.arcade machine: 0.7805, IoU.hovel: 0.1478, IoU.bus: 0.9190, IoU.towel: 0.8168, IoU.light: 0.6308, IoU.truck: 0.4849, IoU.tower: 0.3695, IoU.chandelier: 0.7500, IoU.awning: 0.4027, IoU.streetlight: 0.3951, IoU.booth: 0.4068, IoU.television receiver: 0.8144, IoU.airplane: 0.8738, IoU.dirt track: 0.0902, IoU.apparel: 0.6118, IoU.pole: 0.2983, IoU.land: 0.0341, IoU.bannister: 0.2090, IoU.escalator: 0.6346, IoU.ottoman: 0.4875, IoU.bottle: 0.4422, IoU.buffet: 0.4717, IoU.poster: 0.3303, IoU.stage: 0.2339, IoU.van: 0.4999, IoU.ship: 0.8861, IoU.fountain: 0.3910, IoU.conveyer belt: 0.8009, IoU.canopy: 0.4811, IoU.washer: 0.8378, IoU.plaything: 0.4102, IoU.swimming pool: 0.5318, IoU.stool: 0.5322, IoU.barrel: 0.5834, IoU.basket: 0.4325, IoU.waterfall: 0.5068, IoU.tent: 0.9575, IoU.bag: 0.2833, IoU.minibike: 0.7621, IoU.cradle: 0.8167, IoU.oven: 0.6476, IoU.ball: 0.4711, IoU.food: 0.6921, IoU.step: 0.1422, IoU.tank: 0.7238, IoU.trade name: 0.1956, IoU.microwave: 0.8964, IoU.pot: 0.5875, IoU.animal: 0.6308, IoU.bicycle: 0.6005, IoU.lake: 0.5223, IoU.dishwasher: 0.7820, IoU.screen: 0.4908, IoU.blanket: 0.3407, IoU.sculpture: 0.7158, IoU.hood: 0.6349, IoU.sconce: 0.6439, IoU.vase: 0.4831, IoU.traffic light: 0.3770, IoU.tray: 0.2054, IoU.ashcan: 0.5015, IoU.fan: 0.7162, IoU.pier: 0.5061, IoU.crt screen: 0.0470, IoU.plate: 0.6378, IoU.monitor: 0.3046, IoU.bulletin board: 0.4535, IoU.shower: 0.0171, IoU.radiator: 0.6982, IoU.glass: 0.2163, IoU.clock: 0.5064, IoU.flag: 0.7234, Acc.wall: 0.8991, Acc.building: 0.9368, Acc.sky: 0.9716, Acc.floor: 0.9191, Acc.tree: 0.8962, Acc.ceiling: 0.9458, Acc.road: 0.9273, Acc.bed : 0.9693, Acc.windowpane: 0.8302, Acc.grass: 0.8401, Acc.cabinet: 0.7718, Acc.sidewalk: 0.8376, Acc.person: 0.9410, Acc.earth: 0.5073, Acc.door: 0.7613, Acc.table: 0.8109, Acc.mountain: 0.7419, Acc.plant: 0.6880, Acc.curtain: 0.9030, Acc.chair: 0.7992, Acc.car: 0.9424, Acc.water: 0.7948, Acc.painting: 0.9059, Acc.sofa: 0.9256, Acc.shelf: 0.5966, Acc.house: 0.6007, Acc.sea: 0.7225, Acc.mirror: 0.8472, Acc.rug: 0.7936, Acc.field: 0.6965, Acc.armchair: 0.7703, Acc.seat: 0.8898, Acc.fence: 0.6316, Acc.desk: 0.7910, Acc.rock: 0.7326, Acc.wardrobe: 0.7729, Acc.lamp: 0.8698, Acc.bathtub: 0.8783, Acc.railing: 0.6467, Acc.cushion: 0.8441, Acc.base: 0.5856, Acc.box: 0.4935, Acc.column: 0.6348, Acc.signboard: 0.5860, Acc.chest of drawers: 0.6265, Acc.counter: 0.5432, Acc.sand: 0.7743, Acc.sink: 0.8640, Acc.skyscraper: 0.5857, Acc.fireplace: 0.9241, Acc.refrigerator: 0.9557, Acc.grandstand: 0.8261, Acc.path: 0.4353, Acc.stairs: 0.3749, Acc.runway: 0.9574, Acc.case: 0.8660, Acc.pool table: 0.9837, Acc.pillow: 0.8073, Acc.screen door: 0.8353, Acc.stairway: 0.5331, Acc.river: 0.2671, Acc.bridge: 0.8909, Acc.bookcase: 0.6841, Acc.blind: 0.4440, Acc.coffee table: 0.8867, Acc.toilet: 0.9332, Acc.flower: 0.5913, Acc.book: 0.7691, Acc.hill: 0.1803, Acc.bench: 0.6007, Acc.countertop: 0.8401, Acc.stove: 0.9319, Acc.palm: 0.8299, Acc.kitchen island: 0.8653, Acc.computer: 0.8902, Acc.swivel chair: 0.8075, Acc.boat: 0.9130, Acc.bar: 0.8038, Acc.arcade machine: 0.8310, Acc.hovel: 0.1573, Acc.bus: 0.9692, Acc.towel: 0.8902, Acc.light: 0.7225, Acc.truck: 0.6316, Acc.tower: 0.6735, Acc.chandelier: 0.8558, Acc.awning: 0.4774, Acc.streetlight: 0.5223, Acc.booth: 0.5066, Acc.television receiver: 0.8754, Acc.airplane: 0.9667, Acc.dirt track: 0.1080, Acc.apparel: 0.8499, Acc.pole: 0.3991, Acc.land: 0.0544, Acc.bannister: 0.2546, Acc.escalator: 0.8502, Acc.ottoman: 0.6406, Acc.bottle: 0.6015, Acc.buffet: 0.5489, Acc.poster: 0.4288, Acc.stage: 0.4770, Acc.van: 0.7006, Acc.ship: 0.9622, Acc.fountain: 0.4169, Acc.conveyer belt: 0.9522, Acc.canopy: 0.6763, Acc.washer: 0.8920, Acc.plaything: 0.7640, Acc.swimming pool: 0.7875, Acc.stool: 0.6790, Acc.barrel: 0.7376, Acc.basket: 0.6421, Acc.waterfall: 0.6677, Acc.tent: 0.9797, Acc.bag: 0.3203, Acc.minibike: 0.9171, Acc.cradle: 0.9741, Acc.oven: 0.7681, Acc.ball: 0.5307, Acc.food: 0.8582, Acc.step: 0.1660, Acc.tank: 0.9590, Acc.trade name: 0.2236, Acc.microwave: 0.9621, Acc.pot: 0.6794, Acc.animal: 0.6476, Acc.bicycle: 0.7813, Acc.lake: 0.6362, Acc.dishwasher: 0.8454, Acc.screen: 0.7461, Acc.blanket: 0.4013, Acc.sculpture: 0.9011, Acc.hood: 0.7429, Acc.sconce: 0.7516, Acc.vase: 0.6345, Acc.traffic light: 0.6509, Acc.tray: 0.2540, Acc.ashcan: 0.6495, Acc.fan: 0.8599, Acc.pier: 0.6690, Acc.crt screen: 0.1312, Acc.plate: 0.7700, Acc.monitor: 0.3920, Acc.bulletin board: 0.5127, Acc.shower: 0.0172, Acc.radiator: 0.8506, Acc.glass: 0.2338, Acc.clock: 0.6670, Acc.flag: 0.8224 +2024-06-16 23:28:04,075 - mmseg - INFO - Iter [53050/80000] lr: 1.348e-05, eta: 13:13:39, time: 3.630, data_time: 2.016, memory: 71384, decode.loss_ce: 0.1567, decode.acc_seg: 93.1376, aux.loss_ce: 0.0663, aux.acc_seg: 92.7502, loss: 0.2230 +2024-06-16 23:29:25,324 - mmseg - INFO - Iter [53100/80000] lr: 1.345e-05, eta: 13:12:07, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1674, decode.acc_seg: 92.7804, aux.loss_ce: 0.0704, aux.acc_seg: 92.4028, loss: 0.2378 +2024-06-16 23:30:46,321 - mmseg - INFO - Iter [53150/80000] lr: 1.343e-05, eta: 13:10:35, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1693, decode.acc_seg: 92.8940, aux.loss_ce: 0.0712, aux.acc_seg: 92.5633, loss: 0.2405 +2024-06-16 23:32:07,478 - mmseg - INFO - Iter [53200/80000] lr: 1.340e-05, eta: 13:09:03, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1621, decode.acc_seg: 93.0771, aux.loss_ce: 0.0686, aux.acc_seg: 92.6824, loss: 0.2307 +2024-06-16 23:33:28,459 - mmseg - INFO - Iter [53250/80000] lr: 1.338e-05, eta: 13:07:31, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1493, decode.acc_seg: 93.6568, aux.loss_ce: 0.0630, aux.acc_seg: 93.2711, loss: 0.2123 +2024-06-16 23:34:49,476 - mmseg - INFO - Iter [53300/80000] lr: 1.335e-05, eta: 13:05:59, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1530, decode.acc_seg: 93.3539, aux.loss_ce: 0.0646, aux.acc_seg: 92.9719, loss: 0.2176 +2024-06-16 23:36:10,463 - mmseg - INFO - Iter [53350/80000] lr: 1.333e-05, eta: 13:04:27, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1673, decode.acc_seg: 92.7057, aux.loss_ce: 0.0712, aux.acc_seg: 92.2950, loss: 0.2385 +2024-06-16 23:37:31,514 - mmseg - INFO - Iter [53400/80000] lr: 1.330e-05, eta: 13:02:55, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1536, decode.acc_seg: 93.2727, aux.loss_ce: 0.0646, aux.acc_seg: 92.9089, loss: 0.2183 +2024-06-16 23:38:52,656 - mmseg - INFO - Iter [53450/80000] lr: 1.328e-05, eta: 13:01:23, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1487, decode.acc_seg: 93.6539, aux.loss_ce: 0.0633, aux.acc_seg: 93.2471, loss: 0.2120 +2024-06-16 23:40:13,743 - mmseg - INFO - Iter [53500/80000] lr: 1.325e-05, eta: 12:59:51, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1589, decode.acc_seg: 93.0375, aux.loss_ce: 0.0671, aux.acc_seg: 92.6765, loss: 0.2260 +2024-06-16 23:41:34,807 - mmseg - INFO - Iter [53550/80000] lr: 1.323e-05, eta: 12:58:19, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1610, decode.acc_seg: 93.1341, aux.loss_ce: 0.0682, aux.acc_seg: 92.6574, loss: 0.2291 +2024-06-16 23:42:55,975 - mmseg - INFO - Iter [53600/80000] lr: 1.320e-05, eta: 12:56:48, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1543, decode.acc_seg: 93.3648, aux.loss_ce: 0.0655, aux.acc_seg: 92.9777, loss: 0.2198 +2024-06-16 23:44:16,988 - mmseg - INFO - Iter [53650/80000] lr: 1.318e-05, eta: 12:55:16, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1615, decode.acc_seg: 93.0056, aux.loss_ce: 0.0678, aux.acc_seg: 92.6607, loss: 0.2293 +2024-06-16 23:45:38,007 - mmseg - INFO - Iter [53700/80000] lr: 1.315e-05, eta: 12:53:44, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1608, decode.acc_seg: 93.1786, aux.loss_ce: 0.0680, aux.acc_seg: 92.7163, loss: 0.2288 +2024-06-16 23:46:59,293 - mmseg - INFO - Iter [53750/80000] lr: 1.313e-05, eta: 12:52:12, time: 1.626, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1567, decode.acc_seg: 93.0276, aux.loss_ce: 0.0665, aux.acc_seg: 92.5685, loss: 0.2232 +2024-06-16 23:48:20,327 - mmseg - INFO - Iter [53800/80000] lr: 1.310e-05, eta: 12:50:41, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1689, decode.acc_seg: 92.5705, aux.loss_ce: 0.0707, aux.acc_seg: 92.2373, loss: 0.2396 +2024-06-16 23:49:41,498 - mmseg - INFO - Iter [53850/80000] lr: 1.308e-05, eta: 12:49:09, time: 1.623, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1621, decode.acc_seg: 92.8978, aux.loss_ce: 0.0691, aux.acc_seg: 92.4550, loss: 0.2311 +2024-06-16 23:51:02,592 - mmseg - INFO - Iter [53900/80000] lr: 1.305e-05, eta: 12:47:37, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1739, decode.acc_seg: 92.5613, aux.loss_ce: 0.0728, aux.acc_seg: 92.2356, loss: 0.2466 +2024-06-16 23:52:23,626 - mmseg - INFO - Iter [53950/80000] lr: 1.303e-05, eta: 12:46:05, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1476, decode.acc_seg: 93.6474, aux.loss_ce: 0.0622, aux.acc_seg: 93.3481, loss: 0.2099 +2024-06-16 23:53:44,623 - mmseg - INFO - Saving checkpoint at 54000 iterations +2024-06-16 23:55:09,311 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 23:55:09,311 - mmseg - INFO - Iter [54000/80000] lr: 1.300e-05, eta: 12:45:14, time: 3.314, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1629, decode.acc_seg: 92.7119, aux.loss_ce: 0.0687, aux.acc_seg: 92.2941, loss: 0.2316 +2024-06-16 23:56:44,575 - mmseg - INFO - per class results: +2024-06-16 23:56:44,581 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.68 | 90.8 | +| building | 85.93 | 93.14 | +| sky | 94.89 | 97.32 | +| floor | 85.48 | 92.04 | +| tree | 78.06 | 90.46 | +| ceiling | 87.22 | 93.75 | +| road | 86.67 | 91.63 | +| bed | 92.78 | 97.08 | +| windowpane | 66.3 | 83.11 | +| grass | 66.19 | 76.34 | +| cabinet | 67.6 | 76.64 | +| sidewalk | 71.34 | 85.06 | +| person | 86.25 | 94.53 | +| earth | 39.19 | 55.73 | +| door | 60.53 | 74.69 | +| table | 69.83 | 81.72 | +| mountain | 60.62 | 73.85 | +| plant | 56.6 | 64.33 | +| curtain | 78.35 | 85.43 | +| chair | 68.06 | 78.54 | +| car | 88.52 | 94.69 | +| water | 63.76 | 81.75 | +| painting | 77.12 | 90.87 | +| sofa | 81.71 | 94.88 | +| shelf | 50.66 | 68.4 | +| house | 54.63 | 69.31 | +| sea | 59.75 | 66.97 | +| mirror | 78.71 | 85.32 | +| rug | 68.36 | 75.79 | +| field | 36.16 | 61.7 | +| armchair | 59.56 | 70.42 | +| seat | 68.0 | 88.09 | +| fence | 47.47 | 61.11 | +| desk | 58.87 | 79.56 | +| rock | 50.46 | 76.37 | +| wardrobe | 55.91 | 73.35 | +| lamp | 76.16 | 86.49 | +| bathtub | 86.36 | 88.55 | +| railing | 45.62 | 61.53 | +| cushion | 71.37 | 79.14 | +| base | 35.17 | 50.4 | +| box | 40.48 | 52.44 | +| column | 59.59 | 74.26 | +| signboard | 42.24 | 58.31 | +| chest of drawers | 45.28 | 67.44 | +| counter | 40.11 | 60.04 | +| sand | 49.77 | 76.53 | +| sink | 81.78 | 87.97 | +| skyscraper | 46.59 | 58.81 | +| fireplace | 76.56 | 93.32 | +| refrigerator | 85.8 | 94.08 | +| grandstand | 51.14 | 84.45 | +| path | 32.94 | 44.28 | +| stairs | 37.14 | 45.56 | +| runway | 74.63 | 98.26 | +| case | 57.32 | 71.67 | +| pool table | 94.61 | 98.44 | +| pillow | 69.31 | 82.67 | +| screen door | 81.21 | 83.88 | +| stairway | 44.45 | 53.5 | +| river | 11.27 | 20.96 | +| bridge | 67.74 | 82.57 | +| bookcase | 46.71 | 67.51 | +| blind | 43.84 | 47.24 | +| coffee table | 59.65 | 90.61 | +| toilet | 90.24 | 92.74 | +| flower | 43.81 | 60.69 | +| book | 54.45 | 80.1 | +| hill | 8.04 | 16.28 | +| bench | 57.58 | 63.75 | +| countertop | 66.47 | 86.15 | +| stove | 87.76 | 94.91 | +| palm | 52.91 | 76.9 | +| kitchen island | 60.81 | 85.32 | +| computer | 78.85 | 91.37 | +| swivel chair | 51.87 | 81.74 | +| boat | 81.92 | 90.57 | +| bar | 63.9 | 85.26 | +| arcade machine | 78.88 | 82.96 | +| hovel | 13.32 | 14.21 | +| bus | 92.36 | 97.12 | +| towel | 81.01 | 86.74 | +| light | 63.67 | 75.17 | +| truck | 49.94 | 60.97 | +| tower | 36.07 | 62.83 | +| chandelier | 74.7 | 88.52 | +| awning | 39.22 | 47.03 | +| streetlight | 40.31 | 54.15 | +| booth | 34.98 | 44.31 | +| television receiver | 81.58 | 88.58 | +| airplane | 89.15 | 96.16 | +| dirt track | 10.49 | 51.76 | +| apparel | 64.71 | 83.84 | +| pole | 24.14 | 39.64 | +| land | 3.93 | 6.66 | +| bannister | 21.1 | 26.84 | +| escalator | 66.52 | 83.0 | +| ottoman | 46.1 | 58.57 | +| bottle | 43.47 | 55.99 | +| buffet | 55.29 | 69.59 | +| poster | 32.61 | 41.28 | +| stage | 22.31 | 47.37 | +| van | 50.71 | 67.53 | +| ship | 84.65 | 87.52 | +| fountain | 33.55 | 34.32 | +| conveyer belt | 79.21 | 93.8 | +| canopy | 52.81 | 72.45 | +| washer | 85.49 | 90.83 | +| plaything | 39.25 | 51.92 | +| swimming pool | 53.36 | 77.05 | +| stool | 46.47 | 73.59 | +| barrel | 72.74 | 89.63 | +| basket | 43.95 | 62.37 | +| waterfall | 49.05 | 64.95 | +| tent | 94.46 | 98.47 | +| bag | 27.37 | 32.17 | +| minibike | 76.5 | 90.05 | +| cradle | 90.06 | 97.55 | +| oven | 71.29 | 81.67 | +| ball | 51.92 | 58.77 | +| food | 65.65 | 78.68 | +| step | 19.71 | 25.82 | +| tank | 78.28 | 93.51 | +| trade name | 23.63 | 27.45 | +| microwave | 90.65 | 96.24 | +| pot | 59.79 | 69.09 | +| animal | 61.37 | 62.61 | +| bicycle | 60.31 | 80.6 | +| lake | 40.4 | 76.96 | +| dishwasher | 74.11 | 85.68 | +| screen | 59.12 | 94.74 | +| blanket | 39.87 | 47.16 | +| sculpture | 76.07 | 88.18 | +| hood | 65.09 | 77.54 | +| sconce | 61.03 | 69.45 | +| vase | 47.54 | 64.73 | +| traffic light | 36.68 | 64.31 | +| tray | 20.67 | 26.86 | +| ashcan | 50.48 | 66.01 | +| fan | 71.74 | 84.83 | +| pier | 44.78 | 50.59 | +| crt screen | 2.2 | 3.31 | +| plate | 61.55 | 81.52 | +| monitor | 58.2 | 67.45 | +| bulletin board | 53.06 | 68.65 | +| shower | 8.88 | 9.23 | +| radiator | 69.64 | 81.48 | +| glass | 20.79 | 22.47 | +| clock | 52.1 | 61.75 | +| flag | 72.64 | 82.07 | ++---------------------+-------+-------+ +2024-06-16 23:56:44,581 - mmseg - INFO - Summary: +2024-06-16 23:56:44,582 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.34 | 58.35 | 71.19 | ++-------+-------+-------+ +2024-06-16 23:56:44,582 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-16 23:56:44,583 - mmseg - INFO - Iter(val) [250] aAcc: 0.8634, mIoU: 0.5835, mAcc: 0.7119, IoU.wall: 0.8268, IoU.building: 0.8593, IoU.sky: 0.9489, IoU.floor: 0.8548, IoU.tree: 0.7806, IoU.ceiling: 0.8722, IoU.road: 0.8667, IoU.bed : 0.9278, IoU.windowpane: 0.6630, IoU.grass: 0.6619, IoU.cabinet: 0.6760, IoU.sidewalk: 0.7134, IoU.person: 0.8625, IoU.earth: 0.3919, IoU.door: 0.6053, IoU.table: 0.6983, IoU.mountain: 0.6062, IoU.plant: 0.5660, IoU.curtain: 0.7835, IoU.chair: 0.6806, IoU.car: 0.8852, IoU.water: 0.6376, IoU.painting: 0.7712, IoU.sofa: 0.8171, IoU.shelf: 0.5066, IoU.house: 0.5463, IoU.sea: 0.5975, IoU.mirror: 0.7871, IoU.rug: 0.6836, IoU.field: 0.3616, IoU.armchair: 0.5956, IoU.seat: 0.6800, IoU.fence: 0.4747, IoU.desk: 0.5887, IoU.rock: 0.5046, IoU.wardrobe: 0.5591, IoU.lamp: 0.7616, IoU.bathtub: 0.8636, IoU.railing: 0.4562, IoU.cushion: 0.7137, IoU.base: 0.3517, IoU.box: 0.4048, IoU.column: 0.5959, IoU.signboard: 0.4224, IoU.chest of drawers: 0.4528, IoU.counter: 0.4011, IoU.sand: 0.4977, IoU.sink: 0.8178, IoU.skyscraper: 0.4659, IoU.fireplace: 0.7656, IoU.refrigerator: 0.8580, IoU.grandstand: 0.5114, IoU.path: 0.3294, IoU.stairs: 0.3714, IoU.runway: 0.7463, IoU.case: 0.5732, IoU.pool table: 0.9461, IoU.pillow: 0.6931, IoU.screen door: 0.8121, IoU.stairway: 0.4445, IoU.river: 0.1127, IoU.bridge: 0.6774, IoU.bookcase: 0.4671, IoU.blind: 0.4384, IoU.coffee table: 0.5965, IoU.toilet: 0.9024, IoU.flower: 0.4381, IoU.book: 0.5445, IoU.hill: 0.0804, IoU.bench: 0.5758, IoU.countertop: 0.6647, IoU.stove: 0.8776, IoU.palm: 0.5291, IoU.kitchen island: 0.6081, IoU.computer: 0.7885, IoU.swivel chair: 0.5187, IoU.boat: 0.8192, IoU.bar: 0.6390, IoU.arcade machine: 0.7888, IoU.hovel: 0.1332, IoU.bus: 0.9236, IoU.towel: 0.8101, IoU.light: 0.6367, IoU.truck: 0.4994, IoU.tower: 0.3607, IoU.chandelier: 0.7470, IoU.awning: 0.3922, IoU.streetlight: 0.4031, IoU.booth: 0.3498, IoU.television receiver: 0.8158, IoU.airplane: 0.8915, IoU.dirt track: 0.1049, IoU.apparel: 0.6471, IoU.pole: 0.2414, IoU.land: 0.0393, IoU.bannister: 0.2110, IoU.escalator: 0.6652, IoU.ottoman: 0.4610, IoU.bottle: 0.4347, IoU.buffet: 0.5529, IoU.poster: 0.3261, IoU.stage: 0.2231, IoU.van: 0.5071, IoU.ship: 0.8465, IoU.fountain: 0.3355, IoU.conveyer belt: 0.7921, IoU.canopy: 0.5281, IoU.washer: 0.8549, IoU.plaything: 0.3925, IoU.swimming pool: 0.5336, IoU.stool: 0.4647, IoU.barrel: 0.7274, IoU.basket: 0.4395, IoU.waterfall: 0.4905, IoU.tent: 0.9446, IoU.bag: 0.2737, IoU.minibike: 0.7650, IoU.cradle: 0.9006, IoU.oven: 0.7129, IoU.ball: 0.5192, IoU.food: 0.6565, IoU.step: 0.1971, IoU.tank: 0.7828, IoU.trade name: 0.2363, IoU.microwave: 0.9065, IoU.pot: 0.5979, IoU.animal: 0.6137, IoU.bicycle: 0.6031, IoU.lake: 0.4040, IoU.dishwasher: 0.7411, IoU.screen: 0.5912, IoU.blanket: 0.3987, IoU.sculpture: 0.7607, IoU.hood: 0.6509, IoU.sconce: 0.6103, IoU.vase: 0.4754, IoU.traffic light: 0.3668, IoU.tray: 0.2067, IoU.ashcan: 0.5048, IoU.fan: 0.7174, IoU.pier: 0.4478, IoU.crt screen: 0.0220, IoU.plate: 0.6155, IoU.monitor: 0.5820, IoU.bulletin board: 0.5306, IoU.shower: 0.0888, IoU.radiator: 0.6964, IoU.glass: 0.2079, IoU.clock: 0.5210, IoU.flag: 0.7264, Acc.wall: 0.9080, Acc.building: 0.9314, Acc.sky: 0.9732, Acc.floor: 0.9204, Acc.tree: 0.9046, Acc.ceiling: 0.9375, Acc.road: 0.9163, Acc.bed : 0.9708, Acc.windowpane: 0.8311, Acc.grass: 0.7634, Acc.cabinet: 0.7664, Acc.sidewalk: 0.8506, Acc.person: 0.9453, Acc.earth: 0.5573, Acc.door: 0.7469, Acc.table: 0.8172, Acc.mountain: 0.7385, Acc.plant: 0.6433, Acc.curtain: 0.8543, Acc.chair: 0.7854, Acc.car: 0.9469, Acc.water: 0.8175, Acc.painting: 0.9087, Acc.sofa: 0.9488, Acc.shelf: 0.6840, Acc.house: 0.6931, Acc.sea: 0.6697, Acc.mirror: 0.8532, Acc.rug: 0.7579, Acc.field: 0.6170, Acc.armchair: 0.7042, Acc.seat: 0.8809, Acc.fence: 0.6111, Acc.desk: 0.7956, Acc.rock: 0.7637, Acc.wardrobe: 0.7335, Acc.lamp: 0.8649, Acc.bathtub: 0.8855, Acc.railing: 0.6153, Acc.cushion: 0.7914, Acc.base: 0.5040, Acc.box: 0.5244, Acc.column: 0.7426, Acc.signboard: 0.5831, Acc.chest of drawers: 0.6744, Acc.counter: 0.6004, Acc.sand: 0.7653, Acc.sink: 0.8797, Acc.skyscraper: 0.5881, Acc.fireplace: 0.9332, Acc.refrigerator: 0.9408, Acc.grandstand: 0.8445, Acc.path: 0.4428, Acc.stairs: 0.4556, Acc.runway: 0.9826, Acc.case: 0.7167, Acc.pool table: 0.9844, Acc.pillow: 0.8267, Acc.screen door: 0.8388, Acc.stairway: 0.5350, Acc.river: 0.2096, Acc.bridge: 0.8257, Acc.bookcase: 0.6751, Acc.blind: 0.4724, Acc.coffee table: 0.9061, Acc.toilet: 0.9274, Acc.flower: 0.6069, Acc.book: 0.8010, Acc.hill: 0.1628, Acc.bench: 0.6375, Acc.countertop: 0.8615, Acc.stove: 0.9491, Acc.palm: 0.7690, Acc.kitchen island: 0.8532, Acc.computer: 0.9137, Acc.swivel chair: 0.8174, Acc.boat: 0.9057, Acc.bar: 0.8526, Acc.arcade machine: 0.8296, Acc.hovel: 0.1421, Acc.bus: 0.9712, Acc.towel: 0.8674, Acc.light: 0.7517, Acc.truck: 0.6097, Acc.tower: 0.6283, Acc.chandelier: 0.8852, Acc.awning: 0.4703, Acc.streetlight: 0.5415, Acc.booth: 0.4431, Acc.television receiver: 0.8858, Acc.airplane: 0.9616, Acc.dirt track: 0.5176, Acc.apparel: 0.8384, Acc.pole: 0.3964, Acc.land: 0.0666, Acc.bannister: 0.2684, Acc.escalator: 0.8300, Acc.ottoman: 0.5857, Acc.bottle: 0.5599, Acc.buffet: 0.6959, Acc.poster: 0.4128, Acc.stage: 0.4737, Acc.van: 0.6753, Acc.ship: 0.8752, Acc.fountain: 0.3432, Acc.conveyer belt: 0.9380, Acc.canopy: 0.7245, Acc.washer: 0.9083, Acc.plaything: 0.5192, Acc.swimming pool: 0.7705, Acc.stool: 0.7359, Acc.barrel: 0.8963, Acc.basket: 0.6237, Acc.waterfall: 0.6495, Acc.tent: 0.9847, Acc.bag: 0.3217, Acc.minibike: 0.9005, Acc.cradle: 0.9755, Acc.oven: 0.8167, Acc.ball: 0.5877, Acc.food: 0.7868, Acc.step: 0.2582, Acc.tank: 0.9351, Acc.trade name: 0.2745, Acc.microwave: 0.9624, Acc.pot: 0.6909, Acc.animal: 0.6261, Acc.bicycle: 0.8060, Acc.lake: 0.7696, Acc.dishwasher: 0.8568, Acc.screen: 0.9474, Acc.blanket: 0.4716, Acc.sculpture: 0.8818, Acc.hood: 0.7754, Acc.sconce: 0.6945, Acc.vase: 0.6473, Acc.traffic light: 0.6431, Acc.tray: 0.2686, Acc.ashcan: 0.6601, Acc.fan: 0.8483, Acc.pier: 0.5059, Acc.crt screen: 0.0331, Acc.plate: 0.8152, Acc.monitor: 0.6745, Acc.bulletin board: 0.6865, Acc.shower: 0.0923, Acc.radiator: 0.8148, Acc.glass: 0.2247, Acc.clock: 0.6175, Acc.flag: 0.8207 +2024-06-16 23:58:06,156 - mmseg - INFO - Iter [54050/80000] lr: 1.298e-05, eta: 12:44:29, time: 3.537, data_time: 1.922, memory: 71384, decode.loss_ce: 0.1522, decode.acc_seg: 93.3377, aux.loss_ce: 0.0638, aux.acc_seg: 93.0556, loss: 0.2161 +2024-06-16 23:59:27,315 - mmseg - INFO - Iter [54100/80000] lr: 1.295e-05, eta: 12:42:57, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1658, decode.acc_seg: 92.7538, aux.loss_ce: 0.0699, aux.acc_seg: 92.3844, loss: 0.2357 +2024-06-17 00:00:48,287 - mmseg - INFO - Iter [54150/80000] lr: 1.293e-05, eta: 12:41:25, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1584, decode.acc_seg: 92.9575, aux.loss_ce: 0.0671, aux.acc_seg: 92.5279, loss: 0.2255 +2024-06-17 00:02:09,435 - mmseg - INFO - Iter [54200/80000] lr: 1.290e-05, eta: 12:39:53, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1523, decode.acc_seg: 93.3959, aux.loss_ce: 0.0648, aux.acc_seg: 93.0123, loss: 0.2171 +2024-06-17 00:03:30,497 - mmseg - INFO - Iter [54250/80000] lr: 1.288e-05, eta: 12:38:21, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1613, decode.acc_seg: 93.0232, aux.loss_ce: 0.0681, aux.acc_seg: 92.6515, loss: 0.2294 +2024-06-17 00:04:51,541 - mmseg - INFO - Iter [54300/80000] lr: 1.285e-05, eta: 12:36:50, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1610, decode.acc_seg: 93.1980, aux.loss_ce: 0.0678, aux.acc_seg: 92.8316, loss: 0.2288 +2024-06-17 00:06:15,240 - mmseg - INFO - Iter [54350/80000] lr: 1.283e-05, eta: 12:35:19, time: 1.674, data_time: 0.063, memory: 71384, decode.loss_ce: 0.1456, decode.acc_seg: 93.6124, aux.loss_ce: 0.0619, aux.acc_seg: 93.2551, loss: 0.2074 +2024-06-17 00:07:36,336 - mmseg - INFO - Iter [54400/80000] lr: 1.280e-05, eta: 12:33:47, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1618, decode.acc_seg: 93.1182, aux.loss_ce: 0.0684, aux.acc_seg: 92.7476, loss: 0.2302 +2024-06-17 00:08:57,483 - mmseg - INFO - Iter [54450/80000] lr: 1.278e-05, eta: 12:32:16, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1574, decode.acc_seg: 93.2024, aux.loss_ce: 0.0662, aux.acc_seg: 92.8694, loss: 0.2236 +2024-06-17 00:10:18,457 - mmseg - INFO - Iter [54500/80000] lr: 1.275e-05, eta: 12:30:44, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1553, decode.acc_seg: 93.1233, aux.loss_ce: 0.0659, aux.acc_seg: 92.7155, loss: 0.2212 +2024-06-17 00:11:39,545 - mmseg - INFO - Iter [54550/80000] lr: 1.273e-05, eta: 12:29:12, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1581, decode.acc_seg: 93.1765, aux.loss_ce: 0.0670, aux.acc_seg: 92.7577, loss: 0.2251 +2024-06-17 00:13:00,545 - mmseg - INFO - Iter [54600/80000] lr: 1.270e-05, eta: 12:27:40, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1550, decode.acc_seg: 93.3326, aux.loss_ce: 0.0653, aux.acc_seg: 92.9550, loss: 0.2203 +2024-06-17 00:14:21,521 - mmseg - INFO - Iter [54650/80000] lr: 1.268e-05, eta: 12:26:09, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1483, decode.acc_seg: 93.4012, aux.loss_ce: 0.0630, aux.acc_seg: 93.0617, loss: 0.2114 +2024-06-17 00:15:42,573 - mmseg - INFO - Iter [54700/80000] lr: 1.265e-05, eta: 12:24:37, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1549, decode.acc_seg: 93.2731, aux.loss_ce: 0.0660, aux.acc_seg: 92.8700, loss: 0.2208 +2024-06-17 00:17:03,707 - mmseg - INFO - Iter [54750/80000] lr: 1.263e-05, eta: 12:23:05, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1523, decode.acc_seg: 93.2566, aux.loss_ce: 0.0643, aux.acc_seg: 93.0282, loss: 0.2166 +2024-06-17 00:18:24,749 - mmseg - INFO - Iter [54800/80000] lr: 1.260e-05, eta: 12:21:34, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1536, decode.acc_seg: 93.2337, aux.loss_ce: 0.0649, aux.acc_seg: 92.8682, loss: 0.2185 +2024-06-17 00:19:45,840 - mmseg - INFO - Iter [54850/80000] lr: 1.258e-05, eta: 12:20:02, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1502, decode.acc_seg: 93.3199, aux.loss_ce: 0.0634, aux.acc_seg: 92.9498, loss: 0.2135 +2024-06-17 00:21:06,865 - mmseg - INFO - Iter [54900/80000] lr: 1.255e-05, eta: 12:18:31, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1452, decode.acc_seg: 93.6152, aux.loss_ce: 0.0618, aux.acc_seg: 93.2034, loss: 0.2070 +2024-06-17 00:22:27,901 - mmseg - INFO - Iter [54950/80000] lr: 1.253e-05, eta: 12:16:59, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1585, decode.acc_seg: 93.1356, aux.loss_ce: 0.0669, aux.acc_seg: 92.7734, loss: 0.2254 +2024-06-17 00:23:48,945 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 00:23:48,946 - mmseg - INFO - Iter [55000/80000] lr: 1.250e-05, eta: 12:15:27, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1508, decode.acc_seg: 93.3994, aux.loss_ce: 0.0643, aux.acc_seg: 92.9401, loss: 0.2150 +2024-06-17 00:25:28,461 - mmseg - INFO - per class results: +2024-06-17 00:25:28,467 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.29 | 89.9 | +| building | 85.31 | 92.68 | +| sky | 94.83 | 97.38 | +| floor | 85.55 | 92.35 | +| tree | 77.86 | 90.16 | +| ceiling | 87.45 | 94.05 | +| road | 86.44 | 91.69 | +| bed | 92.78 | 96.75 | +| windowpane | 68.94 | 81.54 | +| grass | 69.48 | 81.81 | +| cabinet | 67.06 | 76.72 | +| sidewalk | 70.23 | 83.75 | +| person | 85.59 | 95.67 | +| earth | 38.53 | 50.41 | +| door | 60.72 | 75.01 | +| table | 69.83 | 79.79 | +| mountain | 60.91 | 75.11 | +| plant | 57.03 | 67.43 | +| curtain | 80.01 | 90.51 | +| chair | 68.93 | 80.64 | +| car | 88.16 | 93.95 | +| water | 62.18 | 77.01 | +| painting | 76.53 | 90.79 | +| sofa | 83.59 | 90.96 | +| shelf | 51.34 | 68.55 | +| house | 52.7 | 68.27 | +| sea | 70.64 | 81.64 | +| mirror | 77.37 | 83.73 | +| rug | 68.45 | 76.99 | +| field | 34.99 | 60.52 | +| armchair | 63.27 | 79.83 | +| seat | 64.83 | 88.94 | +| fence | 47.72 | 62.52 | +| desk | 58.79 | 77.88 | +| rock | 50.21 | 79.37 | +| wardrobe | 53.97 | 77.07 | +| lamp | 76.89 | 87.67 | +| bathtub | 86.22 | 88.14 | +| railing | 43.52 | 62.47 | +| cushion | 72.48 | 83.39 | +| base | 36.29 | 53.73 | +| box | 39.3 | 49.77 | +| column | 57.06 | 68.6 | +| signboard | 42.04 | 56.6 | +| chest of drawers | 40.91 | 68.91 | +| counter | 41.12 | 54.53 | +| sand | 52.14 | 81.41 | +| sink | 80.64 | 84.92 | +| skyscraper | 45.37 | 59.4 | +| fireplace | 72.54 | 95.7 | +| refrigerator | 85.67 | 93.91 | +| grandstand | 51.56 | 77.74 | +| path | 32.2 | 44.61 | +| stairs | 29.39 | 33.88 | +| runway | 75.08 | 96.45 | +| case | 63.06 | 83.98 | +| pool table | 95.1 | 97.71 | +| pillow | 70.01 | 81.63 | +| screen door | 84.98 | 88.03 | +| stairway | 38.7 | 65.92 | +| river | 13.24 | 30.3 | +| bridge | 73.4 | 89.79 | +| bookcase | 51.19 | 61.12 | +| blind | 53.2 | 66.77 | +| coffee table | 61.18 | 86.37 | +| toilet | 89.77 | 93.13 | +| flower | 47.34 | 60.61 | +| book | 56.81 | 81.08 | +| hill | 7.04 | 13.44 | +| bench | 56.52 | 61.96 | +| countertop | 65.89 | 84.3 | +| stove | 87.42 | 92.19 | +| palm | 53.01 | 83.16 | +| kitchen island | 59.75 | 86.31 | +| computer | 78.94 | 91.44 | +| swivel chair | 49.15 | 75.52 | +| boat | 80.61 | 90.46 | +| bar | 66.32 | 83.19 | +| arcade machine | 78.6 | 83.64 | +| hovel | 19.87 | 21.15 | +| bus | 92.73 | 96.81 | +| towel | 81.9 | 89.62 | +| light | 62.77 | 70.27 | +| truck | 49.62 | 63.11 | +| tower | 35.83 | 64.66 | +| chandelier | 74.96 | 88.18 | +| awning | 41.55 | 55.62 | +| streetlight | 39.49 | 54.41 | +| booth | 39.5 | 53.79 | +| television receiver | 83.35 | 89.79 | +| airplane | 88.59 | 96.32 | +| dirt track | 10.84 | 33.42 | +| apparel | 62.14 | 89.33 | +| pole | 25.09 | 36.25 | +| land | 4.81 | 7.78 | +| bannister | 21.7 | 28.53 | +| escalator | 63.96 | 83.8 | +| ottoman | 49.8 | 65.75 | +| bottle | 42.99 | 66.04 | +| buffet | 45.63 | 53.72 | +| poster | 30.77 | 37.54 | +| stage | 22.98 | 35.66 | +| van | 47.37 | 75.36 | +| ship | 73.24 | 75.23 | +| fountain | 38.92 | 41.07 | +| conveyer belt | 82.81 | 93.13 | +| canopy | 53.53 | 74.05 | +| washer | 87.87 | 93.91 | +| plaything | 44.26 | 69.36 | +| swimming pool | 52.42 | 78.01 | +| stool | 52.8 | 68.0 | +| barrel | 72.24 | 92.0 | +| basket | 40.78 | 58.28 | +| waterfall | 58.14 | 76.54 | +| tent | 90.44 | 98.23 | +| bag | 28.14 | 33.36 | +| minibike | 75.27 | 91.99 | +| cradle | 85.98 | 98.17 | +| oven | 64.56 | 79.99 | +| ball | 54.19 | 60.69 | +| food | 63.19 | 72.09 | +| step | 13.92 | 17.05 | +| tank | 72.54 | 89.57 | +| trade name | 22.16 | 26.78 | +| microwave | 89.49 | 96.95 | +| pot | 60.44 | 71.63 | +| animal | 60.78 | 61.76 | +| bicycle | 60.15 | 78.3 | +| lake | 60.84 | 63.69 | +| dishwasher | 75.08 | 85.24 | +| screen | 60.48 | 96.53 | +| blanket | 36.1 | 42.21 | +| sculpture | 73.97 | 89.23 | +| hood | 63.67 | 76.18 | +| sconce | 62.49 | 72.57 | +| vase | 46.29 | 62.08 | +| traffic light | 36.15 | 64.19 | +| tray | 25.16 | 35.71 | +| ashcan | 50.25 | 67.27 | +| fan | 69.2 | 80.53 | +| pier | 47.21 | 53.36 | +| crt screen | 2.76 | 3.33 | +| plate | 61.98 | 82.8 | +| monitor | 62.59 | 77.21 | +| bulletin board | 59.52 | 73.55 | +| shower | 10.11 | 13.16 | +| radiator | 68.99 | 84.28 | +| glass | 20.9 | 22.38 | +| clock | 52.14 | 63.47 | +| flag | 71.98 | 82.67 | ++---------------------+-------+-------+ +2024-06-17 00:25:28,468 - mmseg - INFO - Summary: +2024-06-17 00:25:28,468 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.35 | 58.6 | 71.73 | ++-------+------+-------+ +2024-06-17 00:25:28,469 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 00:25:28,469 - mmseg - INFO - Iter(val) [250] aAcc: 0.8635, mIoU: 0.5860, mAcc: 0.7173, IoU.wall: 0.8229, IoU.building: 0.8531, IoU.sky: 0.9483, IoU.floor: 0.8555, IoU.tree: 0.7786, IoU.ceiling: 0.8745, IoU.road: 0.8644, IoU.bed : 0.9278, IoU.windowpane: 0.6894, IoU.grass: 0.6948, IoU.cabinet: 0.6706, IoU.sidewalk: 0.7023, IoU.person: 0.8559, IoU.earth: 0.3853, IoU.door: 0.6072, IoU.table: 0.6983, IoU.mountain: 0.6091, IoU.plant: 0.5703, IoU.curtain: 0.8001, IoU.chair: 0.6893, IoU.car: 0.8816, IoU.water: 0.6218, IoU.painting: 0.7653, IoU.sofa: 0.8359, IoU.shelf: 0.5134, IoU.house: 0.5270, IoU.sea: 0.7064, IoU.mirror: 0.7737, IoU.rug: 0.6845, IoU.field: 0.3499, IoU.armchair: 0.6327, IoU.seat: 0.6483, IoU.fence: 0.4772, IoU.desk: 0.5879, IoU.rock: 0.5021, IoU.wardrobe: 0.5397, IoU.lamp: 0.7689, IoU.bathtub: 0.8622, IoU.railing: 0.4352, IoU.cushion: 0.7248, IoU.base: 0.3629, IoU.box: 0.3930, IoU.column: 0.5706, IoU.signboard: 0.4204, IoU.chest of drawers: 0.4091, IoU.counter: 0.4112, IoU.sand: 0.5214, IoU.sink: 0.8064, IoU.skyscraper: 0.4537, IoU.fireplace: 0.7254, IoU.refrigerator: 0.8567, IoU.grandstand: 0.5156, IoU.path: 0.3220, IoU.stairs: 0.2939, IoU.runway: 0.7508, IoU.case: 0.6306, IoU.pool table: 0.9510, IoU.pillow: 0.7001, IoU.screen door: 0.8498, IoU.stairway: 0.3870, IoU.river: 0.1324, IoU.bridge: 0.7340, IoU.bookcase: 0.5119, IoU.blind: 0.5320, IoU.coffee table: 0.6118, IoU.toilet: 0.8977, IoU.flower: 0.4734, IoU.book: 0.5681, IoU.hill: 0.0704, IoU.bench: 0.5652, IoU.countertop: 0.6589, IoU.stove: 0.8742, IoU.palm: 0.5301, IoU.kitchen island: 0.5975, IoU.computer: 0.7894, IoU.swivel chair: 0.4915, IoU.boat: 0.8061, IoU.bar: 0.6632, IoU.arcade machine: 0.7860, IoU.hovel: 0.1987, IoU.bus: 0.9273, IoU.towel: 0.8190, IoU.light: 0.6277, IoU.truck: 0.4962, IoU.tower: 0.3583, IoU.chandelier: 0.7496, IoU.awning: 0.4155, IoU.streetlight: 0.3949, IoU.booth: 0.3950, IoU.television receiver: 0.8335, IoU.airplane: 0.8859, IoU.dirt track: 0.1084, IoU.apparel: 0.6214, IoU.pole: 0.2509, IoU.land: 0.0481, IoU.bannister: 0.2170, IoU.escalator: 0.6396, IoU.ottoman: 0.4980, IoU.bottle: 0.4299, IoU.buffet: 0.4563, IoU.poster: 0.3077, IoU.stage: 0.2298, IoU.van: 0.4737, IoU.ship: 0.7324, IoU.fountain: 0.3892, IoU.conveyer belt: 0.8281, IoU.canopy: 0.5353, IoU.washer: 0.8787, IoU.plaything: 0.4426, IoU.swimming pool: 0.5242, IoU.stool: 0.5280, IoU.barrel: 0.7224, IoU.basket: 0.4078, IoU.waterfall: 0.5814, IoU.tent: 0.9044, IoU.bag: 0.2814, IoU.minibike: 0.7527, IoU.cradle: 0.8598, IoU.oven: 0.6456, IoU.ball: 0.5419, IoU.food: 0.6319, IoU.step: 0.1392, IoU.tank: 0.7254, IoU.trade name: 0.2216, IoU.microwave: 0.8949, IoU.pot: 0.6044, IoU.animal: 0.6078, IoU.bicycle: 0.6015, IoU.lake: 0.6084, IoU.dishwasher: 0.7508, IoU.screen: 0.6048, IoU.blanket: 0.3610, IoU.sculpture: 0.7397, IoU.hood: 0.6367, IoU.sconce: 0.6249, IoU.vase: 0.4629, IoU.traffic light: 0.3615, IoU.tray: 0.2516, IoU.ashcan: 0.5025, IoU.fan: 0.6920, IoU.pier: 0.4721, IoU.crt screen: 0.0276, IoU.plate: 0.6198, IoU.monitor: 0.6259, IoU.bulletin board: 0.5952, IoU.shower: 0.1011, IoU.radiator: 0.6899, IoU.glass: 0.2090, IoU.clock: 0.5214, IoU.flag: 0.7198, Acc.wall: 0.8990, Acc.building: 0.9268, Acc.sky: 0.9738, Acc.floor: 0.9235, Acc.tree: 0.9016, Acc.ceiling: 0.9405, Acc.road: 0.9169, Acc.bed : 0.9675, Acc.windowpane: 0.8154, Acc.grass: 0.8181, Acc.cabinet: 0.7672, Acc.sidewalk: 0.8375, Acc.person: 0.9567, Acc.earth: 0.5041, Acc.door: 0.7501, Acc.table: 0.7979, Acc.mountain: 0.7511, Acc.plant: 0.6743, Acc.curtain: 0.9051, Acc.chair: 0.8064, Acc.car: 0.9395, Acc.water: 0.7701, Acc.painting: 0.9079, Acc.sofa: 0.9096, Acc.shelf: 0.6855, Acc.house: 0.6827, Acc.sea: 0.8164, Acc.mirror: 0.8373, Acc.rug: 0.7699, Acc.field: 0.6052, Acc.armchair: 0.7983, Acc.seat: 0.8894, Acc.fence: 0.6252, Acc.desk: 0.7788, Acc.rock: 0.7937, Acc.wardrobe: 0.7707, Acc.lamp: 0.8767, Acc.bathtub: 0.8814, Acc.railing: 0.6247, Acc.cushion: 0.8339, Acc.base: 0.5373, Acc.box: 0.4977, Acc.column: 0.6860, Acc.signboard: 0.5660, Acc.chest of drawers: 0.6891, Acc.counter: 0.5453, Acc.sand: 0.8141, Acc.sink: 0.8492, Acc.skyscraper: 0.5940, Acc.fireplace: 0.9570, Acc.refrigerator: 0.9391, Acc.grandstand: 0.7774, Acc.path: 0.4461, Acc.stairs: 0.3388, Acc.runway: 0.9645, Acc.case: 0.8398, Acc.pool table: 0.9771, Acc.pillow: 0.8163, Acc.screen door: 0.8803, Acc.stairway: 0.6592, Acc.river: 0.3030, Acc.bridge: 0.8979, Acc.bookcase: 0.6112, Acc.blind: 0.6677, Acc.coffee table: 0.8637, Acc.toilet: 0.9313, Acc.flower: 0.6061, Acc.book: 0.8108, Acc.hill: 0.1344, Acc.bench: 0.6196, Acc.countertop: 0.8430, Acc.stove: 0.9219, Acc.palm: 0.8316, Acc.kitchen island: 0.8631, Acc.computer: 0.9144, Acc.swivel chair: 0.7552, Acc.boat: 0.9046, Acc.bar: 0.8319, Acc.arcade machine: 0.8364, Acc.hovel: 0.2115, Acc.bus: 0.9681, Acc.towel: 0.8962, Acc.light: 0.7027, Acc.truck: 0.6311, Acc.tower: 0.6466, Acc.chandelier: 0.8818, Acc.awning: 0.5562, Acc.streetlight: 0.5441, Acc.booth: 0.5379, Acc.television receiver: 0.8979, Acc.airplane: 0.9632, Acc.dirt track: 0.3342, Acc.apparel: 0.8933, Acc.pole: 0.3625, Acc.land: 0.0778, Acc.bannister: 0.2853, Acc.escalator: 0.8380, Acc.ottoman: 0.6575, Acc.bottle: 0.6604, Acc.buffet: 0.5372, Acc.poster: 0.3754, Acc.stage: 0.3566, Acc.van: 0.7536, Acc.ship: 0.7523, Acc.fountain: 0.4107, Acc.conveyer belt: 0.9313, Acc.canopy: 0.7405, Acc.washer: 0.9391, Acc.plaything: 0.6936, Acc.swimming pool: 0.7801, Acc.stool: 0.6800, Acc.barrel: 0.9200, Acc.basket: 0.5828, Acc.waterfall: 0.7654, Acc.tent: 0.9823, Acc.bag: 0.3336, Acc.minibike: 0.9199, Acc.cradle: 0.9817, Acc.oven: 0.7999, Acc.ball: 0.6069, Acc.food: 0.7209, Acc.step: 0.1705, Acc.tank: 0.8957, Acc.trade name: 0.2678, Acc.microwave: 0.9695, Acc.pot: 0.7163, Acc.animal: 0.6176, Acc.bicycle: 0.7830, Acc.lake: 0.6369, Acc.dishwasher: 0.8524, Acc.screen: 0.9653, Acc.blanket: 0.4221, Acc.sculpture: 0.8923, Acc.hood: 0.7618, Acc.sconce: 0.7257, Acc.vase: 0.6208, Acc.traffic light: 0.6419, Acc.tray: 0.3571, Acc.ashcan: 0.6727, Acc.fan: 0.8053, Acc.pier: 0.5336, Acc.crt screen: 0.0333, Acc.plate: 0.8280, Acc.monitor: 0.7721, Acc.bulletin board: 0.7355, Acc.shower: 0.1316, Acc.radiator: 0.8428, Acc.glass: 0.2238, Acc.clock: 0.6347, Acc.flag: 0.8267 +2024-06-17 00:26:49,977 - mmseg - INFO - Iter [55050/80000] lr: 1.248e-05, eta: 12:14:41, time: 3.621, data_time: 2.007, memory: 71384, decode.loss_ce: 0.1465, decode.acc_seg: 93.6335, aux.loss_ce: 0.0617, aux.acc_seg: 93.3259, loss: 0.2083 +2024-06-17 00:28:11,030 - mmseg - INFO - Iter [55100/80000] lr: 1.245e-05, eta: 12:13:10, time: 1.621, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1542, decode.acc_seg: 93.1887, aux.loss_ce: 0.0649, aux.acc_seg: 92.8147, loss: 0.2192 +2024-06-17 00:29:32,302 - mmseg - INFO - Iter [55150/80000] lr: 1.243e-05, eta: 12:11:38, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1484, decode.acc_seg: 93.4739, aux.loss_ce: 0.0630, aux.acc_seg: 93.0599, loss: 0.2114 +2024-06-17 00:30:53,407 - mmseg - INFO - Iter [55200/80000] lr: 1.240e-05, eta: 12:10:07, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1491, decode.acc_seg: 93.5409, aux.loss_ce: 0.0633, aux.acc_seg: 93.1216, loss: 0.2124 +2024-06-17 00:32:14,537 - mmseg - INFO - Iter [55250/80000] lr: 1.238e-05, eta: 12:08:35, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1600, decode.acc_seg: 92.9624, aux.loss_ce: 0.0679, aux.acc_seg: 92.5692, loss: 0.2279 +2024-06-17 00:33:35,622 - mmseg - INFO - Iter [55300/80000] lr: 1.235e-05, eta: 12:07:03, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1565, decode.acc_seg: 92.9858, aux.loss_ce: 0.0661, aux.acc_seg: 92.6328, loss: 0.2226 +2024-06-17 00:34:56,546 - mmseg - INFO - Iter [55350/80000] lr: 1.233e-05, eta: 12:05:32, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1579, decode.acc_seg: 93.1141, aux.loss_ce: 0.0665, aux.acc_seg: 92.7400, loss: 0.2244 +2024-06-17 00:36:17,593 - mmseg - INFO - Iter [55400/80000] lr: 1.230e-05, eta: 12:04:00, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1477, decode.acc_seg: 93.4584, aux.loss_ce: 0.0625, aux.acc_seg: 93.0891, loss: 0.2101 +2024-06-17 00:37:38,654 - mmseg - INFO - Iter [55450/80000] lr: 1.228e-05, eta: 12:02:29, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1622, decode.acc_seg: 93.2296, aux.loss_ce: 0.0680, aux.acc_seg: 92.8447, loss: 0.2303 +2024-06-17 00:38:59,684 - mmseg - INFO - Iter [55500/80000] lr: 1.225e-05, eta: 12:00:57, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1602, decode.acc_seg: 92.9269, aux.loss_ce: 0.0676, aux.acc_seg: 92.5789, loss: 0.2277 +2024-06-17 00:40:20,736 - mmseg - INFO - Iter [55550/80000] lr: 1.223e-05, eta: 11:59:26, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1511, decode.acc_seg: 93.3143, aux.loss_ce: 0.0642, aux.acc_seg: 92.9235, loss: 0.2153 +2024-06-17 00:41:44,380 - mmseg - INFO - Iter [55600/80000] lr: 1.220e-05, eta: 11:57:56, time: 1.673, data_time: 0.056, memory: 71384, decode.loss_ce: 0.1486, decode.acc_seg: 93.4930, aux.loss_ce: 0.0631, aux.acc_seg: 93.1188, loss: 0.2117 +2024-06-17 00:43:05,497 - mmseg - INFO - Iter [55650/80000] lr: 1.218e-05, eta: 11:56:24, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1582, decode.acc_seg: 93.0729, aux.loss_ce: 0.0669, aux.acc_seg: 92.7070, loss: 0.2251 +2024-06-17 00:44:26,686 - mmseg - INFO - Iter [55700/80000] lr: 1.215e-05, eta: 11:54:53, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1422, decode.acc_seg: 93.5401, aux.loss_ce: 0.0606, aux.acc_seg: 93.1482, loss: 0.2028 +2024-06-17 00:45:47,667 - mmseg - INFO - Iter [55750/80000] lr: 1.213e-05, eta: 11:53:21, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1578, decode.acc_seg: 93.0972, aux.loss_ce: 0.0670, aux.acc_seg: 92.6876, loss: 0.2249 +2024-06-17 00:47:08,710 - mmseg - INFO - Iter [55800/80000] lr: 1.210e-05, eta: 11:51:50, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1384, decode.acc_seg: 93.8757, aux.loss_ce: 0.0587, aux.acc_seg: 93.4972, loss: 0.1971 +2024-06-17 00:48:29,707 - mmseg - INFO - Iter [55850/80000] lr: 1.208e-05, eta: 11:50:19, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1570, decode.acc_seg: 93.0158, aux.loss_ce: 0.0663, aux.acc_seg: 92.5823, loss: 0.2233 +2024-06-17 00:49:50,804 - mmseg - INFO - Iter [55900/80000] lr: 1.205e-05, eta: 11:48:47, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1631, decode.acc_seg: 93.0690, aux.loss_ce: 0.0691, aux.acc_seg: 92.6462, loss: 0.2321 +2024-06-17 00:51:12,069 - mmseg - INFO - Iter [55950/80000] lr: 1.203e-05, eta: 11:47:16, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1519, decode.acc_seg: 93.3823, aux.loss_ce: 0.0645, aux.acc_seg: 92.9417, loss: 0.2164 +2024-06-17 00:52:33,113 - mmseg - INFO - Saving checkpoint at 56000 iterations +2024-06-17 00:54:02,045 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 00:54:02,045 - mmseg - INFO - Iter [56000/80000] lr: 1.200e-05, eta: 11:46:23, time: 3.399, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1461, decode.acc_seg: 93.6363, aux.loss_ce: 0.0618, aux.acc_seg: 93.3055, loss: 0.2078 +2024-06-17 00:55:39,095 - mmseg - INFO - per class results: +2024-06-17 00:55:39,101 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.32 | 89.51 | +| building | 85.69 | 93.17 | +| sky | 94.84 | 97.94 | +| floor | 85.34 | 92.34 | +| tree | 78.04 | 89.09 | +| ceiling | 87.85 | 94.82 | +| road | 86.64 | 90.69 | +| bed | 92.57 | 97.13 | +| windowpane | 67.34 | 83.31 | +| grass | 70.7 | 83.67 | +| cabinet | 66.14 | 76.35 | +| sidewalk | 69.5 | 83.73 | +| person | 86.07 | 94.84 | +| earth | 41.72 | 57.19 | +| door | 59.82 | 79.07 | +| table | 69.41 | 81.47 | +| mountain | 61.75 | 73.16 | +| plant | 58.26 | 69.33 | +| curtain | 79.66 | 87.98 | +| chair | 68.93 | 81.0 | +| car | 88.32 | 94.56 | +| water | 62.4 | 77.22 | +| painting | 75.35 | 91.11 | +| sofa | 83.2 | 90.96 | +| shelf | 52.97 | 70.42 | +| house | 57.38 | 73.97 | +| sea | 71.33 | 81.35 | +| mirror | 77.66 | 84.56 | +| rug | 68.01 | 75.68 | +| field | 43.39 | 64.77 | +| armchair | 61.92 | 80.47 | +| seat | 66.03 | 89.37 | +| fence | 48.46 | 61.13 | +| desk | 61.72 | 79.87 | +| rock | 53.99 | 82.3 | +| wardrobe | 54.18 | 75.2 | +| lamp | 76.8 | 87.1 | +| bathtub | 84.84 | 86.87 | +| railing | 44.32 | 59.61 | +| cushion | 72.73 | 84.57 | +| base | 37.26 | 46.78 | +| box | 38.72 | 50.29 | +| column | 57.63 | 63.79 | +| signboard | 42.64 | 61.05 | +| chest of drawers | 42.34 | 63.45 | +| counter | 35.07 | 40.67 | +| sand | 56.02 | 87.93 | +| sink | 80.84 | 86.28 | +| skyscraper | 46.98 | 58.42 | +| fireplace | 72.45 | 95.53 | +| refrigerator | 86.88 | 94.6 | +| grandstand | 52.38 | 84.15 | +| path | 31.31 | 46.78 | +| stairs | 29.28 | 33.44 | +| runway | 75.22 | 97.6 | +| case | 62.2 | 82.58 | +| pool table | 94.39 | 97.99 | +| pillow | 68.9 | 78.31 | +| screen door | 70.9 | 73.58 | +| stairway | 43.06 | 62.72 | +| river | 10.46 | 23.29 | +| bridge | 70.46 | 82.27 | +| bookcase | 50.57 | 57.76 | +| blind | 42.91 | 46.16 | +| coffee table | 61.28 | 88.31 | +| toilet | 90.04 | 93.16 | +| flower | 48.73 | 57.39 | +| book | 55.73 | 82.0 | +| hill | 4.85 | 8.63 | +| bench | 56.61 | 64.95 | +| countertop | 64.88 | 83.19 | +| stove | 87.39 | 93.05 | +| palm | 55.69 | 85.16 | +| kitchen island | 50.87 | 91.57 | +| computer | 78.61 | 90.94 | +| swivel chair | 48.38 | 70.41 | +| boat | 80.71 | 90.93 | +| bar | 62.98 | 90.08 | +| arcade machine | 72.46 | 76.88 | +| hovel | 29.15 | 31.46 | +| bus | 92.08 | 96.46 | +| towel | 81.41 | 89.31 | +| light | 62.49 | 70.65 | +| truck | 50.1 | 64.21 | +| tower | 24.96 | 42.6 | +| chandelier | 75.07 | 88.64 | +| awning | 40.32 | 48.58 | +| streetlight | 38.61 | 51.16 | +| booth | 37.81 | 59.61 | +| television receiver | 80.17 | 88.53 | +| airplane | 86.9 | 94.61 | +| dirt track | 6.78 | 30.35 | +| apparel | 59.85 | 88.84 | +| pole | 24.51 | 33.85 | +| land | 3.57 | 5.69 | +| bannister | 20.59 | 27.49 | +| escalator | 60.47 | 78.4 | +| ottoman | 50.07 | 68.02 | +| bottle | 43.86 | 61.02 | +| buffet | 46.22 | 53.75 | +| poster | 32.6 | 42.51 | +| stage | 27.4 | 47.02 | +| van | 48.05 | 71.77 | +| ship | 76.6 | 80.68 | +| fountain | 35.98 | 36.68 | +| conveyer belt | 79.87 | 93.74 | +| canopy | 52.86 | 72.69 | +| washer | 84.3 | 89.5 | +| plaything | 39.53 | 54.16 | +| swimming pool | 53.31 | 77.43 | +| stool | 52.39 | 67.6 | +| barrel | 76.68 | 93.24 | +| basket | 42.57 | 62.63 | +| waterfall | 53.27 | 69.91 | +| tent | 95.87 | 97.99 | +| bag | 27.13 | 32.04 | +| minibike | 76.07 | 91.1 | +| cradle | 87.19 | 97.57 | +| oven | 70.16 | 81.39 | +| ball | 48.75 | 53.65 | +| food | 65.08 | 79.16 | +| step | 14.26 | 16.66 | +| tank | 73.42 | 85.22 | +| trade name | 21.52 | 25.91 | +| microwave | 90.4 | 96.33 | +| pot | 61.1 | 74.24 | +| animal | 61.87 | 63.13 | +| bicycle | 58.8 | 74.05 | +| lake | 52.1 | 63.78 | +| dishwasher | 73.65 | 85.18 | +| screen | 60.52 | 95.38 | +| blanket | 38.84 | 44.88 | +| sculpture | 77.0 | 88.45 | +| hood | 63.66 | 76.0 | +| sconce | 63.52 | 73.37 | +| vase | 49.1 | 66.42 | +| traffic light | 36.49 | 69.28 | +| tray | 28.08 | 39.5 | +| ashcan | 51.35 | 67.61 | +| fan | 69.76 | 84.9 | +| pier | 39.42 | 48.54 | +| crt screen | 2.43 | 3.41 | +| plate | 61.46 | 79.98 | +| monitor | 63.61 | 80.6 | +| bulletin board | 56.39 | 67.1 | +| shower | 10.68 | 16.33 | +| radiator | 69.12 | 82.66 | +| glass | 22.86 | 25.75 | +| clock | 51.68 | 65.38 | +| flag | 70.86 | 81.16 | ++---------------------+-------+-------+ +2024-06-17 00:55:39,101 - mmseg - INFO - Summary: +2024-06-17 00:55:39,101 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.48 | 58.34 | 71.17 | ++-------+-------+-------+ +2024-06-17 00:55:39,102 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 00:55:39,102 - mmseg - INFO - Iter(val) [250] aAcc: 0.8648, mIoU: 0.5834, mAcc: 0.7117, IoU.wall: 0.8232, IoU.building: 0.8569, IoU.sky: 0.9484, IoU.floor: 0.8534, IoU.tree: 0.7804, IoU.ceiling: 0.8785, IoU.road: 0.8664, IoU.bed : 0.9257, IoU.windowpane: 0.6734, IoU.grass: 0.7070, IoU.cabinet: 0.6614, IoU.sidewalk: 0.6950, IoU.person: 0.8607, IoU.earth: 0.4172, IoU.door: 0.5982, IoU.table: 0.6941, IoU.mountain: 0.6175, IoU.plant: 0.5826, IoU.curtain: 0.7966, IoU.chair: 0.6893, IoU.car: 0.8832, IoU.water: 0.6240, IoU.painting: 0.7535, IoU.sofa: 0.8320, IoU.shelf: 0.5297, IoU.house: 0.5738, IoU.sea: 0.7133, IoU.mirror: 0.7766, IoU.rug: 0.6801, IoU.field: 0.4339, IoU.armchair: 0.6192, IoU.seat: 0.6603, IoU.fence: 0.4846, IoU.desk: 0.6172, IoU.rock: 0.5399, IoU.wardrobe: 0.5418, IoU.lamp: 0.7680, IoU.bathtub: 0.8484, IoU.railing: 0.4432, IoU.cushion: 0.7273, IoU.base: 0.3726, IoU.box: 0.3872, IoU.column: 0.5763, IoU.signboard: 0.4264, IoU.chest of drawers: 0.4234, IoU.counter: 0.3507, IoU.sand: 0.5602, IoU.sink: 0.8084, IoU.skyscraper: 0.4698, IoU.fireplace: 0.7245, IoU.refrigerator: 0.8688, IoU.grandstand: 0.5238, IoU.path: 0.3131, IoU.stairs: 0.2928, IoU.runway: 0.7522, IoU.case: 0.6220, IoU.pool table: 0.9439, IoU.pillow: 0.6890, IoU.screen door: 0.7090, IoU.stairway: 0.4306, IoU.river: 0.1046, IoU.bridge: 0.7046, IoU.bookcase: 0.5057, IoU.blind: 0.4291, IoU.coffee table: 0.6128, IoU.toilet: 0.9004, IoU.flower: 0.4873, IoU.book: 0.5573, IoU.hill: 0.0485, IoU.bench: 0.5661, IoU.countertop: 0.6488, IoU.stove: 0.8739, IoU.palm: 0.5569, IoU.kitchen island: 0.5087, IoU.computer: 0.7861, IoU.swivel chair: 0.4838, IoU.boat: 0.8071, IoU.bar: 0.6298, IoU.arcade machine: 0.7246, IoU.hovel: 0.2915, IoU.bus: 0.9208, IoU.towel: 0.8141, IoU.light: 0.6249, IoU.truck: 0.5010, IoU.tower: 0.2496, IoU.chandelier: 0.7507, IoU.awning: 0.4032, IoU.streetlight: 0.3861, IoU.booth: 0.3781, IoU.television receiver: 0.8017, IoU.airplane: 0.8690, IoU.dirt track: 0.0678, IoU.apparel: 0.5985, IoU.pole: 0.2451, IoU.land: 0.0357, IoU.bannister: 0.2059, IoU.escalator: 0.6047, IoU.ottoman: 0.5007, IoU.bottle: 0.4386, IoU.buffet: 0.4622, IoU.poster: 0.3260, IoU.stage: 0.2740, IoU.van: 0.4805, IoU.ship: 0.7660, IoU.fountain: 0.3598, IoU.conveyer belt: 0.7987, IoU.canopy: 0.5286, IoU.washer: 0.8430, IoU.plaything: 0.3953, IoU.swimming pool: 0.5331, IoU.stool: 0.5239, IoU.barrel: 0.7668, IoU.basket: 0.4257, IoU.waterfall: 0.5327, IoU.tent: 0.9587, IoU.bag: 0.2713, IoU.minibike: 0.7607, IoU.cradle: 0.8719, IoU.oven: 0.7016, IoU.ball: 0.4875, IoU.food: 0.6508, IoU.step: 0.1426, IoU.tank: 0.7342, IoU.trade name: 0.2152, IoU.microwave: 0.9040, IoU.pot: 0.6110, IoU.animal: 0.6187, IoU.bicycle: 0.5880, IoU.lake: 0.5210, IoU.dishwasher: 0.7365, IoU.screen: 0.6052, IoU.blanket: 0.3884, IoU.sculpture: 0.7700, IoU.hood: 0.6366, IoU.sconce: 0.6352, IoU.vase: 0.4910, IoU.traffic light: 0.3649, IoU.tray: 0.2808, IoU.ashcan: 0.5135, IoU.fan: 0.6976, IoU.pier: 0.3942, IoU.crt screen: 0.0243, IoU.plate: 0.6146, IoU.monitor: 0.6361, IoU.bulletin board: 0.5639, IoU.shower: 0.1068, IoU.radiator: 0.6912, IoU.glass: 0.2286, IoU.clock: 0.5168, IoU.flag: 0.7086, Acc.wall: 0.8951, Acc.building: 0.9317, Acc.sky: 0.9794, Acc.floor: 0.9234, Acc.tree: 0.8909, Acc.ceiling: 0.9482, Acc.road: 0.9069, Acc.bed : 0.9713, Acc.windowpane: 0.8331, Acc.grass: 0.8367, Acc.cabinet: 0.7635, Acc.sidewalk: 0.8373, Acc.person: 0.9484, Acc.earth: 0.5719, Acc.door: 0.7907, Acc.table: 0.8147, Acc.mountain: 0.7316, Acc.plant: 0.6933, Acc.curtain: 0.8798, Acc.chair: 0.8100, Acc.car: 0.9456, Acc.water: 0.7722, Acc.painting: 0.9111, Acc.sofa: 0.9096, Acc.shelf: 0.7042, Acc.house: 0.7397, Acc.sea: 0.8135, Acc.mirror: 0.8456, Acc.rug: 0.7568, Acc.field: 0.6477, Acc.armchair: 0.8047, Acc.seat: 0.8937, Acc.fence: 0.6113, Acc.desk: 0.7987, Acc.rock: 0.8230, Acc.wardrobe: 0.7520, Acc.lamp: 0.8710, Acc.bathtub: 0.8687, Acc.railing: 0.5961, Acc.cushion: 0.8457, Acc.base: 0.4678, Acc.box: 0.5029, Acc.column: 0.6379, Acc.signboard: 0.6105, Acc.chest of drawers: 0.6345, Acc.counter: 0.4067, Acc.sand: 0.8793, Acc.sink: 0.8628, Acc.skyscraper: 0.5842, Acc.fireplace: 0.9553, Acc.refrigerator: 0.9460, Acc.grandstand: 0.8415, Acc.path: 0.4678, Acc.stairs: 0.3344, Acc.runway: 0.9760, Acc.case: 0.8258, Acc.pool table: 0.9799, Acc.pillow: 0.7831, Acc.screen door: 0.7358, Acc.stairway: 0.6272, Acc.river: 0.2329, Acc.bridge: 0.8227, Acc.bookcase: 0.5776, Acc.blind: 0.4616, Acc.coffee table: 0.8831, Acc.toilet: 0.9316, Acc.flower: 0.5739, Acc.book: 0.8200, Acc.hill: 0.0863, Acc.bench: 0.6495, Acc.countertop: 0.8319, Acc.stove: 0.9305, Acc.palm: 0.8516, Acc.kitchen island: 0.9157, Acc.computer: 0.9094, Acc.swivel chair: 0.7041, Acc.boat: 0.9093, Acc.bar: 0.9008, Acc.arcade machine: 0.7688, Acc.hovel: 0.3146, Acc.bus: 0.9646, Acc.towel: 0.8931, Acc.light: 0.7065, Acc.truck: 0.6421, Acc.tower: 0.4260, Acc.chandelier: 0.8864, Acc.awning: 0.4858, Acc.streetlight: 0.5116, Acc.booth: 0.5961, Acc.television receiver: 0.8853, Acc.airplane: 0.9461, Acc.dirt track: 0.3035, Acc.apparel: 0.8884, Acc.pole: 0.3385, Acc.land: 0.0569, Acc.bannister: 0.2749, Acc.escalator: 0.7840, Acc.ottoman: 0.6802, Acc.bottle: 0.6102, Acc.buffet: 0.5375, Acc.poster: 0.4251, Acc.stage: 0.4702, Acc.van: 0.7177, Acc.ship: 0.8068, Acc.fountain: 0.3668, Acc.conveyer belt: 0.9374, Acc.canopy: 0.7269, Acc.washer: 0.8950, Acc.plaything: 0.5416, Acc.swimming pool: 0.7743, Acc.stool: 0.6760, Acc.barrel: 0.9324, Acc.basket: 0.6263, Acc.waterfall: 0.6991, Acc.tent: 0.9799, Acc.bag: 0.3204, Acc.minibike: 0.9110, Acc.cradle: 0.9757, Acc.oven: 0.8139, Acc.ball: 0.5365, Acc.food: 0.7916, Acc.step: 0.1666, Acc.tank: 0.8522, Acc.trade name: 0.2591, Acc.microwave: 0.9633, Acc.pot: 0.7424, Acc.animal: 0.6313, Acc.bicycle: 0.7405, Acc.lake: 0.6378, Acc.dishwasher: 0.8518, Acc.screen: 0.9538, Acc.blanket: 0.4488, Acc.sculpture: 0.8845, Acc.hood: 0.7600, Acc.sconce: 0.7337, Acc.vase: 0.6642, Acc.traffic light: 0.6928, Acc.tray: 0.3950, Acc.ashcan: 0.6761, Acc.fan: 0.8490, Acc.pier: 0.4854, Acc.crt screen: 0.0341, Acc.plate: 0.7998, Acc.monitor: 0.8060, Acc.bulletin board: 0.6710, Acc.shower: 0.1633, Acc.radiator: 0.8266, Acc.glass: 0.2575, Acc.clock: 0.6538, Acc.flag: 0.8116 +2024-06-17 00:57:00,791 - mmseg - INFO - Iter [56050/80000] lr: 1.198e-05, eta: 11:45:33, time: 3.575, data_time: 1.959, memory: 71384, decode.loss_ce: 0.1522, decode.acc_seg: 93.4970, aux.loss_ce: 0.0641, aux.acc_seg: 93.1182, loss: 0.2163 +2024-06-17 00:58:21,920 - mmseg - INFO - Iter [56100/80000] lr: 1.195e-05, eta: 11:44:02, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1529, decode.acc_seg: 93.3659, aux.loss_ce: 0.0645, aux.acc_seg: 93.0063, loss: 0.2174 +2024-06-17 00:59:42,984 - mmseg - INFO - Iter [56150/80000] lr: 1.193e-05, eta: 11:42:30, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1458, decode.acc_seg: 93.4091, aux.loss_ce: 0.0620, aux.acc_seg: 93.0149, loss: 0.2078 +2024-06-17 01:01:03,970 - mmseg - INFO - Iter [56200/80000] lr: 1.190e-05, eta: 11:40:59, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1542, decode.acc_seg: 93.3571, aux.loss_ce: 0.0655, aux.acc_seg: 92.9350, loss: 0.2196 +2024-06-17 01:02:25,011 - mmseg - INFO - Iter [56250/80000] lr: 1.188e-05, eta: 11:39:27, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1431, decode.acc_seg: 93.8709, aux.loss_ce: 0.0612, aux.acc_seg: 93.5086, loss: 0.2043 +2024-06-17 01:03:46,021 - mmseg - INFO - Iter [56300/80000] lr: 1.185e-05, eta: 11:37:56, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1507, decode.acc_seg: 93.4893, aux.loss_ce: 0.0637, aux.acc_seg: 93.1034, loss: 0.2144 +2024-06-17 01:05:07,087 - mmseg - INFO - Iter [56350/80000] lr: 1.183e-05, eta: 11:36:25, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1500, decode.acc_seg: 93.5047, aux.loss_ce: 0.0637, aux.acc_seg: 93.1101, loss: 0.2137 +2024-06-17 01:06:28,226 - mmseg - INFO - Iter [56400/80000] lr: 1.180e-05, eta: 11:34:53, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1561, decode.acc_seg: 93.3933, aux.loss_ce: 0.0661, aux.acc_seg: 93.0239, loss: 0.2221 +2024-06-17 01:07:49,282 - mmseg - INFO - Iter [56450/80000] lr: 1.178e-05, eta: 11:33:22, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1653, decode.acc_seg: 93.0952, aux.loss_ce: 0.0693, aux.acc_seg: 92.7049, loss: 0.2347 +2024-06-17 01:09:10,353 - mmseg - INFO - Iter [56500/80000] lr: 1.175e-05, eta: 11:31:50, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1548, decode.acc_seg: 93.3182, aux.loss_ce: 0.0659, aux.acc_seg: 92.9260, loss: 0.2207 +2024-06-17 01:10:31,515 - mmseg - INFO - Iter [56550/80000] lr: 1.173e-05, eta: 11:30:19, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1625, decode.acc_seg: 93.3383, aux.loss_ce: 0.0680, aux.acc_seg: 92.9659, loss: 0.2305 +2024-06-17 01:11:52,521 - mmseg - INFO - Iter [56600/80000] lr: 1.170e-05, eta: 11:28:48, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1437, decode.acc_seg: 93.8183, aux.loss_ce: 0.0611, aux.acc_seg: 93.4490, loss: 0.2048 +2024-06-17 01:13:13,542 - mmseg - INFO - Iter [56650/80000] lr: 1.168e-05, eta: 11:27:17, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1497, decode.acc_seg: 93.3258, aux.loss_ce: 0.0636, aux.acc_seg: 92.9591, loss: 0.2133 +2024-06-17 01:14:34,675 - mmseg - INFO - Iter [56700/80000] lr: 1.165e-05, eta: 11:25:45, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1507, decode.acc_seg: 93.4569, aux.loss_ce: 0.0640, aux.acc_seg: 93.0520, loss: 0.2147 +2024-06-17 01:15:55,790 - mmseg - INFO - Iter [56750/80000] lr: 1.163e-05, eta: 11:24:14, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1485, decode.acc_seg: 93.5231, aux.loss_ce: 0.0628, aux.acc_seg: 93.1797, loss: 0.2113 +2024-06-17 01:17:16,821 - mmseg - INFO - Iter [56800/80000] lr: 1.160e-05, eta: 11:22:43, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1640, decode.acc_seg: 93.1517, aux.loss_ce: 0.0690, aux.acc_seg: 92.7686, loss: 0.2330 +2024-06-17 01:18:40,860 - mmseg - INFO - Iter [56850/80000] lr: 1.158e-05, eta: 11:21:13, time: 1.681, data_time: 0.068, memory: 71384, decode.loss_ce: 0.1520, decode.acc_seg: 93.4707, aux.loss_ce: 0.0646, aux.acc_seg: 93.0879, loss: 0.2166 +2024-06-17 01:20:02,193 - mmseg - INFO - Iter [56900/80000] lr: 1.155e-05, eta: 11:19:42, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1427, decode.acc_seg: 93.7093, aux.loss_ce: 0.0607, aux.acc_seg: 93.2910, loss: 0.2033 +2024-06-17 01:21:23,228 - mmseg - INFO - Iter [56950/80000] lr: 1.153e-05, eta: 11:18:10, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1456, decode.acc_seg: 93.5482, aux.loss_ce: 0.0617, aux.acc_seg: 93.2074, loss: 0.2073 +2024-06-17 01:22:44,231 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 01:22:44,232 - mmseg - INFO - Iter [57000/80000] lr: 1.150e-05, eta: 11:16:39, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1441, decode.acc_seg: 93.7665, aux.loss_ce: 0.0622, aux.acc_seg: 93.2650, loss: 0.2064 +2024-06-17 01:24:22,348 - mmseg - INFO - per class results: +2024-06-17 01:24:22,354 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.86 | 89.9 | +| building | 85.44 | 92.76 | +| sky | 94.93 | 97.32 | +| floor | 85.73 | 92.28 | +| tree | 77.81 | 90.55 | +| ceiling | 87.92 | 95.06 | +| road | 86.46 | 91.07 | +| bed | 92.65 | 96.82 | +| windowpane | 67.3 | 83.16 | +| grass | 69.47 | 83.59 | +| cabinet | 67.11 | 77.23 | +| sidewalk | 70.36 | 85.24 | +| person | 86.56 | 94.51 | +| earth | 41.98 | 56.26 | +| door | 60.77 | 76.8 | +| table | 69.13 | 80.94 | +| mountain | 62.64 | 74.44 | +| plant | 57.36 | 67.08 | +| curtain | 78.79 | 88.12 | +| chair | 69.67 | 82.21 | +| car | 88.54 | 94.15 | +| water | 63.46 | 78.4 | +| painting | 75.94 | 89.72 | +| sofa | 82.86 | 90.82 | +| shelf | 55.0 | 74.95 | +| house | 55.51 | 76.16 | +| sea | 70.94 | 83.15 | +| mirror | 79.28 | 86.28 | +| rug | 68.56 | 76.59 | +| field | 37.08 | 58.32 | +| armchair | 62.02 | 79.54 | +| seat | 67.87 | 88.86 | +| fence | 50.62 | 64.3 | +| desk | 60.75 | 75.48 | +| rock | 53.07 | 82.56 | +| wardrobe | 57.24 | 76.89 | +| lamp | 77.17 | 87.56 | +| bathtub | 85.63 | 88.17 | +| railing | 45.15 | 61.47 | +| cushion | 72.03 | 84.69 | +| base | 37.1 | 55.58 | +| box | 36.69 | 43.53 | +| column | 60.99 | 73.0 | +| signboard | 41.49 | 55.47 | +| chest of drawers | 38.79 | 59.07 | +| counter | 37.76 | 50.82 | +| sand | 53.0 | 88.55 | +| sink | 81.65 | 87.7 | +| skyscraper | 45.73 | 59.76 | +| fireplace | 72.35 | 95.73 | +| refrigerator | 86.42 | 95.45 | +| grandstand | 50.34 | 81.91 | +| path | 28.66 | 36.01 | +| stairs | 34.79 | 42.86 | +| runway | 72.03 | 93.06 | +| case | 61.91 | 83.98 | +| pool table | 94.42 | 98.09 | +| pillow | 68.53 | 77.77 | +| screen door | 74.54 | 76.81 | +| stairway | 42.63 | 58.99 | +| river | 9.81 | 20.63 | +| bridge | 76.23 | 87.2 | +| bookcase | 56.41 | 67.23 | +| blind | 42.49 | 44.95 | +| coffee table | 60.39 | 87.85 | +| toilet | 90.6 | 94.0 | +| flower | 46.42 | 54.83 | +| book | 57.47 | 78.45 | +| hill | 6.73 | 11.33 | +| bench | 56.17 | 63.63 | +| countertop | 64.19 | 83.27 | +| stove | 87.51 | 92.68 | +| palm | 56.04 | 81.73 | +| kitchen island | 56.78 | 87.29 | +| computer | 78.72 | 92.56 | +| swivel chair | 48.51 | 64.73 | +| boat | 79.53 | 91.34 | +| bar | 62.22 | 87.52 | +| arcade machine | 72.24 | 76.41 | +| hovel | 24.8 | 27.75 | +| bus | 92.61 | 97.2 | +| towel | 78.5 | 89.73 | +| light | 63.12 | 72.26 | +| truck | 49.24 | 62.25 | +| tower | 34.63 | 60.24 | +| chandelier | 75.6 | 86.18 | +| awning | 42.43 | 52.86 | +| streetlight | 40.06 | 54.44 | +| booth | 39.44 | 53.64 | +| television receiver | 81.9 | 89.48 | +| airplane | 87.82 | 95.93 | +| dirt track | 8.95 | 43.39 | +| apparel | 61.12 | 79.18 | +| pole | 30.22 | 49.03 | +| land | 3.87 | 5.6 | +| bannister | 22.12 | 28.89 | +| escalator | 63.3 | 80.32 | +| ottoman | 51.7 | 68.57 | +| bottle | 46.27 | 61.05 | +| buffet | 47.1 | 55.48 | +| poster | 30.22 | 34.32 | +| stage | 23.44 | 47.23 | +| van | 49.31 | 73.3 | +| ship | 81.76 | 94.9 | +| fountain | 37.62 | 37.98 | +| conveyer belt | 79.31 | 93.62 | +| canopy | 33.55 | 43.95 | +| washer | 87.61 | 93.21 | +| plaything | 41.65 | 62.53 | +| swimming pool | 52.89 | 76.73 | +| stool | 51.9 | 70.52 | +| barrel | 71.88 | 92.07 | +| basket | 42.25 | 62.73 | +| waterfall | 50.68 | 65.21 | +| tent | 95.86 | 96.98 | +| bag | 29.41 | 34.62 | +| minibike | 77.59 | 89.56 | +| cradle | 87.23 | 97.7 | +| oven | 64.09 | 77.21 | +| ball | 55.61 | 65.7 | +| food | 63.29 | 75.48 | +| step | 14.88 | 17.1 | +| tank | 81.04 | 92.83 | +| trade name | 21.45 | 25.48 | +| microwave | 88.48 | 96.93 | +| pot | 59.7 | 69.85 | +| animal | 59.04 | 60.45 | +| bicycle | 59.48 | 79.73 | +| lake | 46.41 | 63.8 | +| dishwasher | 75.78 | 84.13 | +| screen | 54.88 | 89.36 | +| blanket | 40.95 | 47.23 | +| sculpture | 74.55 | 88.05 | +| hood | 62.68 | 72.87 | +| sconce | 62.54 | 71.17 | +| vase | 45.89 | 67.66 | +| traffic light | 40.95 | 64.11 | +| tray | 26.0 | 32.87 | +| ashcan | 50.95 | 67.09 | +| fan | 69.74 | 86.2 | +| pier | 42.32 | 46.33 | +| crt screen | 2.59 | 3.37 | +| plate | 64.43 | 79.15 | +| monitor | 69.18 | 86.25 | +| bulletin board | 58.7 | 70.85 | +| shower | 12.12 | 12.53 | +| radiator | 69.31 | 81.53 | +| glass | 20.92 | 22.2 | +| clock | 51.39 | 60.46 | +| flag | 71.07 | 80.9 | ++---------------------+-------+-------+ +2024-06-17 01:24:22,354 - mmseg - INFO - Summary: +2024-06-17 01:24:22,355 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 86.57 | 58.54 | 71.3 | ++-------+-------+------+ +2024-06-17 01:24:22,355 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 01:24:22,356 - mmseg - INFO - Iter(val) [250] aAcc: 0.8657, mIoU: 0.5854, mAcc: 0.7130, IoU.wall: 0.8286, IoU.building: 0.8544, IoU.sky: 0.9493, IoU.floor: 0.8573, IoU.tree: 0.7781, IoU.ceiling: 0.8792, IoU.road: 0.8646, IoU.bed : 0.9265, IoU.windowpane: 0.6730, IoU.grass: 0.6947, IoU.cabinet: 0.6711, IoU.sidewalk: 0.7036, IoU.person: 0.8656, IoU.earth: 0.4198, IoU.door: 0.6077, IoU.table: 0.6913, IoU.mountain: 0.6264, IoU.plant: 0.5736, IoU.curtain: 0.7879, IoU.chair: 0.6967, IoU.car: 0.8854, IoU.water: 0.6346, IoU.painting: 0.7594, IoU.sofa: 0.8286, IoU.shelf: 0.5500, IoU.house: 0.5551, IoU.sea: 0.7094, IoU.mirror: 0.7928, IoU.rug: 0.6856, IoU.field: 0.3708, IoU.armchair: 0.6202, IoU.seat: 0.6787, IoU.fence: 0.5062, IoU.desk: 0.6075, IoU.rock: 0.5307, IoU.wardrobe: 0.5724, IoU.lamp: 0.7717, IoU.bathtub: 0.8563, IoU.railing: 0.4515, IoU.cushion: 0.7203, IoU.base: 0.3710, IoU.box: 0.3669, IoU.column: 0.6099, IoU.signboard: 0.4149, IoU.chest of drawers: 0.3879, IoU.counter: 0.3776, IoU.sand: 0.5300, IoU.sink: 0.8165, IoU.skyscraper: 0.4573, IoU.fireplace: 0.7235, IoU.refrigerator: 0.8642, IoU.grandstand: 0.5034, IoU.path: 0.2866, IoU.stairs: 0.3479, IoU.runway: 0.7203, IoU.case: 0.6191, IoU.pool table: 0.9442, IoU.pillow: 0.6853, IoU.screen door: 0.7454, IoU.stairway: 0.4263, IoU.river: 0.0981, IoU.bridge: 0.7623, IoU.bookcase: 0.5641, IoU.blind: 0.4249, IoU.coffee table: 0.6039, IoU.toilet: 0.9060, IoU.flower: 0.4642, IoU.book: 0.5747, IoU.hill: 0.0673, IoU.bench: 0.5617, IoU.countertop: 0.6419, IoU.stove: 0.8751, IoU.palm: 0.5604, IoU.kitchen island: 0.5678, IoU.computer: 0.7872, IoU.swivel chair: 0.4851, IoU.boat: 0.7953, IoU.bar: 0.6222, IoU.arcade machine: 0.7224, IoU.hovel: 0.2480, IoU.bus: 0.9261, IoU.towel: 0.7850, IoU.light: 0.6312, IoU.truck: 0.4924, IoU.tower: 0.3463, IoU.chandelier: 0.7560, IoU.awning: 0.4243, IoU.streetlight: 0.4006, IoU.booth: 0.3944, IoU.television receiver: 0.8190, IoU.airplane: 0.8782, IoU.dirt track: 0.0895, IoU.apparel: 0.6112, IoU.pole: 0.3022, IoU.land: 0.0387, IoU.bannister: 0.2212, IoU.escalator: 0.6330, IoU.ottoman: 0.5170, IoU.bottle: 0.4627, IoU.buffet: 0.4710, IoU.poster: 0.3022, IoU.stage: 0.2344, IoU.van: 0.4931, IoU.ship: 0.8176, IoU.fountain: 0.3762, IoU.conveyer belt: 0.7931, IoU.canopy: 0.3355, IoU.washer: 0.8761, IoU.plaything: 0.4165, IoU.swimming pool: 0.5289, IoU.stool: 0.5190, IoU.barrel: 0.7188, IoU.basket: 0.4225, IoU.waterfall: 0.5068, IoU.tent: 0.9586, IoU.bag: 0.2941, IoU.minibike: 0.7759, IoU.cradle: 0.8723, IoU.oven: 0.6409, IoU.ball: 0.5561, IoU.food: 0.6329, IoU.step: 0.1488, IoU.tank: 0.8104, IoU.trade name: 0.2145, IoU.microwave: 0.8848, IoU.pot: 0.5970, IoU.animal: 0.5904, IoU.bicycle: 0.5948, IoU.lake: 0.4641, IoU.dishwasher: 0.7578, IoU.screen: 0.5488, IoU.blanket: 0.4095, IoU.sculpture: 0.7455, IoU.hood: 0.6268, IoU.sconce: 0.6254, IoU.vase: 0.4589, IoU.traffic light: 0.4095, IoU.tray: 0.2600, IoU.ashcan: 0.5095, IoU.fan: 0.6974, IoU.pier: 0.4232, IoU.crt screen: 0.0259, IoU.plate: 0.6443, IoU.monitor: 0.6918, IoU.bulletin board: 0.5870, IoU.shower: 0.1212, IoU.radiator: 0.6931, IoU.glass: 0.2092, IoU.clock: 0.5139, IoU.flag: 0.7107, Acc.wall: 0.8990, Acc.building: 0.9276, Acc.sky: 0.9732, Acc.floor: 0.9228, Acc.tree: 0.9055, Acc.ceiling: 0.9506, Acc.road: 0.9107, Acc.bed : 0.9682, Acc.windowpane: 0.8316, Acc.grass: 0.8359, Acc.cabinet: 0.7723, Acc.sidewalk: 0.8524, Acc.person: 0.9451, Acc.earth: 0.5626, Acc.door: 0.7680, Acc.table: 0.8094, Acc.mountain: 0.7444, Acc.plant: 0.6708, Acc.curtain: 0.8812, Acc.chair: 0.8221, Acc.car: 0.9415, Acc.water: 0.7840, Acc.painting: 0.8972, Acc.sofa: 0.9082, Acc.shelf: 0.7495, Acc.house: 0.7616, Acc.sea: 0.8315, Acc.mirror: 0.8628, Acc.rug: 0.7659, Acc.field: 0.5832, Acc.armchair: 0.7954, Acc.seat: 0.8886, Acc.fence: 0.6430, Acc.desk: 0.7548, Acc.rock: 0.8256, Acc.wardrobe: 0.7689, Acc.lamp: 0.8756, Acc.bathtub: 0.8817, Acc.railing: 0.6147, Acc.cushion: 0.8469, Acc.base: 0.5558, Acc.box: 0.4353, Acc.column: 0.7300, Acc.signboard: 0.5547, Acc.chest of drawers: 0.5907, Acc.counter: 0.5082, Acc.sand: 0.8855, Acc.sink: 0.8770, Acc.skyscraper: 0.5976, Acc.fireplace: 0.9573, Acc.refrigerator: 0.9545, Acc.grandstand: 0.8191, Acc.path: 0.3601, Acc.stairs: 0.4286, Acc.runway: 0.9306, Acc.case: 0.8398, Acc.pool table: 0.9809, Acc.pillow: 0.7777, Acc.screen door: 0.7681, Acc.stairway: 0.5899, Acc.river: 0.2063, Acc.bridge: 0.8720, Acc.bookcase: 0.6723, Acc.blind: 0.4495, Acc.coffee table: 0.8785, Acc.toilet: 0.9400, Acc.flower: 0.5483, Acc.book: 0.7845, Acc.hill: 0.1133, Acc.bench: 0.6363, Acc.countertop: 0.8327, Acc.stove: 0.9268, Acc.palm: 0.8173, Acc.kitchen island: 0.8729, Acc.computer: 0.9256, Acc.swivel chair: 0.6473, Acc.boat: 0.9134, Acc.bar: 0.8752, Acc.arcade machine: 0.7641, Acc.hovel: 0.2775, Acc.bus: 0.9720, Acc.towel: 0.8973, Acc.light: 0.7226, Acc.truck: 0.6225, Acc.tower: 0.6024, Acc.chandelier: 0.8618, Acc.awning: 0.5286, Acc.streetlight: 0.5444, Acc.booth: 0.5364, Acc.television receiver: 0.8948, Acc.airplane: 0.9593, Acc.dirt track: 0.4339, Acc.apparel: 0.7918, Acc.pole: 0.4903, Acc.land: 0.0560, Acc.bannister: 0.2889, Acc.escalator: 0.8032, Acc.ottoman: 0.6857, Acc.bottle: 0.6105, Acc.buffet: 0.5548, Acc.poster: 0.3432, Acc.stage: 0.4723, Acc.van: 0.7330, Acc.ship: 0.9490, Acc.fountain: 0.3798, Acc.conveyer belt: 0.9362, Acc.canopy: 0.4395, Acc.washer: 0.9321, Acc.plaything: 0.6253, Acc.swimming pool: 0.7673, Acc.stool: 0.7052, Acc.barrel: 0.9207, Acc.basket: 0.6273, Acc.waterfall: 0.6521, Acc.tent: 0.9698, Acc.bag: 0.3462, Acc.minibike: 0.8956, Acc.cradle: 0.9770, Acc.oven: 0.7721, Acc.ball: 0.6570, Acc.food: 0.7548, Acc.step: 0.1710, Acc.tank: 0.9283, Acc.trade name: 0.2548, Acc.microwave: 0.9693, Acc.pot: 0.6985, Acc.animal: 0.6045, Acc.bicycle: 0.7973, Acc.lake: 0.6380, Acc.dishwasher: 0.8413, Acc.screen: 0.8936, Acc.blanket: 0.4723, Acc.sculpture: 0.8805, Acc.hood: 0.7287, Acc.sconce: 0.7117, Acc.vase: 0.6766, Acc.traffic light: 0.6411, Acc.tray: 0.3287, Acc.ashcan: 0.6709, Acc.fan: 0.8620, Acc.pier: 0.4633, Acc.crt screen: 0.0337, Acc.plate: 0.7915, Acc.monitor: 0.8625, Acc.bulletin board: 0.7085, Acc.shower: 0.1253, Acc.radiator: 0.8153, Acc.glass: 0.2220, Acc.clock: 0.6046, Acc.flag: 0.8090 +2024-06-17 01:25:43,896 - mmseg - INFO - Iter [57050/80000] lr: 1.148e-05, eta: 11:15:48, time: 3.593, data_time: 1.979, memory: 71384, decode.loss_ce: 0.1477, decode.acc_seg: 93.6865, aux.loss_ce: 0.0629, aux.acc_seg: 93.2928, loss: 0.2107 +2024-06-17 01:27:04,861 - mmseg - INFO - Iter [57100/80000] lr: 1.145e-05, eta: 11:14:16, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1494, decode.acc_seg: 93.4542, aux.loss_ce: 0.0639, aux.acc_seg: 93.0375, loss: 0.2133 +2024-06-17 01:28:25,925 - mmseg - INFO - Iter [57150/80000] lr: 1.143e-05, eta: 11:12:45, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1481, decode.acc_seg: 93.5964, aux.loss_ce: 0.0630, aux.acc_seg: 93.1867, loss: 0.2111 +2024-06-17 01:29:47,043 - mmseg - INFO - Iter [57200/80000] lr: 1.140e-05, eta: 11:11:14, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1480, decode.acc_seg: 93.4489, aux.loss_ce: 0.0626, aux.acc_seg: 93.0526, loss: 0.2106 +2024-06-17 01:31:08,050 - mmseg - INFO - Iter [57250/80000] lr: 1.138e-05, eta: 11:09:43, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1581, decode.acc_seg: 93.3601, aux.loss_ce: 0.0673, aux.acc_seg: 92.9445, loss: 0.2254 +2024-06-17 01:32:29,278 - mmseg - INFO - Iter [57300/80000] lr: 1.135e-05, eta: 11:08:12, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1517, decode.acc_seg: 93.3875, aux.loss_ce: 0.0640, aux.acc_seg: 93.0735, loss: 0.2157 +2024-06-17 01:33:50,274 - mmseg - INFO - Iter [57350/80000] lr: 1.133e-05, eta: 11:06:40, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1382, decode.acc_seg: 93.9335, aux.loss_ce: 0.0590, aux.acc_seg: 93.5201, loss: 0.1972 +2024-06-17 01:35:11,331 - mmseg - INFO - Iter [57400/80000] lr: 1.130e-05, eta: 11:05:09, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1523, decode.acc_seg: 93.4187, aux.loss_ce: 0.0646, aux.acc_seg: 93.0542, loss: 0.2169 +2024-06-17 01:36:32,373 - mmseg - INFO - Iter [57450/80000] lr: 1.128e-05, eta: 11:03:38, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1451, decode.acc_seg: 93.7305, aux.loss_ce: 0.0612, aux.acc_seg: 93.4041, loss: 0.2063 +2024-06-17 01:37:53,422 - mmseg - INFO - Iter [57500/80000] lr: 1.125e-05, eta: 11:02:07, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1428, decode.acc_seg: 93.7991, aux.loss_ce: 0.0609, aux.acc_seg: 93.3921, loss: 0.2036 +2024-06-17 01:39:14,477 - mmseg - INFO - Iter [57550/80000] lr: 1.123e-05, eta: 11:00:36, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1477, decode.acc_seg: 93.6598, aux.loss_ce: 0.0626, aux.acc_seg: 93.3006, loss: 0.2103 +2024-06-17 01:40:35,361 - mmseg - INFO - Iter [57600/80000] lr: 1.120e-05, eta: 10:59:05, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1492, decode.acc_seg: 93.5146, aux.loss_ce: 0.0632, aux.acc_seg: 93.1317, loss: 0.2124 +2024-06-17 01:41:56,362 - mmseg - INFO - Iter [57650/80000] lr: 1.118e-05, eta: 10:57:34, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1440, decode.acc_seg: 93.7104, aux.loss_ce: 0.0615, aux.acc_seg: 93.3051, loss: 0.2055 +2024-06-17 01:43:17,454 - mmseg - INFO - Iter [57700/80000] lr: 1.115e-05, eta: 10:56:03, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1478, decode.acc_seg: 93.5075, aux.loss_ce: 0.0628, aux.acc_seg: 93.1570, loss: 0.2106 +2024-06-17 01:44:38,545 - mmseg - INFO - Iter [57750/80000] lr: 1.113e-05, eta: 10:54:32, time: 1.622, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1545, decode.acc_seg: 93.2430, aux.loss_ce: 0.0656, aux.acc_seg: 92.8549, loss: 0.2201 +2024-06-17 01:45:59,752 - mmseg - INFO - Iter [57800/80000] lr: 1.110e-05, eta: 10:53:01, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1495, decode.acc_seg: 93.3970, aux.loss_ce: 0.0636, aux.acc_seg: 92.9715, loss: 0.2131 +2024-06-17 01:47:20,765 - mmseg - INFO - Iter [57850/80000] lr: 1.108e-05, eta: 10:51:30, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1555, decode.acc_seg: 93.1664, aux.loss_ce: 0.0659, aux.acc_seg: 92.7392, loss: 0.2213 +2024-06-17 01:48:41,778 - mmseg - INFO - Iter [57900/80000] lr: 1.105e-05, eta: 10:49:59, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1485, decode.acc_seg: 93.5535, aux.loss_ce: 0.0631, aux.acc_seg: 93.1553, loss: 0.2116 +2024-06-17 01:50:02,733 - mmseg - INFO - Iter [57950/80000] lr: 1.103e-05, eta: 10:48:28, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1442, decode.acc_seg: 93.6409, aux.loss_ce: 0.0612, aux.acc_seg: 93.3016, loss: 0.2054 +2024-06-17 01:51:23,719 - mmseg - INFO - Saving checkpoint at 58000 iterations +2024-06-17 01:52:49,541 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 01:52:49,541 - mmseg - INFO - Iter [58000/80000] lr: 1.100e-05, eta: 10:47:29, time: 3.336, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1538, decode.acc_seg: 93.3175, aux.loss_ce: 0.0656, aux.acc_seg: 92.8125, loss: 0.2194 +2024-06-17 01:54:26,056 - mmseg - INFO - per class results: +2024-06-17 01:54:26,063 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.67 | 90.07 | +| building | 85.3 | 93.1 | +| sky | 95.09 | 97.4 | +| floor | 85.71 | 92.3 | +| tree | 78.09 | 91.05 | +| ceiling | 87.32 | 94.16 | +| road | 86.9 | 90.72 | +| bed | 92.63 | 97.26 | +| windowpane | 67.09 | 83.75 | +| grass | 69.21 | 82.78 | +| cabinet | 66.65 | 76.97 | +| sidewalk | 71.02 | 85.96 | +| person | 85.88 | 95.05 | +| earth | 42.81 | 57.81 | +| door | 59.45 | 75.15 | +| table | 69.48 | 81.78 | +| mountain | 62.0 | 73.01 | +| plant | 56.65 | 65.31 | +| curtain | 79.08 | 88.86 | +| chair | 68.96 | 79.26 | +| car | 88.46 | 94.55 | +| water | 62.99 | 77.82 | +| painting | 76.31 | 89.56 | +| sofa | 81.69 | 90.12 | +| shelf | 54.01 | 74.71 | +| house | 49.68 | 64.0 | +| sea | 73.04 | 81.81 | +| mirror | 78.61 | 87.39 | +| rug | 68.68 | 78.3 | +| field | 37.31 | 63.22 | +| armchair | 61.12 | 77.27 | +| seat | 67.73 | 88.32 | +| fence | 53.29 | 64.41 | +| desk | 60.62 | 79.2 | +| rock | 52.05 | 78.05 | +| wardrobe | 53.91 | 74.38 | +| lamp | 76.91 | 85.71 | +| bathtub | 86.4 | 89.35 | +| railing | 43.73 | 60.14 | +| cushion | 72.37 | 82.35 | +| base | 36.69 | 57.08 | +| box | 38.4 | 49.47 | +| column | 58.91 | 69.83 | +| signboard | 42.14 | 54.77 | +| chest of drawers | 40.83 | 61.01 | +| counter | 37.43 | 47.61 | +| sand | 57.12 | 87.15 | +| sink | 80.96 | 85.51 | +| skyscraper | 46.46 | 60.08 | +| fireplace | 75.03 | 93.38 | +| refrigerator | 86.47 | 92.89 | +| grandstand | 51.81 | 80.72 | +| path | 31.48 | 42.61 | +| stairs | 35.28 | 45.74 | +| runway | 74.54 | 98.15 | +| case | 63.83 | 85.53 | +| pool table | 94.28 | 97.76 | +| pillow | 68.11 | 77.52 | +| screen door | 72.42 | 74.33 | +| stairway | 43.46 | 55.92 | +| river | 9.59 | 22.04 | +| bridge | 75.09 | 87.64 | +| bookcase | 55.65 | 66.31 | +| blind | 42.08 | 43.31 | +| coffee table | 61.07 | 89.17 | +| toilet | 90.65 | 93.2 | +| flower | 46.38 | 54.4 | +| book | 56.32 | 77.92 | +| hill | 6.03 | 11.14 | +| bench | 54.84 | 61.37 | +| countertop | 66.61 | 85.37 | +| stove | 87.04 | 94.2 | +| palm | 55.49 | 79.8 | +| kitchen island | 59.72 | 84.4 | +| computer | 76.58 | 88.17 | +| swivel chair | 51.13 | 80.93 | +| boat | 76.72 | 88.66 | +| bar | 61.41 | 83.29 | +| arcade machine | 72.69 | 76.58 | +| hovel | 35.87 | 39.46 | +| bus | 92.19 | 97.04 | +| towel | 79.49 | 88.29 | +| light | 64.27 | 74.48 | +| truck | 50.17 | 61.56 | +| tower | 23.16 | 38.7 | +| chandelier | 75.46 | 87.51 | +| awning | 42.03 | 51.32 | +| streetlight | 38.13 | 50.03 | +| booth | 40.67 | 54.39 | +| television receiver | 82.16 | 87.98 | +| airplane | 85.38 | 95.9 | +| dirt track | 1.51 | 7.12 | +| apparel | 63.35 | 89.4 | +| pole | 25.26 | 35.97 | +| land | 3.34 | 4.4 | +| bannister | 20.17 | 28.7 | +| escalator | 51.5 | 64.25 | +| ottoman | 53.28 | 74.07 | +| bottle | 45.46 | 58.39 | +| buffet | 50.06 | 60.34 | +| poster | 31.21 | 36.73 | +| stage | 25.63 | 45.86 | +| van | 50.25 | 69.22 | +| ship | 86.18 | 91.12 | +| fountain | 43.87 | 46.77 | +| conveyer belt | 80.6 | 92.62 | +| canopy | 57.43 | 73.55 | +| washer | 84.06 | 89.03 | +| plaything | 31.56 | 39.61 | +| swimming pool | 54.01 | 78.3 | +| stool | 50.62 | 75.38 | +| barrel | 72.0 | 92.05 | +| basket | 43.21 | 61.83 | +| waterfall | 45.61 | 59.56 | +| tent | 92.79 | 98.05 | +| bag | 26.48 | 31.06 | +| minibike | 77.77 | 89.94 | +| cradle | 88.52 | 97.29 | +| oven | 66.04 | 80.43 | +| ball | 57.79 | 73.2 | +| food | 65.68 | 78.64 | +| step | 15.81 | 17.91 | +| tank | 74.68 | 87.8 | +| trade name | 25.04 | 29.32 | +| microwave | 91.11 | 95.54 | +| pot | 59.67 | 69.0 | +| animal | 58.12 | 59.09 | +| bicycle | 59.24 | 72.67 | +| lake | 42.93 | 63.76 | +| dishwasher | 76.71 | 84.11 | +| screen | 57.19 | 91.73 | +| blanket | 38.72 | 44.92 | +| sculpture | 76.18 | 87.59 | +| hood | 63.67 | 73.99 | +| sconce | 59.8 | 66.97 | +| vase | 46.86 | 63.6 | +| traffic light | 36.19 | 67.59 | +| tray | 25.74 | 35.65 | +| ashcan | 52.01 | 66.96 | +| fan | 70.48 | 80.54 | +| pier | 44.72 | 53.02 | +| crt screen | 2.61 | 3.76 | +| plate | 64.0 | 79.32 | +| monitor | 60.23 | 81.17 | +| bulletin board | 60.13 | 72.02 | +| shower | 5.9 | 12.76 | +| radiator | 70.64 | 78.99 | +| glass | 21.35 | 23.0 | +| clock | 51.35 | 58.28 | +| flag | 71.09 | 78.21 | ++---------------------+-------+-------+ +2024-06-17 01:54:26,063 - mmseg - INFO - Summary: +2024-06-17 01:54:26,063 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.48 | 58.45 | 70.71 | ++-------+-------+-------+ +2024-06-17 01:54:26,064 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 01:54:26,064 - mmseg - INFO - Iter(val) [250] aAcc: 0.8648, mIoU: 0.5845, mAcc: 0.7071, IoU.wall: 0.8267, IoU.building: 0.8530, IoU.sky: 0.9509, IoU.floor: 0.8571, IoU.tree: 0.7809, IoU.ceiling: 0.8732, IoU.road: 0.8690, IoU.bed : 0.9263, IoU.windowpane: 0.6709, IoU.grass: 0.6921, IoU.cabinet: 0.6665, IoU.sidewalk: 0.7102, IoU.person: 0.8588, IoU.earth: 0.4281, IoU.door: 0.5945, IoU.table: 0.6948, IoU.mountain: 0.6200, IoU.plant: 0.5665, IoU.curtain: 0.7908, IoU.chair: 0.6896, IoU.car: 0.8846, IoU.water: 0.6299, IoU.painting: 0.7631, IoU.sofa: 0.8169, IoU.shelf: 0.5401, IoU.house: 0.4968, IoU.sea: 0.7304, IoU.mirror: 0.7861, IoU.rug: 0.6868, IoU.field: 0.3731, IoU.armchair: 0.6112, IoU.seat: 0.6773, IoU.fence: 0.5329, IoU.desk: 0.6062, IoU.rock: 0.5205, IoU.wardrobe: 0.5391, IoU.lamp: 0.7691, IoU.bathtub: 0.8640, IoU.railing: 0.4373, IoU.cushion: 0.7237, IoU.base: 0.3669, IoU.box: 0.3840, IoU.column: 0.5891, IoU.signboard: 0.4214, IoU.chest of drawers: 0.4083, IoU.counter: 0.3743, IoU.sand: 0.5712, IoU.sink: 0.8096, IoU.skyscraper: 0.4646, IoU.fireplace: 0.7503, IoU.refrigerator: 0.8647, IoU.grandstand: 0.5181, IoU.path: 0.3148, IoU.stairs: 0.3528, IoU.runway: 0.7454, IoU.case: 0.6383, IoU.pool table: 0.9428, IoU.pillow: 0.6811, IoU.screen door: 0.7242, IoU.stairway: 0.4346, IoU.river: 0.0959, IoU.bridge: 0.7509, IoU.bookcase: 0.5565, IoU.blind: 0.4208, IoU.coffee table: 0.6107, IoU.toilet: 0.9065, IoU.flower: 0.4638, IoU.book: 0.5632, IoU.hill: 0.0603, IoU.bench: 0.5484, IoU.countertop: 0.6661, IoU.stove: 0.8704, IoU.palm: 0.5549, IoU.kitchen island: 0.5972, IoU.computer: 0.7658, IoU.swivel chair: 0.5113, IoU.boat: 0.7672, IoU.bar: 0.6141, IoU.arcade machine: 0.7269, IoU.hovel: 0.3587, IoU.bus: 0.9219, IoU.towel: 0.7949, IoU.light: 0.6427, IoU.truck: 0.5017, IoU.tower: 0.2316, IoU.chandelier: 0.7546, IoU.awning: 0.4203, IoU.streetlight: 0.3813, IoU.booth: 0.4067, IoU.television receiver: 0.8216, IoU.airplane: 0.8538, IoU.dirt track: 0.0151, IoU.apparel: 0.6335, IoU.pole: 0.2526, IoU.land: 0.0334, IoU.bannister: 0.2017, IoU.escalator: 0.5150, IoU.ottoman: 0.5328, IoU.bottle: 0.4546, IoU.buffet: 0.5006, IoU.poster: 0.3121, IoU.stage: 0.2563, IoU.van: 0.5025, IoU.ship: 0.8618, IoU.fountain: 0.4387, IoU.conveyer belt: 0.8060, IoU.canopy: 0.5743, IoU.washer: 0.8406, IoU.plaything: 0.3156, IoU.swimming pool: 0.5401, IoU.stool: 0.5062, IoU.barrel: 0.7200, IoU.basket: 0.4321, IoU.waterfall: 0.4561, IoU.tent: 0.9279, IoU.bag: 0.2648, IoU.minibike: 0.7777, IoU.cradle: 0.8852, IoU.oven: 0.6604, IoU.ball: 0.5779, IoU.food: 0.6568, IoU.step: 0.1581, IoU.tank: 0.7468, IoU.trade name: 0.2504, IoU.microwave: 0.9111, IoU.pot: 0.5967, IoU.animal: 0.5812, IoU.bicycle: 0.5924, IoU.lake: 0.4293, IoU.dishwasher: 0.7671, IoU.screen: 0.5719, IoU.blanket: 0.3872, IoU.sculpture: 0.7618, IoU.hood: 0.6367, IoU.sconce: 0.5980, IoU.vase: 0.4686, IoU.traffic light: 0.3619, IoU.tray: 0.2574, IoU.ashcan: 0.5201, IoU.fan: 0.7048, IoU.pier: 0.4472, IoU.crt screen: 0.0261, IoU.plate: 0.6400, IoU.monitor: 0.6023, IoU.bulletin board: 0.6013, IoU.shower: 0.0590, IoU.radiator: 0.7064, IoU.glass: 0.2135, IoU.clock: 0.5135, IoU.flag: 0.7109, Acc.wall: 0.9007, Acc.building: 0.9310, Acc.sky: 0.9740, Acc.floor: 0.9230, Acc.tree: 0.9105, Acc.ceiling: 0.9416, Acc.road: 0.9072, Acc.bed : 0.9726, Acc.windowpane: 0.8375, Acc.grass: 0.8278, Acc.cabinet: 0.7697, Acc.sidewalk: 0.8596, Acc.person: 0.9505, Acc.earth: 0.5781, Acc.door: 0.7515, Acc.table: 0.8178, Acc.mountain: 0.7301, Acc.plant: 0.6531, Acc.curtain: 0.8886, Acc.chair: 0.7926, Acc.car: 0.9455, Acc.water: 0.7782, Acc.painting: 0.8956, Acc.sofa: 0.9012, Acc.shelf: 0.7471, Acc.house: 0.6400, Acc.sea: 0.8181, Acc.mirror: 0.8739, Acc.rug: 0.7830, Acc.field: 0.6322, Acc.armchair: 0.7727, Acc.seat: 0.8832, Acc.fence: 0.6441, Acc.desk: 0.7920, Acc.rock: 0.7805, Acc.wardrobe: 0.7438, Acc.lamp: 0.8571, Acc.bathtub: 0.8935, Acc.railing: 0.6014, Acc.cushion: 0.8235, Acc.base: 0.5708, Acc.box: 0.4947, Acc.column: 0.6983, Acc.signboard: 0.5477, Acc.chest of drawers: 0.6101, Acc.counter: 0.4761, Acc.sand: 0.8715, Acc.sink: 0.8551, Acc.skyscraper: 0.6008, Acc.fireplace: 0.9338, Acc.refrigerator: 0.9289, Acc.grandstand: 0.8072, Acc.path: 0.4261, Acc.stairs: 0.4574, Acc.runway: 0.9815, Acc.case: 0.8553, Acc.pool table: 0.9776, Acc.pillow: 0.7752, Acc.screen door: 0.7433, Acc.stairway: 0.5592, Acc.river: 0.2204, Acc.bridge: 0.8764, Acc.bookcase: 0.6631, Acc.blind: 0.4331, Acc.coffee table: 0.8917, Acc.toilet: 0.9320, Acc.flower: 0.5440, Acc.book: 0.7792, Acc.hill: 0.1114, Acc.bench: 0.6137, Acc.countertop: 0.8537, Acc.stove: 0.9420, Acc.palm: 0.7980, Acc.kitchen island: 0.8440, Acc.computer: 0.8817, Acc.swivel chair: 0.8093, Acc.boat: 0.8866, Acc.bar: 0.8329, Acc.arcade machine: 0.7658, Acc.hovel: 0.3946, Acc.bus: 0.9704, Acc.towel: 0.8829, Acc.light: 0.7448, Acc.truck: 0.6156, Acc.tower: 0.3870, Acc.chandelier: 0.8751, Acc.awning: 0.5132, Acc.streetlight: 0.5003, Acc.booth: 0.5439, Acc.television receiver: 0.8798, Acc.airplane: 0.9590, Acc.dirt track: 0.0712, Acc.apparel: 0.8940, Acc.pole: 0.3597, Acc.land: 0.0440, Acc.bannister: 0.2870, Acc.escalator: 0.6425, Acc.ottoman: 0.7407, Acc.bottle: 0.5839, Acc.buffet: 0.6034, Acc.poster: 0.3673, Acc.stage: 0.4586, Acc.van: 0.6922, Acc.ship: 0.9112, Acc.fountain: 0.4677, Acc.conveyer belt: 0.9262, Acc.canopy: 0.7355, Acc.washer: 0.8903, Acc.plaything: 0.3961, Acc.swimming pool: 0.7830, Acc.stool: 0.7538, Acc.barrel: 0.9205, Acc.basket: 0.6183, Acc.waterfall: 0.5956, Acc.tent: 0.9805, Acc.bag: 0.3106, Acc.minibike: 0.8994, Acc.cradle: 0.9729, Acc.oven: 0.8043, Acc.ball: 0.7320, Acc.food: 0.7864, Acc.step: 0.1791, Acc.tank: 0.8780, Acc.trade name: 0.2932, Acc.microwave: 0.9554, Acc.pot: 0.6900, Acc.animal: 0.5909, Acc.bicycle: 0.7267, Acc.lake: 0.6376, Acc.dishwasher: 0.8411, Acc.screen: 0.9173, Acc.blanket: 0.4492, Acc.sculpture: 0.8759, Acc.hood: 0.7399, Acc.sconce: 0.6697, Acc.vase: 0.6360, Acc.traffic light: 0.6759, Acc.tray: 0.3565, Acc.ashcan: 0.6696, Acc.fan: 0.8054, Acc.pier: 0.5302, Acc.crt screen: 0.0376, Acc.plate: 0.7932, Acc.monitor: 0.8117, Acc.bulletin board: 0.7202, Acc.shower: 0.1276, Acc.radiator: 0.7899, Acc.glass: 0.2300, Acc.clock: 0.5828, Acc.flag: 0.7821 +2024-06-17 01:55:47,673 - mmseg - INFO - Iter [58050/80000] lr: 1.098e-05, eta: 10:46:35, time: 3.563, data_time: 1.947, memory: 71384, decode.loss_ce: 0.1450, decode.acc_seg: 93.5742, aux.loss_ce: 0.0616, aux.acc_seg: 93.1789, loss: 0.2067 +2024-06-17 01:57:11,340 - mmseg - INFO - Iter [58100/80000] lr: 1.095e-05, eta: 10:45:05, time: 1.673, data_time: 0.063, memory: 71384, decode.loss_ce: 0.1461, decode.acc_seg: 93.7686, aux.loss_ce: 0.0620, aux.acc_seg: 93.3862, loss: 0.2081 +2024-06-17 01:58:32,554 - mmseg - INFO - Iter [58150/80000] lr: 1.093e-05, eta: 10:43:34, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1442, decode.acc_seg: 93.7140, aux.loss_ce: 0.0616, aux.acc_seg: 93.2989, loss: 0.2058 +2024-06-17 01:59:53,557 - mmseg - INFO - Iter [58200/80000] lr: 1.090e-05, eta: 10:42:03, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1469, decode.acc_seg: 93.6505, aux.loss_ce: 0.0626, aux.acc_seg: 93.2520, loss: 0.2095 +2024-06-17 02:01:14,697 - mmseg - INFO - Iter [58250/80000] lr: 1.088e-05, eta: 10:40:32, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1469, decode.acc_seg: 93.4839, aux.loss_ce: 0.0624, aux.acc_seg: 93.0375, loss: 0.2093 +2024-06-17 02:02:35,677 - mmseg - INFO - Iter [58300/80000] lr: 1.085e-05, eta: 10:39:00, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1490, decode.acc_seg: 93.6182, aux.loss_ce: 0.0636, aux.acc_seg: 93.1582, loss: 0.2126 +2024-06-17 02:03:56,824 - mmseg - INFO - Iter [58350/80000] lr: 1.083e-05, eta: 10:37:29, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1465, decode.acc_seg: 93.6619, aux.loss_ce: 0.0623, aux.acc_seg: 93.2890, loss: 0.2088 +2024-06-17 02:05:17,990 - mmseg - INFO - Iter [58400/80000] lr: 1.080e-05, eta: 10:35:58, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1407, decode.acc_seg: 93.9143, aux.loss_ce: 0.0600, aux.acc_seg: 93.5234, loss: 0.2007 +2024-06-17 02:06:38,944 - mmseg - INFO - Iter [58450/80000] lr: 1.078e-05, eta: 10:34:27, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1531, decode.acc_seg: 93.5643, aux.loss_ce: 0.0649, aux.acc_seg: 93.1180, loss: 0.2180 +2024-06-17 02:08:00,064 - mmseg - INFO - Iter [58500/80000] lr: 1.075e-05, eta: 10:32:56, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1475, decode.acc_seg: 93.6334, aux.loss_ce: 0.0629, aux.acc_seg: 93.1971, loss: 0.2104 +2024-06-17 02:09:21,198 - mmseg - INFO - Iter [58550/80000] lr: 1.073e-05, eta: 10:31:25, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1462, decode.acc_seg: 93.4880, aux.loss_ce: 0.0620, aux.acc_seg: 93.1495, loss: 0.2082 +2024-06-17 02:10:42,442 - mmseg - INFO - Iter [58600/80000] lr: 1.070e-05, eta: 10:29:55, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1416, decode.acc_seg: 93.6520, aux.loss_ce: 0.0604, aux.acc_seg: 93.2138, loss: 0.2020 +2024-06-17 02:12:03,503 - mmseg - INFO - Iter [58650/80000] lr: 1.068e-05, eta: 10:28:24, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1511, decode.acc_seg: 93.3294, aux.loss_ce: 0.0643, aux.acc_seg: 92.9266, loss: 0.2154 +2024-06-17 02:13:24,759 - mmseg - INFO - Iter [58700/80000] lr: 1.065e-05, eta: 10:26:53, time: 1.625, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1458, decode.acc_seg: 93.5865, aux.loss_ce: 0.0620, aux.acc_seg: 93.1691, loss: 0.2077 +2024-06-17 02:14:45,851 - mmseg - INFO - Iter [58750/80000] lr: 1.063e-05, eta: 10:25:22, time: 1.622, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1440, decode.acc_seg: 93.6478, aux.loss_ce: 0.0612, aux.acc_seg: 93.2333, loss: 0.2052 +2024-06-17 02:16:06,909 - mmseg - INFO - Iter [58800/80000] lr: 1.060e-05, eta: 10:23:51, time: 1.621, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1607, decode.acc_seg: 93.0731, aux.loss_ce: 0.0681, aux.acc_seg: 92.6354, loss: 0.2288 +2024-06-17 02:17:28,023 - mmseg - INFO - Iter [58850/80000] lr: 1.058e-05, eta: 10:22:20, time: 1.622, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1430, decode.acc_seg: 93.6853, aux.loss_ce: 0.0606, aux.acc_seg: 93.3136, loss: 0.2036 +2024-06-17 02:18:49,086 - mmseg - INFO - Iter [58900/80000] lr: 1.055e-05, eta: 10:20:49, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1525, decode.acc_seg: 93.3032, aux.loss_ce: 0.0648, aux.acc_seg: 92.9374, loss: 0.2173 +2024-06-17 02:20:10,276 - mmseg - INFO - Iter [58950/80000] lr: 1.053e-05, eta: 10:19:18, time: 1.624, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1545, decode.acc_seg: 93.2138, aux.loss_ce: 0.0656, aux.acc_seg: 92.8067, loss: 0.2201 +2024-06-17 02:21:31,293 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 02:21:31,293 - mmseg - INFO - Iter [59000/80000] lr: 1.050e-05, eta: 10:17:48, time: 1.620, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1473, decode.acc_seg: 93.4920, aux.loss_ce: 0.0628, aux.acc_seg: 93.0509, loss: 0.2100 +2024-06-17 02:23:10,492 - mmseg - INFO - per class results: +2024-06-17 02:23:10,498 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.77 | 89.82 | +| building | 85.02 | 93.88 | +| sky | 94.91 | 97.46 | +| floor | 86.06 | 92.29 | +| tree | 77.93 | 89.29 | +| ceiling | 87.35 | 94.25 | +| road | 86.01 | 90.97 | +| bed | 92.86 | 97.18 | +| windowpane | 67.78 | 82.61 | +| grass | 69.79 | 83.9 | +| cabinet | 68.49 | 79.51 | +| sidewalk | 70.39 | 84.76 | +| person | 86.2 | 94.99 | +| earth | 40.54 | 54.05 | +| door | 61.52 | 75.59 | +| table | 69.58 | 81.25 | +| mountain | 62.33 | 73.09 | +| plant | 57.31 | 67.06 | +| curtain | 79.5 | 90.33 | +| chair | 69.06 | 78.43 | +| car | 88.45 | 94.04 | +| water | 62.72 | 77.38 | +| painting | 75.36 | 91.31 | +| sofa | 83.43 | 91.69 | +| shelf | 53.11 | 67.8 | +| house | 47.52 | 59.63 | +| sea | 72.95 | 81.5 | +| mirror | 79.81 | 86.85 | +| rug | 68.51 | 77.33 | +| field | 38.26 | 64.63 | +| armchair | 62.74 | 82.0 | +| seat | 64.33 | 89.77 | +| fence | 53.03 | 62.14 | +| desk | 61.27 | 78.57 | +| rock | 53.12 | 78.39 | +| wardrobe | 59.77 | 75.57 | +| lamp | 76.68 | 88.43 | +| bathtub | 88.01 | 90.25 | +| railing | 47.01 | 68.01 | +| cushion | 72.48 | 83.82 | +| base | 38.07 | 63.08 | +| box | 40.31 | 49.54 | +| column | 57.54 | 74.74 | +| signboard | 40.72 | 55.02 | +| chest of drawers | 40.23 | 58.72 | +| counter | 39.34 | 50.79 | +| sand | 57.98 | 85.75 | +| sink | 80.73 | 86.39 | +| skyscraper | 46.26 | 58.57 | +| fireplace | 72.95 | 96.24 | +| refrigerator | 85.89 | 94.53 | +| grandstand | 56.88 | 79.99 | +| path | 26.47 | 34.71 | +| stairs | 41.42 | 53.7 | +| runway | 73.27 | 94.32 | +| case | 63.6 | 81.73 | +| pool table | 93.89 | 98.25 | +| pillow | 70.0 | 80.97 | +| screen door | 82.14 | 85.32 | +| stairway | 48.68 | 60.26 | +| river | 10.27 | 24.39 | +| bridge | 72.62 | 82.23 | +| bookcase | 52.65 | 63.89 | +| blind | 44.06 | 49.54 | +| coffee table | 61.37 | 87.06 | +| toilet | 90.38 | 94.84 | +| flower | 45.98 | 57.86 | +| book | 55.53 | 80.16 | +| hill | 9.25 | 18.0 | +| bench | 52.6 | 59.48 | +| countertop | 64.84 | 81.89 | +| stove | 88.17 | 92.84 | +| palm | 54.88 | 85.08 | +| kitchen island | 54.09 | 90.09 | +| computer | 77.0 | 92.23 | +| swivel chair | 50.11 | 72.65 | +| boat | 80.26 | 88.93 | +| bar | 62.74 | 89.42 | +| arcade machine | 76.54 | 80.29 | +| hovel | 14.07 | 15.09 | +| bus | 92.45 | 97.31 | +| towel | 81.63 | 90.22 | +| light | 62.42 | 70.1 | +| truck | 51.53 | 61.66 | +| tower | 31.7 | 54.36 | +| chandelier | 75.38 | 88.93 | +| awning | 40.52 | 47.66 | +| streetlight | 39.24 | 52.67 | +| booth | 38.26 | 63.79 | +| television receiver | 80.93 | 87.18 | +| airplane | 88.7 | 95.39 | +| dirt track | 1.98 | 7.87 | +| apparel | 65.81 | 89.56 | +| pole | 27.31 | 39.33 | +| land | 3.53 | 5.13 | +| bannister | 22.98 | 28.46 | +| escalator | 64.94 | 83.03 | +| ottoman | 50.09 | 64.84 | +| bottle | 45.49 | 57.42 | +| buffet | 54.17 | 62.57 | +| poster | 29.68 | 34.67 | +| stage | 24.96 | 46.58 | +| van | 48.94 | 67.07 | +| ship | 89.15 | 93.1 | +| fountain | 46.69 | 47.87 | +| conveyer belt | 81.71 | 95.03 | +| canopy | 53.6 | 76.07 | +| washer | 88.6 | 94.91 | +| plaything | 42.27 | 57.65 | +| swimming pool | 54.1 | 79.2 | +| stool | 53.02 | 70.82 | +| barrel | 70.12 | 84.4 | +| basket | 42.67 | 63.3 | +| waterfall | 51.23 | 68.24 | +| tent | 93.37 | 98.44 | +| bag | 27.8 | 32.05 | +| minibike | 77.01 | 91.24 | +| cradle | 86.38 | 97.73 | +| oven | 60.95 | 72.51 | +| ball | 58.14 | 75.79 | +| food | 63.16 | 73.38 | +| step | 15.87 | 18.09 | +| tank | 81.62 | 93.6 | +| trade name | 21.56 | 25.74 | +| microwave | 88.17 | 96.39 | +| pot | 60.8 | 71.91 | +| animal | 59.48 | 61.0 | +| bicycle | 60.06 | 78.5 | +| lake | 48.85 | 63.76 | +| dishwasher | 77.29 | 84.33 | +| screen | 56.43 | 90.46 | +| blanket | 36.86 | 42.07 | +| sculpture | 73.07 | 88.91 | +| hood | 64.51 | 76.39 | +| sconce | 63.87 | 74.84 | +| vase | 47.3 | 67.55 | +| traffic light | 35.83 | 63.48 | +| tray | 26.81 | 35.92 | +| ashcan | 52.26 | 67.37 | +| fan | 71.82 | 82.59 | +| pier | 45.19 | 50.15 | +| crt screen | 1.39 | 3.41 | +| plate | 62.18 | 81.1 | +| monitor | 33.14 | 37.29 | +| bulletin board | 54.07 | 69.63 | +| shower | 13.5 | 14.17 | +| radiator | 70.62 | 80.86 | +| glass | 21.46 | 22.97 | +| clock | 52.84 | 64.62 | +| flag | 71.05 | 80.69 | ++---------------------+-------+-------+ +2024-06-17 02:23:10,498 - mmseg - INFO - Summary: +2024-06-17 02:23:10,499 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.56 | 58.72 | 71.22 | ++-------+-------+-------+ +2024-06-17 02:23:10,499 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 02:23:10,500 - mmseg - INFO - Iter(val) [250] aAcc: 0.8656, mIoU: 0.5872, mAcc: 0.7122, IoU.wall: 0.8277, IoU.building: 0.8502, IoU.sky: 0.9491, IoU.floor: 0.8606, IoU.tree: 0.7793, IoU.ceiling: 0.8735, IoU.road: 0.8601, IoU.bed : 0.9286, IoU.windowpane: 0.6778, IoU.grass: 0.6979, IoU.cabinet: 0.6849, IoU.sidewalk: 0.7039, IoU.person: 0.8620, IoU.earth: 0.4054, IoU.door: 0.6152, IoU.table: 0.6958, IoU.mountain: 0.6233, IoU.plant: 0.5731, IoU.curtain: 0.7950, IoU.chair: 0.6906, IoU.car: 0.8845, IoU.water: 0.6272, IoU.painting: 0.7536, IoU.sofa: 0.8343, IoU.shelf: 0.5311, IoU.house: 0.4752, IoU.sea: 0.7295, IoU.mirror: 0.7981, IoU.rug: 0.6851, IoU.field: 0.3826, IoU.armchair: 0.6274, IoU.seat: 0.6433, IoU.fence: 0.5303, IoU.desk: 0.6127, IoU.rock: 0.5312, IoU.wardrobe: 0.5977, IoU.lamp: 0.7668, IoU.bathtub: 0.8801, IoU.railing: 0.4701, IoU.cushion: 0.7248, IoU.base: 0.3807, IoU.box: 0.4031, IoU.column: 0.5754, IoU.signboard: 0.4072, IoU.chest of drawers: 0.4023, IoU.counter: 0.3934, IoU.sand: 0.5798, IoU.sink: 0.8073, IoU.skyscraper: 0.4626, IoU.fireplace: 0.7295, IoU.refrigerator: 0.8589, IoU.grandstand: 0.5688, IoU.path: 0.2647, IoU.stairs: 0.4142, IoU.runway: 0.7327, IoU.case: 0.6360, IoU.pool table: 0.9389, IoU.pillow: 0.7000, IoU.screen door: 0.8214, IoU.stairway: 0.4868, IoU.river: 0.1027, IoU.bridge: 0.7262, IoU.bookcase: 0.5265, IoU.blind: 0.4406, IoU.coffee table: 0.6137, IoU.toilet: 0.9038, IoU.flower: 0.4598, IoU.book: 0.5553, IoU.hill: 0.0925, IoU.bench: 0.5260, IoU.countertop: 0.6484, IoU.stove: 0.8817, IoU.palm: 0.5488, IoU.kitchen island: 0.5409, IoU.computer: 0.7700, IoU.swivel chair: 0.5011, IoU.boat: 0.8026, IoU.bar: 0.6274, IoU.arcade machine: 0.7654, IoU.hovel: 0.1407, IoU.bus: 0.9245, IoU.towel: 0.8163, IoU.light: 0.6242, IoU.truck: 0.5153, IoU.tower: 0.3170, IoU.chandelier: 0.7538, IoU.awning: 0.4052, IoU.streetlight: 0.3924, IoU.booth: 0.3826, IoU.television receiver: 0.8093, IoU.airplane: 0.8870, IoU.dirt track: 0.0198, IoU.apparel: 0.6581, IoU.pole: 0.2731, IoU.land: 0.0353, IoU.bannister: 0.2298, IoU.escalator: 0.6494, IoU.ottoman: 0.5009, IoU.bottle: 0.4549, IoU.buffet: 0.5417, IoU.poster: 0.2968, IoU.stage: 0.2496, IoU.van: 0.4894, IoU.ship: 0.8915, IoU.fountain: 0.4669, IoU.conveyer belt: 0.8171, IoU.canopy: 0.5360, IoU.washer: 0.8860, IoU.plaything: 0.4227, IoU.swimming pool: 0.5410, IoU.stool: 0.5302, IoU.barrel: 0.7012, IoU.basket: 0.4267, IoU.waterfall: 0.5123, IoU.tent: 0.9337, IoU.bag: 0.2780, IoU.minibike: 0.7701, IoU.cradle: 0.8638, IoU.oven: 0.6095, IoU.ball: 0.5814, IoU.food: 0.6316, IoU.step: 0.1587, IoU.tank: 0.8162, IoU.trade name: 0.2156, IoU.microwave: 0.8817, IoU.pot: 0.6080, IoU.animal: 0.5948, IoU.bicycle: 0.6006, IoU.lake: 0.4885, IoU.dishwasher: 0.7729, IoU.screen: 0.5643, IoU.blanket: 0.3686, IoU.sculpture: 0.7307, IoU.hood: 0.6451, IoU.sconce: 0.6387, IoU.vase: 0.4730, IoU.traffic light: 0.3583, IoU.tray: 0.2681, IoU.ashcan: 0.5226, IoU.fan: 0.7182, IoU.pier: 0.4519, IoU.crt screen: 0.0139, IoU.plate: 0.6218, IoU.monitor: 0.3314, IoU.bulletin board: 0.5407, IoU.shower: 0.1350, IoU.radiator: 0.7062, IoU.glass: 0.2146, IoU.clock: 0.5284, IoU.flag: 0.7105, Acc.wall: 0.8982, Acc.building: 0.9388, Acc.sky: 0.9746, Acc.floor: 0.9229, Acc.tree: 0.8929, Acc.ceiling: 0.9425, Acc.road: 0.9097, Acc.bed : 0.9718, Acc.windowpane: 0.8261, Acc.grass: 0.8390, Acc.cabinet: 0.7951, Acc.sidewalk: 0.8476, Acc.person: 0.9499, Acc.earth: 0.5405, Acc.door: 0.7559, Acc.table: 0.8125, Acc.mountain: 0.7309, Acc.plant: 0.6706, Acc.curtain: 0.9033, Acc.chair: 0.7843, Acc.car: 0.9404, Acc.water: 0.7738, Acc.painting: 0.9131, Acc.sofa: 0.9169, Acc.shelf: 0.6780, Acc.house: 0.5963, Acc.sea: 0.8150, Acc.mirror: 0.8685, Acc.rug: 0.7733, Acc.field: 0.6463, Acc.armchair: 0.8200, Acc.seat: 0.8977, Acc.fence: 0.6214, Acc.desk: 0.7857, Acc.rock: 0.7839, Acc.wardrobe: 0.7557, Acc.lamp: 0.8843, Acc.bathtub: 0.9025, Acc.railing: 0.6801, Acc.cushion: 0.8382, Acc.base: 0.6308, Acc.box: 0.4954, Acc.column: 0.7474, Acc.signboard: 0.5502, Acc.chest of drawers: 0.5872, Acc.counter: 0.5079, Acc.sand: 0.8575, Acc.sink: 0.8639, Acc.skyscraper: 0.5857, Acc.fireplace: 0.9624, Acc.refrigerator: 0.9453, Acc.grandstand: 0.7999, Acc.path: 0.3471, Acc.stairs: 0.5370, Acc.runway: 0.9432, Acc.case: 0.8173, Acc.pool table: 0.9825, Acc.pillow: 0.8097, Acc.screen door: 0.8532, Acc.stairway: 0.6026, Acc.river: 0.2439, Acc.bridge: 0.8223, Acc.bookcase: 0.6389, Acc.blind: 0.4954, Acc.coffee table: 0.8706, Acc.toilet: 0.9484, Acc.flower: 0.5786, Acc.book: 0.8016, Acc.hill: 0.1800, Acc.bench: 0.5948, Acc.countertop: 0.8189, Acc.stove: 0.9284, Acc.palm: 0.8508, Acc.kitchen island: 0.9009, Acc.computer: 0.9223, Acc.swivel chair: 0.7265, Acc.boat: 0.8893, Acc.bar: 0.8942, Acc.arcade machine: 0.8029, Acc.hovel: 0.1509, Acc.bus: 0.9731, Acc.towel: 0.9022, Acc.light: 0.7010, Acc.truck: 0.6166, Acc.tower: 0.5436, Acc.chandelier: 0.8893, Acc.awning: 0.4766, Acc.streetlight: 0.5267, Acc.booth: 0.6379, Acc.television receiver: 0.8718, Acc.airplane: 0.9539, Acc.dirt track: 0.0787, Acc.apparel: 0.8956, Acc.pole: 0.3933, Acc.land: 0.0513, Acc.bannister: 0.2846, Acc.escalator: 0.8303, Acc.ottoman: 0.6484, Acc.bottle: 0.5742, Acc.buffet: 0.6257, Acc.poster: 0.3467, Acc.stage: 0.4658, Acc.van: 0.6707, Acc.ship: 0.9310, Acc.fountain: 0.4787, Acc.conveyer belt: 0.9503, Acc.canopy: 0.7607, Acc.washer: 0.9491, Acc.plaything: 0.5765, Acc.swimming pool: 0.7920, Acc.stool: 0.7082, Acc.barrel: 0.8440, Acc.basket: 0.6330, Acc.waterfall: 0.6824, Acc.tent: 0.9844, Acc.bag: 0.3205, Acc.minibike: 0.9124, Acc.cradle: 0.9773, Acc.oven: 0.7251, Acc.ball: 0.7579, Acc.food: 0.7338, Acc.step: 0.1809, Acc.tank: 0.9360, Acc.trade name: 0.2574, Acc.microwave: 0.9639, Acc.pot: 0.7191, Acc.animal: 0.6100, Acc.bicycle: 0.7850, Acc.lake: 0.6376, Acc.dishwasher: 0.8433, Acc.screen: 0.9046, Acc.blanket: 0.4207, Acc.sculpture: 0.8891, Acc.hood: 0.7639, Acc.sconce: 0.7484, Acc.vase: 0.6755, Acc.traffic light: 0.6348, Acc.tray: 0.3592, Acc.ashcan: 0.6737, Acc.fan: 0.8259, Acc.pier: 0.5015, Acc.crt screen: 0.0341, Acc.plate: 0.8110, Acc.monitor: 0.3729, Acc.bulletin board: 0.6963, Acc.shower: 0.1417, Acc.radiator: 0.8086, Acc.glass: 0.2297, Acc.clock: 0.6462, Acc.flag: 0.8069 +2024-06-17 02:24:31,934 - mmseg - INFO - Iter [59050/80000] lr: 1.048e-05, eta: 10:16:52, time: 3.613, data_time: 2.002, memory: 71384, decode.loss_ce: 0.1472, decode.acc_seg: 93.6456, aux.loss_ce: 0.0631, aux.acc_seg: 93.2480, loss: 0.2104 +2024-06-17 02:25:52,942 - mmseg - INFO - Iter [59100/80000] lr: 1.045e-05, eta: 10:15:21, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1500, decode.acc_seg: 93.4750, aux.loss_ce: 0.0639, aux.acc_seg: 93.0629, loss: 0.2139 +2024-06-17 02:27:14,078 - mmseg - INFO - Iter [59150/80000] lr: 1.043e-05, eta: 10:13:50, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1510, decode.acc_seg: 93.5926, aux.loss_ce: 0.0635, aux.acc_seg: 93.2383, loss: 0.2145 +2024-06-17 02:28:35,174 - mmseg - INFO - Iter [59200/80000] lr: 1.040e-05, eta: 10:12:20, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1500, decode.acc_seg: 93.4478, aux.loss_ce: 0.0633, aux.acc_seg: 93.0766, loss: 0.2133 +2024-06-17 02:29:56,185 - mmseg - INFO - Iter [59250/80000] lr: 1.038e-05, eta: 10:10:49, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1453, decode.acc_seg: 93.6412, aux.loss_ce: 0.0623, aux.acc_seg: 93.2161, loss: 0.2076 +2024-06-17 02:31:17,273 - mmseg - INFO - Iter [59300/80000] lr: 1.035e-05, eta: 10:09:18, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1450, decode.acc_seg: 93.7158, aux.loss_ce: 0.0619, aux.acc_seg: 93.3384, loss: 0.2069 +2024-06-17 02:32:38,307 - mmseg - INFO - Iter [59350/80000] lr: 1.033e-05, eta: 10:07:47, time: 1.621, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1490, decode.acc_seg: 93.4082, aux.loss_ce: 0.0636, aux.acc_seg: 93.0697, loss: 0.2126 +2024-06-17 02:34:01,594 - mmseg - INFO - Iter [59400/80000] lr: 1.030e-05, eta: 10:06:17, time: 1.666, data_time: 0.053, memory: 71384, decode.loss_ce: 0.1389, decode.acc_seg: 93.8785, aux.loss_ce: 0.0592, aux.acc_seg: 93.5346, loss: 0.1981 +2024-06-17 02:35:22,660 - mmseg - INFO - Iter [59450/80000] lr: 1.028e-05, eta: 10:04:46, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1378, decode.acc_seg: 93.8973, aux.loss_ce: 0.0590, aux.acc_seg: 93.5109, loss: 0.1968 +2024-06-17 02:36:43,907 - mmseg - INFO - Iter [59500/80000] lr: 1.025e-05, eta: 10:03:15, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1433, decode.acc_seg: 93.6143, aux.loss_ce: 0.0610, aux.acc_seg: 93.2198, loss: 0.2043 +2024-06-17 02:38:05,022 - mmseg - INFO - Iter [59550/80000] lr: 1.023e-05, eta: 10:01:45, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1471, decode.acc_seg: 93.6559, aux.loss_ce: 0.0627, aux.acc_seg: 93.2521, loss: 0.2098 +2024-06-17 02:39:25,955 - mmseg - INFO - Iter [59600/80000] lr: 1.020e-05, eta: 10:00:14, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1366, decode.acc_seg: 93.9318, aux.loss_ce: 0.0587, aux.acc_seg: 93.5224, loss: 0.1953 +2024-06-17 02:40:47,072 - mmseg - INFO - Iter [59650/80000] lr: 1.018e-05, eta: 9:58:43, time: 1.622, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1599, decode.acc_seg: 93.0852, aux.loss_ce: 0.0672, aux.acc_seg: 92.7449, loss: 0.2271 +2024-06-17 02:42:08,176 - mmseg - INFO - Iter [59700/80000] lr: 1.015e-05, eta: 9:57:12, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1455, decode.acc_seg: 93.5829, aux.loss_ce: 0.0617, aux.acc_seg: 93.2121, loss: 0.2073 +2024-06-17 02:43:29,350 - mmseg - INFO - Iter [59750/80000] lr: 1.013e-05, eta: 9:55:42, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1400, decode.acc_seg: 93.9338, aux.loss_ce: 0.0594, aux.acc_seg: 93.5537, loss: 0.1994 +2024-06-17 02:44:50,334 - mmseg - INFO - Iter [59800/80000] lr: 1.010e-05, eta: 9:54:11, time: 1.620, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1402, decode.acc_seg: 93.7323, aux.loss_ce: 0.0597, aux.acc_seg: 93.3625, loss: 0.1999 +2024-06-17 02:46:11,419 - mmseg - INFO - Iter [59850/80000] lr: 1.008e-05, eta: 9:52:40, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1461, decode.acc_seg: 93.7289, aux.loss_ce: 0.0622, aux.acc_seg: 93.2530, loss: 0.2083 +2024-06-17 02:47:32,566 - mmseg - INFO - Iter [59900/80000] lr: 1.005e-05, eta: 9:51:10, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1360, decode.acc_seg: 93.8454, aux.loss_ce: 0.0580, aux.acc_seg: 93.4204, loss: 0.1940 +2024-06-17 02:48:53,806 - mmseg - INFO - Iter [59950/80000] lr: 1.003e-05, eta: 9:49:39, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1536, decode.acc_seg: 93.5052, aux.loss_ce: 0.0654, aux.acc_seg: 93.1067, loss: 0.2189 +2024-06-17 02:50:14,854 - mmseg - INFO - Saving checkpoint at 60000 iterations +2024-06-17 02:51:41,359 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 02:51:41,359 - mmseg - INFO - Iter [60000/80000] lr: 1.000e-05, eta: 9:48:37, time: 3.351, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1469, decode.acc_seg: 93.6535, aux.loss_ce: 0.0623, aux.acc_seg: 93.2189, loss: 0.2093 +2024-06-17 02:53:18,924 - mmseg - INFO - per class results: +2024-06-17 02:53:18,930 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.78 | 90.6 | +| building | 85.65 | 93.12 | +| sky | 95.04 | 97.3 | +| floor | 85.85 | 92.23 | +| tree | 78.15 | 91.5 | +| ceiling | 87.33 | 94.11 | +| road | 86.63 | 89.4 | +| bed | 92.88 | 96.93 | +| windowpane | 67.16 | 81.38 | +| grass | 70.79 | 84.2 | +| cabinet | 67.64 | 77.36 | +| sidewalk | 71.19 | 88.13 | +| person | 85.98 | 94.36 | +| earth | 41.48 | 54.84 | +| door | 62.02 | 76.17 | +| table | 70.5 | 82.42 | +| mountain | 61.43 | 73.05 | +| plant | 57.32 | 69.14 | +| curtain | 80.06 | 89.2 | +| chair | 69.01 | 79.45 | +| car | 88.71 | 94.26 | +| water | 63.11 | 78.41 | +| painting | 77.44 | 90.1 | +| sofa | 82.39 | 91.05 | +| shelf | 53.91 | 72.03 | +| house | 51.58 | 65.78 | +| sea | 73.61 | 81.09 | +| mirror | 79.97 | 86.87 | +| rug | 67.98 | 76.56 | +| field | 35.7 | 57.43 | +| armchair | 62.79 | 80.54 | +| seat | 65.16 | 90.01 | +| fence | 53.57 | 66.98 | +| desk | 59.36 | 78.7 | +| rock | 53.64 | 76.7 | +| wardrobe | 58.28 | 80.65 | +| lamp | 76.67 | 87.56 | +| bathtub | 87.81 | 90.11 | +| railing | 45.9 | 67.74 | +| cushion | 72.36 | 84.82 | +| base | 35.99 | 53.2 | +| box | 39.48 | 49.84 | +| column | 54.57 | 67.14 | +| signboard | 41.2 | 56.25 | +| chest of drawers | 42.56 | 59.78 | +| counter | 37.53 | 47.53 | +| sand | 57.89 | 86.76 | +| sink | 80.71 | 85.48 | +| skyscraper | 47.69 | 60.0 | +| fireplace | 74.62 | 93.06 | +| refrigerator | 85.65 | 94.3 | +| grandstand | 52.44 | 81.09 | +| path | 31.84 | 44.26 | +| stairs | 29.3 | 34.76 | +| runway | 74.9 | 97.83 | +| case | 63.18 | 83.83 | +| pool table | 94.5 | 98.08 | +| pillow | 69.07 | 79.01 | +| screen door | 83.2 | 86.36 | +| stairway | 42.2 | 63.18 | +| river | 10.01 | 21.62 | +| bridge | 73.95 | 83.01 | +| bookcase | 49.23 | 62.09 | +| blind | 42.13 | 44.46 | +| coffee table | 60.94 | 87.78 | +| toilet | 91.08 | 94.19 | +| flower | 45.87 | 56.89 | +| book | 55.51 | 81.44 | +| hill | 10.3 | 21.31 | +| bench | 56.3 | 65.59 | +| countertop | 64.82 | 86.9 | +| stove | 87.51 | 94.04 | +| palm | 56.09 | 81.44 | +| kitchen island | 61.17 | 87.8 | +| computer | 76.77 | 91.92 | +| swivel chair | 51.83 | 77.99 | +| boat | 80.31 | 92.35 | +| bar | 63.93 | 87.53 | +| arcade machine | 77.64 | 81.9 | +| hovel | 13.41 | 15.48 | +| bus | 93.58 | 95.79 | +| towel | 82.33 | 89.79 | +| light | 63.52 | 74.81 | +| truck | 51.28 | 61.33 | +| tower | 38.58 | 67.78 | +| chandelier | 75.2 | 88.78 | +| awning | 39.97 | 47.6 | +| streetlight | 38.62 | 51.63 | +| booth | 45.61 | 60.51 | +| television receiver | 81.0 | 88.18 | +| airplane | 88.9 | 95.14 | +| dirt track | 5.51 | 30.81 | +| apparel | 64.97 | 87.28 | +| pole | 24.4 | 36.56 | +| land | 3.25 | 4.36 | +| bannister | 23.14 | 30.0 | +| escalator | 64.85 | 85.12 | +| ottoman | 52.0 | 67.64 | +| bottle | 39.87 | 49.25 | +| buffet | 48.99 | 56.69 | +| poster | 34.19 | 40.72 | +| stage | 28.47 | 40.61 | +| van | 50.73 | 71.46 | +| ship | 86.94 | 92.6 | +| fountain | 38.37 | 38.85 | +| conveyer belt | 84.7 | 94.58 | +| canopy | 52.76 | 72.64 | +| washer | 86.99 | 92.36 | +| plaything | 34.42 | 49.73 | +| swimming pool | 54.24 | 78.44 | +| stool | 50.86 | 68.82 | +| barrel | 65.16 | 77.49 | +| basket | 39.51 | 55.42 | +| waterfall | 52.33 | 67.22 | +| tent | 94.64 | 98.15 | +| bag | 26.32 | 29.78 | +| minibike | 78.56 | 90.17 | +| cradle | 86.02 | 95.34 | +| oven | 64.47 | 74.4 | +| ball | 49.7 | 53.4 | +| food | 64.17 | 74.4 | +| step | 17.86 | 20.74 | +| tank | 79.64 | 93.4 | +| trade name | 17.23 | 19.35 | +| microwave | 89.42 | 96.28 | +| pot | 61.52 | 72.93 | +| animal | 59.28 | 60.82 | +| bicycle | 59.43 | 77.69 | +| lake | 50.82 | 63.77 | +| dishwasher | 78.63 | 83.7 | +| screen | 58.71 | 92.58 | +| blanket | 41.84 | 48.78 | +| sculpture | 74.0 | 89.23 | +| hood | 64.1 | 76.42 | +| sconce | 63.92 | 76.01 | +| vase | 48.44 | 65.14 | +| traffic light | 35.88 | 64.3 | +| tray | 25.35 | 32.54 | +| ashcan | 51.54 | 65.05 | +| fan | 71.92 | 86.34 | +| pier | 50.96 | 58.46 | +| crt screen | 2.2 | 3.34 | +| plate | 64.1 | 79.78 | +| monitor | 59.98 | 70.8 | +| bulletin board | 56.94 | 72.48 | +| shower | 12.94 | 13.69 | +| radiator | 69.58 | 80.7 | +| glass | 22.49 | 24.56 | +| clock | 49.55 | 56.63 | +| flag | 71.99 | 78.92 | ++---------------------+-------+-------+ +2024-06-17 02:53:18,930 - mmseg - INFO - Summary: +2024-06-17 02:53:18,930 - mmseg - INFO - ++-------+------+------+ +| aAcc | mIoU | mAcc | ++-------+------+------+ +| 86.65 | 58.9 | 71.2 | ++-------+------+------+ +2024-06-17 02:53:18,931 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 02:53:18,931 - mmseg - INFO - Iter(val) [250] aAcc: 0.8665, mIoU: 0.5890, mAcc: 0.7120, IoU.wall: 0.8278, IoU.building: 0.8565, IoU.sky: 0.9504, IoU.floor: 0.8585, IoU.tree: 0.7815, IoU.ceiling: 0.8733, IoU.road: 0.8663, IoU.bed : 0.9288, IoU.windowpane: 0.6716, IoU.grass: 0.7079, IoU.cabinet: 0.6764, IoU.sidewalk: 0.7119, IoU.person: 0.8598, IoU.earth: 0.4148, IoU.door: 0.6202, IoU.table: 0.7050, IoU.mountain: 0.6143, IoU.plant: 0.5732, IoU.curtain: 0.8006, IoU.chair: 0.6901, IoU.car: 0.8871, IoU.water: 0.6311, IoU.painting: 0.7744, IoU.sofa: 0.8239, IoU.shelf: 0.5391, IoU.house: 0.5158, IoU.sea: 0.7361, IoU.mirror: 0.7997, IoU.rug: 0.6798, IoU.field: 0.3570, IoU.armchair: 0.6279, IoU.seat: 0.6516, IoU.fence: 0.5357, IoU.desk: 0.5936, IoU.rock: 0.5364, IoU.wardrobe: 0.5828, IoU.lamp: 0.7667, IoU.bathtub: 0.8781, IoU.railing: 0.4590, IoU.cushion: 0.7236, IoU.base: 0.3599, IoU.box: 0.3948, IoU.column: 0.5457, IoU.signboard: 0.4120, IoU.chest of drawers: 0.4256, IoU.counter: 0.3753, IoU.sand: 0.5789, IoU.sink: 0.8071, IoU.skyscraper: 0.4769, IoU.fireplace: 0.7462, IoU.refrigerator: 0.8565, IoU.grandstand: 0.5244, IoU.path: 0.3184, IoU.stairs: 0.2930, IoU.runway: 0.7490, IoU.case: 0.6318, IoU.pool table: 0.9450, IoU.pillow: 0.6907, IoU.screen door: 0.8320, IoU.stairway: 0.4220, IoU.river: 0.1001, IoU.bridge: 0.7395, IoU.bookcase: 0.4923, IoU.blind: 0.4213, IoU.coffee table: 0.6094, IoU.toilet: 0.9108, IoU.flower: 0.4587, IoU.book: 0.5551, IoU.hill: 0.1030, IoU.bench: 0.5630, IoU.countertop: 0.6482, IoU.stove: 0.8751, IoU.palm: 0.5609, IoU.kitchen island: 0.6117, IoU.computer: 0.7677, IoU.swivel chair: 0.5183, IoU.boat: 0.8031, IoU.bar: 0.6393, IoU.arcade machine: 0.7764, IoU.hovel: 0.1341, IoU.bus: 0.9358, IoU.towel: 0.8233, IoU.light: 0.6352, IoU.truck: 0.5128, IoU.tower: 0.3858, IoU.chandelier: 0.7520, IoU.awning: 0.3997, IoU.streetlight: 0.3862, IoU.booth: 0.4561, IoU.television receiver: 0.8100, IoU.airplane: 0.8890, IoU.dirt track: 0.0551, IoU.apparel: 0.6497, IoU.pole: 0.2440, IoU.land: 0.0325, IoU.bannister: 0.2314, IoU.escalator: 0.6485, IoU.ottoman: 0.5200, IoU.bottle: 0.3987, IoU.buffet: 0.4899, IoU.poster: 0.3419, IoU.stage: 0.2847, IoU.van: 0.5073, IoU.ship: 0.8694, IoU.fountain: 0.3837, IoU.conveyer belt: 0.8470, IoU.canopy: 0.5276, IoU.washer: 0.8699, IoU.plaything: 0.3442, IoU.swimming pool: 0.5424, IoU.stool: 0.5086, IoU.barrel: 0.6516, IoU.basket: 0.3951, IoU.waterfall: 0.5233, IoU.tent: 0.9464, IoU.bag: 0.2632, IoU.minibike: 0.7856, IoU.cradle: 0.8602, IoU.oven: 0.6447, IoU.ball: 0.4970, IoU.food: 0.6417, IoU.step: 0.1786, IoU.tank: 0.7964, IoU.trade name: 0.1723, IoU.microwave: 0.8942, IoU.pot: 0.6152, IoU.animal: 0.5928, IoU.bicycle: 0.5943, IoU.lake: 0.5082, IoU.dishwasher: 0.7863, IoU.screen: 0.5871, IoU.blanket: 0.4184, IoU.sculpture: 0.7400, IoU.hood: 0.6410, IoU.sconce: 0.6392, IoU.vase: 0.4844, IoU.traffic light: 0.3588, IoU.tray: 0.2535, IoU.ashcan: 0.5154, IoU.fan: 0.7192, IoU.pier: 0.5096, IoU.crt screen: 0.0220, IoU.plate: 0.6410, IoU.monitor: 0.5998, IoU.bulletin board: 0.5694, IoU.shower: 0.1294, IoU.radiator: 0.6958, IoU.glass: 0.2249, IoU.clock: 0.4955, IoU.flag: 0.7199, Acc.wall: 0.9060, Acc.building: 0.9312, Acc.sky: 0.9730, Acc.floor: 0.9223, Acc.tree: 0.9150, Acc.ceiling: 0.9411, Acc.road: 0.8940, Acc.bed : 0.9693, Acc.windowpane: 0.8138, Acc.grass: 0.8420, Acc.cabinet: 0.7736, Acc.sidewalk: 0.8813, Acc.person: 0.9436, Acc.earth: 0.5484, Acc.door: 0.7617, Acc.table: 0.8242, Acc.mountain: 0.7305, Acc.plant: 0.6914, Acc.curtain: 0.8920, Acc.chair: 0.7945, Acc.car: 0.9426, Acc.water: 0.7841, Acc.painting: 0.9010, Acc.sofa: 0.9105, Acc.shelf: 0.7203, Acc.house: 0.6578, Acc.sea: 0.8109, Acc.mirror: 0.8687, Acc.rug: 0.7656, Acc.field: 0.5743, Acc.armchair: 0.8054, Acc.seat: 0.9001, Acc.fence: 0.6698, Acc.desk: 0.7870, Acc.rock: 0.7670, Acc.wardrobe: 0.8065, Acc.lamp: 0.8756, Acc.bathtub: 0.9011, Acc.railing: 0.6774, Acc.cushion: 0.8482, Acc.base: 0.5320, Acc.box: 0.4984, Acc.column: 0.6714, Acc.signboard: 0.5625, Acc.chest of drawers: 0.5978, Acc.counter: 0.4753, Acc.sand: 0.8676, Acc.sink: 0.8548, Acc.skyscraper: 0.6000, Acc.fireplace: 0.9306, Acc.refrigerator: 0.9430, Acc.grandstand: 0.8109, Acc.path: 0.4426, Acc.stairs: 0.3476, Acc.runway: 0.9783, Acc.case: 0.8383, Acc.pool table: 0.9808, Acc.pillow: 0.7901, Acc.screen door: 0.8636, Acc.stairway: 0.6318, Acc.river: 0.2162, Acc.bridge: 0.8301, Acc.bookcase: 0.6209, Acc.blind: 0.4446, Acc.coffee table: 0.8778, Acc.toilet: 0.9419, Acc.flower: 0.5689, Acc.book: 0.8144, Acc.hill: 0.2131, Acc.bench: 0.6559, Acc.countertop: 0.8690, Acc.stove: 0.9404, Acc.palm: 0.8144, Acc.kitchen island: 0.8780, Acc.computer: 0.9192, Acc.swivel chair: 0.7799, Acc.boat: 0.9235, Acc.bar: 0.8753, Acc.arcade machine: 0.8190, Acc.hovel: 0.1548, Acc.bus: 0.9579, Acc.towel: 0.8979, Acc.light: 0.7481, Acc.truck: 0.6133, Acc.tower: 0.6778, Acc.chandelier: 0.8878, Acc.awning: 0.4760, Acc.streetlight: 0.5163, Acc.booth: 0.6051, Acc.television receiver: 0.8818, Acc.airplane: 0.9514, Acc.dirt track: 0.3081, Acc.apparel: 0.8728, Acc.pole: 0.3656, Acc.land: 0.0436, Acc.bannister: 0.3000, Acc.escalator: 0.8512, Acc.ottoman: 0.6764, Acc.bottle: 0.4925, Acc.buffet: 0.5669, Acc.poster: 0.4072, Acc.stage: 0.4061, Acc.van: 0.7146, Acc.ship: 0.9260, Acc.fountain: 0.3885, Acc.conveyer belt: 0.9458, Acc.canopy: 0.7264, Acc.washer: 0.9236, Acc.plaything: 0.4973, Acc.swimming pool: 0.7844, Acc.stool: 0.6882, Acc.barrel: 0.7749, Acc.basket: 0.5542, Acc.waterfall: 0.6722, Acc.tent: 0.9815, Acc.bag: 0.2978, Acc.minibike: 0.9017, Acc.cradle: 0.9534, Acc.oven: 0.7440, Acc.ball: 0.5340, Acc.food: 0.7440, Acc.step: 0.2074, Acc.tank: 0.9340, Acc.trade name: 0.1935, Acc.microwave: 0.9628, Acc.pot: 0.7293, Acc.animal: 0.6082, Acc.bicycle: 0.7769, Acc.lake: 0.6377, Acc.dishwasher: 0.8370, Acc.screen: 0.9258, Acc.blanket: 0.4878, Acc.sculpture: 0.8923, Acc.hood: 0.7642, Acc.sconce: 0.7601, Acc.vase: 0.6514, Acc.traffic light: 0.6430, Acc.tray: 0.3254, Acc.ashcan: 0.6505, Acc.fan: 0.8634, Acc.pier: 0.5846, Acc.crt screen: 0.0334, Acc.plate: 0.7978, Acc.monitor: 0.7080, Acc.bulletin board: 0.7248, Acc.shower: 0.1369, Acc.radiator: 0.8070, Acc.glass: 0.2456, Acc.clock: 0.5663, Acc.flag: 0.7892 +2024-06-17 02:54:40,610 - mmseg - INFO - Iter [60050/80000] lr: 9.975e-06, eta: 9:47:39, time: 3.585, data_time: 1.968, memory: 71384, decode.loss_ce: 0.1442, decode.acc_seg: 93.5620, aux.loss_ce: 0.0622, aux.acc_seg: 93.0834, loss: 0.2064 +2024-06-17 02:56:01,681 - mmseg - INFO - Iter [60100/80000] lr: 9.951e-06, eta: 9:46:09, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1457, decode.acc_seg: 93.6526, aux.loss_ce: 0.0619, aux.acc_seg: 93.2667, loss: 0.2075 +2024-06-17 02:57:22,695 - mmseg - INFO - Iter [60150/80000] lr: 9.926e-06, eta: 9:44:38, time: 1.620, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1431, decode.acc_seg: 93.6066, aux.loss_ce: 0.0608, aux.acc_seg: 93.2575, loss: 0.2039 +2024-06-17 02:58:43,732 - mmseg - INFO - Iter [60200/80000] lr: 9.901e-06, eta: 9:43:07, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1515, decode.acc_seg: 93.3748, aux.loss_ce: 0.0647, aux.acc_seg: 92.9356, loss: 0.2162 +2024-06-17 03:00:04,814 - mmseg - INFO - Iter [60250/80000] lr: 9.876e-06, eta: 9:41:36, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1408, decode.acc_seg: 93.7387, aux.loss_ce: 0.0599, aux.acc_seg: 93.3524, loss: 0.2007 +2024-06-17 03:01:25,722 - mmseg - INFO - Iter [60300/80000] lr: 9.851e-06, eta: 9:40:06, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1378, decode.acc_seg: 93.9431, aux.loss_ce: 0.0589, aux.acc_seg: 93.6155, loss: 0.1967 +2024-06-17 03:02:46,687 - mmseg - INFO - Iter [60350/80000] lr: 9.825e-06, eta: 9:38:35, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1457, decode.acc_seg: 93.3428, aux.loss_ce: 0.0616, aux.acc_seg: 92.9637, loss: 0.2073 +2024-06-17 03:04:07,850 - mmseg - INFO - Iter [60400/80000] lr: 9.800e-06, eta: 9:37:04, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1494, decode.acc_seg: 93.5056, aux.loss_ce: 0.0633, aux.acc_seg: 93.2009, loss: 0.2127 +2024-06-17 03:05:28,910 - mmseg - INFO - Iter [60450/80000] lr: 9.775e-06, eta: 9:35:33, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1414, decode.acc_seg: 93.5950, aux.loss_ce: 0.0602, aux.acc_seg: 93.1952, loss: 0.2016 +2024-06-17 03:06:49,927 - mmseg - INFO - Iter [60500/80000] lr: 9.751e-06, eta: 9:34:03, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1433, decode.acc_seg: 93.6419, aux.loss_ce: 0.0609, aux.acc_seg: 93.2702, loss: 0.2042 +2024-06-17 03:08:11,068 - mmseg - INFO - Iter [60550/80000] lr: 9.726e-06, eta: 9:32:32, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1471, decode.acc_seg: 93.5188, aux.loss_ce: 0.0625, aux.acc_seg: 93.1444, loss: 0.2095 +2024-06-17 03:09:32,047 - mmseg - INFO - Iter [60600/80000] lr: 9.701e-06, eta: 9:31:01, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1366, decode.acc_seg: 93.7921, aux.loss_ce: 0.0583, aux.acc_seg: 93.4421, loss: 0.1949 +2024-06-17 03:10:55,317 - mmseg - INFO - Iter [60650/80000] lr: 9.676e-06, eta: 9:29:32, time: 1.665, data_time: 0.052, memory: 71384, decode.loss_ce: 0.1559, decode.acc_seg: 93.5095, aux.loss_ce: 0.0658, aux.acc_seg: 93.1804, loss: 0.2217 +2024-06-17 03:12:16,307 - mmseg - INFO - Iter [60700/80000] lr: 9.651e-06, eta: 9:28:01, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1470, decode.acc_seg: 93.7515, aux.loss_ce: 0.0625, aux.acc_seg: 93.3666, loss: 0.2095 +2024-06-17 03:13:37,427 - mmseg - INFO - Iter [60750/80000] lr: 9.625e-06, eta: 9:26:30, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1412, decode.acc_seg: 93.7806, aux.loss_ce: 0.0605, aux.acc_seg: 93.3418, loss: 0.2016 +2024-06-17 03:14:58,490 - mmseg - INFO - Iter [60800/80000] lr: 9.600e-06, eta: 9:25:00, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1444, decode.acc_seg: 93.6688, aux.loss_ce: 0.0611, aux.acc_seg: 93.2929, loss: 0.2055 +2024-06-17 03:16:19,527 - mmseg - INFO - Iter [60850/80000] lr: 9.576e-06, eta: 9:23:29, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1429, decode.acc_seg: 93.7028, aux.loss_ce: 0.0607, aux.acc_seg: 93.3074, loss: 0.2035 +2024-06-17 03:17:40,489 - mmseg - INFO - Iter [60900/80000] lr: 9.551e-06, eta: 9:21:59, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1404, decode.acc_seg: 93.8892, aux.loss_ce: 0.0601, aux.acc_seg: 93.4236, loss: 0.2006 +2024-06-17 03:19:01,588 - mmseg - INFO - Iter [60950/80000] lr: 9.526e-06, eta: 9:20:28, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1493, decode.acc_seg: 93.6180, aux.loss_ce: 0.0637, aux.acc_seg: 93.1948, loss: 0.2130 +2024-06-17 03:20:22,573 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 03:20:22,573 - mmseg - INFO - Iter [61000/80000] lr: 9.501e-06, eta: 9:18:58, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1449, decode.acc_seg: 93.7662, aux.loss_ce: 0.0619, aux.acc_seg: 93.3458, loss: 0.2067 +2024-06-17 03:21:59,795 - mmseg - INFO - per class results: +2024-06-17 03:21:59,802 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.57 | 90.42 | +| building | 85.4 | 93.52 | +| sky | 95.09 | 97.94 | +| floor | 85.58 | 93.08 | +| tree | 78.25 | 89.72 | +| ceiling | 87.52 | 95.62 | +| road | 87.06 | 91.33 | +| bed | 92.76 | 97.01 | +| windowpane | 67.58 | 81.4 | +| grass | 70.26 | 85.47 | +| cabinet | 67.91 | 79.45 | +| sidewalk | 71.42 | 84.55 | +| person | 86.12 | 92.75 | +| earth | 40.85 | 53.12 | +| door | 61.85 | 77.26 | +| table | 70.45 | 80.7 | +| mountain | 62.34 | 73.95 | +| plant | 57.47 | 68.57 | +| curtain | 78.77 | 89.74 | +| chair | 69.13 | 80.75 | +| car | 88.92 | 93.73 | +| water | 63.0 | 76.28 | +| painting | 76.0 | 90.03 | +| sofa | 83.05 | 92.97 | +| shelf | 53.19 | 69.39 | +| house | 51.04 | 64.99 | +| sea | 73.96 | 81.93 | +| mirror | 79.03 | 85.11 | +| rug | 67.82 | 75.13 | +| field | 37.64 | 58.27 | +| armchair | 61.06 | 73.49 | +| seat | 67.28 | 88.22 | +| fence | 48.41 | 58.45 | +| desk | 59.74 | 77.53 | +| rock | 53.57 | 79.08 | +| wardrobe | 58.05 | 79.03 | +| lamp | 76.78 | 84.33 | +| bathtub | 87.72 | 90.05 | +| railing | 46.29 | 63.76 | +| cushion | 72.76 | 82.61 | +| base | 36.49 | 54.49 | +| box | 39.8 | 51.72 | +| column | 55.8 | 66.07 | +| signboard | 41.38 | 53.78 | +| chest of drawers | 42.38 | 59.1 | +| counter | 43.09 | 51.82 | +| sand | 56.61 | 86.15 | +| sink | 81.22 | 85.67 | +| skyscraper | 46.92 | 61.24 | +| fireplace | 72.92 | 95.16 | +| refrigerator | 83.83 | 90.94 | +| grandstand | 55.19 | 82.24 | +| path | 33.88 | 47.78 | +| stairs | 34.15 | 42.63 | +| runway | 75.62 | 97.78 | +| case | 64.65 | 86.99 | +| pool table | 94.46 | 98.03 | +| pillow | 68.53 | 77.16 | +| screen door | 82.04 | 84.76 | +| stairway | 40.58 | 53.4 | +| river | 8.49 | 19.32 | +| bridge | 74.9 | 85.14 | +| bookcase | 52.99 | 61.65 | +| blind | 41.94 | 47.82 | +| coffee table | 60.6 | 86.15 | +| toilet | 91.44 | 94.55 | +| flower | 46.18 | 62.62 | +| book | 56.1 | 78.93 | +| hill | 9.41 | 16.93 | +| bench | 54.78 | 63.94 | +| countertop | 64.69 | 82.53 | +| stove | 87.47 | 91.89 | +| palm | 56.28 | 83.82 | +| kitchen island | 70.18 | 88.21 | +| computer | 77.99 | 91.42 | +| swivel chair | 51.86 | 76.56 | +| boat | 83.35 | 91.29 | +| bar | 65.72 | 85.18 | +| arcade machine | 77.17 | 81.93 | +| hovel | 13.55 | 15.29 | +| bus | 93.09 | 97.0 | +| towel | 81.51 | 88.72 | +| light | 60.24 | 67.35 | +| truck | 49.27 | 61.63 | +| tower | 33.57 | 57.2 | +| chandelier | 75.33 | 84.04 | +| awning | 44.23 | 54.42 | +| streetlight | 37.32 | 47.14 | +| booth | 45.4 | 55.53 | +| television receiver | 80.9 | 87.35 | +| airplane | 87.99 | 96.62 | +| dirt track | 4.64 | 21.32 | +| apparel | 66.47 | 89.31 | +| pole | 23.76 | 33.07 | +| land | 3.34 | 3.98 | +| bannister | 23.54 | 29.9 | +| escalator | 66.67 | 83.52 | +| ottoman | 49.06 | 62.31 | +| bottle | 47.04 | 61.56 | +| buffet | 50.49 | 57.73 | +| poster | 29.02 | 36.02 | +| stage | 26.21 | 44.08 | +| van | 50.76 | 72.94 | +| ship | 88.69 | 93.79 | +| fountain | 39.31 | 40.3 | +| conveyer belt | 82.82 | 95.51 | +| canopy | 49.05 | 67.53 | +| washer | 87.95 | 94.29 | +| plaything | 33.98 | 45.94 | +| swimming pool | 52.64 | 79.88 | +| stool | 51.22 | 67.13 | +| barrel | 77.37 | 93.89 | +| basket | 42.16 | 61.62 | +| waterfall | 53.5 | 69.97 | +| tent | 94.6 | 98.55 | +| bag | 27.09 | 30.77 | +| minibike | 79.04 | 89.93 | +| cradle | 88.85 | 96.41 | +| oven | 59.94 | 71.93 | +| ball | 47.14 | 50.18 | +| food | 64.82 | 76.94 | +| step | 16.92 | 18.85 | +| tank | 79.11 | 93.99 | +| trade name | 24.25 | 29.37 | +| microwave | 88.69 | 95.87 | +| pot | 59.83 | 68.57 | +| animal | 61.98 | 63.51 | +| bicycle | 60.06 | 80.88 | +| lake | 39.5 | 63.79 | +| dishwasher | 78.84 | 83.79 | +| screen | 54.36 | 85.64 | +| blanket | 39.84 | 46.14 | +| sculpture | 73.94 | 88.9 | +| hood | 62.97 | 73.9 | +| sconce | 61.96 | 70.59 | +| vase | 47.44 | 63.2 | +| traffic light | 34.98 | 61.2 | +| tray | 23.06 | 29.77 | +| ashcan | 51.87 | 67.54 | +| fan | 69.39 | 79.93 | +| pier | 45.31 | 51.51 | +| crt screen | 1.83 | 3.31 | +| plate | 62.35 | 82.44 | +| monitor | 50.41 | 60.26 | +| bulletin board | 56.32 | 71.1 | +| shower | 17.51 | 19.72 | +| radiator | 66.87 | 80.58 | +| glass | 22.43 | 24.28 | +| clock | 45.39 | 52.12 | +| flag | 71.27 | 78.21 | ++---------------------+-------+-------+ +2024-06-17 03:21:59,802 - mmseg - INFO - Summary: +2024-06-17 03:21:59,802 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.67 | 58.78 | 70.66 | ++-------+-------+-------+ +2024-06-17 03:21:59,803 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 03:21:59,803 - mmseg - INFO - Iter(val) [250] aAcc: 0.8667, mIoU: 0.5878, mAcc: 0.7066, IoU.wall: 0.8257, IoU.building: 0.8540, IoU.sky: 0.9509, IoU.floor: 0.8558, IoU.tree: 0.7825, IoU.ceiling: 0.8752, IoU.road: 0.8706, IoU.bed : 0.9276, IoU.windowpane: 0.6758, IoU.grass: 0.7026, IoU.cabinet: 0.6791, IoU.sidewalk: 0.7142, IoU.person: 0.8612, IoU.earth: 0.4085, IoU.door: 0.6185, IoU.table: 0.7045, IoU.mountain: 0.6234, IoU.plant: 0.5747, IoU.curtain: 0.7877, IoU.chair: 0.6913, IoU.car: 0.8892, IoU.water: 0.6300, IoU.painting: 0.7600, IoU.sofa: 0.8305, IoU.shelf: 0.5319, IoU.house: 0.5104, IoU.sea: 0.7396, IoU.mirror: 0.7903, IoU.rug: 0.6782, IoU.field: 0.3764, IoU.armchair: 0.6106, IoU.seat: 0.6728, IoU.fence: 0.4841, IoU.desk: 0.5974, IoU.rock: 0.5357, IoU.wardrobe: 0.5805, IoU.lamp: 0.7678, IoU.bathtub: 0.8772, IoU.railing: 0.4629, IoU.cushion: 0.7276, IoU.base: 0.3649, IoU.box: 0.3980, IoU.column: 0.5580, IoU.signboard: 0.4138, IoU.chest of drawers: 0.4238, IoU.counter: 0.4309, IoU.sand: 0.5661, IoU.sink: 0.8122, IoU.skyscraper: 0.4692, IoU.fireplace: 0.7292, IoU.refrigerator: 0.8383, IoU.grandstand: 0.5519, IoU.path: 0.3388, IoU.stairs: 0.3415, IoU.runway: 0.7562, IoU.case: 0.6465, IoU.pool table: 0.9446, IoU.pillow: 0.6853, IoU.screen door: 0.8204, IoU.stairway: 0.4058, IoU.river: 0.0849, IoU.bridge: 0.7490, IoU.bookcase: 0.5299, IoU.blind: 0.4194, IoU.coffee table: 0.6060, IoU.toilet: 0.9144, IoU.flower: 0.4618, IoU.book: 0.5610, IoU.hill: 0.0941, IoU.bench: 0.5478, IoU.countertop: 0.6469, IoU.stove: 0.8747, IoU.palm: 0.5628, IoU.kitchen island: 0.7018, IoU.computer: 0.7799, IoU.swivel chair: 0.5186, IoU.boat: 0.8335, IoU.bar: 0.6572, IoU.arcade machine: 0.7717, IoU.hovel: 0.1355, IoU.bus: 0.9309, IoU.towel: 0.8151, IoU.light: 0.6024, IoU.truck: 0.4927, IoU.tower: 0.3357, IoU.chandelier: 0.7533, IoU.awning: 0.4423, IoU.streetlight: 0.3732, IoU.booth: 0.4540, IoU.television receiver: 0.8090, IoU.airplane: 0.8799, IoU.dirt track: 0.0464, IoU.apparel: 0.6647, IoU.pole: 0.2376, IoU.land: 0.0334, IoU.bannister: 0.2354, IoU.escalator: 0.6667, IoU.ottoman: 0.4906, IoU.bottle: 0.4704, IoU.buffet: 0.5049, IoU.poster: 0.2902, IoU.stage: 0.2621, IoU.van: 0.5076, IoU.ship: 0.8869, IoU.fountain: 0.3931, IoU.conveyer belt: 0.8282, IoU.canopy: 0.4905, IoU.washer: 0.8795, IoU.plaything: 0.3398, IoU.swimming pool: 0.5264, IoU.stool: 0.5122, IoU.barrel: 0.7737, IoU.basket: 0.4216, IoU.waterfall: 0.5350, IoU.tent: 0.9460, IoU.bag: 0.2709, IoU.minibike: 0.7904, IoU.cradle: 0.8885, IoU.oven: 0.5994, IoU.ball: 0.4714, IoU.food: 0.6482, IoU.step: 0.1692, IoU.tank: 0.7911, IoU.trade name: 0.2425, IoU.microwave: 0.8869, IoU.pot: 0.5983, IoU.animal: 0.6198, IoU.bicycle: 0.6006, IoU.lake: 0.3950, IoU.dishwasher: 0.7884, IoU.screen: 0.5436, IoU.blanket: 0.3984, IoU.sculpture: 0.7394, IoU.hood: 0.6297, IoU.sconce: 0.6196, IoU.vase: 0.4744, IoU.traffic light: 0.3498, IoU.tray: 0.2306, IoU.ashcan: 0.5187, IoU.fan: 0.6939, IoU.pier: 0.4531, IoU.crt screen: 0.0183, IoU.plate: 0.6235, IoU.monitor: 0.5041, IoU.bulletin board: 0.5632, IoU.shower: 0.1751, IoU.radiator: 0.6687, IoU.glass: 0.2243, IoU.clock: 0.4539, IoU.flag: 0.7127, Acc.wall: 0.9042, Acc.building: 0.9352, Acc.sky: 0.9794, Acc.floor: 0.9308, Acc.tree: 0.8972, Acc.ceiling: 0.9562, Acc.road: 0.9133, Acc.bed : 0.9701, Acc.windowpane: 0.8140, Acc.grass: 0.8547, Acc.cabinet: 0.7945, Acc.sidewalk: 0.8455, Acc.person: 0.9275, Acc.earth: 0.5312, Acc.door: 0.7726, Acc.table: 0.8070, Acc.mountain: 0.7395, Acc.plant: 0.6857, Acc.curtain: 0.8974, Acc.chair: 0.8075, Acc.car: 0.9373, Acc.water: 0.7628, Acc.painting: 0.9003, Acc.sofa: 0.9297, Acc.shelf: 0.6939, Acc.house: 0.6499, Acc.sea: 0.8193, Acc.mirror: 0.8511, Acc.rug: 0.7513, Acc.field: 0.5827, Acc.armchair: 0.7349, Acc.seat: 0.8822, Acc.fence: 0.5845, Acc.desk: 0.7753, Acc.rock: 0.7908, Acc.wardrobe: 0.7903, Acc.lamp: 0.8433, Acc.bathtub: 0.9005, Acc.railing: 0.6376, Acc.cushion: 0.8261, Acc.base: 0.5449, Acc.box: 0.5172, Acc.column: 0.6607, Acc.signboard: 0.5378, Acc.chest of drawers: 0.5910, Acc.counter: 0.5182, Acc.sand: 0.8615, Acc.sink: 0.8567, Acc.skyscraper: 0.6124, Acc.fireplace: 0.9516, Acc.refrigerator: 0.9094, Acc.grandstand: 0.8224, Acc.path: 0.4778, Acc.stairs: 0.4263, Acc.runway: 0.9778, Acc.case: 0.8699, Acc.pool table: 0.9803, Acc.pillow: 0.7716, Acc.screen door: 0.8476, Acc.stairway: 0.5340, Acc.river: 0.1932, Acc.bridge: 0.8514, Acc.bookcase: 0.6165, Acc.blind: 0.4782, Acc.coffee table: 0.8615, Acc.toilet: 0.9455, Acc.flower: 0.6262, Acc.book: 0.7893, Acc.hill: 0.1693, Acc.bench: 0.6394, Acc.countertop: 0.8253, Acc.stove: 0.9189, Acc.palm: 0.8382, Acc.kitchen island: 0.8821, Acc.computer: 0.9142, Acc.swivel chair: 0.7656, Acc.boat: 0.9129, Acc.bar: 0.8518, Acc.arcade machine: 0.8193, Acc.hovel: 0.1529, Acc.bus: 0.9700, Acc.towel: 0.8872, Acc.light: 0.6735, Acc.truck: 0.6163, Acc.tower: 0.5720, Acc.chandelier: 0.8404, Acc.awning: 0.5442, Acc.streetlight: 0.4714, Acc.booth: 0.5553, Acc.television receiver: 0.8735, Acc.airplane: 0.9662, Acc.dirt track: 0.2132, Acc.apparel: 0.8931, Acc.pole: 0.3307, Acc.land: 0.0398, Acc.bannister: 0.2990, Acc.escalator: 0.8352, Acc.ottoman: 0.6231, Acc.bottle: 0.6156, Acc.buffet: 0.5773, Acc.poster: 0.3602, Acc.stage: 0.4408, Acc.van: 0.7294, Acc.ship: 0.9379, Acc.fountain: 0.4030, Acc.conveyer belt: 0.9551, Acc.canopy: 0.6753, Acc.washer: 0.9429, Acc.plaything: 0.4594, Acc.swimming pool: 0.7988, Acc.stool: 0.6713, Acc.barrel: 0.9389, Acc.basket: 0.6162, Acc.waterfall: 0.6997, Acc.tent: 0.9855, Acc.bag: 0.3077, Acc.minibike: 0.8993, Acc.cradle: 0.9641, Acc.oven: 0.7193, Acc.ball: 0.5018, Acc.food: 0.7694, Acc.step: 0.1885, Acc.tank: 0.9399, Acc.trade name: 0.2937, Acc.microwave: 0.9587, Acc.pot: 0.6857, Acc.animal: 0.6351, Acc.bicycle: 0.8088, Acc.lake: 0.6379, Acc.dishwasher: 0.8379, Acc.screen: 0.8564, Acc.blanket: 0.4614, Acc.sculpture: 0.8890, Acc.hood: 0.7390, Acc.sconce: 0.7059, Acc.vase: 0.6320, Acc.traffic light: 0.6120, Acc.tray: 0.2977, Acc.ashcan: 0.6754, Acc.fan: 0.7993, Acc.pier: 0.5151, Acc.crt screen: 0.0331, Acc.plate: 0.8244, Acc.monitor: 0.6026, Acc.bulletin board: 0.7110, Acc.shower: 0.1972, Acc.radiator: 0.8058, Acc.glass: 0.2428, Acc.clock: 0.5212, Acc.flag: 0.7821 +2024-06-17 03:23:21,336 - mmseg - INFO - Iter [61050/80000] lr: 9.476e-06, eta: 9:17:57, time: 3.575, data_time: 1.961, memory: 71384, decode.loss_ce: 0.1442, decode.acc_seg: 93.4707, aux.loss_ce: 0.0614, aux.acc_seg: 93.0502, loss: 0.2056 +2024-06-17 03:24:42,473 - mmseg - INFO - Iter [61100/80000] lr: 9.451e-06, eta: 9:16:27, time: 1.623, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1359, decode.acc_seg: 93.9072, aux.loss_ce: 0.0585, aux.acc_seg: 93.4149, loss: 0.1944 +2024-06-17 03:26:03,604 - mmseg - INFO - Iter [61150/80000] lr: 9.426e-06, eta: 9:14:56, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1401, decode.acc_seg: 93.8843, aux.loss_ce: 0.0598, aux.acc_seg: 93.4613, loss: 0.1999 +2024-06-17 03:27:24,732 - mmseg - INFO - Iter [61200/80000] lr: 9.400e-06, eta: 9:13:26, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1378, decode.acc_seg: 93.7915, aux.loss_ce: 0.0592, aux.acc_seg: 93.3533, loss: 0.1970 +2024-06-17 03:28:45,869 - mmseg - INFO - Iter [61250/80000] lr: 9.376e-06, eta: 9:11:55, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1392, decode.acc_seg: 93.9946, aux.loss_ce: 0.0594, aux.acc_seg: 93.5596, loss: 0.1986 +2024-06-17 03:30:07,026 - mmseg - INFO - Iter [61300/80000] lr: 9.350e-06, eta: 9:10:25, time: 1.623, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1509, decode.acc_seg: 93.5542, aux.loss_ce: 0.0638, aux.acc_seg: 93.2250, loss: 0.2147 +2024-06-17 03:31:28,117 - mmseg - INFO - Iter [61350/80000] lr: 9.326e-06, eta: 9:08:54, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1427, decode.acc_seg: 93.6152, aux.loss_ce: 0.0611, aux.acc_seg: 93.2302, loss: 0.2038 +2024-06-17 03:32:49,391 - mmseg - INFO - Iter [61400/80000] lr: 9.301e-06, eta: 9:07:24, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1539, decode.acc_seg: 93.4473, aux.loss_ce: 0.0651, aux.acc_seg: 93.0655, loss: 0.2190 +2024-06-17 03:34:10,546 - mmseg - INFO - Iter [61450/80000] lr: 9.276e-06, eta: 9:05:53, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1386, decode.acc_seg: 93.8480, aux.loss_ce: 0.0593, aux.acc_seg: 93.4167, loss: 0.1979 +2024-06-17 03:35:31,740 - mmseg - INFO - Iter [61500/80000] lr: 9.251e-06, eta: 9:04:23, time: 1.624, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1468, decode.acc_seg: 93.4401, aux.loss_ce: 0.0621, aux.acc_seg: 93.0509, loss: 0.2089 +2024-06-17 03:36:52,903 - mmseg - INFO - Iter [61550/80000] lr: 9.226e-06, eta: 9:02:53, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1455, decode.acc_seg: 93.6630, aux.loss_ce: 0.0623, aux.acc_seg: 93.2245, loss: 0.2078 +2024-06-17 03:38:13,953 - mmseg - INFO - Iter [61600/80000] lr: 9.200e-06, eta: 9:01:22, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1431, decode.acc_seg: 93.8271, aux.loss_ce: 0.0609, aux.acc_seg: 93.4256, loss: 0.2040 +2024-06-17 03:39:34,929 - mmseg - INFO - Iter [61650/80000] lr: 9.175e-06, eta: 8:59:52, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1402, decode.acc_seg: 93.8402, aux.loss_ce: 0.0602, aux.acc_seg: 93.4555, loss: 0.2004 +2024-06-17 03:40:56,019 - mmseg - INFO - Iter [61700/80000] lr: 9.150e-06, eta: 8:58:21, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1349, decode.acc_seg: 93.7922, aux.loss_ce: 0.0579, aux.acc_seg: 93.3948, loss: 0.1928 +2024-06-17 03:42:17,067 - mmseg - INFO - Iter [61750/80000] lr: 9.126e-06, eta: 8:56:51, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1466, decode.acc_seg: 93.4862, aux.loss_ce: 0.0623, aux.acc_seg: 93.1153, loss: 0.2090 +2024-06-17 03:43:38,056 - mmseg - INFO - Iter [61800/80000] lr: 9.101e-06, eta: 8:55:21, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1416, decode.acc_seg: 93.6671, aux.loss_ce: 0.0605, aux.acc_seg: 93.2539, loss: 0.2021 +2024-06-17 03:44:59,420 - mmseg - INFO - Iter [61850/80000] lr: 9.076e-06, eta: 8:53:50, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1443, decode.acc_seg: 93.7607, aux.loss_ce: 0.0619, aux.acc_seg: 93.3139, loss: 0.2062 +2024-06-17 03:46:23,574 - mmseg - INFO - Iter [61900/80000] lr: 9.051e-06, eta: 8:52:21, time: 1.683, data_time: 0.066, memory: 71384, decode.loss_ce: 0.1474, decode.acc_seg: 93.6535, aux.loss_ce: 0.0621, aux.acc_seg: 93.2067, loss: 0.2095 +2024-06-17 03:47:44,597 - mmseg - INFO - Iter [61950/80000] lr: 9.026e-06, eta: 8:50:51, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1405, decode.acc_seg: 93.7996, aux.loss_ce: 0.0600, aux.acc_seg: 93.3647, loss: 0.2005 +2024-06-17 03:49:05,579 - mmseg - INFO - Saving checkpoint at 62000 iterations +2024-06-17 03:50:30,931 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 03:50:30,931 - mmseg - INFO - Iter [62000/80000] lr: 9.000e-06, eta: 8:49:45, time: 3.327, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1360, decode.acc_seg: 93.7859, aux.loss_ce: 0.0576, aux.acc_seg: 93.4197, loss: 0.1935 +2024-06-17 03:52:06,520 - mmseg - INFO - per class results: +2024-06-17 03:52:06,526 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.74 | 90.29 | +| building | 85.33 | 93.75 | +| sky | 95.13 | 97.72 | +| floor | 86.03 | 92.18 | +| tree | 77.99 | 90.9 | +| ceiling | 87.82 | 94.8 | +| road | 87.0 | 91.66 | +| bed | 92.75 | 96.97 | +| windowpane | 68.07 | 84.08 | +| grass | 70.77 | 84.76 | +| cabinet | 67.54 | 77.99 | +| sidewalk | 70.56 | 84.33 | +| person | 86.38 | 94.51 | +| earth | 42.68 | 56.2 | +| door | 60.31 | 73.35 | +| table | 69.66 | 81.06 | +| mountain | 62.06 | 74.24 | +| plant | 56.34 | 64.55 | +| curtain | 79.24 | 87.79 | +| chair | 69.88 | 80.97 | +| car | 88.61 | 93.95 | +| water | 64.71 | 77.97 | +| painting | 76.21 | 90.14 | +| sofa | 82.94 | 92.41 | +| shelf | 53.58 | 68.49 | +| house | 50.39 | 59.78 | +| sea | 73.04 | 81.72 | +| mirror | 79.71 | 86.78 | +| rug | 68.33 | 76.61 | +| field | 33.64 | 60.5 | +| armchair | 63.99 | 82.66 | +| seat | 68.05 | 89.12 | +| fence | 50.54 | 59.42 | +| desk | 58.39 | 79.81 | +| rock | 53.07 | 77.82 | +| wardrobe | 55.84 | 77.64 | +| lamp | 77.44 | 86.89 | +| bathtub | 86.86 | 89.42 | +| railing | 46.3 | 64.88 | +| cushion | 73.27 | 82.41 | +| base | 40.07 | 61.82 | +| box | 40.49 | 50.2 | +| column | 56.46 | 68.52 | +| signboard | 41.83 | 55.7 | +| chest of drawers | 40.18 | 57.13 | +| counter | 41.97 | 50.02 | +| sand | 58.16 | 86.52 | +| sink | 80.97 | 87.48 | +| skyscraper | 47.72 | 60.67 | +| fireplace | 70.49 | 97.64 | +| refrigerator | 84.64 | 91.11 | +| grandstand | 56.29 | 80.99 | +| path | 31.7 | 41.21 | +| stairs | 32.41 | 38.01 | +| runway | 75.14 | 98.01 | +| case | 63.16 | 84.25 | +| pool table | 95.0 | 98.08 | +| pillow | 70.3 | 80.89 | +| screen door | 83.9 | 87.11 | +| stairway | 40.47 | 65.4 | +| river | 13.23 | 27.99 | +| bridge | 70.94 | 79.37 | +| bookcase | 50.36 | 60.63 | +| blind | 41.15 | 47.21 | +| coffee table | 61.02 | 87.65 | +| toilet | 90.82 | 93.08 | +| flower | 46.84 | 59.08 | +| book | 55.22 | 80.89 | +| hill | 8.2 | 14.49 | +| bench | 55.83 | 63.74 | +| countertop | 64.53 | 84.22 | +| stove | 87.45 | 91.55 | +| palm | 55.26 | 79.86 | +| kitchen island | 61.09 | 88.53 | +| computer | 78.12 | 91.51 | +| swivel chair | 51.4 | 75.19 | +| boat | 81.23 | 90.93 | +| bar | 65.21 | 86.74 | +| arcade machine | 78.58 | 83.77 | +| hovel | 14.07 | 15.5 | +| bus | 93.32 | 96.21 | +| towel | 83.19 | 88.8 | +| light | 62.06 | 69.34 | +| truck | 48.54 | 62.4 | +| tower | 19.2 | 30.2 | +| chandelier | 75.7 | 85.04 | +| awning | 40.2 | 48.31 | +| streetlight | 37.66 | 48.15 | +| booth | 40.57 | 58.91 | +| television receiver | 80.74 | 87.41 | +| airplane | 89.06 | 96.14 | +| dirt track | 3.52 | 17.01 | +| apparel | 65.92 | 87.7 | +| pole | 20.58 | 33.45 | +| land | 3.64 | 4.62 | +| bannister | 23.35 | 29.62 | +| escalator | 64.96 | 81.32 | +| ottoman | 49.89 | 63.31 | +| bottle | 45.1 | 59.97 | +| buffet | 52.71 | 63.8 | +| poster | 31.22 | 37.69 | +| stage | 27.3 | 45.09 | +| van | 47.4 | 68.81 | +| ship | 92.39 | 97.08 | +| fountain | 42.55 | 44.17 | +| conveyer belt | 82.41 | 96.3 | +| canopy | 55.58 | 77.14 | +| washer | 84.48 | 89.63 | +| plaything | 46.29 | 67.86 | +| swimming pool | 54.72 | 82.71 | +| stool | 51.39 | 67.63 | +| barrel | 70.36 | 84.39 | +| basket | 42.4 | 61.49 | +| waterfall | 52.1 | 69.75 | +| tent | 95.14 | 98.35 | +| bag | 29.8 | 34.2 | +| minibike | 77.28 | 91.18 | +| cradle | 86.11 | 97.3 | +| oven | 60.04 | 71.85 | +| ball | 56.68 | 59.77 | +| food | 63.21 | 73.26 | +| step | 14.6 | 16.86 | +| tank | 80.41 | 94.05 | +| trade name | 25.88 | 30.18 | +| microwave | 88.35 | 96.3 | +| pot | 59.19 | 70.77 | +| animal | 62.5 | 64.03 | +| bicycle | 59.38 | 74.85 | +| lake | 44.93 | 68.11 | +| dishwasher | 76.57 | 87.95 | +| screen | 55.08 | 84.98 | +| blanket | 39.66 | 45.37 | +| sculpture | 75.67 | 87.74 | +| hood | 65.49 | 77.99 | +| sconce | 61.01 | 68.15 | +| vase | 47.2 | 67.57 | +| traffic light | 35.75 | 61.47 | +| tray | 25.47 | 32.48 | +| ashcan | 51.52 | 64.93 | +| fan | 69.7 | 77.67 | +| pier | 44.57 | 50.58 | +| crt screen | 1.8 | 3.41 | +| plate | 62.69 | 82.02 | +| monitor | 51.01 | 61.3 | +| bulletin board | 52.35 | 64.09 | +| shower | 13.03 | 14.89 | +| radiator | 66.66 | 80.52 | +| glass | 21.78 | 23.55 | +| clock | 48.35 | 54.42 | +| flag | 71.15 | 80.66 | ++---------------------+-------+-------+ +2024-06-17 03:52:06,527 - mmseg - INFO - Summary: +2024-06-17 03:52:06,527 - mmseg - INFO - ++-------+------+-------+ +| aAcc | mIoU | mAcc | ++-------+------+-------+ +| 86.67 | 58.7 | 70.85 | ++-------+------+-------+ +2024-06-17 03:52:06,527 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 03:52:06,528 - mmseg - INFO - Iter(val) [250] aAcc: 0.8667, mIoU: 0.5870, mAcc: 0.7085, IoU.wall: 0.8274, IoU.building: 0.8533, IoU.sky: 0.9513, IoU.floor: 0.8603, IoU.tree: 0.7799, IoU.ceiling: 0.8782, IoU.road: 0.8700, IoU.bed : 0.9275, IoU.windowpane: 0.6807, IoU.grass: 0.7077, IoU.cabinet: 0.6754, IoU.sidewalk: 0.7056, IoU.person: 0.8638, IoU.earth: 0.4268, IoU.door: 0.6031, IoU.table: 0.6966, IoU.mountain: 0.6206, IoU.plant: 0.5634, IoU.curtain: 0.7924, IoU.chair: 0.6988, IoU.car: 0.8861, IoU.water: 0.6471, IoU.painting: 0.7621, IoU.sofa: 0.8294, IoU.shelf: 0.5358, IoU.house: 0.5039, IoU.sea: 0.7304, IoU.mirror: 0.7971, IoU.rug: 0.6833, IoU.field: 0.3364, IoU.armchair: 0.6399, IoU.seat: 0.6805, IoU.fence: 0.5054, IoU.desk: 0.5839, IoU.rock: 0.5307, IoU.wardrobe: 0.5584, IoU.lamp: 0.7744, IoU.bathtub: 0.8686, IoU.railing: 0.4630, IoU.cushion: 0.7327, IoU.base: 0.4007, IoU.box: 0.4049, IoU.column: 0.5646, IoU.signboard: 0.4183, IoU.chest of drawers: 0.4018, IoU.counter: 0.4197, IoU.sand: 0.5816, IoU.sink: 0.8097, IoU.skyscraper: 0.4772, IoU.fireplace: 0.7049, IoU.refrigerator: 0.8464, IoU.grandstand: 0.5629, IoU.path: 0.3170, IoU.stairs: 0.3241, IoU.runway: 0.7514, IoU.case: 0.6316, IoU.pool table: 0.9500, IoU.pillow: 0.7030, IoU.screen door: 0.8390, IoU.stairway: 0.4047, IoU.river: 0.1323, IoU.bridge: 0.7094, IoU.bookcase: 0.5036, IoU.blind: 0.4115, IoU.coffee table: 0.6102, IoU.toilet: 0.9082, IoU.flower: 0.4684, IoU.book: 0.5522, IoU.hill: 0.0820, IoU.bench: 0.5583, IoU.countertop: 0.6453, IoU.stove: 0.8745, IoU.palm: 0.5526, IoU.kitchen island: 0.6109, IoU.computer: 0.7812, IoU.swivel chair: 0.5140, IoU.boat: 0.8123, IoU.bar: 0.6521, IoU.arcade machine: 0.7858, IoU.hovel: 0.1407, IoU.bus: 0.9332, IoU.towel: 0.8319, IoU.light: 0.6206, IoU.truck: 0.4854, IoU.tower: 0.1920, IoU.chandelier: 0.7570, IoU.awning: 0.4020, IoU.streetlight: 0.3766, IoU.booth: 0.4057, IoU.television receiver: 0.8074, IoU.airplane: 0.8906, IoU.dirt track: 0.0352, IoU.apparel: 0.6592, IoU.pole: 0.2058, IoU.land: 0.0364, IoU.bannister: 0.2335, IoU.escalator: 0.6496, IoU.ottoman: 0.4989, IoU.bottle: 0.4510, IoU.buffet: 0.5271, IoU.poster: 0.3122, IoU.stage: 0.2730, IoU.van: 0.4740, IoU.ship: 0.9239, IoU.fountain: 0.4255, IoU.conveyer belt: 0.8241, IoU.canopy: 0.5558, IoU.washer: 0.8448, IoU.plaything: 0.4629, IoU.swimming pool: 0.5472, IoU.stool: 0.5139, IoU.barrel: 0.7036, IoU.basket: 0.4240, IoU.waterfall: 0.5210, IoU.tent: 0.9514, IoU.bag: 0.2980, IoU.minibike: 0.7728, IoU.cradle: 0.8611, IoU.oven: 0.6004, IoU.ball: 0.5668, IoU.food: 0.6321, IoU.step: 0.1460, IoU.tank: 0.8041, IoU.trade name: 0.2588, IoU.microwave: 0.8835, IoU.pot: 0.5919, IoU.animal: 0.6250, IoU.bicycle: 0.5938, IoU.lake: 0.4493, IoU.dishwasher: 0.7657, IoU.screen: 0.5508, IoU.blanket: 0.3966, IoU.sculpture: 0.7567, IoU.hood: 0.6549, IoU.sconce: 0.6101, IoU.vase: 0.4720, IoU.traffic light: 0.3575, IoU.tray: 0.2547, IoU.ashcan: 0.5152, IoU.fan: 0.6970, IoU.pier: 0.4457, IoU.crt screen: 0.0180, IoU.plate: 0.6269, IoU.monitor: 0.5101, IoU.bulletin board: 0.5235, IoU.shower: 0.1303, IoU.radiator: 0.6666, IoU.glass: 0.2178, IoU.clock: 0.4835, IoU.flag: 0.7115, Acc.wall: 0.9029, Acc.building: 0.9375, Acc.sky: 0.9772, Acc.floor: 0.9218, Acc.tree: 0.9090, Acc.ceiling: 0.9480, Acc.road: 0.9166, Acc.bed : 0.9697, Acc.windowpane: 0.8408, Acc.grass: 0.8476, Acc.cabinet: 0.7799, Acc.sidewalk: 0.8433, Acc.person: 0.9451, Acc.earth: 0.5620, Acc.door: 0.7335, Acc.table: 0.8106, Acc.mountain: 0.7424, Acc.plant: 0.6455, Acc.curtain: 0.8779, Acc.chair: 0.8097, Acc.car: 0.9395, Acc.water: 0.7797, Acc.painting: 0.9014, Acc.sofa: 0.9241, Acc.shelf: 0.6849, Acc.house: 0.5978, Acc.sea: 0.8172, Acc.mirror: 0.8678, Acc.rug: 0.7661, Acc.field: 0.6050, Acc.armchair: 0.8266, Acc.seat: 0.8912, Acc.fence: 0.5942, Acc.desk: 0.7981, Acc.rock: 0.7782, Acc.wardrobe: 0.7764, Acc.lamp: 0.8689, Acc.bathtub: 0.8942, Acc.railing: 0.6488, Acc.cushion: 0.8241, Acc.base: 0.6182, Acc.box: 0.5020, Acc.column: 0.6852, Acc.signboard: 0.5570, Acc.chest of drawers: 0.5713, Acc.counter: 0.5002, Acc.sand: 0.8652, Acc.sink: 0.8748, Acc.skyscraper: 0.6067, Acc.fireplace: 0.9764, Acc.refrigerator: 0.9111, Acc.grandstand: 0.8099, Acc.path: 0.4121, Acc.stairs: 0.3801, Acc.runway: 0.9801, Acc.case: 0.8425, Acc.pool table: 0.9808, Acc.pillow: 0.8089, Acc.screen door: 0.8711, Acc.stairway: 0.6540, Acc.river: 0.2799, Acc.bridge: 0.7937, Acc.bookcase: 0.6063, Acc.blind: 0.4721, Acc.coffee table: 0.8765, Acc.toilet: 0.9308, Acc.flower: 0.5908, Acc.book: 0.8089, Acc.hill: 0.1449, Acc.bench: 0.6374, Acc.countertop: 0.8422, Acc.stove: 0.9155, Acc.palm: 0.7986, Acc.kitchen island: 0.8853, Acc.computer: 0.9151, Acc.swivel chair: 0.7519, Acc.boat: 0.9093, Acc.bar: 0.8674, Acc.arcade machine: 0.8377, Acc.hovel: 0.1550, Acc.bus: 0.9621, Acc.towel: 0.8880, Acc.light: 0.6934, Acc.truck: 0.6240, Acc.tower: 0.3020, Acc.chandelier: 0.8504, Acc.awning: 0.4831, Acc.streetlight: 0.4815, Acc.booth: 0.5891, Acc.television receiver: 0.8741, Acc.airplane: 0.9614, Acc.dirt track: 0.1701, Acc.apparel: 0.8770, Acc.pole: 0.3345, Acc.land: 0.0462, Acc.bannister: 0.2962, Acc.escalator: 0.8132, Acc.ottoman: 0.6331, Acc.bottle: 0.5997, Acc.buffet: 0.6380, Acc.poster: 0.3769, Acc.stage: 0.4509, Acc.van: 0.6881, Acc.ship: 0.9708, Acc.fountain: 0.4417, Acc.conveyer belt: 0.9630, Acc.canopy: 0.7714, Acc.washer: 0.8963, Acc.plaything: 0.6786, Acc.swimming pool: 0.8271, Acc.stool: 0.6763, Acc.barrel: 0.8439, Acc.basket: 0.6149, Acc.waterfall: 0.6975, Acc.tent: 0.9835, Acc.bag: 0.3420, Acc.minibike: 0.9118, Acc.cradle: 0.9730, Acc.oven: 0.7185, Acc.ball: 0.5977, Acc.food: 0.7326, Acc.step: 0.1686, Acc.tank: 0.9405, Acc.trade name: 0.3018, Acc.microwave: 0.9630, Acc.pot: 0.7077, Acc.animal: 0.6403, Acc.bicycle: 0.7485, Acc.lake: 0.6811, Acc.dishwasher: 0.8795, Acc.screen: 0.8498, Acc.blanket: 0.4537, Acc.sculpture: 0.8774, Acc.hood: 0.7799, Acc.sconce: 0.6815, Acc.vase: 0.6757, Acc.traffic light: 0.6147, Acc.tray: 0.3248, Acc.ashcan: 0.6493, Acc.fan: 0.7767, Acc.pier: 0.5058, Acc.crt screen: 0.0341, Acc.plate: 0.8202, Acc.monitor: 0.6130, Acc.bulletin board: 0.6409, Acc.shower: 0.1489, Acc.radiator: 0.8052, Acc.glass: 0.2355, Acc.clock: 0.5442, Acc.flag: 0.8066 +2024-06-17 03:53:28,211 - mmseg - INFO - Iter [62050/80000] lr: 8.975e-06, eta: 8:48:42, time: 3.546, data_time: 1.928, memory: 71384, decode.loss_ce: 0.1371, decode.acc_seg: 93.7792, aux.loss_ce: 0.0584, aux.acc_seg: 93.3611, loss: 0.1955 +2024-06-17 03:54:49,254 - mmseg - INFO - Iter [62100/80000] lr: 8.951e-06, eta: 8:47:12, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1395, decode.acc_seg: 93.8182, aux.loss_ce: 0.0598, aux.acc_seg: 93.4102, loss: 0.1993 +2024-06-17 03:56:10,357 - mmseg - INFO - Iter [62150/80000] lr: 8.925e-06, eta: 8:45:42, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1469, decode.acc_seg: 93.6158, aux.loss_ce: 0.0626, aux.acc_seg: 93.2511, loss: 0.2095 +2024-06-17 03:57:31,433 - mmseg - INFO - Iter [62200/80000] lr: 8.901e-06, eta: 8:44:11, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1413, decode.acc_seg: 93.7332, aux.loss_ce: 0.0603, aux.acc_seg: 93.3141, loss: 0.2016 +2024-06-17 03:58:52,556 - mmseg - INFO - Iter [62250/80000] lr: 8.876e-06, eta: 8:42:41, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1434, decode.acc_seg: 93.9654, aux.loss_ce: 0.0607, aux.acc_seg: 93.5833, loss: 0.2041 +2024-06-17 04:00:13,631 - mmseg - INFO - Iter [62300/80000] lr: 8.851e-06, eta: 8:41:10, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1392, decode.acc_seg: 93.9131, aux.loss_ce: 0.0594, aux.acc_seg: 93.5458, loss: 0.1986 +2024-06-17 04:01:34,766 - mmseg - INFO - Iter [62350/80000] lr: 8.826e-06, eta: 8:39:40, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1430, decode.acc_seg: 93.8203, aux.loss_ce: 0.0608, aux.acc_seg: 93.4015, loss: 0.2038 +2024-06-17 04:02:55,929 - mmseg - INFO - Iter [62400/80000] lr: 8.801e-06, eta: 8:38:10, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1525, decode.acc_seg: 93.3746, aux.loss_ce: 0.0648, aux.acc_seg: 92.9977, loss: 0.2173 +2024-06-17 04:04:16,951 - mmseg - INFO - Iter [62450/80000] lr: 8.775e-06, eta: 8:36:39, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1685, decode.acc_seg: 93.1516, aux.loss_ce: 0.0710, aux.acc_seg: 92.7404, loss: 0.2395 +2024-06-17 04:05:38,057 - mmseg - INFO - Iter [62500/80000] lr: 8.751e-06, eta: 8:35:09, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1467, decode.acc_seg: 93.8232, aux.loss_ce: 0.0619, aux.acc_seg: 93.4670, loss: 0.2087 +2024-06-17 04:06:59,144 - mmseg - INFO - Iter [62550/80000] lr: 8.725e-06, eta: 8:33:39, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1403, decode.acc_seg: 93.8886, aux.loss_ce: 0.0599, aux.acc_seg: 93.4592, loss: 0.2002 +2024-06-17 04:08:20,153 - mmseg - INFO - Iter [62600/80000] lr: 8.701e-06, eta: 8:32:08, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1454, decode.acc_seg: 93.4527, aux.loss_ce: 0.0619, aux.acc_seg: 93.0553, loss: 0.2073 +2024-06-17 04:09:41,259 - mmseg - INFO - Iter [62650/80000] lr: 8.676e-06, eta: 8:30:38, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1504, decode.acc_seg: 93.4815, aux.loss_ce: 0.0640, aux.acc_seg: 93.0851, loss: 0.2144 +2024-06-17 04:11:02,400 - mmseg - INFO - Iter [62700/80000] lr: 8.651e-06, eta: 8:29:08, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1481, decode.acc_seg: 93.4459, aux.loss_ce: 0.0638, aux.acc_seg: 92.9808, loss: 0.2119 +2024-06-17 04:12:23,656 - mmseg - INFO - Iter [62750/80000] lr: 8.626e-06, eta: 8:27:37, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1406, decode.acc_seg: 93.8426, aux.loss_ce: 0.0600, aux.acc_seg: 93.4418, loss: 0.2006 +2024-06-17 04:13:44,607 - mmseg - INFO - Iter [62800/80000] lr: 8.601e-06, eta: 8:26:07, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1380, decode.acc_seg: 94.0567, aux.loss_ce: 0.0588, aux.acc_seg: 93.6224, loss: 0.1968 +2024-06-17 04:15:05,581 - mmseg - INFO - Iter [62850/80000] lr: 8.575e-06, eta: 8:24:37, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1399, decode.acc_seg: 93.7004, aux.loss_ce: 0.0597, aux.acc_seg: 93.3265, loss: 0.1997 +2024-06-17 04:16:26,581 - mmseg - INFO - Iter [62900/80000] lr: 8.550e-06, eta: 8:23:07, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1410, decode.acc_seg: 93.9080, aux.loss_ce: 0.0598, aux.acc_seg: 93.4790, loss: 0.2008 +2024-06-17 04:17:47,761 - mmseg - INFO - Iter [62950/80000] lr: 8.525e-06, eta: 8:21:36, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1386, decode.acc_seg: 93.8448, aux.loss_ce: 0.0592, aux.acc_seg: 93.4638, loss: 0.1979 +2024-06-17 04:19:08,868 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 04:19:08,868 - mmseg - INFO - Iter [63000/80000] lr: 8.501e-06, eta: 8:20:06, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1445, decode.acc_seg: 93.4147, aux.loss_ce: 0.0612, aux.acc_seg: 93.0524, loss: 0.2056 +2024-06-17 04:20:47,536 - mmseg - INFO - per class results: +2024-06-17 04:20:47,542 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.02 | 89.95 | +| building | 85.6 | 93.55 | +| sky | 95.16 | 97.64 | +| floor | 85.8 | 93.62 | +| tree | 78.01 | 90.12 | +| ceiling | 88.08 | 94.54 | +| road | 86.33 | 90.7 | +| bed | 92.82 | 96.87 | +| windowpane | 67.53 | 85.57 | +| grass | 70.87 | 82.43 | +| cabinet | 69.88 | 80.13 | +| sidewalk | 71.14 | 86.92 | +| person | 86.4 | 94.97 | +| earth | 41.61 | 56.13 | +| door | 60.25 | 73.24 | +| table | 70.41 | 81.78 | +| mountain | 62.28 | 72.22 | +| plant | 57.2 | 68.15 | +| curtain | 79.6 | 90.19 | +| chair | 68.54 | 79.22 | +| car | 88.52 | 93.8 | +| water | 63.05 | 79.54 | +| painting | 78.15 | 90.6 | +| sofa | 83.58 | 90.56 | +| shelf | 52.85 | 69.68 | +| house | 53.26 | 64.04 | +| sea | 71.14 | 78.84 | +| mirror | 78.33 | 85.58 | +| rug | 66.83 | 72.77 | +| field | 33.23 | 61.15 | +| armchair | 61.86 | 77.88 | +| seat | 66.09 | 89.51 | +| fence | 56.11 | 70.47 | +| desk | 58.86 | 79.68 | +| rock | 54.83 | 81.22 | +| wardrobe | 59.17 | 68.84 | +| lamp | 77.1 | 86.5 | +| bathtub | 87.01 | 90.0 | +| railing | 45.27 | 63.94 | +| cushion | 73.8 | 82.82 | +| base | 37.21 | 51.51 | +| box | 39.18 | 50.4 | +| column | 58.0 | 66.78 | +| signboard | 40.98 | 58.27 | +| chest of drawers | 40.06 | 63.96 | +| counter | 41.34 | 50.18 | +| sand | 57.04 | 87.07 | +| sink | 81.13 | 86.41 | +| skyscraper | 47.4 | 60.51 | +| fireplace | 72.67 | 95.49 | +| refrigerator | 85.57 | 94.69 | +| grandstand | 55.85 | 84.89 | +| path | 28.02 | 34.99 | +| stairs | 30.66 | 35.88 | +| runway | 74.85 | 97.79 | +| case | 62.46 | 85.91 | +| pool table | 95.27 | 98.33 | +| pillow | 71.14 | 82.64 | +| screen door | 82.66 | 85.6 | +| stairway | 46.15 | 72.98 | +| river | 10.48 | 21.89 | +| bridge | 73.24 | 82.25 | +| bookcase | 51.79 | 63.45 | +| blind | 46.81 | 57.86 | +| coffee table | 61.53 | 87.55 | +| toilet | 90.64 | 93.22 | +| flower | 47.42 | 58.21 | +| book | 55.14 | 78.81 | +| hill | 9.01 | 15.24 | +| bench | 57.9 | 65.28 | +| countertop | 63.49 | 83.36 | +| stove | 87.42 | 92.99 | +| palm | 55.73 | 81.43 | +| kitchen island | 72.44 | 84.93 | +| computer | 79.03 | 91.45 | +| swivel chair | 50.65 | 74.28 | +| boat | 80.86 | 90.59 | +| bar | 64.85 | 88.71 | +| arcade machine | 74.84 | 78.93 | +| hovel | 13.93 | 15.66 | +| bus | 92.81 | 96.67 | +| towel | 79.23 | 85.08 | +| light | 63.78 | 75.47 | +| truck | 49.32 | 62.24 | +| tower | 30.21 | 51.88 | +| chandelier | 76.25 | 88.03 | +| awning | 40.52 | 49.07 | +| streetlight | 39.71 | 52.72 | +| booth | 41.2 | 62.34 | +| television receiver | 79.97 | 83.49 | +| airplane | 88.97 | 96.17 | +| dirt track | 7.35 | 46.31 | +| apparel | 68.62 | 86.3 | +| pole | 24.14 | 37.23 | +| land | 2.97 | 4.83 | +| bannister | 23.05 | 29.05 | +| escalator | 65.88 | 81.63 | +| ottoman | 48.34 | 62.45 | +| bottle | 43.82 | 61.25 | +| buffet | 46.07 | 53.64 | +| poster | 29.78 | 35.1 | +| stage | 28.05 | 44.95 | +| van | 46.87 | 72.13 | +| ship | 90.08 | 96.18 | +| fountain | 45.24 | 46.24 | +| conveyer belt | 83.87 | 93.97 | +| canopy | 50.53 | 70.79 | +| washer | 86.91 | 91.75 | +| plaything | 34.08 | 46.0 | +| swimming pool | 53.58 | 76.99 | +| stool | 53.27 | 64.53 | +| barrel | 73.87 | 85.71 | +| basket | 40.15 | 55.23 | +| waterfall | 47.21 | 60.11 | +| tent | 91.42 | 98.21 | +| bag | 27.22 | 31.58 | +| minibike | 77.46 | 90.72 | +| cradle | 85.55 | 97.62 | +| oven | 62.64 | 78.35 | +| ball | 52.86 | 56.35 | +| food | 58.87 | 69.34 | +| step | 16.25 | 18.9 | +| tank | 80.43 | 89.93 | +| trade name | 21.75 | 25.29 | +| microwave | 89.97 | 96.4 | +| pot | 58.62 | 68.46 | +| animal | 60.38 | 61.54 | +| bicycle | 61.38 | 79.95 | +| lake | 51.71 | 63.78 | +| dishwasher | 77.03 | 85.7 | +| screen | 51.66 | 82.12 | +| blanket | 41.35 | 47.62 | +| sculpture | 76.01 | 87.36 | +| hood | 64.7 | 76.71 | +| sconce | 61.24 | 68.34 | +| vase | 49.74 | 63.96 | +| traffic light | 35.73 | 65.7 | +| tray | 23.32 | 28.54 | +| ashcan | 52.23 | 65.36 | +| fan | 72.1 | 84.14 | +| pier | 49.36 | 56.66 | +| crt screen | 1.45 | 3.43 | +| plate | 64.28 | 79.38 | +| monitor | 37.21 | 44.66 | +| bulletin board | 52.01 | 60.16 | +| shower | 16.34 | 17.84 | +| radiator | 66.46 | 83.93 | +| glass | 21.25 | 22.87 | +| clock | 50.22 | 61.61 | +| flag | 74.28 | 82.87 | ++---------------------+-------+-------+ +2024-06-17 04:20:47,542 - mmseg - INFO - Summary: +2024-06-17 04:20:47,542 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 86.7 | 58.76 | 70.84 | ++------+-------+-------+ +2024-06-17 04:20:47,543 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 04:20:47,543 - mmseg - INFO - Iter(val) [250] aAcc: 0.8670, mIoU: 0.5876, mAcc: 0.7084, IoU.wall: 0.8302, IoU.building: 0.8560, IoU.sky: 0.9516, IoU.floor: 0.8580, IoU.tree: 0.7801, IoU.ceiling: 0.8808, IoU.road: 0.8633, IoU.bed : 0.9282, IoU.windowpane: 0.6753, IoU.grass: 0.7087, IoU.cabinet: 0.6988, IoU.sidewalk: 0.7114, IoU.person: 0.8640, IoU.earth: 0.4161, IoU.door: 0.6025, IoU.table: 0.7041, IoU.mountain: 0.6228, IoU.plant: 0.5720, IoU.curtain: 0.7960, IoU.chair: 0.6854, IoU.car: 0.8852, IoU.water: 0.6305, IoU.painting: 0.7815, IoU.sofa: 0.8358, IoU.shelf: 0.5285, IoU.house: 0.5326, IoU.sea: 0.7114, IoU.mirror: 0.7833, IoU.rug: 0.6683, IoU.field: 0.3323, IoU.armchair: 0.6186, IoU.seat: 0.6609, IoU.fence: 0.5611, IoU.desk: 0.5886, IoU.rock: 0.5483, IoU.wardrobe: 0.5917, IoU.lamp: 0.7710, IoU.bathtub: 0.8701, IoU.railing: 0.4527, IoU.cushion: 0.7380, IoU.base: 0.3721, IoU.box: 0.3918, IoU.column: 0.5800, IoU.signboard: 0.4098, IoU.chest of drawers: 0.4006, IoU.counter: 0.4134, IoU.sand: 0.5704, IoU.sink: 0.8113, IoU.skyscraper: 0.4740, IoU.fireplace: 0.7267, IoU.refrigerator: 0.8557, IoU.grandstand: 0.5585, IoU.path: 0.2802, IoU.stairs: 0.3066, IoU.runway: 0.7485, IoU.case: 0.6246, IoU.pool table: 0.9527, IoU.pillow: 0.7114, IoU.screen door: 0.8266, IoU.stairway: 0.4615, IoU.river: 0.1048, IoU.bridge: 0.7324, IoU.bookcase: 0.5179, IoU.blind: 0.4681, IoU.coffee table: 0.6153, IoU.toilet: 0.9064, IoU.flower: 0.4742, IoU.book: 0.5514, IoU.hill: 0.0901, IoU.bench: 0.5790, IoU.countertop: 0.6349, IoU.stove: 0.8742, IoU.palm: 0.5573, IoU.kitchen island: 0.7244, IoU.computer: 0.7903, IoU.swivel chair: 0.5065, IoU.boat: 0.8086, IoU.bar: 0.6485, IoU.arcade machine: 0.7484, IoU.hovel: 0.1393, IoU.bus: 0.9281, IoU.towel: 0.7923, IoU.light: 0.6378, IoU.truck: 0.4932, IoU.tower: 0.3021, IoU.chandelier: 0.7625, IoU.awning: 0.4052, IoU.streetlight: 0.3971, IoU.booth: 0.4120, IoU.television receiver: 0.7997, IoU.airplane: 0.8897, IoU.dirt track: 0.0735, IoU.apparel: 0.6862, IoU.pole: 0.2414, IoU.land: 0.0297, IoU.bannister: 0.2305, IoU.escalator: 0.6588, IoU.ottoman: 0.4834, IoU.bottle: 0.4382, IoU.buffet: 0.4607, IoU.poster: 0.2978, IoU.stage: 0.2805, IoU.van: 0.4687, IoU.ship: 0.9008, IoU.fountain: 0.4524, IoU.conveyer belt: 0.8387, IoU.canopy: 0.5053, IoU.washer: 0.8691, IoU.plaything: 0.3408, IoU.swimming pool: 0.5358, IoU.stool: 0.5327, IoU.barrel: 0.7387, IoU.basket: 0.4015, IoU.waterfall: 0.4721, IoU.tent: 0.9142, IoU.bag: 0.2722, IoU.minibike: 0.7746, IoU.cradle: 0.8555, IoU.oven: 0.6264, IoU.ball: 0.5286, IoU.food: 0.5887, IoU.step: 0.1625, IoU.tank: 0.8043, IoU.trade name: 0.2175, IoU.microwave: 0.8997, IoU.pot: 0.5862, IoU.animal: 0.6038, IoU.bicycle: 0.6138, IoU.lake: 0.5171, IoU.dishwasher: 0.7703, IoU.screen: 0.5166, IoU.blanket: 0.4135, IoU.sculpture: 0.7601, IoU.hood: 0.6470, IoU.sconce: 0.6124, IoU.vase: 0.4974, IoU.traffic light: 0.3573, IoU.tray: 0.2332, IoU.ashcan: 0.5223, IoU.fan: 0.7210, IoU.pier: 0.4936, IoU.crt screen: 0.0145, IoU.plate: 0.6428, IoU.monitor: 0.3721, IoU.bulletin board: 0.5201, IoU.shower: 0.1634, IoU.radiator: 0.6646, IoU.glass: 0.2125, IoU.clock: 0.5022, IoU.flag: 0.7428, Acc.wall: 0.8995, Acc.building: 0.9355, Acc.sky: 0.9764, Acc.floor: 0.9362, Acc.tree: 0.9012, Acc.ceiling: 0.9454, Acc.road: 0.9070, Acc.bed : 0.9687, Acc.windowpane: 0.8557, Acc.grass: 0.8243, Acc.cabinet: 0.8013, Acc.sidewalk: 0.8692, Acc.person: 0.9497, Acc.earth: 0.5613, Acc.door: 0.7324, Acc.table: 0.8178, Acc.mountain: 0.7222, Acc.plant: 0.6815, Acc.curtain: 0.9019, Acc.chair: 0.7922, Acc.car: 0.9380, Acc.water: 0.7954, Acc.painting: 0.9060, Acc.sofa: 0.9056, Acc.shelf: 0.6968, Acc.house: 0.6404, Acc.sea: 0.7884, Acc.mirror: 0.8558, Acc.rug: 0.7277, Acc.field: 0.6115, Acc.armchair: 0.7788, Acc.seat: 0.8951, Acc.fence: 0.7047, Acc.desk: 0.7968, Acc.rock: 0.8122, Acc.wardrobe: 0.6884, Acc.lamp: 0.8650, Acc.bathtub: 0.9000, Acc.railing: 0.6394, Acc.cushion: 0.8282, Acc.base: 0.5151, Acc.box: 0.5040, Acc.column: 0.6678, Acc.signboard: 0.5827, Acc.chest of drawers: 0.6396, Acc.counter: 0.5018, Acc.sand: 0.8707, Acc.sink: 0.8641, Acc.skyscraper: 0.6051, Acc.fireplace: 0.9549, Acc.refrigerator: 0.9469, Acc.grandstand: 0.8489, Acc.path: 0.3499, Acc.stairs: 0.3588, Acc.runway: 0.9779, Acc.case: 0.8591, Acc.pool table: 0.9833, Acc.pillow: 0.8264, Acc.screen door: 0.8560, Acc.stairway: 0.7298, Acc.river: 0.2189, Acc.bridge: 0.8225, Acc.bookcase: 0.6345, Acc.blind: 0.5786, Acc.coffee table: 0.8755, Acc.toilet: 0.9322, Acc.flower: 0.5821, Acc.book: 0.7881, Acc.hill: 0.1524, Acc.bench: 0.6528, Acc.countertop: 0.8336, Acc.stove: 0.9299, Acc.palm: 0.8143, Acc.kitchen island: 0.8493, Acc.computer: 0.9145, Acc.swivel chair: 0.7428, Acc.boat: 0.9059, Acc.bar: 0.8871, Acc.arcade machine: 0.7893, Acc.hovel: 0.1566, Acc.bus: 0.9667, Acc.towel: 0.8508, Acc.light: 0.7547, Acc.truck: 0.6224, Acc.tower: 0.5188, Acc.chandelier: 0.8803, Acc.awning: 0.4907, Acc.streetlight: 0.5272, Acc.booth: 0.6234, Acc.television receiver: 0.8349, Acc.airplane: 0.9617, Acc.dirt track: 0.4631, Acc.apparel: 0.8630, Acc.pole: 0.3723, Acc.land: 0.0483, Acc.bannister: 0.2905, Acc.escalator: 0.8163, Acc.ottoman: 0.6245, Acc.bottle: 0.6125, Acc.buffet: 0.5364, Acc.poster: 0.3510, Acc.stage: 0.4495, Acc.van: 0.7213, Acc.ship: 0.9618, Acc.fountain: 0.4624, Acc.conveyer belt: 0.9397, Acc.canopy: 0.7079, Acc.washer: 0.9175, Acc.plaything: 0.4600, Acc.swimming pool: 0.7699, Acc.stool: 0.6453, Acc.barrel: 0.8571, Acc.basket: 0.5523, Acc.waterfall: 0.6011, Acc.tent: 0.9821, Acc.bag: 0.3158, Acc.minibike: 0.9072, Acc.cradle: 0.9762, Acc.oven: 0.7835, Acc.ball: 0.5635, Acc.food: 0.6934, Acc.step: 0.1890, Acc.tank: 0.8993, Acc.trade name: 0.2529, Acc.microwave: 0.9640, Acc.pot: 0.6846, Acc.animal: 0.6154, Acc.bicycle: 0.7995, Acc.lake: 0.6378, Acc.dishwasher: 0.8570, Acc.screen: 0.8212, Acc.blanket: 0.4762, Acc.sculpture: 0.8736, Acc.hood: 0.7671, Acc.sconce: 0.6834, Acc.vase: 0.6396, Acc.traffic light: 0.6570, Acc.tray: 0.2854, Acc.ashcan: 0.6536, Acc.fan: 0.8414, Acc.pier: 0.5666, Acc.crt screen: 0.0343, Acc.plate: 0.7938, Acc.monitor: 0.4466, Acc.bulletin board: 0.6016, Acc.shower: 0.1784, Acc.radiator: 0.8393, Acc.glass: 0.2287, Acc.clock: 0.6161, Acc.flag: 0.8287 +2024-06-17 04:22:08,927 - mmseg - INFO - Iter [63050/80000] lr: 8.476e-06, eta: 8:19:03, time: 3.601, data_time: 1.991, memory: 71384, decode.loss_ce: 0.1412, decode.acc_seg: 93.7894, aux.loss_ce: 0.0601, aux.acc_seg: 93.3877, loss: 0.2013 +2024-06-17 04:23:30,256 - mmseg - INFO - Iter [63100/80000] lr: 8.451e-06, eta: 8:17:32, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1446, decode.acc_seg: 93.7560, aux.loss_ce: 0.0620, aux.acc_seg: 93.2497, loss: 0.2066 +2024-06-17 04:24:51,505 - mmseg - INFO - Iter [63150/80000] lr: 8.426e-06, eta: 8:16:02, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1443, decode.acc_seg: 93.5336, aux.loss_ce: 0.0619, aux.acc_seg: 93.1102, loss: 0.2062 +2024-06-17 04:26:14,797 - mmseg - INFO - Iter [63200/80000] lr: 8.401e-06, eta: 8:14:33, time: 1.666, data_time: 0.052, memory: 71384, decode.loss_ce: 0.1368, decode.acc_seg: 93.9012, aux.loss_ce: 0.0582, aux.acc_seg: 93.5276, loss: 0.1950 +2024-06-17 04:27:35,812 - mmseg - INFO - Iter [63250/80000] lr: 8.375e-06, eta: 8:13:02, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1471, decode.acc_seg: 93.4357, aux.loss_ce: 0.0627, aux.acc_seg: 93.0308, loss: 0.2098 +2024-06-17 04:28:56,934 - mmseg - INFO - Iter [63300/80000] lr: 8.350e-06, eta: 8:11:32, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1366, decode.acc_seg: 94.0665, aux.loss_ce: 0.0583, aux.acc_seg: 93.6751, loss: 0.1949 +2024-06-17 04:30:17,871 - mmseg - INFO - Iter [63350/80000] lr: 8.326e-06, eta: 8:10:02, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1424, decode.acc_seg: 93.4362, aux.loss_ce: 0.0605, aux.acc_seg: 93.0454, loss: 0.2029 +2024-06-17 04:31:38,941 - mmseg - INFO - Iter [63400/80000] lr: 8.300e-06, eta: 8:08:32, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1371, decode.acc_seg: 93.9911, aux.loss_ce: 0.0590, aux.acc_seg: 93.5211, loss: 0.1962 +2024-06-17 04:32:59,985 - mmseg - INFO - Iter [63450/80000] lr: 8.276e-06, eta: 8:07:02, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1390, decode.acc_seg: 93.9697, aux.loss_ce: 0.0596, aux.acc_seg: 93.5427, loss: 0.1986 +2024-06-17 04:34:21,119 - mmseg - INFO - Iter [63500/80000] lr: 8.251e-06, eta: 8:05:31, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1382, decode.acc_seg: 93.7878, aux.loss_ce: 0.0586, aux.acc_seg: 93.4134, loss: 0.1968 +2024-06-17 04:35:42,251 - mmseg - INFO - Iter [63550/80000] lr: 8.226e-06, eta: 8:04:01, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1387, decode.acc_seg: 93.8775, aux.loss_ce: 0.0594, aux.acc_seg: 93.4667, loss: 0.1981 +2024-06-17 04:37:03,404 - mmseg - INFO - Iter [63600/80000] lr: 8.201e-06, eta: 8:02:31, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1328, decode.acc_seg: 93.9839, aux.loss_ce: 0.0571, aux.acc_seg: 93.6027, loss: 0.1899 +2024-06-17 04:38:24,461 - mmseg - INFO - Iter [63650/80000] lr: 8.176e-06, eta: 8:01:01, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1442, decode.acc_seg: 93.6398, aux.loss_ce: 0.0615, aux.acc_seg: 93.2304, loss: 0.2057 +2024-06-17 04:39:45,623 - mmseg - INFO - Iter [63700/80000] lr: 8.150e-06, eta: 7:59:31, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1365, decode.acc_seg: 94.0306, aux.loss_ce: 0.0584, aux.acc_seg: 93.6328, loss: 0.1949 +2024-06-17 04:41:06,844 - mmseg - INFO - Iter [63750/80000] lr: 8.125e-06, eta: 7:58:01, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1315, decode.acc_seg: 94.1116, aux.loss_ce: 0.0566, aux.acc_seg: 93.6720, loss: 0.1881 +2024-06-17 04:42:27,784 - mmseg - INFO - Iter [63800/80000] lr: 8.100e-06, eta: 7:56:31, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1436, decode.acc_seg: 93.6123, aux.loss_ce: 0.0612, aux.acc_seg: 93.2373, loss: 0.2048 +2024-06-17 04:43:48,998 - mmseg - INFO - Iter [63850/80000] lr: 8.076e-06, eta: 7:55:01, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1328, decode.acc_seg: 94.1675, aux.loss_ce: 0.0564, aux.acc_seg: 93.7823, loss: 0.1893 +2024-06-17 04:45:10,082 - mmseg - INFO - Iter [63900/80000] lr: 8.051e-06, eta: 7:53:31, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1431, decode.acc_seg: 93.7906, aux.loss_ce: 0.0609, aux.acc_seg: 93.4040, loss: 0.2040 +2024-06-17 04:46:31,342 - mmseg - INFO - Iter [63950/80000] lr: 8.026e-06, eta: 7:52:01, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1388, decode.acc_seg: 93.7336, aux.loss_ce: 0.0595, aux.acc_seg: 93.3096, loss: 0.1983 +2024-06-17 04:47:52,294 - mmseg - INFO - Saving checkpoint at 64000 iterations +2024-06-17 04:49:20,080 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 04:49:20,081 - mmseg - INFO - Iter [64000/80000] lr: 8.001e-06, eta: 7:50:53, time: 3.375, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1438, decode.acc_seg: 93.6898, aux.loss_ce: 0.0613, aux.acc_seg: 93.3002, loss: 0.2051 +2024-06-17 04:50:56,660 - mmseg - INFO - per class results: +2024-06-17 04:50:56,666 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.56 | 89.35 | +| building | 85.72 | 93.66 | +| sky | 95.06 | 97.66 | +| floor | 86.09 | 92.4 | +| tree | 78.15 | 88.41 | +| ceiling | 87.92 | 95.25 | +| road | 86.65 | 90.87 | +| bed | 92.34 | 97.59 | +| windowpane | 67.6 | 82.76 | +| grass | 70.11 | 82.66 | +| cabinet | 67.57 | 77.11 | +| sidewalk | 71.38 | 85.66 | +| person | 86.43 | 94.31 | +| earth | 41.66 | 55.01 | +| door | 60.65 | 78.32 | +| table | 70.41 | 82.49 | +| mountain | 62.86 | 75.82 | +| plant | 57.36 | 69.09 | +| curtain | 78.97 | 88.5 | +| chair | 69.37 | 82.25 | +| car | 88.81 | 94.1 | +| water | 64.47 | 78.72 | +| painting | 75.87 | 91.16 | +| sofa | 83.07 | 90.8 | +| shelf | 52.42 | 69.58 | +| house | 54.14 | 68.67 | +| sea | 72.45 | 80.27 | +| mirror | 78.23 | 86.91 | +| rug | 69.85 | 78.04 | +| field | 32.82 | 61.54 | +| armchair | 61.55 | 79.02 | +| seat | 66.07 | 89.42 | +| fence | 55.47 | 68.11 | +| desk | 60.51 | 78.36 | +| rock | 54.51 | 83.37 | +| wardrobe | 55.78 | 74.02 | +| lamp | 77.42 | 88.4 | +| bathtub | 86.96 | 89.96 | +| railing | 46.46 | 65.34 | +| cushion | 73.1 | 81.32 | +| base | 38.77 | 55.1 | +| box | 39.99 | 50.59 | +| column | 56.15 | 69.32 | +| signboard | 42.02 | 58.21 | +| chest of drawers | 43.6 | 69.88 | +| counter | 42.1 | 50.79 | +| sand | 56.13 | 86.27 | +| sink | 81.36 | 86.72 | +| skyscraper | 46.71 | 58.84 | +| fireplace | 72.24 | 94.41 | +| refrigerator | 85.94 | 94.78 | +| grandstand | 54.95 | 82.57 | +| path | 32.93 | 43.89 | +| stairs | 33.83 | 40.95 | +| runway | 74.23 | 97.04 | +| case | 66.17 | 83.42 | +| pool table | 95.04 | 97.94 | +| pillow | 68.51 | 77.69 | +| screen door | 79.32 | 81.82 | +| stairway | 49.57 | 73.62 | +| river | 11.17 | 24.05 | +| bridge | 69.09 | 78.25 | +| bookcase | 53.31 | 68.93 | +| blind | 45.88 | 54.49 | +| coffee table | 61.97 | 86.9 | +| toilet | 90.62 | 93.22 | +| flower | 45.73 | 60.57 | +| book | 55.29 | 76.62 | +| hill | 8.56 | 15.44 | +| bench | 58.29 | 65.01 | +| countertop | 64.17 | 83.0 | +| stove | 87.06 | 93.56 | +| palm | 55.37 | 85.06 | +| kitchen island | 66.39 | 87.25 | +| computer | 77.86 | 89.51 | +| swivel chair | 46.17 | 70.0 | +| boat | 81.84 | 91.31 | +| bar | 65.0 | 86.57 | +| arcade machine | 73.33 | 77.52 | +| hovel | 13.6 | 15.51 | +| bus | 92.75 | 97.12 | +| towel | 80.2 | 86.72 | +| light | 62.58 | 70.6 | +| truck | 49.2 | 61.36 | +| tower | 35.29 | 62.72 | +| chandelier | 76.24 | 86.75 | +| awning | 39.56 | 46.86 | +| streetlight | 38.99 | 51.5 | +| booth | 38.53 | 62.82 | +| television receiver | 81.83 | 87.51 | +| airplane | 89.24 | 96.02 | +| dirt track | 3.82 | 18.48 | +| apparel | 63.76 | 84.55 | +| pole | 24.31 | 37.89 | +| land | 2.93 | 4.32 | +| bannister | 21.44 | 26.17 | +| escalator | 66.98 | 84.93 | +| ottoman | 49.24 | 64.81 | +| bottle | 43.93 | 63.98 | +| buffet | 56.52 | 68.06 | +| poster | 29.76 | 36.54 | +| stage | 28.99 | 47.58 | +| van | 49.76 | 74.61 | +| ship | 86.97 | 93.47 | +| fountain | 38.77 | 39.37 | +| conveyer belt | 82.49 | 94.82 | +| canopy | 53.16 | 73.32 | +| washer | 87.69 | 92.75 | +| plaything | 39.71 | 57.25 | +| swimming pool | 53.9 | 79.05 | +| stool | 55.32 | 67.85 | +| barrel | 60.67 | 72.47 | +| basket | 43.69 | 63.83 | +| waterfall | 47.51 | 60.62 | +| tent | 93.36 | 98.27 | +| bag | 26.98 | 31.19 | +| minibike | 77.5 | 89.37 | +| cradle | 88.89 | 97.96 | +| oven | 65.3 | 79.94 | +| ball | 55.95 | 66.95 | +| food | 65.94 | 81.25 | +| step | 15.4 | 17.74 | +| tank | 70.29 | 79.13 | +| trade name | 25.91 | 31.52 | +| microwave | 90.53 | 96.48 | +| pot | 58.94 | 68.44 | +| animal | 62.39 | 63.73 | +| bicycle | 60.6 | 81.72 | +| lake | 44.93 | 63.74 | +| dishwasher | 76.43 | 83.25 | +| screen | 51.93 | 79.71 | +| blanket | 36.38 | 41.83 | +| sculpture | 75.85 | 88.6 | +| hood | 65.44 | 76.95 | +| sconce | 63.57 | 74.15 | +| vase | 49.16 | 63.32 | +| traffic light | 36.56 | 64.0 | +| tray | 25.29 | 32.44 | +| ashcan | 52.82 | 67.11 | +| fan | 71.96 | 82.32 | +| pier | 45.61 | 50.53 | +| crt screen | 1.89 | 5.56 | +| plate | 62.77 | 80.18 | +| monitor | 27.7 | 32.69 | +| bulletin board | 55.19 | 68.53 | +| shower | 13.93 | 15.37 | +| radiator | 67.15 | 81.19 | +| glass | 21.3 | 22.75 | +| clock | 48.48 | 59.04 | +| flag | 71.7 | 79.25 | ++---------------------+-------+-------+ +2024-06-17 04:50:56,666 - mmseg - INFO - Summary: +2024-06-17 04:50:56,666 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.62 | 58.66 | 71.11 | ++-------+-------+-------+ +2024-06-17 04:50:56,667 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 04:50:56,668 - mmseg - INFO - Iter(val) [250] aAcc: 0.8662, mIoU: 0.5866, mAcc: 0.7111, IoU.wall: 0.8256, IoU.building: 0.8572, IoU.sky: 0.9506, IoU.floor: 0.8609, IoU.tree: 0.7815, IoU.ceiling: 0.8792, IoU.road: 0.8665, IoU.bed : 0.9234, IoU.windowpane: 0.6760, IoU.grass: 0.7011, IoU.cabinet: 0.6757, IoU.sidewalk: 0.7138, IoU.person: 0.8643, IoU.earth: 0.4166, IoU.door: 0.6065, IoU.table: 0.7041, IoU.mountain: 0.6286, IoU.plant: 0.5736, IoU.curtain: 0.7897, IoU.chair: 0.6937, IoU.car: 0.8881, IoU.water: 0.6447, IoU.painting: 0.7587, IoU.sofa: 0.8307, IoU.shelf: 0.5242, IoU.house: 0.5414, IoU.sea: 0.7245, IoU.mirror: 0.7823, IoU.rug: 0.6985, IoU.field: 0.3282, IoU.armchair: 0.6155, IoU.seat: 0.6607, IoU.fence: 0.5547, IoU.desk: 0.6051, IoU.rock: 0.5451, IoU.wardrobe: 0.5578, IoU.lamp: 0.7742, IoU.bathtub: 0.8696, IoU.railing: 0.4646, IoU.cushion: 0.7310, IoU.base: 0.3877, IoU.box: 0.3999, IoU.column: 0.5615, IoU.signboard: 0.4202, IoU.chest of drawers: 0.4360, IoU.counter: 0.4210, IoU.sand: 0.5613, IoU.sink: 0.8136, IoU.skyscraper: 0.4671, IoU.fireplace: 0.7224, IoU.refrigerator: 0.8594, IoU.grandstand: 0.5495, IoU.path: 0.3293, IoU.stairs: 0.3383, IoU.runway: 0.7423, IoU.case: 0.6617, IoU.pool table: 0.9504, IoU.pillow: 0.6851, IoU.screen door: 0.7932, IoU.stairway: 0.4957, IoU.river: 0.1117, IoU.bridge: 0.6909, IoU.bookcase: 0.5331, IoU.blind: 0.4588, IoU.coffee table: 0.6197, IoU.toilet: 0.9062, IoU.flower: 0.4573, IoU.book: 0.5529, IoU.hill: 0.0856, IoU.bench: 0.5829, IoU.countertop: 0.6417, IoU.stove: 0.8706, IoU.palm: 0.5537, IoU.kitchen island: 0.6639, IoU.computer: 0.7786, IoU.swivel chair: 0.4617, IoU.boat: 0.8184, IoU.bar: 0.6500, IoU.arcade machine: 0.7333, IoU.hovel: 0.1360, IoU.bus: 0.9275, IoU.towel: 0.8020, IoU.light: 0.6258, IoU.truck: 0.4920, IoU.tower: 0.3529, IoU.chandelier: 0.7624, IoU.awning: 0.3956, IoU.streetlight: 0.3899, IoU.booth: 0.3853, IoU.television receiver: 0.8183, IoU.airplane: 0.8924, IoU.dirt track: 0.0382, IoU.apparel: 0.6376, IoU.pole: 0.2431, IoU.land: 0.0293, IoU.bannister: 0.2144, IoU.escalator: 0.6698, IoU.ottoman: 0.4924, IoU.bottle: 0.4393, IoU.buffet: 0.5652, IoU.poster: 0.2976, IoU.stage: 0.2899, IoU.van: 0.4976, IoU.ship: 0.8697, IoU.fountain: 0.3877, IoU.conveyer belt: 0.8249, IoU.canopy: 0.5316, IoU.washer: 0.8769, IoU.plaything: 0.3971, IoU.swimming pool: 0.5390, IoU.stool: 0.5532, IoU.barrel: 0.6067, IoU.basket: 0.4369, IoU.waterfall: 0.4751, IoU.tent: 0.9336, IoU.bag: 0.2698, IoU.minibike: 0.7750, IoU.cradle: 0.8889, IoU.oven: 0.6530, IoU.ball: 0.5595, IoU.food: 0.6594, IoU.step: 0.1540, IoU.tank: 0.7029, IoU.trade name: 0.2591, IoU.microwave: 0.9053, IoU.pot: 0.5894, IoU.animal: 0.6239, IoU.bicycle: 0.6060, IoU.lake: 0.4493, IoU.dishwasher: 0.7643, IoU.screen: 0.5193, IoU.blanket: 0.3638, IoU.sculpture: 0.7585, IoU.hood: 0.6544, IoU.sconce: 0.6357, IoU.vase: 0.4916, IoU.traffic light: 0.3656, IoU.tray: 0.2529, IoU.ashcan: 0.5282, IoU.fan: 0.7196, IoU.pier: 0.4561, IoU.crt screen: 0.0189, IoU.plate: 0.6277, IoU.monitor: 0.2770, IoU.bulletin board: 0.5519, IoU.shower: 0.1393, IoU.radiator: 0.6715, IoU.glass: 0.2130, IoU.clock: 0.4848, IoU.flag: 0.7170, Acc.wall: 0.8935, Acc.building: 0.9366, Acc.sky: 0.9766, Acc.floor: 0.9240, Acc.tree: 0.8841, Acc.ceiling: 0.9525, Acc.road: 0.9087, Acc.bed : 0.9759, Acc.windowpane: 0.8276, Acc.grass: 0.8266, Acc.cabinet: 0.7711, Acc.sidewalk: 0.8566, Acc.person: 0.9431, Acc.earth: 0.5501, Acc.door: 0.7832, Acc.table: 0.8249, Acc.mountain: 0.7582, Acc.plant: 0.6909, Acc.curtain: 0.8850, Acc.chair: 0.8225, Acc.car: 0.9410, Acc.water: 0.7872, Acc.painting: 0.9116, Acc.sofa: 0.9080, Acc.shelf: 0.6958, Acc.house: 0.6867, Acc.sea: 0.8027, Acc.mirror: 0.8691, Acc.rug: 0.7804, Acc.field: 0.6154, Acc.armchair: 0.7902, Acc.seat: 0.8942, Acc.fence: 0.6811, Acc.desk: 0.7836, Acc.rock: 0.8337, Acc.wardrobe: 0.7402, Acc.lamp: 0.8840, Acc.bathtub: 0.8996, Acc.railing: 0.6534, Acc.cushion: 0.8132, Acc.base: 0.5510, Acc.box: 0.5059, Acc.column: 0.6932, Acc.signboard: 0.5821, Acc.chest of drawers: 0.6988, Acc.counter: 0.5079, Acc.sand: 0.8627, Acc.sink: 0.8672, Acc.skyscraper: 0.5884, Acc.fireplace: 0.9441, Acc.refrigerator: 0.9478, Acc.grandstand: 0.8257, Acc.path: 0.4389, Acc.stairs: 0.4095, Acc.runway: 0.9704, Acc.case: 0.8342, Acc.pool table: 0.9794, Acc.pillow: 0.7769, Acc.screen door: 0.8182, Acc.stairway: 0.7362, Acc.river: 0.2405, Acc.bridge: 0.7825, Acc.bookcase: 0.6893, Acc.blind: 0.5449, Acc.coffee table: 0.8690, Acc.toilet: 0.9322, Acc.flower: 0.6057, Acc.book: 0.7662, Acc.hill: 0.1544, Acc.bench: 0.6501, Acc.countertop: 0.8300, Acc.stove: 0.9356, Acc.palm: 0.8506, Acc.kitchen island: 0.8725, Acc.computer: 0.8951, Acc.swivel chair: 0.7000, Acc.boat: 0.9131, Acc.bar: 0.8657, Acc.arcade machine: 0.7752, Acc.hovel: 0.1551, Acc.bus: 0.9712, Acc.towel: 0.8672, Acc.light: 0.7060, Acc.truck: 0.6136, Acc.tower: 0.6272, Acc.chandelier: 0.8675, Acc.awning: 0.4686, Acc.streetlight: 0.5150, Acc.booth: 0.6282, Acc.television receiver: 0.8751, Acc.airplane: 0.9602, Acc.dirt track: 0.1848, Acc.apparel: 0.8455, Acc.pole: 0.3789, Acc.land: 0.0432, Acc.bannister: 0.2617, Acc.escalator: 0.8493, Acc.ottoman: 0.6481, Acc.bottle: 0.6398, Acc.buffet: 0.6806, Acc.poster: 0.3654, Acc.stage: 0.4758, Acc.van: 0.7461, Acc.ship: 0.9347, Acc.fountain: 0.3937, Acc.conveyer belt: 0.9482, Acc.canopy: 0.7332, Acc.washer: 0.9275, Acc.plaything: 0.5725, Acc.swimming pool: 0.7905, Acc.stool: 0.6785, Acc.barrel: 0.7247, Acc.basket: 0.6383, Acc.waterfall: 0.6062, Acc.tent: 0.9827, Acc.bag: 0.3119, Acc.minibike: 0.8937, Acc.cradle: 0.9796, Acc.oven: 0.7994, Acc.ball: 0.6695, Acc.food: 0.8125, Acc.step: 0.1774, Acc.tank: 0.7913, Acc.trade name: 0.3152, Acc.microwave: 0.9648, Acc.pot: 0.6844, Acc.animal: 0.6373, Acc.bicycle: 0.8172, Acc.lake: 0.6374, Acc.dishwasher: 0.8325, Acc.screen: 0.7971, Acc.blanket: 0.4183, Acc.sculpture: 0.8860, Acc.hood: 0.7695, Acc.sconce: 0.7415, Acc.vase: 0.6332, Acc.traffic light: 0.6400, Acc.tray: 0.3244, Acc.ashcan: 0.6711, Acc.fan: 0.8232, Acc.pier: 0.5053, Acc.crt screen: 0.0556, Acc.plate: 0.8018, Acc.monitor: 0.3269, Acc.bulletin board: 0.6853, Acc.shower: 0.1537, Acc.radiator: 0.8119, Acc.glass: 0.2275, Acc.clock: 0.5904, Acc.flag: 0.7925 +2024-06-17 04:52:18,091 - mmseg - INFO - Iter [64050/80000] lr: 7.976e-06, eta: 7:49:47, time: 3.560, data_time: 1.948, memory: 71384, decode.loss_ce: 0.1347, decode.acc_seg: 93.9188, aux.loss_ce: 0.0573, aux.acc_seg: 93.5463, loss: 0.1920 +2024-06-17 04:53:39,071 - mmseg - INFO - Iter [64100/80000] lr: 7.950e-06, eta: 7:48:17, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1428, decode.acc_seg: 93.6438, aux.loss_ce: 0.0613, aux.acc_seg: 93.2148, loss: 0.2041 +2024-06-17 04:55:00,349 - mmseg - INFO - Iter [64150/80000] lr: 7.925e-06, eta: 7:46:46, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1256, decode.acc_seg: 94.3496, aux.loss_ce: 0.0536, aux.acc_seg: 93.9996, loss: 0.1792 +2024-06-17 04:56:21,333 - mmseg - INFO - Iter [64200/80000] lr: 7.900e-06, eta: 7:45:16, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1452, decode.acc_seg: 93.6422, aux.loss_ce: 0.0620, aux.acc_seg: 93.2507, loss: 0.2072 +2024-06-17 04:57:42,480 - mmseg - INFO - Iter [64250/80000] lr: 7.876e-06, eta: 7:43:46, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1418, decode.acc_seg: 93.6577, aux.loss_ce: 0.0606, aux.acc_seg: 93.2576, loss: 0.2024 +2024-06-17 04:59:03,420 - mmseg - INFO - Iter [64300/80000] lr: 7.851e-06, eta: 7:42:16, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1428, decode.acc_seg: 93.6497, aux.loss_ce: 0.0608, aux.acc_seg: 93.3094, loss: 0.2036 +2024-06-17 05:00:24,359 - mmseg - INFO - Iter [64350/80000] lr: 7.826e-06, eta: 7:40:46, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1342, decode.acc_seg: 94.1986, aux.loss_ce: 0.0575, aux.acc_seg: 93.7894, loss: 0.1917 +2024-06-17 05:01:45,533 - mmseg - INFO - Iter [64400/80000] lr: 7.801e-06, eta: 7:39:16, time: 1.623, data_time: 0.009, memory: 71384, decode.loss_ce: 0.1381, decode.acc_seg: 93.9133, aux.loss_ce: 0.0593, aux.acc_seg: 93.4569, loss: 0.1974 +2024-06-17 05:03:09,233 - mmseg - INFO - Iter [64450/80000] lr: 7.776e-06, eta: 7:37:46, time: 1.674, data_time: 0.061, memory: 71384, decode.loss_ce: 0.1371, decode.acc_seg: 93.8855, aux.loss_ce: 0.0587, aux.acc_seg: 93.4571, loss: 0.1958 +2024-06-17 05:04:30,253 - mmseg - INFO - Iter [64500/80000] lr: 7.750e-06, eta: 7:36:16, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1442, decode.acc_seg: 93.7302, aux.loss_ce: 0.0611, aux.acc_seg: 93.4032, loss: 0.2053 +2024-06-17 05:05:51,221 - mmseg - INFO - Iter [64550/80000] lr: 7.725e-06, eta: 7:34:46, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1381, decode.acc_seg: 93.8762, aux.loss_ce: 0.0597, aux.acc_seg: 93.4278, loss: 0.1978 +2024-06-17 05:07:12,303 - mmseg - INFO - Iter [64600/80000] lr: 7.701e-06, eta: 7:33:16, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1366, decode.acc_seg: 93.9554, aux.loss_ce: 0.0583, aux.acc_seg: 93.5439, loss: 0.1950 +2024-06-17 05:08:33,243 - mmseg - INFO - Iter [64650/80000] lr: 7.675e-06, eta: 7:31:46, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1382, decode.acc_seg: 93.8914, aux.loss_ce: 0.0595, aux.acc_seg: 93.4336, loss: 0.1977 +2024-06-17 05:09:54,422 - mmseg - INFO - Iter [64700/80000] lr: 7.651e-06, eta: 7:30:16, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1370, decode.acc_seg: 93.9947, aux.loss_ce: 0.0588, aux.acc_seg: 93.6300, loss: 0.1959 +2024-06-17 05:11:15,409 - mmseg - INFO - Iter [64750/80000] lr: 7.626e-06, eta: 7:28:46, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1405, decode.acc_seg: 93.7813, aux.loss_ce: 0.0604, aux.acc_seg: 93.2737, loss: 0.2009 +2024-06-17 05:12:36,414 - mmseg - INFO - Iter [64800/80000] lr: 7.601e-06, eta: 7:27:16, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1367, decode.acc_seg: 93.8995, aux.loss_ce: 0.0585, aux.acc_seg: 93.5177, loss: 0.1952 +2024-06-17 05:13:57,496 - mmseg - INFO - Iter [64850/80000] lr: 7.576e-06, eta: 7:25:46, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1394, decode.acc_seg: 93.9824, aux.loss_ce: 0.0596, aux.acc_seg: 93.5879, loss: 0.1990 +2024-06-17 05:15:18,559 - mmseg - INFO - Iter [64900/80000] lr: 7.551e-06, eta: 7:24:16, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1375, decode.acc_seg: 93.9277, aux.loss_ce: 0.0591, aux.acc_seg: 93.4922, loss: 0.1966 +2024-06-17 05:16:39,571 - mmseg - INFO - Iter [64950/80000] lr: 7.525e-06, eta: 7:22:46, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1366, decode.acc_seg: 93.7744, aux.loss_ce: 0.0588, aux.acc_seg: 93.3479, loss: 0.1954 +2024-06-17 05:18:00,732 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 05:18:00,732 - mmseg - INFO - Iter [65000/80000] lr: 7.500e-06, eta: 7:21:16, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1323, decode.acc_seg: 94.2304, aux.loss_ce: 0.0569, aux.acc_seg: 93.7483, loss: 0.1892 +2024-06-17 05:19:39,144 - mmseg - INFO - per class results: +2024-06-17 05:19:39,150 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.83 | 90.45 | +| building | 85.46 | 93.69 | +| sky | 95.06 | 97.4 | +| floor | 85.38 | 92.76 | +| tree | 78.22 | 90.02 | +| ceiling | 87.94 | 94.06 | +| road | 87.13 | 91.11 | +| bed | 92.51 | 97.16 | +| windowpane | 67.64 | 83.23 | +| grass | 69.11 | 81.57 | +| cabinet | 67.25 | 77.48 | +| sidewalk | 71.47 | 84.7 | +| person | 86.47 | 94.55 | +| earth | 42.59 | 55.91 | +| door | 60.64 | 75.28 | +| table | 70.24 | 81.2 | +| mountain | 60.95 | 75.14 | +| plant | 57.92 | 67.75 | +| curtain | 79.37 | 90.04 | +| chair | 69.51 | 80.28 | +| car | 88.68 | 94.07 | +| water | 63.76 | 77.68 | +| painting | 75.99 | 90.34 | +| sofa | 83.92 | 92.56 | +| shelf | 53.07 | 70.26 | +| house | 51.12 | 62.91 | +| sea | 73.46 | 82.14 | +| mirror | 79.23 | 89.04 | +| rug | 67.75 | 76.21 | +| field | 31.85 | 59.85 | +| armchair | 63.79 | 79.44 | +| seat | 66.29 | 89.32 | +| fence | 52.42 | 61.87 | +| desk | 60.59 | 80.19 | +| rock | 53.13 | 77.91 | +| wardrobe | 54.49 | 74.68 | +| lamp | 76.51 | 88.15 | +| bathtub | 87.66 | 91.1 | +| railing | 45.39 | 61.69 | +| cushion | 74.37 | 84.72 | +| base | 39.75 | 55.21 | +| box | 39.87 | 51.36 | +| column | 56.25 | 68.39 | +| signboard | 41.9 | 56.27 | +| chest of drawers | 42.48 | 64.57 | +| counter | 42.2 | 53.75 | +| sand | 57.47 | 85.99 | +| sink | 81.45 | 85.97 | +| skyscraper | 46.76 | 58.48 | +| fireplace | 76.83 | 89.16 | +| refrigerator | 85.66 | 93.49 | +| grandstand | 51.65 | 81.16 | +| path | 32.42 | 43.36 | +| stairs | 33.87 | 39.76 | +| runway | 75.19 | 97.73 | +| case | 63.66 | 83.76 | +| pool table | 95.14 | 97.8 | +| pillow | 68.74 | 77.2 | +| screen door | 80.3 | 82.74 | +| stairway | 47.94 | 66.73 | +| river | 10.32 | 22.28 | +| bridge | 64.7 | 76.29 | +| bookcase | 54.42 | 65.16 | +| blind | 46.58 | 51.02 | +| coffee table | 61.34 | 87.59 | +| toilet | 90.79 | 94.06 | +| flower | 47.79 | 59.22 | +| book | 55.54 | 78.23 | +| hill | 6.53 | 9.48 | +| bench | 55.76 | 62.14 | +| countertop | 65.77 | 84.04 | +| stove | 86.49 | 93.09 | +| palm | 56.25 | 85.46 | +| kitchen island | 65.38 | 79.28 | +| computer | 78.32 | 90.82 | +| swivel chair | 45.17 | 70.03 | +| boat | 78.24 | 92.7 | +| bar | 63.63 | 84.96 | +| arcade machine | 78.85 | 83.47 | +| hovel | 14.31 | 15.54 | +| bus | 92.76 | 97.25 | +| towel | 79.02 | 83.54 | +| light | 63.14 | 72.2 | +| truck | 52.13 | 59.57 | +| tower | 37.67 | 65.86 | +| chandelier | 73.93 | 83.65 | +| awning | 38.99 | 45.92 | +| streetlight | 37.99 | 54.03 | +| booth | 38.39 | 63.4 | +| television receiver | 81.87 | 87.32 | +| airplane | 90.04 | 95.56 | +| dirt track | 6.14 | 34.95 | +| apparel | 64.66 | 89.0 | +| pole | 26.1 | 45.45 | +| land | 4.03 | 5.47 | +| bannister | 22.54 | 27.92 | +| escalator | 65.31 | 87.31 | +| ottoman | 52.79 | 67.7 | +| bottle | 44.97 | 61.85 | +| buffet | 54.17 | 64.0 | +| poster | 30.45 | 36.24 | +| stage | 29.26 | 45.69 | +| van | 48.5 | 74.81 | +| ship | 87.19 | 92.38 | +| fountain | 37.87 | 40.11 | +| conveyer belt | 81.93 | 94.52 | +| canopy | 52.08 | 71.37 | +| washer | 88.72 | 94.08 | +| plaything | 37.17 | 55.91 | +| swimming pool | 54.73 | 79.78 | +| stool | 49.91 | 66.76 | +| barrel | 67.84 | 80.2 | +| basket | 42.53 | 62.19 | +| waterfall | 47.99 | 58.49 | +| tent | 93.45 | 98.5 | +| bag | 27.42 | 31.27 | +| minibike | 77.22 | 90.52 | +| cradle | 85.18 | 96.86 | +| oven | 59.73 | 72.41 | +| ball | 50.2 | 53.56 | +| food | 61.51 | 71.87 | +| step | 19.4 | 22.76 | +| tank | 76.9 | 92.54 | +| trade name | 24.34 | 29.28 | +| microwave | 88.64 | 96.87 | +| pot | 58.61 | 70.85 | +| animal | 62.16 | 63.25 | +| bicycle | 61.08 | 78.87 | +| lake | 50.32 | 63.73 | +| dishwasher | 76.41 | 85.03 | +| screen | 60.63 | 93.32 | +| blanket | 37.05 | 43.03 | +| sculpture | 76.32 | 88.71 | +| hood | 64.54 | 77.26 | +| sconce | 62.62 | 72.57 | +| vase | 49.36 | 64.97 | +| traffic light | 39.61 | 62.44 | +| tray | 26.78 | 35.44 | +| ashcan | 52.38 | 67.71 | +| fan | 72.84 | 84.65 | +| pier | 48.78 | 56.56 | +| crt screen | 1.65 | 3.38 | +| plate | 63.01 | 79.54 | +| monitor | 43.74 | 50.74 | +| bulletin board | 57.81 | 69.88 | +| shower | 11.02 | 11.47 | +| radiator | 66.96 | 82.81 | +| glass | 21.96 | 23.5 | +| clock | 51.63 | 61.12 | +| flag | 71.93 | 81.75 | ++---------------------+-------+-------+ +2024-06-17 05:19:39,150 - mmseg - INFO - Summary: +2024-06-17 05:19:39,151 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.64 | 58.82 | 71.13 | ++-------+-------+-------+ +2024-06-17 05:19:39,151 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 05:19:39,151 - mmseg - INFO - Iter(val) [250] aAcc: 0.8664, mIoU: 0.5882, mAcc: 0.7113, IoU.wall: 0.8283, IoU.building: 0.8546, IoU.sky: 0.9506, IoU.floor: 0.8538, IoU.tree: 0.7822, IoU.ceiling: 0.8794, IoU.road: 0.8713, IoU.bed : 0.9251, IoU.windowpane: 0.6764, IoU.grass: 0.6911, IoU.cabinet: 0.6725, IoU.sidewalk: 0.7147, IoU.person: 0.8647, IoU.earth: 0.4259, IoU.door: 0.6064, IoU.table: 0.7024, IoU.mountain: 0.6095, IoU.plant: 0.5792, IoU.curtain: 0.7937, IoU.chair: 0.6951, IoU.car: 0.8868, IoU.water: 0.6376, IoU.painting: 0.7599, IoU.sofa: 0.8392, IoU.shelf: 0.5307, IoU.house: 0.5112, IoU.sea: 0.7346, IoU.mirror: 0.7923, IoU.rug: 0.6775, IoU.field: 0.3185, IoU.armchair: 0.6379, IoU.seat: 0.6629, IoU.fence: 0.5242, IoU.desk: 0.6059, IoU.rock: 0.5313, IoU.wardrobe: 0.5449, IoU.lamp: 0.7651, IoU.bathtub: 0.8766, IoU.railing: 0.4539, IoU.cushion: 0.7437, IoU.base: 0.3975, IoU.box: 0.3987, IoU.column: 0.5625, IoU.signboard: 0.4190, IoU.chest of drawers: 0.4248, IoU.counter: 0.4220, IoU.sand: 0.5747, IoU.sink: 0.8145, IoU.skyscraper: 0.4676, IoU.fireplace: 0.7683, IoU.refrigerator: 0.8566, IoU.grandstand: 0.5165, IoU.path: 0.3242, IoU.stairs: 0.3387, IoU.runway: 0.7519, IoU.case: 0.6366, IoU.pool table: 0.9514, IoU.pillow: 0.6874, IoU.screen door: 0.8030, IoU.stairway: 0.4794, IoU.river: 0.1032, IoU.bridge: 0.6470, IoU.bookcase: 0.5442, IoU.blind: 0.4658, IoU.coffee table: 0.6134, IoU.toilet: 0.9079, IoU.flower: 0.4779, IoU.book: 0.5554, IoU.hill: 0.0653, IoU.bench: 0.5576, IoU.countertop: 0.6577, IoU.stove: 0.8649, IoU.palm: 0.5625, IoU.kitchen island: 0.6538, IoU.computer: 0.7832, IoU.swivel chair: 0.4517, IoU.boat: 0.7824, IoU.bar: 0.6363, IoU.arcade machine: 0.7885, IoU.hovel: 0.1431, IoU.bus: 0.9276, IoU.towel: 0.7902, IoU.light: 0.6314, IoU.truck: 0.5213, IoU.tower: 0.3767, IoU.chandelier: 0.7393, IoU.awning: 0.3899, IoU.streetlight: 0.3799, IoU.booth: 0.3839, IoU.television receiver: 0.8187, IoU.airplane: 0.9004, IoU.dirt track: 0.0614, IoU.apparel: 0.6466, IoU.pole: 0.2610, IoU.land: 0.0403, IoU.bannister: 0.2254, IoU.escalator: 0.6531, IoU.ottoman: 0.5279, IoU.bottle: 0.4497, IoU.buffet: 0.5417, IoU.poster: 0.3045, IoU.stage: 0.2926, IoU.van: 0.4850, IoU.ship: 0.8719, IoU.fountain: 0.3787, IoU.conveyer belt: 0.8193, IoU.canopy: 0.5208, IoU.washer: 0.8872, IoU.plaything: 0.3717, IoU.swimming pool: 0.5473, IoU.stool: 0.4991, IoU.barrel: 0.6784, IoU.basket: 0.4253, IoU.waterfall: 0.4799, IoU.tent: 0.9345, IoU.bag: 0.2742, IoU.minibike: 0.7722, IoU.cradle: 0.8518, IoU.oven: 0.5973, IoU.ball: 0.5020, IoU.food: 0.6151, IoU.step: 0.1940, IoU.tank: 0.7690, IoU.trade name: 0.2434, IoU.microwave: 0.8864, IoU.pot: 0.5861, IoU.animal: 0.6216, IoU.bicycle: 0.6108, IoU.lake: 0.5032, IoU.dishwasher: 0.7641, IoU.screen: 0.6063, IoU.blanket: 0.3705, IoU.sculpture: 0.7632, IoU.hood: 0.6454, IoU.sconce: 0.6262, IoU.vase: 0.4936, IoU.traffic light: 0.3961, IoU.tray: 0.2678, IoU.ashcan: 0.5238, IoU.fan: 0.7284, IoU.pier: 0.4878, IoU.crt screen: 0.0165, IoU.plate: 0.6301, IoU.monitor: 0.4374, IoU.bulletin board: 0.5781, IoU.shower: 0.1102, IoU.radiator: 0.6696, IoU.glass: 0.2196, IoU.clock: 0.5163, IoU.flag: 0.7193, Acc.wall: 0.9045, Acc.building: 0.9369, Acc.sky: 0.9740, Acc.floor: 0.9276, Acc.tree: 0.9002, Acc.ceiling: 0.9406, Acc.road: 0.9111, Acc.bed : 0.9716, Acc.windowpane: 0.8323, Acc.grass: 0.8157, Acc.cabinet: 0.7748, Acc.sidewalk: 0.8470, Acc.person: 0.9455, Acc.earth: 0.5591, Acc.door: 0.7528, Acc.table: 0.8120, Acc.mountain: 0.7514, Acc.plant: 0.6775, Acc.curtain: 0.9004, Acc.chair: 0.8028, Acc.car: 0.9407, Acc.water: 0.7768, Acc.painting: 0.9034, Acc.sofa: 0.9256, Acc.shelf: 0.7026, Acc.house: 0.6291, Acc.sea: 0.8214, Acc.mirror: 0.8904, Acc.rug: 0.7621, Acc.field: 0.5985, Acc.armchair: 0.7944, Acc.seat: 0.8932, Acc.fence: 0.6187, Acc.desk: 0.8019, Acc.rock: 0.7791, Acc.wardrobe: 0.7468, Acc.lamp: 0.8815, Acc.bathtub: 0.9110, Acc.railing: 0.6169, Acc.cushion: 0.8472, Acc.base: 0.5521, Acc.box: 0.5136, Acc.column: 0.6839, Acc.signboard: 0.5627, Acc.chest of drawers: 0.6457, Acc.counter: 0.5375, Acc.sand: 0.8599, Acc.sink: 0.8597, Acc.skyscraper: 0.5848, Acc.fireplace: 0.8916, Acc.refrigerator: 0.9349, Acc.grandstand: 0.8116, Acc.path: 0.4336, Acc.stairs: 0.3976, Acc.runway: 0.9773, Acc.case: 0.8376, Acc.pool table: 0.9780, Acc.pillow: 0.7720, Acc.screen door: 0.8274, Acc.stairway: 0.6673, Acc.river: 0.2228, Acc.bridge: 0.7629, Acc.bookcase: 0.6516, Acc.blind: 0.5102, Acc.coffee table: 0.8759, Acc.toilet: 0.9406, Acc.flower: 0.5922, Acc.book: 0.7823, Acc.hill: 0.0948, Acc.bench: 0.6214, Acc.countertop: 0.8404, Acc.stove: 0.9309, Acc.palm: 0.8546, Acc.kitchen island: 0.7928, Acc.computer: 0.9082, Acc.swivel chair: 0.7003, Acc.boat: 0.9270, Acc.bar: 0.8496, Acc.arcade machine: 0.8347, Acc.hovel: 0.1554, Acc.bus: 0.9725, Acc.towel: 0.8354, Acc.light: 0.7220, Acc.truck: 0.5957, Acc.tower: 0.6586, Acc.chandelier: 0.8365, Acc.awning: 0.4592, Acc.streetlight: 0.5403, Acc.booth: 0.6340, Acc.television receiver: 0.8732, Acc.airplane: 0.9556, Acc.dirt track: 0.3495, Acc.apparel: 0.8900, Acc.pole: 0.4545, Acc.land: 0.0547, Acc.bannister: 0.2792, Acc.escalator: 0.8731, Acc.ottoman: 0.6770, Acc.bottle: 0.6185, Acc.buffet: 0.6400, Acc.poster: 0.3624, Acc.stage: 0.4569, Acc.van: 0.7481, Acc.ship: 0.9238, Acc.fountain: 0.4011, Acc.conveyer belt: 0.9452, Acc.canopy: 0.7137, Acc.washer: 0.9408, Acc.plaything: 0.5591, Acc.swimming pool: 0.7978, Acc.stool: 0.6676, Acc.barrel: 0.8020, Acc.basket: 0.6219, Acc.waterfall: 0.5849, Acc.tent: 0.9850, Acc.bag: 0.3127, Acc.minibike: 0.9052, Acc.cradle: 0.9686, Acc.oven: 0.7241, Acc.ball: 0.5356, Acc.food: 0.7187, Acc.step: 0.2276, Acc.tank: 0.9254, Acc.trade name: 0.2928, Acc.microwave: 0.9687, Acc.pot: 0.7085, Acc.animal: 0.6325, Acc.bicycle: 0.7887, Acc.lake: 0.6373, Acc.dishwasher: 0.8503, Acc.screen: 0.9332, Acc.blanket: 0.4303, Acc.sculpture: 0.8871, Acc.hood: 0.7726, Acc.sconce: 0.7257, Acc.vase: 0.6497, Acc.traffic light: 0.6244, Acc.tray: 0.3544, Acc.ashcan: 0.6771, Acc.fan: 0.8465, Acc.pier: 0.5656, Acc.crt screen: 0.0338, Acc.plate: 0.7954, Acc.monitor: 0.5074, Acc.bulletin board: 0.6988, Acc.shower: 0.1147, Acc.radiator: 0.8281, Acc.glass: 0.2350, Acc.clock: 0.6112, Acc.flag: 0.8175 +2024-06-17 05:21:00,625 - mmseg - INFO - Iter [65050/80000] lr: 7.475e-06, eta: 7:20:09, time: 3.598, data_time: 1.984, memory: 71384, decode.loss_ce: 0.1399, decode.acc_seg: 93.6400, aux.loss_ce: 0.0601, aux.acc_seg: 93.1455, loss: 0.2000 +2024-06-17 05:22:21,649 - mmseg - INFO - Iter [65100/80000] lr: 7.451e-06, eta: 7:18:39, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1446, decode.acc_seg: 93.8108, aux.loss_ce: 0.0621, aux.acc_seg: 93.3703, loss: 0.2067 +2024-06-17 05:23:42,653 - mmseg - INFO - Iter [65150/80000] lr: 7.426e-06, eta: 7:17:09, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1321, decode.acc_seg: 94.0943, aux.loss_ce: 0.0565, aux.acc_seg: 93.6880, loss: 0.1886 +2024-06-17 05:25:03,823 - mmseg - INFO - Iter [65200/80000] lr: 7.401e-06, eta: 7:15:39, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1355, decode.acc_seg: 93.9220, aux.loss_ce: 0.0584, aux.acc_seg: 93.5109, loss: 0.1938 +2024-06-17 05:26:25,139 - mmseg - INFO - Iter [65250/80000] lr: 7.376e-06, eta: 7:14:09, time: 1.626, data_time: 0.009, memory: 71384, decode.loss_ce: 0.1399, decode.acc_seg: 93.9079, aux.loss_ce: 0.0602, aux.acc_seg: 93.5257, loss: 0.2001 +2024-06-17 05:27:46,143 - mmseg - INFO - Iter [65300/80000] lr: 7.351e-06, eta: 7:12:39, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1397, decode.acc_seg: 93.8257, aux.loss_ce: 0.0599, aux.acc_seg: 93.3654, loss: 0.1996 +2024-06-17 05:29:07,310 - mmseg - INFO - Iter [65350/80000] lr: 7.325e-06, eta: 7:11:10, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1400, decode.acc_seg: 93.7264, aux.loss_ce: 0.0595, aux.acc_seg: 93.3565, loss: 0.1995 +2024-06-17 05:30:28,393 - mmseg - INFO - Iter [65400/80000] lr: 7.300e-06, eta: 7:09:40, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1365, decode.acc_seg: 93.8214, aux.loss_ce: 0.0581, aux.acc_seg: 93.4994, loss: 0.1947 +2024-06-17 05:31:49,453 - mmseg - INFO - Iter [65450/80000] lr: 7.276e-06, eta: 7:08:10, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1425, decode.acc_seg: 93.7929, aux.loss_ce: 0.0610, aux.acc_seg: 93.3835, loss: 0.2035 +2024-06-17 05:33:10,405 - mmseg - INFO - Iter [65500/80000] lr: 7.251e-06, eta: 7:06:40, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1410, decode.acc_seg: 93.8320, aux.loss_ce: 0.0602, aux.acc_seg: 93.4088, loss: 0.2012 +2024-06-17 05:34:31,419 - mmseg - INFO - Iter [65550/80000] lr: 7.226e-06, eta: 7:05:10, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1378, decode.acc_seg: 93.9296, aux.loss_ce: 0.0585, aux.acc_seg: 93.5615, loss: 0.1963 +2024-06-17 05:35:52,541 - mmseg - INFO - Iter [65600/80000] lr: 7.201e-06, eta: 7:03:40, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1457, decode.acc_seg: 93.5363, aux.loss_ce: 0.0621, aux.acc_seg: 93.0676, loss: 0.2078 +2024-06-17 05:37:13,577 - mmseg - INFO - Iter [65650/80000] lr: 7.176e-06, eta: 7:02:10, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1504, decode.acc_seg: 93.4916, aux.loss_ce: 0.0642, aux.acc_seg: 93.0503, loss: 0.2146 +2024-06-17 05:38:37,166 - mmseg - INFO - Iter [65700/80000] lr: 7.151e-06, eta: 7:00:41, time: 1.672, data_time: 0.058, memory: 71384, decode.loss_ce: 0.1357, decode.acc_seg: 93.7383, aux.loss_ce: 0.0577, aux.acc_seg: 93.3984, loss: 0.1934 +2024-06-17 05:39:58,202 - mmseg - INFO - Iter [65750/80000] lr: 7.125e-06, eta: 6:59:11, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1358, decode.acc_seg: 93.9613, aux.loss_ce: 0.0581, aux.acc_seg: 93.6094, loss: 0.1939 +2024-06-17 05:41:19,125 - mmseg - INFO - Iter [65800/80000] lr: 7.100e-06, eta: 6:57:41, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1306, decode.acc_seg: 94.1371, aux.loss_ce: 0.0557, aux.acc_seg: 93.7562, loss: 0.1863 +2024-06-17 05:42:40,154 - mmseg - INFO - Iter [65850/80000] lr: 7.075e-06, eta: 6:56:12, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1323, decode.acc_seg: 93.9195, aux.loss_ce: 0.0566, aux.acc_seg: 93.5212, loss: 0.1889 +2024-06-17 05:44:01,297 - mmseg - INFO - Iter [65900/80000] lr: 7.051e-06, eta: 6:54:42, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1395, decode.acc_seg: 93.5454, aux.loss_ce: 0.0602, aux.acc_seg: 93.0699, loss: 0.1997 +2024-06-17 05:45:22,395 - mmseg - INFO - Iter [65950/80000] lr: 7.026e-06, eta: 6:53:12, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1349, decode.acc_seg: 94.2099, aux.loss_ce: 0.0581, aux.acc_seg: 93.7792, loss: 0.1930 +2024-06-17 05:46:43,359 - mmseg - INFO - Saving checkpoint at 66000 iterations +2024-06-17 05:48:13,656 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 05:48:13,656 - mmseg - INFO - Iter [66000/80000] lr: 7.001e-06, eta: 6:52:01, time: 3.425, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1339, decode.acc_seg: 93.9795, aux.loss_ce: 0.0573, aux.acc_seg: 93.5950, loss: 0.1912 +2024-06-17 05:49:49,995 - mmseg - INFO - per class results: +2024-06-17 05:49:50,001 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.74 | 90.28 | +| building | 85.6 | 93.19 | +| sky | 95.01 | 97.54 | +| floor | 85.69 | 92.73 | +| tree | 78.08 | 90.26 | +| ceiling | 87.9 | 94.76 | +| road | 86.89 | 91.32 | +| bed | 92.67 | 97.02 | +| windowpane | 67.73 | 81.11 | +| grass | 68.79 | 80.51 | +| cabinet | 67.44 | 77.22 | +| sidewalk | 70.97 | 84.99 | +| person | 86.56 | 94.56 | +| earth | 41.21 | 55.71 | +| door | 60.55 | 75.7 | +| table | 70.28 | 81.49 | +| mountain | 61.93 | 73.05 | +| plant | 57.25 | 68.3 | +| curtain | 79.41 | 89.73 | +| chair | 69.15 | 81.7 | +| car | 88.98 | 93.93 | +| water | 63.51 | 79.82 | +| painting | 76.26 | 90.18 | +| sofa | 83.44 | 91.26 | +| shelf | 53.7 | 71.36 | +| house | 58.44 | 77.08 | +| sea | 73.53 | 81.59 | +| mirror | 79.44 | 86.9 | +| rug | 68.19 | 74.99 | +| field | 29.41 | 51.34 | +| armchair | 62.54 | 78.47 | +| seat | 67.83 | 89.41 | +| fence | 53.49 | 67.86 | +| desk | 60.09 | 77.48 | +| rock | 54.62 | 82.06 | +| wardrobe | 53.97 | 77.3 | +| lamp | 77.03 | 87.15 | +| bathtub | 86.15 | 88.2 | +| railing | 46.42 | 64.5 | +| cushion | 74.18 | 84.08 | +| base | 37.58 | 53.81 | +| box | 39.24 | 51.23 | +| column | 56.45 | 67.64 | +| signboard | 42.65 | 57.99 | +| chest of drawers | 41.33 | 59.78 | +| counter | 41.73 | 49.28 | +| sand | 54.4 | 86.56 | +| sink | 80.78 | 84.78 | +| skyscraper | 46.86 | 58.72 | +| fireplace | 75.48 | 91.09 | +| refrigerator | 86.54 | 93.61 | +| grandstand | 51.7 | 82.59 | +| path | 33.17 | 45.67 | +| stairs | 33.49 | 40.83 | +| runway | 74.76 | 97.5 | +| case | 62.51 | 85.54 | +| pool table | 95.36 | 98.06 | +| pillow | 69.68 | 79.37 | +| screen door | 83.92 | 86.38 | +| stairway | 49.59 | 70.42 | +| river | 10.53 | 21.66 | +| bridge | 58.76 | 71.88 | +| bookcase | 55.24 | 65.43 | +| blind | 48.92 | 55.99 | +| coffee table | 61.26 | 87.73 | +| toilet | 91.27 | 94.09 | +| flower | 48.59 | 59.02 | +| book | 55.5 | 78.15 | +| hill | 8.05 | 15.41 | +| bench | 53.27 | 59.11 | +| countertop | 65.01 | 85.46 | +| stove | 88.11 | 93.02 | +| palm | 56.04 | 83.14 | +| kitchen island | 62.94 | 88.91 | +| computer | 78.73 | 91.53 | +| swivel chair | 46.59 | 67.5 | +| boat | 81.32 | 91.52 | +| bar | 64.25 | 86.65 | +| arcade machine | 75.9 | 80.11 | +| hovel | 15.47 | 17.62 | +| bus | 92.83 | 97.02 | +| towel | 81.4 | 87.53 | +| light | 63.66 | 73.12 | +| truck | 54.06 | 62.9 | +| tower | 20.16 | 33.53 | +| chandelier | 75.2 | 89.32 | +| awning | 39.06 | 45.27 | +| streetlight | 35.88 | 46.39 | +| booth | 37.04 | 55.26 | +| television receiver | 81.05 | 88.42 | +| airplane | 87.98 | 96.83 | +| dirt track | 5.73 | 31.12 | +| apparel | 56.56 | 68.42 | +| pole | 24.54 | 36.29 | +| land | 2.81 | 4.03 | +| bannister | 22.05 | 27.66 | +| escalator | 66.87 | 85.99 | +| ottoman | 47.12 | 61.29 | +| bottle | 44.99 | 56.65 | +| buffet | 56.46 | 65.97 | +| poster | 30.95 | 36.86 | +| stage | 24.77 | 47.81 | +| van | 50.07 | 75.15 | +| ship | 85.62 | 90.39 | +| fountain | 35.23 | 37.76 | +| conveyer belt | 81.24 | 94.19 | +| canopy | 54.77 | 75.93 | +| washer | 81.97 | 86.62 | +| plaything | 34.51 | 48.09 | +| swimming pool | 53.47 | 79.59 | +| stool | 50.26 | 68.56 | +| barrel | 75.79 | 95.05 | +| basket | 42.51 | 63.78 | +| waterfall | 47.72 | 61.65 | +| tent | 90.99 | 98.49 | +| bag | 29.06 | 33.9 | +| minibike | 76.69 | 91.49 | +| cradle | 89.44 | 97.04 | +| oven | 63.36 | 76.4 | +| ball | 53.11 | 58.05 | +| food | 58.35 | 67.06 | +| step | 18.34 | 21.25 | +| tank | 82.97 | 93.55 | +| trade name | 27.64 | 33.85 | +| microwave | 89.96 | 96.25 | +| pot | 58.8 | 69.74 | +| animal | 62.26 | 63.49 | +| bicycle | 60.42 | 79.35 | +| lake | 52.78 | 63.73 | +| dishwasher | 76.99 | 85.12 | +| screen | 55.91 | 86.63 | +| blanket | 38.74 | 44.59 | +| sculpture | 75.56 | 87.78 | +| hood | 64.61 | 76.46 | +| sconce | 62.83 | 70.93 | +| vase | 48.82 | 65.9 | +| traffic light | 36.46 | 62.19 | +| tray | 26.63 | 34.64 | +| ashcan | 52.25 | 64.52 | +| fan | 71.19 | 82.69 | +| pier | 47.37 | 64.07 | +| crt screen | 1.86 | 3.56 | +| plate | 62.05 | 81.2 | +| monitor | 54.14 | 62.96 | +| bulletin board | 58.42 | 67.66 | +| shower | 10.16 | 13.95 | +| radiator | 68.42 | 81.0 | +| glass | 22.11 | 23.97 | +| clock | 51.02 | 60.8 | +| flag | 72.16 | 81.39 | ++---------------------+-------+-------+ +2024-06-17 05:49:50,002 - mmseg - INFO - Summary: +2024-06-17 05:49:50,002 - mmseg - INFO - ++------+-------+-------+ +| aAcc | mIoU | mAcc | ++------+-------+-------+ +| 86.6 | 58.71 | 70.94 | ++------+-------+-------+ +2024-06-17 05:49:50,003 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 05:49:50,003 - mmseg - INFO - Iter(val) [250] aAcc: 0.8660, mIoU: 0.5871, mAcc: 0.7094, IoU.wall: 0.8274, IoU.building: 0.8560, IoU.sky: 0.9501, IoU.floor: 0.8569, IoU.tree: 0.7808, IoU.ceiling: 0.8790, IoU.road: 0.8689, IoU.bed : 0.9267, IoU.windowpane: 0.6773, IoU.grass: 0.6879, IoU.cabinet: 0.6744, IoU.sidewalk: 0.7097, IoU.person: 0.8656, IoU.earth: 0.4121, IoU.door: 0.6055, IoU.table: 0.7028, IoU.mountain: 0.6193, IoU.plant: 0.5725, IoU.curtain: 0.7941, IoU.chair: 0.6915, IoU.car: 0.8898, IoU.water: 0.6351, IoU.painting: 0.7626, IoU.sofa: 0.8344, IoU.shelf: 0.5370, IoU.house: 0.5844, IoU.sea: 0.7353, IoU.mirror: 0.7944, IoU.rug: 0.6819, IoU.field: 0.2941, IoU.armchair: 0.6254, IoU.seat: 0.6783, IoU.fence: 0.5349, IoU.desk: 0.6009, IoU.rock: 0.5462, IoU.wardrobe: 0.5397, IoU.lamp: 0.7703, IoU.bathtub: 0.8615, IoU.railing: 0.4642, IoU.cushion: 0.7418, IoU.base: 0.3758, IoU.box: 0.3924, IoU.column: 0.5645, IoU.signboard: 0.4265, IoU.chest of drawers: 0.4133, IoU.counter: 0.4173, IoU.sand: 0.5440, IoU.sink: 0.8078, IoU.skyscraper: 0.4686, IoU.fireplace: 0.7548, IoU.refrigerator: 0.8654, IoU.grandstand: 0.5170, IoU.path: 0.3317, IoU.stairs: 0.3349, IoU.runway: 0.7476, IoU.case: 0.6251, IoU.pool table: 0.9536, IoU.pillow: 0.6968, IoU.screen door: 0.8392, IoU.stairway: 0.4959, IoU.river: 0.1053, IoU.bridge: 0.5876, IoU.bookcase: 0.5524, IoU.blind: 0.4892, IoU.coffee table: 0.6126, IoU.toilet: 0.9127, IoU.flower: 0.4859, IoU.book: 0.5550, IoU.hill: 0.0805, IoU.bench: 0.5327, IoU.countertop: 0.6501, IoU.stove: 0.8811, IoU.palm: 0.5604, IoU.kitchen island: 0.6294, IoU.computer: 0.7873, IoU.swivel chair: 0.4659, IoU.boat: 0.8132, IoU.bar: 0.6425, IoU.arcade machine: 0.7590, IoU.hovel: 0.1547, IoU.bus: 0.9283, IoU.towel: 0.8140, IoU.light: 0.6366, IoU.truck: 0.5406, IoU.tower: 0.2016, IoU.chandelier: 0.7520, IoU.awning: 0.3906, IoU.streetlight: 0.3588, IoU.booth: 0.3704, IoU.television receiver: 0.8105, IoU.airplane: 0.8798, IoU.dirt track: 0.0573, IoU.apparel: 0.5656, IoU.pole: 0.2454, IoU.land: 0.0281, IoU.bannister: 0.2205, IoU.escalator: 0.6687, IoU.ottoman: 0.4712, IoU.bottle: 0.4499, IoU.buffet: 0.5646, IoU.poster: 0.3095, IoU.stage: 0.2477, IoU.van: 0.5007, IoU.ship: 0.8562, IoU.fountain: 0.3523, IoU.conveyer belt: 0.8124, IoU.canopy: 0.5477, IoU.washer: 0.8197, IoU.plaything: 0.3451, IoU.swimming pool: 0.5347, IoU.stool: 0.5026, IoU.barrel: 0.7579, IoU.basket: 0.4251, IoU.waterfall: 0.4772, IoU.tent: 0.9099, IoU.bag: 0.2906, IoU.minibike: 0.7669, IoU.cradle: 0.8944, IoU.oven: 0.6336, IoU.ball: 0.5311, IoU.food: 0.5835, IoU.step: 0.1834, IoU.tank: 0.8297, IoU.trade name: 0.2764, IoU.microwave: 0.8996, IoU.pot: 0.5880, IoU.animal: 0.6226, IoU.bicycle: 0.6042, IoU.lake: 0.5278, IoU.dishwasher: 0.7699, IoU.screen: 0.5591, IoU.blanket: 0.3874, IoU.sculpture: 0.7556, IoU.hood: 0.6461, IoU.sconce: 0.6283, IoU.vase: 0.4882, IoU.traffic light: 0.3646, IoU.tray: 0.2663, IoU.ashcan: 0.5225, IoU.fan: 0.7119, IoU.pier: 0.4737, IoU.crt screen: 0.0186, IoU.plate: 0.6205, IoU.monitor: 0.5414, IoU.bulletin board: 0.5842, IoU.shower: 0.1016, IoU.radiator: 0.6842, IoU.glass: 0.2211, IoU.clock: 0.5102, IoU.flag: 0.7216, Acc.wall: 0.9028, Acc.building: 0.9319, Acc.sky: 0.9754, Acc.floor: 0.9273, Acc.tree: 0.9026, Acc.ceiling: 0.9476, Acc.road: 0.9132, Acc.bed : 0.9702, Acc.windowpane: 0.8111, Acc.grass: 0.8051, Acc.cabinet: 0.7722, Acc.sidewalk: 0.8499, Acc.person: 0.9456, Acc.earth: 0.5571, Acc.door: 0.7570, Acc.table: 0.8149, Acc.mountain: 0.7305, Acc.plant: 0.6830, Acc.curtain: 0.8973, Acc.chair: 0.8170, Acc.car: 0.9393, Acc.water: 0.7982, Acc.painting: 0.9018, Acc.sofa: 0.9126, Acc.shelf: 0.7136, Acc.house: 0.7708, Acc.sea: 0.8159, Acc.mirror: 0.8690, Acc.rug: 0.7499, Acc.field: 0.5134, Acc.armchair: 0.7847, Acc.seat: 0.8941, Acc.fence: 0.6786, Acc.desk: 0.7748, Acc.rock: 0.8206, Acc.wardrobe: 0.7730, Acc.lamp: 0.8715, Acc.bathtub: 0.8820, Acc.railing: 0.6450, Acc.cushion: 0.8408, Acc.base: 0.5381, Acc.box: 0.5123, Acc.column: 0.6764, Acc.signboard: 0.5799, Acc.chest of drawers: 0.5978, Acc.counter: 0.4928, Acc.sand: 0.8656, Acc.sink: 0.8478, Acc.skyscraper: 0.5872, Acc.fireplace: 0.9109, Acc.refrigerator: 0.9361, Acc.grandstand: 0.8259, Acc.path: 0.4567, Acc.stairs: 0.4083, Acc.runway: 0.9750, Acc.case: 0.8554, Acc.pool table: 0.9806, Acc.pillow: 0.7937, Acc.screen door: 0.8638, Acc.stairway: 0.7042, Acc.river: 0.2166, Acc.bridge: 0.7188, Acc.bookcase: 0.6543, Acc.blind: 0.5599, Acc.coffee table: 0.8773, Acc.toilet: 0.9409, Acc.flower: 0.5902, Acc.book: 0.7815, Acc.hill: 0.1541, Acc.bench: 0.5911, Acc.countertop: 0.8546, Acc.stove: 0.9302, Acc.palm: 0.8314, Acc.kitchen island: 0.8891, Acc.computer: 0.9153, Acc.swivel chair: 0.6750, Acc.boat: 0.9152, Acc.bar: 0.8665, Acc.arcade machine: 0.8011, Acc.hovel: 0.1762, Acc.bus: 0.9702, Acc.towel: 0.8753, Acc.light: 0.7312, Acc.truck: 0.6290, Acc.tower: 0.3353, Acc.chandelier: 0.8932, Acc.awning: 0.4527, Acc.streetlight: 0.4639, Acc.booth: 0.5526, Acc.television receiver: 0.8842, Acc.airplane: 0.9683, Acc.dirt track: 0.3112, Acc.apparel: 0.6842, Acc.pole: 0.3629, Acc.land: 0.0403, Acc.bannister: 0.2766, Acc.escalator: 0.8599, Acc.ottoman: 0.6129, Acc.bottle: 0.5665, Acc.buffet: 0.6597, Acc.poster: 0.3686, Acc.stage: 0.4781, Acc.van: 0.7515, Acc.ship: 0.9039, Acc.fountain: 0.3776, Acc.conveyer belt: 0.9419, Acc.canopy: 0.7593, Acc.washer: 0.8662, Acc.plaything: 0.4809, Acc.swimming pool: 0.7959, Acc.stool: 0.6856, Acc.barrel: 0.9505, Acc.basket: 0.6378, Acc.waterfall: 0.6165, Acc.tent: 0.9849, Acc.bag: 0.3390, Acc.minibike: 0.9149, Acc.cradle: 0.9704, Acc.oven: 0.7640, Acc.ball: 0.5805, Acc.food: 0.6706, Acc.step: 0.2125, Acc.tank: 0.9355, Acc.trade name: 0.3385, Acc.microwave: 0.9625, Acc.pot: 0.6974, Acc.animal: 0.6349, Acc.bicycle: 0.7935, Acc.lake: 0.6373, Acc.dishwasher: 0.8512, Acc.screen: 0.8663, Acc.blanket: 0.4459, Acc.sculpture: 0.8778, Acc.hood: 0.7646, Acc.sconce: 0.7093, Acc.vase: 0.6590, Acc.traffic light: 0.6219, Acc.tray: 0.3464, Acc.ashcan: 0.6452, Acc.fan: 0.8269, Acc.pier: 0.6407, Acc.crt screen: 0.0356, Acc.plate: 0.8120, Acc.monitor: 0.6296, Acc.bulletin board: 0.6766, Acc.shower: 0.1395, Acc.radiator: 0.8100, Acc.glass: 0.2397, Acc.clock: 0.6080, Acc.flag: 0.8139 +2024-06-17 05:51:12,085 - mmseg - INFO - Iter [66050/80000] lr: 6.976e-06, eta: 6:50:52, time: 3.569, data_time: 1.944, memory: 71384, decode.loss_ce: 0.1463, decode.acc_seg: 93.7085, aux.loss_ce: 0.0622, aux.acc_seg: 93.3170, loss: 0.2085 +2024-06-17 05:52:33,032 - mmseg - INFO - Iter [66100/80000] lr: 6.951e-06, eta: 6:49:22, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1308, decode.acc_seg: 93.9906, aux.loss_ce: 0.0562, aux.acc_seg: 93.6099, loss: 0.1870 +2024-06-17 05:53:54,239 - mmseg - INFO - Iter [66150/80000] lr: 6.926e-06, eta: 6:47:52, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1361, decode.acc_seg: 93.9073, aux.loss_ce: 0.0583, aux.acc_seg: 93.5109, loss: 0.1944 +2024-06-17 05:55:15,349 - mmseg - INFO - Iter [66200/80000] lr: 6.900e-06, eta: 6:46:23, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1381, decode.acc_seg: 94.0949, aux.loss_ce: 0.0592, aux.acc_seg: 93.6886, loss: 0.1973 +2024-06-17 05:56:36,390 - mmseg - INFO - Iter [66250/80000] lr: 6.875e-06, eta: 6:44:53, time: 1.621, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1411, decode.acc_seg: 93.8350, aux.loss_ce: 0.0606, aux.acc_seg: 93.3854, loss: 0.2016 +2024-06-17 05:57:57,579 - mmseg - INFO - Iter [66300/80000] lr: 6.850e-06, eta: 6:43:23, time: 1.624, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1339, decode.acc_seg: 93.7692, aux.loss_ce: 0.0576, aux.acc_seg: 93.3290, loss: 0.1916 +2024-06-17 05:59:18,678 - mmseg - INFO - Iter [66350/80000] lr: 6.826e-06, eta: 6:41:53, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1481, decode.acc_seg: 93.5618, aux.loss_ce: 0.0633, aux.acc_seg: 93.2082, loss: 0.2114 +2024-06-17 06:00:39,772 - mmseg - INFO - Iter [66400/80000] lr: 6.801e-06, eta: 6:40:23, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1264, decode.acc_seg: 94.2741, aux.loss_ce: 0.0544, aux.acc_seg: 93.8632, loss: 0.1808 +2024-06-17 06:02:00,797 - mmseg - INFO - Iter [66450/80000] lr: 6.776e-06, eta: 6:38:53, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1428, decode.acc_seg: 93.8066, aux.loss_ce: 0.0614, aux.acc_seg: 93.3751, loss: 0.2041 +2024-06-17 06:03:22,015 - mmseg - INFO - Iter [66500/80000] lr: 6.751e-06, eta: 6:37:24, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1396, decode.acc_seg: 93.7423, aux.loss_ce: 0.0590, aux.acc_seg: 93.4090, loss: 0.1986 +2024-06-17 06:04:43,193 - mmseg - INFO - Iter [66550/80000] lr: 6.726e-06, eta: 6:35:54, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1343, decode.acc_seg: 94.1049, aux.loss_ce: 0.0575, aux.acc_seg: 93.6785, loss: 0.1918 +2024-06-17 06:06:04,303 - mmseg - INFO - Iter [66600/80000] lr: 6.700e-06, eta: 6:34:24, time: 1.622, data_time: 0.011, memory: 71384, decode.loss_ce: 0.1325, decode.acc_seg: 93.9203, aux.loss_ce: 0.0572, aux.acc_seg: 93.4408, loss: 0.1897 +2024-06-17 06:07:25,350 - mmseg - INFO - Iter [66650/80000] lr: 6.675e-06, eta: 6:32:54, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1323, decode.acc_seg: 94.1063, aux.loss_ce: 0.0569, aux.acc_seg: 93.6677, loss: 0.1892 +2024-06-17 06:08:46,327 - mmseg - INFO - Iter [66700/80000] lr: 6.651e-06, eta: 6:31:25, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1398, decode.acc_seg: 93.7775, aux.loss_ce: 0.0599, aux.acc_seg: 93.3557, loss: 0.1997 +2024-06-17 06:10:07,461 - mmseg - INFO - Iter [66750/80000] lr: 6.626e-06, eta: 6:29:55, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1428, decode.acc_seg: 93.7719, aux.loss_ce: 0.0611, aux.acc_seg: 93.2995, loss: 0.2039 +2024-06-17 06:11:28,450 - mmseg - INFO - Iter [66800/80000] lr: 6.601e-06, eta: 6:28:25, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1329, decode.acc_seg: 93.9677, aux.loss_ce: 0.0569, aux.acc_seg: 93.5850, loss: 0.1898 +2024-06-17 06:12:49,391 - mmseg - INFO - Iter [66850/80000] lr: 6.576e-06, eta: 6:26:55, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1398, decode.acc_seg: 93.9880, aux.loss_ce: 0.0598, aux.acc_seg: 93.5703, loss: 0.1996 +2024-06-17 06:14:10,580 - mmseg - INFO - Iter [66900/80000] lr: 6.551e-06, eta: 6:25:26, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1419, decode.acc_seg: 93.5545, aux.loss_ce: 0.0608, aux.acc_seg: 93.1150, loss: 0.2026 +2024-06-17 06:15:35,760 - mmseg - INFO - Iter [66950/80000] lr: 6.526e-06, eta: 6:23:57, time: 1.704, data_time: 0.086, memory: 71384, decode.loss_ce: 0.1348, decode.acc_seg: 94.0984, aux.loss_ce: 0.0577, aux.acc_seg: 93.7357, loss: 0.1925 +2024-06-17 06:16:56,903 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 06:16:56,904 - mmseg - INFO - Iter [67000/80000] lr: 6.500e-06, eta: 6:22:27, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1356, decode.acc_seg: 94.0903, aux.loss_ce: 0.0584, aux.acc_seg: 93.6327, loss: 0.1939 +2024-06-17 06:18:34,944 - mmseg - INFO - per class results: +2024-06-17 06:18:34,950 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.49 | 89.13 | +| building | 85.39 | 94.06 | +| sky | 94.99 | 97.73 | +| floor | 85.73 | 92.44 | +| tree | 77.52 | 88.69 | +| ceiling | 87.49 | 95.55 | +| road | 86.93 | 91.64 | +| bed | 92.8 | 97.07 | +| windowpane | 67.42 | 83.5 | +| grass | 69.47 | 82.49 | +| cabinet | 68.3 | 79.64 | +| sidewalk | 71.06 | 85.16 | +| person | 86.34 | 95.31 | +| earth | 41.85 | 52.64 | +| door | 60.91 | 76.94 | +| table | 70.61 | 80.97 | +| mountain | 61.72 | 75.45 | +| plant | 55.37 | 64.11 | +| curtain | 79.17 | 89.51 | +| chair | 69.37 | 82.07 | +| car | 88.71 | 94.02 | +| water | 63.52 | 78.78 | +| painting | 75.9 | 91.46 | +| sofa | 84.0 | 91.82 | +| shelf | 52.95 | 70.77 | +| house | 54.97 | 68.79 | +| sea | 74.0 | 81.72 | +| mirror | 77.9 | 84.5 | +| rug | 68.24 | 77.27 | +| field | 28.94 | 55.38 | +| armchair | 63.28 | 79.48 | +| seat | 68.19 | 89.22 | +| fence | 54.87 | 69.21 | +| desk | 61.24 | 80.31 | +| rock | 53.66 | 82.1 | +| wardrobe | 55.98 | 75.55 | +| lamp | 76.91 | 87.64 | +| bathtub | 88.07 | 91.08 | +| railing | 46.67 | 66.64 | +| cushion | 73.99 | 83.6 | +| base | 37.15 | 52.92 | +| box | 38.84 | 48.89 | +| column | 57.03 | 69.01 | +| signboard | 41.15 | 53.61 | +| chest of drawers | 42.14 | 59.68 | +| counter | 42.5 | 51.14 | +| sand | 58.1 | 85.73 | +| sink | 81.15 | 86.66 | +| skyscraper | 46.57 | 58.73 | +| fireplace | 72.57 | 93.7 | +| refrigerator | 86.76 | 94.66 | +| grandstand | 52.13 | 82.46 | +| path | 32.17 | 43.12 | +| stairs | 38.3 | 45.95 | +| runway | 74.44 | 96.98 | +| case | 62.82 | 84.27 | +| pool table | 95.14 | 97.82 | +| pillow | 70.86 | 82.98 | +| screen door | 80.9 | 83.26 | +| stairway | 48.91 | 71.07 | +| river | 9.62 | 20.62 | +| bridge | 69.1 | 78.04 | +| bookcase | 56.11 | 75.91 | +| blind | 45.23 | 49.62 | +| coffee table | 60.56 | 87.33 | +| toilet | 90.75 | 93.64 | +| flower | 48.28 | 60.52 | +| book | 57.41 | 71.7 | +| hill | 8.97 | 17.21 | +| bench | 55.4 | 62.33 | +| countertop | 65.29 | 85.01 | +| stove | 88.84 | 93.72 | +| palm | 56.15 | 82.09 | +| kitchen island | 70.18 | 86.32 | +| computer | 78.87 | 91.13 | +| swivel chair | 45.71 | 64.49 | +| boat | 82.54 | 92.06 | +| bar | 63.41 | 85.63 | +| arcade machine | 73.14 | 77.34 | +| hovel | 14.04 | 15.7 | +| bus | 92.19 | 97.55 | +| towel | 80.64 | 86.67 | +| light | 63.04 | 72.04 | +| truck | 52.43 | 62.75 | +| tower | 24.24 | 41.68 | +| chandelier | 74.55 | 85.46 | +| awning | 39.69 | 47.18 | +| streetlight | 37.61 | 49.48 | +| booth | 36.57 | 60.4 | +| television receiver | 82.4 | 88.62 | +| airplane | 86.6 | 97.14 | +| dirt track | 5.31 | 28.66 | +| apparel | 66.56 | 83.94 | +| pole | 21.59 | 33.78 | +| land | 3.55 | 4.89 | +| bannister | 23.02 | 29.59 | +| escalator | 65.04 | 87.41 | +| ottoman | 50.69 | 66.43 | +| bottle | 44.36 | 59.82 | +| buffet | 51.2 | 58.29 | +| poster | 29.67 | 35.33 | +| stage | 25.95 | 46.03 | +| van | 48.87 | 73.77 | +| ship | 85.37 | 93.64 | +| fountain | 38.22 | 40.0 | +| conveyer belt | 83.05 | 94.51 | +| canopy | 55.27 | 78.35 | +| washer | 87.2 | 92.58 | +| plaything | 41.28 | 60.03 | +| swimming pool | 55.1 | 83.02 | +| stool | 49.02 | 70.66 | +| barrel | 77.13 | 94.57 | +| basket | 41.89 | 63.23 | +| waterfall | 50.33 | 64.64 | +| tent | 87.48 | 98.76 | +| bag | 26.3 | 29.86 | +| minibike | 78.33 | 89.69 | +| cradle | 89.09 | 97.9 | +| oven | 64.49 | 76.58 | +| ball | 41.83 | 43.59 | +| food | 62.01 | 72.24 | +| step | 14.82 | 16.58 | +| tank | 79.58 | 94.46 | +| trade name | 23.88 | 27.56 | +| microwave | 90.23 | 96.31 | +| pot | 58.58 | 68.64 | +| animal | 62.59 | 63.75 | +| bicycle | 60.13 | 77.67 | +| lake | 52.37 | 63.77 | +| dishwasher | 78.33 | 84.68 | +| screen | 55.72 | 89.7 | +| blanket | 38.48 | 45.25 | +| sculpture | 73.65 | 89.8 | +| hood | 64.01 | 74.73 | +| sconce | 63.85 | 72.37 | +| vase | 49.32 | 65.56 | +| traffic light | 35.83 | 61.76 | +| tray | 27.6 | 37.41 | +| ashcan | 52.88 | 66.07 | +| fan | 72.56 | 83.94 | +| pier | 45.34 | 52.6 | +| crt screen | 1.86 | 3.42 | +| plate | 64.07 | 78.82 | +| monitor | 55.47 | 67.95 | +| bulletin board | 58.82 | 71.22 | +| shower | 8.33 | 14.86 | +| radiator | 68.08 | 81.15 | +| glass | 22.43 | 24.3 | +| clock | 51.52 | 60.43 | +| flag | 72.45 | 82.04 | ++---------------------+-------+-------+ +2024-06-17 06:18:34,951 - mmseg - INFO - Summary: +2024-06-17 06:18:34,951 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.58 | 58.85 | 71.23 | ++-------+-------+-------+ +2024-06-17 06:18:34,951 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 06:18:34,952 - mmseg - INFO - Iter(val) [250] aAcc: 0.8658, mIoU: 0.5885, mAcc: 0.7123, IoU.wall: 0.8249, IoU.building: 0.8539, IoU.sky: 0.9499, IoU.floor: 0.8573, IoU.tree: 0.7752, IoU.ceiling: 0.8749, IoU.road: 0.8693, IoU.bed : 0.9280, IoU.windowpane: 0.6742, IoU.grass: 0.6947, IoU.cabinet: 0.6830, IoU.sidewalk: 0.7106, IoU.person: 0.8634, IoU.earth: 0.4185, IoU.door: 0.6091, IoU.table: 0.7061, IoU.mountain: 0.6172, IoU.plant: 0.5537, IoU.curtain: 0.7917, IoU.chair: 0.6937, IoU.car: 0.8871, IoU.water: 0.6352, IoU.painting: 0.7590, IoU.sofa: 0.8400, IoU.shelf: 0.5295, IoU.house: 0.5497, IoU.sea: 0.7400, IoU.mirror: 0.7790, IoU.rug: 0.6824, IoU.field: 0.2894, IoU.armchair: 0.6328, IoU.seat: 0.6819, IoU.fence: 0.5487, IoU.desk: 0.6124, IoU.rock: 0.5366, IoU.wardrobe: 0.5598, IoU.lamp: 0.7691, IoU.bathtub: 0.8807, IoU.railing: 0.4667, IoU.cushion: 0.7399, IoU.base: 0.3715, IoU.box: 0.3884, IoU.column: 0.5703, IoU.signboard: 0.4115, IoU.chest of drawers: 0.4214, IoU.counter: 0.4250, IoU.sand: 0.5810, IoU.sink: 0.8115, IoU.skyscraper: 0.4657, IoU.fireplace: 0.7257, IoU.refrigerator: 0.8676, IoU.grandstand: 0.5213, IoU.path: 0.3217, IoU.stairs: 0.3830, IoU.runway: 0.7444, IoU.case: 0.6282, IoU.pool table: 0.9514, IoU.pillow: 0.7086, IoU.screen door: 0.8090, IoU.stairway: 0.4891, IoU.river: 0.0962, IoU.bridge: 0.6910, IoU.bookcase: 0.5611, IoU.blind: 0.4523, IoU.coffee table: 0.6056, IoU.toilet: 0.9075, IoU.flower: 0.4828, IoU.book: 0.5741, IoU.hill: 0.0897, IoU.bench: 0.5540, IoU.countertop: 0.6529, IoU.stove: 0.8884, IoU.palm: 0.5615, IoU.kitchen island: 0.7018, IoU.computer: 0.7887, IoU.swivel chair: 0.4571, IoU.boat: 0.8254, IoU.bar: 0.6341, IoU.arcade machine: 0.7314, IoU.hovel: 0.1404, IoU.bus: 0.9219, IoU.towel: 0.8064, IoU.light: 0.6304, IoU.truck: 0.5243, IoU.tower: 0.2424, IoU.chandelier: 0.7455, IoU.awning: 0.3969, IoU.streetlight: 0.3761, IoU.booth: 0.3657, IoU.television receiver: 0.8240, IoU.airplane: 0.8660, IoU.dirt track: 0.0531, IoU.apparel: 0.6656, IoU.pole: 0.2159, IoU.land: 0.0355, IoU.bannister: 0.2302, IoU.escalator: 0.6504, IoU.ottoman: 0.5069, IoU.bottle: 0.4436, IoU.buffet: 0.5120, IoU.poster: 0.2967, IoU.stage: 0.2595, IoU.van: 0.4887, IoU.ship: 0.8537, IoU.fountain: 0.3822, IoU.conveyer belt: 0.8305, IoU.canopy: 0.5527, IoU.washer: 0.8720, IoU.plaything: 0.4128, IoU.swimming pool: 0.5510, IoU.stool: 0.4902, IoU.barrel: 0.7713, IoU.basket: 0.4189, IoU.waterfall: 0.5033, IoU.tent: 0.8748, IoU.bag: 0.2630, IoU.minibike: 0.7833, IoU.cradle: 0.8909, IoU.oven: 0.6449, IoU.ball: 0.4183, IoU.food: 0.6201, IoU.step: 0.1482, IoU.tank: 0.7958, IoU.trade name: 0.2388, IoU.microwave: 0.9023, IoU.pot: 0.5858, IoU.animal: 0.6259, IoU.bicycle: 0.6013, IoU.lake: 0.5237, IoU.dishwasher: 0.7833, IoU.screen: 0.5572, IoU.blanket: 0.3848, IoU.sculpture: 0.7365, IoU.hood: 0.6401, IoU.sconce: 0.6385, IoU.vase: 0.4932, IoU.traffic light: 0.3583, IoU.tray: 0.2760, IoU.ashcan: 0.5288, IoU.fan: 0.7256, IoU.pier: 0.4534, IoU.crt screen: 0.0186, IoU.plate: 0.6407, IoU.monitor: 0.5547, IoU.bulletin board: 0.5882, IoU.shower: 0.0833, IoU.radiator: 0.6808, IoU.glass: 0.2243, IoU.clock: 0.5152, IoU.flag: 0.7245, Acc.wall: 0.8913, Acc.building: 0.9406, Acc.sky: 0.9773, Acc.floor: 0.9244, Acc.tree: 0.8869, Acc.ceiling: 0.9555, Acc.road: 0.9164, Acc.bed : 0.9707, Acc.windowpane: 0.8350, Acc.grass: 0.8249, Acc.cabinet: 0.7964, Acc.sidewalk: 0.8516, Acc.person: 0.9531, Acc.earth: 0.5264, Acc.door: 0.7694, Acc.table: 0.8097, Acc.mountain: 0.7545, Acc.plant: 0.6411, Acc.curtain: 0.8951, Acc.chair: 0.8207, Acc.car: 0.9402, Acc.water: 0.7878, Acc.painting: 0.9146, Acc.sofa: 0.9182, Acc.shelf: 0.7077, Acc.house: 0.6879, Acc.sea: 0.8172, Acc.mirror: 0.8450, Acc.rug: 0.7727, Acc.field: 0.5538, Acc.armchair: 0.7948, Acc.seat: 0.8922, Acc.fence: 0.6921, Acc.desk: 0.8031, Acc.rock: 0.8210, Acc.wardrobe: 0.7555, Acc.lamp: 0.8764, Acc.bathtub: 0.9108, Acc.railing: 0.6664, Acc.cushion: 0.8360, Acc.base: 0.5292, Acc.box: 0.4889, Acc.column: 0.6901, Acc.signboard: 0.5361, Acc.chest of drawers: 0.5968, Acc.counter: 0.5114, Acc.sand: 0.8573, Acc.sink: 0.8666, Acc.skyscraper: 0.5873, Acc.fireplace: 0.9370, Acc.refrigerator: 0.9466, Acc.grandstand: 0.8246, Acc.path: 0.4312, Acc.stairs: 0.4595, Acc.runway: 0.9698, Acc.case: 0.8427, Acc.pool table: 0.9782, Acc.pillow: 0.8298, Acc.screen door: 0.8326, Acc.stairway: 0.7107, Acc.river: 0.2062, Acc.bridge: 0.7804, Acc.bookcase: 0.7591, Acc.blind: 0.4962, Acc.coffee table: 0.8733, Acc.toilet: 0.9364, Acc.flower: 0.6052, Acc.book: 0.7170, Acc.hill: 0.1721, Acc.bench: 0.6233, Acc.countertop: 0.8501, Acc.stove: 0.9372, Acc.palm: 0.8209, Acc.kitchen island: 0.8632, Acc.computer: 0.9113, Acc.swivel chair: 0.6449, Acc.boat: 0.9206, Acc.bar: 0.8563, Acc.arcade machine: 0.7734, Acc.hovel: 0.1570, Acc.bus: 0.9755, Acc.towel: 0.8667, Acc.light: 0.7204, Acc.truck: 0.6275, Acc.tower: 0.4168, Acc.chandelier: 0.8546, Acc.awning: 0.4718, Acc.streetlight: 0.4948, Acc.booth: 0.6040, Acc.television receiver: 0.8862, Acc.airplane: 0.9714, Acc.dirt track: 0.2866, Acc.apparel: 0.8394, Acc.pole: 0.3378, Acc.land: 0.0489, Acc.bannister: 0.2959, Acc.escalator: 0.8741, Acc.ottoman: 0.6643, Acc.bottle: 0.5982, Acc.buffet: 0.5829, Acc.poster: 0.3533, Acc.stage: 0.4603, Acc.van: 0.7377, Acc.ship: 0.9364, Acc.fountain: 0.4000, Acc.conveyer belt: 0.9451, Acc.canopy: 0.7835, Acc.washer: 0.9258, Acc.plaything: 0.6003, Acc.swimming pool: 0.8302, Acc.stool: 0.7066, Acc.barrel: 0.9457, Acc.basket: 0.6323, Acc.waterfall: 0.6464, Acc.tent: 0.9876, Acc.bag: 0.2986, Acc.minibike: 0.8969, Acc.cradle: 0.9790, Acc.oven: 0.7658, Acc.ball: 0.4359, Acc.food: 0.7224, Acc.step: 0.1658, Acc.tank: 0.9446, Acc.trade name: 0.2756, Acc.microwave: 0.9631, Acc.pot: 0.6864, Acc.animal: 0.6375, Acc.bicycle: 0.7767, Acc.lake: 0.6377, Acc.dishwasher: 0.8468, Acc.screen: 0.8970, Acc.blanket: 0.4525, Acc.sculpture: 0.8980, Acc.hood: 0.7473, Acc.sconce: 0.7237, Acc.vase: 0.6556, Acc.traffic light: 0.6176, Acc.tray: 0.3741, Acc.ashcan: 0.6607, Acc.fan: 0.8394, Acc.pier: 0.5260, Acc.crt screen: 0.0342, Acc.plate: 0.7882, Acc.monitor: 0.6795, Acc.bulletin board: 0.7122, Acc.shower: 0.1486, Acc.radiator: 0.8115, Acc.glass: 0.2430, Acc.clock: 0.6043, Acc.flag: 0.8204 +2024-06-17 06:19:56,333 - mmseg - INFO - Iter [67050/80000] lr: 6.475e-06, eta: 6:21:17, time: 3.589, data_time: 1.978, memory: 71384, decode.loss_ce: 0.1363, decode.acc_seg: 94.1501, aux.loss_ce: 0.0583, aux.acc_seg: 93.7552, loss: 0.1946 +2024-06-17 06:21:17,466 - mmseg - INFO - Iter [67100/80000] lr: 6.450e-06, eta: 6:19:47, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1325, decode.acc_seg: 93.8827, aux.loss_ce: 0.0569, aux.acc_seg: 93.5210, loss: 0.1893 +2024-06-17 06:22:38,500 - mmseg - INFO - Iter [67150/80000] lr: 6.425e-06, eta: 6:18:17, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1372, decode.acc_seg: 93.9905, aux.loss_ce: 0.0589, aux.acc_seg: 93.5320, loss: 0.1961 +2024-06-17 06:23:59,619 - mmseg - INFO - Iter [67200/80000] lr: 6.401e-06, eta: 6:16:48, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1333, decode.acc_seg: 94.1787, aux.loss_ce: 0.0568, aux.acc_seg: 93.8096, loss: 0.1902 +2024-06-17 06:25:20,648 - mmseg - INFO - Iter [67250/80000] lr: 6.376e-06, eta: 6:15:18, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1350, decode.acc_seg: 93.8879, aux.loss_ce: 0.0577, aux.acc_seg: 93.5213, loss: 0.1928 +2024-06-17 06:26:41,544 - mmseg - INFO - Iter [67300/80000] lr: 6.351e-06, eta: 6:13:48, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1346, decode.acc_seg: 94.1715, aux.loss_ce: 0.0576, aux.acc_seg: 93.7818, loss: 0.1922 +2024-06-17 06:28:02,492 - mmseg - INFO - Iter [67350/80000] lr: 6.326e-06, eta: 6:12:18, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1379, decode.acc_seg: 93.8839, aux.loss_ce: 0.0588, aux.acc_seg: 93.5506, loss: 0.1967 +2024-06-17 06:29:23,411 - mmseg - INFO - Iter [67400/80000] lr: 6.301e-06, eta: 6:10:49, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1299, decode.acc_seg: 94.2029, aux.loss_ce: 0.0561, aux.acc_seg: 93.7428, loss: 0.1860 +2024-06-17 06:30:44,688 - mmseg - INFO - Iter [67450/80000] lr: 6.275e-06, eta: 6:09:19, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1326, decode.acc_seg: 94.0156, aux.loss_ce: 0.0564, aux.acc_seg: 93.6288, loss: 0.1890 +2024-06-17 06:32:05,673 - mmseg - INFO - Iter [67500/80000] lr: 6.250e-06, eta: 6:07:50, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1335, decode.acc_seg: 94.0571, aux.loss_ce: 0.0573, aux.acc_seg: 93.6423, loss: 0.1909 +2024-06-17 06:33:26,678 - mmseg - INFO - Iter [67550/80000] lr: 6.225e-06, eta: 6:06:20, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1456, decode.acc_seg: 93.7162, aux.loss_ce: 0.0615, aux.acc_seg: 93.2919, loss: 0.2071 +2024-06-17 06:34:47,646 - mmseg - INFO - Iter [67600/80000] lr: 6.201e-06, eta: 6:04:50, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1344, decode.acc_seg: 93.9508, aux.loss_ce: 0.0576, aux.acc_seg: 93.5586, loss: 0.1921 +2024-06-17 06:36:08,692 - mmseg - INFO - Iter [67650/80000] lr: 6.176e-06, eta: 6:03:21, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1390, decode.acc_seg: 93.7951, aux.loss_ce: 0.0597, aux.acc_seg: 93.3966, loss: 0.1987 +2024-06-17 06:37:29,920 - mmseg - INFO - Iter [67700/80000] lr: 6.151e-06, eta: 6:01:51, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1304, decode.acc_seg: 94.1240, aux.loss_ce: 0.0557, aux.acc_seg: 93.7933, loss: 0.1861 +2024-06-17 06:38:50,810 - mmseg - INFO - Iter [67750/80000] lr: 6.126e-06, eta: 6:00:22, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1323, decode.acc_seg: 93.9772, aux.loss_ce: 0.0570, aux.acc_seg: 93.4971, loss: 0.1893 +2024-06-17 06:40:11,736 - mmseg - INFO - Iter [67800/80000] lr: 6.101e-06, eta: 5:58:52, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1362, decode.acc_seg: 94.0655, aux.loss_ce: 0.0584, aux.acc_seg: 93.6970, loss: 0.1945 +2024-06-17 06:41:32,767 - mmseg - INFO - Iter [67850/80000] lr: 6.075e-06, eta: 5:57:22, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1344, decode.acc_seg: 94.0154, aux.loss_ce: 0.0580, aux.acc_seg: 93.5628, loss: 0.1923 +2024-06-17 06:42:53,817 - mmseg - INFO - Iter [67900/80000] lr: 6.050e-06, eta: 5:55:53, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1344, decode.acc_seg: 93.9152, aux.loss_ce: 0.0576, aux.acc_seg: 93.4795, loss: 0.1921 +2024-06-17 06:44:14,802 - mmseg - INFO - Iter [67950/80000] lr: 6.025e-06, eta: 5:54:23, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1395, decode.acc_seg: 93.8084, aux.loss_ce: 0.0597, aux.acc_seg: 93.4608, loss: 0.1992 +2024-06-17 06:45:35,828 - mmseg - INFO - Saving checkpoint at 68000 iterations +2024-06-17 06:47:01,159 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 06:47:01,159 - mmseg - INFO - Iter [68000/80000] lr: 6.001e-06, eta: 5:53:09, time: 3.327, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1326, decode.acc_seg: 93.9154, aux.loss_ce: 0.0571, aux.acc_seg: 93.4732, loss: 0.1898 +2024-06-17 06:48:36,277 - mmseg - INFO - per class results: +2024-06-17 06:48:36,283 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.7 | 89.91 | +| building | 85.79 | 93.37 | +| sky | 94.98 | 97.44 | +| floor | 85.46 | 92.61 | +| tree | 78.14 | 90.92 | +| ceiling | 87.67 | 95.44 | +| road | 87.0 | 90.64 | +| bed | 92.88 | 97.18 | +| windowpane | 68.12 | 82.17 | +| grass | 70.11 | 82.85 | +| cabinet | 67.68 | 78.81 | +| sidewalk | 71.35 | 86.51 | +| person | 86.55 | 94.59 | +| earth | 42.78 | 57.01 | +| door | 61.33 | 77.33 | +| table | 70.09 | 82.98 | +| mountain | 62.58 | 73.62 | +| plant | 57.57 | 67.0 | +| curtain | 78.47 | 89.69 | +| chair | 69.31 | 78.79 | +| car | 88.66 | 94.38 | +| water | 63.47 | 79.15 | +| painting | 76.92 | 90.6 | +| sofa | 83.79 | 92.01 | +| shelf | 53.8 | 71.76 | +| house | 59.35 | 75.51 | +| sea | 73.39 | 81.34 | +| mirror | 78.69 | 86.56 | +| rug | 66.51 | 74.35 | +| field | 30.27 | 52.09 | +| armchair | 64.99 | 82.06 | +| seat | 68.7 | 88.99 | +| fence | 52.7 | 65.79 | +| desk | 61.08 | 78.68 | +| rock | 55.87 | 81.44 | +| wardrobe | 54.03 | 73.28 | +| lamp | 77.1 | 85.92 | +| bathtub | 86.48 | 88.57 | +| railing | 46.72 | 62.69 | +| cushion | 74.17 | 85.17 | +| base | 38.21 | 54.14 | +| box | 39.34 | 49.59 | +| column | 57.46 | 68.82 | +| signboard | 42.36 | 56.94 | +| chest of drawers | 41.78 | 60.87 | +| counter | 45.09 | 56.15 | +| sand | 59.11 | 85.09 | +| sink | 80.71 | 84.36 | +| skyscraper | 47.23 | 60.75 | +| fireplace | 70.7 | 93.39 | +| refrigerator | 85.92 | 94.8 | +| grandstand | 50.99 | 83.45 | +| path | 31.39 | 42.89 | +| stairs | 39.53 | 47.74 | +| runway | 74.78 | 97.27 | +| case | 62.75 | 84.25 | +| pool table | 95.12 | 97.72 | +| pillow | 70.67 | 81.09 | +| screen door | 79.21 | 81.62 | +| stairway | 53.03 | 64.34 | +| river | 10.07 | 22.3 | +| bridge | 67.99 | 76.78 | +| bookcase | 54.91 | 68.3 | +| blind | 47.66 | 54.97 | +| coffee table | 60.3 | 87.8 | +| toilet | 90.73 | 93.13 | +| flower | 46.75 | 58.69 | +| book | 56.15 | 74.88 | +| hill | 11.62 | 21.19 | +| bench | 57.2 | 65.36 | +| countertop | 64.69 | 86.54 | +| stove | 88.34 | 92.95 | +| palm | 54.03 | 77.1 | +| kitchen island | 65.8 | 88.69 | +| computer | 76.4 | 87.76 | +| swivel chair | 45.52 | 68.32 | +| boat | 83.7 | 91.43 | +| bar | 68.17 | 85.6 | +| arcade machine | 77.65 | 82.2 | +| hovel | 14.26 | 16.02 | +| bus | 92.81 | 96.66 | +| towel | 81.9 | 88.35 | +| light | 61.05 | 68.72 | +| truck | 52.83 | 61.95 | +| tower | 27.83 | 48.47 | +| chandelier | 75.15 | 84.67 | +| awning | 39.53 | 45.56 | +| streetlight | 38.3 | 51.52 | +| booth | 35.21 | 55.44 | +| television receiver | 81.99 | 87.86 | +| airplane | 89.73 | 95.22 | +| dirt track | 5.9 | 27.1 | +| apparel | 67.82 | 87.91 | +| pole | 23.28 | 41.43 | +| land | 3.57 | 4.65 | +| bannister | 21.25 | 25.3 | +| escalator | 68.28 | 85.51 | +| ottoman | 53.6 | 68.86 | +| bottle | 45.16 | 59.61 | +| buffet | 47.53 | 50.83 | +| poster | 33.6 | 39.93 | +| stage | 27.36 | 46.65 | +| van | 49.98 | 73.26 | +| ship | 84.57 | 92.04 | +| fountain | 36.97 | 38.37 | +| conveyer belt | 81.52 | 93.81 | +| canopy | 51.04 | 71.32 | +| washer | 88.4 | 94.04 | +| plaything | 34.55 | 46.35 | +| swimming pool | 54.25 | 81.28 | +| stool | 49.66 | 71.74 | +| barrel | 78.41 | 95.46 | +| basket | 43.97 | 62.85 | +| waterfall | 47.06 | 60.28 | +| tent | 91.56 | 98.47 | +| bag | 27.82 | 33.22 | +| minibike | 78.02 | 90.33 | +| cradle | 89.91 | 96.4 | +| oven | 62.19 | 74.75 | +| ball | 56.3 | 67.08 | +| food | 60.31 | 70.98 | +| step | 21.11 | 24.88 | +| tank | 79.58 | 88.94 | +| trade name | 24.4 | 28.68 | +| microwave | 89.83 | 96.29 | +| pot | 58.96 | 70.12 | +| animal | 62.34 | 63.65 | +| bicycle | 60.79 | 77.01 | +| lake | 52.68 | 63.7 | +| dishwasher | 78.34 | 84.3 | +| screen | 59.56 | 92.99 | +| blanket | 38.51 | 44.62 | +| sculpture | 76.27 | 87.73 | +| hood | 64.89 | 76.76 | +| sconce | 61.63 | 68.4 | +| vase | 48.35 | 65.53 | +| traffic light | 37.74 | 59.6 | +| tray | 24.96 | 33.12 | +| ashcan | 51.1 | 68.17 | +| fan | 69.51 | 82.23 | +| pier | 46.26 | 52.28 | +| crt screen | 1.91 | 3.42 | +| plate | 64.94 | 77.88 | +| monitor | 48.27 | 63.31 | +| bulletin board | 59.58 | 69.3 | +| shower | 7.39 | 12.0 | +| radiator | 68.0 | 79.8 | +| glass | 21.13 | 22.32 | +| clock | 52.98 | 60.95 | +| flag | 72.94 | 79.59 | ++---------------------+-------+-------+ +2024-06-17 06:48:36,283 - mmseg - INFO - Summary: +2024-06-17 06:48:36,283 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.74 | 59.12 | 71.11 | ++-------+-------+-------+ +2024-06-17 06:48:36,284 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 06:48:36,284 - mmseg - INFO - Iter(val) [250] aAcc: 0.8674, mIoU: 0.5912, mAcc: 0.7111, IoU.wall: 0.8270, IoU.building: 0.8579, IoU.sky: 0.9498, IoU.floor: 0.8546, IoU.tree: 0.7814, IoU.ceiling: 0.8767, IoU.road: 0.8700, IoU.bed : 0.9288, IoU.windowpane: 0.6812, IoU.grass: 0.7011, IoU.cabinet: 0.6768, IoU.sidewalk: 0.7135, IoU.person: 0.8655, IoU.earth: 0.4278, IoU.door: 0.6133, IoU.table: 0.7009, IoU.mountain: 0.6258, IoU.plant: 0.5757, IoU.curtain: 0.7847, IoU.chair: 0.6931, IoU.car: 0.8866, IoU.water: 0.6347, IoU.painting: 0.7692, IoU.sofa: 0.8379, IoU.shelf: 0.5380, IoU.house: 0.5935, IoU.sea: 0.7339, IoU.mirror: 0.7869, IoU.rug: 0.6651, IoU.field: 0.3027, IoU.armchair: 0.6499, IoU.seat: 0.6870, IoU.fence: 0.5270, IoU.desk: 0.6108, IoU.rock: 0.5587, IoU.wardrobe: 0.5403, IoU.lamp: 0.7710, IoU.bathtub: 0.8648, IoU.railing: 0.4672, IoU.cushion: 0.7417, IoU.base: 0.3821, IoU.box: 0.3934, IoU.column: 0.5746, IoU.signboard: 0.4236, IoU.chest of drawers: 0.4178, IoU.counter: 0.4509, IoU.sand: 0.5911, IoU.sink: 0.8071, IoU.skyscraper: 0.4723, IoU.fireplace: 0.7070, IoU.refrigerator: 0.8592, IoU.grandstand: 0.5099, IoU.path: 0.3139, IoU.stairs: 0.3953, IoU.runway: 0.7478, IoU.case: 0.6275, IoU.pool table: 0.9512, IoU.pillow: 0.7067, IoU.screen door: 0.7921, IoU.stairway: 0.5303, IoU.river: 0.1007, IoU.bridge: 0.6799, IoU.bookcase: 0.5491, IoU.blind: 0.4766, IoU.coffee table: 0.6030, IoU.toilet: 0.9073, IoU.flower: 0.4675, IoU.book: 0.5615, IoU.hill: 0.1162, IoU.bench: 0.5720, IoU.countertop: 0.6469, IoU.stove: 0.8834, IoU.palm: 0.5403, IoU.kitchen island: 0.6580, IoU.computer: 0.7640, IoU.swivel chair: 0.4552, IoU.boat: 0.8370, IoU.bar: 0.6817, IoU.arcade machine: 0.7765, IoU.hovel: 0.1426, IoU.bus: 0.9281, IoU.towel: 0.8190, IoU.light: 0.6105, IoU.truck: 0.5283, IoU.tower: 0.2783, IoU.chandelier: 0.7515, IoU.awning: 0.3953, IoU.streetlight: 0.3830, IoU.booth: 0.3521, IoU.television receiver: 0.8199, IoU.airplane: 0.8973, IoU.dirt track: 0.0590, IoU.apparel: 0.6782, IoU.pole: 0.2328, IoU.land: 0.0357, IoU.bannister: 0.2125, IoU.escalator: 0.6828, IoU.ottoman: 0.5360, IoU.bottle: 0.4516, IoU.buffet: 0.4753, IoU.poster: 0.3360, IoU.stage: 0.2736, IoU.van: 0.4998, IoU.ship: 0.8457, IoU.fountain: 0.3697, IoU.conveyer belt: 0.8152, IoU.canopy: 0.5104, IoU.washer: 0.8840, IoU.plaything: 0.3455, IoU.swimming pool: 0.5425, IoU.stool: 0.4966, IoU.barrel: 0.7841, IoU.basket: 0.4397, IoU.waterfall: 0.4706, IoU.tent: 0.9156, IoU.bag: 0.2782, IoU.minibike: 0.7802, IoU.cradle: 0.8991, IoU.oven: 0.6219, IoU.ball: 0.5630, IoU.food: 0.6031, IoU.step: 0.2111, IoU.tank: 0.7958, IoU.trade name: 0.2440, IoU.microwave: 0.8983, IoU.pot: 0.5896, IoU.animal: 0.6234, IoU.bicycle: 0.6079, IoU.lake: 0.5268, IoU.dishwasher: 0.7834, IoU.screen: 0.5956, IoU.blanket: 0.3851, IoU.sculpture: 0.7627, IoU.hood: 0.6489, IoU.sconce: 0.6163, IoU.vase: 0.4835, IoU.traffic light: 0.3774, IoU.tray: 0.2496, IoU.ashcan: 0.5110, IoU.fan: 0.6951, IoU.pier: 0.4626, IoU.crt screen: 0.0191, IoU.plate: 0.6494, IoU.monitor: 0.4827, IoU.bulletin board: 0.5958, IoU.shower: 0.0739, IoU.radiator: 0.6800, IoU.glass: 0.2113, IoU.clock: 0.5298, IoU.flag: 0.7294, Acc.wall: 0.8991, Acc.building: 0.9337, Acc.sky: 0.9744, Acc.floor: 0.9261, Acc.tree: 0.9092, Acc.ceiling: 0.9544, Acc.road: 0.9064, Acc.bed : 0.9718, Acc.windowpane: 0.8217, Acc.grass: 0.8285, Acc.cabinet: 0.7881, Acc.sidewalk: 0.8651, Acc.person: 0.9459, Acc.earth: 0.5701, Acc.door: 0.7733, Acc.table: 0.8298, Acc.mountain: 0.7362, Acc.plant: 0.6700, Acc.curtain: 0.8969, Acc.chair: 0.7879, Acc.car: 0.9438, Acc.water: 0.7915, Acc.painting: 0.9060, Acc.sofa: 0.9201, Acc.shelf: 0.7176, Acc.house: 0.7551, Acc.sea: 0.8134, Acc.mirror: 0.8656, Acc.rug: 0.7435, Acc.field: 0.5209, Acc.armchair: 0.8206, Acc.seat: 0.8899, Acc.fence: 0.6579, Acc.desk: 0.7868, Acc.rock: 0.8144, Acc.wardrobe: 0.7328, Acc.lamp: 0.8592, Acc.bathtub: 0.8857, Acc.railing: 0.6269, Acc.cushion: 0.8517, Acc.base: 0.5414, Acc.box: 0.4959, Acc.column: 0.6882, Acc.signboard: 0.5694, Acc.chest of drawers: 0.6087, Acc.counter: 0.5615, Acc.sand: 0.8509, Acc.sink: 0.8436, Acc.skyscraper: 0.6075, Acc.fireplace: 0.9339, Acc.refrigerator: 0.9480, Acc.grandstand: 0.8345, Acc.path: 0.4289, Acc.stairs: 0.4774, Acc.runway: 0.9727, Acc.case: 0.8425, Acc.pool table: 0.9772, Acc.pillow: 0.8109, Acc.screen door: 0.8162, Acc.stairway: 0.6434, Acc.river: 0.2230, Acc.bridge: 0.7678, Acc.bookcase: 0.6830, Acc.blind: 0.5497, Acc.coffee table: 0.8780, Acc.toilet: 0.9313, Acc.flower: 0.5869, Acc.book: 0.7488, Acc.hill: 0.2119, Acc.bench: 0.6536, Acc.countertop: 0.8654, Acc.stove: 0.9295, Acc.palm: 0.7710, Acc.kitchen island: 0.8869, Acc.computer: 0.8776, Acc.swivel chair: 0.6832, Acc.boat: 0.9143, Acc.bar: 0.8560, Acc.arcade machine: 0.8220, Acc.hovel: 0.1602, Acc.bus: 0.9666, Acc.towel: 0.8835, Acc.light: 0.6872, Acc.truck: 0.6195, Acc.tower: 0.4847, Acc.chandelier: 0.8467, Acc.awning: 0.4556, Acc.streetlight: 0.5152, Acc.booth: 0.5544, Acc.television receiver: 0.8786, Acc.airplane: 0.9522, Acc.dirt track: 0.2710, Acc.apparel: 0.8791, Acc.pole: 0.4143, Acc.land: 0.0465, Acc.bannister: 0.2530, Acc.escalator: 0.8551, Acc.ottoman: 0.6886, Acc.bottle: 0.5961, Acc.buffet: 0.5083, Acc.poster: 0.3993, Acc.stage: 0.4665, Acc.van: 0.7326, Acc.ship: 0.9204, Acc.fountain: 0.3837, Acc.conveyer belt: 0.9381, Acc.canopy: 0.7132, Acc.washer: 0.9404, Acc.plaything: 0.4635, Acc.swimming pool: 0.8128, Acc.stool: 0.7174, Acc.barrel: 0.9546, Acc.basket: 0.6285, Acc.waterfall: 0.6028, Acc.tent: 0.9847, Acc.bag: 0.3322, Acc.minibike: 0.9033, Acc.cradle: 0.9640, Acc.oven: 0.7475, Acc.ball: 0.6708, Acc.food: 0.7098, Acc.step: 0.2488, Acc.tank: 0.8894, Acc.trade name: 0.2868, Acc.microwave: 0.9629, Acc.pot: 0.7012, Acc.animal: 0.6365, Acc.bicycle: 0.7701, Acc.lake: 0.6370, Acc.dishwasher: 0.8430, Acc.screen: 0.9299, Acc.blanket: 0.4462, Acc.sculpture: 0.8773, Acc.hood: 0.7676, Acc.sconce: 0.6840, Acc.vase: 0.6553, Acc.traffic light: 0.5960, Acc.tray: 0.3312, Acc.ashcan: 0.6817, Acc.fan: 0.8223, Acc.pier: 0.5228, Acc.crt screen: 0.0342, Acc.plate: 0.7788, Acc.monitor: 0.6331, Acc.bulletin board: 0.6930, Acc.shower: 0.1200, Acc.radiator: 0.7980, Acc.glass: 0.2232, Acc.clock: 0.6095, Acc.flag: 0.7959 +2024-06-17 06:49:57,750 - mmseg - INFO - Iter [68050/80000] lr: 5.976e-06, eta: 5:51:56, time: 3.532, data_time: 1.919, memory: 71384, decode.loss_ce: 0.1363, decode.acc_seg: 94.2137, aux.loss_ce: 0.0589, aux.acc_seg: 93.7874, loss: 0.1952 +2024-06-17 06:51:18,671 - mmseg - INFO - Iter [68100/80000] lr: 5.951e-06, eta: 5:50:27, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1371, decode.acc_seg: 94.0866, aux.loss_ce: 0.0584, aux.acc_seg: 93.7350, loss: 0.1956 +2024-06-17 06:52:39,509 - mmseg - INFO - Iter [68150/80000] lr: 5.926e-06, eta: 5:48:57, time: 1.617, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1426, decode.acc_seg: 93.6010, aux.loss_ce: 0.0611, aux.acc_seg: 93.1976, loss: 0.2037 +2024-06-17 06:54:00,520 - mmseg - INFO - Iter [68200/80000] lr: 5.901e-06, eta: 5:47:27, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1280, decode.acc_seg: 94.2947, aux.loss_ce: 0.0553, aux.acc_seg: 93.8474, loss: 0.1833 +2024-06-17 06:55:23,878 - mmseg - INFO - Iter [68250/80000] lr: 5.875e-06, eta: 5:45:58, time: 1.667, data_time: 0.051, memory: 71384, decode.loss_ce: 0.1333, decode.acc_seg: 94.0618, aux.loss_ce: 0.0571, aux.acc_seg: 93.6848, loss: 0.1904 +2024-06-17 06:56:44,811 - mmseg - INFO - Iter [68300/80000] lr: 5.850e-06, eta: 5:44:28, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1451, decode.acc_seg: 93.7499, aux.loss_ce: 0.0606, aux.acc_seg: 93.3508, loss: 0.2056 +2024-06-17 06:58:05,716 - mmseg - INFO - Iter [68350/80000] lr: 5.825e-06, eta: 5:42:59, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1450, decode.acc_seg: 93.7157, aux.loss_ce: 0.0617, aux.acc_seg: 93.2732, loss: 0.2068 +2024-06-17 06:59:26,688 - mmseg - INFO - Iter [68400/80000] lr: 5.800e-06, eta: 5:41:29, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1440, decode.acc_seg: 93.4883, aux.loss_ce: 0.0615, aux.acc_seg: 93.0365, loss: 0.2055 +2024-06-17 07:00:47,742 - mmseg - INFO - Iter [68450/80000] lr: 5.776e-06, eta: 5:40:00, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1296, decode.acc_seg: 94.2596, aux.loss_ce: 0.0556, aux.acc_seg: 93.8185, loss: 0.1852 +2024-06-17 07:02:08,878 - mmseg - INFO - Iter [68500/80000] lr: 5.751e-06, eta: 5:38:30, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1490, decode.acc_seg: 93.5666, aux.loss_ce: 0.0636, aux.acc_seg: 93.1318, loss: 0.2126 +2024-06-17 07:03:29,964 - mmseg - INFO - Iter [68550/80000] lr: 5.726e-06, eta: 5:37:01, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1365, decode.acc_seg: 93.9940, aux.loss_ce: 0.0588, aux.acc_seg: 93.5408, loss: 0.1952 +2024-06-17 07:04:51,141 - mmseg - INFO - Iter [68600/80000] lr: 5.701e-06, eta: 5:35:31, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1342, decode.acc_seg: 94.1065, aux.loss_ce: 0.0575, aux.acc_seg: 93.6396, loss: 0.1917 +2024-06-17 07:06:12,085 - mmseg - INFO - Iter [68650/80000] lr: 5.676e-06, eta: 5:34:02, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1293, decode.acc_seg: 94.2920, aux.loss_ce: 0.0559, aux.acc_seg: 93.7981, loss: 0.1853 +2024-06-17 07:07:33,227 - mmseg - INFO - Iter [68700/80000] lr: 5.650e-06, eta: 5:32:32, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1327, decode.acc_seg: 94.1432, aux.loss_ce: 0.0567, aux.acc_seg: 93.7250, loss: 0.1893 +2024-06-17 07:08:54,296 - mmseg - INFO - Iter [68750/80000] lr: 5.625e-06, eta: 5:31:03, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1294, decode.acc_seg: 94.1303, aux.loss_ce: 0.0557, aux.acc_seg: 93.6858, loss: 0.1851 +2024-06-17 07:10:15,310 - mmseg - INFO - Iter [68800/80000] lr: 5.600e-06, eta: 5:29:33, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1291, decode.acc_seg: 94.4091, aux.loss_ce: 0.0556, aux.acc_seg: 94.0090, loss: 0.1847 +2024-06-17 07:11:36,232 - mmseg - INFO - Iter [68850/80000] lr: 5.576e-06, eta: 5:28:04, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1260, decode.acc_seg: 94.3053, aux.loss_ce: 0.0543, aux.acc_seg: 93.8934, loss: 0.1803 +2024-06-17 07:12:57,255 - mmseg - INFO - Iter [68900/80000] lr: 5.551e-06, eta: 5:26:34, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1408, decode.acc_seg: 94.0479, aux.loss_ce: 0.0603, aux.acc_seg: 93.6345, loss: 0.2011 +2024-06-17 07:14:18,221 - mmseg - INFO - Iter [68950/80000] lr: 5.526e-06, eta: 5:25:05, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1357, decode.acc_seg: 93.9424, aux.loss_ce: 0.0580, aux.acc_seg: 93.5229, loss: 0.1937 +2024-06-17 07:15:39,354 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 07:15:39,354 - mmseg - INFO - Iter [69000/80000] lr: 5.501e-06, eta: 5:23:36, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1314, decode.acc_seg: 94.1766, aux.loss_ce: 0.0564, aux.acc_seg: 93.7435, loss: 0.1878 +2024-06-17 07:17:16,862 - mmseg - INFO - per class results: +2024-06-17 07:17:16,868 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.89 | 90.29 | +| building | 85.9 | 93.28 | +| sky | 95.04 | 97.54 | +| floor | 85.3 | 92.26 | +| tree | 78.33 | 90.54 | +| ceiling | 87.82 | 94.85 | +| road | 86.26 | 90.82 | +| bed | 92.83 | 96.86 | +| windowpane | 67.49 | 82.7 | +| grass | 70.02 | 82.59 | +| cabinet | 67.34 | 76.51 | +| sidewalk | 70.25 | 85.11 | +| person | 86.21 | 95.24 | +| earth | 41.94 | 54.96 | +| door | 60.29 | 74.54 | +| table | 70.66 | 82.15 | +| mountain | 63.21 | 72.55 | +| plant | 57.76 | 68.02 | +| curtain | 78.9 | 89.55 | +| chair | 68.92 | 81.12 | +| car | 88.72 | 93.9 | +| water | 60.4 | 78.15 | +| painting | 76.77 | 91.0 | +| sofa | 84.64 | 93.13 | +| shelf | 53.5 | 70.99 | +| house | 56.53 | 71.73 | +| sea | 61.6 | 68.25 | +| mirror | 78.42 | 85.66 | +| rug | 67.3 | 77.26 | +| field | 30.33 | 56.31 | +| armchair | 61.57 | 75.31 | +| seat | 67.33 | 89.35 | +| fence | 52.61 | 66.8 | +| desk | 59.43 | 80.35 | +| rock | 56.35 | 82.31 | +| wardrobe | 53.07 | 73.54 | +| lamp | 76.74 | 88.06 | +| bathtub | 86.28 | 89.13 | +| railing | 46.35 | 63.68 | +| cushion | 73.39 | 81.62 | +| base | 37.99 | 52.2 | +| box | 38.52 | 48.94 | +| column | 57.2 | 67.66 | +| signboard | 41.46 | 58.15 | +| chest of drawers | 41.19 | 66.27 | +| counter | 41.65 | 50.96 | +| sand | 55.27 | 86.9 | +| sink | 80.64 | 85.22 | +| skyscraper | 46.5 | 58.12 | +| fireplace | 72.3 | 94.91 | +| refrigerator | 86.29 | 93.49 | +| grandstand | 51.91 | 82.53 | +| path | 29.25 | 39.54 | +| stairs | 35.78 | 47.96 | +| runway | 74.38 | 96.66 | +| case | 63.56 | 83.93 | +| pool table | 95.07 | 97.76 | +| pillow | 70.93 | 83.39 | +| screen door | 78.45 | 80.41 | +| stairway | 47.97 | 54.87 | +| river | 10.49 | 24.74 | +| bridge | 71.05 | 82.5 | +| bookcase | 51.03 | 66.47 | +| blind | 41.97 | 45.12 | +| coffee table | 61.08 | 87.24 | +| toilet | 90.97 | 93.35 | +| flower | 45.54 | 58.5 | +| book | 56.01 | 76.54 | +| hill | 9.38 | 16.61 | +| bench | 56.89 | 64.34 | +| countertop | 64.9 | 86.08 | +| stove | 88.94 | 94.19 | +| palm | 55.84 | 80.11 | +| kitchen island | 64.39 | 87.28 | +| computer | 79.37 | 91.52 | +| swivel chair | 50.24 | 79.01 | +| boat | 82.2 | 91.72 | +| bar | 64.85 | 89.38 | +| arcade machine | 78.25 | 82.81 | +| hovel | 14.76 | 16.31 | +| bus | 91.82 | 97.13 | +| towel | 80.63 | 87.4 | +| light | 63.08 | 72.23 | +| truck | 50.77 | 61.72 | +| tower | 39.04 | 69.22 | +| chandelier | 74.16 | 85.58 | +| awning | 40.55 | 49.93 | +| streetlight | 40.27 | 54.39 | +| booth | 36.53 | 57.99 | +| television receiver | 82.15 | 88.5 | +| airplane | 89.58 | 95.73 | +| dirt track | 5.63 | 31.98 | +| apparel | 65.0 | 89.63 | +| pole | 24.07 | 42.57 | +| land | 3.41 | 4.78 | +| bannister | 22.28 | 27.6 | +| escalator | 67.63 | 86.07 | +| ottoman | 49.5 | 63.74 | +| bottle | 44.5 | 56.33 | +| buffet | 58.18 | 66.08 | +| poster | 31.17 | 36.94 | +| stage | 26.86 | 45.65 | +| van | 49.55 | 72.66 | +| ship | 84.96 | 92.57 | +| fountain | 36.39 | 37.08 | +| conveyer belt | 81.61 | 93.7 | +| canopy | 52.88 | 72.8 | +| washer | 87.29 | 92.71 | +| plaything | 31.8 | 41.1 | +| swimming pool | 52.63 | 77.05 | +| stool | 50.87 | 72.6 | +| barrel | 75.42 | 95.53 | +| basket | 43.47 | 67.81 | +| waterfall | 47.7 | 63.49 | +| tent | 93.72 | 98.2 | +| bag | 27.23 | 31.05 | +| minibike | 78.45 | 89.83 | +| cradle | 87.14 | 97.57 | +| oven | 65.97 | 77.06 | +| ball | 57.79 | 68.64 | +| food | 64.84 | 76.58 | +| step | 22.44 | 25.86 | +| tank | 78.64 | 84.79 | +| trade name | 19.59 | 22.72 | +| microwave | 90.55 | 96.53 | +| pot | 60.2 | 70.9 | +| animal | 62.35 | 63.77 | +| bicycle | 60.58 | 77.02 | +| lake | 49.23 | 67.64 | +| dishwasher | 78.28 | 84.27 | +| screen | 57.58 | 88.69 | +| blanket | 38.43 | 44.61 | +| sculpture | 73.83 | 88.78 | +| hood | 64.91 | 76.85 | +| sconce | 63.44 | 73.03 | +| vase | 48.74 | 65.12 | +| traffic light | 37.67 | 60.86 | +| tray | 25.92 | 34.99 | +| ashcan | 51.87 | 68.33 | +| fan | 70.38 | 85.21 | +| pier | 47.79 | 54.15 | +| crt screen | 2.3 | 4.05 | +| plate | 64.24 | 78.75 | +| monitor | 53.86 | 64.27 | +| bulletin board | 56.29 | 67.89 | +| shower | 7.0 | 12.59 | +| radiator | 66.89 | 82.64 | +| glass | 21.4 | 22.8 | +| clock | 50.66 | 58.13 | +| flag | 72.62 | 81.82 | ++---------------------+-------+-------+ +2024-06-17 07:17:16,868 - mmseg - INFO - Summary: +2024-06-17 07:17:16,869 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.58 | 58.85 | 71.39 | ++-------+-------+-------+ +2024-06-17 07:17:16,869 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 07:17:16,870 - mmseg - INFO - Iter(val) [250] aAcc: 0.8658, mIoU: 0.5885, mAcc: 0.7139, IoU.wall: 0.8289, IoU.building: 0.8590, IoU.sky: 0.9504, IoU.floor: 0.8530, IoU.tree: 0.7833, IoU.ceiling: 0.8782, IoU.road: 0.8626, IoU.bed : 0.9283, IoU.windowpane: 0.6749, IoU.grass: 0.7002, IoU.cabinet: 0.6734, IoU.sidewalk: 0.7025, IoU.person: 0.8621, IoU.earth: 0.4194, IoU.door: 0.6029, IoU.table: 0.7066, IoU.mountain: 0.6321, IoU.plant: 0.5776, IoU.curtain: 0.7890, IoU.chair: 0.6892, IoU.car: 0.8872, IoU.water: 0.6040, IoU.painting: 0.7677, IoU.sofa: 0.8464, IoU.shelf: 0.5350, IoU.house: 0.5653, IoU.sea: 0.6160, IoU.mirror: 0.7842, IoU.rug: 0.6730, IoU.field: 0.3033, IoU.armchair: 0.6157, IoU.seat: 0.6733, IoU.fence: 0.5261, IoU.desk: 0.5943, IoU.rock: 0.5635, IoU.wardrobe: 0.5307, IoU.lamp: 0.7674, IoU.bathtub: 0.8628, IoU.railing: 0.4635, IoU.cushion: 0.7339, IoU.base: 0.3799, IoU.box: 0.3852, IoU.column: 0.5720, IoU.signboard: 0.4146, IoU.chest of drawers: 0.4119, IoU.counter: 0.4165, IoU.sand: 0.5527, IoU.sink: 0.8064, IoU.skyscraper: 0.4650, IoU.fireplace: 0.7230, IoU.refrigerator: 0.8629, IoU.grandstand: 0.5191, IoU.path: 0.2925, IoU.stairs: 0.3578, IoU.runway: 0.7438, IoU.case: 0.6356, IoU.pool table: 0.9507, IoU.pillow: 0.7093, IoU.screen door: 0.7845, IoU.stairway: 0.4797, IoU.river: 0.1049, IoU.bridge: 0.7105, IoU.bookcase: 0.5103, IoU.blind: 0.4197, IoU.coffee table: 0.6108, IoU.toilet: 0.9097, IoU.flower: 0.4554, IoU.book: 0.5601, IoU.hill: 0.0938, IoU.bench: 0.5689, IoU.countertop: 0.6490, IoU.stove: 0.8894, IoU.palm: 0.5584, IoU.kitchen island: 0.6439, IoU.computer: 0.7937, IoU.swivel chair: 0.5024, IoU.boat: 0.8220, IoU.bar: 0.6485, IoU.arcade machine: 0.7825, IoU.hovel: 0.1476, IoU.bus: 0.9182, IoU.towel: 0.8063, IoU.light: 0.6308, IoU.truck: 0.5077, IoU.tower: 0.3904, IoU.chandelier: 0.7416, IoU.awning: 0.4055, IoU.streetlight: 0.4027, IoU.booth: 0.3653, IoU.television receiver: 0.8215, IoU.airplane: 0.8958, IoU.dirt track: 0.0563, IoU.apparel: 0.6500, IoU.pole: 0.2407, IoU.land: 0.0341, IoU.bannister: 0.2228, IoU.escalator: 0.6763, IoU.ottoman: 0.4950, IoU.bottle: 0.4450, IoU.buffet: 0.5818, IoU.poster: 0.3117, IoU.stage: 0.2686, IoU.van: 0.4955, IoU.ship: 0.8496, IoU.fountain: 0.3639, IoU.conveyer belt: 0.8161, IoU.canopy: 0.5288, IoU.washer: 0.8729, IoU.plaything: 0.3180, IoU.swimming pool: 0.5263, IoU.stool: 0.5087, IoU.barrel: 0.7542, IoU.basket: 0.4347, IoU.waterfall: 0.4770, IoU.tent: 0.9372, IoU.bag: 0.2723, IoU.minibike: 0.7845, IoU.cradle: 0.8714, IoU.oven: 0.6597, IoU.ball: 0.5779, IoU.food: 0.6484, IoU.step: 0.2244, IoU.tank: 0.7864, IoU.trade name: 0.1959, IoU.microwave: 0.9055, IoU.pot: 0.6020, IoU.animal: 0.6235, IoU.bicycle: 0.6058, IoU.lake: 0.4923, IoU.dishwasher: 0.7828, IoU.screen: 0.5758, IoU.blanket: 0.3843, IoU.sculpture: 0.7383, IoU.hood: 0.6491, IoU.sconce: 0.6344, IoU.vase: 0.4874, IoU.traffic light: 0.3767, IoU.tray: 0.2592, IoU.ashcan: 0.5187, IoU.fan: 0.7038, IoU.pier: 0.4779, IoU.crt screen: 0.0230, IoU.plate: 0.6424, IoU.monitor: 0.5386, IoU.bulletin board: 0.5629, IoU.shower: 0.0700, IoU.radiator: 0.6689, IoU.glass: 0.2140, IoU.clock: 0.5066, IoU.flag: 0.7262, Acc.wall: 0.9029, Acc.building: 0.9328, Acc.sky: 0.9754, Acc.floor: 0.9226, Acc.tree: 0.9054, Acc.ceiling: 0.9485, Acc.road: 0.9082, Acc.bed : 0.9686, Acc.windowpane: 0.8270, Acc.grass: 0.8259, Acc.cabinet: 0.7651, Acc.sidewalk: 0.8511, Acc.person: 0.9524, Acc.earth: 0.5496, Acc.door: 0.7454, Acc.table: 0.8215, Acc.mountain: 0.7255, Acc.plant: 0.6802, Acc.curtain: 0.8955, Acc.chair: 0.8112, Acc.car: 0.9390, Acc.water: 0.7815, Acc.painting: 0.9100, Acc.sofa: 0.9313, Acc.shelf: 0.7099, Acc.house: 0.7173, Acc.sea: 0.6825, Acc.mirror: 0.8566, Acc.rug: 0.7726, Acc.field: 0.5631, Acc.armchair: 0.7531, Acc.seat: 0.8935, Acc.fence: 0.6680, Acc.desk: 0.8035, Acc.rock: 0.8231, Acc.wardrobe: 0.7354, Acc.lamp: 0.8806, Acc.bathtub: 0.8913, Acc.railing: 0.6368, Acc.cushion: 0.8162, Acc.base: 0.5220, Acc.box: 0.4894, Acc.column: 0.6766, Acc.signboard: 0.5815, Acc.chest of drawers: 0.6627, Acc.counter: 0.5096, Acc.sand: 0.8690, Acc.sink: 0.8522, Acc.skyscraper: 0.5812, Acc.fireplace: 0.9491, Acc.refrigerator: 0.9349, Acc.grandstand: 0.8253, Acc.path: 0.3954, Acc.stairs: 0.4796, Acc.runway: 0.9666, Acc.case: 0.8393, Acc.pool table: 0.9776, Acc.pillow: 0.8339, Acc.screen door: 0.8041, Acc.stairway: 0.5487, Acc.river: 0.2474, Acc.bridge: 0.8250, Acc.bookcase: 0.6647, Acc.blind: 0.4512, Acc.coffee table: 0.8724, Acc.toilet: 0.9335, Acc.flower: 0.5850, Acc.book: 0.7654, Acc.hill: 0.1661, Acc.bench: 0.6434, Acc.countertop: 0.8608, Acc.stove: 0.9419, Acc.palm: 0.8011, Acc.kitchen island: 0.8728, Acc.computer: 0.9152, Acc.swivel chair: 0.7901, Acc.boat: 0.9172, Acc.bar: 0.8938, Acc.arcade machine: 0.8281, Acc.hovel: 0.1631, Acc.bus: 0.9713, Acc.towel: 0.8740, Acc.light: 0.7223, Acc.truck: 0.6172, Acc.tower: 0.6922, Acc.chandelier: 0.8558, Acc.awning: 0.4993, Acc.streetlight: 0.5439, Acc.booth: 0.5799, Acc.television receiver: 0.8850, Acc.airplane: 0.9573, Acc.dirt track: 0.3198, Acc.apparel: 0.8963, Acc.pole: 0.4257, Acc.land: 0.0478, Acc.bannister: 0.2760, Acc.escalator: 0.8607, Acc.ottoman: 0.6374, Acc.bottle: 0.5633, Acc.buffet: 0.6608, Acc.poster: 0.3694, Acc.stage: 0.4565, Acc.van: 0.7266, Acc.ship: 0.9257, Acc.fountain: 0.3708, Acc.conveyer belt: 0.9370, Acc.canopy: 0.7280, Acc.washer: 0.9271, Acc.plaything: 0.4110, Acc.swimming pool: 0.7705, Acc.stool: 0.7260, Acc.barrel: 0.9553, Acc.basket: 0.6781, Acc.waterfall: 0.6349, Acc.tent: 0.9820, Acc.bag: 0.3105, Acc.minibike: 0.8983, Acc.cradle: 0.9757, Acc.oven: 0.7706, Acc.ball: 0.6864, Acc.food: 0.7658, Acc.step: 0.2586, Acc.tank: 0.8479, Acc.trade name: 0.2272, Acc.microwave: 0.9653, Acc.pot: 0.7090, Acc.animal: 0.6377, Acc.bicycle: 0.7702, Acc.lake: 0.6764, Acc.dishwasher: 0.8427, Acc.screen: 0.8869, Acc.blanket: 0.4461, Acc.sculpture: 0.8878, Acc.hood: 0.7685, Acc.sconce: 0.7303, Acc.vase: 0.6512, Acc.traffic light: 0.6086, Acc.tray: 0.3499, Acc.ashcan: 0.6833, Acc.fan: 0.8521, Acc.pier: 0.5415, Acc.crt screen: 0.0405, Acc.plate: 0.7875, Acc.monitor: 0.6427, Acc.bulletin board: 0.6789, Acc.shower: 0.1259, Acc.radiator: 0.8264, Acc.glass: 0.2280, Acc.clock: 0.5813, Acc.flag: 0.8182 +2024-06-17 07:18:38,369 - mmseg - INFO - Iter [69050/80000] lr: 5.476e-06, eta: 5:22:22, time: 3.580, data_time: 1.968, memory: 71384, decode.loss_ce: 0.1354, decode.acc_seg: 93.9627, aux.loss_ce: 0.0581, aux.acc_seg: 93.5388, loss: 0.1935 +2024-06-17 07:19:59,327 - mmseg - INFO - Iter [69100/80000] lr: 5.450e-06, eta: 5:20:52, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1379, decode.acc_seg: 94.0704, aux.loss_ce: 0.0590, aux.acc_seg: 93.6334, loss: 0.1969 +2024-06-17 07:21:20,630 - mmseg - INFO - Iter [69150/80000] lr: 5.425e-06, eta: 5:19:23, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1341, decode.acc_seg: 93.9753, aux.loss_ce: 0.0576, aux.acc_seg: 93.4974, loss: 0.1917 +2024-06-17 07:22:41,696 - mmseg - INFO - Iter [69200/80000] lr: 5.400e-06, eta: 5:17:53, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1347, decode.acc_seg: 94.0823, aux.loss_ce: 0.0576, aux.acc_seg: 93.6810, loss: 0.1923 +2024-06-17 07:24:02,661 - mmseg - INFO - Iter [69250/80000] lr: 5.376e-06, eta: 5:16:24, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1384, decode.acc_seg: 94.2180, aux.loss_ce: 0.0591, aux.acc_seg: 93.8007, loss: 0.1975 +2024-06-17 07:25:23,637 - mmseg - INFO - Iter [69300/80000] lr: 5.351e-06, eta: 5:14:55, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1321, decode.acc_seg: 94.0230, aux.loss_ce: 0.0567, aux.acc_seg: 93.6092, loss: 0.1888 +2024-06-17 07:26:44,863 - mmseg - INFO - Iter [69350/80000] lr: 5.326e-06, eta: 5:13:25, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1319, decode.acc_seg: 94.1698, aux.loss_ce: 0.0565, aux.acc_seg: 93.7770, loss: 0.1884 +2024-06-17 07:28:05,862 - mmseg - INFO - Iter [69400/80000] lr: 5.301e-06, eta: 5:11:56, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1303, decode.acc_seg: 94.2507, aux.loss_ce: 0.0562, aux.acc_seg: 93.8024, loss: 0.1865 +2024-06-17 07:29:27,084 - mmseg - INFO - Iter [69450/80000] lr: 5.276e-06, eta: 5:10:26, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1295, decode.acc_seg: 94.2059, aux.loss_ce: 0.0558, aux.acc_seg: 93.6735, loss: 0.1853 +2024-06-17 07:30:50,401 - mmseg - INFO - Iter [69500/80000] lr: 5.250e-06, eta: 5:08:57, time: 1.666, data_time: 0.051, memory: 71384, decode.loss_ce: 0.1348, decode.acc_seg: 94.0482, aux.loss_ce: 0.0581, aux.acc_seg: 93.6343, loss: 0.1928 +2024-06-17 07:32:11,439 - mmseg - INFO - Iter [69550/80000] lr: 5.225e-06, eta: 5:07:28, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1331, decode.acc_seg: 93.8665, aux.loss_ce: 0.0571, aux.acc_seg: 93.4338, loss: 0.1902 +2024-06-17 07:33:32,430 - mmseg - INFO - Iter [69600/80000] lr: 5.200e-06, eta: 5:05:59, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1314, decode.acc_seg: 94.0373, aux.loss_ce: 0.0565, aux.acc_seg: 93.6345, loss: 0.1879 +2024-06-17 07:34:53,440 - mmseg - INFO - Iter [69650/80000] lr: 5.175e-06, eta: 5:04:29, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1344, decode.acc_seg: 94.1001, aux.loss_ce: 0.0577, aux.acc_seg: 93.6548, loss: 0.1920 +2024-06-17 07:36:14,428 - mmseg - INFO - Iter [69700/80000] lr: 5.151e-06, eta: 5:03:00, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1315, decode.acc_seg: 94.0393, aux.loss_ce: 0.0567, aux.acc_seg: 93.5783, loss: 0.1882 +2024-06-17 07:37:35,413 - mmseg - INFO - Iter [69750/80000] lr: 5.126e-06, eta: 5:01:31, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1292, decode.acc_seg: 94.3516, aux.loss_ce: 0.0558, aux.acc_seg: 93.9256, loss: 0.1850 +2024-06-17 07:38:56,666 - mmseg - INFO - Iter [69800/80000] lr: 5.101e-06, eta: 5:00:01, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1375, decode.acc_seg: 94.0099, aux.loss_ce: 0.0589, aux.acc_seg: 93.5849, loss: 0.1964 +2024-06-17 07:40:17,614 - mmseg - INFO - Iter [69850/80000] lr: 5.076e-06, eta: 4:58:32, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1331, decode.acc_seg: 94.1595, aux.loss_ce: 0.0574, aux.acc_seg: 93.7305, loss: 0.1905 +2024-06-17 07:41:38,782 - mmseg - INFO - Iter [69900/80000] lr: 5.051e-06, eta: 4:57:03, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1318, decode.acc_seg: 94.2002, aux.loss_ce: 0.0566, aux.acc_seg: 93.7343, loss: 0.1885 +2024-06-17 07:42:59,983 - mmseg - INFO - Iter [69950/80000] lr: 5.025e-06, eta: 4:55:34, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1386, decode.acc_seg: 94.0491, aux.loss_ce: 0.0593, aux.acc_seg: 93.6368, loss: 0.1979 +2024-06-17 07:44:20,948 - mmseg - INFO - Saving checkpoint at 70000 iterations +2024-06-17 07:45:47,386 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 07:45:47,386 - mmseg - INFO - Iter [70000/80000] lr: 5.000e-06, eta: 4:54:17, time: 3.348, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1430, decode.acc_seg: 93.5248, aux.loss_ce: 0.0610, aux.acc_seg: 93.1365, loss: 0.2040 +2024-06-17 07:47:22,768 - mmseg - INFO - per class results: +2024-06-17 07:47:22,774 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.13 | 90.89 | +| building | 85.88 | 93.54 | +| sky | 95.08 | 97.38 | +| floor | 85.5 | 92.49 | +| tree | 78.3 | 90.74 | +| ceiling | 87.86 | 94.75 | +| road | 86.21 | 90.65 | +| bed | 92.6 | 96.92 | +| windowpane | 67.17 | 80.75 | +| grass | 69.86 | 82.99 | +| cabinet | 66.79 | 76.65 | +| sidewalk | 70.28 | 84.89 | +| person | 86.41 | 94.86 | +| earth | 42.05 | 55.91 | +| door | 60.47 | 75.49 | +| table | 70.93 | 81.69 | +| mountain | 62.87 | 73.63 | +| plant | 58.48 | 68.89 | +| curtain | 78.78 | 88.0 | +| chair | 69.15 | 82.36 | +| car | 88.65 | 94.12 | +| water | 60.5 | 78.17 | +| painting | 77.95 | 91.31 | +| sofa | 84.29 | 91.98 | +| shelf | 53.26 | 69.59 | +| house | 51.8 | 61.52 | +| sea | 61.71 | 67.86 | +| mirror | 78.45 | 85.36 | +| rug | 66.73 | 75.58 | +| field | 33.88 | 56.41 | +| armchair | 61.76 | 75.9 | +| seat | 66.89 | 89.91 | +| fence | 49.69 | 63.58 | +| desk | 59.61 | 79.56 | +| rock | 54.43 | 78.68 | +| wardrobe | 54.4 | 76.28 | +| lamp | 76.95 | 88.06 | +| bathtub | 86.48 | 89.75 | +| railing | 46.5 | 66.49 | +| cushion | 73.93 | 84.08 | +| base | 36.6 | 52.52 | +| box | 38.99 | 50.33 | +| column | 59.07 | 69.77 | +| signboard | 41.99 | 59.22 | +| chest of drawers | 43.98 | 64.36 | +| counter | 42.67 | 52.53 | +| sand | 58.16 | 86.29 | +| sink | 80.78 | 86.34 | +| skyscraper | 46.57 | 60.03 | +| fireplace | 74.07 | 91.87 | +| refrigerator | 85.99 | 94.32 | +| grandstand | 51.31 | 81.08 | +| path | 32.21 | 43.83 | +| stairs | 34.04 | 40.51 | +| runway | 74.49 | 97.35 | +| case | 63.15 | 84.21 | +| pool table | 94.98 | 98.01 | +| pillow | 69.56 | 80.3 | +| screen door | 77.77 | 79.2 | +| stairway | 47.45 | 66.4 | +| river | 9.18 | 20.89 | +| bridge | 62.18 | 71.23 | +| bookcase | 52.35 | 61.67 | +| blind | 44.7 | 50.31 | +| coffee table | 62.37 | 86.71 | +| toilet | 91.32 | 94.12 | +| flower | 46.4 | 60.42 | +| book | 57.03 | 80.77 | +| hill | 9.18 | 15.76 | +| bench | 56.41 | 64.18 | +| countertop | 64.28 | 85.33 | +| stove | 88.37 | 93.12 | +| palm | 55.29 | 83.47 | +| kitchen island | 62.38 | 86.86 | +| computer | 78.11 | 91.2 | +| swivel chair | 50.16 | 76.83 | +| boat | 83.79 | 91.09 | +| bar | 65.28 | 87.33 | +| arcade machine | 73.95 | 77.82 | +| hovel | 14.21 | 15.58 | +| bus | 92.22 | 97.26 | +| towel | 80.51 | 86.79 | +| light | 63.2 | 73.1 | +| truck | 49.7 | 58.88 | +| tower | 40.99 | 73.44 | +| chandelier | 74.8 | 89.16 | +| awning | 39.26 | 46.75 | +| streetlight | 39.7 | 52.65 | +| booth | 41.57 | 57.73 | +| television receiver | 80.68 | 86.7 | +| airplane | 87.98 | 96.69 | +| dirt track | 5.41 | 29.15 | +| apparel | 65.37 | 90.14 | +| pole | 22.32 | 37.73 | +| land | 3.49 | 4.63 | +| bannister | 23.34 | 30.89 | +| escalator | 67.81 | 84.77 | +| ottoman | 49.35 | 63.39 | +| bottle | 46.41 | 63.19 | +| buffet | 52.1 | 58.99 | +| poster | 31.23 | 38.05 | +| stage | 21.2 | 32.96 | +| van | 49.41 | 70.17 | +| ship | 82.38 | 90.02 | +| fountain | 36.74 | 37.33 | +| conveyer belt | 82.77 | 93.75 | +| canopy | 55.43 | 78.21 | +| washer | 85.74 | 91.13 | +| plaything | 34.35 | 51.66 | +| swimming pool | 52.82 | 76.33 | +| stool | 52.42 | 71.8 | +| barrel | 76.39 | 94.22 | +| basket | 42.13 | 60.51 | +| waterfall | 49.0 | 64.82 | +| tent | 93.84 | 98.46 | +| bag | 27.76 | 32.0 | +| minibike | 77.85 | 90.85 | +| cradle | 86.61 | 97.41 | +| oven | 60.55 | 70.76 | +| ball | 57.18 | 72.21 | +| food | 64.64 | 75.39 | +| step | 23.97 | 27.44 | +| tank | 84.29 | 91.26 | +| trade name | 21.7 | 25.37 | +| microwave | 90.02 | 96.2 | +| pot | 57.39 | 68.08 | +| animal | 62.8 | 64.56 | +| bicycle | 59.72 | 81.06 | +| lake | 50.35 | 69.03 | +| dishwasher | 76.58 | 84.44 | +| screen | 59.21 | 88.16 | +| blanket | 34.95 | 39.64 | +| sculpture | 74.97 | 87.56 | +| hood | 64.73 | 76.0 | +| sconce | 64.57 | 76.23 | +| vase | 48.88 | 67.64 | +| traffic light | 36.33 | 62.55 | +| tray | 26.8 | 33.82 | +| ashcan | 52.34 | 66.46 | +| fan | 70.09 | 84.17 | +| pier | 49.7 | 56.38 | +| crt screen | 1.7 | 3.45 | +| plate | 62.79 | 81.22 | +| monitor | 51.12 | 60.86 | +| bulletin board | 59.14 | 68.27 | +| shower | 9.41 | 12.54 | +| radiator | 67.67 | 81.72 | +| glass | 22.13 | 23.84 | +| clock | 50.22 | 58.63 | +| flag | 72.7 | 82.13 | ++---------------------+-------+-------+ +2024-06-17 07:47:22,774 - mmseg - INFO - Summary: +2024-06-17 07:47:22,774 - mmseg - INFO - ++-------+------+------+ +| aAcc | mIoU | mAcc | ++-------+------+------+ +| 86.64 | 58.8 | 71.2 | ++-------+------+------+ +2024-06-17 07:47:22,775 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 07:47:22,775 - mmseg - INFO - Iter(val) [250] aAcc: 0.8664, mIoU: 0.5880, mAcc: 0.7120, IoU.wall: 0.8313, IoU.building: 0.8588, IoU.sky: 0.9508, IoU.floor: 0.8550, IoU.tree: 0.7830, IoU.ceiling: 0.8786, IoU.road: 0.8621, IoU.bed : 0.9260, IoU.windowpane: 0.6717, IoU.grass: 0.6986, IoU.cabinet: 0.6679, IoU.sidewalk: 0.7028, IoU.person: 0.8641, IoU.earth: 0.4205, IoU.door: 0.6047, IoU.table: 0.7093, IoU.mountain: 0.6287, IoU.plant: 0.5848, IoU.curtain: 0.7878, IoU.chair: 0.6915, IoU.car: 0.8865, IoU.water: 0.6050, IoU.painting: 0.7795, IoU.sofa: 0.8429, IoU.shelf: 0.5326, IoU.house: 0.5180, IoU.sea: 0.6171, IoU.mirror: 0.7845, IoU.rug: 0.6673, IoU.field: 0.3388, IoU.armchair: 0.6176, IoU.seat: 0.6689, IoU.fence: 0.4969, IoU.desk: 0.5961, IoU.rock: 0.5443, IoU.wardrobe: 0.5440, IoU.lamp: 0.7695, IoU.bathtub: 0.8648, IoU.railing: 0.4650, IoU.cushion: 0.7393, IoU.base: 0.3660, IoU.box: 0.3899, IoU.column: 0.5907, IoU.signboard: 0.4199, IoU.chest of drawers: 0.4398, IoU.counter: 0.4267, IoU.sand: 0.5816, IoU.sink: 0.8078, IoU.skyscraper: 0.4657, IoU.fireplace: 0.7407, IoU.refrigerator: 0.8599, IoU.grandstand: 0.5131, IoU.path: 0.3221, IoU.stairs: 0.3404, IoU.runway: 0.7449, IoU.case: 0.6315, IoU.pool table: 0.9498, IoU.pillow: 0.6956, IoU.screen door: 0.7777, IoU.stairway: 0.4745, IoU.river: 0.0918, IoU.bridge: 0.6218, IoU.bookcase: 0.5235, IoU.blind: 0.4470, IoU.coffee table: 0.6237, IoU.toilet: 0.9132, IoU.flower: 0.4640, IoU.book: 0.5703, IoU.hill: 0.0918, IoU.bench: 0.5641, IoU.countertop: 0.6428, IoU.stove: 0.8837, IoU.palm: 0.5529, IoU.kitchen island: 0.6238, IoU.computer: 0.7811, IoU.swivel chair: 0.5016, IoU.boat: 0.8379, IoU.bar: 0.6528, IoU.arcade machine: 0.7395, IoU.hovel: 0.1421, IoU.bus: 0.9222, IoU.towel: 0.8051, IoU.light: 0.6320, IoU.truck: 0.4970, IoU.tower: 0.4099, IoU.chandelier: 0.7480, IoU.awning: 0.3926, IoU.streetlight: 0.3970, IoU.booth: 0.4157, IoU.television receiver: 0.8068, IoU.airplane: 0.8798, IoU.dirt track: 0.0541, IoU.apparel: 0.6537, IoU.pole: 0.2232, IoU.land: 0.0349, IoU.bannister: 0.2334, IoU.escalator: 0.6781, IoU.ottoman: 0.4935, IoU.bottle: 0.4641, IoU.buffet: 0.5210, IoU.poster: 0.3123, IoU.stage: 0.2120, IoU.van: 0.4941, IoU.ship: 0.8238, IoU.fountain: 0.3674, IoU.conveyer belt: 0.8277, IoU.canopy: 0.5543, IoU.washer: 0.8574, IoU.plaything: 0.3435, IoU.swimming pool: 0.5282, IoU.stool: 0.5242, IoU.barrel: 0.7639, IoU.basket: 0.4213, IoU.waterfall: 0.4900, IoU.tent: 0.9384, IoU.bag: 0.2776, IoU.minibike: 0.7785, IoU.cradle: 0.8661, IoU.oven: 0.6055, IoU.ball: 0.5718, IoU.food: 0.6464, IoU.step: 0.2397, IoU.tank: 0.8429, IoU.trade name: 0.2170, IoU.microwave: 0.9002, IoU.pot: 0.5739, IoU.animal: 0.6280, IoU.bicycle: 0.5972, IoU.lake: 0.5035, IoU.dishwasher: 0.7658, IoU.screen: 0.5921, IoU.blanket: 0.3495, IoU.sculpture: 0.7497, IoU.hood: 0.6473, IoU.sconce: 0.6457, IoU.vase: 0.4888, IoU.traffic light: 0.3633, IoU.tray: 0.2680, IoU.ashcan: 0.5234, IoU.fan: 0.7009, IoU.pier: 0.4970, IoU.crt screen: 0.0170, IoU.plate: 0.6279, IoU.monitor: 0.5112, IoU.bulletin board: 0.5914, IoU.shower: 0.0941, IoU.radiator: 0.6767, IoU.glass: 0.2213, IoU.clock: 0.5022, IoU.flag: 0.7270, Acc.wall: 0.9089, Acc.building: 0.9354, Acc.sky: 0.9738, Acc.floor: 0.9249, Acc.tree: 0.9074, Acc.ceiling: 0.9475, Acc.road: 0.9065, Acc.bed : 0.9692, Acc.windowpane: 0.8075, Acc.grass: 0.8299, Acc.cabinet: 0.7665, Acc.sidewalk: 0.8489, Acc.person: 0.9486, Acc.earth: 0.5591, Acc.door: 0.7549, Acc.table: 0.8169, Acc.mountain: 0.7363, Acc.plant: 0.6889, Acc.curtain: 0.8800, Acc.chair: 0.8236, Acc.car: 0.9412, Acc.water: 0.7817, Acc.painting: 0.9131, Acc.sofa: 0.9198, Acc.shelf: 0.6959, Acc.house: 0.6152, Acc.sea: 0.6786, Acc.mirror: 0.8536, Acc.rug: 0.7558, Acc.field: 0.5641, Acc.armchair: 0.7590, Acc.seat: 0.8991, Acc.fence: 0.6358, Acc.desk: 0.7956, Acc.rock: 0.7868, Acc.wardrobe: 0.7628, Acc.lamp: 0.8806, Acc.bathtub: 0.8975, Acc.railing: 0.6649, Acc.cushion: 0.8408, Acc.base: 0.5252, Acc.box: 0.5033, Acc.column: 0.6977, Acc.signboard: 0.5922, Acc.chest of drawers: 0.6436, Acc.counter: 0.5253, Acc.sand: 0.8629, Acc.sink: 0.8634, Acc.skyscraper: 0.6003, Acc.fireplace: 0.9187, Acc.refrigerator: 0.9432, Acc.grandstand: 0.8108, Acc.path: 0.4383, Acc.stairs: 0.4051, Acc.runway: 0.9735, Acc.case: 0.8421, Acc.pool table: 0.9801, Acc.pillow: 0.8030, Acc.screen door: 0.7920, Acc.stairway: 0.6640, Acc.river: 0.2089, Acc.bridge: 0.7123, Acc.bookcase: 0.6167, Acc.blind: 0.5031, Acc.coffee table: 0.8671, Acc.toilet: 0.9412, Acc.flower: 0.6042, Acc.book: 0.8077, Acc.hill: 0.1576, Acc.bench: 0.6418, Acc.countertop: 0.8533, Acc.stove: 0.9312, Acc.palm: 0.8347, Acc.kitchen island: 0.8686, Acc.computer: 0.9120, Acc.swivel chair: 0.7683, Acc.boat: 0.9109, Acc.bar: 0.8733, Acc.arcade machine: 0.7782, Acc.hovel: 0.1558, Acc.bus: 0.9726, Acc.towel: 0.8679, Acc.light: 0.7310, Acc.truck: 0.5888, Acc.tower: 0.7344, Acc.chandelier: 0.8916, Acc.awning: 0.4675, Acc.streetlight: 0.5265, Acc.booth: 0.5773, Acc.television receiver: 0.8670, Acc.airplane: 0.9669, Acc.dirt track: 0.2915, Acc.apparel: 0.9014, Acc.pole: 0.3773, Acc.land: 0.0463, Acc.bannister: 0.3089, Acc.escalator: 0.8477, Acc.ottoman: 0.6339, Acc.bottle: 0.6319, Acc.buffet: 0.5899, Acc.poster: 0.3805, Acc.stage: 0.3296, Acc.van: 0.7017, Acc.ship: 0.9002, Acc.fountain: 0.3733, Acc.conveyer belt: 0.9375, Acc.canopy: 0.7821, Acc.washer: 0.9113, Acc.plaything: 0.5166, Acc.swimming pool: 0.7633, Acc.stool: 0.7180, Acc.barrel: 0.9422, Acc.basket: 0.6051, Acc.waterfall: 0.6482, Acc.tent: 0.9846, Acc.bag: 0.3200, Acc.minibike: 0.9085, Acc.cradle: 0.9741, Acc.oven: 0.7076, Acc.ball: 0.7221, Acc.food: 0.7539, Acc.step: 0.2744, Acc.tank: 0.9126, Acc.trade name: 0.2537, Acc.microwave: 0.9620, Acc.pot: 0.6808, Acc.animal: 0.6456, Acc.bicycle: 0.8106, Acc.lake: 0.6903, Acc.dishwasher: 0.8444, Acc.screen: 0.8816, Acc.blanket: 0.3964, Acc.sculpture: 0.8756, Acc.hood: 0.7600, Acc.sconce: 0.7623, Acc.vase: 0.6764, Acc.traffic light: 0.6255, Acc.tray: 0.3382, Acc.ashcan: 0.6646, Acc.fan: 0.8417, Acc.pier: 0.5638, Acc.crt screen: 0.0345, Acc.plate: 0.8122, Acc.monitor: 0.6086, Acc.bulletin board: 0.6827, Acc.shower: 0.1254, Acc.radiator: 0.8172, Acc.glass: 0.2384, Acc.clock: 0.5863, Acc.flag: 0.8213 +2024-06-17 07:48:44,435 - mmseg - INFO - Iter [70050/80000] lr: 4.976e-06, eta: 4:53:01, time: 3.541, data_time: 1.925, memory: 71384, decode.loss_ce: 0.1280, decode.acc_seg: 94.2505, aux.loss_ce: 0.0553, aux.acc_seg: 93.8718, loss: 0.1833 +2024-06-17 07:50:05,544 - mmseg - INFO - Iter [70100/80000] lr: 4.951e-06, eta: 4:51:32, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1360, decode.acc_seg: 93.9670, aux.loss_ce: 0.0587, aux.acc_seg: 93.4933, loss: 0.1947 +2024-06-17 07:51:26,503 - mmseg - INFO - Iter [70150/80000] lr: 4.926e-06, eta: 4:50:02, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1313, decode.acc_seg: 94.2829, aux.loss_ce: 0.0568, aux.acc_seg: 93.8443, loss: 0.1881 +2024-06-17 07:52:47,441 - mmseg - INFO - Iter [70200/80000] lr: 4.901e-06, eta: 4:48:33, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1298, decode.acc_seg: 94.1567, aux.loss_ce: 0.0560, aux.acc_seg: 93.7242, loss: 0.1859 +2024-06-17 07:54:08,649 - mmseg - INFO - Iter [70250/80000] lr: 4.876e-06, eta: 4:47:03, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1246, decode.acc_seg: 94.3726, aux.loss_ce: 0.0540, aux.acc_seg: 93.9054, loss: 0.1786 +2024-06-17 07:55:30,031 - mmseg - INFO - Iter [70300/80000] lr: 4.851e-06, eta: 4:45:34, time: 1.628, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1403, decode.acc_seg: 93.8992, aux.loss_ce: 0.0604, aux.acc_seg: 93.4779, loss: 0.2007 +2024-06-17 07:56:50,958 - mmseg - INFO - Iter [70350/80000] lr: 4.825e-06, eta: 4:44:05, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1326, decode.acc_seg: 94.1159, aux.loss_ce: 0.0574, aux.acc_seg: 93.6613, loss: 0.1900 +2024-06-17 07:58:11,936 - mmseg - INFO - Iter [70400/80000] lr: 4.800e-06, eta: 4:42:36, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1284, decode.acc_seg: 94.3278, aux.loss_ce: 0.0552, aux.acc_seg: 93.9265, loss: 0.1835 +2024-06-17 07:59:33,034 - mmseg - INFO - Iter [70450/80000] lr: 4.775e-06, eta: 4:41:06, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1282, decode.acc_seg: 94.3064, aux.loss_ce: 0.0552, aux.acc_seg: 93.8837, loss: 0.1834 +2024-06-17 08:00:54,016 - mmseg - INFO - Iter [70500/80000] lr: 4.751e-06, eta: 4:39:37, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1327, decode.acc_seg: 94.0945, aux.loss_ce: 0.0573, aux.acc_seg: 93.6613, loss: 0.1900 +2024-06-17 08:02:14,959 - mmseg - INFO - Iter [70550/80000] lr: 4.726e-06, eta: 4:38:08, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1275, decode.acc_seg: 94.3071, aux.loss_ce: 0.0548, aux.acc_seg: 93.9115, loss: 0.1823 +2024-06-17 08:03:36,147 - mmseg - INFO - Iter [70600/80000] lr: 4.701e-06, eta: 4:36:38, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1361, decode.acc_seg: 93.8683, aux.loss_ce: 0.0582, aux.acc_seg: 93.4669, loss: 0.1943 +2024-06-17 08:04:57,346 - mmseg - INFO - Iter [70650/80000] lr: 4.676e-06, eta: 4:35:09, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1335, decode.acc_seg: 93.9553, aux.loss_ce: 0.0573, aux.acc_seg: 93.4911, loss: 0.1909 +2024-06-17 08:06:18,381 - mmseg - INFO - Iter [70700/80000] lr: 4.651e-06, eta: 4:33:40, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1292, decode.acc_seg: 94.3620, aux.loss_ce: 0.0559, aux.acc_seg: 93.8943, loss: 0.1851 +2024-06-17 08:07:42,152 - mmseg - INFO - Iter [70750/80000] lr: 4.625e-06, eta: 4:32:11, time: 1.675, data_time: 0.062, memory: 71384, decode.loss_ce: 0.1329, decode.acc_seg: 94.0213, aux.loss_ce: 0.0573, aux.acc_seg: 93.6090, loss: 0.1902 +2024-06-17 08:09:03,211 - mmseg - INFO - Iter [70800/80000] lr: 4.600e-06, eta: 4:30:42, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1244, decode.acc_seg: 94.4539, aux.loss_ce: 0.0539, aux.acc_seg: 93.9918, loss: 0.1784 +2024-06-17 08:10:24,247 - mmseg - INFO - Iter [70850/80000] lr: 4.575e-06, eta: 4:29:13, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1212, decode.acc_seg: 94.6068, aux.loss_ce: 0.0522, aux.acc_seg: 94.2035, loss: 0.1734 +2024-06-17 08:11:45,331 - mmseg - INFO - Iter [70900/80000] lr: 4.550e-06, eta: 4:27:43, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1286, decode.acc_seg: 94.0302, aux.loss_ce: 0.0556, aux.acc_seg: 93.5656, loss: 0.1842 +2024-06-17 08:13:06,442 - mmseg - INFO - Iter [70950/80000] lr: 4.526e-06, eta: 4:26:14, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1320, decode.acc_seg: 94.1470, aux.loss_ce: 0.0566, aux.acc_seg: 93.7395, loss: 0.1886 +2024-06-17 08:14:27,422 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 08:14:27,422 - mmseg - INFO - Iter [71000/80000] lr: 4.501e-06, eta: 4:24:45, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1289, decode.acc_seg: 94.1884, aux.loss_ce: 0.0558, aux.acc_seg: 93.7346, loss: 0.1847 +2024-06-17 08:16:03,449 - mmseg - INFO - per class results: +2024-06-17 08:16:03,456 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.21 | 90.91 | +| building | 85.97 | 93.41 | +| sky | 95.11 | 97.65 | +| floor | 85.46 | 92.35 | +| tree | 78.28 | 89.82 | +| ceiling | 87.95 | 94.26 | +| road | 86.43 | 91.19 | +| bed | 92.92 | 96.68 | +| windowpane | 68.06 | 82.55 | +| grass | 69.22 | 82.14 | +| cabinet | 68.56 | 78.59 | +| sidewalk | 70.24 | 84.09 | +| person | 86.37 | 94.94 | +| earth | 42.35 | 56.38 | +| door | 60.32 | 74.13 | +| table | 71.01 | 82.39 | +| mountain | 62.67 | 73.93 | +| plant | 58.34 | 68.16 | +| curtain | 78.82 | 89.32 | +| chair | 69.28 | 81.85 | +| car | 88.68 | 94.1 | +| water | 62.55 | 78.4 | +| painting | 76.81 | 90.21 | +| sofa | 84.44 | 93.17 | +| shelf | 53.9 | 71.16 | +| house | 56.44 | 72.06 | +| sea | 72.37 | 80.41 | +| mirror | 77.64 | 84.95 | +| rug | 66.74 | 75.6 | +| field | 32.62 | 55.4 | +| armchair | 62.49 | 76.86 | +| seat | 67.76 | 90.0 | +| fence | 53.38 | 65.39 | +| desk | 60.65 | 78.21 | +| rock | 53.51 | 78.93 | +| wardrobe | 55.93 | 74.26 | +| lamp | 77.24 | 86.69 | +| bathtub | 86.66 | 89.15 | +| railing | 45.83 | 63.43 | +| cushion | 74.39 | 84.49 | +| base | 36.96 | 53.6 | +| box | 37.58 | 46.76 | +| column | 58.9 | 67.86 | +| signboard | 41.43 | 58.47 | +| chest of drawers | 43.12 | 61.87 | +| counter | 42.38 | 51.77 | +| sand | 54.04 | 88.0 | +| sink | 81.03 | 86.15 | +| skyscraper | 46.92 | 59.62 | +| fireplace | 74.51 | 91.78 | +| refrigerator | 86.73 | 94.43 | +| grandstand | 51.25 | 82.76 | +| path | 31.32 | 41.57 | +| stairs | 33.05 | 41.43 | +| runway | 74.33 | 96.9 | +| case | 64.62 | 81.96 | +| pool table | 95.13 | 97.74 | +| pillow | 70.99 | 82.25 | +| screen door | 75.43 | 76.77 | +| stairway | 46.07 | 64.1 | +| river | 9.54 | 21.41 | +| bridge | 70.67 | 82.18 | +| bookcase | 55.37 | 66.78 | +| blind | 46.29 | 53.09 | +| coffee table | 62.61 | 86.57 | +| toilet | 91.13 | 93.84 | +| flower | 46.68 | 60.12 | +| book | 58.38 | 80.63 | +| hill | 7.79 | 14.4 | +| bench | 56.19 | 62.88 | +| countertop | 65.01 | 85.22 | +| stove | 87.73 | 92.28 | +| palm | 54.92 | 83.79 | +| kitchen island | 66.59 | 84.19 | +| computer | 76.78 | 87.75 | +| swivel chair | 46.91 | 70.72 | +| boat | 79.92 | 92.49 | +| bar | 64.97 | 88.33 | +| arcade machine | 72.96 | 76.92 | +| hovel | 13.73 | 16.07 | +| bus | 92.17 | 97.39 | +| towel | 81.26 | 88.7 | +| light | 63.3 | 72.58 | +| truck | 51.5 | 59.16 | +| tower | 40.36 | 71.4 | +| chandelier | 75.29 | 87.59 | +| awning | 41.39 | 49.82 | +| streetlight | 39.84 | 52.97 | +| booth | 40.94 | 54.4 | +| television receiver | 80.47 | 88.25 | +| airplane | 87.46 | 95.52 | +| dirt track | 5.9 | 31.03 | +| apparel | 66.8 | 85.42 | +| pole | 20.8 | 35.6 | +| land | 3.84 | 5.52 | +| bannister | 22.17 | 27.43 | +| escalator | 68.12 | 85.88 | +| ottoman | 50.24 | 64.46 | +| bottle | 44.8 | 58.95 | +| buffet | 59.26 | 71.68 | +| poster | 32.21 | 39.58 | +| stage | 27.8 | 45.62 | +| van | 48.63 | 74.31 | +| ship | 82.04 | 93.81 | +| fountain | 35.76 | 37.47 | +| conveyer belt | 82.24 | 95.17 | +| canopy | 51.1 | 71.19 | +| washer | 84.52 | 89.57 | +| plaything | 40.9 | 61.05 | +| swimming pool | 52.29 | 76.0 | +| stool | 53.51 | 73.31 | +| barrel | 78.57 | 94.8 | +| basket | 41.04 | 56.63 | +| waterfall | 46.16 | 59.65 | +| tent | 90.72 | 98.48 | +| bag | 29.18 | 35.13 | +| minibike | 76.28 | 91.6 | +| cradle | 88.75 | 97.36 | +| oven | 65.26 | 80.75 | +| ball | 57.18 | 69.35 | +| food | 66.97 | 80.2 | +| step | 23.49 | 26.91 | +| tank | 83.01 | 90.57 | +| trade name | 20.82 | 24.11 | +| microwave | 91.26 | 96.2 | +| pot | 58.35 | 70.34 | +| animal | 63.05 | 64.61 | +| bicycle | 60.44 | 77.81 | +| lake | 49.17 | 63.72 | +| dishwasher | 77.05 | 84.25 | +| screen | 57.47 | 89.79 | +| blanket | 38.24 | 43.76 | +| sculpture | 74.63 | 88.86 | +| hood | 64.58 | 75.89 | +| sconce | 63.37 | 72.88 | +| vase | 49.25 | 67.43 | +| traffic light | 35.4 | 66.32 | +| tray | 27.12 | 36.12 | +| ashcan | 52.23 | 67.46 | +| fan | 70.56 | 83.58 | +| pier | 48.51 | 54.74 | +| crt screen | 1.42 | 3.4 | +| plate | 62.51 | 81.05 | +| monitor | 34.08 | 43.83 | +| bulletin board | 53.41 | 65.04 | +| shower | 9.3 | 12.58 | +| radiator | 68.78 | 82.2 | +| glass | 23.18 | 25.87 | +| clock | 52.26 | 64.82 | +| flag | 72.44 | 82.23 | ++---------------------+-------+-------+ +2024-06-17 08:16:03,456 - mmseg - INFO - Summary: +2024-06-17 08:16:03,456 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.74 | 58.95 | 71.43 | ++-------+-------+-------+ +2024-06-17 08:16:03,457 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 08:16:03,457 - mmseg - INFO - Iter(val) [250] aAcc: 0.8674, mIoU: 0.5895, mAcc: 0.7143, IoU.wall: 0.8321, IoU.building: 0.8597, IoU.sky: 0.9511, IoU.floor: 0.8546, IoU.tree: 0.7828, IoU.ceiling: 0.8795, IoU.road: 0.8643, IoU.bed : 0.9292, IoU.windowpane: 0.6806, IoU.grass: 0.6922, IoU.cabinet: 0.6856, IoU.sidewalk: 0.7024, IoU.person: 0.8637, IoU.earth: 0.4235, IoU.door: 0.6032, IoU.table: 0.7101, IoU.mountain: 0.6267, IoU.plant: 0.5834, IoU.curtain: 0.7882, IoU.chair: 0.6928, IoU.car: 0.8868, IoU.water: 0.6255, IoU.painting: 0.7681, IoU.sofa: 0.8444, IoU.shelf: 0.5390, IoU.house: 0.5644, IoU.sea: 0.7237, IoU.mirror: 0.7764, IoU.rug: 0.6674, IoU.field: 0.3262, IoU.armchair: 0.6249, IoU.seat: 0.6776, IoU.fence: 0.5338, IoU.desk: 0.6065, IoU.rock: 0.5351, IoU.wardrobe: 0.5593, IoU.lamp: 0.7724, IoU.bathtub: 0.8666, IoU.railing: 0.4583, IoU.cushion: 0.7439, IoU.base: 0.3696, IoU.box: 0.3758, IoU.column: 0.5890, IoU.signboard: 0.4143, IoU.chest of drawers: 0.4312, IoU.counter: 0.4238, IoU.sand: 0.5404, IoU.sink: 0.8103, IoU.skyscraper: 0.4692, IoU.fireplace: 0.7451, IoU.refrigerator: 0.8673, IoU.grandstand: 0.5125, IoU.path: 0.3132, IoU.stairs: 0.3305, IoU.runway: 0.7433, IoU.case: 0.6462, IoU.pool table: 0.9513, IoU.pillow: 0.7099, IoU.screen door: 0.7543, IoU.stairway: 0.4607, IoU.river: 0.0954, IoU.bridge: 0.7067, IoU.bookcase: 0.5537, IoU.blind: 0.4629, IoU.coffee table: 0.6261, IoU.toilet: 0.9113, IoU.flower: 0.4668, IoU.book: 0.5838, IoU.hill: 0.0779, IoU.bench: 0.5619, IoU.countertop: 0.6501, IoU.stove: 0.8773, IoU.palm: 0.5492, IoU.kitchen island: 0.6659, IoU.computer: 0.7678, IoU.swivel chair: 0.4691, IoU.boat: 0.7992, IoU.bar: 0.6497, IoU.arcade machine: 0.7296, IoU.hovel: 0.1373, IoU.bus: 0.9217, IoU.towel: 0.8126, IoU.light: 0.6330, IoU.truck: 0.5150, IoU.tower: 0.4036, IoU.chandelier: 0.7529, IoU.awning: 0.4139, IoU.streetlight: 0.3984, IoU.booth: 0.4094, IoU.television receiver: 0.8047, IoU.airplane: 0.8746, IoU.dirt track: 0.0590, IoU.apparel: 0.6680, IoU.pole: 0.2080, IoU.land: 0.0384, IoU.bannister: 0.2217, IoU.escalator: 0.6812, IoU.ottoman: 0.5024, IoU.bottle: 0.4480, IoU.buffet: 0.5926, IoU.poster: 0.3221, IoU.stage: 0.2780, IoU.van: 0.4863, IoU.ship: 0.8204, IoU.fountain: 0.3576, IoU.conveyer belt: 0.8224, IoU.canopy: 0.5110, IoU.washer: 0.8452, IoU.plaything: 0.4090, IoU.swimming pool: 0.5229, IoU.stool: 0.5351, IoU.barrel: 0.7857, IoU.basket: 0.4104, IoU.waterfall: 0.4616, IoU.tent: 0.9072, IoU.bag: 0.2918, IoU.minibike: 0.7628, IoU.cradle: 0.8875, IoU.oven: 0.6526, IoU.ball: 0.5718, IoU.food: 0.6697, IoU.step: 0.2349, IoU.tank: 0.8301, IoU.trade name: 0.2082, IoU.microwave: 0.9126, IoU.pot: 0.5835, IoU.animal: 0.6305, IoU.bicycle: 0.6044, IoU.lake: 0.4917, IoU.dishwasher: 0.7705, IoU.screen: 0.5747, IoU.blanket: 0.3824, IoU.sculpture: 0.7463, IoU.hood: 0.6458, IoU.sconce: 0.6337, IoU.vase: 0.4925, IoU.traffic light: 0.3540, IoU.tray: 0.2712, IoU.ashcan: 0.5223, IoU.fan: 0.7056, IoU.pier: 0.4851, IoU.crt screen: 0.0142, IoU.plate: 0.6251, IoU.monitor: 0.3408, IoU.bulletin board: 0.5341, IoU.shower: 0.0930, IoU.radiator: 0.6878, IoU.glass: 0.2318, IoU.clock: 0.5226, IoU.flag: 0.7244, Acc.wall: 0.9091, Acc.building: 0.9341, Acc.sky: 0.9765, Acc.floor: 0.9235, Acc.tree: 0.8982, Acc.ceiling: 0.9426, Acc.road: 0.9119, Acc.bed : 0.9668, Acc.windowpane: 0.8255, Acc.grass: 0.8214, Acc.cabinet: 0.7859, Acc.sidewalk: 0.8409, Acc.person: 0.9494, Acc.earth: 0.5638, Acc.door: 0.7413, Acc.table: 0.8239, Acc.mountain: 0.7393, Acc.plant: 0.6816, Acc.curtain: 0.8932, Acc.chair: 0.8185, Acc.car: 0.9410, Acc.water: 0.7840, Acc.painting: 0.9021, Acc.sofa: 0.9317, Acc.shelf: 0.7116, Acc.house: 0.7206, Acc.sea: 0.8041, Acc.mirror: 0.8495, Acc.rug: 0.7560, Acc.field: 0.5540, Acc.armchair: 0.7686, Acc.seat: 0.9000, Acc.fence: 0.6539, Acc.desk: 0.7821, Acc.rock: 0.7893, Acc.wardrobe: 0.7426, Acc.lamp: 0.8669, Acc.bathtub: 0.8915, Acc.railing: 0.6343, Acc.cushion: 0.8449, Acc.base: 0.5360, Acc.box: 0.4676, Acc.column: 0.6786, Acc.signboard: 0.5847, Acc.chest of drawers: 0.6187, Acc.counter: 0.5177, Acc.sand: 0.8800, Acc.sink: 0.8615, Acc.skyscraper: 0.5962, Acc.fireplace: 0.9178, Acc.refrigerator: 0.9443, Acc.grandstand: 0.8276, Acc.path: 0.4157, Acc.stairs: 0.4143, Acc.runway: 0.9690, Acc.case: 0.8196, Acc.pool table: 0.9774, Acc.pillow: 0.8225, Acc.screen door: 0.7677, Acc.stairway: 0.6410, Acc.river: 0.2141, Acc.bridge: 0.8218, Acc.bookcase: 0.6678, Acc.blind: 0.5309, Acc.coffee table: 0.8657, Acc.toilet: 0.9384, Acc.flower: 0.6012, Acc.book: 0.8063, Acc.hill: 0.1440, Acc.bench: 0.6288, Acc.countertop: 0.8522, Acc.stove: 0.9228, Acc.palm: 0.8379, Acc.kitchen island: 0.8419, Acc.computer: 0.8775, Acc.swivel chair: 0.7072, Acc.boat: 0.9249, Acc.bar: 0.8833, Acc.arcade machine: 0.7692, Acc.hovel: 0.1607, Acc.bus: 0.9739, Acc.towel: 0.8870, Acc.light: 0.7258, Acc.truck: 0.5916, Acc.tower: 0.7140, Acc.chandelier: 0.8759, Acc.awning: 0.4982, Acc.streetlight: 0.5297, Acc.booth: 0.5440, Acc.television receiver: 0.8825, Acc.airplane: 0.9552, Acc.dirt track: 0.3103, Acc.apparel: 0.8542, Acc.pole: 0.3560, Acc.land: 0.0552, Acc.bannister: 0.2743, Acc.escalator: 0.8588, Acc.ottoman: 0.6446, Acc.bottle: 0.5895, Acc.buffet: 0.7168, Acc.poster: 0.3958, Acc.stage: 0.4562, Acc.van: 0.7431, Acc.ship: 0.9381, Acc.fountain: 0.3747, Acc.conveyer belt: 0.9517, Acc.canopy: 0.7119, Acc.washer: 0.8957, Acc.plaything: 0.6105, Acc.swimming pool: 0.7600, Acc.stool: 0.7331, Acc.barrel: 0.9480, Acc.basket: 0.5663, Acc.waterfall: 0.5965, Acc.tent: 0.9848, Acc.bag: 0.3513, Acc.minibike: 0.9160, Acc.cradle: 0.9736, Acc.oven: 0.8075, Acc.ball: 0.6935, Acc.food: 0.8020, Acc.step: 0.2691, Acc.tank: 0.9057, Acc.trade name: 0.2411, Acc.microwave: 0.9620, Acc.pot: 0.7034, Acc.animal: 0.6461, Acc.bicycle: 0.7781, Acc.lake: 0.6372, Acc.dishwasher: 0.8425, Acc.screen: 0.8979, Acc.blanket: 0.4376, Acc.sculpture: 0.8886, Acc.hood: 0.7589, Acc.sconce: 0.7288, Acc.vase: 0.6743, Acc.traffic light: 0.6632, Acc.tray: 0.3612, Acc.ashcan: 0.6746, Acc.fan: 0.8358, Acc.pier: 0.5474, Acc.crt screen: 0.0340, Acc.plate: 0.8105, Acc.monitor: 0.4383, Acc.bulletin board: 0.6504, Acc.shower: 0.1258, Acc.radiator: 0.8220, Acc.glass: 0.2587, Acc.clock: 0.6482, Acc.flag: 0.8223 +2024-06-17 08:17:24,967 - mmseg - INFO - Iter [71050/80000] lr: 4.476e-06, eta: 4:23:28, time: 3.551, data_time: 1.937, memory: 71384, decode.loss_ce: 0.1316, decode.acc_seg: 93.9891, aux.loss_ce: 0.0569, aux.acc_seg: 93.5251, loss: 0.1885 +2024-06-17 08:18:45,994 - mmseg - INFO - Iter [71100/80000] lr: 4.451e-06, eta: 4:21:59, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1269, decode.acc_seg: 94.2673, aux.loss_ce: 0.0548, aux.acc_seg: 93.7883, loss: 0.1817 +2024-06-17 08:20:07,076 - mmseg - INFO - Iter [71150/80000] lr: 4.426e-06, eta: 4:20:30, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1287, decode.acc_seg: 94.2276, aux.loss_ce: 0.0555, aux.acc_seg: 93.7878, loss: 0.1842 +2024-06-17 08:21:27,962 - mmseg - INFO - Iter [71200/80000] lr: 4.400e-06, eta: 4:19:00, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1328, decode.acc_seg: 94.2759, aux.loss_ce: 0.0573, aux.acc_seg: 93.8617, loss: 0.1901 +2024-06-17 08:22:49,092 - mmseg - INFO - Iter [71250/80000] lr: 4.375e-06, eta: 4:17:31, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1233, decode.acc_seg: 94.4802, aux.loss_ce: 0.0535, aux.acc_seg: 94.0600, loss: 0.1768 +2024-06-17 08:24:10,189 - mmseg - INFO - Iter [71300/80000] lr: 4.351e-06, eta: 4:16:02, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1318, decode.acc_seg: 94.0694, aux.loss_ce: 0.0566, aux.acc_seg: 93.6468, loss: 0.1883 +2024-06-17 08:25:31,148 - mmseg - INFO - Iter [71350/80000] lr: 4.326e-06, eta: 4:14:33, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1323, decode.acc_seg: 94.0823, aux.loss_ce: 0.0568, aux.acc_seg: 93.7160, loss: 0.1891 +2024-06-17 08:26:52,326 - mmseg - INFO - Iter [71400/80000] lr: 4.301e-06, eta: 4:13:04, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1374, decode.acc_seg: 94.0186, aux.loss_ce: 0.0586, aux.acc_seg: 93.6106, loss: 0.1960 +2024-06-17 08:28:13,224 - mmseg - INFO - Iter [71450/80000] lr: 4.276e-06, eta: 4:11:35, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1232, decode.acc_seg: 94.4470, aux.loss_ce: 0.0529, aux.acc_seg: 94.0630, loss: 0.1761 +2024-06-17 08:29:34,453 - mmseg - INFO - Iter [71500/80000] lr: 4.251e-06, eta: 4:10:05, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1314, decode.acc_seg: 94.1395, aux.loss_ce: 0.0563, aux.acc_seg: 93.7367, loss: 0.1877 +2024-06-17 08:30:55,679 - mmseg - INFO - Iter [71550/80000] lr: 4.226e-06, eta: 4:08:36, time: 1.625, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1335, decode.acc_seg: 94.0101, aux.loss_ce: 0.0572, aux.acc_seg: 93.6006, loss: 0.1907 +2024-06-17 08:32:16,606 - mmseg - INFO - Iter [71600/80000] lr: 4.200e-06, eta: 4:07:07, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1313, decode.acc_seg: 94.2778, aux.loss_ce: 0.0562, aux.acc_seg: 93.8494, loss: 0.1875 +2024-06-17 08:33:37,639 - mmseg - INFO - Iter [71650/80000] lr: 4.175e-06, eta: 4:05:38, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1278, decode.acc_seg: 94.3488, aux.loss_ce: 0.0549, aux.acc_seg: 93.9151, loss: 0.1827 +2024-06-17 08:34:58,852 - mmseg - INFO - Iter [71700/80000] lr: 4.150e-06, eta: 4:04:09, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1229, decode.acc_seg: 94.5434, aux.loss_ce: 0.0534, aux.acc_seg: 94.0969, loss: 0.1763 +2024-06-17 08:36:19,991 - mmseg - INFO - Iter [71750/80000] lr: 4.125e-06, eta: 4:02:40, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1299, decode.acc_seg: 94.0949, aux.loss_ce: 0.0563, aux.acc_seg: 93.6324, loss: 0.1863 +2024-06-17 08:37:40,979 - mmseg - INFO - Iter [71800/80000] lr: 4.101e-06, eta: 4:01:11, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1255, decode.acc_seg: 94.4398, aux.loss_ce: 0.0540, aux.acc_seg: 94.0914, loss: 0.1795 +2024-06-17 08:39:01,934 - mmseg - INFO - Iter [71850/80000] lr: 4.076e-06, eta: 3:59:42, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1375, decode.acc_seg: 93.9749, aux.loss_ce: 0.0594, aux.acc_seg: 93.5179, loss: 0.1969 +2024-06-17 08:40:22,920 - mmseg - INFO - Iter [71900/80000] lr: 4.051e-06, eta: 3:58:13, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1278, decode.acc_seg: 94.1919, aux.loss_ce: 0.0548, aux.acc_seg: 93.7699, loss: 0.1826 +2024-06-17 08:41:44,020 - mmseg - INFO - Iter [71950/80000] lr: 4.026e-06, eta: 3:56:44, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1370, decode.acc_seg: 94.1039, aux.loss_ce: 0.0587, aux.acc_seg: 93.6877, loss: 0.1956 +2024-06-17 08:43:07,405 - mmseg - INFO - Saving checkpoint at 72000 iterations +2024-06-17 08:44:33,256 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 08:44:33,256 - mmseg - INFO - Iter [72000/80000] lr: 4.000e-06, eta: 3:55:25, time: 3.385, data_time: 0.055, memory: 71384, decode.loss_ce: 0.1360, decode.acc_seg: 93.8392, aux.loss_ce: 0.0587, aux.acc_seg: 93.4505, loss: 0.1947 +2024-06-17 08:46:09,705 - mmseg - INFO - per class results: +2024-06-17 08:46:09,711 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.07 | 90.31 | +| building | 85.97 | 93.34 | +| sky | 94.99 | 97.32 | +| floor | 84.96 | 92.64 | +| tree | 78.06 | 91.5 | +| ceiling | 87.88 | 94.93 | +| road | 86.36 | 90.85 | +| bed | 92.95 | 96.93 | +| windowpane | 68.36 | 82.58 | +| grass | 69.13 | 81.52 | +| cabinet | 68.04 | 78.76 | +| sidewalk | 70.67 | 84.81 | +| person | 86.37 | 94.88 | +| earth | 42.32 | 57.4 | +| door | 60.88 | 76.45 | +| table | 70.52 | 81.58 | +| mountain | 62.68 | 72.35 | +| plant | 57.29 | 67.2 | +| curtain | 79.08 | 89.32 | +| chair | 69.16 | 81.83 | +| car | 88.88 | 93.86 | +| water | 62.07 | 77.82 | +| painting | 77.44 | 90.12 | +| sofa | 84.62 | 92.15 | +| shelf | 53.22 | 70.22 | +| house | 57.49 | 72.07 | +| sea | 73.09 | 81.18 | +| mirror | 77.98 | 86.71 | +| rug | 58.17 | 64.27 | +| field | 33.53 | 57.11 | +| armchair | 62.12 | 76.29 | +| seat | 68.02 | 89.48 | +| fence | 53.75 | 64.38 | +| desk | 60.04 | 78.79 | +| rock | 54.38 | 82.99 | +| wardrobe | 56.12 | 73.93 | +| lamp | 77.14 | 87.18 | +| bathtub | 86.92 | 89.24 | +| railing | 46.66 | 66.85 | +| cushion | 74.39 | 86.2 | +| base | 37.26 | 53.65 | +| box | 39.4 | 51.24 | +| column | 59.53 | 68.64 | +| signboard | 42.16 | 57.67 | +| chest of drawers | 43.95 | 66.64 | +| counter | 42.59 | 51.75 | +| sand | 56.44 | 86.69 | +| sink | 80.54 | 85.39 | +| skyscraper | 47.06 | 58.03 | +| fireplace | 74.78 | 92.46 | +| refrigerator | 86.64 | 95.61 | +| grandstand | 51.3 | 81.0 | +| path | 32.4 | 42.7 | +| stairs | 31.47 | 39.11 | +| runway | 74.52 | 97.46 | +| case | 63.57 | 84.57 | +| pool table | 95.08 | 97.88 | +| pillow | 71.68 | 83.08 | +| screen door | 82.5 | 84.72 | +| stairway | 47.82 | 66.57 | +| river | 9.93 | 21.22 | +| bridge | 68.78 | 76.78 | +| bookcase | 54.9 | 68.02 | +| blind | 49.25 | 56.84 | +| coffee table | 60.89 | 87.32 | +| toilet | 91.33 | 94.05 | +| flower | 46.05 | 58.06 | +| book | 58.15 | 79.64 | +| hill | 10.73 | 19.43 | +| bench | 56.86 | 63.75 | +| countertop | 65.21 | 86.5 | +| stove | 87.62 | 91.77 | +| palm | 54.9 | 83.1 | +| kitchen island | 60.15 | 89.71 | +| computer | 79.6 | 91.48 | +| swivel chair | 48.05 | 66.36 | +| boat | 79.46 | 92.12 | +| bar | 65.95 | 87.95 | +| arcade machine | 74.29 | 78.61 | +| hovel | 13.75 | 15.58 | +| bus | 92.6 | 96.86 | +| towel | 81.3 | 88.19 | +| light | 61.7 | 68.91 | +| truck | 52.13 | 60.38 | +| tower | 30.07 | 51.34 | +| chandelier | 74.0 | 83.07 | +| awning | 40.4 | 47.39 | +| streetlight | 40.83 | 54.96 | +| booth | 39.21 | 57.83 | +| television receiver | 81.49 | 88.4 | +| airplane | 88.53 | 95.94 | +| dirt track | 5.33 | 26.6 | +| apparel | 67.87 | 85.9 | +| pole | 23.28 | 39.9 | +| land | 3.31 | 4.9 | +| bannister | 21.83 | 26.88 | +| escalator | 67.72 | 85.28 | +| ottoman | 50.62 | 64.31 | +| bottle | 45.71 | 61.43 | +| buffet | 50.94 | 58.28 | +| poster | 30.84 | 34.8 | +| stage | 27.23 | 44.38 | +| van | 48.83 | 73.53 | +| ship | 82.91 | 93.78 | +| fountain | 35.95 | 36.5 | +| conveyer belt | 79.53 | 96.3 | +| canopy | 51.27 | 71.11 | +| washer | 83.6 | 88.65 | +| plaything | 41.93 | 61.95 | +| swimming pool | 52.28 | 75.6 | +| stool | 55.86 | 71.26 | +| barrel | 76.7 | 95.41 | +| basket | 39.65 | 56.94 | +| waterfall | 45.86 | 60.56 | +| tent | 92.52 | 98.39 | +| bag | 29.36 | 33.95 | +| minibike | 77.9 | 89.83 | +| cradle | 86.71 | 97.28 | +| oven | 67.08 | 80.48 | +| ball | 51.9 | 55.84 | +| food | 63.78 | 73.39 | +| step | 16.2 | 18.23 | +| tank | 83.82 | 92.19 | +| trade name | 24.99 | 29.83 | +| microwave | 91.26 | 96.51 | +| pot | 58.84 | 69.6 | +| animal | 61.71 | 62.73 | +| bicycle | 59.76 | 77.03 | +| lake | 49.09 | 63.74 | +| dishwasher | 78.2 | 84.25 | +| screen | 60.73 | 93.37 | +| blanket | 40.33 | 45.64 | +| sculpture | 76.4 | 86.55 | +| hood | 64.96 | 76.79 | +| sconce | 64.45 | 73.11 | +| vase | 49.15 | 66.98 | +| traffic light | 35.81 | 63.65 | +| tray | 25.49 | 33.15 | +| ashcan | 52.11 | 67.8 | +| fan | 72.03 | 81.57 | +| pier | 47.18 | 51.89 | +| crt screen | 2.29 | 3.39 | +| plate | 63.79 | 78.34 | +| monitor | 64.35 | 79.27 | +| bulletin board | 53.59 | 72.38 | +| shower | 13.21 | 13.93 | +| radiator | 68.61 | 82.67 | +| glass | 22.12 | 23.93 | +| clock | 50.08 | 60.14 | +| flag | 72.1 | 80.34 | ++---------------------+-------+-------+ +2024-06-17 08:46:09,711 - mmseg - INFO - Summary: +2024-06-17 08:46:09,712 - mmseg - INFO - ++------+------+-------+ +| aAcc | mIoU | mAcc | ++------+------+-------+ +| 86.7 | 59.1 | 71.31 | ++------+------+-------+ +2024-06-17 08:46:09,713 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 08:46:09,713 - mmseg - INFO - Iter(val) [250] aAcc: 0.8670, mIoU: 0.5910, mAcc: 0.7131, IoU.wall: 0.8307, IoU.building: 0.8597, IoU.sky: 0.9499, IoU.floor: 0.8496, IoU.tree: 0.7806, IoU.ceiling: 0.8788, IoU.road: 0.8636, IoU.bed : 0.9295, IoU.windowpane: 0.6836, IoU.grass: 0.6913, IoU.cabinet: 0.6804, IoU.sidewalk: 0.7067, IoU.person: 0.8637, IoU.earth: 0.4232, IoU.door: 0.6088, IoU.table: 0.7052, IoU.mountain: 0.6268, IoU.plant: 0.5729, IoU.curtain: 0.7908, IoU.chair: 0.6916, IoU.car: 0.8888, IoU.water: 0.6207, IoU.painting: 0.7744, IoU.sofa: 0.8462, IoU.shelf: 0.5322, IoU.house: 0.5749, IoU.sea: 0.7309, IoU.mirror: 0.7798, IoU.rug: 0.5817, IoU.field: 0.3353, IoU.armchair: 0.6212, IoU.seat: 0.6802, IoU.fence: 0.5375, IoU.desk: 0.6004, IoU.rock: 0.5438, IoU.wardrobe: 0.5612, IoU.lamp: 0.7714, IoU.bathtub: 0.8692, IoU.railing: 0.4666, IoU.cushion: 0.7439, IoU.base: 0.3726, IoU.box: 0.3940, IoU.column: 0.5953, IoU.signboard: 0.4216, IoU.chest of drawers: 0.4395, IoU.counter: 0.4259, IoU.sand: 0.5644, IoU.sink: 0.8054, IoU.skyscraper: 0.4706, IoU.fireplace: 0.7478, IoU.refrigerator: 0.8664, IoU.grandstand: 0.5130, IoU.path: 0.3240, IoU.stairs: 0.3147, IoU.runway: 0.7452, IoU.case: 0.6357, IoU.pool table: 0.9508, IoU.pillow: 0.7168, IoU.screen door: 0.8250, IoU.stairway: 0.4782, IoU.river: 0.0993, IoU.bridge: 0.6878, IoU.bookcase: 0.5490, IoU.blind: 0.4925, IoU.coffee table: 0.6089, IoU.toilet: 0.9133, IoU.flower: 0.4605, IoU.book: 0.5815, IoU.hill: 0.1073, IoU.bench: 0.5686, IoU.countertop: 0.6521, IoU.stove: 0.8762, IoU.palm: 0.5490, IoU.kitchen island: 0.6015, IoU.computer: 0.7960, IoU.swivel chair: 0.4805, IoU.boat: 0.7946, IoU.bar: 0.6595, IoU.arcade machine: 0.7429, IoU.hovel: 0.1375, IoU.bus: 0.9260, IoU.towel: 0.8130, IoU.light: 0.6170, IoU.truck: 0.5213, IoU.tower: 0.3007, IoU.chandelier: 0.7400, IoU.awning: 0.4040, IoU.streetlight: 0.4083, IoU.booth: 0.3921, IoU.television receiver: 0.8149, IoU.airplane: 0.8853, IoU.dirt track: 0.0533, IoU.apparel: 0.6787, IoU.pole: 0.2328, IoU.land: 0.0331, IoU.bannister: 0.2183, IoU.escalator: 0.6772, IoU.ottoman: 0.5062, IoU.bottle: 0.4571, IoU.buffet: 0.5094, IoU.poster: 0.3084, IoU.stage: 0.2723, IoU.van: 0.4883, IoU.ship: 0.8291, IoU.fountain: 0.3595, IoU.conveyer belt: 0.7953, IoU.canopy: 0.5127, IoU.washer: 0.8360, IoU.plaything: 0.4193, IoU.swimming pool: 0.5228, IoU.stool: 0.5586, IoU.barrel: 0.7670, IoU.basket: 0.3965, IoU.waterfall: 0.4586, IoU.tent: 0.9252, IoU.bag: 0.2936, IoU.minibike: 0.7790, IoU.cradle: 0.8671, IoU.oven: 0.6708, IoU.ball: 0.5190, IoU.food: 0.6378, IoU.step: 0.1620, IoU.tank: 0.8382, IoU.trade name: 0.2499, IoU.microwave: 0.9126, IoU.pot: 0.5884, IoU.animal: 0.6171, IoU.bicycle: 0.5976, IoU.lake: 0.4909, IoU.dishwasher: 0.7820, IoU.screen: 0.6073, IoU.blanket: 0.4033, IoU.sculpture: 0.7640, IoU.hood: 0.6496, IoU.sconce: 0.6445, IoU.vase: 0.4915, IoU.traffic light: 0.3581, IoU.tray: 0.2549, IoU.ashcan: 0.5211, IoU.fan: 0.7203, IoU.pier: 0.4718, IoU.crt screen: 0.0229, IoU.plate: 0.6379, IoU.monitor: 0.6435, IoU.bulletin board: 0.5359, IoU.shower: 0.1321, IoU.radiator: 0.6861, IoU.glass: 0.2212, IoU.clock: 0.5008, IoU.flag: 0.7210, Acc.wall: 0.9031, Acc.building: 0.9334, Acc.sky: 0.9732, Acc.floor: 0.9264, Acc.tree: 0.9150, Acc.ceiling: 0.9493, Acc.road: 0.9085, Acc.bed : 0.9693, Acc.windowpane: 0.8258, Acc.grass: 0.8152, Acc.cabinet: 0.7876, Acc.sidewalk: 0.8481, Acc.person: 0.9488, Acc.earth: 0.5740, Acc.door: 0.7645, Acc.table: 0.8158, Acc.mountain: 0.7235, Acc.plant: 0.6720, Acc.curtain: 0.8932, Acc.chair: 0.8183, Acc.car: 0.9386, Acc.water: 0.7782, Acc.painting: 0.9012, Acc.sofa: 0.9215, Acc.shelf: 0.7022, Acc.house: 0.7207, Acc.sea: 0.8118, Acc.mirror: 0.8671, Acc.rug: 0.6427, Acc.field: 0.5711, Acc.armchair: 0.7629, Acc.seat: 0.8948, Acc.fence: 0.6438, Acc.desk: 0.7879, Acc.rock: 0.8299, Acc.wardrobe: 0.7393, Acc.lamp: 0.8718, Acc.bathtub: 0.8924, Acc.railing: 0.6685, Acc.cushion: 0.8620, Acc.base: 0.5365, Acc.box: 0.5124, Acc.column: 0.6864, Acc.signboard: 0.5767, Acc.chest of drawers: 0.6664, Acc.counter: 0.5175, Acc.sand: 0.8669, Acc.sink: 0.8539, Acc.skyscraper: 0.5803, Acc.fireplace: 0.9246, Acc.refrigerator: 0.9561, Acc.grandstand: 0.8100, Acc.path: 0.4270, Acc.stairs: 0.3911, Acc.runway: 0.9746, Acc.case: 0.8457, Acc.pool table: 0.9788, Acc.pillow: 0.8308, Acc.screen door: 0.8472, Acc.stairway: 0.6657, Acc.river: 0.2122, Acc.bridge: 0.7678, Acc.bookcase: 0.6802, Acc.blind: 0.5684, Acc.coffee table: 0.8732, Acc.toilet: 0.9405, Acc.flower: 0.5806, Acc.book: 0.7964, Acc.hill: 0.1943, Acc.bench: 0.6375, Acc.countertop: 0.8650, Acc.stove: 0.9177, Acc.palm: 0.8310, Acc.kitchen island: 0.8971, Acc.computer: 0.9148, Acc.swivel chair: 0.6636, Acc.boat: 0.9212, Acc.bar: 0.8795, Acc.arcade machine: 0.7861, Acc.hovel: 0.1558, Acc.bus: 0.9686, Acc.towel: 0.8819, Acc.light: 0.6891, Acc.truck: 0.6038, Acc.tower: 0.5134, Acc.chandelier: 0.8307, Acc.awning: 0.4739, Acc.streetlight: 0.5496, Acc.booth: 0.5783, Acc.television receiver: 0.8840, Acc.airplane: 0.9594, Acc.dirt track: 0.2660, Acc.apparel: 0.8590, Acc.pole: 0.3990, Acc.land: 0.0490, Acc.bannister: 0.2688, Acc.escalator: 0.8528, Acc.ottoman: 0.6431, Acc.bottle: 0.6143, Acc.buffet: 0.5828, Acc.poster: 0.3480, Acc.stage: 0.4438, Acc.van: 0.7353, Acc.ship: 0.9378, Acc.fountain: 0.3650, Acc.conveyer belt: 0.9630, Acc.canopy: 0.7111, Acc.washer: 0.8865, Acc.plaything: 0.6195, Acc.swimming pool: 0.7560, Acc.stool: 0.7126, Acc.barrel: 0.9541, Acc.basket: 0.5694, Acc.waterfall: 0.6056, Acc.tent: 0.9839, Acc.bag: 0.3395, Acc.minibike: 0.8983, Acc.cradle: 0.9728, Acc.oven: 0.8048, Acc.ball: 0.5584, Acc.food: 0.7339, Acc.step: 0.1823, Acc.tank: 0.9219, Acc.trade name: 0.2983, Acc.microwave: 0.9651, Acc.pot: 0.6960, Acc.animal: 0.6273, Acc.bicycle: 0.7703, Acc.lake: 0.6374, Acc.dishwasher: 0.8425, Acc.screen: 0.9337, Acc.blanket: 0.4564, Acc.sculpture: 0.8655, Acc.hood: 0.7679, Acc.sconce: 0.7311, Acc.vase: 0.6698, Acc.traffic light: 0.6365, Acc.tray: 0.3315, Acc.ashcan: 0.6780, Acc.fan: 0.8157, Acc.pier: 0.5189, Acc.crt screen: 0.0339, Acc.plate: 0.7834, Acc.monitor: 0.7927, Acc.bulletin board: 0.7238, Acc.shower: 0.1393, Acc.radiator: 0.8267, Acc.glass: 0.2393, Acc.clock: 0.6014, Acc.flag: 0.8034 +2024-06-17 08:47:31,131 - mmseg - INFO - Iter [72050/80000] lr: 3.975e-06, eta: 3:54:06, time: 3.558, data_time: 1.947, memory: 71384, decode.loss_ce: 0.1265, decode.acc_seg: 94.2385, aux.loss_ce: 0.0542, aux.acc_seg: 93.8542, loss: 0.1807 +2024-06-17 08:48:52,154 - mmseg - INFO - Iter [72100/80000] lr: 3.950e-06, eta: 3:52:37, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1223, decode.acc_seg: 94.6137, aux.loss_ce: 0.0527, aux.acc_seg: 94.2084, loss: 0.1750 +2024-06-17 08:50:13,283 - mmseg - INFO - Iter [72150/80000] lr: 3.925e-06, eta: 3:51:08, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1393, decode.acc_seg: 93.9174, aux.loss_ce: 0.0596, aux.acc_seg: 93.4606, loss: 0.1988 +2024-06-17 08:51:34,301 - mmseg - INFO - Iter [72200/80000] lr: 3.901e-06, eta: 3:49:39, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1211, decode.acc_seg: 94.5007, aux.loss_ce: 0.0525, aux.acc_seg: 94.0318, loss: 0.1737 +2024-06-17 08:52:55,428 - mmseg - INFO - Iter [72250/80000] lr: 3.876e-06, eta: 3:48:10, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1275, decode.acc_seg: 94.4220, aux.loss_ce: 0.0549, aux.acc_seg: 93.9647, loss: 0.1824 +2024-06-17 08:54:16,460 - mmseg - INFO - Iter [72300/80000] lr: 3.851e-06, eta: 3:46:41, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1301, decode.acc_seg: 94.2639, aux.loss_ce: 0.0558, aux.acc_seg: 93.8394, loss: 0.1860 +2024-06-17 08:55:37,454 - mmseg - INFO - Iter [72350/80000] lr: 3.826e-06, eta: 3:45:11, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1333, decode.acc_seg: 93.9953, aux.loss_ce: 0.0582, aux.acc_seg: 93.5257, loss: 0.1915 +2024-06-17 08:56:58,618 - mmseg - INFO - Iter [72400/80000] lr: 3.801e-06, eta: 3:43:42, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1298, decode.acc_seg: 94.1715, aux.loss_ce: 0.0559, aux.acc_seg: 93.7522, loss: 0.1857 +2024-06-17 08:58:19,652 - mmseg - INFO - Iter [72450/80000] lr: 3.775e-06, eta: 3:42:13, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1236, decode.acc_seg: 94.3468, aux.loss_ce: 0.0535, aux.acc_seg: 93.9419, loss: 0.1771 +2024-06-17 08:59:40,676 - mmseg - INFO - Iter [72500/80000] lr: 3.750e-06, eta: 3:40:44, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1274, decode.acc_seg: 94.3413, aux.loss_ce: 0.0550, aux.acc_seg: 93.9261, loss: 0.1824 +2024-06-17 09:01:01,625 - mmseg - INFO - Iter [72550/80000] lr: 3.725e-06, eta: 3:39:15, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1293, decode.acc_seg: 94.3414, aux.loss_ce: 0.0556, aux.acc_seg: 93.9523, loss: 0.1849 +2024-06-17 09:02:22,816 - mmseg - INFO - Iter [72600/80000] lr: 3.701e-06, eta: 3:37:46, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1238, decode.acc_seg: 94.5256, aux.loss_ce: 0.0534, aux.acc_seg: 94.1395, loss: 0.1773 +2024-06-17 09:03:43,780 - mmseg - INFO - Iter [72650/80000] lr: 3.676e-06, eta: 3:36:17, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1243, decode.acc_seg: 94.4983, aux.loss_ce: 0.0539, aux.acc_seg: 94.0729, loss: 0.1782 +2024-06-17 09:05:04,827 - mmseg - INFO - Iter [72700/80000] lr: 3.651e-06, eta: 3:34:48, time: 1.621, data_time: 0.009, memory: 71384, decode.loss_ce: 0.1249, decode.acc_seg: 94.2859, aux.loss_ce: 0.0540, aux.acc_seg: 93.8741, loss: 0.1789 +2024-06-17 09:06:25,955 - mmseg - INFO - Iter [72750/80000] lr: 3.626e-06, eta: 3:33:19, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1435, decode.acc_seg: 93.7611, aux.loss_ce: 0.0611, aux.acc_seg: 93.3888, loss: 0.2046 +2024-06-17 09:07:46,947 - mmseg - INFO - Iter [72800/80000] lr: 3.601e-06, eta: 3:31:50, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1331, decode.acc_seg: 94.1151, aux.loss_ce: 0.0570, aux.acc_seg: 93.7019, loss: 0.1901 +2024-06-17 09:09:07,825 - mmseg - INFO - Iter [72850/80000] lr: 3.575e-06, eta: 3:30:21, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1295, decode.acc_seg: 94.1766, aux.loss_ce: 0.0560, aux.acc_seg: 93.8143, loss: 0.1855 +2024-06-17 09:10:29,135 - mmseg - INFO - Iter [72900/80000] lr: 3.550e-06, eta: 3:28:52, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1278, decode.acc_seg: 94.2336, aux.loss_ce: 0.0551, aux.acc_seg: 93.7949, loss: 0.1829 +2024-06-17 09:11:50,060 - mmseg - INFO - Iter [72950/80000] lr: 3.525e-06, eta: 3:27:23, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1247, decode.acc_seg: 94.4804, aux.loss_ce: 0.0540, aux.acc_seg: 94.0267, loss: 0.1787 +2024-06-17 09:13:11,008 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 09:13:11,008 - mmseg - INFO - Iter [73000/80000] lr: 3.501e-06, eta: 3:25:54, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1400, decode.acc_seg: 94.1056, aux.loss_ce: 0.0604, aux.acc_seg: 93.6709, loss: 0.2003 +2024-06-17 09:14:48,335 - mmseg - INFO - per class results: +2024-06-17 09:14:48,341 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.11 | 90.12 | +| building | 85.74 | 93.9 | +| sky | 95.02 | 97.49 | +| floor | 85.11 | 92.3 | +| tree | 78.09 | 90.37 | +| ceiling | 87.82 | 95.06 | +| road | 86.17 | 91.16 | +| bed | 92.9 | 97.15 | +| windowpane | 68.21 | 82.13 | +| grass | 69.1 | 82.34 | +| cabinet | 67.41 | 78.18 | +| sidewalk | 69.7 | 82.87 | +| person | 86.46 | 94.53 | +| earth | 41.24 | 54.99 | +| door | 61.21 | 77.17 | +| table | 70.41 | 81.97 | +| mountain | 62.79 | 74.33 | +| plant | 57.06 | 66.01 | +| curtain | 79.07 | 89.51 | +| chair | 68.95 | 81.2 | +| car | 88.24 | 95.16 | +| water | 62.23 | 77.78 | +| painting | 77.68 | 90.62 | +| sofa | 84.25 | 92.79 | +| shelf | 53.24 | 69.73 | +| house | 56.79 | 69.16 | +| sea | 71.26 | 79.52 | +| mirror | 78.12 | 86.22 | +| rug | 65.09 | 75.55 | +| field | 33.28 | 55.7 | +| armchair | 62.44 | 76.65 | +| seat | 67.8 | 89.4 | +| fence | 54.75 | 69.18 | +| desk | 60.63 | 79.38 | +| rock | 53.28 | 79.51 | +| wardrobe | 53.46 | 74.57 | +| lamp | 77.06 | 87.65 | +| bathtub | 87.82 | 90.52 | +| railing | 47.62 | 67.23 | +| cushion | 73.87 | 83.35 | +| base | 37.88 | 58.25 | +| box | 39.02 | 50.35 | +| column | 57.16 | 66.93 | +| signboard | 42.1 | 57.27 | +| chest of drawers | 42.61 | 61.22 | +| counter | 42.43 | 51.07 | +| sand | 57.37 | 85.56 | +| sink | 80.59 | 86.08 | +| skyscraper | 46.57 | 58.36 | +| fireplace | 74.68 | 94.86 | +| refrigerator | 87.27 | 95.25 | +| grandstand | 50.75 | 82.05 | +| path | 31.68 | 46.31 | +| stairs | 31.99 | 38.24 | +| runway | 73.82 | 96.21 | +| case | 63.02 | 83.94 | +| pool table | 95.03 | 98.01 | +| pillow | 70.38 | 80.59 | +| screen door | 81.14 | 82.93 | +| stairway | 48.31 | 66.28 | +| river | 9.11 | 20.6 | +| bridge | 61.38 | 72.38 | +| bookcase | 56.89 | 68.37 | +| blind | 46.3 | 52.63 | +| coffee table | 61.19 | 87.31 | +| toilet | 91.03 | 93.88 | +| flower | 46.44 | 61.37 | +| book | 57.85 | 78.28 | +| hill | 9.66 | 17.7 | +| bench | 55.86 | 63.57 | +| countertop | 65.28 | 84.98 | +| stove | 88.01 | 92.82 | +| palm | 55.68 | 81.82 | +| kitchen island | 63.18 | 88.68 | +| computer | 79.16 | 91.2 | +| swivel chair | 47.68 | 68.45 | +| boat | 78.43 | 92.54 | +| bar | 64.73 | 88.83 | +| arcade machine | 76.34 | 80.52 | +| hovel | 13.98 | 15.37 | +| bus | 92.61 | 96.8 | +| towel | 81.4 | 88.6 | +| light | 63.08 | 72.41 | +| truck | 52.8 | 61.88 | +| tower | 27.82 | 46.98 | +| chandelier | 73.78 | 84.09 | +| awning | 41.62 | 49.06 | +| streetlight | 39.59 | 51.64 | +| booth | 43.98 | 55.14 | +| television receiver | 81.82 | 88.63 | +| airplane | 88.81 | 96.32 | +| dirt track | 5.68 | 28.1 | +| apparel | 67.45 | 86.29 | +| pole | 23.12 | 38.17 | +| land | 2.3 | 3.07 | +| bannister | 22.53 | 28.7 | +| escalator | 68.02 | 85.09 | +| ottoman | 49.06 | 63.25 | +| bottle | 44.18 | 55.65 | +| buffet | 56.05 | 66.72 | +| poster | 31.45 | 37.18 | +| stage | 29.49 | 45.03 | +| van | 48.72 | 64.08 | +| ship | 82.39 | 92.14 | +| fountain | 37.26 | 39.31 | +| conveyer belt | 81.5 | 94.65 | +| canopy | 51.36 | 71.87 | +| washer | 83.64 | 88.75 | +| plaything | 38.49 | 55.23 | +| swimming pool | 53.38 | 77.5 | +| stool | 55.7 | 73.09 | +| barrel | 78.73 | 94.65 | +| basket | 41.74 | 59.45 | +| waterfall | 49.84 | 65.54 | +| tent | 92.74 | 98.53 | +| bag | 27.97 | 31.18 | +| minibike | 78.46 | 89.91 | +| cradle | 88.92 | 97.36 | +| oven | 66.13 | 78.07 | +| ball | 55.05 | 63.45 | +| food | 64.64 | 75.29 | +| step | 17.16 | 19.41 | +| tank | 81.23 | 92.95 | +| trade name | 21.6 | 25.14 | +| microwave | 90.59 | 96.73 | +| pot | 58.58 | 70.08 | +| animal | 61.89 | 63.19 | +| bicycle | 60.69 | 76.98 | +| lake | 50.29 | 64.36 | +| dishwasher | 77.44 | 84.88 | +| screen | 60.6 | 93.46 | +| blanket | 40.54 | 47.17 | +| sculpture | 75.55 | 88.19 | +| hood | 64.93 | 76.3 | +| sconce | 63.91 | 72.1 | +| vase | 49.06 | 66.13 | +| traffic light | 36.15 | 63.48 | +| tray | 26.97 | 35.74 | +| ashcan | 52.63 | 66.33 | +| fan | 73.34 | 84.92 | +| pier | 44.82 | 50.01 | +| crt screen | 1.66 | 3.41 | +| plate | 63.57 | 78.84 | +| monitor | 43.01 | 51.44 | +| bulletin board | 58.05 | 66.87 | +| shower | 12.36 | 17.07 | +| radiator | 69.87 | 82.2 | +| glass | 21.05 | 22.38 | +| clock | 51.67 | 63.05 | +| flag | 72.12 | 80.56 | ++---------------------+-------+-------+ +2024-06-17 09:14:48,341 - mmseg - INFO - Summary: +2024-06-17 09:14:48,341 - mmseg - INFO - ++-------+-------+------+ +| aAcc | mIoU | mAcc | ++-------+-------+------+ +| 86.65 | 59.01 | 71.1 | ++-------+-------+------+ +2024-06-17 09:14:48,342 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 09:14:48,342 - mmseg - INFO - Iter(val) [250] aAcc: 0.8665, mIoU: 0.5901, mAcc: 0.7110, IoU.wall: 0.8311, IoU.building: 0.8574, IoU.sky: 0.9502, IoU.floor: 0.8511, IoU.tree: 0.7809, IoU.ceiling: 0.8782, IoU.road: 0.8617, IoU.bed : 0.9290, IoU.windowpane: 0.6821, IoU.grass: 0.6910, IoU.cabinet: 0.6741, IoU.sidewalk: 0.6970, IoU.person: 0.8646, IoU.earth: 0.4124, IoU.door: 0.6121, IoU.table: 0.7041, IoU.mountain: 0.6279, IoU.plant: 0.5706, IoU.curtain: 0.7907, IoU.chair: 0.6895, IoU.car: 0.8824, IoU.water: 0.6223, IoU.painting: 0.7768, IoU.sofa: 0.8425, IoU.shelf: 0.5324, IoU.house: 0.5679, IoU.sea: 0.7126, IoU.mirror: 0.7812, IoU.rug: 0.6509, IoU.field: 0.3328, IoU.armchair: 0.6244, IoU.seat: 0.6780, IoU.fence: 0.5475, IoU.desk: 0.6063, IoU.rock: 0.5328, IoU.wardrobe: 0.5346, IoU.lamp: 0.7706, IoU.bathtub: 0.8782, IoU.railing: 0.4762, IoU.cushion: 0.7387, IoU.base: 0.3788, IoU.box: 0.3902, IoU.column: 0.5716, IoU.signboard: 0.4210, IoU.chest of drawers: 0.4261, IoU.counter: 0.4243, IoU.sand: 0.5737, IoU.sink: 0.8059, IoU.skyscraper: 0.4657, IoU.fireplace: 0.7468, IoU.refrigerator: 0.8727, IoU.grandstand: 0.5075, IoU.path: 0.3168, IoU.stairs: 0.3199, IoU.runway: 0.7382, IoU.case: 0.6302, IoU.pool table: 0.9503, IoU.pillow: 0.7038, IoU.screen door: 0.8114, IoU.stairway: 0.4831, IoU.river: 0.0911, IoU.bridge: 0.6138, IoU.bookcase: 0.5689, IoU.blind: 0.4630, IoU.coffee table: 0.6119, IoU.toilet: 0.9103, IoU.flower: 0.4644, IoU.book: 0.5785, IoU.hill: 0.0966, IoU.bench: 0.5586, IoU.countertop: 0.6528, IoU.stove: 0.8801, IoU.palm: 0.5568, IoU.kitchen island: 0.6318, IoU.computer: 0.7916, IoU.swivel chair: 0.4768, IoU.boat: 0.7843, IoU.bar: 0.6473, IoU.arcade machine: 0.7634, IoU.hovel: 0.1398, IoU.bus: 0.9261, IoU.towel: 0.8140, IoU.light: 0.6308, IoU.truck: 0.5280, IoU.tower: 0.2782, IoU.chandelier: 0.7378, IoU.awning: 0.4162, IoU.streetlight: 0.3959, IoU.booth: 0.4398, IoU.television receiver: 0.8182, IoU.airplane: 0.8881, IoU.dirt track: 0.0568, IoU.apparel: 0.6745, IoU.pole: 0.2312, IoU.land: 0.0230, IoU.bannister: 0.2253, IoU.escalator: 0.6802, IoU.ottoman: 0.4906, IoU.bottle: 0.4418, IoU.buffet: 0.5605, IoU.poster: 0.3145, IoU.stage: 0.2949, IoU.van: 0.4872, IoU.ship: 0.8239, IoU.fountain: 0.3726, IoU.conveyer belt: 0.8150, IoU.canopy: 0.5136, IoU.washer: 0.8364, IoU.plaything: 0.3849, IoU.swimming pool: 0.5338, IoU.stool: 0.5570, IoU.barrel: 0.7873, IoU.basket: 0.4174, IoU.waterfall: 0.4984, IoU.tent: 0.9274, IoU.bag: 0.2797, IoU.minibike: 0.7846, IoU.cradle: 0.8892, IoU.oven: 0.6613, IoU.ball: 0.5505, IoU.food: 0.6464, IoU.step: 0.1716, IoU.tank: 0.8123, IoU.trade name: 0.2160, IoU.microwave: 0.9059, IoU.pot: 0.5858, IoU.animal: 0.6189, IoU.bicycle: 0.6069, IoU.lake: 0.5029, IoU.dishwasher: 0.7744, IoU.screen: 0.6060, IoU.blanket: 0.4054, IoU.sculpture: 0.7555, IoU.hood: 0.6493, IoU.sconce: 0.6391, IoU.vase: 0.4906, IoU.traffic light: 0.3615, IoU.tray: 0.2697, IoU.ashcan: 0.5263, IoU.fan: 0.7334, IoU.pier: 0.4482, IoU.crt screen: 0.0166, IoU.plate: 0.6357, IoU.monitor: 0.4301, IoU.bulletin board: 0.5805, IoU.shower: 0.1236, IoU.radiator: 0.6987, IoU.glass: 0.2105, IoU.clock: 0.5167, IoU.flag: 0.7212, Acc.wall: 0.9012, Acc.building: 0.9390, Acc.sky: 0.9749, Acc.floor: 0.9230, Acc.tree: 0.9037, Acc.ceiling: 0.9506, Acc.road: 0.9116, Acc.bed : 0.9715, Acc.windowpane: 0.8213, Acc.grass: 0.8234, Acc.cabinet: 0.7818, Acc.sidewalk: 0.8287, Acc.person: 0.9453, Acc.earth: 0.5499, Acc.door: 0.7717, Acc.table: 0.8197, Acc.mountain: 0.7433, Acc.plant: 0.6601, Acc.curtain: 0.8951, Acc.chair: 0.8120, Acc.car: 0.9516, Acc.water: 0.7778, Acc.painting: 0.9062, Acc.sofa: 0.9279, Acc.shelf: 0.6973, Acc.house: 0.6916, Acc.sea: 0.7952, Acc.mirror: 0.8622, Acc.rug: 0.7555, Acc.field: 0.5570, Acc.armchair: 0.7665, Acc.seat: 0.8940, Acc.fence: 0.6918, Acc.desk: 0.7938, Acc.rock: 0.7951, Acc.wardrobe: 0.7457, Acc.lamp: 0.8765, Acc.bathtub: 0.9052, Acc.railing: 0.6723, Acc.cushion: 0.8335, Acc.base: 0.5825, Acc.box: 0.5035, Acc.column: 0.6693, Acc.signboard: 0.5727, Acc.chest of drawers: 0.6122, Acc.counter: 0.5107, Acc.sand: 0.8556, Acc.sink: 0.8608, Acc.skyscraper: 0.5836, Acc.fireplace: 0.9486, Acc.refrigerator: 0.9525, Acc.grandstand: 0.8205, Acc.path: 0.4631, Acc.stairs: 0.3824, Acc.runway: 0.9621, Acc.case: 0.8394, Acc.pool table: 0.9801, Acc.pillow: 0.8059, Acc.screen door: 0.8293, Acc.stairway: 0.6628, Acc.river: 0.2060, Acc.bridge: 0.7238, Acc.bookcase: 0.6837, Acc.blind: 0.5263, Acc.coffee table: 0.8731, Acc.toilet: 0.9388, Acc.flower: 0.6137, Acc.book: 0.7828, Acc.hill: 0.1770, Acc.bench: 0.6357, Acc.countertop: 0.8498, Acc.stove: 0.9282, Acc.palm: 0.8182, Acc.kitchen island: 0.8868, Acc.computer: 0.9120, Acc.swivel chair: 0.6845, Acc.boat: 0.9254, Acc.bar: 0.8883, Acc.arcade machine: 0.8052, Acc.hovel: 0.1537, Acc.bus: 0.9680, Acc.towel: 0.8860, Acc.light: 0.7241, Acc.truck: 0.6188, Acc.tower: 0.4698, Acc.chandelier: 0.8409, Acc.awning: 0.4906, Acc.streetlight: 0.5164, Acc.booth: 0.5514, Acc.television receiver: 0.8863, Acc.airplane: 0.9632, Acc.dirt track: 0.2810, Acc.apparel: 0.8629, Acc.pole: 0.3817, Acc.land: 0.0307, Acc.bannister: 0.2870, Acc.escalator: 0.8509, Acc.ottoman: 0.6325, Acc.bottle: 0.5565, Acc.buffet: 0.6672, Acc.poster: 0.3718, Acc.stage: 0.4503, Acc.van: 0.6408, Acc.ship: 0.9214, Acc.fountain: 0.3931, Acc.conveyer belt: 0.9465, Acc.canopy: 0.7187, Acc.washer: 0.8875, Acc.plaything: 0.5523, Acc.swimming pool: 0.7750, Acc.stool: 0.7309, Acc.barrel: 0.9465, Acc.basket: 0.5945, Acc.waterfall: 0.6554, Acc.tent: 0.9853, Acc.bag: 0.3118, Acc.minibike: 0.8991, Acc.cradle: 0.9736, Acc.oven: 0.7807, Acc.ball: 0.6345, Acc.food: 0.7529, Acc.step: 0.1941, Acc.tank: 0.9295, Acc.trade name: 0.2514, Acc.microwave: 0.9673, Acc.pot: 0.7008, Acc.animal: 0.6319, Acc.bicycle: 0.7698, Acc.lake: 0.6436, Acc.dishwasher: 0.8488, Acc.screen: 0.9346, Acc.blanket: 0.4717, Acc.sculpture: 0.8819, Acc.hood: 0.7630, Acc.sconce: 0.7210, Acc.vase: 0.6613, Acc.traffic light: 0.6348, Acc.tray: 0.3574, Acc.ashcan: 0.6633, Acc.fan: 0.8492, Acc.pier: 0.5001, Acc.crt screen: 0.0341, Acc.plate: 0.7884, Acc.monitor: 0.5144, Acc.bulletin board: 0.6687, Acc.shower: 0.1707, Acc.radiator: 0.8220, Acc.glass: 0.2238, Acc.clock: 0.6305, Acc.flag: 0.8056 +2024-06-17 09:16:09,918 - mmseg - INFO - Iter [73050/80000] lr: 3.476e-06, eta: 3:24:35, time: 3.578, data_time: 1.965, memory: 71384, decode.loss_ce: 0.1265, decode.acc_seg: 94.4571, aux.loss_ce: 0.0545, aux.acc_seg: 94.0330, loss: 0.1810 +2024-06-17 09:17:31,078 - mmseg - INFO - Iter [73100/80000] lr: 3.451e-06, eta: 3:23:06, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1339, decode.acc_seg: 93.9385, aux.loss_ce: 0.0578, aux.acc_seg: 93.4934, loss: 0.1917 +2024-06-17 09:18:52,178 - mmseg - INFO - Iter [73150/80000] lr: 3.426e-06, eta: 3:21:37, time: 1.622, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1300, decode.acc_seg: 94.1661, aux.loss_ce: 0.0558, aux.acc_seg: 93.7545, loss: 0.1858 +2024-06-17 09:20:13,052 - mmseg - INFO - Iter [73200/80000] lr: 3.401e-06, eta: 3:20:08, time: 1.617, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1315, decode.acc_seg: 94.2309, aux.loss_ce: 0.0567, aux.acc_seg: 93.8077, loss: 0.1882 +2024-06-17 09:21:34,048 - mmseg - INFO - Iter [73250/80000] lr: 3.375e-06, eta: 3:18:39, time: 1.620, data_time: 0.009, memory: 71384, decode.loss_ce: 0.1257, decode.acc_seg: 94.2968, aux.loss_ce: 0.0542, aux.acc_seg: 93.8914, loss: 0.1799 +2024-06-17 09:22:58,150 - mmseg - INFO - Iter [73300/80000] lr: 3.350e-06, eta: 3:17:10, time: 1.682, data_time: 0.065, memory: 71384, decode.loss_ce: 0.1232, decode.acc_seg: 94.4942, aux.loss_ce: 0.0534, aux.acc_seg: 94.0919, loss: 0.1766 +2024-06-17 09:24:19,176 - mmseg - INFO - Iter [73350/80000] lr: 3.325e-06, eta: 3:15:41, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1227, decode.acc_seg: 94.6090, aux.loss_ce: 0.0531, aux.acc_seg: 94.1693, loss: 0.1757 +2024-06-17 09:25:40,120 - mmseg - INFO - Iter [73400/80000] lr: 3.300e-06, eta: 3:14:12, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1339, decode.acc_seg: 94.1114, aux.loss_ce: 0.0576, aux.acc_seg: 93.7281, loss: 0.1915 +2024-06-17 09:27:01,313 - mmseg - INFO - Iter [73450/80000] lr: 3.276e-06, eta: 3:12:43, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1255, decode.acc_seg: 94.1573, aux.loss_ce: 0.0542, aux.acc_seg: 93.7023, loss: 0.1797 +2024-06-17 09:28:22,323 - mmseg - INFO - Iter [73500/80000] lr: 3.251e-06, eta: 3:11:14, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1248, decode.acc_seg: 94.5595, aux.loss_ce: 0.0538, aux.acc_seg: 94.1614, loss: 0.1785 +2024-06-17 09:29:43,511 - mmseg - INFO - Iter [73550/80000] lr: 3.226e-06, eta: 3:09:46, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1289, decode.acc_seg: 94.2915, aux.loss_ce: 0.0555, aux.acc_seg: 93.9040, loss: 0.1844 +2024-06-17 09:31:04,655 - mmseg - INFO - Iter [73600/80000] lr: 3.201e-06, eta: 3:08:17, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1386, decode.acc_seg: 94.0741, aux.loss_ce: 0.0596, aux.acc_seg: 93.5974, loss: 0.1983 +2024-06-17 09:32:25,690 - mmseg - INFO - Iter [73650/80000] lr: 3.176e-06, eta: 3:06:48, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1306, decode.acc_seg: 94.1032, aux.loss_ce: 0.0566, aux.acc_seg: 93.6142, loss: 0.1872 +2024-06-17 09:33:46,695 - mmseg - INFO - Iter [73700/80000] lr: 3.150e-06, eta: 3:05:19, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1293, decode.acc_seg: 94.2072, aux.loss_ce: 0.0558, aux.acc_seg: 93.7957, loss: 0.1851 +2024-06-17 09:35:07,640 - mmseg - INFO - Iter [73750/80000] lr: 3.125e-06, eta: 3:03:50, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1329, decode.acc_seg: 94.1893, aux.loss_ce: 0.0575, aux.acc_seg: 93.6850, loss: 0.1904 +2024-06-17 09:36:28,949 - mmseg - INFO - Iter [73800/80000] lr: 3.100e-06, eta: 3:02:21, time: 1.626, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1268, decode.acc_seg: 94.3986, aux.loss_ce: 0.0546, aux.acc_seg: 94.0008, loss: 0.1815 +2024-06-17 09:37:49,976 - mmseg - INFO - Iter [73850/80000] lr: 3.076e-06, eta: 3:00:52, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1336, decode.acc_seg: 94.0330, aux.loss_ce: 0.0574, aux.acc_seg: 93.6119, loss: 0.1910 +2024-06-17 09:39:10,920 - mmseg - INFO - Iter [73900/80000] lr: 3.051e-06, eta: 2:59:24, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1235, decode.acc_seg: 94.4560, aux.loss_ce: 0.0534, aux.acc_seg: 94.0186, loss: 0.1768 +2024-06-17 09:40:31,918 - mmseg - INFO - Iter [73950/80000] lr: 3.026e-06, eta: 2:57:55, time: 1.620, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1344, decode.acc_seg: 93.9762, aux.loss_ce: 0.0576, aux.acc_seg: 93.5454, loss: 0.1920 +2024-06-17 09:41:53,381 - mmseg - INFO - Saving checkpoint at 74000 iterations +2024-06-17 09:43:20,076 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 09:43:20,077 - mmseg - INFO - Iter [74000/80000] lr: 3.001e-06, eta: 2:56:33, time: 3.363, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1305, decode.acc_seg: 94.1875, aux.loss_ce: 0.0563, aux.acc_seg: 93.7654, loss: 0.1869 +2024-06-17 09:44:56,322 - mmseg - INFO - per class results: +2024-06-17 09:44:56,328 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.19 | 90.56 | +| building | 85.86 | 93.36 | +| sky | 95.0 | 97.65 | +| floor | 85.23 | 92.53 | +| tree | 78.18 | 89.92 | +| ceiling | 87.67 | 94.87 | +| road | 86.03 | 90.97 | +| bed | 92.78 | 96.84 | +| windowpane | 68.15 | 81.48 | +| grass | 69.33 | 83.4 | +| cabinet | 67.74 | 78.19 | +| sidewalk | 70.3 | 84.53 | +| person | 86.39 | 94.78 | +| earth | 41.68 | 55.68 | +| door | 61.37 | 77.22 | +| table | 70.77 | 82.1 | +| mountain | 62.64 | 73.15 | +| plant | 57.97 | 67.64 | +| curtain | 79.09 | 89.32 | +| chair | 68.94 | 80.25 | +| car | 88.79 | 94.49 | +| water | 62.78 | 77.3 | +| painting | 78.0 | 90.53 | +| sofa | 83.46 | 92.93 | +| shelf | 53.75 | 72.21 | +| house | 57.78 | 73.94 | +| sea | 72.43 | 81.31 | +| mirror | 77.49 | 84.56 | +| rug | 62.94 | 70.64 | +| field | 34.75 | 56.49 | +| armchair | 61.74 | 75.46 | +| seat | 68.59 | 88.96 | +| fence | 54.96 | 68.75 | +| desk | 60.13 | 79.09 | +| rock | 55.01 | 81.76 | +| wardrobe | 54.32 | 76.29 | +| lamp | 76.89 | 87.88 | +| bathtub | 87.01 | 89.23 | +| railing | 46.03 | 63.23 | +| cushion | 74.02 | 84.12 | +| base | 36.61 | 53.85 | +| box | 38.37 | 48.35 | +| column | 56.04 | 68.16 | +| signboard | 42.3 | 57.98 | +| chest of drawers | 43.95 | 67.17 | +| counter | 43.93 | 53.73 | +| sand | 56.78 | 86.08 | +| sink | 80.63 | 85.49 | +| skyscraper | 45.61 | 58.81 | +| fireplace | 74.07 | 92.5 | +| refrigerator | 86.29 | 94.48 | +| grandstand | 50.53 | 80.9 | +| path | 32.11 | 42.98 | +| stairs | 34.15 | 41.69 | +| runway | 73.77 | 96.42 | +| case | 63.48 | 83.17 | +| pool table | 95.0 | 98.06 | +| pillow | 70.34 | 80.59 | +| screen door | 80.92 | 82.67 | +| stairway | 48.44 | 64.09 | +| river | 9.4 | 21.17 | +| bridge | 71.14 | 82.16 | +| bookcase | 56.57 | 66.13 | +| blind | 46.82 | 52.68 | +| coffee table | 61.02 | 86.93 | +| toilet | 90.97 | 93.96 | +| flower | 47.55 | 59.25 | +| book | 57.54 | 79.21 | +| hill | 11.86 | 20.07 | +| bench | 56.72 | 64.49 | +| countertop | 65.59 | 84.75 | +| stove | 87.85 | 92.36 | +| palm | 54.63 | 80.47 | +| kitchen island | 68.86 | 87.29 | +| computer | 79.03 | 91.42 | +| swivel chair | 47.15 | 70.55 | +| boat | 78.46 | 92.52 | +| bar | 64.98 | 85.52 | +| arcade machine | 78.56 | 82.99 | +| hovel | 14.06 | 15.38 | +| bus | 92.11 | 97.01 | +| towel | 81.24 | 88.35 | +| light | 62.61 | 71.98 | +| truck | 53.05 | 62.65 | +| tower | 30.99 | 53.42 | +| chandelier | 73.93 | 83.02 | +| awning | 40.94 | 49.13 | +| streetlight | 40.58 | 56.53 | +| booth | 42.87 | 57.25 | +| television receiver | 81.83 | 87.71 | +| airplane | 88.72 | 95.63 | +| dirt track | 5.51 | 27.49 | +| apparel | 67.96 | 86.6 | +| pole | 21.32 | 35.95 | +| land | 3.54 | 5.35 | +| bannister | 20.58 | 25.86 | +| escalator | 67.39 | 84.07 | +| ottoman | 48.94 | 63.47 | +| bottle | 43.55 | 53.95 | +| buffet | 52.72 | 61.14 | +| poster | 32.31 | 37.76 | +| stage | 26.99 | 47.32 | +| van | 49.69 | 70.58 | +| ship | 83.12 | 92.47 | +| fountain | 37.41 | 39.12 | +| conveyer belt | 81.48 | 94.27 | +| canopy | 53.16 | 73.7 | +| washer | 84.54 | 89.69 | +| plaything | 37.96 | 54.55 | +| swimming pool | 53.9 | 78.62 | +| stool | 53.12 | 72.75 | +| barrel | 75.63 | 96.21 | +| basket | 42.2 | 59.41 | +| waterfall | 48.97 | 64.52 | +| tent | 92.38 | 98.64 | +| bag | 29.38 | 33.86 | +| minibike | 77.55 | 90.68 | +| cradle | 88.87 | 97.3 | +| oven | 61.91 | 73.27 | +| ball | 49.29 | 53.65 | +| food | 64.26 | 77.01 | +| step | 17.04 | 19.42 | +| tank | 80.23 | 93.36 | +| trade name | 25.96 | 31.93 | +| microwave | 89.8 | 96.49 | +| pot | 59.34 | 70.95 | +| animal | 62.84 | 64.27 | +| bicycle | 60.69 | 78.16 | +| lake | 47.19 | 65.15 | +| dishwasher | 77.53 | 84.22 | +| screen | 61.66 | 93.73 | +| blanket | 38.34 | 44.08 | +| sculpture | 75.18 | 88.22 | +| hood | 65.25 | 76.99 | +| sconce | 64.3 | 73.12 | +| vase | 49.01 | 66.44 | +| traffic light | 36.89 | 65.61 | +| tray | 26.38 | 33.92 | +| ashcan | 52.35 | 66.91 | +| fan | 72.4 | 84.06 | +| pier | 45.47 | 51.03 | +| crt screen | 2.06 | 3.37 | +| plate | 64.22 | 77.91 | +| monitor | 56.11 | 67.59 | +| bulletin board | 57.5 | 66.95 | +| shower | 11.84 | 14.68 | +| radiator | 68.68 | 82.21 | +| glass | 21.87 | 23.4 | +| clock | 51.8 | 61.35 | +| flag | 71.79 | 82.16 | ++---------------------+-------+-------+ +2024-06-17 09:44:56,328 - mmseg - INFO - Summary: +2024-06-17 09:44:56,328 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.73 | 59.13 | 71.33 | ++-------+-------+-------+ +2024-06-17 09:44:56,329 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 09:44:56,329 - mmseg - INFO - Iter(val) [250] aAcc: 0.8673, mIoU: 0.5913, mAcc: 0.7133, IoU.wall: 0.8319, IoU.building: 0.8586, IoU.sky: 0.9500, IoU.floor: 0.8523, IoU.tree: 0.7818, IoU.ceiling: 0.8767, IoU.road: 0.8603, IoU.bed : 0.9278, IoU.windowpane: 0.6815, IoU.grass: 0.6933, IoU.cabinet: 0.6774, IoU.sidewalk: 0.7030, IoU.person: 0.8639, IoU.earth: 0.4168, IoU.door: 0.6137, IoU.table: 0.7077, IoU.mountain: 0.6264, IoU.plant: 0.5797, IoU.curtain: 0.7909, IoU.chair: 0.6894, IoU.car: 0.8879, IoU.water: 0.6278, IoU.painting: 0.7800, IoU.sofa: 0.8346, IoU.shelf: 0.5375, IoU.house: 0.5778, IoU.sea: 0.7243, IoU.mirror: 0.7749, IoU.rug: 0.6294, IoU.field: 0.3475, IoU.armchair: 0.6174, IoU.seat: 0.6859, IoU.fence: 0.5496, IoU.desk: 0.6013, IoU.rock: 0.5501, IoU.wardrobe: 0.5432, IoU.lamp: 0.7689, IoU.bathtub: 0.8701, IoU.railing: 0.4603, IoU.cushion: 0.7402, IoU.base: 0.3661, IoU.box: 0.3837, IoU.column: 0.5604, IoU.signboard: 0.4230, IoU.chest of drawers: 0.4395, IoU.counter: 0.4393, IoU.sand: 0.5678, IoU.sink: 0.8063, IoU.skyscraper: 0.4561, IoU.fireplace: 0.7407, IoU.refrigerator: 0.8629, IoU.grandstand: 0.5053, IoU.path: 0.3211, IoU.stairs: 0.3415, IoU.runway: 0.7377, IoU.case: 0.6348, IoU.pool table: 0.9500, IoU.pillow: 0.7034, IoU.screen door: 0.8092, IoU.stairway: 0.4844, IoU.river: 0.0940, IoU.bridge: 0.7114, IoU.bookcase: 0.5657, IoU.blind: 0.4682, IoU.coffee table: 0.6102, IoU.toilet: 0.9097, IoU.flower: 0.4755, IoU.book: 0.5754, IoU.hill: 0.1186, IoU.bench: 0.5672, IoU.countertop: 0.6559, IoU.stove: 0.8785, IoU.palm: 0.5463, IoU.kitchen island: 0.6886, IoU.computer: 0.7903, IoU.swivel chair: 0.4715, IoU.boat: 0.7846, IoU.bar: 0.6498, IoU.arcade machine: 0.7856, IoU.hovel: 0.1406, IoU.bus: 0.9211, IoU.towel: 0.8124, IoU.light: 0.6261, IoU.truck: 0.5305, IoU.tower: 0.3099, IoU.chandelier: 0.7393, IoU.awning: 0.4094, IoU.streetlight: 0.4058, IoU.booth: 0.4287, IoU.television receiver: 0.8183, IoU.airplane: 0.8872, IoU.dirt track: 0.0551, IoU.apparel: 0.6796, IoU.pole: 0.2132, IoU.land: 0.0354, IoU.bannister: 0.2058, IoU.escalator: 0.6739, IoU.ottoman: 0.4894, IoU.bottle: 0.4355, IoU.buffet: 0.5272, IoU.poster: 0.3231, IoU.stage: 0.2699, IoU.van: 0.4969, IoU.ship: 0.8312, IoU.fountain: 0.3741, IoU.conveyer belt: 0.8148, IoU.canopy: 0.5316, IoU.washer: 0.8454, IoU.plaything: 0.3796, IoU.swimming pool: 0.5390, IoU.stool: 0.5312, IoU.barrel: 0.7563, IoU.basket: 0.4220, IoU.waterfall: 0.4897, IoU.tent: 0.9238, IoU.bag: 0.2938, IoU.minibike: 0.7755, IoU.cradle: 0.8887, IoU.oven: 0.6191, IoU.ball: 0.4929, IoU.food: 0.6426, IoU.step: 0.1704, IoU.tank: 0.8023, IoU.trade name: 0.2596, IoU.microwave: 0.8980, IoU.pot: 0.5934, IoU.animal: 0.6284, IoU.bicycle: 0.6069, IoU.lake: 0.4719, IoU.dishwasher: 0.7753, IoU.screen: 0.6166, IoU.blanket: 0.3834, IoU.sculpture: 0.7518, IoU.hood: 0.6525, IoU.sconce: 0.6430, IoU.vase: 0.4901, IoU.traffic light: 0.3689, IoU.tray: 0.2638, IoU.ashcan: 0.5235, IoU.fan: 0.7240, IoU.pier: 0.4547, IoU.crt screen: 0.0206, IoU.plate: 0.6422, IoU.monitor: 0.5611, IoU.bulletin board: 0.5750, IoU.shower: 0.1184, IoU.radiator: 0.6868, IoU.glass: 0.2187, IoU.clock: 0.5180, IoU.flag: 0.7179, Acc.wall: 0.9056, Acc.building: 0.9336, Acc.sky: 0.9765, Acc.floor: 0.9253, Acc.tree: 0.8992, Acc.ceiling: 0.9487, Acc.road: 0.9097, Acc.bed : 0.9684, Acc.windowpane: 0.8148, Acc.grass: 0.8340, Acc.cabinet: 0.7819, Acc.sidewalk: 0.8453, Acc.person: 0.9478, Acc.earth: 0.5568, Acc.door: 0.7722, Acc.table: 0.8210, Acc.mountain: 0.7315, Acc.plant: 0.6764, Acc.curtain: 0.8932, Acc.chair: 0.8025, Acc.car: 0.9449, Acc.water: 0.7730, Acc.painting: 0.9053, Acc.sofa: 0.9293, Acc.shelf: 0.7221, Acc.house: 0.7394, Acc.sea: 0.8131, Acc.mirror: 0.8456, Acc.rug: 0.7064, Acc.field: 0.5649, Acc.armchair: 0.7546, Acc.seat: 0.8896, Acc.fence: 0.6875, Acc.desk: 0.7909, Acc.rock: 0.8176, Acc.wardrobe: 0.7629, Acc.lamp: 0.8788, Acc.bathtub: 0.8923, Acc.railing: 0.6323, Acc.cushion: 0.8412, Acc.base: 0.5385, Acc.box: 0.4835, Acc.column: 0.6816, Acc.signboard: 0.5798, Acc.chest of drawers: 0.6717, Acc.counter: 0.5373, Acc.sand: 0.8608, Acc.sink: 0.8549, Acc.skyscraper: 0.5881, Acc.fireplace: 0.9250, Acc.refrigerator: 0.9448, Acc.grandstand: 0.8090, Acc.path: 0.4298, Acc.stairs: 0.4169, Acc.runway: 0.9642, Acc.case: 0.8317, Acc.pool table: 0.9806, Acc.pillow: 0.8059, Acc.screen door: 0.8267, Acc.stairway: 0.6409, Acc.river: 0.2117, Acc.bridge: 0.8216, Acc.bookcase: 0.6613, Acc.blind: 0.5268, Acc.coffee table: 0.8693, Acc.toilet: 0.9396, Acc.flower: 0.5925, Acc.book: 0.7921, Acc.hill: 0.2007, Acc.bench: 0.6449, Acc.countertop: 0.8475, Acc.stove: 0.9236, Acc.palm: 0.8047, Acc.kitchen island: 0.8729, Acc.computer: 0.9142, Acc.swivel chair: 0.7055, Acc.boat: 0.9252, Acc.bar: 0.8552, Acc.arcade machine: 0.8299, Acc.hovel: 0.1538, Acc.bus: 0.9701, Acc.towel: 0.8835, Acc.light: 0.7198, Acc.truck: 0.6265, Acc.tower: 0.5342, Acc.chandelier: 0.8302, Acc.awning: 0.4913, Acc.streetlight: 0.5653, Acc.booth: 0.5725, Acc.television receiver: 0.8771, Acc.airplane: 0.9563, Acc.dirt track: 0.2749, Acc.apparel: 0.8660, Acc.pole: 0.3595, Acc.land: 0.0535, Acc.bannister: 0.2586, Acc.escalator: 0.8407, Acc.ottoman: 0.6347, Acc.bottle: 0.5395, Acc.buffet: 0.6114, Acc.poster: 0.3776, Acc.stage: 0.4732, Acc.van: 0.7058, Acc.ship: 0.9247, Acc.fountain: 0.3912, Acc.conveyer belt: 0.9427, Acc.canopy: 0.7370, Acc.washer: 0.8969, Acc.plaything: 0.5455, Acc.swimming pool: 0.7862, Acc.stool: 0.7275, Acc.barrel: 0.9621, Acc.basket: 0.5941, Acc.waterfall: 0.6452, Acc.tent: 0.9864, Acc.bag: 0.3386, Acc.minibike: 0.9068, Acc.cradle: 0.9730, Acc.oven: 0.7327, Acc.ball: 0.5365, Acc.food: 0.7701, Acc.step: 0.1942, Acc.tank: 0.9336, Acc.trade name: 0.3193, Acc.microwave: 0.9649, Acc.pot: 0.7095, Acc.animal: 0.6427, Acc.bicycle: 0.7816, Acc.lake: 0.6515, Acc.dishwasher: 0.8422, Acc.screen: 0.9373, Acc.blanket: 0.4408, Acc.sculpture: 0.8822, Acc.hood: 0.7699, Acc.sconce: 0.7312, Acc.vase: 0.6644, Acc.traffic light: 0.6561, Acc.tray: 0.3392, Acc.ashcan: 0.6691, Acc.fan: 0.8406, Acc.pier: 0.5103, Acc.crt screen: 0.0337, Acc.plate: 0.7791, Acc.monitor: 0.6759, Acc.bulletin board: 0.6695, Acc.shower: 0.1468, Acc.radiator: 0.8221, Acc.glass: 0.2340, Acc.clock: 0.6135, Acc.flag: 0.8216 +2024-06-17 09:46:18,465 - mmseg - INFO - Iter [74050/80000] lr: 2.976e-06, eta: 2:55:12, time: 3.568, data_time: 1.942, memory: 71384, decode.loss_ce: 0.1284, decode.acc_seg: 94.1938, aux.loss_ce: 0.0551, aux.acc_seg: 93.8651, loss: 0.1834 +2024-06-17 09:47:39,530 - mmseg - INFO - Iter [74100/80000] lr: 2.950e-06, eta: 2:53:43, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1259, decode.acc_seg: 94.3213, aux.loss_ce: 0.0547, aux.acc_seg: 93.8511, loss: 0.1806 +2024-06-17 09:49:00,417 - mmseg - INFO - Iter [74150/80000] lr: 2.925e-06, eta: 2:52:14, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1320, decode.acc_seg: 94.0314, aux.loss_ce: 0.0570, aux.acc_seg: 93.6047, loss: 0.1890 +2024-06-17 09:50:21,858 - mmseg - INFO - Iter [74200/80000] lr: 2.900e-06, eta: 2:50:45, time: 1.629, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1232, decode.acc_seg: 94.4491, aux.loss_ce: 0.0535, aux.acc_seg: 93.9614, loss: 0.1767 +2024-06-17 09:51:43,045 - mmseg - INFO - Iter [74250/80000] lr: 2.875e-06, eta: 2:49:16, time: 1.624, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1265, decode.acc_seg: 94.2757, aux.loss_ce: 0.0549, aux.acc_seg: 93.8118, loss: 0.1814 +2024-06-17 09:53:04,042 - mmseg - INFO - Iter [74300/80000] lr: 2.851e-06, eta: 2:47:47, time: 1.620, data_time: 0.009, memory: 71384, decode.loss_ce: 0.1328, decode.acc_seg: 94.1428, aux.loss_ce: 0.0577, aux.acc_seg: 93.6291, loss: 0.1905 +2024-06-17 09:54:24,994 - mmseg - INFO - Iter [74350/80000] lr: 2.826e-06, eta: 2:46:19, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1289, decode.acc_seg: 94.3027, aux.loss_ce: 0.0558, aux.acc_seg: 93.8511, loss: 0.1847 +2024-06-17 09:55:45,858 - mmseg - INFO - Iter [74400/80000] lr: 2.801e-06, eta: 2:44:50, time: 1.617, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1313, decode.acc_seg: 94.1317, aux.loss_ce: 0.0570, aux.acc_seg: 93.6662, loss: 0.1883 +2024-06-17 09:57:06,908 - mmseg - INFO - Iter [74450/80000] lr: 2.776e-06, eta: 2:43:21, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1268, decode.acc_seg: 94.3514, aux.loss_ce: 0.0550, aux.acc_seg: 93.9029, loss: 0.1818 +2024-06-17 09:58:27,789 - mmseg - INFO - Iter [74500/80000] lr: 2.750e-06, eta: 2:41:52, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1274, decode.acc_seg: 94.2286, aux.loss_ce: 0.0550, aux.acc_seg: 93.8052, loss: 0.1824 +2024-06-17 09:59:51,168 - mmseg - INFO - Iter [74550/80000] lr: 2.725e-06, eta: 2:40:23, time: 1.668, data_time: 0.052, memory: 71384, decode.loss_ce: 0.1257, decode.acc_seg: 94.4790, aux.loss_ce: 0.0546, aux.acc_seg: 94.0004, loss: 0.1802 +2024-06-17 10:01:12,119 - mmseg - INFO - Iter [74600/80000] lr: 2.700e-06, eta: 2:38:55, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1254, decode.acc_seg: 94.2266, aux.loss_ce: 0.0546, aux.acc_seg: 93.7420, loss: 0.1800 +2024-06-17 10:02:33,158 - mmseg - INFO - Iter [74650/80000] lr: 2.675e-06, eta: 2:37:26, time: 1.621, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1254, decode.acc_seg: 94.3264, aux.loss_ce: 0.0544, aux.acc_seg: 93.8453, loss: 0.1798 +2024-06-17 10:03:54,492 - mmseg - INFO - Iter [74700/80000] lr: 2.651e-06, eta: 2:35:57, time: 1.627, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1215, decode.acc_seg: 94.5235, aux.loss_ce: 0.0527, aux.acc_seg: 94.1461, loss: 0.1742 +2024-06-17 10:05:15,455 - mmseg - INFO - Iter [74750/80000] lr: 2.626e-06, eta: 2:34:28, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1301, decode.acc_seg: 94.3410, aux.loss_ce: 0.0557, aux.acc_seg: 93.9697, loss: 0.1858 +2024-06-17 10:06:36,335 - mmseg - INFO - Iter [74800/80000] lr: 2.601e-06, eta: 2:32:59, time: 1.618, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1312, decode.acc_seg: 94.2816, aux.loss_ce: 0.0568, aux.acc_seg: 93.7653, loss: 0.1879 +2024-06-17 10:07:57,476 - mmseg - INFO - Iter [74850/80000] lr: 2.576e-06, eta: 2:31:31, time: 1.623, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1231, decode.acc_seg: 94.4904, aux.loss_ce: 0.0531, aux.acc_seg: 94.0553, loss: 0.1762 +2024-06-17 10:09:18,433 - mmseg - INFO - Iter [74900/80000] lr: 2.551e-06, eta: 2:30:02, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1291, decode.acc_seg: 94.1357, aux.loss_ce: 0.0560, aux.acc_seg: 93.7031, loss: 0.1851 +2024-06-17 10:10:39,305 - mmseg - INFO - Iter [74950/80000] lr: 2.525e-06, eta: 2:28:33, time: 1.617, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1260, decode.acc_seg: 94.3432, aux.loss_ce: 0.0547, aux.acc_seg: 93.8721, loss: 0.1807 +2024-06-17 10:12:00,251 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 10:12:00,251 - mmseg - INFO - Iter [75000/80000] lr: 2.500e-06, eta: 2:27:04, time: 1.619, data_time: 0.010, memory: 71384, decode.loss_ce: 0.1230, decode.acc_seg: 94.3837, aux.loss_ce: 0.0533, aux.acc_seg: 93.9593, loss: 0.1763 +2024-06-17 10:13:38,186 - mmseg - INFO - per class results: +2024-06-17 10:13:38,192 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.06 | 90.48 | +| building | 86.01 | 93.51 | +| sky | 95.06 | 97.74 | +| floor | 85.53 | 92.4 | +| tree | 78.05 | 89.99 | +| ceiling | 87.76 | 94.88 | +| road | 86.19 | 90.74 | +| bed | 92.8 | 97.04 | +| windowpane | 67.97 | 81.55 | +| grass | 69.77 | 83.29 | +| cabinet | 67.47 | 76.54 | +| sidewalk | 70.0 | 84.02 | +| person | 86.36 | 94.62 | +| earth | 42.28 | 55.81 | +| door | 61.22 | 76.55 | +| table | 70.36 | 82.41 | +| mountain | 62.47 | 73.39 | +| plant | 57.71 | 67.3 | +| curtain | 79.07 | 89.33 | +| chair | 68.73 | 81.35 | +| car | 88.79 | 94.75 | +| water | 62.76 | 76.98 | +| painting | 77.69 | 91.13 | +| sofa | 84.15 | 93.11 | +| shelf | 54.68 | 74.97 | +| house | 57.45 | 70.95 | +| sea | 72.55 | 81.27 | +| mirror | 76.83 | 84.12 | +| rug | 67.21 | 75.67 | +| field | 35.18 | 56.45 | +| armchair | 61.52 | 75.52 | +| seat | 67.75 | 89.49 | +| fence | 52.74 | 62.82 | +| desk | 59.1 | 78.92 | +| rock | 54.36 | 82.98 | +| wardrobe | 53.95 | 74.65 | +| lamp | 77.13 | 87.62 | +| bathtub | 87.2 | 89.6 | +| railing | 45.74 | 63.33 | +| cushion | 73.89 | 84.01 | +| base | 37.11 | 53.29 | +| box | 38.44 | 48.79 | +| column | 56.36 | 68.32 | +| signboard | 42.2 | 58.6 | +| chest of drawers | 44.1 | 69.49 | +| counter | 45.97 | 56.87 | +| sand | 56.88 | 85.86 | +| sink | 80.74 | 85.69 | +| skyscraper | 46.05 | 59.13 | +| fireplace | 73.72 | 94.25 | +| refrigerator | 86.19 | 94.82 | +| grandstand | 51.51 | 82.09 | +| path | 33.55 | 49.04 | +| stairs | 30.78 | 36.87 | +| runway | 73.99 | 96.31 | +| case | 62.9 | 82.94 | +| pool table | 95.11 | 97.96 | +| pillow | 71.0 | 81.81 | +| screen door | 79.43 | 81.81 | +| stairway | 47.26 | 68.7 | +| river | 10.18 | 23.52 | +| bridge | 66.94 | 82.36 | +| bookcase | 56.64 | 67.55 | +| blind | 48.19 | 56.4 | +| coffee table | 61.57 | 86.98 | +| toilet | 91.06 | 94.1 | +| flower | 47.58 | 60.68 | +| book | 57.68 | 78.85 | +| hill | 10.08 | 17.62 | +| bench | 56.06 | 63.32 | +| countertop | 65.91 | 85.39 | +| stove | 88.39 | 93.26 | +| palm | 55.12 | 80.84 | +| kitchen island | 64.84 | 88.54 | +| computer | 78.95 | 91.43 | +| swivel chair | 47.0 | 69.77 | +| boat | 78.81 | 92.06 | +| bar | 65.02 | 84.31 | +| arcade machine | 77.33 | 81.69 | +| hovel | 14.16 | 15.83 | +| bus | 92.15 | 97.06 | +| towel | 80.68 | 87.02 | +| light | 63.13 | 73.12 | +| truck | 52.63 | 59.66 | +| tower | 37.31 | 64.93 | +| chandelier | 75.58 | 87.45 | +| awning | 40.79 | 49.21 | +| streetlight | 39.59 | 51.48 | +| booth | 39.79 | 58.44 | +| television receiver | 81.32 | 88.35 | +| airplane | 89.26 | 95.81 | +| dirt track | 5.62 | 27.65 | +| apparel | 66.83 | 86.85 | +| pole | 21.96 | 35.82 | +| land | 3.11 | 4.66 | +| bannister | 20.12 | 25.56 | +| escalator | 67.11 | 84.17 | +| ottoman | 48.06 | 60.42 | +| bottle | 44.51 | 56.96 | +| buffet | 57.12 | 67.08 | +| poster | 31.99 | 37.21 | +| stage | 27.13 | 47.89 | +| van | 49.96 | 70.43 | +| ship | 84.71 | 96.1 | +| fountain | 36.88 | 38.03 | +| conveyer belt | 81.69 | 94.34 | +| canopy | 52.52 | 73.39 | +| washer | 84.68 | 89.75 | +| plaything | 38.36 | 54.88 | +| swimming pool | 52.88 | 76.66 | +| stool | 53.96 | 72.25 | +| barrel | 76.54 | 94.99 | +| basket | 42.14 | 58.18 | +| waterfall | 50.28 | 65.48 | +| tent | 94.78 | 98.56 | +| bag | 27.3 | 30.28 | +| minibike | 77.99 | 90.45 | +| cradle | 88.72 | 97.51 | +| oven | 61.63 | 71.96 | +| ball | 55.12 | 64.39 | +| food | 63.79 | 76.31 | +| step | 17.81 | 20.24 | +| tank | 79.77 | 93.27 | +| trade name | 24.51 | 29.07 | +| microwave | 89.72 | 96.6 | +| pot | 59.45 | 69.94 | +| animal | 61.74 | 62.78 | +| bicycle | 60.37 | 77.19 | +| lake | 50.13 | 69.33 | +| dishwasher | 76.87 | 84.32 | +| screen | 61.68 | 93.16 | +| blanket | 35.68 | 40.55 | +| sculpture | 75.01 | 88.75 | +| hood | 65.11 | 76.88 | +| sconce | 64.34 | 73.53 | +| vase | 49.29 | 65.14 | +| traffic light | 36.38 | 63.49 | +| tray | 25.22 | 31.79 | +| ashcan | 52.64 | 66.87 | +| fan | 72.46 | 84.54 | +| pier | 46.02 | 51.85 | +| crt screen | 1.99 | 3.37 | +| plate | 63.36 | 79.25 | +| monitor | 55.24 | 66.21 | +| bulletin board | 56.63 | 66.99 | +| shower | 14.65 | 17.85 | +| radiator | 68.35 | 82.34 | +| glass | 21.88 | 23.42 | +| clock | 51.31 | 61.31 | +| flag | 71.54 | 80.83 | ++---------------------+-------+-------+ +2024-06-17 10:13:38,192 - mmseg - INFO - Summary: +2024-06-17 10:13:38,193 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.74 | 59.15 | 71.53 | ++-------+-------+-------+ +2024-06-17 23:56:16,417 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-17 23:56:16,417 - mmseg - INFO - Iter(val) [250] aAcc: 0.8680, mIoU: 0.5917, mAcc: 0.7122, IoU.wall: 0.8321, IoU.building: 0.8606, IoU.sky: 0.9506, IoU.floor: 0.8550, IoU.tree: 0.7823, IoU.ceiling: 0.8790, IoU.road: 0.8622, IoU.bed : 0.9282, IoU.windowpane: 0.6783, IoU.grass: 0.6958, IoU.cabinet: 0.6761, IoU.sidewalk: 0.7100, IoU.person: 0.8659, IoU.earth: 0.4211, IoU.door: 0.6075, IoU.table: 0.7071, IoU.mountain: 0.6259, IoU.plant: 0.5747, IoU.curtain: 0.7904, IoU.chair: 0.6876, IoU.car: 0.8887, IoU.water: 0.6304, IoU.painting: 0.7800, IoU.sofa: 0.8408, IoU.shelf: 0.5327, IoU.house: 0.5786, IoU.sea: 0.7305, IoU.mirror: 0.7774, IoU.rug: 0.6648, IoU.field: 0.3450, IoU.armchair: 0.6139, IoU.seat: 0.6844, IoU.fence: 0.5484, IoU.desk: 0.6035, IoU.rock: 0.5485, IoU.wardrobe: 0.5371, IoU.lamp: 0.7729, IoU.bathtub: 0.8608, IoU.railing: 0.4517, IoU.cushion: 0.7382, IoU.base: 0.3713, IoU.box: 0.3862, IoU.column: 0.5634, IoU.signboard: 0.4188, IoU.chest of drawers: 0.4318, IoU.counter: 0.4373, IoU.sand: 0.5817, IoU.sink: 0.8053, IoU.skyscraper: 0.4615, IoU.fireplace: 0.7397, IoU.refrigerator: 0.8633, IoU.grandstand: 0.5087, IoU.path: 0.3272, IoU.stairs: 0.3141, IoU.runway: 0.7390, IoU.case: 0.6370, IoU.pool table: 0.9510, IoU.pillow: 0.6944, IoU.screen door: 0.7897, IoU.stairway: 0.4916, IoU.river: 0.0923, IoU.bridge: 0.7355, IoU.bookcase: 0.5495, IoU.blind: 0.4710, IoU.coffee table: 0.6161, IoU.toilet: 0.9086, IoU.flower: 0.4740, IoU.book: 0.5695, IoU.hill: 0.1207, IoU.bench: 0.5648, IoU.countertop: 0.6515, IoU.stove: 0.8821, IoU.palm: 0.5440, IoU.kitchen island: 0.6795, IoU.computer: 0.7997, IoU.swivel chair: 0.4689, IoU.boat: 0.8036, IoU.bar: 0.6550, IoU.arcade machine: 0.7770, IoU.hovel: 0.1408, IoU.bus: 0.9239, IoU.towel: 0.7956, IoU.light: 0.6369, IoU.truck: 0.5429, IoU.tower: 0.3235, IoU.chandelier: 0.7467, IoU.awning: 0.4012, IoU.streetlight: 0.3982, IoU.booth: 0.4435, IoU.television receiver: 0.8218, IoU.airplane: 0.8930, IoU.dirt track: 0.0542, IoU.apparel: 0.6690, IoU.pole: 0.2460, IoU.land: 0.0326, IoU.bannister: 0.2075, IoU.escalator: 0.6699, IoU.ottoman: 0.5054, IoU.bottle: 0.4332, IoU.buffet: 0.5156, IoU.poster: 0.3200, IoU.stage: 0.2696, IoU.van: 0.4913, IoU.ship: 0.8229, IoU.fountain: 0.3856, IoU.conveyer belt: 0.8028, IoU.canopy: 0.5459, IoU.washer: 0.8208, IoU.plaything: 0.4042, IoU.swimming pool: 0.5371, IoU.stool: 0.5456, IoU.barrel: 0.7614, IoU.basket: 0.4227, IoU.waterfall: 0.4883, IoU.tent: 0.9228, IoU.bag: 0.2808, IoU.minibike: 0.7826, IoU.cradle: 0.8770, IoU.oven: 0.6455, IoU.ball: 0.5276, IoU.food: 0.6583, IoU.step: 0.1581, IoU.tank: 0.8147, IoU.trade name: 0.2231, IoU.microwave: 0.9053, IoU.pot: 0.6049, IoU.animal: 0.6235, IoU.bicycle: 0.6013, IoU.lake: 0.5165, IoU.dishwasher: 0.7606, IoU.screen: 0.6244, IoU.blanket: 0.3807, IoU.sculpture: 0.7318, IoU.hood: 0.6529, IoU.sconce: 0.6434, IoU.vase: 0.4801, IoU.traffic light: 0.3744, IoU.tray: 0.2723, IoU.ashcan: 0.5269, IoU.fan: 0.7253, IoU.pier: 0.4563, IoU.crt screen: 0.0172, IoU.plate: 0.6311, IoU.monitor: 0.4614, IoU.bulletin board: 0.5823, IoU.shower: 0.1344, IoU.radiator: 0.6986, IoU.glass: 0.2212, IoU.clock: 0.5065, IoU.flag: 0.7191, Acc.wall: 0.9111, Acc.building: 0.9356, Acc.sky: 0.9764, Acc.floor: 0.9258, Acc.tree: 0.8984, Acc.ceiling: 0.9431, Acc.road: 0.9061, Acc.bed : 0.9707, Acc.windowpane: 0.8155, Acc.grass: 0.8338, Acc.cabinet: 0.7717, Acc.sidewalk: 0.8612, Acc.person: 0.9480, Acc.earth: 0.5597, Acc.door: 0.7561, Acc.table: 0.8202, Acc.mountain: 0.7326, Acc.plant: 0.6755, Acc.curtain: 0.8889, Acc.chair: 0.8120, Acc.car: 0.9438, Acc.water: 0.7840, Acc.painting: 0.9114, Acc.sofa: 0.9319, Acc.shelf: 0.7044, Acc.house: 0.7340, Acc.sea: 0.8080, Acc.mirror: 0.8569, Acc.rug: 0.7517, Acc.field: 0.5668, Acc.armchair: 0.7467, Acc.seat: 0.8928, Acc.fence: 0.7009, Acc.desk: 0.7886, Acc.rock: 0.8149, Acc.wardrobe: 0.7327, Acc.lamp: 0.8765, Acc.bathtub: 0.8901, Acc.railing: 0.6056, Acc.cushion: 0.8378, Acc.base: 0.5410, Acc.box: 0.4975, Acc.column: 0.6761, Acc.signboard: 0.5586, Acc.chest of drawers: 0.6696, Acc.counter: 0.5218, Acc.sand: 0.8617, Acc.sink: 0.8514, Acc.skyscraper: 0.5853, Acc.fireplace: 0.9324, Acc.refrigerator: 0.9460, Acc.grandstand: 0.8176, Acc.path: 0.4364, Acc.stairs: 0.3730, Acc.runway: 0.9607, Acc.case: 0.8258, Acc.pool table: 0.9785, Acc.pillow: 0.7888, Acc.screen door: 0.8064, Acc.stairway: 0.6889, Acc.river: 0.2050, Acc.bridge: 0.8419, Acc.bookcase: 0.6667, Acc.blind: 0.5228, Acc.coffee table: 0.8694, Acc.toilet: 0.9340, Acc.flower: 0.6035, Acc.book: 0.7927, Acc.hill: 0.2074, Acc.bench: 0.6400, Acc.countertop: 0.8561, Acc.stove: 0.9285, Acc.palm: 0.8540, Acc.kitchen island: 0.8773, Acc.computer: 0.9094, Acc.swivel chair: 0.7017, Acc.boat: 0.9103, Acc.bar: 0.8791, Acc.arcade machine: 0.8198, Acc.hovel: 0.1555, Acc.bus: 0.9705, Acc.towel: 0.8577, Acc.light: 0.7423, Acc.truck: 0.6249, Acc.tower: 0.5569, Acc.chandelier: 0.8585, Acc.awning: 0.4674, Acc.streetlight: 0.5303, Acc.booth: 0.5605, Acc.television receiver: 0.8777, Acc.airplane: 0.9606, Acc.dirt track: 0.2667, Acc.apparel: 0.8632, Acc.pole: 0.4018, Acc.land: 0.0455, Acc.bannister: 0.2664, Acc.escalator: 0.8513, Acc.ottoman: 0.6599, Acc.bottle: 0.5414, Acc.buffet: 0.6007, Acc.poster: 0.3676, Acc.stage: 0.4791, Acc.van: 0.6859, Acc.ship: 0.9167, Acc.fountain: 0.3978, Acc.conveyer belt: 0.9484, Acc.canopy: 0.7614, Acc.washer: 0.8699, Acc.plaything: 0.5724, Acc.swimming pool: 0.7761, Acc.stool: 0.7093, Acc.barrel: 0.9648, Acc.basket: 0.6127, Acc.waterfall: 0.6375, Acc.tent: 0.9870, Acc.bag: 0.3211, Acc.minibike: 0.9028, Acc.cradle: 0.9760, Acc.oven: 0.7705, Acc.ball: 0.5767, Acc.food: 0.7862, Acc.step: 0.1779, Acc.tank: 0.9165, Acc.trade name: 0.2555, Acc.microwave: 0.9640, Acc.pot: 0.7032, Acc.animal: 0.6360, Acc.bicycle: 0.7624, Acc.lake: 0.6374, Acc.dishwasher: 0.8469, Acc.screen: 0.9320, Acc.blanket: 0.4388, Acc.sculpture: 0.8928, Acc.hood: 0.7700, Acc.sconce: 0.7335, Acc.vase: 0.6682, Acc.traffic light: 0.6228, Acc.tray: 0.3620, Acc.ashcan: 0.6562, Acc.fan: 0.8483, Acc.pier: 0.5065, Acc.crt screen: 0.0338, Acc.plate: 0.7922, Acc.monitor: 0.5473, Acc.bulletin board: 0.6650, Acc.shower: 0.1621, Acc.radiator: 0.8236, Acc.glass: 0.2385, Acc.clock: 0.5982, Acc.flag: 0.8029 +2024-06-17 23:57:38,115 - mmseg - INFO - Iter [75050/80000] lr: 2.475e-06, eta: 8:48:50, time: 3.568, data_time: 1.953, memory: 71386, decode.loss_ce: 0.1214, decode.acc_seg: 94.4893, aux.loss_ce: 0.0526, aux.acc_seg: 94.0268, loss: 0.1740 +2024-06-17 23:58:59,233 - mmseg - INFO - Iter [75100/80000] lr: 2.451e-06, eta: 8:25:44, time: 1.622, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1276, decode.acc_seg: 94.3691, aux.loss_ce: 0.0550, aux.acc_seg: 93.9438, loss: 0.1826 +2024-06-18 00:00:20,463 - mmseg - INFO - Iter [75150/80000] lr: 2.426e-06, eta: 8:04:32, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1248, decode.acc_seg: 94.3539, aux.loss_ce: 0.0542, aux.acc_seg: 93.8953, loss: 0.1790 +2024-06-18 00:01:41,658 - mmseg - INFO - Iter [75200/80000] lr: 2.401e-06, eta: 7:44:59, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1286, decode.acc_seg: 94.2393, aux.loss_ce: 0.0557, aux.acc_seg: 93.7641, loss: 0.1843 +2024-06-18 00:03:02,920 - mmseg - INFO - Iter [75250/80000] lr: 2.376e-06, eta: 7:26:54, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1299, decode.acc_seg: 94.1821, aux.loss_ce: 0.0563, aux.acc_seg: 93.7352, loss: 0.1862 +2024-06-18 00:04:26,418 - mmseg - INFO - Iter [75300/80000] lr: 2.351e-06, eta: 7:10:13, time: 1.670, data_time: 0.051, memory: 71386, decode.loss_ce: 0.1229, decode.acc_seg: 94.4954, aux.loss_ce: 0.0535, aux.acc_seg: 94.0701, loss: 0.1763 +2024-06-18 00:05:47,607 - mmseg - INFO - Iter [75350/80000] lr: 2.325e-06, eta: 6:54:33, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1344, decode.acc_seg: 94.0524, aux.loss_ce: 0.0582, aux.acc_seg: 93.5737, loss: 0.1926 +2024-06-18 00:07:08,740 - mmseg - INFO - Iter [75400/80000] lr: 2.300e-06, eta: 6:39:54, time: 1.623, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1239, decode.acc_seg: 94.4810, aux.loss_ce: 0.0537, aux.acc_seg: 94.0273, loss: 0.1776 +2024-06-18 00:08:29,911 - mmseg - INFO - Iter [75450/80000] lr: 2.275e-06, eta: 6:26:10, time: 1.623, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1324, decode.acc_seg: 94.3531, aux.loss_ce: 0.0573, aux.acc_seg: 93.9218, loss: 0.1897 +2024-06-18 00:09:51,222 - mmseg - INFO - Iter [75500/80000] lr: 2.250e-06, eta: 6:13:16, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1301, decode.acc_seg: 94.3003, aux.loss_ce: 0.0553, aux.acc_seg: 93.9227, loss: 0.1854 +2024-06-18 00:11:12,507 - mmseg - INFO - Iter [75550/80000] lr: 2.226e-06, eta: 6:01:06, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1260, decode.acc_seg: 94.3989, aux.loss_ce: 0.0548, aux.acc_seg: 93.9298, loss: 0.1808 +2024-06-18 00:12:33,694 - mmseg - INFO - Iter [75600/80000] lr: 2.201e-06, eta: 5:49:37, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1172, decode.acc_seg: 94.6979, aux.loss_ce: 0.0505, aux.acc_seg: 94.2776, loss: 0.1677 +2024-06-18 00:13:54,905 - mmseg - INFO - Iter [75650/80000] lr: 2.176e-06, eta: 5:38:44, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1253, decode.acc_seg: 94.4334, aux.loss_ce: 0.0543, aux.acc_seg: 93.9924, loss: 0.1797 +2024-06-18 00:15:16,223 - mmseg - INFO - Iter [75700/80000] lr: 2.151e-06, eta: 5:28:25, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1185, decode.acc_seg: 94.5995, aux.loss_ce: 0.0515, aux.acc_seg: 94.1336, loss: 0.1699 +2024-06-18 00:16:37,307 - mmseg - INFO - Iter [75750/80000] lr: 2.125e-06, eta: 5:18:37, time: 1.622, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1248, decode.acc_seg: 94.3983, aux.loss_ce: 0.0539, aux.acc_seg: 93.9877, loss: 0.1787 +2024-06-18 00:17:58,607 - mmseg - INFO - Iter [75800/80000] lr: 2.100e-06, eta: 5:09:17, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1235, decode.acc_seg: 94.4691, aux.loss_ce: 0.0539, aux.acc_seg: 93.9816, loss: 0.1774 +2024-06-18 00:19:19,935 - mmseg - INFO - Iter [75850/80000] lr: 2.075e-06, eta: 5:00:23, time: 1.627, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1264, decode.acc_seg: 94.2086, aux.loss_ce: 0.0546, aux.acc_seg: 93.7487, loss: 0.1810 +2024-06-18 00:20:41,313 - mmseg - INFO - Iter [75900/80000] lr: 2.050e-06, eta: 4:51:53, time: 1.628, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1303, decode.acc_seg: 94.0341, aux.loss_ce: 0.0564, aux.acc_seg: 93.5784, loss: 0.1867 +2024-06-18 00:22:02,448 - mmseg - INFO - Iter [75950/80000] lr: 2.026e-06, eta: 4:43:45, time: 1.623, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1286, decode.acc_seg: 94.2922, aux.loss_ce: 0.0559, aux.acc_seg: 93.8286, loss: 0.1845 +2024-06-18 00:23:23,661 - mmseg - INFO - Saving checkpoint at 76000 iterations +2024-06-18 00:24:54,209 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 00:24:54,209 - mmseg - INFO - Iter [76000/80000] lr: 2.001e-06, eta: 4:38:58, time: 3.435, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1325, decode.acc_seg: 94.2747, aux.loss_ce: 0.0571, aux.acc_seg: 93.8700, loss: 0.1896 +2024-06-18 00:26:30,710 - mmseg - INFO - per class results: +2024-06-18 00:26:30,716 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 82.99 | 90.46 | +| building | 85.89 | 93.81 | +| sky | 95.03 | 97.86 | +| floor | 85.47 | 92.32 | +| tree | 78.19 | 89.8 | +| ceiling | 87.91 | 95.18 | +| road | 86.28 | 90.82 | +| bed | 92.71 | 97.28 | +| windowpane | 67.99 | 81.33 | +| grass | 69.54 | 82.9 | +| cabinet | 67.77 | 77.7 | +| sidewalk | 70.79 | 85.0 | +| person | 86.46 | 94.86 | +| earth | 42.72 | 57.52 | +| door | 61.15 | 75.9 | +| table | 70.66 | 81.85 | +| mountain | 62.51 | 74.15 | +| plant | 57.49 | 66.96 | +| curtain | 78.96 | 89.3 | +| chair | 69.07 | 81.59 | +| car | 88.81 | 94.44 | +| water | 62.4 | 78.05 | +| painting | 77.22 | 91.03 | +| sofa | 84.47 | 92.8 | +| shelf | 54.33 | 72.63 | +| house | 57.25 | 71.73 | +| sea | 71.97 | 80.17 | +| mirror | 78.3 | 85.78 | +| rug | 66.65 | 75.1 | +| field | 33.66 | 54.51 | +| armchair | 62.21 | 77.38 | +| seat | 68.44 | 89.39 | +| fence | 54.87 | 68.42 | +| desk | 59.36 | 79.63 | +| rock | 54.26 | 80.28 | +| wardrobe | 54.42 | 75.61 | +| lamp | 77.0 | 87.0 | +| bathtub | 86.91 | 90.05 | +| railing | 45.49 | 61.96 | +| cushion | 73.19 | 81.9 | +| base | 37.1 | 54.4 | +| box | 38.99 | 49.71 | +| column | 56.47 | 66.53 | +| signboard | 41.64 | 56.54 | +| chest of drawers | 42.99 | 64.42 | +| counter | 43.17 | 53.43 | +| sand | 59.04 | 85.93 | +| sink | 80.85 | 85.79 | +| skyscraper | 46.36 | 58.21 | +| fireplace | 74.16 | 94.36 | +| refrigerator | 86.52 | 94.92 | +| grandstand | 51.43 | 81.13 | +| path | 32.64 | 44.79 | +| stairs | 31.09 | 38.21 | +| runway | 73.83 | 95.52 | +| case | 62.8 | 83.13 | +| pool table | 95.14 | 97.87 | +| pillow | 68.89 | 78.44 | +| screen door | 83.42 | 86.13 | +| stairway | 46.13 | 62.91 | +| river | 9.64 | 22.14 | +| bridge | 73.19 | 83.52 | +| bookcase | 57.22 | 69.9 | +| blind | 49.07 | 54.98 | +| coffee table | 61.44 | 87.11 | +| toilet | 91.12 | 93.76 | +| flower | 48.31 | 59.02 | +| book | 57.02 | 76.4 | +| hill | 11.67 | 20.19 | +| bench | 55.96 | 63.11 | +| countertop | 65.95 | 84.44 | +| stove | 88.14 | 92.82 | +| palm | 55.95 | 80.67 | +| kitchen island | 67.34 | 88.53 | +| computer | 79.34 | 91.33 | +| swivel chair | 47.07 | 70.76 | +| boat | 78.71 | 91.07 | +| bar | 64.99 | 86.77 | +| arcade machine | 78.91 | 82.95 | +| hovel | 14.21 | 15.86 | +| bus | 92.44 | 96.93 | +| towel | 79.73 | 84.88 | +| light | 61.89 | 69.69 | +| truck | 53.79 | 62.57 | +| tower | 30.09 | 51.2 | +| chandelier | 73.71 | 84.09 | +| awning | 40.16 | 46.67 | +| streetlight | 39.61 | 52.12 | +| booth | 42.02 | 56.49 | +| television receiver | 82.85 | 88.05 | +| airplane | 89.31 | 95.68 | +| dirt track | 5.39 | 27.77 | +| apparel | 66.32 | 84.72 | +| pole | 23.12 | 38.65 | +| land | 3.05 | 4.32 | +| bannister | 21.43 | 27.64 | +| escalator | 67.23 | 85.77 | +| ottoman | 49.07 | 62.51 | +| bottle | 43.73 | 54.41 | +| buffet | 49.61 | 56.41 | +| poster | 31.95 | 35.94 | +| stage | 28.47 | 47.44 | +| van | 48.56 | 72.07 | +| ship | 81.12 | 92.11 | +| fountain | 36.66 | 37.57 | +| conveyer belt | 80.98 | 93.74 | +| canopy | 53.75 | 74.8 | +| washer | 82.49 | 87.41 | +| plaything | 41.96 | 56.61 | +| swimming pool | 53.84 | 77.76 | +| stool | 54.82 | 70.62 | +| barrel | 76.49 | 96.13 | +| basket | 41.41 | 58.13 | +| waterfall | 49.64 | 65.12 | +| tent | 92.97 | 98.46 | +| bag | 27.31 | 30.59 | +| minibike | 77.9 | 90.55 | +| cradle | 88.46 | 97.34 | +| oven | 61.61 | 74.21 | +| ball | 55.59 | 62.57 | +| food | 64.35 | 76.0 | +| step | 15.68 | 17.52 | +| tank | 82.57 | 92.57 | +| trade name | 22.68 | 26.34 | +| microwave | 89.33 | 96.73 | +| pot | 59.98 | 70.96 | +| animal | 61.81 | 62.9 | +| bicycle | 60.51 | 76.29 | +| lake | 51.73 | 63.77 | +| dishwasher | 77.3 | 84.37 | +| screen | 61.66 | 91.24 | +| blanket | 37.02 | 41.89 | +| sculpture | 75.68 | 87.2 | +| hood | 64.91 | 76.2 | +| sconce | 64.32 | 73.44 | +| vase | 48.92 | 65.62 | +| traffic light | 37.15 | 62.29 | +| tray | 26.3 | 34.29 | +| ashcan | 52.46 | 65.47 | +| fan | 72.22 | 82.51 | +| pier | 45.25 | 50.24 | +| crt screen | 1.85 | 3.38 | +| plate | 63.66 | 78.52 | +| monitor | 51.65 | 60.63 | +| bulletin board | 57.26 | 64.45 | +| shower | 14.4 | 15.26 | +| radiator | 69.77 | 82.92 | +| glass | 21.3 | 22.74 | +| clock | 51.02 | 59.75 | +| flag | 71.96 | 79.54 | ++---------------------+-------+-------+ +2024-06-18 00:26:30,716 - mmseg - INFO - Summary: +2024-06-18 00:26:30,716 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.76 | 59.16 | 70.98 | ++-------+-------+-------+ +2024-06-18 00:26:30,717 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 00:26:30,717 - mmseg - INFO - Iter(val) [250] aAcc: 0.8676, mIoU: 0.5916, mAcc: 0.7098, IoU.wall: 0.8299, IoU.building: 0.8589, IoU.sky: 0.9503, IoU.floor: 0.8547, IoU.tree: 0.7819, IoU.ceiling: 0.8791, IoU.road: 0.8628, IoU.bed : 0.9271, IoU.windowpane: 0.6799, IoU.grass: 0.6954, IoU.cabinet: 0.6777, IoU.sidewalk: 0.7079, IoU.person: 0.8646, IoU.earth: 0.4272, IoU.door: 0.6115, IoU.table: 0.7066, IoU.mountain: 0.6251, IoU.plant: 0.5749, IoU.curtain: 0.7896, IoU.chair: 0.6907, IoU.car: 0.8881, IoU.water: 0.6240, IoU.painting: 0.7722, IoU.sofa: 0.8447, IoU.shelf: 0.5433, IoU.house: 0.5725, IoU.sea: 0.7197, IoU.mirror: 0.7830, IoU.rug: 0.6665, IoU.field: 0.3366, IoU.armchair: 0.6221, IoU.seat: 0.6844, IoU.fence: 0.5487, IoU.desk: 0.5936, IoU.rock: 0.5426, IoU.wardrobe: 0.5442, IoU.lamp: 0.7700, IoU.bathtub: 0.8691, IoU.railing: 0.4549, IoU.cushion: 0.7319, IoU.base: 0.3710, IoU.box: 0.3899, IoU.column: 0.5647, IoU.signboard: 0.4164, IoU.chest of drawers: 0.4299, IoU.counter: 0.4317, IoU.sand: 0.5904, IoU.sink: 0.8085, IoU.skyscraper: 0.4636, IoU.fireplace: 0.7416, IoU.refrigerator: 0.8652, IoU.grandstand: 0.5143, IoU.path: 0.3264, IoU.stairs: 0.3109, IoU.runway: 0.7383, IoU.case: 0.6280, IoU.pool table: 0.9514, IoU.pillow: 0.6889, IoU.screen door: 0.8342, IoU.stairway: 0.4613, IoU.river: 0.0964, IoU.bridge: 0.7319, IoU.bookcase: 0.5722, IoU.blind: 0.4907, IoU.coffee table: 0.6144, IoU.toilet: 0.9112, IoU.flower: 0.4831, IoU.book: 0.5702, IoU.hill: 0.1167, IoU.bench: 0.5596, IoU.countertop: 0.6595, IoU.stove: 0.8814, IoU.palm: 0.5595, IoU.kitchen island: 0.6734, IoU.computer: 0.7934, IoU.swivel chair: 0.4707, IoU.boat: 0.7871, IoU.bar: 0.6499, IoU.arcade machine: 0.7891, IoU.hovel: 0.1421, IoU.bus: 0.9244, IoU.towel: 0.7973, IoU.light: 0.6189, IoU.truck: 0.5379, IoU.tower: 0.3009, IoU.chandelier: 0.7371, IoU.awning: 0.4016, IoU.streetlight: 0.3961, IoU.booth: 0.4202, IoU.television receiver: 0.8285, IoU.airplane: 0.8931, IoU.dirt track: 0.0539, IoU.apparel: 0.6632, IoU.pole: 0.2312, IoU.land: 0.0305, IoU.bannister: 0.2143, IoU.escalator: 0.6723, IoU.ottoman: 0.4907, IoU.bottle: 0.4373, IoU.buffet: 0.4961, IoU.poster: 0.3195, IoU.stage: 0.2847, IoU.van: 0.4856, IoU.ship: 0.8112, IoU.fountain: 0.3666, IoU.conveyer belt: 0.8098, IoU.canopy: 0.5375, IoU.washer: 0.8249, IoU.plaything: 0.4196, IoU.swimming pool: 0.5384, IoU.stool: 0.5482, IoU.barrel: 0.7649, IoU.basket: 0.4141, IoU.waterfall: 0.4964, IoU.tent: 0.9297, IoU.bag: 0.2731, IoU.minibike: 0.7790, IoU.cradle: 0.8846, IoU.oven: 0.6161, IoU.ball: 0.5559, IoU.food: 0.6435, IoU.step: 0.1568, IoU.tank: 0.8257, IoU.trade name: 0.2268, IoU.microwave: 0.8933, IoU.pot: 0.5998, IoU.animal: 0.6181, IoU.bicycle: 0.6051, IoU.lake: 0.5173, IoU.dishwasher: 0.7730, IoU.screen: 0.6166, IoU.blanket: 0.3702, IoU.sculpture: 0.7568, IoU.hood: 0.6491, IoU.sconce: 0.6432, IoU.vase: 0.4892, IoU.traffic light: 0.3715, IoU.tray: 0.2630, IoU.ashcan: 0.5246, IoU.fan: 0.7222, IoU.pier: 0.4525, IoU.crt screen: 0.0185, IoU.plate: 0.6366, IoU.monitor: 0.5165, IoU.bulletin board: 0.5726, IoU.shower: 0.1440, IoU.radiator: 0.6977, IoU.glass: 0.2130, IoU.clock: 0.5102, IoU.flag: 0.7196, Acc.wall: 0.9046, Acc.building: 0.9381, Acc.sky: 0.9786, Acc.floor: 0.9232, Acc.tree: 0.8980, Acc.ceiling: 0.9518, Acc.road: 0.9082, Acc.bed : 0.9728, Acc.windowpane: 0.8133, Acc.grass: 0.8290, Acc.cabinet: 0.7770, Acc.sidewalk: 0.8500, Acc.person: 0.9486, Acc.earth: 0.5752, Acc.door: 0.7590, Acc.table: 0.8185, Acc.mountain: 0.7415, Acc.plant: 0.6696, Acc.curtain: 0.8930, Acc.chair: 0.8159, Acc.car: 0.9444, Acc.water: 0.7805, Acc.painting: 0.9103, Acc.sofa: 0.9280, Acc.shelf: 0.7263, Acc.house: 0.7173, Acc.sea: 0.8017, Acc.mirror: 0.8578, Acc.rug: 0.7510, Acc.field: 0.5451, Acc.armchair: 0.7738, Acc.seat: 0.8939, Acc.fence: 0.6842, Acc.desk: 0.7963, Acc.rock: 0.8028, Acc.wardrobe: 0.7561, Acc.lamp: 0.8700, Acc.bathtub: 0.9005, Acc.railing: 0.6196, Acc.cushion: 0.8190, Acc.base: 0.5440, Acc.box: 0.4971, Acc.column: 0.6653, Acc.signboard: 0.5654, Acc.chest of drawers: 0.6442, Acc.counter: 0.5343, Acc.sand: 0.8593, Acc.sink: 0.8579, Acc.skyscraper: 0.5821, Acc.fireplace: 0.9436, Acc.refrigerator: 0.9492, Acc.grandstand: 0.8113, Acc.path: 0.4479, Acc.stairs: 0.3821, Acc.runway: 0.9552, Acc.case: 0.8313, Acc.pool table: 0.9787, Acc.pillow: 0.7844, Acc.screen door: 0.8613, Acc.stairway: 0.6291, Acc.river: 0.2214, Acc.bridge: 0.8352, Acc.bookcase: 0.6990, Acc.blind: 0.5498, Acc.coffee table: 0.8711, Acc.toilet: 0.9376, Acc.flower: 0.5902, Acc.book: 0.7640, Acc.hill: 0.2019, Acc.bench: 0.6311, Acc.countertop: 0.8444, Acc.stove: 0.9282, Acc.palm: 0.8067, Acc.kitchen island: 0.8853, Acc.computer: 0.9133, Acc.swivel chair: 0.7076, Acc.boat: 0.9107, Acc.bar: 0.8677, Acc.arcade machine: 0.8295, Acc.hovel: 0.1586, Acc.bus: 0.9693, Acc.towel: 0.8488, Acc.light: 0.6969, Acc.truck: 0.6257, Acc.tower: 0.5120, Acc.chandelier: 0.8409, Acc.awning: 0.4667, Acc.streetlight: 0.5212, Acc.booth: 0.5649, Acc.television receiver: 0.8805, Acc.airplane: 0.9568, Acc.dirt track: 0.2777, Acc.apparel: 0.8472, Acc.pole: 0.3865, Acc.land: 0.0432, Acc.bannister: 0.2764, Acc.escalator: 0.8577, Acc.ottoman: 0.6251, Acc.bottle: 0.5441, Acc.buffet: 0.5641, Acc.poster: 0.3594, Acc.stage: 0.4744, Acc.van: 0.7207, Acc.ship: 0.9211, Acc.fountain: 0.3757, Acc.conveyer belt: 0.9374, Acc.canopy: 0.7480, Acc.washer: 0.8741, Acc.plaything: 0.5661, Acc.swimming pool: 0.7776, Acc.stool: 0.7062, Acc.barrel: 0.9613, Acc.basket: 0.5813, Acc.waterfall: 0.6512, Acc.tent: 0.9846, Acc.bag: 0.3059, Acc.minibike: 0.9055, Acc.cradle: 0.9734, Acc.oven: 0.7421, Acc.ball: 0.6257, Acc.food: 0.7600, Acc.step: 0.1752, Acc.tank: 0.9257, Acc.trade name: 0.2634, Acc.microwave: 0.9673, Acc.pot: 0.7096, Acc.animal: 0.6290, Acc.bicycle: 0.7629, Acc.lake: 0.6377, Acc.dishwasher: 0.8437, Acc.screen: 0.9124, Acc.blanket: 0.4189, Acc.sculpture: 0.8720, Acc.hood: 0.7620, Acc.sconce: 0.7344, Acc.vase: 0.6562, Acc.traffic light: 0.6229, Acc.tray: 0.3429, Acc.ashcan: 0.6547, Acc.fan: 0.8251, Acc.pier: 0.5024, Acc.crt screen: 0.0338, Acc.plate: 0.7852, Acc.monitor: 0.6063, Acc.bulletin board: 0.6445, Acc.shower: 0.1526, Acc.radiator: 0.8292, Acc.glass: 0.2274, Acc.clock: 0.5975, Acc.flag: 0.7954 +2024-06-18 00:27:52,489 - mmseg - INFO - Iter [76050/80000] lr: 1.976e-06, eta: 4:34:29, time: 3.566, data_time: 1.948, memory: 71386, decode.loss_ce: 0.1250, decode.acc_seg: 94.2690, aux.loss_ce: 0.0546, aux.acc_seg: 93.8385, loss: 0.1796 +2024-06-18 00:29:13,749 - mmseg - INFO - Iter [76100/80000] lr: 1.951e-06, eta: 4:27:04, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1273, decode.acc_seg: 94.2418, aux.loss_ce: 0.0552, aux.acc_seg: 93.7510, loss: 0.1825 +2024-06-18 00:30:34,968 - mmseg - INFO - Iter [76150/80000] lr: 1.926e-06, eta: 4:19:56, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1267, decode.acc_seg: 94.3486, aux.loss_ce: 0.0550, aux.acc_seg: 93.9438, loss: 0.1816 +2024-06-18 00:31:56,236 - mmseg - INFO - Iter [76200/80000] lr: 1.900e-06, eta: 4:13:05, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1202, decode.acc_seg: 94.4738, aux.loss_ce: 0.0523, aux.acc_seg: 94.0532, loss: 0.1724 +2024-06-18 00:33:17,574 - mmseg - INFO - Iter [76250/80000] lr: 1.875e-06, eta: 4:06:27, time: 1.627, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1257, decode.acc_seg: 94.4329, aux.loss_ce: 0.0548, aux.acc_seg: 93.9957, loss: 0.1805 +2024-06-18 00:34:38,708 - mmseg - INFO - Iter [76300/80000] lr: 1.850e-06, eta: 4:00:04, time: 1.623, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1192, decode.acc_seg: 94.6210, aux.loss_ce: 0.0517, aux.acc_seg: 94.1889, loss: 0.1709 +2024-06-18 00:35:59,851 - mmseg - INFO - Iter [76350/80000] lr: 1.826e-06, eta: 3:53:53, time: 1.623, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1308, decode.acc_seg: 94.1813, aux.loss_ce: 0.0564, aux.acc_seg: 93.7411, loss: 0.1872 +2024-06-18 00:37:21,077 - mmseg - INFO - Iter [76400/80000] lr: 1.801e-06, eta: 3:47:54, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1287, decode.acc_seg: 94.3333, aux.loss_ce: 0.0552, aux.acc_seg: 93.9392, loss: 0.1839 +2024-06-18 00:38:42,300 - mmseg - INFO - Iter [76450/80000] lr: 1.776e-06, eta: 3:42:07, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1250, decode.acc_seg: 94.4173, aux.loss_ce: 0.0541, aux.acc_seg: 93.9985, loss: 0.1791 +2024-06-18 00:40:03,567 - mmseg - INFO - Iter [76500/80000] lr: 1.751e-06, eta: 3:36:30, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1386, decode.acc_seg: 93.9973, aux.loss_ce: 0.0596, aux.acc_seg: 93.5779, loss: 0.1982 +2024-06-18 00:41:27,482 - mmseg - INFO - Iter [76550/80000] lr: 1.726e-06, eta: 3:31:07, time: 1.678, data_time: 0.060, memory: 71386, decode.loss_ce: 0.1320, decode.acc_seg: 94.1410, aux.loss_ce: 0.0567, aux.acc_seg: 93.6994, loss: 0.1887 +2024-06-18 00:42:48,712 - mmseg - INFO - Iter [76600/80000] lr: 1.700e-06, eta: 3:25:50, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1221, decode.acc_seg: 94.4567, aux.loss_ce: 0.0529, aux.acc_seg: 94.0001, loss: 0.1750 +2024-06-18 00:44:09,916 - mmseg - INFO - Iter [76650/80000] lr: 1.675e-06, eta: 3:20:41, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1248, decode.acc_seg: 94.5834, aux.loss_ce: 0.0538, aux.acc_seg: 94.1758, loss: 0.1786 +2024-06-18 00:45:31,232 - mmseg - INFO - Iter [76700/80000] lr: 1.650e-06, eta: 3:15:41, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1177, decode.acc_seg: 94.7579, aux.loss_ce: 0.0509, aux.acc_seg: 94.3497, loss: 0.1686 +2024-06-18 00:46:52,445 - mmseg - INFO - Iter [76750/80000] lr: 1.625e-06, eta: 3:10:49, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1251, decode.acc_seg: 94.4935, aux.loss_ce: 0.0539, aux.acc_seg: 94.0308, loss: 0.1790 +2024-06-18 00:48:13,740 - mmseg - INFO - Iter [76800/80000] lr: 1.601e-06, eta: 3:06:04, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1265, decode.acc_seg: 94.3702, aux.loss_ce: 0.0548, aux.acc_seg: 93.9513, loss: 0.1812 +2024-06-18 00:49:35,340 - mmseg - INFO - Iter [76850/80000] lr: 1.576e-06, eta: 3:01:27, time: 1.632, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1292, decode.acc_seg: 94.2698, aux.loss_ce: 0.0562, aux.acc_seg: 93.7549, loss: 0.1855 +2024-06-18 00:50:56,587 - mmseg - INFO - Iter [76900/80000] lr: 1.551e-06, eta: 2:56:57, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1227, decode.acc_seg: 94.5105, aux.loss_ce: 0.0534, aux.acc_seg: 94.0737, loss: 0.1762 +2024-06-18 00:52:17,803 - mmseg - INFO - Iter [76950/80000] lr: 1.526e-06, eta: 2:52:32, time: 1.624, data_time: 0.011, memory: 71386, decode.loss_ce: 0.1356, decode.acc_seg: 93.9279, aux.loss_ce: 0.0579, aux.acc_seg: 93.5571, loss: 0.1935 +2024-06-18 00:53:39,053 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 00:53:39,053 - mmseg - INFO - Iter [77000/80000] lr: 1.500e-06, eta: 2:48:14, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1216, decode.acc_seg: 94.6228, aux.loss_ce: 0.0523, aux.acc_seg: 94.2472, loss: 0.1739 +2024-06-18 00:55:15,235 - mmseg - INFO - per class results: +2024-06-18 00:55:15,241 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.03 | 90.28 | +| building | 85.97 | 93.41 | +| sky | 95.05 | 97.61 | +| floor | 85.52 | 92.31 | +| tree | 78.31 | 90.43 | +| ceiling | 87.92 | 94.97 | +| road | 86.25 | 90.58 | +| bed | 92.88 | 97.16 | +| windowpane | 68.09 | 82.29 | +| grass | 70.37 | 83.43 | +| cabinet | 67.75 | 77.89 | +| sidewalk | 70.76 | 85.25 | +| person | 86.39 | 94.88 | +| earth | 42.08 | 56.13 | +| door | 60.88 | 75.79 | +| table | 70.62 | 81.7 | +| mountain | 62.8 | 73.84 | +| plant | 57.59 | 69.24 | +| curtain | 78.98 | 89.8 | +| chair | 68.89 | 81.56 | +| car | 88.88 | 94.18 | +| water | 62.46 | 77.71 | +| painting | 78.23 | 90.66 | +| sofa | 84.72 | 92.55 | +| shelf | 54.36 | 73.29 | +| house | 58.1 | 73.7 | +| sea | 72.59 | 80.89 | +| mirror | 77.78 | 85.12 | +| rug | 66.39 | 75.54 | +| field | 34.51 | 53.84 | +| armchair | 62.3 | 77.46 | +| seat | 67.77 | 89.56 | +| fence | 54.37 | 68.45 | +| desk | 60.09 | 78.31 | +| rock | 54.63 | 81.77 | +| wardrobe | 53.65 | 74.02 | +| lamp | 77.43 | 87.41 | +| bathtub | 86.92 | 89.69 | +| railing | 45.96 | 63.92 | +| cushion | 73.81 | 83.72 | +| base | 37.27 | 54.33 | +| box | 39.02 | 50.32 | +| column | 56.38 | 67.51 | +| signboard | 42.15 | 57.28 | +| chest of drawers | 42.78 | 66.06 | +| counter | 43.02 | 51.8 | +| sand | 57.31 | 85.95 | +| sink | 80.96 | 85.97 | +| skyscraper | 46.21 | 58.08 | +| fireplace | 73.73 | 94.09 | +| refrigerator | 86.54 | 94.63 | +| grandstand | 51.55 | 81.13 | +| path | 32.62 | 45.73 | +| stairs | 31.54 | 38.32 | +| runway | 74.18 | 96.24 | +| case | 62.68 | 83.08 | +| pool table | 95.13 | 97.82 | +| pillow | 70.31 | 81.57 | +| screen door | 81.36 | 84.0 | +| stairway | 47.54 | 68.64 | +| river | 9.28 | 20.82 | +| bridge | 75.25 | 88.82 | +| bookcase | 57.27 | 66.97 | +| blind | 46.87 | 52.09 | +| coffee table | 61.77 | 87.04 | +| toilet | 91.23 | 94.06 | +| flower | 48.22 | 60.08 | +| book | 57.81 | 78.47 | +| hill | 11.55 | 19.84 | +| bench | 56.41 | 63.64 | +| countertop | 66.23 | 84.17 | +| stove | 88.17 | 92.75 | +| palm | 55.96 | 82.96 | +| kitchen island | 68.58 | 88.41 | +| computer | 79.47 | 91.4 | +| swivel chair | 47.09 | 70.08 | +| boat | 80.88 | 91.46 | +| bar | 65.41 | 87.76 | +| arcade machine | 78.96 | 83.16 | +| hovel | 14.07 | 15.66 | +| bus | 92.43 | 96.81 | +| towel | 80.34 | 86.67 | +| light | 63.09 | 72.22 | +| truck | 52.9 | 63.06 | +| tower | 35.54 | 61.6 | +| chandelier | 75.39 | 86.88 | +| awning | 41.3 | 49.8 | +| streetlight | 39.68 | 52.41 | +| booth | 41.78 | 57.5 | +| television receiver | 82.48 | 88.48 | +| airplane | 89.13 | 96.09 | +| dirt track | 5.46 | 27.31 | +| apparel | 65.05 | 89.12 | +| pole | 24.31 | 38.88 | +| land | 3.25 | 4.52 | +| bannister | 21.31 | 27.22 | +| escalator | 66.52 | 86.74 | +| ottoman | 48.9 | 62.9 | +| bottle | 44.35 | 56.38 | +| buffet | 50.26 | 57.81 | +| poster | 32.21 | 37.73 | +| stage | 27.74 | 47.95 | +| van | 48.6 | 72.51 | +| ship | 80.21 | 92.17 | +| fountain | 37.32 | 38.24 | +| conveyer belt | 80.06 | 94.42 | +| canopy | 53.81 | 75.19 | +| washer | 83.01 | 87.92 | +| plaything | 42.9 | 60.49 | +| swimming pool | 52.94 | 77.09 | +| stool | 54.89 | 71.15 | +| barrel | 75.46 | 96.64 | +| basket | 41.81 | 59.34 | +| waterfall | 49.08 | 63.95 | +| tent | 92.72 | 98.46 | +| bag | 29.08 | 34.02 | +| minibike | 78.05 | 90.69 | +| cradle | 86.96 | 97.59 | +| oven | 61.67 | 74.04 | +| ball | 57.28 | 65.46 | +| food | 64.43 | 76.45 | +| step | 15.93 | 17.98 | +| tank | 81.74 | 92.5 | +| trade name | 26.19 | 31.26 | +| microwave | 89.64 | 96.96 | +| pot | 60.77 | 72.27 | +| animal | 61.94 | 62.99 | +| bicycle | 60.72 | 77.99 | +| lake | 51.42 | 63.75 | +| dishwasher | 75.86 | 84.79 | +| screen | 60.2 | 89.67 | +| blanket | 38.83 | 44.29 | +| sculpture | 75.78 | 88.23 | +| hood | 65.23 | 77.52 | +| sconce | 64.31 | 73.8 | +| vase | 48.34 | 67.24 | +| traffic light | 37.24 | 63.02 | +| tray | 27.13 | 36.09 | +| ashcan | 52.76 | 68.02 | +| fan | 72.41 | 84.18 | +| pier | 44.89 | 50.64 | +| crt screen | 1.84 | 3.41 | +| plate | 63.44 | 78.73 | +| monitor | 52.17 | 62.83 | +| bulletin board | 57.34 | 67.34 | +| shower | 14.85 | 16.14 | +| radiator | 69.81 | 81.83 | +| glass | 22.09 | 23.89 | +| clock | 51.28 | 61.36 | +| flag | 71.77 | 80.97 | ++---------------------+-------+-------+ +2024-06-18 00:55:15,241 - mmseg - INFO - Summary: +2024-06-18 00:55:15,241 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.79 | 59.32 | 71.64 | ++-------+-------+-------+ +2024-06-18 00:55:15,242 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 00:55:15,242 - mmseg - INFO - Iter(val) [250] aAcc: 0.8679, mIoU: 0.5932, mAcc: 0.7164, IoU.wall: 0.8303, IoU.building: 0.8597, IoU.sky: 0.9505, IoU.floor: 0.8552, IoU.tree: 0.7831, IoU.ceiling: 0.8792, IoU.road: 0.8625, IoU.bed : 0.9288, IoU.windowpane: 0.6809, IoU.grass: 0.7037, IoU.cabinet: 0.6775, IoU.sidewalk: 0.7076, IoU.person: 0.8639, IoU.earth: 0.4208, IoU.door: 0.6088, IoU.table: 0.7062, IoU.mountain: 0.6280, IoU.plant: 0.5759, IoU.curtain: 0.7898, IoU.chair: 0.6889, IoU.car: 0.8888, IoU.water: 0.6246, IoU.painting: 0.7823, IoU.sofa: 0.8472, IoU.shelf: 0.5436, IoU.house: 0.5810, IoU.sea: 0.7259, IoU.mirror: 0.7778, IoU.rug: 0.6639, IoU.field: 0.3451, IoU.armchair: 0.6230, IoU.seat: 0.6777, IoU.fence: 0.5437, IoU.desk: 0.6009, IoU.rock: 0.5463, IoU.wardrobe: 0.5365, IoU.lamp: 0.7743, IoU.bathtub: 0.8692, IoU.railing: 0.4596, IoU.cushion: 0.7381, IoU.base: 0.3727, IoU.box: 0.3902, IoU.column: 0.5638, IoU.signboard: 0.4215, IoU.chest of drawers: 0.4278, IoU.counter: 0.4302, IoU.sand: 0.5731, IoU.sink: 0.8096, IoU.skyscraper: 0.4621, IoU.fireplace: 0.7373, IoU.refrigerator: 0.8654, IoU.grandstand: 0.5155, IoU.path: 0.3262, IoU.stairs: 0.3154, IoU.runway: 0.7418, IoU.case: 0.6268, IoU.pool table: 0.9513, IoU.pillow: 0.7031, IoU.screen door: 0.8136, IoU.stairway: 0.4754, IoU.river: 0.0928, IoU.bridge: 0.7525, IoU.bookcase: 0.5727, IoU.blind: 0.4687, IoU.coffee table: 0.6177, IoU.toilet: 0.9123, IoU.flower: 0.4822, IoU.book: 0.5781, IoU.hill: 0.1155, IoU.bench: 0.5641, IoU.countertop: 0.6623, IoU.stove: 0.8817, IoU.palm: 0.5596, IoU.kitchen island: 0.6858, IoU.computer: 0.7947, IoU.swivel chair: 0.4709, IoU.boat: 0.8088, IoU.bar: 0.6541, IoU.arcade machine: 0.7896, IoU.hovel: 0.1407, IoU.bus: 0.9243, IoU.towel: 0.8034, IoU.light: 0.6309, IoU.truck: 0.5290, IoU.tower: 0.3554, IoU.chandelier: 0.7539, IoU.awning: 0.4130, IoU.streetlight: 0.3968, IoU.booth: 0.4178, IoU.television receiver: 0.8248, IoU.airplane: 0.8913, IoU.dirt track: 0.0546, IoU.apparel: 0.6505, IoU.pole: 0.2431, IoU.land: 0.0325, IoU.bannister: 0.2131, IoU.escalator: 0.6652, IoU.ottoman: 0.4890, IoU.bottle: 0.4435, IoU.buffet: 0.5026, IoU.poster: 0.3221, IoU.stage: 0.2774, IoU.van: 0.4860, IoU.ship: 0.8021, IoU.fountain: 0.3732, IoU.conveyer belt: 0.8006, IoU.canopy: 0.5381, IoU.washer: 0.8301, IoU.plaything: 0.4290, IoU.swimming pool: 0.5294, IoU.stool: 0.5489, IoU.barrel: 0.7546, IoU.basket: 0.4181, IoU.waterfall: 0.4908, IoU.tent: 0.9272, IoU.bag: 0.2908, IoU.minibike: 0.7805, IoU.cradle: 0.8696, IoU.oven: 0.6167, IoU.ball: 0.5728, IoU.food: 0.6443, IoU.step: 0.1593, IoU.tank: 0.8174, IoU.trade name: 0.2619, IoU.microwave: 0.8964, IoU.pot: 0.6077, IoU.animal: 0.6194, IoU.bicycle: 0.6072, IoU.lake: 0.5142, IoU.dishwasher: 0.7586, IoU.screen: 0.6020, IoU.blanket: 0.3883, IoU.sculpture: 0.7578, IoU.hood: 0.6523, IoU.sconce: 0.6431, IoU.vase: 0.4834, IoU.traffic light: 0.3724, IoU.tray: 0.2713, IoU.ashcan: 0.5276, IoU.fan: 0.7241, IoU.pier: 0.4489, IoU.crt screen: 0.0184, IoU.plate: 0.6344, IoU.monitor: 0.5217, IoU.bulletin board: 0.5734, IoU.shower: 0.1485, IoU.radiator: 0.6981, IoU.glass: 0.2209, IoU.clock: 0.5128, IoU.flag: 0.7177, Acc.wall: 0.9028, Acc.building: 0.9341, Acc.sky: 0.9761, Acc.floor: 0.9231, Acc.tree: 0.9043, Acc.ceiling: 0.9497, Acc.road: 0.9058, Acc.bed : 0.9716, Acc.windowpane: 0.8229, Acc.grass: 0.8343, Acc.cabinet: 0.7789, Acc.sidewalk: 0.8525, Acc.person: 0.9488, Acc.earth: 0.5613, Acc.door: 0.7579, Acc.table: 0.8170, Acc.mountain: 0.7384, Acc.plant: 0.6924, Acc.curtain: 0.8980, Acc.chair: 0.8156, Acc.car: 0.9418, Acc.water: 0.7771, Acc.painting: 0.9066, Acc.sofa: 0.9255, Acc.shelf: 0.7329, Acc.house: 0.7370, Acc.sea: 0.8089, Acc.mirror: 0.8512, Acc.rug: 0.7554, Acc.field: 0.5384, Acc.armchair: 0.7746, Acc.seat: 0.8956, Acc.fence: 0.6845, Acc.desk: 0.7831, Acc.rock: 0.8177, Acc.wardrobe: 0.7402, Acc.lamp: 0.8741, Acc.bathtub: 0.8969, Acc.railing: 0.6392, Acc.cushion: 0.8372, Acc.base: 0.5433, Acc.box: 0.5032, Acc.column: 0.6751, Acc.signboard: 0.5728, Acc.chest of drawers: 0.6606, Acc.counter: 0.5180, Acc.sand: 0.8595, Acc.sink: 0.8597, Acc.skyscraper: 0.5808, Acc.fireplace: 0.9409, Acc.refrigerator: 0.9463, Acc.grandstand: 0.8113, Acc.path: 0.4573, Acc.stairs: 0.3832, Acc.runway: 0.9624, Acc.case: 0.8308, Acc.pool table: 0.9782, Acc.pillow: 0.8157, Acc.screen door: 0.8400, Acc.stairway: 0.6864, Acc.river: 0.2082, Acc.bridge: 0.8882, Acc.bookcase: 0.6697, Acc.blind: 0.5209, Acc.coffee table: 0.8704, Acc.toilet: 0.9406, Acc.flower: 0.6008, Acc.book: 0.7847, Acc.hill: 0.1984, Acc.bench: 0.6364, Acc.countertop: 0.8417, Acc.stove: 0.9275, Acc.palm: 0.8296, Acc.kitchen island: 0.8841, Acc.computer: 0.9140, Acc.swivel chair: 0.7008, Acc.boat: 0.9146, Acc.bar: 0.8776, Acc.arcade machine: 0.8316, Acc.hovel: 0.1566, Acc.bus: 0.9681, Acc.towel: 0.8667, Acc.light: 0.7222, Acc.truck: 0.6306, Acc.tower: 0.6160, Acc.chandelier: 0.8688, Acc.awning: 0.4980, Acc.streetlight: 0.5241, Acc.booth: 0.5750, Acc.television receiver: 0.8848, Acc.airplane: 0.9609, Acc.dirt track: 0.2731, Acc.apparel: 0.8912, Acc.pole: 0.3888, Acc.land: 0.0452, Acc.bannister: 0.2722, Acc.escalator: 0.8674, Acc.ottoman: 0.6290, Acc.bottle: 0.5638, Acc.buffet: 0.5781, Acc.poster: 0.3773, Acc.stage: 0.4795, Acc.van: 0.7251, Acc.ship: 0.9217, Acc.fountain: 0.3824, Acc.conveyer belt: 0.9442, Acc.canopy: 0.7519, Acc.washer: 0.8792, Acc.plaything: 0.6049, Acc.swimming pool: 0.7709, Acc.stool: 0.7115, Acc.barrel: 0.9664, Acc.basket: 0.5934, Acc.waterfall: 0.6395, Acc.tent: 0.9846, Acc.bag: 0.3402, Acc.minibike: 0.9069, Acc.cradle: 0.9759, Acc.oven: 0.7404, Acc.ball: 0.6546, Acc.food: 0.7645, Acc.step: 0.1798, Acc.tank: 0.9250, Acc.trade name: 0.3126, Acc.microwave: 0.9696, Acc.pot: 0.7227, Acc.animal: 0.6299, Acc.bicycle: 0.7799, Acc.lake: 0.6375, Acc.dishwasher: 0.8479, Acc.screen: 0.8967, Acc.blanket: 0.4429, Acc.sculpture: 0.8823, Acc.hood: 0.7752, Acc.sconce: 0.7380, Acc.vase: 0.6724, Acc.traffic light: 0.6302, Acc.tray: 0.3609, Acc.ashcan: 0.6802, Acc.fan: 0.8418, Acc.pier: 0.5064, Acc.crt screen: 0.0341, Acc.plate: 0.7873, Acc.monitor: 0.6283, Acc.bulletin board: 0.6734, Acc.shower: 0.1614, Acc.radiator: 0.8183, Acc.glass: 0.2389, Acc.clock: 0.6136, Acc.flag: 0.8097 +2024-06-18 00:56:37,095 - mmseg - INFO - Iter [77050/80000] lr: 1.475e-06, eta: 2:45:35, time: 3.561, data_time: 1.941, memory: 71386, decode.loss_ce: 0.1242, decode.acc_seg: 94.3361, aux.loss_ce: 0.0541, aux.acc_seg: 93.9007, loss: 0.1782 +2024-06-18 00:57:58,586 - mmseg - INFO - Iter [77100/80000] lr: 1.450e-06, eta: 2:41:26, time: 1.630, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1211, decode.acc_seg: 94.6347, aux.loss_ce: 0.0526, aux.acc_seg: 94.1658, loss: 0.1738 +2024-06-18 00:59:19,911 - mmseg - INFO - Iter [77150/80000] lr: 1.425e-06, eta: 2:37:21, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1273, decode.acc_seg: 94.3352, aux.loss_ce: 0.0555, aux.acc_seg: 93.8698, loss: 0.1828 +2024-06-18 01:00:41,204 - mmseg - INFO - Iter [77200/80000] lr: 1.401e-06, eta: 2:33:22, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1291, decode.acc_seg: 94.1857, aux.loss_ce: 0.0556, aux.acc_seg: 93.7174, loss: 0.1848 +2024-06-18 01:02:02,499 - mmseg - INFO - Iter [77250/80000] lr: 1.376e-06, eta: 2:29:27, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1283, decode.acc_seg: 94.2677, aux.loss_ce: 0.0556, aux.acc_seg: 93.8498, loss: 0.1839 +2024-06-18 01:03:23,874 - mmseg - INFO - Iter [77300/80000] lr: 1.351e-06, eta: 2:25:37, time: 1.627, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1278, decode.acc_seg: 94.2028, aux.loss_ce: 0.0554, aux.acc_seg: 93.7399, loss: 0.1832 +2024-06-18 01:04:45,395 - mmseg - INFO - Iter [77350/80000] lr: 1.326e-06, eta: 2:21:52, time: 1.630, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1301, decode.acc_seg: 94.1915, aux.loss_ce: 0.0558, aux.acc_seg: 93.8279, loss: 0.1859 +2024-06-18 01:06:06,573 - mmseg - INFO - Iter [77400/80000] lr: 1.301e-06, eta: 2:18:11, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1251, decode.acc_seg: 94.4575, aux.loss_ce: 0.0541, aux.acc_seg: 93.9999, loss: 0.1792 +2024-06-18 01:07:27,895 - mmseg - INFO - Iter [77450/80000] lr: 1.275e-06, eta: 2:14:34, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1250, decode.acc_seg: 94.3485, aux.loss_ce: 0.0543, aux.acc_seg: 93.9169, loss: 0.1793 +2024-06-18 01:08:49,127 - mmseg - INFO - Iter [77500/80000] lr: 1.250e-06, eta: 2:11:00, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1192, decode.acc_seg: 94.5025, aux.loss_ce: 0.0519, aux.acc_seg: 94.0469, loss: 0.1712 +2024-06-18 01:10:10,628 - mmseg - INFO - Iter [77550/80000] lr: 1.225e-06, eta: 2:07:31, time: 1.630, data_time: 0.011, memory: 71386, decode.loss_ce: 0.1329, decode.acc_seg: 94.0452, aux.loss_ce: 0.0574, aux.acc_seg: 93.5773, loss: 0.1903 +2024-06-18 01:11:31,876 - mmseg - INFO - Iter [77600/80000] lr: 1.200e-06, eta: 2:04:05, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1212, decode.acc_seg: 94.5810, aux.loss_ce: 0.0528, aux.acc_seg: 94.1429, loss: 0.1740 +2024-06-18 01:12:53,215 - mmseg - INFO - Iter [77650/80000] lr: 1.176e-06, eta: 2:00:42, time: 1.627, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1272, decode.acc_seg: 94.3546, aux.loss_ce: 0.0552, aux.acc_seg: 93.9293, loss: 0.1824 +2024-06-18 01:14:14,635 - mmseg - INFO - Iter [77700/80000] lr: 1.151e-06, eta: 1:57:23, time: 1.628, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1228, decode.acc_seg: 94.3616, aux.loss_ce: 0.0534, aux.acc_seg: 93.9262, loss: 0.1762 +2024-06-18 01:15:35,933 - mmseg - INFO - Iter [77750/80000] lr: 1.126e-06, eta: 1:54:07, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1297, decode.acc_seg: 94.2486, aux.loss_ce: 0.0561, aux.acc_seg: 93.8188, loss: 0.1859 +2024-06-18 01:16:59,858 - mmseg - INFO - Iter [77800/80000] lr: 1.101e-06, eta: 1:50:55, time: 1.678, data_time: 0.061, memory: 71386, decode.loss_ce: 0.1263, decode.acc_seg: 94.3340, aux.loss_ce: 0.0548, aux.acc_seg: 93.8659, loss: 0.1811 +2024-06-18 01:18:21,187 - mmseg - INFO - Iter [77850/80000] lr: 1.075e-06, eta: 1:47:45, time: 1.627, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1219, decode.acc_seg: 94.4512, aux.loss_ce: 0.0531, aux.acc_seg: 94.0079, loss: 0.1750 +2024-06-18 01:19:42,500 - mmseg - INFO - Iter [77900/80000] lr: 1.050e-06, eta: 1:44:37, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1246, decode.acc_seg: 94.4177, aux.loss_ce: 0.0536, aux.acc_seg: 94.0034, loss: 0.1782 +2024-06-18 01:21:03,806 - mmseg - INFO - Iter [77950/80000] lr: 1.025e-06, eta: 1:41:32, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1348, decode.acc_seg: 93.7870, aux.loss_ce: 0.0581, aux.acc_seg: 93.3311, loss: 0.1928 +2024-06-18 01:22:25,054 - mmseg - INFO - Saving checkpoint at 78000 iterations +2024-06-18 01:23:54,084 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 01:23:54,084 - mmseg - INFO - Iter [78000/80000] lr: 1.000e-06, eta: 1:39:15, time: 3.406, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1321, decode.acc_seg: 94.0871, aux.loss_ce: 0.0571, aux.acc_seg: 93.6317, loss: 0.1892 +2024-06-18 01:25:29,101 - mmseg - INFO - per class results: +2024-06-18 01:25:29,107 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.03 | 90.43 | +| building | 85.86 | 93.58 | +| sky | 95.03 | 97.78 | +| floor | 85.5 | 92.4 | +| tree | 78.38 | 90.16 | +| ceiling | 88.08 | 94.77 | +| road | 86.33 | 90.56 | +| bed | 92.85 | 97.06 | +| windowpane | 67.78 | 82.39 | +| grass | 70.1 | 82.95 | +| cabinet | 68.15 | 78.0 | +| sidewalk | 70.78 | 85.64 | +| person | 86.37 | 94.71 | +| earth | 42.55 | 57.2 | +| door | 60.65 | 75.7 | +| table | 70.66 | 82.27 | +| mountain | 62.85 | 72.95 | +| plant | 57.75 | 67.54 | +| curtain | 78.92 | 89.44 | +| chair | 68.58 | 81.0 | +| car | 88.87 | 94.25 | +| water | 62.94 | 77.84 | +| painting | 77.9 | 90.98 | +| sofa | 84.7 | 92.82 | +| shelf | 53.57 | 71.04 | +| house | 57.3 | 71.51 | +| sea | 72.92 | 81.02 | +| mirror | 77.43 | 84.46 | +| rug | 66.05 | 75.26 | +| field | 34.71 | 56.05 | +| armchair | 61.83 | 75.7 | +| seat | 67.71 | 89.35 | +| fence | 53.92 | 67.8 | +| desk | 60.13 | 78.48 | +| rock | 55.13 | 82.97 | +| wardrobe | 53.91 | 73.93 | +| lamp | 77.21 | 87.61 | +| bathtub | 86.87 | 89.42 | +| railing | 46.27 | 65.82 | +| cushion | 73.57 | 83.26 | +| base | 37.63 | 54.86 | +| box | 39.26 | 50.71 | +| column | 56.95 | 67.92 | +| signboard | 41.68 | 56.5 | +| chest of drawers | 42.81 | 63.11 | +| counter | 43.06 | 51.65 | +| sand | 56.63 | 86.26 | +| sink | 81.2 | 86.4 | +| skyscraper | 47.04 | 59.98 | +| fireplace | 73.88 | 93.99 | +| refrigerator | 86.29 | 94.59 | +| grandstand | 50.89 | 81.98 | +| path | 32.52 | 44.72 | +| stairs | 30.77 | 39.43 | +| runway | 74.34 | 96.59 | +| case | 63.03 | 83.14 | +| pool table | 95.16 | 97.77 | +| pillow | 70.02 | 80.81 | +| screen door | 79.66 | 81.67 | +| stairway | 43.16 | 59.38 | +| river | 9.23 | 20.73 | +| bridge | 75.6 | 87.13 | +| bookcase | 56.5 | 67.02 | +| blind | 46.71 | 52.39 | +| coffee table | 62.33 | 86.76 | +| toilet | 91.26 | 94.15 | +| flower | 47.66 | 60.77 | +| book | 56.79 | 80.54 | +| hill | 12.44 | 21.47 | +| bench | 56.45 | 63.83 | +| countertop | 65.68 | 83.47 | +| stove | 87.87 | 92.98 | +| palm | 55.64 | 81.66 | +| kitchen island | 69.49 | 86.56 | +| computer | 80.06 | 91.1 | +| swivel chair | 47.32 | 69.25 | +| boat | 80.27 | 91.35 | +| bar | 65.0 | 88.22 | +| arcade machine | 78.77 | 83.21 | +| hovel | 14.08 | 15.77 | +| bus | 92.65 | 96.86 | +| towel | 80.56 | 86.99 | +| light | 63.08 | 71.96 | +| truck | 53.26 | 62.42 | +| tower | 33.65 | 57.71 | +| chandelier | 74.54 | 86.09 | +| awning | 40.63 | 48.02 | +| streetlight | 38.99 | 50.66 | +| booth | 40.7 | 58.38 | +| television receiver | 82.48 | 88.57 | +| airplane | 89.41 | 96.11 | +| dirt track | 5.42 | 27.26 | +| apparel | 66.56 | 87.48 | +| pole | 22.63 | 37.73 | +| land | 3.15 | 4.32 | +| bannister | 21.0 | 26.55 | +| escalator | 67.03 | 86.47 | +| ottoman | 49.91 | 65.1 | +| bottle | 45.71 | 61.58 | +| buffet | 51.55 | 59.46 | +| poster | 31.85 | 37.91 | +| stage | 27.71 | 48.01 | +| van | 48.83 | 73.44 | +| ship | 79.65 | 91.34 | +| fountain | 37.89 | 38.66 | +| conveyer belt | 80.93 | 94.24 | +| canopy | 53.77 | 74.74 | +| washer | 83.2 | 88.2 | +| plaything | 43.63 | 63.31 | +| swimming pool | 54.07 | 78.68 | +| stool | 52.63 | 72.27 | +| barrel | 76.6 | 96.48 | +| basket | 41.52 | 57.59 | +| waterfall | 49.86 | 64.39 | +| tent | 92.64 | 98.52 | +| bag | 28.26 | 32.59 | +| minibike | 77.8 | 90.44 | +| cradle | 87.27 | 97.41 | +| oven | 63.77 | 77.44 | +| ball | 56.79 | 66.45 | +| food | 64.66 | 77.52 | +| step | 16.06 | 18.21 | +| tank | 82.24 | 92.48 | +| trade name | 24.35 | 28.55 | +| microwave | 90.32 | 96.75 | +| pot | 60.17 | 70.32 | +| animal | 62.49 | 63.68 | +| bicycle | 60.57 | 78.15 | +| lake | 50.62 | 65.75 | +| dishwasher | 76.09 | 84.88 | +| screen | 62.16 | 92.7 | +| blanket | 39.04 | 44.72 | +| sculpture | 75.27 | 88.48 | +| hood | 65.19 | 77.29 | +| sconce | 64.33 | 73.19 | +| vase | 48.77 | 66.13 | +| traffic light | 37.62 | 61.54 | +| tray | 27.09 | 35.59 | +| ashcan | 52.75 | 66.84 | +| fan | 72.09 | 82.89 | +| pier | 44.89 | 49.98 | +| crt screen | 2.01 | 3.4 | +| plate | 63.07 | 80.54 | +| monitor | 55.91 | 67.85 | +| bulletin board | 56.63 | 66.21 | +| shower | 13.66 | 16.23 | +| radiator | 69.49 | 81.31 | +| glass | 21.7 | 23.26 | +| clock | 52.13 | 61.31 | +| flag | 71.74 | 80.99 | ++---------------------+-------+-------+ +2024-06-18 01:25:29,107 - mmseg - INFO - Summary: +2024-06-18 01:25:29,107 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.78 | 59.29 | 71.56 | ++-------+-------+-------+ +2024-06-18 01:25:29,108 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 01:25:29,108 - mmseg - INFO - Iter(val) [250] aAcc: 0.8678, mIoU: 0.5929, mAcc: 0.7156, IoU.wall: 0.8303, IoU.building: 0.8586, IoU.sky: 0.9503, IoU.floor: 0.8550, IoU.tree: 0.7838, IoU.ceiling: 0.8808, IoU.road: 0.8633, IoU.bed : 0.9285, IoU.windowpane: 0.6778, IoU.grass: 0.7010, IoU.cabinet: 0.6815, IoU.sidewalk: 0.7078, IoU.person: 0.8637, IoU.earth: 0.4255, IoU.door: 0.6065, IoU.table: 0.7066, IoU.mountain: 0.6285, IoU.plant: 0.5775, IoU.curtain: 0.7892, IoU.chair: 0.6858, IoU.car: 0.8887, IoU.water: 0.6294, IoU.painting: 0.7790, IoU.sofa: 0.8470, IoU.shelf: 0.5357, IoU.house: 0.5730, IoU.sea: 0.7292, IoU.mirror: 0.7743, IoU.rug: 0.6605, IoU.field: 0.3471, IoU.armchair: 0.6183, IoU.seat: 0.6771, IoU.fence: 0.5392, IoU.desk: 0.6013, IoU.rock: 0.5513, IoU.wardrobe: 0.5391, IoU.lamp: 0.7721, IoU.bathtub: 0.8687, IoU.railing: 0.4627, IoU.cushion: 0.7357, IoU.base: 0.3763, IoU.box: 0.3926, IoU.column: 0.5695, IoU.signboard: 0.4168, IoU.chest of drawers: 0.4281, IoU.counter: 0.4306, IoU.sand: 0.5663, IoU.sink: 0.8120, IoU.skyscraper: 0.4704, IoU.fireplace: 0.7388, IoU.refrigerator: 0.8629, IoU.grandstand: 0.5089, IoU.path: 0.3252, IoU.stairs: 0.3077, IoU.runway: 0.7434, IoU.case: 0.6303, IoU.pool table: 0.9516, IoU.pillow: 0.7002, IoU.screen door: 0.7966, IoU.stairway: 0.4316, IoU.river: 0.0923, IoU.bridge: 0.7560, IoU.bookcase: 0.5650, IoU.blind: 0.4671, IoU.coffee table: 0.6233, IoU.toilet: 0.9126, IoU.flower: 0.4766, IoU.book: 0.5679, IoU.hill: 0.1244, IoU.bench: 0.5645, IoU.countertop: 0.6568, IoU.stove: 0.8787, IoU.palm: 0.5564, IoU.kitchen island: 0.6949, IoU.computer: 0.8006, IoU.swivel chair: 0.4732, IoU.boat: 0.8027, IoU.bar: 0.6500, IoU.arcade machine: 0.7877, IoU.hovel: 0.1408, IoU.bus: 0.9265, IoU.towel: 0.8056, IoU.light: 0.6308, IoU.truck: 0.5326, IoU.tower: 0.3365, IoU.chandelier: 0.7454, IoU.awning: 0.4063, IoU.streetlight: 0.3899, IoU.booth: 0.4070, IoU.television receiver: 0.8248, IoU.airplane: 0.8941, IoU.dirt track: 0.0542, IoU.apparel: 0.6656, IoU.pole: 0.2263, IoU.land: 0.0315, IoU.bannister: 0.2100, IoU.escalator: 0.6703, IoU.ottoman: 0.4991, IoU.bottle: 0.4571, IoU.buffet: 0.5155, IoU.poster: 0.3185, IoU.stage: 0.2771, IoU.van: 0.4883, IoU.ship: 0.7965, IoU.fountain: 0.3789, IoU.conveyer belt: 0.8093, IoU.canopy: 0.5377, IoU.washer: 0.8320, IoU.plaything: 0.4363, IoU.swimming pool: 0.5407, IoU.stool: 0.5263, IoU.barrel: 0.7660, IoU.basket: 0.4152, IoU.waterfall: 0.4986, IoU.tent: 0.9264, IoU.bag: 0.2826, IoU.minibike: 0.7780, IoU.cradle: 0.8727, IoU.oven: 0.6377, IoU.ball: 0.5679, IoU.food: 0.6466, IoU.step: 0.1606, IoU.tank: 0.8224, IoU.trade name: 0.2435, IoU.microwave: 0.9032, IoU.pot: 0.6017, IoU.animal: 0.6249, IoU.bicycle: 0.6057, IoU.lake: 0.5062, IoU.dishwasher: 0.7609, IoU.screen: 0.6216, IoU.blanket: 0.3904, IoU.sculpture: 0.7527, IoU.hood: 0.6519, IoU.sconce: 0.6433, IoU.vase: 0.4877, IoU.traffic light: 0.3762, IoU.tray: 0.2709, IoU.ashcan: 0.5275, IoU.fan: 0.7209, IoU.pier: 0.4489, IoU.crt screen: 0.0201, IoU.plate: 0.6307, IoU.monitor: 0.5591, IoU.bulletin board: 0.5663, IoU.shower: 0.1366, IoU.radiator: 0.6949, IoU.glass: 0.2170, IoU.clock: 0.5213, IoU.flag: 0.7174, Acc.wall: 0.9043, Acc.building: 0.9358, Acc.sky: 0.9778, Acc.floor: 0.9240, Acc.tree: 0.9016, Acc.ceiling: 0.9477, Acc.road: 0.9056, Acc.bed : 0.9706, Acc.windowpane: 0.8239, Acc.grass: 0.8295, Acc.cabinet: 0.7800, Acc.sidewalk: 0.8564, Acc.person: 0.9471, Acc.earth: 0.5720, Acc.door: 0.7570, Acc.table: 0.8227, Acc.mountain: 0.7295, Acc.plant: 0.6754, Acc.curtain: 0.8944, Acc.chair: 0.8100, Acc.car: 0.9425, Acc.water: 0.7784, Acc.painting: 0.9098, Acc.sofa: 0.9282, Acc.shelf: 0.7104, Acc.house: 0.7151, Acc.sea: 0.8102, Acc.mirror: 0.8446, Acc.rug: 0.7526, Acc.field: 0.5605, Acc.armchair: 0.7570, Acc.seat: 0.8935, Acc.fence: 0.6780, Acc.desk: 0.7848, Acc.rock: 0.8297, Acc.wardrobe: 0.7393, Acc.lamp: 0.8761, Acc.bathtub: 0.8942, Acc.railing: 0.6582, Acc.cushion: 0.8326, Acc.base: 0.5486, Acc.box: 0.5071, Acc.column: 0.6792, Acc.signboard: 0.5650, Acc.chest of drawers: 0.6311, Acc.counter: 0.5165, Acc.sand: 0.8626, Acc.sink: 0.8640, Acc.skyscraper: 0.5998, Acc.fireplace: 0.9399, Acc.refrigerator: 0.9459, Acc.grandstand: 0.8198, Acc.path: 0.4472, Acc.stairs: 0.3943, Acc.runway: 0.9659, Acc.case: 0.8314, Acc.pool table: 0.9777, Acc.pillow: 0.8081, Acc.screen door: 0.8167, Acc.stairway: 0.5938, Acc.river: 0.2073, Acc.bridge: 0.8713, Acc.bookcase: 0.6702, Acc.blind: 0.5239, Acc.coffee table: 0.8676, Acc.toilet: 0.9415, Acc.flower: 0.6077, Acc.book: 0.8054, Acc.hill: 0.2147, Acc.bench: 0.6383, Acc.countertop: 0.8347, Acc.stove: 0.9298, Acc.palm: 0.8166, Acc.kitchen island: 0.8656, Acc.computer: 0.9110, Acc.swivel chair: 0.6925, Acc.boat: 0.9135, Acc.bar: 0.8822, Acc.arcade machine: 0.8321, Acc.hovel: 0.1577, Acc.bus: 0.9686, Acc.towel: 0.8699, Acc.light: 0.7196, Acc.truck: 0.6242, Acc.tower: 0.5771, Acc.chandelier: 0.8609, Acc.awning: 0.4802, Acc.streetlight: 0.5066, Acc.booth: 0.5838, Acc.television receiver: 0.8857, Acc.airplane: 0.9611, Acc.dirt track: 0.2726, Acc.apparel: 0.8748, Acc.pole: 0.3773, Acc.land: 0.0432, Acc.bannister: 0.2655, Acc.escalator: 0.8647, Acc.ottoman: 0.6510, Acc.bottle: 0.6158, Acc.buffet: 0.5946, Acc.poster: 0.3791, Acc.stage: 0.4801, Acc.van: 0.7344, Acc.ship: 0.9134, Acc.fountain: 0.3866, Acc.conveyer belt: 0.9424, Acc.canopy: 0.7474, Acc.washer: 0.8820, Acc.plaything: 0.6331, Acc.swimming pool: 0.7868, Acc.stool: 0.7227, Acc.barrel: 0.9648, Acc.basket: 0.5759, Acc.waterfall: 0.6439, Acc.tent: 0.9852, Acc.bag: 0.3259, Acc.minibike: 0.9044, Acc.cradle: 0.9741, Acc.oven: 0.7744, Acc.ball: 0.6645, Acc.food: 0.7752, Acc.step: 0.1821, Acc.tank: 0.9248, Acc.trade name: 0.2855, Acc.microwave: 0.9675, Acc.pot: 0.7032, Acc.animal: 0.6368, Acc.bicycle: 0.7815, Acc.lake: 0.6575, Acc.dishwasher: 0.8488, Acc.screen: 0.9270, Acc.blanket: 0.4472, Acc.sculpture: 0.8848, Acc.hood: 0.7729, Acc.sconce: 0.7319, Acc.vase: 0.6613, Acc.traffic light: 0.6154, Acc.tray: 0.3559, Acc.ashcan: 0.6684, Acc.fan: 0.8289, Acc.pier: 0.4998, Acc.crt screen: 0.0340, Acc.plate: 0.8054, Acc.monitor: 0.6785, Acc.bulletin board: 0.6621, Acc.shower: 0.1623, Acc.radiator: 0.8131, Acc.glass: 0.2326, Acc.clock: 0.6131, Acc.flag: 0.8099 +2024-06-18 01:26:50,909 - mmseg - INFO - Iter [78050/80000] lr: 9.755e-07, eta: 1:36:59, time: 3.536, data_time: 1.918, memory: 71386, decode.loss_ce: 0.1199, decode.acc_seg: 94.5104, aux.loss_ce: 0.0521, aux.acc_seg: 94.0595, loss: 0.1720 +2024-06-18 01:28:12,200 - mmseg - INFO - Iter [78100/80000] lr: 9.505e-07, eta: 1:33:58, time: 1.626, data_time: 0.011, memory: 71386, decode.loss_ce: 0.1233, decode.acc_seg: 94.3908, aux.loss_ce: 0.0534, aux.acc_seg: 93.9464, loss: 0.1767 +2024-06-18 01:29:33,465 - mmseg - INFO - Iter [78150/80000] lr: 9.255e-07, eta: 1:31:00, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1261, decode.acc_seg: 94.3678, aux.loss_ce: 0.0546, aux.acc_seg: 93.9430, loss: 0.1806 +2024-06-18 01:30:54,747 - mmseg - INFO - Iter [78200/80000] lr: 9.005e-07, eta: 1:28:04, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1268, decode.acc_seg: 94.3037, aux.loss_ce: 0.0550, aux.acc_seg: 93.8988, loss: 0.1818 +2024-06-18 01:32:16,240 - mmseg - INFO - Iter [78250/80000] lr: 8.755e-07, eta: 1:25:10, time: 1.630, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1210, decode.acc_seg: 94.6425, aux.loss_ce: 0.0523, aux.acc_seg: 94.2385, loss: 0.1733 +2024-06-18 01:33:37,555 - mmseg - INFO - Iter [78300/80000] lr: 8.505e-07, eta: 1:22:19, time: 1.626, data_time: 0.011, memory: 71386, decode.loss_ce: 0.1295, decode.acc_seg: 94.2132, aux.loss_ce: 0.0561, aux.acc_seg: 93.7728, loss: 0.1856 +2024-06-18 01:34:58,898 - mmseg - INFO - Iter [78350/80000] lr: 8.255e-07, eta: 1:19:29, time: 1.627, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1225, decode.acc_seg: 94.4651, aux.loss_ce: 0.0527, aux.acc_seg: 94.0495, loss: 0.1752 +2024-06-18 01:36:20,184 - mmseg - INFO - Iter [78400/80000] lr: 8.005e-07, eta: 1:16:42, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1264, decode.acc_seg: 94.3363, aux.loss_ce: 0.0549, aux.acc_seg: 93.8829, loss: 0.1813 +2024-06-18 01:37:41,487 - mmseg - INFO - Iter [78450/80000] lr: 7.755e-07, eta: 1:13:56, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1222, decode.acc_seg: 94.3580, aux.loss_ce: 0.0532, aux.acc_seg: 93.8798, loss: 0.1753 +2024-06-18 01:39:02,792 - mmseg - INFO - Iter [78500/80000] lr: 7.505e-07, eta: 1:11:13, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1271, decode.acc_seg: 94.3550, aux.loss_ce: 0.0550, aux.acc_seg: 93.9237, loss: 0.1821 +2024-06-18 01:40:24,030 - mmseg - INFO - Iter [78550/80000] lr: 7.255e-07, eta: 1:08:31, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1279, decode.acc_seg: 94.2091, aux.loss_ce: 0.0552, aux.acc_seg: 93.7752, loss: 0.1831 +2024-06-18 01:41:45,315 - mmseg - INFO - Iter [78600/80000] lr: 7.005e-07, eta: 1:05:50, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1216, decode.acc_seg: 94.4740, aux.loss_ce: 0.0528, aux.acc_seg: 94.0713, loss: 0.1744 +2024-06-18 01:43:06,732 - mmseg - INFO - Iter [78650/80000] lr: 6.755e-07, eta: 1:03:12, time: 1.628, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1237, decode.acc_seg: 94.4173, aux.loss_ce: 0.0532, aux.acc_seg: 93.9894, loss: 0.1769 +2024-06-18 01:44:27,992 - mmseg - INFO - Iter [78700/80000] lr: 6.505e-07, eta: 1:00:35, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1241, decode.acc_seg: 94.4936, aux.loss_ce: 0.0538, aux.acc_seg: 94.1037, loss: 0.1779 +2024-06-18 01:45:49,333 - mmseg - INFO - Iter [78750/80000] lr: 6.255e-07, eta: 0:58:00, time: 1.627, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1252, decode.acc_seg: 94.1550, aux.loss_ce: 0.0546, aux.acc_seg: 93.6804, loss: 0.1798 +2024-06-18 01:47:10,647 - mmseg - INFO - Iter [78800/80000] lr: 6.005e-07, eta: 0:55:26, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1326, decode.acc_seg: 94.2280, aux.loss_ce: 0.0569, aux.acc_seg: 93.8034, loss: 0.1895 +2024-06-18 01:48:32,097 - mmseg - INFO - Iter [78850/80000] lr: 5.755e-07, eta: 0:52:54, time: 1.629, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1290, decode.acc_seg: 94.3210, aux.loss_ce: 0.0557, aux.acc_seg: 93.8470, loss: 0.1847 +2024-06-18 01:49:53,322 - mmseg - INFO - Iter [78900/80000] lr: 5.505e-07, eta: 0:50:23, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1249, decode.acc_seg: 94.4635, aux.loss_ce: 0.0543, aux.acc_seg: 94.0746, loss: 0.1792 +2024-06-18 01:51:14,597 - mmseg - INFO - Iter [78950/80000] lr: 5.255e-07, eta: 0:47:54, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1257, decode.acc_seg: 94.3452, aux.loss_ce: 0.0546, aux.acc_seg: 93.8674, loss: 0.1803 +2024-06-18 01:52:35,964 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 01:52:35,965 - mmseg - INFO - Iter [79000/80000] lr: 5.005e-07, eta: 0:45:26, time: 1.627, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1210, decode.acc_seg: 94.5867, aux.loss_ce: 0.0526, aux.acc_seg: 94.1759, loss: 0.1736 +2024-06-18 01:54:18,428 - mmseg - INFO - per class results: +2024-06-18 01:54:18,434 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.02 | 90.49 | +| building | 85.9 | 93.53 | +| sky | 95.05 | 97.67 | +| floor | 85.45 | 92.16 | +| tree | 78.49 | 90.49 | +| ceiling | 88.03 | 94.79 | +| road | 86.27 | 90.62 | +| bed | 92.81 | 97.01 | +| windowpane | 67.74 | 82.08 | +| grass | 70.13 | 82.62 | +| cabinet | 68.05 | 78.23 | +| sidewalk | 70.8 | 85.44 | +| person | 86.36 | 94.98 | +| earth | 42.26 | 56.43 | +| door | 60.69 | 75.7 | +| table | 70.66 | 81.95 | +| mountain | 62.98 | 73.26 | +| plant | 58.24 | 68.81 | +| curtain | 78.76 | 89.18 | +| chair | 68.89 | 81.67 | +| car | 88.98 | 94.02 | +| water | 62.73 | 77.72 | +| painting | 77.82 | 90.78 | +| sofa | 84.26 | 93.06 | +| shelf | 54.03 | 72.26 | +| house | 56.97 | 70.32 | +| sea | 72.95 | 81.13 | +| mirror | 77.55 | 84.29 | +| rug | 66.0 | 75.81 | +| field | 34.78 | 57.77 | +| armchair | 61.54 | 75.08 | +| seat | 68.15 | 89.22 | +| fence | 54.23 | 68.24 | +| desk | 60.01 | 79.2 | +| rock | 55.28 | 82.18 | +| wardrobe | 54.05 | 74.92 | +| lamp | 77.38 | 87.69 | +| bathtub | 87.04 | 89.68 | +| railing | 46.28 | 64.81 | +| cushion | 74.01 | 84.22 | +| base | 37.43 | 55.31 | +| box | 38.64 | 49.34 | +| column | 56.95 | 68.93 | +| signboard | 41.93 | 57.39 | +| chest of drawers | 42.34 | 62.44 | +| counter | 43.34 | 52.49 | +| sand | 56.51 | 86.32 | +| sink | 81.05 | 85.9 | +| skyscraper | 46.35 | 58.23 | +| fireplace | 73.9 | 94.12 | +| refrigerator | 86.69 | 94.35 | +| grandstand | 50.86 | 80.79 | +| path | 32.2 | 43.07 | +| stairs | 30.81 | 39.17 | +| runway | 74.18 | 96.46 | +| case | 62.75 | 83.4 | +| pool table | 95.11 | 97.87 | +| pillow | 70.25 | 80.52 | +| screen door | 79.86 | 81.82 | +| stairway | 42.81 | 58.15 | +| river | 9.26 | 20.79 | +| bridge | 74.29 | 87.71 | +| bookcase | 57.87 | 68.99 | +| blind | 45.79 | 51.14 | +| coffee table | 62.08 | 86.94 | +| toilet | 91.26 | 94.04 | +| flower | 48.02 | 59.53 | +| book | 57.64 | 77.77 | +| hill | 12.67 | 22.31 | +| bench | 56.26 | 63.74 | +| countertop | 65.8 | 84.98 | +| stove | 87.97 | 92.64 | +| palm | 55.66 | 82.84 | +| kitchen island | 69.36 | 86.96 | +| computer | 79.28 | 91.46 | +| swivel chair | 47.28 | 70.22 | +| boat | 80.72 | 91.02 | +| bar | 65.02 | 87.88 | +| arcade machine | 78.87 | 82.88 | +| hovel | 14.14 | 15.7 | +| bus | 92.58 | 96.93 | +| towel | 80.49 | 86.94 | +| light | 63.27 | 72.73 | +| truck | 53.22 | 62.49 | +| tower | 35.7 | 61.54 | +| chandelier | 75.43 | 88.1 | +| awning | 40.54 | 47.72 | +| streetlight | 40.17 | 53.78 | +| booth | 42.18 | 57.47 | +| television receiver | 82.4 | 88.23 | +| airplane | 89.24 | 96.13 | +| dirt track | 5.61 | 28.69 | +| apparel | 66.76 | 84.88 | +| pole | 22.88 | 37.55 | +| land | 3.23 | 4.45 | +| bannister | 21.05 | 26.76 | +| escalator | 67.35 | 86.3 | +| ottoman | 50.8 | 66.28 | +| bottle | 45.28 | 58.65 | +| buffet | 51.58 | 59.75 | +| poster | 31.64 | 36.73 | +| stage | 27.8 | 47.79 | +| van | 49.04 | 73.52 | +| ship | 80.2 | 92.56 | +| fountain | 38.15 | 39.19 | +| conveyer belt | 81.47 | 93.93 | +| canopy | 53.71 | 74.67 | +| washer | 83.14 | 88.15 | +| plaything | 43.88 | 62.52 | +| swimming pool | 53.98 | 78.59 | +| stool | 53.25 | 72.6 | +| barrel | 76.48 | 96.28 | +| basket | 41.37 | 57.28 | +| waterfall | 49.49 | 64.58 | +| tent | 93.13 | 98.46 | +| bag | 27.69 | 31.44 | +| minibike | 77.94 | 90.5 | +| cradle | 86.21 | 97.51 | +| oven | 63.17 | 75.95 | +| ball | 55.52 | 63.13 | +| food | 63.95 | 76.29 | +| step | 15.94 | 18.03 | +| tank | 80.64 | 92.54 | +| trade name | 25.48 | 30.49 | +| microwave | 90.08 | 96.54 | +| pot | 60.36 | 71.08 | +| animal | 62.16 | 63.26 | +| bicycle | 60.53 | 77.62 | +| lake | 49.95 | 64.4 | +| dishwasher | 76.41 | 84.61 | +| screen | 62.17 | 92.38 | +| blanket | 38.5 | 43.94 | +| sculpture | 75.07 | 88.65 | +| hood | 65.09 | 76.9 | +| sconce | 64.52 | 73.7 | +| vase | 48.65 | 66.94 | +| traffic light | 37.23 | 64.47 | +| tray | 26.67 | 34.91 | +| ashcan | 52.86 | 67.48 | +| fan | 72.41 | 83.79 | +| pier | 45.21 | 50.64 | +| crt screen | 2.06 | 3.39 | +| plate | 63.23 | 79.32 | +| monitor | 57.68 | 68.64 | +| bulletin board | 56.61 | 66.46 | +| shower | 13.83 | 16.29 | +| radiator | 69.63 | 81.12 | +| glass | 21.88 | 23.66 | +| clock | 51.72 | 61.38 | +| flag | 71.82 | 81.37 | ++---------------------+-------+-------+ +2024-06-18 01:54:18,434 - mmseg - INFO - Summary: +2024-06-18 01:54:18,435 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.78 | 59.31 | 71.57 | ++-------+-------+-------+ +2024-06-18 01:54:18,435 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 01:54:18,436 - mmseg - INFO - Iter(val) [250] aAcc: 0.8678, mIoU: 0.5931, mAcc: 0.7157, IoU.wall: 0.8302, IoU.building: 0.8590, IoU.sky: 0.9505, IoU.floor: 0.8545, IoU.tree: 0.7849, IoU.ceiling: 0.8803, IoU.road: 0.8627, IoU.bed : 0.9281, IoU.windowpane: 0.6774, IoU.grass: 0.7013, IoU.cabinet: 0.6805, IoU.sidewalk: 0.7080, IoU.person: 0.8636, IoU.earth: 0.4226, IoU.door: 0.6069, IoU.table: 0.7066, IoU.mountain: 0.6298, IoU.plant: 0.5824, IoU.curtain: 0.7876, IoU.chair: 0.6889, IoU.car: 0.8898, IoU.water: 0.6273, IoU.painting: 0.7782, IoU.sofa: 0.8426, IoU.shelf: 0.5403, IoU.house: 0.5697, IoU.sea: 0.7295, IoU.mirror: 0.7755, IoU.rug: 0.6600, IoU.field: 0.3478, IoU.armchair: 0.6154, IoU.seat: 0.6815, IoU.fence: 0.5423, IoU.desk: 0.6001, IoU.rock: 0.5528, IoU.wardrobe: 0.5405, IoU.lamp: 0.7738, IoU.bathtub: 0.8704, IoU.railing: 0.4628, IoU.cushion: 0.7401, IoU.base: 0.3743, IoU.box: 0.3864, IoU.column: 0.5695, IoU.signboard: 0.4193, IoU.chest of drawers: 0.4234, IoU.counter: 0.4334, IoU.sand: 0.5651, IoU.sink: 0.8105, IoU.skyscraper: 0.4635, IoU.fireplace: 0.7390, IoU.refrigerator: 0.8669, IoU.grandstand: 0.5086, IoU.path: 0.3220, IoU.stairs: 0.3081, IoU.runway: 0.7418, IoU.case: 0.6275, IoU.pool table: 0.9511, IoU.pillow: 0.7025, IoU.screen door: 0.7986, IoU.stairway: 0.4281, IoU.river: 0.0926, IoU.bridge: 0.7429, IoU.bookcase: 0.5787, IoU.blind: 0.4579, IoU.coffee table: 0.6208, IoU.toilet: 0.9126, IoU.flower: 0.4802, IoU.book: 0.5764, IoU.hill: 0.1267, IoU.bench: 0.5626, IoU.countertop: 0.6580, IoU.stove: 0.8797, IoU.palm: 0.5566, IoU.kitchen island: 0.6936, IoU.computer: 0.7928, IoU.swivel chair: 0.4728, IoU.boat: 0.8072, IoU.bar: 0.6502, IoU.arcade machine: 0.7887, IoU.hovel: 0.1414, IoU.bus: 0.9258, IoU.towel: 0.8049, IoU.light: 0.6327, IoU.truck: 0.5322, IoU.tower: 0.3570, IoU.chandelier: 0.7543, IoU.awning: 0.4054, IoU.streetlight: 0.4017, IoU.booth: 0.4218, IoU.television receiver: 0.8240, IoU.airplane: 0.8924, IoU.dirt track: 0.0561, IoU.apparel: 0.6676, IoU.pole: 0.2288, IoU.land: 0.0323, IoU.bannister: 0.2105, IoU.escalator: 0.6735, IoU.ottoman: 0.5080, IoU.bottle: 0.4528, IoU.buffet: 0.5158, IoU.poster: 0.3164, IoU.stage: 0.2780, IoU.van: 0.4904, IoU.ship: 0.8020, IoU.fountain: 0.3815, IoU.conveyer belt: 0.8147, IoU.canopy: 0.5371, IoU.washer: 0.8314, IoU.plaything: 0.4388, IoU.swimming pool: 0.5398, IoU.stool: 0.5325, IoU.barrel: 0.7648, IoU.basket: 0.4137, IoU.waterfall: 0.4949, IoU.tent: 0.9313, IoU.bag: 0.2769, IoU.minibike: 0.7794, IoU.cradle: 0.8621, IoU.oven: 0.6317, IoU.ball: 0.5552, IoU.food: 0.6395, IoU.step: 0.1594, IoU.tank: 0.8064, IoU.trade name: 0.2548, IoU.microwave: 0.9008, IoU.pot: 0.6036, IoU.animal: 0.6216, IoU.bicycle: 0.6053, IoU.lake: 0.4995, IoU.dishwasher: 0.7641, IoU.screen: 0.6217, IoU.blanket: 0.3850, IoU.sculpture: 0.7507, IoU.hood: 0.6509, IoU.sconce: 0.6452, IoU.vase: 0.4865, IoU.traffic light: 0.3723, IoU.tray: 0.2667, IoU.ashcan: 0.5286, IoU.fan: 0.7241, IoU.pier: 0.4521, IoU.crt screen: 0.0206, IoU.plate: 0.6323, IoU.monitor: 0.5768, IoU.bulletin board: 0.5661, IoU.shower: 0.1383, IoU.radiator: 0.6963, IoU.glass: 0.2188, IoU.clock: 0.5172, IoU.flag: 0.7182, Acc.wall: 0.9049, Acc.building: 0.9353, Acc.sky: 0.9767, Acc.floor: 0.9216, Acc.tree: 0.9049, Acc.ceiling: 0.9479, Acc.road: 0.9062, Acc.bed : 0.9701, Acc.windowpane: 0.8208, Acc.grass: 0.8262, Acc.cabinet: 0.7823, Acc.sidewalk: 0.8544, Acc.person: 0.9498, Acc.earth: 0.5643, Acc.door: 0.7570, Acc.table: 0.8195, Acc.mountain: 0.7326, Acc.plant: 0.6881, Acc.curtain: 0.8918, Acc.chair: 0.8167, Acc.car: 0.9402, Acc.water: 0.7772, Acc.painting: 0.9078, Acc.sofa: 0.9306, Acc.shelf: 0.7226, Acc.house: 0.7032, Acc.sea: 0.8113, Acc.mirror: 0.8429, Acc.rug: 0.7581, Acc.field: 0.5777, Acc.armchair: 0.7508, Acc.seat: 0.8922, Acc.fence: 0.6824, Acc.desk: 0.7920, Acc.rock: 0.8218, Acc.wardrobe: 0.7492, Acc.lamp: 0.8769, Acc.bathtub: 0.8968, Acc.railing: 0.6481, Acc.cushion: 0.8422, Acc.base: 0.5531, Acc.box: 0.4934, Acc.column: 0.6893, Acc.signboard: 0.5739, Acc.chest of drawers: 0.6244, Acc.counter: 0.5249, Acc.sand: 0.8632, Acc.sink: 0.8590, Acc.skyscraper: 0.5823, Acc.fireplace: 0.9412, Acc.refrigerator: 0.9435, Acc.grandstand: 0.8079, Acc.path: 0.4307, Acc.stairs: 0.3917, Acc.runway: 0.9646, Acc.case: 0.8340, Acc.pool table: 0.9787, Acc.pillow: 0.8052, Acc.screen door: 0.8182, Acc.stairway: 0.5815, Acc.river: 0.2079, Acc.bridge: 0.8771, Acc.bookcase: 0.6899, Acc.blind: 0.5114, Acc.coffee table: 0.8694, Acc.toilet: 0.9404, Acc.flower: 0.5953, Acc.book: 0.7777, Acc.hill: 0.2231, Acc.bench: 0.6374, Acc.countertop: 0.8498, Acc.stove: 0.9264, Acc.palm: 0.8284, Acc.kitchen island: 0.8696, Acc.computer: 0.9146, Acc.swivel chair: 0.7022, Acc.boat: 0.9102, Acc.bar: 0.8788, Acc.arcade machine: 0.8288, Acc.hovel: 0.1570, Acc.bus: 0.9693, Acc.towel: 0.8694, Acc.light: 0.7273, Acc.truck: 0.6249, Acc.tower: 0.6154, Acc.chandelier: 0.8810, Acc.awning: 0.4772, Acc.streetlight: 0.5378, Acc.booth: 0.5747, Acc.television receiver: 0.8823, Acc.airplane: 0.9613, Acc.dirt track: 0.2869, Acc.apparel: 0.8488, Acc.pole: 0.3755, Acc.land: 0.0445, Acc.bannister: 0.2676, Acc.escalator: 0.8630, Acc.ottoman: 0.6628, Acc.bottle: 0.5865, Acc.buffet: 0.5975, Acc.poster: 0.3673, Acc.stage: 0.4779, Acc.van: 0.7352, Acc.ship: 0.9256, Acc.fountain: 0.3919, Acc.conveyer belt: 0.9393, Acc.canopy: 0.7467, Acc.washer: 0.8815, Acc.plaything: 0.6252, Acc.swimming pool: 0.7859, Acc.stool: 0.7260, Acc.barrel: 0.9628, Acc.basket: 0.5728, Acc.waterfall: 0.6458, Acc.tent: 0.9846, Acc.bag: 0.3144, Acc.minibike: 0.9050, Acc.cradle: 0.9751, Acc.oven: 0.7595, Acc.ball: 0.6313, Acc.food: 0.7629, Acc.step: 0.1803, Acc.tank: 0.9254, Acc.trade name: 0.3049, Acc.microwave: 0.9654, Acc.pot: 0.7108, Acc.animal: 0.6326, Acc.bicycle: 0.7762, Acc.lake: 0.6440, Acc.dishwasher: 0.8461, Acc.screen: 0.9238, Acc.blanket: 0.4394, Acc.sculpture: 0.8865, Acc.hood: 0.7690, Acc.sconce: 0.7370, Acc.vase: 0.6694, Acc.traffic light: 0.6447, Acc.tray: 0.3491, Acc.ashcan: 0.6748, Acc.fan: 0.8379, Acc.pier: 0.5064, Acc.crt screen: 0.0339, Acc.plate: 0.7932, Acc.monitor: 0.6864, Acc.bulletin board: 0.6646, Acc.shower: 0.1629, Acc.radiator: 0.8112, Acc.glass: 0.2366, Acc.clock: 0.6138, Acc.flag: 0.8137 +2024-06-18 01:55:40,631 - mmseg - INFO - Iter [79050/80000] lr: 4.755e-07, eta: 0:43:19, time: 3.693, data_time: 2.067, memory: 71386, decode.loss_ce: 0.1292, decode.acc_seg: 94.0853, aux.loss_ce: 0.0556, aux.acc_seg: 93.6871, loss: 0.1848 +2024-06-18 01:57:04,704 - mmseg - INFO - Iter [79100/80000] lr: 4.505e-07, eta: 0:40:53, time: 1.681, data_time: 0.060, memory: 71386, decode.loss_ce: 0.1243, decode.acc_seg: 94.4106, aux.loss_ce: 0.0540, aux.acc_seg: 93.9091, loss: 0.1783 +2024-06-18 01:58:26,004 - mmseg - INFO - Iter [79150/80000] lr: 4.255e-07, eta: 0:38:27, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1222, decode.acc_seg: 94.4715, aux.loss_ce: 0.0526, aux.acc_seg: 94.1003, loss: 0.1748 +2024-06-18 01:59:47,348 - mmseg - INFO - Iter [79200/80000] lr: 4.005e-07, eta: 0:36:03, time: 1.627, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1276, decode.acc_seg: 94.3322, aux.loss_ce: 0.0550, aux.acc_seg: 93.8837, loss: 0.1826 +2024-06-18 02:01:08,563 - mmseg - INFO - Iter [79250/80000] lr: 3.755e-07, eta: 0:33:40, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1271, decode.acc_seg: 94.3350, aux.loss_ce: 0.0550, aux.acc_seg: 93.9412, loss: 0.1821 +2024-06-18 02:02:29,978 - mmseg - INFO - Iter [79300/80000] lr: 3.505e-07, eta: 0:31:18, time: 1.628, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1216, decode.acc_seg: 94.4775, aux.loss_ce: 0.0528, aux.acc_seg: 94.0368, loss: 0.1744 +2024-06-18 02:03:51,272 - mmseg - INFO - Iter [79350/80000] lr: 3.255e-07, eta: 0:28:58, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1261, decode.acc_seg: 94.2600, aux.loss_ce: 0.0549, aux.acc_seg: 93.7677, loss: 0.1810 +2024-06-18 02:05:12,745 - mmseg - INFO - Iter [79400/80000] lr: 3.005e-07, eta: 0:26:38, time: 1.629, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1218, decode.acc_seg: 94.4678, aux.loss_ce: 0.0529, aux.acc_seg: 94.0506, loss: 0.1746 +2024-06-18 02:06:33,980 - mmseg - INFO - Iter [79450/80000] lr: 2.755e-07, eta: 0:24:20, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1270, decode.acc_seg: 94.2889, aux.loss_ce: 0.0548, aux.acc_seg: 93.8923, loss: 0.1818 +2024-06-18 02:07:55,267 - mmseg - INFO - Iter [79500/80000] lr: 2.505e-07, eta: 0:22:02, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1220, decode.acc_seg: 94.2813, aux.loss_ce: 0.0530, aux.acc_seg: 93.8283, loss: 0.1750 +2024-06-18 02:09:16,541 - mmseg - INFO - Iter [79550/80000] lr: 2.255e-07, eta: 0:19:46, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1292, decode.acc_seg: 94.3705, aux.loss_ce: 0.0556, aux.acc_seg: 93.9267, loss: 0.1848 +2024-06-18 02:10:38,166 - mmseg - INFO - Iter [79600/80000] lr: 2.005e-07, eta: 0:17:31, time: 1.632, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1158, decode.acc_seg: 94.8225, aux.loss_ce: 0.0504, aux.acc_seg: 94.3991, loss: 0.1662 +2024-06-18 02:11:59,349 - mmseg - INFO - Iter [79650/80000] lr: 1.755e-07, eta: 0:15:16, time: 1.624, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1264, decode.acc_seg: 94.4253, aux.loss_ce: 0.0551, aux.acc_seg: 93.9254, loss: 0.1816 +2024-06-18 02:13:20,620 - mmseg - INFO - Iter [79700/80000] lr: 1.505e-07, eta: 0:13:03, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1283, decode.acc_seg: 94.2989, aux.loss_ce: 0.0556, aux.acc_seg: 93.9224, loss: 0.1839 +2024-06-18 02:14:41,921 - mmseg - INFO - Iter [79750/80000] lr: 1.255e-07, eta: 0:10:50, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1211, decode.acc_seg: 94.5755, aux.loss_ce: 0.0524, aux.acc_seg: 94.1669, loss: 0.1735 +2024-06-18 02:16:03,218 - mmseg - INFO - Iter [79800/80000] lr: 1.005e-07, eta: 0:08:38, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1236, decode.acc_seg: 94.4573, aux.loss_ce: 0.0536, aux.acc_seg: 94.0073, loss: 0.1772 +2024-06-18 02:17:24,469 - mmseg - INFO - Iter [79850/80000] lr: 7.550e-08, eta: 0:06:27, time: 1.625, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1248, decode.acc_seg: 94.2552, aux.loss_ce: 0.0550, aux.acc_seg: 93.7455, loss: 0.1798 +2024-06-18 02:18:45,791 - mmseg - INFO - Iter [79900/80000] lr: 5.050e-08, eta: 0:04:17, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1203, decode.acc_seg: 94.3664, aux.loss_ce: 0.0524, aux.acc_seg: 93.8795, loss: 0.1727 +2024-06-18 02:20:07,088 - mmseg - INFO - Iter [79950/80000] lr: 2.550e-08, eta: 0:02:08, time: 1.626, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1219, decode.acc_seg: 94.4450, aux.loss_ce: 0.0528, aux.acc_seg: 94.0182, loss: 0.1747 +2024-06-18 02:21:28,409 - mmseg - INFO - Saving checkpoint at 80000 iterations +2024-06-18 02:22:54,161 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 02:22:54,161 - mmseg - INFO - Iter [80000/80000] lr: 5.000e-10, eta: 0:00:00, time: 3.341, data_time: 0.010, memory: 71386, decode.loss_ce: 0.1255, decode.acc_seg: 94.3726, aux.loss_ce: 0.0549, aux.acc_seg: 93.8754, loss: 0.1804 +2024-06-18 02:24:30,832 - mmseg - INFO - per class results: +2024-06-18 02:24:30,838 - mmseg - INFO - ++---------------------+-------+-------+ +| Class | IoU | Acc | ++---------------------+-------+-------+ +| wall | 83.05 | 90.53 | +| building | 85.93 | 93.56 | +| sky | 95.05 | 97.71 | +| floor | 85.37 | 92.1 | +| tree | 78.39 | 90.12 | +| ceiling | 88.03 | 94.81 | +| road | 86.24 | 90.74 | +| bed | 92.82 | 97.09 | +| windowpane | 67.78 | 81.96 | +| grass | 69.95 | 82.82 | +| cabinet | 67.95 | 77.7 | +| sidewalk | 70.77 | 85.33 | +| person | 86.5 | 94.66 | +| earth | 42.2 | 56.77 | +| door | 60.74 | 75.59 | +| table | 70.65 | 81.82 | +| mountain | 62.93 | 73.56 | +| plant | 57.99 | 68.18 | +| curtain | 78.87 | 89.19 | +| chair | 68.88 | 81.5 | +| car | 88.96 | 94.18 | +| water | 62.9 | 78.17 | +| painting | 78.02 | 90.87 | +| sofa | 84.54 | 92.96 | +| shelf | 54.39 | 73.04 | +| house | 56.89 | 70.74 | +| sea | 73.22 | 81.02 | +| mirror | 77.53 | 84.46 | +| rug | 65.97 | 75.7 | +| field | 34.63 | 56.91 | +| armchair | 61.81 | 75.95 | +| seat | 68.37 | 89.33 | +| fence | 54.3 | 68.03 | +| desk | 60.19 | 78.87 | +| rock | 55.27 | 81.64 | +| wardrobe | 54.55 | 75.62 | +| lamp | 77.39 | 87.76 | +| bathtub | 87.04 | 89.73 | +| railing | 46.32 | 65.28 | +| cushion | 73.83 | 83.83 | +| base | 37.28 | 54.36 | +| box | 38.97 | 50.18 | +| column | 57.13 | 68.76 | +| signboard | 41.7 | 57.19 | +| chest of drawers | 42.73 | 64.26 | +| counter | 43.31 | 52.27 | +| sand | 56.5 | 86.35 | +| sink | 81.07 | 86.08 | +| skyscraper | 46.32 | 58.56 | +| fireplace | 73.74 | 94.23 | +| refrigerator | 86.5 | 94.32 | +| grandstand | 51.03 | 80.96 | +| path | 32.3 | 43.45 | +| stairs | 31.37 | 40.06 | +| runway | 74.09 | 96.15 | +| case | 62.95 | 82.99 | +| pool table | 95.12 | 97.84 | +| pillow | 70.28 | 80.64 | +| screen door | 80.37 | 82.39 | +| stairway | 43.31 | 57.99 | +| river | 9.29 | 20.93 | +| bridge | 74.29 | 87.99 | +| bookcase | 57.16 | 67.18 | +| blind | 45.87 | 51.18 | +| coffee table | 61.81 | 87.07 | +| toilet | 91.25 | 94.04 | +| flower | 47.95 | 59.43 | +| book | 57.47 | 78.38 | +| hill | 12.65 | 22.46 | +| bench | 56.29 | 63.65 | +| countertop | 65.68 | 85.55 | +| stove | 88.07 | 92.98 | +| palm | 55.54 | 82.93 | +| kitchen island | 68.55 | 87.41 | +| computer | 79.66 | 91.25 | +| swivel chair | 47.27 | 69.98 | +| boat | 79.56 | 91.44 | +| bar | 65.26 | 87.89 | +| arcade machine | 78.85 | 82.95 | +| hovel | 14.11 | 15.72 | +| bus | 92.59 | 96.98 | +| towel | 80.47 | 87.46 | +| light | 63.2 | 72.59 | +| truck | 53.08 | 62.57 | +| tower | 36.07 | 62.31 | +| chandelier | 75.47 | 87.73 | +| awning | 40.65 | 48.19 | +| streetlight | 40.01 | 53.42 | +| booth | 42.46 | 57.47 | +| television receiver | 82.38 | 88.59 | +| airplane | 89.24 | 96.15 | +| dirt track | 5.64 | 28.82 | +| apparel | 65.87 | 85.38 | +| pole | 22.95 | 37.82 | +| land | 3.15 | 4.42 | +| bannister | 21.39 | 27.17 | +| escalator | 67.23 | 86.15 | +| ottoman | 51.13 | 67.13 | +| bottle | 45.63 | 58.91 | +| buffet | 51.28 | 59.27 | +| poster | 31.88 | 37.18 | +| stage | 28.47 | 47.64 | +| van | 49.18 | 72.76 | +| ship | 80.61 | 92.88 | +| fountain | 38.0 | 39.01 | +| conveyer belt | 81.21 | 93.88 | +| canopy | 53.85 | 75.14 | +| washer | 83.04 | 88.07 | +| plaything | 43.33 | 61.92 | +| swimming pool | 54.05 | 79.13 | +| stool | 53.4 | 73.05 | +| barrel | 77.17 | 95.86 | +| basket | 41.29 | 57.21 | +| waterfall | 49.51 | 64.75 | +| tent | 92.55 | 98.57 | +| bag | 27.95 | 31.86 | +| minibike | 77.82 | 90.6 | +| cradle | 86.51 | 97.62 | +| oven | 63.15 | 75.47 | +| ball | 54.85 | 61.23 | +| food | 64.17 | 76.38 | +| step | 15.94 | 18.06 | +| tank | 81.07 | 92.38 | +| trade name | 24.14 | 28.48 | +| microwave | 89.94 | 96.6 | +| pot | 60.47 | 70.95 | +| animal | 62.47 | 63.67 | +| bicycle | 60.81 | 77.48 | +| lake | 49.88 | 63.78 | +| dishwasher | 76.38 | 84.65 | +| screen | 61.87 | 92.62 | +| blanket | 38.96 | 44.74 | +| sculpture | 75.17 | 88.52 | +| hood | 65.16 | 77.12 | +| sconce | 64.56 | 73.87 | +| vase | 48.75 | 66.78 | +| traffic light | 37.31 | 64.3 | +| tray | 27.01 | 35.77 | +| ashcan | 52.86 | 67.36 | +| fan | 72.37 | 83.82 | +| pier | 45.84 | 52.09 | +| crt screen | 1.97 | 3.4 | +| plate | 63.39 | 79.32 | +| monitor | 55.39 | 66.07 | +| bulletin board | 56.34 | 65.34 | +| shower | 13.78 | 16.26 | +| radiator | 69.65 | 81.17 | +| glass | 21.57 | 23.13 | +| clock | 51.74 | 61.07 | +| flag | 71.81 | 81.28 | ++---------------------+-------+-------+ +2024-06-18 02:24:30,838 - mmseg - INFO - Summary: +2024-06-18 02:24:30,838 - mmseg - INFO - ++-------+-------+-------+ +| aAcc | mIoU | mAcc | ++-------+-------+-------+ +| 86.78 | 59.32 | 71.59 | ++-------+-------+-------+ +2024-06-18 02:24:30,839 - mmseg - INFO - Exp name: upernet_internvit_h6b_384_512_80k_ade20k_bs16_lr4e-5.py +2024-06-18 02:24:30,839 - mmseg - INFO - Iter(val) [250] aAcc: 0.8678, mIoU: 0.5932, mAcc: 0.7159, IoU.wall: 0.8305, IoU.building: 0.8593, IoU.sky: 0.9505, IoU.floor: 0.8537, IoU.tree: 0.7839, IoU.ceiling: 0.8803, IoU.road: 0.8624, IoU.bed : 0.9282, IoU.windowpane: 0.6778, IoU.grass: 0.6995, IoU.cabinet: 0.6795, IoU.sidewalk: 0.7077, IoU.person: 0.8650, IoU.earth: 0.4220, IoU.door: 0.6074, IoU.table: 0.7065, IoU.mountain: 0.6293, IoU.plant: 0.5799, IoU.curtain: 0.7887, IoU.chair: 0.6888, IoU.car: 0.8896, IoU.water: 0.6290, IoU.painting: 0.7802, IoU.sofa: 0.8454, IoU.shelf: 0.5439, IoU.house: 0.5689, IoU.sea: 0.7322, IoU.mirror: 0.7753, IoU.rug: 0.6597, IoU.field: 0.3463, IoU.armchair: 0.6181, IoU.seat: 0.6837, IoU.fence: 0.5430, IoU.desk: 0.6019, IoU.rock: 0.5527, IoU.wardrobe: 0.5455, IoU.lamp: 0.7739, IoU.bathtub: 0.8704, IoU.railing: 0.4632, IoU.cushion: 0.7383, IoU.base: 0.3728, IoU.box: 0.3897, IoU.column: 0.5713, IoU.signboard: 0.4170, IoU.chest of drawers: 0.4273, IoU.counter: 0.4331, IoU.sand: 0.5650, IoU.sink: 0.8107, IoU.skyscraper: 0.4632, IoU.fireplace: 0.7374, IoU.refrigerator: 0.8650, IoU.grandstand: 0.5103, IoU.path: 0.3230, IoU.stairs: 0.3137, IoU.runway: 0.7409, IoU.case: 0.6295, IoU.pool table: 0.9512, IoU.pillow: 0.7028, IoU.screen door: 0.8037, IoU.stairway: 0.4331, IoU.river: 0.0929, IoU.bridge: 0.7429, IoU.bookcase: 0.5716, IoU.blind: 0.4587, IoU.coffee table: 0.6181, IoU.toilet: 0.9125, IoU.flower: 0.4795, IoU.book: 0.5747, IoU.hill: 0.1265, IoU.bench: 0.5629, IoU.countertop: 0.6568, IoU.stove: 0.8807, IoU.palm: 0.5554, IoU.kitchen island: 0.6855, IoU.computer: 0.7966, IoU.swivel chair: 0.4727, IoU.boat: 0.7956, IoU.bar: 0.6526, IoU.arcade machine: 0.7885, IoU.hovel: 0.1411, IoU.bus: 0.9259, IoU.towel: 0.8047, IoU.light: 0.6320, IoU.truck: 0.5308, IoU.tower: 0.3607, IoU.chandelier: 0.7547, IoU.awning: 0.4065, IoU.streetlight: 0.4001, IoU.booth: 0.4246, IoU.television receiver: 0.8238, IoU.airplane: 0.8924, IoU.dirt track: 0.0564, IoU.apparel: 0.6587, IoU.pole: 0.2295, IoU.land: 0.0315, IoU.bannister: 0.2139, IoU.escalator: 0.6723, IoU.ottoman: 0.5113, IoU.bottle: 0.4563, IoU.buffet: 0.5128, IoU.poster: 0.3188, IoU.stage: 0.2847, IoU.van: 0.4918, IoU.ship: 0.8061, IoU.fountain: 0.3800, IoU.conveyer belt: 0.8121, IoU.canopy: 0.5385, IoU.washer: 0.8304, IoU.plaything: 0.4333, IoU.swimming pool: 0.5405, IoU.stool: 0.5340, IoU.barrel: 0.7717, IoU.basket: 0.4129, IoU.waterfall: 0.4951, IoU.tent: 0.9255, IoU.bag: 0.2795, IoU.minibike: 0.7782, IoU.cradle: 0.8651, IoU.oven: 0.6315, IoU.ball: 0.5485, IoU.food: 0.6417, IoU.step: 0.1594, IoU.tank: 0.8107, IoU.trade name: 0.2414, IoU.microwave: 0.8994, IoU.pot: 0.6047, IoU.animal: 0.6247, IoU.bicycle: 0.6081, IoU.lake: 0.4988, IoU.dishwasher: 0.7638, IoU.screen: 0.6187, IoU.blanket: 0.3896, IoU.sculpture: 0.7517, IoU.hood: 0.6516, IoU.sconce: 0.6456, IoU.vase: 0.4875, IoU.traffic light: 0.3731, IoU.tray: 0.2701, IoU.ashcan: 0.5286, IoU.fan: 0.7237, IoU.pier: 0.4584, IoU.crt screen: 0.0197, IoU.plate: 0.6339, IoU.monitor: 0.5539, IoU.bulletin board: 0.5634, IoU.shower: 0.1378, IoU.radiator: 0.6965, IoU.glass: 0.2157, IoU.clock: 0.5174, IoU.flag: 0.7181, Acc.wall: 0.9053, Acc.building: 0.9356, Acc.sky: 0.9771, Acc.floor: 0.9210, Acc.tree: 0.9012, Acc.ceiling: 0.9481, Acc.road: 0.9074, Acc.bed : 0.9709, Acc.windowpane: 0.8196, Acc.grass: 0.8282, Acc.cabinet: 0.7770, Acc.sidewalk: 0.8533, Acc.person: 0.9466, Acc.earth: 0.5677, Acc.door: 0.7559, Acc.table: 0.8182, Acc.mountain: 0.7356, Acc.plant: 0.6818, Acc.curtain: 0.8919, Acc.chair: 0.8150, Acc.car: 0.9418, Acc.water: 0.7817, Acc.painting: 0.9087, Acc.sofa: 0.9296, Acc.shelf: 0.7304, Acc.house: 0.7074, Acc.sea: 0.8102, Acc.mirror: 0.8446, Acc.rug: 0.7570, Acc.field: 0.5691, Acc.armchair: 0.7595, Acc.seat: 0.8933, Acc.fence: 0.6803, Acc.desk: 0.7887, Acc.rock: 0.8164, Acc.wardrobe: 0.7562, Acc.lamp: 0.8776, Acc.bathtub: 0.8973, Acc.railing: 0.6528, Acc.cushion: 0.8383, Acc.base: 0.5436, Acc.box: 0.5018, Acc.column: 0.6876, Acc.signboard: 0.5719, Acc.chest of drawers: 0.6426, Acc.counter: 0.5227, Acc.sand: 0.8635, Acc.sink: 0.8608, Acc.skyscraper: 0.5856, Acc.fireplace: 0.9423, Acc.refrigerator: 0.9432, Acc.grandstand: 0.8096, Acc.path: 0.4345, Acc.stairs: 0.4006, Acc.runway: 0.9615, Acc.case: 0.8299, Acc.pool table: 0.9784, Acc.pillow: 0.8064, Acc.screen door: 0.8239, Acc.stairway: 0.5799, Acc.river: 0.2093, Acc.bridge: 0.8799, Acc.bookcase: 0.6718, Acc.blind: 0.5118, Acc.coffee table: 0.8707, Acc.toilet: 0.9404, Acc.flower: 0.5943, Acc.book: 0.7838, Acc.hill: 0.2246, Acc.bench: 0.6365, Acc.countertop: 0.8555, Acc.stove: 0.9298, Acc.palm: 0.8293, Acc.kitchen island: 0.8741, Acc.computer: 0.9125, Acc.swivel chair: 0.6998, Acc.boat: 0.9144, Acc.bar: 0.8789, Acc.arcade machine: 0.8295, Acc.hovel: 0.1572, Acc.bus: 0.9698, Acc.towel: 0.8746, Acc.light: 0.7259, Acc.truck: 0.6257, Acc.tower: 0.6231, Acc.chandelier: 0.8773, Acc.awning: 0.4819, Acc.streetlight: 0.5342, Acc.booth: 0.5747, Acc.television receiver: 0.8859, Acc.airplane: 0.9615, Acc.dirt track: 0.2882, Acc.apparel: 0.8538, Acc.pole: 0.3782, Acc.land: 0.0442, Acc.bannister: 0.2717, Acc.escalator: 0.8615, Acc.ottoman: 0.6713, Acc.bottle: 0.5891, Acc.buffet: 0.5927, Acc.poster: 0.3718, Acc.stage: 0.4764, Acc.van: 0.7276, Acc.ship: 0.9288, Acc.fountain: 0.3901, Acc.conveyer belt: 0.9388, Acc.canopy: 0.7514, Acc.washer: 0.8807, Acc.plaything: 0.6192, Acc.swimming pool: 0.7913, Acc.stool: 0.7305, Acc.barrel: 0.9586, Acc.basket: 0.5721, Acc.waterfall: 0.6475, Acc.tent: 0.9857, Acc.bag: 0.3186, Acc.minibike: 0.9060, Acc.cradle: 0.9762, Acc.oven: 0.7547, Acc.ball: 0.6123, Acc.food: 0.7638, Acc.step: 0.1806, Acc.tank: 0.9238, Acc.trade name: 0.2848, Acc.microwave: 0.9660, Acc.pot: 0.7095, Acc.animal: 0.6367, Acc.bicycle: 0.7748, Acc.lake: 0.6378, Acc.dishwasher: 0.8465, Acc.screen: 0.9262, Acc.blanket: 0.4474, Acc.sculpture: 0.8852, Acc.hood: 0.7712, Acc.sconce: 0.7387, Acc.vase: 0.6678, Acc.traffic light: 0.6430, Acc.tray: 0.3577, Acc.ashcan: 0.6736, Acc.fan: 0.8382, Acc.pier: 0.5209, Acc.crt screen: 0.0340, Acc.plate: 0.7932, Acc.monitor: 0.6607, Acc.bulletin board: 0.6534, Acc.shower: 0.1626, Acc.radiator: 0.8117, Acc.glass: 0.2313, Acc.clock: 0.6107, Acc.flag: 0.8128